Stroke-associated intergenic variants modulate a human FOXF2 transcriptional enhancer.

SNPs associated with human stroke risk have been identified in the intergenic region between Forkhead family transcription factors FOXF2 and FOXQ1, but we lack a mechanism for the association. FoxF2 is expressed in vascular mural pericytes and is important for maintaining pericyte number and stabilizing small vessels in zebrafish. The stroke-associated SNPs are located in a previously unknown transcriptional enhancer for FOXF2, functional in human cells and zebrafish. We identify critical enhancer regions for FOXF2 gene expression, including binding sites occupied by transcription factors ETS1, RBPJ, and CTCF. rs74564934, a stroke-associated SNP adjacent to the ETS1 binding site, decreases enhancer function, as does mutation of RPBJ sites. rs74564934 is significantly associated with the increased risk of any stroke, ischemic stroke, small vessel stroke, and elevated white matter hyperintensity burden in humans. Foxf2 has a conserved function cross-species and is expressed in vascular mural pericytes of the vessel wall. Thus, stroke-associated SNPs modulate enhancer activity and expression of a regulator of vascular stabilization, FOXF2, thereby modulating stroke risk.

Hox-driven conditional immortalization of myeloid and lymphoid progenitors: Uses, advantages, and future potential.

Those who study macrophage biology struggle with the decision whether to utilize primary macrophages derived directly from mice or opt for the convenience and genetic tractability of immortalized macrophage-like cell lines in in vitro studies. Particularly when it comes to studying phagocytosis and phagosomal maturation-a signature cellular process of the macrophage-many commonly used cell lines are not representative of what occurs in primary macrophages. A system developed by Mark Kamps' group, that utilizes conditionally constitutive activity of Hox transcription factors (Hoxb8 and Hoxa9) to immortalize differentiation-competent myeloid cell progenitors of mice, offers an alternative to the macrophage/macrophage-like dichotomy. In this resource, we will review the use of Hoxb8 and Hoxa9 as hematopoietic regulators to conditionally immortalize murine hematopoietic progenitor cells which retain their ability to differentiate into many functional immune cell types including macrophages, neutrophils, basophils, osteoclasts, eosinophils, dendritic cells, as well as limited potential for the generation of lymphocytes. We further demonstrate that the use of macrophages derived from Hoxb8/Hoxa9 immortalized progenitors and their similarities to bone marrow-derived macrophages. To supplement the existing data, mass spectrometry-based proteomics, flow cytometry, cytology, and in vitro phagosomal assays were conducted on macrophages derived from Hoxb8 immortalized progenitors and compared to bone marrow-derived macrophages and the macrophage-like cell line J774. We additionally propose the use of a standardized nomenclature to describe cells derived from the Hoxb8/Hoxa9 system in anticipation of their expanded use in the study of leukocyte cell biology.

Uncovering spatiotemporal dynamics of the corticothalamic network at ictal onset.

OBJECTIVE: Although the clinical efficacy of deep brain stimulation targeting the anterior nucleus (AN) and centromedian nucleus (CM) of the thalamus has been actively investigated for the treatment of medication-resistant epilepsy, few studies have investigated dynamic ictal changes in corticothalamic connectivity in human electroencephalographic (EEG) recording. This study aims to establish the complex spatiotemporal dynamics of the ictal corticothalamic network associated with various seizure foci. METHODS: We analyzed 10 patients (aged 2.7-28.1 years) with medication-resistant focal epilepsy who underwent stereotactic EEG evaluation with thalamic sampling. We examined both undirected and directed connectivity, incorporating coherence and spectral Granger causality analysis (GCA) between the diverse seizure foci and thalamic nuclei (AN and CM) at ictal onset. RESULTS: In our analysis of 36 seizures, coherence between seizure onset and thalamic nuclei increased across all frequencies, especially in slower bands (delta, theta, alpha). GCA showed increased information flow from seizure onset to the thalamus across all frequency bands, but outflows from the thalamus were mainly in slower frequencies, particularly delta. In the subgroup analysis based on various seizure foci, the delta coherence showed a more pronounced increase at CM than at AN during frontal lobe seizures. Conversely, in limbic seizures, the delta coherence increase was greater at AN compared to CM. SIGNIFICANCE: It appears that the delta frequency plays a pivotal role in modulating the corticothalamic network during seizures. Our results underscore the significance of comprehending the spatiotemporal dynamics of the corticothalamic network at ictal onset, and this knowledge could guide personalized responsive neuromodulation treatment strategies.

Extracellular cathepsin Z signals through the α5 integrin and augments NLRP3 inflammasome activation.

Respiratory silicosis is a preventable occupational disease that develops secondary to the aspiration of crystalline silicon dioxide (silica) into the lungs, activation of the NLRP3 inflammasome, and IL-1ß production. Cathepsin Z has been associated with the development of inflammation and IL-1ß production; however, the mechanism of how cathepsin Z leads to IL-1ß production is unknown. Here, the requirement for cathepsin Z in silicosis was determined using WT mice and mice deficient in cathepsin Z. The activation of the NLRP3 inflammasome in macrophages was studied using WT and cathepsin Z-deficient bone marrow-derived murine dendritic cells and the human monocytic cell line THP-1. The cells were activated with silica, and IL-1ß release was determined using enzyme-linked immunosorbent assay or IL-1ß bioassays. The relative contribution of the active domain or integrin-binding domain of cathepsin Z was studied using recombinant cathepsin Z constructs and the α5 integrin neutralizing antibody. We report that the lysosomal cysteine protease cathepsin Z potentiates the development of inflammation associated with respiratory silicosis by augmenting NLRP3 inflammasome-derived IL-1ß expression in response to silica. The secreted cathepsin Z functions nonproteolytically via the internal integrin-binding domain to impact caspase-1 activation and the production of active IL-1ß through integrin α5 without affecting the transcription levels of NLRP3 inflammasome components. This work reveals a regulatory pathway for the NLRP3 inflammasome that occurs in an outside-in fashion and provides a link between extracellular cathepsin Z and inflammation. Furthermore, it reveals a level of NLRP3 inflammasome regulation that has previously only been found downstream of extracellular pathogens.

Experiences of Women With Interstitial Cystitis/Bladder Pain Syndrome: What Can We Learn From Women's Online Discussions?

PURPOSE: Interstitial cystitis/bladder pain syndrome is a debilitating chronic condition that disproportionately affects women at a ratio of 5:1. We sought to capture women's experiences with interstitial cystitis/bladder pain syndrome by conducting a large-scale digital ethnographic analysis of anonymous posts on Internet forums. MATERIALS AND METHODS: Online posts were identified using condition-specific keywords and data mining extraction services. Once posts were identified, a random sample of 200 online posts was coded and analyzed by hand using qualitative methods. A Latent Dirichlet Allocation probabilistic topic model was applied to the complete dataset to substantiate the qualitative analysis and allow for further thematic discovery. RESULTS: A total of 6,842 posts written by 3,902 unique users from 224 websites were identified. There was a significant overlap between the hand coding and Latent Dirichlet Allocation themes. Our analysis yielded the following themes: online community engagement, triggers and disease etiologies, medical comorbidities, quality of life impact, patient experience with medical care, and alternative therapies and self-management strategies. Additionally, our population appeared to have a high burden of nonurological associated syndromes. We identified barriers to patient-centered care and found that online peer support was important for women. CONCLUSIONS: Our digital ethnographic analysis is a novel application of qualitative methods using online sources. Social media analytics appears to capture a broader patient population than that typically included in clinic-based qualitative studies, such as patient interviews and focus groups. Understanding patient behaviors and concerns are important to guide strategies for improving care and the overall experience with this difficult-to-treat condition.

Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis.

BACKGROUND: Current qualitative literature about the experiences of women dealing with urinary tract infections (UTIs) is limited to patients recruited from tertiary centers and medical clinics. However, traditional focus groups and interviews may limit what patients share. Using digital ethnography, we analyzed free-range conversations of an online community. OBJECTIVE: This study aimed to investigate and characterize the patient perspectives of women dealing with UTIs using digital ethnography. METHODS: A data-mining service was used to identify online posts. A thematic analysis was conducted on a subset of the identified posts. Additionally, a latent Dirichlet allocation (LDA) probabilistic topic modeling method was applied to review the entire data set using a semiautomatic approach. Each identified topic was generated as a discrete distribution over the words in the collection, which can be thought of as a word cloud. We also performed a thematic analysis of the word cloud topic model results. RESULTS: A total of 83,589 posts by 53,460 users from 859 websites were identified. Our hand-coding inductive analysis yielded the following 7 themes: quality-of-life impact, knowledge acquisition, support of the online community, health care utilization, risk factors and prevention, antibiotic treatment, and alternative therapies. Using the LDA topic model method, 105 themes were identified and consolidated into 9 categories. Of the LDA-derived themes, 25.7% (27/105) were related to online community support, and 22% (23/105) focused on UTI risk factors and prevention strategies. CONCLUSIONS: Our large-scale social media analysis supports the importance and reproducibility of using online data to comprehend women's UTI experience. This inductive thematic analysis highlights patient behavior, self-empowerment, and online media utilization by women to address their health concerns in a safe, anonymous way.

Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing.

MOTIVATION: Mining drug-disease association and related interactions are essential for developing in silico drug repurposing (DR) methods and understanding underlying biological mechanisms. Recently, large-scale biological databases are increasingly available for pharmaceutical research, allowing for deep characterization for molecular informatics and drug discovery. However, DR is challenging due to the molecular heterogeneity of disease and diverse drug-disease associations. Importantly, the complexity of molecular target interactions, such as protein-protein interaction (PPI), remains to be elucidated. DR thus requires deep exploration of a multimodal biological network in an integrative context. RESULTS: In this study, we propose BiFusion, a bipartite graph convolution network model for DR through heterogeneous information fusion. Our approach combines insights of multiscale pharmaceutical information by constructing a multirelational graph of drug-protein, disease-protein and PPIs. Especially, our model introduces protein nodes as a bridge for message passing among diverse biological domains, which provides insights into utilizing PPI for improved DR assessment. Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation. We offered an exploratory analysis for finding novel drug-disease associations. Extensive experiments showed that our approach achieved improved performance than multiple baselines for DR analysis. AVAILABILITY AND IMPLEMENTATION: Source code and preprocessed datasets are at: https://github.com/zcwang0702/BiFusion.

Optimizing Spatial Biopsy Sampling for the Detection of Prostate Cancer.

PURPOSE: The appropriate number of systematic biopsy cores to retrieve during magnetic resonance imaging (MRI)-targeted prostate biopsy is not well defined. We aimed to demonstrate a biopsy sampling approach that reduces required core count while maintaining diagnostic performance. MATERIALS AND METHODS: We collected data from a cohort of 971 men who underwent MRI-ultrasound fusion targeted biopsy for suspected prostate cancer. A regional targeted biopsy (RTB) was evaluated retrospectively; only cores within 2 cm of the margin of a radiologist-defined region of interest were considered part of the RTB. We compared detection rates for clinically significant prostate cancer (csPCa) and cancer upgrading rate on final whole mount pathology after prostatectomy between RTB, combined, MRI-targeted, and systematic biopsy. RESULTS: A total of 16,459 total cores from 971 men were included in the study data sets, of which 1,535 (9%) contained csPCa. The csPCa detection rates for systematic, MRI-targeted, combined, and RTB were 27.0% (262/971), 38.3% (372/971), 44.8% (435/971), and 44.0% (427/971), respectively. Combined biopsy detected significantly more csPCa than systematic and MRI-targeted biopsy (p <0.001 and p=0.004, respectively) but was similar to RTB (p=0.71), which used on average 3.8 (22%) fewer cores per patient. In 102 patients who underwent prostatectomy, there was no significant difference in upgrading rates between RTB and combined biopsy (p=0.84). CONCLUSIONS: A RTB approach can maintain state-of-the-art detection rates while requiring fewer retrieved cores. This result informs decision making about biopsy site selection and total retrieved core count.

Social media analytics of overactive bladder posts: what do patients know and want to know?

OBJECTIVE: To assess women's knowledge, patient experience, and treatment decision making regarding overactive bladder (OAB) using digital ethnography. METHODS: Online posts were identified using a data mining service. Two hundred randomized posts were reviewed and coded using grounded theory. We then applied a latent Dirichlet allocation (LDA) probabilistic topic modeling process to review the entire collection of identified posts. RESULTS: A total of 2618 posts by 1867 unique users from 203 different websites were identified. Our analysis yielded six themes: the impact of OAB on quality of life, patient-physician interactions, online engagement, symptom management, patient knowledge acquisition, and alternative therapies. CONCLUSION: Overall, online communities are a source of support for women to self-manage the OAB symptom complex and help overcome treatment pathway challenges. Digital ethnography provides insight into patient knowledge and barriers to patient-centered care, which are important to improve patient outreach. Additionally, we identify similar findings to prior work, indicating the reliability of studying social media.

Women's Experience with Stress Urinary Incontinence: Insights from Social Media Analytics.

PURPOSE: We conducted a large-scale digital ethnographic analysis of anonymous online posts to capture the complete patient experience, knowledge and perceptions among women with stress urinary incontinence. MATERIALS AND METHODS: Online posts were identified through data mining. Overall, 200 randomized posts were analyzed using grounded theory qualitative methods. To ensure full thematic discovery we also applied a Latent Dirichlet Allocation probabilistic topic modeling approach to the entire data set of identified posts. Latent Dirichlet Allocation topics are represented as a distribution of words, similar to a word cloud, which were manually reviewed to identify themes. RESULTS: A total of 985 online posts by 762 unique users were extracted from 98 websites. There was significant overlap between the grounded theory and Latent Dirichlet Allocation identified themes. Our analysis suggests that these online communities help women manage the quality of life impact of their stress urinary incontinence, navigate specialty care and reach a decision regarding surgical vs nonsurgical management. Additionally, we identified risk factors, prevention strategies and treatment recommendations discussed online. CONCLUSIONS: Findings demonstrated patient values that may influence decision making when seeking care for stress urinary incontinence and choosing a treatment. Social media interactions provide insight into patient behaviors that are important in order to improve patient centered care and decision making.

Using phrases and document metadata to improve topic modeling of clinical reports.

Probabilistic topic models provide an unsupervised method for analyzing unstructured text, which have the potential to be integrated into clinical automatic summarization systems. Clinical documents are accompanied by metadata in a patient's medical history and frequently contains multiword concepts that can be valuable for accurately interpreting the included text. While existing methods have attempted to address these problems individually, we present a unified model for free-text clinical documents that integrates contextual patient- and document-level data, and discovers multi-word concepts. In the proposed model, phrases are represented by chained n-grams and a Dirichlet hyper-parameter is weighted by both document-level and patient-level context. This method and three other Latent Dirichlet allocation models were fit to a large collection of clinical reports. Examples of resulting topics demonstrate the results of the new model and the quality of the representations are evaluated using empirical log likelihood. The proposed model was able to create informative prior probabilities based on patient and document information, and captured phrases that represented various clinical concepts. The representation using the proposed model had a significantly higher empirical log likelihood than the compared methods. Integrating document metadata and capturing phrases in clinical text greatly improves the topic representation of clinical documents. The resulting clinically informative topics may effectively serve as the basis for an automatic summarization system for clinical reports.

Comparative analysis of genes regulated by Dzip1/iguana and hedgehog in zebrafish.

BACKGROUND: The zebrafish genetic mutant iguana (igu) has defects in the ciliary basal body protein Dzip1, causing improper cilia formation. Dzip1 also interacts with the downstream transcriptional activators of Hedgehog (Hh), the Gli proteins, and Hh signaling is disrupted in igu mutants. Hh governs a wide range of developmental processes, including stabilizing developing blood vessels to prevent hemorrhage. Using igu mutant embryos and embryos treated with the Hh pathway antagonist cyclopamine, we conducted a microarray to determine genes involved in Hh signaling mediating vascular stability. RESULTS: We identified 40 genes with significantly altered expression in both igu mutants and cyclopamine-treated embryos. For a subset of these, we used in situ hybridization to determine localization during embryonic development and confirm the expression changes seen on the array. CONCLUSIONS: Through comparing gene expression changes in a genetic model of vascular instability with a chemical inhibition of Hh signaling, we identified a set of 40 differentially expressed genes with potential roles in vascular stabilization.

A multitask approach for automated detection and segmentation of thyroid nodules in ultrasound images.

An increase in the incidence and diagnosis of thyroid nodules and thyroid cancer underscores the need for a better approach to nodule detection and risk stratification in ultrasound (US) images that can reduce healthcare costs, patient discomfort, and unnecessary invasive procedures. However, variability in ultrasound technique and interpretation makes the diagnostic process partially subjective. Therefore, an automated approach that detects and segments nodules could improve performance on downstream tasks, such as risk stratification. Ultrasound studies were acquired from 280 patients at UCLA Health, totaling 9888 images, and annotated by collaborating radiologists. Current deep learning architectures for segmentation are typically semi-automated because they are evaluated solely on images known to have nodules and do not assess ability to identify suspicious images. However, the proposed multitask approach both detects suspicious images and segments potential nodules; this allows for a clinically translatable model that aptly parallels the workflow for thyroid nodule assessment. The multitask approach is centered on an anomaly detection (AD) module that can be integrated with any UNet architecture variant to improve image-level nodule detection. Of the evaluated multitask models, a UNet with a ImageNet pretrained encoder and AD achieved the highest F1 score of 0.839 and image-wide Dice similarity coefficient of 0.808 on the hold-out test set. Furthermore, models were evaluated on two external validations datasets to demonstrate generalizability and robustness to data variability. Ultimately, the proposed architecture is an automated multitask method that expands on previous methods by successfully both detecting and segmenting nodules in ultrasound.

Perspectives, experiences, and concerns with cyclical vomiting syndrome: Insights from online targeted-disease forums.

INTRODUCTION: There is limited research examining the biopsychosocial impact of cyclic vomiting syndrome (CVS) on patients. This study aims to assess individuals' experiences, fears, and concerns associated with CVS and the impact of CVS on their daily lives. METHODS: We employed social netnography to analyze publicly available posts related to CVS that were identified from six US online forums and Twitter. A randomly selected sub-cohort of posts per pre-defined criteria was first qualitatively analyzed using an inductive thematic approach. Then, machine learning topic modeling was applied to explore themes in an unsupervised manner for the entire corpus of posts. Afterward, findings from the qualitative and quantitative approaches were integrated to generate a thematic network. RESULTS: Based on the 39,179 collected posts, seven domain themes were identified. Overall, 41.4% of the posts were related to "biopsychosocial burden" of CVS, including physical impact, psychological impact, and social impact. In 22.3% of posts, individuals shared their experience of "interactions with the healthcare system", and 14.2% of posts were related to "perceived CVS triggers." Individuals also shared "solutions to alleviate their symptoms" and "mental health needs" in 10.2% and 8.8% of posts, respectively. Finally, 6.1% of the posts were about "seeking/sharing support" with others. DISCUSSION: This is the first social netnography study to describe the in-depth experiences of individuals living with CVS and the marked impact on their physical, mental, and social health. The study also highlights the unmet need for effective therapies, both pharmacological and non-pharmacological, to alleviate the biopsychosocial impact of CVS.

A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Following endovascular thrombectomy in patients with large-vessel occlusion stroke, successful recanalization from 1 attempt, known as the first-pass effect, has correlated favorably with long-term outcomes. Pretreatment imaging may contain information that can be used to predict the first-pass effect. Recently, applications of machine learning models have shown promising results in predicting recanalization outcomes, albeit requiring manual segmentation. In this study, we sought to construct completely automated methods using deep learning to predict the first-pass effect from pretreatment CT and MR imaging. MATERIALS AND METHODS: Our models were developed and evaluated using a cohort of 326 patients who underwent endovascular thrombectomy at UCLA Ronald Reagan Medical Center from 2014 to 2021. We designed a hybrid transformer model with nonlocal and cross-attention modules to predict the first-pass effect on MR imaging and CT series. RESULTS: The proposed method achieved a mean 0.8506 (SD, 0.0712) for cross-validation receiver operating characteristic area under the curve (ROC-AUC) on MR imaging and 0.8719 (SD, 0.0831) for cross-validation ROC-AUC on CT. When evaluated on the prospective test sets, our proposed model achieved a mean ROC-AUC of 0.7967 (SD, 0.0335) with a mean sensitivity of 0.7286 (SD, 0.1849) and specificity of 0.8462 (SD, 0.1216) for MR imaging and a mean ROC-AUC of 0.8051 (SD, 0.0377) with a mean sensitivity of 0.8615 (SD, 0.1131) and specificity 0.7500 (SD, 0.1054) for CT, respectively, representing the first classification of the first-pass effect from MR imaging alone and the first automated first-pass effect classification method in CT. CONCLUSIONS: Results illustrate that both nonperfusion MR imaging and CT from admission contain signals that can predict a successful first-pass effect following endovascular thrombectomy using our deep learning methods without requiring time-intensive manual segmentation.

Hybrid immunity after BNT162b2 Covid-19 vaccine administration in children aged 5 to 11 years.

BACKGROUND: The immune response to coronavirus disease 2019 (COVID-19) vaccination is stronger among adults with prior infection (hybrid immunity). It is important to understand if children demonstrate a similar response to better inform vaccination strategies. Our study investigated the humoral response after BNT162b2 COVID-19 vaccine doses in SARS-CoV-2 naïve and recovered children (5-11 years). METHODS: A multi-institutional, longitudinal, prospective cohort study was conducted. Children were enrolled in a case-ascertained antibody surveillance study in Ottawa, Ontario from September/2020-March/2021; at least one household member was severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) positive on RT-PCR. In November 2021, BNT162b2 COVID-19 vaccine was authorized for children aged 5-11 in Canada. Children enrolled in the surveillance study intending to receive two vaccine doses were invited to participate in this study from November 2021-April 2022. Main exposure was prior SARS-CoV-2 infection, defined by positive RT-PCR or SARS-CoV-2 anti-N IgG antibody presence. Primary outcome was spike IgG antibody levels measured following the first vaccine dose (2-3 weeks) and second vaccine dose (3-4 weeks). RESULTS: Of the 153 eligible children, 75 participants (median age 8.9 IQR (7.4, 10.2) years; 38 (50.7 %) female; 59 (78.7 %) Caucasian) had complete follow-up. Fifty-four (72 %) children had prior SARS-COV-2 infection. Spike IgG antibody levels are significantly higher in SARS-CoV-2 recovered participants after receiving the first dose (p < 0.001) and the second (p = 0.01) compared to infection naïve children. CONCLUSIONS AND RELEVANCE: SARS-CoV-2 recovered children (5-11 years) demonstrated higher antibody levels following first BNT162b2 vaccine dose compared with naïve children. Most reached antibody saturation two to three weeks after the first dose; a second dose didn't change the saturation level. A single vaccine dose in SARS-CoV-2 recovered children may be equivalent or superior to a 2-dose primary series in naïve children. Further research is needed on the durability and quality of a single vaccine dose in this population.

From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review.

CONTEXT: Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. The greatest challenge is in understanding the best model to apply to one's own patient population, and how to operationalize such a model in practice. EVIDENCE ACQUISITION: A literature search of PubMed and IEEE Xplore was conducted for English-language publications between January 1, 2015 and January 1, 2023, studying diagnostic tests on suspected thyroid nodules that used AI. We excluded articles without prospective or external validation, nonprimary literature, duplicates, focused on nonnodular thyroid conditions, not using AI, and those incidentally using AI in support of an experimental diagnostic outside standard clinical practice. Quality was graded by Oxford level of evidence. EVIDENCE SYNTHESIS: A total of 61 studies were identified; all performed external validation, 16 studies were prospective, and 33 compared a model to physician prediction of ground truth. Statistical validation was reported in 50 papers. A diagnostic pipeline was abstracted, yielding 5 high-level outcomes: (1) nodule localization, (2) ultrasound (US) risk score, (3) molecular status, (4) malignancy, and (5) long-term prognosis. Seven prospective studies validated a single commercial AI; strengths included automating nodule feature assessment from US and assisting the physician in predicting malignancy risk, while weaknesses included automated margin prediction and interobserver variability. CONCLUSION: Models predominantly used US images to predict malignancy. Of 4 Food and Drug Administration-approved products, only S-Detect was extensively validated. Implementing an AI model locally requires data sanitization and revalidation to ensure appropriate clinical performance.

Clinical and serological predictors of post COVID-19 condition-findings from a Canadian prospective cohort study.

Introduction: More than 3 years into the pandemic, there is persisting uncertainty as to the etiology, biomarkers, and risk factors of Post COVID-19 Condition (PCC). Serological research data remain a largely untapped resource. Few studies have investigated the potential relationships between post-acute serology and PCC, while accounting for clinical covariates. Methods: We compared clinical and serological predictors among COVID-19 survivors with (n = 102 cases) and without (n = 122 controls) persistent symptoms ≥12 weeks post-infection. We selected four primary serological predictors (anti-nucleocapsid (N), anti-Spike, and anti-receptor binding domain (RBD) IgG titres, and neutralization efficiency), and specified clinical covariates a priori. Results: Similar proportions of PCC-cases (66.7%, n = 68) and infected-controls (71.3%, n = 87) tested positive for anti-N IgG. More cases tested positive for anti-Spike (94.1%, n = 96) and anti-RBD (95.1%, n = 97) IgG, as compared with controls (anti-Spike: 89.3%, n = 109; anti-RBD: 84.4%, n = 103). Similar trends were observed among unvaccinated participants. Effects of IgG titres on PCC status were non-significant in univariate and multivariate analyses. Adjusting for age and sex, PCC-cases were more likely to be efficient neutralizers (OR 2.2, 95% CI 1.11-4.49), and odds was further increased among cases to report deterioration in quality of life (OR 3.4, 95% CI 1.64-7.31). Clinical covariates found to be significantly related to PCC included obesity (OR 2.3, p = 0.02), number of months post COVID-19 (OR 1.1, p < 0.01), allergies (OR 1.8, p = 0.04), and need for medical support (OR 4.1, p < 0.01). Conclusion: Despite past COVID-19 infection, approximately one third of PCC-cases and infected-controls were seronegative for anti-N IgG. Findings suggest higher neutralization efficiency among cases as compared with controls, and that this relationship is stronger among cases with more severe PCC. Cases also required more medical support for COVID-19 symptoms, and described complex, ongoing health sequelae. More data from larger cohorts are needed to substantiate results, permit subgroup analyses of IgG titres, and explore for differences between clusters of PCC symptoms. Future assessment of IgG subtypes may also elucidate new findings.

COVID-19 vaccine immunogenicity and safety surrounding fourth and subsequent vaccine doses in patients with hematologic malignancies.

BACKGROUND: Immune response to COVID-19 vaccine is diminished in patients with hematologic malignancy. There is limited data regarding response to vaccine doses in these patients. PURPOSE: To quantify the humoral immune response engendered by 4th and subsequent doses of SARS-CoV-2 vaccination as measured by anti-Spike (anti-S) antibody levels, based on dried blood spot (DBS) testing, in patients with hematologic malignancies. Anti-S binds to the spike protein of the SARS-CoV-2 virus and is indicative of vaccine immunogenicity. METHODS: We conducted a prospective study of hematologic malignancies between August 2021 and January 2023 at 12 sites across Canada. Participants were followed longitudinally and submitted finger-prick DBS cards at set intervals associated with vaccination. Samples were processed via high throughput ELISA assay to detect serum antibodies against nucleocapsid (N) and spike (S) proteins. RESULTS: We obtained 3071 samples on 790 unique patients. Of these, 372 unique participants with 1840 samples had anti-S results available post-4th, 5th or 6th COVID-19 vaccine dose and were included for analysis. Three hundred thirty-three patients of the 372 participants submitted a DBS sample post 4th dose. Of these, 257 patients (77.2%) had a positive anti-S antibody. A total of 198 patients had paired samples pre- and post-dose 4, of which 59 (29.7%) had a negative anti-S antibody pre-dose 4. Of these, 20 (33.4%) developed positive anti-S antibody post-dose 4. One hundred forty-nine patients submitted a DBS sample post-dose 5. Of these, 135 patients (90.6%) had positive anti-S antibody. A total of 52 had paired samples pre- and post-dose 5. Six (8.7%) had a negative anti-S antibody pre-dose 5, of which two (33.3%) developed positive anti-S antibody post-dose 5. Of these 372 patients, 123 (34%) reported COVID-19 infection and 4 (1%) had a COVID-19 related hospitalization. There were no reported deaths from COVID-19. CONCLUSIONS: This prospective cohort study showed that humoral immune response improved with subsequent doses of COVID-19 vaccines.

The effect of dose-interval on antibody response to mRNA COVID-19 vaccines: a prospective cohort study.

Background: Vaccination against COVID-19 is highly effective in preventing severe disease and hospitalization, but primary COVID mRNA vaccination schedules often differed from those recommended by the manufacturers due to supply chain issues. We investigated the impact of delaying the second dose on antibody responses to COVID mRNA-vaccines in a prospective cohort of health-care workers in Quebec. Methods: We recruited participants from the McGill University Health Centre who provided serum or participant-collected dried blood samples (DBS) at 28-days, 3 months, and 6 months post-second dose and at 28-days after a third dose. IgG antibodies to SARS-CoV2 spike (S), the receptor-binding domain (RBD), nucleocapsid (N) and neutralizing antibodies to the ancestral strain were assessed by enzyme-linked immunosorbent assay (ELISA). We examined associations between long (≤89 days) versus short (<89 days) between-dose intervals and antibody response through multivariable mixed-effects models adjusted for age, sex, prior covid infection status, time since vaccine dose, and assay batch. Findings: The cohort included 328 participants who received up to three vaccine doses (>80% Pfizer-BioNTech). Weighted averages of the serum (n=744) and DBS (n=216) cohort results from the multivariable models showed that IgG anti-S was 31% higher (95% CI: 12% to 53%) and IgG anti-RBD was 37% higher (95% CI: 14% to 65%) in the long vs. short interval participants, across all time points. Interpretation: Our study indicates that extending the covid primary series between-dose interval beyond 89 days (approximately 3 months) provides stronger antibody responses than intervals less than 89 days. Our demonstration of a more robust antibody response with a longer between dose interval is reassuring as logistical and supply challenges are navigated in low-resource settings.