Neonatal adiposity is associated with microRNAs in adipocyte-derived extracellular vesicles in maternal and cord blood, a discovery analysis.

BACKGROUND: Maternal body size, nutrition, and hyperglycemia contribute to neonatal body size and composition. There is little information on maternal-fetal transmission of messages which influence fetal growth. We analyzed adipocyte-derived small extracellular vesicular (ADsEV) microRNAs in maternal and cord blood to explore their adipogenic potential. METHODS: There were 279 mother-neonate pairs with all phenotypic data (normal glucose tolerant NGT = 148, gestational diabetes mellitus GDM = 131). Neonates with adiposity were those in the highest tertile (T3) of sex-specific sum of skinfolds and those without adiposity (lean) in the lowest tertile T1 of NGT pregnancies. We studied ADsEV miRNAs in 76 and 51 neonates with and without adiposity respectively and their mothers based on power calculations (68 NGT and 59 GDM pregnancies). ADsEV miRNAs from maternal and cord blood plasma samples were profiled on Agilent 8*60 K microarray. Differential expression (DE) of ADsEV miRNAs in adipose vs. lean groups was studied before and after adjustment for maternal GDM, adiposity, and vitamin B12-folate status. RESULTS: Multiple miRNAs were common in maternal and cord blood and positively correlated. We identified 24 maternal and 5 cord blood miRNAs differentially expressed (discovery p ≤ 0.1) in the adipose group in unadjusted, and 19 and 26, respectively, in the adjusted analyses. Even though DE miRNAs were different in maternal and cord blood, they targeted similar adipogenic pathways (e.g., the forkhead box O (FOXO) family of transcription factors, mitogen­activated protein kinase (MAPK) pathway, transforming growth factor beta (TGF-ß) pathway). Maternal GDM and adiposity were associated with many DE ADsEV miRNAs. CONCLUSION: Our results suggest that the ADsEV miRNAs in mothers are potential regulators of fetal adiposity. The expression and functionality of miRNAs appear to be influenced by maternal adiposity, hyperglycemia, and micronutrient status during pregnancy.

Therapeutic efficacy of artemether-lumefantrine for the treatment of uncomplicated Plasmodium falciparum malaria in four malaria endemic states of India.

BACKGROUND: Malaria is a major public health problem in India and accounts for about 88% of malaria burden in South-East Asia. India alone accounted for 2% of total malaria cases globally. Anti-malarial drug resistance is one of the major problems for malaria control and elimination programme. Artemether-lumefantrine (AL) is the first-line treatment of uncomplicated Plasmodium falciparum in north eastern states of India since 2013 after confirming the resistance against sulfadoxine-pyrimethamine. In the present study, therapeutic efficacy of artemether-lumefantrine and k13 polymorphism was assessed in uncomplicated P. falciparum malaria. METHODS: This study was conducted at four community health centres located in Koraput district of Odisha, Bastar district of Chhattisgarh, Balaghat district of Madhya Pradesh and Gondia district of Maharashtra state. Patients with uncomplicated P. falciparum malaria were administered with fixed dose combination (6 doses) of artemether-lumefantrine for 3 days and clinical and parasitological response was recorded up to 28 days as per World Health Organization protocol. Nucleotide sequencing of msp1 and msp2 gene was performed to differentiate between recrudescence and reinfection. Amplification and sequencing of k13 propeller gene region covering codon 450-680 was also carried out to identify the polymorphism. RESULTS: A total 376 malaria patients who fulfilled the enrolment criteria as well as consented for the study were enrolled. Total 356 patients were followed up successfully up to 28 days. Overall, the adequate clinical and parasitological response was 98.9% and 99.4% with and without PCR correction respectively. No case of early treatment failure was observed. However, four cases (1.1%) of late parasitological failure were found from the Bastar district of Chhattisgarh. Genotyping of msp1 and msp2 confirmed 2 cases each of recrudescence and reinfection, respectively. Mutation analysis of k13 propeller gene showed one non-synonymous mutation Q613H in one isolate from Bastar. CONCLUSIONS: The study results showed that artemether-lumefantrine is highly effective in the treatment of uncomplicated P. falciparum malaria among all age groups. No functional mutation in k13 was found in the study area. The data from this study will be helpful in implementation of artemether-lumefantrine in case of treatment failure by artesunate plus sulfadoxine-pyrimethamine.

Characteristics of COVID-19 Patients Admitted to a Tertiary Care Hospital in Pune, India and Predictors of Requirement for Intensive Care Treatment.

OBJECTIVES: 1. To study associations of severity of COVID-19 disease with clinical features and laboratory markers. 2. To develop a model to predict the need for ICU treatment. METHODS: This is an analysis of clinical course in 800 consecutive patients from a dedicated COVID-19 tertiary care hospital in Pune, India (8th April to 15th June 2020). We obtained clinical and laboratory information, severity grading and progress from hospital records. We studied associations of these characteristics with need for ICU management. We developed a predictive model of need for ICU treatment among first 500 patients and tested its sensitivity and specificity in the following 300 patients. RESULTS: Average age was 41 years, 16% were 20 years of age, 55% were male, 50% were asymptomatic and 16% had at least one comorbidity. Using MoHFW India severity guidelines, 73% patients had mild, 6% moderate and 20% severe disease. Severity was associated with higher age, symptomatic presentation, elevated neutrophil and reduced lymphocyte counts and elevated inflammatory markers. Seventy-seven patients needed ICU treatment: they were older (56 years), more symptomatic and had lower SpO2 and abnormal chest X-ray and deranged hematology and biochemistry at admission. A model trained on the first 500 patients, using above variables predicted need for ICU treatment with sensitivity 80%, specificity 88% in subsequent 300 patients; exclusion of expensive laboratory tests (Ferritin, C- Reactive Protein) did not affect accuracy. CONCLUSION: In the early phase of COVID- 19 pendemic, a significant proportion of hospitalized patients were young and asymptomatic. Need for ICU treatment was predicted by simple measures including higher age, symptomatic onset, low SpO2 and abnormal chest X-ray. We propose a simple model for referring patients for treatment at specialized COVID-19 hospitals.

Targeted exome sequencing in anti-factor H antibody negative HUS reveals multiple variations.

BACKGROUND: Genetic susceptibility to atypical hemolytic uremic syndrome (aHUS) may lie within genes regulating or activating the alternate complement and related pathways converging on endothelial cell activation. METHODS: We tested 32 Indian patients of aHUS negative for antibodies to complement factor H for genetic variations in a panel of 15 genes, i.e., CFH, CFHR1-5, CFI, CFB, C3, CD46, MASP2, DGKE, ADAMTS13, THBD and PLG using next-generation DNA sequencing and for copy number variation in CFHR1-3. RESULTS: Despite absence of a public database of exome variations in the Indian population and limited functional studies, we could establish a genetic diagnosis in 6 (18.8%) patients using a stringent scheme of prioritization. One patient carried a likely pathogenic variation. The number of patients carrying possibly pathogenic variation was as follows: 1 variation: 5 patients, 2 variations: 9 patients, 3 variations: 5 patients, 4 variations: 9 patients, 5 variations: 2 patients and 6 variations: 2 patients. Homozygous deletion of CFHR1-3 was present in five patients; none of these carried a diagnostic genetic variation. Patients with or without diagnostic variation did not differ significantly in terms of enrichment of genetic variations that were rare/novel or predicted deleterious, or for possible environmental triggers. CONCLUSION: We conclude that genetic testing for multiple genes in patients with aHUS negative for anti-FH antibodies reveals multiple candidate variations that require prioritization. Population data on variation frequency of the Indian population and supportive functional studies are likely to improve diagnostic yield.

Machine Learning Model Based on Insulin Resistance Metagenes Underpins Genetic Basis of Type 2 Diabetes.

Insulin resistance (IR) is considered the precursor and the key pathophysiological mechanism of type 2 diabetes (T2D) and metabolic syndrome (MetS). However, the pathways that IR shares with T2D are not clearly understood. Meta-analysis of multiple DNA microarray datasets could provide a robust set of metagenes identified across multiple studies. These metagenes would likely include a subset of genes (key metagenes) shared by both IR and T2D, and possibly responsible for the transition between them. In this study, we attempted to find these key metagenes using a feature selection method, LASSO, and then used the expression profiles of these genes to train five machine learning models: LASSO, SVM, XGBoost, Random Forest, and ANN. Among them, ANN performed well, with an area under the curve (AUC) > 95%. It also demonstrated fairly good performance in differentiating diabetics from normal glucose tolerant (NGT) persons in the test dataset, with 73% accuracy across 64 human adipose tissue samples. Furthermore, these core metagenes were also enriched in diabetes-associated terms and were found in previous genome-wide association studies of T2D and its associated glycemic traits HOMA-IR and HOMA-B. Therefore, this metagenome deserves further investigation with regard to the cardinal molecular pathological defects/pathways underlying both IR and T2D.

IoT-Based Reinforcement Learning Using Probabilistic Model for Determining Extensive Exploration through Computational Intelligence for Next-Generation Techniques.

Computing intelligence is built on several learning and optimization techniques. Incorporating cutting-edge learning techniques to balance the interaction between exploitation and exploration is therefore an inspiring field, especially when it is combined with IoT. The reinforcement learning techniques created in recent years have largely focused on incorporating deep learning technology to improve the generalization skills of the algorithm while ignoring the issue of detecting and taking full advantage of the dilemma. To increase the effectiveness of exploration, a deep reinforcement algorithm based on computational intelligence is proposed in this study, using intelligent sensors and the Bayesian approach. In addition, the technique for computing the posterior distribution of parameters in Bayesian linear regression is expanded to nonlinear models such as artificial neural networks. The Bayesian Bootstrap Deep Q-Network (BBDQN) algorithm is created by combining the bootstrapped DQN with the recommended computing technique. Finally, tests in two scenarios demonstrate that, when faced with severe exploration problems, BBDQN outperforms DQN and bootstrapped DQN in terms of exploration efficiency.

Abdominal fat depots and their association with insulin resistance in patients with type 2 diabetes.

BACKGROUND: Asian-Indians show thin fat phenotype, characterized by predominantly central deposition of excess fat. The roles of abdominal subcutaneous fat (SAT), intra-peritoneal adipose tissue, and fat depots surrounding the vital organs (IPAT-SV) and liver fat in insulin resistance (IR), type-2 diabetes (T2D) and metabolic syndrome (MetS) in this population are sparsely investigated. AIMS AND OBJECTIVES: Assessment of liver fat, SAT and IPAT-SV by MRI in subjects with T2D and MetS; and to investigate its correlation with IR, specifically according to different quartiles of HOMA-IR. METHODS: Eighty T2D and the equal number of age sex-matched normal glucose tolerant controls participated in this study. Abdominal SAT, IPAT-SV and liver fat were measured using MRI. IR was estimated by the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). RESULTS: T2D and MetS subjects have higher quantity liver fat and IPAT-SV fat than controls (P = 9 x 10-4 and 4 x 10-4 for T2D and 10-4 and 9 x 10-3 for MetS subjects respectively). MetS subjects also have higher SAT fat mass (P = 0.012), but not the BMI adjusted SAT fat mass (P = 0.48). Higher quartiles of HOMA-IR were associated with higher BMI, W:H ratio, waist circumference, and higher liver fat mass (ANOVA Test P = 0.020, 0.030, 2 x 10-6 and 3 x 10-3 respectively with F-values 3.35, 3.04, 8.82, 4.47 respectively). In T2D and MetS subjects, HOMA-IR showed a moderately strong correlation with liver fat (r = 0.467, P < 3 x 10-5 and r = 0.493, P < 10-7), but not with SAT fat and IPAT-SV. However, in MetS subjects IPAT-SV fat mass showed borderline correlation with IR (r = 0.241, P < 0.05), but not with the BMI adjusted IPAT-SV fat mass (r = 0.13, P = 0.26). In non-T2D and non-MetS subjects, no such correlation was seen. On analyzing the correlation between the three abdominal adipose compartment fat masses and IR according to its severity, the correlation with liver fat mass becomes stronger with increasing quartiles of HOMA-IR, and the strongest correlation is seen in the highest quartile (r = 0.59, P < 10-3). On the other hand, SAT fat mass tended to show an inverse relation with IR with borderline negative correlation in the highest quartile (r = -0.284, P < 0.05). IPAT-SV fat mass did not show any statistically significant correlation with HOMA-IR, but in the highest quartile it showed borderline, but statistically insignificant positive correlation (P = 0.07). CONCLUSION: In individuals suffering from T2D and MetS, IR shows a trend towards positive and borderline negative correlation with liver fat and SAT fat masses respectively. The positive trend with liver fat tends to become stronger with increasing quartile of IR. Therefore, these findings support the theory that possibly exhaustion of protective compartment's capacity to store excess fat results in its pathological deposition in liver as ectopic fat.

Neonatal adiposity is associated with microRNAs in adipocyte-derived extracellular vesicles in maternal and cord blood.

Background: Maternal body size, nutrition, and hyperglycemia contribute to neonatal body size and composition. There is little information on maternal-fetal transmission of messages which influence fetal growth. We analyzed adipocyte-derived small extracellular vesicular (ADsEV) microRNAs in maternal and cord blood to explore their adipogenic potential. Methods: We studied 127 mother-neonate pairs (51 lean and 76 adipose neonates, in 68 NGT and 59 GDM pregnancies). Adiposity refers to the highest tertile (T3) of sum of skinfolds in neonates of normal glucose tolerant (NGT) mothers, lean to the to lowest tertile (T1). ADsEV miRNAs from maternal and cord blood samples were profiled on Agilent 8*60K microarray. Differential expression (DE) of ADsEV miRNAs in adipose vs. lean neonates was studied before and after adjustment for maternal gestational diabetes mellitus (GDM), adiposity, and vitamin B12-folate status. Results: Multiple miRNAs were common in maternal and cord blood and positively correlated. We identified 24 maternal and 5 cord blood miRNAs differentially expressed (p ≤ 0.1) in the adipose neonate group, and 19 and 26 respectively, in the adjusted analyses. Even though DE miRNAs were different in maternal and cord blood, they targeted similar adipogenic pathways (e.g., the forkhead box O (FOXO) family of transcription factors, mitogen-activated protein kinase (MAPK) pathway, transforming growth factor beta (TGF-ß) pathway). Maternal GDM and adiposity were associated with many DE ADsEV miRNAs. Conclusion: Our results suggest that the ADsEV miRNAs in mothers are potential regulators of fetal adiposity. The expression and functionality of miRNAs appears to be influenced by maternal adiposity, hyperglycemia, and micronutrient status during pregnancy.

Structure and Bonding analysis of the cationic electrophilic phosphinidene complexes of iron, ruthenium, and osmium [(η(5)-C5Me5)(CO)2M{PN(i)Pr2}]+, [(η(5)-C5H5)(CO)2M{PNR2}]+ (R = Me, (i)Pr), and [(η(5)-C5H5)(PMe3)2M{PNMe2}]+ (M = Fe, Ru, Os).

Quantum-chemical DFT calculations for the electronic, molecular structure and M-PNR(2) bonding analyses of the experimentally known cationic electrophilic phosphinidene complexes [(η(5)-C(5)Me(5))(CO)(2)M{PN(i)Pr(2)}](+) and of the model complexes [(η(5)-C(5)H(5))(CO)(2)M{PNR(2)}](+) (R = (i)Pr, Me) and [(η(5)-C(5)H(5))(PMe(3))(2)M{PNMe(2)}](+) were carried out using BP86/TZ2P/ZORA level of theory. The calculated geometrical parameters of the studied complexes are in good agreement with the reported experimental values. The short M-P bond distances and calculated Pauling bond orders (range of 1.23-1.68), suggest the presence of M-P multiple bond characters. The Hirshfeld charge analysis shows that the overall charge flows from phosphinidene ligand to metal fragment. The M-P σ-bonding orbitals are well-occupied (>1.80e). The energy decomposition analysis revealed that the contribution of the electrostatic interaction ΔE(elstat) is, in all studied complexes, significantly larger (55.2-62.6%) than the orbital interactions ΔE(orb). The orbital interactions between metal and PNR(2) in [(η(5)-C(5)H(5))(L)(2)M{PNR(2)}](+) arise mainly from M ← PNR(2) σ-donation. The π-bonding contribution (19-36%) is much smaller than the σ-bonding. The interaction energies, as well as bond dissociation energies, depend on the auxiliary ligand framework around the metal and decrease in the order (η(5)-C(5)H(5)) > (η(5)-C(5)Me(5)) and CO > PMe(3). Upon substitution of R = (i)Pr with smaller group R = Me, the M-PNR(2) bond strength slightly decreases.

Left ventricular diastolic dysfunction in COPD may manifest myocardial ischemia.

BACKGROUND: Left ventricular diastolic dysfunction (LVDD) is observed frequently in advanced COPD and it appears unrelated to the co-presence of its common risk factors as hypertension, diabetes, ischemic heart disease, etc. The patho-physiology of this association is yet unclear. We postulated that ischemia not apparent in the routine clinical evaluation could be the cause of such LVDD. METHOD: Advanced COPD (GOLD III & IV) patients being excluded of concomitant presence of hypertension, diabetes, ischemic heart disease, and hypothyroidism by conventional evaluations were looked for the presence of LVDD by tissue Doppler echocardiography. The subjects having LVDD were further evaluated with 99(m)Tc MIBI stress myocardial perfusion imaging to detect the presence of ischemia. RESULTS: 7 out of 14 patients of advanced COPD sufferers without any known risk factors for LVDD had reversible perfusion defect in the stress perfusion imaging predominantly involving the inferior wall myocardium (71.43%). Most of the defects, though significant, were not picked up by stress ECG. CONCLUSION: Reversible ischemic defects are quite common (50%) in advanced COPD patients with LVDD without the presence of common risk factors. This association needs further evaluation.

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing.

One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.

Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring.

Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic's beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article proposes an Intelligent Remote Patient Activity Tracking System system that can monitor patient activities and vitals during those activities based on the attached sensors. An Internet of Things- (IoT-) enabled health monitoring device is designed using machine learning models to track patient's activities such as running, sleeping, walking, and exercising, the vitals during those activities such as body temperature and heart rate, and the patient's breathing pattern during such activities. Machine learning models are used to identify different activities of the patient and analyze the patient's respiratory health during various activities. Currently, the machine learning models are used to detect cough and healthy breathing only. A web application is also designed to track the data uploaded by the proposed devices.

Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition.

A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database and the University of Notre Dame Collection F database for 3D face and 3D ear datasets, respectively. Experimental results reveal that the proposed model achieves an accuracy of 99.25% using the proposed score-level fusion. Comparative analyses show that the proposed method performs better than other state-of-the-art biometric algorithms in terms of accuracy.

Medical Image Captioning Using Optimized Deep Learning Model.

Medical image captioning provides the visual information of medical images in the form of natural language. It requires an efficient approach to understand and evaluate the similarity between visual and textual elements and to generate a sequence of output words. A novel show, attend, and tell model (ATM) is implemented, which considers a visual attention approach using an encoder-decoder model. But the show, attend, and tell model is sensitive to its initial parameters. Therefore, a Strength Pareto Evolutionary Algorithm-II (SPEA-II) is utilized to optimize the initial parameters of the ATM. Finally, experiments are considered using the benchmark data sets and competitive medical image captioning techniques. Performance analysis shows that the SPEA-II-based ATM performs significantly better as compared to the existing models.

Evolving Long Short-Term Memory Network-Based Text Classification.

Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a well-known factory reports dataset. Extensive analyses are performed to evaluate the performance of the proposed ELSTM network. From the comparative analysis, it is found that the LSTM network outperforms the competitive models.

Whole transcriptome RNA-seq reveals key regulatory factors involved in type 2 diabetes pathology in peripheral fat of Asian Indians.

The prevalence of Type 2 Diabetes has reached an epidemic proportion particularly in south Asian countries. We have earlier shown that the anatomical fat distribution, termed 'thin fat phenotype' in this population indeed plays a major role for their T2D-predisposition it is indeed the sick fat or adiposopathy, which is the root cause of metabolic syndrome and diabetes and affects both-peripheral, as well as visceral adipose tissue compartments. In present study, we have attempted to unravel the altered regulatory mechanisms at the level of transcription factors, and miRNAs those may likely accounts to T2D pathophysiology in femoral subcutaneous adipose tissue. We prioritized transcription factors and protein kinases as likely upstream regulators of obtained differentially expressed genes in this RNA-seq study. An inferred network of these upstream regulators was then derived and the role of TFs and miRNAs in T2D pathophysiology was explored. In conclusions, this RNS-Seq study finds that peripheral subcutaneous adipose tissue among Asian Indians show pathology characterized by altered lipid, glucose and protein metabolism, adipogenesis defect and inflammation. A network of regulatory transcription factors, protein kinases and microRNAs have been imputed which converge on the process of adipogenesis. As the majority of these genes also showed altered expression in diabetics and some of them are also circulatory, therefore they deserve further investigation for potential clinical diagnostic and therapeutic applications.

Higher CNV Frequencies in Chromosome 14 of Girls With Turner Syndrome Phenotype.

CONTEXT: Precise genotype-phenotype correlations in Turner syndrome (TS) have not yet been deciphered. The chromosomal basis of the clinical TS phenotype in the absence of X chromosome aberrations on conventional karyotyping remains more and less unexplored. OBJECTIVE: To elucidate the high-resolution chromosomal picture and analyze the genotype-phenotype associations in girls with clinical phenotype of TS by chromosomal microarray. DESIGN AND PATIENTS: Cross sectional observational study conducted between October 2018 and January 2020 on 47 girls presenting the clinical TS phenotype and fulfilling the criteria for chromosomal analysis. SETTING: Outpatient department at Department of Endocrinology and the Molecular Research Lab at tertiary care teaching institution. RESULTS: The copy number variation (CNV) polymorphs were more frequent on autosomes than X chromosomes, and they were detected in 89.3%, 61.7%, and 92.8% of patients, respectively, on chromosome 14 or X or both. A total 445 and 64 CNV polymorphs were discovered on chromosome X and 14, respectively. The latter exhibited either gain at 14q32.33, loss at 14q11.2, or both. Karyotype was available for 27 patients; 55.6% of cases displayed X chromosome abnormalities while 44.4% cases had a normal karyotype. Functional interactomes of the genes that were present in chromosome 14 CNVs and those known to be associated with TS showed an overlap of 67% and enriched various development-related cellular pathways underlying TS phenotype. CONCLUSIONS: On high-resolution karyotype analysis, clinical phenotype of TS can be associated with CNV defects in autosomes, specifically chromosome 14 or X chromosome or both. The syndrome of chromosome 14 CNV defects with and without X-chromosomal defects clinically mimics TS and shares a common genomic network that deserves further investigations.

The common pathophysiologic threads between Asian Indian diabetic's 'Thin Fat Phenotype' and partial lipodystrophy: the peripheral adipose tissue transcriptomic evidences.

T2D is a complex disease with poorly understood mechanisms. In Asian Indians, it is associated with "thin fat" phenotype which resembles with partial lipodystrophy. We hypothesized that disturbed expression of lipodystrophy genes might play a role in T2D pathogenesis. Therefore, we attempted to establish a link between these two diseases by studying the overlap between the network of lipodystrophy genes and the differentially expressed genes (DEGs) in the peripheral subcutaneous adipose tissue of Asian Indians diabetics. We found that 16, out of 138 lipodystrophy genes were differentially regulated in diabetics and around 18% overlap between their network and the DEGs; the expression level of lipodystrophy genes showed an association with disease-related intermediate phenotypic traits among diabetics but not in the control group. We also attempted to individualize the diabetic patients based on ±2 fold altered expression of lipodystrophy genes as compared to their average expression in the control group. In conclusion, significant overlap exists between some of the lipodystrophy genes and their network with DEGs in the peripheral adipose tissue in diabetics. They possibly play a role in the pathogenesis of diabetes and individualization of diabetics is possible based on their altered expression in their peripheral adipose tissue.

The Transcriptomic Evidence on the Role of Abdominal Visceral vs. Subcutaneous Adipose Tissue in the Pathophysiology of Diabetes in Asian Indians Indicates the Involvement of Both.

The roles of abdominal visceral (VAT) and subcutaneous adipose tissue (SAT) in the molecular pathogenesis type-2 diabetics (T2D) among Asian Indians showing a "thin fat" phenotype largely remains obscure. In this study, we generated transcription profiles in biopsies of these adipose depots obtained during surgery in 19 diabetics (M: F ratio, 8:11) and 16 (M: F ratio 5:11) age- and BMI-matched non-diabetics. Gene set enrichment analysis (GSEA) was used for comparing transcription profile and showed that 19 gene sets, enriching inflammation and immune system-related pathways, were upregulated in diabetics with F.D.R. <25% and >25%, respectively, in VAT and SAT. Moreover, 13 out of the 19 significantly enriched pathways in VAT were among the top 20 pathways in SAT. On comparison of VAT vs. SAT among diabetics, none of the gene sets were found significant at F.D.R. <25%. The Weighted Gene Correlation Analysis (WGCNA) analysis of the correlation between measures of average gene expression and overall connectivity between VAT and SAT was significantly positive. Several modules of co-expressed genes in both the depots showed a bidirectional correlation with various diabetes-related intermediate phenotypic traits. They enriched several diabetes pathogenicity marker pathways, such as inflammation, adipogenesis, etc. It is concluded that, in Asian Indians, diabetes pathology inflicts similar molecular alternations in VAT and SAT, which are more intense in the former. Both adipose depots possibly play a role in the pathophysiology of T2D, and whether it is protective or pathogenic also depends on the nature of modules of co-expressed genes contained in them.