Browse Articles

Fractional and stochastic modeling of breast cancer progression with real data validation

This study presents a novel approach to modeling breast cancer dynamics, one of the most significant health threats to women worldwide. Utilizing a piecewise mathematical framework, we incorporate both deterministic and stochastic elements of cancer progression. The model is divided into three distinct phases: (1) initial growth, characterized by a constant-order Caputo proportional operator (CPC), (2) intermediate growth, modeled by a variable-order CPC, and (3) advanced stages, capturing stochastic fluctuations in cancer cell populations using a stochastic operator. Theoretical analysis, employing fixed-point theory for the fractional-order phases and Ito calculus for the stochastic phase, establishes the existence and uniqueness of solutions. A robust numerical scheme, combining the nonstandard finite difference method for fractional models and the Euler-Maruyama method for the stochastic system, enables simulations of breast cancer progression under various scenarios. Critically, the model is validated against real breast cancer data from Saudi Arabia spanning 2004-2016. Numerical simulations accurately capture observed trends, demonstrating the model’s predictive capabilities. Further, we investigate the impact of chemotherapy and its associated cardiotoxicity, illustrating different treatment response scenarios through graphical representations. This piecewise fractional-stochastic model offers a powerful tool for understanding and predicting breast cancer dynamics, potentially informing more effective treatment strategies.

Correction to “Dioxane Bridge Formation during the Biosynthesis of Spectinomycin Involves a Twitch Radical <i>S</i>-Adenosyl Methionine Dehydrogenase That May Have Evolved from an Epimerase”

Effects of kinesio taping on lower limb biomechanical characteristics during dynamic postural control tasks in individuals with chronic ankle instability

Purpose Previous studies have demonstrated significant biomechanical differences between individuals with chronic ankle instability (CAI) and healthy controls during the Y-balance test. This study aimed to examine the effects of kinesio taping (KT) on lower limb biomechanical characteristics during the Y-balance anterior reach task in individuals with CAI. Methods A total of 30 participants were recruited, comprising 15 individuals with CAI and 15 healthy controls. All participants were randomly assigned three taping conditions: no taping (NT), placebo taping (PT), and KT, followed by the Y-balance anterior reach task. Each condition was separated by one-week intervals. Kinematic and kinetic data of the lower limbs during the movement phase were collected using the Vicon motion capture system (Vicon, T40, 200 Hz) and two Kistler force platforms (Kistler, 1000 Hz). Results KT significantly improved the Y-balance anterior reach distance (P = 0.003) and peak ankle eversion angle (P = 0.019) compared to NT. Additionally, KT resulted in increased peak knee flexion angle (P = 0.002, P = 0.011) and peak ankle dorsiflexion angle (P &lt;0.001, P = 0.005) relative to both NT and PT. KT also significantly reduced mediolateral center of pressure (COP) displacement (P = 0.001) and average velocity of mediolateral COP displacement (P = 0.033) in comparison to NT. Furthermore, KT decreased mediolateral center of gravity displacement (P = 0.002, P = 0.003) relative to both NT and PT. Conclusion KT significantly improved abnormal ankle posture by promoting greater ankle dorsiflexion and eversion angles. Additionally, KT reduced mediolateral COP displacement and average velocity to improve postural stability. These changes may contribute to reduced risk of ankle sprains. Therefore, KT may serve as an effective tool for managing recurrent ankle sprains in individuals with CAI.

The advantages of lexicon-based sentiment analysis in an age of machine learning

Assessing whether texts are positive or negative—sentiment analysis—has wide-ranging applications across many disciplines. Automated approaches make it possible to code near unlimited quantities of texts rapidly, replicably, and with high accuracy. Compared to machine learning and large language model (LLM) approaches, lexicon-based methods may sacrifice some in performance, but in exchange they provide generalizability and domain independence, while crucially offering the possibility of identifying gradations in sentiment. We demonstrate the strong performance of lexica using MultiLexScaled, an approach which averages valences across a number of widely-used general-purpose lexica. We validate it against benchmark datasets from a range of different domains, comparing performance against machine learning and LLM alternatives. In addition, we illustrate the value of identifying fine-grained sentiment levels by showing, in an analysis of pre- and post-9/11 British press coverage of Muslims, that binarized valence metrics give rise to different (and erroneous) conclusions about the nature of the post-9/11 shock as well as about differences between broadsheet and tabloid coverage. The code to apply MultiLexScaled is available online.

Clinical and psychosocial context of HIV perinatally infected young mothers in Harare, Zimbabwe: A longitudinal mixed-methods study

Background The lives of adolescents and young people living with HIV (LHIV) are dominated by complex psychological and social stressors. These may be more pronounced among those perinatally infected. This longitudinal mixed-methods study describes the clinical and psychosocial challenges faced by HIV perinatally infected young mothers in Harare, Zimbabwe to inform tailored support. Methods HIV perinatally infected young mothers were recruited in 2013 and followed up in 2019. In 2013, they completed a structured interview, clinical examination, psychological screening and had viral load and drug resistance testing. A subset completed in-depth interviews (n = 10). In 2019, they were re-interviewed and had viral load testing. Data were analyzed using STATA 15.0. and thematic analysis. Results Nineteen mothers aged 17–24 years were recruited in 2013. Eleven (57.9%) were successfully recontacted in 2019; 3 had died, 2 had relocated and 3 were untraceable. In 2013, all 19 mothers were taking antiretroviral therapy (median duration 8 years, range 2–11 years) and median CD4 count was 524 (IQR 272). In 2013, eight mothers (42.1%) had virological failure (≥1000 copies/ml) (3 of whom subsequently died) and 7 (36.8%) had evidence of drug resistance. In 2019, the proportion with virological failure was 2/11 (18.1%). Six of 11 (54.5%) had switched to second line therapy. In 2013, 64.3% were at risk of common mental disorder and this risk was higher at follow-up (72.7%). Qualitative data highlighted three pertinent themes: HIV status disclosure, adherence experiences and, social and emotional support. Conclusions Findings from this study underscore the significant clinical, social and psychological challenges faced by perinatally infected young mothers. The high rates of virological failure, drug resistant mutations, mental health issues and mortality observed in this population indicate the need for tailored and comprehensive health and support services to assist these young mothers.

Experimental analysis of genetic algorithm-enhanced PI controller for power optimization in multi-rotor variable-speed wind turbine systems

Modelling mixed crop-livestock systems and climate impact assessment in sub-Saharan Africa

Abstract Climate change significantly challenges smallholder mixed crop-livestock (MCL) systems in sub-Saharan Africa (SSA), affecting food and feed production. This study enhances the SIMPLACE modeling framework by incorporating crop-vegetation-livestock models, which contribute to the development of sustainable agricultural practices in response to climate change. Applying such a framework in a domain in West Africa (786,500 km 2 ) allowed us to estimate the changes in crop (Maize, Millet, and Sorghum) yield, grass biomass, livestock numbers, and greenhouse gas emission in response to future climate scenarios. We demonstrate that this framework accurately estimated the key components of the domain for the past (1981–2005) and enables us to project their future changes using dynamically downscaled Global Circulation Model (GCM) projections (2020–2050). The results demonstrate that in the future, the northern part of the study area will likely experience a significant decline in crop biomass (up to -56%) and grass biomass (up to -57%) production leading to a decrease in livestock numbers (up to -43%). Consequently, this will impact total emissions (up to -47% CH 4 ) and decrease of -41% in milk production, and − 47% in meat production concentrated in the Sahelian zone. Whereas, in pockets of the Sudanian zone, an increase in livestock population and CH 4 emission of about + 24% has been estimated, indicating that variability in climate change impact is amplifying with no consistent pattern evident across the study domain.

Computational microscopy with coherent diffractive imaging and ptychography

Nicotinamide adenine dinucleotide supplementation fails to enhance anesthetic recovery in rodents

Electrochemical synthesis goes wireless

Association between overt hepatic encephalopathy and liver pathology after transjugular intrahepatic portosystemic shunt creation in cirrhotic patients

The genetic demographic history of the last hunter-gatherer population of the Himalayas

RNF138 contributes to cisplatin resistance in nasopharyngeal carcinoma cells

How frictional ruptures and earthquakes nucleate and evolve

Author Correction: Numerical modeling the process of deep slab dehydration and magmatism

Feasibility of substituting soda pulping with high consistency kneaded chemi mechanical pulping for discarded oyster farming bamboo scaffolding

A super-resolution algorithm to fuse orthogonal CT volumes using OrthoFusion

Quality of life in early breast cancer patients after adjuvant accelerated partial-breast irradiation (APBI) in randomized trial

Quantum mechanics 100 years on: an unfinished revolution

Biogenic synthesis of titanium nanoparticles by Streptomyces rubrolavendulae for sustainable management of Icerya aegyptiaca (Douglas)

AbstractBiosynthesized nanoparticles have a variety of applications, and microorganisms are considered one of the most ideal sources for the synthesis of green nanoparticles. Icerya aegyptiaca (Douglas) is a pest that has many generations per year and can affect 123 plant species from 49 families by absorbing sap from bark, forming honeydew, causing sooty mold, and attracting invasive ant species, leading to significant agricultural losses. The purpose of this work was to synthesize titanium dioxide nanoparticles (TiO2-NPs) from marine actinobacteria and evaluate their insecticidal effects on Icerya aegyptiaca (Hemiptera: Monophlebidae), in addition to explaining their effects on protein electrophoresis analysis of SDS‒PAGE proteins from control and treated insects after 24, 72 and 120 h of exposure. In all, seven actinobacterial isolates, the most potent of which has the potential to produce titanium hydroxide-based nanoparticles (TiO2-NP2), have DNA sequences that are 99.9% like those of Streptomyces rubrolavendulae (MCN2) according to nucleotide alignment and a phylogenetic tree. The produced TiO2-NPs were verified by UV examination and characterized by FT-IR, XRD, TEM, EDX, and DLS analyses. Toxicological results revealed that TiO2-NPs have insecticidal effects and high mortality rates reaching 55, 62.5, 80 and 95% at TiO2-NPs dose 120,250,500 and 1000 ppm respectively. Compared with the control, TiO2-NP spraying caused changes in the protein pattern of I. aegyptiaca, as indicated by the disappearance of normal bands and the appearance of other bands, as well as quantitative and qualitative changes in protein content after 24, 72 and 120 h of exposure. The application of TiO2-NPs by MNC2 offers a new alternative strategy to control I. aegyptiaca and is considered a modern approach to nanotechnology.