Aqueous Angiography with Fluorescein and Indocyanine Green in Bovine Eyes.

PurposeWe characterize aqueous angiography as a real-time aqueous humor outflow imaging (AHO) modality in cow eyes with two tracers of different molecular characteristics.MethodsCow enucleated eyes (n = 31) were obtained and perfused with balanced salt solution via a Lewicky AC maintainer through a 1-mm side-port. Fluorescein (2.5%) or indocyanine green (ICG; 0.4%) were introduced intracamerally at 10 mm Hg individually or sequentially. With an angiographer, infrared and fluorescent images were acquired. Concurrent anterior segment optical coherence tomography (OCT) was performed, and fixable fluorescent dextrans were introduced into the eye for histologic analysis of angiographically positive and negative areas.ResultsAqueous angiography in cow eyes with fluorescein and ICG yielded high-quality images with segmental patterns. Over time, ICG maintained a better intraluminal presence. Angiographically positive, but not negative, areas demonstrated intrascleral lumens with anterior segment OCT. Aqueous angiography with fluorescent dextrans led to their trapping in AHO pathways. Sequential aqueous angiography with ICG followed by fluorescein in cow eyes demonstrated similar patterns.ConclusionsAqueous angiography in model cow eyes demonstrated segmental angiographic outflow patterns with either fluorescein or ICG as a tracer.Translational relevanceFurther characterization of segmental AHO with aqueous angiography may allow for intelligent placement of trabecular bypass minimally invasive glaucoma surgeries for improved surgical results

ALTERATION OF THE EPHA2/EPHRIN-A SIGNALING AXIS IN PSORIATIC EPIDERMIS

EphA2 is a receptor tyrosine kinase (RTK) that triggers keratinocyte differentiation upon activation and subsequently down-regulation by ephrin-A1 ligand. The objective for this study was to determine if the EphA2/ephrin-A1 signaling axis was altered in psoriasis, an inflammatory skin condition where keratinocyte differentiation is abnormal. Microarray analysis of skin biopsies from psoriasis patients revealed increased mRNA transcripts for several members of this RTK family in plaques, including the EphA1, EphA2 and EphA4 subtypes prominently expressed by keratinocytes. Of these, EphA2 showed the greatest up-regulation, a finding that was confirmed by quantitative RT-PCR, IHC analysis and ELISA. In contrast, psoriatic lesions exhibited reduced ephrin-A ligand immunoreactivity. Exposure of primary keratinocytes induced to differentiated in high calcium or a 3-dimensiosnal raft culture of human epidermis to a combination of growth factors and cytokines elevated in psoriasis increased EphA2 mRNA and protein expression while inducing S100A7 and disrupting differentiation. Pharmacological delivery of a soluble ephrin-A1 peptidomimetic ligand led to a reduction in EphA2 expression and ameliorated proliferation and differentiation in raft cultures exposed to EGF and IL-1α. These findings suggest that ephrin-A1-mediated down-regulation of EphA2 supports keratinocyte differentiation in the context of cytokine perturbation

Artificial intelligence for dementia research methods optimization

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation

Realistic measurement uncertainties for marine macronutrient measurements conducted using gas segmented flow and Lab-on-Chip techniques

Highlights • Accounting for systematic bias is required for a realistic analytical uncertainty • Gas segmented flow techniques achieved a combined uncertainties of 1-4 % • Lab-on-Chip nitrate + nitrite analysers achieved a combined uncertainties < 5% Abstract Accurate and precise measurements of marine macronutrient concentrations are fundamental to our understanding of biogeochemical cycles in the ocean. Quantifying the measurement uncertainty associated with macronutrient measurements remains a challenge. Large systematic biases (up to 10 %) have been identified between datasets, restricting the ability of marine biogeochemists to distinguish between the effects of environmental processes and analytical uncertainty. In this study we combine the routine analyses of certified reference materials (CRMs) with the application of a simple statistical technique to quantify the combined (random + systematic) measurement uncertainty associated with marine macronutrient measurements using gas segmented flow techniques. We demonstrate that it is realistic to achieve combined uncertainties of ~1-4 % for nitrate + nitrite (ΣNOx), phosphate (PO43-) and silicic acid (Si(OH)4) measurements. This approach requires only the routine analyses of CRMs (i.e. it does not require inter-comparison exercises). As CRMs for marine macronutrients are now commercially available, it is advocated that this simple approach can improve the comparability of marine macronutrient datasets and therefore should be adopted as ‘best practice’. Novel autonomous Lab-on-Chip (LoC) technology is currently maturing to a point where it will soon become part of the marine chemist’s standard analytical toolkit used to determine marine macronutrient concentrations. Therefore, it is critical that a complete understanding of the measurement uncertainty of data produced by LoC analysers is achieved. In this study we analysed CRMs using 7 different LoC ΣNOx analysers to estimate a combined measurement uncertainty of < 5%. This demonstrates that with high quality manufacturing and laboratory practices, LoC analysers routinely produce high quality measurements of marine macronutrient concentrations

Quantification of a subsea CO2 release with lab-on-chip sensors measuring benthic gradients

We present a novel approach to detecting and quantifying a subsea release of CO2 from within North Sea sed�iments, which mimicked a leak from a subsea CO2 reservoir. Autonomous lab-on-chip sensors performed in situ measurements of pH at two heights above the seafloor. During the 11 day experiment the rate of CO2 release was gradually increased. Whenever the currents carried the CO2-enriched water towards the sensors, the sensors measured a decrease in pH, with a strong vertical gradient within a metre of the seafloor. At the highest release rate, a decrease of over 0.6 pH units was observed 17 cm above the seafloor compared to background mea�surements. The sensor data was combined with hydrodynamic measurements to quantify the amount of CO2 escaping the sediments using an advective mass transport model. On average, we directly detected 43 ± 8% of the released CO2 in the water column. Accounting for the incomplete carbonate equilibration process increases this estimate to up to 61 ± 10%. This technique can provide long-term in situ monitoring of offshore CO2 res�ervoirs and hence provides a tool to support climate change mitigation activities. It could also be applied to characterising plumes and quantifying other natural or anthropogenic fluxes of dissolved solutes

Experiences and Perspectives of Filipino Patients with Stroke on Physical Therapy Telerehabilitation: A Phenomenological Study Protocol

Introduction: Stroke is the third leading cause of death in the Philippines, so these patients must continuously undergo rehabilitation for faster recovery. With the rise of COVID-19, physical therapy (PT) telerehabilitation (TR) has emerged, where services are provided outside the usual rehabilitation setting for patients with stroke to continue their treatment while reducing the risk of acquiring COVID-19. However, it is a relatively new service in the country; hence, further research is needed to identify the factors and needs of these patients during TR, which may help improve PT TR services. Objective: This study aims to explore the experiences and perspectives of Filipino patients with stroke who have undergone PT TR in the Philippines since March 2020. Administrators of healthcare facilities, policy-makers, and other decision-makers involved in evaluating, implementing, and developing PT TR may benefit patients with stroke. This can expand the scope of rehabilitation to patients with stroke who have no access to face-to-face rehabilitation or improve the training or education of Physical Therapists who are providing TR to stroke patients. Methods: This will be a qualitative phenomenological study design that will use purposive sampling to recruit participants. Semi-structured interviews (SSI) will be conducted online using Google Meetings®, Zoom®, or Facebook Messenger® to record their experiences and perspectives. The NVivo data analysis software will be used to create codes and identify themes from the data gathered. The data that will be obtained is about the experiences and perspectives of Filipino patients with stroke regarding PT TR. The insights of the participants will undergo Thematic Analysis until no new information will be discovered from the analyzed data

Morfología y biometría de racimos, frutos y semillas de Attalea bassleriana en Alto Amazonas, Perú

We evaluated the morphology and biometrics of racemes, fruits, and seeds of Attalea bassleriana in the localities of Paraiso, Libertad de Cuiparillo and Santa Lucia in the Alto Amazonas Province, Peru, to understand its interpopulational variability and contribute to the taxonomic clarification of the species. Additionally, we described the environment where the palm grows. To do so, we used 70 descriptors (38 biometric and 32 morphological), which were compared with ANOVA and Kruskal-Wallis, correlated through Spearman, and graphically visualized by Principal Component Analysis (PCA). A data sheet for field samples was used for the description of the environment. In total, 23 biometric descriptors presented significant differences (p &lt; 0.05). The highest correlations were fruit number/raceme weight (0.921) and fruit weight/fruit diameter (0.844). The PCA demonstrates the variability of fruits between populations and denotes Paradise as the least variable and the most differentiated. Likewise, we observed that the species is found in terrace forests, swampy forests, agricultural and livestock lands, between 145-159 m.a.s.l. The data evidence the taxonomic identification of shebon and constitute a reference for both the proper utilization of fruits and seeds and their application to genetic improvement.Se evaluó la morfología y biometría de racimos, frutos y semillas de Attalea bassleriana en las localidades de Paraíso, Libertad de Cuiparillo y Santa Lucía en Alto Amazonas, Perú, para comprender su variabilidad interpoblacional y contribuir al esclarecimiento taxonómico de la especie. Se describió el ambiente donde se desarrolla la palmera mediante 70 descriptores que fueron comparados con ANOVA y Kruskal-Wallis, correlacionados a través Spearman y visualizados gráficamente mediante el Análisis de Componentes Principales (PCA). Para la descripción del ambiente se utilizó una ficha de toma de datos para muestras de campo. En total, 23 descriptores biométricos presentaron diferencias significativas (p &lt; 0,05). Las correlaciones más altas fueron número de frutos/peso de racimo (0,921) y peso del fruto/diámetro del fruto (0,844). El PCA demuestra la variabilidad de los frutos entre las poblaciones y denota a Paraíso como la menos variable y más diferenciada. Asimismo, se observó que la especie se encuentra en bosques de terraza, bosques pantanosos, tierras agrícolas y ganaderas, entre 145 y 159 m s. n. m. Esta información evidencia la identificación taxonómica del shebón y es un referente tanto para el aprovechamiento adecuado de frutos y semillas como para su aplicación en el mejoramiento genético

Can Molecular Motors Drive Distance Measurements in Injured Neurons?

Injury to nerve axons induces diverse responses in neuronal cell bodies, some of which are influenced by the distance from the site of injury. This suggests that neurons have the capacity to estimate the distance of the injury site from their cell body. Recent work has shown that the molecular motor dynein transports importin-mediated retrograde signaling complexes from axonal lesion sites to cell bodies, raising the question whether dynein-based mechanisms enable axonal distance estimations in injured neurons? We used computer simulations to examine mechanisms that may provide nerve cells with dynein-dependent distance assessment capabilities. A multiple-signals model was postulated based on the time delay between the arrival of two or more signals produced at the site of injury–a rapid signal carried by action potentials or similar mechanisms and slower signals carried by dynein. The time delay between the arrivals of these two types of signals should reflect the distance traversed, and simulations of this model show that it can indeed provide a basis for distance measurements in the context of nerve injuries. The analyses indicate that the suggested mechanism can allow nerve cells to discriminate between distances differing by 10% or more of their total axon length, and suggest that dynein-based retrograde signaling in neurons can be utilized for this purpose over different scales of nerves and organisms. Moreover, such a mechanism might also function in synapse to nucleus signaling in uninjured neurons. This could potentially allow a neuron to dynamically sense the relative lengths of its processes on an ongoing basis, enabling appropriate metabolic output from cell body to processes

Effects of climate change on the distribution of plant species and plant functional strategies on the Canary Islands

Aim Oceanic islands possess unique floras with high proportions of endemic species. Island floras are expected to be severely affected by changing climatic conditions as species on islands have limited distribution ranges and small population sizes and face the constraints of insularity to track their climatic niches. We aimed to assess how ongoing climate change affects the range sizes of oceanic island plants, identifying species of particular conservation concern. Location Canary Islands, Spain. Methods We combined species occurrence data from single-island endemic, archipelago endemic and nonendemic native plant species of the Canary Islands with data on current and future climatic conditions. Bayesian Additive Regression Trees were used to assess the effect of climate change on species distributions; 71% (n = 502 species) of the native Canary Island species had models deemed good enough. To further assess how climate change affects plant functional strategies, we collected data on woodiness and succulence. Results Single-island endemic species were projected to lose a greater proportion of their climatically suitable area (x ̃ = −0.36) than archipelago endemics (x ̃ = −0.28) or nonendemic native species (x ̃ = −0.26), especially on Lanzarote and Fuerteventura, which are expected to experience less annual precipitation in the future. Moreover, herbaceous single-island endemics were projected to gain less and lose more climatically suitable area than insular woody single-island endemics. By contrast, we found that succulent single-island endemics and nonendemic natives gain more and lose less climatically suitable area. Main Conclusions While all native species are of conservation importance, we emphasise single-island endemic species not characterised by functional strategies associated with water use efficiency. Our results are particularly critical for other oceanic island floras that are not constituted by such a vast diversity of insular woody species as the Canary Islands

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review

Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias