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Identifying native-like protein-ligand complexes (PLCs) from an abundance of docking decoys is critical for large-scale virtual drug screening in early-stage drug discovery lead searching efforts. Providing reliable prediction is still a challenge for most current affinity predicting models because of a lack of non-binding data during model training, lost critical physical-chemical features, and difficulties in learning abstract information with limited neural layers. In this work, we proposed a deep learning model, DeepBindBC, for classifying putative ligands as binding or non-binding. Our model incorporates information on non-binding interactions, making it more suitable for real applications. ResNet model architecture and more detailed atom type representation guarantee implicit features can be learned more accurately. Here, we show that DeepBindBC outperforms Autodock Vina, Pafnucy, and DLSCORE for three DUD.E testing sets. Moreover, DeepBindBC identified a novel human pancreatic α-amylase binder validated by a fluorescence spectral experiment (K

COVID-19 can lead to severe outcomes in children, including multisystem inflammatory syndrome, hospitalization, and death (1,2). On November 2, 2021, the Advisory Committee on Immunization Practices issued an interim recommendation for use of the BNT162b2 (Pfizer-BioNTech) vaccine in children aged 5-11 years for the prevention of COVID-19; however, vaccination coverage in this age group remains low (3). As of June 7, 2022, 36.0% of children aged 5-11 years in the United States had received ≥1 of COVID-19 vaccine (3). Among factors that might influence vaccination coverage is the availability of vaccine providers (4). To better understand how provider availability has affected COVID-19 vaccination coverage among children aged 5-11 years, CDC analyzed data on active COVID-19 vaccine providers and county-level vaccine administration data during November 1, 2021-April 25, 2022. Among 2,586 U.S. counties included in the analysis, 87.5% had at least one active COVID-19 vaccine provider serving children aged 5-11 years. Among the five assessed active provider types, most counties had at least one pharmacy (69.1%) or public health clinic (61.3%), whereas fewer counties had at least one pediatric clinic (29.7%), family medicine clinic (29.0%), or federally qualified health center (FQHC)* (22.8%). Median county-level vaccination coverage was 14.5% (IQR = 8.9%-23.6%). After adjusting for social vulnerability index (SVI)

Breast cancer is the most common malignancy among women across the globe. Recent studies have revealed that many long non-coding RNAs (lncRNAs) regulate the Wnt/β-catenin signaling pathway in several types of cancer. Hyperactivation of the Wnt/β-catenin pathway has been extensively presented in breast cancer and is involved in breast cancer progression. However, the underlying molecular mechanism remains elusive. In the current study, we found lncRNA RBM5-AS1 was remarkably upregulated in breast cancer cells and tissues. Overexpression of RBM5-AS1 facilitated proliferation, migration, invasion, EMT, and stemness maintenance of breast cancer cells in vitro and in vivo. Mechanism studies suggested that RBM5-AS1 could be transcriptionally activated by hypoxia-induced RUNX2. Upregulated RBM5-AS1 further activated the Wnt/β-catenin signaling by preventing β-catenin degradation and by helping organize β-catenin-TCF4 transcriptional complex. These findings suggested that RBM5-AS1, a regulator of Wnt/β-catenin signaling, plays a vital role in breast cancer initiation and progression, implicating its potential as a new target for breast cancer treatment.

A substantial proportion of patients with unresectable stage III non-small-cell lung cancer (NSCLC) cannot either tolerate or access concurrent chemoradiotherapy, so sequential chemoradiotherapy is commonly used. We assessed the efficacy and safety of sugemalimab, an anti-PD-L1 antibody, in patients with stage III NSCLC whose disease had not progressed after concurrent or sequential chemoradiotherapy.GEMSTONE-301 is a randomised, double-blind, placebo-controlled, phase 3 trial in patients with locally advanced, unresectable, stage III NSCLC, done at 50 hospitals or academic research centres in China. Eligible patients were aged 18 years or older with an Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1 who had not progressed after concurrent or sequential chemoradiotherapy. We randomly assigned patients (2:1, using an interactive voice-web response system) to receive sugemalimab 1200 mg or matching placebo, intravenously every 3 weeks for up to 24 months. Stratification factors were ECOG performance status, previous chemoradiotherapy, and total radiotherapy dose. The investigators, trial coordination staff, patients, and study sponsor were masked to treatment allocation. The primary endpoint was progression-free survival as assessed by blinded independent central review (BICR) in the intention-to-treat population. Safety was assessed in all participants who received at least one dose of assigned study treatment. The study has completed enrolment and the results of a preplanned analysis of the primary endpoint are reported here. The trial is registered with ClinicalTrials.gov, NCT03728556.Between Aug 30, 2018 and Dec 30, 2020, we screened 564 patients of whom 381 were eligible. Study treatment was received by all patients randomly assigned to sugemalimab (n=255) and to placebo (n=126). At data cutoff (March 8, 2021), median follow-up was 14·3 months (IQR 6·4-19·4) for patients in the sugemalimab group and 13·7 months (7·1-18·4) for patients in the placebo group. Progression-free survival assessed by BICR was significantly longer with sugemalimab than with placebo (median 9·0 months [95% CI 8·1-14·1] vs 5·8 months [95% CI 4·2-6·6]; stratified hazard ratio 0·64 [95% CI 0·48-0·85], p=0·0026). Grade 3 or 4 treatment-related adverse events occurred in 22 (9%) of 255 patients in the sugemalimab group versus seven (6%) of 126 patients in the placebo group, the most common being pneumonitis or immune-mediated pneumonitis (seven [3%] of 255 patients in the sugemalimab group vs one [1%] of 126 in the placebo group). Treatment-related serious adverse events occurred in 38 (15%) patients in the sugemalimab group and 12 (10%) in the placebo group. Treatment-related deaths were reported in four (2%) of 255 patients (pneumonia in two patients, pneumonia with immune-mediated pneumonitis in one patient, and acute hepatic failure in one patient) in the sugemalimab group and none in the placebo group.Sugemalimab after definitive concurrent or sequential chemoradiotherapy could be an effective consolidation therapy for patients with stage III NSCLC whose disease has not progressed after sequential or concurrent chemoradiotherapy. Longer follow-up is needed to confirm this conclusion.CStone Pharmaceuticals and the National Key Research and Development Program of China.For the Chinese translation of the abstract see Supplementary Materials section.

Accumulated studies have discovered that circular RNAs (CircRNAs) are closely related to many complex human diseases. Due to this close relationship, CircRNAs can be used as good biomarkers for disease diagnosis and therapeutic targets for treatments. However, the number of experimentally verified circRNA-disease associations are still fewer and also conducting wet-lab experiments are constrained by the small scale and cost of time and labour. Therefore, effective computational methods are required to predict associations between circRNAs and diseases which will be promising candidates for small scale biological and clinical experiments. In this paper, we propose novel computational models based on Graph Convolution Networks (GCN) for the potential circRNA-disease association prediction. Currently most of the existing prediction methods use shallow learning algorithms. Instead, the proposed models combine the strengths of deep learning and graphs for the computation. First, they integrate multi-source similarity information into the association network. Next, models predict potential associations using graph convolution which explore this important relational knowledge of that network structure. Two circRNA-disease association prediction models, GCN based Node Classification (GCN-NC) and GCN based Link Prediction (GCN-LP) are introduced in this work and they demonstrate promising results in various experiments and outperforms other existing methods. Further, a case study proves that some of the predicted results of the novel computational models were confirmed by published literature and all top results could be verified using gene-gene interaction networks.

Consolidation immunotherapy after completion of chemoradiotherapy has become the standard of care for unresectable locally advanced non-small cell lung cancer and can induce potentially severe and life-threatening adverse events, including both immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP), which are very challenging for radiologists to diagnose. Differentiating between CIP and RP has significant implications for clinical management such as the treatments for pneumonitis and the decision to continue or restart immunotherapy. The purpose of this study is to differentiate between CIP and RP by a CT radiomics approach.We retrospectively collected the CT images and clinical information of patients with pneumonitis who received immune checkpoint inhibitor (ICI) only (n = 28), radiotherapy (RT) only (n = 31), and ICI+RT (n = 14). Three kinds of radiomic features (intensity histogram, gray-level co-occurrence matrix [GLCM] based, and bag-of-words [BoW] features) were extracted from CT images, which characterize tissue texture at different scales. Classification models, including logistic regression, random forest, and linear SVM, were first developed and tested in patients who received ICI or RT only with 10-fold cross-validation and further tested in patients who received ICI+RT using clinicians' diagnosis as a reference.Using 10-fold cross-validation, the classification models built on the intensity histogram features, GLCM-based features, and BoW features achieved an area under curve (AUC) of 0.765, 0.848, and 0.937, respectively. The best model was then applied to the patients receiving combination treatment, achieving an AUC of 0.896.This study demonstrates the promising potential of radiomic analysis of CT images for differentiating between CIP and RP in lung cancer, which could be a useful tool to attribute the cause of pneumonitis in patients who receive both ICI and RT.

A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause the major cause of adverse drug reactions and even result in the drug withdrawal from the market and the patient death. Therefore, identifying DDIs has become a key component of the drug development and disease treatment. In this study, we propose a novel method to predict DDIs based on the integrated similarity and semi-supervised learning (DDI-IS-SL). DDI-IS-SL integrates the drug chemical, biological and phenotype data to calculate the feature similarity of drugs with the cosine similarity method. The Gaussian Interaction Profile kernel similarity of drugs is also calculated based on known DDIs. A semi-supervised learning method (the Regularized Least Squares classifier) is used to calculate the interaction possibility scores of drug-drug pairs. In terms of the 5-fold cross validation, 10-fold cross validation and de novo drug validation, DDI-IS-SL can achieve the better prediction performance than other comparative methods. In addition, the average computation time of DDI-IS-SL is shorter than that of other comparative methods. Finally, case studies further demonstrate the performance of DDI-IS-SL in practical applications.

ImportanceSulfonylureas are frequently used as add-on to metformin in type 2 diabetes (T2D), and individual sulfonylurea agents carry different risks of cardiovascular disease. Sulfonylureas’ different affinities to cardiac mitochondrial adenosine triphosphate–sensitive potassium (mitoKATP) channels have been speculated to account for the intraclass difference in cardiovascular risk from in vitro and ex vivo studies; however, this hypothesis has not been assessed in a general population with diabetes receiving sulfonylureas added to metformin.ObjectiveTo compare the risk of myocardial infarction (MI), ischemic stroke, or cardiovascular death in patients with T2D treated with mitoKATP channel high-affinity sulfonylureas and low-affinity sulfonylureas as add-on to metformin.Design, Setting, and ParticipantsThis is a new-user, active-comparator, and propensity score–matched cohort study with analysis of the Taiwanese Diabetes Mellitus Health Database from 2006, to 2017. Data analysis was performed from August 2020 to July 2021.ExposuresCardiac mitoKATP channel high-affinity (glyburide and glipizide) and low-affinity (gliclazide and glimepiride) sulfonylureas combined with metformin.Main Outcomes and MeasuresPrimary outcome was major adverse cardiovascular events (MACEs), a composite of cardiovascular death or hospitalization for either MI or ischemic stroke. Secondary outcomes included individual MACE components, heart failure, arrhythmia, all-cause mortality, and severe hypoglycemia. Cox proportional hazards models were used to estimate adjusted hazard ratios (aHRs).ResultsEach sulfonylurea group comprised 53 714 patients (mean [SD] age, 54.7 [12.1] years; 31 962 men [59.5%]). MitoKATP channel high-affinity sulfonylureas vs low-affinity sulfonylureas when combined with metformin were associated with an increased risk of MACE (aHR, 1.18; 95% CI, 1.03-1.34), MI (aHR, 1.34; 95% CI, 1.04-1.73), all-cause mortality (aHR, 1.27; 95% CI, 1.03-1.57), and severe hypoglycemia (aHR, 1.82; 95% CI, 1.58-2.10), but not with increased risks of ischemic stroke, cardiovascular death, arrhythmia, and heart failure. The duration analyses revealed the highest MACE risk during 1 to 90 days after initiation of mitoKATP channel high-affinity sulfonylureas (aHR, 6.06; 95% CI, 4.86-7.55).Conclusions and RelevanceUse of mitoKATP channel high-affinity sulfonylureas vs low-affinity sulfonylureas was associated with an increased MACE risk in patients with T2D concomitantly receiving metformin, suggesting that high-affinity blockage of the mitoKATP channels could account for sulfonylurea-associated MACEs.

BACKGROUND: Measles is a highly-contagious, serious diseases that can lead to potentially fatal illness, disability and death . We conducted an investigation to calculate the economic burden of measles cases from 2018 to 2019 and to analyze factors that influenced the total costs of measles cases in Fujian Province, China. METHODS: We investigated confirmed cases of measles by telephone interview, from 2018 to 2019, including demographic characteristics, therapeutic measures, self-treatment and nutritional supplement expenditure, transportation fees, and lost wages. Medical expenses in the hospitals were obtained from the hospital service charge system. RESULTS: A total of 131 measles cases were investigated, the average direct cost, indirect cost, and total cost were $518, $284, and $802, respectively. The total cost was 15.5% of Annual Per Capita Disposable Income of Households ($5 162) in Fujian. Hospitalization (OR = 98.6, 95%CI: 21.1 ~ 460.6) and complication (OR = 8.7, 95%CI: 1.3 ~ 58.0) significantly influenced the total cost according to binary logistic regression, and an outbreak of measles may pose a significant threat to household finances in a short term. CONCLUSIONS: The economic burden of measles was high when compared with Annual Per Capita Disposable Income of Households. The resurgence of measles and measles outbreaks increased economic burden of household finances.

Excessive alcohol use* is associated with disease, injury, and poor pregnancy outcomes and is responsible for approximately 95,000 deaths in the United States each year (1). Binge drinking (five or more drinks on at least one occasion for men or four or more drinks for women) is the most common and costly pattern of excessive alcohol use (2). CDC analyzed data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) to estimate past 30-day binge drinking prevalence, frequency, and intensity (number of drinks per occasion), overall and by select characteristics and state. The overall unadjusted prevalence of binge drinking during the past 30 days was 16.6%, representing an estimated 38.5 million U.S. adults aged ≥18 years; prevalence was highest (26.0%) among those aged 25-34 years. The age-standardized binge drinking prevalence was higher among men (22.5%) than among women (12.6%), increased with income, and was highest among non-Hispanic White adults and adults in the Midwest Census region. State-level age-standardized binge drinking prevalence ranged from 10.5% (Utah) to 25.8% (Wisconsin). Among adults who reported binge drinking, 25.0% did so at least weekly, on average, and 25.0% consumed at least eight drinks on an occasion. To reduce binge drinking, the Community Preventive Services Task Force recommends increasing alcohol taxes and implementing strategies that strengthen regulations to reduce alcohol availability.† The U.S. Preventive Services Task Force recommends clinicians screen adults for alcohol misuse in primary care settings and provide counseling as needed.§.

Purpose Gamma knife radiosurgery (GK) is a commonly used approach for the treatment of intracranial lesions. Its radiation response is typically not immediate, but delayed. In this study, we analyzed cases from a prospectively collected database to assess the influence of COVID-19 pandemic on the decision making in patients treated by gamma knife radiosurgery. Methods From January 2019 to August 2021, 540 cases of intracranial lesions were treated by GK with 207 cases before COVID-19 pandemic as a control. During the COVID-19 pandemic, 333 cases were similarly treated on patients with or without the COVID-19 vaccination. All the GK treated parameters as well as time profile in the decision making were analyzed. The parameters included age, sex, characteristic of lesion, targeted volume, peripheral radiation dose, neurological status, Karnofsky Performance Status (KPS), time interval from MRI diagnosis to consultation, time interval from the approval to treatment, frequency of outpatient department (OPD) visit, and frequency of imaging follow-up. Results Longer time intervals from diagnosis to GK consultation and treatment were found in the pandemic group (36.8 ± 25.5/54.5 ± 27.6 days) compared with the pre-COVID control (17.1 ± 22.4/45.0 ± 28.0 days) or vaccination group (12.2 ± 7.1/29.6 ± 10.9 days) (p p p p Conclusions The decision making in patients requiring gamma knife treatment was most likely influenced by the status of the COVID-19 pandemic, while vaccination appeared to attenuate their hesitant behaviors. Patients with pre-treatment neurological deficits and high co-morbidity undergoing the gamma knife treatment were less affected by the COVID-19 pandemic.

Accurately estimating the spatial and temporal distribution of precipitation is crucial for hydrological modeling. However, precipitation products based on a single source have their advantages and disadvantages. How to effectively combine the advantages of different precipitation datasets has become an important topic in developing high-quality precipitation products internationally in recent years. This paper uses the measured precipitation data of Multi-Source Weighted-Ensemble Precipitation (MSWEP) and in situ rainfall observation in the Taihu Lake Basin, as well as the longitude, latitude, elevation, slope, aspect, surface roughness, distance to the coastline, and land use and land cover data, and adopts a two-step method to achieve precipitation fusion: (1) downscaling the MSWEP source precipitation field using the bilinear interpolation method and (2) using the geographically weighted regression (GWR) method and tri-cube function weighting method to achieve fusion. Considering geographical and human activities factors, the spatial and temporal distribution of precipitation errors in MSWEP is detected. The fusion of MSWEP and gauge observation precipitation is realized. The results show that the method in this paper significantly improves the spatial resolution and accuracy of precipitation data in the Taihu Lake Basin.

Introduction While Hodgkin lymphoma (HL) is mostly curable, outcomes for advanced-stage HL remain unsatisfactory. The International Prognostic Score and its modifications were developed to predict HL prognosis; however, more straightforward prognostic factors are needed. This study aimed to identify simpler prognostic factors for advanced-stage newly diagnosed HL (NDHL). Methods This retrospective study used the Taiwan National Health Insurance Research Database and the Taiwan Cancer Registry. Patients with advanced-stage NDHL receiving ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) or ABVD-like regimens between 2009 and 2016 were enrolled. Cox proportional hazards models were used to identify prognostic factors for the time to next treatment (TTNT) and overall survival (OS). We used the time-dependent area under the receiver operating characteristic curve (AUROC) to evaluate model performance. Results The study included 459 patients with advanced-stage NDHL. A bimodal age distribution (peaks 20-44 and >65 years) was observed. Over a median follow-up of 4.7 years, the complete remission and OS rates were 52% and 76%, respectively. Age ≥60 years (adjusted hazard ratio [aHR]: 1.73, 95% confidence interval [CI]: 1.23-2.43), extranodal involvement (1.40, 1.05-1.87), B symptoms (1.53, 1.13-2.06), and Charlson Comorbidity Index (CCI) ≥1 (1.49, 1.08-2.06) were significantly associated with a shorter TTNT. The time-dependent AUROC was .65. With a time-dependent AUROC of .81, age ≥60 years (4.55, 2.90-7.15) and CCI ≥1 (1.86, 1.18-2.91) were risk factors for worse OS. Conclusion Older age and more comorbidities were risk factors for an inferior OS in advanced-stage NDHL, while older age, extranodal involvement, B-symptoms, and higher CCI were significantly associated with disease relapse.

The novel coronavirus (COVID-19) infections have adopted the shape of a global pandemic now, demanding an urgent vaccine design. The current work reports contriving an anti-coronavirus peptide scanner tool to discern anti-coronavirus targets in the embodiment of peptides. The proffered CoronaPep tool features the fast fingerprinting of the anti-coronavirus target serving supreme prominence in the current bioinformatics research. The anti-coronavirus target protein sequences reported from the current outbreak are scanned against the anti-coronavirus target data-sets via CORONAPEP which provides precision-based anti-coronavirus peptides. This tool is specifically for the coronavirus data, which can predict peptides from the whole genome, or a gene or protein's list. Besides it is relatively fast, accurate, userfriendly and can generate maximum output from the limited information. The availability of tools like CORONAPEP will immeasurably perquisite researchers in the discipline of oncology and structure-based drug design.

Homozygous and heterozygous deletions commonly exist in the human genome. For current structural variation detection tools, it is significant to determine whether a deletion is homozygous or heterozygous. However, the problems of sequencing errors, micro-homologies, and micro-insertions prohibit common alignment tools from identifying accurate breakpoint locations, and often result in detecting false structural variations. In this study, we present a novel deletion detection tool called Sprites2. Comparing with Sprites, Sprites2 makes the following modifications: (1) The distribution of insert size is used in Sprites2, which can identify the type of deletions and improve the accuracy of deletion calls. (2) A precise alignment method based on AGE (one algorithm simultaneously aligning 5’ and 3’ ends between two sequences) is adopted in Sprites2 to identify breakpoints, which is helpful to resolve the problems introduced by sequencing errors, micro-homologies, and micro-insertions. In order to test and verify the performance of Sprites2, some simulated and real datasets are adopted in our experiments, and Sprites2 is compared with five popular tools. The experimental results show that Sprites2 can improve the performance of deletion detection. Sprites2 can be downloaded from https://github.com/zhangzhen/sprites2 .

Coronavirus disease 2019 (COVID‐19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), has become a global pandemic worldwide. Long non‐coding RNAs (lncRNAs) are a subclass of endogenous, non‐protein‐coding RNA, which lacks an open reading frame and is more than 200 nucleotides in length. However, the functions for lncRNAs in COVID‐19 have not been unravelled. The present study aimed at identifying the related lncRNAs based on RNA sequencing of peripheral blood mononuclear cells from patients with SARS‐CoV‐2 infection as well as health individuals. Overall, 17 severe, 12 non‐severe patients and 10 healthy controls were enrolled in this study. Firstly, we reported some altered lncRNAs between severe, non‐severe COVID‐19 patients and healthy controls. Next, we developed a 7‐lncRNA panel with a good differential ability between severe and non‐severe COVID‐19 patients using least absolute shrinkage and selection operator regression. Finally, we observed that COVID‐19 is a heterogeneous disease among which severe COVID‐19 patients have two subtypes with similar risk score and immune score based on lncRNA panel using iCluster algorithm. As the roles of lncRNAs in COVID‐19 have not yet been fully identified and understood, our analysis should provide valuable resource and information for the future studies.

Computational drug repositioning, which is an efficient approach to find potential indications for drugs, has been used to increase the efficiency of drug development. The drug repositioning problem essentially is a top-K recommendation task that recommends most likely diseases to drugs based on drug and disease related information. Therefore, many recommendation methods can be adopted to drug repositioning. Collaborative metric learning (CML) algorithm can produce distance metrics that capture the important relationships among objects, and has been widely used in recommendation domains. By applying CML in drug repositioning, a joint metric space is learned to encode drug's relationships with different diseases. In this study, we propose a novel drug repositioning computational method using Collaborative Metric Learning to predict novel drug-disease associations based on known drug and disease related information. Specifically, the proposed method learns latent vectors of drugs and diseases by applying metric learning, and then predicts the association probability of one drug-disease pair based on the learned vectors. The comprehensive experimental results show that CMLDR outperforms the other state-of-the-art drug repositioning algorithms in terms of precision, recall, and AUPR.

Single-cell RNA sequencing (scRNA-seq) technology provides quantitative gene expression profiles at single-cell resolution. As a result, researchers have established new ways to explore cell population heterogeneity and genetic variability of cells. One of the current research directions for scRNA-seq data is to identify different cell types accurately through unsupervised clustering methods. However, scRNA-seq data analysis is challenging because of their high noise level, high dimensionality and sparsity. Moreover, the impact of multiple latent factors on gene expression heterogeneity and on the ability to accurately identify cell types remains unclear. How to overcome these challenges to reveal the biological difference between cell types has become the key to analyze scRNA-seq data. For these reasons, the unsupervised learning for cell population discovery based on scRNA-seq data analysis has become an important research area. A cell similarity assessment method plays a significant role in cell clustering. Here, we present BioRank, a new cell similarity assessment method based on annotated gene sets and gene ranks. To evaluate the performances, we cluster cells by two classical clustering algorithms based on the similarity between cells obtained by BioRank. In addition, BioRank can be used by any clustering algorithm that requires a similarity matrix. Applying BioRank to 12 public scRNA-seq datasets, we show that it is better than or at least as well as several popular similarity assessment methods for single cell clustering.

To evaluate the efficacy and safety of sodium oligomannate in the treatment of Alzheimer's disease. Patients with mild-to-moderate AD were randomly divided into three groups, the scores of ADAS-Cog, ADL, CIBIC-plus, NPI and CSDD were evaluated at the 0th, 12th, 24th, 36th and 48th weeks of medication. Comparing the mean scores of each scale in each cycle of each group. Using SPSS21.0 software for measurement data using t test, Chi-square test was used for counting data. A total of 72 patients with AD were included. The difference of CIBIC-plus score at week 12(P=0.007) and 24(P=0.005), ADAS-Cog scores (P=0.01) at week 24 in GV-971 group was statistically significant compared with that in the control group. The CIBIC-plus score at week 24(P=0.01) and week 48 (P=0.04), CSDD scores at week 48(P=0.02) of GV-971 group was statistically significant compared with that of donepezil group. There were 2 cases of adverse reaction of increased stool frequency in GV-971 (5.67%), and 2 cases of adverse reaction of nausea in donepezil group (8.33%), the difference was statistically significant. GV-971 is as effective as donepezil in the treatment of Alzheimer's disease, and may even be better. It has good safety.

Hashimoto’s thyroiditis (HT) is the main cause of hypothyroidism. We develop a deep learning model called HTNet for diagnosis of HT by training on 106,513 thyroid ultrasound images from 17,934 patients and test its performance on 5051 patients from 2 datasets of static images and 1 dataset of video data. HTNet achieves an area under the receiver operating curve (AUC) of 0.905 (95% CI: 0.894 to 0.915), 0.888 (0.836–0.939) and 0.895 (0.862–0.927). HTNet exceeds radiologists’ performance on accuracy (83.2% versus 79.8%; binomial test, p p p = 0.004) and static-image (AUC, 0.914 versus 0.901; p = 0.08) testing sets, respectively. HTNet may be helpful as a tool for the management of HT.

Triple-negative breast cancer (TNBC) presents special biological behavior and clinicopathological characteristics and leads to a worse prognosis than other types of breast cancer. The development of an effective therapeutic method is significant to improve the survival rate of TNBC cancer patients. In this work, an engineered red blood cell membrane (RBCm)-coating salidroside/indocyanine green nanovesicle (ARISP) is successfully prepared for hypoxic targeting phototherapy of TNBC. Salidroside in ARISP effectively ameliorates hypoxia-induced tumorigenesis by downregulating the expression of hypoxia-inducible factor 1α (HIF-1α), which increases the killing effect of reactive oxygen species on tumor cells during photodynamic therapy (PDT) using the photosensitizer indocyanine green. Besides, ARISP has an anti-LDLR modified RBCm-coating that extends its circulation time in the blood and escapes from immune surveillance and enhances hypoxia-targeted cellular uptake via the overexpressed LDLR receptor in hypoxic tumor sites. Moreover, guided by near-infrared fluorescence imaging and photoacoustic imaging, ARISP can eliminate tumors via high-efficiency phototherapy and inhibit lung and liver metastasis in TNBC models. Cytotoxicity assay of ARISP indicates the excellent biocompatibility with normal cells and tissues. This study provides fulfilling insights into the anticancer mechanism of reducing HIF-1α for enhanced PDT and has a promising therapeutic potential for TNBC treatment.

Purpose: Prediction models for acute myeloid leukemia (AML) are useful, but have considerable inaccuracy and imprecision. No current model includes covariates related to immune cells in the AML microenvironment. Here, an immune risk score was explored to predict the survival of patients with AML. Experimental Design: We evaluated the predictive accuracy of several in silico algorithms for immune composition in AML based on a reference of multi-parameter flow cytometry. CIBERSORTx was chosen to enumerate immune cells from public datasets and develop an immune risk score for survival in a training cohort using least absolute shrinkage and selection operator Cox regression model. Results: Six flow cytometry–validated immune cell features were informative. The model had high predictive accuracy in the training and four external validation cohorts. Subjects in the training cohort with low scores had prolonged survival compared with subjects with high scores, with 5-year survival rates of 46% versus 19% (P < 0.001). Parallel survival rates in validation cohorts-1, -2, -3, and -4 were 46% versus 6% (P < 0.001), 44% versus 18% (P = 0.041), 44% versus 24% (P = 0.004), and 62% versus 32% (P < 0.001). Gene set enrichment analysis indicated significant enrichment of immune relation pathways in the low-score cohort. In multivariable analyses, high-risk score independently predicted shorter survival with HRs of 1.45 (P = 0.005), 2.12 (P = 0.004), 2.02 (P = 0.034), 1.66 (P = 0.019), and 1.59 (P = 0.001) in the training and validation cohorts, respectively. Conclusions: Our immune risk score complements current AML prediction models.

BACKGROUND Most patients with actinic keratosis (AK) present with more than one lesion. Although histopathological examination is the gold standard for diagnosing this condition, performing an invasive skin biopsy for each AK is impractical. Thus, this study aimed to identify AK's morphological characteristics based on harmonic generation microscopy (HGM). Moreover, the correlation between features observed using HGM and histopathological grading of AK was examined. METHODS Lesions of seven patients were examined using HGM (n = 1, ex vivo and n = 6, in vivo), and histopathological examinations of the biopsy specimens were also performed. The features of each AK, based on HGM, were assessed and compared with corresponding standard histopathological findings. RESULTS Using the histopathological findings as a standard reference, HGM's accuracy in detecting features of AK lesions, such as hyperkeratosis, epidermal thinning, abnormal architecture, and atypical honeycomb pattern, was 100%. Approximately five (72%) patients had similar histopathological grades. Moreover, based on HGM, except for one patient with grade 1 AK, six (85.71%) patients had lesions with intraepidermal dendritic cell-like cells, representing melanocytes. CONCLUSION Harmonic generation microscopy can be used in vivo to provide critical diagnostic information with a resolution comparable to histopathological examination. In addition, intralesional melanocytes in AK, which may be correlated with disease severity, can be specifically enhanced using HGM.

Background During psoriasis initiation and development, deregulations in signalling pathways and gene expression are observed. Methods Herein, we downloaded seven sets of microarray mRNA expression profiles showing differentially expressed genes in psoriasis lesion skin and non-lesion skin tissues and three sets of RNA-seq data and analysed these online data attempting to screen for crucial genes related to keratinocyte proliferation and psoriasis development. The expression of CTNNBIP1 in psoriasis patients and IMQ mouse model skin tissues were examined by RT-PCR, immunoblotting and IHC. The functions of CTNNBIP1 on HaCaT cell proliferation, apoptosis and β-catenin/TCF complex were measured by MTT, EdU, flow cytometry, IF, luciferase assays and immunoblotting. Results The expression of catenin beta interacting protein 1 (CTNNBIP1) was remarkably downregulated within psoriasis lesion skin tissue samples compared to that within non-lesion skin tissues based on both online data and experimental results. In response to a period of different therapies, respectively, CTNNBIP1 expression could be rescued in lesion skin tissues. Within IMQ-induced psoriasis-like dermatitis in mice, CTNNBIP1 silence further aggravated psoriatic phenotypes. In human immortalized keratinocytes, HaCaT cells, CTNNBIP1 silence significantly inhibited cell apoptosis and promoted cell proliferation. Regarding the molecular mechanism, CTNNBIP1 silence in HaCaT cells promoted β-catenin nucleus translocation, enhanced the transcriptional activity of TCF4 and increased β-catenin/TCF complex downstream c-Myc and cyclin D1 proteins, and also increased the expression of cell proliferation marker ki-67. In contrast to CTNNBIP1, the expression of c-Myc and cyclin D1 showed to be dramatically upregulated within psoriasis lesion tissue samples than that within non-lesion tissue samples. Within tissues, c-Myc and cyclin D1 showed to be negatively correlated with CTNNBIP1, respectively. Conclusions We identify CTNNBIP1 as an abnormally downregulated gene in psoriasis. CTNNBIP1 silence significantly disturbs the proliferation of keratinocytes through promoting the transcription of β-catenin/TCF complex downstream genes.

The present study examined how visual working memory affects temporal resolution of visual stimuli that match the memorised nontemporal information about stimulus features (e.g., shapes). The results show that active maintenance of a sample shape in visual working memory impaired the concurrent detection of a sufficiently brief temporal gap within a matching visual target stimulus. The impairment of temporal gap detection was not likely due to the mechanism of visual priming from the presentation of the sample shape, because there was no evidence for priming effects when the sample was only passively viewed without working memory demands or when the sample was initially encoded into memory but did not need to be actively maintained in mind by the time the target stimulus appeared. These findings demonstrate that visual working memory can lower the temporal resolution of visual perception and thereby impair performance in the temporal gap detection task that requires fine temporal segregation of visual information, and they suggest the existence of a previously unknown downside to the interplay between working memory and visual perception.

The maintenance of information in visual working memory has been shown to bias the concurrent processing in favor of matching visual input. The present study aimed to examine whether this bias can act at an early stage of processing to enhance target feature perception in single-item displays. Participants were sequentially presented with two distinct colored stimuli as memory samples and a retro-cue indicating which of the two samples should be maintained for subsequent memory test. During the retention interval, they had to discriminate the gap orientation of a Landolt target presented through a single visual stimulus that could match one or neither of the two samples. Across two experiments, we consistently found that discrimination performance was more accurate when the Landolt target was situated within a stimulus that matched the sample being retained in visual working memory, as compared with when the target was not. This effect cannot be attributed to the mechanism of passive priming, because we failed to observe priming effects when the stimulus containing the target matched the sample that was retro-cued to be irrelevant to the working memory task, as compared to when the stimulus matched neither sample. Given the fact that target stimuli were presented in single-item displays wherein external noise was precluded, the present findings demonstrate that the working memory bias of visual attention operating in the absence of stimulus competition facilitates early perceptual processing at the attended location via signal enhancement.

microRNAs (miRNAs) are small and important non-coding RNAs that regulate gene expression in transcriptional and post-transcriptional level by combining with their targets (genes). Predicting miRNA targets is an important problem in biological research. It is expensive and time-consuming to identify miRNA targets by using biological experiments. Many computational methods have been proposed to predict miRNA targets. In this study, we develop a novel method, named miRTRS, for predicting miRNA targets based on a recommendation algorithm. miRTRS can predict targets for an isolated (new) miRNA with miRNA sequence similarity, as well as isolated (new) targets for a miRNA with gene sequence similarity. Furthermore, when compared to supervised machine learning methods, miRTRS does not need to select negative samples. We use 10-fold cross validation and independent datasets to evaluate the performance of our method. We compared miRTRS with two most recently published methods for miRNA target prediction. The experimental results have shown that our method miRTRS outperforms competing prediction methods in terms of AUC and other evaluation metrics.

Prostate cancer is one of the leading causes of death in men worldwide, revealing a substantial heterogeneity in terms of molecular and clinical behaviors. Tumor infiltrating immune cell is associated with prognosis and response to immunotherapy in several cancer types. However, until now, the immune infiltrate profile of distinct subtypes for prostate cancer remains poorly characterized. In this study, using immune infiltration profiles as well as transcriptomic datasets, we characterized this subtype of prostate tumors. We observed that the FLI1 subtype of prostate tumors was highly enriched in immune system processes, immune related KEGG pathways and biological processes. We also expanded this approach to explore the immune infiltration profile of the high FLI1 expression subtype for skin cutaneous melanoma, similar results were found. Investigation of the association of immune infiltration features with the FLI1 expression demonstrated that many important features were associated with the FLI1 expression.

Pulmonary arterial hypertension (PAH) is a rare, but progressive and devastating vascular disease with few treatment options to prevent the advancement to right ventricular dysfunction hypertrophy and failure. Empagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, enhances urinary glucose excretion as well as reduces cardiovascular events and mortality in individuals with type 2 diabetes. While empagliflozin has been reported to lower systemic hypertension due to increased diuresis, the effect of empagliflozin on PAH is unknown. We used monocrotaline (MCT)-treated Sprague-Dawley rats to determine if empagliflozin alters PAH-associated outcomes. Compared to vehicle control, daily empagliflozin administration significantly improved survival in rats with severe MCT-induced PAH. Hemodynamic assessments showed that empagliflozin treatment significantly reduced mean pulmonary artery pressure, right ventricular systolic pressure, and increased pulmonary acceleration time. Empagliflozin treatment resulted in reduced right ventricular hypertrophy and fibrosis. Histological and molecular assessments of lung vasculature revealed significantly reduced medial wall thickening and decreased muscularization of pulmonary arterioles after empagliflozin treatment compared to vehicle-treated rats. In summary, SGLT2 inhibition with empagliflozin lowered mortality, reduced right ventricle systolic pressure, and attenuated maladaptive pulmonary remodeling in MCT-induced PAH. Clinical studies evaluating the efficacy of SGLT-2 inhibition should be considered for patients with PAH.

The working memory Stroop task is to name the color of a rectangular patch with keypress while holding a color word in working memory. Previous studies using this variant of the Stroop task have shown that congruency between the color patch and the color word significantly affects both color naming and working memory, with the worsening of task performance when the color patch is semantically incongruent rather than congruent with the color word. However, it remains unclear with regard to cognitive mechanisms underlying such congruency effects in the working memory Stroop task. By including a control condition among the congruent and incongruent conditions in a working memory Stroop task, the present study showed that nearly all of the working memory Stroop effect can be facilitation, and that interference, if present, is markedly smaller than facilitation in this form of the Stroop effect. There was also a critical contrast between the working memory and classic Stroop effects in terms of facilitation and interference, with larger facilitation and smaller interference in the working memory Stroop effect than in the classic Stroop effect. Moreover, working memory for a color word can be either facilitated or interfered with by the perceptual judgment (color naming) of an interposed color patch during the retention interval, depending on whether the color patch is semantically congruent or incongruent with the color word. Together, these results suggest that both facilitation and interference mechanisms can contribute to the overall congruency effects in the working memory Stroop task.

To establish an immune gene prognostic model of acute myeloid leukemia (AML) and explore its correlation with immune cells in bone marrow microenvironment.Gene expression profile and clinical data of TCGA-AML were downloaded from TCGA database. Immune genes were screened by LASSO analysis to construct prognosis prediction model, and prediction accuracy of the model was quantified by receiver operating characteristic curve and area under the curve. Survival analysis was performed by Log-rank test. Enriched pathways in the different immune risk subtypes were evaluated from train cohort. The relationship between immune prediction model and bone marrow immune microenvironment was verified by flow cytometry in the real world.Patients with low-risk score of immune gene model had better prognosis than those with high-risk score. Multivariate analysis showed that the immune gene risk model was an independent prognostic factor. The risk ratio for AML patients in the training concentration was HR=24.594 (95%CI: 6.180-97.878), and the AUC for 1-year, 3-year, and 5-year overall survival rate was 0.811, 0.815, and 0.837, respectively. In addition, enrichment analysis of differential gene sets indicated activation of immune-related pathways such as cytokines and chemokines as well as autoimmune disease-related pathways. At the same time, real world data showed that patients with high immune risk had lower numbers of CD8We constructed a stable prognostic model for AML, which can not only predict the prognosis of AML, but also reveal the dysregulation of immune microenvironment.急性髓系白血病免疫评分的建立及验证.建立急性髓系白血病（AML）免疫基因预后模型并探索其与骨髓免疫微环境之间的关系.从TCGA数据库下载TCGA-AML基因表达谱以及临床资料数据。通过LASSO分析筛选构建预测模型的免疫基因，模型预测精度通过受试者工作特征曲线和曲线下面积来量化，生存分析采用Log-rank检验。通过基因集富集分析评估不同免疫风险状态下的通路以及功能富集状态，并在真实世界中通过流式分析验证免疫预测模型与骨髓免疫微环境之间的相关性.免疫基因模型风险低的患者预后优于风险高的患者，多因素分析显示，该免疫基因风险模型为独立预后因素。训练集中AML患者的危险比为HR=24.594（95%CI：6.180-97.878），1、3和5年总生存率的AUC分别为0.811、0.815和0.837。此外，差异基因集富集分析提示，细胞因子以及趋化因子等免疫相关通路激活以及自身免疫疾病相关通路激活。同时，真实世界数据显示，免疫风险高的患者CD8本研究构建了一个稳定的AML预后评估模型，它不仅可以用来预测AML的预后，还能进一步揭示了免疫微环境的失调.

The development of new diagnostic imaging and precise treatment methods for glioblastoma multiforme (GBM) is significant to improve patients' quality of life and prolong their survival time. Herein, we proposed a photoacoustic imaging (PAI)-guided GBM high-efficient photothermal therapy (PTT) based on a second near-infrared (NIR-II) absorptive polymer (PDTP-TBZ) conjugated with intense electron donor dithienopyrrole (DTP) and strong electron acceptor thiadiazolobenzotriazole (TBZ). By nanoprecipitation, PDTP-TBZ can form into nanoparticles (PT NPs), and c(RGDfK) cyclic peptide with integrin-specific targeting was then modified on the surface of PT NPs to obtain the ability of active targeting GBM multifunctional nano-reagent (cRGD@PT NPs). Both in vitro and in vivo experiments demonstrated that cRGD@PT NPs as NIR-II GBM phototheranostic reagents can greatly improve the enrichment rate at tumor sites under PAI monitoring, and carry out precise NIR-II PTT with high effective tumor cell phototoxicity and high biological safety. Thus, cRGD@PT NPs have great potential for the future GBM phototheranostic application in clinic. STATEMENT OF SIGNIFICANCE: In this work, we successfully constructed an intense electron donor dithienopyrrole (DTP) with a strong electron acceptor thiadiazolobenzotriazole (TBZ) into a novel NIR-II optical absorptive conjugated polymer (PDTP-TBZ). Then, the c(RGDfK) cyclic peptide was modified on the surface of PT NPs to obtain multifunctional nanodiagnostic reagents (cRGD@PT NPs) that can effectively target GBM neovascularization and tumor cells. Both in vitro and in vivo experiments demonstrate that cRGD@PT NPs possess high photothermal conversion efficiency and practical photoacoustic imaging capability under 1064 nm laser irradiation. The results of this work suggested that cRGD@PT NPs have great potential in efficient NIR-II PTT guided by accurate PAI, which provide a good perspective for the treatment and diagnosis of GBM.

OBJECTIVE Previous studies have revealed an intraclass difference in major adverse cardiovascular events (MACE) among sulfonylureas. In vitro and ex vivo studies reported several sulfonylureas to exhibit high-affinity blockage of cardiac mitochondrial ATP-sensitive potassium (mitoKATP) channels and could interfere with ischemic preconditioning, the most important mechanism of self-cardiac protection. However, no studies have examined whether these varying binding affinities of sulfonylureas could account for their intraclass difference in MACE. We compared mitoKATP channel high-affinity versus low-affinity sulfonylureas regarding the MACE risk in real-world settings. RESEARCH DESIGN AND METHODS Using the Taiwan nationwide health care claims database, patients with type 2 diabetes initiating sulfonylurea monotherapy between 2007 and 2016 were included in the cohort study. A total of 33,727 new mitoKATP channel high-affinity (glyburide and glipizide) and low-affinity (gliclazide and glimepiride) sulfonylurea users, respectively, were identified after 1:1 propensity score matching. Cox proportional hazard models were used to estimate adjusted hazard ratios (aHRs) and 95% CI. RESULTS MitoKATP channel high-affinity sulfonylureas were associated with a significantly increased risk of three-point MACE (aHR 1.21 [95% CI 1.03–1.44]), ischemic stroke (aHR 1.23 [95% CI 1.02–1.50]), and cardiovascular death (aHR 2.61 [95% CI 1.31–5.20]), but not with that of myocardial infarction (aHR 1.04 [95% CI 0.75–1.46]). The duration-response analyses revealed the highest MACE risk to be within 90 days of therapy (aHR 4.67 [95% CI 3.61–6.06]). CONCLUSIONS Cardiac mitoKATP channel high-affinity sulfonylureas were associated with an increased MACE risk compared with low-affinity sulfonylureas in a nationwide population with diabetes.

Previous research has shown that the presence of a distractor object matching the current content of working memory interacts with visual search. Because finding a target and quitting a search without finding a target may be implemented by qualitatively different processes, it is possible that the effects of a memory-matching distractor on target-present trials and on target-absent trials reveal different mechanisms by which the memory-matching distractor interacts with visual search. Although previous studies have well established the effect of attentional capture by a memory-matching distractor when the target object is found in the search display, there remains an open question about whether the presence of a memory-matching distractor can affect the process of search termination when no target is found. In the present study, we showed that search termination times on target-absent trials were delayed by the presence of a distractor matching the content of visual working memory. This delayed quitting effect cannot be conceived of as a more general influence of visual short-term memory, because the presence of a distractor matching the content of passive visual short-term memory (i.e., visual priming) did not influence quitting behavior in visual search. These findings offer a novel perspective that distractors matching the information maintained in visual working memory can cause observers to delay search termination when no target has been found.

Various vaccine strategies have been developed to provide broad protection against diverse influenza viruses. The hemagglutinin (HA) stem is the major potential target of these vaccines. Enhancing immunogenicity and eliciting cross-protective immune responses are critical for HA stem-based vaccine designs. In this study, the A helix (Ah) and CD helix (CDh) from the HA stem were fused with ferritin, individually, or in tandem, yielding Ah-f, CDh-f and (A + CD)h-f nanoparticles (NPs), respectively. These NPs were produced through a prokaryotic expression system. After three immunizations with AS03-adjuvanted NPs in BALB/c mice

Medical supply chain communication networks engender critical information and data. Notably in the COVID era, inner personal and private information is being shared between healthcare providers regarding the medical supply chain. In recent years, multiple cyber-attacks have targeted medical supply chain communication networks due to their lack of security measures. In the era where cyber-attacks are cheaper and easier due to the computational power and various algorithms available for malicious uses, security, and data privacy requires intensive and higher measures. On the other hand, Information Hiding Techniques (IHT) compromise various advanced methods to hide sensitive information from being disclosed to malicious nodes. Moreover, with the support of Blockchain, IHT can bring higher security and the required privacy levels. In this paper, we propose the implementation of Blockchain and smart contract with the information hiding technique to enhance the security and privacy of data communication in critical systems, such as smart healthcare supply chain communication networks. Results show the feasibility of the framework using Hyperledger smart contract along with the desired security level.

Increasing body of recent studies determining the expression of tumor-specific major histocompatibility complex (MHC) class II protein supports its potential role in several malignancies, but little is known in human medullary thyroid cancer (MTC). Here, we report the expression of MHC-II and its clinicopathologic and prognostic relevance in MTC patients. Immunohistochemistry staining revealed a significant reduction in tumor cell-specific MHC-II expression in a higher AJCC stage and its poor prognostic correlation with human MTC development. Further statistical analysis identified the low MHC-II expression as a significant and independent risk factor for MTC recurrence and patient survival. Moreover, in vitro studies showed that the MHC-II expression was remarkably increased by RET inhibitors, which were prescribed to treat advanced MTC. Similarly, inhibitors blocking the MAPK/ERK and AKT/mTOR pathways also augmented MHC-II expression, suggesting their implications in RET-MHC-II signaling axis. Importantly, in vitro assays manifested enhanced peripheral blood leukocytes-mediated cytotoxicity in MTC cells treated with RET inhibitors, which were partially alleviated by HLA knock-down. Together, our study demonstrates that low MHC-II expression levels may serve as a prognostic biomarker for aggressive diseases in MTC patients and indicates that RET activation may promote MTC immune escape through downregulating MHC-II expression.

Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.

IntroductionIdiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterised by progressive and irreversible fibrosis of the lung parenchyma, resulting in reduced lung function. Since conventional medicines can be associated with low effective rates and adverse events, pulmonary rehabilitation may be a promising non-pharmacological therapy for IPF. Thus, we aimed to evaluate the effects of full-body exercise-based pulmonary rehabilitation on patients with IPF by conducting a systematic review and meta-analysis of randomised controlled trials (RCTs).Methods and analysisThis systematic review and meta-analysis has been registered in the International Prospective Register of Systematic Reviews (PROSPERO). From inception to 31 August 2022, electronic databases in English and Chinese were searched, including PubMed, Embase, Web of Science, Cochrane Central Register of Controlled Trials among the English databases. China National Knowledge Infrastructure, Chinese Biomedical Literature, VIP Chinese Science and Technology Periodical, and Wan Fang Data were among the Chinese databases. Two independent reviewers then screened the potential RCT studies, which were analysed according to the Cochrane Handbook criteria. The efficacy and safety of full-body exercise pulmonary rehabilitation for IPF were evaluated based on outcomes, including exercise capacity measured by 6 min walking distance and quality of life measured by St. George’s Respiratory Questionnaire. Lung function was measured based on the forced vital capacity, total lung capacity, diffusing capacity of the lungs for carbon monoxide and dyspnoea assessed by the modified Medical Research Council scale.Ethics and disseminationEthical approval was not required for this systematic review and meta-analysis. Results will be published in a peer-reviewed journal and presented at conferences.PROSPERO registration numberCRD42021284293.

Background Canine distemper virus (CDV), which is highly infectious, has caused outbreaks of varying scales in domestic and wild animals worldwide, so the development of a high-efficiency vaccine has broad application prospects. Currently, the commercial vaccine of CDV is an attenuated vaccine, which has the disadvantages of a complex preparation process, high cost and safety risk. It is necessary to develop a safe and effective CDV vaccine that is easy to produce on a large scale. In this study, sequences of CDV haemagglutinin (HA) from the Yanaka strain were aligned, and three potential linear sequences, termed YaH3, YaH4, and YaH5, were collected. To increase the immunogenicity of the epitopes, ferritin was employed as a self-assembling nanoparticle element. The ferritin-coupled forms were termed YaH3F, YaH4F, and YaH5F, respectively. A full-length HA sequence coupled with ferritin was also constructed as a DNA vaccine to compare the immunogenicity of nanoparticles in prokaryotic expression. Result The self-assembly morphology of the proteins from prokaryotic expression was verified by transmission electron microscopy. All the proteins self-assembled into nanoparticles. The expression of the DNA vaccine YaHF in HEK-293T cells was also confirmed in vitro. After subcutaneous injection of epitope nanoparticles or intramuscular injection of DNA YaHF, all vaccines induced strong serum titres, and long-term potency of antibodies in serum could be detected after 84 days. Strong anti-CDV neutralizing activities were observed in both the YaH4F group and YaHF group. According to antibody typing and cytokine detection, YaH4F can induce both Th1 and Th2 immune responses. The results of flow cytometry detection indicated that compared with the control group, all the immunogens elicited an increase in CD3. Simultaneously, the serum antibodies induced by YaH4F and YaHF could significantly enhance the ADCC effect compared with the control group, indicating that the antibodies in the serum effectively recognized the antigens on the cell surface and induced NK cells to kill infected cells directly. Conclusions YaH4F self-assembling nanoparticle obtained by prokaryotic expression has no less of an immune effect than YaHF, and H4 has great potential to become a key target for the easy and rapid preparation of epitope vaccines. Graphical Abstract

Reactive oxygen species (ROS)-induced vascular endothelial cell apoptosis is strongly associated with atherosclerosis progression. Herein, we aimed to examine whether Kansuinine A (KA), extracted from Euphorbia kansui L., prevents atherosclerosis development in a mouse model and inhibits cell apoptosis through oxidative stress reduction. Atherosclerosis development was analyzed in apolipoprotein E-deficient (ApoE−/−) mice fed a high-fat diet (HFD) using Oil Red O staining and H&amp, E staining. Human aortic endothelial cells (HAECs) were treated with KA, followed by hydrogen peroxide (H2O2), to investigate the KA-mediated inhibition of ROS-induced oxidative stress and cell apoptosis. Oil Red O staining and H&amp, E staining showed that atherosclerotic lesion size was significantly smaller in the aortic arch of ApoE−/− mice in the HFD+KA group than that in the aortic arch of those in the HFD group. Further, KA (0.1–1.0 μM) blocked the H2O2-induced death of HAECs and ROS generation. The H2O2-mediated upregulation of phosphorylated IKKβ, phosphorylated IκBα, and phosphorylated NF-κB was suppressed by KA. KA also reduced the Bax/Bcl-2 ratio and cleaved caspase-3 expression, preventing H2O2-induced vascular endothelial cell apoptosis. Our results indicate that KA may protect against ROS-induced endothelial cell apoptosis and has considerable clinical potential in the prevention of atherosclerosis and cardiovascular diseases.

Contrast-enhanced computed tomography revealed sclerotic enhancement of the T1 vertebral body and the vertebral bodies from C2 to T2.

The Brain Tumor Reporting and Data System (BT-RADS) category 3 is suitable for identifying cases with intermediate probability of tumor recurrence that do not meet the Response Assessment in Neuro-Oncology (RANO) criteria for progression. The aim of this study was to evaluate the added value of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC PWI) and diffusion-weighted imaging (DWI) to BT-RADS for differentiating tumor recurrence from non-recurrence in postoperative high-grade glioma (HGG) patients with category 3 lesions. Patients with BT-RADS category 3 lesions were included. The maximal relative cerebral blood volume (rCBVmax) and the mean apparent diffusion coefficient (ADCmean) values were measured. The added value of DSC PWI and DWI to BT-RADS was evaluated by receiver operating characteristic (ROC) curve analysis. Fifty-one of 91 patients had tumor recurrence, and 40 patients did not. There were significant differences in rCBVmax and ADCmean between the tumor recurrence group and non-recurrence group. Compared to BT-RADS alone, the addition of DSC PWI to BT-RADS increased the area under curve (AUC) from 0.76 (95% confidence interval [CI] 0.66–0.84) to 0.90 (95% CI 0.81–0.95) for differentiating tumor recurrence from non-recurrence. The addition of DWI to BT-RADS increased the AUC from 0.76 (95% CI 0.66–0.84) to 0.88 (95% CI 0.80–0.94). The combination of BT-RADS, DSC PWI, and DWI exhibited the best diagnostic performance (AUC = 0.95; 95% CI 0.88–0.98) for differentiating tumor recurrence from non-recurrence. Adding DSC PWI and DWI to BT-RADS can significantly improve the diagnostic performance for differentiating tumor recurrence from non-recurrence in BT-RADS category 3 lesions.

Napsin A is widely used in the diagnosis of lung adenocarcinoma and has also been reported to be positive in cases of thyroid carcinomas. We investigated napsin A levels through immunohistochemistry on whole sections of 210 primary thyroid tumors of various subtypes and another 41 metastatic thyroid carcinomas, and compared these with 125 primary and 25 metastatic lung adenocarcinomas. The results showed that napsin A was expressed in 23.8% thyroid tumors and 30.3% papillary thyroid carcinomas. Most cases showed a focal and weak to moderate expression. In comparison, 80.8% primary lung adenocarcinomas expressed napsin A, with mostly diffused and strong expression. For metastatic carcinomas of thyroid and lung origin, napsin A was detected in 39.0% of thyroid carcinomas in contrast to 88.0% in cases of lung adenocarcinomas. Comparisons of additional markers, TTF-1, CK7, thyroglobulin, and Pax-8 in metastatic carcinomas showed the overlapping expression of immunomarkers of TTF-1 and CK7. Thyroglobulin and Pax-8 were useful for distinguishing between metastatic carcinomas; however, Pax-8 may be a superior marker due to its higher sensitivity. The clinicopathological analysis of papillary thyroid carcinomas showed that the expression of napsin A was positively correlated with lymph node metastasis (p = 0.030). Here, we focused on the expression of napsin A in thyroid tumors and compared it with that in lung adenocarcinomas. The expression of napsin A is common in thyroid tumors and the combined expression of napsin A and TTF-1 in a metastatic thyroid carcinoma is a cause for concern due to chances of misdiagnosis as lung adenocarcinoma.

Background Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Results We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins. Conclusion We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.

The benefits of combination antiretroviral (ARV) prophylaxis for infants whose HIV exposure is recognized near birth have been established, and the benefits of early ARV therapy are well known. Decisions about ARVs can be supported by the probability that the child has acquired HIV.Using 2005-2010 data from Enhanced Perinatal Surveillance of the Centers for Disease Control and Prevention, we developed a tool for use at birth to help predict HIV acquisition of HIV-exposed infants to support ARV management. A logistic regression model, fit using a fully Bayesian approach, was used to determine maternal variables predictive of infant HIV acquisition. We created a score index from these variables, established the sensitivity and specificity of each possible score, and determined the distribution of scores among infants, with and without HIV, in our study population.Multivariable analysis of data from 8740 HIV-exposed infants (176 infected and 8564 uninfected) yielded 4 maternal variables in the perinatal HIV acquisition prediction model: sexually transmitted infection, substance use, last HIV viral load before delivery and ARV use. Using the regression coefficient estimates, we rescaled each possible score to make the maximum score equal to 100. For each score, sensitivity and specificity were determined; the area under the receiver operating characteristic curve was 0.79. Median index scores for infants with HIV and without HIV were 43 (first quartile 27 and third quartile 60), and 12 (first quartile, 0 and thirs quartile, 29), respectively.Decisions to begin infants on 3 ARVs-whether considered therapeutic or prophylactic-can be supported by data available on the day of birth.

We analyzed the number of circulating tumor cells (CTCs) and Epstein-Barr virus DNA (EBV DNA) for diagnosis, monitoring and prognosis of patients with metastatic nasopharyngeal carcinoma (mNPC). The levels of CTCs and EBV DNA were measured at baseline and after first-line chemotherapy in 148 mNPC patients prospectively enrolled between December 2014 and August 2016. We also collected 122 non-mNPC cases within the same time frame for examining CTCs and EBV DNA at baseline. In 270 NPC patients, we observed improved specificity (86.0% vs. 41.0%) and inferior sensitivity (42.3% vs. 81.3%) of CTCs as compared to EBV DNA for diagnosis of distant metastasis. mNPC patients were stratified into unfavorable and favorable prognostic groups, respectively, based on CTC of 12 at baseline and 1 after first-line chemotherapy and EBV DNA of 10,000 at baseline and 4,000 after first-line chemotherapy. Conversion of baseline unfavorable CTCs and EBV DNA to favorable after first-line chemotherapy was associated with significantly longer progression-free survival (PFS) and overall survival (OS) compared to patients with unfavorable CTCs and EBV DNA at both time points. Among patients with a complete/partial response as per imaging evaluation, favorable CTCs and EBV DNA levels after first-line chemotherapy were associated with significantly longer PFS and OS. In conclusion, our data demonstrated the number of CTCs and EBV DNA before, after and during first-line chemotherapy were strong predictive markers for mNPC patients. When utilized in conjunction with imaging studies, CTCs and EBV DNA could provide additional prognostic information.

The development of noninvasive brain imaging such as resting-state functional magnetic resonance imaging (rs-fMRI) and its combination with AI algorithm provides a promising solution for the early diagnosis of Autism spectrum disorder (ASD). However, the performance of the current ASD classification based on rs-fMRI still needs to be improved. This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI. In the framework, we proposed a novel filter feature selection method based on the difference between step distribution curves (DSDC) to select remarkable functional connectivities (FCs) and utilized a multilayer perceptron (MLP) which was pretrained by a simplified Variational Autoencoder (VAE) for classification. We also designed a pipeline consisting of a normalization procedure and a modified hyperbolic tangent (tanh) activation function to replace the original tanh function, further improving the model accuracy. Our model was evaluated by 10 times 10-fold cross-validation and achieved an average accuracy of 78.12%, outperforming the state-of-the-art methods reported on the same dataset. Given the importance of sensitivity and specificity in disease diagnosis, two constraints were designed in our model which can improve the model's sensitivity and specificity by up to 9.32% and 10.21%, respectively. The added constraints allow our model to handle different application scenarios and can be used broadly., Comment: 15 pages, 11 figures

Postsurgical recurrence of breast cancer is closely related to the inflammatory tumor microenvironment evoked by surgical wounds. Toll-like receptor 4 (TLR4) signaling contributes to NF-κB activation thus secreting various inflammatory cytokines. Herein, we developed an in situ photo-crosslinked hydrogel (D/T gel) concurrently loaded with doxorubicin (DOX) and a TLR4 antagonist, resatorvid (TAK-242). Its therapeutic effect against breast cancer postsurgical relapse was accomplished through remodeling the proinflammatory tumor microenvironment. The obtained gel network exhibited ideal biodegradability and biocompatibility, which motivated dermal wound healing in the full thickness wound model in mice. Despite the initial burst release of DOX, D/T gels exhibited extended-release of both DOX and TAK-242 for up to 21 days in vitro. TAK-242 was demonstrated to inhibit the lipopolysaccharide-induced NF-κB activation and downregulate TLR4 levels in both RAW264.7 and 4T1 cells. In a 4T1-Luc tumor postsurgical recurrence model, D/T gel significantly suppressed recurrent tumor growth by elevating the concentrations of DOX and TAK-242 at the tumor sites and remodeling the TLR4 activation-induced proinflammatory microenvironment. Overall, the D/T gel platform technology is proven to deliver therapeutics directly to the surgical wound bed, attenuating the dual inflammatory responses induced by DOX and surgical wounding thus greatly potentiating its efficacy in preventing postsurgical tumor recurrence. Statement of significance Postsurgical recurrence of breast cancer is closely related to the inflammatory tumor microenvironment (TME) evoked by surgical wounds. Although chemotherapeutics lead to extensive residual tumor cell necrosis, multiple inflammatory cytokines are secreted simultaneously, which are conducive to tumor recurrence. In this work, a TLR4 antagonist, TAK-242, was combined with DOX to reverse the dual inflammatory TME induced by surgical wounding and chemotherapy. To elevate the concentration of therapeutics at the tumor site, a photocrosslinked hydrogel (D/T gel) implant coloaded with TAK-242 and DOX was developed and applied on the postsurgical bed. Consequently, D/T gel attenuated the dual inflammatory responses and greatly potentiated its efficacy in preventing postsurgical tumor recurrence.