Longitudinal clinical data improve survival prediction after hematopoietic cell transplantation using machine learning.

ABSTRACT: Serial prognostic evaluation after allogeneic hematopoietic cell transplantation (allo-HCT) might help identify patients at high risk of lethal organ dysfunction. Current prediction algorithms based on models that do not incorporate changes to patients' clinical condition after allo-HCT have limited predictive ability. We developed and validated a robust risk-prediction algorithm to predict short- and long-term survival after allo-HCT in pediatric patients that includes baseline biological variables and changes in the patients' clinical status after allo-HCT. The model was developed using clinical data from children and young adults treated at a single academic quaternary-care referral center. The model was created using a randomly split training data set (70% of the cohort), internally validated (remaining 30% of the cohort) and then externally validated on patient data from another tertiary-care referral center. Repeated clinical measurements performed from 30 days before allo-HCT to 30 days afterwards were extracted from the electronic medical record and incorporated into the model to predict survival at 100 days, 1 year, and 2 years after allo-HCT. Naïve-Bayes machine learning models incorporating longitudinal data were significantly better than models constructed from baseline variables alone at predicting whether patients would be alive or deceased at the given time points. This proof-of-concept study demonstrates that unlike traditional prognostic tools that use fixed variables for risk assessment, incorporating dynamic variability using clinical and laboratory data improves the prediction of mortality in patients undergoing allo-HCT.

Application of Machine Learning to the Prediction of Cancer-Associated Venous Thromboembolism.

Venous thromboembolism (VTE) is a common and impactful complication of cancer. Several clinical prediction rules have been devised to estimate the risk of a thrombotic event in this patient population, however they are associated with limitations. We aimed to develop a predictive model of cancer-associated VTE using machine learning as a means to better integrate all available data, improve prediction accuracy and allow applicability regardless of timing for systemic therapy administration. A retrospective cohort was used to fit and validate the models, consisting of adult patients who had next generation sequencing performed on their solid tumor for the years 2014 to 2019. A deep learning survival model limited to demographic, cancer-specific, laboratory and pharmacological predictors was selected based on results from training data for 23,800 individuals and was evaluated on an internal validation set including 5,951 individuals, yielding a time-dependent concordance index of 0.72 (95% CI = 0.70-0.74) for the first 6 months of observation. Adapted models also performed well overall compared to the Khorana Score (KS) in two external cohorts of individuals starting systemic therapy; in an external validation set of 1,250 patients, the C-index was 0.71 (95% CI = 0.65-0.77) for the deep learning model vs 0.66 (95% CI = 0.59-0.72) for the KS and in a smaller external cohort of 358 patients the C-index was 0.59 (95% CI = 0.50-0.69) for the deep learning model vs 0.56 (95% CI = 0.48-0.64) for the KS. The proportions of patients accurately reclassified by the deep learning model were 25% and 26% respectively. In this large cohort of patients with a broad range of solid malignancies and at different phases of systemic therapy, the use of deep learning resulted in improved accuracy for VTE incidence predictions. Additional studies are needed to further assess the validity of this model.

Fungal Systematics and Evolution: FUSE 5.

Thirteen new species are formally described: Cortinarius brunneocarpus from Pakistan, C. lilacinoarmillatus from India, Curvularia khuzestanica on Atriplex lentiformis from Iran, Gloeocantharellus neoechinosporus from China, Laboulbenia bernaliana on species of Apenes, Apristus, and Philophuga (Coleoptera, Carabidae) from Nicaragua and Panama, L. oioveliicola on Oiovelia machadoi (Hemiptera, Veliidae) from Brazil, L. termiticola on Macrotermes subhyalinus (Blattodea, Termitidae) from the DR Congo, Pluteus cutefractus from Slovenia, Rhizoglomus variabile from Peru, Russula phloginea from China, Stagonosporopsis flacciduvarum on Vitis vinifera from Italy, Strobilomyces huangshanensis from China, Uromyces klotzschianus on Rumex dentatus subsp. klotzschianus from Pakistan. The following new records are reported: Alternaria calendulae on Calendula officinalis from India; A. tenuissima on apple and quince fruits from Iran; Candelariella oleaginescens from Turkey; Didymella americana and D. calidophila on Vitis vinifera from Italy; Lasiodiplodia theobromae causing tip blight of Dianella tasmanica 'variegata' from India; Marasmiellus subpruinosus from Madeira, Portugal, new for Macaronesia and Africa; Mycena albidolilacea, M. tenuispinosa, and M. xantholeuca from Russia; Neonectria neomacrospora on Madhuca longifolia from India; Nothophoma quercina on Vitis vinifera from Italy; Plagiosphaera immersa on Urtica dioica from Austria; Rinodina sicula from Turkey; Sphaerosporium lignatile from Wisconsin, USA; and Verrucaria murina from Turkey. Multi-locus analysis of ITS, LSU, rpb1, tef1 sequences revealed that P. immersa, commonly classified within Gnomoniaceae (Diaporthales) or as Sordariomycetes incertae sedis, belongs to Magnaporthaceae (Magnaporthales). Analysis of a six-locus Ascomycota-wide dataset including SSU and LSU sequences of S. lignatile revealed that this species, currently in Ascomycota incertae sedis, belongs to Pyronemataceae (Pezizomycetes, Pezizales).

Amorphous Carbon Dots and their Remarkable Ability to Detect 2,4,6-Trinitrophenol.

Apparently mundane, amorphous nanostructures of carbon have optical properties which are as exotic as their crystalline counterparts. In this work we demonstrate a simple and inexpensive mechano-chemical method to prepare bulk quantities of self-passivated, amorphous carbon dots. Like the graphene quantum dots, the water soluble, amorphous carbon dots too, exhibit excitation-dependent photoluminescence with very high quantum yield (~40%). The origin and nature of luminescence in these high entropy nanostructures are well understood in terms of the abundant surface traps. The photoluminescence property of these carbon dots is exploited to detect trace amounts of the nitro-aromatic explosive - 2,4,6-trinitrophenol (TNP). The benign nanostructures can selectively detect TNP over a wide range of concentrations (0.5 to 200 µM) simply by visual inspection, with a detection limit of 0.2 µM, and consequently outperform nearly all reported TNP sensor materials.