Browse Articles

Surface amorphization enables robust catalyst for industrial-level low-potential electrooxidation reactions

Vitamin D and lipopolysaccharide jointly induce a distinct epigenetic and transcriptional program in human monocytes

Abstract Pathogen-associated molecular patterns such as lipopolysaccharide (LPS) mimic immune responses triggered by bacterial infections. The hormonally active form of vitamin D3, 1α,25-dihydroxyvitamin D3 [1,25(OH)2D3], supports innate immunity, but its molecular mechanisms remain incompletely understood. We investigated epigenomic and transcriptomic changes in THP-1 monocytes that were either unprimed or primed for 24 h with 1,25(OH)2D3 or LPS, followed by a second 24-hour stimulation with 1,25(OH)2D3, LPS, or their combination. Epigenome profiling via ATAC-seq revealed that co-stimulation with 1,25(OH)2D3 and LPS induces substantially more chromatin accessibility changes than either treatment alone, with up to 81% of altered regions uniquely responsive to the combination. Motif enrichment analysis highlighted JUN/FOS transcription factors as key regulators of this synergistic response. Transcriptomic analysis via RNA-seq mirrored these findings, though fewer genes than chromatin regions were affected. Notably, under 1,25(OH)2D3-primed conditions, 331 genes exhibited synergistic expression changes upon co-treatment, meaning that their responses significantly deviates from the additive effects of the individual stimulations. This includes 264 genes previously unrecognized as vitamin D targets. Functional annotation revealed that these genes are primarily linked to monocyte and T cell differentiation, in contrast to classical vitamin D targets associated with inflammation. In conclusion, our findings provide mechanistic insight into how vitamin D modulates inflammation through epigenetic and transcriptional reprogramming.

Environmental cues in different host niches shape the survival fitness of Staphylococcus aureus

Exploring synergistic effects of graphene oxide and hydrolyzed polyacrylamide on rheology and thermal stability relevant to enhanced oil recovery

The 18S rRNA methyltransferase DIMT-1 regulates lifespan in the germline later in life

Investigation of Na-ion battery applicability and supercapacitance properties of ZrCr2 alloyed with Na: a first-principles study

Sequence-based virtual screening using transformers

Abstract Protein-ligand interactions play central roles in myriad biological processes and are of key importance in drug design. Deep learning approaches are becoming cost-effective alternatives to high-throughput experimental methods for ligand identification. Here, to predict the binding affinity between proteins and small molecules, we introduce Ligand-Transformer, a deep learning method based on the transformer architecture. Ligand-Transformer implements a sequence-based approach, where the inputs are the amino acid sequence of the target protein and the topology of the small molecule to enable the prediction of the conformational space explored by the complex between the two. We apply Ligand-Transformer to screen and validate experimentally inhibitors targeting the mutant EGFRLTC kinase, identifying compounds with low nanomolar potency. We then use this approach to predict the conformational population shifts induced by known ABL kinase inhibitors, showing that sequence-based predictions enable the characterisation of the population shift upon binding. Overall, our results illustrate the potential of Ligand-Transformer to accurately predict the interactions of small molecules with proteins, including the binding affinity and the changes in the free energy landscapes upon binding, thus uncovering molecular mechanisms and facilitating the initial steps in drug design.

Exploring the correlation between nocturnal awakenings and occupational burnout in internal medicine physicians via online survey

A controllable photoresponsive potassium transporter

Sliding characteristics of chute waste slag in high steep canyon ecologically sensitive areas

3D anthropometry of the nasolabial region in children aged 3 to 9 months as reference database for clinical assessment

Abstract Treatment for a cleft lip can result in significant functional and aesthetic changes to the nasolabial region. Although three-dimensional (3D) measurements are the gold standard for evaluating cleft surgery, most short- and long-term evaluations still rely on subjective assessment or the measurement of patient photographs. To our knowledge, this work establishes the first baseline and reference group for the nasolabial region in children aged 3 to 9 months without cleft lip or palate. This group can be used for future evaluations, such as those of surgical outcomes or NAM therapy, via 3D anthropometric measurement. Data was collected cross-sectionally from 25 children aged 3 to 9 months using a validated intraoral scanner (Trios 4, 3Shape). Scans were analysed according to 3D anthropometric criteria by metrically accurate measurements of distances, surface curves and angles using 3D inspection software (GOM Inspect, Co. Zeiss, Jena, Germany). Results are presented as reference database combined with a step-by-step guide on the measurement methodology. For easy application all data are additionally presented in the form of formulae in which clinical data can be inserted. Based on the data from healthy children, we propose a new classification of alar base types ranging from 1 to 3. Unlike conventional assessment methods, surface curves and other 3D anthropometric tools provide a highly accurate and objective quantification of the anatomy of the nasolabial region and thus serve as a foundation for future clinical research on cleft lip surgery. Alar base type classification may influence future surgical approaches to cleft lip surgery.

SNP rs615552 and lncRNA CDKN2B-AS1 influence brain cancer pathogenesis through multi-omic mechanisms

Opportunistic assessment of variations in tissue composition content using chest QCT

Predicting geriatric environmental safety perception assessment using LightGBM and SHAP framework

Optimisation of Goose oil extraction process by response surface methodology

Umbilical cord serum metabolomics identifies amino acid alterations associated with impaired linear growth in small for gestational age infants

Feasibility of a tailored, combined intervention with mind-body elements to prevent burnout in healthcare professionals (LAGOM) in a mixed-methods multicenter single-arm trial

Abstract Healthcare professionals (HCPs) face high occupational stress, rendering them highly vulnerable to burnout. Given the significant individual and societal impacts, there is an urgent need for tailored approaches to prevent burnout. This study investigated the acceptability and feasibility of a person-and organization-directed 9-week program (LAGOM) with mind-body elements designed to mitigate burnout among HCPs. This single-arm, multicenter study employed a mixed-methods sequential explanatory design. Descriptive analyses evaluated recruitment, adherence, attrition, data collection completeness, and safety. A pre-post online survey assessed demographic and occupational variables, burnout symptoms (using the Maslach Burnout Inventory) and work-related self-efficacy (using the German BSW-5 questionnaire). The feasibility of electrophysiological measures like pulse and respiration activity was assessed. Semi-structured interviews with a subgroup were qualitatively analyzed following a qualitative content analysis approach. Pre to post changes in burnout symptoms and self-efficacy were exploratively analyzed with paired sample t-tests. Out of 29 HCPs screened, 24 (91.3% females, 65.2% nurses, 26.1% physicians) working at the Charité – Universitätsmedizin Berlin or the Immanuel Hospital Berlin, were included. The drop-out rate was 8%, intervention adherence was 79%. Of training completers, 86% filled out the post-assessments and 94.7% would recommend the program. Pre-post exploratory analyses revealed improvement on burnout scales emotional exhaustion (∆±SD=-2.79 ± 5.63, Cohen´s d = 0.5, 95% Confidence Interval (CI) 0.01;0.97), depersonalization (∆±SD=-1.47 ± 3.20, d = 0.46, 95%CI -0.02;0.93), personal accomplishment (∆±SD = 0.16 ± 3.93, d = 0.04, 95%CI -0.41;0.49), and work-related self-efficacy (∆±SD = 0.17 ± 0.39, d = 0.43, 95%CI -0.05;0.90). Semi-structured interviews (n = 4) revealed that participants valued the course content and collegial exchange but suggested allowing more time for discussion and reducing theoretical input. Electrophysiological measures (n = 14) were deemed feasible. The study supports the acceptability and feasibility of implementing LAGOM in the healthcare setting, with initial indications of effectiveness. Program sessions need to be modified to increase time for participant exchange. Securing visible leadership commitment and sufficient resources will be critical for future success. Trial Registration: German Clinical Trials Register: DRKS00032014, registered 17/10/2023, https://drks.de/search/de/trial/DRKS00032014.

Protocol for a randomized crossover study of thigh cuff inflation in experimental hemorrhage: Assessing its potential as a model for zone 3 REBOA

Resuscitative endovascular balloon occlusion of the aorta (REBOA) is a method to provide temporary control of noncompressible torso hemorrhage in trauma patients. Previous research on REBOA has mainly focused on animals and patients. This study aims to explore whether thigh cuff inflation combined with simulated hemorrhage can serve as an experimental human model for zone 3 REBOA. Lower body negative pressure is a model of hypovolemia. A zone 3 REBOA occludes aorta at its bifurcation, essentially excluding the pelvis and lower extremities from the circulation. Bilateral proximal thigh cuffs will occlude blood vessels to and from the lower extremities. Twenty healthy volunteers will be exposed to bilateral proximal thigh cuff inflation to suprasystolic pressures to simulate the hemodynamic effects of REBOA during experimental hemorrhage using lower body negative pressure (LBNP). Each participant will complete two experimental conditions in a randomized order within one study visit. In the one condition, subjects will undergo only 60 mmHg LBNP for six minutes. In the alternate condition, 60 mmHg LBNP will be applied for six minutes, adding thigh cuff inflation during the final three minutes. Continuous, non-invasive monitoring of systemic hemodynamic parameters—including arterial blood pressure, stroke volume, and heart rate—will be conducted. Cerebral hemodynamics will be assessed by measuring middle cerebral artery blood velocity and cerebral oxygenation. Pain related to thigh cuff inflation will be assessed using a verbal numerical rating scale. The impact of thigh cuff inflation on systemic and cerebral hemodynamics will be evaluated using mixed-effects regression modeling. This study aims to examine the systemic and cerebral hemodynamic effects of combined thigh cuff inflation and lower body negative pressure in healthy volunteers. Based on the feasibility and findings, the potential of this combination as a model for zone 3 REBOA in simulated hemorrhage will be discussed.

Harnessing infrared thermography and multi-convolutional neural networks for early breast cancer detection

Abstract Breast cancer is a relatively common carcinoma among women worldwide and remains a considerable public health concern. Consequently, the prompt identification of cancer is crucial, as research indicates that 96% of cancers are treatable if diagnosed prior to metastasis. Despite being considered the gold standard for breast cancer evaluation, conventional mammography possesses inherent drawbacks, including accessibility issues, especially in rural regions, and discomfort associated with the procedure. Therefore, there has been a surge in interest in non-invasive, radiation-free alternative diagnostic techniques, such as thermal imaging (thermography). Thermography employs infrared thermal sensors to capture and assess temperature maps of human breasts for the identification of potential tumours based on areas of thermal irregularity. This study proposes an advanced computer-aided diagnosis (CAD) system called Thermo-CAD to assess early breast cancer detection using thermal imaging, aimed at assisting radiologists. The CAD system employs a variety of deep learning techniques, specifically incorporating multiple convolutional neural networks (CNNs) to enhance diagnostic accuracy and reliability. To effectively integrate multiple deep features and diminish the dimensionality of features derived from each CNN, feature transformation and selection methods, including non-negative matrix factorization and Relief-F, are used leading to a reduction in classification complexity. The Thermo-CAD system is assessed utilising two datasets: the DMR-IR (Database for Mastology Research Infrared Images), for distinguishing between normal and abnormal breast tissues, and a novel thermography dataset to distinguish abnormal instances as benign or malignant. Thermo-CAD has proven to be an outstanding CAD system for thermographic breast cancer detection, attaining 100% accuracy on the DMR-IR dataset (normal versus abnormal breast cancer) using CSVM and MGSVM classifiers, and lower accuracy using LSVM and QSVM classifiers. However, it showed a lower ability to distinguish benign from malignant cases (second dataset), achieving an accuracy of 79.3% using CSVM. Yet, it remains a promising tool for early-stage cancer detection, especially in resource-constrained environments.

Finding the original mass: A machine learning model and its deployment for lithic scrapers

Predicting the original mass of a retouched scraper has long been a major goal in lithic analysis. It is commonly linked to lithic technological organization of past societies along with notions of stone tool general morphology, standardization through the reduction process, use life, and site occupation patterns. In order to obtain a prediction of original stone tool mass, previous studies have focused on attributes that would remain constant or unaltered through retouch episodes. However, these approaches have provided limited success for predictions and have also remained untested in the framework of successive resharpening episodes. In the research presented here, a set of experimentally knapped flint flakes were successively resharpened as scraper types. After each resharpening episode, four attributes were recorded (scraper mass, height of retouch, maximum thickness and the GIUR index). Four machine learning models were trained using these variables in order to estimate the mass of the flake prior to any retouch. A Random Forest model provided the best results with an r2 value of 0.97 when predicting original flake mass, and a r2 value of 0.84 when predicting percentage of mass lost by retouch. The Random Forest model has been integrated into an open source and free to use Shiny app. This allows for the wide spread implementation of a highly precise machine learning model for predicting initial mass of flake blanks successively retouched into scrapers.