Search

Atherosclerosis, a major global health concern with high morbidity and mortality rates, involves complex interactions of chronic inflammation, oxidative stress, and proteolytic enzymes. Conventional imaging methods struggle to capture the dynamic biochemical processes in atherosclerotic plaques. Here, we introduce a novel unimolecular photoacoustic probe (UMAPP) designed with specific binding sites for neutrophil elastase (NE) and the redox pair O2⋅−/GSH, enabling real‐time monitoring of oxidative stress and activated neutrophils in plaques. UMAPP, comprising a boron‐dipyrromethene (BODIPY) core linked to a hydrophilic NE‐cleavable tetrapeptide and dual oxidative stress‐responsive catechol moieties, facilitates NE‐mediated modulation of photoinduced electron transfer impacting photoacoustic intensity at 685 nm (PA685). Furthermore, oxidation and reduction of catechol groups by O2⋅− and GSH induce reversible, ratiometric changes in the photoacoustic spectrum (PA745/PA685 ratio). Initial UMAPP applications successfully distinguished atherosclerotic and healthy mice, evaluated pneumonia's effect on plaque composition and verified the probe's effectiveness in drug‐treatment studies by detecting molecular alterations before visible histopathological changes. The integrated molecular imaging capabilities of UMAPP offer promising advancements in atherosclerosis diagnosis and management, enabling early and accurate identification of vulnerable plaques. [ABSTRACT FROM AUTHOR]

Enhancing patient response to immune checkpoint inhibitors (ICIs) is crucial in cancer immunotherapy. We aim to create a data-driven mathematical model of the tumor immune microenvironment (TIME) and utilize deep reinforcement learning (DRL) to optimize patient-specific ICI therapy combined with chemotherapy (ICC). Using patients' genomic and transcriptomic data, we develop an ordinary differential equations (ODEs)–based TIME dynamic evolutionary model to characterize interactions among chemotherapy, ICIs, immune cells, and tumor cells. A DRL agent is trained to determine the personalized optimal ICC therapy. Numerical experiments with real-world data demonstrate that the proposed TIME model can predict ICI therapy response. The DRL-derived personalized ICC therapy outperforms predefined fixed schedules. For tumors with extremely low CD8 + T cell infiltration ('extremely cold tumors'), the DRL agent recommends high-dosage chemotherapy alone. For tumors with higher CD8 + T cell infiltration ('cold' and 'hot tumors'), an appropriate chemotherapy dosage induces CD8 + T cell proliferation, enhancing ICI therapy outcomes. Specifically, for 'hot tumors', chemotherapy and ICI are administered simultaneously, while for 'cold tumors', a mid-dosage of chemotherapy makes the TIME 'hotter' before ICI administration. However, in several 'cold tumors' with rapid resistant tumor cell growth, ICC eventually fails. This study highlights the potential of utilizing real-world clinical data and DRL algorithm to develop personalized optimal ICC by understanding the complex biological dynamics of a patient's TIME. Our ODE–based TIME dynamic evolutionary model offers a theoretical framework for determining the best use of ICI, and the proposed DRL agent may guide personalized ICC schedules. [ABSTRACT FROM AUTHOR]

This systematic review and meta‐analysis analyzed and summarized the growing literature on the effectiveness of chatbot‐delivered interventions in increasing uptake, intention, and attitudes related to any type of vaccination. We identified randomized controlled studies (RCTs), quasi‐experimental studies, and non‐experimental studies from the following platforms: PubMed, Web of Science, MEDLINE, Global Health, APA PsycInfo, and EMBASE databases. A total of 12 eligible studies published from 2019 to 2023 were analyzed and summarized. In particular, one RCT showed that a chatbot‐delivered tailored intervention was more effective than a chatbot‐delivered non‐tailored intervention in promoting seasonal influenza vaccine uptake among older adults (50.5% versus 35.3%, p = 0.002). Six RCTs were included in the meta‐analysis to evaluate the effectiveness of chatbot interventions to improve vaccination attitudes and intentions. The pooled standard mean difference (SMD) of overall attitude change was 0.34 (95% confidence intervals [CI]: 0.13, 0.55, p = 0.001). We found a non‐significant trivial effect of chatbot interventions on improving intentions of vaccination (SMD: 0.11, 95% CI: −0.13, 0.34, p = 0.38). However, further evidence is needed to draw a more precise conclusion. Additionally, study participants reported high satisfaction levels of using the chatbot and were likely to recommend it to others. The development of chatbots is still nascent and rooms for improvement exist. [ABSTRACT FROM AUTHOR]

ObjectivesInternet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China.DesignWe identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016).ResultsSearch query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks.ConclusionsBaidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention.

BackgroundA large and growing body of "big data" is generated by internet search engines, such as Google. Because people often search for information about public health and medical issues, researchers may be able to use search engine data to monitor and predict public health problems, such as HIV. We sought to assess the feasibility of using Google search data to analyze and predict new HIV diagnoses cases in the United States.Methods and findingsFrom 2007 to 2014, we collected search volume data on HIV-related Google search keywords across the United States. State-level new HIV diagnoses data were collected from the Centers for Disease Control and Prevention (CDC) and AIDSVu.org. We developed a negative binomial model to predict HIV cases using a subset of significant predictor keywords identified by LASSO. The Google search data were combined with state-level HIV case reports provided by the CDC. We use historical data to train the model and predict new HIV diagnoses from 2011 to 2014, with an average R2 value of 0.99 between predicted versus actual cases, and average root-mean-square error (RMSE) of 108.75.ConclusionsResults indicate that Google Trends is a feasible tool to predict new cases of HIV at the state level. We discuss the implications of integrating visualization maps and tools based on these models into public health and HIV monitoring and surveillance.