Search

PURPOSE: Achieving a pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is associated with improved patient outcomes in triple-negative breast cancer (TNBC). Currently, there are no validated predictive biomarkers for the response to NAC in TNBC. We developed and validated a deep convolutional neural network–based artificial intelligence (AI) model to predict the response of TNBC to NAC. MATERIALS AND METHODS: Whole-slide images (WSIs) of hematoxylin and eosin–stained core biopsies from 165 (pCR in 60 and non-pCR in 105) and 78 (pCR in 31 and non-pCR in 47) patients with TNBC were used to train and validate the model. The model extracts morphometric features from WSIs in an unsupervised manner, thereby generating clusters of morphologically similar patterns. Downstream ranking of clusters provided regions of interest and morphometric scores; a low score close to zero and a high score close to one represented a high or low probability of response to NAC. RESULTS: The predictive ability of AI score for the entire cohort of 78 patients with TNBC ascertained by receiver operating characteristic analysis demonstrated an area under the curve (AUC) of 0.75. The AUC for stages I, II, and III disease were 0.88, 0.73, and 0.74, respectively. Using a cutoff value of 0.35, the positive predictive value of the AI score for pCR was 73.7%, and the negative predictive value was 76.2% for non-pCR patients. CONCLUSION: To our knowledge, this study is the first to demonstrate the use of an AI tool on digitized hematoxylin and eosin–stained tissue images to predict the response to NAC in patients with TNBC with high accuracy. If validated in subsequent studies, these results may serve as an ancillary aid for individualized therapeutic decisions in patients with TNBC. Artificial intelligence model on the basis of deep learning has potential to predict response of triple-negative breast cancer to neoadjuvant chemotherapy. [ABSTRACT FROM AUTHOR]

Fuzzy modelling and simulation of a supply chain (SC) in an uncertain environment involves consideration of SC fuzzy models and the SC simulator. Consideration of such permits the study of dynamic behavior of a serial SC in an uncertain environment. Under the SC simulator, a dynamic view of the SC is attained and the influence of decisions proposed by the SC fuzzy models on SC performance is evaluated.

Amyloid fibril formation of proteins is of great concern in neurodegenerative disease and can be detrimental to the storage and stability of biologics. Recent evidence suggests that insulin fibril formation reduces the efficacy of type II diabetes management and may lead to several complications. To develop anti‐amyloidogenic compounds of endogenous origin, we have utilized the hydrogen bond anchoring, π stacking ability of porphyrin, and investigated its role on the inhibition of insulin amyloid formation. We report that hydroxylation and metal removal from the heme moiety yields an excellent inhibitor of insulin fibril formation. Thioflavin T, tyrosine fluorescence, Circular Dichorism (CD) spectroscopy, Field emission scanning electron microscopy (FESEM) and molecular dynamics (MD) simulation studies suggest that hematoporphyrin (HP) having hydrogen bonding ability on both sides is a superior inhibitor compared to hemin and protoporphyrin (PP). Experiments with hen egg white lysozyme (HEWL) amyloid fibril formation also validated the efficacy of endogenous porphyrin based small molecules. Our results will help to decipher a general therapeutic strategy to counter amyloidogenesis. [ABSTRACT FROM AUTHOR]

The charge-density integral method is used to analyse a triode gun which will inject electrons into a standing wave electron linac. The effect of space charge on the gun output beam parameters is estimated. The beam dynamics of the entire linac system is studied. The energy spectrum, beam size and divergence at the end of the linac are evaluated neglecting space charge.

The individual resonant frequencies and the intercavity coupling constants of the cavities which comprise a side coupled linear accelerator chain show small deviations from the mean value, due to fabrication tolerances, and, in some cases, due to design. This paper makes use of a perturbation theory to predict the overall mode spectrum of the linear accelerator chain, given the measured values of the individual frequencies and the intercavity coupling constants. The predictions of the theory show good agreement with experimental measurements.