Effect of short-term exercise intervention on cardiovascular functions and quality of life of chronic heart failure patients: A meta-analysis

AbstractObjectiveThe purpose of this study was to comprehensively evaluate the effect of short-term exercise intervention on the cardiovascular functions and quality of life (QoL) of patients with chronic heart failure (CHF).MethodsThis meta-analysis was analyzed using RevMan5.3 and Stata 13.0. The parameters of cardiovascular functions and QoL were assessed. Weighted mean differences and their corresponding 95% confidence intervals (CIs) were computed for continuous variables.ResultsData from 2533 CHF patients enrolled in 28 published studies of randomized controlled trials (RCTs) were collated. There were significant differences in VO2 max prior to and after exercise intervention in CHF patients who are 50–55 years old (5 RCTs; 95% CI, −4.86 to −2.29; I2 = 50.5%), 60–65 years old (10 RCTs; 95% CI, −2.66 to −2.04; I2 = 0%), and 69–75 years old (5 RCTs; 95% CI, −1.88 to −0.34; I2 = 38.5%). VO2 max was significantly increased by aerobic exercise (9 RCTs; 95% CI, −3.45 to −1.92; I2 = 37.7%) and combined aerobic resistance exercise (4 RCTs; 95% CI, −4.41 to −0.26; I2 = 76.6%). There were significant differences in cardiac output (n = 303; 95% CI, −0.25 to −0.02; I2 = 12%) and QoL (n = 299; 95% CI, 3.19 to 9.70; I2 = 17%) prior to and after short-term exercise.ConclusionAerobic exercise and aerobic with resistance exercise can significantly improve the aerobic capacity of CHF patients, whereas resistance exercise cannot. The improvement in aerobic capacity caused by aerobic exercise and aerobic with resistance exercise decreases with age. Systolic blood pressure and ventricle structures and functions of CHF patients show no significant changes after the short-term exercise intervention

Validation of an adaptive transfer function method to estimate the aortic pressure waveform

Aortic pulse wave reflects cardiovascular status, but, unlike the peripheral pulse wave, is difficult to be measured reliably using noninvasive techniques. Thus, the estimation of aortic pulse wave from peripheral ones is of great significance. This study proposed an adaptive transfer function (ATF) method to estimate the aortic pulse wave from the brachial pulse wave. Aortic and brachial pulse waves were derived from 26 patients who underwent cardiac catheterization. Generalized transfer functions (GTF) were derived based on the autoregressive exogenous model. Then, the GTF was adapted by its peak resonance frequency. And the optional peak resonance frequency for an individual was determined by regression formulas using brachial systolic blood pressure. The method was validated using the leave-one-out cross validation method. Compared with previous studies, the ATF method showed better performance in estimating the aortic pulse wave and predicting the feature parameters. The prediction error of the aortic systolic blood pressure and pulse pressure were 0.2 ± 3.1 and -0.9 ± 3.1 mmHg, respectively. The percentage errors of augmentation index, percentage notch amplitude, and ejection duration were -2.1 ± 32.7%, 12.4 ± 9.2%, and -2.4 ± 3.3%, respectively

Integrative Analyses of Transcriptome Sequencing Identify Functional miRNAs in the Chicken Embryo Fibroblasts Cells Infected With Reticuloendotheliosis Virus

In this study, we found a much higher proportion of reticuloendotheliosis virus (REV) infected chicken embryo fibroblasts (CEF) were in active cell division phase than that of control cells which indicated that REV can affect the fate of CEF. So, we performed high-throughput sequencing and transcriptomic analysis to identify functional miRNAs, in order to figure out the possible mechanism in the interaction of REV with CEF. In total, 50 differentially expressed miRNAs (DEmiRNAs) were identified. Then target genes of DEmiRNAs were predicted and identified by transcriptome profile results. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were conducted to analyze the identified target genes of miRNAs which showed that metabolism, cell cycle, and apoptosis were the most related pathways involved in infection of REV. We analyzed the genes related to cell cycle which indicated that CyclinD1-CDK6 complex played an important role in regulating the transition of the cell cycle from G1 phase to S phase during REV infection. Fluorescence microscope identification showed that REV inhibited the apoptosis of CEF which was in accordance with transcriptome results. A novel miRNA, named novel-72 was found, KEGG analysis was conducted to predict the biological function of its target genes which showed that those target genes were significantly enriched in mTOR signaling pathway and functioned to promote cell cycle and cell growth during the REV infection. In conclusion, REV could induce the up-regulation of cell metabolism, cell cycle and mTOR signaling pathway while inhibit apoptosis of the cell

Role of 18F-MD-PSMA PET/CT in initial stage of intermediate and high risk prostate cancer

Objective·To evaluate the role of 18F-MD-PSMA PET/CT in the initial stage of patients with moderate and high risk prostate cancer (PCa).Methods·A total of 67 patients with moderate and high risk PCa who were treated in Xinhua Hospital, Shanghai Jiao Tong University School of Medicine from September 2017 to June 2022 were initially staged by 18F-MD-PSMA PET/CT. Conventional imaging (CI), including multi-parameter magnetic resonance imaging (mp-MRI) and bone scintigraphy (BS), were performed within two weeks before 18F-MD-PSMA PET/CT. Twenty-five patients underwent 18F-FDG PET/CT at the same time. The sensitivity (SEN), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACU) of 18F-MD-PSMA PET/CT in the initial stage were evaluated, and the results were compared with those of 18F-FDG PET/CT, mp-MRI and BS. The consistency of 18F-MD-PSMA PET/CT and CI in terms of primary lesion, regional lymph node metastasis and bone metastasis was evaluated by Kappa consistency test refering to the postoperative pathological T and N staging results and bone metastasis results of clinical follow-up. Kappa coefficient was calculated and compared.Results·Of the 67 patients with PCa, 38 patients underwent radical prostatectomy and had completed pathological data, with 27 patients undergoing regional lymphadenectomy and 1 patient undergoing expanded pelvic lymphadenectomy at the same time. The pathological results were obtained as gold standard. The detection rates of mp-MRI and 18F-MD-PSMA PET/CT in diagnosing intrathecal lesions were both 100%. The SENs in diagnosing bilateral intralobular lesions were 26.3% and 63.2%, respctively; the SPECs were both 75.0%.The Kappa consistency test showed that the consistency of 18F-MD-PSMA PET/CT in diagnosis of extracapsular extension (EPE), seminal vesicle invasion (SVI), and bladder neck invasion (BNI) was higher than that of mp-MRI. Fisher′s exact test showed that there were no statistically significant differences in SEN (P=0.226, P=0.491) and SPEC (P=1.000, P=0.342) between the two methods for diagnosing EPE and SVI, as well as SEN (P=1.000) for diagnosing BNI. In terms of diagnosis of lymph node metastasis, based on the analysis of lymph node numbers, the consistency between 18F-MD-PSMA PET/CT and pathological results was higher than that of mp-MRI (Kappa coefficients of 0.555 and 0.137, respectively). Fisher′s exact test showed that there were no statistically significant differences in SEN and SPEC between the two examination methods (P=0.562, P=0.829). Based on the patients, the consistency between 18F-MD-PSMA PET/CT and pathological results was higher than that of mp-MRI (Kappa coefficients of 0.850 and 0.313, respectively). There was no statistically significant difference in SEN between the two methods (P=1.000). In terms of diagnosis of bone metastasis, based on the analysis of bone lesion numbers, the consistency between 18F-MD-PSMA PET/CT and clinical follow-up results was higher than that of BS (Kappa coefficients of 0.500 and 0.299, respectively). Fisher′s exact test showed that there was no statistically significant difference in SEN between the two methods (P=0.219). Based on the patients, the consistency between 18F-MD-PSMA PET/CT and clinical follow-up results was higher than that of BS (Kappa coefficients of 0.953 and 0.766, respectively). There was no statistically significant difference in SEN between the two methods (P=1.000). The risks of 21 patients (31.3%) were increased after 18F-MD-PSMA PET/CT detection, with 1 patient (1.5%) decreasing. The initial stage of 32 cases (47.8%) were changed after 18F-MD-PSMA PET/CT detection, with 27 cases (40.3%) upstaged and 5 cases (7.5%) downstaged.Conclusion·18F-MD-PSMA PET/CT is superior to CI in the diagnosis of bilateral intralobular lesions, EPE, SVI, regional lymph node metastasis and bone metastasis in intermediate and high risk PCa, and on this basis, the diagnosis of clinical stage and metastatic status of some patients has been changed

Fetal brain tissue annotation and segmentation challenge results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero