SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection.

Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and intervention. In recent years, self-supervised deep representation pattern learning (SS-DRPL) has emerged as a promising approach for extracting valuable representations from data, offering the potential to enhance the efficiency of voice-based PD detection. This research study focuses on investigating the utilization of SS-DRPL in conjunction with deep learning algorithms for voice-based PD classification. This study encompasses a comprehensive evaluation aimed at assessing the accuracy of various predictive models, particularly deep learning methods when combined with SS-DRPL. Two deep learning architectures, namely hybrid Long Short-Term Memory and Recurrent Neural Networks (LSTM-RNN) and Deep Neural Networks (DNN), are employed and compared in terms of their ability to detect voice-based PD cases accurately. Additionally, several traditional machine learning models are also included to establish a baseline for comparison. The findings of the study reveal that the incorporation of SS-DRPL leads to improved model performance across all experimental setups. Notably, the LSTM-RNN architecture augmented with SS-DRPL achieves the highest F1-score of 0.94, indicating its superior ability to detect PD cases using voice-based data effectively. This outcome underscores the efficacy of SS-DRPL in enabling deep learning models to learn intricate patterns and correlations within the data, thereby facilitating more accurate PD classification.

TSSG-CNN: A Tuberculosis Semantic Segmentation-Guided Model for Detecting and Diagnosis Using the Adaptive Convolutional Neural Network.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium. It primarily impacts the lungs but can also endanger other organs, such as the renal system, spine, and brain. When an infected individual sneezes, coughs, or speaks, the virus can spread through the air, which contributes to its high contagiousness. The goal is to enhance detection recognition with an X-ray image dataset. This paper proposed a novel approach, named the Tuberculosis Segmentation-Guided Diagnosis Model (TSSG-CNN) for Detecting Tuberculosis, using a combined semantic segmentation and adaptive convolutional neural network (CNN) architecture. The proposed approach is distinguished from most of the previously proposed approaches in that it uses the combination of a deep learning segmentation model with a follow-up classification model based on CNN layers to segment chest X-ray images more precisely as well as to improve the diagnosis of TB. It contrasts with other approaches like ILCM, which is optimized for sequential learning, and explainable AI approaches, which focus on explanations. Moreover, our model is beneficial for the simplified procedure of feature optimization from the perspectives of approach using the Mayfly Algorithm (MA). Other models, including simple CNN, Batch Normalized CNN (BN-CNN), and Dense CNN (DCNN), are also evaluated on this dataset to evaluate the effectiveness of the proposed approach. The performance of the TSSG-CNN model outperformed all the models with an impressive accuracy of 98.75% and an F1 score of 98.70%. The evaluation findings demonstrate how well the deep learning segmentation model works and the potential for further research. The results suggest that this is the most accurate strategy and highlight the potential of the TSSG-CNN Model as a useful technique for precise and early diagnosis of TB.

HO-SsNF: heap optimizer-based self-systematized neural fuzzy approach for cervical cancer classification using pap smear images.

Cervical cancer is a significant concern for women, necessitating early detection and precise treatment. Conventional cytological methods often fall short in early diagnosis. The proposed innovative Heap Optimizer-based Self-Systematized Neural Fuzzy (HO-SsNF) method offers a viable solution. It utilizes HO-based segmentation, extracting features via Gray-Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The proposed SsNF-based classifier achieves an impressive 99.6% accuracy in classifying cervical cancer cells, using the Herlev Pap Smear database. Comparative analyses underscore its superiority, establishing it as a valuable tool for precise cervical cancer detection. This algorithm has been seamlessly integrated into cervical cancer diagnosis centers, accessible through smartphone applications, with minimal resource demands. The resulting insights provide a foundation for advancing cancer prevention methods.

Artificial intelligence framework for heart disease classification from audio signals.

As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning (DL) techniques are pivotal in classifying and identifying heart disease from audio signals. This study investigates ML and DL techniques to detect heart disease by analyzing noisy sound signals. This study employed two subsets of datasets from the PASCAL CHALLENGE having real heart audios. The research process and visually depict signals using spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs). We employ data augmentation to improve the model's performance by introducing synthetic noise to the heart sound signals. In addition, a feature ensembler is developed to integrate various audio feature extraction techniques. Several machine learning and deep learning classifiers are utilized for heart disease detection. Among the numerous models studied and previous study findings, the multilayer perceptron model performed best, with an accuracy rate of 95.65%. This study demonstrates the potential of this methodology in accurately detecting heart disease from sound signals. These findings present promising opportunities for enhancing medical diagnosis and patient care.

Evaluating deep learning variants for cyber-attacks detection and multi-class classification in IoT networks.

The Internet of Things (IoT), considered an intriguing technology with substantial potential for tackling many societal concerns, has been developing into a significant component of the future. The foundation of IoT is the capacity to manipulate and track material objects over the Internet. The IoT network infrastructure is more vulnerable to attackers/hackers as additional features are accessible online. The complexity of cyberattacks has grown to pose a bigger threat to public and private sector organizations. They undermine Internet businesses, tarnish company branding, and restrict access to data and amenities. Enterprises and academics are contemplating using machine learning (ML) and deep learning (DL) for cyberattack avoidance because ML and DL show immense potential in several domains. Several DL teachings are implemented to extract various patterns from many annotated datasets. DL can be a helpful tool for detecting cyberattacks. Early network data segregation and detection thus become more essential than ever for mitigating cyberattacks. Numerous deep-learning model variants, including deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), are implemented in the study to detect cyberattacks on an assortment of network traffic streams. The Canadian Institute for Cybersecurity's CICDIoT2023 dataset is utilized to test the efficacy of the proposed approach. The proposed method includes data preprocessing, robust scalar and label encoding techniques for categorical variables, and model prediction using deep learning models. The experimental results demonstrate that the RNN model achieved the highest accuracy of 96.56%. The test results indicate that the proposed approach is efficient compared to other methods for identifying cyberattacks in a realistic IoT environment.

Enhanced Drug Delivery System Using Mesenchymal Stem Cells and Membrane-Coated Nanoparticles.

Mesenchymal stem cells (MSCs) have newly developed as a potential drug delivery system. MSC-based drug delivery systems (MSCs-DDS) have made significant strides in the treatment of several illnesses, as shown by a plethora of research. However, as this area of research rapidly develops, several issues with this delivery technique have emerged, most often as a result of its intrinsic limits. To increase the effectiveness and security of this system, several cutting-edge technologies are being developed concurrently. However, the advancement of MSC applicability in clinical practice is severely hampered by the absence of standardized methodologies for assessing cell safety, effectiveness, and biodistribution. In this work, the biodistribution and systemic safety of MSCs are highlighted as we assess the status of MSC-based cell therapy at this time. We also examine the underlying mechanisms of MSCs to better understand the risks of tumor initiation and propagation. Methods for MSC biodistribution are explored, as well as the pharmacokinetics and pharmacodynamics of cell therapies. We also highlight various promising technologies, such as nanotechnology, genome engineering technology, and biomimetic technology, to enhance MSC-DDS. For statistical analysis, we used analysis of variance (ANOVA), Kaplan Meier, and log-rank tests. In this work, we created a shared DDS medication distribution network using an extended enhanced optimization approach called enhanced particle swarm optimization (E-PSO). To identify the considerable untapped potential and highlight promising future research paths, we highlight the use of MSCs in gene delivery and medication, also membrane-coated MSC nanoparticles, for treatment and drug delivery.

A cross-sectional study on the prevalence of HIV and hepatitis B virus co-infection among students of a tertiary institution in Ekiti State, Southwest Nigeria.

Nigeria aims to eradicate public-health threats such as HIV/AIDS and hepatitis B virus (HBV) by 2030. However, to achieve the short- and medium-term response target, and end the epidemic by 2030, there is the need to monitor and estimate the population level of HIV and HBV epidemic trends to boost the country's strategic framework's chances of success. Hence, we uncovered the prevalence of HIV and HBV among full-time, newly admitted undergraduate university students in Southwestern Nigeria between 2015 and 2017. In this regard, 4 ml of blood samples was collected from each subject into Ethylene Diamine Tetraacetic Acid (EDTA) bottles and were allowed to stand for one hour. Samples were allowed to separate into plasma and corpuscles on the bench. HIV screening was done using an immunochromatographic method via a highly sensitive kit DETERMINE® (Abbott Diagnostic Division, Netherlands) and were later confirmed using Enzyme Linked Immunosorbent Assay (ELISA) Uni-Gold® manufactured by Trinity Biotech Plc, Ireland. HBV screening was carried out using an immunoassay method for the detection of the hepatitis B surface antigen (HBsAg). Out of the 4,623 subjects recruited, 2,545 were male while 2,078 were female. The overall prevalence of HIV was found to be 0.13% while that of HBV was 2.23%. Conclusively, although HIV was found to be less prevalent among the study as compared to HBV; however, the higher transmission propensity of HBV necessitates even more urgent efforts to eradicate the infectious diseases.

On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals.

Epileptic patients suffer from an epileptic brain seizure caused by the temporary and unpredicted electrical interruption. Conventionally, the electroencephalogram (EEG) signals are manually studied by medical practitioners as it records the electrical activities from the brain. This technique consumes a lot of time, and the outputs are unreliable. In a bid to address this problem, a new structure for detecting an epileptic seizure is proposed in this study. The EEG signals obtained from the University of Bonn, Germany, and real-time medical records from the Senthil Multispecialty Hospital, India, were used. These signals were disintegrated into six frequency subbands that employed discrete wavelet transform (DWT) and extracted twelve statistical functions. In particular, seven best features were identified and further fed into k-Nearest Neighbor (kNN), naïve Bayes, Support Vector Machine (SVM), and Decision Tree classifiers for two-type and three-type classifications. Six statistical parameters were employed to measure the performance of these classifications. It has been found that different combinations of features and classifiers produce different results. Overall, the study is a first attempt to find the best combination feature set and classifier for 16 different 2-class and 3-class classification challenges of the Bonn and Senthil real-time clinical dataset.

Studies on the serological markers for hepatitis B virus infection among type 2 diabetic patients.

BACKGROUND: Hepatitis B infection is a public health concern globally. HBV can be associated with type II diabetes mellitus, as HBV outbreaks have been observed among diabetics in healthcare facilities. This study evaluates the prevalence of HBV infection among patients with type II diabetes mellitus. METHOD: A total of one hundred and eighty (180) diabetic patients and one-hundred non-diabetics (Controls) were recruited for this study. Structured questionnaires were administered to the consented participants to obtain relevant data. Sera samples obtained were screened using the HBsAg ELISA kit; CTK Biotech, Inc, while the 5 panel kit-rapid diagnostic test, was used to assay for serological markers. Questionnaires were used to obtain relevant information and demographic data. RESULT: Overall prevalence of HBV infection among diabetes patients was 13.3%. Breakdown showed 9 (5.0%) seropositivity was obtained among male subjects compared to 15(8.3%) recorded among the females, P = .834; P < .05. Subjects aged 41-50 years recorded, 7(3.9%) positivity P = .774; P > .05. Educational status of participants showed 22 (12.2%) positivity among subjects with tertiary level of education P = .032; P < .05). Risk factors considered showed that 5(2.8%).seropositive subjects were alcoholic consumers (P value = .9711; P > .05). Result among non-diabetics (Control) subjects showed (4%) seropositivity among the male subjects compared to (5.0%) seropositivity recorded among the female subjects (P = .739; P > .05). CONCLUSION: There is an indication of higher risk of HBV infection among type 2 diabetic patients when compared to non-diabetics. There is the need for more research on this area of study, to further validate the association between HBV infection and Diabetes Mellitus.

Predictive evaluation of pediatric patients based on their typhoid fever status using linear discriminant model.

Epidemiologic studies have established a relationship between pediatric patients and typhoid fever infection. This study was carried out to ascertain if specific hematological measurements of the pediatric patients discriminate between their positive and negative status to typhoid infection and to produce a rule for classifying other pediatric patients. Discriminant analysis was applied to predict the probability of a specific categorical outcome based on several explanatory variables (predictors). This study analyzed the differentiation between two hundred pediatric patients attending Landmark University Medical Centre based on their typhoid fever status. The hematological parameters considered were Packed Cell Volume, White Blood Cell count; Neutrophil, Erythrocyte level, Hemoglobin and Platelet count, Assay of samples were performed using standard procedures. Fisher's Linear Discriminant Method was used for classification of variables in this study. With the use of the Fisher's Linear Discrimination method for classification of the obtained data, a minimum value of -0.0067 was obtained implying that any new pediatric patient with a discriminant score above -0.0067 would be diagnosed to be typhoid negative; otherwise, they would be classified as typhoid positive pediatric patients. The efficiency of this method of classification was tested using two approaches; Retribution estimate approach and leaving-one out approach which showed a prevalence rate of typhoid positive patients at 75.8% and 74.7% respectively. This data analysis hypotheses that typhoid fever is highly endemic amongst our study subjects. A point-of-care diagnosis with a strong positive predictive value, which improves pediatric enteric fever diagnosis, is strongly advocated.

Diaryl- and triaryl-pyrrole derivatives: inhibitors of the MDM2-p53 and MDMX-p53 protein-protein interactions†Electronic supplementary information (ESI) available: Experimental details for compound synthesis, analytical data for all compounds and intermediates. Details for the biological evaluation. Further details for the modeling. Table of combustion analysis data. See DOI: 10.1039/c3md00161jClick here for additional data file.

Screening identified 2-(3-((4,6-dioxo-2-thioxotetrahydropyrimidin-5(2H)-ylidene)methyl)-2,5-dimethyl-1H-pyrrol-1-yl)-4,5,6,7-tetrahydrobenzo[b]thiophene-3-carbonitrile as an MDM2-p53 inhibitor (IC50 = 12.3 µM). MDM2-p53 and MDMX-p53 activity was seen for 5-((1-(4-chlorophenyl)-2,5-diphenyl-1H-pyrrol-3-yl)methylene)-2-thioxodihydropyrimidine-4,6(1H,5H)-dione (MDM2 IC50 = 0.11 µM; MDMX IC50 = 4.2 µM) and 5-((1-(4-nitrophenyl)-2,5-diphenyl-1H-pyrrol-3-yl)methylene)pyrimidine-2,4,6(1H,3H,5H)-trione (MDM2 IC50 = 0.15 µM; MDMX IC50 = 4.2 µM), and cellular activity consistent with p53 activation in MDM2 amplified cells. Further SAR studies demonstrated the requirement for the triarylpyrrole moiety for MDMX-p53 activity but not for MDM2-p53 inhibition.