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 understand the characteristics of new infections in the newly reported HIV-1 infected population in Luzhou, to find out the characteristics of the high-risk population, which may provide a basis for developing precise prevention and control measures locally.Methods:HIV-1 LAg Avidity EIA test was applied for newly reported cases in Luzhou from 2018 to 2021. The chi-square test was used for univariate analysis, logistic regression model for multivariate analysis, and spatial autocorrelation and hotspot analysis were applied to explore the spatial distribution characteristics of new infections in Luzhou city.Results:A total of 4 494 cases reported in the odd-numbered months were selected for testing, with 673 newly infected cases and the proportion of newly infected cases was 14.98%. Newly infected cases were predominantly male (472/673, 70.13%), married (335/673, 49.78%), heterosexual transmission (621/673, 92.27%), age>50 years (520/673, 77.27%), and junior high school or lower education (599/673, 89.00%). The proportion of new infections in each year were 11.74% (129/1 099), 17.11% (247/1 444), 13.57% (154/1 134) and 17.50% (143/817), respectively, with a statistically significant difference ( χ2=20.024, P<0.001) and an upward trend ( χ2=5.997, P=0.014). There were statistically significant differences in different transmission routes, gender, education level, occupation, sample source and residence (all P<0.05). Logistic regression analysis showed that gender, marital status, transmission route, current address, and sample source were all influencing factors for new infections. There are spatial autocorrelation characteristics of new case incidence in 2018 and 2021, hotspot areas gradually shifted from dispersed to relatively concentrated, with the number fluctuating with reported year. Conclusions:The proportion of new HIV-1 infections in Luzhou is relatively low throughout the province, but it is on the rise. The proportion of new infections is higher among youth, students, homosexual transmission, and retired persons. Attention should be focused on hotspot areas and routine surveillance and testing of key populations in hotspot areas should be strengthened.

Background/Aims: Anti-reflux mucosal ablation (ARMA) is a promising endoscopic intervention for proton pump inhibitor (PPI)-dependent gastroesophageal reflux disease (GERD). However, the effect of ARMA on esophageal motility remains unclear. Methods: Twenty patients with PPI-dependent GERD receiving ARMA were prospectively enrolled. Comprehensive self-report symptom questionnaires, endoscopy, 24-hour impedance-pH monitoring, and high-resolution impedance manometry were performed and analyzed before and 3 months after ARMA. Results: All ARMA procedures were performed successfully. Symptom scores, including GerdQ (11.16 ± 2.67 to 9.11 ± 2.64, P = 0.026) and reflux symptom index (11.63 ± 5.62 to 6.11 ± 3.86, P = 0.001), improved significantly, while 13 patients (65%) reported discontinuation of PPI. Total acid exposure time (5.84 ± 4.63% to 2.83 ± 3.41%, P = 0.024) and number of reflux episodes (73.05 ± 19.34 to 37.55 ± 22.71, P < 0.001) decreased significantly after ARMA. Improved esophagogastric junction (EGJ) barrier function, including increased lower esophageal sphincter resting pressure (13.89 ± 10.78 mmHg to 21.68 ± 11.5 mmHg, P = 0.034), 4-second integrated relaxation pressure (5.75 ± 6.42 mmHg to 9.99 ± 5.89 mmHg, P = 0.020), and EGJ-contractile integral(16.42 ± 16.93 mmHg · cm to 31.95 ± 21.25 mmHg · cm, P = 0.016), were observed. Esophageal body contractility also increased significantly (distal contractile integral, 966.85 ± 845.84 mmHg · s · cm to 1198.8 ± 811.74 mmHg · s · cm, P = 0.023). Patients with symptom improvement had better pre-AMRA esophageal body contractility. Conclusions ARMA effectively improves symptoms and reflux burden, EGJ barrier function, and esophageal body contractility in patients with PPIdependent GERD during short-term evaluation. Longer follow-up to clarify the sustainability of ARMA is needed.

Objective:To investigate the effects of propionic acid produced by Akkermansia muciniphila on the radiosensitivity of colorectal cancer and the underlying mechanism. Methods:Normal human colon mucosal epithelial cells (NCM460) were used to determine the appropriate concentration of propionic acid. Human colorectal cancer cells (HCT-8) were treated with A. muciniphila-conditioned medium or propionic acid, followed by exposure to 6 Gy γ-ray irradiation, and cell survival and proliferation were measured by clone formation assay and Cell Counting Kit-8 (CCK-8) assay, respectively. A mouse model of colorectal cancer was established using azoxymethane/dextran sodium sulfate. The mice were divided into control model group, irradiation group, and irradiation+ propionic acid group. Their body weight, colorectal length, tumor count, and tumor area were recorded. The radiosensitizing effect of propionic acid was assessed with HE staining, immunohistochemical staining, and enzyme-linked immunosorbent assay. The mechanism was explored by using RT-PCR and flow cytometry. Results:CCK-8 assay showed that 1-mmol/L propionic acid had no significant effect on the proliferation of NCM460 cells ( P>0.05), which was used for subsequent experiments. Pretreated with A. muciniphila-conditioned medium or propionic acid, the survival and proliferation abilities of irradiated HCT cells were significantly decreased ( t=3.14-34.98, P<0.05). Compared with the irradiation group, the colorectal cancer mice in the irradiation+ propionic acid group showed a significantly longer colorectal length ( t=3.50, P<0.05) and a significantly smaller number of tumors ( t=3.48, P<0.05); the two groups had significantly smaller tumor areas than the control model group ( t=5.97, 7.30, P<0.05). HE staining and immunohistochemical staining showed that propionic acid restored colorectal structure, and decreased Ki67 expression in colorectal tissue ( t=14.50, 3.40, P<0.05). Propionic acid treatment significantly reduced the levels of the inflammatory factors interleukin-6 and tumor necrosis factor-α, as compared with the mice receiving irradiation alone ( t=4.86, 5.06, P<0.05). Irradiation plus propionic acid treatment significantly increased p53 expression and significantly aggravated G 2/M phase block and cell apoptosis ( t=20.35, 13.05, P<0.05). Conclusions:The A. muciniphila metabolite propionic acid plays a sensitizing role in radiation therapy for colorectal cancer by promoting G 2/M phase block and apoptosis in colorectal cancer cells.

Objective To construct an operational indicator system for infectious disease monitoring and early warning,and provide data support and decision-making basis for monitoring and early warning.Methods A preli-minary indicator framework was constructed through literature analysis and expert interviews,and expert consultation was conducted using the Delphi method.A total of 30 experts were selected.The score of the importance of indica-tors based on the consultation results was calculated,and the weight was determined.Results The response rates of expert consultation questionnaires from two rounds were both 100％,with expert authority coefficients(reliabili-ty coefficient)being 0.87 and 0.88 for the first and second rounds,respectively.Kendall's W coefficients ranged from 0.137 to 0.424,with statistically significant differences(all P＜0.001).Coefficients of variation for all indi-cators were＜0.25,indicating that expert opinions tend to be consistent and the results were authoritative and relia-ble.The finally constructed indicator system encompassed 4 first-level,25 second-level,and 68 third-level indica-tors.Conclusion The operational indicator system for infectious disease monitoring and early warning developed in this study can provide a basis for the practice of infectious disease monitoring and early warning as well as related scientific decision-making.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.