Applications of Machine Learning in Palliative Care: A Systematic Review

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception

Mycobacterium avium subsp. paratuberculosis Proteome Changes Profoundly in Milk

Mycobacterium avium subspecies paratuberculosis (MAP) are detectable viable in milk and other dairy products. The molecular mechanisms allowing the adaptation of MAP in these products are still poorly understood. To obtain information about respective adaptation of MAP in milk, we differentially analyzed the proteomes of MAP cultivated for 48 h in either milk at 37 °C or 4 °C or Middlebrook 7H9 broth as a control. From a total of 2197 MAP proteins identified, 242 proteins were at least fivefold higher in abundance in milk. MAP responded to the nutritional shortage in milk with upregulation of 32% of proteins with function in metabolism and 17% in fatty acid metabolism/synthesis. Additionally, MAP upregulated clusters of 19% proteins with roles in stress responses and immune evasion, 19% in transcription/translation, and 13% in bacterial cell wall synthesis. Dut, MmpL4_1, and RecA were only detected in MAP incubated in milk, pointing to very important roles of these proteins for MAP coping with a stressful environment. Dut is essential and plays an exclusive role for growth, MmpL4_1 for virulence through secretion of specific lipids, and RecA for SOS response of mycobacteria. Further, 35 candidates with stable expression in all conditions were detected, which could serve as targets for detection. Data are available via ProteomeXchange with identifier PXD027444

Applications of Machine Learning in Palliative Care: A Systematic Review.

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception

Convolutional Neural Networks to Detect Vestibular Schwannomas on Single MRI Slices: A Feasibility Study.

In this study. we aimed to detect vestibular schwannomas (VSs) in individual magnetic resonance imaging (MRI) slices by using a 2D-CNN. A pretrained CNN (ResNet-34) was retrained and internally validated using contrast-enhanced T1-weighted (T1c) MRI slices from one institution. In a second step, the model was externally validated using T1c- and T1-weighted (T1) slices from a different institution. As a substitute, bisected slices were used with and without tumors originating from whole transversal slices that contained part of the unilateral VS. The model predictions were assessed based on the categorical accuracy and confusion matrices. A total of 539, 94, and 74 patients were included for training, internal validation, and external T1c validation, respectively. This resulted in an accuracy of 0.949 (95% CI 0.935-0.963) for the internal validation and 0.912 (95% CI 0.866-0.958) for the external T1c validation. We suggest that 2D-CNNs might be a promising alternative to 2.5-/3D-CNNs for certain tasks thanks to the decreased demand for computational power and the fact that there is no need for segmentations. However, further research is needed on the difference between 2D-CNNs and more complex architectures

Longitudinal assessment of cognitive and psychosocial functioning after Hurricanes Katrina and Rita: Exploring disaster impact on middle-aged, older, and oldest-old adults

The authors examined the effects of Hurricanes Katrina and Rita on cognitive and psychosocial functioning in a lifespan sample of adults 6-14 months after the storms. Participants were recruited from the Louisiana Healthy Aging Study. Most were assessed during the immediate impact period and retested for this study. Analyses of pre- and post-disaster cognitive data confirmed that storm-related decrements in working memory for middle-aged and older adults observed in the immediate impact period had returned to pre-hurricane levels in the post-disaster recovery period. Middle-aged adults reported more storm-related stressors and greater levels of stress than the two older groups at both waves of testing. These results are consistent with a burden perspective on post-disaster psychological reactions. © 2012 Wiley Periodicals, Inc

Development of a peer support intervention to encourage dietary behaviour change towards a Mediterranean diet in adults at high cardiovascular risk.

BACKGROUND: Mediterranean diet (MD) interventions are demonstrated to significantly reduce cardiovascular disease (CVD) risk but are typically resource intensive and delivered by health professionals. There is considerable interest to develop interventions that target sustained dietary behaviour change and that are feasible to scale-up for wider public health benefit. The aim of this paper is to describe the process used to develop a peer support intervention to encourage dietary behaviour change towards a MD in non-Mediterranean adults at high CVD risk. METHODS: The Medical Research Council (MRC) and Behaviour Change Wheel (BCW) frameworks and the COM-B (Capability, Opportunity, Motivation, Behaviour) theoretical model were used to guide the intervention development process. We used a combination of evidence synthesis and qualitative research with the target population, health professionals, and community health personnel to develop the intervention over three main stages: (1) we identified the evidence base and selected dietary behaviours that needed to change, (2) we developed a theoretical basis for how the intervention might encourage behaviour change towards a MD and selected intervention functions that could drive the desired MD behaviour change, and (3) we defined the intervention content and modelled outcomes. RESULTS: A theory-based, culturally tailored, peer support intervention was developed to specifically target behaviour change towards a MD in the target population. The intervention was a group-based program delivered by trained peer volunteers over 12-months, and incorporated strategies to enhance social support, self-efficacy, problem-solving, knowledge, and attitudes to address identified barriers to adopting a MD from the COM-B analysis. CONCLUSIONS: The MRC and BCW frameworks provided a systematic and complementary process for development of a theory-based peer support intervention to encourage dietary behaviour change towards a MD in non-Mediterranean adults at high CVD risk. The next step is to evaluate feasibility, acceptability, and diet behaviour change outcomes in response to the peer support intervention (change towards a MD and nutrient biomarkers) using a randomized controlled trial design

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment