Remote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.

BACKGROUND: Raised intracranial pressure (ICP) contributes to approximately 20% of the admissions in the paediatric intensive care unit (PICU) in our setting. Timely identification and treatment of raised ICP is important to prevent brain herniation and death in such cases. The objective of this study was to examine the role of optic nerve sheath diameter (ONSD) in detecting clinically relevant raised ICP in children.
METHODS: A hospital-based observational analytical study in a PICU of a tertiary care institute in India on children aged 2-14 years. ONSD was measured in all children on three time points that is, day 1, day 2 and between day 4 and 7 of admission. ONSD values were compared between children with and without clinical signs of raised ICP.
RESULTS: Out of 137 paediatric patients recruited, 34 had signs of raised ICP. Mean ONSD on day 1 was higher in children with signs of raised ICP (4.99±0.57 vs 4.06±0.40; p<0.01). Mean ONSD on day 2 also was higher in raised ICP patients (4.94±0.55 vs 4.04±0.40; p<0.01). The third reading between days 4 and 7 of admission was less than the first 2 values but still higher in raised ICP patients (4.48±1.26 vs 3.99±0.57; p<0.001). The cut-off ONSD value for detecting raised ICP was 4.46 mm on the ROC curve with an area under curve 0.906 (95% CI 0.844 to 0.968), 85.3% sensitivity and 86.4% specificity. There was no difference in ONSD between the right and the left eyes at any time point irrespective of signs of raised ICP.
CONCLUSIONS: We found that measurement of ONSD by transorbital ultrasound was able to detect clinically relevant raised ICP with an excellent discriminatory performance at the cut-off value of 4.46 mm.

Biomedical research collaborations are to be contextualized in the larger global health agenda which also opens up new information pathways, expands research networks, and brings additional resources. A qualitative inquiry was employed to understand the perceived benefits and challenges of research collaborations by biomedical scientists from India (Global South [GS] country) and the Global North (GN). In-depth interviews were conducted with 47 biomedical scientists from India and 06 from the GN. The data was analyzed using the grounded theory approach. Complementarity of skills and resources, access to funds, improved quality of work, an opportunity to conduct multi-centric studies, development of collaborative networks, better and larger number of publications, mutual learning, opportunity to work with credible researchers, address common interests, leverage interpersonal and trusted relationships and larger societal good were some of the critical factors for eagerness of participants in joint scientific endeavors. However, the challenging aspects of dissent and disagreements were the power imbalance between the collaborators, the development of a trust deficit, and local administrative issues. The challenges reported in the current publication, also echoed in several previous publications can be surmounted and negotiated amicably when the rules of the game, law of the land, sharing of the credits, and interest of the collaborating parties are addressed and agreed up in a fair and just manner before the start of the collaboration. Overall biomedical partnerships are complex collaborations with its challenges, the processes are dynamic and outcomes are emergent. This requires constant and proactive evolution of the preparation, implementation and sustainability of the collaborative efforts be it national or international.

BACKGROUND: The global rise in diabetes, particularly in India, poses a significant public health challenge, with factors such as limited awareness, financial strain, and cultural considerations hindering its effective management. Although lifestyle changes have shown promising results, their consistent implementation and maintenance continue to pose challenges. Most studies have focused primarily on dietary modifications, overlooking other essential aspects of lifestyle intervention. The DiRemI study aims to address these gaps by evaluating the efficacy of a comprehensive one-year program that combines diet, exercise, psychological support, and medical management to achieve weight loss, diabetes remission, and improved glycemic control among patients with type 2 diabetes (T2D) in India, while also considering the unique needs of the Indian population.
METHODS: The DiRemI study is a prospective, open-label, matched-group trial aimed at assessing the impact of a one-year online integrated intensive lifestyle intervention (ILI) comprising dietary modifications, physical activity, psychological support, and medical management on weight loss and remission in adult T2D patients (aged 30-70 years), with a body mass index (BMI) between 25 and 35 kg/m2, and disease duration of <15 years. ILI will be compared with routine medical care (RMC). Participants will be recruited from three clinics: one providing ILI and two others providing RMC. The co-primary outcome will be weight loss and remission at 12 months, with a follow-up at 18 months. The proposed sample size is 360 participants (180 each in intervention and control group).
CONCLUSIONS: The DiRemI study represents the first large-scale remission study in India to show the effectiveness of an integrated approach in the remission and management of T2D and its complications. The findings of this study hold the potential to report evidence-based strategies for managing T2D both in India and globally, thus alleviating the substantial burden of diabetes on public health systems.
BACKGROUND: Clinical Trials Registry, India (Registered Number: CTRI/2023/06/053885).

The present research endeavors to examine the effectiveness of four gridded precipitation datasets, namely Integrated Multi-satellite Retrievals for GPM (IMERG), Tropical Precipitation Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), with the observed rainfall data of eight rain gauge stations of India Meteorological Department (IMD) from 2001 to 2019 in Kosi River basin, India. Various statistical metrics, contingency tests, trend analysis, and rainfall anomaly index were utilized at daily, monthly, seasonal, and annual time scales. The categorical metrics namely probability of detection (POD) and false alarm ratio (FAR) indicate that MERRA-2 and IMERG datasets have the highest level of concurrence with the observed daily data. Statistical analysis of gridded datasets with observed dataset of IMD showed that the performance of the IMERG dataset is better than MERRA-2, PERSIANN, and TRMM datasets with \"very good\" coefficient of determination (R2) and Nash-Sutcliffe Efficiency values for monthly data. Trend analysis of gridded seasonal data of IMERG showed similar trends of observed seasonal data whereas other dataset differs. IMERG also performed well in identifying wet and dry years based on annual data. Discrepancies of the satellite sensor in capturing the precipitation have also been discussed. Thus, the IMERG dataset can be effectively used for hydro-meteorological and climatological investigations in cases of lack of observed datasets.

Garlic (Allium sativum L.), particularly its volatile essential oil, is widely recognized for medicinal properties. We have evaluated the efficacy of Indian Garlic Essential Oil (GEO) for antimicrobial and antibiofilm activity and its bioactive constituents. Allyl sulfur-rich compounds were identified as predominant phytochemicals in GEO, constituting 96.51% of total volatile oils, with 38% Diallyl trisulphide (DTS) as most abundant. GEO exhibited significant antibacterial activity against eleven bacteria, including three drug-resistant strains with minimum inhibitory concentrations (MICs) ranging from 78 to 1250 µg/mL. In bacterial growth kinetic assay GEO effectively inhibited growth of all tested strains at its ½ MIC. Antibiofilm activity was evident against two important human pathogens, S. aureus and P. aeruginosa. Mechanistic studies demonstrated that GEO disrupts bacterial cell membranes, leading to the release of nucleic acids, proteins, and reactive oxygen species. Additionally, GEO demonstrated potent antioxidant activity at IC50 31.18 mg/mL, while its isolated constituents, Diallyl disulphide (DDS) and Diallyl trisulphide (DTS), showed effective antibacterial activity ranging from 125 to 500 µg/mL and 250-1000 µg/mL respectively. Overall, GEO displayed promising antimicrobial and antibiofilm activity against enteric bacteria, suggesting its potential application in the food industry.

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UNASSIGNED: Understanding chronic disease prevalence, patterns, and co-occurrence is pivotal for effective health care planning and disease prevention strategies. In this paper, we aimed to identify the clustering of major non-communicable diseases among Indian adults aged ≥50 years based on their self-reported diagnosed non-communicable disease status and to find the risk factors that heighten the risk of developing the identified disease clusters.
UNASSIGNED: We utilised data from the nationally representative survey Study on Global AGEing and Adult Health (SAGE Wave-2). The eligible sample size was 6298 adults aged ≥50 years. We conducted the latent class analysis to uncover latent subgroups of multimorbidity and the multinomial logistic regression to identify the factors linked to observed latent class membership.
UNASSIGNED: The latent class analysis grouped our sample of men and women >49 years old into three groups - mild multimorbidity risk (41%), moderate multimorbidity risk (30%), and severe multimorbidity risk (29%). In the mild multimorbidity risk group, the most prevalent diseases were asthma and arthritis, and the major prevalent disease in the moderate multimorbidity risk group was low near/distance vision, followed by depression, asthma, and lung disease. Angina, diabetes, hypertension, and stroke were the major diseases in the severe multimorbidity risk category. Individuals with higher ages had an 18% and 15% higher risk of having moderate multimorbidity and severe multimorbidity compared to those in the mild multimorbidity category. Females were more likely to have a moderate risk (3.36 times) and 2.82 times more likely to have severe multimorbidity risk.
UNASSIGNED: The clustering of diseases highlights the importance of integrated disease management in primary care settings and improving the health care system to accommodate the individual\'s needs. Implementing preventive measures and tailored interventions, strengthening the health and wellness centres, and delivering comprehensive primary health care services for secondary and tertiary level hospitalisation may cater to the needs of multimorbid patients.

BACKGROUND: Phosphine resistance in Tribolium castaneum challenges grain storage. This study investigates the impact of cytochrome P450 (CYP) enzymes and CYP346 family genes on phosphine resistance in Indian Tribolium castaneum populations.
METHODS: Seven field populations of T. castaneum were compared with Lab- susceptible population for their resistance to phosphine. The levels of cytochrome P450 enzyme and expression of certain CYP346 family genes were tracked in these populations.
RESULTS: The highly resistant Patiala population showed significantly increased CYP450 activity (11.26 ± 0.14 nmol/min/mg protein, 7.41-fold higher) compared to the lab-susceptible population (1.52 ± 0.09 nmol/min/mg protein) when assayed using 8 mM p-nitroanisole as the substrate. The mRNA expression was measured relative to the standard gene RPS18 and revealed significant upregulation of CYP346B1 and CYP346B3 in highly resistant populations Moga and Patiala (CYP346B1: 12.09 ± 2.19 to 21.74 ± 3.82; CYP346B3: 59.097 ± 10.265 to 50.148 ± 8.272). Patiala\'s CYP346B1 exhibited an impressive 685.76-fold change, and Moga\'s CYP346B3 showed a 361.893-fold change compared to lab-susceptible. Linear regression confirmed robust fits for each gene (R2: 0.693 to 0.756). Principal component analysis (PCA) demonstrated a strong positive correlation between CYP346 genes expression; and cytochrome P450 activity. Patiala, Moga, and Hapur populations showed conformity, associating higher resistance with increased P450 activity and CYP346 gene expression. Cluster analysis highlighted a potential correlation between CYP346B1, CYP346B2, and CYP346B3 and P450 activity, with Patiala and Moga clustering together.
CONCLUSIONS: Variability in CYP346B1 and CYP346B3 in strong resistance populations may contribute to adaptation and resistance mechanisms. The study provides insights into specific CYP346 family genes associated with phosphine resistance, emphasizing the intricate interaction between CYP450 detoxifying enzymes, CYP346 family genes, and resistance mechanisms. The upregulation of CYP346 genes suggests a survival advantage for T. castaneum against phosphine, diminishing phosphine\'s efficacy as a pest control measure.

UNASSIGNED: West Bengal is experiencing an unanticipated risk of eclampsia among pregnant women and it persists as the leading cause of maternal mortality. This study aimed to investigate the predictors for maternal deaths due to eclampsia in West Bengal.
UNASSIGNED: The study adopted retrospective mixed methods covering facility and community-based maternal death review approaches. Facility-based data were used for 317 deceased cases wherein the community-based review approach was used in 40 cases. An in-depth interview was also performed among 12 caregivers.
UNASSIGNED: One-third of maternal deaths occurred due to eclampsia, and this accounted for the leading cause of maternal deaths in West Bengal. A younger age, a primigravida or nulliparous status, absence of antenatal care (ANC), and residence in rural areas appeared to have the highest risk of developing eclampsia. The majority of pregnant women had an irregular antenatal check-up history, particularly during the second trimester of pregnancy. The rate of eclampsia-related maternal death was higher in women residing more than 49 km from the studied hospitals. Most of the deceased women were referred to three or more hospitals before their death. Gravidity, the number of ANC visits, the mode of delivery, and delays at different levels were significant confounders of death due to eclampsia. The risk of death due to eclampsia was two times higher among women without ANC and those who had a cesarean section than that in their counterparts.
UNASSIGNED: Women in West Bengal have a high risk of preeclampsia and eclampsia resulting in maternal mortality and morbidity. Gravidity, the number of ANC visits, the mode of delivery, and delays in recognition of eclampsia contribute to the risk of maternal deaths. The establishment of separate eclampsia units, enhanced screening, and preventive and treatment procedures may optimize managing eclampsia.