Image_1_Identification of cuproptosis-related molecular subtypes and a novel predictive model of COVID-19 based on machine learning.pdf

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.</p

<i>Treh</i> regulates neuroepithelial cell maintenance and differentiation in the optic lobe.

<p>(A-I) Time courses of neuroepithelial growth and expansion. (A-C) Wild-type brains at late-second (A), mid-third (B) and late-third instar (C). (D-F) <i>c768-Gal4/UAS-Treh^RNAi</i> brains at late-second (D), mid-third (E) and late-third instar (F). The OPC neuroepithelium was normal at late-second instar (D), but became gradually disintegrated from mid-third (E) to late-third instar stages (F). (G-I) <i>c855a-Gal4/UAS-Treh^RNAi</i> brains at late-second (G), mid-third (H) and late-third instar (I). The OPC neuroepithelium began to disintegrate around mid-third instar. (J, K) <i>Treh</i> RNAi brains had some enlarged, rounded cells that were Dpn⁺ and localized in the medulla cortex (K, K' indicated by yellow arrows), whereas wild-type brains have medulla neuroblasts localized on the medial surface of the optic lobe (J, J', indicated by white arrowhead). White arrow indicates IPC neuroblasts, which were not analyzed in this study. Scale bar: 20 µm.</p

<i>Treh</i> overexpression does not affect optic lobe development.

<p>(A-E) Late-third instar larval brains expressing five different <i>UAS-Treh</i> lines under the control of <i>c768-Gal4</i>. Overexpression of <i>Treh</i> did not cause defects in the brain; and the proliferation and differentiation of NEs was normal. (F) Quantification of <i>Treh</i> mRNA levels in wild-type and <i>c768-Gal4/UAS-Treh</i> larval CNS by real-time PCR analysis. Scale bar: 20 µm.</p

<i>Treh</i> loss-of-function mutations cause neuroepithelial disintegration and premature neuroblast formation.

<p>(A, B) <i>Treh¹⁸</i> and <i>Treh⁴¹</i> homozygous late-third-instar larval brains had partly disintegrated OPC neuroepithelia, with some NEs transformed to rounded cells that expressed Dpn (indicated by arrow). (C) <i>Treh¹⁸/Treh⁴¹</i> late-third-instar larval brains also had disintegrated NEs and premature formation of NBs (indicated by arrow). (D) Quantification of <i>Treh</i> mRNA levels in wild type and <i>Treh</i> mutants by real-time PCR analysis. Scale bar: 20 µm.</p

<i>Treh</i> suppresses the differentiation of neuroepithelial cells.

<p>Late-third instar larval brains were stained with the antibodies indicated and flip-out clones expressing <i>Treh</i> RNAi were marked by GFP and dashed lines. (A) Cells in <i>Treh</i> RNAi clones in the medulla cortex were large and rounded (indicated by white arrowhead). (B) Multiple cells in each <i>Treh</i> RNAi clone expressed Dpn. (C) <i>Treh</i> RNAi mutant cells had asymmetric Mira localization in the cell cortex. (D) <i>Treh</i> RNAi clones generated only a limited number of neurons as revealed by Elav staining. (E) A wild-type control clone had a large lineage with some neuroblasts localized on the medial surface of the OPC. (F) <i>Treh</i> RNAi mutant cells underwent proliferation as revealed by PH3 staining. (G) No apoptotic cell death of <i>Treh</i> RNAi mutant cells was detected by activated caspase-3 staining. (H) Ectopic neuroblasts in <i>Treh</i> RNAi clones had asymmetric aPKC and Numb localization at opposite poles. The apical and basal poles (H') were reversed as compared with wild-type medulla neuroblasts (H”). (I) Tubulin staining of <i>Treh</i> RNAi mutant cells revealed that the spindle was aligned along the apicobasal axis. In (H) and (I), white and yellow arrows indicate <i>Treh</i> RNAi mutant neuroblast and normal medulla neuroblast, respectively; purple and green arrows indicates apical and basal pole, respectively. (J) Schematic showing <i>Treh</i> RNAi mutant neuroblasts with a reversal of apical and basal poles as compared with normal medulla neuroblasts. Scale bar: 20 µm.</p

<i>Treh</i> is essential for lamina and medulla development.

<p>(A) Schematic diagram of the larval CNS. OL: optic lobe; CB: central brain; OPC: outer proliferation center; IPC: inner proliferation center; LF: lamina furrow; me: medulla; NE: neuroepithelial cell; NB: neuroblast in the optic lobe and central brain; VNC: ventral nerve cord. (B) Magnified view of boxed region in (A). NEs in the medial region of the OPC differentiate into medulla NBs; the NBs divide asymmetrically to generate a neuroblast daughter and a smaller ganglion mother cell (GMC) that generates medulla neurons. (C) Lateral view of the optic lobe showing the visual processing neuropils, the medulla (me), lamina (la) and lobula complex (lo). The optic lobe is connected with the eye imaginal disc (ED) through the optic stalk (OS). (D-F) Brains dissected from late-third instar larvae were stained with Dac and Elav to visualize the lamina and medulla, respectively. (D) Wild-type brains have a crescent-shaped lamina and a dome-shaped medulla. (E) <i>c768-Gal4/UAS-Treh^RNAi</i> brains do not have a lamina. (F) <i>c855a-Gal4/UAS-Treh^RNAi</i> brains do not have a lamina, but have an underdeveloped medulla with regions that contained no differentiated neurons (indicated by arrow). Scale bar: 20 µm.</p

Additional file 1 of Identification of biomarkers and pathways for the SARS-CoV-2 infections in obstructive sleep apnea patients based on machine learning and proteomic analysis

Supplementary 1

Supplemental Material - Moral distress, psychological capital, and burnout in registered nurses

Supplemental Material for Moral distress, psychological capital, and burnout in registered nurses by Bowen Xue, Shujin Wang, Dandan Chen, Zhiguo Hu, Yaping Feng and Hong Luo in Nursing Ethics</p

Funnel plots of exposure to dental X-rays and risk of meningioma for assessment of publication bias.

<p>Funnel plots of exposure to dental X-rays and risk of meningioma for assessment of publication bias.</p

Forest plot of exposure to dental full-mouth X-rays and risk of meningioma.

<p>Studies are pooled with a fixed -effects model.</p