Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data.

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic 'novel' lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.

Effect of COVID-19 Curriculum Changes on Medical Student Exam Performance: A Case Series.

BACKGROUND: The COVID-19 pandemic caused medical schools to convert to an online format, necessitating a swift change in medical education delivery. New teaching methods were adapted, with some schools having greater success than others. Kirk Kerkorian School of Medicine (KSOM) employed a small-group interactive learning style that consists of eight or fewer medical students and one faculty mentor engaging in group problem-based learning (PBL) twice weekly. This style had clear signs of struggle with a significant decrease in exam performance. Rocky Vista University College of Osteopathic Medicine (RVUCOM) employed a large-group didactic lecture style that consisted of one faculty mentor lecturing hundreds of medical students in a pre-recorded setting five times weekly. This style had greater success with its curriculum adaptation leading to minimal effect on their exam performance. This study aims to investigate whether the type of medical school curriculum (small-group interactive vs. large-group didactic) impacts student exam performance during online learning transitions forced by the COVID-19 pandemic. METHODOLOGY: KSOM and RVUCOM students were grouped into above-expectations and below-expectations categories based on each institution's standardized exam performance metrics. Independently sampled t-tests were performed to compare groups. KSOM was classified as a small-group interactive curriculum through its heavy reliance on student-led PBL, whereas RVUCOM was classified as a large-group didactic curriculum through its extensive proctor-led slideshow lectures. RESULTS: KSOM's transition to online PBL resulted in fewer students scoring above the national average on the National Board of Medical Examiners (NBME) exams compared to previous cohorts (55% vs. 77%, respectively; N = 47 and 78; P < 0.01). RVUCOM's transition to online large-group lectures yielded no significant differences between students who performed above expectations and students who performed below expectations between their cohorts (63% vs. 65%, respectively; N = 305 and 300; P > 0.05). CONCLUSIONS: KSOM's COVID-19 cohort performed significantly worse than RVUCOM's COVID-19 cohort during their medical school organ-system exams. We believe that the small-group learning at KSOM is less resilient for online curricula compared to the large-group didactics seen at RVUCOM. Understanding which didactic methods can transition to online learning more effectively than others is vital in guiding effective curriculum adjustments as online delivery becomes more prominent.

Machine learning for predicting cognitive deficits using auditory and demographic factors.

IMPORTANCE: Predicting neurocognitive deficits using complex auditory assessments could change how cognitive dysfunction is identified, and monitored over time. Detecting cognitive impairment in people living with HIV (PLWH) is important for early intervention, especially in low- to middle-income countries where most cases exist. Auditory tests relate to neurocognitive test results, but the incremental predictive capability beyond demographic factors is unknown. OBJECTIVE: Use machine learning to predict neurocognitive deficits, using auditory tests and demographic factors. SETTING: The Infectious Disease Center in Dar es Salaam, Tanzania. PARTICIPANTS: Participants were 939 Tanzanian individuals from Dar es Salaam living with and without HIV who were part of a longitudinal study. Patients who had only one visit, a positive history of ear drainage, concussion, significant noise or chemical exposure, neurological disease, mental illness, or exposure to ototoxic antibiotics (e.g., gentamycin), or chemotherapy were excluded. This provided 478 participants (349 PLWH, 129 HIV-negative). Participant data were randomized to training and test sets for machine learning. MAIN OUTCOME(S) AND MEASURE(S): The main outcome was whether auditory variables combined with relevant demographic variables could predict neurocognitive dysfunction (defined as a score of <26 on the Kiswahili Montreal Cognitive Assessment) better than demographic factors alone. The performance of predictive machine learning algorithms was primarily evaluated using the area under the receiver operational characteristic curve. Secondary metrics for evaluation included F1 scores, accuracies, and the Youden's indices for the algorithms. RESULTS: The percentage of individuals with cognitive deficits was 36.2% (139 PLWH and 34 HIV-negative). The Gaussian and kernel naïve Bayes classifiers were the most predictive algorithms for neurocognitive impairment. Algorithms trained with auditory variables had average area under the curve values of 0.91 and 0.87, F1 scores (metric for precision and recall) of 0.81 and 0.76, and average accuracies of 86.3% and 81.9% respectively. Algorithms trained without auditory variables as features were statistically worse (p < .001) in both the primary measure of area under the curve (0.82/0.78) and the secondary measure of accuracy (72.3%/74.5%) for the Gaussian and kernel algorithms respectively. CONCLUSIONS AND RELEVANCE: Auditory variables improved the prediction of cognitive function. Since auditory tests are easy-to-administer and often naturalistic tasks, they may offer objective measures or predictors of neurocognitive performance suitable for many global settings. Further research and development into using machine learning algorithms for predicting cognitive outcomes should be pursued.

Does ROSE matter? Evaluation of final diagnostic, PD-L1 immunohistochemistry, and molecular testing yields of CT-guided lung biopsies performed before and after the onset of the COVID-19 pandemic.

INTRODUCTION: The onset of the COVID-19 pandemic, with urgent implementation of safety protocols limiting the number of on-site personnel, essentially terminated the use of rapid on-site evaluation (ROSE) for computed tomography (CT)--guided lung biopsies at our institution. The diminished use of ROSE during the pandemic prompted us to reevaluate the potential value of ROSE for CT-guided lung biopsies. MATERIALS AND METHODS: We retrospectively identified all CT-guided lung biopsies from 2017 to 2022. Associations between the use of ROSE, adequate diagnostic and ancillary testing (programmed death-ligand 1 immunohistochemistry and next-generation sequencing) outcomes, and other factors such as the number of passes performed and lesion size, were evaluated. RESULTS: Nine hundred twelve CT-guided lung biopsies were performed from 2017 to 2022; 171 (19%) utilized ROSE. The use of ROSE had been steadily decreasing prior to the pandemic but was essentially eliminated with the onset of the pandemic. By univariable analysis, the employment of ROSE was more likely to be associated with an adequate final diagnosis (odds ratio = 2.14, 95% confidence interval: [1.24-3.70], P = 0.006) and successful molecular testing (odds ratio = 2.16, 95% confidence interval: [1.11-4.21], P = 0.024). However, those associations were not present in multivariable analyses that incorporated the number of passes performed or lesion size. There were no differences in diagnostic adequacy or ancillary testing yields when comparing the periods 2017-2019 and 2020-2022, despite declining use of ROSE. CONCLUSIONS: If ROSE is not requested for CT-guided lung biopsies, proceduralists should err on the side of performing more, rather than fewer, passes, particularly for smaller lesions.

Integration of a deep learning basal cell carcinoma detection and tumor mapping algorithm into the Mohs micrographic surgery workflow and effects on clinical staffing: A simulated, retrospective study.

Background: Artificial intelligence (AI) enabled tools have been proposed as 1 solution to improve health care delivery. However, research on downstream effects of AI integration into the clinical workflow is lacking. Objective: We aim to analyze how integration of an automated basal cell carcinoma detection and tumor mapping algorithm in a Mohs micrographic surgery unit impacts the work efficiency of clinical and laboratory staff. Methods: Slide, staff, and histotechnician waiting times were analyzed over a 20-day period in a Mohs micrographic surgery unit. A simulated AI workflow was created and the time differences between the real and simulated workflows were compared. Results: Simulated nonautonomous algorithm integration led to savings of 35.6% of slide waiting time, 18.4% of staff waiting time, and 18.6% of histotechnician waiting time per day. Algorithm integration on days with increased reconstruction complexity resulted in the greatest time savings. Limitations: One Mohs micrographic surgery unit was analyzed and simulated AI integration was performed retrospectively. Conclusions: AI integration results in reduced staff waiting times, enabling increased productivity and a streamlined clinical workflow. Schedules containing surgical cases with either increased repair complexity or numerous tumor removal stages stand to benefit most. However, significant logistical challenges must be addressed before broad adoption into clinical practice is realistic.

Integrative Co-Registration of Elemental Imaging and Histopathology for Enhanced Spatial Multimodal Analysis of Tissue Sections through TRACE.

Summary: Elemental imaging provides detailed profiling of metal bioaccumulation, offering more precision than bulk analysis by targeting specific tissue areas. However, accurately identifying comparable tissue regions from elemental maps is challenging, requiring the integration of hematoxylin and eosin (H&E) slides for effective comparison. Facilitating the streamlined co-registration of Whole Slide Images (WSI) and elemental maps, TRACE enhances the analysis of tissue regions and elemental abundance in various pathological conditions. Through an interactive containerized web application, TRACE features real-time annotation editing, advanced statistical tools, and data export, supporting comprehensive spatial analysis. Notably, it allows for comparison of elemental abundances across annotated tissue structures and enables integration with other spatial data types through WSI co-registration. Availability and Implementation: Available on the following platforms- GitHub: jlevy44/trace_app , PyPI: trace_app , Docker: joshualevy44/trace_app , Singularity: joshualevy44/trace_app . Contact: joshua.levy@cshs.org. Supplementary information: Supplementary data are available.

Delivering Therapy to the Olfactory Cleft: A Comparison of the Various Methods of Administering Topical Nasal Medications.

Background and objective Chronic rhinosinusitis (CRS) is an inflammatory condition affecting the nasal mucosa, and it causes olfactory dysfunction (OD) in up to 78.2% of patients. Corticosteroids are the mainstay of treatment to shrink nasal polyposis, reduce inflammation, and improve olfactory function. While many delivery methods for topical nasal corticosteroids exist, there is scarce data on the efficacy of the various medication delivery methods to the olfactory cleft (OC). In light of this, this study aimed to compare the following delivery methods to the OC: conventional nasal spray (NS), nasal drops in the Kaiteki position (KP), and exhalation delivery system (EDS). Methods We evaluated 16 sinonasal cavities from eight cadaver specimens in this study. Each sinonasal cavity was administered fluorescein dye solution via NS, KP, and EDS. Following administration, nasal endoscopy was employed to capture staining patterns in the OC. OC staining was rated with scores ranging from 0 (no staining) to 3 (heavy staining) after each administration of dye solution. Mean OC staining ratings were calculated and compared using the Kruskal-Wallis rank sum test and the Wilcoxon signed-rank test. Results The mean OC staining score for the different delivery methods was as follows - NS: 1.095 ± 1.008, EDS: 0.670 ± 0.674, and KP: 2.038 ± 1.097. Nasal drops in the KP had a significantly higher staining score compared to NS (p=0.041) and EDS (p=0.003). However, there was no significant difference in staining scores between NS and EDS. Conclusions Nasal drops in the KP are more effective at reaching the OC than NS or EDS and should be considered as a first-line modality for administering topical medications when treating OD.

Comparison of RT-PCR and antigen test sensitivity across nasopharyngeal, nares, and oropharyngeal swab, and saliva sample types during the SARS-CoV-2 omicron variant.

Limited data highlight the need to understand differences in SARS-CoV-2 omicron (B.1.1.529) variant viral load between the gold standard nasopharyngeal (NP) swab, mid-turbinate (MT)/anterior nasal swabs, oropharyngeal (OP) swabs, and saliva. MT, OP, and saliva samples from symptomatic individuals in Atlanta, GA, in January 2022 and longitudinal samples from a small familial cohort were tested by both RT-PCR and ultrasensitive antigen assays. Higher concentrations in the nares were observed in the familial cohort, but a dominant sample type was not found among 39 cases in the cross-sectional cohort. The composite of positive MT or OP assay for both RT-PCR and antigen assay trended toward higher diagnostic yield but did not achieve significant difference. Our data did not identify a singular preferred sample type for SARS-CoV-2 testing, but higher levels of saliva nucleocapsid, a trend toward higher yield of composite OP/MT result, and association of apparent MT or OP predominance with symptoms warrant further study.

Technical and clinical considerations of a physical liver phantom for CT radiomics analysis.

OBJECTIVE: This study identifies key characteristics to help build a physical liver computed tomography (CT) phantom for radiomics harmonization; particularly, the higher-order texture metrics. MATERIALS AND METHODS: CT scans of a radiomics phantom comprising of 18 novel 3D printed inserts with varying size, shape, and material combinations were acquired on a 64-slice CT scanner (Brilliance 64, Philips Healthcare). The images were acquired at 120 kV, 250 mAs, CTDIvol of 16.36 mGy, 2 mm slice thickness, and iterative noise-reduction reconstruction (iDose, Philips Healthcare, Andover, MA). Radiomics analysis was performed using the Cancer Imaging Phenomics Toolkit (CaPTk), following automated segmentation of 3D regions of interest (ROI) of the 18 inserts. The findings were compared to three additional ROI obtained of an anthropomorphic liver phantom, a patient liver CT scan, and a water phantom, at comparable imaging settings. Percentage difference in radiomic metrics values between phantom and tissue was used to assess the biological equivalency and <10% was used to claim equivalent. RESULTS: The HU for all 18 ROI from the phantom ranged from -30 to 120 which is within clinically observed HU range of the liver, showing that our phantom material (T3-6B) is representative of biological CT tissue densities (liver) with >50% radiomic features having <10% difference from liver tissue. Based on the assessment of the Neighborhood Gray Tone Difference Matrix (NGTDM) metrics it is evident that the water phantom ROI show extreme values compared to the ROIs from the phantom. This result may further reinforce the difference between a structureless quantity such as water HU values and tissue HU values found in liver. CONCLUSION: The 3-D printed patterns of the constructed radiomics phantom cover a wide span of liver tissue textures seen in CT images. Using our results, texture metrics can be selectively harmonized to establish clinically relevant and reliable radiomics panels.

Executive Summary of Clinical Practice Guideline on Immunotherapy for Inhalant Allergy.

OBJECTIVE: Allergen immunotherapy (AIT) is the therapeutic exposure to an allergen or allergens selected by clinical assessment and allergy testing to decrease allergic symptoms and induce immunologic tolerance. Inhalant AIT is administered to millions of patients for allergic rhinitis (AR) and allergic asthma (AA) and is most commonly delivered as subcutaneous immunotherapy (SCIT) or sublingual immunotherapy (SLIT). Despite its widespread use, there is variability in the initiation and delivery of safe and effective immunotherapy, and there are opportunities for evidence-based recommendations for improved patient care. PURPOSE: The purpose of this clinical practice guideline is to identify quality improvement opportunities and provide clinicians trustworthy, evidence-based recommendations regarding the management of inhaled allergies with immunotherapy. Specific goals of the guideline are to optimize patient care, promote safe and effective therapy, reduce unjustified variations in care, and reduce risk of harm. The target patients for the guideline are any individuals aged 5 years and older with AR, with or without AA, who are either candidates for immunotherapy or treated with immunotherapy for their inhalant allergies. The target audience is all clinicians involved in the administration of immunotherapy. This guideline is intended to focus on evidence-based quality improvement opportunities judged most important by the guideline development group. It is not intended to be a comprehensive, general guide regarding the management of inhaled allergies with immunotherapy. The statements in this guideline are not intended to limit or restrict care provided by clinicians based on their experience and assessment of individual patients. ACTION STATEMENTS: The guideline development group made a strong recommendation that (Key Action Statement [KAS] 10) the clinician performing allergy skin testing or administering AIT must be able to diagnose and manage anaphylaxis. The guideline development group made recommendations for the following KASs: (KAS 1) Clinicians should offer or refer to a clinician who can offer immunotherapy for patients with AR with or without AA if their patients' symptoms are inadequately controlled with medical therapy, allergen avoidance, or both, or have a preference for immunomodulation. (KAS 2A) Clinicians should not initiate AIT for patients who are pregnant, have uncontrolled asthma, or are unable to tolerate injectable epinephrine. (KAS 3) Clinicians should evaluate the patient or refer the patient to a clinician who can evaluate for signs and symptoms of asthma before initiating AIT and for signs and symptoms of uncontrolled asthma before administering subsequent AIT. (KAS 4) Clinicians should educate patients who are immunotherapy candidates regarding the differences between SCIT and SLIT (aqueous and tablet) including risks, benefits, convenience, and costs. (KAS 5) Clinicians should educate patients about the potential benefits of AIT in (1) preventing new allergen sensitization, (2) reducing the risk of developing AA, and (3) altering the natural history of the disease with continued benefit after discontinuation of therapy. (KAS 6) Clinicians who administer SLIT to patients with seasonal AR should offer pre- and co-seasonal immunotherapy. (KAS 7) Clinicians prescribing AIT should limit treatment to only those clinically relevant allergens that correlate with the patient's history and are confirmed by testing. (KAS 9) Clinicians administering AIT should continue escalation or maintenance dosing when patients have local reactions to AIT. (KAS 11) Clinicians should avoid repeat allergy testing as an assessment of the efficacy of ongoing AIT unless there is a change in environmental exposures or a loss of control of symptoms. (KAS 12) For patients who are experiencing symptomatic control from AIT, clinicians should treat for a minimum duration of 3 years, with ongoing treatment duration based on patient response to treatment. The guideline development group offered the following KASs as options: (KAS 2B) Clinicians may choose not to initiate AIT for patients who use concomitant beta-blockers, have a history of anaphylaxis, have systemic immunosuppression, or have eosinophilic esophagitis (SLIT only). (KAS 8) Clinicians may treat polysensitized patients with a limited number of allergens.

Upper Airway Epithelial Tissue Transcriptome Analysis Reveals Immune Signatures Associated with COVID-19 Severity in Ghanaians.

The immunological signatures driving the severity of coronavirus disease 19 (COVID-19) in Ghanaians remain poorly understood. We performed bulk transcriptome sequencing of nasopharyngeal samples from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-infected Ghanaians with mild and severe COVID-19, as well as healthy controls to characterize immune signatures at the primary SARS-CoV-2 infection site and identify drivers of disease severity. Generally, a heightened antiviral response was observed in SARS-CoV-2-infected Ghanaians compared with uninfected controls. COVID-19 severity was associated with immune suppression, overexpression of proinflammatory cytokines, including CRNN, IL1A, S100A7, and IL23A, and activation of pathways involved in keratinocyte proliferation. SAMD9L was among the differentially regulated interferon-stimulated genes in our mild and severe disease cohorts, suggesting that it may play a critical role in SARS-CoV-2 pathogenesis. By comparing our data with a publicly available dataset from a non-African (Indians) (GSE166530), an elevated expression of antiviral response-related genes was noted in COVID-19-infected Ghanaians. Overall, the study describes immune signatures driving COVID-19 severity in Ghanaians and identifies immune drivers that could serve as potential prognostic markers for future outbreaks or pandemics. It further provides important preliminary evidence suggesting differences in antiviral response at the upper respiratory interface in sub-Saharan Africans (Ghanaians) and non-Africans, which could be contributing to the differences in disease outcomes. Further studies using larger datasets from different populations will expand on these findings.

Clinical Practice Guideline: Immunotherapy for Inhalant Allergy.

OBJECTIVE: Allergen immunotherapy (AIT) is the therapeutic exposure to an allergen or allergens selected by clinical assessment and allergy testing to decrease allergic symptoms and induce immunologic tolerance. Inhalant AIT is administered to millions of patients for allergic rhinitis (AR) and allergic asthma (AA) and is most commonly delivered as subcutaneous immunotherapy (SCIT) or sublingual immunotherapy (SLIT). Despite its widespread use, there is variability in the initiation and delivery of safe and effective immunotherapy, and there are opportunities for evidence-based recommendations for improved patient care. PURPOSE: The purpose of this clinical practice guideline (CPG) is to identify quality improvement opportunities and provide clinicians trustworthy, evidence-based recommendations regarding the management of inhaled allergies with immunotherapy. Specific goals of the guideline are to optimize patient care, promote safe and effective therapy, reduce unjustified variations in care, and reduce the risk of harm. The target patients for the guideline are any individuals aged 5 years and older with AR, with or without AA, who are either candidates for immunotherapy or treated with immunotherapy for their inhalant allergies. The target audience is all clinicians involved in the administration of immunotherapy. This guideline is intended to focus on evidence-based quality improvement opportunities judged most important by the guideline development group (GDG). It is not intended to be a comprehensive, general guide regarding the management of inhaled allergies with immunotherapy. The statements in this guideline are not intended to limit or restrict care provided by clinicians based on their experience and assessment of individual patients. ACTION STATEMENTS: The GDG made a strong recommendation that (Key Action Statement [KAS] 10) the clinician performing allergy skin testing or administering AIT must be able to diagnose and manage anaphylaxis. The GDG made recommendations for the following KASs: (KAS 1) Clinicians should offer or refer to a clinician who can offer immunotherapy for patients with AR with or without AA if their patients' symptoms are inadequately controlled with medical therapy, allergen avoidance, or both, or have a preference for immunomodulation. (KAS 2A) Clinicians should not initiate AIT for patients who are pregnant, have uncontrolled asthma, or are unable to tolerate injectable epinephrine. (KAS 3) Clinicians should evaluate the patient or refer the patient to a clinician who can evaluate for signs and symptoms of asthma before initiating AIT and for signs and symptoms of uncontrolled asthma before administering subsequent AIT. (KAS 4) Clinicians should educate patients who are immunotherapy candidates regarding the differences between SCIT and SLIT (aqueous and tablet) including risks, benefits, convenience, and costs. (KAS 5) Clinicians should educate patients about the potential benefits of AIT in (1) preventing new allergen sensitizations, (2) reducing the risk of developing AA, and (3) altering the natural history of the disease with continued benefit after discontinuation of therapy. (KAS 6) Clinicians who administer SLIT to patients with seasonal AR should offer pre- and co-seasonal immunotherapy. (KAS 7) Clinicians prescribing AIT should limit treatment to only those clinically relevant allergens that correlate with the patient's history and are confirmed by testing. (KAS 9) Clinicians administering AIT should continue escalation or maintenance dosing when patients have local reactions (LRs) to AIT. (KAS 11) Clinicians should avoid repeat allergy testing as an assessment of the efficacy of ongoing AIT unless there is a change in environmental exposures or a loss of control of symptoms. (KAS 12) For patients who are experiencing symptomatic control from AIT, clinicians should treat for a minimum duration of 3 years, with ongoing treatment duration based on patient response to treatment. The GDG offered the following KASs as options: (KAS 2B) Clinicians may choose not to initiate AIT for patients who use concomitant beta-blockers, have a history of anaphylaxis, have systemic immunosuppression, or have eosinophilic esophagitis (SLIT only). (KAS 8) Clinicians may treat polysensitized patients with a limited number of allergens.

Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study.

Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine's first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students' confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution's preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students' confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.

Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site.

Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Intraoperative and postoperative radial sectioning, the most common form of margin assessment, can lead to incomplete excision and increase the risk of recurrence and repeat procedures. Mohs Micrographic Surgery is associated with complete removal of basal cell and squamous cell carcinoma through real-time margin assessment of 100% of the peripheral and deep margins. Real-time assessment in many tumor types is constrained by tissue size, complexity, and specimen processing / assessment time during general anesthesia. We developed an artificial intelligence platform to reduce the tissue preprocessing and histological assessment time through automated grossing recommendations, mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma as a model system, results demonstrate that this approach can address surgical laboratory efficiency bottlenecks for rapid and complete intraoperative margin assessment.

Accessing the Eustachian tube: Conventional nasal spray vs. exhalation delivery system and the impact of targeted endoscopic sinus surgery on topical distribution patterns.

BACKGROUND: Eustachian tube dysfunction (ETD) may occur distinct from, or in conjunction with, chronic rhinosinusitis (CRS+ETD). Intranasal corticosteroid sprays are often prescribed for ETD, although ET distribution may be limited. To date, no anatomic studies compare nasopharynx (NP) distribution between conventional nasal sprays (NS) and exhalation delivery systems (EDS) after surgery. This study utilizes a cadaver model to examine topical NP delivery using EDS vs. NS before and after targeted endoscopic sinus surgery (ESS). METHODS: Sixteen sinonasal cavities were administered fluorescein solution via NS and EDS before and after maxillary antrostomy and anterior ethmoidectomy, followed by nasal endoscopy of the NP and ET orifice. Seven blinded experts submitted staining ratings of endoscopy images on a 0- to 3-point scale, with ratings averaged for analysis. RESULTS: Interrater reliability was excellent (intraclass correlation, 0.956). EDS was associated with significantly greater NP staining vs. NS in a pooled cohort of nonsurgical and ESS specimens (1.19 ± 0.81 vs. 0.78 ± 1.06; p = 0.043). Using a logistic regression model, EDS significantly outperformed NS in nonsurgical (odds ratio [OR], 3.49; 95% confidence interval [CI], 1.21-10.09; p = 0.021) and post-ESS (OR, 9.00; 95% CI, 1.95-41.5; p = 0.005) specimens, with the greatest relative staining observed for EDS after targeted ESS (OR, 18.99; 95% CI, 3.44-104.85; p = 0.001). CONCLUSIONS: EDS is more effective than NS in topical delivery to the NP and ET orifices in cadavers. Targeted ESS may facilitate greater NP penetration by EDS compared with NS, with possible synergism after ESS for augmented delivery. These findings suggest a role for EDS delivery methods for ETD management and in CRS+ETD patients undergoing sinus surgery.

Investigating the role of imaging factors in the variability of CT-based texture analysis metrics.

OBJECTIVE: This study assesses the robustness of first-order radiomic texture features namely interquartile range (IQR), coefficient of variation (CV) and standard deviation (SD) derived from computed tomography (CT) images by varying dose, reconstruction algorithms and slice thickness using scans of a uniform water phantom, a commercial anthropomorphic liver phantom, and a human liver in-vivo. MATERIALS AND METHODS: Scans were acquired on a 16 cm detector GE Revolution Apex Edition CT scanner with variations across three different nominal slice thicknesses: 0.625, 1.25, and 2.5 mm, three different dose levels: CTDIvol of 13.86 mGy for the standard dose, 40% reduced dose and 60% reduced dose and two different reconstruction algorithms: a deep learning image reconstruction (DLIR-high) algorithm and a hybrid iterative reconstruction (IR) algorithm ASiR-V50% (AV50) were explored, varying one at a time. To assess the effect of non-linear modifications of images by AV50 and DLIR-high, images of the water phantom were also reconstructed using filtered back projection (FBP). Quantitative measures of IQR, CV and SD were extracted from twelve pre-selected, circular (1 cm diameter) regions of interest (ROIs) capturing different texture patterns across all scans. RESULTS: Across all scans, imaging, and reconstruction settings, CV, IQR and SD were observed to increase with reduction in dose and slice thickness. An exception to this observation was found when using FBP reconstruction. Lower values of CV, IQR and SD were observed in DLIR-high reconstructions compared to AV50 and FBP. The Poisson statistics were more stringently noted in FBP than DLIR-high and AV50, due to the non-linear nature of the latter two algorithms. CONCLUSION: Variation in image noise due to dose reduction algorithms, tube current, and slice thickness show a consistent trend across phantom and patient scans. Prospective evaluation across multiple centers, scanners and imaging protocols is needed for establishing quality assurance standards of radiomics.

A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: A retrospective assessment.

Intraoperative margin analysis is crucial for the successful removal of cutaneous squamous cell carcinomas (cSCC). Artificial intelligence technologies (AI) have previously demonstrated potential for facilitating rapid and complete tumour removal using intraoperative margin assessment for basal cell carcinoma. However, the varied morphologies of cSCC present challenges for AI margin assessment. The aim of this study was to develop and evaluate the accuracy of an AI algorithm for real-time histologic margin analysis of cSCC. To do this, a retrospective cohort study was conducted using frozen cSCC section slides. These slides were scanned and annotated, delineating benign tissue structures, inflammation and tumour to develop an AI algorithm for real-time margin analysis. A convolutional neural network workflow was used to extract histomorphological features predictive of cSCC. This algorithm demonstrated proof of concept for identifying cSCC with high accuracy, highlighting the potential for integration of AI into the surgical workflow. Incorporation of AI algorithms may improve efficiency and completeness of real-time margin assessment for cSCC removal, particularly in cases of moderately and poorly differentiated tumours/neoplasms. Further algorithmic improvement incorporating surrounding tissue context is necessary to remain sensitive to the unique epidermal landscape of well-differentiated tumours, and to map tumours to their original anatomical position/orientation.

Miniaturized Capsule System Toward Real-Time Electrochemical Detection of H2 S in the Gastrointestinal Tract.

Hydrogen sulfide (H2 S) is a gaseous inflammatory mediator and important signaling molecule for maintaining gastrointestinal (GI) homeostasis. Excess intraluminal H2 S in the GI tract has been implicated in inflammatory bowel disease and neurodegenerative disorders; however, the role of H2 S in disease pathogenesis and progression is unclear. Herein, an electrochemical gas-sensing ingestible capsule is developed to enable real-time, wireless amperometric measurement of H2 S in GI conditions. A gold (Au) three-electrode sensor is modified with a Nafion solid-polymer electrolyte (Nafion-Au) to enhance selectivity toward H2 S in humid environments. The Nafion-Au sensor-integrated capsule shows a linear current response in H2 S concentration ranging from 0.21 to 4.5 ppm (R2 = 0.954) with a normalized sensitivity of 12.4% ppm-1 when evaluated in a benchtop setting. The sensor proves highly selective toward H2 S in the presence of known interferent gases, such as hydrogen (H2 ), with a selectivity ratio of H2 S:H2 = 1340, as well as toward methane (CH4 ) and carbon dioxide (CO2 ). The packaged capsule demonstrates reliable wireless communication through abdominal tissue analogues, comparable to GI dielectric properties. Also, an assessment of sensor drift and threshold-based notification is investigated, showing potential for in vivo application. Thus, the developed H2 S capsule platform provides an analytical tool to uncover the complex biology-modulating effects of intraluminal H2 S.

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining.

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways, and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Challenges to current methods include limited focus on dermal elastosis variations and reliance on self-reported measures, which can introduce subjectivity and inconsistency. Spatial transcriptomics offers an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene in photoaging and preventing cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and interpatient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal cell and squamous cell keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.