This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools.n = 9708, ages 25–74) and a birth cohort  and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study  were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression.Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys ; low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile.Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata. We recently described how age-associated changes in body-mass index (BMI) are strongly associated with concurrent changes in a wide selection of metabolic measures over 15 years of follow-up in a large-scale population sample , and theThe scientific value of metabolomics has been demonstrated in cardiovascular medicine –10 and iPrevious subgrouping studies have stratified large population cohorts according to cross-sectional biochemical data , 15, 16,n = 1286, ages 24–39 at baseline, three visits between 2001 and 2011) with supporting data from other population surveys . These unique population resources enabled us to apply the SOM framework in a study design that separated subgroup construction from the evaluation of temporal obesity trends and metabolic trajectories  from a Finnish longitudinal dataset . ImThe Cardiovascular Risk in Young Finns Study (YFS) is a population based prospective cohort study . It was The NFBC1966 is a longitudinal birth cohort established to study factors affecting preterm birth and consequent morbidity in the two northernmost provinces of Finland, Oulu and Lapland . The NFBFINRISK surveys are cross-sectional, population-based studies conducted every five years since 1972 to monitor the risk of chronic diseases . For eacThe following data were available at each date of blood collection. A high-throughput nuclear magnetic resonance (NMR) spectroscopy metabolomics platform was used to quantify 164 lipid and metabolite measures from serum . The pla31yrs + 0.53×NFBC196646yrs before training the map. The SOM was fitted to the combined dataset of FINRISK surveys and the interpolated NFBC1966 visit. After training, the YFS participants were placed on the SOM according to the 2007 visit. Neither the 2001 nor the 2011 YFS visit was used for determining the positions of individuals on the map and are thus considered independent time points  was constructed based on the FINRISK cross-sectional data and the NFBC66 data , 30. Thents Fig. . Furtherage were estimated by bootstrapping. In addition, analysis of variance (ANOVA) was conducted for the coefficients by permutation analysis. Analogous ANOVA was calculated for unadjusted subgroup means. Visual trajectories to depict the regression models were created by setting the confounders to zero and plotting the model outputs for the ages between 24 and 49.Temporal trajectories that combined cross-sectional and longitudinal data in the YFS were modelled by linear regression. We tested quadratic and cubic models, however, the results were not substantially different or suffered from instability (data not shown). Each model included chronological age as the regressor, birth year as a covariate, and the quantitative metabolic measure as the dependent variable. The metabolic measure was standardized as implemented in the Numero function nroPreprocess(method = ”standard”), which log transforms skewed data, truncates extreme outliers and scales by standard deviation . Age uniP < 0.0006 to match the previous paper . All statistical analyses were conducted in the R environment version 3.6 (https://www.R-project.org). All P-values are two-sided unless otherwise indicated.Principal component analysis (PCA) of the biochemical data revealed that the first 48 PCA components explained 99% of the total variance when all data were pooled. These results were compatible with earlier work . For conTo explore the structure of the high-dimensional metabolic data, we described and summarized the variation of 174 metabolic measures using the self-organizing map (SOM). In the schematic example, each participant is represented by their individual pre-processed metabolic profile Fig. . The Kohhttps://cran.r-project.org/web/packages/Numero/vignettes/intro.html).Regarding the technical details of the SOM, we highlight extensive supplementary documents in four earlier papers that introduce the basic mathematical concepts and discuss the differences between textbook examples of clustered data and real-world population-like datasets , 33, 34.A self-organizing map (SOM) was fitted to the FINRISK and NFBC66 data and adjusted for age and sex to further elucidate the relationship between cross-sectional metabolic diversity and longitudinal change . To summarize the multivariable profiles, we divided the SOM into four areas and assigned the participants located in each area to separate subgroups. In Fig. We first focused on the area of the map that was characterized by overweight Fig. . After eIn the previous section, we created a data-assisted labelling of the participants into one of four metabolic subgroups. In this section, we characterize the two most important underlying cross-sectional patterns that differentiate the subgroup profiles Fig. . In the Although we ended up with a lipid-focused subgrouping, we emphasize that these patterns are clearly visible across most if not all of the metabolic measures due to extensive collinearity . The full list of coefficients is available in Supplement Linear regression models were fitted to the YFS data to elucidate the slope of temporal trajectories of the SOM-derived metabolic subgroups . The standardized slope was quantified by the coefficient for age in the regression model (βP = 9.3 × 10−12) and unadjusted rates of change (P = 5.9 × 10−11) between subgroups; insulin slopes were substantially different between the TG-rich subgroup  and the TG-poor subgroup  and some difference was observable between the Low lipid  and TG-poor subgroups. The connection between cross-sectional subgroups and longitudinal slopes was also visible in multiple VLDL and HDL measures  and low lipid subgroups  despite a highly significant 1.5-fold difference in concentration . Most lipid measures increased in all subgroups . We observed a qualitative difference between BMI and WHR regarding their temporal associations with the four metabolic subgroups. BMI and particularly insulin exhibited a divergent pattern where initial subgroup differences were weak at age 24 but became more pronounced by the time the individuals were 49 years old . On the other hand, WHR and particularly LDL-C where substantially different between the subgroups at 24 and the stratification remained almost unchanged up to 49 years (i.e. WHR increased at the same rate regardless of the starting point).We characterised the population by four subgroups that we labelled according to established clinically available lipids (triglycerides and LDL cholesterol). These choices were made based on the vital importance the two main circulating lipids play in metabolic processes and cardiometabolic risk assessment in the clinics , 35–37, The subgroups highlight one of the main aspects of the public health challenge of obesity. The gradient from TG-rich to TG-poor is temporally persistent, which supports the idea of biological resistance against an individual’s deviation from a pre-determined trajectory if the (obesogenic) environment stays the same, even if interrupted by episodes of dieting , 41. Fro2 might be insufficient to stabilize insulin in individuals with the TG-rich metabolic profile, whereas accumulation of extra kilos on the waist for someone with a TG-poor profile would be less of a concern. This proposition is best supported by the specific observation that even when the waist-hip ratio in the TG-poor subgroup at age 48 surpassed the TG-rich at age 33 , therefore we propose that focusing on the temporal correlation between adiposity and insulin could be a powerful and well-defined way to stratify the metabolic resilience of overweight individuals. This insulin-centric approach could also help resolve the main weakness of the healthy obesity concept, namely that there is no clear definition of what “healthy” actually means in this context .We also agree with the counter argument: everyone in our study showed consistent deterioration in classical cardiovascular risk factors such as LDL cholesterol and blood pressure regardless of the BMI and WHR trajectories or starting points. Based on this, we speculate that a resilient metabolic phenotype against obesity would not protect against the cholesterol-driven or hemodynamic components of atherosclerosis etiology.Two independent longitudinal cohorts and two independent cross-sectional surveys of the same ethnicity, socioeconomics and time period provide us with robust data and high statistical power, but these strengths also mean that the results may not generalize outside the Northern European context. Furthermore, the results apply to adults under the age of 50 and further studies are needed to establish explicit links to late-life phenomena or how diet, exercise and genetics may influence the ageing trajectories of metabolite concentrations –51. In oThe results from this study do not represent causal evidence. Subgrouping and clustering studies are always at risk of finding patterns where none exist, however, we employed rigorous statistical designs to prevent overfitting and our subgroups are fully compatible with previous studies of other cohorts , 7, whicThe availability of metabolic measures is always limited and selected due to the technical constraints of analytical platforms and due the nature of the biosamples themselves. In this study, our data is lipid-centric, which means that lipids as the defining subgroup indicators can get over-emphasized. On the other hand, we took steps to manage collinearities to get an agnostic fit of the SOM, yet the ratio of triglycerides to cholesterol still emerged as the prominent feature. As these two lipids (and lipoprotein particles more broadly) are true and tested indicators of cardiometabolic health, we maintain our subgroup definitions are biologically relevant and accurate.P = 0.57), which means that practical applications of metabolic ageing are likely to be feasible based on a panel of conventional biomarkers.The subgroup labels were predictable at 53% accuracy (versus 25% for random labels) by the full suite of available metabolic data and at 52% accuracy by nine pre-selected easily available clinical biomarkers (BMI, WHR, systolic and diastolic blood pressure, triglycerides, cholesterol, LDL cholesterol, HDL cholesterol and glucose; please see Supplement Multi-variate subgrouping of longitudinal metabolomics data revealed two temporal features of the obesity pandemic that were captured by the two most commonly used markers for excess adiposity, BMI and WHR. We interpret the stratification and divergence of BMI in adulthood as a modifiable health indicator that segregates low and high-risk cardiometabolic phenotypes by diverging insulin trajectories and associated changes in triglyceride-cholesterol ratio and VLDL-HDL balance. On the other hand, the general increase in WHR may represent a hard-wired decline in cardiometabolic health that affects everyone and features consistently increasing rates of classical heart disease risk factors such as LDL cholesterol and blood pressure.SOM colorings of YFSCorrelationsSubgroup profilesSubgroup profiles for all measuresAgeing slopesAgeing slopes visualizationSubgroup trajectoriesSubgroup prediction


A 33-year-old man was referred to our institution for the management of a percutaneous pancreatic fistula after acute pancreatitis due to SARS-CoV-2 infection. He developed a peripancreatic collection (PPC), which was percutaneously drained because of infection. After resolution of the collection, percutaneous leakage from the main pancreatic duct (MPD) was observed. The patient underwent endoscopic retrograde cholangiopancreatography (ERCP) with biliary and pancreatic sphincterotomy, and placement of both pancreatic and biliary stents; however, the leak persisted.He was then referred to our institution, where initial management included ERCP with placement of two transpapillary pancreatic stents and the removal of the percutaneous catheter; however, fluid continued to leak from the percutaneous fistula . A multVideo 1 Management of pancreato-cutaneous fistula after percutaneous drainage of peripancreatic collection.The procedure included an initial ERCP with replacement of the two pancreatic stents  while tThe next step was to percutaneously insert a double-pigtail (10 Fr) stent, releasing the distal side into the stomach and the proximal side into the MPD, in order to stabilize the newly created fistula. Another transgastric plastic stent was endoscopically placed through the gastropancreatic fistula . InjectIn conclusion, the procedure achieved complete exclusion and resolution of the pancreatic-cutaneous fistula.Endoscopy_UCTN_Code_TTT_1AR_2AI


The minor copper (Cu) particles among major aluminum (Al) particles have been detected by means of an integration of a generative adversarial network and electrical impedance tomography (GAN-EIT) for a wet-type gravity vibration separator (WGS). This study solves the problem of blurred EIT reconstructed images by proposing a GAN-EIT integration system for Cu detection in WGS. GAN-EIT produces two types of images of various Cu positions among major Al particles, which are (1) the photo-based GAN-EIT images, where blurred EIT reconstructed images are enhanced by GAN based on a full set of photo images, and (2) the simulation-based GAN-EIT images. The proposed metal particle detection by GAN-EIT is applied in experiments under static conditions to investigate the performance of the metal detection method under single-layer conditions with the variation of the position of Cu particles. As a quantitative result, the images of detected Cu by GAN-EIT  An effective recycling system is required to meet the high global demand for recycled aluminum (Al), copper (Cu), and plastic particles from electronic waste while considering economic and environmental factors . In ordeGenerally, in order to detect minor Cu particles, magnetic and optical detectors have already been proposed for dry metal separators . Ferrous10 mS/m while Cu has higher conductivity around 10 mS/m  respectively in Numerical simulation studies are employed by a finite element method (FEM) software based on COMSOL Multiphysics v5.3a with AC/DC module at stationary study in order to generate input for training data set . The simurrent i .(18)∇·σ10 mS/m, Cu particles is at 10 mS/m and water are at The simulation focuses on the displacement of particles in a three-dimensional (3D) model under static conditions. Since the measurement technique is based on four-wire impedance measurement, the 3D model is constructed by considering the configuration of each of the four electrodes and the particles in the measurement domain The current applied in the measurement of AC current is The electrical impedance tomography (EIT) technique has been successfully utilized to reconstruct images based on experimental results. However, as shown in The evaluation of the GAN-EIT image is shown in The evaluation of performance between GAN-EIT and original EIT is performed by RGB value analysis. Each image is represented by red, green, and blue (RGB) color values.mpedance , stray cmpedance , and meampedance . On the The limitation of EIT using the adjacent pattern is that in the case electrode 1 acts as the high-current electrode and electrode 2 acts as the low-current electrode, four metal particles on top of them are neglected because the voltage measurement, which detects the metal particle, is done by the other electrodes. Along with the measurement, the optimum metal particles that are able to be detected are in positions 5–9. In the case of the electrode arrangement being flipped, the metal particles in positions 10–13 are detectable. Copper particles mostly pass through the center of the mouth’s channel  with Generative Adversarial Networks (deGAN) for image reconstruction and enhancement in order to distinguish minor Cu particles from major Al particles for a wet-type gravity vibration separator. The results obtained from our experiments have provided valuable insights into the feasibility of this integration in improving the quality and interpretability of EIT reconstructed images. Our findings indicate EIT has been successfully utilized to generate images based on simulation/experimental results, but the reconstructed images were often blurry and lacked clarity, making it difficult to distinguish between the different Cu positions within Al-Cu mixtures. However, through the integration of GAN-EIT, we were able to enhance the images obtained from EIT, resulting in images that exhibited improved visual fidelity and clarity.GAN-EIT reduces Cu detection error of conventional EIT by 40%.The proposed method is reasonably robust to prevent intervention from the noise signal condition of a device. An impedance measurement with a minimum SNR = 60 [dB] is recommended.GAN-EIT is capable of improving the detailed features of the EIT images to detect Cu effectively.The GAN-EIT model effectively improved the quality of the reconstructed images, making them more feasible to interpret and analyze the different Cu positions within Al–Cu mixtures. The conclusions are summarized as follows:


Coronavirus Disease 2019 (COVID-19) is a highly infectious disease that can be treated with antivirals in addition to other antimicrobials in cases of secondary or concomitant infections. This creates potential for antimicrobials misuse, which increases antimicrobial resistance (AMR). Pharmacists are known to undertake prominent roles in combatting AMR.The aim of this review was to characterize pharmacist-driven interventions that have been performed in patients with COVID-19 globally and describe their impact on antimicrobial use.We followed the Joanna Briggs Institutes manual framework for scoping reviews in our study. Studies that reported antimicrobial stewardship (AMS) interventions performed by pharmacists in COVID-19 patients were included. Articles that did not report outcomes or did not mention pharmacists in the intervention were excluded. Restrictions included English-only articles from inception date until June 2022. Articles were searched from four databases.Eleven publications were included in the review. The most common AMS intervention was pharmacist-driven interventions reported in 63.2% of all studies, followed by guideline development and application (26.3%), and medication supply coordination (10.5%), respectively. The outcomes of the interventions were difficult to compare but showed a reduction in antimicrobial use and prevention of adverse drug reactions with a relatively high acceptance rate from physicians.Pharmacists played an important role in performing AMS-related interventions in COVID-19 patients and helped in the fight against the worsening of AMR during the pandemic. The impact of pharmacist-driven AMS interventions in patients with COVID-19 seemed to be positive and improved outcomes related to antimicrobial use.The online version contains supplementary material available at 10.1007/s11096-023-01574-0. Pharmacist-driven AMS interventions can improve outcomes related to antimicrobial use in patients with COVID-19.Pharmacists are well positioned health care professionals who play a vital role in the fight against AMR during the COVID-19 pandemic.The World Health Organization (WHO) declared the Coronavirus Disease 2019 (COVID-19) outbreak a global pandemic in March 2020 . A recenThe aim of this scoping review was to characterize pharmacist-driven interventions that have been performed in patients with COVID-19 globally and describe their impact on antimicrobial use.a priori and was followed without deviation.We followed the Joanna Briggs Institutes manual framework for scoping reviews when conducting our study . A protoStudies conducted on patients with COVID-19 receiving antimicrobials were included in this review.We adopted the concept of AMS interventions performed by pharmacists. The classification was adopted from the CDC 2019 core elements of antibiotic stewardship or labeled by the investigators as deemed appropriate . A survey conducted in the UK showed that more than 60% of the participating centers claimed that COVID-19 negatively affected routine AMS activities by decreasing the number of the multidisciplinary team meetings and medical rounds . It is rASPs can support pandemic response efforts. Pharmacists played an important role in performing AMS-related interventions in COVID-19 patients and helped in the fight against the worsening of AMR during the ongoing pandemic. There seem to be several ASP opportunities that could be performed during the pandemic by pharmacists. Although difficult to aggregate, the impact of pharmacist-driven AMS interventions in patients with COVID-19 appears to be positive with improved outcomes related to antimicrobial use.Supplementary file1 (DOCX 28 kb)Below is the link to the electronic supplementary material.


The DNA sensor cGAS (cyclic GMP-AMP synthase) and its adaptor protein STING (Stimulator of Interferon Genes) detect the presence of cytosolic DNA as a sign of infection or damage. In cancer cells, this pathway can be activated through persistent DNA damage and chromosomal instability, which results in the formation of micronuclei and the exposure of DNA fragments to the cytosol. DNA damage from radio- or chemotherapy can further activate DNA sensing responses, which may occur in the cancer cells themselves or in stromal and immune cells in the tumour microenvironment (TME). cGAS–STING signalling results in the production of type I interferons, which have been linked to immune cell infiltration in ‘hot’ tumours that are susceptible to immunosurveillance and immunotherapy approaches. However, recent research has highlighted the complex nature of STING signalling, with tumours having developed mechanisms to evade and hijack this signalling pathway for their own benefit. In this mini-review we will explore how cGAS–STING signalling in different cells in the TME can promote both anti-tumour and pro-tumour responses. This includes the role of type I interferons and the second messenger cGAMP in the TME, and the influence of STING signalling on local immune cell populations. We examine how alternative signalling cascades downstream of STING can promote chronic interferon signalling, the activation of the transcription factor nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and the production of inflammatory cytokines, which can have pro-tumour functions. An in-depth understanding of DNA sensing in different cell contexts will be required to harness the anti-tumour functions of STING signalling. Our immune system has the capacity to detect and eliminate cancer cells, despite the fact that they are derived from our own body. This is partly because cancerous and pre-cancerous cells – particularly those with a high mutational burden – express proteins that can be detected as neo-antigens by the cells of our adaptive immune system ,2. HowevCytosolic DNA is detected as pathogen-associated molecular pattern (PAMP), during viral infection for instance, but it can also serve as danger- or damage-associated molecular pattern (DAMP) when the cell’s own nuclear or mitochondrial DNA is damaged and gains access to the cytosol. Tumour cells often contain cytosolic DNA, due to persistent DNA damage and chromosomal instability (CIN), which is a hallmark of cancer ,5, and aThe DNA sensor cGAS and its adaptor protein STING constitute the key signalling axis for sensing dsDNA within the cytosol of many different cell types  15–19]..15–19]. cGAS-STING signalling is tightly regulated by post-translational modifications, including ubiquitylation with K48-, K63-, K27- and K11-linked ubiquitin chains, modification with ubiquitin-like proteins as well modification with metabolites and lipids ,47. AddiSTING can also be activated independently of cGAS, such as following membrane fusion or after uptake of cGAMP from neighbouring cells ,58. BactcGAS–STING signalling has been shown to have potent anti-cancer activities in a variety of mouse tumour models A. The prThe first potential point of activation of cGAS–STING pathway is within the cancer cells themselves following detection of cytosolic DNA from micronuclei for instance ,8. This cGAMP production and export by tumour cells can activated STING signalling in neighbouring endothelial cells, to induce further IFN-β and CXCL10 production in the TME. Activation of STING within the endothelium was also shown to be accompanied by increased expression of factors associated with T-cell adhesion to the endothelium and the infiltration of lymphocytes into the TME. The activation of STING in endothelial cells in the tumour vasculature is also thought to protect against angiogenesis and promote vascular normalisation in tumours ,88.Immune cells in the TME can respond to extracellular tumour-derived DNA and activate the DNA sensing pathway. The uptake of tumour DNA has been shown to be particularly important for activating DCs in the TME ,69,89. HActivation of STING signalling has been shown to be a feature of ‘hot’ tumours, which show infiltration of DCs and cytotoxic T cells – a pre-requisite for detection of tumour antigens by adaptive immune responses and for effective immunotherapy . Thus, tDue to of the anti-tumour effects of STING signalling, it was originally proposed that the pathway must be dysfunctional or epigenetically suppressed in cancer cells . AlthougIn addition to its potent anti-tumour activities, STING signalling has also been shown to contribute to cancer cell survival and tumour progression in some contexts. The balance of downstream STING signalling outputs may be differentially regulated in tumour cells, so that anti-tumour IFN-I and cell death responses are limited, while pro-inflammatory and pro-survival programs are promoted.While short-lived interferon responses due to acute STING stimulation have clear anti-tumour effects mediated by IFN-Is, it has been shown that low levels of chronic IFN-β signalling can promote cancer cell survival B 125–12. MechaniThe STING-dependent expression of pro-inflammatory cytokines, and particularly IL-6, can also promote inflammation-driven tumour progression. STING-deficient mice have been found to be resistant to the development of skin tumours induced by the mutagen DMBA, which drives tumorigenesis through IL-6-mediated inflammation . AnalysiThe expression of pro-inflammatory cytokines such as IL-6 is driven by the NF-kB family of transcription factors. In most cells, canonical cGAS–STING signalling induces only modest levels of NF-κB p65 activation, through signalling by TBK1 which favours the activation of the transcription factor IRF3 . HoweverAs an additional immune evasion strategy, tumour cells can also re-wire cGAS-mediated STING signalling towards increased non-canonical NF-κB activation in cancer cells through the NF-κB subunits p52 and RelB ,140. ThiIt is becoming increasingly clear that the ultimate outcome of STING signalling is also highly sensitive to cell context and differs between different cell types. For instance, while cGAMP transport to DCs can stimulate potent anti-tumour responses, its transport into astrocytes can promote brain metastasis through the production of cytokines that are beneficial for cancer cell growth and survival . Activatper se, to promote the anti-tumour functions of DNA sensing in cancer, while limiting adverse effects on tumour progression.The intense study of DNA sensing pathways in the last decade or so has revealed a crucial role of the cGAS–STING–interferon signalling axis in shaping immune responses during infection, autoinflammation and cancer. However, despite the fact that the anti-tumour role of STING signalling is well documented in mouse tumour models and multiple STING agonists have been tested in clinical trials, these approaches have not yet shown efficacy as anti-tumour agents in patients. Indeed, STING signalling has been shown to have both pro- and anti-tumour functions in cancer cells, stromal cells and the immune cells in the TME. While the activation of canonical, acute STING signalling that results in IFN-I induction has been studied in great molecular detail and clearly possess anti-tumour functions, we know much less about how low-grade, chronic STING signalling is induced in response to DNA damage. We will also need to understand how different kinds of DNA damage induced by radio- and chemotherapy regimens affect DNA sensing pathways, so that we can avoid potential pro-tumour effects. There are many open questions about how STING activation drives different cellular outcomes, from the predominant activation of IRF3 to canonical or non-canonical NF-κB activation, as well as inflammasome activation and cell death or senescence pathways. In the future, it may be possible to therapeutically influence the balance of STING signalling outputs, rather than STING activation The detection of cytosolic DNA and DNA damage results in the activation of the innate immune adaptor STING in cancer cells, immune cells or endothelial cells within the tumour microenvironment. All these cell types can contribute to the production of type I interferons which have anti-tumour functions.If the STING signalling response is evaded or diverted towards a more pro-inflammatory cytokine profile, this can instead promote the establishment of an immune-suppressive microenvironment and can aid tumour cell survival and metastasis.A deeper understanding of how different cell contexts and regulatory mechanisms influence signalling outputs downstream of STING will be important for the development of STING-targeting immunotherapies.


RET fusion-positive non-small cell lung cancer (NSCLC) and resulted in the drug’s approval for this indication. A cohort that included neoadjuvant and adjuvant selpercatinib was opened on LIBRETTO-001 for early-stage RET fusion-positive NSCLC with the primary endpoint of major pathologic response. A patient with a stage IB (cT2aN0M0) KIF5B-RET fusion-positive NSCLC received 8 weeks of neoadjuvant selpercatinib at 160 mg twice daily followed by surgery. While moderate regression in the primary tumor  guidelines version 1.1) was observed radiologically, assessment via an Independent Pathologic Review Committee revealed a pathologic complete response (0% viable tumor). This consensus assessment by three independent pathologists was aided by RET fluorescence in situ hybridization testing of a reactive pneumocyte proliferation showing no rearrangement. Neoadjuvant selpercatinib was well-tolerated with only low-grade treatment-emergent adverse events. The activity of prospective preoperative selpercatinib in this case establishes proof of concept of the potential utility of RET inhibitor therapy in early-stage RET fusion-positive NSCLC.The LIBRETTO-001 trial demonstrated the activity of the selective rearrangement during transfection (RET) inhibitor selpercatinib in advanced  In trials randomizing epidermal growth factor receptor  was formally amended in 2021 to include neoadjuvant and adjuvant selpercatinib for early-stage RET fusion-positive NSCLC  kinase inhibitor with central nervous system activity approved in multiple countries for the treatment of ve NSCLC Figure 12KIF5B-RET fusion and no other activating genomic aberrations.A 76-year-old Caucasian woman with a remote, five pack-year history of former smoking was diagnosed with clinical stage IB (T2aN0M0) adenocarcinoma of the lung at the University of California, Los Angeles, in September 2021. Baseline radiologic tumor assessments identified a left upper lobe tumor 3.1 cm, Figure 2Resectability was confirmed by a thoracic surgeon, and the patient consented to LIBRETTO-001 in October 2021, including the consent to publish her study-related information while keeping her identity confidential. In November 2021, neoadjuvant selpercatinib was initiated at the recommended dose of 160 mg twice daily for two 28-day cycles. Treatment was well tolerated; the only treatment-emergent adverse events (TEAEs) were grade 1 fatigue and diarrhea and grade 2 hypertension. During this time, one dose was missed inadvertently, and there was no dose modification planned.After the completion of neoadjuvant selpercatinib, the tumor decreased from 3.1 to 2.9 cm  1.1 stable disease, investigator assessment, The primary endpoint of the LIBRETTO-001 cohort was MPR. Pathologic assessment of the resected primary tumor in hematoxylin and eosin (H&E) staining was conducted by three independent pathologists including two from the Independent Pathologic Review Committee. On initial assessment, 0%, 10%, and 30% viable tumor cells were found.in situ hybridization (FISH) was performed , which did not detect aberrations in the 200 nuclei examined. Alignment on the pathologic assessment of 0% viable tumor cells (pCR) was then reached among the three pathologists. The consensus was atypical cells represented a reactive pneumocyte proliferation (instead of carcinoma) secondary to treatment effect  fluorescence t effect Figure 3KIF5B-RET was undetectable (Guardant360) in plasma throughout the trial  likely due to the fact that fusions are less well-detected by circulating tumor DNA and early-phase disease may have limited shedding. No other significant alterations were identified; a platelet-derived growth factor receptor alpha V193I variant of uncertain significance was found pre-selpercatinib  but not thereafter. This mutation was not detected by next-generation sequencing of the pre-selpercatinib lung cancer biopsy.Plasma circulating tumor DNA samples were collected. 3KIF5B-RET-positive stage IB lung adenocarcinoma, prospective treatment with neoadjuvant selpercatinib in the LIBRETTO-001 trial achieved pCR. This case provides the first proof of concept of the clinical activity of a RET TKI in the neoadjuvant setting and sets a precedent for ongoing and future investigations in early-stage disease. While this cohort was closed after this patient was treated (for reasons unrelated to safety or activity), a separate trial continues to explore the utility of selpercatinib as adjuvant therapy in RET fusion-positive NSCLC   and pathologic (pCR) assessments, conducted within a week of each other, highlights the inherent limitations of RECIST and the importance of pathologic evaluation in fully demonstrating the tumor response to effective therapy, especially in the short timelines of neoadjuvant therapy. Pathologic response in the setting of minimal radiologic response may need to be considered in other settings as well where surgical resection is not performed.This case also highlights interobserver variation in the interpretation of pathologic response due to the challenge of using standard H&E staining to determine reactive versus neoplastic epithelium, especially in the setting of lepidic proliferations. To mitigate this challenge, molecular adjudication via FISH was utilized . A negatRET fusion-positive NSCLC. Neoadjuvant therapy resulted in low-grade TEAEs but did not preclude or interfere with the performance of definitive surgical therapy. The activity of preoperative selpercatinib in this prospective case establishes proof of concept of the potential utility of selpercatinib in early-stage RET fusion-positive NSCLC.In summary, neoadjuvant selpercatinib was found to be active for a patient with The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by UCLA Institutional Review Board (UCLA IRB) – Office of the Human Research Protection Program (OHRPP). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.This study was designed jointly by all authors. The sponsor (Loxo Oncology) collected, analyzed, and interpreted the trial data in collaboration with the authors. All authors were involved in the writing of the manuscript and its revisions. All the authors vouch for the completeness and accuracy of the clinical data and analyses and for the adherence of the trial to the protocol. All authors contributed to the article and approved the submitted version.


Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells. Many expression deconvolution approaches have been developed to estimate % RNA contributions of diverse cell types to mixed RNA measurements. Here, the authors have developed a complementary approach called scProjection to recover cell type-specific expression profiles from mixed RNA measurements. These technologies are now routinely used to generate atlases on the scale of up to millions of cells7, in order to maximize the discovery of novel cell types and characterize the transcriptional heterogeneity of individual cell types within samples. One of the limitations of the sc/snRNA-seq technologies, however, is that they only measure RNA content. To address this limitation, there are a growing number of single-cell resolution assays that simultaneously measure RNA content as well as other cellular annotations and modalities. For example, spatial transcriptomic sequencing assays such as Slide-seq8 and LCM-seq9 record both the spatial position and RNA measurements of individual spots on a sample. There are also multi-modal assays such as Patch-seq11 that measure cellular phenotypes in addition to local RNA content, enabling the identification of connections between molecular and cellular phenotypes of neurons.In recent years, there has been a surge in the number and size of atlasing efforts across tissues, conditions, and species12. This contamination of the measured RNA leads to lower power to identify relationships between gene expression levels and electrophysiological features of neurons. Third, for spatial transcriptome technologies such as MERFISH13, in practice, only a few hundred selected genes in the genome can be measured in a tissue. This prevents the detection of spatial expression patterns of genes not directly selected for measurement.However, single-cell resolution assays often trade precision in their RNA measurements in exchange for collecting additional data modalities; consider the following three examples. First, for spatial transcriptome sequencing assays such as LCM-seq or Slide-seq, RNA is extracted from spots of pre-defined size and location on tissue in order to infer spatial gene expression patterns. Because the spots are not lined up with the locations of individual cells, individual spots often capture RNA from multiple adjacent cells by chance, leading to imprecise spatial expression patterns. Second, for the Patch-seq assay that jointly measures RNA and electrophysiological properties of neurons, the collected RNA can include contributions from the target neuron as well as surrounding cells when performed on in vivo or ex vivo slices13. Finally, we show scProjection can separate RNA contributions of the target neuron from neighboring glial cells when analyzing Patch-seq data, leading to more accurate prediction of one data modality  from another (RNA expression levels). We conclude that integrating deep single-cell atlases with single and multimodal cell resolution assays can therefore combine the advantages of both sequencing approaches to study single cells.Here we present scProjection, a unified framework for addressing imprecise single-cell resolution assays. In summary, scProjection uses deeply sequenced single cell atlases to improve the precision of individual single-cell resolution RNA measurements. It does so by jointly performing two tasks: deconvolution (estimating % RNA contributions of each of a set of cell types to a single RNA measurement) and projection (extracting a cell type-specific gene expression profile for each of a set of cell types from an RNA measurement). We show that scProjection achieves state-of-the-art performance in both the deconvolution and projection tasks. Then, we illustrate the use of scProjection for improving analyses of data generated from the three examples of imprecise single-cell resolution assays described above. First, we show scProjection analysis of spatial transcriptomes increases the detection of cell type-specific spatial gene expression patterns across diverse tissues in the brain and intestinal villus. Second, we show scProjection can impute the expression levels of genes not directly measured via spatial transcriptome technologies such as MERFISHThe scProjection model and workflow is illustrated in Fig. 14 (VAEs) trained on each cell population within the single cell atlas to model within-cell type expression variation and delineate the landscape of valid cell states15, as well as their relative occurrence. Here, a valid cell state for a cell type scProjection uses individual variational autoencoders16, MuSic17, dtangle18, DSA19, NNLS20) as well as the deconvolution approaches implemented by spatial transcriptome methods  across multiple datasets and benchmarks, such as CellBench24, ROSMAP25, and MERFISH26 . When comparing individual projections of RNA mixtures to the ground truth cell states used to generate the mixtures, scProjection overall projection performance score was on average 27% and 79% higher than Tangram and uniPort, respectively -based spatial transcriptome measurements. FISH-based technologies such as MERFISH29 27. Based on the composite score, scProjection outperformed all other imputation methods by an average of 38%  by Codeluppi et al. 29 Fig.  and an u38% Fig. . Looking38% Fig. . Figure 26 on neurons from the hypothalamic preoptic region of the mouse brain, in order to identify novel spatial expression patterns. In this study, 155 marker genes were measured across millions of neurons, while a matched scRNA-seq cell atlas was also generated. We first applied scProjection to perform deconvolution in order to identify the major cell type contributing to each MERFISH measurement. Labeling each MERFISH measurement by the cell type predicted to contribute the most RNA, we found scProjection recovered the spatial organization of oligodendrocytes across slices from the mouse brain defined by Bregma indices  from Yao et al. 5. We used scProjection to infer a revised high-resolution cell type label for each MERFISH measurement by projecting MERFISH measurements to the snRNA-seq atlas and assigning discrete labels based on the taxonomy of Yao et al., which defines 129 cell types under the broader categories of glutamatergic, GABAergic, and non-neuronal subtypes. We defined spatial motifs as spatial neighborhoods consisting of a specific set of cell types that are unlikely to co-occur by chance. We assigned each cell into a spatial neighborhood type based on the observed cell types within a 100um distance. We then counted the number of cells assigned to each spatial neighborhood type, and permuted the cell type labels 1,000,000 times, making sure cell labels only permute across cells of the same layer. Through comparison with simulated neighborhood occurrences, we identified a diverse set of 19 significant neighborhood types  ranging from homogenous L4/5 populations to neighborhoods that exist on the L2/3 and L6 boundaries  of the intestinal villus, and also collected a scRNA-seq cell atlas from replicate intestinal villi. They identified spatial expression patterns in the dominant cell type, enterocytes, by (1) identifying marker genes for each zone using the bulk LCM-seq data, (2) assigning zone labels to the scRNA-seq cells using the marker genes, and (3) predicting zone-specific expression through zone-specific averaging of the labeled scRNA-seq data. We reasoned that the identification of marker genes from LCM-seq data could be difficult since each LCM-seq measurement can capture contributions from multiple cells and cell types, thus yielding poor labeling of the single-cell atlas cells. We therefore avoided this critical marker gene selection step by taking the opposite approach: we use scProjection to project the zone-specific LCM-seq samples to the enterocyte single-cell atlas, in order to extract the enterocyte expression patterns within each zone.We obtained a dataset collected by Moor et al. 43. The LCM-enterocyte projections are proximal to the single cells assigned to the same zone by Moor et al., suggesting our approach is overall consistent with that of Moor et al. However, our approach identifies 3-fold more zone-specific spatial expression patterns compared to the genes identified by Moor et al. , and morphological properties from the same neuron, and is critical for linking the molecular and cellular properties of neurons. Patch-seq navigates a micropipette to a neuron of interest in order to simultaneously fill the neuron with a dye, measure its electrophysiological properties and extract its RNA. When applied to in vivo and ex vivo brain slices, the micropipette navigates through many neurons and glia to the neuron of interest. RNA from surrounding neurons and glial often adheres to the micropipette, resulting in the collection of RNA from both the target neuron as well as surrounding cells45. scProjection analysis of several Patch-seq studies indicates cell type abundances from non-neuronal cells are predicted to be as high as 30%, suggesting significant contamination of RNA from non-target cells  when predicting two ephys features, sag and latency . We also identified 79 genes not annotated as ion channels but that are associated with both electrophysiology and eQTLs  over the cell state space for each cell type. This is advantageous for several reasons. First, this enables scProjection to reason about the relative frequency of a cell state observed in the training data, where more frequently observed states have higher probability of being projected to. Second, it enables scProjection to interpolate between observed cell states when the training data is small, which can be important for training on rare cell types or on data from smaller studies. Third, scProjection can also naturally ignore outlier observed cells in the training data because they will not appear often in the cell atlas. In contrast, a number of other methods such as CIBERSORTx49 so we expect this caveat to not limit the widespread applicability of scProjection.One of the caveats of scProjection and related methods, is that by projecting RNA measurements to a reference single-cell atlas, scProjection assumes that the single-cell atlas contains accurate representations of the cell state of cell populations within the RNA sample. There could be scenarios where this is false; for example, projecting RNA from a spatial transcriptome assay of hepatocellular carcinoma samples to a normal liver atlas would miss expression variation in hepatocytes that is driven by carcinomas. Therefore, if no suitable single-cell atlases are publicly available, it would make sense to collect scRNA-seq data on some biological replicate samples in addition to the spatial transcriptome datasets. This experimental design of collecting both scRNA-seq as well as spatial transcriptome data is commonMany current computation approaches to infer cell type abundances assume that biological mixtures can be modeled through a series of weighted linear equations. This common modeling approach assumes that all cell types present in the biological mixture are represented in the single-cell reference dataset. scProjection also models the biological mixtures as a weighted linear equation but additionally computes the likelihood of each biological mixture under each cell type model Figs. S–S9. InveFinally, we envision applications of RNA projections beyond what we have illustrated here. For example, databases such as the Gene Expression Omnibus (GEO) catalog gene expression data from bulk RNA samples collected since when RNA sequencing was first deployed. Using the increasing number of single cell atlases derived for different tissues and cell types across organisms, scProjection can be used to re-analyze historic bulk RNA samples to extract average cell states for individual cell populations that contribute to the bulk RNA sample. Cell type-specific changes in case-control studies could then be inferred, as could cell type-specific eQTLs from genetic studies of disease, for example.Note our notation follows the convention that scalar variables are italicized, and vectors and matrices are bolded without italics. Our framework, scProjection, projects mentclass2pt{minimOnly th cell sequenced for cell population th VAE and sampling from the output to estimate To perform estimation, scProjection leverages a separate reference single cell atlas in which single cells mentclass2pt{minimscProjection uses VAEs to perform the projection of RNA samples 50, batch normalization51 and L2 weight regularization to ensure robust training. ADAM52 is used for optimization with a decaying learning rate starting at 10−3 and a smooth warmup of the KL term in the ELBO, which has been shown to produce more accurate reconstructions53. We denote the trained VAE parameters by The functions For the experiments in which we infer genome-wide expression measurements from limited sets of marker genes such as those measured by MERFISH, the structure of the VAE can be adjusted to be asymmetric with the input measurements to the encoder defined by a subset of gene expression measurements Here, scProjection projects each RNA sample 52 and a learning rate of 10−3 until convergence. The estimated mixing proportions Then, we estimate the mixture proportions In this step, scProjection re-optimizes the encoders and decoders of the individual VAEs Note in this case, the VAE parameters are initially set to 43 from GSE109413 and 10.5281/zenodo.1320734. We independently normalized the count matrices, then scaled and centered using Seurat’s (v4) NormalizeData (without log transform) and ScaleData functions. We retained the union of the marker genes of each cell type identified from the original study, together with the top 2000 variable genes of both LCM-seq and scRNA-seq.We obtained the gene expression matrices for the LCM-seq, scRNA-seq and spatial reconstructions experiments described in Moor et al. 26 from dryad.8t8s248 and the scRNA-seq count matrix from GSE113576. We independently preprocessed each data modality by using Seurat’s NormalizeData and ScaleData functions. We removed entire cell types from the scRNA-seq data that had no analog in the MERFISH experiments and are defined in Table 26. We retained the union of the marker genes of each cell type identified in the original study, together with the top 2000 variable genes across the entire scRNA-seq atlas.We obtained the processed MERFISH gene luminescence matrix described in Moffitt et al. 54 from https://portal.brain-map.org/explore/classes/multimodal-characterization on January 2019. We discarded samples that did not pass QC as defined in the original paper in both the RNA and electrophysiology modalities. We normalized the count matrix to relative counts (RC) (without log transform), then scaled and centered using Seurat’s NormalizeData and ScaleData functions. We retained the union of the marker genes of each cell type identified from the original study, together with the top 2000 variable genes across each of the cell types defined in the snRNA-seq.We obtained the gene count matrix for the mouse Patch-seq experiments described in Berg et al.5 from the Allen Institute Cell Types database: RNA-Seq data page on the Allen Institute’s webpage. We normalized the count matrix, then scaled and centered using Seurat’s NormalizeData and ScaleData functions. We retained the union of the marker genes of each cell type reported in the original study, together with the top 2000 variable genes across each of the cell types defined in the snRNA-seq.We obtained the gene count matrix for the mouse brain atlas described in Yao et al. We obtained the gene count matrix for the mouse brain atlas described in Tasic et al. from the Allen Institute Cell Types database: RNA-Seq data page on the Allen Institute’s webpage. We normalized the count matrix, then scaled and centered using Seurat’s NormalizeData and ScaleData functions. We retained the union of the marker genes of each cell type reported in the original study, together with the top 2000 variable genes across each of the cell types defined in the snRNA-seq.24 from the R data file mRNAmix_qc.RData available on GitHub (https://github.com/Shians/CellBench). We normalized the count matrix to RC (without log transform), then scaled and centered using Seurat’s NormalizeData and ScaleData functions. We retained the union of the marker genes of each cell type identified by CIBERSORTx, together with the top 3000 variable genes computed separately on the RNA mixtures profiled on CEL-Seq2 and SORT-Seq.We obtained the gene count matrix for the RNA mixture experiments in CellBench described in Tian et al.https://github.com/ellispatrick/CortexCellDeconv). We normalized the count matrix to RC (without log transform), then scaled and centered using Seurat’s NormalizeData and ScaleData functions. We retained the union of the marker genes of each cell type reported in Darmanis et al. 55, together with the top 2000 variable genes.We obtained the gene count matrix for the bulk-RNA experiments and IHC measurements described in Patrick et al. from the R data files available on GitHub  was provided the TPM values for all highly variable genes in the scRNA-seq and mixture data. Proportions from NNLS for cell type In the two sections below on benchmarking deconvolution (cell proportion estimation) methods in different datasets, we compared scProjection against CIBERSORTxS. We treated the scProjection estimates The CellBench dataset provides gene expression profiles obtained from sequencing titrated RNA mixtures from three human lung adenocarcinoma cell lines , as well as single cell RNA profiles from each cell line. Sequencing was performed using either plate based (CEL-Seq2 or Drop-Seq) or droplet based (10x Chromium and Drop-seq Dolomite) protocols. The proportion of RNA from each cell line was recorded for each mixture and defines a baseline for methods aiming to computationally estimate the cell type abundances. We trained scProjection using the RNA mixtures as inputs S. We treated the scProjection estimates To provide a more challenging and realistic deconvolution benchmark, we used the ROSMAP-IHC dataset consisting of 70 bulk RNA samples from the dorsolateral prefrontal cortex (DLPFC), an scRNA-seq atlas derived from the DLPFC, and cell type abundances estimated using IHC from adjacent samples to those samples used for sequencing. The bulk RNA, reference single cell atlas and cell type abundances were collected and estimated in three different studies, thus introducing technical and biological variability between data modalities that does not exist in the CellBench study. We trained scProjection using the RNA mixtures as inputs ove Fig. . Furtherove Fig. .From scProjection’s estimates of cell population specific abundances, treated as probabilistic class assignments, the class with maximal probability is assigned as the cell population label for each sample.We label RNA mixtures using k-NN based on the labels of the neighboring cells in the single cell atlas the mixture was projected into.th quantile of the resulting scores to compare the number of zonated genes across methods.For each gene, we compute the distance from an idealized zone-specific measurement as the difference between S. We treated the scProjection estimates https://github.com/broadinstitute/Tangram), and SpatialDWLS .The MERFISH dataset from Moffitt et al. provides the gene expression profiles of MERFISH samples as well as single cells from the same regions of mouse brain. Animal 1, which had the largest sample size in MERFISH data, along with the scRNA-seq reference data from the same study were selected. The features were matched by intersecting the gene set measured in MERFISH data and the top 2000 highly variable genes from scRNA-seq data called using Seurat’s FindVariableFeatures function, and we retained 115 genes for the cell type abundance estimation experiments. TP10K (without log transform) normalization and z-score centering and scaling were applied to the count matrix for both datasets using Seurat’s NormalizeData and ScaleData functions. We trained scProjection using the MERFISH samples as inputs The deconvolution results from each of scProjection, Tangram, and SpatialDWLS included the sample by cell type annotation matrix reflecting the estimates of cell type abundances of each MERFISH sample. All samples from the same cell type label defined in the original Moffitt et al. study were grouped together to calculate the cell-type-specific abundance estimations by averaging across samples.https://celltypes.brain-map.org/api/v2/well_known_file_download/738607155). The 4 sets of simulated mixed RNA samples were comprised of mixtures of either two (L2/3 IT and Lamp5), four , six , or eight  different neuronal subclasses, respectively. We varied the number of contributing cell types in order to ensure representation of RNA mixtures of varying levels of complexity of cell type composition. Relative counts  normalization was applied to the raw count matrix using Seurat’s NormalizeData function, and the top 2000 highly variable genes were selected using the Seurat’s FindVaribleFeatures function. Then each of the 4 different mixed RNA sample sets were generated by randomly sampling 5000 single cells from each of the component cell subclasses of the mouse MOp dataset, followed by summing the transcriptional profile of each single cell from each subclass to build 5000 mixed RNA samples for each sample set. In total, we generated 20,000 mixed RNA samples.We simulated 4 sets of pseudo-bulk mixed RNA samples by combining single cells from up to eight neuronal subclasses  using the mouse Primary Motor Area (MOp) scRNA-seq data from the Cell Types Database of the Allen Brain Map , respectively.To estimate scProjection performance under realistic conditions, we projected the simulated mixed RNA samples into an independent primary visual cortex (VISp) scRNA-seq cell atlas27 and Singh et al. 56 were used to evaluate the performance of each method. They are correlation coefficient (CC), root-mean-square error (RMSE), Jensen-Shannon distance (JSD), and the “fraction of samples closer than the true match” (FOSCTTM). Figure 27, we defined a “relative projection performance score” by aggregating the ranks of CC, RMSE, JSD, and FOSCTTM as a summary metric to compare the projection performance of each method. More specifically, we rank all methods in increasing order of CC, and decreasing order of RMSE, JSD, and FOSCTTM. We then assign each method a final average rank that represents their composite score, based on the four individual metric rankings.To quantify projection performance, four metrics adapted from Li et al. 29 and leveraged a scRNA-seq dataset containing 3005 mouse cortex cellsas the paired reference. We imported the datasets via a scvi-tools built-in data function57.To evaluate the performance of imputing the spatial distribution of transcripts that are not measured in the spatial transcriptomics datasets, we conducted systemic benchmarking experiments on a gold standard mouse somatosensory cortex osmFISH dataset from the Linnarsson Lab38, Tangram23, gimVI36, and SpaGE37, using the same evaluation metric as developed previously27. Briefly, we adapted the evaluation metrics from the benchmarking paper Li et al.27 including Spearman’s correlation coefficient (SCC), structural similarity index measure (SSIM), root means square error (RMSE), Jensen-Shannon distance (JSD), as well as the overall relative imputation performance score that is based on the average ranking of the 4 metrics. The SCC, RMSE, and JSD metrics are similar to what have been described above but were used to assess the accuracy between the ground truth gene expression and imputed patterns across spots on the held-out test set of spatial data. Figure 27. For each gene, higher SSIM values represent better imputation accuracy.There are 33 genes measured in the spatial osmFISH dataset in total, and we applied leave-one-gene-out procedure to test the imputation performance on each gene individually using scProjection, uniPort27 by Spearman’s correlation coefficient (SCC) because SCC was used as a metric for imputation performance in most tutorials of the methods tested in this gene imputation benchmark experiment38.We first sorted the SCC and SSIM values of each method in ascending order, and then sorted RMSE and JSD values individually in descending order to get their corresponding ranks. Each method was assigned a final rank based on its average rank across the four metrics . We replaced the Pearson’s correlation coefficient (PCC) used originally in Li et al. https://tangram-sc.readthedocs.io/en/latest/tutorial_link.html.Tangram: https://docs.scvi-tools.org/en/stable/tutorials/notebooks/gimvi_tutorial.html.gimVI: https://github.com/tabdelaal/SpaGE/blob/master/SpaGE_Tutorial.ipynb.SpaGE: https://uniport.readthedocs.io/en/latest/examples/MERFISH/MERFISH_impute.htmluniPort: .We followed the guidelines in these tutorials for executing the competing imputation methods:To determine the amount of non-neuronal contamination for each Patch-seq sample, we take the sum of abundances across all non-neuronal cell types  as the percent contamination per sample.https://www.python.org) or R . The detailed statistical tests were indicated in corresponding figure legends where applicable. No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. This study does not involve group allocation that requires blinding.All statistical calculations were implemented in Python (v3.8.13; Further information on research design is available in the Supplementary InformationReporting SummarySource Data


In this study, a novel hybrid additive and subtractive manufacturing method using pulsed arc plasma (PAP-HASM) was developed to better integrate additive and subtractive processes. The PAP-HASM process is based on the flexible application of pulsed arc plasma. In this PAP-HASM method, wire arc additive manufacturing using pulsed arc plasma (PAP-WAAM) and dry electrical discharge machining (EDM) milling were used as additive and subtractive techniques, respectively; both are thermal machining processes based on pulsed arc plasma, and both are dry machining techniques requiring no working fluids. The PAP-HASM can be easily realized by only changing the pulsed power supply and tool electrodes. A key technological challenge is that the recast layer on the part surface after dry EDM milling may have a detrimental effect on the component fabricated by PAP-HASM. Here, the hybrid manufacturing method developed in this study was validated with commonly used 316L stainless steel. Preliminary experimental results showed that the PAP-HASM specimens exhibited excellent tensile properties, with an ultimate tensile strength of 539 ± 8 MPa and elongation of 46 ± 4%, which were comparable to the PAP-WAAM specimens. The recast layer on the surface after dry EDM milling has no significant detrimental effect on the mechanical properties of the parts fabricated by PAP-HASM. In addition, compared with components fabricated by PAP-WAAM, those fabricated by PAP-HASM showed significantly better surface roughness. Metal additive manufacturing is an advanced method enabling the fabrication of three-dimensional metallic parts by progressively adding materials in a layer-by-layer fashion , which aNumerous researchers have focused on developing new HASM methods. Song et al.  proposedThe developed hybrid processes, reported in relevant literature, have shown that HASM can be effective in manufacturing high-quality metal parts. However, current research on HASM has mainly focused on the hybridization of additive processes and conventional cutting manufacturing as a subtractive process . To avoi3~106 Hz) are created between the tool electrode and workpiece to remove unwanted materials by melting and evaporating [Electrical discharge machining (EDM) has been widely used to manufacture metallic and non-metallic functional parts ,22. In pporating . High-prporating . Since dporating . These aA wire arc additive manufacturing method using pulsed arc plasma (PAP-WAAM) has been proposed in our previous study to address the material processing challenges generated by high levels of heat input in WAAM . HoweverTherefore, this study has developed a novel hybrid additive and subtractive manufacturing method using pulsed arc plasma (PAP-HASM) to better integrate the advantages of additive and subtractive processes. In this PAP-HASM method, PAP-WAAM and dry EDM were used as additive and subtractive techniques, respectively. Here, the basic process characteristics of PAP-HASM were first identified to show the advantages of this new hybrid manufacturing method. Then, the PAP-HASM method was used to fabricate 316L stainless steel (SS) parts whose characteristics, including the distribution of chemical composition, surface quality and mechanical properties, were thoroughly analyzed and discussed to assess the effectiveness of this hybrid process.To achieve high-quality manufacturing of metal parts using WAAM, an innovative PAP-WAAM method has been developed in our previous study . In this3 ms) is set to allow sufficient heat energy for the arc plasma to melt the filler wire. The arc plasma is used to melt the metal wire and deposit parts in a layer-by-layer fashion. Argon is used throughout the process to prevent oxidation. Subsequently, the pulsed power supply is switched to perform dry EDM milling, wherein the tool electrode is converted to a pipe electrode, and the discharge duration is reduced for the discharge to occur at high frequencies (103~106 Hz). For each pulse, the materials at the discharge spot are removed through melting and evaporation due to the high energy density of the arc plasma. With the assistance of the tool electrode rotation, a high velocity gas jet from a pipe tool electrode flushes the debris away from the discharge gap. Under continuous discharge, the expected shape or profile can be obtained by the accumulation of the removed material. Dry EDM milling is performed to remove unwanted materials from the deposition process. The PAP-HASM process is based on the flexible application of pulsed arc plasma. To validate the developed PAP-HASM method, it was used to fabricate a 316L SS thin-walled structure with dimensions of 110 mm × 36 mm × 2 mm. The samples required for both metallographic analyses and mechanical testing were cut off from as-built 316L SS parts using wire EDM. The locations of the metallographic and mechanical test samples are shown in Based on our understanding of the PAP-HASM process, there is a technological challenge that needs to be considered to realize this hybrid manufacturing process. As shown in Interestingly, after dry EDM milling under the same processing parameters, the component exhibits a clean top surface without any reattached debris b. Much fThe experimental results obtained in this study are compared with those published in the relevant references. In this study, a novel PAP-HASM method, using PAP-WAAM and dry EDM as additive and subtractive techniques, respectively, is developed. Both additive and subtractive techniques are thermal machining processes based on pulsed arc plasma, which contributes to a better integration of the addition and subtraction processes. The hybrid manufacturing method developed in this study was validated with commonly used 316L stainless steel. PAP-HASM components exhibit excellent mechanical properties and surface roughness, with a mean ultimate tensile strength of 539 ± 8 MPa, mean elongation of 46 ± 4% and Sa of 1.504. The recast layer on the surface after dry EDM milling has no significant detrimental effect on the mechanical properties of the parts fabricated by PAP-HASM. This indicates that PAP-HASM is a potentially useful method for forming high-quality metal parts at a low cost.After dry EDM milling, an amount of debris is distributed uniformly along the scanning path of the tool electrode on the side surface of the as-fabricated part. This phenomenon may be due to some molten material expelled from the molten pool and not completely cooled when reaching the surface of the workpiece, which forms debris reattached to the machined surface. The reattached debris has a detrimental effect on surface quality. Further research is planned to address the issue of the reattached debris by optimizing the processing strategy.


Suicide is recognized as a social problem and the interest in preventive measures to diminish suicide risk is constantly increasing. But scientific research results that distinguish between those who have only suicidal ideation (SI) and those who have a history of Suicidal attempts (SA) are limited. Inhibitory control is regarded as an important ability related to the transition from suicidal ideation to suicide attempts. In event-related potential, patients with dysfunction of inhibitory control demonstrate a reduction in the no-go amplitude.This study aimed to determine the association between the no-go event-related potential component and suicidal behaviors among suicide attempters and ideators who never attempted suicide.Overall, 150 patients who visited the emergency room by suicide attempts or patients who visited the psychiatric department with suicidal ideation were recruited and instructed to perform a go/no-go task during electroencephalography recording. The Beck Depression Inventory, Beck Anxiety Inventory, Barratt Impulsivity Scale, Difficulties in Emotional Regulation Scale, and Acquired Capability for Suicide Scale were used. Individuals were divided into two groups: those with suicidal attempts (SA group) and with suicidal ideation (SI group) without SA. The psychological characteristics and event-related potentials of the two groups were compared. Correlation analyses were conducted to test the association between the clinical characteristics and event-related potentials.The SA group had significantly decreased no-go P3 amplitudes at all electrodes compared to the SI group. In the correlation analysis between the clinical measurements and event-related potentials in all the participants, no-go P3 amplitudes in whole electrode sites were negatively correlated with the scores of the acquired capability for the suicide scale.This study revealed that suicide attempters have dysfunction in controlling inhibition compared to suicide ideators reflected in the no-go P3. Our findings suggested that no-go P3 can be a biomarker associated suicide attempts in suicide ideators.None Declared


Recent developments in fifth-generation (5G) wireless communications networks are creating an increasingly crowded electromagnetic environment at microwave (3–30 GHz) and millimeter-wave (30–300 GHz) frequencies. Radiation at these bands can provide non-destructive testing of defects and shielded structures using non-ionizing signals. In an actual building setting where 5G millimeter-wave communications signals are present, passive imaging of the radiation that is propagating through a wall defect can take place by means of interferometric processing without emitting additional signals in an already-crowded spectrum. We investigate the use of millimeter-wave interferometric imaging of defects in building walls and shielded structures by capturing the transmission of 5G millimeter-wave signals through the defects. We experimentally explore the ability to image defects by capturing the transmission of 38 GHz signals through materials using a 24-element interferometric receiving array. The ability to perform joint sensing and communication with the same electromagnetic resources is becoming an increasingly important part of future millimeter-wave wireless networks due to the increasingly crowded spectrum, significant power consumption, and limited hardware resources due to the ubiquity of wireless devices around us ,2. ThereWe present a new approach that can perform non-destructive testing of walls and shielded structures by performing passive millimeter-wave imaging of the ambient electromagnetic signals that get transmitted through the defects. It is achieved with no additional signal emission in an already crowded environment by performing passive interferometric imaging of the millimeter-wave radiation. Our approach can work when the cracks are not visible to the human eye, when for example they are covered by wallpaper, because millimeter-waves can propagate through very thin layers of paper or other materials that are not conductive.Millimeter-wave imaging is used in a range of applications, including security screening ,7, non-dInterferometric imaging has been used for decades in radio astronomy where significantly sparse antenna arrays capture the signals coming from the stars and other astronomical objects ,17. The B and integration time C is a constant that depends on the receiver architecture, amplifier noise factor, and amplifier gain. This means that in order for a passive interferometric receiver to have the sensitivity necessary to detect low-power thermally generated signals, they require amplifiers with very high gain, low noise factor, wide bandwidth, and long observation times. This can increase the system cost tremendously and make real-time operation challenging.Unfortunately, image reconstruction utilizing the van Cittert–Zernike theorem can only take place when the scene is spatially incoherent. Incoherence in space is not always satisfied; an example of this is seen in active radars and typical non-destructive evaluation systems that utilize coherent signal transmission ,21. Spatime τ by (1)ΔT=CTWe recently presented a new incoherent imaging technique that combats the very high sensitivity requirements by utilizing active incoherent noise transmission at a sparse set of locations ,25. ThisIn this work, we utilize existing wireless millimeter-wave signals in an indoor environment in order to perform imaging of defects and slits as shown in the concept of Interferometric processing is usually referred to as correlation processing because the correlations of the electric fields The total set of cross-correlations between the different antenna locations present in an interferometric aperture is defining the sampling function u and v spatial frequency dimensions.The two-dimensional reconstructed scene intensity Although interferometric image reconstruction is primarily a Fourier-domain process as the correlations are directly mapped to the scene visibility, the spatial-domain interpretation can be very helpful for understanding spatial characteristics such as resolution and field of view. The spatial-domain impulse response of the imaging system is the point-spread function (PSF) of the array, whereI and the PSF in the spatial domain:The PSF usually consists of a synthesized beam in the spatial domain with a number of sidelobes, and is a result of the different baselines included in the array. Because interferometric imaging is incoherent imaging and reconstructs image intensities, we utilize the squared magnitude The array layout, sampling function Inter-element antenna spacings affect the unambiguous field of view of an interferometric imager. Ideally, x and y [The spatial resolution of an interferometric imager in the azimuth and elevation planes can be approximated by the half-power beamwidth  x and y . θHPBW cThe interferometric antenna array used in this work had an asymmetric “Y”-shaped formation with 24 elements in 3based on .ith element, with its conjugate transpose. By doing that, every row of the matrix jth element, and then summed (integrated). After the multiplication between the two matrices takes place, the cross-correlations are translated to visibility samples The 38 GHz signals coming out of the 24 receive antennas were amplified using 23 dB gain analog devices (ADI) HMC1040LP3CE low-noise amplifiers and then quadrature downconverted using ADI HMC6789BLC5A downconverters with integrated local oscillator (LO) frequency doublers fed by a 19 GHz LO. Three 16-channel ATS9416 14-bit, 100 MS/s, AlazarTech waveform digitizers installed on a computer were utilized for the data acquisition of the 48 in-phase and quadrature channels from the 24 antennas. The three digitizers were frequency lockedm and triggering was taking place in the time domain using a common 1 kHz signal in order to eliminate frequency differences or timing jitter between the 48 baseband channels . The signal processing and image reconstruction took place in real time using MATLAB on the host computer. An overview of the image reconstruction algorithm can be seen in rm (IFT) . The recx) of the image x, which can be written asx [A regularizer function is frequently used to mitigate the noise present in the image and compensate for the lost information during the blurring. The regularizer function used here for non-destructive imaging, which captures piecewise constant intensities as a function of millimeter-wave signal transmission, is the total variation TV(ons of x . In thisExperimental measurements took place using two 38 GHz transmitters that utilized ADI HMC6787A upconverters and ADI HMC7229 power amplifiers. The setup is shown in The reconstruction of the transmitted signals can be seen in The next target we examined was a foam board with the same dimensions, but which had a much smaller defect represented by a slit opening of 5 mm by 75 mm simulating a wall defect, as shown in In this article, we presented a new method of detecting and imaging defects and openings in walls utilizing existing 5G wireless network infrastructure and a receive interferometric array. While most other microwave imaging methods utilize reflections from transmit and receive apertures, we utilize only receivers and the existing electromagnetic radiation in the environment which suggests lower power and complexity. The proof-of-concept 5G experimental images of slits show promise for future non-destructive inspections using sparse antenna arrays. We showed experimental measurements of non-destructive imaging at 38 GHz of square openings and slits using non-cooperative signals from third party transmitters. Both reconstructions show good dynamic range, which can lead to further analysis and classification. This work is a first step towards contactless and non-ionizing inspection of buildings and other structures using existing communications signals or other incoherent “beacon” signal architectures. This means that the operating frequency and bandwidth will not be limited by the 5G spectrum, as there is the potential for utilizing incoherent transmit architectures. Future work will include experimental results through different wall materials and thicknesses and discuss imaging performance.


T values) as a proxy for infectious viral litres from individuals infected with SARS-CoV-2 is yet to be fully understood. This retrospective observational cohort study compares quantitative infectious viral litres derived from a focus-forming viral titre assay with SARS-CoV-2 RT-PCR CT values in both unvaccinated and vaccinated individuals infected with the Delta strain. As the COVID-19 pandemic continues, efforts to better understand severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral shedding and transmission in both unvaccinated and vaccinated populations remain critical to informing public health policies and vaccine development. The utility of using real time RT-PCR cycle threshold values (CT value <27 collected from 26 June to 17 October 2021 at the University of Vermont Medical Center Clinical Laboratory for which vaccination records were available were included. Partially vaccinated and individuals <18 years of age were excluded. Infectious viral litres were determined using a micro-focus forming assay under BSL-3 containment. Nasopharyngeal swabs positive for SARS-CoV-2 by RT-PCR with a CT values and viral litres was observed in both unvaccinated and vaccinated groups. No difference in mean CT value or viral titre was detected between vaccinated and unvaccinated groups. Viral litres did not change as a function of time since vaccination. In total, 119 specimens from 22 unvaccinated and 97 vaccinated individuals met all inclusion criteria and had sufficient residual volume to undergo viral titring. A negative correlation between RT-PCR CT values, vaccination does not appear to impact an individual’s potential infectivity when infected with the Delta variant. Our results add to the growing body of knowledge regarding the correlation of SARS-CoV-2 RNA levels and levels of infectious virus. At similar C Patient-level metadata includes identifiers and is available upon request following ethical review. The sequencing data were deposited to the NCBI Sequence Read Archive under the BioProject PRJNA938406 and accession numbers are found in Table S1, available in the online version of this article.The COVID-19 pandemic continues to cause significant morbidity and mortality worldwide. As of January 2023, 6.7 million deaths have been attributed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) . InitialT) values are used as a proxy for infectious viral litres in both clinical and research settings and multiple studies have demonstrated that lower CT values  positively correlate with an increased probability of isolating SARS-CoV-2 in viral culture  of samples with CT values <20. However, in agreement with prior studies, we observed variation at the patient level between the amount of RNA and the amount of infectious virus present, suggesting that CT is an imprecise measurement of infectious viral load [We generally found that direct viral litres increased as Cral load .T values as a function of time since vaccination using a linear regression model and observed no differences  of individuals with a CT <20. The reason for this is unclear and could be related to a combination of both biologic  and preanalytical variables . Ke et al. also demonstrated significant variability of viral dynamics among individuals, and therefore, caution should still be used when making clinical decisions based on CT values alone [T >30, caution should also be used when inferring infectivity of patients based on individual CT values, particularly when complicating factors such as immune-suppression or antiviral drugs (i.e. nirmatrelvir-ritonavir) are present.In this study we assessed the impact of vaccination status on viral RNA levels (as measured by RT-PCR) and quantitative viral litres in individuals infected with the Delta variant of SARS-CoV-2. We found a negative correlation between Cier work . While tes alone . While tT value or direct viral titre between vaccinated and unvaccinated groups and vaccination status did not impact the relationship between CT value and viral litres of individuals infected with the Delta variant. Additionally, no difference in the proportion of individuals with undetectable viral litres was observed between vaccinated and unvaccinated individuals, suggesting that vaccination status alone does not impact the likelihood of viral recovery from a given sample. Few studies have quantified SARS-CoV-2 infectious viral litres using a focus-forming assay, and this study, to our knowledge, is only the second of its kind to compare direct viral litres in unvaccinated and vaccinated individuals [et al. evaluated infectious viral litres of NP swabs with RT-PCR CT values <27 from unvaccinated and vaccinated individuals infected with the Delta variant [T value RNA levels following vaccination; however, the clinical and epidemiologic relevance of the reported difference is unclear [T values due to the neutralizing capabilities of vaccine-induced antibodies, our findings suggest that receipt of original SARS-CoV-2 vaccine formulations does not markedly decrease the potential infectivity of an individual infected with the Delta variant. This is in contrast to earlier data that reported reductions in both CT values and onward transmission of pre-Delta strains following vaccination [T values in vaccinated and unvaccinated cohorts began to emerge, raising concern regarding the impact of vaccination on infectivity and transmission [T values and reductions in the duration of viral shedding in vaccinated individuals provided some hope that vaccination was still capable of limiting widespread community spread of the virus; however, additional epidemiologic studies ultimately revealed similar secondary attacks rates in vaccinated and unvaccinated populations infected with the Delta variant [in vitro evidence that the emergence of Delta and subsequent SARS-CoV-2 variants have ameliorated the impact of vaccination on viral infectivity and subsequent community transmission and supports the continued use of additional public health interventions to mitigate viral transmission. Importantly, these data indicate a clear need for the development of second-generation vaccines targeting mucosal immune responses to reduce viral transmission.Importantly, we observed no difference in mean Cividuals . Similar variant . In agre variant . They al unclear . While wcination . With wismission . Reports variant . We beliT values or viral litres as a function of time since vaccination suggesting that there is no waning effect of vaccination on Delta transmission potential. This is in contrast to several other studies that have demonstrated waning of vaccine-associated reductions in RNA viral load at 6 months post-vaccination [T values and viral litres across our study population.Further, we did not observe any changes in Ccination . It is pet al. demonstrated that vaccinated individuals reach similar peak viral loads as unvaccinated individuals but have a more rapid decline in viral load, which could have a significant impact on onward transmission dynamics at the population level [T values using the Cobas platform on all specimens, which may have impacted the comparability of CT values across our data set. Third, our results are limited by a small sample size and an uneven distribution of specimens from unvaccinated versus vaccinated individuals, with only 18.5 % of cases occurring in unvaccinated individuals. While this was not surprising considering that early vaccination efforts resulted in Vermont being the first state in the United States to reach national vaccination targets and vaccination rates continue to be amongst the highest in the country, it limited the number of unvaccinated cases available for comparison and thus constrained robust statistical comparisons between the groups.Our study has at least three notable limitations. First, we lacked some sample-associated metadata, including time since symptom onset. Although we were able to extract data from our institution’s electronic medical record, most individuals were tested in the outpatient setting, and the test order did not include any details regarding the presence, absence or duration of symptoms. Therefore, we were unable to correlate our data with days post-onset of symptoms or control for differences in symptom status in vaccinated versus unvaccinated patients. This may bias our results and should be considered when interpreting these data. Singanayagam on level . ConsideT values and viral infectivity as well as the impact of vaccination on SARS-CoV-2 transmission potential. Since the time of this work the Delta variant has been surpassed by other strains, most notably those of the Omicron lineages, and viral evolution continues to impact the effect of currently available vaccines. Caution however should continue to be used when using clinical testing data  to infer infectivity of individual patients, regardless of vaccination status and viral strain. Continued generation of quantitative viral culture data is critical to enhance our understanding of the impact of novel viral strains and vaccine formulations on viral transmission potential and to inform effective public health interventions.Despite these limitations, this study contributes to a growing body of literature examining the relationship between CClick here for additional data file.


In the current guidelines, the decision to initiate and intensify lipid lowering therapy is guided by the untreated LDL-C levels of the individual as well as the total cardiovascular risk level.2 As the risk becomes higher, the actions are intensified in a graded manner. The risk of developing a cardiovascular (CV) event depends on the extent of atherosclerosis, which is the result of the complex interplay of genetics, lifestyle, and cumulative LDL-C levels over time. Current risk estimation systems consider major causal risk factors at a single time point to classify individuals into different risk categories. The most recent prevention guidelines in Europe use the contemporary and improved SCORE2 risk estimation system to determine the 10-year risk of CV events.3 However, this approach where risk estimation is based on group averages and applied to individual patients may not reflect genetic vulnerability or resilience, the cumulative exposure of risk factors over time, and interaction with other risk factors. While lifestyle should be recommended for all, there is a large group of patients in the moderate- or low-risk category where treatment decision needs more accurate assessment. Identifying novel risk markers may improve the selection of individuals for preventive strategies. Current risk estimation systems are limited to predicting 10-year risk therefore may underestimate the risk especially in women and younger individuals and overestimate risk in the elderly.4 A recent study applied the 2021 European guideline treatment criteria to a low-risk population and found that <1% of women met eligibility for class I recommendation to statins.5Dyslipidemia is an important modifiable risk factor for atherosclerotic cardiovascular disease (ASCVD) and lipid lowering an integral component of cardiovascular prevention. Apolipoprotein B containing lipoproteins—mainly low-density lipoprotein cholesterol (LDL-C)—have indubitably been shown to be causal for atherosclerosis, and interventions that lower LDL-C can change the trajectory of the disease to improve cardiovascular outcomes.In attempts to refine risk prediction further and tailor therapy in an optimal and cost-effective way, imaging and biomarkers have been utilized. The following debate will focus on whether biomarkers or imaging can help us answer the following questions better:How can we better identify the seemingly low–moderate risk patient who will benefit from lipid lowering therapy and the high risk patient who needs treatment intensification?Which tool will aid our decision to intensify or deescalate lipid lowering therapy?Can imaging or a biomarker help us choose the ideal lipid lowering regimen in a given individual?6 Will adding any other biomarker help tailor therapy? Although not recommended in these guidelines, several other biomarkers have been utilized in an attempt to further define risk and personalize therapy. Recent evidence has shown that increased lipoprotein(a) [Lp(a)] leads to an incremental and continuous increase in absolute CV disease (CVD) risk.7 As Lp(a) levels increase, the LDL-C reduction needed to mitigate the increased risk of major CV events becomes higher, and elevated Lp(a) levels may justify more intensive LDL-C lowering therapy. Biomarkers of inflammation may also help guide therapy.8 High-sensitivity C-reactive protein (hs-CRP) >2 mg/L is considered a risk enhancer in the US and Canadian guidelines especially for intermediate risk patients.9 Newer lipid lowering therapies such as icosapent ethyl and bempedoic acid lower hs-CRP substantially, but whether this can be used to personalize therapy is not known. Other biomarkers, such as N-terminal pro-B-type natriuretic peptide and high-sensitivity cardiac troponin I, have also been shown to predict increased hazard of incident CVD and modestly improve discrimination and reclassification.10 Although biomarkers may help personalize therapy in selected patients, whether or when to use which biomarker in which patient is still debated.The 2021 European Prevention Guidelines base their treatment recommendations on plasma levels of LDL-C, apolipoprotein B, and non-HDL-C.11 The US multisociety guidelines on the management of blood cholesterol recommend to use coronary artery calcium (CAC) for guiding treatment decisions for primary prevention of ASCVD in individuals at borderline or intermediate risk.12 The European prevention guidelines consider CAC score as a risk modifier to reclassify CVD risk upwards or downwards in addition to conventional risk factors but exert caution about presence of noncalcified plaques that are not detected by CAC.6 Assessment of carotid or femoral plaque burden with ultrasound can also predict CV events and may be considered as a risk modifier in patients at intermediate risk when a CAC score is not feasible.13 Because of the cost, low-dose radiation, and need for specialized centers for some of these techniques, who will benefit most and at what stage of life from imaging need to be determined. The decision to utilize imaging or biomarkers should be personalized by carefully weighing the risk of testing—especially low dose radiation—against potential benefit of the intervention. Despite the supportive epidemiology, no randomized trial has yet directly tested the benefit of imaging-guided interventions on top of risk stratification using clinical characteristics and biomarkers. In the future, integrating a large number of patient-related variables over time with artificial intelligence including genetics, omics, biomarkers, imaging, and data from wearables can truly personalize lifetime risk prediction and management.14 Till then, we should utilize the tools we have to identify those who will benefit from lipid lowering meanwhile avoiding unnecessary overtreatment. The following debate will focus on whether imaging or biomarkers can better guide therapy today.On the other hand, imaging can give us the memory of lifetime exposure to risk factors. Non-invasive imaging can detect the presence, extent, and composition of the atherosclerotic plaque; all of which are determinants of CV events. Detection of coronary artery calcification with computed tomography (CT) improves both discrimination and reclassification for CV risk.