Biodiversity and productivity in eastern US forests.

Despite experimental and observational studies demonstrating that biodiversity enhances primary productivity, the best metric for predicting productivity at broad geographic extents-functional trait diversity, phylogenetic diversity, or species richness-remains unknown. Using >1.8 million tree measurements from across eastern US forests, we quantified relationships among functional trait diversity, phylogenetic diversity, species richness, and productivity. Surprisingly, functional trait and phylogenetic diversity explained little variation in productivity that could not be explained by tree species richness. This result was consistent across the entire eastern United States, within ecoprovinces, and within data subsets that controlled for biomass or stand age. Metrics of functional trait and phylogenetic diversity that were independent of species richness were negatively correlated with productivity. This last result suggests that processes that determine species sorting and packing are likely important for the relationships between productivity and biodiversity. This result also demonstrates the potential confusion that can arise when interdependencies among different diversity metrics are ignored. Our findings show the value of species richness as a predictive tool and highlight gaps in knowledge about linkages between functional diversity and ecosystem functioning.

Link prediction using low-dimensional node embeddings: The measurement problem.

Graph representation learning is a fundamental technique for machine learning (ML) on complex networks. Given an input network, these methods represent the vertices by low-dimensional real-valued vectors. These vectors can be used for a multitude of downstream ML tasks. We study one of the most important such task, link prediction. Much of the recent literature on graph representation learning has shown remarkable success in link prediction. On closer investigation, we observe that the performance is measured by the AUC (area under the curve), which suffers biases. Since the ground truth in link prediction is sparse, we design a vertex-centric measure of performance, called the VCMPR@k plots. Under this measure, we show that link predictors using graph representations show poor scores. Despite having extremely high AUC scores, the predictors miss much of the ground truth. We identify a mathematical connection between this performance, the sparsity of the ground truth, and the low-dimensional geometry of the node embeddings. Under a formal theoretical framework, we prove that low-dimensional vectors cannot capture sparse ground truth using dot product similarities (the standard practice in the literature). Our results call into question existing results on link prediction and pose a significant scientific challenge for graph representation learning. The VCMPR plots identify specific scientific challenges for link prediction using low-dimensional node embeddings.

The legacy environmental footprints of manufactured capital.

The foundations of today's societies are provided by manufactured capital accumulation driven by investment decisions through time. Reconceiving how the manufactured assets are harnessed in the production-consumption system is at the heart of the paradigm shifts necessary for long-term sustainability. Our research integrates 50 years of economic and environmental data to provide the global legacy environmental footprint (LEF) and unveil the historical material extractions, greenhouse gas emissions, and health impacts accrued in today's manufactured capital. We show that between 1995 and 2019, global LEF growth outpaced GDP and population growth, and the current high level of national capital stocks has been heavily relying on global supply chains in metals. The LEF shows a larger or growing gap between developed economies (DEs) and less-developed economies (LDEs) while economic returns from global asset supply chains disproportionately flow to DEs, resulting in a double burden for LDEs. Our results show that ensuring best practice in asset production while prioritizing well-being outcomes is essential in addressing global inequalities and protecting the environment. Achieving this requires a paradigm shift in sustainability science and policy, as well as in green finance decision-making, to move beyond the focus on the resource use and emissions of daily operations of the assets and instead take into account the long-term environmental footprints of capital accumulation.

Experimental observation of curved light-cones in a quantum field simulator.

We investigate signal propagation in a quantum field simulator of the Klein-Gordon model realized by two strongly coupled parallel one-dimensional quasi-condensates. By measuring local phononic fields after a quench, we observe the propagation of correlations along sharp light-cone fronts. If the local atomic density is inhomogeneous, these propagation fronts are curved. For sharp edges, the propagation fronts are reflected at the system's boundaries. By extracting the space-dependent variation of the front velocity from the data, we find agreement with theoretical predictions based on curved geodesics of an inhomogeneous metric. This work extends the range of quantum simulations of nonequilibrium field dynamics in general space-time metrics.

A review of methods for predicting DNA N6-methyladenine sites.

Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.

MKMR: a multi-kernel machine regression model to predict health outcomes using human microbiome data.

Studies have found that human microbiome is associated with and predictive of human health and diseases. Many statistical methods developed for microbiome data focus on different distance metrics that can capture various information in microbiomes. Prediction models were also developed for microbiome data, including deep learning methods with convolutional neural networks that consider both taxa abundance profiles and taxonomic relationships among microbial taxa from a phylogenetic tree. Studies have also suggested that a health outcome could associate with multiple forms of microbiome profiles. In addition to the abundance of some taxa that are associated with a health outcome, the presence/absence of some taxa is also associated with and predictive of the same health outcome. Moreover, associated taxa may be close to each other on a phylogenetic tree or spread apart on a phylogenetic tree. No prediction models currently exist that use multiple forms of microbiome-outcome associations. To address this, we propose a multi-kernel machine regression (MKMR) method that is able to capture various types of microbiome signals when doing predictions. MKMR utilizes multiple forms of microbiome signals through multiple kernels being transformed from multiple distance metrics for microbiomes and learn an optimal conic combination of these kernels, with kernel weights helping us understand contributions of individual microbiome signal types. Simulation studies suggest a much-improved prediction performance over competing methods with mixture of microbiome signals. Real data applicants to predict multiple health outcomes using throat and gut microbiome data also suggest a better prediction of MKMR than that of competing methods.

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows.

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

The 2023 Report on the Proteome from the HUPO Human Proteome Project.

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

A novel risk-adjusted metric to compare hospitals on their antibiotic-prescribing at hospital discharge.

BACKGROUND: Antibiotic overuse at hospital discharge is common, but there is no metric to evaluate hospital performance at this transition of care. We built a risk-adjusted metric for comparing hospitals on their overall post-discharge antibiotic use. METHODS: This was a retrospective study across all acute-care admissions within the Veterans Health Administration during 2018-2021. For patients discharged to home, we collected data on antibiotics and relevant covariates. We built a zero-inflated negative binomial mixed-model with two random intercepts for each hospital to predict post-discharge antibiotic exposure and length of therapy (LOT). Data were split into training and testing sets to evaluate model performance using absolute error. Hospital performance was determined by the predicted random intercepts. RESULTS: 1,804,300 patient-admissions across 129 hospitals were included. Antibiotics were prescribed to 41.5% while hospitalized and 19.5% at discharge. Median LOT among those prescribed post-discharge antibiotics was 7 (IQR 4-10). The predictive model detected post-discharge antibiotic use with fidelity, including accurate identification of any exposure (area under the precision-recall curve=0.97) and reliable prediction of post-discharge LOT (mean absolute error = 1.48). Based on this model, 39 (30.2%) hospitals prescribed antibiotics less often than expected at discharge and used shorter LOT than expected. Twenty-eight (21.7%) hospitals prescribed antibiotics more often at discharge and used longer LOT. CONCLUSION: A model using electronically-available data was able to predict antibiotic use prescribed at hospital discharge and showed that some hospitals were more successful in reducing antibiotic overuse at this transition of care. This metric may help hospitals identify opportunities for improved antibiotic stewardship at discharge.

Diffusion tensor imaging metrics as natural markers of multiple sclerosis-induced brain disorders with a low Expanded Disability Status Scale score.

Non-invasive and effective differentiation along with determining the degree of deviations compared to the healthy cohort is important in the case of various brain disorders, including multiple sclerosis (MS). Evaluation of the effectiveness of diffusion tensor metrics (DTM) in 3T DTI for recording MS-related deviations was performed using a time-acceptable MRI protocol with unique comprehensive detection of systematic errors related to spatial heterogeneity of magnetic field gradients. In a clinical study, DTMs were acquired in segmented regions of interest (ROIs) for 50 randomly selected healthy controls (HC) and 50 multiple sclerosis patients. Identical phantom imaging was performed for each clinical measurement to estimate and remove the influence of systematic errors using the b-matrix spatial distribution in the DTI (BSD-DTI) technique. In the absence of statistically significant differences due to age in healthy volunteers and patients with multiple sclerosis, the existence of significant differences between groups was proven using DTM. Moreover, a statistically significant impact of spatial systematic errors occurs for all ROIs and DTMs in the phantom and for approximately 90 % in the HC and MS groups. In the case of a single patient measurement, this appears for all the examined ROIs and DTMs. The obtained DTMs effectively discriminate healthy volunteers from multiple sclerosis patients with a low mean score on the Expanded Disability Status Scale. The magnitude of the group differences is typically significant, with an effect size of approximately 0.5, and similar in both the standard approach and after elimination of systematic errors. Differences were also observed between metrics obtained using these two approaches. Despite a small alterations in mean DTMs values for groups and ROIs (1-3 %), these differences were characterized by a huge effect (effect size ∼0.8 or more). These findings indicate the importance of determining the spatial distribution of systematic errors specific to each MR scanner and DTI acquisition protocol in order to assess their impact on DTM in the ROIs examined. This is crucial to establish accurate DTM values for both individual patients and mean values for a healthy population as a reference. This approach allows for an initial reliable diagnosis based on DTI metrics.

Time is of the essence: A general framework for uncovering temporal structures of communities.

Ecological communities are inherently dynamic: species constantly turn over within years, months, weeks or even days. These temporal shifts in community composition determine essential aspects of species interactions and how energy, nutrients, information, diseases and perturbations 'flow' through systems. Yet, our understanding of community structure has relied heavily on static analyses not designed to capture critical features of this dynamic temporal dimension of communities. Here, we propose a conceptual and methodological framework for quantifying and analysing this temporal dimension. Conceptually, we split the temporal structure into two definitive features, sequence and duration, and review how they are linked to key concepts in ecology. We then outline how we can capture these definitive features using perspectives and tools from temporal graph theory. We demonstrate how we can easily integrate ongoing research on phenology into this framework and highlight what new opportunities arise from this approach to answer fundamental questions in community ecology. As climate change reshuffles ecological communities worldwide, quantifying the temporal organization of communities is imperative to resolve the fundamental processes that shape natural ecosystems and predict how these systems may change in the future.

Classification of Alzheimer's disease: application of a transfer learning deep Q-network method.

Early diagnosis is crucial to slowing the progression of Alzheimer's disease (AD), so it is urgent to find an effective diagnostic method for AD. This study intended to investigate whether the transfer learning approach of deep Q-network (DQN) could effectively distinguish AD patients using local metrics of resting-state functional magnetic resonance imaging (rs-fMRI) as features. This study included 1310 subjects from the Consortium for Reliability and Reproducibility (CoRR) and 50 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) GO/2. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF) and percent amplitude of fluctuation (PerAF) were extracted as features using the Power 264 atlas. Based on gender bias in AD, we searched for transferable similar parts between the CoRR feature matrix and the ADNI feature matrix, resulting in the CoRR similar feature matrix served as the source domain and the ADNI similar feature matrix served as the target domain. A DQN classifier was pre-trained in the source domain and transferred to the target domain. Finally, the transferred DQN classifier was used to classify AD and healthy controls (HC). A permutation test was performed. The DQN transfer learning achieved a classification accuracy of 86.66% (p < 0.01), recall of 83.33% and precision of 83.33%. The findings suggested that the transfer learning approach using DQN could be an effective way to distinguish AD from HC. It also revealed the potential value of local brain activity in AD clinical diagnosis.

The measure of impact: Proposal of quality metrics for solid organ transplant pharmacy practice.

As healthcare continues its transition toward value-based care, it is increasingly important for transplant pharmacists to demonstrate their impact on patient care, health-related outcomes, and healthcare costs. Evidence-based quality and performance metrics are recognized as crucial tools for measuring the value of service. Yet, there is a lack of well-developed and agreed-upon specific metrics for many clinical pharmacy specialties, including solid organ transplantation. To address this need, a panel of transplant pharmacy specialists conducted a detailed literature review and engaged in several panel discussions to identify quality metrics to be considered for assessing the value of clinical pharmacy services provided to solid organ transplant recipients and living donors. The proposed metrics are based on the Donabedian model and are categorized to coincide with the typical phases of transplant care. The measures focus on key issues that arise in transplant recipients related to medication therapy, including adverse drug events, nonadherence, and clinical outcomes attributable to medication therapy management. This article proposes a comprehensive set of measures, any number of which transplant pharmacists can adopt and measure over time to objectively gauge the value of services they are providing to transplant recipients, the transplant center, and the overall healthcare system.

Rejuvenating potato growth and yield in challenging semiarid and saline sandy Cholistan: harnessing PGPB-coated N and P application strategies.

BACKGROUND: Potato serves as a major non-cereal food crop and income source for small-scale growers in Punjab, Pakistan. Unfortunately, improper fertilization practices have led to low crop yields, worsened by challenging environmental conditions and poor groundwater quality in the Cholistan region. To address this, we conducted an experiment to assess the impact of two fertilizer application approaches on potato cv. Barna using plant growth-promoting bacteria (PGPB) coated biofertilizers. The first approach, termed conventional fertilizer application (CFA), involved four split applications of PGPB-coated fertilizers at a rate of 100:75 kg acre-1 (N and P). The second, modified fertilizer application (MFA), employed nine split applications at a rate of 80:40 kg acre-1. RESULTS: The MFA approach significantly improved various plant attributes compared to the CFA. This included increased plant height (28%), stem number (45%), leaf count (46%), leaf area index (36%), leaf thickness (three-folds), chlorophyll content (53%), quantum yield of photosystem II (45%), photosynthetically active radiations (56%), electrochromic shift (5.6%), proton flux (24.6%), proton conductivity (71%), linear electron flow (72%), photosynthetic rate (35%), water use efficiency (76%), and substomatal CO2 (two-folds), and lowered non-photochemical quenching (56%), non-regulatory energy dissipation (33%), transpiration rate (59%), and stomatal conductance (70%). Additionally, the MFA approach resulted in higher tuber production per plant (21%), average tuber weight (21.9%), tuber diameter (24.5%), total tuber yield (29.1%), marketable yield (22.7%), seed-grade yield (9%), specific gravity (9.6%), and soluble solids (7.1%). It also reduced undesirable factors like goli and downgrade yields by 57.6% and 98.8%, respectively. Furthermore, plants under the MFA approach exhibited enhanced nitrogen (27.8%) and phosphorus uptake (40.6%), with improved N (26.1%) and P uptake efficiency (43.7%) compared to the CFA approach. CONCLUSION: The use of PGPB-coated N and P fertilizers with a higher number of splits at a lower rate significantly boosts potato production in the alkaline sandy soils of Cholistan.

Exploring the mechanism of transformation in Acacia nilotica (Linn.) triggered by colchicine seed treatment.

BACKGROUND: Acacia nilotica Linn. is a widely distributed tree known for its applications in post-harvest and medicinal horticulture. However, its seed-based growth is relatively slow. Seed is a vital component for the propagation of A. nilotica due to its cost-effectiveness, genetic diversity, and ease of handling. Colchicine, commonly used for polyploidy induction in plants, may act as a pollutant at elevated levels. Its optimal concentration for Acacia nilotica's improved growth and development has not yet been determined, and the precise mechanism underlying this phenomenon has not been established. Therefore, this study investigated the impact of optimized colchicine (0.07%) seed treatment on A. nilotica's morphological, anatomical, physiological, fluorescent, and biochemical attributes under controlled conditions, comparing it with a control. RESULTS: Colchicine seed treatment significantly improved various plant attributes compared to control. This included increased shoot length (84.6%), root length (53.5%), shoot fresh weight (59.1%), root fresh weight (42.8%), shoot dry weight (51.5%), root dry weight (40%), fresh biomass (23.6%), stomatal size (35.9%), stomatal density (41.7%), stomatal index (51.2%), leaf thickness (11 times), leaf angle (2.4 times), photosynthetic rate (40%), water use efficiency (2.2 times), substomatal CO2 (36.6%), quantum yield of photosystem II (13.1%), proton flux (3.1 times), proton conductivity (2.3 times), linear electron flow (46.7%), enzymatic activities of catalase (25%), superoxide dismutase (33%), peroxidase (13.5%), and ascorbate peroxidase (28%), 2,2-diphenyl-1-picrylhydrazyl-radical scavenging activities(23%), total antioxidant capacity (59%), total phenolic (23%), and flavonoid content (37%) with less number of days to 80% germination (57.1%), transpiration rate (53.9%), stomatal conductance (67.1%), non-photochemical quenching (82.8%), non-regulatory energy dissipation (24.3%), and H2O2 (25%) and O-2 levels (30%). CONCLUSION: These findings elucidate the intricate mechanism behind the morphological, anatomical, physiological, fluorescent, and biochemical transformative effects of colchicine seed treatment on Acacia nilotica Linn. and offer valuable insights for quick production of A. nilotica's plants with modification and enhancement from seeds through an eco-friendly approach.

Screening mammographic performance by race and age in the National Mammography Database: 29,479,665 screening mammograms from 13,181,241 women.

PURPOSE: There are insufficient large-scale studies comparing the performance of screening mammography in women of different races. This study aims to compare the screening performance metrics across racial and age groups in the National Mammography Database (NMD). METHODS: All screening mammograms performed between January 1, 2008, and December 31, 2021, in women aged 30-100 years from 746 mammography facilities in 46 U.S. states in the NMD were included. Patients were stratified by 10-year age intervals and 5 racial groups (African American, American Indian, Asian, White, unknown). Incidence of risk factors (breast density, personal history, family history of breast cancer, age), and time since prior exams were compared. Five screening mammography metrics were calculated: recall rate (RR), cancer detection rate (CDR), positive predictive values for recalls (PPV1), biopsy recommended (PPV2) and biopsy performed (PPV3). RESULTS: 29,479,655 screening mammograms performed in 13,181,241 women between January 1, 2008, and December 31, 2021, from the NMD were analyzed. The overall mean performance metrics were RR 10.00% (95% CI 9.99-10.02), CDR 4.18/1000 (4.16-4.21), PPV1 4.18% (4.16-4.20), PPV2 25.84% (25.72-25.97), PPV3 25.78% (25.66-25.91). With advancing age, RR significantly decreases, while CDR, PPV1, PPV2, and PPV3 significantly increase. Incidence of personal/family history of breast cancer, breast density, age, prior mammogram availability, and time since prior mammogram were mostly similar across all races. Compared to White women, African American women had significantly higher RR, but lower CDR, PPV1, PPV2 and PPV3. CONCLUSIONS: Benefits of screening mammography increase with age, including for women age > 70 and across all races. Screening mammography is effective; with lower RR and higher CDR, PPV2, and PPV3 with advancing age. African American women have poorer outcomes from screening mammography (higher RR and lower CDR), compared to White and all women in the NMD. Racial disparity can be partly explained by higher rate of African American women lost to follow up.

Imaging the dynamics of murine uterine contractions in early pregnancy†.

Uterine muscle contractility is essential for reproductive processes including sperm and embryo transport, and during the uterine cycle to remove menstrual effluent. Even still, uterine contractions have primarily been studied in the context of preterm labor. This is partly due to a lack of methods for studying the uterine muscle contractility in the intact organ. Here, we describe an imaging-based method to evaluate mouse uterine contractility of both the longitudinal and circular muscles in the cycling stages and in early pregnancy. By transforming the image-based data into three-dimensional spatiotemporal contractility maps, we calculate waveform characteristics of muscle contractions, including amplitude, frequency, wavelength, and velocity. We report that the native organ is highly contractile during the progesterone-dominant diestrus stage of the cycle when compared to the estrogen-dominant proestrus and estrus stages. We also observed that during the first phase of uterine embryo movement when clustered embryos move toward the middle of the uterine horn, contractions are dynamic and non-uniform between different segments of the uterine horn. In the second phase of embryo movement, contractions are more uniform and rhythmic throughout the uterine horn. Finally, in Lpar3-/- uteri, which display faster embryo movement, we observe global and regional increases in contractility. Our method provides a means to understand the wave characteristics of uterine smooth muscle in response to modulators and in genetic mutants. Better understanding uterine contractility in the early pregnancy stages is critical for the advancement of artificial reproductive technologies and a possibility of modulating embryo movement during clinical embryo transfers.

IgIDivA: immunoglobulin intraclonal diversification analysis.

Intraclonal diversification (ID) within the immunoglobulin (IG) genes expressed by B cell clones arises due to ongoing somatic hypermutation (SHM) in a context of continuous interactions with antigen(s). Defining the nature and order of appearance of SHMs in the IG genes can assist in improved understanding of the ID process, shedding light into the ontogeny and evolution of B cell clones in health and disease. Such endeavor is empowered thanks to the introduction of high-throughput sequencing in the study of IG gene repertoires. However, few existing tools allow the identification, quantification and characterization of SHMs related to ID, all of which have limitations in their analysis, highlighting the need for developing a purpose-built tool for the comprehensive analysis of the ID process. In this work, we present the immunoglobulin intraclonal diversification analysis (IgIDivA) tool, a novel methodology for the in-depth qualitative and quantitative analysis of the ID process from high-throughput sequencing data. IgIDivA identifies and characterizes SHMs that occur within the variable domain of the rearranged IG genes and studies in detail the connections between identified SHMs, establishing mutational pathways. Moreover, it combines established and new graph-based metrics for the objective determination of ID level, combined with statistical analysis for the comparison of ID level features for different groups of samples. Of importance, IgIDivA also provides detailed visualizations of ID through the generation of purpose-built graph networks. Beyond the method design, IgIDivA has been also implemented as an R Shiny web application. IgIDivA is freely available at https://bio.tools/igidiva.

Evaluation of greenness and analytical performances of separative methods for chiral separation of novel lactam-based P2RX7-antagonists.

In this work, a preparative supercritical fluid chromatography (SFC) method was first developed to separate a series of chiral compounds evaluated as lactam-based P2RX7 antagonists. Subsequently, high-performance liquid chromatography, SFC, and capillary electrophoresis (CE) were comparatively investigated as QC tools to determine the enantiomeric purity of the separated isomers, including analytical performance and greenness. The screening of the best conditions was carried out in liquid and SFC on the nine derivatives and the amylose tris(3,5-dimethylphenylcarbamate)-based chiral stationary phase was found to be highly efficient. The same screening was carried out in CE and very different conditions, either in acidic or basic background electrolyte and different cyclodextrins used as chiral selectors, allowed the separation of six of the nine derivatives. 1-((3,4-Dichlorophenyl)carbamoyl)-5-oxopyrrolidine-2-carboxylic acid (compound 1) was chosen as a probe, and its semi-preparative separation by SFC and enantiomeric verification using the three techniques are presented. Its limit of detection and limit of quantification are calculated for each method. Finally, the greenness of each quality control method was evaluated.

Capillary electrokinetic chromatography for chiral separation of potential SARS-CoV-2 3CL protease inhibitors.

In this work, a capillary electrophoresis method was developed as a quality control tool to determine the enantiomeric purity of a series of five chiral compounds evaluated as potential severe acute respiratory syndrome coronavirus 2 3CL protease inhibitors. The first cyclodextrin tested, that is, highly sulfated-ß-cyclodextrin, at 6% (m/v) in a 25 mM phosphate buffer, using a capillary dynamically coated with polyethylene oxide, at an applied voltage of 15 kV and a temperature of 25°C, was found to successfully separate the five derivatives. The limits of detection and quantification were calculated together with the greenness score of the method in order to evaluate the method in terms of analytical and environmental performance. In addition, it is noteworthy that simultaneously high-performance liquid chromatography separation of the enantiomers of the same compounds with two different columns, the amylose tris(3,5-dimethylphenylcarbamate)-coated and the cellulose tris(3,5-dichlorophenylcarbamate)-immobilized on silica stationary phases, was studied. Neither the former stationary phase nor the latter was able to separate all derivatives in a mobile phase consisting of n-heptane/propan-2-ol 80/20 (v/v).