Search

OBJECTIVE: Goal 3 (SDG) of the 2030 Agenda for Sustainable Development, adopted by India in 2015, seeks to “Ensure healthy lives and promote well-being for all at all ages” by 2030 and covers 13 broad target areas. Diagnosis is a major milestone in disease identification and, hence, planning of management. Charaka Samhita, which is the major treatise of Ayurveda explains the disease diagnostic principles through eight chapters of Nidana Sthana. Another irony is the Ayurveda texts, documented thousands of years ago, offers cures to the diseases of all the times, many of which were not even prevalent at the time of the documentation of the Samhita and hence are not mentioned in the same. The present paper aims to highlight the competence of the Nidana Sthana of Charaka Samhita in diagnosing diseases that are not listed directly in the same. Data Source: Charaka Samhita, along with its available commentaries and related research works. Review Methods: The Charaka Samhita and its available commentaries were thoroughly explored and critically analyzed to understand the rationale behind the Ashta-Adhyaya of Nidana Sthana and their relevance in decoding the pathogenesis of the unstated diseases through the stated. Contemporary research works in the context were explored and analyzed to elicit evidence. Results and Conclusion: The documentation of scientific writing should be pertinent and comprehensive. Charaka Samhita is documented scientifically, where the relevant data are quoted at specific instances, assuring no repetition, undue elaboration, or abridgment. The Nidana Sthana comprising the eight chapters lays the foundation for diagnosing diseases of diverse pathogenesis due to the dominance of three doshas, which can be applied to understand the unstated conditions based on Yukti.

Background: Urbanization, sedentary lifestyles, and dietary changes have all contributed to an increase in the prevalence of metabolic syndrome (MetS) in Indian populations during the past 10 years. Numerous markers have been investigated to determine if a person is at risk for developing MetS, with the bulk of them having to do with adipose tissue. Recently, adiponectin and leptin, two biomarkers with a high correlation to cardiometabolic health or disease, are of particular interest. Methods: In the general population of India, 100 persons were included. Body mass index (BMI), waist circumference, systolic and diastolic blood pressure, fasting blood glucose, plasma lipids, adiponectin, leptin, insulin, and the homeostasis model were measured to assess insulin resistance. We used binary logistic regression analysis to determine the connection between the researched factors and MetS and Spearman’s analyses to evaluate correlations. Results: In all, 200 participants (100 men and 100 women) were enrolled in the study. Men’s and women’s median ages were 53 and 48, respectively (P < 0.05). Men had significantly greater WHR, SBP, and DBP (P < 0.05, respectively). Women had significantly higher levels of triglycerides, LDL, insulin, adiponectin, leptin, and HOMA-IR (P < 0.05, respectively). Leptin-to-adiponectin ratio was significantly and positively correlated with BMI (r = 0.597, P < 0.001), waist circumference (r = 0.576, P < 0.001), triglycerides (r = 0.190, P = 0.001), insulin levels (r = 0.329, P < 0.000), and HOMA-IR (r = 0.301, P < 0.000). Conclusion: In this study, higher levels of LAR, together with higher levels of leptin and lower levels of adiponectin, were found to be significantly linked with MetS. To properly determine whether LAR can be a predictor of MetS, independent of confounding factors, research with adequate design must be conducted.

Abstract Polycystic Ovarian Syndrome (PCOS) is a multifaceted metabolic and hormonal condition that impacts women in their procreative ages, identified by ovarian dysfunction, hyperandrogenaemia overweight and insulin insensitivity. The piperine, an important alkaloid compound of black pepper has shown promise in modulating various physiological processes. In this work, employed computational docking studies to explore the potential of piperine as a treatment for PCOS. Utilizing computational methods, we analyzed the binding interactions between piperine and key molecular targets implicated in PCOS pathogenesis, including hyperandrogenism, and “oligomenorrhea. The network pharmacology analysis report found 988 PCOS-related genes, 108 hyperandrogenism-related genes, and 377 oligomenorrhea-related genes, and we finally shortlisted 5 common genes in PCOS, hyperandrogenism, and “oligomenorrhea”: NR3C1, PPARG, FOS, CYP17A1, and H6PD. Our results reveal favorable binding affinities with PPARG (-8.34 Kcal/mol) and H6PD (-8.70 Kcal/mol) and interaction patterns, suggesting the potential of piperine to modulate these targets. Moreover, the reliability of the piperine-target interactions was revealed by molecular simulations studies. These findings support further experimental investigations to validate the therapeutic efficacy of piperine in PCOS management. The integration of computational approaches with experimental studies has the potential to lay the groundwork for the creation of new therapies specifically targeting PCOS and related endocrine disorders.

The untreated municipal solid wastes (MSWs) dumping and improper management causes major concerns of environmental degradation and human health risks. In this study, we collected soil and vegetable from the MSWs dumpsites of Khulna City Corporation (KCC) in southwestern Bangladesh. Trace metals (Pb, Cd, Co, Cr, Cu, Ni, Zn, Fe, Mn) were measured to explore the health risk of cultivated vegetables from dumpsites. Soil contamination was evaluated by geoaccumulation index (Igeo), enrichment factor (EF), contamination factor (CF), and pollution load index (PLI), while health risk was evaluated by transfer factor (TF), estimated daily intake (EDI), target hazard quotient (THQ). Results exhibited that the average trace metals in soil and vegetables followed in the order of Fe > Pb > Mn > Zn > Cu > Cr > Ni > Cd > Co. The Igeo, EF, CF, and PLI values revealed that the soil contamination was dominated by Pd, Cd, Mn, and Zn. The EDI of metals in vegetables were exceed the maximum daily intake only for Fe, Pb, and Cr. The total THQ was > 1, implying potential health hazards for the local people due to the long-term consumption of the cultivated vegetables. The multivariate analysis reveled that the sources of trace metals in the soils and vegetables of dumpsites were natural and anthropogenic. Overall, the findings suggest that growing vegetables in dumpsite is unsafe for long-term consumption by local inhabitants. Immediate action should be taken to protect the environment and human health from trace metal hazards.

Bioimpedance measurements are becoming important in probing the human body for diagnosis and monitoring. An age old 4-electrode technique called tetrapolar impedance measurement (TPIM), giving transfer impedance, cannot localize a specific zone besides having large zones of negative sensitivity. A new technique named the focused impedance method (FIM) from Dhaka University (DU), Bangladesh used the algebraic average of two concentric and orthogonal TPIMs, localizing a zone of interest and having reduced magnitudes of negative sensitivity. Earlier, this was implemented with electrodes applied from one side of the human body giving information to shallow depths only. To get information from deeper regions, specifically, of the thorax, the same DU group placed two electrodes of a 4-electrode version of FIM at the front and two at the back in a horizontal plane of the thorax, using physics-based visualization. This was followed by a few quantitative studies using point sensitivity, which supported the concept. However, more quantitative studies still need to be performed, particularly using objects of finite sizes, in order to establish the technique on a stronger footing. The present study was taken up with this objective. A simplified approach was used in which the volume conductor was a rectangular non-conducting container filled with saline of uniform conductivity with an embedded spherical object – first an insulator and then a conductor. Electrodes were placed at specific chosen positions following the above visualization. Percentage change in transfer impedance with the object placed at different internal positions, compared to that without the object was obtained first using COMSOL simulation and then through experimental measurements. These were performed for both TPIM and FIM. The new configuration of 4-electrode FIM gave good depth sensitivity supporting the effectiveness of the new placement of electrodes.

Abstract Background Cluster randomized trials (CRTs) are randomized trials where randomization takes place at an administrative level (e.g., hospitals, clinics, or schools) rather than at the individual level. When the number of available clusters is small, researchers may not be able to rely on simple randomization to achieve balance on cluster-level covariates across treatment conditions. If these cluster-level covariates are predictive of the outcome, covariate imbalance may distort treatment effects, threaten internal validity, lead to a loss of power, and increase the variability of treatment effects. Covariate-constrained randomization (CR) is a randomization strategy designed to reduce the risk of imbalance in cluster-level covariates when performing a CRT. Existing methods for CR have been developed and evaluated for two- and multi-arm CRTs but not for factorial CRTs. Methods Motivated by the BEGIN study—a CRT for weight loss among patients with pre-diabetes—we develop methods for performing CR in 2 × 2 factorial cluster randomized trials with a continuous outcome and continuous cluster-level covariates. We apply our methods to the BEGIN study and use simulation to assess the performance of CR versus simple randomization for estimating treatment effects by varying the number of clusters, the degree to which clusters are associated with the outcome, the distribution of cluster level covariates, the size of the constrained randomization space, and analysis strategies. Results Compared to simple randomization of clusters, CR in the factorial setting is effective at achieving balance across cluster-level covariates between treatment conditions and provides more precise inferences. When cluster-level covariates are included in the analyses model, CR also results in greater power to detect treatment effects, but power is low compared to unadjusted analyses when the number of clusters is small. Conclusions CR should be used instead of simple randomization when performing factorial CRTs to avoid highly imbalanced designs and to obtain more precise inferences. Except when there are a small number of clusters, cluster-level covariates should be included in the analysis model to increase power and maintain coverage and type 1 error rates at their nominal levels.

This study explores a novel approach utilizing acoustic emission (AE) signaling technology for pipeline leakage detection and analysis. Pipeline leaks are a significant concern in the liquids and gases industries, prompting the development of innovative detection methods. Unlike conventional methods, which often require contact and visual inspection with the pipeline surface, the proposed time-series-based deep learning approach offers real-time detection with higher safety and efficiency. In this study, we propose an automatic detection system of pipeline leakage for efficient transportation of liquid (water) and gas across the city, considering the smart city approach. We propose an AE-based framework combined with time-series deep learning algorithms to detect pipeline leaks through time-series features. The time-series AE signal detection module is designed to capture subtle changes in the AE signal state caused by leaks. Sequential deep learning models, including long short-term memory (LSTM), bi-directional LSTM (Bi-LSTM), and gated recurrent units (GRUs), are used to classify the AE response into normal and leakage detection from minor seepage, moderate leakage, and major ruptures in the pipeline. Three AE sensors are installed at different configurations on a pipeline, and data are acquired at 1 MHz sample/sec, which is decimated to 4K sample/second for efficiently utilizing the memory constraints of a remote system. The performance of these models is evaluated using metrics, namely accuracy, precision, recall, F1 score, and convergence, demonstrating classification accuracies of up to 99.78%. An accuracy comparison shows that BiLSTM performed better mostly with all hyperparameter settings. This research contributes to the advancement of pipeline leakage detection technology, offering improved accuracy and reliability in identifying and addressing pipeline integrity issues.

Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, and conductivity), and fault detection in the processes and equipment integral to wastewater treatment. The prediction accuracy (R2 value) of AI technologies for pollutant removal has been reported to vary between 0.64 and 1.00. A critical aspect explored in this review is the cost-effectiveness of implementing AI systems in wastewater treatment. Numerous countries and municipalities are actively engaging in pilot projects and demonstrations to assess the feasibility and effectiveness of AI applications in wastewater treatment. Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater treatment on a global scale. Further, insights on the ethical considerations and potential future directions for the use of AI in wastewater treatment plants have also been provided. HIGHLIGHTS Artificial intelligence (AI) has immense potential to optimize the wastewater treatment processes.; Prediction, real-time monitoring, and fault detection using AI have been reviewed.; Cost-effectiveness of implementing AI in wastewater treatment has been discussed.; Adaptability and impact of AI in wastewater treatment globally has been highlighted.;

Abstract Background Worldwide, a significant number of girls become mothers during adolescence. In Bangladesh, adolescent childbirth is highly prevalent and has adverse effects on children’s health and undernutrition. We aimed to identify the relationship between the undernutrition of children and adolescent motherhood, the factors associated with adolescent mothers’ age at first birth, and to examine the programmatic factors and gaps influencing children’s undernutrition in Bangladesh. Methods We analysed the ‘Bangladesh Demographic and Health Survey’ BDHS-17-18 data and desk review. To examine the factors associated with adolescent motherhood and its impact on child undernutrition, data from 7,643 mother-child pairs were selected. Child stunting, wasting, and underweight were measured according to the World Health Organisation (WHO) median growth guidelines based on z-scores − 2. Univariate, bivariate, simple, and multiple logistic regressions were used for analyse. We followed the systematic procedures for the literature review. Results Approximately, 89% of adolescents aged ≤ 19 years were married and 71% of them gave their first childbirth. Children of adolescent mothers (≤ 19 years) were significantly 1.68 times more wasted (aOR: 1.68; 95% CI: 1.08 to 2.64), 1.37 times more underweight (aOR: 1.37; 95% CI: 1.01 to 1.86) and either form 1.32 times more stunting, wasting or underweight (aOR:1.32; 95% Cl: 1.05 to 1.66) compared to the children of adult mothers (> 19 years) after adjusting potential confounders. The factors associated with mothers’ first childbirth during adolescence were the age gap between husband and wife 5–10 years (aOR: 1.81; 95% Cl: 1.57–2.10) and age gap > 10 years (aOR: 2.41; 95% Cl: 1.96–2.97) compared with the age group

Abstract Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls in dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite is added to concrete mixes for the adsorption of toxic metals. The modified design of BPC, as compared to normal concrete, requires a reliable tool to predict its strength. Thus, this study presents a novel attempt at the application of two innovative evolutionary techniques known as multi-expression programming (MEP) and gene expression programming (GEP) and a boosting-based algorithm known as AdaBoost to predict the 28-day compressive strength ( ) of BPC based on its mixture composition. The MEP and GEP algorithms expressed their outputs in the form of an empirical equation, while AdaBoost failed to do so. The algorithms were trained using a dataset of 246 points gathered from published literature having six important input factors for predicting. The developed models were subject to error evaluation, and the results revealed that all algorithms satisfied the suggested criteria and had a correlation coefficient (R) greater than 0.9 for both the training and testing phases. However, AdaBoost surpassed both MEP and GEP in terms of accuracy and demonstrated a lower testing RMSE of 1.66 compared to 2.02 for MEP and 2.38 for GEP. Similarly, the objective function value for AdaBoost was 0.10 compared to 0.176 for GEP and 0.16 for MEP, which indicated the overall good performance of AdaBoost compared to the two evolutionary techniques. Also, Shapley additive analysis was done on the AdaBoost model to gain further insights into the prediction process, which revealed that cement, coarse aggregate, and fine aggregate are the most important factors in predicting the strength of BPC. Moreover, an interactive graphical user interface (GUI) has been developed to be practically utilized in the civil engineering industry for prediction of BPC strength.

Abstract The creation of the National Register of Citizens (NRC) in Assam is indicative of the sharpening tensions surrounding citizenship, belonging and integration in India. Officially aimed at demarcating the “legitimate citizens”, its implementation is believed to have resulted in the partial exclusion of the so-called “Doubtful Voters” and denationalisation of the “illegitimate residents”. These frictions associated with citizenship identity and rights are nowhere as acute as in the northeastern Indian state of Assam, where measures of retroactive revocation, administrative erasure and withdrawal of citizenship rights have been systematically deployed against religious and linguistic minorities. Using new research with some NRC rejected applicants in western Assam and other materials, this article identifies the central aspects of the implementation gap in the crucial, albeit problematic task of locating the rightful “Assamese-Indian” citizens. Linking our work to the idea of the ‘process is the punishment’, we conceptualise these conspicuous inconsistencies in the NRC citizenship determination processes and their results as the “punitive gap”. We have identified the distinctive contours of this gap in terms of the massive economic costs, intensification of social (including gender and religion-based) inequalities, increased control through social suspicion and unpredictable outcomes for the marginal Miya Muslim community. The article highlights how this punitive gap has constantly eroded key components of due process, of procedural and substantive protections of the rights of individuals, during the NRC determination exercise and after the release of the final draft list.

Abstract Soil dissolved organic matter (DOM) is crucial to atmospheric, terrestrial and aquatic environments as well as human life. Here, by characterizing DOM from 89 grassland soils throughout China, we reveal the spatial association between DOM geochemistry in the dry season vs annual ecosystem exchange and cancer cases. The humic-like and high molecular weight (3.4–25 kDa) fractions with lower biodegradability, decline from the northern to the southern regions of China, and are correlated with lower soil respiration and net ecosystem productivity at the continental scale. The

Abstract The utilization of Self-compacting Concrete (SCC) has escalated worldwide due to its superior properties in comparison to normal concrete such as compaction without vibration, increased flowability and segregation resistance. Various other desirable properties like ductile behaviour, increased strain capacity and tensile strength etc. can be imparted to SCC by incorporation of fibres. Thus, this study presents a novel approach to predict 28-day compressive strength (C–S) of FR-SCC using Gene Expression Programming (GEP) and Multi Expression Programming (MEP) for fostering its widespread use in the industry. For this purpose, a dataset had been compiled from internationally published literature having six input parameters including water-to-cement ratio, silica fume, fine aggregate, coarse aggregate, fibre, and superplasticizer. The predictive abilities of developed algorithms were assessed using error metrices like mean absolute error (MAE), a20-index, and objective function (OF) etc. The comparison of MEP and GEP models indicated that GEP gave a simple equation having lesser errors than MEP. The OF value of GEP was 0.029 compared to 0.031 of MEP. Thus, sensitivity analysis was performed on GEP model. The models were also checked using some external validation checks which also verified that MEP and GEP equations can be used to forecast the strength of FR-SCC for practical uses.

Abstract Background Water pollution in densely populated urban areas, mainly from municipal wastewater, poses a significant threat. Pathogenic bacteria, such as Vibrio spp. and fecal coliform, endanger public health and the environment. Additionally, antibiotic-resistant bacteria in wastewater complicate treatment and heighten public health concerns. Methods The study sampled municipal wastewater from ten Dhaka neighborhoods, selecting treatment plants, sewage outlets, and various collection points using meticulous techniques for representative samples. Bacteriological and biochemical analyses were conducted using standardized methods. Antimicrobial susceptibility testing (AST) was performed with the disk diffusion method against 13 widely used antibiotics. Results All sampled areas exhibited positive results for Vibrio spp., fecal coliform, E. coli, and Salmonella spp. Varying bacterial concentrations were observed, with the highest concentration of TVC, total vibrio spp., and total fecal coliform, total E. coli count, and total Salmonella spp. were found in Uttara (1.9 × 104 CFU/ml), Bangshal (1.8 × 102 CFU/ml), and Lalbag (2.1 × 103 CFU/ml), Mirpur (3.70 × 102 CFU/ml), and Lalbag (6 × 102 CFU/ml) respectively. AST results revealed significant resistance among all bacterial species to various antibiotics. Specifically, Vibrio spp. showed 100% resistance to cefuroxime, fecal coliform exhibited 90% resistance to cephradine, E. coli demonstrated 60% resistance to cephradine, and Salmonella spp. displayed 90% resistance to ampicillin. Conclusion The study highlights the existence of multiple antibiotic-resistant bacteria in Dhaka's wastewater. Addressing antibiotic resistance is essential to manage the risks of multiple antibiotic-resistant infections and maintain antibiotic effectiveness. These implications are critical for various stakeholders, including public health officials, policymakers, environmentalists, and urban planners.

Breast cancer is the biggest cause of death among women worldwide. Natural chemicals from medicinal plants offer promise for cancer therapy. This research screens 29 Dryopteris species plant-derived chemicals, mostly phloroglucinols, for breast cancer therapy potential. First, we used Gaussian09 and DFT/B3LYP/6-311+G(d, p) calculations to evaluate compound stability and reactivity. We conducted molecular docking experiments to identify drugs with high binding affinity for the PI3Kα protein’s active pocket. DJ1–DJ22 were found to be the most effective PI3Kα inhibitors, with energies ranging from −8.0 to −9.2 kJ/mol. From in silico pharmacokinetic and bioactivity screening, DJ3, DJ7, and DJ18 were identified as promising PI3Kα inhibitors. PI3Kα backbone stability was tested in a water model using molecular dynamics simulations employing DJ3, DJ7, DJ18, and Trastuzumab as a pharmacological reference. Synthesis of target-hit DJ3, DJ7, and DJ18 derivatives may lead to breast cancer drug-like molecules for related cancers. The work uses in silico methods to find natural phloroglucinols for breast cancer therapy, enabling new chemotherapeutic drugs.

Abstract Microbubble contrast agents in ultrasound/echocardiography are used to increase the echogenicity of the target tissues, thereby raising the contrast resolution of the resultant image. Recently, the trend has shifted toward the development of phase-convertible nanodroplets as ultrasound contrast agents due to their promising theragnostic potential by switching capability at the active site. Herein, we fabricated pre-PGS- perfluoropentane phase convertible nanodroplets and checked their in vitro and in vivo enhancement and safety profile. For this, we performed experiments on 20 male Wistar rats and 2 dogs. Biochemical assays of both rats and dogs included complete blood profiles, liver function tests, and renal function tests. For rat vitals, monitoring and histopathological analysis were also performed. Converted nanodroplets showed excellent contrast enhancement, better than Sonovue upon in vitro testing, with an enhancement time of up to 14 min. In vivo, experiments showed comparable opacification of the ventricles of both rats and dogs. All biochemical assays remained within the normal range during the study period. The histopathological analysis did not show any signs of drug-induced toxicity, showing the safety of these nanodroplets. Pre-PGS-PFP nanodroplets hold great potential for use in echocardiography and abdominal imaging in both human and veterinary applications after clinical trials.

Root-knot nematodes (Meloidogyne spp.) are highly evolved obligate parasites threatening global food security. These parasites have a remarkable ability to establish elaborate feeding sites in roots, which are their only source of nutrients throughout their life cycle. A wide range of nematode effectors have been implicated in modulation of host pathways for defense suppression and/or feeding site development. Plants produce a diverse array of peptide hormones including PLANT PEPTIDE CONTAINING SULFATED TYROSINE (PSY)-family peptides, which promote root growth via cell expansion and proliferation. A sulfated PSY-like peptide RaxX (required for activation of XA21 mediated immunity X) produced by the biotrophic bacterial pathogen (Xanthomonas oryzae pv. oryzae) has been previously shown to contribute to bacterial virulence. Here, we report the identification of genes from root-knot nematodes predicted to encode PSY-like peptides (MigPSYs) with high sequence similarity to both bacterial RaxX and plant PSYs. Synthetic sulfated peptides corresponding to predicted MigPSYs stimulate root growth in Arabidopsis. MigPSY transcript levels are highest early in the infection cycle. Downregulation of MigPSY gene expression reduces root galling and egg production, suggesting that the MigPSYs serve as nematode virulence factors. Together, these results indicate that nematodes and bacteria exploit similar sulfated peptides to hijack plant developmental signaling pathways to facilitate parasitism.

Enterococci are commensal bacteria that inhabit the digestive tracts of animals and humans. The transmission of antibiotic-resistant genes through human-animal contact poses a potential public health risk worldwide, as zoonoses from wildlife reservoirs can occur on every continent. The purpose of this study was to detect Enterococcus spp. in rhesus macaques (Macaca mulatta) and to investigate their resistance patterns, virulence profiles, and biofilm-forming ability. Conventional screening of rectal swabs (n = 67) from macaques was followed by polymerase chain reaction (PCR). The biofilm-forming enterococci were determined using the Congo red agar plate assay. Using the disk diffusion test (DDT), antibiogram profiles were determined, followed by resistance and virulence genes identification by PCR. PCR for bacterial species confirmation revealed that 65.7% (44/67) and 22.4% (15/67) of the samples tested positive for E. faecalis and E. faecium, respectively. All the isolated enterococci were biofilm formers. In the DDT, enterococcal isolates exhibited high to moderate resistance to penicillin, rifampin, ampicillin, erythromycin, vancomycin, and linezolid. In the PCR assays, the resistance gene blaTEM was detected in 61.4% (27/44) of E. faecalis and 60% (9/15) of E. faecium isolates. Interestingly, 88.63 % (39/44) of E. faecalis and 100% (15/15) of E. faecium isolates were phenotypically multidrug-resistant. Virulence genes (agg, fsrA, fsrB, fsrC, gelE, sprE, pil, and ace) were more frequent in E. faecalis compared to E. faecium; however, isolates of both Enterococcus spp. were found negative for the cyl gene. As far as we know, the present study has detected, for the first time in Bangladesh, the presence of virulence genes in MDR biofilm-forming enterococci isolated from rhesus macaques. The findings of this study suggest employing epidemiological surveillance along with the one-health approach to monitor these pathogens in wild animals in Bangladesh, which will aid in preventing their potential transmission to humans.