Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system's exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method's performance was approximately 95.39%. Upon completing all the analysis, the system's classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

Development of maximum relevant prior feature ensemble (MRPFE) index to characterize future drought using global climate models.

Drought is one of the foremost outcomes of global warming and global climate change. It is a serious threat to humans and other living beings. To reduce the adverse impact of drought, mitigation strategies as well as sound projections of extreme events are essential. This research aims to strengthen the robustness of anticipated twenty-first century drought by combining different Global Climate Models (GCMs). In this article, we develop a new drought index, named Maximum Relevant Prior Feature Ensemble index that is based on the newly proposed weighting scheme, called weighted ensemble (WE). In the application, this study considers 32 randomly scattered grid points within the Tibetan Plateau region and 18 GCMs of Coupled Model Intercomparison Project Phase 6 (CMIP6) of precipitation. In this study, the comparative inferences of the WE scheme are made with the traditional simple model averaging (SMA). To investigate the trend and long-term probability of various classes, this research employs Markov chain steady states probability, Mann-Kendall trend test, and Sen's Slope estimator. The outcomes of this research are twofold. Firstly, the comparative inference shows that the proposed weighting scheme has greater efficiency than SMA to conflate GCMs. Secondly, the research indicates that the Tibetan Plateau is projected to experience "moderate drought (MD)" in the twenty-first century.

Unlocking the potential of biofilm-forming plant growth-promoting rhizobacteria for growth and yield enhancement in wheat (Triticum aestivum L.).

Plant growth-promoting rhizobacteria (PGPR) boost crop yields and reduce environmental pressures through biofilm formation in natural climates. Recently, biofilm-based root colonization by these microorganisms has emerged as a promising strategy for agricultural enhancement. The current work aims to characterize biofilm-forming rhizobacteria for wheat growth and yield enhancement. For this, native rhizobacteria were isolated from the wheat rhizosphere and ten isolates were characterized for plant growth promoting traits and biofilm production under axenic conditions. Among these ten isolates, five were identified as potential biofilm-producing PGPR based on in vitro assays for plant growth-promoting traits. These were further evaluated under controlled and field conditions for their impact on wheat growth and yield attributes. Surface-enhanced Raman spectroscopy analysis further indicated that the biochemical composition of the biofilm produced by the selected bacterial strains includes proteins, carbohydrates, lipids, amino acids, and nucleic acids (DNA/RNA). Inoculated plants in growth chamber resulted in larger roots, shoots, and increase in fresh biomass than controls. Similarly, significant increases in plant height (13.3, 16.7%), grain yield (29.6, 17.5%), number of tillers (18.7, 34.8%), nitrogen content (58.8, 48.1%), and phosphorus content (63.0, 51.0%) in grains were observed in both pot and field trials, respectively. The two most promising biofilm-producing isolates were identified through 16 s rRNA partial gene sequencing as Brucella sp. (BF10), Lysinibacillus macroides (BF15). Moreover, leaf pigmentation and relative water contents were significantly increased in all treated plants. Taken together, our results revealed that biofilm forming PGPR can boost crop productivity by enhancing growth and physiological responses and thus aid in sustainable agriculture.

Chia seed-mediated fabrication of ZnO/Ag/Ag2O nanocomposites: structural, antioxidant, anticancer, and wound healing studies.

Plant extract-mediated fabrication of metal nanocomposites is used in cell proliferation inhibition and topical wound treatment, demonstrating significant effectiveness. Salvia hispanica L. (chia) seed extract (CE) is used as the reaction medium for the green fabrication of ecofriendly ZnO(CE) nanoparticles (NPs) and Ag/Ag2O(CE) and ZnO/Ag/Ag2O(CE) nanocomposites. The resultant nanoparticles and nanocomposite materials were characterized using UV-visible, Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy-dispersive X-ray (EDX) techniques. In the context of antioxidant studies, ZnO/Ag/Ag2O(CE) exhibited 57% reducing power and 86% 2,2, diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging. All three materials showed strong antibacterial activity against Staphylococcus aureus (S. aureus), Escherichia coli (E.coli), and Bacillus subtilis (B. subtilis) bacterial strains. Additionally, ZnO(CE), Ag/Ag2O(CE), and ZnO/Ag/Ag2O(CE) also revealed 64.47%, 42.56%, and 75.27% in vitro Michigan Cancer Foundation-7 (MCF7) cancer cell line inhibition, respectively, at a concentration of 100 µg/mL. Selectively, the most effective composite material, ZnO/Ag/Ag2O(CE), was used to evaluate in vivo wound healing potential in rat models. The study revealed 96% wound closure in 10 days, which was quite rapid healing compared to wound healing using clinically available ointment. Therefore, in conclusion, the ZnO/Ag/Ag2O(CE) nanocomposite material could be considered for further testing and formulation as a good anticancer and wound healing agent.

Development of Ridge Ensemble Standardized Drought Index (RESDI) for improving drought characterization and future assessment.

Global warming upsets the environmental balance and leads to more frequent and severe climatic events. These extreme events include floods, droughts, and heatwaves. These widespread extreme events disrupt various sectors of ecosystems directly. However, among all these events, drought is one of the most prolonged climatic events that significantly destroys the ecosystem. Therefore, accurate and efficient assessment of droughts is necessary to mitigate their detrimental impacts. In recent years, several drought indices based on global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) have been proposed to quantify and monitor droughts. However, each index has its advantages and limitations. As each index ensembles different models by using different statistical approaches, it is well known that the margin of error is always a part of statistics. Therefore, this study proposed a new drought index to reduce the uncertainty involved in the assessment of droughts. The proposed index named the Ridge Ensemble Standardized Drought Index (RESDI) is based on the innovative ensemble approach termed ridge parameters and distance-based weighting (RDW) scheme. And the development of this RDW scheme is based on two types of methods i.e., ridge regression and divergence-based method. In this research, we ensemble 18 different GCMs of CMIP6 using the RDW scheme. A comparative analysis of the RDW scheme is performed against the simple model average (SMA) and Bayesian model averaging (BMA) schemes at 32 locations on the Tibetan plateau. The comparison revealed that RDW has less mean absolute error (MAE) and root-mean-square error (RMSE). Therefore, the developed RESDI based on RDW is used to project drought properties under three distinct shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, across seven different time scales (1, 3, 7, 9, 12, 24, and 48). The projected data is then standardized by using the K-components Gaussian mixture model (K-CGMM). In addition, the study employs steady-state probabilities (SSPs) to determine the long-term behavior of drought. The outcome of this research shows that "normal drought (ND)" has the highest probability of occurrence under all scenarios and time scales.

iso-Guttiferone J and Structure Revision of Guttiferone J from Garcinia gummi-gutta: A Combined Experimental and Integrated QM/NMR Approach.

Many polyprenylated acylphloroglucinols with fascinating chemical structures and intriguing biological activities have been identified as key to phytochemicals isolated from Garcinia, Hypericum, and related genera. In the present work, two chiral, tautomeric, highly-oxygenated polyprenylated acylphloroglucinols tethered with acyl and prenyl moieties on a bicyclo[3.3.1]nonanetrione core were isolated from the 95% ethanolic extract of Garcinia gummi-gutta fruit. The structures of both compounds were elucidated based on the NMR and MS data with ambiguity in the exact position of the enol and keto functions at C-1 and C-3 of the core structure. The structures of both polyprenylated acylphloroglucinols were established as a structurally revised guttiferone J and the new iso-guttiferone J with the aid of gauge-independent atomic orbital NMR calculations, CP3 probability analyses, specific rotation calculations, and electronic circular dichroism calculations in combination with the experimental data. The structures of both compounds resemble hyperforin, a potent activator of the human pregnane X receptor. As expected, both compounds showed strong pregnane X receptor activation at 10 µM [7.1-fold (guttiferone J) and 5.0-fold (iso-guttiferone J)], explained by a molecular docking study, necessitating further in-depth investigation to substantiate the herb-drug interaction potential of G. gummi-gutta upon co-administration with pharmaceutical drugs.

Phytochemical analysis of Cichorium bottae root and anti-inflammatory potential assessment of isolates.

The Cichorium plants are particularly notable due to their remarkable therapeutic and medicinal properties, besides being used as food and conventional medication. Although Cichorium plants have been studied for their phytoconstituents and biological activities, there is limited knowledge about the constituents of the roots of C. bottae. A phytochemical study of the 90% MeOH extract of C. bottae roots resulted in the isolation of twelve compounds belonging to guaianolide sesquiterpene lactones, sesquiterpene lactone glucosides, and phenolic derivatives, of which two compounds designated as 9α-hydroxycrepediaside B (1) and cichobotinal (2) were previously undescribed. The isolated compounds were assessed for their anti-inflammatory potential through the inhibition of inducible nitric oxide synthase (iNOS) and resultant decrease in nitric oxide generation in LPS-induced macrophages. Among the isolates, compounds 2 and 11 (8-deoxylactucin) inhibited iNOS activity with IC50 values of 21.0 ± 4 and 6.8 ± 0.1 µM, respectively. The methanolic extract of C. bottae inhibited iNOS with an IC50 of 10.5 ± 0.5 µg/mL.

Dissecting wheat above-ground architecture for enhanced water use efficiency and grain yield in the subtropics.

BACKGROUND: Growing wheat under climate change scenarios challenges, scientists to develop drought and heat-tolerant genotypes. The adaptive traits should therefore be explored and engineered for this purpose. Thus, this study aimed to dissect surface traits and optimizing the leaf architecture to enhance water use efficiency (WUE) and grain yield. Twenty-six wheat genotypes were assessed for five novel leaf traits (NLTs: leaf prickle hairs, groove type, rolling, angle and wettability) under normal, drought and heat conditions following triplicated factorial randomized complete block design (RCBD). The data for NLTs, physiological traits (stomatal conductance, WUE, transpiration, and photosynthesis), and standard morphological and yield traits were recorded. Leaves were sampled at the stem elongation stage (Zadoks 34) to measure the leaf water content (%), contact angle, and to obtain pictures through scanning electron microscopy (SEM). The air moisture harvesting efficiency was evaluated for five selected genotypes. The ideotype concept was applied to evaluate the best-performing genotypes. RESULTS: The correlation analysis indicated that long leaf prickle hairs (> 100 µm), short stomatal aperture and density (40-60 mm- 2), inward to spiral leaf rolling, medium leaf indentation, low contact angle hysteresis (< 10°), and cuticular wax were positively associated with WUE. This, in turn, was significantly correlated to grain yield. Thus, the genotypes (E-1) with these traits and alternate leaf wettability had maximum grain yield (502 g m- 2) and WUE supported with high photosynthesis rate, and relative water content (94 and 75% under normal and stress conditions, respectively). However, the genotype (1-hooded) with dense leaf hairs on edges but droopy leaves, spiral leaf rolling, and lighter groove, also performed better in terms of grain yield (450 g m- 2) under heat stress conditions by maintaining high photosynthesis and WUE with low stomatal conductance and transpiration rate. CONCLUSION: The SEM analysis verified that the density of hairs on the leaf surface and epicuticular wax contributes towards alternate wettability patterns thereby increasing the water-use efficiency and yield of the wheat plant. This study paves a way towards screening and and developing heat and drought-tolerant cultivars that are water-saving and climate-resilient.

Phytochemicals and enzymes inhibitory potentials of leaves and rootbarks of Sarcocephallus latifolius (smith): In vitro and in silico investigations.

In this study, bioactive compounds were isolated and characterized from the leaves and root-barks extracts of S.latifolius, with subsequent in vitro experimental investigations for antihyperglycemic potentials on α-amylase and α-glucosidase enzymes. Thirteen bioactive compounds were identified, including 10-Hydroxystrictosamide (2) and Quinovic acid-3-O-α-L-rhamnosyl-28-O-ß-d-glucopyranosyl ester (8), using chromatographic, nuclear magnetic resonance spectroscopy (NMR), and mass spectrometry (MS) techniques. Experimental assays revealed that compounds 1-4 exhibited potent inhibition of α-amylase and α-glucosidase, with compound 2 demonstrating the most potent α-amylase inhibition (IC50 value of 0.52 ± 0.003 µg/mL). Compound 8 showed a lower IC50 value (0.098 ± 0.016 µg/mL) against α-glucosidase compared to compound 2 and acarbose. Synergistic effects among the compounds could enhance their inhibitory actions on the enzymes, positioning them as potential anti-hyperglycemia agents. Compound 2 displayed the highest binding affinity (-7.970 kcal/mol) when docked against α-amylase (PDB ID: 2QV4), comparable to acarbose (-8.515 kcal/mol). It also ranked among the top ligands against α-glucosidase (PDB ID 3TOP), although compound 13 and acarbose had higher docking scores. All compounds exhibited ideal ADMET properties, suggesting good bioavailability and low toxicity. In conclusion, the isolated compounds exhibit promising antihyperglycemic potential and favourable safety profiles for further exploration.

A novel nor-megastigmane from the leaves of Rhaphiostylis beninensis.

A novel nor-megastigmane, normegastigmane-5α,9-epoxy-3ß,8-diol (1), together with 10 known compounds of diverse classes including megastigmanes, sesquiterpenoids, and triterpenoids were isolated from the leaves of Rhaphiostylis beninensis. The structure of 1 was established by 1D and 2D NMR and HRESIMS data analysis. The known compounds were identified as 5,11-epoxy-3,9-megastigmanediol (2), 7-megastigmene-3,6,9-triol (3), 1,3-dihydroxypropan-2-yl stearate (4), (1E,5E)-1,5-dimethyl-8-(propan-2-ylidene)cyclodeca-1,5-diene germacrene (5) 3,5-dihyroxy-6,7-megastigmadien-9-one (6), squalene (7), ß-amyrin (8), ß-amyrone (9), ß-amyrin eicosanoate (10), and ß-sitosterol (11). Compounds 2, 3, and 6 displayed inhibitory effects on acetylcholinesterase enzyme. This is the first report of these compounds from the plant and their anticholinesterase activity.

Spatiotemporal changes in future precipitation of Afghanistan for shared socioeconomic pathways.

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020-2059) and the distant future (2060-2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

Internet of medical things and blockchain-enabled patient-centric agent through SDN for remote patient monitoring in 5G network.

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.

Phytochemical profiling of Striga asiatica; characterisation and anti-microbial assessment of the isolates.

One undescribed compound, striasinol (1), and twelve previously described compounds were isolated from the aerial parts of Striga asiatica. Structure elucidation of isolated compounds was achieved by the interpretation of 1D and 2D NMR and HRESIMS data. The absolute configuration (1S,5S) of 1 was ascertained based on GIAO NMR calculations, DP4+ probability analysis, and a comparison of the experimental and calculated specific rotation values. The isolated compounds were evaluated for their antimalarial action, and none was found to be effective against the chloroquine-sensitive (D6) or chloroquine-resistant (W2) strains of Plasmodium falciparum. The isolates were found non-toxic to the Vero cell line as well. Subsequent testing of these metabolites for antimicrobial activities against various bacterial and fungal strains (up to 20 µg/mL), revealed that compounds 6 (chryseriol) and 7 (apigenin) showed a reasonable activity towards methicillin-resistant Staphylococcus aureus ATCC 1708 (MRSA), with IC50 values of 5.81 and 3.60 µg/mL, respectively.

Upregulation of TaHSP90A transcripts enhances heat tolerance and increases grain yield in wheat under changing climate conditions.

Plants have certain adaptation mechanisms to combat temperature extremes and fluctuations. The heat shock protein (HSP90A) plays a crucial role in plant defence mechanisms under heat stress. In silico analysis of the eight TaHSP90A transcripts showed diverse structural patterns in terms of intron/exons, domains, motifs and cis elements in the promoter region in wheat. These regions contained cis elements related to hormones, biotic and abiotic stress and development. To validate these findings, two contrasting wheat genotypes E-01 (thermo-tolerant) and SHP-52 (thermo-sensitive) were used to evaluate the expression pattern of three transcripts TraesCS2A02G033700.1, TraesCS5B02G258900.3 and TraesCS5D02G268000.2 in five different tissues at five different temperature regimes. Expression of TraesCS2A02G033700.1 was upregulated (2-fold) in flag leaf tissue after 1 and 4h of heat treatment in E-01. In contrast, SHP-52 showed downregulated expression after 1h of heat treatment. Additionally, it was shown that under heat stress, the increased expression of TaHSP90A led to an increase in grain production. As the molecular mechanism of genes involved in heat tolerance at the reproductive stage is mostly unknown, these results provide new insights into the role of TaHSP90A transcripts in developing phenotypic plasticity in wheat to develop heat-tolerant cultivars under the current changing climate scenario.

Cytotoxic leuconoxine-type diazaspiroindole alkaloids isolated from Cryptolepis dubia.

Two leuconoxine-type diazaspiroindole alkaloids, the known compound, (+)-melodinine E (1), and its new analogue, (+)-11-chloromelodinine E (2), were isolated from the stems of Cryptolepis dubia (Burm.f.) M.R. Almeida (Apocynaceae), collected in Laos. The chemical structures of these compounds were determined by analysis of their spectroscopic data and by comparison of these data with literature values, of which the molecular structure of 1 has been determined previously by analysis of its single-crystal X-ray diffraction data. The absolute configurations of 1 and 2 have been defined by their experimental and simulated electronic circular dichroism (ECD) spectroscopic data and supported by 1H and 13C NMR-based DP4+ probability analysis and specific rotation calculations. When tested against a small panel of human cancer cell lines, these two compounds exhibited selective cytotoxicity toward OVCAR3 human ovarian cancer cells.

Comparative transcript abundance of gibberellin oxidases genes in two barley (Hordeum vulgare) genotypes with contrasting lodging resistance under different regimes of water deficit.

Barley (Hordeum vulgare ) is the world's fourth most important cereal crop, and is particularly well adapted to harsh environments. However, lodging is a major productivity constraint causing 13-65% yield losses. Gibberellic acid (GA) homeostatic genes such as HvGA20ox, HvGA3ox and HvGA2ox are responsible for changes in plant phenotype for height and internodal length that contribute towards lodging resistance. This study explored the expression of different HvGAox transcripts in two contrasting barley genotypes (5-GSBON-18, lodging resistant; and 5-GSBON-70, lodging sensitive), which were sown both under controlled (hydroponic, completely randomised factorial design) and field conditions (split-plot, completely randomised block design) with two irrigation treatments (normal with three irrigation events; and water deficit with one irrigation event). In the hydroponic experiment, expression analysis was performed on seedlings at 0, ¾, 1½, 3 and 6h after application of treatment. In the field experiment, leaf, shoot nodes and internodes were sampled. Downregulation of HvGA20ox.1 transcript and 2-fold upregulation of HvGA2ox.2 transcript were observed in 5-GSBON-18 under water deficit conditions. This genotype also showed a significant reduction in plant height (18-20%), lodging (<10%), and increased grain yield (15-18%) under stress. Utilisation of these transcripts in barley breeding has the potential to reduce plant height, lodging and increased grain yield.

Impact of salinity stress on cotton and opportunities for improvement through conventional and biotechnological approaches.

Excess salinity can affect the growth and development of all plants. Salinization jeopardizes agroecosystems, induces oxidative reactions in most cultivated plants and reduces biomass which affects crop yield. Some plants are affected more than others, depending upon their ability to endure the effects of salt stress. Cotton is moderately tolerant to salt stress among cultivated crops. The fundamental tenet of plant breeding is genetic heterogeneity in available germplasm for acquired characteristics. Variation for salinity tolerance enhancing parameters (morphological, physiological and biochemical) is a pre-requisite for the development of salt tolerant cotton germplasm followed by indirect selection or hybridization programs. There has been a limited success in the development of salt tolerant genotypes because this trait depends on several factors, and these factors as well as their interactions are not completely understood. However, advances in biochemical and molecular techniques have made it possible to explore the complexity of salt tolerance through transcriptomic profiling. The focus of this article is to discuss the issue of salt stress in crop plants, how it alters the physiology and morphology of the cotton crop, and breeding strategies for the development of salinity tolerance in cotton germplasm.

Application of the Boruta algorithm to assess the multidimensional determinants of malnutrition among children under five years living in southern Punjab, Pakistan.

BACKGROUND: Malnutrition causes nutrient deficiencies that have both physical and clinical consequences in severe acute malnutrition children. Globally, there were 47 million wasted children under the age of five in 2019. One in four were located in sub-Saharan Africa, with half being in South Asia. This study aims to apply the Boruta algorithm to identify the determinants of undernutrition among children under five living in Dera Ghazi Khan, one of the marginalized districts of densely populated Punjab Province in Pakistan. METHODS: A multicenter cross-sectional study design was used to collect data from 185 children with severe acute malnutrition aged under five years visiting the OTPs centers located in Dera Ghazi Khan, Punjab, Pakistan. A purposive sampling technique was used to collect data using a pretested structured questionnaire from parents/caregivers regarding family sociodemographic characteristics, child nutrition, and biological and healthcare characteristics. Anthropometric measurements, including height, weight, and mid-upper arm circumference, were collected. The Boruta models were used to incorporate the children's anthropometric, nutritional, and household factors to determine the important predictive variables for undernutrition using the Boruta package in R studio. RESULTS: This study included 185 children, with a mean age of 15.36 ± 10.23 months and an MUAC of 10.19 ± 0.96 cm. The Boruta analysis identifies age, mid-upper arm circumference, weaning practices, and immunization status as important predictors of undernutrition. Income per month, exclusive breastfeeding, and immunization status were found to be key factors of undernutrition in children under the age of five. CONCLUSION: This study highlights age, mid-upper arm circumference, weaning practices, and immunization status as key determinants of weight-for-height and weight-for-age in children under five years. It also suggests that economic context may influence undernutrition. The findings can guide targeted strategies for combating undernutrition.