bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved extraction efficiency of bakuchiol with antioxidant and whitening bioactivities from the seeds of Psoralea corylifolia L.
Authors: Qin Tian , Liu Jinjun , Bi Yongxian , Wang Yanduo , Liu Jianzi , Ding Gang 
Published: November 2023

Abstract

Background
Mining new whitening ingredients from plants is an effective way to develop new cosmetic products. Bakuchiol, as one of the main active ingredients from the seeds of Psoralea corylifolia L, displays whitening, antioxidant, antiaging, and free radical-scavenging activities and attracts wide attention especially in the cosmetics industry. However, environmentally friendly extraction technologies with high efficiency and high product purity remain to be developed for the scale production of bakuchiol.

Objective
In this study, a new approach (acetic acid–steaming–assisted technique) was developed to improve the extraction efficiency of bakuchiol.

Methods
The factors influencing the extraction efficiency of bakuchiol, including seed powder granularity, acetic acid volume, temperature, and time, were optimized based on single-factor experiments.

Results
The extraction conditions were optimized as steaming the seed powder (100 g) of 40–100 meshes with acetic acid (50 mL) for 10 minutes, from which a crude extract (25.1 g) containing bakuchiol was obtained. High purity (99.1) of bakuchiol (6.02 g) was then prepared by silica gel column chromatography in a one-step process. The method established in this study had higher extraction rate than other methods. The antioxidant potential of bakuchiol was evaluated based on the ABTS free radical scavenging activity and ferric reducing antioxidant power. Furthermore, bakuchiol demonstrated stronger tyrosinase inhibitory activity than the positive control (arbutin).

Conclusions
Compared with the available methods, the method established in this study has increased extraction rate of bakuchiol and is environmentally friendly and economical, demonstrating the potential for industrial production.
Efficiency and novelty of using environmental swabs for dry surface biofilm recovery.
Authors: Watson Fergus , Wilks Sandra , Chewins John , Keevil Bill 
Published: November 2023

Studies on the epidemiology of dry surface biofilms within healthcare has shown an almost universal distribution across frequently touched items. Despite a growing body of evidence for dry surface biofilms in hospitals little attention has been paid to the recovery capacity of techniques used to detect these microbial communities. Biofilms are inherently difficult to remove from surfaces due to adhesive substances within their matrix and may act as sources of infection but to what extent is largely unknown. In this study we evaluate the recovery efficiencies of commonly used environmental swabs against dry surface biofilms containing 7.24-Log10 Acinetobacter baumannii/cm2, using a drip flow reactor and desiccation cycle. Biofilm presence was visually confirmed using episcopic differential interference contrast microscopy combined with epifluorescence and quantified using sonicated viable plate counts. The swab materials used comprised of foam, viscose and cotton, all of which were pre-moistened using a buffer solution. The surfaces were vigorously swabbed by each material type and the resultant microbe populations for both swabs and remaining dry surface biofilms were quantified. Our results found foam tipped swabs to be superior, detecting on average 30% of the original dry surface biofilm contamination; followed by viscose (6%) and cotton (3%). However, no distinct difference was revealed in the concentration of microbes remaining on the surface after swabbing for each swab type suggesting there is variation in the capacity for each swab to release biofilm associated microorganisms. We conclude whilst environmental swabs do possess the ability to detect biofilms on dry surfaces; the reduced efficiencies are likely to cause an underestimation of the microbes present and should be considered during clinical application.
Terminally differentiated effector memory T cells associate with cognitive and AD-related biomarkers in an aging-based community cohort
Authors: Winford Edric , Lutshumba Jenny , Martin Barbara J. , Wilcock Donna M. , Jicha Gregory A. , Nikolajczyk Barbara S. , Stowe Ann M , Bachstetter Adam D. 
Published: November 2023

AbstractBackground and PurposeThe immune response changes during aging and the progression of Alzheimer’s disease (AD) and related dementia (ADRD). Terminally differentiated effector memory T cells (called TEMRA) are important during aging and AD due to their cytotoxic phenotype and association with cognitive decline. However, it is not clear if the changes seen in TEMRAsare specific to AD-related cognitive decline specifically or are more generally correlated with cognitive decline. This study aimed to examine whether TEMRAsare associated with cognition and plasma biomarkers of AD, neurodegeneration, and neuroinflammation in a community-based cohort of older adults.MethodsStudy participants from a University of Kentucky Alzheimer’s Disease Research Center (UK-ADRC) community-based cohort of aging and dementia were used to test our hypothesis. There were 84 participants, 44 women and 40 men. Participants underwent physical examination, neurological examination, medical history, cognitive testing, and blood collection to determine plasma biomarker levels (Aβ42/Aβ40 ratio, total tau, Neurofilament Light chain (Nf-L), Glial Fibrillary Acidic Protein (GFAP)) and to isolate peripheral blood mononuclear cells (PBMCs). Flow cytometry was used to analyze PBMCs from study participants for effector and memory T cell populations, including CD4+and CD8+central memory T cells (TCM), Naïve T cells, effector memory T cells (TEM), and effector memory CD45RA+T cells (TEMRA) immune cell markers.ResultsCD8+TEMRAswere positively correlated with Nf-L and GFAP. We found no significant difference in CD8+TEMRAsbased on cognitive scores and no associations between CD8+TEMRAsand AD-related biomarkers. CD4+TEMRAswere associated with cognitive impairment on the MMSE. Gender was not associated with TEMRAs, but it did show an association with other T cell populations.ConclusionThese findings suggest that the accumulation of CD8+TEMRAsmay be a response to neuronal injury (Nf-L) and neuroinflammation (GFAP) during aging or the progression of AD and ADRD. As our findings in a community-based cohort were not clinically- defined AD participants but included all ADRDs, this suggests that TEMRAsmay be associated with changes in systemic immune T cell subsets associated with the onset of pathology.
Generating the sigmoid form in neuroscience: A threshold-crossing model
Authors: Auty Andrew R 
Published: November 2023

AbstractThe conventional interpretation1of a logistic curve in biology is that it indicates the presence of an infection-like mechanism bounded by a saturation limit e.g. saturation occurs when the whole population has been infected. However, a logistic form may be generated by the fluctuating approach of biological systems to, and then exceeding, an effect threshold.The commonly observed trends in frequency and degree of cognitive loss in neurodegenerative disease (NDD) are consistent with a threshold-crossing model. Whereas direct evidence of real life NDD being driven by an infection-like mechanism remains elusive2,3.Variation in susceptibility to NDD may be explained by variation in the rate of biological ageing (e.g. neurodegeneration rate, or, metabolic interference), height of threshold and initial reserve. Each is potentially modifiable and clinically informative.
T-cell receptor sequences correlate with and predict gene expression levels in T cells
Authors: Wang Hao , Ji Zhicheng 
Published: November 2023

ABSTRACTT cells exhibit high heterogeneity in both their gene expression profiles and antigen specificities. We analyzed fifteen single-cell immune profiling datasets to systematically investigate the association between T-cell receptor (TCR) sequences and the gene expression profiles of T cells. Our findings reveal that T cells sharing identical or similar TCR sequences tend to have highly similar gene expression profiles. Additionally, we developed a foundational model that leverages TCR information to predict gene expression levels in T cells.
Convergent evolution in toxin detection and resistance provides evidence for conserved bacterial-fungal interactions
Authors: Dolan Stephen K. , Duong Ashley T. , Whiteley Marvin 
Published: November 2023

AbstractMicrobes rarely exist in isolation, and instead form complex polymicrobial communities. As a result, microbes have developed intricate offensive and defensive strategies that enhance their fitness in these complex communities. Thus, identifying and understanding the molecular mechanisms controlling polymicrobial interactions is critical for understanding the function of microbial communities. In this study, we show that the Gram-negative opportunistic human pathogenPseudomonas aeruginosa, which frequently causes infection alongside a plethora of other microbes including fungi, encodes a genetic network which can detect, and defend against gliotoxin, a potent, disulfide-containing antimicrobial produced by the ubiquitous filamentous fungusAspergillus fumigatus. We show that gliotoxin exposure disruptsP. aeruginosazinc homeostasis, leading to transcriptional activation of a gene encoding a previously uncharacterized dithiol oxidase (DnoP), which detoxifies gliotoxin and structurally related toxins. While the enzymatic activity of DnoP is identical to that used byA. fumigatusto protect itself against gliotoxin, DnoP shares little homology to theA. fumigatusgliotoxin resistance protein. Thus, DnoP and its transcriptional induction by low zinc represent an example of both convergent evolution of toxin defense and environmental cue sensing across kingdoms. Collectively, these data support disulfide-containing natural products as mediators of inter-kingdom interactions and provide evidence thatP. aeruginosahas evolved to survive exposure to these molecules in the natural environment.Significance statementBacteria and fungi frequently exist as complex, polymicrobial communities during infection. Reconstructing ecological structure in the laboratory is challenging and, consequently, the precise molecular mechanisms which underpin microbial interactions remain elusive. Using a pre-clinical model that mimics the cystic fibrosis lung, we discovered that the bacteriumPseudomonas aeruginosadetects and defends against a disulfide-containing toxin produced by the fungusAspergillus fumigatus. In an example of both convergent evolution of toxin defense and environmental cue sensing across kingdoms, we discovered that these organisms use the same cue to produce/sense this toxin, and the same enzymatic mechanism to protect against toxicity. This discovery of convergent evolution provides strong evidence forP. aeruginosaexposure to microbially-produced disulfide-containing toxins in natural environments.
Nanoporous PEGDA ink for High-Resolution Additive Manufacturing of Scaffolds for Organ-on-a-Chip
Authors: Karamzadeh Vahid , Shen Molly L. , Shafique Houda , Lussier Felix , Juncker David 
Published: November 2023

AbstractPolydimethylsiloxane (PDMS), commonly used in organ-on-a-chip (OoC) systems, faces limitations in replicating complex geometries, hindering its effectiveness in creating 3D OoC models. In contrast, poly(ethylene glycol)diacrylate (PEGDA-250), favored for its fabrication ease and resistance to small molecule absorption, is increasingly used for 3D printing microfluidic devices. However, applications in cell culture have been limited due to poor cell adhesion. Here, we introduce a nanoporous PEGDA ink (P-PEGDA) designed to enhance cell adhesion. P-PEGDA is formulated with a porogen, photopolymerized, followed by the porogen removal. Utilizing P-PEGDA, complex microstructures and membranes as thin as 27 µm were 3D-printed. Porogen concentrations from 10-30% were tested yielding constructs with increasing porosity and oxygen permeability surpassing PDMS, without compromising printing resolution. Tests across four cell lines showed >80% cell viability, with a notable 77-fold increase in MDA-MB-231 cell coverage on the porous scaffolds. Finally, we introduce an OoC model comprising a gyroid scaffold with a central opening filled with a cancer spheroid. This setup, after a 14-day co-culture, demonstrated significant endothelial sprouting and integration within the spheroid. The P-PEGDA formulation is suitable for high-resolution 3D printing of constructs for 3D cell culture and OoC owing to its printability, gas permeability, biocompatibility, and cell adhesion.
Rapid estimates of leaf litter chemistry using reflectance spectroscopy
Authors: Kothari Shan , Hobbie Sarah E. , Cavender-Bares Jeannine 
Published: November 2023

AbstractMeasuring the chemical traits of leaf litter is important for understanding plants’ roles in nutrient cycles, including through nutrient resorption and litter decomposition, but conventional leaf trait measurements are often destructive and labor-intensive. Here, we develop and evaluate the performance of partial least-squares regression (PLSR) models that use reflectance spectra of intact or ground leaves to estimate leaf litter traits, including carbon and nitrogen concentration, carbon fractions, and leaf mass per area (LMA). Our analyses included more than 300 samples of senesced foliage from 11 species of temperate trees, including needleleaf and broadleaf species. Across all samples, we could predict each trait with moderate-to-high accuracy from both intact-leaf litter spectra (validationR2= 0.543-0.941; %RMSE = 7.49-18.5) and ground-leaf litter spectra (validationR2= 0.491-0.946; %RMSE = 7.00-19.5). Notably intact-leaf spectra yielded better predictions of LMA. Our results support the feasibility of building models to estimate multiple chemical traits from leaf litter of a range of species. In particular, the success of intact-leaf spectral models allows non-destructive trait estimation in a matter of seconds, which could enable researchers to measure the same leaves over time in studies of nutrient resorption.
High-throughput and scalable single cell proteomics identifies over 5000 proteins per cell
Authors: Ye Zilu , Sabatier Pierre , van der Hoeven Leander , Phlairaharn Teeradon , Hartlmayr David , Izaguirre Fabiana , Seth Anjali , Joshi Hiren J. , Bekker-Jensen Dorte B. , Bache Nicolai , Olsen Jesper V. 
Published: November 2023

AbstractThe emergence of mass spectrometry (MS)-based single-cell proteomics (SCP) promise to revolutionize the study of cellular biology and biomedicine by providing an unparalleled view of the proteome in individual cells. Despite its groundbreaking potential, SCP is nascent and faces challenges including limited sequence depth, throughput, and reproducibility, which have constrained its broader utility. This study introduces key methodological advances, which considerably improve the sensitivity, coverage and dependability of protein identification from single cells. We developed an almost lossless SCP workflow encompassing sample preparation to MS analysis, doubling the number of identified proteins from roughly 2000 to over 5000 in individual HeLa cells. A comprehensive evaluation of analytical software tools, alongside strict false discovery rate (FDR) controls solidified the reliability of our results. These enhancements also facilitated the direct detection of post-translational modifications (PTMs) in single cells, negating the need for enrichment and thereby simplifying the analytical process. Although throughput in MS remains a challenge, our study demonstrates the feasibility of processing up to 80 label-free SCP samples per day. Moreover, an optimized tissue dissociation buffer enabled effective single cell disaggregation of drug-treated cancer cell spheroids, refining the overall proteomic analysis. Our workflow sets a new benchmark in SCP for sensitivity and throughput, with broad applications ranging from the study of cellular development to disease progression and the identification of cell type-specific markers and therapeutic targets.
Establishing syndromic surveillance of gastrointestinal infections in emergency departments using routine emergency department data and validating it against laboratory-based surveillance, Germany, January 2019 – June 2023
Authors: Baum Jonathan Hans Josef , Dörre Achim , Boender Tamara Sonia , Heldt Katharina , Wilking Hendrik , Drynda Susanne , Erdmann Bernadett , Grashey Rupert , Grupp Caroline , Habbinga Kirsten , Hamelmann Eckard , Heining Amrei , Höger-Schmidt Heike , Kill Clemens , Reichert Friedrich , Riße Joachim , Schilling Tobias ,   AKTIN Research Group, Schranz Madlen 
Published: November 2023

2.Structured AbstractBackgroundGastrointestinal infections in Germany account for 24.5 million outpatient visits annually. Surveillance of gastrointestinal infections in emergency departments strengthens timely outbreak detection and disease trend monitoring.AimWe developed a syndrome definition for automated syndromic surveillance of gastrointestinal infections in emergency departments, and validated it against statutory laboratory-based surveillance.MethodsTo develop a syndrome definition, we selected presenting complaints (Canadian Emergency Department Information System) and diagnoses (ICD-10). We validated the definition through time series and cross-correlation analysis, comparing trends between syndromic and laboratory-based surveillance. We analysed German emergency department registry (AKTIN) data and included emergency departments that continuously transferred (01/2019-06/2023) data. As reference we combined statutory norovirus-gastroenteritis, rotavirus-gastroenteritis, campylobacteriosis and salmonellosis notifications.ResultsOur syndrome definition combined presenting complaints (diarrhoea, vomiting and nausea) and diagnoses (Intestinal infectious diseases). Accordingly, in 7 emergency departments withn= 864,353 visits, 2.1% (n= 18,158) were gastrointestinal infection cases. Of those, 57% (n= 10,424) were female, with 23% 0–19 years (n= 4,108) and 23% 20–29 years (n= 4,116) old. We visually observed similar gastrointestinal infection trends in both surveillance systems. The cross-correlation was 0.73 (95%-confidence interval 0.61–0.85;p<0.001) at lag −1, indicating a 1-week relative reporting delay of laboratory-based surveillance.ConclusionThe coherent trends and significant cross-correlation validated our syndrome definition, which adequately captures gastrointestinal infection cases in emergency departments. Our novel automated surveillance complements laboratory-based surveillance, while offering advantages regarding timeliness and reduced workload. Therefore, it will be implemented in national routine surveillance.
Predicting Alzheimer’s Trajectory: A Multi-PRS Machine Learning Approach for Early Diagnosis and Progression Forecasting
Authors: Mustaq Mashiat , Ahmed Naeem , Mahbub Sazan , Li Clara , Miyaoka Yuichiro , TCW Julia , Andrews Shea , Bayzid Md. Shamsuzzoha ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: November 2023

AbstractPredicting the early onset of dementia due to Alzheimer’s Disease (AD) has major implications for timely clinical management and outcomes. Current diagnostic methods, which are reliant on invasive and costly procedures, underscore the need for scalable and innovative approaches. To date, considerable effort has been dedicated to developing machine learning (ML)-based approaches using different combinations of medical, demographic, cognitive, and clinical data, achieving varying levels of accuracy. However, they often lack the scalability required for large-scale screening and fail to identify underlying risk factors for AD progression. Polygenic risk scores (PRS) have shown promise in predicting disease risk from genetic data. Here, we aim to leverage ML techniques to develop a multi-PRS model that captures both genetic and non-genetic risk factors to diagnose and predict the progression of AD in different stages in older adults. We developed an automatic feature selection pipeline that identifies the relevant traits that predict AD. Leveraging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Religious Orders Study and Memory and Aging Project (ROSMAP), and the IEU OpenG-WAS Project, our study presents the first known end-to-end ML-based multi-PRS model for AD. This approach provides an automatic mechanism for harnessing genetic data for AD diagnosis and prognosis for comprehending the role of various traits in AD development and progression. Our method produced AUROC scores of 77% on ADNI and 72% on ROSMAP for predicting the diagnosis of AD, substantially surpassing the performance of the uni-variate PRS models. Our models also showed promise in predicting transitions between various cognitive stages. Moreover, the features identified by our automated feature selection pipeline are closely aligned with the widely recognized, potentially modifiable risk factors for AD.
Mechanism-based classification of SARS-CoV-2 Variants by Molecular Dynamics Resembles Phylogenetic Tree
Authors: Arns Thais , Fouquier d’Hérouël Aymeric , May Patrick , Tkatchenko Alexandre , Skupin Alexander 
Published: November 2023

AbstractThe COVID-19 pandemics has demonstrated the vulnerability of our societies to viral infectious disease. The mitigation of COVID-19 was complicated by the emergence of Variants of Concern (VOCs) with varying properties including increased transmissibility and immune evasion. Traditional population sequencing proved to be slow and not conducive for timely action. To tackle this challenge, we introduce the Persistence Score (PS) that assesses the pandemic potential of VOCs based on molecular dynamics of the interactions between the SARS-CoV-2 Receptor Binding Domain (RBD) and the ACE2 residues. Our mechanism-based classification approach successfully grouped VOCs into clinically relevant subgroups with higher sensitivity than classical affinity estimations and allows for risk assessment of hypothetical new VOCs. The PS-based interaction analysis across VOCs resembled the phylogenetic tree of SARS-Cov-2 demonstrating its predictive relevance for pandemic preparedness. Thus, PS allows for early detection of a variant’s pandemic potential, and an early risk evaluation for data-driven policymaking.
Towards faster identification of MRSA and MSSA: analysis of calorimetric curve parameters from large hospital bacterial collections
Authors: Zunk-Parras Sara , Trampuz Andrej , Arias-Sánchez Flor I. 
Published: November 2023

AbstractThere is a need to develop faster yet precise diagnostic tools for the identification of infectious agents and their levels of antimicrobial susceptibility. One such tool is calorimetry, as previous studies show that bacteria display unique signatures of calorimetric/metabolic activity that such signatures can be used for pathogen identification under controlled laboratory conditions. However, the level of variation of these unique signatures remains largely unexplored. Here, we use real-time isothermal microcalorimetry to disentangle the inter- and intra-species metabolic differences betweenS. aureusandS. epidermidis. We worked with a large collection of bacterial isolates obtained from patients with prosthetic joint infections as well as reference laboratory strains. We analyzed their calorimetric profiles and decomposed the curve parameters derived from them. As a result, we identified six calorimetric parameters that are useful for identification at the intra-species level, with a particular focus on MRSA. Furthermore, we found important differences between the thermograms of ATCC laboratory strains when compared against model thermograms generated from all our clinical isolates. These results indicate that accounting for metabolic variability can impact diagnosis precision. Finally, we discuss our findings and suggest ways to optimize calorimetric diagnostics and treatment approaches.