Exploring the dynamics and interplay of human papillomavirus and cervical tumorigenesis by integrating biological data into a mathematical model

Spatiotemporal Transformer Neural Network for Time-Series Forecasting

Predicting high-dimensional short-term time-series is a difficult task due to the lack of sufficient information and the curse of dimensionality. To overcome these problems, this study proposes a novel spatiotemporal transformer neural network (STNN) for efficient prediction of short-term time-series with three major features. Firstly, the STNN can accurately and robustly predict a high-dimensional short-term time-series in a multi-step-ahead manner by exploiting high-dimensional/spatial information based on the spatiotemporal information (STI) transformation equation. Secondly, the continuous attention mechanism makes the prediction results more accurate than those of previous studies. Thirdly, we developed continuous spatial self-attention, temporal self-attention, and transformation attention mechanisms to create a bridge between effective spatial information and future temporal evolution information. Fourthly, we show that the STNN model can reconstruct the phase space of the dynamical system, which is explored in the time-series prediction. The experimental results demonstrate that the STNN significantly outperforms the existing methods on various benchmarks and real-world systems in the multi-step-ahead prediction of a short-term time-series.

An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection

Motivation:Brucella, the causative agent of brucellosis, is a global zoonotic pathogen that threatens both veterinary and human health. The main sources of brucellosis are farm animals. Importantly, the bacteria can be used for biological warfare purposes, requiring source tracking and routine surveillance in an integrated manner. Additionally, brucellosis is classified among group B infectious diseases in China and has been reported in 31 Chinese provinces to varying degrees in urban areas. From a national biosecurity perspective, research on brucellosis surveillance has garnered considerable attention and requires an integrated platform to provide researchers with easy access to genomic analysis and provide policymakers with an improved understanding of both reported patients and detected cases for the purpose of precision public health interventions.

Results: For the first time in China, we have developed a comprehensive information platform for Brucella based on dynamic visualization of the incidence (reported patients) and prevalence (detected cases) of brucellosis in mainland China. Especially, our study establishes a knowledge graph for the literature sources of Brucella data so that it can be expanded, queried, and analyzed. When similar “epidemiological comprehensive platforms” are established in the distant future, we can use knowledge graph to share its information. Additionally, we propose a software package for genomic sequence analysis. This platform provides a specialized, dynamic, and visual point-and-click interface for studying brucellosis in mainland China and improving the exploration of Brucella in the fields of bioinformatics and disease prevention for both human and veterinary medicine.

Implications of Persistent HPV52 and HPV58 Positivity for the Management of Cervical Lesions

Objective

This study aimed to compare the variability of HPV16/18/52/58 subtype infections in patients with different cervical lesions, to explore the guiding significance of persistent positive HPV subtypes 52 and 58 in the stratified management of cervical lesions, and to determine the appropriate management model.

Method

This study was conducted through a retrospective analysis of 244,218 patients who underwent HPV testing at the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University from September 2014 to December 2020 to examine the distribution of different types of HPV infection. From March 2015 to September 2017, 3,014 patients with known HPV underwent colposcopy to analyze high-risk HPV infection for different cervical lesions. Meanwhile, from September 2014 to December 2020, 1,616 patients positive for HPV16/18/52/58 alone with normal TCT who underwent colposcopy in our hospital were retrospectively analyzed for the occurrence of cervical and vulvovaginal lesions, with colposcopic biopsy pathology results serving as the gold standard for statistical analysis.

Result

Analysis of 244,218 patients who had HPV tested revealed that the top 3 high-risk HPV types were HPV52, HPV58, and HPV16. Further analysis of 3,014 patients showed that 78.04% of patients referred for colposcopy had HPV16/18/52/58 alone. Among high-grade squamous intraepithelial lesions (HSIL) and cervical cancer, the most common is HPV16, followed by HPV58 and then HPV52 (p < 0.05). A total of 1,616 patients with normal TCT who were referred for colposcopy due to HPV16/18/52/58 infection were further analyzed. Based on pathological findings in lesions of HSIL and CC, HPV16 is the most common, followed by HPV58 and then HPV18 (p < 0.05). In the 1,616 patients analyzed, high-grade vulvovaginal lesions were detected, with HPV58 being the most common, followed by HPV16 and then HPV52 (p < 0.05).

Conclusion

1. In patients with positive HPV58 alone and normal TCT, the indications for colposcopy may be relaxed, with particular attention paid to the possibility of vulvar and vaginal lesions.2. Patients with a positive HPV type 52 alone and normal TCT may be considered for a follow-up review and, if necessary, a colposcopy.3. The development of a more suitable HPV vaccine for the Asian population, such as HPV16/18/52/58, may better protect women's health.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.

Prevalence and genotype distribution of high-risk human papillomavirus infection among women with cervical cytological abnormalities in Chongqing, China, 2014-2020

BACKGROUND

Persistent infection with high-risk human papillomavirus (HR-HPV) is the main leading cause of cervical precancerous lesions and cervical cancer. This study aims to explore the epidemiological characteristics of HR-HPV genotypes and their correlation with the ThinPrep cytological test (TCT) results among women in Chongqing, in China.

METHODS

In this retrospective study, cervical exfoliations of 14,548 women who visited Chongqing university cancer hospital were collected for detecting HR-HPV genotypes and TCT.

RESULTS

Overall, the rate of HR-HPV infection was 14.26%. The three most common HR-HPV genotypes are HPV52 (4.39%), HPV58 (2.21%), and HPV16 (1.94%). In this study, the positive rate of cervical TCT was 4.54%. Atypical squamous cells of undetermined significance (ASC-US), atypical squamous cells that could not exclude high-grade squamous intraepithelial lesion (ASU-H), low-grade squamous intraepithelial lesions (LSIL), high-grade squamous intraepithelial lesions (HSIL), and atypical glandular cells of undetermined significance (AGC) were 2.99%, 0.20%, 0.92%, 0.29%, and 0.14%, respectively. Among the several types of cytological lesions, the HR-HPV infection rates of ASC-US, ASC-H, LSIL, HSIL, and (AGC) were 24.82%, 41.38%, 64.18%, 95.24%, and 23.81%, respectively.

CONCLUSIONS

HPV52, HPV 58, and HPV16 are the most common infection subtypes in Chongqing. When implementing HPV vaccine programs in Chongqing, HPV58 and HPV52 should be attached importance as HPV16 and HPV18.

MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction

Mitotic catastrophe (MC) is a form of programmed cell death induced by mitotic process disorders, which is very important in tumor prevention, development, and drug resistance. Because rapidly increased data for MC is vigorously promoting the tumor-related biomedical and clinical study, it is urgent for us to develop a professional and comprehensive database to curate MC-related data. Mitotic Catastrophe Database (MCDB) consists of 1214 genes/proteins and 5014 compounds collected and organized from more than 8000 research articles. Also, MCDB defines the confidence level, classification criteria, and uniform naming rules for MC-related data, which greatly improves data reliability and retrieval convenience. Moreover, MCDB develops protein sequence alignment and target prediction functions. The former can be used to predict new potential MC-related genes and proteins, and the latter can facilitate the identification of potential target proteins of unknown MC-related compounds. In short, MCDB is such a proprietary, standard, and comprehensive database for MC-relate data that will facilitate the exploration of MC from chemists to biologists in the fields of medicinal chemistry, molecular biology, bioinformatics, oncology and so on. The MCDB is distributed on http://www.combio-lezhang.online/MCDB/index_html/.

2019nCoVAS: Developing the Web Service for Epidemic Transmission Prediction, Genome Analysis, and Psychological Stress Assessment for 2019-nCoV

Since the COVID-19 epidemic is still expanding around the world and poses a serious threat to human life and health, it is necessary for us to carry out epidemic transmission prediction, whole genome sequence analysis, and public psychological stress assessment for 2019-nCoV. However, transmission prediction models are insufficiently accurate and genome sequence characteristics are not clear, and it is difficult to dynamically assess the public psychological stress state under the 2019-nCoV epidemic. Therefore, this study develops a 2019nCoVAS web service (http://www.combio-lezhang.online/2019ncov/home.html) that not only offers online epidemic transmission prediction and lineage-associated underrepresented permutation (LAUP) analysis services to investigate the spreading trends and genome sequence characteristics, but also provides psychological stress assessments based on such an emotional dictionary that we built for 2019-nCoV. Finally, we discuss the shortcomings and further study of the 2019nCoVAS web service.

Genetic and Epigenetic Variations of HPV52 in Cervical Precancer

The goal of this study was to identify human papillomavirus (HPV) type 52 genetic and epigenetic changes associated with high-grade cervical precancer and cancer. Patients were selected from the HPV Persistence and Progression (PaP) cohort, a cervical cancer screening program at Kaiser Permanente Northern California (KPNC). We performed a nested case-control study of 89 HPV52-positive women, including 50 cases with predominantly cervical intraepithelial neoplasia grade 3 (CIN3) and 39 controls without evidence of abnormalities. We conducted methylation analyses using Illumina sequencing and viral whole genome Sanger sequencing. Of the 24 CpG sites examined, increased methylation at CpG site 5615 in HPV52 L1 region was the most significantly associated with CIN3, with a difference in median methylation of 17.9% (odds ratio (OR) = 4.8, 95% confidence interval (CI) = 1.9–11.8) and an area under the curve of 0.73 (AUC; 95% CI = 0.62–0.83). Complete genomic sequencing of HPV52 isolates revealed associations between SNPs present in sublineage C2 and a higher risk of CIN3, with ORs ranging from 2.8 to 3.3. This study identified genetic and epigenetic HPV52 variants associated with high risk for cervical precancer, improving the potential for early diagnosis of cervical neoplasia caused by HPV52.

Prevalence and Risk Factors of Infection with High Risk Human Papilloma Viruses among HIV-Positive Women with Clinical Manifestations of Tuberculosis in a Middle-Income Country

Women living with HIV-1 are at high risk of infection with human papillomavirus of high carcinogenic risk (HR HPVs). M. tuberculosis (TB) promotes HPV infection and increases the risk to develop HPV-associated cancer. Our knowledge of persisting HR HPVs genotypes, and of the factors promoting HR HPV infection in people living with HIV-1 with clinical TB manifestations is sparse. Here, we analyzed 58 women living with HIV-1 with clinical TB manifestations (WLWH with TB) followed up in specialized centers in Russia, a middle income country endemic for HIV-1 and TB, for the presence in cervical smears of DNA of twelve HR HPV genotypes. DNA encoding HPV16 E5, E6/E7 was sequenced. Sociodemographic data of patients was collected by questionnaire. All women were at C2-C3 stages of HIV-infection (by CDC). The majority were over 30 years old, had secondary education, were unemployed, had sexual partners, experienced 2–3 pregnancies and at least one abortion, and were smokers. The most prevalent was HPV16 detected in the cervical smears of 38% of study participants. Altogether 34.5% of study participants were positive for HR HPV types other than HPV16; however, but none of these types was seen in more than 7% of tested samples. Altogether, 20.7% of study participants were positive for several HR HPV types. Infections with HPVs other than HPV16 were common among WLWH with generalized TB receiving combined ART/TB-therapy, and associated with their ability to work, indirectly reflecting both their health and lifestyle. The overall prevalence of HR HPVs was associated with sexual activity of women reflected by the number of pregnancies, and of HPV 16, with young age; none was associated to CD4+-counts, route of HIV-infection, duration of life with HIV, forms of TB-infection, or duration of ART, characterizing the immune status. Thus, WLWH with TB—especially young—were predisposed to infection with HPV16, advancing it as a basis for a therapeutic HPV vaccine. Phylogenetic analysis of HPV16 E5, E6/E7 DNA revealed no common ancestry; sequences were similar to those of the European and American HPV16 strains, indicating that HPV vaccine for WLWH could be the same as HPV16 vaccines developed for the general population. Sociodemographic and health correlates of HR HPV prevalence in WLWH deserve further analysis to develop criteria/recommendations for prophylactic catch-up and therapeutic HPV vaccination of this highly susceptible and vulnerable population group.

Main findings and advances in bioinformatics and biomedical engineering- IWBBIO 2018

In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.

Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis

Subarachnoid hemorrhage (SAH) is devastating disease with high mortality, high disability rate, and poor clinical prognosis. It has drawn great attentions in both basic and clinical medicine. Therefore, it is necessary to explore the therapeutic drugs and effective targets for early prediction of SAH. Firstly, we demonstrate that LCN2 can effectively intervene or treat SAH from the perspective of cell signaling pathway. Next, three potential genes that we explored have been validated by manually reviewed experimental evidences. Finally, we turn out that the SAH early ensemble learning predictive model performs better than the classical LR, SVM, and Naïve-Bayes models.