Pathway Selection in Temporal Evolution of Supramolecular Polymers of Ionic π-systems: Amphiphilic Organic Solvent Dictates the Fate of Water.

Understanding solvent-solute interactions is essential to designing and synthesising soft materials with tailor-made functions. Although the interaction of the solute with the solvent mixture is more complex than the single solvent medium, solvent mixtures are exciting to unfold several unforeseen phenomena in supramolecular chemistry. Here we report two unforeseen pathways observed during the hierarchical assembly of cationic perylene diimides (cPDIs) in a water and amphiphilic organic solvent (AOS) mixtures. When the aqueous supramolecular polymers (SPs) of cPDIs are injected into AOS, initially kinetically trapped short SPs are formed, which gradually transform into thermodynamically stable high aspect ratio SP networks. Using various experimental and theoretical investigations, we found that this temporal evolution follows two distinct pathways depending on the nature of the water-AOS interactions. If the AOS is isopropanol (IPA), water is released from cPDIs into bulk IPA due to strong hydrogen bonding interactions, which further decreases the monomer concentration of cPDIs (Pathway-1). In the case of dioxane AOS, cPDIs monomer concentration further increases as water is retained among cPDIs (Pathway-2) due to relatively weak interactions between dioxane and water. Interestingly, these two pathways are accelerated by external stimuli such as heat and mechanical agitation.

The digital divide in rural and regional communities: a survey on the use of digital health technology and implications for supporting technology use.

OBJECTIVE: A digital divide exists for people from rural and regional areas where they are less likely and confident to engage in digital health technologies. The aim of this study was to evaluate the digital health literacy and engagement of people from rural and regional communities, with a focus on identifying barriers and facilitators to using technology. RESULTS: Forty adults living in rural/regional areas completed a survey consisting of the eHealth Literacy Scale (eHEALS) with additional items surveying participants' experience with a range of digital health technologies. All participants had used at least one digital health technology. Most (80%) participants had an eHEALS score of 26 or above indicating confidence in online health information. Commonly reported barriers to digital health technology use centred on product complexity and reliability, awareness of resources, lack of trust, and cost. Effective digital health technology use is becoming increasingly important, there may be a need to prioritise and support people with lower levels of digital health literacy. We present opportunities to support community members in using and accessing digital health technology.

Generative AI in healthcare: an implementation science informed translational path on application, integration and governance.

BACKGROUND: Artificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient care, revolutionizing disease diagnosis and expanding treatment options. However, the utility and impact of generative AI in healthcare remain poorly understood, with concerns around ethical and medico-legal implications, integration into healthcare service delivery and workforce utilisation. Also, there is not a clear pathway to implement and integrate generative AI in healthcare delivery. METHODS: This article aims to provide a comprehensive overview of the use of generative AI in healthcare, focusing on the utility of the technology in healthcare and its translational application highlighting the need for careful planning, execution and management of expectations in adopting generative AI in clinical medicine. Key considerations include factors such as data privacy, security and the irreplaceable role of clinicians' expertise. Frameworks like the technology acceptance model (TAM) and the Non-Adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) model are considered to promote responsible integration. These frameworks allow anticipating and proactively addressing barriers to adoption, facilitating stakeholder participation and responsibly transitioning care systems to harness generative AI's potential. RESULTS: Generative AI has the potential to transform healthcare through automated systems, enhanced clinical decision-making and democratization of expertise with diagnostic support tools providing timely, personalized suggestions. Generative AI applications across billing, diagnosis, treatment and research can also make healthcare delivery more efficient, equitable and effective. However, integration of generative AI necessitates meticulous change management and risk mitigation strategies. Technological capabilities alone cannot shift complex care ecosystems overnight; rather, structured adoption programs grounded in implementation science are imperative. CONCLUSIONS: It is strongly argued in this article that generative AI can usher in tremendous healthcare progress, if introduced responsibly. Strategic adoption based on implementation science, incremental deployment and balanced messaging around opportunities versus limitations helps promote safe, ethical generative AI integration. Extensive real-world piloting and iteration aligned to clinical priorities should drive development. With conscientious governance centred on human wellbeing over technological novelty, generative AI can enhance accessibility, affordability and quality of care. As these models continue advancing rapidly, ongoing reassessment and transparent communication around their strengths and weaknesses remain vital to restoring trust, realizing positive potential and, most importantly, improving patient outcomes.

Quality of Life in the Phase 2/3 Trial of N-803 Plus Bacillus Calmette-Guérin in Bacillus Calmette-Guérin‒Unresponsive Nonmuscle-Invasive Bladder Cancer.

INTRODUCTION: In the phase 2/3 study QUILT-3.032 (NCT03022825), the ability of the IL-15RαFc superagonist N-803 (nogapendekin alfa inbakicept) plus bacillus Calmette-Guérin (BCG) to elicit durable complete responses in patients with BCG-unresponsive nonmuscle-invasive bladder cancer (NMIBC) was demonstrated. As a secondary end point, patient-reported outcomes (PROs) were assessed. METHODS: Both cohort A patients with carcinoma in situ with or without Ta/T1 disease and cohort B patients with high-grade Ta/T1 papillary disease who received N-803 plus BCG therapy completed the EORTC (European Organization for Research and Treatment of Cancer) Core 30 and Quality of Life NMIBC-Specific 24 questionnaires at baseline and months 6, 12, 18, and 24 on study. Scores were analyzed using descriptive statistics, and multivariable analyses were performed to identify baseline variables associated with PROs. RESULTS: On study, mean physical function (PF) and global health (GH) scores remained relatively stable from baseline for cohorts A (n = 86) and B (n = 78). At month 6, cohort A patients with a complete response reported higher PF scores than those without (P = .0659); at month 12, > 3 as compared with ≤ 3 prior transurethral resections of bladder tumor was associated (P = .0729) with lower GH scores. In cohort B, baseline disease type was associated (P = .0738) with PF and race was significantly associated (P = .0478) with GH at month 6. NMIBC-Specific 24 summary scores also remained stable on study for both cohorts. CONCLUSIONS: The overall stability of PROs scores, taken together with the efficacy findings, indicates a favorable risk-benefit ratio and quality of life following N-803 plus BCG.

Case Study: Design of an Approach for Assessing a Novel Health Capability Maturity Model.

Capability maturity models have been developed and are widely used within healthcare aiming to assess the degree of digitization of the organization, but empirical assessments of the models themselves has been undertaken infrequently. We present a mixed-method approach to assessing a novel health capability maturity model developed by a state government responsible for the management of 86 health services. The approach was designed to be suitable for system level assessment of services and pooled the wisdom and experience of subject matter experts and key stakeholders using a combination of survey and interviews to test and tune the proposed assessment approach and parameters. We applied the approach to assess the target capability model across a number of public health services in Victoria, Australia. The result showed sufficient validity to be able to generate recommendations for further improvement of the capability model and the assessment approach to enable broader application within Australia.

Navigating the AI Revolution: The Case for Precise Regulation in Health Care.

Health care is undergoing a profound transformation through the integration of artificial intelligence (AI). However, the rapid integration and expansive growth of AI within health care systems present ethical and legal challenges that warrant careful consideration. In this viewpoint, the author argues that the health care domain, due to its complexity, requires specialized approaches to regulating AI. Precise regulation can provide clear guidelines for addressing these challenges, thereby ensuring ethical and legal AI implementations.

The Morphology and Morphometric Analysis of the Radius Bone: A Study on Freshly Frozen Cadavers in the Indian Population.

Introduction The radial bone and the radioulnar joint are vital for the physiological and physical stability of the elbow. The prostheses and plates used in cases of radius fracture are designed based on the morphology of the Western population. This could result in a bone-implant mismatch when applied to the Indian population, resulting in complications. Hence, the study aimed to record the normal values of radius morphology in the Indian population. Methods A total of 30 (eight male and seven female) freshly frozen cadaveric bilateral upper limbs were chosen. Cadavers with previous surgical scars, deformities, and congenital defects of the upper limb were excluded. The radius was excised, and morphometric parameters were measured with a non-elastic measuring tape and a digital caliper and recorded using GeoGebra software. Results All measuring parameters exhibited no significant difference between the right and left side of the bone (p > 0.05), whereas the difference between males and females for most parameters was statistically significant (p < 0.05). The mean difference between the anteroposterior (AP) diameter and transverse diameter of the radial head for the study sample was 0.89 ± 0.06 mm. Thus, the AP diameter was 4% greater than the transverse diameter. The head of the radius was observed to be almost round. The degree of extent of the safe zone was 124.64°, with an average safe arc length of 3.27 ± 0.55 cm. Conclusion The morphometric measurements of the radius in the Indian population are different from the Western population.

Application of a Comprehensive Evaluation Framework to COVID-19 Studies: Systematic Review of Translational Aspects of Artificial Intelligence in Health Care.

Background: Despite immense progress in artificial intelligence (AI) models, there has been limited deployment in health care environments. The gap between potential and actual AI applications is likely due to the lack of translatability between controlled research environments (where these models are developed) and clinical environments for which the AI tools are ultimately intended. Objective: We previously developed the Translational Evaluation of Healthcare AI (TEHAI) framework to assess the translational value of AI models and to support successful transition to health care environments. In this study, we applied the TEHAI framework to the COVID-19 literature in order to assess how well translational topics are covered. Methods: A systematic literature search for COVID-19 AI studies published between December 2019 and December 2020 resulted in 3830 records. A subset of 102 (2.7%) papers that passed the inclusion criteria was sampled for full review. The papers were assessed for translational value and descriptive data collected by 9 reviewers (each study was assessed by 2 reviewers). Evaluation scores and extracted data were compared by a third reviewer for resolution of discrepancies. The review process was conducted on the Covidence software platform. Results: We observed a significant trend for studies to attain high scores for technical capability but low scores for the areas essential for clinical translatability. Specific questions regarding external model validation, safety, nonmaleficence, and service adoption received failed scores in most studies. Conclusions: Using TEHAI, we identified notable gaps in how well translational topics of AI models are covered in the COVID-19 clinical sphere. These gaps in areas crucial for clinical translatability could, and should, be considered already at the model development stage to increase translatability into real COVID-19 health care environments.

Survey and Evaluation of Hypertension Machine Learning Research.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.

Promising anode materials for alkali metal ion batteries: a case study on cobalt anti-MXenes.

There is continuous demand for energy storage devices with high energy densities in consumer electronics, electric vehicles, and the grid energy market. Although commercial lithium-ion batteries (LIBs) satisfy the current needs, the limited availability of their raw materials and the moderate specific charge capacities (SCCs) of LIBS have motivated scientists to search for alternate anode materials for LIBs and create technologies beyond LIBs. In this work, we studied the potential of six cobalt anti-MXenes (CoAs, CoB, CoP, CoS, CoSe, and CoSi), a class of newly discovered 2D materials, as anode materials for lithium, sodium, and potassium ion batteries (LIBs, NIBs, and KIBs). We found that these materials are good electrical conductors and have high adsorption stability for alkali metal ions, which helps to prevent the formation of dendrites and increase the cycle life of the battery. They also show moderate to low migration energy barriers (MEBs), indicating the potential for faster charge-discharge kinetics. We also explain the slightly counter-intuitive result of observing low MEBs along with high adsorption stability. Furthermore, Co-anti-MXenes can adsorb multiple alkali atoms per formula unit, resulting in high specific charge capacities and low average anodic voltages. For example, as anode materials for lithium-ion batteries, CoP and CoSi have SCC values of 1075.4 mA h g-1 and 934 mA h g-1, and anodic voltages as low as 0.28 V and 0.43 V, respectively. Moreover, even the maximally metalated Co-anti-MXenes did not show agglomeration tendency at room temperature. Furthermore, the volume expansion of these materials is minimum for both Li and Na adsorption. As a whole, we find that Co-anti-MXenes are promising as anode materials for alkali metal ion batteries.

Induced Migration of CO2 from Hydrate Cages to Amorphous Solid Water under Ultrahigh Vacuum and Cryogenic Conditions.

Restricted migration of reactive species limits chemical transformations within interstellar and cometary ices. We report the migration of CO2 from clathrate hydrate (CH) cages to amorphous solid water (ASW) in the presence of tetrahydrofuran (THF) under ultrahigh vacuum (UHV) and cryogenic conditions. Thermal annealing of sequentially deposited CO2 and H2O ice, CO2@H2O, to 90 K resulted in the partitioning of CO2 in 512 and 51262 CH cages (CO2@512, CO2@51262). However, upon preparing a composite ice film composed of CO2@512, CO2@51262 and THF distributed in the water matrix at 90 K, and annealing the mixture for 6 h at 130 K produced mixed CO2-THF CH, where THF occupied the 51264 cages (THF@51264) exclusively while CO2 in 51262 cages (CO2@51262) got transferred to the ASW matrix and CO2 in the 512 cages (CO2@512) remained as is. This cage-matrix exchange may create a more conducive environment for chemical transformations in interstellar environments.

Structure, energetics and dynamics in crowded amino acid solutions: a molecular dynamics study.

A comprehensive understanding of crowding effects on biomolecular processes necessitates investigating the bulk thermodynamic and kinetic properties of the solutions with an accurate molecular representation of the crowded milieu. Recent studies have reparameterized the non-bonded dispersion interaction of solutes to precisely model intermolecular interactions, which would circumvent artificial aggregation as shown by the original force-fields. However, the performance of this reparameterization is yet to be assessed for concentrated crowded solutions in terms of investigating the hydration shell structure, energetics and dynamics. In this study, we perform molecular dynamics simulations of crowded aqueous solutions of five zwitterionic neutral amino acids (Gly, Ala, Thr, Pro, and Ser), mimicking the molecular crowding environment, using a modified AMBER ff99SB-ILDN force-field. We systematically examine and show that the reproducibility of the osmotic coefficients, density, viscosity and self-diffusivity of amino acids improves using the modified force-field in crowded concentrations. The modified force-field also improves the structuring of the solute solvation shells, solute interaction energy and convergence of tails of radial distribution functions, indicating reduction in the artificial aggregation. Our results also indicate that the hydrogen bonding network of water weakens and water molecules anomalously diffuse at small time scales in the crowded solutions. These results underscore the significance of examining the solution properties and anomalous hydration behaviour of water in crowded solutions, which have implications in shaping the structure and dynamics of biomolecules. The findings also illustrate the improvement in predicting bulk solution properties using the modified force-field, thereby providing an approach towards accurate modeling of crowded molecular solutions.

IL-15 Superagonist NAI in BCG-Unresponsive Non-Muscle-Invasive Bladder Cancer.

IL-15 Superagonist NAI in BCG-Unresponsive NMIBCIn this trial, patients with BCG-unresponsive bladder CIS with or without Ta/T1 papillary disease or BCG-unresponsive high-grade Ta/T1 papillary NMIBC were treated with intravesical NAI, an IL-15 superagonist, plus BCG. Primary end points were CR at 3 or 6 months for patients with CIS disease and DFS rate at 12 months for those with high-grade Ta/T1 disease. CR rate was 71% (58 of 82 patients), and the DFS rate was 55.4%.

Trends in Imaging Patterns of Bronchogenic Carcinoma: Reality or a Statistical Variation? A Single-Center Cross-Sectional Analysis of Outcomes.

INTRODUCTION: Bronchogenic carcinoma accounts for more cancer-related deaths than any other malignancy and is the most frequently diagnosed cancer in the world. Bronchogenic carcinoma is by far the leading cause of cancer death among both men and women, making up almost 25% of all cancer deaths. The objective of this study was to identify the changing trends, if any, in radiological patterns of bronchogenic carcinoma to document the various computed tomography (CT) appearances of bronchogenic carcinoma with histopathologic correlation. METHODS: This was a single-center cross-sectional study on 162 patients with clinical or radiological suspicion of bronchogenic carcinoma with histopathological confirmation of diagnosis. RESULTS: There was a male preponderance with bronchogenic carcinoma and smoking being the most common risk factor. Squamous cell carcinoma followed by adenocarcinoma and small cell carcinoma is the most common histologic subtype. Squamous cell carcinoma was noted to be present predominantly in the peripheral location (55.5%), and adenocarcinoma was noted to be present predominantly in the central location (68.4%). CONCLUSION: CT is the imaging modality of choice for evaluating bronchogenic carcinoma and provides for precise characterization of the size, extent, and staging of the carcinoma. Among 162 bronchogenic carcinoma cases evaluated in the current study, a definite changing trend in the radiological pattern of squamous cell carcinoma and adenocarcinoma was observed. Squamous cell carcinoma was predominantly noted to be a peripheral tumor, and adenocarcinoma is predominantly noted to be a central tumor. Surveillance or restaging scans are recommended, considering the high mortality rate in patients with bronchogenic carcinoma.

Cooperative Supramolecular Polymerization Guided by Dispersive Interactions.

Cooperative supramolecular polymerization is important for the synthesis of functional supramolecular homo and block-copolymers of π-systems. Current strategies indicate the need of strong hydrogen bonding (H-bonding) and/or dipolar interactions in the π-systems to achieve cooperativity. In sharp contrast, here we report the cooperative supramolecular polymerization in alkyl chain substituted perylene diimides (alkyl PDIs) driven by dispersive interactions with molecular level understanding. Moreover, alkyl PDIs follow cooperative mechanism with cooperativity similar to the strong H-bonded π-systems (σ ∼10-5 ) despite the lack of strong H-bonding and dipolar interactions. Computer simulations show that this surprising phenomenon in alkyl PDIs is driven by the efficient dispersive interactions among the alkyl chains and π-cores due to their zigzag arrangement in the supramolecular polymer. Importantly, alkyl PDIs display cooperative supramolecular polymerization in both polar and non-polar solvents which is difficult for H-bonded/dipolar π-systems thus highlighting the advantages of dispersive interactions.

Origin of novel coronavirus causing COVID-19: A computational biology study using artificial intelligence.

Origin of the COVID-19 virus (SARS-CoV-2) has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic. Recent research results suggest that bats or pangolins might be the hosts for SARS-CoV-2 based on comparative studies using its genomic sequences. This paper investigates the SARS-CoV-2 origin by using artificial intelligence (AI)-based unsupervised learning algorithms and raw genomic sequences of the virus. More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods. The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined SARS-CoV-2 genomes belong to a cluster that also contains bat and pangolin coronavirus genomes. This provides evidence strongly supporting scientific hypotheses that bats and pangolins are probable hosts for SARS-CoV-2. At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.

Quality use of artificial intelligence in medical imaging: What do radiologists need to know?

The application of artificial intelligence, and in particular machine learning, to the practice of radiology, is already impacting the quality of imaging care. It will increasingly do so in the future. Radiologists need to be aware of factors that govern the quality of these tools at the development, regulatory and clinical implementation stages in order to make judicious decisions about their use in daily practice.

Evaluation framework to guide implementation of AI systems into healthcare settings.

OBJECTIVES: To date, many artificial intelligence (AI) systems have been developed in healthcare, but adoption has been limited. This may be due to inappropriate or incomplete evaluation and a lack of internationally recognised AI standards on evaluation. To have confidence in the generalisability of AI systems in healthcare and to enable their integration into workflows, there is a need for a practical yet comprehensive instrument to assess the translational aspects of the available AI systems. Currently available evaluation frameworks for AI in healthcare focus on the reporting and regulatory aspects but have little guidance regarding assessment of the translational aspects of the AI systems like the functional, utility and ethical components. METHODS: To address this gap and create a framework that assesses real-world systems, an international team has developed a translationally focused evaluation framework termed 'Translational Evaluation of Healthcare AI (TEHAI)'. A critical review of literature assessed existing evaluation and reporting frameworks and gaps. Next, using health technology evaluation and translational principles, reporting components were identified for consideration. These were independently reviewed for consensus inclusion in a final framework by an international panel of eight expert. RESULTS: TEHAI includes three main components: capability, utility and adoption. The emphasis on translational and ethical features of the model development and deployment distinguishes TEHAI from other evaluation instruments. In specific, the evaluation components can be applied at any stage of the development and deployment of the AI system. DISCUSSION: One major limitation of existing reporting or evaluation frameworks is their narrow focus. TEHAI, because of its strong foundation in translation research models and an emphasis on safety, translational value and generalisability, not only has a theoretical basis but also practical application to assessing real-world systems. CONCLUSION: The translational research theoretic approach used to develop TEHAI should see it having application not just for evaluation of clinical AI in research settings, but more broadly to guide evaluation of working clinical systems.

Use and validation of text mining and cluster algorithms to derive insights from Corona Virus Disease-2019 (COVID-19) medical literature.

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) late last year has not only led to the world-wide coronavirus disease 2019 (COVID-19) pandemic but also a deluge of biomedical literature. Following the release of the COVID-19 open research dataset (CORD-19) comprising over 200,000 scholarly articles, we a multi-disciplinary team of data scientists, clinicians, medical researchers and software engineers developed an innovative natural language processing (NLP) platform that combines an advanced search engine with a biomedical named entity recognition extraction package. In particular, the platform was developed to extract information relating to clinical risk factors for COVID-19 by presenting the results in a cluster format to support knowledge discovery. Here we describe the principles behind the development, the model and the results we obtained.