Unraveling Epigenetic Interplay between Inflammation, Thrombosis, and Immune-Related Disorders through a Network Meta-analysis.

Inflammation and thrombosis are two distinct yet interdependent physiological processes. The inflammation results in the activation of the coagulation system that directs the immune system and its activation, resulting in the initiation of the pathophysiology of thrombosis, a process termed immune-thrombosis. Still, the shared underlying molecular mechanism related to the immune system and coagulation has not yet been explored extensively. Inspired to answer this, we carried out a comprehensive gene expression meta-analysis using publicly available datasets of four diseases, including venous thrombosis, systemic lupus erythematosus, rheumatoid arthritis, and inflammatory bowel disease. A total of 609 differentially expressed genes (DEGs) shared by all four datasets were identified based on the combined effect size approach. The pathway enrichment analysis of the DEGs showed enrichment of various epigenetic pathways such as histone-modifying enzymes, posttranslational protein modification, chromatin organization, chromatin-modifying enzymes, HATs acetylate proteins. Network-based protein-protein interaction analysis showed epigenetic enzyme coding genes dominating among the top hub genes. The miRNA-interacting partner of the top 10 hub genes was determined. The predomination of epitranscriptomics regulation opens a layout for the meta-analysis of miRNA datasets of the same four diseases. We identified 30 DEmiRs shared by these diseases. There were 9 common DEmiRs selected from the list of miRNA-interacting partners of top 10 hub genes and shared significant DEmiRs from microRNAs dataset acquisition. These common DEmiRs were found to regulate genes involved in epigenetic modulation and indicate a promising epigenetic aspect that needs to be explored for future molecular studies in the context of immunothrombosis and inflammatory disease.

Identification of potential therapeutic targets for COVID-19 through a structural-based similarity approach between SARS-CoV-2 and its human host proteins.

Background: The COVID-19 pandemic caused by SARS-CoV-2 has led to millions of deaths worldwide, and vaccination efficacy has been decreasing with each lineage, necessitating the need for alternative antiviral therapies. Predicting host-virus protein-protein interactions (HV-PPIs) is essential for identifying potential host-targeting drug targets against SARS-CoV-2 infection. Objective: This study aims to identify therapeutic target proteins in humans that could act as virus-host-targeting drug targets against SARS-CoV-2 and study their interaction against antiviral inhibitors. Methods: A structure-based similarity approach was used to predict human proteins similar to SARS-CoV-2 ("hCoV-2"), followed by identifying PPIs between hCoV-2 and its target human proteins. Overlapping genes were identified between the protein-coding genes of the target and COVID-19-infected patient's mRNA expression data. Pathway and Gene Ontology (GO) term analyses, the construction of PPI networks, and the detection of hub gene modules were performed. Structure-based virtual screening with antiviral compounds was performed to identify potential hits against target gene-encoded protein. Results: This study predicted 19,051 unique target human proteins that interact with hCoV-2, and compared to the microarray dataset, 1,120 target and infected group differentially expressed genes (TIG-DEGs) were identified. The significant pathway and GO enrichment analyses revealed the involvement of these genes in several biological processes and molecular functions. PPI network analysis identified a significant hub gene with maximum neighboring partners. Virtual screening analysis identified three potential antiviral compounds against the target gene-encoded protein. Conclusion: This study provides potential targets for host-targeting drug development against SARS-CoV-2 infection, and further experimental validation of the target protein is required for pharmaceutical intervention.

Structure-based molecular docking and molecular dynamics simulations study for the identification of dipeptidyl peptidase 4 inhibitors in type 2 diabetes.

Inhibition of dipeptidyl peptidase-4 (DPP4) activity has emerged as a promising therapeutic approach for the treatment of type 2 diabetes mellitus (T2DM). Bioinformatics-driven approaches have emerged as crucial tools in drug discovery. Molecular docking and molecular dynamics (MD) simulations are effective tools in drug discovery, as they reduce the time and cost associated with experimental screening. In this study, we employed structure-assisted in-silico methods, including molecular docking and MD simulations, to identify SRT2183, a small molecule that may potentially inhibit the activity of DPP4 enzyme. The interaction between the small molecule "SRT2183" and DPP4 exhibited a binding affinity of -9.9 Kcal/Mol, leading to the formation of hydrogen bonds with the amino acid residues MET348, SER376, and THR351 of DPP4. The MD simulations over a period of 100 ns indicated stable protein-ligand interactions, with no significant conformational rearrangements observed within the simulated timeframe. In conclusion, our results suggest that the small molecule SRT2183 may have the potential to inhibit the DPP4 enzyme and pave the way for the therapeutics of T2DM.Communicated by Ramaswamy H. Sarma.

Identification of New Key Genes and Their Association with Breast Cancer Occurrence and Poor Survival Using In Silico and In Vitro Methods.

Breast cancer is one of the most prevalent types of cancer diagnosed globally and continues to have a significant impact on the global number of cancer deaths. Despite all efforts of epidemiological and experimental research, therapeutic concepts in cancer are still unsatisfactory. Gene expression datasets are widely used to discover the new biomarkers and molecular therapeutic targets in diseases. In the present study, we analyzed four datasets using R packages with accession number GSE29044, GSE42568, GSE89116, and GSE109169 retrieved from NCBI-GEO and differential expressed genes (DEGs) were identified. Protein-protein interaction (PPI) network was constructed to screen the key genes. Subsequently, the GO function and KEGG pathways were analyzed to determine the biological function of key genes. Expression profile of key genes was validated in MCF-7 and MDA-MB-231 human breast cancer cell lines using qRT-PCR. Overall expression level and stage wise expression pattern of key genes was determined by GEPIA. The bc-GenExMiner was used to compare expression level of genes among groups of patients with respect to age factor. OncoLnc was used to analyze the effect of expression levels of LAMA2, TIMP4, and TMTC1 on the survival of breast cancer patients. We identified nine key genes, of which COL11A1, MMP11, and COL10A1 were found up-regulated and PCOLCE2, LAMA2, TMTC1, ADAMTS5, TIMP4, and RSPO3 were found down-regulated. Similar expression pattern of seven among nine genes (except ADAMTS5 and RSPO3) was observed in MCF-7 and MDA-MB-231 cells. Further, we found that LAMA2, TMTC1, and TIMP4 were significantly expressed among different age groups of patients. LAMA2 and TIMP4 were found significantly associated and TMTC1 was found less correlated with breast cancer occurrence. We found that the expression level of LAMA2, TIMP4, and TMTC1 was abnormal in all TCGA tumors and significantly associated with poor survival.

Combined Multiomics and In Silico Approach Uncovers PRKAR1A as a Putative Therapeutic Target in Multi-Organ Dysfunction Syndrome.

Despite all epidemiological, clinical, and experimental research efforts, therapeutic concepts in sepsis and sepsis-induced multi-organ dysfunction syndrome (MODS) remain limited and unsatisfactory. Currently, gene expression data sets are widely utilized to discover new biomarkers and therapeutic targets in diseases. In the present study, we analyzed MODS expression profiles (comprising 13 sepsis and 8 control samples) retrieved from NCBI-GEO and found 359 differentially expressed genes (DEGs), among which 170 were downregulated and 189 were upregulated. Next, we employed the weighted gene co-expression network analysis (WGCNA) to establish a MODS-associated gene co-expression network (weighted) and identified representative module genes having an elevated correlation with age. Based on the results, a turquoise module was picked as our hub module. Further, we constructed the PPI network comprising 35 hub module DEGs. The DEGs involved in the highest-confidence PPI network were utilized for collecting pathway and gene ontology (GO) terms using various libraries. Nucleotide di- and triphosphate biosynthesis and interconversion was the most significant pathway. Also, 3 DEGs within our PPI network were involved in the top 5 significantly enriched ontology terms, with hypercortisolism being the most significant term. PRKAR1A was the overlapping gene between top 5 significant pathways and GO terms, respectively. PRKAR1A was considered as a therapeutic target in MODS, and 2992 ligands were screened for binding with PRKAR1A. Among these ligands, 3 molecules based on CDOCKER score (molecular dynamics simulated-based score, which allows us to rank the binding poses according to their quality and to identify the best pose for each system) and crucial interaction with human PRKAR1A coding protein and protein kinase-cyclic nucleotide binding domains (PKA RI alpha CNB-B domain) via active site binding residues, viz. Val283, Val302, Gln304, Val315, Ile327, Ala336, Ala337, Val339, Tyr373, and Asn374, were considered as lead molecules.

In Silico Analysis of PTP1B Inhibitors and TLC-MS Bioautography-Based Identification of Free Radical Scavenging and α-Amylase Inhibitory Compounds from Heartwood Extract of Pterocarpus marsupium.

Type 2 diabetes mellitus leads to metabolic impairment caused by insulin resistance and hyperglycemia, giving rise to chronic diabetic complications and poor disease prognosis. The heartwood of Pterocarpus marsupium has been used in Ayurveda for a long time, and we sought to find the actual mechanism(s) driving its antidiabetic potential. Methanol was used to prepare the extract using a Soxhlet extraction, and the identification of metabolites was performed by thin-layer chromatography (TLC) and ultraperformance-liquid chromatography and mass spectroscopy (UP-LCMS). The antioxidant potential of methanolic heartwood extract of Pterocarpus marsupium MHPM was determined using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and a reducing power assay. The α-amylase and α-glucosidase enzyme inhibitory potential of MHPM were investigated for their antidiabetic activity against acarbose. TLC-MS-bioautography was performed to identify the compounds responsible for possible antioxidant and antidiabetic activities. Moreover, targeting protein tyrosine phosphatase 1B (PTP1B), a key regulator of insulin resistance, by identified metabolites from MHPM through molecular docking and all-atom molecular dynamics (MD) simulations was also undertaken, suggesting its potential as an antidiabetic herb. The IC50 of free-radical scavenging activity of MHPM against DPPH was 156.342 ± 10.70 µg/mL. Further, the IC50 values of MHPM in α-amylase and α-glucosidase enzymatic inhibitions were 158.663 ± 10.986 µg/mL and 180.21 ± 11.35 µg/mL, respectively. TLC-MS-bioautography identified four free radical scavenging metabolites, and vanillic acid identified by MS analysis showed both free radical scavenging activity and α-amylase inhibitory activity. Among the identified metabolites from MHPM, epicatechin showed significant PTP1B docking interactions, and its MD simulations revealed that PTP1B forms a stable protein-ligand complex with epicatechin throughout the progression, which indicates that epicatechin may be used as a promising scaffold in the development of the antidiabetic drug after isolation from Pterocarpus marsupium. Overall, these findings imply that Pterocarpus marsupium is a source of valuable metabolites that are accountable for its antioxidant and antidiabetic properties.

Integrative multiomics and in silico analysis revealed the role of ARHGEF1 and its screened antagonist in mild and severe COVID-19 patients.

COVID-19 is a sneaking deadly disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid increase in the number of infected patients worldwide enhances the exigency for medicines. However, precise therapeutic drugs are not available for COVID-19; thus, exhaustive research is critically required to unscramble the pathogenic tools and probable therapeutic targets for the development of effective therapy. This study utilizes a chemogenomics strategy, including computational tools for the identification of viral-associated differentially expressed genes (DEGs), and molecular docking of potential chemical compounds available in antiviral, anticancer, and natural product-based libraries against these DEGs. We scrutinized the messenger RNA expression profile of SARS-CoV-2 patients, publicly available on the National Center for Biotechnology Information-Gene Expression Omnibus database, stratified them into different groups based on the severity of infection, superseded by identification of overlapping mild and severe infectious (MSI)-DEGs. The profoundly expressed MSI-DEGs were then subjected to trait-linked weighted co-expression network construction and hub module detection. The hub module MSI-DEGs were then exposed to enrichment (gene ontology + pathway) and protein-protein interaction network analyses where Rho guanine nucleotide exchange factor 1 (ARHGEF1) gene conjectured in all groups and could be a probable target of therapy. Finally, we used the molecular docking and molecular dynamics method to identify inherent hits against the ARHGEF1 gene from antiviral, anticancer, and natural product-based libraries. Although the study has an identified significant association of the ARHGEF1 gene in COVID19; and probable compounds targeting it, using in silico methods, these targets need to be validated by both in vitro and in vivo methods to effectively determine their therapeutic efficacy against the devastating virus.

Anti-HIV and anti-HCV small molecule protease inhibitors in-silico repurposing against SARS-CoV-2 Mpro for the treatment of COVID-19.

The COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is a global health emergency warranting development and implementation of targeted treatment. The enzyme main protease (Mpro; also known as 3C-like protease) is emerging as an attractive drug target. This enzyme plays an indispensable role in processing the translated polyproteins of viral RNA. Inhibiting the activity of Mpro would wedge viral replication. To facilitate the discovery of targeted therapy for COVID-19, we carried out the structure-assisted repurposing of existing protease inhibiting small molecules to target SARS-CoV-2 Mpro. Based on the structure of SARS-CoV-2 Mpro, here we report the small drug molecule namely saquinavir as its potent inhibitor. Findings support the premise that this promising antiviral protease inhibiting small drug molecule can be validated and implemented for the treatment and clinical management of COVID-19 pandemic disease.Communicated by Ramaswamy H. Sarma.

In Silico Integrative Approach Revealed Key MicroRNAs and Associated Target Genes in Cardiorenal Syndrome.

Cardiorenal syndromes constellate primary dysfunction of either heart or kidney whereby one organ dysfunction leads to the dysfunction of another. The role of several microRNAs (miRNAs) has been implicated in number of diseases, including hypertension, heart failure, and kidney diseases. Wide range of miRNAs has been identified as ideal candidate biomarkers due to their stable expression. Current study was aimed to identify crucial miRNAs and their target genes associated with cardiorenal syndrome and to explore their interaction analysis. Three differentially expressed microRNAs (DEMs), namely, hsa-miR-4476, hsa-miR-345-3p, and hsa-miR-371a-5p, were obtained from GSE89699 and GSE87885 microRNA data sets, using R/GEO2R tools. Furthermore, literature mining resulted in the retrieval of 15 miRNAs from scientific research and review articles. The miRNAs-gene networks were constructed using miRNet (a Web platform of miRNA-centric network visual analytics). CytoHubba (Cytoscape plugin) was adopted to identify the modules and the top-ranked nodes in the network based on Degree centrality, Closeness centrality, Betweenness centrality, and Stress centrality. The overlapped miRNAs were further used in pathway enrichment analysis. We found that hsa-miR-21-5p was common in 8 pathways out of the top 10. Based on the degree, 5 miRNAs, namely, hsa-mir-122-5p, hsa-mir-222-3p, hsa-mir-21-5p, hsa-mir-146a-5p, and hsa-mir-29b-3p, are considered as key influencing nodes in a network. We suggest that the identified miRNAs and their target genes may have pathological relevance in cardiorenal syndrome (CRS) and may emerge as potential diagnostic biomarkers.

Circadian clock modulating small molecules repurposing as inhibitors of SARS-CoV-2 Mpro for pharmacological interventions in COVID-19 pandemic.

The COVID-19 pandemic caused by SARS-CoV-2 is a global health emergency warranting the development of targeted treatment. The main protease Mpro is considered as a key drug target in coronavirus infections because of its vital role in the proteolytic processing of two essential polyproteins required for the replication and transcription of viral RNA. Targeting and inhibiting the Mpro activity represents a valid approach to prevent the SARS-CoV-2 replication and spread. Based on the structure-assisted drug designing, here we report a circadian clock-modulating small molecule "SRT2183" as a potent inhibitor of Mpro to block the replication of SARS-CoV-2. The findings are expected to pave the way for the development of therapeutics for COVID-19.

An Integrative Network Approach to Identify Common Genes for the Therapeutics in Tuberculosis and Its Overlapping Non-Communicable Diseases.

Tuberculosis (TB) is the leading cause of death from a single infectious agent. The estimated total global TB deaths in 2019 were 1.4 million. The decline in TB incidence rate is very slow, while the burden of noncommunicable diseases (NCDs) is exponentially increasing in low- and middle-income countries, where the prevention and treatment of TB disease remains a great burden, and there is enough empirical evidence (scientific evidence) to justify a greater research emphasis on the syndemic interaction between TB and NCDs. The current study was proposed to build a disease-gene network based on overlapping TB with NCDs (overlapping means genes involved in TB and other/s NCDs), such as Parkinson's disease, cardiovascular disease, diabetes mellitus, rheumatoid arthritis, and lung cancer. We compared the TB-associated genes with genes of its overlapping NCDs to determine the gene-disease relationship. Next, we constructed the gene interaction network of disease-genes by integrating curated and experimentally validated interactions in humans and find the 13 highly clustered modules in the network, which contains a total of 86 hub genes that are commonly associated with TB and its overlapping NCDs, which are largely involved in the Inflammatory response, cellular response to cytokine stimulus, response to cytokine, cytokine-mediated signaling pathway, defense response, response to stress and immune system process. Moreover, the identified hub genes and their respective drugs were exploited to build a bipartite network that assists in deciphering the drug-target interaction, highlighting the influential roles of these drugs on apparently unrelated targets and pathways. Targeting these hub proteins by using drugs combination or drug repurposing approaches will improve the clinical conditions in comorbidity, enhance the potency of a few drugs, and give a synergistic effect with better outcomes. Thus, understanding the Mycobacterium tuberculosis (Mtb) infection and associated NCDs is a high priority to contain its short and long-term effects on human health. Our network-based analysis opens a new horizon for more personalized treatment, drug-repurposing opportunities, investigates new targets, multidrug treatment, and can uncover several side effects of unrelated drugs for TB and its overlapping NCDs.

Dim Light at Night Induced Neurodegeneration and Ameliorative Effect of Curcumin.

It is a well-known fact that following a proper routine light/dark or diurnal rhythm controls almost all biological processes. With the introduction of modern lighting and artificial illumination systems, continuous exposure to light at night may lead to the disruption of diurnal rhythm. However, the effect of light during the night on brain anatomy, physiology, and human body functions is less explored and poorly understood. In this study, we have evaluated the effect of exposure to dim light (5 lux) at night (dLAN) on Swiss Albino mice over a duration of three consecutive weeks. Results have revealed that exposure to dLAN led to an impairment of cognitive and non-cognitive behaviour, oxidative stress-mediated elevation of lipid peroxidation, and reduction of superoxide dismutase and catalase activity. It also led to the downregulation of hippocampal proteins (BDNF, Synapsin II and DCX) at both protein and mRNA level. Additionally, there was downregulation of CREB and SIRT1 mRNAs and neurodegeneration-associated miRNA21a-5p and miRNA34a-5p. The pyramidal and cortical neurons started showing pyknotic and chromatolysis characteristics. However, a dose of curcumin administered to the mice positively modulated these parameters in our experimental animals. We proposed the modulatory role of curcumin in addressing the deleterious effects of dLAN.

Hospitalization-induced exacerbation of the ill effects of chemotherapy on rest-activity rhythm and quality of life of breast cancer patients: a prospective and comparative cross-sectional follow-up study.

Chemotherapy administration may result in the disruption of circadian rhythms and impairment of quality of life (QoL) of cancer patients. Nevertheless, we have little knowledge on the long-term consequences of chemotherapy and the effects of hospitalization. In the present study, we employed the two-factor repeated-measure cross-sectional design to determine the effects of chemotherapy and hospitalization on rest-activity (RA) rhythm and QoL of breast cancer patients. Initially, we randomly selected 39 inpatients and 42 outpatients, scheduled to receive six cycles of chemotherapy, from the Regional Cancer Center (RCC), Raipur, India. Finally, 30 patients in each group were included in the current study. We monitored circadian RA rhythm and QoL using wrist actigraphy and QLQ-C30 and QLQ-BR23, respectively, during the 1st (C1), 3rd (C3) and 6th (C6) chemotherapy cycles. Results revealed that with the progression of chemotherapy cycles (from C1 to C6), all rhythm parameters, namely mesor, amplitude, acrophase, rhythm quotient (RQ), circadian quotient (CQ), peak activity (PA), dichotomy index and autocorrelation coefficient, significantly decreased in both cancer in- and outpatients. In both groups of patients and during C1-C6, all functional and global QoL measures of QLQ-C30 and QLQ-BR23 significantly decreased and the symptoms significantly increased, except constipation, body image, sexual functioning and future perspectives in outpatients. The hospitalization exacerbated the problems associated with the RA rhythm and the QoL of the patients. In conclusion, the current study highlighted the negative consequences of hospitalization among inpatients, irrespective of the stage of cancer. We, therefore, recommend that cancer patients should be administered with chemotherapy as outpatients. The proposed protocol might have a covert bearing on the expression of better physiological state leading to satisfactory treatment outcomes.

Worsening of rest-activity circadian rhythm and quality of life in female breast cancer patients along progression of chemotherapy cycles.

Chemotherapy and its associated side effects can induce the disruption of circadian rest-activity rhythm and may have negative consequences on health-related quality of life (HRQoL) of cancer patients. In the current study, repeated-measures cross-sectional design was implemented to determine the status of circadian rest-activity rhythm and to assess the HRQoL of newly diagnosed female breast cancer patients those were planned to receive six cycles of chemotherapy. Rest activity and HRQoL were assessed in twenty-five patients during chemotherapy cycles 1st (C1), 3rd (C3), and 6th (C6) immediately after they reported to the outdoor ward of the Regional Cancer Center, Pt. J.N.M. Medical College, Dr. B.R. Ambedkar Memorial Hospital, Raipur, India. Wrist actigraphs for consecutive spans of 3-4 days were used to record the rest-activity rhythm, and its parameters were computed with the help of Cosinor Rhythmometry. Quality of life (QoL) parameters were assessed using EORTC QLQ-C30 and QLQ-BR23. Results revealed that average scores of all rhythm parameters, such as MESOR, amplitude, acrophase, rhythm quotient, circadian quotient, peak activity, dichotomy index, and autocorrelation coefficient; and all functional scales of QLQ-C30, such as physical, role, emotional, cognitive, and social, and global quality of life statistically significantly decreased with the increasing number of chemotherapy cycles (C1 to C3 and C6). Scores of symptom scales of QLQ-C30, such as fatigue, pain, dyspnoea, insomnia, appetite loss, and diarrhea increased significantly from C1 to C6. Among the QLQ-BR23 scales, scores of sexual functioning, sexual enjoyment, breast symptoms, and arm symptoms significantly decreased, whereas scores of systemic therapy side effects, and upset by hair loss significantly increased across the chemotherapy cycles. We conclude that rest-activity rhythm disrupted and HRQoL of breast cancer patients worsened along the increasing number of chemotherapy cycles. We suggest that along with the treatment protocol, level of disruption of these parameters should be assessed and managed with the proper interventions that prominently include timing of the chemotherapy administration. The latter is pivotal for maintenance of these parameters, which are likely to enhance the physiological ability of patients for better treatment responses and may improve the overall QoL and survival of the patients.

Validation of the Hindi version of the Multidimensional Fatigue Inventory-20 (MFI-20) in Indian cancer patients.

OBJECTIVE: The present study was designed to validate the Hindi version of the Multidimensional Fatigue Inventory-20 (MFI-20) in Indian oncology population. METHODS: The original English version of the MFI-20 was translated into Hindi (hMFI-20) using the translation and back translation processes. The hMFI-20 was administered to 200 cancer patients. The item analysis for hMFI-20 was carried out using the corrected item-total correlation. The confirmatory factor analysis (CFA) was employed to test whether the original factor structure of MFI-20 is confirmed for the hMFI-20. Further, convergent and discriminant validities were also tested. The reliability of the hMFI-20 was evaluated by computing composite reliability and Cronbach's α coefficient. RESULTS: Corrected item-total correlation value for each of the items of hMFI-20 was greater than 0.6. Results of the CFA (comparative fit indices (CFI) = 0.91, root mean squared residual (RMR) = 0.04, root mean square error of approximation (RMSEA) = 0.028, and χ (2) = 45.68, p > 0.05) indicated that the five-factor model provided a good fit to the data. The findings indicated that hMFI-20 has a good convergent (composite reliability (CR) >0.7; average variance extracted value (AVE) >0.5) and discriminant (maximum shared variance (MSV) < AVE; average shared variance (ASV) < AVE; square root of AVE > inter-factor correlations) validities. The Cronbach's α coefficient for the total hMFI-20 was 0.8 and was more than 0.7 for each of the five factors. CONCLUSIONS: We conclude that the hMFI-20 has a high internal consistency and reasonable construct validity. Therefore, the hMFI-20 is a reliable and valid tool to assess the multidimensional fatigue in Indian oncology population. However, we recommend further validation of hMFI-20 in population of cancer patients of different linguistic settings and regions of India.

Effect of hospitalization on rest-activity rhythm and quality of life of cancer patients.

Rest-activity rhythm and quality of life (QoL) in three cohorts, namely (1) cancer in-patients, (2) out-patients, and (3) control subjects were studied. The patients of the former two groups were chosen randomly from the Regional Cancer Center, Raipur, India. All patients received chemotherapy for 3-4 consecutive days. The in-patients remained hospitalized for the entire period of chemotherapy plus one day post treatment. The out-patients, unlike the in-patients, went to their homes daily after treatment. Rest-activity rhythm of the patients was monitored using Actical. Quality of life (QoL) and psychological status of patients were assessed using EORTC QLQ-C30 and Hospital Anxiety & Depression Scale, respectively. Each subject exhibited significant circadian rhythm in rest-activity. The average values for Mesor, amplitude, peak activity, autocorrelation coefficient and dichotomy index of all three groups varied significantly between one group to the other in the following order: in-patient < out-patient < control. Further, quality of life, measured from responses on functional and symptom scales, was better off in cancer out-patients compared to the in-patients. It is concluded that hospitalization alters rest-activity rhythm parameters markedly and deteriorates QoL in cancer patients. Nevertheless, further extensive investigation is desirable to support the above speculation and to ascertain if hospitalization produces similar effects on patients suffering from diseases other than cancer.

Molecular analysis of gut microbiota in obesity among Indian individuals.

Obesity is a consequence of a complex interplay between the host genome and the prevalent obesogenic factors among the modern communities. The role of gut microbiota in the pathogenesis of the disorder was recently discovered; however, 16S-rRNA-based surveys revealed compelling but community-specific data. Considering this, despite unique diets, dietary habits and an uprising trend in obesity, the Indian counterparts are poorly studied. Here, we report a comparative analysis and quantification of dominant gut microbiota of lean, normal, obese and surgically treated obese individuals of Indian origin. Representative gut microbial diversity was assessed by sequencing fecal 16S rRNA libraries for each group (n=5) with a total of over 3000 sequences. We detected no evident trend in the distribution of the predominant bacterial phyla, Bacteroidetes and Firmicutes. At the genus level, the bacteria of genus Bacteroides were prominent among the obese individuals, which was further confirmed by qPCR (P less than 0.05). In addition, a remarkably high archaeal density with elevated fecal SCFA levels was also noted in the obese group. On the contrary, the treated-obese individuals exhibited comparatively reduced Bacteroides and archaeal counts along with reduced fecal SCFAs. In conclusion, the study successfully identified a representative microbial diversity in the Indian subjects and demonstrated the prominence of certain bacterial groups in obese individuals; nevertheless, further studies are essential to understand their role in obesity.