Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Objective: To explore the prognostic factors of extracellular NK/T cell lymphoma (ENKTL) treated with pegaspargase/L-asparaginase. Methods: The clinical data of 656 ENKTL patients diagnosed at 11 medical centers in the Huaihai Lymphoma Working Group from March 2014 to April 2021 were retrospectively analyzed. The patients were randomly divided into two groups: a training set (460 cases) and a validation set (196 cases) at 7∶3, and the prognostic factors of the patients were analyzed. A prognostic scoring system was established, and the predictive performance of different models was compared. Results: Patients' median age was 46 (34, 57) years, with 456 males (69.5% ) and 561 nasal involvement (85.5% ). 203 patients (30.9% ) received a chemotherapy regimen based on L-asparaginase combined with anthracyclines, and the 5-year overall survival rate of patients treated with P-GEMOX regimen (pegaspargase+gemcitabine+oxaliplatin) was better than those treated with SMILE regimen (methotrexate+dexamethasone+cyclophosphamide+L-asparaginase+etoposide) (85.9% vs 63.8% ; P=0.004). The results of multivariate analysis showed that gender, CA stage, the Eastern Cooperative Oncology Group performance status (ECOG PS) score, HGB, and EB virus DNA were independent influencing factors for the prognosis of ENKTL patients (P<0.05). In this study, the predictive performance of the prognostic factors is superior to the international prognostic index, Korean prognostic index, and prognostic index of natural killer lymphoma. Conclusion: Gender, CA stage, ECOG PS score, HGB, and EB virus DNA are prognostic factors for ENKTL patients treated with pegaspargase/L-asparaginase.
Male ; Humans ; Middle Aged ; Asparaginase/therapeutic use* ; Prognosis ; Retrospective Studies ; Lymphoma, Extranodal NK-T-Cell/drug therapy* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Etoposide ; Cyclophosphamide ; Methotrexate/therapeutic use* ; DNA/therapeutic use* ; Treatment Outcome

AIM: To explore the application value of deep learning technology in automatic meibomian glands segmentation. METHODS:Infrared meibomian gland images were collected and 193 of them were picked out for establishing the database. The images were manually labeled by three clinicians. UNet++ network and automatic data expansion strategy were introduced to construct the automatic meibomian glands segmentation model. The feasibility and effectiveness of the proposed segmentation model were analyzed by precision, sensitivity, specificity, accuracy and intersection over union.RESULTS: Taking manual labeling as the gold standard, the presented method segment the glands effectively and steadily with accuracy, sensitivity and specificity of 94.31%, 82.15% and 96.13% respectively. On the average, only 0.11s was taken for glands segmentation of single image.CONCLUSIONS: In this paper, deep learning technology is introduced to realize automatic segmentation of meibomian glands, achieving high accuracy, good stability and efficiency. It would be quite useful for calculation of gland morphological parameters, the clinical diagnosis and screening of related diseases, improving the diagnostic efficiency.

BACKGROUND: Extracellular vesicles (EVs) are derived from internal cellular compartments, and have potential as a diagnostic and therapeutic tool in degenerative disease associated with aging. Mesenchymal stem cells (MSCs) have become a promising tool for functional EVs production. This study investigated the efficacy of EVs and its effect on differentiation capacity. METHODS: The characteristics of MSCs were evaluated by flow cytometry and stem cell differentiation analysis, and a production mode of functional EVs was scaled from MSCs. The concentration and size of EVs were quantitated by Nanoparticle Tracking Analysis (NTA). Western blot analysis was used to assess the protein expression of exosomespecific markers. The effects of MSC-derived EVs were assessed by chondrogenic and adipogenic differentiation analyses and histological observation. RESULTS The range of the particle size of adipose-derived stem cells (ADSCs)- and Wharton’s jelly -MSCs-derived EVs were from 130 to 150 nm as measured by NTA, which showed positive expression of exosomal markers. The chondrogenic induction ability was weakened in the absence of EVs in vitro. Interestingly, after EV administration, type II collagen, a major component in the cartilage extracellular matrix, was upregulated compared to the EV-free condition.Moreover, EVs decreased the lipid accumulation rate during adipogenic induction.