Quantitative brain-derived neurotrophic factor lateral flow assay for point-of-care detection of glaucoma

Glaucoma, a ruinous group of eye diseases with progressive degeneration of the optic nerve and vision loss, is the leading cause of irreversible blindness. Accurate and timely diagnosis of glaucoma is critical to promote secondary prevention and early disease-modifying therapies. Reliable, cheap, and rapid tests for measuring disease activities are highly required. Brain-derived neurotrophic factor (BDNF) plays an important role in maintaining the function and survival of the central nervous system. Decreased BDNF levels in tear fluid can be seen in glaucoma patients, which indicates that BDNF can be regarded as a novel biomarker for glaucoma. Conventional ELISA is the standard method to measure the BDNF level, but the multi-step operation and strict storage conditions limit its usage in point-of-care settings. Herein, a one-step and a portable glaucoma detection method was developed based on the lateral flow assay (LFA) to quantify the BDNF concentration in artificial tear fluids. The results of the LFA were analyzed by using a portable and low-cost system consisting of a smartphone camera and a dark readout box fabricated by 3D printing. The concentration of BDNF was quantified by analyzing the colorimetric intensity of the test line and the control line. This assay yields reliable quantitative results from 25 to 300 pg mL-1 with an experimental detection limit of 14.12 pg mL-1. The LFA shows a high selectivity for BDNF and high stability in different pH environments. It can be readily adapted for sensitive and quantitative testing of BDNF in a point-of-care setting. The BDNF LFA strip shows it has great potential to be used in early glaucoma detection

Ophthalmic sensing technologies for ocular disease diagnostics

Point-of-care diagnosis and personalized treatments are critical in ocular physiology and disease. Continuous sampling of tear fluid for ocular diagnosis is a need for further exploration. Several techniques have been developed for possible ophthalmological applications, from traditional spectroscopies to wearable sensors. Contact lenses are commonly used devices for vision correction, as well as for other therapeutic and cosmetic purposes. They are increasingly being developed into ocular sensors, being used to sense and monitor biochemical analytes in tear fluid, ocular surface temperature, intraocular pressure, and pH value. These sensors have had success in detecting ocular conditions, optimizing pharmaceutical treatments, and tracking treatment efficacy in point-of-care settings. However, there is a paucity of new and effective instrumentation reported in ophthalmology. Hence, this review will summarize the applied ophthalmic technologies for ocular diagnostics and tear monitoring, including both conventional and biosensing technologies. Besides applications of smart readout devices for continuous monitoring, targeted biomarkers are also discussed for the convenience of diagnosis of various ocular diseases. A further discussion is also provided for future aspects and market requirements related to the commercialization of novel types of contact lens sensors

Handling loops in bounded model checking of C programs via k-induction

The first attempts to apply the k-induction method to software verification are only recent. In this paper, we present a novel proof by induction algorithm, which is built on the top of a symbolic context-bounded model checker and uses an iterative deepening approach to verify, for each step k up to a given maximum, whether a given safety property ϕϕ holds in the program. The proposed k-induction algorithm consists of three different cases, called base case, forward condition, and inductive step. Intuitively, in the base case, we aim to find a counterexample with up to k loop unwindings; in the forward condition, we check whether loops have been fully unrolled and that ϕϕ holds in all states reachable within k unwindings; and in the inductive step, we check that whenever ϕϕ holds for k unwindings, it also holds after the next unwinding of the system. The algorithm was implemented in two different ways, a sequential and a parallel one, and the results were compared. Experimental results show that both forms of the algorithm can handle a wide variety of safety properties extracted from standard benchmarks, ranging from reachability to time constraints. And by comparison, the parallel algorithm solves more verification tasks in less time. This paper marks the first application of the k-induction algorithm to a broader range of C programs; in particular, we show that our k-induction method outperforms CPAChecker in terms of correct results, which is a state-of-the-art k-induction-based verification tool for C programs

Measures of disease activity in glaucoma

Glaucoma is the leading cause of irreversible blindness globally which significantly affects the quality of life and has a substantial economic impact. Effective detective methods are necessary to identify glaucoma as early as possible. Regular eye examinations are important for detecting the disease early and preventing deterioration of vision and quality of life. Current methods of measuring disease activity are powerful in describing the functional and structural changes in glaucomatous eyes. However, there is still a need for a novel tool to detect glaucoma earlier and more accurately. Tear fluid biomarker analysis and new imaging technology provide novel surrogate endpoints of glaucoma. Artificial intelligence is a post-diagnostic tool that can analyse ophthalmic test results. A detail review of currently used clinical tests in glaucoma include intraocular pressure test, visual field test and optical coherence tomography are presented. The advanced technologies for glaucoma measurement which can identify specific disease characteristics, as well as the mechanism, performance and future perspectives of these devices are highlighted. Applications of AI in diagnosis and prediction in glaucoma are mentioned. With the development in imaging tools, sensor technologies and artificial intelligence, diagnostic evaluation of glaucoma must assess more variables to facilitate earlier diagnosis and management in the future

Fibroblast migration and collagen deposition during dermal wound healing: mathematical modelling and clinical implications,

The extent to which collagen alignment occurs during dermal wound healing determines the severity of scar tissue formation. We have modelled this using a multiscale approach, in which extracellular materials, for example collagen and fibrin, are modelled as continua, while fibroblasts are considered as discrete units. Within this model framework, we have explored the effects that different parameters have on the alignment process, and we have used the model to investigate how manipulation of transforming growth factor-β levels can reduce scar tissue formation. We briefly review this body of work, then extend the modelling framework to investigate the role played by leucocyte signalling in wound repair. To this end, fibroblast migration and collagen deposition within both the wound region and healthy peripheral tissue are considered. Trajectories of individual fibroblasts are determined as they migrate towards the wound region under the combined influence of collagen/fibrin alignment and gradients in a paracrine chemoattractant produced by leucocytes. The effects of a number of different physiological and cellular parameters upon the collagen alignment and repair integrity are assessed. These parameters include fibroblast concentration, cellular speed, fibroblast sensitivity to chemoattractant concentration and chemoattractant diffusion coefficient. Our results show that chemoattractant gradients lead to increased collagen alignment at the interface between the wound and the healthy tissue. Results show that there is a trade-off between wound integrity and the degree of scarring. The former is found to be optimized under conditions of a large chemoattractant diffusion coefficient, while the latter can be minimized when repair takes place in the presence of a competitive inhibitor to chemoattractants

Molecular genotyping of sugarcane clones with microsatellite DNA markers

Molecular genotypes of 27 sugarcane clones (Saccharum hybrids) were produced with nine sugarcane microsatellites. A total of 52 alleles were identified using a capillary electrophoresis system with 41 alleles displaying varying degrees of polymorphism and the remaining 11 being monomorphic. There were eight alleles for sugarcane microsatellite SMC286CS, five for SMC334BS, eight for SMC336BS, four for SMC713BS, five for mSSCIR5, five for mSSCIR33, five for MCSA042E08, four for MCSA053C10, and eight for MCSA068G08. Presence or absence of these 52 alleles from a clone allowed the assignment of its arbitrary microsatellite genotype. The genetic relatedness among these clones was assessed using the CLUSTAL W algorithm with DNAMAN(R) software based on their arbitrary genotypes. With the exception of four clones, CP 70-321, HoCP 91-555, L 97-137 and Q124, six groups of clones were identified that shared at least 76% homology between their microsatellite genotypes. The software program also produced a bootstrapped phylogenetic tree with branch patterns that in general coincided with the putative pedigrees of these clones. The derivation of molecular genotypes such as these has enable sugarcane geneticists and breeders to verify the genetic pedigrees and purity of their sugarcane populations. These microsatellite genotypes can also aid in progeny selection and facilitate studies on allele transmission in this aneu-polyploidy crop

Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography

Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-related macular degeneration (AMD). The development of MA in patients with AMD results in irreversible blindness, and there is currently no effective method of early diagnosis of this condition, despite the recent development of unique treatments. Using OCT dataset of a total of 2211 B-scans from 45 volumetric scans of 8 patients, a convolutional neural network using one-against-all strategy was trained to present all six atrophic features followed by a validation to evaluate the performance of the models. The model predictive performance has achieved a mean dice similarity coefficient score of 0.706 ± 0.039, a mean Precision score of 0.834 ± 0.048, and a mean Sensitivity score of 0.615 ± 0.051. These results show the unique potential of using artificially intelligence-aided methods for early detection and identification of the progression of MA in wet AMD, which can further support and assist clinical decisions

Hemiselmis andersenii and Chlorella stigmatophora as new sources of high‐value compounds: a lipidomic approach

To unlock the potential of Chlorella stigmatophora (Trebouxiophyceae, Chlorophyta) and Hemiselmis andersenii (Cryptophyceae, Cryptophyta) as natural reactors for biotechnological exploitation, their lipophilic extracts were characterized using Fourier Transform Infrared spectroscopy with Attenuated Total Reflectance (FTIR-ATR) and Gas Chromatography-Mass Spectrometry (GC-MS) before and after alkaline hydrolysis. The GC-MS analysis enabled the identification of 62 metabolites-namely fatty acids (27), aliphatic alcohols (17), monoglycerides (7), sterols (4), and other compounds (7). After alkaline hydrolysis, monounsaturated fatty acids increased by as much as 87%, suggesting that the esterified compounds were mainly neutral lipids. Hemiselmis andersenii yielded the highest Σω3/Σω6 ratio (7.26), indicating that it is a good source of ω3 fatty acids, in comparison to C. stigmatophora (Σω3/Σω6 = 1.24). Both microalgae presented significant amounts of aliphatic alcohols (6.81-10.95 mg · g dw-1 ), which are recognized by their cholesterol-lowering properties. The multivariate analysis allowed visualization of the chemical divergence among H. andersenii lipophilic extracts before and after alkaline hydrolysis, as well as species-specific differences. Chlorella stigmatophora showed to be a valuable source of essential fatty acids for nutraceuticals, whereas H. andersenii, due to its high chemical diversity, seems to be suitable for different fields of application.info:eu-repo/semantics/publishedVersio

Multiclasificadores basados en aprendizaje automático como herramienta para la evaluación del perfil neurotóxico de líquidos iónicos

Los líquidos iónicos poseen un perfil fisicoquímico único, el cual los provee de un amplio rango de aplicaciones. Su variabilidad estructural casi ilimitada permite su diseño para tareas específicas. Sin embargo, su sustentabilidad, específicamente su seguridad desde el punto de vista toxicológico, ha sido frecuentemente cuestionada. Este último aspecto limita significativamente el cumplimiento de las regulaciones establecidas por la Unión Europea para el registro, evaluación, autorización y restricción de compuestosquímicos (REACH), así como su aplicación final. Debido a que la mayoría de los líquidos iónicos no han sido sintetizados, se hace evidente la importancia del desarrollo de herramientas quimioinformáticas que, de forma eficiente, permitan evaluar el potencial toxicológico de estos compuestos. En este sentido, el uso combinado de múltiples clasificadores ha demostrado superar las limitaciones de desempeño asociadas al uso de clasificadores individuales. En el presente trabajo fueron evaluadas varias estrategias alternativas de multiclasificadores basados en técnicas de aprendizaje automático supervisado, como herramientas para la evaluación del perfil neurotóxico de líquidos iónicos basado en la inhibición de la enzima acetilcolinesterasa, como indicador de neurotoxicidad. Se obtuvieron dos multiclasificadores con una alta capacidad predictiva sobre un conjunto de validación externa (no utilizado en el proceso de aprendizaje de los modelos). De acuerdo a los resultados obtenidos el 96% de un conjunto de nuevos líquidos iónicos podrá ser correctamente clasificado con la utilizaciónde estos multiclasificadores, los cuales constituyen herramientas de toma de decisión útiles en el campo del diseño y desarrollo de nuevos líquidos iónicos sustentables