When intensive insulin therapy (MDi) fails in patients with type 2 diabetes: Switching to GLP-1 receptor agonist versus insulin pump

Treatment with insulin, alone or with oral or injectable hypoglycemic agents, is becoming increasingly common in patients with type 2 diabetes. However, approximately 40% of patients fail to reach their glycemic targets with the initially prescribed regimen and require intensification of insulin therapy, which increases the risks of weight gain and hypoglycemia. Many of these patients eventually reach a state in which further increases in the insulin dosage fail to improve glycemic control while increasing the risks of weight gain and hypoglycemia. The recently completed OpT2mise clinical trial showed that continuous subcutaneous insulin infusion (CSII) is more effective in reducing glycated hemoglobin (HbA1c) than intensification of multiple daily injection (MDI) insulin therapy in patients with type 2 diabetes who do not respond to intensive insulin therapy. CSII therapy may also be useful in patients who do not reach glycemic targets despite multidrug therapy with basal-bolus insulin and other agents, including glucagon-like peptide (GLP)-1 receptor agonists; current guidelines offer no recommendations for the treatment of such patients. Importantly, insulin and GLP-1 receptor agonists have complementary effects on glycemia and, hence, can be used either sequentially or in combination in the initial management of diabetes. Patients who have not previously failed GLP-1 receptor agonist therapy may show reduction in weight and insulin dose, in addition to moderate improvement in HbA1c, when GLP-1 receptor agonist therapy is added to MDI regimens. In subjects with long-standing type 2 diabetes who do not respond to intensive insulin therapies, switching from MDI to CSII and/or the addition of GLP-1 receptor agonists to MDI have the potential to improve glycemic control without increasing the risk of adverse events

ReFace: Improving Clothes-Changing Re-Identification With Face Features

Person re-identification (ReID) has been an active research field for many years. Despite that, models addressing this problem tend to perform poorly when the task is to re-identify the same people over a prolonged time, due to appearance changes such as different clothes and hairstyles. In this work, we introduce a new method that takes full advantage of the ability of existing ReID models to extract appearance-related features and combines it with a face feature extraction model to achieve new state-of-the-art results, both on image-based and video-based benchmarks. Moreover, we show how our method could be used for an application in which multiple people of interest, under clothes-changing settings, should be re-identified given an unseen video and a limited amount of labeled data. We claim that current ReID benchmarks do not represent such real-world scenarios, and publish a new dataset, 42Street, based on a theater play as an example of such an application. We show that our proposed method outperforms existing models also on this dataset while using only pre-trained modules and without any further training

A Variational Qubit-Efficient MaxCut Heuristic Algorithm

The resolution of hard combinatorial problems is essential in a wide range of industrial applications and theoretical fields. Quantum computers offer a unique platform for addressing such problems, with the Quantum Approximate Optimization Algorithm (QAOA) being a state-of-the-art example. However, due to high levels of noise and limited numbers of qubits in current quantum devices, only very small problem instances can be addressed on actual quantum hardware. Here we present a new variational Qubit-Efficient MaxCut (QEMC) algorithm that is specifically designed to find heuristic solutions for the MaxCut problem, a well-studied NP-hard combinatorial problem. The QEMC method introduces a unique information encoding scheme that requires $\log{N}$ qubits to address graphs with $N$ nodes, an exponential reduction in comparison to QAOA. Each node of the graph is assigned to a unique computational quantum state, and its logical state is depicted by the measurement probability of the corresponding state, using a probability-threshold encoding scheme. Consequently, each partition of the graph is associated with a volume of states, rather than with just a single state. We present noiseless QEMC simulations on regular graphs with up to 2048 nodes (11 qubits). These simulations achieved cut solutions that outperform those obtained by the best-known classical approximation algorithm of Goemans and Williamson (GW), by several percent. Moreover, executing the QEMC algorithm on actual IBM quantum devices achieved leading-edge results for graphs with up to 32 nodes (5 qubits), providing a challenging benchmark for the QAOA algorithm. We analyze the computational complexity of the QEMC algorithm and show that it can be simulated efficiently using classical methods, thereby constituting a new quantum-inspired classical MaxCut heuristic.Comment: 16 pages, 12 figure

Structure of droplet-epitaxy-grown InAs/GaAs quantum dots

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98673/1/ApplPhysLett_98_243115.pd

Diameter-dependent wetting of tungsten disulfide nanotubes

The simple process of a liquid wetting a solid surface is controlled by a plethora of factors—surface texture, liquid droplet size and shape, energetics of both liquid and solid surfaces, as well as their interface. Studying these events at the nanoscale provides insights into the molecular basis of wetting. Nanotube wetting studies are particularly challenging due to their unique shape and small size. Nonetheless, the success of nanotubes, particularly inorganic ones, as fillers in composite materials makes it essential to understand how common liquids wet them. Here, we present a comprehensive wetting study of individual tungsten disulfide nanotubes by water. We reveal the nature of interaction at the inert outer wall and show that remarkably high wetting forces are attained on small, open-ended nanotubes due to capillary aspiration into the hollow core. This study provides a theoretical and experimental paradigm for this intricate problem

Travel- and Community-Based Transmission of Multidrug-Resistant Shigella sonnei Lineage among International Orthodox Jewish Communities

Shigellae are sensitive indicator species for studying trends in the international transmission of antimicrobial-resistant Enterobacteriaceae. Orthodox Jewish communities (OJCs) are a known risk group for shigellosis; Shigella sonnei is cyclically epidemic in OJCs in Israel, and sporadic outbreaks occur in OJCs elsewhere. We generated whole-genome sequences for 437 isolates of S. sonnei from OJCs and non-OJCs collected over 22 years in Europe (the United Kingdom, France, and Belgium), the United States, Canada, and Israel and analyzed these within a known global genomic context. Through phylogenetic and genomic analysis, we showed that strains from outbreaks in OJCs outside of Israel are distinct from strains in the general population and relate to a single multidrug-resistant sublineage of S. sonnei that prevails in Israel. Further Bayesian phylogenetic analysis showed that this strain emerged approximately 30 years ago, demonstrating the speed at which antimicrobial drug–resistant pathogens can spread widely through geographically dispersed, but internationally connected, communities

Effectiveness of core needle biopsy in the diagnosis of thyroid lymphoma and anaplastic thyroid carcinoma: A systematic review and meta-analysis

Both anaplastic thyroid carcinoma (ATC) and thyroid lymphoma (TL) clinically present as rapidly enlarging neck masses. Unfortunately, in this situation, like in any other thyroid swelling, a routine fine-needle aspiration (FNA) cytology is the first and only diagnostic test performed at the initial contact in the average thyroid practice. FNA, however, has a low sensitivity in diagnosing ATC and TL, and by the time the often "inconclusive" result is known, precious time has evolved, before going for core-needle biopsy (CNB) or incisional biopsy (IB) as the natural next diagnostic steps

CSI-OMIM - Clinical Synopsis Search in OMIM

<p>Abstract</p> <p>Background</p> <p>The OMIM database is a tool used daily by geneticists. Syndrome pages include a Clinical Synopsis section containing a list of known phenotypes comprising a clinical syndrome. The phenotypes are in free text and different phrases are often used to describe the same phenotype, the differences originating in spelling variations or typing errors, varying sentence structures and terminological variants.</p> <p>These variations hinder searching for syndromes or using the large amount of phenotypic information for research purposes. In addition, negation forms also create false positives when searching the textual description of phenotypes and induce noise in text mining applications.</p> <p>Description</p> <p>Our method allows efficient and complete search of OMIM phenotypes as well as improved data-mining of the OMIM phenome. Applying natural language processing, each phrase is tagged with additional semantic information using UMLS and MESH. Using a grammar based method, annotated phrases are clustered into groups denoting similar phenotypes. These groups of synonymous expressions enable precise search, as query terms can be matched with the many variations that appear in OMIM, while avoiding over-matching expressions that include the query term in a negative context. On the basis of these clusters, we computed pair-wise similarity among syndromes in OMIM. Using this new similarity measure, we identified 79,770 new connections between syndromes, an average of 16 new connections per syndrome. Our project is Web-based and available at <url>http://fohs.bgu.ac.il/s2g/csiomim</url></p> <p>Conclusions</p> <p>The resulting enhanced search functionality provides clinicians with an efficient tool for diagnosis. This search application is also used for finding similar syndromes for the candidate gene prioritization tool S2G.</p> <p>The enhanced OMIM database we produced can be further used for bioinformatics purposes such as linking phenotypes and genes based on syndrome similarities and the known genes in Morbidmap.</p

Quality of life in the course of a one-year use of an advanced hybrid closed-loop system in adults with type 1 diabetes previously naïve to advanced diabetes technology

AimTo evaluate the effect of a one-year use of an advanced hybrid closed-loop (AHCL) system on the quality of life, level of anxiety, and level of self-efficacy in adults with type 1 diabetes (T1D) previously treated with multiple daily injections (MDI) and naïve to advanced diabetes technologyMethodsA total of 18 participants of a previously published 3-month randomized trial (10 men, 8 women; age 40.9 ± 7.6 years) who were switched directly from MDI/BMG to AHCL completed 12 months of MiniMed 780G™system use (a 3-month randomized trial followed by a 9-month follow-up phase). At month 6 of the study, patients were switched from the sensor GS3 (Continuous Glucose Monitoring) system, powered by Guardian™ Sensor 3) to GS4. Quality of life was assessed using the Polish validated version of the ‘QoL-Q Diabetes’ questionnaire. The level of anxiety was evaluated with the use of the State-Trait Anxiety Inventory (STAI). Self-efficacy was assessed with the General Self-Efficacy Scale (GSES). Results were obtained at baseline and at the end of the study.ResultsSignificant increase in QoL was reported in the global score (p=0.02, Cohen d=0.61) and in as many as 11 out of 23 analyzed areas of life: being physically active (p=0.02, Cohen d = 0.71); feeling well (p&lt;.01, Cohen d = 0.73); feeling in control of my body (p&lt;.01, Cohen d = 0.72); looking good (p&lt;.01, Cohen d = 1.07); working (p&lt;.01, Cohen d = 1.12); sleeping (p=0.01, Cohen d = 0.66); eating as I would like (p&lt;.01, Cohen d = 0.79); looking after or being useful to others (p= 0.02, Cohen d = 0.65); being active with pets/animals (p&lt;.01, Cohen d = 0.95); being spontaneous (p=0.02, Cohen d = 0.67); and doing “normal” things (p=0.02, Cohen d = 0.67). Both state (p=0.04, Cohen d = 0.56) and trait (p=0.02, Cohen d = 0.60) anxiety decreased while the general self-efficacy increased (p=0.03, Cohen d = 0.76). No participant stopped the use of the pump.ConclusionAdult patients with T1D previously treated with MDI and naïve to modern technologies experienced significant improvement in their psychological well-being after transitioning to the AHCL system after 12 months of treatment