Two miniature Schnauzer dogs with chronic pancreatitis were investigated. Both dogs showed systemic hypertension and increased concentrations of triglycerides and C-reactive protein. Abdominal radiography revealed cylindrical calcification in the retroperitoneum, and computed tomography (CT) showed extensive calcification of the abdominal and peripheral arteries in both dogs. Metastases and other dystrophic conditions that can cause arterial calcification were excluded based on the laboratory tests, and the dogs were diagnosed with atherosclerosis ante mortem. Atherosclerosis should be considered when extensive arterial calcification is observed on abdominal radiography or CT in miniature Schnauzers.

Graphene-based approaches have been influential in the design and manipulation of dental implants and tissue regeneration to overcome the problems associated with traditional titanium-based dental implants, such as their low biological affinity. Here, we describe the current progress of graphene-based platforms, which have contributed to major advances for improving cellular functions in in vitro and in vivo applications of dental implants. We also present opinions on the principal challenges and future prospects for new graphene-based platforms for the development of advanced graphene dental implants and tissue regeneration.
Dental Implants* ; Graphite* ; In Vitro Techniques ; Regeneration* ; Titanium

Canine T-zone lymphoma (TZL) is a mature T-cell lymphoma in dogs. The diagnosis and sub-classification are impossible without biopsy or immunophenotyping by flow cytometry. An 11-year-old, spayed, female Golden Retriever presented with lymph node enlargement. Clinical examination was consistent with canine multicentric lymphoma. However, immunophenotyping revealed positive for CD3, CD4, CD5, CD8, CD21, TCRαβ, and MHCII but negative for CD34, CD45, CD79a, and TCRγδ. Histopathology revealed lymphocytes expanding to the cortex-preserving architecture and thinning of the nodal capsule, and CD3 positive but PAX-5 negative. Owing to the indolent nature of TZL, careful monitoring approach without clinical intervention was utilized.

Acute lymphocytic leukemia (ALL) is uncommon lymphoid malignancy in dogs, and its diagnosis is challenging. A 14-year-old spayed female mixed breed dog was transferred to a veterinary medical teaching hospital for an immediate blood transfusion. The dog showed lethargy, pale mucous membranes, and a weak femoral pulse. Complete blood count revealed non-regenerative anemia and severe leukopenia with thrombocytopenia. ALL was tentatively diagnosed based on the predominance of immature lymphoblasts on blood film examination. For confirmation of lymphoid malignancy, PCR for antigen receptor rearrangement (PARR) on a peripheral blood sample and flow cytometry analysis were performed after blood transfusion. Flow cytometry analysis revealed that lymphocyte subsets were of normal composition, but PARR detected a T-cell malignancy. The dog was diagnosed with ALL and survived 1 wk after diagnosis. In conclusion, after blood transfusion, flow cytometry was not a reliable diagnostic method for an ALL dog, whereas PARR could detect lymphoid malignancy. Our results suggest that PARR should be the first-line diagnostic tool to detect canine lymphoid malignancy after a blood transfusion.
Adolescent ; Anemia ; Animals ; Blood Cell Count ; Blood Transfusion* ; Diagnosis* ; Dogs* ; Female ; Flow Cytometry ; Hospitals, Teaching ; Humans ; Lethargy ; Leukopenia ; Lymphocyte Subsets ; Methods ; Mucous Membrane ; Polymerase Chain Reaction* ; Precursor Cell Lymphoblastic Leukemia-Lymphoma* ; Receptors, Antigen* ; T-Lymphocytes ; Thrombocytopenia

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.