We report a case of mucinous adenocarcinoma arising in the perianal soft tissue in association with chronic fistula-in-ano in a 43-year-old man who had a relapse of perianal pain and bloody discharge after six years of defaulted follow-up. He underwent magnetic resonance (MR) and computed tomography imaging with correct identification of the disease entity on imaging. Mesh-like septations and an enhancing solid component with high diffusion-weighted imaging (DWI) and intermediate apparent diffusion coefficient signals were observed. He underwent abdominoperineal resection of the tumour but succumbed due to postoperative complications. Literature on the MR imaging features of this tumour remains scarce. We highlight the MR imaging features, including those seen on DWI, which were useful in making the correct diagnosis. Though uncommon, this would be an important condition to recognise since assessment of fistula-in-ano by MR imaging is considered to be the standard of care in current clinical practice. The clinical features of this entity are also briefly discussed.
Adenocarcinoma, Mucinous ; complications ; diagnosis ; Adult ; Anus Neoplasms ; complications ; diagnosis ; Chronic Disease ; Diagnosis, Differential ; Diffusion Magnetic Resonance Imaging ; Humans ; Male ; Rectal Fistula ; complications ; diagnosis ; Tomography, X-Ray Computed

Background: Echocardiography is a primary tool used by veterinarians to evaluate heart diseases. In recent years, various studies have targeted standard echocardiographic values for different breeds. Reference data are currently lacking in Maltese dogs and it is important to fill this gap as this breed is predisposed to myxomatous mitral valve disease, which is a volume overload disease. Objectives: To establish the normal echocardiographic parameters for Maltese dogs. Methods: In total, 23 healthy Maltese dogs were involved in this study. Blood pressure measurements, thoracic radiography, and complete transthoracic echocardiography were performed. The effects of body weight, age and sex were evaluated, and the correlations between weight and linear and volumetric dimensions were calculated by regression analysis. Results: The mean vertebral heart size was 9.1 ± 0.4. Aside from the ejection fraction, fractional shortening, and the left atrial to aorta root ratio, all the other echocardiographic parameters were significantly correlated with weight. Conclusion This study describes normal echocardiographic parameters that may be useful in the echocardiographic evaluation of Maltese dogs.

Background: Echocardiography is a primary tool used by veterinarians to evaluate heart diseases. In recent years, various studies have targeted standard echocardiographic values for different breeds. Reference data are currently lacking in Maltese dogs and it is important to fill this gap as this breed is predisposed to myxomatous mitral valve disease, which is a volume overload disease. Objectives: To establish the normal echocardiographic parameters for Maltese dogs. Methods: In total, 23 healthy Maltese dogs were involved in this study. Blood pressure measurements, thoracic radiography, and complete transthoracic echocardiography were performed. The effects of body weight, age and sex were evaluated, and the correlations between weight and linear and volumetric dimensions were calculated by regression analysis. Results: The mean vertebral heart size was 9.1 ± 0.4. Aside from the ejection fraction, fractional shortening, and the left atrial to aorta root ratio, all the other echocardiographic parameters were significantly correlated with weight. Conclusion This study describes normal echocardiographic parameters that may be useful in the echocardiographic evaluation of Maltese dogs.

No abstract available.
Fever* ; Humans ; Neuromyelitis Optica*

Organ transplantation from donors with immune thrombocytopenia (ITP), a condition involving the autoantibody-mediated destruction of platelets, is a topic of debate due to the potential for transplantation-mediated autoimmune thrombocytopenia (TMAT), a rare but potentially fatal complication. Previous reports have described transplants from deceased liver donors with ITP who had very low platelet counts and disease largely refractory to treatment. Here, we present the first case of living kidney transplantation from a donor with ITP who underwent preoperative treatment, with concurrent splenectomy performed to reduce the long-term risk of spontaneous hemorrhage.To ensure the safety of the procedure, we monitored perioperative rotational thromboelastometry parameters and platelet counts, leading to the normalization of the donor’s platelet levels. The recipient experienced an uneventful recovery of renal function without perioperative bleeding or the development of TMAT. Our report suggests that kidney transplantation from a donor with well-managed ITP is safe, and such a condition should not be considered a contraindication for donation.

Background: To compare risk of diabetic retinopathy (DR) between patients taking sodium-glucose cotransporter-2 inhibitors (SGLT2is) and those taking glucagon-like peptide-1 receptor agonists (GLP1-RAs) in routine care. Methods: This retrospective cohort study emulating a target trial included patient data from the multi-institutional Chang Gung Research Database in Taiwan. Totally, 33,021 patients with type 2 diabetes mellitus using SGLT2is and GLP1-RAs between 2016 and 2019 were identified. 3,249 patients were excluded due to missing demographics, age <40 years, prior use of any study drug, a diagnosis of retinal disorders, a history of receiving vitreoretinal procedure, no baseline glycosylated hemoglobin, or no follow-up data. Baseline characteristics were balanced using inverse probability of treatment weighting with propensity scores. DR diagnoses and vitreoretinal interventions served as the primary outcomes. Occurrence of proliferative DR and DR receiving vitreoretinal interventions were regarded as vision-threatening DR. Results: There were 21,491 SGLT2i and 1,887 GLP1-RA users included for the analysis. Patients receiving SGLT2is and GLP-1 RAs exhibited comparable rate of any DR (subdistribution hazard ratio [SHR], 0.90; 95% confidence interval [CI], 0.79 to 1.03), whereas the rate of proliferative DR (SHR, 0.53; 95% CI, 0.42 to 0.68) was significantly lower in the SGLT2i group. Also, SGLT2i users showed significantly reduced risk of composite surgical outcome (SHR, 0.58; 95% CI, 0.48 to 0.70). Conclusion Compared to those taking GLP1-RAs, patients receiving SGLT2is had a lower risk of proliferative DR and vitreoretinal interventions, although the rate of any DR was comparable between the SGLT2i and GLP1-RA groups. Thus, SGLT2is may be associated with a lower risk of vision-threatening DR but not DR development.

Human pluripotent stem cells (hPSCs), including embryonic stem cells (ESCs) and induced PSCs (iPSCs), represent potentially unlimited cell sources for clinical applications. Previous studies have suggested that hPSCs may benefit from immune privilege and limited immunogenicity, as reflected by the reduced expression of major histocompatibility complex class-related molecules. Here we investigated the global immune-related gene expression profiles of human ESCs, hiPSCs and somatic cells and identified candidate immune-related genes that may alter their immunogenicity. The expression levels of global immune-related genes were determined by comparing undifferentiated and differentiated stem cells and three types of human somatic cells: dermal papilla cells, ovarian granulosa cells and foreskin fibroblast cells. We identified the differentially expressed genes CD24, GATA3, PROM1, THBS2, LY96, IFIT3, CXCR4, IL1R1, FGFR3, IDO1 and KDR, which overlapped with selected immune-related gene lists. In further analyses, mammalian target of rapamycin complex (mTOR) signaling was investigated in the differentiated stem cells following treatment with rapamycin and lentiviral transduction with specific short-hairpin RNAs. We found that the inhibition of mTOR signal pathways significantly downregulated the immunogenicity of differentiated stem cells. We also tested the immune responses induced in differentiated stem cells by mixed lymphocyte reactions. We found that CD24- and GATA3-deficient differentiated stem cells including neural lineage cells had limited abilities to activate human lymphocytes. By analyzing the transcriptome signature of immune-related genes, we observed a tendency of the hPSCs to differentiate toward an immune cell phenotype. Taken together, these data identify candidate immune-related genes that might constitute valuable targets for clinical applications.
Embryonic Stem Cells ; Female ; Fibroblasts ; Foreskin ; Granulosa Cells ; Humans* ; Induced Pluripotent Stem Cells ; Lymphocyte Culture Test, Mixed ; Lymphocytes* ; Major Histocompatibility Complex ; Phenotype ; Pluripotent Stem Cells* ; RNA ; Signal Transduction ; Sirolimus ; Stem Cells ; Transcriptome

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.