2D Metal Porphyrin-Based MOFs and ZIF-8 Composite-Derived Carbon Materials Containing M-Nx Active Sites as Bifunctional Electrocatalysts for Zinc-Air Batteries.

The main impediment to the development of zinc-air batteries is the sluggish kinetics of the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER). Transition metal N-doped carbon catalysts offer a promising alternative to noble metal catalysts, with metal-organic framework (MOF)-derived carbon material catalysts being particularly noteworthy. Here, we synthesized MxP-Z-C carbon catalysts by combining two-dimensional (2D) metal porphyrin-based MOFs (MxPMFs, x = Fe, Co, Ni, Mn) and three-dimensional zeolitic imidazole framework-8 (ZIF-8) through electrostatic interaction, followed by carbonization. ZIF-8 was inserted between the layers of MxPMFs to prevent its Π-Π stacking, allowing the active sites to become fully exposed. MxP-Z-C demonstrated an impressive catalytic activity for both the ORR and the OER reactions. Among them, FeP-Z-C showed the best catalytic activity. The half-wave potential for ORR was 0.92 V (vs the reversible hydrogen electrode (RHE)), while the overpotential for the OER was 290 mV. In addition, the zinc-air battery assembled by FeP-Z-C exhibited high power density (133.14 mW cm-2) and significant specific capacity (816 mAh gZn-1), indicating considerable potential as a bifunctional catalyst for electronic devices.

Multi-omics approaches to understand pathogenicity during potato early blight disease caused by Alternaria solani.

Potato early blight (PEB), a foliar disease of potato during the growing period, caused by Alternaria sp., is common in major potato-producing areas worldwide. Effective agents to control this disease or completely resistant potato varieties are absent. Large-scale use of fungicides is limited due to possibility of increase in pathogen resistance and the requirements of ecological agriculture. In this study, we focused on the composition and infection characteristics of early blight pathogens in Yunnan Province and screened candidate pathogenesis-related pathways and genes. We isolated 85 strains of Alternaria sp. fungi from typical early blight spots in three potato-growing regions in Yunnan Province from 2018 to 2022, and identified 35 strains of Alternaria solani and 50 strains of Alternaria alternata by morphological characterization and ITS sequence comparison, which were identified as the main and conditional pathogens causing early blight in potato, respectively. Scanning electron microscope analysis confirmed only A. solani producing appressorium at 4 h after inoculation successfully infected the leaf cells. Via genome assembly and annotation, combine transcriptome and proteomic analysis, the following pathogenicity-related unit, transcription factors and metabolic pathway were identified: (1) cell wall-degrading enzymes, such as pectinase, keratinase, and cellulase; (2) genes and pathways related to conidia germination and pathogenicity, such as ubiquitination and peroxisomes; and (3) transcription factors, such as Zn-clus, C2H2, bZIP, and bHLH. These elements were responsible for PEB epidemic in Yunnan.

The impact of the chalcogen-substitution element and initial spectroscopic state on excited-state relaxation pathways in nucleobase photosensitizers: a combination of static and dynamic studies.

The substitution of oxygen with chalcogen in carbonyl group(s) of canonical nucleobases gives an impressive triplet generation, enabling their promising applications in medicine and other emerging techniques. The excited-state relaxation S2(ππ*) → S1(nπ*) → T1(ππ*) has been considered the preferred path for triplet generation in these nucleobase derivatives. Here, we demonstrate enhanced quantum efficiency of direct intersystem crossing from S2 to triplet manifold upon substitution with heavier chalcogen elements. The excited-state relaxation dynamics of sulfur/selenium substituted guanines in a vacuum is investigated using a combination of static quantum chemical calculations and on-the-fly excited-state molecular dynamics simulations. We find that in sulfur-substitution the S2 state predominantly decays to the S1 state, while upon selenium-substitution the S2 state deactivation leads to simultaneous population of the S1 and T2,3 states in the same time scale and multi-state quasi-degeneracy region S2/S1/T2,3. Interestingly, the ultrafast deactivation of the spectroscopic S3 state of both studied molecules to the S1 state occurs through a successive S3 → S2 → S1 path involving a multi-state quasi-degeneracy S3/S2/S1. The populated S1 and T2 states will cross the lowest triplet state, and the S1 → T intersystem crossing happens in a multi-state quasi-degeneracy region S1/T2,3/T1 and is accelerated by selenium-substitution. The present study reveals the influence of both the chalcogen substitution element and initial spectroscopic state on the excited-state relaxation mechanism of nucleobase photosensitizers and also highlights the important role of multi-state quasi-degeneracy in mediating the complex relaxation process. These theoretical results provide additional insights into the intrinsic photophysics of nucleobase-based photosensitizers and are helpful for designing novel photo-sensitizers for real applications.

Noncovalent composites of meso-substituted A2B2-porphyrins and carbon nanotubes for enhanced electrocatalytic oxygen reduction.

Exploring highly active oxygen reduction electrocatalysts with low precious metals content is imperative but remains a considerable challenge. Herein, a series of heterobimetallic multi-walled carbon nanotubes (MWCNTs) electrocatalysts based on metal complexes are presented. These electrocatalysts feature diverse transition metals (M=Mn, Fe, Co, Ni) 5,15-bromophenyl-10, 20-methoxyphenyl porphyrin (MBMP) and tetrakis(triphenylphosphine)palladium (0) (Pd[P(Ph3)4]) anchored non-covalently on its surface. The resulting NiBMP-based MWCNTs with Pd[P(Ph3)4] (PdNiN4/MWCNTs) display outstanding electrocatalytic oxygen reduction activity (onset potential, 0.941 V; half wave potential, 0.830 V) and robust long-term durability in alkaline electrolyte. While in neutral condition, the MnBMP-based MWCNTs with Pd[P(Ph3)4] (PdMnN4/MWCNTs) are the most active heterobimetallic ORR catalyst and produce ultra-low concentration hydrogen peroxide (H2O2yield, 1.2%-1.3%). Synergistically tuning the ORR electrocatalytic activity and electron transfer pathway is achieved by the formation of NiBMP/MnBMP-Pd[P(Ph3)4] active sites. This work indicates such metalloporphyrin-Pd[P(Ph3)4] active sites on MWCNTs have significantly positive influence on electrocatalytic ORR systems and provides facile and mild strategy for designing highly efficient ORR electrocatalysts with ultra-low loading precious metal.

Retraction: Radical Covalent Organic Frameworks Associated with Liquid Na-K toward Dendrite-Free Alkali Metal Anodes.

10.1002/advs.202203058 Adv. Sci. 2022, 9, 2203058 The above article from Advanced Science, published online on 21 July 2022 in Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202203058), has been retracted by agreement between the authors, the Editor-in-Chief, Kirsten Severing, and Wiley-VCH GmbH. The retraction has been agreed as the article is based on research results and data that the authors were not authorized to use. Moreover, the majority of co-authors have been listed despite insufficient qualification for contributorship.

Retraction: Dual-Ion Stabilized Layered Structure of O-V-OforZero-Strain Potassium Insertion and Extraction.

10.1002/advs.202202550 Adv. Sci.2022, 9, 2202550 The above article from Advanced Science, published online on 5 June 2022 in Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202202550), has been retracted by agreement between the authors, the Editor-in-Chief, Kirsten Severing, and Wiley-VCH GmbH. The retraction has been agreed as the article is based on research results and data that the authors were not authorized to use. Moreover, the majority of co-authors have been listed despite insufficient qualification for contributorship.

Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution.

Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability. Cartilage defects are regarded as major manifestations of knee OA, which are visible by magnetic resonance imaging (MRI). Thus early detection and assessment for knee cartilage defects are important for protecting patients from knee OA. In this way, many attempts have been made on knee cartilage defect assessment by applying convolutional neural networks (CNNs) to knee MRI. However, the physiologic characteristics of the cartilage may hinder such efforts: the cartilage is a thin curved layer, implying that only a small portion of voxels in knee MRI can contribute to the cartilage defect assessment; heterogeneous scanning protocols further challenge the feasibility of the CNNs in clinical practice; the CNN-based knee cartilage evaluation results lack interpretability. To address these challenges, we model the cartilages structure and appearance from knee MRI into a graph representation, which is capable of handling highly diverse clinical data. Then, guided by the cartilage graph representation, we design a non-Euclidean deep learning network with the self-attention mechanism, to extract cartilage features in the local and global, and to derive the final assessment with a visualized result. Our comprehensive experiments show that the proposed method yields superior performance in knee cartilage defect assessment, plus its convenient 3D visualization for interpretability.

Reduced Graphene Oxide-Coated Separator to Activate Dead Potassium for Efficient Potassium Batteries.

Potassium (K) metal batteries (KMBs) have the advantages of relatively low electric potential (-2.93 V), high specific capacity (687 mAh g-1), and low cost, which are highly appealing to manufacturers of portable electric products and vehicles. However, the large amounts of "dead K" caused by K dendrite growth and volumetric expansion can cause severe K metal anode deactivation. Here, a thin layer of conductive reduced graphene oxide (rGO) was coated on a GF separator (rGO@GF) to activate the generated dead K. Compared with the batteries adopting an original separator, those adopting a modified separator have significantly improved specific capacity and cycling stability. The life of full-cell of KMBs combining an rGO@GF separator with synthesized K0.51V2O5 is expected to exceed 400 cycles, with an initial capacity of 92 mAh g-1 at 0.5 A g-1 and an attenuation rate per cycle as low as 0.03%. Our work demonstrates that a composite separator of high conductivity is beneficial for high performance KMBs.

Radical Covalent Organic Frameworks Associated with Liquid Na-K toward Dendrite-Free Alkali Metal Anodes.

Liquid sodium-potassium (Na-K) alloy has the characteristics of high abundance, low redox potential, high capacity, and no dendrites, which has become an ideal alternative material for potassium/sodium metal anodes. However, the high surface tension of liquid sodium potassium alloy at room temperature makes it inconvenient in practical use. Here, the Na-K as reducing agent treats with hydrazone linkages of covalent organic frameworks (COFs) and obtain the carbon-oxygen radical COFs (COR-Tf-DHzDM-COFs). The preparation method solves the problems that the preparation process of the traditional Na-K composite anode is complex and has high cost. The structures of the COR-Tf-DHzDM-COFs are characterized by X-ray diffraction （XRD), fourier transform infrared (FT-IR), electron paramagnetic resonance (EPR), and solid-state NMR measurements. It is the first time that carbon-oxygen radical COFs from bulk COFs are constructed by one-step method and the operation is flexible, convenient, and high rate of quality, which is suitable for big production and widely used. The cycle stability of the composite Na-K anode is improved, which provides a new idea for the design of high-performance liquid metal anode.

V4C3TX MXene: First-principles computational and separator modification study on immobilization and catalytic conversion of polysulfide in Li-S batteries.

Many attempts have recently used rationally-designed Ti3C2Tx MXene-based materials to increase sulfur utilization and tackle the detrimental shuttle effect in Li-S batteries (LSBs) due to their merits of high electronic conductivity, considerable catalytic activity, and sulfur immobilization. Nevertheless, the investigation of applying other two-dimensional (2D) transition metal carbides in LSBs is comparatively rare. In this work, the first-principles computations predicted that V4C3Tx could boost the "adsorption-diffusion-conversion" process of lithium polysulfides (LiPSs) over that of most other metal carbides of the MXene family. Inspired by this, we prepared the V4C3Tx MXene by hydrofluoric acid (HF) etching and then used it as a functional material coating on a polypropylene (PP) separator for LSB. As expected, the V4C3Tx modified PP separator (V4C3Tx-PP) can effectively prevent the shuttle effect of LiPSs via physical blocking, chemical adsorption, and catalytic conversion, as confirmed by visual polysulfide adsorption and diffusion tests, XPS analysis, and a series of electrochemical evaluations. As a result, the LSB with a V4C3Tx-PP enabled a high capacity and enhanced cycling performance (927 mAh g-1 at 1 C and 516 mAh g-1 retained for over 800 cycles, 1 C = 1675 mA g-1). More encouragingly, the cell achieves a superior rate capability of 725 mAh g-1 at 2 C and 586 mAh g-1 at 4 C, respectively. In addition, the V4C3Tx-PP-based LSB shows a high areal capacity of 4.3 mAh cm-2, even with the sulfur loading up to 4 mg cm-2. This work expands the application types and scope of MXenes from theoretical and experimental points of view. The first use of the V4C3Tx MXene modified separator in Li-S batteries creates high potential for practical application.

Dual-Ion Stabilized Layered Structure of OVO for Zero-Strain Potassium Insertion and Extraction.

Potassium-ion batteries (KIB) have similar energy storage mechanism with lithium-ion battery, but the potassium (K) resource is rich, which shows great potential for large-scale energy storage system. Recently, the anode materials of KIB studied mainly include carbon materials, transition metal oxides, and alloy materials. The amorphous hard carbon shows the best comprehensive performance, but its intercalation potential is close to 0 V (versus K+ /K), which is easy to cause K dendrite and brings security risks. The oxide materials have high capacity but high intercalation potential, low first cycle efficiency, and unstable cycle. Here, based on the understanding of the K intercalation mechanism of vanadium oxides, a novel zero strain anode material with layered structure of dual-ions (Na+ /K+ ) is designed (NaK(VO3 )2 V2 O5 ). The introduction of Na/K ion contributed to the transmission and further stabilized the structure. It has an excellent rate performance (10 A g-1 , up to 25 000th cycle), and its special K storage mechanism and zero-strain characteristics are revealed for the first time by ex situ scanning electron microscope, X-ray powder diffraction, X-ray photoelectron spectroscopy, and other test methods. Considering the excellent performance endowed by these unique inherent properties, NaK(VO3 )2 V2 O5 shows great potential for commercial anode materials and may promote the innovation of KIB.

Automatic Grading Assessments for Knee MRI Cartilage Defects via Self-ensembling Semi-supervised Learning with Dual-Consistency.

Knee cartilage defects caused by osteoarthritis are major musculoskeletal disorders, leading to joint necrosis or even disability if not intervened at early stage. Deep learning has demonstrated its effectiveness in computer-aided diagnosis, but it is time-consuming to prepare a large set of well-annotated data by experienced radiologists for model training. In this paper, we propose a semi-supervised framework to effectively use unlabeled data for better evaluation of knee cartilage defect grading. Our framework is developed based on the widely-used mean-teacher classification model, by designing a novel dual-consistency strategy to boost the consistency between the teacher and student models. The main contributions are three-fold: (1) We define an attention loss function to make the network focus on the cartilage regions, which can both achieve accurate attention masks and boost classification performance simultaneously; (2) Besides enforcing the consistency of classification results, we further design a novel attention consistency mechanism to ensure the focusing of the student and teacher networks on the same defect regions; (3) We introduce an aggregation approach to ensemble the slice-level classification outcomes for deriving the final subject-level diagnosis. Experimental results show that our proposed method can significantly improve both classification and localization performances of knee cartilage defects. Our code is available on https://github.com/King-HAW/DC-MT.

Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram.

OBJECTIVES: To implement a pipeline to automatically segment the ROI and to use a nomogram integrating the MRI-based radiomics score and clinical variables to predict responses to neoadjuvant chemotherapy (NAC) in osteosarcoma patients. METHODS: A total of 144 osteosarcoma patients treated with NAC were separated into training (n = 101) and test (n = 43) groups. After normalisation, ROIs for the preoperative MRI were segmented by a deep learning segmentation model trained with nnU-Net by using two independent manual segmentations as labels. Radiomics features were extracted using automatically segmented ROIs. Feature selection was performed in the training dataset by five-fold cross-validation. The clinical, radiomics, and clinical-radiomics models were built using multiple machine learning methods with the same training dataset and validated with the same test dataset. The segmentation model was evaluated by the Dice coefficient. AUC and decision curve analysis (DCA) were employed to illustrate the model performance and clinical utility. RESULTS: 36/144 (25.0%) patients were pathological good responders (pGRs) to NAC, while 108/144 (75.0%) were non-pGRs. The segmentation model achieved a Dice coefficient of 0.869 on the test dataset. The clinical and radiomics models reached AUCs of 0.636 with a 95% confidence interval (CI) of 0.427-0.860 and 0.759 (95% CI, 0.589-0.937), respectively, in the test dataset. The clinical-radiomics nomogram demonstrated good discrimination, with an AUC of 0.793 (95% CI, 0.610-0.975), and accuracy of 79.1%. The DCA suggested the clinical utility of the nomogram. CONCLUSION: The automatic nomogram could be applied to aid radiologists in identifying pGRs to NAC. KEY POINTS: • The nnU-Net trained by manual labels enables the use of an automatic segmentation tool for ROI delineation of osteosarcoma. • A pipeline using automatic lesion segmentation and followed by a radiomics classifier could aid the evaluation of NAC response of osteosarcoma. • A predictive nomogram composed of clinical variables and MRI-based radiomics score provides support for individualised treatment planning.

The prevalence and parameters of fabella and its association with medial meniscal tear in China: a retrospective study of 1011 knees.

BACKGROUND: Fabella is a sesamoid bone of knee that has potential biomechanical function. We aimed to examine the fabellar prevalence and parameters in Chinese population and test the hypothesis that fabellar presence and morphology were associated with meniscus tear or ligament injury. METHODS: A total of 1011 knee magnetic resonance imaging scans from 979 patients with knee pain were analyzed retrospectively. The exclusion criteria are postsurgical scans, difficulty in fabella discrimination, conditions not suitable for measurement, and unsatisfied image. The fabellar presence and its parameters (length, width and thickness) were documented. The association between fabellar presence and meniscus tear or ligament injury were assessed by chi-square test, in all knees and subgroups (age, gender, side, lesion part). The correlation of fabellar presence and parameters with advancing age was assessed by Spearman correlation analysis. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to investigate whether factors related with meniscus tear or ligament injury. Diagnostic performance of risk factors was assessed by receiver operating characteristic (ROC) analysis. RESULTS: The overall prevalence of fabellae was 39.8% (402/1011 knees) and increased with the increasing age (r = 0.237, P < 0.001). The size of the fabellae differed according to genders, age, and presence of articulating grooves. Fabella presented more often in knees with medial meniscus (MM) tears (66.7% vs 33.8%; P < 0.001) with a multivariate OR of 2.960 (95% CI, 1.853-3.903). The association remained in all tear parts (anterior, middle, and posterior), and in younger (age < 50 years) and older patients (age ≥ 50 years). Age, fabellar length, width, length/thickness ratio and width/thickness ratio yielded an area under the ROC curve (AUC) of 0.604-0.766 to predict an MM tear. In combination with age, fabellar width and length/thickness ratio, the AUC was improved 0.791 (95% CI, 0.744-0.837), with a sensitivity of 73.0% and a specificity of 74.6%. CONCLUSION: The presence of fabellae, increased fabellar length and width as well as flatter fabellar morphology, are significantly associated with an increased risk for MM tear. These findings might aid clinicians in identifying patients at risk for a MM tear and informing them.

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

PURPOSE: Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application of AI to medical imaging in knee joint. MATERIALS AND METHODS: A Checklist for Artificial Intelligence in Medical Imaging systematic review was conducted from January 1, 2015, to June 1, 2020, using PubMed, EMBASE, and Web of Science databases. A total of 36 articles discussing deep learning applications in knee joint imaging were identified, divided by imaging modality, and characterized by imaging task, data source, algorithm type, and outcome metrics. RESULTS: A total of 36 studies were identified and divided into: X-ray (44.44%) and MRI (55.56%). The mean CLAIM score of the 36 studies was 27.94 (standard deviation, 4.26), which was 66.53% of the ideal score of 42.00. The CLAIM items achieved an average good inter-rater agreement (ICC 0.815, 95% CI 0.660-0.902). In total, 32 studies performed internal cross-validation on the data set, while only 4 studies conducted external validation of the data set. CONCLUSIONS: The overall scientific quality of deep learning in knee imaging is insufficient; however, deep learning remains a promising technology for diagnostic or predictive purpose. Improvements in study design, validation, and open science need to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application, pre-trained scoring procedure, and modification of CLAIM in response to clinical needs are necessary in the future. KEY POINTS: • Limited deep learning studies were established in knee imaging with mean score of 27.94, which was 66.53% of the ideal score of 42.00, commonly due to invalidated results, retrospective study design, and absence of a clear definition of the CLAIM items in detail. • A previous trained data extraction instrument allowed reaching moderate inter-rater agreement in the application of the CLAIM, while CLAIM still needs improvement in scoring items and result reporting to become a wide adaptive tool in reviews of deep learning studies.

Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis.

BACKGROUND: Osteoarthritis is the most common degenerative joint disease. It is associated with significant socioeconomic burden and poor quality of life, mainly due to knee osteoarthritis (KOA), and related total knee arthroplasty (TKA). Since early detection method and disease-modifying drug is lacking, the key of KOA treatment is shifting to disease prevention and progression slowing. The prognostic prediction models are called for to guide clinical decision-making. The aim of our review is to identify and characterize reported multivariable prognostic models for KOA about three clinical concerns: (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA. METHODS: The electronic datasets (PubMed, Embase, the Cochrane Library, Web of Science, Scopus, SportDiscus, and CINAHL) and gray literature sources (OpenGrey, British Library Inside, ProQuest Dissertations & Theses Global, and BIOSIS preview) will be searched from their inception onwards. Title and abstract screening and full-text review will be accomplished by two independent reviewers. The multivariable prognostic models that concern on (1) the risk of developing KOA in the general population, (2) the risk of receiving TKA in KOA patients, and (3) the outcome of TKA in KOA patients who plan to receive TKA will be included. Data extraction instrument and critical appraisal instrument will be developed before formal assessment and will be modified during a training phase in advance. Study reporting transparency, methodological quality, and risk of bias will be assessed according to the TRIPOD statement, CHARMS checklist, and PROBAST tool, respectively. Prognostic prediction models will be summarized qualitatively. Quantitative metrics on the predictive performance of these models will be synthesized with meta-analyses if appropriate. DISCUSSION: Our systematic review will collate evidence from prognostic prediction models that can be used through the whole process of KOA. The review may identify models which are capable of allowing personalized preventative and therapeutic interventions to be precisely targeted at those individuals who are at the highest risk. To accomplish the prediction models to cross the translational gaps between an exploratory research method and a valued addition to precision medicine workflows, research recommendations relating to model development, validation, or impact assessment will be made. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020203543.

A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation.

OBJECTIVES: To assess the methodological quality and risk of bias in radiomics studies investigating diagnosis, therapy response, and survival of patients with osteosarcoma. METHODS: In this systematic review, literatures on radiomics in osteosarcoma were included and assessed for methodological quality through the radiomics quality score (RQS). The risk of bias and concern of application was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A meta-analysis of studies focusing on predicting osteosarcoma response to neoadjuvant chemotherapy was performed. RESULTS: Twelve radiomics studies exploring osteosarcoma were identified, and five were included in meta-analysis. The RQS reached an average of 20.4% (6.92 of 36) with good inter-rater agreement (ICC 0.95, 95% CI 0.85-0.99). Four studies validated results with an internal dataset, none of which used external dataset; one study was prospectively designed, and another one shared part of the dataset. The risk of bias and concern of application were mainly related to index test aspect. The meta-analysis showed a diagnostic odds ratio of 43.68 (95%CI 13.5-141.31) for predicting response to neoadjuvant chemotherapy with high heterogeneity and low methodological quality. CONCLUSIONS: The overall scientific quality of included studies is insufficient; however, radiomics remains a promising technology for predicting treatment response, which might guide therapeutic decision-making and related to prognosis. Improvements in study design, validation, and open science needs to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application of RQS, pre-trained RQS scoring procedure, and modification of RQS in response to clinical needs are necessary. KEY POINTS: • Limited radiomics studies were established in osteosarcoma with mean RQS of 20.4%, commonly due to unvalidated results, retrospective study design, and absence of open science. • Meta-analysis of radiomics studies predicting osteosarcoma response to neoadjuvant chemotherapy showed high diagnostic odds ratio 43.68, while high heterogeneity and low methodological quality were the main concerns. • A previously trained data extraction instrument allowed reaching moderate inter-rater agreement in RQS applications, while RQS still needs improvement to become a wide adaptive tool in reviews of radiomics studies, in routine self-check before manuscript submitting and in study design.

Primary perivascular epithelioid cell tumor (PEComa) in bone: A review of the literature and a case arising in the humerus with multiple metastases.

INTRODUCTION: Perivascular epithelioid cell tumors (PEComas) are a family of mesenchymal tumors that rarely arise as a primary bone tumor. MATERIAL AND METHODS: We report a case of primary malignant bone PEComa. A literature review via PubMed, Embase and Web of Science databases with the keyword "PEComa" and "bone" was performed. RESULTS: We reported a 33-year-old female with primary malignant bone PEComa in right distal humerus. The patient received an inhibitor of the mammalian target of rapamycin (mTOR) protein based on negative molecular investigation result of transcription factor E3 (TFE3) rearrangement, and additional therapies including palliative radiotherapy, anti-angiogenics and immunotherapy when the disease progression was detected. The patient was alive with the disease twenty-three months postoperatively. A total of nineteen related literature cases were retrieved and reviewed. Taking current case into account, ten males and ten females with median age of 24 years (range, 3-93 years) were identified, who were most frequently affected in tibia. The median follow-up duration of 24 months (range, 3-96 months). One patient died due to this disease, and six patients showed metastases. Three patients experienced recurrence, and two of them experienced twice and three times, respectively. CONCLUSION: To our knowledge, this is the first case of primary malignant bone PEComa arising in humerus. Clinicopathological and radiological correlation is mandatory to the correct diagnosis and to determine its malignancy. More studies are required to understand the role of molecular test and imaging in selecting suitable treatment and mechanisms of treatment resistance.

Clarifying prognostic factors of small cell osteosarcoma: A pooled analysis of 20 cases and the literature.

INTRODUCTION: Small cell osteosarcoma (SCOS) is a rare subtype of osteosarcoma, with limited studies mainly focusing on histological features. Our study aims to analyze our own patients and those reported in the literature to increase the recognition of this rare disease, to evaluate patient survival and to further determine potential prognostic factors. MATERIAL AND METHODS: Twenty patients with SCOS were treated in our hospital between 2010 and 2019. Their follow-up data were collected retrospectively. A total of 336 literature cases from 58 manuscripts were retrieved by means of a PubMed search with the key word "small cell osteosarcoma". Data pertaining to treatment and follow-up were extracted. We performed a pooled analysis for the survival of patients and the risk factors for local recurrence (LR), as well as metastatic disease (MD), in a total of 160 patients using the Kaplan-Meier method and Cox regression method. RESULTS: We reported our experience in diagnosing and treating SCOS. In our cases, elevated alkaline phosphatase (P = 0.013) and lactate dehydrogenase (P = 0.001) significantly impaired overall survival. In the pooled analysis, SCOS was diagnosed at the median age of 17 years and affected both sexes almost equally. The median follow-up duration was 19.5 months. In the pooled analysis cases, the 5-year overall survival rate was 38.6%, and 36.4% of patients survived 10 years. However, an increasing trend was detected, indicating recent improvements in management. The surgical margin status (P = 0.024) and metastases (P = 0.008) significantly impaired overall survival, and the response to chemotherapy was related to disease-free survival (P = 0.012). LR and MD were significantly correlated (P = 0.002) and could be observed after 5 years of follow-up. LR was significantly dependent on response to chemotherapy (P = 0.020). The development of MD seemed to be affected by response to chemotherapy (P = 0.060). Correlations between imaging features and prognosis were not detected. CONCLUSIONS: This study suggested that positive margins, poor response to chemotherapy and MD are negative prognostic factors for SCOS, implied the potential role of laboratory examinations in the survival prediction and supported the need for prolonged or more intensive surveillance in patients with MD or LR. More well-documented literatures are encouraged to allow further confirmations.

MOF-derived Ni3S4 Encapsulated in 3D Conductive Network for High-Performance Supercapacitor.

Transition-metal sulfide is a good kind of material for supercapacitors because of the large capacity. Nevertheless, the low electroconductivity, slow reaction kinetics, and limited active centers lead to poor electrochemical properties such as long-term cycling stability. In the present work, nano nickel metal-organic framework (Ni-MOF) was constructed by using the nitrogen-rich functional group ligand 2,4,6-tris(3,5-dicarboxylphenylamino)-1,3,5-triazin and compounded with carbon nanotubes (CNTs) to prepare Ni-MOF/CNTs composite, which was used as a precursor to prepare the MOFs-derived NC/Ni-Ni3S4/CNTs composite with the Ni3S4 uniformly distributed in the three-dimensional (3D) conductive network. The rich nitrogen doping and 3D conductive network constructed by CNTs improved the conductivity, prompted the rapid entry of electrolyte, and improved the reaction kinetics of NC/Ni-Ni3S4/CNTs, thus obtained excellent specific capacitance, coulomb efficiency, and cyclic stability. The specific capacitance of NC/Ni-Ni3S4/CNTs is 1489.9 F/g at 1 A/g, which remains 800 F/g at 10 A/g, showing good rate performance.