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Background The syndemic of HIV and tuberculosis (TB) co-infection remains a critical global health challenge, particularly in resource-limited settings where conventional epidemiological models fail to capture the complex evolutionary dynamics between pathogens, hosts, and interventions. Current approaches lack adaptive mechanisms to account for temporal changes in transmission parameters and quality of life (QoL) impacts, creating an urgent need for innovative modeling frameworks. Methods This study focuses on the Khyber Pakhtunkhwa province of Pakistan, a region with moderate HIV prevalence and high TB incidence. The analysis used de-identified clinical and demographic data (N = 592) collected from tertiary hospitals in KP between 2021 and 2023. We developed a novel hybrid modeling approach integrating empirical clinical data with evolutionary computation through three synergistic components: (1) evolutionary-optimized demographic sampling ( ), (2) time-varying compartmental modeling with adaptive parameters ( ), and (3) multi-objective intervention optimization. The framework was validated through a four-pillar approach incorporating statistical metrics, evolutionary cross-validation, clinical evaluation, and policy impact assessment. Key results Our analysis revealed three critical findings: First, transmission parameters exhibited distinct temporal patterns, with TB showing saturating growth ( ) while HIV declined gradually. Second, gender-specific exposure dynamics were identified, with males having significantly higher transmission risk ( , ). Third, the targeted treatment strategy emerged as optimally cost-effective (ICER = $2,300/quality-adjusted life years (QALY), 95% CI: 1,850–2,750), reducing by 54% while maintaining high feasibility (0.91). Significance This study provides the first comprehensive framework that simultaneously addresses pathogen evolution, host dynamics, and intervention optimization in HIV-TB co-infection. The findings offer actionable insights for public health policy, particularly in balancing cost-effectiveness with implementation feasibility. Our evolutionary-optimized approach establishes a new paradigm for modeling complex disease systems, with potential applications extending beyond HIV-TB to other interacting epidemics.

Understanding the structural and operational characteristics of complex systems is crucial for network science research and analysis. To better understand the dynamics and behaviors of networks, it involves studying them in a variety of settings, including social, biological, and technical domains. This entails modeling and analyzing networks to identify their properties, frequently employing machine learning and statistical techniques. Conventional network models, such Erdős-Renyi (ER), Barabási-Albert (BA), and Stochastic Block Models (SBM), are commonly employed in synthetic network analysis. Real-world networks sometimes include extra complexities, like modularity, clustering, and scale-free features, which pose issues for these models. This study focuses on assessing the effectiveness of machine learning models in examining the structural features of networks across different scales and the related computational expenses. Here we show that Logistic Regression (LR) consistently outperforms Random Forest (RF) in synthetic networks of varying sizes, achieving perfect accuracy, precision, recall, F1 score, and AUC across networks with 100, 500, and 1000 nodes, while Random Forest exhibits lower performance with an accuracy of 80%. These findings call into question the notion that complicated models like Random Forest are inherently superior, indicating that simpler models like Logistic Regression are more effective in larger, more complex networks due to their higher generalization capabilities. The Stochastic Block Model (SBM) closely matches the modularity of real-world networks, while the Barabási-Albert (BA) model accurately replicates the hub-dominated structure of social networks, as confirmed by Kolmogorov-Smirnov (K-S) test statistics of ( ) for BA and ( ) for WS. These findings show that simpler machine learning models can outperform more sophisticated ones in some contexts, offering a more nuanced view of model selection based on network scale and complexity. They also emphasize the significance of balancing computational trade-offs when using machine learning on real-world networks. In a larger sense, this research helps to optimize machine learning techniques for network inference and analysis, which has ramifications for social, biological, and technical applications. The findings imply that future research should concentrate on adapting model selection to the specific characteristics of the network and task, assuring optimal performance and accuracy.

Background Milk can sometimes carry harmful mycotoxins, which pose health risks, especially in hot and humid areas. We collected samples from both farms and households in Khyber Pakhtunkhwa (KP), Pakistan, to see how common these toxins are, whether the genes that make them are present, and which environmental factors might make contamination worse. Methods We tested raw milk for aflatoxins (AFM1, AFM2), ochratoxin A (OTA), and zearalenone (ZEN) using TLC, HPLC, and UHPLC-MS/MS. All testing procedures were validated following ICH Q2(R2), ISO 17025, and FDA guidance. PCR was applied to check for the genes aflC , otaA , and zen1 . We then combined chemical, molecular, microbial, and environmental data and used multivariate statistics and PLS-DA modeling to find the main factors driving contamination. Results Milk from farms had higher mycotoxin levels than household milk. Average AFM1 concentrations were g/kg in farm milk and g/kg in domestic milk (p < 0.001). AFM2 and OTA showed similar patterns, while ZEN was below detection in all samples. UHPLC-MS/MS confirmed the HPLC findings and offered greater sensitivity. The genes aflC and otaA were found in 68% of farm samples and were strongly linked to AFM1 and OTA levels, while zen1 was absent. High temperature (over 28°C) and humidity (over 75%) were associated with increased contamination. PLS-DA modeling effectively distinguished high- and low-risk samples (AUC = 0.92), highlighting AFM1 concentration, aflC presence, and humidity as key predictors. Conclusions Combining chemical testing, gene screening, and environmental monitoring provides a practical way to detect and evaluate mycotoxin risk in milk. Farm milk showed higher contamination than household milk, emphasizing the need for targeted monitoring and preventive measures. Identifying environmental thresholds and risk factors can support early interventions to improve food safety and protect public health.

The escalating challenge of antimicrobial resistance necessitates the development of novel antibacterial agents. In this study, a series of five vanillin Schiff bases (SB-1 to SB-5) were synthesized from vanillin and various aromatic amines. The chemical structures of these compounds were characterized using Thin Layer Chromatography (TLC), Fourier Transform Infrared Spectroscopy (FT-IR), proton nuclear magnetic resonance ( 1 H -NMR), carbon-13 NMR ( 13 C -NMR), and mass spectrometry techniques. Antibacterial efficacy was evaluated against strains of bacteria producing extended-spectrum beta-lactamases (ESBL), including Escherichia coli , Pseudomonas aeruginosa , and Klebsiella pneumoniae using the disc diffusion method. Cytotoxic effects were assessed through haemocompatibility and brine shrimp lethality assays. The Schiff bases demonstrated notable antibacterial activities, with SB-1, SB-2, SB-4, and SB-5 exhibiting zones of inhibition up to 16.0, 16.5, 16.6, and 15.5 mm against ESBL E. coli , respectively. SB-3 showed a maximum inhibition zone of 15.0 mm against ESBL K. pneumoniae . In cytotoxicity assays, the compounds exhibited IC 50 values against red blood cells (RBCs) greater than 200 μg/mL and ranging from 45.7 to 50.5 μg/mL for the brine shrimp assay. While demonstrating potent antibacterial properties, the toxicity towards human RBCs suggests that further toxicity evaluations and structural modifications are essential for developing safer therapeutic agents based on vanillin Schiff bases.

PurposeThe purpose of this study is to analyze the fluctuations in gold prices within the Saudi Arabian market and to develop a reliable forecasting model to aid market participants and policymakers in making informed decisions.Design/methodology/approachIn this study, we employ a rigorous time series analysis methodology, including the ARIMA (Auto Regressive Integrated Moving Average) model, to analyze historical gold price data in the Saudi Arabian market. The approach involves identifying optimal model parameters and assessing forecast accuracy to provide actionable insights for market participants.FindingsThe study showcases that the autoregressive properties of past gold prices play a pivotal role in capturing the inherent serial correlation within the market, enabling the ARIMA model to effectively forecast future gold price movements with accuracy.Research limitations/implicationsOur study primarily focuses on quantitative analysis, whereas few qualitative parameters are not included. Future studies may benefit from incorporating qualitative factors and expert opinions to enhance the robustness of gold price predictions and capture the full spectrum of market dynamics.Social implicationsParticipants and policymakers may find this study helpful in navigating the complicated Saudi Arabian gold market. By understanding financial stability and investment decisions more thoroughly, individuals and institutions may be able to manage their portfolios more effectively.Originality/valueBy combining historical insights with advanced ARIMA modeling techniques, this research provides valuable insight into gold price dynamics in the Saudi Arabian market.

Antimicrobial resistance (AMR) in dairy production systems poses a major public health threat, particularly under the One Health framework. Raw milk can act as a reservoir for multidrug-resistant (MDR) pathogens, especially in regions like Khyber Pakhtunkhwa (KP), Pakistan, where unregulated antibiotic use and insufficient surveillance have promoted high-risk resistance hotspots. This study analyzed 172 bacterial isolates from raw milk, focusing on key pathogens, Staphylococcus aureus and Escherichia coli, to assess their prevalence, resistance profiles, and epidemiological distribution. A total of 172 bacterial isolates were analyzed using: (1) conventional microbiological isolation and identification, (2) antimicrobial susceptibility testing across twelve antibiotic classes, (3) molecular detection of resistance determinants (mecA, vanA, bla , bla , qnrS, aac(6')-Ib-cr) by PCR, and (4) geospatial modeling (GeoDa, R, ArcGIS) to identify AMR hotspots. E. coli was detected in 70.0% of samples, followed by S. aureus in 41.0%. Among E. coli isolates, 32.5% were MDR, with high β-lactam resistance (ampicillin 31.0%, amoxicillin 22.7%). S. aureus exhibited extensive MDR, with 55.2% resistant to three or more antibiotic classes; the Oxacillin-Penicillin-Tetracycline phenotype was most prevalent (28.6%), and 18.4% displayed complex hexa-resistant profiles. Vancomycin resistance was observed in 16.3% of S. aureus isolates, with 8.2% carrying vanA. Molecular screening confirmed mecA in 89.8% of S. aureus, bla in 67.9% of β-lactam-resistant isolates, bla in 21.4% of E. coli, and plasmid-mediated quinolone/aminoglycoside determinants (qnrS 8.2%, aac(6')-Ib-cr 10.9%). Geospatial analysis identified three resistance hotspots across KP, with 68% of vancomycin-resistant S. aureus concentrated in northern districts and a strong correlation between β-lactam and tetracycline resistance ([Formula: see text], [Formula: see text]). Raw milk in KP harbors pathogens with multidrug resistance exceeding previous regional estimates by 2-3 fold, including resistance to last-resort antibiotics such as vancomycin. These findings emphasize the urgent need for enhanced veterinary antibiotic stewardship, targeted surveillance of resistance hotspots, improved dairy hygiene practices, and community education regarding raw milk consumption. Integrated One Health strategies are critical to mitigate the amplification and spread of AMR in dairy production systems.

Purpose This study examines the relationship between energy sector profitability and environmental sustainability in Saudi Arabia, with a focus on carbon emissions and energy efficiency. Design/methodology/approach This study employs a quantitative research design, over the time period of 2010–2023, using ordinary least squares (OLS) regression to analyze the impact of energy sector profitability on environmental sustainability indicators such as carbon emissions and energy efficiency. Findings The emissions model indicates a strong fit (Adj. R2 = 0.68; R2 = 0.82; DW = 2.03; p < 0.05), revealing that improvements in energy productivity (Y1) and overall economic growth (C1) significantly contribute to lowering environmental pressures, while oil-revenue dependency (Y2) and population growth (C2) show no meaningful impact on emissions. The energy-intensity model demonstrates good explanatory power as well (Adj. R2 = 0.65; R2 = 0.80; p = 0.051; DW = 1.58), indicating that reliance on oil revenues (Y2) significantly enhances energy efficiency, while profitability (Y1), economic growth (C1) and population growth (C2) do not exhibit substantial influence. Practical implications The results of this study can help in strategic planning of the country by aligning economic growth with environmental sustainability goals by utilizing the profitability of the energy industry as a tool to improve energy efficiency and lower carbon emissions. Social implications This study supports Saudi Arabia's Vision 2030. Improved efficiency can ease fiscal space for clean-energy investment, supporting SDG 7 and SDG 13. Originality/value This study uniquely explores how profitability in Saudi Arabia's energy sector influences both emissions reduction and energy efficiency.

This study investigated gender disparities in random blood glucose (RBS) levels among Pakistani adults with Type 2 Diabetes (T2D), examining biological and sociocultural determinants. A cross-sectional analysis of 300 age-matched adults with T2D (150 men, 150 women; age 35-60 years) from four tertiary hospitals in Peshawar, Pakistan (February-July 2023). RBS was measured via the Microlab-300 system (Beer-Lambert Law). Multivariate regression and machine learning models (Ridge Regression, Random Forest, Support Vector Regression (SVR), Neural Network, Polynomial Regression) with nested cross-validation were used to analyze associations between demographic factors and RBS. Women had significantly higher mean RBS than men (243.6 vs. 210.8 mg/dL, p < 0.001) and a higher prevalence of severe hyperglycemia (≥260 mg/dL: 38.7% vs. 12.0%). Gender alone explained 16.5% of RBS variance in simple linear regression. Age showed a moderate positive correlation with RBS (r = 0.587, p < 0.001). In multivariate analysis, female gender (β = 24.76, p < 0.001), age (β = 3.01 per year, p < 0.001), and BMI (β = 0.88, p = 0.034) were significant predictors, while family history showed a protective effect (β = -13.36, p < 0.001). Machine learning models using only demographic variables achieved moderate predictive performance (R² = 0.421-0.470), with Ridge Regression performing best (R² = 0.470, MAE = 23.68 mg/dL). Feature importance analysis identified age (70.9%), gender (17.8%), and BMI (8.9%) as the dominant predictors. Significant gender disparities exist in random blood glucose among Pakistani adults with T2D, with women exhibiting higher mean values and greater prevalence of severe hyperglycemia. Age, gender, BMI, and family history are important demographic determinants, but demographic factors alone explain less than half of RBS variance. These findings highlight the need for gender-sensitive diabetes management strategies in South Asia and emphasize the necessity of incorporating direct biomarkers in future prediction efforts.

Biomaterial science is an expanding area, which encompasses a wide range of medical knowledge involving arthroplasty, cochlear implants, heart valves designing, lenses, dental fixation and tissue engineering. Within this context, in vitro cell culture on polymer scaffolds is one of the adopted strategies for tissue creation. It consists of a specific cell line that is seeded onto a particular substrate. This scaffold should provide excellent biocompatibility, controllable biodegradability, appropriate mechanical strength, flexibility as well as the ability to absorb body fluids for delivery of nutrients. Collagen certainly fulfils these demands; therefore, it is often chosen as a biomaterial. Moreover, this protein is abundant in the animal kingdom and plays a vital role in biological functions, such as tissue formation, cell attachment and proliferation.

P53 overexpression plays a critical role in cancer pathogenesis by disrupting the intricate regulation of cellular proliferation. Despite its firmly established function as a tumor suppressor, elevated p53 levels can paradoxically contribute to tumorigenesis, influenced by factors such as exposure to carcinogens, genetic mutations, and viral infections. This phenomenon is observed across a spectrum of cancer types, including bladder (BLCA), ovarian (OV), cervical (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and uterine corpus endometrial carcinoma (UCEC). This broad spectrum of cancers is often associated with increased aggressiveness and recurrence risk. Effective therapeutic strategies targeting tumors with p53 overexpression require a comprehensive approach, integrating targeted interventions aimed at the p53 gene with conventional modalities such as chemotherapy, radiation therapy, and targeted drugs. In this extensive study, we present a detailed analysis shedding light on the multifaceted role of TP53 across various cancers, with a specific emphasis on its impact on disease-free survival (DFS). Leveraging data from the TCGA database and the GTEx dataset, along with GEPIA, UALCAN, and STRING, we identify TP53 overexpression as a significant prognostic indicator, notably pronounced in prostate adenocarcinoma (PRAD). Supported by compelling statistical significance (p < 0.05), our analysis reveals the distinct influence of TP53 overexpression on DFS outcomes in PRAD. Additionally, graphical representations of overall survival (OS) underscore the notable disparity in OS duration between tumors exhibiting elevated TP53 expression (depicted by the red line) and those with lower TP53 levels (indicated by the blue line). The hazard ratio (HR) further emphasizes the profound impact of TP53 on overall survival. Moreover, our investigation delves into the intricate TP53 protein network, unveiling genes exhibiting robust positive correlations with TP53 expression across 13 out of 27 cancers. Remarkably, negative correlations emerge with pivotal tumor suppressor genes. This network analysis elucidates critical proteins, including SIRT1, CBP, p300, ATM, DAXX, HSP 90-alpha, Mdm2, RPA70, 14-3-3 protein sigma, p53, and ASPP2, pivotal in regulating cell cycle dynamics, DNA damage response, and transcriptional regulation. Our study underscores the paramount importance of deciphering TP53 dynamics in cancer, providing invaluable insights into tumor behavior, disease-free survival, and potential therapeutic avenues.