The philosophy of “treating disease before its onset” is a fundamental concept of traditional Chinese medicine (TCM), permeating its diagnostic and therapeutic framework, and is central to clinical practice. However, current TCM diagnostic and treatment models for the “pre-disease to disease” window period face several limitations, including the lack of comprehensive clinical parameters, difficulties in characterizing and integrating heterogeneous multimodal data, and insufficient dynamic precision in interventions and efficacy evaluations. To address these issues, guided by Professor Candong Li’s theory of TCM stateology, this study focuses on integrating objective multimodal data. It proposes a new model for personalized TCM diagnosis and treatment targeting the “pre-disease to disease” window period. This approach first proposes the idea of restructuring the conceptual framework of “symptom” and integrating multi-source heterogeneous data at macroscopic, mesoscopic, and microscopic levels to form a three-dimensional assessment indicator system. By integrating graph neural networks, convolutional neural networks, attention mechanisms, and knowledge graph-guided weight allocation, this approach enables collaborative representation, alignment, and fusion of multi-source data. Subsequently, it plans to construct a multimodal fusion model at both feature and decision levels, in order to establish mappings between indicators and TCM state elements, and to screen key indicators characterizing pathological evolution during the window period. Furthermore, it proposes a technical path for enhancing model interpretability using methods such as SHapley Additive exPlanations (SHAP) and Ablation-CAM++. Finally, with state assessment as the core, it proposes the concept of constructing a dynamic evaluation method for individualized diagnosis and treatment based on time-series data analysis using algorithms such as long short-term memory (LSTM) networks and gated recurrent units (GRUs). Moreover, a causal inference framework and semi-supervised learning strategies are introduced to enable quantitative evaluation of individual intervention effects and to provide interpretable therapeutic feedback, forming a complete technical path from data representation and fusion, weight adjustment, and interpretability analysis, to dynamic diagnosis feedback. This study aims to address deficiencies in the current TCM diagnosis and treatment model during the “pre-disease to disease” window period and to provide an operational framework for the clinical practice of TCM’s “treating disease before its onset”.

OBJECTIVE To construct a risk prediction model for nonattainment plasma concentration of zonisamide in children with epilepsy， and to provide evidence for individualized dosing.METHODS Children with epilepsy who received standardized zonisamide treatment orally in Kunming Children’s Hospital were retrospectively selected as research subjects. Those treated from May 2022 to May 2024 were included in the modeling cohort， and those treated from June 2024 to January 2025 were included in the external validation cohort. According to plasma concentration， children were divided into therapeutic group and nonattainment group. Independent influencing factors for nonattainment plasma concentration of zonisamide were screened by collinearity diagnosis， univariate analysis， Lasso regression and random forest， based on which a nomogram risk prediction model was constructed and validated. Finally， the Shapley additive explanations （SHAP） method was used to interpret the features of the nomogram risk prediction model.RESULTS A total of 945 children with epilepsy were included in this study. There was no statistically significant difference in baseline data between the modeling cohort （531 cases） and the external validation cohort （414 cases） （ P ＞0.05）， indicating comparability. Through multi-stage feature screening， seven influencing factors were finally included to construct the prediction model， namely gender， age， dosage， aspartate transferase， total bilirubin， uric acid and vitamin D. The corrected area under the curve of the model was 0.799 in the modeling cohort and 0.762 in the external validation cohort， showing good calibration and clinical applicability. SHAP analysis showed that gender， dosage， total bilirubin， age and aspartate transferase were the top five important variables affecting model prediction.CONCLUSIONS The nomogram risk prediction model for nonattainment plasma concentration of zonisamide constructed in this study has stable predictive performance. Clinically， the risk of nonattainment plasma concentration can be quickly assessed by combining the child’s gender， age， dosage， liver and kidney function， and vitamin D， and the dosing regimen can be adjusted in time.

BACKGROUND: Pulse indicated continuous cardiac output (PiCCO) has largely replaced Swan-Ganz catheterization in shock patients. However, whether PiCCO monitoring can improve outcomes of shock patients, such as mortality, length of hospital stay, duration of mechanical ventilation, or laboratory parameters, remains unknown. METHODS: This retrospective cohort study included patients with shock in the intensive care unit (ICU) from January 2013 to January 2020. Patients were divided into PiCCO group and non-PiCCO group based on treatment with PiCCO monitoring or not. Demographic characteristics, Acute Physiology and Chronic Health Evaluation (APACHE) II scores, quick Sequential Organ Failure Assessment (qSOFA) scores, 14-day mortality, and N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels at 0, 1, 3 and 7 days after onset of shock, duration of mechanical ventilation, length of hospital stay and hospitalization costs were compiled and analyzed using propensity score matching (PSM). RESULTS: Real-world analysis of 1,583 ICU patients suffering shock after propensity score matching revealed that 14-day mortality did not differ between PiCCO and non-PiCCO groups (36.2% vs. 32.6%, P=0.343). Duration of mechanical ventilation, hospital stay, and hospitalization costs were also similar between the two groups (P>0.05). No differences in changes of NT-proBNP levels on days 0, 1, 3, and 7 as compared to baseline were noted between the two groups (P>0.05). CONCLUSIONS: The results of our real-world indicate that PiCCO monitoring may not shorten the duration of mechanical ventilation, length of hospital stay, or reduce hospitalization costs, nor will it bring survival benefits to ICU patients suffering shock.

The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.

Idiopathic pulmonary fibrosis (IPF), a chronic interstitial lung disease, is characterized by aberrant wound healing, excessive scarring and the formation of myofibroblastic foci. Although the role of alternative splicing (AS) in the pathogenesis of organ fibrosis has garnered increasing attention, its specific contribution to pulmonary fibrosis remains incompletely understood. In this study, we identified an up-regulation of serine/arginine-rich splicing factor 7 (SRSF7) in lung fibroblasts derived from IPF patients and a bleomycin (BLM)-induced mouse model, and further characterized its functional role in both human fetal lung fibroblasts and mice. We demonstrated that enhanced expression of Srsf7 in mice spontaneously induced alveolar collagen accumulation. Mechanistically, we investigated alternative splicing events and revealed that SRSF7 modulates the alternative splicing of pyruvate kinase (PKM), leading to metabolic dysregulation and fibroblast activation. In vivo studies showed that fibroblast-specific knockout of Srsf7 in conditional knockout mice conferred resistance to bleomycin-induced pulmonary fibrosis. Importantly, through drug screening, we identified lomitapide as a novel modulator of SRSF7, which effectively mitigated experimental pulmonary fibrosis. Collectively, our findings elucidate a molecular pathway by which SRSF7 drives fibroblast metabolic dysregulation and propose a potential therapeutic strategy for pulmonary fibrosis.

OBJECTIVES: To analyze bone mass distribution and the factors affecting bone mass in a general Chinese Han cohort undergoing physical examinations at our center. METHODS: We retrospectively collected the data of bone mineral density (BMD) measurements from 30 599 healthy Han Chinese adults (age≥20 years) who underwent dual-energy X-ray absorptiometry scans at our hospital from July, 2013 to July, 2023. Basic parameters including height, body weight, and gender were recorded, and descriptive statistics and correlation analyses were performed using R software. RESULTS: In this cohort, the male individuals had a mean peak BMD of 1.00±0.12 g/cm² in the lumbar vertebrae, 0.94±0.14 g/cm² in the femoral neck, and 0.99±0.13 g/cm² in the total hip, significantly higher than the values in the female individuals [0.99±0.12 g/cm² in the lumbar vertebrae (P=0.022), 0.79±0.11 g/cm² in the femoral neck (P<0.001), and 0.88±0.11 g/cm² in the total hip (P<0.001)]. In the overall cohort, the BMD values of the lumbar spine and femur decreased with age after reaching their peak levels. There was a positive correlation between BMD value and body mass index (BMI) in both male and female individuals. The 2013-2014 period recorded the lowest BMD values in the lumbar, hip, and femoral neck, which tended to increase steadily in the following years (2015-2023). CONCLUSIONS Our data suggest that the BMD values vary among different populations, and future multi-center studies using more accurate BMD detection technology are warranted to capture the variation patterns of BMD with demographic characteristics of specific populations.
Humans ; Bone Density ; Absorptiometry, Photon ; Male ; Female ; Retrospective Studies ; Osteoporosis/diagnostic imaging* ; Adult ; Middle Aged ; Lumbar Vertebrae/diagnostic imaging* ; China/epidemiology* ; Femur Neck/diagnostic imaging* ; Aged ; Beijing/epidemiology* ; Young Adult

The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflow, named Evolutionary-Nanobody (EvoNB), to predict key mutation sites of nanobodies by combining protein language models (PLMs) and molecular dynamic (MD) simulations. By fine-tuning the ESM2 model on a large-scale nanobody dataset, the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced. The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies. Additionally, we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations. MD simulations analyzed the energy changes caused by these mutations to predict their impact on binding affinity to the targets. The results showed that multiple mutations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target, further validating the potential of this workflow for designing and optimizing nanobody mutations. Additionally, sequence-based predictions are generally less dependent on structural absence, allowing them to be more easily integrated with tools for structural predictions, such as AlphaFold 3. Through mutation prediction and systematic analysis of key sites, we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes. The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.

OBJECTIVE: To compare the efficacy and safety of intravenous colistin sulfate combined with nebulized inhalation versus intravenous monotherapy for pulmonary infections caused by carbapenem-resistant organism (CRO). METHODS: A multicenter retrospective cohort study was conducted. Clinical data were collected from patients admitted to the intensive care unit (ICU) of 10 tertiary class-A hospitals in Henan Province between July 2021 and May 2023, who received colistin sulfate for CRO pulmonary infections. Data included baseline characteristics, inflammatory markers [white blood cell count (WBC), neutrophil count (NEU), procalcitonin (PCT), C-reactive protein (CRP)], renal function indicators [serum creatinine (SCr), blood urea nitrogen (BUN)], life support measures, anti-infection regimens, clinical efficacy, microbiological clearance rate, and prognostic outcomes. Patients were divided into two groups: intravenous group (colistin sulfate monotherapy via intravenous infusion) and combination group ((intravenous infusion combined with nebulized inhalation of colistin sulfate). Changes in parameters before and after treatment were analyzed. RESULTS: A total of 137 patients with CRO pulmonary infections were enrolled, including 89 in the intravenous group and 48 in the combination group. Baseline characteristics, life support measures, daily colistin dose, and combination regimens (most commonly colistin sulfate plus carbapenems in both groups) showed no significant differences between two groups. The combination group exhibited higher clinical efficacy [77.1% (37/48) vs. 59.6% (52/89)] and microbiological clearance rate [60.4% (29/48) vs. 39.3% (35/89)], both P < 0.05. Pre-treatment inflammatory and renal parameters showed no significant differences between two groups. Post-treatment, the combination group showed significantly lower WBC and CRP [WBC (×10⁹/L): 8.2±0.5 vs. 10.9±0.6, CRP (mg/L): 14.0 (5.7, 26.6) vs. 52.1 (24.4, 109.6), both P < 0.05], whereas NEU, PCT, SCr, and BUN levels showed no significant between two groups. ICU length of stay was shorter in the combination group [days: 16 (10, 25) vs. 21 (14, 29), P < 0.05], although mechanical ventilation duration and total hospitalization showed no significant differences between two groups. CONCLUSIONS Intravenous colistin sulfate combined with nebulized inhalation improved clinical efficacy and microbiological clearance in CRO pulmonary infections with an acceptable safety profile.
Humans ; Colistin/therapeutic use* ; Retrospective Studies ; Administration, Inhalation ; Anti-Bacterial Agents/therapeutic use* ; Carbapenems/pharmacology* ; Male ; Female ; Middle Aged ; Gram-Negative Bacteria/drug effects* ; Aged ; Treatment Outcome ; Respiratory Tract Infections/drug therapy*

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*