Cellular Communication Research Articles

Maneuvering the mineralization of self-assembled peptide nanofibers for designing mechanically-stiffened self-healable composites toward bone-mimetic ECM.

Extracellular matrix (ECM) elasticity remains a crucial parameter to determine cell-material interactions (viz. adhesion, growth, and differentiation), cellular communication, and migration that are essential to tissue repair and regeneration. Supramolecular peptide hydrogels with their 3-dimensional porous network and tuneable mechanical properties have emerged as an excellent class of ECM-mimetic biomaterials with relevant dynamic attributes and bioactivity. Here, we demonstrate the design of minimalist amyloid-inspired peptide amphiphiles, CnPA (n = 6, 8, 10, 12) with tuneable peptide nanostructures that are efficiently biomineralized and cross-linked using bioactive silicates. Such hydrogel composites, CnBG exhibit excellent mechanical attributes and possess excellent self-healing abilities and collagen-like strain-stiffening ability as desired for bone ECM mimetic scaffold. The composites exhibited the formation of a hydroxyapatite mineral phase upon incubation in a simulated body fluid that rendered mechanical stiffness akin to the hydroxyapatite-bridged collagen fibers to match the bone tissue elasticity eventually. In a nutshell, peptide nanostructure-guided temporal effects and mechanical attributes demonstrate C8BG to be an optimal composite. Finally, such constructs feature the potential for adhesion, proliferation of U2OS cells, high alkaline phosphatase activity, and osteoconductivity.

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.

Hygienic problems of using terahertz electromagnetic radiation (literature review)

The purpose of the work is to review and analyze domestic and foreign scientific works, systematize the scope of application of terahertz electromagnetic radiation (EMR) to determine hygienic problems in the field of health risk prevention in the development and use of modern radioelectronic devices. The literature search was conducted on the databases: eLibrary, Web of Science, and fifty. During the study of scientific literature, from over fifty works were analyzed, there 36 sources were selected 36 sources corresponded to the purpose of the study. Today, the urgent tasks are to predict the parameters of a complex electromagnetic environment in open areas and inside buildings using mobile communication standards 4, 5 and 6G, scientific justification of hygienic standards for the combined effects of the electromagnetic factor, methodological approaches to monitoring EMR levels, including the development of domestic selective EMR meters in a wide range of frequencies (radio frequency and terahertz ranges)

Identification of neutrophil extracellular trap-related biomarkers in non-alcoholic fatty liver disease through machine learning and single-cell analysis

Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver condition globally. Simultaneously, the annual disease burden, particularly liver cirrhosis caused by NAFLD, has increased significantly. Neutrophil Extracellular Traps (NETs) play a crucial role in the progression of this disease and are key to the pathogenesis of NAFLD. However, research into the specific roles of NETs-related genes in NAFLD is still a field requiring thorough investigation. Utilizing techniques like AddModuleScore, ssGSEA, and WGCNA, our team conducted gene screening to identify the genes linked to NETs in both single-cell and bulk transcriptomics. Using algorithms including Random Forest, Support Vector Machine, Least Absolute Shrinkage, and Selection Operator, we identified ZFP36L2 and PHLDA1 as key hub genes. The pivotal role of these genes in NAFLD diagnosis was confirmed using the training dataset GSE164760. This study identified 116 genes linked to NETs across single-cell and bulk transcriptomic analyses. These genes demonstrated enrichment in immune and metabolic pathways. Additionally, two NETs-related hub genes, PHLDA1 and ZFP36L2, were selected through machine learning for integration into a prognostic model. These hub genes play roles in inflammatory and metabolic processes. scRNA-seq results showed variations in cellular communication among cells with different expression patterns of these key genes. In conclusion, this study explored the molecular characteristics of NETs-associated genes in NAFLD. It identified two potential biomarkers and analyzed their roles in the hepatic microenvironment. These discoveries could aid in NAFLD diagnosis and management, with the ultimate goal of enhancing patient outcomes.

THE STATE, PROBLEMS AND PROSPECTS OF PRICING DEVELOPMENT IN THE FIELD OF CONSTRUCTION OF ANTENNA-MAST STRUCTURES OF CELLULAR COMMUNICATIONS

The article examines issues related to the current state and problems of pricing in the field of construction of antenna-mast structures of cellular communications. The authors point out the peculiarity of the considered sphere of construction in terms of the mechanism for determining the estimated cost using tariffers, which differs from the generally accepted method, which implies the use of approved estimated standards, and is due to the lack of prices in the current regulatory framework that allow full marking of special types of work performed during the construction of antenna-mast structures. The disadvantages of this approach are given due to the establishment of prices in the tariff for enlarged work complexes and elements of the structure as a whole, which does not allow taking into account factors that have a significant impact on the design features of the support and, as a result, the cost of its construction and depend on the construction conditions and customer requirements. At the same time, the authors note and clearly confirm the absence of linking the rates of the tariff to the amount of work actually performed and the consumption of resources during the construction of a specific antenna-mast structure, which entails an unreliable estimate of the cost of construction. Separately, a comparison of the cost of materials for the construction of supports of the same height, depending on the projected payload, is provided, which also confirms the need to revise the existing pricing system. The authors provide recommendations aimed at improving the pricing mechanism used: options are proposed for the development and implementation of estimated standards for the construction area under consideration, as well as for improving the tariff rates in the interaction of construction organizations and customers.

Deployment options of AI components for network resource management in 5G‐enabled agile industrial production cell

SummaryOn‐demand manufacturing in Industry 4.0 requires flexibility of the networks which can be provided with the fifth generation (5G) of mobile communications wireless connectivity. A key component in the efficient utilization of the radio resources in a manufacturing scenario is network resource management (NRM). We show how NRM can be automated with artificial intelligence (AI). We introduce several futuristic industrial use cases that require AI in various parts of the process. We analyze the AI components' benefits and disadvantages in several deployment scenarios. The findings can be used by business stakeholders interested in deploying the 5G cellular wireless network to choose the best NRM and AI implementation strategy for a particular use case. We show that there are many viable options for the AI component in the process automation, but the cost of AI has to be considered in all cases. Also, we point out that an essential component, the standardized information flow on the status of the productivity key performance indicators (KPIs), is needed for the successful deployment and application of the 5G AI.

Ccn2 Deletion Reduces Cardiac Dysfunction, Oxidative Markers, and Fibrosis Induced by Doxorubicin Administration in Mice.

Cellular Communication Network Factor 2 (CCN2) is a matricellular protein implicated in cell communication and microenvironmental signaling. Overexpression of CCN2 has been documented in various cardiovascular pathologies, wherein it may exert either deleterious or protective effects depending on the pathological context, thereby suggesting that its role in the cardiovascular system is not yet fully elucidated. In this study, we aimed to investigate the effects of Ccn2 gene deletion on the progression of acute cardiac injury induced by doxorubicin (DOX), a widely utilized chemotherapeutic agent. To this end, we employed conditional knockout (KO) mice for the Ccn2 gene (CCN2-KO), which were administered DOX and compared to DOX-treated wild-type (WT) control mice. Our findings demonstrated that the ablation of CCN2 ameliorated DOX-induced cardiac dysfunction, as evidenced by improvements in ejection fraction (EF) and fractional shortening (FS) of the left ventricle. Furthermore, DOX-treated CCN2-KO mice exhibited a significant reduction in the gene expression and activation of oxidative stress markers (Hmox1 and Nfe2l2/NRF2) relative to DOX-treated WT controls. Additionally, the deletion of Ccn2 markedly attenuated DOX-induced cardiac fibrosis. Collectively, these results suggest that CCN2 plays a pivotal role in the pathogenesis of DOX-mediated cardiotoxicity by modulating oxidative stress and fibrotic pathways. These findings provide a novel avenue for future investigations to explore the therapeutic potential of targeting CCN2 in the prevention of DOX-induced cardiac dysfunction.

Mapping cellular interactions from spatially resolved transcriptomics data.

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.

Quantification of relevant metabolites in apoptotic bodies from HK-2 cells by targeted metabolomics based on liquid chromatography-tandem mass spectrometry

BackgroundApoptotic bodies play an important role in the cellular communication as a consequence of the great variety of biomolecules they harbor. There is evidence that 1st generation apoptotic bodies from HK-2 cells induced by cisplatin or UV light trigger apoptosis in naïve HK-2 cells whereas 2nd generation apoptotic bodies activate cell proliferation showing an opposite effect. Thus, the development of new analytical strategies to quantify the changes in the involved metabolites is imperative to shed light on the biological mechanisms which trigger apoptosis and cell proliferation. ResultsA LC-(Q-Orbitrap)MS method has been developed to quantify the metabolites unequivocally identified in the apoptotic body fluid from HK-2 cells in our previous works based on untargeted metabolomics. Thus, two different columns and gradients were tested and the HILIC column was selected taking into account the retention times and chromatographic separation. Also, different normal collision energies were tested for each metabolite and the parallel reaction monitoring was chosen to carry out the quantitative analysis. Once the method was optimized, it was evaluated in terms of linearity, limits of detection and quantification, matrix effects, accuracy, and precision, for each metabolite. Limits of detection ranged from 0.02 to 1.4 ng mL−1. A total of 9 relevant metabolites proposed as potential biomarkers to reveal metabolic differences among apoptotic bodies from HK-2 cells were quantified and some insights about the biological relevance were discussed. SignificanceThe first targeted metabolomics methodology enabling the quantification of relevant metabolites in apoptotic bodies from HK-2 cells was developed using LC-(Q-Orbitrap)MS. Pyridoxine, kynurenine, and creatine concentrations were determined in apoptotic bodies from HK-2 cells treated with cisplatin and UV light. Phenylacetylglycine, hippuric acid, butyrylcarnitine, acetylcarnitine, carnitine, and phenylalanine were determined in 1st and 2nd generation apoptotic bodies from HK-2 cells treated with cisplatin. Concentrations determined were useful to establish their biological role in the metabolism.

Exosomes of immune cell origin and their therapeutic potential for tumors

AbstractExosomes are nanoscale membrane vesicles identified by electron microscopy in 1946. They are approximately 30–150 nm in size. Originally, exosomes were thought to be used to eliminate excess components from cells to maintain their normal physiology. Later, it was found that the function and targeting of specific cellular components in exosomes have important implications for the regulation of cellular communication. In the last few years, exosomes have been linked to tumors, infections, and other diseases. New advances in cancer immunotherapy have occurred as a result of the identification of exosomes of immune cell origin, expanding the existing anticancer immune response. This article details exosomes derived from dendritic cells, T lymphocytes (CD4+ T cells, CD8+ T cells, and CAR‐T cells), natural killer cells, and their promising applications in tumor therapy.

Exosomes: Key Factors in Ovarian Cancer Peritoneal Metastasis and Drug Resistance.

Ovarian cancer remains a leading cause of death among gynecological cancers, largely due to its propensity for peritoneal metastasis and the development of drug resistance. This review concentrates on the molecular underpinnings of these two critical challenges. We delve into the role of exosomes, the nano-sized vesicles integral to cellular communication, in orchestrating the complex interactions within the tumor microenvironment that facilitate metastatic spread and thwart therapeutic efforts. Specifically, we explore how exosomes drive peritoneal metastasis by promoting epithelial-mesenchymal transition in peritoneal mesothelial cells, altering the extracellular matrix, and supporting angiogenesis, which collectively enable the dissemination of cancer cells across the peritoneal cavity. Furthermore, we dissect the mechanisms by which exosomes contribute to the emergence of drug resistance, including the sequestration and expulsion of chemotherapeutic agents, the horizontal transfer of drug resistance genes, and the modulation of critical DNA repair and apoptotic pathways. By shedding light on these exosome-mediated processes, we underscore the potential of exosomal pathways as novel therapeutic targets, offering hope for more effective interventions against ovarian cancer's relentless progression.

Signaling roles of sphingolipids in the ischemic brain and their potential utility as therapeutic targets

Sphingolipids comprise a class of lipids, which are composed of a sphingoid base backbone and are essential structural components of cell membranes. Beyond their role in maintaining cellular integrity, several sphingolipids are pivotally involved in signaling pathways controlling cell proliferation, differentiation, and death. The brain exhibits a particularly high concentration of sphingolipids and dysregulation of the sphingolipid metabolism due to ischemic injury is implicated in consecutive pathological events. Experimental stroke studies revealed that the stress sphingolipid ceramide accumulates in the ischemic brain post-stroke. Specifically, counteracting ceramide accumulation protects against ischemic damage and promotes brain remodeling, which translates into improved behavioral outcome. Sphingomyelin substantially influences cell membrane fluidity and thereby controls the release of extracellular vesicles, which are important vehicles in cellular communication. By modulating sphingomyelin content, these vesicles were shown to contribute to behavioral recovery in experimental stroke studies. Another important sphingolipid that influences stroke pathology is sphingosine-1-phosphate, which has been attributed a pro-angiogenic function, that is presumably mediated by its effect on endothelial function and/or immune cell trafficking. In experimental and clinical studies, sphingosine-1-phosphate receptor modulators allowed to modify clinically significant stroke recovery. Due to their pivotal roles in cell signaling, pharmacological compounds modulating sphingolipids, their enzymes or receptors hold promise as therapeutics in human stroke patients.

Machine learning and single-cell analysis identify the mitophagy-associated gene TOMM22 as a potential diagnostic biomarker for intervertebral disc degeneration

BackgroundMitophagy selectively eliminates potentially cytotoxic and damaged mitochondria and effectively prevents excessive cytotoxicity from damaged mitochondria, thereby attenuating inflammatory and oxidative responses. However, the potential role of mitophagy in intervertebral disc degeneration remains to be elucidated. MethodsThe GSVA method, two machine learning methods (SVM-RFE algorithm and random forest), the CIBERSORT and MCPcounter methods, as well as the consensus clustering method and the WGCNA algorithm were used to analyze the involvement of mitophagy in intervertebral disc degeneration, the diagnostic value of mitophagy-associated genes in intervertebral disc degeneration, and the infiltration of immune cells, and identify the gene modules that were closely related to mitophagy. Single-cell analysis was used to detect mitophagy scores and TOMM22 expression, and pseudo-temporal analysis was used to explore the function of TOMM22 in nucleus pulposus cells. In addition, TOMM22 expression was compared between human normal and degenerated intervertebral disc tissue samples by immunohistochemistry and PCR. ResultsThis study identified that the mitophagy pathway score was elevated in intervertebral disc degeneration compared with the normal condition. A strong link was present between mitophagy genes and immune cells, which may be used to typify intervertebral disc degeneration. The single-cell level showed that mitophagy-associated gene TOMM22 was highly expressed in medullary cells of the disease group. Further investigations indicated the upregulation of TOMM22 expression in late-stage nucleus pulposus cells and its role in cellular communication. In addition, human intervertebral disc tissue samples established that TOMM22 levels were higher in disc degeneration samples than in normal samples. ConclusionsOur findings revealed that mitophagy may be used in the diagnosis of intervertebral disc degeneration and its typing, and TOMM22 is a molecule in this regard and may act as a potential diagnostic marker in intervertebral disc degeneration.

A Multi-Machine Learning Consensus Model Based on Clinical Features Reveals That Interleukin-10 Derived from Monocytes Leads to a Poor Prognosis in Patients with Coronavirus Disease-2019.

Despite ongoing interventions, SARS-CoV-2 continues to cause significant global morbidity and mortality. Early diagnosis and intervention are crucial for effective clinical management. However, prognostic features based on transcriptional data have shown limited effectiveness, highlighting the need for more precise biomarkers to improve COVID-19 treatment outcomes. We retrospectively analyzed 149 clinical features from 189 COVID-19 patients, identifying prognostic features via univariate Cox regression. The cohort was split into training and validation sets, and 77 prognostic models were developed using seven machine learning algorithms. Among these, the least absolute shrinkage and selection operator (Lasso) method was employed to refine the selection of prognostic variables by ten-fold cross-validation strategy, which were then integrated with random survival forests (RSF) to build a robust COVID-19-related prognostic model (CRM). Model accuracy was evaluated across training, validation, and entire cohorts. The diagnostic relevance of interleukin-10 (IL-10) was confirmed in bulk transcriptional data and validated at the single-cell level, where we also examined changes in cellular communication between mononuclear cells with differing IL-10 expression and other immune cells. Univariate Cox regression identified 43 prognostic features. Among the 77 machine learning models, the combination of Lasso and RSF produced the most robust CRM. This model consistently performed well across training, validation, and entire cohorts. IL-10 emerged as a key prognostic feature within the CRM, validated by single-cell transcriptional data. Transcriptome analysis confirmed the stable diagnostic value of IL-10, with mononuclear cells identified as the primary IL-10 source. Moreover, differential IL-10 expression in these cells was linked to altered cellular communication in the COVID-19 immune microenvironment. The CRM provides accurate prognostic predictions for COVID-19 patients. Additionally, the study underscores the importance of early IL-10 level testing upon hospital admission, which could inform therapeutic strategies.

Integrated single-cell and bulk RNA sequencing analysis reveal immune-related biomarkers in postmenopausal osteoporosis

BackgroundPostmenopausal osteoporosis (PMOP) represents as a significant health concern, particularly as the population ages. Currently, there is a paucity of comprehensive descriptions regarding the immunoregulatory mechanisms and early diagnostic biomarkers associated with PMOP. This study aims to examine immune-related differentially expressed genes (IR-DEGs) in the peripheral blood mononuclear cells of PMOP patients to identify immunological patterns and diagnostic biomarkers. MethodsThe GSE56815 dataset from the Gene Expression Omnibus (GEO) database was used as the training group, while the GSE2208 dataset served as the validation group. Initially, differential expression analysis was conducted after data integration to identify IR-DEGs in the peripheral blood mononuclear cells of PMOP. Subsequently, feature selection of these IR-DEGs was performed using RF, SVM-RFE, and LASSO regression models. Additionally, the expression of IR-DEGs in distinct bone marrow cell subtypes was analyzed using single-cell RNA sequencing (scRNA-seq) datasets, allowing the identification of cellular communication patterns within various cell subgroups. Finally, molecular subtypes and diagnostic models for PMOP were constructed based on these selected IR-DEGs. Furthermore, the expression levels of characteristic IR-DEGs were examined in rat osteoporosis (OP) models. ResultsUsing machine learning, six IR-DEGs (JUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5) were identified. Subsequently, two molecular subtypes of PMOP (subtype 1 and subtype 2) were established, with subtype 1 exhibiting a higher proportion of M1 macrophage infiltration. Analysis of the scRNA-seq dataset revealed 11 distinct cell clusters. It was noted that JUN was significantly overexpressed in M1 macrophages, while HMOX1 showed a marked elevation in endothelial cells and M2 macrophages. Cell communication results suggested that the PMOP microenvironment features increased interactions among M2 macrophages, CD8+ T cells, Tregs, and fibroblasts. The diagnostic model based on these six IR-DEGs demonstrated excellent diagnostic performance (AUC = 0.927). In the OP rat model, the expression of IL1R2 and TNFSF8 were significantly elevated. ConclusionJUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5 may serve as promising molecular targets for diagnosing and subtyping patients with PMOP. These results offer novel perspectives on the early diagnosis of PMOP and the advancement of personalized immune-based therapies.

Assessing the risk of negative effects produced by electromagnetic fields of cellular communication on the central nervous system of children and adolescents (Review). Part 2. Indicators of cognitive processes

This paper continues the authors’ review that dwells on modeling radiofrequency electromagnetic fields (RF EMF) and results obtained by measuring electroencephalography indicators, sensorimotor reactions, fatigue, work capacity, duration of an individual minute and the reproduction of a given rhythm in children and adolescents. Health risk assessment is always based on data obtained by either laboratory tests or epidemiological studies. This paper analyses publications that describe effects of RF EMF exposure, including Wi-Fi, on cognitive processes in children and adolescents as well as methodical approaches to investigating this exposure. However, there are few such studies; in particular, effects produced by Wi-Fi exposure on cognitive indicators of adolescents aged 14–17 years, were found only in two publications. Literature analysis has established that research findings do not always give an unambiguous estimation of RF EMF effects. The review covers the reasons for ambiguous interpretation of research results: a variable range of test-systems used for investigating indicators of cognitive processes; simultaneous analysis of single exposures including descriptions of ‘effect of improvement’ in indicators; changes in cognitive indicators registered for a group of children and adolescents in a wide age range. Nevertheless, most results give evidence of negative changes in attention and memory of children and adolescents. Given that, longitudinal studies are becoming especially relevant since they estimate changes in various indicators in dynamics, including those induced by changes in mobile phone use. The review highlights the relevance of comprehensive investigations with their focus on health outcomes of RF EMF exposure intrinsic to 5G technologies considering their global implementation.

Cellular mobile station positioning algorithm based on IRS

Cellular mobile station positioning algorithm based on IRS

Single-cell sequencing reveals glial cell involvement in development of neuropathic pain via myelin sheath lesion formation in the spinal cord

BackgroundNeuropathic pain (NP), which results from injury or lesion of the somatosensory nervous system, is intimately associated with glial cells. The roles of microglia and astrocytes in NP have been broadly described, while studies on oligodendrocytes have largely focused on axonal myelination. The mechanisms of oligodendrocytes and their interactions with other glial cells in NP development remain uncertain.MethodsTo explore the function of the interaction of the three glial cells and their interactions on myelin development in NP, we evaluated changes in NP and myelin morphology after a chronic constriction injury (CCI) model in mice, and used single-cell sequencing to reveal the subpopulations characteristics of oligodendrocytes, microglia, and astrocytes in the spinal cord tissues, as well as their relationship with myelin lesions; the proliferation and differentiation trajectories of oligodendrocyte subpopulations were also revealed using pseudotime cell trajectory and RNA velocity analysis. In addition, we identified chemokine ligand-receptor pairs between glial cells by cellular communication and verified them using immunofluorescence.ResultsOur study showed that NP peaked on day 7 after CCI in mice, a time at which myelin lesions were present in both the spinal cord and sciatic nerve. Oligodendrocytes, microglia, and astrocytes subpopulations in spinal cord tissue were heterogeneous after CCI and all were involved in suppressing the process of immune defense and myelin production. In addition, the differentiation trajectory of oligodendrocytes involved a unidirectional lattice process of OPC-1-Oligo-9, which was arrested at the Oligo-2 stage under the influence of microglia and astrocytes. And the CADM1-CADM1, NRP1-VEGFA interactions between glial cells are enhanced after CCI and they had a key role in myelin lesions and demyelination.ConclusionsOur study reveals the close relationship between the differentiation block of oligodendrocytes after CCI and their interaction with microglia and astrocytes-mediated myelin lesions and NP. CADM1/CADM1 and NRP-1/VEGFA may serve as potential therapeutic targets for use in the treatment of NP.

Structural and functional characterization of gamma-irradiation-enhanced nanocomposites for colorectal cancer therapy

The purpose of this research was to develop and evaluate a nanocomposite material for the treatment of colorectal cancer. The nanocomposite consisted of manganese-doped silver nanoparticles coated with silica (Si-Ag: Mn3O4). The nanocomposite was synthesized by co-precipitating silver and manganese oxide, followed by a silica coating using the Stöber method. Extensive characterization was conducted to assess the morphological, optical, and stability properties. The key findings of this study are the Si-Ag: Mn3O4 nanocomposite exhibited unexpected and potent cytotoxicity against colorectal cancer cells, exceeding the effects of the positive control, cisplatin. The silica coating increased the Ag: Mn ratio, further enhancing the efficacy of the nanocomposite in cancer treatment; the nanocomposite maintained cellular mobility without agglomeration or sedimentation, indicating its potential for targeted tumor destruction; and gamma irradiation of the nanocomposite further increased its cytotoxic effects on colorectal cancer cells. In conclusion, the unique combination of silver, manganese oxide, and silica in this nanocomposite framework shows great promise for advanced applications in selective cancer therapy. The nanocomposite demonstrated significant cytotoxicity against HT-29 colorectal cancer cells, particularly at a gamma dose of 5.256 kGy. The results of this study open new perspectives in biological research, cancer treatment, and nanomedicine.

Thermal effects and ephaptic entrainment in Hodgkin–Huxley model

The brain is understood as an intricate biological system composed of numerous elements. It is susceptible to various physical and chemical influences, including temperature. The literature extensively explores the conditions that influence synapses in the context of cellular communication. However, the understanding of how the brain’s global physical conditions can modulate ephaptic communication remains limited due to the poorly understood nature of ephapticity. This study proposes an adaptation of the Hodgkin and Huxley (HH) model to investigate the effects of ephaptic entrainment in response to thermal changes (HH-E). The analysis focuses on two distinct neuronal regimes: subthreshold and suprathreshold. In the subthreshold regime, circular statistics are used to demonstrate the dependence of phase differences with temperature. In the suprathreshold regime, the Inter-Spike Interval are employed to estimate phase preferences and changes in the spiking pattern. Temperature influences the model’s ephaptic interactions and can modify its preferences for spiking frequency, with the direction of this change depending on specific model conditions and the temperature range under consideration. Furthermore, temperature enhance the anti-phase differences relationship between spikes and the external ephaptic signal. In the suprathreshold regime, ephaptic entrainment is also influenced by temperature, especially at low frequencies. This study reveals the susceptibility of ephaptic entrainment to temperature variations in both subthreshold and suprathreshold regimes and discusses the importance of ephaptic communication in the contexts where temperature may plays a significant role in neural physiology, such as inflammatory processes, fever, and epileptic seizures.