Dependence of resting-state-based cerebrovascular reactivity (CVR) mapping on spatial resolution.

Cerebrovascular reactivity (CVR) is typically assessed with a carbon dioxide (CO2) stimulus combined with BOLD fMRI. Recently, resting-state (RS) BOLD fMRI has been shown capable of generating CVR maps, providing a potential for broader CVR applications in neuroimaging studies. However, prior RS-CVR studies have primarily been performed at a spatial resolution of 3-4 mm voxel sizes. It remains unknown whether RS-CVR can also be obtained at high-resolution without major degradation in image quality. In this study, we investigated RS-CVR mapping based on resting-state BOLD MRI across a range of spatial resolutions in a group of healthy subjects, in an effort to examine the feasibility of RS-CVR measurement at high resolution. Comparing the results of RS-CVR with the maps obtained by the conventional CO2-inhalation method, our results suggested that good CVR map quality can be obtained at a voxel size as small as 2 mm isotropic. Our results also showed that, RS-CVR maps revealed resolution-dependent sensitivity. However, even at a high resolution of 2 mm isotropic voxel size, the voxel-wise sensitivity is still greater than that of typical task-evoked fMRI. Scan duration affected the sensitivity of RS-CVR mapping, but had no significant effect on its accuracy. These findings suggest that RS-CVR mapping can be applied at a similar resolution as state-of-the-art fMRI studies, which will broaden the use of CVR mapping in basic science and clinical applications including retrospective analysis of previously collected fMRI data.

Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington's disease.

Changes in the brain of patients with Huntington's disease (HD) begin years before clinical onset, so it remains critical to identify biomarkers to track these early changes. Metrics derived from tensor modeling of diffusion-weighted MRIs (DTI), that indicate the microscopic brain structure, can add important information to regional volumetric measurements. This study uses two large-scale longitudinal, multicenter datasets, PREDICT-HD and IMAGE-HD, to trace changes in DTI of HD participants with a broad range of CAP scores (a product of CAG repeat expansion and age), including those with pre-manifest disease (i.e., prior to clinical onset). Utilizing a fully automated data-driven approach to study the whole brain divided in regions of interest, we traced changes in DTI metrics (diffusivity and fractional anisotropy) versus CAP scores, using sigmoidal and linear regression models. We identified points of inflection in the sigmoidal regression using change-point analysis. The deep gray matter showed more evident and earlier changes in DTI metrics over CAP scores, compared to the deep white matter. In the deep white matter, these changes were more evident and occurred earlier in superior and posterior areas, compared to anterior and inferior areas. The curves of mean diffusivity vs. age of HD participants within a fixed CAP score were different from those of controls, indicating that the disease has an additional effect to age on the microscopic brain structure. These results show the regional and temporal vulnerability of the white matter and deep gray matter in HD, with potential implications for experimental therapeutics.

Predicting respiratory decompensation in mechanically ventilated adult ICU patients.

Introduction: Mechanical ventilation is a life-saving treatment in the Intensive Care Unit (ICU), but often causes patients to be at risk of further respiratory complication. We created a statistical model utilizing electronic health record and physiologic vitals data to predict the Center for Disease Control and Prevention (CDC) defined Ventilator Associated Complications (VACs). Further, we evaluated the effect of data temporal resolution and feature generation method choice on the accuracy of such a constructed model. Methods: We constructed a random forest model to predict occurrence of VACs using health records and chart events from adult patients in the Medical Information Mart for Intensive Care III (MIMIC-III) database. We trained the machine learning models on two patient populations of 1921 and 464 based on low and high frequency data availability. Model features were generated using both basic statistical summaries and tsfresh, a python library that generates a large number of derived time-series features. Classification to determine whether a patient will experience VAC one hour after 35 h of ventilation was performed using a random forest classifier. Two different sample spaces conditioned on five varying feature extraction techniques were evaluated to identify the most optimal selection of features resulting in the best VAC discrimination. Each dataset was assessed using K-folds cross-validation (k = 10), giving average area under the receiver operating characteristic curves (AUROCs) and accuracies. Results: After feature selection, hyperparameter tuning, and feature extraction, the best performing model used automatically generated features on high frequency data and achieved an average AUROC of 0.83 ± 0.11 and an average accuracy of 0.69 ± 0.10. Discussion: Results show the potential viability of predicting VACs using machine learning, and indicate that higher-resolution data and the larger feature set generated by tsfresh yield better AUROCs compared to lower-resolution data and manual statistical features.

An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA2 cases.

BACKGROUND: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients. METHODS: Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae. RESULTS: The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set. CONCLUSION: Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases.

[New Advances in the Treatment of Primary Central Nervous System Lymphoma--Review].

Primary central nervous system lymphoma (PCNSL) is a rare aggressive non-Hodgkin's lymphoma outside the lymph nodes. At present, high-dose chemotherapy based on methotrexate is the standard induction therapy for newly diagnosed PCNSL, but the effective therapy of relapse/refractory and elderly PCNSL is still unclear. With the progress of clinical trials, new drugs and combined treatment method appear constantly, such as rituximab and ibrutinib, the remission rate of refractory and relapsed patients increased, while lenalidomide showed a good activity in the maintenance treatment of elderly patients. This review summarized briefly the recent advances of research on immunocheckpoint inhibitors, immunoregulatory agents, bruton tyrosine kinase (BTK) and PI3K/AKT/mTOR pathway inhibitors.

A computed tomography-based clinical-radiomics model for prediction of lymph node metastasis in esophageal carcinoma.

Aims: Evaluation of lymph node metastasis (LNM) is an essential component of preoperative assessment of esophageal carcinoma (EC). This study aimed to develop and validate a computed tomography (CT)-based clinical-radiomics model for the prediction of LNM in patients with EC. Subjects and Methods: This is a retrospective study of 195 patients with biopsy-proven EC. 70% of the included patients were randomly allocated to the training cohort and the remaining 30% of subjects were allocated to the testing cohort. Radiomics models were developed based on features of multi-phase contrast-enhanced CT images using the least absolute shrinkage and selection operator method. The predictive values of these models for LNM were examined in both the training and testing cohorts. Furthermore, the benefits of adding two clinical features (CT report of LNM and tumor location) to the models were also investigated. Results: Seven radiomics models were established based on features identified on single-phase images (plain, P; arterial phase, A; and venous phase, V) and multi-phase images (P + A, P + V, A + V, P + A + V). The model that included 26 features derived from P + A + V had the best predictive value in the training cohort (area under the receiver operator characteristic curve [AUC] 0.783) and testing cohort (AUC: 0.741). The inclusion of CT reports of LNM to the models further improved their performances (AUC 0.814 in the training cohort and AUC 0.813 in the testing cohort). Conclusions: A clinical-radiomics model based on a multi-phase CT study may be useful in predicting LNM in EC.

[Research Progress on New Drug Therapy for Primary Immune Thrombocytopenic Purpura in Adults--Review].

Immune thrombocytopenia (ITP) is an immune disease characterized by an increased risk of hemorrhagic disease caused by a decrease in platelet count. At present, the first line, second-line treatment can not completely or maintain continuous remission of ITP. New treatments in recent research include stimulating platelet-producing drugs, Syk inhibitors, and molecular-targeted drugs, etc., which can play a role in key steps of the progression of the disease. Among them, new types of drugs that stimulate thrombopoiesis shows a better therapeutic prospects with a comparative mechanism and clinical research, Syk inhibitors have a unique role in the treatment of malignant diseases in blood system, and the transplantation of mesenchymal stem cells is a new treatment idea. These treatments show the potential to improve the quality of life in patients with ITP. In this review, the latest research progress of new therapeutic drugs for ITP is summarized briefly.