Gastric Cancer Assembloids Derived from Patient-Derived Xenografts: A Preclinical Model for Therapeutic Drug Screening.

The construction of reliable preclinical models is crucial for understanding the molecular mechanisms involved in gastric cancer and for advancing precision medicine. Currently, existing in vitro tumor models often do not accurately replicate the human gastric cancer environment and are unsuitable for high-throughput therapeutic drug screening. In this study, droplet microfluidic technology is employed to create novel gastric cancer assembloids by encapsulating patient-derived xenograft gastric cancer cells and patient stromal cells in Gelatin methacryloyl (GelMA)-Gelatin-Matrigel microgels. The usage of GelMA-Gelatin-Matrigel composite hydrogel effectively alleviated cell aggregation and sedimentation during the assembly process, allowing for the handling of large volumes of cell-laden hydrogel and the uniform generation of assembloids in a high-throughput manner. Notably, the patient-derived xenograft assembloids exhibited high consistency with primary tumors at both transcriptomic and histological levels, and can be efficiently scaled up for preclinical drug screening efforts. Furthermore, the drug screening results clearly demonstrated that the in vitro assembloid model closely mirrored in vivo drug responses. Thus, these findings suggest that gastric cancer assembloids, which effectively replicate the in vivo tumor microenvironment, show promise for enabling more precise high-throughput drug screening and predicting the clinical outcomes of various drugs.

Integrating Multiple Bacterial Phenotypes and Bayesian Network for Analyzing Health Risks of Pathogens in Plastisphere.

Plastic pollution represents a critical threat to soil ecosystems and even humans, as plastics can serve as a habitat for breeding and refuging pathogenic microorganisms against stresses. However, evaluating the health risk of plastispheres is difficult due to the lack of risk factors and quantification model. Here, DNA sequencing, single-cell Raman-D2O labeling, and transformation assay were used to quantify key risk factors of plastisphere, including pathogen abundance, phenotypic resistance to various stresses (antibiotic and pesticide), and ability to acquire antibiotic resistance genes. A Bayesian network model was newly introduced to integrate these three factors and infer their causal relationships. Using this model, the risk of pathogen in the plastisphere is found to be nearly 3 magnitudes higher than that in free-living state. Furthermore, this model exhibits robustness for risk prediction, even in the absence of one factor. Our framework offers a novel and practical approach to assessing the health risk of plastispheres, contributing to the management of plastic-related threats to human health.

Missing data in amortized simulation-based neural posterior estimation.

Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet, the available approach cannot handle the in experimental studies ubiquitous case of missing data, and might provide incorrect posterior estimates. In this work, we discuss various ways of encoding missing data and integrate them into the training and inference process. We implement the approaches in the BayesFlow methodology, an amortized estimation framework based on invertible neural networks, and evaluate their performance on multiple test problems. We find that an approach in which the data vector is augmented with binary indicators of presence or absence of values performs the most robustly. Indeed, it improved the performance also for the simpler problem of data sets with variable length. Accordingly, we demonstrate that amortized simulation-based inference approaches are applicable even with missing data, and we provide a guideline for their handling, which is relevant for a broad spectrum of applications.

Improving the Mechanical Properties of Hot Rolled Low-Carbon Copper-Containing Steel by Adjusting Quenching Roll Speed.

This paper presents a comprehensive study of the impact of quenching roll speed on enhancing the low-temperature toughness of a low-carbon copper-containing steel. The microstructure characteristics, such as the prior austenite grains, and the distribution and volume fraction of precipitates, are observed using optical microscopy, scanning electron microscopy, transmission electron microscopy, and small-angle scattering X-ray. The results show that a decrease in the quenching roller speed (2 m/min) contributes to the achievement of more excellent low-temperature toughness (the average value is 232 J), although the prior austenite grains exhibit a relatively larger size in this case. The tempering treatment results in the precipitation of a large amount of 9R-type Cu-rich particles, regardless of the quenching roller speed. Reducing the quenching roller speed contributes to the increase in the volume fraction of Cu-rich particles, which is considered to be the main factor contributing to the achievement of excellent low-temperature toughness.

ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities.

The design of nanozymes with superior catalytic activities is a prerequisite for broadening their biomedical applications. Previous studies have exerted significant effort in theoretical calculation and experimental trials for enhancing the catalytic activity of nanozyme. Machine learning (ML) provides a forward-looking aid in predicting nanozyme catalytic activity. However, this requires a significant amount of human effort for data collection. In addition, the prediction accuracy urgently needs to be improved. Herein, we demonstrate that ChatGPT can collaborate with humans to efficiently collect data. We establish four qualitative models (random forest (RF), decision tree (DT), adaboost random forest (adaboost-RF), and adaboost decision tree (adaboost-DT)) for predicting nanozyme catalytic types, such as peroxidase, oxidase, catalase, superoxide dismutase, and glutathione peroxidase. Furthermore, we use five quantitative models (random forest (RF), decision tree (DT), Support Vector Regression (SVR), gradient boosting regression (GBR), and fully connected deep neuron network (DNN)) to predict nanozyme catalytic activities. We find that GBR model demonstrates superior prediction performance for nanozyme catalytic activities (R2 = 0.6476 for Km and R2 = 0.95 for Kcat). Moreover, an open-access web resource, AI-ZYMES, with a ChatGPT-based nanozyme copilot is developed for predicting nanozyme catalytic types and activities and guiding the synthesis of nanozyme. The accuracy of the nanozyme copilot's responses reaches more than 90% through the retrieval augmented generation. This study provides a new potential application for ChatGPT in the field of nanozymes.

Acute kidney injury after radical gastrectomy: incidence, risk factors, and impact on prognosis.

Background: Acute kidney injury (AKI) is a serious adverse event often overlooked following major abdominal surgery. While radical gastrectomy stands as the primary curative method for treating gastric cancer patients, little information exists regarding AKI post-surgery. Hence, this study aimed to ascertain the incidence rate, risk factors, and consequences of AKI among patients undergoing radical gastrectomy. Methods: This was a population-based, retrospective cohort study. The incidence of AKI was calculated. Multivariate logistic regression was used to identify independent predictors of AKI. Survival curves were plotted by using the Kaplan-Meier method and differences in survival rates between groups were analyzed by using the log-rank test. Results: Of the 2,875 patients enrolled in this study, 61 (2.1%) developed postoperative AKI, with AKI Network 1, 2, and 3 in 50 (82.0%), 6 (9.8%), and 5 (8.2%), respectively. Of these, 49 patients had fully recovered by discharge. Risk factors for AKI after radical gastrectomy were preoperative hypertension (odds ratio [OR], 1.877; 95% CI, 1.064-3.311; P = 0.030), intraoperative blood loss (OR, 1.001; 95% CI, 1.000-1.002; P = 0.023), operation time (OR, 1.303; 95% CI, 1.030-1.649; P = 0.027), and postoperative intensive care unit (ICU) admission (OR, 4.303; 95% CI, 2.301-8.045; P < 0.001). The probability of postoperative complications, mortality during hospitalization, and length of stay in patients with AKI after surgery were significantly higher than those in patients without AKI. There was no statistical difference in overall survival (OS) rates between patients with AKI and without AKI (1-year, 3-year, 5-year overall survival rates of patients with AKI and without AKI were 93.3% vs 92.0%, 70.9% vs 73.6%, and 57.1% vs 67.1%, respectively, P = 0.137). Conclusions: AKI following radical gastrectomy is relatively rare and typically self-limited. AKI is linked with preoperative hypertension, intraoperative blood loss, operation time, and postoperative ICU admission. While AKI raises the likelihood of postoperative complications, it does not affect OS.

Nogo-A Drives Alzheimer's Disease Progression by Inducing Tauopathy Vulnerability.

Tauopathies, a group of neurodegenerative disorders, are characterized by disrupted homeostasis of the microtubule binding protein tau. Nogo-A mainly hinders axonal growth and development in neurons, but the underlying mechanism of tau vulnerability has not been determined. Here, to gain more comprehensive insights into the impact of Nogo-A on tau protein expression, we showed that Nogo-A induces tau hyperphosphorylation, synapse loss and cognitive dysfunction. Consistent with the biological function of tau hyperphosphorylation, Nogo-A-induced tau hyperphosphorylation altered microtubule stability, which causes synaptic dysfunction. Mechanistically, Nogo-A-induced tau hyperphosphorylation was abolished by the Nogo-A antagonist NEP1-40 in primary neurons. Surprisingly, downregulation of Nogo-A in the hippocampus of AD mice (hTau. P301S) inhibited tau hyperphosphorylation at the AT8, Thr181, The231 and Ser404 sites and rescued synaptic loss and cognitive impairment in AD mice. Our findings exhibit a strong degree of consistency with Nogo-A-induced tauopathy vulnerability, reinforcing the coherence and reliability of our research. Furthermore, in mice, Nogo-A increases tauopathy vulnerability to exacerbate AD progression via ROCK/AKT/GSK3ß signaling. Together, our findings provide new insight into the function of Nogo-A in regulating tau hyperphosphorylation and reveal an effective treatment strategy for tauopathies.

Identification and characterization of two Bacillus anthracis bacteriophages.

Anthrax is an acute infectious zoonotic disease caused by Bacillus anthracis, a bacterium that is considered a potential biological warfare agent. Bacillus bacteriophages shape the composition and evolution of bacterial communities in nature and therefore have important roles in the ecosystem community. B. anthracis phages are not only used in etiological diagnostics but also have promising prospects in clinical therapeutics or for disinfection in anthrax outbreaks. In this study, two temperate B. anthracis phages, vB_BanS_A16R1 (A16R1) and vB_BanS_A16R4 (A16R4), were isolated and showed siphovirus-like morphological characteristics. Genome sequencing showed that the genomes of phages A16R1 and A16R4 are 36,569 bp and 40,059 bp in length, respectively. A16R1 belongs to the genus Wbetavirus, while A16R4 belongs to the genus Hubeivirus and is the first phage of that genus found to lyse B. anthracis. Because these two phages can comparatively specifically lyse B. anthracis, they could be used as alternative diagnostic tools for identification of B. anthracis infections.

Brain-inspired chaotic spiking backpropagation.

Spiking neural networks (SNNs) have superior energy efficiency due to their spiking signal transmission, which mimics biological nervous systems, but they are difficult to train effectively. Although surrogate gradient-based methods offer a workable solution, trained SNNs frequently fall into local minima because they are still primarily based on gradient dynamics. Inspired by the chaotic dynamics in animal brain learning, we propose a chaotic spiking backpropagation (CSBP) method that introduces a loss function to generate brain-like chaotic dynamics and further takes advantage of the ergodic and pseudo-random nature to make SNN learning effective and robust. From a computational viewpoint, we found that CSBP significantly outperforms current state-of-the-art methods on both neuromorphic data sets (e.g. DVS-CIFAR10 and DVS-Gesture) and large-scale static data sets (e.g. CIFAR100 and ImageNet) in terms of accuracy and robustness. From a theoretical viewpoint, we show that the learning process of CSBP is initially chaotic, then subject to various bifurcations and eventually converges to gradient dynamics, consistently with the observation of animal brain activity. Our work provides a superior core tool for direct SNN training and offers new insights into understanding the learning process of a biological brain.

Immediate loading in partially edentulous patients with fixed implant-supported restorations cases report.

Objectives: This article reports on four rare cases involving multiple trauma-induced adjacent missing anterior teeth in the maxillary or mandibular region. These cases were successfully treated using a 4-axial implant-based alternative insert and an immediate loading protocol. Material and methods: This series of cases was summarized by retrospective study that 4 patients who received a total of 20 immediately loaded implants. These patients had suffered from trauma-induced loss of 8-9 adjacent anterior teeth. The 4-axial-implants were inserted with the assistance of digital pioneer drill guides. The surgical procedure involved alveolar bone trimming or ultrasonic osteotomy, eliminating the need for traditional large-area bone augmentation. Pre- and post-operative CBCT was matched using DTX Studio Implant software, the deviation of implant between actual position and preoperative design was measured and compared using SPSS software package. Results: The average follow-up duration 48 months after implant prostheses, the cumulative retention rate of the implants was 100%, the marginal bone loss averaged 0.53 mm (SD 0.15 mm), and buccal plate bone loss averaged 0.62 mm (SD 0.41 mm). Conclusions: This retrospective clinical report demonstrates the successful treatment of several patients with multiple adjacent maxillary or mandibular anterior teeth using four implant-supported screws to fix the frame and employing immediate loading. The approach resulted in long-term stable clinical outcomes. Moreover, the method not only shortens the period of edentulism but also facilitates easy disassembly, maintenance, and cleaning. Consequently, it emerges as a highly favorable clinical option for patients suffering from extensive tooth loss.

Analysis of four heavy metal concentrations in sediments fromthe Jiaozhou Bay, China by visible and near infrared spectroscopy (225-975 nm).

As an important component ofbiogeochemical cyclein coastal ecosystems, sediments are the sink of heavy metals. Therefore, distribution and dynamics of heavy metals in sediments could assess ecological quality and predict ecological risks. In the new era, rapid and green technology are highly needed, especially that could determine multi-parameters simultaneously. Here, we explored a new method to rapidly determine concentrations of heavy metals in sediments by visible and near infrared reflectance spectroscopy (VIRS).We sampled sediments in the Jiaozhou Bay, China, collected their reflectance spectra, and measured concentrations of four heavy metals (As, Cr, Cu, and Zn). Heavy metal models were established and evaluated using substances highly correlated with heavy metals. This study provides an effective reference for rapid analysis of As, Cr, Cu, and Zn simultaneously in sediments, at least in the Jiaozhou Bay, and for ecological environment protection and resource development of the Jiaozhou Bay.

Computational study on the endocrine-disrupting metabolic activation of Benzophenone-3 catalyzed by cytochrome P450 1A1: A QM/MM approach.

Predicting the metabolic activation mechanism and potential hazardous metabolites of environmental endocrine-disruptors is a challenging and significant task in risk assessment. Here the metabolic activation mechanism of benzophenone-3 catalyzed by P450 1A1 was investigated by using Molecular Dynamics, Quantum Mechanics/Molecular Mechanics and Density Functional Theory approaches. Two elementary reactions involved in the metabolic activation of BP-3 with P450 1A1: electrophilic addition and hydrogen abstraction reactions were both discussed. Further conversion reactions of epoxidation products, ketone products and the formaldehyde formation reaction were investigated in the non-enzymatic environment based on previous experimental reports. Binding affinities analysis of benzophenone-3 and its metabolites to sex hormone binding globulin indirectly demonstrates that they all exhibit endocrine-disrupting property. Toxic analysis shows that the eco-toxicity and bioaccumulation values of the benzophenone-3 metabolites are much lower than those of benzophenone-3. However, the metabolites are found to have skin-sensitization effects. The present study provides a deep insight into the biotransformation process of benzophenone-3 catalyzed by P450 1A1 and alerts us to pay attention to the adverse effects of benzophenone-3 and its metabolites in human livers.

Herbal textual research of Belamcanda chinensis (L.) redouté and screening of quality-markers based on 'pharmacodynamics-substance'.

ETHNOPHARMACOLOGICAL RELEVANCE: Belamcanda chinensis (L.) Redouté is widely distributed in East Asia, such as China, Russia and North Korea. Belamcandae Rhizoma is the sun-dried rhizome of B. chinensis and has a long history of traditional medicinal use. It was first recorded in the Shennong's Herbal Classic, and has the effects of clearing heat and detoxifying, eliminating phlegm and benefiting the pharynx. AIM OF THE STUDY: To systematically study the source of Belamcandae Rhizoma, summarize the evolution of its medicinal properties, efficacy and the application history of its prescriptions, summarize its biological activity, phytochemistry, synthetic metabolic pathway and toxicology, and screen the Quality-Markers of Belamcandae Rhizoma according to the screening principle of traditional Chinese medicine Quality-Markers. MATERIALS AND METHODS: All information available on Belamcandae Rhizoma was collected using electronic search engines, such as Pubmed, Web of Science, CNKI, WFO (www.worldfloraonline.org), MPNS (https://mpsn.kew.org), Changchun University of Traditional Chinese Medicine Library collections, Chinese Medical Classics. RESULTS: The source of Belamcandae Rhizoma is B. chinensis of Iridaceae. It has a long history of application in China. It has the effects of clearing heat and detoxifying, eliminating phlegm and promoting pharynx. Modern pharmacological studies have shown that it has anti-inflammatory, anti-oxidation, anti-tumor and other physiological activities, and is safe and non-toxic at normal application doses. At present, tectoridin, iridin, tectorigenin, irigenin and irisflorentin are identified as the Quality-Markers of Belamcandae Rhizoma. CONCLUSIONS: As a traditional Chinese medicine, Belamcandae Rhizoma has a long history of application, and multifaceted studies have demonstrated that Belamcandae Rhizoma is a promising Chinese medicine with good application prospects. By reviewing and identifying the Quality-Markers of Belamcandae Rhizoma, this study can help to establish the evaluation procedure of it on the one hand, and identify the shortcomings research on the other hand. Currently, there are few studies on the anabolism and toxicology of it, and future studies may focus on its in vivo processes, toxicology and adverse effects.

Characteristics of a pseudolysogenic phage vB_YpM_HQ103 infecting Yersinia pestis.

The plague, caused by Yersinia pestis, is a natural focal disease and the presence of Y. pestis in the environment is a critical ecological concern worldwide. The role of Y. pestis phages in the ecological life cycle of the plague is crucial. Previously, a temperature-sensitive phage named vB_YpM_HQ103 was isolated from plague foci in Yunnan province, China. Upon infecting the EV76 strain of Y. pestis, vB_YpM_HQ103 exhibits lysogenic behavior at 21 °C and lytic behavior at 37 °C. Various methods including continuous passage lysogenic tests, in vitro lysis tests, comparative genomic assays, fluorescence quantitative PCR and receptor identification tests were employed to demonstrate that the lysogenic life cycle of this phage is applicable to wild Y. pestis strains; its lysogeny is pseudolysogenic (carrying but not integrating), allowing it to replicate and proliferate within Y. pestis. Furthermore, we have identified the outer membrane protein OmpA of Y. pestis as the receptor for phage infection. In conclusion, our research provides insight into the characteristics and receptors of a novel Y. pestis phage infection with a pseudolysogenic cycle. The findings of this study enhance our understanding of Y. pestis phages and plague microecology, offering valuable insights for future studies on the conservation and genetic evolution of Y. pestis in nature.

Novel Hydrogel Adjuvant of Chinese Medicine External Preparations for Accelerated Healing of Deep Soft Tissue Injuries.

Traditional Chinese medicine external prescriptions have displayed excellent clinical effects for treating deep soft tissue injuries. However, the effects cannot be fully utilized due to the limitations of their dosage forms and usage methods. It is still a challenge to develop a satisfactory adjuvant of traditional Chinese medicine external prescriptions. Herein, a hydrogel adjuvant was prepared based on gallic acid coupled ε-poly-l-lysine and partially oxidized hyaluronic acid. The resulting adjuvant shows great physicochemical properties, low hemolysis rate (still much less than 5% at 5 mg/mL), excellent antibacterial ability (about 95% at 2 mg/mL), strong antioxidant ability (1.687 ± 0.085 mmol FeSO4/(g hydrogel) at 1 mg/mL), as well as outstanding biocompatibility. A clinically used Chinese medicine external preparation was selected as an example to investigate the effectiveness of the adjuvant in treating deep soft tissue injuries. The results show that the prescription can be evenly dispersed in the adjuvant. Moreover, the introduction of the prescription has not significantly changed these advanced properties of the adjuvant. Importantly, the hydrogel adjuvant significantly improves the effectiveness of the prescription in treating deep soft tissue injuries. This work offers an alternative approach to the development of a new-type adjuvant of Chinese medicine external preparations and also provides a new strategy for the combination of traditional Chinese medicine and hydrogel to treat clinical diseases.

Prevalence and risk factors of early postoperative seizures in patients with glioma: a systematic review and meta-analysis.

Objective: This study aimed to explore the prevalence and risk factors of early postoperative seizures in patients with glioma through meta-analysis. Methods: Case-control studies and cohort studies on the prevalence and risk factors of early postoperative seizures in glioma patients were retrieved from various databases including CNKI, Wanfang, VIP, PubMed, Embase, Cochrane Library, and Web of Science, and the retrieval deadline for the data was 1 April 2023. Stata15.0 was used to analyze the data. Results: This review included 11 studies consisting of 488 patients with early postoperative seizures and 2,051 patients without early postoperative seizures. The research findings suggest that the prevalence of glioma is complicated by seizures (ES = 19%, 95% confidence interval [CI] [14%-25%]). The results also indicated a history of seizures (RR = 1.94, 95% CI [1.76, 2.14], P = 0.001), preoperative dyskinesia (RR = 3.13, 95% CI [1.20, 8.15], P = 0.02), frontal lobe tumor (RR = 1.45, 95% CI [1.16, 1.83], P = 0.001), pathological grade ≤2 (RR = 1.74, 95% CI [1.13, 2.67], P = 0.012), tumor≥ 3 cm (RR = 1.70, 95% CI [1.18, 2.45], P = 0.005), tumor resection (RR = 1.60, 95% CI [1.36, 1.88], P = 0.001), tumor edema ≥ 2 cm (RR = 1.77, 95% CI [1.40, 2.25], P = 0.001), and glioma cavity hemorrhage (RR=3.15, 95% CI [1.85, 5.37], P = 0.001). The multivariate analysis results showed that a history of seizures, dyskinesia, tumor ≥3 cm, peritumoral edema ≥2 cm, and glioma cavity hemorrhage were indicated as risk factors for glioma complicated with early postoperative seizures. Significance: Based on the existing evidence, seizure history, dyskinesia, frontal lobe tumor, pathological grade ≤2, tumor ≥3 cm, partial tumor resection, edema around tumor ≥2 cm, and glioma cavity hemorrhage are indicated as risk factors for glioma complicated with early postoperative seizures.

[Research on mechanism of hypolipidemic effect of Massa Medicata Fermentata based on metabolomics].

This paper aims to study the therapeutic effect of Massa Medicata Fermentata on hyperlipidemia model rats and investigate its mechanism of hypolipidemic effect with the help of non-targeted metabolomics. The mixed hyperlipidemia model rats were constructed by giving high-fat chow. After successful modeling, the rats were divided into the model group, pravastatin sodium group(4.4 mg·kg~(-1)), lipotropic group(0.1 g·kg~(-1)), high-dose group(2.4 g·kg~(-1)), medium-dose group(1.2 g·kg~(-1)), and low-dose group(0.6 g·kg~(-1)) of Massa Medicata Fermentata, and they were administered for four weeks once daily. An equal volume of ultrapure water was given to the blank group and model group. Serum lipid level and liver hematoxylin-eosin(HE) staining were used as indicators to estimate the intervention effect of Massa Medicata Fermentata on mixed hyperlipidemia, and the changes in metabolites in plasma of mixed hyperlipidemia model rats were analyzed by non-targeted metabolomics. The mechanism of the hypolipidemic effect of Massa Medicata Fermentata was analyzed through metabolite pathway enrichment. The results showed that compared with the model group, the Massa Medicata Fermentata administration group, especially the high-dose group, could significantly reduce the content of total cholesterol(TC), triglyceride(TG), and low-density lipoprotein cholesterol(LDL-c)(P<0.05 or P<0.01), and liver HE staining revealed that the number of adipocytes in the high-dose group was reduced to some extent. The potential biomarkers obtained by non-targeted metabolomics screening included glycerol 3-phosphate, sphingomyelin, sphingosine 1-phosphate, and deoxyuridine, which were mainly involved in the sphingolipid metabolism process, glycerophospholipid metabolism process, glycerol ester metabolism pathway, and pyrimidine metabolism pathway, totaling four possible metabolic pathways related to lipid metabolism. This study provides a reference for an in-depth investigation of the hypolipidemic mechanism of Massa Medicata Fermentata, which is of great significance for further promoting the clinical application of Massa Medicata Fermentata and increasing the indications.

Prevalence and impact of viral myocarditis in patients with severe fever with thrombocytopenia syndrome.

To explore the association and impact between viral myocarditis and mortality in patients with severe fever with thrombocytopenia syndrome. A dynamic analysis was conducted between fatal group and nonfatal group regarding the daily epidemiology data, clinical symptoms, and electrocardiogram (ECG), echocardiogram, and laboratory findings. Outcomes of patients with and without viral myocarditis were compared. The association between viral myocarditis and mortality was analyzed. Among 183 severe fever with thrombocytopenia syndrome patients, 32 were in the fatal group and 151 in the nonfatal group; there were 26 (81.25%) with viral myocarditis in the fatal group, 66 (43.70%) with viral myocarditis in the nonfatal group (p < 0.001), 79.35% of patients had abnormal ECG results. The abnormal rate of ECG in the fatal group was 100%, and in the nonfatal group was 74.83%. Univariate analysis found that the number of risk factors gradually increased on Day 7 of the disease course and reached the peak on Day 10. Combined with the dynamic analysis of the disease course, alanine aminotransferase, aspartate aminotransferase, creatine kinase, creatine kinase fraction, lactate dehydrogenase, hydroxybutyrate dehydrogenase, neutrophil count, serum creatinine, Na, Ca, carbon dioxide combining power, amylase, lipase, activated partial thromboplastin time and thrombin time had statistically significant impact on prognosis. The incidence of fever with thrombocytopenia syndrome combined with viral myocarditis is high, especially in the fatal group of patients. Viral myocarditis is closely related to prognosis and is an early risk factor. The time point for changes in myocarditis is Day 7 of the course of the disease.

Construction of an early differentiation diagnosis model for patients with severe fever with thrombocytopenia syndrome and hemorrhagic fever with renal syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with a high mortality rate. Differentiating between SFTS and hemorrhagic fever with renal syndrome (HFRS) is difficult and inefficient. Retrospective analysis of the medical records of individuals with SFTS and HFRS was performed. Clinical and laboratory data were compared, and a diagnostic model was developed based on multivariate logistic regression analyzes. Receiver operating characteristic curve analysis was used to evaluate the diagnostic model. Among the 189 patients, 113 with SFTS and 76 with HFRS were enrolled. Univariate analysis revealed that more than 20 variables were significantly associated with SFTS. Multivariate logistic regression analysis revealed that gender, especially female gender (odds ratio [OR]: 4.299; 95% confidence interval [CI]: 1.163-15.887; p = 0.029), age ≥65 years (OR: 16.386; 95% CI: 3.043-88.245; p = 0.001), neurological symptoms (OR: 12.312; 95% CI: 1.638-92.530; p = 0.015), leukopenia (<4.0 × 109/L) (OR: 17.355; 95% CI: 3.920-76.839; p < 0.001), and normal Cr (OR: 97.678; 95% CI: 15.483-616.226; p < 0.001) were significantly associated with SFTS but not with HFRS. The area under the curve of the differential diagnostic model was 0.960 (95% CI: 0.936-0.984), which was significantly better than that of each single factor. In addition, the model exhibited very excellent sensitivity and specificity (92.9% and 85.5%, respectively). In cases where HFRS and SFTS are endemic, a diagnostic model based on five parameters, such as gender, age ≥65 years, neurological symptoms, leukopenia and normal Cr, will facilitate the differential diagnosis of SFTS and HFRS in medical institutions, especially in primary care settings.

Pure ZrO2 Ferroelectric Thin Film for Nonvolatile Memory and Neural Network Computing.

The recent discovery of ferroelectricity in pure ZrO2 has drawn much attention, but the information storage and processing performances of ferroelectric ZrO2-based nonvolatile devices remain open for further exploration. Here, a ZrO2 (∼8 nm)-based ferroelectric capacitor using RuO2 oxide electrodes is fabricated, and the ferroelectric orthorhombic phase evolution under electric field cycling is studied. A ferroelectric remnant polarization (2Pr) of >30 µC/cm2, leakage current density of ∼2.79 × 10-8 A/cm2 at 1 MV/cm, and estimated polarization retention of >10 years are achieved. When the ferroelectric capacitor is connected with a transistor, a memory window of ∼0.8 V and eight distinct states can be obtained in such a ferroelectric field-effect transistor (FeFET). Through the conductance manipulation of the FeFET, a high object image recognition accuracy of ∼93.32% is achieved on the basis of the CIFAR-10 dataset in the convolutional neural network (CNN) simulation, which is close to the result of ∼94.20% obtained by floating-point-based CNN software. These results demonstrate the potential of ferroelectric ZrO2 devices for nonvolatile memory and artificial neural network computing.