Charge-Switchable nanoparticles to enhance tumor penetration and accumulation.

Nanoparticle-based drug delivery systems hold potential in chemotherapy, but their limited accumulation in tumor tissues hinders effective drug concentration for combating tumor growth. Hence, altering the physicochemical properties of nanoparticles, particularly their surface charge, can enhance their performance. This study utilized a computational model to explore a nanoparticle drug delivery system capable of dynamically adjusting its surface charge. In the model, nanoparticles in the bloodstream were assigned a neutral or positive charge, which, upon reaching the tumor microenvironment, switched to a neutral or negative charge, and releasing chemotherapy drugs into the extracellular space. Results revealed that circulating nanoparticles with a positive surface charge, despite having a shorter circulation and high clearance rate compared to their neutral counterparts, could accumulate significantly in the tissue due to their high transvascular rate. After extravasation, neutralized surface-charged nanoparticles tended to accumulate only near blood microvessels due to their low diffusion rate, resulting in substantial released drug drainage back into the bloodstream. On the other hand, nanoparticles with a negative surface charge in the tumor's extracellular space, due to the reduction of nano-bio interactions, were able to penetrate deeper into the tumor, and increasing drug bioavailability by reducing the volume of drained drugs. Furthermore, the analysis suggested that burst drug release yields a higher drug concentration than sustained drug release, however their creation of bioavailability dependent on nanoparticle accumulation in the tissue. The study's findings demonstrate the potential of this delivery system and offer valuable insights for future research in this area.

Mathematical modeling of hypoxia and adenosine to explore tumor escape mechanisms in DC-based immunotherapy.

Identifying and controlling tumor escape mechanisms is crucial for improving cancer treatment effectiveness. Experimental studies reveal tumor hypoxia and adenosine as significant contributors to such mechanisms. Hypoxia exacerbates adenosine levels in the tumor microenvironment. Combining inhibition of these factors with dendritic cell (DC)-based immunotherapy promises improved clinical outcomes. However, challenges include understanding dynamics, optimal vaccine dosages, and timing. Mathematical models, including agent-based, diffusion, and ordinary differential equations, address these challenges. Here, we employ these models for the first time to elucidate how hypoxia and adenosine facilitate tumor escape in DC-based immunotherapy. After parameter estimation using experimental data, we optimize vaccination protocols to minimize tumor growth. Sensitivity analysis highlights adenosine's significant impact on immunotherapy efficacy. Its suppressive role impedes treatment success, but inhibiting adenosine could enhance therapy, as suggested by the model. Our findings shed light on hypoxia and adenosine-mediated tumor escape mechanisms, informing future treatment strategies. Additionally, identifiability analysis confirms accurate parameter determination using experimental data.

INSCYD physiological performance software is valid to determine the maximal lactate steady state in male and female cyclists.

Introduction: The maximal lactate steady state (MLSS) is defined as the highest workload that can be maintained without blood lactate accumulation over time. The power output at MLSS (PMLSS) is regularly implemented to define training zones, quantify training progress, or predict race performance. The gold standard methodology for MLSS determination requires two to five trials of constant-load exercise, which limits the practical application in training. The INSCYD software can calculate the PMLSS (PMLSSINSCYD) based on physiological data that can be obtained during a ∼1 h laboratory visit. However, to the best of our knowledge, the validity of the most recent software version has not yet been investigated. This study aimed to assess the validity of the software's calculations on PMLSS in cycling. Methods: The data for this study were retrieved from two published scientific sources. Thirty-one cyclists (19 males, 12 females) performed a 15 s sprint to estimate the VLamax, a ramp test for the V˙O2max assessment, and two to five constant-load tests to determine the PMLSS. The INSCYD software was used to calculate the PMLSS based on the V˙O2max, VLamax, sex, body mass, and body composition. Results: The PMLSSINSCYD was higher than the PMLSS in the entire sample (mean difference: 4.6 W, p < 0.05, 95% CI 0.8-8.3 W) and in men (mean difference: 6.6 W, p < 0.05, 95% CI 1.3-11.8 W), but not in women (mean difference: 0.8 W, n.s., 95% CI -3.7 to 5.3 W), which was within the typical error of the PMLSS estimations (∼3%). In 12 subjects (nine males, three females), the PMLSSINSCYD differed by 3.1-7.3% compared to the MLSS. The Pearson correlations between the measured PMLSS and the calculated PMLSS (PMLSSINSCYD) were very strong in men (r = 0.974, p < 0.001, 95% CI 0.933-0.99), women (r = 0.984, p < 0.001, 95% CI 0.931-0.996), and for the entire sample (r = 0.992, p < 0.001, 95% CI 0.982-0.996). Discussion: In conclusion, the PMLSS can be accurately calculated using the INSCYD software, but it still requires advanced testing equipment to collect valid V˙O2max and VLamax data.

A unified mechanism for PARP inhibitor-induced PARP1 chromatin retention at DNA damage sites in living cells.

Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPis) not only suppress PARP1 catalytic activity but also prolong its association to damaged chromatin. Here, through live-cell imaging, we quantify the alterations in PARP1 dynamics and activity elicited by seven PARPis over a wide range of concentrations to deliver a unified mechanism of PARPi-induced PARP1 chromatin retention. We find that gross PARP1 retention at DNA damage sites is jointly governed by catalytic inhibition and allosteric trapping, albeit in a strictly independent manner-catalytic inhibition causes multiple unproductive binding-dissociation cycles of PARP1, while allosteric trapping prolongs the lesion-bound state of PARP1 to greatly increase overall retention. Importantly, stronger PARP1 retention produces greater temporal shifts in downstream DNA repair events and superior cytotoxicity, highlighting PARP1 retention, a complex but precisely quantifiable characteristic of PARPis, as a valuable biomarker for PARPi efficacy. Our approach can be promptly repurposed for interrogating the properties of DNA-repair-targeting compounds beyond PARPis.

Antibiotic dose and nutrient availability differentially drive the evolution of antibiotic resistance and persistence.

Effective treatment of bacterial infections proves increasingly challenging due to the emergence of bacterial variants that endure antibiotic exposure. Antibiotic resistance and persistence have been identified as two major bacterial survival mechanisms, and several studies have shown a rapid and strong selection of resistance or persistence mutants under repeated drug treatment. Yet, little is known about the impact of the environmental conditions on resistance and persistence evolution and the potential interplay between both phenotypes. Based on the distinct growth and survival characteristics of resistance and persistence mutants, we hypothesized that the antibiotic dose and availability of nutrients during treatment might play a key role in the evolutionary adaptation to antibiotic stress. To test this hypothesis, we combined high-throughput experimental evolution with a mathematical model of bacterial evolution under intermittent antibiotic exposure. We show that high nutrient levels during antibiotic treatment promote selection of high-level resistance, but that resistance mainly emerges independently of persistence when the antibiotic concentration is sufficiently low. At higher doses, resistance evolution is facilitated by the preceding or concurrent selection of persistence mutants, which ensures survival of populations in harsh conditions. Collectively, our experimental data and mathematical model elucidate the evolutionary routes toward increased bacterial survival under different antibiotic treatment schedules, which is key to designing effective antibiotic therapies.

Preclinical assessment of a mathematical model for ablation zone prediction in thyroid laser ablation.

To develop and validate a 3D simulation model to calculate laser ablation (LA) zone size and estimate the volume of treated tissue for thyroid applications, a model was developed, taking into account dynamic optical and thermal properties of tissue change. For validation, ten Yorkshire swines were equally divided into two cohorts and underwent thyroid LA at 3 W/1,400 J and 3 W/1,800 J respectively with a 1064-nm multi-source laser (Echolaser X4 with Orblaze™ technology; ElEn SpA, Calenzano, Italy). The dataset was analyzed employing key statistical measures such as mean and standard deviation (SD). Model simulation data were compared with animal gross histology. Experimental data for longitudinal length, width (transverse length), ablation volume and sphericity were 11.0 mm, 10.0 mm, 0.6 mL and 0.91, respectively at 1,400 J and 14.6 mm, 12.4 mm, 1.12 mL and 0.83, respectively at 1,800 J. Gross histology data showed excellent reproducibility of the ablation zone among same laser settings; for both 1,400 J and 1,800 J, the SD of the in vivo parameters was ≤ 0.7 mm, except for width at 1,800 J, for which the SD was 1.1 mm. Simulated data for longitudinal length, width, ablation volume and sphericity were 11.6 mm, 10.0 mm, 0.62 mL and 0.88, respectively at 1,400 J and 14.2 mm, 12.0 mm, 1.06 mL and 0.84, respectively at 1,800 J. Experimental data for ablation volume, sphericity coefficient, and longitudinal and transverse lengths of thermal damaged area showed good agreement with the simulation data. Simulation datasets were successfully incorporated into proprietary planning software (Echolaser Smart Interface, Elesta SpA, Calenzano, Italy) to provide guidance for LA of papillary thyroid microcarcinomas. Our mathematical model showed good predictability of coagulative necrosis when compared with data from in vivo animal experiments.

Astroglial gap junctions strengthen hippocampal network activity by sustaining afterhyperpolarization via KCNQ channels.

Throughout the brain, astrocytes form networks mediated by gap junction channels that promote the activity of neuronal ensembles. Although their inputs on neuronal information processing are well established, how molecular gap junction channels shape neuronal network patterns remains unclear. Here, using astroglial connexin-deficient mice, in which astrocytes are disconnected and neuronal bursting patterns are abnormal, we show that astrocyte networks strengthen bursting activity via dynamic regulation of extracellular potassium levels, independently of glutamate homeostasis or metabolic support. Using a facilitation-depression model, we identify neuronal afterhyperpolarization as the key parameter underlying bursting pattern regulation by extracellular potassium in mice with disconnected astrocytes. We confirm this prediction experimentally and reveal that astroglial network control of extracellular potassium sustains neuronal afterhyperpolarization via KCNQ voltage-gated K+ channels. Altogether, these data delineate how astroglial gap junctions mechanistically strengthen neuronal population bursts and point to approaches for controlling aberrant activity in neurological diseases.

What makes a temperate phage an effective bacterial weapon?

Temperate bacteriophages (phages) are common features of bacterial genomes and can act as self-amplifying biological weapons, killing susceptible competitors and thus increasing the fitness of their bacterial hosts (lysogens). Despite their prevalence, however, the key characteristics of an effective temperate phage weapon remain unclear. Here, we use systematic mathematical analyses coupled with experimental tests to understand what makes an effective temperate phage weapon. We find that effectiveness is controlled by phage life history traits-in particular, the probability of lysis and induction rate-but that the optimal combination of traits varies with the initial frequency of a lysogen within a population. As a consequence, certain phage weapons can be detrimental when their hosts are rare yet beneficial when their hosts are common, while subtle changes in individual life history traits can completely reverse the impact of an individual phage weapon on lysogen fitness. We confirm key predictions of our model experimentally, using temperate phages isolated from the clinically relevant Liverpool epidemic strain of Pseudomonas aeruginosa. Through these experiments, we further demonstrate that nutrient availability can also play a critical role in driving frequency-dependent patterns in phage-mediated competition. Together, these findings highlight the complex and context-dependent nature of temperate phage weapons and the importance of both ecological and evolutionary processes in shaping microbial community dynamics more broadly. IMPORTANCE: Temperate bacteriophages-viruses that integrate within bacterial DNA-are incredibly common within bacterial genomes and can act as powerful self-amplifying weapons. Bacterial hosts that carry temperate bacteriophages can thus gain a fitness advantage within a given niche by killing competitors. But what makes an effective phage weapon? Here, we first use a simple mathematical model to explore the factors determining bacteriophage weapon utility. Our models suggest that bacteriophage weapons are nuanced and context-dependent; an individual bacteriophage may be beneficial or costly depending upon tiny changes to how it behaves or the bacterial community it inhabits. We then confirm these mathematical predictions experimentally, using phages isolated from cystic fibrosis patients. But, in doing so, we also find that another factor-nutrient availability-plays a key role in shaping bacteriophage-mediated competition. Together, our results provide new insights into how temperate bacteriophages modulate bacterial communities.

Temporal multiscale modeling of biochemical regulatory networks: Calcium-regulated hepatocyte lipid and glucose metabolism.

Hepatocyte lipid and glucose metabolism is regulated not only by major hormones like insulin and glucagon but also by many other factors, including calcium ions. Recently, mitochondria-associated membrane (MAM) dysfunction combined with incorrect IP3-receptor regulation has been shown to result in abnormal calcium signaling in hepatocytes. This dysfunction could further lead to hepatic metabolism pathology. However, the exact contribution of MAM dysfunction, incorrect IP3-receptor regulation and insulin resistance to the calcium-insulin-glucagon interplay is not understood yet. In this work, we analyze the role of abnormal calcium signaling and insulin dysfunction in hepatocytes by proposing a model of hepatocyte metabolic regulatory network with a detailed focus on the model construction details besides the biological aspect. In this work, we analyze the role of abnormal calcium signaling and insulin dysfunction in hepatocytes by proposing a model of hepatocyte metabolic regulatory network. We focus on the model construction details, model validation, and predictions. We describe the dynamic regulation of signaling processes by sigmoid Hill function. In particular, we study the effect of both the Hill function slope and the distance between Hill function extremes on metabolic processes in hepatocytes as a model of nonspecific insulin dysfunction. We also address the significant time difference between characteristic time of glucose hepatic processing and a typical calcium oscillation period in hepatocytes. Our modeling results show that calcium signaling dysfunction results in an abnormal increase in postprandial glucose levels, an abnormal glucose decrease in fasting, and a decreased amount of stored glycogen. An insulin dysfunction of glucose phosphorylation, glucose dephosphorylation, and glycogen breakdown also cause a noticeable effect. We also get some insight into the so-called hepatic insulin resistance paradox, confirming the hypothesis regarding indirect insulin action on hepatocytes via dysfunctional adipocyte lipolysis.

Game-theoretic modeling in regulating greenhouse gas emissions.

This research introduces an innovative framework for addressing the escalating issue of greenhouse gas emissions through the integration of game theory with differential equations, proposing a novel model to simulate the regulatory dynamics between emission sources and legislative actions. By blending advanced mathematical modeling with environmental science, this paper underscores the critical necessity for pioneering, proactive strategies in environmental management and policy formulation. Central to our approach is the simulation of interactions within a game-theoretic context, aiming to delineate optimal strategies for emission sources and regulatory bodies, factoring in legislative constraints and environmental ramifications. The methodology employs a system of ordinary differential equations, capturing the dynamic, non-stationary nature of atmospheric processes and offering a realistic portrayal of the challenges in mitigating greenhouse gas emissions. Furthermore, the study introduces a fee-based regulatory mechanism designed to encourage emission reductions, highlighting the economic implications of such strategies. Significantly contributing to environmental management, this research presents a detailed model capable of predicting the trajectory of greenhouse gas emissions over a decade, considering the potential impact of technological innovations in emission control. The conclusion emphasizes the promising role of artificial intelligence in refining environmental governance, acknowledging the complexities and limitations inherent in predictive modeling. Aimed at policymakers and environmental scientists, this paper serves as a strategic tool for informed decision-making, advocating for a multidisciplinary approach to develop sustainable, effective solutions to combat one of the most critical environmental challenges facing the globe today.

Analysis of the multiparametric cell cycle data.

This chapter was originally written in 2011. The idea was to give some history of cell cycle analysis before and after flow cytometry became widely accessible; provide references to educational material for single parameter DNA content analysis, introduce and discuss multiparameter cell cycle analysis in a methodological style, and in a casual style, discuss aspects of the work over the last 40years that we have given thought, performing some experiments, but didn't publish. It feels like there is a linear progression that moves from counting cells for growth curves, to counting labeled mitotic cells by autoradiography, to DNA content analysis, to cell cycle states defined by immunofluorescence plus DNA content analysis, to extraction of cell cycle expression profiles, and finally to probability state modeling, which should be the "right" way to analyze cytometric cell cycle data. This is the sense of this chapter. In 2023, we have updated it, but the exciting, expansive aspects brought about by spectral and mass cytometry are still young and developing, and thus have not been vetted, reviewed, and presented in mature form.

DNA lesion bypass and the stochastic dynamics of transcription-coupled repair.

DNA base damage is a major source of oncogenic mutations and disruption to gene expression. The stalling of RNA polymerase II (RNAP) at sites of DNA damage and the subsequent triggering of repair processes have major roles in shaping the genome-wide distribution of mutations, clearing barriers to transcription, and minimizing the production of miscoded gene products. Despite its importance for genetic integrity, key mechanistic features of this transcription-coupled repair (TCR) process are controversial or unknown. Here, we exploited a well-powered in vivo mammalian model system to explore the mechanistic properties and parameters of TCR for alkylation damage at fine spatial resolution and with discrimination of the damaged DNA strand. For rigorous interpretation, a generalizable mathematical model of DNA damage and TCR was developed. Fitting experimental data to the model and simulation revealed that RNA polymerases frequently bypass lesions without triggering repair, indicating that small alkylation adducts are unlikely to be an efficient barrier to gene expression. Following a burst of damage, the efficiency of transcription-coupled repair gradually decays through gene bodies with implications for the occurrence and accurate inference of driver mutations in cancer. The reinitation of transcription from the repair site is not a general feature of transcription-coupled repair, and the observed data is consistent with reinitiation never taking place. Collectively, these results reveal how the directional but stochastic activity of TCR shapes the distribution of mutations following DNA damage.

Mathematical modeling of the synergistic interplay of radiotherapy and immunotherapy in anti-cancer treatments.

Introduction: While radiotherapy has long been recognized for its ability to directly ablate cancer cells through necrosis or apoptosis, radiotherapy-induced abscopal effect suggests that its impact extends beyond local tumor destruction thanks to immune response. Cellular proliferation and necrosis have been extensively studied using mathematical models that simulate tumor growth, such as Gompertz law, and the radiation effects, such as the linear-quadratic model. However, the effectiveness of radiotherapy-induced immune responses may vary among patients due to individual differences in radiation sensitivity and other factors. Methods: We present a novel macroscopic approach designed to quantitatively analyze the intricate dynamics governing the interactions among the immune system, radiotherapy, and tumor progression. Building upon previous research demonstrating the synergistic effects of radiotherapy and immunotherapy in cancer treatment, we provide a comprehensive mathematical framework for understanding the underlying mechanisms driving these interactions. Results: Our method leverages macroscopic observations and mathematical modeling to capture the overarching dynamics of this interplay, offering valuable insights for optimizing cancer treatment strategies. One shows that Gompertz law can describe therapy effects with two effective parameters. This result permits quantitative data analyses, which give useful indications for the disease progression and clinical decisions. Discussion: Through validation against diverse data sets from the literature, we demonstrate the reliability and versatility of our approach in predicting the time evolution of the disease and assessing the potential efficacy of radiotherapy-immunotherapy combinations. This further supports the promising potential of the abscopal effect, suggesting that in select cases, depending on tumor size, it may confer full efficacy to radiotherapy.

Engineering Tunable, Low Latency Spatial Computation with Dual Input Quorum Sensing Promoters.

Quorum sensing signals have evolved for population-level signaling in bacterial communities and are versatile tools for engineering cell-cell signaling in synthetic biology projects. Here, we characterize the spatial diffusion of a palette of quorum sensing signals and find that their diffusion in agar can be predicted from their molecular weight with a simple power law. We also engineer novel dual- and multi-input promoters that respond to quorum-sensing diffusive signals for use in engineered genetic systems. We engineer a promoter scaffold that can be adapted for activation and repression by multiple diffusers simultaneously. Lastly, we combine the knowledge on diffusion dynamics with the novel genetic components to build a new generation of spatial, stripe-forming systems with a simplified design, improved robustness, tuneability, and response time.

Deciphering and designing microbial communities by genome-scale metabolic modelling.

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

Modelling spatio-temporal interactions between second messengers Ca 2 + and cAMP in a pancreatic ß -cell.

Calcium serves as a widespread second messenger in almost every human and animal cell. The regulation of various cellular processes, such as transcriptional control and the kinetics of membrane channels, is significantly influenced by intracellular calcium ions (Ca 2 + ), and linkages between Ca 2 + and other second messengers should activate signaling networks. The passage of ions across the cell membrane regulates Ca 2 + levels in pancreatic ß -cells and requires the coordinated interaction of various ion transport mechanisms and organelles. The signaling of Ca 2 + in ß -cells and its interactions with the intracellular dynamics of cyclic adenosine monophosphate (cAMP) is poorly understood. Therefore, the current investigation proposes a mathematical model to illustrate the spatiotemporal dynamical interaction between Ca 2 + and cAMP. In order to construct a one-dimensional mathematical model, the fundamental initial and boundary conditions derived from the physiological characteristics of the ß -cell are incorporated. The numerical results were obtained by MATLAB simulations using the finite element method and the Crank-Nicolson method. The current study aims to offer an update on regulation between Ca 2 + and cAMP signaling circuits, with a focus on interactions that occur in localized areas of the ß -cell. The model gives the individual effect of each parameter on the regulation of Ca 2 + and cAMP profiles in a ß -cell. Evidently, impairments in the regulation of messenger pathways contribute to the pathological conditions, as demonstrated by the results obtained.

Flip bifurcation analysis and mathematical modeling of cholera disease by taking control measures.

To study the dynamical system, it is necessary to formulate the mathematical model to understand the dynamics of various diseases which are spread in the world wide. The objective of the research study is to assess the early diagnosis and treatment of cholera virus by implementing remedial methods with and without the use of drugs. A mathematical model is built with the hypothesis of strengthening the immune system, and a ABC operator is employed to turn the model into a fractional-order model. A newly developed system SEIBR, which is examined both qualitatively and quantitatively to determine its stable position as well as the verification of flip bifurcation has been made for developed system. The local stability of this model has been explored concerning limited observations, a fundamental aspect of epidemic models. We have derived the reproductive number using next generation method, denoted as " R 0 ", to analyze its impact rate across various sub-compartments, which serves as a critical determinant of its community-wide transmission rate. The sensitivity analysis has been verified according to its each parameters to identify that how much rate of change of parameters are sensitive. Atangana-Toufik scheme is employed to find the solution for the developed system using different fractional values which is advanced tool for reliable bounded solution. Also the error analysis has been made for developed scheme. Simulations have been made to see the real behavior and effects of cholera disease with early detection and treatment by implementing remedial methods without the use of drugs in the community. Also identify the real situation the spread of cholera disease after implementing remedial methods with and without the use of drugs. Such type of investigation will be useful to investigate the spread of virus as well as helpful in developing control strategies from our justified outcomes.

Modeling of spatiotemporal dynamics of ligand-coated particle flow in targeted drug delivery processes.

Nanoparticles tethered with vasculature-binding epitopes have been used to deliver the drug into injured or diseased tissues via the bloodstream. However, the extent that blood flow dynamics affects nanoparticle retention at the target site after adhesion needs to be better understood. This knowledge gap potentially underlies significantly different therapeutic efficacies between animal models and humans. An experimentally validated mathematical model that accurately simulates the effects of blood flow on nanoparticle adhesion and retention, thus circumventing the limitations of conventional trial-and-error-based drug design in animal models, is lacking. This paper addresses this technical bottleneck and presents an integrated mathematical method that derives heavily from a unique combination of a mechanics-based dispersion model for nanoparticle transport and diffusion in the boundary layers, an asperity model to account for surface roughness of endothelium, and an experimentally calibrated stochastic nanoparticle-cell adhesion model to describe nanoparticle adhesion and subsequent retention at the target site under external flow. PLGA-b-HA nanoparticles tethered with VHSPNKK peptides that specifically bind to vascular cell adhesion molecules on the inflamed vascular wall were investigated. The computational model revealed that larger particles perform better in adhesion and retention at the endothelium for the particle sizes suitable for drug delivery applications and within physiologically relevant shear rates. The computational model corresponded closely to the in vitro experiments which demonstrates the impact that model-based simulations can have on optimizing nanocarriers in vascular microenvironments, thereby substantially reducing in vivo experimentation as well as the development costs.

To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy.

Toxicity and emerging drug resistance pose important challenges in poly-adenosine ribose polymerase inhibitor (PARPi) maintenance therapy of ovarian cancer. We propose that adaptive therapy, which dynamically reduces treatment based on the tumor dynamics, might alleviate both issues. Utilizing in vitro time-lapse microscopy and stepwise model selection, we calibrate and validate a differential equation mathematical model, which we leverage to test different plausible adaptive treatment schedules. Our model indicates that adjusting the dosage, rather than skipping treatments, is more effective at reducing drug use while maintaining efficacy due to a delay in cell kill and a diminishing dose-response relationship. In vivo pilot experiments confirm this conclusion. Although our focus is toxicity mitigation, reducing drug use may also delay resistance. This study enhances our understanding of PARPi treatment scheduling and illustrates the first steps in developing adaptive therapies for new treatment settings. A record of this paper's transparent peer review process is included in the supplemental information.

Development and application of mathematical modeling of thymol release from environmental-responsive potato starch active packaging films.

Traditional active packaging materials are easily affected by the environment, resulting in their inability to release active substances in specified quantities at specified times and locations. In this study, MCM-41 was used as a thymol (THY) carrier and added to the potato starch (PS) matrix to design an intelligent release active packaging film based on storage microenvironment. MCM-41 encapsulation improved thermal stability of THY. THY-MCM-41 addition significantly improved the tensile strength (TS, 7.18 MPa) of the film (P < 0.05) and endowed the film excellent gas and water barrier protection. THY release was responsive to temperature and relative humidity (RH), and the First-order model better explained the THY release pattern (R2 > 0.980). The THY-MCM-41/PS film exhibited long-term antibacterial effect during 10-day storage due to the sustained release of THY. Additionally, strawberries packaged in the THY-MCM-41/PS film exhibited the best sensory characteristics during 5-day storage (25 °C and 50 % RH). Overall, the present THY-MCM-41/PS film provides a novel alternative for the sustained release of active substances in order to achieve the excellent preservation of goods such as fruits and vegetables.