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Spin-Transfer-Torque Magnetic Random Access Memory (STT-MRAM) is a memory which has bit cells made of magnetic tunnel junctions (MTJs), which comprise a storage switchable magnetic layer ("free layer") and, typically, one thin insulating barrier and one stable magnetic layer providing reference spin polarization for read and write operations ("reference layer"). STT-MRAM may compete with conventional dynamic and static RAM on technological nodes below 22 nm, if its switching current is reduced. This goal may be achieved for MTJ, which has two insulating barriers and reference layers. Building such a double-barrier MTJ, however, faces tremendous material challenges. In this work, a new double-barrier MTJ design with a switchable reference layer is introduced. We show that its efficiency is similar to its counterpart with stable reference layers, but it is much easier to be built. [ABSTRACT FROM AUTHOR]

We present a reliable image reconstruction algorithm suitable for a microwave holographic vision system with several sensors coupled to the spin-diode based microwave detector and a single emission source. An objective is, by reconstructing the spatial microwave scattering density on the scene, to detect the presence and the nature of road obstacles impeding driving in the near vehicle zone. The idea of holographic visualization is to reconstruct the spatial microwave scattering density of an object by detecting an amplitude and phase of a reflected signal by lattice of sensors. We discuss versions of an algorithm, determine and analyse its resolution limits for various distances with different number of sensors for a one-dimensional test problem of detecting two walls (or posts) separated by a gap at a fixed distance. The maximal interval between sensors needed for a reliable reconstruction equals approximately Fresnel zone width. We show that maximal resolution achieved by our algorithm with an appropriate number of sensors was about 40% of Fresnel zone width for wall detection and about 30% of zone width for gap detection.

Chronic wounds, defined by their prolonged healing process, significantly impair patient quality of life and impose a hefty financial burden on healthcare systems worldwide. Sex/gender-specific mechanisms regulate inflammation and infection, angiogenesis, matrix synthesis, and cell recruitment contribute to cutaneous wound healing, but remain largely understudied. This review is aimed to spotlight the innovative realm of bioengineering and nanomedicine, which is at the helm of revolutionizing complex chronic wound care. It underscores the significance of integrating patient sex into the development and (pre)clinical testing of these avant-garde treatment modalities, in order to enhance healing prospects for both women and men. Moreover, we explore the representation of both sexes in clinical trials of bioengineered and nanomedicine products. Finally, we examine the primary reasons for the historical neglect in translating sex-specific wound healing research into clinical practice and propose strategic solutions. By tackling these issues, the article advocates for advanced treatment frameworks that could significantly improve healing outcomes for individuals of all sexes, thereby optimizing both efficacy and inclusivity in chronic wound management., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Association of Dermatologists.)

Genetic interactions identify functional connections between genes and pathways, establishing gene functions or druggable targets. Here we use CRISPRi-TnSeq, CRISPRi-mediated knockdown of essential genes alongside TnSeq-mediated knockout of non-essential genes, to map genome-wide interactions between essential and non-essential genes in Streptococcus pneumoniae. Transposon-mutant libraries constructed in 13 CRISPRi strains enabled screening of ~24,000 gene pairs. This identified 1,334 genetic interactions, including 754 negative and 580 positive interactions. Network analyses show that 17 non-essential genes pleiotropically interact with more than half the essential genes tested. Validation experiments confirmed that a 7-gene subset protects against perturbations. Furthermore, we reveal hidden redundancies that compensate for essential gene loss, relationships between cell wall synthesis, integrity and cell division, and show that CRISPRi-TnSeq identifies synthetic and suppressor-type relationships between both functionally linked and disparate genes and pathways. Importantly, in species where CRISPRi and Tn-Seq are established, CRISPRi-TnSeq should be straightforward to implement., (© 2024. The Author(s).)

Salmonella enterica is comprised of genetically distinct 'serovars' that together provide an intriguing model for exploring the genetic basis of pathogen evolution. Although the genomes of numerous Salmonella isolates with broad variations in host range and human disease manifestations have been sequenced, the functional links between genetic and phenotypic differences among these serovars remain poorly understood. Here, we conduct high-throughput functional genomics on both generalist (Typhimurium) and human-restricted (Typhi and Paratyphi A) Salmonella at unprecedented scale in the study of this enteric pathogen. Using a comprehensive systems biology approach, we identify gene networks with serovar-specific fitness effects across 25 host-associated stresses encountered at key stages of human infection. By experimentally perturbing these networks, we characterize previously undescribed pseudogenes in human-adapted Salmonella. Overall, this work highlights specific vulnerabilities encoded within human-restricted Salmonella that are linked to the degradation of their genomes, shedding light into the evolution of this enteric pathogen., (© 2024. The Author(s).)

The emerging large language models (LLMs) are actively evaluated in various fields including healthcare. Most studies have focused on established benchmarks and standard parameters; however, the variation and impact of prompt engineering and fine-tuning strategies have not been fully explored. This study benchmarks GPT-3.5 Turbo, GPT-4, and Llama-7B against BERT models and medical fellows' annotations in identifying patients with metastatic cancer from discharge summaries. Results revealed that clear, concise prompts incorporating reasoning steps significantly enhanced performance. GPT-4 exhibited superior performance among all models. Notably, one-shot learning and fine-tuning provided no incremental benefit. The model's accuracy sustained even when keywords for metastatic cancer were removed or when half of the input tokens were randomly discarded. These findings underscore GPT-4's potential to substitute specialized models, such as PubMedBERT, through strategic prompt engineering, and suggest opportunities to improve open-source models, which are better suited to use in clinical settings., Competing Interests: The authors declare no competing interests., (©2024 AMIA - All rights reserved.)

Idiopathic pulmonary fibrosis (IPF) is a pathological condition wherein lung injury precipitates the deposition of scar tissue, ultimately leading to a decline in pulmonary function. Existing research indicates a notable exacerbation in the clinical prognosis of IPF patients following infection with COVID-19. This investigation employed bulk RNA-sequencing methodologies to describe the transcriptomic profiles of small airway cell cultures derived from IPF and post-COVID fibrosis patients. Differential gene expression analysis unveiled heightened activation of pathways associated with microtubule assembly and interferon signaling in IPF cell cultures. Conversely, post-COVID fibrosis cell cultures exhibited distinctive characteristics, including the upregulation of pathways linked to extracellular matrix remodeling, immune system response, and TGF-β1 signaling. Notably, BMP signaling levels were elevated in cell cultures derived from IPF patients compared to non-IPF control and post-COVID fibrosis samples. These findings underscore the molecular distinctions between IPF and post-COVID fibrosis, particularly in the context of signaling pathways associated with each condition. A better understanding of the underlying molecular mechanisms holds the promise of identifying potential therapeutic targets for future interventions in these diseases.

RNA sequencing (RNA-seq) is a method used for the high-throughput quantification of mRNA in a biological sample. It is widely used to investigate differential gene expression between drug-resistant and sensitive cancers to identify genetic mediators of drug resistance. Here, we describe a comprehensive experimental and bioinformatic pipeline to isolate mRNA from human cell lines, prepare mRNA libraries for next-generation sequencing, and perform post-sequencing bioinformatics analyses., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Detailed knowledge on how bacteria evade antibiotics and eventually develop resistance could open avenues for novel therapeutics and diagnostics. It is thereby key to develop a comprehensive genome-wide understanding of how bacteria process antibiotic stress, and how modulation of the involved processes affects their ability to overcome said stress. Here we undertake a comprehensive genetic analysis of how the human pathogen Streptococcus pneumoniae responds to 20 antibiotics. We build a genome-wide atlas of drug susceptibility determinants and generated a genetic interaction network that connects cellular processes and genes of unknown function, which we show can be used as therapeutic targets. Pathway analysis reveals a genome-wide atlas of cellular processes that can make a bacterium less susceptible, and often tolerant, in an antibiotic specific manner. Importantly, modulation of these processes confers fitness benefits during active infections under antibiotic selection. Moreover, screening of sequenced clinical isolates demonstrates that mutations in genes that decrease antibiotic sensitivity and increase tolerance readily evolve and are frequently associated with resistant strains, indicating such mutations could be harbingers for the emergence of antibiotic resistance., (© 2022. The Author(s).)

Loss of metabolic homeostasis is a hallmark of aging and is characterized by dramatic metabolic reprogramming. To analyze how the fate of labeled methionine is altered during aging, we applied 13 C5-Methionine labeling to Drosophila and demonstrated significant changes in the activity of different branches of the methionine metabolism as flies age. We further tested whether targeted degradation of methionine metabolism components would "reset" methionine metabolism flux and extend the fly lifespan. Specifically, we created transgenic flies with inducible expression of Methioninase , a bacterial enzyme capable of degrading methionine and revealed methionine requirements for normal maintenance of lifespan. We also demonstrated that microbiota-derived methionine is an alternative and important source in addition to food-derived methionine. In this genetic model of methionine restriction (MetR), we also demonstrate that either whole-body or tissue-specific Methioninase expression can dramatically extend Drosophila health- and lifespan and exerts physiological effects associated with MetR. Interestingly, while previous dietary MetR extended lifespan in flies only in low amino acid conditions, MetR from Methioninase expression extends lifespan independently of amino acid levels in the food. Finally, because impairment of the methionine metabolism has been previously associated with the development of Alzheimer's disease, we compared methionine metabolism reprogramming between aging flies and a Drosophila model relevant to Alzheimer's disease, and found that overexpression of human Tau caused methionine metabolism flux reprogramming similar to the changes found in aged flies. Altogether, our study highlights Methioninase as a potential agent for health- and lifespan extension., Competing Interests: The authors declare no competing interest.

Aging is characterized by extensive metabolic reprogramming. To identify metabolic pathways associated with aging, we analyzed age-dependent changes in the metabolomes of long-lived Drosophila melanogaster . Among the metabolites that changed, levels of tyrosine were increased with age in long-lived flies. We demonstrate that the levels of enzymes in the tyrosine degradation pathway increase with age in wild-type flies. Whole-body and neuronal-specific downregulation of enzymes in the tyrosine degradation pathway significantly extends Drosophila lifespan, causes alterations of metabolites associated with increased lifespan, and upregulates the levels of tyrosine-derived neuromediators. Moreover, feeding wild-type flies with tyrosine increased their lifespan. Mechanistically, we show that suppression of ETC complex I drives the upregulation of enzymes in the tyrosine degradation pathway, an effect that can be rescued by tigecycline, an FDA-approved drug that specifically suppresses mitochondrial translation. In addition, tyrosine supplementation partially rescued lifespan of flies with ETC complex I suppression. Altogether, our study highlights the tyrosine degradation pathway as a regulator of longevity., Competing Interests: AP, DR, LW, DL, EF, RB, AO, JA, VC, JR, AB, NP No competing interests declared, (© 2020, Parkhitko et al.)