No abstract available.
Catheter Ablation* ; Thyroid Gland* ; Ultrasonography

YKL-40, a secreted glycoprotein, may serve as an autoantigen, which mediates multiple inflammatory diseases and cancers. A high YKL-40 serum level is correlated with metastasis and poor survival in a variety of human cancers. However, the role of YKL-40 in dogs is still under evaluation. Herein, we examined the associations between plasma YKL-40 level and YKL-40 autoantibody (YAA) titers with malignancy and prognosis in canine cancer. Plasma levels of YKL-40 in healthy dogs (n = 20) and in dogs (n = 82) with cancer were evaluated using enzyme-linked immunosorbent assay. Our results indicated that plasma YKL-40 levels were significantly higher (p < 0.01) in dogs with cancer than in healthy dogs. A significant decrease in the YAA titers was detected in the dogs with cancer when compared with those of the healthy dogs (p < 0.05), although the change was not correlated with the YKL-40 levels. Among the dogs with cancer, plasma YKL-40 levels in the dogs that later relapsed or had metastasis were significantly higher than in the dogs with no signs of relapse (p < 0.01) or metastasis (p <0.05). The relapse and metastasis rates were significantly higher in the high YKL-40 group (> 180 pg/mL) than in the low YKL-40 group (< 180 pg/mL). The results imply that plasma YKL-40 levels might have the potential to be developed as a marker of malignancy progression and prognosis in canine cancers.
Animals ; Autoantibodies ; Autoantigens ; Dogs ; Enzyme-Linked Immunosorbent Assay ; Glycoproteins ; Humans ; Neoplasm Metastasis ; Plasma ; Prognosis ; Recurrence

Background: The initiation of sodium-glucose cotransporter-2 inhibitors (SGLT2i) typically leads to a reversible initial dip in estimated glomerular filtration rate (eGFR). The implications of this phenomenon on clinical outcomes are not well-defined. Methods: We searched MEDLINE, Embase, and Cochrane Library from inception to March 23, 2023 to identify randomized controlled trials and cohort studies comparing kidney and cardiovascular outcomes in patients with and without initial eGFR dip after initiating SGLT2i. Pooled estimates were calculated using random-effect meta-analysis. Results: We included seven studies in our analysis, which revealed that an initial eGFR dip following the initiation of SGLT2i was associated with less annual eGFR decline (mean difference, 0.64; 95% confidence interval [CI], 0.437 to 0.843) regardless of baseline eGFR. The risk of major adverse kidney events was similar between the non-dipping and dipping groups but reduced in patients with a ≤10% eGFR dip (hazard ratio [HR], 0.915; 95% CI, 0.865 to 0.967). No significant differences were observed in the composite of hospitalized heart failure and cardiovascular death (HR, 0.824; 95% CI, 0.633 to 1.074), hospitalized heart failure (HR, 1.059; 95% CI, 0.574 to 1.952), or all-cause mortality (HR, 0.83; 95% CI, 0.589 to 1.170). The risk of serious adverse events (AEs), discontinuation of SGLT2i due to AEs, kidney-related AEs, and volume depletion were similar between the two groups. Patients with >10% eGFR dip had increased risk of hyperkalemia compared to the non-dipping group. Conclusion Initial eGFR dip after initiating SGLT2i might be associated with less annual eGFR decline. There were no significant disparities in the risks of adverse cardiovascular outcomes between the dipping and non-dipping groups.

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.

BACKGROUNDIdentification of hospitalized carbapenem-resistant Enterobacteriaceae (CRE)-positive patient is important in preventing nosocomial transmission. The objective of this study was to illustrate the implementation of proactive infection control measures in preventing nosocomial transmission of CRE in a healthcare region of over 3200 beds in Hong Kong between October 1, 2010 and December 31, 2011.

METHODSThe program included active surveillance culture in patients with history of medical tourism with hospitalization and surgical operation outside Hong Kong within 12 months before admission, and "added test" as an opportunistic CRE screening in all fecal specimens submitted to the laboratory. Outbreak investigation and contact tracing were conducted for CRE-positive patients. Serial quantitative culture was performed on CRE-positive patients and the duration of fecal carriage of CRE was analyzed.

RESULTSDuring the study period, a total of 6533 patients were screened for CRE, of which 76 patients were positive (10 from active surveillance culture, 65 from "added test", and 1 secondary case from contact tracing of 223 patients with no nosocomial outbreak), resulting in an overall rate of CRE fecal carriage of 1.2%. The median time of fecal carriage of CRE was 43 days (range, 13-119 days). Beta-lactam-beta-lactamase-inhibitors, cephalosporins, and fluoroquinolones were associated significantly with high fecal bacterial load when used 90 days before CRE detection, while use of cephalosporins, carbapenems, and fluoroquinolones after CRE detection are significantly associated with longer duration of carriage. The duration of fecal carriage of CRE also correlates significantly with the initial fecal bacterial load (Pearson correlation: 0.53; P = 0.02).

CONCLUSIONProactive infection control measures by enhanced surveillance program identify CRE-positive patients and data obtained are useful for the planning of and resource allocation for CRE control.

Anti-Bacterial Agents ; therapeutic use ; Carbapenems ; therapeutic use ; Cephalosporins ; therapeutic use ; Drug Resistance, Bacterial ; Enterobacteriaceae ; drug effects ; Enterobacteriaceae Infections ; prevention & control ; transmission ; Fluoroquinolones ; therapeutic use ; Humans ; Infection Control ; methods

Objective: To quantitatively assess the pulmonary vasculature using non-contrast computed tomography (CT) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) pre- and post-treatment and correlate CT-based parameters with right heart catheterization (RHC) hemodynamic and clinical parameters. Materials and Methods: A total of 30 patients with CTEPH (mean age, 57.9 years; 53% female) who received multimodal treatment, including riociguat for ≥ 16 weeks with or without balloon pulmonary angioplasty and underwent both noncontrast CT for pulmonary vasculature analysis and RHC pre- and post-treatment were included. The radiographic analysis included subpleural perfusion parameters, including blood volume in small vessels with a cross-sectional area ≤ 5 mm 2 (BV5) and total blood vessel volume (TBV) in the lungs. The RHC parameters included mean pulmonary artery pressure (mPAP), pulmonary vascular resistance (PVR), and cardiac index (CI). Clinical parameters included the World Health Organization (WHO) functional class and 6-minute walking distance (6MWD). Results: The number, area, and density of the subpleural small vessels increased after treatment by 35.7% (P < 0.001), 13.3% (P = 0.028), and 39.3% (P < 0.001), respectively. The blood volume shifted from larger to smaller vessels, as indicated by an 11.3% increase in the BV5/TBV ratio (P = 0.042). The BV5/TBV ratio was negatively correlated with PVR (r = -0.26; P = 0.035) and positively correlated with CI (r = 0.33; P = 0.009). The percent change across treatment in the BV5/TBV ratio correlated with the percent change in mPAP (r = -0.56; P = 0.001), PVR (r = -0.64; P < 0.001), and CI (r = 0.28; P = 0.049).Furthermore, the BV5/TBV ratio was inversely associated with the WHO functional classes I–IV (P = 0.004) and positively associated with 6MWD (P = 0.013). Conclusion Non-contrast CT measures could quantitatively assess changes in the pulmonary vasculature in response to treatment and were correlated with hemodynamic and clinical parameters.