Objectives: . Idiopathic sudden sensorineural hearing loss (ISSNHL) is a significant cause of hearing loss. Intratympanic steroid injection (ITSI) is commonly used as an initial or salvage treatment; however, the lack of a standardized treatment protocol has resulted in variability in clinical practice. In addition, no efficient prediction model currently exists to support personalized management. Therefore, this study aimed to develop tailored management strategies for ISSNHL using a machine-learning model. Methods: . This retrospective multicenter cohort study was conducted between January 2015 and December 2020, with data analysis performed between January 2021 and March 2024. Patients were selected based on the International Classification of Diseases, 10th Revision criteria for ISSNHL, along with relevant medication and procedure codes. Patients with pure-tone audiogram results not meeting ISSNHL criteria, better initial hearing in the affected ear, an identifiable etiology, no post-treatment audiogram, or delayed treatment (>6 weeks) were excluded. We included 770 patients diagnosed with ISSNHL who received ITSI. The primary outcome was the area under the receiver operating characteristic curve for prediction performance. Recovery status was determined using the last pure-tone audiogram. Modeling was conducted on the Quanta for Medical Care AI platform using five machine-learning algorithms and a nested cross-validation framework, in which feature selection and hyperparameter tuning were performed in the inner folds and model performance was evaluated in the outer folds. Results: . A random forest classifier outperformed the other models in predicting hearing outcomes, achieving an area under the receiver operating characteristic curve of 0.788. Time to ITSI was the most influential treatment-related factor, with ITSI administered within 10 days of hearing loss being associated with better outcomes. This model can be used to provide personalized prognostic estimates under different treatment protocols. Conclusion . The machine-learning-based prediction model facilitates personalized treatment strategies and timely treatment adjustments for ISSNHL, thereby optimizing the likelihood of complete recovery.

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.

The MXene/MF microspheres were prepared by coating MXene nanosheets on melamine formaldehyde(MF)resin microspheres.Co(NO3)2 was adsorbed on the surface of the microspheres by impregnation,and then calcined at high temperature in an argon atmosphere.MF pyrolysis generated reducing gases such as CO and NH3,reducing Co2+to elemental Co,which was then used as a catalyst for in situ growth of carbon nanotube(CNT)through chemical vapor deposition(CVD),forming MXene-Co-CNT microspheres(MXene-Co-CNT MS).During this process,the pyrolysis of MF microspheres had dual effects.On one hand,the template was sacrificed to produce an internal hollow structure,and on the other hand,the generated gas worked as carbon source to generate CNT,forming an external sea urchin-like structure.Both of them promoted the formation of a novel structure,which combined the advantages of large specific surface area and good conductivity,thus possessing excellent electrocatalytic activity.The MXene-Co-CNT MS was modified on glassy carbon electrode(GCE)and further used in highly sensitive detection of nitroaromatic compounds(NACs).The detection limits of MXene-Co-CNT MS/GCE for 2,4,6-trinitrotoluene(TNT),1,3,5-nitrobenzamide(TNB),2,4-dinitrotoluene(DNT),1,3-dinitrobenzene(DNB),1-Cl-2,4-dinitrotoluene(Cl-DNB),and 4-nitrophenol(4-NP)were 26.84,31.60,35.03,54.14,43.86 and 28.67 nmol/L,respectively.It also had excellent anti-interference ability,and was used to detect NACs in environmental water samples accurately.

Depression is a heterogeneous mental disorder in which the interaction between genetic susceptibility and environ-mental factors plays a key role in its pathogenesis.Although the specific mechanisms of the disease still need to be thoroughly studied and elucidated,there is now a broad consensus that epi-genetic markers have a central influence on its mechanism of ac-tion.DNA methylation of brain-derived neurotrophic factor(BD-NF)is not only regarded as a promising epigenetic biomarker of pathology,but may also help predict the efficacy of antidepres-sants.In this paper we reviewed the gene structure of BDNF and its DNA methylation regulation mechanism,and also analyzed the changes of DNA methylation of this factor in depression pa-tients and animal models,aiming to provide new ideas and theo-retical support for clinical research.

In this study, we employed a combination of genome mining and heteronuclear single quantum coherence (HSQC)-based small molecule accurate recognition technology (SMART) technology to search for fernane-type triterpenoids. Initially, potential endophytic fungi were identified through genome mining. Subsequently, fine fractions containing various fernane-type triterpenoids were selected using HSQC data collection and SMART prediction. These triterpenoids were then obtained through targeted isolation and identification. Finally, their antifungal activity was evaluated. As a result, three fernane-type triterpenoids, including two novel compounds, along with two new sesquiterpenes and four known compounds were isolated from one potential strain, Diaporthe discoidispora. Their structures were elucidated through analysis of high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) and nuclear magnetic resonance (NMR) spectroscopic data. The absolute configurations were determined using single-crystal X-ray diffraction analysis and electron capture detector (ECD) analysis. Compound 3 exhibited moderate antifungal activity against Candida albicans CMCC 98001 and Aspergillus niger.
Triterpenes/isolation & purification* ; Antifungal Agents/isolation & purification* ; Molecular Structure ; Candida albicans/drug effects* ; Ascomycota/genetics* ; Magnetic Resonance Spectroscopy ; Aspergillus niger/drug effects* ; Genome, Fungal ; Microbial Sensitivity Tests

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

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.

Depression is a heterogeneous mental disorder in which the interaction between genetic susceptibility and environ-mental factors plays a key role in its pathogenesis.Although the specific mechanisms of the disease still need to be thoroughly studied and elucidated,there is now a broad consensus that epi-genetic markers have a central influence on its mechanism of ac-tion.DNA methylation of brain-derived neurotrophic factor(BD-NF)is not only regarded as a promising epigenetic biomarker of pathology,but may also help predict the efficacy of antidepres-sants.In this paper we reviewed the gene structure of BDNF and its DNA methylation regulation mechanism,and also analyzed the changes of DNA methylation of this factor in depression pa-tients and animal models,aiming to provide new ideas and theo-retical support for clinical research.

This study investigated the potential pathogenicity and genetic characteristics of ST314 Salmonella Kentucky(S.Ken-tucky)isolates from a broiler slaughterhouse.Antimicrobial susceptibility testing and whole-genome sequencing(WGS)were used to determine antimicrobial resistance,virulence factors,and the presence of antimicrobial resistance genes(ARGs)and mobile genetic elements(MGEs)among the isolates.The three multidrug resistant(MDR)isolates exhibited high resistance to multiple antimicrobial agents.The F4-2S strain exhibited resistance to 14 drugs across seven categories,whereas the F4T strain showed resistance to 13 drugs in the same number of categories.In contrast,the Y23 strain was resistant to nine drugs in six categories.Notably,F4-2S dem-onstrated high homology with F4T:both possessed 13 ARGs distributed across nine categories,in addition to a wide range of virulence factors,including secretion systems and effector proteins.The presence of IncR and IncX1 plasmids significantly enhanced both the antimicrobial resistance and pathogenicity of the isolates.The genome map of Y23 revealed a chromosome alongside two plasmids.The chromosome containedonly one resistance gene but several virulence factors,including the type III secretion system(T3SS),which is crucial for bacterial invasion.The plasmid pY23-1 contained eight types of 19 ARGs.Comparative analysis indicated that pY23-1 ex-hibited high homology with pZ1323SSL0055 and pSAL-045,all of which contained multiple ARGs,thus suggesting critical roles of these genes in the evolution of bacterial resistance.In conclusion,ST314 S.Kentucky demonstrated a complex mechanism of resis-tance coupled with significant pathogenic potential.The ARGs and MGEs in the plasmid contributed to the emergence and dissemina-tion of antimicrobial resistance.The multiple virulence factors present in the chromosome may be key factors driving the increasing virulence of ST314 S.Kentucky.

This study investigated the infection status,drug resistance,and molecular characteristics of Shiga toxin-producing Escherichia coli(STEC)in domestic goats in Chengkou county,Chongqing.In August 2023,283 fecal samples were collected from households in Chengkou county.After enrichment with EC broth and inoculation onto selective media,samples that tested positive for stx1/stx2 were selected for further isolation.The positive strains were investigated with antimicrobial susceptibility testing and whole genome sequencing.According to the whole genomic sequences,the stx subtypes,serotypes,multi-locus sequence types,virulence genes,drug resistance genes,and phylogenetic relationships of the STEC strains were analyzed.Forty-six strains of STEC were isolated from 283 goat fecal samples,thus resulting in a detection rate of 16.25%.The 46 STEC strains were categorized into 12 O∶H serotypes,among which O76∶H19 and O8∶H7 predominated,each represented by 9 strains.Five STEC strains were identified as serotype O157∶H7.The 46 STEC strains were categorized into 11 sequence types(STs),among which ST675 and ST196 predominated,each represented by nine strains,accounting for a 19.57%proportion.The strains were categorized into 7 stx subtypes,among which stx1c(26/46,56.52%),followed by stx2k(9/46,19.57%)predominated.All nine Stx2k-STEC strains were identified as serotype O8∶H7 and sequence type ST196.In antimicrobial susceptibility testing,2 STEC strains were resistant to ampicillin,one strain was resistant to ampicillin/sulbactam,one strain was resistant to cefazolin,and one strain was resistant to cefoxitin.Nine Stx2k-STEC strains were found to carry the beta-lactam resistance gene blaEC-18.Antimicrobial sensitivity tests revealed that the nine Stx2k-STEC strains were sensitive to all 15 tested antibiotics.Moreover,phylogenetic analysis indicated that the 9 Stx2k-STEC strains were remarkably similar but showed high genetic diversity with respect to that of the Stx2k-STEC strains isolated from other regions in China.Goatsare an important animal reservoir for STEC in theChengkou district of Chongqing,and novel sequence type Stx2k-STEC strains distinct from those found in other regions of China were identified in this region.