Dairong Cao

Background: Multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) are autoimmune neuroinflammatory conditions characterized by widespread brain alterations. However, how cortical hierarchical organization is altered across these disorders and how such alterations relate to underlying molecular architecture remain unclear. Methods: We applied the Morphometric INverse Divergence (MIND) framework to structural MRI data from 62 MS patients, 53 NMOSD patients, and 74 healthy controls. Cortical gradients were constructed based on MIND features to represent the hierarchical organization across the cortex. The derived MIND‐gradient metrics were integrated with multiscale analyses, including clinical and neuropsychological assessments, neurotransmitter system mapping, and transcriptomic profiling coupled with enrichment analyses, to link macroscale structural organization with molecular and functional substrates. Results: MIND‐gradient analyses showed alterations in cortical hierarchical organization in MS and NMOSD, mainly involving frontal and parietal regions. The primary MIND gradient was significantly associated with the spatial distributions of multiple neurotransmitter receptors and transporters. Transcriptomic analyses identified 1978 gradient‐associated genes in MS and 2485 in NMOSD. These genes were enriched in immune‐related, synaptic, and transport‐related biological processes in both disorders, with shared and non‐overlapping enriched terms. Cellular enrichment analysis further highlighted enrichment for myelinating oligodendrocytes, and developmental enrichment analysis suggested different temporal enrichment patterns in MS and NMOSD. Conclusions: These findings provide insights into cortical hierarchical alterations in MS and NMOSD and highlight potential molecular correlates that warrant further investigation in longitudinal studies.

A spectral CT–based nomogram predicted microsatellite instability and deficient mismatch repair status and stratified prognosis in patients with esophagogastric junction adenocarcinoma across retrospective and prospective cohorts.

Schizophrenia (SCZ) is a highly heritable and complex neuropsychiatric disorder. Emerging evidence implicates dysregulated brain iron, particularly in subcortical regions, in SCZ pathophysiology. Here, we systematically dissected the shared genetic architecture between SCZ and subcortical brain susceptibility phenotypes by integrating large-scale genome-wide association study (GWAS) data with quantitative susceptibility mapping (QSM) phenotypes across 16 subcortical regions. Using the MiXeR framework, we quantified the extent and pattern of shared genetic overlap between SCZ and each QSM phenotype, revealing the strongest overlap in the left and right nucleus accumbens. Conditional and conjunction false discovery rate analyses identified 666 unique shared SNPs. Functional annotation highlighted enrichment in pathways related to synaptic function and neuronal development, with pronounced expression in neurons. Additionally, we characterized their spatio-temporal expression patterns of shared genes and identified 89 significant expression-trait associations linked to SCZ-related phenotypes. Furthermore, the virtual drug screen identified 691 potential protein-drug pairs that may contribute to both iron regulation and SCZ. Our findings provide new insights into the complex interaction between subcortical brain susceptibility phenotypes and SCZ, highlighting pathways that may offer novel therapeutic strategies for SCZ and related brain susceptibility-associated conditions.

To evaluate and compare the diagnostic performance of whole-tumor histogram analysis using multiple diffusion MRI models for prediction of B-cell lymphoma-

Background: Schizophrenia (SCZ) and bipolar disorder (BD) share substantial clinical and neuroanatomical features, yet the neurogenetic basis underlying their shared gray matter volume (GMV) deficits remains poorly understood. Methods: We conducted meta-analyses to identify convergent GMV alterations across the two disorders. Genome-wide association studies (GWAS) were performed to uncover genetic variants associated with the shared GMV deficits region in UK Biobank participants. Polygenic risk score (PRS)-GMV associations were analyzed to examine the cumulative influence of genetic risk on GMV in regions with shared deficits. Furthermore, pleiotropic SNPs jointly associated with SCZ, BD, and shared GMV deficits were identified. Spatiotemporal gene expression profiling was utilized to characterize the developmental trajectories, and molecular docking was performed to explore potential drugs. Results: Meta-analysis revealed consistent overlapping GMV reductions in frontal, temporal, and insular regions across SCZ and BD, based on 6,620 patients and 7,762 controls. GWAS identified 14 SNPs associated with the shared GMV deficits. PRS analyses showed that modestly higher SCZ polygenic risk correlated with decreased GMV of shared regions. Two pleiotropic SNPs – rs11191368 and rs79668541 – were linked to both disorders and the shared GMV deficits. Spatiotemporal expression analyses demonstrated distinct developmental trajectories, and molecular docking highlighted 168 drugs with binding interactions for shared genes. Conclusions: This study delineates shared neurogenetic mechanisms linking GMV abnormalities to genetic risk across SCZ and BD. Given the cross-sectional design, future longitudinal studies in independent cohorts are warranted to validate these findings and clarify the temporal relationships.

Objectives: The purpose of this study was to evaluate the role of SWI and DWI in predicting the relapsed/refractory primary central nervous system lymphoma (R/R PCNSL). Materials and methods: Seventy-seven patients with histologically confirmed PCNSL were enrolled. Patients were divided into R/R group ( n = 40) and Non-R/R group ( n = 37), according to follow-up outcomes. Demographics, pathological indicators, conventional MRI characteristics, pre-chemotherapy intratumoral susceptibility signal (ITSS) grade, ADC parameters, including pre-chemotherapy relative minimum ADC (rADC _min−pre ) and relative mean ADC (rADC _mean−pre ), post-chemotherapy relative minimum ADC (rADC _min−post ) and relative mean ADC (rADC _mean−post ), as well as the change in relative minimum ADC (rADC _min−change ) and relative mean ADC (rADC _{mean−change} ), were compared between the two groups. Receiver operating characteristic curves and logistic regression analysis were used to assess the predictive performance. Results: Compared with Non-R/R PCNSL group, R/R PCNSL group showed significantly lower Ki-67 index ( P = 0.004) but higher pre-chemotherapy ITSS grade ( P < 0.001). The comparison of rADC _min−pre and rADC _mean−pre between the two groups showed no significant differences ( P > 0.05). However, among the 50 patients with available post-treatment ADC data, rADC _min−post , rADC _mean−post , rADC _min−change and rADC _{mean−change} in R/R group were significantly lower than in Non-R/R group (all P < 0.001). The predictive performance of rADC _min−post , rADC _mean−post , rADC _min−change and rADC _{mean−change} was comparable to that of pre-chemotherapy ITSS grade for R/R PCNSL (0.894 vs. 0.714, 0.867 vs. 0.714, 0.877 vs. 0.714, 0.814 vs. 0.714, all P > 0.05), but superior to that of Ki-67 index (0.894 vs. 0.665, 0.867 vs. 0.665, 0.877 vs. 0.665, 0.814 vs. 0.665, all P < 0.05). Conclusions: The pre-chemotherapy ITSS grade, rADC _min−post , rADC _mean−post , rADC _min−change and rADC _{mean−change} may serve as preferable imaging biomarkers for predicting R/R PCNSL, and compare favorably with Ki-67 index.