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Protocol for a prospective cohort study on congenital heart disease in neonates across varied altitudes in Sichuan province, China
Congenital heart disease (CHD) is the most prevalent congenital disorder, contributing significantly to neonate mortality. Despite advances in diagnosis and treatment, the incidence and risk factors of CHD remain underexplored, particularly in regions with varied altitudes. Sichuan Province, China, with its diverse topography and altitudes, provides a unique opportunity to investigate the epidemiology of CHD across different environmental settings. This study aims to explore the incidence, subtypes, and risk factors of CHD in neonates across high, middle, and low-altitude regions of Sichuan Province. It also seeks to assess the effectiveness of the Sichuan Province Newborn CHD Free Screening Project, the impact of CHD on family economics and child development, and to provide data-driven recommendations for improving CHD prevention and control measures. We will conduct a multicenter, prospective cohort study involving neonates with positive CHD screening results and their families, recruited from three cities representing different altitude levels: Aba Tibetan and Qiang Autonomous Prefecture, Mianyang City, and Guangyuan City. Data collection will include birth characteristics, CHD screening outcomes, parental and gestational histories, and blood samples for genetic analysis. The study will monitor treatment outcomes, economic impact, and the growth and development of the children over time. This study will provide critical insights into the epidemiology of CHD in Sichuan Province, particularly in relation to altitude. The results will help optimize CHD screening and management programs, ultimately improving outcomes for affected children and their families.
The role of the hippocampus and SLC39A8 in chronic musculoskeletal pain-induced dementia: a Mendelian randomization study
High kinetic inductance cavity arrays for compact band engineering and topology-based disorder meters
Abstract Superconducting microwave metamaterials offer enormous potential for quantum optics and information science, enabling the development of advanced quantum technologies for sensing and amplification. In the context of circuit quantum electrodynamics, such metamaterials can be implemented as coupled cavity arrays (CCAs). In the continuous effort to miniaturize quantum devices for increasing scalability, minimizing the footprint of CCAs while preserving low disorder becomes paramount. In this work, we present a compact CCA architecture using superconducting NbN thin films manifesting high kinetic inductance. The latter enables high-impedance CCA (~1.5 kΩ), while reducing the resonator footprint. We demonstrate its versatility and scalability by engineering one-dimensional CCAs with up to 100 resonators and with structures that exhibit multiple bandgaps. Additionally, we quantitatively investigate disorder in the CCAs using symmetry-protected topological SSH edge modes, from which we extract a resonator frequency scattering of $$0.2{2}_{-0.03}^{+0.04}\%$$ 0.2 2 − 0.03 + 0.04 % . Our platform opens up exciting prospects for analog quantum simulations of many-body physics with ultrastrongly coupled emitters.
Evaluation and Characterization of Acute respiratory distress syndrome in tree shrews through TMT proteomic method
Acute respiratory distress syndrome (ARDS), a common cause of acute fatal respiratory, is characterized by severe inflammatory lung injury as well as hallmarks of increased pulmonary vascular permeability, neutrophil infiltration, and macrophage accumulation. Tree shrew, a squirrel-like small animal model, has been confirmed to have more similar traits to human ARDS with one-hit intratracheal instillation of LPS in our previous study. In this study, we characterized protein profile changes induced by intranasal LPS challenge in the tree shrew model through tandem mass tag (TMT)-based quantitative proteomics and type II alveolar epithelial cells through pathological analysis. In total, 4070 proteins (p < 0.05) were identified from lung tissues of the LPS-induced group and PBS group. Among the differential expression proteins (DEPs) detected by t-test (≥|1.5-fold|), 529 DEPs were identified, of which 304 were upregulated, and 225 were downregulated. The most important pathways involved in the process of ARDS had been identified by enrichment analysis: oxidative stress, apoptosis, inflammatory responses, and vascular endothelial injury. In addition, proteins have been reported in animal models or clinical patients also detail investigated for further analysis, such as ceruloplasmin (CP), hemopexin (HPX), sphingosine kinase 1 (SphK1), lactotransferrin (LTF), and myeloperoxidase (MPO) were upregulated in induced tissues and confirmed by western blot analysis. Overall, this study not only reveals a comprehensive proteomic analysis of the ARDS tree shrew model but also provides novel insights into multi-pathways responses induced by the LPS challenge of tree shrews. We highlight shared and unique proteomic changes in the lungs of ARDS tree shrews and identify novel pathways for acute lung injury, which may promote the model into basic research and translational research.
The individual training history shapes soccer players’ ability to predict teammates’ and opponents’ moves
Redefining closed pores in carbons by solvation structures for enhanced sodium storage
Biochemical characterization of wood decay and metabolization of phenolic compounds by causal fungi of grapevine trunk diseases
Grapevine trunk diseases, such as Esca, Botryosphaeria dieback, and Eutypa dieback, are caused by various Ascomycota and Basidiomycota fungi that colonize wood and form internal lesions. Basidiomycota fungi, such as Fomitiporia species, are associated only with the trunk disease Esca, and are wood-decay fungi. Variation in the extent of lesion development among the fungal pathogens reflects a combination of fungal virulence and host susceptibility. To evaluate factors that may affect lesion development, we compared in vitro wood-decay abilities and tolerance of host secondary metabolites (cell-wall and soluble phenolic compounds) of four fungi that cause trunk diseases: Eutypa lata (Eutypa dieback), Fomitiporia polymorpha (Esca), and Diplodia seriata and Neofusicoccum parvum (Botryosphaeria dieback). Fungi were grown on autoclaved blocks of Vitis vinifera ‘Merlot’ wood for six months, to examine fungal colonization of wood cells and percentages of wood components remaining after decay. Fungi were also grown on medium amended with starch, pectin, lignin, cellulose, hemicellulose, tannic acid, gallic acid, magnesium sulfate, or grape wood powder, to determine cell wall-degrading enzyme activity and impacts on fungal growth. Lastly, to determine tolerance of phenolic compounds, fungi were grown in medium amended with piceid, rutin, epicatechin, or gallic acid. Our novel findings for F. polymorpha include its preferential degradation of hemicellulose and pectin (and detection of corresponding enzymatic activities), but no degradation of lignin, in spite of growth in lignin-amended media and detection of laccase, lignin peroxidase, and peroxidase activities. Together, these findings suggest F. polymorpha has characteristics of both brown-rot and white-rot fungi. The type of wood decay caused by D. seriata and N. parvum, based on their degradation of pectin, cellulose, hemicellulose, and lignin (and detection of corresponding enzymatic activities), is characteristic of a soft rot, similar to that of E. lata. Unique among these three Ascomycetes was induction of N. parvum growth by piceid, rutin, epicatechin, and gallic acid, and efficient metabolism and/or detoxification of these phenolic compounds by N. parvum. As all four fungi metabolize components of the wood as substrate, and also can metabolize/detoxify host-defense compounds, a clearer understanding of their roles as wood-decay fungi might further research on managing the chronic wood infections.
An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD)
Abstract The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) (http://www.gutrhythm.com/public_database) is a new source of electrophysiological big data for drug research. Over the past 2 years, the database has doubled in size, and now contains the electrophysiological profiles of 172 drugs across 11,943 datasets. This study used a state-of-the-art deep-learning model with time-series classification to explore the feasibility of using raw electrophysiological recordings from tissues to predict ADRs. The GIPADD contains the recordings of the electrical activity of various gastrointestinal tissues (stomach, duodenum, ileum, and colon) exposed to a drug at three or more different concentrations, representing the effects of the drug on gastrointestinal pacemaker activity. Each drug in the database is associated with at least 60 recordings. The datasets are divided in a ratio of 8:2 for training and validation. A modified InceptionTime classifier (ICT) was used to predict whether a drug induces ADRs, using data from the SIDER database as the target. Concentrations and tissues were added as covariates and added to the input of the model during forward propagation. We also established a negative control with shuffled target labels, and external validation was conducted using time-shifted recording predictions. The best model for predicting nausea, vomiting, diarrhoea, and constipation achieved by-drug accuracies of 0.87, 0.89, 0.85, and 0.91, respectively; by-drug precision (class 1) of 0.88, 0.90, 0.99, and 0.89, respectively; and area under the receiver operating characteristic curve (AUROC) values of 0.84, 0.87, 0.94, and 0.96, respectively. The best model was an ensemble of five independent ICT classifiers trained on the same dataset. Models trained using shuffled labels (negative controls) exhibited significantly lower accuracy, precision, and AUROC values than models trained using correctly labelled datasets, indicating that ICT classifiers successfully identified latent features in the raw recordings associated with ADRs. The combined benefits of the GIPADD and deep learning may accelerate drug safety testing and drug development by enabling the reliable analysis of electrophysiological drug profiles during the preclinical stage.
A metal ion mediated functional dichotomy encodes plasticity during translation quality control
Zero inflated high dimensional compositional data with DeepInsight
Through the Human Microbiome Project, research on human-associated microbiomes has been conducted in various fields. New sequencing techniques such as Next Generation Sequencing (NGS) and High-Throughput Sequencing (HTS) have enabled the inclusion of a wide range of features of the microbiome. These advancements have also contributed to the development of numerical proxies like Operational Taxonomic Units (OTUs) and Amplicon Sequence Variants (ASVs). Studies involving such microbiome data often encounter zero-inflated and high-dimensional problems. Based on the need to address these two issues and the recent emphasis on compositional interpretation of microbiome data, we conducted our research. To solve the zero-inflated problem in compositional microbiome data, we transformed the data onto the surface of the hypersphere using a square root transformation. Then, to solve the high-dimensional problem, we modified DeepInsight, an image-generating method using Convolutional Neural Networks (CNNs), to fit the hypersphere space. Furthermore, to resolve the common issue of distinguishing between true zero values and fake zero values in zero-inflated images, we added a small value to the true zero values. We validated our approach using pediatric inflammatory bowel disease (IBD) fecal sample data and achieved an area under the curve (AUC) value of 0.847, which is higher than the previous study’s result of 0.83.
Xerostomia correlates with pain sensitivity in burning mouth syndrome patients
Reaction-driven formation of anisotropic strains in FeTeSe nanosheets boosts low-concentration nitrate reduction to ammonia
Deep learning-based acceleration of muscle water T2 mapping in patients with neuromuscular diseases by more than 50% - translating quantitative MRI from research to clinical routine
Background Quantitative muscle water T2 (T2w) mapping is regarded as a biomarker for disease activity and response to treatment in neuromuscular diseases (NMD). However, the implementation in clinical settings is limited due to long scanning times and low resolution. Using artificial intelligence (AI) to accelerate MR image acquisition offers a possible solution. Combining compressed sensing and parallel imaging with AI-based reconstruction, known as CSAI (SmartSpeed, Philips Healthcare), allows for the generation of high-quality, weighted MR images in a shorter scan time. However, CSAI has not yet been investigated for quantitative MRI. Therefore, in the present work we assessed the performance of CSAI acceleration for T2w mapping compared to standard acceleration with SENSE. Methods T2w mapping of the thigh muscles, based on T2-prepared 3D TSE with SPAIR fat suppression, was performed using standard SENSE (acceleration factor of 2; 04:35 min; SENSE) and CSAI (acceleration factor of 5; 01:57 min; CSAI 5x) in ten patients with facioscapulohumeral muscular dystrophy (FSHD). Subjects were scanned in two consecutive sessions (14 days in between). In each dataset, six regions of interest were placed in three thigh muscles bilaterally. SENSE and CSAI 5x acceleration were compared for i) image quality using apparent signal- and contrast-to-noise ratio (aSNR/aCNR), ii) diagnostic agreement of T2w values, and iii) intra- and inter-session reproducibility. Results aSNR and aCNR of SENSE and CSAI 5x scans were not significantly different (p > 0.05). An excellent agreement of SENSE and CSAI 5x T2w values was shown (r = 0.99; ICC = 0.992). T2w mapping with both acceleration methods showed excellent, matching intra-method reproducibility. Conclusion AI-based acceleration of CS data allows for scan time reduction of more than 50% for T2w mapping in the thigh muscles of NMD patients without compromising quantitative validity.
Availability and uncertainty-aware optimal placement of capacitors and DSTATCOM in distribution network using improved exponential distribution optimizer
Hydrogels with prestressed tensegrity structures
Tel Shiqmona during the Iron Age: A first glimpse into an ancient Mediterranean purple dye ‘factory’
Purple-dyed textiles, primarily woolen, were much sought after in the Ancient Near East and the Mediterranean, and they adorned the powerful and wealthy. It is commonly assumed that in antiquity, purple dye—extracted from specific species of marine mollusks—was produced in large quantities and in many places around the Mediterranean. But despite numerous archaeological excavations, direct and unequivocal evidence for locales of purple-dye production remains very limited in scope. Here we present Tel Shiqmona, a small archaeological tell on Israel’s Carmel coast. It is the only site in the Near East or around the Mediterranean—indeed, in the entire world—where a sequence of purple-dye workshops has been excavated and which has clear evidence for large-scale, sustained manufacture of purple dye and dyeing in a specialized facility for half a millennium, during the Iron Age (ca. 1100–600 BCE). The number and diversity of artifacts related to purple dye manufacturing are unparalleled. The paper focuses on the various types of evidence related to purple dye production in their environmental and archaeological contexts. We utilize chemical, mineralogical and contextual analyses to connect several categories of finds, providing for the first time direct evidence of the instruments used in the purple-dye production process in the Iron Age Levant. The artifacts from Shiqmona also serve as a first benchmark for future identification of significant purple-dye production sites around the Mediterranean, especially in the Iron Age.
Risk of hepatocellular carcinoma and cirrhosis decompensation in a large retrospective cohort of cirrhotic patients with autoimmune hepatitis
scPRINT: pre-training on 50 million cells allows robust gene network predictions
Abstract A cell is governed by the interaction of myriads of macromolecules. Inferring such a network of interactions has remained an elusive milestone in cellular biology. Building on recent advances in large foundation models and their ability to learn without supervision, we present scPRINT, a large cell model for the inference of gene networks pre-trained on more than 50 million cells from the cellxgene database. Using innovative pretraining tasks and model architecture, scPRINT pushes large transformer models towards more interpretability and usability when uncovering the complex biology of the cell. Based on our atlas-level benchmarks, scPRINT demonstrates superior performance in gene network inference to the state of the art, as well as competitive zero-shot abilities in denoising, batch effect correction, and cell label prediction. On an atlas of benign prostatic hyperplasia, scPRINT highlights the profound connections between ion exchange, senescence, and chronic inflammation.
Analysis of centre of pressure trajectories and plantar pressure distribution to map development of foot-ground interactions from new to confident walking infants
Independent walking is a crucial milestone, allowing infants to explore their environment efficiently. This phase introduces complex foot-ground interactions. However, previous studies have focused on either center of pressure (CoP) or plantar pressure distribution alone, often losing critical information. The impact of variables like body weight, height, and foot size on pressure distribution in infants remains also underexplored. Our study uses continuous statistical approaches to comprehensively analyze anterior-posterior (AP) and medio-lateral (ML) trajectories of CoP and plantar pressure distribution in infancy, mapping foot-ground interactions from new to confident walking stages. Thirty-nine infants walked across an EMED xl platform as new and then confident walkers. Frames of pressure steps were exported and processed in Matlab 2019b. Upon data normality assessment, AP and ML trajectories of new and confident walking steps were compared with parametric two-sample paired SPM1d t-test. Plantar pressure distribution between new and confident walking were compared using the nonparametric two-sample paired SPM1d t-test. Nonparametric linear regression analysis at pixel-level considered variables like body weight, height, foot dimensions, and walking experience, ensuring only non-correlated items were included. Our analyses revealed significant changes in CoP and plantar pressure distribution from new to confident walkers. New walkers initially contact the ground with the central part of their foot, while confident walkers show a more posterior initial contact. Confident walkers also exhibit more medial heel contact and a progression of CoP trajectories closer to the foot’s longitudinal axis. Regression analysis indicated that increasing walking experience significantly predicts higher pressure in the lateral and central forefoot. These findings underscore the importance of combining multi-segment joint analysis with plantar pressure data to fully understand foot development during infancy. This project highlighted key aspects of the unique biomechanics of infants’ foot development, emphasizing the need for further research to enhance understanding and inform clinical practices.