Brink Truelsen (roofwine6)

001), 9% (p<0.001) and 16% (p<0.001), respectively. The integral dose was reduced by 16% (p<0.001). The dose conformity for the three PTVs was significantly higher with AP plans (p<0.001). The two oncologists chose AP plans in more than 80% of cases. Overall planning times were reduced to <30min for automated optimization. All AP plans passed the 3%/2mm γ-analysis by more than 95%. Complex head-neck plans created using Personalized automated engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues. The Feasibility module allowed OARs dose sparing well beyond the clinical objectives. Complex head-neck plans created using Personalized automated engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues. The Feasibility module allowed OARs dose sparing well beyond the clinical objectives. Existing phantom-less quality assurance (QA) platforms does not provide patient-specific QA for helical tomotherapy (HT). A new system, called TomoEQA, is presented to facilitate this using the leaf open time (LOT) of a binary multi-leaf collimator, as measured by an exit detector. TomoEQA was designed to provide measurement-based LOTs based on detector data and to generate a new digital imaging and communication in medicine (DICOM) dataset that includes the measured LOTs for use by secondary check platforms. To evaluate the system, 20 patient-specific QAs were performed using the program in Mobius3D software, and the results were compared to conventional phantom-based QA results. From our assessment, most of the differences between the planned and measured (or calculated) data, excluding one case, were within the acceptance criteria comparing with those of conventional QA. Sovilnesib Regarding the gamma analysis, all results considered in this study were within the acceptance criteria. In addition, the developed system was performed for a failed case and showed approximately the same trends as the conventional approach. TomoEQA could perform patient-specific QAs of HT using Mobius3D and provide reliable patient-specific QAs results by evaluating point dose errors and 3D gamma passing rates. TomoEQA could also distinguish whether an intensity-modulated radiation therapy plan failed or not. TomoEQA could perform patient-specific QAs of HT using Mobius3D and provide reliable patient-specific QAs results by evaluating point dose errors and 3D gamma passing rates. TomoEQA could also distinguish whether an intensity-modulated radiation therapy plan failed or not.Lung cancer is a prominent type of cancer, which leads to high mortality rate worldwide. The major lung cancers lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) occur mainly due to somatic driver mutations in proteins and screening of such mutations is often cost and time intensive. Hence, in the present study, we systematically analyzed the preferred residues, residues pairs and motifs of 4172 disease prone sites in 195 proteins and compared with 4137 neutral sites. We observed that the motifs LG, QF and TST are preferred in disease prone sites whereas GK, KA and ISL are predominant in neutral sites. In addition, Gly, Asp, Glu, Gln and Trp are preferred in disease prone sites whereas, Ile, Val, Lys, Asn and Phe are preferred in neutral sites. Further, utilizing deep neural networks, we have developed a method for predicting disease prone sites with amino acid sequence based features such as physicochemical properties, conservation scores, secondary structure and di and tri-peptide motifs. The model is able to predict the disease prone sites at an accuracy of 81 % with sensitivity, specificity and AUC of 82 %, 78 % and 0.91, respectively, on 10-fold cross-validation. When the model was tested with a set of 417 disease-causing and 413 neutral sites, we obtained an accuracy and AUC of 80 % and 0.