Real-world outcomes in patients with malignancy and moderate-to-severe psoriasis treated with guselkumab.

Background: The treatment of psoriasis in patients with a personal history of cancer is a matter of debate and limited evidence is available to guide clinicians. Objectives: To report a multicenter real-life experience of a group of patients with psoriasis undergoing treatment with guselkumab and a history of cancer. Methods: We conducted a multicenter retrospective Spanish study enrolling patients with moderate-to-severe plaque psoriasis and neoplasia being treated with guselkumab for their psoriasis. Results: Twenty patients with moderate-to-severe psoriasis and at least 12 weeks of ongoing treatment were included. For the analysis, a 52 week follow-up period was evaluated in terms of efficacy and safety. Most of the malignancies in these patients were solid tumors. The percentage of patients achieving psoriasis area and severity index ≤3 at week 12 and week 52 was 80% and 87.5%, respectively, whereas 68.8% of patients achieved psoriasis area and severity index ≤1. A 52-week survival rate of 100% in the study population was observed (n = 20), including those patients with concomitant active cancers (n = 14). No adverse effects or dropouts related to guselkumab safety profile were detected. Limitations: Modest sample size and the retrospective nature of the study. Conclusion: Guselkumab not only demonstrates high effectiveness in treating psoriasis but also exhibits a favorable safety profile in patients with neoplasms.

An Integrated Process Analysis for Producing Glycerol Carbonate from CO2 and Glycerol.

Glycerol carbonate (GC) is one of the most attractive green chemicals involved in several applications such as polymer synthesis, e. g., the production of polyurethanes and polycarbonates. This relevant chemical can be produced, in a green way, using CO2 (from carbon capture) and glycerol (a byproduct from biodiesel manufacturing). Therefore, in this work, a comprehensive analysis of the GC production process is conducted based on the following synthesis route: urea-dimethyl carbonate-GC using carbon dioxide and glycerol as the main raw materials where the synthesis pathway was efficiently integrated using Aspen Plus. A techno-economic analysis was performed in order to estimate the required capital investment and operating cost for the whole GC process, providing insights on individual capital cost requirements for the urea, dimethyl carbonate, and GC production sections. A total capital cost of $192.1 MM, and a total operating cost of $225.7 MM/y were estimated for the process. The total annualized cost was estimated as $1,558 USD/t of GC produced, competitive with current market price.

Multi-scale techno-economic assessment of nitrogen recovery systems for livestock operations.

Intensive livestock farming generates vast amounts of organic materials, which are an important source of nitrogen releases. These anthropogenic nitrogen releases contribute to multiple environmental problems, including eutrophication of water systems, contamination of drinking water sources, and greenhouse gas emissions. Nitrogen recovery and recycling are technically feasible, and there exists a number of processes for nitrogen recovery from livestock material in the form of different products. In this work, a multi-scale techno-economic assessment of techniques for nitrogen recovery and recycling is performed. The assessment includes a material flow analysis of each process, from material collection to final treatment, to determine nitrogen recovery efficiency, losses, and recovery cost, as well as an environmental cost-benefit analysis to compare the nitrogen recovery cost versus the economic losses derived from its uncontrolled release into the environment. The results show that transmembrane chemisorption process results in the lowest recovery cost, 3.4-10.4 USD per kilogram of nitrogen recovered in the range of studied processing scales. The recovery of nitrogen from livestock material through three technologies, i.e., transmembrane chemisorption, MAPHEX, and stripping in packed bed, reveales to be cost-effective. Since the economic losses due to the harmful effects of nitrogen into the environment are estimated at 32-35 USD per kilogram of nitrogen released, nitrogen recycling is an environmentally and economically beneficial approach to reduce nutrient pollution caused by livestock operations.

Evaluation of the Economic, Environmental, and Social Impact of the Valorization of Grape Pomace from the Wine Industry.

The increase in the world population has led to intensive food production systems that are generating increasing amounts of solid waste. In this work, the valorization of the most important waste generated during wine production, grape pomace, is evaluated. Eight processes are proposed to approach different types of valorization (production of energy and value-added products), from economic, environmental, and social points of view. The best process depends on the budget available, the production capacity, and the weight of each impact produced by the factory (economic, environmental, or social). For small (less than 0.1 kg/s) or very large (greater than 10 kg/s) capacities, the production of high-value-added products outperforms the other processes in all three impacts and in profitability. For intermediate capacities, combustion and gasification stand out as having the highest greenhouse emissions and intermediate economic benefits. Anaerobic digestion is remarkable for its low greenhouse gas emissions, while tannin production is the best-balanced process from both economic and environmental points of view. Pyrolysis is the worst process of all three impacts.

Tracking end-of-life stage of chemicals: A scalable data-centric and chemical-centric approach.

Chemical flow analysis (CFA) can be used for collecting life-cycle inventory (LCI), estimating environmental releases, and identifying potential exposure scenarios for chemicals of concern at the end-of-life (EoL) stage. Nonetheless, the demand for comprehensive data and the epistemic uncertainties about the pathway taken by the chemical flows make CFA, LCI, and exposure assessment time-consuming and challenging tasks. Due to the continuous growth of computer power and the appearance of more robust algorithms, data-driven modelling represents an attractive tool for streamlining these tasks. However, a data ingestion pipeline is required for the deployment of serving data-driven models in the real world. Hence, this work moves forward by contributing a chemical-centric and data-centric approach to extract, transform, and load comprehensive data for CFA at the EoL, integrating cross-year and country data and its provenance as part of the data lifecycle. The framework is scalable and adaptable to production-level machine learning operations. The framework can supply data at an annual rate, making it possible to deal with changes in the statistical distributions of model predictors like transferred amount and target variables (e.g., EoL activity identification) to avoid potential data-driven model performance decay over time. For instance, it can detect that recycling transfers of 643 chemicals over the reporting years (1988 to 2020) are 29.87%, 17.79%, and 20.56% for Canada, Australia, and the U.S. Finally, the developed approach enables research advancements on data-driven modelling to easily connect with other data sources for economic information on industry sectors, the economic value of chemicals, and the environmental regulatory implications that may affect the occurrence of an EoL transfer class or activity like recycling of a chemical over years and countries. Finally, stakeholders gain more context about environmental regulation stringency and economic affairs that could affect environmental decision-making and EoL chemical exposure predictions.

Emergence and retention of a collective memory in cockroaches.

The stability of collective decisions-making in social systems is crucial as it can lead to counterintuitive phenomena such as collective memories, where an initial choice is challenged by environmental changes. Many social species face the challenge to perform collective decisions under variable conditions. In this study, we focused on situations where isolated individuals and groups of the American cockroach (Periplaneta americana) had to choose between two shelters with different luminosities that were inverted during the experiment. The darker shelter was initially preferred, but only groups that reached a consensus within that shelter maintain their choice after the light inversion, while isolated individuals and small groups lacked site fidelity. Our mathematical model, incorporating deterministic and probabilistic elements, sheds light on the significance interactions and their stochasticity in the emergence and retention of a collective memory.

Lignocellulosic biorefineries: A multiscale approach for resource exploitation.

Biomass can become the source for chemicals towards a sustainable production system. However, the challenges it presents such as the variety of species, their widespread and sparse availability, and the expensive transportation claims for an integrated approach to design the novel production system. Multiscale approaches have not been properly extended to biorefineryes design and deployment, due to the comprehensive experimental and modelling work they require. A systems perspective provides the systematic framework to analyze the availability and composition of raw materials across regions, how that affects process design, the portfolio of products that can be obtained by evaluating the strong link between the biomass features and the process design. The use of lignocellulosic materials requires for a multidisciplinary work, that must lead to new process engineers with technical competences in biology, biotechnology but also process engineering, mathematics, computer science and social sciences towards a sustainable process/chemical industry.

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models.

Analyzing chemicals and their effects on the environment from a life cycle viewpoint can produce a thorough analysis that takes end-of-life (EoL) activities into account. Chemical risk assessment, predicting environmental discharges, and finding EoL paths and exposure scenarios all depend on chemical flow data availability. However, it is challenging to gain access to such data and systematically determine EoL activities and potential chemical exposure scenarios. As a result, this work creates quantitative structure-transfer relationship (QSTR) models for aiding environmental managment decision-making based on chemical structure-based machine learning (ML) models to predict potential industrial EoL activities, chemical flow allocation, environmental releases, and exposure routes. Further multi-label classification methods may improve the predictability of QSTR models according to the ML experiment tracking. The developed QSTR models will assist stakeholders in predicting and comprehending potential EoL management activities and recycling loops, enabling environmental decision-making and EoL exposure assessment for new or existing chemicals in the global marketplace.

SARS-CoV-2 Infection During the First and Second Pandemic Waves in Spain: the ENE-COVID Study.

Objectives. To describe participant characteristics associated with severe acute respiratory syndrome coronavirus 2 infection in Spain's first 2 COVID-19 waves per the Spanish National Seroepidemiological Survey of SARS-CoV-2 Infection (ENE-COVID). Methods. A representative cohort of the noninstitutionalized Spanish population, selected through stratified 2-stage sampling, answered a questionnaire and received point-of-care testing April to June 2020 (first wave: n = 68 287); previously seronegative participants repeated the questionnaire and test November 2020 (second wave: n = 44 451). We estimated seropositivity by wave and participant characteristics, accounting for sampling weights, nonresponse, and design effects. Results. We found that 6.0% (95% confidence interval [CI] = 5.7%, 6.4%) of Spain's population was infected by June and 3.8% (95% CI = 3.5%, 4.1%) more by November 2020. Both genders were equally affected. Seroprevalence decreased with age in adults 20 years and older in the second wave; socioeconomic differences increased. Health care workers were affected at 11.1% (95% CI = 9.0%, 13.6%) and 6.1% (95% CI = 4.4%, 8.5%) in the first and second waves, respectively. Living with an infected person increased infection risk to 22.1% (95% CI = 18.9%, 25.6%) in the first and 35.0% (95% CI = 30.8%, 39.4%) in the second wave. Conclusions. ENE-COVID characterized the first 2 pandemic waves, when information from surveillance systems was incomplete. (Am J Public Health. 2023;113(5):533-544. https://doi.org/10.2105/AJPH.2023.307233).

Design and Implementation of a Nationwide Population-Based Longitudinal Survey of SARS-CoV-2 Infection in Spain: The ENE-COVID Study.

Data System. The Spanish National Seroepidemiological Survey of SARS-CoV-2 (or ENE-COVID; SARS-CoV-2 [severe acute respiratory syndrome coronavirus 2] is the causative agent of COVID-19) was funded by the Spanish Ministry of Health, the Instituto de Salud Carlos III, and the Spanish National Health System. Data Collection/Processing. A stratified 2-stage probability sampling was used to select a representative cohort of the noninstitutionalized population of Spain. ENE-COVID collected longitudinal data from epidemiological questionnaires and 2 SARS-CoV-2 IgG antibody tests. From April 27 to June 22, 2020, 68 287 participants (77.0% of contacted persons) received a point-of-care test and 61 095 (68.9%) also underwent a laboratory immunoassay. A second follow-up phase was conducted between November 16 and 30, 2020. Data Analysis/Dissemination. Analyses use weights to adjust for oversampling and nonresponse and account for design effects of stratification and clustering. ENE-COVID data for research purposes will be available upon request from the official study Web page. Public Health Implications. ENE-COVID, a nationwide population-based study, allowed monitoring seroprevalence of antibodies against SARS-CoV-2 at the national and regional levels, providing accurate figures by gender, age (from babies to nonagenarians), and selected risk factors; characterizing symptomatic and asymptomatic infections; and estimating the infection fatality risk during the first pandemic wave. (Am J Public Health. 2023;113(5):525-532. https://doi.org/10.2105/AJPH.2022.307167).

Optimal Integrated Plant for Biodegradable Polymer Production.

An integrated facility for the production of biodegradable polymers from biomass residues has been developed. Lignocellulosic residues (sawdust), CO2, and organic waste such as manure or sludge are the raw materials. Manure and sludge are digested to provide the nutrients needed to grow algae. Algae are used in full to oil and starch production. The oil is transesterified with methanol generated via biogas dry reforming to obtain biodiesel and glycerol. The starch is used together with glycerol and the pretreated sawdust for the production of the biodegradable polymer. A mathematical optimization approach is used to identify the best use of each resource and the optimal operation of the integrated facility for each case. 4732 kt/yr of manure or 4653 kt/yr of sludge was processed to produce 354 kt/yr of biopolymer and 84 Mgal/yr of fatty acid methyl ester, capturing 2.47 kg of CO2 per kg of biopolymer with production costs of 0.89 and 0.95 $/kg, respectively, and an investment capital of 717 and 712 M$, respectively.

ADAM: A web platform for graph-based modeling and optimization of supply chains.

Modeling and optimization are essential tasks that arise in the analysis and design of supply chains (SCs). SC models are essential for understanding emergent behavior such as transactions between participants, inherent value of products exchanged, as well as impact of externalities (e.g., policy and climate) and of constraints. Unfortunately, most users of SC models have limited expertise in mathematical optimization, and this hinders the adoption of advanced decision-making tools. In this work, we present ADAM, a web platform that enables the modeling and optimization of SCs. ADAM facilitates modeling by leveraging intuitive and compact graph-based abstractions that allow the user to express dependencies between locations, products, and participants. ADAM model objects serve as repositories of experimental, technology, and socio-economic data; moreover, the graph abstractions facilitate the organization and exchange of models and provides a natural framework for education and outreach. Here, we discuss the graph abstractions and software design principles behind ADAM, its key functional features and workflows, and application examples.

Targeted Next-Generation Sequencing of Congenital Hypothyroidism-Causative Genes Reveals Unexpected Thyroglobulin Gene Variants in Patients with Iodide Transport Defect.

Congenital iodide transport defect is an uncommon autosomal recessive disorder caused by loss-of-function variants in the sodium iodide symporter (NIS)-coding SLC5A5 gene and leading to dyshormonogenic congenital hypothyroidism. Here, we conducted a targeted next-generation sequencing assessment of congenital hypothyroidism-causative genes in a cohort of nine unrelated pediatric patients suspected of having a congenital iodide transport defect based on the absence of 99mTc-pertechnetate accumulation in a eutopic thyroid gland. Although, unexpectedly, we could not detect pathogenic SLC5A5 gene variants, we identified two novel compound heterozygous TG gene variants (p.Q29* and c.177-2A>C), three novel heterozygous TG gene variants (p.F1542Vfs*20, p.Y2563C, and p.S523P), and a novel heterozygous DUOX2 gene variant (p.E1496Dfs*51). Splicing minigene reporter-based in vitro assays revealed that the variant c.177-2A>C affected normal TG pre-mRNA splicing, leading to the frameshift variant p.T59Sfs*17. The frameshift TG variants p.T59Sfs*17 and p.F1542Vfs*20, but not the DUOX2 variant p.E1496Dfs*51, were predicted to undergo nonsense-mediated decay. Moreover, functional in vitro expression assays revealed that the variant p.Y2563C reduced the secretion of the TG protein. Our investigation revealed unexpected findings regarding the genetics of congenital iodide transport defects, supporting the existence of yet to be discovered mechanisms involved in thyroid hormonogenesis.

Pleiotropic effects on proliferation and mineralization of primary human adipose tissue-derived stromal cells induced by simvastatin.

The circulating low-density lipoprotein concentration in blood can be reduced by the administration of statins. Frequently simvastatin (SV) is prescribed. Due to the reported pleiotropic effects of SV the aim of this study was to evaluate mineralization effects on human adipose tissue-derived stromal cells upon administration of SV. After informed consent human adipose tissue-derived stromal cells were obtained from tissue surplus of regular treatments of 14 individuals. According to established protocols after adding various SV concentrations (0.01 µM, 0.1 µM, 1.0 µM, 2.0 µM), alkaline phosphate (osteoblastic marker), mineralization capability and viability were determined at day 18, 21 and 28. The Kruskal-Wallis test was performed for statistical analysis. After adding SV a dose-dependent significant decreased viability and levels of alkaline phosphatase (p < 0.01) and a significantly increased mineralization (p < 0.01) of the primary cultures was recognized during the late mineralization stage. Mineralization of the human adipose tissue-derived stromal cells was induced by SV, possibly originated from alternative pathways than the alkaline phosphatase pathway. Further investigations should be performed regarding switching into the osteoblastic differentiation and as a possible source of cells that can be used as the basis for a potential bone graft substitute, which may allow an extension of the field of application.

Silent but Not Harmless: A Synonymous SLC5A5 Gene Variant Leading to Dyshormonogenic Congenital Hypothyroidism.

Background: Congenital iodide transport defect (ITD) is an uncommon cause of dyshormonogenic congenital hypothyroidism characterized by the absence of active iodide accumulation in the thyroid gland. ITD is an autosomal recessive disorder caused by loss-of-function variants in the sodium/iodide symporter (NIS)-coding SLC5A5 gene. Objective: We aimed to identify, and if so to functionally characterize, novel ITD-causing SLC5A5 gene variants in a cohort of five unrelated pediatric patients diagnosed with dyshormonogenic congenital hypothyroidism with minimal to absent 99mTc-pertechnetate accumulation in the thyroid gland. Methods: The coding region of the SLC5A5 gene was sequenced using Sanger sequencing. In silico analysis and functional in vitro characterization of a novel synonymous variant were performed. Results: Sanger sequencing revealed a novel homozygous synonymous SLC5A5 gene variant (c.1326A>C in exon 11). In silico analysis revealed that the c.1326A>C variant is potentially deleterious for NIS pre-mRNA splicing. The c.1326A>C variant was predicted to lie within a putative exonic splicing enhancer reducing the binding of splicing regulatory trans-acting protein SRSF5. Splicing minigene reporter assay revealed that c.1326A>C causes exon 11 or exon 11 and 12 skipping during NIS pre-mRNA splicing leading to the NIS pathogenic variants p.G415_P443del and p.G415Lfs*32, respectively. Significantly, the frameshift variant p.G415Lfs*32 is predicted to be subjected to degradation by nonsense-mediated decay. Conclusions: We identified the first exonic synonymous SLC5A5 gene variant causing aberrant NIS pre-mRNA splicing, thus expanding the mutational landscape of the SLC5A5 gene leading to dyshormonogenic congenital hypothyroidism.