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Regulating cardiolipin to maintain mitochondrial homeostasis is a promising strategy for addressing Parkinson's disease (PD). Through a comprehensive screening and validation process involving multiple models, ginsenoside Rg3 (Rg3) as a compound capable of enhancing cardiolipin levels is identified. This augmentation in cardiolipin levels fosters mitochondrial homeostasis by bolstering mitochondrial unfolded protein response, promoting mitophagy, and enhancing mitochondrial oxidative phosphorylation. Consequently, this cascade enhances the survival of tyrosine hydroxylase positive (TH+) dopaminergic neurons, leading to an amelioration in motor performance within PD mouse models. Using limited proteolysis-small-molecule mapping combined with molecular docking analysis, it has confirmed Growth Factor Receptor-Bound Protein 2 (GRB2) as a molecular target for Rg3. Furthermore, these investigations reveal that Rg3 facilitates the interaction between GRB2 and TRKA (Neurotrophic Tyrosine Kinase, Receptor, Type 1), thus promotes EVI1 (Ecotropic Virus Integration Site 1 Protein Homolog) phosphorylation by ERK, subsequently increases CRLS1 (Cardiolipin Synthase 1) gene expression and boosts cardiolipin synthesis. The absence of GRB2 or CRLS1 significantly attenuates the beneficial effects of Rg3 on PD symptoms. Finally, Tenofovir Disoproxil Fumarate (TDF) that also promotes the binding between GRB2 and TRKA is further identified. The identified compounds, Rg3 and TDF, exhibit promising potential for the prevention of PD by bolstering cardiolipin expression and reinstating mitochondrial homeostasis.

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Background: In the field of evidence-based medicine, randomized controlled trials (RCTs) are of critical importance for writing clinical guidelines and providing guidance to practicing physicians. Currently, RCTs rely heavily on manual extraction, but this method has data breadth limitations and is less efficient. Objectives: To expand the breadth of data and improve the efficiency of obtaining clinical evidence, here, we introduce an automated information extraction model for traditional Chinese medicine (TCM) RCT evidence extraction. Methods: We adopt the Evidence-Bidirectional Encoder Representation from Transformers (Evi-BERT) for automated information extraction, which is combined with rule extraction. Eleven disease types and 48,523 research articles from the China National Knowledge Infrastructure (CNKI), WanFang Data, and VIP databases were selected as the data source for extraction. We then constructed a manually annotated dataset of TCM clinical literature to train the model, including ten evidence elements and 24,244 datapoints. We chose two models, BERT-CRF and BiLSTM-CRF, as the baseline, and compared the training effects with Evi-BERT and Evi-BERT combined with rule expression (RE). Results: We found that Evi-BERT combined with RE achieved the best performance (precision score = 0.926, Recall = 0.952, F1 score = 0.938) and had the best robustness. We totally summarized 113 pieces of rule datasets in the regulation extraction procedure. Our model dramatically expands the amount of data that can be searched and greatly improves efficiency without losing accuracy. Conclusion: Our work provided an intelligent approach to extracting clinical evidence for TCM RCT data. Our model can help physicians reduce the time spent reading journals and rapidly speed up the screening of clinical trial evidence to help generate accurate clinical reference guidelines. Additionally, we hope the structured clinical evidence and structured knowledge extracted from this study will help other researchers build large language models in TCM.

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Extracellular vesicles (EVs) are released by many cell types including neurons, carrying cargoes involved in signaling and disease. It is unclear whether EVs promote intercellular signaling or serve primarily to dispose of unwanted materials. We show that loss of multivesicular endosome-generating ESCRT (endosomal sorting complex required for transport) machinery disrupts release of EV cargoes from Drosophila motor neurons. Surprisingly, ESCRT depletion does not affect the signaling activities of the EV cargo Synaptotagmin-4 (Syt4) and disrupts only some signaling activities of the EV cargo Evenness Interrupted (Evi). Thus, these cargoes may not require intercellular transfer via EVs, and instead may be conventionally secreted or function cell autonomously in the neuron. We find that EVs are phagocytosed by glia and muscles, and that ESCRT disruption causes compensatory autophagy in presynaptic neurons, suggesting that EVs are one of several redundant mechanisms to remove cargoes from synapses. Our results suggest that synaptic EV release serves primarily as a proteostatic mechanism for certain cargoes.

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Acute myeloid leukemia (AML) shows multiple chromosomal translocations & point mutations which can be used to refine risk-adapted therapy in AML patients. Ecotropic viral integration site-1 (EVI-1) & myocyte enhancer factor 2 C gene (MEF2C) are key regulatory transcription factors in hematopoiesis and leukemogenesis & both drive immune escape. This prospective study involved 80 adult de novo AML patients recruited from Oncology Center, Mansoura University, between March 2019 and July 2021. The MEF2C and EVI1 expression were measured using a Taqman probe-based qPCR assay. The results revealed that EVI1 and MEF2C expression were significantly elevated in AML patients as compared to control subjects (p = 0.001. 0.007 respectively). Aberrant expressions of EVI1 and MEF2C showed a significant negative correlation with hemoglobin levels (p = 0.034, 0.025 respectively), & bone marrow blasts (p = 0.007, 0.002 respectively). 11q23 translocation was significantly associated with EVI1 and MEF2C (p = 0.004 and 0.02 respectively). Also, t (9;22) was significantly associated with EVI1 and MEF2C (p = 0.01 and 0.03 respectively), higher expression of EVI1 and MEF2C were significantly associated with inferior outcome after induction therapy (p = 0.001 and 0.018 respectively) and shorter overall survival (p = 0.001, 0.014 respectively). In conclusion, EVI1 & MEF2C were significantly expressed in AML cases. EVI1 & MEF2C overexpression were significantly associated with 11q23 rearrangements and t (9;22) and were indicators for poor outcome in adult AML patients; These results could be a step towards personalized therapy in those patients.

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Globally, grazing activities have profound impacts on the structure and function of ecosystems. This study, based on a 20-year MODIS time series dataset, employs remote sensing techniques and the Seasonal-Trend decomposition using Loess (STL) algorithm to quantitatively assess the stability of alpine grassland ecosystems from multiple dimensions, and to reveal the characteristics of grazing activities and environmental conditions on ecosystem stability. The results indicate that only 5.77% of the area remains undisturbed, with most areas experiencing varying degrees of disturbance. Further analysis shows that grazing activities in high vegetation coverage areas are the main source of interference. In areas with concentrated interference, elevation and slope have a positive correlation with resistance stability, but a negative correlation with recovery stability. Precipitation and landscape diversity have positive effects on both resistance stability and recovery stability. Vegetation coverage and grazing intensity have a negative correlation with resistance stability, but a positive correlation with recovery stability. This highlights the complex interactions between human activities, environmental factors, and ecosystem stability. The findings emphasize the need for targeted conservation and management strategies to mitigate disturbances to ecosystems affected by human activities and enhance their stability.

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Nitrate contamination in surface and groundwater remains a widespread problem in agricultural watersheds is primarily associated to high levels of percolation or leakage from fertilized soil, which allows easy infiltration from soil into groundwater. This study was aimed to predict canopy water content to determine the nitrate contamination index resulting from nitrogen fertilizer loss in surface and groundwater. The study used Geographically Weighted Regression (GWR) model using MODIS 006 MOD13Q1-EVI Earth observation data, crop information and rainfall data. Satellite data collection was synchronized with regional crop calendars and calibrated to plant biomass. The average plant biomass during observed plant growth stages was between 0.19 kg/m2 at the minimum and 0.57 kg/m2 at the maximum. These values are based on the growth stages of crops and provide a solid basis for monitoring and validating crop water productivity data. The simulation results were validated with a high correlation coefficient (R2 = 0.996, P < 0.0005) for the observed rainfall in the growing zone compared to the predicted canopy water content. The nitrate contamination index assessment was conducted in 2004, 2008, 2009, 2010, 2011, 2013, 2014, 2015, 2018 and 2020. Canopy water content and root zone seasonal water content were measured in (%) per portion as indicators of the NO-3-N-nitrate contamination index in these years (0.391, 0.316, 0.298, 0.389, 0.380, 0.339, 0.242, 0.342 and 0.356).

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BACKGROUND: Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction. METHODS: In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images. RESULTS: After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs, short-term HSCs, and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments. CONCLUSION: Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into the molecular mechanisms underlying stem cell self-renewal.

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Grasslands cover a substantial portion of the earth's surface and agricultural land and is crucial for human well-being and livestock farming. Ranchers and grassland management authorities face challenges in effectively controlling herders' grazing behavior and grassland utilization due to underdeveloped infrastructure and poor communication in pastoral areas. Cloud-based grazing management and decision support systems (DSS) are needed to address this issue, promote sustainable grassland use, and preserve their ecosystem services. These systems should enable rapid and large-scale grassland growth and utilization monitoring, providing a basis for decision-making in managing grazing and grassland areas. In this context, this study contributes to the objectives of the EU LIFE IMAGINE project, aiming to develop a Web-GIS app for conserving and monitoring Umbria's grasslands and promoting more informed decisions for more sustainable livestock management. The app, called "Praterie" and developed in Google Earth Engine, utilizes historical Sentinel-2 satellite data and harmonic modeling of the EVI (Enhanced Vegetation Index) to estimate vegetation growth curves and maturity periods for the forthcoming vegetation cycle. The app is updated in quasi-real time and enables users to visualize estimates for the upcoming vegetation cycle, including the maximum greenness, the days remaining to the subsequent maturity period, the accuracy of the harmonic models, and the grassland greenness status in the previous 10 days. Even though future additional developments can improve the informative value of the Praterie app, this platform can contribute to optimizing livestock management and biodiversity conservation by providing timely and accurate data about grassland status and growth curves.

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BACKGROUND: Exposure to green space can protect against poor health through a variety of mechanisms. However, there is heterogeneity in methodological approaches to exposure assessments which makes creating effective policy recommendations challenging. OBJECTIVE: Critically evaluate the use of a satellite-derived exposure metric, the Enhanced Vegetation Index (EVI), for assessing access to different types of green space in epidemiological studies. METHODS: We used Landsat 5-8 (30 m resolution) to calculate average EVI for a 300 m radius surrounding 1.4 million households in Wales, UK for 2018. We calculated two additional measures using topographic vector data to represent access to green spaces within 300 m of household locations. The two topographic vector-based measures were total green space area stratified by type and average private garden size. We used linear regression models to test whether EVI could discriminate between publicly accessible and private green space and Pearson correlation to test associations between EVI and green space types. RESULTS: Mean EVI for a 300 m radius surrounding households in Wales was 0.28 (IQR = 0.12). Total green space area and average private garden size were significantly positively associated with corresponding EVI measures (ß = < 0.0001, 95% CI: 0.0000, 0.0000; ß = 0.0001, 95% CI: 0.0001, 0.0001 respectively). In urban areas, as average garden size increases by 1 m2, EVI increases by 0.0002. Therefore, in urban areas, to see a 0.1 unit increase in EVI index score, garden size would need to increase by 500 m2. The very small ß values represent no 'measurable real-world' associations. When stratified by type, we observed no strong associations between greenspace and EVI. IMPACT: It is a widely implemented assumption in epidiological studies that an increase in EVI is equivalent to an increase in greenness and/or green space. We used linear regression models to test associations between EVI and potential sources of green reflectance at a neighbourhood level using satellite imagery from 2018. We compared EVI measures with a 'gold standard' vector-based dataset that defines publicly accessible and private green spaces. We found that EVI should be interpreted with care as a greater EVI score does not necessarily mean greater access to publicly available green spaces in the hyperlocal environment.

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EVI1 expression is associated with poor prognosis in myeloid leukaemia, which can result from Chr.3q alterations that juxtapose enhancers to induce EVI1 expression via long-range chromatin interactions. More often, however, EVI1 expression occurs unrelated to 3q alterations, and it remained unclear if, in these cases, EVI1 expression is similarly caused by aberrant enhancer activation. Here, we report that, in EVI1+3q- myeloid leukaemia cells, the EVI1 promoter interacts via long-range chromatin interactions with promoters of distally located, active genes, rather than with enhancer elements. Unlike in 3q+ cells, EVI1 expression and long-range interactions appear to not depend on CTCF/cohesin, though EVI1+3q- cells utilise an EVI1 promoter-proximal site to enhance its expression that is also involved in CTCF-mediated looping in 3q+ cells. Long-range interactions in 3q- cells connect EVI1 to promoters of multiple genes, whose transcription correlates with EVI1 in EVI1+3q- cell lines, suggesting a shared mechanism of transcriptional regulation. In line with this, CRISPR interference-induced silencing of two of these sites minimally, but consistently reduced EVI1 expression. Together, we provide novel evidence of features associated with EVI1 expression in 3q- leukaemia and consolidate the view that EVI1 in 3q- leukaemia is largely promoter-driven, potentially involving long-distance promoter clustering.

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Radioulnar synostosis with amegakaryocytic thrombocytopenia (RUSAT) type 2, caused by MDS1 and EVI1 complex locus (MECOM) gene mutations, is a rare inherited bone marrow failure syndrome (IBMFS) with skeletal anomalies, characterized by varying presentation of congenital thrombocytopenia (progressing to pancytopenia), bilateral proximal radioulnar synostosis, and other skeletal abnormalities. Due to limited knowledge and heterogenous manifestations, clinical diagnosis of the disease is challenging. Here we reported a novel MECOM mutation in a Chinese boy with typical clinical features for RUSAT-2. Trio-based whole exome sequencing of buccal swab revealed a novel heterozygous missense mutation in exon 11 of the MECOM gene (chr3:168818673; NM_001105078.3:c.2285G > A). The results strongly suggest that the variant was a germline mutation and disease-causing mutation. The patient received matched unrelated donor hematopoetic stem cell transplantation (HSCT). This finding was not only expanded the pathogenic mutation spectrum of MECOM gene, but also provided key information for clinical diagnosis and treatment of RUSAT-2.

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Vegetation patterns on slopes strongly affect the water cycle processes in a basin, especially the water yield and confluence in arid areas. Quantifying and evaluating the effects of hydrological change on the migration and transformation of pollutants are challenging. Based on 4-year stream water quality data of 13 monitoring sites in the Huangshui River basin, a typical arid watershed of the Chinese Loess Plateau, the redundancy analysis (RDA) and structural equation modeling (SEM) analysis tools were used to quantify its relationship with vegetation patterns. In the study, land use and the enhanced vegetation index (EVI) were used as a metric of vegetation patterns; accordingly, the 13 catchments were divided into three groups via the cluster analysis, including large (over 80%), medium (70 ~ 80%), and small (below 70%) proportion vegetation patterns (LVP, MVP, SVP). The results of the LVP group showed that vegetation patterns negatively affected the contamination of total phosphorus (TP), ammonia nitrogen (NH3-N), permanganate index (CODMn), and biochemical oxygen demand (BOD5) in the stream water, and the contribution rates were - 0.57. While the proportion of urban area positively correlated with stream water quality in the groups of MVP and SVP, the contribution rates were 0.46 and 0.36, respectively. Moreover, the precipitation in the groups of MVP and SVP negatively correlated with pollutants (- 0.24 and - 0.26). Those results revealed the response of stream water quality to vegetation patterns on the slope with the consideration of precipitation, land use, and socio-economic factors for the regional water and land resource allocation. This study has important management implications for vegetation patterns on slope of fragile ecosystems in arid areas.

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ABSTRACT Cold sores require Healthcare professionals to employ specific approaches for prevention and management, with the need for effective therapeutic guidelines and ongoing improvement in patient care. Aim To evalúate the methodological quality of Clinical Guidelines (CG), clinical guides and manuals for care of the population affected by herpes labialis, to verify their compliance with evidence-based health standards. Materials and Method A search was conducted for CG on labial herpes in the MedicalLiteratureAnalysis andRetrieval System Online (Medline) database, Google Scholar, Brazilian Virtual Health Library (BVS), and sites of institutions/professional categories, using the descriptors "herpes labialis" or "oral herpes". Document quality was assessed using the Appraisal of Guidelines for Research & Evaluation Instrument (AGREE II). The Kappa test was used to avoid randomness or poor agreement between results. Results Analysis of the 12 selected publications on the management of labial herpes revealed flaws in quality, as the publications did not follow a quality standard. The main quality flaws identified were in "rigor in development" and "applicability. " Conclusions Priorities need to be redefined in the development of CG for clinical practice related to fever blisters to reduce the variability of the quality standard, and generate reliable, applicable recommendations.

RESUMO A Herpes labial requer dos profissionais abordagens específicas para prevenido e manejo, com a ne-cessidade de diretrizes terapéuticas eficazes e continuo aprimoramento do cuidado ao paciente. Objetivo avaliar a qualidade metodológica de documentos que abordaram Diretrizes Clínicas (DC), guias clínicos e manuais para o cuidado da populando afetada pelo herpes labial, verificando sua conformidade com padroes de saúde baseados em evidencias. Materiais e Método As DC sobre herpes labial foram pesquisadas na base de dados Medical Literature Analysis and Retrieval System Online (Medline), Google Académico, Biblioteca Virtual em Saúde (BVS) e em sites de instituigoes/categorias profissionais, utilizando os descritores "herpes labial" ou "herpes oral". Utilizamos aferramenta The Appraisal of Guidelines for Research & Evaluation Instrument (AGREE II) para a avaliagdo da qualidade. O teste Kappa também foi utilizado para evitar aleatoriedade ou baixa concordáncia entre os resultados. Resultados Na análise das 12 publicagoes selecionadas sobre o manejo do herpes labial, foram identificadas falhas na qualidade dos documentos, que ndo seguiram um padrdo de qualidade. As principais falhas de qualidade identificadas foram em "rigor no desenvolvimento" e "aplicabilida-de". Conclusao é necessário um reenfoque para definir prioridades no desenvolvimento de DC para a prática clínica do herpes labial, a fim de reduzir a variabilidade do padrdo de qualidade e gerar recomendagoes que possam ser confiáveis e aplicáveis.

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Neurofibromatosis type 1 (NF1) is an autosomal dominant disease that affects the development and growth of various tissues. NF1 is a major risk factor for the development of malignancies, particularly malignant peripheral nerve sheath tumors, optic gliomas, and leukemia. NF1 encodes a neurofibromin. Three genes, EVI2A, EVI2B, and OMGP, are embedded within intron 27b of NF1. However, the function of these genes remains unclear. EVI2A and EVI2B encode for putative transmembrane proteins. Mouse homologs are associated with viral insertions involved in leukemia in mice. Mouse Evi2b has been identified as a direct target gene of C/EBPα, a transcription factor critical for myeloid differentiation. Also possible is that these genes are related to the leukemia observed in patients with NF1. These genes might act as modifiers of NF1 phenotypic variations. Therefore, we investigated the EVI2B gene in leukemia and NF1 tumors. We analyzed DNA from 10, 20, and 3 patients with NF1, leukemia, and NF1-leukemia, respectively, and six NF1 tumor tissues. DNA sequencing analysis was used to identify the viral integration sequence, and the protein amounts and EVI2B gene expression were analyzed by flow cytometry and quantitative real-time PCR techniques. The EVI2B gene expression was increased in cutaneous neurofibroma compared with the control both at the level of protein and mRNA. However, its expression in plexiform neurofibroma was decreased significantly at protein level and increased at mRNA level compare to control. Moreover, integration of 455 bases near the 3' end of the exon was detected. When this integrated sequence was blasted into the NCBI retroviral genome database, an 87% match with the HIV-1 virus envelope gene was obtained. These preliminary results show that EVI2B might be important in NF1 tumorigenesis and leukemia.

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BACKGROUND: Evidence of the relationship between greenness and sleep is limited, and, given the worsening sleep insufficiency worldwide, this relationship needs elucidation. In this study, we investigated the association of greenness with sleep deprivation using nationwide survey data. METHODS: This study included 1,727,273 participants in the Korea Community Health Survey who resided in all 229 districts of South Korea from 2011 to 2018. Sleep deprivation variables were defined as strong deprivation or mild deprivation, based on average daily sleep duration of <5 or 5-6 h, respectively. District-specific annual average of satellite-derived enhanced vegetation index (EVI) was used as a green space exposure. A logistic regression with complex survey weights was used to estimate the association between greenness and sleep deprivation, and it was further examined by sex, age group, educational status, income level, and population density. The regression analysis was performed annually, and the annual estimates were pooled by a combined data analysis. RESULTS: A higher level of greenness was associated (odds ratio [95 % confidence interval]) with strong and mild sleep deprivation (0.96 [0.93-0.99] and 0.96 [0.95-0.97]), respectively, and males and the younger age group (<65 years) showed a more prominent association with greenness than in females and the elderly group (65 years or older). In addition, only high-population-density areas showed evident associations of greenness with both strong and mild sleep deprivation. CONCLUSIONS: This large population-based study provides important epidemiological evidence for improving sleep quantity through an increase in greenness exposure and supports policymakers in establishing strategies for urban planning.

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Quantification of forest resilience will help us to manage the sustainability of the forest environment and the safety of biodiversity. Measuring forest resilience is crucial for ensuring long-term health of the forest ecosystem in the face of ongoing environmental changes and disturbances. This study focuses on providing a framework to estimate forest resilience scores to assess the vegetation condition after a disturbance. The resilience calculation framework provided uses number of recovery days, the phenological performance level of vegetation in the year when the disturbance took place, long-term mean phenological performance, and greenness levels in subsequent year to calculate the final resilience score at each pixel. Recovery of forests using Landsat data with the help of Normalized Difference Vegetation Index or Normalized Burn Ratio poses a challenge for continuous monitoring of forested landscapes due to cloud cover and availability of scenes at continuous intervals in Landsat datasets. In this regard, MODIS 16-day EVI products were used in this study (2001 to 2020) for monitoring vegetation health before, during, and after the disaster. Bandhavgarh National Park (BNP) located in Madhya Pradesh, India is considered for this study as it witnessed major forest fire breakouts in the second half of March 2018. The objectives of the study are the following: (1) to estimate post-fire recovery days and (2) to formulate new resilience index. The study revealed that the northern part of BNP is more vulnerable and shows slow recovery. The relationship between occupation of people living inside and in the neighboring area with forest resilience is also investigated in this study.

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In acute myeloid leukemia (AML), EVI1 rearrangement represented by inv(3)(q21q26) or t(3;3)(q21;q26) causes EVI1 overexpression via structural rearrangement of an enhancer, and confers poor prognosis. My colleagues and I performed a mutational analysis of EVI1-rearranged myeloid neoplasms and identified SF3B1, a core RNA splicing factor, as the most commonly co-mutated gene. Indeed, latent leukemia development in transgenic mice bearing the humanized inv(3)(q21q26) allele was significantly accelerated by co-occurrence of Sf3b1 mutation. Intriguingly, we found that this SF3B1 mutant induced mis-splicing of EVI1 itself, which generated an aberrant EVI1 isoform with in-frame insertion of 6 amino acids near the DNA-binding domain of EVI1. This aberrant EVI1 isoform exhibited DNA-binding activity different from wild-type EVI1 and significantly enhanced the self-renewal capacity of murine hematopoietic stem cells. We also identified the cryptic branch point and exonic splicing enhancer required for this EVI1 mis-splicing induced by the SF3B1 mutant. These data provide a basis for further elucidation of the molecular mechanism and potential therapeutic candidates for EVI1-rearranged AML.

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Background: Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction. Methods: In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images. Results: After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs (LT-HSCs), short-term HSCs (ST-HSCs), and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments. Conclusion: Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. With ongoing advancements in model algorithms and their integration into various imaging systems, deep learning stands poised to become an invaluable tool, significantly impacting stem cell research.

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Introduction: The MRL mouse strain is one of the few examples of a mammal capable of healing appendage wounds by regeneration, a process that begins with the formation of a blastema, a structure containing de-differentiating mesenchymal cells. HIF-1α expression in the nascent MRL wound site blastema is one of the earliest identified events and is sufficient to initiate the complete regenerative program. However, HIF-1α regulates many cellular processes modulating the expression of hundreds of genes. A later signal event is the absence of a functional G1 checkpoint, leading to G2 cell cycle arrest with increased cellular DNA but little cell division observed in the blastema. This lack of mitosis in MRL blastema cells is also a hallmark of regeneration in classical invertebrate and vertebrate regenerators such as planaria, hydra, and newt. Results and discussion: Here, we explore the cellular events occurring between HIF-1α upregulation and its regulation of the genes involved in G2 arrest (EVI-5, γH3, Wnt5a, and ROR2), and identify epithelial-mesenchymal transition (EMT) (Twist and Slug) and chromatin remodeling (EZH-2 and H3K27me3) as key intermediary processes. The locus of these cellular events is highly regionalized within the blastema, occurring in the same cells as determined by double staining by immunohistochemistry and FACS analysis, and appears as EMT and chromatin remodeling, followed by G2 arrest determined by kinetic expression studies.

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Although gemcitabine (GEM) is the first­line drug for advanced pancreatic adenocarcinoma (PAAD), the development of GEM resistance severely limits the effectiveness of this chemotherapy. This study investigated the mechanisms of ecotropic viral integration site 2 A (EVI2A) for resistance to GEM and immune evasion in PAAD. GEM resistance-related biomarkers were predicted using GEO datasets, and GEM-resistant PAAD cells were generated. EVI2A was found expressed highly in GEM-resistant PAAD cells. Gain-of-function analyses revealed that EVI2A encouraged the proliferation and motility of GEM-resistant cells and prevented apoptosis. In addition, EVI2A reduced T cell effector activation. SMYD2 was overexpressed in GEM-resistant cells, and SMYD2 enhanced H3K36me2 modification of EVI2A, thereby promoting EVI2A expression. SMYD2 reduced the sensitivity of GEM-resistant cells, which was reversed by EVI2A knockdown. SMYD2 increased the amount of M2 macrophages (co-cultured with PAAD cells) and decreased T cell effector activation (co-cultured with macrophage supernatant), and the number of M2 macrophages was decreased and T cell effectors were activated following EVI2A knockdown. Our findings indicate that EVI2A, manipulated by the SMYD2-H3K36me2 epigenetic axis, promoted GEM resistance and M2 macrophage-mediated immune evasion in PAAD. Therefore, EVI2A might represent a therapeutic target for overcoming GEM resistance and immunosuppressive environment in PAAD.