Synergistic Anti-Cancer Effects of Isocnicin and Radiotherapy in Glioblastoma: A Natural Compound's Potential.

Background/objectives: Glioblastoma (GBM) is the most aggressive type of brain tumor in adults. Currently, the only treatments available are surgery, radiotherapy, and chemotherapy based on temozolomide (TMZ); however, the prognosis is dismal. Several natural substances are under investigation for cancer treatment.

M-ClustEHR: A multimodal clustering approach for electronic health records.

Sepsis refers to a potentially life-threatening situation where the immune system of the human body has an extreme response to an infection. In the presence of underlying comorbidities, the situation can become even worse and result in death. Employing unsupervised machine learning techniques, such as clustering, can assist in providing a better understanding of patient phenotypes by unveiling subgroups characterized by distinct sepsis progression and treatment patterns.

Antitumor activity of 5-hydroxy-3',4',6,7-tetramethoxyflavone in glioblastoma cell lines and its antagonism with radiotherapy.

5-Hydroxy-3',4',6,7-tetramethoxyflavone (TMF) is a plant-origin flavone known for its anti-cancer properties. In the present study, the cytotoxic effect of TMF was evaluated in the U87MG and T98G glioblastoma (GBM) cell lines. The effect of TMF on cell viability was assessed with trypan blue exclusion assay and crystal violet staining.

Patients with Hashimoto's thyroiditis present higher immune response to COVID-19 mRNA vaccine compared to normal individuals.

Aim: To evaluate the response (titers of anti-COVID-19 antibodies) to COVID-19 mRNA vaccine of patients with Hashimoto's thyroiditis and normal individuals.

Patients And Methods: Twenty-four patients with Hashimoto's thyroiditis and 51 normal individuals were studied after the third dose of the vaccine.

Results: Patients with Hashimoto's thyroiditis showed significantly higher immune response after the third dose of the COVID-19 mRNA vaccine compared with normal individuals (p = 0.

Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients.

In recent years, machine learning methods have been rapidly adopted in the medical domain. However, current state-of-the-art medical mining methods usually produce opaque, black-box models. To address the lack of model transparency, substantial attention has been given to developing interpretable machine learning models.