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Objective: Laparoscopic repair of vesicovaginal fistula (VVF) is gaining popularity day by day, but we have limited literature on it. This study is to evaluate the safety, feasibility, and outcomes of laparoscopic VVF repair in a tertiary care center in Eastern India. Subjects and Methods: This study is a single-center retrospective study in which 11 patients of supratrigonal VVF were evaluated who underwent laparoscopic repair. Preoperative workup included history, examination, computed tomography urography, cystoscopy, and vaginoscopy. After informed consent, laparoscopic VVF repair was done under general anesthesia. Duration of surgery, the need for conversion to open procedure, intraoperative bowel injury, the need for postoperative blood transfusion, postoperative hospital stay, postoperative urinary leak, and any incontinence after catheter removal were noted. Results: All the patients had primary supratrigonal fistula of approximate size in the range of 1.5–2.0 cm. All the included patients were operated on successfully by laparoscopic technique with a mean duration of surgery of 177.91 ± 6.14 min. The mean duration of postoperative hospital stay was 4.45 ± 0.52 days. There was no incidence of postoperative urine leak in any patient. After urethral catheter removal, all the patients voided well without any incontinence. Conclusions: Laparoscopic VVF repair is a safe and feasible option with satisfactory intraoperative and postoperative outcomes.

Abstract The genetic factors of stroke in South Asians are largely unexplored. Exome-wide sequencing and association analysis (ExWAS) in 75 K Pakistanis identified NM_000435.3(NOTCH3):c.3691 C > T, encoding the missense amino acid substitution p.Arg1231Cys, enriched in South Asians (alternate allele frequency = 0.58% compared to 0.019% in Western Europeans), and associated with subcortical hemorrhagic stroke [odds ratio (OR) = 3.39, 95% confidence interval (CI) = [2.26, 5.10], p = 3.87 × 10−9), and all strokes (OR [CI] = 2.30 [1.77, 3.01], p = 7.79 × 10−10). NOTCH3 p.Arg231Cys was strongly associated with white matter hyperintensity on MRI in United Kingdom Biobank (UKB) participants (effect [95% CI] in SD units = 1.1 [0.61, 1.5], p = 3.0 × 10−6). The variant is attributable for approximately 2.0% of hemorrhagic strokes and 1.1% of all strokes in South Asians. These findings highlight the value of diversity in genetic studies and have major implications for genomic medicine and therapeutic development in South Asian populations.

Abstract Background The evaluation of undergraduate medical curricula plays a crucial role in ensuring effectiveness and helps in continuous improvement of the learning process. This study aims to compare the effectiveness of online and hybrid teaching models of the first-year MBBS curriculum in the COVID-19 era (2019–20) and the para-COVID-19 pandemic (2020–21). Study methodology Mixed methods study with CIPP model was used. Data was collected by administering a survey and focus group discussions (FGDs) with first-year students from the 2019–2020 and 2020–2021 cohorts, faculty and administrators, which were recorded for analysis. Recorded lectures, guidebooks, planners, and question papers were also scrutinized for quality and adequacy. Furthermore, admission merit, module assessments, and professional examination results were compared and correlated. The learning environment was evaluated through the questionnaire (validated and used by Pakistan Medical and Dental council for inspections of medical schools) and the facilities provided in both years were juxtaposed. The study utilized NVIVO for qualitative and SPSS version 23 for quantitative data analysis. Results Contextual analysis underscored the critical need for online teaching during the COVID-19 pandemic, with provided resources being deemed sufficient. Notably, the student-faculty ratio stood at 4:1, and essential resources were readily available. The fully online batch outperformed the hybrid teaching class in 2020–21. Process analysis revealed successful session delivery in hybrid and online through webinars and Zoom, accompanied by timely provision of study guides and punctual assessments. Moreover, examination papers demonstrated acceptable reliability (Cronbach’s alpha: 0.61) in core subjects. Product analysis indicated that the 2020–21 cohort performed better in modular and professional examinations across all subjects (P

Introduction:: The Industrial Internet of Things (IIoT) is a technology that connects devices to collect data and conduct in-depth analysis to provide value-added services to industries. The integration of the physical and digital domains is crucial for unlocking the full potential of the IIoT, and digital twins can facilitate this integration by providing a virtual representation of real-world entities. Objectives:: By combining digital twins with the IIoT, industries can simulate, predict, and control physical behaviors, enabling them to achieve broader value and support industry 4.0 and 5.0. Constituents of cooperative IIoT domains tend to interact and collaborate during their complicated operations. Methods:: To secure such interaction and collaborations, we introduce a blockchain-based cross-domain authentication protocol for IIoT. The blockchain maintains only each domain’s dynamic accumulator, which accumulates crucial materials derived from devices, decreasing the overhead. In addition, we use the on-chain accumulator to effectively validate the unlinkable identities of cross-domain IIoT devices. Results:: The implementation of the concept reveals the fact that our protocol is efficient and reliable. This efficiency and reliability of our protocol is also substantiated through comparison with state-of-the-art literature. In contrast to related protocols, our protocol exhibits a minimum 22.67% increase in computation cost efficiency and a 16.35% rise in communication cost efficiency. Conclusion:: The developed protocol guarantees data transfer security across the domain and thwarts IoT devices from potential physical attacks. Additionally, in order to protect privacy, anonymity and unlinkability are also guaranteed.

Abstract Sustainability in cotton production is inevitable because producing more cotton means more employment, economic acceleration, and industrial expansion. India, China, the United States, Brazil, and Pakistan contribute 74% of worldwide cotton production. Pakistan is contributing only 5%, despite the high potential of cotton. The average yield of cotton in Pakistan is stagnant at 570.99 kg hm−2, whereas it entails the highest cost of production among all other crops. The yield obtained in Pakistan is less than the potential, profitability is drastically lessening, and farmers are abandoning cotton for alternative kharif crops. Some traditional quantitative studies have unveiled different factors that affect cotton production. However, an in-depth qualitative study has never been conducted in Pakistan to explore the root causes of growing cotton crop failure. Following Moustakas’s traditional phenomenological guidelines, this phenomenological study was conducted in the district of Rahim Yar Khan in the core cotton zone of Punjab province. A total of 10 interviews were conducted with purposively selected cotton growers based on a criterion: (i) having more than 10 years of cotton growing experience, (ii) being a cotton grower, and (iii) having at least 10 years of formal schooling. Interviews were conducted face to face on an interview guide. One interview lasted 45–50 min, and responses were recorded and analyzed using a thematic analysis approach. A total of 6 themes emerged from the collected data, including (i) climate change, (ii) varietal problems, (iii) pesticide usage, (iv) sense of institutional services, (v) attitude of farmers and (vi) soil health and environment. These six merging themes contributed to cotton crop failure and yield decline. The deep exploration further summarized that researchers, extensionists, and farmers need to seriously consider variety, sowing time, and the environment to revive cotton crops. The detailed recommendations and policy guidelines are presented in this paper, highlighting the cotton sector’s research, development and investment areas.

Abstract Background and Aim The present study aimed to evaluate and compare the overall and breed‐specific seasonal breeding patterns, fertility rates, cyclicity, and follicular dynamics of Arab, Thoroughbred, and Percheron mares under the subtropical conditions of Pakistan. Materials and Methods A retrospective analysis of climatic data and breeding records of eleven breeding studs spanning four years (2020–2023) was made to find out the overall seasonality in the breeding pattern of mares. Fifty mares of each breed (n = 150 in total) were scanned by ultrasonography for a calendar year to find the cyclicity pattern and follicular dynamics (follicular growth rate, size of ovulatory follicle, and days from estrus till ovulation). Results and Discussion The statistical analysis of breeding records demonstrated a clear pattern of seasonal breeding (p 0.05). Thoroughbred mares experienced significant seasonal declines from spring to winter with a peak conception rate in winter (63.89% ± 27.37, p > 0.05). Percherons showed the most pronounced seasonal effects, especially with a high fall conception rate (73.04% ± 19.61) and a sharp decrease in winter breeding metrics (p

Abstract Human adenoviruses (HAdVs) are a diverse group of viruses associated with respiratory infections in humans worldwide. However, there is a lack of research on the genetic diversity and epidemiology of HAdVs in Pakistan. This study characterized HAdVs in pediatric patients with respiratory tract infections in Karachi, Pakistan, between 2022 and 2023. We analyzed 762 nasopharyngeal samples of children ≤ 5 years. DNA extraction, followed by PCR targeting E2B and hexon genes, was carried out. Data analysis was performed on SPSS 25.0, and phylogenetic analysis of hexon gene was performed on MEGA 11. HAdV was detected in 7.34% (56/762) of patients round the year, but at a significantly higher rate during the winter season. Age was insignificantly associated with HAdV incidence (p = 0.662), but more than 62.5% (35/56) of positive cases were younger than 10 months. The circulating HAdVs were identified as six different types from species B (78.57%) and C (21.42%), with the majority of isolates found to be like B3. HAdV was found to be co-infected with bocavirus (5.4%) and measles (7.14%). These findings revealed a high frequency and genetic diversity of respiratory HAdVs in Karachi, Pakistan. We conclude that periodic and continuous surveillance of adenoviruses and other respiratory pathogens is necessary to improve the prognosis and management of respiratory diseases, thereby reducing the child mortality rate in Pakistan.

Internet of Things (IoT) security is an umbrella term for the strategies and tools that protect devices connected to the cloud, and the network they use to connect. The IoT connects different objects and devices through the internet to communicate with similarly connected machines or devices. An IoT botnet is a network of infected or cooperated IoT devices that can be remotely organized by cyber attackers for malicious purposes such as spreading malware, stealing data, distributed denial of service (DDoS) attacks, and engaging in other types of cybercrimes. The compromised devices can be included in any device connected to the internet and communicate data with, e.g., cameras, smart home appliances, routers, etc. Millions of devices can include an IoT botnet, making it an attractive tool for cyber attackers to launch attacks. Lately, cyberattack detection using deep learning (DL) includes training neural networks on different datasets to automatically detect patterns indicative of cyber threats, which provides an adaptive and proactive approach to cybersecurity. This study presents an evolutionary algorithm with an ensemble DL-based botnet detection and classification (EAEDL-BDC) approach. The goal of the study is to enhance cybersecurity in the cloud-assisted IoT environment via a botnet detection process. In the EAEDL-BDC technique, the primary stage of data normalization using Z-score normalization is performed. For the feature selection process, the EAEDL-BDC technique uses a binary pendulum search algorithm (BPSA). Moreover, a weighted average ensemble of three models, such as the modified Elman recurrent neural network (MERNN), gated recurrent unit (GRU), and long short-term memory (LSTM), are used. Additionally, the hyperparameter choice of the DL approaches occurs utilizing the reptile search algorithm (RSA). The experimental outcome of the EAEDL-BDC approach can be examined on the N-BaIoT database. The extensive comparison study implied that the EAEDL-BDC technique reaches a superior accuracy value of 99.53% compared to other approaches concerning distinct evaluation metrics.

Continuous use of oral NSAIDs can damage mucosal membrane, which results in decreased bioavailability and non-compliance with the therapy. But the use of sustained release drug delivery systems might offer a solution. Objective was to synthesize mucoadhesive SR microspheres by using different combinations of pectin (PEC) and its thiolated derivative (T-PEC3100) for improved loxoprofen (LS) permeation. Thiolated pectin (T-PEC) was synthesized by the esterification method using thioglycolic acid. Thiolation was confirmed by thiol group quantification and charring point determination. Further characterization was done by Fourier Transform Infrared spectroscopy (FTIR), and Scanning electron microscopy (SEM). Ex-vivo mucoadhesion study was performed to confirm the improved characteristics. Microspheres (MS) were prepared using different ratios of PEC/T-PEC by solvent evaporation method and their particle size and surface morphology were evaluated. Mucus permeation study was carried out using the trans-well plate method. Sustained release behavior of prepared microspheres was investigated through the edema inhibition method in albino rats. T-PEC3100 was considered the optimum formulation for further evaluation and contained maximum thiol group content. FTIR spectra showed a characteristic peak of –SH and charring point was also changed considerably confirming the successful thiolation of PEC. SEM results showed spherical microspheres in the size range of 2–10 μm. Thiol-rich formulation of MS exhibited more than 80 % release after 12 h and maximum absorbable dose (MAD) was calculated as 400 μg % inhibition of edema in MS treated group was slowly attained initially but the reduction in inflammation was detected even after 24 h as compared to control group. Promising results from In-vivo edema inhibition study suggest the possible use of these thiolated MS in formulating sustained release formulation for arthritis.

Background: The increased rate of elective cesarean sections (CS) has been a significant health concern worldwide due to which many long-term and short-term neonatal and maternal complications are arising. This study aimed to assess the early neonatal outcomes of elective CS and compare these outcomes with emergency CS. Methods: A cross-sectional study was conducted using convenience sampling from March to August 2023 at Fazaia Ruth Pfau Medical College, Karachi. A total of 184 neonates born between 36 to 42 weeks gestational age delivered via elective c-section for uncomplicated single pregnancy were included. The percentage, frequency, and odds ratio were calculated using SPSS 23 with a p-value

Objective: The aim of our study was to investigate the relationship between clinical indicators and gene dysregulation in different types of epilepsy, while also seeking to identify a diagnostic model capable of distinguishing between focal and generalized seizures. This highlights the critical importance of understanding clinical indicators and gene dysregulation for targeted therapeutic interventions to effectively address the specific seizure types effectively. Materials and methods: In this study, we conducted a comprehensive analysis of the peripheral blood of epilepsy patients (n = 100) and a control group (n = 51) to determine the differential gene expression. Our analysis involved a range of statistical approaches, including correlation analysis to establish the association between clinical indicators and gene dysregulation, and principal component analysis to highlight distinct disease group from control group. Furthermore, we developed diagnostic models using logistic regression to aid in the accurate diagnosis of epilepsy. Results: Among several selected genes in this study such as AVP (AUC = 0.832, p

The purpose of this research is to synthesize the findings of existing studies on the sharing of health information among older adults. This SLR follows the PRISMA model to provide an extensive analysis of the practices of health information sharing among older adults. This review synthesizes the results of the searches in Google Scholar, Web of Science (WoS), and Scopus databases that investigated the purpose to share health related information, technologies used, and challenges faced by older adults in sharing their health information. Most of the studies emphasize that the elderly use computers, mobile phones, and social media apps to discuss their health issues with doctors and families. However, there are challenges like technology constraints, people’s resistance to technology, and lack of funds that hinder information sharing. Moreover, the study shows the variety of technologies used by older people, indicating the necessity for more individualized approaches to the development of interventions. Subsequent studies should focus on tackling these barriers with the help of specific interventions and creating effective technology designs that would enhance the accessibility of healthcare services for elderly people. By bridging the digital divide and enhancing access to technology, stakeholders can foster a more equitable healthcare system that empowers older adults to actively engage in health information sharing, ultimately improving their health outcomes and overall well-being.

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries $({\mathcal {A}}_{d})$ can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system.

Background: Gastroparesis is a serious condition that can be caused by diabetes, surgery or infection, or can be idiopathic. When there is no mechanical obstruction, gastroparesis is characterized by delayed stomach emptying. Itopride, a prokinetic drug, inhibits acetylcholinesterase activity in addition to antagonizing dopamine D2 receptors. Methods: This prospective, multicentre study is based on real-world data from 988 patients with a diagnosis of diabetic gastroparesis for index (PAGI-SYM2) evaluation at baseline and week 4 of treatment for upper gastrointestinal disorder symptoms. Results: Upper gastrointestinal symptom severity scores improved significantly after 4 weeks of treatment (p

Background: Hepatitis C, caused by the Hepatitis C Virus (HCV), is the second most common form of viral hepatitis. The geographical distribution of HCV genotypes can be quite complex, making it challenging to ascertain the most prevalent genotype in a specific area. Methods: To address this, a review was conducted to determine the prevalence of HCV genotypes across various provinces and as a whole in Pakistan. The scientific literature regarding the prevalence, distribution, genotyping, and epidemiology of HCV was gathered from published articles spanning the years 1996–2020. Results: Genotype 1 accounted for 5.1% of the patients, with its predominant subtype being 1a at 4.38%. The frequencies of its other subtypes, 1b and 1c, were observed to be 1.0% and 0.31% respectively. Genotype 2 had a frequency of 2.66%, with the most widely distributed subtype being 2a at 2.11% of the patients. Its other subtypes, 2b and 2c, had frequencies of 0.17% and 0.36% respectively. The most prevalent genotype among all isolates was 3 (65.35%), with the most frequent subtype being 3a (55.15%), followed by 3b (7.18%). The prevalence of genotypes 4, 5, and 6 were scarce in Pakistan, with frequencies of 0.97%, 0.08%, and 0.32% respectively. The prevalence of untypeable and mixed genotypes was 21.34% and 3.53% respectively. Estimating genotypes proves to be a productive method in assisting with the duration and selection of antiviral treatment. Different HCV genotypes can exhibit variations in their response to specific antiviral treatments. Different genotypes may have distinct natural histories, including variations in disease progression and severity. Some genotypes may lead to more rapid liver damage, while others progress more slowly. Conclusions: This information can guide screening and testing strategies, helping to identify individuals at higher risk of developing severe complications. Studying the distribution of HCV genotypes in a population can provide valuable insights into the transmission dynamics of the virus.

Objective: To assess the awareness level and need perception of soft skills among general dental practitioners (GDPs), dental residents and specialists. Study Design: Mixed method study Place and Duration of Study: Armed Forces Institute of Dentistry (AFID), Rawalpindi and 28 Military Dental Centre (28 MDC), Lahore Pakistan, from Mar to Sep 2020. Methodology: A mixed method study research design with descriptive and explorative strategies was used to formulate a survey questionnaire and in-depth interviews for participants aged 24-60, medically fit, with a minimum of one year of practical experience, and willing to participate. Results: 136(74.37%) knew the basic concept of soft skills being non-technical skills, as they classified their awareness of soft skills as ‘Partially Aware’. Only 9(4.91%) responded as being ‘Expert’ in this topic. 38(20.76%) were totally ‘Unaware’. Regarding the need for teaching soft skills to dental students, 171(93.44%) participants wanted soft skills teaching to begin in the undergraduate years. Conclusion: Participating dental professionals understood the basic concept of soft skills but needed a formal knowledge of what it entails. There is a dire need for the dental professional to have a satisfactory knowledge of soft skills to improve their overall job performance.

Introduction: Dentists are still confused about whether to stop anticoagulants during extractions or to practice local hemostatic measures and closely monitor patients' international normalized ratios (INR) levels. Aims & Objectives: To determine and analyze the level of knowledge of practicing students and dental surgeons regarding anticoagulation therapy of patients undergoing dental extractions. Place and Duration of Study: A descriptive cross-sectional study was conducted among dentists and dental students in a teaching dental hospital (Islamabad Dental Hospital) for 5 months between August to December 2021. Material & Method: There were a total of 127 participants including BDS students, house officers, postgraduate trainees, and faculty members of all dental clinical departments who responded to the printed questionnaire. A convenience sampling technique was used to gather the data. After encoding, the data was input into SPSS version 22. For qualitative variables, mean and standard deviation were computed, whereas for quantitative variables, frequencies and percentages were determined., p.value? 0.05 was considered significant. The findings were displayed as tables, charts, and figures. Results: Out of 127 participants, 63.7% (n=81/127) were in favor of consultation with the patient's physician before dental extractions whereas 59% (n=75/127) considered discontinuation of aspirin seven days before extractions. Different international normalized ratio (INR) levels were considered for different medical conditions by 78.7% (n=100/127) while 49.6% (n=63/127) of the participants believed that warfarin should be replaced with low molecular weight heparin before dental extractions. 79% of participants knew the antidote of heparin. Postoperative clot formation was not checked by 25.2% (n=32/127), and 87.4% (n=111/127) gave both verbal and written instructions after dental extractions. Conclusion: It is concluded that there is a good level of awareness and implementation of guidance among dental practitioners. Further research and training programs are advised to enhance the knowledge among dental practitioners and to upgrade the standard protocols for treating patients with anticoagulation therapy.

Abstract Requirement elicitation stands as a pivotal activity within requirement engineering, gaining even greater significance in the context of global software development. Effective communication among stakeholders assumes paramount importance in this arena. Factors such as time zone disparities, cultural variations, and language differences exert a formidable impact on communication within the sphere of global software development. These dynamics inevitably impinge upon timely coordination, potentially compromising the software's quality. In response, researchers have proffered communication models tailored for requirement elicitation within the ambit of global software development. The purpose of this study is to conduct an in-depth critical review of existing communication models for demand elicitation in global software development. Through this comprehensive review, we aim to discern prevailing publication trends, provide an introductory overview, and illuminate the strengths and limitations inherent in the existing communication models. By identifying these limitations, we seek to advance a novel, low-cost communication approach designed primarily for demand elicitation in global software development. To culminate our endeavor, we will undertake a case study-based experiment, meticulously designed to assess the efficacy and practical utility of the proposed techniques.

The outbreak of Corona Virus 2019(Covid-19) is a great threat to the whole world. It is crucial to early detect patients infected with covid-19 and treat them to mitigate the rapid spread of this disease. It is an immediate priority to overcome the traditional screening and develop an accurate as well as speedy covid-19 automatic diagnosis system. Computer Tomography (CT) and Chest X-Ray imaging coupled with deep learning models to develop and test Computer Aided Screening (CAS) of covid-19 images from the normal images. In this paper classification and screening of covid-19 disease are performed by using pre-trained convolutional neural networks and a proposed hybrid model on an available standard dataset of chest X-Ray images. The proposed hybrid model employs the pre-trained Convolutional Neural Network models and Transfer Learning models. Our proposed model consists of three stages where extraction of features is performed in first stage by using pre-trained machine learning model. Deep features are extracted by using the infusion of the Transfer Learning Technique in the second stage of the model. The third stage uses Flatten and Classification layers to diagnose of Covid-19 patients. In order to assure the consistency of the proposed model, by considering standard dataset X-Ray images. Simulation results of performance metrics of Accuracy, F1 Score, Precision, Recall, ROC, and AUC curve, and training and testing loss are used to evaluate and compare the proposed model with existing models. Experimental result demonstrates that the hybrid model improves the screening process for Covid-19 disease by achieving higher accuracy.

Abstract Background Ventriculoperitoneal (VP) shunt infections in adults represent a severe complication and make treatment more challenging. Therefore, drug susceptibility patterns are crucial for therapeutic decisions and infection control in neurosurgical centers. This 7-year retrospective study aimed to identify the bacteria responsible for adult VP shunt infections and determine their drug susceptibility patterns. Methods This single-center study was performed from 2015 to 2021 in Lahore, Pakistan, and included CSF cultures from VP shunt infections. Demographic data, causative organisms, and antimicrobial susceptibility testing results were collected. Multivariate analysis of variance (MANOVA) and two-sample t-tests were used to analyze and compare the antibiotic sensitivity trends over the study period. Results 14,473 isolates recovered from 13,937 CSF samples of VP shunt infections were identified and analyzed for their susceptibility patterns to antimicrobials. The proportion of Gram-negative and Gram-positive bacteria were 11,030 (76%) and 3443 (24)%, respectively. The predominant bacteria were Acinetobacter species (n = 5898, 41%), followed by Pseudomonas species (n = 2368, 16%) and coagulase-negative Staphylococcus (CoNS) (n = 1880, 13%). 100% of Staphylococcus aureus (S.aureus) and CoNS were sensitive to vancomycin and linezolid (n = 2580). However, 52% of S. aureus (719/1,343) were methicillin-resistant Staphylococcus aureus (MRSA). Acinetobacter showed maximum sensitivity to meropenem at 69% (2759/4768). Pseudomonas was 80% (1385/1863 sensitive to piperacillin-tazobactam, Escherichia coli (E. coli) showed 72% to amikacin (748/1055), while Klebsiella spp. was 57% (574/1170) sensitive to piperacillin-tazobactam. The sensitivity of piperacillin-tazobactam and meropenem for Gram-negative bacteria decreased significantly (p

Cutaneous squamous cell carcinoma (cSCC) arises from the abnormal proliferation of keratinocytes of the epidermis, most commonly due to UV-light-induced DNA damage. Although histopathological assessment is the gold standard for diagnosing cSCC, nascent optical imaging diagnostic modalities enable clinicians to perform “optical or virtual biopsy” in real-time. We aim to report advances in optical imaging diagnostics for cSCC, along with an updated review of the literature. A comprehensive literature review was performed using PubMed, Embase, and Cochrane databases for manuscripts published from 2008 to 2022. The search yielded a total of 9581 articles, out of which 136 relevant articles were included in the literature review after fulfilling screening and eligibility criteria. This review highlights the current optical imaging devices used for diagnosing cSCC and their diagnostic features. These devices include in vivo and ex vivo reflectance confocal microscopy, optical coherence tomography, line-field confocal optical coherence tomography, multiphoton tomography, and high-frequency ultrasonography. Although surgical excision or Mohs micrographic surgery is considered the gold standard, the latest developments in nonsurgical management of cSCC are discussed. Based on he review of the literature, we conclude that contemporary optical imaging devices such as confocal microscopy, optical coherence tomography, line-field confocal optical coherence tomography and multiphoton tomography have revolutionized real-time diagnostic imaging in dermatology, particularly within the realm of skin cancer. These devices enable rapid diagnoses and allow for a faster initiation of therapy. The application of newer imaging devices to cSCC management may benefit high-risk patients (e.g., chronic UV radiation exposure or organ transplant recipients) or patients with multifocal cSCC, for whom multiple biopsies would be impractical, thus avoiding unnecessary biopsies. Together with dermoscopy, optical imaging technologies can help to improve the efficiency of diagnosis by reducing the turnaround time and the need for extensive laboratory processing resources.

Abstract Background MLH1 epimutation is characterised by constitutional monoallelic MLH1 promoter hypermethylation, which can cause colorectal cancer (CRC). Tumour molecular profiles of MLH1 epimutation CRCs were used to classify germline MLH1 promoter variants of uncertain significance and MLH1 methylated early-onset CRCs (EOCRCs). Genome-wide DNA methylation and somatic mutational profiles of tumours from two germline MLH1: c.-11C > T and one MLH1: c.-[28A > G; 7C > T] carriers and three MLH1 methylated EOCRCs ( T carriers and MLH1 methylated EOCRCs clustered with the constitutional MLH1 epimutation CRCs but not with the sporadic MLH1 methylated CRCs. Furthermore, monoallelic MLH1 methylation and APC promoter hypermethylation in tumour were observed in both MLH1 epimutation and germline MLH1: c.-11C > T carriers and MLH1 methylated EOCRCs. Mosaic constitutional MLH1 methylation in MLH1: c.-11C > T carriers and 1 of 3 MLH1 methylated EOCRCs was identified by methylation-sensitive ddPCR. Conclusions Mosaic MLH1 epimutation underlies the CRC aetiology in MLH1: c.-11C > T germline carriers and a subset of MLH1 methylated EOCRCs. Tumour profiling and ultra-sensitive ddPCR methylation testing can be used to identify mosaic MLH1 epimutation carriers.

Objective: To determine the percentage completion of individual GQI indicators and the overall percentage of completed predefined Pre-anaesthesia assessment forms and identify any areas for improvement in the documentation of pre-anaesthesia assessment. Study Design: Cross-sectional study. Place and Duration of Study: Department of Anaesthesiology, Combined Military Hospital, Sialkot Pakistan, from Mar to Sep 2021. Methodology: Patients who reported to the operation theatre during the study period for elective surgery after having undergone formal pre-anaesthesia assessment preoperatively based on predefined Pre-anaesthesia assessment form (PAAF) were included. The quality of Pre-anaesthesia assessments from completion was assessed using a modified Global Quality Index with twenty-seven components. Results: The overall completion rate for all pre-anaesthesia assessment form components was 77.10±30.26%. The lowest completed items were local examination (0.00%), airway assessment, neck mobility, mouth opening, thyromental distance (24,10.61%), weight (25, 11.36%), GIT (86, 38.64%) and CNS/GCS (108, 48.48%) assessment, pre-operative vitals (121, 54.55%) and family history (129, 58.33%). Conclusion: We need to improve the quality of pre-anaesthesia assessment forms at our institution by ensuring that all relevant information is collected and documented.

After a decade of research, development, and instrument commercialization, capillary electrophoresis (CE) has firmly established itself as a recognized analytical technique. CE is a separation method that segregates charged species based on their charge and size. In the field of environmental science, CE instruments have emerged as an ideal choice due to their simplicity, efficiency, affordability, and compact design. Furthermore, CE's equipment requirements are straightforward, making it well-suited for easy miniaturization. This review provides a comprehensive overview of the latest advancements in the CE technology, with a focus on its miniaturization. It delves into portable CE and microchip-based CE, exploring their structural characteristics, advantages, and limitations. Additionally, it investigates a modular approach that consolidates all essential components onto a single board, offering a holistic perspective on the innovative possibilities within the realm of miniature capillary electrophoresis.

Ghazal is the most famous genre of literature in Urdu poetry that has maintained its existence and identity in all eras. In this research paper, the poetry collections and magazines published during the last twenty-three years have been discussed. This paper also deals with the general perception that the proliferation of themes in Urdu ghazal has harmed the traditions of Urdu ghazal. Writers from the sixties and seventies have a deep insight into the life of the ghazal and at the same time they have the classical values of the ghazal. In the modern era, the changing behavior of man at the social level is the important topic of this era. This research paper brings forward the trends of Urdu Ghazal in these years.

Aeromonas hydrophila is one of the major pathogenic bacteria responsible for causing severe outbreaks at fish farms and is also a major global public health concern. This bacterium harbors many virulence genes. The current study was designed to evaluate the antidrug and virulence potential of A. hydrophila by amplifying its antimicrobial resistance and virulence genes using PCR and examining their effects on fish tissues and organs. A total of 960 fish samples of Channa marulius and Sperata sarwari were collected from four sites of the rivers of the Punjab, Pakistan. A. hydrophila isolates were subjected to biochemical identification and detection of virulence and antimicrobial resistance (AMR) genes by PCR. We retrieved 181 (6.46%) A. hydrophila isolates from C. marulius and 177 (6.25%) isolates from S. sarwari. Amplification through PCR revealed the incidence of virulence genes in 95.7% of isolates in C. marulius and 94.4% in S. sarwari. Similarly, amplification through PCR also revealed occurrence of AMR genes in 87.1% of isolates in C. marulius and 83.9% in S. sarwari. Histopathological examination revealed congestion (5.2%) and hepatocyte necrosis (4.6%) in liver, lamellar fusion (3.3%) and the presence of bacterial colonies (3.7%) in gills, fin erosion (6%), and the presence of biofilms (3.5%) in tail fins of infected fish. Phylogenetic tree analysis of 16S rRNA and gyrB gene of A. hydrophila revealed 100% and 97% similarity, respectively, with 16S rRNA gene and gyrB of A. hydrophila isolated in previous studies. The results of antimicrobial susceptibility testing showed that all isolates demonstrated resistance to sulfamethoxazole, ampicillin, neomycin, and norfloxacin, while susceptibility to gentamicin, chloramphenicol, and tetracycline, and intermediate resistance was observed against cefotaxime. The results concluded that examined fish samples were markedly contaminated with virulent and multidrug strains of A. hydrophila which may be of a potential health risk. The study emphasizes the responsible antimicrobial use in aquaculture and the urgent need for effective strategies to control the spread of virulence and antimicrobial resistance genes in A. hydrophila.

Water quality monitoring is a critical process in maintaining the well-being of aquatic ecosystems and ensuring growth of the surrounding environment. Clean water supports and maintains the health, livelihoods, and ecological balance of the ecosystem as a whole. Regular assessment of water quality is essential to ensure clean and reliable water is available to everyone. This requires regular measurement of pollutants or contaminants in water that can be monitored in real-time. Hence, this research showcases a system that consists of low-cost sensors used to measure five basic parameters of water quality that are: turbidity, total dissolved solids, temperature, pH, and dissolved oxygen. The system incorporates electronics and IoT technology that are powered by a solar charged lead acid battery. The data gathered from the sensors was stored locally on a micro-SD card with live updates that could be viewed on a mobile device when in proximity to the system. Data was gathered from three different bodies of water over a span of three weeks, precisely during the seasonal transition from autumn to winter. We adopted a water sampling technique since our low-cost sensors were not designed for continuous submersion. The results show that the temperature drops gradually during this period and an inversely proportional relationship between pH and temperature could be observed. The concentration of total dissolved solids decreased during rainy periods with a variation in turbidity. The deployed system was robust and autonomous that effectively monitored the quality of water in real-time with scope of adding more sensors and employing Industry 4.0 paradigm to predict variations in water quality.

Equine embryo transfer (EET) is a prominent technology in the equine breeding industry, and its efficacy is affected by a number of factors. The current study aimed to determine the effects of the breed of donor/recipient mares, estrus/ovulation induction treatment, cooled transportation of embryos, and synchrony between donor and recipient mares on the efficiency of the EET under subtropical conditions of Pakistan. A total of eighty-four (n = 84) Polo-playing donor mares (Argentino-polo = 41 and Anglo-Arab = 43) and seventy (n = 70) recipient mares (light breed = 26 and heavy breed = 44) were used for EET. The donor mares exhibiting natural estrus (n = 28) were detected by teaser a stallion, and corpus luteum (CL) having mares (n = 56) were treated with prostaglandin (150 μg of Cloprostenol) for estrus induction. The mares' follicular growth was monitored through ultrasonography until the dominant follicle's size reached 35 mm or more with a moderate to obvious uterine edema score. Afterward, the mares were treated either with GnRH, i.e., 50 μg of Lecirelin acetate (n = 41) or Ovusyn, i.e., 1500 IU hCG (n = 43). Insemination with chilled semen was performed 24 hours later. The embryos were collected non-surgically, 7 or 8 days after ovulation, from the donor mares. The collected embryos were transferred into the well-synchronized recipient mares as fresh (n = 44) or chilled (n = 26) embryos. The pregnancy after ET was checked through ultrasonography. Statistical analysis revealed that the embryo recovery rate (ERR) remained significantly higher (P0.05) affect the ERR. There was no significant effect of the type (fresh vs chilled), classification, and stage of development of embryo on pregnancy outcomes (P>0.05). ET pregnancy rate was significantly affected by the breed of recipient mares and ovulation synchrony between donor and recipient mares (P

Rye (Secale cereale L.) is an important cereal crop used for food, beverages, and feed, especially in North-Eastern Europe. While rye is generally more tolerant to biotic and abiotic stresses than other cereals, it still can be infected by several diseases, including scald caused by Rhynchosporium secalis. The aims of this study were to investigate the genetic architecture of scald resistance, to identify genetic markers associated with scald resistance, which could be used in breeding of hybrid rye and to develop a model for genomic prediction for scald resistance. Four datasets with records of scald resistance on a population of 251 hybrid winter rye lines grown in 2 years and at 3 locations were used for this study. Four genomic models were used to obtain variance components and heritabilities of scald resistance. All genomic models included additive genetic effects of the parental components of the hybrids and three of the models included additive-by-additive epistasis and/or dominance effects. All models showed moderate to high broad sense heritabilities in the range of 0.31 (SE 0.05) to 0.76 (0.02). The model without non-additive genetic effects and the model with dominance effects had moderate narrow sense heritabilities ranging from 0.24 (0.06) to 0.55 (0.08). None of the models detected significant non-additive genomic variances, likely due to a limited data size. A genome wide association study was conducted to identify markers associated with scald resistance in hybrid winter rye. In three datasets, the study identified a total of twelve markers as being significantly associated with scald resistance. Only one marker was associated with a major quantitative trait locus (QTL) influencing scald resistance. This marker explained 11-12% of the phenotypic variance in two locations. Evidence of genotype-by-environment interactions was found for scald resistance between one location and the other two locations, which suggested that scald resistance was influenced by different QTLs in different environments. Based on the results of the genomic prediction models and GWAS, scald resistance seems to be a quantitative trait controlled by many minor QTL and one major QTL, and to be influenced by genotype-by-environment interactions.

Wireless power transfer (WPT) has emerged as a promising solution for delivering services to low-power Internet of Things (IoT) devices in a demand-driven manner. In this work, we consider the Wireless power-enabled Hybrid Mobile Edge Cloud (WPHMEC) network, which utilizes a dynamic offloading strategy (partial and binary) to maximize computational efficiency while minimizing device energy consumption. To address this challenge, we formulate a convex optimization problem to maximize the number of computational bits and minimize the energy consumption of the IIoT devices in the WPT-based MEC system for Industry 5.0 applications. To achieve this goal, we employ a reformulation approach based on block coordinate descent (BCD) to formulate an optimization problem that addresses the nonconvexity of the problem and propose a solution approach using the Karush–Kuhn–Tucker (KKT) conditions. To validate the effectiveness of the proposed scheme, extensive simulations are carried out using the Matlab software to evaluate the system’s performance and components. The results demonstrate that optimal resource allocation maximizes energy efficiency and enhances computational resource utilization, making WPHMEEC an ideal choice for addressing the evolving demands of Industry 5.0 applications.

Background: Diabetes mellitus is associated with a theoretical increase in risk of development of post-operative infections; however, literature on the subject is conflicted, with some studies negating the effect of high blood sugar on the development of surgical wound site complications.Objectives: To compare the complications of ventral hernia repair in diabetics versus non-diabetics in terms of frequency of surgical site infection, seroma and haematoma formationStudy type,settings& duration:A prospective cohort study conducted Department of General Surgery, Combined Military Hospital,Rawalpindi from November 2019 to December2020.Methodology:Atotal of 182 patients (91 in each group) diagnosed with a ventral abdominal hernia who met the sample selection criteria were included. Patients who were diabetic were placed in Group A, while Group B patients were non-diabetic. All patients were followed up for surgical site infection, seroma and haematoma formation. Data was analyzed using SPSS 26.0.Results:The mean age of patients was 46.54±10.58 years (range 25-70 years). The patients included 113 females (62.1%) and 69 males (37.9%). A total of9 (9.9%) patients developed surgical site infections in Group A versus 2 (2.2%) in Group B, (p=0.029). Seroma formation was seen in 4 (4.4%) patients of Group A versus 2 (2.2%) Group B patients, p=0.406. One(1.1%) case from each group developed haematomas, (p=1.000).The relative riskratios in diabetics for developing surgical site infections, seromas and haematomas were RR: 4.9 (CI 95% 1.03 –23.3), RR: 2.1 (CI 95% 0.37 –11.5) and RR: 1.0 (CI 95% 0.06–16.2).Conclusion:Diabetics are prone to increased rates of surgical site infection, even in clean surgeries, when compared to non-diabetics, but there was no difference in frequencies of other complications.

Abstract Routine screening of tumors for DNA mismatch repair (MMR) deficiency (dMMR) in colorectal (CRC), endometrial (EC) and sebaceous skin (SST) tumors leads to a significant proportion of unresolved cases classified as suspected Lynch syndrome (SLS). SLS cases (n = 135) were recruited from Family Cancer Clinics across Australia and New Zealand. Targeted panel sequencing was performed on tumor (n = 137; 80×CRCs, 33×ECs and 24xSSTs) and matched blood-derived DNA to assess for microsatellite instability status, tumor mutation burden, COSMIC tumor mutational signatures and to identify germline and somatic MMR gene variants. MMR immunohistochemistry (IHC) and MLH1 promoter methylation were repeated. In total, 86.9% of the 137 SLS tumors could be resolved into established subtypes. For 22.6% of these resolved SLS cases, primary MLH1 epimutations (2.2%) as well as previously undetected germline MMR pathogenic variants (1.5%), tumor MLH1 methylation (13.1%) or false positive dMMR IHC (5.8%) results were identified. Double somatic MMR gene mutations were the major cause of dMMR identified across each tumor type (73.9% of resolved cases, 64.2% overall, 70% of CRC, 45.5% of ECs and 70.8% of SSTs). The unresolved SLS tumors (13.1%) comprised tumors with only a single somatic (7.3%) or no somatic (5.8%) MMR gene mutations. A tumor-focused testing approach reclassified 86.9% of SLS into Lynch syndrome, sporadic dMMR or MMR-proficient cases. These findings support the incorporation of tumor sequencing and alternate MLH1 methylation assays into clinical diagnostics to reduce the number of SLS patients and provide more appropriate surveillance and screening recommendations.

The present study was designed to evaluate the effect of pectin supplemented feeds on the growth, gut morphology and immunology of Labeo rohita. Experimental feeds containing various concentrations of pectin including control (0%), T1 (0.5%), T2 (1.0%), T3 (2.0%), and T4 (4.0%) were administered to L. rohita twice per day at a rate of 3% body weight. There was a significant (P < 0.05) decrease in average final weight, weight gain%, net weight gain and specific growth rate observed in treatment groups in order of control > T1 > T2 > T3 > T4 while feed conversion ratio was significantly (P < 0.05) higher in T4 compared to the other groups T3 > T2 > T1 > control. Immunological parameters of peroxidase, protease and antiprotease (trypsin) showed significant (P T2 > T1 > control group. Total protein, protease and antiprotease activity were significantly (P < 0.05) higher in the control compared to treatment groups T1 > T2 > T3 > T4. An increased level of the non-starch polysaccharide pectin in feed caused acute effects on gut morphometry. Villus length, villus width, and an increase in mucosal folds were abnormal but no significant (P > 0.05) abnormality was observed in the control group. In the submucosal part of the intestine, hyperplasia was shown in loose connective tissues and in adipose tissue cells in the treated groups. In conclusion, dietary pectin supplementation from 0.5% to 4% had negative effects on the growth, immunological parameters and gut histomorphology of L. rohita.

Objective: To determine the diagnostic accuracy of RIPASA (Raja Isteri Pengiran Anak Saleha Appendicitis) and Modified ALVARADO score in the diagnosis of acute appendicitis by using histopathology as a gold standard. Study Design: A comparative cross-sectional study. Place and Duration of Study: The study was carried out at the Department of Surgery, Combined Military Hospital (CMH) Rawalpindi, Pakistan from January 2020 to August 2020. Methods: A total of 126 patients suspected of having acute appendicitis were included. All patients who had met the inclusion criteria were chosen via the process of consecutive sampling. Patients who had presented with right iliac fossa pain, migration of pain, anorexia, nausea, and vomiting and were admitted to the hospital were assessed prospectively using RIPASA and Modified ALVARADO scores. RIPASA (Raja Isteri Pengiran Anak Saleha Appendicitis) score > 7.5 and Modified ALVARADO score > 7 were used to diagnose acute appendicitis. The diagnosis was confirmed postoperatively using a histopathological report. Every scoring system was assessed for validity by calculating sensitivity, specificity, diagnostic accuracy, negative predictive value, and positive predictive value. Result: The results obtained from individuals recruited in the study showthat out of 126 patients, 70(55.6%) were male and 56(44.4%) were female. The mean age was 33.25±8.19 years. 83.3% of patients having RIPASA (Raja Isteri Pengiran Anak Saleha Appendicitis) score (≥7.5) and 68.3% of patients having Modified ALVARADO score (≥7) have met the criteria for acute appendicitis. The sensitivity and specificity of the RIPASA (Raja Isteri Pengiran Anak Saleha Appendicitis) score were 90.9% and 73.3% compared with 73.8% and 73.3% of the Modified Alvarado score. Conclusion: The RIPASA (Raja Isteri Pengiran Anak Saleha Appendicitis) scoring system is a more convenient, accurate, and specific scoring system for our population than the Modified ALVARADO scoring system. How to cite this: Qamar R, Mahmood K, Javed R, Sher F, Tufail I.Reliability of Diagnosis of Acute Appendicitis by Clinical Assessment Through Modified Alvarado Vs Ripasa Scoring System. Life and Science.2024; 5(2): 193-198. doi: http://doi.org/10.37185/LnS.1.1.346

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.

Women LIS researchers in Pakistan have been publishing their research since 1977. This paper provides a bibliometric analysis of the 43 years (1977–2020) of their research. This study conducted a bibliometric analysis of the data collected from four data sources, the Web of Science Core Collection, Scopus, LISA, and LISTA, to include the maximum number of scholarly publications by women Pakistani LIS researchers working in Pakistan or abroad. The study findings highlighted an upward trend in publications since 1977. It appears to have gathered momentum after the year 2003. The University of the Punjab was the top-ranked organization in publishing research by woman LIS researchers, and the Department of Information Management at the University of the Punjab was identified as being the most prolific in this regard. The researchers had published over 40% of their research in national journals and the rest in international journals of good repute. Kanwal Ameen led the list of woman researchers with over 100 research publications to her name. The main areas of research were academic libraries, information literacy, and evolving trends in LIS. This is the first research paper highlighting the different dimensions of Pakistani LIS women researchers since they started publishing in 1977. The findings of the study would help future researchers to understand trends and various bibliometric aspects of publications originating from women LIS researchers in Pakistan. The findings could also help LIS researchers and funding organizations in promoting the profession.

The nonlinear optical (NLO) characteristics exhibited by organic compounds hold significant value in the realm of photonics and optoelectronics applications. Herein, the Density Functional Theory method at MPW1PW91/6-311G (d,p) functional was utilized to evaluate the electronic and NLO behavior of novel compounds i.e., MTPBR1 having A-π-D-π-A and MTPBD2-MTPBD9 with D1-π-D2-π-A configurations. Quantum chemical computations revealed that structural tailoring of all the designed chromophores demonstrate reduced bandgaps with greater absorption properties than MTPBR1. The efficacy of charge transfer from the donor to the acceptor through a spacer was investigated using frontier molecular orbitals calculations. These calculations were substantiated by analyses of density of states and transition density matrices. Natural bonding orbitals indicated molecular stability in designed molecules due to hyper-conjugative interactions and extended conjugation. Among all the derivatives, MTPBD8 exhibited the lowest energy bandgap (1.891 eV) with bathochromic shift of 723.868 nm in chloroform and 546.688 nm in gaseous phase, and the highest global softness of 0.529 eV−1. MTPBD5 displayed the highest dipole moment (µtot) at 22.815 D. MTPBD2, MTPBD3, MTPBD5 and MTPBD8 demonstrated consistent linear polarizability 〈α〉 at 3.4 × 10−22 esu. Notably, MTPBD8 calculated the highest first and second hyperpolarizability (βtot, γtot) at 9.6 × 10–27 and 4.302 × 10−32 esu, respectively. To conclude, the novel chromophores, particularly MTPBD8, showed remarkable NLO properties, demonstrating their potential use in advanced nonlinear optical devices.

Introduction Countries are grappling with a rapidly worsening upsurge in the opioid-related overdose deaths, misuse and abuse. There is a dearth of data in Pakistan regarding the practices and competencies of pharmacists in handling opioid-related issues.Study design A cross-sectional study, conducted across Punjab, Pakistan.Method The study deployed a validated survey to evaluate the competencies and practices of the community and hospital pharmacists.Results 504 community pharmacists and 279 hospital pharmacists participated in the survey with an overall response rate of 85.5%. Almost half of the respondents ‘never’ or ‘sometimes’ made clinical notes in a journal or dispensing software to monitor ongoing opioid use. Generally, pharmacists were reluctant to collaborate with physicians or notify police regarding the abuse/misuse of opioids. Hospital pharmacists achieved significantly higher mean competency scores than chain and independent community pharmacists (p

The development of multiple university ranking systems at national and global levels has been driven by increasing interest in improving efficiency in the national educational sector without compromising the demand for international standards. Global university ranking systems play an important role by providing the foundation for competing in this global era. One approach could be developing and evaluating criteria to reduce the unnecessary use of standard, less productive indicators. This study aims to systematically exploit national and global university ranking systems in terms of their indicators and relevance to national educational needs. This study uses two online qualitative focus groups with 10 participants each. The participants were purposively sampled, and the transcribed data from the focus group were thematically analyzed. Findings indicate that university ranking should include indicators like governance and digital presence, as these are missing in global ranking indicators. These findings will guide the development of a university ranking framework that policymakers and universities can implement to improve institutional performance.

Background: Intranasal meningoencephalocele is a rare congenital malformation. It is defined by protrusion of cerebral tissue and meninges through a defect in the skull base. Serious complications usually occur in early childhood, however, symptoms occasionally can wait until adulthood. Case Presentation: We report a case of a 24-year-old male who had a congenital intranasal meningoencephalocele with recurrent episodes of bacterial meningitis. After a delayed diagnosis, he eventually underwent Endoscopic Endonasal Repair of Meningoencephalocele via 'Bath-plug' technique. Conclusions: To prevent improper management, it is crucial that ENT colleagues, to whom these cases are primarily present, are well informed about this unusual condition. For the purpose of preventing further neurological problems, thorough neuroimaging evaluations and successful surgical repair are important. The 'Bath-plug' technique for anterior skull base cerebrospinal fluid leak repair is effective for a wide range of situations.

Laryngeal cancer detection on throat area images is a vital application of medical image diagnosis and computer vision (CV) in the healthcare domain. It contains the analysis and detection of cancerous or abnormal tissues from the larynx, an essential part of the respiratory and vocal systems. Several machine learning (ML) and deep learning (DL) systems are executed for classifying the extraction features as both cancerous and healthy tissue. Convolutional Neural Networks (CNNs) and recurrent neural networks (RNNs) have shown promise in this context. With this motivation, this study designs a new Laryngeal Cancer Detection and Classification using the Aquila Optimization Algorithm with Deep Learning (LCDC-AOADL) technique on neck region images. The purpose of the LCDC-AOADL technique is to examine the histopathological images for the recognition and classification of Laryngeal Cancer. In the presented LCDC-AOADL technique, the Inceptionv3 model is used for the feature extraction process. Besides, the LCDC-AOADL technique employed a deep belief network (DBN) model for the identification and classification of LC. Moreover, the AOA is utilized for the hyperparameter tuning of the DBN model which results in improved detection rate. The simulation analysis of the LCDC-AOADL method is validated on the benchmark Laryngeal dataset. The experimental results pointed out the enhanced detection results of the LCDC-AOADL technique over other recent approaches with a maximum accuracy of 96.02%, precision of 92.10%, recall of 91.87%, and F-score of 91.86%.

The Internet of Things (IoT) refers to a technology enabler to enhance the urban physical architecture and render public services. But, public access to accumulated heterogeneous IoT urban information is prone to hackers attacking connected devices to the internet intellectual property as well. IoT security serves a dynamic part in the smart city. Some IoT devices are connected in smart homes, and these connections were centred on gateways. In smart homes, the gateways gain a lot of significance; but their centralized structure causes many security vulnerabilities like availability, integrity, and certification. Unified “cloud-like” computing networks and Blockchain (BC) type systems should be used to sort out these problems. Therefore, this article develops a Blockchain-Assisted Secure Smart Home Network using Gradient Based Optimizer with Hybrid Deep Learning (BSSHN-GBOHDL) model. The presented BSSHN-GBOHDL technique employs BC technology to improve the confidentiality of the data in the smart home environment. In addition, the BSSHN-GBOHDL technique identifies malicious activities in the smart home environment via three sub-processes namely data preprocessing, hybrid deep learning (HDL)-based malicious activity classification, and GBO-based hyperparameter tuning. The GBO algorithm assists in the proficient hyperparameter selection of the HDL model, which aids in accomplishing increased detection efficiency. The experimental validation of the BSSHN-GBOHDL approach is tested on a benchmark NSL-KDD dataset with 65495 normal and 60743 attack samples. The results highlight the betterment of the BSSHN-GBOHDL approach over other recent methods with maximum accuracy of 98.29%.

The Federated learning (FL) technique resolves the issue of training machine learning (ML) techniques on distributed networks, including the huge volume of modern smart devices. FL clients frequently use Wi-Fi and have to interact in unstable network surroundings. However, as the present FL aggregation approaches receive and send a large number of weights, accuracy can be decreased considerably in unstable network surroundings. Therefore, this study presents a Quantum with Metaheuristics Algorithm Based Minimization of Communication Costs in Federated Learning (QMAMCC-FL) technique. The presented QMAMCC-FL technique is designed a federated hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model with a quantum Aquila optimization (QAO) algorithm. The QMAMCC-FL technique upgrades the global model via weight collection of the learned model, which is commonly used in FL. The proposed model can be employed to increase the performance of network communication and reduce the size of data transmitted from clients to servers such as smartphones and tablets. The experimental analysis of the QMAMCC-FL approach is tested, and the outcomes show better performance over other existing models.

Recently, power systems are drastically developed and shifted towards cyber-physical power systems (CPPS). The CPPS involve numerous sensor devices which generates enormous quantities of information. The data gathered from each sensing component needs to accomplish to reliability which are highly prone to attacks. Amongst various kinds of attacks, false data injection attack (FDIA) can seriously affects energy efficiency of CPPS. Current data driven approach utilized for designing FDIA frequently depends on distinct environmental and assumption conditions making them unrealistic and ineffective. In this paper, we present a modified Red Fox Optimizer with Deep Learning enabled FDIA detection (MRFODL-FDIAD) in the CPPS environment. The presented MRFODL-FDIAD technique mainly detects and classifies FDIAs in the CPPS environment. It encompasses a three stage process namely pre-processing, detection, and parameter tuning. For FDIA detection, the MRFODL-FDIAD technique uses multihead attention-based long short term memory (MBALSTM) technique. To improve the detection performance of the MBALSTM model, the MRFO technique can be exploited in this study. The experimental evaluation of the MRFODL-FDIAD approach was performed on standard IEEE bus system. Extensive set of experimentations highlighted the supremacy of the MRFODL-FDIAD technique.

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm.

Smart farming helps to increase yield by smartly deciding the steps that should be practised in the season. A few components of precision farming are recommending the crops for cultivation, predicting the weather conditions, examining the soil; determining the pesticides, and fertilizers that have to be used. Smart Farming utilizes advanced technologies namely data mining (DM), machine learning (ML), the Internet of Things (IoT), and data analytics for collecting the data, predicting the outcomes and training the system. One of the most significant parameters is proper soil prediction which decides the proper crop and is manually executed by the agriculturalists. Hence, the farmer’s efficacy can be improved by producing automated tools for soil type classification. This study presents a Chaotic Jaya Optimization Algorithm with Computer Vision based Soil Type Classification (CJOCV-STC) for smart farming. The presented CJOCV-STC technique applies CV with metaheuristic algorithms for the automated soil classification process, which identifies the soil into distinct types. To accomplish this, the presented CJOCV-STC technique uses the SqueezeNet model for producing a set of feature vectors. To improve the performance of the SqueezeNet model, the CJO algorithm is used for the hyperparameter tuning process. Moreover, the Elman neural network (ENN) technique is applied for soil type classification and the parameters related to it can be adjusted by the chicken swarm algorithm (CSA). The soil classification performance of the CJOCV-STC method can be studied on the Kaggle dataset and the outcomes stated the better performance of the CJOCV-STC algorithm over other recent approaches with increased accuracy of 98.47%.

Web Services (WSs) are gaining worldwide popularity due to reliable and fast intercommunication services for the development of web and mobile applications. WSs are provided to client application developers through web Application Programming Interfaces (APIs), such as YouTube API, Twitter API, Facebook API, etc. Due to the popularity of WSs, the developers frequently discuss various WSs-based application’ issues on online forums, such as Stack Overflow (SO). This study aims to highlight the problems faced by client developers in the development process of WSs-based applications using the dataset of SO. The comprehension of developers’ conversations on SO can give insight into the frequency, difficulty, and popularity of different WSs-related problems of developers. We downloaded 12,746 posts from SO relevant to WSs-related issues for this article. We used the topic modeling technique (LDA) to extract various topics from the SO dataset. The topics are labeled and organized into categories and sub-categories according to relationships among them. The difficulty and popularity of each topic have been analyzed. Our investigation yield several findings. First, developers focus on six topics related to WSs on SO: Client APIs development, Data Processing, Web services Authorization, Framework Support, Web APIs, and Mobile Applications. Secondly, the advantages and disadvantages of web applications topic (Fused_Popularity=0.39), from the Clients APIs development category have the highest prevalence, followed by Database (DB) and Data Processing in Applications topic (Fused_Popularity=0.38) from the Data Processing category. Third, most WSs-related topics in all categories are evolving promptly on SO, i.e., new questions are added daily about WSs development, deployment, and authorization. Fourth, the questions of type “how” are primarily asked in Framework support, Client APIs development, and Web APIs categories. Although, many questions in other categories are of the kind “What”. It is also observed that WSs developers not only used SO to ask How and What types of questions but they also used SO to ask information-seeking questions (i.e., in Data processing and Client APIs development categories). Fifth, the topics relevant to Web APIs (Fused_Popularity=10.8) and Client API Development ((Fused_Popularity=9.35) categories of WSs are very popular on SO. Sixth, the questions relevant to the Web APIs (Fused_Difficulty =3) & Client APIs development (Fused_Difficulty=2.25) categories are more difficult than the other four categories. The results of our research may be helpful for the following WSs stakeholders: WSs Client application developers, WSs Educators, and WSs researchers. The WSs Educators and investigators can get more knowledge of new methods and discover novel techniques to make challenging WSs topics easy to understand. WSs framework developers can utilize our extracted WSs topics and categories to know the preferences of WSs developers that may support them in upgrading existing frameworks or developing new ones.

Abstract A series of derivatives (DOCD2–DOCD6) with D–π–A configuration was designed by substituting various efficient donor moieties via the structural tailoring of o-DOC6-2F. Quantum-chemical approaches were used to analyze the optoelectronic properties of the designed chromophores. Particularly, M06/6-311G(d,p) functional was employed to investigate the non-linear optical (NLO) response (linear polarizability ⟨α⟩, first (β tot ) and second ( $$\upgamma$$ γ tot ) order hyperpolarizabilities) of the designed derivatives. A variety of analyses such as frontier molecular orbital (FMO), absorption spectra, transition density matrix (TDMs), density of states (DOS), natural bond orbital (NBO) and global reactivity parameters (GRPs) were employed to explore the optoelectronic response of aforementioned chromophores. FMO investigation revealed that DOCD2 showed the least energy gap (1.657 eV) among all the compounds with an excellent transference of charge towards the acceptor from the donor. Further, DOS pictographs and TDMs heat maps also supported FMO results, corroborating the presence of charge separation states along with efficient charge transitions. NBO analysis showed that π-linker and donors possessed positive charges while acceptors retained negative charges confirming the D–π–A architecture of the studied compounds. The λ max values of designed chromophores (659.070–717.875 nm) were found to have broader spectra. The GRPs were also examined utilizing energy band gaps of E HOMO and E LUMO for the entitled compounds. Among all the derivatives, DOCD2 showed the highest values of β tot (7.184 × 10–27 esu) and $$\upgamma$$ γ tot (1.676 × 10–31 esu), in coherence with the reduced band gap (1.657 eV), indicating future potentiality for NLO materials.

The worldwide demand has been raised to use natural dyes in all applied fields of life due to their environmental-friendly and therapeutic nature. For the current study microwave rays as an eco-friendly extraction tool has been used to explore the natural coloring potency of Arjun bark for cotton dyeing. Arjun bark powder was exposed to microwave irradiation for up-to 5 min and was used for extraction of colorant in different media. It has been found that 50 mL of acidified methanol-solubilized extract obtained from 5 min microwave-treated powder has given excellent results when employed onto MW treated cotton at 55°C for 75 min, salt of Fe as chemical mordant and extracts of acacia and turmeric as bio-mordants employed at optimum condition has shown good to the excellent fastness ratings at grey scales. It is concluded that microwave treatment has good potential for the extraction of the colorant from Arjun bark for cotton dyeing and the application of sustainable mordants has made the process more aesthetic and sustainable.