Low-density lipoprotein cholesterol-to-lymphocyte count ratio (LLR) is a promising novel predictor of postoperative new-onset deep vein thrombosis following open wedge high tibial osteotomy: a propensity score-matched analysis.

BACKGROUND: The association of low-density lipoprotein cholesterol (LDL-C) and lymphocyte counts with the development of deep vein thrombosis (DVT) has been demonstrated in many fields but remains lacking in open wedge high tibial osteotomy (OWHTO). This study aimed to assess the predictive value of LDL-C to lymphocyte count ratio (LLR) in screening for postoperative new-onset DVT. METHODS: Clinical data were retrospectively collected from patients who underwent OWHTO between June 2018 and May 2023. The limited restricted cubic spline (RCS) was conducted to evaluate the nonlinear relationship between LLR and the risk of postoperative new-onset DVT. The receiver operating characteristic (ROC) curves were plotted and the predictive value of biomarkers was assessed. After adjusting for intergroup confounders by propensity score matching, the univariate logistic regression was applied to assess the association between LLR and DVT. RESULTS: 1293 eligible patients were included. RCS analysis showed a linear positive correlation between LLR and the risk of DVT (P for overall = 0.008). We identified LLR had an area under the curve of 0.607, accuracy of 74.3%, sensitivity of 38.5%, and specificity of 80.7%, and LLR > 1.75 was independently associated with a 1.45-fold risk of DVT (95% CI: 1.01-2.08, P = 0.045). Furthermore, significant heterogeneities were observed in the subgroups of age, BMI, diabetes mellitus, hypertension, Kellgren-Lawrence grade, the American Society of Anesthesiologists (ASA) score, and intraoperative osteotomy correction size. CONCLUSION: LLR is a valuable biomarker for predicting postoperative new-onset DVT in patients with OWHTO, and routine screening is expected to yield positive benefits.

Automatic segmentation of hemispheric CSF on MRI using deep learning: Quantifying cerebral edema following large hemispheric infarction.

Background and objective: Cerebral edema (CED) is a serious complication of acute ischemic stroke (AIS), especially in patients with large hemispheric infarction (LHI). Herein, a deep learning-based approach is implemented to extract CSF from T2-Weighted Imaging (T2WI) and evaluate the relationship between quantified cerebrospinal fluid and outcomes. Methods: Patients with acute LHI who underwent magnetic resonance imaging (MRI) were included. We used a deep learning algorithm to segment the CSF from T2WI. The hemispheric CSF ratio was calculated to evaluate its relationship with the degree of brain edema and prognosis in patients with LHI. Results: For the 93 included patients, the left and right cerebrospinal fluid regions were automatically extracted with a mean Dice similarity coefficient of 0.830. Receiver operating characteristic analysis indicated that hemispheric CSF ratio was an accurate marker for qualitative severe cerebral edema (area under receiver-operating-characteristic curve 0.867 [95% CI, 0.781-0.929]). Multivariate logistic regression analysis of functional prognosis showed that previous stroke (OR = 5.229, 95% CI 1.013-26.984), ASPECT≤6 (OR = 13.208, 95% CI 1.136-153.540) and low hemispheric CSF ratio (OR = 0.966, 95% CI 0.937-0.997) were significantly associated with higher chances for unfavorable functional outcome in patients with LHI. Conclusions: Automated assessment of CSF volume provides an objective biomarker of cerebral edema that can be leveraged to quantify the degree of cerebral edema and confirm its predictive effect on outcomes after LHI.

Light and reductive dual stimuli-responsive PEI nanoparticles: "AND" logic response and controllable release.

"AND" logic responsive polyetherimide (PEI)-based polymers PENS were constituted by attaching the photoresponsive o-nitrobenzyl phototrigger and reductive responsive disulfide linker to the polymer PEI. This dual-responsive system maintained micelle-like assembly upon single photo or reductive stimuli. The disassembly only occurred when photo and reductive signals were input at the same time. This smart system was therefore applied as a carrier for the controlled release of the anti-cancer drug doxorubicin (DOX), which exhibited effective release only when UV irradiation and GSH reduction were applied simultaneously.