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Non-structural carbohydrates (NSCs) are building blocks for biomass and fuel metabolic processes. However, it remains unclear how tropical forests mobilize, export, and transport NSCs to cope with extreme droughts. We combined drought manipulation and ecosystem 13CO2 pulse-labeling in an enclosed rainforest at Biosphere 2, assessed changes in NSCs, and traced newly assimilated carbohydrates in plant species with diverse hydraulic traits and canopy positions. We show that drought caused a depletion of leaf starch reserves and slowed export and transport of newly assimilated carbohydrates below ground. Drought effects were more pronounced in conservative canopy trees with limited supply of new photosynthates and relatively constant water status than in those with continual photosynthetic supply and deteriorated water status. We provide experimental evidence that local utilization, export, and transport of newly assimilated carbon are closely coupled with plant water use in canopy trees. We highlight that these processes are critical for understanding and predicting tree resistance and ecosystem fluxes in tropical forest under drought.

Introduction: Pre-analytical phase of laboratory testing is the most susceptible phase, as errors in this phase leads to more than 50% of erroneous results and often breaches the trust of the stakeholders on the quality of the laboratory results. Many pre-analytical errors occur during the pre-analytical phase, encompassing sample collection, labelling and transportation- factors often beyond the laboratory’s direct control. Aim: To determine the type and frequency of pre-analytical errors leading to sample rejection in clinical biochemistry laboratory. Materials and Methods: Being a retrospective descriptive study, convenient sampling was used to analyse sample rejection due to pre-analytical errors in clinical biochemistry laboratory of a tertiary care teaching hospital- PSG Institute of Medical Sciences and Research, Coimbatore, Tamil Nadu, India for a period of six months from May 2023 to October 2023. All the cases/blood samples from Outpatient Department (OPD) and Inpatient Department (IPD), received and rejected during this period were included under study. The data collection and analysis was done over a period of five months using the sample rejection and resample description from Laboratory Information System (LIS). Using Statistical Package for the Social Sciences (SPSS) version 28.0, data were summarised using descriptive statistics such as numbers and percentages. Results: During the six months period out of the total of 667454 samples, 1505 (0.23%) samples were rejected due to pre-analytical errors. The majority of the samples which were rejected were from IPD than OPD. Among the pre-analytical errors, haemolysis accounted for 806 (53.6%), clotted samples 256 (17%), delta check 217 (14.4%), insufficient sample 129 (8.6%), contamination 74 (4.9%), identification error 14 (0.9%), sample without request form 3 (0.2%) while missing samples, billing error, inappropriate tube, delay in transport and wrong test selection accounted for

Fucoidan is a naturally occurring sulphate-containing polysaccharide and a major active compound in Turbinaria decurrens, a brown seaweed commonly found in the Indian Ocean. Cancer, particularly lung cancer, poses a huge health burden in terms of incidence and mortality in India and globally. This study used a hot water extraction method. The seaweed extract was cleared by ethanol precipitation, further characterized, and its efficacy tested against lung cancer in vitro. The isolated compound was purified with ion-exchange chromatography, 13C solid-state nuclear magnetic resonance (NMR), attenuated total reflection infrared (ATR-IR), and Fourier transform infrared spectroscopy (FTIR). The antioxidant properties of fucoidan are known but there are few reports about oxidative stress-mediated lung cancer relevant to Turbinaria decurrens. The purified compound was tested against the lung cancer A549 cell line in vitro, and an IC50 concentration of purified fucoidan did not show any cytotoxicity in normal human embryonic kidney (HEK293) cells. The results showed the promising antioxidant potential of fucoidan. Mechanistically, fucoidan-induced cell cycle arrest in the S-M phase in a dose- and time-dependent manner. Fucoidan was able to induce mitochondrial membrane damage in A549 cells. Acridine AO/EB dual staining combined with western blotting revealed that fucoidan has the potential to inhibit anti-apoptotic Bcl-2 protein and significantly (p

Abstract Background The global landscape of public health faces significant challenges attributed to the prevalence of cancer and the emergence of treatment resistance. This study addresses these challenges by focusing on Cyclin-dependent Kinase 2 (CDK2) and employing a systematic computational approach for the discovery of novel cancer therapeutics. Results Initial ligand-based pharmacophore modelling, utilizing a training set of five reported CDK2 inhibitors, yielded a robust model characterized by Aro|Hyd| and |Acc|Don| features. Screening this validated model against the ZINC database identified 1881 hits, which were further subjected to molecular docking studies. The top 10 compounds (Z1–Z10) selected from the docking studies underwent Pharmacokinetic parameters Absorption, Distribution, Metabolism, Excretion and Toxicity profiling, Density Functional Theory (DFT) studies and the top two went for 100ns molecular dynamics (MD) simulations by comparing them with the standard Roscovitine. Compounds Z1 and Z2 emerged as the most promising, with docking scores of − 8.05 kcal/mol and − 8.02 kcal/mol, respectively. DFT analysis of the top 10 compounds revealed minimal variations in highest occupied molecular orbital–lowest unoccupied molecular orbital energy gaps, indicating consistent electronic stability and reactivity across the candidates. MD simulations of Z1 and Z2 confirmed their stable interactions with CDK2, with root mean square deviation (RMSD) values ranging from 1.4 to 2.5 Å for Z1 and 1.5 to 2.4 Å for Z2. Conclusion The current research identified compounds Z1 and Z2, which demonstrated significant potential as potent CDK2 inhibitors for cancer therapy, providing valuable insights into the development of more effective CDK2 inhibitors and addressing the critical need for innovative therapeutic strategies in cancer treatment. Graphical abstract

Introduction Water, sanitation, and hygiene (WASH)-related infectious diseases contribute to approximately 5% of the global disease burden. Despite sanitation being a human right, 673 million people worldwide had limited access to toilets. To tackle the same, Swachh Bharat Mission-Gramin (SBM-G) was launched in 2014 to facilitate the construction of over 100 million individual household latrines (IHHLs) across India. However, literature evidence on acceptance of SBM-G in Tamil Nadu, particularly in Coimbatore, is scarce. Objectives The primary objective was to investigate the utilisation of the SBM-G scheme and its associated factors in rural Coimbatore. Materials and Methods In 2022, a mixed-methods study incorporating quantitative (using a purpose-designed questionnaire) and qualitative (using in-depth interviews and focus group discussions) components was conducted among 60 SBM-G beneficiaries in Kovai Medical Center and Hospital Institute of Health Sciences and Research (KMCH IHSR)’s rural field practice area. Quantitative data were analysed using Statistical Package for the Social Sciences (SPSS) v23 and qualitative data using manual thematic content analysis. Results Before IHHL construction, 93.7% of respondents practiced open-air defecation (OAD). The issues perceived with OAD were distance (28.8%) and privacy concerns (12.5%). After SBM-G implementation, 78.3% reported using the IHHL ‘all the time’, reflecting a substantial shift in behaviour. Most respondents received financial aid within about six months (INR 8,000), often in a single instalment. The majority had constructed their IHHLs before 2015. The expenditures incurred varied significantly, with 58.3% spending extra costs ranging between INR 10,000 and 30,000. Water supply to households significantly influenced IHHL usage. Conclusion While rural villagers appreciate the SBM-G financial assistance for IHHLs, concerns about the inadequacy of the sanctioned amount for proper piped water supply and septic tanks persist. Nonetheless, there is a clear demonstration of positive behavioural change, marked by reduced OAD and increased IHHL usage.

The design of a supersonic combustor ramjet engine has gained significant attention for futuristic air-breathing engines. Achieving efficient fuel mixing and complete combustion within a short period poses a substantial challenge. This study has directed efforts towards improving mixing and minimizing total pressure losses across the supersonic combustor to enhance performance. Experiments were conducted to investigate the influence of fuel injection geometry in the supersonic combustor with an entry Mach number of 2. Two different fuel orifice geometries, inclined at 45°, were considered. The investigation covered four different momentum flux ratios: 0.8, 1.0, 1.2, and 1.4 respectively. Various measurements were conducted to observe flow phenomena inside the supersonic combustion, including wall static pressure measurement, Schlieren visualization, exit Mach number and total pressure loss measurement. Without injection cases exhibited weaker compression and expansion inside the combustor. During injection, the rise in wall pressure indicated that the bow shock formed in front of the arc injection was slightly weaker than that of the circular injection. The impinging bow shock on the opposite wall also exhibited higher strength, resulting in a static pressure rise. As a result, the lower total pressure ratio across the shock indicates a higher momentum exchange between the main flow and the orifice. Therefore, arc injection has proven more effective in exchanging momentum inside the supersonic combustor. Consequently, the Mach number at the exit of the combustor was higher for the arc injection.

Climate warming has been suggested to impact high latitude grasslands severely, potentially causing considerable carbon (C) losses from soil. Warming can also stimulate nitrogen (N) turnover, but it is largely unclear whether and how altered N availability impacts belowground C dynamics. Even less is known about the individual and interactive effects of warming and N availability on the fate of recently photosynthesized C in soil. On a 10-year geothermal warming gradient in Iceland, we studied the effects of soil warming and N addition on CO2 fluxes and the fate of recently photosynthesized C through CO2 flux measurements and a 13 CO2 pulse-labeling experiment. Under warming, ecosystem respiration exceeded maximum gross primary productivity, causing increased net CO2 emissions. N addition treatments revealed that, surprisingly, the plants in the warmed soil were N limited, which constrained primary productivity and decreased recently assimilated C in shoots and roots. In soil, microbes were increasingly C limited under warming and increased microbial uptake of recent C. Soil respiration was increased by warming and was fueled by increased belowground inputs and turnover of recently photosynthesized C. Our findings suggest that a decade of warming seemed to have induced a N limitation in plants and a C limitation by soil microbes. This caused a decrease in net ecosystem CO2 uptake and accelerated the respiratory release of photosynthesized C, which decreased the C sequestration potential of the grassland. Our study highlights the importance of belowground C allocation and C-N interactions in the C dynamics of subarctic ecosystems in a warmer world.

Using computational approaches, the potential efficacy and specificity of 1,3,4 trisubstituted pyrazole derivatives as MtInhA inhibitors which will aid in rational drug design for tubercular therapy were forecasted. QSARINS software was used to investigate the ability of compound to inhibit MtInhA. Three noteworthy descriptors with significant correlations and impressive statistical values were identified by the produced QSAR model: Correlation of coefficient(R2)= 0.8789, Cross-validation leave one out correlation coefficient (Q2LOO)= 0.8402, Cross-validation leave-many-out correlation coefficient(Q2LMO)=0.7321, Concordance Correlation Coefficient for cross-validation(CCCtr)=0.9355, CCCext =0.888. The descriptor generated by the QSAR model includes Centered Broto-Moreau autocorrelation weighted by Sanderson electronegativities (ATSC1e), Radial distribution functions at 15.0 and 2.0 Å inter-atomic distances weighted by relative van der Waals volumes (RDF150v), Radial distribution function – 145/weighted by relative I-state (RDF145s). Using these, three descriptor model was developed and the designed compounds were evaluated for their MtInhA inhibitory activity. Further, ADMET prediction and Molecular docking studies were carried out using Schrodinger's Software. ADMET prediction were used to evaluate drug likeliness and molecular docking was used to determine the interactions of designed compounds with the target protein. After the docking studies, the compounds were subjected for MM-GBSA calculations and MD simulation. Among the designed compounds, AP2 had the strongest binding affinity towards the MtInhA enzyme. The result of this work helps to understand the key interactions between 1,3,4 trisubstituted pyrazole derivatives and MtInhA protein that may be necessary to develop new lead compounds against tuberculosis.

Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. While current cancer vaccines are primarily prophylactic, advancements in targeting tumor-associated antigens (TAAs) and neoantigens have paved the way for therapeutic vaccines. The integration of artificial intelligence (AI) into cancer vaccine development is revolutionizing the field by enhancing various aspect of design and delivery. This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses. By utilizing AI technologies, researchers can navigate complex biological datasets and uncover novel therapeutic targets, thereby improving the precision and efficacy of cancer vaccines. Despite the promise of AI-powered cancer vaccines, significant challenges remain, such as tumor heterogeneity and genetic variability, which can limit the effectiveness of neoantigen prediction. Moreover, ethical and regulatory concerns surrounding data privacy and algorithmic bias must be addressed to ensure responsible AI deployment. The future of cancer vaccine development lies in the seamless integration of AI to create personalized immunotherapies that offer targeted and effective cancer treatments. This review underscores the importance of interdisciplinary collaboration and innovation in overcoming these challenges and advancing cancer vaccine development.

In today’s world, there has been a significant increase in the use of devices, gadgets, and mobile applications in our daily activities. Although this has had a significant impact on the lives of the general public, people who are Partially Visually Impaired SPVI, which includes a much broader range of vision loss that includes mild to severe impairments, and Completely Visually Impaired (CVI), who have no light perception, still face significant obstacles when trying to access and use these technologies. This review article aims to provide an overview of the NUI, Multi-sensory Interfaces and UX Design (NMUD) of apps and devices specifically tailored CVI and PVI individuals. The article begins by emphasizing the importance of accessible technology for the visually impaired and the need for a human-centered design approach. It presents a taxonomy of essential design components that were considered during the development of applications and gadgets for individuals with visual impairments. Furthermore, the article sheds light on the existing challenges that need to be addressed to improve the design of apps and devices for CVI and PVI individuals. These challenges include usability, affordability, and accessibility issues. Some common problems include battery life, lack of user control, system latency, and limited functionality. Lastly, the article discusses future research directions for the design of accessible apps and devices for visually impaired individuals. It emphasizes the need for more user-centered design approaches, adherence to guidelines such as the Web Content Accessibility Guidelines, the application of e-accessibility principles, the development of more accessible and affordable technologies, and the integration of these technologies into the wider assistive technology ecosystem.

Caffeic acid, a potent polyphenol belonging to the hydroxycinnamic acid derivative class, was utilized in the synthesis of silver nanoparticles (AgNPs) at ambient temperature. The resultant conjugates underwent comprehensive characterization employing various analytical techniques, including UV–visible spectroscopy, FTIR, RAMAN spectroscopy, dynamic light scattering (DLS) for size and zeta potential analysis, atomic force microscopy (AFM), and scanning electron microscopy (SEM). Through these analyses, the morphological characteristics of the synthesized nanoparticles were elucidated, providing valuable insights into their structural properties. Subsequently, the cytotoxic effects of the caffeic acid-synthesized silver nanoparticles were assessed against A549 cells over a 48-h period by MTT assay. Remarkably, these nanoparticles exhibited significant toxicity towards the cells, with inhibitory effects observed at concentrations of 141 μg/ml for CA AgNPs. This underscores their potential as potent agents against cancer cells. Furthermore, the profound significance of caffeic acid-synthesized silver nanoparticles was evaluated specifically against A549 lung cancer cells. This was corroborated through cell cycle analysis, which demonstrated the potent anticancer activity of the caffeic acid-synthesized silver nanoparticles. Such findings suggest promising prospects for their utilization in diverse cancer treatment modalities. Overall, the successful synthesis and characterization of caffeic acid-synthesized silver nanoparticles underscore their potential as potent agents against cancer, particularly in combating A549 lung cancer cells. Further research and exploration into their mechanisms of action and potential synergistic effects with existing anticancer therapies could unveil additional avenues for their clinical translation and utilization in cancer management.

ObjectiveCharacterization studies of the phenotypic and genetic diversity of Mongolian goats are limited, despite several goat breeds being registered in the country. This study aimed to evaluate the phenotypic and genetic diversity of 14 cashmere goat populations in Mongolia, consisting largely of identified goat breeds.MethodsBody weight, cashmere quality, and coat color were the phenotypic traits considered in this study. A linear model was used to fit body weight and cashmere traits, and least squares means (LSMs) were estimated for the region and location classes. Genetic diversity and structure were assessed using a goat 50K SNP array.ResultsThe studied populations exhibited greater phenotypic diversity at the regional level. A very small overall differentiation index (Fst: 0.017) was revealed by Wright’s Fst and a very small overall inbreeding index (FROH1:0.019) was revealed based on runs of homozygosity. Genetic clustering of populations by principal components showed large variances for the two goat populations of the Russian admixture (Gobi Gurvan Saikhan and Uuliin Bor), and smaller but differentiated clusters for the remaining populations. Similar results were observed in the admixture analysis, which identified populations with the highest (Govi Gurvan Saikhan and Uuliin Bor) and lowest (Tsagaan Ovoo Khar) exotic admixtures. A genomewide association study (GWAS) of body weight and cashmere traits identified a few significant variants on chromosomes 2, 4, 5, 9, and 15, with the strongest variant for cashmere yield on chromosome 4. The GWAS on coat color yielded nine significant variants, with the strongest variants located on chromosomes 6, 13, and 18 and potential associations with KIT, ASIP, and MC1R genes. These signals were also found in other studies on coat color and patterns in goats.ConclusionMongolian cashmere goats showed relatively low genetic differentiation and low inbreeding levels, possibly caused by the traditional pastoral livestock management system and the practice of trading breeding bucks across provinces, along with a recent increase in the goat population. Further investigation of cashmere traits using larger samples and alternative methods may help identify the genes or genomic regions underlying cashmere quality in goats.

Anthocyanins (ACNs) are natural pigments commonly found in plants which contribute to the vibrant colors of fruits, vegetables, and flowers. The present review aims to cover the ACNs field in terms of sources, extraction/purification techniques, as well as characterization methods that are crucial for assessing their medicinal potential and sustainable applications. Characterization methods e.g. HPLC, UPLC-QTOF-MS, MS, and NMR are discussed as analytical tools for the identification and quantification of ACNs in various vegetable matrices. Their antioxidant, anti-inflammatory, antidiabetic, anti-cancer and cardiovascular properties are, also, highlighted. Besides, the use of ACNs as natural colorants, preservatives, and functional ingredients is discussed considering their impact on the food industry. Likewise, due to their anti-aging and skin-protective properties, their employment in the cosmetic field is reported making them appealing for skincare formulations. This review provides a comprehensive overview, emphasizing the ACNs versatility in medicine, food industry, and cosmetic field, fostering future research and innovation.

IntroductionIn the evolving landscape of healthcare and medicine, the merging of extensive medical datasets with the powerful capabilities of machine learning (ML) models presents a significant opportunity for transforming diagnostics, treatments, and patient care.MethodsThis research paper delves into the realm of data-driven healthcare, placing a special focus on identifying the most effective ML models for diabetes prediction and uncovering the critical features that aid in this prediction. The prediction performance is analyzed using a variety of ML models, such as Random Forest (RF), XG Boost (XGB), Linear Regression (LR), Gradient Boosting (GB), and Support VectorMachine (SVM), across numerousmedical datasets. The study of feature importance is conducted using methods including Filter-based, Wrapper-based techniques, and Explainable Artificial Intelligence (Explainable AI). By utilizing Explainable AI techniques, specifically Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), the decision-making process of the models is ensured to be transparent, thereby bolstering trust in AI-driven decisions.ResultsFeatures identified by RF in Wrapper-based techniques and the Chi-square in Filter-based techniques have been shown to enhance prediction performance. A notable precision and recall values, reaching up to 0.9 is achieved in predicting diabetes.DiscussionBoth approaches are found to assign considerable importance to features like age, family history of diabetes, polyuria, polydipsia, and high blood pressure, which are strongly associated with diabetes. In this age of data-driven healthcare, the research presented here aspires to substantially improve healthcare outcomes.

The world faced different crises, several times, in the last five decades. The COVID-19 pandemic, debt crisis and oil bubble were the most important crises among them. During the crisis, the main financial driving factor was the oil price which transmitted an alarming hit to other economic nexus factors. The objective of this paper was to find out the crisis period, caused by crude oil and its eventuality in different markets’ historical plots. Scopus data represent 605 studies, using the bibliometrix R tool, about crude oil and related crises. Within the context of the contemporary scientific literature, the most significant authors, publications, and research institutions were included in this paper. In the context of the most recent scientific literature, the most significant authors, publications, and research institutions were identified and the relevance of their contributions was established. This research employed disaggregated sectoral analysis and mechanistic analysis on grounded theory, which included moderation analysis. Finally, this bibliometric analysis tried to locate roadblocks in the current literature as well as indicate new directions for investigation.

Introduction: Recently, imidazooxazines have attracted more attention due to their therapeutic potential against tuberculosis (TB). The present study aimed to identify and develop potential inhibitors against Pks13-TE to combat the antimicrobial drug resistance of TB. Methods: Computer-aided drug design is a more highly valued technology than the traditional drug discovery approach. Herein, we computationally investigated a chemical dataset using QSAR models and virtually screened novel leads of imidazooxazines against the thioesterase domain, further subjecting them to molecular docking and dynamics simulation. Results: The present study identified two molecules, 1 and 3, promising leads with minimum energy conformations of −7.63 and −7.62 kcal, respectively, providing structural insight into Pks13 inhibition. The average values of MolSA, SASA, and PSA for molecules 1 and 3 were 382.41 Å, 77.65 Å, and 195.54 Å and 386.24 Å, 71.105 Å, and 184.46 Å, respectively. In conclusion, our research has demonstrated that imidazoxazines are promising leads to combat the resistance problem of TB. Among the two potent molecules 1 and 3, molecule 1 displayed favourable interactions in the active site with good stability, as confirmed by the RMSD, RMSF, RoG, H-bond, and SASA analyses. The Molecule 1 protein complex showed two strong hydrogen bonds, effectively maintained for 80–85 % of the simulation time, indicating its stability and potency. Conclusion: The identified two molecules and their conformations were highly stable; hence, these findings provide valuable insight into the evolution of new therapeutic agents to address the growing problem of TB and its resistance.

Abstract Hydrazides, N-N containing structural motifs, are important due to their presence in a wide variety of biologically significant compounds. While the homo N-N coupling of two NH moieties to form the hydrazide N-N bond is well developed, the cross-dehydrogenative hetero N-N coupling remains very unevolved. Here we present an efficient intermolecular N-N cross-coupling of a series of primary benzamides with broad range of Lewis basic primary and secondary amines using PhI(OAc)2 as both a terminal oxidant and a cross-coupling mediator, without the need for metal catalysts, high temperatures, and inert atmospheres, and with substantial potential for use in the late-stage functionalization of drugs.

Epilepsy is a chronic brain disorder characterized by recurrent unprovoked seizures. The treatment for epilepsy is influenced by the types of seizures. Therefore, developing a reliable, explainable, and automated system to identify seizure types is necessary. This study aims to automate the process of classification of five seizure types: focal non-specific, generalized, complex partial, absence, and tonic-clonic using electroencephalogram (EEG) signals and machine learning algorithms. The EEG signals of 2933 seizures from 327 patients were obtained from the publicly available Temple University Hospital dataset. Initially, the signals were preprocessed using a standard pipeline, and 110 features from the time, frequency, and time-frequency domain were computed from each seizure. Further, the features were ranked using the statistical test and extreme Gradient Boosting (XGBoost) algorithm to identify the significant features. We built binary and multiclass seizure-type classification systems using the identified features and machine learning algorithms. Our study revealed that the EEG band power between 11–13 Hz, 27–29 Hz, intrinsic mode function (IMF) band power 19–21 Hz, and delta band (1-4 Hz) played a crucial role in discriminating the seizures. We achieved an average accuracy of 88.21% and 69.43% for the binary and multiclass seizure-type classification, respectively, using the XGBoost classifier. We also found that the combination of features performed well compared to any single domain. This automated system has the potential to aid neurologists in making diagnosis of epileptic seizure types. The proposed methodology can be applied alongside the established clinical approach of visual evaluation for the classification of seizure-types.

Droughts typically develop gradually, and early prediction is crucial for the government to formulate effective mitigation plans. Our approach does not involve predicting specific drought index values. Instead, we forecast whether a particular year will experience drought. Insufficient investigation has been carried out regarding variations in additional climatic indicators like shortwave radiation, wind speed, sea level, and pollution in the context of droughts in the state of Tamil Nadu, India. In the study period taken from 1995 to 2020, only three years (2002, 2009, and 2017) experienced drought occurrences, resulting in an imbalanced dataset. To enhance the classification performance of this imbalanced dataset, a weighted dataset is constructed using a feature weighting approach known as the Single Objective Scorer (SOS) based Multi-objective PSO(MPSO) in conjunction with the Gradient Boosting Classifier. The proposed model facilitates objective-based multi-population formation and neighborhood learning. Precision and recall are crucial metrics, particularly in measuring imbalanced dataset classification performance. The application of multi-objective optimization techniques helps to strike a suitable balance between precision and recall. In addition to the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), 14 climatic indicators based on land, atmosphere, and sea are utilized. By employing the weighted dataset created with SOS-based MPSO, a significant improvement in recall value of 0.81 is achieved. Based on the weights assigned to the features, it is identified that the Mean Sea Level of the Arabian Sea and CO2 are significant indicators for predicting meteorological drought. The Explainable AI techniques SHAP and LIME are employed for interpreting the drought prediction model, providing insights into its workings.