Superior University

In this research, an assessment of scour depth prediction in meandering channels with spur dikes is made employing machine learning approaches. Efficient determination of the scour depth is therefore vital in the prediction of morphologic aspects and structural stability. The input parameters include sinuosity (S), spur dike locations (Ld), and porosity (P) with experimental data from sinusoidal flumes. Four machine learning models; Extreme Gradient Boosting (XGBoost) with Particle Swarm Optimization (PSO) XGBoost-PSO, Random Forest (RF), k-Nearest Neighbors (k-NN), and Decision Tree-Neural Network (DT-NN) were used and compared. The findings demonstrate an R-value of 0.995 in the case of RF model while XGBoost-PSO gave second-best accuracy with R = 0.988. The results of the SHAP analysis illustrated that porosity and sinuosity are significant factors affecting scour depth (Ds/Yn, Ds: scour depth, Yn: water depth) and had moderate importance assigned to spur dike location. Kernel density plots further supported the RF model regarding error distribution consistency. Even though, both XGBoost-PSO yielded better results because of hyperparameter tuning, k-NN and DT-NN had less precise outcomes specifically predicted for progressive hydraulic procedures. Taylor's diagram even revealed greater accuracy of prediction by RF. Hence, a proper selection of appropriate machine learning models remains the first step in estimating scour depth sufficiently for flood and erosion control.

Difficult-to-treat resistant Pseudomonas aeruginosa (DTR-PA) is an MDR subset resistant to all first-line antipseudomonal agents. It is particularly concerning in respiratory infections like bronchiectasis, leading to poor outcomes, limited treatment options, and higher healthcare costs. This study aimed to investigate the antimicrobial resistance profiles and sequence types (STs) of Pseudomonas aeruginosa isolates, providing insight into their resistance patterns and the factors contributing to resistance. In 2021, 38 multidrug-resistant P. aeruginosa isolates were collected from bronchiectasis patients from a single medical center in Ningbo, China. The isolates were obtained from various clinical samples, including sputum, secretion, urine, and blood. Minimum inhibitory concentration testing revealed that 97.4% of the isolates were sensitive to amikacin and tobramycin, with none showing resistance to polymyxin B. Resistance rates to imipenem and meropenem were 84.2% and 57.9%, respectively, with 44.7% of isolates classified as DTR-PA. Multilocus sequence typing identified ST277 (18.4%), ST1076 (13.2%), and ST3012 (13.2%) as the predominant DTR-PA sequence types. The presence of blaPAO, aph(3′)-IIb, and catB7, in all isolates and blaOXA-50 (16 isolates) and crpP genes (24 isolates) in coexisitance in 11 of 16 isolates, suggested a strong association with the DTR phenotype. Phylogenetic analysis grouped DTR-PA isolates into distinct evolutionary lineages (II and III), underscoring their genetic relatedness and potential for clonal spread. Our findings suggest that co-harboring blaOXA-50 and crpP contributes to the development of DTR-PA, highlighting the need for continuous monitoring of these resistance determinants. While the study provides important insights into antimicrobial resistance in DTR-PA, further research is needed to explore resistance development across different infection sites and clinical settings.

The practice of cutting and pasting portions of one image into another, known as “image splicing,” is commonplace in the field of image manipulation. Image splicing detection using deep learning has been a hot research topic for the past few years. However, there are two problems with the way deep learning is currently implemented: first, it is not good enough for feature fusion, and second, it uses only simple models for feature extraction and encoding, which makes the models vulnerable to overfitting. To tackle these problems, this research proposes CMV2U‐Net, an edge‐weighted U‐shaped network‐based image splicing forgery localization approach. An initial step is the development of a feature extraction module that can process two streams of input images simultaneously, allowing for the simultaneous extraction of semantically connected and semantically agnostic features. One characteristic is that a hierarchical fusion approach has been devised to prevent data loss in shallow features that are either semantically related or semantically irrelevant. This approach implements a channel attention mechanism to monitor manipulation trajectories involving multiple levels. Extensive trials on numerous public datasets prove that CMV2U‐Net provides high AUC and F 1 in localizing tampered regions, outperforming state‐of‐the‐art techniques. Noise, Gaussian blur, and JPEG compression are post‐processing threats that CMV2U‐Net has successfully resisted.

Tumour necrosis factors (TNFs) are key players in processes such as inflammation, cancer development, and autoimmune diseases. However, accurately identifying TNFs remains challenging because of their complex interactions with other cytokines. Although existing machine learning models offer some potential, they often fall short in reliably distinguishing TNFs. To address this issue, the authors developed DEEP‐TNFR, a more advanced model designed specifically to predict TNFR activity. The approach incorporates features such as relative and reverse positions, along with statistical moments, and is tested on a recognised benchmark dataset. The authors explored six different deep learning classifiers, including fully connected networks (FCN), convolutional neural networks (CNN), simple RNN (RNN), long short‐term memory (LSTM), bidirectional LSTM (Bi‐LSTM), and gated recurrent units (GRU). The model's effectiveness was evaluated through multiple methods: self‐consistency, independent set testing, and 5‐ and 10‐fold cross‐validation, using metrics, such as accuracy, specificity, sensitivity, and Matthews correlation coefficient. Among these classifiers, LSTM proved to be the most effective, outperforming the others and setting a new standard compared to previous studies. DEEP‐TNFR is poised to significantly support ongoing research by enhancing the accuracy of TNFR identification.

In human history, metals have had a significant yet ironic function. Their growing industrial usage has made a significant contribution to technological advancement; on the other hand, their requirement for humans, particularly in situations where certain metal ions are lacking, has just now come to light. In this review, we have examined various analytical methods employed in the detection of lithium, spanning from conventional approaches to contemporary technologies. Traditional methods encompass gravimetric analysis, titrimetry, flame testing, chromatography, and colorimetry. On the other hand, modern techniques involve the utilization of sophisticated instruments such as X-ray diffraction, X-ray fluorescence, atomic absorption spectroscopy, inductively coupled plasma optical emission spectroscopy, inductively coupled plasma mass spectrometry, thermal ionization mass spectrometry, laser-induced breakdown spectroscopy, ultraviolet/visible spectroscopy, X-ray photoelectron spectroscopy, and sensor technology.

Introduction The mitochondria are highly dynamic organelles. The mitochondrial morphology and spatial distribution within the cell is determined by fusion and fission processes of mitochondria. Several studies have used mitochondrial division inhibitor-1 (Mdivi.1) to explore the roles of mitochondrial dynamics in various pathological conditions, including diabetic cardiomyopathy, myocardial infarction, cardiac hypertrophy, Alzheimer’s disease, Huntington’s disease and cancers. Purpose The objective of the study was to investigate the role of mitochondrial dynamics in the invasiveness of HCT116 colorectal cancer cells. Material and Methods MTT assay was used to determine the Mdivi.1-induced toxicity in HCT116 cells. Wound healing, cell migration and colony forming assays were adopted to measure the migration and invasion activity of HCT116 cells. Furthermore, flow cytometry was used to determine the Mdivi.1-induced mitochondrial mass quantification, mitochondrial membrane potential and reactive oxygen species generation in HCT116 cells. Additionally, Western Blot analysis was used to determine the expression level of Drp1, p-Drp1, Mnf2, AMPK-α, p-AMPK-α, Cox-2, iNos and MMP9 in HCT116 cells. Results We found that Mdivi.1 induced toxicity and altered the morphology of HCT116 cells in concentration- and time-dependent manners. Mdivi.1 significantly increased mitochondrial mass and dissipated the mitochondrial membrane potential. Furthermore, Mdivi.1 induced reactive oxygen species (ROS) generation and mitochondrial superoxide production, leading to AMPK activation. Moreover, Mdivi.1 decreased dynamin-related protein-1 (Drp1) and phosphorylated-Drp1 expression and increased mitofusin-2 (Mfn2) expression in a concentration-dependent manner at 48 and 72 h post-treatment. Notably, Mdivi.1 induced inhibition of translocation of Drp1 from the cytosol to the outer mitochondrial membrane. Mdivi.1 significantly suppressed the invasion and migration of HCT116 cells and inhibited the formation of HCT116 cell colonies. In addition, Mdivi.1 significantly decreased the expression of metastatic markers including Cox-2, iNos, and MMP-9 in HCT116 cells. Conclusion Collectively, this study revealed that Mdivi.1 downregulates Drp1, upregulates Mfn2, and increases mitochondrial mass with attenuated oxidative metabolism, leading to the inhibition of cell invasion and metastasis in colorectal cancer HCT116 cells. Mitochondrial dynamics are regarded as possible drug targets for interrupting colorectal cancer cell migration and metastasis.

This study explores how economic policy uncertainty (EPU) impacts the leverage structure of firms and considers whether foreign ownership provides a hedge against the EPU. We applied the PCSE and FGLS regressions to panel data from 231 non-financial firms registered in Pakistan for the period 2017–2021. We examined the EPU influences on the firms' leverage structure by considering their capital and debt maturity structures separately. Our research proves a positive correlation between EPU and the total debt ratio regarding capital structure, indicating that firms often take on more leverage amid economic policy uncertainty. Regarding debt maturity, the findings suggest that firms prefer short-term debt financing during EPU for flexibility. Further, the moderating analysis suggests that firms with foreign ownership tend to rely more heavily on long-term debt financing. Also, foreign ownership helps to tackle EPU by providing access to global networks, diversified funding sources and managerial expertise to local firms. Additionally, we did a comparative analysis between large and firms to detect any possible variations in our findings. We recommend that the government should be interested in providing security and a favorable business climate to foreign investors.

This exploratory study uncovers the complexities of socio-cultural barriers and gender stereotypes from the entrepreneurial expeditions of women in Pakistan, and sheds light on how significantly digitalization has altered the ways of doing business. This qualitative study is based on in-depth interviews with Pakistani women entrepreneurs, and interviews were tape recorded and NVIVO software facilitated in conducting thematic analysis. The study highlights the significance of family dynamics in forming directions of businesses and highlights the entrepreneurs’ resourcefulness in the face of hardship, while revealing how e-commerce sites and digitization helped them get beyond the challenges. This study explores how gender prejudices, family dynamics, and cultural standards shape the business directions and accomplishments of female entrepreneurs. It also explores the complexities of these experiences. This study highlights the need for gender-inclusive policies and support systems while offering a comprehensive view of the potential and difficulties particular to women entrepreneurs. This study provides insights into how family dynamics can be used to support women’s economic empowerment, as well as consequences for entrepreneurs and policymakers. It also emphasizes how e-commerce and digitization may be used to support Pakistani women who want to start their own businesses.

This study uses an innovative modified (G′/G²)-expansion method to reveal various soliton solutions to the (2+1)-dimensional Wazwaz Kaur Boussinesq problem. The suggested approach produces new traveling wave solutions by utilizing different derivatives, including conformable, M-truncated, and β-derivative, and unique solutions, including hyperbolic, rational, and trigonometric. The three forms of fractional derivatives used to construct W-type, bright-type, and dark-type soliton wave solutions are compared and contrasted in this work using two-dimensional temporal and spatial plots and three-dimensional graphs. These findings have substantial implications for the technologies currently employed in communication networks, such as optical fiber, magneto-sound, ion-acoustic, and stationary media, as well as for the study of transmission of tidal and tsunami waves.

Type 2 diabetes mellitus (T2D) is a major health problem worldwide having life-threatening complications causing mortality and a rise in prevalence. Effective treatment strategies are vital for managing diabetes and its associated complications including cardiovascular disease (CVD), nephropathy, neuropathy, and retinopathy. This systematic review aims to evaluate effective treatment approaches, focusing on the comparative effects of exercise and GLP-1 receptor agonists (GLP-1RAs) in T2D rat models. Current pharmacological therapies primarily target glycemic control and insulin sensitivity. However, there is a growing concern in non-traditional approaches that involve exercise and GLP-1 RAs for managing T2D. These therapies are crucial as they have the potential to improve pancreatic β-cell efficiency to secrete insulin, control blood glucose levels, decrease insulin resistance, and manage diabetes-related issues. Studies were searched in seven electronic databases including Google Scholar, MEDLINE, PubMed, Cochrane Library, Scopus, PEDro, and Web of Science from inception till 2024. Out of 16,500 documents retrieved between 2020 and 2024, 58 full-text articles were assessed in detail, and 13 studies met the inclusion criteria that include Male Wistar, Male Sprague Dawley and Adult female Wistar albino rats weighing 200-250 grams. These experimental studies examined the effects of exercise and different GLP-1 RAs on 103 diabetics and 103 non-diabetic rats. Overall, synthesized findings revealed a promising effect on glucose control, insulin sensitivity, and metabolic health in diabetic rats. Further research is needed to elucidate the cellular and molecular mechanism(s) through which exercise and GLP-1 RAs manage T2D and its associated complications including cardiovascular disease (CVD), nephropathy, neuropathy, and retinopathy.

Background/Objectives: Breast cancer is among the most frequently diagnosed cancers and leading cause of mortality worldwide. The accurate classification of breast cancer from the histology photographs is very important for the diagnosis and effective treatment planning. Methods: In this article, we propose a DenseNet121-based deep learning model for breast cancer detection and multi-class classification. The experiments were performed using whole-slide histopathology images collected from the BreakHis dataset. Results: The proposed method attained state-of-the-art performance with a 98.50% accuracy and an AUC of 0.98 for the binary classification. In multi-class classification, it obtained competitive results with 92.50% accuracy and an AUC of 0.94. Conclusions: The proposed model outperforms state-of-the-art methods in distinguishing between benign and malignant tumors as well as in classifying specific malignancy subtypes. This study highlights the potential of deep learning in breast cancer diagnosis and establishes the foundation for developing advanced diagnostic tools.

In today’s fast-paced academic landscape, university teachers encounter a considerable workload while managing strict deadlines and diverse responsibilities. In their efforts to maintain high performance, these teachers encounter significant job demands that can affect both their psychological well-being and job performance. Therefore, this study investigates the role of job demand in shaping the job performance of university teachers, with a focus on the mediating roles of Psychological Well-Being (PWB), anxiety and depression, and the moderating role of social support by considering Job Demand-Resource (JD-R) Theory as the foundation. Utilizing a cross-sectional design, quantitative data was collected from university teachers via an electronic structured questionnaire. The sample for this study was obtained through multistage random sampling and comprised 293 teachers from private universities in Lahore, Pakistan. The results of structural equation modeling show that PWB, anxiety, and depression act as mediators in the relationship between job demands and job performance. The study indicates that social support moderates the effects of job demands on anxiety and depression. Social support also moderates the relationship between job demands and PWB. The findings contribute to academic discourse by emphasizing the need for targeted interventions that prioritize social support and mental health resources to improve job outcomes and performance. This study bridges a critical gap in the literature and offers practical implications for university administrators and policymakers, indicating the requirement for well-balanced workloads, mental health initiatives, and supportive work environments.

This study investigates the relationship between students' mental health (MH) and mobile phone addiction (MPA), addressing growing concerns about the impact of excessive phone use on school-age children. Using a quantitative research approach, data was collected through a standardized questionnaire and analyzed using Cronbach's Alpha, Composite Reliability (CR), Average Variance Extracted (AVE), and factor loadings to ensure construct validity and reliability. Structural equation modeling (SEM) was employed to examine the impact of MPA on MH. The findings reveal that mobile phone addiction significantly explains 61.5% of the variance in mental health (R² = 0.615), with a strong positive association between MPA and mental health issues (β = 0.784, p = 0.000). This indicates that excessive mobile phone use negatively affects students' mental health, potentially leading to stress, anxiety, and poor academic performance. The study highlights the need for digital wellness initiatives and interventions to promote responsible phone use and reduce screen time. Focusing specifically on school-level teenagers contributes to understanding the psychological and academic consequences of MPA, offering valuable insights for policymakers, parents, and educational institutions. The results underscore the importance of fostering mindful phone usage to safeguard students' mental well-being. Future research should explore long-term effects and develop targeted strategies to mitigate the adverse impacts of mobile phone addiction. This study emphasizes the urgency of addressing MPA to support healthier mental and academic outcomes for students.

Blood–brain barrier peptides (BBBP) could significantly improve the delivery of drugs to the brain, paving the way for new treatments for central nervous system (CNS) disorders. The primary challenge in treating CNS disorders lies in the difficulty pharmaceutical agent’s face in crossing the BBB. Almost 98% of small molecule drugs and nearly all large molecule drugs fail to penetrate the BBB effectively. Thus, identifying these peptides is vital for advancements in healthcare. This study introduces an enhanced intelligent computational model called BBB-PEP- Evolutionary Scale Modeling (ESM), designed to identify BBBP. The relative positions, reverse position and statistical moment-based features have been utilized on the existing benchmark dataset. For classification purpose, six deep classifiers such as fully connected networks, convolutional neural network, simple recurrent neural networks, long short-term memory (LSTM), bidirectional LSTM, and gated recurrent unit have been utilized. In addition to harnessing the effectiveness of the pre-trained model, a protein language model ESM 2.0 has been fine-tuned on a benchmark dataset for BBBP classification. Three tests such as self-consistency, independent set testing, and five-fold cross-validation have been utilized for evaluation purposes with evaluation metrics includes accuracy, specificity, sensitivity, and Matthews correlation coefficient. The fine-tuned model ESM 2.0 has shown superior results as compared to employed classifiers and surpasses the existing benchmark studies. This system will support future research and the scientific community in the computational identification of BBBP.

Introduction: A high frequency of mental issues has been reported amongst dental students in recent years. The aim of this study was to explore the frequency of depression, stress, and anxiety amongst undergraduate dental students in a developing country and identify factors which may contribute to the poor mental health of dental students. Materials and Methods: After obtaining ethical approval, undergraduate dental students from 14 dental institutions were invited to participate in an online study. Data were collected using two globally validated scales for screening mental health. The survey inventory also included two open-ended items and was administered using Google forms. Results: Complete responses were received from 639 participants, which included 71.67% (n = 458) females and 28.33% (n = 181) males. The overall response rate was 43%. The modal age group was 18-21-year-olds (63.54%, n = 406), followed by 22-25-year-olds (35.52%, n = 227). The mean score on PHQ-9 was 10.37 (SD ± 6.13) and 48.67% of participants showed moderate to severe depression. The mean DASS-21 score was 20.81 (SD ± 14.64) and 48.21% of participants were screened positively for moderate to extremely severe depression, 49.30% for moderate to extremely severe anxiety, and 30.36% of participants showed features of moderate to extremely severe stress. Significantly positive correlations were observed for the whole sample and demographic factors for participant scores on PHQ-9 for Depression, and Depression, Anxiety, and Stress scores on DASS-21. Academic workload, social interactions, personal factors, academic environment, and financial difficulties were reported as the main causes of poor mental health. Discussion: This study shows a high prevalence of depression, anxiety, and stress amongst undergraduate dental students in a country with a unique socio-cultural landscape. The study also identified underlying factors which adversely affect the mental health of dental students and provides recommendations to address these challenges.

This study explores how interventions focused on digital health literacy (DHL) can improve access to healthcare and contribute to achieving Sustainable Development Goal-3 (SDG- 3). We scrutinized information from PubMed (MEDLINE), Scopus, and Web of Science released search articles from March 1, 2020 to January 31, 2024. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review concentrated only on experimental studies that assessed how DHL initiatives have influenced enhancing patient health outcomes and access to healthcare. Research that did not cover DHL or the availability of healthcare, was not included. The analysis was primarily qualitative, focusing on thematic patterns and insights rather than statistical outcomes. Our results showed that DHL interventions typically result in enhanced health literacy, improved medication adherence, and higher self-confidence, particularly benefiting marginalized communities. Limitations to safe & accessible healthcare underscore the need for more focused and culturally appropriate strategies. This review shows that interventions by DHL can greatly enhance healthcare results, highlighting the need to tackle inequalities to ensure marginalized communities also benefit. doi: https://doi.org/10.12669/pjms.41.3.10639 How to cite this: Mukhtar T, Babur MN, Abbas R, Irshad A, Kiran Q. Digital Health Literacy: A systematic review of interventions and their influence on healthcare access and sustainable development Goal-3 (SDG-3). Pak J Med Sci. 2025;41(3):910-918. doi: https://doi.org/10.12669/pjms.41.3.10639 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background Individualization of the therapeutic plan for cancer patients is the essence of modern clinical practice. Standard cancer diagnostic and prognostic factors are invasive, and their value for the stratification of cancer patients with a higher risk of local or distant recurrence is limited. YKL-40 is a protumor glycoprotein linked to the immunosuppressive tumor in a microenvironment and an important biomarker of cell activation, proliferation, and migration. Objective The objective is to update the review, and molecular and clinical research should investigate novel modalities of targeting this glycoprotein for cancer treatment. Methodology Relevant studies published in the English language were identified by searching PubMed, Google Scholar, and MEDLINE from January 2000 to December 2023. Published studies that specifically elicited the role of YKL-40 as a biomarker in different types of tumors were included. Results YKL-40 cancer prognostic effect was reported in various cancer types. Conclusion Since antibodies against YKL-40 can inhibit tumor angiogenesis and cancer progression, it can be suggested as an attractive candidate for chemical cancer therapy and immunomodulation.

The global spread of monkeypox, caused by the double-stranded DNA monkeypox virus (MPXV), has underscored the urgent need for effective antiviral treatments. In this study, we aim to identify a potent inhibitor for MPXV DNA polymerase (DNAP), a critical enzyme in the virus replication process. Using a computational drug repurposing approach, we performed a virtual screening of 1615 FDA-approved drugs based on drug-likeness and molecular docking against DNAP. Among these, 1430 compounds met Lipinski's rule of five for drug-likeness, with Doxycycline emerging as the most promising competitive inhibitor, binding strongly to the DNAP active site with a binding affinity of − 9.3 kcal/mol. This interaction involved significant hydrogen bonds, electrostatic interactions, and hydrophobic contacts, with Doxycycline demonstrating a stronger affinity than established antivirals for smallpox, including Cidofovir, Brincidofovir, and Tecovirimat. Stability and flexibility analyses through a 200 ns molecular dynamics simulation and normal mode analysis confirmed the robustness of Doxycycline binding to DNAP. Overall, our results suggest Doxycycline as a promising candidate for monkeypox treatment, though additional experimental and clinical studies are needed to confirm its therapeutic potential and clinical utility.

The aim of this research is to examine the asymmetric effects of climate uncertainty policy (CU), innovation, and institutional performance on environmental degradation in the BRICS (Brazil, Russia, India, China, and South Africa) countries from 1990 to 2021. The novel study looks at emissions of carbon dioxide (CO2), nitrogen oxide (N2O), sulphur dioxide (SO2), methane (CH4), and ecological footprint (EFP) as proxies for environmental deterioration using the Panel Non-linear Auto Regressive Distributed Lag (NARDL) technique. To evaluate institutional performance and ecological innovations, respectively, Principal Component Analysis (PCA) is used with six and twelve indicators. The Panel NARDL findings affirms that the positive and negative change of climate uncertainty policy, and ecological innovation have positive association with environment (CO2, N2O, SO2, CH4 and EFP), Furthermore, the positive and negative shock of negative change institutional performance has negative and significant relation with CO2, N2O, SO2, CH4 and EFP in long run. In order to attain environmental sustainability, this study conclusion emphasizes the significance of promoting innovation, building institutions, and backing free trade policies among the BRICS nations. These findings offer insightful information on the intricate relationships that these countries' energy use, institutional strength, ecological innovations, and emissions have with one another. Our empirical findings suggest that the fossil fuel industry may see continuous expansion as a result of policy uncertainty related to climate change. Lastly, a few specific policies are recommended in light of the empirical findings.