Dynamic generalized normal distribution optimization for feature selection

High dimensionality of data represents a major problem that affects the accuracy of the classification. This problem related with classification is mainly resulted from the availability of irrelevant features. Feature selection represents a solution to a problem by selecting the most informative features and discard the irrelevant features. Generalized normal distribution optimization (GNDO) represents a newly developed optimization that confirmed its outperformance in comparison with well-known optimization algorithms on parameter extraction for photovoltaic models. As an optimization algorithm, however, GNDO suffers from degraded performance when dealing with a problem with a high dimensionality. The main problems of GNDO include exploitation problem by falling into local optima problem. Also, GNDO has solutions diversity problem when it deals with data with high dimensionality. To alleviate the drawbacks of this algorithm and solve feature selection problems, a local search algorithm (LSA) is used. The new algorithm is called dynamic generalized normal distribution optimization (DGNDO), which includes the following main improvements to GNDO: it can improve the best solution to solve the local optima problem, it can improve solution diversity by improving the randomly selected solution, and it can improve both exploration and exploitation combined. To confirm the outperformance and efficiency of the new DGNDO algorithm, DGNDO algorithm is applied on 20 benchmarked datasets from UCI repository of data. In addition, DGNDO algorithm results are compared with seven well-known optimization algorithms using number of evaluation metrics including classification, accuracy, fitness, the number of selected features, statistical results using Wilcoxon test and convergence curves. The obtained results reveal the superiority of DGNDO algorithm over all other competing algorithms

Improved Reptile Search Optimization Algorithm using Chaotic map and Simulated Annealing for Feature Selection in Medical Filed

The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search Algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles’ encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic optimizer, namely the Improved Reptile Search Algorithm (IRSA), is proposed to overcome these limitations and adapt the RSA to solve the feature selection problem. Two main improvements adding value to the standard RSA; the first improvement is to apply the chaos theory at the initialization phase of RSA to enhance its exploration capabilities in the search space. The second improvement is to combine the Simulated Annealing (SA) algorithm with the exploitation search to avoid the local optima problem. The IRSA performance was evaluated over 20 medical benchmark datasets from the UCI machine learning repository. Also, IRSA is compared with the standard RSA and state-of-the-art optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization algorithm (GOA) and Slime Mould Optimization (SMO). The evaluation metrics include the number of selected features, classification accuracy, fitness value, Wilcoxon statistical test (p-value), and convergence curve. Based on the results obtained, IRSA confirmed its superiority over the original RSA algorithm and other optimized algorithms on the majority of the medical datasets

MicroRNA-150 down Regulation in Acute Myeloid Leukaemia Patients and Its Prognostic Implication

BACKGROUND: MicroRNAs (miRNAs) are small, non-coding RNAs that are important for post-transcriptional gene regulation in both healthy and morbid conditions. Numerous miRNAs promote tumorigenesis, while others have a tumour suppressive effects. Acute myeloid leukaemia (AML) is a heterogeneous group of genetically diverse hematopoietic malignancies with variable response to treatment. AIM: Our study aimed to investigate the possible role of miR-150 in de novo adult AML and the impact of its level on survival, and we used in the silicon analysis to predict the main target genes involved in miR-150 mediated cancer pathway. MATERIAL AND METHODS: We evaluated miR-150 expression profiling assay using TaqMan primer probes RT-PCR in the plasma of 50 adult AML patients, before the start of treatment and at day 28 of treatment, along with 20 normal adult control samples. miR-16 was used as an endogenous reference for standardisation. Follow-up of patients during treatment at day 28 of induction chemotherapy and after one year was done. RESULTS: In this study, we found a significantly lower level of miR-150 in AML patients when compared to controls (p = 0.005) with 0.62 fold change than in healthy controls. Patients were divided into two groups: the low miR-150 group (miR-150 < 1) and the high miR-150 group (miR-150 > 1). A statistically significant difference was found between the two groups regarding initial total leukocytic count and initial PB blast count while for the TLC, HB and PLT count at follow up. No difference in the overall survival between the low and the high miR-150 groups could be demonstrated. CONCLUSION: Our results suggest that miR-150 functions as a tumour suppressor and gatekeeper in inhibiting cell transformation and that its downregulation is required for leukemogenesis

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Role of bronchoalveolar lavage in differentiation between bacterial aspiration pneumonia and gastric aspiration pneumonitis

Background Differentiation between gastric aspiration pneumonitis and bacterial aspiration pneumonia is important and difficult. This study aimed to evaluate the efficacy of bronchoalveolar lavage (BAL) in differentiation between them using different biomarkers. Patients and methods Cases were divided into two groups: group A (study group) included cases admitted with suspected aspiration pneumonia. Furthermore, the cases diagnosed as aspiration pneumonia were grouped as A1 and cases diagnosed as aspiration pneumonitis were grouped as A2. Group B (control group) included cases admitted with pneumonia without risk of aspiration. Patients were subjected to history and examination, plasma C-reactive protein (CRP), serum procalcitonin (PCT), chest radiograph, and flexible bronchoscopy. BAL was collected for pH, culture and sensitivity, lipid-laden alveolar macrophages (LLAM), and starch granules. Results Serum PCT and CRP were significantly higher in group B than group A (P=0.0173 and 0.0058, respectively). BAL-pH was significantly lower in group A than group B (P=0.0115). Group A showed significantly higher frequency of positive cases with LLAM (60%) than what was recorded in group B (only 20%) (P=0.0418). Seven (35%) cases in group A and no cases in group B had positive BAL for starch granules (P=0.035). Serum PCT and CRP were significantly higher in group A1 compared with group A2. BAL-pH was significantly lower in group A2 (P<0.0001). LLAM in group A2 showed highly significant increase in the number of positive cases (P=0.007). Conclusion Analysis of BAL biomarkers (starch granules and LLAM) and cultures has important diagnostic value in differentiation between bacterial aspiration pneumonia and gastric aspiration pneumonitis

Isolation and Characterization of Two Chalcone Derivatives with Anti-Hepatitis B Virus Activity from the Endemic Socotraen Dracaena cinnabari (Dragon&rsquo;s Blood Tree)

Hepatitis B virus (HBV) infection is prevalent and continues to be a global health concern. In this study, we determined the anti-hepatitis B virus (HBV) potential of the Socotra-endemic medicinal plant Dracaena cinnabari and isolated and characterized the responsible constituents. A bioassay-guided fractionation using different chromatographic techniques of the methanolic extract of D. cinnabari led to the isolation of two chalcone derivatives. Using a variety of spectroscopic techniques, including 1H-, 13C-, and 2D-NMR, these derivatives were identified as 2,4&rsquo;-dihydroxy-4-methoxydihydrochalcone (compound 1) and 2,4&rsquo;-dihydroxy-4-methoxyhydrochalcone (compound 2). Both compounds were isolated for the first time from the red resin (dragon&rsquo;s blood) of D. cinnabari. The compounds were first evaluated for cytotoxicity on HepG2.2.15 cells and 50% cytotoxicity concentration (CC50) values were determined. They were then evaluated for anti-HBV activity against HepG2.2.15 cells by assessing the suppression of HBsAg and HBeAg production in the culture supernatants and their half maximum inhibitory concentration (IC50) and therapeutic index (TI) values were determined. Compounds 1 and 2 indicated inhibition of HBsAg production in a dose- and time-dependent manner with IC50 values of 20.56 and 6.36 &mu;g/mL, respectively

Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets

The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets

Ultrasound-guided multilevel paravertebral block versus local anesthesia for medical thoracoscopy

Background: Local anesthetic infiltration for medical thoracoscopy has an analgesic properties for short duration. Single injection thoracic paravertebral block (PVB) provides limited analgesia. Purpose: Comparison between thoracic PVB performed at two or three levels with local infiltration for anesthetic adequacy in adult medical thoracoscopy as a primary outcome and postthoracoscopic analgesia and pulmonary function as secondary outcomes for adult medical thoracoscopy. Patients and Methods: Prospective randomized control study included 63 adult patients with exudative pleural effusion randomly divided into three groups of 21 patients: 3-level PVB, 2-level PVB group, and local infiltration group. Patients with contraindications to regional anesthesia or uncontrolled comorbidities were excluded from the study. Pain visual analog scale and spirometry were used for comparison as anesthetic adequacy in adult medical thoracoscopy as a primary outcome besides prolonged analgesia and improved pulmonary function as secondary outcomes. Results: The anesthetic adequacy was 95.3% in 3-level PVB group, 81% in 2-level PVB group, and 71.5% in local infiltration group. The mean sensory level was 1 ± 0.8 and 1 ± 0.6 segment above and 0.8 ± 0.6 and 0.7 ± 0.7 segment below the injected level in 3-level PVB group and 2-level PVB, respectively. VAS was statistically significant higher in local infiltration compared to the other two groups immediately postthoracoscopic and 1 h after. Two-hour postthoracoscopy, significant increase in forced vital capacity values in the three groups compared to their basal values whereas forced expiratory volume at 1 s (FEV1) only in both PVB groups. Conclusion: Unilateral 3-level TPVB was superior to 2-level TPVB and LA infiltration for anesthetic adequacy for patients undergoing medical thoracoscopy. Moreover, US-guided TPVB was followed by higher FEV1 values and lower pain scores during the next 12 h postthoracoscopy in comparison to local infiltration, so 3-level TPVB is an effective and relatively safe anesthetic technique for adult patients undergoing medical thoracoscopy which may replace local anesthesia

A Path towards Timely VAP Diagnosis: Proof-of-Concept Study on Pyocyanin Sensing with Cu-Mg Doped Graphene Oxide

In response to the urgent requirement for rapid, precise, and cost-effective detection in intensive care units (ICUs) for ventilated patients, as well as the need to overcome the limitations of traditional detection methods, researchers have turned their attention towards advancing novel technologies. Among these, biosensors have emerged as a reliable platform for achieving accurate and early diagnoses. In this study, we explore the possibility of using Pyocyanin analysis for early detection of pathogens in ventilator-associated pneumonia (VAP) and lower respiratory tract infections in ventilated patients. To achieve this, we developed an electrochemical sensor utilizing a graphene oxide-copper oxide-doped MgO (GO - Cu - Mgo) (GCM) catalyst for Pyocyanin detection. Pyocyanin is a virulence factor in the phenazine group that is produced by Pseudomonas aeruginosa strains, leading to infections such as pneumonia, urinary tract infections, and cystic fibrosis. We additionally investigated the use of DNA aptamers for detecting Pyocyanin as a biomarker of Pseudomonas aeruginosa, a common causative agent of VAP. The results of this study indicated that electrochemical detection of Pyocyanin using a GCM catalyst shows promising potential for various applications, including clinical diagnostics and drug discovery.This paper was supported by an International Research Collaboration Co-Fund (IRCC) grant of Qatar University under grant no. IRCC-2022-569. This work was additionally supported by the Qatar National Research Fund under grant no. MME03-1226-210042. The findings achieved and statements made herein are solely the responsibility of the authors.Scopu