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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]

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]

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]

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]

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]

Bismuth borate glasses containing lithium and gadolinium were doped with (0.1–2.5 mol%) of Ce and their optical and radiation response were investigated. These modified glasses exhibited photoluminescent properties in response to the radiations, which were evidenced by their broad-band spectral features around 378 nm due to 5d→4f transitions of the Ce3+ ions. The intensity response of these glasses increased with increasing concentration of cerium up to 2.0 mol% which decreased upon further increase in the concentration of cerium due to the quenching effect and cross-relaxation between the nearest ions. Gamma irradiation of samples with 1 and 2 Gy doses demonstrated an increase in the intensity of photoluminescence peaks with increase in the irradiation dose. The observed increase caused the formation of relatively more electron and hole trap centers that eventually led to more recombinations in the glass matrix. This enhancement in the intensity after irradiation was also proportional to the dose as expected and the photoluminescence peaks observed for the irradiation samples were broader than those of un-irradiated samples. The observed decrease in intensity after irradiation of samples with high cerium concentration (i.e. 2.5 mol%) may be linked to the energy transfer and dissipation occurring during cross-relaxation among the nearest cerium ions. • Optical and radiation response of the Ce3+-doped LGBB glasses were investigated. • γ-rays irradiation (1–2 Gy) was performed for Ce3+ concentrations 0.1–2.5 mol%. • PL emission peaks before and after irradiation at 378 nm due to 5d. → 4f transitions • PL intensity increased up to 2.0 mol% and quenched for higher concentrations. [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]

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]

Recently, the echo state networks (ESNs) have been widely used for time series prediction. To meet the demand of actual applications and avoid the overfitting issue, the online sequential ESN with sparse recursive least squares (OSESN-SRLS) algorithm is proposed. Firstly, the ℓ 0 and ℓ 1 norm sparsity penalty constraints of output weights are separately employed to control the network size. Secondly, the sparse recursive least squares (SRLS) algorithm and the subgradients technique are combined to estimate the output weight matrix. Thirdly, an adaptive selection mechanism for the ℓ 0 or ℓ 1 norm regularization parameter is designed. With the selected regularization parameter, it is proved that the developed SRLS shows comparable or better performance than the regular RLS. Furthermore, the convergence of OSESN-SRLS is theoretically analyzed to guarantee its effectiveness. Simulation results illustrate that the proposed OSESN-SRLS always outperforms other existing ESNs in terms of estimation accuracy and network compactness. • The online sequential ESN with sparse RLS algorithm is studied to improve estimation accuracy and network compactness. • The network size is controlled by the ℓ 0 and ℓ 1 norm sparsity penalty constraints. • The estimation performance is improved by the regularization parameters selection rule. • The algorithm convergence is analyzed to guarantee its effectiveness. [ABSTRACT FROM AUTHOR]