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In this paper, we present a method to achieve exponential stability in a class of impulsive delayed neural networks containing parameter uncertainties, time-varying delays, and impulsive effect and reaction–diffusion terms. By using an integro-differential inequality with impulsive initial conditions and employing the M-matrix theory and the nonlinear measure approach, some new sufficient conditions ensuring the global exponential stability and global robust exponential stability of the considered system are derived. In particular, the results obtained are presented by simple algebraic inequalities, which are certainly more concise than the previous methods. By comparisons and examples, it is shown that the results obtained are effective and useful. [ABSTRACT FROM AUTHOR]

Background: Cosmetic products are widely used in daily life and can cause skin reactions. However, to date, few studies have investigated the prevalence and associated factors of skin reactions to different types of cosmetic products using patch test results. Objectives: To investigate the frequency and associated factors of skin reactions to sunscreens, skin whitening products, and deodorants using patch test results in China. Methods: This cross‐sectional analysis used data collected during 2004–2017 at the Shanghai Skin Disease Hospital, China. Skin reactions were measured using patch tests following internationally standardized protocols. Results: In total, 151 280 patch tests comprising 16 477 cosmetic products conducted in 4029 healthy volunteers were analyzed. The frequencies of skin reactions to different cosmetic products were 1.07%, 1.28%, and 4.41% for sunscreens, skin whitening products, and deodorants, respectively. With increasing age, a higher frequency of reactions to sunscreens in women (p < 0.001) and a lower frequency of reactions to skin whitening products in men (p < 0.05) were observed. In addition, men were more likely to develop skin reactions to deodorants compared to women (p < 0.05). Skin reactions were more frequent in winter (p < 0.05), which was true for different types of cosmetic products. Conclusions: The frequency of patch testing for different cosmetic products was 1.07–4.41%. The effects of age, sex, and season vary among the different types of cosmetic products. We hope that these findings can offer guidance for a healthy skincare concept. [ABSTRACT FROM AUTHOR]

Background: Patch test, as a helpful tool in clinic diagnosis and safety assessment of cosmetics, is affected by many factors. Objectives: To investigate the influencing factors of patch test reactions in a highly standardized large‐scale dataset of Chinese. Methods: Patch test data (n = 151,280) from safety assessments of cosmetic products were obtained following internationally standardized patch testing protocols during 2004–2017 in China. Results: The frequency of patch test reactions was 1.45% (2,191/151,280), with majority of the reactions being "score 1" reactions (also known as doubtful reactions, n = 2,075) and a small number being "score 2" reactions (weak reactions, n = 116). Patch test reactions were 67% more frequent in winter (p < 0.001), associated with temperatures (p < 0.001), rather than relative humidity (P:0.29). The frequency of reactions was higher in men than in women (p:0.001), especially in winter. The risk to develop reactions clearly increased with age in women (p < 0.001), but not in men (p:0.14). In women, the frequency of reactions in the old group (≥50 years old) was 30% more than the young group (<30 years old). Conclusions: The frequency of patch test reactions to cosmetic products was 1.45% in our large‐scale study. The influencing factors of patch test include season, sex, and age, which should be considered when conducting and interpreting patch testing. [ABSTRACT FROM AUTHOR]

Objective: To establish a classification model based on knee MRI radiomics, realize automatic identification of meniscus tear, and provide reference for accurate diagnosis of meniscus injury., Methods: A total of 228 patients (246 knees) with meniscus injury who were admitted between July 2018 and March 2021 were selected as the research objects. There were 146 males and 82 females; the age ranged from 9 to 76 years, with a median age of 53 years. There were 210 cases of meniscus injury in one knee and 18 cases in both knees. All the patients were confirmed by arthroscopy, among which 117 knees with meniscus tear and 129 knees with meniscus non-tear injury. The proton density weighted-spectral attenuated inversion recovery (PDW-SPAIR) sequence images of sagittal MRI were collected, and two doctors performed radiomics studies. The 246 knees were randomly divided into training group and testing group according to the ratio of 7∶3. First, ITK-SNAP3.6.0 software was used to extract the region of interest (ROI) of the meniscus and radiomic features. After retaining the radiomic features with intraclass correlation coefficient (ICC)>0.8, the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were used for filtering the features to establish an automatic identification model of meniscus tear. The receiver operator characteristic curve (ROC) and the corresponding area under the ROC curve (AUC) was obtained; the model performance was comprehensively evaluated by calculating the accuracy, sensitivity, and specificity., Results: A total of 1 316-dimensional radiomic features were extracted from the meniscus ROI, and the ICC within the group and ICC between the groups of the 981-dimensional radiomic features were both greater than 0.80. The redundant information in the 981-dimensional radiomic features was eliminated by mRMR, and the 20-dimensional radiomic features were retained. The optimal feature subset was further selected by LASSO, and 8-dimensional radiomic features were selected. The average ICC within the group and the average ICC between the groups were 0.942 and 0.920, respectively. The AUC of the training group was 0.889±0.036 [95% CI (0.845, 0.942), P <0.001], and the accuracy, sensitivity, and specificity were 0.873, 0.869, and 0.842, respectively; the AUC of the testing group was 0.876±0.036 [95% CI (0.875, 0.984), P <0.001], and the accuracy, sensitivity, and specificity were 0.862, 0.851, and 0.845, respectively., Conclusion: The model established by the radiomics method has good automatic identification performance of meniscus tear.