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Propolis is produced by honey bees from a series of resinous, gummy and balsamic substances collected from the leaf buds of different tree species and mixed with their secretions. It is used as a sealant and antiseptic in the hive. Because of its antimicrobial properties, propolis has become a popular alternative medicine or food for health protection and disease prevention. The presence of a large number of flavonoids, aromatic acids and phenolic compounds has been suggested to be responsible for most biological and pharmacological activities of propolis. This review aims to provide a critical analysis of the different studies which evaluate the activity of propolis against fungi and to identify the chemical components responsible for such activity. The discussion of the methodological approaches used and the issued results is a key point of this review to highlight knowledge gaps. This review will first describe the chemical composition of the propolis and the factors of variability including geographical and botanical origins and then examine its antifungal activities with a focus against phytopathogenic fungi. Finally, it will discuss the main components responsible for such activities and their mechanism of action.

Abstract Background In liver computed tomography (CT), tailoring the contrast injection to the patient’s specific characteristics is relevant for optimal imaging and patient safety. We evaluated a novel algorithm engineered for personalized contrast injection to achieve reproducible liver enhancement centered on 50 HU. Methods From September 2020 to August 31, 2022, CT data from consecutive adult patients were prospectively collected at our multicenter premises. Inclusion criteria consisted of an abdominal CT referral for cancer staging or follow-up. For all examinations, a web interface incorporating data from the radiology information system (patient details and examination information) and radiographer-inputted data (patient fat-free mass, imaging center, kVp, contrast agent details, and imaging phase) were used. Calculated contrast volume and injection rate were manually entered into the CT console controlling the injector. Iopamidol 370 mgI/mL or Iohexol 350 mgI/mL were used, and kVp varied (80, 100, or 120) based on patient habitus. Results We enrolled 384 patients (mean age 61.2 years, range 21.1–94.5). The amount of administered iodine dose (gI) was not significantly different across contrast agents (p = 0.700), while a significant increase in iodine dose was observed with increasing kVp (p

Objective. Several factors are involved in the preservation of graft function after surgical myocardial revascularization. This follow-up study aimed to evaluate the effects of vein graft anastomosis and graft morphology on long-term graft patency a minimum of 10 years after aortocoronary bypass grafting. Setting and Cohorts. This was a sub-analysis of a study that enrolled patients after isolated bypass surgery at the University Hospital Ostrava in order to evaluate the long-term graft patency of the saphenous vein after endoscopic harvest, a minimum of 10 years after aortocoronary bypass grafting. Methods. Fifty angiograms, with a total of 90 grafts, after isolated myocardial revascularization were visualized using coronary computed tomography angiography, with 50% luminal stenosis or greater considered significant. Results. The overall graft patency rate was 72.3%. The differences in occlusion rates between sequential and individual grafts were not statistically significant (P=0.156). All y-grafts were totally occluded. Graft and target artery diameters had a statistically significant influence on patency (P=1.000 and 0.381, respectively). Longer graft length and higher calcium scores were associated with statistically significant graft occlusion (P=0.033 and 0.005, respectively). Conclusion. Sequential grafts can be constructed safely, especially when the goal is complete myocardial revascularization.

Abstract Dose management systems (DMS) are an essential tool for quality assurance and optimising patient radiation exposure. For radiologists and medical physicists, they are important for managing many radiation protection tasks. In addition, they help fulfil the requirements of Directive 2013/59/EURATOM regarding the electronic transmission of dosimetric data and the detection of unintended patient exposures. The EuroSafe Imaging Clinical Dosimetry and Dose Management Working Group launched a questionnaire on the use of DMS in European member states and analysed the results in terms of modalities, frequency of radiological procedures, involvement of medical physics experts (MPEs), legal requirements, and local issues (support by information technology (IT), modality interfaces, protocol mapping, clinical workflow, and associated costs). Critical relevance statement Despite the great advantages of dose management systems for optimising radiation protection, distribution remains insufficient. This questionnaire shows that reasons include: a lack of DICOM interfaces, insufficient harmonisation of procedure names, lack of medical physicist and IT support, and costs. Key Points Quantitative radiation dose information is essential for justification and optimisation in medical imaging. Guidelines are required to ensure radiation dose management systems quality and for acceptance testing. Verifying dose data management is crucial before dose management systems clinical implementation. Medical physics experts are professionals who have important responsibilities for the proper management of dose monitoring. Graphical Abstract

Abstract Background Physical activity is a crucial demand on cystic fibrosis treatment management. The highest value of oxygen uptake (VO2peak) is an appropriate tool to evaluate the physical activity in these patients. However, there are several other valuable CPET parameters describing exercise tolerance (Wpeak, VO2VT1, VO2VT2, VO2/HRpeak, etc.), and helping to better understand the effect of specific treatment (VE, VT, VD/VT etc.). Limited data showed ambiguous results of this improvement after CFTR modulator treatment. Elexacaftor/tezacaftor/ivacaftor medication improves pulmonary function and quality of life, whereas its effect on CPET has yet to be sufficiently demonstrated. Methods We performed a single group prospective observational study of 10 adolescent patients with cystic fibrosis who completed two CPET measurements between January 2019 and February 2023. During this period, elexacaftor/tezacaftor/ivacaftor treatment was initiated in all of them. The first CPET at the baseline was followed by controlled CPET at least one year after medication commencement. We focused on interpreting the data on their influence by the novel therapy. We hypothesized improvements in cardiorespiratory fitness following treatment. We applied the Wilcoxon signed-rank test. The data were adjusted for age at the time of CPET to eliminate bias of aging in adolescent patients. Results We observed significant improvement in peak workload, VO2 peak, VO2VT1, VO2VT2, VE/VCO2 slope, VE, VT, RQ, VO2/HR peak and RR peak. The mean change in VO2 peak was 5.7 mL/kg/min, or 15.9% of the reference value (SD ± 16.6; p= 0.014). VO2VT1 improved by 15% of the reference value (SD ± 0.1; p= 0.014), VO2VT2 improved by 0.5 (SD ± 0.4; p= 0.01). There were no differences in other parameters. Conclusion Exercise tolerance improved after elexacaftor/tezacaftor/ivacaftor treatment initiation. We suggest that the CFTR modulator alone is not enough for recovering physical decondition, but should be supplemented with physical activity and respiratory physiotherapy. Further studies are needed to examine the effect of CFTR modulators and physical therapy on cardiopulmonary exercise tolerance.

Abstract Few published data science tools are ever translated from academia to real-world clinical settings for which they were intended. One dimension of this problem is the software engineering task of turning published academic projects into tools that are usable at the bedside. Given the complexity of the data ecosystem in large health systems, this task often represents a significant barrier to the real-world deployment of data science tools for prospective piloting and evaluation. Many information technology companies have created Machine Learning Operations (MLOps) teams to help with such tasks at scale, but the low penetration of home-grown data science tools in regular clinical practice precludes the formation of such teams in healthcare organizations. Based on experiences deploying data science tools at two large academic medical centers (Beth Israel Deaconess Medical Center, Boston, MA; Mayo Clinic, Rochester, MN), we propose a strategy to facilitate this transition from academic product to operational tool, defining the responsibilities of the principal investigator, data scientist, machine learning engineer, health system IT administrator, and clinician end-user throughout the process. We first enumerate the technical resources and stakeholders needed to prepare for model deployment. We then propose an approach to planning how the final product will work from data extraction and analysis to visualization of model outputs. Finally, we describe how the team should execute on this plan. We hope to guide health systems aiming to deploy minimum viable data science tools and realize their value in clinical practice.

Introduction In recent years, ventilatory efficiency (minute ventilation (V′E)/carbon dioxide production (V′CO2) slope) and partial pressure of end-tidal carbon dioxide (PETCO2) have emerged as independent predictors of postoperative pulmonary complications (PPC). Single parameters may give only partial information regarding periprocedural hazards. Accordingly, our aim was to create prediction models with improved ability to stratify PPC risk in patients scheduled for elective lung resection surgery. Methods This post hoc analysis was comprised of consecutive lung resection candidates from two prior prospective trials. All individuals completed pulmonary function tests and cardiopulmonary exercise testing (CPET). Logistic regression analyses were used for identification of risk factors for PPC that were entered into the final risk prediction models. Two risk models were developed; the first used rest PETCO2 (for patients with no available CPET data), the second used V′E/ V′CO2 slope (for patients with available CPET data). Receiver operating characteristic analysis with the De-Long test and area under the curve (AUC) were used for comparison of models. Results The dataset from 423 patients was randomly split into the derivation (n=310) and validation (n=113) cohorts. Two final models were developed, both including sex, thoracotomy, “atypical” resection and forced expiratory volume in 1 s/forced vital capacity ratio as risk factors. In addition, the first model also included rest PETCO2, while the second model used V′E/V′CO2 slope from CPET. AUCs of risk scores were 0.795 (95% CI: 0.739–0.851) and 0.793 (95% CI: 0.737–0.849); both p

Oils extracted from three exotic fruits, guava, kiwi and passion fruit were analyzed to evaluate the possible commercial interest for these waste materials from fruit juices industry. Results showed interesting fatty acids compositions with high amounts of essential fatty acids such as 62.3% alpha linolenic acid for kiwi seed oil, and respectively 73.4% and 77.0% for omega 6 linoleic acid in passion fruit and guava seed oils. Fatty acids regiodistribution, sterols and tocopherols contents were also analyzed to try to establish the potential nutritional interest of such oils.

Developing alloys with optimal properties involves tuning several compositional and processing parameters. As the parametric space is often high-dimensional, doing so may require a prohibitively large number of experiments, which demand ample time and physical resources. In this study, we examine whether the method of Bayesian optimisation, which involves a sequential function evaluation scheme, applies to designing metallic alloys. We consider the example of bake-hardening ferritic steel and aim to maximise the extent of bake-hardening by tuning more than a dozen parameters while reducing the number of experimental fabrications and measurements. To this end, an existing dataset has been used to create a regression model, which acts as a surrogate for experimental measurements. The Bayesian optimiser has been implemented with three different covariance functions and two acquisition functions. Our results suggest that a design strategy guided by Bayesian optimisation has the potential to substantially reduce the time and cost of developing alloys through experimental routes.

PurposeIn young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population.MethodsA retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS.ResultsAmong the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%).ConclusionTrough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

BACKGROUND: Selected molecular biomarkers were incorporated into the US cancer registry reporting for patients with brain tumors beginning in 2018. We investigated the completeness and validity of these variables and described the epidemiology of molecularly defined brain tumor types. METHODS: Brain tumor patients with histopathologically confirmed diagnosis in 2018 were identified within the Central Brain Tumor Registry of the United States and NCIs Surveillance, Epidemiology, and End Results Incidence databases. The brain molecular markers (BMM) site-specific data item was assessed for coding completeness and validity. 1p/19q status, MGMT promoter methylation, WHO grade data items, and new ICD-O-3 codes were additionally evaluated. These data were used to profile the characteristics and age-adjusted incidence rates per 100 000 population of molecularly defined brain tumors with 95% confidence intervals (95% CI). RESULTS: BMM completeness across the applicable tumor types was 75%-92% and demonstrated favorable coding validity. IDH-wildtype glioblastomas incidence rate was 1.74 (95% CI: 1.69-1.78), as compared to 0.14 for WHO grade 2 (95% CI: 0.12-0.15), 0.15 for grade 3 (95% CI: 0.14-0.16), and 0.07 for grade 4 (95% CI: 0.06-0.08) IDH-mutant astrocytomas. Irrespective of WHO grade, IDH mutation prevalence was highest in adolescent and young adult patients, and IDH-mutant astrocytomas were more frequently MGMT promoter methylated. Among pediatric-type tumors, the incidence rate was 0.06 for H3K27M-mutant diffuse midline gliomas (95% CI: 0.05-0.07), 0.03 for SHH-activated/TP53-wildtype medulloblastomas (95% CI: 0.02-0.03), and

Rib fractures commonly result from traumatic injury and often require hospitalization for pain control and supportive pulmonary care. Although the use of mobile health technology to share patient-generated health data has increased, it remains limited in patients with traumatic injuries. We sought to assess the feasibility of mobile health tracking in patients with rib fractures by using a smartphone app to monitor postdischarge recovery. We encountered patient, institutional, and process-related obstacles that limited app use. The success of future work requires the acknowledgment of these limitations and the use of an implementation science framework to effectively integrate technological tools for personalized trauma care.

BackgroundPost–COVID-19 condition (colloquially known as “long COVID-19”) characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for “Post COVID-19 condition, unspecified” to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear. ObjectiveThis study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems—the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center—against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions. MethodsPatients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19–related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a “core” cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code’s performance was compared across 3 health care systems and across different time periods of the pandemic. ResultsOverall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems. ConclusionsThis is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

The present study examined the relationships between childhood attachment, adult attachment, self-esteem, and psychological distress; specifically, it investigated the multiple mediating roles of self-esteem and adult attachment on the association between childhood attachment and psychological distress. Using 1,708 adult participants, a multiple-mediator model analysis following bootstrapping procedures was conducted in order to investigate the mechanisms among childhood and adult attachment, self-esteem, and psychological distress. As hypothesized, childhood attachment was significantly associated with self-esteem, adult attachment, and psychological distress. Self-esteem was found to be a significant mediator for the relationship between childhood attachment and adult attachment. In addition, adult attachment significantly mediated the relationship between self-esteem and psychological distress. The results provide insight on counseling interventions to increase adults' self-esteem and attachment security, with efforts to decrease the negative impact of insecure childhood attachment on later psychological distress.

BackgroundThe Central Brain Tumor Registry of the United States (CBTRUS) uses a histology grouping model based on the World Health Organization (WHO) classifications to group records for clinically relevant statistical reporting. Newly identified genetic markers more accurately stratify patients than histology alone and were incorporated into the 2016 update to the WHO Classification.MethodsCBTRUS and consulting neuropathologists reviewed and aligned histology groupings with the 2016 WHO update. "Obsolete" (terms not currently in use) histology nomenclature along with their International Classification of Disease, Oncology 3rd edition (ICD-O-3) codes were identified, some histologies were reclassified to 2016 WHO, and new codes found in 2016 WHO were incorporated. An evaluation of the frequency of histology codes affected in the realignment process, and incidence and survival pre- and post-realignment was conducted.ResultsAfter review, 67 codes were noted as obsolete, 51 codes were reclassified, and 12 new codes were incorporated. Histology groups most affected were mesenchymal tumors and neuronal/mixed neuronal-glial tumors. Reorganization resulted in 2588 (0.65%) cases with grouping reassignment or reporting change, indicating that the 2016 WHO Classification revision has impacted the collection and reporting of primary brain and other CNS tumors.ConclusionThis work demonstrates the need to be responsive to changes in classification and coding in order to ensure the most up-to-date and accurate statistics for brain and CNS tumors. This will require collaboration from all stakeholders within the brain tumor community, so to have the ability to reconcile clinical practices and surveillance requirements.

The factors driving therapy resistance in diffuse glioma remain poorly understood. To identify treatment-associated cellular and genetic changes, we analyzed RNA and/or DNA sequencing data from the temporally separated tumor pairs of 304 adult patients with isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant glioma. Tumors recurred in distinct manners that were dependent on IDH mutation status and attributable to changes in histological feature composition, somatic alterations, and microenvironment interactions. Hypermutation and acquired CDKN2A deletions were associated with an increase in proliferating neoplastic cells at recurrence in both glioma subtypes, reflecting active tumor growth. IDH-wild-type tumors were more invasive at recurrence, and their neoplastic cells exhibited increased expression of neuronal signaling programs that reflected a possible role for neuronal interactions in promoting glioma progression. Mesenchymal transition was associated with the presence of a myeloid cell state defined by specific ligand-receptor interactions with neoplastic cells. Collectively, these recurrence-associated phenotypes represent potential targets to alter disease progression.

Context.—The diagnosis and clinical management of patients with diffuse gliomas (DGs) have evolved rapidly over the past decade with the emergence of molecular biomarkers that are used to classify, stratify risk, and predict treatment response for optimal clinical care.Objective.—To develop evidence-based recommendations for informing molecular biomarker testing for pediatric and adult patients with DGs and provide guidance for appropriate laboratory test and biomarker selection for optimal diagnosis, risk stratification, and prediction.Design.—The College of American Pathologists convened an expert panel to perform a systematic review of the literature and develop recommendations. A systematic review of literature was conducted to address the overarching question, "What ancillary tests are needed to classify DGs and sufficiently inform the clinical management of patients?" Recommendations were derived from quality of evidence, open comment feedback, and expert panel consensus.Results.—Thirteen recommendations and 3 good practice statements were established to guide pathologists and treating physicians on the most appropriate methods and molecular biomarkers to include in laboratory testing to inform clinical management of patients with DGs.Conclusions.—Evidence-based incorporation of laboratory results from molecular biomarker testing into integrated diagnoses of DGs provides reproducible and clinically meaningful information for patient management.

Tumors of the central nervous system (CNS) often display a wide morphologic spectrum that has, until recently, been the sole basis for tumor classification. The introduction of the integrated histomolecular diagnostic approach in CNS tumors has facilitated a classification system that is increasingly data-driven and with improved alignment to clinical outcome. Here, we report a previously uncharacterized glioma type (n = 31) using unsupervised clustering analysis of DNA methylation array data from approximately 14,000 CNS tumor samples. Histologic examination revealed circumscribed growth and morphologic similarities to pleomorphic xanthoastrocytoma (PXA), astroblastoma, ependymoma, polymorphous neuroepithelial tumor of the young (PLNTY), and IDH-wildtype glioblastoma (GBM). Median age (46.5 years) was significantly older than other circumscribed gliomas and younger than GBM. Dimensionality reduction with uniform manifold approximation and projection (UMAP) and hierarchical clustering confirmed a methylation signature distinct from known tumor types and methylation classes. DNA sequencing revealed recurrent mutations in TP53 (57%), RB1 (26%), NF1 (26%), and NF2 (14%). BRAF V600E mutations were detected in 3/27 sequenced cases (12%). Copy number analysis showed increased whole chromosome aneuploidy with recurrent loss of chromosome 13 (28/31 cases, 90%). CDKN2A/B deletion (2/31, 6%) and MGMT promoter methylation (1/31, 3%) were notably rare events. Most tumors showed features of a high-grade glioma, yet survival data showed significantly better overall survival compared to GBM (p

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Only a subset of recurrent glioblastoma (rGBM) responds to anti-PD-1 immunotherapy. Previously, we reported enrichment of BRAF/PTPN11 mutations in 30% of rGBM that responded to PD-1 blockade. Given that BRAF and PTPN11 promote MAPK/ERK signaling, we investigated whether activation of this pathway is associated with response to PD-1 inhibitors in rGBM, including patients that do not harbor BRAF/PTPN11 mutations. Here we show that immunohistochemistry for ERK1/2 phosphorylation (p-ERK), a marker of MAPK/ERK pathway activation, is predictive of overall survival following adjuvant PD-1 blockade in two independent rGBM patient cohorts. Single-cell RNA-sequencing and multiplex immunofluorescence analyses revealed that p-ERK was mainly localized in tumor cells and that high-p-ERK GBMs contained tumor-infiltrating myeloid cells and microglia with elevated expression of MHC class II and associated genes. These findings indicate that ERK1/2 activation in rGBM is predictive of response to PD-1 blockade and is associated with a distinct myeloid cell phenotype.

Macrophages and microglia are professional phagocytes that sense and migrate toward “eat-me” signals. The role of phagocytic cells is to maintain homeostasis by engulfing senescent or apoptotic cells, debris, and abnormally aggregated macromolecules. Usually, dying cells send out “find-me” signals, facilitating the recruitment of phagocytes. Healthy cells can also promote or inhibit the phagocytosis phenomenon of macrophages and microglia by tuning the balance between “eat-me” and “don’t-eat-me” signals at different stages in their lifespan, while the “don’t-eat-me” signals are often hijacked by tumor cells as a mechanism of immune evasion. Using a combination of bioinformatic analysis and spatial profiling, we delineate the balance of the “don’t-eat-me” CD47/SIRPα and “eat-me” CALR/STC1 ligand–receptor interactions to guide therapeutic strategies that are being developed for glioblastoma sequestered in the central nervous system (CNS).

The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, is the sixth version of the international standard for the classification of brain and spinal cord tumors. Building on the 2016 updated fourth edition and the work of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy, the 2021 fifth edition introduces major changes that advance the role of molecular diagnostics in CNS tumor classification. At the same time, it remains wedded to other established approaches to tumor diagnosis such as histology and immunohistochemistry. In doing so, the fifth edition establishes some different approaches to both CNS tumor nomenclature and grading and it emphasizes the importance of integrated diagnoses and layered reports. New tumor types and subtypes are introduced, some based on novel diagnostic technologies such as DNA methylome profiling. The present review summarizes the major general changes in the 2021 fifth edition classification and the specific changes in each taxonomic category. It is hoped that this summary provides an overview to facilitate more in-depth exploration of the entire fifth edition of the WHO Classification of Tumors of the Central Nervous System.

ObjectiveThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity.Materials and methodsTwelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site.ResultsThe full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review.DiscussionWe developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions.ConclusionsWe developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

Glioblastoma (GBM) is one of the most difficult cancers to effectively treat, in part because of the lack of precision therapies and limited therapeutic access to intracranial tumor sites due to the presence of the blood-brain and blood-tumor barriers. We have developed a precision medicine approach for GBM treatment that involves the use of brain-penetrant RNA interference-based spherical nucleic acids (SNAs), which consist of gold nanoparticle cores covalently conjugated with radially oriented and densely packed small interfering RNA (siRNA) oligonucleotides. On the basis of previous preclinical evaluation, we conducted toxicology and toxicokinetic studies in nonhuman primates and a single-arm, open-label phase 0 first-in-human trial (NCT03020017) to determine safety, pharmacokinetics, intratumoral accumulation and gene-suppressive activity of systemically administered SNAs carrying siRNA specific for the GBM oncogene Bcl2Like12 (Bcl2L12). Patients with recurrent GBM were treated with intravenous administration of siBcl2L12-SNAs (drug moniker: NU-0129), at a dose corresponding to 1/50th of the no-observed-adverse-event level, followed by tumor resection. Safety assessment revealed no grade 4 or 5 treatment-related toxicities. Inductively coupled plasma mass spectrometry, x-ray fluorescence microscopy, and silver staining of resected GBM tissue demonstrated that intravenously administered SNAs reached patient tumors, with gold enrichment observed in the tumor-associated endothelium, macrophages, and tumor cells. NU-0129 uptake into glioma cells correlated with a reduction in tumor-associated Bcl2L12 protein expression, as indicated by comparison of matched primary tumor and NU-0129-treated recurrent tumor. Our results establish SNA nanoconjugates as a potential brain-penetrant precision medicine approach for the systemic treatment of GBM.