RESULTS: The N-terminal truncated mutants of PhaCBP-M-CPF4 were constructed based on the information of the predicted secondary and tertiary structures using PSIPRED server and AlphaFold2 program, respectively. The N-terminal truncated PhaCBP-M-CPF4 mutants were evaluated in C. necator mutant PHB-4 based on the cell dry weight, PHA content, 3HHx molar composition, molecular weights, and granule morphology of the PHA granules. The results showed that most transformants harbouring the N-terminal truncated PhaCBP-M-CPF4 showed a reduction in PHA content and cell dry weight except for PhaCBP-M-CPF4 G8. PhaCBP-M-CPF4 G8 and A27 showed an improved weight-average molecular weight (Mw) of PHA produced due to lower expression of the truncated PhaCBP-M-CPF4. Transformants harbouring PhaCBP-M-CPF4 G8, A27, and T74 showed a reduction in the number of granules. PhaCBP-M-CPF4 G8 produced higher Mw PHA in mostly single larger PHA granules with comparable production as the full-length PhaCBP-M-CPF4.
CONCLUSION: This research showed that N-terminal truncation had effects on PHA accumulation, substrate specificity, Mw, and granule morphology. This study also showed that N-terminal truncation of the amino acids that did not adopt any secondary structure can be an alternative to improve PhaCs for the production of PHA with higher Mw in mostly single larger granules.
OBJECTIVES: This review aims to provide insights regarding the FOXP3 Tregs involved and their mechanisms in breast cancer prognosis.
METHODS: The literature study method is used from primary and secondary libraries. The library search used online-based search instruments such as NCBI-PubMed, Google Scholar, and Elsevier. The data obtained were then arranged according to the framework, data on the relationship between FOXP3 Regulatory T Cells and breast cancer, and writing a journal review was carried out according to the given format. Regulators (Tregs) can inhibit anti-tumor immunity and promote tumor growth. Tregs also play a role in inhibiting cytotoxic T lymphocyte cells by inhibiting the release of granules from CD8+, where CD8+ is important in killing tumor cells. FOXP3 is a Treg-specific biomarker and plays an important role in the development and function of Tregs.
RESULTS: Studies on the presence of FOXP3+ Tregs in tumors have shown controversial results. Studies in some tumors reported the presence of FOXP3+, indicating a poor prognosis, whereas studies in other tumors found that FOXP3+ correlated with a good prognosis.
CONCLUSION: Regulatory T lymphocytes and TILs in invasive breast carcinoma are still not established. Therefore, further research on the Effect of FOXP3 expression of regulatory T lymphocytes on breast cancer is still important.
METHODS: In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Čech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling.
RESULTS: The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively.
CONCLUSION: 3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features.