Hinokitiol, a natural monoterpenoid from the heartwood of Calocedrus formosana, has been reported to have anticancer effects against various cancer cell lines. However, the detailed molecular mechanisms and the inhibiting roles of hinokitiol on adenocarcinoma A549 cells remain to be fully elucidated. Thus, the current study was designed to evaluate the effect of hinokitiol on the migration of human lung adenocarcinoma A549 cells in vitro. The data demonstrates that hinokitiol does not effectively inhibit the viability of A549 cells at up to a 10 µM concentration. When treated with non-toxic doses (1–5 µM) of hinokitiol, the cell migration is markedly suppressed at 5 µM. Hinokitiol significantly reduced p53 expression, followed by attenuation of Bax in A549 cells. A dose-dependent inhibition of activated caspase-9 and -3 was observed in the presence of hinokitiol. An observed increase in protein expression of matrix metalloproteinases (MMPs) -2/-9 in A549 cells was significantly inhibited by hinokitiol. Remarkably, when A549 cells were subjected to hinokitiol (1–5 µM), there was an increase in the activities of antioxidant enzymes catalase (CAT) and superoxide dismutase (SOD) from the reduction in cells. In addition, the incubation of A549 cells with hinokitiol significantly activated the cytochrome c expression, which may be triggered by activation of caspase-9 followed by caspase-3. These observations indicate that hinokitiol inhibited the migration of lung cancer A549 cells through several mechanisms, including the activation of caspases-9 and -3, induction of p53/Bax and antioxidant CAT and SOD, and reduction of MMP-2 and -9 activities. It also induces cytochrome c expression. These findings demonstrate a new therapeutic potential for hinokitiol in lung cancer chemoprevention.

The neodymium-doped yttrium aluminum garnet (Nd-YAG) laser is used for removal of pigmented skin patches and rejuvenation of skin. However, complications such as hyperpigmentation, hypopigmentation, and petechiae can occur after frequent treatments. Therefore, identifying the risk factors for such complications is important. The development of a multivariable logistic regression model with least absolute shrinkage and selection operator (LASSO) is needed to provide valid predictions about the incidence of post inflammatory hyperpigmentation complication probability (PIHCP) among patients treated with Nd-YAG laser toning. A total of 125 female patients undergoing laser toning therapy between January 2014 and January 2016 were examined for post-inflammatory hyperpigmentation (PIH) complications. Factor analysis was performed using 15 potential predictive risk factors of PIH determined by a physician. The LASSO algorithm with cross-validation was used to select the optimal number of predictive risk factors from the potential factors for a multivariate logistic regression PIH complication model. The optimal number of predictive risk factors for the model was five: immediate endpoints of laser (IEL), α-hydroxy acid (AHA) peels, Fitzpatrick skin phototype (FSPT), acne, and melasma. The area under the receiver operating characteristic curve (AUC) was 0.79 (95% CI, 0.70–0.88) in the optimal model. The overall performance of the LASSO-based PIHCP model was satisfactory based on the AUC, Omnibus, Nagelkerke R², and Hosmer–Lemeshow tests. This predictive risk factor model is useful to further optimize laser toning treatment related to PIH. The LASSO-based PIHCP model could be useful for decision-making.

Background: Radiotherapy-related complications can impair long-term outcomes in nasopharyngeal carcinoma (NPC), while high-volume literature screening remains a bottleneck in evidence synthesis. Methods: Four databases were searched through January 2026. After deduplication, 4916 records underwent two-stage manual screening, yielding 38 studies. Three Bidirectional Encoder Representations from Transformers (BERT) models, BERT-base, BioBERT, and PubMedBERT, were fine-tuned using stratified five-fold cross-validation, and screening performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), the area under the precision-recall curve (PR-AUC), and work saved over sampling (WSS) at the 100% and 85% recall thresholds (WSS@100% and WSS@85%).The clinical evidence was evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST) and synthesized narratively according to endpoint, discrimination metric, time horizon, unit of analysis, validation level, and uncertainty reporting. Results: At complete recall, BioBERT and PubMedBERT achieved mean WSS values of 97.8% and 97.5%, respectively, compared with 89.7% for BERT-base. Separately, the clinical evidence review identified 39 discrimination estimates: 33 conventional binary ROC-AUCs, three Harrell’s C-indices, and three time-dependent AUCs. Only two studies contributed external-validation estimates and one contributed a temporal-validation estimate. Sixteen estimates lacked a usable measure of uncertainty, and PROBAST rated all studies as having a high overall risk of bias, driven by the analysis domain. Conclusions: Within the retrieved and manually annotated corpus, biomedical pretrained language models showed the potential to reduce the title-and-abstract screening burden while maintaining complete recall. Independently, the clinical evidence map identified promising discrimination but insufficiently robust and transportable evidence for routine clinical implementation.

Background: Scabies is typically treated with scabicides like lindane, which poses a risk for acute neural toxicity. Lindane’s prolonged use, particularly in agriculture, is linked to neurodegenerative diseases, including Parkinson’s disease (PD), the second most common neurodegenerative disorder. This study aimed to evaluate whether scabies patients, particularly those treated with topical lindane, are at increased risk of developing PD. Methods: A nationwide population-based cohort study was conducted using data from Taiwan’s National Health Research Institutes claims database from 2000 to 2018. The study included 27,173 patients with scabies, matched to a control group, with both groups followed for up to 18 years. The primary outcome was the incidence of newly diagnosed PD, and the hazard ratio (HR) for PD was calculated, focusing on those treated with topical lindane. Results: Among the 54,346 patients, 1639 (3.0%) were newly diagnosed with PD, with 993 (60.6%) from the scabies group and 646 (39.4%) from the control group. Scabies patients had an adjusted hazard ratio (aHR) of 1.46 (95% CI 1.32–1.63) for developing PD compared to controls. However, patients treated with topical lindane had a significantly lower aHR for PD at 0.15 (95% CI 0.12–0.19; p < 0.001), with a lower cumulative incidence of PD also observed in this group (p < 0.001). Conclusions: Scabies patients are at a 1.46-fold increased risk of developing PD, but those treated with lindane exhibit a significantly lower risk, suggesting potential protective effects of lindane against PD.

Objective: This study aims to develop a preliminary explainable image-based auxiliary assessment framework for radiation pneumonitis (RP) by integrating a generative adversarial network (GAN), Gradient-weighted Class Activation Mapping (Grad-CAM), and ensemble learning to improve image-based classification performance, interpretability, and computational efficiency. Methods: Chest CT images from 46 lung cancer patients who underwent VMAT were retrospectively collected, yielding 542 RP and 1857 non-RP images. Images were preprocessed using Otsu-based segmentation and standardized before being input into the RP-GAN for feature extraction. Grad-CAM was applied to qualitatively visualize attention patterns across convolutional layers and guide feature-layer selection, while PCA reduced the extracted features from 12,288 to 730 dimensions. An ensemble stacking classifier combining RF, SVM, KNN, and XGBoost with logistic regression as a meta-learner was constructed. Model performance was evaluated using AUC, accuracy, PPV, NPV, specificity, recall, and F1-score. Results: Grad-CAM highlighted conv2d_4 and conv2d_5 as the most informative layers. PCA reduced training time from 49 min to 49 s with minimal performance loss. In the internal hold-out test set, the ensemble model achieved the highest point estimates for AUC and accuracy among the evaluated classifiers (AUC = 0.921; accuracy = 87.1%). The model identified RP-positive CT slices and provided qualitative visual cues for suspected RP-related regions. Conclusions: The proposed GAN–Grad-CAM ensemble framework showed promising internal classification performance for post-treatment CT-based RP image assessment with substantially improved computational efficiency. Its qualitative visual outputs may support clinical image review, although further external validation and quantitative localization assessment are required before clinical application as an auxiliary image-review tool.