Study of Cation Distribution and Photocatalytic Activity of Nonthermal Plasma-Modified NiZnFe2O4 Magnetic Nanocomposites.

In this study, NiZnFe2O4 composite was synthesized using a sol-gel route and subjected to nonthermal plasma treatment for tailoring their cations' distribution and physicochemical, magnetic, and photocatalytic properties. Microwave plasma treatment was given to the composites for 60 min in support of postsynthesis sintering at 700 °C for 5 h. X-ray diffraction (XRD) analysis was conducted on pre- and postplasma-modified ferrite composites to identify phase-pure cubic spinel structure and cations' distribution. The cation distributions were measured from the ratio of XRD intensity peaks corresponding to (220), (311), (422) and (440) planes. The intensity ratio of plasma-treated ferrite composites decreased compared to that of pristine composites. The crystallite size and lattice constant were increased on plasma treatment of the composite. The morphological analysis showed nanoflower-like structures of the particles with an increased surface area in the plasma-treated composites. The plasma oxidation and sputtering effects caused a reduction in the nanoflower size. The energy bandgap increased with a decrease in particle size due to plasma treatment. The rhodamine B dye solution was then irradiated with a light source in the presence of the nanocomposites. The dye degradation efficiency of the composite photocatalyst increased from 80 to 96% after plasma treatment.

Design of a novel intelligent computing framework for predictive solutions of malaria propagation model.

The paper presents an innovative computational framework for predictive solutions for simulating the spread of malaria. The structure incorporates sophisticated computing methods to improve the reliability of predicting malaria outbreaks. The study strives to provide a strong and effective tool for forecasting the propagation of malaria via the use of an AI-based recurrent neural network (RNN). The model is classified into two groups, consisting of humans and mosquitoes. To develop the model, the traditional Ross-Macdonald model is expanded upon, allowing for a more comprehensive analysis of the intricate dynamics at play. To gain a deeper understanding of the extended Ross model, we employ RNN, treating it as an initial value problem involving a system of first-order ordinary differential equations, each representing one of the seven profiles. This method enables us to obtain valuable insights and elucidate the complexities inherent in the propagation of malaria. Mosquitoes and humans constitute the two cohorts encompassed within the exposition of the mathematical dynamical model. Human dynamics are comprised of individuals who are susceptible, exposed, infectious, and in recovery. The mosquito population, on the other hand, is divided into three categories: susceptible, exposed, and infected. For RNN, we used the input of 0 to 300 days with an interval length of 3 days. The evaluation of the precision and accuracy of the methodology is conducted by superimposing the estimated solution onto the numerical solution. In addition, the outcomes obtained from the RNN are examined, including regression analysis, assessment of error autocorrelation, examination of time series response plots, mean square error, error histogram, and absolute error. A reduced mean square error signifies that the model's estimates are more accurate. The result is consistent with acquiring an approximate absolute error close to zero, revealing the efficacy of the suggested strategy. This research presents a novel approach to solving the malaria propagation model using recurrent neural networks. Additionally, it examines the behavior of various profiles under varying initial conditions of the malaria propagation model, which consists of a system of ordinary differential equations.

Association of polymorphism in the promotor area of the caprine BMPR1B gene with litter size and body measurement traits in Damani goats.

This study aimed to explore polymorphisms in the promoter region of the caprine BMPR1B (Bone morphogenetic protein receptor 1 beta) gene and its association with body measurement and litter size traits in Damani does. A total of 53 blood samples were collected to analyze the association between the BMPR1B gene polymorphism and 11 phenotypic traits in Damani female goats. The results revealed that three novel SNPs were identified in the promoter region of the caprine BMPR1B gene, including g.67 A > C (SNP1), g.170 G > A(SNP2), and g.501A > T (SNP3), among which the SNP1 and SNP2 were significantly (p < 0.05) associated with litter size and body measurement traits in Damani goats. In SNP1 the AC genotype could be used as a marker for litter size, and the CC genotype for body weight in Damani goats. In SNP2, the genotype GG was significantly (p < 0.05) associated with ear and head length. Therefore, we can conclude from the present study, that genetic variants AC and CC of the caprine BMPR1B gene could be used as genetic markers for economic traits through marker-assisted selection for the breed improvement program of the Damani goat.

Investigating ischemia and reperfusion-induced organ damage in severe cardiac arrest: A comprehensive proteomics perspective.

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Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data.

The 2017 World Health Organization Fact Sheet highlights that coronary artery disease is the leading cause of death globally, responsible for approximately 30% of all deaths. In this context, machine learning (ML) technology is crucial in identifying coronary artery disease, thereby saving lives. ML algorithms can potentially analyze complex patterns and correlations within medical data, enabling early detection and accurate diagnosis of CAD. By leveraging ML technology, healthcare professionals can make informed decisions and implement timely interventions, ultimately leading to improved outcomes and potentially reducing the mortality rate associated with coronary artery disease. Machine learning algorithms create non-invasive, quick, accurate, and economical diagnoses. As a result, machine learning algorithms can be employed to supplement existing approaches or as a forerunner to them. This study shows how to use the CNN classifier and RNN based on the LSTM classifier in deep learning to attain targeted "risk" CAD categorization utilizing an evolving set of 450 cytokine biomarkers that could be used as suggestive solid predictive variables for treatment. The two used classifiers are based on these "45" different cytokine prediction characteristics. The best Area Under the Receiver Operating Characteristic curve (AUROC) score achieved is (0.98) for a confidence interval (CI) of 95; the classifier RNN-LSTM used "450" cytokine biomarkers had a great (AUROC) score of 0.99 with a confidence interval of 0.95 the percentage 95, the CNN model containing cytokines received the second best AUROC score (0.92). The RNN-LSTM classifier considerably beats the CNN classifier regarding AUROC scores, as evidenced by a p-value smaller than 7.48 obtained via an independent t-test. As large-scale initiatives to achieve early, rapid, reliable, inexpensive, and accessible individual identification of CAD risk gain traction, robust machine learning algorithms can now augment older methods such as angiography. Incorporating 65 new sensitive cytokine biomarkers can increase early detection even more. Investigating the novel involvement of cytokines in CAD could lead to better risk detection, disease mechanism discovery, and new therapy options.

A framework for the analysis of skin sores disease using evolutionary intelligent computing approach.

The most common and contagious bacterial skin disease i.e. skin sores (impetigo) mostly affects newborns and young children. On the face, particularly around the mouth and nose area, as well as on the hands and feet, it typically manifests as reddish sores. In this study, a neuro-evolutionary global algorithm is introduced to solve the dynamics of nonlinear skin sores disease model (SSDM) with the help of an artificial neural network. The global genetic algorithm is integrated with local sequential quadratic programming (GA-LSQP) to obtain the optimal solution for the proposed model. The designed differential model of skin sores disease is comprised of susceptible (S), infected (I), and recovered (R) categories. An activation function based neural network modeling is exploited for skin sores system through mean square error to achieve best trained weights. The integrated approach is validated and verified through the comparison of results of reference Adam strategy with absolute error analysis. The absolute error results give accuracy of around 10-11 to 10-5, demonstrating the worthiness and efficacy of proposed algorithm. Additionally, statistical investigations in form of mean absolute deviation, root mean square error, and Theil's inequality coefficient are exhibited to prove the consistency, stability, and convergence criteria of the integrated technique. The accuracy of the proposed solver has been examined from the smaller values of minimum, median, maximum, mean, semi-interquartile range, and standard deviation, which lie around 10-12 to 10-2.

ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction.

Enhancers play an important role in the process of gene expression regulation. In DNA sequence abundance or absence of enhancers and irregularities in the strength of enhancers affects gene expression process that leads to the initiation and propagation of diverse types of genetic diseases such as hemophilia, bladder cancer, diabetes and congenital disorders. Enhancer identification and strength prediction through experimental approaches is expensive, time-consuming and error-prone. To accelerate and expedite the research related to enhancers identification and strength prediction, around 19 computational frameworks have been proposed. These frameworks used machine and deep learning methods that take raw DNA sequences and predict enhancer's presence and strength. However, these frameworks still lack in performance and are not useful in real time analysis. This paper presents a novel deep learning framework that uses language modeling strategies for transforming DNA sequences into statistical feature space. It applies transfer learning by training a language model in an unsupervised fashion by predicting a group of nucleotides also known as k-mers based on the context of existing k-mers in a sequence. At the classification stage, it presents a novel classifier that reaps the benefits of two different architectures: convolutional neural network and attention mechanism. The proposed framework is evaluated over the enhancer identification benchmark dataset where it outperforms the existing best-performing framework by 5%, and 9% in terms of accuracy and MCC. Similarly, when evaluated over the enhancer strength prediction benchmark dataset, it outperforms the existing best-performing framework by 4%, and 7% in terms of accuracy and MCC.

A prospective observational study of estimating drug related problems and clinical outcomes in subtypes of stroke patients.

BACKGROUND: Stroke is a neurological disease and a leading cause of mortality worldwide. Strokes mainly consist of two types: hemorrhage and ischemia. Stroke patients are being administered multiple drug therapy and are at risk of drug-related problems. AIM: To estimate drug-related problems (DRPs) and clinical end outcomes in hospitalized stroke patients. METHODS: Current study was a multicenter, cross-sectional prospective observational study including 250 stroke patients admitted to tertiary care hospitals in Karachi, Pakistan. The study included all clinical subtypes of stroke patients i.e. Stroke, Ischemic stroke, Hemorrhagic stroke, CVA, and TIA. Associations among patient-clinical end outcomes and drug therapy-related variables like DRPs, mortality, and morbidity rates were estimated using Pearson's chi-squared test. Statistical analysis was done by using SPSS software, version 25. RESULTS: A total of 250 patients participated in this study suffering from different clinical subtypes of stroke i.e. Ischemic stroke, hemorrhagic stroke, TIA, and CVA, including 46% male and 54% female patients. The majority of patients' stay at the hospital was between 1-10 days. The overall mortality rate in stroke patients was 51%. HAIs were observed in 70% of patients, HAIs faced by patients were SAP, CAP, UTI, sepsis, and VAP. Drugs were assessed according to NEML i.e. access group antibiotics, watch group antibiotics, reserve group antibiotics, statins, antiepileptics, and proton pump inhibitors. Majorly ceftriaxone was administered to 79% of patients, piperacillin-tazobactam to 52%, and cefixime to 48%, whereas meropenem was administered to 42% of patients along with vancomycin to 39% of total patients. A high mortality rate was observed in the case of Klebsiella pneumoniae and Staphylococcus aureus i.e. 78% and in the case of streptococcus pneumoniae 61% mortality rate was observed. Due to the presence of DRPs and various other clinical factors like comorbidities, DDIs, HAIs, administration of potentially nephrotoxic drugs, and administration of antibiotics without having CST, hospitalized stroke patients faced many problems. CONCLUSION: This study helped determine DRPs along with various clinical factors affecting the clinical end outcomes of patients suffering from any clinical subtype of stroke. Due to the enhancement in the evidence of the incidence of DRPs in tertiary care hospitals, pharmacist-led drug therapy review by interfering with doctors and other medical professionals at the patient bed site is needed and should be done to avoid any negative end outcomes and serious issues related to DRPs.

Facile-Solution-Processed Silicon Nanofibers Formed on Recycled Cotton Nonwovens as Multifunctional Porous Sustainable Materials.

Limited efficiency, lower durability, moisture absorbance, and pest/fungal/bacterial interaction/growth are the major issues relating to porous nonwovens used for acoustic and thermal insulation in buildings. This research investigated porous nonwoven textiles composed of recycled cotton waste (CW) fibers, with a specific emphasis on the above-mentioned problems using the treatment of silicon coating and formation of nanofibers via facile-solution processing. The findings revealed that the use of an economic and eco-friendly superhydrophobic (contact angle higher than 150°) modification of porous nonwovens with silicon nanofibers significantly enhanced their intrinsic characteristics. Notable improvements in their compactness/density and a substantial change in micro porosity were observed after a nanofiber network was formed on the nonwoven material. This optimized sample exhibited a superior performance in terms of stiffness, surpassing the untreated samples by 25-60%. Additionally, an significant enhancement in tear strength was observed, surpassing the untreated samples with an impressive margin of 70-90%. Moreover, the nanofibrous network of silicon fibers on cotton waste (CW) showed significant augmentation in heat resistance ranging from 7% to 24% and remarkable sound absorption capabilities. In terms of sound absorption, the samples exhibited a performance comparable to the commercial standard material and outperformed the untreated samples by 20% to 35%. Enhancing the micro-roughness of fabric via silicon nanofibers induced an efficient resistance to water absorption and led to the development of inherent self-cleaning characteristics. The antibacterial capabilities observed in the optimized sample were due to its superhydrophobic nature. These characteristics suggest that the proposed nano fiber-treated nonwoven fabric is ideal for multifunctional applications, having features like enhanced moisture resistance, pest resistance, thermal insulation, and sound absorption which are essential for wall covers in housing.

Identification of potential natural products derived from fungus growing termite, inhibiting Pseudomonas aeruginosa quorum sensing protein LasR using molecular docking and molecular dynamics simulation approach.

Pseudomonas aeruginosa, the most common opportunistic pathogen, is becoming antibiotic-resistant worldwide. The fate of P. aeruginosa, a multidrug-resistant strain, can be determined by multidrug efflux pumps, enzyme synthesis, outer membrane protein depletion, and target alterations. Microbial niches have long used quorum sensing (QS) to synchronize virulence gene expression. Computational methods can aid in the development of novel P. aeruginosa drug-resistant treatments. The tripartite symbiosis in termites that grow fungus may help special microbes find new antimicrobial drugs. To find anti-quorum sensing natural products that could be used as alternative therapies, a library of 376 fungal-growing termite-associated natural products (NPs) was screened for their physicochemical properties, pharmacokinetics, and drug-likeness. Using GOLD, the top 74 NPs were docked to the QS transcriptional regulator LasR protein. The five lead NPs with the highest gold score and drug-like properties were chosen for a 200-ns molecular dynamics simulation to test the competitive activity of different compounds against negative catechin. Fridamycin and Daidzein had stable conformations, with mean RMSDs of 2.48 and 3.67 Å, respectively, which were similar to Catechin's 3.22 Å. Fridamycin and Daidzein had absolute binding energies of -71.186 and -52.013 kcal/mol, respectively, which were higher than the control's -42.75 kcal/mol. All the compounds within the active site of the LasR protein were kept intact by Trp54, Arg55, Asp67, and Ser123. These findings indicate that termite gut and fungus-associated NPs, specifically Fridamycin and Daidzein, are potent QS antagonists that can be used to treat P. aeruginosa's multidrug resistance.Communicated by Ramaswamy H. Sarma.

Assistive technology-based solutions in learning mathematics for visually-impaired people: exploring issues, challenges and opportunities.

In the absence of vision, visually impaired and blind people rely upon the tactile sense and hearing to obtain information about their surrounding environment. These senses cannot fully compensate for the absence of vision, so visually impaired and blind people experience difficulty with many tasks, including learning. This is particularly true of mathematical learning. Nowadays, technology provides many effective and affordable solutions to help visually impaired and blind people acquire mathematical skills. This paper is based upon a systematic review of technology-based mathematical learning solutions for visually impaired people and discusses the findings and objectives for technological improvements. It analyses the issues, challenges and limitations of existing techniques. We note that audio feedback, tactile displays, a supportive academic environment, digital textbooks and other forms of accessible math applications improve the quality of learning mathematics in visually impaired and blind people. Based on these findings, it is suggested that smartphone-based solutions could be more convenient and affordable than desktop/laptop-based solutions as a means to enhance mathematical learning. Additionally, future research directions are discussed, which may assist researchers to propose further solutions that will improve the quality of life for visually impaired and blind people.

5-Fluorouracil-Loaded Hyaluronic Acid-Coated Niosomal Vesicles: Fabrication and Ex Vivo Evaluation for Skin Drug Delivery.

5-Fluorouracil (5-FU) is one of the most potent drugs against solid tumors. However, its parenteral administration is associated with systemic toxicity, while its topical application has limited percutaneous absorption. To overcome these limitations, the current study undertakes the formulation of 5-FU as niosomal vesicles that were coated with hyaluronic acid to improve its targeting efficiency for cancer cells. The niosomes were prepared by the thin-film hydration method using cholesterol as physiological lipid and nonionic surfactants (Tween 80 and Span 80) in the ratio of 1:1. The niosomal vesicles were characterized for their size, size distribution, viscosity, surface tension, density, and drug entrapment efficiency. The vesicles were within the particle size range of 337-478 nm with relatively homogeneous particle size distribution (PDI ≤ 0.5). The ζ-potential and drug entrapment efficiency of coated formulations (F2 and F4) were comparatively higher than corresponding noncoated formulations (F1 and F3). The release behavior of 5-FU from niosomal vesicles using a dialysis membrane depicts that initial burst drug release was higher for F1 and F3 due to their smaller particle size in comparison to their coated counterparts. However, the release was more controlled for F4 due to the larger particle size, higher viscosity, and entrapped fraction of the formulation. The permeation of the drug through the rat's skin was comparatively higher in the case of noncoated formulations than their coated counterparts (p ≤ 0.05). This could be attributed to their small particle size and lower surface tension. In the case of coated formulations, the hydrophilic hyaluronic acid hinders the permeation of the drug through the lipid bilayer membrane of the skin. The retention of the drug in the skin was found to be in the range of 20-40%, which is sufficient to achieve optimum drug concentration in the tumorous tissue. Overall, the study successfully designed novel niosomal carrier systems for improved 5-FU delivery after topical application.

Neurological Improvement via Lysophosphatidic Acid Administration in a Rodent Model of Cardiac Arrest-Induced Brain Injury.

Lysophosphatidic acid (LPA) serves as a fundamental constituent of phospholipids. While prior studies have shown detrimental effects of LPA in a range of pathological conditions, including brain ischemia, no studies have explored the impact of LPA in the context of cardiac arrest (CA). The aim of this study is to evaluate the effects of the intravenous administration of an LPA species containing oleic acid, LPA (18:1) on the neurological function of rats (male, Sprague Dawley) following 8 min of asphyxial CA. Baseline characteristics, including body weight, surgical procedure time, and vital signs before cardiac arrest, were similar between LPA (18:1)-treated (n = 10) and vehicle-treated (n = 10) groups. There was no statistically significant difference in 24 h survival between the two groups. However, LPA (18:1)-treated rats exhibited significantly improved neurological function at 24 h examination (LPA (18:1), 85.4% ± 3.1 vs. vehicle, 74.0% ± 3.3, p = 0.045). This difference was most apparent in the retention of coordination ability in the LPA (18:1) group (LPA (18:1), 71.9% ± 7.4 vs. vehicle, 25.0% ± 9.1, p < 0.001). Overall, LPA (18:1) administration in post-cardiac arrest rats significantly improved neurological function, especially coordination ability at 24 h after cardiac arrest. LPA (18:1) has the potential to serve as a novel therapeutic in cardiac arrest.

Cattaneo-Christov heat flow model at mixed impulse stagnation point past a Riga plate: Levenberg-Marquardt backpropagation method.

Applications of artificial intelligence (AI) via soft computing procedures have attracted the attention of researchers due to their effective modeling, simulation procedures, and detailed analysis. In this article, the designing of intelligence computing through a neural network that is backpropagated with the Levenberg-Marquardt method (NN-BLMM) to study the Cattaneo-Christov heat flow model at the mixed impulse stagnation point (CCHFM-MISP) past a Riga plate is investigated. The original model CCHFM-MISP in terms of PDEs is converted into non-linear ODEs through suitable similarity variables. A data set is generated for all scenarios of CCHFM-MISP through Lobatto IIIA numerical solver by varying Hartman number, velocity ratio parameter, inverse Darcy number, mixed impulse variable, non-dimensional constraint, Eckert number, heat generation variable, Prandtl number, thermal relaxation variable. To find the physical impacts of parameters of interest associated with the presented fluidic system CCHFM-MISP, the approximate solution of NN-BLMM is carried out by performing training (80 %), testing (10 %), and validation (10 %), and then the results are equated with the reference data to ensure the perfection of the proposed model. Through MSE, state transition, error histogram, and regression analysis, the outcomes of NN-BLMM are presented and analyzed. The graphical illustration and numerical outcomes confirm the authentication and effectiveness of the solver. Moreover, mean square errors for validation, training and testing data points along with performance measures lie around 10-10 and the solution plots generated through deterministic (Lobatto IIIA) approach and stochastic numerical solver are matching up to 10-6, which surely validate the solver NN-BLMM. The outcomes of M and B on velocity present the similar impacts. The velocity of material particles decreases under Da while, it increases through velocity ratio and magnetic parameters.

COVID-19-Associated Portal Vein Thrombosis Post-Cholecystitis.

This case study describes a 45-year-old Caucasian male with a past medical history of obesity, hypertension, and non-insulin-dependent diabetes mellitus, who in the setting of coronavirus disease 2019 (COVID-19) pneumonia, developed portal vein thrombosis (PVT) presenting as an acute abdomen after hospital discharge from a cholecystitis episode. PVT is a very infrequent thromboembolic condition, classically occurring in patients with systemic conditions such as cirrhosis, malignancy, pancreatitis, diverticulitis, autoimmunity, and thrombophilia. PVT can cause serious complications, such as intestinal infarction, or even death, if not promptly treated. Due to the limited number of reports in the literature describing PVT in the COVID-19 setting, its prevalence, natural history, mechanism, and precise clinical features remain unknown. Therefore, clinical suspicion should be high for PVT, in any COVID-19 patient who presents with abdominal pain or associated signs and symptoms. To the best of our knowledge, this is the first report of COVID-19-associated PVT causing extensive thrombosis in the portal vein and its right branch, occurring in the setting of early-stage cirrhosis after a preceding episode of cholecystitis.

Intelligent computing for MHD radiative Von Kármán Casson nanofluid along Darcy-Fochheimer medium with activation energy.

The impact of activation energy in chemical processes, heat radiations, and temperature gradients on non-Darcian steady MHD convective Casson nanofluid flows (NMHD-CCNF) over a radial elongated circular cylinder is investigated in this study. The network of partial differential equations (PDEs) for NMHD-CCNF is developed using the modified Buongiorno framework, and the network of controlling PDEs is then transformed into ordinary differential equations (ODEs) utilizing the Von Karman method. Finally, the resulting non-linear ODEs are computed using the ND-solve approach to produce sets of data to assess the proposed model's skills, which can then be handled using the Bayesian Regularization technique of artificial neural networks (BRT-ANN). A novel stochastic computing-based application is being developed to evaluate the importance of NMHD-CCNF across a spinning disc that is radially stretched. The novelty and significance of results for better understanding, clarity, and highlighting the innovative contributions and significance of the proposed scheme. Further, to check the validity of the defined results for NMHD-CCNF, error charts, validation, and mean squared error suggestions are employed. The impact of multiple physical parameters on concentration, radial and tangential velocities, and temperature profiles is shown via tables and figures. Additionally, the results demonstrate that as the Forchheimer number, Casson nanofluid parameter, magnetic parameter, and porosity parameter are strengthened, the radial and rotational nanofluid mobility drops dramatically. The stretching parameter, on the other hand, has a parallel developmental trend. The heat generation parameter, the thermophoresis process, the thermal radiation parameter, and the Brownian motion of nanoparticles can all be increased to give thermal enhancement. On the other side, with larger estimates in thermophoresis parameters and the activation energy, there is a noticeable increase in the concentration profile.

Exploring transfer learning in chest radiographic images within the interplay between COVID-19 and diabetes.

The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors. Based on previous research, we implemented deep-learning models to diagnose the infection via chest x-ray images in coronavirus patients. Three Thousand (3000) x-rays of the chest are collected through freely available resources. A council-certified radiologist discovered images demonstrating the presence of COVID-19 disease. Inception-v3, ShuffleNet, Inception-ResNet-v2, and NASNet-Large, four standard convoluted neural networks, were trained by applying transfer learning on 2,440 chest x-rays from the dataset for examining COVID-19 disease in the pulmonary radiographic images examined. The results depicted a sensitivity rate of 98 % (98%) and a specificity rate of almost nightly percent (90%) while testing those models with the remaining 2080 images. In addition to the ratios of model sensitivity and specificity, in the receptor operating characteristics (ROC) graph, we have visually shown the precision vs. recall curve, the confusion metrics of each classification model, and a detailed quantitative analysis for COVID-19 detection. An automatic approach is also implemented to reconstruct the thermal maps and overlay them on the lung areas that might be affected by COVID-19. The same was proven true when interpreted by our accredited radiologist. Although the findings are encouraging, more research on a broader range of COVID-19 images must be carried out to achieve higher accuracy values. The data collection, concept implementations (in MATLAB 2021a), and assessments are accessible to the testing group.

Dual S-Scheme Heterojunction CdS/TiO2/g-C3N4 Photocatalyst for Hydrogen Production and Dye Degradation Applications.

This study investigated a ternary CdS/TiO2/g-C3N4 heterojunction for degrading synthetic dyes and hydrogen production from aqueous media through visible light-initiated photocatalytic reactions. CdS, TiO2, and g-C3N4 were combined in different mass ratios through a simple hydrothermal method to create CdS/TiO2/g-C3N4 composite photocatalysts. The prepared heterojunction catalysts were investigated by using FTIR, XRD, EDX, SEM, and UV-visible spectroscopy analysis for their crystal structures, functional groups, elemental composition, microtopography, and optical properties. The rhodamine B dye was then degraded by using fully characterized photocatalysts. The maximum dye degradation efficiency of 99.4% was noted in these experiments. The evolution rate of hydrogen from the aqueous solution with the CdS/TiO2/g-C3N4 photocatalyst remained 2910 µmol·h-1·g-1, which is considerably higher than those of g-C3N4, CdS, CdS/g-C3N4, and g-C3N4/TiO2-catalyzed reactions. This study also proposes a photocatalytic activity mechanism for the tested ternary CdS/TiO2/g-C3N4 heterojunctions.

Assessment of potential drug-related problems (PDRP) and clinical outcomes in bacterial meningitis patients admitted to tertiary care hospitals.

Meningitis is an important cause of morbidity and mortality in children and adults. Its treatment strategy varies with age and gender. To assess potential drug-related problems (PDRP) and clinical outcomes in bacterial meningitis patients, a multicenter, clinical, descriptive, cross-sectional prospective observational study in 120 patients admitted to different tertiary care hospitals in Karachi was conducted. It includes both males 48% and females 52% belonging from all age groups i.e. peadiatrics (01 to 12 years), adults (18 to 65 years), and geriatrics (66 to 75 years). Out of these 72 patients were admitted in the public sector and 48 patients were admitted in private sector hospitals. Nosocomial infections were developed in 41% of patients during their stay at the hospital. Potentially nephrotoxic drugs were administered to all BM patients, these drugs should be administered carefully. Majorly Ceftriaxone was administered to 86% of patients, Vancomycin 71%, and meropenem 73% whereas 68% of patients were administered piperacillin-tazobactam. Organisms involved as causative agents in the majority of patients are Neisseria meningitides, Pseudomonas aeruginosa and, Streptococcus pneumoniae. DRPs impacted patient clinical outcomes in presence of many other factors like comorbidities, DDIs, Nis, administration of potentially nephrotoxic drugs, and administration of watch group and reserve group antibiotics without having culture sensitivity test, even after having CST no principles of de-escalation for antibiotics were done, which is a very important factor for hospitalized patients having IV antibiotics. The mortality rate among BM patients was 66%. The majority of patients (87%) stay at the hospital was 1-10 days. The present study helped in the identification of DRPs along with some other factors affecting the clinical outcomes in patients suffering from bacterial meningitis. Healthcare professionals should receive awareness and education on the importance of CST before initiating antibiotic therapy. Pharmacist-led medication review is necessary and should be followed to avoid negative outcomes and serious consequences related to DRPs.

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection.

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model's resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.