The incidence of diabetes mellitus is increasing among companion animals. This disease has similar characteristics in both humans and animals. Diabetes is frequently identified as an independent risk factor for infections associated with increased mortality. In the present study, homozygous diabetic (db/db) mice were infected with Listeria (L.) monocytogenes and then treated with the anti-diabetic drug exendin-4, a glucagon-like peptide 1 analogue. In aged db/db mice, decreased CD11b+ macrophage populations with higher lipid content and lower phagocytic activity were observed. Exendin-4 lowered high lipid levels and enhanced phagocytosis in macrophages from db/db mice infected with L. monocytogenes. Exendin-4 also ameliorated obesity and hyperglycemia, and improved ex vivo bacteria clearance by macrophages in the animals. Liver histology examined during L. monocytogenes infection indicated that abscess formation was much milder in exendin-4-treated db/db mice than in the control animals. Moreover, mechanistic studies demonstrated that expression of ATP binding cassette transporter 1, a sterol transporter, was higher in macrophages isolated from the exendin-4-treated db/db mice. Overall, our results suggest that exendin-4 decreases the risk of infection in diabetic animals by modifying the interaction between intracellular lipids and phagocytic macrophages.
ATP-Binding Cassette Transporters/metabolism ; Age Factors ; Animals ; Blood Chemical Analysis ; Cholesterol/metabolism ; Diabetes Mellitus, Type 2/*drug therapy/genetics ; Dyslipidemias/drug therapy/genetics ; Female ; Hyperglycemia/drug therapy/genetics ; Hypoglycemic Agents/*therapeutic use ; Injections, Intraperitoneal ; *Lipid Metabolism/drug effects ; Listeria monocytogenes/*drug effects/immunology ; Listeriosis/*drug therapy/immunology/microbiology ; Macrophages/drug effects/*metabolism ; Mice ; Obesity/drug therapy/genetics ; Peptides/*therapeutic use ; Phagocytosis/drug effects ; Venoms/*therapeutic use

Breast metastases from extramammary neoplasms are very rare. We presented a 66 year-old female with metastasis of small cell lung carcinoma to the breast. She presented with consolidation over the left upper lobe of her lung undetermined after endobronchial or video-assisted thoracoscopic surgery (VATS) biopsy, and this was treated effectively after antibiotic therapy at initial stage. The left breast lumps were noted 4 months later, and she underwent a modified radical mastectomy under the impression of primary breast carcinoma. However, the subsequent chest imaging revealed re-growing mass over the left mediastinum and hilum, and cells with the same morphological and staining features were found from specimens of transbronchial brushing and biopsy. An accurate diagnosis to distinguish a primary breast carcinoma from metastatic one is very important because the therapeutic planning and the outcome between them are different.
Aged ; Breast Neoplasms ; secondary ; Bronchoscopy ; Carcinoma, Small Cell ; pathology ; Female ; Humans ; Lung Neoplasms ; pathology

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.

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

We studied a family with Gorlin-Goltz syndrome. The novel mutations of our cases were located on the 21st exon of the PTCH1 gene (c.3450C>G). The father, who received a strategic 56-day vismodegib treatment for disease control, was the first patient with Gorlin syndrome treated with the hedgehog inhibitor in Taiwan. The lesions regressed gradually, with scar formation, and were subsequently removed via a wide excision. Further details are provided below.
Basal Cell Nevus Syndrome* ; Cicatrix ; Exons ; Fathers ; Hedgehogs ; Humans ; Taiwan

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.