Imatinib, a breakpoint cluster region (BCR)-Abelson murine leukemia(ABL) tyrosine kinase inhibitor (TKI), has revolutionized the treatment of chronic myelogenous leukemia (CML). However, development of multidrug resistance(MDR) limits the use of imatinib. In the present study, we aimed to investigate the mechanisms of cellular resistance to imatinib in CML. Therefore, we established an imatinib-resistant human CML cell line(K562-imatinib) through a stepwise selection process. While characterizing the phenotype of these cells, we found that K562-imatinib cells were 124.6-fold more resistant to imatinib than parental K562 cells. In addition, these cells were cross-resistant to second- and third-generation BCR-ABL TKIs. Western blot analysis and reverse transcription-polymerase chain reaction(RT-PCR) demonstrated that P-glycoprotein(P-gp) and MDR1 mRNA levels were increased in K562-imatinib cells. In addition, accumulation of [14C]6-mercaptopurine (6-MP) was decreased, whereas the ATP-dependent efflux of [14C]6-MP and [3H]methotrexate transport were increased in K562-imatinib cells. These data suggest that the overexpression of P-gp may play a crucial role in acquired resistance to imatinib in CML K562-imatinib cells.
ATP Binding Cassette Transporter, Sub-Family B ; genetics ; metabolism ; Antineoplastic Agents ; pharmacology ; Benzamides ; Drug Resistance, Multiple ; Drug Resistance, Neoplasm ; Fusion Proteins, bcr-abl ; antagonists & inhibitors ; Gene Expression Regulation, Neoplastic ; Humans ; Imatinib Mesylate ; K562 Cells ; Mercaptopurine ; metabolism ; Methotrexate ; metabolism ; Piperazines ; pharmacology ; Protein Kinase Inhibitors ; pharmacology ; Protein-Tyrosine Kinases ; antagonists & inhibitors ; Pyrimidines ; pharmacology ; RNA, Messenger ; metabolism

Objectives: To assess the relationship of variation of blood pressure and neurological deterioration (ND) in ischemic stroke patients. Methods: We recruited patients with the fi rst-ever ischemic stroke at a teaching hospital. The National Institutes of Health Stoke Score (NIHSS) of each patient was monitored for 2 months. ND was defi ned as an increase of ≥ 2 points in NIHSS during the fi rst 7 days after stroke. Blood pressure was measured every 6 hours for fi rst 7 days. We analyzed blood pressure data in the fi rst 36 hours to study the relationship between variation of blood pressure and ND. Successive variation of systolic (svSBP) and diastolic (svDBP) blood pressure was calculated as svSBP= |SBPn+1 – SBPn | and svDBP= |DBPn+1 – DBPn | respectively. The largest svSBP in the fi rst 36 hours of hospitalization or before ND was defi ned as maximum variation of systolic blood pressure (maxvSBP). Then, the mean variation of systolic (mvSBP) and diastolic (mvDBP) blood pressure was calculated as mvSBP= svSBP/N and mvDBP= svDBP/N respectively. Results: A total of 121 patients were included in this study, and 38 of them had ND. The mvSBP was higher in the ND Group (17.9±8.4 mmHg vs. 13.7±4.4 mmHg, p=0.006) but the difference in mvDBP did not reach statistical signifi cance (9.8±3.5mmHg vs. 8.6±3.0 mmHg p=0.06). The ND Group had a larger maxvSBP (35.2±17.2 vs. 27.6±11.6 mmHg, p =0.01), which was more frequently over 30mmHg than that in the stable group (P=0.02). Conclusions: A large svSBP is associated with an increased risk for ND. The study highlights the importance of close monitoring of blood pressure in ischemic stroke patients.

Traditional acute kidney injury (AKI) classifications, which are centered around semi-anatomical lines, can no longer capture the complexity of AKI. By employing strategies to identify predictive and prognostic enrichment targets, experts could gain a deeper comprehension of AKI’s pathophysiology, allowing for the development of treatment-specific targets and enhancing individualized care. Subphenotyping, which is enriched with AKI biomarkers, holds insights into distinct risk profiles and tailored treatment strategies that redefine AKI and contribute to improved clinical management. The utilization of biomarkers such as N-acetyl-β-D-glucosaminidase, tissue inhibitor of metalloprotease-2·insulin-like growth factor-binding protein 7, kidney injury molecule-1, and liver fatty acid-binding protein garnered significant attention as a means to predict subclinical AKI. Novel biomarkers offer promise in predicting persistent AKI, with urinary motif chemokine ligand 14 displaying significant sensitivity and specificity. Furthermore, they serve as predictive markers for weaning patients from acute dialysis and offer valuable insights into distinct AKI subgroups. The proposed management of AKI, which is encapsulated in a structured flowchart, bridges the gap between research and clinical practice. It streamlines the utilization of biomarkers and subphenotyping, promising a future in which AKI is swiftly identified and managed with unprecedented precision. Incorporating kidney biomarkers into strategies for early AKI detection and the initiation of AKI care bundles has proven to be more effective than using care bundles without these novel biomarkers. This comprehensive approach represents a significant stride toward precision medicine, enabling the identification of high-risk subphenotypes in patients with AKI.

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.

OBJECTIVETo observe the effect of combining red yeast rice and Lactobacillus casei (L. casei) in lowering cholesterol in patients with primary hyperlipidemia, the later has also been shown to remove cholesterol in in vitro studies.

METHODSA double-blind clinical trial was conducted to evaluate the cholesterol-lowering effect of the combination of red yeast rice and L. casei. Sixty patients with primary hyperlipidemia were recruited and randomized equally to either the treatment group (red yeast rice + L. casei) or the control group (red yeast rice + placebo). One red yeast rice capsule and two L. casei capsules were taken twice a day. The treatment lasted for 8 weeks, with an extended follow-up period of 4 weeks. The primary endpoint was a difference of serum low-density lipoprotein cholesterol (LDL-C) level at week 8.

RESULTSAt week 8, the LDL-C serum level in both groups was lower than that at baseline, with a decrease of 33.85±26.66 mg/dL in the treatment group and 38.11±30.90 mg/dL in the control group; however, there was no statistical difference between the two groups (P>0.05). The total cholesterol was also lower than the baseline in both groups, yet without a statistical difference between the two groups. The only statistically signifificant difference between the two groups was the average diastolic pressure at week 12, which dropped by 2.67 mm Hg in the treatment group and increased by 4.43 mm Hg in the placebo group (P<0.05). The antihypertensive activity may be associated with L. casei. Red yeast rice can signifificantly reduce LDL-C, total cholesterol and triglyceride.

CONCLUSIONThe combination of red yeast rice and L. casei did not have an additional effect on lipid profifiles.

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.

Arsenic trioxide (ATO) is used as a chemotherapeutic agent for the treatment of acute promyelocytic leukemia. However, increasing drug resistance is reducing its efficacy. Therefore, a better understanding of ATO resistance mechanism is required. In this study, we established an ATO-resistant human epidermoid carcinoma cell line, KB/ATO, from its parental KB-3-1 cells. In addition to ATO, KB/ATO cells also exhibited cross-resistance to other anticancer drugs such as cisplatin, antimony potassium tartrate, and 6-mercaptopurine. The arsenic accumulation in KB/ATO cells was significantly lower than that in KB-3-1 cells. Further analysis indicated that neither application of P-glycoprotein inhibitor, breast cancer resistant protein (BCRP) inhibitor, or multidrug resistance protein 1 (MRP1) inhibitor could eliminate ATO resistance. We found that the expression level of ABCB6 was increased in KB/ATO cells. In conclusion, ABCB6 could be an important factor for ATO resistance in KB/ATO cells. The ABCB6 level may serve as a predictive biomarker for the effectiveness of ATO therapy.