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The security and confidentiality of medical images are of utmost importance due to frequent issues such as leakage, theft, and tampering during transmission and storage, which seriously impact patient privacy. Traditional encryption techniques applied to entire images have proven to be ineffective in guaranteeing timely encryption and preserving the privacy of organ regions separated from the background. In response, this study proposes a specialized and efficient local image encryption algorithm for the medical field. The proposed encryption algorithm focuses on the regions of interest (ROI) within massive medical images. Initially, the Laplacian of Gaussian operator and the outer boundary tracking algorithm are employed to extract the binary image and achieve ROI edge extraction. Subsequently, the image is divided into ROI and ROB (regions outside ROI). The ROI is transformed into a row vector and rearranged using the Lorenz hyperchaotic system. The rearranged sequence is XOR with the random sequence generated by the Henon chaotic map. Next, the encrypted sequence is arranged according to the location of the ROI region and recombined with the unencrypted ROB to obtain the complete encrypted image. Finally, the least significant bit algorithm controlled by the key is used to embed binary image into the encrypted image to ensure lossless decryption of the medical images. Experimental verification demonstrates that the proposed selective encryption algorithm for massive medical images offers relatively ideal security and higher encryption efficiency. This algorithm addresses the privacy concerns and challenges faced in the medical field and contributes to the secure transmission and storage of massive medical images. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the correlation between histogram parameters derived from synthetic magnetic resonance imaging (SyMRI) and prognostically relevant factors in nasopharyngeal carcinoma (NPC). Methods: Fifty-nine consecutive NPC patients were prospectively enrolled. Quantitative parameters (T1, T2, and proton density (PD)) were obtained by outlining the three-dimensional volume of interest (VOI) of all lesions. Then, histogram analysis of these quantitative parameters was performed and the correlations with prognostically relevant factors were assessed. By choosing appropriate cutoff, we divided the sample into two groups. Independent-samples t test/Mann–Whitney U test was used and ROC curve analysis was further processed. Results: Histogram parameters of the T1, T2, and PD maps were positively correlated with the Ki-67 expression levels, and PD_mean was the most representative parameter (AUC: 0.861). The PD map exhibited good performance in differentiating epidermal growth factor receptor (EGFR) expression levels (AUC: 0.706~0.732) and histological type (AUC: 0.650~0.660). T2_minimum was highest correlated with Epstein-Barr virus (EBV) DNA levels (r = − 0.419), and PD_75th percentile exhibited the highest performance in distinguishing positive and negative EBV DNA groups (AUC: 0.721). T1_minimum was statistically correlated with EA-IgA expression (r = − 0.313). Additionally, several histogram parameters were negatively correlated with tumor stage (T stage: r = − 0.259 ~ − 0.301; N stage: r = − 0.348 ~ − 0.456; clinical stage: r = − 0.419). Conclusions: Histogram parameters of SyMRI could reflect tissue intrinsic characteristics and showed potential value in assessing the Ki-67 and EGFR expression levels, histological type, EBV DNA level, EA-IgA, and tumor stage. Key Points: • SyMRI combined with histogram analysis may help clinicians to assess different prognostic factor statuses in nasopharyngeal carcinoma. • The PD map exhibited good discriminating performance in the Ki-67 and EGFR expression levels. • Histogram parameters of SyMRI were negatively correlated with EBV-related blood biomarkers and TNM stage. [ABSTRACT FROM AUTHOR]

In this work, we investigate the dynamics of the drug-resistance models with the continuous and impulsive drug treatment. Firstly, a mathematical model with the continuous drug treatment is presented. The existence and stability of the boundary and positive equilibria are investigated. At the same time, the optimal dosing strategy is given according to the Pontryagin's Maximum Principle. Secondly, the impulsive drug treatment is also considered. The stability of the bacteria-free periodic solution and persistance of system are obtained when some conditions are satisfied. The sensitivity analysis is presented to determine the relative importance of different factors responsible for the drug-resistance model. Furthermore, our theoretical results are justified by some numerical simulations. [ABSTRACT FROM AUTHOR]

Background: To investigate the potential of synthetic MRI (SyMRI) in the prognostic assessment of patients with nonmetastatic nasopharyngeal carcinoma (NPC), and the predictive value when combined with diffusion-weighted imaging (DWI) as well as clinical factors. Methods: Fifty-three NPC patients who underwent SyMRI were prospectively included. 10th Percentile, Mean, Kurtosis, and Skewness of T1, T2, and PD maps and ADC value were obtained from the primary tumor. Cox regression analysis was used for analyzing the association between SyMRI and DWI parameters and progression-free survival (PFS), and then age, sex, staging, and treatment as confounding factors were also included. C-index was obtained by bootstrap. Moreover, significant parameters were used to construct models in predicting 3-year disease progression. ROC curves and leave-one-out cross-validation were used to evaluate the performance and stability. Results: Disease progression occurred in 16 (30.2%) patients at a follow-up of 39.6 (3.5, 48.2) months. T1_Kurtosis, T1_Skewness, T2_10th, PD_Mean, and ADC were correlated with PFS, and T1_Kurtosis (HR: 1.093) and ADC (HR: 1.009) were independent predictors of PFS. The C-index of SyMRI and SyMRI + DWI + Clinic models was 0.687 and 0.779. Moreover, the SyMRI + DWI + Clinic model predicted 3-year disease progression better than DWI or Clinic model (p ≤ 0.008). Interestingly, there was no significant difference between the SyMRI model (AUC: 0.748) and SyMRI + DWI + Clinic model (AUC: 0.846, p = 0.092). Conclusion: SyMRI combined with histogram analysis could predict disease progression in NPC patients, and SyMRI + DWI + Clinic model further improved the predictive performance. Key points: SyMRI showed excellent performance in predicting disease progression in NPC. T1_Kurtosis and ADC were independent predictors of progression-free survival. T1_Kurtosis was lower in the non-disease progression group than the disease progression group. Combination of SyMRI, DWI, and clinical factors could improve the predictive performance. [ABSTRACT FROM AUTHOR]

Background: This study evaluated the predictive potential of histogram analysis derived from apparent diffusion coefficient (ADC) maps in radiation-induced temporal lobe injury (RTLI) of nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Results: Pretreatment diffusion-weighted imaging (DWI) of the temporal lobes of 214 patients with NPC was retrospectively analyzed to obtain ADC histogram parameters. Of the 18 histogram parameters derived from ADC maps, 7 statistically significant variables in the univariate analysis were included in the multivariate logistic regression analysis. The final best prediction model selected by backward stepwise elimination with Akaike information criteria as the stopping rule included kurtosis, maximum energy, range, and total energy. A Rad-score was established by combining the four variables, and it provided areas under the curve (AUCs) of 0.95 (95% confidence interval [CI] 0.91–0.98) and 0.89 (95% CI 0.81–0.97) in the training and validation cohorts, respectively. The combined model, integrating the Rad-score with the T stage (p = 0.02), showed a favorable prediction performance in the training and validation cohorts (AUC = 0.96 and 0.87, respectively). The calibration curves showed a good agreement between the predicted and actual RTLI occurrences. Conclusions: Pretreatment histogram analysis of ADC maps and their combination with the T stage showed a satisfactory ability to predict RTLI in NPC after IMRT. Key points: Histogram parameters from pretreatment ADC maps are associated with RTLI in NPC. The ADC and combined models show better performance than the T stage alone. The combined model shows excellent performance in predicting RTLI in different subgroups. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a radiomics-based model for predicting radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) by pretreatment MRI of the temporal lobe. Methods: A total of 216 patients with diagnosed NPC were retrospectively reviewed. Patients were randomly allocated to the training (n = 136) and the validation cohort (n = 80). Radiomics features were extracted from pretreatment contrast-enhanced T1- or fat-suppressed T2 weighted MRI. A radiomics signature was generated by the least absolute shrinkage and selection operator (LASSO) regression algorithm, Pearson correlation analysis, and univariable logistic analysis. Clinical features were selected with logistic regression analysis. Multivariable logistic regression analysis was conducted to develop three models for RTLI prediction in the training cohort: namely radiomics signature, clinical variables, and clinical-radiomics parameters. A radiomics nomogram was used and assessed with respect to calibration, discrimination, reclassification, and clinical application. Results: The radiomics signature, composed of two radiomics features, was significantly associated with RTLI. The proposed radiomics model demonstrated favorable discrimination in both the training (AUC, 0.89) and the validation cohort (AUC, 0.92), outperforming the clinical prediction model (p < 0.05). Combining radiomics and clinical features, higher AUCs were achieved (AUC, 0.93 and 0.95), as well as a better calibration and improved accuracy of the prediction of RTLI. The clinical-radiomics model showed also excellent performance in predicting RTLI in different clinical-pathologic subgroups. Conclusion: A radiomics model derived from pretreatment MRI of the temporal lobe showed persuasive performance for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma. Key Points: • Radiomics features from pretreatment MRI are associated with radiation-induced temporal lobe injury in nasopharyngeal carcinoma. • The radiomics model shows better predictive performance than a clinical model and was similar to a clinical-radiomics model. • A clinical-radiomics model shows excellent performance in the prediction of radiation-induced temporal lobe injury in different clinical-pathologic subgroups. [ABSTRACT FROM AUTHOR]

Voluntary contribution has become the only source of donor lungs in China since 2015. To elaborate the outcomes of patients awaiting lung transplantation (LTx) after the implementation of donation after brain death, we performed a retrospective study that encompassed 205 patients with end-stage lung disease who registered for LTx at Shanghai Pulmonary Hospital from January 1, 2015 to January 1, 2021. A total of 180 patients were enrolled in the study. The median waiting time was 1.25 months. Interstitial lung disease (ILD) (103/180, 57.2%) and chronic obstructive pulmonary disease (COPD) (56/180, 31.1%) were the most common diseases in our study population. The mean pulmonary artery pressure (mPAP) of patients in the died-waiting group was higher than that of the survivors (53.29±21.71 mmHg vs. 42.11±18.58 mmHg, P=0.002). The mortality of patients with ILD (34/103, 33.00%) was nearly twice that of patients with COPD (10/56, 17.86%) while awaiting LTx (P=0.041). In the died-waiting group, patients with ILD had a shorter median waiting time than patients with COPD after being listed (0.865 months vs. 4.720 months, P=0.030). ILD as primary disease and mPAP > 35 mmHg were two significant independent risk factors for waitlist mortality, with hazard ratios (HR) of 3.483 (95% CI 1.311–9.111; P=0.011) and 3.500 (95% CI 1.435–8.536; P=0.006). Hence, LTx is more urgently needed in patients with ILD and pulmonary hypertension. [ABSTRACT FROM AUTHOR]

Semantic segmentation of high-resolution remote sensing (RS) images faces challenges in multi-scale transformation. Although feature fusion is widely used in this task, the existing methods do not fully consider the spatial structure relationship between feature layers in the encoder stage. Firstly, this paper designs BGFNet network, combines the feature extraction in the encoder stage and the topological relationship modeling of graph structure, and proposes the graph structure-guided multi-scale feature fusion module to solve this problem. Secondly, in order to solve the problem of blurred object boundaries in RS image segmentation, we propose a multi-level deformable boundary guidance module, which emphasizes object boundaries by establishing long-range context. Finally, a shared enhanced attention module with shared parameters is proposed to enhance the characteristics of each class to improve the recognition ability of the model. The effectiveness of BGFNet is verified on Potsdam and Vaihingen public RS datasets, and its segmentation performance is obviously better than the existing mainstream methods. The source code will be freely available at https://github.com/zyf-cell/BGF_Net. [ABSTRACT FROM AUTHOR]

Asynchronous coupling force of dual forging manipulator frequently results in poor forging and even equipment failure. In this paper, a synchronous control strategy in dual forging manipulator systems (DFMS) is proposed to stabilize its operation. Kinematic model of the hanging system and finite element model of the forgings are established to investigate the relationships of tension, forging deformation and deformation rate. The rigid-flexible coupling model of DFMS is further established and simulated concerning hydraulics, mechanics and controls. A correction based on the independent feedback state difference is concerned, simulated results show good agreements with experimental data, validating the dead zone compensation algorithm of the proportional valve. Moreover, by the control strategy, the vertical synchronous error of the pincers end is rather small as ± 0.125 mm. The methodology presented in this paper represents a fundamental step towards the cooperation of DFMS and the press to realize collaborative operations. [ABSTRACT FROM AUTHOR]

A subculture of S.epidermidis strain ATCC35984 that is amenable to genetically manipulate was occasionally found in our laboratory. This mutant exhibited susceptibility to methicillin in contrast to its parent strain. To unveil the underlying mechanism, whole-genome sequencing of the mutant was performed. A comparative analysis revealed that a large DNA fragment encompassing the CRISPR–Cas system, type I R-M system and the SCCmec element was deleted from the mutant. The large chromosomal deletion associated with CRISPR–Cas system was also observed to occur spontaneously in S. epidermidis in another independent laboratory, or artificially induced by introducing engineering crRNAs in other bacterial species. These findings imply the CRISPR–Cas systems can affect bacterial genome remodeling through deletion of the integrated MGEs (mobile genetic elements). Further bioinformatics analysis identified a higher carriage rate of SCCmec element in the S. epidermidis strains harboring the CRISPR–Cas system. MLST typing and phylogenetic analysis of those CRIPSR–Cas-positive S. epidermidis strains revealed multiple origins. In addition, distinct types of SCCmec carried in those strains suggested that acquisition of this MGE originated from multiple independent recombination events. Intriguingly, CRISPR–Cas systems are found to be always located in the vicinity of orfX gene among staphylococci. Allelic analysis of CRISPR loci flanking cas genes disclosed that the loci distal to the orfX gene are considerably stable and conserved, which probably serve as recombination hotspot between CRISPR–Cas system and phage or plasmid. Therefore, the findings generally support the notion that incomplete immune protection of CRISPR–Cas system can promote dissemination of its neighboring SCCmec element. [ABSTRACT FROM AUTHOR]

In this work, Ti3C2 MXene, a novel two-dimensional nanosheet, was introduced to waterborne polyurethane (WPU) coatings to prepare a composite coating. First, MAX phase materials were in situ etched by HF acid and further intercalated by water molecules to obtain exfoliated single-layer MXene nanosheet. And then, composite coatings were prepared via solution-blending low addition (0–0.4 wt%) of MXene, self-prepared waterborne polyacrylate emulsion (PAE) and isocyanate hardener, applying on Q235 mild steel. Results of AFM, XRD SEM and SEM–EDS confirm that single-layer MXene nanosheets with large lateral-to-thickness ratio are successfully prepared and achieved homogenous distribution within WPU matrix. With 0.4 wt% MXene incorporated, the WPU/Ti3C2 MXene composite coatings reach a lowest corrosion current of 2.143 × 10–6 A/cm2, a decrease of one order of magnitude compared with blank WPU (1.599 × 10–5 A/cm2) and own an excellent UV-blocking property (almost block the whole UV light). [ABSTRACT FROM AUTHOR]

Purpose: Current approaches to quantification of magnetic particle imaging (MPI) for cell-based therapy are thwarted by the lack of reliable, standardized methods of segmenting the signal from background in images. This calls for the development of artificial intelligence (AI) systems for MPI analysis.Procedures: We utilize a canonical algorithm in the domain of unsupervised machine learning, known as K-means++, to segment the regions of interest (ROI) of images and perform iron quantification analysis using a standard curve model. We generated in vitro, in vivo, and ex vivo data using islets and mouse models and applied the AI algorithm to gain insight into segmentation and iron prediction on these MPI data. In vitro models included imaging the VivoTrax-labeled islets in varying numbers. In vivo mouse models were generated through transplantation of increasing numbers of the labeled islets under the kidney capsule of mice. Ex vivo data were obtained from the MPI images of excised kidney grafts.Results: The K-means++ algorithms segmented the ROI of in vitro phantoms with minimal noise. A linear correlation between the islet numbers and the increasing prediction of total iron value (TIV) in the islets was observed. Segmentation results of the ROI of the in vivo MPI scans showed that with increasing number of transplanted islets, the signal intensity increased with linear trend. Upon segmenting the ROI of ex vivo data, a linear trend was observed in which increasing intensity of the ROI yielded increasing TIV of the islets. Through statistical evaluation of the algorithm performance via intraclass correlation coefficient validation, we observed excellent performance of K-means++-based model on segmentation and quantification analysis of MPI data.Conclusions: We have demonstrated the ability of the K-means++-based model to provide a standardized method of segmentation and quantification of MPI scans in an islet transplantation mouse model. [ABSTRACT FROM AUTHOR]

In this paper, we first introduce the concept of graphical convex metric spaces and some basic properties of the underlying spaces. Different from related literature, we generalize Mann iterative scheme and Agrawal iterative scheme for set-valued mappings to above spaces by introducing the concepts of T-Mann sequences and T-Agrawal sequences. Furthermore, by using the iterative techniques and graph theory, we investigate the existence and uniqueness of fixed points for set-valued G-contractions in a graphical convex metric space. Moreover, we present some notions of well-posedness and G-Mann stability of the fixed point problems in the above space. Additionally, as an application of our main results, we discuss the well-posedness and G-Mann stability of the fixed point problems for set-valued G-contractions in a graphical convex metric space. [ABSTRACT FROM AUTHOR]

Key message: Our results reveal both soil drought and PEG can enhance malate, glutathione and ascorbate metabolism, and proline biosynthesis, whereas soil drought induced these metabolic pathways to a greater degree than PEG. Polyethylene glycol (PEG) is widely used to simulate osmotic stress, but little is known about the different responses of wheat to PEG stress and soil drought. In this study, isobaric tags for relative quantification (iTRAQ)-based proteomic techniques were used to determine both the proteomic and physiological responses of wheat seedlings to soil drought and PEG. The results showed that photosynthetic rate, stomatal conductance, intercellular CO2 concentration, transpiration rate, maximum potential efficiency of PS II, leaf water content, relative electrolyte leakage, MDA content, and free proline content exhibited similar responses to soil drought and PEG. Approximately 15.8% of differential proteins were induced both by soil drought and PEG. Moreover, both soil drought and PEG inhibited carbon metabolism and the biosynthesis of some amino acids by altering the accumulation of glyceraldehyde-3-phosphate dehydrogenase, ribulose-bisphosphate carboxylase, and phosphoglycerate kinase, but they both enhanced the metabolism of malate, proline, glutathione, and ascorbate by increasing the accumulation of key enzymes including malate dehydrogenase, monodehydroascorbate reductase, pyrroline-5-carboxylate dehydrogenase, pyrroline-5-carboxylate synthetase, ascorbate peroxidase, glutathione peroxidase, and glutathione S-transferase. Notably, the latter five of these enzymes were found to be more sensitive to soil drought. In addition, polyamine biosynthesis was specifically induced by increased gene expression and protein accumulation of polyamine oxidase and spermidine synthase under PEG stress, whereas fructose-bisphosphate aldolase and arginase were induced by soil drought. Therefore, present results suggest that PEG is an effective method to simulate drought stress, but the key proteins related to the metabolism of malate, glutathione, ascorbate, proline, and polyamine need to be confirmed under soil drought. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging technology is applied to nondestructive quality determination of agricultural and food products. It has a greater advantage of combining spatial image and spectral measurement which can determine both external and internal quality of the product. To increase the classification accuracy and stability of the prediction model, the spectral correlation analysis of each pixel was used to determine quality of the sample’s hyperspectral image in this study. 400 hyperspectral image ROIs were extracted from 40 apples (20 apples with waxed and the other 20 apples without any waxed) were studied. Two effective wavelengths (806.85 nm, 1073.97 nm) were screened by spectral correlation analysis of pixels in 7 peak wave bands (639.90 nm, 806.85 nm, 973.80 nm, 1073.97 nm, 1197.99 nm, 1269.54 nm, 1441.26 nm). When the spectral correlation degree is less than 0.9 among all the pixels in the same sample, this sample data should be eliminated before inputting into the model training group. The least squares support vector machine (LS-SVM) model and BP neural network were used to establish the classification model between the hyperspectral image and waxed situation. The prediction result showed the classification accuracy was increased from 82% to 100% when the low-quality sample data for training were filtered by spectral correlation analysis. By evaluating the quality of the hyperspectral image measured, more reliable prediction results can be obtained, which can make the noninvasive discrimination of food safety come to the practice application sooner. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer in women worldwide, identification of new biomarkers for early diagnosis and detection will improve the clinical outcome of breast cancer patients. In the present study, we determined serum levels of vitronectin (VN) in 93 breast cancer patients, 30 benign breast lesions, 9 precancerous lesions, and 30 healthy individuals by enzyme-linked immunosorbent assays. Serum VN level was significantly higher in patients with stage 0-I primary breast cancer than in healthy individuals, patients with benign breast lesion or precancerous lesions, as well as those with breast cancer of higher stages. Serum VN level was significantly and negatively correlated with tumor size, lymph node status, and clinical stage ( p < 0.05 in all cases). In addition, VN displayed higher area under curve (AUC) value (0.73, 95 % confidence interval (CI) [0.62-0.84]) than carcinoembryonic antigen (CEA) (0.64, 95 % CI [0.52-0.77]) and cancer antigen 15-3 (CA 15-3) (0.69, 95 % CI [0.58-0.81]) when used to distinguish stage 0-I cancer and normal control. Importantly, the combined use of three biomarkers yielded an improvement in receiver operating characteristic curve with an AUC of 0.83, 95 % CI [0.74-0.92]. Taken together, our current study showed for the first time that serum VN is a promising biomarker for early diagnosis of breast cancer when combined with CEA and CA15-3. [ABSTRACT FROM AUTHOR]

A mixed rain-snow storm associated with a strong burst of cold air and development of an extratropical cyclone occurred over North China from 3 to 5 November 2012. This early snowfall event was characterized by a dramatic drop in temperature, strong winds, high precipitation intensity, broad spatial extent, and coexistence of multi-phase precipitating hydrometeors. This study investigates the multi-scale interactions between the large-scale circulation background and the synoptic-scale weather systems associated with the storm. The results are as follows. (1) The Arctic Oscillation (AO) had been in its negative phase long before the event, leading to southward advection of cold air into North China in advance of the storm. (2) The large-scale atmospheric circulation experienced a decreased number of long waves upstream of North China prior to the storm, resulting in reduced wave velocity and an almost stagnant low pressure system (extratropical cyclone) over North China. (3) An Ω-shaped blocking high over East Asia and the western Pacific obstructed the eastward movement of an upstream trough, allowing the corresponding surface cyclone to stabilize and persist over Beijing and its neighboring areas. This blocking high was a major factor in making this event a historically most severe precipitation event in autumn in Beijing for the past 60 years. (4) Baroclinic instability at lower levels gave rise to rapid development of the cyclone under the classical 'second type' development mechanism for extratropical cyclones. (5) Moisture originated from the Yellow Sea entered the slowly-moving cyclone in a steady stream, creating fairly favorable water vapor supply for the heavy rainfall-snowfall, especially during the later stage of the cyclone development. (6) Moisture transport and frontal lifting triggered low-level instability and updrafts. Intensification of the front enhanced the vertical wind shear, causing conditional symmetric instability (CSI) to expand upward within the unstable lower troposphere, and to eventually gear into the CSI region of the upper troposphere, which facilitated the upward development of low-level updrafts. [ABSTRACT FROM AUTHOR]

The relation between microstructure characteristics and mechanical properties of X80 pipeline steels was investigated using optical microscopy, scanning electron microscopy, etc. It is shown that the structure consists of polygonal ferrite (PF), quasi-polygonal ferrite (QPF), acicular ferrite (AF), and granular bainitic ferrite (GF). With increasing volume fraction of M-A islands (below 3%), the yield strength increases. With increasing content of higher angle grain boundaries(HAGBs), the yield strength, elongation, and DWTT properties at −15° increase, and the volume fraction of M-A islands reaches its highest point in the steel containing the most volume fraction of GF. [ABSTRACT FROM AUTHOR]

High-resolution two-dimensional gel electrophoresis (2-DE), followed by computer-assisted image analysis was used to screen protein patterns of normal and infarcted myocardial tissues for quantitative and qualitative differences in protein expression. In the gels of pH 5–8 immobilized pH gradient (IPG) strips, 851 protein spots were detected in normal myocardial tissue and 1 032 protein spots were resolved in infarcted myocardial tissue. Thirteen protein spots only expressed in normal myocardial tissue, and 14 protein spots only expressed in infarcted myocardial tissue. Results also showed that 49 protein spots displayed quantitative changes in expression between normal and infarcted myocardial tissue. Eleven protein spots were subjected to mass spectrometry (MS) analysis and seven proteins were identified by peptide mass fingerprinting (PMF). These proteins may be involved in cardiovascular injury, and could play an important role in the treatment of coronary heart disease. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concepts of the set of all the common attractive points (CAP(S, T)) and the set of all the common strongly attractive points (CSAP(S, T)) for the set-valued mappings S and T in a Hilbert space. Moreover, some fundamental properties related to the sets A(T), F(T) and CAP(S, T) are given. Furthermore, we generalize the Agarwal iteration for the case of two set-valued mappings and obtain a weak convergence theorem for two (α , β) -generalized hybrid set-valued mappings in a Hilbert space. Moreover, an example of two (α , β) -generalized hybrid set-valued mappings which have a common strongly attractive point is shown. Then we finish the work using the proposed algorithm to find a common element of the set of solutions of an equilibrium problem and the sets of fixed points of two (α , β) -generalized hybrid set-valued mappings. [ABSTRACT FROM AUTHOR]

Mammaglobin A (MGA) is an organ specific molecular biomarker for metastatic breast cancer diagnosis. However, there are still needs to develop optimal monoclonal antibodies (mAbs) to detect MGA expression in breast carcinoma by immunohistochemistry. In this study, we first generated mAbs against MGA. Then, we used epitope prediction and computer-assisted structural analysis to screen five dominant epitopes and identified mAbs against five epitopes. Further immunohistochemical analysis on 42 breast carcinoma specimens showed that MHG1152 and MGD785 had intensive staining mainly in membrane, while CHH11617, CHH995 and MJF656 had more intensive staining within the cytoplasm. MGA scoring results showed that MJF656 had the highest rate (92.8%) of positive staining among five mAbs, including higher staining intensity when compared with that of MHG1152 (p < 0.01) and CHH995 (p < 0.05) and the highest the mean percentage of cells stained among mAbs. Furthermore, we analyzed the relationship of positive staining rate by mAbs with patient clinical characteristics. The results suggest that MJF656 was able to detect MGA expression, especially in early clinical stage, low grade and lymph node metastasis-negative breast carcinoma. In conclusion, our study generated five mAbs against MGA and identified the best candidate for detection of MGA expression in breast cancer tissues. [ABSTRACT FROM AUTHOR]

A correction to the article "A Case Study of the November 2012 Mixed Rain-Snow Storm Over North China" in a 2014 issue of the "Journal of Meteorological Research" is presented.

Background: Postsurgical recurrence is of great concern for papillary thyroid carcinoma (PTC). We aim to investigate the value of computed tomography (CT)-based radiomics features and conventional clinical factors in predicting the recurrence of PTC., Methods: Two-hundred and eighty patients with PTC were retrospectively enrolled and divided into training and validation cohorts at a 6:4 ratio. Recurrence was defined as cytology/pathology-proven disease or morphological evidence of lesions on imaging examinations within 5 years after surgery. Radiomics features were extracted from manually segmented tumor on CT images and were then selected using four different feature selection methods sequentially. Multivariate logistic regression analysis was conducted to identify clinical features associated with recurrence. Radiomics, clinical, and combined models were constructed separately using logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and neural network (NN), respectively. Receiver operating characteristic analysis was performed to evaluate the model performance in predicting recurrence. A nomogram was established based on all relevant features, with its reliability and reproducibility verified using calibration curves and decision curve analysis (DCA)., Results: Eighty-nine patients with PTC experienced recurrence. A total of 1218 radiomics features were extracted from each segmentation. Five radiomics and six clinical features were related to recurrence. Among the 4 radiomics models, the LR-based and SVM-based radiomics models outperformed the NN-based radiomics model (P = 0.032 and 0.026, respectively). Among the 4 clinical models, only the difference between the area under the curve (AUC) of the LR-based and NN-based clinical model was statistically significant (P = 0.035). The combined models had higher AUCs than the corresponding radiomics and clinical models based on the same classifier, although most differences were not statistically significant. In the validation cohort, the combined models based on the LR, SVM, KNN, and NN classifiers had AUCs of 0.746, 0.754, 0.669, and 0.711, respectively. However, the AUCs of these combined models had no significant differences (all P > 0.05). Calibration curves and DCA indicated that the nomogram have potential clinical utility., Conclusions: The combined model may have potential for better prediction of PTC recurrence than radiomics and clinical models alone. Further testing with larger cohort may help reach statistical significance., (© 2023. The Author(s).)

Background: To evaluate the parameters derived from arterial spin labeling (ASL) and multi-b-value diffusion-weighted imaging (DWI) for differentiating retropharyngeal lymph nodes (RLNs) in patients with nasopharyngeal carcinoma (NPC)., Methods: This prospective study included 50 newly diagnosed NPC and 23 healthy control (HC) participants. RLNs of NPC were diagnosed according to the follow-up MRI after radiotherapy. Parameters derived from ASL and multi-b-value DWI, and RLNs axial size on pre-treatment MRI among groups were compared. Receiver operating characteristic curve (ROC) was used to analyze the diagnostic efficiency., Results: A total of 133 RLNs were collected and divided into a metastatic group (n = 71) and two non-metastatic groups (n = 62, including 29 nodes from NPC and 33 nodes from HC). The axial size, blood flow (BF), and apparent diffusion coefficient (ADC) of RLNs were significantly different between the metastasis and the non-metastasis group. For NPC patients with a short axis < 5 mm or < 6 mm, or long axis < 7 mm, if BF > 54 mL/min/100 g or ADC ≤ 0.95 × 10 -3  mm 2 /s, the RLNs were still considered metastatic. Compared with the index alone, a combination of size and functional parameters could improve the accuracy significantly, except the long axis combined with ADC; especially, combined size with BF exhibited better performance with an accuracy of 91.00-92.00%., Conclusions: ASL and multi-b-value DWI could help determine the N stage of NPC, while the BF combination with RLNs size may significantly improve the diagnostic efficiency., (© 2022. The Author(s).)

Background: Accurate pretreatment prediction for disease progression of nasopharyngeal carcinoma is key to intensify therapeutic strategies to high-risk individuals. Our aim was to evaluate the value of baseline MRI-based radiomics machine-learning models in predicting the disease progression in nasopharyngeal carcinoma patients who achieved complete response after treatment., Methods: In this retrospective study, 171 patients with pathologically confirmed nasopharyngeal carcinoma were included. Using hold-out cross validation scheme (7:3), relevant radiomic features were selected with the least absolute shrinkage and selection operator method based on baseline T2-weighted fat suppression and contrast-enhanced T1-weighted images in the training cohort. After Pearson's correlation analysis of selected radiomic features, multivariate logistic regression analysis was applied to radiomic features and clinical characteristics selection. Logistic regression analysis and support vector machine classifier were utilized to build the predictive model respectively. The predictive accuracy of the model was evaluated by ROC analysis along with sensitivity, specificity and AUC calculated in the validation cohort., Results: A prediction model using logistic regression analysis comprising 4 radiomics features (HGLZE&#95;T2H, HGLZE&#95;T1, LDLGLE&#95;T1, and GLNU&#95;T1) and 5 clinical features (histology, T stage, N stage, smoking history, and age) showed the best performance with an AUC of 0.75 in the training cohort (95% CI: 0.66-0.83) and 0.77 in the validation cohort (95% CI: 0.64-0.90). The nine independent impact factors were entered into the nomogram. The calibration curves for probability of 3-year disease progression showed good agreement. The features of this prediction model showed satisfactory clinical utility with decision curve analysis., Conclusions: A radiomics model derived from pretreatment MR showed good performance for predicting disease progression in nasopharyngeal carcinoma and may help to improve clinical decision making., (© 2022. The Author(s).)