Primary immune thrombocytopenia (ITP) can increase the risk of neonatal thrombocytopenia (NT). This study aimed to investigate the key factors for predicting the risk of NT.Data were retrospectively collected from all pregnant women with ITP from 2015 to 2021. Newborns were divided into two groups according to the presence or absence of NT. The parameters between the two groups were then compared. Next, the correlation between maternal platelet-to-lymphocyte ratio (PLR) and neonatal platelet count was analyzed by logistic regression and generalized additive model. Additionally, the relationships among the platelet counts of siblings were also determined.P < 0.001), as the nadir gestational platelet . Correlation analysis of platelet count on postnatal day 1 and the nadir platelet count in 36 sibling neonates showed a positive correlation . PLR was divided into 3 groups via tertiles, and the incidence of NT was dramatically higher in the group with lower PLR during the second and third trimesters than in the other two groups . Moreover, the risk of NT was markedly higher in the first trimester  and the second trimester  compared to the third trimester.A total of 147 maternal cases were included. NT was observed in 46 (31.72%) neonates. A history of previous children with NT appeared to have predictive value for NT (OR 16.484, 95% CI 2.212–122.858, Our findings suggest that a history of previous children with NT is a significant factor for predicting NT in subsequent pregnancies. PLR in the first, second and third trimesters can also be used as a reference to predict NT risk. Primary immune thrombocytopenia (ITP) is an acquired autoimmune thrombocytopenic disorder characterized by persistent or transient reduction in platelet counts. ITP is the most frequent cause of neonatal thrombocytopenia (NT) during early pregnancy. The incidence of ITP at pregnancy is 1/1000–10000, which accounts for 1%-4% of thrombocytopenias during pregnancy . Hence, 9/L), with the exclusion of other causes of thrombocytopenia  syndrome, autoimmune diseases, sepsis, viral infections, and gestational thrombocytopenia) [Ethics Committee of Peking University People's Hospital approved the study protocol (2020PHB396-01), and the need for informed consent was waived because it was a retrospective analysis of patient notes. In this retrospective study, pregnant women with ITP and their babies were recruited at a single institution from July 2015 to June 2021. Patients were included if they met the criteria of ITP during their pregnancy. Maternal ITP was defined as isolated thrombocytopenia (< 150×10topenia) , 13. Ant9/L), moderate (50–100×109/L), and severe (< 50×109/L). To determine ICH (major bleeding), cranial USG was conducted on all neonates with severe thrombocytopenia. The minor sites of hemorrhage , baseline characteristics and clinical outcomes of pregnant women and newborns were analyzed.A full blood count was performed on all newborns within the first 6 h of life. Thrombocytopenia was classified as mild . Meanwhile, the categorical variables were shown as percentage. Univariate analysis of the two groups was conducted by Mann–Whitney and chi-square tests for continuous and categorical data, respectively. Spearman’s correlation coefficients were used to analyze the correlations. The factors associated with NT were identified by multivariate logistic regression analysis. The selection of covariates included in this study was based on our previous work and other relevant published studies.P<0.05 (two-sided) was deemed statistically significant.Since platelet-to-lymphocyte ratio (PLR) is a continuous variable, its nonlinear correlation was analyzed using a generalized additive model (GAM). The inflection point was then measured for the nonlinear relationship. The correlation between the right and left sides of the inflection point was determined using a two-dimensional piecewise model. R package (http: www.R-project.org) was used for data analysis. n=14, 73.7%), second , and third  trimesters.During the study interval, 153 pregnancies with ITP were identified, excluding 4 pregnancies of twins and 2 stillbirths. A total of 147 pregnant women with ITP were ultimately included in this study  were treated for ITP during pregnancy, and the remaining 20 (13.6%) did not receive any treatment. The treatment options for 8 (5.5%), 44 (29.9%), 75 (51%) and 85 (57.8%) pregnant women were intravenous immunoglobulin (IVIG), corticosteroid, IVIG + corticosteroid and platelet transfusion at 24 h before or during delivery, respectively. The median maternal nadir platelet count was 22 × 10The median gestational week of newborns was 37.7 weeks. Of them, 27 (18.37%) newborns were premature. The median birth weight was 3139.4 g. Of them, 16 (10.9%) were low birth weight, and 2 (1.4%) were macrosomia. Two newborns were transferred to other hospital after birth with unknown platelet status. A total of 145 newborns were included in the analysis of platelet count.n=9), corticosteroids only (n=1), IVIG + corticosteroids (n = 6), IVIG + platelet transfusion (n=2), and IVIG + corticosteroids + platelet transfusion (n = 5). In contrast, the remaining 19 (46.4%) neonates did not receive any treatment, and their platelet counts increased spontaneously. The duration for the resolution of thrombocytopenia was 7–120 days.NT was encountered in 46 (31.7%) pregnancies. Among thrombocytopenic newborns, 27 (58.7%), 14 (30.4%) and 5 (10.9%) had severe, moderate and mild thrombocytopenia, respectively. Thirteen (28.3%) neonates suffered from minor bleeding, while none of them had ICH. Since the underlying conditions and treatment status of 5 cases were unknown, only 41 newborns with thrombocytopenia were included. Of them, only 22 (53.6%) neonates received treatment: IVIG only  on postnatal day 1. There were no obvious differences in maternal age, ITP duration, mode of delivery, predelivery platelet count, and previously underwent splenectomy between the two groups. However, maternal nadir platelet counts ‘before pregnancy’ and ‘during pregnancy’ were lower in NT group than in non-NT group. Additionally, a history of previous children with NT was more commonly detected in NT group than in non-NT group (P = 0.005).Table A multivariate logistic regression model was subsequently constructed. Maternal nadir platelet count ‘during pregnancy’ and a history of previous children with NT were identified as significant factors for predicting the risk of NT  .Figure 9/L vs 54 × 109/L, P = 0.007), first-trimester PLR , second-trimester platelet count , second-trimester PLR , third-trimester platelet count , and third-trimester PLR .Compared to newborns without thrombocytopenia, the mother of NT group had much lower levels of first-trimester platelet count  was a risk factor for NT development. The PLR of Q1-Q3 was included as categorical variables in the logistic regression analysis. The same trend also was observed in the adjusted model. This trend was more notable in the second trimester  (Table Univariate logistic regression analysis demonstrated that PLR in Q1 (first trimester: OR 3.491, 95%CI 1.177–10.354, Furthermore, GAM and two-dimensional piecewise model were employed to clarify this nonlinear relationship and determine the potential threshold or saturation effect. The results showed a nonlinear relationship between maternal PLR and NT risk Fig. . NotablyDue to the fact that antiplatelet antibodies can pass through the placenta and lead to thrombocytopenia, the neonates born to mothers with ITP are at risk of NT and bleeding. Thus, managing maternal ITP is of utmost importance to reduce the risk of NT , 15. PlaAlthough remarkable progress has been made in the treatment of maternal ITP, the incidence of NT remains high. In a previous study 15.4–67.0% of babies born to mothers with ITP had thrombocytopenia, and the ratio of severe thrombocytopenia was reported as 8.0–29.9% among these neonates . A totalITP is classified as an immune-mediated disorder. It has been recently reported that multifactorial autoimmune mechanisms, such as faulty CD4 + Tregs, are involved in the pathogenesis of ITP . LymphocThis retrospective cohort study evaluated the relationship between PLR at pregnancy and NT occurrence. The results showed that PLR < 78.51 at the first trimester and PLR < 20.41 at the second trimester were associated with NT risk. However, when the PLR values were > 78.51 and > 20.41 at the first and second trimesters, respectively, the risk of NT was basically the same. Therefore, PLR can serve as a marker for predicting NT occurence. The nonlinear regression analysis of PLR also supported its better clinical utility. The results demonstrated that an increase in PLR was significantly associated with the decreased risk of NT, implying that the clinicians can directly assess the risk of NT based on PLR.Our study has some strengths. Firstly, we introduced PLR into the risk assessment model for NT occurrence, and included different stages of pregnancy. Secondly, PLR was used as a continuous variable as well as a categorical variable, which is useful in evaluating the robustness of data analysis. Thirdly, a logistic regression model was constructed to evaluate the linearity, while GAM was used to explain the nonlinearity. GAM is powerful tool that can be used to determine the actual relationship between PLR and NT risk. However, due to its retrospective nature, our study has several limitations. The patients in this study were predominantly recruited from a tertiary care center, and most of them were followed up by only one of the authors. Hence, the generalizability of the results of this study may be limited due to potential referral bias. Moreover, we did not compare the effectiveness of PLR at the three trimesters in predicting NT risk. Additionally, because PLR is based on the platelet and lymphocyte count, the comparison of performance among PLR, platelet count, and lymphocyte counts should be performed. However, the maternal platelet count can be interfered with by treatment which including specific types of drugs, doses, and treatment start times. Therefore, larger studies are warranted to verify our findings.In summary, newborns with maternal ITP have a high susceptibility to NT. A history of previous children with NT is a significant factor for predicting NT in subsequent pregnancies. PLR in the first, second and third trimesters can also be used as a reference to predict the occurrence of NT.


The aim of this retrospective study was to evaluate baricitinib retention rate in patients affected by rheumatoid arthritis. Secondary aims were to compare the impact on treatment persistence of monotherapy and other variables such as systemic corticosteroid use, line of treatment, disease duration, sex, biomarkers positivity, and Herpes Zoster virus infection.Patients with Rheumatoid Arthritis undergoing baricitinib were consecutively enrolled. Rheumatoid Arthritis diagnosis was performed with 2010 ACR/EULAR classification criteria. The cohort’s demographic, clinical and therapeutical data were retrospectively collected. The whole follow-up duration was 104 weeks.p = 0.638) while patients undergoing a steroidal treatment showed a significantly reduced treatment retention (p = 0.028). Contrarily, patients treated with baricitinib as a first-line b/tsDMARD showed higher drug retention (p = 0.002) compared to further treatment lines. Steroid employment, steroid dosage and previous treatment with bDMARDs correlated with risk of treatment discontinuation and at univariate analysis . Multivariate analysis confirmed significance for higher steroid dosage and previous treatment with bDMARDs (p = 0.002 and p = 0.046). No adverse events such as deep venous thrombosis, pulmonary embolism or tubercular infection/reactivation were reported during the study observation.Ninety-five patients affected by rheumatoid arthritis and treated with baricitinib were consecutively enrolled. At the end of follow-up, the overall retention rate was 69.3%. No statistically significant difference in retention rate was observed between patients treated with baricitinib in monotherapy or in combination with methotrexate . In our cohort, concomitant treatment with methotrexate did not influence treatment persistence while retention was reduced in patients undergoing a steroidal treatment and/or in multi-failure subjects. Rheumatoid arthritis (RA) is a chronic, multi-systemic, immune-mediated disease that can lead to progressive joint damage, functional disability and substantial comorbidity. Early diagnosis and a prompt treatment are required to reduce clinical signs and symptoms, to prevent the development of chronic sequelae and to reduce the burden of disease from comorbidities. Since the European League Against Rheumatism (EULAR) published in 2010 its first recommendations for the management of RA with synthetic and biological disease-modifying anti-rheumatic drugs (bDMARDs), many other drugs with different mechanisms of action have been approved  infections. The whole follow-up period was of 104 weeks. The study was conducted according to the guidelines of the declaration of Helsinki and approved by the Ethics Committee (Rhelabus 22271). Informed consent was obtained from all subjects involved in the study.p < 0.05. Analyses were carried out with SPSS v. 24 software.Descriptive analysis was carried out, quantitative variables were summarized as mean ± standard deviation (SD), while qualitative ones as absolute frequencies and percentages. The association between qualitative variables and treatment interruption was evaluated with the Fisher exact test. To evaluate the mean difference between groups the t test or the non-parametric Mann–Whitney were used, according to the hypothesis of normality distribution and homoscedasticity, tested with Kolmogorov–Smirnov test and Levene’s test, respectively. Interruption rate was estimated using Cox regression and the hazard ratio (HR) and their confidence intervals were estimated. In particular univariate and stepwise multivariate Cox regression were performed. Tests were regarded to be statistically significant when Ninety-five patients affected by rheumatoid arthritis and treated with BAR were consecutively enrolled. Their demographic and clinical information at the baseline are summarized in BAR was given at a dosage of 2 mg in 18 (18.9%) patients and at a dosage of 4 mg in 77 (81%) patients. The mean treatment duration was 49.9 (±31.1) weeks. Forty-nine (51.6%) patients received BAR in monotherapy, while 46 (48.4%) were concomitantly receiving MTX. Fifty-three (55.8%) patients were on treatment with systemic CCs at the start of BAR at a mean dosage of 8.1 (±4.7) mg/day of prednisone or equivalent. BAR represented the first line non-conventional DMARD treatment after MTX failure in 32 (33.7%) patients. At the end of the observation time 71 (74.7%) patients were still treated with BAR. BAR was interrupted in 24 (25.3%) patients: 15 (62.5%) patients discontinued BAR because of inefficacy and 9 (37.5%) because of adverse events. Five (5.3%) patients out of 95 experienced a HZV infection while on treatment with BAR. No adverse events such as deep venous thrombosis (DVT), pulmonary embolism (PE) or tubercular infection/reactivation were reported during the study observation. Fifteen (62.5%) out of the 24 patients interrupting BAR and 38 (53.5%) out of the 71 patients continuing BAR were taking oral CCs. Thirteen (54.2%) out of the 24 patients interrupting and 33 (46.5%) out of the 71 patients continuing BAR were in combination therapy with MTX. Patients undergoing BAR as a first line agent after MTX failure were 30 (42.3%%) and 2 (8.3%) in the BAR persistence and BAR discontinuation group, respectively.p = 0.002). Similarly, the mean disease duration for patients that underwent BAR interruption was significantly increased when compared to patients who did not discontinue the treatment . Data about patients’ sex, BAR dosages, RF and ACPA positivity, HZV infections, CCs or MTX combination therapy and history of previous bDMARDs in patients both continuing or interrupting BAR are summarized in At the end of the follow-up the percentage of subjects retaining the treatment was higher in the b/tsDMARD naïve group compared to patients in which BAR represented the second or a more advanced line of treatment .p = 0.6). No retention rate differences were observed between patients treated with 2 or 4 mg/day (p = 0.226), patients with RF (p = 0.196) and/or ACPA (p = 1.000) positivity or negativity, and patients with or without HZV infection while on treatment (p = 1.000). On the contrary, patients who were previously exposed to bDMARDs as well as patients who were co-administered with steroids had a significantly lower BAR retention rate . BAR retention rate was 75.1% at 52-weeks and 69.3% at 104-week . No signp = 0.028), higher steroid dosage (p < 0.001) and previous exposure to bDMARDs (p = 0.017). More precisely, significance was detected for patients previously exposed to 2 (p = 0.008) and 3-or-more (p = 0.028) biological drugs. Multivariate analysis confirmed the correlation with BAR discontinuation for higher steroid dosage (p = 0.002) and previous bDMARDs exposure (p = 0.046). The output of the univariate and multivariate analysis is reported in Univariate analysis showed a significant correlation between BAR discontinuation and the following variables: steroid employment  found a higher risk of major adverse cardiovascular (CV) events and venous thromboembolic events (TVE) in patients with RA and CV risk factors treated with Xeljanz (tofacitinib) than with Tumor Necrosis Factor inhibitors (TNFi) . Based oHowever, in our cohort of patients no major adverse CV events were observed. Moreover, none of the patients experienced episodes of DVT/PE or tuberculosis infection, thus confirming once more the good BAR safety profile . Our resThis study has some limitations, including the absence of a control group and the lack of data about disease activity. Moreover, the small sample size may negatively affect the detection of less common adverse events such as the occurrence of cancer or thromboembolic diseases.Finally, regarding infectious adverse events, BAR safety profile showed to be satisfactory, thus confirming already available data. Further and larger studies are needed to confirm those results, especially concerning cardiovascular and neoplastic adverse events. These novel findings may provide new insight for the management of BAR treatment in clinical practice.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Azienda Ospedaliero Universitaria Senese. The patients/participants provided their written informed consent to participate in this study.CB and SG designed the study and drafted the manuscript. AC performed the statistical analysis. VB, PF, EC, RD’A, ED’I, MB, MF, LC, and BF collected the data for this study in the final dataset. BF, LC, and SG revised the draft of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


V600E gene encodes a protein-dependent kinase (PDK), which is a key component of the mitogen-activated protein kinase pathway and essential for controlling cell proliferation, differentiation, and death. The BRAFV600E mutation causes PDK to be activated improperly and continuously, resulting in abnormal proliferation and differentiation in PTC. Based on elastography ultrasound (US) radiomic features, this study seeks to create and validate six distinct machine learning algorithms to predict BRAFV6OOE mutation in PTC patients prior to surgery. This study employed routine US strain elastography image data from 138 PTC patients. The patients were separated into two groups: those who did not have the BRAFV600E mutation (n = 75) and those who did have the mutation (n = 63). The patients were randomly assigned to one of two data sets: training (70%), or validation (30%). From strain elastography US images, a total of 479 radiomic features were retrieved. Pearson's Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified tenfold cross-validation were used to decrease the features. Based on selected radiomic features, six machine learning algorithms including support vector machine with the linear kernel (SVM_L), support vector machine with radial basis function kernel (SVM_RBF), logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were compared to predict the possibility of BRAFV600E. The accuracy (ACC), the area under the curve (AUC), sensitivity (SEN), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), decision curve analysis (DCA), and calibration curves of the machine learning algorithms were used to evaluate their performance. ① The machine learning algorithms' diagnostic performance depended on 27 radiomic features. ② AUCs for NB, KNN, LDA, LR, SVM_L, and SVM_RBF were 0.80 , 0.87 (95% CI 0.73–0.95), 0.91(95% CI 0.79–0.98), 0.92 (95% CI 0.80–0.98), 0.93 (95% CI 0.80–0.98), and 0.98 (95% CI 0.88–1.00), respectively. ③ There was a significant difference in echogenicity,vertical and horizontal diameter ratios, and elasticity between PTC patients with BRAFV600E and PTC patients without BRAFV600E. Machine learning algorithms based on US elastography radiomic features are capable of predicting the likelihood of BRAFV600E in PTC patients, which can assist physicians in identifying the risk of BRAFV600E in PTC patients. Among the six machine learning algorithms, the support vector machine with radial basis function (SVM_RBF) achieved the best ACC (0.93), AUC (0.98), SEN (0.95), SPEC (0.90), PPV (0.91), and NPV (0.95).The most common BRAF mutation is thymine (T) to adenine (A) missense mutation in nucleotide 1796 . The BRAF V600E mutation is a significant contributor to the papillary thyroid carcinoma (PTC) phenotype, which aids in the diagnosis and differential diagnoses of PTC before surgery2. BRAFV600E diagnosis requires genetic testing of cell eluate by ultrasound-guided fine-needle aspiration (FNA), which is invasive. Ultrasound-guided FNA cytological examination of thyroid nodules can diagnose PTC before surgery, but 15% to 30% of the cytological results belong to the Bethesda system definition with uncertain detection results , Type IV: follicular tumors/suspected follicular tumors, and Type V: suspected malignant tumors (SUSP)). Therefore, "TBSRTC Classification Malignant Risk and Management Recommendation" recommends FNA cytology combined with BRAFV600E mutation detection, but are all invasive. As a result, it is critical in clinical practice to adopt non-invasive approaches to forecast the status of BRAFV600E mutations, so as to reduce the FNA and molecular detection rate.The BRAFV600E mutation in thyroid tumors is thought to be a sign of severe illness and PTC-related mortality3. It is interesting to note that the presence of the BRAFV600E mutation has become a more reliable molecular marker for PTC recurrence3. Therefore, finding BRAFV600E mutations in thyroid tumors has implications for prognosis and serves as a marker for tumor recurrence. The BRAFV600E mutation is also strongly linked to the existence of extrathyroidal extension (ETE) and cervical lymph node metastasis (CLNM) in PTC patients, suggesting an invasion5. Jin et al.6 observed a substantial relationship between BRAFV600E mutation with CLNM and ETE in a Mayo Clinic research. Xing et al.7 revealed a close link between BRAFV600E mutation and ETE, CLNM, and advanced illness stages in a large comprehensive international multicenter investigation. Even if either gene mutation is capable of identifying aggressive cancer types, the genetic analysis still requires specimen tissue for examination, which is often collected by invasive surgical procedures. Furthermore, identifying tumors using FNA biopsy samples from type 4a nodules is difficult because tumor cells vary in quantity, quality, and purity8. A sensitive and precise BRAFV600E mutation detection method will thus aid in the early detection of PTC9. Because BRAFV600E gene mutations produce 99.8% of malignant nodules, it is a significant tumor marker for PTC.More importantly, According to earlier research, BRAFV600E status in PTC patients is critical for patients to choose therapy methods. Surgery is currently chosen over ablation for highly invasive PTC in China.With the increasing popularity of tumor thermal ablation technology  in China, an increasing number of patients with PTC, particularly those with minimal papillary thyroid cancer (MPTC), are willing to accept thermal ablation as a minimally invasive procedure to maximize the preservation of thyroid function. As a result, proper preoperative detection of BRAF10. Grayscale US has recently been found to be able to predict the mutation of BRAFV600E in PTC. However, the findings are still up for debate12, which may be related to the drawbacks of the conventional US image, such as its dependence on the radiologist's experience and interobserver variation13. The ability of a tissue to resist deformation whenever a force is exerted on it or to restore its initial form when that force is withdrawn is called elasticity, which is what elastography often examines in tissues. Depending on the Doppler US technology, strain images can be presented in grayscale or in colors that reflect the stiffness and elasticity of the tissues17. Elastography is also based on the grayscale US which is also subjective and operator dependent. There have been several significant developments to increase the performance of US elastography such as elastography estimation from Doppler imaging using central difference and least-squares algorithms17.The main imaging method for evaluating thyroid nodules is ultrasound (US)19. Artificial intelligence (AI) has considerably expanded in recent years as a cutting-edge data analysis tool in the medical field20. As a result of its extensive digital data sets, radiology in particular is well suited for AI21. Recently, there has been a lot of interest in the medical area in the use of radiomics in conjunction with machine learning, which is an important subset of AI and plays a tremendous supporting role in increasing diagnostic and prognostic accuracy23. However, there are currently few studies that used machine learning models primarily on elastography US radiomics data to detect the existence of BRAFV6OOE mutation in PTC. There has also been no research that employed different machine learning classifiers in assessing BRAFV6OOE in PTC patients using elastography US radiomic features. Based on elastography US radiomic features, this study seeks to create and validate six distinct machine-learning algorithms to predict BRAFV6OOE mutation in PTC patients prior to surgery.Radiomics analysis using US images has been used to forecast the molecular characteristics of several malignancies, which include PTCV600E mutation diagnosis, and surgery at Jiangsu University Affiliated People's Hospital and traditional Chinese medicine hospital of Nanjing Lishui District between January 2014 and 2021. The enrolling process is displayed in Fig. V600E mutation-free group (n = 75) and BRAFV600E mutation group (n = 63). Using a stratified sample technique at a 7:3 ratio, all patients were randomly assigned to either the training group (n = 96) or the validation group (n = 42). The following criteria were required for inclusion: postoperative pathology indicated PTC; preoperative thyroid US elastography evaluation; related US images and diagnostic outcomes; maximum nodule diameter > 5 mm, and < 5 cm; and unilateral and single focal lesion. The exclusion criteria included a maximum nodule diameter of > 5 cm and indistinct US imaging of nodules caused by artifacts. The clinical details of the enrolled patients were documented, including age, sex, nodule diameter, nodule location, nodular echo, nodule boundary, nodule internal and peripheral blood flow, nodule elastic grading, calcification, CLNM, and BRAFV600E mutation results. The Jiangsu University Affiliated People's Hospital and the traditional Chinese medicine hospital of Nanjing Lishui District Ethics Committee approved this study. Because it was retrospective in nature, it did not require written informed consent.A retrospective analysis was performed on PTC patients who had undergone preoperative thyroid US elastography, BRAFThere were two ultrasonic devices used: the Philips Q5  and the GE LOGIC E20  .To acquire longitudinal and transverse images of the thyroid nodules, continuous longitudinal and transverse scanning was done while the patients were supine. Blood flow in and around the nodule, strain elastic grading of the nodule, calcification, and CLNM were all visible on the coexisting diagram, which also included the nodule diameter, location, echo, and boundary.The cross-sectional image's position and size of the sampling frame were adjusted, and the strain elastic imaging mode was activated. With an ROI that was larger than the nodules , the nodules were placed in the middle of the elastic imaging zone. Pressure was applied steadily  while the probe was perpendicular to the nodule. When the linear strain hint graph (green spring) suggested stability, the freeze key was pressed to get an elastic image; the ROI's color changed , and the nodule's hardness was determined based on elasticity. The elastic image was graded according to the following criteria: one point equals a nodular area that alternates between red, green, and blue; two points equal nodules that are partially red and partially green ; three points equal a nodule area that is primarily green, with surrounding tissues visible in red; four points equal a nodule area that is primarily red, with the red area > 90%; and five points equal a nodule area that is completely covered in red.http://www.itksnap.org), the ROIs were manually drawn on each image . A total of 479 radiomic features were recovered from each ROI's elastography US images. Among those included were first-order Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), Gray Level Dependence Matrix (GLDM), and Neighbouring Gray Tone Difference Matrix (NGTDM) features, as well as features deduced from wavelet filter images containing first-order GLCM, GLRLM, GLSZM, GLDM, and NGTDM features.Radiomic features were extracted using PyRadiomics . Every pair of features with a PCC of more than 0.80 was deemed redundant.24 to choose representative features for the training cohort. During the RFE procedure, the following parameters were taken into consideration  for feature selection was applied to the whole dataset using the Scikit-learn python module24The Support Vector Machine with the linear kernel (SVM_L), Support Vector Machine with radial basis function kernel (SVM_RBF), LogisticRegression (LR), Naïve Bayes (NB), K-nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) classifiers were used to build the prediction models using the RFE’s key features. All six algorithms were implemented using the Scikit-learn machine learning libraryThe same feature sets were chosen and fed into the model during the validation process. Standard clinical statistics like the area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy (ACC) were used to evaluate the model's performance on the training and validation datasets.https://www.python.org/ Accessed 8 July 2021) and IBM SPSS Statistics  for Windows version 26.0 were used for statistical analyses. Pearson's chi-square and Fisher's exact tests were used to compare the differences in categorical characteristics. The independent sample t-test was used for continuous factors with normal distribution, whereas the Mann–Whitney U test was used for continuous factors without normal distribution.Python  and scikit‐learn version 1.224 were used to extract radiomic features and build the prediction models. Each prediction model's AUC, sensitivity, specificity, ACC, NPV, and PPV were calculated.A two‐sided P < 0.05 indicated statistically significant differences. PyRadiomics  was used to perform the decision curve analysis.Medcalc Statistical Software was used to calculate the six models’ AUCs and evaluate the predictions. The DeLong method was used to compare the AUCs of the six machine learning classifiers. To create calibration curves, the sci-kit-learn version 1.2The study was conducted in accordance with the Declaration of Helsinki and approved by the Jiangsu University-Affiliated People’s Hospital and traditional Chinese medicine hospital of Nanjing Lishui District Ethics Committee.Patient consent was waived by the Jiangsu University-Affiliated People’s Hospital and traditional Chinese medicine hospital of Nanjing Lishui District ethics committee due to the retrospective nature of the study.There were 138 PTC patients in all, 87 of whom were women , and 51 of them were men . In Table V600E mutation and ultrasonic imaging characteristics and the predictive performance of the machine learning algorithms is shown in Tables The relationship between the BRAFAfter PCC and RFE with stratified tenfold cross-validation, 27 radiomic features were chosen in the training cohort Figs.  and 4.FiV600E based on six machine learning algorithms , 1.00 (95% CI 0.96–1.00), 1.00 (95% CI 0.96–1.00), 0.96 (95% CI 0.89–0.99), 1.00 (95% CI 0.96–0.1.00), and 1.00 (95% CI 0.96–1.00), respectively , 0.87 (95% CI 0.73–0.95), 0.91(95% CI 0.79–0.98), 0.92 (95% CI 0.80–0.98), 0.93 (95% CI 0.80–0.98), and 0.98 (95% CI 0.88–1.00), respectively. The SVM_RBF model performed the best in the validation cohort, followed by the LR, SVM_L, LDA, KNN, and NB models, in that order  feature selection algorithm. The gray scale US radiomic model did not perform well in the validation cohort , but the elastography model  and combined model , did perform well. Considering that elastic US images superimpose information on the hardness of the mass on the basis of grayscale US images, image feature extraction not only extracts the hardness characteristics of the mass, but also extracts rich features of the internal structure of the mass. So we decided to focus on elastography radiomics features in this current study to explore the performance of multiple machine learning classifiers or algorithms as well as other feature selection methods.In a previous study31 on the use of machine learning to differentiate between benign and malignant thyroid nodules. However, there have been only a few studies using machine learning models to predict BRAFV600E mutation in PTC patients. Additionally, there hasn't been a comparison of the effectiveness of various machine learning algorithms for the prediction of BRAFV600E mutation in PTC patients based on US elastography radiomic features. In the current study, we constructed six machine-learning models to distinguish PTC patients with BRAFV600E gene mutations from PTC patients without BRAFV600E gene mutations using preoperative US elastography radiomic data. There were three noteworthy discoveries. First, based on preoperative US elastography radiomic features, the six machine learning models could differentiate PTC patients with BRAFV600E from non-BRAFV600E PTC patients. Second, when the six machine learning models were compared, SVM_RBF had the best prediction performance. Third the machine learning algorithms' diagnostic performance was based on 27 radiomic features.Machine learning algorithms benefit from learning from input data and automatically recognizing patterns and trends in that data automatically. There have been numerous studiesV600E mutations among patients with PTC, Yoon et al.10 established a radiomic score using a dataset of 527 patients who had undergone surgical treatment for PTC and who had all undergone BRAFV600E mutation analysis on surgical specimen.They reported that radiomics features extracted from US have limited value as a non-invasive biomarker for predicting the presence of BRAFV600E mutation status of PTC, with a c-statistic value  of 0.63 in the validation cohort and 0.72 in the training cohort. In comparison to this study, our study reported AUCs range of (0.96–1.00) and (0.87–0.98) in the training and validation cohorts, respectively, and ACCs range of (0.89–0.98) and (0.81–0.93) in the training and validation cohorts, respectively for the six machine learning algorithms employed in this study.In order to determine whether US-based radiomics could perhaps assess the occurrence of BRAFV600E mutations in PTC patients.The higher AUCs and ACCs in the current study could be attributed to the US elastography radiomic features used, which provided more information than the grayscale US. Also in this study, machine learning algorithms were used in conjunction with radiomics, and several machine learning classifiers were used to build the models. To the best of our knowledge, this is the first study to develop models based on different machine-learning algorithms to predict BRAF28, the ACCs of SVM_L (0.88), LDA (0.88), and LR (0.88) were the same as the ACC of the logistic regression classifier (0.88) utilized in our previous study. However, the KNN (0.81) and NB (0.81) ACCs values were lower than the LR (0.88) method utilized in our previous study.Comparing the current study results to our previous studyThe AUCs of the KNN (0.87), LDA (0.91), LR (0.92), and NB (0.80) classifiers were all lower than the logistic regression classifier (0.93) utilized in our previous study. The SVM_L AUC (0.93) score, on the other hand, was the same as the logistic regression algorithm (0.93) utilized in our previous work. When compared to the logistic regression classifier (ACC: 0.88 and AUC: 0.93) employed in our previous investigation, the SVM_RBF had higher ACC (0.93) and AUC (0.98) values. The disparity in performance could be attributed to the various feature preprocessing techniques and feature selection strategies used in the current investigation.32 discovered that vertical and horizontal diameter ratios greater than one, as well as low echo, were all associated with the BRAFV600E mutation. Similarly, Hahn et al.33 discovered that vertical and horizontal diameter ratios greater than one were linked to BRAFV600E gene mutations. Consistent with these studies, we discovered in this current study that there was a significant difference in echogenicity, vertical and horizontal diameter ratios, and elasticity between PTC patients with BRAFV600E and PTC patients without BRAFV600E.On US examination, Kabaker et al.V600E gene mutation and the predicted probability.The calibration curve of the prediction model is an essential metric for evaluating the probability accuracy of a disease risk model in predicting an individual outcome event in the future. A high degree of calibration shows that the prediction model is accurate, whereas a low degree of calibration indicates that the model may exaggerate or underestimate the risk of illness. The blue line reflects the performance of the machine learning algorithms, while the diagonal dotted line represents an ideal prediction Fig. . A closeV600E gene mutation, the solid black line (negative line) indicates that when no patient accepts intervention or treatment, the net benefit is zero. On the contrary, the solid grey line (positive line) indicates the net benefits when all patients have BRAFV600E and receive treatments or interventions. According to the incidence of BRAFV600E among patients with PTC, the reasonable range of thresholds was set from 0.3 to 0.99. In the entire range, all machine learning-based algorithms showed higher net benefits than the two extreme lines (negative line and positive line). In almost the entire threshold probability range, the SVM_RBF algorithm had the highest net benefit in both the training and validation cohorts (Fig. Furthermore, the DCA was used to evaluate the clinical utility of these models Fig. . Assuminrts Fig. .V600E in PTC patients, the gene in healthy individuals was not analyzed, and the focus was on evaluating the BRAFV600E mutation in PTC patients, which could have resulted in a selection bias. Also, this was a small sample retrospective study conducted at two institutions; thus, a selection bias may exist. In the future, we aim to conduct a multicenter study with a larger sample size.There are some limitations of the study, in the construction of the models to predict BRAFV600E in PTC patients, which can assist physicians in identifying the risk of BRAFV600E in PTC patients. SVM_RBF achieved the greatest prediction performance of the six machine learning models tested.Finally, our study found that machine learning-based US elastography radiomic models performed well in predicting the potential of BRAF


Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, which can progress from simple steatosis to advanced cirrhosis and hepatocellular carcinoma. Clinical diagnosis of NAFLD is crucial in the early stages of the disease. The main aim of this study was to apply machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables. A cross-sectional study was carried out among 513 individuals aged 13 years old or above in Iran. Anthropometric and body composition measurements were performed manually using body composition analyzer InBody 270. Hepatic steatosis and fibrosis were determined using a Fibroscan. ML methods including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease. RF generated the most accurate model for fatty liver (presence of any stage), steatosis stages and fibrosis stages with 82%, 52% and 57% accuracy, respectively. Abdomen circumference, waist circumference, chest circumference, trunk fat and body mass index were among the most important variables contributing to fatty liver disease. ML-based prediction of NAFLD using anthropometric and body composition data can assist clinicians in decision making. ML-based systems provide opportunities for NAFLD screening and early diagnosis, especially in population-level and remote areas. Worldwide prevalence of metabolic syndrome and NAFLD has increased in parallel with increased obesity prevalence5, which is about 20–30% in developed countries and one-third among American adults8.Non-alcoholic fatty liver disease (NAFLD)‒ the hepatic manifestation of metabolic syndrome‒ is the most common chronic liver disease11. The prevalence of NAFLD is directly related to increased body mass index (BMI) and central obesity14. Most studies have shown that visceral fat is an independent factor in generating hepatic steatosis, independent of BMI16. The amount of adipose tissue and its distribution differs between men and women17. Women have higher overall fat tissue with relatively more subcutaneous adipose tissue in the hips and thighs. At the same time, men accumulate visceral and subcutaneous fat mainly in the trunk and abdomen with continuous changes before and after puberty19. The increased fat distribution around the waist (i.e. apple-shaped body) is linked to NAFLD in both genders20. In a pear-shaped body, the subcutaneous fat accumulates mainly in the thighs and buttocks22, which is typical among females but can increase metabolic syndrome in males, which is a risk factor for NAFLD independent of central obesity23. In support of the role of fat distribution and anthropometric measures in NAFLD, studies have found several contributing factors, including abdomen circumferences, waist, neck and fat accrual in trunk and arms29.Obesity is a common metabolic risk factor associated with NAFLD30. Notably, after the development of cirrhosis, different symptoms such as caput medusa, spider angioma, palmar erythema, ascites, and jaundice appear31. Therefore, early diagnosis is critical to prevent severe complications.Most people with NAFLD, including both children or adults, do not have differential symptoms at the early stages of the disease32. Notably, hepatic fibrosis cannot be diagnosed by ultrasonography33. Although typically used to detect fatty liver disease, laboratory tests are not useful for all ages and gender groups due to low accuracy35. Therefore, a precise, cost-effective, and non-invasive method to analyze symptoms of various stages of the fatty liver for NAFLD diagnosis is desirable. Such an approach is important to help with early diagnosis of NAFLD, which could help prevent hepatic steatosis progression to fibrosis, advanced cirrhosis, and hepatocellular carcinoma.Ultrasonography and laboratory tests are typical diagnostic methods for detecting fatty liver disease. Ultrasound technique has relatively high accuracy in detecting the moderate-to-severe steatosis level and lower accuracy in earlier stages of NAFLD39. However, all of these studies have focused mainly on laboratory outcomes and have not considered body composition and anthropometric factors. Therefore, the primary aim of this study is to identify essential ML classifiers of NAFLDs using body composition and anthropometric indices. The secondary aim is to identify feature contributions to the prediction of NAFLDs.In recent years, machine learning (ML) models have been used as a novel approach in predicting NAFLDThis cross-sectional study was conducted to explore NAFLD phenotypes based on body composition and anthropometric indices. Participants were recruited from the eastern (Khorasan Razavi) and southern (Hormozgan) provinces of Iran, through advertisement on the notice boards of the university clinics, as well as via phone or email contact to potential participants. A total of 593 individuals aged above 13 years old were initially recruited. Eighty individuals were excluded from the study and 513 participants were remained. Exclusion criteria were as follows: the presence of underlying liver disease, taking medications , alcoholic patients with more than twice-a-week consumption, previous history of any type of cancer during the last year, history of surgery during the last 6 months, pregnant women. The study was conducted following the Declaration of Helsinki, and ethical approval was granted by Mashhad University of Medical Sciences following (Code: IR.MUMS.fm.REC.1395.64). All participants provided written informed consent. Also, written informed consent was obtained from guardians of participants aged under 18 years.26. Subcutaneous fat in the area below the scapula, arms biceps and triceps and the upper iliac crest was measured using a Saehan calliper . Participants were also examined for acanthosis in the back of the neck and armpits and the presence of subcutaneous fat under the chin and at the back of the neck. A Fibroscan equipped with the M and XL probes  was used to assess both controlled attenuation parameter (CAP) (dB/m) and liver stiffness measurement (LSM) (kPa) values simultaneously. A reliable LSM was defined as the median liver stiffness of the 10 measurements 40. CAP values range from 100 to 400 dB/m and the following cut-off values were used for the diagnosis of steatosis stages: Stage 0, < 238 dB/m, Stage 1, ≥ 238 to 260 dB/m, Stage 2, ≥ 260 to 292 dB/m, and Stage 3, ≥ 292 dB/m41. LSM values range from 1.5 to 75 kPa, and the following cut-off values were used for the diagnosis of liver fibrosis stages: no significant fibrosis or F0 < 6.2 kPa, mild fibrosis or F1 ≥ 6.2 to 7.6 kPa, moderate fibrosis or F2 ≥ 7.6 to 8.8 kPa, severe fibrosis or F3 ≥ 8.8 to 11.8 kPa and cirrhosis or F4 ≥ 11.8 kPa42.At each medical clinic, eligibility, demographics questionnaire, anthropometric, and body composition measurements were assessed by two trained nutritionists. Medical examination and disease diagnosis were performed by a general physician and an internal specialist, respectively. Demographic information, including sex, age, education, disease history and medications, were assessed by researcher using a questionnaire. Weight was measured using a digital weighing scale , height was measured using a wall height chart, and the body composition measures were assessed using InBody 270  body analyzer to measure per cent (%) body fat, total fat mass, muscle mass, as well as fat mass in the right/left leg, right/left arm and trunk with light clothing and without shoes. The circumferences of neck, chest, arm, wrist, waist, hips, abdomen, thighs, and length of ulna and leg were measured using a flexible tape measure with an accuracy of 0.1 cm. BMI was calculated by dividing weight (kilograms) by the height in meters squaredt test and analysis of variance (ANOVA), followed by Tukey’s post hoc test. A P-value of less than 0.05 was considered statistically significant.Descriptive and non-predictive data analysis was performed using SPSS version 21 software . Data were expressed as mean ± standard deviation or frequencies. Between-group comparisons were performed using an independent sample 43. Testing of these models was performed using the Scikit-learn library in Python programming language44.Three label variables were considered: fatty liver , steatosis, and fibrosis stages. Eight ML techniques were applied to the dataset to identify the best modelling approach. To this end, k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), AdaBoost and Naïve Bayes were tested. An extant explanation of these classifiers can be found elsewhereTo comprehensively compare different classifiers, we trained and evaluated dataset 50 times. This is because different classifiers sometimes predict slightly different outputs and initial points are different for a specific classifier in each run. Thus, a reliable output can be estimated by averaging each classifier several times. Model accuracy and area under the curve (AUC) are reported for each ML technique. Importance values are reported for individual feature variables.45. Principal component analysis (PCA) was used to extract the attribute of the data47. Data were divided into two parts, train and test. Processing involved feature selection and classification with the best feature. The model processing involved a variety of models. The model with the highest performance was selected.Pre-processing involved data normalization and segmentation. The few missing values in the numerical results of the experiments were replaced using the Linear Interpolation methodAll patients provided written consent for participation in this study. For participants aged under 18 years, written informed consent was obtained from their guardians.Ethical approval was received from the research ethics committee at Mashhad University of Medical Sciences (Code: IR.MUMS.fm.REC.1395.64).2, respectively. Overall demographic characteristics and biochemical measures are presented in Table In total 513 participants  took part in the study, of whom 169 (74.1%) male and 220 (80.6%) female cases had a degree of hepatic steatosis. The mean age, weight, and BMI were 37.04 ± 15.44 years, 77.26 ± 17.31 kg, and 28.15 ± 4.89 kg mFigures Feature variables with the highest predictability for fatty liver were abdomen circumference , waist circumference (IV = 0.061), chest circumference IV = 0.054), trunk fat (IV = 0.056) and BMI (IV = 0.053); for steatosis, the stage was abdominal circumference (IV = 0.053), waist circumference (IV = 0.052), chest circumference (IV = 0.052), trunk fat (IV = 0.051) and BMI (IV = 0.050); and for fibrosis were abdominal circumference (IV = 0.049), waist circumference (IV = 0.049), chest circumference (IV = 0.043), BMI (IV = 0.045) and weight (IV = 0.045). See Figs. , trunk fFurther assessment identified gender-specific features  and fibrosis. To our knowledge, this is the first study applying ML on body composition and anthropometric data to predict NAFLD. High accuracy (82%) highlights the potential for applying ML techniques for the primary prevention and screening of NAFLD using anthropometric measurements.38, (76.3%) Logistic Regression37, (86.4%) RF39 and (80%) Classification Tree techniques36. However, we tested the predictive value of body composition and anthropometric measurements rather than biochemical variables. Anthropometry as a lower-cost and more feasible approach can be considered a primary screening method for fatty liver disease.Previous studies using ML techniques to predict fatty liver disease have mainly focused on biochemical measurements, with similar levels of accuracy (83.0%) using Bayesian Network27. Waist circumference and trunk fat have been shown to be significantly predicting the risk of NAFLD24. Although BMI is one of the risk factors of NAFLD31, it has been argued that BMI is limited compared to other anthropometric measures  in identifying lean NAFLD individuals25. In a similar vein, the findings of the present study clearly show the importance of these body composition and anthropometric measures and their relative contribution to the prediction of NAFLD.Abdominal obesity is a significant risk factor leading to NAFLD48. A positive correlation has been shown between neck circumference and hepatic steatosis28. Neck circumference showed a positive association with other anthropometric components, such as BMI and waist and waist-to-hip circumference. In the present study, neck circumference contributed almost equally to hepatic steatosis and fibrosis.Neck circumference reflects the amount of subcutaneous fat in the upper body, and is a reliable factor in determining central obesity29. In our study, a strong and positive relationship between arm circumference and the severity of steatosis and fibrosis was detected, validated by the ML model. Rafiee et al. showed that the amount of fat in hips and legs and circumference of hip negatively associated with fatty liver and the severity of the disease. In contrast, the waist-to-hip ratio was closely associated with fatty liver. They also showed that the accuracy of this ratio in predicting NAFLD was greater than BMI and waist-to-height ratio49.A study by Subramanian revealed that the level of arm fat index in both males and females had a negative association with the degree and severity of NAFLD39. Ultrasound is a commonly used method for the diagnosis of hepatic steatosis50. Ultrasonography is a safe, well-tolerated, non-invasive and low-cost technique50; however, there are limitations associated with ultrasound use, including limited capability in detecting fatty infiltration (less than 20% steatosis), operator dependency and subjective assessment52, and ML is expected to minimise some of these. Application of ML techniques on body composition and anthropometric measures as a less time-consuming and easy to undertake method can help physicians in their clinical decision making.Most ML studies for the prediction of NAFLD have used the ultrasonography technique to diagnose fatty liver disease53. NAFLD Fibrosis Score (NFS) and Fibrosis-4 (FIB-4) have been recommended as appropriate methods for the initial assessment of fibrosis in NAFLD patients54. Both of these methods use a combination of variables including age, BMI and biochemical measures (i.e. aspartate aminotransferase (AST), alanine aminotransferase (ALT), platelets, etc.). Graupera et al. concluded that NFS and FIB-4 are not optimal for screening as they correlate poorly with liver stiffness55. In their study, waist circumference was found to be the ideal measure for fibrosis screening among high risk people from general population55. However, other studies found that NFS and FIB-4 have the potential to detect advanced fibrosis and the progression of fibrosis among people with NAFLD56. It seems that NFS and FIB-4 are more useful in the diagnosis of fibrosis in NAFLD but not for fibrosis screening among the general populations. The present study showed suboptimal accuracy (57%) in detecting fibrosis using less expensive and non-invasive factors i.e. anthropometric and body composition measures. Further studies might explore a combination of these methods including anthropometric, body composition and biochemical variables altogether.The presence of liver fibrosis in patients with NAFLD is considered the strongest predictor of long-term outcome57. Moreover, performing the measurement task needs less specialty knowledge therefore can be implemented in several health centres  and also remote areas. Once validated, the resulted assistive technology can serve the clinicians in the prevention of liver diseases. There are limitations of the present research that need to be addressed. A small sample size might have potentially limited the results of ML prediction. Although, the small sample size was accounted for by multiple cross-validations, which reduced potential errors. Future studies with larger sample sizes can allocate separate validation sets and evaluate the model. Moreover, even though the most common method for fatty liver diagnosis, the ultrasound technique is not the gold standard. Using liver biopsy outcomes would generate more valid results. Also, to increase the predictive accuracy of the proposed model for NAFLD prediction, future studies should include other body composition and anthropometric measures such as sagittal abdominal diameter (SAD) and peri-renal fat58.The proposed algorithm identified in this research can be used by the health systems for several reasons. Screening of the presence or absence of NAFLD with the help of non-invasive anthropometric measurements can be achieved with simple and cheap equipmentPresent findings show that applying a ML classification model on anthropometric and body composition variables predicted the presence of fatty liver disease. ML-based decision support systems offer potential to assist physicians with screening, diagnosis and prevention of NAFLD. ML-based decision support systems could be of particular value for providing services at a population level and remote health care where there is a lack of trained specialists.Supplementary Information 1.Supplementary Information 2.


Gene expression profiles and apoptosis-related data were downloaded from the Gene Expression Omnibus and Molecular Signature databases, respectively. Apoptosis-related differentially expressed mRNAs (DEGs) and miRNAs (DEMs) from blood samples between the schizophrenia and healthy control individuals were screened. A diagnostic model was developed using the data from univariate and least absolute shrinkage and selection operator (LASSO) regression analyses, followed by validation using the GSE38485 dataset. Cases were divided into low-risk (LR) and high-risk (HR) groups based on the risk score of the model, and differences in immune gene sets and pathways between these two groups were compared. Finally, a ceRNA network was constructed by integrating long non-coding RNAs (lncRNAs), DEMs, and DEGs.  A diagnostic model containing 15 apoptosis-related genes was developed and its diagnostic efficiency was found to be robust. The HR group was correlated with higher immune scores of chemokines, cytokines, and interleukins; it was also significantly involved in pathways such as pancreatic beta cells and early estrogen response. A ceRNA network composed of 2 lncRNAs, 14 miRNAs, and 5 mRNAs was established.  The established model is a potential tool to improve the diagnostic efficiency of patients with schizophrenia, and the nodes included in the ceRNA network might serve as biomarkers and therapeutic targets for schizophrenia. Schizophrenia is a complex neuropsychiatric syndrome that affects approximately 1% of the world's population and poses a serious health burden . PatientIncreasing attention is being paid to the study of environmental factors associated with alterations in gene expression through epigenetic regulation . MoreoveApoptosis is a regulated form of cell death that remains active during neurodevelopment; it can also be reactivated under a variety of neuropathological conditions . AccumulIn addition to mRNA, non-coding RNAs, such as lncRNAs, may be also involved in the pathogenesis of schizophrenia. A high expression of lncRNAs in the brain contributes to the healthy function of neurons and synapses; these molecular pathways are frequently found to be dysfunctional in schizophrenia . Specifihttps://www.ncbi.nlm.nih.gov/geo/), and the miRNA expression profiles of 211 samples (128 schizophrenia and 83 controls) were extracted from the ARRAYEXPRES database (https://www.ebi.ac.uk/arrayexpress/). Detailed information for each dataset is presented in A total of 310 schizophrenia and 242 NC samples obtained from four public datasets were included in this study. In brief, mRNA-seq expression profiles of the 341 samples (182 schizophrenia and 159 controls) were downloaded from the Gene Expression Omnibus (GEO) database  were merged as the training sets (GSE27383 and GSE54913), and those from whole blood were merged as the validation set (GSE38485). The R package inSilicoMerging v1.14.0) [.0 [14] wBased on the miRNA microarray data from the ARRAYEXPRESS database, the probe number was matched with miRNA using the downloaded platform annotation file to obtain the expression value of miRNA.t test. Genes with p < 0.05 were considered apoptosis-related DEGs.A total of 228 apoptosis-related genes were downloaded from two datasets (HALLMARK_APOPTOSIS and KEGG_APOPTOSIS) hosted on the Molecular Signature (MSigDB) database (v7.1). Next, these genes were mapped to the obtained mRNA matrix, and 189 apoptosis-related genes were screened for further analysis. Differential expression analysis of schizophrenia vs. controls was performed according to the expression level of these genes established using the Student's To understand the relationship between genes, the Pearson correlation coefficient (PCC) between any two apoptosis-related DEGs was calculated. Additionally, these genes were entered into the STRING database, and the parameters were set to human and medium confidence (protein–protein interaction [PPI] score = 4) to obtain PPI pairs. After obtaining PPI pairs, the PPI network was visualized using the Cytoscape software (v3.6.1) .p < 0.05. least absolute shrinkage and selection operator (LASSO) Cox regression with 20-fold cross-validation was conducted to obtain the optimal gene signature and corresponding coefficients using the glmnet package in R (v4.0–2) [RS = ∑βgene × Expgene; where βgene represents the LASSO coefficient, and Expgene represents the expression level of the gene. Based on the median score, the samples were assigned to low-risk (LR) and high-risk (HR) groups. To verify whether this model was effective for whole blood, we used the same formula to construct a diagnostic model for the validation set. In addition, a receiver operating characteristic (ROC) curve was plotted to determine the predictive performance of the model.Univariate logistic regression was performed to identify disease-associated genes and genes with (v4.0–2) . The rist test. PCC between immune gene sets and each gene in the model was calculated and displayed using a heatmap. Moreover, differences in human leukocyte antigen (HLA) family genes between the two risk groups were assessed, followed by a calculation of the PCC between HLA family genes and each gene in the model.Immune-related gene sets were obtained from the ImmPort database , and thep < 0.05 were regarded as significantly enriched.Based on the “h.all.v7.4. symbols.gmt” in the MSigDB database as an enrichment background, the GSVA score of each HALLMARK pathway in samples was calculated using the GSVA algorithm . Differet test, and miRNAs with p < 0.05 were considered to be differentially expressed. The miRWalk 3.0 was used to perform the miRNA prediction of genes in the diagnostic model, with default parameters: binding probability > 95%, binding site position = 3UTR. The predicted miRNAs were then intersected with DEMs, and the overlapping miRNAs were obtained to generate miRNA–mRNA pairs. Furthermore, DIANA-LncBase was used to predict lncRNAs targeted by miRNAs in miRNA–mRNA pairs. These lncRNAs were then matched with the DEGs within schizophrenia vs. controls to obtain the differentially expressed lncRNAs and the corresponding lncRNA–miRNA pairs. Finally, the mRNAs and lncRNAs regulated by the same miRNA were screened, and a ceRNA network based on the mRNA–miRNA–lncRNA pair was constructed using the Cytoscape software.DEMs between schizophrenia and controls were identified using the Student's As described in Materials and Methods section, the data were merged, and batch effects were removed. The distribution of data after removing the batch effect tended to be consistent with the median in a line, indicating that our data were of high quality and could be used for subsequent analyses Figures . In totap < 0.05), 41 apoptosis-related DEGs were identified between the schizophrenia and control groups  were screened and regarded as disease-related genes (EGR3) + (0.292 × PMAIP1) + (0.639 × LGALS3) + (1.665 × DNAJC3) + (0.352 × CD14) + (1.222 × CYLD) + (4.053 × BTG3) − (0.606 × DIABLO) − (1.852 × PIK3CD) − (1.516 × DFFA) − (0.111 × PIK3CA) − (1.103 × IRF1) − (2.532 × HSPB1) − (0.792 × TNFRSF1A) − (0.896 × FASLG).After the univariate logistic regression analysis, 36 genes  family member receptors were higher in the LR group . The coreceptors  and a strleukins .HLA-DOA was increased in the HR group, whereas those of HLA-C, HLA-E, and HLA-F were decreased in the HR group , 14 miRNAs , and 5 mRNAs , with 14 lncRNA–miRNA and 22 miRNA–mRNA pairs  that are involved in the immune response and are associated with neurodevelopmental disorders, including schizophrenia [PIK3CD is downregulated in schizophrenia and may be considered as a potential therapeutic target. PIK3CA also encodes the PI3Ks enzyme and its activity is decreased in patients with schizophrenia [IRF1 is decreased in patients with schizophrenia [HSPB1 directly inhibits apoptotic pathways to increase neuronal survival, thereby protecting against injury-induced nerve death [HSPB1 expression levels were observed in patients with schizophrenia, and HSPB1 polymorphisms were associated with an increased risk of schizophrenia [TNFRSF1A is downregulated in elderly schizophrenia subjects, which may be related to cognitive decline [FASLG acts as a marker of apoptosis, and increased apoptotic signaling occurs at the onset of schizophrenia and is associated with treatment progression [PMAIP1 [DNAJC3 [DFFA [BTG3 [In this study, we screened apoptosis-related DEGs between schizophrenia and control samples and developed a 15-gene-based diagnostic model. Validation analysis was performed to evaluate the diagnostic performance of this model, and the results showed that the AUC values of the models in the training and validation sets were larger than 0.7 , indicatophrenia . The proesponses . A previry state . PIK3CD ophrenia . Etemadiophrenia  reportedophrenia . As a spophrenia . The oveve death . Changesophrenia . TNFRSF1 decline . FASLG agression . Taken t [PMAIP1 , DNAJC3  [DNAJC3 , DFFA [4C3 [DFFA , and BTGFA [BTG3  are invoThe constructed diagnostic model could categorize individuals into LR and HR groups. The immune analysis showed that the immune scores for chemokines, cytokines, and interleukins were significantly higher for patients in the HR group than those in the LR group. Chemokines are promising biomarkers of inflammation and immune activation, which may be associated with psychiatric disorders . A cliniZNF883 and HCP5, may play a role in the pathogenesis of schizophrenia. Gong et al. recently found that ZNF883 is upregulated in hippocampal neurons and could be considered as a biomarker and therapeutic target for epilepsy [HCP5 is mainly detected in immune organs such as the blood, spleen, and thymus [HCP5 can be used as an immune-related marker for various human malignancies [Furthermore, we constructed a ceRNA network based on 5 mRNAs, 14 miRNAs, and 2 lncRNAs. Among these, two lncRNAs, epilepsy . HCP5 isd thymus . HCP5 cagnancies , 49; howIt should be noted that this study analyzed gene expression and miRNA profiles of human blood samples. Although alterations of apoptotic markers in the schizophrenic brains have been reported –12, whetin vivo and in vitro experiments. This will be the focus of future research.Several limitations of our study need to be noted. First, the diagnostic model is only validated in samples from public data, and clinical samples also need to be enrolled to confirm the diagnostic performance of the model. Validation of the results of this study in a new and separate set of clinical data will strengthen the conclusion. Second, ceRNA networks were obtained via a bioinformatics analysis, and whether specific ceRNA regulatory mechanisms exist in patients with schizophrenia needs to be investigated through Through analyzing the gene expressions and the lncRNA and miRNA profiles of blood samples from schizophrenic patients and controls, we constructed a robust diagnostic model based on apoptosis-related genes. Our initial study suggests that this model can be used for risk classification of individuals with schizophrenia and potentially has a diagnostic value. Moreover, a lncRNA-related ceRNA network may offer new insight into the regulatory mechanisms of schizophrenia. These findings may help improve the diagnostic efficiency of schizophrenia and provide guidance for personalized management for schizophrenic patients.


Palbociclib combined with an aromatase inhibitor (AI) has been shown to be effective in clinical trials for people with HR+/HER2− breast cancer that has spread to other areas of the body, such as the lungs or liver. Evidence of palbociclib effectiveness in routine clinical practice can provide complementary support for clinical trial findings. This study used electronic health records of people with breast cancer that had spread to their lungs and/or liver to determine how well palbociclib plus an AI worked compared to an AI alone. The study showed that palbociclib plus an AI compared with an AI alone was associated with a 38% or 27% reduction in the risk of death for patients with breast cancer that had spread to the lungs or liver, respectively. These findings support the use of palbociclib plus an AI for people whose breast cancer has spread to their lungs or liver.p < 0.001) and rwPFS  in patients with lung metastases and numerically longer OS  and significantly longer rwPFS  for those with liver metastases. Overall, PAL + AI versus AI alone was associated with prolonged OS and rwPFS in routine clinical practice, supporting the use of first-line PAL + AI for patients with HR+/HER2− mBC with lung and/or liver metastases.A cyclin-dependent kinase 4/6 inhibitor combined with endocrine therapy is the standard of care for patients with hormone receptor-positive/human epidermal growth factor 2-negative (HR+/HER2−) metastatic breast cancer (mBC), but real-world effectiveness data for patients with lung or liver metastases are limited. This retrospective study included data from the US Flatiron Health database of patients with HR+/HER2− mBC and lung or liver metastases treated with first-line palbociclib (PAL) plus an aromatase inhibitor (AI) or an AI alone in routine clinical practice. Overall survival (OS) and real-world progression-free survival (rwPFS) were assessed. A total of 891 patients were included . After stabilized inverse probability of treatment weighting to balance patient characteristics, PAL + AI versus AI alone was associated with significantly prolonged OS (HR = 0.62;  Viscera = 2940)  for pati = 4096) , while a = 4096) . The med = 4096) ,14,15. Ap < 0.0005) [A cyclin-dependent kinase 4/6 (CDK4/6) inhibitor in combination with an aromatase inhibitor (AI) is a standard first-line treatment for patients with HR+/HER2− mBC ,17. Palb 0.0005)  and a nu 0.0005) . In the  0.0005) ,21.n = 848) treated with palbociclib plus an AI versus an AI alone. However, the subgroup analysis was univariate and did not separately assess outcomes for patients with lung or liver metastasis. Another study (n = 551) demonstrated that palbociclib plus letrozole was associated with significantly prolonged rwPFS (HR = 0.56) and OS (HR = 0.58) for patients with HR+/HER2− mBC with visceral metastasis (lung or liver) compared with letrozole alone [Although real-world evidence supporting the use of CDK4/6 inhibitors, particularly palbociclib, as first-line therapy in patients with HR+/HER2− mBC has grown ,30,31,32le alone . An examle alone , in whicThis retrospective study used electronic health records from the Flatiron Health longitudinal database, which contains de-identified patient data from >280 cancer clinics, representing more than 3.5 million patients with cancer being actively treated in the US. This database has undergone extensive validation and has been used for multiple real-world studies of patients with breast cancer ,34,35,36This study included women and men (≥18 years of age) who were diagnosed with HR+/HER2− mBC and visceral disease and who initiated palbociclib plus an AI or an AI alone as a first-line treatment between February 2015 and March 2020 in routine clinical practice, with a data cut-off date of September 2020. Visceral disease was defined as metastatic disease in the lung and/or liver. Patient follow-up was conducted from the start of therapy to the data cut-off date, death, last visit, or the date of initiation of the next line of therapy for patients with two or more lines of therapy, whichever came first, with a potential minimum follow-up of 6 months from the index date until the data cut-off date. The exclusion criteria were evidence of any prior treatments with CDK4/6 inhibitors, endocrine treatments, or chemotherapy in the mBC setting. Patients with first structured activity >90 days from the mBC diagnosis date or with missing relevant unstructured documents in the Flatiron database were also excluded from the study.The primary outcome of this study was OS, defined as the number of months from the start of palbociclib plus an AI or an AI alone until death due to any cause . A previDescriptive statistics were used for baseline demographic and clinical characteristics. Treatment comparative analyses were conducted for patients with liver, lung, and both liver and/or lung metastases. Three methods were used for comparative analysis: (1) an unadjusted analysis without controlling for baseline patient characteristics, (2) a stabilized inverse probability of treatment weighting (sIPTW) method  to balance baseline demographic and clinical characteristics between the treatment groups and control for confounding variables, and (3) a 1:1 propensity score matching (PSM) method as a sensitivity analysis to assess the robustness of the sIPTW results. Both the sIPTW and PSM are based on propensity scores calculated by a multivariable binomial logistic regression model. Variables included in the model were age group, sex, race/ethnicity, practice type, disease stage at initial diagnosis, Eastern Cooperative Oncology Group (ECOG) performance status score, bone disease, visceral disease, interval from initial breast cancer diagnosis to mBC diagnosis, and number of metastatic sites.The weighted Kaplan–Meier method was used to summarize and display median survival times and 95% CIs for OS and rwPFS endpoints. The weighted Cox proportional hazards model was used to compute the HRs and corresponding 95% CIs. All analyses were performed using SAS version 9.1.4 or higher .Data from 891 patients with HR+/HER2− mBC and lung and/or liver metastases were included in the study. Of these patients, 622 (69.8%) had lung metastasis, 376 (42.2%) had liver metastasis, and 107 (12.0%) had both lung and liver metastases. Overall, 480 patients received palbociclib plus an AI, and 411 received an AI alone. For patients with lung metastases, 326 (52.4%) received palbociclib plus and AI, whereas 296 (47.6%) received an AI alone. For patients with liver metastases, 211 (56.1%) were given palbociclib plus an AI, whereas 165 (43.9%) were given an AI alone.Patient demographic and clinical characteristics are reported by the site of metastasis  and for p < 0.0001; p = 0.0005; In the unadjusted analysis, patients with lung metastasis treated with palbociclib plus an AI had a significantly longer median OS than those treated with an AI alone .For the cohort of patients with lung and/or liver metastases, treatment with palbociclib plus an AI was associated with a significantly prolonged median OS in the unadjusted, sIPTW, and PSM analyses compared with treatment with an AI alone . After sp < 0.0001; p < 0.0001; In the unadjusted analysis of patients with lung metastasis, palbociclib plus AI was associated with a significantly prolonged median rwPFS compared with an AI alone  versus an AI alone .In the overall cohort of patients with lung and/or liver metastases, the unadjusted, sIPTW, and PSM analyses each revealed a significantly longer rwPFS for patients receiving palbociclib plus AI compared with an AI alone . After sLung and liver are the second and third most common sites of breast cancer metastasis and have generally unfavorable prognoses ,4. In thp = 0.902), whereas those with liver metastases had significantly shorter OS  [p < 0.001) [Studies drawing from data preceding the advent of modern therapies, such as CDK4/6 inhibitors, reported survival outcomes ranging from 16 to 28 months for mBC that spreads to lung or liver ,8,39. Ho< 0.001) . Similar< 0.001) . A retro< 0.001) . Althougn = 138) or liver (n = 51) involvement treated with fulvestrant, the median PFS was 9.6 months and 3.9 months, respectively [Many factors contribute to the clinical outcomes of patients with mBC, including their demographics, clinicopathologic features, tumor molecular subtype, treatment selection, and sequencing ,41. Metaectively . Importan = 1390; HR = 0.76; p < 0.001) [n = 2094; HR = 0.58, 95% CI: 0.52–0.65) [In this study, analysis of the overall cohort of patients with lung and/or liver involvement demonstrated superior OS and rwPFS outcomes for patients treated with palbociclib and AI compared with AI monotherapy. These results are consistent with meta- and pooled analyses of clinical trials of CDK4/6 inhibitor plus ET, which demonstrated a survival benefit in both OS (< 0.001)  and PFS 52–0.65)  for pati52–0.65) . HoweverThe current study has several limitations. As an observational study, only associations can be inferred between treatments and outcomes, but not causality. Retrospective database analyses may have incomplete or missing data and inaccurate data capture. Unlike clinical trials, disease progression was not assessed on a schedule and was not based on the Response Evaluation Criteria in Solid Tumors; therefore, rwPFS data were dependent on each oncologist’s interpretation of diagnostic scans and pathology reports. Treatments were not randomly assigned but were selected for each patient based on the treating physician’s judgment, resulting in potential treatment selection bias. Although sIPTW and PSM were used to balance patient characteristics, the effects of potential unmeasured confounders could not be adjusted for in these analyses. Finally, our results may not be generalizable outside of the US-based Flatiron Health network. It should be noted that our study did not separately analyze patients that had mBC spread to both lung and liver due to the small number of patients in this group. Future inquiries could focus on this population because these patients may exhibit worse outcomes than those with single-organ metastasis . SeparatTreatment with palbociclib plus an AI versus an AI alone in routine clinical practice was associated with OS and rwPFS benefits for patients with HR+/HER2− mBC with lung or liver metastasis. This finding is important given that visceral involvement is common in HR+/HER2− mBC, can be difficult to treat, and is associated with a poor prognosis. These data support the use of first-line palbociclib plus an AI for the treatment of patients with HR+/HER2− mBC with lung or liver metastases.


Angiogenesis contributes to the pathophysiology of cardiovascular disease (CVD). Some cardiovascular drugs used in the treatment of CVD have an effect on the process of angiogenesis. in vivo. Zebrafish embryos at a one-cell stage or two-cell stage were cultured with embryo medium containing cardiovascular drugs at a final solvent concentration of 0.5% (V/V) dimethyl sulfoxide (DMSO) for 24 hours in 24-well plates.  Transgenic Tg (flk1: EGFP) zebrafish embryos were used to identify the effects of some cardiovascular drugs on angiogenesis during vertebral development  We found that 6 drugs including isosorbide mononitrate, amlodipine, bisoprolol fumarate, carvedilol, irbesartan, and rosuvastatin calcium may affect angiogenesis by vascular endothelial growth factor (VEGF) signaling pathway.  These new findings of some cardiovascular drugs should improve the treatment of cardiovascular diseases. Angiogenesis is an important part of the pathophysiology of cardiovascular diseases (CVD) –3. Amongin vivo, zebrafish (Danio rerio) provides an excellent model for the transparency of its embryo, which can direct observe vertebral development [In order to study angiogenesis elopment . Transgeelopment . In this2, 0.33 mM Mg2SO4, 0.7 mM HEPES, and 10−5% methylene blue (pH 7.2) with a conductivity of 672 μS and dissolved oxygen of 8.2 mg/l) in an incubator at 28.5°C. All the assays were performed at 25-28°C. Animal experiments were carried out in accordance with the guiding principles of the Animal Experimentation Ethics Committee of Second Military Medical University.Transgenic zebrafish were raised following standard care and maintenance protocols of a 14 : 10 light : dark cycle , and raiV/V) DMSO final solvent concentration for 24 hours in a 24-well plate. Each compound was tested at six concentrations according to human blood concentration  zebrafish embryo, in which GFP was expressed in all endothelial cells of the vasculature in the intersegmental blood vessels (ISVs). Embryos were exposed to a single concentration of reagent for 24 hours and then dechorionated and fixed in methylcellulose . After fixation, the vascular development of embryos was examined by a LEICA 205FA microscope. Antiangiogenic and angiogenic effects are defined as significant inhibition or enhancement of mature ISV formation, which normally connects the dorsal aorta and dorsal longitudinal anastomotic vessel (DLAV) in living embryos. The length of ISVs was calculated by LEICA 205FA software through point-to-point distance. The number of ISVs was calculated from photos taken under a microscope. ISV length and number were used to quantify angiogenesis.μg/ml streptomycin at 37°C with 5% CO2. Then, HUVECs were passaged 3 times a week.Human Umbilical Vein Endothelial Cells (HUVECs)  were cultured in DMEM media (Invitrogen) with 2% fetal bovine serum (FBS), 100 U/ml penicillin and 100 5 HUVECs from passages 3 using TRIzol reagent . Besides, NanoDrop 2000  was utilized to analyze the quality and integrity of RNA. Next, 2 μg RNA of each sample was reverse transcribed into cDNA by the PrimeScript™ 1st Strand cDNA Synthesis Kit (Takara Biotechnology). Subsequently, the amount of target RNA was normalized to that of internal control (GAPDH) and given by 2−△△Ct relative to the control sample. Primers used in qRT-PCR were listed as follows (shown 5′-3′): VEGF F: TGGACCCTGGCTTTACTGCTG and R: GGCAATAGCTGCGCTGGTAGA; GAPDH F: GAEAACTTTGGCATCGTGGA and R: TGCAGGGATGATGTTCTGG.The VEGF expression level in HUVECs was determined by qRT-PCR. Briefly, total RNA was extracted from 1 × 10t-test to compare the vehicle control group with a drug-treated group. All the statistical tests were two-tailed. A P value less than 0.05 was considered to indicate a statistical significance.The data of the current study were analyzed using a mixed design analysis of variance between-subject factors of drugs, drug dose, and timing of drug administration. All experiments were repeated for at least three times. Next, the values were presented as mean ± standard definition (SD). And statistical analysis was performed with Microsoft Excel software. Statistical significance was assessed using Student's To maximize drug dissolution, we used 0.5% DMSO as the drug carrier control. First, we test the effect of DMSO to angiogenesis in zebrafish. As shown in μg/ml isosorbide mononitrate, 100 ng/ml amlodipine, and 500 ng/ml bisoprolol fumarate significantly promoted the growth of angiogenic ISVs in embryos (P < 0.05). The bar graph in To determine the proangiogenic properties of the drug, we used a transgenic Tg (fli1: EGFP) zebrafish as an animal model and 0.5% DMSO as the solvent of the drug. At the same time, VEGF, a known angiogenic compound, was used as a positive control. As a control, VEGF (200 ng/ml) was conducive to the formation of ISVs . Then, aμg/ml) could significantly decrease the number and length of ISVs (P < 0.05), suggesting that carvedilol, irbesartan, and rosuvastatin calcium could suppress the formation of capillary under similar experimental conditions.In the process of drug screening, we also found that the other 3 drugs could inhibit the formation of ISVs. First, we used an antiangiogenic compound PD173074 as a positive control, which suppresses angiogenesis through inhibiting fibroblast growth factor receptor 1 (FGFR1) , 13. PD1 of ISVs .  Figurein vitro HUVEC model was established using drug incubation for 24 hours. The expression of VEGF mRNA was detected by qRT-PCR. Results showed that the expression of VEGF was dramatically increased in HUVEC exposure to 50 μg/ml isosorbide mononitrate, 10 ng/ml amlodipine, and 50 ng/ml bisoprolol fumarate (P < 0.05). However, compared with the control group, the expression of VEGF was decreased in HUVECs incubated with 10 ng/ml carvedilol, 10 ng/ml irbesartan, and 20 ng/ml rosuvastatin calcium (P < 0.05). These data indicated that the above drugs may play a role in promoting angiogenesis and antiangiogenesis through the VEGF signal pathway.Due to the potent pro- and antiangiogenic activities of drugs, its mechanism is worthy of further investigation. Among the known angiogenic factors, VEGF is the most important factor in the process of angiogenesis . To furtResults of this study indicated that isosorbide mononitrate, amlodipine, and bisoprolol fumarate exerted angiogenic effects, whereas carvedilol, irbesartan, and rosuvastatin calcium inhibited angiogenesis. Besides, these drugs might regulate angiogenesis through the VEGF pathway.Zebrafish has become a novel preclinical model that can support rapid decision-making in the early phases of the drug discovery process . Our stuIn the transgenic zebrafish model, we found that isosorbide mononitrate, amlodipine, and bisoprolol fumarate might exert angiogenic effects through upregulating VEGF expression Figures  and 4. A2+) channel blocker used for treatment of hypertension, also shows angiogenic activity in human coronary artery endothelial cells in vitro [Amlodipine, a dihydropyridine calcium (Cain vitro . Moreovein vitro . HoweverBisoprolol fumarate is a beta-selective blocker and an effective drug for the treatment of heart failure and hypertension. A previous study has demonstrated that bisoprolol fumarate displays angiogenic activity in mouse aortic ring assay . Our resα1- and nonselective β-adrenoceptor antagonist activity, which is widely used in hypertension and heart failure [Furthermore, we also identified the antiangiogenic activity of carvedilol, irbesartan, and rosuvastatin calcium in the zebrafish model. Carvedilol is a pharmacological antioxidant with  failure . However failure , 26. ThuIrbesartan is an angiotensin II receptor blocker. Recent research has shown that irbesartan may reduce angiogenesis by decreasing the number of infiltrating cells expressing VEGF in cancer . BesidesRosuvastatin calcium is a member of the statins family. Currently, the effect of statins on angiogenesis remains controversial. Weis et al. concluded that statins have a biphasic dose-dependent effect on angiogenesis . In the However, there were several limitations in this study. We did not address the problem of the potential effects of hydrophilicity that may affect drug absorption and lead to possible false negative results. In addition, like any other animal models, the zebrafish model does not reliably predict the human outcome; nevertheless, it provides valuable insights into the “new” pharmacological effects of the drugs tested. The evidence obtained from this model may enrich our understanding of the pharmacological profiles of the currently used cardiovascular drugs.Results of this study indicated that isosorbide mononitrate, amlodipine, and bisoprolol fumarate exerted angiogenic effects, whereas carvedilol, irbesartan, and rosuvastatin calcium inhibited angiogenesis. Besides, these drugs might regulate angiogenesis through the VEGF pathway. The new functions of these drugs should improve the treatment of cardiovascular diseases.