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Abstract Several bacterial strains have developed resistance against commercial antibiotics, and interestingly, supramolecular nanomaterials have shown considerable advantages for antibacterial applications. However, the main challenges in adopting nanotechnology for antibacterial studies are random aggregation, compromised toxicity, multi-step preparation approaches, and unclear structure-function properties. Herein, we designed the amphiphilic tripeptide that acts as a reducing and capping agent for silver metal to form silver-peptide colloidal nanohybrids with the mild assistance of UV light (254 nm) through the photochemical reduction method. The nanohybrids are characterized by different spectroscopic and microscopic techniques, and non-covalent molecular interactions between metal and peptide building blocks confirm their central role in the formation of nanohybrids. The tripeptide is biocompatible and can reduce the toxicity of silver ions (Ag+) by reducing to Ag0. These colloidal nanohybrids showed antibacterial activity against gram-negative and gram-positive bacterial strains, and the possible mechanism of killing bacterial cells could be membrane disruption. This synthetic strategy is facile and green, which helps avoid using toxic chemicals or reagents and complicated methods for colloidal nanohybrid preparation for biomedical applications.

Abstract Medical image machines serve as a valuable tool to monitor and diagnose a variety of diseases. However, manual and centralized interpretation are both error-prone and time-consuming due to malicious attacks. Numerous diagnostic algorithms have been developed to improve precision and prevent poisoning attacks by integrating symptoms, test methods, and imaging data. But in today’s digital technology world, it is necessary to have a global cloud-based diagnostic artificial intelligence model that is efficient in diagnosis and preventing poisoning attacks and might be used for multiple purposes. We propose the Healthcare Federated Ensemble Internet of Learning Cloud Doctor System (FDEIoL) model, which integrates different Internet of Things (IoT) devices to provide precise and accurate interpretation without poisoning attack problems, thereby facilitating IoT-enabled remote patient monitoring for smart healthcare systems. Furthermore, the FDEIoL system model uses a federated ensemble learning strategy to provide an automatic, up-to-date global prediction model based on input local models from the medical specialist. This assures biomedical security by safeguarding patient data and preserving the integrity of diagnostic processes. The FDEIoL system model utilizes local model feature selection to discriminate between malicious and non-malicious local models, and ensemble strategies use positive and negative samples to optimize the performance of the test dataset, enhancing its capability for remote patient monitoring. The FDEIoL system model achieved an exceptional accuracy rate of 99.24% on the Chest X-ray dataset and 99.0% on the MRI dataset of brain tumors compared to centralized models, demonstrating its ability for precision diagnosis in IoT-enabled healthcare systems.

Abstract Hyperspectral image (HSI) data has a wide range of valuable spectral information for numerous tasks. HSI data encounters challenges such as small training samples, scarcity, and redundant information. Researchers have introduced various research works to address these challenges. Convolution Neural Network (CNN) has gained significant success in the field of HSI classification. CNN’s primary focus is to extract low-level features from HSI data, and it has a limited ability to detect long-range dependencies due to the confined filter size. In contrast, vision transformers exhibit great success in the HSI classification field due to the use of attention mechanisms to learn the long-range dependencies. As mentioned earlier, the primary issue with these models is that they require sufficient labeled training data. To address this challenge, we proposed a spectral-spatial feature extractor group attention transformer that consists of a multiscale feature extractor to extract low-level or shallow features. For high-level semantic feature extraction, we proposed a group attention mechanism. Our proposed model is evaluated using four publicly available HSI datasets, which are Indian Pines, Pavia University, Salinas, and the KSC dataset. Our proposed approach achieved the best classification results in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient. As mentioned earlier, the proposed approach utilized only 5%, 1%, 1%, and 10% of the training samples from the publicly available four datasets.

Abstract Maternal health risks can cause a range of complications for women during pregnancy. High blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health conditions can all lead to pregnancy complications. Proper identification and monitoring of risk factors can assist to reduce pregnancy complications. The primary goal of this research is to use real-world datasets to identify and predict Maternal Health Risk (MHR) factors. As a result, we developed and implemented the Quad-Ensemble Machine Learning framework to predict Maternal Health Risk Classification (QEML-MHRC). The methodology used a vacxsriety of Machine Learning (ML) models, which then integrated with four ensemble ML techniques to improve prediction. The dataset collected from various maternity hospitals and clinics subjected to nineteen training and testing tests. According to the exploratory data analysis, the most significant risk factors for pregnant women include high blood pressure, low blood pressure, and high blood sugar levels. The study proposed a novel approach to dealing with high-risk factors linked to maternal health. Dealing with class-specific performance elaborated further to properly understand the distinction between high, low, and medium risks. All tests yielded outstanding results when predicting the amount of risk during pregnancy. In terms of class performance, the dataset associated with the “HR” class outperformed the others, predicting 90% correctly. GBT with ensemble stacking outperformed and demonstrated remarkable performance for all evaluation measure (0.86) across all classes in the dataset. The key success of the models used in this work is the ability to measure model performance using a class-wise distribution. The proposed approach can help medical experts assess maternal health risks, saving lives and preventing complications throughout pregnancy. The prediction approach presented in this study can detect high-risk pregnancies early on, allowing for timely intervention and treatment. This study’s development and findings have the potential to raise public awareness of maternal health issues.

Abstract Arsenic (As), contamination in drinking groundwater resources is commonly environmental problem in many developing countries including Pakistan, with significant human health risk reports. In order to examine the groundwater quality concerning As contamination, its geochemical behavior along with physicochemical parameters, 42 samples were collected from community tube wells from District Bahawalpur, Punjab, Pakistan. The results showed the concentration of elevated As, its source of mobilization, and associated public health risk. The As concentration detected in groundwater samples varied from 0.12 to 104 µg/L with an average value of 34.7 µg/L. Among 42 groundwater samples, 27 samples were beyond the permitted limit of 10 µg/L recommended by World Health Organization (WHO), for drinking purposes. Statistical analysis result show that the groundwater cations values are in decreasing order such as: Na+ > Mg2+ > Ca2+ > K+, while anions were HCO3 – > SO4 2– > Cl– > NO3 –. Hydrochemical facies result depict that the groundwater samples of the study area, 14 samples belong to CaHCO3 type, 5 samples belong to NaCl type, 20 samples belong to Mixed CaMgCl type, and 3 samples belong to CaCl2 type. It can be accredited due to weathering and recharge mechanism, evaporation processes, and reverse ion exchange. Gibbs diagram shows that rock water interaction controls the hydrochemistry of groundwater resources of the study area. Saturation Index (SI) result indicated the saturation of calcite, dolomite, gypsum, geothite, and hematite mineral due their positive SI values. The principal component analysis (PCA) results possess a total variability of 80.69% signifying the anthropogenic and geogenic source of contamination. The results of the exposure-health-risk-assessment method for measuring As reveal significant potential non-carcinogenic risk (HQ), exceeding the threshold level of (> 1) for children in the study area. Water quality assessment results shows that 24 samples were not suitable for drinking purposes.

Abstract Misuse of antibiotics leads to the worldwide spread of antibiotic resistance, which motivates scientists to create new antibiotics. The recurring UTI due to antibiotics-resistant microorganism’s challenges scientists globally. The biogenic nanoparticles have the potential to meet the escalating requirements of novel antimicrobial agents. The green synthesis of nanoparticles (NPs) gained more attention due to their reliable applications against resistant microbes. The current study evaluates the biogenic ZnO NPs of Mentha piperata extract against resistant pathogens of urinary tract infections by agar well diffusion assay. The biogenic ZnO NPs revealed comparatively maximum inhibition in comparison to synthetic antibiotics against two bacterial strains (Proteus mirabilis, Pseudomonas aeruginosa) and a fungal strain (Candida albicans).The synthesized biogenic ZnO NPs alone revealed maximum activities than the combination of plant extract (PE) and ZnO NPs, and PE alone. The physiochemical features of ZnO NPs characterized through UV–Vis spectroscopy, FTIR, XRD, SEM, and EDX. The UV–Vis spectroscopy revealed 281.85 nm wavelengths; the XRD pattern revealed the crystalline structure of ZnO NPs. The FTIR analysis revealed the presence of carboxylic and nitro groups, which could be attributed to plant extract. SEM analysis revealed spherical hollow symmetry due to electrostatic forces. The analysis via EDX confirmed the presence of Zn and oxygen in the sample. The physiochemical features of synthesized ZnO NPs provide pivotal information such as quality and effectiveness. The current study revealed excellent dose-dependent antimicrobial activity against the pathogenic isolates from UTI-resistant patients. The higher concentration of ZnONPs interacts with the cell membrane which triggers oxidative burst. They may bind with the enzymes and proteins and brings epigenetic alteration which leads to membrane disruption or cell death.

Abstract Diabetic retinopathy (DR) is a diabetes complication that can cause vision loss among patients due to damage to blood vessels in the retina. Early retinal screening can avoid the severe consequences of DR and enable timely treatment. Nowadays, researchers are trying to develop automated deep learning-based DR segmentation tools using retinal fundus images to help Ophthalmologists with DR screening and early diagnosis. However, recent studies are unable to design accurate models due to the unavailability of larger training data with consistent and fine-grained annotations. To address this problem, we propose a semi-supervised multitask learning approach that exploits widely available unlabelled data (i.e., Kaggle-EyePACS) to improve DR segmentation performance. The proposed model consists of novel multi-decoder architecture and involves both unsupervised and supervised learning phases. The model is trained for the unsupervised auxiliary task to effectively learn from additional unlabelled data and improve the performance of the primary task of DR segmentation. The proposed technique is rigorously evaluated on two publicly available datasets (i.e., FGADR and IDRiD) and results show that the proposed technique not only outperforms existing state-of-the-art techniques but also exhibits improved generalisation and robustness for cross-data evaluation.

Human population growth and the accompanying increase in anthropogenic activities pose a significant threat to forest ecosystems by reducing the natural services these ecosystems provide. Malam Jabba, located in the District Swat of Pakistan's Hindukush-Himalayan temperate zone, is known for its ecotourism, skiing, timber-producing tree species, medicinal plants, and unique biodiversity. However, a large portion of Swat Valley's population depends on the Malam Jabba forests for timber and fuelwood. This study investigates how deforestation rates have increased in response to the growing human population in Malam Jabba, District Swat. To monitor forest cover changes, we used remote sensing (RS) and geographic information systems (GIS) tools. Vegetation analysis was conducted using the Normalized Difference Vegetation Index (NDVI) based on multi-temporal satellite imagery from 1980, 2000, and 2020. Using a decay model, we calculated the deforestation rate from 1980 to 2020 and projected future rates using MATLAB, based on anticipated population growth. Our results show that over the last two decades, the average annual deforestation rate rose from 0.7% to 1.93%, coinciding with a population increase from 1.2 million to 2.3 million at a growth rate of 9% per year. Projections indicate that the deforestation rate will increase to 2.5% annually over the next 20 years, given the predicted 11.6% yearly population growth. Population growth in District Swat has severely endangered nearby forest ecosystems, and further increases in human activity, such as unsustainable tourism, fuel and timber collection, and urbanization, will likely exacerbate this trend. Based on our findings, we recommend: (i) the implementation of reforestation programs and sustainable forest resource use; (ii) the development of a long-term forest management plan that maintains equilibrium between forest density and population pressure; and (iii) prioritizing areas with extreme human impact for in-situ conservation efforts.

Abstract Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.

Abstract Vegetation structures and dynamics are the result of interactions between abiotic and biotic factors in an ecosystem. The present study was designed to investigate vegetation structure and species diversity along various environmental variables in the Yakhtangay Hills of the Hindu-Himalayan Mountain Pakistan, by using multivariate statistical analysis. Quadrat quantitative method was used for the sampling of vegetation. PC-ORD version 5 software was used to classify the vegetation into different plants communities using cluster analysis. The results of regression analysis among various edaphic variables shows that soil organic matter, total dissolved solids, electrical conductivity, CaCO3 and moisture contents shows a significant positive correlation with species abundance, while the soil pH has inverse relationship with plant species abundance. Similarly, species richness increases with increase in soil organic matter, CaCO3 and moisture contents, while decrease with increase in soil pH, total dissolved solids and electrical conductivity (p < 0.05). The vegetation was classified into four major plant communities and their respective indicators were identified using indicator species analysis. Indicator species analysis reflects the indicators of the study area are mostly the indicators to the Himalayan or moist temperate ecosystem. These indicators could be considered for micro-habitat conservation and respective ecosystem management plans not only in the study area but also in other region with similar sort of environmental conditions.

Abstract Objective: To assess the efficacy of prophylactic administration of 0.1% nepafenac in the maintenance of trans-operative mydriasis following cataract surgery. Study Design: Quasi-experimental study. Place and Duration of Study: Armed Forces Institute of Ophthalmology, Rawalpindi, from Oct 2018 to Mar 2019. Methodology: A total of 70 patients undergoing cataract surgery phacoemulsification were randomly placed in either the control group or the nepafenac group. Nepafenac eye drops were administered as one drop 8 hourly, 3 days before surgery. Control group didn’t receive any medication preoperatively Mydriasis was measured trans-operatively before surgery, after aspiration of the cortex, and then at the end of surgery. Patients of both groups received tobradex 2 weeks post-surgery. Results: Post-surgery statistically significant (0.001) difference was observed in the mean pupil size between the nepafenac group (7.88 ± 0.49) and the control group (6.57 ± 0.92 mm). Conclusion: Use of nepafenac prophylactically was very effective in maintenance of trans-operative mydriasis in cataract surgery.

The growing issue of demand-supply management between the prosumers and the local energy market requires an efficient and reliable energy management model. The microlayers, such as prosumers, energy districts, and macro players, namely retail dealers and wholesale dealers play a pivotal role in achieving mutual benefits. The stochastic nature of renewable energy generation in energy districts requires an effective model that can contemplate all stochastic complexities. Therefore, this paper proposes a mutual trade model between energy districts and smart grid to authorize the prosumers for mutual energy transactions under the stochastic adaptive-service level agreement. Moreover, multiple smart contacts are developed between the stakeholders to design adaptability and stochastic behavior of wind speed and solar irradiance. The real-time adaptations of the stochastic adaptive-service level agreement are based on technical beneficial feasibility and achieved through stochastic and adaptive functions. The optimized solution based on a genetic algorithm is proposed for the energy cost and energy surplus of prosumers and output parameters of the mutual trade model (grid revenue). In the context of mutual benefits associated with balanced demand and supply, the economic load dispatch and simplex method maximization are used for optimized demand-supply energy management. Moreover, the effectiveness of the proposed adaptive and stochastic mutual trade model is validated through simulation and statistical analysis.

Abstract Objective: To isolate and characterise multidrug resistant strains of Staphylococcus aureus from healthcare workers who are at potential risk of nosocomial infections. Methods: The observational, cross-sectional study was conducted from November 2014 to April 2015 at different hospitals of Haripur and Abbottabad, Pakistan, and comprised ward and operation theatre staff. The isolates were identified on the basis of microbiological and biochemical tests and further confirmed by polymerase chain reaction. Disc diffusion method was used for antibiotic sensitivity testing, and panton valentine leukocidin and methicillin resistance mecA genes were detected using polymerase chain reaction. Results: Of 208 isolates, 108(52%) were from the ward staff and 100(48%) were from the operation theatre staff. Overall, 167(80.3%) isolates were positive for Staphylococcus aureus, and 75(36%) were methicillin-resistant Staphylococcus aureus. The number of antibiotic-resistant isolates was 75(45%) cefoxitin, 60(36%) ofloxacin, 152(91%) erythromycin, 52(31%) doxycycline, 127(76%) lincomycin, 53(32%) amoxicillin-clavulanate, 67(40%) ciprofloxacin, and 89(53%) ceftriaxone. Conclusion: A high number of hospital staff, including those working in operation theatres, were found to be carrying methicillin-resistant Staphylococcus aureus and multidrug resistant strains in their nasal passage that may be a source of infection to patients. Key Words: Methicillin resistance, Nosocomial infections, Vancomycin, MecA gene, Pvl gene. Continuous....

Objective: To find out the association of Cerebrospinal Fluid (CSF) adenosine deaminase (ADA) Levels with tuberculosis meningitis. Study Design: Case control study. Place and Duration of Study: This study was conducted in the department of Medicine, Pak Emirates Military Hospital, Rawalpindi over a period of six months from May to Oct 2015. Material and Methods: One hundred and ten participants i.e. 55 cases and 55 controls were included in the study. Lumbar puncture were done and 2cc CSF was obtained. CSF for routine examination and ADA levels were sent to a single lab and pathologist verified report. Based on CSF report, patients were classified as tuberculous meningitis (TBM) and Non- tuberculous meningitis and comparative analysis of ADA level were done in both groups. Results: Mean age of the patients was 51.33 ± 21.60 and 41.76 ± 18.66 in tuberculosis meningitis and non-tuberculosis meningitis participants. In Tuberculous Meningitis patients, 33 (60.0%) were males while in non Tuberculous Meningitis participants, 35 (63.6%) were males. Mean ADA level in CSF (13.71 ± 4.25 vs. 4.76 ± 3.49, p

This study aims to provide estimates, trends and projections of vision loss burden in Pakistan from 1990 to 2025. Global Burden of Diseases, Injuries, and Risk Factors Study (GBD 2017) was used to observe the vision loss burden in terms of prevalence and Years Lived with Disability (YLDs). As of 2017, out of 207.7 million people in Pakistan, an estimated 1.12 million (95% Uncertainty Interval [UI] 1.07-1.19) were blind (Visual Acuity [VA]

Laboratory diagnosis of sickle cell anemia involves phenotypic testing for the presence of the sickle haemoglobin and genetic analysis. Phenotypic tests include sickling test and sickle solubility test. Sickle cell anemia can be diagnosed by the presence of sickle cells (drepanocytes) in the peripheral blood film with direct microscopic examination.

Background: Dengue is a major health problem around the world and contributes in high rate of morbidity and mortality. The objective of the study was to determine the frequency, time and place distribution of the dengue fever epidemic in District Swat. Material & Methods: This hospital based, cross-sectional study was conducted in District Head Quarter Teaching Hospital Swat, KPK, Pakistan from August 07 to October 31, 2013, during dengue epidemic. Total of 9032 dengue suspected patients were registered on consecutive sampling technique. Demographic variables were gender, age groups and residence. Research variables were Dengue non-hemorrhagic and Dengue hemorrhagic fever. Blood samples were collected from non-hemorrhagic dengue and dengue hemorrhagic fever patients. Serum was obtained by centrifugation at 3000 rpm for 5 minutes after blood samples were properly clotted. Qualitative detection of dengue virus NS1 antigen was done on commercially available ICT-SD Dengue NS1 kit (Standard Diagnostic, South Korea) following manufacturer instructions. Data analysis was carried out by using SPSS version 19. All variables being categorical were analyzed by frequency and percentages. Results: Out of 9032 dengue suspected patients serum samples, 8638(96%) were found positive for dengue NS1 antigen. Out of 8638(96%) positive cases on NSI, 5889(68%) patients were males and 2749(32%) were females. The age group wise distribution of the male and female Dengue virus NS1 Antigen positive cases with the age group 21-30 was the modal age group. Conclusion: The highest incidence of dengue fever was observed in young people. Effective awareness campaigns should be initiated at local, provincial and national level by health departments.