Von Hippel-Lindau disease is an autosomal dominant disorder caused by mutations in the VHL tumor suppressor gene on chromosome 3.
The disease is characterized by the formation of vascular-rich tumors or cysts in multiple organ systems, including the central nervous system.
A 54-year-old man diagnosed with VHL syndrome presented with an intracranial lateral ventricular hemangioblastoma and metastatic renal cell carcinoma.
Postoperative complications included extensive intracranial hemorrhage, leading to fatal brainstem hemorrhage due to the hemangioblastoma's high vascularity.
The case highlights the diagnostic challenges of VHL syndrome and the surgical risks associated with central nervous system lesions.
Cytomegalovirus (CMV)-associated hemolytic anemia (HA) is rare in immunocompetent individuals and can present as mixed autoimmune hemolytic anemia (AIHA).
A 59-year-old man developed severe CMV-associated mixed AIHA after presenting with fever, sore throat, and fatigue, confirmed by comprehensive immunohematologic testing.
The patient exhibited significant laboratory findings, including a hemoglobin level of 40 g/L and evidence of acute hemolysis, alongside positive CMV IgM and negative IgG.
Diagnosis of mixed AIHA requires extensive testing, including extended direct antiglobulin test (DAT) and cold agglutinin titration, beyond routine DAT.
The patient's severe mixed AIHA was managed successfully with antiviral therapy and immunomodulatory treatment, stabilizing hemoglobin without further transfusions.
Perioperative chemotherapy is standard care for locally advanced resectable gastric cancer, supported by landmark studies.
The independent oncological value of treatment timing in gastric cancer remains uncertain due to methodological limitations in trials.
Western studies focus on the benefits of neoadjuvant therapy, while East Asian studies emphasize the role of adjuvant therapy after high-quality surgery.
Landmark trials primarily assessed the addition of systemic therapy rather than the timing of its administration relative to surgery.
Future research should directly compare perioperative and postoperative systemic regimens to clarify the impact of treatment timing.
The study developed a machine learning model to predict early neurological deterioration (END) in patients with acute ischemic stroke.
A total of 1,151 patients treated for acute ischemic stroke were included in the analysis from January 2021 to December 2024.
Five key predictors for END were identified: ischemic stroke subtype, OCSP classification, age, atrial fibrillation history, and previous stroke history.
The logistic regression model demonstrated AUCs of 0.787 and 0.751 in development and validation cohorts, respectively.
An online tool was created to facilitate early risk stratification for END, supporting personalized clinical management.
The study developed a machine learning model to predict postoperative outcomes in patients with biliary tract malignancies using multiple risk factors.
Data from 6,951 patients in the SEER database and 245 patients from a medical center were utilized for model training and validation.
Independent prognostic factors for overall survival included age, sex, marital status, tumor site, differentiation, AJCC stage, and lymph node status.
The random forest model demonstrated strong performance in predicting survival rates, validated by AUC values and decision curve analysis.
Chemotherapy was associated with improved overall survival among patients with positive lymph nodes in both training and test cohorts.
The study assessed language-dependent differences in mechanistic performance of five web-enabled large language models (LLMs) across four languages.
Twenty senior clinician-experts scored 480 responses using the Mechanistic Fidelity Score, demonstrating moderate-to-good inter-rater agreement.
Reviewer-standardized scores indicated a significant interaction between model and language, but no significant model-by-complexity interaction was found.
Kimi K3 and ChatGPT 5.6 Sol achieved the highest mean Mechanistic Fidelity Scores, significantly outperforming other models.
The results suggest that multilingual biomedical evaluations should directly assess mechanistic explanations in the intended language and context.