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Glioblastoma multiforme (GBM)is the most common and aggressive primary brain tumor. Although temozolomide (TMZ)-based radiochemotherapy improves overall GBM patients' survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudo-progression (PsP) is a treatment-related reaction with an increased contrast-enhancing lesion size at the tumor site or resection margins miming tumor recurrence on MRI. The accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate the tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or true tumor progression (TTP) from the Wake dataset. Based on these radiographic features, we conducted a radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were used as features to construct a 2YS (2-year survival rate) logistic regression model. GBM patients were classified into low- and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent The Cancer Genome Atlas Program (TCGA) dataset and found that 2YS scores were significantly associated with the patient's overall survival. We used two cohorts of the TCGA data to train and test our model. Our results show that the 2YS scores-based classification results from the training and testing TCGA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS (Karnofsky performance status), normal cell ratio) and found that these factors were unrelated or weakly correlated with patients' survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting the clinical outcomes of GBM patients after standard therapies. [ABSTRACT FROM AUTHOR]

Fine needle aspiration biopsy (FNAB) is a diagnostic modality for the evaluation of suspicious soft tissue masses. Despite its reasonable sensitivity, specificity and positive predictive value in differentiating benign from malignant neoplasms, the exact subtyping of the primary soft tissue tumours can be challenging. Certain tumours constitute "pitfalls" and add to the diagnostic challenge. This review provides a detailed account of the diagnostic challenges in soft tissue cytopathology, including pitfalls and, more importantly, the ways to overcome these challenges by integrating clinical details, key cytomorphological features and judicious application of ancillary techniques. [ABSTRACT FROM AUTHOR]

Aims: MYC is a proto‐oncogene that is frequently dysregulated in various malignancies, through translocation or amplification. Radiation‐associated angiosarcoma frequently shows MYC amplification, and immunohistochemical expression has been shown to be a reliable surrogate marker for amplification, but less is known about MYC expression in other sarcoma types, despite reports of MYC amplification in some undifferentiated/unclassified radiation‐associated sarcomas (RASs). Distinguishing putative RAS from non‐radiation‐associated sarcoma or sarcomatoid carcinoma can be difficult. The aim of this study was to determine the prevalence and potential diagnostic utility of MYC in this context, by evaluating MYC expression in a cohort of RASs, non‐radiation‐associated sarcomas, and sarcomatoid carcinomas. Methods and results: Three hundred and eighty‐five neoplasms were evaluated, including 81 RASs (18 angiosarcomas; 57 undifferentiated sarcomas; three leiomyosarcomas; and three malignant peripheral nerve sheath tumours), 267 non‐radiation‐associated sarcomas, and 37 sarcomatoid carcinomas. Immunohistochemistry was performed with a monoclonal anti‐MYC antibody. Staining in tumour cells was scored on the basis of extent (focal, 1–4%; multifocal, 5–49%; and diffuse, ≥50%) and intensity (strong, moderate, and weak). One hundred percent of radiation‐associated angiosarcomas expressed MYC diffusely. Expression was infrequent among other types of RAS (9.5%), and the frequency was similar to that in non‐radiation‐associated sarcomas (9.7%). MYC expression was more common in sarcomatoid carcinomas, occurring in 43%. The extent and intensity of staining were variable in all groups. Conclusion: MYC expression is infrequent among RASs other than angiosarcoma, and has a similar prevalence in sporadic sarcomas. Given the frequency of expression in sarcomatoid carcinomas, MYC expression outside the context of radiation‐associated angiosarcoma is of limited diagnostic utility, and should be interpreted with caution after exclusion of sarcomatoid carcinoma where relevant. [ABSTRACT FROM AUTHOR]

Titanium alloys are widely used in the aviation field due to the excellent properties, such as high specific-strength and high-temperature resistance. But the poor machinability of titanium alloys has brought the great difficulty to its machining process. In the cutting process of titanium alloys, tool wear is very serious, and the machining quality is difficult to be guaranteed. As a new type titanium alloy, TC21 alloy has the higher strength and its machinability is less than other titanium alloys. Aiming at the cutting defects in the cutting process of TC21 alloy, three different microstructures, including parallel, perpendicular and wavy grooves, are presented and cut on the rake surface of tools by the laser texturing method. A series of precision turning process of TC21 alloy are implemented on the precision machine using the inserts with different microstructures. The effects of the different microstructures on the chip morphology, turning force, surface morphology, and surface roughness are investigated deeply. The results demonstrates that the tool microstructures have very important role on the turning process of titanium alloys and the wavy microstructure has the best effect in improving the machinability of titanium alloy TC21. [ABSTRACT FROM AUTHOR]

The PPV23 immunizes healthy elderly and other high-risk populations against pneumococcal disease. Immune mechanisms whereby these populations differently mount antibody(Ab) and cellular responses to PPV23 vaccination remain unknown. Here, healthy elderly, those elderly with prior tuberculosis-cured history (TB-cured), and HIV-infected humans were vaccinated with PPV23, and assessed for opsonophagocytic Ab responses and potential cellular mechanisms. PPV23 vaccination elicited hierarchical responses of opsonophagocytic Ab. PPV23-elicited Ab titers were highest in healthy elderly, significantly lower in TB-cured elderly and lowest in HIV-infected subjects. Mechanistically, high PPV23-elicited Ab titers in healthy elderly were associated with increases in CD19 + CD69+ cells and CD19 + CD138 + plasma cells. Surprisingly, TB-cured elderly failed to show PPV23-induced increases in these cells. While HIV-infected subjects showed a depressed CD19 + CD69+ cellular response, PPV23 vaccination uncovered HIV-related over-reactive increases in CD19 + CD138 + cells. For the first time, we demonstrate that PPV23-elicted opsonophagocytic Ab titers correlate with different cellular responses in healthy, TB-cured and HIV statuses. [ABSTRACT FROM AUTHOR]

Ventricle segmentation is a challenging technique for the development of detection system of ischemic stroke in computed tomography (CT), as ischemic stroke regions are adjacent to the brain ventricle with similar intensity. To address this problem, we developed an objective segmentation system of brain ventricle in CT. The intensity distribution of the ventricle was estimated based on clustering technique, connectivity, and domain knowledge, and the initial ventricle segmentation results were then obtained. To exclude the stroke regions from initial segmentation, a combined segmentation strategy was proposed, which is composed of three different schemes: (1) the largest three-dimensional (3D) connected component was considered as the ventricular region; (2) the big stroke areas were removed by the image difference methods based on searching optimal threshold values; (3) the small stroke regions were excluded by the adaptive template algorithm. The proposed method was evaluated on 50 cases of patients with ischemic stroke. The mean Dice, sensitivity, specificity, and root mean squared error were 0.9447, 0.969, 0.998, and 0.219 mm, respectively. This system can offer a desirable performance. Therefore, the proposed system is expected to bring insights into clinic research and the development of detection system of ischemic stroke in CT. [ABSTRACT FROM AUTHOR]

Purpose: Pseudoprogression (PsP) can mimic true tumor progression (TTP) on magnetic resonance imaging in patients with glioblastoma multiform (GBM). The phenotypical similarity between PsP and TTP makes it a challenging task for physicians to distinguish these entities. So far, no approved biomarkers or computer-aided diagnosis systems have been used clinically for this purpose. Methods: To address this challenge, the authors developed an objective classification system for PsP and TTP based on longitudinal diffusion tensor imaging. A novel spatio-temporal discriminative dictionary learning scheme was proposed to differentiate PsP and TTP, thereby avoiding segmentation of the region of interest. The authors constructed a novel discriminative sparse matrix with the classification-oriented dictionary learning approach by excluding the shared features of two categories, so that the pooled features captured the subtle difference between PsP and TTP. The most discriminating features were then identified from the pooled features by their feature scoring system. Finally, the authors stratified patients with GBM into PsP and TTP by a support vector machine approach. Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were used to assess the robustness of the developed system. Results: The average accuracy and AUC values after ten rounds of tenfold CV were 0.867 and 0.92, respectively. The authors also assessed the effects of different methods and factors (such as data types, pooling techniques, and dimensionality reduction approaches) on the performance of their classification system which obtained the best performance. Conclusions: The proposed objective classification system without segmentation achieved a desirable and reliable performance in differentiating PsP from TTP. Thus, the developed approach is expected to advance the clinical research and diagnosis of PsP and TTP. [ABSTRACT FROM AUTHOR]

Purpose: Spring-assisted surgery is an effective and minimally invasive treatment for sagittal craniosynostosis (CSO). The principal barrier to the advancement of spring-assisted surgery is the patient-specific spring selection. The selection of spring force depends on the suture involved, subtypes of sagittal CSO, and age of the infant, among other factors. Clinically, physicians manually judge the subtype of sagittal CSO patients based on their CT image data, which may cause bias from different clinicians. An objective system would be helpful to stratify the sagittal CSO patients and make spring choice less subjective. Methods: The authors developed a novel informatics system to automatically segment and characterize sutures and classify sagittal CSO. The proposed system is composed of three phases: preprocessing, sutures segmentation, and classification. First, the three-dimensional (3D) skull was extracted from the CT images and aligned with the symmetry of the cranial vault. Second, a "hemispherical projection" algorithm was developed to transform 3D surface of the skull to a polar two-dimensional plane. Through the transformation, an "effective" projected region can be obtained to enable easy segmentation of sutures. Then, the different types of sutures, such as coronal sutures, lambdoid sutures, sagittal suture, and metopic suture, obtained from the segmented sutures were further identified by a dual-projection technique of the midline of the sutures. Finally, 108 quantified features of sutures were extracted and selected by a proposed multiclass feature scoring system. The sagittal CSO patients were classified into four subtypes: anterior, central, posterior, and complex with the support vector machine approach. Fivefold cross validation (CV) was employed to evaluate the capability of selected features in discriminating the four subtypes in 33 sagittal CSO patients. Receiver operating characteristics (ROC) curves were used to assess the robustness of the developed system. Results: The segmentation results of the proposed method were clinically acceptable for the qualitative evaluation. For the quantitative evaluation, the fivefold CV accuracy of the classification for the four subtypes was 72.7%. This classification system was reliable with the area under curve (in ROC analysis) being greater than 0.8 for four two-class problems. Conclusions: The proposed hemispherical projection algorithm based on backtracking search can successfully segment sutures of the cranial vault. The classification system can also offer a desirable performance. As a result, the proposed segmentation and classification system is expected to bring insights into clinic research and the selection of the spring force to facilitate widespread application of this minimally invasive treatment. [ABSTRACT FROM AUTHOR]

Purpose: Myocardium segmentation in cardiac magnetic resonance (MR) images plays a vital role in clinical diagnosis of the cardiovascular diseases. Because of the low contrast and large variation in intensity and shapes, myocardium segmentation has been a challenging task. A dynamic programming (DP) based segmentation method, incorporating the likelihood and shape information of the myocardium, is developed for segmenting myocardium in cardiac MR images. Methods: The endocardium, i.e., the left ventricle blood cavity, is segmented for initialization, and then the optimal epicardium contour is determined using the polar-transformed image and DP scheme. In the DP segmentation scheme, three techniques are proposed to improve the segmentation performance: (1) the likelihood image of the myocardium is constructed to define the external cost in the DP, thus the cost function incorporates prior probability estimation; (2) the adaptive search range is introduced to determine the polar-transformed image, thereby excluding irrelevant tissues; (3) the connectivity constrained DP algorithm is developed to obtain an optimal closed contour. Four metrics, including the Dice metric (Dice), root mean squared error (RMSE), reliability, and correlation coefficient, are used to assess the segmentation accuracy. The authors evaluated the performance of the proposed method on a private dataset and the MICCAI 2009 challenge dataset. The authors also explored the effects of the three new techniques of the DP scheme in the proposed method. Results: For the qualitative evaluation, the segmentation results of the proposed method were clinically acceptable. For the quantitative evaluation, the mean (Dice) for the endocardium and epicardium was 0.892 and 0.927, respectively; the mean RMSE was 2.30 mm for the endocardium and 2.39 mm for the epicardium. In addition, the three new techniques in the proposed DP scheme, i.e., the likelihood image of the myocardium, the adaptive search range, and the connectivity constrained DP algorithm, improved the segmentation performance for the epicardium with 0.029, 0.047, and 0.007 in terms of the Dice and 0.98, 1.31, and 0.21 mm in terms of the RMSE, respectively. Conclusions: The three techniques (the likelihood image of the myocardium, the adaptive search range, and the connectivity constrained DP algorithm) can improve the segmentation ability of the DP method, and the proposed method with these techniques has the ability to achieve the acceptable segmentation result of the myocardium in cardiac MR images. [ABSTRACT FROM AUTHOR]

Highlights: [•] A cohort of controls and patients with Alzheimer's disease was genotyped for IL-4 −590C>T, −33C>T and −1098T>G SNPs. [•] The −590C and −1098G alleles of IL-4 are associated with the increased risk for Alzheimer's disease. [•] The CCG haplotype occurred at significantly higher frequencies in patients with Alzheimer's disease. [•] The −590C>T and −1098T>G SNPs have a linkage disequilibrium with multiple potentially functional SNPs inside IL-4 gene. [•] Genetic variation in IL-4 plays a role in the susceptibility of Alzheimer's disease. [Copyright &y& Elsevier]

Clonorchiasis sinensis and Taenia solium taeniasis/cysticercosis are major foodborne parasitoses. Clonorchiasis sinensis is actively transmitted in some areas of China, Korea, Russia, Vietnam, etc. Currently, it is estimated that more than 200 million people are at risk of infection, 15-20 million people are infected, and 1.5-2 million show symptoms or complications. In China, it is relatively heavily transmitted in Zhujiang River Delta, including Hong Kong and Macao, and Northeast China, where many Korean people live. The transmission is related to the unhealthy habits of residents who like to have raw fish or half-raw fish. The infection of Clonorchis sinensis could result in serious liver and biliary system damages, and chronic cases may induce liver and bile duct cancers. T. solium taeniasis/cysticercosis is distributed around the world except the areas where the residents have a taboo against pork for religious reasons. Recent years, the urban inhabitants infected with T. solium/ Cysticercus are increasing in China. T. solium results in intestinal diseases, and cysticercosis is a very serious disease, especially nervous system cysticercosis. Its symptoms include headache, epilepsy, sudden death, etc. Health education and health promotion, environmental reconstruction, and chemotherapy are the main control measures for these diseases. Through several decades of efforts in China, the achievements of control of clonorchiasis and T. solium taeniasis/cysticercosis are great. For example, in one of the main clonorchiasis-endemic provinces, Shandong Province, clonorchiasis has been controlled. In 31 T. solium taeniasis/cysticercosis-endemic counties of Henan Province, through a 6-year control program, the decline rates of T. solium taeniasis and cysticercosis were 90.8 and 96.8 %, respectively. This paper reviews the researches on the control of clonorchiasis and T. solium taeniasis/cysticercosis in China past decades so as to provide references for other countries where these diseases are endemic to improve the control or elimination of clonorchiasis and T. solium taeniasis/cysticercosis. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the cerebrospinal fluid (CSF) in patients with ependymal neoplasms and illustrates the ependymoma cytomorphologic features and its variants. The ependymal neoplasms are rare tumors, which arise from the cerebral ventricles' ependymal lining and remnants of spinal cord's central canal and mostly affects children and young adults. Moreover, the ependymomas exfoliated cells of patients with myxopapillary tumors are recognizable in CSF samples.

Titanium alloys have high corrosion resistance and specific strength, leading to a wide range of uses in a variety of industrial fields. However, machining performance is often very poor, causing serious difficulty during the cutting process. In particular, high cutting temperature and high chemical activity of titanium alloys during the cutting process lead to rapid tool wear. Within this research, specific tool microstructures cut onto the tool rake surface is explored to improve the cutting performance of titanium alloy TC21. In order to isolate the influence of particular tool microstructures on the cutting performance of titanium alloy TC21, a 3D orthogonal finite element model (OFEM) is utilized to simulate the cutting process of TC21 alloy. The impact of tool microstructure on chip formation, cutting force and temperature is thoroughly analyzed through turning simulations and experiments on titanium alloy TC21. Finally, a comprehensive comparison of cutting behaviors between textured and untextured tools during the cutting of titanium alloy TC21 was carried out. Cutting simulations indicate that tool microstructure can improve the cutting properties, reducing cutting temperature and cutting force. Research results confirm that chip serration and tool wear noticeably decreased, indicating tool texture can significantly improve cutting performance of titanium alloy TC21. [ABSTRACT FROM AUTHOR]

As a typical high specific strength and corrosion-resistant alloy, titanium alloy Ti6Al4V is widely used in the aviation, ocean, biomedical, sport, and other fields. The heat treatment method is often used to improve the material mechanical properties. To investigate the dynamic mechanical properties of titanium alloy Ti6Al4V after heat treatment, dynamic compressive experiments under high temperature and high strain rate were carried out using split Hopkinson press bar (SHPB) equipment. The stress–strain curves of Ti6Al4V alloy under different temperatures and strain rates were obtained through SHPB compressive tests. The Johnson–Cook (J–C) constitutive equation was used for expressing the stress–strain relationship of titanium alloy under large deformation. In addition, the material constants of the J–C model were fitted based on the experimental data. An orthogonal cutting simulation was performed to investigate the cutting of Ti6Al4V alloy under two different numerical calculation methods based on the established J–C model using the finite element method (FEM). The simulation results confirm that the adiabatic mode is more suitable to analyze the cutting of Ti6Al4V alloy. [ABSTRACT FROM AUTHOR]

Objectives: For children with Kawasaki disease (KD) at high risk of developing coronary artery lesions and requiring retreatment with intravenous immunoglobulin (IVIG), the availability of accurate prediction models remains limited because of inconsistent variables and unsatisfactory prediction results. We aimed to construct models to predict patient's probability of IVIG retreatment combining children's individual inflammatory characteristics. Methods: Clinical manifestations and laboratory examinations of 266 children with KD were retrospectively analysed to build a development cohort data set (DC) and a validation cohort data set (VC). In the DC, binary logistic regression analyses were performed using R language. Nomograms and receiver operating curves were plotted. The concordance index (C index), net reclassification index, integrated discrimination improvement index and confusion matrix were applied to evaluate and validate the models. Results: Models_5V and _9V were established. Both contained variables including the percentages of CD8+ T cells, CD4+ T cells, CD3+ T cells, levels of interleukin (IL)‐2R and CRP. Model_9V additionally included variables for IL‐6, TNF‐α, NT‐proBNP and sex, with a C index of 0.86 (95% CI 0.79–0.92). When model_9V was compared with model_5V, the NRI and IDI were 0.15 (95% CI 0.01–0.30, P < 0.01) and 0.07 (95% CI 0.02–0.12, P < 0.01). In the VC, the sensitivity, specificity and precision of model_9V were 1, 0.875 and 0.667, while those of model_5V were 0.833, 0.875 and 0.625. Conclusion: Model_9V combined cytokine profiles and lymphocyte subsets with clinical characteristics and was superior to model_5V achieving satisfactory predictive power and providing a novel strategy early to identify patients who needed IVIG retreatment. [ABSTRACT FROM AUTHOR]

Microfluidic 3D cell culture devices that enable the recapitulation of key aspects of organ structures and functions in vivo represent a promising preclinical platform to improve translational success during drug discovery. Essential to these engineered devices is the spatial patterning of cells from different tissue types within a confined microenvironment. Traditional fabrication strategies lack the scalability, cost‐effectiveness, and rapid prototyping capabilities required for industrial applications, especially for processes involving thermoplastic materials. Here, an approach to pattern fluid guides inside microchannels is introduced by establishing differential hydrophilicity using pressure‐sensitive adhesives as masks and a subsequent selective coating with a biocompatible polymer. Optimal coating conditions are identified using polyvinylpyrrolidone, which resulted in rapid and consistent hydrogel flow in both the open‐chip prototype and the fully bonded device containing additional features for medium perfusion. The suitability of the device for dynamic 3D cell culture is tested by growing human hepatocytes in the device under controlled fluid flow for a 14‐day period. Additionally, the study demonstrated the potential of using the device for pharmaceutical high‐throughput screening applications, such as predicting drug‐induced liver injury. The approach offers a facile strategy of rapid prototyping thermoplastic microfluidic organ chips with varying geometries, microstructures, and substrate materials. [ABSTRACT FROM AUTHOR]

Objectives: The SWI/SNF complex core subunit SMARCB1 is inactivated in a variety of neoplasms that share characteristic "rhabdoid" cytomorphology. The aim of this study was to evaluate SMARCB1-deficient soft tissue neoplasms on cytology to identify diagnostic clues.Methods: Eleven SMARCB1-deficient tumors, including six epithelioid sarcomas, three malignant rhabdoid tumors, one epithelioid malignant peripheral nerve sheath tumor (MPNST), and one poorly differentiated chordoma with fine-needle aspiration (FNA), serous effusion, or touch prep (TP) from two institutions, were included. Targeted next-generation sequencing (NGS) was performed in two cases.Results: Evaluation of FNA (n = 4), effusion (n = 4), and TP (n = 3) in nine adult and two pediatric patients demonstrated cellular samples (n = 11), epithelioid cells with rhabdoid morphology (n = 9), eccentrically located nuclei with prominent nucleoli (n = 7), and cytoplasmic bodies (n = 4); two patients were diagnosed on FNA with cell block. Immunohistochemistry (IHC) demonstrated SMARCB1 loss in all cases and keratin and/or EMA expression in all but the epithelioid MPNST; NGS identified SMARCB1 inactivation in both cases.Conclusions: SMARCB1-deficient soft tissue neoplasms comprise a variety of tumors with epithelioid morphology and frequent expression of keratin and/or EMA. Recognition of characteristic rhabdoid morphology on cytology can prompt IHC and/or NGS testing for SMARCB1 deficiency and help establish the diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we introduced an intermittent wave energy generator (IWEG) system with hydraulic power take-off (PTO) including accumulator storage parts. To convert unsteady wave energy into intermittent but stable electrical output power, theoretical models, including wave energy capture, hydraulic energy storage, and torque balance between hydraulic motor and electrical generator, have been developed. Then, the integrated IWEG simulator was constructed and tested at the Ningbo Institute of Technology. Through a series of experimental tests, the relationship between operating flow rates and pressure drops across the hydraulic motor was established. Furthermore, on the basis of the pressure drop signal, we proposed a feedback control method on the basis of the pressure drop database as the feedback control signal to eliminate the disturbance of periodic peak pressure impulse through the regulation of the opening ratio of a proportional flow valve and achieved the effective and stable electric power output, albeit intermittently. Compared with the previous complex control theories and algorithms, this method can keep the power output more stable over a wide range of operating conditions. Furthermore, experimental tests indicate that the IWEG system, with hydraulic PTO, including hydraulic accumulator and proportional flow control valve, is simple, reliable, and easy to control. Most importantly, the real-time power output is stable, and power quality and generation efficiency are significantly improved. [ABSTRACT FROM AUTHOR]

• A generalized method for effective early diagnosis of pancreatic cancer based on multiple instance learning. • A spatial transformation based on anatomical structures is proposed to reduce the background interference and enhance the detection stability for the learned features. • Instance-level contrastive learning is developed to aggregate different types of tumor features, maintain the tumor feature integrity, and enhance the diagnosis stability. • The method achieves remarkable generalization performance in unseen datasets and is sensitive to tumors smaller than 2 cm in diameter. Pancreatic cancer is a highly malignant cancer type with a high mortality rate. As no obvious symptoms are associated with this cancer type, most of the diagnoses are made when the patients are already in a late stage. In this work, we propose an automated method for effective early diagnosis of pancreatic cancer based on multiple instance learning with contrast-enhanced CT images. In this method, diagnosis stability and generalizability were improved through shape normalization based on anatomical structures as well as instance-level contrastive learning. Specifically, anatomically-guided shape normalization were developed to reconstruct the pancreatic regions of interest by spatial transformations, account for larger tumor parts in these regions, and hence enhance the extraction of pancreatic features. Moreover, instance-level contrastive learning was employed to aggregate different types of tumor features within the multiple instance learning framework. This learning approach can maintain the tumor feature integrity and enhance the diagnosis stability. Finally, a balance-adjustment strategy was designed to alleviate the class imbalance problem caused by the scarcity of tumor samples. Extensive experimental results demonstrated remarkable performance of our method when conducted cross-validation on an in-house dataset with 310 patients and independent test on two unseen datasets (a private test set with 316 and a publicly-available test set with 281). The proposed strategies also led to significant improvements in generalizability. Besides, the clinical significance of the proposed method was further verified through two independent test results in which tumors smaller than 2 cm in diameter were identified at accuracies of 80.9% and 90.1%, respectively. Overall, our method provides a potentially successful tool for early diagnosis of pancreatic cancer. Our source codes will be released at https://github.com/SJTUBME-QianLab/MIL_PAdiagnosis. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: Cerebrospinal fluid (CSF) segmentation in computed tomography (CT) is a key step in computer-aided detection (CAD) of acute ischemic stroke. Because of image noise, low contrast and intensity inhomogeneity, CSF segmentation has been a challenging task. A region-based active contour model, which is insensitive to contour initialization and robust to intensity inhomogeneity, was developed for segmenting CSF in brain CT images. Methods: The energy function of the region-based active contour model is composed of a range domain kernel function, a space domain kernel function, and an edge indicator function. By minimizing the energy function, the region of edge elements of the target could be automatically identified in images with less dependence on initial contours. The energy function was optimized by means of the deepest descent method with a level set framework. An overlap rate between segmentation results and the reference standard was used to assess the segmentation accuracy. The authors evaluated the performance of the proposed method on both synthetic data and real brain CT images. They also compared the performance level of our method to those of region-scalable fitting (RSF) and global convex segment (GCS) models. Results: For the experiment of CSF segmentation in 67 brain CT images, their method achieved an average overlap rate of 66% compared to the average overlap rates of 16% and 46% from the RSF model and the GCS model, respectively. Conclusions: Their region-based active contour model has the ability to achieve accurate segmentation results in images with high noise level and intensity inhomogeneity. Therefore, their method has great potential in the segmentation of medical images and would be useful for developing CAD schemes for acute ischemic stroke in brain CT images. [ABSTRACT FROM AUTHOR]

The onset and progression of Parkinson’s disease (PD) gradually affect the patient’s motor functions and quality of life. The PD motor symptoms are usually assessed using the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Automated MDS-UPDRS assessment has been recently required as an invaluable tool for PD diagnosis and telemedicine, especially with the recent novel coronavirus pandemic outbreak. This paper proposes a novel vision-based method for automated assessment of the arising-from-chair task, which is one of the key MDS-UPDRS components. The proposed method is based on a self-supervised metric learning scheme with a graph convolutional network (SSM-GCN). Specifically, for human skeleton sequences extracted from videos, a self-supervised intra-video quadruplet learning strategy is proposed to construct a metric learning formulation with prior knowledge, for improving the spatial-temporal representations. Afterwards, a vertex-specific convolution operation is designed to achieve effective aggregation of all skeletal joint features, where each joint or feature is weighted differently based on its relative factor of importance. Finally, a graph representation supervised mechanism is developed to maximize the potential consistency between the joint and bone information streams. Experimental results on a clinical dataset demonstrate the superiority of the proposed method over the existing sensor-based methods, with an accuracy of 70.60% and an acceptable accuracy of 98.65%. The analysis of discriminative spatial connections makes our predictions more clinically interpretable. This method can achieve reliable automated PD assessment using only easily-obtainable videos, thus providing an effective tool for real-time PD diagnosis or remote continuous monitoring. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS) has gained increasing importance in molecular detection due to its high specificity and sensitivity. Complex biofluids (e.g., cell lysates and serums) typically contain large numbers of different bio-molecules with various concentrations, making it extremely challenging to be reliably and comprehensively characterized via conventional single SERS spectra due to uncontrollable electromagnetic hot spots and irregular molecular motions. The traditional approach of directly reading out the single SERS spectra or calculating the average of multiple spectra is less likely to take advantage of the full information of complex biofluid systems. Herein, we propose to construct a spectral set with unordered multiple SERS spectra as a novel representation strategy to characterize full molecular information of complex biofluids. This new SERS representation not only contains details from each single spectra but captures the temporal/spatial distribution characteristics. To address the ordering-independent property of traditional chemometric methods (e.g., the Euclidean distance and the Pearson correlation coefficient), we introduce Wasserstein distance (WD) to quantitatively and comprehensively assess the quality of spectral sets on biofluids. WD performs its superiority for the quantitative assessment of the spectral sets. Additionally, WD benefits from its independence of the ordering of spectra in a spectral set, which is undesirable for traditional chemometric methods. With experiments on cell lysates and human serums, we successfully achieve the verification for the reproducibility between parallel samples, the uniformity at different positions in the same sample, the repeatability from multiple tests at one location of the same sample, and the cardinality effect of the spectral set. SERS spectral sets also manage to distinguish different classes of human serums and achieve higher accuracy than the traditional prostate-specific antigen in prostate cancer classification. The proposed SERS spectral set is a robust representation approach in accessing full information of biological samples compared to relying on a single or averaged spectra in terms of reproducibility, uniformity, repeatability, and cardinality effect. The application of WD further demonstrates the effectiveness and robustness of spectral sets in characterizing complex biofluid samples, which extends and consolidates the role of SERS. [Display omitted] • A novel SERS representation strategy for dynamic biofluids. • Statistical analysis for unordered spectral data. • Reliability, consistency, and reproducibility of the strategy. • Spectral details and distribution characteristics were captured. [ABSTRACT FROM AUTHOR]

• A causal intervention based automated method for generalized pancreatic cancer diagnosis. • A continuous image generation strategy and a pancreatic feature diversification method are proposed to simulate appearance potential variations on limited dataset and enrich the feature diversity. • A causal intervention strategy is developed for alleviating spurious correlations caused by appearance changes, enhance causal feature extraction, thereby solving the challenges of generalization. • Extensive experiments conducted on a multi-center dataset with more than 1000 subjects showed that the proposed method achieves remarkable generalization performance. Pancreatic cancer (PC) is a severely malignant cancer variant with high mortality. Since PC has no obvious symptoms, most PC patients are belatedly diagnosed at advanced disease stages. Recently, artificial intelligence (AI) approaches have demonstrated promising prospects for early diagnosis of pancreatic cancer. However, certain non-causal factors (such as intensity and texture appearance variations, also called confounders) tend to induce spurious correlation with PC diagnosis. This undermines the generalization performance and the clinical applicability of the AI-based PC diagnosis approaches. Therefore, we propose a causal intervention based automated method for pancreatic cancer diagnosis with contrast-enhanced computerized tomography (CT) images, where a confounding effects reduction scheme is developed for alleviating spurious correlations to achieve unbiased learning, thereby improving the generalization performance. Specifically, a continuous image generation strategy was developed to simulate wide variations of intensity differences caused by imaging heterogeneities, where Monte Carlo sampling is added to further enhance the continuity of simulated images. Then, to enhance the pancreatic texture variability, a texture diversification method was introduced in conjunction with gradient-based data augmentation. Finally, a causal intervention strategy was proposed to alleviate the adverse confounding effects by decoupling the causal and non-causal factors and combining them randomly. Extensive experiments showed remarkable diagnosis performance on a cross-validation dataset. Also, promising generalization performance with an average accuracy of 0.87 was attained on three independent test sets of a total of 782 subjects. Therefore, the proposed method shows high clinical feasibility and applicability for pancreatic cancer diagnosis. [Display omitted] [ABSTRACT FROM AUTHOR]

Allergic rhinitis (AR) is an allergic disease characterized as (immunoglobulin, IgE)-mediated type I hypersensitivity disorder. Regulatory T cells (Tregs) play a crucial role in AR. In the present study, we aimed to investigate the mechanism of how Tregs are regulated by long noncoding RNA HCP5 and the regulatory role of HCP5 in IL-13-induced inflammatory response in nasal epithelial cells (NECs) from AR patients. Peripheral blood mononuclear cells (PBMCs) and NECs were obtained from collected blood samples and nasal epithelial tissues. CD4+ T cells and Tregs were purified using certain cell isolation kits from PBMCs and Tregs were also differentiated from CD4+ T cells using recombinant human IL-2 and TGF-β. The expression levels of HCP5, miR-16, ATXN2L, GM-CSF, eotaxin, and MUC5AC were detected by real-time PCR and western blot. The concentrations of inflammatory cytokines were detected by enzyme-linked immunosorbent assay (ELISA). The interaction among HCP5, miR-16, and ATXN2L were verified by dual-luciferase reporter assay. lncRNA HCP5 expression dramatically downregulated in PBMCs, CD4+ T cells, Tregs, and nasal tissues of AR patients, as well as in IL-13-treated NECs. HCP5 promoted Tregs differentiation and proliferation via targeting miR-16/ATXN2L axis. Additionally, HCP5 inhibited IL-13-induced GM-CSF, eotaxin, and MUC5AC production in NECs. HCP5 sponged miR-16 and negatively regulated its expression, and miR-16 targeted ATXN2L and inhibition of miR-16 suppressed IL-13-induced GM-CSF, eotaxin, and MUC5AC expression. HCP5/miR-16/ATXN2L axis mediated Tregs proliferation and functions in AR. Besides, the regulation of IL-13-induced dysfunction of NECs by lncRNA HCP5 depended on miR-16/ATXN2L in the inflammatory response of AR. [ABSTRACT FROM AUTHOR]

Environmental conditions contribute to plant growth and metabolism. This study aimed to determine a suitable environment and climate for large-scale artificial cultivation of an endangered plant, Tetrastigma hemsleyanum, by investigating the seasonal variations influencing the flavonoid biosynthetic selectivity and antioxidant activity of its major metabolites. Under conditions of precipitation (2.0~6.6 mm), temperature (17.5~24.1°C), humidity (67.3~80.2%), and sunshine duration (3.4~5.8 h) from April to May, the total flavonoid content in T. hemsleyanum reached higher levels between 281.3 and 392.8 μg/g. In the second half of April, the production selectivity (PS) of isoorientin (IsoO), orientin (Or), rutin (Rut), isoquercitin (IsoQ), kaempferol-3-O-rutinoside (Km3rut), astragalin (Ast), quercetin (Qu), apigenin (Ap), and kaempferol (Km) were 0.30, 0.06, 0.07, 0.07, 0.00, 0.04, 0.38, 0.05, and 0.03, respectively. Naringenin was dehydrogenated or hydroxylated to initiate two parallel reaction pathways for flavonoid biosynthesis in T. hemsleyanum: path I subsequently generated flavone derivatives including apigenin, luteolin, orientin, and isoorientin, and path II subsequently generated flavonol derivatives including Km, Qu, IsoQ, Rut, Ast, and Km3rut. The reaction selectivity of path II (RPSII) from January 1 to September 30 was considerably higher than that of path I (RPSI), except for March 16–31. In addition, either the content or antioxidant activity of three major metabolites in T. hemsleyanum followed the order of phenolic compounds > polysaccharides > sterols, and exhibited dynamic correlations with environmental factors. Naringenin favored hydroxylation and derived six flavonol compounds from January to September, and favored dehydrogenation and derived three flavone compounds from October to December. In most months of a year, Km preferentially favored hydroxylation rather than glucosylation. [ABSTRACT FROM AUTHOR]

Background: To investigate the value of 18F-FDG PET/CT molecular radiomics combined with a clinical model in predicting thoracic lymph node metastasis (LNM) in invasive lung adenocarcinoma (≤ 3 cm). Methods: A total of 528 lung adenocarcinoma patients were enrolled in this retrospective study. Five models were developed for the prediction of thoracic LNM, including PET radiomics, CT radiomics, PET/CT radiomics, clinical and integrated PET/CT radiomics-clinical models. Ten PET/CT radiomics features and two clinical characteristics were selected for the construction of the integrated PET/CT radiomics-clinical model. The predictive performance of all models was examined by receiver operating characteristic (ROC) curve analysis, and clinical utility was validated by nomogram analysis and decision curve analysis (DCA). Results: According to ROC curve analysis, the integrated PET/CT molecular radiomics-clinical model outperformed the clinical model and the three other radiomics models, and the area under the curve (AUC) values of the integrated model were 0.95 (95% CI: 0.93–0.97) in the training group and 0.94 (95% CI: 0.89–0.97) in the test group. The nomogram analysis and DCA confirmed the clinical application value of this integrated model in predicting thoracic LNM. Conclusions: The integrated PET/CT molecular radiomics-clinical model proposed in this study can ensure a higher level of accuracy in predicting the thoracic LNM of clinical invasive lung adenocarcinoma (≤ 3 cm) compared with the radiomics model or clinical model alone. [ABSTRACT FROM AUTHOR]

Digital polymerase chain reaction (digital PCR) can provide absolute quantification of target nucleic acids with high sensitivity, excellent precision, and superior resolution. Digital PCR has broad applications in both life science research and clinical molecular diagnostics. However, limited by current fluorescence imaging methods, parallel quantification of multiple target molecules in a single digital PCR remains challenging. Here, we present a multiplex digital PCR method using digital melting curve analysis (digital MCA) with a SlipChip microfluidic system. The self-partitioning SlipChip (sp-SlipChip) can generate an array of nanoliter microdroplets with trackable physical positions using a simple loading-and-slipping operation. A fluorescence imaging adaptor and an in situ thermal cycler can be used to perform digital PCR and digital MCA on the sp-SlipChip. The unique signature melting temperature (Tm) designed for amplification products can be used as a fingerprint to further classify the positive amplification partitions into different subgroups. Amplicons with Tm differences as low as 1.5 degrees celsius were clearly separated, and multiple amplicons in the same partition could also be distinguished by digital MCA. We further demonstrated this digital MCA method with simultaneous digital quantification of five common respiratory pathogens, including Staphylococcus aureus, Acinetobacter baumannii, Streptococcus pneumoniae, Hemophilus influenzae, and Klebsiella pneumoniae. Since digital MCA only requires an intercalation dye instead of sequence-specific hydrolysis probes to perform multiplex digital PCR analysis, it can be less expensive and not limited to the number of fluorescence channels. [ABSTRACT FROM AUTHOR]

The article titled "Rapid Prototyping of Thermoplastic Microfluidic 3D Cell Culture Devices by Creating Regional Hydrophilicity Discrepancy" discusses a new method for creating fluid guides in microchannels, resulting in efficient hydrogel flow in microfluidic devices. The authors demonstrate the potential of these devices for high-throughput screening in the pharmaceutical industry. This approach allows for the rapid prototyping of organ chips with different geometries, microstructures, and substrate materials. The authors of the article are Haiqing Bai, Kristen N. Peters Olson, Ming Pan, Thomas Marshall, Hardeep Singh, Jingzhe Ma, Paige Gilbride, Yu-Chieh Yuan, Jenna McCormack, Longlong Si, Sushila Maharjan, Di Huang, Xiaohua Qian, Carol Livermore, Yu Shrike Zhang, and Xin Xie. [Extracted from the article]

Automated pancreatic cancer segmentation is crucial for successful clinical aid diagnosis and surgical planning. However, the tiny size and inconspicuous boundaries of pancreatic cancer lesions lead to poor segmentation performance with single-modality imaging. The commonly used registration-based multi-modal fusion strategies may not only introduce uncertainties arising from registration, but also underutilize the complementary information between different modalities. Thus, to achieve different modality-based tumor segmentation, we propose for the first time a registration-free multi-modal and multi-scale adversarial segmentation network (MMSA-Net), which consist of a shared encoder and a dual decoder. Specifically, MMSA-Net combine two complementary modules, inter-modality adversarial learning and intra-modality multi-scale adversarial supervision, to obtain mode-specific segmentation results while facilitating multi-modal fusion. The inter-modality adversarial learning module facilitates the fusion of modality-shared features among different modalities by strengthening the similarity of features extracted by the shared encoder. The intra-modality multi-scale adversarial supervision emphasizes modality-specific features in different decoding paths, inherits and fuses modality-shared features while preserving the feature specificity of each modality, thus outputting competitive modality-specific segmentation results for each modality. Quantitative and qualitative experimental results on multi-parametric MRI pancreatic cancer data show that our method can effectively improve the performance of multi-modal segmentation. The method proposed in this work is expected to be another potential paradigm for addressing multi-modal segmentation tasks in addition to registration. Our source codes will be released at https://github.com/SJTUBME-QianLab/PancreaticCancer%5fSeg%5fAdvLearning , once the manuscript is accepted for publication. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is a lethal malignant tumor with one of the worst prognoses. Accurate segmentation of pancreatic cancer is vital in clinical diagnosis and treatment. Due to the unclear boundary and small size of cancers, it is challenging to both manually annotate and automatically segment cancers. Considering 3D information utilization and small sample sizes, we propose a model-driven deep learning method for pancreatic cancer segmentation based on spiral transformation. Specifically, a spiral-transformation algorithm with uniform sampling was developed to map 3D images onto 2D planes while preserving the spatial relationship between textures, thus addressing the challenge in effectively applying 3D contextual information in a 2D model. This study is the first to introduce spiral transformation in a segmentation task to provide effective data augmentation, alleviating the issue of small sample size. Moreover, a transformation-weight-corrected module was embedded into the deep learning model to unify the entire framework. It can achieve 2D segmentation and corresponding 3D rebuilding constraint to overcome non-unique 3D rebuilding results due to the uniform and dense sampling. A smooth regularization based on rebuilding prior knowledge was also designed to optimize segmentation results. The extensive experiments showed that the proposed method achieved a promising segmentation performance on multi-parametric MRIs, where T2, T1, ADC, DWI images obtained the DSC of 65.6%, 64.0%, 64.5%, 65.3%, respectively. This method can provide a novel paradigm to efficiently apply 3D information and augment sample sizes in the development of artificial intelligence for cancer segmentation. Our source codes will be released at https://github.com/SJTUBME-QianLab/ Spiral-Segmentation. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to develop a clinically practical model to predict EGFR mutation in lung adenocarcinoma patients according to radiomics signatures based on PET/CT and clinical risk factors. Methods: This retrospective study included 583 lung adenocarcinoma patients, including 295 (50.60%) patients with EGFR mutation and 288 (49.40%) patients without EGFR mutation. The clinical risk factors associated with lung adenocarcinoma were collected at the same time. We developed PET/CT, CT, and PET radiomics models for the prediction of EGFR mutation using multivariate logistic regression analysis, respectively. We also constructed a combined PET/CT radiomics-clinical model by nomogram analysis. The diagnostic performance and clinical net benefit of this risk-scoring model were examined via receiver operating characteristic (ROC) curve analysis while the clinical usefulness of this model was evaluated by decision curve analysis (DCA). Results: The ROC analysis showed predictive performance for the PET/CT radiomics model (AUC = 0.76), better than the PET model (AUC = 0.71, Delong test: Z = 3.03, p value = 0.002) and the CT model (AUC = 0.74, Delong test: Z = 1.66, p value = 0.098). Also, the PET/CT radiomics-clinical combined model has a better performance (AUC = 0.84) to predict EGFR mutation than the PET/CT radiomics model (AUC = 0.76, Delong test: D = 2.70, df = 790.81, p value < 0.001) or the clinical model (AUC = 0.81, Delong test: Z = 3.46, p value < 0.001). Conclusions: We demonstrated that the combined PET/CT radiomics-clinical model has an advantage to predict EGFR mutation in lung adenocarcinoma. Key Points: • Radiomics from lung tumor increase the efficiency of the prediction for EGFR mutation in clinical lung adenocarcinoma on PET/CT. • A radiomic nomogram was developed to predict EGFR mutation. • Combining PET/CT radiomics-clinical model has an advantage to predict EGFR mutation. [ABSTRACT FROM AUTHOR]

• A vision-based fine-grained classification model is proposed for automated assessment of Parkinsonian finger-tapping tests. • A skeleton-based three-stream fine-grained classification network with Markov chained fusion is developed. • A mini-batch-based balanced algorithm is proposed to ensure that each mini-batch is inter-class balanced, thus reducing the effect of imbalanced data. Movement disorder of Parkinson's disease (PD) is usually quantified by the Movement Disorders Society-sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to evaluate its severity. However, the lack of well-trained experts and subjective inter-rater variability often limit an effective and objective assessment in clinical practice. Hence, developing an automated assessment method for movement disorders in PD is crucial. Here, we present a novel vision-based fine-grained action recognition model to cope with one of the most critical and challenging tasks in clinical scales: the finger-tapping test. Specifically, we establish a three-stream fine-grained classification network with a Markov chain fusion model to aggregate multi-stream information of the skeleton sequence from finger-tapping test videos. Then, we develop a spatial–temporal attention mechanism to capture rich spatial and temporal long-range dependencies from skeleton data and introduce a symmetric bilinear pooling layer to enrich the local feature representation of each stream's output. Besides, a mini-batch-based balanced algorithm is designed to ensure that the samples in each mini-batch are inter-class balanced, thus mitigating the effect of imbalanced data on neural networks. Finally, our three-stream fine-grained classification network achieved an accuracy of 72.4% and an acceptable accuracy of 98.3% on 157 patients and 744 videos. Extensive experiments further confirm our approach's effectiveness and reliability. This method does not require any wearable device and has excellent potential for remote monitoring of PD patients in the future. [ABSTRACT FROM AUTHOR]

Pancreas segmentation in medical imaging is of great significance for clinical pancreas diagnostics and treatment. However, the large population variations in the pancreas shape and volume cause enormous segmentation difficulties, even for state-of-the-art algorithms utilizing fully convolutional neural networks (FCNs). Specifically, pancreas segmentation suffers from the loss of statement temporal information in 2D methods, and the high computational cost of 3D methods. To alleviate these problems, we propose a probabilistic-map-guided bi-directional recurrent UNet (PBR-UNet) architecture, which fuses intra-slice information and inter-slice probabilistic maps into a local 3D hybrid regularization scheme, which is followed by a bi-directional recurrent optimization scheme. The PBR-UNet method consists of an initial estimation module for efficiently extracting pixel-level probabilistic maps and a primary segmentation module for propagating hybrid information through a 2.5D UNet architecture. Specifically, local 3D information is inferred by combining an input image with the probabilistic maps of the adjacent slices into multi-channel hybrid data, and then hierarchically aggregating the hybrid information of the entire segmentation network. Besides, a bi-directional recurrent optimization mechanism is developed to update the hybrid information in both the forward and the backward directions. This allows the proposed network to make full and optimal use of the local context information. Quantitative and qualitative evaluation was performed on the NIH Pancreas-CT and MSD pancreas dataset, and our proposed PBR-UNet method achieved similar segmentation results with less computational cost compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is a malignant form of cancer with one of the worst prognoses. The poor prognosis and resistance to therapeutic modalities have been linked to TP53 mutation. Pathological examinations, such as biopsies, cannot be frequently performed in clinical practice; therefore, noninvasive and reproducible methods are desired. However, automatic prediction methods based on imaging have drawbacks such as poor 3D information utilization, small sample size, and ineffectiveness multi-modal fusion. In this study, we proposed a model-driven multi-modal deep learning scheme to overcome these challenges. A spiral transformation algorithm was developed to obtain 2D images from 3D data, with the transformed image inheriting and retaining the spatial correlation of the original texture and edge information. The spiral transformation could be used to effectively apply the 3D information with less computational resources and conveniently augment the data size with high quality. Moreover, model-driven items were designed to introduce prior knowledge in the deep learning framework for multi-modal fusion. The model-driven strategy and spiral transformation-based data augmentation can improve the performance of the small sample size. A bilinear pooling module was introduced to improve the performance of fine-grained prediction. The experimental results show that the proposed model gives the desired performance in predicting TP53 mutation in pancreatic cancer, providing a new approach for noninvasive gene prediction. The proposed methodologies of spiral transformation and model-driven deep learning can also be used for the artificial intelligence community dealing with oncological applications. Our source codes with a demon will be released at https://github.com/SJTUBME-QianLab/SpiralTransform. [ABSTRACT FROM AUTHOR]

• An ensemble CNN is established to fuse multiple features from different architectures. • Multi-scale capability is equipped with the network by using various-perspective features. • Improved CNN model reliability by feature visualization and expert selection. For patients with glioblastoma multiform (GBM), differentiating pseudoprogression (PsP) from true tumor progression (TTP) is a challenging and time-consuming task for radiologists. Although deep neural networks can automatically diagnose PsP and TTP, lacking of interpretability has always been its major drawback. To overcome these shortcomings and produce more reliable outcomes, we propose a transparency-guided ensemble convolutional neural network (CNN) to automatically discriminate PsP and TTP in magnetic resonance imaging (MRI). A total of 84 patients with GBM were enrolled in the study. First, three typical convolutional neutral networks, namely VGG, ResNet and DenseNet, were trained to distinguish PsP and TTP. Subsequently, we used class-specific gradient information from convolutional layers to highlight the important regions in MRI scans. And radiologists selected the most lesion-relevant layer for each CNN. Finally, the selected layers are utilized to guide the construction of a multi-scale ensemble CNN whose classification accuracy reached 90.20%, and whose specificity is promoted 20% than that of a single CNN. The results demonstrate the presented network can enhance the reliability and accuracy of CNNs. [ABSTRACT FROM AUTHOR]

With the advances in commercial light field cameras, light field image processing has attracted considerable attention from researchers. In this paper, we propose a novel method for generating stereoscopic images from a light field image pair with flexible control over the convergence of the virtual stereo cameras. We have developed a light field image-capturing prototype that consists of two horizontally arranged light field cameras (i.e., with their optical axes being parallel to each other). When using our proposed device for image/video capture, stereo photographers can concentrate on how to capture the desired visual experience without being frequently disturbed by having to manipulate the stereo camera parameters, i.e., the convergence angle of a stereo camera. During postprocessing, our method estimates accurate disparity maps for the light field image pair and then generates the target stereoscopic images that satisfy the desired stereo camera convergence requirements by adopting a novel view synthesis method for light field images. We have conducted extensive experiments to demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Purpose: Pseudoprogression (PsP) occurs in 20–30% of patients with glioblastoma multiforme (GBM) after receiving the standard treatment. PsP exhibits similarities in shape and intensity to the true tumor progression (TTP) of GBM on the follow‐up magnetic resonance imaging (MRI). These similarities pose challenges to the differentiation of these types of progression and hence the selection of the appropriate clinical treatment strategy. Methods: To address this challenge, we introduced a novel feature learning method based on deep convolutional generative adversarial network (DCGAN) and AlexNet, termed DC‐AL GAN, to discriminate between PsP and TTP in MRI images. Due to the adversarial relationship between the generator and the discriminator of DCGAN, high‐level discriminative features of PsP and TTP can be derived for the discriminator with AlexNet. We also constructed a multifeature selection module to concatenate features from different layers, contributing to more powerful features used for effectively discriminating between PsP and TTP. Finally, these discriminative features from the discriminator are used for classification by a support vector machine (SVM). Tenfold cross‐validation (CV) and the area under the receiver operating characteristic (AUC) were applied to evaluate the performance of this developed algorithm. Results: The accuracy and AUC of DC‐AL GAN for discriminating PsP and TTP after tenfold CV were 0.920 and 0.947. We also assessed the effects of different indicators (such as sensitivity and specificity) for features extracted from different layers to obtain a model with the best classification performance. Conclusions: The proposed model DC‐AL GAN is capable of learning discriminative representations from GBM datasets, and it achieves desirable PsP and TTP classification performance superior to other state‐of‐the‐art methods. Therefore, the developed model would be useful in the diagnosis of PsP and TTP for GBM. [ABSTRACT FROM AUTHOR]

Polycaprolactone-g-glycidyl methacrylate (PCL-g-GMA), a reactive interfacial agent for PCL-starch blends, is synthesized using supercritical carbon dioxide (scCO2) as reaction medium and relatively high molecular weight PCL (Mw = 50,000). Higher GMA and radical initiator intakes lead to higher functionalization degrees (FD) for PCL-g-GMA samples. A mathematical model is developed to describe the correlation between monomer and initiator intake and FD values. The model shows an excellent R2-value (0.978), which implies a good fit of the experimental data. Comparison of this model with a similar one for the reaction in the melt clearly indicates a better reaction efficiency in scCO2. Furthermore, GPC results show that less degradation occurred for samples made in scCO2. Finally, the use of the PCL-g-GMA made in scCO2 (as interfacial agent) in ternary blend of PCL/starch/PCL-g-GMA results in better mechanical properties with respect to those obtained by using the same graft-copolymer as prepared in the melt. [ABSTRACT FROM AUTHOR]

Objectives: Accurate diagnosis of malignant peripheral nerve sheath tumor (MPNST) is often challenging on fine-needle aspiration (FNA) or core needle biopsy. Recurrent mutations in EED and SUZ12, which encode subunits of polycomb repressive complex 2 (PRC2), have been identified in 70% to 92% of MPNSTs; PRC2 inactivation leads to loss of trimethylation of lysine 27 of histone H3 (H3K27me3). We evaluated the utility of H3K27me3 immunohistochemistry for distinguishing MPNST from its cytomorphologic mimics.Methods: H3K27me3 immunohistochemistry was performed on 180 cases of spindle cell neoplasms sampled by FNA (n = 66) and needle biopsy (n = 114), and loss of nuclear staining was scored. Tumor types included MPNST, dedifferentiated liposarcoma, schwannoma, solitary fibrous tumor, leiomyosarcoma, melanoma, synovial sarcoma, sarcomatoid carcinoma, gastrointestinal stromal tumor, desmoid fibromatosis, low-grade fibromyxoid sarcoma, and unclassified spindle cell sarcoma/undifferentiated pleomorphic sarcoma.Results: Complete loss of H3K27me3 was observed in 54% (13/24) of MPNSTs. In contrast, only two (of 156) histologic mimics showed complete loss of H3K27me3. Partial loss of H3K27me3 was present in a subset of cases (26/180), including both MPNST and non-MPNSTs.Conclusions: Complete loss of H3K27me3 is a highly specific (98.7%) marker of MPNST that can distinguish MPNST from cytomorphologic mimics in FNA cell block and small biopsy specimens. [ABSTRACT FROM AUTHOR]

• This is the first approach to predict lymph node metastasis in pancreatic cancer based on CT using deep learning scheme for assisted prognosis assessment. • A dual-transformation with contrastive learning framework is developed to overcome the challenges in fine-grained prediction with small sample sizes. • A dynamic surface projection algorithm is developed to transform 3D images into a 2D space, enhancing diversity and complementarity of the dataset. • Dual-transformation-guided contrastive learning is designed to fully mine the additional supervised information for more discriminative features. Pancreatic cancer is a malignant tumor, and its high recurrence rate after surgery is related to the lymph node metastasis status. In clinical practice, a preoperative imaging prediction method is necessary for prognosis assessment and treatment decision; however, there are two major challenges: insufficient data and difficulty in discriminative feature extraction. This paper proposed a deep learning model to predict lymph node metastasis in pancreatic cancer using multiphase CT, where a dual-transformation with contrastive learning framework is developed to overcome the challenges in fine-grained prediction with small sample sizes. Specifically, we designed a novel dynamic surface projection method to transform 3D data into 2D images for effectively using the 3D information, preserving the spatial correlation of the original texture information and reducing computational resources. Then, this dynamic surface projection was combined with the spiral transformation to establish a dual-transformation method for enhancing the diversity and complementarity of the dataset. A dual-transformation-based data augmentation method was also developed to produce numerous 2D-transformed images to alleviate the effect of insufficient samples. Finally, the dual-transformation-guided contrastive learning scheme based on intra-space-transformation consistency and inter-class specificity was designed to mine additional supervised information, thereby extracting more discriminative features. Extensive experiments have shown the promising performance of the proposed model for predicting lymph node metastasis in pancreatic cancer. Our dual-transformation with contrastive learning scheme was further confirmed on an external public dataset, representing a potential paradigm for the fine-grained classification of oncological images with small sample sizes. The code will be released at https://github.com/SJTUBME-QianLab/Dual-transformation. [ABSTRACT FROM AUTHOR]

Background: The term noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) has been proposed to replace noninvasive follicular variant of papillary thyroid carcinoma (FVPTC) in recognition of the indolent behavior of this tumor. The ability to differentiate NIFTP from classical papillary thyroid carcinoma (cPTC) by fine-needle aspiration (FNA) would facilitate conservative management for NIFTP. The aim of this study was to determine if NIFTP can be distinguished prospectively from cPTC. Methods: From June 2015 to January 2016, thyroid FNAs with a diagnosis of 'malignant' or 'suspicious for malignancy' were prospectively scored for features associated with NIFTP/FVPTC (microfollicular architecture) or cPTC (papillae, psammomatous calcifications, sheet-like architecture, and nuclear pseudoinclusions) and categorized as NIFTP/FVPTC, cPTC, or indeterminate. Results were correlated with subsequent histologic diagnoses. Results: The study included 52 patients with 56 resected nodules with a cytologic diagnosis of 'malignant' (43/56) or 'suspicious for malignancy' (13/56). Forty-nine patients (94%) underwent initial total thyroidectomy. Histopathologic diagnoses included 42 cPTC, 8 NIFTP, 3 invasive FVPTC, 2 follicular adenomas, and 1 poorly differentiated carcinoma. Excluding 7 indeterminate cases, 89% (8/9) of nodules classified as NIFTP/FVPTC on FNA demonstrated follicular-patterned lesions on histology (5 NIFTP, 1 invasive FVPTC, 2 follicular adenomas). Cytopathologists prospectively identified cPTC in 95% (38/40) of cases. Conclusions: In thyroid FNAs with cytologic features concerning for PTC, NIFTP/FVPTC can be distinguished from cPTC in most cases by assessing a limited number of features. Therefore, it is both feasible and appropriate to attempt to separate NIFTP/FVPTC from cPTC on FNA to promote appropriate clinical management. [ABSTRACT FROM AUTHOR]

• A tree-structure-guided graph convolutional network with contrastive learning scheme is developed for automated and objective video assessment on Parkinsonian hand movements. • A novel tri-directional skeleton tree scheme is developed for effective fine-grained extraction of spatial features. • A tree max-pooling module is designed to improve the learning ability of the model to salient fine-grained motion features. • A group-sparsity-induced momentum contrast is developed to capture more discriminative spatial-temporal dynamics and realize stable feature learning. Bradykinesia is one of the core motor symptoms of Parkinson's disease (PD). Neurologists typically perform face-to-face bradykinesia assessment in PD patients according to the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). As this human-expert assessment lacks objectivity and consistency, an automated and objective assessment scheme for bradykinesia is critically needed. In this paper, we propose a tree-structure-guided graph convolutional network with contrastive learning scheme to solve the challenge of difficulty in fine-grained feature extraction and insufficient model stability, finally achieving the video-based automated assessment of Parkinsonian hand movements, which represent a vital MDS-UPDRS component for examining upper-limb bradykinesia. Specifically, a tri-directional skeleton tree scheme is proposed to achieve effective fine-grained modeling of spatial hand dependencies. In this scheme, hand skeletons are extracted from videos, and then the spatial structures of these skeletons are constructed through depth-first tree traversal. Afterwards, a tree max-pooling module is employed to establish remote exchange between outer and inner nodes, hierarchically gather the most salient motion features, and hence achieve fine-grained mining. Finally, a group-sparsity-induced momentum contrast is also developed to learn similar motion patterns under different interference through contrastive learning. This can promote stable learning of discriminative spatial-temporal features with invariant motion semantics. Comprehensive experiments on a large clinical video dataset reveal that our method achieves competitive results, and outperforms other sensor-based and RGB-depth methods. The proposed method leads to accurate assessment of PD bradykinesia through videos collected by low-cost consumer cameras of limited capabilities. Hence, our work provides a convenient tool for PD telemedicine applications with modest hardware requirements. [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel dual meta-learning framework based on idle multi-parametric MRIs is developed to enhance the segmentation performance of CT pancreatic cancer, one to explore commonalities in idle multi-parametric MRIs, and the other to obtain salient knowledge of CT images. • Intermediate modalities are used to smoothly fill the gap between different modalities, providing abundant intermediate representations to boost the performance of our meta-learning scheme. • This approach is an effective pancreatic cancer segmentation framework, which can be easily integrated into other segmentation networks. • This method is a potential paradigm for leveraging idle data to mitigate the challenge of data scarcity. Automated segmentation of pancreatic cancer is vital for clinical diagnosis and treatment. However, the small size and inconspicuous boundaries limit the segmentation performance, which is further exacerbated for deep learning techniques with the few training samples due to the high threshold of image acquisition and annotation. To alleviate this issue caused by the small-scale dataset, we collect idle multi-parametric MRIs of pancreatic cancer from different studies to construct a relatively large dataset for enhancing the CT pancreatic cancer segmentation. Therefore, we propose a deep learning segmentation model with the dual meta-learning framework for pancreatic cancer. It can integrate the common knowledge of tumors obtained from idle MRIs and salient knowledge from CT images, making high-level features more discriminative. Specifically, the random intermediate modalities between MRIs and CT are first generated to smoothly fill in the gaps in visual appearance and provide rich intermediate representations for ensuing meta-learning scheme. Subsequently, we employ intermediate modalities-based model-agnostic meta-learning to capture and transfer commonalities. At last, a meta-optimizer is utilized to adaptively learn the salient features within CT data, thus alleviating the interference due to internal differences. Comprehensive experimental results demonstrated our method achieved the promising segmentation performance, with a max Dice score of 64.94% on our private dataset, and outperformed state-of-the-art methods on a public pancreatic cancer CT dataset. The proposed method is an effective pancreatic cancer segmentation framework, which can be easily integrated into other segmentation networks and thus promises to be a potential paradigm for alleviating data scarcity challenges using idle data. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: Cardiac computed tomography (CT) is widely used in clinical diagnosis of cardiovascular diseases. Whole heart segmentation (WHS) plays a vital role in developing new clinical applications of cardiac CT. However, the shape and appearance of the heart can vary greatly across different scans, making the automatic segmentation particularly challenging. The objective of this work is to develop and evaluate a multiatlas segmentation (MAS) scheme using a new atlas ranking and selection algorithm for automatic WHS of CT data. Research on different MAS strategies and their influence on WHS performance are limited. This work provides a detailed comparison study evaluating the impacts of label fusion, atlas ranking, and sizes of the atlas database on the segmentation performance. Methods: Atlases in a database were registered to the target image using a hierarchical registration scheme specifically designed for cardiac images. A subset of the atlases were selected for label fusion, according to the authors' proposed atlas ranking criterion which evaluated the performance of each atlas by computing the conditional entropy of the target image given the propagated atlas labeling. Joint label fusion was used to combine multiple label estimates to obtain the final segmentation. The authors used 30 clinical cardiac CT angiography (CTA) images to evaluate the proposed MAS scheme and to investigate different segmentation strategies. Results: The mean WHS Dice score of the proposed MAS method was 0.918±0.021, and the mean runtime for one case was 13.2 min on a workstation. This MAS scheme using joint label fusion generated significantly better Dice scores than the other label fusion strategies, including majority voting (0.901±0.276, p < 0.01), locally weighted voting (0.905±0.0247, p < 0.01), and probabilistic patch-based fusion (0.909±0.0249, p < 0.01). In the atlas ranking study, the proposed criterion based on conditional entropy yielded a performance curve with higher WHS Dice scores compared to the conventional schemes (p < 0.03). In the atlas database study, the authors showed that the MAS using larger atlas databases generated better performance curves than the MAS using smaller ones, indicating larger atlas databases could produce more accurate segmentation. Conclusions: The authors have developed a new MAS framework for automatic WHS of CTA and investigated alternative implementations of MAS. With the proposed atlas ranking algorithm and joint label fusion, the MAS scheme is able to generate accurate segmentation within practically acceptable computation time. This method can be useful for the development of new clinical applications of cardiac CT. [ABSTRACT FROM AUTHOR]

This work mainly investigated the interferences of NaCl treatment to major metabolite accumulation, individual flavonoid biosynthesis and photosynthetic activity in Tetrastigma hemsleyanum. The results showed that the accumulation of phenolic acids, steroids and polysaccharides in T. hemsleyanum was significantly enhanced after the treatment of 50 or 100 mM NaCl for 12 days, and after 150 mM NaCl treatment for 3 days, the total content of flavonoids reached the highest value of 219.5 μg·g‐1−1 and was higher by 3.8 times in comparison with the control (CK). In addition, the salt stress would generally enhance the reaction selectivity of Path II (RPS II) to produce flavonol derivatives (Rut, IsoQ, Km3rut, Ast, Qu, Km) and reduce the reaction selectivity of Path I (RPS I) to produce flavone derivatives (IsoO, Or, Ap), and after the treatment of 150 mM NaCl for 3 days, the RPS II and the RPS I reached the highest level of 0.785 and the lowest level of 0.215, respectively. Moreover, salt stress demonstrated significant and diverse effects on photosynthetic characteristics of T. hemsleyanum. Compared with CK, the values of Pn , Cond, Tr , Fv/Fm, Y(Ⅱ) and qP increased first and then declined with increasing NaCl concentration and stress time, and the highest level of Pn , Fv/Fm and Y (II)occurred after 50 mM NaCl treatment. These findings clearly showed that 50 mM NaCl treatment results in favorable changes in photosynthesis and secondary metabolites accumulation in T. hemsleyanum. • 50 mM NaCl treatment is favorable for metabolite accumulation. • Salt treatment is beneficial to generate more flavonol derivatives. • T. hemsleyanum is capable to tolerate the stress of 50 mM NaCl. • Mild salt stress is suitable to artificial cultivation before harvest. [ABSTRACT FROM AUTHOR]

Although primary basal cell carcinoma (BCC) represents an extremely common malignancy, metastases derived from BCC are exceedingly rare. The prognosis for metastatic BCC is poor, and little consensus exists regarding predictive factors or optimal treatment strategies. Here, we present the case of a 63-year-old man with BCC of the neck who subsequently developed multiple metastases to subcutaneous tissue, lymph nodes, and the parotid gland. Risk factors and clinical features of metastatic BCC are reviewed, as is the relationship of histopathologic subtype to metastatic behavior. Current chemotherapeutic and targeted therapies also are discussed in the context of recent advances in molecular biology. Gropper AB, Girouard SD, Hojman LP, Huang SJ, Qian X, Murphy GF, Vleugels RA. Metastatic basal cell carcinoma of the posterior neck: case report and review of the literature. [ABSTRACT FROM AUTHOR]