Provision of supportive care by an NGO in the face of a dual challenge: cancer and wartime.

Purpose: In this study, we explored the work of Halasartan (Stop Cancer), an Israeli nongovernmental organization (NGO) and unique social support network for cancer patients and survivors aged 18-44, during a war period. Drawing on the conservation of resources (COR) theory, we examined whether self-efficacy, social support, psychological distress, and participation in activities that were geared toward alleviating the war situation at Time 1 (T1) would predict engagement in such activities at Time 2 (T2).

Methods: A longitudinal design with two time-points was used, and NGO members completed self-report questionnaires.

Cleavage of VAMP2/3 Affects Oligodendrocyte Lineage Development in the Developing Mouse Spinal Cord.

In the developing and adult CNS, new oligodendrocytes (OLs) are generated from a population of cells known as oligodendrocyte precursor cells (OPCs). As they begin to differentiate, OPCs undergo a series of highly regulated changes to morphology, gene expression, and membrane organization. This stage represents a critical bottleneck in oligodendrogliogenesis, and the regulatory program that guides it is still not fully understood.

Unbiased single-cell morphology with self-supervised vision transformers.

Accurately quantifying cellular morphology at scale could substantially empower existing single-cell approaches. However, measuring cell morphology remains an active field of research, which has inspired multiple computer vision algorithms over the years. Here, we show that DINO, a vision-transformer based, self-supervised algorithm, has a remarkable ability for learning rich representations of cellular morphology without manual annotations or any other type of supervision.

Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation.

Reward processing is essential for our mental-health and well-being. In the current study, we developed and validated a scalable, fMRI-informed EEG model for monitoring reward processing related to activation in the ventral-striatum (VS), a significant node in the brain's reward system. To develop this EEG-based model of VS-related activation, we collected simultaneous EEG/fMRI data from 17 healthy individuals while listening to individually-tailored pleasurable music - a highly rewarding stimulus known to engage the VS.

Mapping thalamic innervation to individual L2/3 pyramidal neurons and modeling their 'readout' of visual input.

The thalamus is the main gateway for sensory information from the periphery to the mammalian cerebral cortex. A major conundrum has been the discrepancy between the thalamus's central role as the primary feedforward projection system into the neocortex and the sparseness of thalamocortical synapses. Here we use new methods, combining genetic tools and scalable tissue expansion microscopy for whole-cell synaptic mapping, revealing the number, density and size of thalamic versus cortical excitatory synapses onto individual layer 2/3 (L2/3) pyramidal cells (PCs) of the mouse primary visual cortex.

Spatial dependency of the PPG morphology at right carotid common artery.

PhotoPlethysmoGraphy (PPG) is ubiquitously employed in wearable devices for health monitoring. Photodiode signal inversion is observed in rare occasions, most of the time when the sensor is pressed against the skin. We report in this article such observations made at the right common carotid artery site.

A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex.

Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections.

Synaptic Input and ACh Modulation Regulate Dendritic Ca Spike Duration in Pyramidal Neurons, Directly Affecting Their Somatic Output.

Nonlinear synaptic integration in dendrites is a fundamental aspect of neural computation. One such key mechanism is the Ca spike at the apical tuft of pyramidal neurons. Characterized by a plateau potential sustained for tens of milliseconds, the Ca spike amplifies excitatory input, facilitates somatic action potentials (APs), and promotes synaptic plasticity.

Image-based cell phenotyping with deep learning.

A cell's phenotype is the culmination of several cellular processes through a complex network of molecular interactions that ultimately result in a unique morphological signature. Visual cell phenotyping is the characterization and quantification of these observable cellular traits in images. Recently, cellular phenotyping has undergone a massive overhaul in terms of scale, resolution, and throughput, which is attributable to advances across electronic, optical, and chemical technologies for imaging cells.

No more hypoglycaemia on days with physical activity and unrestricted diet when using a closed-loop system for 12 weeks: A post hoc secondary analysis of the multicentre, randomized controlled Diabeloop WP7 trial.

A post hoc analysis of the Diabeloop WP7 multicentre, randomized controlled trial was performed to investigate the efficacy of the Diabeloop Generation-1 (DBLG1) closed-loop system in controlling the hypoglycaemia induced by physical activity (PA) in real-life conditions. Glycaemic outcomes were compared between days with and without PA in 56 patients with type 1 diabetes (T1D) using DBLG1 for 12 weeks. After the patient announces a PA, DBLG1 reduces insulin delivery and, if necessary, calculates the amount of preventive carbohydrates (CHO).

Unannounced Meal Detection for Artificial Pancreas Systems Using Extended Isolation Forest.

This study aims at developing an unannounced meal detection method for artificial pancreas, based on a recent extension of Isolation Forest. The proposed method makes use of features accounting for individual Continuous Glucose Monitoring (CGM) profiles and benefits from a two-threshold decision rule detection. The advantage of using Extended Isolation Forest (EIF) instead of the standard one is supported by experiments on data from virtual diabetic patients, showing good detection accuracy with acceptable detection delays.

Increasing the safety of unannounced meal detection for artificial pancreas closed-loop with patient's hourly meal schedule.

The daily challenge for people with type 1 diabetes is maintaining glycaemia in the "normal" range after meals, by injecting themselves the correct amount of insulin. Artificial pancreas systems were developed to adjust insulin delivery based on real-time monitoring of glycaemia and meal patient's report. Meal reporting is a heavy burden for patients as it requires carbohydrate estimation several times per day.

Patients with highly unstable type 1 diabetes eligible for islet transplantation can be managed with a closed-loop insulin delivery system: A series of N-of-1 randomized controlled trials.

Aim: To compare the efficacy of the closed-loop Diabeloop for highly unstable diabetes (DBLHU) system with the open-loop predictive low glucose suspend (PLGS) system in patients with highly unstable type 1 diabetes (T1D) who experience acute metabolic events.

Methods: DBLHU-WP10 was an interventional, controlled, randomized, open-label study that comprised two cycles of N-of-1 trials (2-of-1 trials). Each trial consisted of two crossover 4-week periods of treatment with either DBLHU or PLGS in randomized order.

Whole-Neuron Synaptic Mapping Reveals Spatially Precise Excitatory/Inhibitory Balance Limiting Dendritic and Somatic Spiking.

The balance between excitatory and inhibitory (E and I) synapses is thought to be critical for information processing in neural circuits. However, little is known about the spatial principles of E and I synaptic organization across the entire dendritic tree of mammalian neurons. We developed a new open-source reconstruction platform for mapping the size and spatial distribution of E and I synapses received by individual genetically-labeled layer 2/3 (L2/3) cortical pyramidal neurons (PNs) in vivo.

Modeling the variability of insulin sensitivity for people with Type 1 Diabetes based on clinical data from an artificial pancreas study.

Type 1 Diabetes is an autoimmune disease that eliminates endogenous insulin production. Without the crucial hormone insulin, which is necessary to equilibrate the blood glucose level, the patient must inject insulin subcutaneously. Treatment must be personalized (timing and size of insulin delivery) to achieve glycaemic equilibrium and avoid long-term comorbidities.

Efficacy of the Diabeloop closed-loop system to improve glycaemic control in patients with type 1 diabetes exposed to gastronomic dinners or to sustained physical exercise.

Aims: To compare closed-loop (CL) and open-loop (OL) systems for glycaemic control in patients with type 1 diabetes (T1D) exposed to real-life challenging situations (gastronomic dinners or sustained physical exercise).

Methods: Thirty-eight adult patients with T1D were included in a three-armed randomized pilot trial (Diabeloop WP6.2 trial) comparing glucose control using a CL system with use of an OL device during two crossover 72-hour periods in one of the three following situations: large (gastronomic) dinners; sustained and repeated bouts of physical exercise (with uncontrolled food intake); or control (rest conditions).

The safety of amoxicillin and clavulanic acid use during the first trimester of pregnancy.

Aims: The goal of the current study was to assess the risk for major congenital malformations following first-trimester exposure to amoxicillin, or amoxicillin and clavulanic acid (ACA).

Methods: A population-based retrospective cohort study was conducted, by linking 4 computerized databases: maternal and infant hospitalization records, drug dispensing database of Clalit Health Services in Israel and data concerning pregnancy terminations. Multivariate negative-binomial regression was used to assess the risk for major malformations following first-trimester exposure, adjusted for mother's age, ethnicity (Bedouin vs Jewish), parity, diabetes mellitus, lack of perinatal care, and the year of birth.

Closed-loop insulin delivery in adults with type 1 diabetes in real-life conditions: a 12-week multicentre, open-label randomised controlled crossover trial.

Background: Closed-loop insulin delivery systems are expected to become a standard treatment for patients with type 1 diabetes. We aimed to assess whether the Diabeloop Generation 1 (DBLG1) hybrid closed-loop artificial pancreas system improved glucose control compared with sensor-assisted pump therapy.

Methods: In this multicentre, open-label, randomised, crossover trial, we recruited adults (aged ≥18 years) with at least a 2 year history of type 1 diabetes, who had been treated with external insulin pump therapy for at least 6 months, had glycated haemoglobin (HbA) of 10% or less (86 mmol/mol), and preserved hypoglycaemia awareness.

Customization of home closed-loop insulin delivery in adult patients with type 1 diabetes, assisted with structured remote monitoring: the pilot WP7 Diabeloop study.

Aims: Improvement in closed-loop insulin delivery systems could result from customization of settings to individual needs and remote monitoring. This pilot home study evaluated the efficacy and relevance of this approach.

Methods: A bicentric clinical trial was conducted for 3 weeks, using an MPC-based algorithm (Diabeloop Artificial Pancreas system) featuring five settings designed to modulate the reactivity of regulation.

Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry.

Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions.

Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons.

The NMDA spike is a long-lasting nonlinear phenomenon initiated locally in the dendritic branches of a variety of cortical neurons. It plays a key role in synaptic plasticity and in single-neuron computations. Combining dynamic system theory and computational approaches, we now explore how the timing of synaptic inhibition affects the NMDA spike and its associated membrane current.

Personalization of a compartmental physiological model for an artificial pancreas through integration of patient's state estimation.

Artificial Pancreas (AP) are developed for patients with Type 1 diabetes. This medical device system consists in the association of a subcutaneous continuous glucose monitor (CGM) providing a proxy of the patient's glycaemia and a control algorithm offering the real-time modification of the insulin delivery with an automatic command of the subcutaneous insulin pump. The most complex algorithms are based on a compartmental model of the glucoregulatory system of the patient coupled to an approach of MPC (Model-Predictive-Control) for the command.

An original piecewise model for computing energy expenditure from accelerometer and heart rate signals.

Objective: Activity energy expenditure (EE) plays an important role in healthcare, therefore, accurate EE measures are required. Currently available reference EE acquisition methods, such as doubly labeled water and indirect calorimetry, are complex, expensive, uncomfortable, and/or difficult to apply on real time. To overcome these drawbacks, the goal of this paper is to propose a model for computing EE in real time (minute-by-minute) from heart rate and accelerometer signals.