Characterization of three lamp genes from largemouth bass (Micropterus salmoides): molecular cloning, expression patterns, and their transcriptional levels in response to fast and refeeding strategy.

Lysosomes-associated membrane proteins (LAMPs), a family of glycosylated proteins and major constituents of the lysosomal membranes, play a dominant role in various cellular processes, including phagocytosis, autophagy and immunity in mammals. However, their roles in aquatic species remain poorly known. In the present study, three lamp genes were cloned and characterized from Micropterus salmoides. Subsequently, their transcriptional levels in response to different nutritional status were investigated. The full-length coding sequences of lamp1, lamp2 and lamp3 were 1251bp, 1224bp and 771bp, encoding 416, 407 and 256 amino acids, respectively. Multiple sequence alignment showed that LAMP1-3 were highly conserved among the different fish species, respectively. 3-D structure prediction, genomic survey, and phylogenetic analysis were further confirmed that these genes are widely existed in vertebrates. The mRNA expression of the three genes was ubiquitously expressed in all selected tissues, including liver, brain, gill, heart, muscle, spleen, kidney, stomach, adipose and intestine, lamp1 shows highly transcript levels in brain and muscle, lamp2 displays highly expression level in heart, muscle and spleen, but lamp3 shows highly transcript level in spleen, liver and kidney. To analyze the function of the three genes under starvation stress in largemouth bass, three experimental treatment groups (fasted group and refeeding group, control group) were established in the current study. The results indicated that the expression of lamp1 was significant induced after starvation, and then returned to normal levels after refeeding in the liver. The expression of lamp2 and lamp3 exhibited the same trend in the liver. In addition, in the spleen and the kidney, the transcript level of lamp1 and lamp2 was remarkably increased in the fasted treatment group and slightly decreased in the refed treatment group, respectively. Collectively, our findings suggest that three lamp genes may have differential function in the immune and energetic organism in largemouth bass, which is helpful in understanding roles of lamps in aquatic species.

SCANED: Siamese collateral assessment network for evaluation of collaterals from ischemic damage.

This study conducts collateral evaluation from ischemic damage using a deep learning-based Siamese network, addressing the challenges associated with a small and imbalanced dataset. The collateral network provides an alternative oxygen and nutrient supply pathway in ischemic stroke cases, influencing treatment decisions. Research in this area focuses on automated collateral assessment using deep learning (DL) methods to expedite decision-making processes and enhance accuracy. Our study employed a 3D ResNet-based Siamese network, referred to as SCANED, to classify collaterals as good/intermediate or poor. Utilizing non-contrast computed tomography (NCCT) images, the network automates collateral identification and assessment by analyzing tissue degeneration around the ischemic site. Relevant features from the left/right hemispheres were extracted, and Euclidean Distance (ED) was employed for similarity measurement. Finally, dichotomized classification of good/intermediate or poor collateral is performed by SCANED using an optimal threshold derived from ROC analysis. SCANED provides a sensitivity of 0.88, a specificity of 0.63, and a weighted F1 score of 0.86 in the dichotomized classification.

circCPA4 induces malignant behaviors of prostate cancer via miR-491-5p/SHOC2 feedback loop.

OBJECTIVE: circCPA4 has been defined to be an oncogenic gene. This study examined whether circCPA4 regulates Prostate Cancer (PC) development and revealed its molecular mechanism. METHODS: PC tissues and PC cell lines were collected, in which circCPA4/miR-491-5p/SHOC2 levels were evaluated by RT-qPCR and immunoblot. Colony formation assay and EdU assay assessed cell proliferation, flow cytometry measured apoptosis, and Transwell assessed invasion and migration. Ki-67, cleaved caspase-3, E-cadherin, and N-cadherin were evaluated by immunoblot. Based on the luciferase reporter assay and RIP assay the authors investigated the targeting relationship between circCPA4/miR-491-5p/SHOC2. The effect of circCPA4 on tumor growth was evaluated by xenotransplantation in nude mice. RESULTS: circCPA4 and SHOC2 levels were abundant while miR-491-5p expression was low in PC. Loss of circCPA4 decreased the proliferation and EdU-positive rate of PC cells, enhanced apoptosis, and inhibited invasion, migration, and EMT. Upregulation of circCPA4 forced the malignant behaviors of PC cells, and this promotion could be abolished when miR-491-5p was overexpressed or SHOC2 was silenced. CircCAP4 competitively decoyed miR-491-5p mediating SHOC2 expression. circCAP4 suppression inhibited PC tumor growth. CONCLUSION: circCAP4 acts as a novel oncogenic factor in PC, accelerating the malignant behavior of PC cells via miR-491-5p/SHOC2 interaction. This novel ceRNA axis may be a potential target for PC drug development and targeted therapy in the future.

circCPA4 induces malignant behaviors of prostate cancer via miR-491-5p/ SHOC2 feedback loop

ABSTRACT Objective: circCPA4 has been defined to be an oncogenic gene. This study examined whether circCPA4 regulates Prostate Cancer (PC) development and revealed its molecular mechanism. Methods: PC tissues and PC cell lines were collected, in which circCPA4/miR-491-5p/SHOC2 levels were evaluated by RT-qPCR and immunoblot. Colony formation assay and EdU assay assessed cell proliferation, flow cytometry measured apoptosis, and Transwell assessed invasion and migration. Ki-67, cleaved caspase-3, E-cadherin, and N-cadherin were evaluated by immunoblot. Based on the luciferase reporter assay and RIP assay the authors investigated the targeting relationship between circCPA4/miR-491-5p/SHOC2. The effect of circCPA4 on tumor growth was evaluated by xenotransplantation in nude mice. Results: circCPA4 and SHOC2 levels were abundant while miR-491-5p expression was low in PC. Loss of circCPA4 decreased the proliferation and EdU-positive rate of PC cells, enhanced apoptosis, and inhibited invasion, migration, and EMT. Upregulation of circCPA4 forced the malignant behaviors of PC cells, and this promotion could be abolished when miR-491-5p was overexpressed or SHOC2 was silenced. CircCAP4 competitively decoyed miR-491-5p mediating SHOC2 expression. circCAP4 suppression inhibited PC tumor growth. Conclusion: circCAP4 acts as a novel oncogenic factor in PC, accelerating the malignant behavior of PC cells via miR-491-5p/SHOC2 interaction. This novel ceRNA axis may be a potential target for PC drug development and targeted therapy in the future.

PILLAR: ParaspInaL muscLe segmentAtion pRoject - a comprehensive online resource to guide manual segmentation of paraspinal muscles from magnetic resonance imaging.

BACKGROUND: There is an increasing interest in assessing paraspinal morphology and composition in relation to low back pain (LBP). However, variations in methods and segmentation protocols contribute to the inconsistent findings in the literature. We present an on-line resource, the ParaspInaL muscLe segmentAtion pRoject (PILLAR, https://projectpillar.github.io/ ), to provide a detailed description and visual guide of a segmentation protocol by using the publicly available ITK-SNAP software and discuss related challenges when performing paraspinal lumbar muscles segmentations from magnetic resonance imaging (MRI). METHODS: T2-weighted and corresponding fat-water IDEAL axial MRI from 3 males and 3 females (2 chronic LBP and 1 control for each sex) were used to demonstrate our segmentation protocol for each lumbar paraspinal muscle (erector spinae, lumbar multifidus, quadratus lumborum and psoas) and lumbar spinal level (L1-L5). RESULTS: Proper segmentation requires an understanding of the anatomy of paraspinal lumbar muscles and the variations in paraspinal muscle morphology and composition due to age, sex, and the presence of LBP or related spinal pathologies. Other challenges in segmentation includes the presence and variations of intramuscular and epimuscular fat, and side-to-side asymmetry. CONCLUSION: The growing interest to assess the lumbar musculature and its role in the development and recurrence of LBP prompted the need for comprehensive and easy-to-follow resources, such as the PILLAR project to reduce inconsistencies in segmentation protocols. Standardizing manual muscle measurements from MRI will facilitate comparisons between studies while the field is progressively moving towards the automatization of paraspinal muscle measurements for large cohort studies.

Hollow-core pear-shaped conjoined-tube fiber with low loss in the ultraviolet band.

PCTF (pear-shaped conjoined-tube fiber) is presented as a new ultraviolet (UV) guiding fiber with low loss. Results indicate that two PCTFs have better properties than that of previous studies in the UV band. The total loss of two PCTFs is less than 1 dB/km, and its bandwidth exceeds 150 nm between 0.2 and 0.4 µm. Furthermore, PCTF's single-mode performance is very promising, as evidenced by the higher-order mode extinction ratio (HOMER) over 103. The fabrication tolerance is discussed in this paper and results show that the tolerance is good enough to fabricate by normal fiber drawing process. This fiber is promising in applications for nonlinear optics, ultrafast optics, high power laser, and quantum optics.

A population-averaged structural connectomic brain atlas dataset from 422 HCP-aging subjects.

Population-averaged brain atlases, that are represented in a standard space with anatomical labels, are instrumental tools in neurosurgical planning and the study of neurodegenerative conditions. Traditional brain atlases are primarily derived from anatomical scans and contain limited information regarding the axonal organization of the white matter. With the advance of diffusion MRI that allows the modeling of fiber orientation distribution (FOD) in the brain tissue, there is an increasing interest for a population-averaged FOD template, especially based on a large healthy aging cohort, to offer structural connectivity information for connectomic surgery and analysis of neurodegeneration. The dataset described in this article contains a set of multi-contrast structural connectomic MRI atlases, including T1w, T2w, and FOD templates, along with the associated whole brain tractograms. The templates were made using multi-contrast group-wise registration based on 3T MRIs of 422 Human Connectome Project in Aging (HCP-A) subjects. To enhance the usability, probabilistic tissue maps and segmentation of 22 subcortical structures are provided. Finally, the subthalamic nucleus shown in the atlas is parcellated into sensorimotor, limbic, and associative sub-regions based on their structural connectivity to facilitate the analysis and planning of deep brain stimulation procedures. The dataset is available on the OSF Repository: https://osf.io/p7syt.

Poly(ionic liquid)/OPBI Composite Membrane with Excellent Chemical Stability for High-Temperature Proton Exchange Membrane.

Despite the outstanding proton conductivity of phosphoric acid (PA)-doped polybenzimidazole (PBI) membranes as high-temperature proton exchange membranes (HT-PEMs), chemical stability is a critical issue for the operation life of PEM fuel cells (PEMFCs). Herein, we introduced polymerized [HVIM]H2PO4 ionic liquids (PIL) into an OPBI membrane to accelerate proton transfer and enhance the chemical stability of the membrane. Based on the regulation of the intrinsic viscosity of PIL, the entanglement between PIL chains and OPBI chains is enhanced to prevent the loss of PIL and the oxidative degradation of membrane materials. The PIL/OPBI membrane with the intrinsic viscosity of 2.34 dL·g-1 (2.34-PIL/OPBI) exhibited the highest proton conductivity of 113.9 mS·cm-1 at 180 °C, which is 3.5 times that of the original OPBI membrane. The 2.34-PIL/OPBI membrane exhibited the highest remaining weight of 92.1% under harsh conditions (3 wt% H2O2; 4 ppm Fe2+ at 80 °C) for 96 h, and a much lower attenuation amplitude than the OPBI did in mechanical strength and proton conductivity performance. Our present work demonstrates a simple and effective method for blending PIL with OPBI to enhance the chemical durability of the PA-PBI membranes as HT-PEMs.

A dataset of multi-contrast unbiased average MRI templates of a Parkinson's disease population.

Parkinson's disease (PD) is a complex neurodegenerative disorder affecting regions such as the substantia nigra (SN), red nucleus (RN) and locus coeruleus (LC). Processing MRI data from patients with PD requires anatomical structural references for spatial normalization and structural segmentation. Extending our previous work, we present multi-contrast unbiased MRI templates using nine 3T MRI modalities: T1w, T2*w, T1-T2* fusion, R2*, T2w, PDw, fluid-attenuated inversion recovery (FLAIR), susceptibility-weighted imaging, and neuromelanin-sensitive MRI (NM). One mm isotropic voxel size templates were created, along with 0.5 mm isotropic whole brain templates and 0.3 mm isotropic templates of the midbrain. All templates were created from 126 PD patients (44 female; ages=40-87), and 17 healthy controls (13 female; ages=39-84), except the NM template, which was created from 85 PD patients and 13 controls, respectively. The dataset is available on the NIST MNI Repository via the following link: http://nist.mni.mcgill.ca/multi-contrast-pd126-and-ctrl17-templates/. The data is also available on NITRC at the following link: https://www.nitrc.org/projects/pd126/.

The Big Bang of Deep Learning in Ultrasound-Guided Surgery: A Review.

Ultrasound (US) imaging is a paramount modality in many image-guided surgeries and percutaneous interventions, thanks to its high portability, temporal resolution, and cost-efficiency. However, due to its imaging principles, the US is often noisy and difficult to interpret. Appropriate image processing can greatly enhance the applicability of the imaging modality in clinical practice. Compared with the classic iterative optimization and machine learning (ML) approach, deep learning (DL) algorithms have shown great performance in terms of accuracy and efficiency for US processing. In this work, we conduct a comprehensive review on deep-learning algorithms in the applications of US-guided interventions, summarize the current trends, and suggest future directions on the topic.

Deep learning for collateral evaluation in ischemic stroke with imbalanced data.

PURPOSE: Collateral evaluation is typically done using visual inspection of cerebral images and thus suffers from intra- and inter-rater variability. Large open databases of ischemic stroke patients are rare, limiting the use of deep learning methods in treatment decision-making. METHODS: We adapted a pre-trained EfficientNet B0 network through transfer learning to improve collateral evaluation using slice-based and subject-level classification. Our method uses stacking and overlapping of 2D slices from a patient's 4D computed tomography angiography (CTA) and a majority voting scheme to determine a patient's final collateral grade based on all classified 2D MIPs. Class imbalance is handled in the evaluation process by using the focal loss with class weight to penalize the majority class. RESULTS: We evaluated our method using a nine-fold cross-validation performed with 83 subjects. Mean sensitivity of 0.71, specificity of 0.84, and a weighted F1 score of 0.71 in multi-class (good, intermediate, and poor) classification were obtained. Considering treatment effect, a dichotomized decision is also made for collateral scoring of a subject based on two classes (good/intermediate and poor) which achieves a sensitivity of 0.89 and specificity of 0.96 with a weighted F1 score of 0.95. CONCLUSION: An automatic and robust collateral assessment method that mitigates the issues with the small imbalanced dataset was developed. Computer-aided evaluation of collaterals can help decision-making of ischemic stroke treatment strategy in clinical settings.

Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

Robust landmark-based brain shift correction with a Siamese neural network in ultrasound-guided brain tumor resection.

PURPOSE: In brain tumor surgery, tissue shift (called brain shift) can move the surgical target and invalidate the surgical plan. A cost-effective and flexible tool, intra-operative ultrasound (iUS) with robust image registration algorithms can effectively track brain shift to ensure surgical outcomes and safety. METHODS: We proposed to employ a Siamese neural network, which was first trained using natural images and fine-tuned with domain-specific data to automatically detect matching anatomical landmarks in iUS scans at different surgical stages. An efficient 2.5D approach and an iterative re-weighted least squares algorithm are utilized to perform landmark-based registration for brain shift correction. The proposed method is validated and compared against the state-of-the-art methods using the public BITE and RESECT datasets. RESULTS: Registration of pre-resection iUS scans to during- and post-resection iUS images were executed. The results with the proposed method shows a significant improvement from the initial misalignment ([Formula: see text]) and the method is comparable to the state-of-the-art methods validated on the same datasets. CONCLUSIONS: We have proposed a robust technique to efficiently detect matching landmarks in iUS and perform brain shift correction with excellent performance. It has the potential to improve the accuracy and safety of neurosurgery.

DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR.

PURPOSE: Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task. METHODS: We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier-Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration. RESULTS: The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image Normalization (SyN). CONCLUSIONS: Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://github.com/nimamasoumi/DiffeoRaptor .

Fluorescent property of carbon dots extracted from cigarette smoke and the application in bio-imaging.

Cigarette smoke is one of the six major pollution sources in the room air. It contains large number of particles with size less than 10 nm. There exist carbon dots (CDs) in cigarette smoke which have strong fluorescence and with good bio-compatibility and low toxicity. CDs in cigarette smoke can be applied in bio-imaging which has great potential applications in the integration of cancer diagnosis and treatment. In this paper, CDs were extracted from cigarette smoke. Then, sodium borohydride was added to CDs aqueous solution for reduction and the reduced CDs (R-CDs) were used for biological cell imaging. The results indicate that the CDs with the particle size <10 nm in cigarette smoke are self-assembled by the polymerizated polycyclic aromatic hydrocarbons (PAHs) and ammonium nitrite which are disk nano-structure composed of sp2/sp3 carbon and oxygen/nitrogen groups or polymers. Sodium borohydride can reduce the carbonyl group on the surface of CDs to hydroxyl group and increase the ratio of the Na 1s ratio of the CDs from 1.86 to 7.42. The CDs can emit blue fluorescence under ultraviolet irradiation. After reduction, the R-CDS have the intensity of fluorescence 7.2 times than before and the fluorescence quantum yield increase from 6.13% to 8.86%. The photoluminescence (PL) wavelength of R-CDS have red-shift of 7 nm which was due to the increasing of Na element ratio. The onion epidermal cells labeled with R-CDs show that the CDs could pass through the cell wall into the cell and reach the nucleus. The cell wall and the nucleus could be clearly visualized. CDs also shows low toxicity to human bronchial epithelial cells (BEAS-2B) with good biological activity. The obtained results indicate that the CDs and R-CDs have good fluorescent property which could be used as bio-imaging agent.

nnUNet-based Multi-modality Breast MRI Segmentation and Tissue-Delineating Phantom for Robotic Tumor Surgery Planning.

Segmentation of the thoracic region and breast tissues is crucial for analyzing and diagnosing the presence of breast masses. This paper introduces a medical image segmentation architecture that aggregates two neural networks based on the state-of-the-art nnU-Net. Additionally, this study proposes a polyvinyl alcohol cryogel (PVA-C) breast phantom, based on its automated segmentation approach, to enable planning and navigation experiments for robotic breast surgery. The dataset consists of multimodality breast MRI of T2W and STIR images obtained from 10 patients. A statistical analysis of segmentation tasks emphasizes the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. We first use a single class labeling to segment the breast region and then exploit it as an input for three-class labeling to segment fatty, fibroglandular (FGT), and tumorous tissues. The first network has a 0.95 DCS, while the second network has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively. Clinical Relevance-This research is relevant to the breast surgery community as it establishes a deep learning-based (DL) algorithmic and phantomic foundation for surgical planning and navigation that will exploit preoperative multimodal MRI and intraoperative ultrasound to achieve highly cosmetic breast surgery. In addition, the planning and navigation will guide a robot that can cut, resect, bag, and grasp a tissue mass that encapsulates breast tumors and positive tissue margins. This image-guided robotic approach promises to potentiate the accuracy of breast surgeons and improve patient outcomes.

Risk Factors Associated With Social Media Addiction: An Exploratory Study.

The use of social media is becoming a necessary daily activity in today's society. Excessive and compulsive use of social media may lead to social media addiction (SMA). The main aim of this study was to investigate whether demographic factors (including age and gender), impulsivity, self-esteem, emotions, and attentional bias were risk factors associated with SMA. The study was conducted in a non-clinical sample of college students (N = 520), ranging in age from 16 to 23 years, including 277 females (53%) and 243 males (47%). All participants completed a survey measuring impulsivity, self-esteem, anxiety, depression, social anxiety, loneliness, and attentional bias. The final hierarchical regression model indicated significant risk factors for SMA with an accuracy of 38%. The identified set of associated risk factors included female gender (ß = -0.21, t = -4.88, p < 0.001), impulsivity (ß = 0.34, t = 8.50, p < 0.001), self-esteem (ß = -0.20, t = -4.38, p < 0.001), anxiety (ß = 0.24, t = 4.43, p < 0.001), social anxiety (ß = 0.25, t = 5.79, p < 0.001), and negative attentional biases (ß = 0.31, t = 8.01, p < 0.001). Finally, a discussion of the results is presented, followed by corresponding recommendations for future studies.

LGALS1 was related to the prognosis of clear cell renal cell carcinoma identified by weighted correlation gene network analysis combined with differential gene expression analysis.

Understanding the molecular mechanism of clear cell renal cell carcinoma (ccRCC) is essential for predicting the prognosis and developing new targeted therapies. Our study is to identify hub genes related to ccRCC and to further analyze its prognostic significance. The ccRCC gene expression profiles of GSE46699 from the Gene Expression Omnibus (GEO) database and datasets from the Cancer Genome Atlas Database The Cancer Genome Atlas were used for the Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We screened out 397 overlapping genes from the four sets of results, and then performed Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. In addition, the protein-protein interaction (PPI) network of 397 overlapping genes was mapped using the STRING database. We identified ten hub genes (KNG1, TIMP1, ALB, C3, GPC3, VCAN, P4HB, CHGB, LGALS1, EGF) using the CytoHubba plugin of Cytoscape based on the Maximal Clique Centrality (MCC) score. According to Kaplan-Meier survival analysis, higher expression of LGALS1 and TIMP1 was related to poorer overall survival (OS) in patients with ccRCC. Univariate and multivariate Cox proportional hazard analysis showed that the expression of LGALS1 was an independent risk factor for poor prognosis. Moreover, the higher the clinical grade and stage of ccRCC, the higher the expression of LGALS1. LGALS1 may play an important role in developing ccRCC and may be potential a biomarker for prognosis and treatment targets.

Preparation and Fluorescent Wavelength Control of Multi-Color Nitrogen-Doped Carbon Nano-Dots.

It is known that, by taking advantage of heteroatom doping, the electronic states and transition channels in carbon nano-dots (CNDs) can be effectively modulated. Thus, the photoluminescence (PL) properties of CNDs can be changed. For potential applications of CNDs as advanced materials for optoelectronic devices, it is important and significant to develop the practical techniques for doping heteroatoms into CNDs. In this work, we synthesize the multi-color fluorescent by using a fast and effective microwave method where the CNDs are nitrogen-doped. We examine the influence of different ratios of the raw materials on the structure and optical properties of N-CNDs. The results show that the prepared N-CNDs can generate blue (445 nm), green (546 nm), and orange (617 nm) fluorescence or PL with the mass ratio of the raw materials at 1:1, 1:2 and 1:3, respectively. We find that the N content in N-CNDs leads to different surface/edge states in n-π∗ domain. Thus, the wavelength of the PL emission from N-CNDs can be tuned via controlling the N concentration doped into the CNDs. Moreover, it is shown that the intensity of the PL from N-CNDs is mainly positively related to the content of C-O groups attached on the surface/edges of the N-CNDs. This study provides an effective experimental method and technical way to improve the fluorescent emission, and to modulate the color of the PL emission from CNDs.