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Abstract Protein-ligand binding affinity prediction is a key element of computer-aided drug discovery. Most of the existing deep learning methods for protein-ligand binding affinity prediction utilize single models and suffer from low accuracy and generalization capability. In this paper, we train 13 deep learning models from combinations of 5 input features. Then, we explore all possible ensembles of the trained models to find the best ensembles. Our deep learning models use cross-attention and self-attention layers to extract short and long-range interactions. Our method is named Ensemble Binding Affinity (EBA). EBA extracts information from various models using different combinations of input features, such as simple 1D sequential and structural features of the protein-ligand complexes rather than 3D complex features. EBA is implemented to accurately predict the binding affinity of a protein-ligand complex. One of our ensembles achieves the highest Pearson correlation coefficient (R) value of 0.914 and the lowest root mean square error (RMSE) value of 0.957 on the well-known benchmark test set CASF2016. Our ensembles show significant improvements of more than 15% in R-value and 19% in RMSE on both well-known benchmark CSAR-HiQ test sets over the second-best predictor named CAPLA. Furthermore, the superior performance of the ensembles across all metrics compared to existing state-of-the-art protein-ligand binding affinity prediction methods on all five benchmark test datasets demonstrates the effectiveness and robustness of our approach. Therefore, our approach to improving binding affinity prediction between proteins and ligands can contribute to improving the success rate of potential drugs and accelerate the drug development process.

Accurate burst pressure prediction is critical for ensuring oil and gas pipeline safety, guiding maintenance decisions, and lowering costs and risks. Traditional methods have limitations, including high experimental costs, conservative empirical models, and computationally expensive numerical algorithms. Machine learning (ML) models have supplanted traditional methods in recent years. However, small and imbalanced datasets are the big challenge to build a ML model that can generate more accurate results. Moreover, the lack of generalization in ML models trained on a dataset of pipelines with specific material grids prevents them from producing superior results on other pipeline types. First, FEA was used to make a dataset. Then, a new way to improve machine learning (ML) model generalization for burst pressure prediction is suggested: combine publicly available datasets of different pipeline specifications. In this combined dataset, some pipelines have a higher number of data samples, and some have fewer, which causes a class imbalance issue. The Synthetic Minority Oversampling Technique (SMOTE) technique was applied to address the issue of class imbalance. The performance of various ML models, Extra Trees (ET), Extreme Gradient Boosting (XGBR), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Decision Tree (DT), was evaluated to validate the model's prediction and generalization on pipelines of various material grids. Results show that all the selected ML models produced high R-squared, i.e., >0.95, on balanced data compared to the imbalance dataset. These results show that SMOTE-based augmentation is a beneficial way to fix dataset imbalance and make ML models better at predicting burst pressure in oil and gas pipelines.

Amyotrophic Lateral Sclerosis (ALS) is a complex and rare neurodegenerative disorder characterized by significant genetic, molecular, and clinical heterogeneity. Despite numerous endeavors to discover the genetic factors underlying ALS, a significant number of these factors remain unknown. This knowledge gap highlights the necessity for personalized medicine approaches that can provide more comprehensive information for the purposes of diagnosis, prognosis, and treatment of ALS. This work utilizes an innovative approach by employing a machine learning-facilitated, multi-omic model to develop a more comprehensive knowledge of ALS. Through unsupervised clustering on gene expression profiles, 9,847 genes associated with ALS pathways are isolated and integrated with 7,699 genes containing rare, presumed pathogenic genomic variants, leading to a comprehensive amalgamation of 17,546 genes. Subsequently, a Variational Autoencoder is applied to distil complex biomedical information from these genes, culminating in the creation of the proposed Multi-Omics for ALS (MOALS) model, which has been designed to expose intricate genotype-phenotype interconnections within the dataset. Our meticulous investigation elucidates several pivotal ALS signaling pathways and demonstrates that MOALS is a superior model, outclassing other machine learning models based on single omic approaches such as SNV and RNA expression, enhancing accuracy by 1.7 percent and 6.2 percent, respectively. The findings of this study suggest that analyzing the relationships within biological systems can provide heuristic insights into the biological mechanisms that help to make highly accurate ALS diagnosis tools and achieve more interpretable results.

Load inconsistency has disrupted the power system, causing rotor angle fluctuation that leads to angle instability in the system. This research suggests an innovative proportional integral derivative with filter (PIDF)-based thyristor-controlled series compensator (TCSC) controller that utilise an evolutionary programming sine cosine algorithm (EPSCA) for hybrid optimisation to increase the angle stability of the power system. The challenge of the PIDF-TCSC design is transformed into an optimal control problem with respect to performance indices, such as the maximum imaginary part of system eigenvalues, damping ratio and damping factor, where another multi-objective function is utilised to determine the best stabiliser settings. Eigenvalue analysis is used to conduct the stability study in a linearised paradigm of the single-machine infinite-bus (SMIB) network. The resilience of the PIDF controller was tested using a SMIB power network under various operating circumstances. Simulation results are used to evaluate the system's effectiveness with the proposed optimised PIDF-TCSC controller to that of the system using the proportional integral derivative, proportional integral, and base case PIDF-TCSC approaches. The research findings demonstrate the efficacy of EPSCA in implementing PIDF-TCSC motif and its excellent resilient performance for improving power system stability as related to other strategies in various situations.

In vitro embryo production (IVEP) encompasses a series of three basic steps, namely in vitro maturation (IVM), in vitro fertilisation (IVF), and in vitro embryo culture. IVEP approach has shown to be a valuable tool in advancing our comprehension of early mammalian development and expediting genetic enhancement in livestock. The aim of this research was to compare the impact of small and large follicles on the total number of oocytes recovered through the IVEP process. Therefore, 305 Ewe genitalia were collected at the Al-Fallujah abattoir in the Al-Anbar province. The follicles were identified, and their diameter was measured using an automated vernier calliper. Subsequently, the oocytes were classified into three distinct groups, namely excellent (A), acceptable (B), and poor (C). The results of the maturation rate of large follicles were 43.9% (51/116) and small follicles 30.7% (20/65). There was a statistical difference in maturation rate (p

Abstract Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and $$\pi $$ π -stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap. Scientific Contribution Statement This study innovatively introduces distance-based features to predict protein-ligand binding affinity, capitalizing on unique molecular interactions. Furthermore, the incorporation of protein sequence features of specific residues enhances the model’s proficiency in capturing intricate binding patterns. The predictive capabilities are further strengthened through the use of a deep learning architecture with attention mechanisms, and an ensemble approach, averaging the outputs of five models, is implemented to ensure robust and reliable predictions.

Abstract In this paper, we propose a fractional-order mathematical model to explain the role of glucagon in maintaining the glucose level in the human body by using a generalised form of a fractal fractional operator. The existence, boundedness, and positivity of the results are constructed by fixed point theory and the Lipschitz condition for the biological feasibility of the system. Also, global stability analysis with Lyapunov’s first derivative functions is treated. Numerical simulations for fractional-order systems are derived with the help of Lagrange interpolation under the Mittage-Leffler kernel. Results are derived for normal and type 1 diabetes at different initial conditions, which support the theoretical observations. These results play an important role in the glucose-insulin-glucagon system in the sense of a closed-loop design, which is helpful for the development of artificial pancreas to control diabetes in society.

This study presents an innovative framework to examine the interplay between foreign direct investment and economic development from 1996 to 2021. Using the Panel NARDL approach, our study provides an empirical framework for exploring these complex relationships. We enhance the reliability of our results by integrating additional methodologies, such as augmented mean group and common correlated effects mean group. A decline in carbon emissions is commonly hypothesized to be correlated with economic expansion, foreign direct investment inflows, advancements in renewable energy, and implementation of environmental technologies. On the contrary, detrimental disturbances have the potential to result in protracted ecological deterioration, especially in situations where there are setbacks in economic expansion and foreign direct investment. It is worth noting that although short-run and long-run coefficients exhibit comparable directional trends, there are discrepancies in their magnitudes and levels of statistical significance. This sophisticated comprehension empowers countries such as China, among others, to allocate resources strategically towards sustainable energy initiatives, promoting a systematic approach that prioritizes the adoption of renewable energy, advancements in technology, attraction of foreign investment, and transformation of structures.

The underwater environment is harsh and challenging for human life, prompting companies and researchers to develop advanced technologies for exploration. Building on previous work that applied a CNN-based method for underwater object classification, this paper focuses on the design and implementation of an interactive embedded system for a compact Remotely Operated Vehicle (ROV) with specific dimensions and weight. The primary goal is to capture real-time underwater video using remote control communication via Ethernet. The ROV is powered by five brushless motors controlled by a smart PID controller. Precise pulse-width modulation signals enhance stability during movements along three axes, enabling high-resolution video capture. The system utilizes the Raspberry Pi 3's computing power for motion control, positioning, temperature monitoring, and video acquisition. Experimental results demonstrate the system's capability to process 42 frames per second. A user-friendly graphical interface allows for remote ROV control across various operating systems. With a depth rating of 100 meters and speed of 0.148 m/s. This ROV surpasses human divers' limitations and holds significant potential for applications in surveillance, operations, maintenance, and measurement tasks underwater. The proposed ROV contributes to the advancement of underwater exploration technology by combining high performance with cost-effectiveness.

Rolling bearings are essential components in a wide range of equipment, such as aeroplanes, trains, and wind turbines. Bearing failure has the potential to result in complete system failure, and it accounts for approximately 45 %–50 % of failures in rotating machinery. Hence, it is imperative to establish a thorough and accurate predictive maintenance program that can efficiently foresee and prevent mishaps or malfunctions. The literature has employed a variety of techniques and approaches, from conventional methods to contemporary machine learning (ML) and ML-integrated IoT-based solutions, to categorise bearing faults. This article provides an overview of the most recent research and models used in the classification of bearing faults. The literature summary highlights various significant challenges in current models, such as issues with the classification function, complexities in the neural network structure, unrealistic datasets, dynamic working conditions of rotating machines, noise in the dataset, limited data availability, and imbalanced datasets. In order to tackle the problems, researchers have endeavored to improve and apply different methods, such as convolutional neural networks, deep belief neural networks, and LiNet, among others. Researchers have primarily developed these approaches using datasets from publicly accessible sources. This study also identified research gaps and deficiencies, including limited data availability, data imbalance, and difficulties in data integration. The nascent technologies in the field of problem diagnosis and predictive maintenance are acknowledged as Internet of Things-based ML and vision-based deep learning techniques, which are currently in their initial phases of advancement. Ultimately, the study puts forth several prospective suggestions and recommendations.

Objective: To determine the risk factors for early post-operative arrhythmias in children undergoing congenital heart surgeries at a tertiary care hospital. Study Design: Prospective longitudinal study. Place and Duration of Study: National Institute of Cardiovascular Disease, Karachi Pakistan, from Jan to Jul 2020. Methodology: We included 143 patients of both genders undergoing open heart surgery for congenital heart diseases. We noted all patients' pre-operative, intra-operative, and post-operative clinical characteristics. Patients were monitored in the pediatric cardiac intensive care unit. We analyzed the development of postoperative arrhythmias and the factors associated with them. Results: In 143 patients, the mean age at the time of surgery was 7.52±6.0 years. There were 83(58.0%) male patients. Tetralogy of Fallot was the most common type of congenital heart disease noted in 57(39.0%) patients. The mean cardiopulmonary bypass time was 83.9±25.5 minutes, while the mean aortic cross-clamp time was 58.1±21.9 minutes. Post-operatively, arrhythmia was observed in 39(27.3%) children. Intra-operative arrhythmias (p

Objective: To compare the surgical outcome of Tetralogy of Fallot patients with and without right ventricular diastolic dysfunction after repair. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Cardiology, National Institute of Cardiovascular Disease, Karachi Pakistan, from May to Oct 2021. Methodology: Patients above 5 months of age, of either gender, undergoing surgery for Tetralogy of Fallot were enrolled. Patients were divided into two groups according to the Gatzoulis’ criterion for right ventricular restriction i.e. Group-A had right ventricle with restriction, and Group-B had right ventricle without restriction. Velocities of E and A waves was recorded, and their ratio (E/A) was calculated. The outcome such as aortic cross clamp, cardiopulmonary bypass time, ventilation time, intensive care unit stay, drain time, and transannular patch was noted. Results: Of 54 patients, restrictive physiology patients had significantly higher mean for aortic cross-clamp (p-value 0.004, 95%CI 3.54 - 17.35), ventilation time (p-value

As the demand for energy continues to rise unabated, an examination of the constraints posed by finite fossil fuel reserves becomes crucial. There is need to explore and underscore the imperative shift towards renewable energy resources, where, solar energy is widely available and can readily be used in different applications, especially in water desalination applications. In the present research, photothermal performance of water based nanofluids containing graphene oxide (GO), zinc oxide (ZnO), iron oxide (FeO), and their composites (GO-ZnO and GO-FeO) have been studied under the natural sun to determine the potential for water desalination applications. All type of the nanofluid are prepared through a two-step method and are characterized morphologically using a scanning electron microscope (SEM). UV–visible spectroscopy is used to assess the optical absorbance of prepared nanofluids. The outcomes of this study show that pure GO nanofluids exhibited excellent absorption in the visible and near-infrared regions as compared to other nanofluids used in this research study, which makes them efficient nanofluids in converting solar energy into heat. For more assessment, a direct or volumetric solar thermal collector was used to evaluate the photothermal efficiency of water based nanofluids GO, ZnO, FeO, and their binary composites of GO-ZnO, and GO-FeO under natural solar flux at weight concentrations (0.01 wt%). The sensible heating efficiency due to the rise in temperature and latent heat of evaporation efficiency due to mass loss of nanofluid samples contributed towards photothermal efficiency. Pure GO nanofluids showed the highest photothermal efficiency, which is 71 % among all the nanofluids used in this study due to their carbon-based structure, highest optical absorption peak, excellent dispersion stability, and highest thermal conductivity. This experimental work also revealed that binary nanofluids containing composites of (GO-ZnO) and (GO-FeO) showed higher photothermal efficiency as compared to individual nanofluids ZnO and FeO except graphene oxide GO. According to the results and observation of this experimental work, pure GO-based nanofluids are potential candidates for direct absorption solar collection used in water desalination applications.

Groundwater is very important part of life and source of survival on earth; and the clean water is one of the most important issues of the developed or developing world. In addition to other secondary problems such as solid waste and resource waste management, wastewater management is a major environmental obstacle to the development of the textile industry. Textile industry is the largest industrial sector of the world and hold very important part for the development of economy in all countries. Although the industry generates a lot of chemicals, including dyes in the form of wastewater. The textile industry is most fresh water consuming industry; simultaneously it discharges the huge load of contamination in our environment. The textile industry is the largest producer of wastewater during its different production processes. The wastewater creates several problems such as health problems, aquatic life including water pollution. The textile industry wastewater is treated by different methods such as coagulation, adsorption, membrane, and biological treatment. This paper reviews the different treatment technologies for textile wastewater along with its advantages and disadvantages. The objective of this study is to contribute a summary on the best and latest treatment techniques for textile industry effluent.

This review explores the potential of floating waterwheel power generation systems as a sustainable source of energy. With increasing concerns about environmental degradation and the need for renewable energy sources, the utilization of flowing water for power generation presents an attractive solution. By analyzing existing literature and case studies, this review assesses the feasibility and effectiveness of floating waterwheel systems in harnessing energy from rivers and streams. Key metrics, including the total potential of floating hydro generation systems and their contribution to renewable energy, are evaluated to provide insights into the scalability and impact of this technology. Furthermore, the review identifies the most viable types of generating systems and highlights the environmental benefits they offer. This paper outlines the significant potential of floating hydro generation systems, with an estimated contribution of up to 10% of global renewable energy production. Among these systems, tidal barrage technology stands out as the most viable option, offering predictability and high energy density. By harnessing the power of moving rivers and tides, this technology addresses the pressing demand for clean energy while minimizing environmental impact compared to traditional dams. By addressing these aspects, this study aims to contribute to the advancement of eco-friendly energy solutions and provide valuable insights for policymakers, researchers, and industry professionals working in the field of renewable energy.

Drought stress is one of the major threats to food security in the climate change scenario. Reducing the deleterious impacts of drought stress on the productivity of cereal crops is crucial. Hence, limited information has been available about the effect of the combined use of plant growth regulators and mineral fertilisers on promoting drought tolerance in maize seedlings. In this study, a pot experiment was carried out to evaluate the potential of sole or combined application of silicon (Si) and putrescine (Put) to mitigate the detrimental effects of drought on maize. The experimental treatments were, i.e. control (CK), water spray, 4.0 mmol Si, 0.5 mmol Put, and 4.0 mmol Si + 0.5 mmol Put on maize crop grown at two different water-holding capacity levels (80% well-water condition and 40% drought stress). The experiment was arranged in a complete randomised design with factorial arrangements having three replications. Exposure of maize plants to drought stress at the reproductive phase (VT-tasseling) reduced the photosynthetic pigments, including chlorophyll a, chlorophyll b and chlorophyll a + b, relative water contents, leaf area, yield and yield attributes. However, foliar application of Si and Put individually and Si + Put dramatically reduced these negative effects by improving photosynthetic pigments, relative water contents, and activities of enzymatic antioxidant defence. Drought stress-induced lipid peroxidation in the form of more production of malondialdehyde content, hydrogen peroxide and electrolyte leakage significantly declined due to the combined application of Si and Put compared to the respective control. Drought stress boosted the activities of key enzymatic antioxidants (catalase, superoxide dismutase, peroxidase, and ascorbate peroxidase) irrespective of the treatment application. Moreover, it was noted that the accumulation of osmolytes (proline and soluble protein) contents was increased by the combined application of Si and Put. Under drought stress conditions, combined foliar application of Si and Put considerably improved 22.70% cob length, 12.77% number of grains per cob, and 18.30% 100-grain weight, which ultimately enhanced maize's 10.29% grain yield. From the current study's findings, it was concluded that a combined foliar spray of silicon and putrescine at the reproductive phase is an effective strategy to enhance the maize yield in drought-prone areas.

Background and Objectives: Children with disabilities face unique challenges that can affect their well-being and quality of life (QOL). This study aimed to assess the QOL and coping strategies adopted by children with disabilities and explore how socio-demographic factors influence QOL and coping strategies. Materials and Methods: This cross-sectional study, which was conducted in Saudi Arabia with children aged 6 to 18 years, used a stratified random sample to ensure representation from a variety of demographic groups. Short Form-12 (SF-12) was used to assess the QOL in the sample population. The Brief COPE Inventory was used to examine coping strategies among the children. One-way analysis of variance was applied to examine differences in the QOL, coping strategies scores, and demographic variables. Multiple regression analyses were performed to examine the role of demographic variables in predicting QOL and p value was considered statistical significance at p < 0.05. Results: The results of the study clearly revealed significant differences between the mean scores of QOL for gender, age, and type of disability, duration of disability, education qualification, family status, family occupation, and housing status. Female participants showed better QOL in physical functioning than their male counterparts. Children with intellectual disability reported better QOL in general health, vitality, social functioning, and mental health. Participants with seven to eight years of disability reported higher scores in physical functioning, vitality, and mental health. Children whose parents were working in private agencies and living in rented houses scored higher on the general health aspects of quality of life. The findings also revealed that the duration of the disability was a significant predictor of the QOL. The mean scores of different dimensions of coping strategies clearly revealed that male participants used dysfunctional coping (p < 0.01), as compared to problem-focused and emotional-focused coping while female children relied more on emotional-focused coping. Emotion-focused coping was significantly higher in participants with visual (p < 0.01), learning (p < 0.01), and intellectual disabilities (p < 0.01). Dysfunctional coping scores were higher among children with auditory disabilities (p < 0.01) and multiple disabilities (p < 0.01). Conclusions: This study highlights the significance of demographic factors in understanding and improving the well-being of a diverse population of disabled juveniles. It offers valuable insights into the subtle factors affecting quality of life. Future interventions and policies can leverage these findings to enhance the quality of life of individuals with disabilities and to foster a more supportive and inclusive approach.

Objective: To note short-term outcome of patients undergoing tetralogy of fallot repair. Study Design: Retrospective longitudinal study. Place and Duration of Study: Department of Pediatric Cardiology, National Institute of Cardiovascular Disease, Karachi Pakistan, from Sep 2020 to Feb 2021. Methodology: A total of 113 patients under 25 years of age who underwent tetralogy of fallot correction were included. Pre-operative information, echocardiography, investigations and history of the patient were noted. Intra-operative and post-operative information and surgical procedure were also noted. Short-term outcome included data up to discharge or death of the patients enrolled. Results: Mean age was noted to be 9.40+5.01 years, with 59(52.2%) of the patients undergoing tetralogy of fallot repair were male. Mean saturation of peripheral Oxygen (SpO2) at the time of surgery was noted to be 79.27±.34. There were 76(67.3%) patients without history of previous surgery. Mean cardiopulmonary bypass time was noted to be 106.04±40.59 minutes. Mean aortic cross-clamp time was recorded to be 58.79±27.90 minutes. Additional procedures were required in 68(60.2%) patients. Mean duration of hospital stay was 13.96±8.68 days while mean duration of ICU stay was 87.04±69.37 hours. Mortality was reported in 3(2.7%) patients. Conclusion: Low mortality and low post-operative morbidity were noted among patients undergoing tetralogy of fallot repair.

Objective: To analyze the outcome among neonates undergoing transcatheter balloon atrial septostomy for various kinds of congenital heart diseases. Study Design: Prospective longitudinal study. Place and Duration of Study: National Institute of Cardiovascular Diseases, Karachi Pakistan, from Feb 2020 to May 2021. Methodology: A total of 28 neonates of both genders with echocardiography proven diagnosis of Tricuspid Atresia with restrictive Patent Foramen Ovale, Dextro-Transposition of Great Arteries with Intact Ventricular Septum, complex cyanotic congenital heart disease with inadequate mixing or hypoplastic left heart syndrome with restrictive PFO and a non-significant Patent Ductus Arteriosus, undergoing transcatheter balloon atrial septostomy were included. Demographic data along with diagnosis, clinical presentation, echocardiographic findings, procedural outcome (successful/unsuccessful), duration of the procedure, complication (if any), post-procedural echocardiographic findings, duration of hospital stay, further treatment plan and 3-months follow up outcomes were recorded among all neonates. Results: Most common diagnosis was Dextro-Transposition of the Great Arteries with IVS observed in 14(50.0%) neonates. Procedural outcome was found to be successful among 23(82.1%) neonates. There were 19(67.9%) neonates who did not show any post-procedure complication. At 3-months follow up, improvement in blood saturation and weight gain were reported among 12(42.9%) neonates each. Overall, mortality was noted in 7(25.0%) neonates, of whom 4(57.1%) neonates were having hypoplastic left heart syndrome. Conclusion: Transcatheter atrial septostomy done in neonatal period was found to have positive outcomes. Mortality remains high among neonates undergoing transcatheter atrial septostomy having hypoplastic left heart syndrome.

Bacillus atrophaeus HAB-5 is a plant growth-promoting rhizobacterium (PGPR) that exhibits several biotechnological traits, such as enhancing plant growth, colonizing the rhizosphere, and engaging in biocontrol activities. In this study, we conducted whole-genome sequencing of B. atrophaeus HAB-5 using the single-molecule real-time (SMRT) sequencing platform by Pacific Biosciences (PacBio; United States), which has a circular chromosome with a total length of 4,083,597 bp and a G + C content of 44.21%. The comparative genomic analysis of B. atrophaeus HAB-5 with other strains, Bacillus amyloliquefaciens DSM7, B. atrophaeus SRCM101359, Bacillus velezensis FZB42, B. velezensis HAB-2, and Bacillus subtilis 168, revealed that these strains share 2,465 CDSs, while 599 CDSs are exclusive to the B. atrophaeus HAB-5 strain. Many gene clusters in the B. atrophaeus HAB-5 genome are associated with the production of antimicrobial lipopeptides and polypeptides. These gene clusters comprise distinct enzymes that encode three NRPs, two Transat-Pks, one terpene, one lanthipeptide, one T3PKS, one Ripp, and one thiopeptide. In addition to the likely IAA-producing genes (trpA, trpB, trpC, trpD, trpE, trpS, ywkB, miaA, and nadE), there are probable genes that produce volatile chemicals (acoA, acoB, acoR, acuB, and acuC). Moreover, HAB-5 contained genes linked to iron transportation (fbpA, fetB, feuC, feuB, feuA, and fecD), sulfur metabolism (cysC, sat, cysK, cysS, and sulP), phosphorus solubilization (ispH, pstA, pstC, pstS, pstB, gltP, and phoH), and nitrogen fixation (nif3-like, gltP, gltX, glnR, glnA, nadR, nirB, nirD, nasD, narl, narH, narJ, and nark). In conclusion, this study provides a comprehensive genomic analysis of B. atrophaeus HAB-5, pinpointing the genes and genomic regions linked to the antimicrobial properties of the strain. These findings advance our knowledge of the genetic basis of the antimicrobial properties of B. atrophaeus and imply that HAB-5 may employ a variety of commercial biopesticides and biofertilizers as a substitute strategy to increase agricultural output and manage a variety of plant diseases.

Objective: To find out the mode of presentation and severity of cardiac involvement in children presented with established rheumatic heart diseases (RHD) at first presentation at a tertiary care setup. Study Design: Cross-sectional study. Place and Duration of the Study: Department of Pediatric Cardiology, National Institute of Cardiovascular Disease, Karachi Pakistan, from Jul 2020 to Jun 2021. Methodology: One hundred seven children of genders aged 3 to 18 with a confirmed diagnosis of RHD were included. Demographic features, chief presenting complaints, possible reasons for late presentation, and echocardiographic findings were noted. Results: In 107 patients with RHD, 63(58.9%) were male. The sensation of the rapid pounding of heartbeat (palpitation) was observed among 72(67.3%) children, while a similar number of cases, 72(67.3%), presented with breathlessness. There were 33(30.8%) children who were diagnosed for the first time with RHD. The most common reason for late diagnosis was socioeconomic factors in children (18, 26.1%). Primary prophylaxis for acute rheumatic fever (ARF)/RHD was noted to be given in 11(10.3%) patients. Mitral regurgitation was the most common echocardiographic finding in 103(96.3%), followed by aortic regurgitation in 99(92.5%). Conclusion: The majority of children with RHD are diagnosed late, which exposes them to an increased risk of undiagnosed valvular disorders. Palpitation, orthopnea and breathlessness were RHD most frequent presenting clinical features. Mitral regurgitation and aortic regurgitation were the most common echocardiographic findings.

Online group buying emerged as one of the most successful online business models. Online group buying refers to the online buying community's purchase of products and services significantly reduced from the regular retail price. According to previous studies, many factors can affect purchase intention on such a platform. This study developed a model that explains the factors influencing purchase intention in an online group buying website, it also proposed a model to study online group buying sustainability from the customer perspective. It considers the impact of sustainability dimensions, customer satisfaction, and website quality on customers' intention to buy. This study examines three dimensions of social, economic, and environmental factors on customers' intention to buy from online group buying websites for the first time. This study also addressed the related relationship between the sustainability dimensions in such a platform. The results show that each social, economic, and environmental dimension positively affects customer satisfaction. Moreover, the sustainability dimensions positively influence the purchase intention, while the environmental dimension has less impact, and the studied online group buying customers pay less attention to it, also, the satisfaction and quality of the website affect the purchase intention.

A large number of biological and pharmacological effects can be attributed to a class of naturally occurring compounds called terpenoids, also known as terpenes. These methods have shown promise in treating a wide range of infectious disorders, from bacteria and fungi to inflammation and even cancer. Terpenoids are built from two separate five-carbon-atom "backbones" in their most basic form. Monoterpenes (C10), sesquiterpenes (C15), diterpenes (C20), and sesterterpenes (C25) are only a few examples of the various terpenoid classes that may be broken down by the number of carbon atoms in their carbon skeletons. Synthesis may take place either chemically or biologically, and both methods can be employed to manufacture terpenoids. Drugs generated from terpenoids may be synthesised using a number of chemical routes; these routes are all now serving vital roles in modern medicine. This review will cover the multiple medicinal benefits of terpenoids, as well as their extraction and separation, structural elucidation, biosynthesis, chemical synthesis, and biosynthesis.

Abstract Protein–peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein–peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git .

Background and Objective: This study was conducted to evaluate the frequency and pattern of congenital heart defects (CHD) in the infant of pregestational and gestational diabetic mothers presenting at NICU and the causality department of NICH Karachi Pakistan. Methods: It was a cross-sectional study conducted at NICH Karachi, from July to October, 2022. Infants born to diabetic mothers were enrolled in the study and the frequency and pattern of congenital heart diseases were determined by echocardiography which was performed by a pediatric cardiologist who has had expertise in his field for more than 10 years. Results: A total of 147 infants were enrolled in the study with the majority of them being 0 to 15 days of age (n=138, 93.9%) and were male gender (n=88, 59.9%). Nearly a quarter of mothers had pre-gestational diabetes (n=35, 23.8%) while 112 (76.2%) mothers had gestational diabetes. Out of 147, 67 (45.57%) infants had congenital heart defects. The most common heart defect was PFO (n=35, 23%) followed by PDA (n=22, 14.9%), VSD (n=8, 5.4%), ASD (n=4, 2.7%), HCM (n=4, 2.7%), TOF (n=4, 2.7%) and TGA (n=2, 1.36%). A significant difference was seen for ASD (p

Purpose: In recent years, the issue of self-harm has emerged as a critical public health concern globally, drawing attention to its multifaceted nature and the complex interplay of factors contributing to its prevalence. We aim to underpin this cross-country relationship between self-harm and macroeconomic variables. Methods: A balanced annual macroeconomic indicator data spanning two decades (2000-2019) across seven Asian countries. quantile regression analysis was used along with a variety of conventional tests, including those for cross-sectional dependency, panel unit roots, and panel cointegration were conducted. Findings: The findings from quantile regression analysis revealed a variation in association among different indicators during different quantiles. Inflation, poverty, corruption, total labor force participation, and out of pocket expenditures showed a significant association while GDP growth and participants locality showed no significant association with prevalence of self-harm in target regions. Self-harm is also strongly associated with early indicators of Poverty. Implications: This study suggest that social and economic policy measures and programs related to tax cut, corruption control, health care safety and insurance should be implemented both prior to and during economic crises to prevent self-harm and loss of human capital of the society.

Key Clinical Message Cerebrofacial arteriovenous Metameric syndrome (CAMS) typically manifests as types I, II, or III, occasionally presenting as dual types. Our unique case underscores the coexistence of all three CAMS types in one patient. Furthermore, the concurrent acute cerebellar infarct underscores the need to consider CAMS in the differential diagnosis of adolescents experiencing neurological events.

Suspicious events or objects that differ from the norm in data can be discovered using anomaly identification. Identifying anomalies is critical for many applicable domains of life, e.g., preventing credit card theft and spotting intrusions into networks. It is possible to detect anomalies on a global scale as well as at the local level. A global outlier is a data point beyond the norm for the entire dataset, while a local outlier may be inside the norm for the entire dataset but outside the surrounding data points. Data outlier identification methods that are performed locally are inadequate. Therefore, better algorithms are required to investigate the high velocity of data and identify local outliers. Machine learning and data mining techniques need to be investigated to determine the pros and cons of anomaly identification residing inside data. The density based LOF method can be applied as the best choice to identify local outliers. While many variants of LOF exist for low-dimensional data, none are suitable for high-dimensional data. This research study discusses LOF, COF, and CBLOF methods for spotting local outliers in low and high-dimensional data. Regarding the size of the dimension, the performance of density-based algorithms is examined based on accuracy and time complexity. In this scenario, CBLOF achieves outstanding results due to its distinctive method of employing cluster-based local outlier detection.

In IoT environment applications generate continuous non-stationary data streams with in-built problems of concept drift and class imbalance which cause classifier performance degradation. The imbalanced data affects the classifier during concept detection and concept adaptation. In general, for concept detection, a separate mechanism is added in parallel with the classifier to detect the concept drift called a drift detector. For concept adaptation, the classifier updates itself or trains a new classifier to replace the older one. In case, the data stream faces a class imbalance issue, the classifier may not properly adapt to the latest concept. In this survey, we study how the existing work addresses the issues of class imbalance and concept drift while learning from nonstationary data streams. We further highlight the limitation of existing work and challenges caused by other factors of class imbalance along with concept drift in data stream classification. Results of our survey found that, out of 1110 studies, by using our inclusion and exclusion criteria, we were able to narrow the pool of articles down to 35 that directly addressed our study objectives. The study found that issues such as multiple concept drift types, dynamic class imbalance ratio, and multi-class imbalance in presence of concept drift are still open for further research. We also observed that, while major research efforts have been dedicated to resolving concept drift and class imbalance, not much attention has been given to with-in-class imbalance, rear examples, and borderline instances when they exist with concept drift in multi-class data. This paper concludes with some suggested future directions.

Objective This study aimed to assess the International Quality Improvement Collaborative single-site data from a developing country to identify trends in outcomes and factors associated with poor outcomes.Design Retrospective descriptive study.Setting The National Institute of Cardiovascular Diseases, Karachi, Pakistan.Participants Patients undergoing surgery for congenital heart disease (CHD).Outcome measure Key factors were examined, including preoperative, procedural and demographic data, as well as surgical complications and outcomes. We identified risk factors for mortality, bacterial sepsis and 30-day mortality using multivariable logistic regression.Results A total of 3367 CHD surgical cases were evaluated; of these, 59.4% (2001) were male and 82.8% (2787) were between the ages of 1 and 17 years. Only 0.2% (n=6) were infants (≤30 days) and 2.3% (n=77) were adults (≥18 years). The in-hospital mortality rate was 6.7% (n=224), and 4.4% (n=147) and 0.8% (n=27) had bacterial sepsis and surgical site infections, respectively. The 30-day status was known for 90.8% (n=3058) of the patients, of whom 91.6% (n=2800) were alive. On multivariable analysis, the adjusted OR for in-hospital mortality was 0.40 (0.29–0.56) for teenagers compared with infancy/childhood and 1.95 (1.45–2.61) for patients with oxygen saturation

Background: Brain abscess is a serious infection of brain parenchyma in patients with cyanotic congenital heart disease (CCHD) and around 25–46 % of unrepaired CCHD patients develop brain abscess. Aim of this study was to determine the incidence, clinical features, microbiology and factors associated with early and short term outcome of cerebral abscess in CCHD. Method: ology: This is a retrospective study, conducted at Pediatric cardiology department, National Institute of Cardiovascular Diseases (NICVD) Karachi. The data was collected from January 2019 to December 2021. All CCHD patients between 1 and 25 years of age were included. Data of patients with cerebral abscess was reviewed. Results: Among the 544 pediatric patients hospitalized in the last two years, brain abscesses were identified in 51 (9.3 %). Polycythemia (31.4 %) was the most significant contributing factor, especially in patients aged above 10 years. The most frequently seen CCHD was tetralogy of fallot (TOF) 60.8 %. Majority of the patients (84.3 %) had a single abscess while 15.7 % had multiple abscesses. E coli (9.7 %) was the most common isolated pathogen. Immediate complication identified was cerebral edema in 22 (43.1 %). Four patients (7.8 %) died, 47 (92.2 %) patients completed treatment course. 45.1 % had complete recovery however, 17 (33.3 %) had neurological deficits, 8 (15.7 %) had seizures and 2 (3.9 %) patients had residual abscess. Prolonged hospitalization was observed in patients of age group

Hospital Bed Capacity (HBC) planning affects economic and social sustainability in healthcare through bed capacity efficiency and medical treatment accessibility. Conventionally, this problem is solved using programming or simulation models with assumptions and limits. Forecasting the HBC using time series data on bed occupancy has been considered but not with factors such as the Number of Hospitalized Patients (NHP) and patient’s length of stay (LOS). This study proposes a data-driven methodology to forecast the HBC using Machine Learning (ML) and Deep Learning (DL). The LOS classification is performed using several ML techniques, including the Bayesian network, K-nearest neighbor, support vector machine, decision tree, and Linear regression. Also, the seasonal autoregressive integrated moving average, linear regression and Long short-term memory neural network are applied for the NHP forecasting. The forecasting and descriptive analysis outputs based on LOS classes are directly applied to a simple mathematical model to predict the required bed capacity. This methodology is applied in a case study in a heart ward at a public hospital. The data set includes 51231 records, and ML and DL algorithms are developed in Python. Results show that the heart ward’s bed capacity must be raised from 45 to 137 by 2026. In addition, several managerial recommendations are formulated.

This study explored and analyzed fly wings from the perspectives of hadith and science. It used a qualitative, analytical descriptive approach and integrated Harald Motzki's Isnad Cum Matn analysis with pharmaceutical studies. The research question was: How do hadith and pharmacy view fly wings? The results showed that 21 hadith books mention fly wings, including Sahih Bukhari. The hadith of the fly wings is valid, with Utbah bin Muslim as the common link (disseminator of hadith). From the perspective of pharmaceutical science, most microorganisms on flies, such as germs, viruses, and microbes, can cause diseases. However, drowning flies can neutralize these microorganisms against the fluid they perceive. This shows that science is in harmony with the hadith of the Prophet.

Background: Cotton (Gossypium hirsutum L.) is an important fiber crop that has a widespread cultivation in tropical and subtropical regions globally. The decline in cotton production over the last two decades may be attributed to the effects of climate change and imbalances in mineral nutrition. However, mineral nutrition, particularly micronutrients has been less focused in cotton production. Silicon (Si) and boron (B) are considered crucial micronutrients that play diverse functions in the physiological and biochemical development of plants, as well as in enhancing their resistance to abiotic stress. Methods: The present study investigated the impact of individual and combined foliar application of Si and B on the development of transgenic (Bt) cotton, as well as their impacts on boll retention, seed cotton production, and fiber quality indicators. Treatments included individual application of 2.0- and 4.0-mM Si and 0.5- and 1.0-mM B, and combined application 2.0 mM Si + 0.5 mM B, 2.0 mM Si + 1.0 mM B, 4 mM Si + 0.5 mM B and 4.0 mM Si + 1.0 mM B. Water spray and no foliar application were regarded as controls for comparison. Results: Combined application of 4 mM Si + 1.0 mM B resulted in the highest ginning out turn (39%), fiber uniformity (83%) and fiber length (28 mm). The longest plant height and the highest number of closed bolls per plant were recorded with sole application of 0.5 mM B, while sole application of 1 mM B produced the highest number of monopodial branches (20.26), sympodial branches (33.53) and total number of bolls (37.03) per plant. The highest boll weight (18.39 g), boll retention (64.38%) and seed cotton yield (1253.7 kg ha−1) were recorded for sole application of 1.0 mM B. Conclusion: The results revealed that combined foliar application of 4.0 mM Si + 0.5 or 1.0 0.5 mM fiber quality traits, whereas sole application of 1.0 mM B improved growth and yield related traits. Therefore, B and Si can be applied in combination to improve the fiber quality, whereas sole application of B could increase yield-related traits.

OBJECTIVES This study evaluated pain relief and functional outcomes in patients who underwent surgical stabilization following post-operative discitis. METHODOLOGY A cross-sectional was conducted at the Department of Orthopedic and Spine Surgery, Peshawar. The study duration spanned from 2020-2023. The inclusion criteria for participants involved patients who had experienced post-operative discitis and subsequently underwent surgical stabilization, with a stipulated minimum follow-up period of two weeks. Pain relief was measured using the Visual Analogue Score (VAS) for back and leg pain, while functional outcomes were evaluated using the Oswestry Disability Index (ODI). The acquired data were subjected to analysis utilizing SPSS version 26.0. RESULTS Out of the 35, 51.4% were male, and 48.6% were female, with a mean age of 47.5 years (SD ± 10.9 years). The most frequently affected lumbar spine level was L4-L5 in 57.1% of patients. 31.4% were smokers, and 45.7% had comorbidities, primarily consisting of diabetes or hypertension. Significant improvements were observed for back pain (baseline: 9.29 ± 0.71, post-stabilization: 5.37 ± 1.47, p = 0.001) and leg pain (baseline: 3.32 ± 2.17, post-stabilization: 1.91 ± 1.02, p = 0.001). Functional outcomes also showed substantial recovery (baseline: 51.57 ± 6.91, post-stabilization: 34.89 ± 6.85, p = 0.001). Preoperatively, the patients exhibited a spectrum of disability levels, with 29% experiencing severe disability. After two weeks of surgical intervention, substantial improvement was noted, 6% had severe disability. At the last follow-up, only 1% remained severely disabled, underlining the significant enhancement in functional outcomes post-stabilization. CONCLUSION Surgical stabilization is associated with significant pain relief and improved functional outcomes in patients with post-operative discitis.

The hydrothermal characteristics of fluid flow within a hexagonal enclosure hold immense significance due to their wide-ranging applications in various fields. Despite their potential importance, there is currently a limited understanding of free convection flow within these geometries. This study aims to tackle this aspect by considering the effect of cold diamond-shaped obstacles and periodic magnetic field. The cavity top inclined walls are uniformly heated while bottom inclined walls are considered as heaters. The straight top and bottom walls of the cavity are adiabatic. The numerical solution is obtained via finite element method. The results indicate a substantial augmentation of streamlines, x- and y-direction velocities, entropy and average Bejan number with increase in the Rayleigh number. Inducing stronger magnetic field force reduces x- and y-direction velocities, average Bejan number and entropy generation. The average Nusselt number improves by 76.16% for Rayleigh number ranging from 105 to 106 at several lengths of hot surface. Among the cases analyzed in this research, moving the diamonds from the middle (Case A) to top (Case B) and bottom (Case C) enhances and reduces the Nusselt number by 6.3% and 7.5%, respectively.

The prototype development of an automatic and floating structured hydropower plant constitutes a pioneering initiative aimed at addressing the critical issue of energy scarcity in remote areas. This research is centered on the creation of an innovative and sustainable energy solution that capitalizes on diverse water flow conditions. It involves design and construction of a floating hydropower plant that seamlessly integrates a Pico hydro turbine, generator, and control system. The IoT technology is incorporated to enable real-time monitoring and visualization of essential electrical parameters through mobile apps. In light of the escalating global demand for renewable energy solutions, this research holds immense significance. By extending access to electricity to off-grid communities, especially those residing in remote regions where conventional energy infrastructure is impractical, the proposed floating hydropower plant offers a reliable, eco-friendly power source. Its deployment in rivers and water bodies underscores its adaptability and potential to significantly alleviate energy deficits in underserved areas. As the world grapples with the pressing need for a transition toward sustainable energy sources, this project serves as a beacon of ingenuity and progress. By broadening the scope of the original research and exploring avenues for further enhancement, this endeavor underscores the commitment to a future where accessible, clean, and efficient power generation can transform the lives of communities plagued by energy scarcity. The outcomes and implications of this research endeavor not only contribute to the field of renewable energy but also provide a foundation for future innovations in sustainable power generation systems.

Protein structure prediction (PSP) is a vital challenge in bioinformatics, structural biology and drug discovery. Protein secondary structure (SS) prediction is critical since three-dimensional (3D) structures are primarily made up of secondary structures. With the advancement of deep learning approaches, SS classification accuracy has been significantly improved. Many existing methods use an ensemble of complex neural networks to improve SS prediction. Because of the high dimensionality of the hyperparameter space, deep neural networks with complex architectures are typically challenging to train effectively. Also, predicting secondary structures in the boundary regions between different types of SS is challenging. This study presents Multi-S3P, which employs bidirectional Long-Short-Term-Memory (BILSTM) and Convolutional Neural Networks (CNN) with a self-attention mechanism to improve the secondary structure prediction using an effective training strategy to capture the unique characteristics of each type of secondary structure and combine them more effectively. The ensemble of CNN and BILSTM can learn both contextual information and long-range interactions between the residues. In addition, using a self-attention mechanism allows the model to focus on the most important features for improving performance. We used the SPOT-1D dataset for the training and validation of our model using a set of four input features derived from amino acid sequences. Further, the model was tested on four popular independent test datasets and compared with various state-of-the-art predictors. The presented results show that Multi-S3P outperformed the other methods in terms of Q3, Q8 accuracy and other performance metrics, achieving the highest Q3 accuracy of 87.57% and a Q8 accuracy of 77.56% on the TEST2016 test set. More importantly, Multi-S3P demonstrates high performance in SS boundary regions. Our experiment also demonstrates that the combination of different input features and a multi-network-based training strategy significantly improved the performance.

Wheat is an important food crop worldwide, providing substantial calories and nourishment. Genetic variability in wheat germplasm is crucial for the development of cultivars with desirable features. This two years study (2020–21 and 2021–22) was conducted to evaluate 13 diverse wheat genotypes factorially combined with foliar-applied zinc sulphate (0, 0.4, 0.6%) arranged in a triplicate randomized complete block design. Boxplot analysis revealed the significant (P

This research proposes a mathematical model of cancer treatment with chemotherapy using a fractal fractional Mittag-Leffler operator with non-integer order. The model is analyzed both qualitatively and quantitatively. The control of cancer treatment with chemotherapy effects is established using a fractal fractional operator with a Mittag-Leffler kernel and control theory. The effect of global derivative, existence of unique solutions, and boundedness of proposed system is verified. Solutions are produced using a two-step Lagrange polynomial, and numerical simulations are carried out to illustrate the theoretical results. Cancer treatment with chemotherapy effects are verified from our justified results and predictions are made by simulation using MATLAB. Controllability and observability of the proposed system is also treated for linear control system to observe the close loop design with chemotherapy as an input and treated cells as the output.

Aims of the study: to clarify the clinical and histological evaluation of the oral folic acid effect on reduction of drug induced gingival enlargement. Materials and methods: The study was carried out in the college of dentistry /university of Mosul on twelve white local male Rabbits with gingival enlargement weights between (900 g±1.250g). six rabbits considered a control group and the other six rabbits considered a treated group which taking 15 mg orally folic acid once daily for about 14 days then depth of gingival sulcus(gingival pocket) evaluated clinically by a periodontal probe and euthaniused for histological examination. Result: Clinically: after 14 days of drug administration, there is a high clinical difference between two group (mean +- SD), control group have (3-5mm) of gingival enlargement and the experimental group have (1-3 mm) of gingival enlargement. The Histological finding show significant differences in the form of gingival sulcus depth(gingival pocket) and inflammatory cell infiltration and connective tissue in the control group and experimental group. Conclusion: the effect of oral administration of folic acid have higher evaluation on reducing the drug induced gingival enlargement in rabbit

Aims: This study aimed to evaluate the effect of azithromycin in reducing pathological gingival enlargement. Materials and methods: twelve adult New Zealand male rabbits weighing (900gm-1250mg) and almost the same in age and circumstance were chosen in this study. All animals with pathological gingival enlargement were clinically examined by using a periodontal probe and measuring their gingival sulcus depth. Then, six of them were slaughtered and considered the control group and the other six were experimental group, and at the end of study a specimens of oral mucosa were taken for immunohistochemical examinations. Results: Clinical examination shows there is no reduction in gingival enlargement in the control group while this enlargement decreased in the experimental group after use of azithromycin. Immunohistochemical analysis shows mild matrix metalloproteinase-1(MMP1) expression in the control group and moderate to strong MMP1 expression in sub-epithelial of gingiva in the experimental group. Conclusion: This study establishes that azithromycin is clinically and immunohistochemically effective in the reduction of pathological gingival enlargement.

Objective: To determine the relationship between transthoracic and transesophageal echocardiography in the anatomic and hemodynamic assessment of secundum atrial septal defects in children and adults. Study Design: Cross-sectional study. Place and Duration of Study: Department of Paediatric Cardiology, National Institute of Cardiovascular Diseases, Karachi Pakistan, from Jul 2020 to Jun 2021. Methodology: Seventy-seven patients above three years of age with secundum atrial septal defects were included. Linear regression was used to determine any statistically significant difference between transthoracic and transesophageal echocardiography approaches. Results: Of 77 patients, there were 37(48.1%) male and 40(51.9%) female. No significant difference was noted in terms of any of the clinical parameters during transthoracic echocardiography and transesophageal echocardiography. As per transthoracic echocardiography, 35(45.5%) were noted to be deviceable, while in transesophageal echocardiography, 36(46.8%) patients were found to be deviceable (p=0.872). No emergency exploration or deaths occurred. Linear regression showed no statistically significant difference between transthoracic echocardiogram and transesophageal echocardiography findings in terms of atrial septal defects size (p=0.226), total septal length (p=0.930), RV systolic pressure (p=0.440), RV functions and TAPSE (0.748) except LV ejection fraction function findings (p

Background: Spinal anesthesia has emerged as a safe and effective alternative for patients with lumbar spine degenerative disease, particularly those with comorbidities. The aim of this study was to investigate the role of spinal anesthesia in the management of lumbar disc degeneration at the Neurospinal and Cancer Care Institute in Karachi. Methodology: This prospective cohort study was conducted at the Neurospinal and Cancer Care Institute's Department of Neurosurgery. The study included patients with a mean age of 53.43 ± 8.11 years and was carried out from February 2019 to August 2022, following approval from the Institutional Review Board. Results: The study comprised predominantly high-risk patients, with 32 individuals (78%) having associated comorbidities. The American Society of Anesthesiologists (ASA) classification was used to assess the patients' risk level, with 3 patients (7.31%) classified as ASA grade I, 21 patients (51.21%) as ASA grade II, 16 patients (39.02%) as ASA grade III, and 2 patients (4.87%) as ASA grade IV. The most commonly affected level of disc degeneration was L4-L5 (63.41%), followed by L5-S1 (36.58%), with the majority of stenosis occurring at L4-S1. No complications such as urinary retention, vomiting, or dural tear were observed. Pain relief was assessed using the visual analogue scale (VAS), with 23 patients having a preoperative VAS score of 7, 14 patients with a score of 8, and 4 patients with a score of 9. Postoperatively, 18 patients (43.9%) had a VAS score of 2, 23 patients (56%) had a score of 1, and 5 patients (12%) had a score of 0. Conclusion: Based on the findings of this study, spinal anesthesia can be considered a suitable alternative to general anesthesia for patients with comorbidities or those classified as ASA grade I/II. This technique offers several advantages, including cost-effectiveness, shorter anesthesia duration, and fewer complications. These findings support the use of spinal anesthesia in patients with limited spinal pathology in the lumbar spine.

The presence of heavy metal, chromium (VI), in water environments leads to various diseases in humans, such as cancer, lung tumors, and allergies. This review comparatively examines the use of several adsorbents, such as biosorbents, activated carbon, nanocomposites, and polyaniline (PANI), in terms of the operational parameters (initial chromium (VI) concentration (Co), temperature (T), pH, contact time (t), and adsorbent dosage) to achieve the Langmuir's maximum adsorption capacity (qm) for chromium (VI) adsorption. The study finds that the use of biosorbents (fruit bio-composite, fungus, leave, and oak bark char), activated carbons (HCl-treated dry fruit waste, polyethyleneimine (PEI) and potassium hydroxide (KOH) PEI-KOH alkali-treated rice waste-derived biochar, and KOH/hydrochloric acid (HCl) acid/base-treated commercial), iron-based nanocomposites, magnetic manganese-multiwalled carbon nanotubes nanocomposites, copper-based nanocomposites, graphene oxide functionalized amino acid, and PANI functionalized transition metal are effective in achieving high Langmuir's maximum adsorption capacity (qm) for chromium (VI) adsorption, and that operational parameters such as initial concentration, temperature, pH, contact time, and adsorbent dosage significantly affect the Langmuir's maximum adsorption capacity (qm). Magnetic graphene oxide functionalized amino acid showed the highest experimental and pseudo-second-order kinetic model equilibrium adsorption capacities. The iron oxide functionalized calcium carbonate (IO@CaCO3) nanocomposites showed the highest heterogeneous adsorption capacity. Additionally, Syzygium cumini bark biosorbent is highly effective in treating tannery industrial wastewater with high levels of chromium (VI).

Objective: to find out the impact of cardiopulmonary bypass time (CPBT) and aortic cross-clamp time (ACCT) on immediate post-operative outcomes in patients with congenital heart disease (CHD) undergoing open heart surgery at a tertiary care centre. Study Design: Prospective longitudinal study. Place and Duration of the Study: Department of Paediatric, Cardiac Intensive Care Unit, National Institute of Cardiovascular Diseases (NICVD), Karachi Pakistan, from Jul to Dec 2021. Methodology: We enrolled 104 patients of both genders up to any age with CHD undergoing open heart surgeries. At the time of admission, age, gender, body weight, presence of cyanosis and types of CHDs were noted. Duration of mechanical ventilation, pediatric cardiac intensive care unit (PCICU) stay, and mortality was recorded. Baseline characteristics and postsurgery parameters were compared about CPBT and ACCT. Results: Of 104 patients, 58(55.8%) were males. Overall, the mean age was 7.9±5.1 years. Cyanosis was noted in 34(32.7%) cases. Mean CPBT was 74.6±42.9 minutes, while the mean ACCT was 43.5±29.5 minutes. Most types of CHDs were noted to be tetralogy of Fallot (ToF) 28(26.9%). An increase in the duration of CPBT and ACCT was found to have a significant association with post-surgical morbidity (p

Summary: Background: We aimed to explore the epidemiological, clinical, and phenotypic parameters of pediatric patients hospitalized with COVID-19 in Pakistan. Methods: This longitudinal cohort study was conducted in five tertiary care hospitals in Pakistan from March 2020 to December 2021. Data on various epidemiological and clinical variables were collected using Case Report Forms (CRFs) adapted from the WHO COVID-19 clinical data platform at baseline and at monthly follow-ups for 3 months. Findings: A total of 1090 children were included. The median age was 5 years (Interquartile range 1–10), and the majority presented due to new signs/symptoms associated with COVID-19 (57.8%; n = 631), the most common being general and respiratory symptoms. Comorbidities were present in 417 (38.3%) children. Acute COVID-19 alone was found in 932 (85.5%) children, 81 (7.4%) had multisystem inflammatory syndrome (MIS-C), 77 (7.0%) had overlapping features of acute COVID-19 and MIS-C, and severe disease was found in 775/1086 (71.4%). Steroids were given to 351 (32.2%) patients while 77 (7.1%) children received intravenous immunoglobulins. Intensive care unit (ICU) care was required in 334 (31.6%) patients, and 203 (18.3%) deaths were reported during the study period. The largest spike in cases and mortality was from July to September 2021 when the Delta variant first emerged. During the first and second follow-ups, 37 and 10 children expired respectively, and medical care after discharge was required in 204 (25.4%), 94 (16.6%), and 70 (13.7%) children respectively during each monthly follow-up. Interpretation: Our study highlights that acute COVID-19 was the major phenotype associated with high severity and mortality in children in Pakistan in contrast to what has been observed globally. Funding: The study was supported by the World Health Organization (WHO), which was involved in the study design but played no role in its analysis, writeup, or publication.

Background: The world's main crop for producing staple foods is wheat. In South Asia's cotton-wheat cropping system, however, a late cotton harvest causes a delayed wheat planting, which causes significant production losses. Therefore, this study investigated genetic diversity among wheat genotypes for late sowing. Additionally, associations between several yield-related parameters were discovered, along with their direct and indirect implications on the choice of the best genotypes suitable for late planting. Methods: Twenty wheat genotypes/advance lines were evaluated under two sowing times, i.e., normal sowing and late sowing for two consecutive years (2019–2021). Data regarding post anthesis traits were collected. Results: The findings showed that planting dates and genotypes substantially (p

Abstract Rampant rise of multidrug resistant strains among Gram-negative bacteria has necessitated investigation of alternative antimicrobial agents with novel modes of action including antimicrobial proteins such as bacteriocins. The main hurdle in the clinical development of bacteriocin biologics is their narrow specificity and limited strain activity spectrum. Genome mining of bacteria for broadly active bacteriocins have identified a number of promising candidates but attempts to improve these natural multidomain proteins further, for example by combining domains of different origin, have so far met with limited success. We have found that domain swapping of Pseudomonas bacteriocins of porin type, when carried out between phylogenetically related molecules with similar mechanism of activity, allows the generation of highly active molecules with broader spectrum of activity, for example by abolishing strain resistance due to the presence of immunity proteins. The most broadly active chimera engineered in this study, S5-PmnH, exhibits excellent control of Pseudomonas aeruginosa infection in validated murine keratitis and lung infection models.

In developing countries, like Pakistan, the pursuit of urbanization and economic development disrupts the delicate ecosystem, resulting in additional biogeochemical emissions of heavy metals into the human habitat and posing significant health risks. The levels of these trace elements in humans remain unknown in areas at higher risk of pollution in Pakistan. In this investigation, selected trace metals including Copper (Cu), Chromium (Cr), Lead (Pb) Cadmium (Cd), Cobalt (Co), Nickel (Ni), and Arsenic (As) were examined in human hair, urine, and nail samples of different age groups from three major cities (Muzaffargarh, Multan, and Vehari) in Punjab province, Pakistan. The results revealed that the mean concentrations (ppm) of Cr (1.1) and Cu (9.1) in hair was highest in Muzaffargarh. In urine samples, the mean concentrations (μg/L) of Co (93), As (79), Cu (69), Cr (56), Ni (49), Cd (45), and Pb (35) were highest in the Multan region, while As (34) and Cr (26) were highest in Vehari. The mean concentrations (ppm) of Ni (9.2), Cr (5.6), and Pb (2.8), in nail samples were highest in Vehari; however, Multan had the highest Cu (28) concentration (ppm). In urine samples, the concentrations of all the studied metals were within permissible limits except for As (34 µg/L) and Cr (26 µg/L) in Vehari. However, in nail samples, the concentrations of Ni in Multan (8.1 ppm), Muzaffargarh (9 ppm), Vehari (9.2 ppm), and Cd (3.69 ppm) in Muzaffargarh exceeded permissible limits. Overall, the concentrations of metals in urine, nail, and hair samples were higher in adults (39–45 age group). Cr, Cu, and Ni revealed significantly higher concentrations of metals in hair and water in Multan, whereas As in water was significantly (p < 0.001) correlated with urinary As in Multan, indicating that the exposure source was region-specific.