Predicting Suicidal Ideation, Planning, and Attempts among the Adolescent Population of the United States.

Suicide is the second leading cause of death among individuals aged 5 to 24 in the United States (US). However, the precursors to suicide often do not surface, making suicide prevention challenging. This study aims to develop a machine learning model for predicting suicide ideation (SI), suicide planning (SP), and suicide attempts (SA) among adolescents in the US during the coronavirus pandemic. We used the 2021 Adolescent Behaviors and Experiences Survey Data. Class imbalance was addressed using the proposed data augmentation method tailored for binary variables, Modified Synthetic Minority Over-Sampling Technique. Five different ML models were trained and compared. SHapley Additive exPlanations analysis was conducted for explainability. The Logistic Regression model, identified as the most effective, showed superior performance across all targets, achieving high scores in recall: 0.82, accuracy: 0.80, and area under the Receiver Operating Characteristic curve: 0.88. Variables such as sad feelings, hopelessness, sexual behavior, and being overweight were noted as the most important predictors. Our model holds promise in helping health policymakers design effective public health interventions. By identifying vulnerable sub-groups within regions, our model can guide the implementation of tailored interventions that facilitate early identification and referral to medical treatment.

Psychometric evaluation of the Persian version of the Sense of Belonging in Nursing School (SBNS): a quantitative and cross-sectional design.

BACKGROUND: This study aimed to evaluate the psychometric indicators of the Persian version of the Sense of Belonging in Nursing School scale (SBNS). METHODS: The study conducted in Shahroud and Semnan schools of nursing and midwifery in Iran examined nursing students using a cross-sectional approach by convenience sampling method from 3/6/2023 to 24/8/2023. To assess the SBNS scale, the forward-backward procedure was used to translate it into Persian. Face and content validity were evaluated, and exploratory and confirmatory factor analyses were conducted with sample sizes of 200 and 182, respectively. Reliability was assessed using Cronbach's alpha coefficient, MacDonald's omega, and intra-class correlation coefficient. RESULTS: The exploratory factor analysis resulted in the exclusion of four items, leaving a final selection of 15 items. These items were categorized into three factors: classmates, clinical staff, and inclusive educational environment, which accounted for 49.16% of the overall variance. The confirmatory factor analysis indicated that the model was a good fit for the observed data, and the subscales had high internal consistency (Cronbach's alpha coefficient was 0.752 to 0.880) and stability (intra-class correlation coefficient was 0.889 to 0.968). CONCLUSION: According to the results, it can be concluded that the Persian version of the SBNS scale demonstrates sufficient validity and reliability in assessing students' sense of belonging to the nursing school.

Gastric carcinoma's primary presentation as multiple cutaneous nodules throughout the body.

Cutaneous metastasis resulting from internal primary tumors remains a rare phenomenon. The prompt recognition of these metastases is important, as they are an indicator of advanced disease and poor prognosis. We report the case of a 44-year-old Caucasian man presenting with a four-month history of multiple cutaneous nodules on the face, trunk, and upper extremities. Results from skin biopsy revealed strands and cords of atypical cells concerning for poorly differentiated metastatic carcinoma. An esophagogastroduodenoscopy was performed and a poorly differentiated signet-ring type invasive adenocarcinoma at the lesser curvature of the stomach was found. As the cancer had already metastasized to the skin and bones, the patient was started on chemotherapy with an oxaliplatin-based regimen and denosumab for the bone metastases, with resultant objective response and diagnostic control for greater than one year to the present date.

Simultaneous Removal of Heavy Metals from Wastewater Using Modified Sodium Montmorillonite Nanoclay.

Sodium montmorillonite nanoclay particles were modified by 3-aminopropyltrimetoxysilane using a physical vapor deposition method and applied for the simultaneous removal of Cd, Zn, Pb and Ni heavy metals from wastewater samples. Experiments were performed in both batch and column systems. Several parameters that influence the removal efficiency, such as the pH, contact time, amount of adsorbent, interfering ions, flow rate of sample passage and eluent solvent, were evaluated and optimized. The results showed that the adsorption isotherms obeyed Langmuir isotherms for all heavy metals. Also, interfering ions did not influenced the removal efficiency. Finally, modified nanoclay successfully could remove all heavy metals from real wastewater samples to near completeness.