Parallel CNN-Deep Learning Clinical-Imaging Signature for Assessing Pathologic Grade and Prognosis of Soft Tissue Sarcoma Patients.

BACKGROUND: Traditional biopsies pose risks and may not accurately reflect soft tissue sarcoma (STS) heterogeneity. MRI provides a noninvasive, comprehensive alternative. PURPOSE: To assess the diagnostic accuracy of histological grading and prognosis in STS patients when integrating clinical-imaging parameters with deep learning (DL) features from preoperative MR images. STUDY TYPE: Retrospective/prospective. POPULATION: 354 pathologically confirmed STS patients (226 low-grade, 128 high-grade) from three hospitals and the Cancer Imaging Archive (TCIA), divided into training (n = 185), external test (n = 125), and TCIA cohorts (n = 44). 12 patients (6 low-grade, 6 high-grade) were enrolled into prospective validation cohort. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T/Unenhanced T1-weighted and fat-suppressed-T2-weighted. ASSESSMENT: DL features were extracted from MR images using a parallel ResNet-18 model to construct DL signature. Clinical-imaging characteristics included age, gender, tumor-node-metastasis stage and MRI semantic features (depth, number, heterogeneity at T1WI/FS-T2WI, necrosis, and peritumoral edema). Logistic regression analysis identified significant risk factors for the clinical model. A DL clinical-imaging signature (DLCS) was constructed by incorporating DL signature with risk factors, evaluated for risk stratification, and assessed for progression-free survival (PFS) in retrospective cohorts, with an average follow-up of 23 ± 22 months. STATISTICAL TESTS: Logistic regression, Cox regression, Kaplan-Meier curves, log-rank test, area under the receiver operating characteristic curve (AUC)，and decision curve analysis. A P-value <0.05 was considered significant. RESULTS: The AUC values for DLCS in the external test, TCIA, and prospective test cohorts (0.834, 0.838, 0.819) were superior to clinical model (0.662, 0.685, 0.694). Decision curve analysis showed that the DLCS model provided greater clinical net benefit over the DL and clinical models. Also, the DLCS model was able to risk-stratify patients and assess PFS. DATA CONCLUSION: The DLCS exhibited strong capabilities in histological grading and prognosis assessment for STS patients, and may have potential to aid in the formulation of personalized treatment plans. TECHNICAL EFFICACY: Stage 2.

An Artificial Enzyme for Asymmetric Nitrocyclopropanation of α,ß-Unsaturated Aldehydes-Design and Evolution.

The introduction of an abiological catalytic group into the binding pocket of a protein host allows for the expansion of enzyme chemistries. Here, we report the generation of an artificial enzyme by genetic encoding of a non-canonical amino acid that contains a secondary amine side chain. The non-canonical amino acid and the binding pocket function synergistically to catalyze the asymmetric nitrocyclopropanation of α,ß-unsaturated aldehydes by the iminium activation mechanism. The designer enzyme was evolved to an optimal variant that catalyzes the reaction at high conversions with high diastereo- and enantioselectivity. This work demonstrates the application of genetic code expansion in enzyme design and expands the scope of enzyme-catalyzed abiological reactions.

An MRI-Based Radiomics Nomogram to Assess Recurrence Risk in Sinonasal Malignant Tumors.

BACKGROUND: Sinonasal malignant tumors (SNMTs) have a high recurrence risk, which is responsible for the poor prognosis of patients. Assessing recurrence risk in SNMT patients is a current problem. PURPOSE: To establish an MRI-based radiomics nomogram for assessing relapse risk in patients with SNMT. STUDY TYPE: Retrospective. POPULATION: A total of 143 patients with 68.5% females (development/validation set, 98/45 patients). FIELD STRENGTH/SEQUENCE: A 1.5-T and 3-T, fat-suppressed fast spin echo (FSE) T2-weighted imaging (FS-T2WI), FSE T1-weighted imaging (T1WI), and FSE contrast-enhanced T1WI (T1WI + C). ASSESSMENT: Three MRI sequences were used to manually delineate the region of interest. Three radiomics signatures (T1WI and FS-T2WI sequences, T1WI + C sequence, and three sequences combined) were built through dimensional reduction of high-dimensional features. The clinical model was built based on clinical and MRI features. The Ki-67-based and tumor-node-metastasis (TNM) model were established for comparison. The radiomics nomogram was built by combining the clinical model and best radiomics signature. The relapse-free survival analysis was used among 143 patients. STATISTICAL TESTS: The intraclass/interclass correlation coefficients, univariate/multivariate Cox regression analysis, least absolute shrinkage and selection operator Cox regression algorithm, concordance index (C index), area under the curve (AUC), integrated Brier score (IBS), DeLong test, Kaplan-Meier curve, log-rank test, optimal cutoff values. A P value < 0.05 was considered statistically significant. RESULTS: The T1 + C-based radiomics signature had best prognostic ability than the other two signatures (T1WI and FS-T2WI sequences, and three sequences combined). The radiomics nomogram had better prognostic ability and less error than the clinical model, Ki-67-based model, and TNM model (C index, 0.732; AUC, 0.765; IBS, 0.185 in the validation set). The cutoff values were 0.2 and 0.7 and then the cumulative risk rates were calculated. DATA CONCLUSION: A radiomics nomogram for assessing relapse risk in patients with SNMT may provide better prognostic ability than the clinical model, Ki-67-based model, and TNM model. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 5.

Lumbar MR-based radiomics nomogram for detecting minimal residual disease in patients with multiple myeloma.

OBJECTIVES: Minimal residual disease (MRD) is a standard for assessing treatment response in multiple myeloma (MM). MRD negativity is considered to be the most powerful predictor of long-term good outcomes. This study aimed to develop and validate a radiomics nomogram based on magnetic resonance imaging (MRI) of the lumbar spine to detect MRD after MM treatment. METHODS: A total of 130 MM patients (55 MRD negative and 75 MRD positive) who had undergone MRD testing through next-generation flow cytometry were divided into a training set (n = 90) and a test set (n = 40). Radiomics features were extracted from lumbar spinal MRI (T1-weighted images and fat-suppressed T2-weighted images) by means of the minimum redundancy maximum relevance method and the least absolute shrinkage and selection operator algorithm. A radiomics signature model was constructed. A clinical model was established using demographic features. A radiomics nomogram incorporating the radiomics signature and independent clinical factor was developed using multivariate logistic regression analysis. RESULTS: Sixteen features were used to establish the radiomics signature. The radiomics nomogram included the radiomics signature and the independent clinical factor (free light chain ratio) and showed good performance in detecting the MRD status (area under the curve: 0.980 in the training set and 0.903 in the test set). CONCLUSIONS: The lumbar MRI-based radiomics nomogram showed good performance in detecting MRD status in MM patients after treatment, and it is helpful for clinical decision-making. KEY POINTS: • The presence or absence of minimal residual disease status has a strong predictive significance for the prognosis of patients with multiple myeloma. • A radiomics nomogram based on lumbar MRI is a potential and reliable tool for evaluating minimal residual disease status in MM.

Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors.

OBJECTIVES: This study evaluated the ability of a preoperative contrast-enhanced CT (CECT)-based radiomics nomogram to differentiate benign and malignant primary retroperitoneal tumors (PRT). METHODS: Images and data from 340 patients with pathologically confirmed PRT were randomly placed into training (n = 239) and validation sets (n = 101). Two radiologists independently analyzed all CT images and made measurements. Key characteristics were identified through least absolute shrinkage selection combined with four machine-learning classifiers (support vector machine, generalized linear model, random forest, and artificial neural network back propagation) to create a radiomics signature. Demographic data and CECT characteristics were analyzed to formulate a clinico-radiological model. Independent clinical variables were merged with the best-performing radiomics signature to develop a radiomics nomogram. The discrimination capacity and clinical value of three models were quantified by the area under the receiver operating characteristics (AUC), accuracy, and decision curve analysis. RESULTS: The radiomics nomogram was able to consistently differentiate between benign and malignant PRT in the training and validation datasets, with AUCs of 0.923 and 0.907, respectively. Decision curve analysis manifested that the nomogram achieved higher clinical net benefits than did separate use of the radiomics signature and clinico-radiological model. CONCLUSIONS: The preoperative nomogram is valuable for differentiating between benign and malignant PRT; it can also aid in treatment planning. KEY POINTS: • A noninvasive and accurate preoperative determination of benign and malignant PRT is crucial to identifying suitable treatments and predicting disease prognosis. • Associating the radiomics signature with clinical factors facilitates differentiation of malignant from benign PRT with improved diagnostic efficacy (AUC) and accuracy from 0.772 to 0.907 and from 0.723 to 0.842, respectively, compared with the clinico-radiological model alone. • For some PRT with anatomically special locations and when biopsy is extremely difficult and risky, a radiomics nomogram may provide a promising preoperative alternative for distinguishing benignity and malignancy.

Copper-Catalyzed Formal [4 + 1] Annulation toward Diverse Trifunctionalized Indolizines from Pyridinium 1,4-Zwitterionic Thiolates and Diazos.

Pyridinium 1,4-zwitterionic thiolates were regarded as powerful and versatile building blocks to prepare nitrogen- and sulfur-containing heterocycles. Herein, we reported a copper-catalyzed formal [4 + 1] annulation of pyridinium 1,4-zwitterionic thiolates and diazo compounds without any additives to access a library of trifunctionalized indolizines in good yields. Besides, isoquinolinium 1,4-zwitterionic thiolates and imidazolium 1,4-zwitterionic thiolates were also applied to this formal [4 + 1] annulation reaction. Of particular note is that various functional groups such as -CO2R, -CO2NR2, -CF3, -CN, and -(O)P(OR)2 could be easily introduced into cycloaddition products indolizines by this strategy.

Comparative analysis of changes in whey proteins of goat milk throughout the lactation cycle using quantitative proteomics.

The composition and content of goat milk proteins are affected by many factors and have been extensively studied. However, variation in whey protein composition in goat milk throughout the lactation cycle has not been clarified. In the current study, 15 dairy goats were selected, and milk samples were collected at 1, 3, 30, 90, 150, and 240 d after delivery. Whey proteins were separated and digested and then identified using data-independent acquisition (DIA) and data-dependent acquisition proteomics approaches. Protein profiles identified using DIA were consistent with those of the data-dependent acquisition proteomics approach according to clustering and principal component analyses. Significant differences in the abundance of 238 proteins around the lactation cycle were identified using the DIA approach. Developmental changes of the whey proteome corresponding to lactation stage were revealed: plasminogen, α-2-macroglobulin, and fibronectin levels decreased from d 1 to 240, whereas polymeric immunoglobulin receptor, nucleobindin 2, fatty acid-binding protein 3, and lactoperoxidase increased from d 1 to 240. Protein-protein interaction analysis showed that fibronectin with a higher degree of connectivity is a central node. The findings are of great significance to better understanding the potential role of specific proteins and the mechanism of protein biosynthesis or intercellular transport in the mammary glands related to the physiological changes of dairy goats.

Developmental changes in proteins of casein micelles in goat milk using data-independent acquisition-based proteomics methods during the lactation cycle.

Casein micelles (CM) play an important role in milk secretion, stability, and processing. The composition and content of milk proteins are affected by physiological factors, which have been widely investigated. However, the variation in CM proteins in goat milk throughout the lactation cycle has yet to be fully clarified. In the current study, milk samples were collected at d 1, 3, 30, 90, 150, and 240 of lactation from 15 dairy goats. The size of CM was determined using laser light scattering, and CM proteins were separated, digested, and identified using data-independent acquisition (DIA) and data-dependent acquisition (DDA)-based proteomics approaches. According to clustering and principal component analysis, protein profiles identified using DIA were similar to those identified using the DDA approach. Significant differences in the abundance of 115 proteins during the lactation cycle were identified using the DIA approach. Developmental changes in the CM proteome corresponding to lactation stages were revealed: levels of lecithin cholesterol acyltransferase, folate receptor α, and prominin 2 increased from 1 to 240 d, whereas levels of growth/differentiation factor 8, peptidoglycan-recognition protein, and 45 kDa calcium-binding protein decreased in the same period. In addition, lipoprotein lipase, glycoprotein IIIb, and α-lactalbumin levels increased from 1 to 90 d and then decreased to 240 d, which is consistent with the change in CM size. Protein-protein interaction analysis showed that fibronectin, albumin, and apolipoprotein E interacted more with other proteins at the central node. These findings indicate that changes in the CM proteome during lactation could be related to requirements of newborn development, as well as mammary gland development, and may thus contribute to elucidating the physical and chemical properties of CM.

Size- and Emission-Controlled Synthesis of Full-Color Luminescent Metal-Organic Frameworks for Tryptophan Detection.

The development of nanoscaled luminescent metal-organic frameworks (nano-LMOFs) with organic linker-based emission to explore their applications in sensing, bioimaging and photocatalysis is of great interest as material size and emission wavelength both have remarkable influence on their performances. However, there is lack of platforms that can systematically tune the emission and size of nano-LMOFs with customized linker design. Herein two series of fcu- and csq-type nano-LMOFs, with precise size control in a broad range and emission colors from blue to near-infrared, were prepared using 2,1,3-benzothiadiazole and its derivative based ditopic- and tetratopic carboxylic acids as the emission sources. The modification of tetratopic carboxylic acids using OH and NH2 as the substituent groups not only induces significant emission bathochromic shift of the resultant MOFs, but also endows interesting features for their potential applications. As one example, we show that the non-substituted and NH2 -substituted nano-LMOFs exhibit turn-off and turn-on responses for highly selective and sensitive detection of tryptophan over other nineteen natural amino acids. This work sheds light on the rational construction of nano-LMOFs with specific emission behaviours and sizes, which will undoubtedly facilitate their applications in related areas.

Biodegradable Ru-Containing Polycarbonate Micelles for Photoinduced Anticancer Multitherapeutic Agent Delivery and Phototherapy Enhancement.

The lack of selectivity between tumor and healthy cells, along with inefficient reactive oxygen species production in solid tumors, are two major impediments to the development of anticancer Ru complexes. The development of photoinduced combination therapy based on biodegradable polymers that can be light activated in the "therapeutic window" would be beneficial for enhancing the therapeutic efficacy of Ru complexes. Herein, a biodegradable Ru-containing polymer (poly(DCARu)) is developed, in which two different therapeutics (the drug and the Ru complex) are rationally integrated and then conjugated to a diblock copolymer (MPEG-b-PMCC) containing hydrophilic poly(ethylene glycol) and cyano-functionalized polycarbonate with good degradability and biocompatibility. The polymer self-assembles into micelles with high drug loading capacity, which can be efficiently internalized into tumor cells. Red light induces the generation of singlet oxygen and the release of anticancer drug-Ru complex conjugates from poly(DCARu) micelles, hence inhibiting tumor cell growth. Furthermore, the phototherapy of polymer micelles demonstrates remarkable inhibition of tumor growth in vivo. Meanwhile, polymer micelles exhibit good biocompatibility with blood and healthy tissues, which opens up opportunities for multitherapeutic agent delivery and enhanced phototherapy.

Multi-parametric MRI-based radiomics signature for preoperative prediction of Ki-67 proliferation status in sinonasal malignancies: a two-centre study.

OBJECTIVE: To assess the predictive ability of a multi-parametric MRI-based radiomics signature (RS) for the preoperative evaluation of Ki-67 proliferation status in sinonasal malignancies. METHODS: A total of 128 patients with sinonasal malignancies that underwent multi-parametric MRIs at two medical centres were retrospectively analysed. Data from one medical centre (n = 77) were used to develop the predictive models and data from the other medical centre (n = 51) constitute the test dataset. Clinical data and conventional MRI findings were reviewed to identify significant predictors. Radiomics features were determined using maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms. Subsequently, RSs were established using a logistic regression (LR) algorithm. The predictive performance of RSs was assessed using calibration, decision curve analysis (DCA), accuracy, and AUC. RESULTS: No independent predictors of high Ki-67 proliferation were observed based on clinical data and conventional MRI findings. RS-T1, RS-T2, and RS-T1c (contrast enhancement T1WI) were established based on a single-parametric MRI. RS-Combined (combining T1WI, FS-T2WI, and T1c features) was developed based on multi-parametric MRI and achieved an AUC and accuracy of 0.852 (0.733-0.971) and 86.3%, respectively, on the test dataset. The calibration curve and DCA demonstrated an improved fitness and benefits in clinical practice. CONCLUSIONS: A multi-parametric MRI-based RS may be used as a non-invasive, dependable, and accurate tool for preoperative evaluation of the Ki-67 proliferation status to overcome the sampling bias in sinonasal malignancies. KEY POINTS: • Multi-parametric MRI-based radiomics signatures (RSs) are used to preoperatively evaluate the proliferation status of Ki-67 in sinonasal malignancies. • Radiomics features are determined using maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms. • RSs are established using a logistic regression (LR) algorithm.

Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study.

OBJECTIVES: To evaluate the performance of a deep learning radiomic nomogram (DLRN) model at predicting tumor relapse in patients with soft tissue sarcomas (STS) who underwent surgical resection. METHODS: In total, 282 patients who underwent MRI and resection for STS at three independent centers were retrospectively enrolled. In addition, 113 of the 282 patients received additional contrast-enhanced MRI scans. We separated the participants into a development cohort and an external test cohort. The development cohort consisted of patients from one center and the external test cohort consisted of patients from two other centers. Two MRI-based DLRNs for prediction of tumor relapse after resection of STS were established. We universally tested the DLRNs and compared them with other prediction models constructed by using widespread adopted predictors (i.e., staging systems and Ki67) instead of radiomics features. RESULTS: The DLRN1 model incorporated plain MRI-based radiomics signature into the clinical data, and the DLRN2 model integrated radiomics signature extracted from plain and contrast-enhanced MRI with the clinical predictors. Across both study sets, the two MRI-based DLRNs had relatively better prognostic capability (C index ≥ 0.721 and median AUC ≥ 0.746; p < 0.05 compared with most other models and predictors) and less opportunity for prediction error (integrated Brier score ≤ 0.159). The decision curve analysis indicates that the DLRNs have greater benefits than staging systems, Ki67, and other models. We selected appropriate cutoff values for the DLRNs to divide STS recurrence into three risk strata (low, medium, and high) and calculated those groups' cumulative risk rates. CONCLUSION: The DLRNs were shown to be a reliable and externally validated tool for predicting STS recurrence by comparing with other prediction models. KEY POINTS: • The prediction of a high recurrence rate of STS before emergence of local recurrence can help to determine whether more active treatment should be implemented. • Two MRI-based DLRNs for prediction of tumor relapse were shown to be a reliable and externally validated tool for predicting STS recurrence. • We used the DLRNs to divide STS recurrence into three risk strata (low, medium, and high) to facilitate more targeted postoperative management in the clinic.

Research on imbalance machine learning methods for MR[Formula: see text]WI soft tissue sarcoma data.

BACKGROUND: Soft tissue sarcoma is a rare and highly heterogeneous tumor in clinical practice. Pathological grading of the soft tissue sarcoma is a key factor in patient prognosis and treatment planning while the clinical data of soft tissue sarcoma are imbalanced. In this paper, we propose an effective solution to find the optimal imbalance machine learning model for predicting the classification of soft tissue sarcoma data. METHODS: In this paper, a large number of features are first obtained based on [Formula: see text]WI images using the radiomics methods.Then, we explore the methods of feature selection, sampling and classification, get 17 imbalance machine learning models based on the above features and performed extensive experiments to classify imbalanced soft tissue sarcoma data. Meanwhile, we used another dataset splitting method as well, which could improve the classification performance and verify the validity of the models. RESULTS: The experimental results show that the combination of extremely randomized trees (ERT) classification algorithm using SMOTETomek and the recursive feature elimination technique (RFE) performs best compared to other methods. The accuracy of RFE+STT+ERT is 81.57% , which is close to the accuracy of biopsy, and the accuracy is 95.69% when using another dataset splitting method. CONCLUSION: Preoperative predicting pathological grade of soft tissue sarcoma in an accurate and noninvasive manner is essential. Our proposed machine learning method (RFE+STT+ERT) can make a positive contribution to solving the imbalanced data classification problem, which can favorably support the development of personalized treatment plans for soft tissue sarcoma patients.

Imaging features of lipoma arborescens.

BACKGROUND: Lipoma arborescens is characterized by the villous proliferation of the synovium and diffuse hyperplasia of fatty tissue in the subsynovial layer, almost always with a periarticular lesion. According to past articles, fewer cases have depicted the imaging features of lipoma arborescens. PURPOSE: To evaluate the computed tomography (CT) and magnetic resonance imaging (MRI) features of lipoma arborescens. MATERIAL AND METHODS: The imaging features of 15 patients with pathologically proven lipoma arborescens were retrospectively analyzed including lesion number, shape, location, size, margins, attenuation on CT, and signal intensity and enhancement patterns on MR images. RESULTS: All cases (n=15) showed joint or bursa effusion. The primary lesion attached to the bursa wall adjacent to the bone in 13 cases and to the lateral wall in two cases. CT shows a mass with fatty tissue attenuation values in the suprapatellar pouch (n=3) or subdeltoid bursa with an erosion of the humeral head (n=2), among them two cases showed branched slightly high density in the center of the fat density tissue. Fifteen cases on routine MRI display mass-like subsynovial mass with fatty tissue signal on all of the sequences and suppression of the signal on fat-suppression sequences. Among them, five lesions showed branched slightly low T1-weighted imaging, high proton density-weighted imaging, and T2-weighted imaging signals in the center. It showed the enhancement of the linear surface and the bursa wall in contrast-enhanced MRI (n=3). CONCLUSION: Lipoma arborescens have specific CT and MRI features that enable them to make a conclusive diagnosis of this rare condition, which helps the diagnosis before treatment.

BTF3 promotes stemness and inhibits Typeâ Interferon signaling pathway in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a major challenge in clinical practice due to its aggressiveness and lack of targeted treatment. Cancer stem-like traits contribute to tumorigenesis and immune privilege of TNBC. However, the relationship of stemness and immunosurveillance remains unclear. Here, we demonstrate that BTF3 expression is related with stem-like properties in TNBC cells. BTF3 modulates stemness, migration and proliferation of TNBC in vitro. Bioinformatics analysis revealed that interferon signaling pathways and IRF7, both of which participate in the immune escape of TNBC, are closely related to BTF3 in TNBC cells. Knockdown of BTF3 activates IRF7 expression through increased degradation of BMI1, a protein that can represses IRF7 transcription by directly binding to its promotor region. BTF3 links stem-like traits and the interferon signaling pathway, revealing the potential connection of stemness and immunomodulation in TNBC. Clinically, we suggest that BTF3 is predictive of poor prognosis in patients with TNBC. Together, our findings highlight an important role of BTF3 in regulating the progression of TNBC cells.

Magnetic Resonance Imaging-Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two-Center Study.

BACKGROUND: Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed. PURPOSE: To develop and test an magnetic resonance imaging (MRI)-based radiomics nomogram for predicting the grade of STS (low-grade vs. high grade). STUDY TYPE: Retrospective POPULATION: One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N = 109; external validation set, N = 71). FIELD STRENGTH/SEQUENCE: Unenhanced T1-weighted (T1WI) and fat-suppressed T2-weighted images (FS-T2WI) were acquired at 1.5 T and 3.0 T. ASSESSMENT: Clinical-MRI characteristics included age, gender, tumor-node-metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression-free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS-T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS-T1， RS-FST2, and RS-Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors. STATISTICAL TESTS: Clinical-MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS-T1 model, RS-FST2 model, and RS-Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS-Combined model had AUCs of 0.916 (95%CI, 0.866-0.966, training set) and 0.879 (95%CI, 0.791-0.967, external validation set), and demonstrated good calibration and good clinical utility. DATA CONCLUSION: The proposed noninvasive MRI-based radiomics models showed good performance in differentiating low-grade from high-grade STSs. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

An MRI-Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors.

BACKGROUND: Preoperative discrimination between malignant and benign sinonasal tumors is important for treatment plan selection. PURPOSE: To build and validate a radiomic nomogram for preoperative discrimination between malignant and benign sinonasal tumors. STUDY TYPE: Retrospective. POPULATION: In all, 197 patients with histopathologically confirmed 84 benign and 113 malignant sinonasal tumors. FIELD STRENGTH/SEQUENCES: Fast-spin-echo (FSE) T1 -weighted and fat-suppressed FSE T2 -weighted imaging on a 1.5T and 3.0T MRI. ASSESSMENT: T1 and fat-suppressed T2 -weighted images were selected for feature extraction. The least absolute shrinkage selection operator (LASSO) algorithm was applied to establish a radiomic score. Multivariate logistic regression analysis was applied to determine independent risk factors, and the radiomic score was combined to build a radiomic nomogram. The nomogram was assessed in a training dataset (n = 138/3.0T MRI) and tested in a validation dataset (n = 59/1.5T MRI). STATISTICAL TESTS: Independent t-test or Wilcoxon's test, chi-square-test, or Fisher's-test, univariate analysis, LASSO, multivariate logistic regression analysis, area under the curve (AUC), Hosmer-Lemeshow test, decision curve, and the Delong test. RESULTS: In the validation dataset, the radiomic nomogram could differentiate benign from malignant sinonasal tumors with an AUC of 0.91. There was no significant difference in AUC between the combined radiomic score and radiomic nomogram (P > 0.05), and the radiomic nomogram showed a relatively higher AUC than the combined radiomic score. There was a significant difference in AUC between each two of the following models (the radiomic nomogram vs. the clinical model, all P < 0.001; the combined radiomic score vs. the clinical model, P = 0.0252 and 0.0035, respectively, in the training and validation datasets). The radiomic nomogram outperformed the radiomic scores and clinical model. DATA CONCLUSION: The radiomic nomogram combining the clinical model and radiomic score is a simple, effective, and reliable method for patient risk stratification. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

A CT-based radiomics nomogram for differentiation of lympho-associated benign and malignant lesions of the parotid gland.

OBJECTIVES: Preoperative differentiation between benign lymphoepithelial lesion (BLEL) and mucosa-associated lymphoid tissue lymphoma (MALToma) in the parotid gland is important for treatment decisions. The purpose of this study was to develop and validate a CT-based radiomics nomogram combining radiomics signature and clinical factors for the preoperative differentiation of BLEL from MALToma in the parotid gland. METHODS: A total of 101 patients with BLEL (n = 46) or MALToma (n = 55) were divided into a training set (n = 70) and validation set (n = 31). Radiomics features were extracted from non-contrast CT images, a radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factor model. A radiomics nomogram combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The performance levels of the nomogram, radiomics signature, and clinical model were evaluated and validated on the training and validation datasets, and then compared among the three models. RESULTS: Seven features were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature showed favorable predictive value for differentiating parotid BLEL from MALToma, with AUCs of 0.983 and 0.950 for the training set and validation set, respectively. Decision curve analysis showed that the nomogram outperformed the clinical factor model in terms of clinical usefulness. CONCLUSIONS: The CT-based radiomics nomogram incorporating the Rad-score and clinical factors showed favorable predictive efficacy for differentiating BLEL from MALToma in the parotid gland, and may help in the clinical decision-making process. KEY POINTS: • Differential diagnosis between BLEL and MALToma in parotid gland is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of BLEL from MALToma with improved diagnostic efficacy.

Antidepressant-like effect of Ganoderma lucidum spore polysaccharide-peptide mediated by upregulation of prefrontal cortex brain-derived neurotrophic factor.

A 28-kDa polysaccharide-peptide (PGL) with antidepressant-like activities was isolated from spores of the mushroom Ganoderma lucidum. It was unadsorbed on DEAE-cellulose. Its internal amino acid sequences manifested pronounced similarity with proteins from the mushrooms Lentinula edodes and Agaricus bisporus. The monosaccharides present in 28-kDa PGL comprised predominantly of glucose (over 90%) and much fewer galactose, mannose residues, and other residues. PGL manifested antidepressant-like activities as follows. It enhanced viability and DNA content in corticosterone-injured PC12 cells(a cell line derived from a pheochromocytoma of the rat adrenal medulla with an embryonic origin from the neural crest containing a mixture of neuroblastic cells and eosinophilic cells) and reduced LDH release. A single acute PGL treatment shortened the duration of immobility of mice in both tail suspension and forced swimming tests. PGL treatment enhanced sucrose preference and shortened the duration of immobility in mice exposed to chronic unpredictable mild stress (CUMS). Chronic PGL treatment reversed the decline in mouse brain serotonin and norepinephrine levels but did not affect dopamine levels. PGL decreased serum corticosterone levels and increased BDNF mRNA and protein levels and increased synapsin I and PSD95 levels in the prefrontal cortex. This effect was completely blocked by pretreatment with the BDNF antagonist K252a, indicating that PGL increased synaptic proteins in a BDNF-dependent manner.Key points• An antidepressive polysaccharide-peptide PGL was isolated from G. lucidum spores.• PGL protected PC12 nerve cells from the toxicity of corticosterone.• PGL upregulated BDNF expression and influenced key factors in the prefrontal cortex.

Purification and properties of a laccase from the mushroom Agaricus sinodeliciosus.

A homogeneous monomeric laccase (ASL) from Agaricus sinodeliciosus, with a molecular mass of 65 kDa, was isolated using ion-exchange chromatography (CM-cellulose and Q-Sepharose) and gel-filtration chromatography (Superdex 75). This laccase exhibited maximum activity at 50 °C and pH 5.0. Hg2+ and Cd2+ significantly inhibited its activity. The laccase displayed a Km value of 0.9 mM toward 2,2'-azinobis-(3-ethylbenzthiazoline-6-sulfonate) (ABTS). In addition to ABTS, ASL exhibited higher affinity toward o-toluidine and benzidine than other substrates. ASL is able to decolorize malachite green and Eriochrome black T.