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Background: Zinc deficiency poses significant health risks, particularly in low-income settings. This study aims to evaluate the impact of agronomically zinc biofortified (fermented and non-fermented) and post-harvest wheat flour flatbread on zinc status and metabolic health in adolescents and adult women in rural Pakistan. Methods: A four-arm triple-blind randomized controlled trial will be conducted in a rural district of Pakistan. Participants (adolescents aged 10–19 and adult women aged 20–40) will be assigned to receive fermented or unfermented high zinc agronomically biofortified wheat flour flatbread, post-harvest zinc-fortified wheat flour flatbread, or low zinc conventional whole wheat flour flatbread. The meal would be served once a day, six days a week for six months. The study aims to enroll 1000 participants and will be analyzed based on the intention-to-treat principle. The trial is registered with number NCT06092515. Outcomes: Primary outcomes will include serum zinc concentration and metabolic markers, while secondary outcomes include anthropometric measurements, blood pressure, and dietary intake. Conclusion: This trial will provide valuable insights into the efficacy of agronomically zinc biofortified wheat flour in improving zinc status and metabolic health. Findings may inform public health strategies to combat zinc deficiency in resource-limited settings. [ABSTRACT FROM AUTHOR]

This study explores previous research efforts concerning prediction models related to the textile and polymer industry, especially garment seam strength, emphasizing critical parameters such as stitch density, fabric GSM, thread type, thread count, stitch classes, and seam types. These parameters play a pivotal role in determining the durability and overall quality of denim jeans based on cellulosic polymer. A significant focus is dedicated to the mathematical computational models employed for predicting seam strength in five-pocket denim jeans. Herein, the discussion poses the application of AI for manufacturing industries, especially for textile and clothing sectors, and highlights the importance of using a machine learning prediction model for sewing thread consumption, seam strength analysis, and seam performance analysis. Therefore, the authors suggest the significant importance of the machine learning prediction model, as future trends anticipate advancements in AI-driven methodologies, potentially leading to high-profile predictions and superior manufacturing processes. The authors also describe the limitation of AI and address a comprehensive model of risk outlines of AI in the manufacturing-based industries, especially the garments industry. Put simply, this review serves as a bridge between the realms of AI, mathematics, and textile engineering, providing a clear understanding of how artificial-neural-network-based models will be shaping the future of seam strength prediction in the denim manufacturing landscape. This type of evolution, based on ANN, will support and enhance the accuracy and efficiency of seam strength predictions by allowing models to discern intricate patterns and relationships within vast and diverse datasets. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cervical radiculopathy is a common condition encountered in clinical practice and is characterized by compression or irritation of the nerve roots in the cervical spine. OBJECTIVE: To compare the effectiveness of manual intermittent cervical longitudinal mobilization (mICLM) and therapeutic modalities in managing acute, sub-acute and chronic radiculopathy pain. METHODS: This was a multicenter, double-blinded, randomized controlled trial. Individuals with radiculopathies were randomized into interventional; (IG, n = 18) receiving mICLM and control group; (CG, n = 17), receiving Microwave Diathermy (MWD) and transcutaneous electrical nerve stimulation (TENS) at the cervico-dorsal area daily for two weeks. The neck pain disability index (NPDI), numeric pain rating scale (NPRS), and global rating of change score (GRoC) were used as outcome measures. Statistical analysis was conducted using SPSS version 23.0. RESULTS: Both mICLM and MWD with TENS were equally effective in reducing the pain and disability in either group. However, it was noted that mICLM had a better impact on acute, sub-acute and chronic cases of radiculopathy pain comparatively. Mean age was 42.3 (SD = ± 10.9) years. 54.3% were between the age group of 40–60 years out of which 51.4% were male and 48.6% were female. 51.4% were in IG and 48.6% were in CG. The mean comparison of NPAD and NPRS within groups was significant with p < 0.01*. Post treatment mean comparison of NPAD outcomes in IG and CG remain significant with p = 0.004* and p < 0.01* respectively. The post NPAD and NPRS between IG and CG were found statistically insignificant with p = 0.75 and p = 0.57 respectively. CONCLUSION: mICLM and therapeutic modalities showed similar outcomes in managing radiculopathy pain. The study highlights the need for further research to understand the effectiveness of these interventions in larger patient populations. [ABSTRACT FROM AUTHOR]

This study was planned to explore the AFM1‐reducing potential of various heat and acid‐killed lactic acid bacteria (LAB), activated carbon and bentonite (BENT) in milk contaminated with 0.5 μg/L AFM1 and their impact on milk composition. A negligible impact was observed on milk composition with AC while exhibiting a higher AFM1 removal (78.2%) than bentonite (35.8%). Their combination with LAB cells increased AFM1 decontamination for most of the treatments, with highest AFM1 removal (97.6%) exhibited by a treatment involving 0.1% AC and acid‐killed cells of L. cremoris. The results of this study indicate that LAB cells associated with the adsorbents tested are highly effective in reducing milk AFM1 content. [ABSTRACT FROM AUTHOR]