Efficacy and Safety of Bepirovirsen in Chronic Hepatitis B Infection.

BACKGROUND: Bepirovirsen is an antisense oligonucleotide that targets all hepatitis B virus (HBV) messenger RNAs and acts to decrease levels of viral proteins. METHODS: We conducted a phase 2b, randomized, investigator-unblinded trial involving participants with chronic HBV infection who were receiving or not receiving nucleoside or nucleotide analogue (NA) therapy. Participants were randomly assigned (in a 3:3:3:1 ratio) to receive weekly subcutaneous injections of bepirovirsen at a dose of 300 mg for 24 weeks (group 1), bepirovirsen at a dose of 300 mg for 12 weeks then 150 mg for 12 weeks (group 2), bepirovirsen at a dose of 300 mg for 12 weeks then placebo for 12 weeks (group 3), or placebo for 12 weeks then bepirovirsen at a dose of 300 mg for 12 weeks (group 4). Groups 1, 2, and 3 received loading doses of bepirovirsen. The composite primary outcome was a hepatitis B surface antigen (HBsAg) level below the limit of detection and an HBV DNA level below the limit of quantification maintained for 24 weeks after the planned end of bepirovirsen treatment, without newly initiated antiviral medication. RESULTS: The intention-to-treat population comprised 457 participants (227 receiving NA therapy and 230 not receiving NA therapy). Among those receiving NA therapy, a primary-outcome event occurred in 6 participants (9%; 95% credible interval, 0 to 31) in group 1, in 6 (9%; 95% credible interval, 0 to 43) in group 2, in 2 (3%; 95% credible interval, 0 to 16) in group 3, and 0 (0%; post hoc credible interval, 0 to 8) in group 4. Among participants not receiving NA therapy, a primary-outcome event occurred in 7 participants (10%; 95% credible interval, 0 to 38), 4 (6%; 95% credible interval, 0 to 25), 1 (1%; post hoc credible interval, 0 to 6), and 0 (0%; post hoc credible interval, 0 to 8), respectively. During weeks 1 through 12, adverse events, including injection-site reactions, pyrexia, fatigue, and increased alanine aminotransferase levels, were more common with bepirovirsen (groups 1, 2, and 3) than with placebo (group 4). CONCLUSIONS: In this phase 2b trial, bepirovirsen at a dose of 300 mg per week for 24 weeks resulted in sustained HBsAg and HBV DNA loss in 9 to 10% of participants with chronic HBV infection. Larger and longer trials are required to assess the efficacy and safety of bepirovirsen. (Funded by GSK; B-Clear ClinicalTrials.gov number, NCT04449029.).

A dynamic role for transcription factors in restoring transcription through mitosis.

Mitosis involves intricate steps, such as DNA condensation, nuclear membrane disassembly, and phosphorylation cascades that temporarily halt gene transcription. Despite this disruption, daughter cells remarkably retain the parent cell's gene expression pattern, allowing for efficient transcriptional memory after division. Early studies in mammalian cells suggested that transcription factors (TFs) mark genes for swift reactivation, a phenomenon termed 'mitotic bookmarking', but conflicting data emerged regarding TF presence on mitotic chromosomes. Recent advancements in live-cell imaging and fixation-free genomics challenge the conventional belief in universal formaldehyde fixation, revealing dynamic TF interactions during mitosis. Here, we review recent studies that provide examples of at least four modes of TF-DNA interaction during mitosis and the molecular mechanisms that govern these interactions. Additionally, we explore the impact of these interactions on transcription initiation post-mitosis. Taken together, these recent studies call for a paradigm shift toward a dynamic model of TF behavior during mitosis, underscoring the need for incorporating dynamics in mechanistic models for re-establishing transcription post-mitosis.

A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound.

Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.

Evaluation of germicidal UV-C light for suppression of grape powdery mildew and Botrytis bunch rot in Western Oregon.

Germicidal ultraviolet light (UV-C) has been shown to effectively suppress several plant pathogens, as well as some arthropod pests. Recent reports describe the efficacy of nighttime applications of UV-C at doses from 100 to 200 J/m2 in vineyards to reduce grape powdery mildew (Erysiphe necator). Our in vitro studies confirmed efficacy of UV-C to inhibit germination of E. necator and Botrytis cinerea conidia, demonstrated a range of tolerances to UV-C within a collection of E. necator isolates, and showed growth stage-specific effects of UV-C on B. cinerea. Nighttime use of UV-C was evaluated at 48 to 96 J/m2 in small plot trials (<1,000 vines) from 2020 to 2023. Once or twice weekly UV-C applications significantly reduced the incidence of foliar powdery mildew compared to non-UV-C-treated controls (P < 0.02). Suppression of powdery mildew on fruit was less consistent, where once or twice weekly UV-C exposure reduced powdery mildew disease severity in 2020 (P = 0.04), 2021 (P = 0.02) and 2023 (P =0.003), but less so in 2022 (P = 0.07). Bunch rot severity was not significantly reduced with UV-C treatment in any year of the study. Application of UV-C until the onset of fruit color change (veraison) also had a minimal effect on the fruit soluble solids, pH, anthocyanins, or phenolics in harvested fruit at any UV-C dose or frequency (P > 0.10). Suppression of powdery mildew by nighttime application UV-C at lower doses in small plots suggests that such treatments merit further evaluation in larger-scale studies in Western Oregon.

COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring.

While no longer a public health emergency of international concern, COVID-19 remains an established and ongoing global health threat. As the global population continues to face significant negative impacts of the pandemic, there has been an increased usage of point-of-care ultrasound (POCUS) imaging as a low-cost, portable, and effective modality of choice in the COVID-19 clinical workflow. A major barrier to the widespread adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert clinicians who can interpret POCUS examinations, leading to considerable interest in artificial intelligence-driven clinical decision support systems to tackle this challenge. A major challenge to building deep neural networks for COVID-19 screening using POCUS is the heterogeneity in the types of probes used to capture ultrasound images (e.g., convex vs. linear probes), which can lead to very different visual appearances. In this study, we propose an analytic framework for COVID-19 assessment able to consume ultrasound images captured by linear and convex probes. We analyze the impact of leveraging extended linear-convex ultrasound augmentation learning on producing enhanced deep neural networks for COVID-19 assessment, where we conduct data augmentation on convex probe data alongside linear probe data that have been transformed to better resemble convex probe data. The proposed explainable framework, called COVID-Net L2C-ULTRA, employs an efficient deep columnar anti-aliased convolutional neural network designed via a machine-driven design exploration strategy. Our experimental results confirm that the proposed extended linear-convex ultrasound augmentation learning significantly increases performance, with a gain of 3.9% in test accuracy and 3.2% in AUC, 10.9% in recall, and 4.4% in precision. The proposed method also demonstrates a much more effective utilization of linear probe images through a 5.1% performance improvement in recall when such images are added to the training dataset, while all other methods show a decrease in recall when trained on the combined linear-convex dataset. We further verify the validity of the model by assessing what the network considers to be the critical regions of an image with our contribution clinician.

Automatic Radar-Based Step Length Measurement in the Home for Older Adults Living with Frailty.

With an aging population, numerous assistive and monitoring technologies are under development to enable older adults to age in place. To facilitate aging in place, predicting risk factors such as falls and hospitalization and providing early interventions are important. Much of the work on ambient monitoring for risk prediction has centered on gait speed analysis, utilizing privacy-preserving sensors like radar. Despite compelling evidence that monitoring step length in addition to gait speed is crucial for predicting risk, radar-based methods have not explored step length measurement in the home. Furthermore, laboratory experiments on step length measurement using radars are limited to proof-of-concept studies with few healthy subjects. To address this gap, a radar-based step length measurement system for the home is proposed based on detection and tracking using a radar point cloud followed by Doppler speed profiling of the torso to obtain step lengths in the home. The proposed method was evaluated in a clinical environment involving 35 frail older adults to establish its validity. Additionally, the method was assessed in people's homes, with 21 frail older adults who had participated in the clinical assessment. The proposed radar-based step length measurement method was compared to the gold-standard Zeno Walkway Gait Analysis System, revealing a 4.5 cm/8.3% error in a clinical setting. Furthermore, it exhibited excellent reliability (ICC(2,k) = 0.91, 95% CI 0.82 to 0.96) in uncontrolled home settings. The method also proved accurate in uncontrolled home settings, as indicated by a strong consistency (ICC(3,k) = 0.81 (95% CI 0.53 to 0.92)) between home measurements and in-clinic assessments.

Long-Acting Cabotegravir and Rilpivirine Dosed Every 2 Months in Adults With Human Immunodeficiency Virus 1 Type 1 Infection: 152-Week Results From ATLAS-2M, a Randomized, Open-Label, Phase 3b, Noninferiority Study.

BACKGROUND: Cabotegravir (CAB) + rilpivirine (RPV) dosed intramuscularly monthly or every 2 months is a complete, long-acting (LA) regimen for the maintenance of HIV-1 virologic suppression. Here, we report the antiretroviral therapy as long acting suppression (ATLAS)-2M study week 152 results. METHODS: ATLAS-2M is a phase 3b, randomized, multicenter study assessing the efficacy and safety of CAB+RPV LA every 8 weeks (Q8W) versus every 4 weeks (Q4W). Virologically suppressed (HIV-1 RNA <50 copies/mL) individuals were randomized to receive CAB+RPV LA Q8W or Q4W. Endpoints included the proportion of participants with plasma HIV-1 RNA ≥50 copies/mL and <50 copies/mL, incidence of confirmed virologic failure (CVF; 2 consecutive measurements ≥200 copies/mL), safety, and tolerability. RESULTS: A total of 1045 participants received CAB+RPV LA (Q8W, n = 522; Q4W, n = 523). CAB+RPV LA Q8W demonstrated noninferior efficacy versus Q4W dosing, with 2.7% (n = 14) and 1.0% (n = 5) of participants having HIV-1 RNA ≥50 copies/mL, respectively, with adjusted treatment difference being 1.7% (95% CI: 0.1-3.3%), meeting the 4% noninferiority threshold. At week 152, 87% of participants maintained HIV-1 RNA <50 copies/mL (Q8W, 87% [n = 456]; Q4W, 86% [n = 449]). Overall, 12 (2.3%) participants in the Q8W arm and 2 (0.4%) in the Q4W arm had CVF. Eight and 10 participants with CVF had treatment-emergent, resistance-associated mutations to RPV and integrase inhibitors, respectively. Safety profiles were comparable, with no new safety signals observed since week 48. CONCLUSIONS: These data demonstrate virologic suppression durability with CAB+RPV LA Q8W or Q4W for ∼3 years and confirm long-term efficacy, safety, and tolerability of CAB+RPV LA as a complete regimen to maintain HIV-1 virologic suppression.

PKC-ε regulates vesicle delivery and focal exocytosis for efficient IgG-mediated phagocytosis.

Protein kinase C (PKC)-ε is required for membrane addition during IgG-mediated phagocytosis, but its role in this process is ill defined. Here, we performed high-resolution imaging, which reveals that PKC-ε exits the Golgi and enters phagosomes on vesicles that then fuse. TNF and PKC-ε colocalize at the Golgi and on vesicles that enter the phagosome. Loss of PKC-ε and TNF delivery upon nocodazole treatment confirmed vesicular transport on microtubules. That TNF+ vesicles were not delivered in macrophages from PKC-ε null mice, or upon dissociation of the Golgi-associated pool of PKC-ε, implies that Golgi-tethered PKC-ε is a driver of Golgi-to-phagosome trafficking. Finally, we established that the regulatory domain of PKC-ε is sufficient for delivery of TNF+ vesicles to the phagosome. These studies reveal a novel role for PKC-ε in focal exocytosis - its regulatory domain drives Golgi-derived vesicles to the phagosome, whereas catalytic activity is required for their fusion. This is one of the first examples of a PKC requirement for vesicular trafficking and describes a novel function for a PKC regulatory domain. This article has an associated First Person interview with the first author of the paper.

Feasibility of diffusion-tensor and correlated diffusion imaging for studying white-matter microstructural abnormalities: Application in COVID-19.

There has been growing attention on the effect of COVID-19 on white-matter microstructure, especially among those that self-isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single-shell diffusion magnetic resonance imaging (MRI) methods for detecting such effects. In this work, the performances of three single-shell-compatible diffusion MRI modeling methods are compared for detecting the effect of COVID-19, including diffusion-tensor imaging, diffusion-tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self-isolated patients at the study initiation and 3-month follow-up, along with age- and sex-matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single-shell methods to demonstrate COVID-19-related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID-19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID-19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID-19 related white-matter microstructural pathology manifests as a change in tissue diffusivity. Interestingly, different b-values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3-month follow-up, likely due to the limited size of the follow-up cohort. To summarize, correlated diffusion imaging is shown to be a viable single-shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID-19 patients, suggesting the two regions react differently to viral infection.

Modeling the Role of Contour Integration in Visual Inference.

Under difficult viewing conditions, the brain's visual system uses a variety of recurrent modulatory mechanisms to augment feedforward processing. One resulting phenomenon is contour integration, which occurs in the primary visual (V1) cortex and strengthens neural responses to edges if they belong to a larger smooth contour. Computational models have contributed to an understanding of the circuit mechanisms of contour integration, but less is known about its role in visual perception. To address this gap, we embedded a biologically grounded model of contour integration in a task-driven artificial neural network and trained it using a gradient-descent variant. We used this model to explore how brain-like contour integration may be optimized for high-level visual objectives as well as its potential roles in perception. When the model was trained to detect contours in a background of random edges, a task commonly used to examine contour integration in the brain, it closely mirrored the brain in terms of behavior, neural responses, and lateral connection patterns. When trained on natural images, the model enhanced weaker contours and distinguished whether two points lay on the same versus different contours. The model learned robust features that generalized well to out-of-training-distribution stimuli. Surprisingly, and in contrast with the synthetic task, a parameter-matched control network without recurrence performed the same as or better than the model on the natural-image tasks. Thus, a contour integration mechanism is not essential to perform these more naturalistic contour-related tasks. Finally, the best performance in all tasks was achieved by a modified contour integration model that did not distinguish between excitatory and inhibitory neurons.

Association Between Pretreatment Chest Imaging and Immune Checkpoint Inhibitor Pneumonitis Among Patients With Lung Cancer.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are a first-line and perioperative treatment for lung cancer. Pneumonitis is a potentially life-threatening complication of ICI treatment in 2% to 5% of patients; however, risk factors for developing ICI pneumonitis (ICI-p) remain undefined. METHODS: We conducted a retrospective cohort study of consecutive patients with lung cancer who received at least one dose of ICI from 2015 through 2020 at The Ohio State University. Pneumonitis cases were documented by the treating oncologist and retrospectively evaluated for agreement between an oncologist and a pulmonologist. Patient demographic and clinical characteristics were recorded and summarized between those with and without pneumonitis for the overall cohort. Univariate and multivariable survival analyses using the Fine-Gray competing risk model were used to examine the associations. RESULTS: A total of 471 patients with lung cancer were included, of which 402 had non-small cell lung cancer and 69 had small cell lung cancer; 39 (8%) patients in the overall cohort developed ICI-p. Preexisting interstitial abnormalities and prior chest radiation were both significantly associated with ICI-p on univariate analysis (hazard ratio [HR], 8.91; 95% CI, 4.69-16.92; P<.001; and HR, 2.81; 95% CI, 1.50-5.28; P=.001). On multivariable analyses, interstitial abnormalities remained a strong independent risk factor for ICI-p when controlling for chest radiation and type of immunotherapy (HR, 9.77; 95% CI, 5.17-18.46; P<.001). Among patients with ICI-p (n=39), those with severe (grade 3-5) pneumonitis had worse overall survival compared with those with mild (grade 1 or 2) pneumonitis (P=.001). Abnormal pulmonary function test results at both 12 and 18 months prior to ICI initiation were not significantly associated with ICI-p. CONCLUSIONS: Preexisting interstitial abnormalities on chest CT and prior chest radiation are independent risk factors that are strongly associated with ICI-p in patients with lung cancer. These findings highlight a potential need for closer observation for ICI-p among patients with these risk factors.

Patients with treated indolent lymphomas immunized with BNT162b2 have reduced anti-spike neutralizing IgG to SARS-CoV-2 variants, but preserved antigen-specific T cell responses.

Patients with indolent lymphoma undertaking recurrent or continuous B cell suppression are at risk of severe COVID-19. Patients and healthy controls (HC; N = 13) received two doses of BNT162b2: follicular lymphoma (FL; N = 35) who were treatment naïve (TN; N = 11) or received immunochemotherapy (ICT; N = 23) and Waldenström's macroglobulinemia (WM; N = 37) including TN (N = 9), ICT (N = 14), or treated with Bruton's tyrosine kinase inhibitors (BTKi; N = 12). Anti-spike immunoglobulin G (IgG) was determined by a high-sensitivity flow-cytometric assay, in addition to live-virus neutralization. Antigen-specific T cells were identified by coexpression of CD69/CD137 and CD25/CD134 on T cells. A subgroup (N = 29) were assessed for third mRNA vaccine response, including omicron neutralization. One month after second BNT162b2, median anti-spike IgG mean fluorescence intensity (MFI) in FL ICT patients (9977) was 25-fold lower than TN (245 898) and HC (228 255, p = .0002 for both). Anti-spike IgG correlated with lymphocyte count (r = .63; p = .002), and time from treatment (r = .56; p = .007), on univariate analysis, but only with lymphocyte count on multivariate analysis (p = .03). In the WM cohort, median anti-spike IgG MFI in BTKi patients (39 039) was reduced compared to TN (220 645, p = .0008) and HC (p < .0001). Anti-spike IgG correlated with neutralization of the delta variant (r = .62, p < .0001). Median neutralization titer for WM BTKi (0) was lower than HC (40, p < .0001) for early-clade and delta. All cohorts had functional T cell responses. Median anti-spike IgG decreased 4-fold from second to third dose (p = .004). Only 5 of 29 poor initial responders assessed after third vaccination demonstrated seroconversion and improvement in neutralization activity, including to the omicron variant.

Impact of concurrent glomerulonephritis on outcomes of diffuse alveolar haemorrhage in antineutrophil cytoplasmic antibody-associated vasculitis.

OBJECTIVES: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) commonly presents with diffuse alveolar haemorrhage (DAH) and/or glomerulonephritis. Patients who present with DAH but without kidney involvement have been understudied. METHODS: Patients with DAH diagnosed by bronchoscopy and attributed to AAV over 8.5 years were retrospectively identified through electronic medical records and bronchoscopy reporting software. Patients with end-stage kidney disease (ESKD) or prior kidney transplant were excluded. Characteristics, treatments, and outcomes were abstracted. RESULTS: 30 patients were identified with DAH secondary to AAV. Five with ESKD or prior kidney transplant, and one with concomitant anti-glomerular basement membrane disease, were excluded, leaving 24 patients for analysis. At the time of qualifying bronchoscopy, six patients had no apparent kidney involvement by AAV, while eight of 18 with kidney involvement required dialysis. Of the eight patients dialysed during the initial hospitalisation, four were declared to have ESKD and three died in the subsequent year (one of whom did both). None of the 16 patients without initial dialysis requirement developed kidney involvement requiring dialysis in the subsequent year, though three of the six without initial evidence of kidney involvement by AAV ultimately developed it. No patient without initial kidney involvement died during follow-up. CONCLUSIONS: In our cohort, patients with DAH due to AAV without initial kidney involvement did not develop kidney involvement requiring dialysis or die during the follow-up period, though half of patients without initial evidence of kidney involvement subsequently developed it. Larger studies are warranted to better characterise this population and guide medical management.

Characterizing Canadian long-term care home consumed foods and their inflammatory potential: a secondary analysis.

BACKGROUND: Nutrient dense food that supports health is a goal of food service in long-term care (LTC). The objective of this work was to characterize the "healthfulness" of foods in Canadian LTC and inflammatory potential of the LTC diet and how this varied by key covariates. Here, we define foods to have higher "healthfulness" if the are in accordance with the evidence-based 2019 Canada's Food Guide, or with comparatively lower inflammatory potential. METHODS: We conducted a secondary analysis of the Making the Most of Mealtimes dataset (32 LTC homes; four provinces). A novel computational algorithm categorized food items from 3-day weighed food records into 68 expert-informed categories and Canada's Food Guide (CFG) food groups. The dietary inflammatory potential of these food sources was assessed using the Dietary Inflammatory Index (DII). Comparisons were made by sex, diet texture, and nutritional status. RESULTS: Consumption patterns using expert-informed categories indicated no single protein or vegetable source was among the top 5 most commonly consumed foods. In terms of CFG's groups, protein food sources (i.e., foods with a high protein content) represented the highest proportion of daily calorie intake (33.4%; animal-based: 31.6%, plant-based: 1.8%), followed by other foods (31.3%) including juice (9.8%), grains (25.0%; refined: 15.0%, whole: 10.0%), and vegetables/fruits (10.3%; plain: 4.9%, with additions: 5.4%). The overall DII score (mean, IQR) was positive (0.93, 0.23 to 1.75) indicating foods consumed tend towards a pro-inflammatory response. DII was significantly associated with sex (female higher; p<0.0001), and diet (minced higher; p=0.036). CONCLUSIONS: "Healthfulness" of Canadian LTC menus may be enhanced by lowering inflammatory potential to support chronic disease management through further shifts from refined to whole grains, incorporating more plant-based proteins, and moving towards serving plain vegetables and fruits. However, there are multiple layers of complexities to consider when optimising foods aligned with the CFG, and shifting to foods with anti-inflammatory potential for enhanced health benefits, while balancing nutrition and ensuring sufficient food and fluid intake to prevent or treat malnutrition.

COVID-Net USPro: An Explainable Few-Shot Deep Prototypical Network for COVID-19 Screening Using Point-of-Care Ultrasound.

As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent the further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and assess severity through visual inspection of the chest ultrasound images. Combined with the recent advancements in computer science, applications of deep learning techniques in medical image analysis have shown promising results, demonstrating that artificial intelligence-based solutions can accelerate the diagnosis of COVID-19 and lower the burden on healthcare professionals. However, the lack of large, well annotated datasets poses a challenge in developing effective deep neural networks, especially in the case of rare diseases and new pandemics. To address this issue, we present COVID-Net USPro, an explainable few-shot deep prototypical network that is designed to detect COVID-19 cases from very few ultrasound images. Through intensive quantitative and qualitative assessments, the network not only demonstrates high performance in identifying COVID-19 positive cases, using an explainability component, but it is also shown that the network makes decisions based on the actual representative patterns of the disease. Specifically, COVID-Net USPro achieves 99.55% overall accuracy, 99.93% recall, and 99.83% precision for COVID-19-positive cases when trained with only five shots. In addition to the quantitative performance assessment, our contributing clinician with extensive experience in POCUS interpretation verified the analytic pipeline and results, ensuring that the network's decisions are based on clinically relevant image patterns integral to COVID-19 diagnosis. We believe that network explainability and clinical validation are integral components for the successful adoption of deep learning in the medical field. As part of the COVID-Net initiative, and to promote reproducibility and foster further innovation, the network is open-sourced and available to the public.

Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis.

Computer vision and deep learning have the potential to improve medical artificial intelligence (AI) by assisting in diagnosis, prediction, and prognosis. However, the application of deep learning to medical image analysis is challenging due to limited data availability and imbalanced data. While model performance is undoubtedly essential for medical image analysis, model trust is equally important. To address these challenges, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which leverages image features learned through self-supervised learning and utilizes a novel surrogate loss function to build trustworthy models with optimal performance. The framework is validated on three benchmark data sets for detecting pneumonia, COVID-19, and melanoma, and the created models prove to be highly competitive, even outperforming those designed specifically for the tasks. Furthermore, we conduct ablation studies, cross-validation, and result visualization and demonstrate the contribution of proposed modules to both model performance (up to 21%) and model trust (up to 5%). We expect that the proposed framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises, improving patient outcomes, increasing diagnostic accuracy, and enhancing the overall quality of healthcare delivery.

Long-acting cabotegravir and rilpivirine dosed every 2 months in adults with HIV-1 infection (ATLAS-2M), 48-week results: a randomised, multicentre, open-label, phase 3b, non-inferiority study.

BACKGROUND: Phase 3 clinical studies showed non-inferiority of long-acting intramuscular cabotegravir and rilpivirine dosed every 4 weeks to oral antiretroviral therapy. Important phase 2 results of every 8 weeks dosing, and supportive modelling, underpin further evaluation of every 8 weeks dosing in this trial, which has the potential to offer greater convenience. Our objective was to compare the week 48 antiviral efficacy of cabotegravir plus rilpivirine long-acting dosed every 8 weeks with that of every 4 weeks dosing. METHODS: ATLAS-2M is an ongoing, randomised, multicentre (13 countries; Australia, Argentina, Canada, France, Germany, Italy, Mexico, Russia, South Africa, South Korea, Spain, Sweden, and the USA), open-label, phase 3b, non-inferiority study of cabotegravir plus rilpivirine long-acting maintenance therapy administered intramuscularly every 8 weeks (cabotegravir 600 mg plus rilpivirine 900 mg) or every 4 weeks (cabotegravir 400 mg plus rilpivirine 600 mg) to treatment-experienced adults living with HIV-1. Eligible newly recruited individuals must have received an uninterrupted first or second oral standard-of-care regimen for at least 6 months without virological failure and be aged 18 years or older. Eligible participants from the ATLAS trial, from both the oral standard-of-care and long-acting groups, must have completed the 52-week comparative phase with an ATLAS-2M screening plasma HIV-1 RNA less than 50 copies per mL. Participants were randomly assigned 1:1 to receive cabotegravir plus rilpivirine long-acting every 8 weeks or every 4 weeks. The randomisation schedule was generated by means of the GlaxoSmithKline validated randomisation software RANDALL NG. The primary endpoint at week 48 was HIV-1 RNA ≥50 copies per mL (Snapshot, intention-to-treat exposed), with a non-inferiority margin of 4%. The trial is registered at ClinicalTrials.gov, NCT03299049 and is ongoing. FINDINGS: Screening occurred between Oct 27, 2017, and May 31, 2018. Of 1149 individuals screened, 1045 participants were randomised to the every 8 weeks (n=522) or every 4 weeks (n=523) groups; 37% (n=391) transitioned from every 4 weeks cabotegravir plus rilpivirine long-acting in ATLAS. Median participant age was 42 years (IQR 34-50); 27% (n=280) female at birth; 73% (n=763) white race. Cabotegravir plus rilpivirine long-acting every 8 weeks was non-inferior to dosing every 4 weeks (HIV-1 RNA ≥50 copies per mL; 2% vs 1%) with an adjusted treatment difference of 0·8 (95% CI -0·6-2·2). There were eight (2%, every 8 weeks group) and two (<1%, every 4 weeks group) confirmed virological failures (two sequential measures ≥200 copies per mL). For the every 8 weeks group, five (63%) of eight had archived non-nucleoside reverse transcriptase inhibitor resistance-associated mutations to rilpivirine at baseline. The safety profile was similar between dosing groups, with 844 (81%) of 1045 participants having adverse events (excluding injection site reactions); no treatment-related deaths occurred. INTERPRETATION: The efficacy and safety profiles of dosing every 8 weeks and dosing every 4 weeks were similar. These results support the use of cabotegravir plus rilpivirine long-acting administered every 2 months as a therapeutic option for people living with HIV-1. FUNDING: ViiV Healthcare and Janssen.

Computational and Experimental Model to Study Immunobead-Based Assays in Microfluidic Mixing Platforms.

In immunobead-based assays, micro/nanobeads are functionalized with antibodies to capture the target analytes, which can significantly improve the assay's performance. The immunobead-based assays have been recently combined with microfluidic mixing devices and customized for a variety of applications. However, device design and process optimization to achieve the best performance remain a substantial technological challenge. Here, we introduce a computational model that enables the rational design and optimization of the immunobead-based assay in a microfluidic mixing channel. We use numerical methods to examine the effect of the flow rates, channel geometry, bead's trajectory, and the analyte and reagent characteristics on the efficiency of analyte capture on the surface of microbeads. This model accounts for different bead movements inside the microchannel, with the goal of simulating an actual active binding environment. The model is further validated experimentally where different microfluidic channels are tested to capture the target analytes. Our experimental results are shown to meet theoretical predictions. While the model is demonstrated here for the analysis of IgG capture in simple and herringbone-structured microchannels, it can be readily adapted to a broad range of target molecules and different device designs.

Predicting the presence of depressive symptoms in the HIV-HCV co-infected population in Canada using supervised machine learning.

BACKGROUND: Depression is common in the human immunodeficiency virus (HIV)-hepatitis C virus (HCV) co-infected population. Demographic, behavioural, and clinical data collected in research settings may be of help in identifying those at risk for clinical depression. We aimed to predict the presence of depressive symptoms indicative of a risk of depression and identify important classification predictors using supervised machine learning. METHODS: We used data from the Canadian Co-infection Cohort, a multicentre prospective cohort, and its associated sub-study on Food Security (FS). The Center for Epidemiologic Studies Depression Scale-10 (CES-D-10) was administered in the FS sub-study; participants were classified as being at risk for clinical depression if scores ≥ 10. We developed two random forest algorithms using the training data (80%) and tenfold cross validation to predict the CES-D-10 classes-1. Full algorithm with all candidate predictors (137 predictors) and 2. Reduced algorithm using a subset of predictors based on expert opinion (46 predictors). We evaluated the algorithm performances in the testing data using area under the receiver operating characteristic curves (AUC) and generated predictor importance plots. RESULTS: We included 1,934 FS sub-study visits from 717 participants who were predominantly male (73%), white (76%), unemployed (73%), and high school educated (52%). At the first visit, median age was 49 years (IQR:43-54) and 53% reported presence of depressive symptoms with CES-D-10 scores ≥ 10. The full algorithm had an AUC of 0.82 (95% CI:0.78-0.86) and the reduced algorithm of 0.76 (95% CI:0.71-0.81). Employment, HIV clinical stage, revenue source, body mass index, and education were the five most important predictors. CONCLUSION: We developed a prediction algorithm that could be instrumental in identifying individuals at risk for depression in the HIV-HCV co-infected population in research settings. Development of such machine learning algorithms using research data with rich predictor information can be useful for retrospective analyses of unanswered questions regarding impact of depressive symptoms on clinical and patient-centred outcomes among vulnerable populations.

Tobacco smoking and HIV-related immunologic and virologic response among individuals of the Canadian HIV Observational Cohort (CANOC).

We assessed the relationship between tobacco smoking and immunologic and virologic response among people living with HIV (PLWH) initiating combination antiretroviral therapy (cART) in the Canadian HIV Observational Cohort (CANOC). Positive immunologic and virologic response, respectively, were defined as ≥50 cells/mm3 CD4 count increase (CD4+) and viral suppression ≤50 copies/mL (VL+) within 6 months of cART initiation. Using multinomial regression, we examined the relationship between smoking, immunologic, and virologic response category. Model A adjusted for birth sex, baseline age, enrolling province, and era of cohort entry; models B and C further adjusted for neighbourhood level material deprivation and history of injection drug use (IDU), respectively. Among 4267 individuals (32.7%) with smoking status data, concordant positive (CD4+/VL+) response was achieved by 64.2% never, 66.9% former, and 59.4% current smokers. In the unadjusted analysis, current smoking was significantly associated with concordant negative response (odds ratio [OR] 1.85, 95% confidence interval [CI] 1.40-2.45). Similarly, models A and B showed an increased odds of concordant negative response in current smokers (adjusted OR [aOR] 1.78, 95% CI 1.32-2.39 and 1.74, 95% CI 1.29-2.34, respectively). The association between current smoking and concordant negative response was no longer significant in model C (aOR 1.18, 95%CI 0.85-1.65).