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DNA sequencing of whole human cytomegalovirus genomes from formalin-fixed, paraffin-embedded tissues from congenital cytomegalovirus disease cases
Background Congenital cytomegalovirus disease (cCMV) is uncommon but can be severe. Investigations of the role of genome sequence variation in the causative virus (human cytomegalovirus, HCMV) in clinical outcome have to date depended on small sample numbers derived from fresh tissues. Extensive formalin-fixed, paraffin-embedded (FFPE) cCMV biorepositories established worldwide potentially provide much larger sample numbers for future investigations. However, there are no published reports of sequencing whole HCMV genomes from such material. Objective To sequence whole HCMV genomes from cCMV FFPE material Study design Sixteen FFPE samples of foetal kidney or placental tissue were processed from ten cCMV cases in foetuses or neonates. Two commercial kits for extracting DNA from FFPE material were evaluated, HCMV DNA was enriched in the extracts, and the samples were sequenced on the Illumina platform. The sequence read datasets were analysed by genotyping, genome assembly and variant calling using a published software pipeline. Results Whole HCMV genomes were sequenced for five cases using either DNA extraction kit. Conclusions Sequencing whole HCMV genomes from cCMV FFPE material is feasible. This potentially facilitates future studies of the effects of HCMV variation on the clinical outcome of cCMV.
Latent class growth analysis of dynamic PaCo2 patterns and clinical outcomes in acute brain injury
Abstract To analyze dynamic patterns of arterial carbon dioxide partial pressure (PaCO₂) using latent class growth analysis in acute brain injury patients and investigate their associations with 28-day ICU mortality and 60-day in-hospital mortality. This retrospective study utilized the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. We applied latent class growth analysis to analyze PaCO₂ dynamic patterns during the first 72 h after ICU admission in adult patients with acute brain injury. Associations between trajectories and outcomes were evaluated using Cox proportional hazards models. Among the included acute brain injury patients (n = 1,145), three distinct PaCO₂ trajectories were identified: persistent hypocapnia pattern (23.7%), normal-mild regulation pattern (65.9%), and hypercapnia improvement pattern (10.5%). Cox proportional hazards regression analysis revealed that, compared with the normal-mild regulation pattern, the persistent hypocapnia pattern was significantly associated with higher risk of 28-day ICU mortality (HR = 1.28, 95% CI: 1.02–1.60) and 60-day in-hospital mortality (HR = 1.28, 95% CI: 1.03–1.59) after adjusting for confounding factors. The hypercapnia improvement pattern demonstrated a potentially protective association. This study identified distinct patterns of PaCO₂ dynamic changes in acute brain injury patients. Our analysis demonstrated that persistent hypocapnia was associated with higher risk of 28-day ICU and 60-day in-hospital mortality, while the hypercapnia improvement pattern showed potentially protective association. These results suggest the complexity of PaCO₂ management and the need for more individualized respiratory management strategies.
Assessing anthropogenic impact on the habitat of threatened rock cavy (Kerodon rupestris) through its alarm calls
Acoustic monitoring is emerging as a key tool in wildlife conservation, especially for species in inaccessible habitats like the rock cavy (Kerodon rupestris), an endangered species native to Brazil’s threatened Caatinga biome. Emotional stress from threatening situations affects breathing, heart rate, and vocal muscle tension, altering vocal acoustic parameters. This allows researchers to gauge the animal’s environmental perception through its vocalizations. We aimed to evaluate emotional disturbance indicators in free-range rock cavies’ vocalizations to suggest an acoustic index during threats. We compared calls from rock cavies in two areas with similar habitats but that differ in terms of anthropic impacts. Area 1 (A1) is near urban areas and disturbed by livestock and dogs, and Area 2 (A2) is farther from urban areas and free from human disturbance. Data on calls and behaviors were collected ad libitum in both areas. The alarm whistle call, making up 73.5% of total calls, was most common. Across 108 observation hours per area, 392 alarm whistle calls were recorded, with more calls in A1 than A2 (223 vs. 169; Chi-square = 29.44, DF = 1, P < 0.001). This resulted in a 32% higher hourly call rate in A1 (2.6 calls/h vs. 1.6 calls/h). Both male and female cavies in A1 had higher high-frequency (F1, 388 = 7.80, P = 0.005) and peak-frequency calls (F1, 388 = 21.32, P < 0.001). Given the similar landscape and resource availability in both areas, the differences in call emission rate and parameters are likely linked to emotional responses to human disturbances in A1. Thus, alarm whistle calls at an hourly rate of 2.6 calls/h or higher, with high-frequency and peak-frequency at or above 7222 Hz and 2603 Hz, can indicate anthropogenic disturbance in the Caatinga biome, aiding remote monitoring efforts.
Chitosan attenuates titanium dioxide nanoparticles induced hepatic and renal toxicities
Abstract Titanium dioxide nanoparticles (TiO2 NPs) are extensively incorporated in numerous industrial products. Adult male Albino rats received oral TiO2 NPs at a dose of 150 mg/kg body weight for 14 days exhibited both hepatic and renal toxicities manifested by disruption in serum hepatic and renal biomarkers, imbalance in oxidative-antioxidant system, up-regulation of mRNA expression of genes encode inflammation (IL-1β, TNF-α) and apoptosis (Caspase-3, BAX) with down-regulation of PCNA immune-staining density and histological modifications in hepatic and renal architecture. Carboxymethyl chitosan (5 mg/kg BW) significantly improved the harmful effects of nano-titanium particles highlighting its relevance in reducing TiO2 NPs – induced hepatic and renal dysfunction.
A graded neonatal mouse model of necrotizing enterocolitis demonstrates that mild enterocolitis is sufficient to activate microglia and increase cerebral cytokine expression
Necrotizing enterocolitis (NEC) is an inflammatory gastrointestinal process that afflicts approximately 10% of preterm infants born in the United States each year, with a mortality rate of 30%. NEC severity is graded using Bell’s classification system, from stage I mild NEC to stage III severe NEC. Over half of NEC survivors present with neurodevelopmental impairment during adolescence, a long-term complication that is poorly understood. Although multiple animal models exist, none prospectively controls for NEC severity. We bridge this knowledge gap by characterizing a graded murine model of NEC and studying its relationship with neuroinflammation across a range of NEC severities. Postnatal day 3 (P3) C57BL/6 mice were fed a formula containing different concentrations (0% control, 0.25%, 1%, 2%, and 3%) of dextran sodium sulfate (DSS). P3 mice were fed every 3 hours for 72 hours. We collected data on weight gain and behavior (activity, response, body color) during feeding. At the end of feeding, we collected tissues (intestine, liver, plasma, brain) for immunohistochemistry, immunofluorescence, and cytokine and chemokine analysis. Throughout NEC induction, mice fed higher concentrations of DSS died sooner, lost weight faster, and became sick or lethargic earlier. Intestinal characteristics (dilation, color, friability) were worse in mice fed higher DSS concentrations. Histology revealed small intestinal disarray among all mice fed DSS, while higher DSS concentrations resulted in reduced small intestinal cellular proliferation and increased hepatic and systemic inflammation. In the brain, IL-2, G-CSF, and CXCL1 concentrations increased with higher DSS concentrations, and microglial branching in the hippocampus CA1 was significantly reduced in DSS-fed mice. In conclusion, we characterized a novel graded model of NEC that recapitulates the full range of NEC severities. We showed that mild NEC is sufficient to initiate neuroinflammation and microglia activation. This model will facilitate long-term studies on the neurodevelopmental effects of NEC.
Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization
Spatial characteristics and determinants of traditional village distribution in Guizhou Province
This study employs a range of analytical techniques, including the geographical detector, kernel density estimation, imbalance index, geographical concentration index, and nearest neighbor index, all integrated with ArcGIS 10.8, to examine and illustrate the spatial distribution of 757 traditional villages across Guizhou, revealing an aggregated spatial distribution pattern of traditional villages, i.e., “one highly concentrated area and two secondary density clusters.” This pattern is influenced by both natural and socio-cultural factors, with socio-cultural elements such as road network density, GDP, and ethnic minority populations playing a more significant role than natural environmental factors. The results of geodetector analysis indicate that the interaction between these factors generally shows a nonlinear enhancement effect. Based on these findings, this study proposes four main strategies to preserve and enhance traditional villages: (1) establishing regional identities that reflect local ethnic characteristics; (2) improving village infrastructure to enhance accessibility; (3) implementing targeted protection and utilization strategies based on local conditions; and (4) industrial linkage, combining protection and development.
Feasibility and safety of 0.018-inch guidewire-supported distal access catheters in establishing transradial neurointerventional access
Interactive, computer-based, and situated design for innovative formative assessment approaches
Social and teamwork skills are essential for today’s teachers, yet their assessment in authentic contexts is challenging. This study presents the design, development and validation of an innovative computer-based test, designed to assess collaborative and teamwork skills in preprimary and primary school teachers and referred to as the ‘CoTeSt’ (Collaborative and Teamwork Skill test). Based on Situated Action Theory, the test immerses participants in realistic team interactions using conversational agents within a narrative framework. Teachers are asked to solve problems, collaborate with virtual colleagues, and provide feedback. The test consists of 20 dichotomously scored items in both multiple-choice and short answer, and it was administered to 139 Italian teachers. Test validation involved qualitative and quantitative methods, confirming that the items actually assess the skills they were assumed to evaluate. Post-test interviews and group discussions highlighted the tool’s user-friendly design and its potential to foster self-reflection, professional dialogue, and continuous skill development. The CoTeSt represents a meaningful step toward empowering teachers with critical social skills and fostering a culture of collaboration and growth in education.
Prediction of rock type from physical and mechanical properties by data mining implementations
Evaluation of metatranscriptomic sequencing protocols to obtain full-length RNA virus genomes from mammalian tissues
High-throughput sequencing technologies have advanced RNA virus genomics, but recovering viral genomes from mammalian tissues remains challenging due to the predominance of host RNA. We evaluated two metatranscriptomic workflows to address these challenges. Our results demonstrate that the methods differed significantly in performance, with Method B achieving a 5-fold increase in RNA yield and improved RNA integrity over Method A. These differences resulted in the recovery of 4 complete hepacivirus genomes with Method B compared to fragmented or incomplete genomes with Method A. Additionally, Method B’s library preparation workflow, incorporating rRNA depletion, enhanced viral genome recovery by reducing host RNA background. Our novel approach integrates an optimized RNA purification protocol with a customized bioinformatics strategy for improved viral genome recovery. Overall, our findings highlight the critical role of optimized homogenization, RNA purification, and library preparation in metatranscriptomic workflows, facilitating the more effective RNA virus genome recovery from complex mammalian tissues.
rhaFGF promotes acute diabetic wound healing by suppressing chronicity of inflammation
Age-related differences in trust beliefs during middle childhood: Downward-extension and validation of the general trust scale
There are conflicting suggestions concerning the developmental trend of trust beliefs during middle childhood. Across three studies, the current research developed a brief measure of child general trust beliefs, as well as child measures of trust in peers and online, and examined age-related differences in these beliefs. Study 1 explored the appropriateness of downward extending the General Trust Scale. Studies 2 and 3 developed the child version of this scale and adapted the target of trust to construct two additional scales measuring trust beliefs in peers and online. These studies also provide evidence of the psychometric quality of the scales, and that trust beliefs are positively associated with friendship quality and psychosocial well-being outcomes in children. In addition, Study 3 demonstrated small age-related decreases in general and peer trust. This finding suggests children may become more discerning during middle childhood. Implications of these age-related differences and the use of these novel scales is discussed.
A highly sensitive quantitative method of polysialic acid reveals its unique changes in brain aging and neuropsychiatric disorders
Construction of a proximity labeling vector to identify protein-protein interactions in human stem cells
Identification of protein-protein interactions is essential for understanding protein functions in biological processes. While immunoprecipitation has traditionally been used to isolate proteins and their partners, it faces limitations in capturing transient interactions. Proximity labeling, particularly with the biotin ligase TurboID, addresses this challenge by enabling rapid and efficient identification of interacting proteins in vivo. Human induced pluripotent stem cells are valuable models for studying human development, however certain biological processes, such as differentiation, can be difficult to analyze because conventional transfection methods are challenging. Therefore, an alternative strategy for detection of interacting proteins is necessary. Here, we developed a novel system employing TurboID-fusion proteins within an integrative and inducible expression vector to investigate the interactome during stem cell differentiation. We validated our system by using U2AF2 and GFP as bait proteins, generated two distinct cell lines, and determining the minimum induction time required for optimal protein expression. Our results confirmed that the system did not alter the expected localization of U2AF2. Applying our system, we identified significant differences in the interactome of U2AF2 between the pluripotent and mesodermal differentiation stages, demonstrating that U2AF2 interacts with distinct protein sets following cell fate commitment. Our study successfully unveils a new tool for studying protein-protein interaction in human stem cells.
Semi-supervised action recognition using logit aligned consistency and adaptive negative learning
Abstract With the development of the socialized video era, while semi-supervised action recognition can address the increasingly high costs of video annotation, it still faces significant challenges, particularly in the underexplored application of Vision Transformer. In this paper, we present our work on designing Full-SVFormer, a simple yet efficient semi-supervised action recognition architecture based on the Transformer framework. Full-SVFormer uses TimeSformer based on pre-trained weights as its backbone network, which balances the accuracy and speed of Transformers in semi-supervised action recognition. Within the stable pseudo-label framework EMA-Teacher, we introduce KL divergence loss, which has undergone logit standardization preprocessing, as its unsupervised consistency loss. This improves the student’s focus on the inherent relationship between the logit of student and teacher. Furthermore, we incorporate the Adaptive Negative Learning (ANL) method to introduce additional negative pseudo-labels, which dynamically evaluate the Top-k performance of the model to adaptively assign negative labels, thus making better use of ambiguous prediction examples. We conducted a number of experiments on two extensive datasets, UCF-101 and HMDB-51, where our overall experimental results achieved superior performance compared to previous methods. Our work further advances the development of Transformer in the domain of semi-supervised action recognition.
XLLC-Net: A lightweight and explainable CNN for accurate lung cancer classification using histopathological images
Lung cancer imaging plays a crucial role in early diagnosis and treatment, where machine learning and deep learning have significantly advanced the accuracy and efficiency of disease classification. This study introduces the Explainable and Lightweight Lung Cancer Net (XLLC-Net), a streamlined convolutional neural network designed for classifying lung cancer from histopathological images. Using the LC25000 dataset, which includes three lung cancer classes and two colon cancer classes, we focused solely on the three lung cancer classes for this study. XLLC-Net effectively discerns complex disease patterns within these classes. The model consists of four convolutional layers and contains merely 3 million parameters, considerably reducing its computational footprint compared to existing deep learning models. This compact architecture facilitates efficient training, completing each epoch in just 60 seconds. Remarkably, XLLC-Net achieves a classification accuracy of 99.62% ± 0.16%, with precision, recall, and F1 score of 99.33% ± 0.30%, 99.67% ± 0.30%, and 99.70% ± 0.30%, respectively. Furthermore, the integration of Explainable AI techniques, such as Saliency Map and GRAD-CAM, enhances the interpretability of the model, offering clear visual insights into its decision-making process. Our results underscore the potential of lightweight DL models in medical imaging, providing high accuracy and rapid training while ensuring model transparency and reliability.
Sulfonated Schiff base immobilized on magnetite-chitosan as an efficient nanocatalyst for the synthesis of xanthene derivatives
Habitat use and abundance of an introduced population of the Japanese weasel (Mustela itatsi): Comparison with the native population
Understanding habitat use and abundance is essential for elucidating the impact of invasive species. Invasive carnivores affect ecosystems by preying on native animals. In Japan, the Japanese weasel (Mustela itatsi) is native to the mainland but has been intentionally introduced to many small islands, including Miyakejima Island. We investigated the habitat use and abundance of invasive non-native Japanese weasels on Miyakejima Island via fecal surveys, and for comparison, performed similar surveys for their native conspecifics on Izu-Oshima Island. We constructed a generalized linear mixed model and estimated fecal abundance across the entire island based on the effect of vegetation type on their abundance. On Miyakejima Island, deciduous broadleaf and bamboo forests had positive effects on weasel abundance, whereas grasslands had a negative effect. Conversely, on Izu-Oshima Island, bare ground had a negative effect. Further, the estimated average fecal abundance across Miyakejima and Izu-Ohshima Islands, considering vegetation type, were 7.44 and 4.89 feces samples per km, respectively, suggesting that weasels are well adapted to Miyakejima Island. We also analyzed the fecal DNA of weasels in a specific area on Miyakejima Island and estimated non-native weasel density at 20 individuals per km2 (95% CI: 4.9–80) using genetic capture-recapture methods in the area. These findings enhance understanding regarding non-native species and may facilitate the formulation of countermeasures for their control.