Artificial intelligence scale-invariant feature transform algorithm-based system to improve the calculation accuracy of Ki-67 index in invasive breast cancer: a multicenter retrospective study.

Background: Ki-67 is a key indicator of the proliferation activity of tumors. However, no standardized criterion has been established for Ki-67 index calculation. Scale-invariant feature transform (SIFT) algorithm can identify the robust invariant features to rotation, translation, scaling and linear intensity changes for matching and registration in computer vision. Thus, this study aimed to develop a SIFT-based computer-aided system for Ki-67 calculation in breast cancer. Methods: Hematoxylin and eosin (HE)-stained and Ki-67-stained slides were scanned and whole slide images (WSIs) were obtained. The regions of breast cancer (BC) tissues and non-BC tissues were labeled by experienced pathologists. All the labeled WSIs were randomly divided into the training set, verification set, and test set according to a fixed ratio of 7:2:1. The algorithm for identification of cancerous regions was developed by a ResNet network. The registration process between paired consecutive HE-stained WSIs and Ki-67-stained WSIs was based on a pyramid model using the feature matching method of SIFT. After registration, we counted the nuclear-stained Ki-67-positive cells in each identified invasive cancerous region using color deconvolution. To assess the accuracy, the AI-assisted result for each slice was compared with the manual diagnosis result of pathologists. If the difference of the two positive rate values is not greater than 10%, it was a consistent result; otherwise, it was an inconsistent result. Results: The accuracy of the AI-based algorithm in identifying breast cancer tissues in HE-stained slides was 93%, with an area under the curve (AUC) of 0.98. After registration, we succeeded in identifying Ki-67-positive cells among cancerous cells across the entire WSIs and calculated the Ki-67 index, with an accuracy rate of 91.5%, compared to the gold standard pathological reports. Using this system, it took about 1 hour to complete the evaluation of all the tested 771 pairs of HE- and Ki-67-stained slides. Each Ki-67 result took less than 2 seconds. Conclusions: Using a pyramid model and the SIFT feature matching method, we developed an AI-based automatic cancer identification and Ki-67 index calculation system, which could improve the accuracy of Ki-67 index calculation and make the data repeatable among different hospitals and centers.

Biodiesel fuel production from waste cooking oil by the inclusion complex of heteropoly acid with bridged bis-cyclodextrin.

The inclusion complex of Cs2.5H0.5PW12O40 with bridged bis-cyclodextrin (CsPW/B) is prepared as a highly efficient catalyst for the direct production of biodiesel via the transesterification of waste cooking oil. CsPW/B is characterized by X-ray diffraction, and the biodiesel is analyzed by Gas Chromatography-Mass Spectrometer. The conversion rate of waste cooking oil is up to 94.2% under the optimum experimental conditions that are methanol/oil molar ratio of 9:1, catalyst dosage of 3 wt%, temperature of 65 °C and reaction time of 180 min. The physical properties of biodiesel sample satisfy the requirement of ASTM D6751 standards. The novel CsPW/B catalyst used for the transesterification can lead to 96.9% fatty acid methyl esters and 86.5% of the biodiesel product can serve as the ideal substitute for diesel fuel, indicating its excellent potential application in biodiesel production.

Degradation kinetics and mechanism of aniline by heat-assisted persulfate oxidation.

Oxidation of aniline by persulfate in aqueous solutions was investigated and the reaction kinetic rates under different temperature, persulfate concentration and pH conditions were examined in batch experiments. The results showed that, the aniline degradation followed pseudo first-order reaction model. Aniline degradation rate increased with increasing temperature or persulfate concentration. In the pH range of 3 to 11, a low aniline degradation rate was obtained at strong acid system (pH 3), while a high degradation rate was achieved at strong alkalinity (pH 11). Maximum aniline degradation occurred at pH 7 when the solution was in a weak level of acid and alkalinity (pH 5, 7 and 9). Produced intermediates during the oxidation process were identified using liquid chromatography-mass spectrometry technology. And nitrobenzene, 4-4'-diaminodiphenyl and 1-hydroxy-1,2-diphenylhydrazine have been identified as the major intermediates of aniline oxidation by persulfate and the degradation mechanism of aniline was also tentatively proposed.