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Liver cancer is one of the leading causes of cancer deaths worldwide. Among all primary liver cancers, hepatocellular carcinoma (HCC) is the most common type, representing 75%‐85% of all primary liver cancer cases. Median survival following diagnosis of HCC is approximately 6 to 20 months due to late diagnosis in its course and few effective treatment options. Interventional therapy with minimal invasiveness is recognized as a promising treatment for HCC. However, due to the heterogeneity of HCC and the complexity of the tumor microenvironment, the long‐term efficacy of treatment for HCC remains a challenge in the clinic. Tumor microenvironment, including factors such as hypoxia, angiogenesis, low extracellular pH, interstitial fluid pressure, aerobic glycolysis, and various immune responses, has emerged as a key contributor to tumor residual and progression after locoregional treatment for HCC. New approaches to noninvasively assess the treatment response and assist in the clinical decision‐making process are therefore urgently needed. Molecular imaging tools enabling such an assessment may significantly advance clinical practice by allowing real‐time optimization of treatment protocols for the individual patient. This review discusses recent advances in the application of molecular imaging technologies for noninvasively assessing changes occurring in the microenvironment of HCC after locoregional treatment.

Any person that has broken a bone can attest to the existence of sensory innervation of the skeleton. Beyond afferent functions, sensory neurons have been implicated in the orchestration of bone repair via the release of neuroregulatory signals. Yet, these neurosecretory effects have principally been deciphered through loss‐of‐function studies. Indeed, the potential therapeutic benefit of boosting nerve‐to‐bone interactions remains cursorily studied. Here, using a mouse calvarial bone defect model, pharmacologic activation of TrkA with a small molecule partial agonist induced bone‐associated nerve ingrowth and significantly improved calvarial bone healing. Single‐cell RNA sequencing analysis of cells from the defect site revealed shifts in cluster proportions, with enrichment of immune cell populations in TrkA agonist‐treated mice. Within the skeletal cell lineage, TrkA agonism enhanced osteoblast differentiation while suppressing fibroblastic differentiation. Pathway analysis showed increased Hedgehog signaling activity, and interactome analyses between trigeminal ganglia sensory neurons and skeletal cells implicated Hedgehog signaling. The pro‐regenerative effects of TrkA agonism were abolished in conditional knockout mice lacking Smoothened (Smo) in PDGFRα+ skeletal progenitor cells. In summary, boosting sensory nerve signaling enhances membranous bone repair after injury, at least in part via Hedgehog pathway activation in osteoprogenitor cells. Boosting sensory nerve activity via TrkA agonism strongly accelerates calvarial bone repair in adult mice. Furthermore, single‐cell RNA sequencing and neuron–bone interactome analyses identify these sensory neurons as a direct neural source of Hedgehog pathway ligands. Consequently, these ligands drive osteoblast differentiation of skeletal progenitors, enhancing local tissue healing through Smoothened‐dependent signaling.

Abstract Platelet-derived growth factor receptor α (PDGFRα) is often considered as a general marker of mesenchymal cells and fibroblasts, but also shows expression in a portion of osteoprogenitor cells. Within the skeleton, Pdgfrα+ mesenchymal cells have been identified in bone marrow and periosteum of long bones, where they play a crucial role in participating in fracture repair. A similar examination of Pdgfrα+ cells in calvarial bone healing has not been examined. Here, we utilize Pdgfrα-CreERTM;mT/mG reporter animals to examine the contribution of Pdgfrα+ mesenchymal cells to calvarial bone repair through histology and single-cell RNA sequencing (scRNA-Seq). Results showed that Pdgfrα+ mesenchymal cells are present in several cell clusters by scRNA-Seq, and by histology a dramatic increase in Pdgfrα+ cells populated the defect site at early timepoints to give rise to healed bone tissue overtime. Notably, diphtheria toxin-mediated ablation of Pdgfrα reporter+ cells resulted in significantly impaired calvarial bone healing. Our findings suggest that Pdgfrα-expressing cells within the calvarial niche play a critical role in the process of calvarial bone repair. Graphical Abstract Graphical Abstract

AbstractObjectiveTo estimate the effectiveness of mRNA covid-19 vaccines against symptomatic infection and severe outcomes (hospital admission or death).DesignTest negative design study.SettingOntario, Canada between 14 December 2020 and 19 April 2021.Participants324 033 community dwelling people aged ≥16 years who had symptoms of covid-19 and were tested for SARS-CoV-2.InterventionsBNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) vaccine.Main outcome measuresLaboratory confirmed SARS-CoV-2 by reverse transcription polymerase chain reaction (RT-PCR) and hospital admissions and deaths associated with SARS-CoV-2 infection. Multivariable logistic regression was adjusted for personal and clinical characteristics associated with SARS-CoV-2 and vaccine receipt to estimate vaccine effectiveness against symptomatic infection and severe outcomes.ResultsOf 324 033 people with symptoms, 53 270 (16.4%) were positive for SARS-CoV-2 and 21 272 (6.6%) received at least one dose of vaccine. Among participants who tested positive, 2479 (4.7%) were admitted to hospital or died. Vaccine effectiveness against symptomatic infection observed ≥14 days after one dose was 60% (95% confidence interval 57% to 64%), increasing from 48% (41% to 54%) at 14-20 days after one dose to 71% (63% to 78%) at 35-41 days. Vaccine effectiveness observed ≥7 days after two doses was 91% (89% to 93%). Vaccine effectiveness against hospital admission or death observed ≥14 days after one dose was 70% (60% to 77%), increasing from 62% (44% to 75%) at 14-20 days to 91% (73% to 97%) at ≥35 days, whereas vaccine effectiveness observed ≥7 days after two doses was 98% (88% to 100%). For adults aged ≥70 years, vaccine effectiveness estimates were observed to be lower for intervals shortly after one dose but were comparable to those for younger people for all intervals after 28 days. After two doses, high vaccine effectiveness was observed against variants with the E484K mutation.ConclusionsTwo doses of mRNA covid-19 vaccines were observed to be highly effective against symptomatic infection and severe outcomes. Vaccine effectiveness of one dose was observed to be lower, particularly for older adults shortly after the first dose.

The control of heat conduction through the manipulation of phonons as coherent waves in solids is of fundamental interest and could also be exploited in applications, but coherent heat conduction has not been experimentally confirmed. We report the experimental observation of coherent heat conduction through the use of finite-thickness superlattices with varying numbers of periods. The measured thermal conductivity increased linearly with increasing total superlattice thickness over a temperature range from 30 to 150 kelvin, which is consistent with a coherent phonon heat conduction process. First-principles and Green's function-based simulations further support this coherent transport model. Accessing the coherent heat conduction regime opens a new venue for phonon engineering for an array of applications.

Agonists of glucocorticoid receptor (GR) are frequently given to cancer patients with platinum-containing chemotherapy to reduce inflammation, but how GR influences tumor growth in response to platinum-based chemotherapy such as cisplatin through inflammation-independent signaling remains largely unclear. Combined genomics and transcription factor profiling reveal that MAST1, a critical platinum resistance factor that reprograms the MAPK pathway, is upregulated upon cisplatin exposure through activated transcription factor GR. Mechanistically, cisplatin binds to C622 in GR and recruits GR to the nucleus for its activation, which induces MAST1 expression and consequently reactivates MEK signaling. GR nuclear translocation and MAST1 upregulation coordinately occur in patient tumors collected after platinum treatment, and align with patient treatment resistance. Co-treatment with dexamethasone and cisplatin restores cisplatin-resistant tumor growth, whereas addition of the MAST1 inhibitor lestaurtinib abrogates tumor growth while preserving the inhibitory effect of dexamethasone on inflammation in vivo. These findings not only provide insights into the underlying mechanism of GR in cisplatin resistance but also offer an effective alternative therapeutic strategy to improve the clinical outcome of patients receiving platinum-based chemotherapy with GR agonists. Glucocorticoid receptor (GR) agonists - used in the treatment of solid malignant tumors to reduce inflammation - could potentially affect the anti-tumor activity of chemotherapy. Here, the authors identify a mechanism of cisplatin resistance observed with GR agonist treatment, and show the binding and activation of GR by cisplatin, which leads to MAST1 activation and subsequent MAPK re-activation.

Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs.

Scalable quantum computing can become a reality with error correction, provided that coherent qubits can be constructed in large arrays 1 , 2 . The key premise is that physical errors can remain both small and sufficiently uncorrelated as devices scale, so that logical error rates can be exponentially suppressed. However, impacts from cosmic rays and latent radioactivity violate these assumptions. An impinging particle can ionize the substrate and induce a burst of quasiparticles that destroys qubit coherence throughout the device. High-energy radiation has been identified as a source of error in pilot superconducting quantum devices 3 – 5 , but the effect on large-scale algorithms and error correction remains an open question. Elucidating the physics involved requires operating large numbers of qubits at the same rapid timescales necessary for error correction. Here, we use space- and time-resolved measurements of a large-scale quantum processor to identify bursts of quasiparticles produced by high-energy rays. We track the events from their initial localized impact as they spread, simultaneously and severely limiting the energy coherence of all qubits and causing chip-wide failure. Our results provide direct insights into the impact of these damaging error bursts and highlight the necessity of mitigation to enable quantum computing to scale. Cosmic rays flying through superconducting quantum devices create bursts of excitations that destroy qubit coherence. Rapid, spatially resolved measurements of qubit error rates make it possible to observe the evolution of the bursts across a chip.

Realizing the potential of quantum computing requires sufficiently low logical error rates 1 . Many applications call for error rates as low as 10 −15 (refs. 2 – 9 ), but state-of-the-art quantum platforms typically have physical error rates near 10 −3 (refs. 10 – 14 ). Quantum error correction 15 – 17 promises to bridge this divide by distributing quantum logical information across many physical qubits in such a way that errors can be detected and corrected. Errors on the encoded logical qubit state can be exponentially suppressed as the number of physical qubits grows, provided that the physical error rates are below a certain threshold and stable over the course of a computation. Here we implement one-dimensional repetition codes embedded in a two-dimensional grid of superconducting qubits that demonstrate exponential suppression of bit-flip or phase-flip errors, reducing logical error per round more than 100-fold when increasing the number of qubits from 5 to 21. Crucially, this error suppression is stable over 50 rounds of error correction. We also introduce a method for analysing error correlations with high precision, allowing us to characterize error locality while performing quantum error correction. Finally, we perform error detection with a small logical qubit using the 2D surface code on the same device 18 , 19 and show that the results from both one- and two-dimensional codes agree with numerical simulations that use a simple depolarizing error model. These experimental demonstrations provide a foundation for building a scalable fault-tolerant quantum computer with superconducting qubits. Repetition codes running many cycles of quantum error correction achieve exponential suppression of errors with increasing numbers of qubits.

Computational approaches for drug discovery, such as quantitative structure–activity relationship, rely on structural similarities of small molecules to infer biological activity but are often limited to identifying new drug candidates in the chemical spaces close to known ligands. Here we report a biological activity-based modeling (BABM) approach, in which compound activity profiles established across multiple assays are used as signatures to predict compound activity in other assays or against a new target. This approach was validated by identifying candidate antivirals for Zika and Ebola viruses based on high-throughput screening data. BABM models were then applied to predict 311 compounds with potential activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Of the predicted compounds, 32% had antiviral activity in a cell culture live virus assay, the most potent compounds showing a half-maximal inhibitory concentration in the nanomolar range. Most of the confirmed anti-SARS-CoV-2 compounds were found to be viral entry inhibitors and/or autophagy modulators. The confirmed compounds have the potential to be further developed into anti-SARS-CoV-2 therapies. Activity profiles generated from quantitative high-throughput screening improve drug candidate prediction.