Determining arsenic stress tolerance genes in rice (Oryza sativa L.) via genomic insights and QTL mapping with double haploid lines.

Arsenic, a hazardous heavy metal with potent carcinogenic properties, significantly affects key rice-producing regions worldwide. In this study, we present a quantitative trait locus (QTL) mapping investigation designed to identify candidate genes responsible for conferring tolerance to arsenic toxicity in rice (Oryza sativa L.) during the seedling stage. This study identified 17 QTLs on different chromosomes, including qCHC-1 and qCHC-3 on chromosome 1 and 3 related to chlorophyll content and qRFW-12 on chromosome 12 related to root fresh weight. Gene expression analysis revealed eight candidate genes exhibited significant upregulation in the resistant lines, OsGRL1, OsDjB1, OsZIP2, OsMATE12, OsTRX29, OsMADS33, OsABCG29, and OsENODL24. These genes display sequence alignment and phylogenetic tree similarities with other species and engaging in protein-protein interactions with significant proteins. Advanced gene-editing techniques such as CRISPR-Cas9 to precisely target and modify the candidate genes responsible for arsenic tolerance will be explore. This approach may expedite the development of arsenic-resistant rice cultivars, which are essential for ensuring food security in regions affected by arsenic-contaminated soil and water.

Identification of Quantitative Trait Loci Controlling Root Morphological Traits in an Interspecific Soybean Population Using 2D Imagery Data.

Roots are the hidden and most important part of plants. They serve as stabilizers and channels for uptaking water and nutrients and play a crucial role in the growth and development of plants. Here, two-dimensional image data were used to identify quantitative trait loci (QTL) controlling root traits in an interspecific mapping population derived from a cross between wild soybean 'PI366121' and cultivar 'Williams 82'. A total of 2830 single-nucleotide polymorphisms were used for genotyping, constructing genetic linkage maps, and analyzing QTLs. Forty-two QTLs were identified on twelve chromosomes, twelve of which were identified as major QTLs, with a phenotypic variation range of 36.12% to 39.11% and a logarithm of odds value range of 12.01 to 17.35. Two significant QTL regions for the average diameter, root volume, and link average diameter root traits were detected on chromosomes 3 and 13, and both wild and cultivated soybeans contributed positive alleles. Six candidate genes, Glyma.03G027500 (transketolase/glycoaldehyde transferase), Glyma.03G014500 (dehydrogenases), Glyma.13G341500 (leucine-rich repeat receptor-like protein kinase), Glyma.13G341400 (AGC kinase family protein), Glyma.13G331900 (60S ribosomal protein), and Glyma.13G333100 (aquaporin transporter) showed higher expression in root tissues based on publicly available transcriptome data. These results will help breeders improve soybean genetic components and enhance soybean root morphological traits using desirable alleles from wild soybeans.

Exogenous zinc application mitigates negative effects of salinity on barley (Hordeum vulgare) growth by improving root ionic homeostasis.

Detrimental effects of salinity could be mitigated by exogenous zinc (Zn) application; however, the mechanisms underlying this amelioration are poorly understood. This study demonstrated the interaction between Zn and salinity by measuring plant biomass, photosynthetic performance, ion concentrations, ROS accumulation, antioxidant activity and electrophysiological parameters in barley (Hordeum vulgare L.). Salinity stress (200mM NaCl for 3weeks) resulted in a massive reduction in plant biomass; however, both fresh and dry weight of shoots were increased by ~30% with adequate Zn supply. Zinc supplementation also maintained K+ and Na+ homeostasis and prevented H2 O2 toxicity under salinity stress. Furthermore, exposure to 10mM H2 O2 resulted in massive K+ efflux from root epidermal cells in both the elongation and mature root zones, and pre-treating roots with Zn reduced ROS-induced K+ efflux from the roots by 3-4-fold. Similar results were observed for Ca2+ . The observed effects may be causally related to more efficient regulation of cation-permeable non-selective channels involved in the transport and sequestration of Na+ , K+ and Ca2+ in various cellular compartments and tissues. This study provides valuable insights into Zn protective functions in plants and encourages the use of Zn fertilisers in barley crops grown on salt-affected soils.

Advancing crop classification in smallholder agriculture: A multifaceted approach combining frequency-domain image co-registration, transformer-based parcel segmentation, and Bi-LSTM for crop classification.

Agricultural Remote Sensing has the potential to enhance agricultural monitoring in smallholder economies to mitigate losses. However, its widespread adoption faces challenges, such as diminishing farm sizes, lack of reliable data-sets and high cost related to commercial satellite imagery. This research focuses on opportunities, practices and novel approaches for effective utilization of remote sensing in agriculture applications for smallholder economies. The work entails insights from experiments using datasets representative of major crops during different growing seasons. We propose an optimized solution for addressing challenges associated with remote sensing-based crop mapping in smallholder agriculture farms. Open source tools and data are used for inter and intra-sensor image registration, with a root mean square error of 0.3 or less. We also propose and emphasize on the use of delineated vegetation parcels through Segment Anything Model for Geospatial (SAM-GEOs). Furthermore a Bidirectional-Long Short-Term Memory-based (Bi-LSTM) deep learning model is developed and trained for crop classification, achieving results with accuracy of more than 94% and 96% for validation sets of two data sets collected in the field, during 2 growing seasons.

Remote sensing based forest cover classification using machine learning.

Pakistan falls significantly below the recommended forest coverage level of 20 to 30 percent of total area, with less than 6 percent of its land under forest cover. This deficiency is primarily attributed to illicit deforestation for wood and charcoal, coupled with a failure to embrace advanced techniques for forest estimation, monitoring, and supervision. Remote sensing techniques leveraging Sentinel-2 satellite images were employed. Both single-layer stacked images and temporal layer stacked images from various dates were utilized for forest classification. The application of an artificial neural network (ANN) supervised classification algorithm yielded notable results. Using a single-layer stacked image from Sentinel-2, an impressive 91.37% training overall accuracy and 0.865 kappa coefficient were achieved, along with 93.77% testing overall accuracy and a 0.902 kappa coefficient. Furthermore, the temporal layer stacked image approach demonstrated even better results. This method yielded 98.07% overall training accuracy, 97.75% overall testing accuracy, and kappa coefficients of 0.970 and 0.965, respectively. The random forest (RF) algorithm, when applied, achieved 99.12% overall training accuracy, 92.90% testing accuracy, and kappa coefficients of 0.986 and 0.882. Notably, with the temporal layer stacked image of the Sentinel-2 satellite, the RF algorithm reached exceptional performance with 99.79% training accuracy, 96.98% validation accuracy, and kappa coefficients of 0.996 and 0.954. In terms of forest cover estimation, the ANN algorithm identified 31.07% total forest coverage in the District Abbottabad region. In comparison, the RF algorithm recorded a slightly higher 31.17% of the total forested area. This research highlights the potential of advanced remote sensing techniques and machine learning algorithms in improving forest cover assessment and monitoring strategies.

Molecular, genetic, and genomic basis of seed size and yield characteristics in soybean.

Soybean (Glycine max L. Merr.) is a crucial oilseed cash crop grown worldwide and consumed as oil, protein, and food by humans and feed by animals. Comparatively, soybean seed yield is lower than cereal crops, such as maize, rice, and wheat, and the demand for soybean production does not keep up with the increasing consumption level. Therefore, increasing soybean yield per unit area is the most crucial breeding objective and is challenging for the scientific community. Moreover, yield and associated traits are extensively researched in cereal crops, but little is known about soybeans' genetics, genomics, and molecular regulation of yield traits. Soybean seed yield is a complex quantitative trait governed by multiple genes. Understanding the genetic and molecular processes governing closely related attributes to seed yield is crucial to increasing soybean yield. Advances in sequencing technologies have made it possible to conduct functional genomic research to understand yield traits' genetic and molecular underpinnings. Here, we provide an overview of recent progress in the genetic regulation of seed size in soybean, molecular, genetics, and genomic bases of yield, and related key seed yield traits. In addition, phytohormones, such as auxin, gibberellins, cytokinins, and abscisic acid, regulate seed size and yield. Hence, we also highlight the implications of these factors, challenges in soybean yield, and seed trait improvement. The information reviewed in this study will help expand the knowledge base and may provide the way forward for developing high-yielding soybean cultivars for future food demands.

Mechanistic basis for mitigating drought tolerance by selenium application in tobacco (Nicotiana tabacum L.): a multi-omics approach.

The lack of irrigation water in agricultural soils poses a significant constraint on global crop production. In-depth investigation into microRNAs (miRNAs) has been widely used to achieve a comprehensive understanding of plant defense mechanisms. However, there is limited knowledge on the association of miRNAs with drought tolerance in cigar tobacco. In this study, a hydroponic experiment was carried out to identify changes in plant physiological characteristics, miRNA expression and metabolite profile under drought stress, and examine the mitigating effects of selenium (Se) application. The shoot dry weight of drought-stressed plants was approximately half (50.3%) of that in non-stressed (control) conditions. However, plants supplied with Se attained 38.8% greater shoot dry weight as compared to plants with no Se supply under drought stress. Thirteen miRNAs were identified to be associated with drought tolerance. These included 7 known (such as nta-miR156b and nta-miR166a) and 6 novel miRNAs (such as novel-nta-miR156-5p and novel-nta-miR209-5p) with the target genes of squamosa promoter-binding-like protein 4 (SPL4), serine/threonine protein phosphatase 2A (PPP2A), cation/calcium exchanger 4-like (CCX4), extensin-1-like (EXT1) and reduced wall acetylation 2 (RWA2). Further investigation revealed that the expression levels of Ext1 and RWA2 were significantly decreased under drought stress but increased with Se addition. Moreover, key metabolites such as catechin and N-acetylneuraminic acid were identified, which may play a role in the regulation of drought tolerance. The integrated analysis of miRNA sequencing and metabolome highlighted the significance of the novel-nta-miR97-5p- LRR-RLK- catechin pathway in regulating drought tolerance. Our findings provide valuable insights into the molecular mechanisms underlying drought tolerance and Se-induced stress alleviation in cigar tobacco.

Mapping QTL for Mineral Accumulation and Shoot Dry Biomass in Barley under Different Levels of Zinc Supply.

Zinc (Zn) deficiency is a common limiting factor in agricultural soils, which leads to significant reduction in both the yield and nutritional quality of agricultural produce. Exploring the quantitative trait loci (QTL) for shoot and grain Zn accumulation would help to develop new barley cultivars with greater Zn accumulation efficiency. In this study, two glasshouse experiments were conducted by growing plants under adequate and low Zn supply. From the preliminary screening of ten barley cultivars, Sahara (0.05 mg/pot) and Yerong (0.06 mg/pot) showed the lowest change in shoot Zn accumulation, while Franklin (0.16 mg/pot) had the highest change due to changes in Zn supply for plant growth. Therefore, the double haploid (DH) population derived from Yerong × Franklin was selected to identify QTL for shoot mineral accumulation and biomass production. A major QTL hotspot was detected on chromosome 2H between 31.91 and 73.12 cM encoding genes for regulating shoot mineral accumulations of Zn, Fe, Ca, K and P, and the biomass. Further investigation demonstrated 16 potential candidate genes for mineral accumulation, in addition to a single candidate gene for shoot biomass in the identified QTL region. This study provides a useful resource for enhancing nutritional quality and yield potential in future barley breeding programs.

Experiences, awareness, perceptions and attitudes of women and girls towards menstrual hygiene management and safe menstrual products in Pakistan.

Background: The taboo of menstruation, lack of discussion on puberty, misinformation and poor awareness on menstrual hygiene management and limited access to safe menstrual products can negatively impact the physical and mental health of women and girls residing in low middle income countries. Aim: The aim of the study was to explore the experiences, awareness, perceptions and attitudes of women and girls towards menstrual hygiene management in Pakistan. Moreover, the study also assessed consumer satisfaction towards locally manufactured organic menstrual products. Methods: A descriptive cross-sectional study design was used with a sample of 400 women and girls selected through convenience sampling from high schools, universities, outpatient department and gynaecological clinics located in 2 cities, i.e., Islamabad and Rawalpindi, Pakistan. A pre-structured questionnaire was used to explore experiences, awareness, perceptions and attitudes of women and girls towards menstrual hygiene management. Moreover, each respondent was provided with sample of locally designed and manufactured organic menstrual hygiene & wellness kit by a group of women researchers named "FemPure" including organic sanitary pads, feminine wellness mist and feminine wellness wash. The respondents were asked to use the products and a telephonic follow-up was conducted to assess consumer satisfaction for the products after a period of 1 month. Data was analyzed statistically using SPSS 21. Results: The results of the study reported that 86.2% (n = 345) of the respondents had normal periods. Out of 400 respondents, 58.5% (n = 234) knew about any health conditions related to abnormal menstrual cycle while 88.3% (n = 353) were aware of female menstrual hygiene. Majority of the respondents 78.7% (n = 315) felt ashamed while buying sanitary pads. Out of 400 respondents, 5.4% (n = 22) were interested in getting awareness regarding menstrual hygiene. The results of the study showed that all the respondents (100%, n = 400) were satisfied with FemPure organic menstrual products. Conclusion: The study concluded that majority of women and girls faced menstrual hygiene issues during and after the cycle and were eager to receive information on MHM and use organic rash free menstrual wellness products which could be ordered via mobile app. The respondents were satisfied after the use of FemPure organic menstrual products.

How do smoking, vaping, and nicotine affect people with epilepsy and seizures? A scoping review protocol.

BACKGROUND: Epilepsy is a prevalent disease that requires personalized care to control seizures, reduce side effects, and ameliorate the burden of comorbidities. Smoking is a major cause of preventable death and disease. There is evidence that patients with epilepsy smoke at high rates and that smoking may increase seizure frequency. However, there is a lack systematically synthesized evidence on the interactions between epilepsy and seizures and smoking, tobacco use, vaping, and smoking cessation. METHODS AND ANALYSIS: This scoping review protocol guided by the Joanna Briggs Institute Manual for Evidence Synthesis and the PRISMA Extension for Scoping Reviews will investigate what is known about the interactions between smoking and epilepsy. This review will include the population of persons with all types of epilepsy or seizures and examine an inclusive list of concepts including tobacco use, vaping, nicotine replacement, and smoking cessation. The MEDLINE, Embase, APA Psycinfo, CINAHL, Cochrane, Scopus, and Web of Science databases will be searched. Following systematic screening of records, data will be charted, synthesized, and summarized for presentation and publication. ETHICS AND DISSEMINATION: No ethical approval is required for this literature-based study. The results of this scoping review will be submitted for publication in a peer-reviewed journal. This synthesis will be informative to clinicians and direct further research that may improve health outcomes for people with epilepsy. REGISTRATION: This protocol is registered with the Open Science Framework (DOI: https://doi.org/10.17605/OSF.IO/D3ZK8).

Evaluating the Effectiveness of Calcium Silicate in Enhancing Soybean Growth and Yield.

The application of silicon (Si) fertilizer positively impacts crop health, yield, and seed quality worldwide. Si is a "quasi-essential" element that is crucial for plant nutrition and stress response but is less associated with growth. This study aimed to investigate the effect of Si on the yield of cultivated soybean (Glycine max L). Two locations, Gyeongsan and Gunwi, in the Republic of Korea were selected, and a land suitability analysis was performed using QGIS version 3.28.1. The experiments at both locations consisted of three treatments: the control, Si fertilizer application at 2.3 kg per plot (9 m × 9 m) (T1), and Si fertilizer application at 4.6 kg per plot (9 m × 9 m) (T2). The agronomic, root, and yield traits, as well as vegetative indices, were analyzed to evaluate the overall impact of Si. The results demonstrated that Si had consistently significant effects on most root and shoot parameters in the two experimental fields, which led to significantly increased crop yield when compared with the control, with T2 (22.8% and 25.6%, representing an output of 2.19 and 2.24 t ha-1 at Gyeongsan and Gunwi, respectively) showing a higher yield than T1 (11% and 14.2%, representing 1.98 and 2.04 t ha-1 at Gyeongsan and Gunwi, respectively). These results demonstrate the positive impact of exogenous Si application on the overall growth, morphological and physiological traits, and yield output of soybeans. However, the application of the optimal concentration of Si according to the crop requirement, soil status, and environmental conditions requires further studies.

Quantitative Risk for Single-Positive Lupus Anticoagulant Results With Different Anticoagulants.

OBJECTIVES: Clinical experts recommend against testing for lupus anticoagulant (LAC) during anticoagulation. METHODS: We quantitated the risk of a single-positive dilute Russell viper venom time (dRVVT) result or partial thromboplastin time-based phospholipid neutralization (PN) result on anticoagulation. RESULTS: Any anticoagulation led to a fourfold greater likelihood of single-positive results, primarily by rivaroxaban (odds ratio [OR] = 8.6) and warfarin (OR = 6.6), resulting in a positive dRVVT test with a normal PN test. Heparin and apixaban were twofold more likely to show single-positive results, but enoxaparin did not show significant single positivity. CONCLUSIONS: Our results quantitatively support experts' avoidance of LAC testing during anticoagulation.

Long short term memory deep net performance on fused Planet-Scope and Sentinel-2 imagery for detection of agricultural crop.

In view of the challenges faced by organizations and departments concerned with agricultural capacity observations, we collected In-Situ data consisting of diverse crops (More than 11 consumable vegetation types) in our pilot region of Harichand Charsadda, Khyber Pakhtunkhwa (KP), Pakistan. Our proposed Long Short-Term Memory based Deep Neural network model was trained for land cover land use statistics generation using the acquired ground truth data, for a synergy between Planet-Scope Dove and European Space Agency's Sentinel-2. Total of 4 bands from both sentinel-2 and planet scope including Red, Green, Near-Infrared (NIR) and Normalised Difference Vegetation Index (NDVI) were used for classification purpose. Using short temporal frame of Sentinel-2 comprising 5 date images, we propose an realistic and implementable procedure for generating accurate crop statistics using remote sensing. Our self collected data-set consists of a total number of 107,899 pixels which was further split into 70% and 30% for training and testing purpose of the model respectively. The collected data is in the shape of field parcels, which has been further split for training, validation and test sets, to avoid spatial auto-correlation. To ensure the quality and accuracy 15% of the training data was left out for validation purpose, and 15% for testing. Prediction was also performed on our trained model and visual analysis of the area from the image showed significant results. Further more a comparison between Sentinel-2 time series is performed separately from the fused Planet-Scope and Sentinel-2 time-series data sets. The results achieved shows a weighted average of 93% for Sentinel-2 time series and 97% for fused Planet-Scope and Sentinel-2 time series.

Effect of hyper-parameters on the performance of ConvLSTM based deep neural network in crop classification.

Deep learning based data driven methods with multi-sensors spectro-temporal data are widely used for pattern identification and land-cover classification in remote sensing domain. However, adjusting the right tuning for the deep learning models is extremely important as different parameter setting can alter the performance of the model. In our research work, we have evaluated the performance of Convolutional Long Short-Term Memory (ConvLSTM) and deep learning techniques, over various hyper-parameters setting for an imbalanced dataset and the one with highest performance is utilized for land-cover classification. The parameters that are considered for experimentation are; Batch size, Number of Layers in ConvLSTM model, and No of filters in each layer of the ConvLSTM are the parameters that will be considered for our experimentation. Experiments also have been conducted on LSTM model for comparison using the same hyper-parameters. It has been found that the two layered ConvLSTM model having 16-filters and a batch size of 128 outperforms other setting scenarios, with an overall validation accuracy of 97.71%. The accuracy achieved for the LSTM is 93.9% for training and 92.7% for testing.

Pure post-pubertal yolk sac tumor of the testis: An extremely rare and aggressive entity.

CONTEXT: Pure post-pubertal yolk sac tumors (YSTs) are an extremely rare type of malignant germ cell tumor (GCT) that account for <1 % of testicular GCTs. Clinically, they are more aggressive compared to the more common pre-pubertal counterpart. The aim of this study is to analyze the clinical presentation, ancillary tests and clinical outcomes in addition to presenting a spectrum of histomorphological features, in a case series along with a literature review. DESIGN: A retrospective review of 4 cases of pure post-pubertal YST of the testis was performed. Data collected for each patient included demographics, clinical presentation, serum markers, radiology and pathologic findings, treatment, and clinical outcomes. RESULTS: All patients presented with a testicular mass with or without associated pain and elevated serum alpha-feto protein. Mean age at presentation was 36 years (range 25-68 years). Two patients presented with metastatic disease at the time of diagnosis. Histologic patterns and features are as follows: germ cell neoplasia in-situ (n = 4), reticular/microcystic, solid, glandular, papillary, endometrioid, cystic, necrosis and angiolymphatic invasion (n = 3). Fluorescent in-situ hybridization test performed on Case 2, showed presence of isochromosome 12p and next generation sequencing showed gains of 12p. Case 1, 2 and 4 showed metastatic disease on follow-up. CONCLUSIONS: Diagnosis of pure post-pubertal YST remains challenging due to the variety of morphologic patterns often present in these tumors. Extensive sampling along with use of ancillary tests is the key for diagnosis. In this study, 75 % of cases had metastatic disease at or after the diagnosis confirming the aggressive nature of this rare entity.

Silicon as a Smart Fertilizer for Sustainability and Crop Improvement.

Silicon (Si), despite being abundant in nature, is still not considered a necessary element for plants. Si supplementation in plants has been extensively studied over the last two decades, and the role of Si in alleviating biotic and abiotic stress has been well documented. Owing to the noncorrosive nature and sustainability of elemental Si, Si fertilization in agricultural practices has gained more attention. In this review, we provide an overview of different smart fertilizer types, application of Si fertilizers in agriculture, availability of Si fertilizers, and experiments conducted in greenhouses, growth chambers, and open fields. We also discuss the prospects of promoting Si as a smart fertilizer among farmers and the research community for sustainable agriculture and yield improvement. Literature review and empirical studies have suggested that the application of Si-based fertilizers is expected to increase in the future. With the potential of nanotechnology, new nanoSi (NSi) fertilizer applications may further increase the use and efficiency of Si fertilizers. However, the general awareness and scientific investigation of NSi need to be thoughtfully considered. Thus, we believe this review can provide insight for further research into Si fertilizers as well as promote Si as a smart fertilizer for sustainability and crop improvement.

Untargeted metabolomics and comparative flavonoid analysis reveal the nutritional aspects of pak choi.

Pak choi is a leafy vegetable with high economic value. Despite its importance, the information related to its metabolomics profile has still not been well-understood. This study aimed to determine the leaf metabolite composition of seven pak choi. In total, 513 metabolites belonging to 24 separate metabolite groups were detected. Pak choi leaves were rich in organic acids, amino acids, and flavonoids. There were ninety-two flavonoid compounds detected in pak choi leaves. Multivariate analysis revealed a distinct variation in the metabolite and flavonoid profile of green and purple leaved varieties. The flavonoid accumulation was comparatively greater in green leaved than purple leaf cultivar. This work provides novel insights into pak choi metabolomics profile, the flavonoids in particular, thus, to assess the nutritional value of this vegetable for humans.

On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network.

This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country's Tobacco production. In order to analyze the performance of the developed classification framework, a pilot sub-region named Yar Hussain is selected for experimentation work. Yar Hussain is a tehsil of district Swabi, within KP province of Pakistan, having highest contribution to the gross production of the KP Tobacco crop. KP generally consists of a diverse crop land with different varieties of vegetation, having similar phenology which makes crop classification a challenging task. In this study, a temporal convolutional neural network (TempCNNs) model is implemented for crop classification, while considering remotely sensed imagery of the selected pilot region with specific focus on the Tobacco crop. In order to improve the performance of the proposed classification framework, instead of using the prevailing concept of utilizing a single satellite imagery, both Sentinel-2 and Planet-Scope imageries are stacked together to assist in providing more diverse features to the proposed classification framework. Furthermore, instead of using a single date satellite imagery, multiple satellite imageries with respect to the phenological cycle of Tobacco crop are temporally stacked together which resulted in a higher temporal resolution of the employed satellite imagery. The developed framework is trained using the ground truth data. The final output is obtained as an outcome of the SoftMax function of the developed model in the form of probabilistic values, for the classification of the selected classes. The proposed deep learning-based crop classification framework, while utilizing multi-satellite temporally stacked imagery resulted in an overall classification accuracy of 98.15%. Furthermore, as the developed classification framework evolved with specific focus on Tobacco crop, it resulted in best Tobacco crop classification accuracy of 99%.

Highly green fluorescent Nb2C MXene quantum dots.

Here, two dimensional Nb2C quantum dots with green fluorescence were fabricated for the first time with a quantum yield (QY) of up to 19%, the highest reported for Nb2C dots so far with good photostability and pH stability. The S,N doping on Nb2C was considered to be the main reason for the enhanced high QY of the Nb2C dots and was proved by the density functional theory (DFT) calculations. It was demonstrated that the fluorescent probe could be utilized effectively for 3D brain organoid labeling and thus has huge prospects in biological sensing.