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Abstract Background Cost-effectiveness analyses rarely offer useful insights to policy decisions unless their results are compared against a benchmark threshold. The cost-effectiveness threshold (CET) represents the maximum acceptable monetary value for achieving a unit of health gain. This study aimed to identify CET values on a global scale, provide an overview of using multiple CETs, and propose a country-specific CET framework specifically tailored for Egypt. The proposed framework aims to consider the globally identified CETs, analyze global trends, and consider the local structure of Egypt’s healthcare system. Methods We conducted a literature review to identify CET values, with a particular focus on understanding the basis of differentiation when multiple thresholds are present. CETs of different countries were reviewed from secondary sources. Additionally, we assembled an expert panel to develop a national CET framework in Egypt and propose an initial design. This was followed by a multistakeholder workshop, bringing together representatives of different governmental bodies to vote on the threshold value and finalize the recommended framework. Results The average CET, expressed as a percentage of the gross domestic product (GDP) per capita across all countries, was 135%, with a range of 21 to 300%. Interestingly, while the absolute value of CET increased with a country’s income level, the average CET/GDP per capita showed an inverse relationship. Some countries applied multiple thresholds based on disease severity or rarity. In the case of Egypt, the consensus workshop recommended a threshold ranging from one to three times the GDP per capita, taking into account the incremental relative quality-adjusted life years (QALY) gain. For orphan medicines, a CET multiplier between 1.5 and 3.0, based on the disease rarity, was recommended. A two-times multiplier was proposed for the private reimbursement threshold compared to the public threshold. Conclusion The CET values in most countries appear to be closely related to the GDP per capita. Higher-income countries tend to use a lower threshold as a percentage of their GDP per capita, contrasted with lower-income countries. In Egypt, experts opted for a multiple CET framework to assess the value of health technologies in terms of reimbursement and pricing.

Recently, the attention to exploring and applying probiotics has been increased. Probiotics are living microscopic organisms that have valuable impacts on health when consumed in adequate amounts. One of the most popular widespread utilized probiotics for many years until now is the Lactobacillus species which exemplifies the hugest heterogeneous group among the lactic acid bacteria (LAB) that normally occupy many environments that contain high nutrient levels such as soil, humans, waters, plants, animals and even air without causing any illness. The purpose of this work is to review and demonstrate the wide diversity of lactobacilli isolation sources and related health benefits applications of the strains discovered in order to break the familiar sources of lactobacillus spp., thereby encouraging more research into unconventional sources and opening up opportunities for clinical applications of Lactobacillus spp. As a result, the isolation sources of lactobacillus spp. in this review have been split into two groups based on the environmental condition, including fermented and non-fermented habitats described as lactobacilli probiotic sources. The unique Lactobacilli isolated from various sources are regarded as potential therapies for a variety of illnesses and physiological abnormalities, including immunomodulation, suppression of carcinogenesis, and cholesterol reduction, in addition to their capacity to guard against infections. This will be highly significant for revealing updated information about Lactobacillus spp. that has attracted considerable attention due to its potential application in clinical practices.

Antibiotic resistance is a major risk to human health worldwide due to antibiotic- and multidrug-resistant bacteria, especially in the case of serious infections, which limits the availability of antimicrobial treatment options. Focusing on the bacterial resistance mechanisms against antibiotics and the conventional strategies used to combat antimicrobial resistance, this review highlights the history of antibiotics and their target mechanisms, mentions the strategy limitations, provides the most recent novel alternative therapies to combat resistance, and illustrates their mode of action and applications that may treat several infectious diseases caused by bacterial resistance. Finally, this paper mentions future prospects that we believe would make a considerable difference in the microbial resistance battle. Novel antibiotic alternative therapies, including nanomaterial therapy, antimicrobial photodynamic therapy, hybrid antimicrobial therapy, and phage therapy, are covered in this review.

High-resolution cervical auscultation (HRCA) is an emerging noninvasive and accessible option to assess swallowing by relying upon accelerometry and sound sensors. HRCA has shown tremendous promise and accuracy in identifying and predicting swallowing physiology and biomechanics with accuracies equivalent to trained human judges. These insights have historically been available only through instrumental swallowing evaluation methods, such as videofluoroscopy and endoscopy. HRCA uses supervised learning techniques to interpret swallowing physiology from the acquired signals, which are collected during radiographic assessment of swallowing using barium contrast. Conversely, bedside swallowing screening is typically conducted in non-radiographic settings using only water. This poses a challenge to translating and generalizing HRCA algorithms to bedside screening due to the rheological differences between barium and water. To address this gap, we proposed a cross-domain transformation framework that uses cycle generative adversarial networks to convert HRCA signals of water swallows into a domain compatible with the barium swallows-trained HRCA algorithms. The proposed framework achieved a cross-domain transformation accuracy that surpassed 90%. The authenticity of the generated signals was confirmed using a binary classifier to confirm the framework's capability to produce indistinguishable signals. This framework was also assessed for retaining swallow physiological and biomechanical properties in the signals by applying an existing model from the literature that identifies the opening and closure of the upper esophageal sphincter. The outcomes of this model showed nearly identical results between the generated and original signals. These findings suggest that the proposed transformation framework is a feasible avenue to advance HCRA towards clinical deployment for water-based swallowing screenings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Importance: Built environment plays an important role in development of cardiovascular disease. Large scale, pragmatic evaluation of built environment has been limited owing to scarce data and inconsistent data quality., Objective: To investigate the association between image-based built environment and the prevalence of cardiometabolic disease in urban cities., Design, Setting, and Participants: This cross-sectional study used features extracted from Google satellite images (GSI) to measure the built environment and link them with prevalence of cardiometabolic disease. Convolutional neural networks, light gradient-boosting machines, and activation maps were used to assess the association with health outcomes and identify feature associations with coronary heart disease (CHD), stroke, and chronic kidney disease (CKD). The study obtained aerial images from GSI covering census tracts in 7 cities (Cleveland, Ohio; Fremont, California; Kansas City, Missouri; Detroit, Michigan; Bellevue, Washington; Brownsville, Texas; and Denver, Colorado). The study used census tract-level data from the US Centers for Disease Control and Prevention's 500 Cities project. The data were originally collected from the Behavioral Risk Factor Surveillance System that surveyed people 18 years and older across the country. Analyses were conducted from February to December 2022., Exposures: GSI images of built environment and cardiometabolic disease prevalence., Main Outcomes and Measures: Census tract-level estimated prevalence of CHD, stroke, and CKD based on image-based built environment features., Results: The study obtained 31 786 aerial images from GSI covering 789 census tracts. Built environment features extracted from GSI using machine learning were associated with prevalence of CHD (R2 = 0.60), stroke (R2 = 0.65), and CKD (R2 = 0.64). The model performed better at distinguishing differences between cardiometabolic prevalence between cities than within cities (eg, highest within-city R2 = 0.39 vs between-city R2 = 0.64 for CKD). Addition of GSI features both outperformed and improved the model that only included age, sex, race, income, education, and composite indices for social determinants of health (R2 = 0.83 vs R2 = 0.76 for CHD; P <.001). Activation maps from the features revealed certain health-related built environment such as roads, highways, and railroads and recreational facilities such as amusement parks, arenas, and baseball parks., Conclusions and Relevance: In this cross-sectional study, a significant portion of cardiometabolic disease prevalence was associated with GSI-based built environment using convolutional neural networks.

Monitoring of ingestive activities is critically important for managing the health and wellness of individuals with various health conditions, including the elderly, diabetics, and individuals seeking better weight control. Monitoring swallowing events can be an ideal surrogate for developing streamlined methods for effective monitoring and quantification of eating or drinking events. Swallowing is an essential process for maintaining life. This seemingly simple process is the result of coordinated actions of several muscles and nerves in a complex fashion. In this study, we introduce automated methods for the detection and quantification of various eating and drinking activities. Wireless surface electromyography (sEMG) was used to detect chewing and swallowing from sEMG signals obtained from the sternocleidomastoid muscle, in addition to signals obtained from a wrist-mounted IMU sensor. A total of 4675 swallows were collected from 55 participants in the study. Multiple methods were employed to estimate bolus volumes in the case of fluid intake, including regression and classification models. Among the tested models, neural networks-based regression achieved an R 2 of 0.88 and a root mean squared error of 0.2 (minimum bolus volume was 10 ml). Convolutional neural networks-based classification (when considering each bolus volume as a separate class) achieved an accuracy of over 99% using random cross-validation and around 66% using cross-subject validation. Multiple classification methods were also used for solid bolus type detection, including SVM and decision trees (DT), which achieved an accuracy above 99% with random validation and above 94% in cross-subject validation. Finally, regression models with both random and cross-subject validation were used for estimating the solid bolus volume with an R 2 value that approached 1 and root mean squared error values as low as 0.00037 (minimum solid bolus weight was 3 gm). These reported results lay the foundation for a cost-effective and non-invasive method for monitoring swallowing activities which can be extremely beneficial in managing various chronic health conditions, such as diabetes and obesity., (© 2024. The Author(s).)

Background and Aims: Built environment plays an important role in the development of cardiovascular disease. Tools to evaluate the built environment using machine vision and informatic approaches have been limited. This study aimed to investigate the association between machine vision-based built environment and prevalence of cardiometabolic disease in US cities., Methods: This cross-sectional study used features extracted from Google Street View (GSV) images to measure the built environment and link them with prevalence of coronary heart disease (CHD). Convolutional neural networks, linear mixed-effects models, and activation maps were utilized to predict health outcomes and identify feature associations with CHD at the census tract level. The study obtained 0.53 million GSV images covering 789 census tracts in seven US cities (Cleveland, OH; Fremont, CA; Kansas City, MO; Detroit, MI; Bellevue, WA; Brownsville, TX; and Denver, CO)., Results: Built environment features extracted from GSV using deep learning predicted 63% of the census tract variation in CHD prevalence. The addition of GSV features improved a model that only included census tract-level age, sex, race, income, and education or composite indices of social determinant of health. Activation maps from the features revealed a set of neighbourhood features represented by buildings and roads associated with CHD prevalence., Conclusions: In this cross-sectional study, the prevalence of CHD was associated with built environment factors derived from GSV through deep learning analysis, independent of census tract demographics. Machine vision-enabled assessment of the built environment could potentially offer a more precise approach to identify at-risk neighbourhoods, thereby providing an efficient avenue to address and reduce cardiovascular health disparities in urban environments., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Developing K-ion conducting solid-state electrolytes (SSEs) plays a critical role in the safe implementation of potassium batteries. In this work, a chalcogenide-based potassium ion SSE is reported, K 3 SbSe 4 , which adopts a trigonal structure at room temperature. Single-crystal structural analysis reveals a trigonal-to-cubic phase transition at the low temperature of 50 °C, which is the lowest among similar compounds and thus provides easy access to the cubic phase. The substitution of barium for potassium in K 3 SbSe 4 leads to the creation of potassium vacancies, expansion of lattice parameters, and a transformation from a trigonal phase to a cubic phase. As a result, the maximum conductivity of K 3-2 x Ba x SbSe 4 reaches around 0.1 mS cm -1 at 40 °C for K 2.2 Ba 0.4 SbSe 4 , which is over two orders of magnitude higher than that of undoped K 3 SbSe 4 . This novel SSE is successfully employed in a K-O 2 battery operating at room temperature where a polymer-laminated K 2.2 Ba 0.4 SbSe 4 pellet serves as a separator between the oxygen cathode and the potassium metal anode. Effective protection of the K metal anode against corrosion caused by O 2 is demonstrated., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

Objective: Dysphagia management relies on the evaluation of the temporospatial kinematic events of swallowing performed in videofluoroscopy (VF) by trained clinicians. The upper esophageal sphincter (UES) opening distension represents one of the important kinematic events that contribute to healthy swallowing. Insufficient distension of UES opening can lead to an accumulation of pharyngeal residue and subsequent aspiration which in turn can lead to adverse outcomes such as pneumonia. VF is usually used for the temporal and spatial evaluation of the UES opening; however, VF is not available in all clinical settings and may be inappropriate or undesirable for some patients. High resolution cervical auscultation (HRCA) is a noninvasive technology that uses neck-attached sensors and machine learning to characterize swallowing physiology by analyzing the swallow-induced vibrations/sounds in the anterior neck region. We investigated the ability of HRCA to noninvasively estimate the maximal distension of anterior-posterior (A-P) UES opening as accurately as the measurements performed by human judges from VF images., Methods and Procedures: Trained judges performed the kinematic measurement of UES opening duration and A-P UES opening maximal distension on 434 swallows collected from 133 patients. We used a hybrid convolutional recurrent neural network supported by attention mechanisms which takes HRCA raw signals as input and estimates the value of the A-P UES opening maximal distension as output., Results: The proposed network estimated the A-P UES opening maximal distension with an absolute percentage error of 30% or less for more than 64.14% of the swallows in the dataset., Conclusion: This study provides substantial evidence for the feasibility of using HRCA to estimate one of the key spatial kinematic measurements used for dysphagia characterization and management. Clinical and Translational Impact Statement: The findings in this study have a direct impact on dysphagia diagnosis and management through providing a non-invasive and cheap way to estimate one of the most important swallowing kinematics, the UES opening distension, that contributes to safe swallowing. This study, along with other studies that utilize HRCA for swallowing kinematic analysis, paves the way for developing a widely available and easy-to-use tool for dysphagia diagnosis and management.

Disparities in premature cardiovascular mortality (PCVM) have been associated with socioeconomic, behavioral, and environmental risk factors. Understanding the "phenotypes", or combinations of characteristics associated with the highest risk of PCVM, and the geographic distributions of these phenotypes is critical to targeting PCVM interventions. This study applied the classification and regression tree (CART) to identify county phenotypes of PCVM and geographic information systems to examine the distributions of identified phenotypes. Random forest analysis was applied to evaluate the relative importance of risk factors associated with PCVM. The CART analysis identified seven county phenotypes of PCVM, where high-risk phenotypes were characterized by having greater percentages of people with lower income, higher physical inactivity, and higher food insecurity. These high-risk phenotypes were mostly concentrated in the Black Belt of the American South and the Appalachian region. The random forest analysis identified additional important risk factors associated with PCVM, including broadband access, smoking, receipt of Supplemental Nutrition Assistance Program benefits, and educational attainment. Our study demonstrates the use of machine learning approaches in characterizing community-level phenotypes of PCVM. Interventions to reduce PCVM should be tailored according to these phenotypes in corresponding geographic areas., (© 2023. The Author(s).)

Dysphagia occurs secondary to a variety of underlying etiologies and can contribute to increased risk of adverse events such as aspiration pneumonia and premature mortality. Dysphagia is primarily diagnosed and characterized by instrumental swallowing exams such as videofluoroscopic swallowing studies. videofluoroscopic swallowing studies involve the inspection of a series of radiographic images for signs of swallowing dysfunction. Though effective, videofluoroscopic swallowing studies are only available in certain clinical settings and are not always desirable or feasible for certain patients. Because of the limitations of current instrumental swallow exams, research studies have explored the use of acceleration signals collected from neck sensors and demonstrated their potential in providing comparable radiation-free diagnostic value as videofluoroscopic swallowing studies. In this study, we used a hybrid deep convolutional recurrent neural network that can perform multi-level feature extraction (localized and across time) to annotate swallow segments automatically via multi-channel swallowing acceleration signals. In total, we used signals and videofluoroscopic swallowing study images of 3144 swallows from 248 patients with suspected dysphagia. Compared to other deep network variants, our network was superior at detecting swallow segments with an average area under the receiver operating characteristic curve value of 0.82 (95% confidence interval: 0.807-0.841), and was in agreement with up to 90% of the gold standard-labeled segments.

Background: Phelan-McDermid syndrome (PHMDS) is a rare genetic disorder mostly caused by haploinsufficincy of SHANK3 gene, and characterized by neonatal hypotonia, developmental delay, minor dysmorphic features, seizures and behavior problems. Literature of this syndrome is scanty and confusing, and represents a challenge for pediatricians, in terms of finding the correct diagnoses., Case Presentation: In a postnatal case with hypotonia and dysmorphic features a de novo ring chromosome r(22) leading to in parallel microdeletion and micro duplication in 22q13 was diagnosed by banding cytogenetics, and further characterized in detail by molecular cytogenetic and chromosomal microarray., Conclusion: Here a rare PHMDS case caused by a r(22) is presented. Less than 10 comparable cases are reported in the literature. The present case highlights the importance of conducting genetic counseling and appropriate genetic tests for newborns with mild dysmorphic features., (© 2022. The Author(s).)

Clinicians and researchers commonly judge the completeness of hyoid displacement from videofluoroscopic swallow study (VFSS) videos. Judgments made during the clinical exam are often subjective, and post-examination analysis reduces the measure's immediate value. This study aimed to determine the validity and feasibility of a visual, anatomically scaled benchmark for judging complete hyoid displacement during a VFSS. The third and fourth cervical vertebral bodies (C3 and C4) lie at roughly the same vertical position as the hyoid body and are strongly correlated with patient height. We hypothesized that anterior and superior displacement of the hyoid bone would approximate the height of one C3 or C4 body during safe swallows. Trained raters marked points of interest on C3, C4, and the hyoid body on 1414 swallows of adult patients with suspected dysphagia (n = 195) and 50 swallows of age-matched healthy participants (n = 17), and rated Penetration Aspiration Scale scores. Results indicated that the mean displacements of the hyoid bone were greater than one C3 unit in the superior direction for all swallows from patient and healthy participants, though significantly and clinically greater in healthy participant swallows (p < .001, d > .8). The mean anterior and superior displacements from patient and healthy participant swallows were greater than one C4 unit. Results show preliminary evidence that use of the C3 and/or C4 anatomic scalars can add interpretive value to the immediate judgment of hyoid displacement during the conduct of a clinical VFSS examination., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

There is growing enthusiasm to develop inexpensive, non-invasive, and portable methods that accurately assess swallowing and provide biofeedback during dysphagia treatment. High-resolution cervical auscultation (HRCA), which uses acoustic and vibratory signals from non-invasive sensors attached to the anterior laryngeal framework during swallowing, is a novel method for quantifying swallowing physiology via advanced signal processing and machine learning techniques. HRCA has demonstrated potential as a dysphagia screening method and diagnostic adjunct to VFSSs by determining swallowing safety, annotating swallow kinematic events, and classifying swallows between healthy participants and patients with a high degree of accuracy. However, its feasibility as a non-invasive biofeedback system has not been explored. This study investigated 1. Whether HRCA can accurately differentiate between non-effortful and effortful swallows; 2. Whether differences exist in Modified Barium Swallow Impairment Profile (MBSImP) scores (#9, #11, #14) between non-effortful and effortful swallows. We hypothesized that HRCA would accurately classify non-effortful and effortful swallows and that differences in MBSImP scores would exist between the types of swallows. We analyzed 247 thin liquid 3 mL command swallows (71 effortful) to minimize variation from 36 healthy adults who underwent standardized VFSSs with concurrent HRCA. Results revealed differences (p < 0.05) in 9 HRCA signal features between non-effortful and effortful swallows. Using HRCA signal features as input, decision trees classified swallows with 76% accuracy, 76% sensitivity, and 77% specificity. There were no differences in MBSImP component scores between non-effortful and effortful swallows. While preliminary in nature, this study demonstrates the feasibility/promise of HRCA as a biofeedback method for dysphagia treatment., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Introduction: Teleophthalmology has emerged as a convenient and cost-effective intervention to increase access to screening for diabetic retinopathy (DR), a disease that disproportionately affects socially disadvantaged communities. However, a few studies have directly compared the detection of eye disease by teleophthalmology between socially and geographically diverse communities. This study compared the rates and severity of diabetic eye disease, as detected by teleophthalmology, between safety net and non-Safety Net Hospitals (non-SNHs). Methods: Retrospective chart review of patients screened for DR at county Safety Net Hospitals (SNHs) and non-SNHs in 150 cities and 30 states. The rates of DR, macular edema, suspected cataract, suspected glaucoma, and suspected age-related macular degeneration were compared. Relative risk and severity of disease in the county SNH population were calculated. Images were graded by the same group of IRIS readers, who used at least one image per eye with a 45° field centered between the optic disc and the macula. Participants with ungradable screening images were excluded. Results: Ninety-four thousand three hundred twenty-nine participants were screened for eye disease from September 1, 2016 to August 31, 2017. Among the screened participants (54% female; mean [SD] age, 58.7 [12.9] years), overall disease detection was 31% in the county SNH population and 23.6% in the non-SNH population. Compared with the non-SNH population, the county SNH population was twice as likely to screen positive for three or more concurrent eye conditions (1.2% vs. 0.7%) and had increased prevalence of DR (20.2% vs. 16.2%), macular edema (4.9% vs. 3.4%), suspected glaucoma (9.1% vs. 4.3%), suspected cataract (9.6% vs. 4.8%), and proliferative DR (2.1% vs. 1.0%). Conclusions: Increased diabetic eye disease prevalence and severity among people seen at SNHs highlights the need for continued resources to screen, treat, and manage disease. Teleophthalmology continues to be an important tool in efforts to mitigate health inequities and address barriers faced by underserved communities.

Few research studies have investigated temporal kinematic swallow events in healthy adults to establish normative reference values. Determining cutoffs for normal and disordered swallowing is vital for differentially diagnosing presbyphagia, variants of normal swallowing, and dysphagia; and for ensuring that different swallowing research laboratories produce consistent results in common measurements from different samples within the same population. High-resolution cervical auscultation (HRCA), a sensor-based dysphagia screening method, has accurately annotated temporal kinematic swallow events in patients with dysphagia, but hasn't been used to annotate temporal kinematic swallow events in healthy adults to establish dysphagia screening cutoffs. This study aimed to determine: (1) Reference values for temporal kinematic swallow events, (2) Whether HRCA can annotate temporal kinematic swallow events in healthy adults. We hypothesized (1) Our reference values would align with a prior study; (2) HRCA would detect temporal kinematic swallow events as accurately as human judges. Trained judges completed temporal kinematic measurements on 659 swallows (N = 70 adults). Swallow reaction time and LVC duration weren't different (p > 0.05) from a previously published historical cohort (114 swallows, N = 38 adults), while other temporal kinematic measurements were different (p < 0.05), suggesting a need for further standardization to feasibly pool data analyses across laboratories. HRCA signal features were used as input to machine learning algorithms and annotated UES opening (69.96% accuracy), UES closure (64.52% accuracy), LVC (52.56% accuracy), and LV re-opening (69.97% accuracy); providing preliminary evidence that HRCA can noninvasively and accurately annotate temporal kinematic measurements in healthy adults to determine dysphagia screening cutoffs., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

We used the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework to evaluate a Stroke Prevention Team's readiness to prevent strokes in children with sickle cell anemia living in northern Nigeria. The NIH sponsored Stroke Prevention Trial in Nigeria included a goal of a sustainable stroke prevention program. The program's 1-year reach for transcranial Doppler screening was 14.7% (4710/32,000) of which 6.0% (281/4710) had abnormal velocities (≥200 cm/s). All participants with abnormal transcranial Doppler velocities were started on hydroxyurea (effectiveness). The leaders of all 5 hospitals agreed to adopt the program. After 1 year, program-implementation and maintenance rates were 100%, demonstrating the program's feasibility and short-term sustainability., Competing Interests: M.R.D.: had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. M.R.D.: designed the study. S.U.A. and M.A.T.: provided the TCD training. A.H.D., H.B.-M., H.I., A.T., A.A.S., B.W.J., S.G., Y.K., L.H., S.A., M.A.Z., and N.I.: administered the TCD screening. E.T. and L.J.: verified the neurological examinations. A.I.G., F.M.U., M.A.T., A.I.G., S.U.A., B.C.G., J.G., and D.L.G.: collected the data. D.L.G. and M.R.: performed the analyses. M.R.D., S.A., D.L.G., A.A.B., M.R.D.: interpreted the results. D.L.G., A.A.B., L.C.J., E.T., M.H.A., and M.R.D.: wrote the manuscript. Before submission, all authors reviewed the manuscript.The authors declare no conflict of interest., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)