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PURPOSE: To assess ChatGPT, Bard, and BingChat's ability to generate accurate orthopaedic diagnosis or corresponding treatments by comparing their performance on the Orthopaedic In-Training Examination (OITE) to orthopaedic trainees. METHODS: OITE question sets from 2021 and 2022 were compiled to form a large set of 420 questions. ChatGPT (GPT3.5), Bard, and BingChat were instructed to select one of the provided responses to each question. The accuracy of composite questions was recorded and comparatively analyzed to human cohorts including medical students and orthopaedic residents, stratified by post-graduate year. RESULTS: ChatGPT correctly answered 46.3% of composite questions whereas BingChat correctly answered 52.4% and Bard correctly answered 51.4% of questions on the OITE. Upon excluding image-associated questions, ChatGPT, BingChat, and Bard's overall accuracies improved to 49.1%, 53.5%, and 56.8%, respectively. Medical students and orthopaedic residents (PGY1-5) correctly answered 30.8%, 53.1%, 60.4%, 66.6%, 70.0%, and 71.9%, respectively. CONCLUSION: ChatGPT, Bard, and BingChat are AI models that answered OITE questions with an accuracy similar to that of first-year orthopaedic surgery residents. ChatGPT, Bard, and BingChat achieved this result without using images or other supplementary media that human test takers are provided.
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The prevalence of mixed hepatitis C virus (HCV) genotype infection in a representative Canadian HCV cohort is reported and virological response with direct acting antiviral (DAA) treatment was evaluated. 3272 HCV-positive participants were enrolled, of which 2945 (90.0%) initiated DAA therapy. 0.8% were identified with mixed genotype infection. Overall sustained virological response (SVR) was 99.1% and did not differ based on mixed genotype status. Any historical disadvantage to achieving cure with HCV treatment in mixed genotype infection has been overcome by current DAA regimens.
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Germicidal UV 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 the 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 with 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 of UV-C at lower doses in small plots suggests that such treatments merit further evaluation in larger-scale studies in Western Oregon.
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Background: The cascade of care, commonly used to assess HIV and hepatitis C (HCV) health service delivery, has limitations in capturing the complexity of individuals' engagement patterns. This study examines the dynamic nature of engagement and mortality trajectories among people with HIV and HCV. Methods: We used data from the Canadian HIV-HCV Co-Infection Cohort, which prospectively follows 2098 participants from 18 centers biannually. Markov multistate models were used to evaluate sociodemographic and clinical factors associated with transitioning between the following states: (1) lost-to-follow-up (LTFU), defined as no visit for 18 months; (2) reengaged (reentry into cohort after being LTFU); (3) withdrawn from the study (ie, moved); (4) death; otherwise remained (5) engaged-in-care. Results: A total of 1809 participants met the eligibility criteria and contributed 12 591 person-years from 2003 to 2022. LTFU was common, with 46% experiencing at least 1 episode, of whom only 57% reengaged. One in 5 (n = 383) participants died during the study. Participants who transitioned to LTFU were twice as likely to die as those who were consistently engaged. Factors associated with transitioning to LTFU included detectable HCV RNA (adjusted hazards ratio [aHR], 1.37; 95% confidence interval [CI], 1.13-1.67), evidence of HCV treatment but no sustained virologic response result (aHR, 1.99; 95% CI, 1.56-2.53), and recent incarceration (aHR, 1.94; 95% CI, 1.58-2.40). Being Indigenous was a significant predictor of death across all engagement trajectories. Interpretation: Disengagement from clinical care was common and resulted in higher death rates. People LTFU were more likely to require HCV treatment highlighting a priority population for elimination strategies.
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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 RNAâ <â 50 copies/mL; missing-equals-excluded [Mâ =â 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, nâ =â 10; TE, nâ =â 160), with data collected between November 2018 and September 2020. Of the participants, 88% were male, 72% were White, and 90% hadâ ≥â 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 wasâ <â 50 copies/mL in 100% (9/9) of TN-active participants and 97% (140/145) of TE-active participants (Mâ =â E analysis). Median (Q1-Q3) CD4 cell count increased byâ +195 (125-307) cells/µL in TN participants and byâ +â 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.
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Alanina , Amidas , Fármacos Anti-HIV , Infecções por HIV , HIV-1 , Piperazinas , Piridonas , Tenofovir/análogos & derivados , Masculino , Humanos , Pré-Escolar , Pessoa de Meia-Idade , Feminino , Infecções por HIV/tratamento farmacológico , Emtricitabina/efeitos adversos , Estudos Prospectivos , Adenina/uso terapêutico , Resultado do Tratamento , Canadá , Compostos Heterocíclicos com 3 Anéis/uso terapêutico , Fármacos Anti-HIV/efeitos adversos , Combinação de Medicamentos , RNARESUMO
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.
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Imageamento por Ressonância Magnética , Tomografia Computadorizada por Raios X , Humanos , Raios X , Radiografia , UltrassonografiaRESUMO
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.
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COVID-19 , Sistemas de Apoio a Decisões Clínicas , Humanos , Inteligência Artificial , Aprendizagem , UltrassonografiaRESUMO
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.
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Mitose , Fatores de Transcrição , Transcrição Gênica , Humanos , Fatores de Transcrição/metabolismo , Animais , DNA/metabolismo , Regulação da Expressão GênicaRESUMO
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.
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Fragilidade , Humanos , Idoso , Radar , Reprodutibilidade dos Testes , Vida Independente , Velocidade de Caminhada , MarchaRESUMO
Advances in artificial intelligence and machine learning models, like Chat Generative Pre-trained Transformer (ChatGPT), have occurred at a remarkably fast rate. OpenAI released its newest model of ChatGPT, GPT-4, in March 2023. It offers a wide range of medical applications. The model has demonstrated notable proficiency on many medical board examinations. This study sought to assess GPT-4's performance on the Orthopaedic In-Training Examination (OITE) used to prepare residents for the American Board of Orthopaedic Surgery (ABOS) Part I Examination. The data gathered from GPT-4's performance were additionally compared with the data of the previous iteration of ChatGPT, GPT-3.5, which was released 4 months before GPT-4. GPT-4 correctly answered 251 of the 396 attempted questions (63.4%), whereas GPT-3.5 correctly answered 46.3% of 410 attempted questions. GPT-4 was significantly more accurate than GPT-3.5 on orthopedic board-style questions (P<.00001). GPT-4's performance is most comparable to that of an average third-year orthopedic surgery resident, while GPT-3.5 performed below an average orthopedic intern. GPT-4's overall accuracy was just below the approximate threshold that indicates a likely pass on the ABOS Part I Examination. Our results demonstrate significant improvements in OpenAI's newest model, GPT-4. Future studies should assess potential clinical applications as AI models continue to be trained on larger data sets and offer more capabilities. [Orthopedics. 2024;47(2):e85-e89.].
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Internato e Residência , Procedimentos Ortopédicos , Ortopedia , Humanos , Ortopedia/educação , Inteligência Artificial , Avaliação Educacional , Competência ClínicaRESUMO
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.
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Percepção de Forma , Córtex Visual , Córtex Visual/fisiologia , Estimulação Luminosa/métodos , Percepção de Forma/fisiologia , Percepção Visual/fisiologia , AprendizagemRESUMO
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.
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Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Pneumonia , Humanos , Neoplasias Pulmonares/complicações , Neoplasias Pulmonares/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Inibidores de Checkpoint Imunológico/efeitos adversos , Estudos Retrospectivos , Pneumonia/etiologia , Pneumonia/complicaçõesRESUMO
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.
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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.
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COVID-19 , Aprendizado Profundo , Melanoma , Humanos , Inteligência Artificial , COVID-19/diagnóstico , BenchmarkingRESUMO
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.
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Injúria Renal Aguda , COVID-19 , Humanos , SARS-CoV-2 , Injúria Renal Aguda/etiologia , Rim , BiomarcadoresRESUMO
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.
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Aprendizado Profundo , Poluentes Químicos da Água , Microplásticos , Plásticos , Espectroscopia de Infravermelho com Transformada de Fourier , Monitoramento Ambiental/métodos , Ecossistema , Poluentes Químicos da Água/análiseRESUMO
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.
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We report the controlled synthesis of ultra-high molecular weight (UHMW) polymers (Mn ≥ 106 g/mol) via continuous flow in a tubular reactor. At high monomer conversion, UHMW polymers in homogeneous batch polymerization exhibit high viscosities that pose challenges for employing continuous flow reactors. However, under heterogeneous inverse miniemulsion (IME) conditions, UHMW polymers can be produced within the dispersed phase, while the viscosity of the heterogeneous mixture remains approximately the same as the viscosity of the continuous phase. Conducting such IME polymerizations in flow results in a faster rate of polymerization compared to batch IME polymerizations while still providing excellent control over molecular weight up to 106 g/mol. Crucial emulsion parameters, such as particle size and stability under continuous flow conditions, were examined using dynamic light scattering. A range of poly(N,N-dimethylacrylamide) and poly(4-acryloylmorpholine) polymers with molecular weights of 104-106 g/mol (D ≤ 1.31) were produced by this method using water-soluble trithiocarbonates as photoiniferters.
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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.
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Toxinas Marinhas , Microfluídica , Poluentes Químicos da Água , Poluentes Químicos da Água/análise , Ácido Okadáico/análise , Toxinas Marinhas/análiseRESUMO
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.