Search

Objective To construct a model of heart failure risk prediction based on four machine learning algorithms in order to support early diagnosis and intervention. Methods After reviewing the heart failure dataset published on the Kaggle community, feature selection was used to select relevant factors related to heart failure as predictive indicators. Four machine learning algorithms, namely logistic regression, support vector machine, random forest, and XGBoost were selected to establish predictive models. Compared and analyzed its accuracy, precision, recall, F1 score and area under the ROC curve (AUC) to verify the performance of the model. Results The study analyzed 11 features of 918 patients with heart failure and selected 10 feature factors for modeling. After optimizing the hyper-parameters through grid search, the XGBoost model performed the best, with accuracy, precision, recall, and f1_score and AUC values were 87.5%, 90.38%, 89.71%, 90.04% and 0.93, respectively. In addition, data analysis showed that exercise ST slope, chest pain type, and exercise induced angina were main influencing factors for heart failure. Conclusions The XG Boost model has the best predictive tool for heart failure, and machine learning algorithms may support early prevention, early diagnosis as well as control of heart failure.

Mindful attention is characterized by acknowledging the present experience as a transient mental event. Early stages of mindfulness practice may require greater neural effort for later efficiency. Early effort may self-regulate behavior and focalize the present, but this understanding lacks a computational explanation. Here we used network control theory as a model of how external control inputs-operationalizing effort-distribute changes in neural activity evoked during mindful attention across the white matter network. We hypothesized that individuals with greater network controllability, thereby efficiently distributing control inputs, effectively self-regulate behavior. We further hypothesized that brain regions that utilize greater control input exhibit shorter intrinsic timescales of neural activity. Shorter timescales characterize quickly discontinuing past processing to focalize the present. We tested these hypotheses in a randomized controlled study that primed participants to either mindfully respond or naturally react to alcohol cues during fMRI and administered text reminders and measurements of alcohol consumption during 4 wk postscan. We found that participants with greater network controllability moderated alcohol consumption. Mindful regulation of alcohol cues, compared to ones own natural reactions, reduced craving, but craving did not differ from the baseline group. Mindful regulation of alcohol cues, compared to the natural reactions of the baseline group, involved more-effortful control of neural dynamics across cognitive control and attention subnetworks. This effort persisted in the natural reactions of the mindful group compared to the baseline group. More-effortful neural states had shorter timescales than less effortful states, offering an explanation for how mindful attention promotes being present.

Primary biliary cholangitis (PBC) is an immune-mediated liver disease characterized by cholestasis, biliary injuries, liver fibrosis, and chronic non-suppurative cholangitis. The pathogenesis of PBC is multifactorial and involves immune dysregulation, abnormal bile metabolism, and progressive fibrosis, ultimately leading to cirrhosis and liver failure. Ursodeoxycholic acid (UDCA) and obeticholic acid (OCA) are currently used as first- and second-line treatments, respectively. However, many patients do not respond adequately to UDCA, and the long-term effects of these drugs are limited. Recent research has advanced our understanding the mechanisms of pathogenesis in PBC and greatly facilitated development of novel drugs to target mechanistic checkpoints. Animal studies and clinical trials of pipeline drugs have yielded promising results in slowing disease progression. Targeting immune mediated pathogenesis and anti-inflammatory therapies are focused on the early stage, while anti-cholestatic and anti-fibrotic therapies are emphasized in the late stage of disease, which is characterized by fibrosis and cirrhosis development. Nonetheless, it is worth noting that currently, there exists a dearth of therapeutic options that can effectively impede the progression of the disease to its terminal stages. Hence, there is an urgent need for further research aimed at investigating the underlying pathophysiology mechanisms with potential therapeutic effects. This review highlights our current knowledge of the underlying immunological and cellular mechanisms of pathogenesis in PBC. Further, we also address current mechanism-based target therapies for PBC and potential therapeutic strategies to improve the efficacy of existing treatments.

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.

The title of Academician of the Chinese Academy of Sciences (CAS) represents the highest accolade in China's scientific research field. Profiling the characteristics of CAS academicians can provide crucial insights into the current state of scientific research in China and play a vital role in fostering innovation and cultivating talents for the society. This study conducted a comprehensive quantitative analysis of the birthplaces, ages, genders, educational backgrounds, institutional affiliations, and administrative roles of newly elected CAS academicians from 2005 to 2023. Several key findings are identified, including: (1) the majority of newly elected CAS academicians originate from eastern and central regions of China, with about one-quarter from provincial capitals. (2) the average age of newly elected CAS academicians is younger compared to Nobel Prize laureates in natural sciences. (3) the percentage of newly elected CAS academicians who possess doctorate degrees has been increasing year by year. (4) the work units of newly elected CAS academicians are primarily the Chinese Academy of Sciences and its affiliated research institutes, as well as universities directly under the Ministry of Education. The average number of administrative positions held by these academicians is around four, and the number of positions held after their election tends to be higher than before. (5) Newly elected foreign academicians primarily originate from western developed countries, such as the United States and some other European countries. [ABSTRACT FROM AUTHOR]

Objective: Focal to bilateral tonic–clonic seizures (FBTCS) represent a challenging subtype of focal temporal lobe epilepsy (TLE) in terms of both severity and treatment response. Most studies have focused on regional brain analysis that is agnostic to the distribution of white matter (WM) pathways associated with a node. We implemented a more selective, edge‐wise approach that allowed for identification of the individual connections unique to FBTCS. Methods: T1‐weighted and diffusion‐weighted images were obtained from 22 patients with solely focal seizures (FS), 43 FBTCS patients, and 65 age/sex‐matched healthy participants (HPs), yielding streamline (STR) connectome matrices. We used diffusion tensor‐derived STRs in an edge‐wise approach to determine specific structural connectivity changes associated with seizure generalization in FBTCS compared to matched FS and HPs. Graph theory metrics were computed on both node‐ and edge‐based connectivity matrices. Results: Edge‐wise analyses demonstrated that all significantly abnormal cross‐hemispheric connections belonged to the FBTCS group. Abnormal connections associated with FBTCS were mostly housed in the contralateral hemisphere, with graph metric values generally decreased compared to HPs. In FBTCS, the contralateral amygdala showed selective decreases in the structural connection pathways to the contralateral frontal lobe. Abnormal connections in TLE involved the amygdala, with the ipsilateral side showing increases and the contralateral decreases. All the FS findings indicated higher graph metrics for connections involving the ipsilateral amygdala. Data also showed that some FBTCS connectivity effects are moderated by aging, recent seizure frequency, and longer illness duration. Significance: Data showed that not all STR pathways are equally affected by the seizure propagation of FBTCS. We demonstrated two key biases, one indicating a large role for the amygdala in the propagation of seizures, the other pointing to the prominent role of cross‐hemispheric and contralateral hemisphere connections in FBTCS. We demonstrated topographic reorganization in FBTCS, pointing to the specific WM tracts involved. [ABSTRACT FROM AUTHOR]