Background Long working hours, as a common risk factor for occupational stress, is closely related to the occurrence of depressive symptoms. Understanding how long working hours affect occupational stress and depressive symptoms will inform occupational health interventions. Objective To quantify the impact of long working hours exposure on occupational stress and depressive symptoms among Internet industry employees, translate black-box outputs into actionable insights, and demonstrate the value of interpretable machine learning for early-warning occupational-health surveillance. Methods A dataset was derived from a cross-sectional survey involving 2866 internet industry employees in China. This survey was part of the project Risk Assessment Of Long Working Hour Exposure And Its Adverse Health Effects, conducted by the National Institute for Occupational Health and Poisoning Control, Chinese Center for Disease Control and Prevention, from 2021 to 2023. Working hours, occupational stress and depressive symptoms were quantified with a set of structured questionnaires including the Core Occupational Stress Scale and the Patient Health Questionnaire. Pairwise associations were screened by Mantel tests and variance-inflation factors. Key predictors identified through feature selection were fed into six machine-learning risk-prediction models. Visual interpretation was provided by feature importance, Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), while directed causal effects and intervention impacts of prolonged working hours exposure on occupational stress and depressive symptoms were dissected with causal explanation of features techniques. Results The positive rates of occupational stress and depressive symptoms among internet employees were 12.9% and 77.8% respectively. Twelve core features for occupational stress and nine for depressive symptoms were retained after selection. After these features were supplied to six predictive algorithms and evaluated on five metrics, the Light Gradient Boosting Machine (LGBM) achieved the highest accuracy—0.89 for occupational stress and 0.79 for depressive symptoms on the hold-out test set. The feature-importance rankings converged on fatigue accumulation and life satisfaction as dominant drivers for both outcomes, whereas weekly working hours and daily overtime emerged as the principal exposure-related predictors. The SHAP summary plots revealed that longer weekly hours and daily overtime systematically elevated the probability of occupational stress. The causal feature explanation further quantified that ascending one category in weekly working hours increased the probability of occupational stress by 7.04%. Conclusion Exposure to long working hours is associated with both occupational stress and depressive symptoms among internet industry employees. Interpretable machine-learning frameworks translate these associations into transparent, defensible drivers, enabling precise identification of the pivotal factors and their interplay. This evidence base equips occupational-health practitioners with actionable insights for designing targeted prevention and intervention strategies.

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

Objective:To explore the differences in the ability of different parts of the human skin to recognize vibration frequencies in order to provide data for physiological optimization of vibration parameters in pilots′ tactile vests.Methods:Ten healthy volunteers were selected and grouped by age as 20-40 years (6 cases) and 41-50 years (4 cases). All the subjects participated in 3 trials: ① identification test of frequency steps using cutaneous tactile perception: the back of the left hand was chosen as the test area, a dual-oscillator synchronous vibration system was adopted, and the frequency step was gradually increased (initially 0.1 Hz) to determine the threshold of frequency identification; ② maximum frequency threshold test of cutaneous tactile perception: the maximum frequency recognition thresholds in 7 parts of the trunk (the left waist, right waist, chest, back, left shoulder, right shoulder, neck) and the back of the hand were tested; ③ frequency discrimination test of cutaneous tactile perception: 5 equally spaced frequencies (covering the frequency recognition thresholds) were set to evaluate the accuracy of tactile frequency memory on the back of the hand after 3, 15 min and 24 h. The relationships between the adjacent step sizes of cutaneous tactile perception and the average value of the sum of adjacent frequencies were analyzed using simple linear regression. The highest identification thresholds of frequencies in different parts of the subjects as well as the accuracy of frequency discrimination during different periods were compared across the age groups.Results:The maximum thresholds of frequency identification of cutaneous tactile perception among the 10 subjects ranged from 16.20 to 33.55 Hz, while the range of adjacent step sizes was between 3.60 and 7.10 Hz. The adjacent step sizes were positively correlated with the average value of the sum of adjacent frequencies ( r=0.882, F=529.18, P<0.001). In the test on the maximum threshold of frequency identification of cutaneous tactile perception, there was a statistically significant difference in the main effect of an age group between the 2 groups ( F=15.87, P<0.001), but not in the main effect of the site ( P>0.05), or in the interactions between the group and age ( P>0.05). The accuracies of skin vibration frequency memory on the dorsal hand of the subjects at 3, 15 min and 24 h were 88%, 72% and 68% respectively. In the frequency discrimination test, there were no statistically significant differences in the main effect of the group, main effect of time or in their interactions between the 2 groups (all P>0.05). Conclusions:The ability of the human skin to identify vibration frequency is impacted by age, but there is no difference in sensitivity between the trunk and the back of the hand. It is recommended that the tactile vest for pilots be designed for general purposes to make it easy to adapt to vibration frequencies of multiple parts.

Objective:To explore the differences in the ability of different parts of the human skin to recognize vibration frequencies in order to provide data for physiological optimization of vibration parameters in pilots′ tactile vests.Methods:Ten healthy volunteers were selected and grouped by age as 20-40 years (6 cases) and 41-50 years (4 cases). All the subjects participated in 3 trials: ① identification test of frequency steps using cutaneous tactile perception: the back of the left hand was chosen as the test area, a dual-oscillator synchronous vibration system was adopted, and the frequency step was gradually increased (initially 0.1 Hz) to determine the threshold of frequency identification; ② maximum frequency threshold test of cutaneous tactile perception: the maximum frequency recognition thresholds in 7 parts of the trunk (the left waist, right waist, chest, back, left shoulder, right shoulder, neck) and the back of the hand were tested; ③ frequency discrimination test of cutaneous tactile perception: 5 equally spaced frequencies (covering the frequency recognition thresholds) were set to evaluate the accuracy of tactile frequency memory on the back of the hand after 3, 15 min and 24 h. The relationships between the adjacent step sizes of cutaneous tactile perception and the average value of the sum of adjacent frequencies were analyzed using simple linear regression. The highest identification thresholds of frequencies in different parts of the subjects as well as the accuracy of frequency discrimination during different periods were compared across the age groups.Results:The maximum thresholds of frequency identification of cutaneous tactile perception among the 10 subjects ranged from 16.20 to 33.55 Hz, while the range of adjacent step sizes was between 3.60 and 7.10 Hz. The adjacent step sizes were positively correlated with the average value of the sum of adjacent frequencies ( r=0.882, F=529.18, P<0.001). In the test on the maximum threshold of frequency identification of cutaneous tactile perception, there was a statistically significant difference in the main effect of an age group between the 2 groups ( F=15.87, P<0.001), but not in the main effect of the site ( P>0.05), or in the interactions between the group and age ( P>0.05). The accuracies of skin vibration frequency memory on the dorsal hand of the subjects at 3, 15 min and 24 h were 88%, 72% and 68% respectively. In the frequency discrimination test, there were no statistically significant differences in the main effect of the group, main effect of time or in their interactions between the 2 groups (all P>0.05). Conclusions:The ability of the human skin to identify vibration frequency is impacted by age, but there is no difference in sensitivity between the trunk and the back of the hand. It is recommended that the tactile vest for pilots be designed for general purposes to make it easy to adapt to vibration frequencies of multiple parts.

This paper summarized professor ZHANG Boli's experience in treating stubborn bi （痹） with the herbal pair of Ruxiang （Olibanum）- Moyao （Myrrha）. The basic pathogenesis of stubborn bi is channel and collateral stasis and obstruction. Ruxiang and Moyao are thus used in mutual reinforcement to rectify qi and diffuse bi， activate blood and relieve pain， thereby removing static and obstructed qi and blood， unblocking the obstructed channels and colla-terals， which is especially suitable for stubborn bi caused by channel and collateral obstruction. In clinical practice， the herbal pair of Ruxiang-Moyao is used together with qi-moving and blood-activating medicinals to treat chest bi by expelling stasis and diffusing stagnation， dissipating cold and unblocking vessels. To treat long-term wither and weakness in late stage of stroke， the medicinals of boosting qi and invigorating blood， unblocking channels and venting collaterals can be added to the herbal pair so as to soothe and drain vessels and collaterals， harmonize and regulate qi and blood. Simiao Yongan Decoction （四妙勇安汤） can be integrated in the treatment of vessel bi by moving qi and dissolving stasis， and for the long-term stubborn vessel bi， integrated internal and external treatment is suggested by external use of Ruxiang-Moyao to vent bi with aromatics. Moreover， it is emphasized to use the herbal pair of Ruxiang-Moyao in accordance with indications and cautions.

Objective To evaluate clinical efficacy and safety of loratadine combined with desloratadine in the treatment of chronic spontaneous urticaria(CSU)in children. Methods A total of 177 children with CSU were enrolled into this study, and randomly and equally divided into 3 groups:combination group treated with an age?based dose of desloratadine tablet every morning and a weight?based dose of loratadine tablet before sleep every night for consecutive 28 days, loratadine group treated with a half tablet of placebo(starch tablet)every morning and oral loratadine tablet before sleep every night for consecutive 28 days, and desloratadine group treated with a half tablet of placebo (starch tablet) every morning and oral desloratadine tablet before sleep every night for 28 consecutive days. Possible adverse reactions were observed and recorded after the start of treatment, and therapeutic effects were evaluated at the end of treatment. Results A total of 166 patients completed the trial, including 55 in the combination group, 56 in the loratadine group and 55 in the desloratadine group. After 28?day treatment, the total response rate was significantly higher in the combination group(90.9%, 50/55)than in the loratadine group (71.4%[40/56],χ2=6.865, P<0.05)and desloratadine group(74.5%[41/55],χ2=5.153, P<0.05). No significant difference in the incidence of adverse reactions was observed among the combination group (10.9%[6/55]), loratadine group (8.9%[5/56]) and desloratadine group (9.1%[5/55], P > 0.05). Conclusion Combination of loratadine and desloratadine was superior to loratadine or desloratadine alone in the treatment of childhood CSU, and there was no significant difference in the incidence of adverse reactions among the 3 treatment groups.

Sarcolipin (SLN) is a 3 kD membrane protein found in sarcoplasmic reticulum (SR). It has 31 amino acid residues; SLN and phopholamban (PLB) are belong to the same protein family, so they have similar physiological functions. SLN inhibits sarcoplasmic reticulum Ca(2+) ATPase (SERCA) activity and reduces its affinity of Ca(2+), resulting in dysfunction of myocardial contraction and heart failure. However, much remains to be elucidated. SLN independently or in conjunction with PLB affects SERCA activity, imbalancing intracellular calcium homeostasis, and reducing myocardial contractivity; these effects promote the development of heart failure.
Animals ; Calcium-Binding Proteins ; physiology ; Heart Failure ; physiopathology ; Humans ; Muscle Proteins ; metabolism ; physiology ; Myocardial Contraction ; physiology ; Proteolipids ; metabolism ; physiology ; Sarcoplasmic Reticulum ; metabolism ; Sarcoplasmic Reticulum Calcium-Transporting ATPases ; antagonists & inhibitors ; metabolism

OBJECTIVE:To study the general pattern and characteristics of adverse drug reactions(ADR) induced by tinidazole.METHODS:Tinidazole-induced ADR cases reported in the internal pharmaceutical journals retrieved from Jan.1994 to Jun.2006 were analyzed statistically.RESULTS:The ADR induced by tinidazole were diversified in clinical manifestation,but allergic response was the most common type,followed by the lesion of nervous system and digestive system etc.CONC-LUSION:Clinicians should attach great importance to the ADR induced by tinidazole.

OBJECTIVETo examine the clinical application of pulsed Doppler tissue imaging (DTI) for regional left ventricular function assessment in normal subjects.

METHODSWe examined 50 healthy subjects (range 12-42 years, mean age 28.3 +/- 6.9 years) using pulsed Doppler tissue imaging to characterize the diastolic and systolic velocity profiles of mitral annulus. Recordings were made along the long axis in the apical 4-chamber, 2-chamber, and long apical views of 6 sites (posterior-septum, lateral, anterior, inferior, anterior-septum, posterior) at the mitral annulus. Myocardial velocities were determined with use of variance F statistical analysis. Correlation analysis was employed to test the relationship between age and mitral annular velocities.

RESULTSBoth early diastolic and systolic velocities at the septum were lower than other sites. There were no differences in mitral annulus late diastolic velocities. Mean early diastolic and systolic velocities was negatively correlated with age.

CONCLUSIONSDoppler tissue imaging can directly reflect regional left ventricular function.

Adolescent ; Adult ; Age Factors ; Blood Flow Velocity ; Child ; Diastole ; Echocardiography, Doppler, Pulsed ; methods ; Female ; Humans ; Male ; Mitral Valve ; diagnostic imaging ; physiology ; Reference Values ; Systole ; Ventricular Function, Left