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Abstract Objective Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metastatic cancer. Early detection of breast cancer is challenging due to the lack of early warning signs. Generally, a mammogram is recommended by specialists for screening. Existing approaches are not accurate enough for real‐time diagnostic applications and thus require better and smarter cancer diagnostic approaches. This study aims to develop a customized machine‐learning framework that will give more accurate predictions for IDC and metastasis cancer classification. Methods This work proposes a convolutional neural network (CNN) model for classifying IDC and metastatic breast cancer. The study utilized a large‐scale dataset of microscopic histopathological images to automatically perceive a hierarchical manner of learning and understanding. Results It is evident that using machine learning techniques significantly (15%–25%) boost the effectiveness of determining cancer vulnerability, malignancy, and demise. The results demonstrate an excellent performance ensuring an average of 95% accuracy in classifying metastatic cells against benign ones and 89% accuracy was obtained in terms of detecting IDC. Conclusions The results suggest that the proposed model improves classification accuracy. Therefore, it could be applied effectively in classifying IDC and metastatic cancer in comparison to other state‐of‐the‐art models.

Abstract Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

Abstract Background Fatigue has been observed after the outbreaks of several infectious diseases around the world. To explore the fatigue level among physicians working in COVID‐19‐designated hospitals in Bangladesh, a matched case‒control study was conducted on post‐SARS‐CoV‐2 fatigue. Method In this study, 105 physicians diagnosed with COVID‐19 who were declared cured at least 6 weeks before the interview date were recruited as cases, and the same number of age‐ and designation‐matched healthy physicians were recruited as controls from the same hospital at a 1:1 ratio. Diagnosis of COVID‐19 infection was confirmed by detection of SARS CoV‐2 antigen by RT‒PCR from reference laboratories in Bangladesh or by HRCT chest. Result Approximately two‐thirds of the physicians were male (67.6% vs. 32.4%). More than 80% of them were younger than 40 years. The cases had a significantly greater number of comorbid conditions. The fatigue severity scale (FSS) score (mean) was much higher for cases (36.7 ± 5.3 vs. 19.3 ± 3.8) than for the control group, with a statistically significant difference. Similarly, approximately 67.7% of the previously COVID‐19‐positive physicians were in the highest FSS score tertile compared to the respondents in the control group, who had a mean score of

Abstract Introduction Lumpy skin disease, an economically significant bovine illness, is now found in previously unheard‐of geographic regions. Vaccination is one of the most important ways to stop its further spread. Aim Therefore, in this study, we applied advanced immunoinformatics approaches to design and develop an effective lumpy skin disease virus (LSDV) vaccine. Methods The membrane glycoprotein was selected for prediction of the different B‐ and T‐cell epitopes by using the immune epitope database. The selected B‐ and T‐cell epitopes were combined with the appropriate linkers and adjuvant resulted in a vaccine chimera construct. Bioinformatics tools were used to predict, refine and validate the 2D, 3D structures and for molecular docking with toll‐like receptor 4 using different servers. The constructed vaccine candidate was further processed on the basis of antigenicity, allergenicity, solubility, different physiochemical properties and molecular docking scores. Results The in silico immune simulation induced significant response for immune cells. In silico cloning and codon optimization were performed to express the vaccine candidate in Escherichia coli. This study highlights a good signal for the design of a peptide‐based LSDV vaccine. Conclusion Thus, the present findings may indicate that the engineered multi‐epitope vaccine is structurally stable and can induce a strong immune response, which should help in developing an effective vaccine towards controlling LSDV infection.

Abstract Joint diseases, such as osteoarthritis, often require delivery of drugs to chondrocytes residing within the cartilage. However, intra‐articular delivery of drugs to cartilage remains a challenge due to their rapid clearance within the joint. This problem is further exacerbated by the dense and negatively charged cartilage extracellular matrix (ECM). Cationic nanocarriers that form reversible electrostatic interactions with the anionic ECM can be an effective approach to overcome the electrostatic barrier presented by cartilage tissue. For an effective therapeutic outcome, the nanocarriers need to penetrate, accumulate, and be retained within the cartilage tissue. Nanocarriers that adhere quickly to cartilage tissue after intra‐articular administration, transport through cartilage, and remain within its full thickness are crucial to the therapeutic outcome. To this end, we used ring‐opening polymerization to synthesize branched poly(l‐lysine) (BPL) cationic nanocarriers with varying numbers of poly(lysine) branches, surface charge, and functional groups, while maintaining similar hydrodynamic diameters. Our results show that the multivalent BPL molecules, including those that are highly branched (i.e., generation two), can readily adhere and transport through the full thickness of cartilage, healthy and degenerated, with prolonged intra‐cartilage retention. Intra‐articular injection of the BPL molecules in mouse knee joint explants and rat knee joints showed their localization and retention. In summary, this study describes an approach to design nanocarriers with varying charge and abundant functional groups while maintaining similar hydrodynamic diameters to aid the delivery of macromolecules to negatively charged tissues.

Abstract Introduction Gemcitabine (GEM) is often used to treat pancreatic cancer. Many anti‐cancer drugs induce cancer cell death, but some cells survive after cell cycle arrest. Such a response to DNA damage is termed cellular senescence. Certain drugs, including the Bcl‐2‐family inhibitor ABT‐263, kill senescent cells; this is termed senolysis. In this study, we examined the therapeutic benefits of ABT‐263 in GEM‐induced senescence of human pancreatic cancer cells. Methods and Results Of four pancreatic cancer cell lines (PANC‐1, AsPC‐1, CFPAC‐1, and PANC10.05), GEM induced senescent features in PANC‐1 and AsPC‐1 cells, including increases in the cell sizes and expression levels of mRNAs encoding interleukin (IL)‐6/IL‐8 and induction of β‐galactosidase. Successive treatment with GEM and ABT‐263 triggered apoptosis in PANC‐1 and AsPC‐1 cells and suppressed colony formation significantly. Senolysis of GEM‐induced senescent pancreatic cancer cells by ABT‐263 was triggered by a Bcl‐xL inhibitor, but not by a Bcl‐2 inhibitor, suggesting a central role for Bcl‐xL in senolysis. In a xenograft mouse model, combined treatment with GEM and ABT‐737 (an ABT‐263 analog exhibiting the same specificity) suppressed in vivo growth of AsPC‐1 significantly. Conclusion Together, our results indicate that sequential treatment with GEM and senolytic drugs effectively kill human pancreatic cancer cells.

Abstract Background Factors influencing parasitosis in cattle in Bangladesh remain inadequately explored, necessitating a comprehensive investigation for interventions and sustainable livestock farming. Objectives We conducted this study to estimate the prevalence and distribution of gastrointestinal parasites, exploring their intricate relationship with farm management practices across a spectrum of small‐, medium‐, and large‐scale commercial farms. Methods We conducted this study in the Chattogram district of Bangladesh. We collected a total of 189 freshly voided faecal samples from different farms. We recorded the age, breed, milking status, sex, body condition score, and anthelmintic use history of the sampled animals. We processed the samples using the direct smear method, with the identification of one egg per sample being considered positive. Results We estimated the prevalence of gastrointestinal parasite infection in large‐scale (52.1%), medium‐scale (54.5%), and small‐scale farms (70.0%), with statistically significant differences (p ≤ 0.05). Both pregnant and lactating cows, as well as indigenous cattle, were more likely to have gastrointestinal parasites (p ≤ 0.05). The predominant parasites across farms of all sizes were trematodes (Paramphistomum spp. and Schistosomas spp.) and protozoa (Balantidium coli and Coccidia spp.). Conclusion Poor farm management practices, such as no pasture management and inadequate deworming regimens, may contribute to the elevated prevalence and infection load observed on small‐scale farms. The increased parasitosis in previously dewormed animals can be attributed to the development of anthelmintic resistance against gastrointestinal parasites. Implementing proper and effective deworming strategies is crucial to preventing gastrointestinal parasitosis and mitigating the risk of anthelmintic resistance.

Abstract Background Bovine genital campylobacteriosis (BGC) is a venereal disease caused by Campylobacter fetus that has a negative impact on animal reproduction. The bull is considered to be a symptomless carrier that spreads the disease agent to breeding cows, causing infertility and sporadic abortion. Aim The study aims to estimate the prevalence, identify risk factors of Campylobacter fetus (C. fetus) infection and antimicrobial resistance pattern of the C. fetus isolates. Method A cross‐sectional survey was conducted in Mymensingh district of Bangladesh. Bull smegma samples (single sample from each bull) were collected from 300 bulls from four farms and tested via culture, biochemical identification and finally 16S rRNA and cdtA gene‐based molecular assays (PCR) for herd and animal‐level prevalence estimation. Herd‐ and animal‐level data on risk factors were collected from the farmers using a pretested questionnaire and analysed by univariable and multivariable logistic regression models with a p value of

Abstract Bone injuries such as fractures are one major cause of morbidities worldwide. A considerable number of fractures suffer from delayed healing, and the unresolved acute pain may transition to chronic and maladaptive pain. Current management of pain involves treatment with NSAIDs and opioids with substantial adverse effects. Herein, we tested the hypothesis that the purine molecule, adenosine, can simultaneously alleviate pain and promote healing in a mouse model of tibial fracture by targeting distinctive adenosine receptor subtypes in different cell populations. To achieve this, a biomaterial‐assisted delivery of adenosine is utilized to localize and prolong its therapeutic effect at the injury site. The results demonstrate that local delivery of adenosine inhibited the nociceptive activity of peripheral neurons through activation of adenosine A1 receptor (ADORA1) and mitigated pain as demonstrated by weight bearing and open field movement tests. Concurrently, local delivery of adenosine at the fracture site promoted osteogenic differentiation of mesenchymal stromal cells through adenosine A2B receptor (ADORA2B) resulting in improved bone healing as shown by histological analyses and microCT imaging. This study demonstrates the dual role of adenosine and its material‐assisted local delivery as a feasible therapeutic approach to treat bone trauma and associated pain.

Abstract Diabetic Retinopathy (DR) grading into different stages of severity continues to remain a challenging issue due to the complexities of the disease. Diabetic Retinopathy grading classifies retinal images to five levels of severity ranging from 0 to 5, which represents No DR, Mild non‐proliferative diabetic retinopathy (NPDR), Moderate NPDR, Severe NPDR, and proliferative diabetic retinopathy. With the advancement of Deep Learning, studies on the application of the Convolutional Neural Network (CNN) in DR grading have been on the rise. High accuracy and sensitivity are the desired outcome of these studies. This paper reviewed recently published studies that employed CNN for DR grading to 5 levels of severity. Various approaches are applied in classifying retinal images which are, (i) by training CNN models to learn the features for each grade and (ii) by detecting and segmenting lesions using information about their location such as microaneurysms, exudates, and haemorrhages. Public and private datasets have been utilised by researchers in classifying retinal images for DR. The performance of the CNN models was measured by accuracy, specificity, sensitivity, and area under the curve. The CNN models and their performance varies for every study. More research into the CNN model is necessary for future work to improve model performance in DR grading. The Inception model can be used as a starting point for subsequent research. It will also be necessary to investigate the attributes that the model uses for grading.

Abstract Background Staphylococcus spp. are the major causal agents of mastitis in dairy animals worldwide leading to profound economic losses and public health threats. Recently, Staphylococcus aureus has emerged as a multidrug resistant and zoonotic pathogen. This study aimed to characterize S. aureus in subclinical mastitis (SCM) milk samples of riverine buffaloes in Bangladesh through antibiogram and virulence gene(s) profiling, and 16S rRNA gene sequencing. Method We characterized S. aureus in SCM milk samples (N = 500) of riverine buffaloes through antibiogram and virulence gene(s) profiling, and 16S rRNA gene sequencing. Results Out of 500 milk samples tested, 188 (37.6%) were found positive for SCM. From 188 SCM samples, 291 isolates were obtained with a prevalence of S. aureus in 37.4% (109/291) isolates. Phylogenetic analysis revealed the evolutionary divergence of S. aureus isolates in bubaline SCM milk samples. The antibiogram profiling showed that about 96.0% S. aureus isolates were multidrug resistant (MDR). Notably, 29 and 16 isolates harboured methicillin‐resistant (mecA) and panton‐valentine leucocidin (pvl) genes, respectively, and 46 plasmid‐bearing isolates were MDR. Nine Staphylococcal enterotoxins (SEs/SEls) including sea (11.9%), sec (7.4%), sed (4.6%), seg (3.7%), and seh (3.7%) were detected with 72.48% toxinotypes comprising a single gene. Conclusion This study therefore suggests S. aureus as the single‐most aetiology (∼37.0%) of SCM in riverine buffaloes, and emergence of MDR, enterotoxin producing, and virulent S. aureus strains could impose potential threats to animal welfare and public health.

Abstract The winged bean, Psophocarpus tetragonolobus (L.) D.C., is called the four‐angled bean or Goa bean, which is an underutilized, non‐conventional, and multipurpose tropical legume cultivated in Southeast Asia and Papua New Guinea. The objective of this article was to reveal a comprehensive view on the nutritional properties, biochemical compositions, health benefits, processing, and food applications of winged beans. The average moisture, carbohydrate, protein, fat, crude fiber, and ash content of 138 genotypes of winged bean seeds were found as 9.09 ± 1.73%, 26.81 ± 6.88%, 34.98 ± 4.63%, 18.01 ± 2.27%, 10.24 ± 5.49%, and 4.16 ± 0.41%, respectively. Leaves of winged beans are good sources of vitamins such as vitamin C (14.5–128 mg/100 g), thiamine, riboflavin, niacin, vitamin B6, folate, vitamin A (5240–20,800 IU), and vitamin E. The seed of winged beans is rich in calcium, iron, phosphorus, zinc, and copper. The anti‐nutritional factors, namely, trypsin inhibitor, chymotrypsin inhibitor, phytic acid, saponin, tannin, oxalate, and flatulence saccharides of winged bean, were reported to be in the range of 40–99.5 TIU/mg of protein, 86.4–109.6 CIU/mg of protein, 4.09%–9.96%, 0.6%, 0.77%–0.97%, 0.5%, and 0.04%–0.18%, respectively. This bean can prevent diabetes, cancer, and asthma, boost immunity, and promote women's and men's reproductive health. Winged are transformed into consumable form by processing methods such as soaking, cooking, sprouting, fermentation, roasting, and so forth. This bean is processed into traditional products (curries, soup, pickles, etc.) and contemporary products (milk, tofu, tempeh, etc.). Future research should focus on promoting its utilization as an alternative to soybeans toward meeting global food and nutritional security.

Abstract Demyelinating disorders are among the most common and debilitating diseases in neurology. Canavan disease (CD) is a lethal demyelinating disease caused by mutation of the aspartoacylase (ASPA) gene, which leads to the accumulation of its substrate N‐acetyl‐l‐aspartate (NAA), and consequently demyelination and vacuolation in the brain. In this study, hypoimmunogenic human induced pluripotent stem cell (iPSC)‐derived oligodendrocyte progenitor cells (OPC) are developed from a healthy donor as an “off‐the‐shelf” cell therapy. Hypoimmunogenic iPSCs are generated through CRISPR/Cas9 editing of the human leukocyte antigen (HLA) molecules in healthy donor‐derived iPSCs and differentiated into OPCs. The OPCs are engrafted into the brains of CD (nur7) mice and exhibit widespread distribution in the brain. The engrafted OPCs mature into oligodendrocytes that express the endogenous wildtype ASPA gene. Consequently, the transplanted mice exhibit elevated human ASPA expression and enzymatic activity and reduced NAA level in the brain. The transplanted OPCs are able to rescue major pathological features of CD, including defective myelination, extensive vacuolation, and motor function deficits. Moreover, the hypoimmunogenic OPCs exhibit low immunogenicity both in vitro and in vivo. The hypoimmunogenic OPCs can be used as “off‐the‐shelf” universal donor cells to treat various CD patients and many other demyelinating disorders, especially autoimmune demyelinating diseases, such as multiple sclerosis.

Abstract A hexagonal outlined porous cladding with vacant core photonic crystal fibre (HOPC‐VC‐PCF) sensor using Zeonex has been presented, which can detect cyanides at the THz regime. Cyanides are incredibly lethal chemicals to humans as they can rapidly cause impairment or even death; such harmful substances must be identified precisely. The proposed model has been analysed at a broad spectrum of THz regimes for three different analytes, and the numerical investigation is performed using the Finite element method (FEM). Simulation results exhibit that at optimum design parameters, extremely high sensitivity of 99.75%, negligible confinement loss of 0.5 × 10−13 cm−1, extremely low and flat dispersion of 0.12 ps THz/cm, and other excellent features were obtained. The performance analysis and the design methodology have been depicted in detail. Due to the simplistic design, fabrication of the model is easily achievable by current technology. The remarkable sensing capabilities will not only make it suited in chemicals analysis but also will play vital roles in other applications as well.

Abstract Background and Aims Since the beginning of the SARS‐CoV‐2 pandemic, multiple new variants have emerged posing an increased risk to global public health. This study aimed to investigate SARS‐CoV‐2 variants, their temporal dynamics, infection rate (IFR) and case fatality rate (CFR) in Bangladesh by analyzing the published genomes. Methods We retrieved 6610 complete whole genome sequences of the SARS‐CoV‐2 from the GISAID (Global Initiative on Sharing all Influenza Data) platform from March 2020 to October 2022, and performed different in‐silico bioinformatics analyses. The clade and Pango lineages were assigned by using Nextclade v2.8.1. SARS‐CoV‐2 infections and fatality data were collected from the Institute of Epidemiology Disease Control and Research (IEDCR), Bangladesh. The average IFR was calculated from the monthly COVID‐19 cases and population size while average CFR was calculated from the number of monthly deaths and number of confirmed COVID‐19 cases. Results SARS‐CoV‐2 first emerged in Bangladesh on March 3, 2020 and created three pandemic waves so far. The phylogenetic analysis revealed multiple introductions of SARS‐CoV‐2 variant(s) into Bangladesh with at least 22 Nextstrain clades and 107 Pangolin lineages with respect to the SARS‐CoV‐2 reference genome of Wuhan/Hu‐1/2019. The Delta variant was detected as the most predominant (48.06%) variant followed by Omicron (27.88%), Beta (7.65%), Alpha (1.56%), Eta (0.33%) and Gamma (0.03%) variant. The overall IFR and CFR from circulating variants were 13.59% and 1.45%, respectively. A time‐dependent monthly analysis showed significant variations in the IFR (p = 0.012, Kruskal–Wallis test) and CFR (p = 0.032, Kruskal–Wallis test) throughout the study period. We found the highest IFR (14.35%) in 2020 while Delta (20A) and Beta (20H) variants were circulating in Bangladesh. Remarkably, the highest CFR (1.91%) from SARS‐CoV‐2 variants was recorded in 2021. Conclusion Our findings highlight the importance of genomic surveillance for careful monitoring of variants of concern emergence to interpret correctly their relative IFR and CFR, and thus, for implementation of strengthened public health and social measures to control the spread of the virus. Furthermore, the results of the present study may provide important context for sequence‐based inference in SARS‐CoV‐2 variant(s) evolution and clinical epidemiology beyond Bangladesh.

Image processing applications remarkably contributes to modern ophthalmology. This technology is designed to analyse the characteristics of the human eye microvasculature images. The retinal microvasculature is an excellent non‐invasive screening window for the assessment of systemic diseases such as diabetes, hypertension, and stroke. Retinal microvasculature character such as widening vessel diameter is recognised as an analysable feature for stroke or transient ischemic attack for predicting the progression of this pathology. Thus, in this study, a computer‐assisted method has been developed for this task applying the Euclidean distance transform (EDT) technique. This newly developed algorithm computes the Euclidean distance of the remaining white pixels on the area of interest. Central Light Reflex Image Set (CLRIS) and Vascular Disease Image Set (VDIS) of Retinal Vessel Image set for Estimation of Width database were used for the performance evaluation of the proposed algorithm that showed 98.1 and 97.7% accurate result for both CLRIS and VDIS, respectively. The significantly high accuracy in this newly developed vessel diameter quantification algorithm indicates excellent potential for further development, evaluation, validation, and integration into ophthalmic diagnostic instruments.

Abstract Rectal stenosis is a rare variety of rectal atresia. A membrane separates the rectum from the anal canal in the presence of a normal anus. We report a case of rectal stenosis associated with Down's syndrome and hypothyroidism in whom rectal stenosis was diagnosed at the age of 17 years.