Lin Tan

BACKGROUND: We aimed to clarify high-risk factors for coronavirus disease 2019 (COVID-19) with multivariate analysis and establish a predictive model of disease progression to help clinicians better choose a therapeutic strategy. METHODS: All consecutive patients with COVID-19 admitted to Fuyang Second People's Hospital or the Fifth Medical Center of Chinese PLA General Hospital between 20 January and 22 February 2020 were enrolled and their clinical data were retrospectively collected. Multivariate Cox regression was used to identify risk factors associated with progression, which were then were incorporated into a nomogram to establish a novel prediction scoring model. ROC was used to assess the performance of the model. RESULTS: Overall, 208 patients were divided into a stable group (n = 168, 80.8%) and a progressive group (n = 40,19.2%) based on whether their conditions worsened during hospitalization. Univariate and multivariate analyses showed that comorbidity, older age, lower lymphocyte count, and higher lactate dehydrogenase at presentation were independent high-risk factors for COVID-19 progression. Incorporating these 4 factors, the nomogram achieved good concordance indexes of .86 (95% confidence interval [CI], .81-.91) and well-fitted calibration curves. A novel scoring model, named as CALL, was established; its area under the ROC was .91 (95% CI, .86-.94). Using a cutoff of 6 points, the positive and negative predictive values were 50.7% (38.9-62.4%) and 98.5% (94.7-99.8%), respectively. CONCLUSIONS: Using the CALL score model, clinicians can improve the therapeutic effect and reduce the mortality of COVID-19 with more accurate and efficient use of medical resources.

Software defect prediction, which predicts defective code regions, can help developers find bugs and prioritize their testing efforts. To build accurate prediction models, previous studies focus on manually designing features that encode the characteristics of programs and exploring different machine learning algorithms. Existing traditional features often fail to capture the semantic differences of programs, and such a capability is needed for building accurate prediction models.

BACKGROUND: The investigation of intracellular metabolism is the mainstay in the biotechnology and physiology settings. Intracellular metabolic rates are commonly evaluated using labeling pattern of the identified metabolites obtained from stable isotope labeling experiments. The labeling pattern or mass distribution vector describes the fractional abundances of all isotopologs with different masses as a result of isotopic labeling, which are typically resolved using mass spectrometry. Because naturally occurring isotopes and isotopic impurity also contribute to measured signals, the measured patterns must be corrected to obtain the labeling patterns. Since contaminant isotopologs with the same nominal mass can be resolved using modern mass spectrometers with high mass resolution, the correction process should be resolution dependent. RESULTS: Here we present a software tool, ElemCor, to perform correction of such data in a resolution-dependent manner. The tool is based on mass difference theory (MDT) and information from unlabeled samples (ULS) to account for resolution effects. MDT is a mathematical theory and only requires chemical formulae to perform correction. ULS is semi-empirical and requires additional measurement of isotopologs from unlabeled samples. We validate both methods and show their improvement in accuracy and comprehensiveness over existing methods using simulated data and experimental data from Saccharomyces cerevisiae. The tool is available at https://github.com/4dsoftware/elemcor . CONCLUSIONS: We present a software tool based on two methods, MDT and ULS, to correct LC-MS data from isotopic labeling experiments for natural abundance and isotopic impurity. We recommend MDT for low-mass compounds for cost efficiency in experiments, and ULS for high-mass compounds with relatively large spectral inaccuracy that can be tracked by unlabeled standards.

Abstract Although treatment with immune checkpoint inhibitors provides promising benefit for patients with cancer, optimal use is encumbered by high resistance rates and requires a thorough understanding of resistance mechanisms. We observed that tumors treated with PD-1/PD-L1 blocking antibodies develop resistance through the upregulation of CD38, which is induced by all-trans retinoic acid and IFNβ in the tumor microenvironment. In vitro and in vivo studies demonstrate that CD38 inhibits CD8+ T-cell function via adenosine receptor signaling and that CD38 or adenosine receptor blockade are effective strategies to overcome the resistance. Large data sets of human tumors reveal expression of CD38 in a subset of tumors with high levels of basal or treatment-induced T-cell infiltration, where immune checkpoint therapies are thought to be most effective. These findings provide a novel mechanism of acquired resistance to immune checkpoint therapy and an opportunity to expand their efficacy in cancer treatment. Significance: CD38 is a major mechanism of acquired resistance to PD-1/PD-L1 blockade, causing CD8+ T-cell suppression. Coinhibition of CD38 and PD-L1 improves antitumor immune response. Biomarker assessment in patient cohorts suggests that a combination strategy is applicable to a large percentage of patients in whom PD-1/PD-L1 blockade is currently indicated. Cancer Discov; 8(9); 1156–75. ©2018 AACR. See related commentary by Mittal et al., p. 1066. This article is highlighted in the In This Issue feature, p. 1047