Magnetic Resonance Imaging Analysis Research Articles

Evaluation of glial tumors: correlation between magnetic resonance imaging and histopathological analysis.

To determine the correlation of conventional and diffusion-weighted imaging findings on magnetic resonance imaging (MRI) of the brain, based on Visually AcceSAble Rembrandt Images (VASARI) criteria, with the histopathological grading of gliomas: low-grade or high-grade. Preoperative MRI scans of 178 patients with brain gliomas and pathological confirmation were rated by two neuroradiologists for tumor size, location, and tumor morphology, using a standardized imaging feature set based on the VASARI criteria. In the univariate analysis, more than half of the MRI characteristics evaluated showed a significant association with the tumor grade. The characteristics most significantly associated with the tumor grade were hemorrhage; restricted diffusion; pial invasion; enhancement; and a non-contrast-enhancing tumor crossing the midline. In a multivariable regression model, the presence of enhancement and hemorrhage maintained a significant association with high tumor grade. The absence of contrast enhancement and restricted diffusion were associated with the presence of an isocitrate dehydrogenase gene mutation. Our data illustrate that VASARI MRI features, especially intratumoral hemorrhage, contrast enhancement, and multicentricity, correlate strongly with glial tumor grade.

Functional and morphological evaluation of the trapezius muscle after spinal accessory nerve transfer to brachial plexus nerves.

The main innervation of the trapezius muscle is provided by the spinal accessory nerve. Several studies describe the contributions of cervical plexus roots to the trapezius muscle innervation, either directly or through connections with the spinal accessory nerve. There is no adequate understanding of how the trapezius muscle is affected after using the spinal accessory nerve in nerve transfer procedures with the usual technique, preserving at least 1 branch for the upper trapezius. We evaluated 20 patients with sequelae of traumatic brachial plexus injury who underwent surgical procedures for brachial plexus repair or free muscle transfer, which included the spinal accessory nerve transfer technique and were followed for a minimum of 1 year. The three portions trapezius muscle were evaluated by physical examination, magnetic resonance imaging (analysis of fatty degeneration) and electromyography. In all evaluation methods, the middle and lower portions of the trapezius muscle showed more significant morphological and/or functional impairment than the upper portion, in most cases. There was a statistically significant difference in all the complementary exams results, between the affected side (with sacrifice of the nerve) versus the normal side, in the middle and lower portions of the trapezius muscle. Physical examination alone is not sufficient to determine the residual functionality of the trapezius muscle. Magnetic resonance imaging and electromyography are useful tools to assess both morphological involvement of the trapezius muscle and nerve conduction impairment of the trapezius muscle, respectively. The results suggest that the middle and lower portions of the trapezius muscle are affected by previous SAN transfer and should be considered with caution for further muscle transfer procedures.

Cerebral Cortical Surface Structure and Neural Activation Pattern Among Adolescent Football Players

Recurring exposure to head impacts in American football has garnered public and scientific attention, yet neurobiological associations in adolescent football players remain unclear. To examine cortical structure and neurophysiological characteristics in adolescent football players. This cohort study included adolescent football players and control athletes (swimming, cross country, and tennis) from 5 high school athletic programs, who were matched with age, sex (male), and school. Neuroimaging assessments were conducted May to July of the 2021 and 2022 seasons. Data were analyzed from February to November 2023. Playing tackle football or noncontact sports. Structural magnetic resonance imaging (MRI) data were analyzed for cortical thickness, sulcal depth, and gyrification, and cortical surface-based resting state (RS)-functional MRI analyses examined the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and RS-functional connectivity (RS-FC). Two-hundred seventy-five male participants (205 football players; mean [SD] age, 15.8 [1.2] years; 5 Asian [2.4%], 8 Black or African American [3.9%], and 189 White [92.2%]; 70 control participants; mean [SD] age 15.8 [1.2] years, 4 Asian [5.7], 1 Black or African American [1.4%], and 64 White [91.5%]) were included in this study. Relative to the control group, the football group showed significant cortical thinning, especially in fronto-occipital regions (eg, right precentral gyrus: t = -2.24; P = .01; left superior frontal gyrus: -2.42; P = .002). Elevated cortical thickness in football players was observed in the anterior and posterior cingulate cortex (eg, left posterior cingulate cortex: t = 2.28; P = .01; right caudal anterior cingulate cortex 3.01; P = .001). The football group had greater and deeper sulcal depth than the control groups in the cingulate cortex, precuneus, and precentral gyrus (eg, right inferior parietal lobule: t = 2.20; P = .004; right caudal anterior cingulate cortex: 4.30; P < .001). Significantly lower ALFF was detected in the frontal lobe and cingulate cortex of the football group (t = -3.66 to -4.92; P < .01), whereas elevated ALFF was observed in the occipital regions (calcarine and lingual gyrus, t = 3.20; P < .01). Similar to ALFF, football players exhibited lower ReHo in the precentral gyrus and medial aspects of the brain, such as precuneus, insula, and cingulum, whereas elevated ReHo was clustered in the occipitotemporal regions (t = 3.17; P < .001; to 4.32; P < .01). There was no group difference in RS-FC measures. In this study of adolescent athletes, there was evidence of discernible structural and physiological differences in the brains of adolescent football players compared with their noncontact controls. Many of the affected brain regions were associated with mental health well-being.

Joint diffusional kurtosis magnetic resonance imaging analysis of white matter and the thalamus to identify subcortical ischemic vascular disease

Identifying subcortical ischemic vascular disease (SIVD) in older adults is important but challenging. Growing evidence suggests that diffusional kurtosis imaging (DKI) can detect SIVD-relevant microstructural pathology, and a systematic assessment of the discriminant power of DKI metrics in various brain tissue microstructures is urgently needed. Therefore, the current study aimed to explore the value of DKI and diffusion tensor imaging (DTI) metrics in detecting early-stage SIVD by combining multiple diffusion metrics, analysis strategies, and clinical-radiological constraints. This prospective study compared DKI with diffusivity and macroscopic imaging evaluations across the aging spectrum including SIVD, Alzheimer's disease (AD), and cognitively normal (NC) groups. Using a white matter atlas and segregated thalamus analysis with considerations of the pre-identified macroscopic pathology, the most effective diffusion metrics were selected and then examined using multiple clinical-radiological constraints in a two-group or three-group paradigm. A total of 122 participants (mean age, 74.6 ± 7.38 years, 72 women) including 42 with SIVD, 50 with AD, and 30 NC were evaluated. Fractional anisotropy, mean kurtosis, and radial kurtosis were critical metrics in detecting early-stage SIVD. The optimal selection of diffusion metrics showed 84.4–100% correct classification of the results in a three-group paradigm, with an area under the curve of .909–.987 in a two-group paradigm related to SIVD detection (all P < .001). We therefore concluded that greatly resilient to the effect of pre-identified macroscopic pathology, the combination of DKI/DTI metrics showed preferable performance in identifying early-stage SIVD among adults across the aging spectrum.

Noninvasive Assessment of Kidney Injury by Combining Structure and Function Using Artificial Intelligence-Based Manganese-Enhanced Magnetic Resonance Imaging.

Contrast-enhanced magnetic resonance imaging (MRI) is seriously limited in kidney injury detection due to the nephrotoxicity of clinically used gadolinium-based contrast agents. Herein, we propose a noninvasive method for the assessment of kidney injury by combining structure and function information based on manganese (Mn)-enhanced MRI for the first time. As a proof of concept, the Mn-melanin nanoprobe with good biocompatibility and excellent T1 relaxivity is applied in MRI of a unilateral ureteral obstruction mice model. The abundant renal structure and function information is obtained through qualitative and quantitative analysis of MR images, and a brand new comprehensive assessment framework is proposed to precisely identify the degree of kidney injury successfully. Our study demonstrates that Mn-enhanced MRI is a promising approach for the highly sensitive and biosafe assessment of kidney injury in vivo.

Ridge-Type Pretest and Shrinkage Estimation Strategies in Spatial Error Models with an Application to a Real Data Example

Spatial regression models are widely available across several disciplines, such as functional magnetic resonance imaging analysis, econometrics, and house price analysis. In nature, sparsity occurs when a limited number of factors strongly impact overall variation. Sparse covariance structures are common in spatial regression models. The spatial error model is a significant spatial regression model that focuses on the geographical dependence present in the error terms rather than the response variable. This study proposes an effective approach using the pretest and shrinkage ridge estimators for estimating the vector of regression coefficients in the spatial error mode, considering insignificant coefficients and multicollinearity among regressors. The study compares the performance of the proposed estimators with the maximum likelihood estimator and assesses their efficacy using real-world data and bootstrapping techniques for comparison purposes.

Reward Learning as a Potential Mechanism for Improvement in Schizophrenia Spectrum Disorders Following Cognitive Remediation: Protocol for a Clinical, Nonrandomized, Pre-Post Pilot Study.

Cognitive impairment is common with schizophrenia spectrum disorders. Cognitive remediation (CR) is effective in improving global cognition, but not all individuals benefit from this type of intervention. A better understanding of the potential mechanism of action of CR is needed. One proposed mechanism is reward learning (RL), the cognitive processes responsible for adapting behavior following positive or negative feedback. It is proposed that the structure of CR enhances RL and motivation to engage in increasingly challenging tasks, and this is a potential mechanism by which CR improves cognitive functioning in schizophrenia. Our primary objective is to examine reward processing in individuals with schizophrenia before and after completing CR and to compare this with a group of matched clinical controls. We will assess whether RL mediates the relationship between CR and improved cognitive function and reduced negative symptoms. Potential differences in social RL and nonsocial RL in individuals with schizophrenia will also be investigated and compared with a healthy matched control group. We propose a clinical, nonrandomized, pre-post pilot study comparing the impact of CR on RL and neurocognitive outcomes. The study will use a combination of objective and subjective measures to assess neurocognitive, psychiatric symptoms, and neurophysiological domains. A total of 40 individuals with schizophrenia spectrum disorders (aged 18-35 years) will receive 12 weeks of CR therapy (n=20) or treatment as usual (n=20). Reward processing will be evaluated using a reinforcement learning task with 2 conditions (social reward vs nonsocial reward) at baseline and the 12-week follow-up. Functional magnetic resonance imaging responses will be measured during this task. To validate the reinforcement learning task, RL will also be assessed in 20 healthy controls, matched for age, sex, and premorbid functioning. Mixed-factorial ANOVAs will be conducted to evaluate treatment group differences. For the functional magnetic resonance imaging analysis, computational modeling will allow the estimation of learning parameters at each point in time, during each task condition, for each participant. We will use a variational Bayesian framework to measure how learning occurred during the experimental task and the subprocesses that underlie this learning. Second-level group analyses will examine how learning in patients differs from that observed in control participants and how CR alters learning efficiency and the underlying neural activity. As of September 2023, this study has enrolled 15 participants in the CR group, 1 participant in the treatment-as-usual group, and 11 participants in the healthy control group. Recruitment is expected to be completed by September 2024. Data analysis is expected to be completed and published in early 2025. The results of this study will contribute to the knowledge of CR and RL processes in severe mental illness and the understanding of the systems that impact negative symptoms and cognitive impairments within this population. DERR1-10.2196/52505.

Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity.

Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.

Effects of different isochoric freeze-thaw cycles on the physicochemical quality attributes of chicken breast meat

This study investigated the impact of isochoric freezing-thawing (ISF-T) cycles (C0–C5) on the quality of chicken breast. The results indicated a significant increase (P < 0.05) in pH from 5.41 to 5.96 after C1 to C4 before slightly decreasing to 5.72 (C5). Lightness (L*) varied between 50.13 and 44.55 across C1 to C4, with redness notably decreasing from −1.15 after C0 to −2.09 after C5 while yellowness (b) sharply increased from 2.65 (C0) to 4.65 (C5). Moisture content decreased from 74.01% to 68.13% after C5. WHC slightly reduced from 89.25% after C0 to 85.66% after C4, with an 83.78% reduction after C5. Cooking loss increased linearly across the ISF-T cycles from 18.08% reaching 24.14% after C5. Conversely, shear force decreased to 21.83 N after C5 from 25.21 N after C1. Protein solubility slowly increased to 88.79% after C3, then decreased to 72.84 after C5. The myofibrillar index remained stable until C3, then increased from 35.51 after C5. Lipid oxidation slowly increased to 0.27 mg MDA/kg after C3, then sharply increased to 0.44 mg MDA/kg after C5. In addition, Ca2+-ATPase activity showed that the samples obtained after C1 to C3 fluctuated between 23.59% and 24.71%, followed by a substantial reduction to 12.35% after C5. Furthermore, validation of the results by magnetic resonance imaging and nuclear magnetic resonance analysis confirmed the findings, suggesting that isochoric freezing could offer a viable solution to control temperature fluctuations, a challenging problem during meat storage and distribution in the cold chain food industry.

Early detection of cardiac fibrosis in diabetic mice by targeting myocardiopathy and matrix metalloproteinase 2

Early detection of myocardial fibrosis in diabetic cardiomyopathy (DCM) has significant clinical implications for diabetes management. In this study, we identified matrix metalloproteinase 2 (MMP2) as a potential biomarker for early fibrosis detection. Based on this finding, we designed a dual-targeting nanoparticle CHP-SPIO-ab MMP2 to specifically target myocardiopathy and MMP2, enabling sensitive fibrosis detection using magnetic resonance imaging (MRI). Our results demonstrate that collagen hyperplasia (early fiber formation) begins to develop in diabetic mice at 12 weeks old, with observable fibrosis occurring at 16 weeks old. Additionally, MMP2 expression significantly up-regulates around collagen starting from 12 weeks of age. T2 MRI analysis revealed significant T2% enhancement in the hearts of 12-week-old diabetic mice following administration of the CHP-SPIO-ab MMP2 probe, indicating noninvasive detection of fiber formation. Furthermore, after fibrosis treatment, a reduction in T2% signal was observed in the hearts of 16-week-old diabetic mice. These findings were supported by Sirius red and Prussian blue staining techniques. Overall, our study presents a promising strategy for early identification of myocardial fibrosis. Statement of significanceMyocardial damage typically exhibits irreversibility, underscoring the paramount importance of early fibrosis diagnosis. However, the clinical used T1 mapping for fibrosis detection still exhibits limitations in terms of sensitivity. Therefore, it is imperative to develop highly sensitive strategies for early cardiac fibrosis detection. Here, we investigated the development of myocardial fibrosis in diabetic mice, and designed a highly sensitive probe that specifically targets cardiomyopathy and high expression of MMP2 for the early diagnosis of fibrosis. The probe enables non-invasive detection of abnormalities through MRI imaging as soon as fiber deposition appear, which can be detected earlier than T1 mapping. This advancement holds great potential for clinical diagnosis of myocardial fibrosis using cardiac magnetic resonance.

Intracranial tuberculomas: review of MRI findings and clinical features

To present a comprehensive analysis of the clinical features and magnetic resonance imaging (MRI) findings of intracranial tuberculomas in Indonesia. This was a retrospective analysis of brain MRI from 58 patients (29 women), mean age±SD=39±2 years, diagnosed with intracranial tuberculomas. Clinical data, including symptoms, cerebrospinal fluid examination results, and MRI were also analysed. The diagnosis of intracranial tuberculoma was confirmed based on observed MRI changes following anti-tuberculosis therapy. A total of 603 tuberculomas were fully detected via MRI in all patients. Among these lesions, 507 (84%) were located in the cerebrum, 61 (10%) in the cerebellum, 19 (3%) in the basal ganglia, 12 (2%) in the brain stem, and four (1%) in the thalamus. The MRI signals were classified into two groups: tuberculomas with caseating granulomas and tuberculomas with non-caseating granulomas. Among the patients, 29 had concomitant meningitis, seven had hydrocephalus, and four had cerebral infarction. Evidence of pulmonary tuberculosis was found in 16 patients, with one patient having coexisting thoracic vertebral tuberculosis. The present study confirmed the significance of MRI as a valuable tool in the diagnosis of intracranial tuberculomas and the detection of associated abnormalities. The combination of MRI findings with clinical features can enhance the overall diagnostic accuracy for intracranial tuberculomas.

Predictors of Gleason Grading Group Upgrading in Low-Risk Prostate Cancer Patients From Transperineal Biopsy After Radical Prostatectomy

Rational and ObjectivesTo investigate the predictors of Gleason Grading Group (GGG) upgrading in low-risk prostate cancer (PC) (Gleason score=3+3) from transperineal biopsy after radical prostatectomy (RP). Materials and MethodsThe clinical data of 160 patients who underwent transperineal biopsy and RP from January 2017 to December 2022 were retrospectively analyzed. First, univariate and multivariate logistic regression analysis were used to obtain independent predictors of postoperative GGG upgrading. Then receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of predictors. Finally, Linear-by-Linear Association test was used to analyze the risk trends of patients in different predictor groups in the postoperative GGG. ResultsIn this study, there were 81 cases (50.6%) in the GGG concordance group and 79 cases (49.4%) in the GGG upgrading group. Univariate analysis showed age, f/tPSA, proportion of positive biopsies, positive target of magnetic-resonance imaging (MRI) and positive target of contrast-enhanced ultrasound had significant effects on GGG upgrading (all P<0.05). In multivariate logistic regression analysis, age (OR=1.066, 95%CI=1.007~1.127, P=0.027), f/tPSA (OR=0.001, 95%CI=0~0.146, P=0.001) and positive target of MRI (OR=3.005, 95%CI=1.353~76.674, P=0.007) were independent predictors. The prediction model (AUC=0.751 P<0.001)) had higher predictive efficacy than all independent predictors. The proportion of patients in exposed group of different GGG increased with the level of GGG, but decreased in non-exposed group, and the linear trend was significantly different (all P<0.001). ConclusionAge, f/tPSA and positive target of MRI were independent predictors of postoperative GGG upgrading. The predictive model constructed had the best diagnostic efficacy.

Longitudinal MRI and 1H-MRS study of SCA7 mouse forebrain reveals progressive multiregional atrophy and early brain metabolite changes indicating early neuronal and glial dysfunction.

SpinoCerebellar Ataxia type 7 (SCA7) is an inherited disorder caused by CAG triplet repeats encoding polyglutamine expansion in the ATXN7 protein, which is part of the transcriptional coactivator complex SAGA. The mutation primarily causes neurodegeneration in the cerebellum and retina, as well as several forebrain structures. The SCA7140Q/5Q knock-in mouse model recapitulates key disease features, including loss of vision and motor performance. To characterize the temporal progression of brain degeneration of this model, we performed a longitudinal study spanning from early to late symptomatic stages using high-resolution magnetic resonance imaging (MRI) and in vivo 1H-magnetic resonance spectroscopy (1H-MRS). Compared to wild-type mouse littermates, MRI analysis of SCA7 mice shows progressive atrophy of defined brain structures, with the striatum, thalamus and cortex being the first and most severely affected. The volume loss of these structures coincided with increased motor impairments in SCA7 mice, suggesting an alteration of the sensory-motor network, as observed in SCA7 patients. MRI also reveals atrophy of the hippocampus and anterior commissure at mid-symptomatic stage and the midbrain and brain stem at late stage. 1H-MRS of hippocampus, a brain region previously shown to be dysfunctional in patients, reveals early and progressive metabolic alterations in SCA7 mice. Interestingly, abnormal glutamine accumulation precedes the hippocampal atrophy and the reduction in myo-inositol and total N-acetyl-aspartate concentrations, two markers of glial and neuronal damage, respectively. Together, our results indicate that non-cerebellar alterations and glial and neuronal metabolic impairments may play a crucial role in the development of SCA7 mouse pathology, particularly at early stages of the disease. Degenerative features of forebrain structures in SCA7 mice correspond to current observations made in patients. Our study thus provides potential biomarkers that could be used for the evaluation of future therapeutic trials using the SCA7140Q/5Q model.

Whole-orbit radiomics: machine learning-based multi- and fused- region radiomics signatures for intravenous glucocorticoid response prediction in thyroid eye disease

BackgroundRadiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model.MethodsIn this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms.ResultsThe support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions.ConclusionsThe WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result.

Detection of hypoplastic left heart syndrome anatomy from cardiovascular magnetic resonance images using machine learning

ObjectiveThe prospect of being able to gain relevant information from cardiovascular magnetic resonance (CMR) image analysis automatically opens up new potential to assist the evaluating physician. For machine-learning-based classification of complex congenital heart disease, only few studies have used CMR.Materials and methodsThis study presents a tailor-made neural network architecture for detection of 7 distinctive anatomic landmarks in CMR images of patients with hypoplastic left heart syndrome (HLHS) in Fontan circulation or healthy controls and demonstrates the potential of the spatial arrangement of the landmarks to identify HLHS. The method was applied to the axial SSFP CMR scans of 46 patients with HLHS and 33 healthy controls.ResultsThe displacement between predicted and annotated landmark had a standard deviation of 8–17 mm and was larger than the interobserver variability by a factor of 1.1–2.0. A high overall classification accuracy of 98.7% was achieved.DiscussionDecoupling the identification of clinically meaningful anatomic landmarks from the actual classification improved transparency of classification results. Information from such automated analysis could be used to quickly jump to anatomic positions and guide the physician more efficiently through the analysis depending on the detected condition, which may ultimately improve work flow and save analysis time.

A Predictive Model for Cardiomyopathy Progression in a Longitudinal Cohort of DMD Patients

Background: Cardiomyopathy (CM) is currently the leading cause of mortality in patients with Duchenne muscular dystrophy (DMD). DMD CM is asymptomatic during the early stages of disease and age of onset and clinical progression vary. We previously showed that 4D kinematic analysis of cardiovascular magnetic resonance (CMR) imaging is a robust method to visualize strain and function changes in DMD patients. Our objective, using 4D CMR image analysis, is to build a predictive model to quantify disease progression, and predict functional decline as measured by left ventricular ejection fraction (LVEF) in a longitudinal cohort of DMD patients. &#x0D; &#x0D; Methods: We obtained 4D CMR images from 40 DMD patients imaged every 12 months for three years. Localized surface area strain (Ea) values were derived from compiled 4D CMR images. Patient demographics, presence or absence (+/-) of late gadolinium enhancement (LGE) and LVEF were obtained by a trained cardiologist. Principal component analysis was used to identify prominent variables contributing most to variability. These variables, along with clinically relevant variables, were selected to build the predictive model. We built a linear mixed effects model using visit, age, basal Ea, and LGE presence. Each patient was considered a random effect to account for variability in initial CM severity. &#x0D; &#x0D; Results: Study patients were grouped into stages of heart failure according to the American Heart Association: Stage A, LVEF&gt;55%, LGE(-); Stage B, LVEF&gt;55%, LGE(+); Stage C, 40%&lt;LVEF&lt;55%, LGE(+); Stage D, LVEF&lt;40%, LGE(+). Linear mixed effects model showed a good prediction of LVEF on year 3 with a root mean squared error of 3.6% and an R2 value of 0.75 (p&lt;0.0001.). &#x0D; &#x0D; Conclusion: Early detection is crucial for appropriate clinical intervention. This novel predictive model was developed to determine if Ea derived from 4D imaging, LVEF, and (+/-) LGE can be used to predict functional outcomes in DMD CM.

Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.

Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.

Human Adipose-derived Stem Cells Upregulate IGF-1 and Alleviate Osteoarthritis in a Two-stage Rabbit Osteoarthritis Model.

In recent times, it has been recognized that mesenchymal stem cells (MSCs) possess the capability to address osteoarthritis (OA). The objective of this research was to examine the impact of injecting human adipose-derived stem cells (hADSCs) into a novel rabbit osteoarthritis model with dual damage. The OA model was established surgically first by medial collateral ligament and anterior cruciate ligament transection and medial meniscectomy, then by articular cartilage full-thickness defect. Enhanced Green Fluorescence Protein expressing lentivirus FG12 was used to label hADSCs, which were then injected into the knee joints. Every single rabbit was sacrificed after 4 and 8 weeks following the surgical procedure. Macroscopic examination, immunohistochemistry staining, magnetic resonance imaging, qRT-PCR, and ELISA analysis were utilized for the assessments. After 4 and 8 weeks, the injection of hADSCs resulted in reduced cartilage loss, minimal fissures and cracks, and a decrease in the volume of joint effusion and cartilage defect as measured by MRI. Moreover, the application of ELISA and qRT-PCR techniques revealed that the administration of hADSCs resulted in an elevation in the IGF-1 concentration. Based on our findings, it can be inferred that the transplantation of hADSCs facilitates the healing of articular cartilage in the osteoarthritis model of rabbits with double damage. The upregulated IGF-1 may play a crucial part in the process of cartilage repair using hADSCs. The use of hADSC transplantation could potentially be appropriate for clinical implementation in managing osteoarthritis.

Post-transfusion severe headache in a patient with thalassemia with superficial siderosis of the central nervous system: a case report and literature review

BackgroundPatients with severe thalassemia may experience adverse effects from transfusion such as fever, rash, and iron overload after long-term transfusion therapy. Severe headaches as a side effect of blood transfusion in patients with thalassemia are not commonly observed, especially when combined with superficial siderosis of the central nervous system, which is easily misdiagnosed and requires excessive examination and treatment.Case PresentationA 31-year-old woman was admitted with severe headache and vomiting over 3 days following blood transfusion. She was diagnosed with intermediate α-thalassemia at 2 years of age and had a history of irregular blood transfusions. Physical examination revealed horizontal nystagmus with no other abnormal neurological signs. Magnetic resonance (MR) imaging, MR venography, MR arteriography, and cerebrospinal fluid analysis were normal. However, susceptibility-weighted imaging showed abnormal signals in the bilateral and fourth ventricles. Initial antibiotics, antivirals, decompression of intracranial pressure, iron chelation, and symptomatic treatments were administered; subsequently, small intermittent blood transfusions were cautiously administered for severe anemia. The patient’s headache was gradually relieved, and she was discharged on day 9. At the 5-month follow-up, the patient’s headache recurred following another transfusion.ConclusionsSevere post-transfusion headache in patients with thalassemia has not been fully recognized and is easily misdiagnosed, leading to excessive examination and treatment. Understanding the clinical features of transfusion-related headaches can help identify this complication, but the exact pathophysiological mechanism requires further research.

Differences in Brain Atrophy Pattern between People with Multiple Sclerosis and Systemic Diseases with Central Nervous System Involvement Based on Two-Dimensional Linear Measures.

Conventional brain magnetic resonance imaging (MRI) in systemic diseases with central nervous system involvement (SDCNS) may imitate MRI findings of multiple sclerosis (MS). In order to better describe the MRI characteristics of these conditions, in our study we assessed brain volume parameters in MS (n = 58) and SDCNS (n = 41) patients using two-dimensional linear measurements (2DLMs): bicaudate ratio (BCR), corpus callosum index (CCI) and width of third ventricle (W3V). In SDCNS patients, all 2DLMs were affected by age (CCI p = 0.005, BCR p < 0.001, W3V p < 0.001, respectively), whereas in MS patients only BCR and W3V were (p = 0.001 and p = 0.015, respectively). Contrary to SDCNS, in the MS cohort BCR and W3V were associated with T1 lesion volume (T1LV) (p = 0.020, p = 0.009, respectively) and T2 lesion volume (T2LV) (p = 0.015, p = 0.009, respectively). CCI was associated with T1LV in the MS cohort only (p = 0.015). Moreover, BCR was significantly higher in the SDCNS group (p = 0.01) and CCI was significantly lower in MS patients (p = 0.01). The best predictive model to distinguish MS and SDCNS encompassed gender, BCR and T2LV as the explanatory variables (sensitivity 0.91; specificity 0.68; AUC 0.86). Implementation of 2DLMs in the brain MRI analysis of MS and SDCNS patients allowed for the identification of diverse patterns of local brain atrophy in these clinical conditions.