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.

ObjectiveTo investigate the association between immunoglobulin G （IgG）/immunoglobulin M （IgM） ratio and prognosis in patients with initially unresectable hepatocellular carcinoma （iuHCC） receiving TTP triple therapy with transcatheter arterial chemoembolization （TACE）， tyrosine kinase inhibitor （TKI）， and programmed cell death protein-1 （PD-1） inhibitors. MethodsA retrospective analysis was performed for the clinical data of 151 iuHCC patients who received TTP triple therapy in Department of Hepatobiliary Surgery， Guangxi Medical University Cancer Hospital， from November 2019 to December 2022， and according to IgG/IgM ratio， they were divided into high IgG/IgM group （IgG/IgM ratio >13.23） and low IgG/IgM group （IgG/IgM ratio ≤13.23）. The t-test was used for comparison of continuous data between groups， and the chi-square test was used for comparison of categorical data between groups. The Kaplan-Meier method and the log-rank test were used for survival analysis， and the Cox proportional hazards model was used to investigate the potential influencing factors for overall survival （OS）. ResultsThe 151 patients had a median OS of 26.7 months （95% confidence interval ［CI］： 19.8-not reached） and a median progression-free survival of 12.5 months （95%CI： 10.4 — 15.8）. The objective response rate was 83.4% and the disease control rate was 94.0%. There were no significant differences in baseline data between the high IgG/IgM group and the low IgG/IgM group （all P>0.05）. There was a significant difference in median OS between the high IgG/IgM group and the low IgG/IgM group （20.6 months vs not reached， P=0.016）. In both the high IgG/IgM group and the low IgG/IgM group， salvage hepatectomy was significantly associated with the improvement in OS （χ²=8.297 and 10.307， both P<0.05）. The multivariate analysis showed that high IgG/IgM ratio （hazard ratio ［HR］=1.799， 95%CI： 1.077 — 3.006， P=0.025）， baseline alpha-fetoprotein >400 ng/mL （HR=1.762， 95%CI： 1.017 — 3.050， P=0.043）， and BCLC stage （HR=2.265， 95%CI： 1.212 — 4.232， P=0.010） were independent influencing factors for OS. ConclusionHigh IgG/IgM ratio is associated with a poorer prognosis in iuHCC patients receiving TTP triple therapy， and salvage hepatectomy has a potential value in improving the prognosis of patients with a high IgG/IGM ratio.

Objective To explore the predictive value of artificial intelligence (AI)-based lung nodule CT quantitative analysis for the invasion degree of lung adenocarcinoma spectrum lesions. Methods According to the invasion degree of lung adenocarcinoma spectrum lesions, patients with surgically and pathologically confirmed lung adenocarcinoma spectrum lesions from January to June 2023 in Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University were retrospectively collected and divided into a non-invasive group and an invasive group, including atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma patients in the non-invasive group, and invasive adenocarcinoma patients in the invasive group. All enrolled patients underwent chest CT before surgery, and then the lung nodules were quantitatively analyzed using an AI-based computer-aided diagnosis system to compare the related quantitative parameters of lung nodules that have been surgically removed and pathologically confirmed as lung adenocarcinoma spectrum lesions between the two groups. The relationship between various CT quantitative features and the invasion degree of lung adenocarcinoma spectrum lesions was analyzed. Results A total of 149 patients (149 lesions) were included, including 42 males and 107 females, aged 29-81 (56.35±10.75) years. There were 72 patients in the non-invasive group and 77 patients in the invasive group. Statistical differences were observed between the two groups in long diameter, short diameter, volume, surface area, mass, maximum cross-sectional area, 3D long diameter, maximum CT value, minimum CT value, average CT value, entropy, kurtosis, skewness, malignancy probability and other indicators (P<0.05). Multivariate binary logistic regression analysis showed that long diameter [OR=1.687, 95%CI (1.364, 2.085), P<0.001], average CT value [OR=1.006, 95%CI (1.002, 1.009), P=0.002], and malignancy probability [OR=1.034, 95%CI (1.005, 1.063), P=0.020] were independent risk factors for the invasion degree of lung adenocarcinoma. The predictive model combining the above parameters demonstrated optimal performance, with an area under the receiver operating characteristic curve of 0.951, sensitivity of 0.818, and specificity of 0.972. Using a Nomogram to quantify the three independent risk factors, the cross-validation was performed to evaluate the stability of the model, and the average C-index of cross-validation was 0.950, with each fold C-index >0.75, indicating that the prediction performance of the model was stable, and the calibration curve and decision curve indicated good predictive performance. Conclusion The visualization prediction model constructed by AI-based quantitative analysis of lung nodules in CT demonstrates significant discriminative effectiveness in the assessment of invasiveness in lung adenocarcinoma spectrum lesions. This visualization prediction model can provide a quantitative decision-making basis for the preoperative identification of the degree of invasiveness in lung adenocarcinoma spectrum lesions.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

ObjectiveTo compare the morphological characteristics， phenological stages， and chemical components of two traditional Chaenomeles speciosa cultivars—"Luohanqi" and "Pingguohong"， thus providing a basis for new cultivar breeding， standardized cultivation， and quality evaluation of authentic medicinal materials. MethodsThe morphological indices of leaves， floral organs， and fruits of the two cultivars were measured， and the phenological sequences were recorded. Microplate assays and high-performance liquid chromatography （HPLC） were employed to determine the levels of total flavonoids， total phenols， polysaccharides， amino acids， oleanolic acid， and ursolic acid. Finally， comprehensive comparative analysis was conducted. ResultsThe two cultivars exhibited generally similar tree vigor but showed significant differences in internode length， leaf morphology， floral organs， fruit development， and phenological stages. "Luohanqi" had significantly larger leaves ［length： （124.7±11.97） mm vs. （105.06±9.16） mm； width： （47.81±4.68） mm vs. （41.98±5.15） mm］， a wider ovary diameter ［（10.76±1.25） mm vs. （8.28±1.18） mm］， a higher fruit shape index （1.6 vs. 1.2）， and a much higher calyx persistence rate （78.28% vs. 8.02%） than "Pingguohong". Its phenological stages were 7-18 days later than those of "Pingguohong". No significant differences were found in the levels of total flavonoids， total phenols， total polysaccharides， total amino acids， or oleanolic acid， while "Pingguohong" had significantly higher ursolic acid content than "Luohanqi". ConclusionThe two main cultivars of C. speciosa in Xuancheng city， Anhui province exhibit marked differences in leaf morphology， floral organ structure， fruit traits， calyx persistence， phenological stages， and chemical composition. These findings provide a scientific basis for subsequent breeding of new cultivars， targeted cultivation， and studies on quality formation of authentic medicinal materials.

ObjectiveTo compare the morphological characteristics， phenological stages， and chemical components of two traditional Chaenomeles speciosa cultivars—"Luohanqi" and "Pingguohong"， thus providing a basis for new cultivar breeding， standardized cultivation， and quality evaluation of authentic medicinal materials. MethodsThe morphological indices of leaves， floral organs， and fruits of the two cultivars were measured， and the phenological sequences were recorded. Microplate assays and high-performance liquid chromatography （HPLC） were employed to determine the levels of total flavonoids， total phenols， polysaccharides， amino acids， oleanolic acid， and ursolic acid. Finally， comprehensive comparative analysis was conducted. ResultsThe two cultivars exhibited generally similar tree vigor but showed significant differences in internode length， leaf morphology， floral organs， fruit development， and phenological stages. "Luohanqi" had significantly larger leaves ［length： （124.7±11.97） mm vs. （105.06±9.16） mm； width： （47.81±4.68） mm vs. （41.98±5.15） mm］， a wider ovary diameter ［（10.76±1.25） mm vs. （8.28±1.18） mm］， a higher fruit shape index （1.6 vs. 1.2）， and a much higher calyx persistence rate （78.28% vs. 8.02%） than "Pingguohong". Its phenological stages were 7-18 days later than those of "Pingguohong". No significant differences were found in the levels of total flavonoids， total phenols， total polysaccharides， total amino acids， or oleanolic acid， while "Pingguohong" had significantly higher ursolic acid content than "Luohanqi". ConclusionThe two main cultivars of C. speciosa in Xuancheng city， Anhui province exhibit marked differences in leaf morphology， floral organ structure， fruit traits， calyx persistence， phenological stages， and chemical composition. These findings provide a scientific basis for subsequent breeding of new cultivars， targeted cultivation， and studies on quality formation of authentic medicinal materials.

Consciousness, which underpins human perception, cognition, and behavioural responses, has long been a major focus of neuroscience and medicine. Alterations in consciousness occur not only during reversible processes such as sleep and anaesthesia, but also in pathological conditions, including disorders of consciousness (DOC) and brain injury. With recent advances in the synchronized acquisition and analysis of multimodal physiological signals, such as electroencephalography (EEG) and electrocardiography (ECG), the dynamic interactions between the heart and brain have become increasingly apparent. A growing body of evidence indicates that heart-brain interactions are closely associated with the maintenance and transition of consciousness states, offering a novel perspective for elucidating their underlying mechanisms and supporting relevant clinical assessments. Current approaches to heart-brain interaction analysis for consciousness-state assessment can be broadly grouped into three categories: ECG-based methods, EEG-based methods, and heart-brain coupling analysis. These approaches characterize heart-brain interactions during changes in consciousness from the complementary perspectives of autonomic regulation, cortical interoceptive processing, and cross-system information transfer, respectively. Nevertheless, this field remains at an early stage. Most studies have focused on a single experimental context or a fixed analytical method, with insufficient systematic consideration of the applicability and limitations of different approaches. Moreover, the mechanisms underlying the emergence and alteration of consciousness remain incompletely understood. Accordingly, this review examines heart-brain interaction methods employed in recent consciousness-state assessment studies in accordance with progressively deeper analytical perspectives, and systematically evaluates the advantages and limitations of these methods across distinct consciousness-related conditions. Heart rate variability (HRV) is one of the classical ECG-based indices used to investigate heart-brain interactions. It refers to fluctuations in consecutive interbeat intervals over a specified time period and is regarded as a physiological measure of autonomic nervous system regulation that may indirectly reflect the strength of heart-brain interactions. Depending on the analytical dimension, HRV analysis is commonly divided into time-domain, frequency-domain, and complexity-index analyses. These approaches characterize autonomic activity in terms of temporal fluctuations, the distribution of frequency components, and nonlinear dynamical properties, respectively. Although extensive research supports their physiological interpretation, they cannot characterize directional interactions from the heart to the brain. Among EEG-based methods for heart-brain interaction analysis, EEG activity directly related to cardiac fluctuations has received particular attention. One of the most widely used measures is the heartbeat-evoked potential (HEP). HEP-based approaches are considered to directly reflect the capacity of the cerebral cortex to process cardiac afferent signals and may be especially advantageous for characterizing rapidly changing consciousness levels, such as those observed during sleep. However, they do not comprehensively capture dynamic bidirectional information exchange between the brain and heart and are susceptible to cardiac artefacts, respiration, and variation in preprocessing procedures. Heart-brain coupling analysis quantifies functional associations between ECG and EEG through cross-system interaction measures, thereby assessing dynamic interactions between the heart and brain. It emphasizes information transfer, feedback regulation, and cross-timescale coordination between the two systems, offering a more comprehensive perspective for investigating changes in consciousness during sleep, anaesthesia, and DOC. However, the neurophysiological mechanisms and physiological interpretation of individual computational indices have not yet been fully established, and the robustness of their results and their potential for clinical generalization require further validation. Finally, this review summarizes the commonalities and differences among the three classes of methods across research domains involving different consciousness states. It further argues that heart-brain interaction research requires deeper investigation at both the mechanistic level of consciousness and the level of clinical application. Current challenges and key issues are identified, and future directions and feasible pathways for heart-brain interaction research are discussed, with the aim of providing new perspectives for researchers in related fields.

Chinese medicine therapy for removing gallstones is one of the methods for the treatment of cholelithiasis.In the view of Professor Liu Fengbin,attacking of external pathogens,improper diet and emotional disorders contribute to the main causes of cholelithiasis,and the pathogenesis of cholelithiasis is due to qi stagnation of both liver and gallbladder,and internal obstruction of damp-heat.The occurrence of cholelithiasis is closely related to deficiency of spleen and stomach,and is correlated with the pathological factors of turbid phlegm and blood stasis.For the Chinese medicine treatment of cholelithiasis,Professor Liu follows the principle of"treatment in accordance with three categories of etiological factors"(i.e,seasons,environment and body constitution).He advocates the integration of traditional Chinese medicine and western medicine,and is good at utilizing Lingnan herbs and distinctive herbs that can dissolve stones and remove stones.The treatment for cholelithiasis is mainly through the therapies of soothing liver and alleviating depression,clearing heat and removing dampness,and normalizing gallbladder function to remove stones,and is also supplemented by the therapies of invigorating spleen and replenishing qi,regulating qi to resolve phlegm,and activiting qi movement and blood circulation.Modified Da Chaihu Decoction plus Sijin Decoction is often used as a basic formula for treating cholelithiasis,which is mainly composed of Desmodii Styracifolii Herba,Galli Gigerii Endothelium Corneum,Bupleuri Radix,Curcumae Radix,Scutellariae Radix,Aucklandiae Radix,Aurantii Fructus Immaturus stir-fried with bran,Paeoniae Radix Rubra,Linderae Radix,and Rhei Radix et Rhizoma.

Objective To develop a precise method for analyzing urinary peptides based on electromembrane extraction(EME)combined with liquid chromatography-tandem mass spectrometry(LC-MS/MS),and to evaluate its potential applicability in tumor biomarker screening.Methods A total of 15 disease-associated peptides were selected as the target analytes.A supported liquid membrane(SLM)composed of n-octanol containing 5%di(2-ethylhexyl)phosphate was employed,with the donor phase being a 1∶1 mixture of urine and 100 mmol/L formic acid and urine,and the acceptor phase being 20 mmol/L formic acid containing 50%dimethyl sulfoxide(DMSO).After EME at 40 V for 15 min,the acceptor phase solution was analyzed by LC-MS/MS.Subsequently,the method,EME combined with LC-MS/MS(EME-LC-MS/MS),was preliminarily validated utilizing urine samples from 12 healthy controls and 7 patients with urinary system tumors.Results All 15 peptides exhibited excellent linearity in the range of 0.1-100.0 ng/mL(r≥0.995),with the limits of detection(LODs)being 0.01-0.50 ng/mL and the limits of quantification(LOQs)being 0.03-1.50 ng/mL.The spiked recoveries ranged from 21.0%to 71.2%,with relative standard deviations(RSDs)of 0.8%-20.0%(n=3).Small-sample analysis of clinical specimens revealed that the concentration of bradykinin 1-5 in the urine were significantly higher in tumor patients(median:0.65 ng/mL)than that in healthy controls(median:0.37 ng/mL)(P<0.05),suggesting its potential as a specific biomarker for urinary system tumors.Conclusion The EME-LC-MS/MS method established in the study features simplicity,high efficiency,and high sensitivity,enabling precise determination of trace-level peptides in urine samples.Moreover,this approach provides a reliable methodological basis for disease biomarker screening and promotes the clinical application of electromembrane extraction.

Pleckstrin homology-like domain family A member 1(PHLDA1)is a pro-apoptotic factor as well as a key regulator of metabolic diseases.In obesity-related diseases,PHLDA1 can reduce liver triglyceride production through inhibition of the expression of sterol regulatory?element binding proteins?1(SREBP?1),and reduce fat syn?thesis through inhibition of peroxisome proliferator?activated receptor γ(PPARγ).However,in cardiovascular dis?eases,PHLDA1 increases vascular calcification,dysfunction,thereby aggravates ischemia?reperfusion injury in the heart and brain.The dual role of PHLDA1 has also been confirmed in tumors.In summary,PHLDA1,as a multi?functional factor,plays different functional roles through various mechanisms.