Objective: Melatonin has been considered to have an essential role in the pathophysiology of Alzheimer’s disease (AD) for its regulatory function on circadian rhythm and interaction with glutamate for the modulation of learning and memory. Previous studies revealed that melatonin levels decreased in patients with AD. However, melatonin supplement didn’t show promising efficacy for AD. This study compared trough melatonin levels among elderly people with different severities of cognitive deficits. Methods: We enrolled 270 elder individuals (consisting four groups: healthy elderly, amnestic mild cognitive impairment [MCI], mild AD, and moderate-severe AD) in the learning cohort. Trough melatonin levels in plasma were measured using ELISA. Cognitive function was evaluated by Clinical Dementia Rating Scale (CDR) and Mini-Mental State Examination (MMSE). An independent testing cohort, also consisting of four groups, was enrolled for ascertainment. Results: In the learning cohort, trough melatonin levels decreased in the MCI group but elevated in the mild and moderate to severe AD groups. Trough melatonin levels were associated with CDR and MMSE in MCI or AD patients significantly. In the testing cohort, the results were similar to those in the learning cohort. Conclusion This study demonstrated that trough melatonin levels in the peripheral blood were decreased in MCI but increased with the severity of AD. The finding supports the trials indicating that melatonin showed efficacy only in MCI but not in AD. Whether trough melatonin level has potential to be a treatment response biomarker for AD, especially its early phase needs further studies.

Objective: Limited evidence exists regarding real-world 3-monthly paliperidone palmitate (PP3M) treatment retention and associated factors. Methods: We conducted a retrospective, nationwide cohort study using the Taiwan National Health Insurance Research Database between October 2017 and December 2019. Adult patients with schizophrenia initiated on PP3M were enrolled. The primary outcomes were time to PP3M discontinuation, time to psychiatric hospitalization, and the proportions of patients receiving the next PP3M dose within 120 days among first-, second-, and third-dose completers. Key covariates included prior PP1M duration and adequate PP3M initiation. Results: The PP3M treatment retention rates were 79.7%, 66.3%, and 52.5% after 6, 12, and 24 months, respectively, with 86.4%, 90.6%, and 90.0% of respective first-, second-, and third-dose completers receiving the next PP3M dose. Adequate PP3M initiation and prior PP1M treatment duration ＞ 180 days were associated with favorable PP3M treatment retention. In multivariate analyses, PP1M durations of 180−360 days (adjusted relative risk [aRR], 1.76) or ＜ 180 days (aRR, 2.79) were associated with PP3M discontinuation at the second dose. Inadequate PP3M initiation was associated with discontinuation at the third dose (aRR, 2.18). Patients fully adherent to PP3M treatment in the first year had a higher probability of being free from psychiatric hospitalization (86.7% at 2 years), compared with those partially adherent or non-adherent to PP3M in the first year. Conclusion Prior PP1M duration and adequate PP3M initiation are major factors affecting PP3M treatment retention. Higher PP3M treatment retention is associated with a lower risk of psychiatric hospitalization.

The radiologic appearance of multiple discrete pulmonary nodules in immunocompetent patients, with cryptococcal infection, has been rarely described. We describe a case of pulmonary cryptococcosis, presenting with bilaterally and randomly distributed nodules on a computed tomography, mimicking hematogeneous metastases. Positron emission tomography does not demonstrate 18F-fluorodeoxyglucose (FDG) uptake, suggesting a low probability for malignancy, which is a crucial piece of information for clinicians when making a management decision. We find the absence of FDG uptake correlates with the pathologic finding of an infectious nodule, composed of fibrosis and necrosis.
Cryptococcosis/metabolism/*radionuclide imaging ; Fluorodeoxyglucose F18/*diagnostic use/pharmacokinetics ; Humans ; Immunocompetence ; Lung Diseases, Fungal/metabolism/*radionuclide imaging ; Lung Neoplasms/radionuclide imaging ; Male ; Middle Aged ; Multimodal Imaging/*methods ; Multiple Pulmonary Nodules/radionuclide imaging ; Positron-Emission Tomography/*methods ; Radiopharmaceuticals/*diagnostic use/pharmacokinetics ; Tomography, X-Ray Computed/*methods

Human diphyllobothriasis is a parasitic disease caused by ingestion of larvae (plerocercoids) in raw or undercooked fish and commonly found in temperate areas. Rare cases were reported in tropical or subtropical areas especially in children. The first documented case of pediatric diphyllobothriasis in Taiwan had been reported 11 years ago. Here, we report another 8-year-old girl case who presented with a live noodle-like worm hanging down from her anus, with no other detectable symptoms. We pulled the worm out and found the strobila being 260 cm in length. Examination of gravid proglottids showed that they were wider than their lengths, containing an ovoid cirrus sac in the anterior side and the rosette-shaped uterus. Eggs extracted from the uterus were ovoid and operculated. Diphyllobothrium latum was confirmed by molecular analysis of the mitochondrial DNA cytochrome c oxidase subunit 1 (cox1) gene. The girl was treated with a single oral dose of praziquantel, and no eggs or proglottids were observed from her stool in the subsequent 3 months. The reemergence of human diphyllobothriasis in non-endemic countries is probably due to prevalent habit of eating imported raw fish from endemic areas. This pediatric case raised our concern that human diphyllobothriasis is likely underestimated because of unremarkable symptoms.
Anal Canal ; Child ; Diphyllobothriasis ; Diphyllobothrium* ; DNA, Mitochondrial ; Eating ; Eggs ; Electron Transport Complex IV ; Female ; Humans ; Larva ; Ovum ; Parasitic Diseases ; Praziquantel ; Taiwan* ; Uterus

Elevated serum levels of interleukin-10 (IL-10) have been reported in patients with Kawasaki disease (KD). IL-10 reduces the inflammatory actions of macrophages and T cells and it may play a significant role in the regulation of inflammatory vascular damage associated with systemic vasculitis. The aim of this study was to examine whether -592 IL-10 promoter polymorphism is a susceptibility or severity marker of KD in Chinese patients in Taiwan. The study included 105 KD patients and 100 normal controls. Genotype and allelic frequencies for the IL-10 gene polymorphism in both groups were compared. There were no significant between-group differences in the genotype distribution of IL-10 A-592C gene polymorphism (P=0.08). However, the frequency of the -592*A allele was significantly increased in the patients with KD compared with controls (71.9% vs. 61.0%, P=0.019). The odds ratio for developing KD in individuals with IL-10-592*A allele was 1.64 (95% confidence interval, 1.06-2.52) compared to individuals with the IL-10-592*C allele. No significant difference was observed in the genotype and allelic frequencies for the IL-10 A-592C polymorphism between patients with and without coronary artery lesions. The IL-10-592*A allele may be involved in the development of KD in Taiwanese children.
Alleles ; Asian Continental Ancestry Group/*genetics ; Child ; Child, Preschool ; Female ; Gene Frequency ; Genotype ; Humans ; Infant ; Interleukin-10/blood/*genetics ; Male ; Mucocutaneous Lymph Node Syndrome/diagnosis/*genetics ; Polymorphism, Genetic ; Promoter Regions, Genetic ; Taiwan

Elevated serum levels of interleukin-10 (IL-10) have been reported in patients with Kawasaki disease (KD). IL-10 reduces the inflammatory actions of macrophages and T cells and it may play a significant role in the regulation of inflammatory vascular damage associated with systemic vasculitis. The aim of this study was to examine whether -592 IL-10 promoter polymorphism is a susceptibility or severity marker of KD in Chinese patients in Taiwan. The study included 105 KD patients and 100 normal controls. Genotype and allelic frequencies for the IL-10 gene polymorphism in both groups were compared. There were no significant between-group differences in the genotype distribution of IL-10 A-592C gene polymorphism (P=0.08). However, the frequency of the -592*A allele was significantly increased in the patients with KD compared with controls (71.9% vs. 61.0%, P=0.019). The odds ratio for developing KD in individuals with IL-10-592*A allele was 1.64 (95% confidence interval, 1.06-2.52) compared to individuals with the IL-10-592*C allele. No significant difference was observed in the genotype and allelic frequencies for the IL-10 A-592C polymorphism between patients with and without coronary artery lesions. The IL-10-592*A allele may be involved in the development of KD in Taiwanese children.
Alleles ; Asian Continental Ancestry Group/*genetics ; Child ; Child, Preschool ; Female ; Gene Frequency ; Genotype ; Humans ; Infant ; Interleukin-10/blood/*genetics ; Male ; Mucocutaneous Lymph Node Syndrome/diagnosis/*genetics ; Polymorphism, Genetic ; Promoter Regions, Genetic ; Taiwan

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.