Cardiotoxicity detection tool for breast cancer chemotherapy: a retrospective study.

Background: Patients with breast cancer undergoing biological therapy and/or chemotherapy perform multiple radionuclide angiography (RNA) or multigated acquisition (MUGA) scans to assess cardiotoxicity. The association between RNA imaging parameters and left ventricular (LV) ejection fraction (LVEF) remains unclear. Objectives: This study aimed to extract and evaluate the association of several novel imaging biomarkers to detect changes in LVEF in patients with breast cancer undergoing chemotherapy. Methods: We developed and optimized a novel set of MATLAB routines called the "RNA Toolbox" to extract parameters from RNA images. The code was optimized using various statistical tests (e.g., ANOVA, Bland-Altman, and intraclass correlation tests). We quantitatively analyzed the images to determine the association between these parameters using regression models and receiver operating characteristic (ROC) curves. Results: The code was reproducible and showed good agreement with validated clinical software for the parameters extracted from both packages. The regression model and ROC results were statistically significant in predicting LVEF (R2 = 0.40, P < 0.001) (AUC = 0.78). Some time-based, shape-based, and count-based parameters were significantly associated with post-chemotherapy LVEF (ß = 0.09, P < 0.001), LVEF of phase image (ß = 4, P = 0.030), approximate entropy (ApEn) (ß = 11.6, P = 0.001), ApEn (diastolic and systolic) (ß = 39, P = 0.002) and LV systole size (ß = 0.03, P = 0.010). Conclusions: Despite the limited sample size, we observed evidence of associations between several parameters and LVEF. We believe that these parameters will be more beneficial than the current methods for patients undergoing cardiotoxic chemotherapy. Moreover, this approach can aid physicians in evaluating subclinical cardiac changes during chemotherapy, and in understanding the potential benefits of cardioprotective drugs.

Fetal femur length and risk of diabetes in adolescence: a prospective cohort study.

BACKGROUND: Diabetes is more apparent in adulthood but may be dormant in childhood and originates during early fetal development. In fetal biometry, femur length (FL) is crucial for assessing fetal growth and development. This study aimed to assess potential associations between fetal femur growth and prediabetic biomarkers in Bangladeshi children. METHODS: A cohort study embedded in a population-based maternal food and micronutrient supplementation (MINIMat) trial was conducted in Matlab, Bangladesh. The children in the cohort were followed up until 15 years of age. In the original trial, pregnancy was confirmed by ultrasound before 13 gestational weeks (GWs). Afterward, ultrasound assessments were performed at 14, 19, and 30 GWs. FL was measured from one end to the other, capturing a complete femoral image. The FL was standardized by GW, and a z-score was calculated. FBG and HbA1c levels were determined in plasma and whole blood, and the triglyceride-glucose index, a biomarker of insulin resistance, was calculated as Ln [fasting triglycerides (mg/dl) × fasting glucose (mg/dl)/2]. Multivariable linear regression analysis using a generalized linear model was performed to estimate the effects of FL at 14, 19 and 30 GWs on prediabetic biomarkers at 9 and 15 years of age. Maternal micronutrient and food supplementation group, parity, child sex, and BMI at 9 years or 15 years were included as covariates. RESULTS: A total of 1.2% (6/515) of the participants had impaired fasting glucose during preadolescence, which increased to 3.5% (15/433) during adolescence. At 9 years, 6.3% (32/508) of the participants had elevated HbA1c%, which increased to 28% (120/431) at 15 years. Additionally, the TyG index increased from 9.5% (49/515) (during preadolescence) to 13% (56/433) (during adolescence). A one standard deviation decrease in FL at 14 and 19 GWs was associated with increased FBG (ß = - 0.44 [- 0.88, - 0.004], P = 0.048; ß = - 0.59 [- 1.12, - 0.05], P = 0.031) and HbA1c (ß = - 0.01; [- 0.03, -0.005], P = 0.007; ß = - 0.01 [- 0.03, - 0.003], P = 0.018) levels at 15 years. FL was not associated with diabetic biomarkers at 9 years. CONCLUSION: Mid-trimester impaired femur growth may be associated with elevated prediabetic biomarkers in Bangladeshi adolescents.

Nitrate and ammonium removal in constructed wetlands: Experimental insights and zero-dimensional numerical modeling.

Constructed wetlands (CWs) have emerged as effective wastewater treatment systems, mimicked natural wetland processes but engineered for enhanced pollutant removal efficiency. Ammonium (NH4+) and nitrate (NO3-) are among common pollutants in wastewater, posing significant environmental and health risks. The primary objective of this study is to compares the performance of CWs using gravel and three sizes of natural pumice, along with phragmites australis, in horizontal and horizontal-vertical CWs for nitrate and ammonium removal in the complementary treatment of domestic wastewater. Additionally, the study aims to develop and validate a numerical model using MATLAB software to predict the removal efficiency of these pollutants, thereby contributing to the optimization of CW design and operation. The model operates as a zero-dimensional model based on the law of mass conservation, treating the wetland as a completely mixed reactor, thus avoiding complexities associated with solute movement in porous media. It accurately could predict removal efficiency of chemical, biochemical, and biological indicators while considering active and passive absorption mechanisms by plant uptake. Notably, the determination of coefficients in the model equation does not rely on potentially error-prone laboratory measurements due to sampling issues. Instead, optimization techniques alongside field data robustly estimate these coefficients, ensuring reliability and practicality. Results indicate that higher pollutant concentrations increase reaction rates, particularly enhancing CW efficiency in ammonium removal. Pumice, especially in larger sizes, exhibits superior absorption due to increased porosity and surface area. Overall, the model accurately predicts nitrates concentrations, demonstrating its potential for CW performance optimization and confirming the significance of effective pollutant removal strategies in wastewater treatment.

Optimization and evaluation of modified release solid dosage forms using artificial neural network.

This study aims to optimize and evaluate drug release kinetics of Modified-Release (MR) solid dosage form of Quetiapine Fumarate MR tablets by using the Artificial Neural Networks (ANNs). In training the neural network, the drug contents of Quetiapine Fumarate MR tablet such as Sodium Citrate, Eudragit® L100 55, Eudragit® L30 D55, Lactose Monohydrate, Dicalcium Phosphate (DCP), and Glyceryl Behenate were used as variable input data and Drug Substance Quetiapine Fumarate, Triethyl Citrate, and Magnesium Stearate were used as constant input data for the formulation of the tablet. The in-vitro dissolution profiles of Quetiapine Fumarate MR tablets at ten different time points were used as a target data. Several layers together build the neural network by connecting the input data with the output data via weights, these weights show importance of input nodes. The training process optimises the weights of the drug product excipients to achieve the desired drug release through the simulation process in MATLAB software. The percentage drug release of predicted formulation matched with the manufactured formulation using the similarity factor (f2), which evaluates network efficiency. The ANNs have enormous potential for rapidly optimizing pharmaceutical formulations with desirable performance characteristics.

FDI: A MATLAB tool for computing the fractal dimension index of sources reconstructed from EEG data.

BACKGROUND: The fractal dimension (FD) is a valuable tool for analysing the complexity of neural structures and functions in the human brain. To assess the spatiotemporal complexity of brain activations derived from electroencephalogram (EEG) signals, the fractal dimension index (FDI) was developed. This measure integrates two distinct complexity metrics: 1) integration FD, which calculates the FD of the spatiotemporal coordinates of all significantly active EEG sources (4DFD); and 2) differentiation FD, determined by the complexity of the temporal evolution of the spatial distribution of cortical activations (3DFD), estimated via the Higuchi FD [HFD(3DFD)]. The final FDI value is the product of these two measurements: 4DFD × HFD(3DFD). Although FDI has shown utility in various research on neurological and neurodegenerative disorders, existing literature lacks standardized implementation methods and accessible coding resources, limiting wider adoption within the field. METHODS: We introduce an open-source MATLAB software named FDI for measuring FDI values in EEG datasets. RESULTS: By using CUDA for leveraging the GPU massive parallelism to optimize performance, our software facilitates efficient processing of large-scale EEG data while ensuring compatibility with pre-processed data from widely used tools such as Brainstorm and EEGLab. Additionally, we illustrate the applicability of FDI by demonstrating its usage in two neuroimaging studies. Access to the MATLAB source code and a precompiled executable for Windows system is provided freely. CONCLUSIONS: With these resources, neuroscientists can readily apply FDI to investigate cortical activity complexity within their own studies.

Programmed system for fatigue life prediction of excavator turntables based on multi-body dynamics and finite element analysis.

This study focuses on predicting mechanical fatigue in excavator turntables, critical components susceptible to failure due to variable operational loads. While conventional methods like finite element analysis(FEA) and multiaxial fatigue criteria have been used, they are limited by the complexity and cost of obtaining real operational load spectra. To address this challenge, our research presents a comprehensive approach that integrates multi-body dynamics modeling, finite element analysis, and MATLAB-based fatigue life prediction systems. Our methodology involves creating a finite element model for stress analysis, synthesizing load spectra from operational data, and utilizing Weibull distribution to analyze load magnitude probabilities. Subsequently, MATLAB imported the load spectrum and built the fatigue prediction framework to finalize the analysis. Furthermore, we have fully open-sourced our code on an open platform, incorporating default load profiles and predictive models within the code. Key findings pinpoint areas prone to stress concentration and fatigue. Key findings identify stress concentration areas and fatigue-prone regions, providing valuable insights for design optimization and durability improvement.

Comparative Evaluation of Convolutional Neural Network Object Detection Algorithms for Vehicle Detection.

The domain of object detection was revolutionized with the introduction of Convolutional Neural Networks (CNNs) in the field of computer vision. This article aims to explore the architectural intricacies, methodological differences, and performance characteristics of three CNN-based object detection algorithms, namely Faster Region-Based Convolutional Network (R-CNN), You Only Look Once v3 (YOLO), and Single Shot MultiBox Detector (SSD) in the specific domain application of vehicle detection. The findings of this study indicate that the SSD object detection algorithm outperforms the other approaches in terms of both performance and processing speeds. The Faster R-CNN approach detected objects in images with an average speed of 5.1 s, achieving a mean average precision of 0.76 and an average loss of 0.467. YOLO v3 detected objects with an average speed of 1.16 s, achieving a mean average precision of 0.81 with an average loss of 1.183. In contrast, SSD detected objects with an average speed of 0.5 s, exhibiting the highest mean average precision of 0.92 despite having a higher average loss of 2.625. Notably, all three object detectors achieved an accuracy exceeding 99%.

ElecFeX is a user-friendly toolbox for efficient feature extraction from single-cell electrophysiological recordings.

Characterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Technological developments have enabled the collection of hundreds of neural recordings; this calls for new tools capable of performing feature extraction efficiently. To address the urgent need for a powerful and accessible tool, we developed ElecFeX, an open-source MATLAB-based toolbox that (1) has an intuitive graphical user interface, (2) provides customizable measurements for a wide range of electrophysiological features, (3) processes large-size datasets effortlessly via batch analysis, and (4) yields formatted output for further analysis. We implemented ElecFeX on a diverse set of neural recordings; demonstrated its functionality, versatility, and efficiency in capturing electrical features; and established its significance in distinguishing neuronal subgroups across brain regions and species. ElecFeX is thus presented as a user-friendly toolbox to benefit the neuroscience community by minimizing the time required for extracting features from their electrophysiological datasets.

Unveiling bacterial communication with a MATLAB GUI implementing the diffusion-based quorum sensing model.

Bacteria employ quorum sensing as a remarkable mechanism for coordinating behaviors and communicating within their communities. In this study, we introduce a MATLAB Graphical User Interface (GUI) that offers a versatile platform for exploring the dynamics of quorum sensing. Our computational framework allows for the assessment of quorum sensing, the investigation of parameter dependencies, and the prediction of minimum biofilm thickness required for its initiation. A pivotal observation from our simulations underscores the pivotal role of the diffusion coefficient in quorum sensing, surpassing the influence of bacterial cell dimensions. Varying the diffusion coefficient reveals significant fluctuations in autoinducer concentration, highlighting its centrality in shaping bacterial communication. Additionally, our GUI facilitates the prediction of the minimum biofilm thickness necessary to trigger quorum sensing, a parameter contingent on the diffusion coefficient. This feature provides valuable insights into spatial constraints governing quorum sensing initiation. The interplay between production rates and cell concentrations emerges as another critical facet of our study. We observe that higher production rates or cell concentrations expedite quorum sensing, underscoring the intricate relationship between cell communication and population dynamics in bacterial communities. While our simulations align with mathematical models reported in the literature, we acknowledge the complexity of living organisms, emphasizing the value of our GUI for standardizing results and facilitating early assessments of quorum sensing. This computational approach offers a window into the environmental conditions conducive to quorum sensing initiation, encompassing parameters such as the diffusion coefficient, cell concentration, and biofilm thickness. In conclusion, our MATLAB GUI serves as a versatile tool for understanding the diverse aspects of quorum sensing especially for non-biologists. The insights gained from this computational framework advance our understanding of bacterial communication, providing researchers with the means to explore diverse ecological contexts where quorum sensing plays a pivotal role.

SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework.

BACKGROUND: Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US. RESULTS: This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. CONCLUSIONS: We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.

PAHPhysRAD: A Digital Imaging and Communications in Medicine Research Tool for Segmentation and Radiomic Feature Extraction.

Introduction: Segmentation and analysis of organs at risks (OARs) and tumor volumes are integral concepts in the development of radiotherapy treatment plans and prediction of patients' treatment outcomes. Aims: To develop a research tool, PAHPhysRAD, that can be used to semi- and fully automate segmentation of OARs. In addition, the proposed software seeks to extract 3214 radiomic features from tumor volumes and user-specified dose-volume parameters. Materials and Methods: Developed within MATLAB, PAHPhysRAD provides a comprehensive suite of segmentation tools, including manual, semi-automatic, and automatic options. For semi-autosegmentation, meta AI's Segment Anything Model was incorporated using the bounding box methods. Autosegmentation of OARs and tumor volume are implemented through a module that enables the addition of models in Open Neural Network Exchange format. To validate the radiomic feature extraction module in PAHPhysRAD, radiomic features extracted from gross tumor volume of 15 non-small cell lung carcinoma patients were compared against the features extracted from 3D Slicer™. The dose-volume parameters extraction module was validated using the dose volume data extracted from 28 tangential field-based breast treatment planning datasets. The volume receiving ≥20 Gy (V20) for ipsilateral lung and the mean doses received by the heart and ipsilateral lung, were compared against the parameters extracted from Eclipse. Results: The Wilcoxon signed-rank test revealed no significant difference between the majority of the radiomic features derived from PAHPhysRAD and 3D Slicer. The average mean lung and heart doses calculated in Eclipse were 5.51 ± 2.28 Gy and 1.64 ± 1.98 Gy, respectively. Similarly, the average mean lung and heart doses calculated in PAHPhysRAD were 5.45 ± 2.89 Gy and 1.67 ± 2.08 Gy, respectively. Conclusion: The MATLAB-based graphical user interface, PAHPhysRAD, offers a user-friendly platform for viewing and analyzing medical scans with options to extract radiomic features and dose-volume parameters. Its versatility, compatibility, and potential for further development make it an asset in medical image analysis.

EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis.

Background: At the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application. Methods: We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality. Results: EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT. Conclusion: This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.

Finite Element Analysis of Postoperative Stability of Transverse Scaphoid Waist Fracture.

Background and Purpose: Scaphoid waist fractures are often stabilised with compression screws, Kirschner wires (K-wires), or a combination of both. While clinical and bio-mechanical studies evaluating their utility are available, the ideal configuration of implant that would provide adequate stability to permit early use of the hand is debatable. We examined configurations of a single screw, one screw along with a K-wire, and two K-wires used for a transverse scaphoid waist fracture fixation aiming to assess the stability provided by each in the immediate postoperative period. Methods: Computer-aided design (CAD) models of the scaphoid, K-wire, and headless compression screw were created. A transverse fracture was created at the scaphoid waist, and the CAD models of the screw and K-wire were used to fix the fracture in different configurations in a distal to proximal direction. Finite Element Analysis (FEA) was used to examine the strength of configurations when they were subjected to compression and distraction forces. The total maximum deformation (TDef) and factor of safety (FoS) for each configuration were calculated and used as indirect indicators of postoperative stability. Results: When a single screw was used, the configurations with the screw directed posteriorly from either centre or anterior had the best combined TDef and FoS values. For one screw and one K-wire, the configuration with screw and K-wire parallel to each other with the screw located along the long axis in the AP projection and anterior to the K-wire in the lateral projection had the best combined TDef and FoS values. When using two K-wires, configurations with the two wires diverging proximally on the lateral projection had the best combined TDef and FoS values. Conclusions: When fixing a transverse scaphoid waist fracture with a single screw, the screw directed posteriorly from either the centre or anterior aspect of the distal pole has the best stability, a parallel configuration has the best stability when fixing it using a screw and a K-wire, and divergent configuration has the best stability when fixing it with two K-wires only.

Computational study of the performance of a solar dryer for improvement in the shelf life of the food materials.

In this study, the thermal and drying characteristics of a thin layer food sample were investigated. An indirect type, simple, efficient, and economically feasible solar dryer was fabricated and used for food preservation. However, a dynamic model of a fabricated solar dryer was also presented to gain a better insight into the drying and thermal actions. This model consists of thermal modeling of the drying chamber, solar collector, and solar-dried food sample. The law of conservation of energy was applied to evaluate the temperature at different sections of the solar dryer with respect to drying time. All listed model equations were solved in the MATLAB environment. This study helps to examine the influence of solar radiation on the collector plate temperature, drying chamber temperature, food sample temperature, and performance parameters such as thermal efficiency with respect to drying time. Model data was found in good agreement with experimental data within a 4% error. It is concluded that the drying of food material is affected by air temperature, the collector temperature, mode of heat transfer, and material characteristics such as dimension and mass of the food sample.

SynergyAnalyzer: A Matlab toolbox implementing mixed-matrix factorization to identify kinematic-muscular synergies.

BACKGROUND AND OBJECTIVE: A new direction in the study of motor control was opened about two decades ago with the introduction of a model for the generation of motor commands as combination of muscle synergies. Muscle synergies provide a simple yet quantitative framework for analyzing the hierarchical and modular architecture of the human motor system. However, to gain insights on the functional role of muscle synergies, they should be related to the task space. The recently introduced mixed-matrix factorization (MMF) algorithm extends the standard approach for synergy extraction based on non-negative matrix factorization (NMF) allowing to factorize data constituted by a mixture of non-negative variables (e.g. EMGs) and unconstrained variables (e.g. kinematics, naturally including both positive and negative values). The kinematic-muscular synergies identified by MMF provide a direct link between muscle synergies and the task space. In this contribution, we support the adoption of MMF through a Matlab toolbox for the extraction of kinematic-muscular synergies and a set of practical guidelines to allow biomedical researchers and clinicians to exploit the potential of this novel approach. METHODS: MMF is implemented in the SynergyAnalyzer toolbox using an object-oriented approach. In addition to the MMF algorithm, the toolbox includes standard methods for synergy extraction (NMF and PCA), as well as methods for pre-processing EMG and kinematic data, and for plotting data and synergies. RESULTS: As an example of MMF application, kinematic-muscular synergies were extracted from EMG and kinematic data collected during reaching movements towards 8 targets on the sagittal plane. Instructions and command lines to achieve such results are illustrated in detail. The toolbox has been released as an open-source software on GitHub under the GNU General Public License. CONCLUSIONS: Thanks to its ease of use and adaptability to a variety of datasets, SynergyAnalyzer will facilitate the adoption of MMF to extract kinematic-muscular synergies from mixed EMG and kinematic data, a useful approach in biomedical research to better understand and characterize the functional role of muscle synergies.

Wireless Power Transfer Efficiency Optimization Tracking Method Based on Full Current Mode Impedance Matching.

Wireless power transfer (WPT) technology is a contactless wireless energy transfer method with wide-ranging applications in fields such as smart homes, the Internet of Things (IoT), and electric vehicles. Achieving optimal efficiency in wireless power transfer systems has been a key research focus. In this paper, we propose a tracking method based on full current mode impedance matching for optimizing wireless power transfer efficiency. This method enables efficiency tracking in WPT systems and seamless switching between continuous conduction mode and discontinuous mode, expanding the detection capabilities of the wireless power transfer system. MATLAB was used to simulate the proposed method and validate its feasibility and effectiveness. Based on the simulation results, the proposed method ensures optimal efficiency tracking in wireless power transfer systems while extending detection capabilities, offering practical value and potential for widespread applications.

Voxeloc: A time-saving graphical user interface for localizing and visualizing stereo-EEG electrodes.

BACKGROUND: Thanks to its unrivalled spatial and temporal resolutions and signal-to-noise ratio, intracranial EEG (iEEG) is becoming a valuable tool in neuroscience research. To attribute functional properties to cortical tissue, it is paramount to be able to determine precisely the localization of each electrode with respect to a patient's brain anatomy. Several software packages or pipelines offer the possibility to localize manually or semi-automatically iEEG electrodes. However, their reliability and ease of use may leave to be desired. NEW METHOD: Voxeloc (voxel electrode locator) is a Matlab-based graphical user interface to localize and visualize stereo-EEG electrodes. Voxeloc adopts a semi-automated approach to determine the coordinates of each electrode contact, the user only needing to indicate the deep-most contact of each electrode shaft and another point more proximally. RESULTS: With a deliberately streamlined functionality and intuitive graphical user interface, the main advantages of Voxeloc are ease of use and inter-user reliability. Additionally, oblique slices along the shaft of each electrode can be generated to facilitate the precise localization of each contact. Voxeloc is open-source software and is compatible with the open iEEG-BIDS (Brain Imaging Data Structure) format. COMPARISON WITH EXISTING METHODS: Localizing full patients' iEEG implants was faster using Voxeloc than two comparable software packages, and the inter-user agreement was better. CONCLUSIONS: Voxeloc offers an easy-to-use and reliable tool to localize and visualize stereo-EEG electrodes. This will contribute to democratizing neuroscience research using iEEG.

Pioglitazone Phases and Metabolic Effects in Nanoparticle-Treated Cells Analyzed via Rapid Visualization of FLIM Images.

Fluorescence lifetime imaging microscopy (FLIM) has proven to be a useful method for analyzing various aspects of material science and biology, like the supramolecular organization of (slightly) fluorescent compounds or the metabolic activity in non-labeled cells; in particular, FLIM phasor analysis (phasor-FLIM) has the potential for an intuitive representation of complex fluorescence decays and therefore of the analyzed properties. Here we present and make available tools to fully exploit this potential, in particular by coding via hue, saturation, and intensity the phasor positions and their weights both in the phasor plot and in the microscope image. We apply these tools to analyze FLIM data acquired via two-photon microscopy to visualize: (i) different phases of the drug pioglitazone (PGZ) in solutions and/or crystals, (ii) the position in the phasor plot of non-labelled poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs), and (iii) the effect of PGZ or PGZ-containing NPs on the metabolism of insulinoma (INS-1 E) model cells. PGZ is recognized for its efficacy in addressing insulin resistance and hyperglycemia in type 2 diabetes mellitus, and polymeric nanoparticles offer versatile platforms for drug delivery due to their biocompatibility and controlled release kinetics. This study lays the foundation for a better understanding via phasor-FLIM of the organization and effects of drugs, in particular, PGZ, within NPs, aiming at better control of encapsulation and pharmacokinetics, and potentially at novel anti-diabetics theragnostic nanotools.

suMRak: a multi-tool solution for preclinical brain MRI data analysis.

Introduction: Magnetic resonance imaging (MRI) is invaluable for understanding brain disorders, but data complexity poses a challenge in experimental research. In this study, we introduce suMRak, a MATLAB application designed for efficient preclinical brain MRI analysis. SuMRak integrates brain segmentation, volumetry, image registration, and parameter map generation into a unified interface, thereby reducing the number of separate tools that researchers may require for straightforward data handling. Methods and implementation: All functionalities of suMRak are implemented using the MATLAB App Designer and the MATLAB-integrated Python engine. A total of six helper applications were developed alongside the main suMRak interface to allow for a cohesive and streamlined workflow. The brain segmentation strategy was validated by comparing suMRak against manual segmentation and ITK-SNAP, a popular open-source application for biomedical image segmentation. Results: When compared with the manual segmentation of coronal mouse brain slices, suMRak achieved a high Sørensen-Dice similarity coefficient (0.98 ± 0.01), approaching manual accuracy. Additionally, suMRak exhibited significant improvement (p = 0.03) when compared to ITK-SNAP, particularly for caudally located brain slices. Furthermore, suMRak was capable of effectively analyzing preclinical MRI data obtained in our own studies. Most notably, the results of brain perfusion map registration to T2-weighted images were shown, improving the topographic connection to anatomical areas and enabling further data analysis to better account for the inherent spatial distortions of echoplanar imaging. Discussion: SuMRak offers efficient MRI data processing of preclinical brain images, enabling researchers' consistency and precision. Notably, the accelerated brain segmentation, achieved through K-means clustering and morphological operations, significantly reduces processing time and allows for easier handling of larger datasets.

Exploring the acoustic potential of 3D printed micro-perforated panels: A comparative analysis.

In the present study, the sound absorption performance of inhomogeneous Micro-Perforated Panels (MPPs) with multiple cavities is investigated. Two models, a three-cavity system and a four-cavity system, are proposed and a numerical study is performed using MATLAB. The models are validated through experimental analysis in an impedance tube. The study meticulously varies the geometrical parameters, including pore diameter, thickness of the MPP, perforation ratio, and back-cavity length. It is found that MPPs with a greater number of sub-cavities have a better sound absorption coefficient than two-cavity systems. The results suggest that the back air cavity is predominantly responsible for multiple peaks, ensuring wideband sound absorption. It is also found that smaller perforation ratios for sub-cavities with larger pore diameters improve sound absorption performance in the lower frequency region. The study indicates that a pore diameter of less than 0.5 mm should be used for better sound absorption above the range of 800-850 Hz, and back cavity length has greater control than pore diameter between 850 Hz and 2000 Hz to make the curve smooth with less fluctuation. The findings have significant implications for the design of MPPs for real-world applications.