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Tiffy cooks : 88 easy Asian recipes from my family to yours
\"The wildly popular blogger and TikTok sensation @Tiffycooks shares 88 favorite easy everyday and family-style recipes from Asia and beyond\"-- Provided by publisher.
Book
AI-based pathology predicts origins for cancers of unknown primary
Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined
1
,
2
. This poses a considerable challenge, as modern therapeutics are predominantly specific to the primary tumour
3
. Recent research has focused on using genomics and transcriptomics to identify the origin of a tumour
4
–
9
. However, genomic testing is not always performed and lacks clinical penetration in low-resource settings. Here, to overcome these challenges, we present a deep-learning-based algorithm—Tumour Origin Assessment via Deep Learning (TOAD)—that can provide a differential diagnosis for the origin of the primary tumour using routinely acquired histology slides. We used whole-slide images of tumours with known primary origins to train a model that simultaneously identifies the tumour as primary or metastatic and predicts its site of origin. On our held-out test set of tumours with known primary origins, the model achieved a top-1 accuracy of 0.83 and a top-3 accuracy of 0.96, whereas on our external test set it achieved top-1 and top-3 accuracies of 0.80 and 0.93, respectively. We further curated a dataset of 317 cases of CUP for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 61% of cases and a top-3 agreement of 82%. TOAD can be used as an assistive tool to assign a differential diagnosis to complicated cases of metastatic tumours and CUPs and could be used in conjunction with or in lieu of ancillary tests and extensive diagnostic work-ups to reduce the occurrence of CUP.
A deep-learning-based algorithm uses routinely acquired histology slides to provide a differential diagnosis for the origin of the primary tumour for complicated cases of metastatic tumours and cancers of unknown primary origin.
Journal Article
Data-efficient and weakly supervised computational pathology on whole-slide images
Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability. Here we report an interpretable weakly supervised deep-learning method for data-efficient WSI processing and learning that only requires slide-level labels. The method, which we named clustering-constrained-attention multiple-instance learning (CLAM), uses attention-based learning to identify subregions of high diagnostic value to accurately classify whole slides and instance-level clustering over the identified representative regions to constrain and refine the feature space. By applying CLAM to the subtyping of renal cell carcinoma and non-small-cell lung cancer as well as the detection of lymph node metastasis, we show that it can be used to localize well-known morphological features on WSIs without the need for spatial labels, that it overperforms standard weakly supervised classification algorithms and that it is adaptable to independent test cohorts, smartphone microscopy and varying tissue content.
A data-efficient and interpretable deep-learning method for the multi-class classification of whole-slide images that relies only on slide-level labels is applied to the detection of lymph node metastasis and to cancer subtyping.
Journal Article
Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models stratified across subpopulations have revealed inequalities in how patients are diagnosed, treated and billed. In this Perspective, we outline fairness in machine learning through the lens of healthcare, and discuss how algorithmic biases (in data acquisition, genetic variation and intra-observer labelling variability, in particular) arise in clinical workflows and the resulting healthcare disparities. We also review emerging technology for mitigating biases via disentanglement, federated learning and model explainability, and their role in the development of AI-based software as a medical device.
This Perspective discusses fairness in machine learning and how algorithmic biases arising in clinical workflows can cause healthcare disparities.
Journal Article
Synthetic data in machine learning for medicine and healthcare
The proliferation of synthetic data in artificial intelligence for medicine and healthcare raises concerns about the vulnerabilities of the software and the challenges of current policy.
Journal Article
A radical approach to the copper oxidative addition problem: Trifluoromethylation of bromoarenes
Insertion of palladium into an aryl halide bond is the first step in numerous variants of cross-coupling chemistry used to make carbon-carbon bonds. Copper is an appealing alternative catalyst for such reactions because of its abundance and downstream reactivity profile. However, this preliminary step, termed oxidative addition, is often prohibitively slow for the cheaper metal. Le et al. report a photocatalytic way around this problem. A photoredox catalyst paired with a silane can activate aryl bromides to react with copper, likely via aryl radicals. The copper in this case then catalyzes trifluoromethylation of the arenes. Science , this issue p. 1010 A photocatalyst and silane combination activates aryl bromides toward functionalization by copper catalysis. Transition metal–catalyzed arene functionalization has been widely used for molecular synthesis over the past century. In this arena, copper catalysis has long been considered a privileged platform due to the propensity of high-valent copper to undergo reductive elimination with a wide variety of coupling fragments. However, the sluggish nature of oxidative addition has limited copper’s capacity to broadly facilitate haloarene coupling protocols. Here, we demonstrate that this copper oxidative addition problem can be overcome with an aryl radical–capture mechanism, wherein the aryl radical is generated through a silyl radical halogen abstraction. This strategy was applied to a general trifluoromethylation of aryl bromides through dual copper-photoredox catalysis. Mechanistic studies support the formation of an open-shell aryl species.
Journal Article
Cardiovascular manifestations and treatment considerations in COVID-19
Since its recognition in December 2019, covid-19 has rapidly spread globally causing a pandemic. Pre-existing comorbidities such as hypertension, diabetes, and cardiovascular disease are associated with a greater severity and higher fatality rate of covid-19. Furthermore, COVID-19 contributes to cardiovascular complications, including acute myocardial injury as a result of acute coronary syndrome, myocarditis, stress-cardiomyopathy, arrhythmias, cardiogenic shock, and cardiac arrest. The cardiovascular interactions of COVID-19 have similarities to that of severe acute respiratory syndrome, Middle East respiratory syndrome and influenza. Specific cardiovascular considerations are also necessary in supportive treatment with anticoagulation, the continued use of renin-angiotensin-aldosterone system inhibitors, arrhythmia monitoring, immunosuppression or modulation, and mechanical circulatory support.
Journal Article
Older adults’ acceptance of a robot for partner dance-based exercise
Partner dance has been shown to be beneficial for the health of older adults. Robots could potentially facilitate healthy aging by engaging older adults in partner dance-based exercise. However, partner dance involves physical contact between the dancers, and older adults would need to be accepting of partner dancing with a robot. Using methods from the technology acceptance literature, we conducted a study with 16 healthy older adults to investigate their acceptance of robots for partner dance-based exercise. Participants successfully led a human-scale wheeled robot with arms (i.e., a mobile manipulator) in a simple, which we refer to as the Partnered Stepping Task (PST). Participants led the robot by maintaining physical contact and applying forces to the robot's end effectors. According to questionnaires, participants were generally accepting of the robot for partner dance-based exercise, tending to perceive it as useful, easy to use, and enjoyable. Participants tended to perceive the robot as easier to use after performing the PST with it. Through a qualitative data analysis of structured interview data, we also identified facilitators and barriers to acceptance of robots for partner dance-based exercise. Throughout the study, our robot used admittance control to successfully dance with older adults, demonstrating the feasibility of this method. Overall, our results suggest that robots could successfully engage older adults in partner dance-based exercise.
Journal Article
Demographic bias in misdiagnosis by computational pathology models
Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict
IDH1
mutations in gliomas. For example, when using common modeling approaches, we observed performance gaps (in area under the receiver operating characteristic curve) between white and Black patients of 3.0% for breast cancer subtyping, 10.9% for lung cancer subtyping and 16.0% for
IDH1
mutation prediction in gliomas. We found that richer feature representations obtained from self-supervised vision foundation models reduce performance variations between groups. These representations provide improvements upon weaker models even when those weaker models are combined with state-of-the-art bias mitigation strategies and modeling choices. Nevertheless, self-supervised vision foundation models do not fully eliminate these discrepancies, highlighting the continuing need for bias mitigation efforts in computational pathology. Finally, we demonstrate that our results extend to other demographic factors beyond patient race. Given these findings, we encourage regulatory and policy agencies to integrate demographic-stratified evaluation into their assessment guidelines.
In a series of clinically relevant tasks in computational pathology, AI-driven models display marked performance disparities across demographic groups, which can only partially be mitigated by self-supervision on large training datasets and existing debiasing techniques.
Journal Article
Dispersed surface Ru ensembles on MgO(111) for catalytic ammonia decomposition
Ammonia is regarded as an energy vector for hydrogen storage, transport and utilization, which links to usage of renewable energies. However, efficient catalysts for ammonia decomposition and their underlying mechanism yet remain obscure. Here we report that atomically-dispersed Ru atoms on MgO support on its polar (111) facets {denoted as MgO(111)} show the highest rate of ammonia decomposition, as far as we are aware, than all catalysts reported in literature due to the strong metal-support interaction and efficient surface coupling reaction. We have carefully investigated the loading effect of Ru from atomic form to cluster/nanoparticle on MgO(111). Progressive increase of surface Ru concentration, correlated with increase in specific activity per metal site, clearly indicates synergistic metal sites in close proximity, akin to those bimetallic N
2
complexes in solution are required for the stepwise dehydrogenation of ammonia to N
2
/H
2
, as also supported by DFT modelling. Whereas, beyond surface doping, the specific activity drops substantially upon the formation of Ru cluster/nanoparticle, which challenges the classical view of allegorically higher activity of coordinated Ru atoms in cluster form (B
5
sites) than isolated sites.
Ruthenium-based materials show promising performance for ammonia decomposition, yet the underlying mechanism remains to be further explored. Here the authors investigate atomically dispersed Ru atoms on polar (111) on MgO facets to show synergistic metal sites in close proximity are required for the stepwise dehydrogenation of ammonia to N2/H2.
Journal Article