The aim of this prospective, double‐blinded study in patients with aortic sclerosis was to determine whether a new calcification propensity measure in the serum could predict disease progression.We included 129 consecutive patients with aortic sclerosis as assessed during a routine clinical echocardiographic exam. Clinical, echocardiographic, and serum laboratory parameters were collected, including a new blood test providing an overall measure of calcification propensity by monitoring the maturation time of calciprotein particles (T50 test). The echocardiographic exam was repeated after 1 year. Multiple regression analysis was performed to identify independent predictors of the annual increase of peak transvalvular Doppler velocity (∆vmax). Furthermore, the accuracy of the T50 test to detect patients with the most marked stenosis progression was assessed by receiver operating characteristic (ROC)‐analysis.p = ns). The T50 test was not found to be an independent linear predictor in multivariate testing. By ROC‐analysis, however, a T50‐value ≤ 242 min was able to significantly detect a ∆vmax above the 90th percentile .Mean age was 75 ± 9 years, 79% were men. The T50 was 271 ± 58 min. Overall, there was no significant stenosis progression between baseline and follow‐up (∆vmax 3.8 ± 29.8 cm/s, The T50 test showed a modest but significant ability to identify a pronounced aortic stenosis progression in patients with aortic sclerosis. The test could not be established as an independent linear predictor of disease progression, possibly due to the low valvular disease burden and short follow‐up interval. It is present in approximately 25% of all adults aged above 65 years.In the past, several prospective studies were performed to enhance the predictability of disease behavior. Some determinants of rapid progression and adverse outcome have been identified, such as age, gender, cardiovascular risk factors, B‐natriuretic peptide, stenosis severity, degree of valvular calcification, and others. Although it appears that progression is more rapid in degenerative calcific disease than congenital or rheumatic disease,From a biochemical and histological point of view, aortic sclerosis is a valvular disease characterized by focal plaque‐like lesions containing microscopic calcifications.Koos et al. showed that serum levels of the calcification inhibitor fetuin‐A are associated with the progression of aortic valve calcifications and major adverse clinical events, independent of the renal function and inflammation.A novel in vitro blood test developed by Pasch et al. provides an overall measure of calcification propensity by monitoring the maturation time (T50) of calciprotein particles. First published clinical data indicate that the T50 test is a helpful biomarker for the prediction of future vascular calcifications.Fetuin‐A is an important determinant of the T50 test which goes beyond fetuin‐A measurement by functionally integrating the activities of all calcification inhibitors and promotors in the serum into a single readout.The purpose of this study in patients with aortic sclerosis with and without stenosis is to assess the ability of the T50 test to predict progression of aortic stenosis.22.1One hundred twenty‐nine patients referred to our Department of Cardiology for routine echocardiography showing an aortic sclerosis with or without stenosis were prospectively included. Exclusion criteria were: Age <18 years, aortic valve replacement scheduled within 1 year after inclusion, any aortic valvular disease other than degenerative sclerotic, bi‐ or unicuspid valves, known disease with expected survival <1 year, known malignant tumor, subvalvular obstruction (in left ventricular outflow tract) with mean pressure gradient >10 mm Hg. Patients underwent all exams described below during baseline visit, and an additional follow‐up transthoracic echocardiography after 1 year.The study followed a strict double‐blinded design: the patients and the investigators other than A.P. were blinded for the results of the T50 test. The investigator A.P. (as responsible for the T50 test) was blinded for all clinical and echocardiographic data. Unblinding was performed after completion of all follow‐up examinations.The study was approved by the Ethics Committee of the Canton of Berne, Switzerland (KEK‐BE: 087/2014).2.2It was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research.2.3Patients were examined in a left lateral decubitus position using a Philips IE33, Epiq 5, or GE Vingment E95 ultrasonography system. The exam consisted of a comprehensive assessment of cavity and wall dimensions, ventricular and valvular function, morphologic appraisal, and pressure predictions. This procedure was performed in accordance to international guidelines for echocardiography. In particular, aortic stenosis severity was measured using peak aortic jet velocity from using continuous‐wave Doppler tracing, recorded from the window yielding the highest velocity signal.2.4With the blood samples taken during the baseline visit, a T50 test was performed by Calciscon AG. This test consists of adding calcium and phosphate to serum to trigger the formation of primary calciprotein particles (CPP). As nano‐suspension of calcium‐phosphate, these particles represent a defense mechanism of the serum against calcification. Primary CPPs undergo spontaneous transition to secondary CPPs. The formation of these particles represents calcification. In the T50 test, the time elapsed for the transformation of 50% of the particles is measured optically and is specific for individual sera.Other laboratory tests included creatinine, albumin, calcium, phosphate, high‐sensitivity C‐reactive protein, LDL cholesterol, venous blood‐gas analysis, brain natriuretic peptide and hemoglobin. Measurements took place at the central laboratory of our clinic. There was no blinding for these laboratory results because the information was used for routine clinical purposes.2.5Assessment of clinical baseline characteristic included NYHA‐class, angina pectoris CCS‐class, syncope, personal history of coronary artery disease, diabetes mellitus, hypertension, smoking, chronic inflammatory disease and malignancy, family history for cardiovascular disease, and current medication.2.6t test. To assess the independent predictors of ∆vmax, a two‐step approach was chosen: in the first step, univariate analysis relating all available test variables to ∆vmax was performed to restrict the choice of candidates for further multivariate testing. Univariate testing consisted of linear regression analyses for continuous and unpaired Student's t tests for dichotomous, categorical test variables. A threshold of p < .10 was used to select candidates for multivariate analysis, consisting of a standard multiple regression model. Dichotomous variables were used as dummy variables in multiple regression analysis. Assumption testing was performed using the following two statistical procedures: (1) A Goldfeld‐Quandt test was used to test the assumption of homoscedasticity of the error with a p value < .05 considered as mandatory. (2) Collinearity statistics to exclude multicollinearity between the test variables (a variance inflation factor <10 was defined as mandatory). The results of the overall regression fittings were calculated, including the intercept and standardized coefficients of the multiple regression equation (β), adjusted coefficient of determination (adjusted r2), and analysis of variance (ANOVA) F‐ and p values of the overall regression model. Only variables with significant nonzero slopes in the regression equation were considered independent predictors of aortic stenosis progression. These analyses were performed using the R 3.5.2 statistical software.All patient data was collected in a custom data set in REDCap.In addition, patient were assigned to two groups: patients above and below the 90th percentile of ∆vmax. The ability of the T50 test to diagnose the group above the 90th percentile was assessed using a receiver operating characteristic (ROC) analysis. This computation was performed on GraphPad Prism 7 (GraphPad Software).p‐value < .05 was considered statistically significant in all analyses.A 33.1Baseline characteristics are given in the supplementary material. Mean age was 75 ± 9 years, 79% were male. The T50 was 271 ± 58 min, ranging from 142 to 480 min. The mean follow‐up period was 386 ± 66 days.3.22 and known coronary artery disease were excluded from further testing due to excess collinearity.The univariate relations of the initial test variables with ∆vmax are listed in Table 3.3F = 3.03, p value = .01, adjusted r2 = .10). Among the test variables entered in the model, only Aspirin intake was significantly related to ∆vmax , with intake being associated with lower ∆vmax. All other test variables, including T50, were not significantly related to ∆vmax.The results of the multiple regression analysis are listed in Tables 3.4p = .04) with an area under the curve (AUC) of 0.67.Fourteen patients with a ∆vmax value above the upper 90th percentile were depicted. The mean ∆vmax in this subgroup was 58 cm/s (range 43–91 cm/s), and T50 was 241 ± 48 min. In the other group, mean ∆vmax did not significantly differ from zero (range −66 to 42 cm/s), and T50 was 274 ± 59 min. The ROC analysis is shown in Figure 4This prospective, double‐blinded observational study in 129 patients with aortic sclerosis shows that measuring individual serum calcification propensity (as assessed by the T50 test) allows identifying patients with the most pronounced progression of aortic stenosis in advance. The diagnostic accuracy is modest, but statistically significant. We found no linear relation between T50 and stenosis progression.The T50 test has previously been established as predictor of morbidity and mortality, primarily by investigating patient populations with vascular calcification. In 6231 individuals who participated in the PREVEND‐Study in Groningen, Netherlands, Eeldering et al. found an association between T50 and cardiovascular mortality.Fewer data exist on the role of serum‐driven calcification in degenerative aortic valve stenosis. In an in‐vitro study, Rattazzi et al. showed that inhibitors of tissue calcification, such as pyrophosphate, are significantly reduced in calcified aortic valve tissue,Some other predictors of stenosis progression have been identified in previous studies, that is, jet velocity at baseline,Aspirin and statins were both inversely related to disease progression in univariate analysis, whereas only Aspirin emerged as an independent predictor in multivariate analysis, that is, intake being related to less progression. While similar results regarding dyslipidemia have been found previously,The proportion of patients with chronic kidney disease and kidney transplant is relatively high in this study population, reflecting the referral practice in our tertiary center. These patients are known to have a pronounced serum calcification propensity and reduced T50 Therefore, these patients may have contributed to a larger than normal range of T50‐values.In conclusion, serum calcification propensity seems involved in aortic stenosis progression, with the T50 test capable of identifying individuals with the most pronounced stenosis progression with moderate diagnostic performance. A larger study with longer follow‐up interval is needed to elucidate whether the T50 test is of prognostic relevance in aortic sclerosis patients.The authors have respectively contributed to the following task of the article: Reto Kurmann: Conduct. Eric Buffle: Reporting. Andreas Pasch: Conduct. Christian Seiler: Reporting. Stefano F. de Marchi: Planning, conduct, and reporting.AP is an employee and stockholder in Calciscon AG, Nidau, Switzerland, a company that commercializes the T50 test. The authors declare no conflicts of interest.Supplementary information.Click here for additional data file.


Platelets play an important role in homeostasis however, they have also been associated with increased mortality after myocardial infarction. In the present study, we investigated whether platelet count is associated with differences in the short-term prognosis at the time of hospital discharge and early neurological deterioration in ischemic stroke patients.Patients with ischemic stroke were enrolled from among 661 cerebrovascular disease patients admitted between January 2018 and December 2020. Patients who received hyperacute treatment, had a pre-onset modified Rankin scale (mRS) ≥ 3, transient ischemic attack, or active malignant disease were excluded. The platelet count was divided into quartiles (Q1-4) according to the number of patients, and the relationship between platelet count and prognosis was assessed using multivariable analysis.9/L, p = 0.013, ORs: 1.674, 95%CI: 1.253–6.681) group compared to the Q3 (range: 205–242 × 109/L) group even after adjusting for factors with P < 0.2 in univariate analysis. Furthermore, the frequency of neurological deterioration (NIHSS ≥ 4) within 1 week was significantly lower in the Q3 group than in the Q1  and Q4 groups even after adjustment .In total, 385 patients were included in the study. Regarding the functional outcome by platelet count, there was a significant increase in mRS ≥ 3 at discharge in the Q4 (range: 243–1327 × 10Platelet count at onset may affect the prognosis of cerebral infarction and early neurological deterioration. This study may help clarify the pathogenesis of cerebral infarction to improve prognosis. However, a normal platelet count is 150–400 × 109/L, suggesting that platelets have another functions (e.g. homeostasis maintenance) other than hemostasis  level of > 6.8%, or drug treatment for DM), a cardioembolic source according to the Trial of Org 10,172 in Acute Stroke Treatment (TOAST) classification [We retrieved the following information from the medical records of each patient to evaluate the short-term prognosis at the time of hospital discharge and subsequent deterioration: 1) demographic data, 2) vital signs at presentation and laboratory findings on admission, 3) medications being taken upon admission, with particular attention paid to anti-platelets, anti-coagulants, anti-hypertensives, and statins, 4) vascular risk factors for stroke such as hypertension , dyslipidemia , diabetes mellitus  fication , as recoBriefly, according to Japanese stroke guidelines, thrombosis was treated with dual antiplatelet therapy (often use aspirin 200 mg/day and clopidogrel 75 mg), with edaravone and argatroban in acute phase. For cardiogenic embolism, we use edaravone in acute phase, and stated anticoagulant after day 2–5 from stroke onset. For embolic stroke of undetermined source, we treat with aspirin and edaravone.2 test for categorical variables and Kruskal Wallis test for non-parametric analyses. The platelet count was divided into quartiles and used in the multiple logistic regression analysis to estimate the relationship. Variables with a P value < 0.2 on univariate analysis were entered into multiple logistic regression analysis. p-values of < 0.05 were considered significant.The protocol of this retrospective study was approved by the Human Ethics Review Committee of Juntendo University School of Medicine. The data were analyzed with SPSS 17.0  and are expressed as mean ± SD values. All statistical analyses were performed using χn = 91), TIA (n = 41), undergoing malignancy treatment (n = 56), mRS ≥ 3 (n = 46), or receiving intravenous tissue plasminogen activator treatment and endovascular treatment (n = 42) , large-artery atherosclerosis , cardioembolism , and other determined etiology . Early neurological deterioration of NIHSS ≥ 4 within 1 week from onset was noted in 35 patients (9.1%).A total of 385 patients were enrolled in this study after excluding patients who were diagnosed with hemorrhagic stroke n = 9, TIA . Between quartiles, body mass index, smoking habit, and systolic blood pressure significantly differed (p < 0.05), but age, sex, HT, DM, DL, ischemic heart disease (IHD), atrial fibrillation (Af), history of cerebral infarction, and NIHSS on onset were not different. Stroke subtypes  and premedication from stroke onset did not differ, except for ARB medication (p < 0.05). Regarding laboratory findings (Table p < 0.05). However, there were no differences in eGFR, UA, HDL, LDL, TG, blood sugar, or HbA1c at admission. In patient outcome (Table p < 0.05). However, antiplatelet therapy and anticoagulant therapy for secondary prevention were not differed in platelet query groups.The patient background by platelet count quartiles is shown in Table p < 0.2). We previously reported a good prognosis in patients taking angiotensin II receptor blocker (ARB) medication, and another report demonstrated that factors [p < 0.2). Likewise, early neurological deterioration was related (p < 0.2) to the factors of NIHSS ≥ 8, WBC count, Hb count, SVO, LAS, BAD, and anticoagulant premedication in univariate analysis , and the Q1 group tended to have poor prognosis (p = 0.079). In the case of early neurological deterioration, the Q1 and Q4 groups had a higher rate than the Q3 group (p < 0.05).We performed logistic regression analysis of factors related to mRS3-6 and deterioration between platelet count query groups  exhibited a low rate of early neurological deterioration and good prognosis compared with the Q1 and Q4 groups.This study demonstrated the relationship between platelet count and ischemic stroke prognosis at discharge/early neurological deterioration. The Q3 group (range: 205–242 × 109/L) were associated with the development of cardiovascular disease [9/L. A cohort study of 1506 men reported an association between the risk of ischemic stroke and platelet counts in the upper normal range [Our study is consistent with previous studies. One cohort study reported that platelet counts at the upper end of the normal range  was significantly higher than the normal group, without stroke severity at onset. On the other hand, D'Erasmo et al. indicated the difference in platelet counts between the acute and recovery phases of stroke was not significant . SeveralRecently, Vinholt and colleagues concluded that platelet counts are associated with cardiovascular and cerebrovascular disease . In geneOur study has several limitations involving its non-random treatment allocation procedure and retrospective design. It must be emphasized that this study is exploratory; thus, we aimed to generate hypotheses, but not to test them. Second, antiplatelet and anticoagulant therapy were administered in combination with antihypertensive drugs, statins, and antidiabetic drugs in some patients. This treatment approach may have reduced the severity of stroke by reflecting more advanced medical measures and more aggressive risk factor reduction. Third, because the study was retrospective, information on duration of treatment and daily medication compliance was not available. Fourth, we were unable to determine how different treatment and imaging modalities for acute ischemic stroke influenced our results. However, as the patients in this study were treated for ischemic stroke according to Japanese stroke guidelines, the impact of such differences in treatment was estimated to be limited. Fifth, in generally, this type of analysis would be better to use fractional polynomials or restricted cubic splines. The main research question of this paper was the relationship between platelet count and prognosis, we selected categorical data for importance of U-shape curve. Further prospective randomized studies are needed to address these uncertainties.In conclusion, lower and higher platelet counts at onset may affect the prognosis of cerebral infarction. Our clinical study demonstrated a U-curve. Maintaining a healthy platelet count may improve the prognosis of cerebrovascular disease. This may improve our understanding of the pathophysiology to help improve the prognosis of stroke.


We sought to determine associations of early electrocardiogram (ECG) patterns with clinical deterioration (CD) within 5 days and with RV abnormality (abnlRV) by echocardiography in pulmonary embolism (PE).In this prospective, multicenter study of newly confirmed PE patients, early echocardiography and initial ECG were examined. Initial ECG patterns included lead‐specific ST‐segment elevation (STE) or depression (STD), T‐wave inversion (TWI), supraventricular tachycardia (SVT), sinus tachycardia, and right bundle branch block as complete (cRBBB) or incomplete (iRBBB). We defined CD as respiratory failure, hypotension, dysrhythmia, cardiac arrest, escalated PE intervention, or death within 5 days. We calculated odds ratios (ORs) for CD and abnlRV with univariate and full multivariate models in the presence of other variables.p < 0.001) and 0.24 , respectively. By multivariable analyses, one ECG pattern had a significant OR for CD: SVT 2.87 . Significant ORS for abnlRV were: TWI V2–4 4.0 , iRBBB 2.63 , STE aVR 2.42 , S1‐Q3‐T3 2.42 , and sinus tachycardia 1.68 .Of 1676 patients, 1629 (97.2%) had both ECG and GDE; 415/1676 (24.7%) had CD, and 529/1629 (32.4%) had abnlRV. AbnlRV had an OR for CD of 4.25 . By univariable analysis, the absence of abnormal ECG patterns had OR for CD and abnlRV of 0.34 (0.26, 0.44; 2–4, iRBBB, STE aVR, sinus tachycardia, and S1‐Q3‐T3 were independent predictors of abnlRV. Finding one or more of these ECG patterns may increase considerations for performance of echocardiography to look for RV abnormalities and, if present, inform concerns for early clinical deterioration.SVT was an independent predictor of CD. TWI V The electrocardiogram (ECG) is one of the first tests performed in the emergency department (ED) on patients with symptoms of chest discomfort or dyspnea. Abnormal ECG findings can be found in cardiopulmonary conditions outside of acute coronary syndrome. Although no specific ECG pattern is diagnostic of acute PE, abrupt pulmonary arterial occlusion may provoke abnormalities of the right ventricle (RV) due to increased pressure, dilatation, or myocardial injury. Abnormal ECG patterns may also occur due to left ventricular (LV) ischemia from combinations of hypoxia or hypotension (decreased oxygen supply) and due to tachycardia (increased oxygen demand). Abnormal ECG patterns associated with RV abnormality (abnlRV) in PE include rightward axis shifts, repolarization abnormalities, ST‐segment myocardial injury patterns, conduction delays, and rhythm disturbances.There is a lack of agreement in the literature on ECG changes in PE and their relation to abnlRV, and one study found no relationship whatsoever.The primary objective of this study was to prospectively determine differences in proportions of predefined ECG patterns between ED patients with PE who experience early clinical deterioration and those who do not. The null hypothesis was there is no difference in proportions of ECG patterns between these two groups of patients. Our secondary objective was to determine the proportions of specific ECG patterns in PE patients with and without abnlRV by goal‐directed echocardiography (GDE). The secondary null hypothesis was there is no difference in the proportions of ECG patterns between those with and without abnlRV. For exploratory objectives, we evaluated (1) the association of mean number of ECG patterns with classifications by PE triaging strategies, and (2) the association of specific ECG patterns with abnormal heart features .clinicaltrials.gov NCT02883491 and NCT03915925).This study was a preplanned analysis from a previously reported registry database of ED patients with confirmed acute PE (Adult ED patients (18 years or older) with image confirmed acute PE diagnosed within 12 hours of ED presentation were eligible for enrollment. PE was confirmed by the presence of filling defects in the pulmonary arteries with contrast‐enhanced chest CT or detection of high probability on nuclear ventilation perfusion scan. We excluded patients who refused consent for clinical follow‐up and those who had a ventricular paced rhythm or poor quality ECG tracings that were considered uninterpretable.As previously reported, the primary goal of the PE registry was to include early imaging, ECGs, and cardiac biomarkers of ED patients with confirmed PE in the database and determine their association with clinical deterioration.2–4; TWI in leads II, III, and aVF; supraventricular tachycardia ; ST‐segment depressions (STDs) in V4–6; ST‐segment elevation (STE) in lead aVR, left bundle branch block (LBBB); and LV hypertrophy (LVH) with associated TWI.The 12‐lead ECG was completed during the ED management phase. ECGs were performed at the discretion of the treating health care team in response to the patient's symptoms, clinical course, or PE diagnosis. Site investigators interpreted the 12‐lead ECGs using predefined criteria discussed in pre‐enrollment training modules. The 12‐lead ECG was evaluated for each of the following previously reported patterns in PE: sinus tachycardia (≥100 beats per minute); incomplete right bundle branch block (iRBBB) if QRS complex ≥0.10 and <0.12 seconds and complete right bundle branch block (cRBBB) if QRS complex width ≥0.12 seconds; compilation of S wave in lead I, Q wave in lead III, and T wave inversion (TWI) in lead III, referred to as S1‐Q3‐T3 (the McGinne‐White sign); TWIs in precordial leads VThe primary outcome was the incidence of one or more discrete clinical deterioration events that were considered PE‐related complications, including death. Clinical deterioration included any episode that required hospital‐based monitoring or hospital‐based support or intervention. As previously reported, clinical deterioration endpoints included respiratory failure, cardiac arrest, new dysrhythmia, sustained hypotension requiring intravenous volume expansion or adrenergic medication, and escalated PE interventions.In this prospective study, each site had institutional guidelines on PE management. We defined respiratory failure as respiratory distress associated with emergent or unplanned mechanical ventilation . We defined cardiac arrest as new episodes of unstable cardiac rhythm or absent electrical activity with advanced cardiac life support for asystole, pulseless electrical activity, ventricular fibrillation, or unstable ventricular tachycardia. We defined new dysrhythmia as the onset of atrial fibrillation with rapid ventricular response, atrial flutter, SVT, stable ventricular tachycardia, or bradycardia that was not present at the ED presentation. We defined sustained hypotension as systolic blood pressure <90 mm Hg (or >40 mm Hg decrease from baseline) or shock index greater than 1.0 for >15 minutes with administration of >499 ml of intravenous fluids bolus for volume expansion or an infusion of norepinephrine, dopamine, or epinephrine. Escalated PE interventions included reperfusion interventions  and extracorporeal membrane oxygenation interventions.The secondary outcome was the presence of abnlRV as determined by GDE. GDE was performed during the ED visit by emergency medicine physicians trained in GDE image acquisition and subsequently interpreted by site investigators (advanced emergency ultrasound fellowship directors). As previously reported, abnlRV included the presence of one or more of the following echocardiography findings: severe RV dilatation detected as qualitative assessment of RV:LV ratio ≥1:0 and RV apex blunting in two or more views; severe RV systolic dysfunction as qualitatively estimated by diminished longitudinal contraction of the RV ; and flattening or leftward bowing of the interventricular septum.Site investigators (advanced emergency ultrasound fellowship directors) reviewed and interpreted the images according to interpretation guidelines discussed in training modules before the start of the study. GDE for abnlRV in PE was scored on a 0–3 scale, where 0 = no severe RV dilatation, 1 = severe RV dilatation only, 2 = severe RV dilatation with either septal deviation or severe RV systolic dysfunction, and 3 = severe RV dilatation with both septal deviation and severe RV systolic dysfunction. The presence of abnlRV was defined by a score of 1, 2, or 3. The frequency of ECG patterns was compared to abnlRV responses as an ordinal variable (determined by the GDE severity score). As previously reported in the main manuscript from the PE registry database, the ordinal scale showed increased odds of clinical deterioration as scores increased.We compared the mean number of ECG patterns between assigned risk groups within a PE triaging strategy. We stratified each PE triaging strategy into low‐risk and not low‐risk groups. We used three PE triaging strategies: simplified pulmonary embolism severity index (sPESI), modified European Society of Cardiology (ESC), and the pulmonary embolism short‐term clinical outcomes estimator (PE‐SCORE).We compared proportions of the specific ECG patterns to the following abnormal heart features: abnormal LV systolic function (<30%), RV abnormality as measured by CT RV:LV ratio ≥1.0, and elevated cardiac biomarkers (definitions follow). As a surrogate of myocardial stretch, brain natriuretic peptide (BNP) elevation was defined by BNP ≥90 pg/ml or N‐terminal BNP ≥500 pg/ml. We used elevated troponin levels as a surrogate of myocardial injury. Toward the last quarter of the enrollment phase, the central site had an institution‐wide change from troponin I to high sensitivity troponin. As previously reported in the first manuscript of the PE registry, at each participating institution, an elevated troponin I was defined as ≥0.07 ng/ml (99% upper reference limit ≥0.028 ng/ml). An elevated high sensitivity troponin was defined as ≥20 pg/ml for males and 12 pg/ml for females (99% upper reference limit range between 24 and 30 pg/ml). We used i‐STAT BNP test cartridge measured in pg/mL and i‐STAT cardiac troponin test cartridge measured in pg/ml  for troponin I or high sensitivity troponin assay.We conducted univariate analyses for the primary and secondary outcomes. For the secondary outcome, GDE score  was dichotomized to 0 vs. > 0 for ease of interpretation and presentation of results. In addition, we stratified ECG patterns by abnormal LV systolic function, CT RV:LV ratio elevation, troponin elevation, and BNP elevation as binary dependent variables. We stratified the number of abnormal ECG patterns by the three PE triaging strategy classifications as dependent variables. Descriptive statistics included means, standard deviations, median, and interquartile ranges (IQRs) for continuous variables, as well as frequencies and percentages for binary variables. For univariable statistical comparisons, we used two‐sample t‐tests or analysis of variance (ANOVA) for continuous variables and chi‐square tests for categorical variables.p values <0.05 were considered statistically significant.For each primary and secondary outcome, we fit a multivariable logistic regression model with all ECG variables included as primary main effects of interest, and with the following as confounders: race, gender, ethnicity, age, initial heart rate, shock index, initial respiratory rate, initial oxygen saturation, preceding episode of syncope, prior diagnosis of PE, and whether patients had one or more abnormal ECG patterns or not. In addition, in the model for primary composite outcome, we included RV:LV ratio ≥1 as determined by CT, troponin elevation, echocardiography showing abnlRV, and natriuretic peptide elevation as additional predictors. We displayed results for each full regression model in tables with odds ratios (ORs) with 95% confidence intervals (CIs). All As shown in Figure All ECGs and GDE were performed during the immediate ED course and before anticoagulation or escalated PE intervention. Anticoagulation or escalated PE intervention was initiated either during the ED course or immediately on arrival to the hospital floor in 97.2% (1630/1676) of the PE patients. We followed patients via EMR review during the hospital stay and up to 30 days, as previously published.Demographics Table  includedp < 0.0001).Of the 1676 patients, 415 (24.7%) had one or more clinical deterioration events or hospital‐based intervention within 5 days of PE diagnosis. Forty‐seven of the 1676 patients did not have GDE. Of the 1629 patients with ECG and GDE, 529 (32.4%) had abnlRV by GDE. Table p < 0.001). The most common ECG patterns were sinus tachycardia (38.9%), S1‐Q3‐T3 pattern (16.3%), incomplete or complete RBBB (15.1%), and TWI in V2–4 (14%). In comparison to patients without the specific ECG pattern, those with the following patterns were significantly more likely to experience clinical deterioration: sinus tachycardia, TWI in V2–4, STE in aVR, STE in V1, SVT, TWI in II and III and aVF, and STD in V4–6. LVH with TWI (3%) and LBBB were uncommon and not significantly different between outcome groups.Table 2–4, S1–Q3‐T3, iRBBB, and STE in aVR had ORs with the lower boundary of 95% CI above 1.0. For presence and absence of abnlRV by GDE, the mean number of abnormal ECG patterns was 2.20 ± 1.69 and 0.93 ± 1.13, respectively (p < 0.0001). The absence of abnormal ECG patterns had an OR for abnlRV by GDE of 0.24 . The results of full multivariable logistic regression analysis are shown in Table 2–4, incomplete RBBB, S1‐Q3‐T3, and sinus tachycardia. LVH with associated TWI lowered the odds of abnlRV. With multivariable analysis, the absence of any of the ECG patterns was not an independent predictor of clinical deterioration. Clinical predictors were age, shock index, initial heart rate, initial respiratory rate, and preceding episode of syncope.The results of full model multivariable logistic regression analysis are shown in Table Of the 1676 patients, 1090 (65.0%) had at least one of the abnormal ECG patterns. The mean number of abnormal ECG patterns was 1.33 ± 1.4. For patients with and without the primary outcome, the mean numbers of abnormal ECG patterns were 1.97 ± 1.65 and 1.20 ± 1.30, respectively  and 17.4% (219/1261) of those with and without clinical deterioration, respectively. Finally, we did not examine the prevalence of specific ECG patterns in patients without acute PE to compare with prevalence in patients with PE.Several investigators witnessed important but transient changes to ECG patterns in higher acuity acute PE patients that were not fully recorded or available in the EMR for interpretation or analysis. Our report focused on the initial ECG in the ED; however, there were several instances in patients with abnormal RV by GDE where conduction, TWI, or ST‐segment abnormalities were transient. We observed several instances of normalization of initial abnormal ECG patterns and improvements to RV dilatation or systolic function  after administration of thrombolytic agents. Further supporting evidence for this observation is provided by Choi et al., who showed not only an association between TWI and abnlRV, but also found that reversals of T wave abnormalities were temporally associated with resolution of an abnlRV.We prospectively evaluated PE patients in the ED to report on the associations of multiple specific ECG patterns for clinical deterioration and abnlRV by echocardiography using univariable and multivariable analyses. ECGs were performed early and contemporaneously with GDE assessments for abnlRV during the ED work‐up. Abnormal RV (as determined by GDE) had an OR of 4.25  for clinical deterioration. The absence of abnormal ECG patterns on initial early ECG had ORs of 0.34  and 0.24  for clinical deterioration and abnlRV by GDE, respectively. However, on multivariable analysis, absence of any ECG patterns did not qualify as an independent predictor of clinical deterioration or abnlRV.2–4 were significant as independent predictors for abnlRV by GDE. Patients with acute pulmonary embolism may have one or more of the specific ECG abnormalities. In patients with confirmed acute pulmonary embolism, one or more of these ECG patterns should increase considerations for performance of echocardiography to look for RV abnormalities and, if present, increase concerns for early clinical deterioration. By univariable analysis, we found the absence of any of the abnormal ECG patterns was associated with lower probability of clinical deterioration and abnlRV by GDE. However, with full multivariable analysis with other ECG and non‐ECG variables, the number of abnormal ECG patterns was not an independent predictor of clinical deterioration or abnlRV by echocardiography.We found all abnormal ECG patterns had statistically significant increased ORS above unity for clinical deterioration within 5 days of PE diagnosis, except for LBBB and LVH with TWI. Of the ECG patterns, only SVT was significant as an independent predictor for clinical deterioration. For abnlRV by echocardiography, associations were significant for most ECG patterns. Sinus tachycardia, STE in aVR, incomplete RBBB, S1‐Q3‐T3, and TWI VMost previous studies have been retrospective analyses with ECGs completed 10 to 72 hours removed from the time of PE diagnosis. Strengths of our study include its prospective design, a large database of patients representing the full spectrum of PE severity, and early timing of ECG relative to ED diagnosis of PE.1; TWI in leads II, III, and aVF; ST depression in V4–V6; STE in aVR; and SVT  were associated with clinical deterioration.VT Table . However2–4) were independent predictors of abnlRV by echocardiography even in the presence of other clinical variables. By multivariable analyses, the number of abnormal ECG patterns was not an independent predictor of clinical deterioration or abnl RV by echocardiography.In our study, early echocardiography findings of abnormal RV within the ED course were significantly associated with clinical deterioration within 5 days. In turn, abnormal ECG patterns .Reports on the associations between ECG and abnlRV by echocardiography in PE patients are mixed, with important differences in timing of ECG performance studies, patient cohorts, and the ECG patterns selected. A report by Vanni et al. studied ECG and echocardiography performed within 1 hour of PE diagnosis and found that patients with RBBB, precordial repolarization abnormalities, or the S1‐Q3‐T3 pattern complex were twice as likely to have echocardiographic signs of abnlRV than patients without any of the evaluated ECG patterns.1–4 (38%), and STE in aVR (36%).2–V4 was present in 11% of our patients. Third, the prevalence of TWI in inferior leads was as high as 48% of patients in other studies,2–4) were strong predictors of clinical deterioration.Reports on the prevalence of specific PE‐associated ECG abnormalities vary. In a meta‐analysis of 45 studies that involved 8209 PE patients, the most prevalent abnormal ECG patterns were sinus tachycardia (38%), TWI VBy univariate analysis, mean number of abnormal ECG patterns was significantly greater in those with clinical deterioration and abnl RV by echocardiography versus those without. The mean number of abnormal ECG patterns was lower in those with low‐risk classification by any of the PE triaging strategies.2–4 were independent predictors of abnlRV by echocardiography. The absence of any of the abnormal ECG patterns was associated with reduced risk for both clinical deterioration and abnlRV by univariable analysis but not by multivariable analysis. By univariate analyses, higher mean number of abnormal ECG patterns was associated with presence of clinical deterioration, RV abnormality by echocardiography, and PE triaging strategy classification as “not low‐risk” versus the groups without these characteristics.Several ECG patterns are significantly more frequent in those with clinical deterioration versus those without. SVT was the independent predictor of subsequent clinical deterioration. Sinus tachycardia, STE in aVR, incomplete RBBB, S1‐Q3‐T3, and TWI VThis was supported by the Agency for Healthcare Research and Quality [grant number R01HS025979]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.A.J.W. designed the study. J.D.R., A.T., C.C., K.L., and A.J.W. supervised the conduct of the trial and data collection. All PESCOR investigators enrolled patients. N.O. and A.J.W. provided statistical analysis and interpretation of the data. A.J.W. managed the data, including quality control. A.J.W., J.D.R., K.L. drafted the manuscript. All authors contributed substantially to article revision for important intellectual content. A.J.W. takes responsibility for the paper as a whole.All authors report no conflicts of interest.Data S1Click here for additional data file.


After 12 ‐weeks, acute oral nitrate increased both plasma and muscle nitrate , which were correlated , plasma nitrate increase was greater than in muscle (p < 0.01). Nitrite increased in the plasma  but not in the muscle (0.22 nmol.g−1) (p < 0.05 between compartments). Peak walk time (PWT) increased in both groups . Six‐minute walk (6 MW) distance increased only in the (EX+BR) group (BR + 75.4 m). We report no substantial gradient of nitrate (or nitrite) from skeletal muscle to plasma, suggesting a lack of reservoir‐like function in participants with PAD. Oral nitrate supplementation produced increases in skeletal muscle nitrate, but not skeletal muscle nitrite. The related changes in nitrate concentration between plasma and muscle suggests a potential for inter‐compartmental nitrate “communication”. Skeletal muscle did not appear to play a role in within compartment nitrate reduction. Muscle nitrate and nitrite concentrations did not appear to contribute to exercise performance in patients with PAD.Skeletal muscle may act as a reservoir for N‐oxides following inorganic nitrate supplementation. This idea is most intriguing in individuals with peripheral artery disease (PAD) who are unable to endogenously upregulate nitric oxide. This study analyzed plasma and skeletal muscle nitrate and nitrite concentrations along with exercise performance, prior to and following 12‐weeks of exercise training combined with oral inorganic nitrate supplementation (EX+BR) or placebo (EX+PL) in participants with PAD. Non‐supplemented, at baseline, there were no differences in plasma and muscle nitrate. For nitrite, muscle concentration was higher than plasma (+0.10 nmol.g Nitric oxide (NO) affects a plethora of biological functions including blood flow, cellular respiration and has been proposed as an exercise ergogenic. Skeletal muscle may act as a reservoir for N‐oxides following inorganic nitrate supplementation. This idea is appealing in individuals with peripheral artery disease (PAD) who are unable to endogenously upregulate NO and have poor functional capacities. We report no substantial gradient of nitrate (or nitrite) from skeletal muscle to plasma, suggesting a lack of reservoir‐like function in participants with PAD. Oral nitrate supplementation produced increases in skeletal muscle nitrate, but not skeletal muscle nitrite. Changes in nitrate concentration between plasma and muscle suggests a potential for inter‐compartmental nitrate “communication”. Skeletal muscle did not appear to play a role in within compartment nitrate reduction. Muscle nitrate and nitrite concentrations did not appear to contribute to exercise performance in PAD. In cohorts with vascular pathologies, endothelial dysfunction limits the ability to endogenously upregulate NO production. Thus, approaches to exogenously increase NO bioavailability have focused on these populations to improve cardiovascular health and physical function. Oral inorganic nitrate supplementation is an accepted strategy to increase the circulating pool of bioavailable NO. Despite the now well studied entero‐salivary pathway of N‐oxides conversion and transport, our understanding of the potential role of skeletal muscle tissue as a reservoir for NO‐bioactivity is limited.It is established that ingestion of oral inorganic nitrate can substantially increase plasma nitrate and nitrite concentrations, and that plasma nitrite can be subsequently reduced to NO, especially in conditions of low oxygen availability. Recent studies suggest that skeletal muscle tissue may also contribute to the production, storage, and metabolism of nitrate in mammals, however data is limited  dietary nitrate supplementation on plasma and skeletal muscle nitrate concentrations and exercise performance in participants with Peripheral Arterial Disease (PAD) following 12‐weeks of exercise training combined with oral inorganic nitrate supplementation (EX+BR) or placebo (EX+PL). We also examined the association among nitrate and nitrite concentrations in both the plasma and skeletal muscle tissue, and their relationships to exercise performance in these individuals, to clarify potential intercompartmental signaling.2ClinicalTrials.gov (NCT01785524). The full protocol and the main outcomes have been published previously  versus placebo (PL) (James White Drinks) on exercise performance. The study was approved by the appropriate Human Research Ethics Committee and are registered on .gov NCT0785524. T2.1Twenty four participants aged 40–85 years with symptomatic PAD were enrolled in randomized controlled, double‐blind parallel designed trial  6, N‐ethylmaleimide, water, Nonidet P‐40), as described in  as previously reported  beetroot (EX+BR) or an identical nitrate‐depleted placebo (EX+PL), 2.5 hours before each exercise training visit (3× per week for 12 weeks), as previously detailed  test and maximal treadmill exercise test with a 12‐lead ECG to measure peak walk time (PWT) and claudication onset time (COT) . GraphPad Prism was also utilized for the creation of all graphs and figures. Data from both studies are reported as mean ± SD unless otherwise stated, with p < 0.05 required for statistical significance.Data were analyzed using independent samples and repeated measures t‐tests, respectively. Relationships among plasma nitrite and nitrate, muscle nitrate and nitrite, and exercise performance were assessed using Pearson's product moment correlation coefficients. All statistical analysis was conducted using GraphPad Prism Version 9.3  and muscle (0.3 ± 0.14 nmol.g−1) (p = 0.02)  for plasma and muscle nitrite, respectively .The baseline (non‐supplemented) concentrations of nitrate and nitrite in both the plasma and skeletal muscle can be seen in Figure ) Figure . Interes3.2−1, p < 0.01) respectively .Following BR supplementation, there were significant increases in both plasma and skeletal muscle nitrate from baseline Figure . This al−1), (p < 0.05), respectively .Supplementation caused an increase in plasma but not muscle nitrite Figure , which rp < 0.05). This was not the case for plasma and muscle nitrite concentrations which showed no relationship (not shown). In addition, there was no relationship between plasma nitrate and plasma nitrite , or muscle nitrate and muscle nitrite  values (data not shown).There was a correlation between plasma and muscle nitrate concentrations following BR supplementation Figure  (r = 0.63.3p < 0.14) toward an increase (47.6%) in treadmill walking claudication onset time (COT)  at the 12‐week timepoint Figure . Only pa) Figure . This va) Figure .p < 0.05)  to 12 weeks for the EX+BR group and acutely supplemented Figure  was nega) Figure  but ther4This is the first study to examine skeletal muscle nitrate and nitrite concentrations following dietary inorganic nitrate supplementation in participants with diagnosed PAD. We report, at baseline/pre supplementation no differences in nitrate between the plasma and the skeletal muscle tissues. The second major finding of this study is that supplementation with dietary inorganic nitrate in individuals with PAD, leads to significant increases in skeletal muscle nitrate but not skeletal muscle nitrite. Furthermore, there was a correlation between plasma and skeletal muscle nitrate concentration following BR supplementation suggesting a potential for communication between these compartments.Both groups increased peak walking time following 12 weeks of exercise training but only those supplemented with BR increased 6 MW and tended to increase COT which may be more closely associated with the ischemic threshold and possibly increases in skeletal muscle capillary density and oxidative metabolism   there were no differences in nitrate concentrations between the plasma and skeletal muscle of participants with PAD. This is in agreement with findings in a previous report in healthy humans . However, this gradient was small  in comparison to the gradients observed in young healthy individuals (~5.5 nmol.g−1)  eliminating a potential reservoir capacity that may be available in healthy young participants. This potentially reflects: (a) an inability to transport N‐oxides from the plasma to the muscle compartments; (b) a lack of NO production by skeletal muscle eNOS (NOS3) or nNOS (NOS1)  above resting/PL levels. This skeletal muscle nitrate level is similar to those reported in participants with type 2 diabetes mellitus (T2D) and healthy young individuals following a single dose of 12.3 and 12.8 mmol potassium nitrate, respectively   in PAD, skeletal muscle nitrate increased by 77.27 nmol.gThe relatively lower values of skeletal muscle nitrate observed following supplementation, in individuals with PAD may be potentially due to a variety of factors including; lower muscle nNOS‐activity  show increases in ambulation  on the days of muscle biopsies and exercise testing and had undergone blood sampling on repeated days to test for consistency of plasma concentrations. Biopsies were performed on a separate day to the exercise testing. We did not directly measure the variations between these blood and muscle compartments over a prolonged period following supplementation. Interestingly, recent data in healthy young individuals suggests nitrate in the muscle, plasma and saliva may all follow a similar pattern with levels rising rapidly up to 2.5 h post administration before returning to baseline by 9 h  between plasma and muscle exists in participants with PAD, which is in contrast with observations in healthy individuals Figure . This suFuture studies should focus on further elucidating the explicit role the muscle reservoir could play in increasing NO bioavailability in health and disease and further optimize our dosing strategies for health and functional benefits.The experiments on human participants, including muscle biopsy procedures, were performed at Duke University Medical Center, USA. The tissue analyses were completed at the US National Institutes of Health. Barbora Piknova, Joaquin Ortiz de Zevallos, Alan N. Schechter, and Alan N. Schechter. contributed to the conception and design of the work; Barbora Piknova, Mary N. Woessner, Joaquin Ortiz de Zevallos, William E. Kraus, Mitch D. VanBruggen, Alan N. Schechter, and Alan N. Schechter contributed to the acquisition, analysis, or interpretation of data for the work; Barbora Piknova, Mary N. Woessner, Joaquin Ortiz de Zevallos, William E. Kraus, Mitch D. VanBruggen, Alan N. Schechter, and Alan N. Schechter all contributed to drafting the work or revising it critically for important intellectual content. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed.The PAD work was supported by grants to J.D. Allen from the National Heart, Lung, and Blood Institute R21HL111972 and R21HL113717.Alan N. Schechter is listed as a co‐inventor on several patents issued to the National Institutes of Health for the use of nitrite salts for the treatment of cardiovascular diseases. He receives royalties based on NIH licensing of these patents for clinical development but no other compensation. The other authors declare that they have no conflicts of interest.Institutional Ethics Committee Approval number: Duke University Institutional Review Board protocol# Pro00031918 and Pro00039608.


The main aim of this study was to comprehensively explore the relationship among pelvic incidence (PI), inflection point (IP), and apex of lumbar lordosis (LLA), and establish a predictive formula for LLA based on individual PI and IP in asymptomatic Chinese adults.P-value of <0.05 was considered statistically significant.A total of 385 asymptomatic adults with average age 38.3 ± 11.9 years (range 20–73 years) were recruited between November 2020 and October 2021. Full-spine, standing x-rays were then obtained from each participant. Next, the following sagittal parameters were measured: PI, IP, LLA, the horizontal offset between the plumb line of the lumbar apex and that of the posterosuperior corner of S1 (LASO), the upper lumbar lordosis (ULL) and lower lumbar lordosis (LLL), lumbar lordosis (LL), and thoracic kyphosis (TK). Moreover, the association among PI, IP, and the other sagittal parameters was evaluated, followed by linear regression analyses. A sr = −0.629; P < 0.01), LASO , LLL , and LL . On the other hand, IP also showed statistically significant correlations with LLA , LASO , LLL , and LL . The corresponding predictive formulae were displayed as follows: LLA = −0.03 * PI + 0.23 * IP + 14.45 (R2 = 0.669); LASO = 0.38 * PI−2.09 * IP + 53.62 (R2 = 0.460); and LLL = 0.58 * PI−0.88 * IP + 18.86 (R2 = 0.659).PI showed statistically significant correlations with LLA (The specific lumbar shape should be modulated by pelvic morphology and IP level. In addition, we established predictive formulae for ideal sagittal lumbar profile based on individual PI and IP, with the overarching goal of helping surgeons to better comprehend the regulatory mechanisms of the individual sagittal lumbar alignment, and design a precise and personalized corrective plan. In recent years, sagittal balance in spinal curvature has increasingly become a research hotspot. Sagittal imbalance in patients with lumbar degenerative diseases is significantly associated with poor health-related quality of life , 2. MoreRoussouly et al.  proposedThe main aim of this study was to comprehensively explore the relationship among PI, IP, and LLA, and establish a predictive formula for LLA based on individual PI and IP in asymptomatic Chinese adults. By virtue of the predictive model, we aimed at providing the reference values of sagittal lumbar parameters to customize a more precise surgical correction strategy for ASD patients.A total of 412 asymptomatic Chinese adults  were recruited from Hunan Province between November 2020 and October 2021. The following inclusion criteria were used: (1) no spinal pathology or deformity; (2) no history of pelvic, hip, or lower limbs disease; (3) no history of spinal surgery; and (4) no neurological or neuromuscular disorder.Among the enrolled population, 27 subjects were excluded . Consequently, a total of 385 subjects, including 203 males and 182 females, were eventually included in the derivation cohort to develop a novel predictive formula for LLA. The mean age of the subjects was 38.3 ± 11.9 years (range 20–73 years). The composition of participants in each age stratification was as follows: 96  aged <30 years, 148  in their 30s, 65  in their 40s, 51  in their 50s, and 25  aged ≥60 years .Besides, a cohort of 50 asymptomatic Chinese adults were recruited from November 2021 to January 2022 for validation of the newly established formula.It is worth noting that this study was approved by the ethics committee of our institution, and signed informed consent was obtained from all participants prior to the study.Whole-spine x-rays were obtained from each participant in an erectly standing posture at a 90° position (the arms and elbows straight out with hands gently grasping a pole) or clavicle position (the elbows fully bent with hands placed into the supraclavicular fossae) .Vertebrae from T1 to L5 were coded with numbers 1–17 for data collection and statistical analysis. When the point was located between two adjacent vertebrae, it was coded as the number of superior vertebra plus 0.5 . NotablyAll radiographic parameters were measured by two experienced spinal surgeons, and the average of two measurements was used in data analysis. Intra- and inter-observer reliability was evaluated in 50 randomly selected participants using intraclass correlation coefficient (ICC). The intra-observer ICCs for PI, IP, LLA, LASO, LLL, LL,TK were 0.952, 0.904, 0.913, 0.921, 0.930, 0.962 and 0.901, while inter-observer ICCs for the aforementioned variables were 0.915, 0.897, 0.906, 0.927, 0.942, 0.935 and 0.920, respectively. Based on the Shrout and Fleiss criteria , both insr = 0.00–0.190), weak (sr = 0.20–0.39), moderate (sr = 0.40–0.59), strong (sr = 0.60–0.79), or very strong (sr = 0.80–1.00)  software. All data are expressed as mean ± standard deviation. The Kolmogorov-Smirnov/Shapiro-Wilk test was used to determine the normality of variables. Correlations between the sagittal parameters were analyzed using the Spearman or Pearson correlation coefficient, followed by linear regression analyses. Correlation strengths were assessed and characterized as very weak (80–1.00) . IndepenIn this study, none of the variables followed a normal distribution except LL, ULL and LLL(SS), and wide fluctuation was observed in all sagittal parameters. PI averaged 46.8 ± 9.7°, ranging from 22.8° to 82.6°; IP averaged 12.7 ± 1.2 (approximately at L1), ranging from 7 (T7) to 15 (L3); the LLA was 16.1 ± 0.5 (approximately at L4) on average, ranging from 14.0 (L2) to 17.5 (L5/S1); and the mean value of the LASO was 44.9 ± 7.3 mm, ranging from 28.2 to 77.1 mm. P < 0.001), while this trend is less pronounced among males (P = 0.047)  .sr = 0.854) is stronger than that between ULL and LL (sr = 0.594). TK was significantly correlated with ULL , but not with PI and IP.Results showed that PI and IP had statistically significant correlations with LLA, LASO, LLL or SS, and LL . In addiAmong 50 subjects in the validation cohort, 21 were males and 29 were females with an average age of 37.9 ± 12.8 years. Subjects in the derivation cohort and the validation cohort were matched in terms of gender, age, PI and IP . Then, tRestoring the ideal lumbar lordosis not only refers to a proper magnitude, but also a reasonable shape. Given that the position of LLA affects the lumbar spine morphology, correct restoration of its location based on Roussouly classification helps to decrease the occurrence of mechanical complications after ASD surgery. For instance, Sebaaly et al.  found a sr = 0.52, P < 0.01) in 160 asymptomatic Caucasian volunteers, whereas Pan et al.  in 183 asymptomatic Chinese adults. To date, there has been an absence of relevant studies that have comprehensively explored LLA by incorporating the impacts of PI and IP. Therefore, studies should be conducted with the aim of helping surgeons better comprehend the regulatory mechanisms of the individual sagittal lumbar profile and reconstruct an ideal LLA for ASD patients.Previous studies have demonstrated that there is a correlation between PI, an individual anatomical parameter that reflects the pelvic morphology, and the sagittal lumbar parameters , 9. Notan et al.  reportedsr = −0.629, P < 0.01), and between PI and LASO , indicating that the location of the LLA is determined by the pelvis morphology. This suggests that as the PI increases, the LLA moves more cranially and the lumbar curvature becomes prominent. On the other hand, LLA  and LASO  were statistically correlated with IP, suggesting that the location of LLA is also restricted by the IP level. Therefore, as the IP level descends, the LLA moves more caudally and the lordosis becomes flat.In the current study, we described the position of the lumbar apex in two dimensions; the longitudinal vertebra level (LLA) and its horizontal offset to the plumb line of the posterosuperior corner of S1 (LASO) . A signiR2 = 0.669) and LASO = 0.38 * PI − 2.09 * IP + 53.62 (R2 = 0.460). The above models intuitively suggest that the lumbar apex is determined by individual PI and IP. In other words, decreased PI value and IP level should be accompanied by a short and flat lordosis with the LLA located caudally, corresponding to Roussouly type 1. Conversely, a large PI and high IP level suggests that the LLA should be located more superiorly and away from the line of gravity, thereby resulting in a long and ample lumbar curve corresponding to type 4 (The following novel formulae were developed through multiple linear regressions: LLA = −0.03 * PI + 0.23 * IP + 14.45 (o type 4 . In addio type 4 . Notablyo type 4 . TherefoR2) was 0.669, indicating that 66.9% of the total variation observed in LLA can be explained by PI and IP. Thus, the developed formulae outperformed formulae containing a single variable, such as LLA = −0.042 * PI + 6.134 (R2 = 0.306) (R2 = 0.299) , suggesting that the choice of IP level will largely determine the location of LLA since PI is relatively constant for a specific individual. Namely, for every two levels that the IP descends, the LLA should be positioned more caudally by approximate 0.5 segments, and vice versa. Hey et al.  . Moreovey et al.  exploredsr = 0.854) is stronger than that between ULL and LL (sr = 0.594). Notably, it was found that LLL was also modulated by PI and IP , which suggests that a low PI and low IP level is associated with a flat LLL. Conversely, individuals with a larger PI and higher IP level will recruit more vertebrae to form a larger LLL and higher LLA to maintain spinopelvic balance in the sagittal plane. This study also found that the ULL, with an average of 20.9°, is not correlated with PI or IP. Our results are consistent with Roussouly et al. , indicating that ULL is the pedestal of thoracic kyphosis. Hence, the magnitude of TK across individuals is largely determined by the difference in ULL. This needs to be noted during ASD surgery because overcorrected ULL requires a large TK to match with it, which may lead to the occurrence of PJK . It has been reported that LLL, geometrically equal to SS, is the most important part that determines the magnitude of global lordosis , 10. Hery et al. , who repe of PJK .Herein, a quantitative method was used to describe the phenomenon that the lumbar shape is regulated by PI and IP based on the Roussouly classification. Results of the comparison of actual LLA, LASO and LLL(SS) with their predicted values revealed no significant differences, indicating that the developed formulae have good predictive effects. It is worth mentioning that the novel formulae can help surgeons better understand the regulatory mechanisms of the individual sagittal lumbar profile in asymptomatic population, and provide a good reference for restoring an optimal sagittal alignment. Namely, ideal LLA, LASO and LLL (SS) can be predicted using these models after designing the IP based on patient's PI ; then, bTo the best of our knowledge, this is the first study to calculate with more precision the lumbar apex using both PI and IP simultaneously in a relatively large sample of asymptomatic population. However, the study had some limitations. First, given that this is a single center research and the age of the selected population does not follow a normal distribution, which may result in selective bias, the obtained results may not necessarily be applicable to other regions and clinical contexts. Therefore, multicenter studies should be conducted to confirm our conclusions. Second, subjects included in this study were predominantly young and middle-aged population. According to a previous study , the sagThis study has shown that the specific lumbar shape should be modulated by pelvic morphology and IP level. We established predictive formulae for ideal sagittal lumbar profile based on individual PI and IP, with the overarching goal of helping surgeons to better comprehend the regulatory mechanisms of the individual sagittal lumbar alignment, and design a precise and personalized corrective plan.


To evaluate which of previously reported monogenic genes are associated with increased bladder cancer risk, we reviewed published papers on associations of genes and bladder cancer risk and performed a confirmation study of these genes in a large population‐based cohort.A systematic review of published papers prior to June 2022 was performed first to identify all genes where germline mutations were associated with bladder cancer risk. The associations of these candidate genes with bladder cancer risk were then tested among 1695 bladder cancer cases and 186 271 controls in the UK Biobank (UKB). The robust SKAT‐O, a gene‐based analysis that properly controls for type I error rates due to unbalanced case–control ratio, was used for association tests adjusting for age at recruitment, gender, smoking status, and genetic background.p < 0.05), including MUTYH, MSH2, MSH6, MLH1, ATM, BRCA2, ERCC5, TGFB1 and CHEK2. When pathogenic/likely pathogenic mutations were aggregated within each gene, the association was confirmed for three genes in the UKB at p < 0.0056 (Bonferroni correction for nine tests), including CHEK2, ATM and BRCA2, all also known to be associated with hereditary breast cancer. Suggestive evidence of association was found for two other genes, including MLH1 (p = 0.006) and MSH2 (p = 0.007), both known to be associated with Lynch syndrome. Among these five genes, the bladder cancer risks range from 1.60 (ATM) to 4.88 (MLH1), and mutation carrier rates in cases range from 0.06% (MSH2) to 2.01% (CHEK2).The systematic review identified nine genes that were significantly associated with bladder cancer risk in at least one study (This study provides statistical evidence for association of previously reported genes and bladder cancer risk and has clinical utility for risk assessment and genetic counselling. Statistical power to detect the association is limited for bladder cancer due to its relatively low prevalence. The challenge is further compounded by the low frequency of pathogenic mutations in monogenic genes. Their frequency, even aggregated for various mutations, is typically <1% in cases and controls. Although the availability of large population‐based biobanks presents an opportunity to overcome the challenges of small sample size, common statistical methods such as the Fisher's exact test are inadequate. These methods have inflated type I error rate for unbalanced case–control ratios  and cannot adjust covariates such as age, gender, smoking status and genetic background.The primary objective of this study is to assess the validity of reported monogenic genes for their association with urothelial carcinoma of the bladder (bladder cancer). We first systematically reviewed the published studies on association of monogenic genes and bladder cancer risk and identified candidate genes where at least one positive association was reported. We then performed a confirmation study for these genes in a large biobank using the robust SKAT‐O.22.1p < 0.05) in at least one paper were subjected for a confirmation study.We searched PubMed for papers published between 1993 and June 2022 using the keywords of ‘urothelial carcinoma’, ‘bladder cancer’, ‘germline’ and ‘mutations’. The title and abstract of identified papers were reviewed. Three reviewers , working collaboratively, were involved in the review. The inclusion criteria of studies are (1) case–control design, (2) sample size of >100 bladder cancer cases and (3) statistical test for association between germline mutations and bladder cancer. The exclusion criteria are (1) editorial, letter, commentary, review, meta‐analysis, (2) case report, (3) somatic mutations, (4) GWAS/polygenic risk score, (5) not relevant to the current study  or (6) studies limited to upper tract urothelial carcinoma (UTUC). Those three reviewers also worked collaboratively on the data collection, reviewing the entire text, tables, figures and supplementary data for study design, sample size and association results for the remaining papers. All included articles were reviewed for certainty assessment by additional team members. Genes that were significantly associated with bladder cancer risk (2.2Study subjects were from the UK Biobank (UKB) with available whole‐exome sequencing (WES) data. The UKB, initiated in 2014, is a large prospective cohort of individuals from across the United Kingdom aged 40 to 69 at recruitment (Application Number: 50295).Germline variants of studied genes were from the WES data provided by the UKB (PLINK format). All nonsynonymous changes (missense and nonsense) in the targeted genes were extracted. Pathogenic/likely pathogenic (P/LP) mutations were annotated following the general sequence variant interpretation (SVI) guidelines of the American College of Medical Genetics (ACMG).p < 0.0056 was considered significant based on the Bonferroni correction for nine independent tests (0.05/9).The primary association test was a gene‐based analysis for pathogenic mutations using the robust SKAT‐O.Crude odds ratio (OR) and 95% confidence interval (CI) were also estimated for each gene using the Fisher's exact test.33.1p < 0.05) in at least one study, including MUTYH, MSH2, MSH6, MLH1, ATM, BRCA2, CHEK2, ERCC5 and TGFB1. The sample size, odds ratio (OR), p‐value and reference are listed in Table MSH2, MSH6, MLH1 and PMS2), hereditary breast and ovarian cancer (HBOC) syndrome (BRCA2) and hereditary breast cancer (ATM and CHEK2).A PubMed search in June 2022 identified 271 papers on the associations of germline mutations with bladder cancer risk , including CHEK2 (p = 8.75E‐05), ATM (p = 4.95E‐04) and BRCA2 (P = 0.005). In addition, suggestive evidence of the association was found for two other genes at a nominal p < 0.05, including MLH1 (p = 0.006) and MSH2 (p = 0.007). No significant statistical evidence was found for the remaining four genes, including MSH6 (p = 0.08), MUTYH (p = 0.80), ERCC5 (p = 0.90) and TGFB1 (p = 0.84).A gene‐based association test of these nine genes with bladder cancer risk was tested in the UKB using the robust SKAT‐O Table . Based oCHEK2 (2.01% in cases and 1.02% in controls). In contrast, MSH2 had the lowest mutation carrier rate (0.06% in cases and 0.02% in controls). Pathogenic mutations in these five genes were associated with moderate bladder cancer risk, with the crude OR ranging from 1.60 (95% CI: 0.88–2.91) for ATM to 4.88 (95% CI: 1.76–13.04) for MLH1.Mutation carrier rates in these five genes were rare in both cases and controls. The gene with the highest mutation carrier rate was p = 6.33E‐05), 30 pathogenic mutations in these nine genes had p < 0.05 . The mutation carrier rate was 0.24% in cases and 0.02% in controls; crude OR was 14.4 . Three recurrent mutations in cases were found for CHEK2: W93X (p = 6.11E‐04), c.1462‐2A > G (p = 1.09E‐03), and E64K (p = 0.01).At the individual P/LP level, although none reached the study‐wise significant after adjusting for 790 multiple tests . The age at diagnosis for the single MSH2 mutation carrier was 47.5. Pathogenic mutation rate of ATM was significantly higher among aggressive bladder cancer patients (7/208 = 3.37%) than nonaggressive bladder cancer patients (4/1487 = 0.27%), OR = 12.91 (95% CI: 3.71–44.50), p = 8.15E‐05. A trend of higher carrier rate of ATM V2424G in aggressive bladder cancer patients (2/208 = 0.96%) than nonaggressive bladder cancer patients (2/1487 = 0.13%) was observed, p = 0.08.Association of these genes with age at diagnosis , including CHEK2, ATM and BRCA2. All the three genes are known to be involved in hereditary breast cancer risk.p < 0.05, including MLH1 and MSH2. Both of these genes are known to cause Lynch syndrome.By systematically reviewing published papers on the association of germline genes with bladder cancer risk and validating reported candidate genes in a large population‐based cohort, we confirmed the association for three genes at the study‐wise significance level  but, more importantly, reduces selection bias in many previous studies where patients were recruited from hospitals, cancer family registry and genetic testing companies.Second, the same sequencing method and mutation annotation pipeline were used for both cases and controls in our study. This approach considerably reduces false positives due to potential heterogeneity (methods and genetic background) between cases and controls. For example, several published studies compared mutation carrier rate of bladder cancer cases recruited for their studies with that of external public database such as Exome Aggregation Consortium (ExAC).Third, we adopted a novel gene‐based method (robust SKAT‐O) that can overcome several major challenges of commonly used gene‐based methods, including inflated type I error rate for unbalanced case–control ratios and inability to adjust covariates such as age, gender, smoking status and genetic background.CHEK2, ATM and BRCA2) is novel. It added critical data to the debate on association between bladder cancer risk and genes in DNA damage repair and signalling pathways beyond mismatch repair pathway.Lastly, our results not only are consistent with previous findings but also provide additional insights in bladder cancer susceptibility genes. Published studies to date have suggested urothelial carcinoma is a major extracolonic malignancy component of Lynch syndrome caused by pathogenic variants in mismatch repair genes.MSH6 was significantly associated with bladder cancer risk. The meta‐analysis' OR was 5.63 (2.75–11.49), p = 1.33E‐05. However, the association was noticeably different among three large sub cohorts, with OR of 8.30 (2.33–29.54), 18.98 (4.32–83.30) and 2.28 (0.79–6.61) in the Electronic Medical Records and Genomics Sequencing (eMERGEseq), Hereditary Cancer Registry (HCR) and UKB, respectively. Additional studies with larger number of bladder cancer cases are needed to obtain more consistent results.Additional factors may also contribute to the different results between the current and previous studies. In particular, relatively small sample size of bladder cancer patients in studies, including our study, limits statistical power to detect true associations . The rarity of pathogenic mutations in genes further limits the statistical power and can lead to inconsistent findings among studies. For example, in the largest study to date, evaluating 23 hereditary cancer genes among 214 020 subjects from three cohorts (including UKB),The importance of establishing gene–disease associations is emphasized by ClinGen, a National Institutes of Health (NIH)‐funded resource dedicated to building a central resource that defines the clinical relevance of genes and variants for use in precision medicine and research.ATM V2424G and bladder cancer risk is worth noting. This recurrent missense change is relatively common in cases (0.24%) and confers a strong risk for bladder cancer  from a case–control analysis), especially aggressive bladder cancer  from a case–case analysis. It is predicted as a deleterious mutation by multiple proteins in silico tools and is nonfunctional in multiple different protein assays.ATM's role in bladder cancer risk. Furthermore, implication of a specific variant makes it simple for risk assessment and genetic counselling in clinic.The finding for the association between Our study, however, has several limitations. First, only 40% of UKB subjects with available WES data were analysed in the current study. The WES data from the remaining 60% UKB were recently released, and we are in the process of analysing the data. Second, because the variant annotation was based on WES data, potential mutations in promoters and untranslated regions (UTRs) are likely missed. Furthermore, we were unable to evaluate large‐sized germline deletions and insertions in this study. Third, due to the limited bladder cancer‐related clinical and histological information available in the UKB, our study focused primarily on bladder risk based on case–control data. Association of germline mutations with bladder cancer progression or drug treatment response should be evaluated using large bladder cancer cohorts with detailed clinicopathological, drug treatment and follow‐up data. Fourth, with the primary goal of confirming previously reported genes in the literature, we did not perform a comprehensive analysis of all genes in the genome. Although this effort is feasible with the available WES data, larger sample sizes are still needed to obtain statistical power (require more stringent study‐wise significance due to multiple tests). Fifth, functional analysis for identified P/LP mutations was performed. Although this is beyond the scope of this study, it is a major limitation. Finally, it is noted that approximately 95% of study subjects in this study are White; mutations that are unique to minority populations may be overlooked. Large studies conducted within minority populations including Black are needed.In conclusion, we provided statistical evidence for association of previously reported genes and bladder cancer risk in a large population‐based cohort. The implicated genes may help urologists for risk assessment and genetic counselling of bladder cancer.None.Concept and design: Xu and Helfand. Data analysis: Mian, Wei and Shi. Manuscript draft: Mian and Xu. Critical revision of the manuscript for important intellectual content: Mian, Wei, Shi, Rifkin, Zheng, Glasser, Kearns, Helfand and Xu. Supervision: Xu.Xu take responsibility for the integrity of the data and the accuracy of the data analysis. Table S1. P/LP mutations in the nine reported bladder cancer‐risk‐associated genesTable S2. Variant‐based analysis for reported genes in the UKB (Whites)Table S3. Association of mutation carrier status and age at bladder cancer diagnosis in the UKB (Whites)Table S4. Pathogenic mutations of 9 candidate genes in the UKB bladder cancer cases (Whites)Click here for additional data file.


The pandemic COVID‐19 has caused a high mortality rate and poses a significant threat to the population of the entire world. Due to the novelty of this disease, the pathogenic mechanism of the disease and the host cell's response are not yet fully known, so lack of evidence prevents a definitive conclusion about treatment strategies. The current study employed a small RNA deep‐sequencing approach for screening differentially expressed microRNA (miRNA) in blood and bronchoalveolar fluid (BALF) samples of acute respiratory distress syndrome (ARDS) patients.n = 5). Control samples were those with suspected lung cancer candidates for lung biopsy (n = 3). Illumina high‐throughput (HiSeq 2000) sequencing was performed to identify known and novel miRNAs differentially expressed in the blood and BALFs of ARDS patients compared with controls.In this study, BALF and blood samples were taken from patients with ARDS  and Kyoto Encyclopedia of Genes and Genomes (KEGG), in ARDS patients' blood and BALF samples, showed that the target genes were more involved in activating inflammatory and apoptosis process.Based on our results, the transcriptome profile of ARDS patients would be a valuable source for understanding molecular mechanisms of host response and developing clinical guidance on anti‐inflammatory medication. In this research, BALF and blood samples of patients with ARDS were investigated. Data from miRNA experiments were obtained from 3 normal and 5 infected samples. Total numbers of differentially expressed miRNAs in BALF was 8223 and in blood was 2223, which in BALF 825 were upregulated and 432 were downregulated. In blood samples, 543 miRNAs were upregulated and 223 miRNA downregulated. miRTarBase results for 40 high‐score differentially expressed miRNAs showed 232 target mRNA, which have 5872 interactions. The knowledge base was further validated with wet‐lab experiment, where we analyzed the correlation between some miRNAs and their target genes. More detailed studies have shown that severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is the cause of respiratory distress.Little is known about the pathogenesis of the disease. However, it is clear that when the innate immune system recognizes viral RNAs, three major classes of cytoplasmic recognition receptors are activated: Toll‐like receptors (TLRs), RIG‐I‐like receptors (RLRs), and NOD‐like receptors (NLRs), which trigger expression of interferon (IFN) and activation of antiviral effectors such as natural killer cells, T CD8+ cells, and macrophages;22.1n = 5). Patients with obesity, diabetes, heart, kidney, and lung diseases were not included in the study due to many factors involved in the expression of miRNAs. Some characterization of the samples is mentioned in Table Blood and bronchoalveolar fluid samples and whole blood were obtained from five severe COVID‐19 patients at Vellayat Hospital Qazvin, IRAN. The patients were informed about the sample collection process and had signed informed consent forms. The recruited patients were between 55 and 65 years old, with severe shortness of breath or breathlessness, rapid and labored breathing, extreme tiredness, and muscle fatigue, who were admitted to the ICU section. These patients had a positive result in the COVID‐19 nasopharyngeal swab RT‐PCR test  tubes and then stored at −80°C until further analysis. Total RNA, including miRNAs, was extracted from BALFs and whole blood using TRIZOL reagent  as described in the manufacturer's protocol. To evaluate the concentration and purity of RNA, we used NanoDrop w ND‐1000. The RNA integrity number (RIN) was assessed using the Agilent 2100 Bioanalyzer with RNA 6000 series II Nano Lab Chip analysis kit . The 2100 Bioanalyzer generates numerical RIN values. RIN is an incremental scale spanning 0–10, with 10 indicating maximum RNA integrity. All RNA samples extracted from BALFs and blood samples were of high integrity as judged by the obtained RIN values of higher than eight. Then, all samples with a ratio (28S/18S) of above 1.8, OD 260/280 ratio greater than 1.9, and RIN >8 were sent to BGI company in China for sequencing. In this regard, small RNA libraries were generated from the purified RNA using the Illumina Small RNA v1.5 sample preparation kit , according to the manufacturer's instructions.2.3In the first step, the Illumina Small RNA v1.5 sample preparation kit  was used for the purification of small RNA, according to the manufacturer's instructions. Each RNA sample was size‐fractionated, and 18–30 nt RNA was isolated and purified. In the next step, 5′ and 3′ adaptor ligation was done. RNA was reverse‐transcribed and amplified using 14 PCR cycles of 98°C for 10 s and 72°C for 15 s to generate small RNA libraries, and then the library was performed to the numbers of the studied samples. Average molecule length and purity were evaluated by Agilent Technologies 2100 Bioanalyzer, and quantitative PCR was performed for detection of the concentration, using EvaGreen® dye (Jena Bioscience). The validated libraries were subsequently sequenced on an Illumina HiSeq 2000 .2.4https://cutadapt.readthedocs.io/en/stable/) was used for adaptor trimming, low‐quality reads, rRNA, and tRNA removal. The rRNA proportion less than 5% showed that the studied samples had good quality. Bowtie software  was used to map miRNA data to the miRBase . FastQC (version 0.11.9) and multiQC (version v1.8) were used for quality control of the raw read data. Our raw data quality was good, and the quality score for each sample was between 33 and 37. For miRNA‐seq data, two peaks at 22 nt and 33 nt were observed in the read length distribution.In the first step, Cutadapt software  and controls (n = 15) were used; moreover, the expression of miRNAs and their target genes, which had the highest score and significance, examined by the Real‐Time PCR method. We used TRIZOL reagent to isolate the total RNA from the samples based on kit protocols (Invitrogen Life Technology Co). Three steps of phenol/chloroform purification were done to separate the proteins. To evaluate the concentration and purity of RNA, we used NanoDrop w ND‐1000.To confirm miRNA sequencing results, the RNA samples of patients with ARDS  and Revert Aid First Strand cDNA Synthesis Kit  were used to reverse the transcription of RNA. The final volume was 10 μl containing 1 ng/μl of purified total RNA, 5× reaction buffer, Enzyme mix, and nuclease‐free water. The mixture was incubated at 42°C for 60 min and then at 95°C for 5 min (for enzyme inactivation). It was then quickly cooled to 4°C. The primers of 8 miRNAs  and their internal control (miR‐miR30a‐5p) were ordered and synthesized by Qiagen Company (Qiagen) in the form of LNA. Target genes  primers are mentioned in Table 2.6http://miRwalk.uni‐hd.de) and miRBase (http://www.miRbase.org) database. Gene Ontology (GO) was performed using multiple tools and algorithms for identifying miRNA‐target genes and their corresponding pathways. GO pathway enrichment analysis of biological processes was applied for the predicted miRNA‐target genes, and the human genome was a reference set. We used the z test to determine GO terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Furthermore, the Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics tools (http://david.abcc.ncifcrf.gov) were employed to thoroughly examine the functional characterization results of the enriched cognate mRNA target genes via comparative analysis of biological functions, enrichment analysis of signaling pathways, and assignment of the disease categories. Significance P‐values for DEGs were calculated by Student's t test. For identifying miRNA–mRNA interaction, we used multiMiR R package , and then miRNA‐mRNA networks were constructed with Cytoscape . Each miRNA‐mRNA pair was filtered using the most stringent criteria.For bioinformatics analysis of the sequenced miRNAs, we used miRWalk  and controls. Nearly, 20,559,321 clean high‐quality reads were obtained among the 21,221,234 effective reads (97%). The frequencies of the obtained products length were as follows: 21 nt (6.2%), 22 nt (62.1%), 23 nt (31.7%), and 24 nt (1.1%). The rate of degradation was very insignificant because the contribution of rRNA in the reads was <0.29%.3.1p < 0.05, p < 0.01, and p < 0.001. In blood, a total of 2234 differentially expressed miRNAs were found, in which 543 miRNAs are significantly upregulated while 220 miRNAs are downregulated. In BALF samples, 8223 differentially expressed miRNAs were identified, in which 825 miRNAs are significantly upregulated while 432 miRNAs are downregulated. The differentially expressed miRNAs are represented in a scaled heat map; it compares patients and healthy controls for both BALF and blood samples .The 10 most differentially expressed miRNAs in infected blood and BALF samples (up and downregulated) are compared with uninfected samples, as shown in Tables ‐∆ct:0.000011, p: 0.0004)   (Table MiR‐15a‐5p was the most downregulated miRNA in infected BALFs in comparison with noninfected BALF (23.2https://david.ncifcrf.gov/).Since the molecular mechanisms involved in the pathogenesis of the COVID‐19 virus are not yet known, we used the web‐based GO analysis bioinformatics resources tool of DAVID v6.8 (http://miRTarBase.cuhk.edu.cn/) to find the target genes of miRNAs. Hypergeometric Distribution Mathematical Model was used to detect the p‐value of GO. The targets were enriched in the GO when the p‐value was < 0.05. In this regard, the top major miRNAs were enriched in the GO . Virus infection causes changes in gene expression in specific cellular biological processes. In this regard, we performed miRNAs targeted genes functional enrichment analysis in BALF and blood samples to monitor the cellular changes of patients and control samples. In infected BALF samples, the targeted upregulated genes resulting from the downregulation of miRNAs are associated with the regulation of inflammatory response and apoptosis process  pathway analysis. In this regard, the same enrichment rule was applied as in GO, when the P‐value and the Q‐value were < 0.05. The KEGG pathway studied was considered enriched and included in the results for both infected BALF and blood comparisons. Cytokine–cytokine receptor interaction and T‐cell receptor signaling pathway were found to be enriched, among others, in infected blood and BALF samples compared with control  and then constructed an integrated miRNA‐mRNA regulatory network with MIENTURNET package , inhibit cell proliferation, and induce apoptosis.3.5p < 0.001) and miR‐155‐5p (p < 0.01)  and smad‐7 (p < 0.0001)  and miR‐548c‐5p (p < 0.001) BALF  and IL‐12 (p < 0.01) showed significant downregulation and upregulation in BALF samples, respectively.  and miR‐18a‐3p (p < 0001) in blood samples  and Hox‐1 (p < 0.01)) showed a significant downregulation  miRRNA and their target genes in BALF and blood samples. About the BALF samples, our results showed significant upregulation of miR‐4284  Figure  and down) Figure . Also, mF Figure  and thei. Figure . Our ress Figure . The tarn Figure .p< 0.001) and miR‐14a‐3p (p < 0.0001)  and TGF‐β (p < 0.0001) in infected blood samples  in blood and bronchoalveolar fluid (BALF) samples of acute respiratory distress syndrome (ARDS) patients is involved in activating the inflammatory and apoptosis process; workflow of this research is shown in Figure This research was supported by the Cellular and molecular research center of the Qazvin University of Medical Science.The authors declare that there is no conflict of interest.