Twelve-month effectiveness and safety of bictegravir/emtricitabine/tenofovir alafenamide in people with HIV from the Canadian cohort of the observational BICSTaR study

The BICSTaR (BICtegravir Single Tablet Regimen) study is investigating the effectiveness and safety of bictegravir/emtricitabine/tenofovir alafenamide (B/F/TAF) in people with human immunodeficiency virus (HIV) treated in routine clinical practice. BICSTaR is an ongoing, prospective, observational cohort study across 14 countries. Treatment-naïve (TN) and treatment-experienced (TE) people with HIV (≥18 years of age) are being followed for 24 months. We present an analysis of the primary endpoint (HIV-1 RNA < 50 copies/mL; missing-equals-excluded [M = E]) at month 12 in the BICSTaR Canada cohort, including secondary (CD4 count, CD4/CD8 ratio, safety/tolerability) and exploratory (persistence, treatment satisfaction) endpoints. In total, 201 participants were enrolled in the BICSTaR Canada cohort. The analysis population included 170 participants (TN, n = 10; TE, n = 160), with data collected between November 2018 and September 2020. Of the participants, 88% were male, 72% were White, and 90% had ≥ 1 comorbid condition(s). Median (quartile [Q]1-Q3) age was 50 (39-58) years and baseline CD4 count was 391.5 (109.0-581.0) cells/µL in TN participants and 586.0 (400.0-747.0) cells/µL in TE participants. After 12 months of B/F/TAF treatment, HIV-1 RNA was < 50 copies/mL in 100% (9/9) of TN-active participants and 97% (140/145) of TE-active participants (M = E analysis). Median (Q1-Q3) CD4 cell count increased by +195 (125-307) cells/µL in TN participants and by + 30 (-50 to 123) cells/µL in TE participants. Persistence on B/F/TAF was high through month 12 with 10% (1/10) of TN and 7 % (11/160) of TE participants discontinuing B/F/TAF within 12 months of initiation of treatment. No resistance to B/F/TAF emerged. Study drug-related adverse events occurred in 7% (12/169) of participants, leading to B/F/TAF discontinuation in 4 of 169 participants. Improvements in treatment satisfaction were observed in TE participants. B/F/TAF demonstrated high levels of effectiveness, persistence, and treatment satisfaction, and was well tolerated through month 12 in people with HIV treated in routine clinical practice in Canada.

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.

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.

Evaluation and 1-year follow-up of patients presenting at a Lyme borreliosis expertise centre: a prospective cohort study with validated questionnaires

OBJECTIVES

To describe the course of symptoms reported by patients with symptoms attributed to Lyme borreliosis (LB) without being subsequently diagnosed with LB.

METHODS

We performed a prospective cohort study with patients presenting at the outpatient clinic of two clinical LB centres. The primary outcome was the prevalence of persistent symptoms, which were defined as clinically relevant fatigue (CIS, subscale fatigue), pain (SF-36, subscale bodily pain), and cognitive impairment (CFQ) for ≥ 6 months and onset < 6 months over the first year of follow-up. Outcomes were compared with a longitudinal cohort of confirmed LB patients and a general population cohort. Prevalences were standardised to the distribution of pre-defined confounders in the confirmed LB cohort.

RESULTS

Participants (n = 123) reported mostly fatigue, arthralgia, myalgia, and paraesthesia as symptoms. The primary outcome could be determined for 74.8% (92/123) of participants. The standardised prevalence of persistent symptoms in our participants was 58.6%, which was higher than in patients with confirmed LB at baseline (27.2%, p < 0.0001) and the population cohort (21.2%, p < 0.0001). Participants reported overall improvement of fatigue (p < 0.0001) and pain (p < 0.0001) but not for cognitive impairment (p = 0.062) during the follow-up, though symptom severity at the end of follow-up remained greater compared to confirmed LB patients (various comparisons p < 0.05).

CONCLUSION

Patients with symptoms attributed to LB who present at clinical LB centres without physician-confirmed LB more often report persistent symptoms and report more severe symptoms compared to confirmed LB patients and a population cohort.

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.

Achievement of Target Gain Larger than Unity in an Inertial Fusion Experiment

On December 5, 2022, an indirect drive fusion implosion on the National Ignition Facility (NIF) achieved a target gain G_{target} of 1.5. This is the first laboratory demonstration of exceeding "scientific breakeven" (or G_{target}>1) where 2.05 MJ of 351 nm laser light produced 3.1 MJ of total fusion yield, a result which significantly exceeds the Lawson criterion for fusion ignition as reported in a previous NIF implosion [H. Abu-Shawareb et al. (Indirect Drive ICF Collaboration), Phys. Rev. Lett. 129, 075001 (2022)PRLTAO0031-900710.1103/PhysRevLett.129.075001]. This achievement is the culmination of more than five decades of research and gives proof that laboratory fusion, based on fundamental physics principles, is possible. This Letter reports on the target, laser, design, and experimental advancements that led to this result.

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.

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.

Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging

The fast and accurate identification of MPs in environmental samples is essential for the understanding of the fate and transport of MPs in ecosystems. The recognition of MPs in environmental samples by spectral classification using conventional library search routines can be challenging due to the presence of additives, surface modification, and adsorbed contaminants. Further, the thickness of MPs also impacts the shape of spectra when FTIR spectra are collected in transmission mode. To overcome these challenges, PlasticNet, a deep learning convolutional neural network architecture, was developed for enhanced MP recognition. Once trained with 8000 + spectra of virgin plastic, PlasticNet successfully classified 11 types of common plastic with accuracy higher than 95%. The errors in identification as indicated by a confusion matrix were found to be caused by edge effects, molecular similarity of plastics, and the contamination of standards. When PlasticNet was trained with spectra of virgin plastic it showed good performance (92%+) in recognizing spectra that had increased complexity due to the presence of additives and weathering. The re-training of PlasticNet with more complex spectra further enhanced the model's capability to recognize complex spectra. PlasticNet was also able to successfully identify MPs despite variations in spectra caused by variations in MP thickness. When compared with the performance of the library search in identifying MPs in the same complex dataset collected from an environmental sample, PlasticNet achieved comparable performance in identifying PP MPs, but a 17.3% improvement. PlasticNet has the potential to become a standard approach for rapid and accurate automatic recognition of MPs in environmental samples analyzed by FPA FT-IR imaging.

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.

COVID-Net Biochem: an explainability-driven framework to building machine learning models for predicting survival and kidney injury of COVID-19 patients from clinical and biochemistry data

Since the World Health Organization declared COVID-19 a pandemic in 2020, the global community has faced ongoing challenges in controlling and mitigating the transmission of the SARS-CoV-2 virus, as well as its evolving subvariants and recombinants. A significant challenge during the pandemic has not only been the accurate detection of positive cases but also the efficient prediction of risks associated with complications and patient survival probabilities. These tasks entail considerable clinical resource allocation and attention. In this study, we introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization, utilizing clinical and biochemical data in a transparent, systematic approach. The proposed approach advances machine learning model design by seamlessly integrating domain expertise with explainability tools, enabling model decisions to be based on key biomarkers. This fosters a more transparent and interpretable decision-making process made by machines specifically for medical applications. More specifically, the framework comprises two phases: In the first phase, referred to as the "clinician-guided design" phase, the dataset is preprocessed using explainable AI and domain expert input. To better demonstrate this phase, we prepared a benchmark dataset of carefully curated clinical and biochemical markers based on clinician assessments for survival and kidney injury prediction in COVID-19 patients. This dataset was selected from a patient cohort of 1366 individuals at Stony Brook University. Moreover, we designed and trained a diverse collection of machine learning models, encompassing gradient-based boosting tree architectures and deep transformer architectures, specifically for survival and kidney injury prediction based on the selected markers. In the second phase, called the "explainability-driven design refinement" phase, the proposed framework employs explainability methods to not only gain a deeper understanding of each model's decision-making process but also to identify the overall impact of individual clinical and biochemical markers for bias identification. In this context, we used the models constructed in the previous phase for the prediction task and analyzed the explainability outcomes alongside a clinician with over 8 years of experience to gain a deeper understanding of the clinical validity of the decisions made. The explainability-driven insights obtained, in conjunction with the associated clinical feedback, are then utilized to guide and refine the training policies and architectural design iteratively. This process aims to enhance not only the prediction performance but also the clinical validity and trustworthiness of the final machine learning models. Employing the proposed explainability-driven framework, we attained 93.55% accuracy in survival prediction and 88.05% accuracy in predicting kidney injury complications. The models have been made available through an open-source platform. Although not a production-ready solution, this study aims to serve as a catalyst for clinical scientists, machine learning researchers, and citizen scientists to develop innovative and trustworthy clinical decision support solutions, ultimately assisting clinicians worldwide in managing pandemic outcomes.

Neratinib + fulvestrant + trastuzumab for HR-positive, HER2-negative, HER2-mutant metastatic breast cancer: outcomes and biomarker analysis from the SUMMIT trial

BACKGROUND

HER2 mutations are targetable alterations in patients with hormone receptor-positive (HR+) metastatic breast cancer (MBC). In the SUMMIT basket study, patients with HER2-mutant MBC received neratinib monotherapy, neratinib + fulvestrant, or neratinib + fulvestrant + trastuzumab (N + F + T). We report results from 71 patients with HR+, HER2-mutant MBC, including 21 (seven in each arm) from a randomized substudy of fulvestrant versus fulvestrant + trastuzumab (F + T) versus N + F + T.

PATIENTS AND METHODS

Patients with HR+ HER2-negative MBC with activating HER2 mutation(s) and prior cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) therapy received N + F + T (oral neratinib 240 mg/day with loperamide prophylaxis, intramuscular fulvestrant 500 mg on days 1, 15, and 29 of cycle 1 then q4w, intravenous trastuzumab 8 mg/kg then 6 mg/kg q3w) or F + T or fulvestrant alone. Those whose disease progressed on F + T or fulvestrant could cross-over to N + F + T. Efficacy endpoints included investigator-assessed objective response rate (ORR), clinical benefit rate (RECIST v1.1), duration of response, and progression-free survival (PFS). Plasma and/or formalin-fixed paraffin-embedded tissue samples were collected at baseline; plasma was collected during and at end of treatment. Extracted DNA was analyzed by next-generation sequencing.

RESULTS

ORR for 57 N + F + T-treated patients was 39% [95% confidence interval (CI) 26% to 52%); median PFS was 8.3 months (95% CI 6.0-15.1 months). No responses occurred in fulvestrant- or F + T-treated patients; responses in patients crossing over to N + F + T supported the requirement for neratinib in the triplet. Responses were observed in patients with ductal and lobular histology, 1 or ≥1 HER2 mutations, and co-occurring HER3 mutations. Longitudinal circulating tumor DNA sequencing revealed acquisition of additional HER2 alterations, and mutations in genes including PIK3CA, enabling further precision targeting and possible re-response.

CONCLUSIONS

The benefit of N + F + T for HR+ HER2-mutant MBC after progression on CDK4/6is is clinically meaningful and, based on this study, N + F + T has been included in the National Comprehensive Cancer Network treatment guidelines. SUMMIT has improved our understanding of the translational implications of targeting HER2 mutations with neratinib-based therapy.

Impact of Concurrent Targeted Therapy and Immunotherapy on the Incidence of Radiation Necrosis Following Stereotactic Radiosurgery for Brain Metastases

PURPOSE/OBJECTIVE(S)

The management of metastatic disease has evolved with the advent of immunotherapy (IT) and targeted therapies (TT). Yet, there is limited understanding of the toxicity associated with combining these agents with stereotactic radiosurgery (SRS). We retrospectively evaluated the impact of concurrent systemic therapy (ST) on the risk of radiation necrosis (RN) following LINAC-based SRS for brain metastases (BM).

MATERIALS/METHODS

A retrospective study was conducted within an integrated health care system from March 2017 to December 2021 of 313 patients who underwent SRS or fractionated SRS in 3 or 5 treatments to a total of 1,644 intact BM. Post-operative cavity SRS and re-irradiated lesions were excluded. RN was diagnosed using perfusion MRI, contrast clearance MRI, or serial standard MRI and graded using CTCAE (v.5). Concurrent ST was defined as administration within 1 month preceding or following SRS. Overall survival (OS) and risk of RN were estimated by the Kaplan-Meier method. Logistics regression analyses were performed to compare risk of RN in patients who received concurrent systemic therapy to those who did not, adjusted for PTV volume and receipt of whole brain radiotherapy (WBRT).

RESULTS

Median follow-up was 12.2 months. Median age was 64 years (range: 24-92). Primary sites per patient included lung (48.9%), breast (18.2%), melanoma (11.5%), kidney (6.1%), and other (15.3%). Median total lesions treated was 3 (range: 1-44); 65.9% of patients underwent 1 course of SRS, 23.4% underwent 2 courses, 6.2% underwent 3 courses, 4.5% underwent >4 courses. Seventy-six (24.2%) patients received WBRT. Overall, 70.6% of lesions received concurrent ST including chemotherapy (CT) (32.5%), IT (26.8%), and TT (27.6%); 16.4% received a combination of ST. Median OS was 12.9 months (95% CI: 10.4-15.5). RN was observed in 50 (3.0%) lesions in 42 (13.4%) patients. The 1-year risk of RN was 4.0% per lesion and 15.4% per patient. Symptomatic RN (SRN) was observed in 31 (1.9%) lesions in 24 (7.7%) patients. The 1-year risk of SRN was 2.7% per lesion and 10.1% per patient. When compared to lesions treated without concurrent systemic therapy, there was no increased risk of RN observed in lesions treated with concurrent CT (adjusted OR = 0.86 (95% CI: 0.43-1.73) p = 0.68), concurrent IT (adjusted OR = 0.84 (95% CI: 0.41-1.71) p = 0.84), or concurrent TT (adjusted OR = 0.57 (95% CI: 0.25-1.30) p = 0.18). Treatments of SRN included dexamethasone (96.8%), bevacizumab (22.6%), and laser interstitial thermal therapy (6.5%).

CONCLUSION

Concurrent IT and TT appears well-tolerated in patients who undergo SRS for treatment of BM. No increased risk of RN was observed in lesions treated with concurrent IT or TT compared to lesions treated in the absence of concurrent ST. Further prospective and agent-specific evaluation is necessary to confirm these findings.

High-Dose Rate Brachytherapy Alone for Treatment of Unfavorable Intermediate Risk Prostate Cancer: A Propensity-Score Matched Analysis

PURPOSE/OBJECTIVE(S)

To demonstrate the feasibility of high-dose rate brachytherapy (HDR BT) as monotherapy for unfavorable intermediate risk (UIR) prostate cancer by comparing survival outcomes of HDR BT alone against external beam radiation therapy (EBRT) + HDR BT boost, +/- androgen deprivation therapy (ADT) using propensity-score matched (PSM) data.

MATERIALS/METHODS

This retrospective study queried two data registries collecting patient data from 1991 to present. 633 patients with UIR prostate cancer treated with HDR BT alone, HDR BT+EBRT or HDR+EBRT+ADT were included. HDR BT patients received 42-45Gy/6 fractions (fx) or 27 Gy/2 fx. For HDR BT+EBRT, the HDR dose was 20-24 Gy/2 fx, 24 Gy/4 fx, or 15 Gy/1 fx. EBRT patients received 45 Gy/25 fx to the prostate +/- pelvic nodes. GU/GI toxicities were graded according to the Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Time-to-event analyses were carried out to evaluate the relationship between treatments and five primary endpoints of interest: freedom from biochemical recurrence (FFBC), freedom from distant metastasis (FFDM), freedom from local failure (FFLF), cancer specific survival (CSS), and overall survival (OS) at 5 years. PSM was performed with one-to-n matching. Logistic regression was used to estimate the respective propensity scores. The five potential confounders identified were T-stage, Gleason score, pre-treatment PSA, age, and percent positive cores. Balance was checked using the standardized mean difference of covariates. Univariate and multivariate analyses were conducted on the matched data. Toxicity analysis was performed via association between a change in pre- and post-treatment GU/GI toxicity status and the treatment group, as well as incidence of post-treatment severe GI/GU toxicity (grade 3 or higher) and the treatment group.

RESULTS

Univariate analysis with Kaplan-Meier method and log rank test comparison between the three cohorts demonstrated no significant difference in all survival outcomes FFBC, FFDM, FFLF, CSS, OS (p = 0.15, 0.19, 0.29, 0.57, 0.28, respectively). Multivariate analysis with Cox proportional hazard regression showed no differences in HR for FFBC and OS (p = 0.95, 0.11) with addition of EBRT, or with EBRT+ADT (p = 0.17, 0.24); no fit was obtainable for FFDM, CSS, FFLF. Toxicities between the three cohorts were not significantly different when comparing post-treatment and baseline GI/GU symptoms (p = 0.53/1). No Grade 2 or 3 GI toxicities were identified, while 8%/1% HDR patients, 10%/1% HDR+EBRT patients, and 12%/2% HDR+EBRT+ADT patients experienced Grade 2/3 GU toxicities. The incidence of grade 3 or higher GU toxicities between the three groups was not significantly different (p = 0.91).

CONCLUSION

This propensity-score matched study demonstrates the feasibility of HDR BT alone for effective treatment of UIR prostate cancer when compared to HDR+EBRT or HDR+EBRT+ADT, while potentially minimizing the added toxicities of EBRT and the undesirable side effect profile of ADT.

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.

Immune response to COVID-19 vaccination in patients with Waldenström macroglobulinaemia who pause their BTKi therapy

Patients with Waldenström macroglobulinaemia (WM) are at increased risk of severe COVID-19 infection and have poor immune responses to COVID-19 vaccination. This study assessed whether a closely monitored pause in Bruton's Tyrosine Kinase inhibitor (BTKi) therapy might result in an improved humoral response to a 3rd COVID-19 vaccine dose. Improved response was observed in WM patients who paused their BTKi, compared to a group who did not pause their BTKi. However, the response was attenuated after BTKi recommencement. This data contributes to our understanding of vaccination strategies in this patient group and may help inform consensus approaches in the future.

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.

A competitive, bead-based assay combined with microfluidics for multiplexed toxin detection

The requirement for rapid, in-field detection of cyanotoxins in water resources necessitates the developing of an easy-to-use and miniaturized system for their detection. We present a novel bead-based, competitive fluorescence assay for multiplexed detection of two types of toxins: microcystin-LR (MC-LR) and okadaic acid (OA). To automate the detection process, a reusable microfluidic device, termed toxin-chip, was designed and validated. The toxin-chip consists of a micromixer where the target toxins were efficiently mixed with a reagent solution, and a detection chamber for magnetic retainment of beads for downstream analysis. Quantum dots (QDs) were used as the reporter molecules to enhance the sensitivity of the assay and the emitted fluorescence signal from QDs was reversely proportional to the amount of toxins in the solution. An image analysis program was also developed to further automate the detection and analysis steps. Two toxins were simultaneously analyzed on a single microfluidic chip, and the device exhibited a low detection limit of 10^-4 μg ml^-1 for MC-LR and 4 × 10^-5 μg ml^-1 for OA detection. The bead-based, competitive assay also showed remarkable chemical specificity against potential interfering toxins. We also validated the device performance using natural lake water samples from Sunfish Lake of Waterloo. The toxin-chip holds promise as a versatile and simple quantification tool for cyanotoxin detection, with the potential of detecting more toxins.

The rate of hepatitis C reinfection in Canadians coinfected with HIV and its implications for national elimination

BACKGROUND

The World Health Organisation (WHO) has set targets for the rate of new infections as a way to measure progress towards the elimination of hepatitis C virus (HCV) as a public health threat. As more people are successfully treated for HCV, a higher proportion of new infections will be reinfections. We consider whether the reinfection rate has changed since the interferon era and what we can infer about national elimination efforts from the current reinfection rate.

METHODS

The Canadian Coinfection Cohort is representative of HIV HCV coinfected people in clinical care. We selected cohort participants successfully treated for a primary HCV infection either in the interferon era or in the era of direct acting antivirals (DAAs). Selected participants were followed from 12 weeks after completing a successful treatment until the end of 2019 or until their last measured HCV RNA. We estimated the reinfection rate in each treatment era, overall and in participant subgroups, using proportional hazard models appropriate for interval censored data.

RESULTS

Among 814 successfully treated participants with additional HCV RNA measurements, there were 62 reinfections. The overall reinfection rate was 2.6 (95% confidence interval, CI, 1.2-4.1) /100 person years (PY) in the interferon era and 3.4 (95% CI 2.5-4.4) /100 PY in the DAA era. The rate in those reporting injection drug use (IDU) was much higher: 4.7 (95% CI 1.4-7.9) /100 PY and 7.6 (95% CI 5.3-10) /100 PY in the interferon and DAA eras respectively.

CONCLUSION

The overall reinfection rate in our cohort is now above the WHO target set for new infections in people who inject drugs. The reinfection rate in those reporting IDU has increased since the interferon era. This suggests Canada is not on track to achieve HCV elimination by 2030.

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.

Long-acting cabotegravir and rilpivirine dosed every 2 months in adults with HIV-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 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) vs. 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; two consecutive measurements of ≥200 copies/mL), safety, and tolerability.

RESULTS

1045 participants received CAB+RPV LA (Q8W, n=522; Q4W, n=523). CAB+RPV LA Q8W demonstrated noninferior efficacy vs. Q4W dosing, with 2.7% (n=14) and 1.0% (n=5) of participants having HIV-1 RNA ≥50 copies/mL, respectively. The adjusted treatment difference in proportions was 1.7% (95% CI 0.1 to 3.3), meeting the pre-specified noninferiority threshold of 4%. 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 two (0.4%) participants 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 the durability of virologic suppression with CAB+RPV LA Q8W or Q4W for ∼3 years, and confirm the long-term efficacy, safety, and tolerability of CAB+RPV LA as a complete regimen for the maintenance of HIV-1 virologic suppression.

A two-ward acute care hospital outbreak of SARS-CoV-2 delta variant including a point-source outbreak associated with the use of a mobile vital signs cart and sub-optimal doffing of personal protective equipment

BACKGROUND

The arrival of the Delta variant of SARS-CoV-2 was associated with increased transmissibility and illness of greater severity. Reports of nosocomial outbreaks of Delta variant COVID-19 in acute care hospitals have been described but control measures varied widely.

AIM

Epidemiological investigation of a linked two-ward COVID-19 Delta variant outbreak was conducted to elucidate its source, risk factors, and control measures.

METHODS

Investigations included epidemiologic analysis, detailed case review serial SARS-CoV-2 reverse transcriptase-polymerase chain reaction (RT-PCR) testing of patients and healthcare workers (HCWs), viral culture, environmental swabbing, HCW-unaware personal protective equipment (PPE) audits, ventilation assessments, and the use of whole genome sequencing (WGS).

FINDINGS

This linked two-ward outbreak resulted in 17 patient and 12 HCW cases, despite an 83% vaccination rate. In this setting, suboptimal adherence and compliance to PPE protocols, suboptimal hand hygiene, multi-bedded rooms, and a contaminated vital signs cart with potential fomite or spread via the hands of HCWs were identified as significant risk factors for nosocomial COVID-19 infection. Sudden onset of symptoms, within 72 h, was observed in 79% of all Ward 2 patients, and 93% of all cases (patients and HCWs) on Ward 2 occurred within one incubation period, consistent with a point-source outbreak. RT-PCR assays showed low cycle threshold (C_T) values, indicating high viral load from environmental swabs including the vital signs cart. WGS results with ≤3 SNP differences between specimens were observed.

CONCLUSION

Outbreaks on both wards settled rapidly, within 3 weeks, using a `back-to-basics' approach without extraordinary measures or changes to standard PPE requirements. Strict adherence to recommended PPE, hand hygiene, education, co-operation from HCWs, including testing and interviews, and additional measures such as limiting movement of patients and staff temporarily were all deemed to have contributed to prompt resolution of the outbreak.

Hallway Gait Monitoring System Using an In-Package Integrated Dielectric Lens Paired with a mm-Wave Radar

This paper presents a novel hallway gait extraction system that enables an individual's spatiotemporal gait parameter extraction at each gait cycle using a single FMCW (Frequency Modulated Continuous Wave) radar. The purpose of the proposed system is to detect changes in gait that may be the signs of changes in mobility, cognition, and frailty, particularly for older adults in retirement homes. We believe that one of the straightforward applications for gait monitoring using radars is in corridors and hallways, which are commonly available in most retirement and long-term care homes. To achieve in-corridor coverage, we designed an in-package hyperbola-based lens antenna integrated with a radar module package empowered by our fast and easy-to-implement gait extraction method. We validated system functionality by capturing spatiotemporal gait values (e.g., speed, step points, step time, step length, and step count) of people walking in a hallway. The results achieved in this work pave the way to explore the use of stand-alone radar-based sensors in long hallways in retirement apartment buildings or individual's homes for use in day-to-day long-term monitoring of gait parameters of older adults.

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.).

Exploring the dynamic transitions of polysubstance use patterns among Canadian youth using Latent Markov Models on COMPASS data

Background

Understanding what factors lead to youth polysubstance use (PSU) patterns and how the transitions between use patterns can inform the design and implementation of PSU prevention programs. We explore the dynamics of PSU patterns from a large cohort of Canadian secondary school students using machine learning techniques.

Methods

We employed a multivariate latent Markov model (LMM) on COMPASS data, with a linked sample (N = 8824) of three-annual waves, Wave I (W_I, 2016-17, as baseline), Wave II (W_II, 2017-18), and Wave III (W_III, 2018-19). Substance use indicators, i.e., cigarette, e-cigarette, alcohol and marijuana, were self-reported and were categorized into never/occasional/current use.

Outcomes

Four distinct use patterns were identified: no-use (S1), single-use of alcohol (S2), dual-use of e-cigarettes and alcohol (S3), and multi-use (S4). S1 had the highest prevalence (60.5%) at W_I, however, S3 became the prominent use pattern (32.5%) by W_III. Most students remained in the same subgroup over time, particularly S4 had the highest transition probability (0.87) across the three-wave. With time, those who transitioned typically moved towards a higher use pattern, with the most and least likely transition occurring S2→S3 (0.45) and S3→S2 (<0.01), respectively. Among all covariates being examined, truancy, being measured by the # of classes skipped, significantly affected transition probabilities from any low→high (e.g., OR_S2→S4 = 2.41, 95% CI [2.11, 2.72], p < 0.00001) and high→low (e.g., OR_S3→S1 = 0.38, 95% CI [0.33, 0.44], p < 0.00001) use directions over time. Older students, blacks (vs. whites), and breakfast eaters were less likely to transition from low→high use direction. Students with more weekly allowance, with more friends that smoked, longer sedentary time, and attended attended school unsupportive to resist or quit drug/alcohol were more likely to transition from low→high use direction. Except for truancy, all other covariates had inconsistent effects on the transition probabilities from the high→low use direction.

Interpretation

This is the first study to ascertain the dynamics of use patterns and factors in youth PSU utilizing LMM with population-based longitudinal health surveys, providing evidence in developing programs to prevent youth PSU.

Funding

The Applied Health Sciences scholarship; the Microsoft AI for Good grant; the Canadian Institutes of Health Research, Health Canada, the Canadian Centre on Substance Abuse, the SickKids Foundation, the Ministère de la Santé et des Services sociaux of the province of Québec.

Evaluation of a prescribing pharmacist-led heart failure (HF) up-titration clinic

Background/Introduction

Pharmacotherapy is considered the cornerstone of treatment for patients with heart failure with reduced ejection fraction (HFrEF). Modulation of the renin-angiotensin-aldosterone (RAAS) and sympathetic nervous system have been shown to improve survival, reduce the risk of heart failure (HF) hospitalisations and improve quality of life. Unless contraindicated or not tolerated, these patients should be prescribed angiotensin-converting enzyme inhibitor (ACEi)/angiotensin-receptor blocker (ARB)/angiotensin receptor-neprilysin inhibitor (ARNI), beta-blocker, mineralocorticoid receptor antagonist (MRA) and sodium-glucose co-transporter 2 (SGLT2) inhibitor and up-titrated to guideline directed dosages.1 Despite the availability of effective treatments, patients are often sub-optimally dosed and experience a prolonged optimisation process. Pharmacist-led clinics can complement existing services and increase uptake of guideline directed medical therapies (GDMT).

Purpose

To evaluate the effectiveness of pharmacist-led heart failure clinics in the Princess of Wales Hospital, supported by the Heart Failure team, in optimisation of patients with HFrEF.

Methods

Patients who attended HF pharmacist led clinic between November 2020 and November 2021 were included in the analysis. Baseline demographics, left ventricular ejection fraction (LVEF), biomarkers, vital signs and medications were documented at baseline and after optimisation of GDMT. Number of clinics appointments and rate of hospitalisation and mortality were captured during the study period.

Results

76 patients were reviewed over a total of 318 consultations (34% virtual), averaging 4 consultations per patient. The mean age was 71 years, 36% of patients were female, mean LVEF was 32% and NT-proBNP 3078ng/L with 15% of patients on “quadruple therapy” at baseline. 93% of patients were referred by cardiologists and the average time from referral to first appointment was 42 days. Post-optimisation, 67% of patients were receiving “quadruple therapy”, with increased uptake of ACEi/ARB/ARNI, beta-blocker, MRA and SGLT2i from baseline (100%, 77%, 93% and 90% respectively). Mean LVEF increased to 42% and mean NT-proBNP reduced to 1695ng/L. No significant changes in creatinine clearance, blood pressure, heart rate or serum potassium (sK+) were recorded. The hospitalisation rate was 11% for heart failure, 6% for other cardiovascular causes and the mortality rate was 7%.

Conclusions

The pharmacist-led HF clinic demonstrated a high uptake of optimally dosed GDMT and improvements in NT-proBNP and LVEF. Pharmacist-led clinics can enhance the provision of existing heart failure services to improve patient outcomes and heart failure care.

Funding Acknowledgement

Type of funding sources: None.

A single-center comparison of our initial experiences in treating penile and urethral cancer with video-endoscopic inguinal lymphadenectomy (VEIL) and later experiences in melanoma cases

Background

Video-endoscopic inguinal lymphadenectomy (VEIL) is a minimally invasive approach that is increasingly indicated in oncological settings, with mounting evidence for its long-term oncological safety.

Objectives

To present our single-center experience of treating penile and urethral cancer with VEIL, as well as its more recent application in melanoma patients.

Methods

We prospectively recorded our experiences with VEIL from September 2010 to July 2018, registering the patient primary indication, surgical details, complications, and follow-up.

Results

Twenty-nine patients were operated in one (24) or both (5) groins; 18 had penile cancer, 1 had urethral cancer, and 10 had melanoma. A mean 8.62 ± 4.45 lymph nodes were removed using VEIL and of these, an average of 1.00 ± 2.87 were metastatic; 16 patients developed lymphocele and 10 presented some degree of lymphedema; there were no skin or other major complications. The median follow-up was 19.35 months; there were 3 penile cancer patient recurrences in the VEIL-operated side. None of the melanoma patients presented a lymphatic inguinal recurrence.

Conclusions

VEIL is a minimally invasive technique which appears to be oncologically safe showing fewer complications than open surgery. However, complications such as lymphorrhea, lymphocele, or lymphedema were not diminished by using VEIL.

Nationwide retrospective study of hepatitis B virological response and liver stiffness improvement in 465 patients on nucleos(t)ide analogue

BACKGROUND

Hepatitis B virus (HBV) nucleos(t)ide analog (NA) therapy reduces liver disease but requires prolonged therapy to achieve hepatitis B surface antigen (HBsAg) loss. There is limited North American real-world data using non-invasive tools for fibrosis assessment and few have compared 1^st generation NA or lamivudine (LAM) to tenofovir disoproxil fumarate (TDF).

AIM

To assess impact of NA on virological response and fibrosis regression using liver stiffness measurement (LSM) (i.e., FibroScan^®).

METHODS

Retrospective, observational cohort study from the Canadian HBV Network. Data collected included demographics, NA, HBV DNA, alanine aminotransferase (ALT), and LSM. Patients were HBV monoinfected patients, treatment naïve, and received 1 NA with minimum 1 year follow-up.

RESULTS

In 465 (median 49 years, 37% female, 35% hepatitis B e antigen⁺ at baseline, 84% Asian, 6% White, and 9% Black). Percentage of 64 (n = 299) received TDF and 166 were LAM-treated with similar median duration of 3.9 and 3.7 years, respectively. The mean baseline LSM was 11.2 kPa (TDF) vs 8.3 kPa (LAM) (P = 0.003). At 5-year follow-up, the mean LSM was 7.0 kPa in TDF vs 6.7 kPa in LAM (P = 0.83). There was a significant difference in fibrosis regression between groups (i.e., mean -4.2 kPa change in TDF and -1.6 kPa in LAM, P < 0.05). The last available data on treatment showed that all had normal ALT, but more TDF patients were virologically suppressed (< 10 IU/mL) (n = 170/190, 89%) vs LAM-treated (n = 35/58, 60%) (P < 0.05). None cleared HBsAg.

CONCLUSION

In this real-world North American study, approximately 5 years of NA achieves liver fibrosis regression rarely leads to HBsAg loss.

Lawson Criterion for Ignition Exceeded in an Inertial Fusion Experiment

For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition as a proof of principle of various fusion concepts. Following the Lawson criterion, an ignited plasma is one where the fusion heating power is high enough to overcome all the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop with rapidly increasing temperature. In inertially confined fusion, ignition is a state where the fusion plasma can begin "burn propagation" into surrounding cold fuel, enabling the possibility of high energy gain. While "scientific breakeven" (i.e., unity target gain) has not yet been achieved (here target gain is 0.72, 1.37 MJ of fusion for 1.92 MJ of laser energy), this Letter reports the first controlled fusion experiment, using laser indirect drive, on the National Ignition Facility to produce capsule gain (here 5.8) and reach ignition by nine different formulations of the Lawson criterion.

Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images

Background

Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and well-being but also significant economic costs associated with treatment. A crucial step to the treatment and management of skin cancer is effective early detection with key screening approaches such as dermoscopy examinations, leading to stronger recovery prognoses. Motivated by the advances of deep learning and inspired by the open source initiatives in the research community, in this study we introduce Cancer-Net SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images that is open source and available to the general public. To the best of the authors’ knowledge, Cancer-Net SCa comprises the first machine-driven design of deep neural network architectures tailored specifically for skin cancer detection, one of which leverages attention condensers for an efficient self-attention design.

Results

We investigate and audit the behaviour of Cancer-Net SCa in a responsible and transparent manner through explainability-driven performance validation. All the proposed designs achieved improved accuracy when compared to the ResNet-50 architecture while also achieving significantly reduced architectural and computational complexity. In addition, when evaluating the decision making process of the networks, it can be seen that diagnostically relevant critical factors are leveraged rather than irrelevant visual indicators and imaging artifacts.

Conclusion

The proposed Cancer-Net SCa designs achieve strong skin cancer detection performance on the International Skin Imaging Collaboration (ISIC) dataset, while providing a strong balance between computation and architectural efficiency and accuracy. While Cancer-Net SCa is not a production-ready screening solution, the hope is that the release of Cancer-Net SCa in open source, open access form will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis, treatment and follow-up of patients with prostate cancer

The most recent version of the European Society for Medical Oncology (ESMO) Clinical Practice Guidelines for the diagnosis, treatment and follow-up of prostate cancer was published in 2020. It was therefore decided, by both the ESMO and the Singapore Society of Oncology (SSO), to convene a special, virtual guidelines meeting in November 2021 to adapt the ESMO 2020 guidelines to take into account the differences associated with the treatment of prostate cancer in Asia. These guidelines represent the consensus opinions reached by experts in the treatment of patients with prostate cancer representing the oncological societies of China (CSCO), India (ISMPO), Japan (JSMO), Korea (KSMO), Malaysia (MOS), Singapore (SSO) and Taiwan (TOS). The voting was based on scientific evidence and was independent of the current treatment practices and drug access restrictions in the different Asian countries. The latter were discussed when appropriate. The aim is to provide guidance for the optimisation and harmonisation of the management of patients with prostate cancer across the different regions of Asia.

Cross-sectional comparison of age- and gender-related comorbidities in people living with HIV in Canada

Because antiretroviral therapy (ART) is allowing people living with human immunodeficiency virus (PLWH) to survive longer, they are developing more age-related comorbidities. We evaluated the effects of age and gender on the burden of age-related comorbidities among PLWH. In this retrospective real-world study, de-identified data were extracted from the medical charts of 2000 HIV-positive adults on ART across 10 sites in Canada. The prevalence of age-related comorbidities was determined in 6 age subgroups (<30, 30-39, 40-49, 50-59, 60-69, and ≥70 years). The effects of gender on these comorbidities were also examined. Risks of cardiovascular disease and chronic kidney disease (CKD) were calculated using the Framingham and D:A:D equations. Most persons were White (68%), male (87%), and virologically suppressed (94%). The mean age was 50.3 years (57% aged ≥50 years), and mean CD4+ T-cell count was 616 cells/mm3. The most common comorbidities were neuropsychiatric symptoms (61%), overweight/obesity (43%), liver disease (37%), and dyslipidemia (37%). The mean number of comorbidities increased across age subgroups (P < .001). Across all age subgroups, the prevalence of hypertension (P = .04), dyslipidemia (P = .04), CKD (P = .03), bone fragility (P = .03), and depression (P = .02) differed between males and females. Both age (P < .001) and gender (P < .001) impacted cardiovascular disease and CKD risk. Age and gender influenced the burden, types, and risks of age-related comorbidities in PLWH in this Canadian cohort. These comorbidities should be diagnosed and treated in routine clinical practice.

Real-world effectiveness of sofosbuvir/velpatasvir for the treatment of hepatitis C virus in prison settings

Background: People in prison are at high risk of hepatitis C virus (HCV) infection and often have a history of injection drug use and mental health disorders. Simple test-and-treat regimens which require minimal monitoring are critical. Methods: This integrated real-world analysis evaluated the effectiveness of once daily sofosbuvir/velpatasvir (SOF/VEL) in 20 prison cohorts across Europe and Canada. The primary outcome was sustained virological response (SVR) in the effectiveness population (EP), defined as patients with a valid SVR status. Secondary outcomes were reasons for not achieving SVR, adherence and time between HCV RNA diagnosis and SOF/VEL treatment. Results: Overall, 526 people in prison were included with 98.9% SVR achieved in the EP (n = 442). Cure rates were not compromised by drug use or existence of mental health disorders. Conclusion: SOF/VEL for 12 weeks is highly successful in prison settings and enables the implementation of a simple treatment algorithm in line with guideline recommendations and test-and-treat strategies.

COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest X-ray Images

As the COVID-19 pandemic devastates globally, the use of chest X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing continues to grow given its routine clinical use for respiratory complaint. As part of the COVID-Net open source initiative, we introduce COVID-Net CXR-2, an enhanced deep convolutional neural network design for COVID-19 detection from CXR images built using a greater quantity and diversity of patients than the original COVID-Net. We also introduce a new benchmark dataset composed of 19,203 CXR images from a multinational cohort of 16,656 patients from at least 51 countries, making it the largest, most diverse COVID-19 CXR dataset in open access form. The COVID-Net CXR-2 network achieves sensitivity and positive predictive value of 95.5 and 97.0%, respectively, and was audited in a transparent and responsible manner. Explainability-driven performance validation was used during auditing to gain deeper insights in its decision-making behavior and to ensure clinically relevant factors are leveraged for improving trust in its usage. Radiologist validation was also conducted, where select cases were reviewed and reported on by two board-certified radiologists with over 10 and 19 years of experience, respectively, and showed that the critical factors leveraged by COVID-Net CXR-2 are consistent with radiologist interpretations.

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.

POS0230 THE miR-320 FAMILY IS UPREGULATED IN FAST-PROGRESSING RADIOGRAPHIC KNEE OSTEOARTHRITIS: DATA FROM THE OSTEOARTHRITIS INITIATIVE

Background

There is an outstanding need for prognostic biomarkers to reliably detect fast-progressing knee osteoarthritis (OA) such that preventative interventions can be targeted to this patient population. MicroRNA-sequencing is an unbiased approach for comprehensive profiling of circulating microRNAs in liquid biopsies to discover novel biomarkers of disease. As negative regulators of gene expression, microRNAs hold potential not only as biomarkers, but also as mechanistic drivers of knee OA.

Objectives

To apply microRNA-sequencing to identify unique circulating microRNAs as potential biomarkers that distinguish fast-progressing radiographic knee OA from both slow- and non-progressing radiographic knee OA.

Methods

Leveraging the Osteoarthritis Initiative (OAI) longitudinal cohort, we applied our customized microRNA-sequencing pipeline [1] to blood plasma samples collected at both baseline and 4-year follow-up from 106 participants. The disease trajectory for each participant was constructed by plotting their Kellgren-Lawrence (KL) grades over an 8-year follow-up period. Based on these trajectories, we defined fast-progression as an increase from KL 0/1 at baseline to KL 3/4 by 4-year follow-up, slow-progression as an increase from KL 0/1 at baseline to KL 2/3/4 by 8-year follow-up, and non-progression as no increase from KL0/1 at baseline throughout the 8-year follow-up. Following differential expression analysis, we assessed predictive performance and identified putative gene targets for prioritized microRNAs.

Results

Comparing fast-progressors to both slow-progressors and non-progressors, we identified differentially expressed microRNAs within timepoints (i.e., 48 microRNAs at baseline and 2 microRNAs at 4-year follow-up) and across timepoints. Among these microRNAs were four members of the miR-320 family, with miR-320d showing an increase in fast-progressors at both timepoints, compared to both slow- and non-progressors. The predictive models we constructed included miR-320 members and had good accuracy (area under the receiver operating characteristic curves ranging from 82.6 to 91.9) in distinguishing fast-progressors. Putative gene targets of the miR-320 family included members of the 14-3-3 gene family (Table 1), including YWHAE, whose downregulation in OA cartilage was reported to promote deterioration [2].

Table 1.

Predicted gene targets of the miR-320 family include members of the 14-3-3 gene family.

14-3-3 gene family memberhsa-miR-320bhsa-miR-320chsa-miR-320dhsa-miR-320eSFNMMMLYWHABMMMMYWHAEVVVHYWHAGVVVMYWHAHVVVMYWHAQVVVMYWHAZVVVM

All mirDIP results with bold text indicating the prediction was among the top 1% for that microRNA/gene target pair. Letters denote the mirDIP score class with V=very high, H=high, M=medium, and L=low.

Conclusion

This microRNA-sequencing study is the first of its kind, profiling circulating microRNAs at two timepoints in 106 participants with data-driven construction of knee OA trajectories. We identify the miR-320 family of microRNAs to be associated with fast-progressing radiographic knee OA over time. While our data suggest this microRNA family could have applications as prognostic biomarkers for knee OA, and could be regulating gene targets to impact OA severity, validation of these findings in independent longitudinal cohorts is required.

References

[1]Potla P, Ali SA, Kapoor M. A bioinformatics approach to microRNA-sequencing analysis. Osteoarthritis and Cartilage Open. 2021;3(1):100131. doi: https://doi.org/10.1016/j.ocarto.2020.100131.

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Disclosure of Interests

None declared

The impact of the first wave of COVID-19 on stroke admissions across three tertiary hospitals in Brisbane

Background

COVID‐19 has caused a global shift in healthcare‐seeking behaviour; however, presentation rates with serious conditions, such as stroke in low COVID‐19‐prevalence cities, has received less attention.

Aims

To determine if there was a significant reduction in stroke admissions, delivery of acute reperfusion therapies, or increased delays to presentation during the first wave of the COVID‐19 pandemic.

Methods

A multicentre, retrospective, observational cohort study was performed across three tertiary hospitals in Brisbane, Australia. Cases were identified using ICD‐10 codes and then individually reviewed for eligibility using prespecified inclusion and exclusion criteria. All metrics were compared over 3 months from 1 March to 31 May 2020 with two corresponding 3‐month periods in 2018 and 2019.

Results

There was a mean of 2.15 (95% CI 1.87–2.48) stroke admissions per day in the examined pandemic months compared with 2.13 (95% CI 1.85–2.45) and 2.26 (95% CI 1.97–2.59) in March to May 2018 and 2019 respectively, with no significant difference found (P = 0.81). There was also no difference in rates of intravenous thrombolysis (P = 0.82), endovascular thrombectomy (P = 0.93) and time from last known well to presentation (P = 0.54). Conversely, daily emergency department presentations (including non‐stroke presentations) significantly reduced (P < 0.0001).

Conclusions

During the early months of the COVID‐19 pandemic there was no significant reduction in stroke presentations, use of acute reperfusion therapies or delays to presentation, despite a reduction in ED presentations for any cause. Our results differ from the global experience, with possible explanations, including differences in public health messaging and healthcare infrastructure.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases From Chest X-Ray Images

Tuberculosis (TB) remains a global health problem, and is the leading cause of death from an infectious disease. A crucial step in the treatment of tuberculosis is screening high risk populations and the early detection of the disease, with chest x-ray (CXR) imaging being the most widely-used imaging modality. As such, there has been significant recent interest in artificial intelligence-based TB screening solutions for use in resource-limited scenarios where there is a lack of trained healthcare workers with expertise in CXR interpretation. Motivated by this pressing need and the recent recommendation by the World Health Organization (WHO) for the use of computer-aided diagnosis of TB in place of a human reader, we introduce TB-Net, a self-attention deep convolutional neural network tailored for TB case screening. We used CXR data from a multi-national patient cohort to train and test our models. A machine-driven design exploration approach leveraging generative synthesis was used to build a highly customized deep neural network architecture with attention condensers. We conducted an explainability-driven performance validation process to validate TB-Net's decision-making behavior. Experiments on CXR data from a multi-national patient cohort showed that the proposed TB-Net is able to achieve accuracy/sensitivity/specificity of 99.86/100.0/99.71%. Radiologist validation was conducted on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, and showed consistency between radiologist interpretation and critical factors leveraged by TB-Net for TB case detection for the case where radiologists identified anomalies. The proposed TB-Net not only achieves high tuberculosis case detection performance in terms of sensitivity and specificity, but also leverages clinically relevant critical factors in its decision making process. While not a production-ready solution, we hope that the open-source release of TB-Net as part of the COVID-Net initiative will support researchers, clinicians, and citizen data scientists in advancing this field in the fight against this global public health crisis.