Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic.

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Seafood Wholesale Market. Here, we analyze environmental qPCR and sequencing data collected in the Huanan market in early 2020. We demonstrate that market-linked severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genetic diversity is consistent with market emergence and find increased SARS-CoV-2 positivity near and within a wildlife stall. We identify wildlife DNA in all SARS-CoV-2-positive samples from this stall, including species such as civets, bamboo rats, and raccoon dogs, previously identified as possible intermediate hosts. We also detect animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them with those from farms and other markets. This analysis provides the genetic basis for a shortlist of potential intermediate hosts of SARS-CoV-2 to prioritize for serological and viral sampling.

Maternal glucose levels and late pregnancy circulating extracellular vesicle and particle miRNAs in the MADRES pregnancy cohort.

Maternal hyperglycemia during pregnancy adversely affects maternal and child outcomes. While mechanisms are not fully understood, maternal circulating miRNAs may play a role. We examined whether continuous glucose levels and hyperglycemia subtypes (gestational diabetes, type 2 diabetes, and glucose intolerance) were associated with circulating miRNAs during late pregnancy. Seven miRNAs (hsa-miR-107, hsa-let-7b-5p, hsa-miR-126-3p, hsa-miR-181a-5p, hsa-miR-374a-5p, hsa-miR-382-5p, and hsa-miR-337-5p) were associated (p < 0.05) with either hyperglycemia or continuous glucose levels prior to multiple testing correction. These miRNAs target genes involved in pathways relevant to maternal and child health, including insulin signaling, placental development, energy balance, and appetite regulation.

Characterizing Veteran suicide decedents that were not classified as high-suicide-risk.

BACKGROUND: Although the Department of Veterans Affairs (VA) has made important suicide prevention advances, efforts primarily target high-risk patients with documented suicide risk, such as suicidal ideation, prior suicide attempts, and recent psychiatric hospitalization. Approximately 90% of VA patients that go on to die by suicide do not meet these high-risk criteria and therefore do not receive targeted suicide prevention services. In this study, we used national VA data to focus on patients that were not classified as high-risk, but died by suicide. METHODS: Our sample included all VA patients who died by suicide in 2017 or 2018. We determined whether patients were classified as high-risk using the VA's machine learning risk prediction algorithm. After excluding these patients, we used principal component analysis to identify moderate-risk and low-risk patients and investigated demographics, service-usage, diagnoses, and social determinants of health differences across high-, moderate-, and low-risk subgroups. RESULTS: High-risk (n = 452) patients tended to be younger, White, unmarried, homeless, and have more mental health diagnoses compared to moderate- (n = 2149) and low-risk (n = 2209) patients. Moderate- and low-risk patients tended to be older, married, Black, and Native American or Pacific Islander, and have more physical health diagnoses compared to high-risk patients. Low-risk patients had more missing data than higher-risk patients. CONCLUSIONS: Study expands epidemiological understanding about non-high-risk suicide decedents, historically understudied and underserved populations. Findings raise concerns about reliance on machine learning risk prediction models that may be biased by relative underrepresentation of racial/ethnic minorities within health system.

Artificial Intelligence Applications in Cytopathology: Current State of the Art.

The practice of cytopathology has been significantly refined in recent years, largely through the creation of consensus rule sets for the diagnosis of particular specimens (Bethesda, Milan, Paris, and so forth). In general, these diagnostic systems have focused on reducing intraobserver variance, removing nebulous/redundant categories, reducing the use of "atypical" diagnoses, and promoting the use of quantitative scoring systems while providing a uniform language to communicate these results. Computational pathology is a natural offshoot of this process in that it promises 100% reproducible diagnoses rendered by quantitative processes that are free from many of the biases of human practitioners.

Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.

Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic Topic Modeling, a natural language processing method that evaluates topic changes over time, to analyze high-suicide risk Veterans Affairs patients' unstructured electronic health records. Our sample included all high-risk patients that died (cases) or did not (controls) by suicide in 2017 and 2018. Cases and controls shared the same risk, location, and treatment intervals and received nine months of mental health care during the year before the relevant end date. Each case was matched with five controls. We analyzed case records from diagnosis until death and control records from diagnosis until matched case's death date. Our final sample included 218 cases and 943 controls. We analyzed the corpus using a Python-based Dynamic Topic Modeling algorithm. We identified five distinct topics, "Medication," "Intervention," "Treatment Goals," "Suicide," and "Treatment Focus." We observed divergent change patterns over time, with pathology-focused care increasing for cases and supportive care increasing for controls. The case topics tended to fluctuate more than the control topics, suggesting the importance of monitoring lability. Our study provides a method for monitoring risk fluctuation and strengthens the groundwork for time-sensitive risk measurement.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.