Aldridgekromann2020

Ever-harsher service conditions in the future will call for materials with increasing ability to undergo deformation without sustaining damage while retaining high strength. Prime candidates for these conditions are certain high-entropy alloys (HEAs), which have extraordinary work-hardening ability and toughness. By subjecting the equiatomic CrMnFeCoNi HEA to severe plastic deformation through swaging followed by either quasi-static compression or dynamic deformation in shear, we observe a dense structure comprising stacking faults, twins, transformation from the face-centered cubic to the hexagonal close-packed structure, and, of particular note, amorphization. The coordinated propagation of stacking faults and twins along 111 planes generates high-deformation regions, which can reorganize into hexagonal packets; when the defect density in these regions reaches a critical level, they generate islands of amorphous material. These regions can have outstanding mechanical properties, which provide additional strengthening and/or toughening mechanisms to enhance the capability of these alloys to withstand extreme loading conditions.Refractory pain during care procedures causes a real challenge for terminally ill patients. We are hereby publishing three cases of patients who received repeated procedural sedations using propofol during the painful care procedures. AZD9291 datasheet All patients experience pain relief with no side effects although care procedures initially were a traumatic experience to them despite the usual medication. This therapeutic solution, which would need to be assessed on a case-by-case basis by evaluating the benefit-risk balance, could become a suitable comfort treatment used by palliative care teams.

We aimed to identify clusters of people with type 2 diabetes mellitus (T2DM) and to assess whether the frequency of these clusters was consistent across selected countries in Latin America and the Caribbean (LAC).

We analyzed 13 population-based national surveys in nine countries (n=8361). We used k-means to develop a clustering model; predictors were age, sex, body mass index (BMI), waist circumference (WC), systolic/diastolic blood pressure (SBP/DBP), and T2DM family history. The training data set included all surveys, and the clusters were then predicted in each country-year data set. We used Euclidean distance, elbow and silhouette plots to select the optimal number of clusters and described each cluster according to the underlying predictors (mean and proportions).

The optimal number of clusters was 4. Cluster 0 grouped more men and those with the highest mean SBP/DBP. Cluster 1 had the highest mean BMI and WC, as well as the largest proportion of T2DM family history. We observed the smallest values of all predictors in cluster 2. Cluster 3 had the highest mean age. When we reflected the four clusters in each country-year data set, a different distribution was observed. For example, cluster 3 was the most frequent in the training data set, and so it was in 7 out of 13 other country-year data sets.

Using unsupervised machine learning algorithms, it was possible to cluster people with T2DM from the general population in LAC; clusters showed unique profiles that could be used to identify the underlying characteristics of the T2DM population in LAC.

Using unsupervised machine learning algorithms, it was possible to cluster people with T2DM from the general population in LAC; clusters showed unique profiles that could be used to identify the underlying characteristics of the T2DM population in LAC.

The availability of easily accessible continuous glucose monitoring (CGM) metrics can improve glycemic control in diabetes, and they may even become a viable alternative to hemoglobin A1c (HbA1c) laboratory tests in the next years. The REALISM-T1D study (REAl-Life glucoSe Monitoring in Type 1 Diabetes) was aimed at contributing, with real-world data, to a deeper understanding of these metrics, including the time in range (TIR)-HbA1c relationship, to facilitate their adoption by diabetologists in everyday practice.

70 adults affected by type 1 diabetes were monitored for 1 year by means of either flash (FGM) or real-time (rtCGM) glucose monitoring devices. Follow-up visits were performed after 90, 180 and 365 days from baseline and percentage TIR

evaluated for the 90-day time period preceding each visit. HbA1c tests were also carried out in the same occasions and measured values paired with the corresponding TIR data.

A monovariate linear regression analysis confirms a strong correlation between TIR an) in good accordance with previous studies, also show a statistically significant difference in the regression intercept, thus suggesting the need for different models tailored to device characteristics. The predictive power of A1C as a TIR estimator also deserves further investigations.

To determine whether timing of ophthalmic screening influences prevalence of neonatal fundus haemorrhages. We compared the prevalence of fundus haemorrhages in two populations term newborns screened early (less than 72 hours) and preterm newborns screened late (4-11 weeks). Additionally, we reviewed the literature on timing and prevalence of newborn haemorrhages.

Retrospective observational cohort study. Infants who underwent wide-angle ophthalmic digital imaging over one overlapping year in the Newborn Eye Screen Testing (NEST) or Stanford University Network for Diagnosis of Retinopathy of Prematurity (SUNDROP) programme were included. The PubMed database was filtered to include English-language articles dating back to 1950. Nine articles were selected for review based on inclusion of the prevalence of newborn fundus haemorrhages at multiple time points.

A total of 202 patients received early imaging in the NEST cohort and 73 patients received late imaging in the SUNDROP cohort. In the NEST cohort, 20.2% of newborns had haemorrhages. In contrast, we found haemorrhages in only one case or 1.4% of the SUNDROP cohort. Using prevalence data from nine additional studies, we developed a predicted probabilities model of newborn haemorrhages. Per this model, the probability of seeing a haemorrhage if you screen an infant at 1 hour is 18.8%, at 2 weeks is 2.9% and at 1 month is 0.28%.

We found a significant difference in the prevalence of fundus haemorrhages between the early-screened NEST cohort and the late-screened, preterm SUNDROP cohort. Likely, this difference is due to the transient nature of most newborn haemorrhages.

We found a significant difference in the prevalence of fundus haemorrhages between the early-screened NEST cohort and the late-screened, preterm SUNDROP cohort. Likely, this difference is due to the transient nature of most newborn haemorrhages.