Clustering Of Profiles Research Articles

Single-cell sequencing reveals cellular heterogeneity of nucleus pulposus in intervertebral disc degeneration

The nucleus pulposus (NP) plays a vital role in intervertebral disc degeneration (IVDD). Previous studies have revealed cellular heterogeneity in NP tissue during IVDD progression. However, the cellular and molecular alterations of diverse cell clusters during IVDD remain to be fully elucidated. NP tissues were isolated from patients with different grades of IVDD undergoing discectomy, and then subjected to single-cell RNA sequencing (scRNA-seq). Cell subsets were identified based on unbiased clustering of gene expression profiles. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to determine the molecular features of diverse cell clusters. Monocle analysis was used to illustrate the differentiation trajectories of chondrocytes. Additionally, CellPhoneDB analysis revealed potential interactions between chondrocytes and other cells during IVDD. Based on the expression profiles of 47,610 individual cells, eight putative clusters including chondrocytes, endothelial cells, fibroblasts, macrophages, mural cells, osteoclasts, proliferating stromal cells and T cells were identified. The chondrocyte cluster was classified into three subsets, C1-C3, which were associated with stress-resistance, fibrosis and inflammatory responses, respectively. Pseudo-time trajectories suggested that chondrocytes gradually differentiated into fibroblasts during IVDD. Immune cells including cDC2s, macrophages and monocytes were identified. Further analysis showed that chondrocytes might communicate with immune cells via the MIF, TNFSF9, SPP1 and CCL4L2 signaling pathways. In addition, we found that invading endothelial cells might interact with chondrocytes through the COL4A1, CXCL12, VEGFA and SEMA3E signaling pathways. Our results reveal the cellular complexity and phenotypic characteristics of NP tissues at single-cell resolution, which will contribute to the in-depth investigation of preventative and regenerative strategies for IVDD.

Unveiling the shape: A multi-wavelength analysis of the galaxy clusters Abell 76 and Abell 1307

We analyse the dynamical state of the galaxy clusters Abell 76 and Abell 1307 from the optical point of view, presenting a coherent scenario that responds to the X-ray emissions observed in these structures. Our study is based on 231 and 164 spectroscopic redshifts, for the clusters A76 and A1307, respectively, obtained mostly with the Telescopio Nazionale Galileo, and complemented with others collected from the SDSS DR16 spectroscopic database and the literature. We find that A76 and A1307 are two galaxy clusters at z = 0.0390 and 0.0815, respectively, with a velocity dispersion of 650 ± 56 km s−1 and 863 ± 85 km s−1, and they show velocity distributions following, in practice, Gaussian profiles. From our dynamical analysis, X-ray studies and SZ-Planck emission, we obtain a mean total mass M500 = 1.7 ± 0.6 ⋅ 1014 M⊙ and 3.5 ± 1.3 ⋅ 1014 M⊙ for A76 and A1307, respectively. Using the SDSS DR16 photometric database, we find that the spatial distribution of likely cluster members in the case of A76 is very anisotropic, while A1307 shows a compact distribution of galaxies, but it is double peaked and elongated in the south-north direction. Using XMM-Newton X-ray data, we compared the surface brightness maps with galaxy distributions and noticed that both distributions are correlated. We reconstructed the total mass profile and velocity anisotropy of both clusters by analysing the full projected phase space, through the MG-MAMPOSSt code. Our study reveals a slight indication of radial orbits for A76, while A1307 seems to prefer more isotropic orbits in the whole cluster range. In summary, A76 represents a typical young cluster, in an early stage of formation, with a very low X-ray surface brightness but a high temperature showing a very anisotropic galaxy distribution. A1307 is however more consolidated and massive showing in-homogeneous galaxy distribution and an asymmetric X-ray emission, which suggest a scenario characterised by recent minor mergers.

Spatial Transcriptomic Signatures of Early Acute T Cell-mediated Rejection in Kidney Transplants.

Kidney transplantation significantly improves the quality of life for those with end-stage renal failure, yet allograft rejection resulting from immune cell interactions remains a persistent challenge. Although T cell-directed immunosuppressive drugs effectively contain graft rejection in most patients, a notable proportion still experiences acute T cell-mediated rejection (TCMR). Despite an emphasis on suppressing T cell-mediated immune responses, successful control over TCMR is not always achieved, suggesting the potential involvement of factors beyond T cells. Biopsy samples from suspicious (borderline) for acute TCMR (borderline TCMR) and non-TCMR patients were obtained 9 d postsurgery, and spatial transcriptomics profiling was conducted using the GeoMx Digital Spatial Profiler platform. Regions of interest in the glomerulus and interstitium were selected on the basis of immunohistochemistry staining anti-CD3 to identify areas with T-lymphocyte infiltration. Differential gene expression analysis was performed using unpaired t tests. Unbiased clustering of transcriptional profiles across all regions of interest showed distinct transcriptional profiles between glomeruli and interstitium in non-TCMR samples, whereas borderline TCMR samples displayed no distinct transcriptional profiles between these regions. Contrary to the prevailing T cell-centric view, we observed pathways and genes associated with innate immunity-related inflammatory conditions expressed in glomerular regions of borderline TCMR biopsies. Immunofluorescence staining for CD68 confirmed the presence of macrophages in the glomeruli of the post-TCMR sample in a validation cohort, indicating macrophage involvement in the glomerular response after TCMR. Activation of the innate immune response in borderline TCMR appears to impact not only the interstitium but also the glomerulus. Glomerulus-specific immune signatures suggest the role of the innate immune system in rejection. This nuanced understanding proposes the necessity for tailored therapeutic interventions targeting both innate and adaptive immune pathways to enhance transplant outcomes.

Emotional Intelligence Profiles among Lesbians, Gays and Bisexuals (LGB) People: Their Influence on Mental Health, Life Satisfaction, Self-esteem and the Development of Sexual Identity

Abstract Introduction Many studies have focused recently on the negative impact of homophobia or biphobia, seeking to identify protective factors for the LGB (lesbian, gay, and bisexual) community. Methods The present study aims to identify emotional intelligence (EI) profiles among a sample of 1215 LGB people in order to determine whether significant differences exist between them in terms of mental health, life satisfaction, self-esteem, and the development of sexual identity. To analyse the differential effect of EI on the variables, four cluster profiles were defined by combining the three dimensions of EI (attention, clarity, and repair). The first profile had low levels in all three dimensions; the second had high attention and low repair; the third had high general EI and the fourth had low attention and high repair. Results Significant differences were observed among profiles (p < .001; η2 = 0.126). Those with high EI indexes reported less anxiety and depression and had better levels of self-esteem, life satisfaction and acceptance of their non-normative sexual identity. Those with low rates in all areas of EI or high levels of attention were at risk of developing psycho-emotional problems. Discussion We can conclude that different EI profiles exist among LGB community and that these profiles influence mental health, life satisfaction and self-esteem outcomes and identity development in different ways. Policy Implications The findings reported here have practical policy implications for psychoeducational proposals and interventions aimed at improving the experiences of LGB people, particularly in terms of promoting individual characteristics that can help them cope with systematic oppression, such as emotion repair.

Machine learning identification of NK cell immune characteristics in hepatocellular carcinoma based on single-cell sequencing and bulk RNA sequencing.

Hepatocellular carcinoma (HCC) is a highly malignant tumor; however, its immune microenvironment and mechanisms remain elusive. Single-cell sequencing allows for the exploration of immune characteristics within tumor at the cellular level. However, current knowledge regarding the roles of different immune cell populations in liver cancer progression is limited. The main objective of this study is to identify molecular markers with NK cell immune characteristics in hepatocellular carcinoma using various machine learning methods based on Single-Cell Sequencing and Bulk RNA Sequencing. We collected samples from eight normal liver tissues and eight HCC tumor tissues and performed single-cell RNA sequencing for immune cell clustering and expression profile analysis. Using various bioinformatic approaches, we investigated the immune phenotype associated with natural killer (NK) cells expressing high CD7 level. In addition, we verified the role of CD7 in the growth of HCC after NK cell and HCC cells cocultured by RT-qPCR, MTS and Flow cytometer experiments. Finally, we constructed a machine learning model to develop a prognostic prediction system for HCC based on NK cell-related genes. Through single-cell typing, we found that the proportions of hepatocytes and NK cells were significantly elevated in the tumor samples. Moreover, we found that the expression of CD7 was high in HCC and correlated with prognosis. More importantly, Overexpression of CD7 in NK cells significantly inhibited the activity of MHCC97 cells and increased the number of apoptosis of HCC cells (p < 0.05). Furthermore, we observed that NK cells with high CD7 expression were associated with an activated immune phenotype. Our study found that CD7 is an important biomarker for assessing immune status and predicting survival of HCC patients; hence, it is a potential target for immune therapy against HCC.

Profiling consumers for their shopping motivations in modern retail formats in Oman

PurposeConsumer shopping motivations are evolving in tandem with shifts in products, services and the retail landscape. This paper primarily aims to explore what drives shopping behavior among consumers in Oman. Additionally, the paper aims to profile consumers based on their shopping motivations.Design/methodology/approachBased upon the review of literature, we developed a list of 63 items of shopping motivations. Exploratory factor analysis later led to a 12-factor solution involving 50 items, which were refined further into an 11-factor solution involving 44 items using CFA. A combination of hierarchical and k-means cluster analysis was performed for profiling consumers.FindingsFive distinct segments of consumers that emerged in the Omani food and grocery retail market were derived as Practical Value Seekers, Discerning Shoppers, Selective Online Shoppers, Social Brand Seekers and Instant Gratifiers. Theoretical and managerial implications were discussed.Practical implicationsIn theory, this study examines market heterogeneity by considering customer shopping motivations and applies profiling (clustering) techniques to categorize them into distinct market clusters. In practical terms, it provides valuable insights to food and grocery store managers.Originality/valueThis study is novel and the first of its kind, suggesting a typology of food and grocery shoppers in Oman. Research on consumer shopping motivations has primarily focused on consumers in Western countries, particularly in the United States.

Effects of meteorology on bike-sharing: Cases of 13 cities using non-linear analyses

Bike-sharing system (BSS) provides bicycle rental services for short-term use. The association of BSS with meteorological factors has not been studied sufficiently using nonlinear analysis. To bridge these gaps, this study explored the impact of meteorological conditions on bike-sharing system (BSS) usage in 13 cities in Europe and North America, using a combination of nonlinear analyses to avoid the limitations of linear analyses used traditionally. Specifically, self-organizing map (SOM) and decision tree analyses were used. The SOM results revealed six meteorological profiles (clusters). Kruskal–Wallis test was applied to determine the main effect of the cluster on the input variables. The decision tree indicated temperature (34.4 %), followed by clusters (23.3 %), and cities (17.5 %) as the most influential factors on BSS usage. These findings demonstrated the need to generate nonlinear analyses to understand the dynamic interaction of meteorological variables and BSS usage, providing insights for developing sustainable urban transportation policies.

Clustering Based on Laboratory Data in Patients With Heart Failure Admitted to the Intensive Care Unit.

Heart failure (HF) is a common condition that imposes a significant burden on healthcare systems. We aimed to identify subgroups of patients with heart failure admitted to the ICU using routinely measured laboratory biomarkers. A large dataset (N = 1176) of patients with heart failure admitted to the ICU at the Beth Israel Deaconess Medical Center in Boston, USA, between June 1, 2001, and October 31, 2012, was analyzed. We clustered patients to identify laboratory phenotypes. Cluster profiling was then performed to characterize each cluster, using a binary logistic model. Two distinct clusters of patients were identified (N = 679 and 497). There was a significant difference in the mortality rate between Clusters 1 and 2 (50 [7.4%] vs. 109 [21.9%], respectively, p < 0.001). Patients in the Cluster 2 were significantly older (mean [SD] age = 72.35 [14.40] and 76.37 [11.61] years, p < 0.001) with a higher percentage of chronic kidney disease (167 [24.6%] vs. 262 [52.7%], respectively, p < 0.001). The logistic model was significant (Log-likelihood ratio p < 0.001, pseudo R2 = 0.746) with an area under the curve of 0.905. The odds ratio for leucocyte count, mean corpuscular volume (MCV), red blood cell (RBC) distribution width, hematocrit (HcT), lactic acid, blood urea nitrogen (BUN), serum potassium, magnesium, and sodium were significant (all p < 0.05). Laboratory data revealed two phenotypes of ICU-admitted patients with heart failure. The two phenotypes are of prognostic importance in terms of mortality rate. They can be differentiated using blood cell count, kidney function status, and serum electrolyte concentrations.

Comprehensive transcriptomic analysis identifies three distinct subtypes of pituitary adenomas: insights into tumor behavior, prognosis, and stem cell characteristics

BackgroundPituitary adenomas (PAs) are the second most common intracranial tumor. While current diagnostic practices rely primarily on histological testing, they often fail to capture the molecular complexities of pituitary adenomas, underscoring the need for a molecular-based classification to refine therapeutic strategies and prognostic assessments. This study aims to provide a molecularly unbiased classification of pituitary adenomas and explore their unique gene expression patterns and clinical features.MethodsWe performed unsupervised hierarchical clustering of the gene expression profiles of 117 PA samples to identify three distinct molecular subtypes. Subsequently, we analyzed the compiled transcriptomic profiles of each individual subtype for pathway enrichment. We also validated the new classification with a validation set containing 158 PAs and 24 pituitary adenoma stem cells (PASCs).ResultsConsensus clustering of transcriptomic data from 117 pituitary adenoma (PA) samples identified three distinct molecular subtypes, each showing unique gene expression patterns and associated biological processes: Group I is enriched in signaling pathways, such as the cAMP signaling pathway and the calcium signaling pathway. Group II is primarily related to metabolic processes, including nitrogen metabolism and arginine biosynthesis in cancer. Group III predominantly shows enrichment in immune responses and potential malignant transformation of the disease, especially through cancer-related pathways such as the JAK–STAT signaling pathway and the PI3K–Akt signaling pathway. The immune profiling revealed distinct patterns for each subtype: Group I had higher dendritic cells and fewer CD8+ T cells, Group II had more monocytes and macrophages, and Group III had elevated levels of T cells. Additionally, there were differences in clinical characteristics and prognosis among the subtypes, with Group III having a worse prognosis, despite the smaller tumor size compared to other groups. Notably, differences in PASCs correlated with the molecular subtypes, with Group III stem cells being enriched in tumorigenesis pathways, PI3K–Akt signaling pathway and Ras signaling pathway.ConclusionOur study introduces a novel molecular classification for pituitary adenomas, independent of traditional histological methods. Each subtype features distinct genetic, molecular, and immunological profiles. We have isolated pituitary adenoma stem-like cells (PASCs), pairing them with tumor tissues for detailed transcriptomic analysis. These PASCs exhibit diverse molecular traits consistent with the new classification.

Mechanisms of Berberine in anti-pancreatic ductal adenocarcinoma revealed by integrated multi-omics profiling

This study integrates pharmacology databases with bulk RNA-seq and scRNA-seq to reveal the latent anti-PDAC capacities of BBR. Target genes of BBR were sifted through TargetNet, CTD, SwissTargetPrediction, and Binding Database. Based on the GSE183795 dataset, DEG analysis, GSEA, and WGCNA were sequentially run to build a disease network. Through sub-network filtration acquired PDAC-related hub genes. A PPI network was established using the shared genes. Degree algorithm from cytoHubba screened the key cluster in the network. Analysis of differential mRNA expression and ROC curves gauged the diagnostic performance of clustered genes. CYBERSORT uncovered the potential role of the key cluster on PDAC immunomodulation. ScRNA-seq analysis evaluated the distribution and expression profile of the key cluster at the single-cell level, assessing enrichment within annotated cell subpopulations to delineate the target distribution of BBR in PDAC. We identified 425 drug target genes and 771 disease target genes, using 57 intersecting genes to construct the PPI network. CytoHubba anchored the top 10 highest contributing genes to be the key cluster. mRNA expression levels and ROC curves confirmed that these genes showed good robustness for PDAC. CYBERSORT revealed that the key cluster influenced immune pathways predominantly associated with Macrophages M0, CD8 T cells, and naïve B cells. ScRNA-seq analysis clarified that BBR mainly acted on epithelial cells and macrophages in PDAC tissues. BBR potentially targets CDK1, CCNB1, CTNNB1, CDK2, TOP2A, MCM2, RUNX2, MYC, PLK1, and AURKA to exert therapeutic effects on PDAC. The mechanisms of action appear to significantly involve macrophage polarization-related immunological responses.

Frontoparietal atrophy trajectories in cognitively unimpaired elderly individuals using longitudinal Bayesian clustering

IntroductionFrontal and/or parietal atrophy has been reported during aging. To disentangle the heterogeneity previously observed, this study aimed to uncover different clusters of grey matter profiles and trajectories within cognitively unimpaired individuals. MethodsStructural magnetic resonance imaging (MRI) data of 307 Aβ-negative cognitively unimpaired individuals were modelled between ages 60–85 from three cohorts worldwide. We applied unsupervised clustering using a novel longitudinal Bayesian approach and characterized the clusters' cerebrovascular and cognitive profiles. ResultsFour clusters were identified with different grey matter profiles and atrophy trajectories. Differences were mainly observed in frontal and parietal brain regions. These distinct frontoparietal grey matter profiles and longitudinal trajectories were differently associated with cerebrovascular burden and cognitive decline. DiscussionOur findings suggest a conciliation of the frontal and parietal theories of aging, uncovering coexisting frontoparietal GM patterns. This could have important future implications for better stratification and identification of at-risk individuals.

Optimization of field asymmetric ion mobility spectrometry-based assessment of Aphanomyces root rot in pea

When plants are infected with pathogens, disease response can result in changes in the profiles of volatile organic compounds (VOC). These changes in volatile organic compounds (VOC) profiles can be utilized for disease detection and quantification. In this study, field asymmetric ion mobility spectrometry (FAIMS) was used to evaluate the VOC profile variability in a pea near isogenic line (Pisum sativum L.) inoculated with zoospores of Aphanomyces euteiches Drechs, which causes Aphanomyces root rot disease. Pots were filled with silica sand and six plants per pot were grown under controlled conditions in a randomized complete block design with four replications. Four treatments, namely non-inoculated, 1 x 105, 1 x 106, and 2.79 x 106 zoospores ml−1 were applied to plants at 5 and 7 days after emergence. FAIMS was used to collect volatile profiles at 2, 4, 7 and 9 days after inoculation. Specific regions of interest – extracted from the 3D ion current intensity from the FAIMS spectra – were analyzed using ANOVA, Similarly, multiple regions of interest were evaluated using principal component analysis and k-means clustering. Ion current profiles and curvature profiles were incorporated into the analysis using k-means clustering. Other ground reference data such as root rot index and physiological parameters were also recorded. The results showed a biomarker in a specific region of interest demonstrating ample ability to quantify and differentiate treatment effects during non-destructive sampling at 14 DAE (7 DAI). Data from this region could be used for early and non-destructive quantification and differentiation of treatment effects based on zoospore inoculation levels. The k-means clustering of ion current and curvature profiles showed patterns based on the treatments. These findings demonstrated that FAIMS could be used as a tool to assess plant-pathogen interactions using volatile biomarkers to evaluate disease responses and severity under controlled conditions.

Mass scaling relations for dark halos from an analytic universal outer density profile

Context. The average matter density within the turnaround scale, which demarcates where galaxies shift from clustering around a structure to joining the expansion of the Universe, is an important cosmological probe. However, a measurement of the mass enclosed by the turnaround radius is difficult. Analyses of the turnaround scale in simulated galaxy clusters place the turnaround radius at about three times the virial radius in a ΛCDM universe and at a (present-day) density contrast with the background matter density of the Universe of δ ~ 11. Assessing the mass at such extended distances from a cluster’s center is a challenge for current mass measurement techniques. Consequently, there is a need to develop and validate new mass-scaling relations, to connect observable masses at cluster interiors with masses at greater distances. Aims. Our research aims to establish an analytical framework for the most probable mass profile of galaxy clusters, leading to novel mass scaling relations, allowing us to estimate masses at larger scales. We derive such analytical mass profiles and compare them with those from cosmological simulations. Methods. We used excursion set theory, which provides a statistical framework for the density and local environment of dark matter halos, and complement it with the spherical collapse model to follow the non-linear growth of these halos. Results. The profile we developed analytically showed good agreement (better than 30%, and dependent on halo mass) with the mass profiles of simulated galaxy clusters. Mass scaling relations were obtained from the analytical profile with offset better than 15% from the simulated ones. This level of precision highlights the potential of our model for probing structure formation dynamics at the outskirts of galaxy clusters.

Characterization of Molecular Subtypes of Rat Mammary Cancer and Their Association With Environmental Exposures.

Breast cancer is a heterogeneous disease with many subtypes, and the association between these subtypes and exposure to environmental factors such as radiation remains controversial. Although the rat is used widely for research into human breast cancer, the heterogeneity and subtype definitions are unclear. Here, we leveraged an archive of rat mammary cancer samples and gene expression microarray data to classify tumors and examine their association with exposures. Eighty-four mammary cancer and 12 normal mammary tissue samples were obtained from previous experiments in which rats were exposed to different types of radiation, chemical carcinogens, and diets. Tumors were then subjected to immunohistochemical (IHC) analysis of conventional biomarkers, as well as gene expression profiling; they were then classified by three approaches based on IHC results, the PAM50 classifier algorithm, and unsupervised clustering of gene expression profiles. IHC identified four subtypes (luminal A-like, luminal B-like 1, luminal B-like 2, and triple-negative), while PAM50 identified six (luminal A, luminal B, basal-like, HER2-enriched, normal-like, and claudin-low). Unsupervised clustering divided the tumors into three large, statistically significant, groups (named "luminal A", "luminal B", and "non-luminal" clusters). The results of the three approaches were significantly associated with each other. Exposure to radiation and chemical carcinogens during post-pubertal development was significantly associated with an increased risk of developing luminal A tumors, whereas exposure to a high corn-oil diet was associated with a higher likelihood of luminal B tumors. Rat mammary cancer subtypes resemble those in humans and are related to environmental factors.

Clustering Profiles of Domestic Seasonal Workers in the Hospitality Industry Based on Quality of Working Conditions and Return Intentions

ABSTRACT This study aims to examine the perception of work-related attitudes and behavioral outcomes among domestic seasonal hospitality workers, categorized based on their cluster distribution. The research identifies two distinct profiles of domestic seasonal workers (satisfied – SAT, and dissatisfied – DISAT), revealing significant differences in their perceptions of both attitudinal and behavioral outcomes within the hospitality industry. This finding challenges the existing knowledge by highlighting the limitations of a one-size-fits-all approach to managing seasonal workers. The study underscores the necessity for tailored strategies that recognize and cater to the specific needs of different worker profiles. By addressing these distinct needs, HR managers can significantly enhance the likelihood of seasonal domestic workers returning for subsequent seasons or opting for permanent positions within the hospitality industry.

Inferring the Mass Content of Galaxy Clusters with Satellite Kinematics and Jeans Anisotropic Modeling

Satellite galaxies can be used to indicate the dynamic mass of galaxy groups and clusters. In this study, we apply the axisymmetric Jeans Anisotropic Multi-Gaussian Expansion (JAM) modeling to satellite galaxies in 28 galaxy clusters selected from the TNG300-1 simulation with halo masses of log10M200/M⊙>14.3 . If using true bound satellites as tracers, the best constrained total mass within the half-mass radius of satellites, M(<r half), and the virial mass, M 200, have average biases of −0.01 and 0.03 dex, with average scatters of 0.11 dex and 0.15 dex. If selecting companions in the redshift space with a line-of-sight depth of 2000 km s−1, the biases are −0.06 and 0.01 dex, while the scatters are 0.12 and 0.18 dex for M(<r half) and M 200. By comparing the best-fitting and actual density profiles, we find that ∼29% of the best-fitting density profiles show very good agreement with the truth, ∼32% display over/underestimates at most of the radial range with biased M(<r half), and 39% show under/overestimates in central regions and over/underestimates in the outskirts, with good constraints on M(<r half); yet most of the best constraints are still consistent with the true profiles within 1σ statistical uncertainties for the three circumstances. Using a mock DESI Bright Galaxy Survey catalog with the effect of fiber incompleteness, we find DESI fiber assignments and the choice of flux limits barely modify the velocity dispersion profiles and are thus unlikely to affect the dynamical modeling outcomes. Our results show that with current and future deep spectroscopic surveys, JAM can be a powerful tool to constrain the underlying density profiles of individual massive galaxy clusters.

Huaier-induced suppression of cancer-associated fibroblasts confers immunotherapeutic sensitivity in triple-negative breast cancer

BackgroundTriple-negative breast cancer (TNBC) is the most intractable subgroup of breast neoplasms due to its aggressive nature. In recent years, immune checkpoint inhibitors (ICIs) have exhibited potential efficacy in TNBC treatment. However, only a limited fraction of patients benefit from ICI therapy, primarily because of the suppressive tumor immune microenvironment (TIME). Trametes robiniophila Murr (Huaier) is a traditional Chinese medicine (TCM) with potential immunoregulatory functions. However, the underlying mechanism remains unclear. PurposeThe present study aimed to investigate the therapeutic role of Huaier in the TIME of TNBC patients. MethodsSingle-cell RNA sequencing (scRNA-seq) was used to systematically analyze the influence of Huaier on the TNBC microenvironment for the first time. The mechanisms of the Huaier-induced suppression of cancer-associated fibroblasts (CAFs) were assessed via real-time quantitative polymerase chain reaction (qRT‒PCR) and western blotting. A tumor-bearing mouse model was established to verify the effects of the oral administration of Huaier on immune infiltration. ResultsUnsupervised clustering of the transcriptional profiles suggested an increase in the number of apoptotic cancer cells in the Huaier group. Treatment with Huaier induced immunological alterations from a "cold" to a "hot" state, which was accompanied by phenotypic changes in CAFs. Mechanistic analysis revealed that Huaier considerably attenuated the formation of myofibroblastic CAFs (myoCAFs) by impairing transforming growth factor-beta (TGF-β)/SMAD signaling. In mouse xenograft models, Huaier dramatically modulated CAF differentiation, thus synergizing with the programmed cell death 1 (PD1) blockade to impede tumor progression. ConclusionsOur findings demonstrate that Huaier regulates cancer immunity in TNBC by suppressing the transition of CAFs to myoCAFs and emphasize the crucial role of Huaier as an effective adjuvant agent in immunotherapy.

Multidimensional Analysis of Physiological Entropy during Self-Paced Marathon Running.

The pacing of a marathon is arguably the most challenging aspect for runners, particularly in avoiding a sudden decline in speed, or what is colloquially termed a "wall", occurring at approximately the 30 km mark. To gain further insight into the potential for optimizing self-paced marathon performance through the coding of comprehensive physiological data, this study investigates the complex physiological responses and pacing strategies during a marathon, with a focus on the application of Shannon entropy and principal component analysis (PCA) to quantify the variability and unpredictability of key cardiorespiratory measures. Nine recreational marathon runners were monitored throughout the marathon race, with continuous measurements of oxygen uptake (V˙O2), carbon dioxide output (V˙CO2), tidal volume (Vt), heart rate, respiratory frequency (Rf), and running speed. The PCA revealed that the entropy variance of V˙O2, V˙CO2, and Vt were captured along the F1 axis, while cadence and heart rate variances were primarily captured along the F2 axis. Notably, when distance and physiological responses were projected simultaneously on the PCA correlation circle, the first 26 km of the race were positioned on the same side of the F1 axis as the metabolic responses, whereas the final kilometers were distributed on the opposite side, indicating a shift in physiological state as fatigue set in. The separation of heart rate and cadence entropy variances from the metabolic parameters suggests that these responses are independent of distance, contrasting with the linear increase in heart rate and decrease in cadence typically observed. Additionally, Agglomerative Hierarchical Clustering further categorized runners' physiological responses, revealing distinct clusters of entropy profiles. The analysis identified two to four classes of responses, representing different phases of the marathon for individual runners, with some clusters clearly distinguishing the beginning, middle, and end of the race. This variability emphasizes the personalized nature of physiological responses and pacing strategies, reinforcing the need for individualized approaches. These findings offer practical applications for optimizing pacing strategies, suggesting that real-time monitoring of entropy could enhance marathon performance by providing insights into a runner's physiological state and helping to prevent the onset of hitting the wall.

Identification of two heterogeneous subtypes of hepatocellular carcinoma with distinct pathway activities and clinical outcomes based on gene set variation analysis.

High heterogeneity is an essential feature of malignant tumors. This study aims to reveal the drivers of hepatocellular carcinoma heterogeneity for prognostic stratification and to guide individualized treatment. Omics data and clinical data for two HCC cohorts were derived from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Atlas (ICGC), respectively. CNV data and methylation data were downloaded from the GSCA database. GSVA was used to estimate the transcriptional activity of KEGG pathways, and consensus clustering was used to categorize the HCC samples. The pRRophetic package was used to predict the sensitivity of samples to anticancer drugs. TIMER, MCPcounter, quanTIseq, and TIDE algorithms were used to assess the components of TME. LASSO and COX analyses were used to establish a prognostic gene signature. The biological role played by genes in HCC cells was confirmed by in vitro experiments. We classified HCC tissues into two categories based on the activity of prognostic pathways. Among them, the transcriptional profile of cluster A HCC is similar to that of normal tissue, dominated by cancer-suppressive metabolic pathways, and has a better prognosis. In contrast, cluster B HCC is dominated by high proliferative activity and has significant genetic heterogeneity. Meanwhile, cluster B HCC is often poorly differentiated, has a high rate of serum AFP positivity, is prone to microvascular invasion, and has shorter overall survival. In addition, we found that mutations, copy number variations, and aberrant methylation were also crucial drivers of the differences in heterogeneity between the two HCC subtypes. Meanwhile, the TME of the two HCC subtypes is also significantly different, which offers the possibility of precision immunotherapy for HCC patients. Finally, based on the prognostic value of molecular subtypes, we developed a gene signature that could accurately predict patients' OS. The riskscore quantified by the signature could evaluate the heterogeneity of HCC and guide clinical treatment. Finally, we confirmed through in vitro experiments that RFPL4B could promote the progression of Huh7 cells. The molecular subtypes we identified effectively exposed the heterogeneity of HCC, which is important for discovering new effective therapeutic targets.

Integrating Blood Biomarkers and Marine Brown Algae-Derived Inhibitors in Parkinson's Disease: A Multi-scale Approach from Interactomics to Quantum Mechanics.

Parkinson's disease (PD) involves alpha-synuclein accumulation according to Braak's pattern, with diverse clinical progressions that complicate diagnosis and treatment. We aimed to correlate Braak's pattern with rapid progressive PD to identify blood-based biomarkers and therapeutic targets exploiting brown algae-derived bioactives for potential treatment. We implemented a systematic workflow of transcriptomic profiling, co-expression networks, cluster profiling, transcriptional regulator identification, molecular docking, quantum calculations, and dynamic simulations. The transcriptomic analyses exhibited highly expressed genes at each Braak's stage and in rapidly progressive PD. Co-expression networks for Braak's stages were built, and the top five clusters from each stage displayed significant overlap with differentially expressed genes in rapidly progressive PD, indicating shared biomarkers between the blood and the PD brain. Further investigation showed, NF-kappa-B p105 as the master transcriptional regulator of these biomarkers. Molecular docking screened phlorethopentafuhalol-A from brown algae, exhibiting a superior inhibitory effect with p105 (- 7.51kcal/mol) by outperforming PD drugs and anti-inflammatory compounds (- 5.73 to - 4.38kcal/mol). Quantum mechanics and molecular mechanics (QM/MM) calculations and dynamic simulations have confirmed the interactive stability of phlorethopentafuhalol-A with p105. Overall, our combined computational study shows that phlorethopentafuhalol-A derived from brown algae, may have healing properties that could help treat PD.