Identification of cuproptosis-related molecular subtypes and a novel predictive model of COVID-19 based on machine learning

BackgroundTo explicate the pathogenic mechanisms of cuproptosis, a newly observed copper induced cell death pattern, in Coronavirus disease 2019 (COVID-19).MethodsCuproptosis-related subtypes were distinguished in COVID-19 patients and associations between subtypes and immune microenvironment were probed. Three machine algorithms, including LASSO, random forest, and support vector machine, were employed to identify differentially expressed genes between subtypes, which were subsequently used for constructing cuproptosis-related risk score model in the GSE157103 cohort to predict the occurrence of COVID-19. The predictive values of the cuproptosis-related risk score were verified in the GSE163151 cohort, GSE152418 cohort and GSE171110 cohort. A nomogram was created to facilitate the clinical use of this risk score, and its validity was validated through a calibration plot. Finally, the model genes were validated using lung proteomics data from COVID-19 cases and single-cell data.ResultsPatients with COVID-19 had higher significantly cuproptosis level in blood leukocytes compared to patients without COVID-19. Two cuproptosis clusters were identified by unsupervised clustering approach and cuproptosis cluster A characterized by T cell receptor signaling pathway had a better prognosis than cuproptosis cluster B. We constructed a cuproptosis-related risk score, based on PDHA1, PDHB, MTF1 and CDKN2A, and a nomogram was created, which both showed excellent predictive values for COVID-19. And the results of proteomics showed that the expression levels of PDHA1 and PDHB were significantly increased in COVID-19 patient samples.ConclusionOur study constructed and validated an cuproptosis-associated risk model and the risk score can be used as a powerful biomarker for predicting the existence of SARS-CoV-2 infection

Organizational Structure and Process—An Analysis in Decision-Making

It is known that the performance of an organization is highly related to the process through which activities are organized. However, the dyadic relationship between organizational structure and process along with their influence on performance become complicated when faced with complex activities. We explore this relationship and its influence by following three lines of study. First of all, in a setting of product development, we introduce a process model for organizing concurrent activities. We show how to determine an optimal schedule. The results demonstrate the variation of design performance, i.e., lead-time, rework, and total workload, under a set of different overlapping strategies. Although depending on the setting of case incidences, there generally exists no dominant strategy over all the performance measures. As a result, managers should select the strategy based on preference over the measures. Secondly, we address the question of how should an organization be structured in a static as well as dynamic process variation. Organizational form will be changed along two dimensions, i.e., departmentalization and assignment, whereas process evolves in terms of complexity. In addition to improving the alignment of organizational structure with a static process, we emphasize and study strategic guidelines of restructuring in the presence of a dynamic environment. The last line of study is geared towards evaluating a group of organizations which differ in preference. In the form of decision process, team specialty, and communication structure, we show the comparative performance between two stylized decision processes, i.e., hierarchy and polyarchy, with or without communication between agents in an environment where each project must be determined by two features

Tunable dynamical magnetoelectric effect in antiferromagnetic topological insulator MnBi2_2Te4_4 films

More than forty years ago, axion was postulated as an elementary particle with a low mass and weak interaction in particle physics to solve the strong $\mathcal{CP}$ (charge conjugation and parity) puzzle. Axions are also considered as a possible component of dark matter of the universe. However, the existence of axions in nature has not been confirmed. Interestingly, axions arise as pseudoscalar fields derived from the Chern-Simons theory in condensed matter physics. In antiferromagnetic insulators, the axion field can become dynamical induced by spin-wave excitations and exhibits rich exotic phenomena, such as, the chiral magnetic effect, axionic polariton and so on. However, the study of the dynamical axion field is rare due to the lack of real materials. Recently, MnBi$_2$Te$_4$ was discovered to be an antiferromagnetic topological insulator with a quantized axion field protected by the inversion symmetry $\mathcal{P}$ and the magnetic-crystalline symmetry $\mathcal{S}$. Here, we studied MnBi$_2$Te$_4$ films in which both the $\mathcal{P}$ and $\mathcal{S}$ symmetries are spontaneously broken and found that the dynamical axion field and largely tunable dynamical magnetoelectric effects can be realized through tuning the thickness of MnBi$_2$Te$_4$ films, the temperature and the element substitution. Our results open a broad avenue to study axion dynamics in antiferromagnetic topological insulator MnBi$_2$Te$_4$ and related materials, and also is hopeful to promote the research of dark matter.Comment: 6 pages, 4 figure

Evaluating the Information Usefulness of Online Health Information for Third-party Patients

Online health interactions (OHIs) can benefit patients, physicians, and society. However, little research has been conducted that studies the social value of OHIs for third-party patients who view previous OHIs concerning similar health issues to theirs. Drawing on the literature on social support and information uncertainty, this study established a theoretical model to explore the roles of treatment information, prevention information, and emotional support in determining information usefulness perceived by third-party patients, and whether such relationships are contingent on information uncertainty. The model was tested using “health questions and answers” textual data from 1,848 OHIs. The results indicate that prevention information and emotional support significantly improve information usefulness perceived by third-party patients. When the level of information uncertainty regarding physicians’ replies is high, the effect of treatment information is strengthened and the effect of emotional support is weakened, indicating both positive and negative contingent roles of information uncertainty. This study has implications for practitioners and also contributes to the literature on online health information, social support, information science, and information uncertainty