Blue Laser Triggered Hemostatic Peptide Hydrogel for Gastrointestinal Bleeding Treatment.

In an emergency, nonvariceal upper gastrointestinal bleeding (NVUGIB), endoscopic hemostasis is considered the gold standard intervention. However, current endoscopic hemostasis is very challenging to manage bleeding in large-diameter or deep lesions highly prone to rebleeding risk. Herein, a novel hemostatic peptide hydrogel (HPH) is reported, consisting of a self-assembly peptide sequence CFLIVIGSIIVPGDGVPGDG (PFV) and gelatin methacryloyl (GelMA), which can be triggered by blue laser endoscopy (BLE) for nonvariceal upper gastrointestinal bleeding treatment without recurring bleeding concerns. Upon contact with GelMA solution, PFV immediately fibrillates into ß-sheet nanofiber and solvent-induced self-assembly to form HPH gel. HPH nanofiber networks induced ultrafast coagulation by enveloping blood cells and activating platelets and coagulation factors even to the blood with coagulopathy. Besides its remarkable hemostatic performance in artery and liver injury models, HPH achieves instant bleeding management in porcine NVUGIB models within 60 s by preventing the rebleeding risk. This work demonstrates an extraordinary hemostatic agent for NVUGIB intervention by BLE for the first time, broadening potential application scenarios, including patients with coagulopathy and promising clinical prospects.

Development and validation of a machine learning-based interpretable model for predicting sepsis by complete blood cell parameters.

Background: Sepsis, a severe infectious disease, carries a high mortality rate. Early detection and prompt treatment are crucial for reducing mortality and improving prognosis. The aim of this research is to develop a clinical prediction model using machine learning algorithms, leveraging complete blood cell (CBC) parameters, to detect sepsis at an early stage. Methods: The study involved 572 patients admitted to West China Hospital of Sichuan University between July 2020 and September 2021. Among them, 215 were diagnosed with sepsis, while 357 had local infections. Demographic information was collected, and 57 CBC parameters were analyzed to identify potential predictors using techniques such as the Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). The prediction model was built using Logistic Regression and evaluated for diagnostic specificity, discrimination, and clinical applicability including metrics such as the area under the curve (AUC), calibration curve, clinical impact curve, and clinical decision curve. Additionally, the model's diagnostic performance was assessed on a separate validation cohort. Shapley's additive explanations (SHAP), and breakdown (BD) profiles were used to explain the contribution of each variable in predicting the outcome. Results: Among all the machine learning methods' prediction models, the LASSO-based model (λ = min) demonstrated the highest diagnostic performance in both the discovery cohort (AUC = 0.9446, P < 0.001) and the validation cohort (AUC = 0.9001, P < 0.001). Furthermore, upon local analysis and interpretation of the model, we demonstrated that LY-Z, MO-Z, and PLT-I had the most significant impact on the outcome. Conclusions: The predictive model based on CBC parameters can be utilized as an effective approach for the early detection of sepsis.

Renal anemia: from relative insufficiency of EPO to imbalance of erythropoiesis and eryptosis.

Chronic kidney disease has emerged as a major health issue both in China and worldwide. Renal anemia frequently occurs in patients with chronic kidney disease, and its severity and incidence rate increase as the disease progresses. Over the last 30 years, the administration of exogenous EPO and EPO stimulants has been employed to alleviate renal anemia, suggesting that a relative deficiency in EPO may be a primary cause. However, this approach has overshadowed other contributing factors, particularly eryptosis, which results from the reduced lifespan of red blood cells. Numerous studies reveal that there are nephrogenic and extrarenal EPO secretion indicating that an absolute deficiency of EPO is not always present in patients. Therefore, this paper speculates that renal anemia may arise when EPO-driven erythropoiesis fails to adequately compensate for aggravating eryptosis. Other factors including iron metabolism disorder, uremic toxin accumulation, inflammatory state, oxidative stress, and secondary hyperparathyroidism affect EPO reactivity bone marrow hematopoiesis and eryptosis, leading to an imbalance between red blood cell production and destruction, and cause anemia ultimately. More further studies on the pathogenesis and treatment of renal anemia would be expected to provide evidence to support our opinion.

Genome cloning and genetic evolution analysis of eight duck-sourced novel goose parvovirus strains in China in 2023.

Introduction: There are three major categories of waterfowl parvoviruses, namely goose parvovirus (GPV), Muscovy duck parvovirus, and novel goose parvovirus (NGPV). NGPV can infect both Cherry Valley ducks and mule ducks, resulting in short beaks and dwarfism syndrome, and the incidence of short beaks and dwarfism syndrome rises annually, posing a significant threat to the waterfowl breeding and the animal husbandry. Therefore, clarifying the biological characteristics and genetic evolution of NGPV is very important for the prevention and control of NGPV. Methods: Ducks with short beaks and dwarfism syndrome from Shandong and Henan Province were investigated by dissection and the tissue samples were collected for study. The NGPV genome was amplified by PCR, and the genome was analyzed for genetic evolution. Results: Eight strains of NGPV were isolated, which were designated as HZ0512, HZ0527, HZ0714, HZ0723, HZ0726, HZ0811, HZ0815, and HN0403. The nucleotide homology among these strains ranged from 99.9% to 100%. The eight strains, along with other NGPVs, belong to GPV. The eight strains showed a 92.5%-98.9% nucleotide homology with the classical GPV, while a 96.0%-99.9% homology with NGPV.Therefore, it can be deduced that there have been no major mutations of NGPV in Shandong and Henan provinces in recent years. Discussion: This study lays a theoretical foundation for further studying the genetic evolution and pathogenicity of NGPV, thereby facilitating the prevention and control of NGPV.

Dielectric characterization of paraelectric particle-loaded polymer matrix composites and commercial photoresins at W-band frequencies.

This work presents W-band (75-110 GHz) dielectric characterization of commercially available photoresins in their neat state, as well as in polymer matrix composite (PMC) mixtures with various loading concentrations of the paraelectric barium strontium titanate (BST). Due to difficulties 3D printing the BST-loaded PMC resins detailed within, a custom curing and casting process was used to fabricate testable PMC samples, which were synthesized to demonstrate the dielectric functionalization of the underlying polymer matrix. Dielectric characterization of the PMCs confirmed the functionalization of our composites when compared to the commercial photoresins. For example, a volumetric loading concentration of 25 vol % BST increased the dielectric permittivity (εr ) from 2.78 to 9.60 and the loss tangent (tanδ) from 0.022 to 0.114. These results indicate that the realization of UV-cured photoresins with "designer-dielectric" functionalization based on vol % of filler are strong candidates for use in stereolithography (SLA) 3D printing applications. To accomplish this, and with a special interest for radio/microwave/terahertz (RF/MW/THz) applications, we highlight the need for both (a) better photoresin matrix materials with lower intrinsic tanδ and (b) selection criteria related to the size/geometry and electronic properties of potential filler materials to maintain the printability of PMC photoresins in SLA systems.

Significantly Suppressed Dielectric Loss and Enhanced Breakdown Strength in Core@Shell Structured Ni@TiO2/PVDF Composites.

An insulating shell on the surface of conductive particles is vital for restraining the dielectric loss and leakage current of polymer composites. So as to inhibit the enormous loss and conductivity of pristine nickel (Ni)/poly(vinylidene fluoride)(PVDF) composites but still harvest a high dielectric permittivity (εr) when filler loading approaches or exceeds the percolation threshold (fc), pristine Ni particles were covered by a layer of titanium dioxide (TiO2) shell via a sol-gel approach, and then they were composited with PVDF. The impacts of the TiO2 coating on the dielectric performances of the Ni/PVDF composites were explored as a function of the filler concentration, the shell thickness and frequency. In addition, the dielectric performances were fitted using the Havriliak-Negami (H-N) equation in order to further understand the TiO2 shell's effect on polarization mechanism in the composites. The Ni@TiO2/PVDF composites exhibit high εr and enhanced breakdown strength (Eb) but remarkably suppressed loss and conductivity when compared with pristine Ni/PVDF because the TiO2 shell can efficiently stop the direct contact between Ni particles thereby suppressing the long-range electron transportation. Further, the dielectric performances can be effectively tuned through finely adjusting the TiO2 shell' thickness. The resulting Ni@TiO2/PVDF composites with high εr and Eb but low loss show appealing applications in microelectronics and electrical fields.

Core-Shell Engineering of Conductive Fillers toward Enhanced Dielectric Properties: A Universal Polarization Mechanism in Polymer Conductor Composites.

Flexible dielectric and electronic materials with high dielectric constant (k) and low loss are constantly pursued. Encapsulation of conductive fillers with insulating shells represents a promising approach, and has attracted substantial research efforts. However, progress is greatly impeded due to the lack of a fundamental understanding of the polarization mechanism. In this work, a series of core-shell polymer composites is studied, and the correlation between macroscopic dielectric properties (across entire composites) and microscopic polarization (around single fillers) is investigated. It is revealed that the polarization in polymer conductor composites is determined by electron transport across multiple neighboring conductive fillers-a domain-type polarization. The formation of a core-shell filler structure affects the dielectric properties of tpolymer composites by essentially modifying the filler-cluster size. Based on this understanding, a novel percolative composite is prepared with higher-than-normal filler concentration and optimized shell's electrical resistivity. The developed composite shows both high-k due to enlarged cluster size and low loss due to restrained charge transport simultaneously, which cannot be achieved in traditional percolative composites or via simple core-shell filler design. The revealed polarization mechanism and the optimization strategy for core-shell fillers provide critical guidance and a new paradigm, for developing advanced polymer dielectrics with promising property sets.

Digital Analysis of Smart Registration Methods for Magnetic Resonance Images in Public Healthcare.

Brain development and atrophy accompany people's life. Brain development diseases, such as autism and Alzheimer's disease, affect a large part of the population. Analyzing brain development is very important in public healthcare, and image registration is essential in medical brain image analysis. Many previous studies investigate registration accuracy by the "ground truth" dataset, marker-based similarity calculation, and expert check to find the best registration algorithms. But the evaluation of image registration technology only at the accuracy level is not comprehensive. Here, we compare the performance of three publicly available registration techniques in brain magnetic resonance imaging (MRI) analysis based on some key features widely used in previous MRI studies for classification and detection tasks. According to the analysis results, SPM12 has a stable speed and success rate, and it always works as a guiding tool for newcomers to medical image analysis. It can preserve maximum contrast information, which will facilitate studies such as tumor diagnosis. FSL is a mature and widely applicable toolkit for users, with a relatively stable success rate and good performance. It has complete functions and its function-based integrated toolbox can meet the requirements of different researchers. AFNI is a flexible and complex tool that is more suitable for professional researchers. It retains most details in medical image analysis, which makes it useful in fine-grained analysis such as volume estimation. Our study provides a new idea for comparing registration tools, where tool selection strategy mainly depends on the research task in which the selected tool can leverage its unique advantages.

Detection and Grading of Gliomas Using a Novel Two-Phase Machine Learning Method Based on MRI Images.

The early detection and grading of gliomas is important for treatment decision and assessment of prognosis. Over the last decade numerous automated computer analysis tools have been proposed, which can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. In this paper, we used the gradient-based features extracted from structural magnetic resonance imaging (sMRI) images to depict the subtle changes within brains of patients with gliomas. Based on the gradient features, we proposed a novel two-phase classification framework for detection and grading of gliomas. In the first phase, the probability of each local feature being related to different types (e.g., diseased or healthy for detection, benign or malignant for grading) was calculated. Then the high-level feature representing the whole MRI image was generated by concatenating the membership probability of each local feature. In the second phase, the supervised classification algorithm was used to train a classifier based on the high-level features and patient labels of the training subjects. We applied this framework on the brain imaging data collected from Zhongnan Hospital of Wuhan University for glioma detection, and the public TCIA datasets including glioblastomas (WHO IV) and low-grade gliomas (WHO II and III) data for glioma grading. The experimental results showed that the gradient-based classification framework could be a promising tool for automatic diagnosis of brain tumors.

Thermally Stable Low-Loss Polymer Dielectrics Enabled by Attaching Cross-Linkable Antioxidant to Polypropylene.

Polymer dielectrics with low-loss and high-temperature tolerance are extremely desirable as electrical energy storage materials for advanced electronics and electrical power applications. They can allow fast switching rates during power conversion and therefore achieve high power densities without thermal issues. Here, we explore polypropylene (PP), the state of the art dielectric polymer, and present an innovative approach to substantially improve the thermal stability and concurrently reduce the dielectric loss of PP. In particular, cross-linkable antioxidant groups, hindered phenol (HP), are incorporated into PP via well-controlled chemical synthesis. The grafted HP can simultaneously serve as radical scavenger and cross-linker, thereby constraining thermally decomposed radicals and charge transport in the synthesized PP-HP copolymer. As a result, the upper-temperature limit of PP-HP is greatly extended to 190 °C and the electrical loss is even gradually reduced upon thermal annealing. The copolymer after heating under 190 °C exhibits better dielectric properties than the PP without any thermal treatment. The experimental results indicate that the PP-HP copolymers are promising materials for high-temperature, low-loss, and high-voltage dielectric applications.

The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain. OBJECTIVE: This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics. METHODS: We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature. RESULTS: On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation. CONCLUSIONS: Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.