To clone the first anion channel from Xenopus laevis (X. laevis), we isolated a calcium-activated chloride channel (CLCA)-like membrane protein 6 gene (CMP6) in X. laevis. As a first step in gene isolation, an expressed sequence tags database was screened to find the partial cDNA fragment. A putative partial cDNA sequence was obtained by comparison with rat CLCAs identified in our laboratory. First stranded cDNA was synthesized by reverse transcription polymerase-chain reaction (RT-PCR) using a specific primer designed for the target cDNA. Repeating the 5' and 3' rapid amplification of cDNA ends, full-length cDNA was constructed from the cDNA pool. The full-length CMP6 cDNA completed via 5'- and 3'-RACE was 2,940 bp long and had an open reading frame (ORF) of 940 amino acids. The predicted 940 polypeptides have four major transmembrane domains and showed about 50% identity with that of rat brain CLCAs in our previously published data. Semi-quantification analysis revealed that CMP6 was most abundantly expressed in small intestine, colon and liver. However, all tissues except small intestine, colon and liver had undetectable levels. This result became more credible after we did real-time PCR quantification for the target gene. In view of all CLCA studies focused on human or murine channels, this finding suggests a hypothetical protein as an ion channel, an X. laevis CLCA.
Amino Acids ; Animals ; Brain ; Chloride Channels ; Clone Cells ; Colon ; DNA, Complementary ; Expressed Sequence Tags ; Humans ; Intestine, Small ; Ion Channels ; Liver ; Membrane Proteins ; Membranes ; Open Reading Frames ; Peptides ; Rats ; Real-Time Polymerase Chain Reaction ; Resin Cements ; Reverse Transcription ; Staphylococcal Protein A ; Tissue Distribution ; Xenopus ; Xenopus laevis

Boron neutron capture therapy and Y-90 radioembolization are emerging therapeutic methods for uncontrolled brain cancers and hepatic cancers, respectively. These advanced radiation therapies are heavily relied on theranostic nuclear medicine imaging before the therapy for the eligibility of patients and the prescribed-dose simulation, as well as the post-therapy scanning for assessing the treatment efficacy. In Taiwan, the Taipei Veterans General Hospital is the only institute performing the BNCT and also the leading institute performing Y-90 radioembolization. In this article, we present our single institute experiences and associated theranostic nuclear medicine approaches for these therapies.
Boron Neutron Capture Therapy ; Brain Neoplasms ; Hospitals, General ; Humans ; Liver Neoplasms ; Nuclear Medicine ; Taiwan ; Theranostic Nanomedicine ; Treatment Outcome ; Veterans

Boron neutron capture therapy and Y-90 radioembolization are emerging therapeutic methods for uncontrolled brain cancers and hepatic cancers, respectively. These advanced radiation therapies are heavily relied on theranostic nuclear medicine imaging before the therapy for the eligibility of patients and the prescribed-dose simulation, as well as the post-therapy scanning for assessing the treatment efficacy. In Taiwan, the Taipei Veterans General Hospital is the only institute performing the BNCT and also the leading institute performing Y-90 radioembolization. In this article, we present our single institute experiences and associated theranostic nuclear medicine approaches for these therapies.

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.