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Diabetes mellitus is a complex and heterogeneous metabolic disorder which is often pre- or post-existent with complications such as cardiovascular disease, hypertension, inflammation, chronic kidney disease, diabetic retino- and nephropathies. However, the frequencies of these co-morbidities vary among individuals and across populations. It is, therefore, not unlikely that certain genetic variants might commonly contribute to these conditions. Here, we identified four single nucleotide polymorphisms (rs5186, rs1800795, rs1799983 and rs1800629 in AGTR1, IL6, NOS3 and TNFA genes, respectively) to be commonly associated with each of these conditions. We explored their possible interplay in diabetes and associated complications. The variant allele and haplotype frequencies at these polymorphic loci vary among different super-populations (African, European, admixed Americans, South and East Asians). The variant alleles are particularly highly prevalent in different European and admixed American populations. Differential distribution of these variants in different ethnic groups suggests that certain drugs might be more effective in selective populations rather than all. Therefore, population specific genetic architectures should be considered before considering a drug for these conditions.

Nutritional programming is the idea that early nutrient contributions can influence organismal structure or function and is documented in a variety of vertebrates, yet studies in fish are largely lacking. Tilapia are an important foodfish, with global production having increased rapidly since the 1990s. They exhibit high disease-resistance and grow well on formulated feeds which makes them an ideal aquaculture species, however incorporating high quality proteins into feeds can be costly. As feed constitutes 50–70% of total production costs in aquaculture, reducing protein content could curb these costs and increase revenue. Thus, we examined the effects of feeding Nile tilapia ( O . niloticus ) fry a restricted protein diet for the first 7–21 days on growth, gut microbial flora, and the intestinal transcriptome. Fish were fed either a 25% restricted or 48% control crude protein starter (ST) diet for up to 21 days and then switched to a 25% or 38% control crude protein growout (GO) diet. Fish fed a 25% ST diet for 14 days followed by a 38% GO diet had significantly higher lengths and weights and better feed efficiency than fish fed the control 48% ST and 38% GO diet after 56 days of culture. Growth of fry on the 25% ST, 7-day/38% GO and the 25% ST,7-day/25% GO diets did not differ from the those fed the control protein diets, while fish fed the 25% ST diet for 21 days had significantly lower growth and survival rates. We observed no significant differences in either alpha or beta diversity of the gut microbial flora between diets, however species richness (Shannon Index) was higher in fry fed the 25% protein ST diet regardless of the GO diet. Similarly, fish fed the 25% ST diet for 14 days followed by the 38% GO diet had minimal changes to the intestinal transcriptome relative to fish fed the control 48% ST and 38% GO diet. However, those fed 25% ST and GO diets for the entire 56 days exhibited substantial differences in the gut transcriptome from other groups showing gene expression profiles characteristic of detrimental changes to gut physiology, protein metabolism and immune function. Results suggest protein restriction for up to 14 days early in development leads to enhanced growth and feed efficiency with minimal effects on gut microbes or intestinal function. Protein restriction beyond this period appears detrimental to fish growth and health as underscored by expression of disease related genes and higher mortality rates.

Background Increased awareness amongst large population groups is a major determinant for the prevention of diabetes and its complications as well as related metabolic disorders. Knowledge and attitude are the principal markers of awareness that need to be studied in various population groups in specific racial and cultural contexts. The present study was undertaken to explore knowledge, attitude and practice (KAP) regarding -diabetes mellitus (DM) among nondiabetic (nonDM) and type 2 diabetes mellitus (T2DM) patients in Bangladesh. Methods A cross-sectional study was conducted among 18,697 adults (aged 18 years and above; 7796 male and 10,901 female; 6780 nonDM and 11,917 T2DM) selected purposively from the OPD of 19 healthcare centres in and around Dhaka and in northern parts of Bangladesh. KAP were assessed by a pre-structured, interviewer-administered questionnaire and categorised using predefined scores of poor (mean + 1 SD). Univariate and bivariate statistical analysis were done as appropriate. Multivariate linear regression was done to examine the association between diabetes related KAP and other covariates. Results The mean (±SD) age (years) of all the study participants was 46 ± 14, mean BMI 24.4 ± 4.1 and mean waist-hip ratio (WHR) was 0.93 ± 0.07. The proportion of poor, average and good knowledge scores among T2DM subjects were 17%, 68% and 15% respectively. The corresponding values for attitude score were 23%, 67% and 10% respectively. The KAP regarding diabetes was found to be better among people who were living with diabetes compared to their counterparts. DM males showed better knowledge and practice regarding diabetes, compared to nonDM counterparts (M ± SD; 44.18 ± 16.13 vs 40.88 ± 15.62, p = <0.001; 66.00 ± 29.68 vs 64.21 ± 31.79, p  < 0.001, respectively). Females showed better attitude score compared to males. Overall KAP were found to be significantly higher ( p  < 0.001) in middle aged (31–50 years) participants in each group. Participants from urban residents, higher educational background and upper socio-economic class demonstrated significantly greater score in terms of KAP in both nonDM and T2DM groups ( p  < 0.001). On linear regression analysis, knowledge scores correlated strongly with education, income, residence, diabetic state, BMI and attitude. Conclusions The overall level of knowledge and practice concerning diabetes among Bangladeshi population is average, but the overall level of attitude is good both in nonDM and T2DM subjects. To prevent diabetes and its complications there is an urgent need for coordinated educational campaigns with a prioritized focus on poorer, rural and less educated groups.

Prostate cancer (PrCa) incidence and severity vary across ancestries; men of African ancestry (AA) are more likely to be diagnosed and die from PrCa than those of European ancestry (EA). Current polygenic risk scores, even from multi-ancestry GWAS, do not fully capture population-specific genetic mechanisms, especially those mediated by non-coding regulatory single nucleotide polymorphisms (SNPs). Using a deep learning model of prostate enhancers, we identify ~ 2000 SNPs, potentially affecting enhancer function, with higher alternate allele frequency in AA men, that may affect PrCa risk. These SNPs may promote cancer via two mechanisms: increased enhancer activity leading to immune suppression and telomere elongation or decreased activity causing de-differentiation and apoptosis inhibition. Identified SNPs predominantly modulate binding of key transcription factors such as FOX, HOX, and AR – the first was experimentally validated. Incorporating these SNPs into a polygenic risk score improves PrCa risk assessment beyond existing GWAS-identified variants. Current polygenic risk scores for prostate cancer do not leverage biological mechanisms and remain inadequate for patients with African ancestry. Here, the authors employ a deep learning model to identify 2,407 non-coding polymorphisms with greater frequency in African American individuals that may affect enhancer activity in prostate cancer-related pathways, leading to more accurate polygenic risk scores.

Bangladesh is undertaking a major transformation towards digitalization in every sector, and healthcare is no exception. Digitalization of the health sector is expected to improve healthcare services while reducing human effort and ensuring the satisfaction of patients and health professionals. However, for practical and successful digitalization, it is necessary to understand the perceptions of health professionals. Therefore, we conducted a cross-sectional survey in Bangladesh to investigate health professionals’ perceptions in relation to various socio–demographic variables such as age, gender, location, profession and institution. We also evaluated their competencies, as digital health-related competencies are required for digitalization. Additionally, we identified major digitalization challenges. Quantitative survey data were analyzed with Python Pandas, and qualitative data were classified using Valence-Aware Dictionary and Sentiment Reasoner (VADER). This study found significant relationships between age χ2(12,N=701)=82.02,p<0.001; location χ2(4,N=701)=18.78,p<0.001; and profession χ2(16,N=701)=71.02,p<0.001; with technical competency. These variables also have similar influences on psychological competency. According to VADER, 88.1% (583/701) of respondents have a positive outlook toward digitalization. The internal consistency of the survey was confirmed by Cronbach’s alpha score (0.746). This study assisted in developing a better understanding of how professionals perceive digitalization, categorizes professionals based on competency, and prioritizes the major digitalization challenges.

Productivity susceptibility analysis (PSA) is a semi-quantitative ecological risk assessment tool, widely used to determine the relative vulnerability of target and non-target species to fishing impacts. Considering the available information on species-specific life-history and fishery-specific attributes, we used PSA to assess the relative risk of the 60 species interacting with the shrimp trawl fishery in the Bay of Bengal, Bangladesh. Penaeus monodon, the most important target, and Metapenaeus monoceros, the highest catch contributor, along with other 15 species were in the moderate-risk category, while seven non-target bycatch species were in the high-risk category. PSA-derived vulnerability results were validated with IUCN extinction risk, exploitation rate and stocks’ catch trend. The majority of the identified species showed high productivity (37%) and high susceptibility (46%), and all the moderately and highly vulnerable species were subjected to overfishing conditions by shrimp trawl fishery, which coincided with the vulnerability scores (V ≥ 1.8). Species with V ≥ 1.8 mostly showed a decreasing catch trend, while the species with a stable or increasing catch trend had a V ≤ 1.72. Data quality analysis of productivity and susceptibility attributes indicated that the majority of species were considered data-limited, which emphasizes the acquisition of data on spatio-temporal abundance, catch and effort, and biological information specifically relating to species age, growth, and reproduction. However, our findings can assist fishery administrators in implementing an ecosystem approach to ensure the sustainability and conservation of marine biodiversity in the Bay of Bengal.

Drought stress provokes plants to change their growth pattern and biochemical contents to overcome adverse situations. Soybean was grown under 40 (drought) and 80% (control) of field capacity (FC) to determine the morpho-physiological and biochemical alterations that occur under drought conditions. The experiment was conducted following a randomized complete block design with three replications. The results showed that drought exerted detrimental effects on photosynthetic attributes, leaf production, pigment and water content, plant growth, and dry matter production of soybean. However, drought favored producing a higher amount of proline and malondialdehyde in soybean leaf than in the control. The pod and seed production, grain size, and seed yield of soybean were also adversely affected by the drought, where genotypic variations were conspicuous. Interestingly, the studied morpho-physiological and biochemical parameters of AGS383 were minimally affected by drought. This genotype was capable of maintaining healthier root and shoot growth, greater leaf area, preserving leaf greenness and cell membrane stability, higher photosynthesis, absorbing water and sustaining leaf water potential, and lower amount of proline and malondialdehyde production under drought conditions. The heavier grains of AGS383 make it out yielder under both growth conditions. Considering the changes in morpho-physiological, biochemical, and yield contributing parameters, the genotype AGS383 could be cultivated as a relatively drought-tolerant, high-yielding soybean variety. Further study is needed to uncover the genes responsible for the adaptation of AGS383 to drought-stress environments, and this genotype might be used as parent material in a breeding program to develop a high-yielding, drought-tolerant soybean variety.

Endophytic bacteria, recognized as eco-friendly biofertilizers, have demonstrated the potential to enhance crop growth and yield. While the plant growth-promoting effects of endophytic bacteria have been extensively studied, the impact of weed endophytes remains less explored. In this study, we aimed to isolate endophytic bacteria from native weeds and assess their plant growth-promoting abilities in rice under varying chemical fertilization. The evaluation encompassed measurements of mineral phosphate and potash solubilization, as well as indole-3-acetic acid (IAA) production activity by the selected isolates. Two promising strains, tentatively identified as Alcaligenes faecalis (BTCP01) from Eleusine indica (Goose grass) and Metabacillus indicus (BTDR03) from Cynodon dactylon (Bermuda grass) based on 16S rRNA gene phylogeny, exhibited noteworthy phosphate and potassium solubilization activity, respectively. BTCP01 demonstrated superior phosphate solubilizing activity, while BTDR03 exhibited the highest potassium (K) solubilizing activity. Both isolates synthesized IAA in the presence of L-tryptophan, with the detection of nifH and ipdC genes in their genomes. Application of isolates BTCP01 and BTDR03 through root dipping and spraying at the flowering stage significantly enhanced the agronomic performance of rice variety CV. BRRI dhan29. Notably, combining both strains with 50% of recommended N, P, and K fertilizer doses led to a substantial increase in rice grain yields compared to control plants receiving 100% of recommended doses. Taken together, our results indicate that weed endophytic bacterial strains BTCP01 and BTDR03 hold promise as biofertilizers, potentially reducing the dependency on chemical fertilizers by up to 50%, thereby fostering sustainable rice production.

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.

Bangladesh is a deltaic country in Asia, and its riverine systems ultimately drain into the Bay of Bengal. Plastic is a severe environmental issue for coastal-marine ecosystems due to the indiscriminate usage and discarding of plastic items in the upstream river that eventually find their route into the Bay of Bengal. Microplastics (MPs) are widespread pollutants in almost all environmental compartments, including aquatic environments. This study aimed to quantify and understand the distribution of microplastics in surface water and sediments of the river Karnaphuli, a tidal confluence river adjacent to the Chattogram seaport city of Bangladesh, a highly inhabited and industrial area on the southeast coast of the Bay of Bengal. A manta trawl net (300-µm mesh size) was used to collect surface water samples, while an Ekman dredge was used to collect sediment samples. The concentrations of microplastics in the surface water of the river Karnaphuli during late monsoon, winter, and early summer were recorded to be 120,111.11, 152,222.22, and 164,444.44 items/km 2 , respectively, while in sediments, those were recorded to be 103.83, 137.50, and 103.67 items/kg, respectively. A higher abundance of microplastics was observed in downstream surface water (228,888.88 items/km 2 ) and sediments (164.17 items/kg). Smaller sizes (0.3 to 0.5 mm) of microplastics were predominant, fibers or threads were the frequent types, and black was the most common color in the river Karnaphuli. The Fourier transform infrared analysis revealed that polyethylene terephthalate (surface water: 22%, sediments: 19%), polyamide (surface water: 15%, sediments: 13%), polyethylene (surface water: 12%, sediments: 18%), polystyrene (surface water: 13%, sediments: 11%), and alkyd resin (surface water: 13%, sediments: 10%) were the most prevalent polymers in the river Karnaphuli. Moreover, there was a moderate positive correlation between MPs abundance in surface water and sediments. Therefore, improved long-term research (in different seasons with horizontal and vertical monitoring) is necessary in order to accurately determine the flux of microplastics from the river Karnaphuli to the Bay of Bengal.