Raman Flow Cytometry and Its Biomedical Applications.

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Reconciling the Cooperative-Competitive Patterns among Tumor and Immune Cells for Triple-Negative Breast Cancer Treatment Using Multimodule Nanocomplexes.

Targeting the competitive-cooperative relationships among tumor cells and various immune cells can efficiently reverse the immune-dysfunction microenvironment to boost the immunotherapies for the triple-negative breast cancer treatment. Hence, a bacterial outer membrane vesicle-based nanocomplex is designed for specifically targeting malignant cells and immune cells to reconcile the relationships based on metabolic-immune crosstalk. By uniquely utilizing the property of charge-reversal polymers to realize function separation, the nanocomplexes could synergistically regulate tumor cells and immune cells. This approach could reshape the immunosuppressive competition-cooperation pattern into one that is immune-responsive, showcasing significant potential for inducing tumor remission in TNBC models.

Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning.

Objective. Precise hepatocellular carcinoma (HCC) detection is crucial for clinical management. While studies focus on computed tomography-based automatic algorithms, there is a rareness of research on automatic detection based on dynamic contrast enhanced (DCE) magnetic resonance imaging. This study is to develop an automatic detection and segmentation deep learning model for HCC using DCE.Approach: DCE images acquired from 2016 to 2021 were retrospectively collected. Then, 382 patients (301 male; 81 female) with 466 lesions pathologically confirmed were included and divided into an 80% training-validation set and a 20% independent test set. For external validation, 51 patients (42 male; 9 female) in another hospital from 2018 to 2021 were included. The U-net architecture was modified to accommodate multi-phasic DCE input. The model was trained with the training-validation set using five-fold cross-validation, and furtherly evaluated with the independent test set using comprehensive metrics for segmentation and detection performance. The proposed automatic segmentation model consisted of five main steps: phase registration, automatic liver region extraction using a pre-trained model, automatic HCC lesion segmentation using the multi-phasic deep learning model, ensemble of five-fold predictions, and post-processing using connected component analysis to enhance the performance to refine predictions and eliminate false positives.Main results. The proposed model achieved a mean dice similarity coefficient (DSC) of 0.81 ± 0.11, a sensitivity of 94.41 ± 15.50%, a precision of 94.19 ± 17.32%, and 0.14 ± 0.48 false positive lesions per patient in the independent test set. The model detected 88% (80/91) HCC lesions in the condition of DSC > 0.5, and the DSC per tumor was 0.80 ± 0.13. In the external set, the model detected 92% (58/62) lesions with 0.12 ± 0.33 false positives per patient, and the DSC per tumor was 0.75 ± 0.10.Significance.This study developed an automatic detection and segmentation deep learning model for HCC using DCE, which yielded promising post-processed results in accurately identifying and delineating HCC lesions.

Altered whole-brain functional network in patients with frontal low-grade gliomas: a resting-state functional MRI study.

PURPOSE: Gliomas are the most common primary brain tumor. Currently, topological alterations of whole-brain functional network caused by gliomas are not fully understood. The work here clarified the topological reorganization of the functional network in patients with unilateral frontal low-grade gliomas (LGGs). METHODS: A total of 45 patients with left frontal LGGs, 19 with right frontal LGGs, and 25 healthy controls (HCs) were enrolled. All the resting-state functional MRI (rs-fMRI) images of the subjects were preprocessed to construct the functional network matrix, which was used for graph theoretical analysis. A two-sample t-test was conducted to clarify the differences in global and nodal network metrics between patients and HCs. A network-based statistic approach was used to identify the altered specific pairs of regions in which functional connectivity in patients with LGGs. RESULTS: The local efficiency, clustering coefficient, characteristic path length, and normalized characteristic path length of patients with unilateral frontal LGGs were significantly lower than HCs, while there were no significant differences of global efficiency and small-worldness between patients and HCs. Compared with the HCs, betweenness centrality, degree centrality, and nodal efficiency of several brain nodes were changed significantly in patients. Around the tumor and its adjacent areas, the inter- and intra-hemispheric connections were significantly decreased in patients with left frontal LGGs. CONCLUSION: The patients with unilateral frontal LGGs have altered global and nodal network metrics and decreased inter- and intra-hemispheric connectivity. These topological alterations may be involved in functional impairment and compensation of patients.

Intra-articular injection of platelet-rich plasma vs hyaluronic acid as an adjunct to TMJ arthrocentesis: A systematic review and meta-analysis.

OBJECTIVE: We aimed to find out if there is any difference in outcomes with the use of platelet-rich plasma (PRP) or hyaluronic acid (HA) intra-articular injections after temporomandibular joint arthrocentesis. METHODS: A systematic search of the electronic databases of PubMed, Embase, and Scopus was undertaken up to 5th May 2023. Randomized controlled trials (RCTs) comparing PRP with HA after TMJ arthrocentesis were included. RESULTS: Seven RCTs were eligible. Pooled analysis failed to demonstrate any significant difference in MMO between PRP and HA groups at 1 month (MD: 0.21 95 % CI: -1.29, 1.70), 3 months (MD: 0.92 95 % CI: -2.96, 4.80), and 6 months (MD: -0.05 95 % CI: -2.08, 1.97). The inter-study heterogeneity was high with I2 values of 85 %, 98 %, and 81 % respectively. Similarly, there was no statistically significant difference in pain scores between the PRP and HA groups at 1 month (MD: 0.42 95 % CI: -2.25, 3.10), 3 months (MD: 0.90 95 % CI: -1.60, 3.41), and 6 months (MD: 0.06 95 % CI: -0.92, 1.04) with inter-study heterogeneity of 99 %, 99 %, and 92 % respectively. CONCLUSION: Intra-articular use of PRP or HA after TMJ arthrocentesis may lead to comparable clinical outcomes. The current evidence is low-quality and fraught with high heterogeneity.

Mesoporous Nickel Sulfide Microsphere Encapsulated in Nitrogen, Sulfur Dual-Doped Carbon with Large Subsurface Region for Enhanced Sodium Storage.

Nickel sulfides are promising anode candidates in sodium ion batteries (SIBs) due to high capacity and abundant reserves. However, their applications are restricted by poor cycling stability and slow reaction kinetics. Thus, mesoporous nickel sulfide microsphere encapsulated in nitrogen, sulfur dual-doped carbon (MNS@NSC) is prepared. The packaged structure and carbon matrix restrain the volume variation together, the N, S dual-doping improves the electronic conductivity and offers extra active sites for sodium storage. Ex-situ X-ray diffraction  appeals copper collector adsorbs polysulfide to inhibit the polysulfide accumulation and enhance conductivity. Moreover, the large subsurface attributed to C-S-S-C bonding further boosts pseudocapacitive capacity, conducive to charge transfer. As a result, MNS@NSC delivers a high reversible capacity of 640.2 mAh g-1 after 100 cycles at 0.1 A g-1 , an excellent rate capability (569.8 mAh g-1 at 5 A g-1 ), and a remained capacity of 513.8 mAh g-1 after undergoing 10000 circulations at 10 A g-1 . The MNS@NSC|| Na3 V2 (PO4 )3 full cell shows a cycling performance of specific capacity of 230.8 mAh g-1 after 100 cycles at 1 A g-1 . This work puts forward a valid strategy of combing structural design and heteroatom doping to synthesize high-performance nickel sulfide materials in SIBs.

BTSC-TNAS: A neural architecture search-based transformer for brain tumor segmentation and classification.

Glioblastoma (GBM), isolated brain metastasis (SBM), and primary central nervous system lymphoma (PCNSL) possess a high level of similarity in histomorphology and clinical manifestations on multimodal MRI. Such similarities have led to challenges in the clinical diagnosis of these three malignant tumors. However, many existing models solely focus on either the task of segmentation or classification, which limits the application of computer-aided diagnosis in clinical diagnosis and treatment. To solve this problem, we propose a multi-task learning transformer with neural architecture search (NAS) for brain tumor segmentation and classification (BTSC-TNAS). In the segmentation stage, we use a nested transformer U-shape network (NTU-NAS) with NAS to directly predict brain tumor masks from multi-modal MRI images. In the tumor classification stage, we use the multiscale features obtained from the encoder of NTU-NAS as the input features of the classification network (MSC-NET), which are integrated and corrected by the classification feature correction enhancement (CFCE) block to improve the accuracy of classification. The proposed BTSC-TNAS achieves an average Dice coefficient of 80.86% and 87.12% for the segmentation of tumor region and the maximum abnormal region in clinical data respectively. The model achieves a classification accuracy of 0.941. The experiments performed on the BraTS 2019 dataset show that the proposed BTSC-TNAS has excellent generalizability and can provide support for some challenging tasks in the diagnosis and treatment of brain tumors.

Carbon-coated iron selenide derived from double-framework as an advance anode for Na-ion battery.

Owing to the desirable nano-morphology, controllable structure, and ease of preparation, metal-organic frameworks (MOFs) are widely used as the precursors for electrodes in Na-ion battery (NIB). However, MOF structures are prone to fracture and collapse during the reactions. Additionally, MOF-derived electrodes often exhibit a high expansion rate, which negatively impacts the long cyclic capability of NIBs. Herein, we employed a stable covalent-organic framework (COF) as a protective coating for the first time to preserve the MOF structure. A shuttle-like iron selenide (Fe3Se4) coated with N-doped carbon (NC) was synthesized using a simple hydrothermal method, surface coating, and subsequent selenizing process. Due to its large specific surface area and well-developed porosity, the double-framework derived Fe3Se4/NC electrode provides abundant active sites for Na+ storage. The COF and COF-derived NC protect the structure of Fe3Se4/NC during synthesis and cyclic process, respectively. The high conductivity of the NC coating enhances the electron/ion conductivity of Fe3Se4/NC, thereby beneficial the rate performance. As the material anode for NIB, the Fe3Se4/NC electrode exhibits a high initial charging/discharging capacity (425.7/478.4 mAh·g-1 with an initial Coulombic efficiency of 89.0 %), excellent rate performance (333.5 mAh·g-1 at 12 A·g-1), long-durable cycle capability (290.8 mAh·g-1 after 1000 cycles at 8 A·g-1) and fast charging ability (143 s). This work provides a novel strategy of "COF on MOF" to prepare high-performance electrode materials for NIB.

Development and validation of a radiomics-based prediction pipeline for the response to stereotactic radiosurgery therapy in brain metastases.

OBJECTIVES: The first treatment strategy for brain metastases (BM) plays a pivotal role in the prognosis of patients. Among all strategies, stereotactic radiosurgery (SRS) is considered a promising therapy method. Therefore, we developed and validated a radiomics-based prediction pipeline to prospectively identify BM patients who are insensitive to SRS therapy, especially those who are at potential risk of progressive disease. METHODS: A total of 337 BM patients (277, 30, and 30 in the training set, internal validation set, and external validation set, respectively) were enrolled in the study. 19,377 radiomics features (3 masks × 3 MRI sequences × 2153 features) extracted from 9 ROIs were filtered through LASSO and Max-Relevance and Min-Redundancy (mRMR) algorithms. The selected radiomics features were combined with 4 clinical features to construct a two-stage cascaded model for the prediction of BM patients' response to SRS therapy using SVM and an ensemble learning classifier. The performance of the model was evaluated by its accuracy, specificity, sensitivity, and AUC curve. RESULTS: Radiomics features were integrated with the clinical features of patients in our optimal model, which showed excellent discriminative performance in the training set (AUC: 0.95, 95% CI: 0.88-0.98). The model was also verified in the internal validation set and external validation set (AUC 0.93, 95% CI: 0.76-0.95 and AUC 0.90, 95% CI: 0.73-0.93, respectively). CONCLUSIONS: The proposed prediction pipeline could non-invasively predict the response to SRS therapy in patients with brain metastases thus assisting doctors to precisely designate individualized first treatment decisions. CLINICAL RELEVANCE STATEMENT: The proposed prediction pipeline combines the radiomics features of multi-modal MRI with clinical features to construct machine learning models that noninvasively predict the response of patients with brain metastases to stereotactic radiosurgery therapy, assisting neuro-oncologists to develop personalized first treatment plans. KEY POINTS: • The proposed prediction pipeline can non-invasively predict the response to SRS therapy. • The combination of multi-modality and multi-mask contributes significantly to the prediction. • The edema index also shows a certain predictive value.

Gradient tumor microenvironment-promoted penetrating micelles for hypoxia relief and immunosuppression reversion in pancreatic cancer treatment.

The tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC) is the main block for the penetration of chemotherapy. In the tumor microenvironment, a dense matrix composed of fibrin is formed on the exterior, while the interior is featured by high reduction, hypoxia and low pH. How to match the special microenvironment to on-demand drug release is the key to improve chemotherapeutic efficacy. Herein, a microenvironment-responsive micellar system is developed to deepen tumoral penetration. Briefly, the conjugation of a fibrin-targeting peptide to PEG-poly amino acid has been utilized to achieve accumulation of micelles in the tumor stroma. By modification of micelles with hypoxia-reducible nitroimidazole which becomes protonated under acidic conditions, their surface charge is more positive, facilitating deeper penetration into tumors. Paclitaxel was loaded onto the micelles via a disulfide bond to enable glutathione (GSH)-responsive release. Therefore, the immunosuppressive microenvironment is relived through the alleviation of hypoxia and depletion of GSH. Hopefully, this work could establish paradigms by designing sophisticated drug-delivery systems to tactfully employ and retroact the tamed tumoral microenvironment to improve the therapeutic efficacy based on understanding the multiple hallmarks and learning the mutual regulation. STATEMENT OF SIGNIFICANCE: Tumor microenvironment(TME) is an unique pathological feature of pancreatic cancer and an inherent barrier to chemotherapy. Numerous studies regard TME as the targets for drug delivery. In this work, we propose a hypoxia-responsive nanomicellar drug delivery system that aiming hypoxia TME of pancreatic cancer. The nanodrug delivery system could respond to the hypoxic microenvironment and enhance the penetration of the inner tumor at the same time preserving the outer tumor stroma, thus achieving targeted treatment of PDAC by preserving the integrity of the outer stroma. Simultaneously, the responsive group can reverse the degree of hypoxia in TME by disrupting the redox balance in the tumor region, thus achieving precise treatment of PDAC by matching the pathological characteristics of TME. We believe our article would provide new design ideas for the future treatments for pancreatic cancer.

Nanomaterials with dual immunomodulatory functions for synergistic therapy of breast cancer brain metastases.

A long-standing paucity of effective therapies results in the poor outcomes of triple-negative breast cancer brain metastases. Immunotherapy has made progress in the treatment of tumors, but limited by the non-immunogenicity of tumors and strong immunosuppressive environment, patients with TNBC brain metastases have not yet benefited from immunotherapy. Dual immunoregulatory strategies with enhanced immune activation and reversal of the immunosuppressive microenvironment provide new therapeutic options for patients. Here, we propose a cocktail-like therapeutic strategy of microenvironment regulation-chemotherapy-immune synergistic sensitization and construct reduction-sensitive immune microenvironment regulation nanomaterials (SIL@T). SIL@T modified with targeting peptide penetrates the BBB and is subsequently internalized into metastatic breast cancer cells, releasing silybin and oxaliplatin responsively in the cells. SIL@T preferentially accumulates at the metastatic site and can significantly prolong the survival period of model animals. Mechanistic studies have shown that SIL@T can effectively induce immunogenic cell death of metastatic cells, activate immune responses and increase infiltration of CD8+ T cells. Meanwhile, the activation of STAT3 in the metastatic foci is attenuated and the immunosuppressive microenvironment is reversed. This study demonstrates that SIL@T with dual immunomodulatory functions provides a promising immune synergistic therapy strategy for breast cancer brain metastases.

BTMF-GAN: A multi-modal MRI fusion generative adversarial network for brain tumors.

Image fusion techniques have been widely used for multi-modal medical image fusion tasks. Most existing methods aim to improve the overall quality of the fused image and do not focus on the more important textural details and contrast between the tissues of the lesion in the regions of interest (ROIs). This can lead to the distortion of important tumor ROIs information and thus limits the applicability of the fused images in clinical practice. To improve the fusion quality of ROIs relevant to medical implications, we propose a multi-modal MRI fusion generative adversarial network (BTMF-GAN) for the task of multi-modal MRI fusion of brain tumors. Unlike existing deep learning approaches which focus on improving the global quality of the fused image, the proposed BTMF-GAN aims to achieve a balance between tissue details and structural contrasts in brain tumor, which is the region of interest crucial to many medical applications. Specifically, we employ a generator with a U-shaped nested structure and residual U-blocks (RSU) to enhance multi-scale feature extraction. To enhance and recalibrate features of the encoder, the multi-perceptual field adaptive transformer feature enhancement module (MRF-ATFE) is used between the encoder and the decoder instead of a skip connection. To increase contrast between tumor tissues of the fused image, a mask-part block is introduced to fragment the source image and the fused image, based on which, we propose a novel salient loss function. Qualitative and quantitative analysis of the results on the public and clinical datasets demonstrate the superiority of the proposed approach to many other commonly used fusion methods.

Bioactive PEEK: Surface Enrichment of Vitronectin-Derived Adhesive Peptides.

Polyetheretherketone (PEEK) is a thermoplastic polymer that has been recently employed for bone tissue engineering as a result of its biocompatibility and mechanical properties being comparable to human bone. PEEK, however, is a bio-inert material and, when implanted, does not interact with the host tissues, resulting in poor integration. In this work, the surfaces of 3D-printed PEEK disks were functionalized with: (i) an adhesive peptide reproducing [351-359] h-Vitronectin sequence (HVP) and (ii) HVP retro-inverted dimer (D2HVP), that combines the bioactivity of the native sequence (HVP) with the stability toward proteolytic degradation. Both sequences were designed to be anchored to the polymer surface through specific covalent bonds via oxime chemistry. All functionalized PEEK samples were characterized by Water Contact Angle (WCA) measurements, Atomic Force Microscopy (AFM), and X-ray Photoelectron Spectroscopy (XPS) to confirm the peptide enrichment. The biological results showed that both peptides were able to increase cell proliferation at 3 and 21 days. D2HVP functionalized PEEK resulted in an enhanced proliferation across all time points investigated with higher calcium deposition and more elongated cell morphology.

A neutrophil-biomimic platform for eradicating metastatic breast cancer stem-like cells by redox microenvironment modulation and hypoxia-triggered differentiation therapy.

Metastasis accounts for 90% of breast cancer deaths, where the lethality could be attributed to the poor drug accumulation at the metastatic loci. The tolerance to chemotherapy induced by breast cancer stem cells (BCSCs) and their particular redox microenvironment further aggravate the therapeutic dilemma. To be specific, therapy-resistant BCSCs can differentiate into heterogeneous tumor cells constantly, and simultaneously dynamic maintenance of redox homeostasis promote tumor cells to retro-differentiate into stem-like state in response to cytotoxic chemotherapy. Herein, we develop a specifically-designed biomimic platform employing neutrophil membrane as shell to inherit a neutrophil-like tumor-targeting capability, and anchored chemotherapeutic and BCSCs-differentiating reagents with nitroimidazole (NI) to yield two hypoxia-responsive prodrugs, which could be encapsulated into a polymeric nitroimidazole core. The platform can actively target the lung metastasis sites of triple negative breast cancer (TNBC), and release the escorted drugs upon being triggered by the hypoxia microenvironment. During the responsiveness, the differentiating agent could promote transferring BCSCs into non-BCSCs, and simultaneously the nitroimidazole moieties conjugated on the polymer and prodrugs could modulate the tumor microenvironment by depleting nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) and amplifying intracellular oxidative stress to prevent tumor cells retro-differentiation into BCSCs. In combination, the BCSCs differentiation and tumor microenvironment modulation synergistically could enhance the chemotherapeutic cytotoxicity, and remarkably suppress tumor growth and lung metastasis. Hopefully, this work can provide a new insight in to comprehensively treat TNBC and lung metastasis using a versatile platform.

Compound Nanoemulsion Combined with Differentiation/Cytotoxicity Drugs for Modulating Breast Cancer Stemness.

Breast cancer stem cells (BCSCs) are the culprit of triple-negative breast cancer invasiveness and are heterogeneous. It is recognized that the combination of chemotherapy and differentiation therapy for killing BCSCs and non-BCSCs simultaneously is a reliable strategy. In this study, an oil-in-water nanoemulsion was prepared by high-pressure homogenization with coencapsulation of all-trans retinoic acid (ATRA) and doxorubicin (DOX). The preparation process was simple, and the production was easy to scale up. The particle size of the nanoemulsion was 127.2 ± 2.0 nm. Cellular toxicity assay showed that the composite index of the ATRA and DOX was less than 1 and exhibited a fine combined effect. In vivo antitumor efficacy showed that the compound nanoemulsion could reduce the proportion of BCSCs to 1.18% by inhibiting the expression of Pin1. In addition, the combination of ATRA and DOX could reduce the cardiotoxicity of DOX and had higher safety. Hopefully, this work can provide a new insight into developing pharmaceutically acceptable technology for treating BCSCs.

Bonding iron chalcogenides in a hierarchical structure for high-stability sodium storage.

Owing to price-boom and low-reserve of Lithium ion batteries (LIBs), cost-cutting and well-stocked sodium ion batteries (SIBs) attract a lot of attention, aiming to develop an effective energy storage and conversion equipment. As a typical anode for SIBs, Iron sulfide (FeS) is difficult to maintain the high theoretical capacity. Structural instability and inherent low conductivity limit the cyclic and rate performance of FeS. Herein, hierarchical architecture of FeS-FeSe2 coated with nitrogen-doped carbon (NC) is obtained by single-step solvothermal method and two-stage high-temperature treatments. Specifically, lattice imperfections provided by heterogeneous interfaces increase the Na+ storage sites and fasten ion/electron transfer. Synergistic effect induced by the hierarchical architecture effectively enhances the electrochemical activity and reduces the resistance, which contributes to the transfer kinetics of Na+. In addition, the phenomenon that heterogeneous interfaces provide more active site and extra migration Na+ path is also proved by density functional theory (DFT). As an anode for SIBs, FeS-FeSe2/NC (FSSe/C) delivers highly reversible capacity (704.5 mAh·g-1 after 120 cycles at 0.2 A·g-1), excellent rate performance (326.3 mAh·g-1 at 12 A·g-1) and long-lasting durability (492.3 mAh·g-1 after 1000 cycles at 4 A·g-1 with 100 % capacity retention). Notably, the full battery, assembled with FSSe/C and Na3V2(PO4)3/C (NVP/C), delivers reversible capacity of 252.1 mAh·g-1 after 300 cycles at 1 A·g-1. This work provides a facile method to construct a hierarchical architecture anode for high-performance SIBs.

An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach.

BACKGROUND: The histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of tumor cells are major factors in the diagnosis, prognosis, and treatment management of patients. Many existing studies primarily focused on the classification of two classes of brain tumors and the Ki-67LI of gliomas. This study aimed to develop a preoperative non-invasive radiomics pipeline based on multiparametric-MRI to classify-three types of brain tumors, glioblastoma (GBM), metastasis (MET) and primary central nervous system lymphoma (PCNSL), and to predict their corresponding Ki-67LI. METHODS: In this retrospective study, 153 patients with malignant brain tumors were involved. The radiomics features were extracted from three types of MRI (T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI)) with three masks (tumor core, edema, and whole tumor masks) and selected by a combination of Pearson correlation coefficient (CORR), LASSO, and Max-Relevance and Min-Redundancy (mRMR) filters. The performance of six classifiers was compared and the top three performing classifiers were used to construct the ensemble learning model (ELM). The proposed ELM was evaluated in the training dataset (108 patients) by 5-fold cross-validation and in the test dataset (45 patients) by hold-out. The accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-Score, and the area under the receiver operating characteristic curve (AUC) indicators evaluated the performance of the models. RESULTS: The best feature sets and ELM with the optimal performance were selected to construct the tri-categorized brain tumor aided diagnosis model (training dataset AUC: 0.96 (95% CI: 0.93, 0.99); test dataset AUC: 0.93) and Ki-67LI prediction model (training dataset AUC: 0.96 (95% CI: 0.94, 0.98); test dataset AUC: 0.91). The CE-T1WI was the best single modality for all classifiers. Meanwhile, the whole tumor was the most vital mask for the tumor classification and the tumor core was the most vital mask for the Ki-67LI prediction. CONCLUSION: The developed radiomics models led to the precise preoperative classification of GBM, MET, and PCNSL and the prediction of Ki-67LI, which could be utilized in clinical practice for the treatment planning for brain tumors.

A Triple Therapeutic Strategy with Antiexosomal Iron Efflux for Enhanced Ferroptosis Therapy and Immunotherapy.

Ferroptosis is a form of regulated cell death which can not only kill tumor cells but also enhance immunogenicity of tumor cells, and it is evidenced to be involved in a variety of tumor treatments, especially in cancer immunotherapy. Tumor cell-derived exosomes are reported to influence the progression and metastasis process of tumors. In the process of ferroptosis, exosomes are also demonstrated as mediators to export iron under high intracellular iron concentration and resist ferroptosis. Under this regard, the combined application of ferroptosis inducer and the inhibitor of iron-containing exosomes may enhance the ferroptosis. Herein, biocompatible hybrid nanoparticles composed of the iron oxide nanoparticles, polymers with oxaliplatin attached, and siProminin2 are constructed. The siProminin2 mediated exosomal inhibition can restore the intracellular iron concentration, which can also inhibit the secretion of tumor cell-derived exosomes. The combination of immunotherapy with oxaliplatin, ferroptosis-based cancer therapy and inhibition of tumor cell-derived exosomes can enhance the immune activation effects. The nanoparticles represent an excellent triple therapeutic strategy for enhancing ferroptosis-based cancer therapy and immunotherapy.

Analysis of Key Factors Associated with Response to Salvage High-Dose Methotrexate Rechallenge in Primary Central Nervous System Lymphoma with First Relapse.

BACKGROUND: Primary central nervous system lymphoma (PCNSL) is a rare extranodal non-Hodgkin's lymphoma that occurs in the central nervous system. Although sensitive to chemotherapy, 35-60% of PCNSL patients still relapse within 2 years after the initial treatment. High-dose methotrexate (HD-MTX) rechallenge is generally used in recurrent PCNSL, especially for patients who have achieved a response after initial methotrexate (MTX) treatment. However, the overall remission rate (ORR) of HD-MTX rechallenge is about 70-80%. Additionally, the side effects of HD-MTX treatment endanger the health of patients and affect their quality of life. METHODS: This is a retrospective study of patients with first relapse PCNSL at Huashan Hospital, Fudan University between January 2000 and November 2020. By comparing the clinical characteristics and radiological manifestations of first relapsed PCNSL patients with remission and non-remission after receiving HD-MTX rechallenge, we screened out the key factors associated with HD-MTX rechallenge treatment response, to provide some help for the selection of salvage treatment strategies for patients with recurrent PCNSL. Additionally, patients with remission after HD-MTX rechallenge were followed up to identify the factors related to progression-free survival of the second time (PFS2) (time from the first relapse to second relapse/last follow-up). The Kruskal-Wallis and Pearson chi-square tests were performed to examine the univariate association. Further, multivariable logistic regression analysis was used to study the simultaneous effect of different variables. RESULTS: A total of 207 patients were enrolled in the study based on the inclusion criteria, including 114 patients in the remission group (RG) and 81 patients in the non-remission group (nRG), and 12 patients were judged as having a stable disease. In Kruskal-Wallis and Pearson chi-square tests, progression-free survival rates for first time (PFS1) and whether the initial treatment was combined with consolidated whole brain radiotherapy (WBRT) were related to the response to HD-MTX rechallenge treatment, which was further validated in regression analysis. Further, after univariate analysis and regression analysis, KPS was related to PFS2. CONCLUSIONS: For PCNSL patients in their first relapse, HD-MTX rechallenge may be an effective salvage treatment. PFS1 and whether initial treatment was combined with consolidation WBRT were associated with HD-MTX rechallenge treatment response. In addition, patients with higher KPS at the time of the first relapse had a longer PFS2 after HD-MTX rechallenge treatment.

Analysis of Key Clinical Variables and Radiological Manifestations Associated with the Treatment Response of Patients with Brain Metastases to Stereotactic Radiosurgery.

BACKGROUND: Stereotactic radiosurgery (SRS) is considered a promising treatment for brain metastases (BM) with better healing efficacy, relatively faster treatment time, and lower neurotoxicity, which can achieve local control rates above 70%. Although SRS improves the local control of BM, this may not translate into improvements in survival time. Thus, screening out the key factors influencing the treatment response to SRS, instead of the survival time following SRS, might be of more significance. This may assist doctors when making adjustments to treatment strategies for patients with BM. METHODS: This is a retrospective review of 696 patients with BM who were treated with SRS at Huashan Hospital, Fudan University between June 2015 and February 2020. According to the patients' treatment response to SRS, the patients were divided into an improved group (IG) and a progressive group (PG). The clinical data and magnetic resonance imaging (MRI) performed pre- and post-treatment were collected for the two groups. Five clinical variables (gender, age, Karnofsky performance status (KPS), primary tumor type, and extracranial lesion control) and seven radiological manifestations (location, number, volume, maximum diameter, edema index (EI), diffusion weighted imaging (DWI) sequence signal, and enhanced pattern) were selected and compared. A stepwise regression analysis was performed in order to obtain the best prediction effect of a group of variables and their regression coefficients, and finally to build an SRS treatment response scoring model based on the coefficients. The performance of the model was evaluated by calculating the AUC and performing the Hosmer-Lemeshow test. RESULTS: A total of 323 patients were enrolled in the study based on the inclusion and exclusion criteria, including 209 patients in the IG and 114 patients in the PG. In the Chi-square test and t-test analysis, the significant p values of KPS, extracranial lesion control, volume, and EI were less than 0.05. Moreover, the cut-off values for volume and EI were 1801.145 mm3 and 3.835, respectively. The scoring model that was based on multivariate logistic regression coefficients performed better, achieving AUCs of 0.755 ± 0.062 and 0.780 ± 0.061 for the internal validation and validation cohorts, with p values of 0.168 and 0.073 for the Hosmer-Lemeshow test. CONCLUSIONS: KPS, extracranial lesion control, tumor volume, and EI had a certain correlation with the treatment response to SRS. Scoring models that are based on these variables can accurately predict the treatment response of patients with BM to SRS, thereby assisting doctors to make an appropriate first treatment strategy for patients with BM to a certain degree.