Glucagon-like peptide 1 improved glycemic control in type 1 diabetes

BACKGROUND: Glucagon-like peptide-1 (GLP-1) and its agonists are under assessment in treatment of type 2 diabetes, by virtue of their antidiabetic actions, which include stimulation of insulin secretion, inhibition of glucagon release, and delay of gastric emptying. We examined the potential of GLP-1 to improve glycemic control in type 1 diabetes with no endogenous insulin secretion. METHODS: Dose-finding studies were carried out to establish mid range doses for delay of gastric emptying indicated by postponement of pancreatic polypeptide responses after meals. The selected dose of 0.63 micrograms/kg GLP-1 was administered before breakfast and lunch in 8-hour studies in hospital to establish the efficacy and safety of GLP-1. In outside-hospital studies, GLP-1 or vehicle was self-administered double-blind before meals with usual insulin for five consecutive days by five males and three females with well-controlled C-peptide-negative type 1 diabetes. Capillary blood glucose values were self-monitored before meals, at 30 and 60 min after breakfast and supper, and at bedtime. Breakfast tests with GLP-1 were conducted on the day before and on the day after 5-day studies. Paired t-tests and ANOVA were used for statistical analysis. RESULTS: In 8-hour studies time-averaged incremental (delta) areas under the curves(AUC) for plasma glucose through 8 hours were decreased by GLP-1 compared to vehicle (3.2 ± 0.9, mean ± se, vs 5.4 ± 0.8 mmol/l, p < .05), and for pancreatic polypeptide, an indicator of gastric emptying, through 30 min after meals (4.0 ± 3.1 vs 37 ± 9.6 pmol/l, p < .05) with no adverse effects. Incremental glucagon levels through 60 min after meals were depressed by GLP-1 compared to vehicle (-3.7 ± 2.5 vs 3.1 ± 1.9 ng/l, p < .04). In 5-day studies, AUC for capillary blood glucose levels were lower with GLP-1 than with vehicle (-0.64 ± 0.33 vs 0.34 ± 0.26 mmol/l, p < .05). No assisted episode of hypoglycaemia or change in insulin dosage occurred. Breakfast tests on the days immediately before and after 5-day trials showed no change in the effects of GLP-1. CONCLUSION: We have demonstrated that subcutaneous GLP-1 can improve glucose control in type 1 diabetes without adverse effects when self-administered before meals with usual insulin during established intensive insulin treatment programs

Neutral particle Mass Spectrometry with Nanomechanical Systems

Current approaches to Mass Spectrometry (MS) require ionization of the analytes of interest. For high-mass species, the resulting charge state distribution can be complex and difficult to interpret correctly. In this article, using a setup comprising both conventional time-of-flight MS (TOF-MS) and Nano-Electro-Mechanical-Systems-based MS (NEMS-MS) in situ, we show directly that NEMS-MS analysis is insensitive to charge state: the spectrum consists of a single peak whatever the species charge state, making it significantly clearer than existing MS analysis. In subsequent tests, all charged particles are electrostatically removed from the beam, and unlike TOF-MS, NEMS-MS can still measure masses. This demonstrates the possibility to measure mass spectra for neutral particles. Thus, it is possible to envisage MS-based studies of analytes that are incompatible with current ionization techniques and the way is now open for the development of cutting edge system architectures with unique analytical capability

COMPARING ESTIMATED AND MEASURED MUSCLE ACTIVATION DURING HIGHLY DYNAMIC AND MULTIDIRECTIONAL MOVEMENTS - A VALIDATION STUDY

The purpose of this study was to validate muscle activation of the lower extremities computed in AnyBodyMT with measured muscle activations (EMG) in highly dynamic movement tasks. Ten participants performed walking, jogging, sprinting and cutting tasks. Kinetic, kinematic and EMG data were captured for 8 muscles of the dominant leg. The average correlation coefficient (CC) was 0.51 (max.: 0.83, min.: -0.05) with 71% of all trials showing moderate to very good compliance. The average mean absolute error (MAE) was 1.32 (max.: 3.71, min.: 0.17). Co-contraction, precision of the muscle recruitment algorithm, electromechanical delay and anthropometrical measures may have affected the results. The estimation of computed muscle activation can be a suitable method for certain muscles considering highly dynamic movement tasks

Hip joint load and muscle stress in soccer inside passing

Studies investigating the mechanisms of adductor injuries in soccer have concentrated on full effort kicks. Purpose of this study was a kinetic analysis of the inside pass. Using infrared cameras and inverse dynamics, hip joint moments and adductor muscle stress was calculated during the swing phase of the pass. Moments in the transverse plane were nearly as high as in full effort kicks reported previously. Muscle stress in the m. gracilis reached up to 450 kPa. Considering the repetitive nature of inside passes in modern sot; cer, adductor muscles undergo high loads in matches and training. This might contribute to the explanation of the high incidences of adductor injuries. Practitioners should therefore consider the load-recovery-relation even in inside pass training. Specific strength training programs for the adductor and abductor muscle groups should be developed

The Trial to Reduce IDDM in the Genetically at Risk (TRIGR) study: recruitment, intervention and follow-up

Peer reviewe

Benchopt: Reproducible, efficient and collaborative optimization benchmarks

Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several challenges: researchers are confronted with a profusion of methods to compare, limited transparency and consensus on best practices, as well as tedious re-implementation work. As a result, validation is often very partial, which can lead to wrong conclusions that slow down the progress of research. We propose Benchopt, a collaborative framework to automate, reproduce and publish optimization benchmarks in machine learning across programming languages and hardware architectures. Benchopt simplifies benchmarking for the community by providing an off-the-shelf tool for running, sharing and extending experiments. To demonstrate its broad usability, we showcase benchmarks on three standard learning tasks: $\ell_2$-regularized logistic regression, Lasso, and ResNet18 training for image classification. These benchmarks highlight key practical findings that give a more nuanced view of the state-of-the-art for these problems, showing that for practical evaluation, the devil is in the details. We hope that Benchopt will foster collaborative work in the community hence improving the reproducibility of research findings.Comment: Accepted in proceedings of NeurIPS 22; Benchopt library documentation is available at https://benchopt.github.io