The prevalence and genotype distribution of high-risk human papillomaviruses among women in Xianning, China.

BACKGROUND: The persistent infection of high-risk Human papillomavirus(HPV) is considered the main cause of cervical intraepithelial neoplasia and cervical cancer. But various cervical lesions caused by HPV infection can be properly prevented by timely vaccination. However, the distribution of HPV genotypes varies geographically. METHODS: Retrospective analysis of high-risk HPV prevalence of 16,150 women from 2020 to 2022 in xianning of China. HPV genotyping was performed using a PCR-RDB Kit that can detect 18 high-risk HPV genotypes recommended by China's National Medical Products Administration. The prevalence of 18 high-risk HPV genotypes and their relationship with cervical lesions as well as vaccine efficacy were analyzed. RESULTS: A total of 2431 women were confirmed to have different types of high-risk HPV infections. The overall positive rate reached 15.05%(2431/16,150). The most prevalent high-risk HPV genotypes were HPV52, 16, 58, 53, and 51. The prevalence of high-risk HPV reached peak at age ≤ 20(20.95%) and age ≥ 61(20.56%). The most prevalent high-risk HPV genotypes were HPV16, 58, 18, 33 and 52 in cervical cancer cases, HPV16, 52, 58, 33 and 18 in CIN2/3 cases, and HPV52, 58, 16, 53 and 18 in CIN1 cases, respectively. CONCLUSION: HPV16, 58 and 18 are the most dangerous and carcinogenic genotypes in xianning, China. Conducting epidemiological investigations on high-risk HPV has significant clinical value in guiding HPV vaccination work.

Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network.

Changes in the geological environment and track wear, and deterioration of train bogies may lead to the looseness of subway fasteners. Identifying loose fasteners randomly distributed along the subway line is of great significance to avoid train derailment. This paper presents a convolutional autoencoder (CAE) network-based method for identifying fastener loosening features from the distributed vibration responses of track beds detected by an ultra-weak fiber Bragg grating sensing array. For an actual subway tunnel monitoring system, a field experiment used to collect the samples of fastener looseness was designed and implemented, where a crowbar was used to loosen or tighten three pairs of fasteners symmetrical on both sides of the track within the common track bed area and the moving load of a rail inspection vehicle was employed to generate 12 groups of distributed vibration signals of the track bed. The original vibration signals obtained from the on-site test were converted into two-dimensional images through the pseudo-Hilbert scan to facilitate the proposed two-stage CAE network with acceptable capabilities in feature extraction and recognition. The performance of the proposed methodology was quantified by accuracy, precision, recall, and F1-score, and displayed intuitively by t-distributed stochastic neighbor embedding (t-SNE). The raster scan and the Hilbert scan were selected to compare with the pseudo-Hilbert scan under a similar CAE network architecture. The identification performance results represented by the four quantification indicators (accuracy, precision, recall, and F1-score) based on the scan strategy in this paper were at least 23.8%, 9.5%, 20.0%, and 21.1% higher than those of the two common scan methods. As well as that, the clustering visualization by t-SNE further verified that the proposed approach had a stronger ability in distinguishing the feature of fastener looseness.

CrowdMed-II: a blockchain-based framework for efficient consent management in health data sharing.

The healthcare industry faces serious problems with health data. Firstly, health data is fragmented and its quality needs to be improved. Data fragmentation means that it is difficult to integrate the patient data stored by multiple health service providers. The quality of these heterogeneous data also needs to be improved for better utilization. Secondly, data sharing among patients, healthcare service providers and medical researchers is inadequate. Thirdly, while sharing health data, patients' right to privacy must be protected, and patients should have authority over who can access their data. In traditional health data sharing system, because of centralized management, data can easily be stolen, manipulated. These systems also ignore patient's authority and privacy. Researchers have proposed some blockchain-based health data sharing solutions where blockchain is used for consensus management. Blockchain enables multiple parties who do not fully trust each other to exchange their data. However, the practice of smart contracts supporting these solutions has not been studied in detail. We propose CrowdMed-II, a health data management framework based on blockchain, which could address the above-mentioned problems of health data. We study the design of major smart contracts in our framework and propose two smart contract structures. We also introduce a novel search contract for searching patients in the framework. We evaluate their efficiency based on the execution costs on Ethereum. Our design improves on those previously proposed, lowering the computational costs of the framework. This allows the framework to operate at scale and is more feasible for widespread adoption.

Neutrophil dysregulation during sepsis: an overview and update.

Sepsis remains a leading cause of death worldwide, despite advances in critical care, and understanding of the pathophysiology and treatment strategies. No specific therapy or drugs are available for sepsis. Neutrophils play a critical role in controlling infection under normal conditions, and it is suggested that their migration and antimicrobial activity are impaired during sepsis which contribute to the dysregulation of immune responses. Recent studies further demonstrated that interruption or reversal of the impaired migration and antimicrobial function of neutrophils improves the outcome of sepsis in animal models. In this review, we provide an overview of the associated mediators and signal pathways involved which govern the survival, migration and antimicrobial function of neutrophils in sepsis, and discuss the potential of neutrophils as a target to specifically diagnose and/or predict the outcome of sepsis.

The long noncoding RNA colon cancer-associated transcript-1/miR-490 axis regulates gastric cancer cell migration by targeting hnRNPA1.

Colon cancer-associated transcript-1 (CCAT1) is a highly conserved long noncoding RNA that is deregulated in several cancers. However, its role in gastric carcinoma and its post-transcriptional regulation remain poorly understood. In this study, we provide the first evidence that CCAT1 regulates miR-490 in gastric cancer (GC) cells. Interestingly, miR-490 can also repress CCAT1 expression. CCAT1 expression was significantly upregulated, and miR-490 expression was downregulated in GC. The negative correlation between miR-490 and CCAT1 expression was observed in GC tissues. Importantly, CCAT1 contains a putative miR-490-binding site, and deletion of this binding site abolishes their miR-490 responsiveness. Post-transcriptional CCAT1 silencing by miR-490 significantly suppressed GC cell migration. Furthermore, miR-490 directly bound to the hnRNPA1 mRNA 3'-UTR to repress its translation. Inhibition of miR-490 rescued CCAT1 siRNA-mediated suppression of cell migration. hnRNPA1 expression was significantly upregulated in GC specimens, and there was a negative correlation between miR-490 and hnRNPA1 expression and also a positive correlation between hnRNAP1 expression level and CCAT1 level. Taken together, we show for the first time that the CCAT1/miR-490/hnRNPA1 axis promotes GC migration, and it may have a possible diagnostic and therapeutic potential in GC.

Interactive effects of glutamine and gamma-aminobutyric acid on growth performance and skeletal muscle amino acid metabolism of 22-42-day-old broilers exposed to hot environment.

The present experiment was conducted to investigate the interactive effects between dietary glutamine (Gln, 0 and 5 g/kg) and gamma-aminobutyric acid (GABA, 0 and 100 mg/kg) on growth performance and amino acid (AA) metabolism of broilers under hot environment. A total of 360 22-day-old Arbor Acres male chickens were randomly assigned to five treatment groups under thermoneutral chamber (PC, 23 °C) and cyclic heat stress (HS, 30-34 °C cycling) conditions. Compared with the PC group, cyclic HS decreased (P < 0.05) daily weight gain (DWG), daily feed consumption (DFC), the concentrations of Gln, glutamate (Glu), and GABA, and the activities of glutaminase and glutamic acid decarboxylase (GAD) in breast muscle at 28, 35, and 42 days, while it increased (P < 0.05) the activities of glutamine synthetase (GS) and gamma-aminobutyric acid transaminase (GABA-T) at 28, 35, and 42 days. Dietary Gln and GABA improved (P < 0.05) DWG and DFC of broilers under cyclic HS during 28-42 days. In breast muscle, the Gln supplementation increased (P < 0.05) the concentrations of Gln (28, 35, and 42 days), Glu (28, 35, and 42 days), and GABA (42 days) and the activities of glutaminase (28, 35, and 42 days) and GAD (28, 35, and 42 days) but decreased (P < 0.05) GS activities at 28, 35, and 42 days and GABA-T activities at 28 days. The addition of GABA increased (P < 0.05) the concentrations of Gln and Glu and activities of glutaminase and GAD, while it decreased (P < 0.05) GABA-T activities at 28, 35, and 42 days. Significant interactions (P < 0.05) between Gln and GABA were found on breast skeletal muscle Gln concentrations, glutaminase activities, GS activities at 28 and 35 days, and DWG, GABA concentrations, and GABA-T activities at 28, 35, and 42 days in broilers under cyclic HS. In conclusion, the present results indicated that the interactions of exogenous Gln and GABA could offer a potential nutritional strategy to prevent HS-related depression in skeletal muscle Gln and GABA metabolism of broilers.

Treatment of atrazine-containing wastewater by algae-bacteria consortia: Signal transmission and metabolic mechanism.

Atrazine is a toxic endocrine disruptor. Biological treatment methods are considered to be effective. In the present study, a modified version of the algae-bacteria consortia (ABC) was established and a control was simultaneously set up to investigate the synergistic relationship between bacteria and algae and the mechanism by which atrazine is metabolized by those microorganisms. The total nitrogen (TN) removal efficiency of the ABC reached 89.24% and the atrazine concentration was reduced to below the level recommended by the Environment Protection Agency (EPA) regulatory standards within 25 days. The protein signal released from the extracellular polymeric substances (EPS) secreted by the microorganisms triggered the resistance mechanism of the algae, and the conversion of humic acid to fulvic acid and electron transfer constituted the synergistic mechanism between the bacteria and algae. The mechanism by which atrazine is metabolized by the ABC mainly consists of hydrogen bonding, H-pi interactions, and cation exchange with atzA for hydrolysis, followed by a reaction with atzC for decomposition to non-toxic cyanuric acid. Proteobacteria was the dominant phylum for bacterial community evolution under atrazine stress, and the analysis revealed that the removal of atrazine within the ABC was mainly dependent on the proportion of Proteobacteria and the expression of degradation genes (p < 0.01). EPS played a major role in the removal of atrazine within the single bacteria group (p < 0.01).

Investigating pharmacological mechanisms of andrographolide on non-alcoholic steatohepatitis (NASH): A bioinformatics approach of network pharmacology.

Objective: To investigate the mechanisms of andrographolide against non-alcoholic steatohepatitis (NASH) based on network pharmacology, so as to provide a reference for further study of andrographolide in the treatment of NASH and other metabolic diseases. Methods: The methionine- and choline-deficient (MCD) diet-induced NASH mice were treated by administration of andrographolide, and serum transaminase and pathological changes were analyzed. The network pharmacology-based bioinformatic strategy was then used to search the potential targets, construct protein-protein interaction (PPI) network, analyze gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment, and conduct molecular docking to explore the molecular mechanisms. Results: The predicted core targets TNF, MAPK8, IL6, IL1B and AKT1 were enriched in non-alcoholic fatty liver disease (NAFLD) signaling pathway and against NASH by regulation of de novo fatty acids synthesis, anti-inflammation and anti-oxidation. Conclusion: This work provides a scientific basis for further demonstration of the anti-NASH mechanisms of andrographolide.

Immunological Significance of Prognostic DNA Methylation Sites in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is a tumor with high morbidity and high mortality worldwide. DNA methylation, one of the most common epigenetic changes, might serve a vital regulatory role in cancer. Methods: To identify categories based on DNA methylation data, consensus clustering was employed. The risk signature was yielded by systematic bioinformatics analyses based on the remarkably methylated CpG sites of cluster 1. Kaplan-Meier analysis, variable regression analysis, and ROC curve analysis were further conducted to validate the prognosis predictive ability of risk signature. Gene set enrichment analysis (GSEA) was performed for functional annotation. To uncover the context of tumor immune microenvironment (TIME) of HCC, we employed the ssGSEA algorithm and CIBERSORT method and performed TIMER database exploration and single-cell RNA sequencing analysis. Additionally, quantitative real-time polymerase chain reaction was employed to determine the LRRC41 expression and preliminarily explore the latent role of LRRC41 in prognostic prediction. Finally, mutation data were analyzed by employing the "maftools" package to delineate the tumor mutation burden (TMB). Results: HCC samples were assigned into seven subtypes with different overall survival and methylation levels based on 5'-cytosine-phosphate-guanine-3' (CpG) sites. The risk prognostic signature including two candidate genes (LRRC41 and KIAA1429) exhibited robust prognostic predictive accuracy, which was validated in the external testing cohort. Then, the risk score was significantly correlated with the TIME and immune checkpoint blockade (ICB)-related genes. Besides, a prognostic nomogram based on the risk score and clinical stage presented powerful prognostic ability. Additionally, LRRC41 with prognostic value was corroborated to be closely associated with TIME characterization in both expression and methylation levels. Subsequently, the correlation regulatory network uncovered the potential targets of LRRC41 and KIAA1429. Finally, the methylation level of KIAA1429 was correlated with gene mutation status. Conclusion: In summary, this is the first to identify HCC samples into distinct clusters according to DNA methylation and yield the CpG-based prognostic signature and quantitative nomogram to precisely predict prognosis. And the pivotal player of DNA methylation of genes in the TIME and TMB status was explored, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development.

BACKGROUND: Knowledge graph embedding is an effective semantic representation method for entities and relations in knowledge graphs. Several translation-based algorithms, including TransE, TransH, TransR, TransD, and TranSparse, have been proposed to learn effective embedding vectors from typical knowledge graphs in which the relations between head and tail entities are deterministic. However, in medical knowledge graphs, the relations between head and tail entities are inherently probabilistic. This difference introduces a challenge in embedding medical knowledge graphs. OBJECTIVE: We aimed to address the challenge of how to learn the probability values of triplets into representation vectors by making enhancements to existing TransX (where X is E, H, R, D, or Sparse) algorithms, including the following: (1) constructing a mapping function between the score value and the probability, and (2) introducing probability-based loss of triplets into the original margin-based loss function. METHODS: We performed the proposed PrTransX algorithm on a medical knowledge graph that we built from large-scale real-world electronic medical records data. We evaluated the embeddings using link prediction task. RESULTS: Compared with the corresponding TransX algorithms, the proposed PrTransX performed better than the TransX model in all evaluation indicators, achieving a higher proportion of corrected entities ranked in the top 10 and normalized discounted cumulative gain of the top 10 predicted tail entities, and lower mean rank. CONCLUSIONS: The proposed PrTransX successfully incorporated the uncertainty of the knowledge triplets into the embedding vectors.

Real-world data medical knowledge graph: construction and applications.

OBJECTIVE: Medical knowledge graph (KG) is attracting attention from both academic and healthcare industry due to its power in intelligent healthcare applications. In this paper, we introduce a systematic approach to build medical KG from electronic medical records (EMRs) with evaluation by both technical experiments and end to end application examples. MATERIALS AND METHODS: The original data set contains 16,217,270 de-identified clinical visit data of 3,767,198 patients. The KG construction procedure includes 8 steps, which are data preparation, entity recognition, entity normalization, relation extraction, property calculation, graph cleaning, related-entity ranking, and graph embedding respectively. We propose a novel quadruplet structure to represent medical knowledge instead of the classical triplet in KG. A novel related-entity ranking function considering probability, specificity and reliability (PSR) is proposed. Besides, probabilistic translation on hyperplanes (PrTransH) algorithm is used to learn graph embedding for the generated KG. RESULTS: A medical KG with 9 entity types including disease, symptom, etc. was established, which contains 22,508 entities and 579,094 quadruplets. Compared with term frequency - inverse document frequency (TF/IDF) method, the normalized discounted cumulative gain (NDCG@10) increased from 0.799 to 0.906 with the proposed ranking function. The embedding representation for all entities and relations were learned, which are proven to be effective using disease clustering. CONCLUSION: The established systematic procedure can efficiently construct a high-quality medical KG from large-scale EMRs. The proposed ranking function PSR achieves the best performance under all relations, and the disease clustering result validates the efficacy of the learned embedding vector as entity's semantic representation. Moreover, the obtained KG finds many successful applications due to its statistics-based quadruplet. where Ncomin is a minimum co-occurrence number and R is the basic reliability value. The reliability value can measure how reliable is the relationship between Si and Oij. The reason for the definition is the higher value of Nco(Si, Oij), the relationship is more reliable. However, the reliability values of the two relationships should not have a big difference if both of their co-occurrence numbers are very big. In our study, we finally set Ncomin = 10 and R = 1 after some experiments. For instance, if co-occurrence numbers of three relationships are 1, 100 and 10000, their reliability values are 1, 2.96 and 5 respectively.

A retrospective study of 34 patients with unicentric and multicentric Castleman's disease: Experience from a single institution.

The aim of the present study was to share the experience of a single institute in the diagnosis, use of accessory examinations and treatment strategies of Castleman's disease (CD). The present study analyzed 34 patients (13 males and 21 females) with CD who were hospitalized between January 2006 and September 2014. The patients were divided into two groups based on the anatomical distribution of the disease: Unicentric CD (UCD) and multicentric CD (MCD). Histological data was obtained from lymph node biopsies. All clinical data were acquired by reviewing patients' medical records and contacting patients by telephone. A total of 27 patients had UCD and 7 patients had MCD. All 27 patients with UCD with benign symptoms underwent complete diagnostic surgical resection and survived, with the exception of 1 patient who succumbed to pancreatic head carcinoma 13 months after surgery. A total of 7 patients with MCD presented with systemic symptoms and 2 of these patients declined treatment following the definite diagnosis of CD. The remaining 5 patients were treated with various strategies, including surgical resection and further glucocorticoid treatment, intravenous siltuximab, rituximab in combination with cyclophosphamide, doxorubicin, vincristine and prednisone chemotherapy or hematopoietic stem cell transplantation. A total of 3 patients with MCD survived, with a median follow-up period of 69 months. The present study indicates that complete surgical resection is currently the standard treatment for UCD. Perioperative use of multidetector computed tomography and the laparoscopic approach have certain advantages in UCD. Molecular target therapy is effective in patients with stable MCD, and hematopoietic stem cell transplantation may be beneficial in certain patients with MCD and disease progression.

Abnormal Expression of TRAIL Receptors in Decidual Tissue of Chlamydia trachomatis-Infected Rats During Early Pregnancy Loss.

Chlamydia trachomatis is the scientific name of pathogenic bacteria causing infection that has been linked to spontaneous abortion. In this study, the expression pattern of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL; a cytokine related to cell apoptosis) and its receptors was monitored in the decidua of C trachomatis-infected pregnant rats during early gestation to investigate the potential role of this molecular system in C trachomatis-induced spontaneous abortion. The data showed that C trachomatis infection significantly altered the messenger RNA (mRNA) expression of the receptors; death receptor (DR) 4 and DR5 increased, but decoy receptor (DcR) 1 and DcR2 decreased. Consistent with mRNA data, immunohistochemical staining of TRAIL and its receptors indicated that both DR4 and DR5 protein levels were elevated in infected tissues, primarily, decidual cells, decidual vessel wall, and uterine glands, whereas DcR1 and DcR2 showed lower levels compared to the noninfected group. Although receptor expression was altered, there was no difference detected in TRAIL expression. The observed altered expression of TRAIL receptors in C trachomatis-infected rats compared to noninfected rats during the embryo implantation phase suggests a possible mechanism for spontaneous abortion due to apoptosis and therefore failed embryo implantation. In addition, the observed increase in caspase-3 levels in infected cells further supports this finding. Taken together, the data presented in this study suggests C trachomatis infection altered the expression of TRAIL receptors, thus representing a general mechanism for C trachomatis-induced spontaneous abortion in C trachomatis-infected rats during early pregnancy loss.

Phosphatase Wip1 in Immunity: An Overview and Update.

Wild-type p53-induced phosphatase 1 (Wip1) is a newly identified serine/threonine phosphatase, which belongs to the PP2C family. Due to its involvement in stress-induced networks and overexpression in human tumors, primary studies have mainly focused on the role of Wip1 in tumorigenesis. It now has also been implicated in regulating several other physiological processes such as organism aging and neurogenesis. Recent evidence highlights a new role of Wip1 in controlling immune response through regulating immune cell development and function, as well as through the interplay with inflammatory signaling pathways such NF-κB and p38 mitogen-activated protein kinase. In this short review, we will give an overview of Wip1 in immunity to better understand this important phosphatase.

Donor-Specific Regulatory T Cells Acquired from Tolerant Mice Bearing Cardiac Allograft Promote Mixed Chimerism and Prolong Intestinal Allograft Survival.

The induction of donor-specific transplant tolerance has always been a central problem for small bowel transplantation (SBT), which is thought to be the best therapy for end-stage bowel failure. With the development of new tolerance-inducing strategies, mixed chimerism induced by co-stimulation blockade has become most potent for tolerance of allografts, such as skin, kidney, and heart. However, a lack of clinically available co-stimulation blockers has hindered efficient application in humans. Furthermore, unlike those for other types of solid organ transplantation, strategies to induce robust mixed chimerism for intestinal allografts have not been fully developed. To improve current mixed chimerism induction protocols for future clinical application, we developed a new protocol using donor-specific regulatory T (Treg) cells from mice with heart allograft tolerance, immunosuppressive drugs which could be used clinically and low doses of irradiation. Our results demonstrated that donor-specific Treg cells acquired from tolerant mice after in vitro expansion generate stable chimerism and lead to acceptance of intestinal allograft. Increased intragraft Treg cells and clonal deletion contribute to the development of SBT tolerance.

[Strategy to solve cofactor issues in oxidoreductase catalyzed biocatalytic applications].

NAD(P)(H)-dependent oxidoreductase catalyzes the reduction of ketones or aldehydes to prepare a wide variety of valuable chiral alcohols or amines. However, expensive cofactors are absolutely required for the biocatalytic processes with oxidoreductases, which severely hinder their industrial applications. Consequently, the issue on reducing cofactor costs has become one of the major focuses in the field of biocatalysis. With the substantial development in recent years, a number of strategies have been proposed and implemented to solve the cofactor issues in the oxidoreductase catalyzed biocatalysis, including the establishment of cofactor regeneration system, the improvement of endogenous cofactor availability via metabolic engineering and the development of biomimetic agents to replace cofactors. In this review, recent trends and advances on these strategies are presented, and respective advantages and shortcomings are also discussed with a number of examples.