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Background Smoking is a well‐established risk factor of stroke and smoking cessation has been recommended for stroke prevention; however, the impact of smoking status on stroke recurrence has not been well studied to date. Methods and Results Patients with first‐ever stroke were enrolled and followed in the NSRP (Nanjing Stroke Registry Program). Smoking status was assessed at baseline and reassessed at the first follow‐up. The primary end point was defined as fatal or nonfatal recurrent stroke after 3 months of the index stroke. The association between smoking and the risk of stroke recurrence was analyzed with multivariate Cox regression model. At baseline, among 3069 patients included, 1331 (43.4%) were nonsmokers, 263 (8.6%) were former smokers, and 1475 (48.0%) were current smokers. At the first follow‐up, 908 (61.6%) patients quit smoking. After a mean follow‐up of 2.4±1.2 years, 293 (9.5%) patients had stroke recurrence. With nonsmokers as the reference, the adjusted hazard ratios for stroke recurrence were 1.16 (95% CI , 0.75–1.79) in former smokers, 1.31 (95% CI , 0.99–1.75) in quitters, and 1.93 (95% CI , 1.43–2.61) in persistent smokers. Among persistent smokers, hazard ratios for stroke recurrence ranged from 1.68 (95% CI , 1.14–2.48) in those who smoked 1 to 20 cigarettes daily to 2.72 (95% CI , 1.36–5.43) in those who smoked more than 40 cigarettes daily ( P for trend <0.001). Conclusions After an initial stroke, persistent smoking increases the risk of stroke recurrence. There exists a dose–response relationship between smoking quantity and the risk of stroke recurrence.

Background and Purpose: Symptomatic intracranial hemorrhage (sICH), potentially associated with poor prognosis, is a major complication of endovascular thrombectomy (EVT) for ischemic stroke patients. We aimed to develop and validate a risk model for predicting sICH after EVT in Chinese patients due to large-artery occlusions in the anterior circulation. Methods: The derivation cohort recruited patients with EVT from the Endovascular Treatment for Acute Anterior Circulation Ischemic Stroke Registry in China. sICH was diagnosed according to the Heidelberg Bleeding Classification within 24 hours of EVT. Stepwise logistic regression was performed to derive the predictive model. The discrimination and calibration of the risk model were assessed using the C index and the calibration plot. An additional cohort of 503 patients from 2 stroke centers was prospectively enrolled to validate the new model. Results: We enrolled 629 patients who underwent EVT as the derivation cohort, among whom 87 developed sICH (13.8%). In the multivariate adjustment, Alberta Stroke Program Early CT Score (odds ratio [OR], 0.85; P =0.005), baseline glucose (OR, 1.13; P =0.001), poor collateral circulation (OR, 3.06; P =0.001), passes with retriever (OR, 1.52; P =0.001), and onset-to-groin puncture time (OR, 1.79; P =0.024) were independent factors of sICH and were incorporated as the Alberta Stroke Program Early CT Score, Baseline Glucose, Poor Collateral Circulation, Passes With Retriever, and Onset-to-Groin Puncture Time (ASIAN) score. The ASIAN score demonstrated good discrimination in the derivation cohort (C index, 0.771 [95% CI, 0.716–0.826]), as well as the validation cohort (C index, 0.758 [95% CI, 0.691–0.825]). Conclusions: The ASIAN score reliably predicts the risk of sICH in Chinese ischemic stroke patients treated by EVT.

Background and Purpose— This study aimed to develop and validate a nomogram for predicting the risk of stroke recurrence among young adults after ischemic stroke. Methods— Patients aged between 18 and 49 years with first-ever ischemic stroke were selected from the Nanjing Stroke Registry Program. A stepwise Cox proportional hazards regression model was employed to develop the best-fit nomogram. The discrimination and calibration in the training and validation cohorts were used to evaluate the nomogram. All patients were classified into low-, intermediate-, and high-risk groups based on the risk scores generated from the nomogram. Results— A total of 604 patients were enrolled in this study. Hypertension (hazard ratio [HR], 2.038 [95% CI, 1.504–3.942]; P =0.034), diabetes mellitus (HR, 3.224 [95% CI, 1.848–5.624]; P <0.001), smoking status (current smokers versus nonsmokers; HR, 2.491 [95% CI, 1.304–4.759]; P =0.006), and stroke cause (small-vessel occlusion versus large-artery atherosclerosis; HR, 0.325 [95% CI, 0.109–0.976]; P =0.045) were associated with recurrent stroke. Educational years (>12 versus 0–6; HR, 0.070 [95% CI, 0.015–0.319]; P =0.001) were inversely correlated with recurrent stroke. The nomogram was composed of these factors, and successfully stratified patients into low-, intermediate-, and high-risk groups ( P <0.001). Conclusions— The nomogram composed of hypertension, diabetes mellitus, smoking status, stroke cause, and education years may predict the risk of stroke recurrence among young adults after ischemic stroke.

With the purpose of further mastering and grasping the course of speech signal processing, a novel Android-based, mobile-assisted educational platform (AEPS) is proposed in this paper. The goal of this work was to design AEPS as an educational signalprocessing auxiliary system by simulating signal analysis methods commonly used in speech signal processing and bridging the gap for transition from undergraduate study to industry practice or academic research. The educational platform is presented in a highly intuitive, easy-to-interpret and strongly maneuverable graphical user interface. It also has the characteristics of high portability, strong affordability, and easy adoptability for application extension and popularization. Through adequate intuitive user interface, rich visual information, and extensive hands-on experiences, it greatly facilitates students in authentic, interactive, and creative learning. This paper details a subjective evaluation of AEPS's effectiveness as an educational tool. The result of the experiences shows that the proposed platform not only promotes the students' learning interest and practical ability but also consolidates their understanding and impression of theoretical concepts.

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Background Evaluating tumor‐infiltrating lymphocytes (TILs) in patients with breast cancer using radiomics has been rarely explored. Purpose To establish a radiomics nomogram based on dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) for preoperatively evaluating TIL level. Study Type Retrospective. Population A total of 154 patients with breast cancer were divided into a training cohort (N = 87) and a test cohort (N = 67), who were further divided into low TIL (<50%) and high TIL (≥50%) subgroups according to the histopathological results. Field Strength/Sequence 3.0 T; axial T2‐weighted imaging (fast spin echo), diffusion‐weighted imaging (spin echo‐echo planar imaging), and the volume imaging for breast assessment DCE sequence (gradient recalled echo). Assessment A radiomics signature was developed from the training dataset and independent risk factors were selected by multivariate logistic regression to build a clinical model. A nomogram model was built by combining radiomics score and risk factors. The performance of the nomogram was assessed using calibration curves and decision curves. The area under the receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity were calculated. Statistical Tests The least absolute shrinkage and selection operator, univariate and multivariate logistic regression analysis, t‐tests and chi‐squared tests or Fisher's exact test, Hosmer–Lemeshow test, ROC analysis, and decision curve analysis were conducted. P < 0.05 was considered statistically significant. Results The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (radiomics: area under the curve [AUC] 0.86; nomogram: AUC 0.88) and test (radiomics: AUC 0.83; nomogram: AUC 0.84) datasets compared with clinical model (training: AUC 0.76; test: AUC 0.72). The decision curve demonstrated that the nomogram model exhibited better performance than the clinical model, with a threshold probability between 0.15 and 0.9. Data Conclusion The nomogram model based on preoperative MRI exhibited an excellent ability for the noninvasive evaluation of TILs in breast cancer. Level of Evidence 4 Technical Efficacy Stage 2

This paper systematically demonstrated a variety of experimental phenomena of random lasers (RLs) of N,N′-di-(3-(isobutyl polyhedral oligomeric silsesquioxanes)propyl) perylene diimide (DPP) organic/inorganic hybrid laser dye, which is composed of perylene diimide (PDI) as gain media and polyhedral oligomeric silsesquioxanes (POSS) as scattering media at a mole ratio of 1:2. In this work, we observe the transition from incoherent RL in the DPP-doped solutions and polymer membrane systems using dip-coating method to coherent RL in the polymer membrane system with defect waveguide using semi-polymerization (SP) coating method. Meanwhile, we found that the hybrid dye-DPP has a long lasing lifetime compared with the traditional laser dyes, which indicates that the POSS group can suppress the photo-bleaching effect to extend the working life of laser dyes.