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Purpose: Damage Control Surgery (DCS) refers to a staged laparotomy performed in patients who have suffered severe blunt or penetrating abdominopelvic trauma with the goal of managing critical injuries while avoiding life threatening metabolic derangements. Within 24 h of the initial laparotomy, computed tomography (CT) is used to assess the full extent of injuries. The purpose of this study was to assess the incidence of clinically significant unknown abdominopelvic injuries which required further dedicated surgical or interventional radiology management and failed surgical repairs identified on CT following initial laparotomy. Methods: CT findings were correlated with surgical findings from the initial and subsequent staged laparotomy to determine known and unknown injuries. Frequency and percentage analyses was performed. Results: Out of 63 patients who underwent DCS with an open abdomen following initial laparotomy and subsequent CT within 24 h, a total of 13 clinically significant abdominopelvic injuries were identified in 12 patients. Seven clinically significant injuries were identified in seven patients (11.1% of patients) in surgically explored areas. Six clinically significant injuries were identified in six patients (9.5%) in surgically unexplored areas. Four instances of failed initial surgical repair were identified in four patients (6.3%) involving the liver and gastrointestinal tract. Overall, 23.8% of the DCS patient population had an actionable finding on the post laparotomy CT. Conclusion: CT demonstrated value for identifying the extent of clinically significant abdominopelvic injuries and evidence of failed initial surgical repair, which informed surgical planning for subsequent laparotomy. The authors advocate for performing CT in post-DCS patients with an open abdomen as soon as possible following correction of metabolic and hemodynamic derangements. [ABSTRACT FROM AUTHOR]

Purpose: We wished to evaluate if an open-source artificial intelligence (AI) algorithm (https://www.childfx.com) could improve performance of (1) subspecialized musculoskeletal radiologists, (2) radiology residents, and (3) pediatric residents in detecting pediatric and young adult upper extremity fractures. Materials and methods: A set of evaluation radiographs drawn from throughout the upper extremity (elbow, hand/finger, humerus/shoulder/clavicle, wrist/forearm, and clavicle) from 240 unique patients at a single hospital was constructed (mean age 11.3 years, range 0–22 years, 37.9% female). Two fellowship-trained musculoskeletal radiologists, three radiology residents, and two pediatric residents were recruited as readers. Each reader interpreted each case initially without and then subsequently 3–4 weeks later with AI assistance and recorded if/where fracture was present. Results: Access to AI significantly improved area under the receiver operator curve (AUC) of radiology residents (0.768 [0.730–0.806] without AI to 0.876 [0.845–0.908] with AI, P < 0.001) and pediatric residents (0.706 [0.659–0.753] without AI to 0.844 [0.805–0.883] with AI, P < 0.001) in identifying fracture, respectively. There was no evidence of improvement for subspecialized musculoskeletal radiology attendings in identifying fracture (AUC 0.867 [0.832–0.902] to 0.890 [0.856–0.924], P = 0.093). There was no evidence of difference between overall resident AUC with AI and subspecialist AUC without AI (resident with AI 0.863, attending without AI AUC 0.867, P = 0.856). Overall physician radiograph interpretation time was significantly lower with AI (38.9 s with AI vs. 52.1 s without AI, P = 0.030). Conclusion: An openly accessible AI model significantly improved radiology and pediatric resident accuracy in detecting pediatric upper extremity fractures. [ABSTRACT FROM AUTHOR]

Data categorization is a top concern in medical data to predict and detect illnesses; thus, it is applied in modern healthcare informatics. In modern informatics, machine learning and deep learning models have enjoyed great attention for categorizing medical data and improving illness detection. However, the existing techniques, such as features with high dimensionality, computational complexity, and long-term execution duration, raise fundamental problems. This study presents a novel classification model employing metaheuristic methods to maximize efficient positives on Chronic Kidney Disease diagnosis. The medical data is initially massively pre-processed, where the data is purified with various mechanisms, including missing values resolution, data transformation, and the employment of normalization procedures. The focus of such processes is to leverage the handling of the missing values and prepare the data for deep analysis. We adopt the Binary Grey Wolf Optimization method, a reliable subset selection feature using metaheuristics. This operation is aimed at improving illness prediction accuracy. In the classification step, the model adopts the Extreme Learning Machine with hidden nodes through data optimization to predict the presence of CKD. The complete classifier evaluation employs established measures, including recall, specificity, kappa, F-score, and accuracy, in addition to the feature selection. Data related to the study show that the proposed approach records high levels of accuracy, which is better than the existing models. [ABSTRACT FROM AUTHOR]

Objective: Determine the utility of ZTE as an adjunct to routine MR for assessing degenerative disease in the cervical spine. Methods: Retrospective study on 42 patients with cervical MR performed with ZTE from 1/1/2022 to 4/30/22. Fellowship trained radiologists evaluated each cervical disc level for neural foraminal (NF) narrowing, canal stenosis (CS), facet arthritis (FA), and presence of ossification of the posterior longitudinal ligament (OPLL). When NF narrowing and CS were present, the relative contributions of bone and soft disc were determined and a confidence level for doing so was assigned. Comparisons were made between assessments on routine MR without and with ZTE. Results: With ZTE added, bone contribution as a cause of NF narrowing increased in 47% (n = 110) of neural foramina and decreased in 12% (n = 29) (p = < 0.001). Bone contribution as a cause of CS increased in 25% (n = 33) of disc levels and decreased in 10% (n = 13) (p = 0.013). Confidence increased in identifying the cause of NF narrowing (p = < 0.001)) and CS (p = 0.009) with ZTE. The cause of NF narrowing (p = 0.007) and CS (p = 0.041) changed more frequently after ZTE was added when initial confidence in making the determination was low. There was no change in detection of FA or presence of OPLL with ZTE. Conclusion: Addition of ZTE to a routine cervical spine MR changes the assessment of the degree of bone involvement in degenerative cervical spine pathology. [ABSTRACT FROM AUTHOR]

The study was designed to analyze the quality of education between public and PEF School at elementary level in southern Punjab. Main objective of the study was: To identify standard of quality education between Public and PEF school: To compare the quality of education between Public and PEF school. This research was quantitative and descriptive in nature. The target population was 120 Public & PEF elementary schools in five districts (Bahawalpur, Bahawalnagar, Multan, Rahim Yar Khan & Lodhran) of the southern Punjab. A sample size of 410 respondents was used over random sampling techniques. The results of study provide significant information in reaching the conclusion. The study concluded that Public & PEF schools also provides teacher training programs to enhance the skills of teachers. The training programs provided by public and PEF schools have helped the concerned teachers to improve their teaching techniques, resulting in the better learning experience for the students. Therefore, it is essential to continue evaluating their efforts to ensure that all children have access to quality education. [ABSTRACT FROM AUTHOR]

Glaucoma is a progressive eye disease that can lead to blindness if left untreated. Early detection is crucial to prevent vision loss, but current manual scanning methods are expensive, time-consuming, and require specialized expertise. This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine (EGWO-SVM) method. The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter (AMF) and feature extraction using the previously processed speeded-up robust feature (SURF), histogram of oriented gradients (HOG), and Global features. The enhanced Grey Wolf Optimization (GWO) technique is then employed with SVM for classification. To evaluate the proposed method, we used the online retinal images for glaucoma analysis (ORIGA) database, and it achieved high accuracy, sensitivity, and specificity rates of 94%, 92%, and 92%, respectively. The results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of Glaucoma. This study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: A radial basis function neural network (RBFNN) is proposed for identifying and diagnosing non-linear systems. The neural network developed was optimized not only for RBFNN output layer parameters, including centers, width, and weights, but also for network size (the count of neurons stored in the hidden layer). Two optimization methods, namely, particle swarm optimization (PSO) and hybrid PSO coupled with a spiral-shaped mechanism (HPSO-SSM), were used to train the RBFNN hyperparameters and network size. Simulation results showed that the suggested technique outperformed other current approaches with respect to prediction precision and network compactness. A method is proposed for recognizing and predicting non-linear systems employing a radial basis function neural network (RBFNN) and robust hybrid particle swarm optimization (HPSO) approach. A PSO is coupled with a spiral-shaped mechanism (HPSO-SSM) to optimize the PSO performance by mitigating its constraints, such as sluggish convergence and the local minimum dilemma. Three advancements are incorporated into the hypothesized HPSO-SSM algorithms to achieve remarkable results. First, the diversity of the search process is promoted to update the inertial weight ω based on the logistic map sequence. Then, two distinct parameters are trained in the original position update algorithm to enhance the work efficiency of the successive generation. Finally, the proposed approach employs a spiral-shaped mechanism as a local search operator inside the optimum solution space. Moreover, the HPSO-SSM method concurrently improves the RBFNN parameters and network size, building a model with a compact configuration and higher precision. Two non-linear benchmark functions and the total phosphorus (TP) modelling issue in a waste water treatment process (WWTP) are utilized to assess the overall efficacy of the creative technique. The results of testing indicate that the projected HPSO-SSM-RBFNN algorithm performed very effectively. [ABSTRACT FROM AUTHOR]

Background and aim: The importance of L-lysine as an essential amino acid in the nutrition of human beings has made it desirable supplement of the diet in recent years. It can be produced in different ways among them fermentation is the most economical and practical means of producing lysine. In this method low temperature, low pressure and low-cost carbon sources are used and a biological form of lysine (L-lysine) is produced. Methods: In the present study, the production of L-lysine was achieved through fermentation developed from locally isolated bacterial strains. In total, twenty- nine (29) bacterial strains were isolated and tested using paper chromatographic technique. Six different parameters for optimization were scrutinized for improved bacterial growth and significant yield of lysine was obtained using selected strains. Results: For Streptococcus sp. molasses media with vitamins (w) formed 24.4 g/L, 40°C generated 24.4 g/L, addition of 1 mM solution of metal ion (mg) yielded 20.4 g/L, pH 6.5 delivered 6 g/L, fermentation period of 96 hours engendered 24.4 g/L, and 0.3 mL of inoculum results in 9.2 g/L of lysine. Conclusion: Laboratory scale production of L-lysine was carried out using 1 L Erlenmeyer flask. For Streptococcus sp. 23.4 g/L of lysine was produced after optimized conditions. [ABSTRACT FROM AUTHOR]

As the largest rotator cuff muscle, the subscapularis plays a major role in stabilizing the glenohumeral joint, in conjunction with surrounding rotator cuff structures. Injury to the subscapularis tendon can be isolated, but more commonly is seen in conjunction with supraspinatus tendon pathology. Injury can be associated with biceps pulley instability, superior labral anterior-posterior (SLAP) tears, humeral head subluxation, and anterosuperior and coracoid impingements. The involvement of the rotator interval can lead to what is called "the hidden lesion," due to its difficulty to diagnose during arthroscopy. Understanding the anatomical relations of the subscapularis tendon with the rest of the rotator cuff and rotator interval, as well as common patterns of injury that involve the subscapularis tendon, can aid in proper diagnosis of these injuries leading to prompt surgical repair. This review describes the anatomy of the subscapularis muscle and tendon, and the magnetic resonance imaging (MRI) patterns of subscapularis tendon injury. [ABSTRACT FROM AUTHOR]

This paper examines Islamic and Scientific sources to understand the perspectives of both worldviews about the plagues like COVID-19. Focusing primarily on the scientific worldview, it informs about the history, spread, and the treatment of the disease. Afterward, it explores Muslim scriptures bringing forth precepts suggested to cope with such disasters. It is argued that precautionary measures, including isolation, quarantine, sanitation, and necessary immunization recommended by the World Health Organization, are the religious duties of Muslims. Additionally, Islamic scriptures advise various spiritual practices to its adherents, saving them from problems such as depression and mental distortion. [ABSTRACT FROM AUTHOR]

The suborder Heteroptera contains insects known as true bugs. They are ranked as the 5 most diverse group of insects on the planet in terms of number of species. True bugs are economically important due to certain species being phytophagous pests of various commercially grown crops and trees. However, many other species are predators and their importance in natural and biological control programmes cannot be neglected. This research aimed to identify the species of terrestrial true bugs from the regions of Rawalpindi and Islamabad. A total of 23 species under 22 genera belonging to 12 families were identified in this study. Family Pentatomidae was the most dominant and had 9 species, 3 species were identified for family Cydnidae, 2 each for the families Pyrrhocoridae and Coreidae, whereas the remaining families had 1 species each. [ABSTRACT FROM AUTHOR]

Software piracy is globally a major issue of using software without proper permission as enforced by the software license and agreement. Currently, software industries and organizations face a huge loss of money due to software piracy. Most of the educational institutions/organizations have also been affected from software piracy. To facilitate educational institutions regarding software piracy, it would be ideal to have a detailed study of the awareness of piracy level in education institutions for future development and to help academia to get rid of problems occur from pirated software. The present research aims to study and determine the awareness of piracy level in educational institutions. This study will eventually help academia in determination of level of piracy and awareness of pirated software. [ABSTRACT FROM AUTHOR]