Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.

Multiple sclerosis (MS) is a demyelinating neurological disorder with a highly heterogeneous clinical presentation and course of progression. Disease-modifying therapies are the only available treatment, as there is no known cure for the disease. Careful selection of suitable therapies is necessary, as they can be accompanied by serious risks and adverse effects such as infection. Magnetic resonance imaging (MRI) plays a central role in the diagnosis and management of MS, though MRI lesions have displayed only moderate associations with MS clinical outcomes, known as the clinico-radiological paradox. With the advent of machine learning (ML) in healthcare, the predictive power of MRI can be improved by leveraging both traditional and advanced ML algorithms capable of analyzing increasingly complex patterns within neuroimaging data. The purpose of this review was to examine the application of MRI-based ML for prediction of MS disease progression. Studies were divided into five main categories: predicting the conversion of clinically isolated syndrome to MS, cognitive outcome, EDSS-related disability, motor disability and disease activity. The performance of ML models is discussed along with highlighting the influential MRI-derived biomarkers. Overall, MRI-based ML presents a promising avenue for MS prognosis. However, integration of imaging biomarkers with other multimodal patient data shows great potential for advancing personalized healthcare approaches in MS.

Atlas-Based Structural Disconnectomes Are Associated with Cognitive Performance in Brain Tumors.

Background: Brain tumors are associated with impaired cognitive functioning, which may result from disruptions in brain structural connectivity. Estimating structural disconnections is a more advantageous representation of tumor impact and can be performed indirectly through normative brain atlases. Materials and Methods: Using a publicly available dataset of glioma and meningioma patient MRI scans and tumor masks, latent correlations were estimated between measures of structural disconnection and attention-based cognitive functioning. These measures included gray matter (GM) parcel damage, white matter tract damage, GM parcel-to-parcel disconnections, and reaction time (RTI) as part of the Cambridge Neuropsychological Test Automated Battery to assess attention. Results: Preprocessing pipelines with two different methods of minimizing the pathology impact on MRI normalization were utilized: cost-function masking and lesion filling. The results across both pipelines were nearly consistent, with significant correlations mainly found between RTI measures and the damage to the left inferior fronto-occipital and uncinate fasciculus, as well as the left prefrontal-visual disconnections. Conclusions: This alludes to the importance of left-hemispheric prefrontal-visual coupling in attention-based tasks, particularly those involving object- and feature-based attention.

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers.

Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.

Inflammation, oxidative stress and mitochondrial dysfunction in the progression of type II diabetes mellitus with coexisting hypertension.

Introduction: Type II diabetes mellitus (T2DM) is a metabolic disorder that poses a serious health concern worldwide due to its rising prevalence. Hypertension (HT) is a frequent comorbidity of T2DM, with the co-occurrence of both conditions increasing the risk of diabetes-associated complications. Inflammation and oxidative stress (OS) have been identified as leading factors in the development and progression of both T2DM and HT. However, OS and inflammation processes associated with these two comorbidities are not fully understood. This study aimed to explore changes in the levels of plasma and urinary inflammatory and OS biomarkers, along with mitochondrial OS biomarkers connected to mitochondrial dysfunction (MitD). These markers may provide a more comprehensive perspective associated with disease progression from no diabetes, and prediabetes, to T2DM coexisting with HT in a cohort of patients attending a diabetes health clinic in Australia. Methods: Three-hundred and eighty-four participants were divided into four groups according to disease status: 210 healthy controls, 55 prediabetic patients, 32 T2DM, and 87 patients with T2DM and HT (T2DM+HT). Kruskal-Wallis and χ2 tests were conducted between the four groups to detect significant differences for numerical and categorical variables, respectively. Results and discussion: For the transition from prediabetes to T2DM, interleukin-10 (IL-10), C-reactive protein (CRP), 8-hydroxy-2'-deoxyguanosine (8-OHdG), humanin (HN), and p66Shc were the most discriminatory biomarkers, generally displaying elevated levels of inflammation and OS in T2DM, in addition to disrupted mitochondrial function as revealed by p66Shc and HN. Disease progression from T2DM to T2DM+HT indicated lower levels of inflammation and OS as revealed through IL-10, interleukin-6 (IL-6), interleukin-1ß (IL-1ß), 8-OHdG and oxidized glutathione (GSSG) levels, most likely due to antihypertensive medication use in the T2DM +HT patient group. The results also indicated better mitochondrial function in this group as shown through higher HN and lower p66Shc levels, which can also be attributed to medication use. However, monocyte chemoattractant protein-1 (MCP-1) levels appeared to be independent of medication, providing an effective biomarker even in the presence of medication use. The results of this study suggest that a more comprehensive review of inflammation and OS biomarkers is more effective in discriminating between the stages of T2DM progression in the presence or absence of HT. Our results further indicate the usefulness of medication use, especially with respect to the known involvement of inflammation and OS in disease progression, highlighting specific biomarkers during disease progression and therefore allowing a more targeted individualized treatment plan.

Nanomaterial-Based Label-Free Electrochemical Aptasensors for the Detection of Thrombin.

Thrombin plays a central role in hemostasis and its imbalances in coagulation can lead to various pathologies. It is of clinical significance to develop a fast and accurate method for the quantitative detection of thrombin. Electrochemical aptasensors have the capability of combining the specific selectivity from aptamers with the extraordinary sensitivity from electrochemical techniques and thus have attracted considerable attention for the trace-level detection of thrombin. Nanomaterials and nanostructures can further enhance the performance of thrombin aptasensors to achieve high sensitivity, selectivity, and antifouling functions. In highlighting these material merits and their impacts on sensor performance, this paper reviews the most recent advances in label-free electrochemical aptasensors for thrombin detection, with an emphasis on nanomaterials and nanostructures utilized in sensor design and fabrication. The performance, advantages, and limitations of those aptasensors are summarized and compared according to their material structures and compositions.

Vertebral Malalignment among Male Dentists with Work-related Musculoskeletal Pain in the United Arab Emirates.

AIM: Musculoskeletal pain (MSP) is a highly prevalent and debilitating disorder among dentists, often leading to early retirement. Poor ergonomics in the dental practice not only causes MSP, but can also lead to postural deviations as a result of an imbalance in muscle function. The aim of this study was to observe the relationship between MSP and vertebral deviations among male dentists in the United Arab Emirates (UAE). MATERIALS AND METHODS: A total of 60 male dentists participated in this cross-sectional study. Standardized Nordic questionnaire (SNQ) was used to record MSP and PostureScreen Mobile® (PSM) application was used to observe postural deviations in the vertebral regions, including the neck, shoulders, upper back, lower back, and hips. RESULTS: About 83% of participants were suffering from MSP, with the lower back as the most commonly reported region (29%). The pain characteristics reported were chronicity (63%) and dullness (72%). As for postural deviations, the most common site recorded was the neck region (72%). The correlation between the regions of deviation and the regions of pain showed that the shoulder region was more significant than other regions. CONCLUSION: Musculoskeletal pain and vertebral malalignment are common among practicing dentists, probably because of bad postural habits and lack of awareness. Preventive strategies to minimize the risk of developing these health problems are highly recommended. CLINICAL SIGNIFICANCE: Work-related MSP (WRMSP) is a significant health problem among dentists and may be linked to the later development of vertebral deviations. Posture screen analysis is a quick, yet objective postural and movement screening tool in which findings can quickly be rendered to prospective dentists, so that custom postural and functional exercises may be correctly practiced.