Adolescent e-cigarette use has risen sharply worldwide and poses emerging threats to respiratory health. As adolescence represents a critical window for lung growth and aerobic capacity, inhalation of toxicants during this period may disrupt pulmonary development and exercise performance. This narrative review synthesizes current evidence on the physiological and behavioral consequences of vaping in youth. Clinical and epidemiological studies indicate that adolescents who vape report higher rates of respiratory symptoms, such as chronic cough, wheezing, and shortness of breath, and may exhibit early declines in lung function and exercise tolerance. Experimental and mechanistic studies implicate nicotine, ultrafine particles, aldehydes, and flavoring agents in oxidative stress, airway inflammation, immune suppression, and impaired alveolarization. Dual users of e-cigarettes and combustible cigarettes appear at even greater risk, with lower cardiorespiratory fitness and muscular endurance compared with non-users. Despite these concerns, research is limited by cross-sectional designs, self-reported data, and a lack of longitudinal cohorts. Further high-quality studies are needed to determine the long-term impact of vaping on adolescent lung development and physical fitness. Preventive efforts in public health, education, and policy are critical to protect youth from the potential pulmonary hazards of e-cigarette use.

The problem tackled is the determination of sample size for a given level and power in the context of a simple linear regression model. The standard approach deals with planned experiments in which the predictor X is observed for a number n of times and the corresponding observations on the response variable Y are to be drawn. The statistic that is used is built on the least squares’ estimator of the slope parameter. Its conditional distribution given the data on the predictor X is utilized for sample size calculations. This is problematic. The sample size n is already presaged and the data on X is fixed. In unplanned experiments, in which both X and Y are to be sampled simultaneously, we do not have data on the predictor X yet. This conundrum has been discussed in several papers and books with no solution proposed. We overcome the problem by determining the exact unconditional distribution of the test statistic in the unplanned case. We have provided tables of critical values for given levels of significance following the exact distribution. In addition, we show that the distribution of the test statistic depends only on the effect size, which is defined precisely in the paper.

Systematic Biopsy (SBx) has been and continues to be the standard staple for detecting prostate cancer. The more expensive MRI guided biopsy (MRITBx) is a better way of detecting cancer. The prostatectomy can provide an accurate condition of the prostate. The goal is to assess how reliable SBx and MRITBx are vis à vis prostatectomy. Graded Gleason scores are used for comparison. Cohen’s Kappa index and logistic regression after binarization of the graded Gleason scores are some of the methods used to achieve our goals. Machine learning methods, such as classification trees, are employed to improve predictability clinically. The Cohen’s Kappa index is 0.31 for SBx versus prostatectomy, which means a fair agreement. The index is 0.34 for MRITBx versus prostatectomy, which again means a fair agreement. A direct comparison of SBx versus prostatectomy via binarized graded scores gives sensitivity 0.83 and specificity 0.50. On the other hand, a direct comparison of MRITBx versus prostatectomy gives sensitivity 0.78 and specificity 0.67, putting MRITBx on a higher level of accuracy. The SBx and MRITBx do not yet match the findings of prostatectomy completely, but they are useful. We have developed new biomarkers, considering other pieces of information from the patients, to improve the accuracy of SBx and MRITBx. From a clinical point of view, we provide a prediction model for prostatectomy Gleason grades using classification tree methodology.

Background: Depression is a leading cause of global disability. Timely identification of patients at risk for clinical worsening remains a major challenge. Electronic health records (EHRs) facilitate large-scale, real-world analyses of disease trajectories. However, standardized symptom scale data such as the Patient Health Questionnaire-9 are often unavailable or recorded only as unstructured text. In this context, International Classification of Diseases (ICD10) diagnostic-code based severity progression provides a pragmatic alternative for developing predictive tools to identify worsening depression. Objective: We aim to develop and evaluate machine-learning and deep-learning models for predicting ICD10-defined progression from mild to moderate/severe depression using EHR data curated by the MedStar Health Research Institute (MHRI). Methods: We conducted a multi-institutional retrospective cohort analysis using the MHRI EHR database, which integrates data from 10 hospitals and 300 outpatient sites across the mid-Atlantic. Adults (≥18 years) with an initial ICD10 diagnosis of mild depression between 2017 and 2023 were included (N=2131). Nonprogressors were defined as patients whose mild major depressive disorder remained mild for 24 months (N=270). Progressors were defined as patients who developed moderate or severe ICD10 depression within 24 months of the index diagnosis (N=533). Data were stratified and split into (60%) training, (20%) validation, and (20%) test subsets. A heterogeneous feature set spanning demographics, healthcare utilization, socioeconomic indices, diagnostic context, and laboratory measurements were available. Logistic regression utilized elastic net regularization with fivefold cross validation, and random forest hyperparameters were tuned by grid search. XGBoost, CatBoost, and a deep neural network (DNN) were trained with standard learning rate, depth, class weighting, and early stopping. A deterministic top model selection framework applied prespecified thresholds of sensitivity at least 0.70 and AUC at least 0.70, and composite rankings integrated accuracy, sensitivity, specificity, and the overfitting gap. Results: The analytic cohort included 803 patients with complete two-year follow-up. Under the selection criteria, the DNN failed to meet the AUC threshold (0.671) and was excluded. Among the remaining models, XGBoost achieved the top composite score (accuracy = 0.72; AUC = 0.776; sensitivity = 0.77; specificity = 0.63; overfit gap = 0.112). Logistic regression ranked second (accuracy = 0.71; AUC = 0.797; sensitivity = 0.79; specificity = 0.61; overfit gap = 0.052), followed by CatBoost and random forest, the latter penalized for overfitting (gap = 0.278). The TinyLlama audit note, generated through a local Hugging Face pipeline, confirmed XGBoost as the most balanced model. Conclusions: Using EHR data from a multi-institutional regional health system, we developed and validated machine-learning models that predicted progression of depression. XGBoost demonstrated the most reliable composite performance. These findings support the feasibility of leveraging socioeconomic and EHR data to predict worsening depression and emphasize the importance of transparent model-selection frameworks for trustworthy clinical artificial intelligence.

Background and Objectives: In 2022, the Centers for Disease Control and Prevention (CDC) discovered that 2.55 million middle and high school students were using electronic cigarettes (e-cigarettes) in the US alone. E-cigarettes allow people to inhale a nicotine solution (e-liquid) into their bodies. While e-cigarettes are often advertised as a way to help people quit traditional tobacco products, the World Health Organization (WHO) has stated that there is no proof of e-cigarettes being effective at reducing an individual’s tobacco use. The objective of this study is to determine why adolescents start smoking e-cigarettes. Materials and Methods: For the following study, the National Youth Tobacco Survey (NYTS) 2021 was used. This is a nationally representative cross-sectional survey that includes middle and high school students. The data were analyzed using SAS v.9.4. The odds ratio for smoking e-cigarettes was evaluated for both sex and race via a logistic regression. Results: It was discovered that the percentage of only e-cigarette smokers (12.10%) was 5.5 times the percentage of only traditional smokers (2.19%). Additionally, the median age at which adolescents started smoking e-cigarettes was fifteen years with an IQR (Inter Quartile Range) of two. We used a logistic regression to show that biological sex and race were significant predictors of e-cigarette usage. Conclusions: In general, we saw that adolescents were mostly starting to smoke e-cigarettes because their friends were doing it, out of curiosity, they were depressed or anxious, and/or to get a “high”, implying that adolescents mostly started smoking in order to fit in and/or to numb themselves. Interestingly, reasons like cost, using them to quit smoking, seeing them in the media, and ease of attainment were ranked the lowest when it came to smoking e-cigarettes. However, their lower costs naturally lead to increased accessibility compared to traditional cigarettes, making them easier to reach the hands of teenagers. Overall, in this paper, we aim to identify if adolescents are choosing e-cigarettes as their first foray into tobacco products and why.

A biodegradable hybrid polymer patch was invented at the University of Cincinnati to cover gaps on the skin over the spinal column of a growing fetus, characterized by the medical condition spina bifida. The inserted patch faces amniotic fluid (AF) on one side and cerebrospinal fluid on the other side. The goal is to provide a profile of the roughness of a patch over time at 0, 4, 8, 12, and 16 weeks with a 95% confidence band. The patch is soaked in a test tube filled with either amniotic fluid (AF) or phosphate-buffered saline (PBS) in the lab. If roughness is measured at any time point for a patch, the patch is destroyed. Thus, it is impossible to measure roughness at all weeks of interest for any patch. It is important to assess the roughness of a patch because the rougher the patch is, the faster the skin grows under the patch. We use a model-based approach with Monte Carlo simulations to estimate the profile over time with a 95% confidence band. The roughness profiles are similar with both liquids. The profile can be used as a template for future experiments on the composition of patches.