Citations

Abstract BACKGROUND [ 18 F] flortaucipir (FTP) binding to paired helical filament (PHF) tau in Alzheimer's disease (AD) is well accepted. Binding to 3R and 4R tau in frontotemporal lobar degeneration (FTLD) is controversial. We aimed to investigate whether an FTP fluorescent analog (T726) can help shed light on this controversy. METHOD We assessed T726 binding to amyloid beta (Aβ) and different tau isoforms in nine subjects (one control, three with Alzheimer's disease [AD], and five with FTLD) with different 3R and 4R tauopathies using fluorescence confocal microscopy. RESULTS T726 did not colocalize with Aβ but showed significant co‐localization with PHF tau in AD. We also observed some, albeit limited, co‐localization of T726 with 3R and 4R tau lesions in FTLD. DISCUSSION This study's findings support FTP binding to some 3R and 4R tau lesions in FTLD. Further studies are needed to understand the biology of why FTP binds some but not all FTLD tau lesions. Highlights Flortaucipir analog (T726) showed significant co‐localization with paired helical filament (PHF) tau in Alzheimer's disease (AD). Colocalization between T726 with 3R and 4R tau lesions was observed in frontotemporal lobar degeneration (FTLD). Not all 4R tau lesions bind to T726 across different FTLD brain regions.

AbstractEndometriosis is a complex gynecologic disorder characterized primarily by symptoms of pelvic pain, infertility, and altered quality of life. National and international guidelines highlight the diagnostic difficulties and lack of conclusive diagnostic tools for endometriosis. Furthermore, guidelines are becoming questionable at an increasingly rapid rate as new diagnostic techniques emerge. This work aims to provide a knowledge synthesis of the relevance of various diagnostic tools and to assess areas of improvement of conventional algorithms. MEDLINE and Cochrane Library databases were searched from January 2021 to December 2023 using relevant key words. Articles evaluating the diagnostic relevance and performance of various tools were included and independently reviewed by the authors for eligibility. Included studies were assessed using the GRADE and QUADAS‐2 tools. Of the 4204 retrieved articles, 26 were included. While anamnesis and clinical examination do contribute to diagnostic accuracy, their level of evidence and impact on the diagnostic process remains limited. Although imaging techniques are recommended to investigate endometriosis, ultrasonography remains highly operator dependent. Magnetic resonance imaging appears to exhibit higher sensitivities than ultrasound. However, concerns persist with regards to the terminology, anatomical definition of lesions, and accuracies of both ultrasound and magnetic resonance imaging. Recently, several biological markers have been studied and cumulative evidence supports the contribution of noncoding RNAs to the diagnosis of endometriosis. Marginal improvements have been suggested for anamnesis, clinical examination, and imaging examinations. Conversely, some biomarkers, including the saliva microRNA signature for endometriosis, have emerged as diagnostic tools which inspire reflection on the revision of conventional diagnostic algorithms.

Abstract The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient’s demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.