Browse Articles

Effect of HER2-low expression on the efficacy of neoadjuvant chemotherapy in triple-negative breast cancer

SEIQRS model analysis and optimal control with two delays

This paper addresses the limitations of assuming a bilinear infection rate in computer virus propagation models by proposing a more realistic nonlinear dual-delay SEIQRS model. The key focus of the research includes analyzing the local stability of the disease-free equilibrium point and the existence of positive equilibrium points with different delays. To further enhance control effectiveness, time-varying control terms are introduced, and a corresponding Hamiltonian function is constructed. The optimal control strategy is derived using the Pontryagin’s maximum principle. Numerical simulation experiments are conducted to validate the dynamic characteristics of the model, and the model’s properties are verified on real-time propagation networks. The experimental results demonstrate that appropriate control strategies can effectively suppress the spread of computer viruses.

Anisotropic persistent random walk model simulates T-cells migration over curved landscapes

Abstract Cell migration is an important cellular process to study, as it plays a fundamental role in tissue structuring and development, while abnormal cell migration may be the cause of certain diseases. Among the known factors influencing cell migration, substrate curvature is one, with cells naturally moving towards concave areas while avoiding convex ones. The underlying causes of migration guidance by curvature remain unclear, and in particular, the way in which cell persistence is affected is still not well understood. We introduce an anisotropic persistent random walk model which includes cell heterogeneity to simulate T-cell migration across various corrugate landscapes. We compared the trajectories generated by the model with in vitro T-cells trajectories over the same topographies. The model accurately captures key features of cell trajectories on flat surfaces as well as on curved surfaces, such as a directional bias toward concave regions. The model also reveals a superdiffusive behavior on curvature, demonstrating more efficient movement compared to flat surfaces. The anisotropic randomness incorporated in the model appears as a critical feature which shapes T-cells persistence mechanisms by increasing cellular activity in the axis of concave valleys and promoting migration towards concave areas.

Changes in working and health conditions in different sectors between 2010 and 2019 in a representative sample of French workers

Background Studies on evolution of working conditions and workers’ health in developed countries show conflicting results, and often fail to identify evolutions by occupational sector. This study aimed to describe recent evolutions of working conditions and health status of French workers over a decade across different activity sectors. Methods and findings Data from a large national French observatory of working conditions and health were used. This observatory collects, for a random sample of employees, data on their working conditions and health. Data on working conditions and health status of employees in the 13 main French activity sectors in 2010–2011 and 2018–2019 were used. A 2 steps weighting method was used to allow results to be extrapolated to the French employees. Overall, 23,951 and 26,538 questionnaires were collected for the 2010–2011 and 2018–2019 periods, respectively. Job demand significantly decreased and job resources significantly increased between 2010–2011 and 2018–2019 while physical workload decreased. Neck, wrist, elbow and back pain disorders significantly decreased as well as anxiety, whereas shoulder disorders, fatigue and sleep disorders increased. However, there were large and significant disparities in evolutions between sectors, for exposures and health status. The most worrisome sector was the Human health and social work sector with no improvement in the working conditions and increasing health problems. Conclusion Overall, evolutions and prevalence of physical exposure or health problems do not bring the same information. It is important to understand the extent to which psychosocial, physical working conditions and health status differ among various industry categories, and to study the determinants of such disparities such that policy and practice interventions to reduce disparities can be appropriately directed. It is also important to continue monitoring health of workers, as the impact of changes in working conditions may not have an immediate effect, particularly for conditions with a long latency period.

Huangqi fuling decoction inhibits the invasion and metastasis of gastric cancer via the TNF signaling pathway

Probing the binding hypothesis of Smad3 modulators by molecular dynamic simulations for Atherosclerosis Cardiovascular Disease (ASCVD)

Transforming Growth Factor β (TGFβ) pathway has been recognized as one of the major processes involved in fibrotic diseases including the Atherosclerosis Cardiovascular Disease (ASCVD). Many drugs have been proposed and are under clinical trials for modulation of the TGFβ pathway by targeting TGFβ receptor. Recently, various proof of the concept studies have advocated that the inhibition of TGFβ-mediated Smad pathway could produce more focused effect with less off target toxicities in ASCVD. As these studies lack the mechanism and binding profile of Smad3 modulators, characterization of binding pattern for Smad3 inhibitors can provide a platform for the lead optimization against ASCVD. We utilized dimeric inhibitors from the PubChem dataset (PubChem ID: 630) of Smad3-FoxH1 binding inhibitors to generate binding hypothesis of Smad3 inhibitors. The selected compounds from the dataset were docked and ligand-protein complexes were simulated for 250 ns for further sampling of conformational space and to obtain stable binding hypothesis. Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF) and hydrogen bond analysis of ligand-protein complexes after simulations revealed that Asn320 in Smad3 provides an efficient inhibition site for the two most potent small inhibitors (hereby named SM1 and SM2) of Smad3-FoxH1 binding. Although the diverse nature of compounds produce variable interaction patterns with FoxH1 binding site in Smad3, Gln315, Gln364 and Arg367 were observed to be the most common hydrogen bond interaction points in this binding site. Additionally, two compounds (hereby named SM8 and SM19) detached from the FoxH1 binding site and formed a highly stable complex around Tyr323 via hydrophobic complementarity, suggesting a new binding site for modulation of Smad3 activity.

Effects of change in dysfunctional beliefs in avatar-based cognitive therapy for depressive symptoms: a randomized parallel trial

Abstract This study evaluated the effect of an avatar-based intervention on depressive symptoms and self-esteem. Participants (N = 151) with subclinical depressive symptoms were instructed to challenge an avatar over three sessions. While participants within the intervention group challenged their personal dysfunctional beliefs, participants in the control group challenged nonsense statements. Allocation to treatment groups was randomized. Data collection took place pre-intervention and post-intervention. Statistical analysis revealed a significant decrease in depressive symptoms, which was more pronounced for the intervention group (p < .01), as well as a significant group × time interaction for self-esteem (p < .05). The effect on depression symptom strength was large in the experimental group (d = − 1.19) and medium (d = − 0.72) in the control group, while the effect on self-esteem was moderate (d = 0.54) in the intervention and small (d = 0.29) in the control group. Our findings on symptom reduction align with prior research, while positive effects on self-esteem are a novelty. These results demonstrate the intervention’s potential for reducing the symptoms of mental illness.

Immune-mediated enterocolitis is associated with immune checkpoint inhibitors: A pharmacovigilance study from the FDA Adverse Event Reporting System (FAERS) database

Purpose Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment by demonstrating significant efficacy across multiple malignancies. However, by interfering with immune regulatory pathways, they can lead to immune-related adverse events (irAEs), including immune-mediated enterocolitis. This study aimed to evaluate the real-world risk of immune-mediated enterocolitis across different ICIs using data from the FDA’s Adverse Event Reporting System (FAERS). Methods A disproportionality analysis was conducted using FAERS data to assess the association between different ICIs and the risk of immune-mediated enterocolitis. The risk was analyzed across three ICI classes: cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitors, programmed death-1 (PD-1) inhibitors, and programmed death-ligand 1 (PD-L1) inhibitors. Results The analysis revealed significant variability in the risk of immune-mediated enterocolitis among ICIs. CTLA-4 inhibitors, particularly tremelimumab and ipilimumab, exhibited the strongest association with enterocolitis. Among PD-1 inhibitors, nivolumab demonstrated the highest risk, while PD-L1 inhibitors, including durvalumab and atezolizumab, had a lower but still notable association. Conclusions These findings underscore the need for vigilant monitoring and early intervention in patients receiving ICIs. The differential risk profile among ICIs suggests that physicians should consider enterocolitis risk when selecting and managing immunotherapy regimens.

Community environment, psychological perceptions, and physical activity among older adults

Biased perceptions of public opinion don’t define echo chambers but reveal systematic differences in political awareness

Echo chambers are widely acknowledged as a feature of online discourse and current politics: a phenomenon arising when people selectively engage with like-minded others and are shielded from opposing ideas. Various studies have operationalized the concept through studying opinions, interactions, reinforcement or group identity. Echo chambers both feed and are fed by the false consensus effect, whereby people overestimate the degree to which others share their views, with algorithmic filtering of social media also a contributing factor. Although there is strong evidence that meta-opinions - that is, people’s perceptions of others’ opinions - often fail to reflect reality, no attempt has been made to explore the space of meta-opinions, or detect echo chambers within this space. We created a new, information-theoretic method for directly quantifying the information content of meta-opinions, allowing detailed exploratory analysis of their relationships with demographic factors and underlying opinions. In a gamified survey (presented as a quiz) of 476 UK respondents, we found both the liberal left, and also people at both extremes of the left/right scale, to have more accurate knowledge of others’ opinions. Surprisingly however, we found that meta-opinions, although displaying significant false consensus effects, were not divided into any strong clusters representative of echo chambers. We suggest that the metaphor of discrete echo chambers may be inappropriate for meta-opinions: while measures of meta-opinion accuracy and its influences can reveal echo chamber characteristics where other metrics confirm their presence, the presence or absence of meta-opinion clusters is not itself sufficient to define an echo chamber. We publish both data and analysis code as supplementary material.

Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques

Breaking Structural Instability and Orbital Symmetry Mismatch in <i>p</i>-Block Metal Monochalcogenides for CO₂ Electroreduction via Noninvasive van der Waals Doping

Discursive strategies of Chinese elders in intergenerational conflicts: A critical discourse analysis of mediation television programs in China

Research on conflict discourse highlights the value of a cross-cultural perspective and the need to explore its features across different cultural contexts. However, there is limited research on conflict discourse among Chinese elders, particularly within the framework of building an Age-Friendly Society. To address this gap, this study adopts Fairclough’s dialectical-relational approach in critical discourse analysis to examine the strategies Chinese elders employ in intergenerational conflicts. Drawing on Li’s framework of conflict strategies—competition, cooperation, avoidance, and compromise—this study identifies how Chinese elders use discourse to safeguard their rights and foster mutual understanding within family interactions. The findings reveal that these four strategies serve as discursive means for elders to achieve self-fulfillment, protect their rights, and sustain family harmony amidst social change. This study offers a systematic analysis of conflict resolution strategies in intergenerational discourse, highlighting the nuanced equilibrium that elders navigate between upholding traditional values and accommodating the evolving dynamics of contemporary family life.

Stacked hybrid model for load forecasting: integrating transformers, ANN, and fuzzy logic

Abstract Modern energy management systems must include load forecasting in order for utilities to plan and optimize electricity distribution, lower operating costs, and improve grid stability. With the addition of renewable energy sources and the advancement of smart grid technology, energy systems have become increasingly complex, making accurate forecasting increasingly challenging. Conventional techniques, including regression models and ARIMA, frequently perform less well because they are unable to capture the complex multivariate relationships and temporal dependencies present in energy data. Furthermore, these techniques are prone to errors in the presence of noisy data and have scalability issues when used on big, high-dimensional datasets. This paper presents a hybrid forecasting framework that combines artificial neural networks with Time Series Transformers and Fuzzy Logic Transform in order to overcome these drawbacks. The Transformer architecture excels in capturing long-term dependencies and interdependencies between features through its self-attention mechanism. Meanwhile, FLT + ANN effectively preprocesses noisy, irregular data and models short-term nonlinear patterns. The combination of these techniques creates a robust framework capable of handling complex energy datasets while maintaining high accuracy. Extensive tests on actual energy datasets show that the suggested hybrid model outperforms both conventional and stand-alone methods. With RMSE and MAE reductions of up to 15–20%, the model outperforms baseline models such as Random Forests, Decision Trees, and Linear Regression. These findings demonstrate how the suggested paradigm has the potential to transform load forecasting and enable more intelligent, effective energy systems.

Rapid risk assessment to address emerging concerns of HPAI in raw and pasteurized milk

An outbreak of highly pathogenic avian influenza A, subtype H5N1, first reported in U.S. dairy cattle in March 2024, raised concerns of an emerging food safety threat from the virus in the milk supply. To support potential regulatory responses, we conducted a rapid assessment of the predicted risk to U.S. consumers of cow’s milk with two complementary and parallel approaches: a “bottom-up” quantitative risk assessment model that integrated data on virus levels in milk, milk consumption, and dose response; and a “top-down” epidemiological analysis that linked current novel flu illness detection to the consumption of raw and pasteurized milk. The dynamic use of the approaches accommodated rapidly evolving data in a range of risk scenarios. The risk assessment model identified pasteurization as a critical control for H5N1 in milk and highlighted the need for i) the targeted sampling of bulk tank raw milk in affected states pre-pasteurization, ii) raw milk herd surveillance and sampling, and iii) a better understanding of ingestion as a route of H5N1 infections for humans. This novel approach and the findings from this study promoted informed decision-making in an evolving outbreak investigation. This methodology can be leveraged in the conduct of future risk assessments to address emerging pathogen outbreaks that impact the food supply.

Identification of key proteins and pathways in myocardial infarction using machine learning approaches

Security importance of edge-IoT ecosystem: An ECC-based authentication scheme

Despite the many outstanding benefits of cloud computing, such as flexibility, accessibility, efficiency, and cost savings, it still suffers from potential data loss, security concerns, limited control, and availability issues. The experts introduced the edge computing paradigm to perform better than cloud computing for the mentioned issues and challenges because it is directly connected to the Internet-of-Things (IoT), sensors, and wearables in a decentralized manner to distribute processing power closer to the data source, rather than relying on a central cloud server to handle all computations; this allows for faster data processing and reduced latency by processing data locally at the ‘edge’ of the network where it’s generated. However, due to the resource-constrained nature of IoT, sensors, or wearable devices, the edge computing paradigm endured numerous data breaches due to sensitive data proximity, physical tampering vulnerabilities, and privacy concerns related to user-near data collection, and challenges in managing security across a large number of edge devices. Existing authentication schemes didn’t fulfill the security needs of the edge computing paradigm; they either have design flaws, are susceptible to various known threats—such as impersonation, insider attacks, denial of service (DoS), and replay attacks—or experience inadequate performance due to reliance on resource-intensive cryptographic algorithms, like modular exponentiations. Given the pressing need for robust security mechanisms in such a dynamic and vulnerable edge-IoT ecosystem, this article proposes an ECC-based robust authentication scheme for such a resource-constrained IoT to address all known vulnerabilities and counter each identified threat. The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. The performance metrics have been measured by considering computational time complexity, communication cost, and storage overheads, further reinforcing the confidence in the proposed solution. The comparative analysis results demonstrated that the proposed ECC-based authentication protocol is 90.05% better in terms of computation cost, 62.41% communication cost, and consumes 67.42% less energy compared to state-of-the-art schemes. Therefore, the proposed protocol can be recommended for practical implementation in the real-world edge-IoT ecosystem.

3D printing individualized augments prosthesis and acetabular implant for the treatment of Crowe type III developmental dysplasia of the hip

Inferring internal states across mice and monkeys using facial features

Abstract Animal behaviour is shaped to a large degree by internal cognitive states, but it is unknown whether these states are similar across species. To address this question, here we develop a virtual reality setup in which male mice and macaques engage in the same naturalistic visual foraging task. We exploit the richness of a wide range of facial features extracted from video recordings during the task, to train a Markov-Switching Linear Regression (MSLR). By doing so, we identify, on a single-trial basis, a set of internal states that reliably predicts when the animals are going to react to the presented stimuli. Even though the model is trained purely on reaction times, it can also predict task outcome, supporting the behavioural relevance of the inferred states. The relationship of the identified states to task performance is comparable between mice and monkeys. Furthermore, each state corresponds to a characteristic pattern of facial features that partially overlaps between species, highlighting the importance of facial expressions as manifestations of internal cognitive states across species.

Speech-in-noise recognition as a predictor of academic achievement

Background The capacity to listen effectively greatly influences learning. Speech understanding in noise, a crucial element of listening, involves the ability to comprehend and interpret speech despite surrounding auditory distractions or background noise. While numerous studies have examined the effect of noise on academic success, there is a noticeable lack of research focusing on how a student’s skill in understanding speech amidst noise connects to their unique educational achievements and overall learning journey. This study explores the relationship between listening ability and educational achievement. Design 108 primary school aged children; 55 from Grade 3 (mean age of 8 years 9 months) and 53 from Grade 5 (mean age of 10 years 7 months) participated in this study. Children completed both a listening skills assessment, using the Sound Scouts platform and the National Assessment Program - Literacy and Numeracy (NAPLAN). Interactions between results were explored. Results A significant interaction between speech-in-noise ability and reading was seen in the combined cohort. When exploring interactions between variables within year levels, significant correlations between literacy (reading, grammar/punctuation, writing and spelling) outcomes, but not numeracy, were found in the Grade 3 children. No significant interaction between listening skills and academic achievement were observed in the Grade 5 cohort. Summary The link between speech-in noise ability and literacy development provides insight into overlapping processes in both skills and their developmental trajectories. This highlights the importance of identifying not only the role that noise itself plays in learning, but the skills that support the ability to manage classroom listening environments. This research has implications for early hearing screening programs, teaching approaches, and interventions focused on enhancing the learning environment for all students.