Vulnerability detection in Java source code using a quantum convolutional neural network with self-attentive pooling, deep sequence, and graph-based hybrid feature extraction.

Software vulnerabilities pose a significant threat to system security, necessitating effective automatic detection methods. Current techniques face challenges such as dependency issues, language bias, and coarse detection granularity. This study presents a novel deep learning-based vulnerability detection system for Java code. Leveraging hybrid feature extraction through graph and sequence-based techniques enhances semantic and syntactic understanding. The system utilizes control flow graphs (CFG), abstract syntax trees (AST), program dependencies (PD), and greedy longest-match first vectorization for graph representation. A hybrid neural network (GCN-RFEMLP) and the pre-trained CodeBERT model extract features, feeding them into a quantum convolutional neural network with self-attentive pooling. The system addresses issues like long-term information dependency and coarse detection granularity, employing intermediate code representation and inter-procedural slice code. To mitigate language bias, a benchmark software assurance reference dataset is employed. Evaluations demonstrate the system's superiority, achieving 99.2% accuracy in detecting vulnerabilities, outperforming benchmark methods. The proposed approach comprehensively addresses vulnerabilities, including improper input validation, missing authorizations, buffer overflow, cross-site scripting, and SQL injection attacks listed by common weakness enumeration (CWE).

Detection of offensive terms in resource-poor language using machine learning algorithms.

The use of offensive terms in user-generated content on different social media platforms is one of the major concerns for these platforms. The offensive terms have a negative impact on individuals, which may lead towards the degradation of societal and civilized manners. The immense amount of content generated at a higher speed makes it humanly impossible to categorise and detect offensive terms. Besides, it is an open challenge for natural language processing (NLP) to detect such terminologies automatically. Substantial efforts are made for high-resource languages such as English. However, it becomes more challenging when dealing with resource-poor languages such as Urdu. Because of the lack of standard datasets and pre-processing tools for automatic offensive terms detection. This paper introduces a combinatorial pre-processing approach in developing a classification model for cross-platform (Twitter and YouTube) use. The approach uses datasets from two different platforms (Twitter and YouTube) the training and testing the model, which is trained to apply decision tree, random forest and naive Bayes algorithms. The proposed combinatorial pre-processing approach is applied to check how machine learning models behave with different combinations of standard pre-processing techniques for low-resource language in the cross-platform setting. The experimental results represent the effectiveness of the machine learning model over different subsets of traditional pre-processing approaches in building a classification model for automatic offensive terms detection for a low resource language, i.e., Urdu, in the cross-platform scenario. In the experiments, when dataset D1 is used for training and D2 is applied for testing, the pre-processing approach named Stopword removal produced better results with an accuracy of 83.27%. Whilst, in this case, when dataset D2 is used for training and D1 is applied for testing, stopword removal and punctuation removal were observed as a better preprocessing approach with an accuracy of 74.54%. The combinatorial approach proposed in this paper outperformed the benchmark for the considered datasets using classical as well as ensemble machine learning with an accuracy of 82.9% and 97.2% for dataset D1 and D2, respectively.

Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer.

Cervical cancer is one of the most common types of malignant tumors in women. In addition, it causes death in the latter stages. Squamous cell carcinoma is the most common and aggressive form of cervical cancer and must be diagnosed early before it progresses to a dangerous stage. Liquid-based cytology (LBC) swabs are best and most commonly used for cervical cancer screening and are converted from glass slides to whole-slide images (WSIs) for computer-assisted analysis. Manual diagnosis by microscopes is limited and prone to manual errors, and tracking all cells is difficult. Therefore, the development of computational techniques is important as diagnosing many samples can be done automatically, quickly, and efficiently, which is beneficial for medical laboratories and medical professionals. This study aims to develop automated WSI image analysis models for early diagnosis of a cervical squamous cell dataset. Several systems have been designed to analyze WSI images and accurately distinguish cervical cancer progression. For all proposed systems, the WSI images were optimized to show the contrast of edges of the low-contrast cells. Then, the cells to be analyzed were segmented and isolated from the rest of the image using the Active Contour Algorithm (ACA). WSI images were diagnosed by a hybrid method between deep learning (ResNet50, VGG19 and GoogLeNet), Random Forest (RF), and Support Vector Machine (SVM) algorithms based on the ACA algorithm. Another hybrid method for diagnosing WSI images by RF and SVM algorithms is based on fused features of deep-learning (DL) models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet). It is concluded from the systems' performance that the DL models' combined features help significantly improve the performance of the RF and SVM networks. The novelty of this research is the hybrid method that combines the features extracted from deep-learning models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet) with RF and SVM algorithms for diagnosing WSI images. The results demonstrate that the combined features from deep-learning models significantly improve the performance of RF and SVM. The RF network with fused features of ResNet50-VGG19 achieved an AUC of 98.75%, a sensitivity of 97.4%, an accuracy of 99%, a precision of 99.6%, and a specificity of 99.2%.

Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas.

Malignant lymphoma is one of the most severe types of disease that leads to death as a result of exposure of lymphocytes to malignant tumors. The transformation of cells from indolent B-cell lymphoma to B-cell lymphoma (DBCL) is life-threatening. Biopsies taken from the patient are the gold standard for lymphoma analysis. Glass slides under a microscope are converted into whole slide images (WSI) to be analyzed by AI techniques through biomedical image processing. Because of the multiplicity of types of malignant lymphomas, manual diagnosis by pathologists is difficult, tedious, and subject to disagreement among physicians. The importance of artificial intelligence (AI) in the early diagnosis of malignant lymphoma is significant and has revolutionized the field of oncology. The use of AI in the early diagnosis of malignant lymphoma offers numerous benefits, including improved accuracy, faster diagnosis, and risk stratification. This study developed several strategies based on hybrid systems to analyze histopathological images of malignant lymphomas. For all proposed models, the images and extraction of malignant lymphocytes were optimized by the gradient vector flow (GVF) algorithm. The first strategy for diagnosing malignant lymphoma images relied on a hybrid system between three types of deep learning (DL) networks, XGBoost algorithms, and decision tree (DT) algorithms based on the GVF algorithm. The second strategy for diagnosing malignant lymphoma images was based on fusing the features of the MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models and classifying them by XGBoost and DT algorithms based on the ant colony optimization (ACO) algorithm. The color, shape, and texture features, which are called handcrafted features, were extracted by four traditional feature extraction algorithms. Because of the similarity in the biological characteristics of early-stage malignant lymphomas, the features of the fused MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models were combined with the handcrafted features and classified by the XGBoost and DT algorithms based on the ACO algorithm. We concluded that the performance of the two networks XGBoost and DT, with fused features between DL networks and handcrafted, achieved the best performance. The XGBoost network based on the fused features of MobileNet-VGG16 and handcrafted features resulted in an AUC of 99.43%, accuracy of 99.8%, precision of 99.77%, sensitivity of 99.7%, and specificity of 99.8%. This highlights the significant role of AI in the early diagnosis of malignant lymphoma, offering improved accuracy, expedited diagnosis, and enhanced risk stratification. This study highlights leveraging AI techniques and biomedical image processing; the analysis of whole slide images (WSI) converted from biopsies allows for improved accuracy, faster diagnosis, and risk stratification. The developed strategies based on hybrid systems, combining deep learning networks, XGBoost and decision tree algorithms, demonstrated promising results in diagnosing malignant lymphoma images. Furthermore, the fusion of handcrafted features with features extracted from DL networks enhanced the performance of the classification models.

Correlation between Post-COVID-19, Chemosensitive Function, Blood Group, and Oral Health-Related Quality of Life.

Materials and Methods: A cross-sectional information on demographics, symptomatic disease status, ABO blood group, and oral health-related quality of life (OHRQoL) was collected among 100 patients who were earlier tested positive for COVID-19 reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and were now reporting to the College of Dentistry for routine treatment after recovery. Objective evaluation of olfactory and gustatory disturbances was elicited using the Connecticut Chemosensory Clinical Research Center (CCCRC) test and gustatory function testing. Furthermore, OHRQoL was assessed using Oral Health Impact Profile (OHIP-14). Results: More than half of the patients (62%) had some form of olfactory dysfunction/alteration, and 42% had poor CCCRC scores. About 14% reported ageusia, while 68% reported some form of taste alterations, and 55% reported poor OHRQoL. A statistically significant difference was reported between different ABO blood groups and subjective loss of smell (p < 0.05). The subjective loss of taste, CCCRC score, and dysgeusia were found to be independent of OHIP-14 (p > 0.05), but the taste intensity score was dependent on OHIP 14 (p < 0.05). Moreover, a majority (70.8% and 70.0%) with poor OHIP-14 scores had taste intensity scores of 3 and 4, respectively, while those with moderate (68.4% and 48.6%) OHIP-14 had scored 1 and 2, respectively. Conclusion: Olfactory and gustatory disturbances were found to be a long-term feature in post-COVID-19 patients. The blood group is a predisposing factor for persistent smell alterations in post-COVID-19 patients.

A Comparison of Pre-Emptive Co-Amoxiclav, Postoperative Amoxicillin, and Metronidazole for Prevention of Postoperative Complications in Dentoalveolar Surgery: A Randomized Controlled Trial.

OBJECTIVE: To compare the effectiveness of different oral antibiotics for prevention of dry socket and infection in adults following the surgical extraction of teeth under LA. METHODS: This randomized controlled study was conducted from 10 September 2020 until 10 May 2021. Forty-six patients were randomly allocated to three groups. Sixteen patients were in the postoperative co-amoxiclav (625 mg) group, fifteen in the preoperative co-amoxiclav (625 mg) plus postoperative metronidazole (500 mg) group and fifteen in the preoperative co-amoxiclav (625 mg) plus postoperative amoxicillin (500 mg) group. Evaluation of the postoperative signs of alveolar osteitis and infection was made by a dental surgeon five days postoperatively. Evaluation of the post-surgical extraction pain was made by patients immediately and five days postoperatively on standard 100 mm visual analogue scales (VAS). Furthermore, difficulty of surgery was recorded for all patients immediately postoperatively using (VAS). RESULTS: all antibiotics used in this study were effective. Only 15% of patients had painful alveolar osteitis and 2% had oral infections. There was no significant decrease in the number of patients with severe alveolar osteitis or infection for co-amoxiclav plus metronidazole and co-amoxiclav plus amoxicillin groups compared to co-amoxiclav group at 5 days post-operation (p-values: 0.715, 0.819 & 0.309). Clinically, metronidazole was more effective in protecting the extracted tooth socket from alveolar osteitis compared to co-amoxiclav and amoxicillin. Moreover, there were significant decreases in mean pain scores at 5 days post-operation compared with the levels of pain immediately after surgery (p-value: 0.001). CONCLUSIONS: Administration of a single preoperative dose of co-amoxiclav with a full postoperative dose of amoxicillin or metronidazole was more effective than conventional treatment with postoperative co-amoxilcalv in reducing the incidence of both alveolar osteitis and infection after surgical extractions. However, these differences were not statistically significant. Interestingly, patients in metronidazole group had the lowest incidence of dry socket.

An efficient method for synthesis, characterization and molecular docking study of new sulfamethoxazole derivatives as antibacterial agents.

A new series of sulfamethoxazole derivatives bearing some biologically active heterocycles such as pyrazole (2), 3,4-dihydropyrimidin (3-7, 11, 12), pyrrole (9) and 1,3-dihydropyrimidin (10) rings were successfully synthesized. Identification of designed compounds was done by physicochemical properties and spectral measurements (1H-NMR, 13C-NMR and FT-IR). New prepared derivatives were assay for their (in vitro) antibacterial efficacy against four types of pathogenic bacterial isolates. Significant of the newly prepared compounds appeared promising activity comparison to the cephalexin standard drug. Most of the active compounds are docked into the effective site of tested bacterial enzymes obtained by crystal structure; results reveal the binding template to enzymes of bacteria, which closely related to the laboratory results.

The colostomy complications in anorectal malformation: a retrospective study

Background: A colostomy is a surgical approach that creates an opening for the colon, or/and large intestine through the abdomen. Anorectal malformations are a group of abnormalities of the rectum and anus that are present at birth. Objective: To analyze the common complications of colostomy in anorectal formations. Methods: This was a retrospective study conducted on 50 temporary colostomies performed in children at the Surgical Department of the Abu Ghraib General Hospital in the period from January 2018 to January 2020. Information was collected regarding the patients' age, sex, body weight, associated anomalies, colostomy types and sites, and the indications and complications of colostomies. Results: A total of 44 (88%) cases were reported in the children's 1st month of life. The ratio of male to female was 1:1. Pelvic colostomy was performed in 48 (96%) patients, as 40 (80%) children underwent a loop-type, and 8 (16%) patients underwent doublebarrel colostomy. Transverse colostomy was performed on two patients. Prolapse occurred in 50% of the patients, and skin excoriations occurred in 22% . A total of 10% of the children developed sepsis. Bleeding was seen in 4% of the children after colostomy performance. Stenosis presented in 6% of the children, and this was corrected by repeated dilatation and re-fashioning. Obstruction of intestines was observed in one patient. The retraction developed in 6% of patients. Conclusions: Imperforate anus was themost common indication for stoma formation in the pediatric age group. Loop colostomy was the most common type used, and it had the highest rate of complications. Prolapses and skin excoriation were the most common complications found. (AU)

Anticoagulantes, Antiplaquetarios y sus implicaciones en cirugía: una revisión de la literatura / Anticoagulants, Anti-platelets, and their implications in Surgery: A Literature review

All health care providers should be aware of the impact of bleeding disorders on their patients during any surgical procedures. The knowledge of the mechanisms of hemostasis and optimized management are very important. Initial recognition of a bleeding disorder, in such patients with a systemic pathologic process, may occur in surgical practice. The surgical treatment of those patients might be complicated during the surgery due to the use of anticoagulant and/or antiplatelet medications raises a challenge in the daily practice of surgical professionals. Adequate hemostasis is critical for the success of any surgical procedure because bleeding problems can give rise to complications associated with important morbidity-mortality. Besides, prophylactic, restorative, and surgical care of patients with any bleeding disorders is handled skillfully by practitioners who are well educated regarding the pathology, complications which could arise, and surgical options associated with these conditions. The purpose of this paper is to review common bleeding disorders and their effects on the surgical aspect. Many authors consider that patient medication indicated for the treatment of background disease should not be altered or suspended unless so indicated by the prescribing physician. Local hemostatic measures have been shown to suffice for controlling possible bleeding problems resulting from surgery.

Knowledge and attitudes toward vaccination among Saudi medical students.

BACKGROUND: Studies have identified healthcare providers as an important determinant of vaccination acceptance. However, knowledge and attitudes toward vaccination have not been sufficiently studied in Saudi Arabia, especially among medical students. Therefore, we conducted this study to explore vaccination knowledge and attitudes among medical students at a large Saudi university. METHODS: A cross-sectional survey was conducted on 182 Saudi medical students between February 2019 and May 2019. Participants were invited to fill out a self-administered questionnaire assessing knowledge and attitudes toward vaccination. The statistical analysis included descriptive analysis, Chi-square test, independent samples t-test, and analysis of variance (ANOVA). The relationship between knowledge and attitudes was assessed using Pearson's correlation test. RESULTS: A total of 182 respondents completed the questionnaires, giving a response rate of 91%. The study included male (52.7%) and female (47.3%) students from study years 2, 3, 4, 5, and 6. The overall mean knowledge score was under average [3.05/9, standard deviation (SD) = 1.86] and the respondents showed generally moderate attitudes toward vaccination (mean = 30.60/45, SD = 6.07). While there was no sex difference in both the scores on knowledge and attitudes domains, the year of study was significantly associated with the mean knowledge score (F = 6.48, P < 0.01) and attitudes score (F = 7.12, P < 0.01). As predicted, there was a significant linear relationship between vaccination knowledge and attitudes (r = 0.71, P < 0.01). CONCLUSION: The study revealed generally moderate attitudes of Saudi medical students toward vaccination. However, several knowledge gaps were detected. The implications of the current findings are discussed.