Targeting WEE1 Kinase in Gynecological Malignancies.

WEE1 kinase is involved in the G2/M cell cycle checkpoint control and DNA damage repair. A functional G2/M checkpoint is crucial for DNA repair in cancer cells with p53 mutations since they lack a functional G1/S checkpoint. Targeted inhibition of WEE1 kinase may cause tumor cell apoptosis, primarily, in the p53-deficient tumor, via bypassing the G2/M checkpoint without properly repairing DNA damage, resulting in genome instability and chromosomal deletion. This review aims to provide a comprehensive overview of the biological role of WEE1 kinase and the potential of WEE1 inhibitor (WEE1i) for treating gynecological malignancies. We conducted a thorough literature search from 2001 to September 2023 in prominent databases such as PubMed, Scopus, and Cochrane, utilizing appropriate keywords of WEE1i and gynecologic oncology. WEE1i has been shown to inhibit tumor activity and enhance the sensitivity of chemotherapy or radiotherapy in preclinical models, particularly in p53-mutated gynecologic cancer models, although not exclusively. Recently, WEE1i alone or combined with genotoxic agents has confirmed its efficacy and safety in Phase I/II gynecological malignancies clinical trials. Furthermore, it has become increasingly clear that other inhibitors of DNA damage pathways show synthetic lethality with WEE1i, and WEE1 modulates therapeutic immune responses, providing a rationale for the combination of WEE1i and immune checkpoint blockade. In this review, we summarize the biological function of WEE1 kinase, development of WEE1i, and outline the preclinical and clinical data available on the investigation of WEE1i for treating gynecologic malignancies.

Development of an indirect competitive ELISA based on the common epitope of fish parvalbumin for its detection.

A common epitope (AGSFDHKKFFKACGLSGKST) of parvalbumin from 16 fish species was excavated using bioinformatics tools combined with the characterization of fish parvalbumin binding profile of anti-single epitope antibody in this study. A competitive enzyme-linked immunosorbent assay (ELISA) based on the common epitope was established with a limit of detection of 10.15 ng/mL and a limit of quantification of 49.29 ng/mL. The developed ELISA exhibited a narrow range (71% to 107%) of related cross-reactivity of 15 fish parvalbumin. Besides, the recovery, the coefficient of variations for the intra-assay and the inter-assay were 84.3% to 108.2%, 7.4% to 13.9% and 8.5% to 15.6%. Our findings provide a novel idea for the development of a broad detection method for fish allergens and a practical tool for the detection of parvalbumin of economic fish species in food samples.

A novel framework for high resolution air quality index prediction with interpretable artificial intelligence and uncertainties estimation.

Accurate air quality index (AQI) prediction is essential in environmental monitoring and management. Given that previous studies neglect the importance of uncertainty estimation and the necessity of constraining the output during prediction, we proposed a new hybrid model, namely TMSSICX, to forecast the AQI of multiple cities. Firstly, time-varying filtered based empirical mode decomposition (TVFEMD) was adopted to decompose the AQI sequence into multiple internal mode functions (IMF) components. Secondly, multi-scale fuzzy entropy (MFE) was applied to evaluate the complexity of each IMF component and clustered them into high and low-frequency portions. In addition, the high-frequency portion was secondarily decomposed by successive variational mode decomposition (SVMD) to reduce volatility. Then, six air pollutant concentrations, namely CO, SO2, PM2.5, PM10, O3, and NO2, were used as inputs. The secondary decomposition and preliminary portion were employed as the outputs for the bidirectional long short-term memory network optimized by the snake optimization algorithm (SOABiLSTM) and improved Catboost (ICatboost), respectively. Furthermore, extreme gradient boosting (XGBoost) was applied to ensemble each predicted sub-model to acquire the consequence. Ultimately, we introduced adaptive kernel density estimation (AKDE) for interval estimation. The empirical outcome indicated the TMSSICX model achieved the best performance among the other 23 models across all datasets. Moreover, implementing the XGBoost to ensemble each predicted sub-model led to an 8.73%, 8.94%, and 0.19% reduction in RMSE, compared to SVM. Additionally, by utilizing SHapley Additive exPlanations (SHAP) to assess the impact of the six pollutant concentrations on AQI, the results reveal that PM2.5 and PM10 had the most notable positive effects on the long-term trend of AQI. We hope this model can provide guidance for air quality management.

I3Net: Inter-Intra-slice Interpolation Network for Medical Slice Synthesis.

Medical imaging is limited by acquisition time and scanning equipment. CT and MR volumes, reconstructed with thicker slices, are anisotropic with high in-plane resolution and low through-plane resolution. We reveal an intriguing phenomenon that due to the mentioned nature of data, performing slice-wise interpolation from the axial view can yield greater benefits than performing super-resolution from other views. Based on this observation, we propose an Inter-Intra-slice Interpolation Network (I3Net), which fully explores information from high in-plane resolution and compensates for low through-plane resolution. The through-plane branch supplements the limited information contained in low through-plane resolution from high in-plane resolution and enables continual and diverse feature learning. In-plane branch transforms features to the frequency domain and enforces an equal learning opportunity for all frequency bands in a global context learning paradigm. We further propose a cross-view block to take advantage of the information from all three views online. Extensive experiments on two public datasets demonstrate the effectiveness of I3Net, and noticeably outperforms state-of-the-art super-resolution, video frame interpolation and slice interpolation methods by a large margin. We achieve 43.90dB in PSNR, with at least 1.14dB improvement under the upscale factor of ×2 on MSD dataset with faster inference.

Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound.

Objective.Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line.Approach.The novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to qualitatively evaluate the lesion degree of pleural line in LUS images.Main results.We prospectively evaluated 3770 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the proposed pleural line extraction and evaluation methods all have good performance, with dice and accuracy of 0.87 and 94.47%, respectively, and the comparison with previous methods found statistical significance (P< 0.001 for all). Meanwhile, the generalization verification proved the feasibility of the proposed method in multiple data scenarios.Significance.The proposed method has great application potential for assessment of pleural line in LUS images and aiding lung disease diagnosis and treatment.

Unsupervised domain adaptation for histopathology image segmentation with incomplete labels.

Stain variations pose a major challenge to deep learning segmentation algorithms in histopathology images. Current unsupervised domain adaptation methods show promise in improving model generalization across diverse staining appearances but demand abundant accurately labeled source domain data. This paper assumes a novel scenario, namely, unsupervised domain adaptation based segmentation task with incompletely labeled source data. This paper propose a Stain-Adaptive Segmentation Network with Incomplete Labels (SASN-IL). Specifically, the algorithm consists of two stages. The first stage is an incomplete label correction stage, involving reliable model selection and label correction to rectify false-negative regions in incomplete labels. The second stage is the unsupervised domain adaptation stage, achieving segmentation on the target domain. In this stage, we introduce an adaptive stain transformation module, which adjusts the degree of transformation based on segmentation performance. We evaluate our method on a gastric cancer dataset, demonstrating significant improvements, with a 10.01% increase in Dice coefficient compared to the baseline and competitive performance relative to existing methods.

Characteristics of germline DNA damage response gene mutations in ovarian cancer in Southwest China.

DNA damage response (DDR) pathways are responsible for repairing endogenous or exogenous DNA damage to maintain the stability of the cellular genome, including homologous recombination repair (HRR) pathway, mismatch repair (MMR) pathway, etc. In ovarian cancer, current studies are focused on HRR genes, especially BRCA1/2, and the results show regional and population differences. To characterize germline mutations in DDR genes in ovarian cancer in Southwest China, 432 unselected ovarian cancer patients underwent multi-gene panel testing from October 2016 to October 2020. Overall, deleterious germline mutations in DDR genes were detected in 346 patients (80.1%), and in BRCA1/2 were detected in 126 patients (29.2%). The prevalence of deleterious germline mutations in BRCA2 is higher than in other studies (patients are mainly from Eastern China), and so is the mismatch repair genes. We identified three novel BRCA1/2 mutations, two of which probably deleterious (BRCA1 p.K1622* and BRCA2 p.L2987P). Furthermore, we pointed out that deleterious mutations of FNACD2 and RECQL4 are potential ovarian cancer susceptibility genes and may predispose carriers to ovarian cancer. In conclusion, our study highlights the necessity of comprehensive germline mutation detection of DNA damage response genes in ovarian cancer patients, which is conducive to patient management and genetic counseling.

PARP inhibitor maintenance treatment for newly diagnosed ovarian cancer patients: a real-world study from China.

Purpose: This study evaluated the efficacy and safety in a real-world population of epithelial ovarian cancer (EOC) treated with poly (ADP-ribose) polymerase inhibitor (PARPi) as first-line maintenance therapy in the largest gynecologic oncology center in Western China. Methods: This study included patients newly diagnosed EOC who received PARPi as first-line maintenance therapy in West China Second University Hospital from August 1, 2018 to September 31, 2022. The primary endpoints were progression-free survival (PFS) and safety evaluated by Common Terminology Criteria for Adverse Events Version 5.0(CTCAE 5.0). The secondary endpoints were overall survival (OS) and prognostic factors influencing the PFS of patients in real world. Results: Among the eligible 164 patients, 104 patients received olaparib and 60 patients received niraparib. 100 patients (61.0%) had mutations in breast cancer susceptibility gene (BRCA). 87 patients (53.0%) received primary debulking surgery (PDS) while 77 patients (47.0%) received interval debulking surgery (IDS). 94 patients (94/164, 57.3%) achieved R0 and 39 patients (23.8%) achieved R1 after PDS/IDS. 112 (68.3%) achieved complete response (CR) after first-line chemotherapy, while 49 (29.9%) achieved partial response (PR). The median follow-up time was 17.0 months (95% CI 15.6-18.4), and the median PFS has not been reached yet. Multivariate analysis demonstrated that BRCA mutations and CR/PR after platinum-based chemotherapy were independent factors associated with prolonged PFS. Hematologic toxicity was the most common grade≥3 AE. There were no incidence of myelodysplastic syndromes/acute myelogenous leukemia (MDS/AML). Conclusion: Focusing on PARPi as first-line maintenance therapy for patients with EOC, this study represented the largest single-center real-world study in China to date. Two independent factors were identified to prolong the PFS of patients: BRCA mutated type and CR/PR after primary treatment, which should be further confirmed with long-term follow-up and large sample sizes.

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning.

Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. We chose four standard views in pediatric cardiac ultrasound to identify atrial septal defects; the four standard views were as follows: subcostal sagittal view of the atrium septum (subSAS), apical four-chamber view (A4C), the low parasternal four-chamber view (LPS4C), and parasternal short-axis view of large artery (PSAX). We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). In our model, we present a block random selection, maximal agreement decision, and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. We validate our model using our private dataset by five cross-validation. For ASD detection, we achieve 89.33 ± 3.13 AUC, 84.95 ± 3.88 accuracy, 85.70 ± 4.91 sensitivity, 81.51 ± 8.15 specificity, and 81.99 ± 5.30 F1 score. The proposed model is a multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors.

A pancancer analysis of the clinical and genomic characteristics of multiple primary cancers.

Multiple primary cancer (MPC) denotes individuals with two or more malignant tumors occurring simultaneously or successively. Herein, a total of 11,000 pancancer patients in TCGA database (1993-2013) were divided into MPC or non-MPC groups based on their history of other malignant tumors. The incidence of MPC has risen to 8.5-13.1% since 2000. Elderly individuals, males, early-stage cancer patients, and African Americans and Caucasians are identified as independent risk factors (p < 0.0001). Non-MPC patients exhibit significantly longer overall survival (OS) and disease-free survival (DFS) (p = 0.0038 and p = 0.0014). Age (p < 0.001) and tumor staging at initial diagnosis (p < 0.001) contribute to this difference. In our center, MPC was identified in 380 out of 801 tumor events based on SEER criteria. The peak occurrence of secondary primary was about 1-5 years after the first primary tumor, with a second small peak around 10-15 years. Multiple tumors commonly occur in the same organ (e.g., breast and lung), constituting 12.6%. Certain cancer types, notably skin cutaneous melanoma (SKCM), exhibit significantly higher tumor mutational burden (TMB) in the MPC group (17.31 vs. 6.55 mutations/MB, p < 0.001), with high TMB associated with improved survival (p < 0.001). High TMB in MPC may serve as a predictor for potential immunotherapy application.

CrossU-Net: Dual-modality cross-attention U-Net for segmentation of precancerous lesions in gastric cancer.

Gastric precancerous lesions (GPL) significantly elevate the risk of gastric cancer, and precise diagnosis and timely intervention are critical for patient survival. Due to the elusive pathological features of precancerous lesions, the early detection rate is less than 10%, which hinders lesion localization and diagnosis. In this paper, we provide a GPL pathological dataset and propose a novel method for improving the segmentation accuracy on a limited-scale dataset, namely RGB and Hyperspectral dual-modal pathological image Cross-attention U-Net (CrossU-Net). Specifically, we present a self-supervised pre-training model for hyperspectral images to serve downstream segmentation tasks. Secondly, we design a dual-stream U-Net-based network to extract features from different modal images. To promote information exchange between spatial information in RGB images and spectral information in hyperspectral images, we customize the cross-attention mechanism between the two networks. Furthermore, we use an intermediate agent in this mechanism to improve computational efficiency. Finally, we add a distillation loss to align predicted results for both branches, improving network generalization. Experimental results show that our CrossU-Net achieves accuracy and Dice of 96.53% and 91.62%, respectively, for GPL lesion segmentation, providing a promising spectral research approach for the localization and subsequent quantitative analysis of pathological features in early diagnosis.

Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype.

Thyroid cancer, a common endocrine malignancy, is one of the leading death causes among endocrine tumors. The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures. Therefore, we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma (PTC) diagnosis and characterize PTC characteristics. To alleviate any concerns pathologists may have about using the model, we conducted an analysis of the used bands that can be interpreted pathologically. A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients. The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue. Clinical data of the corresponding patients were collected for subsequent model interpretability analysis. The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University. The spectral preprocessing method was used to process the spectra, and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models. The PTC detection model using mean centering (MC) and multiple scattering correction (MSC) has optimal performance, and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide. For model interpretable analysis, the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak, and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients. Moreover, the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients. In addition, the correlation analysis was performed between the selected wavelengths and the clinical data, and the results show: the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure, nucleus size and free thyroxine (FT4), and has a strong correlation with triiodothyronine (T3); the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine (FT3).

Clinical Microfluidic Chip Platform for the Isolation of Versatile Circulating Tumor Cells.

Circulating tumor cells (CTCs) are significant in cancer prognosis, diagnosis, and anti-cancer therapy. CTC enumeration is vital in determining patient disease since CTCs are rare and heterogeneous. CTCs are detached from the primary tumor, enter the blood circulation system, and potentially grow at distant sites, thus metastasizing the tumor. Since CTCs carry similar information to the primary tumor, CTC isolation and subsequent characterization can be critical in monitoring and diagnosing cancer. The enumeration, affinity modification, and clinical immunofluorescence staining of rare CTCs are powerful methods for CTC isolation because they provide the necessary elements with high sensitivity. Microfluidic chips offer a liquid biopsy method that is free of any pain for the patients. In this work, we present a list of protocols for clinical microfluidic chips, a versatile CTC isolating platform, that incorporate a set of functionalities and services required for CTC separation, analysis, and early diagnosis, thus facilitating biomolecular analysis and cancer treatment. The program includes rare tumor cell counting, clinical patient blood preprocessing, which includes red blood cell lysis, and the isolation and recognition of CTCs in situ on microfluidic chips. The program allows the precise enumeration of tumor cells or CTCs. Additionally, the program includes a tool that incorporates CTC isolation with versatile microfluidic chips and immunofluorescence identification in situ on the chips, followed by biomolecular analysis.

Chemotherapy effect on myocardial fibrosis markers in patients with gynecologic cancer and low cardiovascular risk.

Background: Patients with gynecologic cancers experience side effects of chemotherapy cardiotoxicity. We aimed to quantify cardiac magnetic resonance (CMR) markers of myocardial fibrosis in patients with gynecologic cancer and low cardiovascular risk who undergo chemotherapy. Methods: This study is part of a registered clinical research. CMR T1 mapping was performed in patients with gynecologic cancer and low cardiovascular risk undergoing chemotherapy. The results were compared with those of age-matched healthy control subjects. Results: 68 patients (median age = 50 years) and 30 control subjects were included. The median number of chemotherapy cycles of patients was 9.0 (interquartile range [IQR] 3.3-17.0). Extracellular volume fraction (ECV) (27.2% ± 2.7% vs. 24.5% ± 1.7%, P < 0.001) and global longitudinal strain (-16.2% ± 2.8% vs. -17.4% ± 2.0%, P = 0.040) were higher in patients compared with controls. Patients with higher chemotherapy cycles (>6 cycles) (n=41) had significantly lower intracellular mass indexed (ICMi) compared with both patients with lower chemotherapy cycles (≤6 cycles) (n=27) (median 27.44 g/m2 [IQR 24.03-31.15 g/m2] vs. median 34.30 g/m2 [IQR 29.93-39.79 g/m2]; P = 0.002) and the control group (median 27.44 g/m2 [IQR 24.03-31.15 g/m2] vs. median 32.79 g/m2 [IQR 27.74-35.76 g/m2]; P = 0.002). Patients with two or more chemotherapy regimens had significantly lower ICMi compared with both patients with one chemotherapy regimen (27.45 ± 5.16 g/m2 vs. 33.32 ± 6.42 g/m2; P < 0.001) and the control group (27.45 ± 5.16 g/m2 vs. 33.02 ± 5.52 g/m2; P < 0.001). The number of chemotherapy cycles was associated with an increase in the ECV (Standard regression coefficient [ß] = 0.383, P = 0.014) and a decrease in the ICMi (ß = -0.349, P = 0.009). Conclusion: Patients with gynecologic cancer and low cardiovascular risk who undergo chemotherapy have diffuse extracellular volume expansion, which is obvious with the increase of chemotherapy cycles. Myocyte loss may be part of the mechanism in patients with a higher chemotherapy load. Clinical trial registration: http://www.chictr.org.cn, identifier ChiCTR-DDD-17013450.

A framework for immunofluorescence image augmentation and classification based on unsupervised attention mechanism.

Autoimmune encephalitis (AE) is a common neurological disorder. As a standard method for neuroautoantibody detection, pathologists use tissue matrix assays (TBA) for initial disease screening. In this study, microscopic fluorescence imaging was combined with deep learning to improve AE diagnostic accuracy. Due to the inter-class imbalance of medical data, we propose an innovative generative adversarial network supplemented with attention mechanisms to highlight key regions in images to synthesize high-quality fluorescence images. However, securing annotated medical data is both time-consuming and costly. To circumvent this problem, we employ a self-supervised learning approach that utilizes unlabeled fluorescence data to support downstream classification tasks. To better understand the fluorescence properties in the data, we introduce a multichannel input convolutional neural network that adds additional channels of fluorescence intensity. This study builds an AE immunofluorescence dataset and obtains the classification accuracy of 88.5% using our method, thus confirming the effectiveness of the proposed method.

Recurrent choriocarcinoma complicated with leprosy during chemotherapy: A case report and literature review.

RATIONALE: The global prevalence of leprosy has decreased substantially, and cases of leprosy infection are extremely rare in China. In this report, we present a case of recurrent choriocarcinoma complicated by leprosy infection during chemotherapy. PATIENT CONCERNS: A 24-year-old Chinese woman (gravida 3, para 2) presented to a local hospital with vaginal bleeding. Her medical history included a previous diagnosis of hydatidiform mole. DIAGNOSES, INTERVENTIONS AND OUTCOMES: The patient was diagnosed with choriocarcinoma and received chemotherapy in 6 cycles. Shortly after the initial treatment was completed, the disease recurred twice with resistance to multiple chemotherapeutic agents. In her second recurrence of choriocarcinoma, she was diagnosed with leprosy with many cutaneous nodules throughout her entire body. The patient was administered chemical treatment for leprosy with the multidrug therapy regimen after being diagnosed. To prevent exacerbating the infection, no immunotherapy was utilized to treat cancer, and the infection was well-controlled at the conclusion of anticancer therapy. LESSONS: Because of immunological reduction, cancer patients are susceptible to a variety of infections. For patients with cancer, prevention and early detection of rare infectious diseases should receive special attention. Immunotherapy must be used with caution when treating patients with cancer and infections.

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.

BACKGROUND AND AIMS: Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens. METHODS: HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model. RESULTS: A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei. CONCLUSIONS: An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.

Constitutive Model for Grouted Rock Mass by Macro-Meso Damage.

Rock fractures have a significant impact on the stability of geotechnical engineering, and grouting is currently the most commonly used reinforcement method to address this issue. To ensure the stability of grouted rock mass, it is necessary to study its deformation law and mechanical properties. In this study, theoretical analyses and laboratory experiments were conducted, and the fracture width, Weibull model and effective bearing area were introduced to improve the applicability and accuracy of the original damage constitutive model. Moreover, the constitutive model of grouted rock mass was derived by combining it with the mixing law of composite materials. The main conclusions are summarized as follows: (1) Based on macroscopic damage tensor theory, the fracture width parameter was introduced, which effectively described the variation law of macroscopic damage with fracture width to improve the accuracy of the original damage constitutive model. (2) The effective bearing area was used to optimize the original Weibull model to match the stress-strain curve of the rock mass with fractures. (3) The grouting-reinforced rock mass was considered to be a composite material, the original equivalent elastic modulus model was improved by combining macroscopic damage with the Reuss model, and the constitutive damage model of the grouted rock mass was deduced.

Comparative Study of the Myocardium of Patients from Four COVID-19 Waves.

BACKGROUND: Few studies have compared COVID-19 patients from different waves. This study aims to conduct a clinical and morphological analysis of patients who died from COVID-19 during four waves. METHODS: The study involved 276 patients who died from COVID-19 during four waves, including 77 patients in the first wave, 119 patients in the second wave, and 78 patients in the third wave. We performed a histological examination of myocardium samples from autopsies and additionally analyzed the samples by PCR. We conducted immunohistochemistry of the myocardium for 21 samples using antibodies against CD3, CD45, CD8, CD68, CD34, Ang1, VWF, VEGF, HLA-DR, MHC1, C1q, enteroviral VP1, and SARS-CoV-2 spike protein. We also did immunofluorescent staining of three myocardial specimens using VP1/SARS-CoV-2 antibody cocktails. Further, we ran RT-ddPCR analysis for 14 RNA samples extracted from paraffin-embedded myocardium. Electron microscopic studies of the myocardium were also performed for two samples from the fourth wave. RESULTS: Among the 276 cases, active myocarditis was diagnosed in 5% (15/276). Of these cases, 86% of samples expressed VP1, and individual cells contained SARS-CoV-2 spike protein in 22%. Immunofluorescence confirmed the co-localization of VP1 and SARS-CoV-2 spike proteins. ddPCR did not confidently detect SARS-CoV-2 RNA in the myocardium in any myocarditis cases. However, the myocardium sample from wave IV detected a sub-threshold signal of SARS-CoV-2 by qPCR, but myocarditis in this patient was not confirmed. Electron microscopy showed several single particles similar to SARS-CoV-2 virions on the surface of the endothelium of myocardial vessels. A comparison of the cardiovascular complication incidence between three waves revealed that the incidence of hemorrhage (48 vs. 24 vs. 17%), myocardial necrosis (18 vs. 11 vs. 4%), blood clots in the intramural arteries (12 vs. 7 vs. 0%), and myocarditis (19 vs. 1 vs. 6%) decreased over time, and CD8-T-killers appeared. Immunohistochemistry confirmed the presence of endotheliitis in all 21 studied cases. CONCLUSIONS: This study compared myocardial damage in patients who died during three COVID-19 waves and showed a decrease in the incidence of endotheliitis complications (thrombosis, hemorrhage, necrosis) and myocarditis over time. However, the connection between myocarditis and SARS-CoV-2 infection remains unproven.

Automatic detection of A-line in lung ultrasound images using deep learning and image processing.

BACKGROUND: Auxiliary diagnosis and monitoring of lung diseases based on lung ultrasound (LUS) images is important clinical research. A-line is one of the most common indicators of LUS that can offer support for the assessment of lung diseases. A traditional A-line detection method mainly relies on experienced clinicians, which is inefficient and cannot meet the needs of these areas with backward medical level. Therefore, how to realize the automatic detection of A-line in LUS image is important. PURPOSE: In order to solve the disadvantages of traditional A-line detection methods, realize automatic and accurate detection, and provide theoretical support for clinical application, we proposed a novel A-line detection method for LUS images with different probe types in this paper. METHODS: First, the improved Faster R-CNN model with a selection strategy of localization box was designed to accurately locate the pleural line. Then, the LUS image below the pleural line was segmented for independent analysis excluding the influence of other similar structures. Next, image-processing methods based on total variation, matched filter, and gray difference were applied to achieve the automatic A-line detection. Finally, the "depth" index was designed to verify the accuracy by judging whether the automatic measurement results belong to corresponding manual results (±5%). In experiments, 3000 convex array LUS images were used for training and validating the improved pleural line localization model by five-fold cross validation. 850 convex array LUS images and 1080 linear array LUS images were used for testing the trained pleural line localization model and the proposed image-processing-based A-line detection method. The accuracy analysis, error statistics, and Harsdorff distance were employed to evaluate the experimental results. RESULTS: After 100 epochs, the mean loss value of training and validation set of improved Faster R-CNN model reached 0.6540 and 0.7882, with the validation accuracy of 98.70%. The trained pleural line localization model was applied in the testing set of convex and linear probes and reached the accuracy of 97.88% and 97.11%, respectively, which were 3.83% and 8.70% higher than the original Faster R-CNN model. The accuracy, sensitivity, and specificity of A-line detection reached 95.41%, 0.9244%, 0.9875%, and 94.63%, 0.9230%, and 0.9766% for convex and linear probes, respectively. Compared to the experienced clinicians' results, the mean value and p value of depth error were 1.5342 ± 1.2097 and 0.9021, respectively, and the Harsdorff distance was 5.7305 ± 1.8311. In addition, the accumulated accuracy of the two-stage experiment (pleural line localization and A-line detection) was calculated as the final accuracy of the whole A-line detection system. They were 93.39% and 91.90% for convex and linear probes, respectively, which were higher than these previous methods. CONCLUSIONS: The proposed method combining image processing and deep learning can automatically and accurately detect A-line in LUS images with different probe types, which has important application value for clinical diagnosis.