Search

Identifying essential proteins is of utmost importance in the field of biomedical research due to their essential functions in cellular activities and their involvement in mechanisms related to diseases. In this research, a novel approach called AttentionEP for predicting essential proteins (EP) is introduced by attention mechanisms. This method leverages both cross-attention and self-attention frameworks, focusing on enhancing prediction accuracy through the integration of features across diverse scales. Spatial characteristics of proteins are obtained from the protein-protein interaction (PPI) network by employing Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Following this, Bidirectional Long Short-Term Memory networks (BiLSTM) are employed to derive temporal features from gene expression datasets. Furthermore, spatial characteristics are derived by integrating data on subcellular localization with the application of Deep Neural Networks (DNN). In order to effectively integrate features across multiple scales, initial steps involve the application of self-attention techniques to derive essential insights from each unique data set. Following this, mechanisms involving self-attention and cross-attention are employed to enhance the interaction between diverse information sources. To identify essential proteins, a classifier based on the ResNet architecture is developed. The findings from the experiments indicate that the method introduced here shows superior performance in identifying essential proteins, recording an Area Under the Curve (AUC) value of 0.9433. This approach shows a considerable advantage over established techniques. The findings of this study provide a significant advancement in the comprehension of critical proteins, revealing promising potential for applications in the development of therapeutics and addressing various diseases.

Abstract Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I–III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p

BackgroundStroke is one of the major chronic non-communicable diseases (NCDs) with high morbidity, disability and mortality. The key to preventing stroke lies in controlling risk factors. However, screening risk factors and quantifying stroke risk levels remain challenging.MethodsA novel prediction model for stroke risk based on two-level feature selection and deep fusion network (SRPNet) is proposed to solve the problem mentioned above. First, the two-level feature selection method is used to screen comprehensive features related to stroke risk, enabling accurate identification of significant risk factors while eliminating redundant information. Next, the deep fusion network integrating Transformer and fully connected neural network (FCN) is utilized to establish the risk prediction model SRPNet for stroke patients.ResultsWe evaluate the performance of the SRPNet using screening data from the China Stroke Data Center (CSDC), and further validate its effectiveness with census data on stroke collected in affiliated hospital of Jining Medical University. The experimental results demonstrate that the SRPNet model selects features closely related to stroke and achieves superior risk prediction performance over benchmark methods.ConclusionsSRPNet can rapidly identify high-quality stroke risk factors, improve the accuracy of stroke prediction, and provide a powerful tool for clinical diagnosis.

Abstract Background SARS-CoV-2 infection is described as asymptomatic, mild, or moderate disease in most children. SARS-CoV-2 infection related death in children and adolescents is rare according to the current reports. COVID-19 cases increased significantly in China during the omicron surge, clinical data regarding pediatric critical patients infected with the omicron variant is limited. In this study, we aim to provide an overview of the clinical characteristics and outcomes of critically ill children admitted to a national children’s medical center in Guangdong Province, China, during the outbreak of the omicron variant infection. Methods We conducted a retrospective study from November 25, 2022, to February 8, 2023, which included 63 critically ill children, under the age of 18, diagnosed with SARS-CoV-2 infection. The patients were referred from medical institutions of Guangdong province. The medical records of these patients were analyzed and summarized. Results The median age of patients was 2 years (Interquartile Range, IQR: 1.0–8.0), sex-ratio (male/female) was 1.52. 12 (19%) patients (age ≥ 3 years) were vaccinated. The median length of hospital stay was 14 days (IQR: 6.5–23) in 63 cases, and duration of fever was 5 days (IQR: 3-8.5), pediatric intensive care unit (PICU) stay was 8 days (IQR 4.0–14.0) in 57 cases. 30 (48%) cases had clear contact history with family members who were infected with SARS-CoV-2. Three children who tested positive for SARS-CoV-2 infection did not show any abnormalities on chest imaging examination. Out of the total patients, 33 (52%) had a bacterial co-infection, with Staphylococcus aureus being the most commonly detected bacterial pathogen. Our cohort exhibited respiratory and nervous system involvement as the primary features. Furthermore, fifty (79%) patients required mechanical ventilation, with a median duration of 7 days (IQR 3.75–13.0). Among these patients, 35 (56%) developed respiratory failure, 16 (25%) patients experienced a deteriorating progression of symptoms and ultimately succumbed to the illness, septic shock was the most common condition among these patients (15 cases), followed by multiple organ failure in 12 cases, and encephalopathy identified in 7 cases. Conclusion We present a case series of critically ill children infected with the SARS-CoV-2 omicron variant. While there is evidence suggesting that Omicron may cause less severe symptoms, it is important to continue striving for measures that can minimize the pathogenic impact of SARS-CoV-2 infection in children.

Abstract Assessing changes in protein–protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the complexity of the biological mechanisms involved. In the present work, a geometric deep learning framework called MuToN is introduced for quantifying protein binding affinity change upon residue mutations. The method, designed with geometric attention networks, is mechanism‐aware. It captures changes in the protein binding interfaces of mutated complexes and assesses the allosteric effects of amino acids. Experimental results highlight MuToN's superiority compared to existing methods. Additionally, MuToN's flexibility and effectiveness are illustrated by its precise predictions of binding affinity changes between SARS‐CoV‐2 variants and the ACE2 complex.

Abstract Background Class attendance is important for academic performance. Personal interactions between teachers and students are difficult in large classes; the number of medical undergraduate students in China ranges from dozens to over 100. It is important for teachers to control the teaching process to improve student attendance and participation. Methods Two classes of fourth-year undergraduate medical students, with each class comprising 115 students, participated in the study. One class, the trial group, was taught by the block-based teaching method based on cybernetics. This study was conducted with three of the courses in the Introduction to Oncology subject, and the trial group’s courses included several blocks. Each block had a test paper that the students responded to immediately in class using the Internet. The teacher obtained feedback from the students when the rate of correct responses to block-test questions was less than 90%. The teacher adjusted the teaching in the following blocks according to the feedback information. The other class, the control group, was taught using the traditional lecture-based teaching method. Results The average attendance in the trial group was 104/115 (90.43%), and that in the control group was 83/115 (72.17%) (p = 0.0003). The teacher adjusted the teaching three times in the radiotherapy course owing to the complex ideas. After feedback, information on chemotherapy for the upper body was adjusted once, as was that on chemotherapy for the lower body, owing to students’ attitudes. The average total score of the trial group was 86.06 ± 17.46 and that of the control group was 80.38 ± 6.97 (p = 0.041). Questionnaire I showed that the trial group students’ attendance and participation were better than in the control group. Questionnaire II showed that the block-based teaching method based on cybernetics was approved by the students. Conclusions The block-based teaching method based on cybernetics used in medical classes with large numbers of Chinese undergraduate students had positive effects.

Summary: Background: Improved markers for predicting recurrence are needed to stratify patients with localised (stage I–III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities—clinical, genomic, and histopathological—to improve the predictive accuracy for localised renal cell carcinoma recurrence. Methods: In this retrospective analysis and validation study, we developed a histopathological whole-slide image (WSI)-based score using deep learning allied to digital scanning of conventional haematoxylin and eosin-stained tumour tissue sections, to predict tumour recurrence in a development dataset of 651 patients with distinctly good or poor disease outcome. The six single nucleotide polymorphism-based score, which was detected in paraffin-embedded tumour tissue samples, and the Leibovich score, which was established using clinicopathological risk factors, were combined with the WSI-based score to construct a multimodal recurrence score in the training dataset of 1125 patients. The multimodal recurrence score was validated in 1625 patients from the independent validation dataset and 418 patients from The Cancer Genome Atlas set. The primary outcome measured was the recurrence-free interval (RFI). Findings: The multimodal recurrence score had significantly higher predictive accuracy than the three single-modal scores and clinicopathological risk factors, and it precisely predicted the RFI of patients in the training and two validation datasets (areas under the curve at 5 years: 0·825–0·876 vs 0·608–0·793; p

Abstract Numerical methods are widely used to calculate the secure key rate of many quantum key distribution protocols in practice, but they consume many computing resources and are too time-consuming. In this work, we take the homodyne detection discrete-modulated continuous-variable quantum key distribution (CV-QKD) as an example, and construct a neural network that can quickly predict the secure key rate based on the experimental parameters and experimental results. Compared to traditional numerical methods, the speed of the neural network is improved by several orders of magnitude. Importantly, the predicted key rates are not only highly accurate but also highly likely to be secure. This allows the secure key rate of discrete-modulated CV-QKD to be extracted in real time on a low-power platform. Furthermore, our method is versatile and can be extended to quickly calculate the complex secure key rates of various other unstructured quantum key distribution protocols.

This paper describes an ensemble method with supervised module detection and further module prioritization for reliable network-based biomarker discovery. We design a module detection and ranking method called mRank to discover reliable network modules as cancer diagnostic biomarkers, with two procedures: (1) an iterative supervised module detection guided by phenotypic states in a specific network, (2) a block-based module ranking locally and globally via network topological centrality. We validate its effectiveness and efficiency by identifying hepatocellular carcinoma (HCC) network modules on a comprehensive gene regulatory network with specifying gene interactions by HCC RNA-seq data from the Cancer Genome Atlas (TCGA). These top-ranked modules by mRank get a mean AUC of 0.995 on TCGA HCC dataset with 371 tumor samples and 50 controls by cross-validation SVM. Based on the prior knowledge of cancer dysfunctions enriched in top-ranked modules, 69 genes are identified as HCC candidate biomarkers. They are further validated in independent cohorts with a classifier trained on TCGA HCC dataset. A mean AUC of 0.846 is achieved in distinguishing 976 disease samples from 827 controls. Moreover, some known HCC signatures such as AFP and SPP1 are also included in our identified biomarkers. mRank enables us to find more reliable network modules for cancer diagnosis. For a proof-of-concept study, we validate it in identifying HCC network biomarkers and it is generalizable to other cancers or complex disease. The overall results have demonstrated that mRank can find effective network biomarkers for cancer diagnosis which result in less false positives.

Abstract Background Mining gene regulatory network (GRN) is an important avenue for addressing cancer mechanism. Mutations in cancer genome perturb GRN and cause a rewiring in an orchestrated network. Hence, the exploration of gene regulatory network rewiring is significant to discover potential biomarkers and indicators for discriminating cancer phenotypes. Results Here, we propose a new bioinformatics method of identifying biomarkers based on network rewiring in different states. It firstly reconstructs GRN in different phenotypic conditions from gene expression data with a priori background network. We employ the algorithm based on path consistency algorithm and conditional mutual information to delete false-positive regulatory interactions between independent nodes/genes or not closely related gene pairs. And then a differential gene regulatory network (D-GRN) is constructed from the rewiring parts in the two phenotype-specific GRNs. Community detection technique is then applied for D-GRN to detect functional modules. Finally, we apply logistic regression classifier with recursive feature elimination to select biomarker genes in each module individually. The extracted feature genes result in a gene set of biomarkers with impressing ability to distinguish normal samples from controls. We verify the identified biomarkers in external independent validation datasets. For a proof-of-concept study, we apply the framework to identify diagnostic biomarkers of breast cancer. The identified biomarkers obtain a maximum AUC of 0.985 in the internal sample classification experiments. And these biomarkers achieve a maximum AUC of 0.989 in the external validations. Conclusion In conclusion, network rewiring reveals significant differences between different phenotypes, which indicating cancer dysfunctional mechanisms. With the development of sequencing technology, the amount and quality of gene expression data become available. Condition-specific gene regulatory networks that are close to the real regulations in different states will be established. Revealing the network rewiring will greatly benefit the discovery of biomarkers or signatures for phenotypes. D-GRN is a general method to meet this demand of deciphering the high-throughput data for biomarker discovery. It is also easy to be extended for identifying biomarkers of other complex diseases beyond breast cancer.

Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quantum neural computing using (classically controlled) single-qubit operations and measurements on real-world quantum systems with naturally occurring environment-induced decoherence, which greatly reduces the difficulties of physical implementations. Our model circumvents the problem that the state-space size grows exponentially with the number of neurons, thereby greatly reducing memory requirements and allowing for fast optimization with traditional optimization algorithms. We benchmark our model for handwritten digit recognition and other nonlinear classification tasks. The results show that our model has an amazing nonlinear classification ability and robustness to noise. Furthermore, our model allows quantum computing to be applied in a wider context and inspires the earlier development of a quantum neural computer than standard quantum computers.

Abstract Background The successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group. Methods Regularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, $$L_{0}$$ L 0 -RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts. Results The nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ( $$P

Continuous-variable quantum key distribution offers simple, stable and easy-to-implement key distribution systems. The discrete modulation scheme further reduces the technical difficulty. The main regret is that the security of discrete modulation schemes has not been sufficiently demonstrated. Schemes with different signal state distributions use various physical conditions to obtain the key rate formula, resulting in different security levels, computation complexities and implementation difficulties. Therefore, a relatively systematic and logically consistent security proof against most general attacks is worth exploring. On the other hand, extending the discrete modulation scheme and its variants to different applications, such as satellite-to-earth communication, can further activate and advance this field. Here, we briefly review the achievements that have been made in discrete-modulated continuous-variable quantum key distribution, and openly discuss some issues worthy of further research.

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common cancers. The discovery of specific genes severing as biomarkers is of paramount significance for cancer diagnosis and prognosis. The high-throughput omics data generated by the cancer genome atlas (TCGA) consortium provides a valuable resource for the discovery of HCC biomarker genes. Numerous methods have been proposed to select cancer biomarkers. However, these methods have not investigated the robustness of identification with different feature selection techniques. Methods We use six different recursive feature elimination methods to select the gene signiatures of HCC from TCGA liver cancer data. The genes shared in the six selected subsets are proposed as robust biomarkers. Akaike information criterion (AIC) is employed to explain the optimization process of feature selection, which provides a statistical interpretation for the feature selection in machine learning methods. And we use several methods to validate the screened biomarkers. Results In this paper, we propose a robust method for discovering biomarker genes for HCC from gene expression data. Specifically, we implement recursive feature elimination cross-validation (RFE-CV) methods based on six different classication algorithms. The overlaps in the discovered gene sets via different methods are referred as the identified biomarkers. We give an interpretation of the feature selection process based on machine learning using AIC in statistics. Furthermore, the features selected by the backward logistic stepwise regression via AIC minimum theory are completely contained in the identified biomarkers. Through the classification results, the superiority of interpretable robust biomarker discovery method is verified. Conclusions It is found that overlaps among gene subsets contain different quantitative features selected by the RFE-CV of 6 classifiers. The AIC values in the model selection provide a theoretical foundation for the feature selection process of biomarker discovery via machine learning. What’s more, genes containing in more optimally selected subsets make better biological sense and implication. The quality of feature selection is improved by the intersections of biomarkers selected from different classifiers. This is a general method suitable for screening biomarkers of complex diseases from high-throughput data.

Gene regulatory network (GRN) provides abundant information on gene interactions, which contributes to demonstrating pathology, predicting clinical outcomes, and identifying drug targets. Existing high-throughput experiments provide rich time-series gene expression data to reconstruct the GRN to further gain insights into the mechanism of organisms responding to external stimuli. Numerous machine-learning methods have been proposed to infer gene regulatory networks. Nevertheless, machine learning, especially deep learning, is generally a “black box,” which lacks interpretability. The causality has not been well recognized in GRN inference procedures. In this article, we introduce grey theory integrated with the adaptive sliding window technique to flexibly capture instant gene–gene interactions in the uncertain regulatory system. Then, we incorporate generalized multivariate Granger causality regression methods to transform the dynamic grey association into causation to generate directional regulatory links. We evaluate our model on the DREAM4 in silico benchmark dataset and real-world hepatocellular carcinoma (HCC) time-series data. We achieved competitive results on the DREAM4 compared with other state-of-the-art algorithms and gained meaningful GRN structure on HCC data respectively.

Background We showed that Beclin‐1‐dependent autophagy protects the heart in young and adult mice that underwent endotoxemia. Herein, we compared the potential therapeutic effects of Beclin‐1 activating peptide, TB‐peptide, on endotoxemia‐induced cardiac outcomes in young adult and aged mice. We further evaluated lipopolysaccharide (lipopolysaccharide)‐induced and TB‐peptide treatment‐mediated alterations in myocardial metabolism. Methods and Results C57BL/6J mice that were 10 weeks and 24 months old were challenged by lipopolysaccharide using doses at which cardiac dysfunction occurred. Following the treatment of TB‐peptide or control vehicle, heart contractility, circulating cytokines, and myocardial autophagy were evaluated. We detected that TB‐peptide boosted autophagy, attenuated cytokines, and improved cardiac performance in both young and aged mice during endotoxemia. A targeted metabolomics assay was designed to detect a pool of 361 known metabolites, of which 156 were detected in at least 1 of the heart tissue samples. Lipopolysaccharide‐induced impairments were found in glucose and amino acid metabolisms in mice of all ages, and TB‐peptide ameliorated these alterations. However, lipid metabolites were upregulated in the young group but moderately downregulated in the aged by lipopolysaccharide, suggesting an age‐dependent response. TB‐peptide mitigated lipopolysaccharide‐mediated trend of lipids in the young mice but had little effect on the aged. (Study registration: Project DOI: https://doi.org/10.21228/M8K11W). Conclusions Pharmacological activation of Beclin‐1 by TB‐peptide is cardiac protective in both young and aged population during endotoxemia, suggest a therapeutic potential for sepsis‐induced cardiomyopathy. Metabolomics analysis suggests that an age‐independent protection by TB‐peptide is associated with reprograming of energy production via glucose and amino acid metabolisms.

Abstract Background Fecundity is important for farm blue fox (Vulpes lagopus), who with asthenospermia have be a problem in some of farms in China. A key symptom of asthenospermia is decreased sperm motility. The decreased secreting beta-defensin108 (vBD108) of blue fox is speculated be related to asthenospermia. To clarify this idea, the mRNA expression of vBD108 in testis and epididymis of blue foxes with asthenospermia were detected and compared to the healthy one. The antibody was prepared and analyzed by immunohistochemistry. Results The vBD108 in testis and epididymis was found both in blue fox with asthenospermia and healthy group by the method of immunohistochemistry. The expression of vBD108 mRNA in testes (P

In this work, we provide a computational method of regularized logistic regression for discovering biomarkers of spontaneous preterm birth (SPTB) from gene expression data. The successful identification of SPTB biomarkers will greatly benefit the interference of infant gestational age for reducing the risks of pregnant women and preemies. In recent years, various approaches have been proposed for the feature selection of identifying the subset of meaningful genes that can achieve accurate classification for disease samples from controls. Here, we comprehensively summarize the regularized logistic regression with seven effective penalties developed for the selection of strongly indicative genes of SPTB from microarray data. We compare their properties and assess their classification performances in multiple datasets. It shows that elastic net, lasso, L1/2 and SCAD penalties get the better performance than others and can be successfully used to identify biomarkers of SPTB. Particularly, we make a functional enrichment analysis on these biomarkers and construct a logistic regression classifier based on them. The classifier generates an indicator of preterm risk score (PRS) for predicting SPTB. Based on the trained predictor, we verify the identified biomarkers on an independent dataset. The biomarkers achieve the AUC value of 0.933 in the SPTB classification. The results demonstrate the effectiveness and efficiency of the built-up strategy of biomarker discovery with regularized logistic regression. Obviously, the proposed method of discovering biomarkers for SPTB can be easily extended for other complex diseases.

lncRNA–protein interactions play essential roles in a variety of cellular processes. However, the experimental methods for systematically mapping of lncRNA–protein interactions remain time-consuming and expensive. Therefore, it is urgent to develop reliable computational methods for predicting lncRNA–protein interactions. In this study, we propose a computational method called LncPNet to predict potential lncRNA–protein interactions by embedding an lncRNA–protein heterogenous network. The experimental results indicate that LncPNet achieves promising performance on benchmark datasets extracted from the NPInter database with an accuracy of 0.930 and area under ROC curve (AUC) of 0.971. In addition, we further compare our method with other eight state-of-the-art methods, and the results illustrate that our method achieves superior prediction performance. LncPNet provides an effective method via a new perspective of representing lncRNA–protein heterogenous network, which will greatly benefit the prediction of lncRNA–protein interactions.

Background: Mitochondrial deficiency is a known pathology in sepsis-induced organ failure. We previously found that mitochondria-associated membranes (MAMs), a subcellular domain supporting mitochondrial status, are impaired in the heart during endotoxemia, suggesting a mechanism of mitochondrial damage occurred in sepsis. Mitophagy pathway via E3 ubiquitin ligase Parkin and PTEN-induced kinase 1 (PINK1) controls mitochondrial quality. Studies described here examined the impact of Parkin on cardiac MAMs and endotoxemia-induced cardiomyopathy. Additionally, point mutation W403A in Parkin was previously identified as a constitutively active mutation in vitro. In vivo effects of forced expression of this mutation were evaluated in the endotoxemia model.Methods: Mice of wild type (WT), Parkin-deficiency (Park2−/−), and knock-in expression of Parkin W402A (human Parkin W403A) were given lipopolysaccharide (LPS) challenge. Cardiac function was evaluated by echocardiography. In the harvested heart tissue, MAM fractions were isolated by ultracentrifugation, and their amount and function were quantified. Ultrastructure of MAMs and mitochondria was examined by electron microscopy. Mitochondrial respiratory activities were measured by enzyme assays. Myocardial inflammation was estimated by levels of pro-inflammatory cytokine IL-6. Myocardial mitophagy was assessed by levels of mitophagy factors associated with mitochondria and degrees of mitochondria-lysosome co-localization. Parkin activation, signified by phosphorylation on serine 65 of Parkin, was also evaluated.Results: Compared with WT, Park2−/− mice showed more severely impaired cardiac MAMs during endotoxemia, characterized by disrupted structure, reduced quantity, and weakened transporting function. Endotoxemia-induced cardiomyopathy was intensified in Park2−/− mice, shown by worsened cardiac contractility and higher production of IL-6. Mitochondria from the Park2−/− hearts were more deteriorated, indicated by losses in both structural integrity and respiration function. Unexpectedly, mice carrying Parkin W402A showed similar levels of cardiomyopathy and mitochondrial damage when compared with their WT counterparts. Further, Parkin W402A mutation neither enhanced mitophagy nor increased Parkin activation in myocardium under the challenge of endotoxemia.Conclusion: our results suggest that Parkin/PINK1 mitophagy participates in the regulation of cardiac MAMs during endotoxemia. Point mutation W402A (human W403A) in Parkin is not sufficient to alleviate cardiomyopathy induced by endotoxemia in vivo.

The androgen receptor (AR) plays a central role in prostate cancer. Development of castration resistant prostate cancer (CRPC) requires androgen-independent activation of AR, which involves its large N-terminal domain (NTD) and entails extensive epigenetic changes depending in part on histone lysine demethylases (KDMs) that interact with AR. The AR-NTD is rich in low-complexity sequences, including a polyQ repeat. Longer polyQ sequences were reported to decrease transcriptional activity and to protect against prostate cancer, although they can lead to muscular atrophy. However, the molecular mechanisms underlying these observations are unclear. Using NMR spectroscopy, here we identify weak interactions between the AR-NTD and the KDM4A catalytic domain, and between the AR ligand-binding domain and a central KDM4A region that also contains low-complexity sequences. We also show that the AR-NTD can undergo liquid-liquid phase separation in vitro, with longer polyQ sequences phase separating more readily. Moreover, longer polyQ sequences hinder nuclear localization in the absence of hormone and increase the propensity for formation of AR-containing puncta in the nucleus of cells treated with dihydrotestosterone. These results lead us to hypothesize that polyQ-dependent liquid-liquid phase separation may provide a mechanism to decrease the transcriptional activity of AR, potentially opening new opportunities to design effective therapies against CRPC and muscular atrophy.

AIM: To investigate the secretion levels of serum tumor markers neuron specific enolase(NSE), glycoprotein antigen CA153 and CA199 in patients with retinoblastoma(RB).METHODS: Data of 42 RB patients who received chemotherapy at Shenzhen People's Hospital between October 2017 and October 2019 were collected. The RB group was regrouped according to early and mid-term/advanced stages, and single/double eye involvement, the levels of serum tumor markers were compared between these subgroups and different genders.RESULTS: The levels of tumor markers NSE, CA153 and CA199 in the advanced group were higher than those in the early and mid-term groups(49.69±18.45ng/mL vs 36.18±14.92ng/mL, 22.38±12.03U/mL vs 15.10±8.32U/mL, 46.44±18.76U/mL vs 30.21±24.03U/mL, P0.05).CONCLUSION: Serum NSE, CA153 and CA199 are significantly increased in advanced group than early and mid-term group, and they are of great significance in the diagnosis and treatment of RB. The difference between NSE in single and double eyes is worthy of further study.

Six piperazine derivatives 6a–f containing 1,4-benzodioxan moiety have been synthesized and characterized by 1H NMR, ESI-MS and elemental analysis. The structure of 6d was further confirmed by single crystal X-ray diffraction. All these novel compounds were screened for their in vivo anti-inflammatory activity employing classical para-xylene-induced mice ear-swelling model. The results revealed that most of the target compounds showed significant anti-inflammatory activities, especially compound 6a with ortho-substituted methoxy group on the phenylpiperazine ring exhibited the best activity among the designed compounds.

Researchers have shown that adult zebrafish have the potential to regenerate 20% of the ventricular muscle within two months of apex resection, and neonatal mice have the capacity to regenerate their heart after apex resection up until day 7 after birth. The goal of this study was to determine if large mammals (porcine heart model) have the capability to fully regenerate a resected portion of the left ventricular apex during the neonatal stage, and if so, how long the regenerative potential persists. A total of 36 piglets were divided into the following groups: 0-day control and surgical groups and seven-day control and surgical groups. For the apex removal groups, each piglet was subjected to a partial wall thickness resection (~30% of the ventricular wall thickness). Heart muscle function was assessed via transthoracic echocardiograms; the seven-day surgery group experienced a decrease in ejection fraction and fractional shortening. Upon gross necropsy, for piglets euthanized four weeks post-surgery, all 0-day-old hearts showed no signs of scarring or any indication of the induced injury. Histological analysis confirmed that piglets in the 0-day surgery group exhibited various degrees of regeneration, with half of the piglets showing full regeneration and the other half showing partial regeneration. However, each piglet in the seven-day surgery group demonstrated epicardial fibrosis along with moderate to severe dissecting interstitial fibrosis, which was accompanied by an abundant collagenous extracellular matrix as the result of a scar formation in the resection site. Histology of one 0-day apex resection piglet (briefly lain on and accidentally killed by the mother sow three days post-surgery) revealed dense, proliferative mesenchymal cells bordering the fibrin and hemorrhage zone and differentiating toward immature cardiomyocytes. We further examined the heart explants at 5-days post-surgery (5D PO) and 1-week post-surgery (1W PO) to assess the repair progression. For the 0-day surgery piglets euthanized at 5D PO and 1W PO, half had abundant proliferating mesenchymal cells, suggesting active regeneration, while the other half showed increased extracellular collagen. The seven-day surgery piglets euthanized at 5D PO, and 1W PO showed evidence of greatly increased extracellular collagen, while some piglets had proliferating mesenchymal cells, suggesting a regenerative effort is ongoing while scar formation seems to predominate. In short, our qualitative findings suggest that the piglets lose the full myocardial regenerative potential by 7 days after birth, but greatly preserve the regenerative potential within 1 day post-partum.

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput technique that can measure gene expression, reveal cell heterogeneity, rare and complex cell populations, and discover cell types and their relationships. The analysis of scRNA-seq data is challenging because of transcripts sparsity, replication noise, and outlier cell populations. A gene coexpression network (GCN) analysis effectively deciphers phenotypic differences in specific states by describing gene–gene pairwise relationships. The underlying gene modules with different coexpression patterns partially bridge the gap between genotype and phenotype. This study presents a new framework called scGENA (single-cell gene coexpression network analysis) for GCN analysis based on scRNA-seq data. Although there are several methods for scRNA-seq data analysis, we aim to build an integrative pipeline for several purposes that cover primary data preprocessing, including data exploration, quality control, normalization, imputation, and dimensionality reduction of clustering as downstream of GCN analysis. To demonstrate this integrated workflow, an scRNA-seq dataset of the human diabetic pancreas with 1600 cells and 39,851 genes was implemented to perform all these processes in practice. As a result, scGENA is demonstrated to uncover interesting gene modules behind complex diseases, which reveal biological mechanisms. scGENA provides a state-of-the-art method for gene coexpression analysis for scRNA-seq data.

Harmful algal blooms are a major type of marine disaster that endangers the marine ecological environment and human health. Since the algal bloom is a complex nonlinear process with time characteristics, traditional statistical methods often cannot provide good predictions. In this paper, we propose a method based on machine learning with the aim to predict the occurrence of algal blooms by environmental parameters. The features related to algal bloom growth have been experimented for achieving a good prediction of algal concentrations by a combination strategy. We validate the prediction performance on two real datasets from two locations in US and China, i.e., Scripps Pier, California and Sishili Bay, Shandong, respectively. The models and feature subsets have been selected to complete the missing data and predict the phytoplankton concentration. The results demonstrate the efficiency of our method in the short-term prediction of concentrations by selecting appropriate features. The comparison studies prove the advantage of our developed machine learning method. The importance of every features for the prediction performance reveals crucial factors for the outbreak of harmful algal blooms.

Education is essential to cultivate the innovations. The Nobel prize of physiology or medicine (medicine in short) has been awarded to the breakthrough achievement in medicine. It is of paramount interest to investigate the educational backgrounds of Nobel prize laureates in medicine for discovering their education characteristics. To this end, we compile an integrative and comprehensive dataset about the educational experiences of all the Nobel awardees focusing on their undergraduate and doctoral majors. Then we implement the summary statistics of numerous majors in current university disciplinary system and calculate the proportion of each major accordingly. The results demonstrate that 49.5% of the laureates of Nobel prize in medicine have multidisciplinary educational background and 69% of their undergraduate majors are not directly in medicine. After removing the laureates who graduated from the countries that do not provide undergraduate medical education, the percentage is still as high as 52%. To further check our findings, we compare the similarity and difference between the laureates in medicine and those in physics and chemistry. The percentages in the other two sciences are significantly different from those in medicine, which illustrates a distinct degree of specificity. The comparison confirms the uniqueness of academic backgrounds in major medical innovations, such as those awarded by Nobel prizes. The data-driven analysis highlights the specific pattern and inspires the urgent necessity of introducing science education in medicine for cultivating the original and cutting-edge medical innovations, especially in the undergraduate program.

The accurate prediction of gross box-office markets is of great benefit for investment and management in the movie industry. In this work, we propose a machine learning-based method for predicting the movie box-office revenue of a country based on the empirical comparisons of eight methods with diverse combinations of economic factors. Specifically, we achieved a prediction performance of the relative root mean squared error of 0.056 in the US and of 0.183 in China for the two case studies of movie markets in time-series forecasting experiments from 2013 to 2016. We concluded that the support-vector-machine-based method using gross domestic product reached the best prediction performance and satisfies the easily available information of economic factors. The computational experiments and comparison studies provided evidence for the effectiveness and advantages of our proposed prediction strategy. In the validation process of the predicted total box-office markets in 2017, the error rates were 0.044 in the US and 0.066 in China. In the consecutive predictions of nationwide box-office markets in 2018 and 2019, the mean relative absolute percentage errors achieved were 0.041 and 0.035 in the US and China, respectively. The precise predictions, both in the training and validation data, demonstrate the efficiency and versatility of our proposed method.

Histone lysine demethylases (KDMs) can mediate transcriptional reprogramming in disease states. Here the authors show that KDM3A promotes left ventricular hypertrophy and cardiac fibrosis by activating the transcription of Timp1, and that pharmacological inhibition of KDM3A attenuates cardiac remodeling induced by pressure overload.

Abstract Altered mean platelet volume (MPV) is found in several malignancies. Remarkably, there is little consensus on using the value of MPV in the prognostic evaluations of renal cell carcinoma (RCC). The aim of this study is to examine the feasibility of MPV value as a prognostic indicator of RCC. The retrospective study recruited 306 consecutive RCC patients between January 2009 and December 2009. The relationships between MPV and clinicopathological characteristics were analyzed. Kaplan-Meier method and Cox regression were used to evaluate the prognostic impact of MPV. Of the 306 RCC patients, low MPV levels were detected in 61 (19.9%) patients. Reduced MPV was associated with histology types, T classification, UCLA Integrated Scoring System (UISS) category, and Mayo clinic stage, size, grade, and necrosis score (SSIGN) category (P

Abstract Activated platelets promote cancer progression and metastasis. Nevertheless, the prognostic value of platelet indices in melanoma had been rarely reported. The aim of this study was to investigate the predictive significance of platelet indices in melanoma. A total of 220 consecutive patients with melanoma were retrospectively enrolled between January 2009 and December 2009. The relationship between PDW and clinicopathological characteristics were analyzed. Kaplan-Meier method and Cox regression were used to evaluate the prognostic impact of PDW. Of the 220 patients, high platelet distribution width (PDW) levels were observed in 63 (28.6%) patients. Increased PDW was associated with tumor subtype (P

Alzheimer's disease (AD) is a neurodegenerative and progressive disease, which often causes irreversible damages to the cerebrum. The pathogenesis of AD is far from being fully understood, while there are some popular hypotheses. So far, the diagnosis of AD relies only on clinical screening in the form of imaging techniques or cerebrospinal fluid analysis, which may lead to inaccurate evaluation and then cause the delay of suitable treatments. While molecular biomarkers provide promising alternatives of establishing correct relationships between genotypes and phenotypes of clinical symptoms. In this paper, we propose a machine-learning-based method of identifying potential diagnostic biomarkers of AD based on gene coexpression network by integrating gene expression profiles in six brain regions. After building an integrated gene coexpression network of multiple brain regions, we decompose the differential network into some subnetwork modules. The module candidates from these coexpressed gene communities are then identified by screening their discriminative powers in control from disease samples. The potential biomarkers are then validated by multiple cross-validations and functional enrichment analyses. If the biomarkers successfully pass clinical significance tests, they can be used as a reference for clinical diagnosis after wet-experimental validations.

In this work, we provide a comparative study of the main available association measures for characterizing gene regulatory strengths. Detecting the association between genes (as well as RNAs, proteins, and other molecules) is very important to decipher their functional relationship from genomic data in bioinformatics. With the availability of more and more high-throughput datasets, the quantification of meaningful relationships by employing association measures will make great sense of the data. There are various quantitative measures have been proposed for identifying molecular associations. They are depended on different statistical assumptions, for different intentions, as well as with different computational costs in calculating the associations in thousands of genes. Here, we comprehensively summarize these association measures employed and developed for describing gene regulatory relationships. We compare these measures in their consistency and specificity of detecting gene regulations from both simulation and real gene expression profiling data. Obviously, these measures used in genes can be easily extended in other biological molecules or across them.

Current approaches to study transcriptional profiles post influenza infection typically rely on tissue sampling from one or two sites at a few time points, such as spleen and lung in murine models. In this study, we infected female C57/BL6 mice intranasally with mouse-adapted H3N2/Hong Kong/X31 avian influenza A virus, and then analyzed the gene expression profiles in four different compartments (blood, lung, mediastinal lymph nodes, and spleen) over 11 consecutive days post infection. These data were analyzed by an advanced statistical procedure based on ordinary differential equation (ODE) modeling. Vastly different lists of significant genes were identified by the same statistical procedure in each compartment. Only 11 of them are significant in all four compartments. We classified significant genes in each compartment into co-expressed modules based on temporal expression patterns. We then performed functional enrichment analysis on these co-expression modules and identified significant pathway and functional motifs. Finally, we used an ODE based model to reconstruct gene regulatory network (GRN) for each compartment and studied their network properties.

The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.

High-throughput biological data offer an unprecedented opportunity to fully characterize biological processes. However, how to extract meaningful biological information from these datasets is a significant challenge. Recently, pathway-based analysis has gained much progress in identifying biomarkers for some phenotypes. Nevertheless, these so-called pathway-based methods are mainly individual-gene-based or molecule-complex-based analyses. In this paper, we developed a novel module-based method to reveal causal or dependent relations between network modules and biological phenotypes by integrating both gene expression data and protein-protein interaction network. Specifically, we first formulated the identification problem of the responsive modules underlying biological phenotypes as a mathematical programming model by exploiting phenotype difference, which can also be viewed as a multi-classification problem. Then, we applied it to study cell-cycle process of budding yeast from microarray data based on our biological experiments, and identified important phenotype- and transition-based responsive modules for different stages of cell-cycle process. The resulting responsive modules provide new insight into the regulation mechanisms of cell-cycle process from a network viewpoint. Moreover, the identification of transition modules provides a new way to study dynamical processes at a functional module level. In particular, we found that the dysfunction of a well-known module and two new modules may directly result in cell cycle arresting at S phase. In addition to our biological experiments, the identified responsive modules were also validated by two independent datasets on budding yeast cell cycle.