Search Results (4,867)

Phenol red is a widely used, low-cost, label-free colorimetric pH indicator that bridges traditional colorimetric assays with modern quantitative imaging and cell-based screening platforms. Its protonation-dependent absorbance shift (430–560 nm) allows for the real-time monitoring of extracellular acidification, which indirectly reflects cellular metabolism, growth, and respiration. Although phenol red lacks the molecular specificity of genetically encoded or fluorogenic biosensors, it remains useful in systems where pH changes are effective proxies for physiological processes. Existing tissue digestion protocols often overlook key parameters, especially pH control and enzyme cofactor use. This study presents a straightforward, spectrophotometric method to monitor and adjust the pH of low-volume (1 mL) buffered enzymatic dissociation media using phenol red and a plate reader. We titrated dissociation solutions to physiological pH (~7.4) using spectrophotometric pH measurements validated against conventional glass pH probe readings, confirming method reliability. Accurate pH assessment is critical for isolating viable primary cells for downstream applications such as tissue engineering, single-cell omics, and neurophysiological assays. We highlight that papain-based dissociation media supplemented with L-cysteine can be acidic (pH 6.6) if unadjusted, compromising cell viability. This accessible approach enhances reproducibility by promoting pH documentation concerning dissociation conditions that contribute to advancing consistency in biomedical, cellular, neuronal, and tissue engineering research. Full article

The economic value of goose down is attributed to its extensive application in the production of down-based clothing and related products. The primordium formation stage governs the proper morphogenesis of the feather follicle, while the Wnt signaling pathway serves a positive regulatory function during this stage. To identify critical miRNAs and molecular mechanisms regulating the development of goose feather follicle primordium, we performed transcriptomic sequencing of skin tissues collected from six geese at pre- and post-feather follicle primordium developmental stages. Bioinformatics analysis identified 350 differentially expressed miRNAs (DE miRNAs), which were functionally enriched in processes related to system development and multicellular organismal development, etc. As demonstrated by dual-luciferase reporter experiments, miR-200a binds directly to PITX2’s 3′ untranslated region (3′UTR). Furthermore, overexpression of miR-200a decreased the expression levels of genes linked to the Wnt pathway and suppressed the proliferation of GEDFs, as validated by RT-qPCR, CCK8, and EdU assays. Notably, co-transfection experiments demonstrated that miR-200a-mediated regulation of GEDF proliferation through the Wnt pathway is functionally dependent on PITX2. Collectively, this work expands the regulatory network underlying feather follicle development and provides a genetic foundation aimed at breeding geese with enhanced down production quality. Full article

Salicylic acid (SA) is a key phytohormone that coordinates plant innate immunity and systemic acquired resistance. Because SA levels and signaling are highly dynamic in space and time, a suite of SA-focused tools, including SA-specific microbial biosensors and SA-responsive transcriptional and chemical reporters, has been developed to study them. This review compares three classes of tools in terms of sensitivity, specificity, temporal resolution, invasiveness, quantifiability, and suitability across species. We describe developing genetically encoded sensors that can directly sense salicylic acid and report it, for example, via a fluorescence resonance energy transfer signal or another real-time output. We offer recommendations on method selection by research goal and plant species, as well as combined protocols (long-term autoluminescence plus local probes/biosensors) for cross-validation. Future work should prioritize substrate-free, quantitative SA reporters deployable in crops and the field; coupled with CRISPR-based editing and screening, these tools would enable reporter-guided discovery of immunity genes and rapid engineering of durable disease resistance. Full article

Phosphate (Pi) is a vital macronutrient for plant growth and development, and precise monitoring of its cellular dynamics is essential to understanding Pi homeostasis and its interaction with stress responses. Genetically encoded FRET-based biosensors such as FLIPPi enable real-time, non-invasive visualization of cytosolic Pi levels in living tissues. In this study, Arabidopsis and rice lines expressing a FLIPPi biosensor were used to monitor cytosolic Pi dynamics in root epidermal cells. Sensor functionality was confirmed by measuring FRET responses to graded Pi supplies, revealing a consistent reduction in FRET ratios with increasing Pi concentrations, reflecting elevated cytosolic Pi levels. Comparisons with a Pi-insensitive FLIPPi variant confirmed the specificity of the observed changes. Furthermore, live imaging demonstrated rapid and dynamic alterations in cytosolic Pi upon treatment with defense-related hormones and elicitors of immune responses supporting a link between Pi signaling and plant immunity. Finally, the application of phosphite, an analog of Pi, altered Pi dynamics in both Arabidopsis and rice, suggesting an interference with Pi accumulation. Collectively, our findings establish FLIPPi as a reliable tool for in vivo monitoring of Pi in Arabidopsis and rice plants, the model systems for studies in dicotyledonous and monocotyledonous species, respectively. Full article

Introduction: Gene therapy using siRNA is a current area of research in oncology. Although siRNA formulations have not yet been approved for cancer therapy, numerous studies have demonstrated their therapeutic potential for tumor remission. Objective: To provide an overview of the formulations designed and developed to date based on synthetic siRNA for systemic administration to silence cancer genes. Methodology: A thorough search was conducted using the keywords “siRNA”, “therapy”, and “cancer”, with further classification of the resulting works into the various topics addressed in this review. Results: This review encompasses a wide range of aspects, from the design of siRNA using bioinformatics tools to the primary cellular signals and mechanisms targeted for inhibition in cancer therapy. It describes the primary chemical modifications made to siRNA chains to enhance stability, improve bioavailability, and ensure their binding to nanocarrier systems. siRNA formulations ranging from simple conjugates with biomolecules and small molecules to organic, inorganic, and hybrid nanoparticles, which are examined focusing on their advantages and disadvantages. The significance of nanosystems in dual therapy, including siRNA, for developing personalized treatments that achieve better outcomes is emphasized. Conclusions: Personalized cancer therapy appears to be the preferred approach for oncological treatments. To progress, strategies need to be tailored to the patient’s genetic profile. siRNA therapies provide a flexible platform for targeting and inhibiting critical oncogenes, enhancing the prospects of genomics-guided, patient-specific therapies. Full article

Distant hybridization is key to trait innovation and speciation, with Cyprinidae hybrid phylogeny helping to clarify diversification mechanisms. Yet, a major gap persists in Cyprinidae studies: the stabilization mechanisms of interspecific distant hybrid lineages. To address this, we systematically analyzed the molecular phylogeny of seven Cyprinidae distant hybrid lineages and their parental species, using an integrative genetic framework encompassing four mitochondrial genes (Cytb, COI, 16S rRNA, D-loop) and five nuclear genes (EGR2b, IRBP2, RAG1, RAG2, RH2). Homologous sequences of 41 representative Cyprinidae species (85 samples) were retrieved from GenBank to supplement the dataset. Phylogenies were reconstructed from concatenated sequences, complemented by haplotype networks. Intra-/interspecific divergence was quantified using two mitochondrial genes (COI, Cytb) and two nuclear (RAG1, RH2). The results showed that these hybrid lineages exhibited variation patterns analogous to other Cyprinidae species. Both ML and BI trees reconstructed exhibited congruent topologies with high support (bootstrap/BPP > 80%), resolving genus/species-level relationships. While most hybrids clustered intermediately between their parental species, they typically displayed maternal affinity. A notable exception was the 2nNCRC (a homodiploid hybrid from Cyprinus carpio ♀ × Megalobrama amblycephala ♂), which displayed convergent evolution toward Carassius auratus. COI-based K2P genetic distance analysis revealed 2nNCRC had a much closer relationship with C. auratus (0.0119) than with its parents (0.1249 to C. carpio, 0.1552 to M. amblycephala). These nine genes elucidate the genetic relationships between Cyprinid hybrid lineages and progenitors, serving as pivotal molecular markers for parentage tracing and genetic dissection of distant hybridization mechanisms. The integrated mitochondrial–nuclear marker system in this study advances understanding of cytonuclear coadaptation and the stabilization of interspecific distant hybrid lineages in Cyprinidae. Specifically, it provides a precise tool for parentage tracing, Cyprinid germplasm conservation, and targeted regulation of hybrid breeding—laying a foundation for exploring hybrid speciation and developing elite aquaculture germplasms. Full article

Inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous spectrum of disorders that lead to progressive and irreversible vision loss. Gene therapy is the most promising emerging treatment for IRDs. While gene augmentation strategies have demonstrated clinical benefit and results within the first approved ocular gene therapy, their application is restricted by adeno-associated virus (AAV) packaging capacity and limited efficacy for dominant mutations. Recent breakthroughs in precision genome editing, particularly base editing (BE) and prime editing (PE), have provided alternatives capable of directly correcting pathogenic variants. BE enables targeted single-nucleotide conversions, whereas PE further allows for precise insertions and deletions, both circumventing the double-strand DNA cleavage or repair processes typically induced by conventional CRISPR–Cas editing systems, thereby offering advantages in post-mitotic retinal cells. Preclinical investigations across murine and non-human primate models have demonstrated the feasibility, molecular accuracy, and preliminary safety profiles of these platforms in targeting IRD-associated mutations. However, critical challenges remain before clinical application can be realized, including limited editing efficiency in photoreceptors, interspecies variability in therapeutic response, potential risks of off-target effects, and barriers in large-scale vector manufacturing. Moreover, the delivery of genome editors to the outer retina remains suboptimal, prompting intensive efforts in capsid engineering and the development of non-viral delivery systems. This review synthesizes the current progress in BE and PE optimization, highlights innovations in delivery platforms that encompass viral and emerging non-viral systems and summarizes the major barriers to clinical translation. We further discuss AI-driven strategies for the rational design of BE/PE systems, thereby outlining their future potential and perspectives in the treatment of IRDs. Full article

Arid-region water resource management is hindered by severely inadequate river discharge monitoring, with effective observations of hydrological processes particularly lacking in narrow river channels. To overcome this bottleneck, this study proposes an integrated multi-model remote sensing retrieval framework and systematically evaluates the applicability of Manning’s equation, the At-Many-Stations Hydraulic Geometry (AHG) model, and the AHG’s relaxed form (AMHG) in typical arid-region rivers on the northern slope of the Kunlun Mountains. Runoff was estimated by integrating multi-source remote sensing imagery (Sentinel-2, Landsat-8, and Gaofen-1) on the Google Earth Engine platform and combining it with genetic algorithms for parameter optimization. The results indicate that Manning’s equation performed the best overall (RMSE = 21.78 m³/s, NSE = 0.94) and was highly robust to river width extraction errors, with Manning’s roughness coefficient having a significantly greater impact than the hydraulic slope. The AHG model can construct long-term discharge series based on limited measured data but is sensitive to the accuracy of river width extraction. Although the AMHG model improved the retrieval performance, its effectiveness was constrained by systematic biases in proxy variables. The study also found that the AHG exponent b in the rivers of this region exhibits high stability (coefficient of variation < 0.09), providing a theoretical basis for constructing a sustainable discharge monitoring system. The integrated method developed in this study offers a reliable technical pathway for dynamic hydrological monitoring and quantitative water resource management in data-scarce arid regions. Full article

COVID-19 vaccination has played a pivotal role in mitigating the global health crisis and reducing morbidity and mortality associated with SARS-CoV-2 infection. While its public health benefits are unequivocal, the unprecedented scale of vaccination—reaching billions worldwide—has also enabled the detection of rare autoimmune events, including systemic lupus erythematosus, rheumatoid arthritis, type 1 diabetes, and Guillain–Barré syndrome. Although such events occur in only a small subset of individuals, often influenced by genetic, environmental, or dosage-related factors, they underscore the importance of understanding immune tolerance mechanisms in vaccination. This review synthesizes clinical observations and immunological findings from the COVID-19 vaccination era, highlighting key mechanisms such as molecular mimicry, adjuvant-induced inflammation, bystander activation, epitope spreading, and polyclonal B cell activation. We also consider how novel vaccine platforms, particularly mRNA-based technologies, may influence immune regulation and self-tolerance. Importantly, we discuss the therapeutic management of vaccine-associated autoimmunity, including the use of corticosteroids, intravenous immunoglobulin (IVIG), plasma exchange, disease-modifying anti-rheumatic drugs (DMARDs), and other immunosuppressive agents, many of which have led to favorable clinical outcomes. By integrating mechanistic insights with treatment strategies, this review emphasizes that the overall benefits of COVID-19 vaccination overwhelmingly outweigh the risks, while advocating for continued surveillance, mechanistic research, and risk stratification to inform safer and more targeted vaccination strategies in future pandemics. Full article

Background/Objectives: Non-syndromic tooth agenesis (NSTA) is a congenital condition that causes the absence of one or more teeth without accompanying systemic abnormalities, which significantly affects quality of life. Genetic factors, including mutations in several specific genes, contribute to the pathogenesis of NSTA. This study investigates a novel EVC2 mutation in a patient with NSTA and explores its potential pathogenic mechanism, with the aim of enriching the spectrum of pathogenic genes. Methods: Whole-exome sequencing (WES) was performed on peripheral blood samples from a patient diagnosed with NSTA. Bioinformatics analysis was utilized to identify the mutation and assess its potential impact on protein structure and function. Molecular dynamics simulations were conducted to analyze structural alterations in the EVC2 protein. The binding affinity between EVC2, EVC, and Smoothened (SMO) was to determine the effect of mutation on protein–protein interaction. Protein localization and expression were analyzed using immunofluorescence and Western blotting. Reverse transcription quantitative PCR (RT-qPCR) was employed to evaluate downstream signaling pathway alterations. Results: A novel EVC2 mutation (c.1657_1660delinsA, p.Glu553_leu554delinsMet) was identified in the proband, and the mutation was maternally inherited. Molecular dynamics simulations revealed that the mutation resulted in a decrease in α-helical content and significant conformational changes in the protein structure. This led to reduced binding affinity between EVC2 and its ligands EVC and SMO, destabilizing the structural integrity of the protein complex. Despite these structural changes, EVC2 protein localization and expression were unaffected. Furthermore, a downregulation of GLI1 and SHH expression was observed, indicating impaired Hedgehog (Hh) signaling. The downregulation of the Hh signaling pathway impairs the tooth development process and may lead to the occurrence of tooth agenesis. Conclusions: A novel EVC2 mutation was identified in a patient with NSTA. Based on molecular dynamics simulations, it is hypothesized that this EVC2 variant could contribute to the pathogenesis of NSTA by impairing the EVC2-EVC-SMO complex formation, which may lead to downregulation of downstream GLI1 and SHH. These findings provide new insights into the molecular mechanisms underlying EVC2-mediated NSTA, suggesting that disruption of Hh signaling may represent a critical pathogenic mechanism. Full article

Myelodysplastic neoplasms represent a diverse group of clonal hematopoietic stem cell disorders characterized by ineffective hematopoiesis, peripheral cytopenias, and an inherent risk of progression to acute myeloid leukemia. Accurate risk assessment and patient stratification are critical to optimizing therapeutic approaches and clinical outcomes. In 2022, significant advancements reshaped both the classification and prognostic stratification of MDSs. The revised WHO Classification introduced crucial genetically defined subtypes, particularly those involving biallelic TP53 inactivation and SF3B1 mutations, shifting the emphasis from traditional morphology-based criteria to molecular ones. Simultaneously, morphological subtypes such as hypoplastic and hyperfibrotic MDSs were established as distinct entities with unique prognostic implications. At the same time, the introduction of the International Molecular Prognostic Scoring System (IPSS-M) provided a more precise prognostic stratification by integrating comprehensive molecular data alongside traditional clinical and cytogenetic parameters. Several validation studies have confirmed IPSS-M’s superior discriminative power compared to previous models, notably IPSS-R, improving predictions regarding overall survival and leukemia transformation. Nevertheless, practical considerations regarding the widespread application of IPSS-M have emerged, including concerns over economic feasibility and accessibility of advanced molecular testing methods, such as extensive Next-Generation Sequencing panels. This review synthesizes the recent literature and critical studies validating these classification and prognostic updates, discussing their clinical impact, practical considerations, and implications for targeted therapeutic strategies. By focusing on molecular pathogenesis, the latest classification systems and prognostic models promise significant advances in patient-specific management, setting the stage for future innovations in treatment and improved patient outcomes. Full article

Background/Objectives: African Swine Fever (ASF), caused by the African Swine Fever Virus (ASFV), is a highly contagious and lethal disease in pigs, for which no recognized safe and effective vaccine is currently available. The ASFV EP153R gene, expressed during both early and late infection stages, exhibits strong protective potential. Utilizing advances in genetic engineering, recombinant PRRSV vector vaccines carrying ASFV exogenous genes were constructed. This study aims to prepare pEP153R-based polyclonal antibodies and an iELISA detection method using the constructed rPRRSV-EP153R as a specific target to verify the iELISA’s specificity and effectiveness. Methods: A prokaryotic plasmid, pCold-TF-EP153R, was constructed to express protein in BL21 (DE3). The purified soluble protein (2 mg/mL) was used to generate a murine polyclonal antibody and establish an indirect ELISA. The EP153R gene was inserted between ORF1b and ORF2a of PRRSV via reverse genetics, yielding recombinant rPRRSV-EP153R. Its biological properties were assessed in vitro and in vivo. Results: The pEP153R was specifically detected by both anti-His antibody and generated polyclonal antibodies. An established iELISA showed high specificity, sensitivity, and 98.18% accuracy. The antibodies specifically recognized pEP153R expressed in recombinant virus and eukaryotic systems. Additionally, the recombinant virus stably maintained EP153R without changes in virological characteristics relative to vHuN4-F112. In vaccinated piglets, the rPRRSV-EP153R induced a specific, consistent, and detectable immune response. Conclusions: The established iELISA, characterized by high specificity, sensitivity, and accuracy, furnishes reliable technical support for the serological diagnosis of ASFV. Meanwhile, the recombinant virus rPRRSV-EP153R demonstrates potential as a novel live vectored vaccine candidate, with the capability to induce specific immunity against both ASFV and PRRSV. Full article

Amid growing challenges to global food security, high-throughput crop phenotyping has become an essential tool, playing a critical role in genetic improvement, biomass estimation, and disease prevention. Unlike controlled laboratory environments, field-based phenotypic data collection is highly vulnerable to unpredictable factors, significantly complicating the data acquisition process. As a result, the choice of appropriate data collection equipment and processing methods has become a central focus of research. Currently, three key technologies for extracting crop phenotypic parameters are Light Detection and Ranging (LiDAR), Multi-View Stereo (MVS), and depth camera systems. LiDAR is valued for its rapid data acquisition and high-quality point cloud output, despite its substantial cost. MVS offers the potential to combine low-cost deployment with high-resolution point cloud generation, though challenges remain in the complexity and efficiency of point cloud processing. Depth cameras strike a favorable balance between processing speed, accuracy, and cost-effectiveness, yet their performance can be influenced by ambient conditions such as lighting. Data processing techniques primarily involve point cloud denoising, registration, segmentation, and reconstruction. This review summarizes advances over the past five years in 3D reconstruction technologies—focusing on both hardware and point cloud processing methods—with the aim of supporting efficient and accurate 3D phenotype acquisition in high-throughput crop research. Full article

Acute myocarditis (AM) is an inflammatory cardiac condition resulting from infections, toxic exposures, or immune-mediated mechanisms, with clinical presentations ranging from mild symptoms to heart failure (HF) or cardiogenic shock. Although viral infections remain the predominant cause, both the absolute prevalence and the relative distribution of different etiologies may change over time and across regions depending on endemic diseases. Immune checkpoint inhibitor (ICI)-associated myocarditis has emerged as a newly recognized entity, with diagnostic rates increasing in parallel with growing awareness and the expanding population of cancer patients eligible for ICI therapy. Additionally, genetic predisposition—particularly mutations linked to arrhythmogenic cardiomyopathy—is also being increasingly acknowledged as a susceptibility factor. Recent advances have markedly improved the diagnostic approach to AM. The availability of high-sensitivity cardiac troponins and the widespread use of cardiac magnetic resonance imaging (CMRI) have enhanced early detection and tissue characterization. CMRI, especially following the updated Lake Louise Criteria (2018), which incorporate T1 and T2 mapping, enables accurate assessment of myocardial inflammation and fibrosis. Endomyocardial biopsy (EMB) remains essential in complicated cases, particularly to identify histologic subtypes that may benefit from immunosuppressive therapy. Early EMB (within 48 h) has been associated with better outcomes in fulminant presentations. The use of immunohistochemistry with leukocyte-specific markers has further increased the sensitivity of EMB. Therapeutic strategies now integrate etiology-specific approaches. Immunosuppressive therapy is indicated for distinct histological forms such as eosinophilic (EM) and giant cell myocarditis (GCM) or cases associated with systemic autoimmune disease. Conversely, in most patients with acute myocarditis complicated by acute HF or cardiogenic shock, no specific treatment is currently recommended beyond evidence-based management of acute HF and general supportive therapy. Full article

Introduction: Biological systems inherently exhibit metabolic variability that functions within optimal ranges, as described by the Constrained Disorder Principle (CDP). Deviations from these ranges, whether excessive or insufficient, are linked to adverse health outcomes. This review examines how signatures of metabolic variability can enhance GLP-1 receptor agonist therapy using artificial intelligence platforms. Methods: We conducted a comprehensive literature review examining metabolic variability across various parameters, including heart rate, blood pressure, lipid levels, glucose control, body weight, and metabolic rate. We focused on studies investigating the relationship between variability patterns and treatment responses, particularly in the context of GLP-1 receptor agonist therapy and the use of CDP-based AI systems. Results: Increased variability in metabolic parameters consistently predicts adverse outcomes, such as cardiovascular events, mortality, and disease progression. Heart rate variability shows a U-shaped association with outcomes, while blood pressure, lipid, and glucose variability demonstrate predominantly linear relationships with risk. Body weight variability is associated with cognitive decline and an increased risk of cardiovascular complications. Additionally, genetic polymorphisms and baseline metabolic profiles can influence responses to GLP-1 receptor agonists. CDP-based AI platforms have successfully enhanced therapeutic outcomes in conditions like heart failure, cancer, and multiple sclerosis by leveraging biological variability rather than suppressing it. Summary: The identification of metabolic variability signatures offers valuable predictive insights for personalizing therapy with GLP-1 receptor agonists. Artificial intelligence systems based on clinical data patterns that include these variabilities represent a significant shift toward dynamic and individualized treatment approaches. This can enhance therapeutic efficacy and help counteract drug resistance in chronic metabolic disorders, potentially improving the response to GLP-1-based therapies. Full article