Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation.

This study proposes a novel hybrid computational approach that integrates the artificial dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal representation of the Exact Boolean kSatisfiability (EBkSAT) logical rule. The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EBkSAT logic representation. To assess the performance of the proposed hybrid computational model, a specific Exact Boolean kSatisfiability problem is constructed, and simulated data sets are generated. The evaluation metrics employed include the global minimum ratio (GmR), root mean square error (RMSE), mean absolute percentage error (MAPE), and network computational time (CT) for EBkSAT representation. Comparative analyses are conducted between the results obtained from the proposed model and existing models in the literature. The findings demonstrate that the proposed hybrid model, ADA-HNN-EBkSAT, surpasses existing models in terms of accuracy and computational time. This suggests that the ADA algorithm exhibits effective compatibility with the HNN for achieving an optimal representation of the EBkSAT logical rule. These outcomes carry significant implications for addressing intricate optimization problems across diverse domains, including computer science, engineering, and business.

Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods.

OBJECTIVES: Various techniques for dorsal hand vein (DHV) pattern extraction have been introduced using small datasets with poor and inconsistent segmentation. This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem. METHODS: Manual segmentation involved selecting a region-of-interest (ROI) in images from the Bosphorus dataset to generate ground truth data. The HHM combined histogram equalization and morphological and thresholding-based algorithms to localize veins from hand images. The data were divided into training, validation, and testing sets with an 8:1:1 ratio before training AlexNet. We considered three image augmentation strategies to enlarge our training sets. The best training hyperparameters were found using the manually segmented dataset. RESULTS: We obtained a good test accuracy (91.5%) using the model trained with manually segmented images. The HHM method showed slightly inferior performance (76.5%). Considerable improvement was observed in the test accuracy of the model trained with the inclusion of automatically segmented and augmented images (84%), with low false acceptance and false rejection rates (0.00035% and 0.095%, respectively). A comparison with past studies further demonstrated the competitiveness of our technique. CONCLUSIONS: Our technique can be feasible for extracting the ROI in DHV images. This strategy provides higher consistency and greater efficiency than the manual approach.

Efficacy of Ivermectin Treatment on Disease Progression Among Adults With Mild to Moderate COVID-19 and Comorbidities: The I-TECH Randomized Clinical Trial.

Importance: Ivermectin, an inexpensive and widely available antiparasitic drug, is prescribed to treat COVID-19. Evidence-based data to recommend either for or against the use of ivermectin are needed. Objective: To determine the efficacy of ivermectin in preventing progression to severe disease among high-risk patients with COVID-19. Design, Setting, and Participants: The Ivermectin Treatment Efficacy in COVID-19 High-Risk Patients (I-TECH) study was an open-label randomized clinical trial conducted at 20 public hospitals and a COVID-19 quarantine center in Malaysia between May 31 and October 25, 2021. Within the first week of patients' symptom onset, the study enrolled patients 50 years and older with laboratory-confirmed COVID-19, comorbidities, and mild to moderate disease. Interventions: Patients were randomized in a 1:1 ratio to receive either oral ivermectin, 0.4 mg/kg body weight daily for 5 days, plus standard of care (n = 241) or standard of care alone (n = 249). The standard of care consisted of symptomatic therapy and monitoring for signs of early deterioration based on clinical findings, laboratory test results, and chest imaging. Main Outcomes and Measures: The primary outcome was the proportion of patients who progressed to severe disease, defined as the hypoxic stage requiring supplemental oxygen to maintain pulse oximetry oxygen saturation of 95% or higher. Secondary outcomes of the trial included the rates of mechanical ventilation, intensive care unit admission, 28-day in-hospital mortality, and adverse events. Results: Among 490 patients included in the primary analysis (mean [SD] age, 62.5 [8.7] years; 267 women [54.5%]), 52 of 241 patients (21.6%) in the ivermectin group and 43 of 249 patients (17.3%) in the control group progressed to severe disease (relative risk [RR], 1.25; 95% CI, 0.87-1.80; P = .25). For all prespecified secondary outcomes, there were no significant differences between groups. Mechanical ventilation occurred in 4 (1.7%) vs 10 (4.0%) (RR, 0.41; 95% CI, 0.13-1.30; P = .17), intensive care unit admission in 6 (2.4%) vs 8 (3.2%) (RR, 0.78; 95% CI, 0.27-2.20; P = .79), and 28-day in-hospital death in 3 (1.2%) vs 10 (4.0%) (RR, 0.31; 95% CI, 0.09-1.11; P = .09). The most common adverse event reported was diarrhea (14 [5.8%] in the ivermectin group and 4 [1.6%] in the control group). Conclusions and Relevance: In this randomized clinical trial of high-risk patients with mild to moderate COVID-19, ivermectin treatment during early illness did not prevent progression to severe disease. The study findings do not support the use of ivermectin for patients with COVID-19. Trial Registration: ClinicalTrials.gov Identifier: NCT04920942.

Cytomegalovirus DNAemia and disease: current-era epidemiology, clinical characteristics and outcomes in cancer patients other than allogeneic haemopoietic transplantation.

BACKGROUND: High-intensity chemotherapy and advances in novel immunotherapies have seen the emergence of cytomegalovirus (CMV) infections in cancer patients other than allogeneic haemopoietic cell transplantation (HCT). Aim To evaluate the epidemiology, clinical characteristics and outcomes of CMV infection in this population. METHODS: A retrospective review of cancer patients other than allogeneic HCT who had CMV DNAemia and/or disease from July 2013 till May 2020 at a quaternary cancer centre was performed. RESULTS: Of 11 485 cancer patients who underwent treatment during this period, 953 patients had CMV DNA testing performed and 238 of them had CMV DNAemia. After excluding patients with allogeneic HCT, 62 patients with CMV DNAemia were identified, of whom 10 had concurrent CMV disease. The most frequent underlying malignancies were B-cell lymphoproliferative disease (LPD) (31%; 19/62), T-cell LPD (21%; 13/62), chronic lymphocytic leukaemia (11%; 7/62) and multiple myeloma (10%; 6/62). Most patients had lymphopenia (77%; 48/62), multiple cancer therapies (63%; 39/62 received ≥2 previous therapies), co-infection (56%; 35/62 had ≥1 co-infection) and corticosteroid therapy (48%; 30/62) within 1 month before CMV diagnosis. CMV DNAemia and disease were observed in patients receiving novel immunotherapies, including bispecific antibody therapy, chimeric-antigen receptor T-cell therapy and immune checkpoint inhibitors. CONCLUSION: Patients with haematological malignancy, particularly B-cell LPD, T-cell LPD, chronic lymphocytic leukaemia and multiple myeloma, were frequently identified to have CMV DNAemia and disease. Lymphopenia, multiple cancer therapies, co-infection and recent receipt of systemic corticosteroids were also commonly observed. Future studies are necessary to determine optimal identification and management of CMV in these patients.

Impact and Implications of the COVID-19 Pandemic on Urological Training.

INTRODUCTION: COVID-19 has forever impacted health care in the U.S. Changes to health and hospital policies led to disruptions to both patient care and medical training. There is limited understanding of the impact on urology resident training across the U.S. Our aim was to examine trends in urological procedures, as captured by the Accreditation Council for Graduate Medical Education resident case logs, throughout the COVID-19 pandemic. METHODS: Retrospective review of publicly available urology resident case logs between July 2015 and June 2021 was performed. Average case numbers were analyzed via linear regression with different models specifying different assumptions regarding the impact of COVID-19 on procedure in 2020 and onward. Statistical calculations utilized R (version 4.0.2). RESULTS: Analysis favored models which assumed the impact of COVID-related disruptions were specific to 2019-2020. Analysis of procedures performed indicate an average upward trend of urology cases nationally. An average annual increase of 26 procedures between 2016 and 2021 was noted, except for 2020 which saw an average drop of approximately 67 cases. However, in 2021 case volume dramatically increased to the same rate as projected had there not been a disruption in 2020. Stratifying by category of urology procedure revealed evidence for variability between categories in the magnitude of the 2020 decrease. CONCLUSIONS: Despite widespread pandemic-related disruptions in surgical care, urological volume has rebounded and increased, likely having minimal detriment to urological training over time. Urological care is essential and in high demand as evidenced by the uptick in volume across the U.S.

Five-Class Classification of Cervical Pap Smear Images: A Study of CNN-Error-Correcting SVM Models.

OBJECTIVES: Different complex strategies of fusing handcrafted descriptors and features from convolutional neural network (CNN) models have been studied, mainly for two-class Papanicolaou (Pap) smear image classification. This paper explores a simplified system using combined binary coding for a five-class version of this problem. METHODS: This system extracted features from transfer learning of AlexNet, VGG19, and ResNet50 networks before reducing this problem into multiple binary sub-problems using error-correcting coding. The learners were trained using the support vector machine (SVM) method. The outputs of these classifiers were combined and compared to the true class codes for the final prediction. RESULTS: Despite the superior performance of VGG19-SVM, with mean ± standard deviation accuracy and sensitivity of 80.68% ± 2.00% and 80.86% ± 0.45%, respectively, this model required a long training time. There were also false-negative cases using both the VGGNet-SVM and ResNet-SVM models. AlexNet-SVM was more efficient in terms of running speed and prediction consistency. Our findings also showed good diagnostic ability, with an area under the curve of approximately 0.95. Further investigation also showed good agreement between our research outcomes and that of the state-of-the-art methods, with specificity ranging from 93% to 100%. CONCLUSIONS: We believe that the AlexNet-SVM model can be conveniently applied for clinical use. Further research could include the implementation of an optimization algorithm for hyperparameter tuning, as well as an appropriate selection of experimental design to improve the efficiency of Pap smear image classification.

Concomitant abducens and facial nerve palsies: A rare presentation in anti-aquaporin-4 antibody-positive neuromyelitis optica.

Over the past decade, the discovery of disease-specific aquaporin-4 antibodies has led to a better understanding of the diverse spectrum of disorders that are associated with neuromyelitis optica. Brainstem manifestations have been increasingly recognized in this disease. However, multiple cranial nerve palsies as an initial presentation of neuromyelitis optica are uncommon. We report a rare case of anti-aquaporin-4 antibody-positive neuromyelitis optica that presented with unilateral abducens and facial nerve palsies. Notably, this case did not involve the optic nerve or the spinal cord. Diagnosing neuromyelitis optica that presents as an isolated acute brainstem syndrome is challenging, but the outcome may be devastating if the diagnosis is delayed.

Multi-Faceted Intervention to Improve the Antibiotic Prescriptions among Doctors for Acute URI and Acute Diarrhoea Cases: The Green Zone Antibiotic Project.

BACKGROUND: Antimicrobial resistance is a global problem that is perpetuated by the inappropriate use of antibiotics among doctors. This study aims to assess the antibiotic prescription rate for patients with acute upper respiratory infection (URI) and acute diarrhoea. METHODS: A completed clinical audit cycle was conducted in 2018 in the busy emergency department of a public hospital in Malaysia. Pre- and post-intervention antibiotic prescription data were collected, and changes were implemented through a multifaceted intervention similar to Thailand's Antibiotics Smart Use programme. RESULTS: Data from a total of 1,334 pre-intervention and 1,196 post-intervention patients were collected from the hospital's electronic medical records. The mean (SD) age of participants was 19.88 (17.994) years. The pre-intervention antibiotic prescription rate was 11.2% for acute diarrhoea and 29.1% for acute URI, both of which are above the average national rates. These antibiotic prescription rates significantly reduced post-intervention to 6.2% and 13.7%, respectively, falling below national averages. Antibiotic prescription rate was highest for young children. There were no significant changes in rates of re-attendance or hospital admission following the intervention. CONCLUSION: The multifaceted intervention, which included continuing medical education, physician reminders and patient awareness, was effective in improving the antibiotic prescription rates for these two conditions.