1.Holistic Consideration of Patients with Schizophrenia to Improve Medication Adherence and Outcomes.
Lan Ting LEE ; Kao Chin CHEN ; Wei Hung CHANG ; Po See CHEN ; I Hui LEE ; Yen Kuang YANG
Clinical Psychopharmacology and Neuroscience 2015;13(2):138-143
Although several algorithms have been applied to treat patients with schizophrenia, their clinical use remains still limited, because most emphasize the prescription of antipsychotics. A new algorithm with a more holistic approach to treating patients with schizophrenia, to be used before applying traditional prescribing guidelines, was thus proposed by an expert team of Taiwanese psychiatrists. In this algorithm, several important treatment tasks/modalities are proposed, including long-acting injection anti-psychotics, shared decision-making, a case management system, compulsory treatment by law, community rehabilitation programs, the patients' feeling about their health care professionals (patients' behaviors) and their attitude/knowledge of their conditions/illness. This study proposes that evaluating the medication adherence of patients can be determined by two key domains, namely patients' behaviors and attitudes. Based on different levels of their behaviors (X-axis) and attitude/knowledge (Y-axis), it is possible to categorize patients with schizophrenia into six subgroups, for which various different interventions, including the use of antipsychotics, could be applied and integrated. Further research is needed to assess the applicability of this treatment algorithm in clinical settings.
Antipsychotic Agents
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Case Management
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Delivery of Health Care
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Holistic Health
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Humans
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Jurisprudence
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Medication Adherence*
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Prescriptions
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Psychiatry
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Rehabilitation
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Schizophrenia*
2.Nerve growth factor upregulates sirtuin 1 expression in cholestasis: a potential therapeutic target
Ming Shian TSAI ; Po Huang LEE ; Cheuk Kwan SUN ; Ting Chia CHIU ; Yu Chun LIN ; I Wei CHANG ; Po Han CHEN ; Ying Hsien KAO
Experimental & Molecular Medicine 2018;50(1):e426-
This study investigated the regulatory role of nerve growth factor (NGF) in sirtuin 1 (SIRT1) expression in cholestatic livers. We evaluated the expression of NGF and its cognate receptors in human livers with hepatolithiasis and the effects of NGF therapy on liver injury and hepatic SIRT1 expression in a bile duct ligation (BDL) mouse model. Histopathological and molecular analyses showed that the hepatocytes of human diseased livers expressed NGF, proNGF (a precursor of NGF), TrkA and p75NTR, whereas only p75NTR was upregulated in hepatolithiasis, compared with non-hepatolithiasis livers. In the BDL model without NGF therapy, p75NTR, but not TrkA antagonism, significantly deteriorated BDL-induced liver injury. By contrast, the hepatoprotective effect of NGF was abrogated only by TrkA and not by p75NTR antagonism in animals receiving NGF therapy. Intriguingly, a positive correlation between hepatic SIRT1 and NGF expression was found in human livers. In vitro studies demonstrated that NGF upregulated SIRT1 expression in mouse livers and human Huh-7 and rodent hepatocytes. Both NGF and proNGF induced protective effects against hydrogen peroxide-induced cytotoxicity in Huh-7 cells, whereas inhibition of TrkA and p75NTR activity prevented oxidative cell death. Mechanistically, NGF, but not proNGF, upregulated SIRT1 expression in human Huh-7 and rodent hepatocytes via nuclear factor (NF)-κB activity, whereas NGF-induced phosphoinositide-3 kinase/Akt, extracellular signal–regulated kinase and NF-κB signaling and SIRT1 activity were involved in its hepatoprotective effects against oxidative injury. These findings suggest that pharmacological manipulation of the NGF/SIRT1 axis might serve as a novel approach for the treatment of cholestatic disease.
Animals
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Bile Ducts
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Cell Death
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Cholestasis
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Hepatocytes
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Humans
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Hydrogen
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In Vitro Techniques
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Ligation
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Liver
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Mice
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Nerve Growth Factor
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Phosphotransferases
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Rodentia
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Sirtuin 1
3.Prevalence of Enterobius vermicularis Infection among Preschool Children in Kindergartens of Taipei City, Taiwan in 2008.
Tso Kang CHANG ; Chien Wei LIAO ; Ying Chieh HUANG ; Chun Chao CHANG ; Chia Mei CHOU ; Hsin Chieh TSAY ; Alice HUANG ; Shu Fen GUU ; Ting Chang KAO ; Chia Kwung FAN
The Korean Journal of Parasitology 2009;47(2):185-187
The prevalence of Enterobius vermicularis infection among preschool children was reported to be low based on a 5-year screening program in Taipei City, Taiwan. The Taipei City government intended to terminate the E. vermicularis screening program among preschool children. Thus, we were entrusted with confirming whether pinworm infections among preschool children in Taipei City had truly declined. From each of 12 administrative districts 2-3 kindergartens were randomly selected for investigation. In total, 4,349 children were examined, of which 2,537 were boys and 1,812 were girls. The cellophane tape adhered to a glass slide was used, and all examinations were done by certified medical technologists. Results indicated that the overall prevalence rate of pinworm infections was 0.62% (27/4,349). Although the infection rate was higher among boys (0.67%, 17/2,537) than in girls (0.55%, 10/1,812), no significant difference was found (chi2 = 0.399, P = 0.62). According to the administrative district, the infection rate ranged from no positive cases of E. vermicularis infection in the Xinyi, Zhongzhen, and Wanhua Districts (0%; 0/299, 0/165, and 0/358, respectively), to 0.26% (1/131) in Songshan District, with the highest rate of 1.88% (7/373) in Wenshan District. Because the overall infection rate (0.62%, 27/4,349) in the present study was unchanged compared to that (0.40%, 197/49,541) previously reported in 2005, we propose that regular pinworm screening and treatment programs should be continued in some parts of Taipei City.
Animals
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Child Day Care Centers
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Child, Preschool
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Enterobiasis/*epidemiology
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Enterobius/*isolation & purification
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Female
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Humans
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Male
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Microscopy/methods
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Prevalence
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Taiwan/epidemiology
4.Taiwan Association for the Study of the Liver-Taiwan Society of Cardiology Taiwan position statement for the management of metabolic dysfunction- associated fatty liver disease and cardiovascular diseases
Pin-Nan CHENG ; Wen-Jone CHEN ; Charles Jia-Yin HOU ; Chih-Lin LIN ; Ming-Ling CHANG ; Chia-Chi WANG ; Wei-Ting CHANG ; Chao-Yung WANG ; Chun-Yen LIN ; Chung-Lieh HUNG ; Cheng-Yuan PENG ; Ming-Lung YU ; Ting-Hsing CHAO ; Jee-Fu HUANG ; Yi-Hsiang HUANG ; Chi-Yi CHEN ; Chern-En CHIANG ; Han-Chieh LIN ; Yi-Heng LI ; Tsung-Hsien LIN ; Jia-Horng KAO ; Tzung-Dau WANG ; Ping-Yen LIU ; Yen-Wen WU ; Chun-Jen LIU
Clinical and Molecular Hepatology 2024;30(1):16-36
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.
5.Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program
Ming-Ying LU ; Chung-Feng HUANG ; Chao-Hung HUNG ; Chi‐Ming TAI ; Lein-Ray MO ; Hsing-Tao KUO ; Kuo-Chih TSENG ; Ching-Chu LO ; Ming-Jong BAIR ; Szu-Jen WANG ; Jee-Fu HUANG ; Ming-Lun YEH ; Chun-Ting CHEN ; Ming-Chang TSAI ; Chien-Wei HUANG ; Pei-Lun LEE ; Tzeng-Hue YANG ; Yi-Hsiang HUANG ; Lee-Won CHONG ; Chien-Lin CHEN ; Chi-Chieh YANG ; Sheng‐Shun YANG ; Pin-Nan CHENG ; Tsai-Yuan HSIEH ; Jui-Ting HU ; Wen-Chih WU ; Chien-Yu CHENG ; Guei-Ying CHEN ; Guo-Xiong ZHOU ; Wei-Lun TSAI ; Chien-Neng KAO ; Chih-Lang LIN ; Chia-Chi WANG ; Ta-Ya LIN ; Chih‐Lin LIN ; Wei-Wen SU ; Tzong-Hsi LEE ; Te-Sheng CHANG ; Chun-Jen LIU ; Chia-Yen DAI ; Jia-Horng KAO ; Han-Chieh LIN ; Wan-Long CHUANG ; Cheng-Yuan PENG ; Chun-Wei- TSAI ; Chi-Yi CHEN ; Ming-Lung YU ;
Clinical and Molecular Hepatology 2024;30(1):64-79
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.