Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Direct electrical stimulation of the human cortex can produce subjective visual sensations, yet these sensations are unstable. The underlying mechanisms may stem from differences in electrophysiological activity within the distributed network outside the stimulated site. To address this problem, we recruited 69 patients who experienced visual sensations during invasive electrical stimulation while intracranial electroencephalography (iEEG) data were recorded. We found significantly flattened power spectral slopes in distributed regions involving different brain networks and decreased integrated information during elicited visual sensations compared with the non-sensation condition. Further analysis based on minimum information partitions revealed that the reconfigured network interactions primarily involved the inferior frontal cortex, posterior superior temporal sulcus, and temporoparietal junction. The flattened power spectral slope in the inferior frontal gyrus was also correlated with integrated information. Taken together, this study indicates that the altered electrophysiological signatures provide insights into the neural mechanisms underlying subjective visual sensations.
Humans ; Male ; Female ; Adult ; Visual Perception/physiology* ; Electric Stimulation ; Middle Aged ; Young Adult ; Electrocorticography ; Electroencephalography ; Brain Mapping

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Resistance to poly adenosine diphosphate (ADP)-ribose polymerase inhibitor (PARPi) presents a considerable obstacle in the treatment of ovarian cancer. F-box and tryptophan-aspartic (WD) repeat domain containing 11 (FBXW11) modulates the ubiquitination of growth-and invasion-related factors in lung cancer, colorectal cancer, and osteosarcoma. The function of FBXW11 in PARPi therapy is still ambiguous. In this study, RNA sequencing (RNA-seq) showed that FBXW11 expression was raised in ovarian cancer cells that had been treated with PARPi. FBXW11 was abnormally expressed at low levels in high-grade serous ovarian cancer (HGSOC) tissues, and low levels of FBXW11 were associated with shorter overall survival (OS) and progression-free survival (PFS) in HGSOC patients. Overexpressing FBXW11 made ovarian cancer more sensitive to PARPi, while knocking down FBXW11 made it less sensitive. The four-dimensional (4D) label-free quantitative proteomic analysis revealed that FBXW11 targeted S100 calcium binding protein A11 (S100A11) and promoted its degradation through ubiquitination. The increased degradation of S100A11 led to less efficient DNA damage repair, which in turn contributed to increased PARPi-induced DNA damage. The role of FBXW11 in promoting PARPi sensitivity was also confirmed in xenograft mouse models. In summary, our study confirms that FBXW11 promotes the susceptibility of ovarian cancer cells to PARPi via affecting S100A11-mediated DNA damage repair.

Efficient drug response prediction is crucial for reducing drug development costs and time, but current computational models struggle with limited experimental data and out-of-distribution issues between in vitro and in vivo settings. To address this, we introduced drug response prediction meta-learner (metaDRP), a novel few-shot learning model designed to enhance predictive accuracy with limited sample sizes across diverse drug-tissue tasks. metaDRP achieves performance comparable to state-of-the-art models in both genomics of drug sensitivity in cancer (GDSC) drug screening and in vivo datasets, while effectively mitigating out-of-distribution problems, making it reliable for translating findings from controlled environments to clinical applications. Additionally, metaDRP's inherent interpretability offers reliable insights into drug mechanisms of action, such as elucidating the pathways and molecular targets of drugs like epothilone B and pemetrexed. This work provides a promising approach to overcoming data scarcity and out-of-distribution challenges in drug response prediction, while promoting the integration of few-shot learning in this field.

Objective: To investigate the molecular mechanisms and pathways of action of total flavonoids of Dracocephalum moldavica L.(TFDM) in treating vascular cognitive impairment(VCI) based on network pharmacology and in vivo animal experiments. Methods : The swiss target prediction database, literature, and PubChem were used to screen the active components and action targets of TFDM. The online mendelian inheritance in man(OMIM) and GeneCards databases were utilized to screen for possible VCI targets. Venny software was used to obtain the intersection target of TFDM and VCI. The search tool for recurring instances of neighbouring genes(String) database and Cytoscape software was used to construct the PPI network. The database for annotation, visualization and integrated discovery(DAVID) database was utilized to screen for the kyoto encyclopedia of genes and genomes(KEGG) pathway and gene ontology(GO) enrichment analyses to explore the molecular mechanism and signaling pathway of TFDM for VCI. 24 rats were divided into Sham, Model, Donepezil, and TFDM groups. Except for the Sham group, the VCI model was created using modified bilateral common carotid artery ligation. After continuous gavage for 21 days, the Morris water maze test was used to evaluate the spatial learning and memory ability of rats. Hematoxy-lineosin(HE) staining was used to observe the pathological changes in the hippocampal CA1 and cortex region of the animals and immunohistochemistry detection of zonula occludens-1(ZO-1) content in the brains of the rats. Western blot was used to detect nuclear factor kappa-B p65(NF-κB p65) and tumor necrosis factor-α(TNF-α) in rat brains. Results : A total of 39 active ingredients of TFDM were screened, 209 corresponding targets, 10 417 gene targets of VCI, and 193 intersecting targets. According to the results of the GO enrichment of function analysis, TFDM could improve the response of reactive oxygen species and metabolic processes of reactive oxygen species, etc. KEGG pathway enrichment analysis suggested that TFDM might regulate TNF, IL-17 signing pathway, etc. The results of animal experiments showed that TFDM improved learning and memory while reduced pathological damage in the brains of VCI rats. In addition, TFDM upregulated the positive expression of ZO-1 and downregulated the protein levels of TNF-α and NF-κB p65(P<0.05). Conclusion TFDM can improve the cognitive function of VCI through multi-components and multi-targets, and its key mechanism may be related to inhibiting TNF-α/NF-κB p65 signaling pathway,reducing neuroinflammation,and improvement of blood-brain barrier permeability.

BACKGROUND: Double-stranded RNA-specific adenosine deaminase 1 (ADAR1) binds to double-stranded RNA and catalyzes the deamination of adenosine (A) to inosine (I). The functional mechanism of ADAR1 in non-small cell lung cancer (NSCLC) remains incompletely understood. This study aimed to investigate the prognostic significance of ADAR1 in NSCLC and to elucidate its potential role in regulating tumor cell proliferation and migration. METHODS: Data from The Cancer Genome Atlas (TCGA) and cBioPortal were analyzed to assess the correlation between high ADAR1 expression and clinicopathological features as well as prognosis in lung cancer. We performed Western blot (WB), cell proliferation assays, Transwell invasion/migration assays, and nude mouse xenograft modeling to examine the phenotypic changes and molecular mechanisms induced by ADAR1 knockdown. Furthermore, the ADAR1 p150 overexpression model was utilized to validate the proposed mechanism. RESULTS: ADAR1 expression was significantly elevated in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tissues compared with adjacent non-tumor tissues (LUAD: P=3.70×10-15, LUSC: P=0.016). High ADAR1 expression was associated with poor prognosis (LUAD: P=2.03×10-2, LUSC: P=2.81×10-2) and distant metastasis (P=0.003). Gene Set Enrichment Analysis (GSEA) indicated that elevated ADAR1 was associated with mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) pathway activation, matrix metalloproteinase-9 (MMP-9) expression, and cell adhesion. ADAR1 and MMP-9 levels showed a strongly positive correlation (P=6.45×10-34) in 10 lung cancer cell lines, highest in H1581. Knockdown of ADAR1 in H1581 cells induced a rounded cellular morphology with reduced pseudopodia. Concomitantly, it suppressed cell proliferation, invasion, migration, and in vivo tumorigenesis. It also suppressed ERK phosphorylation and downregulated cellular Finkel-Biskis-Jinkins murine osteosarcoma viral oncogene homolog (c-FOS), MMP-9, N-cadherin, and Vimentin. Conversely, ADAR1 p150 overexpression in PC9 cells enhanced ERK phosphorylation and increased c-FOS and MMP-9 expression. CONCLUSIONS High ADAR1 expression is closely associated with poor prognosis and distant metastasis in NSCLC patients. Mechanistically, ADAR1 may promote proliferation, invasion, migration, and tumorigenesis in lung cancer cells via the ERK/c-FOS/MMP-9 axis.
Humans ; Lung Neoplasms/physiopathology* ; Adenosine Deaminase/genetics* ; Matrix Metalloproteinase 9/genetics* ; Cell Proliferation ; Carcinoma, Non-Small-Cell Lung/physiopathology* ; Cell Movement ; Animals ; Mice ; RNA-Binding Proteins/genetics* ; Female ; Male ; Cell Line, Tumor ; Proto-Oncogene Proteins c-fos/genetics* ; Middle Aged ; MAP Kinase Signaling System ; Gene Expression Regulation, Neoplastic ; Mice, Nude ; Extracellular Signal-Regulated MAP Kinases/genetics*