Role and significance of virus-bacteria interactions in disease progression.

Understanding disease pathogenesis caused by bacteria/virus, from the perspective of individual pathogen has provided meaningful insights. However, as viral and bacterial counterparts might inhabit the same infection site, it becomes crucial to consider their interactions and contributions in disease onset and progression. The objective of the review is to highlight the importance of considering both viral and bacterial agents during the course of coinfection. The review provides a unique perspective on the general theme of virus-bacteria interactions, which either lead to colocalized infections that are restricted to one anatomical niche, or systemic infections that have a systemic effect on the human host. The sequence, nature, and underlying mechanisms of certain virus-bacteria interactions have been elaborated with relevant examples from literature. It also attempts to address the various applied aspects, including diagnostic and therapeutic strategies for individual infections as well as virus-bacteria coinfections. The review aims to aid researchers in comprehending the intricate interplay between virus and bacteria in disease progression, thereby enhancing understanding of current methodologies and empowering the development of novel health care strategies to tackle coinfections.

EnsembleSeq: a workflow towards real-time, rapid, and simultaneous multi-kingdom-amplicon sequencing for holistic and resource-effective microbiome research at scale.

Bacterial communities are often concomitantly present with numerous microorganisms in the human body and other natural environments. Amplicon-based microbiome studies have generally paid skewed attention, that too at a rather shallow genus level resolution, to the highly abundant bacteriome, with interest now forking toward the other microorganisms, particularly fungi. Given the generally sparse abundance of other microbes in the total microbiome, simultaneous sequencing of amplicons targeting multiple microbial kingdoms could be possible even with full multiplexing. Guiding studies are currently needed for performing and monitoring multi-kingdom-amplicon sequencing and data capture at scale. Aiming to address these gaps, amplification of full-length bacterial 16S rRNA gene and entire fungal internal-transcribed spacer (ITS) region was performed for human saliva samples (n = 96, including negative and positive controls). Combined amplicon DNA libraries were prepared for nanopore sequencing using a major fraction of 16S molecules and a minor fraction of ITS amplicons. Sequencing was performed in a single run of an R10.4.1 flow cell employing the latest V14 chemistry. An approach for real-time monitoring of the species saturation using dynamic rarefaction was designed as a guiding determinant of optimal run time. Real-time saturation monitoring for both bacterial and fungal species enabled the completion of sequencing within 30 hours, utilizing less than 60% of the total nanopores. Approximately 5 million high quality (HQ) taxonomically assigned reads were generated (~4.2 million bacterial and 0.7 million fungal), providing a wider (beyond bacteriome) snapshot of human oral microbiota at species-level resolution. Among the more than 400 bacterial and 240 fungal species identified in the studied samples, the species of Streptococcus (e.g., Streptococcus mitis and Streptococcus oralis) and Candida (e.g., Candida albicans and Candida tropicalis) were observed to be the dominating microbes in the oral cavity, respectively. This conformed well with the previous reports of the human oral microbiota. EnsembleSeq provides a proof-of-concept toward the identification of both fungal and bacterial species simultaneously in a single fully multiplexed nanopore sequencing run in a time- and resource-effective manner. Details of this workflow, along with the associated codebase, are provided to enable large-scale application for a holistic species-level microbiome study. IMPORTANCE: Human microbiome is a sum total of a variety of microbial genomes (including bacteria, fungi, protists, viruses, etc.) present in and on the human body. Yet, a majority of amplicon-based microbiome studies have largely remained skewed toward bacteriome as an assumed proxy of the total microbiome, primarily at a shallow genus level. Cost, time, effort, data quality/management, and importantly lack of guiding studies often limit progress in the direction of moving beyond bacteriome. Here, EnsembleSeq presents a proof-of-concept toward concomitantly capturing multiple-kingdoms of microorganisms (bacteriome and mycobiome) in a fully multiplexed (96-sample) single run of long-read amplicon sequencing. In addition, the workflow captures dynamic tracking of species-level saturation in a time- and resource-effective manner.

A comprehensive review on antibacterial analysis of natural extract-based metal and metal oxide nanoparticles.

Pharmaceutical, food packing, cosmetics, agriculture, energy storage devices widely utilize metal and metal oxide nanoparticles prepared via different physical and chemical methods. It resulted in the release of several dangerous compounds and solvents as the nanoparticles were being formed. Currently, Researchers interested in preparing nanoparticles (NPs) via biological approach due to their unique physiochemical properties which took part in reducing the environmental risks. However, a number of microbial species are causing dangerous illnesses and are a threat to the entire planet. The metal and metal oxide nanoparticles played a significant role in the identification and elimination of microbes when prepared using natural extract. Its biological performance is thus also becoming exponentially more apparent than it was using in conventional techniques. Despite the fact that they hurt germs, their small size and well-defined shape encourage surface contact with them. The generation of Reactive Oxygen Species (ROS), weakens the bacterial cell membrane by allowing internal cellular components to seep out. The bacterium dies as a result of this. Numerous studies on different nanoparticles and their antibacterial efficacy against various diseases are still accessible. The main objective of the biogenic research on the synthesis of key metals and metal oxides (such as gold, silver, titanium dioxide, nickel oxide, and zinc oxide) using various plant extracts is reviewed in this study along with the process of nanoparticle formation and the importance of phytochemicals found in the plant extract.

BactInt: A domain driven transfer learning approach for extracting inter-bacterial associations from biomedical text.

BACKGROUND: The healthy as well as dysbiotic state of an ecosystem like human body is known to be influenced not only by the presence of the bacterial groups in it, but also with respect to the associations within themselves. Evidence reported in biomedical text serves as a reliable source for identifying and ascertaining such inter bacterial associations. However, the complexity of the reported text as well as the ever-increasing volume of information necessitates development of methods for automated and accurate extraction of such knowledge. METHODS: A BioBERT (biomedical domain specific language model) based information extraction model for bacterial associations is presented that utilizes learning patterns from other publicly available datasets. Additionally, a specialized sentence corpus has been developed to significantly improve the prediction accuracy of the 'transfer learned' model using a fine-tuning approach. RESULTS: The final model was seen to outperform all other variations (non-transfer learned and non-fine-tuned models) as well as models trained on BioGPT (a domain trained Generative Pre-trained Transformer). To further demonstrate the utility, a case study was performed using bacterial association network data obtained from experimental studies. CONCLUSION: This study attempts to demonstrate the applicability of transfer learning in a niche field of life sciences where understanding of inter bacterial relationships is crucial to obtain meaningful insights in comprehending microbial community structures across different ecosystems. The study further discusses how such a model can be further improved by fine tuning using limited training data. The results presented and the datasets made available are expected to be a valuable addition in the field of medical informatics and bioinformatics.

Risk Prediction and Management of Chronic Kidney Disease in People Living with Type 2 Diabetes Mellitus.

People with type 2 diabetes mellitus have increased risk of chronic kidney disease and atherosclerotic cardiovascular disease. Improved care delivery and implementation of guideline-directed medical therapy have contributed to the declining incidence of atherosclerotic cardiovascular disease in high-income countries. By contrast, the global incidence of chronic kidney disease and associated mortality is either plateaued or increased, leading to escalating direct and indirect medical costs. Given limited resources, better risk stratification approaches to identify people at risk of rapid progression to end-stage kidney disease can reduce therapeutic inertia, facilitate timely interventions and identify the need for early nephrologist referral. Among people with chronic kidney disease G3a and beyond, the kidney failure risk equations (KFRE) have been externally validated and outperformed other risk prediction models. The KFRE can also guide the timing of preparation for kidney replacement therapy with improved healthcare resources planning and may prevent multiple complications and premature mortality among people with chronic kidney disease with and without type 2 diabetes mellitus. The present review summarizes the evidence of KFRE to date and call for future research to validate and evaluate its impact on cardiovascular and mortality outcomes, as well as healthcare resource utilization in multiethnic populations and different healthcare settings.

A cross-sectional study on the nasopharyngeal microbiota of individuals with SARS-CoV-2 infection across three COVID-19 waves in India.

Background: Multiple variants of the SARS-CoV-2 virus have plagued the world through successive waves of infection over the past three years. Independent research groups across geographies have shown that the microbiome composition in COVID-19 positive patients (CP) differs from that of COVID-19 negative individuals (CN). However, these observations were based on limited-sized sample-sets collected primarily from the early days of the pandemic. Here, we study the nasopharyngeal microbiota in COVID-19 patients, wherein the samples have been collected across the three COVID-19 waves witnessed in India, which were driven by different variants of concern. Methods: The nasopharyngeal swabs were collected from 589 subjects providing samples for diagnostics purposes at the Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India and subjected to 16s rRNA gene amplicon - based sequencing. Findings: We found variations in the microbiota of symptomatic vs. asymptomatic COVID-19 patients. CP showed a marked shift in the microbial diversity and composition compared to CN, in a wave-dependent manner. Rickettsiaceae was the only family that was noted to be consistently depleted in CP samples across the waves. The genera Staphylococcus, Anhydrobacter, Thermus, and Aerococcus were observed to be highly abundant in the symptomatic CP patients when compared to the asymptomatic group. In general, we observed a decrease in the burden of opportunistic pathogens in the host microbiota during the later waves of infection. Interpretation: To our knowledge, this is the first analytical cross-sectional study of this scale, which was designed to understand the relation between the evolving nature of the virus and the changes in the human nasopharyngeal microbiota. Although no clear signatures were observed, this study shall pave the way for a better understanding of the disease pathophysiology and help gather preliminary evidence on whether interventions to the host microbiota can help in better protection or faster recovery.

Environmental insults and compensative responses: when microbiome meets cancer.

Tumor microenvironment has recently been ascribed a new hallmark-the polymorphic microbiome. Accumulating evidence regarding the tissue specific territories of tumor-microbiome have opened new and interesting avenues. A pertinent question is regarding the functional consequence of the interface between host-microbiome and cancer. Given microbial communities have predominantly been explored through an ecological perspective, it is important that the foundational aspects of ecological stress and the fight to 'survive and thrive' are accounted for tumor-micro(b)environment as well. Building on existing evidence and classical microbial ecology, here we attempt to characterize the ecological stresses and the compensative responses of the microorganisms inside the tumor microenvironment. What insults would microbes experience inside the cancer jungle? How would they respond to these insults? How the interplay of stress and microbial quest for survival would influence the fate of tumor? This work asks these questions and tries to describe this underdiscussed ecological interface of the tumor and its microbiota. It is hoped that a larger scientific thought on the importance of microbial competition sensing vis-à-vis tumor-microenvironment would be stimulated.

In vitro and in silico studies of silver nanoparticles (AgNPs) from Allium sativum against diabetes.

In the present study, the silver nanoparticles (AgNPs) were synthesized from the bulbs of Allium sativum, characterized by UV-visible spectroscopy, FT-IR, SEM, HR-TEM, EDAX analysis and investigated its action on the inhibition of starch digestion. The results proved that the biosynthesized nanoparticles were uniformly dispersed, spherical shaped with the size ranging from 10 to 30 nm. The phytochemical and FT-IR analysis showed the presence of phenols, terpenoids, and amino acids in the synthesized AgNPs. The cytotoxicity analysis revealed that the synthesized AgNPs were non-toxic to the normal cells. The synthesized AgNPs exhibited significant free radical scavenging activity. The in vitro antidiabetic activity showed that the synthesized AgNPs increased glucose utilization, decreased hepatic glucose production, inhibited the activity of starch digestive enzymes such as α-amylase and α-glucosidase, and were not involved in the stimulation of pancreatic cells for the secretion of insulin. The in silico antidiabetic activity analysis (molecular docking) also revealed that the silver atoms of the AgNPs interacted with the amino acid residues of α-amylase, α-glucosidase, and insulin. The present study proved that the AgNPs synthesized from A. sativum have prominent antidiabetic activity in terms of reducing the hyperglycemia through the increased glucose utilization, decreased hepatic glucose production, and the inhibition of α-amylase and α-glucosidase enzymes. So it can be used as a promising nanomedicine for the treatment of diabetes.

Host-microbiome interactions: Gut-Liver axis and its connection with other organs.

An understanding of connections between gut microbiome and liver has provided important insights into the pathophysiology of liver diseases. Since gut microbial dysbiosis increases gut permeability, the metabolites biosynthesized by them can reach the liver through portal circulation and affect hepatic immunity and inflammation. The immune cells activated by these metabolites can also reach liver through lymphatic circulation. Liver influences immunity and metabolism in multiple organs in the body, including gut. It releases bile acids and other metabolites into biliary tract from where they enter the systemic circulation. In this review, the bidirectional communication between the gut and the liver and the molecular cross talk between the host and the microbiome has been discussed. This review also provides details into the intricate level of communication and the role of microbiome in Gut-Liver-Brain, Gut-Liver-Kidney, Gut-Liver-Lung, and Gut-Liver-Heart axes. These observations indicate a complex network of interactions between host organs influenced by gut microbiome.

Plasmonic nanogels for naked-eye sensing of food adulterants.

Cellulose based nanoplasmonic sensors gained immense attention for various applications due to their advantageous physicochemical characteristics such as ease of fabrication, low density, chirality, surface functionalization and disposal. Herein, a hydrogel based nanoplasmonic sensor probe was fabricated and evaluated for the detection of melamine (MA). Plasmonic nanomaterials (AuNPs) were synthesized by the redox reaction using a dual reducing agent (ß-cyclodextrin (ßCD) and citrate). The physicochemical characteristics of the synthesized AuNPs were extensively determined by various spectroscopic and microscopic techniques. The colorimetric sensing of melamine (MA) was carried out in solution and hydrogel phases. Upon the addition of MA, AuNPs tend to aggregate and exhibit color changes from orange-red to purple due to surface plasmon resonance (SPR) coupling. This nanosensor probe showed high selectivity and sensitivity for detection of MA with a detection limit of 3 × 10-7 M. Plasmonic hydrogels were prepared using the cellulose acetate (CA) polymer and optimized for stability and interaction with melamine. The ßCD-citrate stabilized AuNPs showed color changes with the CA hydrogels. The hydrogel-based sensor probe exhibits similar characteristics with respect to the selective and sensitive detection of MA under optimized conditions. The fabricated nanoreactor based sensor probe has high potential for food sensor applications.

Tracking mutational semantics of SARS-CoV-2 genomes.

Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while dealing with dynamic or temporally evolving datasets (e.g., historical literature). Biological datasets of genome-sequences are interesting since they are sequential as well as dynamic. Here we describe how SARS-CoV-2 genomes and mutations thereof can be processed using fundamental algorithms in NLP to reveal the characteristics and evolution of the virus. We demonstrate applicability of NLP in not only probing the temporal mutational signatures through dynamic topic modelling, but also in tracing the mutation-associations through tracing of semantic drift in genomic mutation records. Our approach also yields promising results in unfolding the mutational relevance to patient health status, thereby identifying putative signatures linked to known/highly speculated mutations of concern.

A compendium of predicted growths and derived symbiotic relationships between 803 gut microbes in 13 different diets.

Gut health is intimately linked to dietary habits and the microbial community (microbiota) that flourishes within. The delicate dependency of the latter on nutritional availability is also strongly influenced by interactions (such as, parasitic or mutualistic) between the resident microbes, often affecting their growth rate and ability to produce key metabolites. Since, cultivating the entire repertoire of gut microbes is a challenging task, metabolic models (genome-based metabolic reconstructions) could be employed to predict their growth patterns and interactions. Here, we have used 803 gut microbial metabolic models from the Virtual Metabolic Human repository, and subsequently optimized and simulated them to grow on 13 dietary compositions. The presented pairwise interaction data (https://osf.io/ay8bq/) and the associated bacterial growth rates are expected to be useful for (a) deducing microbial association patterns, (b) diet-based inference of personalised gut profiles, and (c) as a steppingstone for studying multi-species metabolic interactions.

Perioperative cardiovascular outcome in patients with coronary artery disease undergoing major vascular surgery: A retrospective cohort study.

Background: Major adverse cardiac events (MACE) are a major contributor to morbidity and mortality in patients undergoing major vascular surgeries. We aim to assess the incidence, risk factors, and outcome of MACE in patients with coronary artery disease (CAD) undergoing aortic surgeries. Methods: In this retrospective observational study, we included patients with CAD who underwent elective major vascular surgery, namely, thoracoabdominal aortic aneurysm repairs and vascular bypass surgeries for aorto-occlusive disease, in our institute from January 2010 to December 2019. The association of preoperative risk factors including revised cardiac risk index factors, functional status of patients, severity of CAD, and its treatment status and technique of anesthesia with occurrence of MACE was analyzed. Results: Medical records of 141 patients were studied. The incidence of perioperative MACE was 11.3% (16/141) and overall in-hospital mortality was 6.4% (9/141), all of them related to MACE; implicating a 56.2% mortality in patients who develop MACE. The odds of a patient who had undergone preoperative coronary revascularization to develop a MACE was higher than a nonrevascularized patient (odds ratio: 3.9; 95% confidence interval [CI], 1.34-11.34). There was found to be no benefit in the addition of epidural analgesia to general anesthesia in reducing perioperative MACE. Conclusions: Major vascular surgeries in patients with CAD are a highly morbid procedure and a perioperative MACE places them at a significantly high risk of mortality. Early detection of CAD and preoperative medical optimization can play a major role in reducing the risk of MACE.

Variability of ENSO Forecast Skill in 2-Year Global Reforecasts Over the 20th Century.

In order to explore temporal changes of predictability of El Niño Southern Oscillation (ENSO), a novel set of global biennial climate reforecasts for the historical period 1901-2010 has been generated using a modern initialized coupled forecasting system. We find distinct periods of enhanced long-range skill at the beginning and at the end of the twentieth century, and an extended multi-decadal epoch of reduced skill during the 1930s-1950s. Once the forecast skill extends beyond the first spring barrier, the predictability limit is much enhanced and our results provide support for the feasibility of skillful ENSO forecasts up to 18 months. Changes in the mean state, variability (amplitude), persistence, seasonal cycle and predictability suggest that multi-decadal variations in the dynamical characteristics of ENSO rather than the data coverage and quality of the observations have primarily driven the reported non-monotonic skill modulations.

Sensing Host Health: Insights from Sensory Protein Signature of the Metagenome.

The human microbiota, which comprises an ensemble of taxonomically and functionally diverse but often mutually cooperating microorganisms, benefits its host by shaping the host immunity, energy harvesting, and digestion of complex carbohydrates as well as production of essential nutrients. Dysbiosis in the human microbiota, especially the gut microbiota, has been reported to be linked to several diseases and metabolic disorders. Recent studies have further indicated that tracking these dysbiotic variations could potentially be exploited as biomarkers of disease states. However, the human microbiota is not geography agnostic, and hence a taxonomy-based (microbiome) biomarker for disease diagnostics has certain limitations. In comparison, (microbiome) function-based biomarkers are expected to have a wider applicability. Given that (i) the host physiology undergoes certain changes in the course of a disease and (ii) host-associated microbial communities need to adapt to this changing microenvironment of their host, we hypothesized that signatures emanating from the abundance of bacterial proteins associated with the signal transduction system (herein referred to as sensory proteins [SPs]) might be able to distinguish between healthy and diseased states. To test this hypothesis, publicly available metagenomic data sets corresponding to three diverse health conditions, namely, colorectal cancer, type 2 diabetes mellitus, and schizophrenia, were analyzed. Results demonstrated that SP signatures (derived from host-associated metagenomic samples) indeed differentiated among healthy individual and patients suffering from diseases of various severities. Our finding was suggestive of the prospect of using SP signatures as early biomarkers for diagnosing the onset and progression of multiple diseases and metabolic disorders. IMPORTANCE The composition of the human microbiota, a collection of host-associated microbes, has been shown to differ among healthy and diseased individuals. Recent studies have investigated whether tracking these variations could be exploited for disease diagnostics. It has been noted that compared to microbial taxonomies, the ensemble of functional proteins encoded by microbial genes are less likely to be affected by changes in ethnicity and dietary preferences. These functions are expected to help the microbe adapt to changing environmental conditions. Thus, healthy individuals might harbor a different set of genes than diseased individuals. To test this hypothesis, we analyzed metagenomes from healthy and diseased individuals for signatures of a particular group of proteins called sensory proteins (SP), which enable the bacteria to sense and react to changes in their microenvironment. Results demonstrated that SP signatures indeed differentiate among healthy individuals and those suffering from diseases of various severities.

MarkerML - Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning.

Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis testing and black-box machine learnt models which are conventionally used for identification of these features are generally not exhaustive, especially because they generally do-not provide any quantifiable relevance (context) of/between the identified features. We present MarkerML web-server, that seeks to leverage the emergence of interpretable machine learning for facilitating the contextual discovery of metagenomic features of interest. It does so through a comprehensive and automated application of the concept of Shapley Additive Explanations in companionship to the compositionality accounted hypothesis testing for the multi-variate microbiome datasets. MarkerML not only helps in identification of marker-features, but also enables insights into the role and inter-dependence of the identified features in driving the decision making of the supervised machine learnt model. Generation of high quality and intuitive visualizations spanning prediction effect plots, model performance reports, feature dependency plots, Shapley and abundance informed cladograms (Sungrams), hypothesis tested violin plots along-with necessary provisions for excluding the participant bias and ensuring reproducibility of results, further seek to make the platform a useful asset for the scientists in the field of microbiome (and even beyond). The MarkerML web-server is freely available for the academic community at https://microbiome.igib.res.in/markerml/.

Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

MOTIVATION: Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any). RESULTS: We explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97 ± 0.01, Europe:0.94 ± 0.01, N.America:0.92 ± 0.02, Africa:0.94 ± 0.07, S.America:0.93 ± 03). Although virus-genotype alone could enable high predictivity (0.97 ± 0.01, 0.89 ± 0.02, 0.86 ± 0.04, 0.95 ± 0.06, 0.9 ± 0.04), the performance was not found to be consistent and the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH < 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (>0.87 ± 0.03, 0.91 ± 0.01, 0.87 ± 0.03, 0.84 ± 0.08, 0.89 ± 0.05). High-performance models were employed for inferring the important mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a 'temporal-modeling approach' to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learning can play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.

A perspective on the benefits of consumption of parboiled rice over brown rice for glycaemic control.

PURPOSE: Rice is a staple food for over 3.5 billion people worldwide. The nutritional content of rice varies with different post-harvest processing techniques. Major varieties include brown rice (BR), white rice (WR) and parboiled rice (PBR). While consumption of BR is advocated due to its higher nutritional content compared to other varieties, some studies have indicated lower post-prandial blood glucose (PPBG) levels when PBR is consumed. This apparent benefit of PBR consumption is not well publicised and no commentaries on underlying mechanisms are available in literature. METHODS: In this review, we looked into differential nutrient content of PBR, as compared to BR and WR, and tried to understand how their consumption could be associated with glycaemic control. Various roles played by these nutrients in mechanisms of insulin secretion, insulin resistance, nutrient absorption and T2DM-associated inflammation were reviewed from literature-based evidence. RESULTS: We report differential nutritional factors in PBR, with respect to BR (and WR), such as higher calcium and selenium content, lower phytic acids, and enriched vitamin B6 which might aid PBR's ability to provide better glycaemic control than BR. CONCLUSION: Our interpretation of reviewed literature leads us to suggest the possible benefits of PBR consumption in glycaemic control and its inclusion as the preferred rice variant in diets of T2DM patients and at-risk individuals.

Healthcare Needs and Perceptions of People Living With Inflammatory Bowel Disease in Australia: A Mixed-Methods Study.

Background: Crohn's disease (CD), ulcerative colitis (UC), and indeterminate colitis are inflammatory bowel diseases (IBDs) that adversely affect the healthcare needs and quality of life (QoL) of people with IBD. The aim of this study was to explore the needs and perceptions of people with IBD in a primary care setting. Methods: This sequential explanatory mixed-methods study consisted of a cross-sectional survey (included validated tools), followed by semistructured interviews on participants' perceptions: IBD management, healthcare professionals, IBD care, flare management, and pharmacist's IBD roles. Results: Sixty-seven participants completed the survey, and 8 completed interviews. Quantitative findings: Age at diagnosis had significant association with medication nonadherence (P = .04), QoL (P = .04), and disease control (P = .01) among the respondents. The odds of medication nonadherence were 8 times (adjusted odds ratio [AOR] = 8.04, 95% confidence interval [CI] = 1.08, 60.10) higher among younger participants aged <30 years. Those diagnosed with CD (P = .02) reported more likely to have unfavorable perceptions of pharmacists' role in managing their IBD (AOR = 9.45, 95% CI = 1.57, 56.62) than those with UC and indeterminate colitis. Qualitative findings: General practitioners were considered the most important care provider and the first point of contact for patients in managing all aspects of IBD. Participants identified their key need to be timely access to specialized IBD care and found that other primary healthcare professionals lacked disease-specific knowledge for managing IBD. Conclusions: Primary healthcare professionals are well positioned but need targeted training to influence the needs of IBD patients. The specialty role of an IBD educator could complement existing services to deliver and address patient-specific care.

Tracking mutational semantics of SARS-CoV-2 genomes

Genomes have an inherent context dictated by the order in which the nucleotides and higher order genomic elements are arranged in the DNA/RNA. Learning this context is a daunting task, governed by the combinatorial complexity of interactions possible between ordered elements of genomes. Can natural language processing be employed on these orderly, complex and also evolving datatypes (genomic sequences) to reveal the latent patterns or context of genomic elements (e.g Mutations)? Here we present an approach to understand the mutational landscape of Covid-19 by treating the temporally changing (continuously mutating) SARS-CoV-2 genomes as documents. We demonstrate how the analogous interpretation of evolving genomes to temporal literature corpora provides an opportunity to use dynamic topic modeling (DTM) and temporal Word2Vec models to delineate mutation signatures corresponding to different Variants-of-Concerns and tracking the semantic drift of Mutations-of-Concern (MoC). We identified and studied characteristic mutations affiliated to Covid-infection severity and tracked their relationship with MoCs. Our ground work on utility of such temporal NLP models in genomics could supplement ongoing efforts in not only understanding the Covid pandemic but also provide alternative strategies in studying dynamic phenomenon in biological sciences through data science (especially NLP, AI/ML).