Universal principles of lineage architecture and stem cell identity in renewing tissues.

Adult tissues in multicellular organisms typically contain a variety of stem, progenitor and differentiated cell types arranged in a lineage hierarchy that regulates healthy tissue turnover. Lineage hierarchies in disparate tissues often exhibit common features, yet the general principles regulating their architecture are not known. Here, we provide a formal framework for understanding the relationship between cell molecular 'states' and cell 'types', based on the topology of admissible cell state trajectories. We show that a self-renewing cell type - if defined as suggested by this framework - must reside at the top of any homeostatic renewing lineage hierarchy, and only there. This architecture arises as a natural consequence of homeostasis, and indeed is the only possible way that lineage architectures can be constructed to support homeostasis in renewing tissues. Furthermore, under suitable feedback regulation, for example from the stem cell niche, we show that the property of 'stemness' is entirely determined by the cell environment, in accordance with the notion that stem cell identities are contextual and not determined by hard-wired, cell-intrinsic characteristics. This article has an associated 'The people behind the papers' interview.

Evaluation of antifungal activity of visible light-activated doped TiO2 nanoparticles.

Titanium dioxide (TiO2) is a well-known material for its biomedical applications, among which its implementation as a photosensitizer in photodynamic therapy has attracted considerable interest due to its photocatalytic properties, biocompatibility, high chemical stability, and low toxicity. However, the photoactivation of TiO2 requires ultraviolet light, which may lead to cell mutation and consequently cancer. To address these challenges, recent research has focused on the incorporation of metal dopants into the TiO2 lattice to shift the band gap to lower energies by introducing allowed energy states within the band gap, thus ensuring the harnessing of visible light. This study presents the synthesis, characterization, and application of TiO2 nanoparticles (NPs) in their undoped, doped, and co-doped forms for antimicrobial photodynamic therapy (APDT) against Candida albicans. Blue light with a wavelength of 450 nm was used, with doses ranging from 20 to 60 J/cm2 and an NP concentration of 500 µg/ml. It was observed that doping TiO2 with Cu, Fe, Ag ions, and co-doping Cu:Fe into the TiO2 nanostructure enhanced the visible light photoactivity of TiO2 NPs. Experimental studies were done to investigate the effects of different ions doped into the TiO2 crystal lattice on their structural, optical, morphological, and chemical composition for APDT applications. In particular, Ag-doped TiO2 emerged as the best candidate, achieving 90-100% eradication of C. albicans.

Enhancing 3D human pose estimation with NIR single-pixel imaging and time-of-flight technology: a deep learning approach.

The extraction of 3D human pose and body shape details from a single monocular image is a significant challenge in computer vision. Traditional methods use RGB images, but these are constrained by varying lighting and occlusions. However, cutting-edge developments in imaging technologies have introduced new techniques such as single-pixel imaging (SPI) that can surmount these hurdles. In the near-infrared spectrum, SPI demonstrates impressive capabilities in capturing a 3D human pose. This wavelength can penetrate clothing and is less influenced by lighting variations than visible light, thus providing a reliable means to accurately capture body shape and pose data, even in difficult settings. In this work, we explore the use of an SPI camera operating in the NIR with time-of-flight (TOF) at bands 850-1550 nm as a solution to detect humans in nighttime environments. The proposed system uses the vision transformers (ViT) model to detect and extract the characteristic features of humans for integration over a 3D body model SMPL-X through 3D body shape regression using deep learning. To evaluate the efficacy of NIR-SPI 3D image reconstruction, we constructed a laboratory scenario that simulates nighttime conditions, enabling us to test the feasibility of employing NIR-SPI as a vision sensor in outdoor environments. By assessing the results obtained from this setup, we aim to demonstrate the potential of NIR-SPI as an effective tool to detect humans in nighttime scenarios and capture their accurate 3D body pose and shape.

Stratification of asthma by lipidomic profiling of induced sputum supernatant.

BACKGROUND: Asthma is a chronic respiratory disease with significant heterogeneity in its clinical presentation and pathobiology. There is need for improved understanding of respiratory lipid metabolism in asthma patients and its relation to observable clinical features. OBJECTIVE: We performed a comprehensive, prospective, cross-sectional analysis of the lipid composition of induced sputum supernatant obtained from asthma patients with a range of disease severities, as well as from healthy controls. METHODS: Induced sputum supernatant was collected from 211 adults with asthma and 41 healthy individuals enrolled onto the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study. Sputum lipidomes were characterized by semiquantitative shotgun mass spectrometry and clustered using topologic data analysis to identify lipid phenotypes. RESULTS: Shotgun lipidomics of induced sputum supernatant revealed a spectrum of 9 molecular phenotypes, highlighting not just significant differences between the sputum lipidomes of asthma patients and healthy controls, but also within the asthma patient population. Matching clinical, pathobiologic, proteomic, and transcriptomic data helped inform the underlying disease processes. Sputum lipid phenotypes with higher levels of nonendogenous, cell-derived lipids were associated with significantly worse asthma severity, worse lung function, and elevated granulocyte counts. CONCLUSION: We propose a novel mechanism of increased lipid loading in the epithelial lining fluid of asthma patients resulting from the secretion of extracellular vesicles by granulocytic inflammatory cells, which could reduce the ability of pulmonary surfactant to lower surface tension in asthmatic small airways, as well as compromise its role as an immune regulator.

Cryo-EM Map Anisotropy Can Be Attenuated by Map Post-Processing and a New Method for Its Estimation.

One of the most important challenges in cryogenic electron microscopy (cryo-EM) is the substantial number of samples that exhibit preferred orientations, which leads to an uneven coverage of the projection sphere. As a result, the overall quality of the reconstructed maps can be severely affected, as manifested by the presence of anisotropy in the map resolution. Several methods have been proposed to measure the directional resolution of maps in tandem with experimental protocols to address the problem of preferential orientations in cryo-EM. Following these works, in this manuscript we identified one potential limitation that may affect most of the existing methods and we proposed an alternative approach to evaluate the presence of preferential orientations in cryo-EM reconstructions. In addition, we also showed that some of the most recently proposed cryo-EM map post-processing algorithms can attenuate map anisotropy, thus offering alternative visualization opportunities for cases affected by moderate levels of preferential orientations.

An information-theoretic approach to single cell sequencing analysis.

BACKGROUND: Single-cell sequencing (sc-Seq) experiments are producing increasingly large data sets. However, large data sets do not necessarily contain large amounts of information. RESULTS: Here, we formally quantify the information obtained from a sc-Seq experiment and show that it corresponds to an intuitive notion of gene expression heterogeneity. We demonstrate a natural relation between our notion of heterogeneity and that of cell type, decomposing heterogeneity into that component attributable to differential expression between cell types (inter-cluster heterogeneity) and that remaining (intra-cluster heterogeneity). We test our definition of heterogeneity as the objective function of a clustering algorithm, and show that it is a useful descriptor for gene expression patterns associated with different cell types. CONCLUSIONS: Thus, our definition of gene heterogeneity leads to a biologically meaningful notion of cell type, as groups of cells that are statistically equivalent with respect to their patterns of gene expression. Our measure of heterogeneity, and its decomposition into inter- and intra-cluster, is non-parametric, intrinsic, unbiased, and requires no additional assumptions about expression patterns. Based on this theory, we develop an efficient method for the automatic unsupervised clustering of cells from sc-Seq data, and provide an R package implementation.

Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search.

Fragment merging is a promising approach to progressing fragments directly to on-scale potency: each designed compound incorporates the structural motifs of overlapping fragments in a way that ensures compounds recapitulate multiple high-quality interactions. Searching commercial catalogues provides one useful way to quickly and cheaply identify such merges and circumvents the challenge of synthetic accessibility, provided they can be readily identified. Here, we demonstrate that the Fragment Network, a graph database that provides a novel way to explore the chemical space surrounding fragment hits, is well-suited to this challenge. We use an iteration of the database containing >120 million catalogue compounds to find fragment merges for four crystallographic screening campaigns and contrast the results with a traditional fingerprint-based similarity search. The two approaches identify complementary sets of merges that recapitulate the observed fragment-protein interactions but lie in different regions of chemical space. We further show our methodology is an effective route to achieving on-scale potency by retrospective analyses for two different targets; in analyses of public COVID Moonshot and Mycobacterium tuberculosis EthR inhibitors, potential inhibitors with micromolar IC50 values were identified. This work demonstrates the use of the Fragment Network to increase the yield of fragment merges beyond that of a classical catalogue search.

Deep-learning blurring correction of images obtained from NIR single-pixel imaging.

In challenging scenarios characterized by low-photon conditions or the presence of scattering effects caused by rain, fog, or smoke, conventional silicon-based cameras face limitations in capturing visible images. This often leads to reduced visibility and image contrast. However, using near-infrared (NIR) light within the range of 850-1550 nm offers the advantage of reduced scattering by microparticles, making it an attractive option for imaging in such conditions. Despite NIR's advantages, NIR cameras can be prohibitively expensive. To address this issue, we propose a vision system that leverages NIR active illumination single-pixel imaging (SPI) operating at 1550 nm combined with time of flight operating at 850 nm for 2D image reconstruction, specifically targeting rainy conditions. We incorporate diffusion models into the proposed system to enhance the quality of NIR-SPI images. By simulating various conditions of background illumination and droplet size in an outdoor laboratory scenario, we assess the feasibility of utilizing NIR-SPI as a vision sensor in challenging outdoor environments.

Ultra-high-speed liquid chromatography combined with mass spectrometry detection analytical methods for the determination of nitrosamine drug substance-related impurities.

In the present study, five simple, feasible, and sensitive Ultra-high-speed liquid chromatography combined with mass spectrometry detection methods, using electrospray ionization are proposed. These methods were developed and validated for the determination of four different nitrosamine drug substance-related impurities-N-nitrosoacebutolol, N-nitrosobisoprolol, N-nitrosometoprolol, and N-nitrososotalol-in five beta blockers active pharmaceutical ingredients-acebutolol HCl, bisoprolol fumarate, metoprolol tartrate, metoprolol succinate, and sotalol HCl. The proposed methods were validated as per regulatory guidelines. Acquity HSS T3 (3.0 × 100 mm, 1.8 µm) column and formic acid 0.1% in water combined with methanol or acetonitrile were used for chromatographic separation in all methods. The limit of detection and the limit of quantification were found to be in the range of 0.02-1.2 and 2-20 parts per billion, respectively. The accuracy and precision of the five methods have been demonstrated in the working range of each one, giving values of recovery within the range of 64.1%-113.3%, and the regression coefficients (R) were found to be in the range of 0.9978-0.9999. These methods could be used for controlling nitrosamine drug substance-related impurities content for beta blockers drug substances batches manufactured at Moehs group.

3DBionotes COVID-19 edition.

SUMMARY: The web platform 3DBionotes-WS integrates multiple web services and an interactive web viewer to provide a unified environment in which biological annotations can be analyzed in their structural context. Since the COVID-19 outbreak, new structural data from many viral proteins have been provided at a very fast pace. This effort includes many cryogenic electron microscopy (cryo-EM) studies, together with more traditional ones (X-rays, NMR), using several modeling approaches and complemented with structural predictions. At the same time, a plethora of new genomics and interactomics information (including fragment screening and structure-based virtual screening efforts) have been made available from different servers. In this context, we have developed 3DBionotes-COVID-19 as an answer to: (i) the need to explore multiomics data in a unified context with a special focus on structural information and (ii) the drive to incorporate quality measurements, especially in the form of advanced validation metrics for cryo-EM. AVAILABILITY AND IMPLEMENTATION: https://3dbionotes.cnb.csic.es/ws/covid19. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Efficient ordering of the Hadamard basis for single pixel imaging.

Single-pixel imaging is a technique that can reconstruct an image of a scene by projecting a series of spatial patterns on an object and capturing the reflected light by a single photodetector. Since the introduction of the compressed sensing method, it has been possible to use random spatial patterns and reduce its number below the Nyquist-Shannon limit to form a good quality image but with lower spatial resolution. On the other hand, Hadamard pattern based methods can reconstruct large images by increasing the acquisition measurement time. Here, we propose an efficient strategy to order the Hadamard basis patterns from higher to lower relevance, and then to reconstruct an image at very low sampling rates of at least 8%. Our proposal is based on the construction of generalized basis vectors in two dimensions and then ordering in zigzag fashion. Simulation and experimental results show that the sampling rate, image quality and computational complexity of our method are competitive to the state of the art methods.

Stoichiometric network analysis in reaction networks yielding spontaneous mirror symmetry breaking in a prebiotic atmosphere.

The generation of amino acid homochirality under prebiotic atmosphere conditions is a relevant issue in the study of the origin of life. This research is based on the production of amino acids via Strecker synthesis and how it is adjusted to the Kondepudi-Nelson autocatalytic model. The spontaneous mirror symmetry breaking (SMSB) of the new Kondepudi-Nelson-Strecker model, subject to two modifications (with Limited Enantioselective and Cross Inhibition), and also their combination were studied using the stoichiometric network analysis (SNA). In the calculations, the values obtained from the literature for alanine were considered. A total production of alanine of 7.56 × 109 mol year-1 was determined under prebiotic atmosphere conditions and starting from that value, the reaction rates for the models studied were estimated. Only the model with cross inhibition or achiral dimer formation is driven by stochastic fluctuations during SMSB. The stochastic fluctuation was estimated for a value of 2.619 × 10-15 mol L-1. This perturbation was sufficient to trigger SMSB. Finally, the results of SMSB were used to calculate the entropy production for the cross inhibition model.

Discriminative deep learning based benignity/malignancy diagnosis of dermatologic ultrasound skin lesions with pretrained artificial intelligence architecture.

BACKGROUND: Deep-learning algorithms (DLAs) have been used in artificial intelligence aided ultrasonography diagnosis of thyroid and breast lesions. However, its use has not been described in the case of dermatologic ultrasound lesions. Our purpose was to train a DLA to discriminate benign form malignant lesions in dermatologic ultrasound images. MATERIALS AND METHODS: We trained a prebuilt neural network architecture (EfficientNet B4) in a commercial artificial intelligence platform (Peltarion, Stockholm, Sweden) with 235 color Doppler images of both benign and malignant ultrasound images of 235 excised and histologically confirmed skin lesions (84.3% training, 15.7% validation). An additional 35 test images were used for testing the algorithm discrimination for correct benign/malignant diagnosis. One dermatologist with more than 5 years of experience in dermatologic ultrasound blindly evaluated the same 35 test images for malignancy or benignity. RESULTS: EfficientNet B4 trained dermatologic ultrasound algorithm sensitivity; specificity; predictive positive values, and predicted negative values for validation algorithm were 0.8, 0.86, 0.86, and 0.8, respectively for malignancy diagnosis. When tested with 35 previously unevaluated images sets, the algorithm´s accuracy for correct benign/malignant diagnosis was 77.1%, not statistically significantly different from the dermatologist's evaluation (74.1%). CONCLUSION: An adequately trained algorithm, even with a limited number of images, is at least as accurate as a dermatologic-ultrasound experienced dermatologist in the evaluation of benignity/malignancy of ultrasound skin tumor images devoid of clinical data.

Evaluation of desensitization protocols to betalactam antibiotics.

WHAT IS KNOWN AND OBJECTIVE: Betalactam antibiotics are the most frequent cause of hypersensitivity reactions. Rapid drug desensitization (RDD) is a technique that induces temporary tolerance to a drug allowing a patient to receive the optimal agent. The increased use of RDD and the lack of standardization among available protocols in terms of formulation, starting dose, number of steps and dosing frequency make it essential to determine the safety and appropriate management of these protocols, especially regarding reconstitution, diluents, stability and drug administration in order to guarantee reproducibility. We reviewed betalactam desensitization protocols in a tertiary hospital, in accordance with currently published practices and evaluated its use on patients over a period of three years. METHODS: (a) We performed a literature search in PubMed, MEDLINE and Google Scholar databases for case reports and/or systematic reviews describing desensitization protocols for betalactam antibiotics. Pharmacokinetic parameters and physicochemical stability were checked for each antibiotic. (b) We retrospectively reviewed inpatients undergoing our antibiotic desensitization protocols from February 2018 to January 2021. Data and outcomes of desensitization procedures were analysed. RESULTS: We developed nine RDD protocols: meropenem, ceftriaxone, ceftazidime, ampicillin, ceftolozane/tazobactam, cloxacillin, piperacillin/tazobactam, amoxicillin/clavulanate and penicillin G sodium. Five antibiotics have RDD protocols for two different doses, adjusted to patients with impaired renal function. Detailed data (diluent, total dose, volume, concentrations, duration and stability) of the protocol of each antibiotic used are provided. 28 desensitizations were performed in 17 patients, three of them with confirmed allergies by skin test. 26 out of 28 (92.9%) of them were successfully completed, including those three with positive skin results. The pathogens most frequently involved were E. faecalis and P. aeruginosa; both frequently associated with bacterial resistance. Meropenem, ceftriaxone and ceftazidime were the antibiotics most desensitized. 25 out of 26 (96.1%) procedures were successful in resolving the infection. WHAT IS NEW AND CONCLUSIONS: Detailed information about compounding, dilution and stability is crucial to ensure safe and successful desensitization processes, as well as good coordination between the Allergy and Pharmacy departments. The increase in bacterial resistance to many of the commercially available antibiotics limits the therapeutic options for treating multidrug-resistant infections; in those situations, antibiotic desensitization may be a key therapeutic option. Although there is a broad consensus in limiting the use of RDD to patients with confirmed allergy, in usual clinical practice its application in those strongly suspected of having type I hypersensitivity is still observed. Our betalactam desensitization protocols have shown themselves to be safe and effective, as evidenced by data from the 17 patients on whom they have been tested.

Transformative learning experience among nursing students with patients acting as teachers: Mixed methods, non-randomized, single-arm study.

AIM: To examine the effects of expert HIV patients acting as teachers to Spanish nursing students both on their HIV-related knowledge, attitudes and practices and on their approach to the care model as well as to explore their learning experience. DESIGN: Non-randomized, single-arm study with quantitative before and after measurements and qualitative data. METHODS: The intervention consisted of five 90-min workshops led by two women living with HIV. Thirty-four nursing students participated, and quantitative and qualitative data were gathered from February to June 2018. We used the Patient-Practitioner Orientation Scale (PPOS) and the KAP questionnaire on HIV/AIDS to collect quantitative data. RESULTS: Statistically significant differences were found in the global score for care orientation and its two dimensions, caring and sharing. About the changes resulting from the workshops, the quantitative results-more patient-centred care perception and better attitudes towards people living with HIV-match the qualitative findings in all the aspects studied, except in sharing. CONCLUSION: Incorporating expert patients as teachers in the nursing bachelor's degree resulted in more patient-centred care and improved knowledge, attitudes and practices. The workshops conducted by qualified expert patients showed transformative learning power, as the participants improved professional and personal aspects.

A Multi-Scale Entropy Approach to Study Collapse and Anomalous Diffusion in Shared Mobility Systems.

In this paper, we study the phenomena of collapse and anomalous diffusion in shared mobility systems. In particular, we focus on a fleet of vehicles moving through a stations network and analyse the effect of self-journeys in system stability, using a mathematical simplex under stochastic flows. With a birth-death process approach, we find analytical upper bounds for random walk and we monitor how the system collapses by super diffusing under different randomization conditions. Using the multi-scale entropy metric, we show that real data from a bike-sharing fleet in the city of Salamanca (Spain) present a complex behaviour with more of a 1/f signal than a disorganized system with a white noise signal.

Q-CHAT-NAO: A robotic approach to autism screening in toddlers.

The use of humanoid robots as assistants in therapy processes is not new. Several projects in the past several years have achieved promising results when combining human-robot interaction with standard techniques. Moreover, there are multiple screening systems for autism; one of the most used systems is the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), which includes ten questions to be answered by the parents or caregivers of a child. We present Q-CHAT-NAO, an observation-based autism screening system supported by a NAO robot. It includes the six questions of the Q-CHAT-10 that can be adapted to work in a robotic context; unlike the original system, it obtains information from the toddler instead of from an indirect source. The detection results obtained after applying machine learning models to the six questions in the Autistic Spectrum Disorder Screening Data for Toddlers dataset were almost equivalent to those of the original version with ten questions. These findings indicate that the Q-CHAT-NAO could be a screening option that would exploit all the benefits related to human-robot interaction.

Effect of hydroxychloroquine, azithromycin and lopinavir/ritonavir on the QT corrected interval in patients with COVID-19.

BACKGROUND: Administration of Hydroxychloroquine and Azithromycin in patients with coronavirus disease 2019 (COVID-19) prolongs QTc corrected interval (QTc). The effect and safety of Lopinavir/Ritonavir in combination with these therapies have seldom been studied. OBJECTIVES: Our aim was to evaluate changes in QTc in patients receiving double (Hydroxychloroquine + Azithromycin) and triple therapy (Hydroxychloroquine + Azithromycin + Lopinavir/Ritonavir) to treat COVID-19. Secondary outcome was the incidence of in-hospital all-cause mortality. METHODS: Patients under treatment with double (DT) and triple therapy (TT) for COVID-19 were consecutively included in this prospective observational study. Serial in-hospital electrocardiograms were performed to measure QTc at baseline and during therapy. RESULTS: 168 patients (±66.2 years old) were included: 32.1% received DT and 67.9% received TT. The mean baseline QTc was 410.33 ms. Patients under DT and TT prolonged QTc interval respect baseline values (p < 0.001), without significant differences between both therapy groups (p = 0.748). Overall, 33 patients (19.6%) had a peak QTc and/or an increase QTc 60 ms from baseline, with a higher prevalence among those with hypokalemia (p = 0.003). All-cause mortality was similar between both strategy groups (p = 0.093) and high risk QTc prolongation was no related to clinical events in this series. CONCLUSIONS: DT and TT prolong the QTc in patients with COVID-19. Addition of Lopinavir/Ritonavir on top of Hydroxychloroquine and Azithromycin did not increase QTc compared to DT.

Advances in Xmipp for Cryo-Electron Microscopy: From Xmipp to Scipion.

Xmipp is an open-source software package consisting of multiple programs for processing data originating from electron microscopy and electron tomography, designed and managed by the Biocomputing Unit of the Spanish National Center for Biotechnology, although with contributions from many other developers over the world. During its 25 years of existence, Xmipp underwent multiple changes and updates. While there were many publications related to new programs and functionality added to Xmipp, there is no single publication on the Xmipp as a package since 2013. In this article, we give an overview of the changes and new work since 2013, describe technologies and techniques used during the development, and take a peek at the future of the package.

MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning.

Cryo-EM Single Particle Analysis workflows require tens of thousands of high-quality particle projections to unveil the three-dimensional structure of macromolecules. Conventional methods for automatic particle picking tend to suffer from high false-positive rates, hampering the reconstruction process. One common cause of this problem is the presence of carbon and different types of high-contrast contaminations. In order to overcome this limitation, we have developed MicrographCleaner, a deep learning package designed to discriminate, in an automated fashion, between regions of micrographs which are suitable for particle picking, and those which are not. MicrographCleaner implements a U-net-like deep learning model trained on a manually curated dataset compiled from over five hundred micrographs. The benchmarking, carried out on approximately one hundred independent micrographs, shows that MicrographCleaner is a very efficient approach for micrograph preprocessing. MicrographCleaner (micrograph_cleaner_em) package is available at PyPI and Anaconda Cloud and also as a Scipion/Xmipp protocol. Source code is available at https://github.com/rsanchezgarc/micrograph_cleaner_em.