s2fieldsofstudy: Biology Psychology

text:
Lower synaptic density is associated with depression severity and network alterations

Synaptic loss and deficits in functional connectivity are hypothesized to contribute to symptoms associated with major depressive disorder (MDD) and post-traumatic stress disorder (PTSD). The synaptic vesicle glycoprotein 2A (SV2A) can be used to index the number of nerve terminals, an indirect estimate of synaptic density. Here, we used positron emission tomography (PET) with the SV2A radioligand [11C]UCB-J to examine synaptic density in n = 26 unmedicated individuals with MDD, PTSD, or comorbid MDD/PTSD. The severity of depressive symptoms was inversely correlated with SV2A density, and individuals with high levels of depression showing lower SV2A density compared to healthy controls (n = 21). SV2A density was also associated with aberrant network function, as measured by magnetic resonance imaging (MRI) functional connectivity. This is the first in vivo evidence linking lower synaptic density to network alterations and symptoms of depression. Our findings provide further incentive to evaluate interventions that restore synaptic connections to treat depression.

The main finding is that SV2A density was negatively correlated with severity of depressive symptoms clinical subjects in dlPFC (r=-0.633, p=0.001), ACC (r=-0.634, p=0.001) and hippocampus (r=-0.487, p=0.012). As noted below, it was not immediately clear to me this these findings are the results of a prior hypothesis testing or if the study is meant to be exploratory/hypothesis generating. This is an important point in the context of rigor. Either approach is reasonable, but it is incumbent upon the authors to present the analyses in light of one of the two approaches, I believe. The supportive analyses related to group effects of fMRI are interesting and appropriately presented as exploratory. Overall this is a very interesting paper with some potential limitation related to sample size and lack of clarity about pre-specified features of the statistical plan.
I have a few specific comments below.
1. Intro -maybe a minor quibble, but the paper starts with, "the human brain displays remarkable neuroplasticity in response to stress...Persisting high levels of stress or repeated exposure to extreme stress results in loss of synapses in circuits underlying affective and cognitive processes1." But the data the authors are referring to is in rodent models, and I do not know if we know what happens re synaptic plasticity in humans under stress.
2. Intro, last paragraph, no specific hypotheses are stated. Authors refer to an exploratory approach, which is OK but should be consistent throughout. For example in the Results there is finding presented as primary of correlations between SV2A and a few select brain regions (where they defined a prior prior to data collection?) Then there is what is presented in the results as 'exploratory' but it is unclear if the authors are using these terms in the technical/statistical sense or just general language to indicate what they feel is most important. Since these terms do have technical meanings in this context, I suggest using the terms in that way. appropriate to combine MDD, PTSD and MDD+PTSD in this first study; [2] limited samples sizes that include the number of "pure" PTSD (n=5), sizes of the LS and HS groups, and only a subset of HC with fMRI; [3] significantly different unlabeled mass doses administered across groups; [4] no correction for non-displaceable distribution volume of radiotracer; and [5] the use of "[C-11]UCB-J SV2A VT" and "synaptic density" as equivalent terms (e.g., axis labels). In reference 53 (Fig 1D), it appears that the correlation between SV2A VT and SV2A OD is r2=0.31. How might this translate to the present work -is this lower correlation a concern?
Despite the authors' efforts, some of the work seems a bit exploratory (as noted by the authors) and larger sample sizes and further investigation are likely needed to establish the major findings. A few additional comments/questions remain. Did the authors observe any input function differences across the groups, e.g., did in vivo radiotracer metabolism differ across groups? Could gray matter atrophy impact data interpretation (beyond PVC for the PET and in terms of the MRI ICD metric)? Were regional gray matter volumes compared across groups or considered in the statistical analysis? The authors should describe UCB-J SV2A VT and VT/fp test-retest variability. Which outcome is considered more accurate or reliable, VT or VT/fp? Has outcome reliability been assessed yet in the relevant patient groups?
Overall, it is very exciting novel research.

Methods
• VT (line 423) also includes blood volume? • Is the PVC method related to other methods more commonly applied in the past (e.g. GTM)? Please offer more clarification.
• fMRI: how many functional volumes were acquired? Were the PET data acquisition parameters listed (e.g dynamic acquisition)? Please provide further evidence that the parametric data were well fit. • how does 5 mm fMRI resolution relate to PET resolution?

Results
• Scatter graphs would be more informative, if different symbols or colors indicated HC and/or patient sub-group membership • It would be more informative to show scatter graphs rather than bar graphs when sample sizes are small • Please list sample sizes in figure legends, when possible • Why not show all results on the same y-axis scale? Please include "SV2A" on y-axis scale, although it really is "[C-11]UCB-J VT" • Fig, 2b: Image planes appear to capture different anatomy for the 3 subjects (not spatially normalized?) • Fig, 5: Graphs E and F are less convincing in terms of a linear relationship Discussion • line 226, ". . . exploratory analysis of additional brain regions points to a global effect." Were these data shown? • The discussion is long and sometimes speculative (e.g., the paragraph on sleep, line 229)consider shortening and further focusing the Discussion • It is not always clear how the individuals in the current study (in a major depressive episode at screening) compare to subjects studied in some of the literature findings cited • Line 305, the word "Second" may be missing here • Line 322 The third aspect of the limitation discussion could be more clearly presented/described Supplemental Data • Table S3: Thalamus values appear to be missing Reviewer #3 (Remarks to the Author): The authors investigated SV2A with the radioligand [(11)C]UCB-J in healthy controls and an unmedicated clinical sample comprising subjects with either MDD, PTSD, or with comorbid MDD and PTSD. Both groups were matched for smoking and the ratio of male to female participants. In addition to PET imaging, functional brain connectivity was measured in the clinical sample and a fraction of the healthy controls with fMRI. Participants received a clinical assessment and computerized cognitive tasks. The full clinical sample and the controls did not differ in SV2A VT. In the clinical sample increasing depressive symptom load corresponded to decreasing SV2A VT. When split into subjects with high and low depressive symptom load based on their HAMD score the high load group displayed lower SV2A VT than controls in the DLPFC, ACC, and hippocampus. The full clinical sample displayed reduced functional connectivity of the DLPFC. Lower DLPFC SV2A VT corresponded to a higher DLPFC-PCC connectivity. Higher DLPFC-PCC connectivity corresponded to higher depressive symptom load and lower performance in a working memory task. This carefully conducted investigation uses a novel radiotracer and presents the first in vivo evidence for altered SV2A VT in MDD. It bears a high potential impact for the field and would be of significant interest to the readership of this journal. Consider the points below to improve readability and address some important points in discussing the exciting findings of this study. Major points Stratification for symptom severity resulted in an uneven distribution of diagnoses across groups, with no subjects with PTSD without comorbid MDD in the high depressive symptom load group. This could be emphasized more clearly in the text. The findings on the relation between depressive symptoms and SV2A VT are impressive. An additional aspect of the results would be that no relation was found between SV2A VT and symptoms of PTSD, or anxiety symptoms in general. Consistently, the very informative explorative analysis involving SV2A VT and the factorial structure of HAMD, revealed that decreased SV2A VT corresponds to higher scores on the depression, insomnia, and somatic, but not on the anxiety factor of HAMD. Consider expanding on this in the discussion. Minor points Information on the sensitivity and specificity of [(11)C]UCB-J, such as on page 13, lines 326-328, is central for understanding and appreciating the results. Therefore, consider moving it from the discussion to the introduction and extending it. In the introduction the authors state that "binding to SV2A provides an estimate of the density of nerve terminals" (p. 4, line 94). However, in the results and discussion they use the terms "SV2A density" and "SV2A VT" synonymously, whereas in the figures they only use "SV2A VT" / "VT". Consider using "SV2A VT" throughout, or explicitly stating that alterations in SV2A VT indicate impaired SV2A density. The discussion is interesting and informative but rather long and not very focussed. Consider shortening the text on page 10 (inflammation, HPA axis, mTORC1) to topics directly related to the specific methods and results of the present investigation. The authors report a comparison of SV2A VT ACC-HIP correlation between subjects with high and low depressive symptom load on page 7, lines 160-165 (figure 4), and discuss it on page 9, lines 217-228. This analysis is refreshing, informative, and deserves more highlighting. Analyses on the cross-regional signal pattern in PET studies are highly underrepresented in spite of their potential to open new perspectives -they deserve more attention and appreciation.

Reviewer comments, author responses and manuscript changes (NCOMMS-18-32857-T)
We are grateful to each reviewer for the attentive reading of our work and their constructive comments. The comments are encouraging and all reviewers agree that the presented research is exciting and novel. Below is a point by point response to the reviewers' comments. We have carried out additional data analyses and updated the manuscript according to the reviewers' comments, and believe this makes for an improved manuscript.

Reviewer #1
This is a very interesting study using an indirect measure of synaptic density in patients with MDD and PTSD. The translational relevance of the study is high and the experiment was carried out by an established group of investigators. The investigators take an interesting transdiagnostic approach, examining the SV2A marker in MDD, PTSD and comorbid, as well as HC. The sample sizes are small with "the clinical group" comprised of 11 individuals with MDD, 5 with PTSD and 10 with comorbid MDD and PTSD, which is a limitation.
The main finding is that SV2A density was negatively correlated with severity of depressive symptoms clinical subjects in dlPFC (r=-0.633, p=0.001), ACC (r=-0.634, p=0.001) and hippocampus (r=-0.487, p=0.012). As noted below, it was not immediately clear to me this these findings are the results of a prior hypothesis testing or if the study is meant to be exploratory/hypothesis generating. This is an important point in the context of rigor. Either approach is reasonable, but it is incumbent upon the authors to present the analyses in light of one of the two approaches, I believe. The supportive analyses related to group effects of fMRI are interesting and appropriately presented as exploratory. Overall this is a very interesting paper with some potential limitation related to sample size and lack of clarity about pre-specified features of the statistical plan.
I have a few specific comments below.

Comment 1:
Intro -maybe a minor quibble, but the paper starts with, "the human brain displays remarkable neuroplasticity in response to stress...Persisting high levels of stress or repeated exposure to extreme stress results in loss of synapses in circuits underlying affective and cognitive processes1." But the data the authors are referring to is in rodent models, and I do not know if we know what happens re synaptic plasticity in humans under stress.

Response:
The reference cited (Duman et al. 2016 1 ) refers to both clinical and preclinical studies. It is well established that the human brain demonstrates neuroplastic changes in response to stress. For example, imaging studies show stress-induced functional changes in stress paradigms, and both functional and structural changes have been consistently shown in chronic stress disorders such as MDD and PTSD. We acknowledge that our statement that persisting levels of stress results in loss of synapses is, whilst directly supported by preclinical work, only indirectly supported by human studies (e.g. post-mortem studies in MDD). We have therefore changed the second sentence to "persisting high levels of stress are thought to result in loss of synapses in circuits… ". Comment 2: Intro, last paragraph, no specific hypotheses are stated. Authors refer to an exploratory approach, which is OK but should be consistent throughout. For example in the Results there is finding presented as primary of correlations between SV2A and a few select brain regions (where they defined a prior to data collection?) Then there is what is presented in the results as 'exploratory' but it is unclear if the authors are using these terms in the technical/statistical sense or just general language to indicate what they feel is most important. Since these terms do have technical meanings in this context, I suggest using the terms in that way.

Response:
We have restructured the introduction to clarify that investigating the relationship between depression severity and synaptic density in a transdiagnostic sample was the primary focus of the study. We have also added our primary hypothesis to the end of the introduction: "Our primary hypothesis was that synaptic density, as measured by [ 11 C]UCB-J PET, would be negatively associated with severity of depressive symptoms transdiagnostically".
The regions were selected a priori based on existing research -the introduction outlines this research and justifies our a priori choice of regions. We also state this in the last paragraph of the introduction.
We have now clarified the primary and secondary analyses (see the data analysis section of the methods -pg. 19). Primary analyses included the transdiagnostic association between depression severity and synaptic density; and the group differences in synaptic density. Secondary analyses included the MRI results and the associations between synaptic density, cognitive function and the symptom clusters. Findings from the primary analyses were corrected for multiple comparisons. The secondary analyses relating to cognitive function and symptom clusters were not corrected for multiple comparisons, which is stated in the methods. It is important to note that although exploratory, the fMRI data were processed according to current best practices for multiple comparisons correction due to the high risk of type 1 error in fMRI research 2 .

Comment 3:
Unclear if the p values presented in results section (e.g., for correlations between SVA and severity in 3 different regions, Fig 1) are corrected for multiple comparisons.
Response: These correlations survived correction for multiple comparisons, which is stated in the results (pg. 5).

Comment 4:
There is no explicit statement of hypothesis testing, sample size determination and the like. It is not explicitly stated if the hypotheses and the analytic plan for this experiment as presented was formulated before the data was collected. This is important in evaluating the results and in particular the risk of type II error. It is also important for general transparency. It would be helpful if the accompanying research protocol were available.

Response:
We have added our primary hypothesis to the introduction and have outlined the primary and secondary analyses in the methods. The plan to adopt a transdiagnostic approach was formulated prior to data collection, and investigating the transdiagnostic association between synaptic density and depression severity was the primary aim of the study. Given that this is the first study of its kind there was no existing data on synaptic density and depression severity, and a precise power calculation was not possible. However, with anticipated large effect sizes based on pilot data and preclinical data on synaptic loss in models of depression, we determined that a clinical sample size of 24 would be sufficiently powered to detect a statistically significant (i.e., p<0.05) association between synaptic density and depressive symptoms. The protocol is not available for distribution as it incorporates several other studies and other confidential information.

Reviewer #2
This is an impressive study by a world-class imaging group. This is very novel work but there are concerns relevant to data interpretation, although it is important to note that the authors did well to address some key concerns through Supplemental data and the Discussion section. Example concerns: Comment 1: Need for further consideration of why it may be and/or why it might not be appropriate to combine MDD, PTSD and MDD+PTSD in this first study; Response: Consistent with emerging theoretical frameworks in psychiatry (e.g., NIMH Research Domain Criteria Project) we intentionally employed a transdiagnostic, dimensional approach instead of a categorical approach. Diagnostic categories are inherently heterogenous; a dimensional approach aims to understand the neurobiology underlying symptoms, which may lead to the discovery of targets and novel treatments that are effective for specific symptoms, in this case, depression. Indeed, depressive symptoms are common in PTSD, which is often comorbid with MDD, yet current antidepressants are often ineffective. We believe our transdiagnostic approach is a strength of this study, and that demonstrating an association between SV2A V T , which is indicative of synaptic density, and depressive symptoms in a transdiagnostic sample of individuals with MDD and/or PTSD in a first study highlights the potential importance of synaptic plasticity in relation to depressive symptoms specifically. However, we acknowledge that larger PET studies investigating synaptic density in distinct categorical disorders are vital in further unravelling the role of synaptic density in relation to psychiatric symptoms, and have specifically addressed this in the Discussion.

Comment 2:
Limited samples sizes that include the number of "pure" PTSD (n=5), sizes of the LS and HS groups, and only a subset of HC with fMRI; Response: We acknowledge that the number of individuals with 'pure' PTSD is relatively small. However, given the transdiagnostic approach, we were able to detect a significant association between synaptic density and symptoms of depression. We would also like to highlight that 26 individuals with psychiatric illness (and 21 healthy controls; 47 subjects in total) is relatively large for a PET study. Further, all 26 individuals were medication-free, which is rare to see in a PET study of such size. Given that the study is sufficiently powered to detect a significant association between synaptic density and depressive symptoms transdiagnostically and that this is the first study of its kind, we believe that despite the relatively small samples of each diagnostic category alone, these findings add important and novel insight into the neurobiological mechanisms underlying symptoms of depression -symptoms that span multiple psychiatric and neurological disorders that are often inadequately treated. Saying this, as stated previously, we do acknowledge that larger PET studies investigating synaptic density in discrete categorical disorders are necessary to determine whether there are distinct synaptic density patterns across disorders, and we have highlighted this in the Discussion.
In relation to not having fMRI data in the full HC sample, we have highlighted this in the Discussion. Furthermore, we took several steps to improve power. For example, a ROI-based approach based on the PET findings was used to examine global connectivity in the dlPFC -indeed this finding replicates previous studies showing abnormal PFC connectivity in MDD and PTSD. Further, we combined PET and fMRI modalities to explore a relationship between lower synaptic density and a 'blurring' of 2 typically opposed networks in the clinical sample, which provides novel insight into the possible molecular underpinnings of network dysfunction.

Comment 3: Significantly different unlabelled mass doses administered across groups;
Response: Based on our previously reported validation study 3 (https://journals.sagepub.com/doi/abs/10.1177/0271678X17724947), the mass doses in this study were determined to occupy <1% of receptors. Further, there was no observed correlation between injected mass and V T . We are therefore confident that the different mass doses have not substantially impacted our results. It must also be noted that any effect of a lower mass dose in high severity clinical group would have resulted in an underestimation of our observed group difference. This is because lower mass is associated with higher specific activity . Therefore, any effect of lower mass would have resulted in higher V T and thus would have minimized the difference between high severity patients and controls. We have addressed this issue in the Discussion (pg.12).

Comment 4:
No correction for non-displaceable distribution volume of radiotracer Response: Given the observed difference in centrum semiovale V T, and that MDD and PTSD have been associated with white matter pathology, we determined that V T was the more appropriate outcome measure over BP ND . V T has been previously validated as a reliable and accurate outcome measure for assessing synaptic density using [ 11 C]UCB-J 3,4 . Further, the non-displaceable uptake is very small compared to the grey matter uptake, especially after PVC and so nearly all of the grey matter V T value represents specific binding. We address this in the Discussion (pg. 11).
Response: Thank you for pointing this out -we have changed all axis labels to [ 11 C]UCB-J V T and use [ 11 C]UCB-J V T consistently throughout the results. We explicitly state that [ 11 C]UCB-J V T is indicative of SV2A (synaptic) density, as validated previously 3,4 .

Comment 6:
In reference 53 (Fig 1D), it appears that the correlation between SV2A VT and SV2A OD is r2=0.31. How might this translate to the present work -is this lower correlation a concern?
Response: The relatively low correlation between SV2A OD and SV2A V T for the GM was due to the relative low difference in SV2A signal across the GM regions. Further, we'd like to highlight that Western blot quantification is semi-quantitative and the relatively small sample size for V T (9 GM regions in one baboon). Considering the low sample size, it is important to also consider the high correlation between SV2A OD and SYN OD (direct comparison with the same method) and the good correlation between V T and the SV2A density (B max ) from the homogenate binding assay (a more accurate assay for SV2A). Further, blockade of [ 11 C]UCB-J binding by levetiracetam confirms SV2A density in vivo. We are thus comfortable that V T relates to SV2A density and that SV2A and SYN expression was highly correlated across the brain regions. Comment 7: Did the authors observe any input function differences across the groups, e.g., did in vivo radiotracer metabolism differ across groups?

Response:
We observed no between-group differences in plasma parent fraction at either 30 or 60 minutes post-injection. Further, there was no difference in the metabolite-corrected input function (40-60 minutes post-injection) across groups. We have added this to the manuscript and now report these data in SI (Table S4; shown below). Note that if there was a group difference in the tracer availability, the use of kinetic modelling would have provided a correction for this effect. Response: It is unlikely that gray matter atrophy could account for the ICD findings. As described in the response below, we did not detect any group differences in gray matter volume for the a priori ROIs. In our two previous papers using ICD in MDD and PTSD, we showed that regions of group differences in ICD were not the same regions as group differences in gray matter volume 5,6 . Further, we have shown that the correlation between gray matter volume and ICD is not significantly correlated across the gray matter in MDD 5 . Finally, ICD involves several steps to account for potential confounds related to brain size, include high resolution non-linear registrations, and within subject normalization of connectivity values.
Comment 9: Were regional gray matter volumes compared across groups or considered in the statistical analysis? Response: Regional gray matter volumes were calculated using tensor-based morphometry (TBM) as in our previous studies 5, 6 . The determinant of the Jacobian of the deformation field generated from a high resolution non-linear transformation into MNI common space was used to quantify local volume differences between the registered images and the template 7 . This metric provided an estimate of voxel-wise volume changes for all transformed images with respect to the group averaged template and was used for further analysis. As shown in Response Table 1, below, there were no differences in gray matter volume for these regions, suggesting that there were no differences in gray matter volume for the three regions of interest. As such, we did not consider gray matter volume in any further statistical analysis. Comment 10: The authors should describe UCB-J SV2A VT and VT/fp test-retest variability. Which outcome is considered more accurate or reliable, VT or VT/fp? Response: Our previous paper 3 describes test-retest variability of [ 11 C]UCB-J V T and V T /f p . The testretest reproducibility of [ 11 C]UCB-J V T was exceptionally good, TRV characteristics were virtually identical for ROI and voxel-based analyses. For voxel-based analysis, the regional mean TRV and aTRV for V T were -0.7±5.2% and 4.2±2. 6. Correction for f p worsened the TRV and ICC. Indeed the ICC was above 0.6 (the commonly used as a threshold) for V T /f p , but <0.6 for V T. Therefore, we chose V T as our primary outcome measure, but report V T /f p in SI for completeness. We have added a statement on test-retest variability of these outcomes to the methods section (under 'PET image analysis').
Comment 11: Has outcome reliability been assessed yet in the relevant patient groups?
Response: Although test-retest studies are not typically performed in psychiatric populations, we conducted repeat scans in two adults with MDD and observed test-retest reliability similar to


Oxidative stress mediated neurodegeneration; A cellular perspective

Background: Environmental toxins, nutritional discrepancies, genetic predispositions, and lifestyle modifications induce a variety of stresses on the human brain and body. Such chronic stressors can influence the onset and progression of neurodegeneration via cellular alterations. In this systematic review, we have discussed the role of oxidative stress in causing neurodegeneration. Moreover, the chemical, metallic and other etiological factors that stimulate oxidative stress and results in the development of neurodegenerative disorders are also explored and summarized. Methodology: A systematic review was conducted to investigate the impact of oxidative stress on the human brain in relation to neurodegeneration. The human studies focusing on neurobiology and cellular changes in brain, investigating the pathway of oxidative stress leading to neurodegeneration through mitochondrial dysfunction and cellular alterations, published during 1st January 2000 to 31st March 2019 were identified and included in this systematic review. Results: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 13 out of 1169 studies met the eligibility criteria and were included in the quantitative synthesis and screening. These studies focused on the features like cellular factors, metal exposure, and aging, inducing oxidative stress-related neurodegeneration. Six studies described mitochondrial alterations resulting in oxidative stress due to an increase in the production of reactive oxygen species (ROS), oxidation of proteins, peroxidation of lipid and damages to the deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). While the data from five studies suggested the loss of mitochondrial activity in response to metal exposure. Two experimental studies specified the effect of aging and self-implication of oxidative stress in the brain. Conclusion: Although there are sufficient evidence in favour of the hypothesis that stress is significantly associated with neurodegeneration but it is still not considered as a primary instigator in neurodegenerative disorders. However, it can be concluded on the basis of this review that it has been involved in the propagation of cellular alterations and mitochondrial dysfunction that further leads to neurodegeneration.

Introduction
Evidences suggest that the primary pathogenic factors associated with neurodegenerative disorders include oxidative stress, and mitochondrial damage 1 . The pathway of oxidative stress to neurodegeneration is linked to the overproduction of ROS 2 , that stimulates mitochondrial DNA mutation, damages the mitochondrial respiratory chain, alters membrane permeability, Ca 2+ homeostasis, and mitochondrial defense system. All these cellular changes not only cause amplification in the neuronal dysfunction but also triggers neurodegeneration 1,3,4 . It is a physiological fact that the nervous system is reliant on the energy supply by mitochondrial for normal functioning 4 . Therefore, damage to one or more of the mitochondrial respiratory chain complexes leads to the impairment of cellular ATP synthesis leading to energy dearth [5][6][7] .
The altered ROS level, decreases capability of biological system to detoxify reactive species, impair restoration tendencies, and subsequent damage that leads to the generation of oxidative stress with subsequent outcomes including cellular destruction, tissue necrosis, depletion of ATP, and apoptotic cell death [8][9][10] . The alterations drastically threat neuronal integrity after proteins 8 , lipids 11 , and DNA damage that actually create an imbalance in the normal redox state via production of free radicals and peroxides which leads to toxicity 10,12 . Moreover, neurodegeneration is also linked to the excessive generation of nitric oxide (NO), and its toxic metabolite peroxynitrite (ONOO), that effects the mitochondrial respiratory chain ultimately resulting in neuronal cell death 13 . Alzheimer's Disease (AD), Parkinson's Disease (PD), and Amyotrophic Lateral Sclerosis (ALS), are few of the common neurodegenerative disorders that are caused by the mitochondrial dysfunction and oxidative stress while among the other less associated to oxidative damage are Multiple Sclerosis (MS), Huntington's Disease (HD), and Friedreich's Ataxia (FRDA) 14 .
Oxidative stress and Neurodegenerative disorders AD, PD, and ALS, are the common neurodegenerative disorders that are caused by the mitochondrial dysfunction and oxidative stress while among the other less associated to oxidative damage are MS, HD, and FRDA 14 . AD, PD, and ALS, are 90% sporadic, while only 10% exists in familial forms. The mixture of hereditary, environmental and lifestyle factors leads to the development of sporadic forms. In contrast, HD is inherited and strictly an autosomal disorder 10 .
AD contribute to the progressive neuronal loss and cognitive decline by any damage to the structure, and components of mitochondria, increased oxidative stress, and diverse stressors, which is accountable for the degeneration in Alzheimer's brain 15 . Deficiency in various key enzymes of the Tricarboxylic Acid Cycle (TCA), involved in oxidative metabolism, and in the rate-limiting step of the TCA, cycle is responsible for reducing molecular oxygen, and cause consistent defect in mitochondrial components in brain 16 . Same as AD, ALS is found in both familial (fALS), and sporadic (sALS) forms with increased rate of sALS i.e. 90%. The familial form is less common and occurs due to the mutation in the gene encoding for enzyme Copper/Zinc (Cu/Zn)-Superoxide Dismutase (SOD-1) [17][18][19] . Being a multifactorial pathogenetic disorder, its etiology is not only restricted to oxidative stress but the excitotoxic reactions, formation of clumps, inflammation, deficiency of Growth Factor (GF), and disarrangements of neurofilaments precipitate the disease condition increasing the treatment complexity 20 .
The second most common neurodegenerative disease after AD is PD 21 . PD pathogenesis involves the loss of mitochondrial Complex I (CI) in nigral neurons 22,15 , that may be due to decreased production of certain CI subunits, disassembly of CI, and self-inflicted oxidative damage [23][24][25] . It is evident that inflammation, and oxidative stress cause cessation of events involved in the generation of dopaminergic neurons leading to neurodegeneration 26,27 .
The progressive decay of the caudate nucleus, putamen, and globus pallidus due to the loss of long projecting neurons is involved in HD pathogenesis 28 . It results from the mutation caused by the expansion of CAG (Cytosine, adenine, & guanine), repeated sequence in the gene encoding huntingtin protein on chromosome 4 which leads to the striatal as well as cortical atrophy 29 . As the CAG encodes for glutamine, however, a repeated sequence results in polyglutamine chain that causes an abnormal protein-protein interaction, and hence the mutant Huntingtin Gene (HTT) causes degeneration 30 , which has been associated with defected mitochondrial metabolic activity resulting in impaired energy metabolism 31 , increasing the oxidative damage though an increased production of free radicals.
Based on previous literature, MS pathogenesis is associated with oxidative/nitrosative stress that occurs due to excessive production of ROS and Reactive Nitrogen Species (RNS) 32 . ROS results in the upregulation of the genes involved in MS (such as tumor necrosis factorα and nitric oxide synthase) through activation of Nuclear Transcription Factor-Kappa (NFk) resulting in energy loss that leads to mitochondrial dysfunction, and calcium overloading 32 . Frataxin is responsible for mitochondrial Iron-Sulphur Cluster (ISC) synthesis, its deficiency leads to impaired synthesis of ISC which in turn causes iron overload in mitochondria, and multiple deficiencies related to ISC-containing proteins (ISPs) 33 . The resulting free radicals results in mitochondrial dysfunction, and activation of stress pathway leading to apoptosis, and energy deprivation 33 .
All the above mention neurodegenerative disorders include oxidative stress as a primary key factor in their pathogenesis. An extensive research has been conducted previously, in order to understand the correlation between mitochondrial dysfunction, oxidative stress, and neurodegeneration. In this review, we have summarized the causes of oxidative stress, and the mechanism leading to various neurodegenerative, and neurological changes.

Methodology
A search strategy was employed on electronic databases including PubMed, and Google scholar to find associations between neurodegenerative conditions, mitochondrial dysfunction, cellular modifications, and oxidative stress. The terms such as oxidative stress, mitochondrial dysfunction, cellular modification, and neurodegeneration were utilized for search. Objectives, and outcomes of all articles were examined for potential relevance to the research question before inclusion. Furthermore, reference lists of the journal articles were thoroughly screened to identify additional studies that were relevant.
The articles fulfilling the eligibility criteria, published in between 1st January 2000 to 31st March 2019 were all included in the screening process. Original full-text articles, involving adult subjects within age group of 19 to 45 years or above were all included. While studies conducted on non-human subjects, pediatric studies, meta-analysis, case-reports, and duplicate publications, and studies with any risk of bias were excluded. The articles that correspond to the criteria were collected, and reviewed thoroughly for gathering information regarding the study aim. Only published studies were included to increase the robustness of the article.
The data was synthesized and a systematic summary was developed through extraction, and management of the studies, the results were then screened, and combined according to the PRISMA guidelines 34 .
Mechanism underlying oxidative stress involvement in neurodegenerative diseases Numerous environmental, psychosocial, and physical factors stimulate the nervous system which then collectively generate stress response 35,36 mediated by the stress system, a part of which is located in the Central Nervous System (CNS), and also in the peripheral organs. The interconnected effectors of the CNS include the hypothalamic hormones arginine, vasopressin, corticotropin-releasing hormone (CRH), pro-opiomelanocortin derived peptides (POMC), locus ceruleus, and autonomic norepinephrine centers in the brainstem 36 . The executive, and cognitive reward, and fear systems, wake-sleep centers, growth system, reproductive and thyroid hormone axis, and the gastrointestinal, cardiorespiratory, metabolic, and immune systems are the targeted areas of the brain 36 . A profound effect of stress on brain development, and functioning is evident, it induces neurochemical changes, and disrupts normal neuronal circuitry. A wide variety of fundamental physiological processes are influenced by stress through the Hypothalamic-Pituitary-Adrenal (HPA) axis, and Glucocorticoid (GC) action 37 . Generating  stressful stimuli in the brain involves the activation of neurons of paraventricular nucleus of hypothalamus (PVH), which releases Corticotrophin-Releasing Factor (CRF), and arginine vasopressin. The anterior pituitary gland is then stimulated to secrete Adrenocorticotropic Hormone (ACTH) into the general circulation, which results in Cortisol (stress hormone) production by the Adrenal Cortex [37][38][39] . Excessive synthesis, and secretion of GCs due to stress-induced disruption of HPA axis can lead to altered neuronal connectivity, synaptic loss, and neuronal atrophy. Such modifications can alter brain function, and contribute to development, and progression of neurodegenerative diseases 40,41 . Stress stimulates the stress system in the brain which in turn activates the effector organ which results in the release of hormones from hypothalamus and locus cerules and autonomic norepinephrine center in order to generate stress response by targeting areas for generating stress response.

Generation of Reactive Oxygen Species (ROS)
The source of ROS can be both exogenous and endogenous 42,43 , among the exogenous sources environmental toxins, chemical metabolism, ultraviolet (UV) radiations, ionizing radiation, drugs whose activity is mediated by ROS production 43 . While the endogenous production is either associated with mitochondria or non-mitochondrial enzymes like Nicotinamide Adenine Dinucleotide Phosphate (NADPH) Oxidase (Nox), Xanthine Oxidase (XO), cytochrome P450, cytochrome b5, quinone oxidase, oxalate oxidase, amine oxidases, flavin oxidases etc., are involved in the production of ROS 42,43 . However, ROS is chiefly produced by mitochondria, and Nox system [44][45][46] .
ROS exists in numerous partially reduced forms like Super Oxides (O2.-), Hydrogen Peroxide (H2O2), and Hydroxyl Radicals (OH.). ROS includes both oxygen radicals as well as non-radicals that can be easily converted into free radicals 47 . Moreover, evidences indicate that excessive generation of Nitric Oxide (NO), and its toxic metabolite Peroxynitrite (ONOO-) also plays significant role in neurodegeneration, it inhibits the mitochondrial electron transport chain leading to energy failure, and apoptotic cell death 48 .
Brain cell display varying susceptibility towards NO/ONOO-, and is also affected by status of cellular antioxidants, and the maintenance of energy requirement ability 48 .
Pathway leading oxidative stress to neurodegeneration It is suggested that all the neurodegenerative diseases are to some extent multifactorial, and oxidative stress is inevitably intertwined with the disease mechanisms. It is intimately linked with an integrated series of cellular phenomena, which contribute to neuronal cell death, and neuronal atrophy 49 . Mitochondria is considered as the endogenous source of oxidative stress as it causes an increased production of ROS. Although much research has been conducted on the mechanism behind the increased ROS, and RNS production causing oxidative stress but still the researchers are involved in finding out the primary cause, and consequences associated with neurodegenerative process due to oxidative stress, as the etiology is multifactorial, and hence there is likelihood for deviation in the previous findings. ROS, highly toxic to neuronal cells had been associated with neurodegeneration 48 .
Neurodegenerative disorders, such as PD, AD, and ALS clearly indicate increased ROS production which leads to neurodegeneration 49 .  The disturbed balance between the production of ROS, and antioxidants level mainly results in the generation of oxidative stress which leads to cellular damage. The formation of free radicals by increased ROS generation, contributes to the development of neurodegeneration by modulating the function of several biomolecules at cellular level. Furthermore, ROS targets several different substrates within the cells, causing protein, DNA, and RNA oxidation, and lipid peroxidation. ROS attacks proteins, and oxidizes the backbone and the side chain. It further reacts with amino acid side chains to form carbonyl formations, and results in protein fragmentation, and cross linkages are formed 50 .
Factors causing oxidative stress related neurodegeneration According to past, and recent studies, it is evident that oxidative stress has a fundamental role in the pathophysiology, and pathogenesis of neurodegenerative diseases. There are numerous environmental toxins, nutritional discrepancies, genetic predispositions, and  changes in lifestyle that induce a variety of stresses on the brain, and body, ultimately resulting in cellular modification, and mitochondrial dysfunction. This section highlights some of the causative factors that cause oxidative stress, leading to mitochondrial alteration, cellular changes, and ultimately results in neurodegeneration.
Neurodegeneration associated with Cellular factors Table 1 summarizes data for each study, including sample, cellular factors, alterations, and main outcomes of the studies included in this review. According to the findings, there are numerous cellular changes that occur within the brain producing homeostatic alterations in neurons, and causing neuronal cell death. One of the factors altering brain hemostasis is cholesterol. The CNS is rich in cholesterol, which is essential to neuronal development and survival, synapse maturation, and optimal synaptic activity. Alterations in brain cholesterol is strongly associated with neurodegeneration 51 . Amira Zarrouk, and her colleagues assessed the triggering capacity of oxysterols (cholesterol) for producing cellular modifications leading to neurodegeneration, by treating Human Neuronal Cells (SK-N-BE) with 7-ketocholesterol, 7α, and 7βhydroxycholesterol, 6α-, and 6βhydroxycholesterol, 4α-, and 4βhydroxycholesterol, 24(S)-hydroxycholesterol, and 27-hydroxycholesterol (50-100 lM, 24h) 52 . The findings indicated that 7ketocholesterol, 7β-hydroxycholesterol, 24(S)-hydroxycholesterol, and 27hydroxycholesterol favor neurodegeneration by inducing mitochondrial dysfunction, oxidative stress, and/or cell death, associated with or without a rise in cytosolic calcium level 52 .
Neurodegenerative disorders share a common pathogenetic mechanism involving aggregation, and deposition of protein aggregates 53 . These aggregates are formed due to the misfolding of the normal, soluble proteins which initiates a series of consequences associated with neurodegeneration 54 (Table 1). Glutathione, an important antioxidant whose role has been proposed in the pathogenesis of some neurodegenerative diseases 55 , is one of the important factors as discussed in table 1. Rotenone, an important chemical factor related to neurodegeneration, it alters cell viability, and causes mitochondrial dysfunction 57 . In 2018, Rekha, and her colleague observed that rotenone induces mitochondrial dysfunction, and generates endoplasmic reticulum (ER) stress by the inhibition of cell viability, dissipation of mitochondrial transmembrane potential, increases ROS level, and inhibits the activity of complex-1 of mitochondrial electron transport chain 57 . Furthermore, increased rotenone reduces intracellular ATP concentration, and reduces glutathione level, which results in nuclear fragmentation, and destruction thus causing neuronal apoptosis, and necrosis 57,58 .
Furthermore, salivary cortisol level also increases in response to acute stressful events and can be marked as a factor inducing oxidative stress 59 . Increased cortisol level in response to oxidative stress has been studied by various researchers, Aschbacher K. et al., 2013 studied three markers of oxidative damage including 8-Iso-Prostaglandin F2a (IsoP), 8hydroxyguanosine (8-oxoG) and, 8-hydroxy-20-deoxyguanosine (8-OHdG) 60 . The results suggested that anticipatory stress arousal may accelerate the accumulation of oxidative damage, and biological aging, particularly among chronically stressed individuals through anticipatory cortisol reactivity, and oxidative damage 60 .
Drugs can induce a type of stress like in case of Methamphetamine (METH), it induces oxidative stress along with mitochondrial dysfunction, and cellular alteration. METH is a psychostimulant, highly addictive, and produces long-lasting, and strong euphoric effects 61,62 . METH addiction results in attention deficit, memory decline, and decreased decision-making abilities in human, its abuse is becoming a major public health concern 62 . Moreover, it results in the significant loss of dopamine in the striatum of the brain by causing damage to the dopaminergic neurons in the substantia nigra 63 . Borgmann, and Ghorpade reported that prolonged exposure to low level of METH can cause morphological dysregulation of mitochondrial astrocytes, and also effects their function, and hence increases antioxidant capacity, and oxidative burden which leads to oxidative stress associated with neurodegeneration 64 .
It is suggested that the changes caused by oxidative stress in the functioning of mitochondrial leukocyte can be utilized for evaluation of bioenergetics health among individuals 65 . A study investigated the bioenergetics profiles of monocytes isolated from healthy human subjects, and the impact of the redox cycling agent, 2,3-Dimethoxynaphthoquinone (DMNQ) on it 66 . The results indicated that monocytes are affected by oxidative stress mediated by DMNQ. Furthermore, these monocytes displayed significant decrease in ATP-linked respiration, reduced reserved capacity, increased non-mitochondrial respiration, and a decline in Bioenergetics Health Index (BHI) 66 .
Overall, there are 6 cellular factors discussed in Table 1. The outcomes of the studies show that all of these factors induce oxidative stress by mediating mitochondrial dysfunction that involves a decrease in mitochondrial respiration along with disruption in mitochondrial morphology. Furthermore, all the above discussed factors participate in the increased production of reactive species (ROS and RNS), and decreased ATP production. Neurodegeneration associated with Metal Exposure Metals are responsible for maintenance of cellular structure, neurotransmission, cell adhesion, regulation of gene expression, and metabolism of proteins and carbohydrates in human body 67,68 . There are numerous unfavorable events such as fragmentation of DNA, oxidative stress, mitochondrial dysfunction, misfolding of protein, apoptosis etc., associated with elevated metal exposure [69][70][71] . One of the metals discussed in Table 2 is manganese (Mn). Mn facilitates the activity of numerous enzymes like arginases, hydrolases, lyases, glutamine synthetases, and Superoxide Dismutase (SOD) 72 .
Heavy, and transitional metal also have profound effects on the neurophysiology of brain. Lead (Pb), and cadmium (Cd) are nonessential heavy metals and a ubiquitously present pollutant in the ecosystem 67 . Chronic exposure to Cd, and Pb might restrict normal brain functioning 67 . To evaluate the relationships between oxidative stress, and heavy metal exposure, Xiaoyi et al., examined the health of female subjects belonging to an agricultural community in Japan. The results indicated that continuous exposure to heavy metals increases oxidative stress. Heavy metal exposure, and the concurrent presence of lifestyle factors, and age are significantly associated with oxidative stress 73 . Moreover, long-term exposure to heavy metals, such as Pb, contributes to the accumulation of peroxidative damage, lipid peroxidation, deteriorated cell membrane, and fibrillation of α-synuclein which leads to neurodegenerative cell death 74 . Similarly, transition metals are considered as the crucial players in the pathogenesis of neurodegenerative diseases 75  Residual Oil Fly Ash (ROFA) is a particulate air pollutant generated from the combustion of fuel oil 77 . The chemical composition of ROFA includes sulfates, silicates, compounds containing carbon and nitrogen, and metals 78 . Jee Young Kim investigated the association between exposures to particulate matter, metal fumes, ROFA, and oxidative DNA damage, and repair in boilermakers working at a power plant. Acute lung injury, airways inflammation, and oxidative DNA damage by ROS were observed in the study subjects, indicating increased risk of developing oxidative DNA injury after exposure to high levels of metalcontaining particulate matter 79 . Recently in 2019, the effect of particulate matters was also studied by Ying Wang, in which changes in dysfunction, and oxidative stress.
in the pathophysiology of neurodegeneration.
The studies in Table 2 demonstrated that excessive metal exposure in human body or brain results in neurotoxicity. Furthermore, it has also been evaluated that toxicity of metals in brain cause oxidative stress, mitochondrial dysfunction, and protein misfolding. Abbreviations: ROFA -Residual oil fly ash, DNA -deoxyribose nucleic acid, mtDNA -mitochondrial DNA, Aβ-42amyloid beta-42, NF-κB -Nuclear Factor kappa-light-chain-enhancer of activated B, p53 -tumor protein 53, PD -Parkinson disease, ROS -reactive oxygen species, ATP -adenosine tri phosphate, SOD -superoxide dismutase, mRNA -messenger ribonucleic acid, Pb -lead, Cd -cadmium, PBL -peripheral blood lymphocytes Neurodegeneration associated with increasing age In the last 20 years, it has been recognized that oxidative stress plays an important role in etiology of a variety of onset of neurodegenerative diseases. Aging is by far the greatest risk factor for neurodegenerative diseases such as AD, PD, and ALS 81,82 . Most theories of aging centers suggest that increasing oxidative stress leads to mitochondrial mutation, mitochondrial dysfunction, and oxidative damage 70 . Oxidative stress increases with age in the brain, and neurons might particularly be affected because they are postmitotic 83 . The ability of cells to respond to oxidative protein damage also seems to decline with age and possibly contribute to protein buildup 84 . A decline in induction of heat shock proteins (HSPs), for instance, can result in an increase in oxidatively damaged proteins that might be resistant to ubiquitinylation, and degradation by the 26S proteasome 49 . Table 3 summarizes the studies, showing association between age, and neurodegenerative diseases. Venkateshappa, and his colleagues, in their study demonstrated that the brain aging contributes to the vulnerability of midbrain to neurodegeneration in PD 86 . Increase in protein oxidation, loss of mitochondrial CI activity, increased astrocytic proliferation indicated by glial fibrillary acidic protein (GFAP) expression, decreased SOD activity, and low GSH level were observed during physiological aging in human brain 85,86,87 . Furthermore, there was a significant protein oxidation, and nitration with increasing age. The findings of the included studies indicate that extensive oxidative damage, loss of antioxidant, and mitochondrial function during physiological aging are responsible for neurotoxic activity thus contributing to selective degeneration during evolution of PD 49 . Furthermore, the author continued in another study that a distinct increase in oxidative damage, and Glial Fibrillary Acidic Protein (GFAP) expression, decreased antioxidant response, and insignificant trend towards a decreasing activity of mitochondrial Complex I (CI) in hippocampus region of human brain with increasing age 88 .

Discussion
Neurodegenerative conditions associated with neuronal cell death has long been linked to oxidative stress but the primary events involved in the initiation of oxidative stress with neurodegeneration in PD, AD, and ALS is still uncertain 49 . Further research regarding mechanism behind neurodegeneration, and its possible link to oxidative stress with respect to the contributing factors is required for advancement in the field of molecular and cellular physiology.
In this review, we have highlighted the etiology behind mitochondrial dysfunction, and cellular alterations influencing the production of reactive species, and other associated events with respect to oxidative stress leading to neurodegeneration. Despite of the rapid scientific evolution, a number of stimulators that have shown association with oxidative stress, and neurodegeneration among animals are still unexplored in humans. A study discovered Menadione (Vitamin K3) as one of the important etiological factors for oxidative stress in animal model 89 . Rodrigues MD et al., measured the alterations induced by menadione in mitochondrial bioenergetics, redox homeostasis, and cell viability. Decreased activity of mitochondrial dehydrogenase, decreased Glutathione (GSH), reduced antioxidants, and Superoxide dismutase (SOD), while increased production of ROS and proinflammatory response was observed in cortical astrocytes of wild-type (Gcdh+/+) mice. The authors concluded that redox, and mitochondrial bioenergetics homeostasis is disturbed when triggered by menadione, and it therefore increases the death risk 89 . Though the indicator showed positive response but the study is limited to the animal species.
Many animal models have been utilized for exploring the connection of cellular factors with brain neuronal homeostasis 49 .
α-Synuclein (ASN) oligomer is another widely studied fact in animals. ANS is a multifunctional E3 ubiquitin ligase that is associated with the pathophysiology of PD 90 , its excessive release contributes to neuronal injury leads to neuronal cell death through oxidative-nitrosative stress induction, mitochondrial impairment, and synaptic dysfunction 91 . In 2018, Anna Wilkaniec investigated the effect of extracellular ASN oligomers on Parkin expression and Snitrosylation activity by using rat pheochromocytoma (PC12) cell line treated with exogenous oligomeric ASN as well as PC12 cells with parkin overexpression, and parkin knock-down 92 . The study concluded that the ASN oligomers induce oxidative/nitrosative stress leading to Parkin S-nitrosylation, resulting in neuropathological changes 92 .
Altered calcium-independent phospholipase A2γ (iPLA2γ) level is one of the substantial causes of neurodegeneration, it results in increased mitochondrial lipid peroxidation, and dysfunction 97 . According to Chao H. et al., 2017, decreased functioning of iPLA2γ results in the reduction of dopamine, and its metabolites by stimulating the Parkinsonian phenotype, which subsequently results in locomotory deficiencies, and hypersensitivity to rotenone-induced oxidative stress 97 . Furthermore, it also escalates the ratio of mitochondrial irregularities, reducing ATP synthesis, and levels of glutathione 97 . Bianca Seminotti et al., 2014 evaluated the lysine induced oxidative stress, a marked increase in 2,7-Dichlorodihydrofluorescein (DCFH) oxidation was observed. Lysine overload decreases the brain antioxidant defense mechanism and induces protein oxidation by generating reactive species 95 . Glia Maturation Factor (GMF), a neuroinflammatory protein in the brain has been studied in relation to PD pathophysiology. Selvakumar GP et al., 2018 found that GMF increases oxidative stress, mitochondrial dysfunction, and results in apoptotic cell death by altering the expression of Peroxisome Proliferator-Activated Receptor γ Coactivator 1-α (PGC-1α) and inhibits the mitochondrial activity 98 . Which further causes decreased antioxidant enzyme function, increased intracellular ROS level, increased depolarization of mitochondrial membrane potential, and inhibition of Oxidative phosphorylation (OXPHOS) protein complex 98 . All these studies highlight impact of oxidative stress on human brain system, and its functions with regard to specific causative factors resulting in neuronal cell death or neurodegenerative disorders.

Conclusion
This review concluded that it has been made evident by research studies that oxidative stress is involved in the propagation of cellular alteration and mitochondrial dysfunction that further leads to neurodegeneration. Although the neurodegenerative diseases are multifactorial but the oxidative stress is inevitably associated with the disease mechanisms and itself is linked with an integrated series of cellular variations that all contribute to neuronal death. Moreover, cellular factors, exposure to excessive metals, brain injuries and aging have an important role in the progression of oxidative stress leading to neurodegenerative diseases.

Conflicts of Interest
None.


A Descriptive Study of Gender Difference in Digit Ratio among Bini Ethnic Group of southern Nigeria

There are divers works on digit ratio as a subject matter insinuating that it expresses dimorphism in sexes. If this is true, it means that it could serve as a means of identification in forensic and population studies. Digit ratio has also been used to investigate interpopulation affinity stating tribal or population similarity. [2] It has been speculated that sexual differences in 2D:4D are mainly caused by the shift along the common allometric line with non-zero intercept, which means 2D:4D necessarily decreases with increasing finger length, and the fact that men have longer fingers than women (which may be the basis for the sex difference in digit ratios and/or any putative hormonal influence on the ratios [3-5].

INTRODUCTION
Digit ratio is defined as the ratio of the second digit length (2D) to the fourth digit length (4D) of the hand. [1] There are divers works on digit ratio as a subject matter insinuating that it expresses dimorphism in sexes. If this is true, it means that it could serve as a means of identification in forensic and population studies. Digit ratio has also been used to investigate interpopulation affinity stating tribal or population similarity. [2] It has been speculated that sexual differences in 2D:4D are mainly caused by the shift along the common allometric line with non-zero intercept, which means 2D:4D necessarily decreases with increasing finger length, and the fact that men have longer fingers than women (which may be the basis for the sex difference in digit ratios and/or any putative hormonal influence on the ratios [3][4][5].
This gender difference has not been investigated amongst Bini tribe before, as such the study was done to know whether the ethnic group under investigation has this feature has have been reported by other authors. This study will add to the body of knowledge information on the population characteristics of the indigenous populations especially in Sub-Saharan Africa.
The Edo or Bini (from the word "Benin") people are an ethnic group primarily found in Edo State, and spread across the Delta and Ondo states of Nigeria. They speak the Edo languages and are the descendants of the founders of the Benin Empire. They are closely related to other ethnic groups that speak Edo languages, such as the Esan, the Anemia and the Owan. They have been estimated to have a population of 2,208,700 million [6][7][8]. There are some reports on investigations by other researchers on digit ratios in various indigenous populations. [9][10][11][12][13][14][15][16][17][18][19][20] There is paucity of information on gender difference in digit ratio in Bini tribe.

Aim and Objective
This study was aimed at gender variation in digit ratio of the Bini ethnic group.

Scope of the Study
This study was done specifically on the secondand fourth-digit lengths.

Significance of the Study
This study will benefit the body of knowledge on population studies of ethnic groups in Nigeria which will be significant to historians, sociologists, anthropologists, and the Bini people of Nigeria specifically.

Research Design
The study was descriptive and cross-sectional. For the purpose of this study, an individual was considered to be a Nigeria of a particular ethnic group if the parents and four grandparents are of the same ethnic group. Volunteers with age ranging from 18-60 years from the Bini extractions were recruited for this study by purposive sampling. The study was conducted from March 6-October 20, 2019.

Data Collection
The lengths of the second and fourth digits of the left and right hands were measured on the ventral (inferior) surface of the hand from the basal crease of the 2nd and 4th digits to the tip of the finger of the subjects. In situations where there was a band of creases at the base of the digit, the most proximal crease was used. All measurements were done with a digital vernier caliper having an accuracy of ±0.2mm [1][2][3].The measurements were done three times and the average value was recorded. The lengths of the second digits were divided by the lengths of the fourth digits to obtain the digit ratios [1][2][3].

Data Analysis
The data obtained were analysed using SPSS version 21; independent t-test was used to determine the mean values and comparison between both sexes.

Criteria for Subject Selection
Volunteers recruited were indigenes of the ethnic groups under investigation with no form of anatomical abnormality of the hands.

Ethical Consideration
Ethical clearance was obtained from the Research Ethics Committee of the University of Port Harcourt with REC Number: UPH/CEREMAD/REC/MM59/036before commencement of the study.

RESULTS
In

DISCUSSIONS
The ability to distinctively tell the difference between males and females is a desired feature of an anthropological study. The result of the study depicted sexual dimorphism in the digit ratios with the females having higher digit ratios than the males. This dimorphism could have beena result of the difference in androgen level between both genders. The more exposure to testosterone, the lower the digit ratio. This further strengthens what has been reported in the digit lengths. The result of this present study agrees strongly with the reports of previous investigators who have expressly stated that there is sexual dimorphism in the digit ratios. [9][10][11][12][13][14] in this study, it was found that the males repeatedly had low digit ratios compared to the females. This low digit ratio seen in men is considered a positive trait as several other authors who have worked on this subject reported that low digit ratios are typical masculine traits. Alonso et al. [15] reported that low digit ratio correlates with high social preference. Again, Aycinena et al. [16] in his study asserted that women with low digit ratio tend to be more patient; motioned that a low digit ratio positively correlates with static and explosive strength, speed and agility which is a typical masculine trait exemplified in digit ratio. Furthermore, it was also mentioned that a study on sports activities showed that those who got to the highest level of competition in their respective games had low digit ratios [17] which were mostly men. All these reports from the previous authors affirm the results got from this present study which showed that males had lower digit ratios than the females.

CONCLUSION
The Bini ethnic group has shown a marked difference in the digit ratio between both sexes. The shorter digit length and a lower digit ratio in males are considered to be typical masculine features, as such could be used as a marker for gender differentiation in a forensic investigation.


extfieldsofstudy: Medicine Biology

s2fieldsofstudy: Biology Medicine

text:
Diversity of metallo-β-lactamase-encoding genes found in distinct species of Acinetobacter isolated from the Brazilian Amazon Region

BACKGROUND The multidrug resistance (MDR) phenotype is frequently observed in Acinetobacter baumannii, the most clinically relevant pathogenic species of its genus; recently, other species belonging to the A. calcoaceticus-A. baumannii complex have emerged as important MDR nosocomial pathogens. OBJECTIVES The present study aimed to verify the occurrence of metallo-β-lactamase genes among distinct Acinetobacter species in a hospital located in the Brazilian Amazon Region. METHODS Antimicrobial susceptibility profiles were determined by broth microdilution. The genetic relationships among these isolates were assessed by pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST). Pyrosequencing reads of plasmids carrying the bla NDM-1 gene were generated using the Ion Torrent™ platform sequencing. FINDINGS A total of six isolates carried bla NDM-1: A. baumannii (n = 2), A. nosocomialis (n = 3), and A. pittii (n = 1); three carried bla IMP-1: A. baumannii, A. nosocomialis, and A. bereziniae. Resistance to colistin was observed for an NDM-1-producing A. nosocomialis isolate. Diverse PFGE patterns and sequence types were found among A. nosocomialis and A. baumannii isolates. The bla NDM-1 sequence was inserted in a Tn125 transposon, while the bla IMP-1 was found as a gene cassette of the class 1 integron In86. MAIN CONCLUSIONS To the best of our knowledge, this is the first report describing the dissemination of bla NDM-1 among distinct Acinetobacter species recovered from the same hospital in South America.

The World Health Organization (WHO) has recognised carbapenem-resistant Acinetobacter baumannii as a critical priority pathogen for antimicrobial development and research. (1) The multidrug resistance (MDR) phenotype is frequently observed in A. baumannii, which is the most clinically relevant species of its genus, (2) and recently, other species belonging to the A. calcoaceticus-A. baumannii complex have emerged as important MDR nosocomial pathogens. (3,4,5,6) According to the most recent report from the Brazilian Health Surveillance Agency (ANVISA), Acinetobacter spp. were ranked as the fourth most frequent pathogen (n = 2,129; 12.6%) causing catheter-related bloodstream infections (CR-BSI) in Brazilian intensive care units (ICUs). (7) Eighty-five percent of Acinetobacter spp. reported to ANVISA was resistant to carbapen- ems. Therefore, polymyxins have constituted the first therapeutic option for the treatment of serious carbapenem-resistant Acinetobacter spp. infections. In Brazil, carbapenem resistance among A. baumannii clinical isolates is mainly associated with the production of the class D carbapenemase (CHDL) variants OXA-23, OXA-72, and OXA-143 at a lower frequency. (3,8,9) The spread of high-risk clones of producers of OXA-23 or OXA-72 explain the high carbapenem resistance rates observed among Acinetobacter spp. recovered from adults diagnosed with CR-BSI in Brazilian ICUs. (7) In contrast, carbapenem resistance due to the production of metallo-β-lactamases (MβLs) has rarely been reported in A. baumannii as well as in non-baumannii species. (4,10,11) IMP-type MβLs have only been reported in Acinetobacter spp. isolated from hospitals located in the state of São Paulo, (4,10) south-eastern Brazil, while NDM-1-producing isolates have been sporadically described in hospitals located in the three different states of the Southern region. (5,6,11) Herein, we report the spread of NDM-1 and IMP-1-producing Acinetobacter spp. causing complicated nosocomial infections at a Brazilian tertiary hospital located in the Brazilian Amazon Region (Northern Brazilian Region), and the emergence of polymyxin resistance in this group of pathogens.

Bacterial isolates and phenotypic detection of MβL
production -A total of 478 non-duplicated Acinetobacter spp. clinical isolates were obtained between January 2012 and June 2016 at a tertiary teaching hospital with 408 beds located in the city of Belém, state of Pará, Brazil. The isolates were sent to Evandro Chagas Institute (IEC) for further characterisation. Among those isolates, nine (1.9%) showed reduced susceptibility to carbapenems and were positive by imipenem/imipenem+EDTA (0.5 M) double disk synergy test. Subsequently, the MβL phenotype was confirmed using imipenem/imipenem+EDTA Etest ® strips (bioMérieux, Solna, Sweden) according to the manufacturer's instructions.
Detection of carbapenemase-encoding genes -Polymerase chain reaction (PCR) followed by DNA sequencing was performed for the detection of acquired CHDL (bla OXA-23 -like, bla OXA-24/40 -like, bla OXA-58 -like, bla OXA-143 -like) and MβL-encoding genes (bla NDM -like, bla IMPlike, bla VIM -like, bla SIM -like and bla SPM -like) using specific primers, as previously described. (8,9,10) Amplicons were purified using the QIAquick Gel Extraction Kit (Qiagen, Courtaboeuf, France) according to the manufacturer's instructions, and sequencing reactions were prepared using the BigDye Terminator Cycle Sequencing Kit (Applied Biosystems, Foster City, USA) and the ABI 3500 Genetic Analyser (Applied Biosystems, Perkin Elmer, USA). The nucleotide sequences and the derived protein sequences were analysed using the Lasergene Software Package (DNASTAR, Madison, USA), and then compared with those deposited in the GenBank database.

Characterisation of plasmids and genetic context of
MβL-encoding genes -Genomic and plasmid DNA were extracted using the Wizard Genomic DNA purification kit (Promega, Madison, WI, USA) based on the manufacturer's recommendations. The genetic structures surrounding the MβL-encoding genes were evaluated by PCR mapping followed by DNA sequencing using specific primers based on previously reported sequences. (4,10) The plasmids carrying bla NDM-1 were digested with endonuclease S1 and resolved by pulsed-field gel electrophoresis (PFGE), followed by DNA-DNA hybridisation assay that was assessed by Southern blotting using a Hybond™-N + nylon transfer membrane (GE Healthcare, Little Chalfont, UK). (10) DIG-labelling of a bla NDM-1 -spe-cific probe and signal detection was performed using the DIG DNA Labelling and Detection Kit (Roche Diagnostics GmbH, Penzberg, Germany).

Genotyping by PFGE and multilocus sequence typing (MLST) -The clonal relationships of MβL-producing
Acinetobacter spp. isolates belonging to the same species were investigated by PFGE using the CHEF DR II system (Bio-Rad, Hercules, California) and ApaI restriction enzyme (New England BioLabs, MA, USA), as previously described. (9) MLST was performed for those isolates belonging to the A. calcoaceticus-A. baumannii complex by double-stranded DNA sequencing of internal regions of seven housekeeping genes (cpn60, fusA, gltA, pyrG, recA, rplB, and rpoB) according to the Institute Pasteur scheme. Determination of the sequence type (ST) was performed through the A. baumannii MLST website (http://pubmlst.org/abaumannii/). The relationships between novel and existing STs were surveyed using the eBURST program (http://eburst.mlst.net/).
Genomic DNA sequencing, assembly, and plasmid sequence analysis -Genomic DNA from representatives of each Acinetobacter species carrying bla NDM-1 was extracted and purified using the Wizard Genomic DNA purification kit (Promega, Madison, WI, USA) according to the manufacturer's protocol. Single-end pyrosequencing reads of plasmids were generated using the Ion Torrent™ Personal Genome Machine™ (PGM) platform sequencing (Thermo Fisher Scientific, Carlsbad, CA). Raw sequence reads were trimmed and assembled de novo using plas-midSPAdes version 3.9.0 (http://bioinf.spbau.ru/spades) and MIRA v.1.0.4 software, respectively, followed by gap filling by manual assembly. Each draft plasmid was annotated by Rapid Annotations using Subsystems Technology (RAST) (http://rast.nmpdr.org) and further manually curated by Geneious v.9.1.6 (Biomatters Limited, Auckland, New Zealand) using Basic Local Alignment Search Tool (BLAST) against the non-redundant NCBI database (http://blast.ncbi.nlm.nih.gov/Blast.cgi).
Nucleotide sequencing accession numbers -The complete nucleotide sequences of plasmids pIEC383, pIEC37710, and pIEC38057 were submitted to GenBank under accession numbers MK053932, MK053933, and MK053934, respectively.
Expectedly, MβL-producing Acinetobacter spp. isolates showed high MIC values to all β-lactams tested (Table). Contrastingly, minocycline and tigecycline were the most active antimicrobial agents against the isolates in vitro, with MIC 90 values of ≤ 0.25 μg/mL and 0.5 μg/ mL, respectively. Such results corroborated previous data that also showed good in vitro activity of minocycline against IMP-1-and IMP-10-producing Acinetobacter spp. isolates in Brazil. (4,10) Despite its potential activity, the minocycline formulation for intravenous administration (Minocin ® ) is not yet available in Brazil. The NDM-1-producing Acinetobacter spp. isolates were also susceptible to ciprofloxacin and aminoglycosides. In contrast, most of the IMP-1-producing Acinetobacter spp. isolates were resistant to these drugs (Table). Interestingly, an IMP-1-producing A. nosocomialis isolate (IEC195) showed a high resistance rate to colistin (MIC, > 64 μg/mL). To the best of our knowledge, this is the first report of a colistin-resistant A. nosocomialis strain carrying bla NDM-1 . Notably, a worrisome decrease in the susceptibility to polymyxins was observed among all NDM-1-producing A. nosocomialis and IMP-1-producing A. bereziniae isolates (MICs varying from 1-2 μg/mL). According to a study conducted by Vila-Farrés and colleagues, who evaluated induced colistin-resistant A. nosocomialis mutants, the loss of LPS due to mutations in the lpxD gene was found to be the main mechanism associated with this resistance phenotype in this species. (13) In another study, Wang and colleagues reported high resistance rates to colistin (21.4%; n = 58/271) among A. nosocomialis isolates recovered from BSI in a hospital located in Taiwan during an 11-year period. (14) The authors also reported that the colistin resistance could not be transferred by a conjugation assay, (14) indicating a chromosomal mechanism that corroborated those findings previously reported by Vila-Farrés and colleagues. (13) In addition, all 58 colistin-resistant A. nosocomialis isolates evaluated by Wang and colleagues were susceptible to ciprofloxacin, (14) which was also observed in the colistin-resistant IEC195 strain (MIC 0.5 μg/mL) in the present study.
PFGE analysis showed that the three MβL-producing A. baumannii isolates were not clonally related. Contrastingly, the isolates IEC383 and IEC429 carrying the bla NDM-1 and bla IMP-1 genes, respectively, belonged to the same ST (ST464/CC464), while the IEC304 were included in ST216/CC216, as shown in Table. To date, only one NDM-1-producing A. baumannii isolate has been report- ed in Brazil, and it belonged to ST25/CC25. (11) The other two NDM-1-producing A. baumannii strains reported in the continent were both from Colombia, and one of them also belonged to ST464. (15,16) Interestingly, none of the STs associated with NDM-1-producing A. baumannii strains described in South America (Fig. 1) belonged to the major clonal complexes CC1, CC15, or CC79, which was responsible for the spread of OXA-23, and more recently, of OXA-72 in A. baumannii isolated from this geographic region. (3) Furthermore, the four MβL-producing A. nosocomialis isolates showed distinct PFGE patterns and belonged to CC410 (ST71 and ST1075), CC782 (ST433), and to the singleton ST279 (Table). Rojas and colleagues reported an NDM-1-producing A. nosocomialis isolate belonging to ST322 harbouring the same plasmid as that found in an A. baumannii strain recovered in Colombia (Fig. 1). (16) Furthermore, the NDM-1-producing A. pittii isolate (IEC37710) reported in the present study was included in ST63/CC63 (Table), differing from the two previous studies that described this MβL in A. pittii strains recovered from Brazil (ST119) (5) and Paraguay (ST320 and ST321). (17) According to S1 nuclease-PFGE/hybridisation analysis, bla NDM-1 was located in a ~45-kb plasmid in all six Acinetobacter spp. isolates. Previous reports described similar plasmids (~45-55 kb) carrying bla NDM-1 in distinct Acinetobacter species in South America (Fig. 1). This MβL-encoding gene was also reported in a ~100-kb plasmid found in A. baumannii, (11) and in the chromosome of A. pittii strains recovered from other Brazilian medical centres. (5) Initially, the sequencing of a 5,251-bp fragment flanking bla NDM-1 revealed the presence of the aminoglycoside-modifying enzyme (AME)-encoding gene aphA6, followed by ISAba125 upstream of bla NDM-1 , which was followed downstream by ble MBL (bleomycin resistance) and ΔtrpF (phosphoribosylanthranilate isomerase) genes in all NDM-1-producing Acinetobacter spp. isolates. This conserved genetic structure (IS-Aba125-bla NDM-1 -ble-ΔtrpF) suggests that bla NDM-1 was inserted in the composite transposon Tn125, which has been commonly associated with the mobilisation of this MβL-encoding gene. (5,15,16,17,18,19,20) To date, Tn125 has been described among distinct NDM-1-producing Acinetobacter species (Fig. 1) recovered from Argentina, (18,19,20) Brazil, (5,20) Colombia, (15,16,20) and Paraguay. (17,20) Notably, plasmid sequence analysis revealed that bla NDM-1 was carried by a 47,283-bp plasmid (pIEC383; Fig. 2A) in the A. baumannii IEC383 strain and in a 41,085-bp plasmid (pIEC38057; Fig. 2B) in the A. nosocomialis IEC38057 strain that displayed 86% identity at the nucleotide level, corroborating the results obtained by S1 nuclease-PFGE/hybridisation assays. The pIEC383 and pIEC38057 plasmids displayed 95% and 100% identity with pAbNDM-1 and pNDM-BJ01 isolated from A. baumannii (JN377410) and A. lwoffii (JQ060896) recovered in China, respectively. In addition, both plasmids harboured genes encoding a type IV secretion system that could be related to plasmid conjugation and for a Z toxin of unknown function ( Fig.  2A-B). For A. pittii IEC37710, only a fragment (13,363 bp; Fig. 2C) of the plasmid pIEC37710 was obtained, displaying 99% identity with pNDM-GJ01 (30,293 bp) isolated from A. towneri (KT965092). Interestingly, the three NDM-1-producing Acinetobacter species showed distinct Tn125 backbones (Fig. 1). The Tn125 structures found in A. baumannii IEC383 and in A. pittii IEC37710 strains (Fig. 1A,C) were identical to those commonly described among distinct Acinetobacter species carrying bla NDM-1 recovered in Argentina and Colombia (Fig.  1). (15,16,18,20) However, the comparison between the Tn125 structures found in A. pittii IEC37710 and the other Brazilian A. pittii strain carrying bla NDM-1 in the chromosome was not be possible ( Fig. 1), since the Tn125 had not been fully sequenced. (5) Moreover, the Tn125 found in A. nosocomialis pIEC38057 showed an unusual structure composed of a truncated cutA with a resolvase-encoding gene tnpR downstream of tat, and the complete absence of groES-groEL-insE, as well as the right-side copy of ISAba125 (Fig. 1). This deletion of 6,198 bp explains the differences in the plasmid sizes observed between pIEC383 (47,283 bp; Fig. 2A) and pIEC38057 (41,085 bp; Fig. 2B). These results demonstrate a high variability of the Tn125 genetic backbones among distinct Acinetobacter species in South America (Fig. 1), contrasting with the high homology (99-100%) verified among plasmids carrying bla NDM-1 worldwide. (15,16,17) The analysis of the genetic context of bla IMP-1 showed that it was carried by the class 1 integron In86 in all Acinetobacter spp. isolates. The cassette arrangement structure of In86 is composed of bla IMP-1 at the first position, followed by two AME-encoding genes (aacA31 and aadA1e), as previously described. (4) The In86 has been described among distinct Acinetobacter species, and in other gram-negative bacilli recovered from hospitals in the southeastern Brazil. (4) Interestingly, the distance between the cities of Belém and São Paulo, where IMP-like-producing Acinetobacter spp. strains had been geographically restricted, (4,10) is 2,465 km (1,532 miles). In conclusion, we reported the spread of NDM-1 and IMP-1 among distinct Acinetobacter species in Brazil. The acquisition of MβL-encoding genes by non-baumannii Acinetobacter species is of great concern, drastically limiting the therapeutic options for infections caused by such pathogens. In addition, the production of class B carbapenemases seems to be the main mechanism of carbapenem resistance among non-baumannii Acinetobacter species in the Brazilian Amazon Region, as well as in other South American countries. The successful dissemination of bla NDM-1 among different hosts seems to be mainly associated with the plasmid-mediated composite transposon Tn125 in this geographic region. Therefore, attention should be paid to control the spread of these emerging multidrug-resistant pathogens, particularly among patients hospitalised at ICUs.

AUTHORS' CONTRIBUTION
DB, RC and ACG conceived and supervised the project; RS provided the Acinetobacter isolates for the study; DB, RC, APS, CSN, RRB, PSL and JMM carried out the experimental procedures; YC and IM collected patient clinical data; DB, RC, APS, CSN and ACG drafted the manuscript. All authors read and approved the final manuscript. Competing interests: ACG recently received research funding and/or consultation fees from Bayer, Eurofarma, Pfizer, and MSD. Other authors have nothing to declare. This study was not financially supported by any Diagnostic/Pharmaceutical company.