Amino acids, including asparagine, aspartate, glutamine, and glutamate, play important roles in the purine and pyrimidine biosynthesis as well as serve as anaplerotic sources fueling the tricarboxylic acid (TCA) cycle for mitochondrial energy generation in mammalian cells. Despite extensive studies on glutamine and glutamate in CHO cell cultures, the roles of asparagine and aspartate, especially in feed media, remain underexplored. In this study, we utilized the CHO genome scale model to first deeply characterize the intracellular metabolic states of CHO cells cultured in different combinations of basal and feed media to understand the traits of asparagine/aspartate-dependent and glutamate-dependent feeds. Subsequently, we identified the critical role of asparagine and aspartate in the feed media as anaplerotic sources and conduct in silico simulations to ascertain their optimal ratios to improve cell culture performance. Finally, based on the model simulations, we reformulated the feed media by tailoring the concentrations of asparagine and aspartate. Our experimental data reveal a CHO cell preference for asparagine compared with aspartate, and thus maintaining an optimal ratio of these amino acids is a key factor for achieving optimal CHO cell culture performance in biopharmaceutical production.

Singlet and triplet spin state energies for three-dimensionalHooke atoms, i .e. electrons in a quadratic confinement, with even number of electrons (2, 4, 6, 8, 10) is discussed using Full-CI and CASSCF type wavefunctions with a variety of basis sets and considering perturbative corrections up to second order. The effect of the screening of the electron-electron interaction is also discussed by using a Yukawa-type potential with different values of the Yukawa screening parameter (λee =0.2, 0.4, 0.6, 0.8, 1.0). Our results show that the singlet state is the ground state for 2 and 8 electron Hooke atoms, whereas the triplet is the ground spin state for 4, 6 and 10 electron systems. This suggests the following Auf bau structure 1s < 1p < 1d with singlet ground spin states for systems in which the generation of the triplet implies an inter-shell one electron promotion, and triplet ground states in cases when there is a partial filling of electrons of a given shell. It is also observed that the screening of electronelectron interactions has a sizable quantitative effect on the relative energies of both spin states, specially in the case of 2 and 8 electron systems, favouring the singlet state over the triplet. However, the screening of the electron-electron interaction does not provoke a change in the nature of the ground spin state of these systems. By analyzing the different components of the energy, we have gained a deeper understanding of the effects of the kinetic, confinement and electron-electron interaction components of the energy.

\papertype Original Article Large language models (LLMs), like the GPT series, have recently emerged as transformative tools in the medical field due to their human-like language generation and understanding. This systematic review examines the evolution, applications, and challenges of medical LLMs in digital health and clinical technology. A structured search was conducted across ScienceDirect, PubMed, Scopus, and manual sources from 2007 to 2024, following PRISMA 2020 guidelines. After applying inclusion and exclusion criteria, 179 studies were selected from an initial pool of 698 papers. Among the 30 papers reviewed, most research centered on GPT-based models, with over 81% demonstrating strong performance in language generation, diagnostic assistance, and clinical documentation, based on automated metrics and human feedback. Notably, some models achieved up to 90% satisfaction from healthcare professionals. The findings reveal LLMs’ potential to enhance patient interaction, decision support, and overall healthcare efficiency. This review contributes by synthesizing key advancements, assessing model performance, and outlining ethical challenges such as trust, privacy, and safe deployment. It offers novel insights for researchers and practitioners seeking to adopt or improve LLM integration in healthcare. Future directions include improving transparency, developing domain-specific models, and establishing regulatory frameworks for responsible use.

Version of record in Molecular Ecology Resources:  https://doi.org/10.1111/1755-0998.13402 The Maroni is one of the most speciose basins of the Guianas and hosts a megadiverse freshwater fish community. Although taxonomical references exist for both the Surinamese and Guyanese parts of the basin, these lists were mainly based on morphological identification and there are still taxonomical uncertainties concerning the status of several fish species. Here we present a barcode dataset of 1,284 COI sequences from 199 freshwater fish species (68.86% of the total number of strictly freshwater fishes from the basin) from 124 genera, 36 families, and 8 orders. DNA barcoding allowed for fast and efficient identification of all specimens studied as well as unveiling a consequent cryptic diversity, with the detection of 20 putative cryptic species and 5 species flagged for re-identification. In order to explore global genetic patterns across the basin, genetic divergence landscapes were computed for 128 species, showing a global trend of high genetic divergence between the Surinamese south-west (Tapanahony and Paloemeu), the Guianese south-east (Marouini, Litany, Tampok, Lawa…), and the river mouth in the north. This could be explained either by lower levels of connectivity between these three main parts or by the exchange of individuals with the surrounding basins. A new method of ordination of genetic landscapes successfully assigned species into cluster groups based on their respective pattern of genetic divergence across the Maroni Basin: genetically homogenous species across the basin were effectively discriminated from species showing high spatial genetic fragmentation and possible lower capacity for dispersal.

The ATP-binding cassette (ABC) transporter superfamily comprises membrane proteins that efflux various substrates across extra- and intra-cellular membranes. Mutations in ABC genes cause 21 human disorders or phenotypes with Mendelian inheritance, including cystic fibrosis, adrenoleukodystrophy, retinal degeneration, cholesterol, and bile transport defects. Common polymorphisms and rare variants in ABC genes are associated with several complex phenotypes such as gout, gallstones, and cholesterol levels. Overexpression or amplification of specific drug efflux genes contributes to chemotherapy multidrug resistance. Conservation of the ATP-binding domains of ABC transporters defines the superfamily members, and phylogenetic analysis groups the 48 human ABC transporters into seven distinct subfamilies. While the conservation of ABC genes across most vertebrate species is high, there is also considerable gene duplication, deletion, and evolutionary diversification.

Oxidative polymerization of plant oils and lipids is poorly understood yet widely encountered. Oil oxidation is accelerated at high temperatures, typically above 110°C, where tri-acylglycerides are converted into toxic compounds and viscous deleterious polymers. Polymerization of mono-unsaturated oil (210°C, 3h, open to air) was investigated by comparing four similar sized molecules with different functional groups: oleic acid, methyl oleate, trans-7-tetradecene and stearic acid. Non-volatile products identified by NMR spectroscopy are minor ketones for saturated fatty acid (stearic acid), epoxides for acyl chains without acid groups (methyl oleate, tetradecane) and copious oligomerization, through ester cross-links, for acyl chains with acid and olefinic groups (oleic acid). Long range C-H coupling clearly shows ester (not ether) cross-links, contradicting long held beliefs. Chain fragmentation also occurs as revealed by species with methylene groups bonded to oxygen, -CH2-O-C(=O)-R. Large size (slow diffusion) of the first oligomer (trimer) formed by thermal oxidation of oleic acid, (representing hydrolyzed vegetable oil) was evidenced by DOSY (diffusion ordered spectroscopy). Since the first oligomers formed still have reactive groups (olefin, carboxylic acid), poly-ester formation is inevitable at longer oxidation times. Model oil reactions monitored by NMR spectroscopy are important for resolving the complex chemistry of vegetable oil polymerization.

There has been a compelling body of evidence of COVID-19 causing cardiac complications such as myocarditis and increased risk of coronary thrombosis. Acute coronary syndrome due to coronary vasospasm is an unusual occurrence.

The soil contact model (SCM) is widely used in practice for off-road wheeled vehicle mobility studies when simulation speed is important and highly accurate results are not a main concern. In practice, the SCM parameters are obtained via a bevameter test, which requires a complex apparatus and experimental procedure. Here, we advance the idea of running a virtual bevameter test using a high-fidelity terramechanics simulation. The latter employs the “continuous representation model” (CRM), which regards the deformable terrain as an elasto-plastic continuum that is spatially discretized using the smoothed particle hydrodynamics (SPH) method. The approach embraced is as follows: a virtual bevameter test is run in simulation using CRM terrain to generate “ground truth” data; in a Bayesian framework, this data is subsequently used to calibrate the SCM terrain. We show that (i) the resulting SCM terrain, while leading to fast terramechanics simulations, serves as a good proxy for the more complex CRM terrain; and (ii) the SCM-over-CRM simulation speedup is roughly one order of magnitude. These conclusions are reached in conjunction with two tests: a single wheel test, and a full rover simulation. The SCM and CRM simulations are run in an open-source software called Chrono. The calibration is performed using PyMC, which is a Python package that interactively communicates with Chrono to calibrate SCM. The models and scripts used in this contribution are available as open source for unfettered use and distribution in a public repository.

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has serious negative effects on health, social life, and economics. Recently, vaccines from various companies have been urgently approved to control SARS-CoV-2 infections. However, any specific antiviral drug has not been confirmed so far for regular treatment. An important target is the main protease (Mpro), which plays a major role in replication of the virus. In this study, Gaussian and residue network models are employed to reveal two distinct potential allosteric sites on Mpro that can be evaluated as drug targets besides the active site. Then, FDA-approved drugs are docked to three distinct sites with flexible docking using AutoDock Vina to identify potential drug candidates. 14 best molecule hits for the active site of Mpro are determined. 6 of these also exhibit high docking scores for the potential allosteric regions. Full-atom molecular dynamics simulations with MM-GBSA method indicate that compounds docked to active and potential allosteric sites form stable interactions with high binding free energy (∆Gbind) values. ∆Gbind values reach -52.06 kcal/mol for the active site, -51.08 kcal/mol for the potential allosteric site 1, and -42.93 kcal/mol for the potential allosteric site 2. Energy decomposition calculations per residue elucidate key binding residues stabilizing the ligands that can further serve to design pharmacophores. This systematic and efficient computational analysis successfully determines ivermectine, diosmin and selinexor currently subjected to clinical trials, and further proposes bromocriptine, elbasvir as Mpro inhibitor candidates to be evaluated against SARS-CoV-2 infection

Background: Peanut allergy is among the most severe and common food allergies. The diagnosis has a significant impact on the quality of life for patients and their families. An effective management approach depends on accurate, safe, and easily implementable diagnostic methods. We previously developed a cell-based assay using Hoxb8 mast cells (Hoxb8 MCs) aimed at improving clinical allergy diagnosis. In this study we assessed its diagnostic performance by measuring blinded sera from a prospectively enrolled and pre-validated peanut allergy cohort. Methods: Hoxb8 MCs were passively sensitized with sera from peanut-allergic and peanut tolerant children and adolescents (n=96). Degranulation of Hoxb8 MCs was quantified upon stimulation with dose-titrated peanut extract by means of flow cytometry, using CD107a as activation marker. The results from the Hoxb8 mast cell activation test (Hoxb8 MAT) were compared to established diagnostic assays such as the skin prick test (SPT), specific IgE (sIgE) levels, and the basophil activation test (BAT). Additionally, serum samples from BAT non-responders were assessed with the Hoxb8 MAT. Results: Hoxb8 MAT displayed a robust dose-dependent activation to peanut extract, with a cut-off value of ≤5.2% CD107a positive cells. The diagnostic accuracy was highest at allergen concentrations ≥100 ng/ml, with an area under the receiver operating characteristic curve (AUROC) of 0.97, 93% sensitivity, and 96% specificity, outperforming traditional SPT and sIgE tests. When compared to BAT, Hoxb8 MAT exhibited comparable diagnostic efficacy. However, sera from BAT non-responders were accurately classified into allergics and non-allergics by the Hoxb8 MAT. Conclusions: The Hoxb8 MAT demonstrated a very good diagnostic precision in patients prospectively assessed for peanut allergy comparable to the fresh blood based BAT. It also demonstrated its value for accurate classification of BAT non-responders into allergic and non-allergic individuals. Further investigations into its utility in the routine clinical setting are warranted.

Carbon quantum dots (C-QDs) show great potential to replace traditional semiconductive quantum dots as the next generation of fluorescent probes. We demonstrate here a new C-QD production process using lignin, a high-volume but low market-value industrial waste and/or environmental hazards, as the starting carbon source. By adding a small amount of inorganic acid, the rich phenolic components in lignin were successfully converted to C-QDs through a coking formation mechanism similar to what happens on solid acid catalysts in traditional fossil fuel cracking process. The aqueous solution presence of the received lignin C-QDs is beneficial for brain cell imaging applications, attributing to their fast internalization, low toxicity, tunable photoluminescence by appropriate acidity and reaction temperature during hydrothermal synthesis. This method not only provides a low-cost C-QDs production route, but also helps gain extra profit and/or improve environment for many small agricultural business and paper and pulp industry located in rural area.

Molecular simulation has emerged as an important sub-field of chemical engineering, due in no small part to the leadership of Keith Gubbins. A characteristic of the chemical engineering molecular simulation community is the commitment to freely share simulation codes and other key software components required to perform a molecular simulation under open-source licenses and distribution on public repositories such as GitHub. Here we provide an overview of open-source molecular modeling software in Chemical Engineering, with focus on the Molecular Simulation Design Framework (MoSDeF). MoSDeF is an open-source Python software stack that enables facile use of multiple open-source molecular simulation engines, while at the same time ensuring maximum reproducibility.

The COVID-19 outbreak is a global pandemic with rapid community spread. Patients with multi-morbidities are particularly vulnerable during this time. The number of cases soared in early February 2020, and Singapore declared escalation of the Disease Outbreak Response System Condition (DORSCON) level to Orange. Multiple measures have been taken to combat the spread of this highly contagious infection. Despite our medical manpower being diverted to the wards, our hospital aims to maintain nearly full operations at the clinic, balancing against concern about the spread of the virus and exposing healthcare workers to potential risks. We describe the measures taken in a tertiary hospital in Singapore to mitigate the risk of infection in the outpatient setting while ensuring that continuing clinical care of patients with chronic diseases is not compromised.

Aim: We hypothesize that the efficacy of COVID-19 therapeutic candidates will be better predicted by understanding their effects at various points on a viral cell cycle, in particular, the specific rate constants, and that drugs acting independently of these specific discrete sites may not yield expected efficacy. We hypothesize that drugs, or combinations of drugs that act at specific multiple sites on the viral life cycle have the highest probability of success in the treatment of early infection phase in COVID-19 patients. Methods: Using a target cell limited model structure that had been used to characterize viral load dynamics from COVID-19 patients, we performed simulations to show that combinations of therapeutics targeting specific rate constants have greater probability of efficacy and supportive rationale for clinical trial evaluation. Results: Based on the known kinetics of the SARS-CoV-2 life cycle, we rank ordered potential targeted approaches involving repurposed, low-potency agents. We suggest that targeting multiple points central to viral replication within infected host cells or release from those cells is a viable strategy for reducing both viral load and host cell infection. In addition, we observed that the time-window opportunity for a therapeutic intervention to effect duration of viral shedding exceeds the effect on sparing epithelial cells from infection or impact on viral load AUC. Furthermore, the impact on reduction on duration of shedding may extend further in patients who exhibit a prolonged shedder phenotype. Conclusions: Our work highlights the use of model-informed tools to better rationalize effective treatments for COVID-19.

Objective: To investigate hair cortisol concentrations (HCC) monthly in pregnant women and to explore the effect of parity. Design: Prospective cohort study from gestational week (GW) 26, at childbirth and postpartum. Setting: An antenatal care clinic in southeast Sweden. Sample: 390 pregnant women. Methods: Cortisol was measured using radioimmunoassay in methanol extracts of ground hair samples. Main outcome measures: Hair cortisol concentrations Results: Both primi- and multiparae exhibited an increase in HCC throughout pregnancy. Primiparae had significantly higher HCC in the latter part of the last trimester compared to multiparae (one month p=0.003 and two months p=0.038). The use of psychotropic medication in the first trimester correlated to HCC postpartum (p<0.001). HCC in GW 14-17 were associated with HCC in GWs 18-21 (primiparae and multiparae, p<0.001), GW 22-25 (primiparae p=0.036 and multiparae p=0.033), and two months postpartum (primiparae p=0.049). HCC in GW 18-21 was associated with GW 22-25 among both primiparae (p<0.001) and multiparae (p<0.001) as well as two months prior to childbirth among primiparae (<0.037). In general, all estimates of HCC in pregnancy and postpartum showed a significant association between HCC for a specific month and the HCC in the previous month (all p<0.001), except for the association of HCC among primiparae in GW 22-25 and three months prior to childbirth. Conclusions: Increased cortisol concentrations in hair were observed during pregnancy, which decreased three months prior to childbirth in multiparae. The results indicate a quicker suppression of the hypothalamic CRH production by placenta CRH in multiparous women.

A document by Shivendra Jha. Click on the document to view its contents.

Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5’/3’ splice sites, branch sites or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on machine learning approach, comprehensive assessment of variant effect on different splicing motifs. We gathered a curated set of 4,616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. Bayesian analysis provided us the number of control variants, i.e. variants without impact on splicing, to mimic the deluge of variants from high throughput sequencing data. Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, significantly better than 0.965 spliceAI for the same dataset. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: [https://sourceforge.net/projects/splicing-prediction-pipeline/](https://sourceforge.net/projects/splicing-prediction-pipeline/)

Neural network (NN) models have made a significant impact on fatigue-related engineering communities and are expected to increase rapidly soon due to the recent advancements in machine learning and artificial intelligence. A comprehensive review of fatigue modeling methods using NNs is lacking and will help to recognize past achievements and suggest future research directions. Thus, this paper presents a survey of 251 publications between 1990 and July 2021. The NN modeling in fatigue is classified into five applications: fatigue life prediction, fatigue crack, fatigue damage diagnosis, fatigue strength, and fatigue load. A wide range of NN architectures are employed in the literature and are summarized in this review. An overview of important considerations and current limitations for the application of NNs in fatigue is provided. Statistical analysis for the past and the current trend is provided with representative examples. Existing gaps and future research directions are also presented based on the reviewed articles.