The timing of breeding is a key component of a species' realised niche and has been shown to be flexible to environmental change. Using a network of time-lapse cameras around Patagonia and the sub-Antarctic, along with citizen science assisted AI data extraction, we demonstrate that semi-automated extraction of phenological parameters is feasible without extensive human presence at colonies. Our comparative analysis of two related penguin species across the Polar Front reveals shifts in breeding phenology linked to environmental variation. The Southern Rockhopper Penguin showed a potential contraction of its breeding season, with consistent arrival but earlier departures in recent years. In contrast, Macaroni Penguins arrived and departed earlier, suggesting a temporal shift rather than a shorter season. These phenological changes correlated with winter-integrated SST rather than late-winter conditions, while reduced SST during pre-moult periods extended pre-moult duration and delayed migration. Despite marked phenological shifts in one species, breeding success remained stable, suggesting that timing changes may reflect flexible responses to environmental shifts rather than hidden survival costs. Species-specific differences in baseline phenology and temporal trends suggest that while some seabirds show phenological plasticity to ocean warming, stable breeding success may mask subtle but consequential impacts on sub-Antarctic population dynamics.
Geobacillus stearothermophilus is a thermophilic bacterium widely used in food sterilization and industrial processes. Although it has long been treated as a single, well-defined species, its internal genomic diversity has not been systematically evaluated. Here, we analyzed 36 strains using comparative genomics to clarify the structure of diversity within this species. Phylogenetic analyses consistently revealed two major genomic groups. Genome similarity measurements showed that most strains met current species-level criteria, yet clear internal differentiation was present. The two groups differed in ecological origin and genome composition. Strains associated with food-related environments tended to have smaller genomes and fewer metabolic genes, whereas strains from natural thermal habitats possessed larger genomes and broader metabolic capabilities, including genes for carbohydrate and fatty acid utilization. A small number of strains displayed intermediate positions, suggesting gradual diversification rather than sharp separation. Despite pronounced internal structuring, the strains remain within accepted species boundaries. These findings demonstrate that substantial ecological and functional divergence can accumulate within a single bacterial species. Our results provide a genomic framework for understanding intraspecific diversity in thermophilic bacteria and illustrate the importance of interpreting genome similarity thresholds in the context of population structure.
Cancer-associated fibroblasts (CAFs) are key drivers of extracellular matrix (ECM) deposition and remodeling in the tumor microenvironment (TME), processes that facilitate tumor progression and metastasis. However, CAFs exhibit significant heterogeneity and functional complexity, posing challenges for effective therapeutic targeting. In this study, we evaluated the production of three important ECM proteins - type I (PRO-C1), type III (PRO-C3), and type VI (PRO-C6) collagen - by CAFs in vitro. Four distinct CAFs were cultured in Ficoll-containing media supplemented with ascorbic acid for up to 12 days. Cells were stimulated with profibrotic or inflammatory factors (TGF-β1, PDGF-AB, IL-1α, IL-6) and/or treated with antifibrotic compounds (ALK5i, Fresolimumab). Collagen production was quantified in collected cell culture media using competitive ELISA. Our results reveal distinct fibrotic responses among CAFs. Two CAFs displayed high intrinsic fibrotic activity and minimal additional fibrotic responsiveness to profibrotic stimuli, whereas two CAFs exhibited low intrinsic fibrotic activity and significant increases in PRO-C1, PRO-C3, and PRO-C6 upon profibrotic stimulation. Notably, TGF-β1 was the primary driver of PRO-C3, PDGF-AB was the primary driver of PRO-C6, while IL-1α and IL-6 had no effect on PRO-C1, PRO-C3 and PRO-C6 levels. Antifibrotic treatments with ALK5i and Fresolimumab effectively reduced collagen biomarkers elevated by TGF-β1 to baseline levels or below.These results underscore the heterogeneity of CAFs in ECM remodeling, highlighting the need for tailored therapeutic strategies to target tumors exhibiting high fibrotic activity.
Identifying environmental factors associated with local adaptation and traits under selection is key to linking evolutionary processes to the environment. While reciprocal transplantation studies and provenance experiments often have demonstrated adaptation at relatively large spatial scales, adaptation can also occur at very small spatial scales. Combining a crossing experiment with field transplantations, we investigated whether Cerastium fontanum has adapted to geothermally induced small-scale soil temperature differences. Offspring representing a wide range of parental soil temperatures were transplanted across the same temperature range, and traits and fitness components were measured over 2 yr. We evaluated the relationship between plant performance and soil temperatures, the degree of adaptation to their source thermal environment, and the dependence of adaptation on flowering time. Survival, flowering incidence, and overall fitness were lower in warmer soils. However, adaptation to temperature was asymmetric; while all plants performed well at colder sites, individuals from colder origins performed poorly at warmer sites. Flowering time and fitness varied in relation to soil temperature, as well as the difference between planting and source thermal environments. Our findings indicate that small-scale variation in soil temperature underlies fine-scale adaptation and provides important knowledge to understand evolutionary effects of microclimatic variation.
In recent years, the application of gas injection, particularly carbon dioxide (CO2) dissolved in water, known as carbonated water (CW), has gained increasing attention. In this context, the current study is designed to examine the effect of CO2 dissolution in water under pressures ranging from 500 to 4500 psi, covering subcritical to supercritical conditions, and at temperatures between 25°C-65°C. Additionally, the synergistic effects of surfactants, namely dioctyl sulfosuccinate sodium salt (AOT) and sodium dodecyl polyoxyethylene ether sulfate (AES), were examined at concentrations ranging from 0 to 700 ppm, along with the dissolved CO2 on IFT and swelling factors. The measurements revealed that as the pressure increased, the swelling factor reached a maximum value of 19.3% when it was contacted with crude oil, while the maximum swelling factor for the solutions contacted with synthetic mixed resinous and asphaltenic oil (SMRAO) was reached at a value of 22.3%. The second oil type was selected as SMRAO since crude oil comprises thousands of components, making it hard to extract any generalized conclusions based on the obtained results. In this way, using only one or two specific fractions, especially resin and asphaltene which acts as natural surfactants, providing the chance to examine the generalized interactions between chemicals and oil fractions. The measurements revealed that the presence of surfactant in the carbonated water (CW) reduced the swelling factor up to 50% for AOT and 38% for AES as the pressure and temperature and surfactant concentration increases. The reason of this observed trend was correlated to the bulky structure of AOT compared with the linear chain-like structure of AES. Besides, the measurements revealed the positive impact of pressure and temperature on a higher swelling factor regardless of the used surfactants, which can be due to the higher dissolution of CO2 under higher pressures and better movement and migration of CO2 molecules, which means a penetration of higher amount of CO2 into the oil drop leading to higher swelling factors. In the next stage, the IFT of different solutions under different temperatures (25 °C-65 °C) and pressures (0-4500 psi) was measured. The obtained IFT values showed that using SMRAO instead of crude oil has a reducing impact on the IFT values with minimum value of 19.2 mN/m, while the IFT value for similar thermodynamic condition and crude oil was 23.1 mN/m. Besides, further IFT measurements revealed that although increasing pressure has a reducing impact on the IFT, increasing temperature increases the IFT values regardless of the presence of surfactant or even the type of surfactant. The measurements also revealed that the effect of AES on the IFT reduction was better than AOT, leading to a minimum IFT value of 1.1 mN/m for AES concentration of 700 ppm dissolved in CW with pressure and temperature of 4500 psi and 65 °C, respectively due to longer alkyl chain length and easier packing in the interface compared with AOT which has a bulky structure prevents the high number of AOT molecules to be packed in the interface. The measured IFT values revealed the linear IFT variation behavior for the systems were in contact with SMRAO compared with crude oil due to this fact that the SMRAO has less complexities than crude oil comprises of thousands of components makes the IFT variations more straightforward for SMRAO.
Probiotic supplements are marketed for diverse health benefits, yet species inclusion often lacks functional rationale. Our survey of 352 over-the-counter probiotic products available in the USA revealed 36 unique microbial species. However, there is no clear link between species inclusion and the intended health benefit. Here, to address this gap, we developed HaPaPro, a collection of 1,012 genome-scale metabolic models spanning pathogenic, probiotic and host-associated bacteria, constructed from publicly available genome sequences. Flux balance analysis revealed that probiotic species fail to capture the metabolic diversity of host-associated microbes. Focusing on vaginal health, we computationally identified vaginal microbes with metabolic profiles overlapping Gardnerella vaginalis. In vitro spent media assays using 11 vaginal isolates showed variable inhibition of G. vaginalis, primarily driven by D-lactic acid production, which was also produced by non-Lactobacillus species. These findings highlight the need for function-based probiotic design and demonstrate a scalable framework integrating metabolic modelling with experimental validation.
Hepatocellular carcinoma represents a major global health challenge, with its link to the commensal microbiota being clearly established. However, developing reproducible microbial biomarkers for early-stage hepatocellular carcinoma diagnosis across diverse populations remains challenging. We conducted an integrative analysis of 13 studies, examining 16S rRNA sequencing data from 607 fecal samples and 263 liver tissue samples. Data processing utilized VSEARCH, QIIME, and R packages (vegan, phyloseq, cooccur, random forest), with PICRUSt for functional prediction. Alpha diversity analysis revealed significant differences in liver microbiota but not in gut microbiota between hepatocellular carcinoma patients and non-cancer individuals. Linear Discriminant Analysis Effect Size identified Blautia and Streptococcus as biomarker shared across the gut and liver micro-niches. Based on the internal data, the models constructed using gut and liver microbiome characteristics demonstrated high discriminative ability (gut model AUC = 0.8064; liver model AUC = 0.9645). Mendelian randomization analysis revealed a potential association between Streptococcus and the development of hepatocellular carcinoma. KEGG enrichment analysis further indicated marked functional differences in microbiota, primarily linked to metabolic irregularities, between cancer patients and controls. Therefore, this study reveals unique gut-liver microbial community features in patients with hepatocellular carcinoma, identifies potential cross-site diagnostic biomarkers, and constructs gut and liver predictive model with good performance, providing preliminary evidence for the application of microbial biomarkers in the early diagnosis and screening of hepatocellular carcinoma.
To examine the surgical outcomes of surgical aortic valve replacement in the transcatheter aortic valve replacement era and propose a novel patient-specific prognostic model. We randomly divided 772 patients with aortic stenosis who underwent surgical aortic valve replacement in 2016-2021 into two cohorts (derivation, 515; validation, 257). In the derivation cohort, no data were missing for any patients for the candidate predictors including age, sex, body mass index, left ventricular ejection fraction, levels of albumin, hemoglobin, and serum creatinine, presence of chronic atrial fibrillation, and end-stage renal disease requiring hemodialysis. We developed possible scoring models using Cox proportional hazards regression with overall survival as the endpoint and calculated the cross-validated 5-year C-statistics to assess accuracy. The mean patient age was 74.2 years, and 46.9% were female. Kaplan-Meier analysis revealed overall 1- and 5-year survival rates of 96.6 and 88.7%, respectively. The 5-year C-statistic of the derivation cohort was 0.785 (95% confidence interval: 0.716-0.853), while the estimated 1-, 3-, and 5-year C-statistics of the validation cohort were 0.885 (0.806-0.965), 0.888 (0.824-0.953), and 0.801 (0.702-0.901), respectively. Calibration plots revealed good agreement between predicted and actual 5-year survival (intraclass correlation coefficient = 0.955; 95% confidence interval: 0.827-0.989). This novel survival prediction model after isolated surgical aortic valve replacement in the transcatheter aortic valve replacement era showed good survival prediction, and may guide the decision-making process for surgical aortic valve replacement versus transcatheter aortic valve replacement with lifetime management.
The manchette is a transient microtubule (MT)-based structure that is vital for the correct shaping of sperm during spermiogenesis. Throughout spermiogenesis, the manchette retains structural integrity for several days, raising the question of how its MTs are regulated. Here, using cryo-electron tomography of manchettes isolated from rat testes, we find that manchette MT ends are structurally diverse. We show that the MT-binding protein CLASP2 is present throughout the manchette and likely regulates both MT ends. Using cryo-electron microscopy single particle analysis and super-resolution microscopy, we reveal that SPACA9 and MNMIP1 (SH3D21) bind to the seam of manchette MTs from the luminal side. SPACA9 binds to both α- and β-tubulin of protofilament 1 but does not interact directly with protofilament 13, while MNMIP1 binds directly to protofilament 13. MNMIP1 further extends and threads through the MT lattice at the seam. Our study reveals a novel seam MT inner protein complex with a unique binding mode, providing a plausible explanation for MT regulation that maintains manchette integrity over an extended period.
Acute myeloid leukemia (AML) is a genetically and phenotypically heterogeneous hematological malignancy. Here, to better define this clinically taxing and translationally challenging malignancy, we applied a multiomics approach, consisting of 13 modalities to analyze 173 treatment-naive individuals with AML. By integrating these 'omes', we identified distinct AML subtypes, genotype-phenotype associations, biomarkers and pathobiological mechanisms. Across the spectrum of primitive and committed AML, we found extensive metabolomic and lipidomic reprogramming driven by divergent MYC and mTOR activity. We linked metabolic changes to striking hyperacetylation of mitochondrial proteins in CEBPA-mutant AML. Protein-centric subtyping revealed a distinct NPM1-mutant subset characterized by outlier expression of FOXC1 and HOXB8/9. To nominate therapeutic targets across subtypes, we developed a multiomic machine-learning approach and validated MTA1 as a contributor to panobinostat resistance. Altogether our findings underscore the complex nature of AML and provide a clinically and translationally informed unified view that reveals coalescent phenotypes across multiomic layers.
This study explores the performance, combustion, and emission parameters of a neem biodiesel enhanced with zinc oxide (ZnO) Nanoparticles (NPs) synthesized through a green method in a compression ignition (CI) engine. Neem oil was transesterified to produce biodiesel, while ZnO NPs were synthesized using neem leaf extract and dispersed at concentrations of 50 ppm and 100 ppm, denoted as NB25Zn50 and NB25Zn100, respectively. The outcomes showed that the addition of ZnO NPs significantly enhanced combustion efficiency, resulting in Brake Thermal Efficiency (BTE) increases of 1.51% for NB25Zn50 and 3.64% for NB25Zn100, along with Specific Fuel Consumption (SFC) decreases of 2.54% and 5.28%, respectively. Combustion analysis revealed a higher peak cylinder pressure (CP), increased Heat Release Rate (HRR), and quicker Mass Fraction Burning (MFB) progression for fuels blended with NPs, indicating a shorter Ignition Delay (ID) and more complete combustion. Emission analysis showed considerable reductions in Carbon monoxide (CO), Hydrocarbons (HC), and smoke opacity, whereas the optimal ZnO concentration (100 ppm) effectively controlled Nitrogen Oxides (NOx) emissions. Additionally, machine learning models such as K-Nearest Neighbors (KNN), Rrandom Forest (RF), Ssupport Vector Regression (SVR), and Extreme Gradient Boosting (XGB) were used to predict engine performance, emissions, and energy parameters. Among these, extreme gradient boosting demonstrated superior predictive accuracy with high correlation coefficients (R ≈ 0.99) and minimal error. Overall, neem biodiesel blended with ZnO NPs, especially at 100 ppm, exhibited strong potential as a sustainable fuel for enhanced overall engine performance and cleaner emissions.
Maple syrup urine disease (MSUD) is an inherited metabolic disorder requiring protein restriction, often limiting intake of animal-derived foods. This raises concerns about long-chain polyunsaturated fatty acid (LC-PUFA) status. The primary aim of this study is to evaluate plasma n-3 and n-6 fatty acid levels in MSUD patients. This single-center, cross-sectional study included 16 MSUD patients and 22 unaffected siblings sharing a similar household and environmental background. Dietary intake was recorded and plasma fatty acid profiles were analyzed. Dietary assessments revealed significantly lower intakes of total fat and cholesterol (both p < 0.001) and n-3 PUFAs (p = 0.036) in MSUD patients, with alpha-linolenic acid (ALA) intake as the only individual LC-PUFA significantly reduced (p = 0.048). Plasma analysis showed significantly lower docosahexaenoic acid (DHA) levels in patients despite similar dietary DHA intake (p < 0.001), while arachidonic acid and mead acid were significantly elevated (both p < 0.001). Although plasma DHA concentrations showed a moderate positive correlation with dietary ALA intake (r = 0.516, p = 0.041), regression analysis showed that neither dietary ALA intake (B = 0.005, p = 0.584) nor the dietary n-6/n-3 PUFA ratio (B = -0.552, p = 0.706) independently predicted plasma DHA levels. In the MSUD group, plasma DHA levels were positively associated with dietary leucine intake (r = 0.635, p = 0.008) and plasma isoleucine concentrations (r = 0.524, p = 0.037). Our findings provide evidence of DHA deficiency in MSUD patients, which may result from both inadequate dietary intake and changes in n-3 PUFA metabolism, highlighting the need to investigate additional contributing mechanisms.
Systemic lupus erythematosus (SLE) is a complex and heterogeneous systemic autoimmune disease associated with poor treatment outcomes. While previous studies have indicated a genetic predisposition to SLE, the underlying mechanisms remain poorly understood. This study aimed to identify diagnostic targets with potential genetic associations to SLE by leveraging bioinformatics and the Mendelian randomization (MR) approach. Six datasets (GSE30153, GSE39088, GSE50635, GSE50772, GSE61635, and GSE110169) were obtained from the GEO database for differential expression analysis to identify differentially expressed genes (DEGs). Weighted gene coexpression network analysis (WGCNA) was then performed, and the most relevant module was intersected with the DEGs to identify candidate genes with potential diagnostic value. Subsequently, machine learning algorithms were applied to screen diagnostic genes, and their performance was evaluated using receiver operating characteristic (ROC) curves and confusion matrices. MR analysis was conducted to identify diagnostic genes with genetic associations. A protein-protein interaction (PPI) network was constructed to identify core genes. Finally, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed. Differential expression analysis identified 244 DEGs, and WGCNA revealed a highly relevant module. Intersecting this module with the DEGs produced 136 candidate genes. Machine learning algorithms and MR analysis further refined the selection, identifying five diagnostic genes: GBP1, IFI6, KLHDC8B, OAS3, and ZCCHC2, all of which were shown to be well-aligned with their respective drugs. The PPI network highlighted GBP1, IFI6, and OAS3 as core genes, which showed significant correlations with immune cell infiltration. Our study identified GBP1, IFI6, and OAS3 as core genes implicated in SLE pathogenesis, providing novel insights into its molecular mechanisms and potential therapeutic targets.
The basal ganglia (BG) are central to voluntary action, yet how they organize complex behavior remains unclear. Using a novel operant counting task, we trained mice to perform a specific number of lever presses to obtain a reward, enabling quantification of continuous kinematics and discrete actions. Stimulation of direct pathway and indirect pathway neurons (dSPNs and iSPNs) exert bidirectional and dissociable influences on both movement steering and press count: activation of dSPNs steers mice contraversively and extends press sequences, whereas activation of iSPNs steers mice ipsiversively and prematurely terminates press sequences. Calcium imaging reveals dSPNs and iSPNs that tracked physical approach or count progress, with ramping activity patterns consistent with accumulation and discharge dynamics. The difference between dSPN and iSPN population activity scales with proximity to spatial and numerical goals. These findings show that the BG implement a push-pull controller to integrate kinematics and action counting to steer progress toward goals.
The growing interest in plant-based therapeutics has led to increased exploration of medicinal flora for their nutritional and pharmacological potential. The objective of this study was to determine the nutritional composition, phytochemical profile, and antioxidant activity of Cotoneaster microphyllus from Shimla, Himachal Pradesh. The proximate analysis revealed high levels of ash and fat in the leaves, while high fiber levels in the fruits. According to mineral profiling, leaves showed an abundance of Mg, Ca, Na, and Zn, while fruits indicated predominant presence of P and K. Phytochemical extractions were performed using hydromethanol, methanol, and aqueous solvents, with hydromethanol extract exhibiting the highest phytochemical content and antioxidant activity, followed by methanol and aqueous extracts. DPPH and FRAP antioxidant assays confirmed that C. microphyllus scavenges free radicals and reducing antioxidant potential effectively. Based on GC-MS and LC-MS analyses, cyclosiloxanes and phthalate ester compounds were identified via GC-MS and 50 unique compounds were identified via LC-MS, reported for the first time in Cotoneaster. UHPLC was also used to quantify chlorogenic acid, with fruit extracts showing the highest concentration. In this study, we provide a novel insight into the phytochemical composition and bioactive potential of C. microphyllus. There is a significant lack of systematic biochemical and functional evaluation of this species, so this study represents the first comprehensive integration of nutrition profiling, multi-solvent phytochemical quantification, and advanced characterization (GC-MS, LC-MS, and UHPLC) of different plant parts. These findings provide new insight into phytochemical composition of C. microphyllus and point to its potential as a source of bioactive chemicals with potential pharmacological and nutraceutical applications, which need for more biological validation.
Temporal lobe epilepsy (TLE) is one of the most common forms of epilepsy, characterized by significant reorganization of hippocampal neuronal circuits. While changes in GABAergic inhibitory circuits are a major feature of this reorganization, other prominent alterations include hippocampal myelination disruption, neuronal loss, and pronounced glial and inflammatory responses. Recent studies have established that parvalbumin (PV +) interneurons in the hippocampus are partially myelinated under normal conditions, representing a notable structural feature of this interneuron subtype. However, whether interneuron myelination becomes altered in TLE has remained unexplored. Using a mouse TLE model, we investigated CA1 PV+ interneurons during epileptogenesis and chronic disease phases, examining numerical density and myelination patterns. We simultaneously tracked microglial responses and oligodendrocyte lineage cell populations (mature oligodendrocytes and their precursors). Our findings reveal that CA1 PV+ interneurons maintain their numerical density throughout disease progression but undergo marked myelination alterations. This dysmyelination occurs concurrently with pronounced microgliosis and changes in the dynamics of oligodendrocyte lineage cell populations, predominantly during epileptogenesis. Notably, pharmacological intervention with GW2580, a CSF1R inhibitor, administered for 8 days around the status epilepticus, prevented both PV+ interneuron myelination alterations and changes in oligodendrocyte lineage cell populations by blocking microglial proliferation. These results establish a link between microgliosis and alterations of myelin patterning in CA1 PV+ interneurons, advancing our understanding of interneuronopathy and TLE pathophysiology. The mechanisms identified share remarkable similarities with those observed in dysmyelinating and neurodegenerative diseases, including multiple sclerosis, suggesting potential common therapeutic targets.
Cellulose fibrils are a renewable and biodegradable resource, but their extraction typically requires complete destruction of the original wooden matrix. We present a targeted strategy that enables selective liberation of cellulose microfibrils while preserving the integrity of the surrounding native wood structure. By combining partial delignification with localized surface modification using the ionic liquid 1-butyl-3-methylimidazolium acetate ([Bmim][OAc]), we enable selective liberation and separation of cellulose microfibrils without bulk dissolution or structural damage. This spatially confined treatment exploits the intrinsic anisotropy of the native cellulose architecture, allowing controlled fibril release while maintaining the original orientation and structural framework. These findings reveal how precise chemical interventions can expand the toolbox of cellulose chemistry and unlock new opportunities for advanced wood-based material engineering.
The propylene methoxycarbonylation is a potential atom-economical reaction for the synthesis of butyrate esters, which minimizes wastewater discharge and alleviates subsequent separation difficulties. However, to the best of our knowledge, the mechanism of this reaction has not been thoroughly investigated, and the regioselectivity enhancement remains challenging. Herein, we studied the structure-performance relationship of Palladium-based homogeneous catalysts with different monophosphine and bisphosphine ligands by combining experiments and theoretical calculations. The ligands primarily influence the catalytic activity through electronic effects, while regulating product regioselectivity through steric effects. Using an optimized catalyst, the turnover frequency reaches 12,500 h-1 with a normal-to-iso selectivity ratio of 15.8, representing 13.9-fold and 6.6-fold improvements over a conventional palladium-phosphine catalyst. Mechanistic calculations reveal that regioselectivity is determined by the hydrogen insertion position in the first elementary step. Our work fills a gap in propylene methoxycarbonylation research and provides a basis for rational design of regioselective catalysts.
Accurate prediction of drug-target interactions (DTIs) is a fundamental challenge in early-stage drug discovery, particularly in the absence of reliable three-dimensional structural information. In this study, we propose a fully sequence-based DTI prediction framework that eliminates dependence on structural data while achieving docking-comparable predictive performance. The proposed framework introduces a unified representation that systematically integrates physicochemical protein descriptors, protein 3-gram sequence motifs, and sequence-like drug encodings into a single feature space, enabling effective learning across heterogeneous models. A diverse set of machine learning, deep learning, and ensemble classifiers is evaluated under stratified five-fold cross-validation with class imbalance correction using Synthetic Minority Over-sampling Technique (SMOTE). Beyond individual models, the framework incorporates advanced ensemble strategies, including a stacking classifier that combines Random Forest, Support Vector Machine, and Logistic Regression, resulting in robust performance with ROC-AUC values exceeding 0.90 and a maximum AUC of 0.914. Importantly, the framework explicitly addresses model interpretability through feature importance analysis, revealing biologically meaningful protein sequence motifs associated with binding interactions. To further substantiate the reliability of the proposed approach, molecular docking experiments are conducted on a subset of predicted drug-target pairs, and the observed agreement between docking scores and predicted binding probabilities provides independent validation. Collectively, this study demonstrates that carefully engineered sequence-derived representations, coupled with optimized ensemble learning, constitute a scalable, interpretable, and computationally efficient alternative to structure-dependent DTI prediction methods.