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Coxsackievirus B3 is an Enterovirus implicated in diverse human pathologies, from viral myocarditis to neurological disorders. There isn’t a medicinal agent or vaccine for CVB3 in clinical use at the moment, despite the possibility that vaccination could lower the prevalence of these illnesses. This study focuses on the in vitro production and characterization of the viral protein 1 (VP1) in the objective to use it as subunit vaccine and/or immunodiagnostic reagent. VP1 is considered as the most immunogenic capsid protein of the CVB3 surface. We amplified the VP1 whole gene by RT-PCR from the extracted wild type Nancy strain RNA, then cloned it into the pUC19 plasmid expression vector, and expressed it in E. coli DH5a prokaryotic cells. The obtained recombinant proteins were then analyzed by SDS-PAGE and characterized by Bioinformatic software tools. Our results revealed that the produced recombinant amino acid VP1 (rVP1) is highly identical to the VP1 of the CVB3 wild-type strain and has very similar physicochemical properties. In addition, we demonstrated that rVP1 has the highest number of phosphorylation sites which means that rVP1 can translate the host cell signal via the phosphorylation mechanism. Moreover, The Linear B cell epitope analysis showed that the rVP1 contains many epitope regions that should be recognized by the humoral host immune response. Taken together, results demonstrate that the cloned and recombined expressed viral protein could be used to carry out any studies concerning the development a protein subunit vaccine against CVB3 infections or an immunodiagnostic reagent for detecting the virus in samples.

Abstract Background In COVID-19 management, a variety of pharmaceutical interventions (PI) and non- pharmaceutical interventions (NPI) were adopted to limit the spread of the disease and its associated deaths. We aimed to evaluate the impact of PI and NPI on risks of COVID-19 transmission and deaths. Method We collected aggregate data from March 2nd, 2020, to December 1, 2022 from the Tunisian Ministry of Health's website and OurWorldInData.org site. NPI Periods (NPIP: March 2020 to March 2021) and PI Periods (PIP) were distributed to NPIP1, 2, 3 and 4 and to PIP1, 2, 3 and 4, respectively. We calculated the Relative Risks (RR) and 95% Confidence Intervals (CI) by comparing the subsequent period with previous one. Results The risk of SARS-CoV-2 transmission increased progressively from the zero cases period (NPIP2) to the mitigate strategy period (NPIP3) (RR = 14.0; 95% CI: 12.4–15.8) and to the stop-and-go epidemic control period (NPIP4) (RR = 23.1 (95% CI: 22.4–23.9). It was stabilized in the targeted vaccination period (PIP1) (RR = 1.08, 95% CI: 1.07–1.08) and reduced during the mass vaccination period (PIP2) (RR: 0.50, 95% CI: 0.50–0.51). SARS-CoV-2 transmission, increased during PIP3 concomitant with the Omicron wave (RR = 2.65, 95% CI: 2.64–2.67). It remained at a low level in PIP4 (RR = 0.18; 95% CI: 0.18–0.18). Compared to NPIP2, NPIP3 and NPIP4 were associated with a higher risk of COVID-19 mortality (RR = 3.337; 95% CI: 1.797–6.195) and (RR = 72.63 (95% CI: 54.01–97.68), respectively. Since the start of the immunization program, the risk of COVID-19 death has consistently decreased. In comparison to each previous period, the risk transitioned in PIP1 to RR = 0.91; 95% CI: 0.88–0.93, then to RR = 0.85; 95% CI: 0.83–0.88 in PIP2, to RR = 0.47; 95% CI: 0.45–0.50 in PIP3, and to RR = 0.19; 95% CI: 0.18–0.24 during PIP4. Conclusion In terms of lowering the risk of transmission and mortality, the NP strategy at the beginning of the epidemic outperformed the IP strategy during the outbreak.

Internet of Things (IoT) sensor networks are connected systems of physical devices set with actuators, sensors, and communication abilities, allowing them to gather, spread, and exchange information with centralized methods. These networks are essential in numerous businesses, such as healthcare, manufacturing, agriculture, and smart cities, as they deliver real-time observation, data-driven insights, and automation. Threat recognition in IoT sensor networks is a vital feature of safeguarding the protection and consistency of interconnected systems in the IoT. As IoT sensor networks endure to increase across various industries, the vulnerability to malicious actions and cyber-attacks increases. Threat recognition utilizing deep learning (DL) leverages neural networks to examine complex patterns and anomalies in data, permitting the identification of potential safety threats. DL techniques like convolutional neural networks (CNNs) or recurrent neural networks (RNNs) excel at learning complex representations of data and feature extraction, making them suitable for identifying sophisticated attacks in different fields, including cybersecurity. This research develops a Hybrid Sine-Cosine Chimp Optimization Feature Selection with a Deep Learning (HSCCOFS-DL) approach for Threat Recognition in IoT Sensor Networks. The foremost aim of the HSCCOFS-DL system lies in the automated detection of threats using DL models. To accomplish this, the HSCCOFS-DL approach undergoes a data normalization process. Besides, the selection of features can be performed using the HSCCO algorithm. Meanwhile, the symmetrical autoencoder (SAE) technique effectively classifies threats. Finally, the sparrow search algorithm (SSA) can be applied to the selection of the hyperparameter of the SAE system. The experimental assessment of the HSCCOFS-DL technique takes place on a benchmark dataset. The simulation results indicated that the HSCCOFS-DL approach attains enhanced performance over other methods.

Objectives: We aimed to compare inflammatory markers and determine their potential role in distinguishing secondary leukocytoclastic vasculitis (SLV) from idiopathic leukocytoclastic vasculitis (ILV). Materials and Methods: We included in this cross-sectional study patients with cutaneous leukocytoclastic vasculitis (CLV) diagnosed on cutaneous biopsy. We assessed clinical and laboratory data and then calculated platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP)-to-albumin ratio (CAR), and fibrinogen-to-albumin ratio (FAR). We have also defined the number of positive etiological examination (NPE) as the sum in a unique patient of the positive paraclinical examinations involved in the etiological assessment of CLV. Results: In total 77 patients were included, with 52 SLV group patients and 25 in the ILV group, mean age was 44+/-18 vs 49+/-21, and gender ratio was 29/23 vs 11/14. Comparison of PLR, NLR, CAR, and FAR showed significant differences in mean values between SLV and ILV groups with 199.1 (117.3-309.8) vs 126.8 (79-193) (P = 0.01) for PLR, 3.6 (1.9-5.1) vs 2.3 (1.7-3.4) (P = 0.048) for NLR, 1.9 mg.g-1 (0.4-3.6) vs 0.6 mg g-1 (0.2-1.9) (P = 0.043) for CAR, and 155.8 mg.g-1 (90.7-192.3) vs 108.7 mg.g-1 (82.2-148.1) (P = 0.034) for FAR. PLR, CAR, and FAR were positively correlated to NPE (r = 0.463, P < 0.001; r = 0.434, P < 0.001; and r = 0.411, P < 0.001, respectively), and there was no significant correlation between NLR and NPE (r = 0.165, P = 0.151). Conclusion: This is the first study to investigate PLR, NLR, CAR, and FAR in CLV, and it demonstrates that elevation of these ratios is associated with SLV, which leads us to suggest to exhaustively explore patients with elevated ratios.

Abstract Background There is a growing demand for colonoscopy, worldwide, resulting in increased rate of inappropriate referrals. This “overuse” of colonoscopies has become a major burden for health care. Objectives to assess the appropriateness of colonoscopies performed at the endoscopy unit of the university hospital of Sousse and to compare these results of appropriateness according to the European Panel of Appropriateness of Gastrointestinal Endoscopy (EPAGE) I and EPAGE II criteria. Patients and methods this cross-sectional study included all consecutive patients referred for a diagnostic colonoscopy, between January 2017 and December 2018. Patients referred for exclusively therapeutic indications, those with incomplete colonoscopies were not included. Patients with poor bowel preparation or missing data were also excluded. Indications were assessed using the EPAGE I and EPAGE II criteria. Results From 1972 consecutive patients, 1307 were included. Overall, 986 (75.4%) of all referrals were for out-patients. The majority of patients were referred by gastroenterologists (n = 1026 patients; 78.5%), followed by general surgeons (n = 85; 6.5%). The commonest indications were lower abdominal symptoms (275; 21%) followed by uncomplicated diarrhea (152; 11.6%). Relevant findings were present in 363 patients (27.7%). Neoplastic lesions were the dominant finding in 221 patients (16.9%). EPAGE I and EPAGE II criteria were applicable for 1237 (88.8%) and 1276 (97.7%) patients respectively. Hematochezia and abdominal pain recorded the highest inappropriate rates with both sets of criteria. Appropriate colonoscopies increased to 76.4% when EPAGE II criteria were applied; whereas uncertain and inappropriate procedures decreased to 10.3% and 10.9% respectively Appropriateness of indication was significantly higher in hospitalized patients. For the EPAGE II criteria, the specialty of the referring physician was also significantly associated to the appropriate use. The agreement between EPAGE I and EPAGE II criteria was slight using the weighted version of k (k = 0.153). Conclusions The updated and improved EPAGE II guidelines are a simple and valid tool for assessing the appropriateness of colonoscopies. They decreased the inappropriate rate and the possibility of missing potentially severe diagnoses.

With the fast growth of mobile phone usage, malicious threats against Android mobile devices are enhanced. The Android system utilizes a wide range of sensitive apps like banking apps; thus, it develops the aim of malware that uses the vulnerability of safety measures. Identifying Android malware in smartphones is a vital target for the cyber community to eliminate menacing malware instances. Drawing stimulus from the adaptability and efficacy of biological systems, these methods emulate nature's problem-solving systems for identifying malicious software. By integrating principles, namely, swarm intelligence (SI), neural networks (NN), and genetic algorithms (GA), these bioinspired systems reveal exceptional efficiency in identifying both known and developing Android malware attacks. This bioinspired system provides a capable avenue for robust Android malware detection from an ever-shifting threat landscape. This article designs a Bioinspired Artificial Intelligence-based Android Malware Detection and Classification (BAI-AMDC) technique for Cybersecurity Applications. The BAI-AMDC technique exploits the concept of bioinspired algorithms with a DL approach for the classification and detection of Android malware. In the BAI-AMDC technique, an improved cockroach swarm optimization algorithm-based feature selection (ICSOA-FS) technique can be applied to choose optimum features. The BAI-AMDC technique employs a bidirectional gated recurrent unit (BiGRU) model for Android malware detection. An arithmetic optimization algorithm (AOA) can be utilized to enhance the detection performance of the BAI-AMDC technique. The experimental validation of the BAI-AMDC system can be performed on the CICAndMal2017 database with 10,000 instances. The simulation values highlighted the productive ability of the BAI-AMDC system on the Android malware recognition process.

Rim Sghiri,1,&ast; Hana Ben Hassine,2,&ast; Mariam Ghozzi,2 Nejla El Amri,3 Khadija Baccouche,3 Rihab Sarraj,3 Zahid Shakoor,1 Adel Almogren,1 Elyes Bouajina3 1Immunology Unit, Department of Pathology, College of Medicine, King Saud University, Riyadh, Saudi Arabia; 2Immunology Laboratory, Farhat Hached Hospital, Sousse, Tunisia; 3Department of Rheumatology, Farhat Hached Hospital, Sousse, Tunisia&ast;These authors contributed equally to this workCorrespondence: Rim Sghiri, Immunology Unit, Department of Pathology, College of Medicine, King Saud University, Riyadh, Saudi Arabia, Email rimsghiri2023@gmail.comPurpose: FRAX® is a tool used for evaluation of risk of fracture in RA and non-RA patients and to identify those eligible for intervention. One of the limitations of FRAX in RA settings is that it does not consider factors known to contribute to osteoporosis such as autoantibodies. This study analysed the association of anti-mutated citrullinated vimentin antibody (anti-MCV), anti-cyclic citrullinated peptide antibody (anti-CCP), IgM rheumatoid factor (RF), IgA RF with 10-year risk of major osteoporosis and hip fracture.Methods: FRAX® tool was used to estimate 10-year risk of major osteoporosis fracture and hip fracture in 189 RA patients over 40 years of age. Anti-MCV, anti-CCP, IgM RF and IgA RF were tested using enzyme immunoassay and analysed at different levels. Results were adjusted for various confounders including disease activity.Results: Fifty-one (26.9%) RA patients had high (≥ 20%) 10-year risk of major osteoporosis fracture and 67 (35.4%) had high (> 3%) 10-year risk of hip fracture. Among all the tested autoantibodies, only IgM RF at elevated levels was associated with high 10-year risk of major osteoporosis fracture (adjusted OR = 4.1, 95% CI = 1.5– 11.3, p = 0.006) and of hip fracture (adjusted OR = 17.4, 95% CI = 3.7– 81.3, p < 0.0001). There was no agreement between FRAX and femoral neck (FN) BMD. None of the autoantibodies tested were associated with FN osteopenia or osteoporosis including IgM RF at high levels.Conclusion: Our study highlights the importance of quantitative measurement of autoantibodies in assessment of risk for fractures among RA patients. Our preliminary findings need to be assessed in prospective studies to determine the actual predictive value of high IgM RF levels among patients with RA.Keywords: osteoporosis, rheumatoid arthritis, 10-years risk of major osteoporosis fracture, 10-years risk of hip fracture, FRAX, anti-mutated citrullinated vimentin antibody, anti-cyclic citrullinated peptide antibody, rheumatoid factor, rheumatoid factor isotypes

Abstract This work is devoted to the study of three-dimensional compressible Navier–Stokes equations on unstructured meshes. The approach used is based on separating the convection and diffusion parts. The convective flux is computed using the Godunov method. For the diffusive part, we present a new finite volume scheme. Numerical results are provided to demonstrate the efficiency of the developed technique.

This article presents two patients who were diagnosed with autoimmune liver disease (autoimmune hepatitis and primary biliary cholangitis overlap syndrome) during anti-tuberculosis therapy, which is a rarely reported occurrence. It highlights the challenges in distinguishing drug-induced liver injury from authentic autoimmune liver disease. It also points out the importance of considering autoimmune liver disease as a potential diagnosis revealed by the setting of drug-induced liver injury.

Reports from the literature have discussed patients presenting Hepatitis A virus infection with hemolytic anemia, specifically with glucose-6-phosphate dehydrogenase deficiency. However, autoimmune hemolytic anemia (AIHA) has been rarely reported. We present a challenging case of Coombs-negative hemolytic anemia as initial manifestation of hepatitis A virus infection in a silent carrier of α-thalassemia.

Background Maternal mortality is the main indicator of maternal health worldwide. The aims of your study were to determine the Maternal Mortality Ratio (MMR) in the governorate of Tunis and to identify the main causes of maternal death. Methods We included all maternal deaths between January 2017 and December 2023, reported to the Tunis Regional Health Directorate. The data collected included the MMR per 100000 live birth, sociodemographic characteristics, causes of death, circumstances of death, autopsy findings, and preventability. Results Sixty one maternal deaths were recorded. The average of MMR was 46, 88 per 100000 live births. Two peaks in the MMR were noted in 2017 and 2020. The average age of the patients was 34, 1 years (±5.1). We noted that 43, 75 % of the patients were not residents of the governorate of Tunis. The postpartum period was the most critical. In fact, 83, 33 % of maternal deaths occurred postpartum. Hemorrhage was the main etiology in 20.8% of cases. The second cause was COVID- 19complicationsin 16, 6% of cases. Conclusion This study of maternal mortality in Tunis opens the debate on the effectiveness of maternal health policies in Tunisia and the areas for improvement.

We investigate the continuous annealing of orthorhombic κ-Ga2O3 films on AlN/c-plane sapphire templates grown by Mist Chemical Vapor Deposition (mist-CVD) using in situ high-temperature x-ray diffraction (HT-XRD). Increasing the annealing temperature from 400 to 1100 °C in both vacuum and ambient air reveals a phase transition onset at 825 °C. High-resolution transmission electron microscopy and XRD demonstrated that annealing within the stability window of 650 to 775 °C effectively improves the crystal quality of the κ-Ga2O3 thin film. Optical transmittance and low-loss electron energy loss spectroscopy (EELS) show the pristine film’s bandgaps to be 4.96 and 4.67 eV, respectively, with reduced bandgaps in annealed films due to increased defect density. EELS-derived optical joint density of states indicates that air-annealing fosters sub-bandgap radiative processes, while vacuum annealing suppresses them, qualitatively correlated with the observed photoluminescence intensity variations. The results of this comprehensive high-temperature annealing study offer crucial insight into the influence of annealing ambient conditions on the crystallographic properties of κ-Ga2O3 films and the associated evolution of extended sub-bandgap states.

IntroductionKnee osteoarthritis (KOA) is a major health issue affecting millions worldwide. This study employs machine learning algorithms to analyze human gait using kinematic data, aiming to enhance the diagnosis and detection of KOA. By adopting this approach, we contribute to the development of an effective diagnostic methods for KOA, a prevalent joint condition.MethodsThe methodology is structured around several critical steps to optimize the model’s performance. These steps include extracting kinematic features from video data to capture essential gait dynamics, applying data filtering and reduction techniques to remove noise and enhance data quality, and calculating key gait parameters to boost the model’s predictive power. The machine learning model trains on these refined features, validates through cross-validation for robust performance assessment, and tests on unseen data to ensure generalizability.ResultsOur approach demonstrates significant improvements in classification accuracy, highlighting its potential for early and precise KOA detection. The model achieves a high classification accuracy, indicating its effectiveness in distinguishing KOA-related gait patterns.DiscussionFurthermore, a comparative analysis with another model trained on the same dataset demonstrates the superiority of our method, suggesting that the proposed approach serves as a reliable tool for early KOA detection and potentially improves clinical diagnostic workflows.

Background: Mistreatment in healthcare is defined by the set of behaviors, acts, and omissions committed by the healthcare providers on patients. Pregnant women can be exposed to this behavior during pregnancy, childbirth, and the postpartum period. It can have several aspects and affects the women's mental health, social and professional life, and also their newborns and families. Objective: This study was made to estimate the frequency of mistreatment during the peripartum period in a Tunisian maternity unit, determine its impact on the parturient and her entourage, and draw up recommendations for the prevention of this public health problem. Study Design: We conducted a cross-sectional survey in Department C of the Tunis Maternity and Neonatology Center from July 2022 to September 30, 2022. Our questionnaire encompassed verbal and physical abuse, patient information, consent, unprofessional conduct, poor communication, and discrimination. Results: This study included 400 patients. The average age was 29.3+–5.65. Single women represented 12.3% of the cases. Seventy-five percent of women reported having been victims of at least one type of violence during childbirth. Verbal abuse was the most frequent type observed. Eighty-two percent of women reported verbal abuse, while 23.25% underwent physical violence. After the delivery, 391 women (97.8%) stated that the delivery was a source of anxiety and that they were not ready to repeat the experience. Six of them were followed up in psychiatry. Mistreatment was expressed by a lack of information and/or respect for consent, unprofessional conduct, or poor communication between the caregiver and the patient. Conclusion: This study emphasizes the significance of including women in decision-making processes regarding their care. Establishing systematic approaches for providing information and obtaining consent is crucial, ensuring a dynamic approach that promotes women's freedom of choice.