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Most tissues are continuously renovated through the division of stem cells and the death of old or damaged cells, which is known as cell turnover rate (CTOR). Despite being in steady state, tissues have different population dynamics and leading to diverse clonality levels. Here, we propose and test that cell population dynamics can be a cancer driver. We employed the evolutionary software esiCancer to show that CTOR, within a range comparable to what is observed in human tissues, can amplify the risk of a mutation due to ancestral selection (ANSEL). In a high CTOR tissue, a mutated ancestral cell is likely to be selected and persist over generations, which leads to a scenario of elevated ANSEL profile, characterized by few niches of large clones, which does not occur in low CTOR. We found that CTOR is significantly associated with the risk of developing cancer, even when correcting for mutation load, indicating that population dynamics per se is a cancer driver. This concept is central to understanding cancer risk and for the design of new therapeutic interventions that minimize the contribution of ANSEL in cancer growth.

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One of the cognitive abilities most affected by substance abuse is decision-making. Behavioral tasks such as the Iowa Gambling Task (IGT) provide a means to measure the learning process involved in decision-making. To comprehend this process, three hypotheses have emerged: (1) participants prioritize gains over losses, (2) they exhibit insensitivity to losses, and (3) the capacity of operational storage or working memory comes into play. A dynamic model was developed to examine these hypotheses, simulating sensitivity to gains and losses. The Linear Operator model served as the learning rule, wherein net gains depend on the ratio of gains to losses, weighted by the sensitivity to both. The study further proposes a comparison between the performance of simulated agents and that of substance abusers (n = 20) and control adults (n = 20). The findings indicate that as the memory factor increases, along with high sensitivity to losses and low sensitivity to gains, agents prefer advantageous alternatives, particularly those with a lower frequency of punishments. Conversely, when sensitivity to gains increases and the memory factor decreases, agents prefer disadvantageous alternatives, especially those that result in larger losses. Human participants confirmed the agents' performance, particularly when contrasting optimal and sub-optimal outcomes. In conclusion, we emphasize the importance of evaluating the parameters of the linear operator model across diverse clinical and community samples.

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When updating beliefs, humans tend to integrate more desirable information than undesirable information. In stable environments (low uncertainty and high predictability), this asymmetry favors motivation towards action and perceived self-efficacy. However, in changing environments (high uncertainty and low predictability), this process can lead to risk underestimation and increase unwanted costs. Here, we examine how people (n = 388) integrate threatening information during an abrupt environmental change (mandatory quarantine during the COVID-19 pandemic). Given that anxiety levels are associated with the magnitude of the updating belief asymmetry; we explore its relationship during this particular context. We report a significant reduction in asymmetrical belief updating during a large environmental change as individuals integrated desirable and undesirable information to the same extent. Moreover, this result was supported by computational modeling of the belief update task. However, we found that the reduction in asymmetrical belief updating was not homogeneous among people with different levels of Trait-anxiety. Individuals with higher levels of Trait-anxiety maintained a valence-dependent updating, as it occurs in stable environments. On the other hand, updating behavior was not associated with acute anxiety (State-Anxiety), health concerns (Health-Anxiety), or having positive expectations (Trait-Optimism). These results suggest that highly uncertain environments can generate adaptive changes in information integration. At the same time, it reveals the vulnerabilities of individuals with higher levels of anxiety to adapt the way they learn.

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This paper presents a comprehensive review of natural fiber-reinforced composites (NFRCs) for lower-limb prosthetic designs. It covers the characteristics, types, and properties of natural fiber-reinforced composites as well as their advantages and drawbacks in prosthetic designs. This review also discusses successful prosthetic designs that incorporate NFRCs and the factors that make them effective. Additionally, this study explores the use of computational biomechanical models to evaluate the effectiveness of prosthetic devices and the key factors that are considered. Overall, this document provides a valuable resource for anyone interested in using NFRCs for lower-limb prosthetic designs.

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Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease with the major symptoms comprising loss of movement coordination (motor dysfunction) and non-motor dysfunction, including gastrointestinal symptoms. Alterations in the gut microbiota composition have been reported in PD patients vs. controls. However, it is still unclear how these compositional changes contribute to disease etiology and progression. Furthermore, most of the available studies have focused on European, Asian, and North American cohorts, but the microbiomes of PD patients in Latin America have not been characterized. To address this problem, we obtained fecal samples from Colombian participants (n = 25 controls, n = 25 PD idiopathic cases) to characterize the taxonomical community changes during disease via 16S rRNA gene sequencing. An analysis of differential composition, diversity, and personalized computational modeling was carried out, given the fecal bacterial composition and diet of each participant. We found three metabolites that differed in dietary habits between PD patients and controls: carbohydrates, trans fatty acids, and potassium. We identified six genera that changed significantly in their relative abundance between PD patients and controls, belonging to the families Lachnospiraceae, Lactobacillaceae, Verrucomicrobioaceae, Peptostreptococcaceae, and Streptococcaceae. Furthermore, personalized metabolic modeling of the gut microbiome revealed changes in the predicted production of seven metabolites (Indole, tryptophan, fructose, phenylacetic acid, myristic acid, 3-Methyl-2-oxovaleric acid, and N-Acetylneuraminic acid). These metabolites are associated with the metabolism of aromatic amino acids and their consumption in the diet. Therefore, this research suggests that each individual's diet and intestinal composition could affect host metabolism. Furthermore, these findings open the door to the study of microbiome-host interactions and allow us to contribute to personalized medicine.

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Breast cancer is one of the leading causes of death among women worldwide and can be classified into four major distinct molecular subtypes based on the expression of specific receptors. Despite significant advances, the lack of biomarkers for detailed diagnosis and prognosis remains a major challenge in the field of oncology. This study aimed to identify short single-stranded oligonucleotides known as aptamers to improve breast cancer diagnosis. The Cell-SELEX technique was used to select aptamers specific to the MDA-MB-231 tumor cell line. After selection, five aptamers demonstrated specific recognition for tumor breast cell lines and no binding to non-tumor breast cells. Validation of aptamer specificity revealed recognition of primary and metastatic tumors of all subtypes. In particular, AptaB4 and AptaB5 showed greater recognition of primary tumors and metastatic tissue, respectively. Finally, a computational biology approach was used to identify potential aptamer targets, which indicated that CSKP could interact with AptaB4. These results suggest that aptamers are promising in breast cancer diagnosis and treatment due to their specificity and selectivity.

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The persistence and potential toxicity of emergent pollutants pose significant threats to biodiversity and human health, emphasizing the need for sensors capable of detecting these pollutants at extremely low concentrations before treatment. This study focuses on the development of glassy carbon electrodes (GCEs) modified by films of poly-tris(4-(4-(carbazol-9-yl)phenyl)silanol (PTPTCzSiOH), poly-4,4'-Di(carbazol-9-yl)-1,1'-biphenyl (PCBP), and poly-1,3,5-tri(carbazol-9-yl)benzene (PTCB) for the detection of metronidazole (MNZ) in aqueous media. The films were characterized using electrochemical, microscopy, and spectroscopy techniques, including scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). Monomers were electropolymerized through cyclic voltammetry and chronoamperometry techniques. Computational methods at the B3LYP/def2-TZVP level were employed to investigate the structural and electrochemical properties of the monomers. The electrochemical detection of MNZ utilized the linear sweep voltammetry technique. Surface characterization through SEM and XPS confirmed the proper electrodeposition of polymer films. Notably, MPN-GCEs exhibited higher detection signals compared to bare GCEs up to 3.6 times in the case of PTPTCzSiOH-GCEs. This theoretical study provides insights into the structural, chemical, and electronic properties of the polymers. The findings suggest that polymer-modified GCEs hold promise as candidates for the development of electrochemical sensors.

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It is reported the synthesis, characterization by elemental analysis, thermogravimetry; electronic absorption, infrared, excitation, and emission spectroscopies of the [Eu(12C4)(phen)2(X)n]X2 complexes, where 12C4 = 12-crown-4, phen = 1,10-phenanthroline, and X  = F-, Cl-, Br-, SCN-, ClO4-, and NO3-. It is verified that the polarizability of the anion X- exerts remarkable effects on the emission process. As a general trend, lower wavenumbers for the 7F0→5L6, 7F0→5D2 and 7F0→5D1 transitions are associated with the anions with higher volumes and, consequently, higher polarizability. The molecular modeling results performed with quantum methods (RHF and DFT) suggest some relationships between the calculated structures, electronic, and luminescence properties with the presence of the LMCT (ligand-to-metal charge transfer) states, which explains the differences in the emission spectra of these complexes due to the coordinated anion.

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Objective: Transcranial direct current stimulation (tDCS) has mixed effects for major depressive disorder (MDD) symptoms, partially owing to large inter-experimental variability in tDCS protocols and their correlated induced electric fields (E-fields). We investigated whether the E-field strength of distinct tDCS parameters was associated with antidepressant effect. Methods: A meta-analysis was performed with placebo-controlled clinical trials of tDCS enrolling MDD patients. PubMed, EMBASE, and Web of Science were searched from inception to March 10, 2023. Effect sizes of tDCS protocols were correlated with E-field simulations (SimNIBS) of brain regions of interest (bilateral dorsolateral prefrontal cortex [DLPFC] and bilateral subgenual anterior cingulate cortex [sgACC]). Moderators of tDCS responses were also investigated. Results: A total of 20 studies were included (21 datasets, 1,008 patients), using 11 distinct tDCS protocols. Results revealed a moderate effect for MDD (g = 0.41, 95%CI 0.18-0.64), while cathode position and treatment strategy were found to be moderators of response. A negative association between effect size and tDCS-induced E-field magnitude was seen, with stronger E-fields in the right frontal and medial parts of the DLPFC (targeted by the cathode) leading to smaller effects. No association was found for the left DLPFC and the bilateral sgACC. An optimized tDCS protocol is proposed. Conclusions: Our results highlight the need for a standardized tDCS protocol in MDD clinical trials. Registration number: PROSPERO CRD42022296246.

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OBJECTIVE: Transcranial direct current stimulation (tDCS) has mixed effects for major depressive disorder (MDD) symptoms, partially owing to large inter-experimental variability in tDCS protocols and their correlated induced electric fields (E-fields). We investigated whether the E-field strength of distinct tDCS parameters was associated with antidepressant effect. METHODS: A meta-analysis was performed with placebo-controlled clinical trials of tDCS enrolling MDD patients. PubMed, EMBASE, and Web of Science were searched from inception to March 10, 2023. Effect sizes of tDCS protocols were correlated with E-field simulations (SimNIBS) of brain regions of interest (bilateral dorsolateral prefrontal cortex [DLPFC] and bilateral subgenual anterior cingulate cortex [sgACC]). Moderators of tDCS responses were also investigated. RESULTS: A total of 20 studies were included (21 datasets, 1,008 patients), using 11 distinct tDCS protocols. Results revealed a moderate effect for MDD (g = 0.41, 95%CI 0.18-0.64), while cathode position and treatment strategy were found to be moderators of response. A negative association between effect size and tDCS-induced E-field magnitude was seen, with stronger E-fields in the right frontal and medial parts of the DLPFC (targeted by the cathode) leading to smaller effects. No association was found for the left DLPFC and the bilateral sgACC. An optimized tDCS protocol is proposed. CONCLUSION: Our results highlight the need for a standardized tDCS protocol in MDD clinical trials.

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Beyond words and gestures, people have a remarkable capacity to communicate indirectly through everyday objects: A hat on a chair can mean it is occupied, rope hanging across an entrance can mean we should not cross, and objects placed in a closed box can imply they are not ours to take. How do people generate and interpret the communicative meaning of objects? We hypothesized that this capacity is supported by social goal inference, where observers recover what social goal explains an object being placed in a particular location. To test this idea, we study a category of common ad-hoc communicative objects where a small cost is used to signal avoidance. Using computational modeling, we first show that goal inference from indirect physical evidence can give rise to the ability to use object placement to communicate. We then show that people from the U.S. and the Tsimane'-a farming-foraging group native to the Bolivian Amazon-can infer the communicative meaning of object placement in the absence of a pre-existing convention, and that people's inferences are quantitatively predicted by our model. Finally, we show evidence that people can store and retrieve this meaning for use in subsequent encounters, revealing a potential mechanism for how ad-hoc communicative objects become quickly conventionalized. Our model helps shed light on how humans use their ability to interpret other people's behavior to embed social meaning into the physical world.

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Ovarian cancer is among the seven most common types of cancer in women, being the most fatal gynecological tumor, due to the difficulty of detection in early stages. Aptamers are important tools to improve tumor diagnosis through the recognition of specific molecules produced by tumors. Here, aptamers and their potential targets in ovarian cancer cells were analyzed by in silico approaches. Specific aptamers were selected by the Cell-SELEX method using Caov-3 and OvCar-3 cells. The five most frequent aptamers obtained from the last round of selection were computationally modeled. The potential targets for those aptamers in cells were proposed by analyzing proteomic data available for the Caov-3 and OvCar-3 cell lines. Overexpressed proteins for each cell were characterized as to their three-dimensional model, cell location, and electrostatic potential. As a result, four specific aptamers for ovarian tumors were selected: AptaC2, AptaC4, AptaO1, and AptaO2. Potential targets were identified for each aptamer through Molecular Docking, and the best complexes were AptaC2-FXYD3, AptaC4-ALPP, AptaO1-TSPAN15, and AptaO2-TSPAN15. In addition, AptaC2 and AptaO1 could detect different stages and subtypes of ovarian cancer tissue samples. The application of this technology makes it possible to propose new molecular biomarkers for the differential diagnosis of epithelial ovarian cancer.

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This article evaluates a hip joint socket design by finite element method (FEM). The study was based on the needs and characteristics of a patient with an oncological amputation; however, the solution and the presented method may be generalized for patients with similar conditions. The research aimed to solve a generalized problem, taking a typical case from the study area as a reference. Data were collected on the use of the current improving prosthesis-specifically in interaction with its socket-to obtain information on the new approach design: this step constituted the work's starting point, where the problems to be solved in conventional designs were revealed. Currently, the development of this type of support does not consider the functionality and comfort of the patient. Research has reported that 58% of patients with sockets have rejected their use, because they do not fit comfortably and functionally; therefore, patients' low acceptance or rejection of the use of the prosthesis socket has been documented. In this study, different designs were evaluated, based on the FEM as scientific support for the results obtained, for the development of a new ergonomic fit with a 60% increase in patient compliance, that had correct gait performance when correcting postures, improved fit-user interaction, and that presented an esthetic fit that met the usability factor. The validation of the results was carried out through the physical construction of the prototype. The research showed how the finite element method improved the design, analyzing the structural behavioral, and that it could reduce cost and time instead of generating several prototypes.

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Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.

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BACKGROUND: Telehealth has been widely used for new case detection and telemonitoring during the COVID-19 pandemic. It safely provides access to health care services and expands assistance to remote, rural areas and underserved communities in situations of shortage of specialized health professionals. Qualified data are systematically collected by health care workers containing information on suspected cases and can be used as a proxy of disease spread for surveillance purposes. However, the use of this approach for syndromic surveillance has yet to be explored. Besides, the mathematical modeling of epidemics is a well-established field that has been successfully used for tracking the spread of SARS-CoV-2 infection, supporting the decision-making process on diverse aspects of public health response to the COVID-19 pandemic. The response of the current models depends on the quality of input data, particularly the transmission rate, initial conditions, and other parameters present in compartmental models. Telehealth systems may feed numerical models developed to model virus spread in a specific region. OBJECTIVE: Herein, we evaluated whether a high-quality data set obtained from a state-based telehealth service could be used to forecast the geographical spread of new cases of COVID-19 and to feed computational models of disease spread. METHODS: We analyzed structured data obtained from a statewide toll-free telehealth service during 4 months following the first notification of COVID-19 in the Bahia state, Brazil. Structured data were collected during teletriage by a health team of medical students supervised by physicians. Data were registered in a responsive web application for planning and surveillance purposes. The data set was designed to quickly identify users, city, residence neighborhood, date, sex, age, and COVID-19-like symptoms. We performed a temporal-spatial comparison of calls reporting COVID-19-like symptoms and notification of COVID-19 cases. The number of calls was used as a proxy of exposed individuals to feed a mathematical model called "susceptible, exposed, infected, recovered, deceased." RESULTS: For 181 (43%) out of 417 municipalities of Bahia, the first call to the telehealth service reporting COVID-19-like symptoms preceded the first notification of the disease. The calls preceded, on average, 30 days of the notification of COVID-19 in the municipalities of the state of Bahia, Brazil. Additionally, data obtained by the telehealth service were used to effectively reproduce the spread of COVID-19 in Salvador, the capital of the state, using the "susceptible, exposed, infected, recovered, deceased" model to simulate the spatiotemporal spread of the disease. CONCLUSIONS: Data from telehealth services confer high effectiveness in anticipating new waves of COVID-19 and may help understand the epidemic dynamics.

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Online experiments are an alternative for researchers interested in conducting behavioral research outside the laboratory. However, an online assessment might become a challenge when long and complex experiments need to be conducted in a specific order or with supervision from a researcher. The aim of this study was to test the computational validity and the feasibility of a remote and synchronous reinforcement learning (RL) experiment conducted during the social-distancing measures imposed by the pandemic. An additional feature of this study was to describe how a behavioral experiment originally created to be conducted in-person was transformed into an online supervised remote experiment. Open-source software was used to collect data, conduct statistical analysis, and do computational modeling. Python codes were created to replicate computational models that simulate the effect of working memory (WM) load over RL performance. Our behavioral results indicated that we were able to replicate remotely and with a modified behavioral task the effects of working memory (WM) load over RL performance observed in previous studies with in-person assessments. Our computational analyses using Python code also captured the effects of WM load over RL as expected, which suggests that the algorithms and optimization methods were reliable in their ability to reproduce behavior. The behavioral and computational validation shown in this study and the detailed description of the supervised remote testing may be useful for researchers interested in conducting long and complex experiments online.

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Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, whereas deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.

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OBJECTIVE: We created a finite element model to predict the probability of dissection based on imaging-derived aortic stiffness and investigated the link between stiffness and wall tensile stress using our model. METHODS: Transthoracic echocardiogram measurements were used to calculate aortic diameter change over the cardiac cycle. Aortic stiffness index was subsequently calculated based on diameter change and blood pressure. A series of logistic models were developed to predict the binary outcome of aortic dissection using 1 or more series of predictor parameters such as aortic stiffness index or patient characteristics. Finite element analysis was performed on a subset of diameter-matched patients exhibiting patient-specific material properties. RESULTS: Transthoracic echocardiogram scans of patients with type A aortic dissection (n = 22) exhibited elevated baseline aortic stiffness index when compared with aneurysmal patients' scans with tricuspid aortic valve (n = 83, P < .001) and bicuspid aortic valve (n = 80, P < .001). Aortic stiffness index proved an excellent discriminator for a future dissection event (area under the curve, 0.9337, odds ratio, 2.896). From the parametric finite element study, we found a correlation between peak longitudinal wall tensile stress and stiffness index (ρ = .6268, P < .001, n = 28 pooled). CONCLUSIONS: Noninvasive transthoracic echocardiogram-derived aortic stiffness measurements may serve as an impactful metric toward predicting aortic dissection or quantifying dissection risk. A correlation between longitudinal stress and stiffness establishes an evidence-based link between a noninvasive stiffness parameter and stress state of the aorta with clinically apparent dissection events.

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The main goal of this mini-review is to provide an updated state-of-the-art of the hybrid organic-inorganic materials focusing mainly on interface phenomena involving ionic and electronic transport properties. First, we review the most relevant preparation techniques and the structural features of hybrid organic-inorganic materials prepared by solution-phase reaction of inorganic/organic precursor into organic/inorganic hosts and vapor-phase infiltration of the inorganic precursor into organic hosts and molecular layer deposition of organic precursor onto the inorganic surface. Particular emphasis is given to the advances in joint experimental and theoretical studies discussing diverse types of computational simulations for hybrid-organic materials and interfaces. We make a specific revision on the separately ionic, and electronic transport properties of these hybrid organic-inorganic materials focusing mostly on interface phenomena. Finally, we deepen into mixed ionic-electronic transport properties and provide our concluding remarks and give some perspectives about this growing field of research.