Objective: To assess the clinical application value of a prediction model for red blood cell (RBC) demand in surgical procedures. Methods: Demographic data, laboratory parameters, anesthesia and transfusion records, and model prediction data were retrospectively collected from surgical patients at the First Medical Center of Chinese PLA General Hospital between 2018 and 2024. Statistical analysis was performed using the Chi-square test, t-test, and Mann-Kendall trend test. Results: From 2018 to 2024, the predictive model for RBC demand in surgical procedures was used to evaluate a total of 112 293 surgeries. During this period, the model call rate (77.49%-98.91%, P<0.05), compliance rate (56.81%-84.92%, P<0.05), and prediction accuracy rate (66.82%-94.17%, P<0.05) all showed significant upward trends. The total blood usage across the hospital (13645.4-7723.5 units, P<0.05) and the average blood usage per surgery (0.21-0.1 units, P<0.05) exhibited overall downward trends. Postoperative average hemoglobin levels in the non-compliance group (112.1-105.3 g/L in the non-compliance group vs 106.9-92.7 g/L in the compliance group, P<0.05) and the intraoperative excessive transfusion rate (5.06%-6.05% in the non-compliance group vs 0.09%-0.04% in the compliance group, P<0.05) were significantly higher in the non-compliance group compared to the compliance group. Conclusion: The predictive model for RBC demand in surgical procedures has played a positive role in conserving blood resources, optimizing blood resource allocation, and reducing intraoperative risks.

Background Meteorological factors are among the key extrinsic triggers for the onset and exacerbation of cardiovascular and cerebrovascular diseases (CVD). Against the backdrop of sustained global warming, elucidating the impact of ambient temperature and atmospheric pressure on CVD, especially on pre-hospital CVD emergent events, has become imperative for evidence-based prevention and emergency preparedness. Objective To quantify the temporal trends of daily mean temperature and atmospheric pressure and their associations with pre-hospital CVD emergent events in Yantai, and to explore effect modification by demographic subgroups and geographic areas, thereby providing an empirical basis for the rational allocation of emergency medical resources. Methods Pre-hospital CVD emergency data from January 1, 2016 to December 31, 2022 were selected from the Yantai 120 Emergency Medical Command System. Synchronous meteorological factors and environmental pollutant data were obtained from the websites of the National Oceanic and Atmospheric Administration and the National Centers for Environmental Information of the United States. Time-series analysis combined with distributed lag non-linear model was used to analyze the association between daily temperature, atmospheric pressure, and pre-hospital CVD emergencies. Average annual percentage changes (AAPC) were calculated using Joinpoint (version 5.2.0.0) to reflect temporal trends. Spearman correlation analysis was employed to screen variables with low collinearity for inclusion in the multi-pollutant adjusted models. Results From 2016 to 2022, a total of 268275 pre-hospital CVD emergency cases were recorded in Yantai City, showing an increasing annual trend (AAPC=7.16%, P=0.002). Emergency volume, daily temperature, atmospheric pressure, and PM₁₀ displayed marked seasonal patterns, with summer bottoms and winter peaks for emergencies, daily temperature higher in summer and lower in winter, and the inverse for atmospheric pressure. Correlation analysis showed that both daily temperature and atmospheric pressure were positively correlated with O₃, with correlation coefficients of 0.745 and 0.723, respectively; negatively correlated with PM_2.5, with correlation coefficients of −0.246 and −0.235, respectively; and negatively correlated with PM₁₀, with correlation coefficients of −0.241 and −0.229, respectively (P < 0.05). A U-shaped correlation was revealed between daily temperature and pre-hospital CVD emergency risk. Using 24 °C as the reference, risk was elevated at both low and high daily temperatures, with the strongest cold effect at –11 ℃ (RR=1.53, 95%CI: 1.35, 1.73) and strongest heat effect at 30 ℃ (RR=1.06, 95%CI: 1.01, 1.11); cold effects were prolonged (lag0–14d) compared to heat effects. Atmospheric pressure exhibited a positive association with emergencies; using a median of 1012.2 hPa as the reference, the maximum risk occurred at 1037 hPa (RR=1.25, 95%CI: 1.03, 1.52), with a 0–5 d lag. In the subgroup analysis, the impact of low temperature was more pronounced among females (RR_{−9 ℃}=1.66), people over 60 years (RR_{−9 ℃}=1.91), and rural residents (RR_{−9 ℃}=1.76). High atmospheric pressure significantly increased the risk of pre-hospital CVD emergency among females (RR_{1035 hPa}=1.54), people under 18 years (RR_{1035 hPa}=3.62), and rural residents (RR_{1035 hPa}=1.46). The sensitivity analysis results indicated that, after excluding the impact of pandemic and the degrees of freedom for time variations, the effects of daily average temperature and atmospheric pressure on pre-hospital CVD emergency volume were consistent with the primary analysis findings. Conclusion Both non-optimal temperature and atmospheric pressure significantly increase the pre-hospital CVD emergency volume; therefore, emergency medical services should develop targeted response protocols, optimize resource allocation, and intensify public health education to mitigate the pre-hospital burden attributable to temperature and pressure fluctuations.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and the second leading cause of cancer-related mortality worldwide. Despite therapeutic advancements over recent decades, the prognosis for patients with metastatic CRC (mCRC) remains poor. Approximately 2%-4% of mCRC cases exhibit human epidermal growth factor receptor 2 (HER2) amplification or overexpression, defining a distinct molecular subtype. This HER2-positive status is strongly associated with primary resistance to anti-epidermal growth factor receptor (EGFR) therapies, which are the standard of care for patients with RAS wild-type tumors. Beyond its well-established role in breast and gastric cancers, HER2 has emerged as a pivotal biomarker and actionable therapeutic target in mCRC. However, selecting appropriate treatment strategies remains challenging due to patient heterogeneity and diverse molecular subtypes. This review systematically summarizes the molecular biology, diagnostic strategies, and advances in targeted therapies for HER2-positive mCRC. On the diagnostic front, we discuss the applications of immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), next-generation sequencing (NGS), and circulating tumor DNA (ctDNA) detection technologies. We highlight discrepancies in diagnostic criteria across key clinical trials—such as HERACLES, DESTINY, and MOUNTAINEER—underscoring the urgent need for standardized, CRC-specific definitions to ensure consistent patient selection and comparability of efficacy data across studies. Although NGS enables comprehensive genomic profiling, its cost-effectiveness relative to traditional methods must be carefully considered. Therapeutically, we summarize clinical trial data for HER2-directed agents, including tyrosine kinase inhibitors (TKIs) such as tucatinib and lapatinib, monoclonal antibodies like trastuzumab, bispecific antibodies, and antibody-drug conjugates (ADCs) such as trastuzumab deruxtecan. We review dual-targeting strategies and note recent FDA approvals that represent significant milestones in second-line treatment. Additionally, we explore the potential of combining immune checkpoint inhibitors with HER2-targeted therapies to enhance antitumor immunity through mechanisms including antibody-dependent cellular cytotoxicity (ADCC) and modulation of the tumor microenvironment. ADCs enable precise delivery of cytotoxic payloads, reducing off-target toxicity while effectively inhibiting oncogenic pathways. A substantial portion of this review is dedicated to dissecting the molecular mechanisms underlying primary and acquired resistance to HER2-targeted therapies—persistent challenges that limit clinical benefit. These mechanisms include reactivation of downstream signaling pathways such as PI3K/AKT/mTOR and MAPK, concurrent mutations in genes like KRAS or BRAF, and alterations in HER2 expression that compromise treatment efficacy. For instance, specific HER2 mutations (e.g., L755S) can reduce drug binding affinity, while ctDNA monitoring facilitates early detection of emerging resistance clones during disease progression, thereby enabling timely therapeutic adjustments. Tumor heterogeneity and dynamic interactions with the microenvironment further complicate resistance patterns observed in clinical practice. HER2-targeted therapy represents a new frontier in precision oncology for mCRC, offering renewed hope for improving patient outcomes. Realizing this potential will require continued optimization of diagnostic algorithms and treatment workflows. Future efforts must focus on overcoming resistance, validating liquid biopsy approaches for dynamic monitoring, and establishing unified clinical guidelines. HER2 has become an essential biomarker for stratifying mCRC patients beyond traditional RAS and BRAF status, underscoring the shift from empiric treatment to biomarker-driven precision medicine. International, multidisciplinary collaboration will be critical to validate emerging biomarkers and refine treatment algorithms globally.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

ObjectiveTo characterize the quality differences among different germplasm and introduced varieties of Bupleurum scorzonerifolium roots（BSR）， and explore the underlying molecular mechanisms， providing a basis for high-quality production and quality control. MethodsWild BSR from Yulin（YLW） served as the quality reference， we conducted comparative analysis among YLW， locally domesticated wild germplasm in Yulin（YLC3）， Daqing germplasm introduced and cultivated in Yulin（YLDQC3）， and locally cultivated germplasm in Daqing（DQC3）. A combination of traditional pharmacognostic methods and modern multi-omics analyses was employed， including macroscopic traits（appearance， odor）， microscopic features（proportions of cork， phloem， xylem）， cell wall component contents（hemicellulose， cellulose， lignin）， carbohydrate contents（starch， water-soluble polysaccharides）， marker compound contents（ethanol-soluble extracts， total saponins， liposoluble extracts， and saikosaponins A， B₂， C， D）， metabolomics， and transcriptomics， in order to systematically characterize quality differences and investigate molecular mechanisms among these samples. ResultsMacroscopically， Yulin-produced BSR（YLW， YLC3， YLDQC3） exhibited significantly greater weight， length， and upper and middle diameters than Daqing-produced BSR（DQC3）. Odor-wise， YLW and YLC3 had a a fragrance taste， YLDQC3 had a rancid oil odor， and DQC3 had a sweet and fragrant taste. Microscopically， Yulin germplasm（YLW， YLC3） and Daqing germplasm（YLDQC3， DQC3） shared similar structural features， respectively. However， Yulin germplasm showed significantly higher proportions of cork and phloem， as well as stronger xylem vessel staining intensity compared to Daqing germplasm. Regarding various component contents， Yulin germplasm contained significantly higher levels of ethanol-soluble extracts， total saponins， and saikosaponins A， B₂， C， D， while Daqing germplasm had significantly higher levels of hemicellulose， starch， and liposoluble extracts. After introduction to Yulin， the Daqing germplasm（YLDQC3） showed increased starch， water-soluble polysaccharides and liposoluble extracts contents， decreased cell wall component content， but no significant difference in other component contents. Metabolomics revealed that saponins and terpenes accumulated significantly in Yulin germplasm， while alcohols and aldehydes accumulated predominantly in Daqing germplasm. Transcriptomics indicated similar gene expression patterns within the same germplasm but specificity between different germplasms. Integrative metabolomic-transcriptomic analysis identified 145 potential key genes associated with the saikosaponin biosynthesis pathway， including one acetyl-coenzyme A（CoA） acetyltransferase gene（ACAT）， one 3-hydroxy-3-methylglutaryl-coenzyme A synthase gene（HMGS）， two hydroxymethylglutaryl-CoA（HMG-CoA） reductase genes（HMG）， one phosphomevalonate kinase gene（PMK）， one 1-deoxy-D-xylose-5-phosphate synthase gene（CLA）， one hydroxymethylbuten-1-aldol synthase gene（HDR）， two farnesyl pyrophosphate synthase genes（FPPS）， one squalene synthase gene（SQS）， one β-amyrin synthase gene（BAS）， 102 cytochrome P450（CYP450） gene family members， and 32 uridine diphosphate-glucuronosyltransferase（UGT） gene family members. ConclusionAmong the three cultivated types， YLC3 most closely resembles YLW in appearance， microscopic features， contents of major bioactive constituents， metabolomic and transcriptomic profiles. Yulin germplasm exhibits superior saponin synthesis capability compared to Daqing germplasm， and Yulin region is more suitable for the growth of B. scorzonerifolium. Based on these findings， it is recommended that artificial cultivation in northern Shaanxi and similar regions utilize the local Yulin germplasm source cultivated for at least three years.

Periodontitis is a chronic inflammatory disease. The heterotopic colonization of periodontal pathogens results in the development of several systemic diseases. Porphyromonas gingivalis (P. gingivalis), a key pathogen for periodontitis, has been linked to the development of various cancers, such as oral squamous cell carcinoma (OSCC), lung cancer, esophageal cancer, pancreatic cancer, colorectal cancer, cervical cancer, and prostate cancer. P. gingivalis promote the progression of tumor through various mechanisms, P. gingivalis regulates proteins targeting cell cycle and apoptosis to promote proliferation of tumor cells directly, enhances tumor stemness by upregulating the expression of cluster of differentiation 44 (CD44) and cluster of differentiation 133 (CD133), activates inflammasome and p38/c-Jun N-terminal kinase 1(JNK) pathways, regulates tumor-associated neutrophil (TAN) polarization to remodel the tumor microenvironment, regulates epithelial-mesenchymal transition (EMT) to promote tumor metastasis, remodel macrophage function to evade host immune response, and regulates multi-communicating with symbiotic bacteria. In addition, P. gingivalis accelerates the progression of esophageal cancer, pancreatic cancer, colorectal cancer, and prostate cancer by promoting cell proliferation, inhibiting apoptosis, inducing chronic inflammation, and escaping immunity. However, the oral microbiome is a complex system, whether the interactions between oral bacteria affect tumor progression needs to be further investigated.

ObjectiveHistorically documented Zhejiang Atractylodis Macrocephalae Rhizoma（Baizhu） possesses premium characteristics such as phoenix-like head and crane-like neck， pronounced sweetness， and fragrant aroma. However， its current market circulation is low， and the processed products with Zhejiang-style characteristics are at the risk of being lost. This study aims to preserve the ancient Zhejiang-style processing techniques and evaluate them using modern scientific methods. MethodsMultidimensional intelligent sensory evaluation was used to digitally characterize the "quality-structure" of the external appearance of nine-steamed and nine-processed Baizhu medicinal materials（intermediate processed products） and the "odor-taste" of the internal quality of its decoction pieces（slices）， and the appearance parameters were digitally characterized by colorimeter， texture analyzer， electronic nose and electronic tongue， the chemical composition was analyzed via ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS）. Then， cluster analysis on the differences in odor between the medicinal materials（intermediate processed products） and decoction pieces（slices） of nine-steamed and nine-processed Baizhu was conducted， as well as the differences in taste between water-soluble and alcohol-soluble extracts of the decoction pieces（slices）， and the correlation analysis of chroma value-alcohol-soluble extract content-component response value. ResultsThe nine-steamed and nine-processed Baizhu had a dark brown to black epidermis， a brownish-yellow to brownish-gray cross-section， a slightly tough texture， a faint odor， and a slightly sweet， bitter and pungent taste. Texture analyzer measurements revealed minimal adhesion and maximum recovery in the middle section of the characteristic processed Baizhu， consistent with the processing endpoint of thorough steaming and cooking. The head section showed the highest internal hardness， elasticity and chewiness， indicating a denser texture in this area. The electronic nose sensor could clearly distinguish the difference between the medicinal materials and its decoction pieces， with a more significant clustering effect at 60 ℃ for 30 minutes compared to ambient temperature headspace for 2 hours， highlighting the significant impact of the baking degree before slicing on the quality. The electronic tongue taste signal map clearly distinguished the differences between water-soluble and alcohol-soluble extracts of nine-steamed and nine-processed Baizhu decoction pieces， and the addition of auxiliary materials during processing could enhance its alcohol-soluble extract content. A total of 82 chemical components were identified in the characteristic processed Baizhu. After processing， the contents of 58 components increased， while 24 components decreased. Correlation analysis revealed significant negative correlations（P<0.01） between ethanol-soluble extract content and colorimetric values of brightness（L^*）， yellow-bule value（b^*）， and total color difference（E^*_ab）. E^*_ab showed marked negative correlations（P<0.05） with the response values of isochlorogenic acid A and C. ConclusionThis study establishes a modern intelligent sensory evaluation model for multidimensional holographic characterization of nine-steamed and nine-processed Baizhu， clarifying the correlation between increased isochlorogenic acid content and the visual color appearance after different steaming cycles， as well as its intrinsic alcohol-soluble extracts. This provides a reference for quality evaluation and processing standards of the Zhejiang-style characteristic processed products.