Follicular dendritic cell (FDC) sarcoma is rare and is classified either as conventional type or inflammatory pseudotumor (IPT)-like variant. Extranodal presentation is uncommon and nearly all gastrointestinal FDC tumors are of the conventional type. IPT-like variant tumors occur almost exclusively in the liver and spleen and are consistently associated with Epstein-Barr virus (EBV). Here we report the case of a 78-year-old woman with an IPT-like FDC sarcoma presenting as a pedunculated colonic polyp. Histologically, scanty atypical ovoid to spindle cells were mixed with a background of florid lymphoplasmacytic infiltrate, which led to an initial misdiagnosis of pseudolymphoma. These atypical cells expressed CD21, CD23, CD35, and D2-40, and were positive for EBV by in situ hybridization, confirming the diagnosis. The patient was free of disease five months after polypectomy without adjuvant therapy. Although extremely rare, the differential diagnosis for colonic polyp should include FDC sarcoma to avoid an erroneous diagnosis. A review of the 24 cases of IPT-like FDC sarcoma reported in the literature reveal that this tumor occurs predominantly in females with a predilection for liver and spleen, and has a strong association with EBV.
Aged ; Colonic Polyps* ; Dendritic Cell Sarcoma, Follicular* ; Dendritic Cells, Follicular ; Diagnosis ; Diagnosis, Differential ; Diagnostic Errors ; Female ; Granuloma, Plasma Cell ; Herpesvirus 4, Human ; Humans ; In Situ Hybridization ; Liver ; Pseudolymphoma ; Sarcoma ; Spleen ; Taiwan

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.

OBJECTIVE: Central 5-HT1A receptor is involved in the modulation of sensorimotor gating function. However, its precise role is not clearly defined in developmentally social deprived (isolation rearing, IR) rats featured with impaired sensorimotor gating ability. We therefore aimed to examine the effects of 5HT1A activation on acoustic startle response (ASR) and prepulse inhibition (PPI) in IR rats in a condition of compromised presynaptic 5-HT functions. METHODS: Social control (SOC) and IR rats received an intracerebraoventricular (ICV) injection of 5-HT depletor, 5,7-DHT. Seven days later rats entered a protocol of 8-OH-DPAT, a 5-HT1A agonist, in which locomotor activity, ASR and PPI and their tissue levels of 5-HT were measured. RESULTS: Our results found that both IR and 5,7-DHT decreased the tissue concentration of 5-HT. IR-induced hyperactivity and gating impairment were unaffected by 5-HT depletion. 8-OH-DPAT strengthened the ASR in IR but not SOC rats and the drug-reduced PPI could be adjusted by 5,7-DHT pretreatment. 8-OH-DPAT at 100 μg/kg enhanced PPI in 5-HT-depleted SOC rats. However for IR rats, 8-OH-DPAT strengthened PPI in sham rats but downgraded it in depletion condition. CONCLUSION: The integrity of central 5-HT system is important to 5-HT1A-modulated sensorimotor gating in isolation-reared rats.
8-Hydroxy-2-(di-n-propylamino)tetralin ; Acoustics ; Animals ; Motor Activity ; Prepulse Inhibition ; Rats ; Receptor, Serotonin, 5-HT1A ; Reflex, Startle ; Sensory Gating ; Serotonin ; Serotonin 5-HT1 Receptor Agonists ; Social Control, Formal

Objective: To retrospectively compare the safety and efficacy of ultrasound-guided radiofrequency ablation (RFA) with parotidectomy for superficial pleomorphic adenoma (PA). Materials and Methods: From March 2022 to October 2023, 88 patients diagnosed with superficial parotid PA underwent either RFA (n = 12; mean age, 47.1 years) or parotidectomy (n = 76; mean age, 47.8 years). Patients in the RFA group were matched to those in the surgery group in a 1:1 ratio using propensity scores based on age, sex, tumor volume, diameter, location, and comorbidities. Ultrasound characteristics, cosmetic scores (0–4), numerical rating scale scores (0–10), and complications were assessed before the procedures and at 1-, 3-, and 6-month follow-ups. Outcomes were compared between baseline and follow-up in the RFA group and between the RFA and surgery groups. Results: In the RFA group, significant reductions in tumor volume were observed between baseline (median, 2.02 cm 3 ) and the 1-month follow-up (median, 1.21 cm 3 ; P = 0.015), between the 1-month and 3-month follow-ups (median, 0.53 cm 3 ; P= 0.002), and between the 3- and 6-month follow-ups (median, 0.23 cm 3 ; P = 0.003). The volume reduction ratios at 1, 3, and 6 months were 39.7%, 79.9%, and 88.0%, respectively. The cosmetic score was significantly lower at 3- and 6-month followup compared to baseline (median 1 and 1 vs. 4, P = 0.04). The numerical rating scale scores did not differ significantly from baseline throughout follow-up. In the propensity score-matched analysis (12 patients per group), RFA was associated with a shorter median procedure time (61.5 vs. 253.3 minutes; P < 0.001), shorter hospital stay (0 vs. 4 days; P < 0.001), and lower cost (1859.9 vs. 3512.4 USD; P < 0.001) than parotidectomy, with no significant difference in overall complication rates (33.3% [4/12] vs. 41.7% [5/12]; P = 1.000). Conclusion RFA may be a safe and effective alternative to surgery for superficial parotid PA, offering a shorter median procedure time, shorter hospital stay, and lower costs.

Objective: To retrospectively compare the safety and efficacy of ultrasound-guided radiofrequency ablation (RFA) with parotidectomy for superficial pleomorphic adenoma (PA). Materials and Methods: From March 2022 to October 2023, 88 patients diagnosed with superficial parotid PA underwent either RFA (n = 12; mean age, 47.1 years) or parotidectomy (n = 76; mean age, 47.8 years). Patients in the RFA group were matched to those in the surgery group in a 1:1 ratio using propensity scores based on age, sex, tumor volume, diameter, location, and comorbidities. Ultrasound characteristics, cosmetic scores (0–4), numerical rating scale scores (0–10), and complications were assessed before the procedures and at 1-, 3-, and 6-month follow-ups. Outcomes were compared between baseline and follow-up in the RFA group and between the RFA and surgery groups. Results: In the RFA group, significant reductions in tumor volume were observed between baseline (median, 2.02 cm 3 ) and the 1-month follow-up (median, 1.21 cm 3 ; P = 0.015), between the 1-month and 3-month follow-ups (median, 0.53 cm 3 ; P= 0.002), and between the 3- and 6-month follow-ups (median, 0.23 cm 3 ; P = 0.003). The volume reduction ratios at 1, 3, and 6 months were 39.7%, 79.9%, and 88.0%, respectively. The cosmetic score was significantly lower at 3- and 6-month followup compared to baseline (median 1 and 1 vs. 4, P = 0.04). The numerical rating scale scores did not differ significantly from baseline throughout follow-up. In the propensity score-matched analysis (12 patients per group), RFA was associated with a shorter median procedure time (61.5 vs. 253.3 minutes; P < 0.001), shorter hospital stay (0 vs. 4 days; P < 0.001), and lower cost (1859.9 vs. 3512.4 USD; P < 0.001) than parotidectomy, with no significant difference in overall complication rates (33.3% [4/12] vs. 41.7% [5/12]; P = 1.000). Conclusion RFA may be a safe and effective alternative to surgery for superficial parotid PA, offering a shorter median procedure time, shorter hospital stay, and lower costs.