Traditional Chinese medicine （TCM） diagnostics is a discipline that studies the basic theories and fundamental skills of diagnostic methods， disease diagnosis， and differentiation in accordance with the theories of TCM. The artificial intelligence （AI） technology has gained remarkable achievements in the intelligentization of the four diagnostic methods in TCM and the standardization of differentiation and diagnosis. However， it still faces many challenges. The standardization of clinical data collection is difficult， and the data quality is uneven， which affects the usability of the data. The integration of the four diagnostic information is insufficient. Most instruments can only collect data from a single diagnostic method， lacking overall integrity. The scientific nature of the diagnostic model needs to be improved. The existing models lack dynamics and the reasoning logic of TCM differentiation. The accuracy of intelligent methods needs to be improved， and the existing evaluation indicators cannot fully reflect the practical application effect of the model. Furthermore， the relevant laws and regulations are still not perfect， and data security and patient privacy lack guarantees. The cultivation of compound talents is insufficient， and there is a lack of interdisciplinary talents who are proficient in both TCM and AI. On this basis， this paper expounded on the current development status， difficulties， and bottlenecks of AI in TCM diagnosis and then explored the development trend of AI in the field of TCM diagnosis. It proposed solutions such as optimizing the data collection process， constructing multimodal diagnostic models， facilitating multi-disciplinary exchanges and cooperation， improving laws and regulations， and cultivating compound talents. It is hoped that modern， standardized， normalized， and intelligent TCM diagnosis can be further promoted， thereby providing new impetus and methods for the inheritance and innovation of TCM.

Objective: To develop a RHCE genotyping assay based on kompetitive allele-specific PCR (KASP) and assess its clinical accuracy for RhCE blood group determination. Methods: KASP primers were designed to interrogate three RHCE loci: the 109 bp insertion/deletion in intron 2, c. 307T>C, and c. 676C>G. A total of 1 194 RhD-positive inpatients from Chengdu were typed by both KASP genotyping and manual tube serology. Discordant samples (n=10) were retested by both methods and further resolved by Sanger sequencing. An additional 377 cases were tested for the c. 48C>G locus to evaluate the predictive accuracy of individual loci and combined locus testing for RhC antigen. Results: Genotyping concordance with serology was 100.0% for both the c. 676C>G locus (RhE/Rhe) and the c. 307T>C locus (Rhc). For RhC prediction using the 109 bp insertion, overall accuracy was 99.7% (1 191/1 194); the 3 discordant cases were confirmed by Sanger sequencing to be false negatives attributable to 109 bp deletion in intron 2. Testing the c. 48C>G allele for RhC prediction yielded 7 false positives, with an accuracy of 98.1% (370/377). RhC antigen status was determined by combining the 109 bp insertion and the c. 48C allele. After excluding 10 samples with inconsistent results between the two loci, the accuracy reached 100% in the remaining 367 samples. When both loci were applied in combination, accuracy reached 100% in the 367 cases with concordant results. Among the 1 194 patients, CCee (45.8%) and CcEe (31.7%) were the most common RhCE phenotypes. The e antigen had the highest positivity rate (92.2%), and the Ce haplotype was the most frequent (66.9%). Conclusion: The KASP-based RHCE genotyping method achieves high accuracy for clinical RhCE typing. Combining the 109 bp insertion/deletion with the c. 48C allele significantly improves RhC antigen prediction compared with either locus alone. This method was applied to RhCE genotyping of 1 194 RhD-positive inpatients in Chengdu, providing local RhCE phenotype and haplotype distribution data to support RhCE-matched transfusion practice.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the development context and evolution mechanism of family doctor contract service mode in rural areas,and provide reference for relevant departments to continuously optimize the family doctor contract service policy.Methods:Based on the principle of theoretical sampling,J City in S Province,which is listed as one of the national primary health comprehensive pilot areas,was selected as the case study object,and the data were collected through interviews and literature review,and then grounded and coded with NVivo11 software.Results:74 initial concepts,24 sub categories and 9 main categories were sorted out,and the core categories were formed through selective coding.The signing service of family doctors in rural areas has gone through three stages:individual signing of rural doctors,team signing and team signing under the county medical community.Its evolution conditions have gone through the formalization of signing,service capability and efficiency synergy in turn.The three elements of the system adaptation action successively presents differential execution under the system de embedding,collaborative block under the structure embedding and collaborative execution under the system coupling,so as to gradually realize the system coupling from responsibility construction to ability improvement,and then to responsibility,ability and interest.Conclusion:the evolution mechanism of the contracted service mode of family doctors in rural areas reflects the dynamic adaptation under the conditions of political situation,social situation and policy situation,and gradually realizes the closed-loop of the three elements of responsibility,ability and interest based on the differentiation of the three elements of the system.

Objective:To present a set of clinically representative quality assurance (QA) test cases for stereotactic radiosurgery (SRT) plans of multiple intracranial metastases, in order to assess the plan quality and machine execution capabilities.Methods:Based on the clinical characteristics of multiple brain metastases, four groups of test cases with three target volumes (TVs), six TVs, nine TVs, and TVs near organs at risk (OARs) were designed. For these cases, SRT plans were developed, and plan quality was assessed using metrics including the Radiation Therapy Oncology Group conformality index (RTOG CI), gradient index (GI), homogeneity index (HI), and the volume of normal brain tissue receiving a dose of 24 Gy ( V24 Gy), which was defined as the volume enclosed by the 24 Gy isodose line around the Brain-PTV ( V24 Gy of Brain-PTV). Verification plans were generated for each test case, including the verification of point doses, planar doses (PD), and SRS MapCHECK (SMC) semiconductor matrix planar doses. Compared with the calculated result of the treatment planning system (TPS), the criteria for the γ analysis of planar doses were set at 1 mm/2% and 2 mm/2%. Results:For the four groups of test cases, the mean CI, GI, HI, and V24 Gy of Brain-PTV were 1.04±0.03, 3.79±0.40, 0.73±0.01 and (7.46±3.80) cm 3, respectively. The mean deviations of the point doses were 0.88%±0.98%, 1.47%±0.79%, 1.52%± 0.76%, and 1.17% ± 0.38%, respectively. The mean γ passing rates of the single fields for PDs were greater than 98% at 2 mm/2% and exceeding 96% at 1 mm/2%, and the mean γ pass rates of the SMC semiconductor matrix for PDs were 97.75% ± 2.31% and 99.33% ± 0.62%, at 1 mm/2% and 2 mm/2% respectively. Conclusions:The proposed QA test cases for SRT of multiple intracranial metastases allow for the effective assessments of the plan quality and machine execution capabilities and, thus, can assist various centers in clinical applications.

Objective: To investigate the protective effects and underlying mechanisms of sodium pyruvate (SP) on RBC storage lesions using an oxidative damage model. Methods: Six units of leukocyte-depleted suspended RBCs (discarded for non-infectious reasons within three days post-collection) were randomly assigned to four groups: negative control (NS), positive control (PS), experimental group 1 (SP1), and experimental group 2 (SP2). Oxidative stress was induced in the PS group by the addition of hydrogen peroxide (H O ), while SP1 and SP2 received SP supplementation at different concentrations (25 mM and 50 mM, respectively) in the presence of H O . After 1 hour of incubation, RBC morphology was assessed microscopically, and biochemical indicators including glutathione (GSH), malondialdehyde (MDA), methemoglobin (MetHb), adenosine triphosphate (ATP), and Na /K -ATPase activity were measured. Results: RBCs in the PS group exhibited pronounced morphological damage, including cell shrinkage and echinocyte formation, whereas both SP-treated groups showed significantly reduced structural injury. SP treatment led to elevated GSH levels and decreased concentrations of MDA and MetHb, suggesting attenuation of oxidative stress. Additionally, SP enhanced intracellular ATP levels and Na /K -ATPase activity, thereby contributing to membrane stability. Notably, the SP2 group (50 mM) demonstrated superior protective effects compared to SP1 (25 mM). Conclusion: Sodium pyruvate effectively attenuates oxidative storage lesions in RBCs, primarily through its antioxidant properties, energy metabolism supporting ability, and celluar membrane stabilizing function. These findings suggest SP as a promising additive for enhancing the quality and safety of stored RBCs.

Objective:To investigate the nutritional risk status of children in PICU and analyze its influencing factors.Methods:From July 2021 to February 2023,all children aged 1 to 18 years admitted to PICU of Beijing Children's Hospital were investigated by using the pediatric Yorkhill Malnutrition Scoring tool(PYMS)and the clinical data questionnaire.Results:A total of 492 children in PICU were enrolled.The first nutritional risk screening results showed that there were 32 cases of no/low nutritional risk(6.5%),76 cases of medium risk(15.4%),and 384 cases of high risk(78.1%).The incidence of medium/high nutritional risk was as high as 93.5%.The PYMS score of nutritional risk in PICU was(2.61±1.42).The results of multiple linear regression analysis showed that weight,fever time before admission,white blood cells,body mass index,primary diagnosis,father's education,and diet before illness were the main influencing factors of nutritional risk of children in PICU(P<0.05).Conclusion:Children in PICU are in a state of high nutritional risk.It is suggested that children in PICU should carry out nutritional screening in a standardized manner,identify children with high nutritional risk and its influencing factors early.To actively conduct nutritional assessment and nutritional intervention could improve the clinical outcome of children in PICU.

Metachromatic leukodystrophy (MLD) is an inherited disease caused by a deficiency of the enzyme arylsulfatase A (ARSA). Lentivirus-modified autologous hematopoietic stem cell gene therapy (HSCGT) has recently been approved for clinical use in pre and early symptomatic children with MLD to increase ARSA activity. Unfortunately, this advanced therapy is not available for most patients with MLD who have progressed to more advanced symptomatic stages at diagnosis. Patients with late-onset juvenile MLD typically present with a slower neurological progression of symptoms and represent a significant burden to the economy and healthcare system, whereas those with early onset infantile MLD die within a few years of symptom onset. We conducted a pilot study to determine the safety and benefit of HSCGT in patients with postsymptomatic juvenile MLD and report preliminary results. The safety profile of HSCGT was favorable in this long-term follow-up over 9 years. The most common adverse events (AEs) within 2 months of HSCGT were related to busulfan conditioning, and all AEs resolved. No HSCGT-related AEs and no evidence of distorted hematopoietic differentiation during long-term follow-up for up to 9.6 years. Importantly, to date, patients have maintained remarkably improved ARSA activity with a stable disease state, including increased Functional Independence Measure (FIM) score and decreased magnetic resonance imaging (MRI) lesion score. This long-term follow-up pilot study suggests that HSCGT is safe and provides clinical benefit to patients with postsymptomatic juvenile MLD.
Humans ; Leukodystrophy, Metachromatic/genetics* ; Pilot Projects ; Genetic Therapy/methods* ; Hematopoietic Stem Cell Transplantation ; Male ; Follow-Up Studies ; Female ; Lentivirus/genetics* ; Child ; Child, Preschool ; Hematopoietic Stem Cells/metabolism* ; Cerebroside-Sulfatase/metabolism* ; Adolescent

Objective: To understand the moderating effect of cohabitation with parents on the association between peer dating behavior and sexual behaviors among secondary vocational school students, so as to provide a scientific basis for preventing sexual behaviors among secondary vocational school students. Methods: From March to April 2021, an electronic questionnaire survey was conducted among 3 180 students from 6 vocational schools in Shanghai (urban, suburban, exurban) and Shaanxi (Shangluo, Ankang, Baoji) using cluster sampling. Spearman correlation analysis was used to investigate the relationship of cohabitation with parents, peer dating behavior and sexual behaviors among secondary vocational school students. Binary Logistic regression analysis was performed to investigate the role of cohabitation with parents on peer dating behavior and sexual behaviors among secondary vocational students. Results: There was a significant negative between cohabitation with parents and sexual ( r =-0.04); and there was a positive correlation between peer dating behavior and sexual behaviors ( r =0.24), as well as cohabitation with parents and peer dating behavior ( r =0.04)( P <0.05). Multivariable Logistic regression analysis showed an association between peer dating behavior and the occurrence of sexual behaviors ( OR=2.79-12.95, P <0.05). Cohabitation with parents played a moderating role in the association between peer dating behavior and sexual behaviors, and a signification interaction was found between cohabitation with parents and reporting that a small part or about half of their peers had dating behavior ( OR =0.48, P <0.05). Conclusions The more peers dating behavior are associated with a higher risk of sexual behaviors among secondary vocational school students, and cohabitation with parents can partly reduce this risk. School and family sexuality education for secondary vocational students should be strengthened to improve their interpersonal skills and decision-making, and ability to resist peer pressure, so as to reduce their risk of sexual behaviors.