Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis.

BACKGROUND: With the global spread of COVID-19, the world has seen many patients, including many severe cases. The rapid development of machine learning (ML) has made significant disease diagnosis and prediction achievements. Current studies have confirmed that omics data at the host level can reflect the development process and prognosis of the disease. Since early diagnosis and effective treatment of severe COVID-19 patients remains challenging, this research aims to use omics data in different ML models for COVID-19 diagnosis and prognosis. We used several ML models on omics data of a large number of individuals to first predict whether patients are COVID-19 positive or negative, followed by the severity of the disease. RESULTS: On the COVID-19 diagnosis task, we got the best AUC of 0.99 with our multilayer perceptron model and the highest F1-score of 0.95 with our logistic regression (LR) model. For the severity prediction task, we achieved the highest accuracy of 0.76 with an LR model. Beyond classification and predictive modeling, our study founds ML models performed better on integrated multi-omics data, rather than single omics. By comparing top features from different omics dataset, we also found the robustness of our model, with a wider range of applicability in diverse dataset related to COVID-19. Additionally, we have found that omics-based models performed better than image or physiological feature-based models, proving the importance of the omics-based dataset for future model development. CONCLUSIONS: This study diagnoses COVID-19 positive cases and predicts accurate severity levels. It lowers the dependence on clinical data and professional judgment, by leveraging the utilization of state-of-the-art models. our model showed wider applicability across different omics dataset, which is highly transferable in other respiratory or similar diseases. Hospital and public health care mechanisms can optimize the distribution of medical resources and improve the robustness of the medical system.

Analysis of topological properties and drug discovery for bipolar disorder and associated diseases: A bioinformatics approach.

With the advancement and development of sophisticated bioinformatics tools, the area of computational bioinformatics and systems biology analysis is expanding day by day. The bipolar or manic-depressive disorder might be characterized as one of the most crippling mental problems that affect the people of early age and grown-ups. The objective of the present study was to investigate the association between genetic mutations in the four above listed diseases and to create a Protein-protein interaction (PPI) network or common pathways. Firstly, we need to find out the genetic relationship between them. Thus it will help us to understand the genetic association between them and help to develop the drug design for all the diseases. Genes responsible for these diseases are gathered, pre-processed, processed and mining using python scripts. This exploration is expected to carry out further measurements in the field of drug structure and also contributes to the biological and biomedical sectors.

Carbon nanotube-based aptasensor for sensitive electrochemical detection of whole-cell Salmonella.

In this study, an amino-modified aptasensor using multi-walled carbon nanotubes (MWCNTs)-deposited ITO electrode was prepared and evaluated for the detection of pathogenic Salmonella bacteria. An amino-modified aptamer (ssDNA) which binds selectively to whole-cell Salmonella was immobilised on the COOH-rich MWCNTs to produce the ssDNA/MWCNT/ITO electrode. The morphology of the MWCNT before and after interaction with the aptamers were observed using scanning electron microscopy (SEM). Cyclic voltammetry and electrochemical impedance spectroscopy techniques were used to investigate the electrochemical properties and conductivity of the aptasensor. The results showed that the impedance measured at the ssDNA/MWCNT/ITO electrode surface increased after exposure to Salmonella cells, which indicated successful binding of Salmonella on the aptamer-functionalised surface. The developed ssDNA/MWCNT/ITO aptasensor was stable and maintained linearity when the scan rate was increased from 10 mV s-1 to 90 mV s-1. The detection limit of the ssDNA/MWCNT/ITO aptasensor, determined from the sensitivity analysis, was found to be 5.5 × 101 cfu mL-1 and 6.7 × 101 cfu mL-1 for S. Enteritidis and S. Typhimurium, respectively. The specificity test demonstrated that Salmonella bound specifically to the ssDNA/MWCNT/ITO aptasensor surface, when compared with non-Salmonella spp. The prepared aptasensor was successfully applied for the detection of Salmonella in food samples.

Decentralized and Secure Collaborative Framework for Personalized Diabetes Prediction.

Diabetes is a global epidemic with severe consequences for individuals and healthcare systems. While early and personalized prediction can significantly improve outcomes, traditional centralized prediction models suffer from privacy risks and limited data diversity. This paper introduces a novel framework that integrates blockchain and federated learning to address these challenges. Blockchain provides a secure, decentralized foundation for data management, access control, and auditability. Federated learning enables model training on distributed datasets without compromising patient privacy. This collaborative approach facilitates the development of more robust and personalized diabetes prediction models, leveraging the combined data resources of multiple healthcare institutions. We have performed extensive evaluation experiments and security analyses. The results demonstrate good performance while significantly enhancing privacy and security compared to centralized approaches. Our framework offers a promising solution for the ethical and effective use of healthcare data in diabetes prediction.

MADE-for-ASD: A multi-atlas deep ensemble network for diagnosing Autism Spectrum Disorder.

In response to the global need for efficient early diagnosis of Autism Spectrum Disorder (ASD), this paper bridges the gap between traditional, time-consuming diagnostic methods and potential automated solutions. We propose a multi-atlas deep ensemble network, MADE-for-ASD, that integrates multiple atlases of the brain's functional magnetic resonance imaging (fMRI) data through a weighted deep ensemble network. Our approach integrates demographic information into the prediction workflow, which enhances ASD diagnosis performance and offers a more holistic perspective on patient profiling. We experiment with the well-known publicly available ABIDE (Autism Brain Imaging Data Exchange) I dataset, consisting of resting state fMRI data from 17 different laboratories around the globe. Our proposed system achieves 75.20% accuracy on the entire dataset and 96.40% on a specific subset - both surpassing reported ASD diagnosis accuracy in ABIDE I fMRI studies. Specifically, our model improves by 4.4 percentage points over prior works on the same amount of data. The model exhibits a sensitivity of 82.90% and a specificity of 69.70% on the entire dataset, and 91.00% and 99.50%, respectively, on the specific subset. We leverage the F-score to pinpoint the top 10 ROI in ASD diagnosis, such as precuneus and anterior cingulate/ventromedial. The proposed system can potentially pave the way for more cost-effective, efficient and scalable strategies in ASD diagnosis. Codes and evaluations are publicly available at https://github.com/hasan-rakibul/MADE-for-ASD.

Kinetic study and modelling of drying of Chlorella vulgaris.

Microalgae, a versatile source of biofuels, chemicals, and nutraceuticals, necessitates efficient drying for subsequent applications. Extensive studies have been done on the benefits and uses of microalgae, but very few are focusing on drying. This research focused on a specific microalga, Chlorella vulgaris, to analyze the drying kinetics involved in the moisture removal process. Data on drying behavior were collected using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). As the temperature rose, the moisture content of the biomass rapidly decreased and peaked between 65 and 80 °C. From four widely used drying kinetics models, which are typically used to analyze the drying kinetics of agricultural goods, four non-isothermal drying models were derived. These models were assessed using the coefficient of determination (R2) and reduced chi-square (χ2). Page's model emerged as the best fit for describing drying kinetics. This study introduces a novel approach to characterize the intrinsic properties of freshly harvested Chlorella vulgaris by employing TGA and DSC. Unlike other studies focusing on conventional drying methods, our investigation provides real-time insights into the microalgae's thermal behavior during drying.

Optimal growth conditions to enhance Chlorella vulgaris biomass production in indoor phyto tank and quality assessment of feed and culture stock.

Commercial microalgae cultivation is a dynamic field with ongoing efforts to improve efficiency, reduce costs, and explore new applications. We conducted a study to examine how different light exposure periods affect Chlorella vulgaris's growth. We employed a Phyto tank batch system of approximately 3.5 L with LED light control, controlled airflow, and sterilized bags, maintained at 22.0 ± 2.0 °C indoors. Various methods, including spectrophotometry, and cell counter were employed to monitor Chlorella vulgaris growth under different light exposure cycles. Additionally, quality analysis as feed source was employed by proximate, amino acid, beta-glucan, and microbial content analysis. The results revealed significant variations in C. vulgaris biomass production based on light exposure duration. Notably, the 16:8-h light-dark photoperiod exhibited the highest biomass concentration, reaching 6.48 × 107 ± 0.50 cells/mL with an optical density (OD) of 1.165 absorbance at 682 nm. The 12:12-h light-dark photoperiod produced the second-highest biomass concentration, with 2.305 × 106 ± 0.60 cells/mL at an OD of 0.489. Proximate analysis of dry algae powder revealed low lipid content (0.48 %), high protein content (37.61 %), variable ash concentration (average 10.75 %), and a significant carbohydrate fraction (51.16 %) during extended daylight and shorter dark periods. Amino acid analysis identified nine essential amino acids, with glutamic acid being the most abundant (17.7 %) and methionine the least (0.4 %). Furthermore, quality analysis and microbiological assays demonstrated that the C. vulgaris biomass is well-suited for fish and livestock use as a feed source and possibility as human nutraceuticals. These findings can be considered more environmentally friendly and ethically sound due to the absence of genetic modification.

Role of bacteriophages in shaping gut microbial community.

Phages are the most diversified and dominant members of the gut virobiota. They play a crucial role in shaping the structure and function of the gut microbial community and consequently the health of humans and animals. Phages are found mainly in the mucus, from where they can translocate to the intestinal organs and act as a modulator of gut microbiota. Understanding the vital role of phages in regulating the composition of intestinal microbiota and influencing human and animal health is an emerging area of research. The relevance of phages in the gut ecosystem is supported by substantial evidence, but the importance of phages in shaping the gut microbiota remains unclear. Although information regarding general phage ecology and development has accumulated, detailed knowledge on phage-gut microbe and phage-human interactions is lacking, and the information on the effects of phage therapy in humans remains ambiguous. In this review, we systematically assess the existing data on the structure and ecology of phages in the human and animal gut environments, their development, possible interaction, and subsequent impact on the gut ecosystem dynamics. We discuss the potential mechanisms of prophage activation and the subsequent modulation of gut bacteria. We also review the link between phages and the immune system to collect evidence on the effect of phages on shaping the gut microbial composition. Our review will improve understanding on the influence of phages in regulating the gut microbiota and the immune system and facilitate the development of phage-based therapies for maintaining a healthy and balanced gut microbiota.

Hypolipidemic effect and modulation of hepatic enzymes by different edible oils in obese Wistar rats.

The current study assessed the hypolipidemic effect and modulation of hepatic enzymes by different edible oils in obese Wistar rats. In order to conduct this study, 36 Wistar rats that were collected at 5 weeks of age and weighed an average of 70 g were split into two groups: 28 of them were fed a high-fat diet (HFD) and 8 of them were fed a control diet. After 5 weeks of feeding, rats from the HFD (obese, n = 4) and the control diet group (n = 4) were sacrificed. Subsequently, the rest of obese rats (n = 24) were separated into six groups, including the continuing high-fat (CHF) diet group, rice bran oil (RBO) diet group, olive oil (OO) diet group, soybean oil (SO) diet group, cod liver oil (CLO) diet group, and sunflower oil (SFO) diet group, and the continuing control diet group (n = 4). Rats from each group were sacrificed following an additional 5 weeks, and all analytical tests were carried out. The results found that the interventions of RBO, CLO, and SFO in obese rats reduced their body weight non-significantly when compared with CHF. It was also observed that a non-significant reduction in weight of the heart, AAT, and EAT occurred by RBO, OO, SO, and CLO, while SFO reduced the AAT level significantly (p < 0.05). Besides, RBO, OO, SO, CLO, and SFO decreased IBAT and liver fat significantly compared to CHF. Similarly, the administration of RBO, OO, SO, and CLO reduced ALT significantly. RBO reduced GGT (p < 0.05) significantly, but other oils did not. The given oil has the efficiency to reduce TC, TAG, and LDL-C but increase HDL-C significantly. These findings suggest that different edible oils can ameliorate obesity, regulate lipid profiles, and modulate hepatic enzymes.

RAB23 regulates musculoskeletal development and patterning.

RAB23 is a small GTPase which functions at the plasma membrane to regulate growth factor signaling. Mutations in RAB23 cause Carpenter syndrome, a condition that affects normal organogenesis and patterning. In this study, we investigate the role of RAB23 in musculoskeletal development and show that it is required for patella bone formation and for the maintenance of tendon progenitors. The patella is the largest sesamoid bone in mammals and plays a critical role during movement by providing structural and mechanical support to the knee. Rab23 -/- mice fail to form a patella and normal knee joint. The patella is formed from Sox9 and scleraxis (Scx) double-positive chondroprogenitor cells. We show that RAB23 is required for the specification of SOX9 and scleraxis double-positive patella chondroprogenitors during the formation of patella anlagen and the subsequent establishment of patellofemoral joint. We find that scleraxis and SOX9 expression are disrupted in Rab23 -/- mice, and as a result, development of the quadriceps tendons, cruciate ligaments, patella tendons, and entheses is either abnormal or lost. TGFß-BMP signaling is known to regulate patella initiation and patella progenitor differentiation and growth. We find that the expression of TGFßR2, BMPR1, BMP4, and pSmad are barely detectable in the future patella site and in the rudimentary tendons and ligaments around the patellofemoral joint in Rab23 -/- mice. Also, we show that GLI1, SOX9, and scleraxis, which regulate entheses establishment and maturation, are weakly expressed in Rab23 -/- mice. Further analysis of the skeletal phenotype of Rab23 -/- mice showed a close resemblance to that of Tgfß2 -/- mice, highlighting a possible role for RAB23 in regulating TGFß superfamily signaling.

A systematic review on predicting PV system parameters using machine learning.

Due to the growing demand, assessing performance has become obligatory for photovoltaic (PV) energy harvesting systems. Performance assessment involves estimating different PV system parameters. Traditional ways, such as calculating solar radiation using satellite data and the IV characteristics approach as assessment methods, are no longer reliable enough to provide a reasonable projection of PV system parameters. Estimating system parameters using machine learning (ML) approaches has become a reliable and popular method because of its speed and accuracy. This paper systematically reviewed ML-based PV parameter estimation studies published in the last three years (2020 - 2022). Studies were analyzed using several criteria, including ML algorithm, outcome, experimental setup, sample data size, and error metric. The analysis revealed several interesting factors. The neural network was the most popular ML method (32.55%), followed by random vector functional link (13.95%) and support vector machine (9.30%). Dataset was sourced from hardware tests and computer-based simulations: 66% of the studies used data from only computer simulation, 18% used data from only hardware setup, and the 16% experiments used data from both hardware and simulations to evaluate different system parameters. The top three most commonly used error metrics were root mean square error (29.1%), mean absolute error (17.5%), and coefficient of determination (15.9%). Our systematic review will help researchers assess ML algorithms' projection in PV system parameters estimation. Consequently, scopes shall be created to establish more robust governmental frameworks, expand private financing in the PV industry, and optimize PV system parameters.

Identification of B and T Cell Epitopes to Design an Epitope-Based Peptide Vaccine against the Cell Surface Binding Protein of Monkeypox Virus: An Immunoinformatics Study.

Background: Although the monkeypox virus-associated illness was previously confined to Africa, recently, it has started to spread across the globe and become a significant threat to human lives. Hence, this study was designed to identify the B and T cell epitopes and develop an epitope-based peptide vaccine against this virus's cell surface binding protein through an in silico approach to combat monkeypox-associated diseases. Results: The analysis revealed that the cell surface binding protein of the monkeypox virus contains 30 B cell and 19 T cell epitopes within the given parameter. Among the T cell epitopes, epitope "ILFLMSQRY" was found to be one of the most potential peptide vaccine candidates. The docking analysis revealed an excellent binding affinity of this epitope with the human receptor HLA-B∗15:01 with a very low binding energy (-7.5 kcal/mol). Conclusion: The outcome of this research will aid the development of a T cell epitope-based peptide vaccine, and the discovered B and T cell epitopes will facilitate the creation of other epitope and multi-epitope-based vaccines in the future. This research will also serve as a basis for further in vitro and in vivo analysis to develop a vaccine that is effective against the monkeypox virus.

Vertical distribution, contamination status and ecological risk assessment of heavy metals in core sediments from a mangrove-dominated tropical river.

Understanding the vertical distribution of heavy metals aids in tracing the contamination history, however, it is limited for mangrove-dominated river. Thus, this study aimed to assess the vertical distribution of nine heavy metals and their possible ecological risk in several layers of core sediments from a mangrove-dominated river inside the Sundarban mangrove forest (World heritage and Ramsar site), Bangladesh. 45 core samples from five stations were analyzed using Flame Atomic Absorption Spectrophotometer (FAAS). The findings showed that, with the exception of Fe, Ni, and Cu, which suggested increased metal release in recent times, higher metal levels were recorded in the surface layer (0-10 cm), followed by the middle layer (10-20 cm), and inner layer (20-30 cm) of sediment cores. When compared to non-industrial forest sediment, core sediment from industrial sites contained noticeably more metals (p < 0.05). Overall, the mean metal concentration (mg/kg) followed the increasing order of Cd < As < Pb < Cu < Cr < Zn < Ni < Mn < Fe. Among the studied metals, the levels of Cd and Ni exceeded the average shale value. Contamination indices such as enrichment factor (EF), contamination factor (CF), and geo-accumulation index (Igeo) showed that the studied sediments were only contaminated by Cd. Ecological risk assessed by ecological risk factor (Eri) and risk index (RI) suggested that the analyzed heavy metals, with the exception of Cd, posed no significant ecological threats. All of the heavy metals analyzed might have originated from similar anthropogenic sources, according to the correlation matrix, cluster analysis (CA), and principal component analysis (PCA).

The effect of COVID-19 on the mental health of the people in the Indian subcontinent: A scoping review.

Indian subcontinent has high mental heath burden and low resources to cope the mental health challenges. Assessment of impact of COVID-19 pandemic on the mental health would help to prioritize the resource allocations. We aimed to assess the impact of COVID-19 on the mental health of people in the Indian subcontinent. Following the PRISMA 2020 guideline, a scoping review was performed by systematically searching the PubMed, Scopus, and Embase databases to identify original studies that assessed mental health conditions during the COVID-19 pandemic in the Indian subcontinent. In this review, a total of 34 studies conducted between 2020 and 2022 were analyzed. The prevalence of anxiety disorders was found to range widely, from 2.5% in North Indian urban slum to 53% in Bangladesh and 21.7% in Pakistan. Similarly, the prevalence of depression varied widely, with rates ranging from 3.5% in North India to 29.8% in Pakistan. The prevalence of stress-related problems ranged from 18.3% in Pakistan to 59.7% in Bangladesh. Factors such as female gender, married status, healthcare workers, and mental illness were identified as important predictors of anxiety and depressive disorders. The impact of COVID-19 pandemic on mental health in Indian subcontinent varies widely based on study population and methods. Therefore, a cautious interpretation is needed while generalizing the study results.

Demographic and Clinical Characteristics of Persons With Spinal Cord Injury in Bangladesh: Database for the International Spinal Cord Injury Community Survey 2023.

The study aims to explore the demographic and clinical characteristics of persons with spinal cord injury (SCI) in Bangladesh. A total of 3035 persons with SCI spanning from 2018 to 2022 were included in this cross-sectional study. Information about demographic and clinical variables was obtained from the medical records and verified through telephone calls to ensure accuracy and consistency. Approximately half (48.30%) of the study participants were located in Dhaka Division. The average age of persons with SCI was 38.3 years, with a standard deviation of 15.9 years, and the largest proportion (33.4%) fell within the age range of 18-30 years. Males outnumbered females by nearly 2.5 times. In the study, 59.6% had suffered traumatic injuries, whereas 40.4% had SCI attributable to disease-related causes; 58.1% were diagnosed with tetraplegia and 40.1% with paraplegia. Fall from height (42.1%) and road traffic trauma (27%) were the most common causes of traumatic injuries. Degenerative myelopathy (41.1%) was the most frequent cause of non-traumatic SCI, followed by tumors (27.7%) and tuberculosis (TB; 14.8%). Both traumatic (58.3%) and degenerative (56.7%) causes of SCI commonly affected the cervical spine, whereas TB (24.4%) and tumors (47.5%) had a higher incidence of affecting the dorsal spine. In the absence of a registry or national database for patients with SCI in Bangladesh, this study would serve as representative data for future studies.

Structural and Functional Annotation and Molecular Docking Analysis of a Hypothetical Protein from Neisseria gonorrhoeae: An In-Silico Approach.

Background: Worldwide, Neisseria gonorrhoeae-related sexually transmitted infections (STIs) continue to be of significant public health concern. This obligate-human pathogen has developed a number of defenses against both innate and adaptive immune responses during infection, some of which are mediated by the pathogen's proteins. Hence, the uncharacterized proteins of N. gonorrhoeae can be annotated to get insight into the unique functions of this organism related to its pathogenicity and to find a more efficient therapeutic target. Methods: In this study, a hypothetical protein (HP) of N. gonorrhoeae was chosen for analysis and an in-silico approach was used to explore various properties such as physicochemical characteristics, subcellular localization, secondary structure, 3D structures, and functional annotation of that HP. Finally, a molecular docking analysis was performed to design an epitope-based vaccine against that HP. Results: This study has identified the potential role of the chosen HP of N. gonorrhoeae in plasmid transfer, cell cycle control, cell division, and chromosome partitioning. Acidic nature, thermal stability, cytoplasmic localization of the protein, and some of its other physicochemical properties have also been identified through this study. Molecular docking analysis has demonstrated that one of the T cell epitopes of the protein has a significant binding affinity with the human leukocyte antigen HLA-B∗15 : 01. Conclusions: The in-silico characterization of this protein will help us understand molecular mechanism of action of N. gonorrhoeae and get an insight into novel therapeutic identification processes. This research will, therefore, enhance our knowledge to find new medications to tackle this potential threat to humankind.

AD-CovNet: An exploratory analysis using a hybrid deep learning model to handle data imbalance, predict fatality, and risk factors in Alzheimer's patients with COVID-19.

Alzheimer's disease (AD) is the leading cause of dementia globally, with a growing morbidity burden that may exceed diagnosis and management capabilities. The situation worsens when AD patient fatalities are exposed to COVID-19. Because of differences in clinical features and patient condition, a patient's recovery from COVID-19 with or without AD varies greatly. Thus, this situation stimulates a spectrum of imbalanced data. The inclusion of different features in the class imbalance offers substantial problems for developing of a classification framework. This study proposes a framework to handle class imbalance and select the most suitable and representative datasets for the hybrid model. Under this framework, various state-of-the-art resample techniques were utilized to balance the datasets, and three sets of data were finally selected. We developed a novel hybrid deep learning model AD-CovNet using Long Short-Term Memory (LSTM) and Multi-layer Perceptron (MLP) algorithms that delineate three unique datasets of COVID-19 and AD-COVID-19 patient fatality predictions. This proposed model achieved 97% accuracy, 97% precision, 97% recall, and 97% F1-score for Dataset I; 97% accuracy, 97% precision, 96% recall, and 96% F1-score for Dataset II; and 86% accuracy, 88% precision, 88% recall, and 86% F1-score for Dataset III. In addition, AdaBoost, XGBoost, and Random Forest models were utilized to evaluate the risk factors associated with AD-COVID-19 patients, and the outcome outperformed diagnostic performance. The risk factors predicted by the models showed significant clinical importance and relevance to mortality. Furthermore, the proposed hybrid model's performance was evaluated using a statistical significance test and compared to previously published works. Overall, the uniqueness of the large dataset, the effectiveness of the deep learning architecture, and the accuracy and performance of the hybrid model showcase the first cohesive work that can formulate better predictions and help in clinical decision-making.

CDKAL1 gene rs7756992 A/G and rs7754840 G/C polymorphisms are associated with gestational diabetes mellitus in a sample of Bangladeshi population: implication for future T2DM prophylaxis.

BACKGROUND: Association of single nucleotide polymorphisms (SNP) rs7756992 A/G and rs7754840 G/C of cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) gene with the susceptibility of gestational diabetes mellitus (GDM) has been studied in a group of Bangladeshi women. METHODS: In this case-control study, 212 GDM patients and 256 control subjects were genotyped for rs7756992 and rs7754840 by PCR-RFLP and TaqMan™ allelic discrimination assay method respectively. Genotyping results were confirmed by DNA sequencing and replicated TaqMan™ assay. The odds ratios and their 95% confidence interval were calculated by logistic regression to determine the associations between genotypes and GDM. RESULTS: The genotype frequencies of rs7756992-AA/AG/GG in the GDM group and the control group were 37%/48%, 53%/45%, 10%/7% and those of rs7754840-CC/CG/GG were 51%/55%, 40.1%/39.8%, 9%/5% respectively. Under dominant and log additive models rs7756992 was revealed significantly associated with GDM after being adjusted for family history of diabetes (FHD) and gravidity. Conversely, rs7754840 was significantly associated (P = 0.047) with GDM only under the recessive model after the same adjustment. The risk allele frequency of both SNPs was higher in the GDM group but significantly (P = 0.029) increased prevalence was observed in the rs7756992 G allele. When positive FHD and risk alleles of these SNPs were synergistically present in any pregnant woman, the chance of developing GDM was augmented by many folds. The codominant model revealed 2.5 and 2.1 folds increase in odds by AG (rs7756992) and GC (rs7754840) genotypes and 3.7 and 4.5 folds by GG (rs7756992) and CC (rs7754840) genotypes respectively. A significant 2.7 folds (P = 0.038) increase in odds of GDM resulted from the interaction of rs7756992 and family history of diabetes under the dominant model. The cumulative effect of multigravidity and risk alleles of these SNPs increased the odds of GDM more than 1.5 folds in different genotypes. CONCLUSION: This study not only revealed a significant association between rs7756992 and rs7754840 with GDM but also provided the possibility as potential markers for foretelling about GDM and type 2 diabetes mellitus in Bangladeshi women.

Exploring UK Knife crime and its associated factors: A content analysis of online newspapers.

Knife crime has become a common phrase used by the media, but it is not always clear what it refers to or what they mean when they use the term. Knife crime can cover many offences, making it challenging to define and estimate its prevalence. This review aimed to evaluate potential knife crimes in the UK from 2011 to 2021 and analyse the causes and risk factors associated with the crimes. Six UK online news portals were purposefully chosen to be included in the study, and knife crime news was searched retrospectively. The term "knife crime" was used to search. The news portals were the: Metro, the Sun, the Guardian, Daily Mail, Daily Mirror and the Evening Standard. In the assigned news portals, 692 reports were found between January 2011 and December 2021. The study revealed that the 11-20 years of age group individuals are more vulnerable as victims, and males are more reported as victims when compared to females. About 61.8% of knife crimes are reported from South England. Knife crime risk is higher in early adulthood and among males. Street violence, fights/gang attacks, family issues and robbery are the leading causes of knife crime and have all been identified as risk factors that must be addressed with caution.

Impact of gut microbiome on skin health: gut-skin axis observed through the lenses of therapeutics and skin diseases.

The human intestine hosts diverse microbial communities that play a significant role in maintaining gut-skin homeostasis. When the relationship between gut microbiome and the immune system is impaired, subsequent effects can be triggered on the skin, potentially promoting the development of skin diseases. The mechanisms through which the gut microbiome affects skin health are still unclear. Enhancing our understanding on the connection between skin and gut microbiome is needed to find novel ways to treat human skin disorders. In this review, we systematically evaluate current data regarding microbial ecology of healthy skin and gut, diet, pre- and probiotics, and antibiotics, on gut microbiome and their effects on skin health. We discuss potential mechanisms of the gut-skin axis and the link between the gut and skin-associated diseases, such as psoriasis, atopic dermatitis, acne vulgaris, rosacea, alopecia areata, and hidradenitis suppurativa. This review will increase our understanding of the impacts of gut microbiome on skin conditions to aid in finding new medications for skin-associated diseases.