Improving Seed Shattering Resistance in Wild O. alta Rice with Mesoporous Silica Nanoparticle Delivery Systems.

To address food security challenges and climate change, the polyploid wild rice Oryza alta has been explored as a potential crop, although it suffers from seed shattering. We employed mesoporous silica nanoparticles (MSNs) to deliver small interfering RNAs (siRNAs) for targeted gene silencing. Foliar spraying of MSN-siRNA complexes effectively delivered siRNA, resulting in up to 70% gene silencing of the PDS gene and 75% silencing of the transgenic Ruby gene. Additionally, MSN-siRNAs were infiltrated into the panicles of O. alta to target four seed shattering major genes every other day for 2 weeks until heading outdoors. This method silenced all four shattering genes ranging from 10.7% to 49.4% and significantly reduced the formation of the abscission layer between rice grains and pedicels, which enhanced pedicel tensile strength. Our MSN-siRNA system provides a flexible, nonpermanent approach to modifying crop traits, offering a promising tool for sustainable agricultural practices.

Systematic evaluation of multiple NGS platforms for structural variants detection.

Structural variations (SV) are critical genome changes affecting human diseases. Although many hybridization-based methods exist, evaluating SVs through next-generation sequencing (NGS) data is still necessary for broader research exploration. Here, we comprehensively compared the performance of 16 SV callers and multiple NGS platforms using NA12878 whole genome sequencing (WGS) datasets. The results indicated that several SV callers performed well relatively, such as Manta, GRIDSS, LUMPY, TARDIS, FermiKit, and Wham. Meanwhile, all NGS platforms have a similar performance using a single software. Additionally, we found that the source of undetected SVs was mostly from long reads datasets, therefore, the more appropriate strategy for accurate SV detection will be an integration of long and shorter reads in the future. At present, in the period of NGS as a mainstream method in bioinformatics, our study would provide helpful and comprehensive guidelines for specific categories of SV research.

Comparative performance of the GenoLab M and NovaSeq 6000 sequencing platforms for transcriptome and LncRNA analysis.

BACKGROUND: GenoLab M is a recently established next-generation sequencing platform from GeneMind Biosciences. Presently, Illumina sequencers are the globally leading sequencing platform in the next-generation sequencing market. Here, we present the first report to compare the transcriptome and LncRNA sequencing data of the GenoLab M sequencer to NovaSeq 6000 platform in various types of analysis. RESULTS: We tested 16 libraries in three species using various library kits from different companies. We compared the data quality, genes expression, alternatively spliced (AS) events, single nucleotide polymorphism (SNP), and insertions-deletions (InDel) between two sequencing platforms. The data suggested that platforms have comparable sensitivity and accuracy in terms of quantification of gene expression levels with technical compatibility. CONCLUSIONS: Genolab M is a promising next-generation sequencing platform for transcriptomics and LncRNA studies with high performance at low costs.

Knockdown of miR-543 Inhibits the Proliferation and Migration of Small-Cell Lung Carcinoma Cells and Induces Apoptosis.

This article aims to explore the effects and possible mechanism of miR-543 on small-cell lung carcinoma (SCLC) cells. The respective levels of miR-543 in lung carcinoma tissues, para-cancerous tissues, human normal lung cells MRC-9, and SCLC cells were detected by RT-qPCR. The proliferation, apoptosis, and migration of SCLC cells were detected after the miR-543 level in SCLC cells was altered by miRNA mimics and inhibitors. The levels of apoptosis-related proteins and potential downstream targeted proteins of miR-543 were detected by western blots. The study revealed that KNTC1 was highly expressed in lung carcinoma tissues and SCLC cells (P < 0.01). It also showed that knockdown of miR-543 can inhibit the proliferation and migration of SCLC cells, induce apoptosis, and increase the level of apoptosis-related proteins. These changes were reversed by the addition of mimics that increased miR-543 levels. The level of miR-543 was positively correlated with the protein expression level of downstream MUC1, ß-catenin, and CDC42 in SCLC cells, suggesting that miR-543 may play a role through them. Thus this study concludes that MiR-543 can affect the function of SCLC cells, which may play a crucial role in the presence and development of SCLC.

Correlation between Plasma Cellular Retinoic Acid-Binding Protein 2 and Proliferation, Migration, and Invasion of Non-Small-Cell Lung Cancer Cells.

BACKGROUND: Our primary aim of the current study was to explore the correlation between plasma CRABP2 and migration, proliferation and invasion of non-small cell lung cancer (NSCLC) cells. METHODS: Human lung cancer cell line A549 was used in the present study, which was cultured in 6-well plates (1 × 106 cells/well) and then transfected with pcDNA-CRABP2 and pcDNA, siRNA with the use of Lipofectamine 2000 based on the manufacturer's protocol. The expression of CRABP2 mRNA was detected through real-time PCR. Proliferation was further detected using MTT assays, and apoptosis was monitored and recorded with the application of flow cytometry. The expression of E-cadherin, MMP9, vimentin and related pathway proteins was detected by Western blotting assays. Transwell assays and cell scratch assays were utilized for the detection of migration and invasion ability of A549 cells. RESULTS: RT-PCR results showed The CRABP2 mRNA transcript levels in the CRABP2 overexpression group were higher when comparing those of the empty vector group (P < 0.05). By MTT assays, CRABP2 overexpression promoted cellular proliferation, while CRABP2 downregulation inhibited cellular proliferation. CRABP2 overexpression inhibited cell apoptosis and promoted cellular proliferation. The number of TUNEL staining positive cells was the lowest in the CRABP2 overexpression group, and the siRNA transfection group had increased apoptosis. CRABP2 downregulation reduced EMT in cells and cell migration and invasion reflected from western blotting results and cell migration and invasion assay results, respectively. CONCLUSION: Inhibition of plasma CRABP2 expression offers the potential in terms of reducing the expression of MAPKs and proteins in the NF-κB pathway and inhibiting the proliferation and migration of NSCLC cells, which is ideally suited for further treatment for NSCLC.

Mycobacterium vaccae nebulization protects Balb/c mice against bronchial asthma through neural mechanisms.

OBJECTIVES: Bronchial asthma can be effectively controlled but not be cured, its etiology and pathogenesis are still unclear, and there are no effective preventive measures. The key characteristic of asthma is chronic airway inflammation, and recent research has found that airway neurogenic inflammation plays an important role in asthma. We previously found that Mycobacterium vaccae nebulization protects against asthma. Therefore, this objective of this study is to explore the effect of M. vaccae nebulization on asthmatic neural mechanisms. METHODS: A total 18 of female Balb/c mice were randomized into normal, asthma control, and M. vaccae nebulization (Neb.group) groups, and mice in the Neb.group were nebulized with M. vaccae one month before the asthmatic model was established. Then, 1 month later, the mice were sensitized and challenged with ovalbumin. Twenty-four hours after the last challenge, mouse airway responsiveness; pulmonary brain-derived neurotropic factor (BDNF), neurofilament-medium length (NF-M, using NF09 antibody), and acetylcholine expression; and nerve growth factor (NGF) mRNA level were determined. RESULTS: We found that the BDNF, NF09, acetylcholine expression, and NGF mRNA level were decreased in the Neb.group compared with levels in the asthma control group. CONCLUSION: M. vaccae nebulization may protected in Balb/c mice against bronchial asthma through neural mechanisms.

High Engraftment and Metastatic Rates in Orthotopic Xenograft Models of Gastric Cancer via Direct Implantation of Tumor Cell Suspensions.

Although the implantation of intact tumor fragments is a common practice to generate orthotopic xenografts to study tumor invasion and metastasis, the direct implantation of tumor cell suspensions is necessary when prior manipulations of tumor cells are required. However, the establishment of orthotopic xenografts using tumor cell suspensions is not mature, and a comparative study directly comparing their engraftment and metastatic capabilities is lacking. It is unclear whether tumor fragments are superior to cell suspensions for successful engraftment and metastasis. In this study, we employed three GC cell lines with varying metastatic capacities to stably express firefly luciferase for monitoring tumor progression in real time. We successfully minimized the risk of cell leakage during the orthotopic injection of tumor cell suspensions without Corning Matrigel by systematically optimizing the surgical procedure, injection volume, and needle size options. Comparable high engraftment and metastatic rates between these two methods were demonstrated using MKN-45 cells with a strong metastatic ability. Importantly, our approach can adjust the rate of tumor progression flexibly and cuts the experimental timeline from 10-12 weeks (for tumor fragments) to 4-5 weeks. Collectively, we provided a highly reproducible procedure with a shortened experimental timeline and low cost for establishing orthotopic GC xenografts via the direct implantation of tumor cell suspensions.

Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity.

Arthropods contribute importantly to ecosystem functioning but remain understudied. This undermines the validity of conservation decisions. Modern methods are now making arthropods easier to study, since arthropods can be mass-trapped, mass-identified, and semi-mass-quantified into 'many-row (observation), many-column (species)' datasets, with homogeneous error, high resolution, and copious environmental-covariate information. These 'novel community datasets' let us efficiently generate information on arthropod species distributions, conservation values, uncertainty, and the magnitude and direction of human impacts. We use a DNA-based method (barcode mapping) to produce an arthropod-community dataset from 121 Malaise-trap samples, and combine it with 29 remote-imagery layers using a deep neural net in a joint species distribution model. With this approach, we generate distribution maps for 76 arthropod species across a 225 km2 temperate-zone forested landscape. We combine the maps to visualize the fine-scale spatial distributions of species richness, community composition, and site irreplaceability. Old-growth forests show distinct community composition and higher species richness, and stream courses have the highest site-irreplaceability values. With this 'sideways biodiversity modelling' method, we demonstrate the feasibility of biodiversity mapping at sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Development and Evaluation of a Retrieval-Augmented Large Language Model Framework for Ophthalmology.

Importance: Although augmenting large language models (LLMs) with knowledge bases may improve medical domain-specific performance, practical methods are needed for local implementation of LLMs that address privacy concerns and enhance accessibility for health care professionals. Objective: To develop an accurate, cost-effective local implementation of an LLM to mitigate privacy concerns and support their practical deployment in health care settings. Design, Setting, and Participants: ChatZOC (Sun Yat-Sen University Zhongshan Ophthalmology Center), a retrieval-augmented LLM framework, was developed by enhancing a baseline LLM with a comprehensive ophthalmic dataset and evaluation framework (CODE), which includes over 30 000 pieces of ophthalmic knowledge. This LLM was benchmarked against 10 representative LLMs, including GPT-4 and GPT-3.5 Turbo (OpenAI), across 300 clinical questions in ophthalmology. The evaluation, involving a panel of medical experts and biomedical researchers, focused on accuracy, utility, and safety. A double-masked approach was used to try to minimize bias assessment across all models. The study used a comprehensive knowledge base derived from ophthalmic clinical practice, without directly involving clinical patients. Exposures: LLM response to clinical questions. Main Outcomes and Measures: Accuracy, utility, and safety of LLMs in responding to clinical questions. Results: The baseline model achieved a human ranking score of 0.48. The retrieval-augmented LLM had a score of 0.60, a difference of 0.12 (95% CI, 0.02-0.22; P = .02) from baseline and not different from GPT-4 with a score of 0.61 (difference = 0.01; 95% CI, -0.11 to 0.13; P = .89). For scientific consensus, the retrieval-augmented LLM was 84.0% compared with the baseline model of 46.5% (difference = 37.5%; 95% CI, 29.0%-46.0%; P < .001) and not different from GPT-4 with a value of 79.2% (difference = 4.8%; 95% CI, -0.3% to 10.0%; P = .06). Conclusions and Relevance: Results of this quality improvement study suggest that the integration of high-quality knowledge bases improved the LLM's performance in medical domains. This study highlights the transformative potential of augmented LLMs in clinical practice by providing reliable, safe, and practical clinical information. Further research is needed to explore the broader application of such frameworks in the real world.

Artificial intelligence in corneal diseases: A narrative review.

Corneal diseases represent a growing public health burden, especially in resource-limited settings lacking access to specialized eye care. Artificial intelligence (AI) offers promising solutions for automating the diagnosis and management of corneal conditions. This narrative review examines the application of AI in corneal diseases, focusing on keratoconus, infectious keratitis, pterygium, dry eye disease, Fuchs endothelial corneal dystrophy, and corneal transplantation. AI models integrating diverse imaging modalities (e.g., corneal topography, slit-lamp, and anterior segment OCT images) and clinical data have demonstrated high diagnostic accuracy, often outperforming human experts. Emerging trends include the incorporation of biomechanical data to enhance keratoconus detection, leveraging in vivo confocal microscopy for diagnosing infectious keratitis, and employing multimodal approaches for comprehensive disease analysis. Additionally, AI has shown potential in predicting disease progression, treatment outcomes, and postoperative complications in corneal transplantation. While challenges remain such as population heterogeneity, limited external validation, and the "black box" nature of some models, ongoing advancement in explainable AI, data augmentation, and improved regulatory frameworks can serve to address these limitations.

Latest developments of generative artificial intelligence and applications in ophthalmology.

The emergence of generative artificial intelligence (AI) has revolutionized various fields. In ophthalmology, generative AI has the potential to enhance efficiency, accuracy, personalization and innovation in clinical practice and medical research, through processing data, streamlining medical documentation, facilitating patient-doctor communication, aiding in clinical decision-making, and simulating clinical trials. This review focuses on the development and integration of generative AI models into clinical workflows and scientific research of ophthalmology. It outlines the need for development of a standard framework for comprehensive assessments, robust evidence, and exploration of the potential of multimodal capabilities and intelligent agents. Additionally, the review addresses the risks in AI model development and application in clinical service and research of ophthalmology, including data privacy, data bias, adaptation friction, over interdependence, and job replacement, based on which we summarized a risk management framework to mitigate these concerns. This review highlights the transformative potential of generative AI in enhancing patient care, improving operational efficiency in the clinical service and research in ophthalmology. It also advocates for a balanced approach to its adoption.

Extracellular vesicles from human urine-derived stem cells delay aging through the transfer of PLAU and TIMP1.

Aging increases the risks of various diseases and the vulnerability to death. Cellular senescence is a hallmark of aging that contributes greatly to aging and aging-related diseases. This study demonstrates that extracellular vesicles from human urine-derived stem cells (USC-EVs) efficiently inhibit cellular senescence in vitro and in vivo. The intravenous injection of USC-EVs improves cognitive function, increases physical fitness and bone quality, and alleviates aging-related structural changes in different organs of senescence-accelerated mice and natural aging mice. The anti-aging effects of USC-EVs are not obviously affected by the USC donors' ages, genders, or health status. Proteomic analysis reveals that USC-EVs are enriched with plasminogen activator urokinase (PLAU) and tissue inhibitor of metalloproteinases 1 (TIMP1). These two proteins contribute importantly to the anti-senescent effects of USC-EVs associated with the inhibition of matrix metalloproteinases, cyclin-dependent kinase inhibitor 2A (P16INK4a), and cyclin-dependent kinase inhibitor 1A (P21cip1). These findings suggest a great potential of autologous USC-EVs as a promising anti-aging agent by transferring PLAU and TIMP1 proteins.

Extracting abundance information from DNA-based data.

The accurate extraction of species-abundance information from DNA-based data (metabarcoding, metagenomics) could contribute usefully to diet analysis and food-web reconstruction, the inference of species interactions, the modelling of population dynamics and species distributions, the biomonitoring of environmental state and change, and the inference of false positives and negatives. However, multiple sources of bias and noise in sampling and processing combine to inject error into DNA-based data sets. To understand how to extract abundance information, it is useful to distinguish two concepts. (i) Within-sample across-species quantification describes relative species abundances in one sample. (ii) Across-sample within-species quantification describes how the abundance of each individual species varies from sample to sample, such as over a time series, an environmental gradient or different experimental treatments. First, we review the literature on methods to recover across-species abundance information (by removing what we call "species pipeline biases") and within-species abundance information (by removing what we call "pipeline noise"). We argue that many ecological questions can be answered with just within-species quantification, and we therefore demonstrate how to use a "DNA spike-in" to correct for pipeline noise and recover within-species abundance information. We also introduce a model-based estimator that can be used on data sets without a physical spike-in to approximate and correct for pipeline noise.

Anti-apoptotic protein BCL-XL as a therapeutic vulnerability in gastric cancer.

BACKGROUND: New therapeutic targets are needed to improve the outcomes for gastric cancer (GC) patients with advanced disease. Evasion of programmed cell death (apoptosis) is a hallmark of cancer cells and direct induction of apoptosis by targeting the pro-survival BCL2 family proteins represents a promising therapeutic strategy for cancer treatment. Therefore, understanding the molecular mechanisms underpinning cancer cell survival could provide a molecular basis for potential therapeutic interventions. METHOD: Here we explored the role of BCL2L1 and the encoded anti-apoptotic BCL-XL in GC. Using Droplet Digital PCR (ddPCR) technology to investigate the DNA amplification of BCL2L1 in GC samples and GC cell lines, the sensitivity of GC cell lines to selective BCL-XL inhibitors A1155463 and A1331852, pan-inhibitor ABT-263, and VHL-based PROTAC-BCL-XL was analyzed using (CellTiter-Glo) CTG assay in vitro. Western Blot (WB) was used to detect the protein expression of BCL2 family members in GC cell lines and the manner in which PROTAC-BCL-XL kills GC cells. Co-immunoprecipitation (Co-IP) was used to investigate the mechanism of A1331852 and ABT-263 kills GC cell lines. DDPCR, WB, and real-time PCR (RTPCR) were used to investigate the correlation between DNA, RNA, protein levels, and drug activity. RESULTS: The functional assay showed that a subset of GC cell lines relies on BCL-XL for survival. In gastric cancer cell lines, BCL-XL inhibitors A1155463 and A1331852 are more sensitive than the pan BCL2 family inhibitor ABT-263, indicating that ABT-263 is not an optimal inhibitor of BCL-XL. VHL-based PROTAC-BCL-XL DT2216 appears to be active in GC cells. DT2216 induces apoptosis of gastric cancer cells in a time- and dose-dependent manner through the proteasome pathway. Statistical analysis showed that the BCL-XL protein level predicts the response of GC cells to BCL-XL targeting therapy and BCL2L1 gene CNVs do not reliably predict BCL-XL expression. CONCLUSION: We identified BCL-XL as a promising therapeutic target in a subset of GC cases with high levels of BCL-XL protein expression. Functionally, we demonstrated that both selective BCL-XL inhibitors and VHL-based PROTAC BCL-XL can potently kill GC cells that are reliant on BCL-XL for survival. However, we found that BCL2L1 copy number variations (CNVs) cannot reliably predict BCL-XL expression, but the BCL-XL protein level serves as a useful biomarker for predicting the sensitivity of GC cells to BCL-XL-targeting compounds. Taken together, our study pinpointed BCL-XL as potential druggable target for specific subsets of GC.

Evaluation of environmental factors and microbial community structure in an important drinking-water reservoir across seasons.

The composition of microbial communities varies in water and sediments, and changes in environmental factors have major effects on microbiomes. Here, we characterized variations in microbial communities and physicochemical factors at two sites in a large subtropical drinking water reservoir in southern China. The microbiomes of all sites, including the diversity and abundance of microbial species, were determined via metagenomics, and the relationships between microbiomes and physicochemical factors were determined via redundancy analysis. The dominant species in sediment and water samples differed; Dinobryon sp. LO226KS and Dinobryon divergens were dominant in sediment samples, whereas Candidatus Fonsibacter ubiquis and Microcystis elabens were dominant in water. The diversity was also significantly different in microbial alpha diversity between water and sediment habitats (p < 0.01). The trophic level index (TLI) was the major factor affecting the microbial community in water samples; Mycolicibacterium litorale and Mycolicibacterium phlei were significantly positively related to TLI. Furthermore, we also studied the distribution of algal toxin-encoding genes and antibiotic-resistant genes (ARGs) in the reservoir. It found that water samples contained more phycotoxin genes, with the cylindrospermopsin gene cluster most abundant. We found three genera highly related to cylindrospermopsin and explored a new cyanobacteria Aphanocapsa montana that may produce cylindrospermopsin based on the correlation through network analysis. The multidrug resistance gene was the most abundant ARG, while the relationship between ARGs and bacteria in sediment samples was more complicated than in water. The results of this study enhance our understanding of the effects of environmental factors on microbiomes. In conclusion, research on the properties, including profiles of algal toxin-encoding genes and ARGs, and microbial communities can aid water quality monitoring and conservation.

DeepQuality improves infant retinopathy screening.

Image quality variation is a prominent cause of performance degradation for intelligent disease diagnostic models in clinical applications. Image quality issues are particularly prominent in infantile fundus photography due to poor patient cooperation, which poses a high risk of misdiagnosis. Here, we developed a deep learning-based image quality assessment and enhancement system (DeepQuality) for infantile fundus images to improve infant retinopathy screening. DeepQuality can accurately detect various quality defects concerning integrity, illumination, and clarity with area under the curve (AUC) values ranging from 0.933 to 0.995. It can also comprehensively score the overall quality of each fundus photograph. By analyzing 2,015,758 infantile fundus photographs from real-world settings using DeepQuality, we found that 58.3% of them had varying degrees of quality defects, and large variations were observed among different regions and categories of hospitals. Additionally, DeepQuality provides quality enhancement based on the results of quality assessment. After quality enhancement, the performance of retinopathy of prematurity (ROP) diagnosis of clinicians was significantly improved. Moreover, the integration of DeepQuality and AI diagnostic models can effectively improve the model performance for detecting ROP. This study may be an important reference for the future development of other image-based intelligent disease screening systems.

A Simulation Experiment on Quality Dynamics of Reclaimed Water under Different Flow Exchanges.

Reclaimed water plays an important role in maintaining urban aquatic ecosystems, especially in areas with water shortages. However, there is little information on water quality dynamics and its driving mechanism in reclaimed water bodies. The simulated experiments were conducted to investigate the effect of flow exchange on water quality dynamics and soil microbial diversity for 100% reclaimed water and mixed water (50% reclaimed and 50% stream water), and the exchange periods ranged from 2 to 40 days. The results showed that the degradation coefficients (K) of CODMn and NH3-N were 0.015 d-1 and 0.001 d-1 for the mixed water, while their K values were negative for the reclaimed water. The flow exchange had little effect on water quality dynamics for the mixed water, which may be attributed to the relatively low concentration of TP in this reclaimed water. A small or great exchange period led to a relatively high fluctuation in K during the experimental period and corresponded to a worse soil microbial diversity. These results indicate that it is not recommended to fill an isolated urban lake with 100% reclaimed water and that a suitable flow exchange period of 5~10 days could help self-purify the water quality.

Staple line lockstitch reinforcement decreases clinically relevant pancreatic fistula following distal pancreatectomy: Results of a propensity score matched retrospective analysis.

Background: Postoperative pancreatic fistula (POPF) remains the primary complication of distal pancreatectomies. We aimed to review whether staple line reinforcement with continuous lockstitches would lead to decreased grade B and C pancreatic fistula in patients undergoing distal pancreatectomy. Methods: This retrospective study enrolled consecutive patients scheduled to undergo distal pancreatectomy at a large tertiary hospital. A comparison was conducted between lockstitch reinforcement and non-reinforcement for remnant closure during distal pancreatectomies from August 2016 to February 2021. Propensity score matching was applied to balance the two groups with covariates including abdominal and back pain, diabetes mellitus, and estimated blood loss. The primary outcome was POPF rate. Results: A total of 153 patients were enrolled in the study (89 lockstitch reinforcements, 64 non-reinforcements), of whom 128 patients (64 per group) were analyzed after propensity score matching (1:1). The total POPF rate was 21.9%. POPF was identified in 12.5% (8/64) of the patients who underwent resection with lockstitch reinforcement and 31.2% (20/64) of the patients without reinforcement (odds ratio 0.314, 95% confidence interval 0.130-0.760, P=0.010). No deaths occurred in either group. Neither the major complication rate nor the length of hospital stay after surgery differed between the groups. Conclusions: Compared with the use of stapler alone, staple line lockstitch reinforcement for remnant closure during distal pancreatectomy could reduce the POPF rate. Further multicenter randomized clinical trials are required to confirm these results.