ObjectiveTo construct and validate a clinical prediction model for Qi deficiency and blood stasis syndrome in chronic heart failure （CHF），aiming to assist clinical diagnosis and provide tools and methods for individualized treatment of CHF. MethodsThe clinical data of patients with chronic heart failure treated at Dongzhimen Hospital of Beijing University of Chinese Medicine from January 2022 to January 2024 were retrospectively collected. The patients were randomly divided into a training group and a validation group with a ratio of 7∶3. First， the least absolute shrinkage and selection operator （LASSO） regression analysis was used to preliminarily screen the predictive factors affecting the diagnosis of Qi deficiency and blood stasis syndrome in CHF. Subsequently， the Logistic regression method was applied to conduct a more in-depth and detailed analysis of these factors. Variables with P<0.05 in the results of the multi-factor Logistic regression were carefully selected and included. Based on the regression coefficients obtained from this analysis， a model was constructed， and a nomogram was accurately drawn. Using R software，the receiver operating characteristic （ROC） curve，calibration curve，and decision curve analysis （DCA） were precisely drawn. These analyses were used to comprehensively evaluate the model from three crucial aspects： discrimination，calibration，and clinical applicability. Additionally， the accuracy，specificity，sensitivity，positive predictive value，and negative predictive value of the model were meticulously calculated to conduct a more all-round and comprehensive assessment. ResultsIn total， 168 cases were successfully obtained in the training group， and 71 cases were included in the validation group. After a thorough comparison， it was found that there were no statistically significant differences in the baseline data between the two groups. After being rigorously screened by the LASSO-multivariate logistic regression method， dark red tongue，smoking history，cardiac troponin I，and N-terminal pro-B-type natriuretic peptide （NT-ProBNP） were identified as the influencing factors for diagnosing patients with the Qi deficiency and blood stasis syndrome in CHF. The constructed model demonstrated an area under the curve （AUC） of 0.812 in the training group and 0.719 in the validation group. The calibration curve showed that the predicted curve of the model was close to the actual observed curve. DCA indicated that the model could provide substantial clinical benefits for patients at the decision thresholds ranging from 0.2 to 0.9. ConclusionThe clinical prediction model for Qi deficiency and blood stasis syndrome in chronic heart failure constructed in this study shows good performance. It has certain application value in clinical practice， which may contribute to the improvement of the diagnosis and treatment of CHF patients with this syndrome.

Objective:To develop an artificial intelligence system using fully convolutional neural networks（FCN）to assist echocardiographers in the quantitative assessment of mitral regurgitation（MR）severity.Methods:From August 2021 to June 2024，echocardiographic images of 441 patients with MR were prospectively collected from Renmin Hospital of Wuhan University and the Central Hospital of Wuhan. After screening，a total of 269 patients（4 917 frames）were included in the study. Of these，3 644 frames（128 patients）of apical four-chamber color Doppler MR flow convergence images from Renmin Hospital of Wuhan University were selected as the training/validation set，while images from 121 patients（813 frames）were used as the internal test set. Additionally，images from 20 patients（460 frames）from the Central Hospital of Wuhan were selected as the external test set. The FCN algorithm was employed to capture features and segment the MR color region on the left atrial side，simultaneously outputting the regurgitant radius（r）for the calculation of the effective regurgitant orifice area and regurgitant volume. The severity of MR was then classified according to the 2017 guidelines of the American Society of Echocardiography. The segmentation and classification performance of the model was evaluated，and the measurement results of the AI system was compared with that of both senior and junior physicians.Results:In the internal test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.48，0.81，0.86，and 0.87，respectively. In the external test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.60，0.77，0.64，and 0.77，respectively. The average accuracy of MR classification in the internal and external test sets was 0.91 and 0.88，respectively.Conclusions:The FCN model is capable of segmenting the left atrial side regurgitant areas in apical four-chamber heart color Doppler images，aiding physicians in obtaining quantitative assessment parameters for MR，and assisting junior physicians in accurately assessing the severity of MR.

Objective This study investigated feasible cases and their significance in promoting the"generalized"development of inspection through digital-intelligent equipment.Methods A qualitative research approach was used,involving interviews conducted between February 2025 and March 2025 with experts in traditional Chinese medicine diagnostics,clinical research methodology,medical engineering integration,and related disciplines,using both online and offline methods.In accordance with the Consolidated Criteria for Reporting Qualitative Research,feasible cases involving the specific application of digital equipment in various parts of observation were collected through item enrichment.The significance of extending observation capabilities via these cases was analyzed,along with the overall implications of integrating digital technologies with traditional inspection method.Results Interviews were completed with 11 experts from domestic universities and research institutes in the fields of traditional Chinese medicine diagnosis,medical engineering integration,and related disciplines.A total of 78 feasible cases of digital-intelligent inspection were identified,along with 69 insights regarding the significance of enhancing the inspection capabilities.These insights were synthesized into two dimensions and 23 holistic meanings.The first dimension is to expand the scope of inspection,including obtaining internal environmental characteristics,observing external environmental characteristics,expanding thermodynamic characteristic data,and crossing time and space.The second dimension is to improve the quality of observation and diagnosis information collection and analysis,including 19 specific meanings,such as standardized collection environment,objective quantification,and refined observation.Conclusion Digital-intelligent equipment plays a significant role in expanding the scope of inspection content and achieving high-quality acquisition and analysis of extensive inspection information.These advancements extend and enrich the capabilities of traditional inspection method in traditional Chinese medicine.

Objective This study investigated feasible cases and their significance in promoting the"generalized"development of inspection through digital-intelligent equipment.Methods A qualitative research approach was used,involving interviews conducted between February 2025 and March 2025 with experts in traditional Chinese medicine diagnostics,clinical research methodology,medical engineering integration,and related disciplines,using both online and offline methods.In accordance with the Consolidated Criteria for Reporting Qualitative Research,feasible cases involving the specific application of digital equipment in various parts of observation were collected through item enrichment.The significance of extending observation capabilities via these cases was analyzed,along with the overall implications of integrating digital technologies with traditional inspection method.Results Interviews were completed with 11 experts from domestic universities and research institutes in the fields of traditional Chinese medicine diagnosis,medical engineering integration,and related disciplines.A total of 78 feasible cases of digital-intelligent inspection were identified,along with 69 insights regarding the significance of enhancing the inspection capabilities.These insights were synthesized into two dimensions and 23 holistic meanings.The first dimension is to expand the scope of inspection,including obtaining internal environmental characteristics,observing external environmental characteristics,expanding thermodynamic characteristic data,and crossing time and space.The second dimension is to improve the quality of observation and diagnosis information collection and analysis,including 19 specific meanings,such as standardized collection environment,objective quantification,and refined observation.Conclusion Digital-intelligent equipment plays a significant role in expanding the scope of inspection content and achieving high-quality acquisition and analysis of extensive inspection information.These advancements extend and enrich the capabilities of traditional inspection method in traditional Chinese medicine.

Objective:To develop an artificial intelligence system using fully convolutional neural networks（FCN）to assist echocardiographers in the quantitative assessment of mitral regurgitation（MR）severity.Methods:From August 2021 to June 2024，echocardiographic images of 441 patients with MR were prospectively collected from Renmin Hospital of Wuhan University and the Central Hospital of Wuhan. After screening，a total of 269 patients（4 917 frames）were included in the study. Of these，3 644 frames（128 patients）of apical four-chamber color Doppler MR flow convergence images from Renmin Hospital of Wuhan University were selected as the training/validation set，while images from 121 patients（813 frames）were used as the internal test set. Additionally，images from 20 patients（460 frames）from the Central Hospital of Wuhan were selected as the external test set. The FCN algorithm was employed to capture features and segment the MR color region on the left atrial side，simultaneously outputting the regurgitant radius（r）for the calculation of the effective regurgitant orifice area and regurgitant volume. The severity of MR was then classified according to the 2017 guidelines of the American Society of Echocardiography. The segmentation and classification performance of the model was evaluated，and the measurement results of the AI system was compared with that of both senior and junior physicians.Results:In the internal test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.48，0.81，0.86，and 0.87，respectively. In the external test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.60，0.77，0.64，and 0.77，respectively. The average accuracy of MR classification in the internal and external test sets was 0.91 and 0.88，respectively.Conclusions:The FCN model is capable of segmenting the left atrial side regurgitant areas in apical four-chamber heart color Doppler images，aiding physicians in obtaining quantitative assessment parameters for MR，and assisting junior physicians in accurately assessing the severity of MR.

China has classified the Corona Virus Disease 2019(COVID-19) as a statutory category B infectious disease and managed it according to Category B since January 8, 2023.In view that Omicron variant is currently the main epidemic strain in China, in order to guide the treatment of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) infection in children with the times, refer to the Diagnosis and Treatment Protocol for Novel Coronavirus Infection (Trial 10 th Edition), Expert Consensus on Diagnosis, Treatment and Prevention of Novel Coronavirus Infection in Children (Fourth Edition) and the Diagnosis and Treatment Strategy for Pediatric Related Viral Infections.The Expert Consensus on the Diagnosis, Treatment and Prevention of Novel Coronavirus Infection in Children (Fifth Edition) has been formulated and updated accordingly on related etiology, epidemiology, pathogenic mechanism, clinical manifestations, auxiliary examination, diagnosis and treatment, and added key points for the treatment of COVID-19 related encephalopathy, fulminating myocarditis and other serious complications for clinical reference.

Monkeypox is a zoonotic disease.Previous studies have shown that children are vulnerable to monkeypox and are also at high risk for severe disease or complications.In order to improve pediatricians′ understanding of monkeypox and achieve early detection, early diagnosis, early treatment and early disposal, the committee composed of more than 40 experts in the related fields of infectious diseases, pediatrics, infection control and public health formulate this expert consensus, on the basis of the latest clinical management and infection prevention and control for monkeypox released by the World Health Organization (WHO), the guidelines for diagnosis and treatment of monkeypox (version 2022) issued by National Health Commission of the People′s Republic of China and other relevant documents.During the development of this consensus, multidisciplinary experts have repeatedly demonstrated the etiology, epidemiology, transmission, clinical manifestations, laboratory examinations, diagnosis and differential diagnosis, treatment, discharge criteria, prevention, case management process and key points of prevention and control about monkeypox.

Since December 2019, severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) infections have raged globally for more than 2 years.China has always adopted scientific and effective prevention and control measures to achieved some success.However, with the continuous variation of SARS-CoV-2 cases and imported cases from abroad, the prevention and control work has become more difficult and complex.With the variation of the mutant strain, the number of cases in children changed, and some new special symptoms and complications were found, which proposed a new topic for the prevention and treatment of SARS-CoV-2 infection in children in China.Based on the third edition, the present consensus according to the characteristics of the new strain, expounded the etiology, pathology, pathogenesis, and according to the clinical characteristics and experience of children′s cases, and puts forward recommendations on the diagnostic criteria, laboratory examination, treatment, prevention and control of children′s cases for providing reference for further guidance of effective prevention and treatment of SARS-CoV-2 infection in children in China.

Hypoxemia is a common complication of pneumonia, asthma, and bronchopulmonary dysplasia in children.Rapid identification of hypoxemia is of great significance for the disposal and management of critical children.Pulse oximetry is recognized by the World Health Organization as the best way to monitor hypoxemia in children, and it can monitor pulse oxygen saturation noninvasively and continuously.Based on the related literature at home and abroad, combined with the clinical needs of pediatrics, the " Expert consensus on clinical application of pulse oximetry in children" is formulated to improve the understanding of pediatricians and nurses on the application in pediatric clinical practice, principle, operation techniques, and limitations of pulse oximetry.

Objective:To investigate the knowledge, attitudes, and practices (KAP) of pulse oximetry among pediatric healthcare providers in China and analyze the factor influencing the KAP.Methods:A self-developed questionnaire was used for an online research on the KAP of 11 849 pediatric healthcare providers from 31 provinces, autonomous regions, and municipalities of China from March 11 to 14, 2022.The factors influencing the KAP of pulse oximetry among pediatric healthcare providers were examined by Logistic regression. Results:The scores of KAP, of pulse oximetry were 5.57±0.96, 11.24±1.25 and 11.19±4.54, respectively.The corresponding scoring rates were 69.61%, 74.95%, and 55.99%, respectively. Logistic regression results showed that the gender and working years of pediatric healthcare providers, the region they were located, and whether their medical institution was equipped with pulse oximeters were the main factors affecting the knowledge score (all P<0.05). Main factors influencing the attitude score of pediatric healthcare providers included their knowledge score, gender, educational background, working years, region, medical institution level, and whether the medical institution was equipped with pulse oximeters (all P<0.05). For the practice score, the main influencing factors were the knowledge score, gender, age, and whether the medi-cal institution was equipped with pulse oximeters (all P<0.05). Conclusions:Chinese pediatric healthcare providers need to further improve their knowledge about and attitudes towards pulse oximetry.Pulse oximeters are evidently under-used.It is urgent to formulate policies or guidelines, strengthen education and training, improve knowledge and attitudes, equip more institutions with pulse oximeters, and popularize their application in medical institutions.