Intrahepatic portal venous systems in adult patients with cavernous transformation of portal vein: Imaging features and a new classification

Background: Cavernous transformation of the portal vein(CTPV) due to portal vein obstruction is a rare vascular anomaly defined as the formation of multiple collateral vessels in the hepatic hilum. This study aimed to investigate the imaging features of intrahepatic portal vein in adult patients with CTPV and establish the relationship between the manifestations of intrahepatic portal vein and the progression of CTPV. Methods: We retrospectively analyzed 14 CTPV patients in Beijing Tsinghua Changgung Hospital. All patients underwent both direct portal venography(DPV) and computed tomography angiography(CTA) to reveal the manifestations of the portal venous system. The vessels measured included the left portal vein(LPV), right portal vein(RPV), main portal vein(MPV) and the portal vein bifurcation(PVB). Results: Nine males and 5 females, with a median age of 40.5 years, were included in the study. No significant difference was found in the diameters of the LPV or RPV measured by DPV and CTA. The visualization in terms of LPV, RPV and PVB measured by DPV was higher than that by CTA. There was a significant association between LPV/RPV and PVB/MPV in term of visibility revealed with DPV( P = 0.01), while this association was not observed with CTA. According to the imaging features of the portal vein measured by DPV, CTPV was classified into three categories to facilitate the diagnosis and treatment. Conclusions: DPV was more accurate than CTA for revealing the course of the intrahepatic portal vein in patients with CTPV. The classification of CTPV, that originated from the imaging features of the portal vein revealed by DPV, may provide a new perspective for the diagnosis and treatment of CTPV.

Alterations in the gut microbiome after transjugular intrahepatic portosystemic shunt in patients with hepatitis B virus-related portal hypertension

BACKGROUND Gut microbiota(GM)affects the progression and response to treatment in liver diseases.The GM composition is diverse and associated with different etiologies of liver diseases.Notably,alterations in GM alterations are observed in patients with portal hypertension(PH)secondary to cirrhosis,with hepatitis B virus(HBV)infection being a major cause of cirrhosis in China.Thus,understanding the role of GM alterations in patients with HBV infection-related PH is essential.AIM To evaluate GM alterations in patients with HBV-related PH after transjugular intrahepatic portosystemic shunt(TIPS)placement.METHODS This was a prospective,observational clinical study.There were 30 patients(with a 100%technical success rate)recruited in the present study.Patients with esophagogastric variceal bleeding due to HBV infection-associated PH who underwent TIPS were enrolled.Stool samples were obtained before and one month after TIPS treatment,and GM was analyzed using 16S ribosomal RNA amplicon sequencing.RESULTS One month after TIPS placement,8 patients developed hepatic encephalopathy(HE)and were assigned to the HE group;the other 22 patients were assigned to the non-HE group.There was no substantial disparity in the abundance of GM at the phylum level between the two groups,regardless of TIPS treatment(all,P>0.05).However,following TIPS placement,the following results were observed:(1)The abundance of Haemophilus and Eggerthella increased,whereas that of Anaerostipes,Dialister,Butyricicoccus,and Oscillospira declined in the HE group;(2)The richness of Eggerthella,Streptococcus,and Bilophila increased,whereas that of Roseburia and Ruminococcus decreased in the non-HE group;and(3)Members from the pathogenic genus Morganella appeared in the HE group but not in the non-HE group.CONCLUSION Intestinal microbiota-related synergism may predict the risk of HE following TIPS placement in patients with HBVrelated PH.Prophylactic microbiome therapies may be useful for preventing and treating HE after TIPS placement.

Risk of hepatitis B virus reactivation in oncological patients treated with tyrosine kinase inhibitors:A case report and literature analysis

Hepatitis B virus(HBV)reactivation(HBVr)represents a severe and potentially life-threatening condition,and preventive measures are available through blood test screening or prophylactic therapy administration.The assessment of HBVr traditionally considers factors such as HBV profile,including hepatitis B surface antigen(HBsAg)and antibody to hepatitis B core antigen,along with type of medication(chemotherapy;immunomodulants).Nevertheless,consideration of possible patient’s underlying tumor and the specific malignancy type(solid or hematologic)plays a crucial role and needs to be assessed for decision-making process.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

Perioperative remedial antiviral therapy in hepatitis B virus-related hepatocellular carcinoma resection:How to achieve a better outcome

BACKGROUND Although the benefits of antiviral therapy for hepatitis B virus(HBV)-related hepatocellular carcinoma(HCC)have been proven,researchers have not con-firmed the differences in patient outcomes between patients who received preoperative antiviral therapy for a period of time(at least 24 wk)and patients who received remedial antiviral therapy just before radical resection for HBV-related HCC.AIM To investigate the efficacy of perioperative remedial antiviral therapy in patients with HBV-related HCC.METHODS A retrospective study of patients who underwent radical resection for HBV-related HCC at the First Affiliated Hospital of Xi’an Jiaotong University from January 2016 to June 2019 was conducted.Considering the history of antiviral therapy,patients were assigned to remedial antiviral therapy and preoperative antiviral therapy groups.RESULTS Kaplan–Meier analysis revealed significant differences in overall survival(P<0.0001)and disease-free survival(P=0.035)between the two groups.Multivariate analysis demonstrated that a history of preoperative antiviral treatment was independently related to improved survival(hazard ratio=0.27;95%confidence interval:0.08-0.88;P=0.030).CONCLUSION In patients with HBV-related HCC,it is ideal to receive preoperative long-term antiviral therapy,which helps patients tolerate more extensive hepatectomy;however,remedial antiviral therapy,which reduces preoperative HBV-DNA levels to less than 4 Log10 copies DNA/mL,can also result in improved outcomes.

Antiviral treatment standards for hepatitis B:An urgent need for expansion

The present letter to the editor is related to the review with the title“Past,present,and future of long-term treatment for hepatitis B virus.”Chronic hepatitis B(CHB)represents an important and pressing public health concern.Timely identification and effective antiviral therapy hold the potential to reduce liver-related mortality attributable to chronic infection with hepatitis B virus(HBV)substantially.However,the current global treatment rates for CHB remain conspicuously low,with the excessively stringent treatment criteria advocated by national CHB guidelines being a contributing factor to these low rates.Nevertheless,recent strides in comprehending this malady and the emergence of novel antiviral agents prompt the imperative re-evaluation of treatment standards to extend the sphere of potential beneficiaries.An impending need arises for a novel paradigm for the classification of patients with CHB,the expansion of antiviral treatment eligibility for HBV-infected individuals,and even the streamlining of the diagnostic process for CHB to amplify cost-effectiveness and augment survival prospects.

Autoimmune hepatitis and primary sclerosing cholangitis after direct-acting antiviral treatment for hepatitis C virus:A case report

BACKGROUND Chronic hepatitis C virus(HCV)infection is a major global health concern that leads to liver fibrosis,cirrhosis,and cancer.Regimens containing direct-acting antivirals(DAAs)have become the mainstay of HCV treatment,achieving a high sustained virological response(SVR)with minimal adverse events.CASE SUMMARY A 74-year-old woman with chronic HCV infection was treated with the DAAs ledipasvir,and sofosbuvir for 12 wk and achieved SVR.Twenty-four weeks after treatment completion,the liver enzyme and serum IgG levels increased,and antinuclear antibody became positive without HCV viremia,suggesting the development of autoimmune hepatitis(AIH).After liver biopsy indicated AIH,a definite AIH diagnosis was made and prednisolone was initiated.The treatment was effective,and the liver enzyme and serum IgG levels normalized.However,multiple strictures of the intrahepatic and extrahepatic bile ducts with dilatation of the peripheral bile ducts appeared on magnetic resonance cholangiopancreatography after 3 years of achieving SVR,which were consistent with primary sclerosing cholangitis.CONCLUSION The potential risk of developing autoimmune liver diseases after DAA treatment should be considered.

An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to be in favor of the majority class(usually defined as the negative class),which may do harm to the accuracy of the minority class(usually defined as the positive class),and then lead to poor overall performance of the model.A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article,which is based on a new hybrid resampling approach(MSHR)and a new fine cost-sensitive support vector machine(CS-SVM)classifier(FCSSVM).The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples,based on which,the so-called pseudo-negative samples are screened out to generate new positive samples(over-sampling step)through linear interpolation and are deleted finally(under-sampling step).This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline,without changing the overall scale of the dataset.The FCSSVM is an improved version of the traditional CS-SVM.It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously,and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice(RIME)algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline.To verify the effectiveness of the proposed method,a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets.The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases,and both the MSHR and the FCSSVM played significant roles.

Data-driven casting defect prediction model for sand casting based on random forest classification algorithm

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.

Network traffic classification:Techniques,datasets,and challenges

In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the quality of service,preventing application choke points,and facilitating malicious behavior identification.In this paper,we review existing network classification techniques,such as port-based identification and those based on deep packet inspection,statistical features in conjunction with machine learning,and deep learning algorithms.We also explain the implementations,advantages,and limitations associated with these techniques.Our review also extends to publicly available datasets used in the literature.Finally,we discuss existing and emerging challenges,as well as future research directions.

Chronic hepatitis B virus infection in Eastern Ethiopia:Clinical characteristics and determinants of cirrhosis

BACKGROUND Chronic hepatitis B(CHB)virus infection is a major cause of liver-associated morbidity and mortality,particularly in low-income countries.A better understanding of the epidemiological,clinical,and virological characteristics of CHB will guide appropriate treatment strategies and improve the control and management of CHB in Ethiopia.AIM To investigate the characteristics of CHB in Eastern Ethiopia and assess the efficacy and safety of antiviral treatment.METHODS This cohort study included 193 adults who were human immunodeficiency virus-negative with CHB between June 2016 and December 2019.Baseline assessments included chemistry,serologic,and viral markers.χ^(2) tests,Mann-Whitney U tests,and logistic regression analyses were used to identify the determinants of cirrhosis.Tenofovir disoproxil fumarate(TDF)was initiated using treatment criteria from the Ethiopian CHB pilot program.RESULTS A total of 132 patients(68.4%)were men,with a median age of 30 years[interquartile range(IQR):24-38].At enrollment,60(31.1%)patients had cirrhosis,of whom 35(58.3%)had decompensated cirrhosis.Khat use,hepatitis B envelope antigen positivity,and a high viral load were independently associated with cirrhosis.Additionally,66 patients(33.4%)fulfilled the treatment criteria and 59(30.6%)started TDF.Among 29 patients who completed 24 months of treatment,the median aspartate aminotransferase to platelet ratio index declined from 1.54(IQR:0.66-2.91)to 1.10(IQR:0.75-2.53)(P=0.002),and viral suppression was achieved in 80.9%and 100%of patients after 12 months and 24 months of treatment,respectively.Among the treated patients,12(20.3%)died within the first 6 months of treatment,of whom 8 had decompensated cirrhosis.CONCLUSION This study highlights the high prevalence of cirrhosis,initial mortality,and the efficacy of TDF treatment.Scaling up measures to prevent and control CHB infections in Ethiopia is crucial.

Evaluation of slope stability through rock mass classification and kinematic analysis of some major slopes along NH-1A from Ramban to Banihal, North Western Himalayas

The network of Himalayan roadways and highways connects some remote regions of valleys or hill slopes,which is vital for India’s socio-economic growth.Due to natural and artificial factors,frequency of slope instabilities along the networks has been increasing over last few decades.Assessment of stability of natural and artificial slopes due to construction of these connecting road networks is significant in safely executing these roads throughout the year.Several rock mass classification methods are generally used to assess the strength and deformability of rock mass.This study assesses slope stability along the NH-1A of Ramban district of North Western Himalayas.Various structurally and non-structurally controlled rock mass classification systems have been applied to assess the stability conditions of 14 slopes.For evaluating the stability of these slopes,kinematic analysis was performed along with geological strength index(GSI),rock mass rating(RMR),continuous slope mass rating(CoSMR),slope mass rating(SMR),and Q-slope in the present study.The SMR gives three slopes as completely unstable while CoSMR suggests four slopes as completely unstable.The stability of all slopes was also analyzed using a design chart under dynamic and static conditions by slope stability rating(SSR)for the factor of safety(FoS)of 1.2 and 1 respectively.Q-slope with probability of failure(PoF)1%gives two slopes as stable slopes.Stable slope angle has been determined based on the Q-slope safe angle equation and SSR design chart based on the FoS.The value ranges given by different empirical classifications were RMR(37-74),GSI(27.3-58.5),SMR(11-59),and CoSMR(3.39-74.56).Good relationship was found among RMR&SSR and RMR&GSI with correlation coefficient(R 2)value of 0.815 and 0.6866,respectively.Lastly,a comparative stability of all these slopes based on the above classification has been performed to identify the most critical slope along this road.

Overview of the immunological mechanisms in hepatitis B virus reactivation:Implications for disease progression and management strategies

Hepatitis B virus(HBV)reactivation is a clinically significant challenge in disease management.This review explores the immunological mechanisms underlying HBV reactivation,emphasizing disease progression and management.It delves into host immune responses and reactivation’s delicate balance,spanning innate and adaptive immunity.Viral factors’disruption of this balance,as are interac-tions between viral antigens,immune cells,cytokine networks,and immune checkpoint pathways,are examined.Notably,the roles of T cells,natural killer cells,and antigen-presenting cells are discussed,highlighting their influence on disease progression.HBV reactivation’s impact on disease severity,hepatic flares,liver fibrosis progression,and hepatocellular carcinoma is detailed.Management strategies,including anti-viral and immunomodulatory approaches,are critically analyzed.The role of prophylactic anti-viral therapy during immunosuppressive treatments is explored alongside novel immunotherapeutic interventions to restore immune control and prevent reactivation.In conclusion,this compre-hensive review furnishes a holistic view of the immunological mechanisms that propel HBV reactivation.With a dedicated focus on understanding its implic-ations for disease progression and the prospects of efficient management stra-tegies,this article contributes significantly to the knowledge base.The more profound insights into the intricate interactions between viral elements and the immune system will inform evidence-based approaches,ultimately enhancing disease management and elevating patient outcomes.The dynamic landscape of management strategies is critically scrutinized,spanning anti-viral and immunomodulatory approaches.The role of prophylactic anti-viral therapy in preventing reactivation during immunosuppressive treatments and the potential of innovative immunotherapeutic interventions to restore immune control and proactively deter reactivation.

Influence of nonalcoholic fatty liver disease on response to antiviral treatment in patients with chronic hepatitis B:A meta-analysis

BACKGROUND Although hepatitis B virus infection is the leading cause of chronic liver injury globally,nonalcoholic fatty liver disease(NAFLD)is gradually gaining attention as another major chronic liver disease.The number of patients having chronic hepatitis B(CHB)with concomitant hepatic steatosis has increased.AIM To analyze the effect of NAFLD on the response to antiviral treatment in patients with CHB.METHODS Relevant English studies were systematically searched across PubMed,EMBASE,Web of Science,and Cochrane Library until October 2023.Studies in which the treatment outcomes were compared between patients with CHB only and those with CHB and hepatic steatosis were included.RESULTS Of the 2502 retrieved studies,11 articles were finally included.Biochemical response until 48 wk(OR=0.87,95%CI:0.50–1.53,P=0.000)and 96 wk(OR=0.35,95%CI:0.24–0.53,P=0.24)and virological response until 96 wk(OR=0.80,95%CI:0.43–1.49,P=0.097)were lower in patients with hepatic steatosis than in patients with CHB alone.CONCLUSION Hepatic steatosis lowers the biochemical response to antiviral treatment in patients with CHB.

BSTFNet:An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Current perspectives of viral hepatitis

Viral hepatitis represents a major danger to public health,and is a globally leading cause of death.The five liver-specific viruses:Hepatitis A virus,hepatitis B virus,hepatitis C virus,hepatitis D virus,and hepatitis E virus,each have their own unique epidemiology,structural biology,transmission,endemic patterns,risk of liver complications,and response to antiviral therapies.There remain few options for treatment,in spite of the increasing prevalence of viral-hepatitiscaused liver disease.Furthermore,chronic viral hepatitis is a leading worldwide cause of both liver-related morbidity and mortality,even though effective treatments are available that could reduce or prevent most patients’complications.In 2016,the World Health Organization released its plan to eliminate viral hepatitis as a public health threat by the year 2030,along with a discussion of current gaps and prospects for both regional and global eradication of viral hepatitis.Today,treatment is sufficiently able to prevent the disease from reaching advanced phases.However,future therapies must be extremely safe,and should ideally limit the period of treatment necessary.A better understanding of pathogenesis will prove beneficial in the development of potential treatment strategies targeting infections by viral hepatitis.This review aims to summarize the current state of knowledge on each type of viral hepatitis,together with major innovations.

Hepatic Elastometry in the Management of Hepatitis B at National Hospital of Niamey

Introduction: Viral hepatitis B is a public health problem in Niger which is classified as a high endemicity area with a prevalence ranging from 8 to 17% depending on the studies [1] and that of HBV-related cirrhosis is about 40.26% in 2024. The decision to treat is based on a combination of three parameters: viral load, ALT values and the degree of hepatic fibrosis [2]. The latter is assessed by hepatic elastography (Fibroscan), which is a decisive factor in treatment. In Niger, until 2024, the decision to treat or not to treat a patient with HBV was based on the determination of viral load B and transaminases, and no work evaluating the contribution of this third element, liver elasticity, has been done, hence the interest of our study. Objective: To study the contribution of Fibroscan in measuring hepatic elasticity in the management of patients with HBV. Methodology: This was a prospective descriptive study conducted from January 05 to November 30, i.e. a period of 11 months, on clinically asymptomatic HBsAg-positive patients who had undergone FibroScan liver elasticity measurement. The examination was carried out by a hepatogastroenterologist who had received training in the Fibroscan. The median of ten measures of liver elasticity at the same point with an IQR of less than 30% was considered the valid measure and no or minimal fibrosis was defined as a value ˂7 Kpa, moderate fibrosis as a value between 7 and 10 kpa, severe fibrosis as a value greater than 10 Kpa, and the existence of cirrhosis as a value greater than 14 Kpa were analyzed using SPSS version 20 software. Results: Out of 398 patients monitored for HBV, 60 cases met the inclusion criteria, i.e., a frequency of 15.07%. The mean age of the patients was 35.63 years, with extremes of 18 and 70 years. They were predominantly male, with a sex ratio of 3.2. Married patients accounted for 61.67% (n = 37). Jaundice was absent in 91.67% (n = 55). The circumstances of discovery of HBV were the routine health check-up, followed by blood donation with 50% and 46.67%, respectively. The viral load was >2000 UI/ml in 32.7% (n = 17). HBeAg was negative in 93.33% of cases (n = 56). ALT levels were normal in 47 patients (78.33%). Mean liver elasticity was 6.7 KPa. Fibrosis was classified as F0 - F1 in 75% (n = 45), F1 - F2 in 18.33% (n = 11) and F3 - F4 in 6.67% (n = 4) of patients. There was no significant relationship between viremia value, liver activity, degree of fibrosis and quantitative HBsAg. Conclusion: Measurement of hepatic elasticity has made it possible to diagnose cases of compensated cirrhosis and significant fibrosis in patients considered to be inactive carriers (viral load ˂2000 IU/ml and normal transaminases) in asymptomatic HBV+ patients. This made it possible to put these patients on Tenofovir in order to avoid decompensation for the first group and for the second the progression to cirrhosis. It is an excellent tool to aid in the decision to start treatment.

Hepatitis B Surface Antigen and Hepatitis C Virus Antibodies among Drug Users in Burkina Faso

Introduction: The epidemiology of both hepatitis B virus (HBV) and hepatitis C virus (HCV) infections among drug users (DUs) is little known in West Africa. The study aimed to assess the prevalence of hepatitis B and C viruses among drug users in Burkina Faso. Methodology: This was a cross-sectional biological and behavioral survey conducted between June and August 2022, among drug users in Ouagadougou and Bobo Dioulasso, the two main cities of Burkina Faso. A respondent-driven sampling (RDS) was used to recruit drug users. Hepatitis B surface antigen was determined using lateral flow rapid test kits and antibodies to hepatitis C virus in serum determined using an Enzyme-Linked Immunosorbent Assay. Data were entered and analyzed using Stata 17 software. Weighted binary logistic regression was used to identify the associated factors of hepatitis B and C infections and a p-value Results: A total of 323 drug users were recruited with 97.5% males. The mean age was 32.7 years old. The inhaled or smoked mode was the most used by drug users. The adjusted hepatitis B and hepatitis C prevalence among study participants were 11.1% and 2.3% respectively. The marital status (p = 0.001), and the nationality (p = 0.011) were significantly associated with hepatitis B infection. The type of drug used was not significantly associated with hepatitis B infection or hepatitis C infection. Conclusion: The prevalence of HBsAg and anti-HCV antibodies among DUs are comparable to those reported in the general population in Burkina Faso. This result suggests that the main routes of contamination by HBV and HCV among DUs are similar to those in the population, and could be explained by the low use of the injectable route by DUs in Burkina Faso.

Performance evaluation of seven multi-label classification methods on real-world patent and publication datasets

Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution.