Muhammad Ashad Kabir

This study aims to evaluate the quality and food safety parameters of chicken eggs in Dhaka. A total of 508 chicken eggs were collected from four diverse market types: wholesale market (WM), super shop (SS), grocery shop (GS), and open shop (OS). Significant differences ( p < 0.05) were observed among markets for all external quality traits except the shape index, with SS showing the highest cleanliness score (3.14). Similarly, a significant variation was found internally with the highest Haugh unit value (79.99) recorded in GS, indicating superior internal quality. The lowest microbial contamination was observed in eggs from the SS with eggshell TVC and Escherichia coli levels of 3.99 and 2.42 log ₁₀ CFU/mL, respectively, Salmonella spp. detected in 14.29% of samples, and egg yolk TVC of 2.90 log ₁₀ CFU/mL, indicating that air‐conditioning facilities may reduce the bacterial growth and metabolic activity on the eggshell surface. Concentrations of heavy metals such as Pb and Cr were below detection limits in all samples. This study concludes that eggs from SS and GS exhibit comparatively superior quality based on quality attributes and food safety parameters; however, continuous surveillance by competent authorities remains essential for monitoring quality and potential health risks to ensure consumer safety and confidence.

Glyphosate-resistant annual ryegrass (Lolium rigidum) is a major problem in Australian grain production. Early detection of resistant populations is important for effective management, yet current bioassay-based detection takes 21–28 days. Here, this proof-of-concept study tested whether consumer-grade smartphone imaging, combined with machine learning and deep learning, can compress that timeline. One confirmed glyphosate-resistant (GR) and one susceptible (GS) L. rigidum population were sprayed with glyphosate at 540 g ae ha⁻¹. RGB images were captured with a Samsung Galaxy S21 at 0, 3 and 9 days post-application (DPA) and split at the plant level using stratified 5-fold cross-validation. A pretrained EfficientNet-B0, fine-tuned via transfer learning, achieved 95.45% binary accuracy at 3 DPA, outperforming a custom CNN (90.91%) and Random Forest (81.82%). By 9 DPA, EfficientNet-B0 reached 100%. For four-class classification (biotype × treatment), EfficientNet-B0 scored 60.98% at 3 DPA and 94.44% at 9 DPA, exceeding both CNN and RF. Baseline accuracy of 54.55–72.73% before spraying points to intrinsic population differences; the post-glyphosate gains suggest the herbicide sharpens the contrast between biotypes. Because only one population pair was tested, these results should be treated as proof-of-concept; validation across multiple independent populations in the field is the obvious next step.

Background: Childhood undernutrition remains highly prevalent in low- and middle-income countries and continues to be a major public health concern, contributing to increased morbidity, mortality, and impaired cognitive development. Although many studies have examined factors associated with childhood undernutrition, methodological and analytical gaps remain. This scoping review aimed to synthesize evidence on factors associated with childhood undernutrition in Bangladesh and identify methodological and analytical gaps to inform future research and predictive modeling. Methods: We used the Population-Concept-Context framework to define the eligibility criteria and structure the review question. The population included children under 5 years of age, the concept focused on factors associated with undernutrition, including stunting, wasting, and underweight, and the context was Bangladesh. Studies with data collection periods between 2000 and 2025 were included. PubMed, MEDLINE, Web of Science, Scopus, and CINAHL were searched using keywords developed from the research questions. Supplementary searching, including Google Scholar, manual reference checking, and citation tracking, was also conducted. Results: A total of 128 studies were included. Most studies used cross-sectional designs and secondary datasets. Logistic regression was the most commonly used analytical method, while six studies applied machine learning approaches. Across the included studies, child age, birth weight, maternal education, maternal nutritional status, and household socioeconomic status were among the factors most frequently reported to be associated with childhood undernutrition. Morbidity-related factors, feeding practices, antenatal care, sanitation indicators, food insecurity measures, regional variation, and household size were also reported as associated factors across studies. Conclusions: Multiple factors associated with childhood undernutrition in Bangladesh were identified; however, most studies relied on conventional analytical approaches with limited attention to prediction, validation, and advanced modeling techniques. Future studies may benefit from incorporating advanced analytical techniques, improved feature engineering, and external validation to strengthen predictive modeling. Integrating environmental and contextual data may further support future research and policy planning on childhood undernutrition.

Objective: To examine whether a VR rural health experience, co‐created using an established process and research protocol, could achieve similar impacts on rural practice intent and reflective engagement in a different geographic and organisational context. Setting: Metropolitan Sydney and Central Tablelands of New South Wales, Australia. Participants: Forty‐one health science students and professionals recruited at careers‐related events or while on placement. Most participants (81.1%) were from metropolitan areas, and 39% had no prior rural work exposure. Design: A parallel convergent mixed‐methods study. Participants completed pre‐ and post‐viewing surveys assessing intended practice location and presence in the VR environment. A subset ( n = 38) participated in semi‐structured interviews exploring their experiences. Quantitative data were analysed descriptively and using Wilcoxon Signed Rank tests; interviews underwent reflexive thematic analysis. Results: Post‐VR, there was a significant shift in intended practice location ( p = 0.035, r = 0.233), with increased interest in inner regional and small rural communities. Participants reported high levels of presence and engagement. Three themes and four subthemes were developed, summarised as: (1) VR as immersive and convenient, though limited in interactivity; (2) a rural education and awareness tool that contextualised the rural environment and challenged assumptions; and (3) a career decision‐making aid that reduced uncertainty, supported exploration and helped assess person–environment fit. Conclusion: A locally tailored VR rural health experience reproduced, and built on the key outcomes of an earlier study in a different context, supporting its adaptability and potential as a scalable tool for rural workforce development through critical reflection and informed career exploration.

Background: The goal of automated radiology report generation is to help radiologists in their task of creating descriptive reports from chest radiographs. However, the process of creating coherent and contextually accurate reports has been challenging, mainly due to the intricacies of medical language and the need to correlate visual data with textual descriptions. Methods: This study presents LLaMA-XR, a novel framework that integrates Meta LLaMA 3.1 Large Language Model with DenseNet-121-based image embeddings and Quantized Low-Rank Adaptation (QLoRA) fine-tuning. Results: The experiment conducted on the IU X-ray dataset demonstrates that LLaMA-XR outperforms a range of state-of-the-art methods. It achieves an ROUGE-L score of 0.433 and a METEOR score of 0.336, establishing new performance benchmarks in the domain. Conclusions: These results underscore LLaMA-XR’s potential as an effective artificial intelligence system for automated radiology reporting, offering enhanced performance.