Unraveling the Predictors of Enlarged Perivascular Spaces: A Comprehensive Logistic Regression Approach in Cerebral Small Vessel Disease.

Background: This study addresses the predictive modeling of Enlarged Perivascular Spaces (EPVS) in neuroradiology and neurology, focusing on their impact on Cerebral Small Vessel Disease (CSVD) and neurodegenerative disorders. Methods: A retrospective analysis was conducted on 587 neurology inpatients, utilizing LASSO regression for variable selection and logistic regression for model development. The study included comprehensive demographic, medical history, and laboratory data analyses. Results: The model identified key predictors of EPVS, including Age, Hypertension, Stroke, Lipoprotein a, Platelet Large Cell Ratio, Uric Acid, and Albumin to Globulin Ratio. The predictive nomogram demonstrated strong efficacy in EPVS risk assessment, validated through ROC curve analysis, calibration plots, and Decision Curve Analysis. Conclusion: The study presents a novel, robust EPVS predictive model, providing deeper insights into EPVS mechanisms and risk factors. It underscores the potential for early diagnosis and improved management strategies in neuro-radiology and neurology, highlighting the need for future research in diverse populations and longitudinal settings.

Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach.

Background: Acute ischemic stroke (AIS) remains a leading cause of disability and mortality globally among adults. Despite Intravenous Thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) emerging as the standard treatment for AIS, approximately 6-40% of patients undergoing IVT experience Early Neurological Deterioration (END), significantly impacting treatment efficacy and patient prognosis. Objective: This study aimed to develop and validate a predictive model for END in AIS patients post rt-PA administration using the Least Absolute Shrinkage and Selection Operator (LASSO) regression approach. Methods: In this retrospective cohort study, data from 531 AIS patients treated with intravenous alteplase across two hospitals were analyzed. LASSO regression was employed to identify significant predictors of END, leading to the construction of a multivariate predictive model. Results: Six key predictors significantly associated with END were identified through LASSO regression analysis: previous stroke history, Body Mass Index (BMI), age, Onset to Treatment Time (OTT), lymphocyte count, and glucose levels. A predictive nomogram incorporating these factors was developed, effectively estimating the probability of END post-IVT. The model demonstrated robust predictive performance, with an Area Under the Curve (AUC) of 0.867 in the training set and 0.880 in the validation set. Conclusion: The LASSO regression-based predictive model accurately identifies critical risk factors leading to END in AIS patients following IVT. This model facilitates timely identification of high-risk patients by clinicians, enabling more personalized treatment strategies and optimizing patient management and outcomes.

Lathyrane diterpenoids from Euphorbia lathyris induce cell-cycle arrest and apoptosis in human hypertrophic scar cells.

One new lathyrane-type diterpenoid, euphlathin A (1), and 11 known analogues (2-12), were isolated from the fruits of Euphorbia lathyris. Their structures were elucidated by spectroscopic data. The absolute configurations of 1 were established by single-crystal X-ray crystallography. All diterpenoids (1-12) were evaluated for antiproliferative activity against the human hypertrophic scar (HTS) cells. Compound 1 exhibited significantly against HTS cells growth with an IC50 value of 6.33 µM. Morphological features of apoptosis were evaluated in 1-treated HTS cells. Wound healing assays indicated that 1 significantly inhibited the migration of HTS at 24 h and 48 h. Compound 1 effectively induced apoptosis of HTS, which was associated with G2/M or S phase cell cycle arrest. Flow cytometric analysis showed that the treatment by 1 significantly induced HTS cell apoptosis in a dose-dependent manner. Overall, euphlathin A (1) has the potential to be a therapeutic agent for the treatment of hyperplastic scar therapy.

Lathyrane diterpenoids from Jatropha podagrica and their antitumor activities in human osteosarcoma cells.

Two new lathyrane-type diterpenoids, jatropodagins A and B (1 and 2), and five known analogues (3-7), were isolated from the stems of Jatropha podagrica. Their structures and absolute configurations were elucidated by spectroscopic data and calculated ECD analyses. The cytotoxicities of all the lathyrane-type diterpenoids (1-7) were evaluated against two human osteosarcoma cell lines (Saos-2 and MG-63). Compound 1 exhibited significant cytotoxic effects against Saos-2 and MG-63 with IC50 values of 8.08 and 14.64 µM, respectively. The IC50 values for the positive control 5-FU against the Saos-2 and MG-63 cell lines were 19.01 and 25.00 µM, respectively. Morphological features of apoptosis activities were evaluated in 1-treated Saos-2 cells and the results confirmed apoptosis in a dose-dependent manner.