Inflammation alters the neural stem cell (NSC) lineage from neuronal to astrogliogenesis. However, the underlying mechanism is elusive. Autophagy contributes to the decline in adult hippocampal neurogenesis under E. coli lipopolysaccharide (LPS) stimulation. SRY-box transcription Factor 2 (SOX2) is critical for NSC self-renewal and proliferation. In this study, we investigated the role of SOX2 in induced autophagy and hippocampal adult neurogenesis under LPS stimulation. LPS (5 ng•100 g -1 •hour -1 for 7 days) was intraperitoneally infused into male Sprague–Dawley rats (8 weeks old) to induce mild systemic inflammation. Beclin 1 and autophagy protein 12 (Atg12) were significantly upregulated concurrent with decreased numbers of Ki67- and doublecortin (DCX)-positive cells in the dentate gyrus. Synchronically, the levels of phospho(p)-mTOR, the p-mTOR/mTOR ratio, p-P85s6k, and the p-P85s6k/P85s6k ratio were suppressed. In contrast, SOX2 expression was increased. The fluorescence micrographs indicated that the colocalization of Beclin 1 and SOX2 was increased in the subgranular zone (SGZ) of the dentate gyrus. Moreover, increased S100β-positive astrocytes were colocalized with SOX2 in the SGZ. Intracerebroventricular infusion of 3-methyladenine (an autophagy inhibitor) effectively prevented the increases in Beclin 1, Atg12, and SOX2. The SOX2 + -Beclin 1 + and SOX2 + -S100β + cells were reduced. The levels of p-mTOR and p-P85s6k were enhanced. Most importantly, the number of DCX-positive cells was preserved. Altogether, these data suggest that LPS induced autophagy to inactivate the mTOR/P85s6k pathway, resulting in a decline in neural differentiation. SOX2 was upregulated to facilitate the NSC lineage, while the autophagy milieu could switch the SOX2-induced NSC lineage from neurogenesis to astrogliogenesis.

This study evaluated the value of procalcitonin (PCT) levels in pleural effusion to differentiate the etiology of parapneumonic effusion (PPE). Forty-one consecutive PPE patients were enrolled and were divided into bacterial and non-bacterial PPE. Blood and pleural effusion samples were collected for PCT measurement on admission and analyzed for diagnostic evaluation. PCT of pleural fluid was significantly increased in the bacterial PPE group (0.24 ng/mL) compared to the non-bacterial PPE group (0.09 ng/mL), but there was no significant difference for serum PCT. A PCT concentration of pleural fluid >0.174 ng/mL (best cut-off value) was considered positive for a diagnosis of bacterial PPE (sensitivity, 80%; specificity, 76%; AUC, 0.84). Pleural effusion PCT in the bacterial PPE is significantly different from those of the non-bacterial PPE and control groups, so the diagnostic use of PCT still warrants further investigation.
Aged ; Bacterial Infections/*diagnosis ; Calcitonin/*analysis/blood ; Diagnosis, Differential ; Female ; Humans ; Male ; Middle Aged ; Pleural Effusion/*diagnosis ; Pneumonia/*diagnosis ; Predictive Value of Tests ; Protein Precursors/*analysis/blood ; ROC Curve

INTRODUCTIONCardiopulmonary resuscitation (CPR) guidelines were revised in 2005 based on new evidence and expert consensus. However, the benefits of the new guidelines remain undetermined and their influence has not been published in Asia. This study aimed to evaluate the impact of implementing the new resuscitation guidelines and identify factors that influence the discharge survival of out-of-hospital cardiac arrest (OHCA) patients in an Asian metropolitan city.

MATERIALS AND METHODSThis was an observational cohort study of all OHCA patients seen by the emergency medical service during the period before (Nov 2003 to Oct 2005) and after (May 2006 to Oct 2008) implementing the new resuscitation guidelines. Detailed clinical information was recorded using the Ustein style template. Statistical analysis was done using X2 test or t-test for univariate analysis and the logistic regression model for multivariate analysis.

RESULTSThere were 463 patients before and 430 patients after the new guidelines who received resuscitation. The rate of recovery of spontaneous circulation (ROSC), survival-to-intensive care unit (ICU) admission, and survival-to-hospital discharge all showed no benefits regarding the new resuscitation guidelines (ROSC: 42% vs 39%, P = 0.32; Survival-to-ICU admission: 33% vs 30%, P = 0.27; survival-to-hospital discharge: 10% vs 7%, P = 0.09). The rate of ventricular fibrillation/pulseless ventricular tachycardia (VF/pulseless VT), rate of witnessed arrest, and rate of bystander CPR were much lower than in Western studies. After multivariate logistic regression, factors related to discharge survival were witnessed arrest and initial rhythm with VF/pulseless VT. The new resuscitation guidelines did not significantly influence the discharge survival.

CONCLUSIONSWe did not observe any improvement in survival after implementing the new guidelines. Independent factors of survival-to-hospital discharge are witnessed arrest and initial rhythm with VF/pulseless VT. Because the rates of VF/pulseless VT and bystander CPR in Asia are low, popularising CPR training programmes and increasing the rate of bystander CPR may be more important for improving OHCA survival rates than frequent guideline changes.

Aged ; Aged, 80 and over ; Cardiopulmonary Resuscitation ; methods ; standards ; Emergency Service, Hospital ; statistics & numerical data ; Female ; Hospitals, University ; statistics & numerical data ; Humans ; Male ; Middle Aged ; Out-of-Hospital Cardiac Arrest ; mortality ; therapy ; Patient Discharge ; statistics & numerical data ; Practice Guidelines as Topic ; Survival Analysis ; Taiwan ; epidemiology

Mutations in the synaptic nuclear envelope protein 1 (SYNE1) gene are associated with substantial clinical heterogeneity. Here, we report the first case of SYNE1 ataxia in Taiwan due to two novel truncating mutations. Our patient, a 53-year-old female, exhibited pure cerebellar ataxia with c.1922del in exon 18 and c. C3883T mutations in exon 31. Previous studies have indicated that the prevalence of SYNE1 ataxia among East Asian populations is low. In this study, we identified 27 cases of SYNE1 ataxia from 22 families in East Asia. Of the 28 patients recruited in this study (including our patient), 10 exhibited pure cerebellar ataxia, and 18 exhibited ataxia plus syndromes. We could not find an exact correlation between genotypes and phenotypes. Additionally, we established a precise molecular diagnosis in our patient’s family and extended the findings on the ethnic, phenotypic, and genotypic diversity of the SYNE1 mutational spectrum.

Objective: A meta-analysis of locus-based genome-wide association studies recently identified a relationship between AXIN1 and Parkinson’s disease (PD). Few studies of Asian populations, however, have reported such a genetic association. The influences of rs13337493, rs758033, and rs2361988, three PD-associated genetic variants of AXIN1, were investigated in the present study because AXIN1 is related to Wnt/β-catenin signaling. Methods: A total of 2,418 individuals were enrolled in our Taiwanese cohort for analysis of the genotypic and allelic frequency. Polymerase chain reaction–restriction fragment length polymorphism analysis was employed for rs13337493 genotyping, and the Agena MassARRAY platform (Agena Bioscience, San Diego, CA, USA) was used for rs758033 and rs2361988 genotyping in 672 patients with PD and 392 controls. Taiwan Biobank data of another 1,354 healthy controls were subjected to whole-genome sequencing performed using Illumina platforms at approximately 30× average depth. Results: Our results revealed that rs758033 {odds ratios [OR] (95% confidence interval [CI]) = 0.267 [0.064, 0.795], p = 0.014} was associated with the risk of PD, and there was a trend toward a protective effect of rs2361988 (OR [95% CI] = 0.296 [0.071, 0.884], p = 0.026) under the recessive model. The TT genotype of rs758033 (OR [95% CI] = 0.271 [0.065, 0.805], p = 0.015) and the CC genotype of rs2361988 (OR [95% CI] = 0.305 [0.073, 0.913], p = 0.031) were less common in the PD group than in the non-PD group. Conclusion Our findings indicate that the rs758033 and rs2361988 polymorphisms of AXIN1 may affect the risk of PD in the Taiwanese population.

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.