Relationship between multi-pool model-based chemical exchange saturation transfer imaging, intravoxel incoherent motion MRI, and 11C-methionine uptake on PET/CT in patients with gliomas

PURPOSE

Magnetization transfer ratio asymmetry (MTRasym) analysis is used for chemical exchange saturation transfer (CEST) in patients with gliomas; however, this approach has limitations. CEST imaging using a multi-pool model (MPM) may allow a more detailed assessment of gliomas; however, its mechanism remains unknown. This study aimed to assess the relationship between CEST imaging by MPM, intravoxel incoherent motion (IVIM), and 11C-methionine (11C-MET) uptake on positron emission tomography/computed tomography (PET/CT) to clarify the clinical significance of CEST imaging using MPM in gliomas.

METHODS

This retrospective study included 17 patients with gliomas who underwent 11C-MET PET/CT at our institution between January 2020 and January 2022. Two-dimensional axial CEST imaging was conducted using single-shot fast-spin echo acquisition at 3 T. The apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), f, MTRasym (3.5 ppm), parameters of MPM-based CEST imaging, and tumor-to-contralateral normal brain tissue (T/N) ratio were calculated using a region-of-interest analysis. Shapiro-Wilk test, weighted kappa coefficient, and Spearman's rank correlation coefficients were used for statistical analysis.

RESULTS

Significant correlations were found between APT_T1 and T/N ratio (ρ = 0.87, p < 0.001), APT_T2 and T/N ratio (ρ = 0.47, p < 0.05), MTRasym and T/N ratio (ρ = 0.55, p < 0.01), and T2/T1 and T/N ratio (ρ = -0.36, p < 0.05). Furthermore, significant correlations were observed between APT_T1 and ADC (ρ = -0.67, p < 0.001), APT_T1 and D (ρ = -0.70, p < 0.001), APT_T2 and D* (ρ = -0.45, p < 0.05), and T2/T1 and D (ρ = 0.39, p < 0.05).

CONCLUSION

These preliminary findings indicate that MPM-based CEST imaging parameters correlate with IVIM and 11C-MET uptake on PET/CT in patients with gliomas. In particular, the new parameter APT_T1 correlated more strongly with 11C-MET uptake compared to the traditional CEST parameter MTRasym.

Disrupted Maturation of White Matter Microstructure After Concussion Is Associated With Internalizing Behavior Scores in Female Children

BACKGROUND

Some children who experience concussions, particularly females, develop long-lasting emotional and behavioral problems. Establishing the potential contribution of preexisting behavioral problems and disrupted white matter maturation has been challenging due to a lack of preinjury data.

METHODS

From the Adolescent Brain Cognitive Development cohort, 239 (90 female) children age 12.1 ± 0.6 years who experienced a concussion after study entry at 10.0 ± 0.6 years were compared to 6438 (3245 female) children without head injuries who were age 9.9 ± 0.6 years at baseline and 12.0 ± 0.6 years at follow-up. The Child Behavior Checklist was used to assess internalizing and externalizing behavior at study entry and follow-up. In the children with magnetic resonance imaging data available (concussion n = 134, comparison n = 3520), deep and superficial white matter was characterized by neurite density from restriction spectrum image modeling of diffusion magnetic resonance imaging. Longitudinal ComBat harmonization removed scanner effects. Linear regressions modeled 1) behavior problems at follow-up controlling for baseline behavior, 2) impact of concussion on white matter maturation, and 3) contribution of deviations in white matter maturation to postconcussion behavior problems.

RESULTS

Only female children with concussion had higher internalizing behavior problem scores. The youngest children with concussion showed less change in superficial white matter neurite density over 2 years than children with no concussion. In females with concussion, less change in superficial white matter neurite density was correlated with increased internalizing behavior problem scores.

CONCLUSIONS

Concussions in female children are associated with emotional problems beyond preinjury levels. Injury to superficial white matter may contribute to persistent internalizing behavior problems in females.

The prediction of pCR and chemosensitivity for breast cancer patients using DLG3, RADL and Pathomics signatures based on machine learning and deep learning

BACKGROUND

Limited studies have investigated the predictive value of multiomics signatures (radiomics, deep learning features, pathological features and DLG3) in breast cancer patients who underwent neoadjuvant chemotherapy (NAC). However, no study has explored the relationships among radiomic, pathomic signatures and chemosensitivity. This study aimed to predict pathological complete response (pCR) using multiomics signatures, and to evaluate the predictive utility of radiomic and pathomic signatures for guiding chemotherapy selection.

METHODS

The oncogenic function of DLG3 was explored in breast cancer cells via DLG3 knockdown. Immunohistochemistry (IHC) was used to evaluate the relationship between DLG3 expression and docetaxel/epirubin sensitivity. Machine learning (ML) and deep learning (DL) algorithms were used to develop multiomics signatures. Survival analysis was conducted by K-M curves and log-rank. Multivariate logistic regression analysis was used to develop nomograms.

RESULTS

A total of 311 patients with malignant breast tumours who underwent NAC were retrospectively included in this multicentre study. Multiomics (DLG3, RADL and PATHO) signatures could accurately predict pCR (AUC: training: 0.900; testing: 0.814; external validation: 0.792). Its performance is also superior to that of clinical TNM staging and the single RADL signature in different cohorts. Patients in the low DLG3 group more easily achieved pCR, and those in the high RADL Signature_pCR and PATHO_Signature_pCR (OR = 7.93, 95 % CI: 3.49-18, P < 0.001) groups more easily achieved pCR. In the TEC regimen NAC group, patients who achieved pCR had a lower DLG3 score (4.00 ± 2.33 vs. 6.43 ± 3.01, P < 0.05). Patients in the low RADL_Signature_DLG3 and PATHO_Signature_DLG3 groups had lower DLG3 IHC scores (P < 0.05). Patients in the high RADL signature, PATHO signature and DLG3 signature groups had worse DFS and OS.

CONCLUSIONS

Multiomics signatures (RADL, PATHO and DLG3) demonstrated great potential in predicting the pCR of breast cancer patients who underwent NAC. The RADL and PATHO signatures are associated with DLG3 status and could help doctors or patients choose proper neoadjuvant chemotherapy regimens (TEC regimens). This simple, structured, convenient and inexpensive multiomics model could help clinicians and patients make treatment decisions.

Establishment of diagnostic model and identification of diagnostic markers between liver cancer and cirrhosis based on multi-chip and machine learning

OBJECTIVE

Most cases of hepatocellular carcinoma (HCC) arise as a consequence of cirrhosis. In this study, our objective is to construct a comprehensive diagnostic model that investigates the diagnostic markers distinguishing between cirrhosis and HCC.

METHODS

Based on multiple GEO datasets containing cirrhosis and HCC samples, we used lasso regression, random forest (RF)-recursive feature elimination (RFE) and receiver operator characteristic analysis to screen for characteristic genes. Subsequently, we integrated these genes into a multivariable logistic regression model and validated the linear prediction scores in both training and validation cohorts. The ssGSEA algorithm was used to estimate the fraction of infiltrating immune cells in the samples. Finally, molecular typing for patients with cirrhosis was performed using the CCP algorithm.

RESULTS

The study identified 137 differentially expressed genes (DEGs) and selected five significant genes (CXCL14, CAP2, FCN2, CCBE1 and UBE2C) to construct a diagnostic model. In both the training and validation cohorts, the model exhibited an area under the curve (AUC) greater than 0.9 and a kappa value of approximately 0.9. Additionally, the calibration curve demonstrated excellent concordance between observed and predicted incidence rates. Comparatively, HCC displayed overall downregulation of infiltrating immune cells compared to cirrhosis. Notably, CCBE1 showed strong correlations with the tumour immune microenvironment as well as genes associated with cell death and cellular ageing processes. Furthermore, cirrhosis subtypes with high linear predictive scores were enriched in multiple cancer-related pathways.

CONCLUSION

In conclusion, we successfully identified diagnostic markers distinguishing between cirrhosis and hepatocellular carcinoma and developed a novel diagnostic model for discriminating the two conditions. CCBE1 might exert a pivotal role in regulating the tumour microenvironment, cell death and senescence.

Evaluating the role of 7-Tesla magnetic resonance imaging in neurosurgery: Trends in literature since clinical approval

BACKGROUND

After approval for clinical use in 2017, early investigations of ultra-high-field abdominal magnetic resonance imaging (MRI) have demonstrated its feasibility as well as diagnostic capabilities in neuroimaging. However, there are no to few systematic reviews covering the entirety of its neurosurgical applications as well as the trends in the literature with regard to the aforementioned application.

AIM

To assess the impact of 7-Tesla MRI (7T MRI) on neurosurgery, focusing on its applications in diagnosis, treatment planning, and postoperative assessment, and to systematically analyze and identify patterns and trends in the existing literature related to the utilization of 7T MRI in neurosurgical contexts.

METHODS

A systematic search of PubMed was conducted for studies published between January 1, 2017, and December 31, 2023, using MeSH terms related to 7T MRI and neurosurgery. The inclusion criteria were: Studies involving patients of all ages, meta-analyses, systematic reviews, and original research. The exclusion criteria were: Pre-prints, studies with insufficient data (e.g., case reports and letters), non-English publications, and studies involving animal subjects. Data synthesis involved standardized extraction forms, and a narrative synthesis was performed.

RESULTS

We identified 219 records from PubMed within our defined period, with no duplicates or exclusions before screening. After screening, 125 articles were excluded for not meeting inclusion criteria, leaving 94 reports. Of these, 2 were irrelevant to neurosurgery and 7 were animal studies, resulting in 85 studies included in our systematic review. Data were categorized by neurosurgical procedures and diseases treated using 7T MRI. We also analyzed publications by country and the number of 7T MRI facilities per country was also presented. Experimental studies were classified into comparison and non-comparison studies based on whether 7T MRI was compared to lower field strengths.

CONCLUSION

7T MRI holds great potential in improving the characterization and understanding of various neurological and psychiatric conditions that may be neurosurgically treated. These include epilepsy, pituitary adenoma, Parkinson's disease, cerebrovascular diseases, trigeminal neuralgia, traumatic head injury, multiple sclerosis, glioma, and psychiatric disorders. Superiority of 7T MRI over lower field strengths was demonstrated in terms of image quality, lesion detection, and tissue characterization. Findings suggest the need for accelerated global distribution of 7T magnetic resonance systems and increased training for radiologists to ensure safe and effective integration into routine clinical practice.

Charged Gold Nanoparticles for Target Identification-Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma

Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification-alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable in vitro aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an in situ sHCC mouse model was established for in vivo CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART.

Exosomes-based immunotherapy for cancer: Effective components in the naïve and engineered forms

Today, cancer treatment is one of the main challenges for researchers. The main cause of tumor cell formation is mutations that lead to uncontrolled proliferation and inhibition of apoptosis in malignant cells. Tumor cells also create a microenvironment that can suppress the immune system cells' responses through various methods, including producing soluble factors and cell-to-cell communication. After being produced from tumor cells, exosomes can also affect the functions of other cells in this microenvironment. Various studies have shown that exosomes from different sources, including tumor cells and immune cells, can be used to treat cancers due to their characteristics. Since tumor cells are rich sources of various types of tumor peptides, they can induce anti-tumor responses. Immune cells also produce exosomes that mimic the functions of their cells of origin, such that exosomes derived from NK cells and CTLs can directly lead to their apoptosis after merging with tumor cells. However, many researchers have pointed out that naïve exosomes have a limited therapeutic function, and their therapeutic potential can be increased by manipulating and engineering them. There are various methods to modify exosomes and improve their therapeutic potential. In general, these methods are divided into two parts, which include changing the cell of origin of the exosome and encapsulating the exosome to carry different drugs. In this review, we will discuss the studies on the therapeutic use of naive and engineered exosomes and provide an update on new studies in this field.

High-resolution diffusion magnetic resonance imaging and spatial-transcriptomic in developing mouse brain

Brain development is a highly complex process regulated by numerous genes at the molecular and cellular levels. Brain tissue exhibits serial microstructural changes during the development process. High-resolution diffusion magnetic resonance imaging (dMRI) affords a unique opportunity to probe these changes in the developing brain non-destructively. In this study, we acquired multi-shell dMRI datasets at 32 µm isotropic resolution to investigate the tissue microstructure alterations, which we believe to be the highest spatial resolution dMRI datasets obtained for postnatal mouse brains. We adapted the Allen Developing Mouse Brain Atlas (ADMBA) to integrate quantitative MRI metrics and spatial transcriptomics. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI) metrics were used to quantify brain development at different postnatal days. We demonstrated that the differential evolutions of fiber orientation distributions contribute to the distinct development patterns in white matter (WM) and gray matter (GM). Furthermore, the genes enriched in the nervous system that regulate brain structure and function were expressed in spatial correlation with age-matched dMRI. This study is the first one providing high-resolution dMRI, including DTI, DKI, and NODDI models, to trace mouse brain microstructural changes in WM and GM during postnatal development. This study also highlighted the genotype-phenotype correlation of spatial transcriptomics and dMRI, which may improve our understanding of brain microstructure changes at the molecular level.

Application of biomaterials in the treatment of intracerebral hemorrhage

Although the current surgical hematoma removal treatment saves patients' lives in critical moments of intracerebral hemorrhage (ICH), the lethality and disability rates of ICH are still very high. Due to the individual differences of patients, postoperative functional improvement is still to be confirmed, and the existing drug treatment has limited benefits for ICH. Recent advances in biomaterials may provide new ideas for the therapy of ICH. This review first briefly describes the pathogenic mechanisms of ICH, including primary and secondary injuries such as inflammation and intracerebral edema, and briefly describes the existing therapeutic approaches and their limitations. Secondly, existing nanomaterials and hydrogels for ICH, including exosomes, liposomes, and polymer nanomaterials, are also described. In addition, the potential challenges and application prospects of these biomaterials for clinical translation in ICH treatment are discussed.

Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI

It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted radiomic features of subcortical structures in high-resolution T1-weighted MRI. Different combinations of four feature selection methods (analysis of variance, Kruskal-Wallis, recursive feature elimination and relief) and ten classification algorithms were used to identify the most robust combined models for distinguishing IGD patients from HCs. Furthermore, a nomogram incorporating radiomic signatures and independent clinical factors was developed. Calibration curve and decision curve analyses were used to evaluate the nomogram. The combination of analysis of variance selector and logistic regression classifier identified that the radiomic model constructed with 20 features from the right caudate nucleus and amygdala showed better IGD screening performance. The radiomic model produced good areas under the curves (AUCs) in the training, validation and test cohorts (AUCs of 0.961, 0.903 and 0.895, respectively). In addition, sex, internet addiction test scores and radiomic scores were included in the nomogram as independent risk factors for IGD. Analysis of the correction curve and decision curve showed that the clinical-radiomic model has good reliability (C-index: 0.987). The nomogram incorporating radiomic features of subcortical structures and clinical characteristics achieved satisfactory classification performance and could serve as an effective tool for distinguishing IGD patients from HCs.

Whole-tumor histogram analysis of multiple non-Gaussian diffusion models at high b values for assessing cervical cancer

PURPOSE

To assess the diagnostic potential of whole-tumor histogram analysis of multiple non-Gaussian diffusion models for differentiating cervical cancer (CC) aggressive status regarding of pathological types, differentiation degree, stage, and p16 expression.

METHODS

Patients were enrolled in this prospective single-center study from March 2022 to July 2023. Diffusion-weighted images (DWI) were obtained including 15 b-values (0 ~ 4000 s/mm2). Diffusion parameters derived from four non-Gaussian diffusion models including continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM) were calculated, and their histogram features were analyzed. To select the most significant features and establish predictive models, univariate analysis and multivariate logistic regression were performed. Finally, we evaluated the diagnostic performance of our models by using receiver operating characteristic (ROC) analyses.

RESULTS

89 women (mean age, 55 ± 11 years) with CC were enrolled in our study. The combined model, which incorporated the CTRW, DKI, FROC, and IVIM diffusion models, offered a significantly higher AUC than that from any individual models (0.836 vs. 0.664, 0.642, 0.651, 0.649, respectively; p < 0.05) in distinguishing cervical squamous cell cancer from cervical adenocarcinoma. To distinguish tumor differentiation degree, except the combined model showed a better predictive performance compared to the DKI model (AUC, 0.839 vs. 0.697, respectively; p < 0.05), no significant differences in AUCs were found among other individual models and combined model. To predict the International Federation of Gynecology and Obstetrics (FIGO) stage, only DKI and FROC model were established and there was no significant difference in predictive performance among different models. In terms of predicting p16 expression, the predictive ability of DKI model is significantly lower than that of FROC and combined model (AUC, 0.693 vs. 0.850, 0.859, respectively; p < 0.05).

CONCLUSION

Multiple non-Gaussian diffusion models with whole-tumor histogram analysis show great promise to assess the aggressive status of CC.

Combining multimodal diffusion-weighted imaging and morphological parameters for detecting lymph node metastasis in cervical cancer

BACKGROUND

Accurate detection of lymph node metastasis (LNM) is crucial for determining the tumor stage, selecting optimal treatment, and estimating the prognosis for cervical cancer. This study aimed to assess the diagnostic efficacy of multimodal diffusion-weighted imaging (DWI) and morphological parameters alone or in combination, for detecting LNM in cervical cancer.

METHODS

In this prospective study, we enrolled consecutive cervical cancer patients who received multimodal DWI (conventional DWI, intravoxel incoherent motion DWI, and diffusion kurtosis imaging) before treatment from June 2022 to June 2023. The largest lymph node (LN) observed on each side on imaging was matched with that detected on pathology to improve the accuracy of LN matching. Comparison of the diffusion and morphological parameters of LNs and the primary tumor between the positive and negative LN groups. A combined diagnostic model was constructed using multivariate logistic regression, and the diagnostic performance was evaluated using receiver operating characteristic curves.

RESULTS

A total of 93 cervical cancer patients were enrolled: 35 with LNM (48 positive LNs were collected), and 58 without LNM (116 negative LNs were collected). The area under the curve (AUC) values for the apparent diffusion coefficient, diffusion coefficient, mean diffusivity, mean kurtosis, long-axis diameter, short-axis diameter of LNs, and the largest primary tumor diameter were 0.716, 0.720, 0.716, 0.723, 0.726, 0.798, and 0.744, respectively. Independent risk factors included the diffusion coefficient, mean kurtosis, short-axis diameter of LNs, and the largest primary tumor diameter. The AUC value of the combined model based on the independent risk factors was 0.920, superior to the AUC values of all the parameters mentioned above.

CONCLUSION

Combining multimodal DWI and morphological parameters improved the diagnostic efficacy for detecting cervical cancer LNM than using either alone.

A data-driven intravoxel mean diffusivities distribution approach for molecular classifications and MIB-1 prediction of gliomas

BACKGROUND

Measuring non-parametric intravoxel mean diffusivity distributions (MDDs) using magnetic resonance imaging (MRI) is a sensitive method for detecting intracellular diffusivity changes during physiological alterations. Histological and molecular glioma classifications are essential for prognosis and treatment, with distinct water diffusion dynamics among subtypes.

PURPOSE

We developed a data-driven approach using a fully connected network (FCN) to enhance the speed and stability of calculating MDDs across varying SNRs, enable tumor microstructural mapping, and test its reliability in identifying MIB-1 labeling index (LI) levels and molecular status of gliomas.

METHODS

An FCN was trained to learn the mapping between the simulated diffusion decay curves and the ground truth MDDs. We performed 5 000 000 simulation curves with various diffusivity components and random SNR∈ [ 30 , 300 ] $ \in [ {30,\ 300} ]$ . Eighty percent of simulation curves were used for the FCN training, 10% for validation, and the others were external tests for the FCN performance evaluation. In vivo data were collected to evaluate its clinical reliability. One hundred one patients (44 years ± $ \pm $ 14, 67 men) with gliomas and six healthy controls underwent a 3.0 T MRI examination with a spin echo-echo planar imaging (SE-EPI) diffusion-weighted imaging (DWI) sequence. The trained FCN was employed to calculate MDDs of each brain voxel by voxel. We used the Fuzzy C-means algorithm to cluster the MDDs of tumor voxels, facilitating the characterization of distinct glioma tissues. Quantitative assessments were conducted through sectional integrals of the MDDs, demarcated by six bands to derive signal fractions (f n , n = 1 - 6 ${{f}_n},\ n = 1 -6$ ) and diffusivities of the maximum peaks (D p e a k ${{D}_{peak}}$ ). Cosine similarity scores (CSS) were used for MDD similarity. ANOVA and Mann-Whitney U test were used for difference analysis. Logistic regression and area under the receiver operator characteristic curve (AUC) were used for classification evaluation.

RESULTS

The simulation results showed that the FCN-based MDD approach (FCN-MDD) achieved higher CSS than non-negative least squares-based MDD (NNLS-MDD). For in vivo data, the spectra of ET and NET obtained by FCN-MDD are more distinguishable than NNLS-MDD. Fraction maps delineate the characteristics of different tumor tissues (enhancing and non-enhancing tumor, edema, and necrosis).f 3 , f 4 , D p e a k ${{f}_3},\ {{f}_4},{{D}_{peak}}$ showed a positive and negative correlation with MIB-1 respectively (r = 0.568 , r = - 0.521 , r = - 0.654 $r = 0.568,\ r = - 0.521,\ r = - 0.654$ , allp < 0.001 $p < 0.001$ ). The AUC ofD p e a k ${{D}_{peak}}$ for predicting MIB-1 LI levels was 0.900 (95% CI, 0.826-0.974), versus 0.781 (0.677-0.886) of ADC. The highest AUC of isocitrate dehydrogenase (IDH) mutation status, assessed by a logistic regression model (f 1 + f 3 ${{f}_1} + {{f}_3}$ ) was 0.873 (95% CI, 0.802-0.944).

CONCLUSION

The proposed FCN-MDD method was more robust to variations in SNR and less reliant on empirically set regularization values than the NNLS-MDD method. FCN-MDD also enabled qualitative and quantitative evaluation of the composition of gliomas.

Study of Iron Complex Photosensitizer with Hollow Double-Shell Nano Structure Used to Enhance Ferroptosis and Photodynamic Therapy

Ferroptosis therapy, which uses ferroptosis inducers to produce lethal lipid peroxides and induce tumor cell death, is considered a promising cancer treatment strategy. However, challenges remain regarding how to increase the accumulation of reactive oxygen species (ROS) in the tumor microenvironment (TME) to enhance antitumor efficacy. In this study, a hyaluronic acid (HA) encapsulated hollow mesoporous manganese dioxide (H-MnO2) with double-shell nanostructure is designed to contain iron coordinated cyanine near-infrared dye IR783 (IR783-Fe) for synergistic ferroptosis photodynamic therapy against tumors. The nano photosensitizer IR783-Fe@MnO2-HA, in which HA actively targets the CD44 receptor, subsequently dissociates and releases Fe3+ and IR783 in acidic TME. First, Fe3+ consumes glutathione to produce Fe2+, which promotes the Fenton reaction in cells to produce hydroxyl free radicals (·OH) and induce ferroptosis of tumor cells. In addition, MnO2 catalyzes the production of O2 from H2O2 and enhances the production of singlet oxygen (1O2) by IR783 under laser irradiation, thus increasing the production and accumulation of ROS to provide photodynamic therapy. The highly biocompatible IR783-Fe@MnO2-HA nano-photosensitizers have exhibited tumor-targeting ability and efficient tumor inhibition in vivo due to the synergistic effect of photodynamic and ferroptosis antitumor therapies.

Supramolecular dye nanoassemblies for advanced diagnostics and therapies

Dyes have conventionally been used in medicine for staining cells, tissues, and organelles. Since these compounds are also known as photosensitizers (PSs) which exhibit photoresponsivity upon photon illumination, there is a high desire towards formulating these molecules into nanoparticles (NPs) to achieve improved delivery efficiency and enhanced stability for novel imaging and therapeutic applications. Furthermore, it has been shown that some of the photophysical properties of these molecules can be altered upon NP formation thereby playing a major role in the outcome of their application. In this review, we primarily focus on introducing dye categories, their formulation strategies and how these strategies affect their photophysical properties in the context of photothermal and non-photothermal applications. More specifically, the most recent progress showing the potential of dye supramolecular assemblies in modalities such as photoacoustic and fluorescence imaging, photothermal and photodynamic therapies as well as their employment in photoablation as a novel modality will be outlined. Aside from their photophysical activity, we delve shortly into the emerging application of dyes as drug stabilizing agents where these molecules are used together with aggregator molecules to form stable nanoparticles.

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The 'CMRxRecon' dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.

Application of machine learning in the analysis of multiparametric MRI data for the differentiation of treatment responses in breast cancer: retrospective study

OBJECTIVE

The objective of this study is to develop and validate a multiparametric MRI model employing machine learning to predict the effectiveness of treatment and the stage of breast cancer.

METHODS

The study encompassed 400 female patients diagnosed with breast cancer, with 200 individuals allocated to both the control and experimental groups, undergoing examinations in Shenzhen, China, during the period 2017-2023. This study pertains to retrospective research. Multiparametric MRI was employed to extract data concerning tumor size, blood flow, and metabolism.

RESULTS

The model achieved high accuracy, predicting treatment outcomes with an accuracy of 92%, sensitivity of 88%, and specificity of 95%. The model effectively classified breast cancer stages: stage I, 38% ( P  = 0.027); stage II, 72% ( P  = 0.014); stage III, 50% ( P  = 0.032); and stage IV, 45% ( P  = 0.041).

CONCLUSIONS

The developed model, utilizing multiparametric MRI and machine learning, exhibits high accuracy in predicting the effectiveness of treatment and breast cancer staging. These findings affirm the model's potential to enhance treatment strategies and personalize approaches for patients diagnosed with breast cancer. Our study presents an innovative approach to the diagnosis and treatment of breast cancer, integrating MRI data with machine learning algorithms. We demonstrate that the developed model exhibits high accuracy in predicting treatment efficacy and differentiating cancer stages. This underscores the importance of utilizing MRI and machine learning algorithms to enhance the diagnosis and individualization of treatment for this disease.

Next-generation biomarkers for prognostic and potential therapeutic enhancement in Triple negative breast cancer

Triple-negative breast carcinoma (TNBC) is one of the most challenging subtypes of breast carcinoma and it has very limited therapeutic options as it is highly aggressive. The prognostic biomarkers are crucial for early diagnosis of the tumor, it also helps in anticipating the trajectory of the illness and optimizing the therapy options. Several therapeutic biomarkers are being used. Among them, the next-generation biomarkers that include Circulating tumor (ct) DNA, glycogen, lipid, and exosome biomarkers provide intriguing opportunities for enhancing the prognosis of TNBC. Lipid and glycogen biomarkers serve as essential details on the development of the tumor along with the efficacy of the treatment, as it exhibits metabolic alteration linked to TNBC. Several types of biomarkers have predictive abilities in TNBC. Elevated levels are associated with worse outcomes. ctDNA being a noninvasive biomarker reveals the genetic composition of the tumor, as well as helps to monitor the progression of the disease. Traditional therapies are ineffective in TNBC due to a lack of receptors, targeted drug delivery provides a tailored approach to overcome drug resistance and site-specific action by minimizing the side effects in TNBC treatment. This enhances therapeutic outcomes against the aggressive nature of breast cancer. This paper includes all the recent biomarkers which has been researched so far in TNBC and the state of art for TNBC which is explored.

Recent Progress of Multifunctional Molecular Probes for Triple-Negative Breast Cancer Theranostics

Breast cancer (BC) poses a significant threat to women's health, with triple-negative breast cancer (TNBC) representing one of the most challenging and aggressive subtypes due to the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. Traditional TNBC treatments often encounter issues such as low drug efficiency, limited tumor enrichment, and substantial side effects. Therefore, it is crucial to explore novel diagnostic and treatment systems for TNBC. Multifunctional molecular probes (MMPs), which integrate target recognition as well as diagnostic and therapeutic functions, introduce advanced molecular tools for TNBC theranostics. Using an MMP system, molecular drugs can be precisely delivered to the tumor site through a targeted ligand. Real-time dynamic monitoring of drug release achieved using imaging technology allows for the evaluation of drug enrichment at the tumor site. This approach enables accurate drug release, thereby improving the therapeutic effect. Therefore, this review summarizes the recent advancements in MMPs for TNBC theranostics, encompassing the design and synthesis of MMPs as well as their applications in the field of TNBC theranostics.

Association of cortical gyrification, white matter microstructure, and phenotypic profile in medication-naïve obsessive-compulsive disorder

BACKGROUND

Obsessive-compulsive disorder (OCD) is thought to arise from dysconnectivity among interlinked brain regions resulting in a wide spectrum of clinical manifestations. Cortical gyrification, a key morphological feature of human cerebral cortex, has been considered associated with developmental connectivity in early life. Monitoring cortical gyrification alterations may provide new insights into the developmental pathogenesis of OCD.

METHODS

Sixty-two medication-naive patients with OCD and 59 healthy controls (HCs) were included in this study. Local gyrification index (LGI) was extracted from T1-weighted MRI data to identify the gyrification changes in OCD. Total distortion (splay, bend, or twist of fibers) was calculated using diffusion-weighted MRI data to examine the changes in white matter microstructure in patients with OCD.

RESULTS

Compared with HCs, patients with OCD showed significantly increased LGI in bilateral medial frontal gyrus and the right precuneus, where the mean LGI was positively correlated with anxiety score. Patients with OCD also showed significantly decreased total distortion in the body, genu, and splenium of the corpus callosum (CC), where the average distortion was negatively correlated with anxiety scores. Intriguingly, the mean LGI of the affected cortical regions was significantly correlated with the mean distortion of the affected white matter tracts in patients with OCD.

CONCLUSIONS

We demonstrated associations among increased LGI, aberrant white matter geometry, and higher anxiety in patients with OCD. Our findings indicate that developmental dysconnectivity-driven alterations in cortical folding are one of the neural substrates underlying the clinical manifestations of OCD.

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.

Azulene-Containing Bis(squaraine) Dyes: Design, Synthesis and Aggregation Behaviors

The relationship among chemical structure, physicochemical property and aggregation behavior of organic functional material is an important research topic. Here, we designed and synthesized three bis(squaraine) dyes BSQ1, BSQ2 and BSQ3 through the combination of two kinds of unsymmetrical azulenyl squaraine monomers. Their physicochemical properties were investigated in both molecular and aggregate states. Generally, BSQ1 displayed different assembly behaviors from BSQ2 and BSQ3. Upon fabrication into nanoparticles, BSQ1 tend to form J-aggregates while BSQ2 and BSQ3 tend to form H-aggregates in aqueous medium. When in the form of thin films, three bis(squaraine) dyes all adopted J-aggregation packing modes while only BSQ1 presented the most significant rearrangement of aggregate structures as well as the improvement in the carrier mobilities upon thermal annealing. Our research highlights the discrepancy of aggregation behaviors originating from the molecular structure and surrounding circumstances, providing guidance for the molecular design and functional applications of squaraines.

Emergence of Quantum Dots as Innovative Tools for Early Diagnosis and Advanced Treatment of Breast Cancer

Quantum dots (QDs) semiconducting nanomaterials, have garnered attention due to their distinctive properties, including small size, high luminescence, and biocompatibility. In the context of triple-negative breast cancer (TNBC), notorious for its resistance to conventional treatments, QDs exhibit promising potential for enhancing diagnostic imaging and providing targeted therapies. This review underscores recent advancements in the utilization of QDs in imaging techniques, such as fluorescence tomography and magnetic resonance imaging, aiming at the early and precise detection of tumors. Emphasis is placed on the significance of QD design, synthesis and functionalization processes as well as their use in innovative strategies for targeted drug delivery, capitalizing on their ability to selectively deliver therapeutic agents to cancer cells. As the research in this field advances rapidly, this review covers a classification of QDs according to their composition, the characterization techniques than can be used to determine their properties and, subsequently, emphasizes recent findings in the field of TNBC-targeting, highlighting the imperative need to address challenges, like potential toxicity or methodologies standardization. Collectively, the findings explored thus far suggest that QDs could pave the way for early diagnosis and effective therapy of TNBC, representing a significant stride toward precise and personalized strategies in treating TNBC.

Intravoxel incoherent motion assessment of liver fibrosis staging in MASLD

PURPOSE

Partial correlation analysis was performed to account for the interference of steatosis changes and inflammatory factors, to determine the true correlation between fibrosis and IVIM parameters (Dfast, Dslow, and F), and to evaluate the diagnostic efficacy of IVIM for liver fibrosis.

METHODS

A total of 106 patients with metabolic dysfunction-associated steatotic liver disease (MASLD) examined by IVIM from November 2016 to November 2023 at our hospital were retrospectively included. Preliminary analysis of each IVIM parameter and correlations with pathological findings were performed using Spearman correlation analysis, and partial correlation analysis was used to exclude the interference of other pathological factors, thus yielding the true correlations between IVIM parameters (Dfast, Dslow, and F) and pathology. The diagnostic efficacy of IVIM parameters for diagnosing MASLD was assessed via receiver operating characteristic (ROC) curve analysis.

RESULTS

Spearman correlation analysis of all the IVIM parameters revealed correlations with steatosis, lobular inflammation, and ballooning. Partial correlation analysis indicated that Dfast was correlated with the pathological fibrosis stage (r = - 0.593, P < 0.001), Dslow was correlated with the pathological steatosis score (r = - 0.313, P < 0.05), and F was correlated with the pathological fibrosis stage and steatosis score (r = - 0.456 and 0.255, P < 0.001 and P < 0.05). In the diagnosis of hepatic fibrosis, significant hepatic fibrosis, advanced liver fibrosis and cirrhosis, Dfast achieved areas under the ROC curve of 0.763, 0.801, 0.853, and 0.897, respectively. The threshold values for diagnosing different fibrosis stages using Dfast (10-3 mm2/s) were 57.613, 54.587, 52.714, and 51.978, respectively.

CONCLUSION

According to our partial correlation analysis, there was a moderate correlation between Dfast and F according to fibrosis stage, and Dfast was not influenced by inflammation or steatosis when diagnosing fibrosis in MASLD patients. A relatively close Dfast threshold is insufficient for accurately and noninvasively assessing various stages of MASLD fibrosis. In clinical practice, this approach can be considered an alternative method for the preliminary assessment of fibrosis in MASLD patients.

Chimeric Peptide-Engineered Self-Delivery Nanomedicine for Photodynamic-Triggered Breast Cancer Immunotherapy by Macrophage Polarization

A systemic treatment strategy is urgently demanded to suppress the rapid growth and easy metastasis characteristics of breast cancer. In this work, a chimeric peptide-engineered self-delivery nanomedicine (designated as ChiP-CeR) for photodynamic-triggered breast cancer immunotherapy by macrophage polarization. Among these, ChiP-CeR is composed of the photosensitizer of chlorine e6 (Ce6) and the TLR7/8 agonist of lmiquimod (R837), which is further modified with tumor matrix targeting peptide (Fmoc-K(Fmoc)-PEG8-CREKA. ChiP-CeR is preferred to actively accumulate at the tumor site via specific recognition of fibronectin, which can eradicate primary tumor growth through photodynamic therapy (PDT). Meanwhile, the destruction of primary tumors would trigger immunogenic cell death (ICD) effects to release high-mobility group box-1(HMGB1) and expose calreticulin (CRT). Moreover, ChiP-CeR can also polarize M2-type tumor-associated macrophages (TAMs) into M1-type TAMs, which can activate T cell antitumor immunity in combination with ICD. Overall, ChiP-CeR possesses superior antitumor effects against primary and lung metastatic tumors, which provide an applicable nanomedicine and a feasible strategy for the systemic management of metastatic breast cancer.

Insights into the Inherited Basis of Valvular Heart Disease

PURPOSE OF REVIEW: Increases in the availability of genetic data and advances in the tools and methods for their analyses have enabled well-powered genetic association studies that have significantly enhanced our understanding of the genetic factors underlying both rare and common valve diseases. Valvular heart diseases, such as congenital valve malformations and degenerative valve lesions, increase the risk of heart failure, arrhythmias, and sudden death. In this review, we provide an updated overview of our current understanding of the genetic mechanisms underlying valvular heart diseases. With a focus on discoveries from the past 5 years, we describe recent insights into genetic risk and underlying biological pathways. RECENT FINDINGS: Recently acquired knowledge around valvular heart disease genetics has provided important insights into novel mechanisms related to disease pathogenesis. Newly identified risk loci associated valvular heart disease mainly regulate the composition of the extracellular matrix, accelerate the endothelial-to-mesenchymal transition, contribute to cilia formation processes, and play roles in lipid metabolism. Large-scale genomic analyses have identified numerous risk loci, genes, and biological pathways associated with degenerative valve disease and congenital valve malformations. Shared risk genes suggest common mechanistic pathways for various valve pathologies. More recent studies have combined cardiac magnetic resonance imaging and machine learning to offer a novel approach for exploring genotype-phenotype relationships regarding valve disease. Progress in the field holds promise for targeted prevention, particularly through the application of polygenic risk scores, and innovative therapies based on the biological mechanisms for predominant forms of valvular heart diseases.

Patients with chronic ankle instability exhibit increased sensorimotor cortex activation and correlation with poorer lateral balance control ability during single-leg stance: a FNIRS study

Introduction

Chronic Ankle Instability (CAI) is a musculoskeletal condition that evolves from acute ankle sprains, and its underlying mechanisms have yet to reach a consensus. Mounting evidence suggests that neuroplastic changes in the brain following ankle injuries play a pivotal role in the development of CAI. Balance deficits are a significant risk factor associated with CAI, yet there is a scarcity of evidence regarding the sensorimotor cortical plasticity related to balance control in affected individuals. This study aims to evaluate the differences in cortical activity and balance abilities between patients with CAI and uninjured individuals during a single-leg stance, as well as the correlation between these factors, in order to elucidate the neurophysiological alterations in balance control among patients with CAI.

Methods

The study enrolled 24 patients with CAI and 24 uninjured participants. During single-leg stance, cortical activity was measured using a functional near-infrared spectroscopy (fNIRS) system, which included assessments of the pre-motor cortex (PMC), supplementary motor area (SMA), primary motor cortex (M1), and primary somatosensory cortex (S1). Concurrently, balance parameters were tested utilizing a three-dimensional force platform.

Results

Independent sample t-tests revealed that, compared with the uninjured individuals, the patients with CAI exhibited a significant increase in the changes of oxyhemoglobin concentration (ΔHbO) during single-leg stance within the left S1 at Channel 5 (t = 2.101, p = 0.041, Cohen's d = 0.607), left M1 at Channel 6 (t = 2.363, p = 0.022, Cohen's d = 0.682), right M1 at Channel 15 (t = 2.273, p = 0.029, Cohen's d = 0.656), and right PMC/SMA at Channel 11 (t = 2.467, p = 0.018, Cohen's d = 0.712). Additionally, the center of pressure root mean square (COP-RMS) in the mediolateral (ML) direction was significantly greater (t = 2.630, p = 0.012, Cohen's d = 0.759) in the patients with CAI. Furthermore, a moderate positive correlation was found between ML direction COP-RMS and ΔHbO2 in the M1 (r = 0.436; p = 0.033) and PMC/SMA (r = 0.488, p = 0.016), as well as between anteroposterior (AP) direction COP-RMS and ΔHbO in the M1 (r = 0.483, p = 0.017).

Conclusion

Patients with CAI demonstrate increased cortical activation in the bilateral M1, ipsilateral PMC/SMA, and contralateral S1. This suggests that patients with CAI may require additional brain resources to maintain balance during single-leg stance, representing a compensatory mechanism to uphold task performance amidst diminished lateral balance ability in the ankle joint.

Lawrence K, Hicks JW. Frugal and Translatable [15O]O2 Production for Human Inhalation with Direct Delivery from the Cyclotron to a Hybrid PET/MR

Oxygen-15 (β+, t1/2 = 122 s) radiolabeled diatomic oxygen, in conjunction with positron emission tomography, is the gold standard to quantitatively measure the metabolic rate of oxygen consumption in the living human brain. We present herein a protocol for safe and effective delivery of [15O]O2 over 200 m to a human subject for inhalation. A frugal quality control testing procedure was devised and validated. This protocol can act as a blueprint for other sites seeking to implement similar imaging programs.

Recent advances in therapeutic engineered extracellular vesicles

Extracellular vesicles (EVs) are natural particles secreted by living cells, which hold significant potential for various therapeutic applications. Native EVs have specific components and structures, allowing them to cross biological barriers, and circulate in vivo for a long time. Native EVs have also been bioengineered to enhance their therapeutic efficacy and targeting affinity. Recently, the therapeutic potential of surface-engineered EVs has been explored in the treatment of tumors, autoimmune diseases, infections and other diseases by ongoing research and clinical trials. In this review, we will introduce the modified methods of engineered EVs, summarize the application of engineered EVs in preclinical and clinical trials, and discuss the opportunities and challenges for the clinical translation of surface-engineered EVs.

Brain-Derived Exosomal CircRNAs in Plasma Serve as Diagnostic Biomarkers for Acute Ischemic Stroke

Acute ischemic stroke (AIS), commonly known as stroke, is a debilitating condition characterized by the interruption of blood flow to the brain, resulting in tissue damage and neurological deficits. Early diagnosis is crucial for effective intervention and management, as timely treatment can significantly improve patient outcomes. Therefore, novel methods for the early diagnosis of AIS are urgently needed. Several studies have shown that bioactive molecules contained in extracellular vesicles, especially circRNAs, could be ideal markers for the diagnosis of many diseases. However, studies on the effects of exosomes and their circRNAs on the development and prognosis of AIS have not been reported extensively. Therefore, we explored the feasibility of using circRNAs in plasma brain-derived exosomes as biomarkers for AIS. By high-throughput sequencing, we first identified 358 dysregulated circRNAs (including 23 significantly upregulated circRNAs and 335 significantly downregulated circRNAs) in the plasma brain-derived exosomes of the brain infarct patient group compared to those of the noninfarct control group. Five upregulated circRNAs (hsa_circ_0007290, hsa_circ_0049637, hsa_circ_0000607, hsa_circ_0004808, and hsa_circ_0000097) were selected for further validation via Real-Time Quantitative Reverse Transcription PCR (qRT‒PCR) in a larger cohort based on the exclusion criteria of log2FC > 1, p < 0.05 and measurable expression. We found that the expression levels of hsa_circ_0007290, hsa_circ_0049637, hsa_circ_0000607, hsa_circ_0004808 and hsa_circ_0000097 were significantly upregulated in AIS patients and could serve as potential biomarkers for AIS with high specificity and sensitivity. Moreover, the expression levels of hsa_circ_0007290, hsa_circ_0049637, hsa_circ_0000607, hsa_circ_0004808 and hsa_circ_0000097 were also found to be positively correlated with National Institutes of Health Stroke Scale (NISS) and modified Rankin scale (mRS) scores, which indicated that the presence of these circRNAs in plasma brain-derived exosomes could also determine the progression of AIS.

Role of radiomics in staging liver fibrosis: a meta-analysis

BACKGROUND

Fibrosis has important pathoetiological and prognostic roles in chronic liver disease. This study evaluates the role of radiomics in staging liver fibrosis.

METHOD

After literature search in electronic databases (Embase, Ovid, Science Direct, Springer, and Web of Science), studies were selected by following precise eligibility criteria. The quality of included studies was assessed, and meta-analyses were performed to achieve pooled estimates of area under receiver-operator curve (AUROC), accuracy, sensitivity, and specificity of radiomics in staging liver fibrosis compared to histopathology.

RESULTS

Fifteen studies (3718 patients; age 47 years [95% confidence interval (CI): 42, 53]; 69% [95% CI: 65, 73] males) were included. AUROC values of radiomics for detecting significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4) were 0.91 [95%CI: 0.89, 0.94], 0.92 [95%CI: 0.90, 0.95], and 0.94 [95%CI: 0.93, 0.96] in training cohorts and 0.89 [95%CI: 0.83, 0.91], 0.89 [95%CI: 0.83, 0.94], and 0.93 [95%CI: 0.91, 0.95] in validation cohorts, respectively. For diagnosing significant fibrosis, advanced fibrosis, and cirrhosis the sensitivity of radiomics was 84.0% [95%CI: 76.1, 91.9], 86.9% [95%CI: 76.8, 97.0], and 92.7% [95%CI: 89.7, 95.7] in training cohorts, and 75.6% [95%CI: 67.7, 83.5], 80.0% [95%CI: 70.7, 89.3], and 92.0% [95%CI: 87.8, 96.1] in validation cohorts, respectively. Respective specificity was 88.6% [95% CI: 83.0, 94.2], 88.4% [95% CI: 81.9, 94.8], and 91.1% [95% CI: 86.8, 95.5] in training cohorts, and 86.8% [95% CI: 83.3, 90.3], 94.0% [95% CI: 89.5, 98.4], and 88.3% [95% CI: 84.4, 92.2] in validation cohorts. Limitations included use of several methods for feature selection and classification, less availability of studies evaluating a particular radiological modality, lack of a direct comparison between radiology and radiomics, and lack of external validation.

CONCLUSION

Although radiomics offers good diagnostic accuracy in detecting liver fibrosis, its role in clinical practice is not as clear at present due to comparability and validation constraints.

Clinical Assessment of Magnetic Resonance Spectroscopy and Diffusion-Weighted Imaging in Diffuse Glioma: Insights Into Histological Grading and IDH Classification

PURPOSE

To assess the diagnostic utility of clinical magnetic resonance spectroscopy (MRS) and diffusion-weighted imaging (DWI) in distinguishing between histological grading and isocitrate dehydrogenase (IDH) classification in adult diffuse gliomas.

METHODS

A retrospective analysis was conducted on 247 patients diagnosed with adult diffuse glioma. Experienced radiologists evaluated DWI and MRS images. The Kruskal-Wallis test examined differences in DWI and MRS-related parameters across histological grades, while the Mann-Whitney U test assessed molecular classification. Receiver Operating Characteristic (ROC) curves evaluated parameter effectiveness. Survival curves, stratified by histological grade and IDH classification, were constructed using the Kaplan-Meier test.

RESULTS

The cohort comprised 141 males and 106 females, with ages ranging from 19 to 85 years. The Kruskal-Wallis test revealed significant differences in ADC mean, Cho/NAA, and Cho/Cr concerning glioma histological grade (P < .01). Subsequent application of Dunn's test showed significant differences in ADC mean among each histological grade (P < .01). Notably, Cho/NAA exhibited a marked distinction between grade 2 and grade 3/4 gliomas (P < .01). The Mann-Whitney U test indicated that only ADC mean showed statistical significance for IDH molecular classification (P < .01). ROC curves were constructed to demonstrate the effectiveness of the specified parameters. Survival curves were also delineated to portray survival outcomes categorized by histological grade and IDH classification. Conclusions: Clinical MRS demonstrates efficacy in glioma histological grading but faces challenges in IDH classification. Clinical DWI's ADC mean parameter shows significant distinctions in both histological grade and IDH classification.

Acute pain impairs retention of locomotor learning

Despite abundant evidence that pain alters movement performance, considerably less is known about the potential effects of pain on motor learning. Some of the brain regions involved in pain processing are also responsible for specific aspects of motor learning, indicating that the two functions have the potential to interact, yet it is unclear if they do. In experiment 1, we compared the acquisition and retention of a novel locomotor pattern in young, healthy individuals randomized to either experience pain via capsaicin and heat applied to the lower leg during learning or no stimulus. On day 1, participants learned a new asymmetric walking pattern using distorted visual feedback, a paradigm known to involve mostly explicit re-aiming processes. Retention was tested 24 h later. Although there were no differences in day 1 acquisition between groups, individuals who experienced pain on day 1 demonstrated reduced retention on day 2. Furthermore, the degree of forgetting between days correlated with pain ratings during learning. In experiment 2, we examined the effects of a heat stimulus alone, which served as a control for (nonpainful) cutaneous stimulation, and found no effects on either acquisition or retention of learning. Thus, pain experienced during explicit, strategic locomotor learning interferes with motor memory consolidation processes and does so most likely through a pain mechanism and not an effect of distraction. These findings have important implications for understanding basic motor learning processes and for clinical rehabilitation, in which painful conditions are often treated through motor learning-based interventions.NEW & NOTEWORTHY Pain is a highly prevalent and burdensome experience that rehabilitation practitioners often treat using motor learning-based interventions. Here, we showed that experimental acute pain, but not a heat stimulus, during locomotor learning impaired 24-h retention of the newly learned walking pattern. The degree of retention loss was related to the perceived pain level during learning. These findings suggest important links between pain and motor learning that have significant implications for clinical rehabilitation.

Theranostic Fluorescent Probes

The ability to gain spatiotemporal information, and in some cases achieve spatiotemporal control, in the context of drug delivery makes theranostic fluorescent probes an attractive and intensely investigated research topic. This interest is reflected in the steep rise in publications on the topic that have appeared over the past decade. Theranostic fluorescent probes, in their various incarnations, generally comprise a fluorophore linked to a masked drug, in which the drug is released as the result of certain stimuli, with both intrinsic and extrinsic stimuli being reported. This release is then signaled by the emergence of a fluorescent signal. Importantly, the use of appropriate fluorophores has enabled not only this emerging fluorescence as a spatiotemporal marker for drug delivery but also has provided modalities useful in photodynamic, photothermal, and sonodynamic therapeutic applications. In this review we highlight recent work on theranostic fluorescent probes with a particular focus on probes that are activated in tumor microenvironments. We also summarize efforts to develop probes for other applications, such as neurodegenerative diseases and antibacterials. This review celebrates the diversity of designs reported to date, from discrete small-molecule systems to nanomaterials. Our aim is to provide insights into the potential clinical impact of this still-emerging research direction.

Attenuation of neuronal ferroptosis in intracerebral hemorrhage by inhibiting HDAC1/2: Microglial heterogenization via the Nrf2/HO1 pathway

AIM

The class I histone deacetylases (HDACs) implicate in microglial heterogenization and neuroinflammation following Intracerebral hemorrhage (ICH). Ferroptosis has also been reported in the ICH model. However, the relationship between HDAC1/2's role in microglial heterogenization and neuronal ferroptosis remains unclear.

METHODS

In both in vivo and in vitro models of ICH, we used Romidepsin (FK228), a selective HDAC1/2 inhibitor, to investigate its effects on microglial heterogenization and neuronal ferroptosis. In the in vitro ICH model using Hemin, a transwell system was utilized to examine how microglia-driven inflammation and ICH-triggered neuronal ferroptosis interact. Immunostaining, Western blotting and RT-qPCR were used to evaluate the microglial heterogenization and neuronal ferroptosis. Microglial heterogenization, neuronal ferroptosis, and neurological dysfunctions were assessed in vivo ICH mice model performed by autologous blood injection.

RESULTS

HDAC1/2 inhibition altered microglial heterogenization after ICH, as showing the reducing neuroinflammation and shifting microglia towards an anti-inflammatory phenotype by immunostaining and qPCR results. HDAC1/2 inhibition reduced ferroptosis, characterized by high ROS and low GPx4 expression in HT22 cells, and reduced iron and lipid deposition post-ICH in vivo. Additionally, the Nrf2/HO1 signaling pathway, especially acetyl-Nrf2, activated in the in vivo ICH model due to HDAC1/2 inhibition, plays a role in regulating microglial heterogenization. Furthermore, HDAC1/2 inhibition improved sensorimotor and histological outcomes post-ICH, offering a potential mechanism against ICH.

CONCLUSION

Inhibition of HDAC1/2 reduces neuro-ferroptosis by modifying the heterogeneity of microglia via the Nrf2/HO1 pathway, with a particular focus on acetyl-Nrf2. Additionally, this inhibition aids in the faster removal of hematomas and lessens prolonged neurological impairments, indicating novel approach for treating ICH.

Hype or hope? A review of challenges in balancing tumor control and treatment toxicity in breast cancer from the perspective of the radiation oncologist

The aim of this article is to discuss the challenges and new strategies in managing breast cancer patients, with a specific focus on radiation oncology and the importance of balancing oncologic outcomes with quality of life and post-treatment morbidity. A comprehensive literature review was conducted to identify advances in the management of breast cancer, exploring de-escalation strategies, hypofractionation schemes, predictors and tools for reducing toxicity (radiation-induced lymphocyte apoptosis, deep inspiration breath-hold, adaptive radiotherapy), enhancer treatments (hyperthermia, immunotherapy) and innovative diagnostic modalities (PET-MRI, omics). Balancing oncologic outcomes with quality of life and post-treatment morbidity is crucial in the era of personalized medicine. Radiotherapy plays a critical role in the management of breast cancer patients. Large randomized trials are necessary to generalize some practices and cost remains the main obstacle for many innovations that are already applicable.

Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis

Background

Artificial intelligence-derived software technologies have been developed that are intended to facilitate the review of computed tomography brain scans in patients with suspected stroke.

Objectives

To evaluate the clinical and cost-effectiveness of using artificial intelligence-derived software to support review of computed tomography brain scans in acute stroke in the National Health Service setting.

Methods

Twenty-five databases were searched to July 2021. The review process included measures to minimise error and bias. Results were summarised by research question, artificial intelligence-derived software technology and study type. The health economic analysis focused on the addition of artificial intelligence-derived software-assisted review of computed tomography angiography brain scans for guiding mechanical thrombectomy treatment decisions for people with an ischaemic stroke. The de novo model (developed in R Shiny, R Foundation for Statistical Computing, Vienna, Austria) consisted of a decision tree (short-term) and a state transition model (long-term) to calculate the mean expected costs and quality-adjusted life-years for people with ischaemic stroke and suspected large-vessel occlusion comparing artificial intelligence-derived software-assisted review to usual care.

Results

A total of 22 studies (30 publications) were included in the review; 18/22 studies concerned artificial intelligence-derived software for the interpretation of computed tomography angiography to detect large-vessel occlusion. No study evaluated an artificial intelligence-derived software technology used as specified in the inclusion criteria for this assessment. For artificial intelligence-derived software technology alone, sensitivity and specificity estimates for proximal anterior circulation large-vessel occlusion were 95.4% (95% confidence interval 92.7% to 97.1%) and 79.4% (95% confidence interval 75.8% to 82.6%) for Rapid (iSchemaView, Menlo Park, CA, USA) computed tomography angiography, 91.2% (95% confidence interval 77.0% to 97.0%) and 85.0 (95% confidence interval 64.0% to 94.8%) for Viz LVO (Viz.ai, Inc., San Fransisco, VA, USA) large-vessel occlusion, 83.8% (95% confidence interval 77.3% to 88.7%) and 95.7% (95% confidence interval 91.0% to 98.0%) for Brainomix (Brainomix Ltd, Oxford, UK) e-computed tomography angiography and 98.1% (95% confidence interval 94.5% to 99.3%) and 98.2% (95% confidence interval 95.5% to 99.3%) for Avicenna CINA (Avicenna AI, La Ciotat, France) large-vessel occlusion, based on one study each. These studies were not considered appropriate to inform cost-effectiveness modelling but formed the basis by which the accuracy of artificial intelligence plus human reader could be elicited by expert opinion. Probabilistic analyses based on the expert elicitation to inform the sensitivity of the diagnostic pathway indicated that the addition of artificial intelligence to detect large-vessel occlusion is potentially more effective (quality-adjusted life-year gain of 0.003), more costly (increased costs of £8.61) and cost-effective for willingness-to-pay thresholds of £3380 per quality-adjusted life-year and higher.

Limitations and conclusions

The available evidence is not suitable to determine the clinical effectiveness of using artificial intelligence-derived software to support the review of computed tomography brain scans in acute stroke. The economic analyses did not provide evidence to prefer the artificial intelligence-derived software strategy over current clinical practice. However, results indicated that if the addition of artificial intelligence-derived software-assisted review for guiding mechanical thrombectomy treatment decisions increased the sensitivity of the diagnostic pathway (i.e. reduced the proportion of undetected large-vessel occlusions), this may be considered cost-effective.

Future work

Large, preferably multicentre, studies are needed (for all artificial intelligence-derived software technologies) that evaluate these technologies as they would be implemented in clinical practice.

Study registration

This study is registered as PROSPERO CRD42021269609.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR133836) and is published in full in Health Technology Assessment; Vol. 28, No. 11. See the NIHR Funding and Awards website for further award information.

A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer

This study aims to develop an MRI-based radiomics model to assess the likelihood of recurrence in luminal B breast cancer. The study analyzed medical images and clinical data from 244 patients with luminal B breast cancer. Of 244 patients, 35 had experienced recurrence and 209 had not. The patients were randomly divided into the training set (51.5 ± 12.5 years old; n = 171) and the test set (51.7 ± 11.3 years old; n = 73) in a ratio of 7:3. The study employed univariate and multivariate Cox regression along with the least absolute shrinkage and selection operator (LASSO) regression methods to select radiomics features and calculate a risk score. A combined model was constructed by integrating the risk score with the clinical and pathological characteristics. The study identified two radiomics features (GLSZM and GLRLM) from DCE-MRI that were used to calculate a risk score. The AUCs were 0.860 and 0.868 in the training set and 0.816 and 0.714 in the testing set for 3- and 5-year recurrence risk, respectively. The combined model incorporating the risk score, pN, and endocrine therapy showed improved predictive power, with AUCs of 0.857 and 0.912 in the training set and 0.943 and 0.945 in the testing set for 3- and 5-year recurrence risk, respectively. The calibration curve of the combined model showed good consistency between predicted and measured values. Our study developed an MRI-based radiomics model that integrates clinical and radiomics features to assess the likelihood of recurrence in luminal B breast cancer. The model shows promise for improving clinical risk stratification and treatment decision-making.

Multiparametric MR-based feature fusion radiomics combined with ADC maps-based tumor proliferative burden in distinguishing TNBC versus non-TNBC

Objective.To investigate the incremental value of quantitative stratified apparent diffusion coefficient (ADC) defined tumor habitats for differentiating triple negative breast cancer (TNBC) from non-TNBC on multiparametric MRI (mpMRI) based feature-fusion radiomics (RFF) model.Approach.466 breast cancer patients (54 TNBC, 412 non-TNBC) who underwent routine breast MRIs in our hospital were retrospectively analyzed. Radiomics features were extracted from whole tumor on T2WI, diffusion-weighted imaging, ADC maps and the 2nd phase of dynamic contrast-enhanced MRI. Four models including the RFFmodel (fused features from all MRI sequences), RADCmodel (ADC radiomics feature), StratifiedADCmodel (tumor habitas defined on stratified ADC parameters) and combinational RFF-StratifiedADCmodel were constructed to distinguish TNBC versus non-TNBC. All cases were randomly divided into a training (n= 337) and test set (n= 129). The four competing models were validated using the area under the curve (AUC), sensitivity, specificity and accuracy.Main results.Both the RFFand StratifiedADCmodels demonstrated good performance in distinguishing TNBC from non-TNBC, with best AUCs of 0.818 and 0.773 in the training and test sets. StratifiedADCmodel revealed significant different tumor habitats (necrosis/cysts habitat, chaotic habitat or proliferative tumor core) between TNBC and non-TNBC with its top three discriminative parameters (p <0.05). The integrated RFF-StratifiedADCmodel demonstrated superior accuracy over the other three models, with higher AUCs of 0.832 and 0.784 in the training and test set, respectively (p <0.05).Significance.The RFF-StratifiedADCmodel through integrating various tumor habitats' information from whole-tumor ADC maps-based StratifiedADCmodel and radiomics information from mpMRI-based RFFmodel, exhibits tremendous promise for identifying TNBC.

Neonatal Nucleus Accumbens Microstructure Modulates Individual Susceptibility to Preconception Maternal Stress in Relation to Externalizing Behaviors

OBJECTIVE

Maternal stress influences in utero brain development and is a modifiable risk factor for offspring psychopathologies. Reward circuitry dysfunction underlies various internalizing and externalizing psychopathologies. This study examined (1) the association between maternal stress and microstructural characteristics of the neonatal nucleus accumbens (NAcc), a major node of the reward circuitry, and (2) whether neonatal NAcc microstructure modulates individual susceptibility to maternal stress in relation to childhood behavioral problems.

METHOD

K-means longitudinal cluster analysis was performed to determine trajectories of maternal stress measures (Perceived Stress Scale [PSS], hair cortisol) from preconception to the third trimester. Neonatal NAcc microstructural measures (orientation density index [ODI] and intracellular volume fraction [ICVF]) were compared across trajectories. We then examined the interaction between maternal stress and neonatal NAcc microstructure on child internalizing and externalizing behaviors, assessed between ages 3 and 4 years.

RESULTS

Two trajectories of maternal stress magnitude ("low"/"high") were identified for both PSS (n = 287) and hair cortisol (n = 336). Right neonatal NAcc ODI (rNAcc-ODI) was significantly lower in "low" relative to "high" PSS trajectories (n = 77, p = .04). PSS at preconception had the strongest association with rNAcc-ODI (r = 0.293, p = .029). No differences in NAcc microstructure were found between hair cortisol trajectories. A significant interaction between preconception PSS and rNAcc-ODI on externalizing behavior was observed (n = 47, p = .047).

CONCLUSION

Our study showed that the preconception period contributes to in utero NAcc development, and that NAcc microstructure modulates individual susceptibility to preconception maternal stress in relation to externalizing problems.

An "all-in-one" treatment and imaging nanoplatform for breast cancer with photothermal nanoparticles

Drug delivery systems based on nanoparticles still face challenges of low efficacy and an inability to track treatment effects in tumor therapy due to biological barriers. This limitation hinders clinicians' ability to determine treatment effects and proper drug dosages, thus, ultimately impeding the further application and transformation of nanoplatforms. To address this challenge, an all-in-one nanoplatform for therapy and imaging is proposed. The nanoplatform is constructed by using nanoparticles through the co-encapsulation of the photothermal therapeutic agent IR780, the passively targeted drug OA@Fe3O4, and the chemotherapeutic drug paclitaxel. Under the guidance of magnetic navigation, the nanoparticles can enhance local enrichment of the drug, while the luminescence properties of IR780 enable drug tracking at the same time. Remarkably, the nanoparticles exhibit improved photothermal-chemotherapy synergy under magnetic targeting guidance, demonstrating antitumor effects in both in vitro and in vivo experiments. It is demonstrated that the use of these polymeric nanoparticles has significant potential for future biomedical applications and clinical decisions.

Exosomes: A Cellular Communication Medium That Has Multiple Effects On Brain Diseases

Exosomes, as membranous vesicles generated by multiple cell types and secreted to extracellular space, play a crucial role in a range of brain injury-related brain disorders by transporting diverse proteins, RNA, DNA fragments, and other functional substances. The nervous system's pathogenic mechanisms are complicated, involving pathological processes like as inflammation, apoptosis, oxidative stress, and autophagy, all of which result in blood-brain barrier damage, cognitive impairment, and even loss of normal motor function. Exosomes have been linked to the incidence and progression of brain disorders in recent research. As a result, a thorough knowledge of the interaction between exosomes and brain diseases may lead to the development of more effective therapeutic techniques that may be implemented in the clinic. The potential role of exosomes in brain diseases and the crosstalk between exosomes and other pathogenic processes were discussed in this paper. Simultaneously, we noted the delicate events in which exosomes as a media allow the brain to communicate with other tissues and organs in physiology and disease, and compiled a list of natural compounds that modulate exosomes, in order to further improve our understanding of exosomes and propose new ideas for treating brain disorders.

Neurite density of the hippocampus is associated with trace eyeblink conditioning latency in 4- to 6-year-olds

Limited options exist to evaluate the development of hippocampal function in young children. Research has established that trace eyeblink conditioning (EBC) relies on a functional hippocampus. Hence, we set out to investigate whether trace EBC is linked to hippocampal structure, potentially serving as a valuable indicator of hippocampal development. Our study explored potential associations between individual differences in hippocampal volume and neurite density with trace EBC performance in young children. We used onset latency of conditioned responses (CR) and percentage of conditioned responses (% CR) as measures of hippocampal-dependent associative learning. Using a sample of typically developing children aged 4 to 6 years (N = 30; 14 girls; M = 5.70 years), participants underwent T1- and diffusion-weighted MRI scans and completed a 15-min trace eyeblink conditioning task conducted outside the MRI. % CR and CR onset latency were calculated based on all trials involving tone-puff presentations and tone-alone trials. Findings revealed a connection between greater left hippocampal neurite density and delayed CR onset latency. Children with higher neurite density in the left hippocampus tended to blink closer to the onset of the unconditioned stimulus, indicating that structural variations in the hippocampus were associated with more precise timing of conditioned responses. No other relationships were observed between hippocampal volume, cerebellum volume or neurite density, hippocampal white matter connectivity and any EBC measures. Preliminary results suggest that trace EBC may serve as a straightforward yet innovative approach for studying hippocampal development in young children and populations with atypical development.

Altered orientation dispersion index of white matter in individuals with insomnia during the COVID-19 pandemic: A study combining neuroimaging technique and Mendelian randomization

STUDY OBJECTIVES

Coronavirus disease 2019 (COVID-19) can lead to insomnia. However, associations between COVID-19-caused insomnia and white matter (WM) changes are unclear.

METHODS

All subjects had ever been infected with COVID-19. We investigated 89 insomniacs (29 chronic insomniacs, 33 new-onset insomniacs, 27 aggravated insomniacs) and 44 matched non-insomnia participants. Neurite orientation dispersion and density imaging (NODDI) was performed to identify micro-structural alterations of WM, and twelve scales related to sleeping status, memory, attention, learning, emotional status, and executive functions were used. Then, correlations between insomnia/cognitive-behavioral functions and diffusion metrics were tested. To eliminate influence of pre-COVID-19 factors on insomnia, causal relationships between COVID-19 and WM changes were validated by Mendelian randomization (MR) analysis. The significant brain regions of COVID-19-caused insomnia were intersected results of tract-based spatial statistics (TBSS) and MR analyses.

RESULTS

Compared to non-insomnia group, insomnia group and its subgroups including post-COVID-19 aggravated or unchanged chronic insomnia group had higher orientation dispersion index (ODI) in extensive brain regions. The left superior longitudinal fasciculus (SLF), left posterior thalamic radiation (PTR), and left cingulate gyrus (CG) were specific brain regions in COVID-19-induced insomnia aggravation. After Bonferroni correction, partial correlation analyses within insomnia group showed that ODI in left SLF was positively correlated with Pittsburgh sleep quality index (PSQI), insomnia severity index (ISI), and self-rating anxiety scale (SAS) scores; ODI in the left PTR was positively correlated with PSQI and ISI scores.

CONCLUSIONS

This study is a continuation of our previous research, which provided potential biomarkers for COVID-19-induced insomnia.

Tatton-Brown-Rahman syndrome: Novel pathogenic variants and new neuroimaging findings

Tatton-Brown-Rahman syndrome (TBRS) or DNMT3A-overgrowth syndrome is characterized by overgrowth and intellectual disability associated with minor dysmorphic features, obesity, and behavioral problems. It is caused by variants of the DNMT3A gene. We report four patients with this syndrome due to de novo DNMT3A pathogenic variants, contributing to a deeper understanding of the genetic basis and pathophysiology of this autosomal dominant syndrome. Clinical and magnetic resonance imaging assessments were also performed. All patients showed corpus callosum anomalies, small posterior fossa, and a deep left Sylvian fissure; as well as asymmetry of the uncinate and arcuate fascicles and marked increased cortical thickness. These results suggest that structural neuroimaging anomalies have been previously overlooked, where corpus callosum and brain tract alterations might be unrecognized neuroimaging traits of TBRS syndrome caused by DNMT3A variants.

Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion in differentiating invasive placentas

PURPOSE

To investigate the diagnostic value of monoexponential, biexponential, and diffusion kurtosis MR imaging (MRI) in distinguishing invasive placentas.

METHODS

A total of 53 patients with invasive placentas and 47 patients with noninvasive placentas undergoing conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) were retrospectively enrolled. The mean, minimum, and maximum parameters including the apparent diffusion coefficient (ADC) and exponential ADC (eADC) from standard DWI, diffusion kurtosis (MK), and diffusion coefficient (MD) from DKI and pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from IVIM were measured and compared from the volumetric analysis. Receiver operating characteristics (ROC) curve and logistic regression analyses were conducted to evaluate the diagnostic efficiency of different diffusion parameters for distinguishing invasive placentas.

RESULTS

Comparisons between accreta lesions in patients with invasive placentas (AL) and lower 1/3 part of the placenta in patients with noninvasive placentas (LP) demonstrated that MD mean, D mean, and D* mean were significantly lower while ADC max and D max were significantly higher in invasive placentas (all p < 0.05). Multivariate analysis demonstrated that D mean, D max and D* mean differed significantly among all the studied parameters for invasive placentas. A combined use of these three parameters yielded an AUC of 0.86 with sensitivity, specificity, and accuracy of 84.91%, 76.60%, and 80%, respectively.

CONCLUSION

The combined use of different IVIM parameters is helpful in distinguishing invasive placentas.

Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer

Pharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration-time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was used to divide the whole tumor into three tumor subregions with different kinetic patterns. Radiomic features were calculated from the tumor subregion and whole tumor-based PK parameter maps. The optimal features determined by the fivefold cross-validation method were used to build random forest classifiers to predict molecular subtypes, Ki-67, and tumor grade. The diagnostic performance evaluated by the area under the receiver operating characteristic curve (AUC) was compared between the subregion and whole tumor-based PK parameters. The results showed that the DEC method obtained more accurate PK parameters than the Tofts method. Moreover, the results showed that the subregion-based Ktrans (best AUCs = 0.8319, 0.7032, 0.7132, 0.7490, 0.8074, and 0.6950) achieved a better diagnostic performance than the whole tumor-based Ktrans (AUCs = 0.8222, 0.6970, 0.6511, 0.7109, 0.7620, and 0.5894) for molecular subtypes, Ki-67, and tumor grade. These findings indicate that DEC-based Ktrans in the subregion has the potential to accurately predict molecular subtypes, Ki-67, and tumor grade.

Role of the glymphatic system and perivascular spaces as a potential biomarker for post-stroke epilepsy

Stroke is one of the most common causes of acquired epilepsy, which can also result in disability and increased mortality rates particularly in elderly patients. No preventive treatment for post-stroke epilepsy is currently available. Development of such treatments has been greatly limited by the lack of biomarkers to reliably identify high-risk patients. The glymphatic system, including perivascular spaces (PVS), is the brain's waste clearance system, and enlargement or asymmetry of PVS (ePVS) is hypothesized to play a significant role in the pathogenesis of several neurological conditions. In this article, we discuss potential mechanisms for the role of perivascular spaces in the development of post-stroke epilepsy. Using advanced MR-imaging techniques, it has been shown that there is asymmetry and impairment of glymphatic function in the setting of ischemic stroke. Furthermore, studies have described a dysfunction of PVS in patients with different focal and generalized epilepsy syndromes. It is thought that inflammatory processes involving PVS and the blood-brain barrier, impairment of waste clearance, and sustained hypertension affecting the glymphatic system during a seizure may play a crucial role in epileptogenesis post-stroke. We hypothesize that impairment of the glymphatic system and asymmetry and dynamics of ePVS in the course of a stroke contribute to the development of PSE. Automated ePVS detection in stroke patients might thus assist in the identification of high-risk patients for post-stroke epilepsy trials. PLAIN LANGUAGE SUMMARY: Stroke often leads to epilepsy and is one of the main causes of epilepsy in elderly patients, with no preventative treatment available. The brain's waste removal system, called the glymphatic system which consists of perivascular spaces, may be involved. Enlargement or asymmetry of perivascular spaces could play a role in this and can be visualised with advanced brain imaging after a stroke. Detecting enlarged perivascular spaces in stroke patients could help identify those at risk for post-stroke epilepsy.

TGN-020 Alleviate Inflammation and Apoptosis After Cerebral Ischemia-Reperfusion Injury in Mice Through Glymphatic and ERK1/2 Signaling Pathway

Post-stroke acute inhibition of aquaporin 4 (AQP4) is known to exacerbate inflammation and apoptosis, yet the underlying mechanisms are not fully understood. The objective of this study was to investigate the specific mechanism of inflammation and apoptosis following cerebral ischemia-reperfusion (I/R) injury using the AQP4-specific inhibitor, N-(1,3,4-thiadiazol-2-yl) pyridine-3-carboxamide dihydrochloride (TGN-020). Ischemic stroke was induced in mice using the middle cerebral artery occlusion (MCAO) model. The C57/BL6 mice were randomly divided into three groups as follows: sham operation, I/R 48 h, and TGN-020 + I/R 48 h treatment. All mice were subjected to a series of procedures. These procedures encompassed 2,3,5-triphenyltetrazolium chloride (TTC) staining, neurological scoring, fluorescence tracing, western blotting, immunofluorescence staining, and RNA sequencing (RNA-seq). The glymphatic function in the cortex surrounding cerebral infarction was determined using tracer, glial fibrillary acid protein (GFAP), AQP4 co-staining, and beta-amyloid precursor protein (APP) staining; differential genes were detected using RNA-seq. The influence of TGN-020 on the extracellular signal-regulated kinase 1/2 (ERK) 1/2 pathway was confirmed using the ERK1/2 pathway agonists Ro 67-7467. Additionally, we examined the expression of inflammation associated with microglia and astrocytes after TGN-020 and Ro 67-7467 treatment. Compared with I/R group, TGN-020 alleviated glymphatic dysfunction by inhibiting astrocyte proliferation and reducing tracer accumulation in the peri-infarct area. RNA-seq showed that the differentially expressed genes were mainly involved in the activation of astrocytes and microglia and in the ERK1/2 pathway. Western blot and immunofluorescence further verified the expression of associated inflammation. The inflammation and cell apoptosis induced by I/R are mitigated by TGN-020. This mitigation occurs through the improvement of glymphatic function and the inhibition of the ERK1/2 pathway.

The Crosstalk Between Immune Cells After Intracerebral Hemorrhage

The inflammatory mechanism of intracerebral hemorrhage (ICH) has been widely studied, and it is believed that the regulation of this mechanism is of great significance to the prognosis. In the early stage of the acute phase of ICH, the release of a large number of inflammatory factors around the hematoma can recruit more inflammatory cells to infiltrate the area, further release inflammatory factors, cause an inflammatory cascade reaction, aggravate the volume of cerebral hematoma and edema and further destroy the blood-brain barrier (BBB), according to this, the crosstalk between cells may be of great significance in secondary brain injury (SBI). Because most of the cells recruited are inflammatory immune cells, this paper mainly discusses the cells based on the inflammatory mechanism to discuss their functions after ICH, we found that among the main cells inherent in the brain, glial cells account for the majority, of which microglia are the most widely studied and it can interact with a variety of cells, which is reflected in the literature researches on its pathogenesis and treatment. We believe that exploring multi-mechanism and multi-cell regulated drugs may be the future development trend, and the existing research, the comparison and unification of modeling methods, and the observation of long-term efficacy may be the first problem that researchers need to solve.