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.

Neutralizing antibodies have been designed to specifically target and bind to the receptor binding domain (RBD) of spike (S) protein to block severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus from attaching to angiotensin converting enzyme 2 (ACE2). This study reports a distinctive nanobody, designated as VHH21, that directly catalyzes the S-trimer into an irreversible transition state through postfusion conformational changes. Derived from camels immunized with multiple antigens, a set of nanobodies with high affinity for the S1 protein displays abilities to neutralize pseudovirion infections with a broad resistance to variants of concern of SARS-CoV-2, including SARS-CoV and BatRaTG13. Importantly, a super-resolution screening and analysis platform based on visual fluorescence probes was designed and applied to monitor single proteins and protein subunits. A spontaneously occurring dimeric form of VHH21 was obtained to rapidly destroy the S-trimer. Structural analysis via cryogenic electron microscopy revealed that VHH21 targets specific conserved epitopes on the S protein, distinct from the ACE2 binding site on the RBD, which destabilizes the fusion process. This research highlights the potential of VHH21 as an abzyme-like nanobody (nanoabzyme) possessing broad-spectrum binding capabilities and highly effective anti-viral properties and offers a promising strategy for combating coronavirus outbreaks.
Single-Domain Antibodies/immunology* ; Spike Glycoprotein, Coronavirus/metabolism* ; SARS-CoV-2/immunology* ; Animals ; Humans ; Antibodies, Neutralizing/immunology* ; Camelus ; COVID-19/immunology* ; Antibodies, Viral/immunology* ; Angiotensin-Converting Enzyme 2

Gentianopsis barbata (G. barbata) represents a significant plant species with considerable ornamental and medicinal value in China. This investigation sought to elucidate the primary constituents within the plant and investigate their pharmacological properties. Fifty triterpenoids (1-50), including nine previously undescribed compounds (1, 2, 7, 10, 20, 28, 29, 37, and 41) were isolated and characterized from the whole plants of G. barbata. Notably, compounds 1 and 2 exhibited the novel 3,4;9,10-diseco-24-homo-cycloartane triterpenoid skeleton. The isolated triterpenoids demonstrated substantial anti-inflammatory activity through inhibition of tumor necrosis factor α (TNF-α) and interleukin-6 (IL-6) cytokine secretion in LPS-induced RAW264.7 macrophages, and hepatoprotective effects by preventing tert-butyl hydroperoxide (t-BHP)-induced oxidative injury in HepG2 cells. These results demonstrate both the presence of diverse triterpenoids in G. barbata and their therapeutic potential for inflammatory and hepatic conditions, providing scientific evidence supporting the clinical application of this traditional Mongolian medicinal plant.
Triterpenes/isolation & purification* ; Mice ; Anti-Inflammatory Agents/isolation & purification* ; Animals ; Humans ; RAW 264.7 Cells ; Hep G2 Cells ; Interleukin-6/genetics* ; Tumor Necrosis Factor-alpha/genetics* ; Medicine, Mongolian Traditional ; Macrophages/immunology* ; Protective Agents/isolation & purification* ; Liver/drug effects* ; Gentianaceae/chemistry* ; Plant Extracts/chemistry* ; Molecular Structure

OBJECTIVE: To identify prognostic genes associated with lysosome-dependent cell death (LDCD) in patients with gastric cancer (GC). METHODS: Differentially expressed genes (DEGs) were identified using The Cancer Genome Atlas - Stomach Adenocarcinoma. Weighted gene co-expression network analysis was performed to identify the key module genes associated with LDCD score. Candidate genes were identified by DEGs and key module genes. Univariate Cox regression analysis, and least absolute shrinkage and selection operator regression and multivariate Cox regression analyses were performed for the selection of prognostic genes, and risk module was established. Subsequently, key cells were identified in the single-cell dataset (GSE183904), and prognostic gene expression was analyzed. Cell proliferation and migration were assessed using the Cell Counting Kit-8 assay and the wound healing assay. RESULTS: A total of 4,465 DEGs, 95 candidate genes, and 4 prognostic genes, including C19orf59, BATF2, TNFAIP2, and TNFSF18, were identified in the analysis. Receiver operating characteristic curves indicated the excellent predictive power of the risk model. Three key cell types (B cells, chief cells, and endothelial/pericyte cells) were identified in the GSE183904 dataset. C19orf59 and TNFAIP2 exhibited predominant expression in macrophage species, whereas TNFAIP2 evolved over time in endothelial/pericyte cells and chief cells. Functional experiments confirmed that interfering with C19orf59 inhibited proliferation and migration in GC cells. CONCLUSION C19orf59, BATF2, TNFAIP2, and TNFSF18 are prognostic genes associated with LDCD in GC. Furthermore, the risk model established in this study showed robust predictive power.
Stomach Neoplasms/pathology* ; Humans ; Prognosis ; Lysosomes/physiology* ; RNA-Seq ; Cell Death ; Single-Cell Analysis ; Gene Expression Regulation, Neoplastic ; Cell Proliferation ; Single-Cell Gene Expression Analysis

Objective To investigate the diagnostic value of 4 novel inflammatory markers related to routine blood tests,namely neutrophil-to-lymphocyte ratio(NLR),red blood cell distribution width(RDW),hemoglobin-to-RDW ratio(HRR)and systemic immune-inflammation index(SII),in elderly patients with chronic cardiovascular disease(CVD)complicated with frailty.Methods Retrospectively analyze 110 patients with chronic stable CVD who were hospitalized in the cadre ward of cardiovascular medicine at the Air Force Characteristic Medical Center from January 2022 to June 2023.According to the assessment results of the Fried scale,they were divided into three groups:non-frailty group(Fried score=0,n=30),the pre-frailty group(Fried score 1 or 2,n=40)and frailty group(Fried score≥3,n=40).The differences in general information,the impairment rate of daily living activities,miniature nutritional assessment-short form(MNA-SF)scores,mini-mental state examination(MMSE)scores,and the indicators such as NLR,RDW,HRR,and SII among the three groups were compared.Spearman rank correlation was used to analyze the correlation between NLR,RDW,HRR,SII and frailty scores as well as each frailty indicator.Multivariate logistic regression analysis was performed to identify the independent risk factors for frailty in elderly patients with chronic CVD,and the receiver operating characteristic(ROC)curve was used to assess the clinical diagnostic value of NLR and HRR in elderly patients with chronic CVD complicated with frailty.Results Compared with non-frailty group and pre-frailty group,patients in frailty group were older,with higher impaired rates of daily living activities,NLR,RDW,and SII,and lower MNA-SF scores,MMSE scores,and HRR,and differences were statistically significant(P<0.05).Spearman rank correlation analysis showed that the frailty score was positively correlated with NLR(rs=0.354,P<0.001),and RDW(rs=0.448,P<0.001),negatively correlated with HRR(rs=-0.232,P=0.024),and had no significant correlation with SII(rs=0.144,P=0.167).Further analysis of the correlation between the above novel inflammatory markers and the 5 components of frailty showed that NLR was positively correlated with fatigue(rs=0.228,P=0.017),slowed walking speed(rs=0.299,P<0.001),and low physical function(rs=0.319,P<0.001);RDW was positively correlated with decreased grip strength(rs=0.321,P<0.001),slowed walking speed(rs=0.422,P<0.001),and low physical function(rs=0.246,P=0.001);and HRR was negatively correlated with slowed walking speed(rs=-0.230,P=0.025),and low physical function(rs=-0.299,P=0.003).Multivariate logistic regression analysis showed that MNA-SF score(OR=0.577,95%CI 0.342-0.973)was an independent protective factor for pre-frailty in elderly patients with chronic CVD(P<0.05);NLR(OR=7.866,95%CI 1.101-56.185)was an independent risk factor for frailty,while HRR(OR=0.344,95%CI 0.120-0.983)and MNA-SF score(OR=0.292,95%CI 0.146-0.580)were independent protective factors for frailty in elderly CVD patients(P<0.05).The area under the ROC curve of NLR and HRR for diagnosing frailty in elderly patients with chronic CVD were 0.778 and 0.749,respectively.Conclusion NLR and HRR have high clinical diagnostic value for frailty in elderly patients with chronic CVD,and are expected to become effective inflammatory markers for screening elderly patients with chronic CVD complicated with frailty.

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.

Objective:To evaluate the effect of the radiation dose of thoracic cage bone structure on clinical prognosis in patients with locally advanced non‐small cell lung cancer (LA‐NSCLC) receiving chemoradiotherapy, and to develop and verify a combined model combining the radiation dose of bone structure, the estimated radiation dose of immune cells (EDRIC) and other related factors to predict the prognosis of LA‐NSCLC.Methods:Clinical data of 197 patients with LA‐NSCLC who underwent chemoradiotherapy were retrospectively analyzed. All patients were randomly divided into the training set and testing set at a ratio of 7:3 using computer random partitioning. The EDRIC value was calculated using the model developed by Jin et al. and modified by Ladbury et al. The scope of the thoracic cage structure includes the ribs, sternal manubrium, sternal body, thoracic vertebral body, thoracic vertebral appendages, and thoracic vertebrae. The tumor volume, ERDIC, and average bone structure dose (D mean) were categorized into two groups using the P25, P50, P75 value from the quartile method. Univariate and multivariate Cox proportional hazards regression were used to analyze the influencing factors of overall survival (OS), local progression‐free survival (LPFS), and distant metastasis‐free survival (DMFS) for predicting the outcome, and significant correlated variables were retained to construct a combined prediction model with EDRIC. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curves were plotted for subjects at the 2‐year time point of the combined model to evaluate the predictive performance. The model was visualized through a nomograph. Results:In the thoracic cage bone structure, D mean > 47.3 Gy of the sternal manubrium was an independent risk factor of OS, LPFS, and DMFS of LA-NSCLC patients. D mean > 23.1 Gy of thoracic vertebral body was an independent risk factor of OS, and D mean > 14.4 Gy of thoracic vertebral body was an independent risk factor of DMFS. Among other variables, gross tumor volume (GTV) >50.2 cm 3 was a risk factor for OS, and GTV >87.0 cm 3 was a risk factor for LPFS. Planning target volume >571.9 cm 3 was a risk factor for DMFS. A combined prediction model for OS, LPFS, and DMFS was established with EDRIC using features significantly associated with these three predicted outcomes. The area under the ROC curve (AUC) of OS combined model in the training set and test set were 0.708 and 0.696, respectively, and the AUC of DMFS combined model were 0.675 and 0.639, respectively. The calibration curve and DCA curve of the two prediction endpoints showed that the combined model had good prediction accuracy and clinical benefit. However, the LPFS model was not good in accuracy and clinical applicability. Conclusions:The radiation dose of sternal manubrium and thoracic vertebral body in the thoracic cage bone structure is an independent influencing factor for the prognosis of LA‐NSCLC patients after chemoradiotherapy. The combined model has good predictive performance for OS and DMFS.

Objective:To evaluate the effect of the radiation dose of thoracic cage bone structure on clinical prognosis in patients with locally advanced non‐small cell lung cancer (LA‐NSCLC) receiving chemoradiotherapy, and to develop and verify a combined model combining the radiation dose of bone structure, the estimated radiation dose of immune cells (EDRIC) and other related factors to predict the prognosis of LA‐NSCLC.Methods:Clinical data of 197 patients with LA‐NSCLC who underwent chemoradiotherapy were retrospectively analyzed. All patients were randomly divided into the training set and testing set at a ratio of 7:3 using computer random partitioning. The EDRIC value was calculated using the model developed by Jin et al. and modified by Ladbury et al. The scope of the thoracic cage structure includes the ribs, sternal manubrium, sternal body, thoracic vertebral body, thoracic vertebral appendages, and thoracic vertebrae. The tumor volume, ERDIC, and average bone structure dose (D mean) were categorized into two groups using the P25, P50, P75 value from the quartile method. Univariate and multivariate Cox proportional hazards regression were used to analyze the influencing factors of overall survival (OS), local progression‐free survival (LPFS), and distant metastasis‐free survival (DMFS) for predicting the outcome, and significant correlated variables were retained to construct a combined prediction model with EDRIC. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curves were plotted for subjects at the 2‐year time point of the combined model to evaluate the predictive performance. The model was visualized through a nomograph. Results:In the thoracic cage bone structure, D mean > 47.3 Gy of the sternal manubrium was an independent risk factor of OS, LPFS, and DMFS of LA-NSCLC patients. D mean > 23.1 Gy of thoracic vertebral body was an independent risk factor of OS, and D mean > 14.4 Gy of thoracic vertebral body was an independent risk factor of DMFS. Among other variables, gross tumor volume (GTV) >50.2 cm 3 was a risk factor for OS, and GTV >87.0 cm 3 was a risk factor for LPFS. Planning target volume >571.9 cm 3 was a risk factor for DMFS. A combined prediction model for OS, LPFS, and DMFS was established with EDRIC using features significantly associated with these three predicted outcomes. The area under the ROC curve (AUC) of OS combined model in the training set and test set were 0.708 and 0.696, respectively, and the AUC of DMFS combined model were 0.675 and 0.639, respectively. The calibration curve and DCA curve of the two prediction endpoints showed that the combined model had good prediction accuracy and clinical benefit. However, the LPFS model was not good in accuracy and clinical applicability. Conclusions:The radiation dose of sternal manubrium and thoracic vertebral body in the thoracic cage bone structure is an independent influencing factor for the prognosis of LA‐NSCLC patients after chemoradiotherapy. The combined model has good predictive performance for OS and DMFS.

Isocitrate dehydrogenase 1 (IDH1) R132H is the most common mutated gene in grade II-III gliomas and oligodendrogliomas. Instead of activating telomerase (a reverse transcriptase which using RNA as a template to extend telomere length), the majority of IDH1^R132H mutant glioma maintain telomere length through an alternative mechanism that relies on homologous recombination (HR), which is known as alterative lengthening of telomere (ALT).The phenotype of ALT mechanism include: ALT associated promyelocytic leukemia protein (PML) bodies (APBs); extrachromosomal telomeric DNA repeats such as C- and T-loops; telomeric sister chromatid exchange (T-SCE), etc. The mechanism of ALT activation is not fully understood. Recent studies have shown that mutation IDH1 contributes to ALT phenotype in glioma cells in at least three key ways. Firstly, the IDH1^R132H mutation mediates RAP1 down-regulation leading to telomere dysfunction, thus ensuring persistent endogenous telomeric DNA damage, which is important for ALT activation. Spontaneous DNA damage at telomeres may provide a substrate for mutation break-induced replication (BIR)‑mediated ALT telomere lengthening, and it has been demonstrated that RAP1 inhibits telomeric repeat-containing RNA, transcribed from telomeric DNA repeat sequences (TERRA) transcription to down-regulate ALT telomere DNA replication stress and telomeric DNA damage, thereby inhibiting ALT telomere synthesis. Similarly, in ALT cells, knockdown of telomere-specific RNaseH1 nuclease triggers TERRA accumulation, which leads to increased replication pressure. Overexpression of RNaseH1, on the other hand, attenuates the recombination capacity of ALT telomeres, leading to telomere depletion, suggesting that RAP1 can regulate the level of replication pressure and thus ALT activity by controlling TERRA expression. Secondly, the IDH1^R132H also alters the preference of the telomere damage repair pathway by down-regulating XRCC1, which inhibits the alternative non-homologous end joining (A-NHEJ) pathway at telomeres and alters cellular preference for the HR pathway to promote ALT. Finally, the IDH1^R132H has a decreased affinity for isocitric acid and NADP+ and an increased affinity for α ketoglutarate (α‑KG) and NADPH, so that the mutant IDH1^R132H catalyzes the hydrogenation of α‑KG to produce 2-hydroxyglutarate (2-HG)in a NADPH-dependent manner. Because 2-HG is structurally similar to α‑KG, which maintains the trimethylation level of H3k9me3 by competitively inhibiting the activity of the α‑KG-dependent histone demethylase KDM4B, and recruits heterochromatin protein HP1α to heterochromatinize telomeres, and promote ALT phenotypes in cooperation with the inactivating of ATRX. In addition, it has been shown that APBs contain telomeric chromatin, which is essentially heterochromatin, and HP1α is directly involved in the formation of APBs. Based on these studies, this article reviews the mechanism of IDH1^R132H mediated telomere dysfunction and the preference of DNA repair pathway at telomeres in cooperate with ATRX loss to promote ALT, which may provide references for clinical targeted therapy of IDH1^R132H mutant glioma.

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing. Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing. Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing. Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.