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.

Percutaneous transluminal coronary angioplasty with metal stent placement has become a well-developed treatment modality for coronary stenotic lesions. Although infection involving implanted stents is rare, it can, however, occur with high morbidity and mortality. We describe herein a case of an inserted coronary stent that was infected and complicated with recurrent stent thrombosis, pseudoaneurysm formation and severe sepsis. Despite repeated intervention and bypass surgery, the patient died from severe sepsis.
Aneurysm, False ; Angioplasty, Balloon, Coronary ; Humans ; Mortality ; Myocardial Infarction ; Sepsis ; Stents* ; Thrombosis*

Objective: Limited evidence exists regarding real-world 3-monthly paliperidone palmitate (PP3M) treatment retention and associated factors. Methods: We conducted a retrospective, nationwide cohort study using the Taiwan National Health Insurance Research Database between October 2017 and December 2019. Adult patients with schizophrenia initiated on PP3M were enrolled. The primary outcomes were time to PP3M discontinuation, time to psychiatric hospitalization, and the proportions of patients receiving the next PP3M dose within 120 days among first-, second-, and third-dose completers. Key covariates included prior PP1M duration and adequate PP3M initiation. Results: The PP3M treatment retention rates were 79.7%, 66.3%, and 52.5% after 6, 12, and 24 months, respectively, with 86.4%, 90.6%, and 90.0% of respective first-, second-, and third-dose completers receiving the next PP3M dose. Adequate PP3M initiation and prior PP1M treatment duration ＞ 180 days were associated with favorable PP3M treatment retention. In multivariate analyses, PP1M durations of 180−360 days (adjusted relative risk [aRR], 1.76) or ＜ 180 days (aRR, 2.79) were associated with PP3M discontinuation at the second dose. Inadequate PP3M initiation was associated with discontinuation at the third dose (aRR, 2.18). Patients fully adherent to PP3M treatment in the first year had a higher probability of being free from psychiatric hospitalization (86.7% at 2 years), compared with those partially adherent or non-adherent to PP3M in the first year. Conclusion Prior PP1M duration and adequate PP3M initiation are major factors affecting PP3M treatment retention. Higher PP3M treatment retention is associated with a lower risk of psychiatric hospitalization.

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.

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

A 58-year-old male patient presented with a recurrent true malignant mixed tumor of the parotid gland. Patchy pulmonary opacities were identified with a chest radiograph. Subsequently, a CT scan of the chest showed pulmonary parenchymal consolidation with amorphous calcifications. This abnormality was confirmed to be the result of a metastatic true malignant mixed tumor by using CT-guided biopsy. The current case demonstrated an extremely rare example of atypical pulmonary metastases from a true malignant mixed tumor of the parotid gland showing an air-space pattern and calcification.
Biopsy, Fine-Needle ; Humans ; Lung Neoplasms/*secondary ; Male ; Middle Aged ; Mixed Tumor, Malignant/*pathology ; Parotid Neoplasms/*pathology ; Radiography, Interventional ; Tomography, X-Ray Computed