OBJECTIVE: The objective was to identify risk factors that were associated with the progression from endometriosis to ovarian cancer based on medical insurance data. METHODS: The study was performed on a dataset obtained from the National Health Insurance Research Database, which covered all the inpatient claim data from 2000 to 2013 in Taiwan. The International Classification of Diseases (ICD) code 617 was used to screen the dataset for the patients who were admitted to hospital due to endometriosis. They were then tracked for subsequent diagnosis of ovarian cancer, and available biological, socioeconomic and clinical information was also collected. Univariate and multivariate analyses were then performed based on the Cox regression model to identify risk factors. C-index was calculated and cross validated. RESULTS: A total of 229,617 patients who were admitted to hospital due to endometriosis from 2000 to 2013 were included in the study, out of whom 1,473 developed ovarian cancer by the end of 2013. A variety of factors, including age, residence, hospital stratification, premium range, and various comorbidities had significant impact on the progression (p < 0.05). Among them, age, urbanization of residence, hospital stratification, premium range, post-endometriosis childbearing, pelvic inflammation, and depression all had independent, significant impact (p < 0.05). The validated C-index was 0.69. CONCLUSION: For a woman diagnosed with endometriosis, increased age, residing in a highly urbanized area, low or high income, depression, pelvic inflammation, and absence of childbearing post-endometriosis all put her at high-risk to develop ovarian cancer. The findings may be of help to gynecologists to identify high-risk patients.
Cohort Studies* ; Comorbidity ; Dataset ; Depression ; Diagnosis ; Endometriosis* ; Female ; Humans ; Inflammation ; Inpatients ; Insurance* ; International Classification of Diseases ; Multivariate Analysis ; National Health Programs ; Ovarian Neoplasms* ; Risk Factors* ; Taiwan ; Urbanization

Humans ; Male ; Middle Aged ; Radiography ; Rupture, Spontaneous ; diagnostic imaging ; etiology ; physiopathology ; Ureter ; injuries ; Urolithiasis ; complications ; diagnosis ; diagnostic imaging

PURPOSE: Urinary catheterization is a common technique in clinical practice. There is, however, no consensus on management prior to removal of the indwelling catheter for short-term patients. This systematic review examined the necessity of clamping before removal of an indwelling urinary catheter in short-term patients. METHODS: A systematic literature review was conducted using eight databases and predetermined keywords-guided searches. Some 2,515 studies were evaluated. Ten studies that met the inclusion criteria were selected. RESULTS: The quality of the studies was assessed using the Jadad scoring system. Only 40.0% of studies were rated as high quality. This review found that catheter clamping prior to removal was not necessary for the short-term patient. When made a comparison with the unclamping group, there was no significant difference in recatheterization risk, risk of urine retention, patients' subjective perceptions and rate of urinary tract infection. CONCLUSIONS: This review indicated that bladder training by clamping prior to removal of urinary catheters is not necessary in short-term catheter patients. In addition, clamping carries the risk of complications such as prolonging urinary catheter retention and urinary tract injury. Further investigation requires higher quality methodologies and more diverse study designs.
Attitude to Health ; Catheters, Indwelling ; Constriction ; Device Removal ; Humans ; Patient Education as Topic/methods ; Perception ; Randomized Controlled Trials as Topic ; Retreatment ; Urinary Catheterization/*methods ; Urinary Catheters ; Urinary Retention/psychology ; Urinary Tract Infections/therapy ; Urination/physiology

D-psicose 3-epimerase (DPEase) is demonstrated to be useful in the bioproduction of D-psicose, a rare hexose sugar, from D-fructose, found plenty in nature. Clostridium cellulolyticum H10 has recently been identified as a DPEase that can epimerize D-fructose to yield D-psicose with a much higher conversion rate when compared with the conventionally used DTEase. In this study, the crystal structure of the C. cellulolyticum DPEase was determined. The enzyme assembles into a tetramer and each subunit shows a (β/α)(8) TIM barrel fold with a Mn(2+) metal ion in the active site. Additional crystal structures of the enzyme in complex with substrates/products (D-psicose, D-fructose, D-tagatose and D-sorbose) were also determined. From the complex structures of C. cellulolyticum DPEase with D-psicose and D-fructose, the enzyme has much more interactions with D-psicose than D-fructose by forming more hydrogen bonds between the substrate and the active site residues. Accordingly, based on these ketohexose-bound complex structures, a C3-O3 proton-exchange mechanism for the conversion between D-psicose and D-fructose is proposed here. These results provide a clear idea for the deprotonation/protonation roles of E150 and E244 in catalysis.
Binding Sites ; Biocatalysis ; Catalytic Domain ; Clostridium cellulolyticum ; enzymology ; Hexoses ; chemistry ; Manganese ; chemistry ; Protein Structure, Quaternary ; Racemases and Epimerases ; chemistry ; metabolism ; Substrate Specificity

Background: Therapeutic interventions for diabetes are most effective when administered in the newly onset phase, yet determining the exact onset moment can be elusive in practice. Spontaneous autoimmune diabetes among NOD mice appears randomly between 12 and 32 weeks of age with an incidence range from 60 to 90%. Furthermore, the disease often progresses rapidly to severe diabetes within days, resulting in a very short window of newly onset phase, that poses significant challenge in early diagnosis. Conventionally, extensive blood glucose (BG) testing is typically required on large cohorts throughout several months to conduct prospective survey. We incorporated ultrasensitive urine glucose (UG) testing into an ordinary BG survey process, initially aiming to elucidate the lag period required for excessive glucose leaking from blood to urine during diabetes progression in the mouse model. Results: The observations unexpectedly revealed that small amounts of glucose detected in the urine often coincide with, sometimes even a couple days prior than elevated BG is diagnosed. Accordingly, we conducted the UG-based survey protocol in another cohort that was validated to accurately identified every individual near onset, who could then be confirmed by following few BG tests to fulfill the consecutive BG + criteria. This approach required fewer than 95 BG tests, compared to over 700 tests with traditional BG survey, to diagnose all the 37–38 diabetic mice out of total 60. The average BG level at diagnosis was slightly below 350 mg/dl, lower than the approximately 400 mg/dl observed with conventional BG monitoring. Conclusions We demonstrated a near perfect correlation between BG + and ultrasensitive UG + results in prospective survey with no lag period detected under twice weekly of testing frequency. This led to the refined protocol based on surveying with noninvasive UG testing, allowing for the early identification of newly onset diabetic mice with only a few BG tests required per mouse. This protocol significantly reduces the need for extensive blood sampling, lancet usage, labor, and animal distress, aligning with the 3Rs principle. It presents a convenient, accurate, and animal-friendly alternative for early diabetes diagnosis, facilitating research on diagnosis, pathogenesis, prevention, and treatment.

Background/Aims: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. Methods: The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. Results: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.

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.

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: Particulate matter (PM) < 2.5 μm (PM METHODS: We obtained DNA methylation and exercise data of 496 participants (aged between 30 and 70 years) from the Taiwan Biobank (TWB) database. We also extracted PM RESULTS: DLEC1 methylation and PM CONCLUSIONS We found significant positive associations between PM
Adult ; Aged ; Air Pollutants/adverse effects* ; DNA Methylation/drug effects* ; Environmental Exposure/adverse effects* ; Exercise ; Female ; Humans ; Male ; Middle Aged ; Particulate Matter/adverse effects* ; Taiwan ; Tumor Suppressor Proteins/metabolism*