Non-tubecrulosis mycobacterium infections were increasingly reported either pulmonary or extrapulmonary in the past decades. In Taiwan, we noticed several reports about the soft tissue infections caused by rapid growing mycobacterium such as Mycobacterium abscessus, Mycobacterium chelonae, on newspaper, magazines, or the multimedia. Most of them occurred after a plastic surgery, and medical or non-medical procedures. Here, we reported two cases of these infections following medical procedures. We also discussed common features and the clinical course of the disease, the characteristics of the infected site, and the treatment strategy. The literatures were also reviewed, and the necessity of the treatment guidelines was discussed.
Multimedia ; Mycobacterium chelonae ; Mycobacterium Infections ; Mycobacterium* ; Periodicals ; Periodicals as Topic ; Soft Tissue Infections* ; Surgery, Plastic ; Taiwan

Biomarkers, Tumor ; analysis ; Biopsy, Needle ; Humans ; Keratins ; analysis ; Male ; Oligonucleotide Array Sequence Analysis ; Prostate-Specific Antigen ; analysis ; Prostatic Hyperplasia ; diagnosis ; Prostatic Intraepithelial Neoplasia ; diagnosis ; Prostatic Neoplasms ; diagnosis ; Racemases and Epimerases ; analysis

INTRODUCTIONFew studies in Asia have assessed the burden of hypercholesterolaemia based on the global cardiovascular risk assessment. This study determines the burden of hypercholesterolaemia in an Asian population based on the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATPIII) guidelines, and examines predictors of low-density lipoprotein cholesterol (LDL-C) goal attainment.

MATERIALS AND METHODSFive thousand and eighty-three Chinese, Malays and Asian-Indians living in Singapore were assigned to coronary heart disease (CHD)-risk category based on the NCEP-ATPIII guidelines. Awareness, treatment and control of hypercholesterolaemia based on risk- specific LDL-C goal were determined, including the use of lipid-lowering therapy (LLT). Cox-regression models were used to identify predictors of LDL-C above goal among those who were aware and unaware of hypercholesterolaemia.

RESULTSOne thousand five hundred and sixty-eight (30.8%) participants were aware of hypercholesterolaemia and 877 (17.3%) were newly diagnosed (unaware). For those who were aware, 39.3% participants received LLT. Among those with 2 risk factors, only 59.7% attained LDL-C goal. The majority of them were taking statin monotherapy, and the median dose of statins was similar across all CHD risk categories. Among participants with 2 risk factors and not receiving LLT, 34.1% would require LLT. Malays or Asian-Indians, higher CHD risk category, increasing body mass index (BMI), current smoking and lower education status were associated with higher risk of LDL-C above goal. Being on LLT reduced the risk of having LDL-C above goal.

CONCLUSIONThe burden of hypercholesterolaemia is high in this multi-ethnic population especially those in the higher CHD risk categories, and might be partly contributed by inadequate titration of statins therapy. Raising awareness of hypercholesterolaemia, appropriate LLT initiation and titration, weight management and smoking cessation may improve LDL-C goal attainment in this population.

Adult ; Aged ; Aged, 80 and over ; Asian Continental Ancestry Group ; Cholesterol, LDL ; blood ; Cross-Sectional Studies ; Female ; Humans ; Hypercholesterolemia ; blood ; epidemiology ; prevention & control ; Male ; Middle Aged ; Practice Guidelines as Topic ; Prevalence ; Singapore ; epidemiology ; Young Adult

Objective Familial hypertrophic cardiomyopathy (FHCM) is a primary myocardial disease characterized by unexplained ventricular hypertrophy. The application of the techniques of reverse genetics has identified at least five chromosomal loci as the major causes for FHCM in diverse ethnic populations, suggesting substantial genetic heterogeneity for FHCM. Recently, the defective gene loci of two Chinese families with FHCM have been mapped to chromosome 11 and 14q1, respectively. For further understanding of the molecular basis of FHCM in Chinese, we analyzed the linkage between four other Chinese kindreds and DNA markers from chromosome 14q1. Methods Six unrelated Chinese families with FHCM, including two previously reported, were studied. Totally 90 family members were included for analysis. DNA from 80 individuals was extracted and polymerase chain reactions were performed using the primers designed according to the sequences derived from the α and β myosin heavy chain gene. Totally four polymorphisms were studied, including three polymorphic microsatellite sequences and one single strand conformation polymorphism. Genetic linkage analysis were performed using the Linkage program.Results In the six studied families, 39 of the 90 family members were found to be affected diagnosed either by echocardiography or by clinical evaluation. The pattern of inheritance in all six studied families was most consistent with an autosomal dominant trait with a high degree of penetrance. Genetic linkage analysis using polymorphisms on the α and β MHC genes showed a combined maximal lod score of 6.2 for trinucleotide repeat polymorphism AMHC-I 15 at θ=0.00 for three studied families without recombination. Exclusion of linkage to the chromosome 14q1 location was noted in two of three other families with the maximal lod score of -2 or less.Conclusions These results provide further evidence that FHCM in Chinese is genetically heterogeneous. Chromosome 14q1 locus, probably the β myosin heavy chain gene, is important as the molecular basis for FHCM in Chinese.

Bipolar disorder (BD) is a major psychiatric disorder that is easily misdiagnosed. Patient adherence to a treatment regimen is of utmost importance for successful outcomes in BD. Several trials of antipsychotics suggested that depot antipsychotics, including long-acting first- and second-generation agents, are effective in preventing non-adherence, partial adherence, and in reducing relapse in BD. Various long-acting injectable (LAI) antipsychotics are available, including fluphenazine decanoate, haloperidol decanoate, olanzapine pamoate, risperidone microspheres, paliperidone palmitate, and aripiprazole monohydrate. Due to the increasing number of BD patients receiving LAI antipsychotics, treatment guidelines have been developed. However, the clinical applicability of LAI antipsychotics remains a global cause for concern, particularly in Asian countries. Expert physicians from Taiwan participated in a consensus meeting, which was held to review key areas based on both current literature and clinical practice. The purpose of this meeting was to generate a practical and implementable set of recommendations for LAI antipsychotic use to treat BD; target patient groups, dosage, administration, and adverse effects were considered. Experts recommended using LAI antipsychotics in patients with schizophrenia, rapid cycling BD, BD I, and bipolar-type schizoaffective disorder. LAI antipsychotic use was recommended in BD patients with the following characteristics: multiple episodes and low adherence; seldom yet serious episodes; low adherence potential per a physician's clinical judgment; preference for injectable agents over oral agents; and multiple oral agent users still experiencing residual symptoms.
Antipsychotic Agents* ; Asian Continental Ancestry Group ; Bipolar Disorder* ; Consensus* ; Fluphenazine ; Haloperidol ; Humans ; Judgment ; Microspheres ; Patient Compliance ; Psychotic Disorders ; Recurrence ; Risperidone ; Schizophrenia ; Taiwan ; Aripiprazole ; Paliperidone Palmitate

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.