Repetitive transcranial magnetic stimulation in Alzheimer's disease: effects on neural and synaptic rehabilitation.

Alzheimer's disease is a neurodegenerative disease resulting from deficits in synaptic transmission and homeostasis. The Alzheimer's disease brain tends to be hyperexcitable and hypersynchronized, thereby causing neurodegeneration and ultimately disrupting the operational abilities in daily life, leaving patients incapacitated. Repetitive transcranial magnetic stimulation is a cost-effective, neuro-modulatory technique used for multiple neurological conditions. Over the past two decades, it has been widely used to predict cognitive decline; identify pathophysiological markers; promote neuroplasticity; and assess brain excitability, plasticity, and connectivity. It has also been applied to patients with dementia, because it can yield facilitatory effects on cognition and promote brain recovery after a neurological insult. However, its therapeutic effectiveness at the molecular and synaptic levels has not been elucidated because of a limited number of studies. This study aimed to characterize the neurobiological changes following repetitive transcranial magnetic stimulation treatment, evaluate its effects on synaptic plasticity, and identify the associated mechanisms. This review essentially focuses on changes in the pathology, amyloidogenesis, and clearance pathways, given that amyloid deposition is a major hypothesis in the pathogenesis of Alzheimer's disease. Apoptotic mechanisms associated with repetitive transcranial magnetic stimulation procedures and different pathways mediating gene transcription, which are closely related to the neural regeneration process, are also highlighted. Finally, we discuss the outcomes of animal studies in which neuroplasticity is modulated and assessed at the structural and functional levels by using repetitive transcranial magnetic stimulation, with the aim to highlight future directions for better clinical translations.

Causal associations between the gut microbiota and multiple myeloma: a two-sample Mendelian randomization study.

Background: Previous observational studies have indicated a potential association between the gut microbiota and multiple myeloma (MM). However, the relationship between the gut microbiota and MM remains unclear. This study aimed to ascertain the existence of a causal link between the gut microbiota and MM. Methods: To investigate the potential causal relationship between gut microbiota and MM, a two-sample Mendelian randomization (MR) analysis was conducted. Exposure data was obtained from the MiBioGen consortium, which provided genetic variants associated with 211 bacterial traits. MM outcome data was obtained from the FinnGen consortium. The selection of Single nucleotide polymorphisms estimates was performed through meta-analysis using inverse-variance weighting, and sensitivity analyses were conducted using weighted median, MR Egger, Simple mode, and MR-PRESSO. Results: The results of the study demonstrated a significant positive correlation between the genus Eubacterium ruminantium group and the risk of MM (OR 1.71, 95% CI 1.21 to 2.39). Conversely, the genus: Dorea (OR 0.46, 95% CI 0.24 to 0.86), Coprococcus1 (OR 0.47, 95% CI 0.22 to 1.00), RuminococcaceaeUCG014 (OR 0.57, 95% CI 0.33 to 0.99), Eubacterium rectale group (OR 0.37, 95% CI 0.18 to 0.77), and order: Victivallales (OR 0.62, 95% CI 0.41-0.94), class: Lentisphaeria (OR 0.62, 95% CI 0.41 to 0.94), exhibited a negative association with MM. The inverse variance weighting analysis provided additional support for these findings. Conclusion: This study represents an inaugural exploration of MR to investigate the connections between gut microbiota and MM, thereby suggesting potential significance for the prevention and treatment of MM.

Development of graphitic carbon nitride quantum dots-based oxygen self-sufficient platforms for enhanced corneal crosslinking.

Keratoconus, a disorder characterized by corneal thinning and weakening, results in vision loss. Corneal crosslinking (CXL) can halt the progression of keratoconus. The development of accelerated corneal crosslinking (A-CXL) protocols to shorten the treatment time has been hampered by the rapid depletion of stromal oxygen when higher UVA intensities are used, resulting in a reduced cross-linking effect. It is therefore imperative to develop better methods to increase the oxygen concentration within the corneal stroma during the A-CXL process. Photocatalytic oxygen-generating nanomaterials are promising candidates to solve the hypoxia problem during A-CXL. Biocompatible graphitic carbon nitride (g-C3N4) quantum dots (QDs)-based oxygen self-sufficient platforms including g-C3N4 QDs and riboflavin/g-C3N4 QDs composites (RF@g-C3N4 QDs) have been developed in this study. Both display excellent photocatalytic oxygen generation ability, high reactive oxygen species (ROS) yield, and excellent biosafety. More importantly, the A-CXL effect of the g-C3N4 QDs or RF@g-C3N4 QDs composite on male New Zealand white rabbits is better than that of the riboflavin 5'-phosphate sodium (RF) A-CXL protocol under the same conditions, indicating excellent strengthening of the cornea after A-CXL treatments. These lead us to suggest the potential application of g-C3N4 QDs in A-CXL for corneal ectasias and other corneal diseases.

Enhanced Intrusion of Exogenous Airborne Fine Particles toward Eyes in Humans and Animals: Where Damaged Blood-Ocular Barrier Plays a Crucial Role.

Emerging data suggest a close correlation between ambient fine particle (AFP) exposure and eye disorders and pinpoint potential threats of AFPs to eye health in humans. However, the possible passage (including direct intrusion) and the interactions of AFPs with the eye microenvironment in addition to morphological and physiological injuries remain elusive. To this end, the likely transport of AFPs into the eyes via blood-ocular barrier (BOB) in humans and animals was investigated herein. Exogenous particles were recognized inside human eyes with detailed structural and chemical fingerprints. Importantly, comparable AFPs were found in sera with constant structural and chemical fingerprints, hinting at the translocation pathway from blood circulation into the eye. Furthermore, we found that the particle concentrations in human eyes from patients with diabetic retinopathy were much higher than those from patients with no fundus pathological changes (i.e., myopia), indicating that the damaged BOB increased the possibility of particle entrance. Our diseased animal model further corroborated these findings. Collectively, our results offer a new piece of evidence on the intrusion of exogenous particles into human eyes and provide an explanation for AFP-induced eye disorders, with substantially increased risk in susceptible individuals with BOB injuries.

Crystallographic phase identifier of a convolutional self-attention neural network (CPICANN) on powder diffraction patterns.

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

Generation of an induced pluripotent stem cell line (CSBZZUi001-A) from a female Alzheimer's patient carrying the PSEN1 709 T > C heterozygous mutation.

Pluripotent stem cells were generated through the electroporation of episomal plasmids, containing crucial reprogramming factors, into skin fibroblasts extracted from a female Alzheimer's patient harboring the PSEN1 709 T > C (p.Phe237Leu) heterozygous mutation. The pluripotent stem cells exhibit a normal karyotype and express pivotal stem cell markers including TRA-1-60, Nanog, SOX2, and OCT4. Furthermore, their capacity to differentiate into the three germ layers in in vivo teratoma experiments has been substantiated. The pluripotent stem cell line can serve as a cellular model for Alzheimer's disease, offering significant value in elucidating the pathogenesis and therapeutic strategies of the disease.

Residential quality, physical activity, and restorative perceptions: A seasonal longitudinal study.

While the restorative benefits of residential environments are known, the influence of residents' physical activity on their perceptions of restorativeness in different settlements is unclear. This study aimed to investigate the mediating and moderating roles of residents' physical activities and seasons on restorative perceptions using survey data from three settlements in Harbin, China, involving a baseline survey conducted in June 2023 and questionnaires administered at 30-day intervals from July to December 2023 (534 interviews). Residents' restorative perceptions and physical activity levels were highest in autumn, with settlement quality having a seasonal moderating effect and physical activity having a mediating effect.

Widely Targeted Metabolomic Analysis Reveals the Improvement in Panax notoginseng Triterpenoids Triggered by Arbuscular Mycorrhizal Fungi via UPLC-ESI-MS/MS.

Panax notoginseng is a highly valued perennial medicinal herb in China and is widely used in clinical treatments. The main purpose of this study was to elucidate the changes in the composition of P. notoginseng saponins (PNSs), which are the main bioactive substances, triggered by arbuscular mycorrhizal fungi (AMF) via ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS). A total of 202 putative terpenoid metabolites were detected, of which 150 triterpene glycosides were identified, accounting for 74.26% of the total. Correlation analysis, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of the metabolites revealed that the samples treated with AMF (group Ce) could be clearly separated from the CK samples. In total, 49 differential terpene metabolites were identified between the Ce and CK groups, of which 38 and 11 metabolites were upregulated and downregulated, respectively, and most of the upregulated differentially abundant metabolites were mainly triterpene glycosides. The relative abundances of the two major notoginsenosides (MNs), ginsenosides Rd and Re, and 13 rare notoginsenosides (RNs), significantly increased. The differential saponins, especially RNs, were more easily clustered into one branch and had a high positive correlation. It could be concluded that the biosynthesis and accumulation of some RNs share the same pathways as those triggered by AMF. This study provides a new way to obtain more notoginsenoside resources, particularly RNs, and sheds new light on the scientization and rationalization of the use of AMF agents in the ecological planting of medicinal plants.

Application of Artificial Intelligence in rehabilitation science: A scientometric investigation Utilizing Citespace.

This study presents a scientometric analysis of the intersection between rehabilitation science and artificial intelligence (AI) technologies, using data from the Web of Science (WOS) database from 2002 to 2022. The analysis employed a comprehensive search query with key AI-related terms, focusing on a wide range of publications in rehabilitation science. Utilizing the Citespace tool, the study visualizes and quantifies the relationships between key terms, identifies research trends, and assesses the impact of AI technologies in rehabilitation science. Findings reveal a significant increase in AI-related research in this field, particularly from 2017 onwards, peaking in 2021. The United States has been a leading contributor, followed by countries like England, Australia, Germany, and Canada. Major institutional contributions come from Harvard University and the Pennsylvania Commonwealth System of Higher Education, among others. A keyword co-occurrence network constructed through Citespace identifies nine distinct hot topics and various research frontiers, highlighting evolving focus areas within the field. Burst analysis of keywords indicates a shift from performance and injury-related research to an increasing emphasis on AI and deep learning in recent years. The study also predicts the potential impact of papers, spotlighting works by Kunze KN and others as significantly influencing future research directions. Additionally, it examines the evolution of knowledge bases in AI-related rehabilitation science research, revealing a multidisciplinary core that includes neurology, rehabilitation, and ophthalmology, extending to complementary fields such as medicine and social sciences. This scientometric analysis provides a comprehensive overview of AI's application in rehabilitation science, offering insights into its evolution, impact, and emerging trends over the past two decades. The findings suggest strategic directions for future research, policy-making, and interdisciplinary collaboration in rehabilitation science and AI.

Role of ZNF334 in cervical cancer: implications for EMT reversal and tumor suppression.

Zinc-finger proteins are involved in many biological processes. However, the role of Zinc-finger protein 334 (ZNF334) in cervical cancer remains unidentified. This study showed that promoter methylation of ZNF334 was responsible for its reduced expression. ZNF334 suppressed malignant biological behaviors in cervical cancer. Notably, ZNF334 reversed the EMT process both in vitro and in vivo. RNA-seq coupled with bioinformatics analysis caught P3H3 which is upregulated by ZNF334. Dual-luciferase reporter and Chromatin immunoprecipitation assays illustrated that ZNF334 directly regulate P3H3. Knockdown of P3H3 attenuated the reversal of EMT induced by ZNF334. Additionally, ZNF334 overexpression sensitized cervical cancer cells to the cytotoxic effects of paclitaxel, cyclosporine and sunitinib. In conclusions, this study illustrated that DNA methylation-based silencing ZNF334 played a vital role in cervical cancer, by regulating P3H3 in turn affects EMT. ZNF334 has the potential to become a novel diagnostic biomarker and a potential treatment target for cervical cancer.

Alterations in neural circuit dynamics between the limbic network and prefrontal/default mode network in patients with generalized anxiety disorder.

BACKGROUND: Widespread functional alterations have been implicated in patients with generalized anxiety disorder (GAD). However, most studies have primarily focused on static brain network features in patients with GAD. The current research focused on exploring the dynamics within functional brain networks among individuals diagnosed with GAD. METHODS: Seventy-five participants were divided into patients with GAD and healthy controls (HCs), and resting-state functional magnetic resonance imaging data were collected. The severity of symptoms was measured using the Hamilton Anxiety Scale and the Patient Health Questionnaire. Co-activation pattern (CAP) analysis, centered on the bed nucleus of the stria terminalis, was applied to explore network dynamics. The capability of these dynamic characteristics to distinguish between patients with GAD and HCs was evaluated using a support vector machine. RESULTS: Patients with GAD exhibited disruptions in the limbic-prefrontal and limbic-default-mode network circuits. Particularly noteworthy was the marked reduction in dynamic indicators such as occurrence, EntriesFromBaseline, ExitsToBaseline, in-degree, out-degree, and resilience. Moreover, these decreased dynamic features effectively distinguished the GAD group from the HC in this study. CONCLUSIONS: The current findings revealed the underlying brain networks associated with compromised emotion regulation in individuals with GAD. The dynamic reduction in connectivity between the limbic-default mode network and limbic-prefrontal networks could potentially act as a biomarker and therapeutic target for GAD in the future.

Ruxolitinib Plus Basiliximab Therapy for Steroid-Refractory Acute Graft-Versus-Host Disease in Unrelated Cord Blood Transplantation: A Large-Scale Study.

Steroid-refractory (SR) acute graft-versus-host disease (aGVHD) is a major cause of mortality after allogeneic hematopoietic stem cell transplantation. We aimed to evaluate the effectiveness and safety of ruxolitinib plus basiliximab for treating SR-aGVHD after unrelated cord blood transplantation (UCBT). Among the 1154 patients with hematological malignancies who underwent UCBT between February 2014 and May 2022, 198 patients with grade II to IV SR-aGVHD were enrolled, 112 of whom were treated with basiliximab alone (basiliximab group) and 86 of whom received basiliximab plus ruxolitinib (combined therapy group). The combined therapy group demonstrated a significantly higher complete response rate (CRR) on day 28 (36.0%) than did the basiliximab group (12.5%, P < .001). SR-aGVHD patients were further stratified into standard-risk and high-risk groups using the refined Minnesota aGVHD risk score. For standard-risk patients, combined therapy significantly improved the CRR (51.1% versus 13.6%, P < .001) and 3-year overall survival (74.5% versus 52.4%, P = .033). However, high-risk patients did not exhibit the same benefits. Compared with basiliximab monotherapy, ruxolitinib plus basiliximab therapy was an effective therapy for patients with standard-risk SR-aGVHD following UCBT. The effectiveness of combined therapy in high-risk patients was not apparent, indicating the need for other treatments.

Application of predictive model based on CT radiomics and machine learning in diagnosis for occult locally advanced esophageal squamous cell carcinoma before treatment: A two-center study.

PURPOSE: Development and validation of a radiomics model for predicting occult locally advanced esophageal squamous cell carcinoma (LA-ESCC) on computed tomography (CT) radiomic features before implementation of treatment. METHODS: The study retrospectively collected 574 patients with esophageal squamous cell carcinoma (ESCC) from two medical centers, which were divided into three cohorts for training, internal and external validation. After delineating volume of interest (VOI), radiomics features were extracted and subjected to feature selection using three robust methods. Subsequently, 10 machine learning models were constructed, among which the optimal model was utilized to establish a radiomics signature. Furthermore, a predictive nomogram incorporating both clinical and radiomics signatures was developed. The performance of these models was evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis as well as measures including accuracy, sensitivity, and specificity. RESULTS: A total of 19 radiomics features were selected. The multilayer perceptron (MLP), which was found to be optimal, achieved an AUC of 0.919, 0.864 and 0.882 in the training, internal and external validation cohorts, respectively. Similarly, MLP showed good accuracy in distinguish occult LA-ESCC in subgroup of cT1-2N0M0 diagnosed by clinicians with 0.803 and 0.789 in two validation cohorts respectively. By incorporating the radiomics signature with clinical signature, a predictive nomogram demonstrated superior prediction performance with an AUC of 0.877 and accuracy of 0.85 in external validation cohort. CONCLUSION: The radiomics and machine learning model can offers improved accuracy in prediction of occult LA-ESCC, providing valuable assistance to clinicians when choosing treatment plans.

Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.

INTRODUCTION: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C2h), as an indicator of safety and efficacy, are important for optimizing therapy. OBJECTIVE: The objective of this study was to establish machine learning (ML) models to predict the C2h, that can be used for establishing an individualized dosing regimen in clinical practice. METHODS: Published population pharmacokinetic (PopPK) models for adults were searched based on PubMed and ultimately four reliable models were selected for simulating individual C2h datasets under different conditions (demographics, genotype, ethnicity, etc.). Machine learning models were trained on simulated C2h obtained from the four PopPK models. Five different algorithms were used for ML model building to predict C2h. Real-world data were used for predictive performance evaluations. Virtual trials were used to compare ML-optimized doses with PopPK model-optimized doses. RESULTS: Categorical boosting (CatBoost) exhibited the highest prediction ability. Target C2h can be predicted using the ML model combined with the dosing regimen and three covariates (N-acetyltransferase 2 [NAT2] genotypes, weight and race [Asians and Africans]). Real-world data validation results showed that the ML model can achieve an overall prediction accuracy of 93.4%. Using the final ML model, the mean absolute prediction error value decreased by 45.7% relative to the average of PopPK models. Using the ML-optimized dosing regimen, the probability of target attainment increased by 43.7% relative to the PopPK model-optimized dosing regimens. CONCLUSION: Machine learning models were developed with great predictive performance, which can be used to determine the individualized initial dose of isoniazid in adult patients.

Depressive and anxiety symptoms among schizophrenia patients.

BACKGROUND: Depressive and anxiety symptoms commonly manifested throughout the progression of schizophrenia. However, the prevalence of these symptoms, alongside their co-occurrence, remains uncertain, and clinical correlates remain elusive. OBJECTIVES: This study seeks to investigate the prevalence of such symptoms and their demographic and clinical associations among patients diagnosed with schizophrenia. METHODS: The study included 19,623 patients diagnosed with schizophrenia based on the ICD-10 criteria. Participants were recruited from community-dwelling patients registered in the local health system in Hangzhou of China between August 1 and October 30, 2022. RESULTS: The prevalence rates of depressive and anxiety symptoms, as well as their co-occurrence, were determined to be 19 % (95%CI = 18.5-19.6 %), 37.4 % (95%CI = 36.8-38.0 %), and 17.7 % (95%CI = 17.2-18.2 %), respectively. Patients prescribed quetiapine, olanzapine, and risperidone exhibited significantly lower prevalence rates of these symptoms (P < 0.01). Spearman's correlation analysis revealed a significant correlation between depressive symptoms and anxiety symptoms (r = 0.60, P = 0.006). Additionally, age, social relationships, and sleep status were significantly associated with depressive and anxiety symptoms, and their co-occurrence, in both univariate and multivariate analyses. CONCLUSION: Given the pervasive nature and detrimental consequences of these symptoms among individuals diagnosed with schizophrenia, comprehensive evaluation and implementation of efficacious interventions are highly recommended.

Comparative single-cell analysis reveals IFN-γ as a driver of respiratory sequelae after acute COVID-19.

Postacute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) represent an urgent public health challenge and are estimated to affect more than 60 million individuals globally. Although a growing body of evidence suggests that dysregulated immune reactions may be linked with PASC symptoms, most investigations have primarily centered around blood-based studies, with few focusing on samples derived from affected tissues. Furthermore, clinical studies alone often provide correlative insights rather than causal mechanisms. Thus, it is essential to compare clinical samples with relevant animal models and conduct functional experiments to understand the etiology of PASC. In this study, we comprehensively compared bronchoalveolar lavage fluid single-cell RNA sequencing data derived from clinical PASC samples and a mouse model of PASC. This revealed a pro-fibrotic monocyte-derived macrophage response in respiratory PASC, as well as abnormal interactions between pulmonary macrophages and respiratory resident T cells, in both humans and mice. Interferon-γ (IFN-γ) emerged as a key node mediating the immune anomalies in respiratory PASC. Neutralizing IFN-γ after the resolution of acute SARS-CoV-2 infection reduced lung inflammation and tissue fibrosis in mice. Together, our study underscores the importance of performing comparative analysis to understand the cause of PASC and suggests that the IFN-γ signaling axis might represent a therapeutic target.

Response of soil fungal communities and their co-occurrence patterns to grazing exclusion in different grassland types.

Overgrazing and climate change are the main causes of grassland degradation, and grazing exclusion is one of the most common measures for restoring degraded grasslands worldwide. Soil fungi can respond rapidly to environmental stresses, but the response of different grassland types to grazing control has not been uniformly determined. Three grassland types (temperate desert, temperate steppe grassland, and mountain meadow) that were closed for grazing exclusion for 9 years were used to study the effects of grazing exclusion on soil nutrients as well as fungal community structure in the three grassland types. The results showed that (1) in the 0-5 cm soil layer, grazing exclusion significantly affected the soil water content of the three grassland types (P < 0.05), and the pH, total phosphorous (TP), and nitrogen-to-phosphorous ratio (N/P) changed significantly in all three grassland types (P < 0.05). Significant changes in soil nutrients in the 5-10 cm soil layer after grazing exclusion occurred in the mountain meadow grasslands (P < 0.05), but not in the temperate desert and temperate steppe grasslands. (2) For the different grassland types, Archaeorhizomycetes was most abundant in the montane meadows, and Dothideomycetes was most abundant in the temperate desert grasslands and was significantly more abundant than in the remaining two grassland types (P < 0.05). Grazing exclusion led to insignificant changes in the dominant soil fungal phyla and α diversity, but significant changes in the ß diversity of soil fungi (P < 0.05). (3) Grazing exclusion areas have higher mean clustering coefficients and modularity classes than grazing areas. In particular, the highest modularity class is found in temperate steppe grassland grazing exclusion areas. (4) We also found that pH is the main driving factor affecting soil fungal community structure, that plant coverage is a key environmental factor affecting soil community composition, and that grazing exclusion indirectly affects soil fungal communities by affecting soil nutrients. The above results suggest that grazing exclusion may regulate microbial ecological processes by changing the soil fungal ß diversity in the three grassland types. Grazing exclusion is not conducive to the recovery of soil nutrients in areas with mountain grassland but improves the stability of soil fungi in temperate steppe grassland. Therefore, the type of degraded grassland should be considered when formulating suitable restoration programmes when grazing exclusion measures are implemented. The results of this study provide new insights into the response of soil fungal communities to grazing exclusion, providing a theoretical basis for the management of degraded grassland restoration.

A general pHLA-CD80 scaffold fusion protein to promote efficient antigen-specific T cell-based immunotherapy.

Inadequate antigen-specific T cells activation hampers immunotherapy due to complex antigen presentation. In addition, therapeutic in vivo T cell expansion is constrained by slow expansion rates and limited functionality. Herein, we introduce a model fusion protein termed antigen-presenting cell-mimic fusion protein (APC-mimic), designed to greatly mimicking the natural antigen presentation pattern of antigen-presenting cells and directly expand T cells both in vitro and in vivo. The APC-mimic comprises the cognate peptide-human leukocyte antigen (pHLA) complex and the co-stimulatory marker CD80, which are natural ligands on APCs. Following a single stimulation, APC-mimic leads to an approximately 400-fold increase in the polyclonal expansion of antigen-specific T cells compared with the untreated group in vitro without the requirement for specialized antigen-presenting cells. Through the combination of single-cell TCR sequencing (scTCR-seq) and single-cell RNA sequencing (scRNA-seq), we identify an approximately 600-fold monoclonal expansion clonotype among these polyclonal clonotypes. It also exhibits suitability for in vivo applications confirmed in the OT-1 mouse model. Furthermore, T cells expanded by APC-mimic effectively inhibits tumor growth in adoptive cell transfer (ACT) murine models. These findings pave the way for the versatile APC-mimic platform for personalized therapeutics, enabling direct expansion of polyfunctional antigen-specific T cell subsets in vitro and in vivo.

Deciphering the spatiotemporal transcriptional landscape of intestinal diseases (Review).

The intestines are the largest barrier organ in the human body. The intestinal barrier plays a crucial role in maintaining the balance of the intestinal environment and protecting the intestines from harmful bacterial invasion. Single­cell RNA sequencing technology allows the detection of the different cell types in the intestine in two dimensions and the exploration of cell types that have not been fully characterized. The intestinal mucosa is highly complex in structure, and its proper functioning is linked to multiple structures in the proximal­distal intestinal and luminal­mucosal axes. Spatial localization is at the core of the efforts to explore the interactions between the complex structures. Spatial transcriptomics (ST) is a method that allows for comprehensive tissue analysis and the acquisition of spatially separated genetic information from individual cells, while preserving their spatial location and interactions. This approach also prevents the loss of fragile cells during tissue disaggregation. The emergence of ST technology allows us to spatially dissect enzymatic processes and interactions between multiple cells, genes, proteins and signals in the intestine. This includes the exchange of oxygen and nutrients in the intestine, different gradients of microbial populations and the role of extracellular matrix proteins. This regionally precise approach to tissue studies is gaining more acceptance and is increasingly applied in the investigation of disease mechanisms related to the gastrointestinal tract. Therefore, this review summarized the application of ST in gastrointestinal diseases.