Collagen Membrane as Artificial Dura Substitute: A Comprehensive In Vivo Study of Efficiency and Substitution Compared to Durepair.

BACKGROUND AND OBJECTIVES: The dura mater is a barrier between the brain and the surrounding environment. Injuries to the dura can lead to serious complications, therefore, ensuring a hermetic closure of the dura is a primary task for a neurosurgeon. The aim of the study is to compare the effectiveness of applying the newly developed ViscollDURA collagen membrane (VDCM), with the commercially available Durepair (xenogeneic collagen) in animal model. METHODS: A dural tear model was utilized in rats with membrane implantation using an application method. The sample size consisted of 24 rats. Group I underwent VDCM implantation, while Group II underwent Durepair implantation. Results were evaluated at 30, 60, and 90 days. The study was assessed using magnetic resonance imaging, histology, electron scanning microscopy, and immunohistochemistry. The obtained results underwent statistical analysis. RESULTS: In the clinical presentation, there were no difference between groups. Histologically, Group 1 showed comparable results to Group 2. The integration process of the membrane statistically differed between the groups. In Group 1, neovascularization and tissue replacement showed better results than in Group 2. Magnetic resonance imaging differences were observed at later stages, with group 2 showing adhesion and brain deformation in the implantation area. CONCLUSIONS: Both membranes showed safety and compatibility. The collagen membrane produced under sterile conditions demonstrated better regeneration with minimal inflammatory reaction. The study suggests that VDCM exhibits biocompatibility comparable to Durepair, providing prospects for potential applications in neurosurgery.

Review on the Use of Brain Computer Interface Rehabilitation Methods for Treating Mental and Neurological Conditions.

This review provides a comprehensive examination of recent developments in both neurofeedback and brain-computer interface (BCI) within the medical field and rehabilitation. By analyzing and comparing results obtained with various tools and techniques, we aim to offer a systematic understanding of BCI applications concerning different modalities of neurofeedback and input data utilized. Our primary objective is to address the existing gap in the area of meta-reviews, which provides a more comprehensive outlook on the field, allowing for the assessment of the current landscape and developments within the scope of BCI. Our main methodologies include meta-analysis, search queries employing relevant keywords, and a network-based approach. We are dedicated to delivering an unbiased evaluation of BCI studies, elucidating the primary vectors of research development in this field. Our review encompasses a diverse range of applications, incorporating the use of brain-computer interfaces for rehabilitation and the treatment of various diagnoses, including those related to affective spectrum disorders. By encompassing a wide variety of use cases, we aim to offer a more comprehensive perspective on the utilization of neurofeedback treatments across different contexts. The structured and organized presentation of information, complemented by accompanying visualizations and diagrams, renders this review a valuable resource for scientists and researchers engaged in the domains of biofeedback and brain-computer interfaces.

Collagen-Based Artificial Cornea for Lamellar Keratoplasty: An Early Case Report.

PURPOSE: The aim of this study was to evaluate Viscoll collagen membrane (VCM) for lamellar keratoplasty. METHODS: A 54-year-old man with grade 4 recurrent pterygium underwent lamellar keratoplasty using VCM as the graft material. Standard keratoplasty postoperative treatments, including topical antibiotic-corticosteroid and artificial tears, were administered. RESULTS: Complete graft epithelialization was achieved after 4 days of the surgery. Follow-up at 9 months showed no pterygium recurrence as well as good integration of the VCM graft into the surrounding tissues with preserved transparency. CONCLUSIONS: This case demonstrated that VCM possesses great potential as an alternative to the human donor cornea for lamellar keratoplasty; however, the application of VCM for lamellar keratoplasty still needs additional evaluation.

Light regulation of rhodopsin distribution during outer segment renewal in murine rod photoreceptors.

Vision under dim light relies on primary cilia elaborated by rod photoreceptors in the retina. This specialized sensory structure, called the rod outer segment (ROS), comprises hundreds of stacked, membranous discs containing the light-sensitive protein rhodopsin, and the incorporation of new discs into the ROS is essential for maintaining the rod's health and function. ROS renewal appears to be primarily regulated by extrinsic factors (light); however, results vary depending on different model organisms. We generated two independent transgenic mouse lines where rhodopsin's fate is tracked by a fluorescently labeled rhodopsin fusion protein (Rho-Timer) and show that rhodopsin incorporation into nascent ROS discs appears to be regulated by both external lighting cues and autonomous retinal clocks. Live-cell imaging of the ROS isolated from mice exposed to six unique lighting conditions demonstrates that ROS formation occurs in a periodic manner in cyclic light, constant darkness, and artificial light/dark cycles. This alternating bright/weak banding of Rho-Timer along the length of the ROS relates to inhomogeneities in rhodopsin density and potential points of structural weakness. In addition, we reveal that prolonged dim ambient light exposure impacts not only the rhodopsin content of new discs but also that of older discs, suggesting a dynamic interchange of material between new and old discs. Furthermore, we show that rhodopsin incorporation into the ROS is greatly altered in two autosomal recessive retinitis pigmentosa mouse models, potentially contributing to the pathogenesis. Our findings provide insights into how extrinsic (light) and intrinsic (retinal clocks and genetic mutation) factors dynamically regulate mammalian ROS renewal.

Matrix-Assisted Cell Transplantation for the Treatment of Limbal Stem Cell Deficiency in a Rabbit Model.

With the development of regenerative medicine in ophthalmology, the identification of cells with high proliferative potential in the limbal area has attracted the attention of ophthalmologists and offered a new option for treatment in clinical practice. Limbal stem cell deficiency (LSCD) is an identified eye disease with a difficult and negative outcome, for which the traditional treatment is keratoplasty. This study sought to evaluate the efficacy of matrix-assisted cell transplantation consisting of in vitro-cultured autologous limbal stem cells (LSCs) and type I collagen for the treatment of LSCD in rabbits. LSCD was induced in 10 rabbits by a combination of mechanical limbectomy and alkali burns. Cells were cultured on a plate for 14 days before being transferred to a collagen-based matrix for another 7 days. Rabbits were divided into two groups as follows: the experimental group (five rabbits) received matrix-assisted cell transplantation, while the control group (five rabbits) received only conservative therapy with anti-inflammatory eye drops. During the postoperative period, all rabbits were examined using slit-lamp biomicroscopy with photo-registration and fluorescent staining, impression cytology and anterior segment optical coherence tomography (AS-OCT). Rabbits were euthanized at 30 and 120 days, and their corneas were processed for histology and immunohistochemistry. As a consequence, rabbits in the experimental group demonstrated the restoration of the corneal epithelium and transparency without epithelial defects. Moreover, goblet cells were absent in the central zone of the corneal epithelium. In conclusion, our new method of treatment enhanced the corneal surface and is an effective method of treatment for LSCD in rabbits.

Topology switching during window thresholding fMRI-based functional networks of patients with major depressive disorder: Consensus network approach.

We present a novel method for analyzing brain functional networks using functional magnetic resonance imaging data, which involves utilizing consensus networks. In this study, we compare our approach to a standard group-based method for patients diagnosed with major depressive disorder (MDD) and a healthy control group, taking into account different levels of connectivity. Our findings demonstrate that the consensus network approach uncovers distinct characteristics in network measures and degree distributions when considering connection strengths. In the healthy control group, as connection strengths increase, we observe a transition in the network topology from a combination of scale-free and random topologies to a small-world topology. Conversely, the MDD group exhibits uncertainty in weak connections, while strong connections display small-world properties. In contrast, the group-based approach does not exhibit significant differences in behavior between the two groups. However, it does indicate a transition in topology from a scale-free-like structure to a combination of small-world and scale-free topologies. The use of the consensus network approach also holds immense potential for the classification of MDD patients, as it unveils substantial distinctions between the two groups.

Toward interpretability of machine learning methods for the classification of patients with major depressive disorder based on functional network measures.

We address the interpretability of the machine learning algorithm in the context of the relevant problem of discriminating between patients with major depressive disorder (MDD) and healthy controls using functional networks derived from resting-state functional magnetic resonance imaging data. We applied linear discriminant analysis (LDA) to the data from 35 MDD patients and 50 healthy controls to discriminate between the two groups utilizing functional networks' global measures as the features. We proposed the combined approach for feature selection based on statistical methods and the wrapper-type algorithm. This approach revealed that the groups are indistinguishable in the univariate feature space but become distinguishable in a three-dimensional feature space formed by the identified most important features: mean node strength, clustering coefficient, and the number of edges. LDA achieves the highest accuracy when considering the network with all connections or only the strongest ones. Our approach allowed us to analyze the separability of classes in the multidimensional feature space, which is critical for interpreting the results of machine learning models. We demonstrated that the parametric planes of the control and MDD groups rotate in the feature space with increasing the thresholding parameter and that their intersection increases with approaching the threshold of 0.45, for which classification accuracy is minimal. Overall, the combined approach for feature selection provides an effective and interpretable scenario for discriminating between MDD patients and healthy controls using measures of functional connectivity networks. This approach can be applied to other machine learning tasks to achieve high accuracy while ensuring the interpretability of the results.

First steps into the cloud: Using Amazon data storage and computing with Python notebooks.

With the oncoming age of big data, biologists are encountering more use cases for cloud-based computing to streamline data processing and storage. Unfortunately, cloud platforms are difficult to learn, and there are few resources for biologists to demystify them. We have developed a guide for experimental biologists to set up cloud processing on Amazon Web Services to cheaply outsource data processing and storage. Here we provide a guide for setting up a computing environment in the cloud and showcase examples of using Python and Julia programming languages. We present example calcium imaging data in the zebrafish brain and corresponding analysis using suite2p software. Tools for budget and user management are further discussed in the attached protocol. Using this guide, researchers with limited coding experience can get started with cloud-based computing or move existing coding infrastructure into the cloud environment.

High-Power Quantum Cascade Lasers Emitting at 8 µm: Technology and Analysis.

In this work, we demonstrate the features of a two-stage epitaxial growth technique and show the results of power and efficiency measurements for three different designs of quantum cascade lasers with a record-high peak power in the 8 µm spectral region. The time-resolved QCL spectral study proves that InP-based upper cladding paired with an InP contact layer provides better heat dissipation and allows one to reach better power characteristics in comparison with InGaAs-based contact, even with short pulse pumping.

Forecasting macroscopic dynamics in adaptive Kuramoto network using reservoir computing.

Forecasting a system's behavior is an essential task encountering the complex systems theory. Machine learning offers supervised algorithms, e.g., recurrent neural networks and reservoir computers that predict the behavior of model systems whose states consist of multidimensional time series. In real life, we often have limited information about the behavior of complex systems. The brightest example is the brain neural network described by the electroencephalogram. Forecasting the behavior of these systems is a more challenging task but provides a potential for real-life application. Here, we trained reservoir computer to predict the macroscopic signal produced by the network of phase oscillators. The Lyapunov analysis revealed the chaotic nature of the signal and reservoir computer failed to forecast it. Augmenting the feature space using Takkens' theorem improved the quality of forecasting. RC achieved the best prediction score when the number of signals coincided with the embedding dimension estimated via the nearest false neighbors method. We found that short-time prediction required a large number of features, while long-time prediction utilizes a limited number of features. These results refer to the bias-variance trade-off, an important concept in machine learning.

Corneal Stroma Regeneration with Collagen-Based Hydrogel as an Artificial Stroma Equivalent: A Comprehensive In Vivo Study.

Restoring the anatomical and functional characteristics of the cornea using various biomaterials is especially relevant in the context of a global shortage of donor tissue. Such biomaterials must be biocompatible, strong, and transparent. Here, we report a Viscoll collagen membrane with mechanical and optical properties suitable for replacing damaged stromal tissue. After removing a portion of the stroma, a Viscoll collagen membrane was implanted into the corneas of rabbits. After 6 months, the active migration of host cells into Viscoll collagen membranes was noted, with the preservation of corneal transparency in all experimental animals. Effective integration of the Viscoll collagen membrane with corneal tissue promoted nerve regeneration in vivo, as confirmed by in vivo confocal microscopy. We also demonstrated the safety and efficacy of the Viscoll collagen membrane for corneal stroma regeneration. Thus, in combination with the proposed packaging format that provides long-term storage of up to 10 months, this material has great potential for replacing and regenerating damaged stromal tissues.

Welcome to the lab.

Having a formal onboarding procedure for new lab members can lead to a happier and more productive working environment.

Magnetic structure and internal field nuclear magnetic resonance of cobalt nanowires.

The magnetic properties of cobalt metal nanowires grown by electrodeposition in porous membranes depend largely on the synthesis conditions. Here, we focus on the role of electrolyte additives on the magnetic anisotropy of the electrodeposited nanowires. Through magnetometry and internal field nuclear magnetic resonance (IF NMR) studies, we compared both the magnetic and crystalline structures of 50 and 200 nm diameter Co nanowires synthesized in the presence or absence of organic additives. The spectral characteristics of IF NMR were compared structurally to X-ray diffraction patterns, and the anisotropy of the NMR enhancement factor in ferromagnetic multidomain structures to magnetometry results. While the magnetic behavior of the 50 nm nanowires was dominated, as expected, by shape anisotropy with magnetic domains oriented on axis, the analysis of the 200 nm proved to be more complex. 59Co IF NMR revealed that the determining difference between the samples electrodeposited in the presence or in absence of organic additives was not the dominant crystalline system (fcc or hcp) but the coherent domain sizes and boundaries. In the presence of organic additives, the cobalt crystal domains are smaller and with defective grain boundaries, as revealed by resonances below 210 MHz. This prevented the development in the Co hcp part of the sample of the strong magnetocrystalline anisotropy that was observed in the absence of organic additives. In the presence of organic additives, even in nanowires as wide as 200 nm, the magnetic behavior remained determined by the shape anisotropy with a positive effective magnetic anisotropy and strong anisotropy of the NMR enhancement factor.

Extreme Enhancement of Carbon Hydrogasification via Mechanochemistry.

Carbon hydrogasification is the slowest reaction among all carbon-involved small-molecule transformations. Here, we demonstrate a mechanochemical method that results in both a faster reaction rate and a new synthesis route. The reaction rate was dramatically enhanced by up to 4 orders of magnitude compared to the traditional thermal method. Simultaneously, the reaction exhibited very high selectivity (99.8 % CH4 , versus 80 % under thermal conditions) with a cobalt catalyst. Our study demonstrated that this extreme increase in reaction rate originates from the continuous activation of reactive carbon species via mechanochemistry. The high selectivity is intimately related to the activation at low temperature, at which higher hydrocarbons are difficult to form. This work is expected to advance studies of carbon hydrogasification, and other solid-gas reactions.

Regional synapse gain and loss accompany memory formation in larval zebrafish.

Defining the structural and functional changes in the nervous system underlying learning and memory represents a major challenge for modern neuroscience. Although changes in neuronal activity following memory formation have been studied [B. F. Grewe et al., Nature 543, 670-675 (2017); M. T. Rogan, U. V. Stäubli, J. E. LeDoux, Nature 390, 604-607 (1997)], the underlying structural changes at the synapse level remain poorly understood. Here, we capture synaptic changes in the midlarval zebrafish brain that occur during associative memory formation by imaging excitatory synapses labeled with recombinant probes using selective plane illumination microscopy. Imaging the same subjects before and after classical conditioning at single-synapse resolution provides an unbiased mapping of synaptic changes accompanying memory formation. In control animals and animals that failed to learn the task, there were no significant changes in the spatial patterns of synapses in the pallium, which contains the equivalent of the mammalian amygdala and is essential for associative learning in teleost fish [M. Portavella, J. P. Vargas, B. Torres, C. Salas, Brain Res. Bull 57, 397-399 (2002)]. In zebrafish that formed memories, we saw a dramatic increase in the number of synapses in the ventrolateral pallium, which contains neurons active during memory formation and retrieval. Concurrently, synapse loss predominated in the dorsomedial pallium. Surprisingly, we did not observe significant changes in the intensity of synaptic labeling, a proxy for synaptic strength, with memory formation in any region of the pallium. Our results suggest that memory formation due to classical conditioning is associated with reciprocal changes in synapse numbers in the pallium.

A new collagen scaffold for the improvement of corneal biomechanical properties in a rabbit model.

Although collagen based materials are widely used in corneal tissue engineering with promising results. The usage of such materials for the improvement of corneal biomechanical properties is still unclear. In this study, we aimed to investigate a new Viscoll collagen-based membrane for the improvement of corneal biomechanical characteristics.The right eyes of 15 Chinchilla rabbits were implanted with the membrane via an intrastromal pocket, with the contralateral intact eyes as controls. At 7, 30, 90, and 180 days post-surgery, the rabbits underwent anterior segment optical coherence topography, clinical examination, and slit-lamp microscopy. Additionally, the corneal samples also underwent histological examination followed by the assessment of the biomechanical characteristics of four treated and non-treated corneas at 30, 90, and 180 days, including keratometry at 180 days, post operation. Data are presented as means ± confidence intervals with a 95% confidence level. All the operated corneas retained their transparency throughout the study. Implantation approximately doubled the central corneal thickness. Corneas became stronger by approximately 87% between 1 and 6 months after surgery (maximum fracture load, 13.3 ± 0.8 and 24.9 ± 1.4 N, respectively), and their elasticity increased by approximately 27% over the same time frame (maximum slope of the elastic region of the stress-strain curve, 11.5 ± 0.2 and 14.6 ± 1.4 N/mm respectively). We have thus proposed a new method to increase corneal thickness and strengthen the corneal tissues while preserving their transparency and demonstrated its safety and efficacy in a rabbit model over 6 months. This may be a suitable alternative to the existing corneal collagen crosslinking procedure.

Superparamagnetic behaviour of metallic Co nanoparticles according to variable temperature magnetic resonance.

Investigating the size distributions of Co nanoparticle ensembles is an important problem, which has no straightforward solution. In this work, we use the combination of 59Co internal field nuclear magnetic resonance (59Co IF NMR) and ferromagnetic resonance (FMR) spectroscopies on a metallic Co nanoparticle sample with a narrow Co nanoparticle size distribution due to encapsulation within the inner channels of carbon nanotubes. High-resolution transmission electron microscopy (TEM) images showed that the nanoparticles can be represented as prolate spheroids, with the majority of particles having an aspect ratio between 1 and 2. This observation has increased the accuracy of superparamagnetic blocking size calculations from Néel relaxation model by introducing the actual volume of the ellipsoids taken from the image processing. 59Co IF NMR and FMR experiments conducted under different temperatures allowed us to observe the thermal blocking of superparamagnetic particles in full accordance with the TEM particle volume distribution. This proved that these magnetic resonance techniques can be used jointly for characterization of Co nanoparticles in the bulk of the sample.