Cocaine Use Prediction with Tensor-based Machine Learning on Multimodal MRI Connectome Data

This paper considers the use of machine learning algorithms for predicting cocaine use based on magnetic resonance imaging (MRI) connectomic data. The study utilized functional MRI (fMRI) and diffusion MRI (dMRI) data collected from 275 individuals, which was then parcellated into 246 regions of interest (ROIs) using the Brainnetome atlas. After data preprocessing, the datasets were transformed into tensor form. We developed a tensor-based unsupervised machine learning algorithm to reduce the size of the data tensor from $275$ (individuals) $\times 2$ (fMRI and dMRI) $\times 246$ (ROIs) $\times 246$ (ROIs) to $275$ (individuals) $\times 2$ (fMRI and dMRI) $\times 6$ (clusters) $\times 6$ (clusters). This was achieved by applying the high-order Lloyd algorithm to group the ROI data into 6 clusters. Features were extracted from the reduced tensor and combined with demographic features (age, gender, race, and HIV status). The resulting dataset was used to train a Catboost model using subsampling and nested cross-validation techniques, which achieved a prediction accuracy of 0.857 for identifying cocaine users. The model was also compared with other models, and the feature importance of the model was presented. Overall, this study highlights the potential for using tensor-based machine learning algorithms to predict cocaine use based on MRI connectomic data and presents a promising approach for identifying individuals at risk of substance abuse

Prevalence and significance of clonal hematopoiesis of indeterminate potential in lung transplant recipients

Abstract Background Clonal hematopoiesis of indeterminate potential (CHIP), the age-related acquisition of somatic mutations that leads to an expanded blood cell clone, has been associated with development of a pro-inflammatory state. An enhanced or dysregulated inflammatory response may contribute to rejection after lung transplantation, however the prevalence of CHIP in lung recipients and influence of CHIP on allograft outcomes is unknown. Methods We analyzed whole-exome sequencing data in 279 lung recipients to detect CHIP, defined by pre-specified somatic mutations in 74 genes known to promote clonal expansion of hematopoietic stem cells. We compared the burden of acute rejection (AR) over the first post-transplant year in lung recipients with vs. without CHIP using multivariable ordinal regression. Multivariate Cox proportional hazards models were used to assess the association between CHIP and CLAD-free survival. An exploratory analysis evaluated the association between the number of CHIP-associated variants and chronic lung allograft dysfunction (CLAD)-free survival. Results We detected 64 CHIP-associated mutations in 45 individuals (15.7%), most commonly in TET2 (10.8%), DNMT3A (9.2%), and U2AF1 (9.2%). Patients with CHIP tended to be older but did not significantly differ from patients without CHIP in terms of race or native lung disease. Patients with CHIP did not have a higher incidence of AR over the first post-transplant year (p = 0.45) or a significantly increased risk of death or CLAD (adjusted HR 1.25, 95% CI 0.88–1.78). We did observe a significant association between the number of CHIP variants and CLAD-free survival, specifically patients with 2 or more CHIP-associated variants had an increased risk for death or CLAD (adjusted HR 3.79, 95% CI 1.98–7.27). Conclusions Lung recipients have a higher prevalence of CHIP and a larger variety of genes with CHIP-associated mutations compared with previous reports for the general population. CHIP did not increase the risk of AR, CLAD, or death in lung recipients

Gnidimacrin, a Potent Anti-HIV Diterpene, Can Eliminate Latent HIV‑1 Ex Vivo by Activation of Protein Kinase C β

HIV-1-latency-reversing agents, such as histone deacetylase inhibitors (HDACIs), were ineffective in reducing latent HIV-1 reservoirs ex vivo using CD4 cells from patients as a model. This deficiency poses a challenge to current pharmacological approaches for HIV-1 eradication. The results of this study indicated that gnidimacrin (GM) was able to markedly reduce the latent HIV-1 DNA level and the frequency of latently infected cells in an ex vivo model using patients peripheral blood mononuclear cells. GM induced approximately 10-fold more HIV-1 production than the HDACI SAHA or romidepsin, which may be responsible for the effectiveness of GM in reducing latent HIV-1 levels. GM achieved these effects at low picomolar concentrations by selective activation of protein kinase C βI and βII. Notably, GM was able to reduce the frequency of HIV-1 latently infected cells at concentrations without global T cell activation or stimulating inflammatory cytokine production. GM merits further development as a clinical trial candidate for latent HIV-1 eradication