OBJECTIVE: To explore the safety and efficacy of high-dose etoposide (VP-16) combined with recombinant human granulocyte colony-stimulating factor (rhG-CSF) as salvage mobilization for peripheral blood stem cells (PBSC) in newly diagnosed multiple myeloma (NDMM) patients. METHODS: From April 2021 to May 2023, eight NDMM patients who had failed to yield sufficient PBSC during initial mobilization with high-dose cyclophosphamide (CTX) combined with rhG-CSF underwent salvage mobilization with 1.2 g/m2 etoposide combined with rhG-CSF 10 μg/(kg·d). The effects and adverse reactions of initial mobilization and salvage mobilization were analyzed. RESULTS: For salvage mobilization and initial mobilization, the numbers of PBSC collections were 16 and 18, respectively. The mean value of total collected CD34⁺ cells were (11.90±5.75)×10⁶/kg and (1.67±0.75)×10⁶/kg (P =0.0010) in salvage mobilization group and initial mobilization group, respectively. The proportion of patients with a total collection of CD34⁺ cell count≥2×10⁶/kg were 100% and 37.5% (P =0.0625), and the proportion of patients with a total collection of CD34⁺ cell count≥5×10⁶/kg were 87.5% and 0% (P =0.0156) in salvage mobilization group and initial mobilization group, respectively. For five patients who underwent high-dose CTX initial mobilization but had a total CD34⁺ cell count < 2×10⁶/kg, successful collection was achieved through salvage mobilization with high-dose VP-16. Salvage mobilization with high-dose VP-16 was scheduled 2-3 weeks after failure of CTX mobilization. Adverse reactions of high-dose VP-16 mobilization did not increase compared to the initial mobilization with high-dose CTX. CONCLUSION As a salvage mobilization regimen, VP-16 1.2 g/m² combined with rhG-CSF is safe and highly effective in NDMM patients who failed to initial mobilization with high-dose CTX combined with rhG-CSF.
Humans ; Multiple Myeloma/therapy* ; Etoposide/therapeutic use* ; Hematopoietic Stem Cell Mobilization/methods* ; Cyclophosphamide/therapeutic use* ; Granulocyte Colony-Stimulating Factor ; Salvage Therapy ; Peripheral Blood Stem Cells ; Male ; Middle Aged ; Female ; Peripheral Blood Stem Cell Transplantation

This study aimed to characterize and identify the non-volatile components in aqueous and ethanolic extracts of the stems and leaves of Rhododendron tomentosum by using sensitive and efficient ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS) combined with a self-built information database. By comparing with reference compounds, analyzing fragment ion information, searching relevant literature, and using a self-built information database, 118 compounds were identified from the aqueous and ethanolic extracts of R. tomentosum, including 35 flavonoid glycosides, 15 phenolic glycosides, 12 flavonoids, 7 phenolic acids, 7 phenylethanol glycosides, 6 tannins, 6 phospholipids, 5 coumarins, 5 monoterpene glycosides, 6 triterpenes, 3 fatty acids, and 11 other types of compounds. Among them, 102 compounds were reported in R. tomentosum for the first time, and 36 compounds were identified by comparing them with reference compounds. The chemical components in the ethanolic and aqueous extracts of R. tomentosum leaves and stems showed slight differences, with 84 common chemical components accounting for 71.2% of the total 118 compounds. This study systematically characterized and identified the non-volatile chemical components in the ethanolic and aqueous extracts of R. tomentosum for the first time. The findings provide a reference for active ingredient research, quality control, and product development of R. tomentosum.
Rhododendron/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Mass Spectrometry/methods* ; Plant Leaves/chemistry*

BACKGROUND AND OBJECTIVE: Hypertension (HT) and atrial fibrillation (AF) are highly prevalent cardiovascular conditions that frequently coexist. Coronary artery disease (CAD) is a major global cause of mortality. The co-occurrence of HT, AF, and CAD presents significant management challenges. This study aims to explore the clinical characteristics and risk factors associated with CAD in patients with HT and persistent AF (HT-AF). METHODS: In this retrospective cross-sectional study, data were collected from 384 hospitalized HT-AF patients at the People's Liberation Army General Hospital between January 2010 and December 2019. CAD diagnosis was confirmed by coronary angiography or computed tomography angiography. Clinical characteristics and comorbidities were compared between patients with and without CAD. Multivariate logistic regression analyses were performed to identify independent risk factors associated with CAD development. RESULTS: The prevalence of CAD among HT-AF patients was 66.41% (255/384). Cardiovascular complications, particularly heart failure (44.7% vs 25.6%, P < 0.05), were significantly more prevalent in the CAD group than in the non-CAD group. Only age was identified as an independent risk factor for CAD (adjusted OR: 1.047; 95% CI: 1.022-1.073; P = 0.000). Of all HT-AF patients, 54.7% had a CHA₂DS₂-VASc score of ≥4, indicating high stroke risk. There was a slightly higher anticoagulant usage rate in the CAD group than those without CAD (8.6% vs 4.7%, P = 0.157), and the overall anticoagulant usage remained low. CONCLUSION: There is a high prevalence of CAD among hospitalized HT-AF patients, among whom age is the sole independent risk factor for CAD. Despite a high stroke risk, the utilization of oral anticoagulants is alarmingly low.
Humans ; Atrial Fibrillation/epidemiology* ; Coronary Artery Disease/etiology* ; Hypertension/epidemiology* ; Male ; Female ; Risk Factors ; Middle Aged ; Retrospective Studies ; Cross-Sectional Studies ; Aged ; Prevalence

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To determine whether the Rb1 of ginsenoside has protective effects on PA induced oxidative stress in endothelial cells.Methods:Established a model of palmitic acid-induced oxidative stress injury in human umbilical vein endothelial cells(HUVECs).Using MTT assay,flow cytometry,fluorescent probe staining,and Western blot analysis to detect whether Rb1 of ginsenoside has effects on the cell viability,apoptosis rate,ROS and NO production,mitochondrial membrane potential,and the expression levels of related proteins.Results:MTT assay and flow cytometry revealed that ginsenoside Rb1 can reduce PA-induced apoptosis in HUVECs(P＜0.05).The mechanism may be related to the following two points:(1)reducing ROS production and increasing NO levels,thereby enhancing the antioxidant capacity of HUVECs;(2)regulating the expression of Bcl-2 family proteins,increasing the BCL-2/Bax ratio(P＜0.05),modulating mitochondrial membrane permeability,reducing cytochrome C release(P＜0.001),and decreasing Caspase protein activation(P＜0.01),thereby attenuating PA-induced apoptosis.Conclusion:After the stimulation with PA,ROS production in human umbilical vein endothelial cells increased while NO content and cell activity decreased,oxidative stress induced apoptosis in cells.By regulating the production of ROS and NOx stabilizing the mitochondrial transmembrane potential,reducing the leakage of cytochrome C,Ginsenoside Rb1 can reduce HUVECs apoptosis induced by PA.

Objective:To determine whether the Rb1 of ginsenoside has protective effects on PA induced oxidative stress in endothelial cells.Methods:Established a model of palmitic acid-induced oxidative stress injury in human umbilical vein endothelial cells(HUVECs).Using MTT assay,flow cytometry,fluorescent probe staining,and Western blot analysis to detect whether Rb1 of ginsenoside has effects on the cell viability,apoptosis rate,ROS and NO production,mitochondrial membrane potential,and the expression levels of related proteins.Results:MTT assay and flow cytometry revealed that ginsenoside Rb1 can reduce PA-induced apoptosis in HUVECs(P＜0.05).The mechanism may be related to the following two points:(1)reducing ROS production and increasing NO levels,thereby enhancing the antioxidant capacity of HUVECs;(2)regulating the expression of Bcl-2 family proteins,increasing the BCL-2/Bax ratio(P＜0.05),modulating mitochondrial membrane permeability,reducing cytochrome C release(P＜0.001),and decreasing Caspase protein activation(P＜0.01),thereby attenuating PA-induced apoptosis.Conclusion:After the stimulation with PA,ROS production in human umbilical vein endothelial cells increased while NO content and cell activity decreased,oxidative stress induced apoptosis in cells.By regulating the production of ROS and NOx stabilizing the mitochondrial transmembrane potential,reducing the leakage of cytochrome C,Ginsenoside Rb1 can reduce HUVECs apoptosis induced by PA.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Using China National Knowledge Infrastructure, Wanfang, and PubMed database, literature search was conducted with the keywords ^“pregnancy^” and ^“monkeypox^”, and 27 related research articles were selected for analysis. Through a comprehensive review of the related literature, we aim to improve our knowledge of this viral disease, better our prevention, treatment and responses to future monkeypox outbreaks in China, so as to better protect the safety of mothers and infants. Maternal monkeypox can be prevented and controlled, if active and effective measures are taken in time. Drawing on the experience and lessons from monkeypox outbreaks at home and abroad, it is suggested that hospitals and public health agencies at all levels should raise awareness, and establish an effective emergency preparedness system for the prevention and control of potential future outbreaks.

The clinical practice guidelines of traditional Chinese medicine （TCM） have problems such as limited clinical application and unclear implementation effects， which may be related to the lack of clinical practice evidence. To provide reliable and precise evidence for clinical practice， this article proposes a model of combining TCM guidelines with real-world study， which includes 4 steps. Firstly， during the implementation process of the guidelines， a high-quality research database is established. Secondly， the recommendations in the guidelines are evaluated based on the established database in multiple dimensions， including applicability， effectiveness， safety， and cost-effectiveness， and thus their effectiveness in practical applications can be determined. Thirdly， based on the established database， core prescriptions are identified， and the targeted populations and medication plans are determined. That is， the best treatment regimen is established based on the analysis of abundant clinical data regarding the effects of different medication frequencies， dosages， and duration on efficacy. Fourthly， the guidelines are updated according to the real-world evidence. The research based on this model can provide real-world evidence for ancient and empirical prescriptions， improving their application in clinical practice. Moreover， this model can reduce research costs and improve research efficiency. When applying this model， researchers need to pay attention to the quality of real-world evidence， ensuring that it can truly reflect the situation in clinical practice. In addition， importance should be attached to the clinical application of guideline recommendations， ensuring that doctors can conduct standardized diagnosis and treatment according to the guidelines. Finally， full-process participation of multidisciplinary experts is encouraged to ensure the comprehensiveness and scientificity of the study. In conclusion， the application of this model will contribute to the development of TCM guidelines responsive to the needs of clinical practice and achieve the goal of promoting the homogenization of TCM clinical diagnosis and treatment.

The classification of traditional Chinese medicine （TCM） syndromes is one of the core technical elements in the industry standard of Specification of Diagnosis and Therapeutic Effect Evaluation of Diseases and Syndromes in TCM. In the past，when clinical standards for TCM were formulated，the determination of TCM syndrome classification relied heavily on textbooks and expert experience，lacking systematic research. This approach thus failed to reflect the advancement and scientificity of the standards，thereby affecting their implementation and application. This article reviewed the presentation forms and technical methods of TCM syndrome classification，including the two-tier syndrome classification model with primary and secondary symptoms，as well as the application of modern literature research，ancient literature research，Delphi method，in-depth expert interviews，consensus conferences，and real-world research. When syndrome classification standards are developed，it is necessary to build upon modern literature research，adopt a mixed approach combining qualitative research and quantitative analysis results，and reach expert consensus through consensus conferences. Through systematic research，the scientificity，applicability，and coordination of TCM syndrome classification standards can be enhanced，providing guidance for the standardization of TCM.