RAD51 foci as a biomarker predictive of platinum chemotherapy response in ovarian cancer

PURPOSE: To determine the ability of RAD51 foci to predict platinum chemotherapy response in high-grade serous ovarian cancer (HGSOC) patient-derived samples. EXPERIMENTAL DESIGN: RAD51 and γH2AX nuclear foci were evaluated by immunofluorescence in HGSOC patient-derived cell lines (n = 5), organoids (n = 11), and formalin-fixed, paraffin-embedded tumor samples (discovery n = 31, validation n = 148). Samples were defined as RAD51-High if \u3e10% of geminin-positive cells had ≥5 RAD51 foci. Associations between RAD51 scores, platinum chemotherapy response, and survival were evaluated. RESULTS: RAD51 scores correlated with in vitro response to platinum chemotherapy in established and primary ovarian cancer cell lines (Pearson r = 0.96, P = 0.01). Organoids from platinum-nonresponsive tumors had significantly higher RAD51 scores than those from platinum-responsive tumors (P \u3c 0.001). In a discovery cohort, RAD51-Low tumors were more likely to have a pathologic complete response (RR, 5.28; P \u3c 0.001) and to be platinum-sensitive (RR, ∞; P = 0.05). The RAD51 score was predictive of chemotherapy response score [AUC, 0.90; 95% confidence interval (CI), 0.78-1.0; P \u3c 0.001). A novel automatic quantification system accurately reflected the manual assay (92%). In a validation cohort, RAD51-Low tumors were more likely to be platinum-sensitive (RR, ∞; P \u3c 0.001) than RAD51-High tumors. Moreover, RAD51-Low status predicted platinum sensitivity with 100% positive predictive value and was associated with better progression-free (HR, 0.53; 95% CI, 0.33-0.85; P \u3c 0.001) and overall survival (HR, 0.43; 95% CI, 0.25-0.75; P = 0.003) than RAD51-High status. CONCLUSIONS: RAD51 foci are a robust marker of platinum chemotherapy response and survival in ovarian cancer. The utility of RAD51 foci as a predictive biomarker for HGSOC should be tested in clinical trials

Suppressing quantum errors by scaling a surface code logical qubit

Practical quantum computing will require error rates that are well below what is achievable with physical qubits. Quantum error correction offers a path to algorithmically-relevant error rates by encoding logical qubits within many physical qubits, where increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low in order for logical performance to improve with increasing code size. Here, we report the measurement of logical qubit performance scaling across multiple code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, both in terms of logical error probability over 25 cycles and logical error per cycle ($2.914\%\pm 0.016\%$ compared to $3.028\%\pm 0.023\%$). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a $1.7\times10^{-6}$ logical error per round floor set by a single high-energy event ($1.6\times10^{-7}$ when excluding this event). We are able to accurately model our experiment, and from this model we can extract error budgets that highlight the biggest challenges for future systems. These results mark the first experimental demonstration where quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.Comment: Main text: 6 pages, 4 figures. v2: Update author list, references, Fig. S12, Table I

Non-Abelian braiding of graph vertices in a superconducting processor

Indistinguishability of particles is a fundamental principle of quantum mechanics. For all elementary and quasiparticles observed to date - including fermions, bosons, and Abelian anyons - this principle guarantees that the braiding of identical particles leaves the system unchanged. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well developed mathematical description of non-Abelian anyons and numerous theoretical proposals, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. While efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasi-particles, superconducting quantum processors allow for directly manipulating the many-body wavefunction via unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons, we implement a generalized stabilizer code and unitary protocol to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of employing the anyons for quantum computation and utilize braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and - through the future inclusion of error correction to achieve topological protection - could open a path toward fault-tolerant quantum computing

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

Conic System Analysis of Network Control Systems with a Human Controller

One approach to network control of nonlinear systems has been to use the framework of passivity along with the wave variable transformation. While this has been appealing for telemanipulation systems and other human controlled systems, one shortcoming is that the reaction-time delay of a human operator is neglected. The current paper considers the problem of a human operator controlling a possibly unstable plant over a delayed network. Two transformations are presented here that can be inserted to stabilize a network control system with both network delays and human operator delay. The first result in the paper shows that a rotational transformation can be used to stabilize an unstable system assuming that it is a conic system, i.e. that it has passivity indices. The second result shows that it is possible to use a transformation to shape the human response dynamics in order to stabilize the network control system. In this result, both the network delays and human delay are allowed but the size of the delay need not be known. This delay-independent result can be readily applied to many human controlled systems

Late surgical explantation and aortic valve replacement after transcatheter aortic valve implantation

Transcatheter aortic valve implantation (TAVI) in patients with bicuspid aortic valve disease is associated with higher rates of paravalvular aortic regurgitation, which may require subsequent surgical correction. We report a case of successful late surgical CoreValve explantation 1,389 days after TAVI in a patient with bicuspid aortic valve stenosis and McArdle's disease who developed severe paravalvular aortic regurgitation. We confirm that neoendothelialization and incorporation of the nitinol cage into the aortic wall had occurred at nearly 4 years postimplantation, although explantation with careful endarterectomy could still be performed without requiring simultaneous aortic root replacement.3 page(s

Automated syndrome diagnosis by three-dimensional facial imaging.

PurposeDeep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.MethodsWe analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.ResultsUnrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.ConclusionDeep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance