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The systemic lupus erythematosus-associated NCF190H allele synergizes with viral infection to cause mouse lupus but also limits virus spread

Abstract Studying how single nucleotide polymorphisms (SNPs) crosstalk with non-autologous factors to cause complex autoimmune diseases is challenging. An amino acid replacement in the neutrophil cytosolic factor 1 (NCF1-339/NCF1 R90H ) leading to lower reactive oxygen species induction has been reported as the major SNP for systemic lupus erythematosus (SLE). Here we show that infection with the murine norovirus (MNV) contributes to the induction of lupus in Ncf1 90H mice. Mutant NCF190H upregulates the IFN-α/JAK1/STAT1 pathway in macrophages and anti-MNV-antibody production. In parallel, the MNV infection of NCF190H mice upregulates Toll-like receptor 7 in macrophages, plasmacytoid dendritic cells and B220+ splenocytes, thereby promoting germinal center formation and lupus-associated autoantibodies production. These compounded effects lead to protection against MNV infection but also glomeruloneph ritis with proteinuria and lupus arthritis in the absence of chemical inducers such as pristane. Our data thus suggest that this SLE-associated SNP, NCF190H, synergizes with MNV infection to induce the development of mouse lupus.

SugarViT—Multi-objective regression of UAV images with Vision Transformers and Deep Label Distribution Learning demonstrated on disease severity prediction in sugar beet

Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, and disease control. This work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation for the use case of disease severity scoring for CLS in sugar beet. With concepts of DLDL, special loss functions, and a tailored model architecture, we develop an efficient Vision Transformer based model for disease severity scoring called SugarViT. One novelty in this work is the combination of remote sensing data with environmental parameters of the experimental sites for disease severity prediction. Although the model is evaluated on this special use case, it is held as generic as possible to also be applicable to various image-based classification and regression tasks. With our framework, it is even possible to learn models on multi-objective problems, as we show by a pretraining on environmental metadata. Furthermore, we perform several comparison experiments with state-of-the-art methods and models to constitute our modeling and preprocessing choices.

Expanding tracer space for positron emission tomography with high molar activity 18F-labeled α,α-difluoromethylalkanes

Abstract Positron emission tomography (PET) is an advanced biomedical imaging modality that relies on well-designed radiotracers to report on specific protein targets and processes occurring in living animals and humans. Cyclotron-produced short-lived fluorine-18 (t 1/2 = 109.8 min) is widely used to radiolabel tracers for PET. Herein we aim to expand the chemical space available for PET tracer development to include structures with 18F-labeled α,α-difluoromethylalkyl groups. We report an efficient and broad-scope method for labeling such groups with high molar activities based on a single-step radiofluorination of α-bromo-α-fluoroalkanes. The method is applicable to bioactive compounds and drug-like molecules, and is readily automated for radiotracer production. The unique physical and biochemical features of the α,α-difluoromethyl group can now be exploited in the design of new PET tracers.

Magnetic effects of thaumatin crystals; observation of crystal growth by magneto-Archimedes levitation and magnetic orientation

We examined the magnetic effect of thaumatin crystals, which are a well known model of protein crystals but which have hardly been studied for that effect. We succeeded in crystallizing thaumatin by magnetic levitation based on the magneto-Archimedes effect by the addition of the paramagnetic substance gadolinium chloride. We also carried out a chronological observation of the levitation process in a superconducting magnet, and visualized the magnetic orientation of the crystals by applying a magnetic field along the horizontal direction. In another major result, we carried out a diffraction experiment and performed a structural analysis of the crystals. We noticed from the results that no electron density from the gadolinium ion could be observed in the crystals. This suggests the possibility that the paramagnetic substance of the aqueous precipitant solution helps only to promote the crystals’ levitation, and has little effect on thaumatin crystallization.

The neuroimmune connectome in health and disease

Author Correction: ReaL-MGE is a tool for enhanced multiplex genome engineering and application to malonyl-CoA anabolism

Socioeconomic and academic consequences of COVID-19 pandemic on medical students from the University of Rwanda

Introduction Little is known about the consequences of the COVID-19 pandemic on the life of university students in Sub Saharan Africa (SSA). The objective of this study was to evaluate the socioeconomic and academic consequences of the COVID-19 pandemic on medical students studying at the University of Rwanda. Methods This was a cross-sectional study. An online survey using google form was sent to medical students in clinical training (year 3 till year 5) using convenience sampling followed by snowball sampling method. We collected data on participants’ demographics, general knowledge on the COVID-19 pandemic and perception on mitigation measures, and socio-economic and academic consequences of the COVID-19 pandemic. Descriptive statistics were used in excel 2015 software to calculate participants’ responses and categorical data were presented using frequencies and percentages. Results A total 187 participants completed the survey. Most participants described disruption in routine activities (72.7%), reduced travelling (69%), church closing (64.2%), and loss of freedom (57.2%) as examples of negative social consequences. While financial uncertainty (64.7%), decrease in income (49.7%), and increase in poverty rate (42.2%) were the main economic consequences. Issues with academic progress (95.7%), limited social life (56.1%), and repeating the year (42.8%) were examples of negative academic consequences. Conclusion The results of this study suggest that the COVID-19 had a negative social, economic, and academic consequences on medical students at the University of Rwanda. These finding may guide the design of interventions to mitigate the consequences of COVID-19 and to protect medical students against future pandemics and crises.

Author Correction: Physics-informed deep generative learning for quantitative assessment of the retina

Biomonitoring and Health Risk Assessment of Arsenic Contamination in Drinking Water among Rural Residents in Western Tehran

Arsenic is a widespread environmental contaminant that poses a significant threat to global health due to its toxicity and carcinogenicity. Given the high levels of arsenic found in the drinking water of western areas of Tehran, the objective of this study was to analyze levels of arsenic in multiple biological samples (blood, hair, and nails) collected from residents living in these areas. This cross-sectional study was conducted over three weeks in November 2022 in five villages. A total of 67 residents from these villages were included in the exposure group. Analysis of arsenic was carried out by using the Perkin Elmer Optima 8000 ICP-OES instrument coupled with the FIAS 100 flow injection module after sample digestion. The average concentration of arsenic in people’s blood was 4.19 μg/l, which exceeds the standard limit of ATSDR (1 μg/l) by about 4 times. Additionally, 47.8% of blood samples exceeded the standard, while for nail and hair samples, the percentages were 22.4% and 13.4%, respectively. Water samples showed the highest percentage above the standard, with 67.2%. There is no significant relationship between arsenic levels in drinking water, hair and blood. However, a significant positive correlation was observed between the concentration of arsenic in drinking water and nail samples. The mean of hazard quotient (HQ) and carcinogenic risk (CR) indices of arsenic in drinking water suggest that the daily intake levels of the examined arsenic in the study area exceeded the acceptable thresholds ((HQ < 1) and (CR < 1 × 10−4)). Although this study demonstrated elevated arsenic exposure among the population in western Tehran, our findings showed no significant correlation between arsenic concentrations in drinking water and biological samples. Therefore, further research is required to identify other potential exposure pathways and develop targeted intervention strategies. Additionally, remediation measures to improve water quality remain essential in this rural area.

X-ray telescope casts a sharper gaze on galaxy clusters

Unraveling microglial spatial organization in the developing human brain with DeepCellMap, a deep learning approach coupled with spatial statistics

Effects of biobanding on training loads and technical performance of young football players

Background In adolescent sports, grouping by chronological age can advantage athletes born early in the year due to maturation differences. Early maturing athletes often achieve greater performance gains, are perceived as more talented, and receive more specialized training and workloads. This study aimed to assess the effects of biobanding on internal and external training loads, as well as technical performance during small-sided games (SSGs). Methods Twenty male footballers (11.8 ± 1.15 years) participated in this study. Athletes engaged in small-sided games (SSGs) under two conditions: (1) CA–teams formed based on chronological age; and (2) BIO–teams formed based on age relative to peak height velocity (pre-PHV = -2.5 to -1.5, and PHV = -1.5 to -0.5). External load (ETL) was quantified using the PlayerLoad method, while internal load (ITL) was measured using both training impulse (TRIMP) and session-RPE. Player involvement was determined by summing all technical actions performed during the SSGs, with involvement in the game assessed through video analysis of the sessions. Results BIO games significantly increased ETL for pre-PHV (EM = 415.5; 95%CI = 381.5–449.5 a.u.) compared to the CA games (EM = 388.8; 95% CI = 354.8–422.8 a.u.). PHV players had lower ETL (EM = 354.4; 95% CI = 320.4–388.4 a.u.) in BIO compared to CA games (EM = 366.0; 95% CI = 332.0–400.0 a.u.). No significant ITL differences were observed. BIO lowered steals among pre-PHV players vs. CA. Conclusion Biobanded games significantly increased external load (ETL) for pre-PHV players; however, this increase was not substantial enough to affect internal load (as measured by session-RPE and TRIMP) or player involvement.

Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging

Abstract Normative representation learning focuses on understanding the typical anatomical distributions from large datasets of medical scans from healthy individuals. Generative Artificial Intelligence (AI) leverages this attribute to synthesize images that accurately reflect these normative patterns. This capability enables the AI allowing them to effectively detect and correct anomalies in new, unseen pathological data without the need for expert labeling. Traditional anomaly detection methods often evaluate the anomaly detection performance, overlooking the crucial role of normative learning. In our analysis, we introduce novel metrics, specifically designed to evaluate this facet in AI models. We apply these metrics across various generative AI frameworks, including advanced diffusion models, and rigorously test them against complex and diverse brain pathologies. In addition, we conduct a large multi-reader study to compare these metrics to experts’ evaluations. Our analysis demonstrates that models proficient in normative learning exhibit exceptional versatility, adeptly detecting a wide range of unseen medical conditions. Our code is available at https://github.com/compai-lab/2024-ncomms-bercea.git .

Protective effects of Colla Corii Asini Collagen Peptides on D-galactose injection combined with UVB irradiation-induced aging in mice

Skin aging, autonomic mobility, memory function and physical deterioration are important features of aging, and effective anti-aging treatments are important in slowing down these processes. The objective of this research was to evaluate the protective effect of Colla Corii Asini (Ejiao) Collagen Peptides (CCACPs) on D-galactose (D-gal) injection combined with UV irradiation-induced senescence in mice. BY-HEALTH collagen oral solution (Bcos) was used as a positive control. Behavioural experiments showed that CCACPs significantly improved voluntary activity, learning memory and exercise endurance in aging mice. Elisa results showed that CCACPs reduced the levels of matrix metalloproteinase-1 (MMP-1) and MMP-3 in the skin, acetylcholinesterase (AChE) in the brain, and alanine aminotransferase (ALT) and azelaic aminotransferase (AST) in the liver of mice, while increasing the levels of collagen I in the skin and SOD in the brain. RT-qPCR revealed that CCACPs reduced the expression of p16, p19 and p21 genes in the liver and hippocampus, as well as the expression of IL-6 in the skin. Histological analysis of brain hippocampus, liver and skin confirmed the protective effects of CCACPs. The findings indicated that CCACPs may potentially slow the aging effects caused by D-galactose and UVB exposure in mice by reducing cellular senescence and oxidative stress levels. The results of this research provide the scientific basis for continuing to advance the extraction of collagen peptides from Colla Corii Asini as a potential anti-aging therapy.

Re-evaluation of the psychometric properties of ATE following changes in euthanasia regulations in Spain

Understanding healthcare professionals’ attitudes toward euthanasia, especially within teams assisting patients who request it, is essential for providing appropriate support throughout the process. The objectives of this study were to re-adapt and validate the Attitude Towards Euthanasia Scale for the Spanish context, to examine its dimensional structure, and to estimate its reliability, particularly in light of the 2021 regulation of euthanasia in Spain. A cross-sectional study was conducted with a non-probabilistic sample of 778 healthcare professionals from the Balearic Health Service. Data collection involved a self-reported sociodemographic questionnaire and the Attitude Towards Euthanasia Scale. The scale’s psychometric properties were assessed through reliability and validity tests, including confirmatory factor analysis. The Attitude Towards Euthanasia Scale demonstrated strong reliability, with a Cronbach’s alpha of α = 0.889 and item homogeneity ranging from 0.66 to 0.78. Factor loadings were reported for four models, including a unidimensional model and models with two, three, and four factors. The two-factor model explained 64.51% of the variance, with a Kaiser-Meyer-Olkin (KMO) value of 0.873. Fit indices indicated good model fit: root mean square residual = 0.040, goodness-of-fit index = 0.960, adjusted goodness-of-fit index = 0.927, and root mean square error of approximation = 0.069. After performing a Parallel Analysis, two loading factor were found. The adapted version of the Attitude Towards Euthanasia Scale, aligned with the current societal and legislative context, is a valid and reliable tool for assessing healthcare professionals’ attitudes toward euthanasia, with psychometric properties consistent with the original version.

The lost long tail of early bird evolution

Identification of Cocconeis neothumensis var. marina using a polyphasic approach including ultrastructure and gene annotation

Several microalgae, including marine diatoms, significantly contribute to the global primary production and play a vital role in the food webs of benthic and planktonic ecosystems. Diatoms of the genus Cocconeis frequently inhabit benthic substrates, including the leaves of seagrasses. They are seasonally dominant in the leaf epiphytic layer of the Mediterranean seagrass Posidonia oceanica L. Delile, and have been proposed as model organisms for chemical ecology studies. However, the genome of Cocconeis spp. has not been sequenced. Consequently, their low-level molecular identification is currently impossible, besides a few examples. To address this gap, a polyphasic identification of C. neothumensis has been employed, combining ultra-morphological data with DNA barcoding markers. A strain of diatoms was isolated from P. oceanica leaves. It has been cultured in the laboratory and examined under Scanning Electron Microscopy (SEM). The 18S ribosomal RNA gene (18S rRNA, nrDNA) and the ribulose 1,5-biphosphate carboxylase (rbcL, cpDNA) gene were analysed for DNA barcoding characterisation. Since ultra-morphology data unambiguously identified the isolated strain as C. neothumensis Krammer, 1991, the molecular sequences herein reported will facilitate its rapid and accurate identification. In addition, our comparative analyses will facilitate the evaluation of these molecular markers for identification of closely related benthic diatoms.

FORCETRACKER: A versatile tool for standardized assessment of tissue contractile properties in 3D Heart-on-Chip platforms

Engineered heart tissues (EHTs) have shown great potential in recapitulating tissue organization, functions, and cell-cell interactions of the human heart in vitro. Currently, multiple EHT platforms are used by both industry and academia for different applications, such as drug discovery, disease modelling, and fundamental research. The tissues’ contractile force, one of the main hallmarks of tissue function and maturation level of cardiomyocytes, can be read out from EHT platforms by optically tracking the movement of elastic pillars induced by the contractile tissues. However, existing optical tracking algorithms which focus on calculating the contractile force are customized and platform-specific, often not available to the broad research community, and thus hamper head-to-head comparison of the model output. Therefore, there is the need for robust, standardized and platform-independent software for tissues’ force assessment. To meet this need, we developed ForceTracker: a standalone and computationally efficient software for analyzing contractile properties of tissues in different EHT platforms. The software uses a shape-detection algorithm to single out and track the movement of pillars’ tips for the most common shapes of EHT platforms. In this way, we can obtain information about tissues’ contractile performance. ForceTracker is coded in Python and uses a multi-threading approach for time-efficient analysis of large data sets in multiple formats. The software efficiency to analyze circular and rectangular pillar shapes is successfully tested by analyzing different format videos from two EHT platforms, developed by different research groups. We demonstrate robust and reproducible performance of the software in the analysis of tissues over time and in various conditions. ForceTracker’s detection and tracking shows low sensitivity to common incidental defects, such as alteration of tissue shape or air bubbles. Detection accuracy is determined via comparison with manual measurements using the software ImageJ. We developed ForceTracker as a tool for standardized analysis of contractile performance in EHT platforms to facilitate research on disease modeling and drug discovery in academia and industry.

Your brain is full of microplastics: are they harming you?

Regional and institutional trends in assessment for academic promotion

Abstract The assessment of research performance is widely seen as a vital tool in upholding the highest standards of quality, with selection and competition believed to drive progress. Academic institutions need to take critical decisions on hiring and promotion, while facing external pressure by also being subject to research assessment 1–4 . Here we present an outlook on research assessment for career progression with specific focus on promotion to full professorship, based on 314 policies from 190 academic institutions and 218 policies from 58 government agencies, covering 32 countries in the Global North and 89 countries in the Global South. We investigated how frequently various promotion criteria are mentioned and carried out a statistical analysis to infer commonalities and differences across policies. Although quantitative methods of assessment remain popular, in agreement with what is found in more geographically restricted studies 5–9 , they are not omnipresent. We find differences between the Global North and the Global South as well as between institutional and national policies, but less so between disciplines. A preference for bibliometric indicators is more marked in upper-middle-income countries. Although we see some variation, many promotion policies are based on the assumption of specific career paths that become normative rather than embracing diversity. In turn, this restricts opportunities for researchers. These results challenge current practice and have strategic implications for researchers, research managers and national governments.