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Background Triple negative breast cancer (TNBC) remains one of the most aggressive subtypes of cancer with a poor prognosis and limited treatment options. Building on our previous findings of elevated Interleukin-3-Receptor-α (IL-3Rα) expression in TNBC, this study investigates the mechanisms underpinning IL-3-mediated actions in TNBC. Methods GEO database (GSE25066) was interrogated to evaluate the expression of IL-3. RNAseq data were acquired from the TCGA-BRCA (Breast Carcinoma) project. Seven TNBC cell lines were used to validate the expression of IL-3 by ELISA assay. Chromatin immunoprecipitation assay was performed to evaluate the binding of STAT5A to the miR-155-5p promoter in TNBC cells. FACS analysis and ALDH activity were performed to evaluate the expansion of ALDH-1A1 + and CD44 high /CD24 low subpopulations. Mammosphere formation efficiency (MFE) was evaluated using the standard assay, while chemoresistance by applying the incucyte cell viability assay. miR155-5p silencing served to validate the expression of all target proteins both in vitro and in vivo. Results Bioinformatic analysis of breast cancer patient gene datasets revealed significant upregulation of the IL-3 gene in TNBC patient samples compared to the non-TNBC group (GEO: p  = 0.004: TCGA p  = 2.7e−30 respectively). We also found that TNBC cells secrete IL-3, which activates STAT5A promoting miR-155-5p expression by binding to its promoter in TNBC cells. Correlation analysis based on TCGA-BRCA confirmed elevated miR-155-5p levels in TNBC compared to non-tumoral tissues ( p  = 2.1e−33) and non-TNBC ( p  = 6.5e−30), with positive correlations between the IL-3 and miR-155-5p ( r  = 0.157, p  < 0.001), as well as between miR-155-5p and miR-155-3p and STAT5A ( r  = 0.250, p  = 0.002; r  = 0.245, p  < 0.005 respectively). Functional studies demonstrated that miR-155-5p downregulates programmed cell death 4, APC, and GSK-3β, enhancing β-catenin nuclear translocation and c-myc expression. Silencing miR-155-5p reversed all these effects. IL-3, via miR-155-5p, also drives ALDH-1A1 + and CD44 high /CD24 low subpopulation expansion and ALDH activity, enhances MFE and chemoresistance. Notably, blocking IL-3 impaired MFE, suggesting an autocrine loop sustaining IL-3 action in TNBC. In vivo, IL-3 promoted tumour growth, β-catenin activity, and metastasis, while miR-155-5p silencing mitigated these effects. Conclusions Overall, our results underscore the crucial role of IL-3 in tumour progression, thereby advocating IL-3/IL-3Rα axis targeting as a promising therapeutic approach for TNBC. Graphical abstract

p140Cap is an adaptor protein involved in assembling multi-protein complexes regulating several cellular processes. p140Cap acts as a tumor suppressor in breast cancer (BC) and neuroblastoma patients, where its expression correlates with a better prognosis. The role of p140Cap in tumor metabolism remains largely unknown. Here we study the role of p140Cap in the modulation of the mevalonate (MVA) pathway in BC cells. The MVA pathway is responsible for the biosynthesis of cholesterol and non-sterol isoprenoids and is often deregulated in cancer. We found that both in vitro and in vivo, p140Cap cells and tumors show an increased flux through the MVA pathway by positively regulating the pace-maker enzyme of the MVA pathway, the 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR), via transcriptional and post-translational mechanisms. The higher cholesterol synthesis is paralleled with enhanced cholesterol efflux. Moreover, p140Cap promotes increased cholesterol localization in the plasma membrane and reduces lipid rafts-associated Rac1 signalling, impairing cell membrane fluidity and cell migration in a cholesterol-dependent manner. Finally, p140Cap BC cells exhibit decreased cell viability upon treatments with statins, alone or in combination with chemotherapeutic at low concentrations in a synergistic manner. Overall, our data highlight a new perspective point on tumor suppression in BC by establishing a previously uncharacterized role of the MVA pathway in p140Cap expressing tumors, thus paving the way to the use of p140Cap as a potent biomarker to stratify patients for better tuning therapeutic options.

Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. The main engine for solving MILPs is the branch-and-bound algorithm. Adding to the enormous algorithmic progress in MILP solving of the past decades, in more recent years there has been an explosive development in the use of machine learning for enhancing all main tasks involved in the branch-and-bound algorithm. These include primal heuristics, branching, cutting planes, node selection and solver configuration decisions. This article presents a survey of such approaches, addressing the vision of integration of machine learning and mathematical optimization as complementary technologies, and how this integration can benefit MILP solving. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency. We also address appropriate MILP representations, benchmarks and software tools used in the context of applying learning algorithms.

The architecture of spaces for people on the autistic spectrum is evolving toward inclusive design, which should fit the requirements for independent, autonomous living, and proper support for relatives and caregivers. The use of smart sensor systems represents a valuable support to internal design in order to achieve independent living for impaired people. Accordingly, these devices can monitor or prevent hazardous situations, ensuring security and privacy. Acoustic sensor systems, for instance, could be used in order to realize a passive monitoring system. The correct functioning of such devices needs optimal indoor acoustic criteria. Nevertheless, these criteria should also comply with dedicated acoustic requests that autistic individuals with hearing impairment or hypersensitivity to sound could need. Thus, this research represents the first attempt to balance, integrate, and develop these issues, presenting (i) a wide literature overview related to both topics, (ii) a focused analysis on real facility, and (iii) a final optimization, which takes into account, merges, and elucidates all the presented unsolved issues.

People on the autism spectrum have a different perception of the environment than neurotypical people and often require support in various activities of daily living. Assistive technology can support those affected, but very few smart-home-like technologies exist. To support people on the autism spectrum in their autonomy and safety and to help caregivers, a smart home and interior design environment was developed. Requirements were gathered by employing a holistic human-centered design approach through interactive workshops and questionnaires to create a useful and user-friendly solution. From this process, requirements for a comprehensive solution (the SENSHOME environment) emerged. These requirements include a set of functionalities tailored to the needs of people on the autism spectrum, such as a crowd warning that informs when many people are in a certain area (for example, the entrance), an automatic light regulation system, or a daily life planner that supports task completion. Furthermore, inclusive furniture elements such as a refuge seat or a table with dividers can support wellbeing, autonomy, and safety. This paper demonstrates a consequent and considerable participatory research approach and the story from the target group and context of use through design requirements to the initial design solution of the SENSHOME environment.

Background Lutzomyia longipalpis , the vector for the causal agent of visceral leishmaniasis (VL), has extended its distribution in the southern cone in the Americas. The first urban record of Lu. longipalpis in Argentina was from the City of Clorinda in 2004. The aim of this study was to analyse the monthly distribution and abundance of Lu. longipalpis and to evaluate its association with environmental and climatic variables in Clorinda City, Province of Formosa. Methods Phlebotominae sampling was performed using CDC light mini-traps that were placed in different sites of the city between January 2012 and December 2013. Environmental variables including the normalised difference vegetation index, normalized difference water index, land surface temperature and precipitation were evaluated using a spatiotemporal model. Results A total of 4996 phlebotomine sandflies were captured during the study period, and eight species were reported: Lu. longipalpis , Migonemyia migonei , Nyssomyia whitmani , Ny. neivai , Brumptomyia guimaraesi , Evandromyia cortelezzii/sallesi , Psathyromyia bigeniculata and Expapillata firmatoi . This is the first urban record of Ex. firmatoi in Argentina. Lutzomyia longipalpis was the most abundant species between 2012 and 2013, and it appeared in all the sampled sites. Moreover, the model applied showed that ground humidity and temperature were significantly associated with the abundance of Lu. longipalpis . Conclusions This longitudinal approach at city scale allows for modelling that explains more than 60% of the temporal variability of the abundance of Lu . longipalpis based exclusively on satellite obtained data. The results support the hypothesis of steady ‘hot spots’ of abundance with time, while other sites could change its abundance due to eventual microenvironment changes. The Lu . longipalpis abundance driving factors are breeding site-related variables, highlighting the importance both for modelling and surveillance to use lag data.

Background Health disparities have been highlighted among patient with prostate adenocarcinoma (PRAD) due to ethnicity. Mexican men present a more aggressive disease than other patients resulting in less favorable treatment outcome. We aimed to identify the mutational landscape which could help to reduce the health disparities among minority groups and generate the first genomics exploratory study of PRAD in Mexican patients. Methods Paraffin-embedded formalin-fixed tumoral tissue from 20 Mexican patients with early-stage PRAD treated at The Instituto Nacional de Cancerología, Mexico City from 2017 to 2019 were analyzed. Tumoral DNA was prepared for whole exome sequencing, the resulting files were mapped against h19 using BWA-MEM. Strelka2 and Lancet packages were used to identify single nucleotide variants (SNV) and insertions or deletions. FACETS was used to determine somatic copy number alterations (SCNA). Cancer Genome Interpreter web interface was used to determine the clinical relevance of variants. Results Patients were in an early clinical stage and had a mean age of 59.55 years (standard deviation [SD]: 7.1 years) with 90% of them having a Gleason Score of 7. Follow-up time was 48.50 months (SD: 32.77) with recurrences and progression in 30% and 15% of the patients, respectively. NUP98 (20%), CSMD3 (15%) and FAT1 (15%) were the genes most frequently affected by SNV; ARAF (75%) and ZNF419 (70%) were the most frequently affected by losses and gains SNCA’s. One quarter of the patients had mutations useful as biomarkers for the use of PARP inhibitors, they comprise mutations in BRCA , RAD54L and ATM . SBS05, DBS03 and ID08 were the most common mutational signatures present in this cohort. No associations with recurrence or progression were identified. Conclusions This pilot study reveals the mutational landscape of early-stage prostate adenocarcinoma in Mexican men, providing a first approach to understand the mutational patterns and actionable mutations in early prostate cancer can inform personalized treatment approaches and reduce the underrepresentation in genomic cancer studies.

Bladder cancer (BC) is the most common neoplasm of the urinary tract, which originates in the epithelium that covers the inner surface of the bladder. The molecular BC profile has led to the development of different classifications of non-muscle invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). However, the genomic BC landscape profile of the Mexican population, including NMIBC and MIBC, is unknown. In this study, we aimed to identify somatic single nucleotide variants (SNVs) and copy number variations (CNVs) in Mexican patients with BC and their associations with clinical and pathological characteristics. We retrospectively evaluated 37 patients treated between 2012 and 2021 at the National Cancer Institute—Mexico (INCan). DNA samples were obtained from paraffin-embedded tumor tissues and exome sequenced. Strelka2 and Lancet packages were used to identify SNVs and insertions or deletions. FACETS was used to determine CNVs. We found a high frequency of mutations in TP53 and KMT2D, gains in 11q15.5 and 19p13.11-q12, and losses in 7q11.23. STAG2 mutations and 1q11.23 deletions were also associated with NMIBC and low histologic grade.

Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. During the past decades, enormous algorithmic progress has been made in solving MILPs, and many commercial and academic software packages exist. Nevertheless, the availability of data, both from problem instances and from solvers, and the desire to solve new problems and larger (real-life) instances, trigger the need for continuing algorithmic development. MILP solvers use branch and bound as their main component. In recent years, there has been an explosive development in the use of machine learning algorithms for enhancing all main tasks involved in the branch-and-bound algorithm, such as primal heuristics, branching, cutting planes, node selection and solver configuration decisions. This paper presents a survey of such approaches, addressing the vision of integration of machine learning and mathematical optimization as complementary technologies, and how this integration can benefit MILP solving. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency. We also address how to represent MILPs in the context of applying learning algorithms, MILP benchmarks and software.