Search Results (5,701)

Background: Metabolic syndrome is associated with early impairments in cellular bioenergetics that are not fully captured by conventional body composition measures. Molecular hydrogen, produced endogenously through gut microbial fermentation and measurable in breath, has been implicated in redox and mitochondrial regulation. Whether breath hydrogen relates to preservation of intracellular, metabolically active tissue in metabolic syndrome remains unclear. Objectives: To examine the association between breath hydrogen concentration and an integrated cellular bioenergetic phenotype derived from intracellular body composition indices in sedentary adults with metabolic syndrome. Methods: Twenty-eight sedentary, middle-aged adults (51.2 ± 7.9 years, 19 females) with metabolic syndrome underwent fasting breath hydrogen assessment and multifrequency bioelectrical impedance analysis. A composite cellular bioenergetic phenotype was derived using principal component analysis of body cell mass, intracellular water, total body potassium, and glycogen. Associations between breath hydrogen and the composite phenotype were evaluated using Spearman correlation with bootstrapped confidence intervals, Theil-Sen regression, and Bayesian linear regression adjusted for age, sex, and waist circumference. Sensitivity analyses included fat-free mass. Results: A single principal component explained 98.6% of the variance across intracellular variables, indicating a highly coherent cellular bioenergetic phenotype. Breath hydrogen concentration was positively associated with this phenotype (ρ = 0.43, p = 0.021; BCa 95% CI 0.07–0.70). Theil-Sen regression confirmed a robust positive association (β = 0.017 per ppm hydrogen; 95% CI 0.002–0.046). Bayesian models showed posterior distributions centered on positive effect sizes, independent of central adiposity. In contrast, the association with fat-free mass alone was borderline. Conclusions: Breath hydrogen concentration reflects an integrated intracellular bioenergetic phenotype in sedentary adults with metabolic syndrome, tracking cellular quality rather than lean mass quantity. Breath hydrogen may serve as a non-invasive biomarker of cellular bioenergetic integrity and a potential tool for phenotype-guided metabolic interventions. Full article

Cycloheptylprodigiosin is a promising anticancer candidate that induces cancer cell death accompanied by severe Golgi stress. Although the soybean oil-based optimized MB2216 medium produced a total prodiginine titer approximately three times that of the basal MB2216 medium, the overall production level remained limited. In addition, a substantial fraction of the pigments partitioned into floating oil droplets, hindering efficient recovery by simple centrifugation. In this study, a novel medium was rationally formulated based on genomic insights derived from homology analysis of conserved biosynthetic genes involved in cycloheptylprodigiosin production in Spartinivicinus ruber MCCC 1K03745^T. Sequential optimization through single-factor experiments, full factorial designs, steepest ascent experiments and response surface methodology identified an optimal medium consisting of peptone (5 g/L), yeast extract (1 g/L), peanut meal (7.611 g/L), and L-Proline (0.695 g/L) prepared in seawater at pH 7.6. Under the optimized conditions, the total prodiginine titer reached 53.33 mg/L, which was 11.37 times that of the basal MB2216 medium and 3.29 times that of the soybean oil-based MB2216 medium. Moreover, the pigment-associated biomass could be efficiently recovered by centrifugation. This study provides a genomics-informed strategy for improving prodiginine production in S. ruber and facilitates downstream pigment recovery. Full article

Porphyra suborbiculata exhibits strong heat tolerance and has considerable commercial potential under rising sea temperatures; however, its bioactive components remain insufficiently explored. In this study, a heat-tolerant new strain of P. suborbiculata (PS-M4), cultivated by the College of Fisheries, was used as the experimental material. Polysaccharides were extracted using an ultrasound-assisted composite enzymatic method, and extraction conditions were optimized through single-factor experiments and response surface methodology, yielding a maximum extraction yield of 12.45 ± 0.09%. Crude polysaccharides were further purified using a purification apparatus, yielding two fractions, designated PSP-I and PSP-II. Preliminary structural characterization showed that PSP-I possessed a weight-average molecular weight (Mw) of 26.149 kDa, a number-average molecular weight (Mn) of 11.267 kDa, and a polydispersity index of 2.321. Monosaccharide composition analysis indicated that PSP-I was predominantly composed of galactose. Fourier transform infrared spectroscopy (FT-IR) revealed typical polysaccharide functional groups, and scanning electron microscopy (SEM) analysis revealed a porous lamellar morphology. In vitro cell-based assays demonstrated that PSP-I significantly alleviated ultraviolet B (UVB)-induced damage in HaCaT cells by reducing intracellular reactive oxygen species (ROS) levels, enhancing antioxidant enzyme activities, inhibiting apoptosis, and downregulating the expression of matrix metalloproteinases (MMPs). These results suggest that PSP-I has potential as a functional ingredient for mitigating UVB-induced skin damage. Full article

Currently, the B-cell response patterns induced by viral antigens in avian disease models and their detailed immunological characteristics still require comprehensive elucidation at the single-cell level. In this study, we employed single-cell sequencing (scRNA-seq) and B cell library technology to conduct an in-depth analysis of B cells in the spleens of mice with inactivated duck hepatitis A virus type 1 (DHAV-1) as model antigen. This study aimed to investigate the immunological characteristics of the virus antigen in the mouse model and characteristics of B-Cell Receptors. The results showed that the DHAV-1 group had distinct changes in splenic B cell subset counts, proportions, and intercellular communication. Additionally, an increased trend in communication strength between Gm26917+B and Gm11837+B cells was observed, with enriched expression of C-X-C motif chemokine ligand (CXCL) and lymphotoxin (LT) detected in the DHAV-1 group. Furthermore, the DHAV-1 group exhibited a prominent combination of the IGHV1 family and IGHV3-1/IGHJ3 in the heavy (H) chain variable region. Compared with the CK group (negative control group), the amino acid sequence length and diversity of the CDR3 region in the DHAV-1 group exhibited a decreasing trend. In summary, our findings characterize the immunological features of splenic B cells in mice after immunization with inactivated DHAV-1, and provide a preliminary characterization of DHAV-1-induced B cell transcriptional states and BCR repertoire features, generating testable hypotheses for subsequent mechanistic investigations of B cell-mediated immune responses to viral antigens. Full article

Lithium metal batteries (LMBs) have been widely studied due to their high energy density; however, the practical implementation of LMBs is limited by issues of uncontrolled dendrite growth, continuous electrolyte decomposition, and poor Coulombic efficiency (CE). Highly concentrated electrolytes (HCEs) have emerged as a promising approach to addressing the above issues. In this work, we propose a new HCE system based on a single carbonate solvent of 2,2,2-trifluoroethyl methyl carbonate (FEMC) with a high concentration of lithium bis(fluorosulfonyl)imide (LiFSI). The resulting electrolytes exhibit enhanced anodic stability and improved compatibility with lithium metal anodes and high-voltage cathodes. The optimized 4 M LiFSI–FEMC HCE achieved the highest CE for Li plating/stripping in Li/Cu cell and lowest overpotential in Li/Li symmetric cells, outperforming both low-concentration FEMC and ethyl methyl carbonate (EMC)-based electrolytes. In full-cell configurations with LiNi_0.8Co_0.1Mn_0.1O₂ (NCM811) cathodes, the HCE demonstrates stable cycling with minimal capacity fade over 250 cycles. Importantly, the HCE enables stable operation of 4.6 V high-voltage NCM811/Li cells for more than 120 cycles with a high-capacity retention of 88.61%. Post-mortem analysis revealed a more uniform and compact solid electrolyte interphase and a thinner cathode electrolyte interphase, contributing to the enhanced cycling performance. Full article

CAR-T cell therapy has shown substantial efficacy in haematological malignancies, but its application to solid tumours remains limited by poor effector-cell infiltration, functional exhaustion, antigenic heterogeneity, and an immunosuppressive microenvironment. In this study, we develop a new spatiotemporal mathematical model of CAR-T therapy for solid tumours that integrates these resistance mechanisms within a single reaction–diffusion framework. The model is formulated as a system of partial differential equations describing functional and exhausted CAR-T cells, antigen-positive and antigen-low tumour subpopulations, and chemokine, immunosuppressive, and hypoxic fields. Steady-state analysis and finite-difference simulations showed that therapeutic outcome is governed by the interplay between CAR-T cell infiltration, exhaustion, and antigen escape. The model reproduces partial tumour regression followed by residual tumour persistence, therapy-driven enrichment of antigen-low cells, and reduced efficacy under stronger immunosuppressive and hypoxic conditions. In the combination therapy scenario considered here, repeated simulated CAR-T cell administration together with attenuation of the suppressive microenvironment improves tumour control. The proposed model provides a mechanistic basis for analysing resistance and for future optimisation studies of CAR-T therapy in solid tumours. Full article

Transcriptome profiling is a cornerstone of functional genomics, enabling the detailed characterization of gene expression in health and disease. Bulk RNA sequencing (bulk RNAseq) remains the most widely used approach in clinical and large-cohort studies due to its cost-effectiveness, robustness, and comprehensive transcriptome coverage. However, bulk RNAseq inherently averages gene expression signals across heterogeneous cell populations, thereby masking cellular diversity and obscuring rare cell types. In contrast, single-cell RNA sequencing (scRNAseq) enables a high-resolution analysis of cellular heterogeneity, allowing the identification of distinct cell types, transitional states, and developmental trajectories. Nevertheless, scRNAseq is associated with higher cost, limited scalability, increased technical noise, sparse expression matrices, and protocol-dependent biases introduced during tissue dissociation or nuclear isolation. In this review, we summarize the conceptual and methodological foundations of integrating bulk RNAseq and scRNAseq data, emphasizing their complementary strengths and limitations. We discuss how scRNAseq-derived cell-type atlases can serve as reference matrices for computational reconstruction (deconvolution) of bulk RNAseq profiles and examine key sources of technical and biological variability. Furthermore, we outline major integration strategies, including reference-based deconvolution, pseudobulk aggregation, and Bayesian joint modeling to provide an overview of widely used analytical tools and essential components of scRNAseq data processing workflows. Full article

The widespread occurrence of macrolide antibiotics (MLs) in aquatic environments poses potential ecological risks; however, the interactive effects of MLs, especially combined MLs on microalgae and their removal mechanisms, remain poorly understood. This study investigated the removal efficiency, physiological–biochemical responses, and molecular mechanisms of Chlorella pyrenoidosa under single and combined exposure to erythromycin (ERY) and roxithromycin (ROX) over 14 days. The results demonstrated that antibiotic removal efficiency was concentration-dependent and higher in low-concentration treatment. The removal rates of ERY (0.15 mg/L) and ROX (0.02 mg/L) reached 100% and 66.86%, respectively. Notably, in the combined low-concentration group, the presence of ROX promoted the degradation of ERY, with the removal being 11.06–14.77% higher than in single treatment. Conversely, in high-concentration combined treatments (1.63 mg/L ERY + 0.5 mg/L ROX), the removal of ERY was inhibited and the removal of ROX was comparable with the corresponding single treatment. High-concentration treatment groups and combined-treatment groups significantly inhibited microalgae growth and total chlorophyll content, modified the chlorophyll composition, and induced severe oxidative stress. Correlation analysis revealed that antibiotic removal was positively correlated with cell density, chlorophyll content, CAT, CYP450, and GST activities while negatively correlated with SOD, ROS, and MDA. Transcriptomic analysis revealed significant disruption of xenobiotic metabolism pathways, photosynthesis-related processes, and DNA replication/mismatch repair pathways. Key genes involved in stress signaling (e.g., MKK3, MPK3), detoxification (e.g., CYP97, GSTP), and photosynthesis (e.g., HemL) were differentially regulated, providing molecular evidence for the observed physiological responses and removal behaviors. These findings provide valuable insights for the ecological risk assessment of antibiotic mixtures and the development of microalgae-based wastewater treatment technologies. Full article

Breast cancer remains a major global health challenge, requiring novel therapeutic strategies that can overcome drug resistance and improve treatment efficacy. This study investigates the synergistic antitumor effects of etoposide, a conventional chemotherapeutic agent, and resveratrol, a natural polyphenol with anticancer properties, in human breast cancer cell lines, with particular focus on their ability to activate the parthanatos cell death pathway. Using MCF-7 (estrogen receptor-positive) and MDA-MB-231 (triple-negative) breast cancer cells, we assessed cell viability via MTT assays and evaluated parthanatos activation through multiple complementary approaches including AIF translocation determined by subcellular fractionation, NAD+ depletion measurement, and gene expression analysis. Synergy was quantified using the Chou–Talalay method across multiple effect levels (ED50, ED75, ED90). To establish causality, Olaparib PARP inhibitor experiments were performed to confirm that PARP-1 hyperactivation is essential for the observed cytotoxic effects. The results demonstrated that the etoposide–resveratrol combination significantly enhanced cell death and inhibited proliferation compared to single-agent treatments, with combination index (CI) values indicating strong synergism (CI = 0.62–0.75 for MCF-7; CI = 0.58–0.71 for MDA-MB-231). This synergy was associated with robust parthanatos activation, evidenced by increased PARP-1 expression, AIF nuclear translocation confirmed by subcellular fractionation, and significant NAD+ depletion. Critically, Olaparib pre-treatment (3 µM) significantly rescued cells from combination-induced death, restored NAD+ levels to near-control values, and prevented AIF translocation, establishing a causal link between PARP-1 hyperactivation and parthanatos-mediated cytotoxicity. The combination also induced significant DNA fragmentation, elevated oxidative stress, and cell death with morphological features consistent with parthanatos, while caspase activity remained low, confirming caspase-independent cell death. These findings suggest that targeting parthanatos with etoposide and resveratrol could offer a promising therapeutic strategy for breast cancer, potentially overcoming resistance and improving efficacy. Further in vivo studies and clinical investigations are needed to validate these results and explore translational applications. Full article

Background: A major technical challenge in single-cell transcriptomics is the absence of an integrative analytic pipeline that can simultaneously leverage gene regulatory network (GRN) architecture, AI-assisted gene panel discovery, and functional relevance analyses to generate coherent biological insights. Existing approaches often treat these components independently, focusing on clusters, marker genes, or predictive features without integrating them into a mechanistically grounded framework. Consequently, comprehensive screening that links regulatory association, gene signature screening, and functional interpretation within single-cell datasets remains limited, underscoring the need for an integrated strategy. Methods: We developed an integrative bioinformatics pipeline based on Gene regulatory network–AI–Functional Analysis (GAFA), combining latent-space integration, unsupervised clustering, diffusion pseudotime analysis, lineage-resolved generalized additive modeling, GRN inference, and machine learning-based gene panel discovery. This framework enables systematic mapping of cell-state structure, reconstruction of differentiation and effector trajectories, and identification of transcriptional and regulatory features strongly associated with clinical outcomes. As a case study, we applied the pipeline to NK cell transcriptomes from six CML patients (two early relapse, two late relapse, two durable treatment-free remission—TFR; 15 samples) collected at TKI discontinuation and 6–12 months after therapy cessation. Results: We reanalyzed publicly available scRNA-seq data from a previously published CML cohort to evaluate NK-cell transcriptional programs associated with treatment-free remission and relapse. We resolved six transcriptionally distinct NK cell states spanning CD56^bright-like cytokine-responsive, early activated, terminally mature, cytotoxic, lymphoid trafficking, and HLA-DR⁺ immunoregulatory populations, each exhibiting outcome-specific compositional differences. Pseudotime analysis revealed two major NK cell lineages—a maturation trajectory and a cytotoxic effector trajectory. TFR samples displayed balanced occupancy of both lineages, whereas early relapse samples showed marked depletion of the maturation branch and preferential accumulation in cytotoxic end states. AI-guided feature selection and random forest modeling identified an 18-gene panel that distinguished NK cells from TFR and relapse samples in an exploratory manner. Among them, CST7, FCER1G, GNLY, GZMA, and HLA-C were conventional NK-associated genes, whereas ACTB, CYBA, IFITM2, IFITM3, LYZ, MALAT1, MT2A, MYOM2, NFKBIA, PIM1, S100A8, S100B, and TSC22D3 were novel. The GRN inference further uncovered outcome-specific regulatory modules, with RUNX3, EOMES, ELK4, and REL regulons enriched in TFR, whereas FOSL2 and MAF regulons were enriched in relapse, and their downstream targets linked to IFN-γ signaling, metabolic reprogramming, and immunoregulatory feedback circuits. Conclusions: This AI-enabled single-cell analysis demonstrates how NK cell state composition, differentiation trajectories, and regulatory network rewiring collectively shape TFR versus relapse following TKI discontinuation in CML. The integrative pipeline provides a modular framework that could be extended to additional datasets for data-driven biomarker discovery and mechanistic stratification, and highlights candidate transcriptional regulators and NK cell programs that may be leveraged to improve remission durability, pending validation in larger patient cohorts. Full article

Background and Objectives: Cutaneous squamous cell carcinoma (cSCC) displays heterogeneous metastatic potential, and the role of sentinel lymph node biopsy (SLNB) in clinically node-negative patients remains debated. To evaluate tumor thickness and histological grade as predictors of sentinel lymph node (SLN) metastasis in high-risk cSCC and to assess the performance of a simplified pathology-based predictive model. Materials and Methods: This retrospective single-center study included consecutive patients with high-risk cSCC and clinically N0 status who underwent SLNB. Associations were examined using univariate and multivariable logistic regression, ROC analysis with bootstrap internal validation (2000 iterations), and decision curve analysis. Results: Thirty-four patients were analyzed; 12 (35.3%) had SLN metastases. SLN-positive patients had greater tumor thickness (median 5.5 mm vs. 3.0 mm, p = 0.006) and higher frequency of G2–G3 histological grade (91.7% vs. 45.5%, p = 0.011). Histological grade was the strongest independent predictor in multivariable analysis (OR 14.61, 95% CI 1.63–131.12). The combined model demonstrated apparently high discrimination in this small cohort (AUC 0.91; bootstrap 95% CI 0.79–0.99), though this estimate should be interpreted with caution given the limited number of events. A 4.0-mm threshold yielded sensitivity 83.3% and NPV 86.7%. Conclusions: In this exploratory single-center study, tumor thickness and histological grade were complementary predictors of SLN metastasis in cSCC. These findings are preliminary and require validation in larger prospective cohorts. Full article

The musk gland of male forest musk deer (Moschus berezovskii) secretes musk enriched with sex steroid hormones. The testes mainly produce these hormones; however, whether glandular cells can autonomously synthesize them remains unexplored. This study aimed to utilize an in vitro-cultured musk gland cell model to investigate whether musk gland cells possess the capability for autonomous synthesis of sex steroid hormones. We used single-cell RNA sequencing (scRNA-seq), reverse transcription quantitative real-time polymerase chain reaction, and liquid chromatography–mass spectrometry (LC-MS) to verify the steroidogenic potential of musk gland cells. scRNA-seq revealed that during the secretion period, 18 cholesterol and 6 sex steroid hormone biosynthesis genes were significantly expressed in the cells. In vitro experiments demonstrated that these genes were expressed without exogenous cholesterol supplementation. LC-MS analysis confirmed stable synthesis of nine sex steroid hormones. Increasing cholesterol concentration to 20 mg/L significantly upregulated SRD5A3 and AKR1D1, with AKR1C3 expression showing an upward trend. Elevated cholesterol increased several sex steroid hormone levels: pregnenolone, progesterone, 17α-hydroxypregnenolone, androstenedione, androsterone, and etiocholanolone by 4.12-, 1.46-, 33.42-, 2.06-, 3.11-, and 5.65-fold, respectively. These results collectively indicate that the musk glandular cells can synthesize sex steroid hormones de novo and suggest that cholesterol may regulate their biosynthesis in these cells. Full article

Collective cell migration plays essential roles in morphogenesis, wound healing, angiogenesis, and cancer invasion, yet quantitative measurement of leader–follower interaction strength and range remains challenging due to the lack of direct and scalable methods. Here, we present a microfabricated branch selection platform combined with a probabilistic analysis framework to quantitatively measure intercellular coupling in migrating single-cell trains. Cells migrate through microchannels with a width of one cell and encounter symmetric T-junctions at which each follower cell selects either the same branch as the preceding cell or the opposite branch. We show that branch selection sequences are captured by a first-order Markov process, with the resulting run length (cluster size) statistics following a geometric form determined by an interaction-dependent transition probability. This relationship enables direct estimation of an effective interaction parameter without requiring force measurements or molecular labeling. Monte Carlo simulations confirm that interaction strength is primarily encoded in run length statistics rather than overall left/right occupancy in symmetric junctions. Experiments with epithelial MDCK cells and endothelial MS-1 cells reveal distinct interaction signatures: MS-1 cells show significant repulsive coupling, whereas MDCK cells exhibit at most a weak attractive tendency at the leader-first follower interface, while rear clusters display repulsive signatures. Cluster order-resolved analysis further indicates that interaction effects are spatially localized near the front and do not propagate as sustained attraction along the train. These results establish the proposed platform as a scalable method for quantitative measurement of interaction strength and interaction localization in collective cell migration. Full article

Human infertility affects approximately 17.5% of the global population, with male factors accounting for nearly half of all cases. Identifying reliable molecular biomarkers is crucial for improving the diagnosis and assessment of male fertility. This study established and refined an untargeted high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (HPLC-ESI-MS/MS) protocol for a comprehensive lipidomic and metabolomic analysis of human spermatozoa, using only 1.25 million cells per sample. Compared with previous reports, our optimized method achieved an unparalleled level of analytical depth, identifying 473 lipid species and 955 structurally annotated metabolites. This corresponds to nearly a 7600-fold improvement in detection efficiency per cell compared with previously published approaches. Lipidomic analysis revealed that the most abundant lipid classes were glycerophospholipids (39%), cholesterol (20%) and fatty acids (19%), with cholesterol representing the single most abundant compound. This observation is consistent with the structural complexity of the sperm plasma membrane. Metabolomic profiling similarly identified glycerophospholipids (44%), eicosanoids (14%) and N-acyl amino acids (12%) as the major metabolite classes. The integration of lipidomic and metabolomic data highlighted functionally interconnected pathways related to membrane dynamics, energy metabolism, and hormone biosynthesis. Overall, this work establishes a robust, sensitive, and scalable analytical framework that enables the high-coverage molecular characterization of spermatozoa from limited sample material, laying the groundwork for future biomarker discovery and clinical applications in male infertility research. Full article

The prediction of drug response would significantly improve the treatment of lung cancer. Tumor heterogeneity and complex signal transduction pathways lead to varied treatment effects among patients, but traditional computational approaches struggle to model the nonlinear, high-dimensional relationship between genes and drug responses. In order to develop a Generative Adversarial Network (GAN)-based model that can predict drug-induced gene expression profiles from lung cancer cell lines, we developed GRIP-Lung (Generative Model of Response to Drug-Induced Perturbation in Lung Cancer). By making use of biologically informed embeddings of cell line identity as well as drug treatment conditions, this model is able to gain a fairly good understanding of cell types and their transcriptional perturbations induced by different drugs. The GRIP-Lung model displayed reasonably good prediction ability in terms of predictive accuracy and showed high concordance between the predicted and experimental expression profiles. We not only predicted transcriptional changes induced by drug therapy but also used single-sample Gene Set Enrichment Analysis (ssGSEA) to classify post-treatment response states based on characteristic molecular biomarkers, offering a means for selecting effective drugs to target specific heterogeneity within lung tumors. The proposed GRIP-Lung framework faithfully reproduces drug-induced transcriptional perturbations in lung cell line models. By integrating biologically informed embeddings and adversarial learning, the model advances drug response prediction. This makes it a flexible computational tool for drug repositioning. Full article