15,212 research outputs found

    The structure and dynamics of massive high-z cosmic-web filaments: three radial zones in filament cross-sections

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    We analyse the internal structure and dynamics of cosmic-web filaments connecting massive high-z haloes. Our analysis is based on a high-resolution AREPO cosmological simulation zooming-in on three Mpc-scale filaments feeding three massive haloes of at z ∌ 4, embedded in a large-scale sheet. Each filament is surrounded by a cylindrical accretion shock of radius ⁠. The post-shock gas is in virial equilibrium within the potential well set by an isothermal dark-matter filament. The filament line-mass is ⁠, the gas fraction within rshock is the universal baryon fraction, and the virial temperature is ∌7 × 105 K. These all match expectations from analytical models for filament properties as a function of halo mass and redshift. The filament cross-section has three radial zones. In the outer ‘thermal’ (T) zone, ⁠, inward gravity, and ram-pressure forces are overbalanced by outward thermal pressure forces, decelerating the inflowing gas and expanding the shock outwards. In the intermediate ‘vortex’ (V) zone, 0.25 ≀ r/rshock ≀ 0.65, the velocity field is dominated by a quadrupolar vortex structure due to offset inflow along the sheet through the post-shock gas. The outward force is dominated by centrifugal forces associated with these vortices, with additional contributions from global rotation and thermal pressure. Shear and turbulent forces associated with the vortices act inwards. The inner ‘stream’ (S) zone, ⁠, is a dense isothermal core, and ⁠, defining the cold streams that feed galaxies. The core is formed by an isobaric cooling flow and is associated with a decrease in outward forces, though exhibiting both inflows and outflows

    Treatment Patterns and Use of Immune Checkpoint Inhibitors Among Patients with Metastatic Bladder Cancer in a Dutch Nationwide Cohort

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    Since 2017, two immune checkpoint inhibitors (ICIs) have become the standard of care for the treatment of metastatic urothelial carcinoma in Europe: pembrolizumab as second-line therapy and avelumab as maintenance therapy. Our aim was to describe the use of ICIs as first and later lines of treatment in patients with metastatic bladder cancer (mBC) in the Netherlands. We identified all patients diagnosed with primary mBC between 2018 and 2021 in the Netherlands from the Netherlands Cancer Registry (NCR). NCR data were supplemented with data from the Dutch nationwide Prospective Bladder Cancer Infrastructure (ProBCI) collected from medical files, with follow-up until death or end of data collection on January 1, 2023. A total of 1525 patients were diagnosed with primary mBC between 2018 and 2021 in the Netherlands. Of these, 34.7% received at least one line of systemic treatment with chemotherapy or ICI. After first-line platinum-based chemotherapy, 34.1% received second-line ICI and 3.9% received maintenance ICI. Among patients who completed or discontinued first-line cisplatin- or carboplatin-based chemotherapy after approval of maintenance ICI in the Netherlands, 40.7% and 19.7% received second-line ICI, and 9.3% and 14.1% received maintenance ICI, respectively. ICI use for mBC treatment has not increased considerably since their introduction in 2017. Future research should assess whether the introduction of maintenance avelumab (available since April 2021 in the Netherlands) has led to increases in the proportion of patients with mBC patients receiving systemic treatment and the proportion receiving ICI. Patient summary: We assessed the rate of immunotherapy use for patients with metastatic bladder cancer in the Netherlands. Since its introduction, immunotherapy has been used in a minority of patients, mostly as second-line treatment after platinum-based chemotherapy.</p

    Personalized neck irradiation guided by sentinel lymph node biopsy in patients with squamous cell carcinoma of the oropharynx, larynx or hypopharynx with a clinically negative neck:(Chemo)radiotherapy to the PRIMary tumor only. Protocol of the PRIMO study

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    Background: Elective neck irradiation (ENI) is performed in head and neck cancer patients treated with definitive (chemo)radiotherapy. The aim is to eradicate nodal metastases that are not detectable by pretreatment imaging techniques. It is conceivable that personalized neck irradiation can be performed guided by the results of sentinel lymph node biopsy (SLNB). It is expected that ENI can be omitted to one or both sides of the neck in 9 out of 10 patients, resulting in less radiation side effects with better quality of life. Methods/design: This is a multicenter randomized controlled trial aiming to compare safety and efficacy of treatment with SLNB guided neck irradiation versus standard bilateral ENI in 242 patients with cN0 squamous cell carcinoma of the oropharynx, larynx or hypopharynx for whom bilateral ENI is indicated. Patients randomized to the experimental-arm will undergo SLNB. Based on the histopathologic status of the SLNs, patients will receive no ENI (if all SLNs are negative), unilateral neck irradiation only (if a SLN is positive at one side of the neck) or bilateral neck irradiation (if SLNs are positive at both sides of the neck). Patients randomized to the control arm will not undergo SLNB but will receive standard bilateral ENI. The primary safety endpoint is the number of patients with recurrence in regional lymph nodes within 2 years after treatment. The primary efficacy endpoint is patient reported xerostomia-related quality of life at 6 months after treatment. Discussion: If this trial demonstrates that the experimental treatment is non-inferior to the standard treatment in terms of regional recurrence and is superior in terms of xerostomia-related quality of life, this will become the new standard of care.</p

    Dyslexia and Comorbid Dyscalculia: rate of comorbidity and underlying cognitive and learning profile

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    PURPOSE OF THE STUDY. Children diagnosed with a specific learning disorder (SLD) have four to five times higher chances of developing a comorbid condition. In particular, the high prevalence of comorbid dyscalculia (MD) in children with dyslexia (RD) has been documented. Nevertheless, the exact rate of MD comorbidity and the causes underlying the overlap remain unclear since most research has focused on studying them in isolation. Given the relevance of early identification and evidence-based interventions for further compensation of SLD, there is a need for studies on this matter. The study intended to fill this gap. METHOD. The study was a secondary data analysis of the standardised test scores of 215 neuropsychological assessments administered to grade 1 to 3 schoolchildren in Argentina who had a prior diagnosis of RD. For the purposes of the study, they were classified into 2 groups (RD only and comorbid RDMD). Scores were analyzed using SPSS Statistics to (i) explore the rate of MD comorbidity in children with RD; (ii) contrast the cognitive and learning profiles of the RD and the RDMD group; and (iii) assess the predictive value of each cognitive factor to the development of the RDMD comorbidity. RESULTS AND CONCLUSION. The study found that children with RD developed RDMD at a frequency of 33.5%. There was a significant difference in the two groups' learning and cognitive factors scores, with the comorbid group worst affected in all domains. Among these, verbal working memory, spatial skills, semantic long-term memory and phonological awareness were the most sensitive predictors; together they could account for 35% of the MD comorbidity. These findings are evidence of the high incidence of MD comorbidity in the population with RD and highlight the predictive value of specific cognitive markers

    Antiparasitic Meroterpenoids Isolated from Memnoniella dichroa CF-080171

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    Memnoniella is a fungal genus from which a wide range of diverse biologically active compounds have been isolated. A Memnoniella dichroa CF-080171 extract was identified to exhibit potent activity against Plasmodium falciparum 3D7 and Trypanosoma cruzi Tulahuen whole parasites in a high-throughput screening (HTS) campaign of microbial extracts from the Fundaci&oacute;n MEDINA&rsquo;s collection. Bioassay-guided isolation of the active metabolites from this extract afforded eight new meroterpenoids of varying potencies, namely, memnobotrins C-E (1&ndash;3), a glycosylated isobenzofuranone (4), a tricyclic isobenzofuranone (5), a tetracyclic benzopyrane (6), a tetracyclic isobenzofuranone (7), and a pentacyclic isobenzofuranone (8). The structures of the isolated compounds were established by (+)-ESI-TOF high-resolution mass spectrometry and nuclear magnetic resonance spectroscopy. Compounds 1, 2, and 4 exhibited potent antiparasitic activity against P. falciparum 3D7 (EC50 0.04&ndash;0.243 &mu;M) and T. cruzi Tulahuen (EC50 0.266&ndash;1.37 &mu;M) parasites, as well as cytotoxic activity against HepG2 tumoral liver cells (EC50 1.20&ndash;4.84 &mu;M). The remaining compounds (3, 5&ndash;8) showed moderate or no activity against the above-mentioned parasites and cells

    Machine learning to improve false-positive results in the Dutch newborn screening for congenital hypothyroidism

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    Objective: The Dutch Congenital hypothyroidism (CH) Newborn Screening (NBS) algorithm for thyroidal and central congenital hypothyroidism (CH-T and CH-C, respectively) is primarily based on determination of thyroxine (T4) concentrations in dried blood spots, followed by thyroid-stimulating hormone (TSH) and thyroxine-binding globulin (TBG) measurements enabling detection of both CH-T and CH-C, with a positive predictive value (PPV) of 21%. A calculated T4/TBG ratio serves as an indirect measure for free T4. The aim of this study is to investigate whether machine learning techniques can help to improve the PPV of the algorithm without missing the positive cases that should have been detected with the current algorithm. Design & methods: NBS data and parameters of CH patients and false-positive referrals in the period 2007–2017 and of a healthy reference population were included in the study. A random forest model was trained and tested using a stratified split and improved using synthetic minority oversampling technique (SMOTE). NBS data of 4668 newborns were included, containing 458 CH-T and 82 CH-C patients, 2332 false-positive referrals and 1670 healthy newborns. Results: Variables determining identification of CH were (in order of importance) TSH, T4/TBG ratio, gestational age, TBG, T4 and age at NBS sampling. In a Receiver-Operating Characteristic (ROC) analysis on the test set, current sensitivity could be maintained, while increasing the PPV to 26%. Conclusions: Machine learning techniques have the potential to improve the PPV of the Dutch CH NBS. However, improved detection of currently missed cases is only possible with new, better predictors of especially CH-C and a better registration and inclusion of these cases in future models

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    Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies

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    Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.</p
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