Search

IntroductionPartial breast irradiation (PBI) has increased in utilization, with the postoperative lumpectomy cavity and clips used to guide target volumes. The ideal timing to perform computed tomography (CT)–based treatment planning for this technique is unclear. Prior studies have examined change in volume over time from surgery but not the effect of patient characteristics on lumpectomy cavity volume. We sought to investigate patient and clinical factors that may contribute to larger postsurgical lumpectomy cavities and therefore predict for larger PBI volumes.MethodsA total of 351 consecutive women with invasive or in situ breast cancer underwent planning CT after breast-conserving surgery at a single institution during 2019 and 2020. Lumpectomy cavities were contoured, and volume was retrospectively computed using the treatment planning system. Univariate and multivariate analyses were performed to evaluate the associations between lumpectomy cavity volume and patient and clinical factors.ResultsMedian age was 61.0 years (range, 30–91), 23.9% of patients were Black people, 52.1% had hypertension, the median body mass index (BMI) was 30.4 kg/m², 11.4% received neoadjuvant chemotherapy, 32.5% were treated prone, mean interval from surgery to CT simulation was 54.1 days ± 45.9, and mean lumpectomy cavity volume was 42.2 cm3 ± 52.0. Longer interval from surgery was significantly associated with smaller lumpectomy cavity volume on univariate analysis, p = 0.048. Race, hypertension, BMI, the receipt of neoadjuvant chemotherapy, and prone position remained significant on multivariate analysis (p < 0.05 for all). Prone position vs. supine, higher BMI, the receipt of neoadjuvant chemotherapy, the presence of hypertension, and race (Black people vs. White people) were associated with larger mean lumpectomy cavity volume.DiscussionThese data may be used to select patients for which longer time to simulation may result in smaller lumpectomy cavity volumes and therefore smaller PBI target volumes. Racial disparity in cavity size is not explained by known confounders and may reflect unmeasured systemic determinants of health. Larger datasets and prospective evaluation would be ideal to confirm these hypotheses.

Background: In recent years, tumor immunotherapy has become a viable treatment option for triple negative breast cancer (TNBC). Among these, immune checkpoint inhibitors (ICIs) have demonstrated good efficacy in advanced TNBC patients with programmed death-ligand 1 (PD-L1) positive expression. However, only 63% of PD-L1-positive individuals showed any benefit from ICIs. Therefore, finding new predictive biomarkers will aid in identifying patients who are likely to benefit from ICIs. In this study, we used liquid biopsies and next-generation sequencing (NGS) to dynamically detect changes in circulating tumor DNA (ctDNA) in the blood of patients with advanced TNBC treated with ICIs and focused on its potential predictive value.Methods: From May 2018 to October 2020, patients with advanced TNBC treated with ICIs at Shandong Cancer Hospital were included prospectively. Patient blood samples were obtained at the pretreatment baseline, first response evaluation, and disease progression timepoints. Furthermore, 457 cancer-related genes were evaluated by NGS, and patients’ ctDNA mutations, gene mutation rates, and other indicators were determined and coupled with clinical data for statistical analysis.Results: A total of 11 TNBC patients were included in this study. The overall objective response rate (ORR) was 27.3%, with a 6.1-month median progression-free survival (PFS) (95% confidence interval: 3.877–8.323 months). Of the 11 baseline blood samples, 48 mutations were found, with the most common mutation types being frame shift indels, synonymous single-nucleotide variations (SNVs), frame indel missenses, splicing, and stop gains. Additionally, univariate Cox regression analysis revealed that advanced TNBC patients with one of 12 mutant genes (CYP2D6 deletion and GNAS, BCL2L1, H3F3C, LAG3, FGF23, CCND2, SESN1, SNHG16, MYC, HLA-E, and MCL1 gain) had a shorter PFS with ICI treatment (p < 0.05). To some extent, dynamic changes of ctDNA might indicate the efficacy of ICIs.Conclusion: Our data indicate that ICI efficacy in patients with advanced TNBC may be predicted by 12 mutant ctDNA genes. Additionally, dynamic alterations in peripheral blood ctDNA might be used to track the effectiveness of ICI therapy in those with advanced TNBC.

SUMMARY: Objectives: The aim of this study was to characterise a novel mutation in the gene encoding RhAG in order to elucidate a molecular mechanism for Rh antigen expression and spherocytosis. Background: Rhesus‐associated glycoprotein (RhAG) is critical for maintaining the structure and stability of erythrocytes. Single missense mutations in the gene encoding RhAG are sufficient to induce spherocytosis and deficiencies in Rh complex formation. We report a novel missense mutation that incompletely disrupts Rh antigen expression and selectively knocks out RhD antigen expression. Methods: Blood samples were taken from a 38‐year‐old male, his brother, his wife and his daughter in Xi'an, China. To detect the proband's RhAG and D antigen expression, the RBC were stained with anti‐D and anti‐RhAG and analysed by flow cytometry. Red blood cell morphology was detected with atomic force microscopy (AFM). Genomic DNA was isolated from whole blood samples, and the RHD, RHCE and RHAG alleles were sequenced and analysed. The mutation was mapped onto a predicted crystal structure of RhAG by the I‐TASSER server and visualised using PyMOL. Results: Morphological testing by AFM found clear evidence of spherocytosis in the proband's erythrocytes. RHAG gene sequencing identified the mutation at sequence 236G > A, resulting in a serine to asparagine substitution at residue 79 (S79N). Family survey indicated that inheriting this allele is necessary and sufficient to cause the condition. Mapping the mutation onto a predicted crystal structure of RhAG revealed the proximity of the mutation to the critical structural elements of the protein. Conclusions: A novel RHAG mutation significantly lowers RhAG antigen expression and antigen‐mediated agglutination intensity. [ABSTRACT FROM AUTHOR]

In the bioremediation process, coexistence of lead (Pb) and cadmium causes complex toxicity, resulting in the difficulty of bioremediation. This study investigated the physiological responses and bioaccumulation mechanisms of the typical filamentous fungus Aspergillus niger under the coexistence of Pb and Cd. Four treatments were set up, i.e., control, sole Pb, sole Cd, and coexistence of Pb and Cd. The morphology of A. niger were observed by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), respectively. Then, nano-scale secondary ion mass spectrometry (NanoSIMS) was applied to accurately investigate the distribution of heavy metals in the fungal cells under the coexistence of Pb and Cd. Finally, the metallogenic process and mineral types were simulated by Geochemist’s Workbench (GWB). The electron microscopic and NanoSIMS imaging showed that Pb and Cd were accumulated in both the extracellular and intracellular regions of the A. niger cells. In particular, the accumulated Pb content was ten times higher than that of Cd. However, Cd showed stronger toxicity than Pb to A. niger. Compared with the control treatment, Cd stress resulted in a two-fold increase of cell diameter and more extracellular substances, whereas the cell diameter increased nearly four times in the coexistence treatment. Moreover, the bioaccumulation of Pb was more intense than that of Cd during competitive sorption. The GWB simulation confirmed that Pb2+ can form multiple minerals (e.g., PbC2O4, PbHPO4, and Pb3(PO4)2, etc.), which significantly weakened its toxicity on the cell surface. This study elucidated the morphological characteristics of A. niger and competitive bioaccumulation under the coexistence of Pb and Cd, which would facilitate the application of microorganisms to the bioremediation of coexisted metals.

Traditional methods for detecting maize leaf diseases (such as leaf blight, sooty blotch, brown spot, rust, and purple leaf sheaf) are typically labor-intensive and strongly subjective. With the aim of achieving high accuracy and efficiency in the identification of maize leaf diseases from digital imagery, this article proposes a novel multichannel convolutional neural network (MCNN). The MCNN is composed of an input layer, five convolutional layers, three subsampling layers, three fully connected layers, and an output layer. Using a method that imitates human visual behavior in video saliency detection, the first and second subsampling layers are connected directly with the first fully connected layer. In addition, the mixed modes of pooling and normalization methods, rectified linear units (ReLU), and dropout are introduced to prevent overfitting and gradient diffusion. The learning process corresponding to the network structure is also illustrated. At present, there are no large-scale images of maize leaf disease for use as experimental samples. To test the proposed MCNN, 10,820 RGB images containing five types of disease were collected from maize planting areas in Shandong Province, China. The original images could not be used directly in identification experiments because of noise and irrelevant regions. They were therefore denoised and segmented by homomorphic filtering and region of interest (ROI) segmentation to construct a standard database. A series of experiments on 8 GB graphics processing units (GPUs) showed that the MCNN could achieve an average accuracy of 92.31% and a high efficiency in the identification of maize leaf diseases. The multichannel design and the integration of different innovations proved to be helpful methods for boosting performance. [ABSTRACT FROM AUTHOR]

Abstract We examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior–anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.

This paper introduces the technical transformation and system realization of the hoister gap monitoring and protection device in Hongyang No.2 Coal Mine. Through the high-precision laser displacement sensor, we detected displacement signals, such as brake clearance, brake wear value, spring fatigue and brake disc deflection, to convert into 4-20 mA current signal to transmit to the PLC analog input port. PLC can read, analyze and convert the data in real time, and convert it into numerical value which is easy to display. Sound and light alarm signal is sent when the set value is exceeded. The practical application proves that the device has the technical characteristics of high sensitivity, strong anti-interference ability and reliable operation.

Background Breast cancer is a common cause of brain metastasis. Although breast cancer has relatively high survival rates, its survival rate after metastasis to the brain is lower. Conventional two-dimensional cell culture models and animal models are widely used in metastatic cancer research, and these models have tremendously contributed to the understanding of this disease. However, these models have some limitations, such as different physiological features and genetic backgrounds. Methods We established a simple metastatic breast cancer model using human pluripotent stem cell-derived cerebral organoids (COs)—in this case, breast cancer cerebral organoids (BC-COs). Results Using the BC-CO model, we induced the metastasis of MDA-MB-231 cells into COs by co-culture of cells with COs and compared the differences between adapted cancer cells in BC-COs and non-adapted cells. Our results showed that the proliferative capacity increased in adapted cells. Additionally, the expression levels of endothelial-mesenchymal transition markers and cancer stem cells were significantly higher in adapted cancer cells. Conclusion We conclude that metastasis promotes the metastatic capacity of breast cancer cells. Our results also showed that the BC-CO model could be a novel tool for research on brain metastasis in breast cancer.

Abstract Super-enhancers or stretch enhancers (SEs) consist of large clusters of active transcription enhancers which promote the expression of critical genes that define cell identity during development and disease. However, the role of many super-enhancers in tumor cells remains unclear. This study aims to explore the function and mechanism of a new super-enhancer in various tumor cells. A new super-enhancer that exists in a variety of tumors named EphA2-Super-enhancer (EphA2-SE) was found using multiple databases and further identified. CRISPR/Cas9-mediated deletion of EphA2-SE results in the significant downregulation of its target gene EphA2. Mechanistically, we revealed that the core active region of EphA2-SE comprises E1 component enhancer, which recruits TCF7L2 and FOSL2 transcription factors to drive the expression of EphA2, induce cell proliferation and metastasis. Bioinformatics analysis of RNA-seq data and functional experiments in vitro illustrated that EphA2-SE deletion inhibited cell growth and metastasis by blocking PI3K/AKT and Wnt/β-catenin pathway in HeLa, HCT-116 and MCF-7 cells. Overexpression of EphA2 in EphA2-SE−/− clones rescued the effect of EphA2-SE deletion on proliferation and metastasis. Subsequent xenograft animal model revealed that EphA2-SE deletion suppressed tumor proliferation and survival in vivo. Taken together, these findings demonstrate that EphA2-SE plays an oncogenic role and promotes tumor progression in various tumors by recruiting FOSL2 and TCF7L2 to drive the expression of oncogene EphA2.

Acute trauma is a common cause of mortality in individuals aged < 40 years. As organ preservation has become important in treating trauma patients, the treatment is shifting from surgical management to non-operative management. A multidisciplinary team approach, including interventional radiology (IR), is essential for the optimal management of trauma patients, as IR plays an important role in injury evaluation and management. IR also contributes significantly to achieving the best clinical outcomes in critically ill trauma patients. This pictorial essay aims to present and summarize various interventional treatments in trauma patients requiring critical care.

Abstract To study the usefulness of virtual reality (VR)-based training for diagnosing strabismus. Fourteen residents in ophthalmology performed at least 30 VR training sessions to diagnose esotropia and exotropia. Examinations of real patients with esotropia or exotropia before and after the VR training were video-recorded and presented to a strabismus expert to assess accuracy and performance scores for measuring the deviation angle and diagnosing strabismus with anonymization. A feedback survey regarding the usefulness and ease of use of the VR application was conducted for participants. The mean age of the 14 ophthalmology residents (10 men and 4 women), was 29.7 years. Before VR training, participants showed a mean accuracy score of 14.50 ± 5.45 and a performance score of 9.64 ± 4.67 for measuring the deviation angle and diagnosing strabismus in real patients with strabismus. After VR training, they showed a significantly improved accuracy score of 22.14 ± 4.37 (p = 0.012) and a performance score of 15.50 ± 1.99 (p = 0.011). According to the survey, most participants agreed on the usefulness of VR applications. This study suggests that VR-based training improved ophthalmology residents’ clinical diagnostic skills for strabismus in a short period.

Functional connectivity analysis of intracranial electroencephalography (iEEG) plays an important role in understanding the mechanism of epilepsy and seizure dynamics. However, existing connectivity analysis is only suitable for low-frequency bands below 80 Hz. High-frequency oscillations (HFOs) and high-frequency activity (HFA) in the high-frequency band (80–500 Hz) are thought to be specific biomarkers in epileptic tissue localization. However, the transience in duration and variability of occurrence time and amplitudes of these events pose a challenge for conducting effective connectivity analysis. To deal with this problem, we proposed skewness-based functional connectivity (SFC) in the high-frequency band and explored its utility in epileptic tissue localization and surgical outcome evaluation. SFC comprises three main steps. The first step is the quantitative measurement of amplitude distribution asymmetry between HFOs/HFA and baseline activity. The second step is functional network construction on the basis of rank correlation of asymmetry across time. The third step is connectivity strength extraction from the functional network. Experiments were conducted in two separate datasets which consist of iEEG recordings from 59 patients with drug-resistant epilepsy. Significant difference (p<0.001) in connectivity strength was found between epileptic and non-epileptic tissue. Results were quantified via the receiver operating characteristic curve and the area under the curve (AUC). Compared with low-frequency bands, SFC demonstrated superior performance. With respect to pooled and individual epileptic tissue localization for seizure-free patients, AUCs were 0.66 (95% confidence interval (CI): 0.63–0.69) and (0.63 95% CI 0.56–0.71), respectively. For surgical outcome classification, the AUC was 0.75 (95% CI 0.59–0.85). Therefore, SFC can act as a promising assessment tool in characterizing the epileptic network and potentially provide better treatment options for patients with drug-resistant epilepsy.

The high-complexity, high-reward, and high-risk characteristics of financial markets make them an important and interesting study area. Elliott’s wave theory describes the changing models of financial markets categorically in terms of wave models and is an advanced feature representation of financial time series. Meanwhile, deep learning is a breakthrough technique for nonlinear intelligent models, which aims to discover advanced feature representations of data and thus obtain the intrinsic laws underlying the data. This study proposes an innovative combination of these two concepts to create a deep learning + Elliott wave principle (DL-EWP) model. This model achieves the prediction of future market movements by extracting and classifying Elliott wave models from financial time series. The model’s effectiveness is empirically validated by running it on financial data from three major markets and comparing the results with those of the SAE, MLP, BP network, PCA-BP, and SVD-BP models. Interestingly, the DL-EWP model based on deep confidence networks outperforms other models in terms of stability, convergence speed, and accuracy and has a higher forecasting performance. Thus, the DL-EWP model can improve the accuracy of financial forecasting models that incorporate Elliott’s wave theory.

Abstract Background Caregivers of patients with Alzheimer’s disease or a related dementia (ADRD) report high levels of distress, including symptoms of anxiety and depression, caregiving burden, and existential suffering; however, those with support and healthy coping strategies have less stress and burden. Acceptance and Commitment Therapy (ACT) aims to foster greater acceptance of internal events while promoting actions aligned with personal values to increase psychological flexibility in the face of challenges. The objective of this single-arm pilot, Telephone Acceptance and Commitment Therapy Intervention for Caregivers (TACTICs), was to evaluate the feasibility, acceptability, and preliminary effects of an ACT intervention on ADRD caregiver anxiety, depressive symptoms, burden, caregiver suffering, and psychological flexibility. Methods ADRD caregivers ≥21 years of age with a Generalized Anxiety Disorder Scale (GAD-7) score ≥ 10 indicative of moderate or higher symptoms of anxiety were enrolled (N = 15). Participants received a telephone-based ACT intervention delivered by a non-licensed, bachelor’s-prepared trained interventionist over 6 weekly 1-h sessions that included engaging experiential exercises and metaphors designed to increase psychological flexibility. The following outcome measures were administered at baseline (T1), immediately post-intervention (T2), 3 months post-intervention (T3), and 6 months post-intervention (T4): anxiety symptoms (GAD-7; primary outcome); secondary outcomes of depressive symptoms (Patient Health Questionnaire–9), burden (Zarit Burden Interview), suffering (The Experience of Suffering measure), psychological flexibility/experiential avoidance (Acceptance and Action Questionnaire-II), and coping skills (Brief COPE). Results All 15 participants completed the study and 93.3% rated their overall satisfaction with their TACTICs experience as “completely satisfied.” At T2, caregivers showed large reduction in anxiety symptoms (SRM 1.42, 95% CI [0.87, 1.97], p

This multicenter retrospective study aimed to compare the effects of HES and gelatin (GEL) on the risk of post-OLT AKI.

Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses.Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated.Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival.Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.

The flexible bearing is one of the key components of wave generator（ WG） in Harmonic Drive（ HD）. Its load and life studies are key to the reliable operation of HD. The flexible bearing has a significant flexural deformation of the ferrule after assembly which is different from the cam profile. Assumed that the amount of deformation of the inner ring is unknown. A set of deformation coordination equations was established. The load distribution of flexible bearings was studied. Furthermore,the fatigue life was calculated. Deformation characteristics of the flexible bearing after assembly were analyzed by finite element method（ FEM）. The comparison of results shows that the assumption is consistent with FEM and the load distribution is consistent. The fatigue life is affected by the load. It provides a reference for load analysis and fatigue life assessment of flexible bearings.

Abstract Background It is generally believed that DNA methylation, as one of the most important epigenetic modifications, participates in the regulation of gene expression and plays an important role in the development of cancer, and there exits epigenetic heterogeneity among cancers. Therefore, this study tried to screen for reliable prognostic markers for different cancers, providing further explanation for the heterogeneity of cancers, and more targets for clinical transformation studies of cancer from epigenetic perspective. Methods This article discusses the epigenetic heterogeneity of cancer in detail. Firstly, DNA methylation data of seven cancer types were obtained from Illumina Infinium HumanMethylation 450 K platform of TCGA database. Then, differential methylation analysis was performed in the promotor region. Secondly, pivotal gene markers were obtained by constructing the DNA methylation correlation network and the gene interaction network in the KEGG pathway, and 317 marker genes obtained from two networks were integrated as candidate markers for the prognosis model. Finally, we used the univariate and multivariate COX regression models to select specific independent prognostic markers for each cancer, and studied the risk factor of these genes by doing survival analysis. Results First, the cancer type-specific gene markers were obtained by differential methylation analysis and they were found to be involved in different biological functions by enrichment analysis. Moreover, specific and common diagnostic markers for each type of cancer was sorted out and Kaplan-Meier survival analysis showed that there was significant difference in survival between the two risk groups. Conclusions This study screened out reliable prognostic markers for different cancers, providing a further explanation for the heterogeneity of cancer at the DNA methylation level and more targets for clinical conversion studies of cancer.

BackgroundThe DNA-binding protein RFX6 was overexpressed in hepatocellular carcinoma, and its expression level was correlated with the prognosis and immune cell infiltration in liver hepatocellular carcinoma. However, the mechanism of the abnormal expression and the biological effects of RFX6 in liver cancer remains unknown.MethodsTo understand the specific expression mechanism of RFX6 in liver cancer, we performed bioinformatic prediction, CHIP-qPCR assay, co-IP, and dual-luciferase assay to assess the regulating mechanism of RFX6. In the meantime, a series of biological experiments in vivo and in vitro were conducted to analyze the biological significance of RFX6 in hepatocellular carcinoma.ResultsWe demonstrated that knockdown of RFX6 in liver cancer cells significantly suppressed the proliferation, migration, and invasion of cancer cells. Moreover, inhibition of RFX6 could affect the immune response of T cells. Among a number of interacting proteins, we revealed that RFX6 directly binds to DTX2, a regulator of the Notch signaling pathway by targeting NOTCH1, and helps in its transcription stability. Furthermore, we discovered that miRNA-542-3p, the expression of which was decreased in hepatocellular carcinoma, was directly involved in the negative regulation of the expression of RFX6.ConclusionIn summary, we discovered that the miRNA-542-3p–RFX6–DTX2–NOTCH1 regulatory pathway played significant roles in the tumor progression of liver hepatocellular carcinoma.

In recent years, churn rates in industries such as finance have increased, and the cost of acquiring new users is more than five times the cost of retaining existing users. To improve the intelligent prediction accuracy of customer churn rate, artificial intelligence is gradually used. In this paper, the bidirectional long short-term memory convolutional neural network (BiLSTM-CNN) model is integrated with recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in parallel, which well solves the defective problem that RNNs and CNNs run separately, and it also solves the problem that the output results of a long short-term memory network (LSTM) layer in a densely-connected LSTM-CNN (DLCNN) model will ignore some local information when input to the convolutional layer. In order to explore whether the attention bidirectional long short-term memory convolutional neural network (AttnBLSTM-CNN) model can perform better than BiLSTM-CNN, this paper uses bank data to compare the two models. The experimental results show that the accuracy of the AttnBiLSTM-CNN model is improved by 0.2%, the churn rate is improved by 1.3%, the F1 value is improved by 0.0102, and the AUC value is improved by 0.0103 compared with the BLSTM model. Therefore, introducing the attention mechanism into the BiLSTM-CNN model can further improve the performance of the model. The improvement of the accuracy of the user churn prediction model can warn of the possibility of user churn in advance and take effective measures in advance to prevent user churn and improve the core competitiveness of financial institutions.