Symptom response and episodic disability of long COVID in people with spinal cord injury: A case-control study.

BACKGROUND: Spinal cord injury (SCI) is a consequence of significant disability and health issues globally, and long COVID represents the symptoms of neuro-musculoskeletal, cardiovascular and respiratory complications. PURPOSE: This study aimed to identify the symptom responses and disease burden of long COVID in individuals with spinal cord injury. METHODS: This case-control study was conducted on patients with SCI residing at a specialised rehabilitation centre in Bangladesh. Forty patients with SCI with and without long COVID symptoms (LCS) were enrolled in this study at a 1:1 ratio according to WHO criteria. RESULT: Twelve LCS were observed in patients with SCI, including fatigue, musculoskeletal pain, memory loss, headache, respiratory problems, anxiety, depression, insomnia, problem in ADL problem in work, palpitation, and weakness. The predictors of developing long COVID include increasing age (p<0.002), increasing BMI (p<0.03), and longer duration of spinal cord injury (p<0.004). A significant difference (p<0.01) in overall years of healthy life lost due to disability (YLD) for non-long COVID cases was 2.04±0.596 compared to long COVID (LC) cases 1.22±2.09 was observed. CONCLUSION: Bangladeshi patients of SCI presented 12 long COVID symptoms and have a significant disease burden compared to non long COVID cases.

Exploring the nutraceutical potential: Evaluating the nutritional and bioactive functions of five pomelo fruit varieties in Bangladesh.

Pomelo (Citrus maxima), the largest citrus fruit, provides a variety of nutrients that have several health benefits, including antioxidant and antidiabetic functions. Antioxidants help combat oxidative stress by neutralizing reactive oxygen species (ROS) and reducing cellular damage. On the other hand, antidiabetic properties involve mechanisms such as enhancing insulin secretion, improving insulin sensitivity, inhibiting carbohydrate digestion and absorption, and regulating glucose metabolism. However, there is a lack of data on the comparative analysis of the physicochemical composition, bioactive properties, and antidiabetic effects of pomelo fruits grown in Bangladesh. To address this issue, the most common and popular high-yielding five cultivars of pomelo fruits grown in Bangladesh including LOCAL, BARI-2 (BARI: Bangladesh Agricultural Research Institute, Batabi Lebu-2), BARI-3, BARI-4, and BARI-6 were evaluated concerning proximate, minerals, and physicochemical properties with their antidiabetic and antioxidant properties. Research has revealed that all pomelo varieties contained a significant amount of proximate compositions and major minerals (Ca, Mg, K, Na, and Fe). The highest juice yield (75.37 ± 0.33 %), vitamin C content (79.56 ± 2.26 mg/100 mL of fresh juice), and carotenoid content (919.33 ± 0.62 µM ß-Carotene Equivalent/g DM) were found in BARI-3 pomelo fruit and adhered to the sequence (p < 0.05): BARI-3 > LOCAL > BARI-4 > BARI-6 > BARI-2; BARI-3 > LOCAL > BARI-2 > BARI-4 > BARI-6, and BARI-3 > BARI-2 > BARI-6 > LOCAL > BARI-4, respectively. The anthocyanin content and inhibitory activity of α-glucosidase were found to be at their peak in the BARI-2 pomelo variety and the values were 50.65 ± 2.27 µg cyanidin 3-glucoside equivalents/100 g DM and 85.57 ± 0.00 µM acarbose equivalents/g DM, respectively. BARI-3 pomelo variety showed highest DPPH antioxidant capacity (170.47 ± 0.01 µM Trolox equivalents/g DM), while the BARI-6 pomelo variety exhibited the highest total phenolic content (6712.30 ± 1.84 µg gallic acid equivalents/g DM), and ferric-reducing antioxidant power activity (183.16 ± 0.01 µM Fe(II) equivalents/g DM). Therefore, this study explores the nutritional value and bioactivity of five popular pomelo varieties in Bangladesh, offering valuable insights for utilizing high-value citrus resources and understanding their health-promoting functions.

Spatiotemporal distribution, ecological risk assessment, and human health implications of currently used pesticide (CUP) residues in the surface water of Feni River, Bangladesh.

Spatiotemporal monitoring of pesticide residues in river water is urgently needed due to its negative environmental and human health consequences. The present study is to investigate the occurrence of multiclass pesticide residue in the surface water of the Feni River, Bangladesh, using an optimized salting-out assisted liquid-liquid microextraction (SALLME) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS). The optimized SALLME method was developed and validated following the SANTE/11312/2021 guidelines. A total of 42 water samples were collected and analyzed to understand the spatiotemporal distribution of azoxystrobin (AZ), buprofezin (BUP), carbofuran (CAR), pymetrozine (PYM), dimethoate (DMT), chlorantraniliprole (CLP), and difenoconazole (DFN). At four spike levels (n = 5) of 20, 40, 200, and 400 µg/L, the recovery percentages were satisfactory, ranging between 71.1 % and 107.0 % (RSD ≤13.8 %). The residues ranged from below the detection level (BDL) to 14.5 µg/L. The most frequently detected pesticide was DMT (100 %), followed by CLP (52.3809-57.1429), CAR (4.7619-14.2867), and PYM (4.7619-9.5238). However, AZ and BUP were below the detection limit in the analyzed samples of both seasons. Most pesticides and the highest concentrations were detected in March 2023, while the lowest concentrations were present in August 2023.Furthermore, ecological risk assessment based on the general-case scenario (RQm) and worst-case scenario (RQex) indicated a high (RQ > 1) risk to aquatic organisms, from the presence of PYM and CLP residue in river water. Human health risk via dietary exposure was estimated using the hazard quotient (HQ). Based on the detected residues, the HQ (<1) value indicated no significant health risk. This report provides the first record of pesticide residue occurrences scenario and their impact on the river environment of Bangladesh.

Correction: Exploration of modern contraceptive methods using patterns among later reproductive-aged women in Bangladesh.

[This corrects the article DOI: 10.1371/journal.pone.0291100.].

Pattern of contraceptive use among reproductive-aged women with disabilities in Bangladesh: Evidence from multiple indicator cluster survey 2019.

BACKGROUND: Contraception is crucial for reproductive-aged women with disabilities, empowering them to manage reproductive choices and enhancing overall health, autonomy, and well-being. OBJECTIVE: The objective of this study was to examine the usage patterns of contraceptive methods among reproductive-aged women with disabilities in Bangladesh. METHODS: We analyzed data from 47,465 reproductive-aged women from the 2019 Bangladesh Multiple Indicator Cluster Survey. Outcome variables included contraceptive usage patterns, grouped into any contraceptive methods, any modern contraceptive methods, and any traditional contraceptive methods. The primary explanatory variable considered was disabilities level (women with no disabilities, women with moderate disabilities, and women with severe disabilities), along with types of disabilities. A multilevel mixed-effects logistic regression model was used to assess associations between outcomes and explanatory variables while accounting for confounding. RESULTS: The prevalence of any contraceptive methods use was 66.4 %, declining to 54 % among women with severe disabilities. The odd of modern contraception use was 31 % lower (aOR, 0.69, 95 % CI, 0.65-0.73) among women with moderate disabilities and 47 % lower (aOR, 0.53, 95 % CI, 0.47-0.60) among those with severe disabilities, compared to women with no disabilities. Within the individual domains of disabilities, those with vision, walking, cognitive, and self-care-related disabilities reported lower odds of modern contraception uptake than those with no disabilities. CONCLUSIONS: The study highlights that women with disabilities use contraceptives less often, increasing vulnerability to unintended and short interval pregnancies and unsafe abortion. Strengthening family planning and prioritizing women with disabilities for modern contraceptives are vital.

Comparing effectiveness of physiotherapy versus drug management on fatigue, physical functioning, and episodic disability for myalgic encephalomyelitis in post-COVID-19 condition: a study protocol of randomized control trial.

BACKGROUND: Physiotherapy interventions effectively improved fatigue and physical functioning in non-COVID patients with myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS). There is a research gap on the effectiveness of physiotherapy interventions versus drug management on ME/CFS in post-COVID-19 conditions (PCC). METHODS: We planned a three-arm prospective randomized control trial on 135 PCC cases with ME/CFS who are diagnosed between 20 November 2023 and 20 May 2024 from a population-based cohort. The study aims to determine the effectiveness of physiotherapy interventions as adapted physical activity and therapeutic exercise (APTE) provided in institution-based care versus telemedicine compared with drug management (DM). Participants will be assigned to three groups with the concealed location process and block randomization with an enrollment ratio of 1:1:1. The post-treatment evaluation will be employed after 2 months of interventions, and follow-up will be taken after 6 months post-intervention. The Chalder fatigue scale will measure the primary outcome of fatigue. SF-36 and the disability-adjusted life years (DALYs) will measure the secondary outcome of physical functioning and episodic disability. DISCUSSION: This study will address the research gap to determine the appropriate approach of physiotherapy or drug management for ME/CFS in PCC cases. The future direction of the study will contribute to developing evidence-based practice in post-COVID-19 condition rehabilitation. TRIAL REGISTRATION: The trial is registered prospectively from a primary Clinical Trial Registry side of WHO CTRI/2024/01/061987. Registered on 29 January 2024.

The PreQuine Platform: A novel diagnostic tool for measuring glucose-6-phosphate dehydrogenase (G6PD) activity and hemoglobin concentration.

Quantitative diagnosis of glucose-6-phosphate dehydrogenase (G6PD) deficiency is essential for the safe administration of 8-aminoquinoline based radical cure for the treatment of Plasmodium vivax infections. Here, we present the PreQuine Platform (IVDS, USA), a quantitative biosensor that uses a dual-analyte assay for the simultaneous measurement of Hemoglobin (Hgb) levels and G6PD enzyme activity within the same sample. The platform relies on a downloadable mobile application. The device requires 10µl of whole blood and works with a reflectance-based meter. Comparing the G6PD measurement normalized by Hgb of 12 samples from the PreQuine Platform with reference measurements methods (spectrophotometry, Pointe Scientific, USA and hemoglobin meter, HemoCue, Sweden) showed a positive and significant agreement with a slope of 1.0091 and an intercept of -0.0379 under laboratory conditions. Next steps will be to conduct field trials in Bangladesh, Cambodia, and the USA to assess diagnostic performance, user friendliness and acceptance.

Tea leaf age quality: Age-stratified tea leaf quality classification dataset.

The "Tea Leaf Age Quality" dataset represents a pioneering agricultural and machine-learning resource to enhance tea leaf classification, detection, and quality prediction based on leaf age. This comprehensive collection includes 2208 raw images from the historic Malnicherra Tea Garden in Sylhet and two other gardens from Sreemangal and Moulvibajar in Bangladesh. The dataset is systematically categorized into four distinct classes (T1: 1-2 days, T2: 3-4 days, T3: 5-7 days, and T4: 7+ days) according to age-based quality criteria. This dataset helps to determine how tea quality changes with age. The most recently harvested leaves (T1) exhibited superior quality, whereas the older leaves (T4) were suboptimal for brewing purposes. It includes raw, unannotated images that capture the natural diversity of tea leaves, precisely annotated versions for targeted analysis, and augmented data to facilitate advanced research. The compilation process involved extensive on-ground data collection and expert consultations to ensure the authenticity and applicability of the dataset. The "Tea Leaf Age Quality" dataset is a crucial tool for advancing deep learning models in tea leaf classification and quality assessment, ultimately contributing to the technological evolution of the agricultural sector by providing detailed age-stratified tea leaf categorization.

Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data.

In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex high-dimensional data like RNA sequencing data. In this study, we propose the binarilization technique as a novel way to treat RNA sequencing data and used it to construct explainable cancer prediction models. We tested our proposed data processing technique on five different models, namely neural network, random forest, xgboost, support vector machine, and decision tree, using four cancer datasets collected from the National Cancer Institute Genomic Data Commons. Since our datasets are imbalanced, we evaluated the performance of all models using metrics designed for imbalance performance like geometric mean, Matthews correlation coefficient, F-Measure, and area under the receiver operating characteristic curve. Our approach showed comparative performance while relying on less features. Additionally, we demonstrated that data binarilization offers higher explainability by revealing how each feature affects the prediction. These results demonstrate the potential of data binarilization technique in improving the performance and explainability of RNA sequencing based cancer prediction models.

Evaluation of radioactivity concentration in farm fresh milk and concomitant dose to consumer.

In this study, activity concentrations of natural radionuclides in 28 raw milk samples collected from different dairy farms in Dhaka city of Bangladesh were measured using a high-purity germanium (HPGe) detector for the first time. The activity concentration of 226Ra, 232Th, and 40K in the investigated fresh milk samples ranged from BDL (Below detection level) to 26 ± 1.6 Bq/kg, BDL to 11.7 ± 3.3 Bq/kg and 101 ± 17 to 384 ± 32 Bq/kg, respectively. No artificial radionuclides were found in the investigated samples. Present results show inline within the range of available data in the literature. Annual committed effective doses were estimated following the consumption characteristics of raw milk by city population, values are found within the limiting range recommended by international organizations due to consumption of foodstuffs. Additionally, real-time gamma-ray dose rate in the farms/sampling locations was found in the range of 0.12 ± 0.01-0.20 ± 0.01 µSv/h by using a digital gamma survey meter (Gamma Scout) and the calculated maximum annual effective dose due to outdoor absorbed dose was found to be 0.25 mSv/y, which shows lower than the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) recommended limit of 2.4 mSv/y. This study indicates that the concentration of radionuclides in the farm fresh milk of Dhaka city does not pose any unwanted risk to public health, and it is safe to consume by both children and adults with the current intake level.

Availability and readiness of healthcare facilities and their effects on antenatal care services uptake in Bangladesh.

BACKGROUND: Sustainable Development Goal (SDG) 3.7 aims to ensure universal access to sexual and reproductive healthcare services, where antenatal care (ANC) is a core component. This study aimed to examine the influence of health facility availability and readiness on the uptake of four or more ANC visits in Bangladesh. METHODS: The 2017/18 Bangladesh Demographic and Health Survey data were linked with the 2017 Health Facility Survey and analyzed in this study. The associations of health facility-level factors with the recommended number of ANC uptakes were determined. A multilevel mixed-effect logistic regression model was used to determine the association, adjusting for potential confounders. RESULTS: Nearly 44% of mothers reported four or more ANC uptakes, with significant variations across several areas in Bangladesh. The average distance of mothers' homes from the nearest health facilities was 6.36 km, higher in Sylhet division (8.25 km) and lower in Dhaka division (4.45 km). The overall uptake of the recommended number of ANC visits was positively associated with higher scores for the management (adjusted odds ratio (aOR) 1.85; 95% CI, 1.16-2.82) and infrastructure (aOR, 1.59; 95% CI, 1.09-2.19) of health facilities closest to mothers' homes. The odds of using the recommended number of ANC in mothers increased by 3.02 (95% CI, 2.01-4.19) and 2.36 (95% CI, 2.09-3.16) folds for each unit increase in the availability and readiness scores to provide ANC services at the closest health facilities, respectively. Every kilometer increase in the average regional-level distance between mothers' homes and the nearest health facilities reduced the likelihood of receiving the recommended number of ANC visits by nearly 42% (aOR, 0.58, 95% CI, 0.42-0.74). CONCLUSION: The availability of healthcare facilities close to residence, as well as their improved management, infrastructure, and readiness to provide ANC, plays a crucial role in increasing ANC services uptake. Policies and programs should prioritize increasing the availability, accessibility, and readiness of health facilities to provide ANC services.

Exploration of modern contraceptive methods using patterns among later reproductive-aged women in Bangladesh.

BACKGROUND: With the rapid increase in the number of women in their later reproductive years (aged 35 and above) in the present decade, the concern surrounding their contraceptive considerations has reached a critical point of importance. This study aims to examine the trends and determinants of modern contraceptive uptake among later reproductive-aged women in Bangladesh. METHODS: A total of 17,736 women aged 35 and above were included in the analysis, utilizing data from three consecutives Bangladesh Demographic and Health Surveys conducted in 2011, 2014, and 2017-18. The outcome variable was the uptake of modern contraceptive methods (yes or no). The explanatory variables encompassed survey years, individual characteristics of the women, as well as characteristics of their partners and the community. Multilevel logistic regression model was used to explore the association of the outcome variable with explanatory variables. RESULTS: We found that approximately 54% of women aged 35 and more do not use modern contraceptive methods, and there have been no significant shifts in their usage observed over the survey years. Compared to women aged 35-39, women aged 40-45 (aOR = 0.53, 95% CI: 0.49-0.57) and 45-49 (aOR = 0.24, 0.22-0.26) reported lower likelihoods of modern contraceptive method uptake. Higher education correlated with increased uptake of modern contraceptive methods (112%-142%), while partner's education showed a negative association. Later reproductive-aged women in richer (aOR = 0.83, 95% CI: 0.74-0.94) and richest (aOR = 0.76, 95% CI: 0.66-0.88) quintiles reported lower uptake of modern contraceptive methods compared to their counterparts in the poorest quintile. Later reproductive-aged women in Dhaka (aOR = 1.22, 95% CI: 1.07-1.38) and Rajshahi (aOR = 1.37, 95% CI: 1.19-1.59) regions had higher uptake of modern contraception than those residing in the Barishal division. Modern contraceptive methods uptake was 1.22 times higher among women who reported exposure to mass media and 1.19 times higher among women who reported engagement in paid work compared to among women who reported no exposure to mass media and participation in no formal work, respectively. Modern contraceptive methods uptake was 43% higher (aOR = 1.43, 95% CI: 1.32-1.55) in women with more than 2 children compared to those with ≤2 children. CONCLUSION: The study highlights no significant change in modern contraception uptake among later reproductive-aged women in Bangladesh. This raises concerns about the elevated risk of unintended pregnancies and shorter birth intervals, emphasizing the need for targeted interventions to address the specific needs and preferences of this demographic.

Clinical presentation of post-COVID pain and its impact on quality of life in long COVID patients: a cross-sectional household survey of SARS-CoV-2 cases in Bangladesh.

BACKGROUND: Pain is one of the prevalent Long COVID Symptoms (LCS). Pain interferes with the quality of life (QoL) and induces disease burden. PURPOSE: The study aimed to elicit the clinical presentation of pain and determine the relationships between QoL and pain in LCS. METHODS: This household cross-sectional study of 12,925 SARS-CoV-2 cases between July and December 2021 was carried out in eight administrative divisions of Bangladesh. Stratified random sampling from the cases retrieved from the Ministry of Health was employed. Symptom screening was performed through COVID-19 Yorkshire Rehabilitation Scale, and long COVID was diagnosed according to World Health Organization (WHO) criteria. The analyses were conducted using IBM SPSS (Version 20.00). RESULTS: The prevalence of pain in long COVID was between 01 and 3.1% in the studied population. The study also found five categories of pain symptoms as LCS in Bangladesh: muscle pain 3.1% (95% CI; 2.4-3.8), chest pain 2.4% (95% CI; 1.8-3.1), joint pain 2.8% (95% CI; 2.2-2.3), headache 3.1% (95% CI; 2.4-3.8), and abdominal pain 0.3% (95% CI; 0.01-0.5). People with LCS as pain, multiple LCS, and longer duration of LCS had significantly lower quality of life across all domains of the WHOQOL-BREF (P < 0.001) compared to asymptomatic cases. CONCLUSION: Three out of ten people with long COVID experience painful symptoms, which can significantly reduce their quality of life. Comprehensive rehabilitation can improve the symptoms and reduce the burden of the disease.

RETRACTED: Nobel et al. Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis. Tomography 2024, 10, 105-132.

The Tomography Editorial Office retracts the article "Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis" [...].

A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM.

Sentiment analysis is an essential task in natural language processing that involves identifying a text's polarity, whether it expresses positive, negative, or neutral sentiments. With the growth of social media and the Internet, sentiment analysis has become increasingly important in various fields, such as marketing, politics, and customer service. However, sentiment analysis becomes challenging when dealing with foreign languages, particularly without labelled data for training models. In this study, we propose an ensemble model of transformers and a large language model (LLM) that leverages sentiment analysis of foreign languages by translating them into a base language, English. We used four languages, Arabic, Chinese, French, and Italian, and translated them using two neural machine translation models: LibreTranslate and Google Translate. Sentences were then analyzed for sentiment using an ensemble of pre-trained sentiment analysis models: Twitter-Roberta-Base-Sentiment-Latest, bert-base-multilingual-uncased-sentiment, and GPT-3, which is an LLM from OpenAI. Our experimental results showed that the accuracy of sentiment analysis on translated sentences was over 86% using the proposed model, indicating that foreign language sentiment analysis is possible through translation to English, and the proposed ensemble model works better than the independent pre-trained models and LLM.

Discovery and Characterization of a Novel Cereblon-Recruiting PRC1 Bridged PROTAC Degrader.

Bridged PROTAC is a novel protein complex degrader strategy that exploits the target protein's binding partner to degrade undruggable proteins by inducing proximity to an E3 ubiquitin ligase. In this study, we discovered for the first time that cereblon (CRBN) can be employed for the bridged PROTAC approach and report the first-in-class CRBN-recruiting and EED-binding polycomb repressive complex 1 (PRC1) degrader, compound 1 (MS181). We show that 1 induces preferential degradation of PRC1 components, BMI1 and RING1B, in an EED-, CRBN-, and ubiquitin-proteosome system (UPS)-dependent manner. Compound 1 also has superior antiproliferative activity in multiple metastatic cancer cell lines over EED-binding PRC2 degraders and can be efficacious in VHL-defective cancer cells. Altogether, compound 1 is a valuable chemical biology tool to study the role of PRC1 in cancer. Importantly, we show that CRBN can be utilized to develop bridged PROTACs, expanding the bridged PROTAC technology for degrading undruggable proteins.

Discovery of the First-in-Class G9a/GLP PROTAC Degrader.

Aberrantly expressed lysine methyltransferases G9a and GLP, which catalyze mono- and dimethylation of histone H3 lysine 9 (H3K9), have been implicated in numerous cancers. Recent studies have uncovered both catalytic and noncatalytic oncogenic functions of G9a/GLP. As such, G9a/GLP catalytic inhibitors have displayed limited anticancer activity. Here, we report the discovery of the first-in-class G9a/GLP proteolysis targeting chimera (PROTAC) degrader 10 (MS8709), as a potential anticancer therapeutic. 10 induces G9a/GLP degradation in a concentration-, time-, and ubiquitin-proteasome system (UPS)-dependent manner. Futhermore, 10 does not alter the mRNA expression of G9a/GLP and is selective for G9a/GLP over other methyltransferases. Moreover, 10 displays superior cell growth inhibition to the parent G9a/GLP inhibitor UNC0642 in prostate, leukemia, and lung cancer cells and has suitable mouse pharmacokinetic properties for in vivo efficacy studies. Overall, 10 is a valuable chemical biology tool to further investigate the functions of G9a/GLP and a potential therapeutic for treating G9a/GLP-dependent cancers.

Trends and challenges of fruit by-products utilization: insights into safety, sensory, and benefits of the use for the development of innovative healthy food: a review.

A significant portion of the human diet is comprised of fruits, which are consumed globally either raw or after being processed. A huge amount of waste and by-products such as skins, seeds, cores, rags, rinds, pomace, etc. are being generated in our homes and agro-processing industries every day. According to previous statistics, nearly half of the fruits are lost or discarded during the entire processing chain. The concern arises when those wastes and by-products damage the environment and simultaneously cause economic losses. There is a lot of potential in these by-products for reuse in a variety of applications, including the isolation of valuable bioactive ingredients and their application in developing healthy and functional foods. The development of novel techniques for the transformation of these materials into marketable commodities may offer a workable solution to this waste issue while also promoting sustainable economic growth from the bio-economic viewpoint. This approach can manage waste as well as add value to enterprises. The goal of this study is twofold based on this scenario. The first is to present a brief overview of the most significant bioactive substances found in those by-products. The second is to review the current status of their valorization including the trends and techniques, safety assessments, sensory attributes, and challenges. Moreover, specific attention is drawn to the future perspective, and some solutions are discussed in this report.

Effects of disability on adverse health outcomes and anthropometric deficits among under-five children in South Asian countries: evidence from multiple indicator cluster surveys.

Background: Children with disabilities face an increased risk of adverse health outcomes and poor anthropometric deficits, although the focus on them is limited in the South Asian context thus far and need newer and more evidence. This study investigates the effects of disability on adverse health outcomes and anthropometric deficits among 2-4 years aged children in South Asian countries. Methods: We analyzed data from 93,180 children aged 2-4 years across Bangladesh, Nepal, Pakistan, and Afghanistan using Multiple Indicator Cluster Surveys (2017-2023). Disability status was the primary exposure, and outcomes included adverse health outcome (acute respiratory infection, diarrhea, fever), anthropometric deficit (stunting, wasting, underweight), and healthcare service sources during adverse health events (care received from skilled healthcare personnel, care received from non-professional personnel, and care received from health facility workers other than skilled healthcare personnel). Using multilevel and multinomial logistic regression models, we examined associations between exposure and outcome variables, adjusting for covariates. Findings: We found average disability prevalence in South Asia was 8.7% (8.3-9.0; n = 8072), varying from 3.4% (3.0-3.8; n = 446) in Bangladesh to 12.3% (11.4-13.3; n = 1259) in Afghanistan. Common health issues included fever (n = 24,982, 26.8%, 26.2-27.4) and diarrhea (n = 14,081, 15.1%, 14.7-15.6), while prevalent poor anthropometric deficits were stunting (n = 39,766, 42.7%, 42.0-43.3) and underweight (n = 22,390, 24.0%, 23.5-24.5). Children with disability had 1.30 (95% CI: 1.21-1.40) to 1.60 (95% CI: 1.47-1.75) times and 1.17 (95% CI: 1.05-1.29) to 1.39 (95% CI: 1.30-1.48) times higher likelihoods of adverse health outcomes and anthropometric deficits, respectively, with variations observed among countries and different disability types. Individuals with disability were 1.16 (95% CI: 1.00-1.35) to 1.26 (95% CI: 1.01-1.58) times more likely to receive healthcare services from skilled healthcare personnel compared to health facility workers other than skilled healthcare personnel. Interpretation: This study findings emphasizes the need for community-level awareness programs to improve anthropometric well-being and healthcare of the children with disability. Funding: This research did not receive any specific funds.

Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review.

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.