Palatine tonsillar metastasis of lung adenocarcinoma: An unusual immunohistochemical phenotype and a potential diagnostic pitfall

Metastasis rarely occurs to the palatine tonsils. Herein, we present an exceedingly rare case of palatine tonsillar metastasis from poorly differentiated lung adenocarcinoma with anaplastic lymphoma kinase (ALK) mutation in a 51-year-old woman. The patient manifested clinically as pharyngalgia without obvious respiratory symptoms, with swelling tonsil histomorphologically resembling lymphoma and partially expressing the markers of epithelial and squamous cell carcinoma (CK5/6, P63, and P40). Due to the non-specific immunohistochemical expression, it is easily misdiagnosed as a primary poorly differentiated squamous cell carcinoma of the tonsil. This case highlights the importance of a comprehensive assessment of suspicious tonsillar lesions, that may be a sign of a primary malignancy elsewhere in the body

Treatment-emergent neuroendocrine prostate cancer: A clinicopathological and immunohistochemical analysis of 94 cases

Purpose: This study aimed to evaluate the pathological characteristics, immunophenotype, and prognosis of treatment-emergent neuroendocrine prostate cancer (T-NEPC). Materials and Methods: We collected 231 repeated biopsy specimens of castration-resistant prostate cancer (CRPC) cases between 2008 and 2019. We used histopathological and immunohistochemical evaluations of Synaptophysin (SYN), ChromograninA (CgA), CD56, androgen receptor (AR), and prostate Results: Among the 231 CRPC cases, 94 (40.7%) cases were T-NEPC. T-NEPC were more likely to present with negative immunohistochemistry for AR (30.9%) and PSA (47.9%) than that of CRPC (8.8% and 17.5%, respectively). Kaplan-Meier analysis revealed that patients with T-NEPC (median overall survival [OS]: 17.6 months, 95% CI: 15.3-19.9 months) had significantly worse survival compared with usual CRPC patients (median OS: 23.6 months, 95% CI: 21.3-25.9 months, log-rank Conclusion: T-NEPC was associated with an unfavorable prognosis, negative immunohistochemistry for PSA in T-NEPC and serum PSA level ≤ 4 ng/ml had a worse prognosis. Urologists and pathologists should recognize the importance of the second biopsy in CRPC to avoid unnecessary diagnosis and treatment delays

Mechanism of homocysteine-mediated endothelial injury and its consequences for atherosclerosis

Homocysteine (Hcy) is an intermediate amino acid formed during the conversion from methionine to cysteine. When the fasting plasma Hcy level is higher than 15 μmol/L, it is considered as hyperhomocysteinemia (HHcy). The vascular endothelium is an important barrier to vascular homeostasis, and its impairment is the initiation of atherosclerosis (AS). HHcy is an important risk factor for AS, which can promote the development of AS and the occurrence of cardiovascular events, and Hcy damage to the endothelium is considered to play a very important role. However, the mechanism by which Hcy damages the endothelium is still not fully understood. This review summarizes the mechanism of Hcy-induced endothelial injury and the treatment methods to alleviate the Hcy induced endothelial dysfunction, in order to provide new thoughts for the diagnosis and treatment of Hcy-induced endothelial injury and subsequent AS-related diseases

Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network

(1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the position of the missing part of the data is calibrated, and the trained random forest algorithm is used for the first data interpolation. The output value of the random forest algorithm is used as the input value of the generative adversarial interpolation network, and the generative adversarial interpolation network is used to calibrate the position. The data are interpolated for the second time, and the advantages of the two algorithms are combined to make the interpolation result closer to the true value. (3) Results: The filling effect of the algorithm is tested on a certain bearing data set, and the root mean square error (RMSE) is used to evaluate the interpolation results. The results show that the RMSE of the interpolation results based on the random forest and generative adversarial interpolation network algorithms in the case of single-segment and multi-segment missing data is only 0.0157, 0.0386, and 0.0527, which is better than the random forest algorithm, generative adversarial interpolation network algorithm, and K-nearest neighbor algorithm. (4) Conclusions: The proposed algorithm performs well in each data set and provides a reference method in the field of data filling

Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder

Marine centrifugal pumps (MCPs) are widely used in ships, so it is important to identify their status accurately for their maintenance. Due to the influence of load, friction, and other non-linear factors, the vibration signal of an MCP shows non-linear and non-stationary characteristics, and it is difficult to extract the state characteristics contained in the vibration signal. To solve the difficulty of feature extraction of non-linear non-stationary vibration signals generated by MCPs, a novel MCP frequency domain signal feature extraction method based on a stacked sparse auto-encoder (SSAE) is proposed. The characteristic parameters of MCP frequency domain signals are extracted via the SSAE model for classification training, and different statuses of MCPs are identified. The vibration signals in different MCP statuses were collected for feature extraction and classification training, and the MCP status recognition accuracy based on the time domain feature and fuzzy entropy feature was compared. According to the test data, the accuracy of MCP status recognition based on the time domain feature is 71.2%, the accuracy of MCP status recognition based on the fuzzy entropy feature is 87.7%, and the accuracy of MCP status recognition based on the proposed method is 100%. These results show that the proposed method can accurately identify each status of an MCP under test conditions

Multiform Informed Machine Learning Based on Piecewise and Weibull for Engine Remaining Useful Life Prediction

Informed machine learning (IML), which strengthens machine learning (ML) models by incorporating external knowledge, can get around issues like prediction outputs that do not follow natural laws and models, hitting optimization limits. It is therefore of significant importance to investigate how domain knowledge of equipment degradation or failure can be incorporated into machine learning models to achieve more accurate and more interpretable predictions of the remaining useful life (RUL) of equipment. Based on the informed machine learning process, the model proposed in this paper is divided into the following three steps: (1) determine the sources of the two types of knowledge based on the device domain knowledge, (2) express the two forms of knowledge formally in Piecewise and Weibull, respectively, and (3) select different ways of integrating them into the machine learning pipeline based on the results of the formal expression of the two types of knowledge in the previous step. The experimental results show that the model has a simpler and more general structure than existing machine learning models and that it has higher accuracy and more stable performance in most datasets, particularly those with complex operational conditions, which demonstrates the effectiveness of the method in this paper on the C-MAPSS dataset and assists scholars in properly using domain knowledge to deal with the problem of insufficient training data

Large Eddy Simulation of Cavitation Jets from an Organ-Pipe Nozzle: The Influence of Cavitation on the Vortex Coherent Structure

High-speed water jets are widely used in deep mining and the in-depth study of jet characteristics helps to improve drilling efficiency. Three-dimensional Large Eddy Simulation is used to simulate turbulent flows generated by an organ-pipe nozzle. The simulation is validated with existing experimental data and is focused on the evolution and interaction of cavitation bubbles and vortices. Dynamic mode decomposition is performed to extract structural information about the different motion modes and their stability. Results show that the dominant fluid frequency is positively correlated with inlet pressure while unrelated to the divergence angle. Meanwhile, jets’ oscillation is amplified by a large divergence angle, which facilitates the occurrence of cavitation. Results about the flow field outside of an organ-pipe nozzle advance the understanding of the basic mechanism of cavitation jets

Concentration Quantification of Oil Samples by Three-Dimensional Concentration-Emission Matrix (CEM) Spectroscopy

Developing fast and accurate fluorescence detection technology of oil spill is significant for quantitative analysis in unexpected oil spill events. As the oil sample concentration increases, the fluorescence spectrum produces red-shift behavior, which seriously affects the quantitative detection of concentration. In this work, a three-dimensional concentration-emission matrix (CEM) was constructed by using a series of emission spectra with different levels of concentration at the excitation wavelength of 266 nm. The database is the interpolated CEM of six samples using bicubic interpolation in the concentration dimension. With matrix similarity matching, the database was used to achieve quantification of the concentration of oil samples. The recovery rates of prediction for test samples and weathering samples of six oil samples were between 86.8% and 116.11%, with relative errors of predictions ranging from 2.09% to 15.2%. The results show that this method can provide accurate quantitative determination of the concentration of different oil samples