pK prediction in non-aqueous solvents.

Acid dissociation constants ( ) are widely measured and studied, most typically in water. Comparatively few datasets and models for non-aqueous values exist. In this work, we demonstrate how the in one solvent can be accurately determined using reference data in another solvent, corrected by solvation energy calculations from the COSMO-RS method.

Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions.

Electrochemical C-H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet identifying suitable substrates and optimizing synthesis remain challenging. Here, we report an integrated approach combining machine learning and large language models to streamline the exploration of electrochemical C-H oxidation reactions. Utilizing a batch rapid screening electrochemical platform, we evaluated a wide range of reactions, initially classifying substrates by their reactivity, while LLMs text-mined literature data to augment the training set.

Widespread Misinterpretation of p Terminology for Zwitterionic Compounds and Its Consequences.

The acid dissociation constant (p), which quantifies the propensity for a solute to donate a proton to its solvent, is crucial for drug design and synthesis, environmental fate studies, chemical manufacturing, and many other fields. Unfortunately, the terminology used for describing acid-base phenomena is sometimes inconsistent, causing large potential for misinterpretation. In this work, we examine a systematic confusion underlying the definition of "acidic" and "basic" p values for zwitterionic compounds.

Uncertainties in evaluating the health-related quality of life and disease burden of people with rare diseases and their caregivers in NICE HST submissions.

Background And Aims: The NICE Highly Specialised Technology (HST) programme evaluates interventions for very rare conditions within the UK. This review aimed to analyse previous NICE HST appraisals and determine commonly used methods to overcome uncertainties relating to health-related quality of life (HRQoL) and disease burden for people with rare diseases and their caregivers. The review also aimed to identify areas where further methodological development is required.

Depletion of monocytic myeloid-derived suppressor cells in LP-BM5 murine retroviral infection has a positive impact on virus-induced host immunodeficiency.

We have shown the induction of CD11bLy6C monocytic myeloid-derived suppressor cells (M-MDSCs) during infection of B6 mice by LP-BM5 immunodeficiency-inducing retrovirus. We published that the molecular mechanisms of these M-MDSCs vary, and depend on the cell type targeted by the suppression -defined by use of biochemical inhibitors, mouse M-MDSCs knock-out strains and blocking antibodies. These M-MDSCs suppressed proliferation and function of T cells, via nitric oxide synthase/nitric oxide; and that of B cells, ∼50% via INOS/NO along with the negative checkpoint regulator VISTA, reactive nitrogen and oxygen species, and other soluble mediators.

Accurately Predicting Barrier Heights for Radical Reactions in Solution Using Deep Graph Networks.

Quantitative estimates of reaction barriers and solvent effects are essential for developing kinetic mechanisms and predicting reaction outcomes. Here, we create a new data set of 5,600 unique elementary radical reactions calculated using the M06-2X/def2-QZVP//B3LYP-D3(BJ)/def2-TZVP level of theory. A conformer search is done for each species using TPSS/def2-TZVP.

When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?

Deep graph neural networks are extensively utilized to predict chemical reactivity and molecular properties. However, because of the complexity of chemical space, such models often have difficulty extrapolating beyond the chemistry contained in the training set. Augmenting the model with quantum mechanical (QM) descriptors is anticipated to improve its generalizability.

In vitro activity of olorofim against 507 filamentous fungi including antifungal drug-resistant strains at a tertiary laboratory in Australia: 2020-2023.

Background: New antifungal agents are required to mitigate against azole-resistant Aspergillus and drug-resistant non-Aspergillus moulds. The novel orotomide, olorofim (F2G, Manchester, UK), has potent fungicidal activity against Aspergillus including azole-resistant Aspergillus fumigatus, Lomentospora prolificans and Scedosporium spp. Development of olorofim-specific clinical breakpoints/epidemiological cut-off values requires reliable MIC data.

Epitranscriptomic mechanisms of androgen signalling and prostate cancer.

Prostate cancer (PCa) is the second most common cancer diagnosed in men. While radical prostatectomy and radiotherapy are often successful in treating localised disease, post-treatment recurrence is common. As the androgen receptor (AR) and androgen hormones play an essential role in prostate carcinogenesis and progression, androgen deprivation therapy (ADT) is often used to deprive PCa cells of the pro-proliferative effect of androgens.

Virtual fit assessment of U.S. army body armor using NASA spacesuit techniques.

Fit and accommodation are critical design goals for a body armor system to maximize Soldiers' protection, comfort, mobility, and performance. The aim of this study is to assess fit and accommodation of body armor plates for the US Army. A virtual fit assessment technique, developed, validated, and deployed by NASA for spacesuit design, was adopted for this work.

10-Year Cumulative Incidence and Indications for Revision Total Joint Arthroplasty for Patients Who Have Ehlers-Danlos Syndrome.

Background: Long-term complications following total joint arthroplasty are not well established for patients who have Ehlers-Danlos syndrome (EDS), a group of connective tissue disorders. This study compared 10-year incidence of revision surgery after total hip arthroplasty (THA) and total knee arthroplasty (TKA) in patients who have and do not have EDS.

Methods: A retrospective cohort analysis was conducted using a national all-payer claims database from 2010 to 2021 to identify patients who underwent primary TKA or THA.

Health care resource utilization in Medicare beneficiaries obtaining medication synchronization.

Background: An appointment-based medication synchronization (ABMS) is a service whuch aligns patients' chronic medications to a predetermined routine pickup date and includes a comprehensive medication review or other clinical appointment at the pharmacy.

Objective(s): We compared healthcare utilization outcomes (outpatient, inpatient, emergency department visits, and pharmacy utilization) of Medicare beneficiaries enrolled in a med-sync program to beneficiaries not enrolled in such a program.

Methods: This retrospective cohort study included Medicare beneficiaries obtaining medications from pharmacies providing ABMS.

Toward Accurate Quantum Mechanical Thermochemistry: (1) Extensible Implementation and Comparison of Bond Additivity Corrections and Isodesmic Reactions.

Obtaining accurate enthalpies of formation of chemical species, Δ, often requires empirical corrections that connect the results of quantum mechanical (QM) calculations with the experimental enthalpies of elements in their standard state. One approach is to use atomization energy corrections followed by bond additivity corrections (BACs), such as those defined by Petersson et al. or Anantharaman and Melius.

High spatiotemporal resolution scintillation imaging of pulsed pencil beam scanning proton beams produced by a gantry-mounted synchrocyclotron.

Background: A dosimeter with high spatial and temporal resolution would be of significant interest for pencil beam scanning (PBS) proton beams' characterization, especially when facing small fields and beams with high temporal dynamics. Optical imaging of scintillators has potential in providing sub-millimeter spatial resolution with pulse-by-pulse basis temporal resolution when the imaging system is capable of operating in synchrony with the beam-producing accelerator.

Purpose: We demonstrate the feasibility of imaging PBS proton beams as they pass through a plastic scintillator detector to simultaneously obtain multiple beam parameters, including proton range, pencil beam's widths at different depths, spot's size, and spot's position on a pulse-by-pulse basis with sub-millimeter resolution.

A Web-Based Physical Activity Promotion Intervention for Inactive Parent-Child Dyads: Protocol for a Randomized Controlled Trial.

Background: Low levels of physical activity are associated with numerous adverse health outcomes, yet sedentary lifestyles are common among both children and adults. Physical activity levels tend to decline steeply among children aged between 8 and 12 years, even though children's behavioral patterns are largely governed by familial structures. Similarly, parents' activity levels have been generally reported as lower than those of nonparents of comparable age.

Researching big IT in the UK National Health Service: A systematic review of theory-based studies.

Objective: To identify and discuss theory-based studies of large-scale health information technology programs in the UK National Health Service.

Materials And Methods: Using the PRISMA systematic review framework, we searched Scopus, PubMed and CINAHL databases from inception to March 2022 for theory-based studies of large-scale health IT implementations. We undertook detailed full-text analyses of papers meeting our inclusion criteria.

Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates.

Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28 000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔ, ΔΔ) for a solution phase reaction.

Upregulated selenoprotein I during lipopolysaccharide-induced B cell activation promotes lipidomic changes and is required for effective differentiation into IgM-secreting plasma B cells.

The mechanisms driving metabolic reprogramming during B cell activation are unclear, particularly roles for enzymatic pathways involved in lipid remodeling. We found that murine B cell activation with lipopolysaccharide (LPS) led to a 1.6-fold increase in total lipids that included higher levels of phosphatidylethanolamine (PE) and plasmenyl PE.

Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back.

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds.

EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions.

Enzymatic reactions are an ecofriendly, selective, and versatile addition, sometimes even alternative to organic reactions for the synthesis of chemical compounds such as pharmaceuticals or fine chemicals. To identify suitable reactions, computational models to predict the activity of enzymes on non-native substrates, to perform retrosynthetic pathway searches, or to predict the outcomes of reactions including regio- and stereoselectivity are becoming increasingly important. However, current approaches are substantially hindered by the limited amount of available data, especially if balanced and atom mapped reactions are needed and if the models feature machine learning components.