OBJECTIVE To evaluate the clinical efficacy of Chaihu anxin capsules in the treatment of mild-to-moderate depression， analyze the influencing factors of its efficacy, construct a nomogram prediction model.METHODS Patients with mild-to-moderate depression treated at The First Hospital of Hebei Medical University from May 1， 2023 to March 10， 2025 were retrospectively included and categorized into Chaihu anxin capsules group and milnacipran group according to treatment regimen. Propensity score matching （PSM） was used to balance baseline characteristics， and treatment efficacy was compared after 8 weeks， including 24 Hamilton Depression Scale （HAMD-24） scores and changes， Hamilton Anxiety Scale （HAMA） scores and changes， and treatment response rate after 8 weeks of treatment. Patients receiving Chaihu anxin capsules were further classified as responders or non-responders based on the occurrence of treatment response. Univariate and multivariate Logistic regression analyses were conducted to identify the influencing factors of treatment response， and a nomogram model was constructed to predict the probability of treatment response， its predictive performance was evaluated using receiver operating characteristic （ROC） curves， calibration curves， and decision curve analysis.RESULTS A total of 297 patients were included before PSM， including 196 patients in the Chaihu anxin capsules group and 101 patients in the milnacipran group. After PSM， each group included 44 patients. After PSM， the HAMD-24 scores and HAMA scores at 8 weeks after treatment in the Chaihu anxin capsules group， as well as the HAMD-24 scores in the milnacipran group， were significantly lower than their respective baseline values （ P ＜0.05）； the change in HAMA score in the Chaihu anxin capsules group was significantly greater than that in the milnacipran group （ P ＝0.04）. Multivariable Logistic regression identified age， total bilirubin， and baseline HAMD-24 score as independent predictors of treatment response （ P ＜0.05）. The area under the ROC curve of the constructed nomogram prediction model was 0.72 （95% confidence interval： 0.637-0.799）， and the corrected Brier score was 0.143 （95% confidence interval： 0.112-0.176）； compared with the “all intervention” and “all non intervention” strategies， applying this nomogram prediction model could achieve higher net benefits within the threshold probability range of 8% to 37%.CONCLUSIONS The efficacy of Chaihu anxin capsules in improving mild-to-moderate depression is comparable to that of milnacipran， and it has certain advantages in improving anxiety symptoms； age， total bilirubin， and baseline HAMD-24 score are independent influencing factors for the treatment response of Chaihu anxin capsules. The nomogram prediction model constructed based on these factors has good discrimination， calibration， and clinical applicability.

Objective:To explore the correlation between abnormal vital signs (e.g., heart rate, oxygen saturation, and body temperature) and acute exacerbations in patients with chronic obstructive pulmonary disease (COPD), as well as to evaluate the clinical value of continuous monitoring via wearable devices for the early warning and intervention.Methods:A multicenter cross-sectional study enrolled 335 patients with stable chronic obstructive pulmonary disease (COPD) from 12 community health centers in Beijing and Chengdu between June 2023 and May 2024. General demographic and clinical data were collected, and each participant underwent continuous monitoring of resting heart rate, oxygen saturation, and body temperature using wearable devices for 21 days. Based on whether participants had experienced acute exacerbations requiring outpatient, emergency, or inpatient treatment within the previous year, they were categorized into the acute exacerbation group and the non-exacerbation group. Differences in physiological parameters between the acute exacerbation group and non-exacerbation group were analyzed, and contributing factors were assessed using logistic regression analysis.Results:A total of 335 patients with stable COPD were enrolled, including 252 cases (75.22%) in the acute exacerbation group and 83 cases (24.78%) in the non-acute exacerbation group. There were no statistically significant differences in age, sex distribution, comorbidities, or baseline lung function between the two groups (all P>0.05). Compared with the non-acute exacerbation group, patients in the acute exacerbation group had a faster resting heart rate((76.01 ± 7.78) beats/min vs. (72.72 ± 7.35) beats/min, t=3.126, P=0.002), a higher proportion of patients with decreased oxygen saturation (1.75% (0.97%, 3.03%) vs. 0.86% (0.44%, 1.65%), Z=11.086, P=0.001), and a higher proportion of patients with elevated body temperature (0.60% (0.39%, 1.03%) vs. 0.31% (0.17%, 0.54%), Z=7.314, P=0.007). Logistic regression analysis showed that advanced age ( OR=1.051, 95% CI: 1.023-1.080), increased heart rate ( OR=1.055, 95% CI:1.013-1.098), decreased oxygen saturation ( OR=1.197, 95% CI:1.023-1.400), and elevated body temperature ( OR=1.777, 95% CI:1.148-2.752) were positively associated factors for acute exacerbation of COPD. Conclusions:Abnormalities in physiological indicators such as heart rate, oxygen saturation, and body temperature are associated with acute exacerbations in COPD patients. Continuous monitoring using wearable devices may provide a new method for early warning and timely intervention in COPD exacerbations.

The prefrontal cortex (PFC) plays a pivotal role in orchestrating higher-order emotional and cognitive processes, a function that depends on the precise modulation of synaptic activity. Although pharmacological studies have demonstrated that dopamine signaling through dopamine D1 receptor (DRD1) in the PFC is essential for these functions, the cell-type-specific and molecular mechanisms underlying the neuromodulatory effects remain elusive. Using cell-type-specific knockout mice and patch-clamp recordings, we investigated the regulatory role of DRD1 on neurons and astrocytes in synaptic transmission and plasticity. Furthermore, we explored the mechanisms by which DRD1 on astrocytes regulate synaptic transmission and plasticity at the cellular level, as well as emotional and cognitive functions at the behavioral level, through two-photon imaging, microdialysis, high-performance liquid chromatography, transcriptome sequencing, and behavioral testing. We found that conditional knockout of the Drd1 in astrocytes (CKO^AST) increased glutamatergic synaptic transmission and long-term potentiation (LTP) in the medial prefrontal cortex (mPFC), whereas Drd1 deletion in pyramidal neurons did not affect synaptic transmission. The elevated level of d-serine in the mPFC of CKO^AST mice increased glutamatergic transmission and LTP through NMDA receptors. In addition, CKO^AST mice exhibited abnormal emotional and cognitive function. Notably, these behavioral changes in CKO^AST mice could be reversed through the administration of d-serine degrease to the mPFC. These results highlight the critical role of the astrocytic DRD1 in modulating mPFC synaptic transmission and plasticity, as well as higher brain functions through d-serine, and may shed light on the treatment of mental disorders.

OBJECTIVE: To investigate the predictive value of oxygenation index (PaO₂/FiO₂) at intensive care unit (ICU) admission on 30-day mortality in patients with sepsis. METHODS: A retrospective study was conducted. Patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to October 2023 were enrolled. The demographic information, comorbidities, sites of infection, vital signs and laboratory test indicators at the time of admission to the ICU, disease severity scores within 24 hours of admission to the ICU, treatment process and prognostic indicators were collected. According to the PaO₂/FiO₂ at ICU admission, patients were divided into Q1 group (PaO₂/FiO₂ of 4.1-16.4 cmHg, 1 cmHg ≈ 1.33 kPa), Q2 group (PaO₂/FiO₂ of 16.5-22.6 cmHg), Q3 group (PaO₂/FiO₂ of 22.7-32.9 cmHg), and Q4 group (PaO₂/FiO₂ of 33.0-94.8 cmHg). Differences in the indicators across the four groups were compared. Multifactorial Cox regression analysis was used to assess the relationship between PaO₂/FiO₂ and 30-day mortality of patients with sepsis. The predictive value of PaO₂/FiO₂, sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) on 30-day prognosis of patients with sepsis was analyzed by receiver operator characteristic curve (ROC curve). RESULTS: A total of 1 711 patients with sepsis were enrolled, including 428 patients in Q1 group, 424 patients in Q2 group, 425 patients in Q3 group, and 434 patients in Q4 group. 622 patients died at 30-day, the overall 30-day mortality was 36.35%. There were statistically significant differences in age, body mass index (BMI), history of smoking, history of alcohol consumption, admission heart rate, respiratory rate, APACHE II score, SOFA score, Glasgow coma score (GCS), site of infection, Combined chronic obstructive pulmonary disease (COPD), blood lactic acid (Lac), prothrombin time (PT), albumin (Alb), total bilirubin (TBil), pH, proportion of mechanical ventilation, duration of mechanical ventilation, proportion of vasoactive medication used, and maximal concentration, length of ICU stay, hospital stay, incidence of acute kidney injury, in-hospital mortality, 30-day mortality among the four groups. Multivariate Cox regression analysis showed that after adjusting for confounding factors, for every 1 cmHg increase in PaO₂/FiO₂ at ICU admission, the 30-day mortality risk decreased by 2% [hazard ratio (HR) = 0.98, 95% confidence interval (95%CI) was 0.98-0.99, P < 0.001]. The 30-day mortality risk in the Q4 group was reduced compared with the Q1 group by 41% (HR = 0.59, 95%CI was 0.46-0.76, P < 0.001). The fitted curve showed that a curvilinear relationship between PaO₂/FiO₂ and 30-day mortality after adjustment for confounders. In the inflection point analysis, for every 1 cmHg increase in PaO₂/FiO₂ at PaO₂/FiO₂ < 28.55 cmHg, the risk of 30-day death in sepsis patients was reduced by 5% (HR = 0.95, 95%CI was 0.94-0.97, P < 0.001); when PaO₂/FiO₂ ≥ 28.55 cmHg, there was no statistically significant association between PaO2/FiO2 and the increase in the risk of 30-day death in sepsis (HR = 1.01, 95%CI was 0.99-1.02, P = 0.512). ROC curve analysis showed that the area under the curve (AUC) for the prediction of 30-day mortality by admission PaO₂/FiO₂ in ICU sepsis patients was 0.650, which was lower than the predictive ability of the SOFA score (AUC = 0.698) and APACHE II score (AUC = 0.723). CONCLUSION In patients with sepsis, PaO₂/FiO₂ at ICU admission is strongly associated with 30-day mortality risk, alerting healthcare professionals to pay attention to patients with low PaO₂/FiO₂ for timely interventions.
Humans ; Sepsis/mortality* ; Intensive Care Units ; Retrospective Studies ; Prognosis ; Hospital Mortality ; Oxygen ; Male ; Predictive Value of Tests ; Female ; Middle Aged ; Aged

Objective:To explore the correlation between abnormal vital signs (e.g., heart rate, oxygen saturation, and body temperature) and acute exacerbations in patients with chronic obstructive pulmonary disease (COPD), as well as to evaluate the clinical value of continuous monitoring via wearable devices for the early warning and intervention.Methods:A multicenter cross-sectional study enrolled 335 patients with stable chronic obstructive pulmonary disease (COPD) from 12 community health centers in Beijing and Chengdu between June 2023 and May 2024. General demographic and clinical data were collected, and each participant underwent continuous monitoring of resting heart rate, oxygen saturation, and body temperature using wearable devices for 21 days. Based on whether participants had experienced acute exacerbations requiring outpatient, emergency, or inpatient treatment within the previous year, they were categorized into the acute exacerbation group and the non-exacerbation group. Differences in physiological parameters between the acute exacerbation group and non-exacerbation group were analyzed, and contributing factors were assessed using logistic regression analysis.Results:A total of 335 patients with stable COPD were enrolled, including 252 cases (75.22%) in the acute exacerbation group and 83 cases (24.78%) in the non-acute exacerbation group. There were no statistically significant differences in age, sex distribution, comorbidities, or baseline lung function between the two groups (all P>0.05). Compared with the non-acute exacerbation group, patients in the acute exacerbation group had a faster resting heart rate((76.01 ± 7.78) beats/min vs. (72.72 ± 7.35) beats/min, t=3.126, P=0.002), a higher proportion of patients with decreased oxygen saturation (1.75% (0.97%, 3.03%) vs. 0.86% (0.44%, 1.65%), Z=11.086, P=0.001), and a higher proportion of patients with elevated body temperature (0.60% (0.39%, 1.03%) vs. 0.31% (0.17%, 0.54%), Z=7.314, P=0.007). Logistic regression analysis showed that advanced age ( OR=1.051, 95% CI: 1.023-1.080), increased heart rate ( OR=1.055, 95% CI:1.013-1.098), decreased oxygen saturation ( OR=1.197, 95% CI:1.023-1.400), and elevated body temperature ( OR=1.777, 95% CI:1.148-2.752) were positively associated factors for acute exacerbation of COPD. Conclusions:Abnormalities in physiological indicators such as heart rate, oxygen saturation, and body temperature are associated with acute exacerbations in COPD patients. Continuous monitoring using wearable devices may provide a new method for early warning and timely intervention in COPD exacerbations.

Objective To evaluate the utilization efficiency of beds in clinical departments based on time consumption index.Methods Selecting data from the 2019 annual report of medical work statistics and data from the Beijing inpatient medical service performance evaluation platform of a tertiary grade A general hospital of Traditional Chinese Medicine,the time consumption index of departments was calculated,based on which the regional average inpatient day of each department was calculated.and the number of standard bed turnover was calculated based on the target bed utilization rate.The bed utilization model was constructed based on the actual bed utilization in the department.Results Beds are open for a certain number of days,based on the target bed occupancy rate of each department and the average inpatient days within the DRG region of each department can be calculated based on the annual standard bed turnover number of the department based on the DRG disease group,on the basis of which the bed utilisation model is constructed.59.09%of the departments were efficiency departments,and the departments of press bed type,idle type and weekly transition all accounted for 13.64%respectively.Conclusion Introducing the time consumption index into the evaluation of bed utilization efficiency is in line with the actual work situation.Shortening the time consumption index is the most important thing to improve the bed utilization efficiency.The evaluation results can be used as the basis for the allocation of hospital bed resources.

Objective To evaluate the utilization efficiency of beds in clinical departments based on time consumption index.Methods Selecting data from the 2019 annual report of medical work statistics and data from the Beijing inpatient medical service performance evaluation platform of a tertiary grade A general hospital of Traditional Chinese Medicine,the time consumption index of departments was calculated,based on which the regional average inpatient day of each department was calculated.and the number of standard bed turnover was calculated based on the target bed utilization rate.The bed utilization model was constructed based on the actual bed utilization in the department.Results Beds are open for a certain number of days,based on the target bed occupancy rate of each department and the average inpatient days within the DRG region of each department can be calculated based on the annual standard bed turnover number of the department based on the DRG disease group,on the basis of which the bed utilisation model is constructed.59.09%of the departments were efficiency departments,and the departments of press bed type,idle type and weekly transition all accounted for 13.64%respectively.Conclusion Introducing the time consumption index into the evaluation of bed utilization efficiency is in line with the actual work situation.Shortening the time consumption index is the most important thing to improve the bed utilization efficiency.The evaluation results can be used as the basis for the allocation of hospital bed resources.

Objective To evaluate the utilization efficiency of beds in clinical departments based on time consumption index.Methods Selecting data from the 2019 annual report of medical work statistics and data from the Beijing inpatient medical service performance evaluation platform of a tertiary grade A general hospital of Traditional Chinese Medicine,the time consumption index of departments was calculated,based on which the regional average inpatient day of each department was calculated.and the number of standard bed turnover was calculated based on the target bed utilization rate.The bed utilization model was constructed based on the actual bed utilization in the department.Results Beds are open for a certain number of days,based on the target bed occupancy rate of each department and the average inpatient days within the DRG region of each department can be calculated based on the annual standard bed turnover number of the department based on the DRG disease group,on the basis of which the bed utilisation model is constructed.59.09%of the departments were efficiency departments,and the departments of press bed type,idle type and weekly transition all accounted for 13.64%respectively.Conclusion Introducing the time consumption index into the evaluation of bed utilization efficiency is in line with the actual work situation.Shortening the time consumption index is the most important thing to improve the bed utilization efficiency.The evaluation results can be used as the basis for the allocation of hospital bed resources.

Objective To evaluate the utilization efficiency of beds in clinical departments based on time consumption index.Methods Selecting data from the 2019 annual report of medical work statistics and data from the Beijing inpatient medical service performance evaluation platform of a tertiary grade A general hospital of Traditional Chinese Medicine,the time consumption index of departments was calculated,based on which the regional average inpatient day of each department was calculated.and the number of standard bed turnover was calculated based on the target bed utilization rate.The bed utilization model was constructed based on the actual bed utilization in the department.Results Beds are open for a certain number of days,based on the target bed occupancy rate of each department and the average inpatient days within the DRG region of each department can be calculated based on the annual standard bed turnover number of the department based on the DRG disease group,on the basis of which the bed utilisation model is constructed.59.09%of the departments were efficiency departments,and the departments of press bed type,idle type and weekly transition all accounted for 13.64%respectively.Conclusion Introducing the time consumption index into the evaluation of bed utilization efficiency is in line with the actual work situation.Shortening the time consumption index is the most important thing to improve the bed utilization efficiency.The evaluation results can be used as the basis for the allocation of hospital bed resources.

Objective To evaluate the utilization efficiency of beds in clinical departments based on time consumption index.Methods Selecting data from the 2019 annual report of medical work statistics and data from the Beijing inpatient medical service performance evaluation platform of a tertiary grade A general hospital of Traditional Chinese Medicine,the time consumption index of departments was calculated,based on which the regional average inpatient day of each department was calculated.and the number of standard bed turnover was calculated based on the target bed utilization rate.The bed utilization model was constructed based on the actual bed utilization in the department.Results Beds are open for a certain number of days,based on the target bed occupancy rate of each department and the average inpatient days within the DRG region of each department can be calculated based on the annual standard bed turnover number of the department based on the DRG disease group,on the basis of which the bed utilisation model is constructed.59.09%of the departments were efficiency departments,and the departments of press bed type,idle type and weekly transition all accounted for 13.64%respectively.Conclusion Introducing the time consumption index into the evaluation of bed utilization efficiency is in line with the actual work situation.Shortening the time consumption index is the most important thing to improve the bed utilization efficiency.The evaluation results can be used as the basis for the allocation of hospital bed resources.