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Objective: To identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI. Methods: A total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer's Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort. Results: Based on multivariate logistic regression, clinical dementia rating and Alzheimer's disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P < 0.05), yet no significant difference between the joint model and the white matter signature (P = 0.341). Conclusion: The present results demonstrate that a joint model combining neuropsychological scales can be constructed by using machine learning and DTI technology to identify MCI patients who are at high-risk of progressing to AD. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown the importance of energy deficiency and malfunctioning mitochondria in the pathophysiology of hypertrophic cardiomyopathy (HCM). There has been a little research into the relationship between plasma free fatty acids (FFA), one of the heart's main energy sources, and HCM. We evaluated its clinical importance in HCM to see if there was a link between plasma FFA metabolism and HCM. Methods: In a single-center retrospective observational study, we investigated 420 HCM patients diagnosed at Beijing Anzhen Hospital between January 1, 2018, and December 31, 2022. Meanwhile, 1372 individuals without HCM (non-HCM) were recruited. 391 non-HCM patients were chosen as controls via a propensity score matching (PSM) study with a 1:1 ratio. Results: FFA in HCM patients showed statistically significant correlations with creatinine (r = 0.115, p = 0.023), estimated GFR (r=-0.130, p = 0.010), BNP (r = 0.152, p = 0.007), LVEF (r=-0.227, p < 0.001), LVFS (r=-0.160, p = 0.002), and LAD (r = 0.112, p = 0.028). Higher FFA levels were found in HCM patients who had atrial fibrillation and NYHY functional classes III or IV (p = 0.015 and p = 0.022, respectively). In HCM patients, multiple linear regression analysis revealed that BNP and LVEF had independent relationships with increasing FFA (Standardized = 0.139, p = 0.013 and =-0.196, p < 0.001, respectively). Conclusions: Among HCM patients, the plasma FFA concentration was lower, and those with AF and NYHY functional class III or IV had higher FFA levels, and LVEF and BNP were independently associated with increasing FFA. The findings of the study should help inspire future efforts to better understand how energy deficiency contributes to hypertrophic cardiomyopathy (HCM) development. [ABSTRACT FROM AUTHOR]

Purpose This study aims to evaluate the efficacy and safety of a new combination treatment of vinorelbine and pyrotinib in human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (MBC) and provide higher level evidence for clinical practice. Materials and Methods This was a prospective, single-arm, phase 2 trial conducted at three institutions in China. Patients with HER2-positive MBC, who had previously been treated with trastuzumab plus a taxane or trastuzumab plus pertuzumab combined with a chemotherapeutic agent, were enrolled between March 2020 and December 2021. All patients received pyrotinib 400 mg orally once daily plus vinorelbine 25 mg/m2 intravenously or 60-80 mg/m2 orally on day 1 and day 8 of 21-day cycle. The primary endpoint was progression-free survival (PFS), and the secondary endpoints included the objective response rate (ORR), disease control rate (DCR), overall survival, and safety. Results A total of 39 patients were enrolled. All patients had been pretreated with trastuzumab and 23.1% (n=9) of them had accepted trastuzumab plus pertuzumab. The median follow-up time was 16.3 months (95% confidence interval [CI], 5.3 to 27.2), and the median PFS was 6.4 months (95% CI, 4.0 to 8.8). The ORR was 43.6% (95% CI, 27.8% to 60.4%) and the DCR was 84.6% (95% CI, 69.5% to 94.1%). The median PFS of patients with versus without prior pertuzumab treatment was 4.6 and 8.3 months (p=0.017). The most common grade 3/4 adverse events were diarrhea (28.2%), neutrophil count decreased (15.4%), white blood cell count decreased (7.7%), vomiting (5.1%), and anemia (2.6%). Conclusion Pyrotinib plus vinorelbine showed promising efficacy and tolerable toxicity as second-line treatment in patients with HER2-positive MBC. [ABSTRACT FROM AUTHOR]