In vitro analysis of postbiotic antimicrobial activity against Candida Species in a minimal synthetic model simulating the gut mycobiota in obesity.

Gut fungal imbalances, particularly increased Candida spp., are linked to obesity. This study explored the potential of Lactiplantibacillus plantarum cell-free extracts (postbiotics) to modulate the growth of Candida albicans and Candida kefyr, key members of the gut mycobiota. A minimal synthetic gut model was employed to evaluate the effects of Lactiplantibacillus plantarum postbiotics on fungal growth in mono- and mixed cultures. Microreactors were employed for culturing, fungal growth was quantified using CFU counting, and regression analysis was used to evaluate the effects of postbiotics on fungal growth. Postbiotics at a concentration of 12.5% significantly reduced the growth of both Candida species. At 24 h, both C. albicans and C. kefyr in monocultures exhibited a decrease in growth of 0.11 log CFU/mL. In contrast, mixed cultures showed a more pronounced antifungal effect, with C. albicans and C. kefyr reductions of 0.62 log CFU/mL and 0.64 log CFU/mL, respectively. Regression analysis using the Gompertz model supported the antifungal activity of postbiotics and revealed species-specific differences in growth parameters. These findings suggest that L. plantarum postbiotics have the potential to modulate the gut mycobiota by reducing Candida growth, potentially offering a therapeutic approach for combating fungal overgrowth associated with obesity.

Associations between bacterial and fungal communities in the human gut microbiota and their implications for nutritional status and body weight.

This study examined the interplay between bacterial and fungal communities in the human gut microbiota, impacting on nutritional status and body weight. Cohorts of 10 participants of healthy weight, 10 overweight, and 10 obese individuals, underwent comprehensive analysis, including dietary, anthropometric, and biochemical evaluations. Microbial composition was studied via gene sequencing of 16S and ITS rDNA regions, revealing bacterial (bacteriota) and fungal (mycobiota) profiles. Bacterial diversity exceeded fungal diversity. Statistically significant differences in bacterial communities were found within healthy-weight, overweight, and obese groups. The Bacillota/Bacteroidota ratio (previously known as the Firmicutes/Bacteroidetes ratio) correlated positively with body mass index. The predominant fungal phyla were Ascomycota and Basidiomycota, with the genera Nakaseomyces, Kazachstania, Kluyveromyces, and Hanseniaspora, inversely correlating with weight gain; while Saccharomyces, Debaryomyces, and Pichia correlated positively with body mass index. Overweight and obese individuals who harbored a higher abundance of Akkermansia muciniphila, demonstrated a favorable lipid and glucose profiles in contrast to those with lower abundance. The overweight group had elevated Candida, positively linked to simple carbohydrate consumption. The study underscores the role of microbial taxa in body mass index and metabolic health. An imbalanced gut bacteriota/mycobiota may contribute to obesity/metabolic disorders, highlighting the significance of investigating both communities.

State identification for a class of uncertain switched systems by differential neural networks.

This paper presents a non-parametric identification scheme for a class of uncertain switched nonlinear systems based on continuous-time neural networks. This scheme is based on a continuous neural network identifier. This adaptive identifier guaranteed the convergence of the identification errors to a small vicinity of the origin. The convergence of the identification error was determined by the Lyapunov theory supported by a practical stability variation for switched systems. The same stability analysis generated the learning laws that adjust the identifier structure. The upper bound of the convergence region was characterized in terms of uncertainties and noises affecting the switched system. A second finite-time convergence learning law was also developed to describe an alternative way of forcing the identification error's stability. The study presented in this paper described a formal technique for analysing the application of adaptive identifiers based on continuous neural networks for uncertain switched systems. The identifier was tested for two basic problems: a simple mechanical system and a switched representation of the human gait model. In both cases, accurate results for the identification problem were achieved.

Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication.

The increasing demand for water and worsening climate change place significant pressure on this vital resource, making its preservation a global priority. Water quality monitoring programs are essential for effectively managing this resource. Current programs rely on traditional monitoring approaches, leading to limitations such as low spatiotemporal resolution and high operational costs. Despite the adoption of novel monitoring approaches that enable better data resolution, the public's comprehension of water quality matters remains low, primarily due to communication process deficiencies. This study explores the advantages and challenges of using Internet of Things (IoT) and citizen science as alternative monitoring approaches, emphasizing the need for enhancing public communication of water quality data. Through a systematic review of studies implemented on-field, we identify and propose strategies to address five key challenges that IoT and citizen science monitoring approaches must overcome to mature into robust sources of water quality information. Additionally, we highlight three fundamental problems affecting the water quality communication process and outline strategies to convey this topic effectively to the public.

Modeling bacteria pairwise interactions in human microbiota by Sparse Identification of Nonlinear Dynamics (SINDy).

The gut microbiota is a community of high complexity; its composition changes due to ecological interactions, these are studied to understand the relationship with the human health. External stimuli like the administration of probiotics, prebiotics, or drugs are known to modify these interactions. The high complexity of microbiota composition can be studied by considering pairwise interactions. Pairwise interactions in bacterial communities consider each species' directionality and impact on one another, e.g., commensalism (unidirectional positive interaction) or competition (bidirectional negative interaction). These interactions can either be interspecies or intraspecies. The Lotka-Volterra (LV) model has been implemented to characterize these bacteria interactions, considering the ecological relationship among the different species presented. One of the main challenges is determining the specific interaction parameters in LV structure from experimental data. This study implemented a novel approach based on the sparse identification of nonlinear dynamic method (SINDy). One of the assumptions in SINDy method implies the knowledge of the data derivative structure. To fulfill this requirement, a differential neural network algorithm was implemented. We assessed the performance of this approach considering both a simulated and experimental interspecies scenario. A two-species bacterial LV model was simulated in the initial validation stage, and the resulting kinetic growth data was recorded. This data was utilized for training a differential neural network algorithm, which was used to derive a time-derivative structure for the dataset. After this step, SINDy method was implemented to calculate the interaction parameters. Three conditions were evaluated in intraspecies competition, obtaining an average identification parametric error of less than 2%. For experimental data, parametric analysis results are sensitive to detect the influence of a drug presence over the intraspecies interaction with a reduction of 50% in its typical values.Clinical Relevance- In this study, we devised a strategy to determine how two species of the human gastrointestinal microbiota interact and the impact of drug administration on these interactions.

Electromyographic signals analysis to assess the response of a proprioceptive neuromuscular facilitation pattern execution.

Proprioceptive Neuromuscular Facilitation is a rehabilitation technique that consists of the stimulation of a healthy muscle in one extremity of the body to produce an activation effect of a damaged muscle in another extremity, laterally or contralaterally. The use of the analysis of the electromyographic response during the process allows us to describe and evaluate if the damaged muscle produces an activation. This paper presents the progress of the results of a clinical protocol where PNF is explored in healthy subjects, manipulating the upper limb, and recording the electromyographic response of the lower limbs in three different muscles in both inferior limbs. Four activation patterns (movement sequence) with three different stages with different intensities of resistance are considered. Lateral plane video recording analysis is performed to extract the trajectory of the manipulated limb and correlate this information with electromyography signals. The electromyography signals are analyzed in their temporal response by integrating the rectified and smoothed signal according to the beginning and end of the trajectory of the manipulated superior limb. Additionally, the spectrogram analysis of the signals is performed. The partial results of the protocol allow us to establish that the response in the most distal muscles to the manipulated upper limb has been non-significant for the subjects, compared to the response of the proximal muscles, where a relationship with activation and trajectory of the arm is observed. particularly in the process with greater resistance. The observed changes in signal frequency across the spectrogram correspond to low-frequency changes in the 15 to 20Hz band.Clinical Relevance- This work presents a strategy to assess the rehabilitation process based on proprioceptive neuromuscular facilitation by analyzing the time and time-frequency response of the electromyographic signal.

Design and Analysis of a Rotational Mixer to produce 3D Bioprinting Gyroid-Helical-Patterned Scaffolds for Tissue Engineering Applications.

Tissue engineering scaffolds require complex networks for nutrient diffusion and cell attachment. They must have specific surface area and curvature, and often need a multimaterial composition, demanding advanced micro-fabrication methods. 3D extrusion bioprinting offers versatility to manufacture different scaffold, and strategies for multimaterial printing have been introduced. We propose a method to fabricate scaffolds based on gyroid-helical-patterned microfibers, providing a platform to study the effect of the gyroid minimum curvature on cellular processes, since the geometry wont be layer-by-layer approximated. The pattern is obtained by mixing inks using a gyroid-helix shaped rotational mixer, modifying the extruder of a conventional 3D printer. The mixer was simulated using computational fluid dynamics tools, varying the volumetric flow to obtain different gyroid-thickness. Due to its surface area minimization, it shows lower energy requirements than state-of-art fluid mixers, with a pressure drop of 1.7%, a power number of 39, and a rotation-induced shear stress of ∼400 Pa, enabling the use of cell-embedded bioinks.

Review on Porous Scaffolds Generation Process: A Tissue Engineering Approach.

Porous scaffolds have been widely explored for tissue regeneration and engineering in vitro three-dimensional models. In this review, a comprehensive literature analysis is conducted to identify the steps involved in their generation. The advantages and disadvantages of the available techniques are discussed, highlighting the importance of considering pore geometrical parameters such as curvature and size, and summarizing the requirements to generate the porous scaffold according to the desired application. This paper considers the available design tools, mathematical models, materials, fabrication techniques, cell seeding methodologies, assessment methods, and the status of pore scaffolds in clinical applications. This review compiles the relevant research in the field in the past years. The trends, challenges, and future research directions are discussed in the search for the generation of a porous scaffold with improved mechanical and biological properties that can be reproducible, viable for long-term studies, and closer to being used in the clinical field.

Creation and Analysis of Technological Intelligence Reports as Educative Tool in Biomedical Engineering.

Identifying and analyzing quality information sources of technical information is a fundamental skill to create and maintain a technological intelligence system. In the present work, we propose the intentional training of undergraduate biomedical engineering students to create a simple technological intelligence report by analyzing scientific publications and patents. Computerized Maintenance Management Systems (CMMS) focused on the medical devices industry is proposed as the central topic, results suggest that the creation of technological intelligence report generates value to the undergraduate biomedical engineering students given them a tool to identify the impact of new technology in healthcare and medical devices.

Measuring Choroid Thickness as a Marker of Systemic Inflammation in Patients With Ankylosing Spondylitis.

BACKGROUND AND OBJECTIVE: Ankylosing spondylitis (AS) is an inflammatory disease, and choroidal thickness (CT) has been proposed and evaluated as a potential marker of systemic inflammation associated with AS and other inflammatory diseases. This study compared CT measurements taken from patients with severe AS disease activity without eye inflammation with those taken from healthy subjects. METHODS: This cross-sectional, multicenter study compared CT in 44 patients with high AS disease activity, and no history of eye inflammation with CT in 44 matched healthy subjects aged between 18 and 65 years. In the AS group, the correlation between CT and C-reactive protein, human leukocyte antigen (HLA) B27 positivity, disease duration, and disease activity was calculated. RESULTS: Mean CT values of patients with AS were significantly higher in the right eye, the left eye, and the thickest choroid eye. The right eye mean CT was 338.3 ± 82.8 µm among patients with AS and 290.5 ± 71.2 µm among healthy subjects (p = 0.005). The left eye mean CT was 339.5 ± 84.7 µm for patients with AS and 298.4 ± 68.9 µm for healthy subjects (P = 0.015). The thickest choroid eye CT was 358.4 ± 82.1 µm among patients with AS and 314.1 ± 65.2 µm among healthy subjects (P = 0.006). We did not find a significant correlation between CT and disease activity, C-reactive protein, human leukocyte antigen B27 positivity, or disease duration. CONCLUSIONS: Patients with active AS but without a history of eye inflammation had a thicker choroid than healthy subjects. This finding suggests that CT is a marker of systemic inflammation in patients with inflammatory disease, regardless of known eye symptoms.

Assessment of the water quality of a subtropical lake using the NSF-WQI and a newly proposed ecosystem specific water quality index.

A Water Quality Index (WQI) is a formulation that enables the estimation of the overall quality of a water body based on significant parameters. One example of this is the well-known and widely accepted NSF-WQI, which is frequently used to assess chemical, physical, and microbiologic features of waterbodies in temperate latitudes. In this work, a well-structured method, completely based on multivariate statistical methods and historical data distributions, was used to develop an ecosystem specific water quality index (ES-WQI). Lake Cajititlán, a subtropical Mexican lake located in Tlajomulco de Zúñiga, was selected as a case of study because it is an endorheic shallow lake that shows signs of high levels of eutrophication due to anthropogenic contamination. As a result of the contamination, and its sensibility to changes in the water level, it undergoes important changes in its water features, such as turbidity and intense green color, and experiences massive events of fish mortality. The proposed ES-WQI describes the changes in water quality over the year well and correlates with the capability of the lake to support aquatic life, as the lowest estimated values coincide with the biggest events of massive fish mortality in the lake. Furthermore, the ES-WQI clearly differentiates between typical cyclic behaviors and actual deteriorating trends and is capable of tracking incremental changes all over the range of the possible concentration values of the water quality parameters.

Choroidal Thickness Is a Biomarker Associated With Response to Treatment in Ankylosing Spondylitis.

OBJECTIVE: Choroidal thickness (CT) has been evaluated as a marker of systemic inflammation in ankylosing spondylitis (AS). This study evaluates the CT of AS patients before and after 6 months of biological treatment. METHODS: This longitudinal multicenter study evaluated CT in 44 AS patients. The correlations between CT and C-reactive protein (CRP) with disease activity indices were calculated. The concordance between CT and CRP was determined. We assessed factors associated with response to treatment. Clinically important improvement was defined as a decrease in Ankylosing Spondylitis Disease Activity Score of 1.1 points or greater. RESULTS: Forty-four eyes in patients aged 18 to 65 years were included. Mean CT values were significantly higher at baseline than after 6 months of treatment (baseline: 355.28 ± 80.46 µm; 6 months: 341.26 ± 81.06 µm; p < 0.001). There was a 95% concordance between CT and CRP at baseline and 6 months. Clinically important improvement was associated with lower baseline CT and age as independent factors (odds ratios, 0.97 [95% confidence interval, 0.91-0.93; p = 0.009] and 0.81 [95% confidence interval, 0.7-0.95; p = 0.005]), with baseline CT of less than 374 µm (sensitivity 78%, specificity 78%, area under the curve 0.70, likelihood ratio 3.6). CONCLUSIONS: Choroidal thickness decreased significantly after 6 months of biological treatment in all treatment groups. Choroidal thickness and CRP had a 95% concordance. A high CT was associated with a risk of biological treatment failure. Choroidal thickness can be considered a useful biomarker of inflammation and a factor associated with response to treatment in AS.

Multivariate water quality analysis of Lake Cajititlán, Mexico.

Lake Cajititlán is a shallow body of water located in an endorheic basin in western Mexico. This lake receives excess fertilizer runoff from agriculture and approximately 2.3 Hm3 per year of poorly treated wastewater from three municipal treatment plants. Thirteen water quality parameters were monitored at five sampling points within the lake over 9 years. The objective of this work was to characterize the spatial and temporal variations of the water quality and to identify the sources of data variability in order to assess the influence and the impact of different natural and anthropogenic processes. One-way ANOVA tests, principal component analysis (PCA), cluster analysis (CA), and discriminant analysis (DA) were implemented. The one-way ANOVA showed that biochemical oxygen demand and pH present statistically significant spatial variations and that alkalinity, total chloride, conductivity, chemical oxygen demand, total hardness, ammonia, pH, total dissolved solids, and temperature present statistically significant temporal variations. PCA results explained both natural and anthropogenic processes and their relationship with water quality data. The CA results suggested there is no significant spatial variation in the water quality of the lake because of lake mixing caused by wind. The most significant parameters for spatial variations were pH, NO3-, and NO2-, consistent with the configuration of point and nonpoint sources that affect the lake's water quality. The temporal DA results suggested that conductivity, hardness, NO2-, pH, and temperature were the most significant parameters to discriminate between seasons. The temporal behavior of these parameters was associated with the transport pathways of seasonal contaminants.

Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL classification by immunohistochemistry (IHC)-based decision tree algorithms is a simpler reported technique than gene expression profiling (GEP). There is a significant discrepancy between IHC-decision tree algorithms when they are compared to GEP. METHODS: To address these inconsistencies, we applied the machine learning approach considering the same combinations of antibodies as in IHC-decision tree algorithms. Immunohistochemistry data from a public DLBCL database was used to perform comparisons among IHC-decision tree algorithms, and the machine learning structures based on Bayesian, Bayesian simple, Naïve Bayesian, artificial neural networks, and support vector machine to show the best diagnostic model. We implemented the linear discriminant analysis over the complete database, detecting a higher influence of BCL6 antibody for GCB classification and MUM1 for non-GCB classification. RESULTS: The classifier with the highest metrics was the four antibody-based Perfecto-Villela (PV) algorithm with 0.94 accuracy, 0.93 specificity, and 0.95 sensitivity, with a perfect agreement with GEP (κ = 0.88, P < 0.001). After training, a sample of 49 Mexican-mestizo DLBCL patient data was classified by COO for the first time in a testing trial. CONCLUSIONS: Harnessing all the available immunohistochemical data without reliance on the order of examination or cut-off value, we conclude that our PV machine learning algorithm outperforms Hans and other IHC-decision tree algorithms currently in use and represents an affordable and time-saving alternative for DLBCL cell-of-origin identification.

Hemodynamic Pattern Recognition During Deception Process Using Functional Near-infrared Spectroscopy.

Deception is considered a psychological process by which one individual deliberately attempts to convince another person to accept as true what the liar knows to be false. This paper presents the use of functional near-infrared spectroscopy for deception detection. This technique measures hemodynamic variations in the cortical regions induced by neural activations. The experimental setup involved a mock theft paradigm with ten subjects, where the subjects responded to a set of questions, with each of their answers belonging to one of three categories: Induced Lies, Induced Truths, and Non-Induced responses. The relative changes of the hemodynamic activity in the subject's prefrontal cortex were recorded during the experiment. From this data, the changes in blood volume were derived and represented as false color topograms. Finally, a human evaluator used these topograms as a guide to classify each answer into one of the three categories. His performance was compared with that of a support vector machine (SVM) classifier in terms of accuracy, specificity, and sensitivity. The human evaluator achieved an accuracy of 84.33 % in a tri-class problem and 92 % in a bi-class problem (induced vs. non-induced responses). In comparison, the SVM classifier correctly classified 95.63 % of the answers in a tri-class problem using cross-validation for the selection of the best features. These results suggest a tradeoff between accuracy and computational burden. In other words, it is possible for an interviewer to classify each response by only looking at the topogram of the hemodynamic activity, but at the cost of reduced prediction accuracy.

Platform for the study of virtual task-oriented motion and its evaluation by EEG and EMG biopotentials.

This paper presents a platform to study the relationship between upper limb kinematic and biopotential measurements. The platform comprises of a haptic joystick, biopotential acquisition systems and 3D rendered virtual tasks that require user interaction. The haptic joystick, named TeeR, reproduces the pronation-supination and flexion-extension movements of the human arm, which are directly mapped to a 2D graphic display. The biopotential acquisition system is able to record electroencephalography (EEG) and electromyography (EMG) signals and synchronize them with kinematic data obtained from the Tee-R. The 3D virtual tasks are designed to obtain performance measurements from the user interaction. We include an example that depicts the possibilities of application for the study of event-related (de)synchronization (ERD/ERS) based on EEG during motor tasks.