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Drug repurposing - identifying new therapeutic uses for approved drugs - is often serendipitous and opportunistic, expanding the use of drugs for new diseases. The clinical utility of drug repurposing AI models remains limited because the models focus narrowly on diseases for which some drugs already exist. Here, we introduce T x GNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, T x GNN utilizes a graph neural network and metric-learning module to rank drugs as potential indications and contraindications across 17,080 diseases. When benchmarked against eight methods, T x GNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, T x GNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form T x GNN's predictive rationales. Human evaluation of T x GNN's Explainer showed that T x GNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of T x GNN's novel predictions align with off-label prescriptions clinicians make in a large healthcare system. T x GNN's drug repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.
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Plant-derived extracellular vesicles (PLEVs), as a type of naturally occurring lipid bilayer membrane structure, represent an emerging delivery vehicle with immense potential due to their ability to encapsulate hydrophobic and hydrophilic compounds, shield them from external environmental stresses, control release, exhibit biocompatibility, and demonstrate biodegradability. This comprehensive review analyzes engineering preparation strategies for natural vesicles, focusing on PLEVs and their purification and surface engineering. Furthermore, it encompasses the latest advancements in utilizing PLEVs to transport active components, serving as a nanotherapeutic system. The prospects and potential development of PLEVs are also discussed. It is anticipated that this work will not only address existing knowledge gaps concerning PLEVs but also provide valuable guidance for researchers in the fields of food science and biomedical studies, stimulating novel breakthroughs in plant-based therapeutic options.
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Vesículas Extracelulares , Plantas , Vesículas Extracelulares/metabolismo , Vesículas Extracelulares/química , Plantas/química , Plantas/metabolismo , Humanos , AnimalesRESUMEN
Therapeutics Data Commons (tdcommons.ai) is an open science initiative with unified datasets, AI models, and benchmarks to support research across therapeutic modalities and drug discovery and development stages. The Commons 2.0 (TDC-2) is a comprehensive overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new multimodal tasks and model frameworks, and comprehensive benchmarks. TDC-2 introduces over 1,000 multimodal datasets spanning approximately 85 million cells, pre-calculated embeddings from 5 state-of-the-art single-cell models, and a biomedical knowledge graph. TDC-2 drastically expands the coverage of ML tasks across therapeutic pipelines and 10+ new modalities, spanning but not limited to single-cell gene expression data, clinical trial data, peptide sequence data, peptidomimetics protein-peptide interaction data regarding newly discovered ligands derived from AS-MS spectroscopy, novel 3D structural data for proteins, and cell-type-specific protein-protein interaction networks at single-cell resolution. TDC-2 introduces multimodal data access under an API-first design using the model-view-controller paradigm. TDC-2 introduces 7 novel ML tasks with fine-grained biological contexts: contextualized drug-target identification, single-cell chemical/genetic perturbation response prediction, protein-peptide binding affinity prediction task, and clinical trial outcome prediction task, which introduce antigen-processing-pathway-specific, cell-type-specific, peptide-specific, and patient-specific biological contexts. TDC-2 also releases benchmarks evaluating 15+ state-of-the-art models across 5+ new learning tasks evaluating models on diverse biological contexts and sampling approaches. Among these, TDC-2 provides the first benchmark for context-specific learning. TDC-2, to our knowledge, is also the first to introduce a protein-peptide binding interaction benchmark.
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Background and objectives: To develop a nomogram for mild cognitive impairment (MCI) in patients with subjective cognitive decline (SCD) undergoing physical examinations in China. Methods: We enrolled 370 patients undergoing physical examinations at the Medical Center of the First Hospital of Jilin University, Jilin Province, China, from October 2022 to March 2023. Of the participants, 256 were placed in the SCD group, and 74 were placed in the MCI group. The population was randomly divided into a training set and a validation set at a 7:3 ratio. A least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. The performance and clinical utility of the nomogram were determined using Harrell's concordance index, calibration curves, and decision curve analysis (DCA). Results: Cognitive reserve (CR), age, and a family history of hypertension were associated with the occurrence of MCI. The predictive nomogram showed satisfactory performance, with a concordance index of 0.755 (95% CI: 0.681-0.830) in internal verification. The Hosmer-Lemeshow test results suggested that the model exhibited good fit (p = 0.824). In addition, DCA demonstrated that the predictive nomogram had a good clinical net benefit. Discussion: We developed a simple nomogram that could help secondary preventive health care workers to identify elderly individuals with SCD at high risk of MCI during physical examinations to enable early intervention.
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Probiotics are indispensable for maintaining the structure of gut microbiota and promoting human health, yet their survivability is frequently compromised by environmental stressors such as temperature fluctuations, pH variations, and mechanical agitation. In response to these challenges, microfluidic technology emerges as a promising avenue. This comprehensive review delves into the utilization of microfluidic technology for the encapsulation and delivery of probiotics within the gastrointestinal tract, with a focus on mitigating obstacles associated with probiotic viability. Initially, it elucidates the design and application of microfluidic devices, providing a precise platform for probiotic encapsulation. Moreover, it scrutinizes the utilization of carriers fabricated through microfluidic devices, including emulsions, microspheres, gels, and nanofibers, with the intent of bolstering probiotic stability. Subsequently, the review assesses the efficacy of encapsulation methodologies through in vitro gastrointestinal simulations and in vivo experimentation, underscoring the potential of microfluidic technology in amplifying probiotic delivery efficiency and health outcomes. In sum, microfluidic technology represents a pioneering approach to probiotic stabilization, offering avenues to cater to consumer preferences for a diverse array of functional food options.
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Microfluídica , Probióticos , Probióticos/administración & dosificación , Humanos , Microfluídica/instrumentación , Microfluídica/métodos , Animales , Tracto Gastrointestinal/microbiología , Tracto Gastrointestinal/metabolismo , Microbioma Gastrointestinal , Composición de Medicamentos/métodos , Composición de Medicamentos/instrumentaciónRESUMEN
AIM: To pool existing studies to assess the overall effectiveness of integrated care for older adults (ICOPE)-based interventions in improving depressive symptoms in older adults. DESIGN: A systematic review and meta-analysis. DATA SOURCES: Ten databases were systematically searched from inception to 15 July 2023 and the search was last updated on 2 September 2023. METHODS: Standardized mean difference (SMD) was calculated using random effects models. RoB 2 and GRADEpro GDT were used to assess the methodological quality and confidence in the cumulative evidence. Funnel plots, egger's test and begg's test were used to analyse publication bias. Sensitivity, subgroup and meta-regression analyses were performed to explore potential sources of heterogeneity. RESULTS: The results of 18 studies showed ICOPE-based interventions had a significant effect on improving depressive symptoms (SMD = -.84; 95% CI, -1.20 to -.3647; p < .001; 18 RCTs, 5010 participants; very low-quality evidence). Subgroup analysis showed the intervention group was characterized by mean age (70-80 years old), intervention duration between 6 to 12 months, gender (female <50%), non-frail older adults, depressed older adults and mixed integration appeared to be more effective. Sensitivity analysis found the results to be robust. CONCLUSION: ICOPE-based interventions may be a potentially effective alternative approach to reduce depressive symptoms in the older adults. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Healthcare professionals are expected to use ICOPE as one of the interventions for depressive symptoms in older adults, and this ICOPE could provide more comprehensive care services for older adults to reduce depressive symptoms. IMPACT: ICOPE-based interventions may be a potentially effective alternative approach to reduce depressive symptoms in the older adults. ICOPE-based interventions had a significant effect on reducing depressive symptoms in the older adults. The intervention group characterized by mean age of older adults, intervention duration, gender ratio, health condition and integration types may influence the effect size. REPORTING METHOD: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.
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Prestación Integrada de Atención de Salud , Depresión , Humanos , Anciano , Depresión/terapia , Anciano de 80 o más Años , Femenino , MasculinoRESUMEN
The rapid development of computer vision technologies and applications has brought forth a range of social and ethical challenges. Due to the unique characteristics of visual technology in terms of data modalities and application scenarios, computer vision poses specific ethical issues. However, the majority of existing literature either addresses artificial intelligence as a whole or pays particular attention to natural language processing, leaving a gap in specialized research on ethical issues and systematic solutions in the field of computer vision. This paper utilizes bibliometrics and text-mining techniques to quantitatively analyze papers from prominent academic conferences in computer vision over the past decade. It first reveals the developing trends and specific distribution of attention regarding trustworthy aspects in the computer vision field, as well as the inherent connections between ethical dimensions and different stages of visual model development. A life-cycle framework regarding trustworthy computer vision is then presented by making the relevant trustworthy issues, the operation pipeline of AI models, and viable technical solutions interconnected, providing researchers and policymakers with references and guidance for achieving trustworthy CV. Finally, it discusses particular motivations for conducting trustworthy practices and underscores the consistency and ambivalence among various trustworthy principles and technical attributes.
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Inteligencia Artificial , Humanos , Inteligencia Artificial/ética , Inteligencia Artificial/tendencias , Confianza , Procesamiento de Lenguaje Natural , Minería de Datos/ética , BibliometríaRESUMEN
In this study, a novel one-step coaxial electrospinning process is employed to fabricate shell-core structure fibers choosing Chlorella pyrenoidosa proteins (CP) as the core material. These nanofibers, serving as the wall material for probiotic encapsulation, aimed to enhance the stability and antioxidant activity of probiotics in food processing, storage, and gastrointestinal environments under sensitive conditions. Morphological analysis was used to explore the beads-on-a-string morphology and core-shell structure of the electrospun fibers. Probiotics were successfully encapsulated within the fibers (7.97 log CFU/g), exhibiting a well-oriented structure along the distributed fibers. Compared to free probiotics and uniaxial fibers loaded with probiotics, encapsulation within microalgae proteins/alginate core-shell structure nanofibers significantly enhanced the probiotic cells' tolerance to simulated gastrointestinal conditions (p < 0.05). Thermal analysis indicated that microalgae proteins/alginate core-shell structure nanofibers displayed superior thermal stability compared to uniaxial fibers. The introduction of CP resulted in a 50 % increase in the antioxidant capacity of probiotics-loaded microalgae proteins/alginate nanofibers compared to uniaxial alginate nanofibers, with minimal loss of viability (0.8 log CFU/g) after 28 days of storage at 4 °C. In summary, this dual-layer carrier holds immense potential in probiotic encapsulation and enhancing their resistance to harsh conditions.
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Alginatos , Microalgas , Nanofibras , Probióticos , Alginatos/química , Nanofibras/química , Probióticos/química , Microalgas/química , Antioxidantes/química , Antioxidantes/farmacología , Chlorella/química , Viabilidad Microbiana/efectos de los fármacosRESUMEN
Current large language models (LLMs) rely on word prediction as their backbone pretraining task. Although word prediction is an important mechanism underlying language processing, human language comprehension occurs at multiple levels, involving the integration of words and sentences to achieve a full understanding of discourse. This study models language comprehension by using the next sentence prediction (NSP) task to investigate mechanisms of discourse-level comprehension. We show that NSP pretraining enhanced a model's alignment with brain data especially in the right hemisphere and in the multiple demand network, highlighting the contributions of nonclassical language regions to high-level language understanding. Our results also suggest that NSP can enable the model to better capture human comprehension performance and to better encode contextual information. Our study demonstrates that the inclusion of diverse learning objectives in a model leads to more human-like representations, and investigating the neurocognitive plausibility of pretraining tasks in LLMs can shed light on outstanding questions in language neuroscience.
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Encéfalo , Comprensión , Lenguaje , Humanos , Comprensión/fisiología , Encéfalo/fisiologíaRESUMEN
Depression constitutes a pervasive global mental health concern and stands as a principal determinant of elevated suicide rates worldwide. Recent empirical investigations have showcased the significant potential of visual art therapy (VAT) in ameliorating symptoms among individuals with depression. Nevertheless, specific studies have yielded findings marked by inconclusiveness, underscoring the imperative need for further research to comprehensively establish its efficacy. This study is a systematic review and meta-analysis of extant research, to ascertain the efficacy and effect size of VAT as an intervention for adults with depressive symptoms. A comprehensive search was conducted across 10 databases. The search encompassed articles published from the inception of these databases up until October 18, 2023. Two researchers screened the literature in accordance with inclusion and exclusion criteria and performed a thorough quality assessment. The original data and the data obtained from the literature were extracted for further analysis. The statistical analysis of the data was performed using Stata 17.0 software. fifteen studies were included, encompassing a total of 932 participants. The outcomes of meta-analysis unveiled a statistically significant effect of VAT in diminishing depressive symptoms among adults (SMD = -0.73; 95% CI, -1.07 to -0.39; p < 0.001; 15 randomised controlled trials (RCTs); low-quality evidence). The subgroup analysis indicated that VAT exhibited heightened effectiveness among adults below 65 years of age, with interventions lasting ≤12 weeks demonstrating superior efficacy. Additionally, sensitivity analysis underscored the robustness and reliability of the findings. VAT appears to alleviate depressive symptoms among adults. Existing research indicates that the effectiveness of VAT is influenced by factors, such as intervention population characteristics and intervention duration. However, to comprehensively probe the efficacy of VAT, future studies should strive for larger sample sizes, multicentre collaborations, and long-term follow-ups.
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Inclusions containing TAR DNA binding protein 43 (TDP-43) are a pathological hallmark of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). One of the disease-specific features of TDP-43 inclusions is the aberrant phosphorylation of TDP-43 at serines 409/410 (pS409/410). Here, we developed rabbit monoclonal antibodies (mAbs) that specifically detect pS409/410-TDP-43 in multiple model systems and FTD/ALS patient samples. Specifically, we identified three mAbs (26H10, 2E9 and 23A1) from spleen B cell clones that exhibit high specificity and sensitivity to pS409/410-TDP-43 peptides in an ELISA assay. Biochemical analyses revealed that pS409/410 of recombinant TDP-43 and of exogenous 25 kDa TDP-43 C-terminal fragments in cultured HEK293T cells are detected by all three mAbs. Moreover, the mAbs detect pS409/410-positive TDP-43 inclusions in the brains of FTD/ALS patients and mouse models of TDP-43 proteinopathy by immunohistochemistry. Our findings indicate that these mAbs are a valuable resource for investigating TDP-43 pathology both in vitro and in vivo.
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Esclerosis Amiotrófica Lateral , Demencia Frontotemporal , Proteinopatías TDP-43 , Ratones , Animales , Humanos , Esclerosis Amiotrófica Lateral/genética , Demencia Frontotemporal/patología , Anticuerpos Monoclonales , Células HEK293 , Proteínas de Unión al ADN/genéticaRESUMEN
The selective hydrogenation of furfural (FFA) to furfuryl alcohol (FA) is regarded as attractive transformation to achieve the sustainable synthesis of value-added chemicals from biomass resources. However, the conventional supported catalysts are significantly restricted by their narrow pore size, ununiform dispersion and easy leaching or aggregation of catalytic sites. Herein, we designed hollow UiO-66-NH2 as the support to encapsulate Pd nanoparticles (Pd@H-UiO-66-NH2) to achieve the highly active and selective conversion of FFA to FA. Benefiting from the void-confinement effect and substrate enrichment of hollow structure, as well as the surface wrinkles, the as-prepared catalyst Pd@H-UiO-66-NH2 exhibited 96.8 % conversion of FFA with satisfactory selectivity reaching up to 92.4 % at 80 °C, 0.5â MPa H2 in isopropanol solvent within 6â h. More importantly, as-prepared Pd@H-UiO-66-NH2 catalyst exhibited excellent long-term stability, as well as good universality toward a series of hydrogenation of unsaturated hydrocarbons.
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High performance organic solar cells (OSCs) are usually realized by using post-treatment and/or additive, which can induce the formation of metastable morphology, leading to unfavorable device stability. In terms of the industrial production, the development of high efficiency as-cast OSCs is crucially important, but it remains a great challenge to obtain appropriate active layer morphology and high power conversion efficiency (PCE). Here, efficient as-cast OSCs are constructed via introducing a new polymer acceptor PY-TPT with a high dielectric constant into the D18:L8-BO blend to form a double-fibril network morphology. Besides, the incorporation of PY-TPT enables an enhanced dielectric constant and lower exciton binding energy of active layer. Therefore, efficient exciton dissociation and charge transport are realized in D18:L8-BO:PY-TPT-based device, affording a record-high PCE of 18.60% and excellent photostability in absence of post-treatment. Moreover, green solvent-processed devices, thick-film (300 nm) devices, and module (16.60 cm2) are fabricated, which show PCEs of 17.45%, 17.54%, and 13.84%, respectively. This work brings new insight into the construction of efficient as-cast devices, pushing forward the practical application of OSCs.
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Dry eye disease (DED) is a common and frequent ocular surface disease worldwide, which can cause severe ocular surface discomfort and blurred vision. Inflammation and reactive oxygen species (ROS) play decisive roles in the development of DED. However, existing treatments usually focus on anti-inflammation while ignore the role of ROS in DED. Ever worse, the clinical preparations are easily cleared by nasolacrimal ducts, resulting in poor therapeutic effect. To circumvent these obstacles, here we designed a phenylboronic acid (PBA) modified liposome co-loading immunosuppressant cyclosporin A (CsA) and antioxidant crocin (Cro). The CsA/Cro PBA Lip achieved mucoadhesion through the formation of covalent bonds between PBA and the sialic acid residues on mucin, and consequently improved the retention of drugs on the ocular surface. By inhibiting ROS production and blocking NF-κB inflammatory pathway, CsA/Cro PBA Lip successfully promoted the healing of damaged corneal epithelium, eventually achieving the goal of relieving DED. CsA/Cro PBA Lip is proven a simple yet effective dual-drug delivery system, exhibiting superior antioxidant and anti-inflammatory effects both in vitro and in vivo. This approach holds great potential in the clinical treatment of DED and other related mucosal inflammations.
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Síndromes de Ojo Seco , Liposomas , Humanos , Liposomas/uso terapéutico , Antioxidantes/uso terapéutico , Especies Reactivas de Oxígeno , Soluciones Oftálmicas , Síndromes de Ojo Seco/tratamiento farmacológico , Inflamación/tratamiento farmacológico , CiclosporinaRESUMEN
In recent years, short-form social media videos have emerged as an important source of health-related advice. In this study, we investigate whether experts or ordinary users in such videos are more effective in debunking the common misperception that talking about suicide should be avoided. We also explore a new trend on TikTok and other platforms, in which users attempt to back up their arguments by displaying scientific articles in the background of their videos. To test the effect of source type (expert vs. ordinary user) and scientific references (present or absent), we conducted a 2 × 2 between-subject plus control group experiment (n = 956). In each condition, participants were shown a TikTok video that was approximately 30 seconds long. Our findings show that in all four treatment groups, participants reduced their misperceptions on the topic. The expert was rated as being more authoritative on the topic compared to the ordinary user. However, the expert was also rated as being less credible compared to the ordinary user. The inclusion of a scientific reference did not make a difference. Thus, both experts and ordinary users may be similarly persuasive in a short-form video environment.
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The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.
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Aging entails gradual functional decline influenced by interconnected factors. Multiple hallmarks proposed as common and conserved underlying denominators of aging on the molecular, cellular and systemic levels across multiple species. Thus, understanding the function of aging hallmarks and their relationships across species can facilitate the translation of anti-aging drug development from model organisms to humans. Here, we built AgeAnnoMO (https://relab.xidian.edu.cn/AgeAnnoMO/#/), a knowledgebase of multi-omics annotation for animal aging. AgeAnnoMO encompasses an extensive collection of 136 datasets from eight modalities, encompassing 8596 samples from 50 representative species, making it a comprehensive resource for aging and longevity research. AgeAnnoMO characterizes multiple aging regulators across species via multi-omics data, comprehensively annotating aging-related genes, proteins, metabolites, mitochondrial genes, microbiotas and age-specific TCR and BCR sequences tied to aging hallmarks for these species and tissues. AgeAnnoMO not only facilitates a deeper and more generalizable understanding of aging mechanisms, but also provides potential insights of the specificity across tissues and species in aging process, which is important to develop the effective anti-aging interventions for diverse populations. We anticipate that AgeAnnoMO will provide a valuable resource for comprehending and integrating the conserved driving hallmarks in aging biology and identifying the targetable biomarkers for aging research.
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Envejecimiento , Bases del Conocimiento , Multiómica , Animales , Humanos , Envejecimiento/genética , Biomarcadores , Longevidad/genéticaRESUMEN
Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in DRMref are browsable and searchable, with detailed annotations provided. Meanwhile, DRMref includes analyses of cellular composition, intratumoral heterogeneity, epithelial-mesenchymal transition, cell-cell interaction and differentially expressed genes in resistant cells. Notably, DRMref investigates the drug resistance mechanisms (e.g. Aberration of Drug's Therapeutic Target, Drug Inactivation by Structure Modification, etc.) in resistant cells. Additional enrichment analysis of hallmark/KEGG (Kyoto Encyclopedia of Genes and Genomes)/GO (Gene Ontology) pathways, as well as the identification of microRNA, motif and transcription factors involved in resistant cells, is provided in DRMref for user's exploration. Overall, DRMref serves as a unique single-cell-based resource for studying drug resistance, drug combination therapy and discovering novel drug targets.
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Bases de Datos Factuales , Resistencia a Medicamentos , MicroARNs , Neoplasias , Humanos , Resistencia a Medicamentos/genética , Perfilación de la Expresión Génica , Regulación Neoplásica de la Expresión Génica , MicroARNs/genética , Neoplasias/tratamiento farmacológico , Neoplasias/genética , InternetRESUMEN
Despite the significant progress in immunotherapy for certain cancers, including cervical cancer, most patients remain unresponsive or derive limited benefits from combined radiotherapy and chemotherapy. The factors underlying treatment resistance are unknown and there are few reliable predictive biomarkers. BATF2 is a member of the basic leucine zipper transcription factor family and is involved in immune response and immune cell development. However, the role of BATF2 in the immune microenvironment of patients with cervical cancer after radiotherapy remains unclear. In this study, immunohistochemistry and multicolour immunofluorescence analyses of patient tumor samples were used to assess BATF2 expression. We found that cervical cancer patients with high BATF2 expression had higher infiltration levels of CD4+ T cells, CD8+ T cells, and macrophages within the tumor than those with low expression levels. Furthermore, BATF2 expression was positively correlated with the prognosis of patients after concurrent chemoradiotherapy. A wild-type mouse model with BATF2-knockdown U14 cell-derived subcutaneous tumors and a Batf2-/- mouse model with wild-type U14 cell-derived subcutaneous tumors were used to assess CD8+ T cell infiltration and function. As expected, the knockdown of BATF2 in the U14 cell line substantially promoted tumor growth, which was mediated by a reduction in CD8+ T cell infiltration and antitumor function in vivo. Additionally, the Batf2-/- mouse model demonstrated that host BATF2 is also involved in controlling tumor growth. Furthermore, the combination of radiotherapy and anti-PD-1 therapy showed synergistic antitumour effects. These findings collectively suggest that BATF2 may serve as a potent positive regulator of the tumor immune microenvironment of cervical cancer after radiotherapy, and has the potential to be a prognostic biomarker to guide the application of a combination of radiotherapy and immunotherapy.
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Biomarcadores de Tumor , Neoplasias del Cuello Uterino , Femenino , Humanos , Ratones , Animales , Biomarcadores de Tumor/genética , Neoplasias del Cuello Uterino/genética , Neoplasias del Cuello Uterino/terapia , Linfocitos T CD8-positivos/metabolismo , Factores de Transcripción con Cremalleras de Leucina de Carácter Básico/genética , Factores de Transcripción con Cremalleras de Leucina de Carácter Básico/metabolismo , Inmunoterapia , Microambiente TumoralRESUMEN
StemDriver is a comprehensive knowledgebase dedicated to the functional annotation of genes participating in the determination of hematopoietic stem cell fate, available at http://biomedbdc.wchscu.cn/StemDriver/. By utilizing single-cell RNA sequencing data, StemDriver has successfully assembled a comprehensive lineage map of hematopoiesis, capturing the entire continuum from the initial formation of hematopoietic stem cells to the fully developed mature cells. Extensive exploration and characterization were conducted on gene expression features corresponding to each lineage commitment. At the current version, StemDriver integrates data from 42 studies, encompassing a diverse range of 14 tissue types spanning from the embryonic phase to adulthood. In order to ensure uniformity and reliability, all data undergo a standardized pipeline, which includes quality data pre-processing, cell type annotation, differential gene expression analysis, identification of gene categories correlated with differentiation, analysis of highly variable genes along pseudo-time, and exploration of gene expression regulatory networks. In total, StemDriver assessed the function of 23 839 genes for human samples and 29 533 genes for mouse samples. Simultaneously, StemDriver also provided users with reference datasets and models for cell annotation. We believe that StemDriver will offer valuable assistance to research focused on cellular development and hematopoiesis.