Haris Khurram

Heavy metal pollution in aquatic environments represents a significant global challenge. The Na Thap River, a tidal river connected to the Gulf of Southern Thailand, represents a vulnerable tropical estuarine ecosystem subject to contamination from multiple pollution sources. This study aimed to quantify ten heavy metals; Iron (Fe), Manganese (Mn), Zinc (Zn), Cadmium (Cd), Lead (Pb), Chromium (Cr), Copper (Cu), Nickel (Ni), Arsenic (As), and Mercury (Hg) in Na Thap River surface water from 2017 to 2021, to analyze spatial and temporal distribution patterns. Surface water samples were collected from 10 sampling sites and analyzed using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) and Hydride Generation-Inductively Coupled Plasma (HG-ICP) methods. Exploratory data analysis was used to assess contamination levels and identify unsafe month-site combinations. Correlation analysis was used to measure the relationship among heavy metals. Principal component analysis (PCA) was employed to explore the variation of different heavy metals in different dimensions. Results revealed that several heavy metals exceeded Thailand’s surface water quality standards, with high toxic metals (Cd, Pb) and medium toxic metals (Cu, Ni) showing concerning levels across different sites and time periods. Specific unsafe zones and time periods were identified throughout the Na Thap River system, revealing critical contamination hotspots posing risks to ecosystem health. Temporal and month wise analyses indicated a clear seasonal pattern that contamination risk was concentrated in February, March, June, and July, with February showing unsafe conditions across all monitoring sites simultaneously. Elevated concentrations of Fe, Mn, Cd, and Pb were most frequently observed during the southwest monsoon period, suggesting that seasonal hydrological processes played an important role in controlling heavy-metal transport and accumulation throughout the river system. Spatial sites 2, 3, 5, 6, and 7 emerged as persistent contamination hotspots requiring immediate intervention. PCA identified distinct metal groupings with Dimension 1 (43.7% variance) dominated by Ni, Cu, Cr, and Cd, while Dimension 2 (17.2% variance) was characterized by Mn, Fe, and Pb, indicating different pollution sources. Spatially, recurring exceedances were concentrated at sites 2, 3, 5, 6, and 7, indicating priority locations for management action. These findings provide essential baseline data for developing targeted pollution control strategies to protect this vulnerable ecosystem and dependent communities.

Farmers are the ultimate beneficiaries of the ecosystem service of crop pollination, and their indigenous knowledge can be used to address the current challenges of pollinator decline. However, their perception and knowledge about pollinators is rarely investigated, particularly for orchard-based agroecosystems. This study aimed to assess the farmers’ knowledge and perception about pollinators and pollination in the mango agroecosystem. Also, statistical modelling identified the factors that affect the farmers’ knowledge. We interviewed 280 mango farmers from four districts (70 from each district) of South Punjab, Pakistan, to assess their knowledge about pollinators and pollination. Overall, 80% of respondents had no knowledge about pollination. Two native honeybee species, Apis dorsata and Apis florea, were well known among farmers, but only 13.57% of respondents showed some knowledge about non-bee pollinators. The results of the logistic regression model showed that farmers’ age (B = -0.051, SE = 0.017, p = .003), farming experience (B = 0.071, SE = 0.018, p = .000), primary education (B = 0.671, SE = 0.333, p = .044) and matric education (B = 0.830, SE = 0.398, p = .037) significantly impacted their knowledge about pollinators and pollination. The knowledge gap recognized in this study warrants the capacity building of farmers about the pollinators and their pollination services. By using indigenous and local knowledge, the conservation strategies for pollinators can be established in the region.

Urban air pollution remains a critical global issue, particularly fine particulate matter (PM), which has direct and significant impacts on human health. Understanding PM patterns is challenging due to the large number of associated variables, many of which are highly correlated and must lead to multicollinearity issues in predictive modeling. To address this problem, this study applies Principal Component Analysis (PCA) as a dimensionality reduction technique and develops forecasting models for PM 2 . 5 and PM 10 concentrations. The used air pollution data were collected from 4 monitoring stations in Bangkok, Thailand. Prior to analysis, the datasets were preprocessed through imputation, transformation, and standardization to ensure consistency across variables. PCA results identified 4 principal components for each station based on cumulative variance and eigenvalue criteria. Among these, relative humidity (RH) and zonal wind direction (ZWD) consistently contributed most to the principal components. The selected components were then used as predictors in 4 machine learning models, and their performance was compared with models using all original variables. Model evaluation was conducted using mean absolute error (MAE), mean squared error (MSE), and mean absolute percentage error (MAPE). The results indicate that models based on reduced predictors consistently outperform those using the full set of variables across all stations. These findings offer an alternate effective approach for addressing multicollinearity and improving model performance in urban air pollution forecasting.

Informed decision making is an important aspect based on improved and reliable forecasting. Modeling and forecasting complex real data are crucial for policymaking. In this paper, we have presented an auxiliary-lag dependent Gaussian process, a Bayesian non-parametric machine learning model, for improved forecasting using auxiliary lags. We also introduced some new multi-featured kernel functions that are versatile in dealing with seasonal and non-seasonal data. For comparison of the proposed model, the autoregressive random forest model, autoregressive artificial neural network model, seasonal autoregressive moving average models, and exponential smoothing models were used for forecasting rainfall data, climate modeling, and sugarcane production data, agriculture crop modeling, to show the versatility of the proposed models. Results confirmed the superiority of the proposed model over conventional models. The proposed methodology will be helpful for other researchers and local experts in making more reliable forecasting, which will be helpful in policymaking relevant to agriculture systems, water management systems, climate change, and natural disasters such as droughts and floods.

Backgrounds: Breast cancer presents multifaceted challenges that extend beyond physical illness, profoundly influencing patients’ psychological well-being, treatment adherence, and social relationships. Marital and parental status further affect these experiences by influencing coping capacity, emotional stability, and perceived support systems. This study aimed to assess how marital and parental status influence the lives of women with breast cancer, particularly their psychological well-being, support, treatment adherence, and overall quality of life (QoL). Methods: This cross-sectional study of 503 patients utilized validated research instruments for data collection: The Depression, Anxiety, and Stress Scale (DASS-21), the Multidimensional Scale of Perceived Social Support (MSPSS), the Medication Adherence Report Scale (MARS-5), and the WHOQOL-BREF. Descriptive statistics, Kruskal-Wallis tests, and Spearman’s correlation (95% CI) were used as an exploratory data analysis. Multiple linear regression was utilized to measure the effect of different variables on the domains of QoL. The p < 0.05 was considered significant. Results: Across marital status, psychological distress, notably depression (median = 16.5, Q1-Q3 = 15–22.8, p > 0.001 ), was predominantly observed in divorced/widowed women, whereas single women exhibited better social support (median = 72, Q1-Q3 = 60–81.2, p > 0.001 ) and overall WHOQOL (median = 86, Q1-Q3 = 79–93, p > 0.001 ). Relating to parental status, mothers, particularly those with multiple children, experienced greater psychological distress (median = 16, Q1-Q3 = 12–18, p > 0.001 ). At the same time, childless women exhibited better social support (median = 72, Q1-Q3 = 60–76, p > 0.001 ) and QoL (median = 86, Q1-Q3 = 79–93, p > 0.001 ). A negative relationship of medication adherence with both social support and QoL was observed, while it showed a positive correlation with psychological distress in breast cancer patients, with a significant value ( p > 0.001 ). Conclusions: In breast cancer patients, depression was highest among divorced/widowed women and mothers with multiple children, while single and childless women reported greater social support and QoL. Medication adherence showed a positive association with psychological distress and a negative correlation with both social support and QoL.

Evaluating growth and development is crucial to understanding the health status of adolescents. This assessment, through physical examination, requires healthcare experts and can be challenging due to subjectivity and privacy concerns. Similarly, psychosocial growth is subjective and also requires complex evaluations by professionals. This complexity makes physical growth assessment based on anthropometric measures a more useful and convenient indicator for monitoring growth. Therefore, anthropometric measurements should be assessed with more precise methods and calibrated equipment. Considerations of precision, reliability, and validity are essential for growth assessments when planning health strategies, nutritional status, and overall well-being. The study design should be carefully selected based on the objectives. An adequate sample size is also critical in capturing the true variability of the study population. Adolescents undergo a growth spurt due to prepuberty and puberty; thus, the development of growth centile charts necessitates appropriate assessment methodology and validations to effectively evaluate growth status and capture its variations. The differences, velocity, and acceleration charts are essential in creating reference growth centiles for a population, especially for adolescents. The appropriate statistical methodology, based on exploratory data analysis and modeling for estimating centiles, has fundamental implications. GAMLSS is a more flexible model that can capture the growth spurt due to puberty in adolescents, utilizing various distribution families, smoothing functions, and the ability to model each parameter with multiple covariates. Cross-validation enhances confidence in the generalizability of the statistical model and growth centiles for the target population. These growth centile charts are vital for early detection of growth disorders, nutritional assessments, monitoring, and informed decision-making in clinical and public health contexts.

COVID-19 spread worldwide after emerging at the end of 2020 with a higher spread and death among the elderly and varies across the continent and country. This study aimed to investigate the spatio-temporal trends in COVID-19 morbidity and mortality due to the elderly. COVID-19 infected cases and death data were converted into morbidity and mortality rates per 1,000 population. Thematic maps were used for spatial illustration over time. Linear and Theil-Sen regression models were used to estimate the rate of change in COVID-19 morbidity and mortality due to the elderly. The results revealed a significant relation and change in the global COVID-19 morbidity and mortality rates due to the elderly population (p-value < 0.05). Europe experienced the highest rate of change in COVID-19 morbidity due to the elderly population, followed by Oceania and North America. Moreover, the elderly proportion had the greatest rate of change in COVID-19 mortality in North America, followed by Europe and Africa. The results of this study will enable researchers and experts for data-driven decisions that are more resilient. Moreover, it enables healthcare experts and policymakers to develop guidelines to improve preparedness for future pandemics.

Background: Gestational diabetes mellitus (GDM) and preeclampsia (PE) are two of the most prevalent high-risk pregnancy problems, with significant consequences for maternal health, emotional well-being, and quality of life (QoL). Aim: This study aimed to compare the medication adherence, depression, and QoL among women with GDM and PE. Patients and Methods: This was a cross-sectional study conducted in 1306 women with GDM and PE. A total of 7 hospitals were included across Punjab, Pakistan, conducted between November 2024 to June 2025. Data were collected using the Medication Adherence Report Scale (MARS-5), Patient Health Questionnaire-9 (PHQ-9) and WHOQOL-BREF. Statistical analyses, including frequency, Mann–Whitney U-tests and Kruskal–Wallis test to compare the median across different factors. Exploratory data analysis (EDA) was used to assess the relation between medication adherence, depression, and QoL for different conditions. Linear regression models were employed to assess the medication adherence, depression, and QoL, among women with GDM and PE. Results: The preeclamptic women showed higher medication adherence than those with GDM (Median=14, Q1-Q3=12-16, p=0.012). Depression scores were significant in physical health domain of QoL in preeclamptic patient (p=0.001). QoL scores (p< 0.05) were higher in physical, psychological, social, and environmental domains, particularly among women with GDM than those with PE. Overall education level, parity, and social support were significant (p< 0.05) predictor of adherence and QoL across both conditions. Conclusion: Preeclamptic women were more likely to take their medication as prescribed than GDM women. However, our study highlights the depression as a predictor of physical health in preeclamptic patients with WHOQOL scores while women with GDM reported a better overall QoL than PE.

The beet webworm, Loxostege sticticalis Linnaeus (Lepidoptera: Pyralidae), is a devastating insect pest of agricultural crops in northern parts of China. Ultraviolet-A (UVA) radiation, which ranges between 315 and 400 nm, has been broadly used as an illuminance source in light traps since it induces oxidative stress in insects by increasing reactive oxygen species production. The current study aimed to investigate the impact of UVA (365 nm) LED light on oxidative stress marker enzymes, such as superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), glutathione-S-transferase (GST), and glutathione reductase (GR), in male and female adults of L. sticticalis under different exposure times: 0 (control), 15, 30, 60, and 120 min. For both sexes, total antioxidant capacity (T-AOC) was also calculated. We observed that T-AOC and the activities of oxidative enzymes increased steadily from baseline, peaking at 60 min and then returning to baseline at 120 min for both sexes. This indicates that prolonged exposure to UVA LED light can activate detoxifying enzymes which may hinder L. sticticalis’ ability to break down pesticides, resulting in increased sensitivity to insecticides. The degree of response to oxidative stress also differed across different exposure times for both sexes. We concluded that extended exposure to UVA LED light can induce oxidative stress, affect protein functionality, and disturb the physiology of L. sticticalis. Our findings may help establish the theoretical and scientific basis for understanding the mechanism that drives phototaxis in nocturnal moths like L. sticticalis and contribute to the development of novel, sub-lethal, and eco-friendly pesticides.