Background The quality of occupational health technical services is directly linked to the protection of workers' health rights and the efficacy of occupational disease prevention and control. However, the industry still faces critical challenges: sporadic instances of institutional non-compliance and persistent irregularities in professional practice continue to undermine overall service performance. Objective To assess the current quality status of occupational health technical services in Zhejiang Province and propose countermeasures for quality improvement, providing a scientific basis for policy optimization and service delivery quality enhancement. Methods A total of 69 occupational health technical service institutions in Zhejiang Province that obtained formal accreditation as of April 30, 2024, were sampled, including 3 public institutions and 66 private institutions (comprising 3 formerly Class-A, 28 formerly Class-B, 11 formerly Class-C, and 24 newly certified institutions). Following the Technical Protocol for Quality Monitoring of Occupational Health Technical Service in Zhejiang Province and the Technical Protocol for Proficiency Testing of Occupational Health Detection in Zhejiang Province, a quality assessment task force comprising national and provincial experts was established. Evaluation was conducted across four dimensions: qualification maintenance and compliance, standardization of technical services, authenticity of technical services, and proficiency testing, utilizing a combination of document review, on-site inspections, and technical skill assessments. Results The occupational health technical service institutions in Zhejiang Province were predominantly private entities (82.5%), with significant disparities in overall service quality. The pass rates for qualification maintenance and compliance, technical service standardization, technical service authenticity, and the excellence rate for laboratory proficiency testing were 81.5%, 80.7%, 97.3%, and 90.4%, respectively. Regarding qualification maintenance, the pass rates for "environmental conditions" (49.8%, 56.7%) and "instrumentation and equipment" (58.2%、65.6%) were significantly lower for formerly Class-C and newly certified institutions compared to other categories. In terms of technical standardization, "standardized on-site inspections" recorded the lowest pass rate (67.4%), with newly certified institutions at only 48.0%. Regarding technical service authenticity, formerly Class-C institutions exhibited issues such as missing raw chromatograms for blank samples (85.7% pass rate). In laboratory proficiency testing, public and formerly Class-A institutions achieved 100% excellence rates, but the performance of formerly Class-C and newly certified institutions was comparatively weak; specifically, the failure rate for organic analysis in formerly Class-C institutions reached 20%; the failure rate for dust testing items in newly certified institutions was 10.3%. Conclusion The overall quality of occupational health technical services in Zhejiang Province still requires significant improvement, particularly in basic institutional conditions, the standardization of on-site inspections, and laboratory proficiency in organic and dust analysis. Formerly Class-C and newly certified institutions should be the primary focus of quality management efforts. Differentiated regulatory strategies are recommended, alongside strengthening interim and ex-post supervision to gradually enhance the quality of occupational health technical services across all institutions.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Objective To investigate the effects of combined exposure to benzene series, dust and noise on occupational high-frequency hearing loss (HFHL) in the electronics manufacturing industry. Methods A total of 740 employees from an electronic manufacturing enterprise were selected as the study subjects. According to the detection of occupational hazard factors, they were divided into noise group, noise benzene group, and noise dust group, with 325, 167, and 248 people, respectively. Occupational health examinations were conducted on the study participants to analyze their HFHL detection status and influencing factors. Results Among all participants (n=740), 456 (61.62%) were male, 284 (38.38%) were female, and 123 (15.62%) were detected with HFHL. The detection rate of HFHL increased with age and length of service (P<0.01). According to the exposure factor grouping, the detection rates of HFHL in the noise group, noise benzene group, and noise dust group were 12.00%, 19.16%, and 20.97%, respectively (P<0.05). Multivariate logistic regression analysis showed that workers aged ≥ 40 years, with 10-20 years of work experience, ≥ 20 years of work experience, exposure to noise and benzene series, and exposure to noise and dust had an increased risk of developing HFHL to 1.734, 1.923, 3.071, 1.706, and 1.527, respectively (P<0.05). Conclusion Combined exposure to noise and benzene series, combined exposure to noise and dust, as well as age and extended length of service may increase the risk of HFHL among workers.

Objective We reviewed and analyzed the epidemiological data of 83 imported dengue fever cases reported in Fengtai District, Beijing, from 2007 to 2025, to delineate their epidemiological characteristics and guide the dengue fever prevention and control strategy in the future. Methods Data on dengue fever cases in Fengtai District from 2007 to 2025 were collected to identify the epidemic factors and characteristics using descriptive epidemiological method. Results All 83 dengue fever cases reported in this region over the past two decades have been imported from abroad or other domestic areas, with Southeast Asian countries being identified as the primary source of imported infections, accounting for 42 cases (50.60%), followed by 15 cases (18.07%) from Chinese provinces other than Beijing. The remaining import sources included 13 cases from South Asia,8 from African countries, and each from Oceania, South America, and Europe. Disease onset was found concentrated between May and November. The sex ratio (male-to-female) is 1.59:1. The age of onset ranged from 14 to 61 years old, with a median age of 36. The largest proportion of cases was observed among cadres and office workers (31 cases, 37.35%), followed by businessmen (13 cases, 15.67%), and householders or the unemployed (10 cases,12.05%). Travel visit was identified as the primary reason for their exposure history. Conclusions All dengue fever cases reported in Fengtai from 2007 to 2025 were imported, with an increasing trend of imported cases in Beijing originating from other Chinese provinces. It is essential to strengthen health education for foreign travelers and returning residents, enhance clinical diagnosis and treatment training for medical personnel, eliminate mosquito breeding sites, and reduce mosquito density through active patriotic health campaigns.

Neoadjuvant therapy has become the standard treatment for locally advanced resectable esophageal cancer, significantly improving long-term survival compared to surgery alone. Neoadjuvant therapy has evolved to include various strategies, such as concurrent chemoradiotherapy, chemotherapy, immunotherapy, or targeted combination therapy. This enriches clinical treatment options and provides a more personalized and scientific treatment approach for patients. This article aims to comprehensively summarize current academic research hot topics, review the rationale and evaluation measures of neoadjuvant therapy, discuss challenges in restaging methods after neoadjuvant therapy, and identify the advantages and disadvantages of various neoadjuvant therapeutic strategies.

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

Following the release of the Technical Requirements on Quality Control and Standard Establishment of Traditional Chinese Medicine Formula Granules by the National Medical Products Administration in 2021, Chinese Pharmacopoeia Commission has promulgated 296 national drug standards so far, and most provinces have started the work of establishing provincial standards as supplements. The promulgation of standards fostered high-quality development of the industry. Since the implementation of national and provincial standards for more than three years, enterprises have gained deep understanding and hands-on experiences on the characteristics, technical requirements, production process, and quality control of traditional Chinese medicine(TCM) formula granules. Meanwhile, challenges have emerged restricting the high-quality development of this industry, including how to formulate quality control strategies for medicinal materials and decoction pieces, how to reduce manufacturing costs, and how to improve the pass rate and product stability under high standards. Based on the work experiences from standard management and process research, this article analyzed the distribution of sources, processing methods, dry extract rate ranges, process requirements for volatile oil-containing decoction pieces, control measures of safety indices, characteristics and trends of setting characteristic chromatograms or fingerprints, characteristics and trends of setting content ranges, and main differences between national standards and provincial standards. On the one hand, this article aims to present main characteristics for deeply understanding different indicators in standards and provide basic ideas for establishing quality and process control systems. On the other hand, from the perspective of industrial practice, suggestions are put forward on the important aspects that need to be focused on in the quality and process control of TCM formula granules.
Drugs, Chinese Herbal/chemistry* ; Quality Control ; Medicine, Chinese Traditional/standards* ; China ; Drug Industry/standards*

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Objective: To assess the effectiveness of vertebral cement augmentation (VCA) at upper instrumented vertebra (UIV) and UIV+1 in preventing proximal junction complications in correction surgery for adult spinal deformity patients. Methods: A literature search was conducted on Web of Science, PubMed, and Cochrane Library databases for comparative studies published before December 30th, 2024. Two reviewers independently screened eligible articles based on the inclusion and exclusion criteria, assessed study quality with Newcastle-Ottawa scale, and extracted data like study characteristics, surgical details, primary and secondary outcomes. Data analysis was performed using Review Manager 5.4 and Stata software. Results: Of all 513 papers screened, a meta-analysis was conducted on 7 articles, which included 333 cases in the VCA group and 827 cases in the control group. Patients in the VCA group had significantly older age and lower T score than patients in the control group. Although there was no statistically significant difference in the incidence of proximal junctional failure between the 2 groups, the results of the meta-analysis showed that the incidence of proximal junctional failure and the need for revision surgery were reduced by 36% and 71%, respectively, in the VCA group. One study reported 2 clinically silent pulmonary cement embolism and 1 patient requiring surgical decompression for cement leak into the spinal canal. Conclusion This meta-analysis supported the use of VCA in corrective surgery for spinal deformities patients, especially in patients with advanced age and osteoporosis.

Objective To investigate the predictive value of temperature gradients on the mortality of sepsis patients and their correlation with fluid input.Methods By means of a prospective observational method,154 patients with sepsis or septic shock admitted to the Department of Critical Care Medicine at Nanfang Hospital,Southern Medical University from November 2019 to November 2021 were included as research subjects.They were divided into a survivor group(n=118)and a non-survivor group(n=36)according to whether they survived within 28 days.The core-to-toe temperature gradient(CTTG)and toe-to-room temperature gradient(TRTG)were monitored and calculated immediately upon admission to the intensive care unit(ICU)and 6 hours after admission.Receiver operating characteristic(ROC)curve was used to explore the predictive value of temperature gradients on mortality,and multivariate Cox regression analysis was performed to explore the risk factors of 28-day mortality in sepsis patients.The results were verified through survival analysis.Correlation analysis and multivariate analysis of variance were used to explore the correlation between temperature gradients and fluid input,as well as noradrenaline doses.Results Among the 154 patients,118 survived within 28 days(survivor group),and 36 died(non-survivor group).ROC curve and multivariate Cox regression analysis showed that a toe-to-room temperature gradient of≤5.35℃within 6 hours after admission was a risk factor for 28-day mortality.Compared with patients with a high toe-to-room temperature gradient(>5.35℃),patients with a low toe-to-room temperature gradient(≤5.35℃)had a 2.74-fold increase in the risk of 28-day mortality(P=0.004,95%CI 1.54,9.12).The CTTG and TRTG upon admission to the ICU and 6 hours after admission were not significantly associated with fluid input or noradrenaline doses(P>0.05).Conclusions A toe-to-room temperature gradient of less than or equal to 5.35℃within 6 hours after ICU admission is a risk factor for 28-day mortality in sepsis patients.The improvement of temperature gradients at different time points is not associated with fluid input.