The development of traditional Chinese medicine (TCM) diagnosis and treatment has undergone multiple paradigms, evolving from sporadic experiential practices to systematic approaches in syndrome differentiation and treatment and further integration of disease and syndrome frameworks. TCM is a vital component of the medical system, valued alongside Western medicine. Treatment based on syndrome differentiation embodies both personalized treatment and holistic approaches; however, the inconsistency and lack of stability in syndrome differentiation limit clinical efficacy. The existing integration of diseases and syndromes primarily relies on patchwork and embedded systems, where the full advantages of synergy between Chinese and Western medicine are not fully realized. Recently, driven by the development of diagnosis and treatment concepts and advances in analytical technology, Western medicine has been rapidly transforming from a traditional biological model to a precision medicine model. TCM faces a similar need to progress beyond traditional syndrome differentiation and disease-syndrome integration toward a more precise diagnosis and treatment paradigm. Unlike the micro-level precision trend of Western medicine, precision diagnosis and treatment in TCM is primarily reflected in data-driven applications that incorporate information at various levels, including precise syndrome differentiation, medication, disease management, and efficacy evaluation. The current priority is to accelerate the development of TCM precision diagnosis and treatment technology platforms and advance discipline construction in this area.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

As the brain's resident immune cells, microglia perform crucial functions such as phagocytosis, neuronal network maintenance, and injury restoration by adopting various phenotypes. Dynamic imaging of these phenotypes is essential for accessing brain diseases and therapeutic responses. Although numerous probes are available for imaging pro-inflammatory microglia, no PET tracers have been developed specifically to visualize anti-inflammatory microglia. In this study, we present an ¹⁸F-labeled PET tracer (QTFT) that targets the P2Y₁₂, a receptor highly expressed on anti-inflammatory microglia. [¹⁸F]QTFT exhibited high binding affinity to the P2Y₁₂ (14.43 nmol/L) and superior blood-brain barrier permeability compared to other candidates. Micro-PET imaging in IL-4-induced neuroinflammation models showed higher [¹⁸F]QTFT uptake in lesions compared to the contralateral normal brain tissues. Importantly, this specific uptake could be blocked by QTFT or a P2Y₁₂ antagonist. Furthermore, [¹⁸F]QTFT visualized brain lesions in mouse models of epilepsy, glioma, and aging by targeting the aberrantly expressed P2Y₁₂ in anti-inflammatory microglia. In a pilot clinical study, [¹⁸F]QTFT successfully located epileptic foci, showing enhanced radioactive signals in a patient with epilepsy. Collectively, these studies suggest that [¹⁸F]QTFT could serve as a valuable diagnostic tool for imaging various brain disorders by targeting P2Y₁₂ overexpressed in anti-inflammatory microglia.

Combined with elastic network model (ENM), the perturbation response scanning (PRS) has emerged as a robust technique for pinpointing allosteric interactions within proteins. Here, we proposed the PRS analysis of drug-target networks (DTNs), which could provide a promising avenue in network medicine. We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework, for drug repurposing of multiple sclerosis (MS). First, the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes. Then, based on topological analysis and functional annotation, the neurotransmission module was identified as the "therapeutic module" of MS. Further, perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis, giving a list of repurposable drugs for MS. Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of serotonin 2B receptor (HTR2B). Finally, we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex. These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS. As a useful systematic method, our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.

OBJECTIVE: To reveal the effects and potential mechanisms by which synaptic vesicle glycoprotein 2A (SV2A) influences the distribution of amyloid precursor protein (APP) in the trans-Golgi network (TGN), endolysosomal system, and cell membranes and to reveal the effects of SV2A on APP amyloid degradation. METHODS: Colocalization analysis of APP with specific tagged proteins in the TGN, ensolysosomal system, and cell membrane was performed to explore the effects of SV2A on the intracellular transport of APP. APP, β-site amyloid precursor protein cleaving enzyme 1 (BACE1) expressions, and APP cleavage products levels were investigated to observe the effects of SV2A on APP amyloidogenic processing. RESULTS: APP localization was reduced in the TGN, early endosomes, late endosomes, and lysosomes, whereas it was increased in the recycling endosomes and cell membrane of SV2A-overexpressed neurons. Moreover, Arl5b (ADP-ribosylation factor 5b), a protein responsible for transporting APP from the TGN to early endosomes, was upregulated by SV2A. SV2A overexpression also decreased APP transport from the cell membrane to early endosomes by downregulating APP endocytosis. In addition, products of APP amyloid degradation, including sAPPβ, Aβ _1-42, and Aβ _1-40, were decreased in SV2A-overexpressed cells. CONCLUSION These results demonstrated that SV2A promotes APP transport from the TGN to early endosomes by upregulating Arl5b and promoting APP transport from early endosomes to recycling endosomes-cell membrane pathway, which slows APP amyloid degradation.
Amyloid beta-Protein Precursor/genetics* ; Membrane Glycoproteins/genetics* ; Animals ; Protein Transport ; Nerve Tissue Proteins/genetics* ; Humans ; Mice ; Endosomes/metabolism* ; trans-Golgi Network/metabolism*

Background Hypertension is one of the chronic diseases with the highest prevalence in China, and a history of hypertension may potentially exacerbate hearing loss. Investigating the association between long-term blood pressure trends and hearing thresholds could contribute to hearing protection efforts for occupationally noise-exposed populations. Objective By investigating hearing thresholds and blood pressure levels among occupationally noise-exposed workers in an urban rail transit enterprise, and conducting a comprehensive analysis of the association between long-term blood pressure changes and hearing thresholds, to provide data references for health management strategies targeting occupationally noise-exposed workers. Methods Workers exposed to occupational noise at a rail transit enterprise were enrolled as study subjects and underwent pure-tone audiometry. Group-based trajectory modeling was employed to identify blood pressure trajectories. Categorical data were compared using chi-square tests, while normally distributed continuous variables were analyzed via t-tests and analysis of variance (ANOVA). Generalized linear mixed models (GLMMs) were subsequently applied toexamine associations between these trajectory groups and high-frequency hearing thresholds. Results Among 2 002 occupationally noise-exposed workers, the median (P₂₅, P₇₅) age was 32 (28, 35) years, with a median (P₂₅, P₇₅) working tenure of 7 (3, 10) years. In 2019, the positive hypertension rate was 9.04%, with a mean systolic blood pressure (SBP) of (122.97±11.60) mmHg and a mean diastolic blood pressure (DBP) of (76.37±9.02) mmHg. The hearing loss prevalence was 10.1%, showing bilateral high-frequency average hearing thresholds of (17.18±8.71) dB and speech-frequency average thresholds of (13.79±3.46) dB. Three distinct trajectory groups were identified for both SBP and DBP. Compared with other trajectory groups, the high-stable DBP group exhibited significantly higher hearing loss prevalence (χ²=6.34, P=0.042) and elevated high-frequency hearing thresholds (all Ps<0.05). Specifically, within the 30-39 age subgroup, the moderate-stable DBP group demonstrated 1.96 dB lower high-frequency thresholds than the high-stable group [β(95%CI): −1.96 (−3.61, −0.32), P=0.020]. Conclusion Among occupationally noise-exposed workers in a municipal rail transit enterprise, DBP trajectories demonstrated a positive association with high-frequency hearing thresholds. Notably, in young and middle-aged occupationally noise-exposed populations, DBP may exert a more critical influence than SBP on the progression of hearing loss.

Objective To analyze the classification characteristics of rheumatoid arthritis(RA)-related antibodies and to investigate the factors influencing the development of RA-related interstitial pulmonary diseases(RA-ILD)in RA patients using latent class analysis(LCA).Methods A retrospective analysis of 712 RA patients treated at the Department of Rheumatology and Immunology of the Second Hospital of Shanxi Medical University from December 2019 to October 2022 was conducted.According to whether patients had RA-ILD or not,they were divided into simple RA group(n=523)and RA-ILD group(n=189).Then,the differences in general data,clinical features,medication use and laboratory indicators were compared between the two groups.Based on the differences in RA-related antibody indicators,712 patients were divided into 3 latent categories using LCA:high-risk group(n=364),medium-risk group(n=205),and low-risk group(n=143).One-way analysis of variance was employed to compare clinical characteristics of the 3 groups,and the prevalence of RA-ILD was calculated.Multivariate logistic regression analysis was utilized to identify independent affect factors of RA-ILD.Results Significant differences in gender,age,and smoking history were observed between simple RA group and RA-ILD group(P<0.05).The high,medium and low risk groups exhibited significant differences in gender,age,prednisone(PRED)and methotrexate(MTX)medication history,Red blood cell count(RBC),interleukin-2(IL-2),IL-4,IL-10,IL-17,TNF-α,interferon-γ(INF-γ),serum globulins,and white blood albumins(P<0.05).The high-risk group had a higher proportion of males,RBC,IL-2,IL-4,IL-10,IL-17,TNF-α,INF-γ,and serum globulin levels,and a lower proportion of MTX medication compared with medium-and low-risk groups(P<0.05 or P<0.01).The medium-risk group had a higher proportion of MTX administration than that in high-and low-risk groups(P<0.05 or P<0.01).The low-risk group had a higher proportion of females and older age than those in other two groups(P<0.05 or P<0.01).The prevalence of RA-ILD was 30.5%,23.9%and 20.3%in the three groups,respectively.Multivariate logistic regression analysis indicated that male(OR=2.920,95%CI 1.722-4.952),age(OR=1.055,95%CI 1.035-1.074),IL-17(OR=1.013,95%CI 1.003-1.023),TNF-α(OR=1.050,95%CI 1.017-1.083),INF-γ(OR=0.962,95%CI 0.932-0.993),Serum albumin(OR=0.919,95%CI 0.869-0.971)and high risk antibody indicators(OR=1.725,95%CI 1.084-2.745)were independent predictors for RA-ILD.Conclusions RA patients exhibit distinct categories of antibody indicators,with a higher prevalence in high-risk patients with RA-ILD.RA-ILD is more likely to occur in male,elderly patients with abnormal liver function and high-risk antibody indictors.More attention should be paid to these patients and individualized interventions should be developed and implemented in a timely manner to improve the quality of patient survival.

Objective To explore the predictive value of automated breast ultrasound(ABUS)features combined with Ki-67 in predicting pathological complete response(pCR)after neoadjuvant chemotherapy(NAC)in triple-negative breast cancer(TNBC).Methods A retrospective analysis was conducted on 127 female TNBC patients treated at Xijing Hospital,Air Force Medical University from March 2019 to December 2023.All patients underwent NAC and surgical treatment after ABUS examination.Based on postoperative pathological results,patients were divided into pCR group(n=60)and non-pathological complete response(npCR)group(n=67).Differences in various parameters before NAC were compared between the two groups.LASSO regression was used to identify independent factors influencing pCR after NAC in TNBC patients,and a predictive model was constructed using multivariate logistic regression.The prediction model was internally validated using the Bootstrap method(1000 resamples).The discriminative ability of the model was evaluated using receiver operating characteristic(ROC)curves,and the area under the curves(AUCs)of different prediction models were compared using De-long's test.The accuracy of the model was assessed using calibration curves,and the clinical benefit of the model was evaluated using clinical decision curve analysis(DCA).Results Significant differences were observed between two groups in terms of age,Ki-67,menopausal status,tumor type,posterior echo,coronal plane convergence sign,coronal plane skip sign,and coronal plane white wall sign before NAC(P<0.05).LASSO regression analysis showed that Ki-67,coronal plane convergence sign,and coronal plane white wall sign were independent influencing factors of pCR after NAC in TNBC patients(P<0.05).The AUC of the multivariate logistic regression model based on Ki-67 was 0.733(95%CI 0.646-0.819),the AUC of ABUS model was 0.777(95%CI 0.695-0.858),and the AUC of ABUS combined with Ki-67 model was 0.816(95%CI 0.741-0.890).De-long's test showed that the AUC of the combined model was higher than those of ABUS feature model and Ki-67 model,with statistically significant differences(P<0.05).There was no significant difference in the AUC between ABUS feature model and Ki-67 model(P=0.40).Hosmer-Lemeshow test indicated that the combined model had a good fit(P=0.304).Internal validation results showed that the combined model had a good stability with a consistency index(C-index)of 0.820(95%CI 0.726-0.879).The calibration curve demonstrated good consistency between the predicted and actual probabilities of the combined prediction model,and the DCA curve indicated that the model had favorable clinical benefit.Conclusion The combined ABUS feature and Ki-67 model can be used to predict the probability of pCR after NAC in TNBC patients,providing a reference for the formulation of clinical treatment plans in TNBC patients.