Background: Machine learning (ML) is a type of artificial intelligence strategy. Its algorithms are used on big data sets to see patterns, learn from their results, and perform tasks autonomously without being instructed on how to address problems. New diseases like COVID-19 provide important data for ML. Therefore, all relevant parameters should be explicitly quantified and modeled. Objective: The purpose of this study was to determine (1) the overall preclinical characteristics, (2) the cumulative cutoff values and risk ratios (RRs), and (3) the factors associated with COVID-19 severity in unidimensional and multidimensional analyses involving 2173 SARS-CoV-2 patients. Methods: The study population consisted of 2173 patients (1587 mild status [mild group] and asymptomatic patients, 377 moderate status patients [moderate group], and 209 severe status patients [severe group]). The status of the patients was recorded from September 2021 to March 2022. Two correlation tests, relative risk, and RR were used to eliminate unbalanced parameters and select the most remarkable parameters. The independent methods of hierarchical cluster analysis and k-means were used to classify parameters according to their r values. Finally, network analysis provided a 3-dimensional view of the results. Results: COVID-19 severity was significantly correlated with age (mild-moderate group: RR 4.19, 95% CI 3.58-4.95; P<.001), scoring index of chest x-ray (mild-moderate group: RR 3.29, 95% CI 2.76-3.92; P<.001; moderate-severe group: RR 3.03, 95% CI 2.4023-3.8314; P<.001), percentage of neutrophils (mild-moderate group: RR 3.18, 95% CI 2.73-3.70; P<.001; moderate-severe group: RR 3.32, 95% CI 2.6480-4.1529; P<.001), quantity of neutrophils (moderate-severe group: RR 3.15, 95% CI 2.6153-3.8025; P<.001), albumin (moderate-severe group: RR 0.46, 95% CI 0.3650-0.5752; P<.001), C-reactive protein (mild-moderate group: RR 3.4, 95% CI 2.91-3.97; P<.001), and ratio of lymphocytes (moderate-severe group: RR 0.34, 95% CI 0.2743-0.4210; P<.001). Significant inversion of correlations among the severity groups is important. Alanine transaminase and leucocytes showed a significant negative correlation (r=−1; P<.001) in the mild group and a significant positive correlation in the moderate group (r=1; P<.001). Transferrin and anion Cl showed a significant positive correlation (r=1; P<.001) in the mild group and a significant negative correlation in the moderate group (r=−0.59; P<.001). The clustering and network analysis showed that in the mild-moderate group, the closest neighbors of COVID-19 severity were ferritin and age. C-reactive protein, scoring index of chest x-ray, albumin, and lactate dehydrogenase were the next closest neighbors of these 3 factors. In the moderate-severe group, the closest neighbors of COVID-19 severity were ferritin, fibrinogen, albumin, quantity of lymphocytes, scoring index of chest x-ray, white blood cell count, lactate dehydrogenase, and quantity of neutrophils. Conclusions: This multidimensional study in Vietnam showed possible correlations between several elements and COVID-19 severity to provide clinical reference markers for surveillance and diagnostic management.

Background: The etiology of ischemic stroke is multifactorial. Several gene mutations have been identified as leading causes of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), a hereditary disease that causes stroke and other neurological symptoms. Objective: We aimed to identify the variants of NOTCH3 and thrombophilia genes, and their complex interactions with other factors. Methods: We conducted a hierarchical cluster analysis (HCA) on the data of 100 patients diagnosed with ischemic stroke. The variants of NOTCH3 and thrombophilia genes were identified by polymerase chain reaction with confronting 2-pair primers and real-time polymerase chain reaction. The overall preclinical characteristics, cumulative cutpoint values, and factors associated with these somatic mutations were analyzed in unidimensional and multidimensional scaling models. Results: We identified the following optimal cutpoints: creatinine, 83.67 (SD 9.19) µmol/L; age, 54 (SD 5) years; prothrombin (PT) time, 13.25 (SD 0.17) seconds; and international normalized ratio (INR), 1.02 (SD 0.03). Using the Nagelkerke method, cutpoint 50% values of the Glasgow Coma Scale score; modified Rankin scale score; and National Institutes of Health Stroke Scale scores at admission, after 24 hours, and at discharge were 12.77, 2.86 (SD 1.21), 9.83 (SD 2.85), 7.29 (SD 2.04), and 6.85 (SD 2.90), respectively. Conclusions: The variants of MTHFR (C677T and A1298C) and NOTCH3 p.R544C may influence the stroke severity under specific conditions of PT, creatinine, INR, and BMI, with risk ratios of 4.8 (95% CI 1.53-15.04) and 3.13 (95% CI 1.60-6.11), respectively (Pfisher<.05). It is interesting that although there are many genes linked to increased atrial fibrillation risk, not all of them are associated with ischemic stroke risk. With the detection of stroke risk loci, more information can be gained on their impacts and interconnections, especially in young patients.

Background: Hepatitis B virus (HBV) can be transmitted from mother to child either through transplacental infection or via blood-to-blood contact during or immediately after delivery. Early and accurate risk assessments are essential for guiding clinical decisions and implementing effective preventive measures. Data mining techniques are powerful tools for identifying key predictors in medical diagnostics. Objective: This study aims to develop a robust predictive model for mother-to-child transmission (MTCT) of HBV using decision tree algorithms, specifically Iterative Dichotomiser 3 (ID3) and classification and regression trees (CART). The study identifies clinically and paraclinically relevant predictors, particularly hepatitis B e antigen (HBeAg) status and peripheral blood mononuclear cell (PBMC) concentration, for effective risk stratification and prevention. Additionally, we will assess the model’s reliability and generalizability through cross-validation with various training-test split ratios, aiming to enhance its applicability in clinical settings and inform improved preventive strategies against HBV MTCT. Methods: This study used decision tree algorithms—ID3 and CART—on a data set of 60 hepatitis B surface antigen (HBsAg)–positive pregnant women. Samples were collected either before or at the time of delivery, enabling the inclusion of patients who were undiagnosed or had limited access to treatment. We analyzed both clinical and paraclinical parameters, with a particular focus on HBeAg status and PBMC concentration. Additional biochemical markers were evaluated for their potential contributory or inhibitory effects on MTCT risk. The predictive models were validated using multiple training-test split ratios to ensure robustness and generalizability. Results: Our analysis showed that 20 out of 48 (based on a split ratio of 0.8 from a total of 60 cases, 42%) to 27 out of 57 (based on a split ratio of 0.95 from a total of 60 cases, 47%) training cases with HBeAg-positive status were associated with a significant risk of MTCT of HBV (χ28=21.16, P=.007, df=8). Among HBeAg-negative women, those with PBMC concentrations ≥8 × 106 cells/mL exhibited a low risk of MTCT, whereas individuals with PBMC concentrations <8 × 106 cells/mL demonstrated a negligible risk. Across all training-test split ratios, the decision tree models consistently identified HBeAg status and PBMC concentration as the most influential predictors, underscoring their robustness and critical role in MTCT risk stratification. Conclusions: This study demonstrates that decision tree models are effective tools for stratifying the risk of MTCT of HBV by integrating key clinical and paraclinical markers. Among these, HBeAg status and PBMC concentration emerged as the most critical predictors. While the analysis focused on untreated patients, it provides a strong foundation for future investigations involving treated populations. These findings offer actionable insights to support the development of more targeted and effective HBV MTCT prevention strategies.

BACKGROUND&AIMS: Hepatitis B virus (HBV) infection remains a major public health problem. The interaction between HBV and the host inflammatory response is an important factor contributing to liver damage and disease development. We compared the correlation between the subclinical index and PBMCs concentration in two groups of pregnant women (HBsAg positive), which are different in HBV DNA concentration in Vietnam. METHODS: The Hierarchical cluster analysis (HCA) was run with 20 different clustering methods on data collected from 80 Vietnamese pregnant women and their babies (60/80 cord blood). RESULTS: In the high viral load group (HBV DNA ≥ 5x10^7 copies/ml), a strong correlation between CBMCs with serum maternal Haemoglobin concentration and maternal platelet and maternal ALT. Their R values are: -0.88, 0.82, and 0.84 with p=8.97E-03, 2.41E-02 and 1.75E-01, respectively. CONCLUSIONS: We found a significant correlation shift of subclinical index between the two groups, which may be important in diagnosing pregnant women with chronic hepatitis B virus infection.

Mục tiêu: Mô tả thực trạng sức khoẻ tâm thần và các rối loạn tình dục ở các cặp vợ chồng điều trị vô sinh tại Trung tâm hỗ trợ sinh sản Quốc gia, Bệnh viện Phụ sản Trung ương năm 2024. Đối tượng và phương pháp nghiên cứu: Đây là nghiên cứu mô tả cắt ngang trên 159 cặp vợ chồng đến điều trị vô sinh tại Trung tâm Hỗ trợ sinh sản Quốc gia, Bệnh viện Phụ sản Trung ương trong thời gian từ tháng 5 đến tháng 11 năm 2024. Để đánh giá sức khoẻ tâm thần chúng tôi sử dụng bộ câu hỏi DASS-21. Để đánh giá về rối loạn tình dục chúng tôi sử dụng bộ công cụ FSFI cho vợ và PEDT, IIEF cho chồng. Kết quả: Tuổi trung bình của vợ là 32,3±5,1 tuổi. Tuổi trung bình của chồng là 35,8±5,8 tuổi. Tỷ lệ người vợ có dấu hiệu lo âu, trầm cảm, stress lần lượt là 24,5%, 13,8% và 8,8%. Tỷ lệ người chồng có dấu hiệu lo âu, trầm cảm, stress lần lượt là 11,9%, 7,5% và 3,8%. Tỷ lệ bệnh nhân có rối loạn chức năng tình dục chiếm tỷ lệ lớn với 57,9%. Tỷ lệ người chồng có rối loạn cương dương chiếm tỷ lệ 10,7%. Tỷ lệ người chồng có xuất tinh sớm chiếm tỷ lệ 11,9%. Kết luận: Các vấn đề về sức khoẻ tâm thần như lo âu, trầm cảm, stress vẫn chiếm một tỷ lệ nhất định trong quần thể những cặp vợ chồng vô sinh. Tần suất gặp các rối loạn tình dục ở nữ chiếm hơn 50% tuy nhiên các rối loạn tình dục ở chồng như rối loạn cương dương và xuất tinh sớm chiếm tỷ lệ thấp hơn.

Đặt vấn đề: Bước đầu thử nghiệm phân biệt độ đục của thuốc thử trong xét nghiệm OF test bằng phương pháp quang phổ hấp phụ phân tử. Nghiên cứu hướng tới việc định lượng hóa xét nghiệm định tính từ đó có thể đưa ra các chỉ số đánh giá độ chính xác của xét nghiệm như độ nhạy độ đặc hiệu của phương pháp đo. Mục tiêu nghiên cứu: Đánh giá giá trị xét nghiệm sàng lọc người mang gen bệnh tan máu bẩm sinh sử dụng quang phổ hấp thụ phân tử. Đối tượng, phương pháp nghiên cứu: 80 mẫu máu tĩnh mạch người bình thường và người mang gen bệnh thalassemia, thực hiện tại labo Bộ môn Sinh hóa - và labo bộ môn Vật lý – Lý sinh trường Đại Học Y Dược. Kết quả: Tuổi trung bình đối tượng nghiên cứu là 30±4,5. Độ hấp thụ quang trung bình tại đỉnh có bước sóng 416 nm của mẫu âm tính là 3,45 ± 0,31, của mẫu dương tính là 2,1±0,78, sự khác biệt có ý nghĩa thống kê. OF test cho độ nhạy và độ đặc hiệu lần lượt là 85,2% và 80,0%. Diện tích dưới đường cong ROC (AUC) của Abs416=0,9; AUC của OF test là 0,725 (p<0,05). Kết luận: Đỉnh hấp phụ ở bước sóng 416 nm cho giá trị tiên lượng tốt nhất với diện tích dưới đường cong ROC = 0,90 trong khi của OF test chỉ đạt 0,725 khi so sánh với đường chuẩn. Sự khác biệt là có ý nghĩa thống kê.