National biological standards for antibiotics are critical components of the antibiotic quality control system and serve as reference materials for measuring and calibrating the biological activity of antibiotics. This article systematically reviews the classification, definition of potency units, and current research status of commercially available national antibiotic biological standards in China. At present, these standards can be categorized based on chemical structure, number of components, and development methods. The definition of potency units has evolved from an early “arbitrarily assigned unit” to “being represented by the mass of the antibiotic salt” and, more recently, to the current mainstream approach of “being represented by the mass of the active ingredient”. This evolution reflects a shift in quality control philosophy from primarily biological analysis to a system dominated by chemical analysis supplemented by biological methods. Current research focuses on optimizing potency determination methods, studying the unification of content and potency, and implementing dual quality control of both the potency and the ratio/content of active components in multi-component antibiotics. For complex multi-component antibiotics, the microbiological assay based on biological activity remains irreplaceable in quality control. Future efforts should emphasize further method optimization, ensuring batch-to-batch consistency of standards, and advancing precision quality control as key research priorities for antibiotic biological standards.

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.

Antimicrobial resistance (AMR) poses a critical global health threat. This review systematically examines AMR surveillance technology advances, from conventional culture methods to modern molecular diagnostics (e.g., whole-genome sequencing) and artificial intelligence-assisted approaches. It focuses on the current application of mass spectrometry, machine learning predictive models, and real-time surveillance networks. To address challenges including inadequate technical standardization, clinical translation barriers, and data-sharing limitations, we propose integrated ^"genotype-phenotype^" strategies and global standardization framework, while exploring future applications of CRISPR-based portable detection, single-cell sequencing, and blockchain technologies.

Objective To explore the incidence and risk factors of sarcopenia in non-dialysis patients with stage 5 chronic kidney disease (CKD). Methods Non-dialysis stage 5 CKD patients in Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences from October 2023 to October 2024 were enrolled and divided into the non-sarcopenia group and the sarcopenia group according to lean tissue index (LTI) and handgrip strength (HGS). Demographic data of patients were collected and biochemical indicators were detected. Serum advanced glycation end products (AGEs) were detected by competitive ELISA. Multivariate logistic regression analysis was conducted to analyze the risk factors of sarcopenia in these patients. The diagnostic efficacy of the risk factors was evaluated by the receiver operating characteristic (ROC) curve. Results A total of 102 non-dialysis stage 5 CKD patients with (68.52±7.39) years, were enrolled, including 61 male (59.8%). Among them, 24 were diagnosed with sarcopenia (23.5%). Compared with patients in non-sarcopenia group, patients in sarcopenia group were older and had higher interleukin-6 (IL-6) and AGEs, lower body mass index (BMI) and lower serum albumin (sAlb; P＜0.05). Multivariate logistic regression showed that advanced age (OR＝1.153, 95%CI 1.045–1.273, P＝0.005), higher IL-6 (OR＝1.165, 95%CI 1.042–1.302, P＝0.007)、lower sAlb (OR＝0.675, 95%CI 0.542–0.840, P＝0.001), and higher serum AGEs (OR＝1.105, 95%CI 1.013–1.206, P＝0.024) were independent risk factors of sarcopenia in non-dialysis CKD 5 patients. The areas under the curve (AUC) for the age, IL-6, sAlb, and serum AGEs were 0.896, 0.643, 0.658, and 0.724, respectively. Conclusions Sarcopenia is common in non-dialysis stage 5 CKD patients, and the risk of sarcopenia is higher in non-dialysis stage 5 CKD patients with advanced age, higher IL-6, lower sAlb, and higher serum AGEs.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

Objective This study compared organ doses and effective doses in typical adult patients during dental CBCT examinations using a head-and-neck anthropomorphic phantom and a whole-body phantom. It aimed to determine an appropriate method for evaluating effective dose in dental CBCT. Methods A domestically manufactured dental CBCT unit was used to scan an adult male anthropomorphic phantom under simulated clinical conditions. Thermoluminescent dosimeters were placed on organs and tissues of a whole-body anthropomorphic phantom. Organ equivalent doses were measured, and effective doses were calculated using two approaches: one based on all organ doses from the whole-body phantom, and the other based only on head-and-neck organ doses. Entrance surface air kerma (K_a,e) was also measured at the lens of the eye, thyroid, thymus, and gonads. Results The effective dose estimated with the whole-body phantom was 382.03 μSv. The effective dose estimated with the head-and-neck phantom was 354.03 μSv. The radiation dose to head-and-neck organs and tissues accounted for 92.7% of the total effective dose. The highest K_a,e values were found in the parotid glands, followed by the lens of the eye. Conclusion During dental CBCT scanning, the contribution of organ below the head and neck to the total effective dose is limited. To simplify dose assessment procedures, the effective dose for the patient can be evaluated using only a head-and-neck phantom.

The establishment of endometrial receptivity is a critical prerequisite for successful embryo implantation and early pregnancy. However, current clinical methods for assessing receptivity are insufficiently validated, and their reliability is inadequate for guiding precision medicine. Although microfluidic organ-on-a-chip models have emerged as promising in vitro platforms garnering significant academic interest, their practical clinical translation has been hindered by challenges in accurately replicating patient-specific microenvironments and the lack of standardized quantitative evaluation systems. Recently, Lee et al. published a groundbreaking study in Nature Communications, which effectively addresses these critical limitations by integrating a patient-derived vascularized endometrium-on-a-chip (EoC) with a newly developed endometrial receptivity scoring system (ERS²). This commentary offers a comprehensive analysis of the key findings from this work, systematically examining the design, construction, dynamic simulation validation, and translational potential of the EoC-ERS² platform. This innovative approach successfully transforms conventional subjective assessments of endometrial receptivity into a digitized, objective, and quantitative evaluation framework. As a result, it establishes a robust foundation for a paradigm shift toward personalized infertility management and precision medicine in reproductive health.

ObjectiveTo address the key challenges in breast electrical impedance tomography (EIT) reconstruction including the severe ill‑posedness of the inverse problem, limited measurement information from the electrodes, low contrast of small lesions, as well as the common drawbacks of traditional reconstruction methods such as boundary blurring, low spatial resolution, and weak small-target detection capability, and to overcome the limitations of existing deep learning approaches namely low training efficiency, insufficient physical consistency, and substantial information loss during the low‑to‑high dimensional mapping process, this study proposes a multi‑scale attention and dynamic sparse mask fusion network (MADS‑Net). The proposed method aims to enhance high‑resolution reconstruction accuracy, small-lesion detectability, noise robustness, and computational efficiency in breast‑screening-oriented EIT, thereby providing a feasible solution for the application of EIT in breast functional imaging. MethodsThe proposed network adopts a strategy of “locate first, reconstruct later”. It realizes adaptive allocation of computational resources through a dynamic mask module, alleviates information loss in the low-to-high dimensional mapping process via a multi-scale attention mechanism, and embeds physical constraints to improve the physical rationality of reconstruction results. The performance of the proposed method was validated through systematic numerical simulations, agar phantom experiments, and porcine tissue experiments. Comparisons were made with mainstream methods including the Gauss-Newton algorithm, convolutional neural networks, U-Net, and Deep D-Bar. The image correlation coefficient (ICC), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) were adopted as quantitative evaluation metrics. Furthermore, lesion‑level evaluation metrics were introduced to assess the capability of different methods in reconstructing abnormal target boundaries and small‑sized target regions. ResultsExperimental results demonstrate that MADS-Net outperforms all comparison methods across all test scenarios. In numerical simulations, it achieves higher accuracy in target morphology and spatial location recovery as well as better background suppression. In agar phantom experiments, its detection and imaging reliability for small targets is significantly superior to the comparison methods. In porcine tissue experiments, the model maintains good generalization performance and can adapt to complex biological tissue imaging scenarios. Quantitative results show that MADS-Net achieves an average ICC of 0.917 6±0.048 8, an RMSE as low as0.060 6±0.031 6, and a PSNR of (23.893 9±2.685 3) dB. It successfully detects tiny targets as small as 5 mm within a 100 mm diameter sensing field, reduces the total training duration by 23.5% compared with the baseline method, and maintains stable reconstruction performance even under strong noise interference. ConclusionThe results demonstrate that MADS-Net achieves favorable EIT reconstruction performance in numerical simulations, agar phantom experiments, and porcine tissue experiments, even under common practical imaging challenges including low signal-to-noise ratio and blurred tissue boundaries. This study provides a methodological reference for further research on EIT in breast-screening-related scenarios and other medical functional imaging tasks, and also offers a low-cost, radiation-free alternative for breast lesion screening that aligns with the clinical demand for non-invasive diagnostic tools. It should be noted that real clinical breast data have not yet been used for validation in this study, and the clinical applicability of the proposed method requires further investigation. Future work will prioritize collecting multi-center real clinical breast datasets, and further optimize the model’s generalization ability to adapt to individual differences in breast tissue composition to promote its clinical application.

The recombinant human thyroid-stimulating hormone (rhTSH) can be used for differentiated thyroid cancer (DTC) preparation before ¹³¹I therapy, and follow-up. Since its approval by the American Food and Drug Administration (FDA) in 1998, rhTSH has been clinically used in European countries, the United States, and other countries for nearly 30 years. In April 2024, China’s first independently developed rhTSH agent was approved. Based on clinical experience and evidence from domestic and international studies, an expert consensus on the application of rhTSH has been developed following thorough consultations among experts. The consensus offers evidence-based recommendations to guide the appropriate use of rhTSH in clinical practice.