The assimilation of large suddenly ingested quantities of liquid <die bewaltigung ein- malig zugefuhrter grosser flussigkeitsmengen<
Assimilation of large suddenly ingested quantities of water by patients
PATIENT↗
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Human Systems Integration: Managing Risk in Anesthesia
The practice of anesthesia relies on clinicians’ ability to safely manage increasingly complex equipment. Devices such as ventilators, drug infusion pumps, and physiologic monitors use sophisticated algorithms to deliver care, but most clinicians are only trained to manage automated systems during normal operation. Few if any receive training on how to manage system failures. Although manufacturers are required to consult with human factors engineers as part of the equipment design process, most pieces of equipment are ultimately brought to market without extensive input from clinicians. Systems in the operating room can be as simple as an oxygen tank, or as complex as a multi-institutional healthcare organization. Humans are also a complex system, and play a critical role in the domains of operations, design, fabrication, maintenance, repair, and ultimately, dismantling and closeout. HSI seeks to provide a means for advocating the human side of the system. Human Systems Integration (HSI) is the cross disciplinary process used as part of the Systems Engineering process to reduce risk in systems. HSI professionals consider the human, hardware, and software elements of system design to optimize system performance and improve safety. HSI professionals work in domains of study that include training, management, human factors engineering, safety, and occupational health, among others. This article discusses the role of systems in the practice of anesthesia, and how consideration of the human during all phases of the system life cycle helps manage risks and promote a better patient outcome.
Human Factors↗
Expanding Structural Space for Immunomodulatory Nucleic Acid Nanoparticles via Spatial Arrangement of Their Therapeutic Moieties
Different therapeutic nucleic acids (TNAs) can be unified in a single structure by their elongation with short oligonucleotides designed to self-assemble into nucleic acid nanoparticles (NANPs). With this approach, therapeutic cocktails with precisely controlled composition and stoichiometry of active ingredients can be delivered to the same diseased cells for enhancing pharmaceutical action. In this study, an additional nanotechnology-based therapeutic option that enlists a biocompatible NANP-encoded platform for their controlled patient-specific immunorecognition is explored. For this, a set of representative functional NANPs is extensively characterized in vitro, ex vivo, and in vivo and then further analyzed for immunostimulation of human peripheral blood mononuclear cells freshly collected from healthy donor volunteers. The results of the study present the advancement of the current TNA approach toward personalized medicine and offer a new strategy to potentially address top public health challenges related to drug overdose and safety through the biodegradable nature of the functional platform with immunostimulatory regulation.
60 APPLIED LIFE SCIENCES↗
Overcoming Barriers Associated with Oral Delivery of Differently Sized Fluorescent Core‐Shell Silica Nanoparticles
Oral delivery, while a highly desirable form of nanoparticle-drug administration, is limited by challenges associated with overcoming several biological barriers. Here, in this study, the authors study how fluorescent and poly(ethylene glycol)-coated (PEGylated) core-shell silica nanoparticles sized 5 to 50 nm interact with major barriers including intestinal mucus, intestinal epithelium, and stomach acid. From imaging fluorescence correlation spectroscopy studies using quasi-total internal reflection fluorescence microscopy, diffusion of nanoparticles through highly scattering mucus is progressively hindered above a critical hydrodynamic size around 20 nm. By studying Caco-2 cell monolayers mimicking the intestinal epithelia, it is observed that ultrasmall nanoparticles below 10 nm diameter (Cornell prime dots, [C’ dots]) show permeabilities correlated with high absorption in humans from primarily enhanced passive passage through tight junctions. Particles above 20 nm diameter exclusively show active transport through cells. After establishing C’ dot stability in artificial gastric juice, in vivo oral gavage experiments in mice demonstrate successful passage through the body followed by renal clearance without protein corona formation. Results suggest C’ dots as viable candidates for oral administration to patients with a proven pathway towards clinical translation and may generate renewed interest in examining silica as a food additive and its effects on nutrition and health.
36 MATERIALS SCIENCE↗
High‐Concentration Antibody Formulation via Solvent‐Based Dehydration
Abstract Although subcutaneous (SC) delivery is the preferred administration route for immunotherapies and other biologics for improved patient compliance and lower healthcare costs, it necessitates high‐concentration antibody formulations. However, high‐concentration antibody solutions face significant instabilities and prohibitively high viscosities. Other approaches for high‐concentration formulations have been developed, including non‐aqueous solutions, which can be irritating or painful, and antibody‐laden hydrogel microparticles, which require centrifugation and are limited to concentrations <300 mg mL −1 . This work presents a new formulation process wherein the antibody is concentrated and encapsulated into hydrogel microparticles via solvent‐based dehydration. The final dosage form is an aqueous particle suspension with a formulation concentration of 360 mg mL −1 . In this process, microparticles are synthesized continuously, and antibody precipitation is realized simultaneously to dehydration, which allows for higher antibody concentrations. Antibody phase behavior and precipitation–dehydration kinetics are analyzed. The antibody is structurally and functionally stable in the microparticle post‐processing and after 4 months. Injectability of the suspension meets clinical standards with glide force <20 N. For the first time, an aqueous antibody formulation at high concentrations comparable to non‐aqueous formulations is presented, ideal for subcutaneous administration. The process is envisioned to be generalizable as a platform for SC delivery in multiple clinical applications.
Zheng, Talia [Department of Chemical Engineering M↗
Neutralizing Antibodies to SARS-CoV-2 Selected from a Human Antibody Library Constructed Decades Ago
Combinatorial antibody libraries not only effectively reduce antibody discovery to a numbers game, but enable documentation of the history of antibody responses in an individual. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has prompted a wider application of this technology to meet the public health challenge of pandemic threats in the modern era. Herein, a combinatorial human antibody library constructed 20 years before the coronavirus disease 2019 (COVID-19) pandemic is used to discover three highly potent antibodies that selectively bind SARS-CoV-2 spike protein and neutralize authentic SARS-CoV-2 virus. Compared to neutralizing antibodies from COVID-19 patients with generally low somatic hypermutation (SHM), these three antibodies contain over 13–22 SHMs, many of which are involved in specific interactions in their crystal structures with SARS-CoV-2 spike receptor binding domain. The identification of these somatically mutated antibodies in a pre-pandemic library raises intriguing questions about the origin and evolution of these antibodies with respect to their reactivity with SARS-CoV-2.
60 APPLIED LIFE SCIENCES↗
Molecularly Imprinted Polymer Sensor Empowered by Bound States in the Continuum for Selective Trace-Detection of TGF-beta
The integration of advanced materials and photonic nanostructures can lead to enhanced biodetection capabilities, crucial in clinical scenarios and point-of-care diagnostics, where simplified strategies are essential. Herein, a molecularly imprinted polymer (MIP) photonic nanostructure is demonstrated, which selectively binding to transforming growth factor-beta (TGF-β), in which the sensing transduction is enhanced by bound states in the continuum (BICs). The MIP operating as a synthetic antibody matrix and coupled with BIC resonance, enhances the optical response to TGF-β at imprinted sites, leading to an augmented detection capability, thoroughly evaluated through spectral shift and optical lever analogue readout. The validation underscores the MIP-BIC sensor capability to detect TGF-β in spiked saliva, achieving a limit of detection of 10 fM and a resolution of 0.5 pM at physiological concentrations, with a precision of two orders of magnitude above discrimination threshold in patients. The MIP tailored selectivity is highlighted by an imprinting factor of 52, showcasing the sensor resistance to interference from other analytes. The MIP-BIC sensor architecture streamlines the detection process eliminating the need for complex sandwich immunoassays and demonstrates the potential for high-precision quantification. This positions the system as a robust tool for biomarker detection, especially in real-world diagnostic scenarios.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Association of clonal hematopoiesis mutations with clinical outcomes: A systematic review and meta-analysis
Clonal hematopoiesis (CH) mutations are common among individuals without known hematologic disease. CH mutations have been associated with numerous adverse clinical outcomes across many different studies. We systematically reviewed the available literature for clinical outcomes associated with CH mutations in patients without hematologic disease. We searched PubMed, EMBASE, and Scopus for eligible studies. Three investigators independently extracted the data, and each study was verified by a second author. Risk of bias was assessed using the Newcastle-Ottawa Scale. We identified 32 studies with 56 cohorts that examine the association between CH mutations and clinical outcomes. We conducted meta-analyses comparing outcomes among individuals with and without detectable CH mutations. We conducted meta-analyses for cardiovascular diseases (nine studies; HR = 1.61, 95% CI = 1.26–2.07, p = .0002), hematologic malignancies (seven studies; HR = 5.59, 95% CI = 3.31–9.45, p < .0001), therapy-related myeloid neoplasms (four studies; HR = 7.55, 95% CI = 4.3–13.57, p < .001), and death (nine studies; HR = 1.34, 95% CI = 1.2–1.5, p < .0001). The cardiovascular disease analysis was further stratified by variant allele fraction (VAF) and gene, which showed a statistically significant association only with a VAF of ≥ 10% (HR = 1.42, 95% CI = 1.24–1.62, p < .0001), as well as statistically significant associations for each gene examined with the largest magnitude of effect found for CH mutations in JAK2 (HR = 3.5, 95% CI = 1.84–6.68, p < .0001). Analysis of the association of CH mutations with hematologic malignancy demonstrated a numeric stepwise increase in risk with increasing VAF thresholds. This analysis strongly supports the association of CH mutations with a clinically meaningful increased risk of adverse clinical outcomes among individuals without hematologic disease, particularly with increasing VAF thresholds.
60 APPLIED LIFE SCIENCES↗
Harmonizing tau positron emission tomography in Alzheimer's disease: The CenTauR scale and the joint propagation model
Abstract INTRODUCTION Tau‐positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis. METHODS Using head‐to‐head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed “CenTauR” [CTR]). JPM is a statistical model that simultaneously models the relationships between head‐to‐head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale. RESULTS A strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach. DISCUSSION Preliminary findings using the JPM support the development and adoption of a universal scale for tau‐PET quantification. Highlights Tested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed “CenTauRs”. Tested a Centiloid‐like linear regression approach. Using five cohorts with head‐to‐head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions.
Neurosciences & Neurology↗
Quantum AI Based Enhanced Detection of Dementia
Quantum computing has the potential to significantly improve the early detection of Alzheimer's Disease and Related Dementias (ADRD). Quantum-enhanced machine learning can be used to perform an early screening of Alzheimer's disease using brain imaging data based on dataset of MRI scans from both healthy individuals and those diagnosed with Alzheimer's. This study aims to demonstrate the potential of quantum transfer learning to enhance the performance of the classical deep learning model for dementia detection. Using the MRI sagittal images available in the OASIS-2 (64 demented and 72 non-demented subjects between 60 and 96 years), we show how quantum techniques can transform a suboptimal classical model into a more effective solution for dementia detection, highlighting their potential impact on advancing healthcare technology. We begin with a simple classical deep learning model with a significantly smaller number of parameters, which gives suboptimal performance on the problem. Then, we apply different configurations of quantum transfer learning based on the pre-trained weak classifier (Figure 1). We fix the weak classifier's initial convolutional layers at their fixed pre-trained parameters and replace the last set of dense layers with a dressed quantum circuit (DQN), which we train to enhance performance. We performed 4-fold cross-validation for both the classical and the hybrid quantum models and trained them using Pennylane's `default.qubit' simulator and IonQ's Aria-1 simulator (noisy simulation). We showed that with significantly fewer parameters, the quantum transfer learning-based hybrid models showed significant performance enhancement over the base weak classical deep learning model for dementia detection. To classify between a demented and non-demented subject, the accuracy of quantum-based AI methods improved by 6 to 14% compared to classical methods. The sensitivity of the models improved by 4 to 17%. This shows that there are fewer chances of misclassifying demented patients. Figure 2 compares the performance of the hybrid quantum models and their base classical model, and Table 1 summarizes the results. We illustrated that with assistance from quantum machine learning, it is possible to enhance detection for dementia based on brain images. This shows the potential for practical utility of quantum computing in ADRD research.
Bhowmik, Sounak [University of Tennessee, Knoxvill↗
Crystal Structure of an i‐Motif from the HRAS Oncogene Promoter
Abstract An i‐motif is a non‐canonical DNA structure implicated in gene regulation and linked to cancers. The C‐rich strand of the HRAS oncogene, 5′‐CG CCC GTG CCC TGCG CCC GCAA CCC GA‐3′ (herein referred to as iHRAS), forms an i‐motif in vitro but its exact structure was unknown. HRAS is a member of the RAS proto‐oncogene family. About 19 % of US cancer patients carry mutations in RAS genes. We solved the structure of iHRAS at 1.77 Å resolution. The structure reveals that iHRAS folds into a double hairpin. The two double hairpins associate in an antiparallel fashion, forming an i‐motif dimer capped by two loops on each end and linked by a connecting region. Six C−C + base pairs form each i‐motif core, and the core regions are extended by a G−G base pair and a cytosine stacking. Extensive canonical and non‐canonical base pairing and stacking stabilizes the connecting region and loops. The iHRAS structure is the first atomic resolution structure of an i‐motif from a human oncogene. This structure sheds light on i‐motifs folding and function in the cell.
Li, Kevin S.↗
Crystal Structure of an i‐Motif from the HRAS Oncogene Promoter
Abstract An i‐motif is a non‐canonical DNA structure implicated in gene regulation and linked to cancers. The C‐rich strand of the HRAS oncogene, 5′‐CG CCC GTG CCC TGCG CCC GCAA CCC GA‐3′ (herein referred to as iHRAS), forms an i‐motif in vitro but its exact structure was unknown. HRAS is a member of the RAS proto‐oncogene family. About 19 % of US cancer patients carry mutations in RAS genes. We solved the structure of iHRAS at 1.77 Å resolution. The structure reveals that iHRAS folds into a double hairpin. The two double hairpins associate in an antiparallel fashion, forming an i‐motif dimer capped by two loops on each end and linked by a connecting region. Six C−C + base pairs form each i‐motif core, and the core regions are extended by a G−G base pair and a cytosine stacking. Extensive canonical and non‐canonical base pairing and stacking stabilizes the connecting region and loops. The iHRAS structure is the first atomic resolution structure of an i‐motif from a human oncogene. This structure sheds light on i‐motifs folding and function in the cell.
Li, Kevin S.↗
Tumor‐pH‐value responsive non‐peripheral substituted phthalocyanines: Synthesis, investigation of photophysical and photochemical properties
Cancer is one of the diseases with the highest mortality rate worldwide. Although PDT has recently produced encouraging outcomes, there are still many areas that need to be improved. The first of these is the negative consequences faced by patients treated with the PDT method when exposed to sunlight. For this reason, a new PDT method has been developed in recent years, and it is aimed at using photosensitizer molecules that can be active in acidic conditions. Since the pH values of tumor tissues are more acidic than normal tissues, preparing molecules that act effectively in acidic conditions will allow for more effective results in treating cancer with PDT. In this context, within the scope of this study, 3‐(4‐propionylphenoxy)phthalonitrile ( 1 ) and its non‐peripheral tetra‐substituted phthalocyanine derivatives [( 2 ), ( 3 ), and ( 4 )] were prepared. With these phthalocyanine derivatives, the novel compounds ( 5 ), ( 6 ), and ( 7 ) were synthesized for the first time. The aggregation tendencies of newly synthesized phthalocyanines ( 5–7 ) were investigated in solvent media. The effects of pH changes upon UV–Vis and fluorescence spectra were performed. The electronic and emission spectra of synthesized phthalocyanine derivatives are highly sensitive to pH changes. Formation constant (Log K) values of mono‐ and di‐protonated phthalocyanine forms were calculated by the Henderson–Hasselback equation. The mono‐ and di‐protonated species' equilibrium constants (logK 1 and logK 2 ) were calculated as ~5.0. This value may be promising for pH‐sensitizing photosensitizers. Also, the photophysical and photochemical properties of synthesized metallophthalocyanine derivatives ( 2 ) and ( 5 ) were studied at different pH values. The singlet oxygen quantum yield of ( 2 ) and ( 5 ) was calculated to be 0.78 and 0.81 in DMSO, respectively. When pH = 6.4, that is, tumor‐pH‐values, this value for ( 5 ) has increased to 0.92. The newly synthesized phthalocyanines are suitable photosensitizers for PDT applications, especially with high singlet oxygen quantum yield at pH 6.4.
Genc Bilgicli, Hayriye↗
Breast tumor-targeted drug delivery via polymer nanocarriers: Endogenous and exogenous strategies
Breast cancer, one of the most common types of cancer worldwide, has a high degree of malignancy while lacking efficient treatments. The off-target events frequently occurring in drug administration have been a major obstacle, which causes severe side effects to patients and reduces the therapeutic efficacy. An ideal medication should precisely target the specific site with precise control over concentration and duration. In recent years, there has been immense progress in polymer nanocarriers that demonstrate great potential in the targeted delivery. These nanocarriers are constructed from materials sensitive to a wide range of endogenous (e.g., pH, enzyme, redox) and exogenous stimuli (e.g., light, ultrasound, magnetic waves), driving cargo release to the targeted tissue or cells. Here, we elucidate the mechanism of current targeting strategies and the engineering methods to construct these targeting systems or to improve their targeting efficiency. Here we capture the recent progress in the targeted nanocarriers derived from polymers which span hydrogels and micelles, demonstrating their design and applications in chemo, hyperthermia, immuno, and gene therapies.
36 MATERIALS SCIENCE↗
Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances
Purpose: Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. Methods: We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. Results: Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. Conclusions: Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.
60 APPLIED LIFE SCIENCES↗
A Systematic Assessment of US Food and Drug Administration Dosing Recommendations For Drug Development Programs Amenable to Response‐Guided Titration
A key goal in drug development is optimized dosing for patients. Interactions between drug developers and regulatory scientists throughout development are important for the optimization of dosing and serve as a forum to discuss approaches for optimal dosing, such as precision or individualized dosing. To date, there has not been a systematic assessment of the advice provided by the US Food and Drug Administration (FDA) to drug developers from an individualized dosing perspective. Here, we reviewed FDA recommendations on dose selection for efficacy trials at end‐of‐phase meetings between the FDA and drug developers for 76 new molecular entities approved between 2013 and 2017 that are considered amenable for an individualized dosing method, response‐guided titration. Forty FDA dosing recommendations were identified as specific to dose selection and design of the respective efficacy trials and subsequently: (i) characterized based on if they were supportive of individualized dosing and (ii) compared with dosing regimens used in efficacy trials and labeling at approval to evaluate if FDA recommendations were implemented. Of these 40 recommendations for efficacy trials, 35 (88%) were considered supportive of individualized dosing. Eighteen of these 40 recommendations (45%) were incorporated into efficacy trials and 11 (28%) were incorporated into labeling. This research suggests that early FDA‐sponsor interactions can support the study of doses in efficacy trials that may lead to individualized dosing strategies in labeling.
Wang, Lingshan↗
Calibration and Validation of a Mechanistic COVID-19 Model for Translational Quantitative Systems Pharmacology – A Proof-of-Concept Model Development for Remdesivir
With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an urgent need to accelerate the traditional drug development process. Many studies identified potential COVID-19 therapies based on promising nonclinical data. However, the poor translatability from nonclinical to clinical settings has led to failures of many of these drug candidates in the clinical phase. In this study, we propose a mechanism-based, quantitative framework to translate nonclinical findings to clinical outcome. Adopting a modularized approach, this framework includes an in silico disease model for COVID-19 (virus infection and human immune responses) and a pharmacological component for COVID-19 therapies. Here, the disease model was able to reproduce important longitudinal clinical data for patients with mild and severe COVID-19, including viral titer, key immunological cytokines, antibody responses, and time courses of lymphopenia. Using remdesivir as a proof-of-concept example of model development for the pharmacological component, we developed a pharmacological model that describes the conversion of intravenously administered remdesivir as a prodrug to its active metabolite nucleoside triphosphate through intracellular metabolism and connected it to the COVID-19 disease model. After being calibrated with the placebo arm data, our model was independently and quantitatively able to predict the primary endpoint (time to recovery) of the remdesivir clinical study, Adaptive Covid-19 Clinical Trial (ACTT). Our work demonstrates the possibility of quantitatively predicting clinical outcome based on nonclinical data and mechanistic understanding of the disease and provides a modularized framework to aid in candidate drug selection and clinical trial design for COVID-19 therapeutics.
60 APPLIED LIFE SCIENCES↗
Development of a Translational Model to Assess the Impact of Opioid Overdose and Naloxone Dosing on Respiratory Depression and Cardiac Arrest
In response to a surge of deaths from synthetic opioid overdoses, there have been increased efforts to distribute naloxone products in community settings. Prior research has assessed the effectiveness of naloxone in the hospital setting; however, it is challenging to assess naloxone dosing regimens in the community/first‐responder setting, including reversal of respiratory depression effects of fentanyl and its derivatives (fentanyls). Here, we describe the development and validation of a mechanistic model that combines opioid mu receptor binding kinetics, opioid agonist and antagonist pharmacokinetics, and human respiratory and circulatory physiology, to evaluate naloxone dosing to reverse respiratory depression. Validation supports our model, which can quantitatively predict displacement of opioids by naloxone from opioid mu receptors in vitro , hypoxia‐induced cardiac arrest in vivo , and opioid‐induced respiratory depression in humans from different fentanyls. After validation, overdose simulations were performed with fentanyl and carfentanil followed by administration of different intramuscular naloxone products. Carfentanil induced more cardiac arrest events and was more difficult to reverse than fentanyl. Opioid receptor binding data indicated that carfentanil has substantially slower dissociation kinetics from the opioid receptor compared with nine other fentanyls tested, which likely contributes to the difficulty in reversing carfentanil. Administration of the same dose of naloxone intramuscularly from two different naloxone products with different formulations resulted in differences in the number of virtual patients experiencing cardiac arrest. This work provides a robust framework to evaluate dosing regimens of opioid receptor antagonists to reverse opioid‐induced respiratory depression, including those caused by newly emerging synthetic opioids.
Pharmacology & Pharmacy↗