Changes of Acylating Stimulating Protein (ASP) and Blood Lipid in Patients with Acute Myocardial Infarction

Objective: To study the changes of acylating stimulating protein (ASP) and blood lipid in patients with acute myocardial infarction. Method: There were three groups,25 cases of acute myocardial infarction patients (acute myocardial infarction group), 32 cases of coronary heart disease patients without myocardial infarction (CHD group) and 30 cases of healthy people (control group). They respectively detected the ASP, low density lipoprotein cholesterol (LDL-C), triglyceride (TG), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C), and analyzed the correlation between them. Results: (1) ASP, TG, TC and LDL-C of acute myocardial infarction group and coronary heart disease group were significantly higher than those of control group, while HDL-C was lower than control group, the difference was statistically significant (P < 0.05). (2) TG in coronary heart disease group was higher than that in acute myocardial infarction group, while ASP, TC, LDL-C and HDL-C had no significant difference. Conclusion: ASP and blood lipid are risk factors of CHD, ASP can be used as risk index of CHD. There was no significant difference in plasma ASP between patients with acute myocardial infarction and patients with coronary heart disease without myocardial infarction. ASP cannot be used as a surrogate marker of acute myocardial infarction

Conservation of complete trimethylation of lysine-43 in the rotor ring of c-subunits of metazoan adenosine triphosphate (ATP) synthases.

The rotors of ATP synthases turn about 100 times every second. One essential component of the rotor is a ring of hydrophobic c-subunits in the membrane domain of the enzyme. The rotation of these c-rings is driven by a transmembrane proton-motive force, and they turn against a surface provided by another membrane protein, known as subunit a. Together, the rotating c-ring and the static subunit a provide a pathway for protons through the membrane in which the c-ring and subunit a are embedded. Vertebrate and invertebrate c-subunits are well conserved. In the structure of the bovine F1-ATPase-c-ring subcomplex, the 75 amino acid c-subunit is folded into two transmembrane α-helices linked by a short loop. Each bovine rotor-ring consists of eight c-subunits with the N- and C-terminal α-helices forming concentric inner and outer rings, with the loop regions exposed to the phospholipid head-group region on the matrix side of the inner membrane. Lysine-43 is in the loop region and its ε-amino group is completely trimethylated. The role of this modification is unknown. If the trimethylated lysine-43 plays some important role in the functioning, assembly or degradation of the c-ring, it would be expected to persist throughout vertebrates and possibly invertebrates also. Therefore, we have carried out a proteomic analysis of c-subunits across representative species from different classes of vertebrates and from invertebrate phyla. In the twenty-nine metazoan species that have been examined, the complete methylation of lysine-43 is conserved, and it is likely to be conserved throughout the more than two million extant metazoan species. In unicellular eukaryotes and prokaryotes, when the lysine is conserved it is unmethylated, and the stoichiometries of c-subunits vary from 9-15. One possible role for the trimethylated residue is to provide a site for the specific binding of cardiolipin, an essential component of ATP synthases in mitochondria

Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China:the PLANS (platelet lymphocyte age neutrophil sex) model

Background Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. Methods Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). Results The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. Conclusions The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality

Risk factors for posttraumatic stress reactions among chinese students following exposure to a snowstorm disaster

<p>Abstract</p> <p>Background</p> <p>It is important to understand which factors increase the risk of posttraumatic stress disorder (PTSD) in adolescents. Previous studies have shown that the most important risk factors for PTSD include the type, severity, and duration of exposure to the traumatic events.</p> <p>Methods</p> <p>A cross-sectional survey was used to investigate the psychological symptoms associated with the aftermath of a snowstorm disaster in the Hunan province of China in January 2008. Students living in Hunan were surveyed at a three<b>-</b>month follow-up after the disaster. The questionnaire battery included the Impact of Event Scale-Revised (IES-R, trauma and symptoms associated with PTSD), the Chinese version of the Life Orientation Test-Revised (LOT-R, optimism and pessimism), the Chinese version of the Eysenck Personality Questionnaire (EPQ, neuroticism and extraversion), the Chinese Trait Coping Style Questionnaire (TCSQ, positive and negative coping styles), and a range of questions addressing social demographic characteristics and factors relating to the snowstorm. The survey was administered in school, and 968 students completed and returned the questionnaires.</p> <p>Results</p> <p>The results showed that 14.5% of the students had a total IES-R score ≥20. Students with greater school-to-home distances showed higher levels of posttraumatic stress symptoms than students who lived shorter distances from school. Students with emotional support from their teachers reported higher levels of posttraumatic stress symptoms (21.20%) than students without a teacher's emotional support (11.07%). The IES-R total and subscale scores correlated with all variables except extraversion. The binary logistic regression analysis results showed that the teacher's emotional support [odds ratio (OR) = 1.72, 95% confidence interval (CI) = 1.13-2.62], school-to-home distance (OR = 1.01, 95% CI = 1.00-1.01), negative coping (OR = 1.05; 95% CI = 1.02-1.08), and neuroticism (OR = 1.04, 95% CI = 1.02-1.06) were risk factors that predicted PTSD frequency and severity (percentage correct = 85.5%).</p> <p>Conclusions</p> <p>The risk factors that significantly impacted the onset of posttraumatic stress reactions in students living in Hunan, China following a snowstorm disaster were the school-to-home distance, negative coping, neuroticism, and teacher's emotional support.</p

Machine learning‐based identification of cuproptosis‐related markers and immune infiltration in severe community‐acquired pneumonia

Abstract Background Severe community‐acquired pneumonia (SCAP) is one of the world's most common diseases and a major etiology of acute respiratory distress syndrome (ARDS). Cuproptosis is a novel form of regulated cell death that can occur in various diseases. Methods Our study explored the degree of immune cell infiltration during the onset of severe CAP and identified potential biomarkers related to cuproptosis. Gene expression matrix was obtained from GEO database indexed GSE196399. Three machine learning algorithms were applied: The least absolute shrinkage and selection operator (LASSO), the random forest, and the support vector machine‐recursive feature elimination (SVM‐RFE). Immune cell infiltration was quantified by single‐sample gene set enrichment analysis (ssGSEA) scoring. Nomogram was constructed to verify the applicability of using cuproptosis‐related genes to predict the onset of severe CAP and its deterioration toward ARDS. Results Nine cuproptosis‐related genes were differentially expressed between the severe CAP group and the control group: ATP7B, DBT, DLAT, DLD, FDX1, GCSH, LIAS, LIPT1, and SLC31A1. All 13 cuproptosis‐related genes were involved in immune cell infiltration. A three‐gene diagnostic model was constructed to predict the onset of severe CAP: GCSH, DLD, and LIPT1. Conclusion Our study confirmed the involvement of the newly discovered cuproptosis‐related genes in the progression of SCAP

A matched case-control study of early cervical spondylotic myelopathy based on diffusion magnetic resonance imaging

Abstract Background Early cervical spondylotic myelopathy (CSM) is challenging to diagnose and easily missed. Diffusion MRI (dMRI) has the potential to identify early CSM. Methods Using diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI), a 1:1 matched case-control study was conducted to evaluate the potential of dMRI in identifying early CSM and assessing uncompressed segments of CSM patients. CSM patients and volunteers were matched by age and spinal location. The differences in dMRI parameters between groups were assessed by the paired t-test, the multicollinearity of the dMRI parameters was evaluated by the variance inflation factor (VIF), and the value of dMRI parameters in distinguishing controls from CSM patients was determined by logistic regression. The univariate t-test was used to analyse differences between CSM patients and volunteers in adjacent uncompressed areas. Results In total, 56 CSM patients and 56 control volunteers were included. Paired t-tests revealed significant differences in nine dMRI parameters between groups. Multicollinearity calculated through VIF and combined with logistic regression showed that the orientation division index (ODI) was significantly positively correlated (r = 2.12, p = 0.035), and the anisotropic water fraction (AWF) was significantly negatively correlated (r = −0.98, p = 0.015). The fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), isotropic volume fraction (ISOVF), ODI, and AWF were significantly different in the upper and lower uncompressed areas at all ages. Conclusion dMRI can noninvasively identify early CSM patients and potentially identify the extent of CSM lesions involving the cervical spinal cord. Critical relevance statement Diffusion MRI (dMRI) can identify early cervical spondylotic myelopathy (CSM) and has the potential to help determine the extent of CSM involvement. The application of dMRI can help screen for early CSM and develop clinical surgical and rehabilitation treatment plans. Key points • Diffusion MRI can differentiate between normal and early-stage cervical spondylotic myelopathy patients. • Diffusion MRI has the ability to identify the extent of spinal cord involvement in cervical spondylotic myelopathy. • Diffusion MRI enables the early screening of cervical spondylotic myelopathy and helps guide clinical treatment. Graphical Abstrac

The Synthesis and Assembly of a Truncated Cyanophage Genome and Its Expression in a Heterogenous Host

Cyanophages play an important role in regulating the dynamics of cyanobacteria communities in the hydrosphere, representing a promising biological control strategy for cyanobacterial blooms. Nevertheless, most cyanophages are host-specific, making it difficult to control blooming cyanobacteria via single or multiple cyanophages. In order to address the issue, we explore the interaction between cyanophages and their heterologous hosts, with the aim of revealing the principles of designing and constructing an artificial cyanophage genome towards multiple cyanobacterial hosts. In the present study, we use synthetic biological approaches to assess the impact of introducing a fragment of cyanophage genome into a heterologous cyanobacterium under a variety of environmental conditions. Based on a natural cyanophage A-4L genome (41,750 bp), a truncated cyanophage genome Syn-A-4-8 is synthesized and assembled in Saccharomyces cerevisiae. We found that a 351&ndash;15,930 bp area of the A-4L genome has a fragment that is lethal to Escherichia coli during the process of attempting to assemble the full-length A-4L genome. Syn-A-4-8 was successfully introduced into E. coli and then transferred into the model cyanobacterium Synechococcus elongatus PCC 7942 (Syn7942) via conjugation. Although no significant phenotypes of Syn7942 carrying Syn-A-4-8 (LS-02) could be observed under normal conditions, its growth exhibited a prolonged lag phase compared to that of the control strain under 290-millimolar NaCl stress. Finally, the mechanisms of altered salt tolerance in LS-02 were revealed through comparative transcriptomics, and ORF25 and ORF26 on Syn-A-4-8 turned out to be the key genes causing the phenotype. Our research represents an important attempt in designing artificial cyanophages towards multiple hosts, and offers new future insights into the control of cyanobacterial blooms