Objective To explore the correlation between healthcare-associated infection(HAI)and partial inde-xes in the diagnosis-related groups(DRGs)of patients in thoracic surgery intensive care unit(ICU).Methods DRGs,case mix index(CMI),relative weight(RW),and HAI of patients in thoracic surgery ICU and four subspe-cialty departments(pulmonary surgery group,esophageal surgery group,mediastinum group[mainly thymic sur-gery],and trachea group)in a tertiary chest hospital in Shanghai from January to December 2022 were retrospec-tively analyzed and compared through DRGs index grouping.Results A total of 1 429 patients in the department of thoracic surgery ICU were analyzed,including 59 HAI cases,with a HAI rate of 4.13％.The incidences of HAI in pulmonary surgery group,esophageal surgery group,mediastinum group and trachea group were 3.74％(30/803),5.84％(25/428),1.27％(2/157)and 4.88％(2/41),respectively.There was no statistically significant differ-ence in the incidences of HAI among different subspecialty groups(P＞0.05).A total of 35 DRGs were involved,with CMI of 2.75,3.41,2.35 and 1.25 in pulmonary surgery group,esophageal surgery group,mediastinum group and trachea group,respectively,and RW ranged from 0.53 to 12.62.In the pulmonary surgery group,inci-dence of HAI in male patients was higher than that in female patients.Higher RW score level was associated with higher incidence of HAI.Differences were all statistically significant(all P 0.05).Among patients in the esophageal surgery group,the age of HAI group was higher than that of the non-HAI group(P＜0.05).Higher RW score level was associated with higher incidence of HAI(P＜0.05).Among patients in the mediastinum sur-gery group,the age of patients in the infected group was higher than that in the non-infected group(P＜0.05).Among the 59 HAI cases,31 were infected with MDROs.Conclusion Focusing on CMI and RW in the DRGs in-dex system,analyzing HAI from the perspectives of disease complexity and overall technical difficulties of medical services can provide reference for the precise management of HAI in the new era.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Organoids ; Biomedical Research

To develop a caregiver parenting behavior scale for children aged 2 to 6 years, and to verify its reliability and validity. This study recruited 1 350 caregivers of children aged 2 to 6 years. The item discrimination analysis and exploratory factor analysis were used to analyze the structure, dimensions and items of the scale. Homogeneity reliability, split-half reliability and test-retest reliability were used to analyze the reliability of the scale. Content validity and construct validity were used to analyze the validity of the scale. The results showed that the final scale contained 7 dimensions and 45 items. Cronbach's α coefficient of the total scale was 0.945; the coefficient of split half was 0.899; the test-retest reliability analysis showed that the correlation coefficients between the two tests were 0.893 (total score), 0.854 (social), 0.832 (language), 0.871 (gross motor), 0.893 (fine motor), 0.862 (cognitive), 0.832 (self-care), and 0.872 (sensory). The content validity analysis was carried out by two rounds of expert argumentation using Delphi expert consultation method. The Kendall coefficient of the items score in two rounds of Delphi expert consultation was 0.813 (P<0.01). The structure validity analysis showed that there were significant correlations between each dimension and the total scale, also between each dimension of the scale, and the extracted average variance values of each dimension was greater than the correlation coefficients between this dimension and other dimensions. In conclusion, the reliability and validity of the scale are qualified. It can be used as a tool to evaluate and guide the parenting behavior of caregivers of children aged 2 to 6 years.
Humans ; Child ; Caregivers/psychology* ; Reproducibility of Results ; Parenting ; Surveys and Questionnaires ; Factor Analysis, Statistical ; Psychometrics/methods*

Objective: This study systematically explore the efficacy and safety of fourth-generation chimeric antigen receptor T-cells (CAR-T), which express interleukin 7 (IL7) and chemokine C-C motif ligand 19 (CCL19) and target CD19, in relapsed or refractory large B-cell lymphoma. Methods: Our center applied autologous 7×19 CAR-T combined with tirelizumab to treat 11 patients with relapsed or refractory large B-cell lymphoma. The efficacy and adverse effects were explored. Results: All 11 enrolled patients completed autologous 7×19 CAR-T preparation and infusion. Nine patients completed the scheduled six sessions of tirolizumab treatment, one completed four sessions, and one completed one session. Furthermore, five cases (45.5%) achieved complete remission, and three cases (27.3%) achieved partial remission with an objective remission rate of 72.7%. Two cases were evaluated for disease progression, and one died two months after reinfusion because of uncontrollable disease. The median follow-up time was 31 (2-34) months, with a median overall survival not achieved and a median progression-free survival of 28 (1-34) months. Two patients with partial remission achieved complete remission at the 9th and 12th months of follow-up. Therefore, the best complete remission rate was 63.6%. Cytokine-release syndrome and immune effector cell-associated neurotoxicity syndrome were controllable, and no immune-related adverse reactions occurred. Conclusion: Autologous 7×19 CAR-T combined with tirelizumab for treating relapsed or refractory large B-cell lymphoma achieved good efficacy with controllable adverse reactions.
Humans ; Antibodies, Monoclonal/therapeutic use* ; Antigens, CD19 ; Chemokine CCL19 ; Immunotherapy, Adoptive ; Interleukin-7 ; Lymphoma, Large B-Cell, Diffuse/therapy* ; Programmed Cell Death 1 Receptor ; Receptors, Chimeric Antigen