Hygroscopicity research has long been a key focus and hot topic in Chinese materia medica（CMM）. Elucidating hygroscopic mechanisms plays a vital role in formulation design， process optimization， and storage condition selection. Hygroscopic models serve as essential tools for characterizing CMM hygroscopic mechanisms， with various types available. The double exponential model is a kinetic mathematical model constructed based on the law of conservation of energy and Fick's first law of diffusion， tailored to the physical properties of CMM extracts. In recent years， this model has been extensively applied to simulate the dynamic moisture absorption behavior of CMM extracts and solid dosage forms under varying humidity conditions. It has revealed the correlation between moisture absorption kinetic parameters and material properties， offering a new perspective for characterizing the moisture uptake behavior of CMM. This paper systematically reviews the application progress of this model in the field of CMM， analyzes its advantages， disadvantages， and challenges in this domain， and explores its potential application trends in other fields. It aims to provide references for elucidating the moisture absorption mechanisms of CMM and researching moisture-proofing technologies， while also offering insights for its broader application in food and polymer materials.

ObjectiveThis paper aims to clarify the material basis of Gualou Niubangtang and establish a quantitative analysis method for its main constituents， providing a reference for the overall quality control of this preparation. MethodsThe constituents in the formula were systematically characterized based on ultra-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry （UPLC-Q-TOF-MS/MS）. Identification was performed by matching with the UNIFI 9.6 software and utilizing database platforms such as PubChem， ChemicalBook， and ChemSpider， combined with relevant literature reports. A quantitative analysis method for the seven main constituents in Gualou Niubangtang was established by using high performance liquid chromatography （HPLC）. ResultsUPLC-Q-TOF-MS/MS analysis identified 155 constituents， including 69 flavonoids， 36 terpenoids， 23 phenylpropanoids， 8 phenylethanoid glycosides， and 19 other types of constituents. In the established quantitative analysis method， the seven main constituents showed good linearity within their respective linear ranges. The precision， repeatability， stability， and spike recovery all met the required standards. The results showed that the content ranges of geniposide， liquiritin， hesperidin， arctiin， baicalin， oroxylin A-7-O-β-D-glucuronide， and wogonoside in 15 batches of Gualou Niubangtang were 13.67-21.25， 1.20-7.64， 5.45-7.45， 22.97-33.51， 29.95-39.07， 2.58-4.80， and 6.56-9.31 mg·g^-1， respectively. ConclusionThis study successfully characterizes and attributes multi-category constituents in Gualou Niubangtang， clarifying that its material basis is primarily composed of flavonoids， terpenoids， phenylethanoid glycosides， and phenylpropanoids. Furthermore， it enables the quantification of seven constituents within the formula. This work lays a foundation for research on the quality control， action mechanism， and clinical application of this formula.

ObjectiveTo delineate imaging findings using an imaging platform and investigate the correlation between MRI characteristics of spinal cord atrophy and clinical diagnosis in children with spinal cord injury (SCI). MethodsImaging data of 150 children with SCI admitted to Beijing Bo'ai Hospital, China Rehabilitation Research Center, from January, 2002 to March, 2024 were collected and imported into the imaging platform. The anteroposterior and transverse diameters of the middle part of the spinal cord at the cross-section with the most severe atrophy were measured, and the relevant indicators of the previous normal spinal cord segment were measured as controls; the radiomic features were extracted. Clinical data of the children including gender, age, cause of injury, sensory level, motor level, spinal cord injury level, injury severity and disease course were collected. ResultsSpinal cord atrophy was identified in 81 cases (54%), among which 78 cases (96%) were American Spinal Injury Association Impairment Scale (AIS) grade A and 3 cases (4%) were AIS grade C. The upper boundary of the spinal cord atrophy site strongly correlated with the injury level, motor level and sensory level (r > 0.8, P < 0.001). ConclusionMore than half of children with SCI may develop secondary spinal cord atrophy, the vast majority of whom suffer from complete spinal cord injury; the upper boundary of spinal cord atrophy is correlated with the injury level.

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.

Objective: To validate the performance of the Procleix UltrioPlex E assay (Grifols, Spain) on the Procleix Panther automated nucleic acid detection platform, which employs the TMA method to simultaneously detect HIV-1/HIV-2/HCV/HBV/HEV viruses, and to evaluate its value for screening transfusion-associated infectious diseases. Methods: In accordance with the requirements of ISO15189"Application of the Guidelines for the Accreditation of Quality and Capabilities of Medical Laboratories in the Field of Molecular Diagnostics (CNAS-CL02-A009: 2018)", "Guidelines for Performance Validation of Molecular Diagnostic Testing Procedures (CNAS-GL039: 2019)", and the "Technical Operating Procedures for Blood Banks (2019 Edition)", this study validated the reagent's performance in terms of analytical sensitivity validation, performance consistency validation, interference resistance, and cross-contamination resistance. Results: Probit analysis revealed that the 95% detection limits (95% confidence interval) for HBV, HCV, HIV, and HEV were 2.0 IU/mL, 1.5 IU/mL, 18.0 IU/mL and 3.7 IU/mL, respectively, which were consistent with the minimum detection limits stated in the kit's package insert and were comparable to the Procleix Ultrio Elite kit. Both kits were used to test the performance validation serum plate simultaneously, yielding results consistent with the serum plate (Kappa=1), indicating stable performance. Detection of medium-and low-concentration lipemia and weakly positive hemolysis samples demonstrated good interference resistance. Cross-contamination performance validation showed that the kit exhibited excellent cross-contamination resistance. Conclusion: The Procleix UltrioPlex E nucleic acid detection kit enables combined detection of HIV-1, HIV-2, HCV, HBV, and HEV, allowing single-test screening for multiple viruses in donor blood. The kit's analytical performance is stable and meets basic laboratory requirements, making it suitable for screening transfusion-associated infectious diseases in blood banks.

Objective To investigate the perioperative differences between video-assisted thoracoscopic surgery (VATS) and thoracotomy after neoadjuvant therapy in patients with non-small cell lung cancer (NSCLC). Methods Clinical data of NSCLC patients who underwent VATS or thoracotomy after neoadjuvant therapy at Shanghai Pulmonary Hospital from June 2020 to May 2022 were retrospectively collected. Perioperative outcomes were compared between the two groups. Results A total of 260 patients were enrolled, 184 (70.8%) patients underwent VATS and 76 (29.2%) patients underwent thoracotomy. After propensity matching, there were 113 (62.4%) patients in the VATS group and 68 (37.6%) patients in the thoracotomy group. VATS had similar lymph node dissection ability and postoperative complication rate with thoracotomy (P>0.05), with the advantage of having shorter operative time (146.00 min vs. 165.00 min, P=0.006), less intraoperative blood loss (50.00 mL vs. 100.00 mL, P<0.001), lower intraoperative blood transfusion rate (0.0% vs. 7.4%, P=0.003), less 3-day postoperative drainage (250.00 mL vs. 350.00 mL, P=0.011; 180.00 mL vs. 250.00 mL, P=0.002; 150.00 mL vs. 235.00 mL, P<0.001), and shorter postoperative drainage time (9.34 d vs. 13.84 d, P<0.001) and postoperative hospitalization time (6.19 d vs. 7.94 d, P=0.006). Conclusion VATS after neoadjuvant therapy for NSCLC is safer than thoracotomy and results in better postoperative recovery.

ObjectiveTo analyze the distribution, drug resistance characteristics, and changing trends of Acinetobacter baumannii (AB) isolated from environmental surfaces and healthcare workers’ hands in a grade Ⅱ level A general hospital in Xuhui District of Shanghai from 2018 to 2023, and to provide reference for infection control in the hospital. MethodsEnvironmental samples were collected quarterly from critical surfaces and healthcare workers’ hands in the intensive care unit (ICU), geriatrics, and respiratory departments from 2018 to 2023. Clinical isolates were obtained from all patients with AB infections in ICU, geriatrics, respiratory department, rehabilitation department, infectious diseases department, emergency department, cardiology department, and orthopedics of the hospital from 2018 to 2023. Retrospective analyses were performed on AB detection rates, strain origins, resistance rates to commonly used antimicrobial agents, and resistance gene features, comparing the antimicrobial resistance between clinically isolated strains and environmentally isolated strains. ResultsFrom 2018 to 2023, a total of 1 416 samples were collected from the hospital and a total of 272 strains of AB were detected, with a positive detection rate of 19.21%. The detection rate gradually decreased year-on-year (χ²_trend=45.290, P<0.001). The majority of samples originated from patient-contacted items (34.56%, 94/272), followed by shared items (26.84%, 73/272) and healthcare worker-contacted items (15.07%, 41/272). From 2018 to 2023, the resistance rate of AB on environmental surfaces and healthcare workers’ hands to commonly tested antibiotics in the hospital ranged from 10% to 40%. The resistance rates to cefotaxime (42.52%) and piperacillin (38.58%) were relative high, while the resistance to polymyxin E (1.57%), polymyxin B (2.36%), and doxycycline (3.94%) maintained low. The annual fluctuations in resistance to cefotaxime, piperacillin, ceftriaxone, tobramycin, doxycycline, minocycline and cotrimoxazole were statistically significant (all P<0.05). There were statistically significant differences in the resistance of clinical and environmental isolates to ampicillin/sulbactam, cefepime, ceftazidime, subamphetamine, meropenem, piperacillin, aztreonam, gentamicin, tobramycin, minocycline, ciprofloxacin, levofloxacin, and cotrimoxazole in the hospital from 2018 to 2023 (all P<0.05). The resistance rate of clinical isolates was generally high, especially to β-lactam and quinolone drugs, which were mostly above 80% [such as cefepime (93.86%), cefotaxime (97.37%), imipenem (98.25%), and ciprofloxacin (99.12%)]. The resistance rate of environmental isolated strains to similar antibiotics was relatively lower, mostly concentrated at 10%‒30%. The whole-genome sequencing of 34 carbapenem-resistant Acinetobacter baumannii (CRAB) strains isolated from the hospital environment in 2023 revealed that the main resistance mechanism was overexpression of efflux pumps (51.97%), followed by changes in target sites (32.46%). Among the 34 CRAB strains, carbapenem resistance genes OXA-23 and OXA-51 were detected in 6 strains (17.65%), while genes such as KPC, IMP, VIM, and SIM were not detected. ConclusionFrom 2018 to 2023, AB in the hospital environment exhibited high resistance rates to certain antimicrobial agents and carried multiple resistance genes, indicating a potential transmission risk. It is necessary to further strengthen bacterial resistance monitoring and hospital infection control, and use antibiotics reasonably.

Objective: To investigate the hematological characteristics of the rare McLeod phenotype associated with X-linked chronic granulomatous disease, KEL and XK gene analysis, identification of unexpected antibodies, serological characteristics of high-frequency antigen antibodies, and transfusion strategies. Methods: Serological methods were employed to determine the ABO, Rh, and other blood group system antigen phenotypes of the child, along with screening and identification of unexpected antibodies. The titers of high-frequency antigen antibodies were measured using tube antihuman globulin and microcolumn gel card techniques. Kell blood group typing was performed using serological and genotyping methods, while XK gene sequencing was conducted via next-generation sequencing. Peripheral blood smears from the child's mother were examined for erythrocyte morphology. Results: The child's serological results were as follows: blood group O, ccDEE, MM, Le(a-b+), JK(a+b+), Fy(a+b-), and Kell phenotype K-k+, Kp(a-b+). Plasma analysis revealed alloantibodies anti-C、e, as well as a high-frequency antigen antibody anti-KL, with titers of 512 (tube method) and 2 048 (microcolumn gel method). Genotyping results showed KEL genotype K-k+, Kp(a-b+), Js(a-b+), while XK gene NGS identified a hemizygous deletion of exons 1-3 (XK N. 01), consistent with XK: -1 or Kx-(McLeod). The mother's peripheral blood smear exhibited prominent acanthocytes. Conclusion: The hematological features of this rare McLeod phenotype with X-CGD include weakened Kell antigen expression and a complete exon deletion in the XK gene. Early clinical attention should be given to the symptoms and laboratory diagnosis of X-linked chronic granulomatous disease in pediatric patients. XK genotyping for McLeod phenotype should be prioritized to guide cautious transfusion strategies, preventing life-threatening complications due to incompatible blood products.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.