In the present study, a biomimetic nanoconstruct (BNc) with a multimodal imaging system is engineered using tumor homing natural killer cell membrane (NKM), near‐infrared (NIR) fluorescent dye, and gadolinium (Gd) conjugate‐based magnetic resonance imaging contrast agent onto the surface of a polymeric nanoparticle. The engineered BNc is 110 ± 20 nm in size and showed successful retention of NKM proteins. The magnetic properties of the BNc are found to be tunable from 2.1 ± 0.17 to 5.3 ± 0.5 mm−1 s−1 under 14.1 T, by adjusting the concentration of Gd‐lipid conjugate onto the surface of the BNc. Confocal imaging and cell sorting analysis reveal a distinguishable cellular interaction of the BNc with MCF‐7 cells in comparison to that of bare polymeric nanoparticles suggesting the tumor homing properties of NKM camouflage system. The in vitro cellular interaction results are further confirmed by in vivo NIR fluorescent tumor imaging and ex vivo MR imaging, respectively. Pharmacokinetics and biodistribution analysis of the BNc show longer circulation half‐life (≈9.5 h) and higher tumor accumulation (10% of injected dose) in MCF‐7 induced tumor‐bearing immunodeficient NU/NU nude mice. Owing to the proven immunosurveillance potential of NK‐cell in the field of immunotherapy, the BNc engineered herein would hold promises in the design consideration of nanomedicine engineering.

Nanoparticles (NPs) are easily contaminated by bacterial endotoxin (lipopolysaccharide [LPS]). The presence of LPS can be responsible for many immune/inflammatory effects attributed to NPs. In this study, we examined the effects of LPS adsorption on the NP surface on the formation of a biocorona in biological fluids and on the subsequent inflammation-inducing activity of NPs. Different gold (Au) NPs with sizes ranging from 10 to 80 nm and with different surface functionalization (sodium citrate, lipoic acid, and branched polyethyleneimine (BPEI), or polyethylene glycol (PEG)) were exposed to E. coli LPS under different conditions. The binding capacity of LPS to the surface of AuNPs was dose-and time-dependent. LPS attached to sodium citrate and lipoic acid coatings, but did not adhere to BPEI-or PEG-coated NPs. By computational simulation, the binding of LPS to AuNPs seems to follow the Langmuir absorption isotherm. The presence of LPS on AuNP surface interfered and caused a decrease in the formation of the expected biomolecular corona upon incubation in human plasma. LPS-coated AuNPs, but not the LPS-free NPs, induced significant inflammatory responses in vitro. Notably, while free LPS did also induce an anti-inflammatory response, LPS bound to NPs appeared unable to do so. In conclusion, the unintentional adsorption of LPS onto the NP surface can affect the biocorona formation and the inflammatory properties of NPs. Thus, for an accurate interpretation of NP interactions with cells, it is extremely important to be able to distinguish the intrinsic NP biological effects from those caused by biologically active contaminants such as endotoxin. ARTICLE HISTORY

Many physiologically based pharmacokinetic (PBPK) models for environmental chemicals, drugs, and nanomaterials have been developed to aid risk and safety assessments using acslX. However, acslX has been rendered sunset since November 2015. Alternative modeling tools and tutorials are needed for future PBPK applications. This forum article aimed to: (1) demonstrate the performance of 4 PBPK modeling software packages (acslX, Berkeley Madonna, MATLAB, and R language) tested using 2 existing models (oxytetracycline and gold nanoparticles); (2) provide a tutorial of PBPK model code conversion from acslX to Berkeley Madonna, MATLAB, and R language; (3) discuss the advantages and disadvantages of each software package in the implementation of PBPK models in toxicology, and (4) share our perspective about future direction in this field. Simulation results of plasma/tissue concentrations/amounts of oxytetracycline and gold from different models were compared visually and statistically with linear regression analyses. Simulation results from the original models were correlated well with results from the recoded models, with time-concentration/amount curves nearly superimposable and determination coefficients of 0.86-1.00. Step-by-step explanations of the recoding of the models in different software programs are provided in the Supplementary Data. In summary, this article presents a tutorial of PBPK model code conversion for a small molecule and a nanoparticle among 4 software packages, and a performance comparison of these software packages in PBPK model implementation. This tutorial helps beginners learn PBPK modeling, provides suggestions for selecting a suitable tool for future projects, and may lead to the transition from acslX to alternative modeling tools.

Defining the role of T-cell avidity and killing efficacy in forming immunological response(s), leading to relapse-remission and autoantibody release in autoimmune type 1 diabetes (T1D), remains incompletely understood. Using competition-based population models of T- and B-cells, we provide a predictive tool to determine how these two parametric quantities, namely, avidity and killing efficacy, affect disease outcomes. We show that, in the presence of T-cell competition, successive waves along with cyclic fluctuations in the number of T-cells are exhibited by the model, with the former induced by transient bistability and the latter by transient periodic orbits. We hypothesize that these two immunological processes are responsible for making T1D a relapsing-remitting disease within prolonged but limited durations. The period and the number of peaks of these two processes differ, making them potential candidates to determine how plausible waves and cyclic fluctuations are in producing such effects. By assuming that T-cell and B-cell avidities are correlated, we demonstrate that autoantibodies associated with the higher avidity T-cell clones are first to be detected, and they reach their detectability level faster than those associated with the low avidity clones, independent of what T-cell killing efficacies are. Such outcomes are consistent with experimental observations in humans and they provide a rationale for observing rapid and slow progressors of T1D in high risk subjects. Our analysis of the models also reveals that it is possible to improve disease outcomes by unexpectedly increasing the avidity of certain subclones of T-cells. The decline in the number of -cells in these cases still occurs, but it terminates early, leaving sufficient number of functioning -cells in operation and the affected individual asymptomatic. These results indicate that the models presented here are of clinical relevance because of their potential use in developing predictive algorithms of rapid and slow progression to clinical T1D.

BackgroundDengue is one of the most geographically significant mosquito-borne viral diseases transmitted by Aedes mosquitoes. During blood feeding, mosquitoes deposit salivary proteins that induce antibody responses. These can be related to the intensity of exposure to bites. Some mosquito salivary proteins, such as D7 proteins, are known as potent allergens. The antibody response to D7 proteins can be used as a marker to evaluate the risk of exposure and disease transmission and provide critical information for understanding the dynamics of vector–host interactions.MethodsThe study was conducted at the Los Patios Hospital, Cucuta, Norte de Santander, Colombia. A total of 63 participants were enrolled in the study. Participants were categorized into three disease status groups, age groups, and socioeconomic strata. The level of IgG antibodies against D7 Aedes proteins was determined by ELISA. We used a statistical approach to determine if there is an association between antibody levels and factors such as age, living conditions, and dengue virus (DENV) infection.ResultsWe found that IgG antibodies against D7 proteins were higher in non-DENV infected individuals in comparison to DENV-infected participants. Also, the age factor showed a significant positive correlation with IgG antibodies against D7 proteins, and the living conditions (socioeconomic stratification), in people aged 20 years or older, are a statistically significant factor in the variability of IgG antibodies against D7 proteins.ConclusionThis pilot study represents the first approximation to elucidate any correlation between the antibody response against mosquito D7 salivary proteins and its correlation with age, living conditions, and DENV infection in a dengue endemic area.

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In type 1 diabetes, an autoimmune disease mediated by autoreactive T-cells that attack insulin-secreting pancreatic beta-cells, it has been suggested that disease progression may additionally require protective mechanisms in the target tissue to impede such auto-destructive mechanisms. We hypothesize that the autoimmune attack against beta-cells causes endoplasmic reticulum stress by forcing the remaining beta-cells to synthesize and secrete defective insulin. To rescue beta-cell from the endoplasmic reticulum stress, beta-cells activate the unfolded protein response to restore protein homeostasis and normal insulin synthesis. Here we investigate the compensatory role of unfolded protein response by developing a multi-state model of type 1 diabetes that takes into account beta-cell destruction caused by pathogenic autoreactive T-cells and apoptosis triggered by endoplasmic reticulum stress. We discuss the mechanism of unfolded protein response activation and how it counters beta-cell extinction caused by an autoimmune attack and/or irreversible damage by endoplasmic reticulum stress. Our results reveal important insights about the balance between beta-cell destruction by autoimmune attack (beta-cell homicide) and beta-cell apoptosis by endoplasmic reticulum stress (beta-cell suicide). It also provides an explanation as to why the unfolded protein response may not be a successful therapeutic target to treat type 1 diabetes.

Objectives: The current demographic information from China reports that 10%-19% of patients hospitalized with coronavirus disease (COVID-19) were diabetic. Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) are considered firstline agents in patients with diabetes because of their nephroprotective effects, but administration of these drugs leads to upregulation of angiotensin-converting enzyme 2 (ACE2), which is responsible for the viral entry of severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2). Data are lacking to determine what pulmonary effects ACEIs or ARBs may have in patients with diabetes, which could be relevant in the management of patients infected with SARS-CoV-2. This study aims to assess the prevalence of pulmonary adverse drug effects (ADEs) in patients with diabetes who were taking ACEI or ARBs to provide guidance as to how these medications could affect outcomes in acute respiratory illnesses such as SARS-CoV-2 infection. Methods: 1DATA, a unique data platform resulting from collaboration across veterinary and human health care, used an intelligent medicine recommender system (1DrugAssist) developed using several national and international databases to evaluate all ADEs reported to the Food and Drug Administration for patients with diabetes taking ACEIs or ARBs. Results: Mining of this data elucidated the proportion of a cluster of pulmonary ADEs associated with specific medications in these classes, which may aid health care professionals in understanding how these medications could worsen or predispose patients with diabetes to infections affecting the respiratory system, specifically COVID-19. Based on this data mining process, captopril was found to have a statistically significantly higher incidence of pulmonary ADEs compared with other ACEIs (P ¼ 0.005) as well as ARBs (P ¼ 0.012), though other specific drugs also had important pulmonary ADEs associated with their use. Conclusion: These analyses suggest that pharmacists and clinicians will need to consider the specific medication's adverse event profile, particularly captopril, on how it may affect infections and other acute disease states that alter pulmonary function, such as COVID-19.