ObjectiveTo clarify whether METTL14 mediates the core role of acupuncture at Neiguan (PC6) in promoting myelination and improving behavior in young autistic rats through gene intervention technology. MethodsThe ASD model was established by intraperitoneal injection of valproic acid (VPA) in pregnant rats. Male offspring were intracerebroventricularly injected with adenovirus-packaged METTL14 shRNA (sh-METTL14) or its control (sh-NC) on postnatal day 1, with a model group set as well. Subsequently, the juvenile rats were divided into model group, acupuncture group, acupuncture+sh-NC group, and acupuncture+sh-METTL14 group. The acupuncture group received acupuncture at Neiguan (PC6) from postnatal day 7, once daily for 21 consecutive days. Neurobehavioral changes were evaluated by behavioral tests; METTL14 knockdown efficiency and the expression of METTL14, METTL3, and PTEN were detected by quantitative real-time PCR (qRT-PCR) and Western blot (WB); PTEN m⁶A levels were measured by RNA immunoprecipitation-qPCR (RIP-qPCR); myelin ultrastructure, expression of myelin basic protein (MBP) and neurofascin 155 (NF155), and dendritic spine density were observed using transmission electron microscopy (TEM), enzyme-linked immunosorbent assay (ELISA), immunofluorescence, qRT-PCR, and primary neuron culture. ResultsBehaviorally, knockdown of METTL14 significantly counteracted the beneficial effects of acupuncture in improving self-grooming, open field exploration, three-chamber social interaction, and Morris water maze learning and memory (P<0.05, P<0.01). Compared with the acupuncture+sh-NC group, the acupuncture+sh-METTL14 group showed significantly decreased mRNA and protein expression of hippocampal METTL14 (P<0.01), and the upregulating effects of acupuncture on METTL3 and PTEN expression were reversed (P<0.01). Meanwhile, knockdown of METTL14 significantly inhibited the acupuncture-induced increase in PTEN m⁶A levels (P<0.01). Morphologically, knockdown of METTL14 attenuated the improvement of myelin structure by acupuncture, reversed the downregulation of MBP and upregulation of NF155 induced by acupuncture, and blocked the increase in dendritic spine density (P<0.05, P<0.01). ConclusionMETTL14 is a key molecule mediating the therapeutic effect of acupuncture at Neiguan. Acupuncture at Neiguan upregulates METTL14, thereby enhancing m⁶A methylation modification of PTEN mRNA to stabilize its expression, ultimately promoting myelin development and improving behavioral symptoms in ASD juvenile rats. This preliminarily reveals the modern biological connotation of “opening Xuanfu and dredging myelin”.

ObjectiveTo clarify whether METTL14 mediates the core role of acupuncture at Neiguan (PC6) in promoting myelination and improving behavior in young autistic rats through gene intervention technology. MethodsThe ASD model was established by intraperitoneal injection of valproic acid (VPA) in pregnant rats. Male offspring were intracerebroventricularly injected with adenovirus-packaged METTL14 shRNA (sh-METTL14) or its control (sh-NC) on postnatal day 1, with a model group set as well. Subsequently, the juvenile rats were divided into model group, acupuncture group, acupuncture+sh-NC group, and acupuncture+sh-METTL14 group. The acupuncture group received acupuncture at Neiguan (PC6) from postnatal day 7, once daily for 21 consecutive days. Neurobehavioral changes were evaluated by behavioral tests; METTL14 knockdown efficiency and the expression of METTL14, METTL3, and PTEN were detected by quantitative real-time PCR (qRT-PCR) and Western blot (WB); PTEN m⁶A levels were measured by RNA immunoprecipitation-qPCR (RIP-qPCR); myelin ultrastructure, expression of myelin basic protein (MBP) and neurofascin 155 (NF155), and dendritic spine density were observed using transmission electron microscopy (TEM), enzyme-linked immunosorbent assay (ELISA), immunofluorescence, qRT-PCR, and primary neuron culture. ResultsBehaviorally, knockdown of METTL14 significantly counteracted the beneficial effects of acupuncture in improving self-grooming, open field exploration, three-chamber social interaction, and Morris water maze learning and memory (P<0.05, P<0.01). Compared with the acupuncture+sh-NC group, the acupuncture+sh-METTL14 group showed significantly decreased mRNA and protein expression of hippocampal METTL14 (P<0.01), and the upregulating effects of acupuncture on METTL3 and PTEN expression were reversed (P<0.01). Meanwhile, knockdown of METTL14 significantly inhibited the acupuncture-induced increase in PTEN m⁶A levels (P<0.01). Morphologically, knockdown of METTL14 attenuated the improvement of myelin structure by acupuncture, reversed the downregulation of MBP and upregulation of NF155 induced by acupuncture, and blocked the increase in dendritic spine density (P<0.05, P<0.01). ConclusionMETTL14 is a key molecule mediating the therapeutic effect of acupuncture at Neiguan. Acupuncture at Neiguan upregulates METTL14, thereby enhancing m⁶A methylation modification of PTEN mRNA to stabilize its expression, ultimately promoting myelin development and improving behavioral symptoms in ASD juvenile rats. This preliminarily reveals the modern biological connotation of “opening Xuanfu and dredging myelin”.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To explore the effects of dodecanoylcarnitine(DA)and myristoleic acid(MA)on the function of mouse alveolar epithelial cell line MLE-12 and their underlying mechanisms.Methods An inflammatory model was established by stimulating MLE-12 cells with IL-4.The expression levels of DA,MA,and sphingosine-1-phosphate(S1P)in the cell supernatant were detected by ELISA.MLE-12 cells were separately intervened with DA and MA.RT-PCR and flow cytometry were used to detect the expression changes of inflammatory factors IL-6 and tumor necrosis factor-α(TNF-α)and the level of intracellular reactive oxygen species(ROS).Additionally,Western blot was performed to detect the expression of key proteins such as p38 mitogen-activated protein kinase(p-38 MAPK)and src homology 2 domain-containing phosphatase 1(SHP-1).To explore the role of S1PR2 in the effects of DA and MA,MLE-12 cells were pretreated with the S1PR2 inhibitor JTE-013,and the above experiments were repeated.Results IL-4 stimulation significantly upregulated the levels of DA,MA,and S1P in MLE-12 cells(P<0.05).DA/MA treatment groups exhibited significantly increased expression of IL-6 and TNF-α compared with the control group(P<0.05),along with elevated ROS levels(P<0.05).Western blot analysis revealed that DA/MA promoted SHP-1 dephosphorylation and phosphorylated p38 MAPK activation in MLE-12 cells.Notably,JTE-013 pre-treatment completely reversed these effects(P<0.05).Conclusion Asthma-related metabolites DA and MA exacerbate the inflammatory and oxidative stress responses of MLE-12 cells by activating the S1PR2 receptor,promoting the dephosphorylation of SHP-1 and the activation of the p-p38 MAPK pathway.This study reveals the core regulatory role of S1PR2 in this pathway as well.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To explore the effects of dodecanoylcarnitine(DA)and myristoleic acid(MA)on the function of mouse alveolar epithelial cell line MLE-12 and their underlying mechanisms.Methods An inflammatory model was established by stimulating MLE-12 cells with IL-4.The expression levels of DA,MA,and sphingosine-1-phosphate(S1P)in the cell supernatant were detected by ELISA.MLE-12 cells were separately intervened with DA and MA.RT-PCR and flow cytometry were used to detect the expression changes of inflammatory factors IL-6 and tumor necrosis factor-α(TNF-α)and the level of intracellular reactive oxygen species(ROS).Additionally,Western blot was performed to detect the expression of key proteins such as p38 mitogen-activated protein kinase(p-38 MAPK)and src homology 2 domain-containing phosphatase 1(SHP-1).To explore the role of S1PR2 in the effects of DA and MA,MLE-12 cells were pretreated with the S1PR2 inhibitor JTE-013,and the above experiments were repeated.Results IL-4 stimulation significantly upregulated the levels of DA,MA,and S1P in MLE-12 cells(P<0.05).DA/MA treatment groups exhibited significantly increased expression of IL-6 and TNF-α compared with the control group(P<0.05),along with elevated ROS levels(P<0.05).Western blot analysis revealed that DA/MA promoted SHP-1 dephosphorylation and phosphorylated p38 MAPK activation in MLE-12 cells.Notably,JTE-013 pre-treatment completely reversed these effects(P<0.05).Conclusion Asthma-related metabolites DA and MA exacerbate the inflammatory and oxidative stress responses of MLE-12 cells by activating the S1PR2 receptor,promoting the dephosphorylation of SHP-1 and the activation of the p-p38 MAPK pathway.This study reveals the core regulatory role of S1PR2 in this pathway as well.

Objective To explore the effect and predictive value of ultrasound-guided needling assisted motor evoked potentials(MEP)and electromyography(EMG)monitoring on neurological recovery in spinal surgery.Methods A retrospective analysis was conducted on the clinical data of 80 patients who underwent spinal surgery at Jiangsu Province Hospital of Chinese Medicine from January 2020 to December 2024.A total of 41 patients in the observation group received ultrasound-guided needling assisted MEP and EMG monitoring,and 39 patients in the control group received conventional method for MEP and EMG monitoring.The operative time,intraoperative blood loss,and the proportions of intraoperative MEP and EMG warnings were compared between the two groups,and the sensitivity and specificity of intraoperative MEP monitoring were compared between the two groups.The receiver operating characteristic(ROC)curve was plotted,and the area under the curve(AUC)was calculated to analyze the efficiency of MEP warning in predicting the dysfunction of postoperative spinal cord.Results There were no significant differences in the operative time,intraoperative blood loss,or the proportions of intraoperative MEP and EMG warnings(P>0.05).The sensitivity,specificity and AUC of intraoperative MEP monitoring in the observation group were significantly higher than those in the control group,with statistically significant differences(P<0.05).The sensitivity,specificity,and AUC of postoperative MEP warning in predicting the dysfunction of spinal cord in the observation group were higher than those in the control group,with statistically significant differences(P<0.05).Conclusion Ultrasound-guided needling assisted MEP and EMG monitoring can effectively enhance the intraoperative neural monitoring accuracy,and postoperative MEP warning demonstrates superior predictive value for postoperative neurological dysfunction.

Objective To explore the effect and predictive value of ultrasound-guided needling assisted motor evoked potentials(MEP)and electromyography(EMG)monitoring on neurological recovery in spinal surgery.Methods A retrospective analysis was conducted on the clinical data of 80 patients who underwent spinal surgery at Jiangsu Province Hospital of Chinese Medicine from January 2020 to December 2024.A total of 41 patients in the observation group received ultrasound-guided needling assisted MEP and EMG monitoring,and 39 patients in the control group received conventional method for MEP and EMG monitoring.The operative time,intraoperative blood loss,and the proportions of intraoperative MEP and EMG warnings were compared between the two groups,and the sensitivity and specificity of intraoperative MEP monitoring were compared between the two groups.The receiver operating characteristic(ROC)curve was plotted,and the area under the curve(AUC)was calculated to analyze the efficiency of MEP warning in predicting the dysfunction of postoperative spinal cord.Results There were no significant differences in the operative time,intraoperative blood loss,or the proportions of intraoperative MEP and EMG warnings(P>0.05).The sensitivity,specificity and AUC of intraoperative MEP monitoring in the observation group were significantly higher than those in the control group,with statistically significant differences(P<0.05).The sensitivity,specificity,and AUC of postoperative MEP warning in predicting the dysfunction of spinal cord in the observation group were higher than those in the control group,with statistically significant differences(P<0.05).Conclusion Ultrasound-guided needling assisted MEP and EMG monitoring can effectively enhance the intraoperative neural monitoring accuracy,and postoperative MEP warning demonstrates superior predictive value for postoperative neurological dysfunction.

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.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.