Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural Network.

Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing on heart rate variability (HRV), to detect early SCD risk factors. In this study, we expand the comparison group dataset to include five groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD. ECG signals were recorded for 30 min and segmented into 5 min intervals, following the recommended HRV feature analysis guidelines. We introduce an innovative approach to HRV signal analysis by utilizing Convolutional Neural Networks (CNN). The CNN model was optimized by tuning hyperparameters such as the number of layers, learning rate, and batch size, significantly impacting the prediction accuracy. The findings demonstrate that the HRV approach, in conjunction with linear features and the DL method, achieved a higher accuracy rate, averaging 99.30%, reaching 97% sensitivity, 99.60% specificity, and 97.87% precision. Future research should focus on further exploring and refining DL methods in the context of HRV analysis to improve SCD prediction.

Effect of Extra-Virgin Olive Oil on Hand Foot Syndrome and hs-CRP in Patients Receiving Capecitabine: A Randomized Trial.

BACKGROUND: Hand Foot Syndrome (HFS) is a frequent adverse effect observed in patients undergoing capecitabine chemotherapy, often leading to treatment disruptions and dose adjustments. Elevated C-Reactive Protein (hs-CRP) levels have been associated with the development of HFS. This study aimed to assess the potential of unrefined Extra Virgin Olive Oil (EVOO) supplementation in mitigating HFS and hs-CRP elevation among individuals receiving capecitabine chemotherapy. METHODS: Between November 2022 and May 2023, forty-five eligible participants were enrolled in this randomized trial. Patients with advanced colorectal or breast cancer were randomly allocated into three groups: an intervention group receiving unrefined EVOO supplementation (30 mL per day) alongside capecitabine, a placebo group receiving refined extra light olive oil (ELOO) supplementation (30 mL per day) alongside capecitabine, and a control group receiving capecitabine alone. The masking of both placebo and intervention groups was ensured through identical packaging and instructions, maintaining participant and physician blindness to the assigned treatments. Randomization, achieved via computer-generated sequences, ensured even distribution among the three groups. RESULTS: HFS incidences were notably lower in the EVOO group (13.3%) compared to the placebo (66.7%) and control (80%) groups. Instances of Grade 2 or more severe HFS were observed in 20% of placebo and 40% of control group patients. No cases of severe HFS were reported in the EVOO group. Moreover, EVOO supplementation led to a significant reduction in hs-CRP levels when contrasted with the placebo and control groups. These findings suggest that EVOO may serve as a preventive measure against HFS and exhibit anti-inflammatory effects in patients undergoing capecitabine chemotherapy. CONCLUSION: This study demonstrates the potential benefits of incorporating unrefined EVOO into the regimen of patients undergoing capecitabine chemotherapy. EVOO supplementation was associated with lower incidences of HFS and a reduction in hs-CRP levels, indicating its possible role in preventing HFS development and mitigating inflammation.

Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification.

Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on signal quality and the number of leads. ECG signal classification based on deep learning (DL) has produced an automatic interpretation; however, the proposed method is used for specific abnormality conditions. When the ECG signal morphology change to other abnormalities, it cannot proceed automatically. To generalize the automatic interpretation, we aim to delineate ECG waveform. However, the output of delineation process only ECG waveform duration classes for P-wave, QRS-complex, and T-wave. It should be combined with a medical knowledge rule to produce the abnormality interpretation. The proposed model is applied for atrial fibrillation (AF) identification. This study meets the AF criteria with RR irregularities and the absence of P-waves in essential oscillations for even more accurate identification. The QT database by Physionet is utilized for developing the delineation model, and it validates with The Lobachevsky University Database. The results show that our delineation model works properly, with 98.91% sensitivity, 99.01% precision, 99.79% specificity, 99.79% accuracy, and a 98.96% F1 score. We use about 4058 normal sinus rhythm records and 1804 AF records from the experiment to identify AF conditions that are taken from three datasets. The comprehensive testing has produced higher negative predictive value and positive predictive value. This means that the proposed model can identify AF conditions from ECG signal delineation. Our approach can considerably contribute to AF diagnosis with these results.

Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection.

Accurate segmentation of fetal heart in echocardiography images is essential for detecting the structural abnormalities such as congenital heart defects (CHDs). Due to the wide variations attributed to different factors, such as maternal obesity, abdominal scars, amniotic fluid volume, and great vessel connections, this process is still a challenging problem. CHDs detection with expertise in general are substandard; the accuracy of measurements remains highly dependent on humans' training, skills, and experience. To make such a process automatic, this study proposes deep learning-based computer-aided fetal heart echocardiography examinations with an instance segmentation approach, which inherently segments the four standard heart views and detects the defect simultaneously. We conducted several experiments with 1149 fetal heart images for predicting 24 objects, including four shapes of fetal heart standard views, 17 objects of heart-chambers in each view, and three cases of congenital heart defect. The result showed that the proposed model performed satisfactory performance for standard views segmentation, with a 79.97% intersection over union and 89.70% Dice coefficient similarity. It also performed well in the CHDs detection, with mean average precision around 98.30% for intra-patient variation and 82.42% for inter-patient variation. We believe that automatic segmentation and detection techniques could make an important contribution toward improving congenital heart disease diagnosis rates.

Clinical Outcome of Multiple Platelet-Rich Plasma Injection and Correlation with PDGF-BB in the Treatment of Knee Osteoarthritis.

(1) Background: Current treatments for knee osteoarthritis (KOA), such as intra-articular corticosteroids or hyaluronic acid (HA) injections, are controversial due to their ineffectiveness in preventing disease progression. Platelet-rich plasma (PRP) has become a promising and possible treatment for KOA. It is thought to enhance articular cartilage regeneration and reduce OA-related impairment. PRP contains growth factors such as PDGF-BB, which stimulates growth and inhibits joint damage. Based on numerous studies, after a certain amount of time, it was found that multiple PRP treatments reduced pain more than a single injection. This study evaluates the efficacy of multiple PRP (m-PRP) injections compared to multiple HA (m-HA) injections for KOA treatment, focusing on their correlation with PDGF-BB levels. (2) Methods: In this single-center, open-label, randomized, comparative clinical trial, 30 KOA patients received m-PRP and m-HA injections. VAS and WOMAC were used to evaluate clinical outcomes and PDGF-BB concentrations. (3) Results: The study analysis revealed a statistically significant reduction in pain indices. In both the m-PRP and m-HA groups after 12 weeks, m-PRP showed superior results. PDGF-BB concentrations also increased, with a strong negative correlation and statistical significance using Spearman's rho. (4) Conclusions: Multiple PRP injections are safe and associated with elevated PDGF-BB, reduced VAS and WOMAC scores, providing the potential for articular cartilage regeneration and inhibiting knee osteoarthritis progression.

Umbilical Cord Mesenchymal Stem Cell Secretome Improves Clinical Outcomes and Changes Biomarkers in Knee Osteoarthritis.

(1) Background: The current treatment for osteoarthritis is ineffective due to its focus on pain relief and lack of cartilage repair. Viscosupplementation such as hyaluronic acid improves symptoms but remains unnoticed for several months. Researchers are exploring cell-based therapies such as mesenchymal stem cells secretome and mesenchymal stem cells, which can repair cartilage damage. The objective of the research is to evaluate and compare the effectiveness of the secretome derived from umbilical cord mesenchymal stem cells (UC-MSCs) with hyaluronic acid (HA). (2) Methods: An open-label clinical trial involving 30 knee osteoarthritis patients divided into two groups received UC-MSC secretome and hyaluronic acid doses. The study assessed clinical outcomes using VAS and WOMAC and measured MMP-3 and TGF-ß1 levels before and after treatment. (3) Results: A study of 30 subjects found that the UC-MSC secretome group showed a decrease in pain in the OA knee compared to the HA group. The therapy was most effective after the third injection, and the group showed a decrease in the MMP-3 ratio and an increase in TGF-ß1 compared to the hyaluronic acid group. (4) Conclusions: UC-MSC secretome intra-articular injections showed superior clinical improvement, biomarker changes, and no side effects compared to hyaluronic acid over a 5-week interval.

CervicoXNet: an automated cervicogram interpretation network.

Visual inspection with acetic acid (VIA) is a pre-cancerous screening program for low-middle-income countries (LMICs). Due to the limited number of oncology-gynecologist clinicians in LMICs, VIA examinations are performed mainly by medical workers. However, the inability of the medical workers to recognize a significant pattern based on cervicograms, VIA examination produces high inter-observer variance and high false-positive rate. This study proposed an automated cervicogram interpretation using explainable convolutional neural networks named "CervicoXNet" to support medical workers decision. The total number of 779 cervicograms was used for the learning process: 487 with VIA ( +) and 292 with VIA ( -). We performed data augmentation process under a geometric transformation scenario, such process produces 7325 cervicogram with VIA ( -) and 7242 cervicogram with VIA ( +). The proposed model outperformed other deep learning models, with 99.22% accuracy, 100% sensitivity, and 98.28% specificity. Moreover, to test the robustness of the proposed model, colposcope images used to validate the model's generalization ability. The results showed that the proposed architecture still produced satisfactory performance, with 98.11% accuracy, 98.33% sensitivity, and 98% specificity. It can be proven that the proposed model has been achieved satisfactory results. To make the prediction results visually interpretable, the results are localized with a heat map in fine-grained pixels using a combination of Grad-CAM and guided backpropagation. CervicoXNet can be used an alternative early screening tool with VIA alone.

Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases.

Early prenatal screening with an ultrasound (US) can significantly lower newborn mortality caused by congenital heart diseases (CHDs). However, the need for expertise in fetal cardiologists and the high volume of screening cases limit the practically achievable detection rates. Hence, automated prenatal screening to support clinicians is desirable. This paper presents and analyses potential deep learning (DL) techniques to diagnose CHDs in fetal USs. Four convolutional neural network architectures were compared to select the best classifier with satisfactory results. Hence, dense convolutional network (DenseNet) 201 architecture was selected for the classification of seven CHDs, such as ventricular septal defect, atrial septal defect, atrioventricular septal defect, Ebstein's anomaly, tetralogy of Fallot, transposition of great arteries, hypoplastic left heart syndrome, and a normal control. The sensitivity, specificity, and accuracy of the DenseNet201 model were 100%, 100%, and 100%, respectively, for the intra-patient scenario and 99%, 97%, and 98%, respectively, for the inter-patient scenario. We used the intra-patient DL prediction model to validate our proposed model against the prediction results of three expert fetal cardiologists. The proposed model produces a satisfactory result, which means that our model can support expert fetal cardiologists to interpret the decision to improve CHD diagnostics. This work represents a step toward the goal of assisting front-line sonographers with CHD diagnoses at the population level.

Seluang Fish (Rasbora Spp.) Oil Decreases Inflammatory Cytokines Via Increasing Vitamin D Level in Systemic Lupus Erythematosus.

BACKGROUND: Systemic Lupus Erythematosus (SLE) is an autoimmune disorder mediated by inflammatory cytokines. Decreasing vitamin D levels is a common feature in SLE patients. Vitamin D has the capacity in suppressing inflammatory cascade. Seluang fish (Rasbora spp.) contained a high level of vitamin D with the potential as a new therapeutic modality. AIM: This study aimed to assess the efficacy of Seluang fish oil against proinflammatory cytokines, vitamin D levels, and clinical conditions of SLE. METHODS: A randomised, double-blind, clinical trial study design was conducted. The subjects were 16 SLE subjects treated with 500uL Seluang fish oil capsules and 16 SLE subjects with placebo capsules. Measurement of vitamin D, IL-1, IL-6 and IL-17 levels were performed with ELISA. Clinical assessment of SLE was performed with MEX-SLEDAI. Bivariate analysis, T-test, was performed. Data were presented in the form of mean ± SD. RESULTS: The administration of Seluang fish oil was clinically able to show efficacy assessed by the MEX SLEDAI score. Significant results were also shown by increased vitamin D levels and reduced levels of IL-1, IL-6 and IL-17, in Seluang fish oil group. CONCLUSION: Seluang fish oil possessed the efficacy of reducing the inflammatory response in SLE patients by increasing serum vitamin D levels.