Changes in iron load in specific brain areas lead to neurodegenerative diseases of the central nervous system.

The causes of neurodegenerative diseases remain largely elusive, increasing their personal and societal impacts. To reveal the causal effects of iron load on Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis and multiple sclerosis, we used Mendelian randomisation and brain imaging data from a UK Biobank genome-wide association study of 39,691 brain imaging samples (predominantly of European origin). Using susceptibility-weighted images, which reflect iron load, we analysed genetically significant brain regions. Inverse variance weighting was used as the main estimate, while MR Egger and weighted median were used to detect heterogeneity and pleiotropy. Nine clear associations were obtained. For AD and PD, an increased iron load was causative: the right pallidum for AD and the right caudate, left caudate and right accumbens for PD. However, a reduced iron load was identified in the right and left caudate for multiple sclerosis, the bilateral hippocampus for mixed vascular dementia and the left thalamus and bilateral accumbens for subcortical vascular dementia. Thus, changes in iron load in different brain regions have causal effects on neurodegenerative diseases. Our results are crucial for understanding the pathogenesis and investigating the treatment of these diseases.

An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration.

BACKGROUND: The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM. AIM: Our study presents a novel and significant contribution by developing an interpretable fusion model that effectively integrates both free-text medical record data and structured laboratory data to predict LM in postoperative CRC patients. METHODS: We used a robust dataset of 1463 patients and leveraged state-of-the-art natural language processing (NLP) and machine learning techniques to construct a two-layer fusion framework that demonstrates superior predictive performance compared to single modal models. Our innovative two-tier algorithm fuses the results from different data modalities, achieving balanced prediction results on test data and significantly enhancing the predictive ability of the model. To increase interpretability, we employed Shapley additive explanations to elucidate the contributions of free-text clinical data and structured clinical data to the final model. Furthermore, we translated our findings into practical clinical applications by creating a novel NLP score-based nomogram using the top 13 valid predictors identified in our study. RESULTS: The proposed fusion models demonstrated superior predictive performance with an accuracy of 80.8%, precision of 80.3%, recall of 80.5%, and an F1 score of 80.8% in predicting LMs. CONCLUSION: This fusion model represents a notable advancement in predicting LMs for postoperative CRC patients, offering the potential to enhance patient outcomes and support clinical decision-making.

Bidirectional Mendelian randomization study of insulin-related traits and risk of ovarian cancer.

Background: It is well known that the occurrence and development of ovarian cancer are closely related to the patient's weight and various endocrine factors in the body. Aim: Mendelian randomization (MR) was used to analyze the bidirectional relationship between insulin related characteristics and ovarian cancer. Methods: The data on insulin related characteristics are from up to 5567 diabetes free patients from 10 studies, mainly including fasting insulin level, insulin secretion rate, peak insulin response, etc. For ovarian cancer, UK Biobank data just updated in 2021 was selected, of which the relevant gene data was from 199741 Europeans. Mendelian randomization method was selected, with inverse variance weighting (IVW) as the main estimation, while MR Pleiotropy, MR Egger, weighted median and other methods were used to detect the heterogeneity of data and whether there was multi validity affecting conclusions. Results: Among all insulin related indicators (fasting insulin level, insulin secretion rate, peak insulin response), the insulin secretion rate was selected to have a causal relationship with the occurrence of ovarian cancer (IVW, P < 0.05), that is, the risk of ovarian cancer increased with the decrease of insulin secretion rate. At the same time, we tested the heterogeneity and polymorphism of this indicator, and the results were non-existent, which ensured the accuracy of the analysis results. Reverse causal analysis showed that there was no causal effect between the two (P>0.05). Conclusion: The impairment of the insulin secretion rate has a causal effect on the risk of ovarian cancer, which was confirmed by Mendel randomization. This suggests that the human glucose metabolism cycle represented by insulin secretion plays an important role in the pathogenesis of ovarian cancer, which provides a new idea for preventing the release of ovarian cancer.

The effect of dobutamine on ocular blood flow of healthy adults: A 3D pseudocontinuous aterial spin labelling study.

Purpose: Ocular blood flow (OBF) is an important risk factor for incidence, prevalence and progression of some ocular disorders. To date, there are very limited therapeutic options to increase OBF. This study investigated the effect of dobutamine on OBF of heathy adults using 3D pseudocontinuous arterial spin labelling (3D-pcASL), and explored the risk factors associated with OBF. Methods: Forty-three healthy participants (86 eyes) were given an intravenous injection of dobutamine. We measured OBF using 3D-pcASL with a 3.0T- MRI scanner, OBF values were independently obtained by two doctors from the OBF map. We also collected physiological parameters using a vital signs monitor. The OBF and physiological parameters in the in the period before and after dobutamine injection states were obtained. Results: OBF increased significantly after dobutamine injection using paired t test method (from 22.43 ± 9.87 to 47.73 ± 14.02 ml/min/100g, p < 0.001). Age, heart rate and systolic blood pressure were the main risk factors affecting OBF using logistic regression analysis (all p values < 0.05). Conclusion: To the best of our knowledge, this is the first study observing the effect of dobutamine on OBF. Our findings indicated that intravenously injected dobutamine increased OBF, making it a possible option to counteract ocular vascular ischaemia in the future.

A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas.

Purpose: We aim to develop and validate a machine learning model by enhanced MRI to determine the pathological grading of meningiomas with unsupervised clustering image analysis method, which are multihabitat to reflect the inherent heterogeneity of tumors. Materials and Methods: A total of 120 patients with meningiomas confirmed by postoperative pathology were included in the study, including 60 patients with low-grade meningiomas (WHO grade I) and 60 patients with high-grade meningiomas (WHO grade II and WHO grade III). All patients underwent complete head enhanced magnetic resonance scans before surgery or any anti-tumor treatment. Enrolled patients in the group received surgical resection and obtained postoperative pathological data. The patients in the training group (84 people) and the test group (36 people) were randomly divided into two groups according to the ratio of 7 to 3. Multi-habitat features were extracted from MRI images based on enhanced T1. Machine learning method was used to model, which was used to distinguish high-grade meningioma from low-grade meningioma. At the same time, the obtained machine learning model was calibrated and evaluated. Results: In patients with low-grade meningioma and high-grade meningioma, we found significant differences in Silhouette coefficient (P<0.05). In the machine learning model, the area under the curve was 0.838 in the training group (sensitivity, 67.65%; specificity, 88.82%) and 0.73 in the test group (sensitivity, 69.05%; specificity, 71.43%). After the analysis of calibration curve and decision curve analysis, the model had shown the potential of great application value. Conclusions: Multi-habitat analysis based on enhanced MRI (T1) could accurately predict the pathological grading of meningiomas. This unsupervised image-based method could reflect the direct heterogeneity between high-grade meningiomas and low-grade meningiomas, which is of great significance for patients' treatment and prevention of recurrence.

Prognostic value of nicotinamide adenine dinucleotide (NAD+) metabolic genes in patients with stomach adenocarcinoma based on bioinformatics analysis.

Background: Because stomach adenocarcinoma (STAD) has a poor prognosis, it is necessary to explore new prognostic genes to stratify patients to guide existing individualized treatments. Methods: Survival and clinical information, RNA-seq data and mutation data of STAD were acquired from The Cancer Genome Atlas (TCGA) database. Fifty-one nicotinamide adenine dinucleotide (NAD+) metabolism-related genes (NMRGs) were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Differentially expressed NMRGs (DE-NMRGs) between STAD and normal samples were screened, and consistent clustering analysis of STAD patients was performed based on the DE-NMRGs. Survival analysis, Gene Set Enrichment Analysis (GSEA), mutation frequency analysis, immune microenvironment analysis and drug prediction were performed among different clusters. Additionally, the differentially expressed genes (DEGs) among different clusters were selected, and the intersections of DEGs and DE-NMRGs were selected as the prognostic genes. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was performed on a human gastric mucosa epithelial cell line and cancer cell line to verify the expression of the prognostic genes. Results: A total of 27 DE-NMRGs and two clusters were selected. There was a difference in survival between clusters 1 and 2. Furthermore, 18 DE-NMRGs were significantly different between clusters 1 and 2. The different Gene Ontology (GO) biological processes and KEGG pathways between clusters 1 and 2 were mainly enriched in cyclic nucleotide mediated signaling, synaptic signaling and hedgehog signaling pathway, etc. The somatic mutation frequencies were different between the two clusters, and TTN was the highest mutated gene in the patients of the clusters 1 and 2. Additionally, eight immune cells, immune score, stromal score, and estimate score were different between clusters 1 and 2. The patients in cluster 2 were sensitive to CTLA4 inhibitor treatment. Furthermore, the top five drugs (AP.24534, BX.795, Midostaurin, WO2009093927 and CCT007093) were significantly higher in cluster 1 than in cluster 2. Finally, three genes (AOX1, NNMT and PTGIS) were acquired as prognostic, and their expressions were consistent with the results of bioinformatics analysis. Conclusions: Three prognostic genes related to NAD+ metabolism in STAD were screened out, which provides a theoretical basis and reference value for future treatment and prognosis of STAD.

Modulating Polymer Dynamics via Supramolecular Interaction with Ultrasmall Nanocages for Recyclable Gas Separation Membranes with Intrinsic Microporosity.

The engineering of mixed-matrix membranes is severely hindered by the trade-off between mechanical performance and effective utilization of inorganic fillers' microporosity. Herein, we report a feasible approach for optimal gas separation membranes through the fabrication of coordination nanocages with poly(4-vinylpyridine) (P4VP) via strong supramolecular interactions, enabling the homogeneous dispersion of nanocages in polymer matrixes with long-term structural stability. Meanwhile, suggested from dynamics studies, the strong attraction between P4VP and nanocages slows down polymer dynamics and rigidifies the polymer chains, leading to frustrated packing and lowered densities of the polymer matrix. This effect allows the micropores of nanocages to be accessible to external gas molecules, contributing to the intrinsic microporosity of the nanocomposites and the simultaneous enhancement of permselectivities. The facile strategy for supramolecular synthesis and polymer dynamics attenuation paves avenues to rational design of functional hybrid membranes for gas separation applications.