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A review paper from the Department of Chemical Engineering in Lar, Iran, discusses the current state of modeling the transport and margination of micro-/nanoparticles in blood vessels for targeted drug delivery. The review examines single-particle and multi-particle models, considering factors such as geometry, particle properties, blood flow, and mathematical modeling approach. The findings of this review can provide insights for researchers working on more effective drug delivery strategies. The research was supported by the University of Larestan, Isfahan University of Technology, and Shiraz University. [Extracted from the article]

Researchers from the University of Coimbra in Portugal have developed a numerical analysis method for studying light-controlled drug delivery systems. The researchers used a finite difference method in space and the implicit midpoint method in time to solve a non-linear reaction-diffusion system with mixed boundary conditions. The method was found to have second-order convergence in space and time, and numerical examples supported the theoretical results. This research has been peer-reviewed and published in the Journal of Computational and Applied Mathematics. [Extracted from the article]

Researchers from Imam Hossein University in Tehran, Iran, have published a study on biomathematics, focusing on modeling infectious diseases like HIV using mathematical tools such as differential equations. The study explores the dynamical behavior of an HIV viral infection model, analyzing the stability of equilibrium points based on the basic reproduction number (R-0). The research also introduces a new threshold parameter by including the effects of drug therapy on the model. This peer-reviewed study provides valuable insights into global dynamics, numerical simulation, and drug therapy control in the context of infectious diseases. [Extracted from the article]

Researchers from Beijing University of Chinese Medicine have published a report on the use of numerical simulation technology in advancing drug preparation. The study highlights the application scenarios and examples of mechanistic modeling in pharmacy research, emphasizing the concept of quality by design. The research concludes that numerical simulation saves experimental cost and time, speeding up the experimental process and guiding formulation with the help of mechanistic models. This study provides valuable insights for drug researchers looking to optimize preparation parameters and predict potential quality problems in drug products. [Extracted from the article]

New research from the University of Alberta explores the use of joint mixture models to identify distinct patient subpopulations based on baseline electroencephalograph (EEG) patterns and their response to antidepressant treatment. The study proposes a model that incorporates matrix-variate data and probabilistic multilinear principle component analysis. By applying this model to a depression study, the researchers identified four distinct patient subpopulations with different baseline EEG patterns and varying responses to antidepressant treatment. This research provides insights into the potential for using clustering methods to uncover patient heterogeneity and improve treatment outcomes. [Extracted from the article]

A recent study conducted by researchers at Georgia State University explores different scenarios for controlling epidemics through theoretical analysis and numerical simulations. The study focuses on two types of control measures: nonmedical interventions, which aim to reduce disease transmission through behavioral changes, and medical treatments, which aim to lower the period of infectiousness through antiviral medications and other forms of medical care. The researchers found that implementing these control strategies can reduce the number of daily cumulative cases and "flatten the curve" of the epidemic. However, they also noted that beyond a certain point, both controls may decline, necessitating the addition of new tools such as vaccines, therapeutics, testing, and air ventilation to effectively combat the virus. The study provides valuable insights for public health organizations in managing epidemics. [Extracted from the article]

A study conducted by researchers at the Department of Mathematics in Karur, India, explores the use of mathematical models to predict the physical, chemical, and biological features of anticancer medications used to treat blood, breast, and skin cancer. The researchers developed distance-based topological indices (TIs) using a suggested KP-polynomial with blood cancer drugs. The results of the study suggest that this theoretical method could potentially allow chemists and healthcare professionals to anticipate the characteristics of blood cancer drugs without the need for actual experimental tests. The research also discusses the potential for drug discovery and the use of multicriteria decision-making techniques for ranking disease treatment drugs. [Extracted from the article]

A recent report from Ludong University in Yantai, China discusses the advancements in drug delivery systems using hydrogels. Hydrogels are attractive for medical applications due to their mechanical properties, low cytotoxicity, and biocompatibility. The report summarizes the types of drugs that can be encapsulated in hydrogels and their interactions. It also explores the control mechanisms and factors affecting drug release, with a focus on applications in various body sites. The research was supported by the National Natural Science Foundation of China and other organizations. [Extracted from the article]

A recent study conducted at Pennsylvania State University (Penn State) focused on Alzheimer's disease and the challenges of drug delivery to the brain. The researchers proposed a mathematical model that incorporates two mechanisms of blood-brain barrier crossing: transcytosis and diffusion. The model was based on experimental observations of the drug donepezil hydrochloride, which is used to treat Alzheimer's disease. The study's numerical simulations suggest that drug delivery methods should be tailored to the severity of Alzheimer's disease and the fitness of the blood-brain barrier. These findings may inspire the development of improved therapies for Alzheimer's disease. [Extracted from the article]

A recent study conducted by researchers at the University of Tenaga Nas in Selangor, Malaysia, explores the dynamics of HIV infection and the effects of combination antiretroviral therapy (cART) on the virus. The study utilizes a numerical approach to analyze the complex behavior of HIV infection in CD4(+)T cells and highlights the interaction between the immune system and the virus. The research provides insights into the intricate dynamics of HIV infection, including its chaotic and periodic behavior. This study has been peer-reviewed and offers valuable information for understanding and managing HIV/AIDS. [Extracted from the article]

A report from Nankai University in Tianjin, China discusses the use of two-dimensional (2D) designs in combination experiments of drugs. The research focuses on testing the synergistic or antagonistic actions between two drugs and proposes a dose-response model that encompasses a wide range of potential actions. The study demonstrates the advantages of the 2D optimal design over the traditional one-dimensional (1D) design through a combination study of two anticancer drugs. This research has been peer-reviewed and provides valuable insights for the development of more effective drug treatments. [Extracted from the article]

A recent report from the University of Illinois discusses the use of nanoparticles in targeted drug delivery processes. The researchers highlight the need for a better understanding of how blood flow dynamics affect the retention of nanoparticles at the target site after adhesion. To address this, they developed a computational model that simulates the effects of blood flow on nanoparticle adhesion and retention. The model was validated through in vitro experiments and showed promise in optimizing nanocarriers in vascular microenvironments, potentially reducing the need for in vivo experimentation and development costs. [Extracted from the article]

Researchers at Brock University have conducted a study on drug development, specifically focusing on the design of effective molecules that can bind to specific protein targets. The study explores the use of artificial intelligence algorithms, particularly many-objective metaheuristics, in combination with a latent Transformer-based model for molecular generation. The researchers compared the performance of different models and metaheuristics and found that a multi-objective evolutionary algorithm based on dominance and decomposition was the most effective in finding molecules that satisfied multiple objectives, such as high binding affinity and low toxicity. This study demonstrates the potential of integrating Transformers and many-objective computational intelligence for drug design. [Extracted from the article]

A study conducted at Sultan Moulay Slimane University in Beni Mellal, Morocco, focuses on the computational modeling of the flow of nano-blood through an inclined and tapered saturated porous artery with overlapping stenosis. The study uses the Carreau fluid model to simulate the non-Newtonian behavior of blood and incorporates a suspension of biocompatible nanoparticles and external magnetic and electric fields. The research aims to enhance understanding of hemodynamic flow and its implications for cardiovascular disease treatment. The simulations have potential applications in targeted magnetic therapy and the delivery of nanomedicines. [Extracted from the article]

Researchers from Babes-Bolyai University in Cluj-Napoca, Romania have developed a novel null model called MuAn that uses Evolutionary Algorithms to generate synthetic networks with high values of Assortativity Degree and Local Clustering Coefficient, which are important for identifying network motifs. The proposed model has been shown to be effective in generating networks with motifs and can be used in various domains such as gene regulatory networks, drug-target networks, and artificial neuronal networks. This research has been peer-reviewed and published in Swarm and Evolutionary Computation. [Extracted from the article]

This article discusses the use of artificial intelligence (AI) in improving the reproducibility of mathematical disease models, specifically focusing on a case study of retinitis pigmentosa (RP). The researchers utilized a large language model (LLM) called Chat GPT 4, customized by Mathpix, to extract equations from a PDF and convert them into a human-readable language called Antimony. The model was then converted into Systems Biology Markup Language (SBML) and uploaded onto the jinko platform for simulation. The study demonstrates the potential of AI in re-implementing mathematical models to ensure reliability and reproducibility of results. However, it is important to note that this preprint has not yet undergone peer review. [Extracted from the article]

A study conducted by researchers at Southwest University in Chongqing, China, explores the selection of prostate cancer therapy strategies under early androgen suppression treatment. The researchers developed a mathematical model that incorporates individual differences and the pharmacodynamics of drugs to determine the optimal design of intermittent treatment. The study concludes that the results provide valuable insights for clinical practice. The research was supported by the National Natural Science Foundation of China and has been peer-reviewed. [Extracted from the article]

Researchers from Shaoyang University in China have conducted a study on the solubility of digitoxin in supercritical carbon dioxide (CO2) using ensemble learning techniques and various regression models. The study found that Polynomial Regression (PR) was the most effective model for predicting both solvent density and digitoxin solubility. The results demonstrate that ensemble learning and polynomial regression can accurately predict solubility and solvent density, providing insights into digitoxin's behavior in supercritical CO2. This research contributes to the field of artificial intelligence and its application in optimizing drug solubility. [Extracted from the article]

A recent study conducted by researchers at Arizona State University explores the dynamics of the opioid crisis in the United States. The study utilizes a mathematical model to analyze illicit opioid use disorder (IOUD), initiation, casual use, treatment, relapse, recovery, and opioid overdose deaths. The findings suggest that increasing the availability of treatment facilities is crucial in addressing the crisis. Additionally, the study highlights the importance of considering the influence of casual users in driving the all-illicit opioid epidemic. The results of this research may inform health and policy officials in developing more effective treatment options and prevention efforts. [Extracted from the article]

A new study from King Abdulaziz University in Saudi Arabia introduces two novel topological indices, the forgotten eccentric neighborhood index (FENI) and the modified forgotten eccentric neighborhood index (MFENI), which are designed to predict the boiling points of chemical substances. These indices offer a nuanced capture of structural features that are important for determining boiling points, making them valuable tools in drug design and chemical analysis. The study also explores the properties of FENI and MFENI in relation to different graph types and operations. The findings deepen our understanding of these indices and establish their practical versatility in various chemical and pharmaceutical applications. [Extracted from the article]