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Abstract Soil-transmitted helminth (STH) infections account for a significant global health burden, necessitating mass drug administration with benzimidazole-class anthelmintics, such as albendazole (ALB), for morbidity control. However, ALB efficacy shows substantial variability, presenting challenges for achieving consistent treatment outcomes. We have explored the potential impact of the baseline gut microbiota on ALB efficacy in hookworm-infected individuals through microbiota profiling and machine learning (ML) techniques. Our investigation included 89 stool samples collected from hookworm-infected individuals that were analyzed by microscopy and quantitative PCR (qPCR). Of these, 44 were negative by microscopy for STH infection using the Kato-Katz method and qPCR 21 days after treatment, which entails a cure rate of 49.4%. Microbiota characterization was based on amplicon sequencing of the V3–V4 16S ribosomal RNA gene region. Alpha and beta diversity analyses revealed no significant differences between participants who were cured and those who were not cured, suggesting that baseline microbiota diversity does not influence ALB treatment outcomes. Furthermore, differential abundance analysis at the phylum, family and genus levels yielded no statistically significant associations between bacterial communities and ALB efficacy. Utilizing supervised ML models failed to predict treatment response accurately. Our investigation did not provide conclusive insights into the relationship between gut microbiota and ALB efficacy. However, the results highlight the need for future research to incorporate longitudinal studies that monitor changes in the gut microbiota related to the infection and the cure with ALB, as well as functional metagenomics to better understand the interaction of the microbiome with the drug, and its role, if there is any, in modulating anthelmintic treatment outcomes in STH infections. Interdisciplinary approaches integrating microbiology, pharmacology, genetics and data science will be pivotal in advancing our understanding of STH infections and optimizing treatment strategies globally. Graphical Abstract

Abstract Background Polygenic risk scores (PRS) derived from European individuals have reduced portability across global populations, limiting their clinical implementation at worldwide scale. Here, we investigate the performance of a wide range of PRS models across four ancestry groups (Africans, Europeans, East Asians, and South Asians) for 14 conditions of high-medical interest. Methods To select the best-performing model per trait, we first compared PRS performances for publicly available scores, and constructed new models using different methods (LDpred2, PRS-CSx and SNPnet). We used 285 K European individuals from the UK Biobank (UKBB) for training and 18 K, including diverse ancestries, for testing. We then evaluated PRS portability for the best models in Europeans and compared their accuracies with respect to the best PRS per ancestry. Finally, we validated the selected PRS models using an independent set of 8,417 individuals from Biobank of the Americas-Genomelink (BbofA-GL); and performed a PRS-Phewas. Results We confirmed a decay in PRS performances relative to Europeans when the evaluation was conducted using the best-PRS model for Europeans (51.3% for South Asians, 46.6% for East Asians and 39.4% for Africans). We observed an improvement in the PRS performances when specifically selecting ancestry specific PRS models (phenotype variance increase: 1.62 for Africans, 1.40 for South Asians and 0.96 for East Asians). Additionally, when we selected the optimal model conditional on ancestry for CAD, HDL-C and LDL-C, hypertension, hypothyroidism and T2D, PRS performance for studied populations was more comparable to what was observed in Europeans. Finally, we were able to independently validate tested models for Europeans, and conducted a PRS-Phewas, identifying cross-trait interplay between cardiometabolic conditions, and between immune-mediated components. Conclusion Our work comprehensively evaluated PRS accuracy across a wide range of phenotypes, reducing the uncertainty with respect to which PRS model to choose and in which ancestry group. This evaluation has let us identify specific conditions where implementing risk-prioritization strategies could have practical utility across diverse ancestral groups, contributing to democratizing the implementation of PRS.

Abstract Purpose To determine the factors influencing the likelihood of biochemical pregnancy loss (BPL) after transfer of a euploid embryo from preimplantation genetic testing for aneuploidy (PGT-A) cycles. Methods The study employed an observational, retrospective cohort design, encompassing 6020 embryos from 2879 PGT-A cycles conducted between February 2013 and September 2021. Trophectoderm biopsies in day 5 (D5) or day 6 (D6) blastocysts were analyzed by next generation sequencing (NGS). Only single embryo transfers (SET) were considered, totaling 1161 transfers. Of these, 49.9% resulted in positive pregnancy tests, with 18.3% experiencing BPL. To establish a predictive model for BPL, both classical statistical methods and five different supervised classification machine learning algorithms were used. A total of forty-seven factors were incorporated as predictor variables in the machine learning models. Results Throughout the optimization process for each model, various performance metrics were computed. Random Forest model emerged as the best model, boasting the highest area under the ROC curve (AUC) value of 0.913, alongside an accuracy of 0.830, positive predictive value of 0.857, and negative predictive value of 0.807. For the selected model, SHAP (SHapley Additive exPlanations) values were determined for each of the variables to establish which had the best predictive ability. Notably, variables pertaining to embryo biopsy demonstrated the greatest predictive capacity, followed by factors associated with ovarian stimulation (COS), maternal age, and paternal age. Conclusions The Random Forest model had a higher predictive power for identifying BPL occurrences in PGT-A cycles. Specifically, variables associated with the embryo biopsy procedure (biopsy day, number of biopsied embryos, and number of biopsied cells) and ovarian stimulation (number of oocytes retrieved and duration of stimulation), exhibited the strongest predictive power.

Abstract Background To investigate the reproducibility of automated volumetric bone mineral density (vBMD) measurements from routine thoracoabdominal computed tomography (CT) assessed with segmentations by a convolutional neural network and automated correction of contrast phases, on diverse scanners, with scanner-specific asynchronous or scanner-agnostic calibrations. Methods We obtained 679 observations from 278 CT scans in 121 patients (77 males, 63.6%) studied from 04/2019 to 06/2020. Observations consisted of two vBMD measurements from Δdifferent reconstruction kernels (n = 169), Δcontrast phases (n = 133), scan Δsessions (n = 123), Δscanners (n = 63), or Δall of the aforementioned (n = 20), and observations lacking scanner-specific calibration (n = 171). Precision was assessed using root-mean-square error (RMSE) and root-mean-square coefficient of variation (RMSCV). Cross-measurement agreement was assessed using Bland-Altman plots; outliers within 95% confidence interval of the limits of agreement were reviewed. Results Repeated measurements from Δdifferent reconstruction kernels were highly precise (RMSE 3.0 mg/cm3; RMSCV 1.3%), even for consecutive scans with different Δcontrast phases (RMSCV 2.9%). Measurements from different Δscan sessions or Δscanners showed decreased precision (RMSCV 4.7% and 4.9%, respectively). Plot-review identified 12 outliers from different scan Δsessions, with signs of hydropic decompensation. Observations with Δall differences showed decreased precision compared to those lacking scanner-specific calibration (RMSCV 5.9 and 3.7, respectively). Conclusion Automatic vBMD assessment from routine CT is precise across varying setups, when calibrated appropriately. Low precision was found in patients with signs of new or worsening hydropic decompensation, what should be considered an exclusion criterion for both opportunistic and dedicated quantitative CT. Relevance statement Automated CT-based vBMD measurements are precise in various scenarios, including cross-session and cross-scanner settings, and may therefore facilitate opportunistic screening for osteoporosis and surveillance of BMD in patients undergoing routine clinical CT scans. Key Points Artificial intelligence-based tools facilitate BMD measurements in routine clinical CT datasets. Automated BMD measurements are highly reproducible in various settings. Reliable, automated opportunistic osteoporosis diagnostics allow for large-scale application. Graphical Abstract

Abstract Background Comprehensive cancer care requires effective collaboration by interprofessional healthcare teams. The need to develop educational initiatives to improve interprofessional collaboration is increasingly recognised. However, there is no agreement regarding the interprofessional competencies required for effective cancer care leading to much variation on the focus of research, planning and managing change. A scoping review was conducted to identify the current status of IPE in cancer care and to summarise the results of previous research in order to guide the development of interprofessional education in cancer care. Methods The JBI Scoping Review guidelines were used to guide the process of the review. A search of the available literature was conducted in CINAHL, MEDLINE (Ovid), PubMed, PsycInfo, Scopus databases from January 2012 to March 2023 to investigate IPE for health professional clinicians working in cancer care. Results Of the 825 initial references and 153 studies imported for screening, a total of 28 studies were included in the final review. From those studies, seven focused on the need for IPE and interprofessional competence for oncology healthcare professionals, four reviewed existing IPE programs and 17 described the development and evaluation of interprofessional education. Findings show variation and lack of concept definitions underpinning research in IPE in cancer care settings. Variation also exists in the range of research activities in IPE, most notably related to communication, teamwork and the development of interprofessional practice. The evaluation of impact of IPE is mainly focused on health care professionals’ self-evaluation and general feedback. Impact on patient care was only evaluated in one study. Conclusions Based on the results, interprofessional education research in the field of cancer care is limited in Europe. Thus, there is a significant increase in publications in the last five years. A more systematic focus on the theoretical framework and definition of concepts would be of value. Research and programme development should be based on a shared understanding on what constitutes the interprofessional competences and IPE. Programmes to develop interprofessional practice should be developed and implemented systematically with inclusion of validated assessment methods, and evaluated and improved regularly.

Abstract Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona’s bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system’s predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security.

Highlights: • Some archaeological results obtained using SAR-X images received through the PAZ satellite and applied to a part of what was called Ager Cosanus are shown in this article. • The study has been completed with the analysis of multispectral images TripleSAT and Sentinel-2A and the historical aerial photos from 1944 and 1954. • The possibilities of using PAZ images treated multi-temporally as a high-resolution panchromatic image applicable to multispectral optical images of the type Sentinel-2 were tested. Abstract: Some archaeological results are shown in this article, which have been generated from the use of Synthetic Aperture Radar (SAR)-X images obtained from the PAZ satellite and applied to part of what was called Ager Cosanus, that is, the territory of the city of Cosa, which was one of the first maritime colonies of Rome in the heart of Etruscan territory. Our study has been carried out mainly based on previous works in which a set of images was used to improve the quality of the resulting image, reducing the noise caused by the speckle of the radar images and maintaining the quality of the spatial resolution that can be obtained from these images (1.25 m/pixel). More specifically, a set of images obtained between 2019 and 2021 was used. The study has been completed with the analysis of multispectral images TripleSAT and Sentinel-2A, the historical aerial photos taken from 1944 and 1954, and the use of the historical cadastre of Tuscany, prepared at the beginning of the 19th century. As an addition, the Digital Terrain Model (DTM) Light Detection And Ranging (LiDAR) of the Region of Tuscany was used, on which various functions of the Relief Visualization Tool (RVT) programme have been applied, complementing or contrasting the results. It can be confirmed that the multi-temporal treatment of SAR PAZ images provides better results than an individualised analysis of the image. Finally, it is of great interest to verify the results of studies using new technologies, where it was previously possible to resort only to prospecting on the ground and to analogical aerial photography in black and white. In this case, the Sinistra Decumano I (SDI) structure was seen, which Castagnoli observed in the aerial photography, but of which he only located materials on the ground and it was visualised both in individualised PAZ images and in Sentinel-2.

Background: Epidemic intelligence (EI) ensures early detection, assessment, and communication of public health threats. Threat reporting defines priorities and mobilize resources for surveillance, prevention, and control. In Portugal, the Directorate-General of Health (DGS) is responsible for EI and publishes a weekly public health threat report (RONDA). Changes in threats in regular threat reports since COVID-19 have not been previously described. We analysed changes in non-COVID threat reporting in the weekly threat report. Methods: Using the DGS Emergency Operations Centre’s threat reporting database, we compared threats reported in RONDAs from 2016 to 2022 in three sequential periods: P1 before COVID-19 (January 2016–March 2020), P2 during acute COVID-19 restrictions (April 2020–February 2022), and P3 in post-acute COVID-19 phase (February 2022–September 2022). We described the monthly average frequency of reports on non-COVID-19 threats in those periods considering different disease groups, geographical focus, and information sources. We estimated expected non-COVID-19 reports on threats using a forecast model fitted to the time series until March 2020 and compared observed and expected values. Results: Non-COVID-19 threats had a decrease in the monthly average frequency of reporting in period 2 (x¯1: 4.7 vs. x¯: 2.3, p < 0.001) compared to period 1. Using the forecast methods, there were 114 fewer non-COVID threats than the 162 expected (−70%) in period 2. In period 3, there were 105 more threats than expected (+256%). The ECDC and the WHO were the most frequent sources of information followed by national Public Health sources. Conclusions: During COVID-19, there was a decrease in reports on non-COVID threats in Portugal. COVID-19 possibly affected global EI, by shifting attention and resources from other threats to the pandemic. However, the number of threats that warrant follow-up and communication is increasing. Further research is necessary to inform the EI research and development agenda, to ensure that all relevant threats are detected, accessed, and communicated according to evolving EI objectives and priorities while resources and preparedness are guaranteed.