High-resolution anoscopy predictive modeling of anal canal cancer response after definitive chemoradiotherapy in COVID19 era

Purpose: To develop a predictive index model, integrating both clinical and high-resolution anoscopy (HRA) features to further personalize the decision making process in anal canal carcinoma in COVID19 era.Methods and materials: We assess HRA parameters after definitive chemoradiotherapy in patients with anal canal malignant lesions.Results: HRA features could be important to assess the effect of CRT and a risk stratification system should be introduced in clinical practice to better allocate therapeutic interventions.Conclusion: To our knowledge this is the first proposal for HRA findings in anal canal cancer after definitive CRT. We believe that a risk score can be useful to estimate the risk of treatment failure (in term of persistence disease and/or recurrence) and its clinical relevance should not to be underestimated

Behavior of Triton X-114 cloud point in the presence of inorganic electrolytes

Nonionic surfactants display different behaviors in aqueous solution, depending on their concentration and the presence of small amounts of salts and/or ionic surfactants. Here, cloud point values of the aqueous two-phase micellar system formed by the nonionic surfactant Triton X-114 were determined in water and in McIlvaine buffer (pH 6.5), either in the presence or absence of various salts at different concentrations. The presence of salts lowered the cloud point values according to the sequence: 0.25 M MgSO4 center dot 7H(2)O > 0.5 M NaCl > 0.05 M MgSO4 center dot 7H2O approximate to 0.5 M CaCl2 center dot 2H(2)O approximate to 0.1 M NaCl > 0.1 M CaCl2 center dot 2H(2)O. These results demonstrate that the presence of electrolytes influences the cloud point of Triton X-114 system and that knowledge of this parameter is paramount before starting partitioning studies. (C) 2013 Elsevier B.V. All rights reserved.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Prediction of recurrence by Machine learning in salivary gland cancer patients after adjuvant (chemo)radiotherapy

To investigate survival outcomes and recurrence patterns using machine learning in patients with salivary gland malignant tumor (SGMT) undergoing adjuvant chemoradiotherapy (CRT)