Reghann G LaFrance-Corey, Haidara Kherbek, Nimalan Harinesan, Margherita Milone, Naveen K Paramasivan, Pallab Sarker, Andrew M Knight, Carley Karsten, Surendra Dasari, Teerin Liewluck, William J Litchy, Sean J Pittock, John R Mills, Divyanshu Dubey
Cavin-4 was identified as a potential autoantigen for immune-mediated rippling muscle disease (iRMD). To validate this, we developed and tested various immunoassays, including a cell-based assay (CBA), cavin-4 recombinant protein ELISA, and multi-peptide ELISA. Among 19 iRMD patients, all exhibited muscle rippling, and 13 had percussion-induced mounding. All immunoassays demonstrated clinical and analytical specificities greater than 95%. The protein ELISA had the highest sensitivity (94.7%) and specificity (99.9%), outperforming CBA (sensitivity 89.5%, specificity 99.6%) and the multi-peptide ELISA (sensitivity 79.0%, specificity 97.2%). Our results suggest that the cavin-4 protein ELISA is a promising tool for high-throughput clinical testing in iRMD.
{"title":"High-Throughput Immunoassays for Cavin-4 IgG: A Diagnostic Tool for Immune-Mediated Rippling Muscle Disease.","authors":"Reghann G LaFrance-Corey, Haidara Kherbek, Nimalan Harinesan, Margherita Milone, Naveen K Paramasivan, Pallab Sarker, Andrew M Knight, Carley Karsten, Surendra Dasari, Teerin Liewluck, William J Litchy, Sean J Pittock, John R Mills, Divyanshu Dubey","doi":"10.1002/acn3.70012","DOIUrl":"https://doi.org/10.1002/acn3.70012","url":null,"abstract":"<p><p>Cavin-4 was identified as a potential autoantigen for immune-mediated rippling muscle disease (iRMD). To validate this, we developed and tested various immunoassays, including a cell-based assay (CBA), cavin-4 recombinant protein ELISA, and multi-peptide ELISA. Among 19 iRMD patients, all exhibited muscle rippling, and 13 had percussion-induced mounding. All immunoassays demonstrated clinical and analytical specificities greater than 95%. The protein ELISA had the highest sensitivity (94.7%) and specificity (99.9%), outperforming CBA (sensitivity 89.5%, specificity 99.6%) and the multi-peptide ELISA (sensitivity 79.0%, specificity 97.2%). Our results suggest that the cavin-4 protein ELISA is a promising tool for high-throughput clinical testing in iRMD.</p>","PeriodicalId":126,"journal":{"name":"Annals of Clinical and Translational Neurology","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143432120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Asier Erramuzpe, Ane Murueta-Goyena, Antonio Jimenez-Marin, Marian Acera, Sara Teijeira-Portas, Rocío Del Pino, Tamara Fernández-Valle, Ibai Diez, Unai Sainz-Lugarezaresti, Naroa Ibarretxe-Bilbao, Unai Ayala, Maitane Barrenechea, Alberto Cabrera-Zubizarreta, Jesús Cortés, Juan Carlos Gómez-Esteban, Iñigo Gabilondo
Objective: Visual disability in Parkinson's disease (PD) is not fully explained by retinal neurodegeneration. We aimed to delineate the brain substrate of visual dysfunction in PD and its association with retinal thickness.
Methods: Forty-two PD patients and 29 controls underwent 3-Tesla MRI, retinal spectral-domain optical coherence tomography, and visual testing across four domains. Voxel-level associations between gray matter volume and visual outcomes were used to define a visual impairment region (visualROI). Functional connectivity of the visualROI with brain networks was analyzed. Covariance analysis of brain regions associated with retinal thinning (retinalROI) was conducted using hierarchical clustering to develop a model of retinal and brain neurodegeneration linked to disease progression.
Results: The amygdala was the primary component of the visualROI, comprising 32.3% and 14.6% of its left and right volumes. Functional connectivity analysis revealed significant disruptions between the visualROI and medial/lateral visual networks in PD. Covariance analysis identified three clusters within retinalROI: (1) the thalamic nucleus, (2) the amygdala and lateral/occipital visual regions, and (3) frontal regions, including the anterior cingulate cortex and frontal attention networks. Hierarchical clustering suggested a two-phase progression: early amygdala damage (Braak 1-3) disrupting visual network connections, followed by retinal and frontal atrophy (Braak 4-5) exacerbating visual dysfunction.
Interpretation: Our findings support a novel, amygdala-centric two-phase model of visual dysfunction in PD. Early amygdala degeneration disrupts visual pathways, while advanced-stage disconnection between the amygdala and frontal regions and retinal neurodegeneration contributes to further visual disability.
{"title":"Amygdala Neurodegeneration: A Key Driver of Visual Dysfunction in Parkinson's Disease.","authors":"Asier Erramuzpe, Ane Murueta-Goyena, Antonio Jimenez-Marin, Marian Acera, Sara Teijeira-Portas, Rocío Del Pino, Tamara Fernández-Valle, Ibai Diez, Unai Sainz-Lugarezaresti, Naroa Ibarretxe-Bilbao, Unai Ayala, Maitane Barrenechea, Alberto Cabrera-Zubizarreta, Jesús Cortés, Juan Carlos Gómez-Esteban, Iñigo Gabilondo","doi":"10.1002/acn3.70007","DOIUrl":"https://doi.org/10.1002/acn3.70007","url":null,"abstract":"<p><strong>Objective: </strong>Visual disability in Parkinson's disease (PD) is not fully explained by retinal neurodegeneration. We aimed to delineate the brain substrate of visual dysfunction in PD and its association with retinal thickness.</p><p><strong>Methods: </strong>Forty-two PD patients and 29 controls underwent 3-Tesla MRI, retinal spectral-domain optical coherence tomography, and visual testing across four domains. Voxel-level associations between gray matter volume and visual outcomes were used to define a visual impairment region (visualROI). Functional connectivity of the visualROI with brain networks was analyzed. Covariance analysis of brain regions associated with retinal thinning (retinalROI) was conducted using hierarchical clustering to develop a model of retinal and brain neurodegeneration linked to disease progression.</p><p><strong>Results: </strong>The amygdala was the primary component of the visualROI, comprising 32.3% and 14.6% of its left and right volumes. Functional connectivity analysis revealed significant disruptions between the visualROI and medial/lateral visual networks in PD. Covariance analysis identified three clusters within retinalROI: (1) the thalamic nucleus, (2) the amygdala and lateral/occipital visual regions, and (3) frontal regions, including the anterior cingulate cortex and frontal attention networks. Hierarchical clustering suggested a two-phase progression: early amygdala damage (Braak 1-3) disrupting visual network connections, followed by retinal and frontal atrophy (Braak 4-5) exacerbating visual dysfunction.</p><p><strong>Interpretation: </strong>Our findings support a novel, amygdala-centric two-phase model of visual dysfunction in PD. Early amygdala degeneration disrupts visual pathways, while advanced-stage disconnection between the amygdala and frontal regions and retinal neurodegeneration contributes to further visual disability.</p>","PeriodicalId":126,"journal":{"name":"Annals of Clinical and Translational Neurology","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143432114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmed Bayoumi, Joseph A. Thomas, Breanna R. Alonzo, Juan Jimenez, Christopher M. Orlando, Carlos A. Pérez, Khader M. Hasan, Jerry S. Wolinsky, John A. Lincoln