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Neurologic or immune-mediated conditions have been evaluated as potential adverse events (AEs) in coronavirus disease 2019 (COVID-19) vaccine safety surveillance. To contextualize United States (US) surveillance findings, it is important to quantify the association of AEs with COVID-19 diagnoses among US adults before the introduction of COVID-19 vaccines. Cohort and self-controlled risk interval (SCRI) designs were used in 2 US administrative claims data sources-Merative™ MarketScan® Commercial Database (ages 18-64 years) and Medicare fee-for-service data (ages ≥ 65 years). AEs included Guillain-Barré syndrome (GBS), Bell's palsy, encephalitis/encephalomyelitis, narcolepsy, immune thrombocytopenia (ITP), and transverse myelitis. The cohort (study period, 1 April 2020-10 December 2020) included adults with COVID-19 diagnoses and matched comparators. Inverse probability of treatment-weighted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated. The SCRI (study period, 1 June 2020-10 December 2020) identified the AEs in risk windows after COVID-19 diagnosis and pre- and postexposure reference windows. Relative incidences (RIs) and 95% CIs were estimated with seasonality-adjusted conditional Poisson regression models accounting for outcome-dependent observation windows. The study observed a consistent association between COVID-19 diagnosis and GBS: MarketScan HR = 9.57 (95% CI, 1.23-74.74), RI = 8.53 (95% CI, 2.45-29.7); Medicare HR = 1.97 (95% CI, 1.04-3.74), RI = 4.63 (95% CI, 1.78-12.01). For ITP, the association was weaker, but still consistently elevated: MarketScan HR = 2.06 (95% CI, 1.20-3.53), RI = 1.74 (95% CI, 1.01-3.00); Medicare HR = 1.36 (95% CI, 1.18-1.57), RI = 1.91 (95% CI, 1.60-2.28). For all remaining AEs, there was not consistent evidence of an association with COVID-19, with estimates that were generally modest, imprecise, or varying by study design. COVID-19 diagnoses were associated with an increased risk of GBS and ITP in both data sources and study designs. Increased risks of other neurologic/immune-mediated AEs cannot be ruled out.

Precise biomarkers are needed to guide better diagnostics and therapeutics for basal-like breast cancer, for which DNA-dependent protein kinase catalytic subunit (DNA-PKcs) has been recently reported by the Clinical Proteomic Tumor Analysis Consortium as the most specific biomarker. We evaluated DNA-PKcs expression in clinically-annotated breast cancer tissue microarrays and correlated results with immune biomarkers (training set: n  = 300; validation set: n  = 2401). Following a pre-specified study design per REMARK criteria, we found that high expression of DNA-PKcs was significantly associated with stromal and CD8 + tumor infiltrating lymphocytes. Within the basal-like subtype, tumors with low DNA-PKcs and high tumor-infiltrating lymphocytes displayed the most favourable survival. DNA-PKcs expression by immunohistochemistry identified estrogen receptor-positive cases with a basal-like gene expression subtype. Non-silent mutations in PRKDC were significantly associated with poor outcomes. Integrating DNA-PKcs expression with validated immune biomarkers could guide patient selection for DNA-PKcs targeting strategies, DNA-damaging agents, and their combination with an immune-checkpoint blockade.

Purpose Alterations to mismatch repair (MMR) pathways are a known cause of cancer, particularly colorectal and endometrial carcinomas. Recently, checkpoint inhibitors have been approved for use in MMR-deficient cancers of any type (Prasad et al. in JAMA Oncol 4:157–158, 2018). Functional studies in breast cancer have shown associations between MMR loss, resistance to aromatase inhibitors and sensitivity to palbociclib (Haricharan et al. in Cancer Discov 7:1168–1183, 2017). Herein, we investigate the clinical meaning of MMR deficiency in breast cancer by immunohistochemical assessment of MSH2, MSH6, MLH1 and PMS2 on a large series of breast cancers linked to detailed biomarker and long-term outcome data. Methods Cases were classified as MMR intact when all four markers expressed nuclear reactivity, but MMR-deficient when at least one of the four biomarkers displayed loss of nuclear staining in the presence of positive internal stromal controls on the tissue microarray core. Results Among the 1635 cases with interpretable staining, we identified 31 (1.9%) as MMR-deficient. In our cohort, MMR deficiency was present across all major breast cancer subtypes, and was associated with high-grade, low-progesterone receptor expression and high tumor-infiltrating lymphocyte counts. MMR deficiency is significantly associated with inferior overall (HR 2.29, 95% CI 1.02–5.17, p  = 0.040) and disease-specific survival (HR 2.71, 95% CI 1.00–7.35, p  = 0.042) in the 431 estrogen receptor-positive patients who were uniformly treated with tamoxifen as their sole adjuvant systemic therapy. Conclusion Overall, this study supports the concept that breast cancer patients with MMR deficiency as assessed by immunohistochemistry may be good candidates for alternative treatment approaches such as immune checkpoint or CDK4 inhibitors.

Colony-stimulating factor-1 receptor (CSF-1R) signaling promotes an immune suppressive microenvironment enriched in M2 macrophages. Given that CSF-1R inhibitors are under investigation in clinical trials, including in breast cancer, CSF-1R expression and association with immune biomarkers could identify patients who derive greater benefit from combination with immunotherapies. TIMER2.0 and bc-GenExMiner v4.7 were used to assess the correlation of CSF1R mRNA with immune infiltrates and prognosis. Following a prespecified training–validation approach, an optimized immunohistochemistry assay was applied to assess CSF-1R on carcinoma cells and macrophages on breast cancer tissue microarray series representing 2384 patients, coupled to comprehensive clinicopathological, biomarker, and outcome data. Significant positive correlations were observed between CSF1R mRNA and immune infiltrates. High carcinoma CSF-1R correlated with grade 3 tumors >2 cm, hormone receptor negativity, high Ki67, immune checkpoint biomarkers, and macrophages expressing CSF-1R and CD163. High carcinoma CSF-1R was significantly associated with poor survival in univariate and multivariate analyses. Adverse prognostic associations were retained in ER+ cases regardless of the presence of CD8+ T cells. CSF-1R+ macrophages were not prognostic. High carcinoma CSF-1R is associated with aggressive breast cancer biology and poor prognosis, particularly in ER+ cases, and identifies patients in whom biomarker-directed CSF-1R therapies may yield superior therapeutic responses.

In the original publication of the article, the funding statement was published incompletely. The corrected funding statement should read as below.In the original publication of the article, the funding statement was published incompletely. The corrected funding statement should read as below.

IntroductionNeurologic or immune-mediated conditions have been evaluated as potential adverse events (AEs) in coronavirus disease 2019 (COVID-19) vaccine safety surveillance. To contextualize United States (US) surveillance findings, it is important to quantify the association of AEs with COVID-19 diagnoses among US adults before the introduction of COVID-19 vaccines.MethodsCohort and self-controlled risk interval (SCRI) designs were used in 2 US administrative claims data sources-Merative™ MarketScan® Commercial Database (ages 18-64 years) and Medicare fee-for-service data (ages ≥ 65 years). AEs included Guillain-Barré syndrome (GBS), Bell's palsy, encephalitis/encephalomyelitis, narcolepsy, immune thrombocytopenia (ITP), and transverse myelitis. The cohort (study period, 1 April 2020-10 December 2020) included adults with COVID-19 diagnoses and matched comparators. Inverse probability of treatment-weighted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated. The SCRI (study period, 1 June 2020-10 December 2020) identified the AEs in risk windows after COVID-19 diagnosis and pre- and postexposure reference windows. Relative incidences (RIs) and 95% CIs were estimated with seasonality-adjusted conditional Poisson regression models accounting for outcome-dependent observation windows.ResultsThe study observed a consistent association between COVID-19 diagnosis and GBS: MarketScan HR = 9.57 (95% CI, 1.23-74.74), RI = 8.53 (95% CI, 2.45-29.7); Medicare HR = 1.97 (95% CI, 1.04-3.74), RI = 4.63 (95% CI, 1.78-12.01). For ITP, the association was weaker, but still consistently elevated: MarketScan HR = 2.06 (95% CI, 1.20-3.53), RI = 1.74 (95% CI, 1.01-3.00); Medicare HR = 1.36 (95% CI, 1.18-1.57), RI = 1.91 (95% CI, 1.60-2.28). For all remaining AEs, there was not consistent evidence of an association with COVID-19, with estimates that were generally modest, imprecise, or varying by study design.ConclusionsCOVID-19 diagnoses were associated with an increased risk of GBS and ITP in both data sources and study designs. Increased risks of other neurologic/immune-mediated AEs cannot be ruled out.

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.

Some active asteroids have been proposed to be formed as a result of impact events 1 . Because active asteroids are generally discovered by chance only after their tails have fully formed, the process of how impact ejecta evolve into a tail has, to our knowledge, not been directly observed. The Double Asteroid Redirection Test (DART) mission of NASA 2 , in addition to having successfully changed the orbital period of Dimorphos 3 , demonstrated the activation process of an asteroid resulting from an impact under precisely known conditions. Here we report the observations of the DART impact ejecta with the Hubble Space Telescope from impact time T  + 15 min to T  + 18.5 days at spatial resolutions of around 2.1 km per pixel. Our observations reveal the complex evolution of the ejecta, which are first dominated by the gravitational interaction between the Didymos binary system and the ejected dust and subsequently by solar radiation pressure. The lowest-speed ejecta dispersed through a sustained tail that had a consistent morphology with previously observed asteroid tails thought to be produced by an impact 4 , 5 . The evolution of the ejecta after the controlled impact experiment of DART thus provides a framework for understanding the fundamental mechanisms that act on asteroids disrupted by a natural impact 1 , 6 . Observations with the Hubble Space Telescope reveal a complex evolution of the ejecta produced by the Double Asteroid Redirection Test (DART) spacecraft impacting Dimorphos.