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Abstract Background Echocardiography-based ultrasomics analysis aids Kawasaki disease (KD) diagnosis but its role in predicting coronary artery lesions (CALs) progression remains unknown. We aimed to develop and validate a predictive model combining echocardiogram-based ultrasomics with clinical parameters for CALs progression in KD. Methods Total 371 KD patients with CALs at baseline were enrolled from a retrospective cohort (cohort 1, n = 316) and a prospective cohort (cohort 2, n = 55). CALs progression was defined by increased Z scores in any coronary artery branch at the 1-month follow-up. Patients in cohort 1 were split randomly into training and validation set 1 at the ratio of 6:4, while cohort 2 comprised validation set 2. Clinical parameters and ultrasomics features at baseline were analyzed and selected for models construction. Model performance was evaluated by area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) and decision curve analysis (DCA) in the training and two validation sets. Results At the 1-month follow-ups, 65 patients presented with CALs progression. Three clinical parameters and six ultrasomics features were selected to construct the model. The clinical-ultrasomics model exhibited a good predictive capability in the training, validation set 1 and set 2, achieving AUROCs of 0.83 (95% CI, 0.75–0.90), 0.84 (95% CI, 0.74–0.94), and 0.73 (95% CI, 0.40–0.86), respectively. Moreover, the AUPRC values and DCA of three model demonstrated that the clinical-ultrasomics model consistently outperformed both the clinical model and the ultrasomics model across all three sets, including the training set and the two validation sets. Conclusions Our study demonstrated the effective predictive capacity of a prediction model combining echocardiogram-based ultrasomics features and clinical parameters in predicting CALs progression in KD.

Ovarian cancer is the second most common cause of gynecologic cancer death in women around the world. Around 75% of patients present with stage Ⅲ/Ⅳ disease at diagnosis, with five-year survival rates below 45%. Ovarian cancer is the main adnexal malignant mass. Thus, accurate non-invasive risk stratification of adnexal masses is essential for optimal management and outcomes. In recent years, the field of artificial intelligence is developing rapidly. As a branch of artificial intelligence, machine learning could learn efficiently from complex and large amounts of data, which has infinite potential to differentiate benign and malignant adnexal masses. Logistic regression(LR), artificial neural network (ANN), support vector machine (SVM), deep learning convolution neural networks (DCNN) have been widely applied in this field, and achieved good diagnostic performance. This paper will review the history and progress of machine learning in ultrasound diagnosis of benign and malignant adnexal masses.

Objective To investigate the prevalence of mild cognitive impairment (MCI) and related influencing factors among elderly population in urban community of Tianjin. Methods The cross-sectional survey and cluster sampling method were used for prevalence survey. Total 3200 elderly people aged ≥ 60 years old who underwent routine physical examination from four communities in Heping District of Tianjin from September 2019 to May 2020 were investigated by questionnaire survey and cognitive screening test, finally 3194 elderly people were recruited. Univariate and multivariate Logistic regression analyses were conducted to explore related influencing factors of MCI. Results Total 340 subjects were diagnosed with MCI among 3194 elderly subjects, with a prevalence rate of 10.64%. Logistic regression analysis showed 70-79 years old (OR = 1.670, 95%CI: 1.275-2.187; P = 0.000), ≥ 80 years old (OR = 3.168, 95%CI: 2.264-4.433; P = 0.000) and body mass index (BMI) < 18.50 kg/m² (OR = 3.602, 95%CI: 1.899-6.833; P = 0.000) were independent risk factors of MCI; and primary school (OR = 0.342, 95%CI: 0.148-0.790; P = 0.012), junior high school (OR = 0.150, 95%CI: 0.066-0.337; P = 0.000), senior high school (OR = 0.131, 95%CI: 0.058-0.294; P = 0.000), college and above (OR = 0.068, 95%CI: 0.030-0.155; P = 0.000) and having exercise habits (OR = 0.786, 95%CI: 0.621-0.995; P = 0.045) were protective factors. Conclusions The prevalence of MCI among elder people ≥ 60 years old is 10.64% in urban community of Tianjin, and mainly influenced by age, body weight, educational level and exercise habits. [ABSTRACT FROM AUTHOR]

Previously, we identified the genetic variant −241 (−/G) (rs11453459) in the PP2A-Aα gene (PPP2R1A) promoter and demonstrated that this variant influences the DNA-binding affinity of nuclear factor-kappa B (NF-κB). In this study, we further confirmed that the transcriptional activity of PPP2R1A may be regulated by NF-κB through the functional genetic variant −241 (−/G). Moreover, we also demonstrated that the methylation status of CpG islands in the promoter of PPP2R1A influences the activity of this gene promoter. Few studies have examined the role of this −241 (−/G) variant in genetic or epigenetic regulation in hepatocellular carcinoma (HCC). To investigate whether this functional variant in the PPP2R1A promoter is associated with the risk of HCC and confirm the function of the −241 (−/G) variant in the HCC population, we conducted a case-control study involving 251 HCC cases and 252 cancer-free controls from a Han population in southern China. Compared with the −241 (−−) homozygote, the heterozygous −241 (−G) genotype (adjusted OR  = 0.32, 95% confidence interval (CI)  = 0.17–0.58, P<0.001) and the −241 (−G)/(GG) genotypes (adjusted OR  = 0.38, 95% CI  = 0.22–0.67, P  = 0.001) were both significantly associated with a reduced risk of HCC. Stratification analysis indicated that the protective role of −241 (−G) was more pronounced in individuals who were ≤ 40 years of age, female and HBV-negative. Our data suggest that the transcriptional activity of PPP2R1A is regulated by NF-κB through the −241 (−/G) variant and by the methylation of the promoter region. Moreover, the functional −241 (−/G) variant in the PPP2R1A promoter contributes to the decreased risk of HCC. These findings contribute novel information regarding the gene transcription of PPP2R1A regulated by the polymorphism and methylation in the promoter region through genetic and epigenetic mechanisms in hepatocarcinogenesis. [ABSTRACT FROM AUTHOR]

Expression of mitochondrial proton transporter uncoupling protein 1 (UCP1) in brown adipose tissue (BAT) is essential for mammalian thermogenesis. While human UCP1 mRNA exists in a long form only, alternative polyadenylation creates two different isoforms in mice with 10% of UCP1 mRNA found in the long form (Ucp1L) and ~90% in the short form (Ucp1S). We generated a mouse model expressing only Ucp1S and found that it showed impaired thermogenesis due to a 60% drop in UCP1 protein levels, suggesting that Ucp1L is more efficiently translated than Ucp1S. In addition, we found that β3 adrenergic receptor signaling promoted the translation of mouse Ucp1L and human Ucp1 in a manner dependent on cytoplasmic polyadenylation element binding protein 2 (CPEB2). CPEB2‐knockout mice showed reduced UCP1 levels and impaired thermogenesis in BAT, which was rescued by ectopic expression of CPEB2. Hence, long 3′‐UTR Ucp1 mRNA translation activated by CPEB2 is likely conserved and important in humans to produce UCP1 for thermogenesis. Synopsis: Mitochondrial proton transporter Uncoupling protein 1 (Ucp1) exists in two isoforms that show differential translation efficiency in brown adipose tissue. Adrenergic receptor signaling leads Cytoplasmic polyadenylation element binding protein 2 (CPEB2) to bind the longer form of Ucp1 mRNA and activate its translation, thereby promoting thermogenesis. Alternative polyadenylation generates Ucp1 mRNAs with short (Ucp1S) and long (Ucp1L) 3′‐UTRs in mice.Ucp1L, the only form expressed in humans, is translated ˜15‐fold more efficiently than Ucp1S in vivo.CPEB2 promotes polyadenylation‐induced Ucp1L translation in response to β3 adrenergic signaling.Deficiency of CPEB2 or Ucp1L impairs UCP1 expression and thermogenesis. Isoform‐specific translation of Ucp1, stimulated by cytoplasmic polyA‐binding protein CPEB2 and adrenergic signaling, is required for thermogenesis in vivo. [ABSTRACT FROM AUTHOR]