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Host responses to S. pneumoniae in wild type and Mertk mutant mice

Streptococcus pneumoniae is the leading cause of community-acquired pneumonia. Mertk is a receptor tyrosine kinase and a member of the TAM family. It serves as an efferocytosis receptor involved in the recognition and removal of apoptotic debris by phagocytic cells, dampening the inflammatory response. Here we show that at 24h post-inoculation with S. pneumoniae, Mertk-/- mice generated through homologous recombination and backcrossed (HRB-Mertk-/- mice) have fewer bacteria present in their pneumonic lung than wild type mice. This enhanced clearance was not observed in Mertk-/- mice generated by CRISPR technology. The enhanced clearance of HRB-Mertk-/- mice was associated with fewer neutrophils and more IFNγ in the bronchoalveolar lavage, but was not prevented by a neutralizing IFNγ antibody. Mertk is highly expressed on alveolar macrophages. Transcriptomic changes observed in HRB-Mertk-/- alveolar macrophages were associated with leukocyte activation, cellular motility, and response to stimulus, suggesting that they are primed for an inflammatory response. HRB-Mertk-/- mice similarly had enhanced host defense pathways in S. pneumoniae-stimulated alveolar macrophages in vitro and in pneumonic lung tissue. However, HRB-Mertk-/- alveolar macrophages demonstrated no defect in phagocytosis and acidification in vivo, and genes and gene sets describing phagocytic pathways were not enriched, suggesting that the enhanced clearance may be through alterations in the lung microenvironment. HRB-Mertk-/- mice are reported to have a long 129P2 DNA insert (~645 genes) in chromosome 2 adjacent to Mertk, as well as other alterations at multiple sites. Thus, while Mertk deficiency may contribute to the enhanced bacterial clearance, it is not solely responsible, because the phenotype is not seen in the CRISPR-Mertk-/- mice. The 129P2 DNA insert in the HRB-Mertk-/- mice must be mediating at least some of this phenotype. Understanding the mechanistic differences and the means by which this 129P2 DNA insert enhances bacterial clearance remains critically important.

Improving air quality prediction using hybrid BPSO with BWAO for feature selection and hyperparameters optimization

Time-motion study in primary health care in moldova: How do family doctors and medical assistants spend their work time?

Introduction Despite efforts to strengthen primary health care, Moldova faces continues challenges related to health personnel shortages and distribution disparities, resulting in high workloads. To better understand the current situation, we sought to assess the work-time allocation of family doctors and medical assistants in primary health care centers in Moldova. Methods An observational, cross-sectional study was conducted to investigate health personnel’s time allocated to activity categories, the number and duration of consultations for different health conditions, and the reasons for patient’s visits in nine primary health centers in Moldova. 24 family doctors and 24 medical assistants were observed for five consecutive working days, covering in total 233 workdays and 4,916 consultations. Results Family doctors used 49.2% of their time on direct patient care, 20.4% on administrative tasks and 9.8% on outreach activities. Medical assistants spent 33.6% on administration tasks, 30.9% on direct patient care and 13.6% on outreach activities. Of their direct patient care time, family doctors and medical assistants used 49.8% and 51.7%, respectively, on non-communicable disease consultations. The median duration of doctors’ consultations was 8.55 minutes, whereas medical assistants’ consultations lasted 5.65 minutes. Hypertensive disease and pediatric visits were the most frequent and time-consuming consultations. Health personnel contracted for 1.5 full-time equivalents worked over 10 hours per week shorter than required by their working contracts. Conclusions Family doctors used nearly half of their work time on direct patient care, while medical assistants spent less than one third, with administrative tasks taking up most of medical assistants’ time. Consultation durations were short for both cadres, especially among medical assistants, reflecting the high workload in primary health care in Moldova. Additionally, health personnel contracted for 1.5 full-time equivalents worked consistently fewer hours than required by their contracts, indicating this system does not improve patient access or resolve personnel shortages.

Grid connected improved sepic converter with intelligent mppt strategy for energy storage system in railway applications

Attention and spatial navigation in everyday life: Physical activity is associated with subjective aspects of cognitive function

Efforts to understand the effects of physical activity on cognitive health have long relied on employing objective measures that assess the efficacy of the mechanics of cognition. However, this perspective overlooks complementary dimensions of cognitive functioning, namely one’s subjective appraisal of the efficacy of their cognitive mechanics. In a set of four investigations (N =  2965), we sought to discern whether physical activity (PA), and other health and demographic factors, contribute to subjective experiences of cognitive mechanics (SCF) and to map for future investigations domains of function that are sensitive to health factors. We employed linear multiple regression analyses to examine survey data collected online from four large samples of young adults who responded to measures of health behaviours and SCF. PA contributed to subjective experiences of attentional control and spatial navigation but not memory, executive function, or general cognitive functioning. Further, sleep, diet, and stress were each consistently associated with selective measures of subjective experiences of cognition. Taken together, these studies indicate the importance of PA, as well as additional health behaviours, as significant contributors to SCF.

Vibrational circular dichroism of plasmonic nanostructures embedding chiral drugs

Validation of new equipment for SARS-CoV-2 diagnosis in Ecuador: Detection of the virus and antibodies generated by disease and vaccines with one POC device

The COVID-19 pandemic underscored the critical need to enhance screening capabilities and streamline diagnosis. Point-of-care (POC) tests offer a promising solution by decentralizing testing. We aimed to validate the PLUM device (LSK Technologies Inc.), a portable optical reader, to detect SARS-CoV-2 RNA using direct RT-LAMP targeting the ORF1a and E1 genes and patient antibodies by ELISA. The direct RT-LAMP assays employ nasopharyngeal swabs and bypass RNA extraction protocols through a brief chemical and physical lysis step. Test sensitivity and specificity were assessed against gold-standard detection methods in laboratory and field conditions. For samples with Ct values below 25, direct RT-LAMP showed 83% sensitivity and 90% specificity under laboratory conditions and 91% sensitivity and 92% specificity under field conditions. The nucleocapsid antigen antibody assay had 99% positive percent agreement (PPA) and 97% negative percent agreement (NPA), outperforming spike-RBD antigen (98% PPA, 92% NPA) and seroprevalence (98% PPA, 88% NPA) under laboratory conditions. Under field conditions, similar results were found for antibody detection for the nucleocapsid antigen (93% PPA; 100% NPA), spike-RBD (100% PPA; 94% NPA), and seroprevalence (90% PPA; 94% NPA). This study validated the PLUM device as a dual POC tool for direct RT-LAMP-based SARS-CoV-2 and ELISA-based COVID-19 antibody detection.

ResM-FusionNet for efficient landslide detection algorithm with a hybrid architecture

Introducing “Validated entheses-Based reconstruction of activity 2.0” (VERA 2.0): Semi-automated 3D analysis of bone surface changes

In archaeological sciences, the macroscopic morphology of distinct dry bone structures, such as tubercles, ridges, epicondyles, and fossae, is routinely used to infer habitual activity patterns in past human populations, extinct hominins, and other animals. This study introduces “Validated Entheses-based Reconstruction of Activity 2.0” (VERA 2.0), a new method for precisely quantifying 3D surface irregularities on enthesis-bearing bone structures. Building on VERA 1.0, first introduced by the same author in 2016 and later named in a 2021 literature review, VERA 2.0 enhances the previous approach by incorporating a semi-automated image segmentation technique that reduces manual input while maintaining accuracy. The method involves selecting a predefined broad bone surface region, after which an algorithm automatically detects subtle surface irregularities (see example video in the step-by-step protocol at dx.doi.org/10.17504/protocols.io.5jyl82z8dl2w/v3). Validation analyses confirm VERA 2.0’s precision and reliability for activity reconstruction through intra- and inter-observer repeatability tests, experimental research comparing activity and control laboratory specimens, and analyses of historical human skeletons with extensively detailed long-term occupational data. Moreover, while this anthropological 3D measuring protocol paper cannot and does not aim to analyze the anatomical and histological nature of bone surface irregularities, preliminary anatomical dissection and virtual analysis of a cadaveric thumb enthesis suggest a possible association with attaching muscles and ligaments. Future anatomical and histological research aiming to explore soft-hard tissue interactions could clarify how these identified surface changes exactly relate to the attaching tissues. Overall, VERA 2.0 provides a robust, efficient quantitative tool for inferring activity patterns from skeletal remains, with applications across paleontological, paleoanthropological, and bioarchaeological contexts.

Evaluation of the Toumai robotic system in partial nephrectomy and key system features

The impact of soil acidification on cementing substances and aggregate stability

The excessive utilization of chemical fertilizers, particularly nitrogen fertilizers, is leading to decline in the pH level of the black soil in Jilin Province. Acidification of black soil leads to reduced salt base saturation, decreased organic matter content, and increased soil degradation, which, in turn, leads to diminshed aggregate stability and poor soil structure, negatively affecting soil fertility. As a result, the sustainability of food production and farmland ecosystem stability are at risk. The precise relationship between alterations in cementing substances and changes in soil aggregate stability during the acidification of black soil remains unclear, and the ultrasonic thermal difference method allows for the quantitative description of changes in soil aggregate stability. Therefore, this study employed the ultrasonic thermal difference method to investigate the impact of acidification on the stability of black soil aggregates and their cementing substances through a simulated fertilizer drenching experiment, thus elucidate the relationship between primary cementing materials and the stability of aggregates under varying degrees of black soil acidification, and to provides theoretical basis and data for alleviating and preventing acidification of black soil in Jilin Province. The results disclosed a gradual decline in soil organic carbon (SOC) levels during the acidification experiment, while water-soluble organic carbon (WSOC) first increased and then decreased. After 25 years of simulated leaching, SOC decreased by 1.34% and WSOC declined by 15.63%. Acidification has a minimal impact on Fe-Al bonded organic carbon but significantly reduces calcium-bonded organic carbon by 17.07% over 25 years. The content of exchangeable Ca2+ and Mg²⁺ decreases as acidification intensifies. After 25 years, exchangeable Ca2+ and Mg²⁺ decreased by 9.42% and 7.00%, respectively. The acidification of the test soil resulted in a 46.5% reduction in the aggregate stability energy (E) of water-stable microaggregates, with an average decrease of 14.04 J/g for every 0.1 unit decrease in pH. Additionally, the soil critical stabilization energy (Ecrit) exhibited a 51.48% reduction. The results demonstrated that a decrease of 0.32 J/g in E was associated with a 0.1 unit decrease in pH on average. Furthermore, the multivariate linear regression analysis revealed that the reduction in soil organic carbon (SOC) content contributed the most to the decline in E, followed by Calcium bond-bound soil organic carbon (Ca-SOC). Notably, Ca-SOC exerted the greatest influence on the reduction in sand grain Ecrit, followed by SOC.

An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images

Abstract The importance of gastric cancer (GC) and the role of deep learning techniques in categorizing GC histopathology images have recently increased. Identifying the drawbacks of traditional deep learning models, including lack of interpretability, inability to capture complex patterns, lack of adaptability, and sensitivity to noise. A multi-channel attention mechanism-based framework is proposed that can overcome the limitations of conventional deep learning models by dynamically focusing on relevant features, enhancing extraction, and capturing complex relationships in medical data. The proposed framework uses three different attention mechanism channels and convolutional neural networks to extract multichannel features during the classification process. The proposed framework’s strong performance is confirmed by competitive experiments conducted on a publicly available Gastric Histopathology Sub-size Image Database, which yielded remarkable classification accuracies of 99.07% and 98.48% on the validation and testing sets, respectively. Additionally, on the HCRF dataset, the framework achieved high classification accuracy of 99.84% and 99.65% on the validation and testing sets, respectively. The effectiveness and interchangeability of the three channels are further confirmed by ablation and interchangeability experiments, highlighting the remarkable performance of the framework in GC histopathological image classification tasks. This offers an advanced and pragmatic artificial intelligence solution that addresses challenges posed by unique medical image characteristics for intricate image analysis. The proposed approach in artificial intelligence medical engineering demonstrates significant potential for enhancing diagnostic precision by achieving high classification accuracy and treatment outcomes.

EU enlargements, Brexit and value-added trade: A structural gravity approach

We revisit the topic of trade effects of past EU enlargements. We employ a structural gravity model to flows of domestic value added embedded in international trade and absorbed in the foreign final demand for the period of 1995–2018. Our model takes into account the current state of the art in estimation of gravity models: PPML estimation and multi-dimensional fixed effects. We base our estimations on the TiVA dataset and provide aggregate and sectoral estimates. Our results show stable estimates of EU enlargement on value-added trade of the order of 13.9% depending on the specification. These results are also robust to a choice of an alternative dataset (WIOD database). The advantage of our approach is the direct calculation of the trade impact on the GDP level. Based on these estimates we also provide quantitative insights into the effects of Brexit which varies depending on the country-specific exposure to the value added trade with the UK.

Assessing the capability of electrical capacitance tomography for monitoring chopped maize mass flow rates in field forage harvesters

Metagenomic and proteomic analyses reveal similar reproductive microbial profiles and shared functional pathways in uterine immune regulation in mares and jennies

This study aims to unveil potential differences in the vaginal and uterine microbiomes in mares and jennies, and to identify possible mechanisms involved in uterine immune homeostasis. The microbiota was characterized using 16S rRNA sequencing, and the uterine proteome was analyzed using UHPLC/MS/MS in 18 samples from healthy mares and 14 from jennies. While taxonomic analysis revealed high interspecies similarities, β-diversity analysis showed distinct clustering, with only two vaginal taxa and five uterine taxa differing between species. Despite compositional differences, PICRUSt analysis suggested minimal variations in predicted functional pathways across species. Comparing vaginal and uterine microbiota within the same species revealed overlapping bacterial taxa, but significant differences in α- and β-diversity and functional pathways. The uterine microbiota of both species was dominated by Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria, with abundant taxa like Streptococcus, Pseudomonas, Bacillus, Corynebacterium, and Staphylococcus, many of which are frequently associated with endometritis. The presence of Lactobacillus in the equine reproductive tract was minimal or non-existent. KEGG functional pathway analysis predicted that uterine microbiota of both species utilize metabolic pathways with potential immunomodulatory effects. Proteomic enrichment analysis showed that numerous overexpressed uterine proteins in both species are linked to adaptive and innate immune regulation and defense mechanisms against symbionts. Gene enrichment analysis identified several enriched Gene Ontology terms, including response to bacterial stimuli, humoral immune regulation, and TGF-beta receptor signaling, underscoring microbial-host interactions. The uterine microbiota may play a vital role in maintaining immune balance. Further research is required to confirm its interaction with the uterine immune system and clarify the mechanisms involved.

Effect of elastic modulus of tumour and non-tumour cells on vibration-induced behaviours

Reducing Obesity Using Social Ties (ROBUST): Protocol for a randomized control trial of a social network lifestyle intervention

Aim The aim of the Reducing Obesity Using Social Ties (ROBUST) study is to address negative and positive social network processes that may influence weight loss among individuals enrolled in an evidence-based, 24-week lifestyle intervention modeled after the Diabetes Prevention Program. Methods This is a randomized controlled trial (RCT) of a tailored social network-enhanced lifestyle intervention compared to an individual-level lifestyle intervention (control group) in adults (N=132) who self-identify as Black or Hispanic and have obesity (BMI ≥ 30 kg/m2). Intervention participants will recruit up to two members (n=132) of their social network to participate in skill-building sessions. Intervention and control participants will receive fourteen nutrition and lifestyle coaching sessions over 24 weeks. Diet, physical activity, positive affect, social network processes, communal coping, and anthropometrics will be measured at baseline and after intervention completion at 24 weeks. Discussion This study will provide the first pragmatic evidence of the efficacy of activating a communal coping process within personal networks to optimize weight loss among Black and Hispanic adults. Our study explicitly targets a population that has been less responsive to other lifestyle approaches to weight loss. Trial registration The study protocol was prospectively registered on ClinicalTrials.gov (NCT06335810).

DeepGuard: real-time threat recognition using Golden Jackal optimization with deep learning model

Enabling transparent toxicokinetic modeling for public health risk assessment

Toxicokinetic modeling describes the absorption, distribution, metabolism, and elimination of chemicals by the body. Chemical-specific in vivo toxicokinetic data is often unavailable for the thousands of chemicals in commerce. However, predictions from generalized toxicokinetic models allow for extrapolation from in vitro toxicological data, obtained via new approach methods (NAMs), to predict in vivo human health outcomes and provide key information on chemicals for public health risk assessment. The httk R package provides an open-source software tool containing a suite of generalized toxicokinetic models covering various exposure scenarios, a library of chemical-specific data from peer-reviewed high-throughput toxicokinetic (HTTK) studies, and other utility functions to parameterize and evaluate toxicokinetic models. Generalized HTTK models in httk use the open-source language MCSim to describe the compartmental and physiologically based toxicokinetics (PBTK). New HTTK models may be integrated into httk with a model description code file (C script generated via MCSim) and a model documentation file (R script). httk provides a series of functionalities such as model parameterization, in vivo -derived data for evaluating model predictions, unit conversion, Monte Carlo simulations for uncertainty propagation and biological variability, and other model utilities. Here, we describe in detail how to add new HTTK models into the httk package to leverage its pre-existing data and functionality. As a demonstration, we describe the integration of a gas inhalation PBTK model. The intention of httk is to provide a transparent, open-source tool for toxicokinetics, bioinformatics, and public health risk assessment that makes use of publicly available data on more than one thousand chemicals.

First crustal magnetic anomaly estimates from the magnetometer observations of the Korea pathfinder lunar orbiter