Showing papers by "Bar-Ilan University published in 2022"

Pre-infection 25-hydroxyvitamin D3 levels and association with severity of COVID-19 illness

Abstract: Studies have demonstrated a potential correlation between low vitamin D status and both an increased risk of infection with SARS-CoV-2 and poorer clinical outcomes. This retrospective study examines if, and to what degree, a relationship exists between pre-infection serum 25-hydroxyvitamin D (25(OH)D) level and disease severity and mortality due to SARS-CoV-2.The records of individuals admitted between April 7th, 2020 and February 4th, 2021 to the Galilee Medical Center (GMC) in Nahariya, Israel, with positive polymerase chain reaction (PCR) tests for SARS-CoV-2 (COVID-19) were searched for historical 25(OH)D levels measured 14 to 730 days prior to the positive PCR test.Patients admitted to GMC with COVID-19 were categorized according to disease severity and level of 25(OH)D. An association between pre-infection 25(OH)D levels, divided between four categories (deficient, insufficient, adequate, and high-normal), and COVID-19 severity was ascertained utilizing a multivariable regression analysis. To isolate the possible influence of the sinusoidal pattern of seasonal 25(OH)D changes throughout the year, a cosinor model was used.Of 1176 patients admitted, 253 had records of a 25(OH)D level prior to COVID-19 infection. A lower vitamin D status was more common in patients with the severe or critical disease (<20 ng/mL [87.4%]) than in individuals with mild or moderate disease (<20 ng/mL [34.3%] p < 0.001). Patients with vitamin D deficiency (<20 ng/mL) were 14 times more likely to have severe or critical disease than patients with 25(OH)D ≥40 ng/mL (odds ratio [OR], 14; 95% confidence interval [CI], 4 to 51; p < 0.001).Among hospitalized COVID-19 patients, pre-infection deficiency of vitamin D was associated with increased disease severity and mortality.

Inequalities in initiation of COVID19 vaccination by age and population group in Israel- December 2020-July 2021

Abstract: COVID19 vaccination coverage in Israel varies among population groups. Comparing crude coverage between groups is misleading because of different age structures and socio-economic differences. To describe inequalities in COVID19 vaccine initiation in Israel we analysed the interaction of age and population groups in terms of dose 1 vaccine coverage.We calculated cumulative age-specific first COVID19 vaccine coverage by population group (Ultra-Orthodox Jewish, Arab, General Jewish). We calculated the relative differences in vaccine coverage between population groups within each age group, and between age groups within each population, using ANOVA and binomial regression after adjusting for socio-economic status.8,507,723 individuals in 268 cities were included. Compared with the general Jewish population, coverage was lowest in the Ultra-Orthodox population in all age groups (range -12% among 60+ to -52.8% among 10-19 years olds, p<0.001). In all groups, the proportion of vaccinated individuals in younger age groups relative to those aged 60+ decreased with decreasing age and were smallest in the Ultra-Orthodox groups. For example, within the general Jewish population, people aged 20-29 were 14% less likely to be vaccinated than those aged 60+ while within the Ultra-Orthodox population it was 34.5.In all age groups, the Ultra-Orthodox population had the lowest vaccine coverage. Differences persisted after adjusting for socio-economic status. The younger the age group, the more Ultra-Orthodox Jews were diverging from age peers in terms of initiating COVID19 vaccination, suggesting a generational effect. Tailored approaches are urgently required to encourage vaccination among under-immunized groups in Israel.No specific funding was received.

Inequalities in initiation of COVID19 vaccination by age and population group in Israel- December 2020-July 2021.

Abstract: Summary Background COVID19 vaccination coverage in Israel varies among population groups. Comparing crude coverage between groups is misleading because of different age structures and socio-economic differences. To describe inequalities in COVID19 vaccine initiation in Israel we analysed the interaction of age and population groups in terms of dose 1 vaccine coverage Methods We calculated cumulative age-specific first COVID19 vaccine coverage by population group (Ultra-Orthodox Jewish, Arab, General Jewish). We calculated the relative differences in vaccine coverage between population groups within each age group, and between age groups within each population, using ANOVA and binomial regression after adjusting for socio-economic status Findings 8,507,723 individuals in 268 cities were included. Compared with the general Jewish population, coverage was lowest in the Ultra-Orthodox population in all age groups (range -12% among 60+ to -52.8% among 10-19 years olds, p Interpretation In all age groups, the Ultra-Orthodox population had the lowest vaccine coverage. Differences persisted after adjusting for socio-economic status. The younger the age group, the more Ultra-Orthodox Jews were diverging from age peers in terms of initiating COVID19 vaccination, suggesting a generational effect. Tailored approaches are urgently required to encourage vaccination among under-immunized groups in Israel Funding No specific funding was received

Beyond the Conventional Civil–Military “Gap”: Cleavages and Convergences in Israel:

Abstract: This article modifies the framework for the analysis of civil–military “gaps” proposed in Armed Forces & Society (Vol. 38, 2012) by Rahbek-Clemmensen, Archer, Barr, Belkin, Guerro, Hall, and Swain,...

Precious-Group-Metal-Free Energy-Efficient Urea Electrolysis: Membrane Electrode Assembly Cell Using Ni₃N Nanoparticles as Catalyst

Abstract: The sluggish kinetics of the anodic oxygen evolution reaction (OER) limit the overall efficiency of green hydrogen production. The proposed strategy to overcome this is to replace OER with other kinetically favorable anodic reactions like urea oxidation reaction (UOR). Herein, we develop an organometallic synthesis of nickel nitride nanoparticles supported on carbon (Ni3N–C) as the catalyst for both UOR and hydrogen evolution reaction (HER). A precious group metal-free electrolyzer based on Ni3N–C catalyst (as both anode and cathode) is implemented for the first time, and the urea electrolyzer cell has a 200 mV lower overpotential compared to that of the water electrolyzer.

A cost-effective water-in-salt electrolyte enables highly stable operation of a 2.15-V aqueous lithium-ion battery

Abstract: Extensive efforts are currently underway to develop safe and cost-effective electrolytes for large-scale energy storage. In this regard, water-based electrolytes may be an attractive option, but their narrow electrochemical stability window hinders their realization. Although highly concentrated fluorinated electrolytes have been shown to be highly effective in suppression of water splitting, enabling significant widening of the applied potential range, they utilize expensive salts (e.g., lithium bis(trifluoromethane sulfonyl) imide [LiTFSI] or lithium trifluoromethane sulfonate [LiOTf]); hence, they cannot be considered for practical applications. Here, we demonstrate a cost-effective aqueous electrolyte solution combining 14 M LiCl and 4 M CsCl that allows stable operation of a 2.15-V battery comprising a TiO2 anode and LiMn2O4 cathode. Addition of CsCl to the electrolyte plays a double role in system stabilization: the added chloride anions interact with the free water molecules, whereas the chaotropic cesium cations adsorb at the electrified interface, preventing hydrogen formation.

Preparation/processing of polymer-graphene composites by different techniques

Abstract: Graphene is a ubiquitously demanding 2D nanomaterial not only restricted in research periphery but also widely dispersed in the commercial community. Polymer composite is not a new area of culture, but mingling of graphene into polymer matrices synergistically improves the desired properties to a lump-sum extent. In this chapter the aim is to discuss the significantly used areas of fabrication strategies though to cover the whole area is beyond the scope. The total fabrication was discussed in two different ways, one is physical and another one is chemical. Under these categories, the whole chapter elucidates the methods, reaction pathways, and their end product formation.

Autonomous Vehicles

Abstract: The first car was invented in 1870 by Siegfried Marcus. Actually, it was just a wagon with an engine but without a steering wheel and without brakes. Instead, it was controlled by the legs of the driver. Converting traditional vehicles into autonomous vehicles was not just one step. The first step was just 28 years after the invention of cars, that is to say 1898. This step's concept was moving a vehicle by a remote controller. Since this first step and as computers have been becoming advanced and sophisticated, many functions of modern vehicles have been converted to be entirely automatic with no need of even remote controlling. Changing gears was one of the first actions that could be done automatically without an involvement of the driver, so such cars got the title of “automatic cars”; however, nowadays there are vehicles that can completely travel by themselves although they are not yet allowed to travel on public roads in most of the world. Such vehicles are called “autonomous vehicles” or “driverless cars”.

Stabilizing High-Voltage Lithium-Ion Battery Cathodes Using Functional Coatings of 2D Tungsten Diselenide

Abstract: Functional surface coatings were applied on high voltage spinel (LiNi0.5Mn1.5O4; LNMO) and Ni-rich (LiNi0.85Co0.1Mn0.05O2; NCM851005) NCM cathode materials using few-layered 2H tungsten diselenide (WSe2). Simple liquid-phase mixing with WSe2 in 2-propanol and low-temperature (130 °C) heat treatment in nitrogen flow dramatically improved electrochemical performance, including stable cycling, high-rate performance, and lower voltage hysteresis in Li coin cells at 30 and 55 °C. Significantly improved capacity retention at 30 °C [Q401/Q9 of 99% vs 38% for LNMO and Q322/Q23 of 64% vs 46% for NCM851005] indicated efficient functionality. TEM and XPS clarified the coating distribution and coordination with the cathode surface, while postcycling studies revealed its sustainability, enabling lower transition metal dissolution and minor morphological deformation/microcrack formation. A modified and stable SEI was apparently formed owing to W and Se deposition on the Li anode during cycling. The synergistic functionalization provided a significant dual benefit of cathodic and anodic stability.

Evaluation of a new dataset for visual detection of cervical precancerous lesions

Abstract: Automated visual evaluation (AVE) is an emerging method to detect and diagnose cervical precancerous lesions by imaging and analysis via a deep learning classifier. Challenges in AVE development come from not only the limited data available, but also a proper design of the learning protocol. The most analyzed dataset (PEG) is traced to a clinical trial at a single site, where all the images were captured in a well controlled environment. Recently, cervical images have been captured by a light-weight mobile solution where the screening images were collected from a wider user pool at many sites. This paper introduces a new data resource (EVA dataset), collected by providers using a mobile colposcope during their routine practice. Compared to PEG, EVA images contain higher levels of data variations and exhibits a different distribution over multiple image attributes including image sharpness, brightness and colorfulness. In order to evaluate the practical value of EVA dataset for cervical high-grade squamous intraepithelial lesions (SIL) diagnosis, we further present an analysis of how a deep learning based framework can be used with both datasets by evaluating three key technical components: (1) region-of-interest (ROI) detection, (2) data augmentation and (3) pre-trained deep learning model selection. Our results indicate that ROI detection and shallow deep learning models usually help the detection on both datasets. While most data augmentations are effective on the EVA dataset, the improvement is less pronounced on PEG. Overall, using a deep-learning based framework looks promising for high-grade SIL diagnosis but there is still large room for improvements, especially on the EVA dataset. The noted differences indicate that the EVA dataset presents more practical challenges for high-grade SIL diagnosis and AVE classifier development.

Nitrogen-doped carbon dots as a highly selective and sensitive fluorescent probe for sensing Mg2+ ions in aqueous solution, and their application in the detection and imaging of intracellular Mg2+ ions

Abstract: The magnesium (Mg2+) ion is the second most abundant intracellular cation after potassium, and it is involved in a variety of biological processes and physiological functions. Because of the different effects which are dependent on Mg2+ ion concentration, it is critical to monitor Mg2+ ion levels in biological systems. Here, we report the hydrothermal synthesis of photoluminescent N-doped carbon dots (NCDs) using 4-Hydroxybenzaldehyde and 1, 2, 4, 5-benzenetetramine tetrahydrochloride as carbon and nitrogen sources, respectively. The as-synthesized NCDs demonstrated excitation dependent photoluminescence (PL) with a quantum yield of 16.2%. Because of water dispersibility and chelating functional groups, NCDs were used for highly selective detection of Mg2+ ions using ratiometric PL enhancement with a detection limit of 60 μM. Following that, based on highly biocompatibility and sensing of Mg2+ ions in aqueous solutions, NCDs were employed as photoluminescent probe to detect the Mg2+ ions of mammalian cell lines such as J774, HeLa, and Hek293T, which is most likely due to effective complex formation between NCDs and the intracellular Mg2+ ions. As far as we could possibly know, this is the first report of aqueous solution dispersed carbon dots for intracellular sensing and imaging of Mg2+ ions based solely on an increase in NCDs PL intensity.

An Engineered Nanocomplex with Photodynamic and Photothermal Synergistic Properties for Cancer Treatment

Abstract: Photodynamic therapy (PDT) and photothermal therapy (PTT) are promising therapeutic methods for cancer treatment; however, as single modality therapies, either PDT or PTT is still limited in its success rate. A dual application of both PDT and PTT, in a combined protocol, has gained immense interest. In this study, gold nanoparticles (AuNPs) were conjugated with a PDT agent, meso-tetrahydroxyphenylchlorin (mTHPC) photosensitizer, designed as nanotherapeutic agents that can activate a dual photodynamic/photothermal therapy in SH-SY5Y human neuroblastoma cells. The AuNP-mTHPC complex is biocompatible, soluble, and photostable. PDT efficiency is high because of immediate reactive oxygen species (ROS) production upon mTHPC activation by the 650-nm laser, which decreased mitochondrial membrane potential (∆ψm). Likewise, the AuNP-mTHPC complex is used as a photoabsorbing (PTA) agent for PTT, due to efficient plasmon absorption and excellent photothermal conversion characteristics of AuNPs under laser irradiation at 532 nm. Under the laser irradiation of a PDT/PTT combination, a twofold phototoxicity outcome follows, compared to PDT-only or PTT-only treatment. This indicates that PDT and PTT have synergistic effects together as a combined therapeutic method. Our study aimed at applying the AuNP-mTHPC approach as a potential treatment of cancer in the biomedical field.

Allostery-driven changes in dynamics regulate the activation of bacterial copper transcription factor.

Abstract: Metalloregulators bind and respond to metal ions by regulating the transcription of metal homeostasis genes. Copper efflux regulator (CueR) is a copper-responsive metalloregulator that is found in numerous Gram-negative bacteria. Upon Cu(I) coordination, CueR initiates transcription by bending the bound DNA promoter regions facilitating interaction with RNA polymerase. The structure of Escherichia coli CueR in presence of DNA and metal ion has been reported using X-ray crystallography and cryo-EM, providing information about the mechanism of action. However, the specific role of copper in controlling this transcription mechanism remains elusive. Herein, we use room temperature electron paramagnetic resonance (EPR) experiments to follow allosterically driven dynamical changes in E. coli CueR induced by Cu(I) binding. We suggest that more than one Cu(I) ion binds per CueR monomer, leading to changes in site-specific dynamics at the Cu(I) binding domain and at the distant DNA binding site. Interestingly, Cu(I) binding leads to an increase in dynamics about 27 Å away at the DNA binding domain. These changes in the dynamics of the DNA binding domain are important for exact coordination with the DNA. Thus, Cu(I) binding is critical to initiate a series of conformational changes that regulate and initiate gene transcription. BROAD AUDIENCE STATEMENT: The dynamics of metal transcription factors as a function of metal and DNA binding are complex. In this study, we use EPR spectroscopy to measure dynamical changes of Escherichia coli CueR as a function of copper and DNA binding. We show that copper controls the activation of the transcription processes by initiation a series of dynamical changes over the protein.

Electrocatalysis of Oxygen Reduction Reaction in a Polymer Electrolyte Fuel Cell with a Covalent Framework of Iron Phthalocyanine Aerogel

Abstract: Carbon aerogels have been studied in the context of fuel cell electrodes mainly as catalyst support materials due to their high surface area, porosity, and electrical conductivity. Recently, aerogels composed solely of inorganic molecular complexes have shown to be promising materials for the electrocatalysis of oxygen reduction reaction (ORR). These aerogels consist of atomically dispersed catalytic sites. Herein, we report on the synthesis and characterization of an aerogel-based catalyst: iron phthalocyanine aerogel. It was synthesized by coupling of ethynyl-terminated phthalocyanine monomers and then heat-treated at 800 °C to increase its electrical conductivity and catalytic activity. The aerogels reported here were tested as catalysts for ORR in acidic conditions for the first time and found to have a ultra-high number of atomically dispersed catalytic sites (7.11 × 1020 sites g–1) and very good catalytic activity (Eonset = 0.9 V vs RHE and TOF = 9.2 × 10–3 e– s–1 site–1 at 0.8 V vs RHE). The iron phthalocyanine aerogel was also studied in a proton exchange membrane fuel cell, reaching a peak power density of 292 mW cm–2 and an open circuit voltage of 0.83 V.

Left alone outside: A prospective observational cohort study on mental health outcomes among relatives of COVID-19 hospitalized patients

Abstract: Hospitalization due to COVID-19 bears many psychological challenges. While focusing on infected patients, their relatives are being largely neglected. Here, we investigated the mental health implications of hospitalization among relatives, over a one-month course. A single center study was conducted to assess relatives of COVID-19 patients during the first month from their admission to the hospital, and elucidate risk and protective factors for mental health deterioration. Ninety-one relatives of the first patients to be hospitalized in Israel were contacted by phone and screened for anxiety, depression, and posttraumatic stress symptoms (PTSS) at three time points (25–72 hours, 7–18 days, and one month). We found that anxiety and depression decreased significantly during the first month from their admission. Risk factors for deteriorated mental health at one month included feelings of mental exhaustion, financial concerns, and social disconnection. Being an ultra-orthodox was a protective factor for anxiety and depression but not for PTSS. Our findings emphasize the importance of addressing the mental health status of close relatives and adjust support for the unique setting of COVID-19.

DistilProtBert: a distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts

Abstract: Abstract Summary Recently, deep learning models, initially developed in the field of natural language processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameters needed to be fitted and the amount of computational resources they require. Recently, ‘distilled’ models using the concept of student and teacher networks have been widely used in NLP. Here, we adapted this concept to the problem of protein sequence analysis, by developing DistilProtBert, a distilled version of the successful ProtBert model. Implementing this approach, we reduced the size of the network and the running time by 50%, and the computational resources needed for pretraining by 98% relative to ProtBert model. Using two published tasks, we showed that the performance of the distilled model approaches that of the full model. We next tested the ability of DistilProtBert to distinguish between real and random protein sequences. The task is highly challenging if the composition is maintained on the level of singlet, doublet and triplet amino acids. Indeed, traditional machine-learning algorithms have difficulties with this task. Here, we show that DistilProtBert preforms very well on singlet, doublet and even triplet-shuffled versions of the human proteome, with AUC of 0.92, 0.91 and 0.87, respectively. Finally, we suggest that by examining the small number of false-positive classifications (i.e. shuffled sequences classified as proteins by DistilProtBert), we may be able to identify de novo potential natural-like proteins based on random shuffling of amino acid sequences. Availability and implementation https://github.com/yarongef/DistilProtBert.

Polymer-graphene composite in aerospace engineering

Abstract: Currently, polymer nanocomposites are occupying a major domain in high performance lightweight materials. Fiber reinforced polymer composites always remain in the first row among all the composite materials because of their high Young’s modulus and strength to weight ratio with respect to conventionally used metallic components for aerospace material. However, over the last few years, the motivation has been concentrated in the direction of nanocomposites material to improve longevity, and fuel efficiency of the aircrafts. This chapter focuses on the current status of research on the properties and applications of graphene-based polymer composite structures used in aerospace engineering. The polymers and fillers used in aerospace applications; the reason for graphene and its derivatives suitability in aerospace engineering were also illustrated.