Future Updates Research Articles

928. Concordance with Institutional Guidelines for Letermovir Cytomegalovirus Infection Prophylaxis in Allogeneic Hematopoietic Stem Cell Transplant Recipients

Abstract Background Cytomegalovirus (CMV) is an opportunistic infection in immunocompromised patients associated with significant morbidity and mortality. Letermovir (LET) is used for prophylaxis for CMV-seropositive allogeneic hematopoietic stem cell transplant (HSCT) recipients. An institutional guideline was developed to provide guidance on the appropriate use of letermovir. The purpose of this study was to determine overall concordance with letermovir institutional guidelines and to identify areas for quality improvement. Methods Our evaluation included adult hospitalized patients admitted from November 2019 to July 2022 for allogeneic HSCT who received LET, identified via medication administration records. The primary outcome was concordance with the institutional guideline criteria for use, as outlined in Figure 1. Secondary outcomes included incidence of breakthrough CMV viremia, use of concomitant HSV prophylaxis, and appropriate management of drug interactions (Figure 1). Results We identified 41 patients who received LET during the study period. After excluding patients who received a solid organ transplant, 32 patients were included in the study. The median time to LET initiation post-transplant was 7 days and the median time to discontinuation was 105 days post-transplant. Overall, 9 patients (28%) were fully concordant with our guidelines. Of the remaining patients, reasons for discordance are summarized in Table 1, mainly driven by continuation of LET >110 days post-transplant (n=12, 37%). Three (9%) patients received LET despite CMV recipient negative serostatus (CMV R-). Breakthrough CMV viremia occurred in 3 patients (9%). All patients received concomitant HSV prophylaxis and had drug interactions managed appropriately. Conclusion Our evaluation identified a lower than expected rate of letermovir guideline concordance, primarily related to the recommended duration of drug therapy. We found that our guideline has supported providers to ensure that concomitant HSV prophylaxis is given and drug interactions are managed appropriately. A further investigation into the use of letermovir in CMV R- subjects and a cost analysis is planned to guide future institutional guideline updates. Disclosures Carolyn D. Alonso, MD, Academy for Continued Healthcare Learning: Honoraria|AiCuris: Advisor/Consultant|American Society of Healthcare Pharmacists: Honoraria|Cidara Therapeutics: Advisor/Consultant|Clinical Care Options: Honoraria|Merck: Advisor/Consultant|Merck: Grant/Research Support

Optimized thermodynamic properties of REE aqueous species (REE3+ and REEOH2+) and experimental database for modeling the solubility of REE phosphate minerals (monazite, xenotime, and rhabdophane) from 25 to 300 °C

Rare earth elements (REE) are critical elements found in monazite, xenotime, and hydrated REE phosphates which typically form in hydrothermal mineral deposits. Accurate predictions of the solubility of these REE phosphates and the speciation of REE in aqueous fluids are both key to understanding the controls on the transport, fractionation, and deposition of REE in natural systems. Previous monazite and xenotime solubility experiments indicate the presence of large discrepancies between experimentally derived solubility constants versus calculated solubilities by combining different data sources for the thermodynamic properties of minerals and aqueous species at hydrothermal conditions. In this study, these discrepancies were resolved by using the program GEMSFITS to optimize the standard partial molal Gibbs energy of formation (ΔfG°298) of REE aqueous species (REE3+ and REE hydroxyl complexes) at 298.15 K and 1 bar while keeping the thermodynamic properties fixed for the REE phosphates. A comprehensive experimental database was compiled using solubility data available between 25 and 300 °C. The latter permits conducting thermodynamic parameter optimization of ΔfG°298 for REE aqueous species. Optimal matching of the rhabdophane solubility data between 25 and 100 °C requires modifying the ΔfG°298 values of REE3+ by 1–6 kJ/mol, whereas matching of the monazite solubility data between 100 and 300 °C requires modifying the ΔfG°298 values of both REE3+ and REEOH2+ by ~15–31 kJ/mol and ~ 2–10 kJ/mol, respectively. For xenotime, adjustments of ΔfG°298 values by 1–26 kJ/mol are only necessary for the REE3+ species. The optimizations indicate that the solubility of monazite in acidic solutions is controlled by the light (L)REE3+ species at <150 °C and the LREEOH2+ species at >150 °C, whereas the solubility of xenotime is controlled by the heavy (H)REE3+ species between 25 and 300 °C. Based on the optimization results, we conclude that the revised Helgeson-Kirkham-Flowers equation of state does not reliably predict the thermodynamic properties of REE3+, REEOH2+, and likely other REE hydroxyl species at hydrothermal conditions. We therefore provide an experimental database (ThermoExp_REE) as a basic framework for future updates, extensions with other ligands, and optimizations as new experimental REE data become available. The optimized thermodynamic properties of aqueous species and minerals are available open access to accurately predict the solubility of REE phosphates in fluid-rock systems.

User evaluation and feasibility test of an app designed for smoking cessation in Italian people who smoke: preliminary findings from an uncontrolled pre-test post-test open study

BackgroundmHealth is a public health practice that exploits the use of mobile devices, including smartphone applications. We will describe an uncontrolled pre-test post-test open pilot study concerning the feasibility evaluation of a smartphone App designed to help in smoking cessation. The aim of this study is to evaluate the feasibility of a smartphone app as a tool for smoking cessation. This study is necessary to the literature because smoking is a major public health concern and has been linked to various health issues such as cardiovascular disease, respiratory disease, and cancer. While there are several smoking cessation interventions available, the use of mobile devices to aid in smoking cessation is a relatively new and innovative approach that requires further investigation.MethodsThe App “Smoke Free” was configured on the devices of N = 30 participants who smoked combustible cigarette, 13 males and 17 females aged 18 to 55 years, with the indications to use it for 90 days, describe their experience, suggest new features, and report any critical aspect. The study consisted of an initial screening visit to select participants that reflected the inclusion criteria and 4 study visits: a baseline visit, two follow-up visits, and one final visit. We used descriptive stats to summarize results. Repeated measures ANOVA and Wilcoxon test were used to test differences in smoking consumption, self-reported craving, and measured eCO level. Statistical software Jamovi was used for analysis. Interviews were conducted via phone or in-person and analyzed using qualitative description principles.ResultsParticipants evaluated the app as having good aesthetic appeal and user-friendliness but being moderately useful, despite some quitting or reducing their smoking behavior. To improve it, participants have proposed features such as more notifications, social network integration, and damage caused by smoking to the body over time for future app updates.ConclusionsThe application was moderately useful with good feasibility, with several suggestions for future updates that could improve its effectiveness.

The capabilities of the adjoint of GEOS-Chem model to support HEMCO emission inventories and MERRA-2 meteorological data

Abstract. The adjoint of the GEOS-Chem (Goddard Earth Observing System with Chemistry) model has been widely used to constrain the sources of atmospheric compositions. Here, we designed a new framework to facilitate emission inventory updates in the adjoint of the GEOS-Chem model. The major advantage of this new framework is good readability and extensibility, which allows us to support Harmonized Emissions Component (HEMCO) emission inventories conveniently and to easily add more emission inventories following future updates in GEOS-Chem forward simulations. Furthermore, we developed new modules to support MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) meteorological data, which allows us to perform long-term analyses with consistent meteorological data for the period 1979–present. The performances of the developed capabilities were evaluated with the following steps: (1) diagnostic outputs of carbon monoxide (CO) sources and sinks to ensure the correct reading and use of emission inventories, (2) forward simulations to compare the modeled surface and column CO concentrations among various model versions, (3) backward simulations to compare adjoint gradients of global CO concentrations to CO emissions with finite-difference gradients, and (4) observing system simulation experiments (OSSEs) to evaluate the model performance in 4D variational (4D-Var) assimilations. Finally, an example application of 4D-Var assimilation was presented to constrain anthropogenic CO emissions in 2015 by assimilating Measurement of Pollution in the Troposphere (MOPITT) CO observations. The capabilities developed in this work are important for better applications of the adjoint of the GEOS-Chem model in the future. These capabilities will be submitted to the standard GEOS-Chem adjoint code base for better development of the community of the adjoint of the GEOS-Chem model.

Computation of a probabilistic uranium concentration map of Norway: A digital expert elicitation approach employing random forests and artificial neural networks

We compute the first probabilistic uranium concentration map of Norway. Such a map can support mineral exploration, geochemical mapping, or the assessment of the health risk to the human population. We employ multiple non-linear regression to fill the information gaps in sparse airborne and ground-borne uranium data sets. We mimic an expert elicitation by employing Random Forests and Multi-layer Perceptrons as digital agents equally qualified to find regression models. In addition to the regression, we use supervised classification to produce conservative and alarmistic classified maps outlining regions with different potential for the local occurrence of uranium concentration extremes. Embedding the introduced digital expert elicitation in a Monte Carlo approach we compute an ensemble of plausible uranium concentrations maps of Norway discretely quantifying the uncertainty resulting from the choice of the regression algorithm and the chosen parametrization of the used regression algorithms. We introduce digitated glyphs to visually integrate all computed maps and their associated uncertainties in a loss-free manner to fully communicate our probabilistic results to map perceivers. A strong correlation between mapped geology and uranium concentration is found, which could be used to optimize future sparse uranium concentration sampling to lower extrapolation components in future map updates.

Functional dependency-based group decision-making with incomplete information under social media influence: An application to automobile

In today’s complex decision-making environment, accounting for attribute interdependencies and expert relationships is crucial. Traditional models often assume attribute independence and overlook the significant impact of expert relationships on decision outcomes. Also, amidst the dynamic and ever-changing decision-making landscape, the effect of news and real-time updates on alternative rankings is significant. In complex decision-making environments, information is constantly evolving, and staying up-to-date with the latest developments is paramount. To overcome these limitations, this study aims to develop a novel model that effectively captures attribute dependencies and incorporates the influence of social media on alternative ordering. To establish the model, the Decision-making trial and evaluation laboratory (DEMATEL) method and regression analysis are integrated to capture attribute dependencies. Furthermore, social network analysis (SNA) is employed to develop a trust propagation model for determining experts’ weights. Additionally, we present a two-stage multi-skilled and high potential multi-criteria decision-making (MCDM) framework, where the base-criterion method (BCM) is adopted to evaluate attribute weights and the well-known traditional Vlekriterijumsko KOmpromisno Rangiranje (VIKOR) method is redefined using Heronian mean (HM) operator to capture the relationships between arguments. Despite uncertainties, the proposed fuzzy-BCM-VIKOR-Heronian (F-BCM-VIKOR-H) approach enhances flexibility by addressing inconsistent data in complex decision-making problems. Similarly, certain news or future updates about any alternative or attribute can significantly affect the ranking. Acknowledging the significance of timely information, the proposed approach actively considers the effect of such news through the formation of an updated matrix. By factoring in the latest developments, we ensure that the proposed decision-making model remains relevant and adaptable, capturing the most current insights into alternative performance. To demonstrate the model’s effectiveness, we apply the proposed approach to a numerical illustration in the electronics industry, specifically for ranking cars. Sensitivity analysis evaluates the model’s stability, and comparing the results with existing approaches showcases its advantage and superiority.

Extrapolating a uranium map of Norway: Implications for country-scale geogenic radon risk mapping

Radon, a radioactive gas produced through the decay of uranium in the earth's crust, poses a significant health risk when it accumulates to high concentrations indoors. This study focuses on identifying areas at higher risk of radon accumulation in Norway by employing a data-driven approach based on geogenic factors, particularly the distribution of uranium on the ground surface.Utilizing two types of uranium measurements and employing a statistical methodology, we classify bedrock geology based on their average uranium content. The classification process integrates Self-organizing maps (SOM) with K-means clustering, facilitating the creation of a country-scale extrapolation. The resulting uranium map is merged from the high-resolution airborne uranium map and the extrapolated uranium map.While acknowledging the presence of uncertainties, our study offers valuable insights into geogenic radon risk, serving as a valuable resource for radon studies and mitigation efforts. Furthermore, the methodology employed in this study is characterized by its flexibility and scalability, enabling future updates and refinements to enhance radon risk assessment and management strategies.

Effect of short-term compression therapy after thermal ablation for varicose veins: study protocol for a prospective, multicenter, non-inferiority, randomized controlled trial

BackgroundFor patients with varicose veins, the goal is to relieve pain and swelling, reduce the severity of edema, improve skin changes, and heal ulcers associated with venous disease. Compression therapy is the cornerstone of their management. Several studies have shown that wearing an elastic bandage for the first 24 h and then a compression stocking for a week can effectively reduce the pain after thermal ablation. However, in clinical practice, patient compliance with this treatment could be better, considering difficulties in pulling up and removing the compression stocking, tightness, and skin irritation because these must be worn for a prolonged period. A potential solution to battling these barriers is short-term compression therapy. Besides, the effect and necessity of wearing compression stockings after thermal ablation have been questioned. Based on current clinical experience and limited evidence, although some scholars have suggested that compression therapy may be an unnecessary adjunctive therapy after thermal ablation, there is still a great deal of uncertainty in the absence of compression therapy after thermal ablation compared to compression therapy. Therefore, we advocate further research to evaluate the clinical effect of short-term postoperative compression therapy. Furthermore, well-designed randomized controlled trials are needed.MethodsA prospective, multicenter, non-inferiority randomized controlled trial is designed to evaluate the non-inferiority of target vein occlusion rate at 3 months. Three hundred and sixty patients will be randomly assigned in a 1:1 ratio to one of the following treatments: (A) 3 M™ Coban™ elastic bandage for 48 h or (B) 3 M™ Coban™ elastic bandage for the first 24 h and then a class II compression full-length stocking (23–32 mm Hg) for 1 week. The two groups will be compared on several variables, including target vein occlusion rate at 3 months (primary outcome indicator), pain, quality of life, clinical severity of varicose veins, postoperative complications, time to return to regular work, and compliance.DiscussionSuppose the effect of the 3 M™ Coban™ elastic bandage for 48 h proves to be non-inferior to long-term compression therapy. In that case, this short-term treatment may contribute to a future update of clinical guidelines for compression therapy after thermal ablation of varicose veins, resulting in higher patient compliance and better postoperative quality of life.Trial registrationClinical Trials NCT05840991. Registered on May 2023.

HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks.

The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signals with low abundance and high interference. Although multiple peak-picking software packages are available, they often fail to detect peaks with low quality and do not report cases with low confidence. Furthermore, visual examination of all chromatograms is still necessary to identify uncertain or erroneous cases. This study introduces HeapMS, a web service that uses artificial intelligence to assist with peak picking and the quality assessment of MRM chromatograms. HeapMS applies a rule-based filter to remove chromatograms with low interference and high-confidence peak boundaries detected by Skyline. Additionally, it transforms two histograms (representing light and heavy peptides) into a single encoded heatmap and performs a two-step evaluation (quality detection and peak picking) using image convolutional neural networks. HeapMS offers three categories of peak picking: uncertain peak picking that requires manual inspection, deletion peak picking that requires removal or manual re-examination, and automatic peak picking. HeapMS acquires the chromatogram and peak-picking boundaries directly from Skyline output. The output results are imported back into Skyline for further manual inspection, facilitating integration with Skyline. HeapMS offers the benefit of detecting chromatograms that should be deleted or require human inspection. Based on defined categories, it can significantly reduce human workload and provide consistent results. Furthermore, by using heatmaps instead of histograms, HeapMS can adapt to future updates in image recognition models. The HeapMS is available at: https://github.com/ccllabe/HeapMS.

Esophageal motility disorder - has Chicago classification v4.0 simplified our management?

Addressing dysphagia is vital due to its prevalence and impact on healthcare expenditure. While high resolution manometry (HRM) effectively evaluates esophageal dysphagia, its role in oropharyngeal dysphagia and upper esophageal sphincter (UES) dysfunction remains debated. The fourth iteration of the Chicago classification (CC) offers an algorithmic approach for diagnosing abnormal motor patterns via HRM. This review assesses the CC's impact on dysphagia management. The Chicago classification version 4.0 emphasizes auxiliary and provocative techniques when the algorithm falls short of a conclusive diagnosis. It introduces stricter criteria for previously ambiguous conditions like ineffective motility and esophagogastric junction outflow obstruction. This version also introduces the concept of conclusive and inconclusive classifications based on symptoms, provocation maneuvers, and supportive testing minimizing ambiguity. The Chicago classification v4.0 remains a useful tool for the diagnosis of well characterized esophageal motility disorders. However, major limitations include reliance on HRM and a focus on distal esophagus contractile characteristics without considering proximal esophagus or upper esophageal sphincter, both of which can sometimes be the only evident abnormality in patients with dysphagia. Despite efforts to reduce ambiguity, diagnostic challenges persist. These limitations can be addressed in future updates.

Soil pH mapping as a function of land use, elevation, and rainfall in the lake tana basin, northwestern of ethiopia

AbstractSoil acidity has become a serious problem in the northern highlands of Ethiopia, limiting land productivity mainly that of crop yield. Research had been done to try to come up with results showing the area was affected by severe acidity, but based on few watersheds and districts in a haphazard way. The objective of this research was therefore to develop a comprehensive soil pH map that covers a broader area of 27 districts covering 2,810,055 ha of land to assess the degree of soil acidity in the Tana Sub‐basin, northwestern highlands of Ethiopia. Using GPS (3‐m precision), 2652 soil point data were collected. Precipitation, elevation, and land use cover were taken into account when creating an interpolated soil pH map. Using adjacent probed pH values, ordinary kriging interpolation (R2 = 0.65; root‐mean‐square error = 0.37125) technique was used to estimate the unmeasured locations, while the spatial pH variability was mapped using the geostatistical (GS+10) software. All Food and Agriculture Organization of the United Nations soil pH categories, with 11.22%, 19.97%, 18.77%, 14.59%, 9.64%, and 6.29% of the soil samples being extremely acidic, strongly acidic, moderately acidic, slightly acidic, neutral, moderately alkaline and strongly alkaline, respectively, were observed. The interpolation result showed a moderate coefficient of variation (10.52%) indicating that the research area had medium variability. In addition, the nugget‐to‐total semivariance ratio ranged from 25% to 75%, indicating moderate geographic dependence. The pH semivariograms best fitted the exponential model, which means the one with maximum (R2 = 0.834) and with minimum residual sum of squares (1.819E‐06) value for mapping of pH. Our result showed that soil pH was inversely dependent on precipitation and altitude, while it varies with land use type. The research result/map could give useful tools for effective integrated land management to minimize soil acidification by policymakers, agriculturalists, and other stakeholder groups. We also recommended future routine updates on the size and distribution of surface soil acidity in the study area.

Integration of Machine Learning and Structural Analysis for Predicting Peptide Antibiofilm Effects: Advancements in Drug Discovery for Biofilm-Related Infections.

The rise of antibiotic resistance has become a major concern, signaling the end of the golden age of antibiotics. Bacterial biofilms, which exhibit high resistance to antibiotics, significantly contribute to the emergence of antibiotic resistance. Therefore, there is an urgent need to discover new therapeutic agents with specific characteristics to effectively combat biofilm-related infections. Studies have shown the promising potential of peptides as antimicrobial agents. This study aimed to establish a cost-effective and streamlined computational method for predicting the antibiofilm effects of peptides. This method can assist in addressing the intricate challenge of designing peptides with strong antibiofilm properties, a task that can be both challenging and costly. A positive library, consisting of peptide sequences with antibiofilm activity exceeding 50%, was assembled, along with a negative library containing quorum-sensing peptides. For each peptide sequence, feature vectors were calculated, while considering the primary structure, the order of amino acids, their physicochemical properties, and their distributions. Multiple supervised learning algorithms were used to classify peptides with significant antibiofilm effects for subsequent experimental evaluations. The computational approach exhibited high accuracy in predicting the antibiofilm effects of peptides, with accuracy, precision, Matthew's correlation coefficient (MCC), and F1 score of 99%, 99%, 0.97, and 0.99, respectively. The performance level of this computational approach was comparable to that of previous methods. This study introduced a novel approach by combining the feature space with high antibiofilm activity. In this study, a reliable and cost-effective method was developed for predicting the antibiofilm effects of peptides using a computational approach. This approach allows for the identification of peptide sequences with substantial antibiofilm activities for further experimental investigations. Accessible source codes and raw data of this study can be found online (hiABF), providing easy access and enabling future updates.

Development and Evaluation of a Mobile App for Radiotherapy Patient Education

Radiotherapy is an area that is at times complex, with multiple steps to take and great variety of side effect profiles between treatments. It is also an area that patients know little about before their first consultation. This makes it confusing and due to time constraints physicians may not always have the time to fully explain everything that should be covered. Patients may then persist with doubts or recur to online information, which is at times inaccurate. It was hypothesized that there is a high informational need among radiotherapy patients that could be aided by using a mobile app. A mobile app for Android and IOS was developed to address patient informational needs called Canswer. The mobile app format was chosen due to ease of access for patients and to integrate all the information formats (infographics, videos, and 3D models) on a single hub. Patients that came to their first radio-oncology consultation were gifted the app through the app store to review at home and asked to fill out a survey in a third level public hospital in Mexico. Likert scales were used to analyze patient perception using validated questionnaires. A subsequent survey was performed after the start of their treatment to evaluate how their perceptions changed and their final opinion on the app. On the analysis of 100 patients the average age of respondents was 50 years. Regarding the medical consultation 45% agreed/strongly agreed that it was too short, 33% that everything was not fully explained, and 31% had doubts after it ended. 24% reported they were not sure which symptoms were urgent or when to visit the ER. Concerning informational needs 81% of patients reported they would like to receive more information to review at home and 93% that the app will be useful. The patients wished to know more about: alarm signs during treatment (70%), treatment process (70%), managing side effects (67%), and anxiety/depression (38%). The app currently holds a 5.0 score with 58 reviews on the app store and 90% of patients would recommend the app to a family member. 75% of patients reported using the app after their initial consultation. The average content reviewed was 67% of the total content. The section that was the most well received was the infographics. 78% reported they felt calmer after reviewing the information. Regarding feedback patients wished more information on nutrition was included and more images were added. Based on the results there is a high informational need among patients and the app helped them review the most important information at home. A common finding among the patients was that they persisted with doubts after their appointment and that it was too short to fully explain everything radiotherapy entails. In general, the app was well received but there is still room for improvement. Future updates and investigations based on these initial findings are warranted to expand into other areas of cancer care.

Ground Motion Duration Patterns for Vrancea (Romania) Intermediate-Depth Earthquakes

This study is focused on evaluating ground motion durations of Vrancea intermediate-depth earthquakes in Romania, in the context of future updates to the Romanian seismic design code P100-1/2013. The ground motion database compiled for this study consists of about 200 ground motions recorded during five moderate and large Vrancea intermediate-depth earthquakes that occurred in the period of 1977–2004 and had moment magnitudes of MW ≥ 6.0. Two empirical models were derived in this study for the significant ground motion duration considering two time intervals (5–75% and 5–95%) for the accumulation of the Arias Intensity IA. An analysis of the data shows that the mean ratio between D5-95 and D5-75 is about 2.8. Moreover, the regression also shows that the largest share of variability is due to the within-event component (site term). Among the regression coefficients, the hypocentral distance and the soil conditions appear to have a larger impact on the ground motion duration compared to the earthquake magnitude. It was also observed that the median ground motion durations predicted using the empirical model proposed in this study were much smaller than the ones from the proposed Eurocode 8 draft for the same magnitude range. Finally, geographic trends related to the distribution of residuals were also evaluated using the data from the three earthquakes with the largest number of available ground motion recordings.

The Unified Cluster Catalogue: towards a comprehensive and homogeneous data base of stellar clusters

ABSTRACT We introduce the Unified Cluster Catalogue, the largest catalogue of stellar clusters currently listing nearly 14 000 objects. In this initial release, it exclusively contains Milky Way open clusters, with plans to include other objects in future updates. Each cluster is processed using a novel probability membership algorithm, which incorporates the coordinates, parallax, proper motions, and their associated uncertainties for each star into the probability assignment process. We employ Gaia Data Release 3 (DR3) up to a G magnitude of 20, resulting in the identification of over a million probable members. The catalogue is accompanied by a publicly accessible website designed to simplify the search and data exploration of stellar clusters. The website can be accessed at https://ucc.ar.

OS09.4.A RANO 2.0: UPDATE TO THE RESPONSE ASSESSMENT IN NEURO-ONCOLOGY (RANO) CRITERIA FOR HIGH- AND LOW-GRADE GLIOMAS IN ADULTS

Abstract BACKGROUND The Response Assessment in Neuro-Oncology (RANO) criteria for high-grade gliomas (RANO-HGG) and low-grade gliomas (RANO-LGG) were developed to improve reliability of response assessment in glioma trials. Over time some limitations of these criteria were identified, and uncertainty emerged regarding integrating features of the modified RANO (mRANO) or the immunotherapy RANO (iRANO) criteria. MATERIAL AND METHODS Informed by data from a cohort of glioblastoma patients and other evaluations of RANO criteria that allowed evaluation of features of the different criteria, the RANO working group developed updates to the RANO criteria (RANO 2.0). RESULTS Based on the 2021 WHO classification of gliomas, we recommend a standard set of criteria for both high and low-grade gliomas, to be used for all trials regardless of the treatment modalities being evaluated. In the newly diagnosed setting, the post-radiotherapy MRI, rather than the post-surgical MRI, will be used as the baseline for future comparison. Since the incidence of pseudoprogression is high in the 12 weeks following radiotherapy, continuation of treatment and confirmation of progression during this period with a repeat MRI, or histopathologic evidence of unequivocal recurrent tumor, is required to define tumor progression. However, confirmation scans are not mandatory after this period nor for recurrent tumors. For treatments with a high likelihood of pseudoprogression, mandatory confirmation of progression with a repeat MRI is highly recommended. The primary measurement remains the maximum cross-sectional area of tumor (2-dimensional) but volumetric measurements are an option. For IDH-wildtype glioblastoma, the non-enhancing disease will no longer be evaluated. In IDH-mutated tumors with a significant non-enhancing component, clinical trials may require evaluating both the enhancing and non-enhancing tumor components for response assessment. CONCLUSION These criteria represent a work in progress that will hopefully improve the assessment of response in glioma trials. Future updates will incorporate novel developments, advanced imaging techniques, and endpoints as they become validated.

107 OPEN to Change: Can District General Hospitals Deal with Open Fractures Too?

Abstract Introduction Fractures compromising soft tissues puts patients at risk of significant morbidity, therefore require timely multidisciplinary management. The British Orthopaedic Association Standard for Trauma (BOAST) Guidelines, updated December 2017, state “Patients with open fractures of long bones, hind foot or midfoot should be taken directly or transferred to a specialist centre that can provide Orthoplastic care.” Northern Ireland’s population is estimated at 1.8 million, within this there are between 370 and 500 cases of major trauma each year. Method This review looks at patients presenting to Craigavon Area Hospital in Northern Ireland, between October 2021 to October 2022, with open fractures. Results Over the study period, 41 Open fractures presented, 12 meeting criteria for transfer to a major trauma centre with orthopastic’s present as per BOAST Guidelines point 1. However, 11 patients were managed in Craigavon Area Hospital and 1 patient required transfer to a site with plastics available. Conclusions Can we learn from this same subgroup of patients? There is an ever-growing demand to major trauma centres in Northern Ireland some of the burden on which can be dealt with by DGH’s. This data should help contribute to any future updates of BOAST’s guidelines.

Characterizing SARS-CoV-2 neutralization profiles after bivalent boosting using antigenic cartography

Since emergence of the initial SARS-CoV-2 BA.1, BA.2 and BA.5 variants, Omicron has diversified substantially. Antigenic characterization of these new variants is important to analyze their potential immune escape from population immunity and implications for future vaccine composition. Here, we describe an antigenic map based on human single-exposure sera and live-virus isolates that includes a broad selection of recently emerged Omicron variants such as BA.2.75, BF.7, BQ, XBB and XBF variants. Recent Omicron variants clustered around BA.1 and BA.5 with some variants further extending the antigenic space. Based on this antigenic map we constructed antibody landscapes to describe neutralization profiles after booster immunization with bivalent mRNA vaccines based on ancestral virus and either BA.1 or BA.4/5. Immune escape of BA.2.75, BQ, XBB and XBF variants was also evident in bivalently boosted individuals, however, cross-neutralization was improved for those with hybrid immunity. Our results indicate that future vaccine updates are needed to induce cross-neutralizing antibodies against currently circulating variants.

Damage classification after the 2009 L’Aquila earthquake using multinomial logistic regression and neural networks

Post-earthquake surveys represent a fundamental tool for managing the emergency phase after a strong earthquake. In Italy, the evaluation of the post-earthquake functionality of ordinary buildings is based on the AeDES forms (Agibilitá e Danno nell’Emergenza Sismica, or equivalently, Rapid Post-Earthquake Damage evaluation forms). This form includes information on the building and records of the observed damage classified according to type and intensity in 60 subclasses. Based on the observed damage and expert judgment, the buildings are clustered into six risk classes, from A to F. The assigned class is used to calculate the maximum economic reimbursement owed for the reconstruction or repair of the building. However, often the cluster assignment is not entirely objective due to the inherent responsibility associated with a less conservative assessment. This paper uses the data from the 2009 L’Aquila earthquake to develop classification models based on multinomial logistic regression (MLR) and artificial neural networks (ANN) calibrated with data theoretically less influenced by personal biases. The proposed models, particularly the MLR, are intended to support the decision-making of the evaluation team in future updates of the AeDES forms. This approach cannot substitute expert evaluation, which is always necessary for complex scenarios but may mitigate the impact of subjectivity and can provide an indication of the expected outcome of the survey.

Guidelines of the Polish Society of Gynecologists and Obstetricians, the Polish Society for Vaccinology, and the Polish Society for Family Medicine on vaccinating women with reproductive plans and pregnant or breastfeeding women.

The following Guidelines present the most up-to-date treatment and management recommendations, which may be modified and altered after detailed analysis of a specific clinical situation, which in turn might lead to future modifications and updates.