OBJECTIVE: To evaluate the efficacy and safety of Shexiang Tongxin Dropping Pill (STDP) in treating stable angina patients with phlegm-heat and blood-stasis syndrome by exercise duration and metabolic equivalents. METHODS: This multicenter, randomized, double-blind, placebo-controlled clinical trial enrolled stable angina patients with phlegm-heat and blood-stasis syndrome from 22 hospitals. They were randomized 1:1 to STDP (35 mg/pill, 6 pills per day) or placebo for 56 days. The primary outcome was the exercise duration and metabolic equivalents (METs) assessed by the standard Bruce exercise treadmill test after 56 days of treatment. The secondary outcomes included the total angina symptom score, Chinese medicine (CM) symptom scores, Seattle Angina Questionnaire (SAQ) scores, changes in ST-T on electrocardiogram and adverse events (AEs). RESULTS: This trial enrolled 309 patients, including 155 and 154 in the STDP and placebo groups, respectively. STDP significantly prolonged exercise duration with an increase of 51.0 s, compared to a decrease of 12.0 s with placebo (change rate: -11.1% vs. 3.2%, P<0.01). The increase in METs was significantly greater in the STDP group than in the placebo group (change: -0.4 vs. 0.0, change rate: -5.0% vs. 0.0%, P<0.01). The improvement of total angina symptom scores (25.0% vs. 0.0%), CM symptom scores (38.7% vs. 11.8%), reduction of nitroglycerin consumption (100.0% vs. 11.3%), and all domains of SAQ, were significantly greater with STDP than placebo (all P<0.01). The changes in Q-T intervals at 28 and 56 days from baseline were similar between the two groups (both P>0.05). Twenty-five participants (16.3%) with STDP and 16 (10.5%) with placebo experienced AEs (P=0.131), with no serious AEs observed. CONCLUSION STDP could improve exercise tolerance in patients with stable angina and phlegm-heat and blood stasis syndrome, with a favorable safety profile. (Registration No. ChiCTR-IPR-15006020).
Humans ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Middle Aged ; Angina, Stable/physiopathology* ; Aged ; Syndrome ; Treatment Outcome ; Placebos ; Tablets

Objective:To systematically evaluate the application of artificial intelligence (AI) in cervical cytopathology diagnosis.Methods:A systematic search was conducted using the keywords ′′cervical cancer′′ ′′cytology′′ ′′artificial intelligence′′ ′′sensitivity′′ and ′′specificity′′ (in both English and Chinese) across databases including PubMed, Web of Science, Embase, Cochrane Library, IEEE Xplore, CNKI, Wanfang, VIP Chinese Science and Technology Journals, and SinoMed. The search covered literature from inception until January 1, 2024, on the application of AI in cervical cytopathological diagnosis. Data were extracted using a predefined data extraction form to compile the contingency table data, from which sensitivity, specificity and area under the curve (AUC) were calculated.Results:A total of 1 616 articles were initially retrieved, and 27 articles were finally included in this study according to the inclusion and exclusion criteria. Five researches were conducted on the diagnosis of cytopathology slides, with pooled AUC, sensitivity and specificity of 0.92 (95% CI: 0.89-0.94), 0.91 (95% CI: 0.77-0.97) and 0.84 (95% CI: 0.77-0.90), respectively. About 22 researches were conducted on the diagnosis of cytopathology images (individual cells or cell clusters), with pooled AUC, sensitivity and specificity of 1.00 (95% CI: 0.99-1.00), 0.98 (95% CI: 0.97-0.99) and 0.98 (95% CI: 0.97-0.99), respectively. Conclusion:The application of AI in the field of cervical cytopathology shows certain diagnostic performance and potential clinical application value.

Objective:To investigate the completion rate of tumor evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients in the national multicenter real-world database.Methods:The prospective real-world study was conducted. The clinicopathological data of 1 074 patients who underwent surgical treatment for mid and low rectal cancer in 47 national medical institutions, including Peking Union Medical College Hospital et al, from May 12,2023 to May 11,2024 were collected. Observation indicators: (1) clinical characteristics of patients with mid and low rectal cancer; (2) initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer; (3) initial imaging evaluation of patients with mid and low rectal cancer; (4) imaging evaluation after neoadjuvant therapy for patients with mid and low rectal cancer. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M( Q1, Q3). Count data were described as absoluter numbers and/or percentages. Results:(1) Clinical characteristics of patients with mid and low rectal cancer. Of the 1 074 patients, there were 713 males and 361 females, aged 63(56,70)years. The body mass index of 1 074 patients was 24(21,26)kg/m 2.For American Society of Anesthesiologists classification, there were 147 cases of stage Ⅰ, 641 cases of stage Ⅱ, 157 cases of stage Ⅲ, 2 cases of stage Ⅳ, and there were 127 cases missing data. (2) Initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer. Of the 1 074 patients, there were 787 cases (73.28%) undergoing complete colonoscopy, and there were only 197 cases (18.34%) undergoing immunohistochemical evaluation of all four mismatch repair proteins. (3) Initial imaging evaluation of patients with mid and low rectal cancer. Of the 1 074 patients, there were 842(78.40%) patients completing magnetic resonance imaging (MRI) or ultrasound evaluation, and there were 914(85.10%) patients completing chest, abdomen, and pelvis enhanced computed tomography (CT) evaluation. In the 149 patients completing rectal ultrasound evaluation, there were 122 cases (81.88%) comple-ting T staging evaluation, and there were 81 cases (54.36%) completing N staging evaluation. In the 808 patients completing rectal MRI evaluation, there were 708 cases (87.62%) completing T staging evaluation, and there were 590 cases (73.02%) completing N staging evaluation. (4) Imaging evalua-tion after neoadjuvant therapy for patients with mid and low rectal cancer. Of the 388 patients with neoadjuvant therapy, there were 332 patients (85.57%) completing MRI or ultrasound evaluation, and there were 327 patients (84.28%) completing chest, abdomen, and pelvis enhanced CT evalua-tion. In the 70 patients completing rectal ultrasound evaluation, there were 65 cases (92.86%) com-pleting T staging evaluation, and there were 49 cases (70.00%) completing N staging evaluation. In the 327 patients completing rectal MRI evaluation, there were 246 cases (75.23%) completing T staging, and there were 228 cases (69.72%) completing N staging evaluation. Conclusion:The com-pletion rate of tumor imaging evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients on a national scale is relatively good.

Clinical prediction models, which utilize clinical data and statistical methods, aim to enhance the accuracy and efficiency of medical decision-making and improve patient health outcomes. These models play a crucial role in optimizing healthcare decisions and tailoring treatments to individual needs. However, many studies currently face systemic challenges during the development process, including unclear model design objectives, redundant model construction, lack of clinical relevance in variable selection, and irregular data preprocessing. These issues finally lead to reduced model performance and limited clinical applicability. To address these challenges, this study systematically reviews relevant literature, including articles from the BMJ, and draws on practical research experience to propose a structured preparation process. This process aims to provide a scientific guiding framework for model development, ensuring the efficiency of subsequent model construction and the accuracy of predictions, thus laying a foundation for the application and advancement of clinical prediction models.

Clinical prediction models are statistical tools that incorporate multiple variables to predict the likelihood of specific outcomes, by which the accuracy and efficiency of medical decision-making can be facilitated and patient health outcomes can be improved. However, many current studies face problems, such as model construction and reporting irregularities, as well as questionable reliability, which limit their clinical application of clinical prediction model. Therefore, this study systematically reviews relevant literatures, including publications from journals like BMJ, and outline the steps involved in constructing clinical prediction models based on practical research experience. It also provides an in-depth comparison of commonly used methods during the construction process and proposes a comprehensive guiding framework to help researchers in the field to better understand and master the core concepts and practical skills of clinical prediction models for the purpose of improving their professional capabilities in the development, validation, and application of clinical prediction models.

Clinical prediction models are statistical tools that incorporate multiple variables to predict the likelihood of specific outcomes, by which the accuracy and efficiency of medical decision-making can be facilitated and patient health outcomes can be improved. However, many current studies face problems, such as model construction and reporting irregularities, as well as questionable reliability, which limit their clinical application of clinical prediction model. Therefore, this study systematically reviews relevant literatures, including publications from journals like BMJ, and outline the steps involved in constructing clinical prediction models based on practical research experience. It also provides an in-depth comparison of commonly used methods during the construction process and proposes a comprehensive guiding framework to help researchers in the field to better understand and master the core concepts and practical skills of clinical prediction models for the purpose of improving their professional capabilities in the development, validation, and application of clinical prediction models.

Clinical prediction models, which utilize clinical data and statistical methods, aim to enhance the accuracy and efficiency of medical decision-making and improve patient health outcomes. These models play a crucial role in optimizing healthcare decisions and tailoring treatments to individual needs. However, many studies currently face systemic challenges during the development process, including unclear model design objectives, redundant model construction, lack of clinical relevance in variable selection, and irregular data preprocessing. These issues finally lead to reduced model performance and limited clinical applicability. To address these challenges, this study systematically reviews relevant literature, including articles from the BMJ, and draws on practical research experience to propose a structured preparation process. This process aims to provide a scientific guiding framework for model development, ensuring the efficiency of subsequent model construction and the accuracy of predictions, thus laying a foundation for the application and advancement of clinical prediction models.

Objective:To systematically evaluate the application of artificial intelligence (AI) in cervical cytopathology diagnosis.Methods:A systematic search was conducted using the keywords ′′cervical cancer′′ ′′cytology′′ ′′artificial intelligence′′ ′′sensitivity′′ and ′′specificity′′ (in both English and Chinese) across databases including PubMed, Web of Science, Embase, Cochrane Library, IEEE Xplore, CNKI, Wanfang, VIP Chinese Science and Technology Journals, and SinoMed. The search covered literature from inception until January 1, 2024, on the application of AI in cervical cytopathological diagnosis. Data were extracted using a predefined data extraction form to compile the contingency table data, from which sensitivity, specificity and area under the curve (AUC) were calculated.Results:A total of 1 616 articles were initially retrieved, and 27 articles were finally included in this study according to the inclusion and exclusion criteria. Five researches were conducted on the diagnosis of cytopathology slides, with pooled AUC, sensitivity and specificity of 0.92 (95% CI: 0.89-0.94), 0.91 (95% CI: 0.77-0.97) and 0.84 (95% CI: 0.77-0.90), respectively. About 22 researches were conducted on the diagnosis of cytopathology images (individual cells or cell clusters), with pooled AUC, sensitivity and specificity of 1.00 (95% CI: 0.99-1.00), 0.98 (95% CI: 0.97-0.99) and 0.98 (95% CI: 0.97-0.99), respectively. Conclusion:The application of AI in the field of cervical cytopathology shows certain diagnostic performance and potential clinical application value.

Objective:To investigate the completion rate of tumor evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients in the national multicenter real-world database.Methods:The prospective real-world study was conducted. The clinicopathological data of 1 074 patients who underwent surgical treatment for mid and low rectal cancer in 47 national medical institutions, including Peking Union Medical College Hospital et al, from May 12,2023 to May 11,2024 were collected. Observation indicators: (1) clinical characteristics of patients with mid and low rectal cancer; (2) initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer; (3) initial imaging evaluation of patients with mid and low rectal cancer; (4) imaging evaluation after neoadjuvant therapy for patients with mid and low rectal cancer. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M( Q1, Q3). Count data were described as absoluter numbers and/or percentages. Results:(1) Clinical characteristics of patients with mid and low rectal cancer. Of the 1 074 patients, there were 713 males and 361 females, aged 63(56,70)years. The body mass index of 1 074 patients was 24(21,26)kg/m 2.For American Society of Anesthesiologists classification, there were 147 cases of stage Ⅰ, 641 cases of stage Ⅱ, 157 cases of stage Ⅲ, 2 cases of stage Ⅳ, and there were 127 cases missing data. (2) Initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer. Of the 1 074 patients, there were 787 cases (73.28%) undergoing complete colonoscopy, and there were only 197 cases (18.34%) undergoing immunohistochemical evaluation of all four mismatch repair proteins. (3) Initial imaging evaluation of patients with mid and low rectal cancer. Of the 1 074 patients, there were 842(78.40%) patients completing magnetic resonance imaging (MRI) or ultrasound evaluation, and there were 914(85.10%) patients completing chest, abdomen, and pelvis enhanced computed tomography (CT) evaluation. In the 149 patients completing rectal ultrasound evaluation, there were 122 cases (81.88%) comple-ting T staging evaluation, and there were 81 cases (54.36%) completing N staging evaluation. In the 808 patients completing rectal MRI evaluation, there were 708 cases (87.62%) completing T staging evaluation, and there were 590 cases (73.02%) completing N staging evaluation. (4) Imaging evalua-tion after neoadjuvant therapy for patients with mid and low rectal cancer. Of the 388 patients with neoadjuvant therapy, there were 332 patients (85.57%) completing MRI or ultrasound evaluation, and there were 327 patients (84.28%) completing chest, abdomen, and pelvis enhanced CT evalua-tion. In the 70 patients completing rectal ultrasound evaluation, there were 65 cases (92.86%) com-pleting T staging evaluation, and there were 49 cases (70.00%) completing N staging evaluation. In the 327 patients completing rectal MRI evaluation, there were 246 cases (75.23%) completing T staging, and there were 228 cases (69.72%) completing N staging evaluation. Conclusion:The com-pletion rate of tumor imaging evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients on a national scale is relatively good.

Aim To investigate the involvement of mesencephalic astrocyte-derived neurotrophic factor(MANF)in rifampicin(RFP)-induced cholestatic liv-er injury.Methods We investigated the impact of MANF gene deletion on rifampicin-induced BAT ex-pression in mice by the MANF gene knockout mouse model.We investigated the influence of MANF knock-down on nuclear factor erythroid 2-related factor 2(Nrf2)in HepG2 cells by the MANF knockdown cell model.Results Compared with the wild-type(WT)mice,the mRNA and protein expression levels of bile salt export pump(BSEP)and multidrug-resistant asso-ciated protein4(MRP4)significantly increased in WT mice treated with RFP.However,compared to the WT mice treated with RFP,the mRNA and protein expres-sion levels of BSEP,Multidrug resistance protein 1(MDR1),multidrug resistance associated proteins 2/3/4(MRP2/3/4),and organic solute transport pro-teins α(OST α)were significantly reduced.In cell experiments we found that MANF knockdown weakened the expression of Nrf2 and its nuclear translocation by RFP.Conclusion MANF may regulate adaptive BAT expression by modulating Nrf2 expression,thereby pla-ying a protective role in RFP-induced liver injury.