A follow-up study of post-COVID-19 syndrome in hospitalized children with Omicron variant infection in Wuhan.

Background: Since the Chinese government changed its COVID-19 prevention and control policies, the rapid spread of the omicron variant resulted in a pervasive surge of infections throughout the nation, particularly affecting children. Although the acute symptoms of children infected with COVID-19 are milder compared to adults, the impact of post-COVID-19 syndromes (PCS) on the growth and development of children should not be ignored. The clinical manifestations, treatment methods, and long-term effects of children are significantly different from those of adults, making it necessary to understand the phenotype of children with PCS in order to effectively manage their health. Methods: The study focuses on hospitalized children infected with omicron variant in Zhongnan Hospital of Wuhan University from December 7, 2022, to January 5, 2023. Three telephone follow-ups with the guardians was conducted at 4-5 weeks, 12-13 weeks, and 24-25 weeks after the patients' discharge to understand their prevalence, clinical characteristics, and risk factors of PCS. Results: The age range of the 112 hospitalized pediatric patients was 0-13 years, with a median age of 19 months. After three follow-ups, 49.1% patients had PCS, while the incidence of PCS persisting 3 month was 21.4%, with a prevalence of PCS persisting 6 month of 10.7%. From the first follow-up phase to the third phase, there was a significant decrease in the incidence of PCS. In infants, the most common persistent symptom was sleep disorder (19.2%), followed by respiratory symptoms, diarrhea (8.2%), and decreased appetite (6.8%). In children and adolescents, decreased appetite was the most common persistent symptom (30.8%), followed by respiratory symptoms, fatigue (15.4%), and mood changes (15.4%). Decreased appetite was more common in the children and adolescents, while diarrhea and sleep disorders were more common in the infants. Binary logistic regression analysis and ordered logistic regression analysis showed that times of illness (OR = 1.671, 95% CI: 1.339-2.086) were positively correlated with the duration of symptoms. Times of illness was positively correlated with cough/expectoration (OR = 1.491, 95% CI: 1.039-2.138). Age (OR = 0.844, 95% CI: 0.755-0.944) and re-hospitalization (OR = 0.146, 95% CI: 0.022-0.969) were positively correlated with sleep disorders. Conclusions: Children with Omicron variant may still experience PCS, but the incidence is lower compared to adults and compared to other variants and the incidence of PCS will gradually decrease over time. The symptoms of PCS differ between older children and infants and it is necessary to prevent recurrent illness for at least half a year after COVID-19 recovery. In order to further understand and ameliorate the impact of PCS on the health of children infected with COVID-19, subsequent follow-up studies will expand the scope, combine with objective follow-up contents, and establish an assessment and management system especially for children of different ages.

GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.

Objective.Vital rules learned from fluorodeoxyglucose positron emission tomography (FDG-PET) radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (gray wolf optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer.Approach.Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (ΔSUVmean ⩾ 20% decline) for classification and a continuous response measure (ΔSUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression.Main results.GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC: 0.58-0.86 vs. 0.52-0.78,p= 0.170-0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE: 0.162-0.192) performed better numerically for low-dimensional models (p= 0.097-0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE: 0.189-0.219,p< 0.004).Significance. The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.

Prediction of Vacuum Ultraviolet/Ultraviolet Gas-Phase Absorption Spectra Using Molecular Feature Representations and Machine Learning.

Ultraviolet (UV) absorption spectroscopy is a widely used tool for quantitative and qualitative analyses of chemical compounds. In the gas phase, vacuum UV (VUV) and UV absorption spectra are specific and diagnostic for many small molecules. An accurate prediction of VUV/UV absorption spectra can aid the characterization of new or unknown molecules in areas such as fuels, forensics, and pharmaceutical research. An alternative to quantum chemical spectral prediction is the use of artificial intelligence. Here, different molecular feature representation techniques were used and developed to encode chemical structures for testing three machine learning models to predict gas-phase VUV/UV absorption spectra. Structure data files (.sdf) and VUV/UV absorption spectra for 1397 volatile and semivolatile chemical compounds were used to train and test the models. New molecular features (termed ABOCH) were introduced to better capture pi-bonding, aromaticity, and halogenation. The incorporation of these new features benefited spectral prediction and demonstrated superior performance compared to computationally intensive molecular-based deep learning methods. Of the machine learning methods, the use of a Random Forest regressor returned the best accuracy score with the shortest training time. The developed machine learning prediction model also outperformed spectral predictions based on the time-dependent density functional theory.

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum.

The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations such as large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In this study, we aim to elucidate the unique protein features associated with Cas9 and Cas12 families and identify the features distinguishing each family from non-Cas proteins. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,494 features) encoding various physiochemical, topological, constitutional, and coevolutionary information on Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and non-Cas proteins. All the models were evaluated rigorously on the test and independent data sets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 92% and 95% on their respective independent data sets, while the multiclass classifier achieved an F1 score of close to 0.98. We observed that Quasi-Sequence-Order (QSO) descriptors like Schneider.lag and Composition descriptors like charge, volume, and polarizability are predominant in the Cas12 family. Conversely Amino Acid Composition descriptors, especially Tripeptide Composition (TPC), predominate the Cas9 family. Four of the top 10 descriptors identified in Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all Cas9 proteins and located within different catalytically important domains of the Streptococcus pyogenes Cas9 (SpCas9) structure. Among these, DHI and HHA are well-known to be involved in the DNA cleavage activity of the SpCas9 protein. Mutation studies have highlighted the significance of the PWN tripeptide in PAM recognition and DNA cleavage activity of SpCas9, while Y450 from the PYY tripeptide plays a crucial role in reducing off-target effects and improving the specificity in SpCas9. Leveraging our machine learning (ML) pipeline, we identified numerous Cas9 and Cas12 family-specific features. These features offer valuable insights for future experimental and computational studies aiming at designing Cas systems with enhanced gene-editing properties. These features suggest plausible structural modifications that can effectively guide the development of Cas proteins with improved editing capabilities.

Predominance of A2063G mutant strains in the Mycoplasma pneumoniae epidemic in children: A clinical and epidemiological study in 2023 in Wuhan, China.

OBJECTIVES: The prevalence of respiratory infectious diseases has changed in the post-COVID-19 epidemic era, and mycoplasma pneumoniae (MP) infection in children has attracted wide attention. METHODS: Children hospitalized for pneumonia in Wuhan, China, in 2023 were enrolled. Respiratory secretions were obtained for the targeted next-generation sequencing (tNGS) including mutation of MP. Pulmonary inflammation was divided into bronchopneumonia and pulmonary consolidation/atelectasis according to lung computed tomography imaging. RESULTS: Of the 667 pediatric pneumonia, 478 were MP positive (72%). The positive rate of MP detected by tNGS increased from April, and MP had become the primary pathogen of pneumonia in children in 2023. The 23S rRNA mutations were all A2063G, accounting for 85% of detected MP. The clinical symptoms of the mutant and wild-type strains were similar, with half of them experiencing atelectasis and lung consolidation. Early bronchoscopic lavage combined with azithromycin in pediatric pulmonary consolidation was an effective therapy strategy, which could be an alternative selection to MP pneumonia treatment. CONCLUSIONS: A2063G mutant strain MP was the primary pathogen of mycoplasma pneumoniae in children recently, which was often complicated by extra-pulmonary symptoms and complications.

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum.

The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations like large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In the current study, we aim to elucidate the unique protein attributes associated with Cas9 and Cas12 families and identify the features that distinguish each family from the other. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,495 features) encoding various physiochemical, topological, constitutional, and coevolutionary information of Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and Non-Cas proteins. All the models were evaluated rigorously on the test and independent datasets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 95% and 97% on their respective independent datasets, while the multiclass classifier achieved a high F1 score of 0.97. We observed that Quasi-sequence-order descriptors like Schneider-lag descriptors and Composition descriptors like charge, volume, and polarizability are essential for the Cas12 family. More interestingly, we discovered that Amino Acid Composition descriptors, especially the Tripeptide Composition (TPC) descriptors, are important for the Cas9 family. Four of the identified important descriptors of Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all the Cas9 proteins and were located within different catalytically important domains of the Cas9 protein structure. Among these four tripeptides, tripeptides DHI and HHA are well-known to be involved in the DNA cleavage activity of the Cas9 protein. We therefore propose the the other two tripeptides, PWN and PYY, may also be essential for the Cas9 family. Our identified important descriptors enhanced the understanding of the catalytic mechanisms of Cas9 and Cas12 proteins and provide valuable insights into design of novel Cas systems to achieve enhanced gene-editing properties.

Effects of light color and intensity on discrimination of red objects in broilers.

Poultry are sensitive to red objects, such as comb and blood on the body surface, likely inducing injurious pecking in flocks. Light is an important factor that affects the pecking behavior of poultry. A wooden box was built to investigate the effects of Light Emitting Diode (LED) light color (warm white and cold white) and intensity (5 and 50 lux) of background light on the discrimination of red objects in broilers. A piece of red photographic paper (Paper 1) was used to simulate a red object and paired with another piece of paper (Paper 2 to 8) with a different color. Bigger number of the paired paper indicated greater color difference. The experiment consisted of three phases: adaptation, training, and test. In the adaptation phase, birds were selected for the adaptation to reduce the stress from the box. In the training phase, birds were trained to discriminate and peck at Paper 1 when paired with Paper 8 under one type of background light. Twenty-three birds were tested when the paired paper was changed from Paper 7 to 2. Each pair of paper included 12 trials for every bird, and response time to peck and proportion of choices of Paper 1 in the last 10 trials were collected. The results showed that broilers tested under 5 lux light had longer response times than broilers tested under 50 lux light (P < 0.05). When Paper 1 was paired with paper 7, broilers tested under warm white light had lower proportion of choices of Paper 1 than those tested under cold white light (P < 0.05). Color difference had a significant effect on response time of broilers (P < 0.05). Moreover, the proportion of choices of Paper 1 decreased to 50% (chance-level performance) when color of the paired paper was gradually similar to Paper 1. Conclusively, rearing broilers in warm white rather than cold white light with appropriate light intensity should be recommended to reduce damaging pecking behavior in broiler production.

Poultry are sensitive to red objects, such as comb and blood on the body surface, likely inducing injurious pecking in flocks. We built a wooden box to investigate the effects of light color (reddish and bluish) and intensity (5 and 50 lux) of background light on the discrimination of red objects in broilers. A piece of red photographic paper (Paper 1) was used and paired with another piece of paper (Paper 2 to 8) with a different color. Every bird was trained to discriminate and peck at Paper 1 when paired with Paper 8 under one type of background light. Then, Paper 8 was changed from Paper 7 to 2. Response time to peck and proportion of choices of Paper 1 were collected. We found that broilers under 5 lux light had longer response times than under 50 lux light. Broilers under reddish light had lower proportion of choices than under bluish light. Moreover, color difference had a significant effect on the response time and the proportion of choices. Conclusively, rearing broilers under reddish rather than bluish light with appropriate intensity should be recommended to reduce damaging pecking behavior in broiler production.

In vivo prediction of abdominal fat and breast muscle in broiler chicken using live body measurements based on machine learning.

The purpose of this study was to predict the carcass characteristics of broilers using support vector regression (SVR) and artificial neural network (ANN) model methods. Data were obtained from 176 yellow feather broilers aged 100-day-old (90 males and 86 females). The input variables were live body measurements, including external measurements and B-ultrasound measurements. The predictors of the model were the weight of abdominal fat and breast muscle in male and female broilers, respectively. After descriptive statistics and correlation analysis, the datasets were randomly divided into train set and test set according to the ratio of 7:3 to establish the model. The results of this study demonstrated that it is feasible to use machine learning methods to predict carcass characteristics of broilers based on live body measurements. Compared with the ANN method, the SVR method achieved better prediction results, for predicting breast muscle (male: R2 = 0.950; female: R2 = 0.955) and abdominal fat (male: R2 = 0.802; female: R2 = 0.944) in the test set. Consequently, the SVR method can be considered to predict breast muscle and abdominal fat of broiler chickens, except for abdominal fat in male broilers. However, further revaluation of the SVR method is suggested.

Gradient Boosted Trees for Spatial Data and Its Application to Medical Imaging Data.

Boosting Trees are one of the most successful statistical learning approaches that involve sequentially growing an ensemble of simple regression trees ("weak learners"). This paper proposes a gradient Boosted Trees algorithm for Spatial Data (Boost-S) with covariate information. Boost-S integrates the spatial correlation into the classical framework of eXtreme Gradient Boosting. Each tree is constructed by solving a regularized optimization problem, where the objective function takes into account the underlying spatial correlation and involves two penalty terms on tree complexity. A computationally-efficient greedy heuristic algorithm is proposed to obtain an ensemble of trees. The proposed Boost-S is applied to the spatially-correlated FDG-PET (fluorodeoxyglucose-positron emission tomography) imaging data collected from clinical trials of cancer chemoradiotherapy. Our numerical investigations successfully demonstrate the advantages of the proposed Boost-S over existing approaches for this particular application.

EEG Source Imaging using GANs with Deep Image Prior.

Brain source localization from electroencephalogram (EEG) signals is an challenging problem for noninvasively localizing the brain activity. Conventional methods use handcrafted regularization terms based on neural-physiological assumptions by exploiting the spatial-temporal structure on the source signals. In recent years, deep learning frameworks have demonstrated superior performance for solving the inverse problems in the natural and medical imaging field. This study proposes a novel unsupervised learning training-free framework based on Generative Adversarial Networks and deep image prior (GANs-DIP) as a generative model simulating spatially structured source signal. The proposed framework can faithfully recover extended source patches activation patterns of the brain in an unsupervised manner. Numerical experiments on a realistic brain model are performed under different levels of signal-to-noise ratio (SNR). The proposed model shows satisfactory performance in recovering the underlying source activation.

Inhibitory Effect of miR-339-5p on Glioma through PTP4A1/HMGB1 Pathway.

Objective: Finding miR-339-5p inhibitory functions in glioma through PTP4A1/HMGB1 pathway. Methods: From May 2020 to August 2021, 20 glioblastoma and para cancer tissues were chosen for qRT-PCR analysis. The miR-NC, miR-con, miR-339-5PMIMIC, and miR-con + groups were transfected into human glioma U251 cells. The capacity of cell vascular-like structure construction was found by simulating angiogenesis, and the ability of cell movement was examined by cell scratching. The twofold luciferase reporter gene method determined that miR-339-5p targets PTP4A1, and the protein expression levels of PTP4A1 and HMGB1 were examined using Western blot. Results: MiR-339-5P expression was substantially lower in cancer samples than noncancer samples (P < 0.05). PTP4A1 expression in cancer samples was higher than in healthy controls (P < 0.05). The miR-339-5p group produced significantly less vascular-like structures than the NC and miR-con groups (P < 0.05). The miR-339-5p group lowered the invasive index and migratory rate of U251 cells (P < 0.05). PTP4A1 inhibited the luciferase activity of the pTP4A1-WT reporter gene (P < 0.05) but not the PTP4A1-MUT (P > 0.05). The miR-339-5p group had lower protein levels of PTP4A1 and HMGB1 than the NC and miR-con groups (P < 0.05). The development of vascular-like structures was substantially more significant in the miR-con +PTP4A1 group than in the miR-con and miR-339-5p +PTP4A1 groups (P < 0.05). In terms of migration and invasion index, there was a substantial difference between the miR-339-5p +PTP4A1 and the miR-con +PTP4A1 groups (P < 0.05). The miR-con +PTP4A1 group had a greater migration rate and invasive index than the miR-con and miR-339-5p +PTP4A1 groups (P < 0.05). Conclusion: MiR-339-5P inhibits angiogenic mimicry, migration, and invasion of brain glioma U251 cells by inhibiting the PTP4A1/HMGB1 signal pathway.

The Expression Profile of miRNA in Glioma and the Role of miR-339-5p in Glioma.

Objective: To reveal the expression profile of miRNA in glioma and the effects of microRNA-339-5p (miR-339-5p) on glioma. Methods: The glioma and normal tissues were randomly selected for miRNA gene chip detection and qRT-PCR verification. The U87 cells were separated into miR-NC, miR-339-5p mimic, and miR-339-5p suppressor group. Clonogenesis test, flow cell technique, Transwell, and cell scratch assay were utilized to verify the roles of miR-339-5p in cell proliferation, cell apoptosis, cell invasion, and cell migration. The epithelial-meso-transformation-associated proteins was verified by Western blot. Results: A total of 49 miRNAs (16 upregulated and 33 downregulated) were differentially expressed in glioma tissues, and miR-339-5p was the most downregulated. The clone number, invasion number, and healing rate of cells in miR-339-5p mimic group were decreased compared with miR-NC group (P < 0.05); the clone quantity, invasion number, and healing rate of cells in miR-339-5p inhibitor group were increased compared with miR-NC group (P < 0.05). The apoptosis rate of human glioma U87 cells in miR-339-5P mimic group was compared with miR-NC group (P < 0.05); the apoptosis rate of human glioma U87 cells in miR-339-5p suppressor group decreased compared with miR-NC group (P < 0.05). Compared with miR-NC group, the protein expression of Twist, Snsnail, N-cadherin, and Vimentin in miR-339-5p mimic group was considerably decreased, whereas E-cadherin was elevated (P < 0.05). Compared with miR-NC group, the protein expression of Twist, Snsnail, N-cadherin, and Vimentin in miR-339-5p suppressor group was considerably increased, whereas E-cadherin was considerably decreased (P < 0.05). Conclusion: Forty-nine glioma-related miRNAs were screened out, and miRNA expression was significantly different between glioma and normal tissues. The downregulated miR-339-5p in glioma can regulate the proliferative, apoptotic, invasive, and migratory abilities of glioma U87 cells and might suppress the occurrence and development of glioma.

Sparse group selection and analysis of function-related residue for protein-state recognition.

Machine learning methods have helped to advance wide range of scientific and technological field in recent years, including computational chemistry. As the chemical systems could become complex with high dimension, feature selection could be critical but challenging to develop reliable machine learning based prediction models, especially for proteins as bio-macromolecules. In this study, we applied sparse group lasso (SGL) method as a general feature selection method to develop classification model for an allosteric protein in different functional states. This results into a much improved model with comparable accuracy (Acc) and only 28 selected features comparing to 289 selected features from a previous study. The Acc achieves 91.50% with 1936 selected feature, which is far higher than that of baseline methods. In addition, grouping protein amino acids into secondary structures provides additional interpretability of the selected features. The selected features are verified as associated with key allosteric residues through comparison with both experimental and computational works about the model protein, and demonstrate the effectiveness and necessity of applying rigorous feature selection and evaluation methods on complex chemical systems.

Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer.

Medical imaging provides quantitative and spatial information to evaluate treatment response in the management of patients with non-small cell lung cancer (NSCLC). High throughput extraction of radiomic features on these images can potentially phenotype tumors non-invasively and support risk stratification based on survival outcome prediction. The prognostic value of radiomics from different imaging modalities and time points prior to and during chemoradiation therapy of NSCLC, relative to conventional imaging biomarker or delta radiomics models, remains uncharacterized. We investigated the utility of multitask learning of multi-time point radiomic features, as opposed to single-task learning, for improving survival outcome prediction relative to conventional clinical imaging feature model benchmarks. Survival outcomes were prospectively collected for 45 patients with unresectable NSCLC enrolled on the FLARE-RT phase II trial of risk-adaptive chemoradiation and optional consolidation PD-L1 checkpoint blockade (NCT02773238). FDG-PET, CT, and perfusion SPECT imaging pretreatment and week 3 mid-treatment was performed and 110 IBSI-compliant pyradiomics shape-/intensity-/texture-based features from the metabolic tumor volume were extracted. Outcome modeling consisted of a fused Laplacian sparse group LASSO with component-wise gradient boosting survival regression in a multitask learning framework. Testing performance under stratified 10-fold cross-validation was evaluated for multitask learning radiomics of different imaging modalities and time points. Multitask learning models were benchmarked against conventional clinical imaging and delta radiomics models and evaluated with the concordance index (c-index) and index of prediction accuracy (IPA). FDG-PET radiomics had higher prognostic value for overall survival in test folds (c-index 0.71 [0.67, 0.75]) than CT radiomics (c-index 0.64 [0.60, 0.71]) or perfusion SPECT radiomics (c-index 0.60 [0.57, 0.63]). Multitask learning of pre-/mid-treatment FDG-PET radiomics (c-index 0.71 [0.67, 0.75]) outperformed benchmark clinical imaging (c-index 0.65 [0.59, 0.71]) and FDG-PET delta radiomics (c-index 0.52 [0.48, 0.58]) models. Similarly, the IPA for multitask learning FDG-PET radiomics (30%) was higher than clinical imaging (26%) and delta radiomics (15%) models. Radiomics models performed consistently under different voxel resampling conditions. Multitask learning radiomics for outcome modeling provides a clinical decision support platform that leverages longitudinal imaging information. This framework can reveal the relative importance of different imaging modalities and time points when designing risk-adaptive cancer treatment strategies.

Simulation Study of the Use of AlGaN/GaN Ultra-Thin-Barrier HEMTs with Hybrid Gates for Achieving a Wide Threshold Voltage Modulation Range.

In this work, novel hybrid gate Ultra-Thin-Barrier HEMTs (HG-UTB HEMTs) featuring a wide modulation range of threshold voltages (VTH) are proposed. The hybrid gate structure consists of a p-GaN gate part and a MIS-gate part. Due to the depletion effect assisted by the p-GaN gate part, the VTH of HG-UTB HEMTs can be significantly increased. By tailoring the hole concentration of the p-GaN gate, the VTH can be flexibly modulated from 1.63 V to 3.84 V. Moreover, the MIS-gate part enables the effective reduction in the electric field (E-field) peak at the drain-side edge of the p-GaN gate, which reduces the potential gate degradation originating from the high E-field in the p-GaN gate. Meanwhile, the HG-UTB HEMTs exhibit a maximum drain current as high as 701 mA/mm and correspond to an on-resistance of 10.1 Ω mm and a breakdown voltage of 610 V. The proposed HG-UTB HEMTs are a potential means to achieve normally off GaN HEMTs with a promising device performance and featuring a flexible VTH modulation range, which is of great interest for versatile power applications.

Associação da hipertensão com a gravidade e a mortalidade de pacientes hospitalizados com COVID-19 em Wuhan, China: Estudo unicêntrico e retrospectivo / Association of hypertension with severity and mortality in hospitalized patients with COVID-19 in wuhan, China: A single-centered, retrospective study

Resumo Fundamento A doença Coronavírus 2019 (COVID-19), causada pela síndrome respiratória aguda grave Coronavírus 2 (SARS-CoV-2), espalhou-se pelo mundo. Objetivo Investigar a associação entre a hipertensão e a gravidade/mortalidade de pacientes hospitalizados com COVID-19 em Wuhan, China. Métodos Um total de 337 pacientes diagnosticados com COVID-19 no Sétimo Hospital da cidade de Wuhan, de 20 de janeiro a 25 de fevereiro de 2020, foram inseridos e analisados em um estudo de caso unicêntrico e retrospectivo. O nível de significância adotado para a análise estatística foi 0,05. Resultados Dos 337 pacientes com diagnóstico confirmado de COVID-19, 297 (87.8%) tiveram alta do hospital e 40 pacientes (22,9%) morreram. A idade média foi de 58 anos (variando de 18 a 91 anos). Havia 112 (33,2%) pacientes diagnosticados com hipertensão no momento da internação (idade média, 65,0 anos [variação, 38-91 anos]; sendo 67 homens [59,8%, IC95%: 50,6%-69,0%], p=0,0209). Pacientes com hipertensão apresentaram uma porção significativamente maior de casos graves (69 [61,6%, IC95%: 52,5%-70,8%] vs. 117 [52,0%, IC95%: 45,4%-58,6%] em pacientes graves e 23 [19,3%, IC95%: 12,9%-28,1%] vs. 27 [12,0%, IC95%: 7,7%-16,3%] em pacientes críticos, p=0,0014) e maiores taxas de mortalidade (20 [17,9%, IC95%: 10,7%-25,1%] vs. 20 [8,9%, IC95%: 5,1%-12,6%, p=0,0202). Além disso, pacientes hipertensos apresentaram níveis anormais de vários indicadores, como linfopenia e inflamação, e nas funções cardíacas, hepáticas, renais e pulmonares no momento da internação. O grupo de pacientes com hipertensão também demonstrou níveis maiores de TNT e creatinina próximo da alta. Conclusão A hipertensão está altamente associada à gravidade ou mortalidade da COVID-19. Um tratamento agressivo deve ser considerado para pacientes hipertensos com COVID-19, principalmente com relação a lesões cardíacas e dos rins.

Abstract Background Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread worldwide. Objective To investigate the association between hypertension and severity/mortality in hospitalized patients with COVID-19 in Wuhan, China. Methods A total of 337 patients diagnosed with COVID-19 at the Seventh Hospital of Wuhan City, from January 20 to February 25, 2020, were enrolled and analyzed in a retrospective, single-center case study. The significance level adopted in the statistical analysis was 0.05. Results Of the 337 patients with confirmed diagnosis of COVID-19, 297 (87.8%) were discharged from the hospital and 40 patients (22.9%) died. The median age was 58 years (range, 18-91 years). There were 112 (33.2%) patients diagnosed with hypertension at admission (median age, 65.0 years [range, 38-91 years]; 67 [59.8%, 95%CI: 50.6%-69.0%] men, p=0.0209). Patients with hypertension presented a significantly higher portion of severe cases (69 [61.6%, 95%CI:52.5%-70.8%] vs. 117 [52.0%, 95%CI: 45.4%-58.6%] in severe patients and 23 [19.3%, 95%CI:12.9%-28.1%] vs. 27 [12.0%, 95%CI: 7.7%-16.3%] in critical patients, p=0.0014) and higher mortality rates (20 [17.9%, 95%CI: 10.7%-25.1%] vs. 20 [8.9%, 95%CI: 5.1%-12.6%, p=0.0202). Moreover, hypertensive patients presented abnormal levels of multiple indicators, such as lymphopenia, inflammation, heart, liver, kidney, and lung function at admission. The hypertension group still displayed higher levels of TnT and creatinine at approaching discharge. Conclusion Hypertension is strongly associated with severity or mortality of COVID-19. Aggressive treatment may be considered for COVID-19 patients with hypertension, especially regarding cardiac and kidney injury.

Association of Hypertension with Severity and Mortality in Hospitalized Patients with COVID-19 in Wuhan, China: A Single-centered, Retrospective Study. / Associação da Hipertensão com a Gravidade e a Mortalidade de Pacientes Hospitalizados com COVID-19 em Wuhan, China: Estudo Unicêntrico e Retrospectivo.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread worldwide. OBJECTIVE: To investigate the association between hypertension and severity/mortality in hospitalized patients with COVID-19 in Wuhan, China. METHODS: A total of 337 patients diagnosed with COVID-19 at the Seventh Hospital of Wuhan City, from January 20 to February 25, 2020, were enrolled and analyzed in a retrospective, single-center case study. The significance level adopted in the statistical analysis was 0.05. RESULTS: Of the 337 patients with confirmed diagnosis of COVID-19, 297 (87.8%) were discharged from the hospital and 40 patients (22.9%) died. The median age was 58 years (range, 18-91 years). There were 112 (33.2%) patients diagnosed with hypertension at admission (median age, 65.0 years [range, 38-91 years]; 67 [59.8%, 95%CI: 50.6%-69.0%] men, p=0.0209). Patients with hypertension presented a significantly higher portion of severe cases (69 [61.6%, 95%CI:52.5%-70.8%] vs. 117 [52.0%, 95%CI: 45.4%-58.6%] in severe patients and 23 [19.3%, 95%CI:12.9%-28.1%] vs. 27 [12.0%, 95%CI: 7.7%-16.3%] in critical patients, p=0.0014) and higher mortality rates (20 [17.9%, 95%CI: 10.7%-25.1%] vs. 20 [8.9%, 95%CI: 5.1%-12.6%, p=0.0202). Moreover, hypertensive patients presented abnormal levels of multiple indicators, such as lymphopenia, inflammation, heart, liver, kidney, and lung function at admission. The hypertension group still displayed higher levels of TnT and creatinine at approaching discharge. CONCLUSION: Hypertension is strongly associated with severity or mortality of COVID-19. Aggressive treatment may be considered for COVID-19 patients with hypertension, especially regarding cardiac and kidney injury.

FUNDAMENTO: A doença Coronavírus 2019 (COVID-19), causada pela síndrome respiratória aguda grave Coronavírus 2 (SARS-CoV-2), espalhou-se pelo mundo. OBJETIVO: Investigar a associação entre a hipertensão e a gravidade/mortalidade de pacientes hospitalizados com COVID-19 em Wuhan, China. MÉTODOS: Um total de 337 pacientes diagnosticados com COVID-19 no Sétimo Hospital da cidade de Wuhan, de 20 de janeiro a 25 de fevereiro de 2020, foram inseridos e analisados em um estudo de caso unicêntrico e retrospectivo. O nível de significância adotado para a análise estatística foi 0,05. RESULTADOS: Dos 337 pacientes com diagnóstico confirmado de COVID-19, 297 (87.8%) tiveram alta do hospital e 40 pacientes (22,9%) morreram. A idade média foi de 58 anos (variando de 18 a 91 anos). Havia 112 (33,2%) pacientes diagnosticados com hipertensão no momento da internação (idade média, 65,0 anos [variação, 38-91 anos]; sendo 67 homens [59,8%, IC95%: 50,6%-69,0%], p=0,0209). Pacientes com hipertensão apresentaram uma porção significativamente maior de casos graves (69 [61,6%, IC95%: 52,5%-70,8%] vs. 117 [52,0%, IC95%: 45,4%-58,6%] em pacientes graves e 23 [19,3%, IC95%: 12,9%-28,1%] vs. 27 [12,0%, IC95%: 7,7%-16,3%] em pacientes críticos, p=0,0014) e maiores taxas de mortalidade (20 [17,9%, IC95%: 10,7%-25,1%] vs. 20 [8,9%, IC95%: 5,1%-12,6%, p=0,0202). Além disso, pacientes hipertensos apresentaram níveis anormais de vários indicadores, como linfopenia e inflamação, e nas funções cardíacas, hepáticas, renais e pulmonares no momento da internação. O grupo de pacientes com hipertensão também demonstrou níveis maiores de TNT e creatinina próximo da alta. CONCLUSÃO: A hipertensão está altamente associada à gravidade ou mortalidade da COVID-19. Um tratamento agressivo deve ser considerado para pacientes hipertensos com COVID-19, principalmente com relação a lesões cardíacas e dos rins.

Association of diabetes with severity and mortality in hospitalized patients with COVID-19 in Wuhan, China: a single-centered, retrospective study.

OBJECTIVE: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread worldwide. The aim this study was to investigate the association of diabetes with severity and mortality among hospitalized patients with COVID-19 in Wuhan, China. METHODS: This retrospective, single-center case study enrolled a total of 564 patients diagnosed with COVID-19 at the Seventh Hospital of Wuhan City, between January 20 and March 15, 2020. RESULTS: Among the 564 patients with confirmed COVID-19, 509 (85.1%) were discharged and 55 (9.8%) died. The median age was 59 years (range, 10-93 years). A total of 85 (15.1%) patients were diagnosed with diabetes on admission (median age, 65.0 [range, 34-91] years). Patients with diabetes had significantly higher proportions of critical cases (24 [28.2%] vs. 66 [13.8%]) and in-hospital mortality (17 [20%] vs. 38 [7.9%]). Moreover, patients with diabetes presented abnormal levels of multiple indicators concerning lymphopenia, inflammation, heart, liver, kidney, and lung function on admission, while diabetic patient group still display higher troponin T (TnT) levels when approaching discharge. The Kaplan-Meier survival curve indicated a trend toward poorer survival in diabetic patients compared to non-diabetic patients, also evidenced by abnormal laboratory biomarker changes regarding multiple system impairments among COVID-19 patients with diabetes with in-hospital death. CONCLUSION: The detailed clinical investigation of 564 hospitalized patients with COVID-19 indicated a considerable association between diabetes and COVID-19 severity or mortality. Thus, more intensive treatment may be considered for COVID-19 patients with diabetes, especially regarding to cardiac injury.

Profiling of contemporary beer styles using liquid chromatography quadrupole time-of-flight mass spectrometry, multivariate analysis, and machine learning techniques.

Although all beer is brewed using the same four classes of ingredients, contemporary beer styles show wide variation in flavor and color, suggesting differences in their chemical profiles. A selection of 32 beers covering five styles (India pale ale, blonde, stout, wheat, and sour) were investigated to determine chemical features, which discriminate between popular beer styles. The beers were analyzed in an untargeted fashion using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). The separation and detection method were tuned to include compounds from important beer components, namely iso-α-acids and phenolic compounds. Due to the sheer number of unknown compounds in beer, multivariate analysis and machine learning techniques were used to pinpoint some of the compounds most influential in distinguishing beer styles. It was determined that while many phenols and iso-α-acids were present in the beers, they were not the compounds most responsible for the variations between styles. However, it was possible to discriminate each beer style using multivariate analysis. Principal component analysis (PCA) was able to separate and cluster the individual beer samples by style. A combination of statistical tools were used to predict formulas for some of the most influential metabolites from each style. Machine learning models accurately classified patterns in the five beer styles, indicating that they can be precisely distinguished by their nonvolatile chemical profile.

LncRNA FER1L4 Promotes Oral Squamous Cell Carcinoma Progression via Targeting miR-133a-5p/Prx1 Axis.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is a common cancer especially young people in the world. The long non-coding RNA Fer-1-like protein 4 (FER1L4) has been reported to be closely associated with the progression of various human cancers. However, the role of FER1L4 in OSCC remains unclear. METHODS: The expression level of FER1L4 in OSCC tissues and cancer cell lines was detected by using quantitative real-time polymerase chain reaction (qRT-PCR). Cell proliferation was evaluated by cell counting kit-8 (CCK-8) assay and EdU staining assay. Cell invasion and migration were evaluated by Transwell assay. Cell apoptosis was detected by flow cytometry. Luciferase reporter assay was performed to determine the targeting relationship between FER1L4, miR-133a-5p and Prx1. The protein expression of Prx1 was detected by Western blot. In addition, a xenograft tumor model in vivo was constructed to confirm the function of FER1L4. RESULTS: FERIL4 was significantly upregulated in OSCC tissues and cancer cell lines. Moreover, high level of FER1L4 predicted a poor prognosis of OSCC patients. Silencing of FER1L4 not only significantly inhibited cell growth, invasion, migration and induced apoptosis in SCC-9 and HN4 cells in vitro, but also effectively suppressed the tumorigenesis of OSCC cells in vivo. Knockdown of FER1L4 significantly enhanced the expression of miR-133a-5p by sponging it, and then downregulated Prx1 expression. CONCLUSION: Our study elucidated a new mechanism of lncRNA FER1L4 that promoting OSCC progression by directly targeting miR-133a-5p/Prx1 axis and provided novel therapeutic targets for OSCC.