Research hotspots and trends of spinal cord stimulation for neuropathic pain: a bibliometric analysis from 2004 to 2023.

The purpose of this study is to systematically analyze the development trend, research hotspots, and future development direction on the treatment of neuropathic pain (NP) with spinal cord stimulation through bibliometric method. We extracted the literature related to the treatment of NP with spinal cord stimulation from January 2004 to December 2023 from the Web of Science database. As a result, a total of 264 articles were retrieved. By analyzing the annual published articles, authors, countries, institutions, journals, co-cited literature, and keywords, we found that the count of publication in this field has been experiencing an overall growth, and the publications within the past 5 years accounted for 42% of the total output. Experts from the United States and the UK have made significant contributions in this field and established a stable collaborative team, initially establishing an international cooperation network. Pain is the frequently cited journal in this field. The study on spinal cord stimulation therapy for NP especially the study on spinal cord stimulation therapy for back surgery failure syndrome (FBSS) and its potential mechanisms are the research hotspots in this field, while the study on novel paradigms such as high-frequency spinal cord stimulation and spinal cord burst stimulation represents the future development directions. In short, spinal cord stimulation has been an effective treatment method for NP. The novel paradigms of spinal cord stimulation are the key point of future research in this field.

The Molecular Mechanism of Radix Paeoniae Rubra.-Cortex Moutan. Herb Pair in the Treatment of Atherosclerosis: A Work Based on Network Pharmacology and In Vitro Experiments.

Radix Paeoniae Rubra. (Chishao, RPR) and Cortex Moutan. (Mudanpi, CM) are a pair of traditional Chinese medicines that play an important role in the treatment of atherosclerosis (AS). The main objective of this study was to identify potential synergetic function and underlying mechanisms of RPR-CM in the treatment of AS. The main active ingredients, targets of RPR-CM and AS-related genes were obtained from public databases. A Venn diagram was utilized to screen the common targets of RPR-CM in treating AS. The protein-protein interaction network was established based on STRING database. Biological functions and pathways of potential targets were analyzed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Cytoscape was used to construct the drug-compound-target-signal pathway network. Molecular docking was performed to verify the binding ability of the bioactive ingredients and the target proteins. The endothelial inflammation model was constructed with human umbilical vein endothelial cells stimulated with ox-LDL, and the function of RPR-CM in treating AS was verified by CCK-8 assay, enzyme-linked immunosorbent assay, and qPCR. In this study, 12 active components and 401 potential target genes of RPR-CM were identified, among which quercetin, kaempferol and baicalein were considered to be the main active components. A total of 1903 AS-related genes were identified through public databases and four GEO datasets (GSE57691, GSE72633, GSE6088 and GSE199819). There are 113 common target genes of RPR-CM in treating AS. PPI network analysis identified 17 genes in cluster 1 as the core targets. Bioinformatics analysis showed that RPR-CM in AS treatment was associated with multiple downstream biological processes and signal pathways. PTGS2, JUN, CASP3, TNF, IL1B, IL6, FOS, STAT1 were identified as the core targets of RPR-CM, and molecular docking showed that the main bioactive components of RPR-CM had good binding ability with the core targets. RPR-CM extract significantly inhibited the levels of inflammatory factors TNF-α, IL-6, IL-1ß, MCP-1, VCAM-1 and ICAM-1 in HUVECs, and inhibited endothelial inflammation. This study revealed the active ingredients of RPR-CM, and identified the key downstream targets and signaling pathways in the treatment of AS, providing theoretical basis for the application of RPR-CM in prevention and treatment of AS.

Understanding competitive Cu2+ and Zn2+ adsorption onto functionalized cellulose fiber via experimental and theoretical approach.

Amidoxime groups were successfully introduced to develop a novel amidoxime-functionalized cellulose fiber (AO-Cell) for absorptive removal of heavy metal ions in wastewater. The chemical structure, and the competitive adsorption of Cu2+ and Zn2+ by AO-Cell were investigated by experiments study, Density functional theory (DFT) and molecular dynamic (MD) simulation. The results showed the N and O atoms in the amidoxime group can spontaneously interact with Cu2+ and Zn2+ through sharing long pair electrons to generate stable coordination structure, which was the dominant adsorption mechanism. Besides, the enlarged surface area, improved hydrophilicity and dispersion offered by AO-Cell facilitate the adsorption process by increasing the accessibility of absorption sites. As results of these synergetic modification, AO-Cell can remain effective in a wide pH range (1-6) and reach adsorption equilibrium within 60 min. At optimal conditions, the achieved theoretical adsorption capacity is as high as 84.81 mg/g for Cu2+ and 61.46 mg/g for Zn2+ in the solution with multiple ions. The competition between Cu2+ and Zn2+ in occupying the absorption sites arises from the difference in the metallic ion affinity and covalent index with the adsorbent as demonstrated by the MD analysis. Importantly, AO-Cell demonstrated favorable recyclability after up to 10 adsorption-desorption cycles.

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.

Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma.

BACKGROUND: To develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression. METHODS: We retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis. RESULTS: For the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was - 0.03 and cumulative risk rates were calculated. DATA CONCLUSION: To assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.

Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors.

BACKGROUND: To explore the potential of different quantitative dynamic contrast-enhanced (qDCE)-MRI tracer kinetic (TK) models and qDCE parameters in discriminating benign from malignant soft tissue tumors (STTs). METHODS: This research included 92 patients (41females, 51 males; age range 16-86 years, mean age 51.24 years) with STTs. The qDCE parameters (Ktrans, Kep, Ve, Vp, F, PS, MTT and E) for regions of interest of STTs were estimated by using the following TK models: Tofts (TOFTS), Extended Tofts (EXTOFTS), adiabatic tissue homogeneity (ATH), conventional compartmental (CC), and distributed parameter (DP). We established a comprehensive model combining the morphologic features, time-signal intensity curve shape, and optimal qDCE parameters. The capacities to identify benign and malignant STTs was evaluated using the area under the curve (AUC), degree of accuracy, and the analysis of the decision curve. RESULTS: TOFTS-Ktrans, EXTOFTS-Ktrans, EXTOFTS-Vp, CC-Vp and DP-Vp demonstrated good diagnostic performance among the qDCE parameters. Compared with the other TK models, the DP model has a higher AUC and a greater level of accuracy. The comprehensive model (AUC, 0.936, 0.884-0.988) demonstrated superiority in discriminating benign and malignant STTs, outperforming the qDCE models (AUC, 0.899-0.915) and the traditional imaging model (AUC, 0.802, 0.712-0.891) alone. CONCLUSIONS: Various TK models successfully distinguish benign from malignant STTs. The comprehensive model is a noninvasive approach incorporating morphological imaging aspects and qDCE parameters, and shows significant potential for further development.

Integrating Tumor and Nodal Radiomics to Predict the Response to Neoadjuvant Chemotherapy and Recurrence Risk for Locally Advanced Gastric Cancer.

AIMS: To develop and evaluate machine learning models using tumor and nodal radiomics features for predicting the response to neoadjuvant chemotherapy (NAC) and recurrence risk in locally advanced gastric cancer (LAGC). BACKGROUND: Early and accurate response prediction is vital to stratify LAGC patients and select proper candidates for NAC. OBJECTIVE: A total of 218 patients with LAGC undergoing NAC followed by gastrectomy were enrolled in our study and were randomly divided into a training cohort (n = 153) and a validation cohort (n = 65). METHODS: We extracted 1316 radiomics features from the volume of interest of the primary lesion and maximal lymph node on venous phase CT images. We built 3 radiomics signatures for distinguishing good responders and poor responders based on tumor radiomics (TR), nodal radiomics (NR), and a combination of the two (TNR), respectively. A nomogram was then developed by integrating the radiomics signature and clinical factors. Kaplan- Meier survival curves were used to evaluate the prognostic value of the nomogram. RESULTS: The TNR signature achieved improved predictive value, with AUCs of 0.755 and 0.744 in the training and validation cohorts. Our proposed nomogram model (TNRN) showed a good performance for GR prediction in the prediction efficacy, calibration ability, and clinical benefit, with AUCs of 0.779 and 0.732 in the training and validation cohorts, superior to the clinical model. Moreover, the TNRN could accurately classify the patients into high-risk and low-risk groups in both training and validation cohorts with regard to postoperative recurrence and metastasis. CONCLUSION: The TNRN performed well in identifying good responders and provided valuable information for predicting progression-free survival time (PFS) in patients with LAGC who underwent NAC.

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.

Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study.

OBJECTIVE: To establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients. METHODS: We retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model's performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan-Meier survival curve to analyze the prognosis of BCa patients. RESULTS: The combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659-1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival. CONCLUSION: The proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively. CRITICAL RELEVANCE STATEMENT: Bladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients. KEY POINTS: • Conventional imaging is not sufficiently accurate to determine lymph node status. • Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis. • The proposed method showed satisfactory patient risk stratification for progression-free survival.

Deep learning signature based on multiphase enhanced CT for bladder cancer recurrence prediction: a multi-center study.

Background: Accurate stratification of recurrence risk for bladder cancer (BCa) is essential for precise individualized therapy. This study aimed to develop and validate a model for predicting the risk of recurrence in BCa patients postoperatively using 3-phase enhanced CT images. Methods: We retrospectively enrolled 874 BCa patients across four centers between January 2006 and December 2021. Patients from one center were used as training set, while the remaining patients went into the validation set. We trained a deep learning (DL) model based on convolutional neural networks using 3-phase enhanced CT images. The resulting prediction scores were entered into Cox regression analysis to obtain DL scores and construct a DL signature. DL scores and clinical features were then used as deep learning radioclinical signature. The predictive performance of DL signature was assessed according to concordance index and area under curve compared with deep learning radioclinical signature, clinical model and a widely accepted staging grading system. Recurrence-free survival (RFS) and overall survival (OS) were also predicted in order to further assess survival benefits. Findings: DL signature showed strong power for predicting recurrence (concordance index, 0.869; area under curve, 0.889) in validation set, outperforming other models and system. In addition, we divided RFS and OS into high and low risk groups by selecting appropriate cutoff values for DL signature, and calculated cumulative recurrence risk rates for both groups. Interpretation: Our proposed DL signature shows promising potential as clinical aid for predicting postoperative recurrence risk in BCa and for stratifying the risk of RFS and OS, which can be applied to guide personalized precision therapy. Funding: There are no sources of funding for this manuscript.

Multiplicative enhancement of stereoenrichment by a single catalyst for deracemization of alcohols.

Stereochemical enrichment of a racemic mixture by deracemization must overcome unfavorable entropic effects as well as the principle of microscopic reversibility; recently, photochemical reaction pathways unveiled by the energetic input of light have led to innovations toward this end, most often by ablation of a stereogenic C(sp3)-H bond. We report a photochemically driven deracemization protocol in which a single chiral catalyst effects two mechanistically different steps, C-C bond cleavage and C-C bond formation, to achieve multiplicative enhancement of stereoinduction, which leads to high levels of stereoselectivity. Ligand-to-metal charge transfer excitation of a titanium catalyst coordinated by a chiral phosphoric acid or bisoxazoline efficiently enriches racemic alcohols that feature adjacent and fully substituted stereogenic centers to enantiomeric ratios up to 99:1. Mechanistic investigations support a pathway of sequential radical-mediated bond scission and bond formation through a common prochiral intermediate and reveal that, although the overall stereoenrichment is high, the selectivity in each individual step is moderate.

CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study.

BACKGROUND: To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively. METHODS: We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: The support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature. CONCLUSION: The proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.

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.

Resurgence and advancement of photochemical hydrogen atom transfer processes in selective alkane functionalizations.

The selective functionalization of alkanes has long been recognized as a prominent challenge and an arduous task in organic synthesis. Hydrogen atom transfer (HAT) processes enable the direct generation of reactive alkyl radicals from feedstock alkanes and have been successfully employed in industrial applications such as the methane chlorination process, etc. Nevertheless, challenges in the regulation of radical generation and reaction pathways have created substantial obstacles in the development of diversified alkane functionalizations. In recent years, the application of photoredox catalysis has provided exciting opportunities for alkane C-H functionalization under extremely mild conditions to trigger HAT processes and achieve radical-mediated functionalizations in a more selective manner. Considerable efforts have been devoted to building more efficient and cost-effective photocatalytic systems for sustainable transformations. In this perspective, we highlight the recent development of photocatalytic systems and provide our views on current challenges and future opportunities in this field.

Lead detoxification of edible fungi Auricularia auricula and Pleurotus ostreatus: the purification of the chelation substances and their effects on rats.

Lead is a global pollutant that causes widespread concern. When a lead enters the body, it is distributed throughout the body and accumulates in the brain, bone, and soft tissues such as the kidney, liver, and spleen. Chelators used for lead poisoning therapy all have side effects to some extent and other drawbacks including high cost. Exploration and utilization of natural antidotes become necessary. To date, few substances originating from edible fungi that are capable of adsorbing lead have been reported. In this study, we found that two commonly eaten mushrooms Auricularia auricula and Pleurotus ostreatus exhibited lead adsorption capacity. A. auricula active substance (AAAS) and P. ostreatus active substance (POAS) were purified by hot-water extraction, ethanol precipitation from its fruiting bodies followed by ion exchange chromatography, ultrafiltration, and gel filtration chromatography, respectively. AAAS was 3.6 kDa, while POAS was 4.9 kDa. They were both constituted of polysaccharides and peptides. The peptide sequences obtained by liquid chromatography combined with tandem mass spectrometry (LC-MS/MS) proved that they were rich in amino acids with side chain groups such as hydroxyl, carboxyl, carbonyl, sulfhydryl, and amidogen. Two rat models were established, but only a chronic lead-induced poisoning model was employed to determine the detoxification of AAAS/POAS and their fruiting body powder. For rats receiving continuous lead treatment, either AAAS or POAS could reduce the lead levels in the blood. They also promoted the elimination of the burden of lead in the spleen and kidney. The fruiting bodies were also proved to have lead detoxification effects. This is the first study to identify new functions of A. auricula and P. ostreatus in reducing lead toxicity and to provide dietary strategies for the treatment of lead toxicity.

A multi-view co-training network for semi-supervised medical image-based prognostic prediction.

Prognostic prediction has long been a hotspot in disease analysis and management, and the development of image-based prognostic prediction models has significant clinical implications for current personalized treatment strategies. The main challenge in prognostic prediction is to model a regression problem based on censored observations, and semi-supervised learning has the potential to play an important role in improving the utilization efficiency of censored data. However, there are yet few effective semi-supervised paradigms to be applied. In this paper, we propose a semi-supervised co-training deep neural network incorporating a support vector regression layer for survival time estimation (Co-DeepSVS) that improves the efficiency in utilizing censored data for prognostic prediction. First, we introduce a support vector regression layer in deep neural networks to deal with censored data and directly predict survival time, and more importantly to calculate the labeling confidence of each case. Then, we apply a semi-supervised multi-view co-training framework to achieve accurate prognostic prediction, where labeling confidence estimation with prior knowledge of pseudo time is conducted for each view. Experimental results demonstrate that the proposed Co-DeepSVS has a promising prognostic ability and surpasses most widely used methods on a multi-phase CT dataset. Besides, the introduction of SVR layer makes the model more robust in the presence of follow-up bias.

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.

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.

A novel mushroom (Auricularia polytricha) glycoprotein protects against lead-induced hepatoxicity, promotes lead adsorption, inhibits organ accumulation of lead, upregulates detoxifying proteins, and enhances immunoregulation in rats.

Introduction: Lead is a ubiquitous environmental and industrial pollutant. Its nonbiodegradable toxicity induces a plethora of human diseases. A novel bioactive glycoprotein containing 1.15% carbohydrate, with the ability of adsorbing lead and effecting detoxification, has been purified from Auricularia polytricha and designated as APL. Besides, its mechanisms related to regulation of hepatic metabolic derangements at the proteome level were analyzed in this study. Methods: Chromatographic techniques were utilized to purify APL in the current study. For investigating the protective effects of APL, Sprague-Dawley rats were given daily intraperitoneal injections of lead acetate for establishment of an animal model, and different dosages of APL were gastrically irrigated for study of protection from lead detoxification. Liver samples were prepared for proteomic analyses to explore the detoxification mechanisms. Results and discussion: The detoxifying glycoprotein APL displayed unique molecular properties with molecular weight of 252-kDa, was isolated from fruiting bodies of the edible fungus A. polytricha. The serum concentrations of lead and the liver function biomarkers aspartate and alanine aminotransferases were significantly (p<0.05) improved after APL treatment, as well as following treatment with the positive control EDTA (300 mg/kg body weight). Likewise, results on lead residue showed that the clearance ratios of the liver and kidneys were respectively 44.5% and 18.1% at the dosage of APL 160 mg/kg, which was even better than the corresponding data for EDTA. Proteomics disclosed that 351 proteins were differentially expressed following lead exposure and the expression levels of 41 proteins enriched in pathways mainly involved in cell detoxification and immune regulation were normalized after treatment with APL-H. The results signify that APL ameliorates lead-induced hepatic injury by positive regulation of immune processing, and suggest that APL can be applied as a therapeutic intervention of lead poisoning in clinical practice. This report represents the first demonstration of the protective action of a novel mushroom protein on lead-elicited hepatic toxicity.