Lead (Pb) and cadmium (Cd) in milk were determined by atomic absorption spectrometry, using milk sample inorganication on sand-separated stove and sample inorganication equipment with graphit-Digi PREP Jr heat stabilization system. Results: sample inorganication equipment with heat stabilization system has the advantage of saving chemical and time. Atomic absorption spectrometry method and atomization technique in graphite furnace combined with Zeeman standardization technique allows determining Pb and Cd with limits of 0.0005 μg/ml and 0.005 μg/ml, respectively. This method has been used to check the quality of milk in general as well as to evaluate the effect of Pb and Cd contamination in epidemiology
Milk ; Lead ; Cadmium

Sphaeranthus africanus is commonly used as a traditional remedy for sore throats and pain treatment in Vietnam. The aerial parts have been studied for its anti-inflammatory and anti-proliferative properties. However, the antioxidant and antidiabetic potential of the plant has not been explored. In this work, hydrophilic extracts of the plant's aerial parts were prepared in order to investigate its antioxidant and anti-diabetic properties. Also, the cytotoxicity of the root was evaluated and compared to that of the aerial parts. All of the extracts inhibited lipid peroxidation with IC 50 values ranging from 2.05 to 3.56 µg/mL, indicating substantial antioxidant activity. At an IC 50 value of 4.80 μg/mL, the 50% ethanol extract exhibited the most potent inhibition of α-glucosidase. The cytotoxic activity of root extracts is 2 to 5-fold less than that of the aerial parts. Nevertheless, dichloromethane and ethyl acetate extracts of the root demonstrated a selective effect on leukemia cells, with no harm towards the normal HEK-293 cell line. This work provides a scientific support for the antioxidant and antidiabetic activity of the plant. Hence, it may find a promising material for the development of novel antioxidant and antidiabetic agents. More research can be conducted on the phytochemistry and anticancer activities of the plant’s root.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Background: Suicide continues to be a leading cause of death worldwide. Recently, more than 45,000 children in the age group of 10 to 19 years died by suicide, making it the second leading cause of death in the age group of 15 to 19, surpassed only by traffic accidents, tuberculosis, and fighting. Objective: To determine the prevalence of suicidal ideation among adolescents whose parents are separated/divorced; and to explore the factors associated with suicidal ideation and describe adverse experiences among adolescents. Methods: A cross-sectional descriptive study was conducted in 309 adolescents with separated/separated parents in Hue City. Data was collected through direct interviews using a structured questionnaire. Suicidal ideation was defined as the presence of thoughts or plans related to suicide within the last 12 months. Multivariate logistic regression was applied to identify factors associated with suicidal ideation in adolescents with separated/ separated parents. Results: The study found that 15.5% (95% CI:11.7 - 20.1) of adolescents with separated /separated parents reported experiencing suicidal thoughts, in which men accounted 8.4% (95% CI:5.6 – 11.2) and women accounted 7.1% (95% CI:4.5 - 10.6). Several factors were identified as increasing the risk of suicidal ideation, including alcohol use (OR = 3.24; 95% CI:1.42 - 7.42), hyperactivity/inattention (OR = 4.96; 95% CI:1.58 - 15.605), and a poor quality of family relationships (OR = 4.82; 95% CI:1.26 - 18.50). On the contrary, certain factors were found to reduce the risk, including being in the 14-15 age group of 14-15 (OR = 0.26; 95% CI:0.10 - 0.69) and participating in physical activity (OR = 0.44; 95% CI:0.21 – 0.94). Conclusions: The research highlights a significant percentage of adolescents with separated / divided parents who experience suicidal ideation. Therefore, it is imperative for families, schools, and society to develop comprehensive strategies to monitor and address various risky behaviours among students simultaneously.