Trajectory Analysis Research Articles

Study on the groove geometry of pad in water dissolution polishing of soft brittle materials based on trajectory analysis

During the water dissolution polishing process of soft brittle materials, the pad's groove geometry plays a significant role in determining the performance. This not only affects the flow of the slurry but also directly impacts the overall distribution of the water core on the pad surface, thereby influencing the polishing effect. A trajectory model of the water core on the workpiece surface was established through kinematic analysis and the characteristics and effects of the pad's groove geometry in circular, radial, composite, and grid were studied combined with the trajectory analysis. The coefficient of variation and power spectral density were established to evaluate the trajectory uniformity. The trajectory density distribution was quantitatively analyzed to obtain a better understanding of the material removal uniformity. The simulation and experiment results indicated that the existence of groove geometry will greatly affect the flow of slurry and the surface quality. The grid groove had a better distribution of water core, which means better material removal. There exists a tradeoff between the number of grooves and their spacing. These insights offer a fresh perspective and serve as a valuable reference for further research and analysis into the groove characteristics of pads.

Na Vacancy-Driven Phase Transformation and Fast Ion Conduction in W-Doped Na3SbS4 from Machine Learning Force Fields.

Solid-state sodium batteries require effective electrolytes that conduct at room temperature. The Na3PnCh4 (Pn = P, Sb; Ch = S, Se) family has been studied for their high Na ion conductivity. The population of Na vacancies, which mediate ion diffusion in these materials, can be enhanced through aliovalent doping on the pnictogen site. To probe the microscopic role of extrinsic doping and its impact on diffusion and phase stability, we trained a machine learning force field for Na3-x W x Sb1-x S4 based on an equivariant graph neural network. Analysis of large-scale molecular dynamics trajectories shows that an increased Na vacancy population stabilizes the global cubic phase at lower temperatures with enhanced Na ion diffusion and that the explicit role of the substitutional W dopants is limited. In the global cubic phase, we observe large and long-lived deviations of atoms from the averaged symmetry, echoing recent experimental suggestions. Evidence of correlated Na ion diffusion is also presented that underpins the suggested superionic nature of these materials.

Factors associated with loneliness, depression, and anxiety during the early stages of the COVID-19 pandemic.

The COVID-19 pandemic was an unparalleled stressor that enhanced isolation. Loneliness has been identified as an epidemic by the US Surgeon General. This study aimed to: (1) characterize longitudinal trajectories of loneliness during the acute phase of the COVID-19 pandemic; (2) identify longitudinal mediators of the relationship of loneliness with anxiety and depression; and (3) examine how loneliness naturally clusters and identify factors associated with high loneliness. Two hundred and twenty-nine adults (78% female; mean age=39.5±13.8) completed an abbreviated version of the UCLA Loneliness Scale, Perceived Stress Scale, Emotion Regulation Questionnaire, State Anxiety Inventory, and Patient Health Questionnaire-8 longitudinally between April 2020 and 2021. Trajectory analyses demonstrated relatively stable loneliness over time, while anxiety and depression symptoms declined. Longitudinal analyses indicated that loneliness effects on anxiety and depression were both partially mediated by perceived stress, while emotion regulation capacity only mediated effects on anxiety. Three stable clusters of loneliness trajectories emerged (high, moderate, and low). The odds of moderate or high loneliness cluster membership were positively associated with higher perceived stress and negatively associated with greater cognitive reappraisal use. Our results demonstrate the important interconnections between loneliness and facets of mental health throughout the early phases of the pandemic and may inform targeted future interventions for loneliness work.

Extreme low‐level wind jets in Storm Ciarán

AbstractSatellite imagery of Storm Ciarán as it approached the UK early on 1 November 2023 showed characteristic banding that fuelled media reports that Ciarán could contain a sting jet, with enhanced likelihood of damaging surface winds and gusts. Here operational numerical weather prediction model output is used with trajectory analysis to reveal the existence of at least two wind speed maxima attributed to sting jets. The small‐scale sting jets are located beneath, and likely affected by, far broader overrunning strong wind regions attributed to a deeply descending dry intrusion, thus highlighting the likely interaction of these airstreams at low levels.

Gene-level alignment of single-cell trajectories

AbstractSingle-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.

Immune and inflammatory insights in atherosclerosis: development of a risk prediction model through single-cell and bulk transcriptomic analyses.

Investigation into the immune heterogeneity linked with atherosclerosis remains understudied. This knowledge gap hinders the creation of a robust theoretical framework essential for devising personalized immunotherapies aimed at combating this disease. Single-cell RNA sequencing (scRNA-seq) analysis was employed to delineate the immune cell-type landscape within atherosclerotic plaques, followed by assessments of cell-cell interactions and phenotype characteristics using scRNA-seq datasets. Subsequently, pseudotime trajectory analysis was utilized to elucidate the heterogeneity in cell fate and differentiation among macrophages. Through integrated approaches, including single-cell sequencing, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, we identified hallmark genes. A risk score model and a corresponding nomogram were developed and validated using these genes, confirmed through Receiver Operating Characteristic (ROC) curve analysis. Additionally, enrichment and immune characteristic analyses were conducted based on the risk score model. The model's applicability was further corroborated by in vitro and in vivo validation of specific genes implicated in atherosclerosis. This comprehensive scRNA-seq analysis has shed new light on the intricate immune landscape and the role of macrophages in atherosclerotic plaques. The presence of diverse immune cell populations, with a particularly enriched macrophage population, was highlighted by the results. Macrophage heterogeneity was intricately characterized, revealing four distinct subtypes with varying functional attributes that underscore their complex roles in atherosclerotic pathology. Intercellular communication analysis revealed robust macrophage interactions with multiple cell types and detailed pathways differing between proximal adjacent and atherosclerotic core groups. Furthermore, pseudotime trajectories charted the developmental course of macrophage subpopulations, offering insights into their differentiation fates within the plaque microenvironment. The use of machine learning identified potential diagnostic markers, culminating in the identification of RNASE1 and CD14. The risk score model based on these biomarkers exhibited high accuracy in diagnosing atherosclerosis. Immune characteristic analysis validated the risk score model's efficacy in defining patient profiles, distinguishing high-risk individuals with pronounced immune cell activities. Finally, experimental validation affirmed RNASE1's involvement in atherosclerotic progression, suggesting its potential as a therapeutic target. Our findings have advanced our understanding of atherosclerosis immunopathology and paved the way for novel diagnostic and therapeutic strategies.

Optimization of MMX relative quasi-satellite transfer trajectories using primer vector theory

Quasi-satellite orbits (QSO) are stable retrograde parking orbits around Phobos that are currently being considered for JAXA’s upcoming robotic sample return mission Maritan Moons Exploration (MMX). During the proximity operations of MMX, the spacecraft inserted in a high altitude QSO will gradually descend to lower altitude QSOs with suitable transfer and station-keeping techniques between different relative QSOs. Preliminary analysis of two-impulsive planar transfers between relative retrograde orbits utilizing the bifurcated QSOs families is studied to estimate the ΔV costs and time of flights of the transfers. In this paper, differently from previous works, we utilize the initial guesses found through the preliminary results that provide two-impulsive transfer ΔV execution points and optimize the transfers between relative QSOs around Phobos. Primer vector theory is applied to investigate the primer vector of the MMX transfer trajectories to evaluate whether intermediate maneuver or initial/final coasting times along the trajectories can minimize the total ΔV cost between the transfers. Based on the primer vector analysis of the impulse transfer trajectories, it is found that departing and arriving at the same periphobian sides with an additional mid-course impulse results in the optimal impulse solution.

Landscape of epithelial cell subpopulations in the human esophageal squamous cell carcinoma microenvironment

AimsWe sought to reveal the landscape of epithelial cell subpopulations in the human esophageal squamous cell carcinoma microenvironment and investigate their parts on esophageal squamous carcinoma (ESCC) development. BackgroundEpithelial cells play an important role in the occurrence and development of ESCC through multiple mechanisms. While the landscape of epithelial cell subpopulations in ESCC, remains unclear. ObjectiveExploring the role of epithelial cell subpopulations in ESCC progression. MethodsSeurat R package was used for single-cell RNA sequencing (scRNA-seq) data filtering, dimensionality reduction, clustering and differentially expressed genes analysis. Cellmarker database was adopted for cell cluster annotation. Functional enrichment analysis was carried out by Gene Ontology (GO) analysis. InferCNV package was conducted for copy number variation (CNV) of epithelial cell subpopulations in all chromosomal regions. Pseudotime trajectory analysis was implemented for exploring differentiation trajectory of epithelial cells subgroups during the cancer progression. CellChat analysis was used for probing the interactions between epithelial cells and NK/T cells. cellular experiments were performed using Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR), Wound-Healing Assay and transwell. Results11 major cell subpopulations were identified in ESCC and adjunct tissues. Further reclassification of epithelial cells uncovered 4 subpopulations. Enrichment analysis revealed that highly expressed genes in 4 epithelial cell subpopulations were related to cell proliferation, immune response and angiogenesis. CNV analysis found that UBD + epithelial cells and GAS2L3+ epithelial cells had a higher proportion of CNV. Cell differentiation trajectories disclosed that KRT6C+ and GSTA1+ epithelial cells were in an intermediate state of differentiation, while UBD+ and GAS2L3+ epithelial cells are in an end state of differentiation during ESCC progression. Finally, we found that four epithelial cell subpopulations all inhibited NK/T cells through NECTIN2-TIGIT and CLEC2B-KLRB1. Low ATF3 and DDIT3 mRNA expression inhibited ESCC cell migration and invasion. ConclusionHere, we obtained a through epithelial cell atlas of ESCC at single-cell resolution, explored the role of epithelial cell in ESCC progression, and unveiled immunosuppressive signals to NK/T cells in promoting ESCC. Our findings expand the comprehension of epithelial cells and offer a theoretical guidance for future anti-epithelial cell treatment of ESCC.

Microplastics in glaciers of Tibetan Plateau: Characteristics and potential sources

Microplastics (MPs) in glaciers of remote areas are a hot topic linking the global transport of atmospheric MPs. The Tibetan Plateau (TP) holds large volume of glaciers, providing an effective way to trace MPs transport. Moreover, MPs in glaciers may have adverse effects on the local ecosystem and human health. In this study, we investigate MPs in snowpits collected from six glaciers across the different domain of the TP. The average abundance of MPs in six snowpits is 339.22 ± 51.85 items L−1 (with size ≥10 μm) measured by Agilent 8700 Laser Direct Infrared Chemical Imaging System (LDIR), represented by relatively high MPs abundance in the southern TP and low in the northern TP. The polymers with lower density, namely polyethylene (PE), polyamide (PA), and rubber, are the main MPs types, which are predominated by fragments with sizes smaller than 100 μm in each snowpit. Sources of MPs on glaciers include local tourism and vehicle traffic emissions of MPs. Meanwhile, long-range atmospheric transport of MPs from surrounded regions cannot be ignored. Backward trajectory analysis indicates cross-boundary transport of atmospheric MPs from South Asia play an important role on MPs deposited onto TP glaciers. Analysis further reveals that MPs in glaciers are associated with atmospheric mineral dust deposition. This study provides new data for the investigation of MPs in glaciers of remote areas, and a reference for studying MPs in the ice cores of TP glaciers.

Health expenditure trajectory and gastric cancer incidence in the National Health Insurance Senior Cohort: a nested case-control study

BackgroundGastric cancer is the fourth most common cancer and highly prevalent in South Korea. As one of the predictors of gastric cancer, we focused on health utilization patterns and expenditures, as the surrogate variables of health conditions. This nested case-control study aimed to identify the association between health expenditure trajectory and incidence of gastric cancer.MethodsData from the National Health Insurance Service Senior Cohort of South Korea were used. Individuals diagnosed with gastric cancer (N = 14,873) were matched to a non-diagnosed group (N = 44,619) in a 1:3 ratio using a nested case-control design. A latent class trajectory analysis was performed to identify the patterns of health expenditure among the matched participants. Furthermore, conditional logistic regression analysis was conducted to examine the relationship between healthcare expenditure trajectories and gastric cancer incidence.ResultsSeven distinct health expenditure trajectories for five years were identified; consistently lowest (13.8%), rapidly increasing (5.9%), gradually increasing (13.8%), consistently second-highest (21.4%), middle-low (18.8%), gradually decreasing (13.1%), and consistently highest (13.2%). Compared to the middle-low group, individuals in the rapidly increasing [odds ratio (OR) = 2.11, 95% confidence interval (CI); 1.94–2.30], consistently lowest (OR = 1.40, 95% CI; 1.30–1.51), and gradually increasing (OR = 1.26, 95% CI; 1.17–1.35) groups exhibited a higher risk of developing gastric cancer.ConclusionsOur findings suggest that health expenditure trajectories are predictors of gastric cancer. Potential risk groups can be identified by monitoring health expenditures.

Cell therapy using ex vivo reprogrammed macrophages enhances antitumor immune responses in melanoma

BackgroundMacrophage-based cell therapies have shown modest success in clinical trials, which can be attributed to their phenotypic plasticity, where transplanted macrophages get reprogrammed towards a pro-tumor phenotype. In most tumor types, including melanoma, the balance between antitumor M1-like and tumor-promoting M2-like macrophages is critical in defining the local immune response with a higher M1/M2 ratio favoring antitumor immunity. Therefore, designing novel strategies to increase the M1/M2 ratio in the TME has high clinical significance and benefits macrophage-based cell therapies.MethodsIn this study, we reprogrammed antitumor and proinflammatory macrophages ex-vivo with HDAC6 inhibitors (HDAC6i). We administered the reprogrammed macrophages intratumorally as an adoptive cell therapy (ACT) in the syngeneic SM1 murine melanoma model and patient-derived xenograft bearing NSG-SGM3 humanized mouse models. We phenotyped the tumor-infiltrated immune cells by flow cytometry and histological analysis of tumor sections for macrophage markers. We performed bulk RNA-seq profiling of murine bone marrow-derived macrophages treated with vehicle or HDAC6i and single-cell RNA-seq profiling of SM1 tumor-infiltrated immune cells to determine the effect of intratumor macrophage ACT on the tumor microenvironment (TME). We further analyzed the single-cell data to identify key cell-cell interactions and trajectory analysis to determine the fate of tumor-associated macrophages post-ACT.ResultsMacrophage ACT resulted in diminished tumor growth in both mouse models. We also demonstrated that HDAC6 inhibition in macrophages suppressed the polarization toward tumor-promoting phenotype by attenuating STAT3-mediated M2 reprogramming. Two weeks post-transplantation, ACT macrophages were viable, and inhibition of HDAC6 rendered intratumor transplanted M1 macrophages resistant to repolarization towards protumor M2 phenotype in-vivo. Further characterization of tumors by flow cytometry, single-cell transcriptomics, and single-cell secretome analyses revealed a significant enrichment of antitumor M1-like macrophages, resulting in increased M1/M2 ratio and infiltration of CD8 effector T-cells. Computational analysis of single-cell RNA-seq data for cell-cell interactions and trajectory analyses indicated activation of monocytes and T-cells in the TME.ConclusionsIn summary, for the first time, we demonstrated the potential of reprogramming macrophages ex-vivo with HDAC6 inhibitors as a viable macrophage cell therapy to treat solid tumors.

Single-cell RNA sequencing reveals the differentiation and regulation of endplate cells in human intervertebral disc degeneration

Low back pain (LBP) is largely attributed to intervertebral disc degeneration (IVDD), of which the endplate changes are an important component. However, the alterations in cell fate and properties within the endplates during degeneration remain unknown. Here, we firstly performed the single-cell RNA-sequencing analysis (scRNA-seq) of the cells focusing on degenerative human endplates. By unsupervised clustering of the 8,534 single-cell based on the gene expression, we identified nine distinct cell types. We employed Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, and the single-cell regulatory network inference and clustering (SCENIC) to determine the enriched pathways and transcriptional activities across seven chondrocyte subpopulations. Furthermore, two cell fates of chondrocyte differentiation were found by trajectory analysis, one was enriched in inflammation-related genes, and the other was related to extracellular matrix (ECM). Additionally, the intercellular interactions of macrophages (MA) and chondrocytes, T cells/natural killer cells (T/NK) and chondrocytes were examined by ligand-receptor pairs analysis, showing the important regulative function of FN1 from MA and CD74 from T/NK during endplate degeneration. Overall, our findings provide novel perspectives on the endplate degeneration at the single-cell level and a whole-transcriptome size.

Latent Trajectories of Cerebral Perfusion Pressure and Risk Prediction Models Among Patients with Traumatic Brain Injury: Based on an Interpretable Artificial Neural Network

This study aimed to characterize long-term cerebral perfusion pressure (CPP) trajectory in traumatic brain injury (TBI) patients and construct an interpretable prediction model to assess the risk of unfavorable CPP evolution patterns. TBI patients with CPP records were identified from the Medical Information Mart for the Intensive Care (MIMIC)-IV 2.1, eICU Collaborative Research Database (eICU-CRD) 2.0 and HiRID dataset 1.1.1. The research process consisted of two stages. First, group-based trajectory modeling (GBTM) was used to identify different CPP trajectories. Second, different ANN algorithms were employed to predict the trajectories of CPP. A total of 331 eligible patients' records from MIMIC-IV 2.1 and eICU-CRD 2.0 were used for trajectory analysis and model development. Additionally, 310 patients' data from HiRID were used for external validation. The GBTM identified 5 CPP trajectory groups, group 1 and group 5 were merged into class 1 based on unfavorable in-hospital mortality. The best 6 predictors were invasive systolic blood pressure coefficient of variation (ISBPCV), venous blood chloride ion concentration, PaCO2, PT (Prothrombin Time), CPP coefficient of variation (CPPCV), and mean CPP. Compared with other algorithms, Scaled Conjugate Gradient (SCG) performed relatively better in identifying class 1. This study identified 2 CPP trajectory groups associated with elevated risk and 3 with reduced risk. PaCO2 might be a strong predictor for the unfavorable CPP class. The ANN model achieved the primary goal of risk stratification, which is conducive to early intervention and individualized treatment.

Free Energy Analysis of Peptide-Induced Pore Formation in Lipid Membranes by Bridging Atomistic and Coarse-Grained Simulations.

Antimicrobial peptides (AMPs) are attractive materials for combating the antimicrobial resistance crisis because they can kill target microbes by directly disrupting cell membranes. Although thousands of AMPs have been discovered, their molecular mechanisms of action are still poorly understood. One broad mechanism for membrane disruption is the formation of membrane-spanning hydrophilic pores which can be stabilized by AMPs. In this study, we use molecular dynamics simulations to investigate the thermodynamics of pore formation in model single-component lipid membranes in the presence of one of three AMPs: aurein 1.2, melittin and magainin 2. To overcome the general challenge of modeling long time scale membrane-related behaviors, including AMP binding, clustering, and pore formation, we develop a generalizable methodology for sampling AMP-induced pore formation. This approach involves the long equilibration of peptides around a pore created with a nucleation collective variable by performing coarse-grained simulations, then backmapping equilibrated AMP-membrane configurations to all-atom resolution. We then perform all-atom simulations to resolve free energy profiles for pore formation while accurately modeling the interplay of lipid-peptide-solvent interactions that dictate pore formation free energies. Using this approach, we quantify free energy barriers for pore formation without direct biases on peptides or whole lipids, allowing us to investigate mechanisms of pore formation for these 3 AMPs that are a consequence of unbiased peptide diffusion and clustering. Further analysis of simulation trajectories then relates variations in pore lining by AMPs, AMP-induced lipid disruptions, and salt bridges between AMPs to the observed pore formation free energies and corresponding mechanisms. This methodology and mechanistic analysis have the potential to generalize beyond the AMPs in this study to improve our understanding of pore formation by AMPs and related antimicrobial materials.

Integrative genomics unveils basement membrane-related diagnostic markers and therapeutic targets in esophageal squamous cell carcinoma

BackgroundEsophageal squamous cell carcinoma (ESCC) is often diagnosed at advanced stages due to the inherent limitations of current screening methodologies. Central to evaluating tumor invasion and prognostic assessment in ESCC is the integrity of the basement membrane (BM). However, current research on the implications of BM-related genes (BMRGs) in diagnosing ESCC remains sparse.MethodsWe performed a comprehensive analysis using single-cell RNA-sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, alongside gene expression profiles acquired from GEO and The Cancer Genome Atlas (TCGA) databases. This identified differentially expressed BMRGs in ESCC. Employing LASSO, RF, and SVM-RFE, we selected potential BM biomarkers and crafted a diagnostic nomogram for ESCC, validated by ROC curves and AUC values. We also explored immune infiltration and biological mechanisms through consensus clustering and GSVA, and utilized single cell trajectory analysis and GSCALite to study gene distributions and pathways. In vitro experiments further elucidated the role of these genes in ESCC carcinogenesis.ResultsHere, we discovered that ESCC cell types exhibited markedly elevated BM-related scores. Our analysis pinpointed seven BM genes upregulated and linked to immune infiltration, showcasing unique gene expression profiles and varying immune cell densities across the BM-related subtypes. Furthermore, a robust positive correlation was observed between these genes expression and EMT activity. The knockdown of BGN significantly suppressed cell proliferation, migration, invasion, while also augmenting cell viability following chemotherapy drug treatment.ConclusionOur study identified seven key BMRGs (BGN, LAMB3, SPARC, MMP1, LUM, COL4A1, and NELL2) and established a diagnostic nomogram for ESCC. Of noteworthy significance is the discovery of BGN as a promising drug target, indicating a novel strategy for future clinical combination therapies in ESCC.

Germinal center B-cell subgroups in the tumor microenvironment cannot be overlooked: Their involvement in prognosis, immunotherapy response, and treatment resistance in head and neck squamous carcinoma

BackgroundMore than 60 % of patients with head and neck squamous carcinoma (HNSCC) are diagnosed at advanced stages and miss radical treatment. This has prompted the need to find new biomarkers to achieve early diagnosis and predict early recurrence and metastasis of tumors. MethodsSingle-cell RNA sequencing (scRNA-seq) data from HNSCC tissues and peripheral blood samples were obtained through the Gene Expression Omnibus (GEO) database (GSE164690) to characterize the B-cell subgroups, differentiation trajectories, and intercellular communication networks in HNSCC and to construct a prognostic model of the associated risks. In addition, this study analyzed the differences in clinical features, immune cell infiltration, functional enrichment, tumor mutational burden (TMB), and drug sensitivity between the high- and low-risk groups. ResultsUsing scRNA-seq of HNSCC, we classified B and plasma cells into a total of four subgroups: naive B cells (NBs), germinal center B cells (GCBs), memory B cells (MBs), and plasma cells (PCs). Pseudotemporal trajectory analysis revealed that NBs and GCBs were at the early stage of B cell differentiation, while MBs and PCs were at the end. Cellular communication revealed that GCBs acted on tumor cells through the CD99 and SEMA4 signaling pathways. The independent prognostic value, immune cell infiltration, TMB and drug sensitivity assays were validated for the MEF2B+ GCB score groups. ConclusionsWe identified GCBs as B cell-specific prognostic biomarkers for the first time. The MEF2B+ GCB score fills the research gap in the genetic prognostic prediction model of HNSCC and is expected to provide a theoretical basis for finding new therapeutic targets for HNSCC.

Understanding the stability and dynamics of influenza a H5N1 polymerase PB2 CAP-Binding domain in complex with natural compounds for antiviral drug discovery

Influenza A virus, particularly the H5N1 strain, poses a significant threat to public health due to its ability to cause severe respiratory illness and its high mortality rate. Traditional antiviral drugs targeting influenza A virus have faced challenges such as drug resistance and limited efficacy. Therefore, new antiviral compounds are needed to be discovered and developed. This study concentrated on examining the stability and behavior of the H5N1 polymerase PB2 CAP-binding domain when interacting with natural compounds, aiming to identify potential candidates for antiviral drug discovery. Through the virtual screening process, four lead compounds, ZINC000096095464, ZINC000044404209, ZINC000001562130, and ZINC000059779788, were selected, and these compounds showed binding energies −9.6, −9.4, −9.3, and −9.2 kcal/mol, respectively. When complexed with PB2, the ligand showed acceptable binding stability due to significant bond formation. However, during the 200ns MD simulation analysis, three (ZINC000096095464, ZINC000044404209, and ZINC000059779788) showed significant stability, which was proven by the trajectory analysis. The Rg-RMSD-based FEL plot showed significant structural stability due to stable conformers. The free-binding energy calculation also validates the stability of these complexes. This study offers valuable insights into the stability and dynamics of the H5N1 polymerase PB2 CAP-binding domain in complexes with natural compounds. These findings highlight the potential of these natural compounds as antiviral agents against the H5N1 influenza virus. Furthermore, this research contributes to the broader field of influenza virus treatment by demonstrating the effectiveness of computational methods in predicting and evaluating the stability and dynamics of potential drug candidates.

Does change in area-level deprivation, change health outcomes? A latent class growth analysis of population data.

BackgroundWhile the link between deprivation and health is established, little is known about the association of persistent deprivation or deprivation trajectories over time with adverse health outcomes. AimTo examine latent class trajectories of area level-deprivation from 2010-2016 and their association with social distress indicators from 2017-2021. MethodAnalysis was based on linked administrative data (N=1,569,110) from NI GP Registrations Index, emergency department records (2017-21), enhanced prescribing data (2017-21) and biennial area-level deprivation (2010-2016). Latent class growth analysis (LCGA) identified deprivation trajectories between 2010 and 2016; and regression-based models, adjusted for age, sex and urbanicity, examined their association with indicators of individual level distress: occurrence of death, any accident and emergency (A&E) visit, and any psychotropic medication between 2017 and 2021. ResultsBased on comparison of models and fit statistics, a non-linear LGCA model identified six trajectories: consistently deprived (21.2%); slight improvement (7.1%); substantial improvement (1.2%); consistently average deprivation (31.7%); slight worsening (18.5%); and consistently non-deprived (20.4%). Consistently living in a deprived area was associated with higher likelihood of death, A&E visits and receipt of hypnotics, anxiolytics, and anti-depressants. Compared to consistent deprivation, slight improvement was associated with lower likelihood of death (OR=0.94, CI: 0.91-0.97) and all psychotropic medication in the follow-up period. ImplicationsFindings indicate that even a slight improvement in deprivation status can have a positive influence on health outcomes. Further population-wide analysis of deprivation trajectories, their determinants and associated health impact will inform targeted interventions that promote social mobility and improved health.

Hepatic stellate cells promote hepatocellular carcinoma development by regulating histone lactylation: Novel insights from single-cell RNA sequencing and spatial transcriptomics analyses

This study evaluated the cellular heterogeneity and molecular mechanisms of hepatocellular carcinoma (HCC). Single cell RNA sequencing (scRNA-seq), transcriptomic data, histone lactylation-related genes were collected from public databases. Cell-cell interaction, trajectory, pathway, and spatial transcriptome analyses were executed. Differential expression and survival analyses were conducted. Western blot, Real-time reverse transcription PCR (qRT-PCR), and Cell Counting Kit 8 (CCK8) assay were used to detect the expression of αSMA, AKR1B10 and its target genes, and verify the roles of AKR1B10 in HCC cells. Hepatic stellate cell (HSC) subgroups strongly interacted with tumor cell subgroups, and their spatial distribution was heterogeneous. Two candidate prognostic genes (AKR1B10 and RMRP) were obtained. LONP1, NPIPB3, and ZSWIM6 were determined as AKR1B10 targets. Besides, the expression levels of AKR1B10 and αSMA were significantly increased in LX-2 + HepG2 and LX-2 + HuH7 groups compared to those in LX-2 group, respectively. sh-AKR1B10 significantly inhibited the HCC cell proliferation and change the expression of AKR1B10 target genes, Bcl-2, Bax, Pan Kla, and H3K18la at protein levels. Our findings unveil the pivotal role of HSCs in HCC pathogenesis through regulating histone lactylation.

Analysis of ballistic trajectories and its association with clinical outcomes in civilian penetrating brain injury.

Civilian penetrating brain injuries (PBI) caused by firearms are a medical emergency with high rates of morbidity and mortality. The aim of this study was to evaluate the association between trajectory vectors in CT brain angiography and clinical outcomes in patients with civilian gunshots. This is a retrospective analytical cross-sectional study that includes patients over 15 years of age with PBI due to firearms, admitted from January 2019 to December 2021 at a University Hospital in Cali, Colombia. A brain CT with angio-CT was performed the first day of admission. An XYZ coordinate system centered on the Turk's saddle was developed. Trajectories of projectiles were plotted and compared to a patient 0 in a 3D-Slicer software. A bivariate analysis of the clinical and geometric characteristics of the trajectory was performed. Primary outcomes include mortality and disability at 6 months. Twenty-eight patients with a mean age of 27.39 ± 11.66 years were included. The vectors of non-survivors show a trend, crossing at a specific area. This area was designated as a "potential lethal zone" and inside this area, injuries around 25.3mm from the circle of Willis, were associated with greater mortality (p < 0.005). In our study PBI avoiding the ventricular system, brain stem, dorsum sellae and the circle of Willis were associated with more survivability. A "potential lethal zone" was detected and associated with poor outcome after civilian PBI due to firearms. A better evaluation of the performance of this "potential lethal zone" in larger studies will be required.