The Utility of AI in Writing a Scientific Review Article on the Impacts of COVID-19 on Musculoskeletal Health.

PURPOSE OF REVIEW: There were two primary purposes to our reviews. First, to provide an update to the scientific community about the impacts of COVID-19 on musculoskeletal health. Second, was to determine the value of using a large language model, ChatGPT 4.0, in the process of writing a scientific review article. To accomplish these objectives, we originally set out to write three review articles on the topic using different methods to produce the initial drafts of the review articles. The first review article was written in the traditional manner by humans, the second was to be written exclusively using ChatGPT (AI-only or AIO), and the third approach was to input the outline and references selected by humans from approach 1 into ChatGPT, using the AI to assist in completing the writing (AI-assisted or AIA). All review articles were extensively fact-checked and edited by all co-authors leading to the final drafts of the manuscripts, which were significantly different from the initial drafts. RECENT FINDINGS: Unfortunately, during this process, it became clear that approach 2 was not feasible for a very recent topic like COVID-19 as at the time, ChatGPT 4.0 had a cutoff date of September 2021 and all articles published after this date had to be provided to ChatGPT, making approaches 2 and 3 virtually identical. Therefore, only two approaches and two review articles were written (human and AI-assisted). Here we found that the human-only approach took less time to complete than the AI-assisted approach. This was largely due to the number of hours required to fact-check and edit the AI-assisted manuscript. Of note, the AI-assisted approach resulted in inaccurate attributions of references (about 20%) and had a higher similarity index suggesting an increased risk of plagiarism. The main aim of this project was to determine whether the use of AI could improve the process of writing a scientific review article. Based on our experience, with the current state of technology, it would not be advised to solely use AI to write a scientific review article, especially on a recent topic.

SARS-CoV-2 and its Multifaceted Impact on Bone Health: Mechanisms and Clinical Evidence.

PURPOSE OF REVIEW: SARS-CoV-2 infection, the culprit of the COVID-19 pandemic, has been associated with significant long-term effects on various organ systems, including bone health. This review explores the current understanding of the impacts of SARS-CoV-2 infection on bone health and its potential long-term consequences. RECENT FINDINGS: As part of the post-acute sequelae of SARS-CoV-2 infection, bone health changes are affected by COVID-19 both directly and indirectly, with multiple potential mechanisms and risk factors involved. In vitro and preclinical studies suggest that SARS-CoV-2 may directly infect bone marrow cells, leading to alterations in bone structure and osteoclast numbers. The virus can also trigger a robust inflammatory response, often referred to as a "cytokine storm", which can stimulate osteoclast activity and contribute to bone loss. Clinical evidence suggests that SARS-CoV-2 may lead to hypocalcemia, altered bone turnover markers, and a high prevalence of vertebral fractures. Furthermore, disease severity has been correlated with a decrease in bone mineral density. Indirect effects of SARS-CoV-2 on bone health, mediated through muscle weakness, mechanical unloading, nutritional deficiencies, and corticosteroid use, also contribute to the long-term consequences. The interplay of concurrent conditions such as diabetes, obesity, and kidney dysfunction with SARS-CoV-2 infection further complicates the disease's impact on bone health. SARS-CoV-2 infection directly and indirectly affects bone health, leading to potential long-term consequences. This review article is part of a series of multiple manuscripts designed to determine the utility of using artificial intelligence for writing scientific reviews.

Bayesian latent class models for identifying canine visceral leishmaniosis using diagnostic tests in the absence of a gold standard.

BACKGROUND: Like many infectious diseases, there is no practical gold standard for diagnosing clinical visceral leishmaniasis (VL). Latent class modeling has been proposed to estimate a latent gold standard for identifying disease. These proposed models for VL have leveraged information from diagnostic tests with dichotomous serological and PCR assays, but have not employed continuous diagnostic test information. METHODS/PRINCIPAL FINDINGS: In this paper, we employ Bayesian latent class models to improve the identification of canine visceral leishmaniasis using the dichotomous PCR assay and the Dual Path Platform (DPP) serology test. The DPP test has historically been used as a dichotomous assay, but can also yield numerical information via the DPP reader. Using data collected from a cohort of hunting dogs across the United States, which were identified as having either negative or symptomatic disease, we evaluate the impact of including numerical DPP reader information as a proxy for immune response. We find that inclusion of DPP reader information allows us to illustrate changes in immune response as a function of age. CONCLUSIONS/SIGNIFICANCE: Utilization of continuous DPP reader information can improve the correct discrimination between individuals that are negative for disease and those with clinical VL. These models provide a promising avenue for diagnostic testing in contexts with multiple, imperfect diagnostic tests. Specifically, they can easily be applied to human visceral leishmaniasis when diagnostic test results are available. Also, appropriate diagnosis of canine visceral leishmaniasis has important consequences for curtailing spread of disease to humans.

Bayesian compartmental models and associated reproductive numbers for an infection with multiple transmission modes.

Zoonotic visceral leishmaniasis (ZVL) is a serious neglected tropical disease that is endemic in 98 countries. ZVL is primarily transmitted via a sand fly vector. In the United States, it is enzootic in some canine populations; it is transmitted from infectious mother to pup transplacentally, and vector-borne transmission is absent. This absence affords a unique opportunity to study (1) vertical transmission dynamics in dogs and (2) the importance of vertical transmission in maintaining an infectious reservoir in the presence of a vector. In this paper, we present Bayesian compartmental models and reproductive number formulations to examine (1) and (2), providing a mechanism to plan and evaluate interventions in regions where both transmission modes are present. First, we propose an individual-level susceptible, infectious, removed (SIR) model to study the effect of maternal infection status during pregnancy on pup infection progression. We provide evidence that pups born to diagnostically positive mothers during pregnancy are more likely to become diagnostically positive both earlier in life, and at some point during their lifetime, than those born to diagnostically negative mothers. Second, we propose a population-level SIR model to study the impact of a vertically maintained reservoir on propagating infection in a naive canine population through emergent vector transmission using simulation studies. We also present reproductive numbers to quantify contributions of vertically infected and vector-infected dogs to maintaining infection in the population. We show that a vertically maintained canine reservoir can propagate infection in a theoretical naive population in the presence of a vector.

Bayesian compartmental model for an infectious disease with dynamic states of infection.

Population-level proportions of individuals that fall at different points in the spectrum [of disease severity], from asymptomatic infection to severe disease, are often difficult to observe, but estimating these quantities can provide information about the nature and severity of the disease in a particular population. Logistic and multinomial regression techniques are often applied to infectious disease modeling of large populations and are suited to identifying variables associated with a particular disease or disease state. However, they are less appropriate for estimating infection state prevalence over time because they do not naturally accommodate known disease dynamics like duration of time an individual is infectious, heterogeneity in the risk of acquiring infection, and patterns of seasonality. We propose a Bayesian compartmental model to estimate latent infection state prevalence over time that easily incorporates known disease dynamics. We demonstrate how and why a stochastic compartmental model is a better approach for determining infection state proportions than multinomial regression is by using a novel method for estimating Bayes factors for models with high-dimensional parameter spaces. We provide an example using visceral leishmaniasis in Brazil and present an empirically-adjusted reproductive number for the infection.

Comorbid infections induce progression of visceral leishmaniasis.

BACKGROUND: Visceral leishmaniasis (VL) is a vector borne zoonotic disease endemic in humans and dogs in Brazil. Due to the increased risk of human infection secondary to the presence of infected dogs, public health measures in Brazil mandate testing and culling of infected dogs. Despite this important relationship between human and canine infection, little is known about what makes the dog reservoir progress to clinical illness, significantly tied to infectiousness to sand flies. Dogs in endemic areas of Brazil are exposed to many tick-borne pathogens, which are likely to alter the immune environment and thus control of L. infantum. RESULTS: A cross-sectional study of 223 dogs from an area of Natal, in the Rio Grande do Norte, Brazil, were studied to determine the association between comorbid tick-borne disease and Leishmania infection in this endemic area. The risk of Leishmania seropositivity was 1.68× greater in dogs with tick-borne disease seropositivity compared to those without (Adjusted RR: 1.68, 95% CI: 1.09-2.61, P = 0.019). A longitudinal study of 214 hunting dogs in the USA was conducted to determine the causal relationship between infection with tick-borne diseases and progression of VL. Hunting dogs were evaluated three times across a full tick season to detect incident infection with tick-borne diseases. A logistic regression model with generalized estimating equations to estimate the parameters was used to determine how exposure to tick-borne disease altered VL progression over these three time points when controlling for other variables. Dogs infected with three or more tick-borne diseases were 11× more likely to be associated with progression to clinical VL than dogs with no tick-borne disease (Adjusted RR: 11.64, 95% CI: 1.22-110.99, P = 0.03). Dogs with exposure to both Leishmania spp. and tick-borne diseases were five times more likely to die during the study period (RR: 4.85, 95% CI: 1.65-14.24, P = 0.0051). CONCLUSIONS: Comorbid tick-borne diseases dramatically increased the likelihood that a dog had clinical L. infantum infection, making them more likely to transmit infection to sand flies and people. As an important consequence, reduction of tick-borne disease exposure through topical or oral insecticides may be an important way to reduce progression and transmissibility of Leishmania infection from the canine reservoir to people.

Delayed changes in auditory status in cochlear implant users with preserved acoustic hearing.

This retrospective review explores delayed-onset hearing loss in 85 individuals receiving cochlear implants designed to preserve acoustic hearing at the University of Iowa Hospitals and Clinics between 2001 and 2015. Repeated measures of unaided behavioral audiometric thresholds, electrode impedance, and electrically evoked compound action potential (ECAP) amplitude growth functions were used to characterize longitudinal changes in auditory status. Participants were grouped into two primary categories according to changes in unaided behavioral thresholds: (1) stable hearing or symmetrical hearing loss and (2) delayed loss of hearing in the implanted ear. Thirty-eight percent of this sample presented with delayed-onset hearing loss of various degrees and rates of change. Neither array type nor insertion approach (round window or cochleostomy) had a significant effect on prevalence. Electrode impedance increased abruptly for many individuals exhibiting precipitous hearing loss; the increase was often transient. The impedance increases were significantly larger than the impedance changes observed for individuals with stable or symmetrical hearing loss. Moreover, the impedance changes were associated with changes in behavioral thresholds for individuals with a precipitous drop in behavioral thresholds. These findings suggest a change in the electrode environment coincident with the change in auditory status. Changes in ECAP thresholds, growth function slopes, and suprathreshold amplitudes were not correlated with changes in behavioral thresholds, suggesting that neural responsiveness in the region excited by the implant is relatively stable. Further exploration into etiology of delayed-onset hearing loss post implantation is needed, with particular interest in mechanisms associated with changes in the intracochlear environment.