Objective: Although both partners of a married couple can have mental disorders, the concordant and cross-concordant categories of disorders in couples remain unclear. Using national psychiatric population-based data only from patients with mental disorders, we examined married couples with mental disorders to examine spousal concordance and cross-disorder concordance across the full spectrum of mental disorders. Methods: Data from the 1997 to 2012 Taiwan Psychiatric Inpatient Medical Claims data set were used and a total of 662 married couples were obtained. Concordance of mental disorders was determined if both spouses were diagnosed with mental disorder of an identical category in the International Classification of Diseases, Ninth Revision, Clinical Modification; otherwise, cross-concordance was reported. Results: According to Cohen’s kappa coefficient, the most concordant mental disorder in couples was substance use disorder, followed by bipolar disorder. Depressive and anxiety disorders were the most common cross-concordant mental disorders, followed by bipolar disorder. The prevalence of the spousal concordance of mental disorders differed by monthly income and the couple’s age disparity. Conclusion Evidence of spousal concordance and cross-concordance for mental disorders may highlight the necessity of understanding the social context of marriage in the etiology of mental illness. Identifying the risk factors from a common environment attributable to mental disorders may enhance public health strategies to prevent and improve chronic mental illness of married couples.

Melanoma is the most serious type of skin cancer and one of the most common cancers in the world. Advanced melanoma is often resistant to conventional therapies and has high potential for metastasis and low survival rates. Vemurafenib is a small molecule inhibitor of the BRAF serine-threonine kinase recently approved by the United States Food and Drug Administration to treat patients with metastatic and unresectable melanomas that carry an activating BRAF (V600E) mutation. Many clinical trials evaluating other therapeutic uses of vemurafenib are still ongoing. The ATP-binding cassette (ABC) transporters are membrane proteins with important physiological and pharmacological roles. Collectively, they transport and regulate levels of physiological substrates such as lipids, porphyrins and sterols. Some of them also remove xenobiotics and limit the oral bioavailability and distribution of many chemotherapeutics. The overexpression of three major ABC drug transporters is the most common mechanism for acquired resistance to anticancer drugs. In this review, we highlight some of the recent findings related to the effect of ABC drug transporters such as ABCB1 and ABCG2 on the oral bioavailability of vemurafenib, problems associated with treating melanoma brain metastases and the development of acquired resistance to vemurafenib in cancers harboring the BRAF (V600E) mutation.

The Aeromonas species, belonging to the family Aeromonadaceae, are opportunistic pathogens found in humans with an incidence rate of 76 cases per million inhabitants in Southern Taiwan. The incidence of Aeromonas septicemia is relatively low, accounting for less than 15% of cases. Patients diagnosed with Aeromonas hydrophila bacteremia who were presented with skin blisters and septic shock have been reported to have a mortality rate of 100%. Aeromonas infection must be considered in the differential diagnosis of gangrene-like tissue damage or skin lesions in patients with end-stage renal disease, due to the potential sources of infections. A 49-year-old Taiwanese diabetic woman with end-stage renal disease had underwent regular hemodialysis. She was referred to our hospital due to a one-day course of fever, dyspnea, hypotension, and fulminant hemorrhagic blisters covering her whole body. A physical examination uncovered multiple hemorrhagic blisters, along with a ruptured blister over the lower left leg. Laboratory tests revealed an elevation of liver enzymes, impaired renal function, lactatemia, and high anion-gap metabolic acidosis. Cultures of both blood and hemorrhagic blister fluid grew Aeromonas hydrophila. However, she experienced persistent shock despite aggressive intravenous fluid, empiric antibiotics, and inotropic agents with norepinephrine and dopamine. Early diagnosis and prompt management using intravenous fluids, antibiotics and surgical debridement is recommended in order to improve a patient's survival rate.

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.

PURPOSE: Lovastatin is an effective inhibitor of cholesterol synthesis. A previous study demonstrated that lovastatin can also suppress airway hyperresponsiveness (AHR) in murine model of asthma. We aimed to investigate the effect of lovastatin on mucus secretion and inflammation-associated gene expression in the lungs of murine model of asthma. METHODS: Female BALB/c mice were sensitized and challenged with ovalbumin (OVA) by intraperitoneal injection, and orally administered lovastatin from days 14 to 27 post-injection. Gene expression in lung tissues was analyzed using real-time polymerase chain reaction. AHR and goblet cell hyperplasia were also examined. BEAS-2B human bronchial epithelial cells were used to evaluate the effect of lovastatin on the expression of cell adhesion molecules, chemokines, and proinflammatory cytokines in vitro. RESULTS: We showed that lovastatin inhibits the expression of Th2-associated genes, including eotaxins and adhesion molecules, in the lungs of murine model of asthma. Mucin 5AC expression, eosinophil infiltration and goblet cell hyperplasia were significantly decreased in the lung tissue of murine model of asthma treated with lovastatin. Furthermore, lovastatin inhibited AHR and expression of Th2-associated cytokines in bronchoalveolar lavage fluid. However, a high dose (40 mg/kg) of lovastatin was required to decrease specific IgE to OVA levels in serum, and suppress the expression of Th2-associated cytokines in splenocytes. Activated BEAS-2B cells treated with lovastatin exhibited reduced IL-6, eotaxins (CCL11 and CCL24), and intercellular adhesion molecule-1 protein expression. Consistent with this, lovastatin also suppressed the ability of HL-60 cells to adhere to inflammatory BEAS-2B cells. CONCLUSIONS: These data suggest that lovastatin suppresses mucus secretion and airway inflammation by inhibiting the production of eotaxins and Th2 cytokines in murine model of asthma.
Animals ; Asthma* ; Bronchoalveolar Lavage Fluid ; Cell Adhesion Molecules ; Chemokines ; Cholesterol ; Cytokines ; Eosinophils ; Epithelial Cells ; Female ; Gene Expression ; Goblet Cells ; HL-60 Cells ; Humans ; Hyperplasia ; Immunoglobulin E ; Inflammation* ; Injections, Intraperitoneal ; Intercellular Adhesion Molecule-1 ; Interleukin-6 ; Lovastatin* ; Lung ; Mice ; Mucin 5AC ; Mucus* ; Ovalbumin ; Ovum ; Real-Time Polymerase Chain Reaction

OBJECTIVE: Several cell line studies have demonstrated thioridazine’s anticancer, multidrug resistance-reversing and apoptosis-inducing properties in various tumors. We conducted this nationwide population-based study to investigate the association between thioridazine use and cancer risk among adult patients with schizophrenia. METHODS: Based on the Psychiatric Inpatient Medical Claim of the National Health Insurance Research Database of Taiwan, a total of 185,689 insured psychiatric patients during 2000 to 2005 were identified. After excluding patients with prior history of schizophrenia, only 42,273 newly diagnosed patients were included. Among them, 1,631 patients ever receiving thioridazine for more than 30 days within 6 months were selected and paired with 6,256 randomly selected non-thioridazine controls. These patients were traced till 2012/12/31 to see if they have any malignancy. RESULTS: The incidence rates of hypertension and cerebrovascular disease were higher among cases than among matched controls. The incidence of hyperlipidemia, coronary artery disease and chronic pulmonary disease did not differ between the two groups. By using Cox proportional hazard model for cancer incidence, the crude hazard ratio was significantly higher in age, hypertension, hyperlipidemia, cerebrovascular disease, coronary artery disease and chronic pulmornary disease. However, after adjusting for other covariates, only age and hypertension remained significant. Thioridazine use in adult patients with schizophrenia had no significant association with cancer. CONCLUSION: Despite our finding that thioridazine use had no prevention in cancer in adult patients with schizophrenia. Based on the biological activity, thioridazine is a potential anticancer drug and further investigation in human with cancer is warranted.
Adult* ; Cell Line ; Cerebrovascular Disorders ; Coronary Artery Disease ; Humans ; Hyperlipidemias ; Hypertension ; Incidence ; Inpatients ; Lung Diseases ; National Health Programs ; Proportional Hazards Models ; Schizophrenia ; Taiwan ; Thioridazine*