ObjectiveTo identify the pathogen species responsible for the wilt disease of Tetradium ruticarpum in Chongqing， investigate there biological characteristics， and screen effective fungicides， so as to provide a theoretical basis for disease control in production. MethodsThe pathogen was isolated via the tissue culture method. Pathogenicity was verified according to Koch's postulates. The pathogen was identified based on morphological characteristics and multi-gene phylogenetic analysis. The mycelial growth rate method was used for biological characterization of the pathogen and fungicide screening. ResultsThe pathogen colonies were nearly circular with irregular edges， white， short， velvety aerial hyphae， and pale purple undersides. Macroconidia were colorless， sickle-shaped， with 3-5 septa， while microconidia were transparent， elliptical， aseptate or with 1-2 septa. Multi-gene phylogenetic analysis showed that the pathogen clustered in the same clade as Fusarium fujikuroi with 100% support， which， combined with morphological characteristics， identified the pathogen causing wilt of T. ruticarpum in Chongqing as F. fujikuroi. The optimal conditions for the mycelial growth of F. fujikuroi were mung bean agar （MBA） with glucose as the carbon source， beef extract and yeast powder as nitrogen sources， 28 ℃， pH 7.0， and alternating light/dark conditions. The optimal conditions for sporulation were potato dextrose agar （PDA） with glucose as the carbon source， beef extract as the nitrogen source， 28 ℃， pH 7.0， and complete darkness. Among chemical fungicides， phenazine-1-carboxylic acid exhibited the strongest inhibitory effect on F. fujikuroi. Shenqinmycin and tetramycin were the most effective bio-fungicides. ConclusionThis study is the first to report F. fujikuroi as the causal agent of wilt disease in T. rutaecarpa. The chemical fungicide phenazine-1-carboxylic acid and the bio-fungicides shenqinmycin and tetramycin showed strong inhibitory effects against F. fujikuroi.

Objectives: . The prognostic value of the pretreatment albumin-to-alkaline phosphatase ratio (AAPR) in head and neck cancer (HNC) remains uncertain. This meta-analysis aimed to evaluate the predictive role of AAPR for survival outcomes in patients with HNC. Methods: . A comprehensive search of the Cochrane Library, PubMed, and Embase databases was conducted to identify relevant studies published up to July 30, 2024. We included studies on AAPR and survival outcomes in HNC patients. Results: . Eight studies comprising 1,737 HNC patients were analyzed using random-effects models. Lower AAPR values were significantly correlated with worse overall survival (hazard ratio [HR], 2.08), progression-free survival (HR, 2.00), and disease-free survival (HR, 2.18). Sensitivity analyses confirmed the robustness of these results, with no significant publication bias detected. Conclusion . Our findings suggest that pretreatment AAPR could serve as a valuable and cost-effective prognostic indicator in HNC, potentially aiding clinicians in risk stratification and treatment decision-making. However, additional validation studies are warranted to confirm its clinical applicability.

Objectives: . This study aimed to investigate the regulatory functions of gamma-aminobutyric acid (GABA)ergic neural circuits from the inferior colliculus (IC) to the medial geniculate body (MGB) in salicylate-induced tinnitus. Methods: . Mice were treated with salicylate to induce tinnitus, and tinnitus-like behaviors were evaluated via gap prepulse inhibition of acoustic startle. Using combined viral tracing methodologies, we identified and mapped the pathways and connections from the IC to the MGB. Furthermore, we employed Gq-coupled human M3 designer receptors exclusively activated by designer drugs (DREADDs) and Gi-coupled human M4 DREADDs to achieve targeted excitation or suppression of GABAergic neurons in the IC and MGB. Following the administration of clozapine N-oxide, which binds to these receptors, we modulated these neural circuits to assess their impact on tinnitus severity in a mouse model. Results: . Our findings demonstrated that mice exposed to salicylate exhibited tinnitus-like behaviors. GABAergic neurons projecting retrogradely from the MGB to the IC were primarily concentrated in the external nucleus of the IC. After clozapine N-oxide administration, chemogenetic activation of IC-MGB GABAergic neurons aggravated salicylate-induced tinnitus. Additionally, activation of GABAergic neurons between the IC and MGB induced the perception of tinnitus even without salicylate. However, chemogenetic inhibition of the IC-MGB GABAergic circuit did not reverse salicylate-induced tinnitus. Conclusion . These findings suggest that activation of the IC-MGB GABAergic neural circuit may contribute to tinnitus generation through a mechanism distinct from that of salicylate-induced tinnitus. This study provides novel insights into the mechanisms underlying tinnitus.

Objective To analyze the overall characteristics, research hotspots, and development trends of immune cell-related studies in radiotherapy for rectal cancer. Methods A systematic search was conducted by using Web of Science Core Collection to retrieve articles related to radiation therapy and immune cells in rectal cancer published from 1991 to 2024. Advanced bibliometric tools, such as VOSviewer and CiteSpace, were utilized to facilitate analysis and describe publication trends, geographic contributions, institutional affiliations, journal prominence, author collaboration, and prominent keywords. Results The annual number of relevant publications has increased steadily, showing remarkable growth over the past five years. Studies focusing on adaptive immune cells (T and B cells) account for the largest proportion of works and form the most extensive collaboration networks, reflecting the central role of T cell–mediated antitumor immunity in radiotherapy. Meanwhile, innate immune cells, including myeloid-derived suppressor cells, macrophages, monocytes, and neutrophils, are increasingly investigated for their regulatory roles in shaping the tumor immune microenvironment and influencing therapeutic responses. Among countries, China and the United States have contributed the highest number of publications and demonstrated strong academic influence. While several stable research groups have been formed, intergroup collaboration remains limited. Keyword analysis revealed that radiotherapy-induced immune modulation, tumor immune microenvironment remodeling, and treatment response have emerged as major research hotspots. Conclusion Research on immune cells in rectal cancer radiotherapy has progressed rapidly in recent years. The emphasis of this field has gradually shifted from single-treatment approaches to mechanistic studies investigating the interaction between radiotherapy and the tumor immune microenvironment. The field still demonstrates considerable potential for future research and clinical translational applications.

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.

Objective:To investigate the therapeutic efficacy of autologous hematopoietic stem cell transplantation (auto-HSCT) for treatment of diffuse large B-cell lymphoma (DLBCL) and the factors affecting the prognosis.Methods:A retrospective case series study was conducted. The clinical data of 51 patients with DLBCL who underwent auto-HSCT in the First Affiliated Hospital of Xinjiang Medical University from March 2019 to January 2024 were retrospectively analyzed. Patients were divided into high-risk group (19 cases) and non-high-risk group (low-risk, low-moderate-risk and moderate-high-risk groups, 32 cases) based on different risk stratifications; patients were divided into the germinal center B-cell (GCB) group (29 cases) and non-GCB group (22 cases) based on different cellular origins; patients were divided into BEAM group (39 cases) and BeEAM group (12 cases) based on different conditioning regimens before auto-HSCT; patients were divided into auto-HSCT consolidation therapy group (41 cases) and auto-HSCT after relapsed/refractory group (10 cases) based on different transplantation timings. The Kaplan-Meier method was used for survival analysis and log-rank was used for subgroup comparison.Results:All 51 patients achieved the hematopoietic reconstitution with no transplantation-related death within 100 d. Before auto-HSCT, 39 cases achieved complete remission and 12 cases (23.5%) achieved partial remission. After auto-HSCT, all cases achieved complete remission. Follow-up was until May 31, 2024, and the median follow-up time [ M ( Q1, Q3)] of 51 DLBCL patients was 33 (8, 43) months. After 51 DLBCL patients receiving auto-HSCT, 7 patients relapsed and 6 cases died including 3 cases with relapse-related death and 3 cases with non relapse-related death. The 3-year progression-free survival (PFS) and overall survival (OS) rates were 78.5% (95% CI: 64.4%-92.6%) and 85.5% (95% CI: 73.2%-97.8%), respectively. The 3-year PFS rate was 94.7% (95% CI: 84.7%-104.7%) in the high-risk group, 82.2% (95% CI: 67.9%-96.5%) in the non-high-risk group, and the difference in the PFS was not statistically significant between the high-risk group and the non-high-risk group ( P = 0.158). The 3-year PFS rate was 80.1% (95% CI: 64.4%-95.8%) in the GCB group, 88.1% (95% CI: 72.3%-104.2%) in the non-GCB group, and the difference in PFS was not statistically significant between the 2 groups ( P = 0.803). The 3-year PFS rate was 84.9% (95% CI: 72.6%-97.2%) in BEAM group, 61.1% (95% CI: 25.0%-97.2%) in the BeEAM group, and the difference in PFS was not statistically significant between the 2 groups ( P = 0.106). The 3-year PFS rate was 85.4% (95% CI: 73.4%-97.4%) in the auto-HSCT consolidation therapy group, 64.3% (95% CI: 31.4%-96.4%) in the auto-HSCT after relapsed/refractory group, and the difference in PFS was not statistically significant between the 2 groups ( P = 0.171). Conclusions:auto-HSCT is an effective therapy method for DLBCL.

Objective:To examine the distribution of pathogens that cause postoperative nosocomial infections in patients with rectal cancer(RC)and to construct a predictive nomogram for nosocomial infection.Methods:The clinical data of 1537 RC patients admitted to Sir Run Run Shaw Hospital between January 2021 and December 2022 were collected.Patients were assigned 1∶1 by propensity score matching(PSM)to the infection group(n=83)and control group(n=83)based on the occurrence of nosocomial infection.The dis-tribution and drug resistance of bacteria in patients with nosocomial infection were analyzed.Risk factors for postoperative nosocomial infection were identified by least absolute shrinkage and selection operator(LASSO)regression,and a predictive nomogram was con-structed using multivariate logistics regression.The predictive performance of the model was evaluated by receiver operating character-istic(ROC)curve,calibration curve,and decision curve analysis(DCA).Results:A total of 93 strains of pathogens were isolated from the 83 infected patients,including 62 strains of Gram-negative bacteria(66.67%;predominantly Escherichia coli and Pseudomonas ae-ruginosa),25 strains of Gram-positive bacteria(26.88%;mainly Enterococcus faecalis),and 6 strains of fungi(6.45%;all Candida albicans).LASSO and multivariate logistics regression showed that smoking(odds ratio[OR]=3.97,95%CI=1.27-12.43),the dwelling time of drainage tube(OR=1.19,95%CI=1.08-1.30),difference in preoperative and postoperative neutrophil counts(OR=1.23,95%CI=1.01-1.49),and difference between preoperative and postoperative C-reactive protein levels(OR=1.05,95%CI=1.03-1.07)were inde-pendent risk factors for postoperative nosocomial infection in RC patients.The area under the ROC curve of the nomogram constructed based on the above factors was 0.933(95%CI=0.896-0.969).The calibration curve showed that the predicted risk was in good agree-ment with the actual observed risk of infection.In addition,DCA demonstrated that the nomogram has good clinical utility and high net clinical benefits in predicting nosocomial infection.Conclusion:The nomogram constructed in this study has a good predictive perfor-mance in postoperative nosocomial infection in RC patients.

OBJECTIVE To explore the expression,regulation,and downstream pathways of miR-21-5p in nasal type 2 inflammatory diseases.METHODS The expression levels of miR-21-5p were evaluated in nasal mucosa of patients with chronic rhinosinusitis with nasal polyps(CRSwNP)and healthy controls,as well as in nasal mucosa of CRSwNP and allergic rhinitis(AR)mouse models by real-time PCR.Human primary nasal epithelial cells were cultured and treated with different cytokines.The expression of miR-21-5p were then detected after 48 hours treatment.miR-21-5p lentivirus were used for overexpression in nasal epithelial cells.After 72 hours,cells were collected,and RNA was extracted for transcriptome analysis.Differentially expressed genes were further analyzed for pathway enrichment.RESULTS Compared with healthy controls,the expression of miR-21-5p was significantly increased in the nasal mucosa of CRSwNP patients(P<0.01).The expression of miR-21-5p was also significantly increased in mouse models of CRSwNP and AR respectively,compared to the control groups(P<0.01,respectively).The type 2 inflammation cytokines IL-4 and IL-13 can significantly upregulate miR-21-5p expression in nasal epithelial cells(P<0.01,respectively).Overexpression by lentivirus can significantly increase the expression of miR-21-5p in nasal epithelial cells,and lead to 118 differentially expressed genes.Those genes are significantly enriched in pathways associated with posttranscriptional gene silencing,WNT signaling and MAPKinase signaling.CONCLUSION miR-21-5p is upregulated in nasal type 2 inflammatory diseases partially controlled by IL-4 and IL-13.Overexpression of miR-21-5p lead to the changes of downstream pathways associated with WNT signaling and MAPKinase signaling in nasal epithelial cells.

On the basis of that the fluorescence wavelength of copper nanoclusters(CuNCs)could cover the entire visible region,multicolor fluorescent CuNCs/starch composites were prepared and applied in fingermark development.With L-glutathione as the reducing agent and protective ligand,blue emissive and orange emissive CuNCs solutions were obtained in alkaline solutions at 90℃and 25℃,respectively.With the aggregation-induced emission effect induced by ethanol as a poor solvent,the fluorescence of orange emissive CuNCs with a higher intensity was achieved in an ethanol-water solution.With ascorbic acid as the reducing agent and 3-mercaptopropionic acid as the protective agent,green emissive CuNCs solution was prepared in an acid solution.Particle morphologies,chemical compositions and optical properties of these three CuNCs above were investigated using physical characterization and spectroscopic analysis,indicating that well-dispersed CuNCs had excellent photoluminescent properties.These CuNCs solutions were combined with starch to form composite powders by simply drying.The influences of the type of CuNCs and the ratio of CuNCs to starch on the emission wavelength and fluorescence intensity of the products were studied.The obtained CuNCs/starch composites could emit blue,green and orange fluorescence under 365 nm ultraviolet light,respectively,which were suitable for fingermark development.Minutiae and partial level-3 features of latent fingermarks could be effectively developed.High-quality fluorescence fingermark images would be captured using appropriate optical filters to eliminate background interference of various substrates.

A deep learning-based method for recognizing the minutiae in fingerprint,as well as a Python programming-based evaluation system for quantifying the evidence value of fingerprint was proposed.Firstly,latent fingerprints,which were developed using a series of fluorescent nanomaterials synthesized by chemical methods,were used as unknown fingerprint(UKFP),while ink impressed fingerprints were used as known fingerprint(KFP).Then,the bifurcations and terminations in minutiae were recognized using the improved YOLOv8 deep learning model.After that,the similarity index(Sim.)of UKFP vs KFP were calculated by analyzing the angle similarity factor(α)and the curve similarity factor(β)between UKFP and KFP,meanwhile,the sensitivity index(Sen.)were calculated by analyzing the fineness factor(γ)between UKFP and KFP.The evidence value(EV)of fingerprint was thus obtained by the combination of Sim.and Sen..The calculation formulas for above evaluation factors(i.e.α,β and γ),evaluation indexes(i.e.Sim.and Sen.),and EV were also put forward.Finally,the evaluation system for quantifying the evidence value of fingerprint was established,the feasibility and reliability of this system were verified,and the external factors that impacted on Sim.,Sen.,and EV were investigated in detail.The Python-based evaluation system for quantifying the evidence value of fingerprint could achieve the goals objectively,comprehensively,accurately and efficiently,exhibiting easy operability,high efficiency,responsiveness and reliability.This research was expected to provide beneficial references for quantitatively evaluating and thoroughly developing the evidence value.