Abstract Traditional Chinese medicine (TCM) auscultation has a long history, and with advancements in equipment and analytical methods, the quantitative analysis of auscultation parameters has determined. However, the complexity and diversity of auscultation, along with variations in devices, analytical methods, and applications, bring challenges to its standardization and deeper application. This review presents the advancements in auscultation equipment and systems, auscultation characteristic parameters, and their application in the diagnosis of pulmonary diseases and syndromes over the past 10 years, while also exploring the progress and challenges of current digital research of auscultation. This review also proposes the establishment of standardized protocols for the collection and analysis of auscultation data, the incorporation of advanced artificial intelligence (AI) auscultation analysis methods, and an exploration of the diagnostic utility of auscultatory features in pulmonary diseases and syndromes, so as to provide more precise decision support for intelligent diagnosis of pulmonary diseases and syndromes

Objective: To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features, providing novel insights into the early screening of lung cancer. Methods: This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1, 2019 to December 31, 2024, as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period. The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument, and the facial diagnosis features were extracted from it by deep learning technology. Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics, and the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables. Based on the screened feature variables, four machine learning methods: random forest, logistic regression, support vector machine (SVM), and gradient boosting decision tree (GBDT) were used to establish lung cancer classification models independently. Meanwhile, the model performance was evaluated by indicators such as sensitivity, specificity, F1 score, precision, accuracy, the area under the receiver operating characteristic (ROC) curve (AUC), and the area under the precision-recall curve (AP). Results: A total of 1 275 patients with pulmonary nodules and 1 623 patients with lung cancer were included in this study. After propensity score matching (PSM) to adjust for gender and age, 535 patients were finally included in the pulmonary nodule group and the lung cancer group, respectively. There were significant differences in multiple color space metrics (such as R, G, B, V, L, a, b, Cr, H, Y, and Cb) and texture metrics [such as gray-levcl co-occurrence matrix (GLCM)-contrast (CON) and GLCM-inverse different moment (IDM)] between the two groups of individuals with pulmonary nodules and lung cancer (P < 0.05). To construct a classification model, LASSO regression was used to select 63 key features from the initial 136 facial features. Based on this feature set, the SVM model demonstrated the best performance after 10-fold stratified cross-validation. The model achieved an average AUC of 0.8729 and average accuracy of 0.799 0 on the internal test set. Further validation on an independent test set confirmed the model’s robust performance (AUC = 0.823 3, accuracy = 0.729 0), indicating its good generalization ability. Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components (including color-B-0, wholecolor-Cr, and lipcolor-Cr) were the core factors in the model’s classification decisions, while texture indicators [GLCM-angular second moment (ASM)_2, GLCM-IDM_1, GLCM-CON_1, GLCM-entropy (ENT)_2] played an important auxiliary role. Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas. The various models constructed based on facial image features all demonstrate good performance, indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction, providing a non-invasive and feasible new approach for early lung cancer screening.

Objective To investigate the types of latent profiles and influencing factors of knowledge,attitudes and practice(KAP)in curriculum-based ideological and political education in undergraduate nursing students and to provide evidence for design of ideological and political courses tailored to different types of nursing undergraduates.Methods A convenience sampling method was used to recruit 671 undergraduate nursing students from our school between March and July 2024.Data were collected using a general data survey form,a KAP questionnaire on nursing curriculum-based ideological and political education,a professional identity survey for nursing undergraduates,and the Jefferson empathy scale nursing student edition(JSPE-NS).Latent profile analysis(LPA)was conducted using 22 items of the KAP questionnaire as observed variables,and a logistic regression analysis was used to assess the influencing factors of different KAP latent profiles in ideological and political education.Results A total of 663 nursing undergraduate students completed the survey.They spread in three latent groups:a mediocre group(172/663,25.9%),a competent group(378/663,57.0%),and an exceptional group(113/663,17.1%).From a single-child family,grade point average(GPA),professional identity and empathy were identified as the factors that influenced KAP latent profiles in curriculum based ideological and political education among the undergraduate nursing students(all P<0.05).Conclusion There exists a heterogeneity in KAP in curriculum based ideological and political education among the undergraduate nursing students.Nursing educators should develop targeted enhancement measures according to the characteristics of latent profile groups therefore to facilitate the comprehensive development of the undergraduate nursing students.

Objective To investigate the types of latent profiles and influencing factors of knowledge,attitudes and practice(KAP)in curriculum-based ideological and political education in undergraduate nursing students and to provide evidence for design of ideological and political courses tailored to different types of nursing undergraduates.Methods A convenience sampling method was used to recruit 671 undergraduate nursing students from our school between March and July 2024.Data were collected using a general data survey form,a KAP questionnaire on nursing curriculum-based ideological and political education,a professional identity survey for nursing undergraduates,and the Jefferson empathy scale nursing student edition(JSPE-NS).Latent profile analysis(LPA)was conducted using 22 items of the KAP questionnaire as observed variables,and a logistic regression analysis was used to assess the influencing factors of different KAP latent profiles in ideological and political education.Results A total of 663 nursing undergraduate students completed the survey.They spread in three latent groups:a mediocre group(172/663,25.9%),a competent group(378/663,57.0%),and an exceptional group(113/663,17.1%).From a single-child family,grade point average(GPA),professional identity and empathy were identified as the factors that influenced KAP latent profiles in curriculum based ideological and political education among the undergraduate nursing students(all P<0.05).Conclusion There exists a heterogeneity in KAP in curriculum based ideological and political education among the undergraduate nursing students.Nursing educators should develop targeted enhancement measures according to the characteristics of latent profile groups therefore to facilitate the comprehensive development of the undergraduate nursing students.

Objective:To investigate the clinical manifestation, therapy, and prognosis of Susac syndrome and enhance the understanding of this disease.Methods:A case summary was made.The clinical data of two siblings with Susac syndrome treated at Children′s Medical Center, Peking University First Hospital in January 2024 were summarized.Reported cases of pediatric Susac syndrome were reviewed.Results:The onset of the disease in the two siblings was at the age of 3.00 and 6.75 years, with recurrent headaches, tinnitus, hearing loss and encephalopathy symptoms.Cranial magnetic resonance imaging showed multiple cerebral microbleeding and microinfarction lesions, " snowball like" in the corpus callosum and diffuse white matter edema in the brain.Audiometry revealed sensorineural hearing loss.In one case, ophthalmic fluorescein angiography revealed ischemic changes due to branch retinal artery occlusions.No pathogenic variants were detected in gene testing.This child was diagnosed with Susac syndrome, and the symptoms were improved after treatment with Corticosteroids and Rituximab.No relapse was observed during the 9-month follow-up.A total of 20 pediatric cases of Susac syndrome were retrieved, including 18 reported previously and 2 cases from this study.There were 2 boys and 18 girls, with the age of onset ranging from 2.5 to 17.0 years.The common initial symptoms included headache (19 cases), vertigo and tinnitus or hearing loss (9 cases), and vision impairment or visual field defect (4 cases). The symptoms were improved after immunotherapy.Conclusions:With a low incidence, Susac syndrome is rare in children and difficult to diagnose.There may be a genetic predisposition in such disease.Early diagnosis and immunotherapy can low the relapse and improve the prognosis.

Objective To conduct a scoping review of studies related to latent class trajectory analysis of glucose metabolism indicators in diabetes mellitus patients,so as to clarify the method of latent category trajectory analysis,evaluation indicators and the changing trajectories of different glucose metabolism indicators in diabetes mellitus patients,and to establish foundation for diabetes management and research.Methods Based on Arksey's framework for scoping study,a literature search was performed in CNKI,Wanfang,CBM,Web of Science,PubMed,Embase,Cochrane Library,CINAHL database inception to January 28,2024.Totally 2 researchers independently screened the literature and extracted data.Results A total of 22 pieces of the literature were included.Latent class growth model was the most commonly used model in latent class trajectory analysis,and 4 softwares are available for implementation.The current evaluation indicators for trajectory results are various,with the Bayesian information criterion and average posterior probability as the most frequently utilized.Hemoglobin is the most commonly used glucose metabolism indicators for trajectory analysis,and the number of identified trajectories ranged from 2 to 5.Most studies showed that diabetic patients exhibited a"low-stable"pattern in glucose metabolism indicators.Conclusion Latent class trajectory analysis represents a novel longitudinal data analysis approach that can identify potential subgroups of diabetic patients with similar development trajectory.The models and evaluation indicators employed are diverse,and the stability test of the models can be enhanced in the future.Through trajectory identification,it is recommended that clinical staff pay greater attention to the longitudinal trend of the glucose metabolism indicators and incorporate it into the long-term blood glucose management goal of diabetic patients.

Objective To conduct a scoping review of studies related to latent class trajectory analysis of glucose metabolism indicators in diabetes mellitus patients,so as to clarify the method of latent category trajectory analysis,evaluation indicators and the changing trajectories of different glucose metabolism indicators in diabetes mellitus patients,and to establish foundation for diabetes management and research.Methods Based on Arksey's framework for scoping study,a literature search was performed in CNKI,Wanfang,CBM,Web of Science,PubMed,Embase,Cochrane Library,CINAHL database inception to January 28,2024.Totally 2 researchers independently screened the literature and extracted data.Results A total of 22 pieces of the literature were included.Latent class growth model was the most commonly used model in latent class trajectory analysis,and 4 softwares are available for implementation.The current evaluation indicators for trajectory results are various,with the Bayesian information criterion and average posterior probability as the most frequently utilized.Hemoglobin is the most commonly used glucose metabolism indicators for trajectory analysis,and the number of identified trajectories ranged from 2 to 5.Most studies showed that diabetic patients exhibited a"low-stable"pattern in glucose metabolism indicators.Conclusion Latent class trajectory analysis represents a novel longitudinal data analysis approach that can identify potential subgroups of diabetic patients with similar development trajectory.The models and evaluation indicators employed are diverse,and the stability test of the models can be enhanced in the future.Through trajectory identification,it is recommended that clinical staff pay greater attention to the longitudinal trend of the glucose metabolism indicators and incorporate it into the long-term blood glucose management goal of diabetic patients.

Objective:To investigate the clinical manifestation, therapy, and prognosis of Susac syndrome and enhance the understanding of this disease.Methods:A case summary was made.The clinical data of two siblings with Susac syndrome treated at Children′s Medical Center, Peking University First Hospital in January 2024 were summarized.Reported cases of pediatric Susac syndrome were reviewed.Results:The onset of the disease in the two siblings was at the age of 3.00 and 6.75 years, with recurrent headaches, tinnitus, hearing loss and encephalopathy symptoms.Cranial magnetic resonance imaging showed multiple cerebral microbleeding and microinfarction lesions, " snowball like" in the corpus callosum and diffuse white matter edema in the brain.Audiometry revealed sensorineural hearing loss.In one case, ophthalmic fluorescein angiography revealed ischemic changes due to branch retinal artery occlusions.No pathogenic variants were detected in gene testing.This child was diagnosed with Susac syndrome, and the symptoms were improved after treatment with Corticosteroids and Rituximab.No relapse was observed during the 9-month follow-up.A total of 20 pediatric cases of Susac syndrome were retrieved, including 18 reported previously and 2 cases from this study.There were 2 boys and 18 girls, with the age of onset ranging from 2.5 to 17.0 years.The common initial symptoms included headache (19 cases), vertigo and tinnitus or hearing loss (9 cases), and vision impairment or visual field defect (4 cases). The symptoms were improved after immunotherapy.Conclusions:With a low incidence, Susac syndrome is rare in children and difficult to diagnose.There may be a genetic predisposition in such disease.Early diagnosis and immunotherapy can low the relapse and improve the prognosis.

Objective:To compare the quality of sleep and rehabilitation after routine surgery versus day surgery in pediatric patients with snoring.Methods:This was a prospective study.Seventy pediatric patients with snoring, aged 4-6 yr, of American Society of Anesthesiologists Physical Status classification Ⅰ or Ⅱ, undergoing adenoidectomy and/or tonsillectomy in the Guangzhou Women and Children′s Medical Center from March to June 2023, were divided into routine surgery group (group R) and day surgery group (group D), with 35 cases in each group. A sleep-detecting bracelet was used to monitor the sleep status. The proportion of deep sleep, light sleep and rapid eye movement, sleep continuity score and the maximum and minimum heart rate were recorded on the night before surgery, the first and second night after surgery. Rehabilitation was assessed using Quality of Recovery-15 at 1 day before surgery and 24 and 48 h and 28 days after surgery.Results:Compared with group R, the maximum heart rate at the night before surgery was significantly decreased, the proportion of light sleep on the second night after surgery was increased, the proportion of rapid eye movement sleep on the second night after surgery was decreased, the scores for items on " getting support from hospital doctors and nurses", " feeling comfortable and in control", and " having a feeling of general well-being" and the total score of Quality of Recovery-15 were significantly decreased at 24 and 48 h after operation, and the score for items on " feeling rested" and " have had a good sleep" at 48 h after surgery was decreased in group D ( P<0.05). Conclusions:Pediatric patients with snoring have poor quality of early sleep and rehabilitation after day surgery compared with routine surgery.

Objective To comprehensively characterize the diterpene alkaloids in Radix Aconiti Kusnezoffii.Methods The diterpene alkaloids were isolated and purified by strong acid cation exchange resin solid phase extraction column(SCX-SPE),and identified by ultra high performance liquid chromatography-Q-Exactive Orbitrap mass spectrometry(UHPLC-Q-Exactive Orbitrap MS).Results A total of 99 diterpene alkaloids were identified from Radix Aconiti Kusnezoffii,including 27 diester diterpene alkaloids(DDA),29 monoester diterpene alkaloids(MDA),40 amide diterpene alkaloids(ADA),2 polyester diterpene alkaloids(PDA)and 1 long-chain ester diterpene alkaloid(LDA).Conclusion The SCX-SPE combined with UHPLC-Q-Exactive Orbitrap MS method,established in this paper,can rapidly identify a large number of diterpene alkaloids in Radix Aconiti Kusnezoffii,which provides scientific proof for the study of pharmacodynamic substance basis and quality control of Radix Aconiti Kusnezoffii.