Ritesh Kumar

India reports very high biochemical vitamin D deficiency when global cut-offs are applied, yet the corresponding disease burden appears low. Whether current thresholds are appropriate for Indian populations, therefore, remains uncertain. In this review, we aimed to systematically analyze the literature on vitamin D and non-skeletal outcomes and critically evaluate whether current deficiency thresholds are appropriate for India. We searched PubMed, Scopus, and the Cochrane databases (Jan 1, 2010, to Feb 29, 2024), focusing on randomized trials (RCTs), meta-analyses, and observational studies addressing vitamin D and disease outcomes. Indian-specific modifiers, including sunlight, skin pigmentation, calcium intake, and parathyroid hormone (PTH) sensitivity, were analyzed. Two reviewers independently screened records, assessed risk of bias (Cochrane Risk of Bias 2.0 (RoB 2.0) for RCTs and Newcastle-Ottawa Scale (NOS) for observational studies), and performed a narrative synthesis, with prespecified quantitative pooling conducted when studies were homogeneous.Out of 22,435 records, 78 studies were included. High-quality RCTs (VITAL, D-Health) consistently showed no benefit from supplementation for non-skeletal outcomes. Indian prevalence data, using a 12 ng/mL are sufficient to maintain normocalcemia and suppress secondary hyperparathyroidism, thereby questioning the validity of global thresholds. Global cut-offs have therefore created an inflated burden of vitamin D deficiency in India. Thresholds must be recalibrated using Indian outcome-linked data, not extrapolated norms. Evidence suggests that a threshold of 12 ng/mL is more physiologically valid for Indian populations. Mass screening and supplementation in asymptomatic populations should therefore be discouraged.

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Northern leaf blight (NLB) caused by Exserohilum turcicum is a devastating foliar disease of maize. Deploying resistant varieties is considered a sustainable and eco-friendly option for long-term agriculture sustainability. However, challenges arise with the durability of host resistance, prompting the need for integrated approaches. While NLB management in Nepal involves the use of traditional fungicides, our study focuses on evaluating the efficacy of newly registered fungicides concerning’ disease control and post-harvest seed infestation levels in the inner Terai region of Nepal. Altogether four chemical fungicides, two commonly used (Mancozate and SAAF) and two newly registered (Tilt, and Nativo) were evaluated under in vitro (Poisoned food technique) and field conditions (Randomized complete block design). Tilt and Nativo at 50 ppm were found strikingly effective in completely inhibiting the fungal pathogen’s mycelial growth. Also under field conditions, the treatment (Seed treatment with carbestin-50 + Foliar application of Tilt (0.1%) showed the lowest disease incidence (81.95%), percent disease index (PDI) (33.37%), sporulation (0.52 × 104 spores/ml), AUDPC (1316.98) with highest yield (4.39 t/ha) which was at par with Nativo (0.05%). These results revealed that Tilt or Nativo could be applied in knee high or before the tasseling stage and can also be used alternatively to overcome fungicide resistance issues. No incidence of E. turcicum was detected in any treatment using the freezing blotter technique, indicating either very low natural seed infection or limitations in detection sensitivity.

In order to ensure sustainable agricultural productivity, a reliable diagnostic framework to identify mango leaf diseases through interpretable visual symptoms is necessary. Deep learning models have high classification accuracy, but the “black box” nature of the deep learning models often makes it difficult to understand the underlying rationale of a prediction. To overcome this limitation, an explainable artificial intelligence (XAI) agent is computationally developed based on a two-stage diagnostic strategy. The proposed framework first employs hybrid vision transformer architecture for leaf-level classification, and employs local interpretability methods to determine the specific image patches that influence the decision. In the second stage, a feature detection model scans the identified regions to link the classification to visible pathological indicators such as necrotic regions, holes, and discoloration. By bridging the gap between global predictions and local geometric causes, this dual-model approach mimics the selective attention of a human specialist. The result analysis demonstrates that this strategy effectively transforms opaque diagnostic processes into a transparent and human-understandable format, thereby enhancing the reliability of automated systems for early crop management in hazardous or large-scale agricultural environments. The empirical results reveal that the suggested framework is capable of accurately classifying leaves while simultaneously localizing symptoms in an interpretable way, which can further be applied to diagnose diseases in different plants.