Vibration compensation control strategy of composite cage rotor bearingless induction motor based on fuzzy coefficient adaptive-linear-neuron method.

To address the vibration problem induced by rotor eccentricity in a composite cage rotor bearingless induction motor(CCR-BIM), a vibration compensation control approach based on the fuzzy coefficient adaptive-linear-neuron is proposed. Firstly, the CCR-BIM mathematical model and the mechanism of unbalanced vibration are investigated, obtaining the expression of rotor displacement when the rotor is unbalanced. Afterwards, the displacement is decomposed by the fuzzy coefficient adaptive-linear-neuron algorithm to obtain the harmonic component related to vibration, and the value range of the weight coefficient is determined using stability analysis. Furthermore, through analyzing the shortcomings of the traditional PID vibration compensation method, a rotor vibration compensation method based on the fuzzy coefficient adaptive-linear-neuron is put forward to achieve high-performance vibration compensation control. Finally, the PID method and the proposed fuzzy coefficient adaptive-linear-neuron algorithm are simulated and verified by experiments. The findings demonstrate that the proposed algorithm successfully not only suppresses rotor unbalanced vibration but also exhibiting great dynamic performance.

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study.

Background: The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability. Methods: We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance. Results: The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05). Conclusion: The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Risk assessment of pneumothorax in colorectal lung metastases treated by percutaneous thermal ablation: a multicenter retrospective cohort study.

PURPOSE: To evaluate the risk of pneumothorax in the percutaneous image-guided thermal ablation (IGTA) treatment of colorectal lung metastases (CRLM). METHODS: Data regarding patients with CRLM treated with IGTA from five medical institutions in China from 2016 to 2023 were reviewed retrospectively. Pneumothorax and non-pneumothorax were compared using the Student's t -test, χ 2 test and Fisher's exact test. Univariate logistic regression analysis was conducted to identify potential risk factors, followed by multivariate logistic regression analysis to evaluate the predictors of pneumothorax. Interactions between variables were examined and used for model construction. Receiver operating characteristic curves and nomograms were generated to assess the performance of the model. RESULTS: A total of 254 patients with 376 CRLM underwent 299 ablation sessions. The incidence of pneumothorax was 45.5%. The adjusted multivariate logistic regression model, incorporating interaction terms, revealed that tumour number [odds ratio (OR)=8.34 (95% CI: 1.37-50.64)], puncture depth [OR=0.53 (95% CI: 0.31-0.91)], pre-procedure radiotherapy [OR=3.66 (95% CI: 1.17-11.40)], peribronchial tumour [OR=2.32 (95% CI: 1.04-5.15)], and emphysema [OR=56.83 (95% CI: 8.42-383.57)] were significant predictive factors of pneumothorax (all P <0.05). The generated nomogram model demonstrated a significant prediction performance, with an area under the receiver operating characteristic curve of 0.800 (95% CI: 0.751-0.850). CONCLUSIONS: Pre-procedure radiotherapy, tumour number, peribronchial tumour, and emphysema were identified as risk factors for pneumothorax in the treatment of CRLM using percutaneous IGTA. Puncture depth was found to be a protective factor against pneumothorax.

Discrimination of ground-glass nodular lung adenocarcinoma pathological subtypes via transfer learning: A multicenter study.

BACKGROUND: The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground-glass nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them. METHODS: We developed and validated a three-dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre-training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end-to-end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers. RESULTS: The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group). CONCLUSIONS: Our 3D deep transfer learning model provides a noninvasive, low-cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.

A Sequential Stein's Method for Faster Training of Additive Index Models.

The additive index models (AIMs) can be viewed as a kind of artificial neural networks based on nonparametric activation or so-called ridge functions. Recently, they are shown to achieve enhanced explainability after incorporating various interpretability constraints. However, the training of AIMs by either the backfitting algorithm or the joint stochastic optimization is known to be very slow for especially high dimensional inputs. In this article, we propose a novel sequential approach based on the celebrated Stein's lemma. The proposed SeqStein method can successfully decouple the training of AIMs into two separable steps, namely, the following: 1) Stein's estimation of the projection indices and 2) nonparametric estimation of ridge functions using the smoothing splines. We show through numerical experiments that the SeqStein algorithm is not only more efficient for training AIMs, but also inclined to produce more interpretable models that have smooth ridge functions with sparse and nearly orthogonal projection indices.

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models.

Background: This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). Methods: In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation. Results: A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04). Conclusions: A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.

Proteomic analysis of small extracellular vesicles from the plasma of patients with hepatocellular carcinoma.

PURPOSE: Liver cancer is one of the most common tumors with the seventh-highest incidence and the third-highest mortality. Many studies have shown that small extracellular vesicles (sEVs) play an important role in liver cancer. Here, we report comprehensive signatures for sEV proteins from plasma obtained from patients with hepatocellular carcinoma (HCC), which might be valuable for the evaluation and diagnosis of HCC. METHODS: We extracted sEVs from the plasma of controls and patients with HCC. Differentially expressed proteins in the sEVs were analyzed using label-free quantification and bioinformatic analyses. Western blotting (WB) was used to validate the abovementioned sEV proteins. RESULTS: Proteomic analysis was performed for plasma sEVs from 21 patients with HCC and 15 controls. Among the 335 identified proteins in our study, 27 were significantly dysregulated, including 13 upregulated proteins that were involved predominantly in the complement cascade (complement C1Q subcomponent subunit B (C1QB), complement C1Q subcomponent subunit C (C1QC), C4B-binding protein alpha chain (C4BPA), and C4B-binding protein beta chain (C4BPB)) and the coagulation cascade (F13B, fibrinogen alpha chain (FGA), fibrinogen beta chain (FGB), and fibrinogen gamma chain (FGG)). We verified increased levels of the C1QB, C1QC, C4BPA, and C4BPB proteins in the plasma sEVs from patients with HCC in both the discovery cohort and validation cohort. CONCLUSIONS: The complement cascade in sEVs was significantly involved in HCC progression. C1QB, C1QC, C4BPA, and C4BPB were highly abundant in the plasma sEVs from patients with HCC and might represent molecular signatures.

Development characteristics of the rock fracture field in strata overlying a mined coal seam group.

The fracture development of the overlying strata after coal mining is an important guarantee of efficient gas drainage. In order to explore the fracture evolution characteristics close to a mined coal seam group, the F15.16-24130 working face in the Pingdingshan No. 10 coal mine was taken as the research background. The FLAC3D numerical simulation software was used to study the migration and failure characteristics of the overlying strata during mining of a coal seam group, and the fracture evolution process of the stope was investigated. The results show that as the advancing distance increased, the fracture density and fracture height increased continuously due to deformation and failure of the overlying rock. The displacement of the overlying rock initially increased and then decreased, and the displacement of the floor rock initially decreased and then increased. When working face F15.16 of the coal seam advanced to 75 m, a saddle-shaped plastic zone gradually formed in the upper part of the goaf and the floor of the goaf was formed. The pressure relief depth was proportional to the advancement distance. As the advancement distance of the working face increased, the pressure relief depth gradually extended to the F17 coal seam, which was conducive to the development and penetration of the fractures in the coal floor and rock mass and was convenient for pressure relief gas drainage from the F17 coal seam.

S1PR1 induces metabolic reprogramming of ceramide in vascular endothelial cells, affecting hepatocellular carcinoma angiogenesis and progression.

Angiogenesis is a fundamental process underlying the occurrence, growth and metastasis of hepatocellular carcinoma (HCC), a prevalent tumour type with an extremely poor prognosis due to abundant vasculature. However, the underlying mechanism of angiogenesis in HCC remains largely unknown. Herein, we found that sphingosine-1-phosphate receptor 1 (S1PR1) plays an important role in HCC angiogenesis. S1PR1 was found to be selectively and highly expressed in the blood vessels of HCC tissues compared with those of paratumour tissues. Functionally, high expression of S1PR1 in endothelial cells (ECs) promoted angiogenesis and progression of HCC in vitro and in vivo. Mechanistically, proangiogenic factors (S1P, IL-6, VEGFA) in conditioned medium from HCC cells induced the upregulation of S1PR1 in ECs via the phosphorylation of STAT3 at Y705. Further study also revealed that S1PR1 promotes angiogenesis by decreasing ceramide levels via CerS3 downregulation. Interestingly, we demonstrated that S1PR1 downregulates CerS3 by inducing CerS6 translocation into the nucleus to inhibit CerS3 at the transcriptional level in ECs. In addition, we found that a high concentration of Lenvatinib significantly downregulated the expression of S1PR1 and obviously enhanced S1PR1 knockdown-mediated angiogenesis inhibition, indicating that S1PR1 may be a target by which Lenvatinib combats angiogenesis in HCC. Thus, S1PR1 may be an important target for suppressing angiogenesis in HCC, and inhibiting S1PR1 is a promising approach to antitumor therapy in HCC.

An Endogenous H2S-Activated Nanoplatform for Triple Synergistic Therapy of Colorectal Cancer.

Overproduced hydrogen sulfide (H2S) is a highly potential target for precise colorectal cancer (CRC) therapy; herein, a novel 5-Fu/Cur-P@HMPB nanomedicine is developed by coencapsulation of the natural anticancer drug curcumin (Cur) and the clinical chemotherapeutic drug 5-fluorouracil (5-Fu) into hollow mesoporous Prussian blue (HMPB). HMPB with low Fenton-catalytic activity can react with endogenous H2S and convert into high Fenton-catalytic Prussian white (PW), which can generate in situ a high level of •OH to activate chemodynamic therapy (CDT) and meanwhile trigger autophagy. Importantly, the autophagy can be amplified by Cur to induce autophagic cell death; moreover, Cur also acted as a specific chemosensitizer of the chemotherapy drug 5-Fu, achieving a good synergistic antitumor effect. Such a triple synergistic therapy based on a novel nanomedicine has been verified both in vitro and in vivo to have high efficacy in CRC treatment, showing promising potential in translational medicine.