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Background: Hepatitis C is a major public health issue across the globe, with approximately 58 million individuals estimated to have chronic Hepatitis C virus infection while every year 1.5 million new cases emerge. Locally in Pakistan, the prevalence of Hepatitis C in the general population has recently been reported to be 16.4%. As there are currently no effective vaccines available for precaution against Hepatitis C infection, the role of prevention such as increasing awareness among the masses is even more significant. Objective: To assess the awareness regarding Hepatitis C among the residents of Union Council 3, Gadap Town, District Malir, Karachi. Methods: A cross-sectional study was conducted at Gadap Town, District Mlir, Karachi from September 2022 to November 2023. The study population comprised people aged 18 years or above of either gender living in Union Council 3, Gadap Town, District Malir, Karachi. Data were collected by using a pre-tested questionnaire. Data were analyzed on SPSS version 20. Results: A total of 601 participants were included in the study. The mean age of respondents was 30.99±10.58 years and 268 (44.6%) of them belonged to the 26 to 39 year age group, 326 (54.2%) of them were male whereas 236 (39.2%) of them had a positive family history of Hepatitis C. Moreover, 395 (65.7%) of the study participants were found to have inadequate awareness regarding Hepatitis C. Furthermore, on multivariable analysis, both education level (p

Abstract The Upper Indus Basin (UIB) of Pakistan is home to three largest mountain ranges: Karakoram, Hindukush, and the Himalayas. There’s a hovering danger of reduced water resources due to climate-induced warming, since the Indus Basin relies mostly on snow and glacier melt runoff from these mountains. In this study, the hydrology of these areas is studied to estimate variability in water resources under changing climate. A coupled hydrologic/hydraulic model, MIKE SHE/MIKE HYDRO RIVER, was used to calibrate and validate the model using streamflow data at the Shatial gauge station. The bias-corrected multi-model ensemble dataset accessed from the coordinated regional climate downscaling experiment (CORDEX) for two scenarios RCP 4.5 and RCP 8.5 were used up to the end of twenty-first century for the projection of stream flow. The calibrated model depicts an increase of 86% for RCP4.5 and 97% for RCP8.5, dominated by increased snow melt contribution due to consistent warming trends (average increase of 2.3 and 4 °C, annually for RCP 4.5 and 8.5, respectively). The most prominent increase was observed in winter months when predicted flows increased by 300% from historic. With predicted annual average available water of 4500 m3/s under RCP8.5, the study concludes that the available water would relieve some of the water stress in the country. However, the increased availability of water can also cause catastrophic floods. More flood defense and storage structures are needed to improve management practices in the downstream areas, particularly during wet and dry years.

Abstract Yersinia pestis, a Gram-negative bacterium is the causative agent of the fatal communicable disease plague. The disease had a profound impact on human history. Plague bacteria are usually transmitted to humans through the bite of an infected rat flea. Earlier studies have indicated that Y. pestis can survive in environmental matrices e.g. water and soil. This study aimed to generate a peptide-based screen for identification of Y. pestis particularly from environmental matrices. We employed a shotgun proteomic approach using nano-liquid chromatography-tandem mass spectrometry (nLC-MS/MS) to discover Y. pestis-specific peptides. The pure cultures of Y. pestis and related species were grown, their proteome were delineated and analyzed by in silico tools to discover 61 Y. pestis specific peptides. Additionally, 148 peptides were discovered from proteins of Y. pestis-specific plasmids and chromosomal-associated virulence markers. To validate this screen of 209 peptides, various concentrations of Y. pestis (ranging from 1.3 × 108 to 1.3 × 105 cfu) were spiked into garden soil. Y. pestis could be identified in all samples except un-spiked negative control soil sample. This study offers a valuable method for the identification of Y. pestis, by tandem mass spectrometry which may be used in environmental and clinical matrices.

Abstract The traditional optimization approaches suffer from certain problems like getting stuck in local optima, low speed, susceptibility to local optima, and searching unknown search spaces, thus requiring reliance on single-based solutions. Herein, an Improved Aquila Optimizer (IAO) is proposed, which is a unique meta-heuristic optimization method motivated by the hunting behavior of Aquila. An improved version of Aquila optimizer seeks to increase effectiveness and productivity. IAO emulates the hunting behaviors of Aquila, elucidating each step of the hunting process. The IAO algorithm contains innovative elements to boost its optimization capabilities. It combines a combination of low flight with a leisurely descent for exploitation, high-altitude vertical dives, contour flying with brief gliding attacks for exploration, and controlled swooping maneuvers for effective prey capture. To assess the effectiveness of IAO, Herein, numerous experiments were carried out. Firstly, IAO was compared using 23 classical optimization functions. The achieved results demonstrate that the proposed model outperforms various champion algorithms. Secondly, the proposed algorithm is applied to five real-world engineering problems. The achieved results prove effectiveness in diverse application domains. The key findings of the research work highlight IAO’s resilience and adaptability in solving challenging optimization issues and its importance as a strong optimization tool for real-world engineering applications. Convergence curves compare the speed of proposed algorithms with selected algorithms for 1000 iterations. Time complexity analysis shows that the best time is 0.00015225 which is better as compared to other algorithms also Wilcoxon ranksum test is carried out to calculate the p-value is less than 0.05 rejecting the null hypothesis. The research emphasizes the potential of IAO as a tool for tackling real-world optimization challenges by explaining its efficacy and competitiveness compared to other optimization procedures via comprehensive testing and analysis.

Abstract The Internet of Things (IoT) consist of a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Intrusion detection systems using deep learning are a common method used for providing security in IoT. However, traditional deep learning IDS systems do not accurately classify the attack and also require high computation time. Thus, to solve this issue, herein, we propose an advance Intrusion detection framework using Self-Attention Progressive Generative Adversarial Network (SAPGAN) framework for detecting security threats in IoT networks. In our proposed framework, at first, the IoT data are gathered. Then, the data are fed to pre-processing. In pre-processing, it restored the missing value using Local least squares. Then the preprocessing output is fed to feature selection. At feature selection, the optimum features are compiled using a modified War Strategy Optimization Algorithm (WSOA). Based upon the optimum features, the intruders were categorized into two categories named Anomaly and Normal using the proposed framework. Numerous attacks are assembled, including camera-based flood, DDoS, RTSP brute force, etc. We have compared our proposed framework using state of the art model and efficiency of 23.19%, 27.55%, and 18.35% higher accuracy and 14.46%, 26.76%, and 13.65% lower computational time compared to traditional models.

The Urban Heat Island (UHI) phenomenon exacerbates thermal discomfort in urban areas and significantly contributes to urban overheating when combined with climate change. This study investigates the spatiotemporal patterns of Surface Urban Heat Island Intensity (SUHII) in six major cities of Pakistan, focusing on the interplay between urban expansion, vegetation cover, and SUHII. To quantify SUHII dynamics, the impact of urban sprawl and vegetation changes was analyzed. The study offers critical insights into the implications for urban planning and policymaking in Pakistan. Using remote sensing data from Landsat satellites, analyzed with Geographic Information Systems (GIS) techniques, estimates of SUHII, urban expansion, and vegetation cover were derived. Specifically, imagery from Landsat-5 (2010−2013) and Landsat-8 (2014–2022), obtained from the US Geological Survey (USGS), was employed. Statistical analyses, including Pearson's correlation and linear regression, were conducted to assess relationships between these variables from 2010 to 2022. SUHII was found to increase annually by 0.18 °C in Islamabad and 0.19 °C in Peshawar, with corresponding urban expansion rates of 8.07 km2 (8967.75 pixels) and 1.67 km2 (1860.42 pixels) per year, respectively. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC) were inversely correlated with SUHII, explaining up to 50 % of the variance in Peshawar. However, weaker correlations in Lahore suggest the presence of additional factors influencing SUHII. A distinct spatial relationship between increased vegetation and cooler areas was observed. For instance, Islamabad has greater vegetation cover and cool zones over 41.5 km2. In contrast, Lahore's hot spots spanned 127.1 km2, compared to Abbottabad's 10.4 km2, underscoring the thermal impact of reduced vegetation. The findings emphasize the effectiveness of urban greening, particularly in Islamabad's neutral thermal regions, in mitigating SUHII. This study offers important insights for urban planners in developing sustainable, climate-resilient cities within similar urban contexts. While the results are specific to Pakistani cities, the role of vegetation in mitigating SUHII may hold broader relevance for urban planning strategies in comparable settings.

ObjectivesThis study aimed to assess the prevalence of non-communicable diseases (NCDs) among Saudi adolescents, focusing on obesity, conduct disorder, asthma, and anxiety, and to identify potential risk factors associated with these conditions.MethodsA retrospective cross-sectional analysis was conducted using data from 2,160 adolescents sourced from official government databases and peer-reviewed literature. Statistical methods included one-way ANOVA, chi-square tests, logistic regression, and Pearson’s correlation coefficient, with significance set at p

Objective: Dental trauma poses a significant threat to the oral health and well-being of school-aged children worldwide. Prompt and appropriate management of dental trauma is crucial to prevent infection, abscess formation, and pulp necrosis. Methods: The descriptive cross-sectional study was carried out among 320 male and female teachers via cluster random sampling from October 2023 to May 2024 to assess the level of knowledge among school teachers regarding emergency management of dental injuries, to evaluate the preparedness of school teachers in handling dental injuries in the school setting and to determine the association between demographics with knowledge and preparedness regarding emergency management of dental injuries. Data was collected about socio-demographic characteristics and emergency preparedness for dental trauma. Results: Out of 320 individuals, the level of the teachers' knowledge was moderate (53.3%). Only a few teachers were found to have undergone first-aid training in TDI (19.4%) and to have encountered TDI incidents (21.9%) before. 93(50.5%) females correctly identified fracture (p-value 0.014). A significant association was observed for identifying fractured teeth and managing avulsed teeth with first aid training with a p-value of 0.017 and 0.008 respectively. A significant association was observed between the age group 41-50 years and the Importance of seeking emergency management for dental trauma (p-value 0.018) and knowledge status (p-value 0.007).52.3% of teachers correctly identified a solution for washing a re-implanted tooth(p-value 0.000).46% of teachers whose service span was more than 19 years, had better self-rated knowledge( p-value 0.002) Conclusion: A moderate number of teachers had adequate knowledge among school teachers regarding dental trauma management. While a substantial proportion of teachers exhibit a commendable understanding and readiness to address dental emergencies, there remains a notable gap between certain aspects of knowledge and preparedness. Keywords: Emergency management, Preparedness, School teachers.

Abstract Cloud-based digital twins use real-time data from various data sources to simulate the behavior and performance of their physical counterparts, enabling monitoring and analysis. However, one restraining factor in the use of cloud computing for digital twins is its users’ concerns about the security of their data. This data may be located anywhere in the cloud, with very limited control of the user to ensure its security. Cloud-based digital twins provide opportunities for researchers to collaborate yet security of such digital twins requires measures specific to cloud computing. To overcome this shortcoming, we need to devise a mechanism that not only ensures essential security safeguards but also computes a Trustworthiness value for Cloud Service Providers (CSP). This would give confidence to cloud users and enable them to choose the right CSP for their data-related interaction. This research proposes a solution, whereby the Trustworthiness of CSPs is calculated based on their Compliance with data security controls, User Feedback, and Auditor Rating. Two additional factors, Accuracy of Compliance Measurement and Control Significance Factor have been built in, to cater for other nonstandard conditions. Our implementation of Data Security Compliance Monitor and Data Trust as a Service, along with three CSPs, each with ten different settings, has supported our proposition through the devised formula. Experimental outcomes show changes in the trustworthiness value with changes in compliance level, user feedback and auditor rating. CSPs with better compliance have better trustworthiness values. However, if the Accuracy of Compliance Measurement and Control Significance Factor are low the trustworthiness is also proportionately less. This creates a balance and realism in our calculations. This model is unique and will help in creating users’ trust in cloud-based digital twins.

Background and Objectives: This case report highlights an aggressive and resistant biological course in a patient with mycosis fungoides, which resulted in a poor prognosis despite multiple therapies. The patient ultimately required aggressive chemotherapy due to systemic involvement.Case Presentation: A 64-year-old Hispanic male presented to the dermatology clinic for evaluation of hardened, erythematous lesions distributed across his trunk and upper extremities, accompanied by generalized pruritus. He reported that the lesions had been present for approximately two years and had increased in size over time. Pathology results from punch biopsies taken from the lesions confirmed a diagnosis of mycosis fungoides. Treatment with topical glucocorticoids and systemic retinoids was initiated; however, after six months, the disease progressed to involve lymph nodes. The patient underwent multiple rounds of radiation therapy for advanced lesions and was subsequently referred to an oncologist for further treatment due to the poor prognosis.Discussion: Mycosis fungoides is a form of T-cell lymphoma that is typically confined to the cutaneous tissue. A high expression of CD30+ lymphocytes in the epidermis is indicative of transformation into advanced large cell lymphoma, suggesting an aggressive clinical course and an overall poor prognosis. While mycosis fungoides is generally considered a low-grade, indolent malignancy, this case demonstrates an aggressive and resistant biological course, leading to a significantly poorer outcome.

ABSTRACT Congenital insensitivity to pain with anhidrosis (CIPA) is a rare autosomal recessive disorder because of NTRK1 gene mutations, leading to an inability to perceive pain and temperature and lack of sweating. Its rarity and unique clinical challenges, such as severe injuries from the inability to sense pain, make reporting cases critical. A 5‐year‐old boy, the third child of consanguineous parents, was referred for a fractured femur. His history includes recurrent fevers, pain insensitivity, self‐mutilation, and anhidrosis with compensatory hyperhidrosis. Examination showed multiple ulcers, dry skin, missing digits, dental issues, and corneal ulcers. The neurological assessment confirmed loss of pain and temperature sensation. Genetic testing confirmed NTRK1 mutations, diagnosing CIPA. The femur fracture was treated with a hip spica cast, and injury prevention and temperature management were advised to the parents. This case underscores the importance of early diagnosis and comprehensive management of CIPA, highlights the need for genetic counseling for at‐risk families, and provides insights into managing the condition's complex challenges. A multidisciplinary approach is essential to improve patient outcomes and quality of life.

Diagnosing Autism Spectrum Disorder (ASD) presents a multifaceted challenge, demanding accurate and efficient screening methods. Applying machine learning techniques offers a promising avenue for enhancing diagnostic accuracy and efficiency. This research investigates the efficiency of machine learning in distinguishing individuals with ASD from those without, utilizing a comprehensive dataset comprising screening questions, demographic factors, and ASD related diagnostic classifications. We applied chi-square feature selection technique and also tested Random Forest, Logistic Regression, Gradient Boosting Classifier, and Extra Trees Classifier. Each model showed optimal performance and exhibit high precision, recall, and F1-score for both ASD-positive and ASD-negative instances. Additionally, AUROC curves further validated the models’ exceptional discriminatory abilities, with exceptional results. Our findings highlight the potential of machine learning algorithms for enhancing ASD diagnosis accuracy and efficiency in clinical settings. Further research and validation on larger datasets are required to understand the importance of machine learning methods in ASD diagnosis.

Unmanned aerial vehicle (UAV) pesticide application in recent years owing to its importance such as time saving, reduction in human drudgery and also reduction in pesticides application rate. UAV has a great potential to address the problem involved in manual chemicals spraying in tall crops like coconut plantation where at present operation performed by manual climbing involves lots of drudgery and life risk. The current study aimed to understand the most influencing spraying parameters, such as spray height and spray time of the UAV sprayer on droplet characteristics such as spray droplet size, spray coverage and spray deposition at different layers (spindle, middle and bottom) of coconut tree canopy. The selected spray height (1, 2 and 3 m) and spray time (5, 8 and 11 s) significantly affects (p

Abstract In this work, we demonstrate that a simple argentometric titration is a scalable, fast, green and robust approach for producing AgCl/antibiotic hybrid antimicrobial materials. We titrated AgNO3 into tetracycline hydrochloride (TCH) aqueous solution, thus forming AgCl/TCH in a one-step procedure. Furthermore, we investigated the one-pot synthesis of triply synergistic super-nanoantimicrobials, combining an inorganic source of Ag+ ions (AgCl), a disinfecting agent (benzyl-dimethyl-hexadecyl-ammonium chloride, BAC) and a molecular antibiotic (tetracycline hydrochloride, TCH). Conventional antimicrobial tests, industrial biofilm detection protocols, and in situ IR-ATR microbial biofilm monitoring, have been adapted to understand the performance of the synthesized super-nanoantimicrobial. The resulting hybrid AgCl/BAC/TCH nanoantimicrobials are found to be synergistically active in eradicating Salmonella enterica and Lentilactobacillus parabuchneri bacteria and biofilms. This study paves the way for the development of a new class of super-efficient nanoantimicrobials that combine relatively low amounts of multiple active species into a single (nano)formulation, thus preventing the development of antimicrobial resistance towards a single active principle.

Abstract Background The disability and significant economic costs accredited to Low back pain (LBP) are likely to rise which is an essential problem in low and middle-income countries like Pakistan. The associated factors of LBP are age, sex, and race including physical activity, high spinal load, lifting, bending, and twisting occupations. The literature highlighted there is substantial differences in associated factors of LBP within available studies in developing countries. The objective is to investigate the association of demographic factors and lumbar range of motion with disability in patients with chronic low back. Methods A baseline data analysis was performed as an analytical cross-sectional study among 150 patients with chronic low back in a randomized controlled trial with a duration from March 2020 and January 2021. After recording demographics, Modified-Modified Schober’s test was used to measure lumbar flexion and extension and Oswestry disability index for disability. After the descriptive analysis the continuous variables, age and pain were analyzed with Spearman’s correlation. Variables that were significant in bivariate analysis were then fitted in a multivariable linear regression. The Kruskal–Wallis test was used to analyze variations of disability in gender, marital status, work status, education level, and duration of pain. The p-value of 0.05 was significant. Results The results showed a significant correlation between age and pain in sitting (rh=-0.189, p = 0.021 and rh = 0.788, p 0.05). Conclusions The female gender and unmarried marital status are associated with functional disability. Decreased lumbar range of motion accompanies more disability, while age, education level, and work status do not effect on disability.

Abstract Background Indonesia has the world’s second-highest tuberculosis (TB) burden, with 969,000 annual TB infections. In 2017, Indonesia faced significant challenges in TB care, with 18% of cases missed, 29% of diagnosed cases unreported, and 55.4% of positive results not notified. The government is exploring a new approach called “strategic purchasing” to improve TB detection and treatment rates and offer cost-effective service delivery. Objectives We aimed to analyze the financial impact of implementing a TB purchasing pilot in the city of Medan and assess the project’s affordability and value for money. Methods We developed a budget impact model to estimate the cost-effectiveness of using strategic purchasing to improve TB reporting and treatment success rates. We used using data from Medan’s budget impact model and the Ministry of Health’s guidelines to predict the total cost and the cost per patient. Results The model showed that strategic purchasing would improve TB reporting by 63% and successful treatments by 64%. While this would lead to a rise in total spending on TB care by 60%, the cost per patient would decrease by 3%. This is because more care would be provided in primary healthcare settings, which are more cost-effective than hospitals. Conclusions While strategic purchasing may increase overall spending, it could improve TB care in Indonesia by identifying more cases, treating them more effectively, and reducing the cost per patient. This could potentially lead to long-term cost savings and improved health outcomes.

This study presents a comprehensive investigation into the application of reservoir simulation and machine learning techniques to improve the understanding and prediction of reservoir behavior, focusing on the Sarir C-Main field. The research uses various data sources to develop robust reservoir static and dynamic models, including seismic cubes, well logs, base maps, check shot data, and production history. The methodology involves data quality control, log interpretation, seismic interpretation, horizon and surface interpretation, fault interpretation, gridding, domain conversion, property and petrophysical modeling, well completion, fluid model definition, and rock physics functions. History matching and prediction are performed using simulation cases, and machine learning techniques such as data gathering, cleaning, dynamic time warping (DTW), long short-term memory (LSTM), and transfer learning are applied. The results obtained through the Petrel simulation demonstrate the effectiveness of depletion strategy, history matching, and completion in capturing reservoir behavior. Furthermore, the machine learning techniques, specifically DTW and LSTM, exhibit promising results in predicting oil production. The study concludes that machine learning approaches, such as the LSTM model, offer distinct advantages. They require significantly less time and can yield reliable predictions. By leveraging the power of transfer learning, accurate predictions can be achieved efficiently when limited data are available, offering a more streamlined and practical alternative to traditional reservoir simulation methods.

Two groups of cotton growers participated in this study; the first, referred as “conventional cotton producers” (CCPs), continues to use traditional techniques. The second group, known as sustainable cotton producers (SCPs), adheres to practices that are socially acceptable, economically feasible, and environment friendly. This study was designed to undertake a thorough socio-economic analysis of CCP and SCP in terms of cost of production, yield, and adaptations for climate change. The data were collected from a total of 275 farming households (138 SCPs and 137 CCPs) from two districts, namely, Bahawalpur and Rajanpur, Punjab, Pakistan. The data were analyzed using the benefit–cost ratio (BCR), paired sampled t-test, and log–log regression model in SPSS. The results of the study revealed that the cost of production for SCP was significantly less than CCP and the value of BCR for SCP is higher than CCP. The results of the study also revealed that the land preparation costs, irrigation costs, and fertilizer and pesticide costs decrease the cotton yield of CCP, while land area owned and picking costs showed no significant influence on the yield of CCP. On the other hand, all cost items except fertilizer cost have a positive impact on CCP’s cotton yield. The results also indicated that SCP farmers adopted more of adaptation practices for climate change than CCP. This study concluded that SCP has significant advantages over CCP. Therefore, it is recommended that the government should prioritize incentives for SCP adoption to enhance yield and environmental sustainability in cotton farming.

In this study, biocrude was successfully produced by the hydrothermal liquefaction of municipal solid waste collected from the landfill site of Lahore, the capital of Punjab, Pakistan, boasting a population of 12 million and an annual waste collection of 10 million tons. The hydrothermal liquefaction process was performed at reaction parameters of 350 °C and 165 bars with 15 min of residence time. The solid waste was found to have 78 % dry matter, 22 % moisture contents, 22.2 % ash, 22.69 MJ/kg higher heating value, 52.062 % C, 8.007 % H, 0.764 % N, and 39.164 % O. Non-catalytic process only produced 10.57 % oil, however when using the catalytic process, the biocrude yield improved to 17.61 %, with 22.61 % energy recovery for biocrude and 12.14 % for solids, when using 2 g dose of K2CO3. The resultant biocrude has a 28.61 MJ/kg higher heating value, having 60.28 % C and 9.28 % H. In contrast, the aqueous phase generated had 4.43 pH, 71.5 g/L TOC, and 1.35 g/L Total Nitrogen. TGA indicated that biocrude contains approximately 80 % of volatile fractions of different fuels. The organic compounds having the six highest peak areas in GC-MS were Ethyl ether 25.74 %, 2-pentanone, 4-hydroxy-4-methyl 9.08 %, 2-propanone, 1,1-dimethoxy 5.62 %, Silane, dimethyl (docosyloxy) butoxy 5.08 %, 1-Hexanol, 2-ethyl 4.53 %, and. Phenol 4.07 %. This work makes the first-ever successful use of indigenous solid waste from a landfill dumping site in Lahore to successfully produce useful biocrude with aims of waste reduction and management, circular economy, and energy recovery.

Objective: Skin cancer refers to the pathological condition characterized by the proliferation of atypical skin cells in an uncontrolled manner. Plant-based products such as bixin although show promising anticancer properties, but maintaining their stability in a formulation is a difficult task. The objective of the research is to formulate a silver nanoparticle gel preparation of bixin and evaluate its anticancer properties. Methods: The extract from Bixa orellana seed was prepared by hot extraction technique to isolate the active ingredient, bixin. A green synthesis approach was utilized for preparing the silver nanoparticle gel of bixin (BOAgNPs). Characterization of silver nanoparticles was done using FTIR, scanning electron microscopy, compatibility study, homogeneity testing, pH evaluation, and drug content determination. The in-vitro anticancer activity was performed using cell lines (B16F10) and in-vivo by chemical carcinogen (7,12-dimethylbenz (a) anthracene) in mice. Results: The BOAgNPs-loaded topical gel was found to be homogeneous (clear orange color) and pH-compatible (pH ≈ 6.66) with the skin. The characterization studies indicated the presence of all functional groups in the formulation. An optimized batch of bixin-nano gel showed about 60% inhibitory effects on B16F10 cell lines (in-vitro activity) when equated with a reference drug, 5-fluorouracil. The in-vivo anticancer study suggested suppression of tumorigenesis and promotion of the healing process with bixin-nano gel application on the skin. Conclusion: The results suggested the promising anticancer property of bixin when formulated in silver nanoparticle gel. The preparation of silver particles nano gel with bixin might provide an effective alternative option for treating skin cancers, provided more research complements the findings of the present study.

This study intends to determine the elements for adopting corporate social responsibility (CSR) practices that would boost the organization's performance (OP) either directly or indirectly in Pakistan's oil and gas sectors. This research demonstrates how companies may improve their performance by participating in various CSR efforts, for example, companies can encourage their workers by providing them with information about CSR practices, and they can support multiple facilitators of CSR integration to enhance OP.

Widespread ecosystem degradation from noxious substances like industrial waste, toxic dyes, pesticides, and herbicides poses serious environmental risks. For remediation of these hazardous problems, present study introduces an innovative Cu-doped Ce₂Zr₂O₇ nano-photocatalyst, fabricated via a simple, eco-friendly hydrothermal method, designed to degrade toxic textile dye methylene blue. Harnessing Cu doping for pyrochlore Ce2Zr2O7, structure engineering carried out through a hydrothermal synthesis method to achieve superior photocatalytic performance, addressing limitations of rapid charge carrier recombination in existing photocatalysts. Photoluminescence analysis showed that doped pyrochlore slows charge carrier recombination, boosting dye degradation efficiency. UV–Visible analysis demonstrated an impressive 96 % degradation of methylene blue by Cu-doped Ce2Zr2O7 within 50 min, far exceeding the performance of pristine materials. Trapping experiments clarified the charge transfer mechanism, deepening our understanding of the photocatalytic process. These findings highlight the potential for developing innovative, highly efficient photocatalysts for environmental remediation, offering sustainable solutions to combat pollution. This study not only addresses the limitations of existing photocatalysts but also opens new avenues for enhancing photocatalytic performance through strategic material design.

Background. Preterm birth is a heterogeneous condition with multiple underlying causes, and periodontal diseases are one of them. Approximately 900000 preterm births are reported in Pakistan each year. Oral infections such as periodontitis during pregnancy are associated with adverse pregnancy outcomes such as low birth weight and preterm births. However, different studies have reported contradictory findings. We conducted a cross-sectional study to assess the association of preterm birth with oral infection in pregnancy. Methods. We conducted a cross-sectional analytical study on 400 postpartum pregnant women in Khyber Teaching Hospital, Peshawar. Only women within the age bracket of 18‒40 years were recruited. Data were collected by an interview-based structured questionnaire. The extent and severity index were used to assess the periodontal health of participants. Frequency tables were generated, and the chi-squared test was used to determine associations between different categorical variables. Results. The mean age of the participants was 25.8±4.9 years. Approximately 87.5% of the women had generalized periodontitis. Approximately 68% of mothers had moderate severity of periodontitis. The extent index showed no notable difference between the preterm and full-term birth groups. In contrast, the severity index displayed a statistically significant difference between the preterm and full-term birth groups. Conclusion. The majority of women had generalized periodontitis. The severity index demonstrated a significant association between maternal periodontitis and preterm births. There was no association between the age of mothers and preterm births. Complications in pregnancy were not associated with preterm births.

Abstract Metabolic and bariatric surgery (MBS) is widely considered the most effective option for treating obesity, a chronic, relapsing, and progressive disease. Recently, the American Society of Metabolic and Bariatric Surgery (ASMBS) and the International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO) issued new guidelines on the indications for MBS, which have superseded the previous 1991 National Institutes of Health guidelines. The aim of this study is to establish the first set of consensus guidelines for selecting procedures in Class I and II obesity, using an Expert Modified Delphi Method. In this study, 78 experienced bariatric surgeons from 32 countries participated in a two-round Modified Delphi consensus voting process. The threshold for consensus was set at an agreement or disagreement of ≥ 70.0% among the experts. The experts reached a consensus on 54 statements. The committee of experts reached a consensus that MBS is a cost-effective treatment option for Class II obesity and for patients with Class I obesity who have not achieved significant weight loss through non-surgical methods. MBS was also considered suitable for patients with Type 2 diabetes mellitus (T2DM) and a body mass index (BMI) of 30 kg/m2 or higher. The committee identified intra-gastric balloon (IGB) as a treatment option for patients with class I obesity and endoscopic sleeve gastroplasty (ESG) as an option for patients with class I and II obesity, as well as for patients with T2DM and a BMI of ≥ 30 kg/m2. Sleeve gastrectomy (1) and Roux-en-Y gastric bypass (RYGB) were also recognized as viable treatment options for these patient groups. The committee also agreed that one anastomosis gastric bypass (OAGB) is a suitable option for patients with Class II obesity and T2DM, regardless of the presence or severity of obesity-related medical problems. The recommendations for selecting procedures in Class I and II obesity, developed through an Expert Modified Delphi Consensus, suggest that the use of standard primary bariatric endoscopic (IGB, ESG) and surgical procedures (SG, RYGB, OAGB) are acceptable in these patient groups, as consensus was reached regarding these procedures. However, randomized controlled trials are still needed in Class I and II Obesity to identify the best treatment approach for these patients in the future.

Background: In patients with left bundle branch block (LBBB), the diagnosis of ischemia by noninvasive modalities is cumbersome. Most of them such as stress tests, nuclear imaging, and magnetic resonance imaging have limitations in the detection of ischemia in this subset. The postsystolic motion (PSM) during the isovolumetric relaxation period on tissue Doppler imaging (TDI) is a sensitive and specific marker of ischemia. We aimed to see whether the TDI parameters can detect coronary artery disease (CAD) in patients with LBBB. Materials and Methods: Patients with LBBB (n = 64) who underwent coronary angiography were divided into two groups. Group A (n = 30) included patients with left anterior descending (LAD) artery stenosis of ≥70%, and group B (n = 34) included patients without LAD stenosis. All patients underwent TDI and various myocardial tissue velocity parameters were analyzed to detect the presence of CAD. P < 0.05 was considered significant. Results: The TDI of the mid-interventricular septum showed a higher delayed amplitude of PSM (>100 ms after aortic valve closure), lower myocardial systolic (Sm), and early diastolic (Em) velocities, and a higher late diastolic (Am) velocity in group A when compared to group B (all were significant, P < 0.0001). Both the ratios Sm/PSM and Em/Am were significantly lower in group A compared to group B (P < 0.0001). On receiver operating characteristic curve analysis to predict the presence of significant LAD stenosis, the value of Sm/PSM ratio

In today’s digital age, customer feedback, particularly gathered from various sources like mobile application reviews, has emerged as a critical resource for service-providing organizations to gain valuable insights into their customers’ experiences. As the key objective of service-providing organizations is to facilitate their customers with better services, customer feedback or opinion is a vital resource for such organizations to improve and enhance their services for the betterment of their customers. Explicitly mentioned opinions have been widely studied in research, while a significant gap exists in addressing implicitly described views. Furthermore, most existing research focuses on product-oriented corpora, emphasizing specific product aspects and features. This article presents a novel study on performing end-to-end aspect-based sentiment analysis (ABSA) by extracting implicit opinion terms, categorizing them, and assigning polarity to each term from mobile app reviews in English. Through this study, we developed a domain-specific, service-oriented, and aspect-based annotated dataset and introduced a novel two-step hybrid approach. The first step involves extracting multiple opinion terms using a rule-based approach. The second step employs machine learning and deep learning algorithms to classify the extracted opinion terms into general aspect categories. This two-step approach effectively addresses the double-implicit problem commonly encountered in the previous work on implicit aspects and opinion mining. In addition to traditional machine learning and deep learning models, we fine-tuned BERT to carry out the ABSA task. This approach utilized a pipeline method, where each task’s output serves as the subsequent task’s input, ensuring a seamless flow of information and improved performance. This multi-step pipeline begins with the extracted opinion terms classification into aspect categories and ends with the assignment of sentiment polarity. Experiments with a hold-out test set for the first step (opinion term extraction using a rule-based approach) achieved an accuracy and precision score of 81.4% and an F1 score of 0.99 %, outperforming several baselines. Further experiments with a range of machine learning and deep learning algorithms for classifying extracted opinion terms into general aspect categories yielded accuracy scores ranging from 0.68% to 0.74 % and F1 scores ranging from 0.23% to 0.28%. Experiments on sentiment classification using various machine learning algorithms showed accuracy ranges from 0.58% to 0.68% and F1 scores from 0.47% to 0.49%. This two-step approach for implicit opinion term, aspect extraction, and classification outperforms many baseline systems. By leveraging BERT’s contextual understanding and fine-tuning it for our specific domain, we significantly improved the accuracy and robustness of our aspect-based sentiment analysis. This approach effectively captured both explicit and implicit opinions from mobile app reviews. Specifically, our method achieved an accuracy of 0.80% and an F1 score of 0.78% for aspect categorization, and an accuracy of 0.79% and an F1 score of 0.70% for sentiment classification, demonstrating substantial improvements over traditional methods.

This article presents an extensive examination of antennas rooted in nature and biology, showcasing their remarkable performance across a wide spectrum of frequencies—from microwave to terahertz. The limitations of traditional antenna design have become increasingly evident in the face of burgeoning demands for novel communication technologies. Conventional analytical-equation-based approaches struggle to deliver the combined performance characteristics– encompassing bandwidth, gain, radiation pattern, and miniaturization– that emerging technologies necessitate. This has fueled an interest in bio-inspired antenna designs, a paradigm shift drawing inspiration from the ingenious structural solutions found in the living and non-living world, from plant leaves to bird feathers. These bio-inspired designs offer distinct advantages such as broader bandwidth and reduced sizes, making them highly appealing alternatives to the limitations of conventional antenna designs. This review explores a diverse range of bio-inspired designs. Among them are fractal geometries, inspired by self-repeating patterns in nature, which achieve optimal performance. Numerous designs in this category draw inspiration from nature, incorporating patterns observed in snowflakes, tree branches, clouds, and butterflies. Furthermore, nano-antennas have attracted significant attention for their vast potential applications in microwave and optical frequencies, playing a pivotal role in high-resolution spectroscopy, biomedical diagnosis and sensing, quantum photonics, and solar cell applications. By examining design methodologies and potential benefits, this article highlights the transformative potential of nature-inspired antennas. The compelling advantages of bio-inspired approaches necessitate a thorough exploration of their potential, paving the way for the development of next-generation communication systems with unprecedented capabilities.

A new actuation technique is introduced for overcoming the complexity and radiation performance degradation issues of actuators-based reconfigurable antennas. We employ shape memory alloy (SMAlloy) spring actuators to control the positioning of the fluidic channels, in contrast to the manual filling/loading and evacuating/offloading of dielectric fluids employed in conventional fluidically reconfigurable antennas. To demonstrate this new actuation method, a simple low-cost frequency reconfigurable microstrip patch antenna is fabricated, and two SMAlloy spring actuators are incorporated along the microfluidic channels in the axes of the radiating slots. Electrical control of the pitch of the two SMAlloy springs enables mechanical movement/transformation of the microfluidic channels in two directions along the axes of the radiating slots to attain the required frequency-switching characteristics of the antenna. It should be noted that in contrast to the traditional switching techniques where switches are positioned over the radiating aperture, the proposed method does not deteriorate the performance of the antenna because SMAlloy springs are underneath the radiating patch.

Introduction: Self-medication with antibiotics (SMA)—the use of these drugs without a physician’s diagnosis, supervision, or a medical prescription—has emerged as a significant crisis in many societies, being a particular concern in low- and middle-income countries. SMA practices hinder global efforts to reduce antibiotic consumption in the human health sector and to control antimicrobial resistance (AMR). The aims of the study were to explore the motivations, perspectives, and personal experiences of the Malaysian population related to SMA, analyzing their subjective viewpoints, attitudes, and behaviors. Methods: The present qualitative study applied an interpretivism paradigm to explore the complex topic of SMA; the interview questions were developed and confirmed by experts in AMR and pharmacy practice. Qualitative data analysis was carried out through a thematic approach. Results: Out of twenty-seven (n = 27) eligible individuals, eleven (n = 11) agreed to participate in in-depth interviews. Three main themes and seven subthemes were identified. Participants revealed directly purchasing antibiotics from pharmacies, due to doctors prescribing similar medications for comparable illnesses in the past. Trust issues related to physicians prescribing unnecessary additional medications and suspected personal motives were revealed. Budgetary constraints, timesaving, and aspects of self-care were identified as some of the key drivers for SMA. Conclusions: The current study’s findings contribute to the understanding of the complexities surrounding SMA and provides insights into the public perceptions and behaviors regarding unregulated antibiotic use in Malaysia. Understanding these dynamics may inform targeted public health interventions to address SMA to mitigate the development of AMR.

Background: Lung cancer, also known as lung carcinoma, has a high mortality rate; however, an early prediction helps to reduce the risk. In the current literature, various approaches have been developed for the prediction of lung carcinoma (at an early stage), but these still have various issues, such as low accuracy, high noise, low contrast, poor recognition rates, and a high false-positive rate, etc. Thus, in this research effort, we have proposed an advanced algorithm and combined two different types of deep neural networks to make it easier to spot lung melanoma in the early phases. Methods: We have used WDSI (weakly supervised dense instance-level lung segmentation) for laborious pixel-level annotations. In addition, we suggested an SS-CL (deep continuous learning-based deep neural network) that can be applied to the labeled and unlabeled data to improve efficiency. This work intends to evaluate potential lightweight, low-memory deep neural net (DNN) designs for image processing. Results: Our experimental results show that, by combining WDSI and LSO segmentation, we can achieve super-sensitive, specific, and accurate early detection of lung cancer. For experiments, we used the lung nodule (LUNA16) dataset, which consists of the patients’ 3D CT scan images. We confirmed that our proposed model is lightweight because it uses less memory. We have compared them with state-of-the-art models named PSNR and SSIM. The efficiency is 32.8% and 0.97, respectively. The proposed lightweight deep neural network (DNN) model archives a high accuracy of 98.2% and also removes noise more effectively. Conclusions: Our proposed approach has a lot of potential to help medical image analysis to help improve the accuracy of test results, and it may also prove helpful in saving patients’ lives.

Abstract Background Schwannomas are solitary well-circumscribed encapsulated benign tumors that exhibit Schwann cell differentiation, and arise directly from myelinated peripheral or central nerves. Although they are usually asymptomatic and found incidentally, schwannomas can cause symptoms due to compression of nearby structures which, depending on the location, can make clinical presentations widely variable. Despite their rarity, schwannomas have been documented in a number of locations including the limbs, cerebellopontine angle, posterior mediastinum, and, far more infrequently, the lungs. Case presentation In this article, we report an incidental finding of an intrapulmonary schwannoma in a 59-year-old Pakistani woman who was grossly asymptomatic upon presentation to the cardiothoracic surgery clinic. An [18F]fluorodeoxyglucose positron emission tomography/computed tomography scan revealed a lobulated soft-tissue lesion measuring 23 mm × 23 mm in the lower lobe of the right lung. A computed tomography-guided core biopsy of the mass was performed, which revealed a benign spindle cell lesion based on histopathological examination and immunohistochemical staining. The mass was surgically resected via a right lower lobectomy, and subsequently confirmed to be an encapsulated neoplastic lesion composed of well-differentiated Schwann cells. There were no short- or long-term complications, morbidities, or recurrences based on 1-year follow-up. Conclusion This report underscores the predominantly asymptomatic nature of schwannomas and reemphasizes the efficacy of surgical resection as a safe and curative procedure for a tumor of this nature. Albeit very rare, intrapulmonary schwannomas can be considered a differential diagnosis when encountering solitary asymptomatic pulmonary nodules or masses.

Abstract Religious fasting in Ramadan the 9th month of the lunar year is one of five pillars in Islam and is practiced for a full month every year. There may be risks with fasting in patients with a history of metabolic/bariatric surgery (MBS). There is little published evidence on the possible complications during fasting and needs stronger recommendations and guidance to minimize them. An international survey was sent to surgeons to study the types of complications occurring during religious fasting in patients with history of MBS to evaluate the risk factors to manage and prepare more evidence-based recommendations. In total, 21 centers from 11 countries participated in this survey and reported a total of 132 patients with complications occurring during religious fasting after MBS. The mean age of patients with complications was 36.65 ± 3.48 years and mean BMI was 43.12 ± 6.86 kg/m2. Mean timing of complication occurring during fasting after MBS was 14.18 months. The most common complications were upper GI (gastrointestinal) symptoms including [gastroesophageal reflux disease (GERD), abdominal pain, and dyspepsia], marginal ulcers and dumping syndrome in 24% (32/132), 8.3% (11/132) and 23% (31/132) patients respectively. Surgical management was necessary in 4.5% of patients presenting with complications (6/132) patients due to perforated marginal or peptic ulcer in Single Anastomosis Duodenoileostomy with Sleeve gastrectomy (SADI-S), one anastomosis gastric bypass (OAGB) and sleeve gastrectomy (SG), obstruction at Jejunojenostomy after Roux-en-Y gastric bypass (RYGB) (1/6) and acute cholecystitis (1/6). Patients after MBS should be advised about the risks while fasting including abdominal pain, dehydration, and peptic ulcer disease exacerbation, and a thorough review of their medications is warranted to minimize complications.

This article presents a semantic web-based solution for extracting the relevant information automatically from the annual financial reports of the banks/financial institutions and presenting this information in a queryable form through a knowledge graph. The information in these reports is significantly desired by various stakeholders for making key investment decisions. However, this information is available in an unstructured format making it much more complex and challenging to understand and query manually or even through digital systems. Another challenge that makes the understanding of information more complex is the variation of terminologies among financial reports of different banks or financial institutions. The solution presented in this article signifies an ontological approach to solving the standardization problems of the terminologies in this domain. It further addresses the issue of semantic differences to extract relevant data sharing common semantics. Such semantics are then incorporated by implementing their representation as a Knowledge Graph to make the information understandable and queryable. Our results highlight the usage of Knowledge Graph in search engines, recommender systems and question-answering (Q-A) systems. This financial knowledge graph can also be used to serve the task of financial storytelling. The proposed solution is implemented and tested on the datasets of various banks and the results are presented through answers to competency questions evaluated on precision and recall measures.

Abstract Introduction One of the most serious complications associated with antiplatelet agents is antiplatelet‐associated intracranial hemorrhage (AA‐ICH). Desmopressin is a synthetic antidiuretic hormone (ADH) analog. It has been linked to improving patient outcomes in antiplatelet‐induced intracranial hemorrhage. The secondary outcomes included the incidence of thrombotic complications and neurological outcomes. Methods A systematic search was conducted on three databases (PubMed, Cochrane, and ClinicalTrials.gov) to find eligible literature that compares desmopressin (DDAVP) versus controls in patients with AA‐ICH. The Mantel–Haenszel statistic was used to determine an overall effect estimate for each outcome by calculating the risk ratios and 95% confidence intervals (CI). Heterogeneity was measured using the I2 test. The risk of bias in studies was calculated using the New Castle Ottowa Scale. Results Five studies were included in the analysis with a total of 598 patients. DDAVP was associated with a nonsignificant decrease in the risk of hematoma expansion (RR = .8, 95% CI,.51–1.24; p = .31, I2 = 44%). It was also associated with a non‐significant decrease in the risk of thrombotic events (RR,.83; 95% CI,.25–2.76; p = .76, I2 = 30%). However, patients in the DDAVP group demonstrated a significant increase in the risk of poor neurological outcomes (RR, 1.31; 95% CI, 1.07–1.61; p = .01, I2 = 0%). The risk of bias assessment showed a moderate to low level of risk. Conclusion DDAVP was associated with a nonsignificant decrease in hematoma expansion and thrombotic events. However, it was also associated with a significantly poor neurological outcome in the patients. Thus, until more robust clinical trials are conducted, the use of DDAVP should be considered on a case‐to‐case basis.

Background: Laparoscopic cholecystectomy is the procedure of choice for cholecystitis. There are variable rates of conversion of laparoscopic cholecystectomy to open cholecystectomy. Various studies have highlighted gall bladder wall thickness and age as independent risk factors for conversion from laparoscopic to open procedure. This study was done to determine the frequency of conversion of laparoscopic cholecystectomy to open cholecystectomy in patients undergoing laparoscopic cholecystectomy with a gall bladder wall thickness of more than 3mm as determined on pre operative ultrasound. Objective: To find frequency of conversion to open procedure in patients undergoing laparoscopic cholecystectomy with a gall bladder wall thickness of more than 3mm as determined on pre operative Ultrasound. Methods: This Case series was conducted at the Surgical Unit IV Civil Hospital Karachi, Pakistan from 15th July 2012 to 15th January 2013. A total of 129 patients were included in the study with gall bladder wall thickness of more than 3mm on Ultrasound. Patients were stratified according to age, sex, gall bladder wall thickness, frequency of difficult cholecystectomy and duration of disease. Results: A total of 129 patients were included in the study. Female to male ratio is 5.7:1. The mean age of patients was 43.33±11.5 years. The average gall bladder wall thickness was 4.07±0.63 mm and duration of disease was 4.53±4.67 years. Regarding preoperative findings, bleeding during separation from liver bed was the commonest finding in general i.e. 35.7% followed by local adhesions 33.3% and perforation of gall bladder was observed in 7.8% cases. All (n=13) patients who had conversion to an open procedure had wall thickness between 4.1 to 6 mm. Similarly rate of conversion was found to be in higher in patients with symptoms for more than 5 years (i.e. 9 out of 24). Conclusion: Ultrasound for gall bladder wall thickness is a good predictor for difficult cholecystectomy. It should be used as anticipative measure for difficult cases and patient should be properly counseled regarding chances of conversion. Key Words: Difficult Laparoscopic Cholecystectomy, Gall Bladder Wall Thickness, Conversion To Open Cholecystectomy.

Background & objectives: Pink salt and monosodium glutamate (MSG) are two typical food additives used in cooking to enhance flavour. However, excessive use of them has been associated to a variety of metabolic problems, including weight gain and hyperglycemia. The current study aimed to assess the metabolic changes caused by submaximal dosages of MSG and pink salt in experimental rats. Methods: Twenty-four 120–150 g Wister rats of both sexes were divided into three groups: control, pink salt-treated (0.8 g/kg daily for three weeks), and MSG-treated (3.6 g/kg daily for three weeks). The body weight, amount of food and water consumed, and blood glucose levels of animals were measured and recorded as indicators of their metabolic changes. Furthermore, after salt treatments at intervals such as week 1, week 2, and week 3, the survival rate and general toxicity manifestations were determined. The results were statistically analysed using one-way ANOVA, with p

Abstract Background Metabolic and bariatric surgery (MBS) in patients with low body mass index patients is a topic of debate. This study aimed to address all aspects of controversies in these patients by using a worldwide survey. Methods An online 35-item questionnaire survey based on existing controversies surrounding MBS in class 1 obesity was created by 17 bariatric surgeons from 10 different countries. Responses were collected and analysed by authors. Results A total of 543 bariatric surgeons from 65 countries participated in this survey. 52.29% of participants agreed with the statement that MBS should be offered to class-1 obese patients without any obesity related comorbidities. Most of the respondents (68.43%) believed that MBS surgery should not be offered to patients under the age of 18 with class I obesity. 81.01% of respondents agreed with the statement that surgical interventions should be considered after failure of non-surgical treatments. Conclusion This survey demonstrated worldwide variations in metabolic/bariatric surgery in patients with class 1 obesity. Precise analysis of these results is useful for identifying different aspects for future research and consensus building.

Background: Overactive bladder (OAB) is characterized by urinary urgency, frequency, and nocturia, often accompanied by urinary incontinence. OAB significantly impacts the quality of life of affected individuals. Transcutaneous Tibial Nerve Stimulation (TTNS) is a non-invasive treatment option used to manage overactive bladder (OAB) symptoms. Objective: to determine the effects of Transcutaneous Tibial Nerve Stimulation on overactive bladder symptoms in adults. Methods: A randomized controlled trial was held at the Sindh Institute of Physical Medicine and Rehabilitation with a non-probability purposive sampling technique. After screening for inclusion criteria 60 patients were randomly allocated into two Group A received Transcutaneous tibial nerve stimulation (TTNS) combined with traditional physiotherapy, while Group B just received traditional physiotherapy. Both interventions were given for six weeks. The overactive Bladder Symptom Score was used as an outcome measure tool. Results: Group A improved in all OAB parameters, including daytime frequency (p=0.008), nocturia (p=0.006), urinary urgency (p=0.002), and urge urinary incontinence (p=0.008) with a significant improvement p

Background: Neurodegenerative diseases primarily afflict the elderly and are characterized by a progressive loss of neurons. Oxidative stress is intricately linked to the advancement of these conditions. This study focuses on Phoenix dactylifera (P. dactylifera; Family: Arecaceae), commonly known as “Ajwa,” a globally cultivated herbal plant renowned for its potent antioxidant properties and reported neuroprotective effects in pharmacological studies. Method: This comprehensive systematic review delves into the antioxidant properties of plant extracts and their phytochemical components, with a particular emphasis on P. dactylifera and its potential neuroprotective benefits. Preferred reporting items for systemic reviews and meta-analysis (PRISMA) were employed to review the articles. Results: The study includes 269 articles published in the literature and 17 were selected after qualitative analysis. The growing body of research underscores the critical role of polyphenolic compounds found in P. dactylifera, which significantly contribute to its neuroprotective effects through antioxidant mechanisms. Despite emerging insights into the antioxidant actions of P. dactylifera, further investigation is essential to fully elucidate the specific pathways through which it confers neuroprotection. Conclusions: Like many other plant-based supplements, P. dactylifera’s antioxidant effects are likely mediated by synergistic interactions among its diverse bioactive compounds, rather than by any single constituent alone. Therefore, additional preclinical and clinical studies are necessary to explore P. dactylifera’s therapeutic potential comprehensively, especially in terms of its targeted antioxidant activities aimed at mitigating neurodegenerative processes. Such research holds promise for advancing our understanding and potentially harnessing the therapeutic benefits of P. dactylifera in neuroprotection.

The range of clinical manifestations associated with the infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encompasses a broad spectrum, ranging from flu-like symptoms to the occurrence of multiple organ failure and death. The severity of the coronavirus disease 2019 (COVID-19) is categorized based on clinical presentation and is divided into three distinct levels of severity identified as non-severe, severe, and critical. Although individuals of all age groups are susceptible to SARS-CoV-2 infection, middle-aged and older adults are more frequently impacted, with the latter being more likely to develop severe illness. Various laboratory characteristics observed in hospitalized COVID-19 patients have been correlated with adverse outcomes. These include elevated levels of D-dimer, liver enzymes, lactate dehydrogenase, C-reactive protein, ferritin, prothrombin time, and troponin, as well as decreased lymphocyte and platelets counts. This review investigated the relationship between baseline clinical characteristics, initial laboratory parameters upon hospital admission, and the severity of illness and mortality rates among COVID-19 patients. Although the COVID-19 pandemic has concluded, understanding the laboratory predictors of virus severity and mortality remains crucial, and examining these predictors can have long-term effects. Such insights can help healthcare systems manage resources more effectively and deliver timely and appropriate care by identifying and targeting high-risk individuals. This knowledge can also help us better prepare for future pandemics. By examining these predictors, we can take steps to protect public health and mitigate the impact of future pandemics.

Diabetes has emerged as a prevalent disease, affecting millions of individuals annually according to statistics. Numerous studies have delved into identifying key genes implicated in the causal mechanisms of diabetes. This paper specifically concentrates on 20 functional genes identified in various studies contributing to the complexities associated with Type 2 diabetes (T2D), encompassing complications such as nephropathy, retinopathy, cardiovascular disorders, and foot ulcers. These functional genes serve as a foundation for identifying regulatory genes, their regulators, and protein-protein interactions.The current study introduces a multi-layer Knowledge Graph (DbKB based on MSNMD: Multi-Scale Network Model for Diabetes), encompassing biological networks such as gene regulatory networks and protein-protein interaction networks. This Knowledge Graph facilitates the visualization and querying of inherent relationships between biological networks associated with diabetes, enabling the retrieval of regulatory genes, functional genes, interacting proteins, and their relationships.Through the integration of biologically relevant genetic, molecular, and regulatory information, we can scrutinize interactions among T2D candidate genes [1] and ascertain diseased genes [2]. The first layer of regulators comprises direct regulators to the functional genes, sourced from the TRRUST database in the human transcription factors dataset, thereby forming a multi-layered directed graph. A comprehensive exploration of these direct regulators reveals a total of 875 regulatory transcription factors, constituting the initial layer of regulating transcription factors. Moving to the second layer, we identify 550 regulatory genes.These functional genes engage with other proteins to form complexes, exhibiting specific functions. Leveraging these layers, we construct a Knowledge Graph aimed at identifying interaction-driven sub-networks involving (i) regulating functional genes, (ii) functional genes, and (iii) protein-protein interactions.

This study explores how a simple argentometric titration-like approach could be evolved into a versatile, scalable, fast, and robust strategy for the production of AgCl/quaternary ammonium compounds (QACs) colloidal nanoantimicrobials (NAMs). These systems, which are green, stable, cost-effective, and reproducible are found to be effective against a wide range of food pathogenic bacteria and biofilms. The option of a large-scale production for such colloidal suspensions was explored via the use of a peristaltic pump. The utilization of various types of biosafe QACs and a wide range of solvents including aqueous and organic ones renders this system green and versatile. Nanocolloids (NCs) were characterized using UV–Vis, X-ray photoelectron and Fourier transform infrared (FTIR) spectroscopies. Their morphology and crystalline nature were investigated by transmission electron microscopy (TEM) and selected area diffraction pattern (SAED). Nanoparticle (NP) size distribution and hydrodynamic radius were measured by dynamic light scattering (DLS), while the ζ-potential was found to be highly positive, thus indicating significant colloidal stability and antimicrobial activity. In fact, the higher the NP surface charge, the stronger was their bioactivity. Furthermore, the antibacterial and antibiofilm effects of the as-prepared NCs were tested against Gram-positive bacteria, such as Staphylococcus aureus (ATCC 29213) and Listeria monocytogenes 46, and Gram-negative bacteria, such as Escherichia coli (ATCC 25922) and Pseudomonas aeruginosa (ATCC 27853). The results clearly indicate that AgCl/QACs provide pronounced antibiofilm activity with long-term bacteriostatic effects against foodborne pathogenic bacteria rendering them an ideal choice for active food packaging systems.

Background: Polycystic ovarian syndrome (PCOS) is one of the known causes of anovulatory fertility in the world. Previous research has linked oxidative stress could contribute to PCOS, and vanillic acid has shown antioxidant potential. Hence, the present study evaluated the effect of vanillic acid on letrozole-induced polycystic ovarian syndrome in female rats. Materials and methods: PCOS was induced in Wistar female rats with letrozole (1 mg/kg, orally) in carboxymethoxycellulose (1 % w/v), administered for 21 days. After induction, the standard group received clomiphene citrate (1 mg/kg, orally) while other treatment groups were administered with vanillic acid at doses 25, 50, and 100 mg/kg, orally for 15 days, and without treatment was considered a negative control group. Different parameters studied were body weight, ovary weight, blood glucose, lipid profile, hormonal levels [luteinizing hormone (LH), follicle-stimulating hormone (FSH), and testosterone], markers for oxidative stress (superoxide dismutase, reduced glutathione, catalase, and malonaldehyde), and histopathology of the ovary. Statistical analysis was done for the results and p

Accident rates in academic laboratories are increasing rapidly day to day. The incorporation of complex activities with the inclusion of modern technologies and human behavior during experiments often makes it difficult to manage health and safety risks. Failure modes and effect analysis (FMEA) is widely used risk assessment technique that consider (03) risk parameters which include severity (S), occurrence (O), and detection (D), and calculate a risk priority number (R.P.N), which facilitates in failure modes ranking. However, there are some shortcomings associated with this method. This study aims to propose a framework for prioritizing the health and safety risks (failure modes) in an experiment using a widely used Multi-criteria decision-making method, i.e., Fuzzy VIKOR supported by criteria objective weights determined using the Shannon entropy concept. The fuzzy VIKOR method is employed to rank the failure modes based on the maxi group utility and the minimum individual regret. In this study, an experiment related to preparing rock core samples is taken as an example for implementing the proposed technique. Core sample preparation is one of the most common and dangerous experiments in the petroleum industry. The performance of the experiment allows rock sample preparation of desired shape and size to be tested for its hydrocarbon producing potential. The final results indicate that applying the proposed method in a fuzzy environment can efficiently prioritize the experiment’s health and safety risks, which is further considered for the implementation of control measure prioritization. The proposed approach can be applied to other experiments with the same or different domains.

IntroductionThe ActiGraph (AG) accelerometer is widely used to assess physical activity (PA) in heart failure (HF) patients. However, the validity of the AG in this population remains unexplored.ObjectiveTherefore, this study examined the criterion validity of the AG-GT9X for measuring step counts (SC) and energy expenditure (EE) among HF patients.Methods16 patients with HF with preserved ejection fraction (mean age = 60.3±12.1yrs) completed a total of 41 symptom-limited cardiopulmonary exercise tests on a treadmill across multiple time points (median (IQR) = 2.5 (1.5-3.5)). All participants wore the AG (model: GT9X) on both the right ankle and waist locations during the test. Manually counted steps and indirect calorimetry-derived EE served as criterion measures. AG-derived EE was estimated using six different prediction equations previously developed for waist-worn AG. AG-derived measurements were compared with criterion measurements by calculating correlation coefficients, equivalence tests with two one-sided tests, mean absolute percentage error (MAPE), percentage bias, and Bland-Altman plots using mixed models to account for the nested nature of repeated measures within subjects.ResultsAnkle-worn AG-SC was significantly equivalent to the criterion (p < .05) and had lower MAPE (ConclusionsThe ankle-worn AG-SC and Sasaki-EE showed better accuracy among HF patients in laboratory settings. Further research is warranted to cross-validate the results in different settings.

Asphaltene precipitation and its subsequent deposition always remain a major concern for Oil industry. Formulation of a comprehensive and reliable risk management system for asphaltene prone wells and fields is a challenging task because of the influence of diverse factors. In this study, a decision support system is developed for the asphaltene risk assessment in wells and fields. Since, the data present in the literature is scarce and not consistent, therefore, a hypothetical data of fields and their wells was considered to conduct the study comprehensively. Three hypothetical fields namely; Field A, Field B and Field C were assumed and in each field ten wells were taken into consideration. A decision support system for assessing the risk of asphaltene prone wells was developed using one of the popular and powerful multi-criteria decision making technique i.e. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) coupled with Analytic Hierarchy process (AHP). The risk of wells was evaluated using three criteria namely; Detection, Severity and Controls. These criteria were further sub-divided into sub-criteria and their data was assumed. The assumed data was transformed into Triangular fuzzy numbers for calculations. According to the final outcomes, Field A was proved be the most risky field followed by Field B and in the last comes Field C. The outcomes were further validated by other method namely; Fuzzy Complex Proportional Assessment (COPRAS) and all TOPSIS outcomes were found in good relationship with Fuzzy COPRAS. The proposed methodology proposed in this study will be landmark in risk ranking of asphaltene prone wells and fields.