BACKGROUND: Several factors, including clinical manifestations and laboratory data, have been used to evaluate the disease activity of Sjögren's syndrome (SS). We investigated saliva indicators of disease activity in primary SS patients. METHODS: We enrolled 138 Taiwanese patients with primary SS and 100 Taiwanese normal controls. Interleukin (IL)-6, IL-17A, tumor necrosis factor-alpha (TNF-α), and rheumatoid factor (RF)-IgA levels in saliva samples were measured using ELISA or fluorescent enzyme-linked immunoassay. Serum IgG, IgA, and IgM levels were measured by nephelometry. Erythrocyte sedimentation rate (ESR) was measured with an automatic ESR analyzer. The t-test and Pearson correlation test were used. RESULTS: IL-6 level was higher in primary SS patients than in normal controls (14.23±14.77 vs 9.87±7.32, P=0.012), but there were no significant differences in IL-17A, TNF-α, and RF-IgA levels. In primary SS patients, IL-6 level correlated weakly with ESR and IgG levels (r=0.252, P=0.015, and r=0.248, P=0.017, respectively), and TNF-α level correlated weakly with IgG level (r=0.231, P=0.024). CONCLUSIONS: IL-6 may play a role in SS pathogenesis. Saliva IL-6 might be an indicator of disease activity in primary SS patients.
Blood Sedimentation ; Enzyme-Linked Immunosorbent Assay ; Humans ; Immunoassay ; Immunoglobulin A ; Immunoglobulin G ; Immunoglobulin M ; Interleukin-17 ; Interleukin-6 ; Interleukins ; Nephelometry and Turbidimetry ; Rheumatoid Factor ; Saliva ; Tumor Necrosis Factor-alpha

PURPOSE: Cockroaches are the second leading allergen in Taiwan. Sensitization to Per a 2, the major American cockroach allergen, correlates with clinical severity among patients with airway allergy, but there is limited information on IgE epitopes and tissue localization of Per a 2. This study aimed to identify Per a 2 linear IgE-binding epitopes and its distribution in the body of a cockroach. METHODS: The cDNA of Per a 2 was used as a template and combined with oligonucleotide primers specific to the target areas with appropriate restriction enzyme sites. Eleven overlapping fragments of Per a 2 covering the whole allergen molecule, except 20 residues of signal peptide, were generated by PCR. Mature Per a 2 and overlapping deletion mutants were affinity-purified and assayed for IgE reactivity by immunoblotting. Three synthetic peptides comprising the B cell epitopes were evaluated by direct binding ELISA. Rabbit anti-Per a 2 antibody was used for immunohistochemistry. RESULTS: Human linear IgE-binding epitopes of Per a 2 were located at the amino acid sequences 57-86, 200-211, and 299-309. There was positive IgE binding to 10 tested Per a 2-allergic sera in 3 synthetic peptides, but none in the controls. Immunostaining revealed that Per a 2 was localized partly in the mouth and midgut of the cockroach, with the most intense staining observed in the hindgut, suggesting that the Per a 2 allergen might be excreted through the feces. CONCLUSIONS: Information on the IgE-binding epitope of Per a 2 may be used for designing more specific diagnostic and therapeutic approaches to cockroach allergy.
Amino Acid Sequence ; Cockroaches ; DNA Primers ; DNA, Complementary ; Enzyme-Linked Immunosorbent Assay ; Epitope Mapping* ; Epitopes ; Epitopes, B-Lymphocyte ; Feces ; Humans ; Hypersensitivity ; Immunoblotting ; Immunoglobulin E ; Immunohistochemistry ; Mouth ; Peptides ; Periplaneta* ; Polymerase Chain Reaction ; Protein Sorting Signals ; Taiwan

Objective: Predicting disease relapse and early intervention could reduce symptom severity. We attempted to identify potential indicators that predict the duration to next admission for an acute affective episode in patients with bipolar I disorder. Methods: We mathematically defined the duration to next psychiatric admission and performed single-variate regressions using historical data of 101 patients with bipolar I disorder to screen for potential variables for further multivariate regressions. Results: Age of onset, total psychiatric admissions, length of lithium use, and carbamazepine use during the psychiatric hospitalization contributed to the next psychiatric admission duration positively. The all-in-one found that hyperlipidemia during the psychiatric hospitalization demonstrated a negative contribution to the duration to next psychiatric admission; the last duration to psychiatric admission, lithium and carbamazepine uses during the psychiatric hospitalization, and heart rate on the discharge day positively contributed to the duration to next admission. Conclusion We identified essential variables that may predict the duration of bipolar I patients’ next psychiatric admission. The correlation of a faster heartbeat and a normal lipid profile in delaying the next onset highlights the importance of managing these parameters when treating bipolar I disorder.

BACKGROUND/OBJECTIVES: Increased levels of uremic toxins and decreased antioxidant capacity have a significant impact on the progression of chronic kidney disease (CKD). However, it remains unclear whether they interact with each other to mediate the damage of kidney function. The purpose of this study was to investigate whether uremic toxins (i.e., homocysteine and indoxyl sulfate [IS]), as well as glutathione-dependent antioxidant enzyme activities are dependently or independently associated with kidney function during different stages of CKD patients. SUBJECTS/METHODS: One hundred thirty-two patients diagnosed with CKD at stages 1 to 5 participated in this cross-sectional study. RESULTS: Patients who had reached an advanced CKD stage experienced an increase in plasma uremic toxin levels, along with decreased glutathione peroxidase (GSH-Px) activity.Plasma homocysteine, cysteine, and IS concentrations were all positively associated with each other, but negatively correlated to GSH-Px activity levels after adjusting for potential confounders in all CKD patients. Although plasma homocysteine, cysteine, IS, and GSHPx levels were significantly associated with kidney function, only plasma IS levels still had a significant association with kidney function after these parameters were simultaneously adjusted. In addition, plasma IS could interact with GSH-Px activity to be associated with kidney function. CONCLUSIONS IS plays a more dominant role than homocysteine and GSH-Px activity in relation to kidney function.

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.

There is growing evidence that the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is associated with increased risks of psychiatric sequelae. Depression, anxiety, cognitive impairments, sleep disturbance, and fatigue during and after the acute phase of COVID-19 are prevalent, long-lasting, and exerting negative consequences on well-being and imposing a huge burden on healthcare systems and society. This current review presented timely updates of clinical research findings, particularly focusing on the pathogenetic mechanisms underlying the neuropsychiatric sequelae, and identified potential key targets for developing effective treatment strategies for long COVID. In addition, we introduced the Formosa Long COVID Multicenter Study (FOCuS), which aims to apply the inflammation theory to the pathogenesis and the psychosocial and nutrition treatments of post-COVID depression and anxiety.