Accurate segmentation of left ventricle (LV) is a fundamental step in evaluation of cardiac function. Cardiac CT angiography (CCTA) has become an important clinical diagnostic method for cardio-vascular disease (CVD) due to its non-invasive, short exam time, and low cost. find more To obtain the segmentation of the LV in CCTA scans, we present a deep learning method based on an 8-layer residual U-Net with deep supervision.

Based on the original 4-layer U-Net, our method deepened the network to eight layers, which increased the fitting capacity of the network, thus greatly improved its LV recognition capability. Residual blocks were incorporated to optimize the network from the increased depth. Auxiliary paths as deep supervision were introduced to supervise the intermediate information to improve the segmentation quality. In this study, we collected CCTA scans of 100 patients. Eighty patients with 1600 discrete slices were used to train the LV segmentation and the remaining 20 patients with 400 discrete slices wered has potential advantages to be a reliable segmentation method and useful for the evaluation of cardiac function in the future study.

The proposed 8-layer residual U-Net with deep supervision accurately and efficiently segments the LV in CCTA scans. This method has potential advantages to be a reliable segmentation method and useful for the evaluation of cardiac function in the future study.

Cardiac magnetic resonance (CMR) imaging is a well-established technique for diagnosis of hypertrophic obstructive cardiomyopathy (HOCM) and evaluation of cardiac function, but the process is complicated and time consuming. Therefore, this paper proposes a cardiomyopathy recognition algorithm using a multi-task learning mechanism and a double-branch deep learning neural network.

We implemented a double-branch neural network CMR-based HOCM recognition algorithm. Compared with the traditional classification algorithms such as the ResNet, DenseNet network, contrast the accuracy of network classification of cardiomyopathy is higher by 10.11%.

The loss curve of the algorithm basically converges in 100 rounds, and the convergence speed of the algorithm is twice that of the traditional algorithm. The accuracy of this algorithm to classify cardiomyopathy is 96.79%, and the sensitivity is 95.24%, which is 10.11% higher than the conventional algorithm.

The CMR imaging automatic recognition algorithm for HOCM capture static morphological and motion characteristics of the heart, and comprehensively enhances recognition accuracy when the sample size is limited.

The CMR imaging automatic recognition algorithm for HOCM capture static morphological and motion characteristics of the heart, and comprehensively enhances recognition accuracy when the sample size is limited.Understanding the frequency of bacteraemia of dental origin that is implicated in severe infective endocarditis (IE) will further our understanding of the disease’s pathoaetiology and help us take steps to reduce its prevalence. A total of 78 patients from the Royal Papworth Hospital, Cambridge, who had valve surgery due to IE (as confirmed by the Modified Duke Criteria) were included. Case notes were retrospectively reviewed for microorganisms that were implicated in the bacteraemia and IE. Associated factors were also recorded to determine whether they were different if a dental or non-dental pathogen was inoculated. A dental pathogen was implicated in 24 of the patients with IE; 20 had non-dental pathogens, and 30 were culture negative. This was not deemed statistically significant (p=0.54). Of the associated factors, only smoking was statistically significant with a greater proportion of non-smokers having bacteraemia of dental origin (p=0.03). No other associated factor was appreciably different based on the aetiology of the microorganism. Our results indicate that dental pathogens are not more likely to cause severe IE. We therefore advocate the stance adopted by the current national guidance on the judicious prescription of antibiotic prophylaxis for IE with regard to dental procedures.

Dialysis patients report a low health-related quality of life (HRQOL) due to high disease burden and far-reaching consequences of dialysis treatment. This study examined several cognitive-behavioral and social factors, with a focus on negative outcome expectancies, that might be relevant for HRQOL in end-stage kidney disease (ESKD) patients treated with dialysis.

Patients treated with hemodialysis or peritoneal dialysis were recruited from Dutch hospitals and dialysis centers. Patients completed self-report questionnaires at baseline (n = 175) and six months follow-up (n = 130). Multiple regression analyses were performed.

Higher scores on factors related to negative outcome expectancies at baseline, especially helplessness and worrying, and less perceived social support were significantly related to worse HRQOL six months later. When controlling for baseline HRQOL, besides sex and comorbidity, helplessness remained significantly predictive of worse HRQOL six months later, indicating that helplessness is associated with changes in HRQOL over time.

Negative outcome expectancies and social support are relevant markers for HRQOL and/or changes in HRQOL over time.

Negative outcome expectancies could be prevented or diminished by enhanced treatment information, an improved patient-clinician relationship, and interventions that promote adaptive and realistic expectations. Additionally, increasing supportive social relationships could be a relevant treatment focus.

Negative outcome expectancies could be prevented or diminished by enhanced treatment information, an improved patient-clinician relationship, and interventions that promote adaptive and realistic expectations. Additionally, increasing supportive social relationships could be a relevant treatment focus.Effective communication is critical for therapeutic work with individuals, for the interdisciplinary team, and for leadership in a substance use disorder (SUD) treatment program. Prior to the COVID-19 pandemic, over a two-year period Thomas Jefferson University’s Maternal Addiction Treatment, Education and Research (MATER) program, an SUD treatment program serving pregnant and parenting women living in an urban environment, implemented Mindfulness Dialogue for Life (MDfL) to deepen communication, encourage courageous conversations, bring more compassion to staff and patients, and improve trust among leadership. MDfL focuses on three stages-connecting, exploring, and discovering-and it uses mindfulness practices to enhance communication. Here we describe our efforts to implement MDfL on a virtual platform and how the COVID-19 pandemic affected staff’s work experience, as identified during their MDfL sessions.