Marker Of Progression Research Articles

Epilepsy in Patients With Primary CNS Lymphoma: Prevalence, Risk Factors, and Prognostic Significance.

Epilepsy is a common comorbidity of brain tumors; however, little is known about the prevalence, onset time, semiology, and risk factors of seizures in primary CNS lymphoma (PCNSL). Our objectives were to determine the prevalence of epilepsy in PCNSL, to identify factors associated with epilepsy, and to investigate the prognostic significance of seizures in PCNSL. We performed an observational, retrospective single-center study at a tertiary neuro-oncology center (2011-2023) including immunocompetent patients with PCNSL and no history of seizures. We collected clinical, imaging, and treatment data; seizure status over the course of PCNSL; and oncological and seizure outcome. The primary outcome was to determine the prevalence of epilepsy. Furthermore, we aimed to identify clinical, radiologic, and treatment-related factors associated with epilepsy. Univariate analyses were conducted using the χ2 test for categorical variables and unpaired t test for continuous variables. Predictors identified in the unadjusted analysis were included in backward stepwise logistic regression models. We included 330 patients, 157 (47.6%) were male, median age at diagnosis was 68 years, and the median Karnofsky Performance Status score was 60. Eighty-three (25.2%) patients had at least 1 seizure from initial diagnosis to the last follow-up, 40 (12.1%) as the onset symptom, 16 (4.8%) during first line of treatment, 27 (8.2%) at tumor progression and 6 (1.8%) while in remission. Focal aware seizures were the most frequent seizure type, occurring in 43 (51.8%) patients. Seizure freedom under antiseizure medication was observed in 97.6% patients. Cortical contact (odds ratio [OR] 8.6, 95% CI 4.2-15.5, p < 0.001) and a higher proliferation index (OR 5.7, 95% CI 1.3-26.2, p = 0.02) were identified as independent risk factors of epilepsy. Patients with PCNSL and epilepsy had a significantly shorter progression-free survival (median progression-free survival 9.6 vs 14.1 months, adjusted hazard ratio 1.4, 95% CI 1.0-1.9, p = 0.03), but not a significantly shorter overall survival (17 vs 44.1 months, log-rank test, p = 0.09). Epilepsy affects a quarter of patients with PCNSL, with half experiencing it at the time of initial presentation and potentially serving as a marker of disease progression. Further research is necessary to assess the broader applicability of these findings because they are subject to the constraints of a retrospective design and tertiary center setting.

Non-Coding RNAs Involved in the Regulation of Signaling Pathways as Possible Markers of Non-Alcoholic Fatty Liver Disease Progression

Non-Coding RNAs Involved in the Regulation of Signaling Pathways as Possible Markers of Non-Alcoholic Fatty Liver Disease Progression

Long-Term Dementia Risk in Parkinson Disease.

It is widely cited that dementia occurs in up to 80% of patients with Parkinson disease (PD), but studies reporting such high rates were published over two decades ago, had relatively small samples, and had other limitations. We aimed to determine long-term dementia risk in PD using data from two large, ongoing, prospective, observational studies. Participants from the Parkinson's Progression Markers Initiative (PPMI), a multisite international study, and a long-standing PD research cohort at the University of Pennsylvania (Penn), a single site study at a tertiary movement disorders center, were recruited. PPMI enrolled de novo, untreated PD participants and Penn a convenience cohort from a large clinical center. For PPMI, a cognitive battery is administered annually, and a site investigator makes a cognitive diagnosis. At Penn, a comprehensive cognitive battery is administered either annually or biennially, and a cognitive diagnosis is made by expert consensus. Interval-censored survival curves were fit for time from PD diagnosis to stable dementia diagnosis for each cohort, using cognitive diagnosis of dementia as the primary end point and Montreal Cognitive Assessment (MoCA) score <21 and Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I cognition score ≥3 as secondary end points for PPMI. In addition, estimated dementia probability by PD disease duration was tabulated for each study and end point. For the PPMI cohort, 417 participants with PD (mean age 61.6 years, 65% male) were followed, with an estimated probability of dementia at year 10 disease duration of 9% (site investigator diagnosis), 15% (MoCA), or 12% (MDS-UPDRS Part I cognition). For the Penn cohort, 389 participants with PD (mean age 69.3 years, 67% male) were followed, with 184 participants (47% of cohort) eventually diagnosed with dementia. The interval-censored curve for the Penn cohort had a median time to dementia of 15 years (95% CI 13-15); the estimated probability of dementia was 27% at 10 years of disease duration, 50% at 15 years, and 74% at 20 years. Results from two large, prospective studies suggest that dementia in PD occurs less frequently, or later in the disease course, than previous research studies have reported.

Evolving cartilage strain with pain progression and gait: a longitudinal study post-ACL reconstruction at six and twelve months.

Anterior cruciate ligament (ACL) injuries are prevalent musculoskeletal conditions often resulting in long-term degenerative outcomes such as osteoarthritis (OA). Despite surgical advances in ACL reconstruction, a significant number of patients develop OA within ten years post-surgery, providing a patient population that may present early markers of cartilage degeneration detectable using noninvasive imaging. This study aims to investigate the temporal evolution of cartilage strain and relaxometry post-ACL reconstruction using displacement under applied loading MRI and quantitative MRI. Specifically, we examined the correlations between MRI metrics and pain, as well as knee loading patterns during gait, to identify early candidate markers of cartilage degeneration. Twenty-five participants (female/male = 15/10; average age = 25.6 yrs) undergoing ACL reconstruction were enrolled in a prospective longitudinal cohort study between 2022 and 2023. MRI scans were conducted at 6- and 12-months post-surgery, assessing T2, T2*, and T1ρ relaxometry values, and intratissue cartilage strain. Changes in pain were evaluated using standard outcome scores, and gait analysis assessed the knee adduction moment (KAM). Regressions were performed to evaluate relationships between MRI metrics in cartilage contact regions, patient-reported pain, and knee loading metrics. Increases in axial and transverse strains in the tibial cartilage were significantly correlated with increased pain, while decreases in shear strain were associated with increased pain. Changes in strain metrics were also significantly related to KAM at12 months. Changes in cartilage strain and relaxometry are related to heightened pain and altered knee loading patterns, indicating potential early markers of osteoarthritis progression. These findings underscore the importance of using advanced MRI for early monitoring in ACL-reconstructed patients to optimize treatment outcomes, while also highlighting KAM as a modifiable intervention through gait retraining that may positively impact the evolution of cartilage health and patient pain. Increased axial and transverse strains in the tibial cartilage from 6 to 12 months post-ACL reconstruction were significantly correlated with increased pain, suggesting evolving changes in cartilage biomechanical properties over time.Decreases in shear strain in inner femoral and central tibial cartilage regions were linked to increased pain, indicating alterations in joint loading patterns.Decreases in shear strain in the inner femoral cartilage were significantly associated with decreased 12-month knee adduction moment (KAM), a surrogate for medial cartilage knee loading during walking.

Enhancing early Parkinson’s disease detection through multimodal deep learning and explainable AI: insights from the PPMI database

Parkinson’s is the second most common neurodegenerative disease, affecting nearly 8.5M people and steadily increasing. In this research, Multimodal Deep Learning is investigated for the Prodromal stage detection of Parkinson's Disease (PD), combining different 3D architectures with the novel Excitation Network (EN) and supported by Explainable Artificial Intelligence (XAI) techniques. Utilizing data from the Parkinson’s Progression Markers Initiative, this study introduces a joint co-learning approach for multimodal fusion, enabling end-to-end training of deep neural networks and facilitating the learning of complementary information from both imaging and clinical modalities. DenseNet with EN outperformed other models, showing a substantial increase in accuracy when supplemented with clinical data. XAI methods, such as Integrated Gradients for ResNet and DenseNet, and Attention Heatmaps for Vision Transformer (ViT), revealed that DenseNet focused on brain regions believed to be critical to prodromal pathophysiology, including the right temporal and left pre-frontal areas. Similarly, ViT highlighted the lateral ventricles associated with cognitive decline, indicating their potential in the Prodromal stage. These findings underscore the potential of these regions as early-stage PD biomarkers and showcase the proposed framework's efficacy in predicting subtypes of PD and aiding in early diagnosis, paving the way for innovative diagnostic tools and precision medicine.

SVA Regulation of Transposable Element Clustered Transcription within the Major Histocompatibility Complex Genomic Class II Region of the Parkinson's Progression Markers Initiative.

SINE-VNTR-Alu (SVA) retrotransposons can regulate expression quantitative trait loci (eQTL) of coding and noncoding genes including transposable elements (TEs) distributed throughout the human genome. Previously, we reported that expressed SVAs and human leucocyte antigen (HLA) class II genotypes on chromosome 6 were associated significantly with Parkinson's disease (PD). Here, our aim was to follow-up our previous study and evaluate the SVA associations and their regulatory effects on the transcription of TEs within the HLA class II genomic region. We reanalyzed the transcriptome data of peripheral blood cells from the Parkinson's Progression Markers Initiative (PPMI) for 1530 subjects for TE and gene RNAs with publicly available computing packages. Four structurally polymorphic SVAs regulate the transcription of 20 distinct clusters of 235 TE loci represented by LINES (37%), SINES (28%), LTR/ERVs (23%), and ancient transposon DNA elements (12%) that are located in close proximity to HLA genes. The transcribed TEs were mostly short length, with an average size of 389 nucleotides. The numbers, types and profiles of positive and negative regulation of TE transcription varied markedly between the four regulatory SVAs. The expressed SVA and TE RNAs in blood cells appear to be enhancer-like elements that are coordinated differentially in the regulation of HLA class II genes. Future work on the mechanisms underlying their regulation and potential impact is essential for elucidating their roles in normal cellular processes and disease pathogenesis.

An efficient ranking-based ensembled multiclassifier for neurodegenerative diseases classification using deep learning.

Neurodegenerative diseases are group of debilitating and progressive disorders that primarily affect the structure and functions of nervous system, leading to gradual loss of neurons and subsequent decline in cognitive, and behavioral activities. The two frequent diseases affecting the world's significant population falling in the above category are Alzheimer's disease (AD) and Parkinson's disease (PD). These disorders substantially impact the quality of life and burden healthcare systems and society. The demographic characteristics, and machine learning approaches have now been employed to diagnose these illnesses; however, they possess accuracy limitations. Therefore, the authors have developed ranking-based ensemble approach based on the weighted strategy of deep learning classifiers. The whole modeling procedure of the proposed approach incorporates three phases. In phase I, preprocessing techniques are applied to clean the noise in datasets to make it standardized according to deep learning models as it significantly impacts their performance. In phase II, five deep learning models are selected for classification and calculation of prediction results. In phase III, a ranking-based ensemble approach is proposed to ensemble the results of the five models after calculating the ranks and weights of them. In addition, the Magnetic Resonance Imaging (MRI) datasets named Alzheimer's Disease Neuroimaging Initiative (ADNI) for AD classification and Parkinson's Progressive Marker Initiative (PPMI) for PD classification are selected to validate the proposed approach. Furthermore, the proposed method achieved the classification accuracy on AD- Cognitive Normals (CN) at 97.89%, AD- Mild Cognitive Impairment (MCI) at 99.33% and CN-MCI at 99.44% and on PD-CN at 99.22%, PD- Scans Without Evidence of Dopaminergic Effect (SWEDD) at 97.56% and CN-SWEDD at 98.22% respectively. Also, the multi-class classification shows the promising accuracy of 97.18% for AD and 97.85% for PD for the proposed framework. The findings of the study show that the proposed deep learning-based ensemble technique is competitive for AD and PD prediction in both multiclass and binary class classification. Furthermore, the proposed approach enhances generalization performance in diagnosing neurodegenerative diseases and performs better than existing approaches.

Socioenvironmental Factors are Associated With Dopamine Transporter Availability in Healthy Individuals but not in Parkinson's Disease.

Social factors can influence the brain's dopaminergic function. This study investigated the relationship between socioenvironmental factors and dopamine transporter (DaT) availability in healthy individuals (n = 74) and those with Parkinson's disease (PD) (n = 240). All single photon emission computed tomography (SPECT) DaT data and clinical data used in this study were obtained from the Parkinson's Progression Markers Initiative (PPMI) dataset. Socioenvironmental data was obtained from Social Explorer analyses of the American Community Survey (2014-2018) using the residential ZIP codes of the subjects available in the PPMI dataset. Participants resided in 302 ZIP code tabulation areas across 38 U.S. states. In healthy individuals, DaT signals were significant and negatively correlated in the caudate with median household income (r = -0.27, P = 0.02) and educational level of the living area (r = -0.23, P = 0.04), but not significant in the putamen (r = -0.21, P = 0.08; r = -0.11, P = 0.37 respectively). Also, there was a significant positive correlation between DaT signals in caudate and poverty rates (r = 0.29, P = 0.01), but not in the putamen (r = 0.16, P = 0.19) in healthy subjects. No significant associations were observed in the PD group for any variables. The study findings suggest that socioenvironmental factors, such as median household income, education level, and poverty rate, are significantly associated with DaT availability in the caudate of healthy individuals but not in those with PD. This indicates that PD might disrupt the connection between the social environment and dopaminergic function. These results underscore the importance of considering socioenvironmental variables when studying dopaminergic function in the human brain.

Multi-modality radiomics of conventional T1 weighted and diffusion tensor imaging for differentiating Parkinson’s disease motor subtypes in early-stages

This study aimed to develop and validate a multi-modality radiomics approach using T1-weighted and diffusion tensor imaging (DTI) to differentiate Parkinson's disease (PD) motor subtypes, specifically tremor-dominant (TD) and postural instability gait difficulty (PIGD), in early disease stages. We analyzed T1-weighted and DTI scans from 140 early-stage PD patients (70 TD, 70 PIGD) and 70 healthy controls from the Parkinson's Progression Markers Initiative database. Radiomics features were extracted from 16 brain regions of interest. After harmonization and feature selection, four machine learning classifiers were trained and evaluated for both three-class (HC vs TD vs PIGD) and binary (TD vs PIGD) classification tasks. The light gradient boosting machine (LGBM) classifier demonstrated the best overall performance. For the three-class classification, LGBM achieved an accuracy of 85% and an area under the receiver operating characteristic curve (AUC) of 0.94 using combined T1 and DTI features. In the binary classification task, LGBM reached an accuracy of 95% and AUC of 0.95. Key discriminative features were identified in the Thalamus, Amygdala, Hippocampus, and Substantia Nigra for the three-group classification, and in the Pallidum, Amygdala, Hippocampus, and Accumbens for binary classification. The combined T1 + DTI approach consistently outperformed single-modality classifications, with DTI alone showing particularly low performance (AUC 0.55–0.62) in binary classification. The high accuracy and AUC values suggest that this approach could significantly improve early diagnosis and subtyping of PD. These findings have important implications for clinical management, potentially enabling more personalized treatment strategies based on early, accurate subtype identification.

Modeling of Parkinson's Disease Progression and Implications for Detection of Disease Modification in Treatment Trials.

Objectively measuring Parkinson's disease (PD) signs and symptoms over time is critical for the successful development of treatments aimed at halting the disease progression of people with PD. To create a clinical trial simulation tool that characterizes the natural history of PD progression and enables a data-driven design of randomized controlled studies testing potential disease-modifying treatments (DMT) in early-stage PD. Data from the Parkinson's Progression Markers Initiative (PPMI) were analyzed with nonlinear mixed-effect modeling techniques to characterize the progression of MDS-UPDRS part I (non-motor aspects of experiences of daily living), part II (motor aspects of experiences of daily living), and part III (motor signs). A clinical trial simulation tool was built from these disease models and used to predict probability of success as a function of trial design. MDS-UPDRS part III progresses approximately 3 times faster than MDS-UPDRS part II and I, with an increase of 3 versus 1 points/year. Higher amounts of symptomatic therapy is associated with slower progression of MDS-UPDRS part II and III. The modeling framework predicts that a DMT effect on MDS-UPDRS part III could precede effect on part II by approximately 2 to 3 years. Our clinical trial simulation tool predicted that in a two-year randomized controlled trial, MDS-UPDRS part III could be used to evaluate a potential novel DMT, while part II would require longer trials of a minimum duration of 3 to 5 years underscoring the need for innovative trial design approaches including novel patient-centric measures.

Rest Tremor in Parkinson's Disease Is Associated with Ipsilateral Striatal Dopamine Transporter Binding.

The cardinal motor symptoms of Parkinson's disease (PD) include rigidity, bradykinesia, and rest tremor. Rigidity and bradykinesia correlate with contralateral nigrostriatal degeneration and striatal dopamine deficit, but association between striatal dopamine function and rest tremor has remained unclear. The aim of this study was to investigate the possible link between dopamine function and rest tremor using Parkinson's Progression Markers Initiative dataset, the largest prospective neuroimaging cohort of patients with PD. Clinical, [123I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane ([123I]FP-CIT) single photon emission computed tomography (SPECT), and structural magnetic resonance imaging data from 354 early PD patients and 166 healthy controls were included in this study. We employed a novel approach allowing nonlinear registration of individual scans accurately to a standard space and voxelwise analyses of the association between motor symptoms and striatal dopamine transporter (DAT) binding. Severity of both rigidity and bradykinesia was negatively associated with contralateral striatal DAT binding (PFWE < 0.05 [FWE, family-wise error corrected]). However, rest tremor amplitude was positively associated with increased ipsilateral DAT binding (PFWE < 0.05). The association between rest tremor and binding remained the same controlling for Hoehn & Yahr stage, Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III score, bradykinesia-rigidity score, or motor phenotype. The association between rest tremor and binding was independent of bradykinesia-rigidity and replicated using 2-year follow-up data (PFWE < 0.05). In agreement with the existing literature, we did not find a consistent association between rest tremor and contralateral dopamine defect. However, our results demonstrate a link between rest tremor and increased or less decreased ipsilateral DAT binding. Our findings provide novel information about the association between dopaminergic function and parkinsonian rest tremor. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Epithelial-Mesenchymal Transition Indexes in Triple-Negative Breast Cancer Progression and Metastases.

Background Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer characterized by the lack of expression of estrogen and progesterone receptors and the absence of HER2 protein overexpression or gene amplification. How TNBC becomes so aggressive at the molecular level is not yet fully understood. The epithelial-mesenchymal transition (EMT) has been increasingly recognized as playing a pivotal role in cancer progression and metastasis. This study aimed to elucidate the connection between TNBC progression with EMT-related markers, including vimentin, beta-catenin, and E-cadherin. Methodology Rigorous immunohistochemical analysis was employed to assess the expression of vimentin, beta-catenin, and E-cadherin in primary tumors, tumor buds, and lymph node metastases (LNMs) from 137 cases with an invasive ductal carcinoma triple-negative phenotype diagnosed between 2018 and 2024. The EMT index, which was especially important in our work, is the sum of vimentin and beta-catenin expression divided by that of E-cadherin. Estimated Pearson correlation, multiple linear regression, and Kruskal-Wallis tests were used to determine the relationships of the EMT index with tumor buds and tumor-infiltrating lymphocytes (TILs). Results Vimentin highly correlated within separate regions of interest with Pearson correlation ranging from 0.90 to 0.92 (p < 0.001). Strong negative correlations between E-cadherin and vimentin (r = -0.81 to - 0.89, p < 0.001) showed its role in preserving the epithelial phenotype. The presence of tumor buds, aggregates, or clusters of cancer cells shed from the primary tumor mass invading the connective tissue showed very strong associations with the EMT index (r = 0.91, p < 0.001). Its presence is suggestive of aggressive disease and may identify a high-risk subpopulation that may benefit from more active surveillance or adjuvant treatment. Similarly, TILs correlated inversely with the EMT index (r = -0.90, p < 0.001). The most significant predictor of the EMT index, i.e., vimentin, had a model R-squared value of 1.000 in the regression analysis. Conclusions This study brings to light the importance of EMT-related markers in TNBC progression, with special emphasis on tumor buds as possible prognostic indicators for aggressive disease. The negative correlation of TILs with the EMT index indicates that an effective immune response could antagonize EMT-mediated tumor progression. These results suggest that EMT-based treatments in TNBC should be designed from a multimarker perspective by including interactions among several markers to optimize predictions and therapeutics. The results hold the potential to set future research directions and actionable outcomes that could influence clinical utility in the battle against TNBC.

Association of Platelet Count with Severity of Chronic Liver Disease

Introduction: Chronic liver disease (CLD) is a significant global health concern, encompassing a range of liver pathologies with varying degrees of severity. This cross- sectional study aims to investigate the relationship between platelet count and the severity of CLD. Platelet count is a readily available and cost-effective clinical parameter that may serve as a valuable prognostic marker for CLD progression.  Materials and Method: The study included 122 patients in Chitwan Medical College Teaching Hospital (CMCTH) diagnosed with CLD during one year starting from March 2022. Data were collected using the proforma specially designed for this study. Parameters like age and gender, as well as brief clinical data, including liver function tests, imaging findings, and platelet count, were collected. Patients were then classified into different stages of severity based on established criteria, i.e. child Pugh Score system (CTP score). Statistical analyses were performed using a statistical package for the social science (SPSS); statistical program version 20.0, to explore the association between platelet count and CLD severity.  Results: The majority of patients who had chronic liver disease were found to be high in the age group of 40-60 years. Thrombocytopenia was found in 72.1% of chronic liver disease patients. After classifying the severity of chronic liver disease, it was found that 55.7% of patients had a severe disease that was CTP-C, 36.1% were found to be CTP-B and 8.2% were CTP-A. In patients with normal platelet count, 44.1% had CTP-B, 41.2 had CTPC and 14.7% had CTP-A score. In patients with thrombocytopenia, 61.4% had CTP-C, 33.0% had CTP- B and 5.7% had CTP-A score.  Conclusion: This study provides a valuable snapshot of the potential association between platelet count and the severity of chronic liver disease. The prevalence of thrombocytopenia across different disease stages underscores its clinical relevance. The platelet count is decreased in chronic liver disease, however, it cannot be defined as the sole predictor for the severity of CLD.

Shared and distinct cortical morphometric alterations in five neuropsychiatric symptoms of Parkinson’s disease

Neuropsychiatric symptoms (including anxiety, depression, apathy, impulse–compulsive behaviors and hallucinations) are among the most common non-motor features of Parkinson’s disease. Whether these symptoms should be considered as a direct consequence of the pathophysiologic mechanisms of Parkinson’s disease is controversial. Morphometric similarity network analysis and epicenter mapping approach were performed on T1-weighted images of 505 patients with Parkinson’s disease and 167 age- and sex-matched healthy participants from Parkinson’s Progression Markers Initiative database to reveal the commonalities and specificities of distinct neuropsychiatric symptoms. Abnormal cortical co-alteration pattern in patients with neuropsychiatric symptoms was in somatomotor, vision and frontoparietal regions, with epicenters in somatomotor regions. Apathy, impulse–compulsive behaviors and hallucinations shares structural abnormalities in somatomotor and vision regions, with epicenters in somatomotor regions. In contrast, the cortical abnormalities and epicenters of anxiety and depression were prominent in the default mode network regions. By embedding each symptom within their co-alteration space, we observed a cluster composed of apathy, impulse–compulsive behaviors and hallucinations, while anxiety and depression remained separate. Our findings indicate different structural mechanisms underlie the occurrence and progression of different neuropsychiatric symptoms. Based upon these results, we propose that apathy, impulse-compulsive behaviors and hallucinations are directly related to damage of motor circuit, while anxiety and depression may be the combination effects of primary pathophysiology of Parkinson’s disease and psychosocial causes.

Exploring causal effects of sarcopenia on risk and progression of Parkinson disease by Mendelian randomization

Previous observational studies suggested that sarcopenia is associated with Parkinson disease (PD), but it is unclear whether this association is causal. The objective of this study was to examine causal associations between sarcopenia-related traits and the risk or progression of PD using a Mendelian randomization (MR) approach. Two-sample bidirectional MR analyses were conducted to evaluate causal relationships. Genome-wide association study (GWAS) summary statistics for sarcopenia-related traits, including right handgrip strength (n = 461,089), left handgrip strength (n = 461,026), and appendicular lean mass (n = 450,243), were retrieved from the IEU OpenGWAS database. GWAS data for the risk of PD were derived from the FinnGen database (4235 cases; 373,042 controls). Summary-level data for progression of PD, including progression to Hoehn and Yahr stage 3, progression to dementia, and development of levodopa-induced dyskinesia, were obtained from a recent GWAS publication on progression of PD in 4093 patients from 12 longitudinal cohorts. Significant causal associations identified in MR analysis were verified through a polygenic score (PGS)-based approach and pathway enrichment analysis using genotype data from the Parkinson’s Progression Markers Initiative. MR results supported a significant causal influence of right handgrip strength (odds ratio [OR] = 0.152, 95% confidence interval [CI] = 0.055–0.423, adjusted P = 0.0036) and appendicular lean mass (OR = 0.597, 95% CI = 0.440–0.810, adjusted P = 0.0111) on development of levodopa-induced dyskinesia. In Cox proportional hazard analysis, higher PGSs for right handgrip strength (hazard ratio [HR] = 0.225, 95% CI = 0.095–0.530, adjusted P = 0.0019) and left handgrip strength (HR = 0.303, 95% CI = 0.121–0.59, adjusted P = 0.0323) were significantly associated with a lower risk of developing levodopa-induced dyskinesia, after adjusting for covariates. Pathway enrichment analysis revealed that genome-wide significant single-nucleotide polymorphisms for right handgrip strength were substantially enriched in biological pathways involved in the control of synaptic plasticity. This study provides genetic evidence of the protective role of handgrip strength or appendicular lean mass on the development of levodopa-induced dyskinesia in PD. Sarcopenia-related traits can be promising prognostic markers for levodopa-induced dyskinesia and potential therapeutic targets for preventing levodopa-induced dyskinesia in patients with PD.

Diffusion tensor metrics, motor and non-motor symptoms in de novo Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neurons' degeneration of the substantia nigra, presenting with motor and non-motor symptoms. We hypothesized that altered diffusion metrics are associated with clinical symptoms in de novo PD patients. Fractional Anisotropy (FA) and Mean (MD), Axial (AD), and Radial Diffusivity (RD) were assessed in 55 de novo PD patients (58.62 ± 9.85 years, 37 men) and 55 age-matched healthy controls (59.92 ± 11.25 years, 34 men). Diffusion-weighted images and clinical variables were collected from the Parkinson's Progression Markers Initiative study. Tract-based spatial statistics were used to identify white matter (WM) changes, and fiber tracts were localized using the JHU-WM tractography atlas. Motor and non-motor symptoms were evaluated in patients. We observed higher FA values and lower RD values in patients than controls in various fiber tracts (p-TFCE < 0.05). No significant MD or AD difference was observed between groups. Diffusion metrics of several regions significantly correlated with non-motor (state and trait anxiety and daytime sleepiness) and axial motor symptoms in the de novo PD group. No correlations were observed between diffusion metrics and other clinical symptoms evaluated. Our findings suggest microstructural changes in de novo PD fiber tracts; however, limited associations with clinical symptoms reveal the complexity of PD pathology. They may contribute to understanding the neurobiological changes underlying PD and have implications for developing targeted interventions. However, further longitudinal research with larger cohorts and consideration of confounding factors are necessary to elucidate the underlying mechanisms of these diffusion alterations in de novo PD.

The Faces of "Too Late"-A Surprisingly Progressive Cohort of "Stable" Relapsing Remitting Multiple Sclerosis Patients.

Background and Objectives: Although available therapies have changed the natural evolution of multiple sclerosis (MS), in time some patients assume a progressive course and no longer respond to treatment. There is no definitive clinical or laboratory parameter to certify MS progression from relapsing remitting MS (RRMS) to secondary progressive MS (SPMS) in early phases of transition. Our study aims to evaluate the value of clinical parameters and serum neurofilament light chain levels (sNfLs) as early warning signs of conversion to SPMS. Materials and Methods: The Expanded Disability Status Scale (EDSS), Nine-Hole Peg Test (9HPT), 25-foot walk test (25FWT) and Symbol Digit Modalities Test (SDMT) were evaluated at 12 months apart in a cohort of 83 RRMS treated patients. sNfLs were evaluated at the second time point. Results: sNfLs correlate with EDSS and SDMT, with EDSS change and disease duration. Clinical parameters correlate among themselves and perform well in supporting the diagnosis of SPMS in logistic regression and ROC curves analysis. Eighty percent of the RRMS patients in our study (of which 65% are treated with high-efficacy disease-modifying drugs) showed some type of progression independent of relapses (PIRA) after 12 months, with one in five patients experiencing isolated cognitive worsening and almost two-thirds some type of motor worsening. We found no differences in terms of progression between patients treated with platform drugs versus high-efficacy drugs. Conclusions: An elevated level of progression independent of relapses (PIRA) was found in our cohort, with high-efficacy drugs providing no supplementary protection. As sNfL levels were correlated with the progression of EDSS (the main clinical progression marker), they may be considered potential prognostic markers, but further studies are necessary to precisely define their role in this direction. The lack of early sensitive markers for risk of progression may contribute to therapeutic delay and failure.

Trajectories of behavior and social cognition in behavioral variant frontotemporal dementia and primary psychiatric disorders: A call for better operationalization of socioemotional changes.

Behavioral variant frontotemporal dementia (bvFTD) and primary psychiatric disorders (PPD), such as mood, psychotic, and autism spectrum disorders, share similar clinical characteristics of behavior and social cognition. Better understanding of clinical progression in bvFTD and PPD is essential for adequate disease monitoring and trial design. In this longitudinal study (N = 89), patients with bvFTD and PPD with at least one follow-up assessment were included from the Social Brain Project of the Alzheimer Center Amsterdam. Behavioral change and social cognitive decline were assessed via informant-rated questionnaires (Cambridge Behavioral Inventory-Revised, Frontal Behavioral Inventory [FBI], Stereotypy Rating Inventory, Frontotemporal Dementia Rating Scale, Revised Self-Monitoring Scale [RSMS]-caregiver) and patient assessment (Ekman 60-Faces Test, RSMS-patient, Emotional Contagion Scale). Clinical trajectories (median = 1.4 years, interquartile range = 1.0-2.2) were examined using linear mixed models. In a subsample, associations with baseline serum neurofilament light (sNfL) were examined. At baseline, behavioral and social cognitive symptoms were similar between diagnosis groups, except for poorer emotion recognition in bvFTD. Over time, behavioral symptoms worsened in bvFTD, whereas most measures remained stable and the FBI improved in PPD. Regarding social cognition, emotion recognition and caregiver-reported socioemotional sensitivity worsened in bvFTD and remained stable in PPD. Patient-reported social cognitive measures did not change over time. Higher sNfL was associated with faster behavioral change. Trajectories of behavior and social cognition differentiate bvFTD from PPD, provided that social cognition is not patient-reported. Therefore, we stress the need to optimize longitudinal social cognitive assessment in bvFTD. sNfL may be a useful prognostic marker of behavioral progression in neuropsychiatric populations.

Risk factors and cognitive domain markers of progression in subjective cognitive decline.

We evaluated risk factor differences and cognitive domain markers associated with progression in subjects with subjective cognitive decline (SCD) at baseline from the NYU Alzheimer Disease Research Center. We included SCD non-decliners (n = 27), who remained stable, and decliners (n = 24), who progressed to mild cognitive impairment or worse, between the second to sixth yearly follow-up visits. Adjusted mixed-effects models examined group differences and associations between demographic, APOE status, psychometric test performance and comorbidities with longitudinal-decline. Overall, mean (SD) age was 67.4 (9.2) and total follow-up time was 5.1 (1.8) years. Lower education (14.9 (3.2) vs. 17.3 (2.1)), Hispanic ethnicity (50.0% vs. 11.0%), and hypercholesterolemia (aOR: 6.67), p ≤ 0.05 for all, were risk factors for progression in SCD, whereas APOE status was not. Notably, SCD decliners were at increased risk for both amnestic and non-amnestic cognitive-decline with psychometric changes in memory, executive, and language domains (p < 0.001 for all). These findings inform further work on SCD outcomes and related biomarkers, as well as preventive studies that target modifiable risk factors for SCD progression.

Novel benzenesulfonamides as dual VEGFR2/FGFR1 inhibitors targeting breast cancer: Design, synthesis, anticancer activity and in silico studies

In the current study, a new series of benzenesulfonamides 6a-r was designed and synthesized as dual VEGFR-2 and FGFR1 kinase inhibitors with anti-cancer activity. The 4-trifluoromethyl benzenesulfonamide 6l exhibited the highest dual VEGFR-2/FGFR1 inhibitory activity with IC50 values of 0.025 and 0.026 µM, respectively. It showed a higher activity than sorafenib and staurosporine by 1.8- and 1.3-fold, respectively. Furthermore, compound 6l was further tested on EGFR and PDGFR-β kinases showing IC50 values of 0.106 and 0.077 µM, respectively. The target compounds were tested for their anticancer activity against NCI-60 panel of cancer cell lines at 10 µM concentration, where compound 6l displayed the highest mean growth inhibition percent % (GI%) of 60.38%. Compounds 6a, 6b, 6e, 6f, 6h-l, and 6n-r revealed promising GI% on breast cancer cell lines (MCF-7, T-47D, and MDA-MB-231), and were subjected to IC50 determination on these cell lines. The tested compounds showed a higher activity on T-47D and MCF-7 cell lines over MDA-MB-231 cell line compared to the used reference standard; sorafenib. Compounds 6e, 6h-j, 6l and 6o revealed IC50 values ≤ 20 µM against T-47D cell line, furthermore, they were found to be non-cytotoxic on Vero normal cell line. Furthermore, the effect of the most active compounds 6i, and 6l in T-47D cells on cell cycle analysis progression, cell apoptosis, and apoptosis markers was investigated. Both compounds arrested cell cycle progression at G1 phase, furthermore, they enhanced early and late apoptosis, as well as necrosis. The capability of compounds 6i, and 6l to induce apoptosis was further confirmed by their ability to raise BAX/BCl-2 ratio and caspase-3 level in the treated cells. Cell migration assay revealed that both compounds 6i and 6l have anti-migratory effects compared to control T-47D cells after 24, and 48 h. Molecular docking studies for compounds 6a-r on VEGFR-2 and FGFR1 binding sites showed that they exhibit an analogous binding mode in both target kinases which agrees with that of type II kinase inhibitors.