Objective To analyze the genomic characteristics of Legionella pneumophila (L. pneumophila) isolated from industrial circulating cooling water in workplace. Methods A total of 20 L. pneumophila strains isolated from industrial circulating cooling water collected from eight chemical enterprises were selected as the research subjects. Whole-genome sequencing was performed on the strains, followed by multilocus sequence typing (MLST), whole-genome single nucleotide polymorphism analysis, virulence factor profiling, and antibiotic resistance gene detection. Results The dominant MLST typing among the 20 L. pneumophila strains was ST2. Serogroup 1 (Lp1) strains exhibited high genetic diversity. Strains from different enterprises showed substantial genomic diversity, suggesting possible inter-enterprise dissemination. Each isolate harbored 175-295 virulence genes; among them, virulence genes were identified, and 139 genes were detected in all the 20 strains. These genes primarily included those related to the Dot/Icm type Ⅳ bacterial secretion system, flagellar synthesis and motility, and type Ⅳ pili. The number of virulence genes carried by L. pneumophila strains of different ST types differed (P<0.01), with unidentified sequence types harboring more virulence genes than other STs (P<0.01). A total of four resistance genes were predicted, including aminoglycoside phosphotransferase-encoding gene APH(9)-Ia, carbapenemase-encoding gene OXA-29, and efflux pump genes-encoding genes LpeA and LpeB, with detection rates of 100.00%, 100.00%, 10.00%, and 10.00%, respectively. These genes may confer resistance to cephalosporins, penicillins, and aminoglycosides, and all strains were multidrug resistance. Conclusion L. pneumophila isolated from industrial recirculating cooling water exhibits high genetic diversity, strong pathogenicity, and significant multidrug resistance potential.

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.

Nano liquid chromatography-high resolution tandem mass spectrometry(LC-HRMS/MS)is widely used for body fluid peptidome analysis,yet the impact of replicate analyses remains overlooked.Using salivary peptidome,in this work,10 replicate analyses of the same sample were conducted.It was found that although m/z and molecular weight ranges were consistent across replicates,single runs identified only 348-576 unique peptides from 32-39 degraded proteins.Merging all replicates revealed 1237 peptides from 77 proteins,a 2.5 folds increase in peptides and 2 folds increase in proteins.Instrument stability was confirmed via intensity/retention time of 12 peptides.Merged peptides primarily derived from high-abundance proteins(e.g.,Statherin,PRP1/2).Analysis of 20 peptides showed that the some peptides were detected in single analysis but confidence varying(19%-99%),low-confidence peptides(<95%)exceeded 95%after replicate.signal intensity alone didn't determine confidence and peptides spanning the sequence region between the longest and the shortest detected fragments could be identified.The above findings suggested that single-run LC-MS peptidomics analysis carried inherent false negatives and uncertainties.However,by integrating the results of a single analysis with the mechanism of enzymatic hydrolysis for endogenous peptides,it was possible to make reasonable inferences and extrapolations regarding peptides derived from highly abundant degraded proteins.

Integrated metabolomic and lipidomic profiling,utilizing liquid chromatography coupled with high-resolution mass spectrometry(LC-HRMS),has emerged as a pivotal strategy for biomarker discovery.However,the inherent polarity disparity between metabolites and lipids complicates simultaneous analysis.To address this,a dual-stationary phase polarity-extended liquid chromatography(PELC)system was developed,which surpassed conventional one-dimensional LC(1D-LC)by enabling comprehensive coverage of both polar and non-polar compounds within a single injection.This system enhanced chromatographic resolution,peak capacity,and throughput while minimizing analytical variability.Extracellular vesicles(EVs),lipid bilayer-enclosed nanoparticles ubiquitously present in biofluids,had gained prominence as reservoirs of cancer biomarkers due to their cargo stability and pathophysiological relevance.Herein,the application of PELC-HRMS for concurrent metabolome-lipidome profiling in EVs was pioneered.A total of 193 metabolites were identified using this technique coupled with MS-DIAL software and Human Metabolome Database.Subsequently,this technique was employed to explore potential biomarkers for prostate cancer(PCa).Multivariate analysis identified 17 differentially abundant metabolites in PCa,implicating dysregulated pathways including purine metabolism,starch and sucrose metabolism,galactose metabolism,cysteine and methionine metabolism,and biosynthesis of unsaturated fatty acids.Notably,creatine(AUC=0.92)and DG 42:5(AUC=0.80)demonstrated robust diagnostic efficacy,attributable to their broad polarity ranges and EV-specific enrichment.This study established PELC as a high-fidelity platform for multi-omics integration in complex biospecimens,advancing mechanistic insights into metabolic rewiring and disease pathophysiology.

Objective To evaluate the value of tripartite motif-containing protein 21(TRIM21)mRNA expression in alveolar macrophages for the serverity and prognosis of patients with severe pneumonia complicated by acute respiratory distress syndrome(SP-ARDS).Methods A retrospective analysis was conducted on 42 SP-ARDS patients(SP-ARDS group)and 15 outpatient healthy controls(control group)admitted to the Department of Respiratory and Critical Care Medicine,Jinling Hospital Affilicated to Medical School of Nanjing University,from November 2023 to June 2024.Bronchoalveolar lavage fluid was collected,and alveolar macrophages were isolated.The expression levels of TRIM21 mRNA were quantified using qPCR.Differences in TRIM21 mRNA expression levels,clinical characteristics,and relevant laboratory test results were compared between the two groups.Correlations between TRIM21 mRNA expression and SP-ARDS severity,28-day mortality,inflammatory indicators,acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)scores,duration of mechanical ventilation,and ICU stay were analyzed using Pearson or Spearman correlation analysis.Logistic regression analysis was used to identify risk factors for 28-day mortality,and the predictive value of each factor was evaluated using receiver operating characteristic(ROC)curves.Results Compared with control group,the expression levels of TRIM21 mRNA,white blood cell counts,neutrophil-to-lymphocyte ratio(NLR),and C-reactive protein(CRP),and procalcitonin(PCT)levels increased(P<0.05),and hemoglobin levels decreased(P<0.05)in SP-ARDS group.No significant differences were observed in gender,age,smoking history,alcohol consumption,underlying disease history,and platelet count between the two groups(P>0.05).TRIM21 mRNA expression level in SP-ARDS patients was positively correlated with ARDS severity(P<0.05).Additionally,non-survivors within 28 days had a significantly higher expression level of TRIM21 mRNA compared to survivors(P<0.001).Correlation analysis indicated that the relative expression level of TRIM21 mRNA in SP-ARDS patients was positively correlated with CRP(r=0.309,P<0.05),NLR(r=0.422,P<0.01),PCT(r=0.561,P<0.001),APACHE Ⅱ score(r=0.615,P<0.001),and duration of mechanical ventilation(r=0.665,P<0.001).Logistic regression analysis revealed that elevated expression levels of TRIM21 mRNA(OR=2.886,P=0.043)and higher APACHE Ⅱ scores(OR=1.546,P=0.037)were independent risk factors for 28-day mortality in SP-ARDS patients.The areas under the ROC curves(AUCs)for predicting 28-day mortality using TRIM21 mRNA expression level and APACHE Ⅱ score were 0.889 and 0.874,respectively,with optimal cut-off values of 5.21 and 20.5 points,respectively.The combined AUC for prediction was 0.962.Conclusion Increased TRIM21 mRNA expression in alveolar macrophages of SP-ARDS patients is positively correlated with disease severity and may serve as a potential predictive marker for 28-day survival in SP-ARDS patients.

Objective To investigate the epidemiological characteristics of knee osteoarthritis(KOA)among military personnel in plateau regions and to explore its risk factors.Methods From July 2023 to July 2024,a multi-stage stratified cluster random sampling method was employed to survey the prevalence of KOA and related risk factors among military personnel in the northwest plateau regions of China,covering different altitudes(1500-4500 m)and geographical areas(Gansu,Qinghai,Tibet,and Xinjiang).All study subjects were divided into KOA and non-KOA groups based on the presence or absence of KOA.Variables including age,gender,body mass index(BMI),education level,smoking status,military rank,military branch,service duration,regional altitude,annual average temperature,training duration,perceived training intensity,and history of knee injury were selected for univariate analyses between groups.Variables with P<0.05 in the univariate analyses were included in the binary multifactor logistic regression to identify risk factors for KOA.Results A total of 3000 questionnaires were distributed,and 2854 valid questionnaires were collected,with a response rate of 95.13%.The sample included 2584 males and 270 females,with 510 cases of KOA,resulting in a prevalence rate of 17.9%.Univariate analysis showed that there were statistically significant differences between KOA and non-KOA groups in terms of age,BMI,smoking status,military rank,military branch,service duration,regional altitude,annual average temperature,training duration,perceived training intensity,and history of knee injury(P<0.05).However,no significant differences were found in gender and education level(P>0.05).Binary multivariate logistic regression analysis revealed that older age(OR=1.382,P=0.017),higher BMI(P<0.01),smoking(OR=1.929,P<0.01),higher military rank(OR=1.485,P=0.007),being a member of the Armed Police(P<0.01),longer service duration(P<0.01),higher regional altitude(OR=1.459,P<0.01),lower annual average temperature(OR=1.188,P=0.001),longer training duration(P<0.01),higher perceived training intensity(OR=2.450,P<0.01),and history of knee injury(OR=2.768,P=0.002)were independent risk factors for KOA.Conclusions Older age,overweight/obesity,smoking,higher military rank,being a member of the Armed Police,longer service duration,higher altitude,cold climate,longer training duration,higher training intensity,and history of knee injury are independent risk factors for KOA among military personnel in the northwest plateau regions of China.

Peptide receptor radionuclide therapy (PRRT) with radiolabeled SSTR2 agonists is a treatment option that is highly effective in controlling metastatic and progressive neuroendocrine tumors (NETs). Previous studies have shown that an SSTR2 agonist combined with albumin binding moiety Evans blue (denoted as ¹⁷⁷Lu-EB-TATE) is characterized by a higher tumor uptake and residence time in preclinical models and in patients with metastatic NETs. This study aimed to enhance the in vivo stability, pharmacokinetics, and pharmacodynamics of ¹⁷⁷Lu-EB-TATE by replacing the maleimide-thiol group with a polyethylene glycol chain, resulting in a novel EB conjugated SSTR2-targeting radiopharmaceutical, ¹⁷⁷Lu-LNC1010, for PRRT. In preclinical studies, ¹⁷⁷Lu-LNC1010 exhibited good stability and SSTR2-binding affinity in AR42J tumor cells and enhanced uptake and prolonged retention in AR42J tumor xenografts. Thereafter, we presented the first-in-human dose escalation study of ¹⁷⁷Lu-LNC1010 in patients with advanced/metastatic NETs. ¹⁷⁷Lu-LNC1010 was well-tolerated by all patients, with minor adverse effects, and exhibited significant uptake and prolonged retention in tumor lesions, with higher tumor radiation doses than those of ¹⁷⁷Lu-EB-TATE. Preliminary PRRT efficacy results showed an 83% disease control rate and a 42% overall response rate after two ¹⁷⁷Lu-LNC1010 treatment cycles. These encouraging findings warrant further investigations through multicenter, prospective, and randomized controlled trials.

As global greenhouse gases continue rising, the urgency of more ambitious action is clearer than ever before. China is the world's biggest emitter of greenhouse gases and one of the countries affected most by climate change. The evidence about the impacts of climate change on the environment and human health may encourage China to take more decisive action to mitigate greenhouse gas emissions and adapt to climate impacts. This article aimed to review the evidence of environmental damages and health risks posed by climate change and to provide a new science-based perspective for the delivery of sustainable development goals. Over recent decades, China has experienced a strong warming pattern with a growing frequency of extreme weather events, and the impacts of climate change on China's environment and human health have been consistently observed, with increasing O ₃ air pollution, decreases in water resources and availability, land degradation, and increased risks for both communicable and non-communicable diseases. Therefore, China's climate policy should target the key factors driving climate change and scale up strategic measures to curb carbon emissions and adapt to inevitable increasing climate impacts. It provides new insights for not only China but also other countries, particularly developing and emerging economies, to ensure climate and environmental sustainability whilst pursuing economic growth.
Climate Change ; China ; Humans ; Greenhouse Gases ; Air Pollution ; Sustainable Development ; Environment

OBJECTIVE: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance. METHODS: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument. Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System (FDAS) and Tongue Diagnosis Analysis System version 2.0 (TDAS v2.0). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Ten machine learning models and one deep learning model (ResNet50V2 + Conv1D) were developed and evaluated. RESULTS: Anemic women showed lower a-values, higher L- and b-values across all age groups. Texture features analysis showed that women aged 30-39 with anemia had higher angular second moment (ASM)and lower entropy (ENT) values in facial images, while those aged 40-49 had lower contrast (CON), ENT, and MEAN values in tongue images but higher ASM. Anemic women exhibited age-related trends similar to healthy women, with decreasing L-values and increasing a-, b-, and ASM-values. LASSO identified 19 key features from 62. Among classifiers, the Artificial Neural Network (ANN) model achieved the best performance [area under the curve (AUC): 0.849, accuracy: 0.781]. The ResNet50V2 model achieved comparable results [AUC: 0.846, accuracy: 0.818]. CONCLUSION Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
Humans ; Female ; Tongue/diagnostic imaging* ; Adult ; Anemia/diagnosis* ; Middle Aged ; Face/diagnostic imaging* ; Young Adult ; Machine Learning