G-protein signaling modulators (GPSMs) are a class of proteins involved in the regulation of G protein-coupled receptors, the most abundant family of cell-surface receptors that are crucial in the development of various tumors, including breast cancer. This study aims to identify the potential therapeutic and prognostic roles of GPSMs in breast cancer. Oncomine and UALCAN databases were queried to determine GPSM expression levels in breast cancer tissues compared to normal samples. Survival analysis was conducted to reveal the prognostic significance of GPSMs in individuals with breast cancer. Functional enrichment analysis was performed using cBioPortal and MetaCore platforms. Finally, the association between GPSMs and immune infiltration cells in breast cancer was identified using the TIMER server. The experimental results then showed that all GPSM family members were significantly differentially expressed in breast cancer according to Oncomine and UALCAN data. Their expression levels were also associated with advanced tumor stages, and GPSM2 was found to be related to worse distant metastasis-free survival in patients with breast cancer. Functional enrichment analysis indicated that GPSMs were largely involved in cell division and cell cycle pathways. Finally, GPSM3 expression was correlated with the infiltration of several immune cells. Members of the GPSM class were differentially expressed in breast cancer. In conclusion, expression of GPSM2 was linked with worse distant metastasis-free outcomes, and hence could potentially serve as a prognostic biomarker. Furthermore, GPSM3 has potential to be a possible target for immunotherapy for breast cancer.

Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. To this end, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes’ biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely CRNDE, NRXN3, POPDC3, PTPRN, PTPRN2, SLC46A2, TIMP1, and TNFSF9. The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and MGMT status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM.

Alzheimer’s disease (AD) is a highly heterogeneous disorder. Untangling this variability could lead to personalized treatments and improve participant recruitment for clinical trials. We investigated the cognitive subgroups by using a data-driven clustering technique in an AD cohort. People with mild–moderate probable AD from Taiwan was included. Neuropsychological test results from the Cognitive Abilities Screening Instrument were clustered using nonnegative matrix factorization. We identified two clusters in 112 patients with predominant deficits in memory (62.5%) and non-memory (37.5%) cognitive domains, respectively. The memory group performed worse in short-term memory and orientation and better in attention than the non-memory group. At baseline, patients in the memory group had worse global cognitive status and dementia severity. Linear mixed effect model did not reveal difference in disease trajectory within 3 years of follow-up between the two clusters. Our results provide insights into the cognitive heterogeneity in probable AD in an Asian population.

Background: Mini-Mental State Exam (MMSE) is widely used for the cognitive assessment in Alzheimer’s disease (AD), but its interpretation could be affected by acute medical conditions, depression, or anxiety. Conversely, brain magnetic resonance imaging (MRI) is an independent diagnostic tool to evaluate specific cerebral pathology. Many studies have investigated the correlation between MRI visual scales such as hippocampal atrophy and baseline MMSE in AD patients. However, the correlation between a comprehensive MRI visual scale and baseline MMSE in people with AD remains less well known. Method: We retrospectively collected records of outpatients diagnosed with probable AD according to DSM -5 criteria. The comprehensive visual rating scale (CVRS) was used to semiquantitatively measure structural changes and vascular lesions. The relationship between MRI changes and baseline MMSE was evaluated using Bayesian model averaging (BMA). Results: A total of 65 patients, among whom 21 (32.31%) had early-onset AD, were included. Lateral temporal atrophy, level of education, and late age at onset were the strongest independent predictors of baseline MMSE. Furthermore, hippocampal atrophy was only correlated with delayed recall, while temporal atrophy was correlated with orientation, attention, language, and visual-spatial items of the MMSE. The cerebral atrophy and small vessel lesions scores of the late-onset AD group were significantly higher than those of the early-onset group, despite negligible differences in education and MMSE. Conclusion: Our study suggests lateral temporal atrophy correlates with baseline MMSE scores in people with AD better than the hippocampal atrophy. Age-related atrophy and silent small vessel disease lesions may have negligible impact on AD patients’ cognitive impairment.

Objectives: Cognitive aging entails diverse patterns of cognitive profiles, brain imaging, and biomarkers. Yet, few studies have explored the performance of multiple clustering algorithms on a single data set. Here, we employ data-driven methods to analyze neuropsychological performance in older individuals with normal cognition (NC) and mild cognitive impairment (MCI). Methods: A total of 311 older adults without dementia completed a comprehensive assessment, consisting of 17 cognitive tests and a memory complaint questionnaire. We utilized 2 clustering algorithms: nonnegative matrix factorization (NMF) and model-based clustering (MBC). Cluster characteristics were examined in demographic, clinical, and brain morphometric data. Results: Both NMF and MBC uncovered two- and three-cluster solutions, with satisfactory data fit. The two-cluster profiles encompassed a cognitively intact (CI) group and a cognitively suboptimal (CS) group, distinguished by cognitive performance. The 3-cluster solutions included CI–memory proficient, CI–nonmemory proficient, and CS groups. Remarkably, patterns of cognitive heterogeneity and their association with demographic and neuroimaging variables were highly comparable across NMF and MBC. Phenotypic homogeneity improved after identifying participants with consistent and mismatched memberships from the 2 algorithms. Discussion: The results indicate that 2 distinct data-driven algorithms, with different heuristics, generated comparable patterns regarding cognitive heterogeneity within NC and MCI. These findings may inform future subtyping studies in cognitive aging, where replication of stratifications found across different methods is strongly recommended.

Background: The global prevalence of chronic kidney disease (CKD) continues to increase, with the arteriovenous fistula (AVF) as the most preferred vascular access for hemodialysis. Whether routine preoperative ultrasound mapping improves the outcome of AVF formation compared with clinical examination alone remains controversial. Methods: This retrospective study included patients undergoing AVF surgery with and without preoperative ultrasound at our center between September 2017 and August 2020. Outcome measures included AVF early and mid-term outcome. Cox regression analysis was performed to identify independent predictors of favorable AVF outcome. Results: A total of 158 patients received an AVF during the study period. Both groups with (n = 79) and without (n = 79) ultrasound mapping were similar regarding baseline characteristics (age, sex, comorbidities). Patency rates were comparable between the 2 groups at 30 days, 3 months, and 6 months after AVF surgery, although there was a trend toward more favorable outcome for the mapping group ( P = .07). Kaplan-Meier analysis showed that at the end of study, the ultrasound mapping group had a higher mid-term patency rate; however, the improvement was not significant ( P = .07). Cox regression analysis did not reveal age, gender, comorbidities, and ultrasound as predictors of AVF survival. Conclusion: Our study did not find a significant benefit from routine preoperative ultrasound mapping in creating AVFs for hemodialysis. Further well-designed and adequately powered trials are needed to demonstrate the beneficial role of routine preoperative ultrasound mapping for vascular access in CKD coupled with clinical evaluation in short- and long-term AVF outcome.