Unlocking the Future of Patient Education: ChatGPT vs. LexiComp® as sources of patient education materials.

BACKGROUND: ChatGPT is a conversational artificial intelligence (AI) technology that has shown application in various facets of healthcare. With the increased use of AI, it is imperative to assess the accuracy and comprehensibility of AI platforms. OBJECTIVE: This pilot project aimed to assess the understandability, readability, and accuracy of ChatGPT as a source of medication-related patient education as compared with an evidence-based medicine tertiary reference resource, LexiComp®. METHODS: Patient education materials (PEMs) were obtained from ChatGPT and LexiComp® for eight common medications (albuterol, apixaban, atorvastatin, hydrocodone/acetaminophen, insulin glargine, levofloxacin, omeprazole, and sacubitril/valsartan). PEMs were extracted, blinded, and assessed by two investigators independently. The primary outcome was a comparison of the Patient Education Materials Assessment Tool-printable (PEMAT-P). Secondary outcomes included Flesch reading ease, Flesch Kincaid grade level, percent passive sentences, word count, and accuracy. A 7-item accuracy checklist for each medication was generated by expert consensus among pharmacist investigators, with LexiComp® PEMs serving as the control. PEMAT-P interrater reliability was determined via intraclass correlation coefficient (ICC). Flesch reading ease, Flesch Kincaid grade level, percent passive sentences, and word count were calculated by Microsoft® Word®. Continuous data were assessed using the Student's t-test via SPSS (version 20.0). RESULTS: No difference was found in the PEMAT-P understandability score of PEMs produced by ChatGPT versus LexiComp® [77.9% (11.0) vs. 72.5% (2.4), P=0.193]. Reading level was higher with ChatGPT [8.6 (1.2) vs. 5.6 (0.3), P<0.001). ChatGPT PEMs had a lower percentage of passive sentences and lower word count. The average accuracy score of ChatGPT PEMs was 4.25/7 (61%), with scores ranging from 29-86%. CONCLUSION: Despite comparable PEMAT-P scores, ChatGPT PEMs did not meet grade level targets. Lower word count and passive text with ChatGPT PEMs could benefit patients, but the variable accuracy scores prevent routine use of ChatGPT to produce medication-related PEMs at this time.

A survey on awareness, knowledge and preferences toward genetic testing among the United States general public.

Aim: To understand awareness, knowledge and preferences regarding genetic testing among the USA general public. Methods: A cross-sectional online survey using a Qualtrics Panel. Results: Among 1600 respondents, 545 (34%) were White, 411 (26%) Black, 412 (26%) Hispanic or Latin(x) and 232 (15%) Asian. Most had heard of ancestry testing (87%) and genetic health risk testing (69%), but a third thought inherited genes were only a little or not at all responsible for obesity (36%) and mental health (33%). The majority preferred pre-emptive pharmacogenetic testing (n = 74%) compared with reactive testing. Statistically significant differences between racial/ethnic groups and rural-urban respondents were observed. Conclusion: Most preferred pre-emptive pharmacogenetic testing; however, about one-quarter preferred reactive testing. Preferences should be discussed during patient-clinician interactions.

What is this study about? This study presents a large online survey among the USA general public to understand their awareness, knowledge and preferences about genetic testing and how this may vary by racial/ethnic group and rural/urban status. What were the results? Most survey respondents had heard of ancestry testing (87%) and genetic health risk testing (69%). However, over a third of respondents thought that inherited genes may be only a little or not at all responsible for obesity (36%) and mental health (33%). When asked about preferences for pre-emptive compared with reactive pharmacogenetic testing, the majority preferred pre-emptive testing (n = 74%). Statistically significant differences between racial/ethnic groups as well as rural-urban respondents were seen. What do the results mean? The US general public may have a different understanding of genetic testing for different diseases, and have different preferences when it comes to the timing of testing. Appropriate educational content targeting the link between genetics and specific diseases should be prepared, and preferences for pre-emptive or reactive testing should be discussed during visits with healthcare providers.

The Influence of Kynurenine Metabolites on Neurodegenerative Pathologies.

As the kynurenine pathway's links to inflammation, the immune system, and neurological disorders became more apparent, it attracted more and more attention. It is the main pathway through which the liver breaks down Tryptophan and the initial step in the creation of nicotinamide adenine dinucleotide (NAD+) in mammals. Immune system activation and the buildup of potentially neurotoxic substances can result from the dysregulation or overactivation of this pathway. Therefore, it is not shocking that kynurenines have been linked to neurological conditions (Depression, Parkinson's, Alzheimer's, Huntington's Disease, Schizophrenia, and cognitive deficits) in relation to inflammation. Nevertheless, preclinical research has demonstrated that kynurenines are essential components of the behavioral analogs of depression and schizophrenia-like cognitive deficits in addition to mediators associated with neurological pathologies due to their neuromodulatory qualities. Neurodegenerative diseases have been extensively associated with neuroactive metabolites of the kynurenine pathway (KP) of tryptophan breakdown. In addition to being a necessary amino acid for protein synthesis, Tryptophan is also transformed into the important neurotransmitters tryptamine and serotonin in higher eukaryotes. In this article, a summary of the KP, its function in neurodegeneration, and the approaches being used currently to target the route therapeutically are discussed.

Patient Reported Outcomes of Transperineal Prostate Biopsy With Tumescent Local Anesthesia.

OBJECTIVE: To report the outcomes of performing transperineal prostate biopsy in the office setting using the novel anesthetic technique of tumescent local anesthesia. We report anxiety, pain, and embarrassment of patients who underwent this procedure compared to patients who underwent a transrectal prostate biopsy using standard local anesthesia. MATERIALS AND METHODS: Consecutive patients undergoing either a transperineal prostate biopsy under tumescent local anesthesia or a transrectal prostate biopsy with standard local anesthetic technique were prospectively enrolled. The tumescent technique employed dilute lidocaine solution administered using a self-filling syringe. Patients were asked to rate their pain before, during, and after their procedure using a visual analog scale. Patient anxiety and embarrassment was assessed using the Testing Modalities Index Questionnaire. RESULTS: Between April 2021 and June 2022, 430 patients underwent a transperineal prostate biopsy using tumescent local anesthesia and 65 patients underwent a standard transrectal prostate biopsy. Patients who underwent a transperineal biopsy had acceptable but significantly higher pain scores than those who underwent a transrectal prostate biopsy (3.9 vs 1.6, P-value <.01). These scores fell to almost zero immediately following their procedure. Additionally, transperineal biopsy patients were more likely to experience anxiety (71% vs 45%, P < .01) and embarrassment (32% vs 15%, P < .01). CONCLUSION: Transperineal biopsy using local tumescent anesthesia is safe and well-tolerated. Despite the benefits, patients undergoing a transperineal prostate biopsy under tumescent anesthesia still experienced worse procedural pain, anxiety, and embarrassment. Additional studies examining other adjunctive interventions to improve patient experience during transperineal prostate biopsy are needed.

Projecting the future incidence and burden of dengue in Southeast Asia.

The recent global expansion of dengue has been facilitated by changes in urbanisation, mobility, and climate. In this work, we project future changes in dengue incidence and case burden to 2099 under the latest climate change scenarios. We fit a statistical model to province-level monthly dengue case counts from eight countries across Southeast Asia, one of the worst affected regions. We project that dengue incidence will peak this century before declining to lower levels with large variations between and within countries. Our findings reveal that northern Thailand and Cambodia will show the biggest decreases and equatorial areas will show the biggest increases. The impact of climate change will be counterbalanced by income growth, with population growth having the biggest influence on increasing burden. These findings can be used for formulating mitigation and adaptation interventions to reduce the immediate growing impact of dengue virus in the region.

A wearable device-enabled therapeutic approach to improve joint attention in autism spectrum disorder: a prospective pilot study.

It has been previously proposed that interventions aimed at integrating and co-activating music processing and motor control systems could have therapeutic potential for priming social skill development in children with autism spectrum disorder (ASD). In this study, we assessed this hypothesis through a wearable sensor platform called Ambulosono ("Ambulo"-walk; "sono"-sound) in which pleasurable children's musical stimuli are contingently linked to effortful motor action (locomotor step size), thus creating a motivational state proposed to be conducive to joint attention (JA) operation. Five participants were recruited from a community-based partner and were assessed by scoring responses following therapist-directed bids for JA. Multiple assessment sessions through a repeated time-series design were conducted to determine baseline and post-intervention scores. The intervention session consisted of approximately 15 min of Ambulosono exposure. Baseline and post-intervention data were aggregated and analyzed using a linear mixed-effect model. The wearable sensor and wireless headphones of the Ambulosono system were tolerated by the participants, and there were no adverse effects associated with the use of the device. We found an increase in the average responses to bids for JA during the Ambulosono intervention phase compared to baseline across participants. This increase did not appear to result from enhanced general arousal. Our pilot data support feasibility and further testing of Ambulosono as a therapeutic aid for integration into community-based ASD programs to augment shared child-therapist social interactions.

Using spatial and population mobility models to inform outbreak response approaches in the Ebola affected area, Democratic Republic of the Congo, 2018-2020.

The Democratic Republic of the Congo's (DRC) 10th known Ebola virus disease (EVD) outbreak occurred between August 1, 2018 and June 25, 2020, and was the largest EVD outbreak in the country's history. During this outbreak, the DRC Ministry of Health initiated traveller health screening at points of control (POC, locations not on the border) and points of entry (POE) to minimize disease translocation via ground and air travel. We sought to develop a model-based approach that could be applied in future outbreaks to inform decisions for optimizing POC and POE placement, and allocation of resources more broadly, to mitigate the risk of disease translocation associated with ground-level population mobility. We applied a parameter-free mobility model, the radiation model, to estimate likelihood of ground travel between selected origin locations (including Beni, DRC) and surrounding population centres, based on population size and drive-time. We then performed a road network route analysis and included estimated population movement results to calculate the proportionate volume of travellers who would move along each road segment; this reflects the proportion of travellers that could be screened at a POC or POE. For Beni, the road segments estimated to have the highest proportion of travellers that could be screened were part of routes into Uganda and Rwanda. Conversely, road segments that were part of routes to other population centres within the DRC were estimated to have relatively lower proportions. We observed a posteriori that, in many instances, our results aligned with locations that were selected for actual POC or POE placement through more time-consuming methods. This study has demonstrated that mobility models and simple spatial techniques can help identify potential locations for health screening at newly placed POC or existing POE during public health emergencies based on expected movement patterns. Importantly, we have provided methods to estimate the proportionate volume of travellers that POC or POE screening measures would assess based on their location. This is critical information in outbreak situations when timely decisions must be made to implement public health interventions that reach the most individuals across a network.

Regional connectivity drove bidirectional transmission of SARS-CoV-2 in the Middle East during travel restrictions.

Regional connectivity and land travel have been identified as important drivers of SARS-CoV-2 transmission. However, the generalizability of this finding is understudied outside of well-sampled, highly connected regions. In this study, we investigated the relative contributions of regional and intercontinental connectivity to the source-sink dynamics of SARS-CoV-2 for Jordan and the Middle East. By integrating genomic, epidemiological and travel data we show that the source of introductions into Jordan was dynamic across 2020, shifting from intercontinental seeding in the early pandemic to more regional seeding for the travel restrictions period. We show that land travel, particularly freight transport, drove introduction risk during the travel restrictions period. High regional connectivity and land travel also drove Jordan's export risk. Our findings emphasize regional connectedness and land travel as drivers of transmission in the Middle East.

Peripheral circadian gene activity is altered during hibernation in the thirteen-lined ground squirrel.

Many small mammals living in seasonally cold environments rely on hibernation, utilizing strong metabolic rate suppression and a slow consumption of adipose reserves to survive the winter months. The circannual rhythm of hibernation is well known but less is known about the role of the circadian clock while animals are in torpor for weeks at a time. We hypothesized that due to strong global suppression of transcription and translation in the torpid state, that circadian clock activity would likewise be suppressed in peripheral tissues during hibernation. However, the present study indicates that peripheral circadian clock activity persists during torpor. Using 13-lined ground squirrels (Ictidomys tridecemlineatus) as the model, this study analyzed transcript and protein responses by clock components, comparing euthermic control animals with squirrels in deep torpor for >3 days (subcutaneous body temperature 5-8 °C). The data show tissue specific responses by mRNA transcript levels: (a) no significant changes in transcript abundance in liver of control versus torpid squirrels, (b) a strong increase in Nr1d1 levels in white adipose during torpor, and (c) five significant transcript changes in skeletal muscle during torpor (increased Bmal1, Clock, Cry1 and Nr1d1 but decreased Per1). Levels of core clock proteins (BMAL1, CRY2, PER2, and casein kinases CK1δ and CK1ε) were also assessed across five time points of the torpor/arousal cycle showing both tissue- and time-dependent changes in clock proteins that were most prominent in liver and white adipose and indicating that peripheral clocks are still active in tissues over the torpor/arousal cycle.

Tracking the international spread of SARS-CoV-2 lineages B.1.1.7 and B.1.351/501Y-V2 with grinch.

Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501Y-V2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected.

Emergence and spread of SARS-CoV-2 lineage B.1.620 with variant of concern-like mutations and deletions.

Distinct SARS-CoV-2 lineages, discovered through various genomic surveillance initiatives, have emerged during the pandemic following unprecedented reductions in worldwide human mobility. We here describe a SARS-CoV-2 lineage - designated B.1.620 - discovered in Lithuania and carrying many mutations and deletions in the spike protein shared with widespread variants of concern (VOCs), including E484K, S477N and deletions HV69Δ, Y144Δ, and LLA241/243Δ. As well as documenting the suite of mutations this lineage carries, we also describe its potential to be resistant to neutralising antibodies, accompanying travel histories for a subset of European cases, evidence of local B.1.620 transmission in Europe with a focus on Lithuania, and significance of its prevalence in Central Africa owing to recent genome sequencing efforts there. We make a case for its likely Central African origin using advanced phylogeographic inference methodologies incorporating recorded travel histories of infected travellers.

Emergence of an early SARS-CoV-2 epidemic in the United States.

The emergence of the COVID-19 epidemic in the United States (U.S.) went largely undetected due to inadequate testing. New Orleans experienced one of the earliest and fastest accelerating outbreaks, coinciding with Mardi Gras. To gain insight into the emergence of SARS-CoV-2 in the U.S. and how large-scale events accelerate transmission, we sequenced SARS-CoV-2 genomes during the first wave of the COVID-19 epidemic in Louisiana. We show that SARS-CoV-2 in Louisiana had limited diversity compared to other U.S. states and that one introduction of SARS-CoV-2 led to almost all of the early transmission in Louisiana. By analyzing mobility and genomic data, we show that SARS-CoV-2 was already present in New Orleans before Mardi Gras, and the festival dramatically accelerated transmission. Our study provides an understanding of how superspreading during large-scale events played a key role during the early outbreak in the U.S. and can greatly accelerate epidemics.

Air Passenger Travel and International Surveillance Data Predict Spatiotemporal Variation in Measles Importations to the United States.

Measles incidence in the United States has grown dramatically, as vaccination rates are declining and transmission internationally is on the rise. Because imported cases are necessary drivers of outbreaks in non-endemic settings, predicting measles outbreaks in the US depends on predicting imported cases. To assess the predictability of imported measles cases, we performed a regression of imported measles cases in the US against an inflow variable that combines air travel data with international measles surveillance data. To understand the contribution of each data type to these predictions, we repeated the regression analysis with alternative versions of the inflow variable that replaced each data type with averaged values and with versions of the inflow variable that used modeled inputs. We assessed the performance of these regression models using correlation, coverage probability, and area under the curve statistics, including with resampling and cross-validation. Our regression model had good predictive ability with respect to the presence or absence of imported cases in a given state in a given year (area under the curve of the receiver operating characteristic curve (AUC) = 0.78) and the magnitude of imported cases (Pearson correlation = 0.84). By comparing alternative versions of the inflow variable averaging over different inputs, we found that both air travel data and international surveillance data contribute to the model's ability to predict numbers of imported cases and individually contribute to its ability to predict the presence or absence of imported cases. Predicted sources of imported measles cases varied considerably across years and US states, depending on which countries had high measles activity in a given year. Our results emphasize the importance of the relationship between global connectedness and the spread of measles. This study provides a framework for predicting and understanding imported case dynamics that could inform future studies and outbreak prevention efforts.

Emergence of an early SARS-CoV-2 epidemic in the United States.

The emergence of the early COVID-19 epidemic in the United States (U.S.) went largely undetected, due to a lack of adequate testing and mitigation efforts. The city of New Orleans, Louisiana experienced one of the earliest and fastest accelerating outbreaks, coinciding with the annual Mardi Gras festival, which went ahead without precautions. To gain insight into the emergence of SARS-CoV-2 in the U.S. and how large, crowded events may have accelerated early transmission, we sequenced SARS-CoV-2 genomes during the first wave of the COVID-19 epidemic in Louisiana. We show that SARS-CoV-2 in Louisiana initially had limited sequence diversity compared to other U.S. states, and that one successful introduction of SARS-CoV-2 led to almost all of the early SARS-CoV-2 transmission in Louisiana. By analyzing mobility and genomic data, we show that SARS-CoV-2 was already present in New Orleans before Mardi Gras and that the festival dramatically accelerated transmission, eventually leading to secondary localized COVID-19 epidemics throughout the Southern U.S.. Our study provides an understanding of how superspreading during large-scale events played a key role during the early outbreak in the U.S. and can greatly accelerate COVID-19 epidemics on a local and regional scale.

Estimating internationally imported cases during the early COVID-19 pandemic.

Early in the COVID-19 pandemic, predictions of international outbreaks were largely based on imported cases from Wuhan, China, potentially missing imports from other cities. We provide a method, combining daily COVID-19 prevalence and flight passenger volume, to estimate importations from 18 Chinese cities to 43 international destinations, including 26 in Africa. Global case importations from China in early January came primarily from Wuhan, but the inferred source shifted to other cities in mid-February, especially for importations to African destinations. We estimate that 10.4 (6.2 - 27.1) COVID-19 cases were imported to these African destinations, which exhibited marked variation in their magnitude and main sources of importation. We estimate that 90% of imported cases arrived between 17 January and 7 February, prior to the first case detections. Our results highlight the dynamic role of source locations, which can help focus surveillance and response efforts.

Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK.

The United Kingdom's COVID-19 epidemic during early 2020 was one of world's largest and was unusually well represented by virus genomic sampling. We determined the fine-scale genetic lineage structure of this epidemic through analysis of 50,887 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes, including 26,181 from the UK sampled throughout the country's first wave of infection. Using large-scale phylogenetic analyses combined with epidemiological and travel data, we quantified the size, spatiotemporal origins, and persistence of genetically distinct UK transmission lineages. Rapid fluctuations in virus importation rates resulted in >1000 lineages; those introduced prior to national lockdown tended to be larger and more dispersed. Lineage importation and regional lineage diversity declined after lockdown, whereas lineage elimination was size-dependent. We discuss the implications of our genetic perspective on transmission dynamics for COVID-19 epidemiology and control.

Impact of a Public Policy Restricting Staff Mobility Between Nursing Homes in Ontario, Canada During the COVID-19 Pandemic.

OBJECTIVES: To assess changes in the mobility of staff between nursing homes in Ontario, Canada, before and after enactment of public policy restricting staff from working at multiple homes. DESIGN: Pre-post observational study. SETTING AND PARTICIPANTS: 623 nursing homes in Ontario, Canada, between March 2020 and June 2020. METHODS: We used GPS location data from mobile devices to approximate connectivity between all 623 nursing homes in Ontario during the 7 weeks before (March 1-April 21) and after (April 22-June 13) the policy restricting staff movement was implemented. We constructed a network diagram visualizing connectivity between nursing homes in Ontario and calculated the number of homes that had a connection with another nursing home and the average number of connections per home in each period. We calculated the relative difference in these mobility metrics between the 2 time periods and compared within-home changes using McNemar test and the Wilcoxon rank-sum test. RESULTS: In the period preceding restrictions, 266 (42.7%) nursing homes had a connection with at least 1 other home, compared with 79 (12.7%) homes during the period after restrictions, a drop of 70.3% (P < .001). Including all homes, the average number of connections in the before period was 3.90 compared to 0.77 in the after period, a drop of 80.3% (P < .001). In both periods, mobility between nursing homes was higher in homes located in larger communities, those with higher bed counts, and those part of a large chain. CONCLUSIONS AND IMPLICATIONS: Mobility between nursing homes in Ontario fell sharply after an emergency order by the Ontario government limiting long-term care staff to a single home, though some mobility persisted. Reducing this residual mobility should be a focus of efforts to reduce risk within the long-term care sector during the COVID-19 pandemic.

Modelling airport catchment areas to anticipate the spread of infectious diseases across land and air travel.

Air travel is an increasingly important conduit for the worldwide spread of infectious diseases. However, methods to identify which airports an individual may use to initiate travel, or where an individual may travel to upon arrival at an airport is not well studied. This knowledge gap can be addressed by estimating airport catchment areas: the geographic extent from which the airport derives most of its patronage. While airport catchment areas can provide a simple decision-support tool to help delineate the spatial extent of infectious disease spread at a local scale, observed data for airport catchment areas are rarely made publicly available. Therefore, we evaluated a probabilistic choice behavior model, the Huff model, as a potential methodology to estimate airport catchment areas in the United States in data-limited scenarios. We explored the impact of varying input parameters to the Huff model on estimated airport catchment areas: distance decay exponent, distance cut-off, and measures of airport attractiveness. We compared Huff model catchment area patterns for Miami International Airport (MIA) and Harrisburg International Airport (MDT). We specifically compared our model output to observed data sampled for MDT to align model parameters with an established, observed catchment area. Airport catchment areas derived using the Huff model were highly sensitive to changes in model parameters. We observed that a distance decay exponent of 2 and a distance cut-off of 500 km represented the most realistic spatial extent and heterogeneity of the MIA catchment area. When these parameters were applied to MDT, the Huff model produced similar spatial patterns to the observed MDT catchment area. Finally, our evaluation of airport attractiveness showed that travel volume to the specific international destinations of interest for infectious disease importation risks (i.e., Brazil) had little impact on the predicted choice of airport when compared to all international travel. Our work is a proof of concept for use of the Huff model to estimate airport catchment areas as a generalizable decision-support tool in data-limited scenarios. While our work represents an initial examination of the Huff model as a method to approximate airport catchment areas, an essential next step is to conduct a quantitative calibration and validation of the model based on multiple airports, possibly leveraging local human mobility data such as call detail records or online social network data collected from mobile devices. Ultimately, we demonstrate how the Huff model could be potentially helpful to improve the precision of early warning systems that anticipate infectious disease spread, or to incorporate when local public health decision makers need to identify where to mobilize screening infrastructure or containment strategies at a local level.