Magnetic Resonance Imaging Analysis Research Articles

Differentiation Between Parotid Adenolymphoma and Malignant Tumor Based on Multimodal Functional MRI of Radiomics and Intratumoral Vascular ITSS Classification.

Differentiating between parotid adenolymphoma and malignant tumors remains challenging. This study aims to improve preoperative diagnosis accuracy by evaluating the role of multimodal functional magnetic resonance imaging (MRI) and advanced radiomics analysis. We retrospectively analyzed 124 patients with adenolymphoma and malignant parotid tumors, divided into primary (n = 84) and test (n = 40) cohorts. Tumor regions were manually labeled on susceptibility-weighted imaging (SWI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI). Seven radiomics models were constructed using logistic regression. We also incorporated intratumoral susceptibility signal (ITSS) grading and performed histogram analysis of apparent diffusion coefficient (ADC) maps. The united radiomics model combining SWI, DWI, and CE-T1WI showed the highest diagnostic performance (area under the curve (AUC) = 0.95, accuracy = 0.93, specificity = 0.93) in the primary cohort, outperforming single-sequence and double-sequence models. The test set validated the model's good diagnostic performance (AUC = 0.9). ITSS grading significantly differed between adenolymphomas and malignant tumors (p < 0.001). ADC histogram analysis revealed significant differences in mean, 10th percentile, and kurtosis values between the two groups. The multisequence radiomics model combining DWI, SWI, and CE-T1WI provides a comprehensive and accurate noninvasive approach for differentiating parotid adenolymphoma from malignant tumors. This method helps avoid the risks associated with invasive procedures, such as tumor cell implantation and metastasis, while guiding personalized surgical decision-making. By offering a novel diagnostic tool, this study enhances the precision of preoperative tumor characterization and supports more effective treatment planning and prognosis assessment for patients with parotid gland tumors.

How can spinous process splitting approach in lumbar spine surgery preserve the para-spinal muscles? Quantitative pre- and post-operative Dixon MRI assessment

BackgroundThe spinous process splitting procedure is a muscle sparing approach that could be considered as minimally invasive for para-spinal muscles allowing decompression of the spinal canal without significant muscle violation. T1-Dixon MRI could effectively assess the para-spinal musculature for volume reduction or intra-muscular fat infiltration. The aim of this study was to assess the influence of spinous process splitting procedure utilized in spine decompression surgery in preservation of para-spinal muscle status using T1-Dixon MRI with an emphasis on quantitative histogram analysis of muscle volume and signals. MethodsThirty-three patients candidate for lumbar decompression surgery underwent pre- and postoperative T1-Dixon MRI of lumbosacral spine with quantitative histogram of the fat only images and analysis of signal-oriented and volume-oriented features. All patients underwent decompression surgery employing the spinous process splitting approach.ResultsThere was significant postoperative improvement of Oswestry disability index and visual analogue scale scores. The 6-min walk distance showed significant postoperative improvement from (282.58 ± 69.03) meters to (628.79 ± 79.09) meters. The quantitative histogram analysis of fat-only Dixon MR images showed minimal statistically insignificant postoperative decrease in volume and minimal statistically insignificant postoperative increase in histogram skewness values which represent signal alteration of the muscle fibers. At 6-week follow-up, all patients returned to their pre-symptomatic level of activity and work. At 6-month follow-up, there is no radiographic evidence for instability in either case or either level (total number of cases 33 patients with 42 levels). There is no recorded postoperative infection or neurologic deficits.ConclusionsT1-Dixon MRI is a reliable and valuable imaging method with a proper diagnostic performance in para-spinal muscle evaluation; it could prove the less invasiveness property of the spinous process splitting approach employed in spine decompression surgery to the para-spinal muscle with excellent preservation of their volume and signal.

REVOLUTIONIZING ALZHEIMER’S THERAPEUTICS: FROM MICROTECHNOLOGIES TO AI-DRIVEN INNOVATIONS

This review article dives deep into Alzheimer's Disease (AD), a progressive and incurable brain disorder. It aims to equip readers with a comprehensive understanding of AD by exploring its history, classification, causes, and risk factors. The article explores the emerging field of Artificial Intelligence (AI) and its potential to revolutionize AD management. It examines how AI can impact diagnostics, treatment strategies, and, particularly, the development of targeted therapies. AI-powered imaging tools, such as Deep Learning-based Positron Emission Tomography (PET)/Magnetic Resonance Imaging (MRI) analysis, have achieved over 90% accuracy in early AD detection by identifying subtle brain changes years before clinical symptoms appear. Machine learning models have also enhanced precision medicine by predicting patient responses to therapies with 85–92% accuracy, optimizing treatment regimens based on genetic and biomarker profiles. These novel delivery systems enhance drug efficacy, improve patient compliance, and reduce systemic toxicity, addressing key challenges in AD treatment. Future developments will focus on AI-guided personalized medicine, smart nanocarriers responsive to AD biomarkers, and AI-powered neuroprosthetics for cognitive rehabilitation. Next, it compares established management methods with the latest investigational drug therapies for AD. This analysis sheds light on promising future directions and potential breakthroughs in AD treatment. However, the article emphasizes the importance of patient safety and highlights the rigorous processes of clinical trials, and regulatory hurdles that new AD therapies and delivery systems must overcome. The review concludes by summarizing the key takeaways and identifying the most promising avenues for future research and development in AD treatment. It emphasizes the potential of these cutting-edge approaches to transform AD care and significantly improve patient quality of life.

Evaluating the effectiveness of preoperative embolization for meningioma with pial feeders: Analysis of perfusion magnetic resonance imaging.

Background and purposeMeningiomas often contain a pial supply, and devascularization through preoperative transarterial embolization (TAE) is useful. However, the efficacy indicators of TAE for meningiomas with pial feeders remain unclear owing to their challenging quantification. Therefore, we propose a practical and quantitative intratumoral cerebral blood volume (CBV) from the pial supply, called pial-nCBV. This study aimed to identify the efficacy indicators of TAE against meningiomas in pial feeders using pial-nCBV.Materials and methodsBetween 2015 and 2021, 25 patients who underwent preoperative TAE and perfusion magnetic resonance imaging (MRI) for meningiomas with pial feeders were included in the study. We set 5 mm diameter round Region Of Interests as many as possible within the tumor at the border with the brain parenchyma, and calculated the intratumoral CBV from pial supply. The reduction rate was defined as primary outcome, termed Δ pial-nCBV. Additionally, we collected data on the injection behavior of the n-Butyl-Cyanoacrylate (n-BCA), including intratumoral embolization (IE) and feeder occlusion. We then analyzed the association between each variable and Δ pial-nCBV.ResultsThe median Δ pial-nCBV was 25%. The n-BCA was applied in 22 (88%) cases, of which nine (36%) cases were injected into pial feeders. The use of n-BCA (p = .037), treatment with pial feeders (p = .002), and IE in meningeal feeders (p = .049) were associated with an increase in Δ pial-nCBV.ConclusionWe proposed a practical indicator, pial-nCBV, and increasing Δ pial-nCBV was associated with treating pial feeder using n-BCA.

Focal epilepsy with sensory seizures associated with arginine:glycine amidinotransferase deficiency: A clinical and advanced magnetic resonance imaging study.

We aim to determine whether epilepsy can be considered part of the arginine:glycine amidinotransferase (AGAT) deficiency syndrome phenotype and to identify its associated electroclinical signatures. We reviewed clinical data from our center, identifying individuals with AGAT deficiency. Each individual underwent a dedicated epilepsy assessment with electroencephalography and 3-T brain magnetic resonance imaging (MRI). Additionally, 30 age- and sex-matched healthy controls (18 females, 28.2 ± 3.7 years old) were recruited for advanced MRI analysis. A family with four affected members carrying homozygous AGAT c.446>A:p.Trp149* variant was identified. Two individuals had focal epilepsy with sensory seizures characterized by a prominent "tingling" sensation. Three experienced febrile seizures plus and marked temperature sensitivity. Corpus callosum dysmorphisms were observed in three cases. Despite creatine supplementation, cortical thickness was significantly reduced across multiple brain regions compared to controls, as indicated by Z-scores. A brain map of AGAT mRNA expression revealed lower expression in the parieto-occipital areas. Our findings suggest that focal epilepsy with sensory seizures and temperature-related seizures may be part of the AGAT deficiency spectrum. Furthermore, significant brain atrophy was demonstrated, despite creatine supplementation. The sensory-predominant epilepsy phenotype aligns with observed atrophy and AGAT-mRNA regional expression patterns, supporting its biological plausibility.

Quantifying Leg Muscle Disuse Atrophy During Bed Rest Using DXA, CT, and MRI.

This study evaluated whether dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI) provide comparable outcomes in quantifying disuse-induced skeletal muscle atrophy. Although the calculation of muscle volume using MRI analysis may be considered the gold standard, the method remains labor intense and, as such, less practical and more costly. In this context, we also evaluated the efficacy of a commercially available automated MRI analysis method to measure changes in leg muscle volume after two weeks of bed rest. Twelve healthy, male adults (age: 24±3years, BMI: 23.7±3.1kg/m2) were subjected to 2weeks of strict bed rest. Leg muscle assessments were performed before and after bed rest using DXA, single slice (thigh) CT, and MRI. MRI data analyses were performed using both a manual and automated (AMRA) method. Leg lean mass, as assessed with DXA, declined by 5% following bed rest (from 10.2±1.6 to 9.7±1.6kg; p<0.001). The thigh muscle cross-sectional area, as assessed with CT, declined by 6% following bed rest (from 155±26 to 146±24cm2; p<0.001). Muscle volume, as assessed using MRI, declined by 5% following bed rest, both when assessed manually (from 7.1±1.1 to 6.7±1.0L; p<0.001) and automatically (from 7.2±1.1 to 6.8±1.0L; p<0.001). A very strong correlation (r=0.96; p<0.001) with a low bias (-0.11±0.29L) was observed between manual and automated muscle volume analysis. DXA, CT, and MRI all show a ∼5% decline in leg muscle quantity following two weeks of bed rest in healthy adults. When using MRI, disuse muscle atrophy can be accurately quantified using an automated approach, rendering time-consuming manual analysis obsolete.

380. A Study of Lead Exposure and Early Brain Development Using Retrospective Analysis of Electronic Medical Record Data and Brain Magnetic Resonance Imaging

380. A Study of Lead Exposure and Early Brain Development Using Retrospective Analysis of Electronic Medical Record Data and Brain Magnetic Resonance Imaging

Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.

Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.

10-Year Clinical Review of Revision Hip Arthroplasty Following Previous Metal on Metal Hip Procedures with Preoperative Magnetic Resonance Imaging and Metal Ion Analysis: Abductor Tendon Avulsion Predicts Dislocation.

10-Year Clinical Review of Revision Hip Arthroplasty Following Previous Metal on Metal Hip Procedures with Preoperative Magnetic Resonance Imaging and Metal Ion Analysis: Abductor Tendon Avulsion Predicts Dislocation.

Detecting and Diagnosing Brain Tumors Using ML

Brain tumors are among the most severe and life-threatening medical conditions, often leading to significant morbidity and mortality if not detected and treated promptly. Early and accurate diagnosis plays a crucial role in improving patient outcomes and survival rates. Traditionally, the detection of brain tumors relies on the manual analysis of Magnetic Resonance Imaging (MRI) scans by radiologists. While effective, this process is inherently time-consuming and subject to variability in interpretation due to human fatigue or inexperience, potentially leading to diagnostic delays or errors. To address these challenges, this project introduces an Automated Brain Tumor Detection and Medicine Suggestion System powered by Convolutional Neural Networks (CNNs) — a class of deep learning models particularly effective in image classification tasks. The proposed system is capable of automatically analyzing MRI scans and accurately classifying them into two categories: “Tumor Detected” and “No Tumor Detected.” By automating the detection process, the system significantly reduces the workload of medical professionals and minimizes the chances of oversight, thereby enhancing diagnostic reliability and efficiency. Beyond tumor detection, the system is designed to provide preliminary medicine suggestions based on the type and severity of the tumor identified, referencing medical databases and treatment protocols. This feature aims to support healthcare professionals by offering instant insights into potential treatment pathways, helping expedite the decision-making process in clinical settings. The integration of AI in medical imaging through CNN-based models not only speeds up the diagnostic process but also democratizes access to expert-level analysis, especially in remote or under-resourced regions. With continuous training on diverse and large datasets, the system can improve its accuracy and robustness, making it a valuable tool in modern healthcare. In conclusion, the proposed system demonstrates the potential of artificial intelligence in revolutionizing medical diagnostics. By merging advanced image processing with intelligent recommendation systems, it offers a promising solution for early brain tumor detection and effective treatment planning, ultimately contributing to better patient care and outcomes.

Volumetric examination of the cerebellum and cortical visual centres in individuals with nonarteritic anterior ischemic optic neuropathy using automatic segmentation

Objective: Non-arteritic anterior ischemic optic neuropathy (NAION) is a clinical condition caused by acute ischemic damage to the anterior part of the optic nerve. In this study, brain magnetic resonance imaging (MRI) images of NAION patients and healthy control subjects (HC) were analyzed and compared using an automatic segmentation method. Materials and Methods: The study included 14 patients diagnosed with NAION and 14 HC. VolBrain method, which provides brain parcellation with Voxel-Based Morphometry (VBM), was used for volumetric analysis of brain MR images. In the statistical analysis of the findings, the Mann-Whitney U test was employed. Results: We found that cerebrum, gray and white matter, frontal lobe and middle frontal gyri, occipital lobe, calcarine cortex, lingual gyrus, superior and middle occipital gyri volumes were significantly lower in NAION patients compared to the HC (p &amp;lt; 0.05). There was no statistically significant difference between the two groups in the volume analysis of the cerebellum and its parts (p &amp;gt; 0.05). Conclusions: Our study reveals that NAION induces volumetric changes in visual and non-visual regions of the cerebral cortex, but not in the volume of the cerebellum.

Epitranscriptomic Analysis of the Ventral Hippocampus in a Mouse Model of Post-Traumatic Stress Disorder Following Deep Brain Stimulation Treatment of the Basolateral Amygdala

Background: Basolateral amygdala (BLA) deep brain stimulation (DBS) has been shown to alleviate the symptoms of post-traumatic stress disorder (PTSD), but the specific mechanisms remain incompletely understood. The hippocampus, a brain region closely connected to the amygdala, plays a key role in the pathological processes of PTSD. The N6-methyladenosine (m6A) methylation of RNAs in the hippocampus is known to play a significant role in regulating the brain’s response to stress and emotional disorders. Methods: This study aimed to comprehensively analyze the roles of transcriptome-wide m6A modifications of the hippocampus in the BLA DBS treatment of a PTSD mouse model using m6A sequencing. Results: Significant alterations in functional connectivity between the ventral hippocampus (vHPC) and BLA were observed in foot shock (FS) mice through functional magnetic resonance imaging (fMRI) analysis. Furthermore, we observed that the expression of the key m6A methyltransferase enzyme, METTL3, in the FS and BLA DBS groups was higher than that in the control group. At the same time, both FS and BLA DBS induced the widespread m6A methylation of RNAs in the vHPC. Gene ontology (GO) enrichment analysis revealed that FS altered methylation in metabolic, developmental, and cytoskeletal pathways, while BLA DBS targeted metabolic, cell cycle, and neuroplasticity-related genes. Additionally, BLA DBS reversed the aberrant methylation of genes associated with multiple functional pathways induced by FS, including those related to cholinergic transmission, sodium and calcium ion homeostasis, and stress hormone responsiveness. We identified a set of RNAs with methylation changes that were reversed by BLA DBS in the FS vs. Ctrl (control) comparison, including those associated with cholinergic transmission, sodium and calcium ion balance, and stress hormone response. Additionally, we detected several specific BLA DBS-related genes through MeRIP-qPCR, indicating that DBS influences crucial genes linked to calcium signaling and synaptic plasticity. Conclusions: We draw two conclusions from these findings: BLA DBS may alleviate PTSD-like symptoms by reversing FS-induced methylation changes and by altering the methylation levels of crucial genes. These findings indicate that epigenetic m6A modifications in the vHPC may play an important role in the amelioration of PTSD using BLA DBS.

Quantitative MRI texture analysis of the lateral pterygoid muscle in unilateral temporomandibular joint disorders

BackgroundThis study utilized MRI-based texture analysis to quantify structural alterations in the lateral pterygoid muscle (LPM) among MR images with unilateral anterior disc displacement (uADD) of the temporomandibular joint. Retrospective analysis of anonymized MR images from 232 uADD patients and 123 healthy individuals (January 2022– December 2024), approved by the Ethics Committee of Wenzhou Medical University Affiliated School of Stomatology (Ethics Number: WYKQ 2024008) with waived informed consent due to the use of de-identified retrospective data, was conducted to identify diagnostic markers and possible related pathological changes of disc displacement. According to the inclusion and exclusion criteria four groups of MR images were included in this study: the healthy temporomandibular joints (H-TMJ) of individuals with uADD, joints with anterior disc displacement with reduction (ADDwR), joints with anterior disc displacement without reduction (ADDwoR), and MR images from normal volunteers as the healthy group (HG). Four texture parameters were used for analysis: the angular second moment (ASM), Contrast, inverse difference moment (IDM) and Entropy.ResultsStatistically significant differences (P < 0.05) were found between groups for the ASM, Contrast, IDM, and Entropy variables, indicating their potential as diagnostic markers. Additionally, Entropy values differed significantly between the ADDwoR and ADDwR groups (P < 0.05), highlighting its diagnostic potential in distinguishing these two conditions. The severity of ADD disease showed varying degrees of correlation with specific texture parameters, with significant associations observed for ASM, Contrast, IDM, and Entropy (P < 0.05).ConclusionsThe texture parameters of the LPM exhibit significant changes in MR images with anterior disc displacement(ADD). Notably, the Entropy value of the LPM demonstrates high diagnostic utility in distinguishing ADDwoR from ADDwR, particularly in cases of complex disc displacement involving deformation or remodeling. Furthermore, the severity of ADD disease shows varying degrees of correlation with specific texture parameters. However, further research is required to validate the relationship between numerical texture changes in the LPM and their corresponding pathological alterations.Clinical trial numberNot applicable.

Advanced neuroimaging in systemic lupus erythematosus: identifying biomarkers for cognitive dysfunction.

Cognitive dysfunction (CD) is a common manifestation of central nervous system involvement in patients with systemic lupus erythematosus (SLE). Patients with SLE may develop CD insidiously at an early stage of the disease, and the lack of a standardized diagnostic test poses a major challenge in prompt diagnosis and management of these patients. This review summaries the current application of various magnetic resonance imaging (MRI) techniques for patients with SLE complicated with CD, aiming to identify potential quantitative neuroimaging biomarkers for patients with SLE and CD. We systematically searched several databases between January 2003 to December 2024. We screened retrospective and prospective studies based on search criteria keywords (including structural or functional MRI, cognitive function, lupus, and systemic lupus erythematosus) to identify peer-reviewed articles that reported advanced structural and functional MRI metrics and evaluated CD in human patients with SLE. 123 studies (19 Bold-MRI studies, 9 DTI studies, 2 ASL studies, 4 MTI studies, 5 machine learning, and 84 other studies) were identified. Neuroimaging findings show that patients with CD have abnormal manifestations in the limbic system, hippocampus, corpus callosum, and frontal cortex, and these manifestations are closely related to cognitive functions. The most commonly affected cognitive domains are memory, attention, and executive ability. Multimodal MRI, integrating structural, functional, and perfusion parameters, combined with machine learning, can effectively predict cognitive function. Advanced MRI analysis can identify the abnormalities in the whole brain and local brain regions associated with CD in patients with SLE. The integration of machine learning and multimodal MRI offers new perspectives for early identification and mechanistic studies of CD in SLE patients. More studies are needed to identify potential neuroimaging biomarkers to facilitate early diagnosis, timely treatment, and accurate prognosis for SLE patients with CD.

Open-source quality assurance for multi-parametric MRI: a diffusion analysis update for the magnetic resonance biomarker assessment software (MR-BIAS).

To validate the automated analysis of magnetic resonance imaging (MRI) diffusion phantoms with an updated version of the magnetic resonance biomarker assessment software (MR-BIAS), an open-source tool initially developed for the analysis of MRI relaxometry phantoms. The updated MR-BIAS was validated against two published diffusion weighted MRI datasets: (i) a single-site study (n = 48) was used for validation of apparent diffusion coefficients (ADC) and to identify optimal region of interest (ROI) selection, and (ii) a multi-centre multi-vendor study including diffusion imaging from a shared benchmark protocol (n = 49) and site-specific protocols (n = 43). ADC analysis compared both datasets with ROIs manually matched to the original studies, and with automatically detected optimal ROIs. MR-BIAS ADC values were statistically equivalent (p < 0.05) to original studies within tolerances (manual ROI, automatic ROI) for the single-site study (± 0.01, ± 6 μm2/s) and for the multi-vendor study for benchmark (± 4, ± 7 μm2/s) and site-specific (± 3, ± 6 μm2/s) protocols. The optimal ROI was a central cylinder (height = 10mm, diameter = 10mm). MR-BIAS ADC summary metrics were comparable to those of the original studies. MR-BIAS can automatically and accurately perform ADC analysis of diffusion phantoms, making the software suitable for the quality assurance of multi-centre studies of multi-parametric MRI.

Effectiveness and safety of multiple injections of human placenta-derived MSCs for knee osteoarthritis: a nonrandomized phase I trial

ObjectiveThis study investigates the safety and efficacy of three intra-articular (IA) injections of cryopreserved human placenta-derived mesenchymal stem cells (hP-MSCs) for knee osteoarthritis (KOA) over a 1-year follow-up period.MethodsA total of 26 patients with stage II-III KOA were enrolled in this non-randomized, open-label study. Patients received either conventional therapy with hyaluronic acid (HA) alone (Control group, n = 11) or in combination with hP-MSCs (MSC group, n = 15) via three intra-articular injections with 4-week intervals. Clinical outcomes were assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Visual Analogue Scale (VAS), and magnetic resonance imaging (MRI) at 6 and 12 months following the first injection. Blood samples were analyzed for cytokine levels.ResultsThree injections of hP-MSCs combined with HA were well-tolerated, with no severe adverse events observed. Significant improvements in WOMAC and VAS scores were noted in the MSC group compared to the Control group at both 6 and 12 months. MRI analysis revealed no significant differences in cartilage thickness or optical density index between the groups. Additionally, serum cytokine analysis showed a significant decrease in interleukin-2 (IL-2) levels in the MSC group, indicating an anti-inflammatory effect of hP-MSCs. However, no significant changes were observed in other cytokines.ConclusionThis study demonstrates that three intra-articular injections of cryopreserved hP-MSCs in combination with HA are safe and effective for treating KOA, providing sustained clinical improvement at the 1-year follow-up.Trial registrationNCT04453111, #7/09.26.2018. Registered 02 January 2020, https://www.clinicaltrials.gov/study/NCT04453111.

Causal effects of obesity on estimated glomerular filtration rate: a Mendelian randomization and image data analysis study.

Obesity has been associated with onset and progression of chronic kidney disease (CKD) but causal relationship remains uncertain. This study investigated how obesity causally affects estimated glomerular filtration rate. Cross-sectional and magnetic resonance imaging (MRI) data analyses were performed within the Prospective Investigation of Obesity, Energy, and Metabolism (POEM) study (502 participants, all aged 50 years). Additionally Mendelian randomization was performed using published summary data. Outcomes were creatinine- and cystatin C-based eGFR. Body mass index (BMI) and waist circumference (WC) were used as exposure variables in the cross-sectional and Mendelian randomization analyses. In the imaging data analyses, eGFR was regressed non-parametrically on tissue volume for each 3D voxel and visualized as a correlation "Imiomics" map. Negative correlations were shown between cystatin C-based eGFR and BMI [beta = -0.190 (95% CI: -0.280 to -0.100)] and WC [beta = -0.160 (95% CI: -0.250 to -0.060)] in an adjusted model. In contrast, a positive association was found for creatinine-based eGFR [BMI beta=1.20 (95% CI: 0.030 to 0.210) and WC beta=0.160 (95% CI: 0.070 to 0.260)]. Similar patterns were found using MRI analysis (Imiomics map). Mendelian randomization implied a negative causal effect of obesity-related measures on cystatin C-based eGFR [BMI beta = -0.031 (95% CI: -0.037 to -0.026) and WC beta = -0.038 (95% CI: -0.045 to -0.031)], but no statistically significant effect was found for creatinine-based eGFR. This study suggests a causal negative effect of obesity on cystatin C-based, but not creatinine-based eGFR. These findings warrant further research regarding estimations of kidney function when assessing obesity and CKD.

Mesenchymal stem cells improve osteoarthritis by secreting superoxide dismutase to regulate oxidative stress response

BackgroundTo investigate the therapeutic effect of intraarticular injection of mesenchymal stem cells (MSC) in a rabbit osteoarthritis (OA) model. And to suppose whether MSC play a pivotal role in OA therapy by improving oxidative stress through secreting superoxide dismutase (SOD).MethodsMSC and chondrocytes were isolated and cultured in vitro. SOD gene of MSC was silenced by siRNA technology to prepare the SOD-siRNA-MSC for in-vivo study. Twenty healthy adult New Zealand white rabbits underwent papain injection to induce OA and then received intra-articular injection with MSC, siRNA-MSC, or normal saline. The rabbits were divided into 4 groups (n = 5), such as the control group, the model group, the MSC group, the siRNA-MSC group. Cytokines determination was performed 2 and 4 weeks after treatment. Magnetic resonance imaging (MRI) and histopathology and immunohistochemistry determination were performed 4 weeks after treatment. Normal chondrocytes, OA chondrocytes, OA chondrocytes + MSC group, and OA chondrocytes + siRNA-MSC were incubated for 24 h. Then β-galactosidase staining and reactive oxygen species (ROS) level was detected to establish the senescence of the cells.ResultsCOMP, TNF-α, MMP-2 and MMP-13 in the MSC group were significantly decreased compared to those in model group (P < 0.05). However, MMP2 and MMP13 in the siRNA-MSC group were not significantly decreased compared to the model group (P < 0.05). Magnetic resonance results revealed a significant improvement in cartilage and synovial membrane 4 weeks after MSC injection. Histopathology determination showed that cartilage structure was also significantly improved in MSC group. Immunohistochemical analysis revealed amelioration in the expression levels of proteoglycan, COL-2, P21 and P53 in MSC group. On the other hand, MRI, histopathologic and immunohistochemical analysis also indicated a decreased therapeutic effect with SOD-siRNA -MSC. The positive rate of β-galactosidase staining and ROS level of OA chondrocytes were significantly higher than those in normal chondrocytes, which was decreased in OA chondrocytes + MSC (P < 0.05). In addition, it was increased in OA chondrocytes + siRNA-MSC (P < 0.05).ConclusionOur study demonstrated for the first time that MSC might be a promising therapy in OA through anti-apoptosis and regeneration in chondrocyte by secreting SOD and improving oxidative stress.

Benchmarking Machine Learning Approaches for Breast Cancer Detection: A Performance Analysis

Introduction: Artificial intelligence and machine learning are transforming breast cancer detection by employing computational algorithms to scrutinize medical images, genetic profiles, and clinical datasets, pinpointing patterns suggestive of malignancy. This process, however, demands robust models and meticulous preprocessing, especially for complex imaging modalities. Recent strides in AI and ML have yielded more accurate and efficient analytical tools, significantly enhancing diagnostic capabilities. The synergistic application of Magnetic Resonance Imaging (MRI) and Convolutional Neural Networks (CNNs) has emerged as a particularly potent strategy, offering improved detection and preventative measures. These advanced techniques have shown considerable potential in accurately identifying cancerous cells, contributing to earlier diagnosis and improved patient outcomes. Objectives: The objective of this research is to assess the performance of various machine learning algorithms—Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Classifier, and Linear Support Vector Classifier—in breast cancer detection. This assessment will be based on a large dataset of 3002 merged mammography images from 1501 individuals, obtained from Kaggle and spanning data from February 2007 to May 2015. Methods: The methodology involves a comprehensive data preprocessing pipeline. Initially, duplicate values are removed, and the dataset is balanced. Feature extraction follows, preparing the data for model training. The dataset is split into 70% training and 30% testing sets. Standard Scaler is applied for feature resizing, ensuring optimal model performance. Feature selection, implemented using scikit-learn, prioritizes informative features, reducing dimensionality. Various machine learning classifiers, including Decision Trees, Random Forest, Logistic Regression, Support Vector Classifier, and K-Nearest Neighbors, are employed. These models are then trained and evaluated on the prepared dataset to assess their breast cancer detection accuracy. The methodology focuses on rigorous data preparation and a comparative analysis of established ML algorithms. Results: The CNNI-BCC model assists in breast cancer detection by classifying subtypes using a trained deep neural network. Overcoming detection challenges requires interdisciplinary collaboration between clinicians, data scientists, and regulatory bodies to create robust, ethical ML solutions. Deep learning and transfer learning advancements offer potential improvements in accuracy, generalization, and interpretability. These techniques can address existing limitations, enhancing the effectiveness of breast cancer detection models. Future progress hinges on validated, ethically developed AI systems that integrate seamlessly into clinical workflows, ultimately improving patient outcomes. Conclusions: This study evaluated six ML classifiers on the Breast Cancer Wisconsin dataset, revealing Random Forest as the most accurate, followed by Decision Tree and KNN. Preprocessing, including standardization and feature selection, significantly impacted results, reducing potential false positives. The research underscores the potential of AI and ML to enhance mammography and MRI analysis, highlighting the need for continued development of deep learning models. Future work should explore advanced techniques and feature correlations to improve diagnostic accuracy. Crucially, interdisciplinary collaboration between data scientists and medical professionals is essential for translating these advancements into clinical practice. Employing confusion matrices and performance metrics like accuracy and F1-score provided a robust evaluation, emphasizing the importance of comprehensive analysis in developing effective ML-based breast cancer detection tools.

IGF-1 Provides Protective Role in Arteriosclerotic Cerebral Small Vessel Disease.

Hypertension and advanced age are risk factors for arteriosclerotic cerebral small vessel disease (cSVD), a common cause of vascular dementia in elderly individuals. Circulating IGF-1 (insulin-like growth factor 1) levels decrease with age and are linked to age-related cognitive impairment. This study assessed the relationship between serum IGF-1 and arteriosclerotic cSVD severity in patients and the therapeutic effects and underlying mechanisms of exogenous IGF-1 supplementation in a cSVD rat model. Serum and MR images were collected from healthy subjects (n=26) and patients with arteriosclerotic cSVD (n=86). Stroke-prone renovascular hypertensive rats were used as cSVD animal models and subjected to the Morris water maze test, magnetic resonance imaging, immunohistochemistry, and biochemical analysis. hCMEC/D3 cells were utilized to validate the underlying mechanisms in vitro. Serum IGF-1 concentration was significantly reduced in patients and rats with arteriosclerotic cSVD. Lower serum IGF-1 was associated with an increased cSVD burden and cognitive impairment. Compared with cSVD rats, IGF-1-treated rats had lighter white matter lesions, greater global cerebral blood flow, greater cerebrovascular density, less blood-brain barrier leakage, and better cognitive function. In vitro, IGF-1 administration promoted endothelial proliferation, migration, tube formation, and barrier function. Mechanistically, IGF-1 exerts neuroprotective effects by activating the IGF-1R (IGF-1 receptor)/Wnt7b/β-catenin pathway in vivo and in vitro. Low serum IGF-1 was associated with greater arteriosclerotic cSVD severity. IGF-1 treatment improved cerebral perfusion, blood-brain barrier integrity, and cognitive function in cSVD rats by activating the IGF-1R/Wnt7b/β-catenin pathway, suggesting a potential therapeutic strategy for patients with arteriosclerotic cSVD, particularly those with low IGF-1 levels.