Citations

Abstract Background Training with virtual reality (VR) simulation is increasingly used for health professions training to allow novices to practice in a learner-centered, risk-free environment. This review was performed to evaluate the effectiveness of VR simulation training in gastrointestinal endoscopy. Aims To determine whether virtual reality simulation training can supplement and/or replace early conventional endoscopy training in diagnostic endoscopy for health professions trainees with limited or no prior endoscopic experience. Methods We searched 17 databases from inception until October 18, 2023. We included randomised and quasi-randomised trials that compared VR simulation training with no training, conventional training, another form of simulation training, or an alternative method of VR training. We screened and abstracted data through Cochrane methodology. The primary outcome was composite score of competency. Secondary outcomes were procedure completion, procedure time, adverse events, patient discomfort, global rating of competency, and mucosal visualization. We calculated risk ratio for dichotomous outcomes and mean difference (MD) or standardised mean difference (SMD) for continuous outcomes with 95% CI. We used GRADE to assess the certainty of evidence. Results We included 20 trials (500 participants; 3975 endoscopic procedures). There was insufficient evidence to determine the effect of VR training on composite score of competency compared to no training or conventional training. VR training was advantageous over no training based for independent procedure completion (RR 1.62, 95% CI 1.15 to 2.26; moderate certainty evidence), overall rating of performance (mean difference [MD] 0.45, 95 %CI 0.15 - 0.75, very low certainty evidence), and mucosal visualization (MD 0.60, 95 %CI 0.20 - 1.00, very low certainty evidence). VR training resulted in fewer independent procedure completions compared to conventional training (RR = 0.45, 95 %CI 0.27 - 0.74, low certainty evidence). We found no differences between VR training and no training or conventional training for other outcomes. Based on qualitative analysis, we found no significant differences between VR training and other forms of simulation training. VR curricula based in educational theory provided benefit with respect to composite score of competency, compared with unstructured curricula. Based on qualitative analysis, we found no significant differences between VR training and other forms of simulation training. VR curricula based in educational theory provided benefit with respect to composite score of competency, compared with unstructured curricula. Conclusions VR simulation training in endoscopy can supplement conventional endoscopy training and provides benefit compared to no training. Risk of bias summary Funding Agencies None

Abstract Background Real-time endoscopic optical diagnosis involves detailed visualization of the digestive mucosal and microvascular pattern, allowing for prediction of histology. This is advantageous as it can guide endoscopic resection decisions, surgical referrals and provide cost savings to histopathology. There is, however, no standardized or widely implemented curriculum for endoscopists or trainees to learn optical diagnosis skills. Aims To evaluate the impact of a course for the optical diagnosis of esophageal squamous cell carcinoma (ESCC), early gastric cancer (EGC), Barrett’s esophagus (BE), and colorectal polyps Methods 19 gastroenterology trainees were invited to the 2-day course in Kingston, Canada. Number of procedures previously performed was recorded. The impact of the course on lesion diagnosis accuracy was evaluated using a pre-test, immediate post-test, and delayed post-test administered 6 weeks after the course. A repeated measures ANOVA was performed to compare the mean score (%) between the three tests. Results 13 trainees completed all the tests (Table 1). Mean score % values for the overall tests and test scores for esophageal, gastric, and colon lesions were summarized on Table 1. The repeated measures ANOVA determined that the mean score % had a statistically significant difference between the three tests (F (2,24)=5.63, P=0.01). Pairwise comparisons revealed that there was a statistically significant increase in mean scores between pre- and post-tests (13.86 (95% confidence interval (CI) of 4.36 to 23.33), p<0.01) with no significant difference between post and delayed post-tests (8.46 (95% CI of -0.12 to 17.04), p>0.05) (Figure 1). The mean correct % from the delayed post-test was higher than the pre-test but there was no statistical significance (5.39 (95% CI of -3.72 to 14.49), p>0.05). Conclusions This novel optical diagnosis course improved trainee accuracy in diagnosing ESCC, EGC, BE and colorectal polyps. Learning retention was evident, with no significant score difference between immediate and delayed post-tests. This study was limited by the low sample size and low number of test items. Further investigation is required to validate the course for international trainees and explore its clinical transferability. Demography and Mean Score % Figure 1. Mean score %. (*) denotes p<0.05. Funding Agencies None

AbstractBackgroundIn this pilot study, the diagnostic agreement for sleep biomarker‐based neurodegenerative disorder (NDD) risk probabilities was evaluated in patients with Alzheimer’s disease dementia (AD), Lewy body disease (LBD), mild cognitive impairment (MCI), and controls (CG) with a Mini‐Mental State Exam scores ≥28.MethodsSleep biomarkers recorded with the Sleep Profiler (Advanced Brain Monitoring, Inc.) were used as inputs to a 4‐class machine learning algorithm trained to assign NDD risk probabilities to AD=27, LBD=19, isolated REM sleep behavior disorder=15, and CG=58. Input variables included age, time‐REM, non‐REM hypertonia, autonomic‐activation index, spindle‐duration, atypical‐N3, time‐supine, sleep‐efficiency, relative‐theta, and theta/alpha. Distributions of NDD risk were then assigned to AD=37, LBD=19, MCI=71, and CG=106 records.Records with CG output probabilities ≥70% were labeled Probably‐Normal, and from 45‐70% Likely‐Normal with indications of a NDD added when the LBD, AD or prodromal synucleinopathy (pSYN) risks exceeded approximately 25%. Similar thresholds were used to assign Likely‐ or Probably‐ LBD, AD, pSYN, or Mixed based on the group probabilities. Likely‐Normal with indications consistent with the diagnosis trended toward agreement.ResultsThe proportions of each group with classifications consistent with the diagnosis or trended toward agreement were CG=86%, MCI=75%, LBD=79%, and AD=87%.In the CG group, NDD risk assignments were Probably‐Normal=68% and Likely‐Normal with NDD indications=18%, Likely‐NDD=10%, and Probable‐NDD=4%.The MCI group was assigned Probable‐NDD=38% (AD=26%, Mixed=10%, LBD/pSYN=2%), Likely‐NDD=17% (AD=7%, Mixed=6%, LBD=3%, pSYN=1%), Likely‐Normal with indications=20% (AD=9%, pSYN=7%, Mixed=4%) and Probably‐Normal=25%The LBD group’s assigned risks consistent with the diagnoses included Probable‐NDD=74% and Likely‐Normal with pSYN indications=6% but included inconsistencies from Probable‐AD=5%, Likely‐Normal with AD indications=5%, and Probably‐Normal=11%.AD group’s consistent risk classifications included Probable‐NDD=60%, Likely‐NDD=11%, Likely‐Normal with AD/Mixed indications=13% and countered by inconsistencies from Probable‐LBD/pSYN=8%, Likely‐Normal with pSYN indications=3%, and Probably‐Normal=5%.ConclusionsConsistencies between the diagnoses and assigned NDD risks agreements exceeded 75% in all four groups. Studies are underway to confirm the test‐retest reliability and/or evaluate NDD risk probabilities in other patient populations.Support: NIH grants R44AG050326, R44AG054256, RO1AG060477, R33AG064271, ULRR024150, R34AG056639.

Energy drinks are a commonly consumed beverage, and studies suggest a possible performance-enhancing effect. A Google Scholar search using the keywords “energy drinks” and “exercise” yields numerous results, underscoring the voluminous research on this topic. However, there are questions regarding the effectiveness and safety of energy drinks. These questions include, but are not limited to: (1) What are the main active ingredients in energy drinks? (2) Do energy drinks assist in weight management? (3) Do energy drinks enhance aerobic performance? (4) Do energy drinks enhance athletic speed? (5) Do energy drinks improve reaction time? (6) Do energy drinks enhance lean tissue mass? (7) Can energy drinks improve cognitive performance? (8) Does the acute consumption of energy drinks elevate resting energy expenditure? (9) Is there any evidence to suggest that energy drinks are more effective than an identical serving of caffeine alone? (10) Are there sex differences in the response to energy drink consumption? (11) Do energy drinks affect sleep or sleepiness? (12) Should pregnant women avoid energy drinks? (13) Do energy drinks adversely affect cardiovascular function? (14) Does consuming energy drinks cause brain damage? (15) What are other safety considerations regarding energy drinks? (16) Is there any evidence to suggest that energy drinks are more effective than an identical serving of caffeine alone? (17) If caffeine is the main active ingredient in energy drinks and coffee, why is there a discrepancy in the adverse events reported for each? To address these questions, we performed an evidence-based scientific evaluation of the literature on energy drink supplementation.

Abstract Biomarkers that aid in early detection of neurodegeneration are needed to enable early symptomatic treatment and enable identification of people who may benefit from neuroprotective interventions. Increasing evidence suggests that sleep biomarkers may be useful, given the bi-directional relationship between sleep and neurodegeneration and the prominence of sleep disturbances and altered sleep architectural characteristics in several neurodegenerative disorders. This study aimed to demonstrate that sleep can accurately characterize specific neurodegenerative disorders (NDD). A four-class machine-learning algorithm was trained using age and nine sleep biomarkers from patients with clinically-diagnosed manifest and prodromal NDDs, including Alzheimer’s disease dementia (AD = 27), Lewy body dementia (LBD = 18), and isolated REM sleep behavior disorder (iRBD = 15), as well as a control group (CG = 58). The algorithm was validated in a total of 381 recordings, which included the training data set plus an additional AD = 10, iRBD = 18, Parkinson disease without dementia (PD = 29), mild cognitive impairment (MCI = 78) and CG = 128. Test-retest consistency was then assessed in LBD = 10, AD = 9, and CG = 46. The agreement between the NDD profiles and their respective clinical diagnoses exceeded 75% for the AD, LBD, and CG, and improved when NDD participants classified Likely Normal with NDD indications consistent with their clinical diagnosis were considered. Profiles for iRBD, PD and MCI participants were consistent with the heterogeneity of disease severities, with the majority of overt disagreements explained by normal sleep characterization in 27% of iRBD, 21% of PD, and 26% of MCI participants. For test-retest assignments, the same or similar NDD profiles were obtained for 88% of LBD, 86% in AD, and 98% of CG participants. The potential utility for NDD subtyping based on sleep biomarkers demonstrates promise and requires further prospective development and validation in larger NDD cohorts.

Introduction Multiple Myeloma (MM) precursors Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM) have variable risk of progression to active MM, and identifying which patients may progress remains a clinical challenge. Deep proteome profiling of peripheral blood (PB) plasma may advance non-invasive precursor disease staging, monitoring and characterization. Here, we performed plasma proteomic profiling on patients across the MM disease continuum as well as progressive and stable disease to provide insights into MM disease biology and identify protein-based high-risk disease features that may improve prognostication. Methods We performed high-throughput profiling of >5400 proteins using the Olink® Explore HT library and Proximity Extension Assay (PEA) technology. We profiled 529 PB plasma samples from 485 individuals, including MGUS (n=100), SMM (n=203), MM (n=100), and healthy donors (HDs) (n=82). Sequential samples from SMM-MM progressors (n=32) and stable SMM-SMM non-progressors (n=32) were also profiled, where precursor samples ranged 1.04-6.91 years (median 2.33 years) prior to MM progression. T-tests, ANOVAs, and linear mixed effect (LME) models were used to identify significant proteins across disease stages, progression status, and time. Results were adjusted for multiple testing using Benjamini-Hochberg procedure. A subset of 86 individuals (13 HDs, 14 MGUS, 41 SMM, 18 MM) also had single-cell RNA sequencing (scRNA-seq) performed on tumor and immune cells from paired PB/BM collected at the same timepoint to enable cellular mapping of signals detected in plasma. Results We successfully captured significantly dysregulated proteins including proteins highly expressed on the surface of plasma cells, such as BCMA, SLAMF7 and CD38, highlighting the utility of PEA technology to monitor soluble levels of clinically relevant targets. Given SMM patients are clinically heterogeneous and can resemble MGUS or MM, we aimed to improve discrimination of disease states using biological features obtained from the plasma proteome. We developed a classifier using an Elastic Net model for each stage, where plasma proteins such as SLAMF7, BCMA, TACI, FCRL5 and others had the highest importance scores for the model. We demonstrated 97% SMM/MM samples could be identified from healthy samples (AUC=0.82), indicating our classifier could reliably screen disease-related cases. Moreover, 84% of SMM cases were correctly classified as clinically defined SMM, while misclassified samples were labelled as MM cases. Interestingly, the misclassified patients had consistently rising M-spike levels during clinical follow-up but without a shift in clinical stage or risk status, suggesting the plasma proteome may provide earlier indication of evolving disease. Since proteomic information may also hold value for improved risk stratification, we evaluated protein levels in precursor stage timepoint samples from SMM-MM progressors and stable SMM non-progressors. We identified a prognostic five-protein signature that was significantly elevated in SMM-MM progressors, of which BCMA and TACI were top proteins, as well as proteins vital for calcium homeostasis and integrin-mediated cell adhesion. BCMA and TACI levels also had a strong positive correlation with BM plasma cell infiltration, which suggests these proteins may be useful surrogates of BM tumor burden during routine blood-based assessment of precursor patients. Lastly, integrative analysis of scRNA-seq of tumor and immune cells was used to elucidate cell origin level information of the prognostic signature proteins. Four proteins were highly expressed in malignant versus non-malignant plasma cells, suggesting our prognostic signature partially provides a readout of plasma cell biology. Meanwhile, one protein was constitutively expressed on most leukocytes, suggesting that the interplay of other cell types in the immune microenvironment involved in progression may also be reflected in the plasma. Conclusion We performed the first comprehensive analysis of the plasma proteome of MM and its precursor conditions. Overall, we developed a classifier that utilizes plasma proteins alone to accurately classify disease stages and identified a prognostic protein signature associated with progressive disease.

Background: Individual symptoms are predictors of mortality in adults with heart failure (HF). However, symptom clusters may be more predictive of future risks than isolated symptoms, yet research on these symptom clusters in older adults with HF is limited. Aims: To explore the extent to which symptom cluster profiles predict all-cause mortality among community-dwelling older adults with HF, while adjusting for known risk factors. Methods: We conducted a secondary analysis of data of the 2008 to 2016 surveys of the U.S. Health and Retirement Study using latent class analysis to identify baseline cluster profiles, and survival analysis for time to death with Cox proportional hazard (Cox PH) models, and Kaplan Meier survival analyses. Results: We included 684 participants [mean age=74.9, (SD=10) years, 56.6% female, 16.2% Black/African American]. Three baseline symptom cluster profiles were identified: high-burden (pain, shortness of breath, fatigue, swelling, depressive symptoms, dizziness), low-burden, and cardiopulmonary-depressive (shortness of breath, pain, and dizziness). The estimated median time-to-death was 71 (95% CI=64, 79) months. Of the 364 participants in the cardiopulmonary-depressive profile, 240 (65.9%) died (median time= 65 months, 95% CI= 55, 73) compared to those in the low-burden profile, of which 49% died. Approximately 61% in the high burden profile died (median time=67 months, 95% CI= 51, 90). A significant difference in survival times between the 3 cluster profiles was found (Log-rank= 9.13, p = 0.01). In the adjusted Cox PH model, participants in the high symptom burden and respiratory-depressive distress profiles had adjusted HR of 1.48 (95% CI=1.15, 1.94) and 1.44 (95% CI=1.14, 1.80) compared to those in the low burden symptom cluster profile, after controlling for age, gender, smoking, and comorbidities. Conclusions: The identified symptom cluster profiles predict 8-year all-cause mortality in older adults with HF, after controlling for known risk factors. An evaluation of symptom clusters, rather than individual symptoms, may provide additive prognostic information.

ABSTRACT Medically important pathogenic fungi invade vertebrate tissue and are considered primary when part of their nature life cycle is associated with an animal host and are usually able to infect immunocompetent hosts. Opportunistic fungal pathogens complete their life cycle in environmental habitats or occur as commensals within or on the vertebrate body, but under certain conditions can thrive upon infecting humans. The extent of host damage in opportunistic infections largely depends on the portal and modality of entry as well as on the host’s immune and metabolic status. Diseases caused by primary pathogens and common opportunists, causing the top approximately 80% of fungal diseases [D. W. Denning, Lancet Infect Dis, 24:e428–e438, 2024, https://doi.org/10.1016/S1473-3099(23)00692-8 ], tend to follow a predictive pattern, while those by occasional opportunists are more variable. For this reason, it is recommended that diseases caused by primary pathogens and the common opportunists are named after the etiologic agent, for example, histoplasmosis and aspergillosis, while this should not be done for occasional opportunists that should be named as [causative fungus] [clinical syndrome], for example, Alternaria alternata cutaneous infection. The addition of a descriptor that identifies the location or clinical type of infection is required, as the general name alone may cover widely different clinical syndromes, for example, “rhinocerebral mucormycosis.” A list of major recommended human and animal disease entities (nomenclature) is provided in alignment with their causative agents. Fungal disease names may encompass several genera of etiologic agents, consequently being less susceptible to taxonomic changes of the causative species, for example, mucormycosis covers numerous mucormycetous molds.

Abstract Aims Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on electrocardiogram (ECG) remain unknown. The objective is to assess the effects of GAHT on ECG changes in TG. Methods and results Twelve-lead ECGs of TG on GAHT at the Mayo Clinic were inspected using a validated artificial intelligence (AI) algorithm. The algorithm assigns a patient’s ECG male pattern probability on a scale of 0 (female) to 1 (male). In the primary analysis, done separately for transgender women (TGW) and transgender men (TGM), 12-lead ECGs were used to estimate the male pattern probability before and after GAHT. In a subanalysis, only patients with both pre- and post-GAHT EGCs were included. Further, the autopopulated PR, QRS, and QTc intervals were compared before and after GAHT. Among TGW (n = 86), the probability (mean ± SD) of an ECG male pattern was 0.84 ± 0.25 in the pre-GAHT group, and it was lowered to 0.59 ± 0.36 in the post-GAHT group (n = 173, P < 7.8 × 10−10). Conversely, among TGM, male pattern probability was 0.16 ± 0.28 (n = 47) in the pre-GAHT group, and it was higher at 0.41 ± 0.38 in the post-GAHT group (n = 53, P < 2.4×10−4). The trend persisted in the subanalysis. Furthermore, both the PR (P = 5.68 × 10−4) and QTc intervals (P = 6.65×10−6) prolonged among TGW. Among TGM, the QTc interval shortened (P = 4.8 × 10−2). Conclusion Among TG, GAHT is associated with ECG changes trending towards gender congruence, as determined by the AI algorithm and ECG intervals. Prospective studies are warranted to understand GAHT effects on cardiac structure and function.