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Abstract Polycystic ovary syndrome (PCOS) is a heterogeneous condition, defined by oligo-/anovulation, hyper-androgenism and/or polycystic ovaries. Metabolic complications are common in patients suffering PCOS, including obesity, insulin resistance and type-2 diabetes, which severely compromise the clinical course of affected women. Yet, therapeutic options remain mostly symptomatic and of limited efficacy for the metabolic and reproductive alterations of PCOS. We report here the hormonal, metabolic and gonadal responses to the glucagon-like peptide-1 (GLP1)-based multi-agonists, GLP1/Estrogen (GLP1/E), GLP1/gastric inhibitory peptide (GLP1/GIP) and GLP1/GIP/Glucagon, in two mouse PCOS models, with variable penetrance of metabolic and reproductive traits, and their comparison with metformin. Our data illustrate the superior efficacy of GLP1/E vs. other multi-agonists and metformin in the management of metabolic complications of PCOS; GLP1/E ameliorates also ovarian cyclicity in an ovulatory model of PCOS, without direct estrogenic uterotrophic effects. In keeping with GLP1-mediated brain targeting, quantitative proteomics reveals changes in common and distinct hypothalamic pathways in response to GLP1/E between the two PCOS models, as basis for differential efficiency. Altogether, our data set the basis for the use of GLP1-based multi-agonists, and particularly GLP1/E, in the personalized management of PCOS.

Abstract Background Colorectal cancer (CRC) is a highly prevalent disease in developed countries. Inherited Mendelian causes account for approximately 5% of CRC cases, with Lynch syndrome and familial adenomatous polyposis being the most prevalent forms. Scientific efforts are focused on the discovery of new candidate genes associated with CRC and new associations of phenotypes with well-established cancer-related genes. BRCA1-associated ring domain (BARD1) gene deleterious germline variants are associated with a moderate increase in the relative risk of breast cancer, but their association with other neoplasms, such as CRC, remains unclear. Case presentation We present the case of a 49-year-old male diagnosed with rectal adenocarcinoma whose maternal family fulfilled Amsterdam clinical criteria for Lynch syndrome. Genetic test confirmed the presence in heterozygosis of a germline pathogenic deletion of exons 8–11 in BARD1 gene. The predictive genetic study of the family revealed the presence of this pathogenic variant in his deceased cancer affected relatives, confirming co-segregation of the deletion with the disease. Conclusions To the best of our knowledge, this is the first published work in which this BARD1 deletion is detected in a family with familial colorectal cancer type X (FCCTX) syndrome, in which the clinical criteria for Lynch syndrome without alteration of the DNA mismatch repair (MMR) system are fulfilled. Whether this incidental germline finding is the cause of familial colorectal aggregation remains to be elucidated in scientific forums. Patients should be carefully assessed in specific cancer genetic counseling units to account for hypothetical casual findings in other genes, in principle unrelated to the initial clinical suspicion, but with potential impact on their health.

Summary: Background: Anti-COVID-19 hyperimmune immunoglobulin (hIG) can provide standardized and controlled antibody content. Data from controlled clinical trials using hIG for the prevention or treatment of COVID-19 outpatients have not been reported. We assessed the safety and efficacy of subcutaneous anti-COVID-19 hyperimmune immunoglobulin 20% (C19-IG20%) compared to placebo in preventing development of symptomatic COVID-19 in asymptomatic individuals with SARS-CoV-2 infection. Methods: We did a multicentre, randomized, double-blind, placebo-controlled trial, in asymptomatic unvaccinated adults (≥18 years of age) with confirmed SARS-CoV-2 infection within 5 days between April 28 and December 27, 2021. Participants were randomly assigned (1:1:1) to receive a blinded subcutaneous infusion of 10 mL with 1 g or 2 g of C19-IG20%, or an equivalent volume of saline as placebo. The primary endpoint was the proportion of participants who remained asymptomatic through day 14 after infusion. Secondary endpoints included the proportion of individuals who required oxygen supplementation, any medically attended visit, hospitalisation, or ICU, and viral load reduction and viral clearance in nasopharyngeal swabs. Safety was assessed as the proportion of patients with adverse events. The trial was terminated early due to a lack of potential benefit in the target population in a planned interim analysis conducted in December 2021. ClinicalTrials.gov registry: NCT04847141. Findings: 461 individuals (mean age 39.6 years [SD 12.8]) were randomized and received the intervention within a mean of 3.1 (SD 1.27) days from a positive SARS-CoV-2 test. In the prespecified modified intention-to-treat analysis that included only participants who received a subcutaneous infusion, the primary outcome occurred in 59.9% (91/152) of participants receiving 1 g C19-IG20%, 64.7% (99/153) receiving 2 g, and 63.5% (99/156) receiving placebo (difference in proportions 1 g C19-IG20% vs. placebo, −3.6%; 95% CI -14.6% to 7.3%, p = 0.53; 2 g C19-IG20% vs placebo, 1.1%; −9.6% to 11.9%, p = 0.85). None of the secondary clinical efficacy endpoints or virological endpoints were significantly different between study groups. Adverse event rate was similar between groups, and no severe or life-threatening adverse events related to investigational product infusion were reported. Interpretation: Our findings suggested that administration of subcutaneous human hyperimmune immunoglobulin C19-IG20% to asymptomatic individuals with SARS-CoV-2 infection was safe but did not prevent development of symptomatic COVID-19. Funding: Grifols.

Abstract Purpose The therascreen PIK3CA mutation assay and the alpha-specific PI3K inhibitor alpelisib are FDA-approved for identifying and treating patients with advanced PIK3CA-mutated (PIK3CAmut) breast cancer (BC). However, it is currently unknown to what extend this assay detects most PIK3CA mutations in BC. This information is critical as patients and clinicians are using this and other genomic assays to indicate alpelisib. Methods Data from 6338 patients with BC was explored across 10 publicly available studies. The primary objective was to evaluate the proportion and distribution of PIK3CA mutations in BC. Secondary objectives were (1) to evaluate in silico the spectrum of PIK3CA mutations in BC that would be captured by the therascreen panel; (2) to evaluate the proportion and distribution of PIK3CA mutations in hormone receptor-positive/HER2-negative (HR+/HER2−), HER2+, and triple-negative BC (TNBC); and (3) to explore the identification of PIK3CA mutations in a cohort of 48 HR+/HER2− advanced BC patients by the Guardant B360 circulating tumor DNA (ctDNA) assay. Results Patients with PIK3CAmut tumors represented 35.7% (2261/6338). Five PIK3CA mutations comprised 73% of all PIK3CA mutations: H1047R (35%), E545K (17%), E542K (11%), N345K (6%), and H1047L (4%). Therascreen gene list would capture 72% of all PIK3CA mutations and 80% of patients with a known PIK3CAmut BC. Among patients with double PIK3CAmut tumors (12% of all PIK3CAmut), the therascreen panel would capture 78% as harboring 1 single PIK3CA mutation, 17% as PIK3CAmut undetected, and 5% as PIK3CA double-mut. PIK3CA mutation rates were lower in TNBC (16%) compared to HR+/HER2 (42%) and HER2+ (31%) BC; however, the distribution of the 4 main PIK3CA mutations across subtypes was similar. Finally, 28% of PIK3CA mutations identified in ctDNA in 48 patients with advanced HR+/HER2− BC were not part of the therascreen panel. Conclusion PIK3CA mutations in BC are heterogenous and ~ 20% of patients with a known PIK3CA mutation, and 95% with a known double PIK3CAmut tumor, would not be captured by the therascreen panel. Finally, the clinical utility of PIK3CA mutations not present in the therascreen companion diagnostic assay or identified by other sequencing-based assays needs further investigation.

Bluetooth monitoring systems (BTMS) have opened a new era in traffic sensing, providing a reliable, economical, and easy-to-deploy solution to uniquely identify vehicles. Raw data from BTMS have traditionally been used to calculate travel time and origin–destination matrices. However, we could extend this to include other information like the number of vehicles or their residence times. This information, together with their temporal components, can be applied to the complex task of forecasting traffic. Level of service (LOS) prediction has opened a novel research line that fulfills the need to anticipate future traffic states, based on a standard link-based variable, accepted for both researchers and practitioners. In this paper, we incorporate BTMS’s extended variables and temporal information to an LOS classifier based on a Random Undersampling Boost algorithm, which is proven to efficiently respond to the data unbalance intrinsic to this problem. By using this approach, we achieve an overall recall of 87.2% for up to 15-min prediction horizons, reaching 96.6% predicting congestion, and improving the results for the intermediate traffic states, especially complex given their intrinsic instability. Additionally, we provide detailed analyses on the impact of temporal information on the LOS predictor’s performance, observing improvements up to a separation of 50 min between last features and prediction horizons. Furthermore, we study the predictor importance resulting from the classifiers to highlight those features contributing the most to the final achievements.

Transport agencies require accurate and updated information about public transport systems for the optimal decision-making processes regarding design and operation. In addition to assessing topology and service components, users’ behaviors must be considered. To this end, a data-driven performance evaluation based on passengers’ actual routes is key. Automatic fare collection platforms provide meaningful smart card data (SCD), but these are incomplete when gathered by entry-only systems. To obtain origin–destination (OD) matrices, we must manage complete journeys. In this paper, we use an adapted trip chaining method to reconstruct incomplete multi-modal journeys by finding spatial similarities between the outbound and inbound routes of the same user. From this dataset, we develop a performance evaluation framework that provides novel metrics and visualization utilities. First, we generate a space-time characterization of the overall operation of transport networks. Second, we supply enhanced OD matrices showing mobility patterns between zones and average traversed distances, travel times, and operation speeds, which model the real efficacy of the public transport system. We applied this framework to the Comunidad de Madrid (Spain), using 4 months’ worth of real SCD, showing its potential to generate meaningful information about the performance of multi-modal public transport systems.

COVID-19 has dramatically struck each section of our society: health, economy, employment, and mobility. This work presents a data-driven characterization of the impact of COVID-19 pandemic on public and private mobility in a mid-size city in Spain (Fuenlabrada). Our analysis used real data collected from the public transport smart card system and a Bluetooth traffic monitoring network, from February to September 2020, thus covering relevant phases of the pandemic. Our results show that, at the peak of the pandemic, public and private mobility dramatically decreased to 95% and 86% of their pre-COVID-19 values, after which the latter experienced a faster recovery. In addition, our analysis of daily patterns evidenced a clear change in the behavior of users towards mobility during the different phases of the pandemic. Based on these findings, we developed short-term predictors of future public transport demand to provide operators and mobility managers with accurate information to optimize their service and avoid crowded areas. Our prediction model achieved a high performance for pre- and post-state-of-alarm phases. Consequently, this work contributes to enlarging the knowledge about the impact of pandemic on mobility, providing a deep analysis about how it affected each transport mode in a mid-size city.

Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own right. To do so, we build a mathematical framework for the classification of BSS trips. Using trajectory information, we create the trip index, which characterizes the intrinsic purpose of the use of BSS as transport or leisure. The construction of the trip index required a specific analysis of the BSS shortest path, which cannot be directly calculated from the topology of the network given that cyclists can find shortcuts through traffic lights, pedestrian crossings, etc. to reduce the overall traveled distance. Adding a layer of complication to the problem, these shortcuts have a non-trivial existence in terms of being intermittent, or short lived. We applied the proposed methodology to empirical data from BiciMAD, the public BSS in Madrid (Spain). The obtained results show that the trip index correctly determines transport and leisure categories, which exhibit distinct statistical and operational features. Finally, we inferred the underlying BSS public transport network and show the fundamental trajectories traveled by users. Based on this analysis, we conclude that 90.60% of BiciMAD’s use fall in the category of transport, which demonstrates our first statement.

Bicycle sharing systems (BSSs) have established a new shared-economy mobility model. After a rapid growth they are evolving into a fully-functional mobile sensor platform for cities. The viability of BSSs is floored by their operational costs, mainly due to rebalancing operations. Rebalancing implies transporting bicycles to and from docking stations in order to guarantee the service. Rebalancing performs clustering to group docking stations by behaviour and proximity. In this paper we propose a Hierarchical Agglomerative Clustering based on an Ultra-Light Edge Computing Algorithm (HAC-ULECA). We eliminate the proximity and let Hierarchical Agglomerative Clustering (HAC) focus on behaviour. Behaviour is represented by ULECA as an activity profile based on the net flow of arrivals and departures in a docking station. This drastically reduces the computing requirements which allows ULECA to run as an edge computing functionality embedded into the physical layer of the Internet of Shared Bikes (IoSB) architecture. We have applied HAC-ULECA to real data from BiciMAD, the public BSS in Madrid (Spain). Our results, presented as dendograms, graphs, geographical maps, and colour maps, show that HAC-ULECA is capable of separating behaviour profiles related to business and residential areas and extracting meaningful spatio-temporal information about the BSS and the city’s mobility.

The explosion of the Internet of Things has dramatically increased the data load on networks that cannot indefinitely increment their capacity to support these new services. Edge computing is a viable approach to fuse and process data on sensor platforms so that information can be created locally. However, the integration of complex heterogeneous sensors producing a great amount of diverse data opens new challenges to be faced. Rather than generating usable data straight away, complex sensors demand prior calculations to supply meaningful information. In addition, the integration of complex sensors in real applications requires a coordinated development from hardware and software teams that need a common framework to reduce development times. In this work, we present an edge and fog computing platform capable of providing seamless integration of complex sensors, with the implementation of an efficient data fusion strategy. It uses a symbiotic hardware/software design approach based on a novel messaging system running on a modular hardware platform. We have applied this platform to integrate Bluetooth vehicle identifiers and radar counters in a specific mobility use case, which exhibits an effective end-to-end integration using the proposed solution.

Wireless Sensor Networks (WSNs) must face the challenge of producing a vast plethora of applications from the least possible number of distributed sensors. In this paper we describe the Neuromorphic Sensor Network (NSN) platform, which implements a bioinspired approach to the development of growing applications in WSNs. NSNs follow an analogy with the neurophysiology of vertebrates, compressing the information coming from sets of sensors in wireless nodes and processing it by means of a set of processing units (PUs) that perform individual general purpose functions. Applications are then constructed through specific connections of these PUs, generating application pathways that allow the reuse of the same distributed NSN to give response to any desired output, thus achieving an application scalability . We illustrate the detailed process of growing applications using the NSN platform through an object tracking tool that mimics the behavior of the vertebrates visual system to detect and track objects. Finally, we describe a real implementation of the NSN platform in a road traffic monitoring and information system currently in operation in the cities of Madrid and Seville (Spain).