Comprehensive clinical and genetic analyses of circulating bile acids and their associations with diabetes and its indices.

Bile acids (BAs) are cholesterol-derived compounds that regulate glucose, lipid, and energy metabolism. Despite their significance in glucose homeostasis, the association between specific BA molecular species and their synthetic pathways with diabetes mellitus (DM) is unclear. Here, we used a recently validated stable-isotope dilution highperformance liquid chromatography with tandem mass spectrometry (LC-MS/MS) method to quantify a panel of BAs in fasting plasma from subjects (n=2,145) and explored structural and genetic determinants of BAs linked to DM, insulin resistance and obesity. Multiple 12α-hydroxylated BAs were associated with DM [adjusted odds ratios (aORs):1.3-1.9 (all P<0.05)] and insulin resistance [aORs:1.3-2.2 (all P<0.05)]. Conversely, multiple 6a-hydroxylated BAs and isolithocholic acid (Iso-LCA) were inversely associated with DM and obesity [aORs:0.3-0.9 (all P<0.05)]. Genome-wide association studies (GWAS) revealed multiple genome-wide significant loci linked with nine of the 14 DM-associated BAs, including a locus for Iso-LCA (rs11866815). Mendelian randomization analyses showed genetically elevated DCA levels were causally associated with higher BMI, and Iso-LCA levels were causally associated with reduced BMI and DM risk. In conclusion, comprehensive large-scale quantitative mass spectrometry and genetics analyses show circulating levels of multiple structurally specific BAs, especially DCA and Iso-LCA, are clinically associated with and genetically linked to obesity and DM.

Patient-Derived Models of Cancer in the NCI PDMC Consortium: Selection, Pitfalls, and Practical Recommendations.

For over a century, early researchers sought to study biological organisms in a laboratory setting, leading to the generation of both in vitro and in vivo model systems. Patient-derived models of cancer (PDMCs) have more recently come to the forefront of preclinical cancer models and are even finding their way into clinical practice as part of functional precision medicine programs. The PDMC Consortium, supported by the Division of Cancer Biology in the National Cancer Institute of the National Institutes of Health, seeks to understand the biological principles that govern the various PDMC behaviors, particularly in response to perturbagens, such as cancer therapeutics. Based on collective experience from the consortium groups, we provide insight regarding PDMCs established both in vitro and in vivo, with a focus on practical matters related to developing and maintaining key cancer models through a series of vignettes. Although every model has the potential to offer valuable insights, the choice of the right model should be guided by the research question. However, recognizing the inherent constraints in each model is crucial. Our objective here is to delineate the strengths and limitations of each model as established by individual vignettes. Further advances in PDMCs and the development of novel model systems will enable us to better understand human biology and improve the study of human pathology in the lab.

Exploratory Transcriptomic Profiling Reveals the Role of Gut Microbiota in Vascular Dementia.

Stroke is the second most common cause of cognitive impairment and dementia. Vascular dementia (VaD), a cognitive impairment following a stroke, is common and significantly impacts the quality of life. We recently demonstrated via gut microbe transplant studies that the gut microbe-dependent trimethylamine-N-oxide (TMAO) pathway impacts stroke severity, both infarct size and long-term cognitive outcomes. However, the molecular mechanisms that underly the role of the microbiome in VaD have not been explored in depth. To address this issue, we performed a comprehensive RNA-sequencing analysis to identify differentially expressed (DE) genes in the ischemic cerebral cortex of mouse brains at pre-stroke and post-stroke day 1 and day 3. A total of 4016, 3752 and 7861 DE genes were identified at pre-stroke and post-stroke day 1 and day 3, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated pathways of neurodegeneration in multiple diseases, chemokine signaling, calcium signaling, and IL-17 signaling as the key enriched pathways. Inflammatory response genes interleukin-1 beta (Il-1ß), chemokines (C-X-C motif chemokine ligand 10 (Cxcl10), chemokine ligand 2 (Ccl2)), and immune system genes (S100 calcium binding protein 8 (S100a8), lipocalin-2 (Lcn2)) were among the most significantly upregulated genes. Hypocretin neuropeptide precursor (Hcrt), a neuropeptide, and transcription factors such as neuronal PAS domain protein 4 (Npas4), GATA binding protein 3 (Gata3), and paired box 7 (Pax7) were among the most significantly downregulated genes. In conclusion, our results indicate that higher plasma TMAO levels induce differential mRNA expression profiles in the ischemic brain tissue in our pre-clinical stroke model, and the predicted pathways provide the molecular basis for regulating the TMAO-enhanced neuroinflammatory response in the brain.

Efficient on-site confirmatory testing for atrial fibrillation with derived 12-lead ECG in a wireless body area network.

Smartphones that can support and assist the screening of various cardiovascular diseases are gaining popularity in recent years. The timely detection, diagnosis, and treatment of atrial fibrillation (AF) are critical, especially for those who are at risk of stroke. AF detection via screening with wearable devices should always be confirmed by a standard 12-lead electrocardiogram (ECG). However, the inability to perform on-site AF confirmatory testing results in increased patient anxiety, followed by unnecessary diagnostic procedures and treatments. Also, the delay in confirmation procedure may conclude the condition as non-AF while it was indeed present at the time of screening. To overcome these challenges, we propose an efficient on-site confirmatory testing for AF with 12-lead ECG derived from the reduced lead set (RLS) in a wireless body area network (WBAN) environment. The reduction in the number of leads enhances the comfort level of patients as well as minimizes the hurdles associated with continuous telemonitoring applications such as data transmission, storage, and bandwidth of the overall system. The proposed method is characterized by segment-wise regression and a lead selection algorithm, facilitating improved P-wave reconstruction. Further, an efficient AF detection algorithm is proposed by incorporating a novel three-level P-wave evidence score with an RR irregularity evidence score. The proposed on-site AF confirmation test reduces false positives and false negatives by 88% and 53% respectively, compared to single lead screening. In addition, the proposed lead derivation method improves accuracy, F1-score, and Matthews correlation coefficient (MCC) for the on-site AF detection compared to existing related methods.

Development and validation of a prediction model for outcomes after transaxillary first rib resection for neurogenic thoracic outlet syndrome following strict Society for Vascular Surgery diagnostic criteria.

OBJECTIVE: Neurogenic thoracic outlet syndrome (NTOS) is the most common form of thoracic outlet syndrome. However, NTOS has remained difficult to diagnose and treat successfully. The purpose of the present study was to generate a predictive clinical calculator for postoperative outcomes after first rib resection (FRR) for NTOS. METHODS: We performed a retrospective review of patients who had undergone FRR for NTOS at a single tertiary care institution between 2016 and 2020. A multivariate stepwise logistic regression analysis was performed to assess the association of the percentage of improvement after FRR with the patient baseline characteristics, pertinent clinical characteristics, and diagnostic criteria set by the Society for Vascular Surgery. The primary outcome was subjective patient improvement after FRR. A prediction risk calculator was developed using backward stepwise multivariate logistic regression coefficients. Bootstrapping was used for internal validation. RESULTS: A total of 208 patients (22.2% male; mean age, 35.8 ± 12.8 years; median follow-up, 44.9 months) had undergone 243 FRRs. Of the 208 patients, 94.7% had had symptoms localized to the supraclavicular area, and 97.6% had had symptoms in the hand. All the patients had had positive symptoms reproduced by the elevated arm stress test and upper limb tension test. Another reasonably likely diagnosis was absent for all the patients. Of the 196 patients who had received a lidocaine injection, 180 (93.3%) had experienced improvement of NTOS symptoms. Of the 95 patients who had received a Botox injection, 82 (74.6%) had experienced improvement of NTOS symptoms. Receiver operating characteristic curve analysis was used to assess the model. The area under the curve for the backward stepwise multivariate logistic regression model was 0.8. The multivariate logistic regression analyses revealed that the significant predictors of worsened clinical outcomes included hand weakness (adjusted odds ratio [aOR], 4.28; 95% confidence interval [CI], 1.04-17.74), increasing age (aOR, 0.93; 95% CI, 0.88-0.99), workers' compensation or litigation case (aOR, 0.09; 95% CI, 0.01-0.82), and symptoms in the dominant hand (aOR, 0.20; 95% CI, 0.05-0.88). CONCLUSIONS: Using retrospective data from a single-institution database, we have developed a prediction calculator with moderate to high predictive ability, as demonstrated by an area under the curve of 0.8. The tool (available at: https://jhhntosriskcalculator.shinyapps.io/NTOS_calc/) is an important adjunct to clinical decision-making that can offer patients and providers realistic and personalized expectations of the postoperative outcome after FRR for NTOS. The findings from the present study have reinforced the diagnostic criteria set by the Society for Vascular Surgery. The calculator could aid physicians in surgical planning, referrals, and counseling patients on whether to proceed with surgery.

Comparison of Neurological Manifestations in the Two Waves of COVID-19 Infection: A Cross-Sectional Study.

Introduction: Coronavirus Disease-19 (COVID-19) is an ongoing pandemic caused by highly contagious virus severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) that has infected millions of people across the world. Most of the countries have seen two wave patterns of the pandemic. The second wave is potentially more challenging due to high influx of cases, differing properties of the emerging mutants, and other dynamics of the evolving pandemic. Neurological manifestations are common among COVID-19 positive patients. In this context, the present study attempts to compare the neurological manifestation in the first and second waves of COVID-19. Methodology: A single-center retrospective observational study was undertaken to compare neurological manifestations in the first and second waves of COVID-19. A sample of 1500 patients in the second wave admitted with COVID-19 were included in this study and the findings were compared with 1700 patients in the first wave (data derived from a former study in the same center). A detailed questionnaire addressing co-morbidities, admission details, and clinical features was employed to collect data from the hospital records. Results: Out of 1500 COVID-19 patients in the second wave of COVID-19, 355 (23.7%) of them had one or more neurological manifestations during their in-patient stay. The most common neurological symptom in the 2nd wave of COVID-19 was headache reported in 216 (14.4%) of patients followed by fatigue in 130 (8.7%), myalgia in 120 (8.0%), smell and taste disorders (STD) in 90 (6.0%), altered sensorium in 40 (2.7%), dizziness in 24 (1.6%), seizures in 34 (2.3%), encephalopathy in 26 (1.7%), strokes in 13 (0.9%), etc., Compared to the first wave of COVID-19, dizziness (P < 0.001), myalgia (P = 0.001), headache (P < 0.001) and meningoencephalitis (P = 0.01) were more common while cerebrovascular syndromes (P = 0.001) were less common in the second wave. The mortality in the 2nd wave neurological subgroup was higher [66 (18.6%)] than 1st wave neurological subgroup [23 (10%)]. Conclusion: Meningoencephalitis, headache, and seizures were found to be more common in second wave as compared to first wave. The severity and mortality rate were higher in the second wave.

Bacterial biofilm colonization and succession in tropical marine waters are similar across different types of stone materials used in seawall construction.

Seawalls are important in protecting coastlines from currents, erosion, sea-level rise, and flooding. They are, however, associated with reduced biodiversity, due to their steep orientation, lack of microhabitats, and the materials used in their construction. Hence, there is considerable interest in modifying seawalls to enhance the settlement and diversity of marine organisms, as microbial biofilms play a critical role facilitating algal and invertebrate colonization. We assessed how different stone materials, ranging from aluminosilicates to limestone and concrete, affect biofilm formation. Metagenomic assessment of marine microbial communities indicated no significant impact of material on microbial diversity, irrespective of the diverse surface chemistry and topography. Based on KEGG pathway analysis, surface properties appeared to influence the community composition and function during the initial stages of biofilm development, but this effect disappeared by Day 31. We conclude that marine biofilms converged over time to a generic marine biofilm, rather than the underlying stone substrata type playing a significant role in driving community composition.

Data Velocity in HIV-Related Implementation Research: Estimating Time From Funding to Publication.

BACKGROUND: Given available effective biomedical and behavioral prevention and treatment interventions, HIV-related implementation research (IR) is expanding. The rapid generation and dissemination of IR to inform guidelines and practice has the potential to optimize the impact of the Ending the Epidemic Initiative and the HIV pandemic response more broadly. METHODS: We leveraged a prior mapping review of NIH-funded awards in HIV and IR from January 2013 to March 2018 and identified all publications linked to those grants in NIH RePORTER through January 1, 2021 (n = 1509). Deduplication and screening of nonoriginal research reduced the count to 1032 articles, of which 952 were eligible and included in this review. Publication volume and timing were summarized; Kaplan-Meier plots estimated time to publication. RESULTS: Among the 215 NIH-funded IR-related awards, 127 of 215 (59%) published original research directly related to the grant, averaging 2.0 articles (SD: 3.3) per award, largely in the early IR phases. Many articles (521 of 952, 55%) attributed to grants did not report grant-related data. Time from article submission to publication averaged 205 days (SD: 107). The median time-to-first publication from funding start was 4 years. Data dissemination velocity varied by award type, trending toward faster publication in recent years. Delays in data velocity included (1) time from funding to enrollment, (2) enrollment length, and (3) time from data collection completion to publication. CONCLUSION: Research publication was high overall, and time-to-publication is accelerating; however, over 40% of grants have yet to publish findings from grant-related data. Addressing bottlenecks in the production and dissemination of HIV-related IR would reinforce its programmatic and policy relevance in the HIV response.

Association between Race and Perioperative Outcomes after Carotid Endarterectomy for Asymptomatic Carotid Artery Stenosis in NSQIP.

BACKGROUND: Previous studies have documented that Black patients have worse outcomes after lower extremity revascularization procedures compared with White patients. However, the association of race on carotid endarterectomy (CEA) outcomes is not well described. The aim of this study was to compare perioperative outcomes of CEA for Black vs White patients with asymptomatic carotid artery stenosis. STUDY DESIGN: All patients who underwent CEA for asymptomatic carotid stenosis in the ACS-NSQIP targeted vascular database (2011-2019) were included. Perioperative (30-day) outcomes were compared for Black vs White patients using multivariable logistic regression adjusting for age/sex, comorbidities, and disease characteristics. RESULTS: Of 16,764 asymptomatic CEA patients, 95.2% (N = 15,960) were White and 4.8% (N = 804) were Black. Black patients were slightly younger (mean age 71.4 ± 0.1 vs 69.9 ± 0.3 years, P < 0.001) and more frequently had high-grade carotid artery stenosis compared to White patients (79.5% vs 74.0%, p = 0.001). Comorbidities including hypertension, diabetes, kidney disease, congestive heart failure, and coronary artery disease were all more prevalent among Black patients (p ≤ 0.01). Crude perioperative stroke (2.4% vs 1.3%, p = 0.007) and stroke/death (2.6% vs 1.4%, p = 0.003) were higher for Black patients, but myocardial infarction (1.7% vs 1.5%, p = 0.67) and death (0.4% vs 0.2%, p = 0.12) were similar. After adjusting for baseline differences between groups, the risk of perioperative stroke (odds ratio 1.66, 95% CI 1.01 to 2.73) and stroke/death (odds ratio 1.75, 95% CI 1.10 to 2.81) remained significantly higher for Black patients compared with White patients. CONCLUSIONS: Black patients undergoing CEA for asymptomatic carotid artery stenosis had more severe stenosis, more comorbidities, and worse perioperative outcomes compared to White patients. Overall, our data suggest substantial differences in the treatment and outcomes of asymptomatic carotid artery stenosis based on race.

A systematic review of sutureless vascular anastomosis technologies.

Vascular anastomoses typically involve a handsewn technique requiring significant surgical training, expertise, and time. The aim of our systematic review was to identify and describe sutureless vascular anastomosis techniques. We performed a systematic review of all sutureless vascular anastomosis technologies published in MEDLINE, PubMed, Embase, CINAHL, Cochrane, Web of Science, and Scopus Library databases and a patent review using US Patent and Trade Office Application, US Patent and Trademark Office Patent, Google Patents, Lens, Patent Quality Through Artificial Intelligence, SureChEMBL, and E-Space Net. Data from inclusion studies and patents published between January 1, 1980 and July 15, 2021 were abstracted to describe their category, anastomosis type and configuration, study types, and advantages and disadvantages encountered with each technology. Two hundred eleven original studies and 475 patents describing sutureless vascular anastomosis technologies were identified. In the literature, stents/stent-grafts/grafts (n = 61), lasers (n = 53), and couplers (n = 27) were the predominant device categories. In the patent review, adhesive technologies (n = 103), stents/stent-grafts/grafts (n = 68), and mechanical connectors (n = 61) predominated. The majority of studies involved in vivo animal studies (n = 193); 32.2% (n = 68) of investigations involved human trials; and 17.9% (n = 85) of patent technologies were approved by the US Food and Drug Administration. The main advantages described for sutureless anastomosis technologies included faster procedure time and greater patency rates compared with handsewn anastomoses. The main disadvantages included reduced vessel compliance, stenosis, leakage, and device costs. The appeal of sutureless technology is substantiated by numerous animal trials, but their use in humans remains limited. This may be a reflection of strict regulatory criteria and/or vascular complications associated with currently available technologies.

Age differences in predicting working memory performance from network-based functional connectivity.

Deterioration in working memory capacity (WMC) has been associated with normal aging, but it remains unknown how age affects the relationship between WMC and connectivity within functional brain networks. We therefore examined the predictability of WMC from fMRI-based resting-state functional connectivity (RSFC) within eight meta-analytically defined functional brain networks and the connectome in young and old adults using relevance vector machine in a robust cross-validation scheme. Particular brain networks have been associated with mental functions linked to WMC to a varying degree and are associated with age-related differences in performance. Comparing prediction performance between the young and old sample revealed age-specific effects: In young adults, we found a general unpredictability of WMC from RSFC in networks subserving WM, cognitive action control, vigilant attention, theory-of-mind cognition, and semantic memory, whereas in older adults each network significantly predicted WMC. Moreover, both WM-related and WM-unrelated networks were differently predictive in older adults with low versus high WMC. These results indicate that the within-network functional coupling during task-free states is specifically related to individual task performance in advanced age, suggesting neural-level reorganization. In particular, our findings support the notion of a decreased segregation of functional brain networks, deterioration of network integrity within different networks and/or compensation by reorganization as factors driving associations between individual WMC and within-network RSFC in older adults. Thus, using multivariate pattern regression provided novel insights into age-related brain reorganization by linking cognitive capacity to brain network integrity.

Hippocampus co-atrophy pattern in dementia deviates from covariance patterns across the lifespan.

The hippocampus is a plastic region and highly susceptible to ageing and dementia. Previous studies explicitly imposed a priori models of hippocampus when investigating ageing and dementia-specific atrophy but led to inconsistent results. Consequently, the basic question of whether macrostructural changes follow a cytoarchitectonic or functional organization across the adult lifespan and in age-related neurodegenerative disease remained open. The aim of this cross-sectional study was to identify the spatial pattern of hippocampus differentiation based on structural covariance with a data-driven approach across structural MRI data of large cohorts (n = 2594). We examined the pattern of structural covariance of hippocampus voxels in young, middle-aged, elderly, mild cognitive impairment and dementia disease samples by applying a clustering algorithm revealing differentiation in structural covariance within the hippocampus. In all the healthy and in the mild cognitive impaired participants, the hippocampus was robustly divided into anterior, lateral and medial subregions reminiscent of cytoarchitectonic division. In contrast, in dementia patients, the pattern of subdivision was closer to known functional differentiation into an anterior, body and tail subregions. These results not only contribute to a better understanding of co-plasticity and co-atrophy in the hippocampus across the lifespan and in dementia, but also provide robust data-driven spatial representations (i.e. maps) for structural studies.

Synthesis, phase transformation, and morphology of hausmannite Mn3O4 nanoparticles: photocatalytic and antibacterial investigations.

Nano structured Hausmannite (Mn3O4) has efficacious applications in numerous fields, such as catalytic, medical, biosensors, waste water remediation, energy storage devices etc. The potential application in wastewater treatment is due to its distinct structural features combined with fascinating physicochemical properties. Another area of interest is the oxidative properties imparted due to its reduction potential. Larger surface to volume ratio and high reactivity than the bulk form shows great progress as antimicrobial agent to control drug resistant microbial population. The distinct surface morphologies, crystalline forms, reaction conditions and synthetic methods exerts significant impact on the photo catalytic and bactericidal efficiency. Hence, the present paper focuses on a concise review of the multifarious study on synthetic methods of Mn3O4, growth mechanisms, structural forms, phase transformation and phase control, shape and dimensionality. The review also confers its applications towards photo catalytic and bactericidal studies.

Plug and play self-configurable IoT gateway node for telemonitoring of ECG.

In the era of IoT and hyperconnection, an efficient electrocardiogram (ECG) telemonitoring system in wireless body area network (WBAN) demands an easy to use, self-configurable, secure, plug and play system with minimum hardware and computational complexities. The compression and quantization parameters required for an efficient representation of ECG signal will vary from patient to patient, from lead to lead, and from time to time. To this end, we propose a compressed sensing based WBAN with self-configurable gateway node (CS-SCGN) using deterministic binary block diagonal (DBBD) measurement matrix. The self-configurability is brought in through a low complex method for adaptive tuning of parameters with a careful choice of measurement matrix and data length. The redundant data transfer between sensor nodes and gateway node is avoided by addressing the diverse requirements in ECG remote health monitoring through three modes of configuration in the proposed system. A further reduction in communication and storage cost is achieved by optimizing the number of bits transmitted by sensor nodes by automatically tuning the compression ratio and quantization depth based on the dynamics of ECG signal. The self-configuration algorithm is designed to run at the gateway node in such a way as to optimize the power efficiency of sensor nodes without causing an extra power drain at the gateway node. Also, we investigate the feasibility of using smartphone as an IoT gateway node for performing primary processing to provide local utility before sending the received data to the remote server. The energy efficiency and real-time feasibility of the proposed algorithm are evaluated by implementing the gateway node on Odroid-XU4 board which runs on the same processor as in the latest smartphones. The experimental results indicate that our proposed self-configurable system at the gateway node makes the entire ECG telemonitoring system flexible, plug and play, patient independent and power-efficient.

Evaluation of non-negative matrix factorization of grey matter in age prediction.

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging.

Predicting personality from network-based resting-state functional connectivity.

Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC-personality relations should not be considered independently of gender.

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc.

Resting-state test-retest reliability of a priori defined canonical networks over different preprocessing steps.

Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology. The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity. Several methods exist to address this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we here investigated the effect of various confound removal approaches on the test-retest reliability of functional-connectivity estimates in two previously defined functional brain networks. Our results showed that gray matter masking improved the reliability of connectivity estimates, whereas denoising based on principal components analysis reduced it. We additionally observed that refraining from using any correction for global signals provided the best test-retest reliability, but failed to reproduce anti-correlations between what have been previously described as antagonistic networks. This suggests that improved reliability can come at the expense of potentially poorer biological validity. Consistent with this, we observed that reliability was proportional to the retained variance, which presumably included structured noise, such as reliable nuisance signals (for instance, noise induced by cardiac processes). We conclude that compromises are necessary between maximizing test-retest reliability and removing variance that may be attributable to non-neuronal sources.

A study of 1177 odontogenic lesions in a South Kerala population.

CONTEXT: A study on odontogenic cysts and tumors. AIMS: The aim of this study is to determine the frequency of odontogenic cysts and tumors and their distribution according to age, gender, site and histopathologic types of those reported over a period of 1998-2012 in a Tertiary Health Care Center at South Kerala. SETTINGS AND DESIGN: The archives of Department of Oral Pathology and Microbiology, were retrospectively analyzed. SUBJECTS AND METHODS: Archival records were reviewed and all the cases of odontogenic cysts and tumors were retrieved from 1998 to 2012. STATISTICAL ANALYSIS USED: Descriptive statistical analysis was performed using the computer software, Statistical Package for Social Sciences (SPSS) IBM SPSS Software version 16. RESULTS: Of 7117 oral biopsies, 4.29% were odontogenic tumors. Ameloblastoma was the most common odontogenic tumor comprising 50.2% of cases, followed by keratocystic odontogenic tumor (24.3%). These tumors showed a male predilection (1.19: 1). Odontogenic tumors occurred in a mean age of 33.7 ± 16.8 years. Mandible was the most common jaw affected (76.07%). Odontogenic cysts constituted 12.25% of all oral biopsies. Radicular cyst comprised 75.11% of odontogenic cysts followed by dentigerous cyst (17.2%). CONCLUSIONS: This study showed similar as well as contradictory results compared to other studies, probably due to geographical and ethnic variations which is yet to be corroborated.