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.

Patients suffering from nerve injury often experience exacerbated pain responses and complain of memory deficits. The dorsal hippocampus (dHPC), a well-defined region responsible for learning and memory, displays maladaptive plasticity upon injury, which is assumed to underlie pain hypersensitivity and cognitive deficits. However, much attention has thus far been paid to intracellular mechanisms of plasticity rather than extracellular alterations that might trigger and facilitate intracellular changes. Emerging evidence has shown that nerve injury alters the microarchitecture of the extracellular matrix (ECM) and decreases ECM rigidity in the dHPC. Despite this, it remains elusive which element of the ECM in the dHPC is affected and how it contributes to neuropathic pain and comorbid cognitive deficits. Laminin, a key element of the ECM, consists of α-, β-, and γ-chains and has been implicated in several pathophysiological processes. Here, we showed that peripheral nerve injury downregulates laminin β1 (LAMB1) in the dHPC. Silencing of hippocampal LAMB1 exacerbates pain sensitivity and induces cognitive dysfunction. Further mechanistic analysis revealed that loss of hippocampal LAMB1 causes dysregulated Src/NR2A signaling cascades via interaction with integrin β1, leading to decreased Ca²⁺ levels in pyramidal neurons, which in turn orchestrates structural and functional plasticity and eventually results in exaggerated pain responses and cognitive deficits. In this study, we shed new light on the functional capability of hippocampal ECM LAMB1 in the modulation of neuropathic pain and comorbid cognitive deficits, and reveal a mechanism that conveys extracellular alterations to intracellular plasticity. Moreover, we identified hippocampal LAMB1/integrin β1 signaling as a potential therapeutic target for the treatment of neuropathic pain and related memory loss.
Animals ; Laminin/genetics* ; Hippocampus/metabolism* ; Neuralgia/metabolism* ; Cognitive Dysfunction/etiology* ; Male ; Peripheral Nerve Injuries/metabolism* ; Extracellular Matrix/metabolism* ; Integrin beta1/metabolism* ; Pyramidal Cells/metabolism* ; Signal Transduction

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. It has a high prevalence and poor prognosis. The application of antiarrhythmic drugs and even surgery cannot completely treat the disease, and there are many sequelae. AF can be classified into the category of "palpitation" in Chinese medicine according to its symptoms. Acupuncture has a significant effect on AF. The authors find that an important mechanism of acupuncture in AF treatment is to regulate the cardiac vagus nerve. Therefore, this article intends to review the distribution and function of vagus nerve in the heart, the application and the regulatroy effect for the treatment of AF.
Atrial Fibrillation/physiopathology* ; Humans ; Acupuncture Therapy ; Vagus Nerve/physiology* ; Animals

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 analyze the molecular genetic spectrum of children with acute myeloid leukemia(AML),and explore its correlation with clinical characteristics and prognosis.Methods:The clinical and molecular genetic data of 116 children with newly diagnosed AML in Wuhan Children's Hospital from September 2015 to August 2022 were retrospectively analyzed.The Fisher's exact test was used to analyze the correlation of gene mutations with clinical features,and Kaplan-Meier curve was used to analyze the influences of gene mutations on the prognosis.Results:NRAS(22％),KRAS(14.9％),and KIT(14.7％)mutations were the most common genetic abnormalities in 116 children with AML.Children with KIT,CEBPA and GATA2 mutations showed a higher median onset-age than those without mutations(all P＜0.05).Children with FLT3-ITD mutation exhibited a higher white blood cell count at initial diagnosis compared to those without mutations(P＜0.05).Children with ASXL2 mutation had lower platelet count and hemoglobin at initial diagnosis than those without mutations(both P＜0.05).KIT mutations were often co-occurred with t(8;21)(q22;q22).There was no significant relationship between gene mutation and minimal residual disease(MRD)remission rate after the first and second induction therapy(P＞0.05).KIT and NRAS mutations were not associated with prognosis significantly(P＞0.05).The overall survival(OS)rates of children with CEBPA and FLT3-ITD mutations were superior to those without mutations,but the differences were not statistically significant(P＞0.05).The 3-year OS rate of 61 children treated by allogeneic hematopoietic stem cell transplantation was 89.8％,which was significantly higher than 55.2％of those only treated by chemotherapy(P＜0.001).Conclusions:Gene mutations are common in children with AML,and next-generation sequencing can significantly improve the detection rate of gene mutations,which can guide the risk stratification therapy.In addition,FLT3-ITD and KIT mutations may no longer be poor prognostic factors.

Objective:To investigate the causal relationship between trimethylamine N-oxide (TMAO) and its precursors (betaine, carnitine, and choline) and pancreatic diseases based on the Mendelian randomization (MR) method.Methods:Genome-wide association study data of TMAO, betaine, carnitine, choline, acute pancreatitis (AP), chronic pancreatitis (CP), pancreatic cancer (PC), and circulating immune cell characteristics (white blood cell, lymphocyte, monocyte, neutrophil, eosinophil and basophil) were collected. According to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)-MR reporting guidelines, the available genetic variants [single nucleotide polymorphism (SNP)] were strictly screened. The causal relationship between exposure (TMAO and its precursors) and outcomes (pancreatic diseases and circulating immune cell characteristics) was evaluated using inverse variance weighting (IVW), MR-Egger regression and weighted median. The reliability of the results was evaluated by sensitivity analysis based on MR-Egger regression, MR-PRESSO, Cochrane's Q test and leave-one-out method. Results:A total of 36 SNP associated with TMAO and its precursors were included. Five of these were associated with TMAO, 13 with betaine, 12 with carnitine, and 6 with choline. ① MR analysis showed that TMAO may increase the risk of AP [odds ratio ( OR) = 1.100, 95% confidence interval (95% CI) was 1.008-1.200, P = 0.032], and choline may reduce the risk of alcoholic acute pancreatitis (AAP; OR = 0.743, 95% CI was 0.585-0.944, P = 0.015). The analysis results of MR-Egger regression and weighted median were consistent with the IVW results. There is no evidence to support a causal relationship between TMAO and its precursors and the risk of CP and PC. Sensitivity analysis indicated that SNP analyzed by MR showed no heterogeneity and low pleiotropy. The leave-one-out method analysis determined that after excluding any SNP, the effect intervals of the remaining SNP on the results were similar to the overall effect intervals, which suggested the robustness of MR results. ② There was a positive causal relationship between plasma TMAO level and circulating monocyte count ( OR = 1.017, 95% CI was 1.000*-1.034, P = 0.048, * represented that the data was obtained by correcting to 3 decimal places from 1.000 1). The causal effect obtained by MR-Egger regression and weighted median analysis was consistent with the results of IVW. Sensitivity analysis illustrated SNP analyzed by MR showed no heterogeneity and pleiotropy. The leave-one-out method analysis determined that after excluding any SNP, the effect intervals of the remaining SNP on the results were similar to the overall effect intervals, which suggested the robustness of MR results. Conclusion:TMAO and choline may change the risk of AP, and TMAO may contribute to the increase of circulating monocyte count in AP.

Objective:To investigate the clinical value of preoperative gadolinium ethoxy-benzyldiethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) in predicting microvascular invasion (MVI) and intratumoral tertiary lymphoid structures (TLSs) in hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinicopathological data of 304 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and 10 HCC patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University from June 2021 to June 2023 were collected. There were 272 males and 42 females, aged (56±11)years. Using a random number table method, patients were divided into a training set including 220 cases and a validation set including 94 cases in a 7:3 ratio. Among the 314 patients, 106 cases had MVI and TLSs-positive HCC (MT-HCC), and 208 cases had non-MT-HCC. All patients underwent preoperative Gd-EOB-DTPA-enhanced MRI and radical resection. Observation indicators: (1) clinicopathological characteristics of MT-HCC and non-MT-HCC patients; (2) imaging characteristics of MT-HCC and non-MT-HCC patients; (3) imaging features associated with MT-HCC diagnosis; (4) nomogram predictive model construction and evaluation for MT-HCC. Comparison of measurement data with normal distribution between groups was analyzed using the t test. Comparison of measurement data with skewed distribution between groups was analyzed using the nonpara-meter rank sum test. Univariate analysis was conducted using the corresponding statistical methods based on data type. Multivariate analysis was conducted using the logistic regression model. A nomo-gram predictive model was constructed based on results of multivariate analysis, and receiver operating characteristic (ROC) curves were plotted to evaluate the model's performance with the area under curve (AUC). Calibration curve and decision curve analyses were used to assess the calibration and clinical validity of nomogram predictive model. Results:(1) Clinicopathological characteristics of MT-HCC and non-MT-HCC patients. In the training set, there were significant differences between MT-HCC and non-MT-HCC patients in terms of age, white blood cell count, and alpha fetoprotein level ( t=2.488, Z=-2.515, χ2=4.014, P<0.05). (2) Imaging characteristics of MT-HCC and non-MT-HCC patients. In the training set, there were significant differences in tumor morphology, intratumoral hemorrhage, peritumoral abnormal enhancement in arterial phase, capsule presence, intratumoral necrosis or ischemia >20%, intratumoral necrosis or ischemia >50%, peritumoral hypointensity in the hepatobiliary phase, intravascular tumor thrombus, arterial phase rim-like hyperenhancement, and mosaic architecture between MT-HCC and non-MT-HCC patients ( χ2=8.811, 5.586, 13.962, 31.616, 10.154, 4.835, 5.111, 14.425, 7.112, 5.526, P<0.05). (3) Imaging features associated with MT-HCC diagnosis. Results of multivariate analysis identified the absence of intratumoral hemorrhage, incom-plete capsule, and mosaic architecture as independent risk factors for diagnosing MT-HCC ( hazard ratio=3.846, 7.827, 2.345, P<0.05). (4) Nomogram predictive model construction and evaluation for MT-HCC. A nomogram predictive model for MT-HCC was constructed based on the independent risk factors (absence of intratumoral hemorrhage, incomplete capsule, and mosaic architecture) iden-tified in the multivariate analysis. The ROC curve analysis showed that AUC of nomogram predictive model was 0.778 (95% confidence interval as 0.714-0.843), with sensitivity and specificity of 0.857 and 0.573 in the training set. In the validation set, the area under the curve, sensitivity, and specifi-city were 0.825 (95% confidence interval as 0.745-0.926), 0.655, and 0.877, respectively. The calibra-tion curves for both the training set and the validation set closely aligned with the standard curve, indicating high calibration accuracy. The decision curve analysis demonstrated net clinical benefits at thresholds of 0.130-0.690 in the training set and 0.060-0.750 in the validation set. Conclusions:The absence of intratumoral hemorrhage, incomplete capsule, and mosaic architecture are independent risk factors for diagnosing MT-HCC. A nomogram model based on imaging features can predict MT-HCC in HCC patients.

Objective:To investigate the clinical value of preoperative gadolinium ethoxy-benzyldiethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) in predicting microvascular invasion (MVI) and intratumoral tertiary lymphoid structures (TLSs) in hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinicopathological data of 304 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and 10 HCC patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University from June 2021 to June 2023 were collected. There were 272 males and 42 females, aged (56±11)years. Using a random number table method, patients were divided into a training set including 220 cases and a validation set including 94 cases in a 7:3 ratio. Among the 314 patients, 106 cases had MVI and TLSs-positive HCC (MT-HCC), and 208 cases had non-MT-HCC. All patients underwent preoperative Gd-EOB-DTPA-enhanced MRI and radical resection. Observation indicators: (1) clinicopathological characteristics of MT-HCC and non-MT-HCC patients; (2) imaging characteristics of MT-HCC and non-MT-HCC patients; (3) imaging features associated with MT-HCC diagnosis; (4) nomogram predictive model construction and evaluation for MT-HCC. Comparison of measurement data with normal distribution between groups was analyzed using the t test. Comparison of measurement data with skewed distribution between groups was analyzed using the nonpara-meter rank sum test. Univariate analysis was conducted using the corresponding statistical methods based on data type. Multivariate analysis was conducted using the logistic regression model. A nomo-gram predictive model was constructed based on results of multivariate analysis, and receiver operating characteristic (ROC) curves were plotted to evaluate the model's performance with the area under curve (AUC). Calibration curve and decision curve analyses were used to assess the calibration and clinical validity of nomogram predictive model. Results:(1) Clinicopathological characteristics of MT-HCC and non-MT-HCC patients. In the training set, there were significant differences between MT-HCC and non-MT-HCC patients in terms of age, white blood cell count, and alpha fetoprotein level ( t=2.488, Z=-2.515, χ2=4.014, P<0.05). (2) Imaging characteristics of MT-HCC and non-MT-HCC patients. In the training set, there were significant differences in tumor morphology, intratumoral hemorrhage, peritumoral abnormal enhancement in arterial phase, capsule presence, intratumoral necrosis or ischemia >20%, intratumoral necrosis or ischemia >50%, peritumoral hypointensity in the hepatobiliary phase, intravascular tumor thrombus, arterial phase rim-like hyperenhancement, and mosaic architecture between MT-HCC and non-MT-HCC patients ( χ2=8.811, 5.586, 13.962, 31.616, 10.154, 4.835, 5.111, 14.425, 7.112, 5.526, P<0.05). (3) Imaging features associated with MT-HCC diagnosis. Results of multivariate analysis identified the absence of intratumoral hemorrhage, incom-plete capsule, and mosaic architecture as independent risk factors for diagnosing MT-HCC ( hazard ratio=3.846, 7.827, 2.345, P<0.05). (4) Nomogram predictive model construction and evaluation for MT-HCC. A nomogram predictive model for MT-HCC was constructed based on the independent risk factors (absence of intratumoral hemorrhage, incomplete capsule, and mosaic architecture) iden-tified in the multivariate analysis. The ROC curve analysis showed that AUC of nomogram predictive model was 0.778 (95% confidence interval as 0.714-0.843), with sensitivity and specificity of 0.857 and 0.573 in the training set. In the validation set, the area under the curve, sensitivity, and specifi-city were 0.825 (95% confidence interval as 0.745-0.926), 0.655, and 0.877, respectively. The calibra-tion curves for both the training set and the validation set closely aligned with the standard curve, indicating high calibration accuracy. The decision curve analysis demonstrated net clinical benefits at thresholds of 0.130-0.690 in the training set and 0.060-0.750 in the validation set. Conclusions:The absence of intratumoral hemorrhage, incomplete capsule, and mosaic architecture are independent risk factors for diagnosing MT-HCC. A nomogram model based on imaging features can predict MT-HCC in HCC patients.

Objective: To establish a nomogram prognostic model for predicting the 5-, 10-, and 15-year overall survival (OS) of non-metastatic renal cell carcinoma patients managed with radical nephrectomy (RN), compare the modelled results with the results of pure pathologic staging, the Karakiewicz nomogram and the Mayo Clinic Stage, Size, Grade, and Necrosis (SSIGN) score commonly used in foreign countries, and stratify the patients into different prognostic risk subgroups. Methods: A total of 1 246 non-metastatic renal cell carcinoma patients managed with RN in Sun Yat-sen University Cancer Center (SYSUCC) from 1999 to 2020 were retrospectively analyzed. Multivariate Cox regression analysis was used to screen the variables that influence the prognosis for nomogram establishment, and the bootstrap random sampling was used for internal validation. The time-receiver operating characteristic curve (ROC), the calibration curve and the clinical decision curve analysis (DCA) were applied to evaluate the nomogram. The prediction efficacy of the nomogram and that of the pure pathologic staging, the Karakiewicz nomogram and the SSIGN score was compared through the area under the curve (AUC). Finally, patients were stratified into different risk subgroups according to our nomogram scores. Results: A total of 1 246 patients managed with RN were enrolled in this study. Multivariate Cox regression analysis showed that age, smoking history, pathological nuclear grade, sarcomatoid differentiation, tumor necrosis and pathological T and N stages were independent prognostic factors for RN patients (all P<0.05). A nomogram model named SYSUCC based on these factors was built to predict the 5-, 10-, and 15-year survival rate of the participating patients. In the bootstrap random sampling with 1 000 iterations, all these factors occurred for more than 800 times as independent predictors. The Harrell's concordance index (C-index) of SYSUCC was higher compared with pure pathological staging [0.770 (95% CI: 0.716-0.823) vs 0.674 (95% CI: 0.621-0.728)]. The calibration curve showed that the survival rate as predicted by the SYSUCC model simulated the actual rate, while the clinical DCA showed that the SYSUCC nomogram has a benefit in certain probability ranges. In the ROC analysis that included 857 patients with detailed pathological nuclear stages, the nomogram had a larger AUC (5-/10-year AUC: 0.823/0.804) and better discriminating ability than pure pathological staging (5-/10-year AUC: 0.701/0.658), Karakiewicz nomogram (5-/10-year AUC: 0.772/0.734) and SSIGN score (5-/10-year AUC: 0.792/0.750) in predicting the 5-/10-year OS of RN patients (all P<0.05). In addition, the AUC of the SYSUCC nomogram for predicting the 15-year OS (0.820) was larger than that of the SSIGN score (0.709), and there was no statistical difference (P<0.05) between the SYSUCC nomogram, pure pathological staging (0.773) and the Karakiewicz nomogram (0.826). The calibration curve was close to the standard curve, which indicated that the model has good predictive performance. Finally, patients were stratified into low-, intermediate-, and high-risk subgroups (738, 379 and 129, respectively) according to the SYSUCC nomogram scores, among whom patients in intermediate- and high-risk subgroups had a worse OS than patients in the low-risk subgroup (intermediate-risk group vs. low-risk group: HR=4.33, 95% CI: 3.22-5.81, P<0.001; high-risk group vs low-risk group: HR=11.95, 95% CI: 8.29-17.24, P<0.001), and the high-risk subgroup had a worse OS than the intermediate-risk group (HR=2.63, 95% CI: 1.88-3.68, P<0.001). Conclusions: Age, smoking history, pathological nuclear grade, sarcomatoid differentiation, tumor necrosis and pathological stage were independent prognostic factors for non-metastasis renal cell carcinoma patients after RN. The SYSUCC nomogram based on these independent prognostic factors can better predict the 5-, 10-, and 15-year OS than pure pathological staging, the Karakiewicz nomogram and the SSIGN score of patients after RN. In addition, the SYSUCC nomogram has good discrimination, agreement, risk stratification and clinical application potential.
Humans ; Nomograms ; Retrospective Studies ; Carcinoma, Renal Cell/pathology* ; Prognosis ; Risk Factors ; Nephrectomy ; Kidney Neoplasms/pathology* ; Necrosis

Objective: To establish a nomogram prognostic model for predicting the 5-, 10-, and 15-year overall survival (OS) of non-metastatic renal cell carcinoma patients managed with radical nephrectomy (RN), compare the modelled results with the results of pure pathologic staging, the Karakiewicz nomogram and the Mayo Clinic Stage, Size, Grade, and Necrosis (SSIGN) score commonly used in foreign countries, and stratify the patients into different prognostic risk subgroups. Methods: A total of 1 246 non-metastatic renal cell carcinoma patients managed with RN in Sun Yat-sen University Cancer Center (SYSUCC) from 1999 to 2020 were retrospectively analyzed. Multivariate Cox regression analysis was used to screen the variables that influence the prognosis for nomogram establishment, and the bootstrap random sampling was used for internal validation. The time-receiver operating characteristic curve (ROC), the calibration curve and the clinical decision curve analysis (DCA) were applied to evaluate the nomogram. The prediction efficacy of the nomogram and that of the pure pathologic staging, the Karakiewicz nomogram and the SSIGN score was compared through the area under the curve (AUC). Finally, patients were stratified into different risk subgroups according to our nomogram scores. Results: A total of 1 246 patients managed with RN were enrolled in this study. Multivariate Cox regression analysis showed that age, smoking history, pathological nuclear grade, sarcomatoid differentiation, tumor necrosis and pathological T and N stages were independent prognostic factors for RN patients (all P<0.05). A nomogram model named SYSUCC based on these factors was built to predict the 5-, 10-, and 15-year survival rate of the participating patients. In the bootstrap random sampling with 1 000 iterations, all these factors occurred for more than 800 times as independent predictors. The Harrell's concordance index (C-index) of SYSUCC was higher compared with pure pathological staging [0.770 (95% CI: 0.716-0.823) vs 0.674 (95% CI: 0.621-0.728)]. The calibration curve showed that the survival rate as predicted by the SYSUCC model simulated the actual rate, while the clinical DCA showed that the SYSUCC nomogram has a benefit in certain probability ranges. In the ROC analysis that included 857 patients with detailed pathological nuclear stages, the nomogram had a larger AUC (5-/10-year AUC: 0.823/0.804) and better discriminating ability than pure pathological staging (5-/10-year AUC: 0.701/0.658), Karakiewicz nomogram (5-/10-year AUC: 0.772/0.734) and SSIGN score (5-/10-year AUC: 0.792/0.750) in predicting the 5-/10-year OS of RN patients (all P<0.05). In addition, the AUC of the SYSUCC nomogram for predicting the 15-year OS (0.820) was larger than that of the SSIGN score (0.709), and there was no statistical difference (P<0.05) between the SYSUCC nomogram, pure pathological staging (0.773) and the Karakiewicz nomogram (0.826). The calibration curve was close to the standard curve, which indicated that the model has good predictive performance. Finally, patients were stratified into low-, intermediate-, and high-risk subgroups (738, 379 and 129, respectively) according to the SYSUCC nomogram scores, among whom patients in intermediate- and high-risk subgroups had a worse OS than patients in the low-risk subgroup (intermediate-risk group vs. low-risk group: HR=4.33, 95% CI: 3.22-5.81, P<0.001; high-risk group vs low-risk group: HR=11.95, 95% CI: 8.29-17.24, P<0.001), and the high-risk subgroup had a worse OS than the intermediate-risk group (HR=2.63, 95% CI: 1.88-3.68, P<0.001). Conclusions: Age, smoking history, pathological nuclear grade, sarcomatoid differentiation, tumor necrosis and pathological stage were independent prognostic factors for non-metastasis renal cell carcinoma patients after RN. The SYSUCC nomogram based on these independent prognostic factors can better predict the 5-, 10-, and 15-year OS than pure pathological staging, the Karakiewicz nomogram and the SSIGN score of patients after RN. In addition, the SYSUCC nomogram has good discrimination, agreement, risk stratification and clinical application potential.
Humans ; Nomograms ; Retrospective Studies ; Carcinoma, Renal Cell/pathology* ; Prognosis ; Risk Factors ; Nephrectomy ; Kidney Neoplasms/pathology* ; Necrosis