ObjectiveTo analyze the development status and quality of clinical practice guidelines for the treatment of dominant diseases with Chinese patent medicines （CPMs）. MethodsDatabases were searched from Jan. 2019 to Dec.2023 to collect the published clinical practice guidelines of CPMs for the treatment of dominant diseases. The information about the title， the participants， clinical problems， outcomes， evidence grade， recommendations， and recommendation strength in the included clinical practice guidelines were collected， for which the development status was analyzed， and the quality was evaluated with the Scientific， Transparent and Applicable Rankings （STAR） tool for clinical practice guidelines. ResultsTotally， 34 guidelines were included， involving 273 kinds of CPMs. One to ten （with the medium five） clinical problems were proposed from 29 clinical practice guidelines respectively. All the guidelines divided the evidence into four grades according to Grade of Recommendation Assessment， Deve-lopement an Evaluation. And 28 guidelines had five levels of recommendation strength. A total of 344 recommendations were extracted， including 86 strong-recommendations， 191 weak-recommendations （including 36 weak recommendations only based on expert consensus） and 67 recommendations with unclear recommendation strength. All guidelines had high scores in the three areas of “clinical questions （94.20%）”， “evidence （91.45%）” and “recommendations （89.06%）”， while the scores in the three areas of “registry （22.06%）”， “protocol （19.00%）” and “accessibility （31.51%）” were low. The STAR recommended stars of 8 guidelines were 5.0~4.0 stars， while that of 18 guidelines were 3.5~2.5 stars， and 8 guidelines were 2.0~1.0 stars. The three guidelines with the highest recommended stars were depressive disorder， community-acquired pneumonia， and influenza in adult. ConclusionThere is a certain gap in the quality of the published clinical practice guidelines of CPMs， and the quality of the guidelines could be further improved in registry， protocols， funds， and accessibility.

Community-acquired pneumonia （CAP） is a common respiratory infectious disease in the elderly， and traditional Chinese medicine （TCM） and integrated TCM and western medicine showed effectiveness. According to the clinical characteristics of CAP， the staging concepts of CAP infection and recovery are proposed， and the prevention and treatment goals and strategies of staging and grading are put forward in combination with the disease seve-rity of mild， moderate and severe infections. The main objectives are to increase the cure rate of mild and moderate pneumonia， to reduce the case fatality rate of severe pneumonia， and to reduce the incidence of new upper and lower respiratory tract infections during the recovery period， respectively. TCM prevention and treatment strategies： for mild pneumonia， treatments should scatter and dissipate external pathogens， diffuse and descend lung qi， dispel dampness and dissolve phlegm， or clear lung and dissolve phlegm； for moderate pneumonia， treatments should clear lungs and resolve toxins， dry dampness and dissolve phlegm， and supplementing with tonifying lungs and strengthen spleen， or benefit qi and nourish yin； for severe pneumonia， the treatment of dispelling pathogen and supporting healthy qi should be emphasised according to the primary and secondary levels of excess pathogen and deficiency healthy qi； and for the recovery period， treatments should mainly support healthy qi， and supplement with dispelling pathogen. Relevant clinical studies have been conducted for practical verification， and the results showed that integrated TCM and Western medicine treatment can improve the cure rate of mild to moderate pneumonia， decrease the case fatality rate of severe pneumonia， and reduce the re-hospitalisation of pneumonia during the recovery period， indicating that the staged and graded prevention and treatment strategy has important guiding value for improving the diagnosis and treatment of CAP.

TCM believes that spleen deficiency is the root cause of emotional abnormalities in chronic fatigue syndrome (CFS), and clinical treatment often involves the heart, liver and kidney. TCM therapy has a significant efficacy in CFS emotional abnormalities. It is mostly treated with oral administration of TCM, acupuncture, moxibustion and massage therapy. It may play a therapeutic role by improving oxidative stress and immune inflammation, regulating nerve-endocrine, controlling energy metabolism and other ways. It is suggested to establish the syndrome differentiation standard of CFS emotional abnormality in the future, so as to improve the accuracy of syndrome differentiation and treatment; form a perfect treatment guide or expert consensus to guide the standardized application of various internal and external treatment methods; explore objective indicators based on the pathogenesis, and focus on the morphological and functional changes of disease target brain regions with the help of neuroimaging techniques, so as to improve the diagnosis and prognosis evaluation of CFS; based on the guidance of TCM theory, improve the CFS emotional abnormal animal modeling method.

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection with an extremely high mortality rate,and it is the main risk factor for acute lung injury(ALI).However,the pathophysiology and pathogenesis of sepsis-associated ALI are not fully understood,and effective drugs are extremely limited.Therefore,it is urgent that we explore the pathogenesis of sepsis-associated ALI and attempt to discover effective intervention measures to improve the prognosis of sepsis-associated ALI patients.In recent years,ferroptosis has been considered closely related to the pathological and physiological processes of sepsis-associated ALI,and inhibiting related cell ferroptosis can effectively slow down the occurrence and development of the disease.In this paper,therapeutic strategies targeting ferroptosis in related cells are reviewed to provide a reference for future research on ferroptosis in sepsis-associated ALI and provide a new perspective on potential treatments.

Chronic obstructive pulmonary disease (COPD) has a high global morbidity and mortality and a severe disease burden, yet progress in treatment and prevention has been slow in recent decades. Early COPD has few symptoms and is severely underdiagnosed and undertreated; it is crucial to search for effective clues of early COPD and provide management interventions. By reviewing the definition, risk factors, diagnosis and management interventions, this study explores the disease evolution of early-stage COPD, which can help clinical practice to develop more effective preventive and therapeutic strategies for stopping or slowing down the natural progression of the disease, improving the long-term prognosis, and reducing the disease burden.
Humans ; Pulmonary Disease, Chronic Obstructive/drug therapy* ; Prognosis ; Risk Factors

Objective:To construct the risk prediction model of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and verify its effectiveness based on deep learning and back propagation algorithm neural network (BP neural network).Methods:Based on the relevant data of 1 326 patients with chronic obstructive pulmonary disease (COPD) in the team's previous clinical study, the acute exacerbation, and its risk factors during the stable period and 6 months of follow-up were recorded and analyzed. Combined with previous clinical research data and expert questionnaire results, the independent risk factors of AECOPD after screening and optimization by multivariate Logistic regression including gender, body mass index (BMI) classification, number of acute exacerbation, duration of acute exacerbation and forced expiratory volume in one second (FEV1) were used to build the BP neural network by Python 3.6 programming language and Tensorflow 1.12 deep learning framework. The patients were randomly selected according to the ratio of 4∶1 to generate the training group and the test group, of which, the training group had 1 061 sample data while the test group had 265 pieces of sample data. The training group was used to establish the prediction model of neural network, and the test group was used for back-substitution test. When using the training group data to construct the neural network model, the training group was randomly divided into training set and verification set according to the ratio of 4∶1. There were 849 training samples in the training set and 212 verification samples in the verification set. The optimal model was screened by adjusting the parameters of the neural network and combining the area under the receiver operator characteristic curve (AUC), and the sample data of the test group was substituted into the model for verification.Results:The independent risk factors including gender, BMI classification, number of acute exacerbation, duration of acute exacerbation and FEV1 were collected from the team's previous clinical research, and the AECOPD risk prediction model was constructed based on deep learning and BP neural network. After 10 000 training sessions, the accuracy of the AECOPD risk prediction model in the validation set of the training group was 83.09%. When the number of training times reached 8 000, the accuracy basically tended to be stable and the prediction ability reached the upper limit. The AECOPD risk prediction model trained for 10 000 times was used to predict the risk of the validation set data, and the receiver operator characteristic curve (ROC curve) analysis showed that the AUC was 0.803. When using this model to predict the risk of the data of the test group, the accuracy rate was 81.69%.Conclusion:The risk prediction model based on deep learning and BP neural network has a medium level of prediction efficiency for acute exacerbation within 6 months in COPD patients, which can evaluate the risk of AECOPD and assist the clinic in making accurate treatment decisions.

Objective:To establish a risk prediction model for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) using regression analysis and verify the model.Methods:The risk factors and acute exacerbation of 1 326 patients with chronic obstructive pulmonary disease (COPD) who entered the stable phase and followed up for 6 months in the four completed multi-center large-sample randomized controlled trials were retrospectively analyzed. Using the conversion-random number generator, about 80% of the 1 326 cases were randomly selected as the model group ( n = 1 074), and about 20% were the verification group ( n = 252). The data from the model group were selected, and Logistic regression analysis was used to screen independent risk factors for AECOPD, and an AECOPD risk prediction model was established; the model group and validation group data were substituted into the model, respectively, and the receiver operating characteristic (ROC) curve was drawn to verify the effectiveness of the risk prediction model in predicting AECOPD. Results:There were no statistically significant differences in general information (gender, smoking status, comorbidities, education level, etc.), body mass index (BMI) classification, lung function [forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), etc.], disease status (the number and duration of acute exacerbation in the past year, duration of disease, etc.), quality of life scale [COPD assessment test (CAT), etc.] and clinical symptoms (cough, chest tightness, etc.) between the model group and the validation group. It showed that the two sets of data had good homogeneity, and the cases in the validation group could be used to verify the effectiveness of the risk prediction model established through the model group data to predict AECOPD. Logistic regression analysis showed that gender [odds ratio ( OR) = 1.679, 95% confidence interval (95% CI) was 1.221-2.308, P = 0.001], BMI classification ( OR = 0.576, 95% CI was 0.331-1.000, P = 0.050), FEV1 ( OR = 0.551, 95% CI was 0.352-0.863, P = 0.009), number of acute exacerbation ( OR = 1.344, 95% CI was 1.245-1.451, P = 0.000) and duration of acute exacerbation ( OR = 1.018, 95% CI was 1.002-1.034, P = 0.024) were independent risk factors for AECOPD. A risk prediction model for AECOPD was constructed based on the results of regression analysis: probability of acute exacerbation ( P) = 1/(1+ e- x), x = -3.274 + 0.518×gender-0.552×BMI classification + 0.296×number of acute exacerbation + 0.018×duration of acute exacerbation-0.596×FEV1. The ROC curve analysis verified that the area under ROC curve (AUC) of the model group was 0.740, the AUC of the verification group was 0.688; the maximum Youden index of the model was 0.371, the corresponding best cut-off value of prediction probability was 0.197, the sensitivity was 80.1%, and the specificity was 57.0%. Conclusion:The AECOPD risk prediction model based on the regression analysis method had a moderate predictive power for the acute exacerbation risk of COPD patients, and could assist clinical diagnosis and treatment decision in a certain degree.

Objective:To explore the risk factors of intensive care unit acquired weakness (ICUAW) in patients with sepsis, and to evaluate the predictive value of each risk factor for ICUAW.Methods:A case control study was conducted, 60 septic patients admitted to the intensive care unit (ICU) of Henan Provincial People's Hospital from October 20, 2020 to February 20, 2021 were enrolled. The patients were divided into two groups: sepsis ICUAW group and sepsis non-ICUAW group. The data of gender, age, body mass index (BMI), acute physiology and chronic health evaluationⅡ(APACHEⅡ) score, complications, mechanical ventilation, duration of ICUAW, length of stay in ICU, fasting blood glucose, blood lactic acid (Lac), procalcitonin (PCT), C-reactive protein (CRP), sequential organ failure assessment (SOFA) score, outcome, antimicrobial agent, glucocorticoid, sedatives and analgesics drugs and vasoactive drugs were collected. Risk factors were screened by univariate Logistic regression analysis, and odds ratio ( OR) was adjusted by multivariate binary logistic regression, P < 0.05 was considered as independent risk factors. Finally, the receiver operating characteristic curve (ROC curve) was drawn to analyze the predictive value of independent risk factors. Results:The APACHEⅡ score of the sepsis ICUAW group was significantly higher than that of the sepsis non-ICUAW group (23.05±8.17 vs. 15.33±4.89, P < 0.05), the total length of stay in the ICU was significantly longer than that of the sepsis non-ICUAW group (days: 15.1±9.2 vs. 8.5±3.4, P < 0.05), the improvement rate of patients was significantly lower than that of the sepsis non-ICUAW group [45.0% (9/20) vs. 95.0% (38/40), P < 0.05]. After univariate Logistic regression and multicollinearity test analysis, 7 factors including APACHEⅡ score, average SOFA score, blood lactic acid, proportion of mechanical ventilation, sedatives and analgesics drugs, type of antibiotics and type of vasoactive drugs were included in the binary Logistic regression model [ OR: 1.21, 2.05, 2.26, 0.21, 1.54, 2.07, 1.38, 95% confidence interval (95% CI): 1.09-1.35, 1.42-2.94, 1.12-4.57, 0.05-0.66, 1.03-2.29, 1.27-3.37, 0.96-2.00, all P < 0.05]. Hosmer-Lemchaw test P = 0.901, and the correct percentage of prediction was 85%, indicating good model fit. Multivariate binary Logistic regression analysis showed that APACHEⅡ score and average SOFA score were independent risk factors for the occurrence of ICUAW in septic patients (APACHEⅡscore: OR = 1.17, 95% CI was 1.004-1.376, P = 0.044; average SOFA score: OR = 1.86, 95% CI was 1.157-2.981, P = 0.01). ROC curve analysis showed that the mean value of APACHEⅡ score, average SOFA score and their combined detection had a certain predictive value for the occurrence of ICUAW in sepsis patients, areas under ROC curve (AUC) were 0.787, 0.881, 0.905, 95% CI was 0.646-0.928, 0.791-0.972, 0.828-0.982, all P < 0.05. When the cut-off value was 19.500, 6.225, 0.375, the sensitivity was 75%, 90%, 90%, and the specificity were 80%, 80%, 85%, respectively. Conclusion:APACHEⅡ score and average SOFA score can be used as independent risk factors for the occurrence of ICUAW in sepsis, and their combined predictive value is better than that of individual index.

Bronchial asthma (asthma) is a complex heterogeneous disease, with a high rate of missed diagnosis and misdiagnosis. Repeated attacks of bronchial asthma can cause complications such as chronic obstructive pulmonary disease, emphysema, and pulmonary heart disease. In recent years, mass-spectrometry-based metabolomics has developed rapidly. It can sensitively identify metabolic fluctuations and pathological changes in patients with asthma. By analyzing the molecules produced by various metabolic pathways, it can help us to find relevant biomarkers and provide a better method for early diagnosis and severity assessment of asthma. We reviewed and analyzed the literature of metabolomics technology in disease progression, early diagnosis and severity assessment, so as to provide reference for asthma research.

More efficient drug delivery system and formulation with less adverse effects are needed for the clinical application of broad-spectrum antineoplastic agent doxorubicin (DOX). Here we obtained outer-membrane vesicles (OMVs), a nano-sized proteoliposomes naturally released by Gram-negative bacteria, from attenuated and prepared doxorubicin-loaded O0MVs (DOX-OMV). Confocal microscopy and distribution study observed that DOX encapsulated in OMVs was efficiently transported into NSCLC A549 cells. DOX-OMV resulted in intensive cytotoxic effects and cell apoptosis as evident from MTT assay, Western blotting and flow cytometry due to the rapid cellular uptake of DOX. In A549 tumor-bearing BALB/c nude mice, DOX-OMV presented a substantial tumor growth inhibition with favorable tolerability and pharmacokinetic profile, and TUNEL assay and H&E staining displayed extensive apoptotic cells and necrosis in tumor tissues. More importantly, OMVs' appropriate immunogenicity enabled the recruitment of macrophages in tumor microenvironment which might synergize with their cargo DOX . Our results suggest that OMVs can not only function as biological nanocarriers for chemotherapeutic agents but also elicit suitable immune responses, thus having a great potential for the tumor chemoimmunotherapy.