Feasibility of digital phenotyping based on continuous glucose monitoring to support personalized lifestyle medicine in type 2 diabetes.

Objectives: Type 2 diabetes is a highly prevalent age-related chronic condition, with complex and heterogeneous pathogenesis. A 5-point oral glucose tolerance test can identify type 2 diabetes subtypes or "diabetypes" based on the degree of insulin resistance in muscle and/or liver, and beta-cell dysfunction. Due to its costly and invasive nature, the oral glucose tolerance test is not scalable.

Acute effects of motor learning models on technical efficiency in strength-coordination exercises: a comparative analysis of Olympic snatch biomechanics in beginners.

Despite the development of various motor learning models over many decades, the question of which model is most effective under which conditions to optimize the acquisition of skills remains a heated and recurring debate. This is particularly important in connection with learning sports movements with a high strength component. This study aims to examine the acute effects of various motor learning models on technical efficiency and force production during the Olympic snatch movement.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

Evaluating growth-factor release in leukocyte- and platelet-rich fibrin, advanced platelet-rich fibrin, and injectable platelet-rich fibrin protocols: a narrative review.

Since its introduction in 2001, multiple platelet-rich fibrin (PRF) centrifugation protocols have emerged, but the variations in growth factor release that result from these protocols remain unclear. This review aimed to evaluate growth factor release across three PRF protocols: leukocyte-PRF (L-PRF), advanced-PRF (A-PRF/+), and injectable-PRF (i-PRF). A comprehensive search was conducted using the MEDLINE and Embase databases, identifying 14 studies that met the inclusion criteria.

Modelling individual variation in human walking gait across populations and walking conditions via gait recognition.

Human walking gait is a personal story written by the body, a tool for understanding biological identity in healthcare and security. Gait analysis methods traditionally diverged between these domains but are now merging their complementary strengths to unlock new possibilities. Using large ground reaction force (GRF) datasets for gait recognition is a way to uncover subtle variations that define individual gait patterns.

Reducing Behavioral Problems and Treatment Duration of Adolescents in Secure Residential Care: A Multiple Single-Case Experimental Design Study.

Secure residential care (SRC) is criticized for several reasons. Therefore, in many countries, the general policy is to limit the length of stay of adolescents in SRC. However, research on length of stay and treatment effects of SRC on adolescents' behavioral problems is sparse.

FLASH Bragg-Peak Irradiation With a Therapeutic Carbon Ion Beam: First In Vivo Results.

Purpose: In recent years, ultra-high dose rate (UHDR) irradiation has emerged as a promising innovative approach to cancer treatment. Characteristic feature of this regimen, commonly referred to as FLASH effect, demonstrated primarily for electrons, photons, or protons, is the improved normal tissue sparing, whereas the tumor control is similar to the one of the conventional dose-rate (CDR) treatments. The FLASH mechanism is, however, unknown.

Lack of genotoxicity of iron oxide maghemite (γ-Fe2O3) and magnetite (Fe3O4) nanoparticles to Oreochromis niloticus after acute exposures.

Iron oxide nanoparticles (FeO-NPs) are widely used in scientific and technological fields. Environmental concerns have been raised about residual FeO-NPs levels as their toxicity and bioaccumulative potential are not well understood. Oreochromis niloticus were exposed to nanoparticles of γ-Fe2O3 and Fe3O4.

Unveiling individuality in the early phase of motor learning: a machine learning approach for analysing weightlifting technique in novices.

Introduction: Despite the growing body of evidence highlighting the individuality in movement techniques, predominant models of motor learning, particularly during the acquisition phase, continue to emphasise generalised, person-independent approaches. Biomechanical studies, coupled with machine learning approaches, have demonstrated the uniqueness of movement techniques exhibited by individuals. However, this evidence predominantly pertains to already stabilised movement techniques, particularly evident in cyclic daily activities such as walking, running, or cycling, as well as in expert-level sports movements.

Absolute energy-dependent scintillating screen calibration for real-time detection of laser-accelerated proton bunches.

Laser-plasma accelerators (LPAs) can deliver pico- to nanosecond long proton bunches with ≳100 nC of charge dispersed over a broad energy spectrum. Increasing the repetition rates of today's LPAs is a necessity for their practical application. This, however, creates a need for real-time proton bunch diagnostics.

Road Blocks or Building Blocks? A Qualitative Study on Challenges and Resilience in Romantic Relationships of Youth Exposed to Family Violence.

Romantic relationships are an important part of many people's lives and at least partly shaped by experiences during childhood. Youth exposed to family violence during childhood are more likely to experience difficulties in their later romantic relationships. However, a more holistic perspective on the romantic relationships of youth with a history of family violence is lacking.

Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images.

Background And Objective: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinical observations. Unfortunately, most segmentation methods known today are limited to nuclei and cannot segment the cytoplasm.

The American contribution to attachment theory: John Bowlby's WHO trip to the USA in 1950 and the development of his ideas on separation and attachment.

This paper explores John Bowlby's foundational contributions to attachment theory, particularly his fascination with 'separation' and its impact on child development. Tracing the origins of Bowlby's interest to his personal experiences and his exposure to ideas of mental hygiene and child guidance in the 1930s, it underscores the alignment of his ideas with key figures in the English school of psychoanalysis. The central narrative of this paper unfolds during Bowlby's 1950 WHO research trip, investigating orphaned and separated children in Europe and the USA.

-establishment of small animal proton and photon image-guided radiation experiments.

Theevolution of radiotherapy necessitates innovative platforms for preclinical investigation, bridging the gap between bench research and clinical applications. Understanding the nuances of radiation response, specifically tailored to proton and photon therapies, is critical for optimizing treatment outcomes. Within this context, preclinicalexperimental setups incorporating image guidance for both photon and proton therapies are pivotal, enabling the translation of findings from small animal models to clinical settings.

CellViT: Vision Transformers for precise cell segmentation and classification.

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain.

Dose and dose rate dependence of the tissue sparing effect at ultra-high dose rate studied for proton and electron beams using the zebrafish embryo model.

Purpose: A better characterization of the dependence of the tissue sparing effect at ultra-high dose rate (UHDR) on physical beam parameters (dose, dose rate, radiation quality) would be helpful towards a mechanistic understanding of the FLASH effect and for its broader clinical translation. To address this, a comprehensive study on the normal tissue sparing at UHDR using the zebrafish embryo (ZFE) model was conducted.

Methods: One-day-old ZFE were irradiated over a wide dose range (15-95 Gy) in three different beams (proton entrance channel, proton spread out Bragg peak and 30 MeV electrons) at UHDR and reference dose rate.

Analog Resistive Switching Devices for Training Deep Neural Networks with the Novel Tiki-Taka Algorithm.

A critical bottleneck for the training of large neural networks (NNs) is communication with off-chip memory. A promising mitigation effort consists of integrating crossbar arrays of analogue memories in the Back-End-Of-Line, to store the NN parameters and efficiently perform the required synaptic operations. The "" algorithm was developed to facilitate NN training in the presence of device nonidealities.

The DRESDEN PLATFORM is a research hub for ultra-high dose rate radiobiology.

The recently observed FLASH effect describes the observation of normal tissue protection by ultra-high dose rates (UHDR), or dose delivery in a fraction of a second, at similar tumor-killing efficacy of conventional dose delivery and promises great benefits for radiotherapy patients. Dedicated studies are now necessary to define a robust set of dose application parameters for FLASH radiotherapy and to identify underlying mechanisms. These studies require particle accelerators with variable temporal dose application characteristics for numerous radiation qualities, equipped for preclinical radiobiological research.

Ground-based passive generation of Solar Particle Event spectra: Planning and manufacturing of a 3D-printed modulator.

The generation of space radiation on Earth is essential to study and predict the effects of radiation on space travelers, electronics, or materials during future long-term space missions. Next to the heavy ions of the galactic cosmic rays, solar particle events play a major role concerning the radiation risk in space, which consist of intermediate-energy protons with broad spectra and energies up to a few hundred MeV. This work describes an approach for the ground-based generation of solar particle events.

Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning.

Purpose: Quantifying treatment response to gastroesophageal junction (GEJ) adenocarcinomas is crucial to provide an optimal therapeutic strategy. Routinely taken tissue samples provide an opportunity to enhance existing positron emission tomography-computed tomography (PET/CT)-based therapy response evaluation. Our objective was to investigate if deep learning (DL) algorithms are capable of predicting the therapy response of patients with GEJ adenocarcinoma to neoadjuvant chemotherapy on the basis of histologic tissue samples.

Identification of subject-specific responses to footwear during running.

Placing a stronger focus on subject-specific responses to footwear may lead to a better functional understanding of footwear's effect on running and its influence on comfort perception, performance, and pathogenesis of injuries. We investigated subject-specific responses to different footwear conditions within ground reaction force (GRF) data during running using a machine learning-based approach. We conducted our investigation in three steps, guided by the following hypotheses: (I) For each subject x footwear combination, unique GRF patterns can be identified.