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Abstract The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gmctool, a freely accessible online tool that allows us to accomplish this task in a simple, efficient and intuitive environment. gmctool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. gmctool introduces qualitative and quantitative improvements over our previously developed algorithms to predict, visualize and analyze metabolic vulnerabilities in cancer, demonstrating a superior performance than competing algorithms. A detailed illustration of gmctool is presented for multiple myeloma (MM), an incurable hematological malignancy. We provide in vitro experimental evidence for the essentiality of CTPS1 (CTPS synthase) and UAP1 (UDP-N-Acetylglucosamine Pyrophosphorylase 1) in specific MM patient subgroups.

Abstract It has been suggested that having a reputation for being prosocial is a critical part of social status across all human societies. It has also been argued that prosocial behavior confers benefits, whether physiological, such as stress reduction, or social, such as building allies or becoming more popular. Here, we investigate the relationship between helping reputation (being named as someone others would go to for help), and hair-derived chronic stress (hair cortisol concentration). In a sample of 77 women and 62 men, we found that perceived helping reputation was not related to chronic stress. Overall, the results of our study suggest that, in an egalitarian society with fluid camp membership and widely practiced generosity such as the Hadza, helping reputation does not necessarily boost stress-related health benefits through prestige-signaling mechanisms observed in hierarchical, large-scale societies.

Abstract Malignant transformation (MT) is commonly seen in IDH-mutant gliomas. There has been a growing research interest in revealing its underlying mechanisms and intervening prior to MT at the early stages of the transforming process. Here we established a unique pair of matched 3D cell models: 403L, derived from a low-grade glioma (LGG), and 403H, derived from a high-grade glioma (HGG), by utilizing IDH-mutant astrocytoma samples from the same patient when the tumor was diagnosed as WHO grade 2 (tumor mutational burden (TMB) of 3.96/Mb) and later as grade 4 (TMB of 70.07/Mb), respectively. Both cell models were authenticated to a patient’s sample retaining endogenous expression of IDH1 R132H. DNA methylation profiles of the parental tumors referred to LGG and HGG IDH-mutant glioma clusters. The immunopositivity of SOX2, NESTIN, GFAP, OLIG2, and beta 3-Tubulin suggested the multilineage potential of both models. 403H was more prompt to cell invasion and developed infiltrative HGG in vivo. The differentially expressed genes (DEGs) from the RNA sequencing analysis revealed the tumor invasion and aggressiveness related genes exclusively upregulated in the 403H model. Pathway analysis showcased an enrichment of genes associated with epithelial-mesenchymal transition (EMT) and Notch signaling pathways in 403H and 403L, respectively. Mass spectrometry-based targeted metabolomics and hyperpolarized (HP) 1-13C pyruvate in-cell NMR analyses demonstrated significant alterations in the TCA cycle and fatty acid metabolism. Citrate, glutamine, and 2-HG levels were significantly higher in 403H. To our knowledge, this is the first report describing the development of a matched pair of 3D patient-derived cell models representative of MT and temozolomide (TMZ)-induced hypermutator phenotype (HMP) in IDH-mutant glioma, providing insights into genetic and metabolic changes during MT/HMP. This novel in vitro model allows further investigation of the mechanisms of MT at the cellular level. Graphic Abstract

This paper examines business model implementations in three leading European smart cities: London, Amsterdam, and Berlin. Through a systematic literature review and comparative analysis, the study identifies and analyzes various business models employed in these urban contexts. The findings reveal a diverse array of models, including public–private partnerships, build–operate–transfer arrangements, performance-based contracts, community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies. Each city leverages a unique combination of these models to address its specific urban challenges and priorities. The study highlights the role of PPPs in large-scale infrastructure projects, BOT arrangements in transportation solutions, and performance-based contracts in driving efficiency and accountability. It also explores the benefits of community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies in enhancing the sustainability, efficiency, and livability of smart cities. The paper offers valuable insights for policymakers, urban planners, and researchers seeking to advance smart city development worldwide.

With the concept of Industry 4.0 production processes are moving towards autonomy and intelligence. Technologies equipped with artificial intelligence (AI) are involved into processes that are more and more digitized. Collaborative technologies are a feature of discrete processes. The automotive industry has achieved many successes in the process innovation towards smart factories. Other plants, such as smelters or coal mining are also striving to develop smart manufacturing with integrated computer systems to support processes. A continuous production is different from a discrete or batch production. Industry 4.0 concept is focused on discrete production (with high level of automation and robotization of manufacturing) meanwhile there is a gap in implementation of these approach in the continuous production. The objective of the publication is to prepare and design the integrated computer management system based on processes realized in coal and steel manufacturing. Coal and steel production are key elements in a chain of any industrial manufacturing e.g. automotive or machinery engineering. These processes are crucial in building of smart value chain. In our paper we present the structure of processes for the continuous production. Based the processes model we proposed the next steps to build the smart manufacturing for continuous production.

Abstract Access to Hepatis C treatment in Sub-Saharan Africa is a clinical, public health and ethical concern. The multi-country open-label trial TAC ANRS 12311 allowed assessing the feasibility, safety, efficacy of a specific care model of HCV treatment and retreatment in patients with hepatitis C in Sub Saharan Africa. Between November 2015 and March 2017, with follow-up until mid 2019, treatment-naïve patients with HCV without decompensated cirrhosis or liver cancer were recruited to receive 12 week-treatment with either sofosbuvir + ribavirin (HCV genotype 2) or sofosbuvir + ledipasvir (genotype 1 or 4) and retreatment with sofosbuvir + velpatasvir + voxilaprevir in case of virological failure. The primary outcome was sustained virological response at 12 weeks after end of treatment (SVR12). Secondary outcomes included treatment adherence, safety and SVR12 in patients who were retreated due to non-response to first-line treatment. The model of care relied on both viral load assessment and educational sessions to increase patient awareness, adherence and health literacy. The study recruited 120 participants, 36 HIV-co-infected, and 14 cirrhotic. Only one patient discontinued treatment because of return to home country. Neither death nor severe adverse event occurred. SVR12 was reached in 107 patients (89%): (90%) in genotype 1 or 2, and 88% in GT-4. All retreated patients (n = 13) reached SVR12. HCV treatment is highly acceptable, safe and effective under this model of care. Implementation research is now needed to scale up point-of-care HCV testing and SVR assessment, along with community involvement in patient education, to achieve HCV elimination in Sub-Saharan Africa.

Children with autism spectrum disorder (ASD) are often placed in inclusive mainstream education (ME) or exclusive special education (SE) settings. While ME settings usually offer less-intensive and structured intervention programs than SE settings, they offer more exposure to typically developing peers. A total of 121 children (2-5 years old) with ASD, 85 in SE and 36 in ME, completed two Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2) assessments. Repeated-measures analysis of covariance (ANCOVA) analyses were used to assess longitudinal changes in ADOS-2 calibrated severity scores (CSS) and language production (estimated from the ADOS-2), while accounting for baseline cognitive scores, age of diagnosis, and parent-reported intensity of intervention. Longitudinal changes in ADOS CSS did not differ significantly across educational settings but were strongly associated with the age of diagnosis, demonstrating that children diagnosed earlier improved more regardless of educational settings. These findings suggest that children with ASD placed in SE and ME exhibit similar longitudinal changes in core ASD symptoms. Further studies comparing additional outcome measures such as cognitive abilities and adaptive behaviors are highly warranted for establishing placement recommendations and public health policies.

We explore asynchronous programming with algebraic effects. We complement their conventional synchronous treatment by showing how to naturally also accommodate asynchrony within them, namely, by decoupling the execution of operation calls into signalling that an operation's implementation needs to be executed, and interrupting a running computation with the operation's result, to which the computation can react by installing interrupt handlers. We formalise these ideas in a small core calculus and demonstrate its flexibility using examples ranging from a multi-party web application, to pre-emptive multi-threading, to (cancellable) remote function calls, to a parallel variant of runners of algebraic effects. In addition, the paper is accompanied by a formalisation of the calculus's type safety proofs in Agda, and a prototype implementation in OCaml.

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Caffeine consumption outcomes on Amyotrophic Lateral Sclerosis (ALS) including progression, survival and cognition remain poorly defined and may depend on its metabolization influenced by genetic variants. 378 ALS patients with a precise evaluation of their regular caffeine consumption were monitored as part of a prospective multicenter study. Demographic, clinical characteristics, functional disability as measured with revised ALS Functional Rating Scale (ALSFRS-R), cognitive deficits measured using Edinburgh Cognitive and Behavioural ALS Screen (ECAS), survival and riluzole treatment were recorded. 282 patients were genotyped for six single nucleotide polymorphisms tagging different genes involved in caffeine intake and/or metabolism: CYP1A1 (rs2472297), CYP1A2 (rs762551), AHR (rs4410790), POR (rs17685), XDH (rs206860) and ADORA2A (rs5751876) genes. Association between caffeine consumption and ALSFRS-R, ALSFRS-R rate, ECAS and survival were statistically analyzed to determine the outcome of regular caffeine consumption on ALS disease progression and cognition. No association was observed between caffeine consumption and survival (p = 0.25), functional disability (ALSFRS-R; p = 0.27) or progression of ALS (p = 0.076). However, a significant association was found with higher caffeine consumption and better cognitive performance on ECAS scores in patients carrying the C/T and T/T genotypes at rs2472297 (p-het = 0.004). Our results support the safety of regular caffeine consumption on ALS disease progression and survival and also show its beneficial impact on cognitive performance in patients carrying the minor allele T of rs2472297, considered as fast metabolizers, that would set the ground for a new pharmacogenetic therapeutic strategy.

Abstract Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

The research presented in this article analyzes whether the use of sports contexts in problems can increase the motivation of Primary School students towards mathematics. For this purpose, a case study research has been carried out with fifth grade students of a school in the Autonomous Community of the Basque Country in the 2022/2023 school year. Mathematical problems in a sports context have been designed and treated in the school, using real data and incorporating Basque sports. The students state that they like sports, not so much mathematics, and less mathematical problems. Significant gender differences were observed in the study. In conclusion, it can be stated that, in general, the use of close and realistic sports contexts has increased the students' motivation towards mathematics.

Abstract Social support has been proposed as an important determinant of women's physical and emotional well-being during pregnancy and after childbirth. Our study aimed to examine the association between the risk of postpartum depression (PPD) and perceived social support during pregnancy. A web-based prospective study survey was conducted among Polish women. The level of social support was measured with the Berlin Social Support Scales during pregnancy. Four weeks after the birth the risk of PPD was assessed using the Edinburgh Postpartum Depression Scale. Data from 932 mothers aged 19–43 (mean 30.95; SD 3.83) were analyzed using multinomial logistic regression. Higher perceived available support (emotional and instrumental), currently received support (emotional, instrumental and informational), satisfaction with the support, and sum of score were all associated with lower risk of PPD, after controlling for selected covariates (woman's age, socioeconomic status, parity status, place of residency, education, child's Apgar score, type of delivery, complications during birth, kin assisting the labor, breastfeeding). Our results suggest that the more social support the pregnant woman receives, the lower is her risk of PPD. Since humans evolved as cooperative breeders, they are inherently reliant on social support to raise children and such allomaternal help could improve maternal well-being.