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Simultaneous Resolution of 77 Substances of Rhodiola rosea Acquire by Supercritical CO2-Extraction and HPLC-ESI-MS/MS Spectrometry.
Objective To study the utility of immunohistochemistry (IHC) in differential diagnosis between trichoblastoma (TB) and basal cell carcinoma (BCC). Methods Fifty-eight cases of TB and 40 cases of BCC were collected at Fudan University Shanghai Cancer Center from January 2009 to December 2019 and retrospectively analyzed by IHC for bcl-2, Ber-EP4, CD10, CK20 and Ki-67. Fisher exact test was performed for statistical analysis. Results Twenty-five (43.1%) TBs and 5 (12.5%) BCCs showed bcl-2 staining in the outermost layer of the epithelial nests, the difference was statistically significant (P75%, 51%-75% of epithelial cells than TB group (12.5% vs. 1.7%, 37.5% vs. 8.6%;P less then 0.05). Fifty-five (94.8%) TBs demonstrated CD10 expression in the follicular stroma, while only 16 (40.0%) BCCs showed focal or scattered CD10 expression in reactive fibrous stroma (P less then 0.01). CK20 expression was present in 37 (63.8%) TBs with scattered pattern, but BCCs exhibited no CK20 staining except for only one case (2.5%) showing focal staining (P less then 0.01). Compared with TB group, the BCC group included more cases with Ki-67 labeling index ≥15% on average and ≥25% in hotspot areas (P less then 0.05). Conclusion IHC is helpful in differential diagnosis between TB and BCC. Scattered CK20 staining pattern and stromal CD10 expression support the diagnosis of TB. Bcl-2 staining limited to the outermost layer of the proliferation is more likely to be found in TB. In contrast, Ber-EP4 positivity and higher Ki-67 labeling index tend to be present in BCC.Objective To analyze the expression of mismatch repair (MMR) proteins in colorectal cancers (CRC) and to evaluate the feasibility and potential pitfalls of immunohistochemistry (IHC) analysis for MMR. Methods The IHC sections for MMR proteins were reviewed in 3 428 cases of resected CRC without neoadjuvant therapy at Tianjin Medical University Cancer Institute and Hospital from July 2014 to October 2018. For the cases with unclear MMR IHC results during the initial review, IHC staining was repeated and microsatellite instability (MSI) analysis was performed. Relationships between the expression of MMR proteins and MSI status as well as the clinicopathological parameters were analyzed. Results IHC staining for MMR was repeated in 28 (0.8%) cases due to poor quality of original IHC sections. Inconsistent results between the original diagnosis and re-diagnosis were found in 119 (3.5%) cases, mainly resulting from PMS2 and MLH1. Finally, 261 (7.6%) cases of CRC showed mismatch repair deficiency (dMMR), mainly frot, IHC staining is a clinically effective and convenient method to detect MMR expression, but the operating process and result assessment remain variable and need to be standardized. MSI analysis can be performed in the difficult-to-evaluate cases for MMR to enhance prognostic evaluation and treatment option.Objective To construct a prediction model of gastric cancer related methylation using machine learning algorithms based on genomic data. Methods The gene mutation data, gene expression data and methylation chip data of gastric cancer were downloaded from The Caner Genome Atlas database, feature selection was conducted, and support vector machine (radial basis function), random forest and error back propagation (BP) neural network models were constructed; the model was verified in the new data set. Results Among the three machine learning models, BP neural network had the highest test efficiency (F1 score=0.89,Kappa=0.66, area under curve=0.93). Conclusion Machine learning algorithms, particularly BP neural network, can be used to take advantages of the genomic data for discovering molecular markers, and to help identify characteristic methylation sites of gastric cancer.Objective To develop a convolutional neural network based model for assisting pathological diagnoses on thyroid liquid-based cytology specimens. Methods Seven-hundred thyroid TCT slides were collected, scanned for whole slide imaging (WSI), and divided into training and test sets after labeling the correct diagnosis (benign versus malignant). The extracted regions of interest after noise filtering were cropped into pieces of 512 × 512 patch on 10 × and 40 × magnifications, respectively. A classification model was constructed using deeply learning algorithms, and applied to the training set, then automatically tuned in the test set. After data enhancement and parameters optimization, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the model were calculated. Results The training set with 560 WSI contained 4 926 cell clusters (11 164 patches), while the test set with 140 WSI contained 977 cell clusters (1 402 patches). YOLO network was selected to establish a detection model, and ResNet50 was used as a classification model. With 40 epochs training, results from 10× magnifications showed an accuracy of 90.01%, sensitivity of 89.31%, specificity of 92.51%, positive predictive value of 97.70% and negative predictive value of 70.82%. The area under curve was 0.97. The average diagnostic time was less than 1 second. click here Although the model for data of 40× magnifications was very sensitive (98.72%), but its specificity was poor, suggesting that the model was more reliable at 10× magnification. Conclusions The performance of a deep-learning based model is equivalent to pathologists' diagnostic performance, but its efficiency is far beyond. The model can greatly improve consistency and efficiency, and reduce the missed diagnosis rate. In the future, larger studies should have more morphology diversity, improve model's accuracy and eventually develop a model for direct clinical use.Objective To propose a method of cervical cytology screening based on deep convolutional neural network and compare it with the diagnosis of cytologists. Method The deep segmentation network was used to extract 618 333 regions of interest (ROI) from 5, 516 cytological pathological images. Combined with the experience of physicians, the deep classification network with the ability to analyze ROI was trained. The classification results were used to construct features, and the decision model was used to complete the classification of cytopathological images. Results The sensitivity and specificity were 89.72%, 58.48%, 33.95% and 95.94% respectively. Among the smears derived from four different preparation methods, this algorithm had the best effect on natural fallout with a sensitivity of 91.10%, specificity of 69.32%, positive predictive rate of 41.41%, and negative predictive rate of 97.03%. Conclusion Deep convolutional neural network image recognition technology can be applied to cervical cytology screening.
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