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.

Background and Objectives: The Fractional Flow Reserve and Intravascular UltrasoundGuided Intervention Strategy for Clinical Outcomes in Patients with Intermediate Stenosis (FLAVOUR) trial demonstrated non-inferiority of fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) compared with intravascular ultrasound (IVUS)-guided PCI. We sought to investigate the cost-effectiveness of FFR-guided PCI compared to IVUS-guided PCI in Korea. Methods: A 2-part cost-effectiveness model, composed of a short-term decision tree model and a long-term Markov model, was developed for patients who underwent PCI to treat intermediate stenosis (40% to 70% stenosis by visual estimation on coronary angiography).The lifetime healthcare costs and quality-adjusted life-years (QALYs) were estimated from the healthcare system perspective. Transition probabilities were mainly referred from the FLAVOUR trial, and healthcare costs were mainly obtained through analysis of Korean National Health Insurance claims data. Health utilities were mainly obtained from the Seattle Angina Questionnaire responses of FLAVOUR trial participants mapped to EQ-5D. Results: From the Korean healthcare system perspective, the base-case analysis showed that FFR-guided PCI was 2,451 U.S. dollar lower in lifetime healthcare costs and 0.178 higher in QALYs compared to IVUS-guided PCI. FFR-guided PCI remained more likely to be cost-effective over a wide range of willingness-to-pay thresholds in the probabilistic sensitivity analysis. Conclusions Based on the results from the FLAVOUR trial, FFR-guided PCI is projected to decrease lifetime healthcare costs and increase QALYs compared with IVUS-guided PCI in intermediate coronary lesion, and it is a dominant strategy in Korea.

Peptide-based radiopharmaceuticals targeting integrin α5β1 show promise for precise tumor diagnosis and treatment. However, current peptide-based radioligands that target α5β1 demonstrate inadequate in vivo performance owing to limited tumor retention. The use of PEGylation to enhance the tumor retention of radiopharmaceuticals by prolonging blood circulation time poses a risk of increased blood toxicity. Therefore, a PEGylation strategy that boosts tumor retention while minimizing blood circulation time is urgently needed. Here, we developed a PEGylation-enabled peptide multidisplay platform (PEGibody) for PR_b, an α5β1 targeting peptide. PEGibody generation involved PEGylation and self-assembly. [⁶⁴Cu]QM-2303 PEGibodies displayed spherical nanoparticles ranging from 100 to 200 nm in diameter. Compared with non-PEGylated radioligands, [⁶⁴Cu]QM-2303 demonstrated enhanced tumor retention time due to increased binding affinity and stability. Importantly, the biodistribution analysis confirmed rapid clearance of [⁶⁴Cu]QM-2303 from the bloodstream. Administration of a single dose of [¹⁷⁷Lu]QM-2303 led to robust antitumor efficacy. Furthermore, [⁶⁴Cu]/[¹⁷⁷Lu]QM-2303 exhibited low hematological and organ toxicity in both healthy and tumor-bearing mice. Therefore, this study presents a PEGibody-based radiotheranostic approach that enhances tumor retention time and provides long-lasting antitumor effects without prolonging blood circulation lifetime. The PEGibody-based radiopharmaceutical [⁶⁴Cu]/[¹⁷⁷Lu]QM-2303 shows great potential for positron emission tomography imaging-guided targeted radionuclide therapy for α5β1-overexpressing tumors.

Bacterial biofilms can make traditional antibiotics impenetrable and even promote the development of antibiotic-resistant strains. Therefore, non-antibiotic strategies to effectively penetrate and eradicate the formed biofilms are urgently needed. Here, we demonstrate the development of self-propelled biohybrid microrobots that can enhance the degradation and penetration effects for Pseudomonas aeruginosa biofilms in minimally invasive strategy. The biohybrid microrobots (CR@Alg) are constructed by surface modification of Chlamydomonas reinhardtii (CR) microalgae with alginate lyase (Alg) via biological orthogonal reaction. By degrading the biofilm components, the number of CR@Alg microrobots with fast-moving capability penetrating the biofilm increases by around 2.4-fold compared to that of microalgae. Massive reactive oxygen species are subsequently generated under laser irradiation due to the presence of chlorophyll, inherent photosensitizers of microalgae, thus triggering photodynamic therapy (PDT) to combat bacteria. Our algae-based microrobots with superior biocompatibility eliminate biofilm-infections efficiently and tend to suppress the inflammatory response in vivo, showing huge promise for the active treatment of biofilm-associated infections.

The activation proteins released by fibroblasts in the tumor microenvironment regulate tumor growth, migration, and treatment response, thereby influencing tumor progression and therapeutic outcomes. Owing to the proliferation and metastasis of tumors, fibroblast activation protein (FAP) is typically highly expressed in the tumor stroma, whereas it is nearly absent in adult normal tissues and benign lesions, making it an attractive target for precision medicine. Radiolabeled agents targeting FAP have the potential for targeted cancer diagnosis and therapy. This comprehensive review aims to describe the evolution of FAPI-based radiopharmaceuticals and their structural optimization. Within its scope, this review summarizes the advances in the use of radiolabeled small molecule inhibitors for tumor imaging and therapy as well as the modification strategies for FAPIs, combined with insights from structure-activity relationships and clinical studies, providing a valuable perspective for radiopharmaceutical clinical development and application.

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

Background and Objectives: The Fractional Flow Reserve and Intravascular UltrasoundGuided Intervention Strategy for Clinical Outcomes in Patients with Intermediate Stenosis (FLAVOUR) trial demonstrated non-inferiority of fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) compared with intravascular ultrasound (IVUS)-guided PCI. We sought to investigate the cost-effectiveness of FFR-guided PCI compared to IVUS-guided PCI in Korea. Methods: A 2-part cost-effectiveness model, composed of a short-term decision tree model and a long-term Markov model, was developed for patients who underwent PCI to treat intermediate stenosis (40% to 70% stenosis by visual estimation on coronary angiography).The lifetime healthcare costs and quality-adjusted life-years (QALYs) were estimated from the healthcare system perspective. Transition probabilities were mainly referred from the FLAVOUR trial, and healthcare costs were mainly obtained through analysis of Korean National Health Insurance claims data. Health utilities were mainly obtained from the Seattle Angina Questionnaire responses of FLAVOUR trial participants mapped to EQ-5D. Results: From the Korean healthcare system perspective, the base-case analysis showed that FFR-guided PCI was 2,451 U.S. dollar lower in lifetime healthcare costs and 0.178 higher in QALYs compared to IVUS-guided PCI. FFR-guided PCI remained more likely to be cost-effective over a wide range of willingness-to-pay thresholds in the probabilistic sensitivity analysis. Conclusions Based on the results from the FLAVOUR trial, FFR-guided PCI is projected to decrease lifetime healthcare costs and increase QALYs compared with IVUS-guided PCI in intermediate coronary lesion, and it is a dominant strategy in Korea.

BACKGROUND: Generalized pustular psoriasis (GPP), a rare and recurrent autoinflammatory disease, imposes a substantial burden on patients and society. Awareness of GPP in China remains limited. METHODS: This cross-sectional survey, conducted between September 2021 and May 2023 across 14 hospitals in China, included GPP patients of all ages and disease phases. Data collected encompassed demographics, clinical characteristics, economic impact, disease severity, quality of life, and treatment-related complications. Risk factors for GPP recurrence were analyzed. RESULTS: Among 127 patients (female/male ratio = 1.35:1), the mean age of disease onset was 25 years (1st quartile [Q1]-3rd quartile [Q3]: 11-44 years); 29.2% had experienced GPP for more than 10 years. Recurrence occurred in 75.6% of patients, and nearly half reported no identifiable triggers. Younger age at disease onset ( P  = 0.021) and transitioning to plaque psoriasis ( P  = 0.022) were associated with higher recurrence rates. The median diagnostic delay was 8 months (Q1-Q3: 2-41 months), and 32.3% of patients reported misdiagnoses. Comorbidities were present in 53.5% of patients, whereas 51.1% experienced systemic complications during treatment. Depression and anxiety affected 84.5% and 95.6% of patients, respectively. During GPP flares, the median Dermatology Life Quality Index score was 19.0 (Q1-Q3: 13.0-23.5). This score showed significant differences between patients with and without systemic symptoms; it demonstrated correlations with both depression and anxiety scores. Treatment costs caused financial hardship in 55.9% of patients, underscoring the burden associated with GPP. CONCLUSIONS The substantial disease and economic burdens among Chinese GPP patients warrant increased attention. Patients with early onset disease and those transitioning to plaque psoriasis require targeted interventions to mitigate the high recurrence risk.
Humans ; Male ; Female ; Psoriasis/pathology* ; Adult ; Cross-Sectional Studies ; Adolescent ; Child ; Young Adult ; Quality of Life ; Middle Aged ; China/epidemiology* ; Recurrence ; Risk Factors ; Surveys and Questionnaires ; East Asian People

In the clinical stage, suspected hemolytic plasma may cause hemolysis illness, manifesting as symptoms such as heart failure, severe anemia, etc. Applying a deep learning method to plasma images significantly improves recognition accuracy, so that this paper proposes a plasma quality detection model based on improved "You Only Look Once" 5th version (YOLOv5). Then the model presented in this paper and the evaluation system ‌were introduced‌ into the plasma datasets, and ‌the average accuracy of the final classification reached 98.7%‌. The results of this paper's experiment were obtained through the combination of several key algorithm modules including‌ omni-dimensional dynamic convolution, pooling with separable kernel attention, residual bi-fusion feature pyramid network, ‌and‌ re-parameterization convolution. The method of this paper‌ obtains the feature information of spatial mapping efficiently, and enhances the average recognition accuracy of plasma quality detection. This paper presents a high-efficiency detection method for plasma images, aiming to provide a practical approach to prevent hemolysis illnesses caused by external factors.
Algorithms ; Humans ; Hemolysis ; Plasma ; Deep Learning ; Image Processing, Computer-Assisted/methods*