https://repositorio.fleni.org.ar/xmlui/handle/123456789/807 Sun, 05 Apr 2026 20:00:49 GMT 2026-04-05T20:00:49Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/1484 Capacity building in dementia research: insights from the World Young Leaders in Dementia Morello-García, Florentina; Corvalán, Nicolás; Llibre-Guerra, Jorge; Arruabarrena, Micaela; Clarens, María Florencia; Keller, Greta; De Los Santos, Loana; Martin, María Eugenia; Schaffer Aguzzoli, Cristiano; Allegri, Ricardo Francisco; Amaral, Livia; Ardohain Cristalli, Carolina; Bellaver, Bruna; Ngozi Best, Merci; Bloomquist, Madeleine; Chen, Kevin; Surace, Ezequiel Ignacio; Wilks, Hannah; Zimmer, Eduardo; Crivelli, Lucía; Hernández, Micaela Anahí; Magrath Guimet, Nahuel Early-career researchers from low- and middle-income countries face systemic barriers to professional development and leadership growth. This article presents results from an initiative led by the World Young Leaders in Dementia (WYLD), including a leadership-focused session at the Alzheimer's Association International Conference 2024 and a global survey completed by 130 dementia researchers from 17 countries. The survey explored five capacity-building domains critical for leadership development. Over half of the survey respondents stated that scientific research in their country was not prioritized in public policy. Additionally, only 39% report holding full-time academic positions. The most cited challenges included lack of funding sources, training opportunities, and physical workspace. These findings highlight the urgent need to invest in research, training, and infrastructure to support future scientific leaders. As dementia incidence rises, prioritizing capacity building is essential to ensure global equity in research. HIGHLIGHTS: Early-career dementia researchers face major barriers, especially in LMICs. A networking session and a global survey explored capacity-building needs in dementia research. Key obstacles: lack of funding, training, workspace, and protected research time. Leadership development is a critical component of sustainable research capacity. Mon, 01 Dec 2025 00:00:00 GMT https://repositorio.fleni.org.ar/xmlui/handle/123456789/1484 2025-12-01T00:00:00Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/1478 Thyrotropin Modulates Calcium Handling and Contractility in Adult Cardiac Myocytes Sepúlveda, Marisa; Racioppi, Florencia; Burgos, Juan Ignacio; Murillo, Alexandra; Fernandez-Ruocco, Julieta; Gonano, Luis; Neiman, Gabriel; Miriuka, Santiago Gabriel; Fellet, Andrea; Casis, Oscar; Medei, Emiliano; Colareda, German; Vila Petroff, Martín Hypothyroidism is an independent risk factor for cardiovascular disease and, if chronically sustained, leads to myocardial contractile dysfunction resulting in heart failure. However, the subcellular mechanisms underlying contractile dysfunction are not completely understood. It has been suggested that abnormal gene expression in the myocardium triggered by decreased thyroid hormone plasma levels reduces the expression of sarco-/endoplasmic reticulum calcium ion (Ca2+) adenosine triphosphatase (SERCA), resulting in slower sarcoplasmic reticulum (SR) Ca2+ uptake, a decrease in SR Ca2+ content and reduced SR Ca2+ release that mediates contractile dysfunction [1]. However, in addition to the decrease in thyroid hormones, hypothyroidism is characterised by increased thyrotropin (TSH) levels. Interestingly, subclinical hypothyroidism, which is defined by increased TSH with normal T3 and T4 levels, is also associated with altered contractile dysfunction [2], suggesting that TSH could contribute to reduced contractility observed in hypothyroidism. However, whether and how TSH impacts adult cardiac myocyte contractile function has never been addressed. This study aims to investigate whether TSH affects Ca2+ dynamics, Ca2+ handling protein expression, and contractile function in adult rat cardiac myocytes and in human-induced pluripotent stem cell-derived cardiac myocytes. Wed, 15 Oct 2025 00:00:00 GMT https://repositorio.fleni.org.ar/xmlui/handle/123456789/1478 2025-10-15T00:00:00Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/1467 A RoadMap for Neuropsychological Assessment of the Right Temporal Variant of Frontotemporal Dementia (rtvFTD): Case Studies and Practical Applications De Los Santos, Loana; Morello García, Florentina; Ardohain Cristalli, Carolina Agata; Tabernero, María Eugenia; Clarens, María Florencia; Crivelli, Lucía; Magrath Guimet, Nahuel Background: The right temporal variant of frontotemporal dementia (rtvFTD) is a neurodegenerative condition characterized by progressive atrophy of the right anterior temporal lobe (rATL), significantly impairing semantic-pragmatic comprehension and social cognition. In Latin America, although magnetic resonance imaging (MRI) and computed tomography (CT) are widely available, there is still a need for neuropsychological tools to assess cognitive and social changes in rtvFTD. Currently, this condition remains a subject of debate due to diagnostic challenges stemming from a lack of consensus in terminology and variability in assessment tools (Ulugut et al., 2024; Younes et al., 2022). The aim of this study is to propose neuropsychological tools to characterize both the profile and cognitive changes of rtvFTD and present a structured roadmap to help differentiate rtvFTD from other dementias. Additionally, this roadmap contributes to the design of personalized therapeutic interventions. Method: Two clinical cases diagnosed with rtvFTD at FLENI (Buenos Aires, Argentina) were studied. Both patients underwent standard neuropsychological evaluations focused on semantic-pragmatic language and social cognition, using locally adapted tests for naming, semantic verbal fluency, semantic association, prosody, pragmatics, and speech intentionality. Findings were correlated with MRI scans to validate the proposed roadmap. Result: The patients exhibited severe deficits in naming, semantic verbal fluency, semantic-pragmatic impairments, and alterations in emotional prosody, theory ofmind, and facial emotion recognition. Executive attentional systems, visuospatial abilities, and memory remained preserved. These findings aligned with patterns of atrophy and hypometabolism observed in the rATL and were consistent with current literature on the neuropsychological and clinical profiles of the rtvFTD. Figure 1 shows the proposed neuropsychological assessment approach, using a regionally adapted cognitive battery designed to capture rtvFTD symptoms in Spanish-speaking populations and to guide differentiation from other dementia variants. Conclusion: This roadmap provides a practical guide that includes neuropsychological tests for the assessment of rtvFTD, particularly in Spanish-speaking countries. By integrating evaluations targeting semantic-pragmatic language and social cognition, the roadmap allows for precise differentiation of rtvFTD from other frontotemporal dementia variants. Furthermore, it contributes to the development of personalized therapeutic interventions, aiming to improve patient quality of life and support clinical practices in Spanish-speaking regions. Wed, 01 Jan 2025 00:00:00 GMT https://repositorio.fleni.org.ar/xmlui/handle/123456789/1467 2025-01-01T00:00:00Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/1463 Comparative diagnostic performance of artificial intelligence models in structural MRI for schizophrenia: A systematic review and meta-analysis Kotochinsky, Martin; Oliveira Fonseca, Pandora Eloa; Ramirez Lopera, Veronica; Mora, Laura; Wellgner Fernandes Oliveira, Amador; Cesar Teixeira Sirena, Eduardo; Bandeira de Melo Guimarães, Felipe; Lahitou Herlyn, Delfina; Norbu Sherpa, Nima; Gonzalez Lezana, Andrea; Pardini Fagundes, Thales Introduction: Timely diagnosis of schizophrenia is essential to ensure prompt treatment initiation and adherence. Structural magnetic resonance imaging (sMRI), when combined with artificial intelligence (AI), offers a promising avenue to enhance diagnostic accuracy. However, its performance and clinical use is a matter of debate. Methods: PubMed, Embase, and Cochrane databases were searched for studies using AI models with sMRI to diagnose schizophrenia in adults. Eligible models encompass traditional machine learning methods and deep learning (DL) architectures, utilizing diverse neuroanatomical inputs, including gray matter (GM) features and whole-brain (WB) structural data. The outcomes of interest were diagnostic performance metrics as: sensitivity (SE), specificity (SP), area under the curve (AUC). Results: A total of 16 studies were included, comprising 3601 participants. Overall pooled SE and SP were 0.76 (95 % CI: 0.71-0.80) and 0.78 (95 % CI: 0.73-0.82), respectively. When compared, DL models outperformed Support Vector Machine (SVM), achieving higher SP of 0.83 (95 % CI: 0.80-0.86) vs. 0.78 (95 % CI: 0.72-0.83), and AUC of 0.892 (95 % CI: 0.81-0.90) vs. 0.782 (95 % CI: 0.70-0.82). WB input models also outperformed GM performance, with SP of 0.86 (95 % CI: 0.78-0.92) vs. 0.80 (95 % CI: 0.73-0.85), and AUC of 0.89 (95 % CI: 0.70-0.93) vs. 0.816 (95 % CI: 0.71-0.84). Conclusion: AI models using sMRI show promising but provisional diagnostic performance for schizophrenia. Across studies, DL architectures and WB inputs generally achieved higher specificity and AUC than SVM and GM features. Prospective, multi-site external validation cohorts are needed before routine clinical implementation. Tue, 04 Nov 2025 00:00:00 GMT https://repositorio.fleni.org.ar/xmlui/handle/123456789/1463 2025-11-04T00:00:00Z