Oral processing behavior and dental caries; an insight into a new relationship.

INTRODUCTION: Previous evidence suggests an individual variation in the preferred oral processing behavior. Individuals can be classified as firm processing(FPL) or soft processing likers(SPL). FPL(crunchers and chewers) prefer using their teeth while SPL(smooshers and suckers) prefer using the tongue and the palate when processing different food items. Variation in the preferred oral processing behavior has been associated with differences in food texture preference and eating time. Time is one of the factors directly related to the development of dental caries(tooth decay). Oral retention and eating times are associated with greater caries experience. This study aims to explore if a relationship exists between the preferred oral processing behavior and the individual's caries experience. MATERIALS AND METHODS: This was a cross-sectional, dental center-based study conducted at Jordan University of Science and Technology. Five hundred participants consented to fill out the preferred oral processing behavior(POPB) questionnaire. Anthropometric measurements (including weight, height, and waist circumference) were recorded. A single trained and calibrated dentist registered each participant's caries experience and plaque levels using the DMFS index and plaque index of Silness and Loe. RESULTS: A total of 351(70.2%) and 149(29.8%) participants were typed as FPL and SPL, respectively. SPL demonstrated higher levels of dental caries experience compared to FPL. The mean DMFS score for SPL was 28.8(±25.43) while for FPL was 18.71(± 18.34). This difference remained significant after adjustment for confounders(P<0.001). SPL exhibited a significantly higher mean score for the "M" component(P <0.001) while no significant difference in the mean score of the "D"(P = 0.076) and "F"(P = 0.272) components was observed when compared to FPL. CONCLUSION: The current findings provide new insight into a possible relationship between the preferred oral processing behavior and an individual's caries experience. A relationship in which the preferred oral processing behavior can potentially affect and/or be affected by the dental caries experience.

Salivary oxidative stress biomarkers in periodontitis-free smokers: a cross sectional study.

BACKGROUND: Salivary oxidative stress has been extensively studied with attempts to correlate changes in the oxidative stress markers with local and systemic factors, including smoking. The objective of this study was to evaluate the influence of two forms of smoking, cigarettes and waterpipe smoking (WPS), on selected oxidative stress biomarkers in saliva. METHODS: Three groups of participants were enrolled into the study, controls (never smokers), cigarette smokers and WPS. Participants were clinically free from periodontitis and systemic conditions known to affect the saliva constituents. Unstimulated whole saliva samples were collected according to a standard protocol and concentrations of 8-hydroxy-2'-deoxyguanosine (8-OHdG), myeloperoxidase (MPO), malondialdehyde (MDA), total antioxidant capacity (TAC), and cortisol. The one-way ANOVA test was used to compare the levels of each oxidative stress biomarker between the three study groups and the hierarchical linear regression analysis was used to test the levels of salivary cortisol for prediction of other oxidative stress biomarkers. Significance levels were set at 95% confidence intervals and probability values ≤0.05. RESULTS: 8-OHdG was highest in WPS group (mean±SE 11,030.35±1829.16 pg/mL) while MDA and cortisol levels were highest in the cigarette smokers group (mean±SE 3.33±0.52 µM and 3.99±0.48 ng/mL, respectively) and MPO was highest in the control group (mean±SE 7.760±1.55 ng/mL). WPS group showed the highest TAC (mean±SE 0.3±0.03 mM). However, none of the tested makers reached a statistically significant difference. CONCLUSIONS: Despite subtle changes in some biomarkers, the salivary oxidative stress does not appear to be significantly influenced by smoking habits in periodontitis-free smokers.

A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems.

We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.

Lactic Acid Fermentation Ameliorates Intrinsic Toxicants in Brassica campestris L. Leaves Harvested at Different Growth Stages.

Brassica campestris (syn. Brassica rapa) is often known as mustard and is grown worldwide owing to its health-promoting characteristics associated with the presence of nutrients and phytochemicals. Along with the nutritional components, B. campestris also contains anti-nutrients (phytates, oxalates, tannins, alkaloids, saponins) that can cause adverse severe health effects to consumers, including rashes, nausea, headaches, bloating and nutritional deficiencies. In the present study, heating (blanching) and fermentation (Lactiplantibacillus plantarum) treatments were applied to reduce the load of the anti-nutrients of B. campestris leaves harvested at three different growth stages: the first stage (fourth week), the second stage (sixth week) and the third stage (eighth week). Results revealed that fermentation treatment using Lp. plantarum increases the ash (5.4 to 6%), protein (9 to 10.4%) and fiber (9.6 to 10.7%) contents, whereas moisture (0.91 to 0.82%), fat (9.9 to 9.1%) and carbohydrate (64.5 to 64.2%) contents decreased among B. campestris samples, and the trend was similar for all three stages. Blanching and fermentation lead to the reduction in phytates (46, 42%), saponins (34, 49%), tannins (1, 10%), oxalates (15, 7%) and alkaloids (10, 6%), separately as compared to raw samples of B. campestris leaves. In contrast, fermentation had no considerable effect on phytochemical contents (total phenolic and total flavonoids) and antioxidant potential (DPPH and FRAP). The action of blanching followed by fermentation caused more decline in the aforementioned toxicants load as compared to blanching or fermentation alone. Structural modifications in blanching and the biochemical conversions in fermentation lead to enhanced stability of nutrients and antioxidant potential. Taken together, these findings suggest blanching followed by fermentation treatments as a reliable, cost-effective and safer approach to curtail the anti-nutrient load without affecting the proximate composition, phytochemical attributes and antioxidant activity.

Circulating tumor DNA (ctDNA) application in investigation of cancer: Bench to bedside.

Now, genomics forms the core of the precision medicine concept. Comprehensive investigations of tumor genomes have made it possible to characterize tumors at the molecular level and, specifically, to identify the fundamental processes that cause condition. A variety of kinds of tumors have seen better outcomes for patients as a result of the development of novel medicines to tackle these genetic-driving processes. Since therapy may exert selective pressure on cancers, non-invasive methods such as liquid biopsies can provide the opportunity for rich reservoirs of crucial and real-time genetic data. Liquid biopsies depend on the identification of circulating cells from tumors, circulating tumor DNA (ctDNA), RNA, proteins, lipids, and metabolites found in patient biofluids, as well as cell-free DNA (cfDNA), which exists in those with cancer. Although it is theoretically possible to examine biological fluids other than plasma, such as pleural fluid, urine, saliva, stool, cerebrospinal fluid, and ascites, we will limit our discussion to blood and solely cfDNA here for the sake of conciseness. Yet, the pace of wider clinical acceptance has been gradual, partly due to the increased difficulty of choosing the best analysis for the given clinical issue, interpreting the findings, and delaying proof of value from clinical trials. Our goal in this review is to discuss the current clinical value of ctDNA in cancers and how clinical oncology systems might incorporate procedures for ctDNA testing.

Fuzzy-Based Bioengineering System for Predicting and Diagnosing Diseases of the Nervous System Triggered by the Interaction of Industrial Frequency Electromagnetic Fields.

The study aims to enhance the standard of medical care for individuals working in the electric power industry who are exposed to industrial frequency electromagnetic fields and other relevant risk factors. This enhancement is sought through the integration of fuzzy mathematical models with contemporary information and intellectual technologies. The study addresses the challenges of forecasting and diagnosing illnesses within a specific demographic characterized by a combination of poorly formalized issues with interconnected conditions. To tackle this complexity, a methodological framework was developed for synthesizing hybrid fuzzy decision rules. This approach combines clinical expertise with artificial intelligence methodologies to promote innovative problem-solving strategies. Additionally, the researchers devised an original method to evaluate the body's protective capacity, which was integrated into these decision rules to enhance the precision and efficacy of medical decision-making processes. The research findings indicate that industrial frequency electromagnetic fields contribute to illnesses of societal significance. Additionally, it highlights that these effects are worsened by other risk factors such as adverse microclimates, noise, vibration, chemical exposure, and psychological stress. Diseases of the neurological, immunological, cardiovascular, genitourinary, respiratory, and digestive systems are caused by these variables in conjunction with unique physical traits. The development of mathematical models in this study makes it possible to detect and diagnose disorders in workers exposed to electromagnetic fields early on, especially those pertaining to the autonomic nervous system and heart rhythm regulation. The results can be used in clinical practice to treat personnel in the electric power industry since expert evaluation and modeling showed high confidence levels in decision-making accuracy.

Depth of focus and intensity distribution of a lensacon illuminated by a partially coherent Gaussian Schell vortex beam.

Using the extended Huygens-Fresnel principle, a cross-spectral density formula was developed for a Gaussian Schell model vortex (PCGSMV) beam diffracted through a lensacon (lens with an axicon). The intensity and depth of focus (DOF) shaped by the lensacon were calculated. Our numerical results show the relationship between the intensity distribution and depth of focus with the beam waist width as well as the spatial correlation of the coherence length. Furthermore, the relationship between the beam spot size and propagation distance was investigated. In the case of the lensacon tandem, the maximum intensity was greater than that attained by the axicon alone for the same beam parameters, and the DOF was smaller than that of the axicon alone. The vortex structure canceled out the low value of the spatial degree of coherence length. Our numerical model exhibited high-intensity values and high-quality Bessel rings along the DOF, which are critical for various applications.

Cost-Effectiveness of Increasing Access to Colorectal Cancer Diagnosis: Analysis From Thailand.

OBJECTIVES: The purpose of this study is to evaluate the cost-effectiveness of increasing access to colorectal cancer (CRC) diagnosis, considering resource limitations in Thailand. METHODS: We analyzed the cost-effectiveness of increasing access to fecal immunochemical test screening (strategy I), symptom evaluation (strategy II), and their combination through healthcare and societal perspectives using Colo-Sim, a simulation model of CRC care. We extended our analysis by adding a risk-stratification score (RS) to the strategies. We analyzed all strategies under the currently limited annual colonoscopy capacity and sufficient capacity. We estimated quality-adjusted life-years (QALYs) and costs over 2023 to 2047 and performed sensitivity analyses. RESULTS: Annual costs for CRC care will increase over 25 years in Thailand, resulting in a cumulative cost of 323B Thai baht (THB). Each strategy results in higher QALYs gained and additional costs. With the current colonoscopy capacity and willingness-to-pay threshold of 160 000 THB, strategy I with and without RS is not cost-effective. Strategy II + RS is the most cost-effective, resulting in 0.68 million QALYs gained with additional costs of 66B THB. Under sufficient colonoscopy capacity, all strategies are deemed cost-effective, with the combined approach (strategy I + II + RS) being the most favorable, achieving the highest QALYs (1.55 million) at an additional cost of 131 billion THB. This strategy also maintains the highest probability of being cost-effective at any willingness-to-pay threshold above 96 000 THB. CONCLUSIONS: In Thailand, fecal immunochemical test screening, symptom evaluation, and RS use can achieve the highest QALYs; however, boosting colonoscopy capacity is essential for cost-effectiveness.

Hedgehog signaling mastery: R51211's promise in augmenting the therapeutic efficacy of sorafenib.

Sorafenib is a multikinase inhibitor employed for managing hepatocellular carcinoma (HCC). The emergence of sorafenib resistance presents an obstacle to its therapeutic efficacy. One notable approach to overcoming sorafenib resistance is the exploration of combination therapies. The role of hedgehog signaling in sorafenib resistance has been also examined in HCC. R51211, known as itraconazole, has been safely employed in clinical practice. Through in vitro and in vivo investigations, we assessed the potential of R51211 to enhance the therapeutic efficacy of sorafenib by inhibiting the hedgehog signaling. The zero-interaction potency synergy model demonstrated a synergistic interaction between R51211 and sorafenib, a phenomenon reversed by the action of a smoothened receptor agonist. This dual therapy exhibited an increased capacity to induce apoptosis, as evidenced by alterations in the Bax/BCL-2 ratio and caspase-3, along with a propensity to promote autophagy, as indicated by changes in BECN1, p62, and the LC3I/LC3II ratio. Furthermore, the combination therapy resulted in significant reductions in biomarkers associated with liver preneoplastic alterations, improved liver microstructure, and mitigated changes in liver function enzymes. The substantial decrease in hedgehog components (Shh, SMO, GLI1, and GLI2) following R51211 treatment appears to be a key factor contributing to the increased efficacy of sorafenib. In conclusion, our study highlights the potential of R51211 as an adjunct to sorafenib, introducing a new dimension to this combination therapy through the modulation of the hedgehog signaling pathway. Further investigations are essential to validate the therapeutic efficacy of this combined approach in inhibiting the development of liver cancer.

Optimal Treatment of Tumor in Upper Human Respiratory Tract Using Microaerosols.

Lung cancer is a frequently diagnosed respiratory disease caused by particulate matter in the environment, especially among older individuals. For its effective treatment, a promising approach involves administering drug particles through the inhalation route. Multiple studies have investigated the flow behavior of inhaled particles in the respiratory airways of healthy patients. However, the existing literature lacks studies on the precise understanding of the transportation and deposition (TD) of inhaled particles through age-specific, unhealthy respiratory tracts containing a tumor, which can potentially optimize lung cancer treatment. This study aims to investigate the TD of inhaled drug particles within a tumorous, age-specific human respiratory tract. The computational model reports that drug particles within the size range of 5-10 µm are inclined to deposit more on the tumor located in the upper airways of a 70-year-old lung. Conversely, for individuals aged 50 and 60 years, an optimal particle size range for achieving the highest degree of particle deposition onto upper airway tumor falls within the 11-20 µm range. Flow disturbances are found to be at a maximum in the airway downstream of the tumor. Additionally, the impact of varying inhalation flow rates on particle TD is examined. The obtained patterns of airflow distribution and deposition efficiency on the tumor wall for different ages and tumor locations in the upper tracheobronchial airways would be beneficial for developing an efficient and targeted drug delivery system.

Study of Association of Erectile Dysfunction with Metabolic Syndrome and Its Correlation with Endothelial Dysfunction in an Indian Population.

BACKGROUND: Metabolic syndrome (MS) has emerged as a new health risk, and its associated metabolic derangements have detrimental effects on the cardiovascular system. In recent years, MS has been reported to affect reproductive health in males. It has been reported to be associated with erectile dysfunction (ED) and has been attributed to be due to endothelial dysfunction. Poor endothelial function in ED usually affects small-sized vasculature, so it can be looked at as a predictor for the endothelial dysfunction of macro vasculature. The aim of the present study was to determine the association of ED in patients with MS and to determine its correlation with endothelial dysfunction. MATERIALS AND METHODS: It was a hospital-based case-control study in which 120 male patients with MS and 120 age-matched controls were enroled. Demographic profiles, anthropometry, past illnesses, and medical history of patients were obtained. MS was diagnosed according to the International Diabetes Federation (IDF) criteria and was measured using the flow-mediated dilation (FMD) method with the help of ultrasound used to assess endothelial dysfunction. Diagnosis of ED was based on the International Index of Erectile Function (IIEF) scale. RESULTS: The study participants had a mean age of 40.91 ± 11.41 years. The majority of cases (57.5%) had ≤6 months of history of MS. The prevalence of ED was 31.7% in cases compared to 5% in controls, thus showing a significant difference between cases and controls. Mean IIEF scores were significantly lower in cases (18.82 ± 5.59) compared to those in controls (23.00 ± 2.57). A moderate positive and significant correlation was observed between FMD and IIEF scores. With an increasing number of MS components, there was a significant increase in the prevalence of ED. Those with ED had significantly lower mean FMD values (5.1 ± 1.1%) compared to those not having ED (10.9 ± 3.3%). CONCLUSION: The findings of the present study showed that there is a significant association between ED and MS. We observed that the increase in components of MS increased the prevalence of ED in MS. Endothelial dysfunction measured by FMD was correlated with ED.

Primary spinal oligodendroglioma with intracranial extension: a case report.

Introduction: Primary spinal cord oligodendrogliomas (PSO) are sporadic tumors that arise from oligodendrocytes in the central nervous system (CNS). They can affect adults and children and make up about 2% of all intramedullary (IM) spinal tumors. Here, the authors present the second case in the literature of a primary spinal oligodendroglioma with intracranial extension. Presentation: A 28-year-old right-handed female presented to our emergency room severely malaised with left-sided hemiparesis, numbness, tingling, and urinary retention with positive Babinski and negative Hoffmann. MRI showed a widespread heterogeneous mass extending from the medulla to C7 with syringomyelia inferior to the mass. The mass was removed surgically, and her neurological condition improved rapidly. The gross, pathological exams, and immunohistochemistry confirmed the diagnosis of oligodendroglioma. Discussion: Up until 2017, there have been 60 documented cases of PSO in the literature and we have found two more cases in our search between 2017 and 2023. Also, there has been only one case recorded with an intracranial extension, making our case the 63rd PSO case and the second one with cranial extension. Conclusion: The golden standard for imaging is MRI. Surgical excision is the main treatment in the literature. Single-stage laminectomy showed promising results and surgical resection was the critical intervention to which the patient responded. This matches what was stated in the literature that surgery is the primary mode of treatment in PSO patients.

Alzheimer's disease and drug delivery across the blood-brain barrier: approaches and challenges.

Alzheimer's disease (AD) is a diverse disease with a complex pathophysiology. The presence of extracellular ß-amyloid deposition as neuritic plaques and intracellular accumulation of hyper-phosphorylated tau as neurofibrillary tangles remain the core neuropathologic criteria for diagnosing Alzheimer's disease. Nonetheless, several recent basic discoveries have revealed significant pathogenic roles for other essential cellular and molecular processes. Previously, there were not so many disease-modifying medications (DMT) available as drug distribution through the blood-brain barrier (BBB) is difficult due to its nature, especially drugs of polypeptides nature and proteins. Recently FDA has approved lecanemab as DMT for its proven efficacy. It is also complicated to deliver drugs for diseases like epilepsy or any brain tumor due to the limitations of the BBB. After the advancements in the drug delivery system, different techniques are used to transport the medication across the BBB. Other methods are used, like enhancement of brain blood vessel fluidity by liposomes, infusion of hyperosmotic solutions, and local intracerebral implants, but these are invasive approaches. Non-invasive approaches include the formulation of nanoparticles and their coating with polymers. This review article emphasizes all the above-mentioned techniques, procedures, and challenges to transporting medicines across the BBB. It summarizes the most recent literature dealing with drug delivery across the BBB.

Global Analysis of RTS, S/AS01 Malaria Vaccine Acceptance Rates and Influencing Factors: A Systematic Review.

Malaria remains a significant global health challenge, with Plasmodium parasites transmitted by Anopheles mosquitoes causing substantial morbidity and mortality. Despite historical efforts, malaria continues to affect millions worldwide, particularly in tropical regions. This systematic review aimed to assess the acceptability of the RTS, S/AS01 malaria vaccine among diverse populations. A comprehensive search strategy was employed across databases such as Cochrane Library, Embase, Google Scholar, and Medline. Studies were included based on specific criteria, including observational and cross-sectional designs involving adults. Data extraction and analysis were conducted meticulously, encompassing key variables related to vaccine acceptance rates and influencing factors. Analysis of 18 studies involving 18,561 participants revealed an overall malaria vaccine acceptance rate of 87.51%, ranging from 32.26% to 99.30%. Significant variations were observed based on demographics, with Ghana and Nigeria reporting high acceptance rates. Factors influencing acceptance included knowledge levels, past vaccination experiences, community preferences, and engagement in malaria prevention behaviors. Concerns about adverse reactions and regional disparities were noted as potential barriers to acceptance. This review highlights the importance of understanding public perceptions and concerns regarding malaria vaccines to enhance vaccine coverage and uptake. Tailored communication strategies, advocacy efforts, and targeted education interventions are crucial for addressing misconceptions and increasing vaccine acceptance. Policy recommendations should consider demographic and regional factors to ensure effective implementation of malaria vaccination programs, ultimately contributing to global malaria prevention efforts and public health initiatives.

Assessment of Epidemiologic Data and Surveillance in South Korean Substance Use Research: Insights and Future Directions.

Objectives: Effective data collection and surveillance of epidemiological trends are essential in confronting the growing challenges associated with substance use (SU), especially in light of emerging trends and underreporting of cases. However, research and data are scarce regarding SU and substance use disorder (SUD) in Korea. Methods: We conducted a scoping review to identify data sources and surveillance methods used in SU research in Korea up to December 2023. This review was complemented by semi-structured consultations with experts in this area in Korea, whose feedback led to revisions of previously identified data sources and assessments. Results: Our review identified 32 publications conducting secondary analyses on existing data to examine the epidemiology of SU and SUD in Korea. Of these, 14 studies utilized clinical databases to explore the prescription patterns of addictive substances, particularly opioids. Eleven data sources showed promise for advancing SU research; however, they face substantial limitations, including a lack of available data, missing data, the absence of key variables, the exclusion of marginalized populations not captured within the clinical system, and complexities in matching individual-level data across time points and datasets. Conclusions: Current surveillance methods for SU in Korea face considerable challenges in accessibility, usability, and standardization. Moreover, existing data repositories may fail to capture information on populations not served by clinical or judicial systems. To systematically improve surveillance approaches, it is necessary to develop a robust and nationally representative survey, refine the use of existing clinical data, and ensure the availability of data on treatment facilities.

The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.

PURPOSE OF REVIEW: This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies. RECENT FINDINGS: In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.

Modeling approaches to inform travel-related policies for COVID-19 containment: a scoping review and future directions.

BACKGROUND: Travel-related strategies to reduce the spread of COVID-19 evolved rapidly in response to changes in the understanding of SARS-CoV-2 and newly available tools for prevention, diagnosis, and treatment. Modeling is an important methodology to investigate the range of outcomes that could occur from different disease containment strategies. METHODS: We examined 43 articles published from December 2019 through September 2022 that used modeling to evaluate travel-related COVID-19 containment strategies. We extracted and synthesized data regarding study objectives, methods, outcomes, populations, settings, strategies, and costs. We used a standardized approach to evaluate each analysis according to 26 criteria for modeling quality and rigor. RESULTS: The most frequent approaches included compartmental modeling to examine quarantine, isolation, or testing. Early in the pandemic, the goal was to prevent travel-related COVID-19 cases with a focus on individual-level outcomes and assessing strategies such as travel restrictions, quarantine without testing, social distancing, and on-arrival PCR testing. After the development of diagnostic tests and vaccines, modeling studies projected population-level outcomes and investigated these tools to limit COVID-19 spread. Very few published studies included rapid antigen screening strategies, costs, explicit model calibration, or critical evaluation of the modeling approaches. CONCLUSION: Future modeling analyses should leverage open-source data, improve the transparency of modeling methods, incorporate newly available prevention, diagnostics, and treatments, and include costs and cost-effectiveness so that modeling analyses can be informative to address future SARS-CoV-2 variants of concern and other emerging infectious diseases (e.g., mpox and Ebola) for travel-related health policies.