Notes

Framework and system with the mammalian fructose transporter GLUT5.
efficacy results are promising and further recruitment is underway.Amyloid-β (Aβ), a peptide implicated in Alzheimer's disease, was shown to cause specific fragmentation of lamin proteins, which was mediated by an unidentified protease named nuclear scaffold protease (NSP) independently of caspase-6. Because caspase-6 is responsible for the fragmentation process in many other damage-induced apoptosis, here we further investigated possible involvement of caspase-6 in Aβ-induced lamin fragmentation under various conditions. We found that lamin A fragment generated by NSP (named fragment b) disappeared in cells incubated with Aβ42 for prolonged periods and this product was preserved by a caspase-6 inhibitor. Furthermore, caspase-6 could remove fragment b in nuclei isolated from Aβ42-treated cells (ANU). Lamin B in ANU was fragmented by caspase-6 only after treatment with an alkaline phosphatase. The caspase-mediated fragmentation of lamin B was also achieved with nuclei isolated from cells incubated with Aβ42 plus a Cdk5 inhibitor. The results indicate that Aβ42 induces NSP-mediated fragmentation of lamin A and the following removal process of fragment b by caspase-6 and an Aβ-induced phosphorylation prevents the fragmentation of lamin B by caspase-6. The pathway leading to lamin protein fragmentation in this investigation appears to be specific for Aβ and thus the data will provide novel insights into the toxicity of the peptide.
An estimated 2.8 million neonatal deaths occur each year globally, which accounts for at least 45% of deaths in children aged less than 5 years. Birthweight and gestational age-specific mortality estimates are limited in low-resource countries like Uganda. A deeper analysis of mortality by birthweight and gestational age is critical in identifying the cause and potential solutions to decrease neonatal mortality.

We studied mortality before discharge in relation to birthweight and gestational age using a large sample size from selected tertiary care facilities in Uganda.

We used secondary data from the East Africa Preterm Birth Initiative study conducted in six tertiary care facilities. Birth records of infants born between October 2016 and March 2019 with a gestational age greater than or equal to 24 weeks and/or birthweight greater than or equal to 500 g were reviewed for inclusion in the analysis. Newborn death before discharge was the outcome variable of interest. Multivariable Poisson regression modeling was used to explore birthweight and gestational age-specific mortality rate.

We analysed 50 278 birth records. this website Among these 95.3% (47 913) were live births and 4.8% (2365) were stillbirths. Of the 47 913 live births, 50% (24 147) were males. Overall, pre-discharge mortality was 13.0 per 1000 live births. For each 1 kg increase in birthweight, mortality before discharge decreased by -0.016. As birthweight increases, the mortality before discharge decreased from 336 per 1000 live births among infants born between 500 and 999 g, to 4.7 per 1000 live births among infants born weighing 3500 to 3999 g, and increased again to 11.2 per 1000 live births among infants weighing more than 4500 g.

Our study highlights the need for further research to understand newborn survival across different birthweight and gestational categories.
Our study highlights the need for further research to understand newborn survival across different birthweight and gestational categories.Exacerbations of COPD are one of the commonest causes of admission and readmission to hospital. The role of digital interventions to support self-management in improving outcomes is uncertain. We conducted an open, randomised controlled trial of a digital health platform application (app) in 41 COPD patients recruited following hospital admission with an acute exacerbation. Subjects were randomised to either receive usual care, including a written self-management plan (n = 21), or the myCOPD app (n = 20) for 90 days. The primary efficacy outcome was recovery rate of symptoms measured by COPD assessment test (CAT) score. Exacerbations, readmission, inhaler technique quality of life and patient activation (PAM) scores were also captured by a blinded team. The app was acceptable in this care setting and was used by 17 of the 20 patients with sustained use over the study period. The treatment effect on the CAT score was 4.49 (95% CI -8.41, -0.58) points lower in the myCOPD arm. Patients' inhaler technique improved in the digital intervention arm (101 improving to 20 critical errors) compared to usual care (100 to 72 critical errors). Exacerbations tended to be less frequent in the digital arm compared to usual care; 34 vs 18 events. Hospital readmissions risk was numerically lower in the digital intervention arm OR for readmission 0.383 (95% CI 0.074, 1.987; n = 35). In this feasibility study of the digital self-management platform myCOPD, the app has proven acceptable to patients to use and use has improved exacerbation recovery rates, with strong signals of lower re-exacerbation and readmission rates over 90 days. myCOPD reduced the number of critical errors in inhaler technique compared to usual care with written self-management. This provides a strong basis for further exploration of the use of app interventions in the context of recently hospitalised patients with COPD and informs the potential design of a large multi-centre trial.Missed fractures are the most common diagnostic error in emergency departments and can lead to treatment delays and long-term disability. Here we show through a multi-site study that a deep-learning system can accurately identify fractures throughout the adult musculoskeletal system. This approach may have the potential to reduce future diagnostic errors in radiograph interpretation.Artificial intelligence (AI) based on deep learning has shown excellent diagnostic performance in detecting various diseases with good-quality clinical images. Recently, AI diagnostic systems developed from ultra-widefield fundus (UWF) images have become popular standard-of-care tools in screening for ocular fundus diseases. However, in real-world settings, these systems must base their diagnoses on images with uncontrolled quality ("passive feeding"), leading to uncertainty about their performance. Here, using 40,562 UWF images, we develop a deep learning-based image filtering system (DLIFS) for detecting and filtering out poor-quality images in an automated fashion such that only good-quality images are transferred to the subsequent AI diagnostic system ("selective eating"). In three independent datasets from different clinical institutions, the DLIFS performed well with sensitivities of 96.9%, 95.6% and 96.6%, and specificities of 96.6%, 97.9% and 98.8%, respectively. Furthermore, we show that the application of our DLIFS significantly improves the performance of established AI diagnostic systems in real-world settings.
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