Global Research Trends and Hotspots for Ferroptosis, Necroptosis, and Pyroptosis in Alzheimer's Disease from the Past to 2023: A Combined Bibliometric Review.

Background: Alzheimer's disease (AD) is a genetically intricate neurodegenerative disorder. Studies on "Ferroptosis in AD", "Pyroptosis in AD", and "Necroptosis in AD" are becoming more prevalent and there is increasing evidence that they are closely related to AD. However, there has not yet been a thorough bibliometrics-based investigation on this subject. Objective: This study uses a bibliometric approach to visualize and analyze the literature within the field of three distinct types of cell death in AD and explores the current research hotspots and prospective research directions. Methods: We collected relevant articles from the Web of Science and used CiteSpace, VOS viewer, and Pajek to perform a visual analysis. Results: A total of 123, 95, and 84 articles were published in "Ferroptosis in AD", "Pyroptosis in AD", and "Necroptosis in AD", respectively. Based on keywords analysis, we can observe that "oxidative stress" and "lipid peroxidation", "cell death" and "activation", and "Nlrp3 inflammasome" and "activation" were the three most prominent words in the field of "Ferroptosis in AD", "Pyroptosis in AD", and "Necroptosis in AD", respectively. Focusing on the breakout words in the keyword analysis, we reviewed the mechanisms of ferroptosis, pyroptosis, and necroptosis in AD. By mapping the time zones of the keywords, we speculated on the evolutionary trends of ferroptosis, pyrotosis, and necroptosis in AD. Conclusions: Our findings can help researchers grasp the research status of three types of cell death in AD and determine new directions for future research as soon as possible.

A bibliometric and visual analysis of epigenetic research publications for Alzheimer's disease (2013-2023).

Background: Currently, the prevalence of Alzheimer's disease (AD) is progressively rising, particularly in developed nations. There is an escalating focus on the onset and progression of AD. A mounting body of research indicates that epigenetics significantly contributes to AD and holds substantial promise as a novel therapeutic target for its treatment. Objective: The objective of this article is to present the AD areas of research interest, comprehend the contextual framework of the subject research, and investigate the prospective direction for future research development. Methods: ln Web of Science Core Collection (WOSCC), we searched documents by specific subject terms and their corresponding free words. VOSviewer, CiteSpace and Scimago Graphica were used to perform statistical analysis on measurement metrics such as the number of published papers, national cooperative networks, publishing countries, institutions, authors, co-cited journals, keywords, and visualize networks of related content elements. Results: We selected 1,530 articles from WOSCC from January 2013 to June 2023 about epigenetics of AD. Based on visual analysis, we could get that China and United States were the countries with the most research in this field. Bennett DA was the most contributed and prestigious scientist. The top 3 cited journals were Journal of Alzheimer's Disease, Neurobiology of Aging and Molecular Neurobiology. According to the analysis of keywords and the frequency of citations, ncRNAs, transcription factor, genome, histone modification, blood DNA methylation, acetylation, biomarkers were hot research directions in AD today. Conclusion: According to bibliometric analysis, epigenetic research in AD was a promising research direction, and epigenetics had the potential to be used as AD biomarkers and therapeutic targets.

Pan-cancer analysis of forkhead box Q1 as a potential prognostic and immunological biomarker.

Forkhead box Q1 (FOXQ1) is a member of the forkhead transcription factor family involved in the occurrence and development of different tumors. However, the specific expression patterns and functions of FOXQ1 in pan-cancer remain unclear. Therefore, we collected the expression, mutation, and clinical information data of 33 tumors from The Cancer Genome Atlas database. Via public pan-cancer transcriptome data analysis, we found that FOXQ1 is differentially expressed in various tumors at tissue and cell levels, such as liver hepatocellular carcinoma, colon adenocarcinoma, lung adenocarcinoma, lung squamous cell carcinoma, thyroid carcinoma, and kidney renal clear cell carcinoma. Kaplan-Meier and Cox analyses suggested that FOXQ1 expression was associated with poor overall survival of cutaneous melanoma and thymoma. Its expression was also associated with good disease-specific survival (DSS) in prostate adenocarcinoma but poor DSS in liver hepatocellular carcinoma. In addition, FOXQ1 expression was associated with poor disease-free survival of pancreatic adenocarcinoma. Moreover, FOXQ1 expression was closely related to the tumor mutational burden in 14 tumor types and microsatellite instability (MSI) in 8 tumor types. With an increase in stromal and immune cells, FOXQ1 expression was increased in breast invasive carcinoma, pancreatic adenocarcinoma, thyroid carcinoma, lung adenocarcinoma, and ovarian serous cystadenocarcinoma, while its expression was decreased in pancreatic adenocarcinoma, bladder urothelial carcinoma, and stomach adenocarcinoma. We also found that FOXQ1 expression was related to the infiltration of 22 immune cell types in different tumors (p < 0.05), such as resting mast cells and resting memory CD4 T cells. Last, FOXQ1 was coexpressed with 47 immune-related genes in pan-cancer (p < 0.05). In conclusion, FOXQ1 expression is closely related to prognosis, clinicopathological parameters, cancer-related pathway activity, the tumor mutational burden, MSI, the tumor microenvironment, immune cell infiltration, and immune-related genes and has the potential to be a diagnostic and prognostic biomarker as well as an immunotherapy target for tumors. Our findings provide important clues for further mechanistic research into FOXQ1.

Deep CTS: a Deep Neural Network for Identification MRI of Carpal Tunnel Syndrome.

Carpal tunnel syndrome (CTS) is a common peripheral nerve disease in adults; it can cause pain, numbness, and even muscle atrophy and will adversely affect patients' daily life and work. There are no standard diagnostic criteria that go against the early diagnosis and treatment of patients. MRI as a novel imaging technique can show the patient's condition more objectively, and several characteristics of carpal tunnel syndrome have been found. However, various image sequences, heavy artifacts, small lesion characteristics, high volume of imagine reading, and high difficulty in MRI interpretation limit its application in clinical practice. With the development of automatic image segmentation technology, the algorithm has great potential in medical imaging. The challenge is that the segmentation target is too small, and there are two categories of images with the proximal border of the carpal tunnel as the boundary. To meet the challenge, we propose an end-to-end deep learning framework called Deep CTS to segment the carpal tunnel from the MR image. The Deep CTS consists of the shape classifier with a simple convolutional neural network and the carpal tunnel region segmentation with simplified U-Net. With the specialized structure for the carpal tunnel, Deep CTS can segment the carpal tunnel region efficiently and improve the intersection over union of results. The experimental results demonstrated that the performance of the proposed deep learning framework is better than other segmentation networks for small objects. We trained the model with 333 images, tested it with 82 images, and achieved 0.63 accuracy of intersection over union and 0.17 s segmentation efficiency, which indicate great promise for the clinical application of this algorithm.

Gut Microbiota Interact With the Brain Through Systemic Chronic Inflammation: Implications on Neuroinflammation, Neurodegeneration, and Aging.

It has been noticed in recent years that the unfavorable effects of the gut microbiota could exhaust host vigor and life, yet knowledge and theory are just beginning to be established. Increasing documentation suggests that the microbiota-gut-brain axis not only impacts brain cognition and psychiatric symptoms but also precipitates neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS). How the blood-brain barrier (BBB), a machinery protecting the central nervous system (CNS) from the systemic circulation, allows the risky factors derived from the gut to be translocated into the brain seems paradoxical. For the unique anatomical, histological, and immunological properties underpinning its permeable dynamics, the BBB has been regarded as a biomarker associated with neural pathogenesis. The BBB permeability of mice and rats caused by GM dysbiosis raises the question of how the GM and its metabolites change BBB permeability and causes the brain pathophysiology of neuroinflammation and neurodegeneration (NF&ND) and brain aging, a pivotal multidisciplinary field tightly associated with immune and chronic systemic inflammation. If not all, gut microbiota-induced systemic chronic inflammation (GM-SCI) mainly refers to excessive gut inflammation caused by gut mucosal immunity dysregulation, which is often influenced by dietary components and age, is produced at the interface of the intestinal barrier (IB) or exacerbated after IB disruption, initiates various common chronic diseases along its dispersal routes, and eventually impairs BBB integrity to cause NF&ND and brain aging. To illustrate the immune roles of the BBB in pathophysiology affected by inflammatory or "leaky" IB resulting from GM and their metabolites, we reviewed the selected publications, including the role of the BBB as the immune barrier, systemic chronic inflammation and inflammation influences on BBB permeability, NF&ND, and brain aging. To add depth to the bridging role of systemic chronic inflammation, a plausible mechanism indispensable for BBB corruption was highlighted; namely, BBB maintenance cues are affected by inflammatory cytokines, which may help to understand how GM and its metabolites play a major role in NF&ND and aging.

A deep learning approach for medical waste classification.

As the demand for health grows, the increase in medical waste generation is gradually outstripping the load. In this paper, we propose a deep learning approach for identification and classification of medical waste. Deep learning is currently the most popular technique in image classification, but its need for large amounts of data limits its usage. In this scenario, we propose a deep learning-based classification method, in which ResNeXt is a suitable deep neural network for practical implementation, followed by transfer learning methods to improve classification results. We pay special attention to the problem of medical waste classification, which needs to be solved urgently in the current environmental protection context. We applied the technique to 3480 images and succeeded in correctly identifying 8 kinds of medical waste with an accuracy of 97.2%; the average F1-score of five-fold cross-validation was 97.2%. This study provided a deep learning-based method for automatic detection and classification of 8 kinds of medical waste with high accuracy and average precision. We believe that the power of artificial intelligence could be harnessed in products that would facilitate medical waste classification and could become widely available throughout China.

Two-stage neural network via sensitivity learning for 2D photonic crystal bandgap maximization.

We propose a two-stage neural network method to maximize the bandgap of 2D photonic crystals. The proposed model consists of a fully connected deep feed-forward neural network (FNN) and U-Net, which are employed, respectively, to generate the shape function and learn the sensitivity. The shape is generated by the FNN during the entire optimization process, and obtaining the sensitivity can be split into two steps. In the first step of the optimization, the sensitivity is calculated by finite element analysis (FEA) and the result is used as a sample to train the U-Net. Second, the optimization procedure is adopted instead of FEA, where a trained U-Net is used to generate the corresponding sensitivity. The main advantage of such an approach is that the shape function and sensitivity can be obtained by neural networks without solving a partial differenital equation. Therefore, the computational cost can be reduced by the proposed method without using large training sets. The effectiveness of the proposed method is verified in the numerical experiments in terms of the optimized shape and time consumption.

Using an artificial neural network to predict the residual stress induced by laser shock processing.

With the purpose of using the artificial neural network (ANN) method to predict the residual stresses induced by laser shock processing (LSP), the Ni-Cr-Fe-based precipitation-hardening superalloy GH4169 was selected as the experimental material in this work, and the experimental samples were treated by LSP with laser power densities of 4.24GW/cm2, 7.07GW/cm2, and 9.90GW/cm2 and overlap rates of 10%, 30%, and 50%. The depth-wise residual stresses of experimental samples prior to and after LSP were taken according to the x-ray diffraction sin2ψ method and electrolytic-polished layer by layer. The ANN model for residual stress prediction was established, and the laser power density, overlap rate, and depth were set as input parameters, while residual stress was set as the output parameter. The residual stresses of untreated samples and those treated with laser power densities of 4.24GW/cm2 and 9.90GW/cm2 were selected as the training sets, and the data of experimental samples treated with a laser power density of 7.07GW/cm2 were reserved as testing sets for validating the trained network. After LSP, beneficial stable compressive residual stresses were introduced in the material's near surface, and the overall maximum compressive residual stresses were formed on the top surface (surface residual stress). Depending on the LSP process parameters, the surface residual stresses ranged from -236MPa to -799MPa, and the compressive residual stress depths of all treated samples were over 0.50 mm. According to the results obtained by ANN, the coefficient of determination R2 of the training sets is 0.9948, which shows a good fitness for the training network. The R2 of the testing sets is 0.9931, which is less than that of the training sets but still shows high accuracy. This work proves that the ANN method can be applied to predict the residual stress of metallic materials by LSP treatment with high accuracy and provides a guiding value for the optimization of the LSP process.

Impact on Mechanical Properties and Microstructural Response of Nickel-Based Superalloy GH4169 Subjected to Warm Laser Shock Peening.

Laser shock peening (LSP), as an innovative surface treatment technology, can effectively improve fatigue life, surface hardness, corrosion resistance, and residual compressive stress. Compared with laser shock peening, warm laser shock peening (WLSP) is a newer surface treatment technology used to improve materials' surface performances, which takes advantage of thermal mechanical effects on stress strengthening and microstructure strengthening, resulting in a more stable distribution of residual compressive stress under the heating and cyclic loading process. In this paper, the microstructure of the GH4169 nickel superalloy processed by WLSP technology with different laser parameters was investigated. The proliferation and tangling of dislocations in GH4169 were observed, and the dislocation density increased after WLSP treatment. The influences of different treatments by LSP and WLSP on the microhardness distribution of the surface and along the cross-sectional depth were investigated. The microstructure evolution of the GH4169 alloy being shocked with WLSP was studied by TEM. The effect of temperature on the stability of the high-temperature microstructure and properties of the GH4169 alloy shocked by WLSP was investigated.

Deep HT: A deep neural network for diagnose on MR images of tumors of the hand.

BACKGROUND: There are many types of hand tumors, and it is often difficult for imaging diagnosticians to make a correct diagnosis, which can easily lead to misdiagnosis and delay in treatment. Thus in this paper, we propose a deep neural network for diagnose on MR Images of tumors of the hand in order to better define preoperative diagnosis and standardize surgical treatment. METHODS: We collected MRI figures of 221 patients with hand tumors from one medical center from 2016 to 2019, invited medical experts to annotate the images to form the annotation data set. Then the original image is preprocessed to get the image data set. The data set is randomly divided into ten parts, nine for training and one for test. Next, the data set is input into the neural network system for testing. Finally, average the results of ten experiments as an estimate of the accuracy of the algorithm. RESULTS: This research uses 221 images as dataset and the system shows an average confidence level of 71.6% in segmentation of hand tumors. The segmented tumor regions are validated through ground truth analysis and manual analysis by a radiologist. CONCLUSIONS: With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder decoder deep architectures. Therefore, in this paper, we propose an automatic segmentation method based on DeepLab v3+ and achieved a good diagnostic accuracy rate.

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model.

BACKGROUND AND OBJECTIVE: Peripherally inserted central catheter (PICC) is a novel drug delivery mode which has been widely used in clinical practice. However, long-term retention and some improper actions of patients may cause some severe complications of PICC, such as the drift and prolapse of its catheter. Clinically, the postoperative care of PICC is mainly completed by nurses. However, they cannot recognize the correct position of PICC from X-ray chest images as soon as the complications happen, which may lead to improper treatment. Therefore, it is necessary to identify the position of the PICC catheter as soon as these complications occur. Here we proposed a novel multi-task deep learning framework to detect PICC automatically through X-ray images, which could help nurses to solve this problem. METHODS: We collected 348 X-ray chest images from 326 patients with visible PICC. Then we proposed a multi-task deep learning framework for line segmentation and tip detection of PICC catheters simultaneously. The proposed deep learning model is composed of an extraction structure and three routes, an up-sampling route for segmentation, an RPNs route, and an RoI Pooling route for detection. We further compared the effectiveness of our model with the models previously proposed. RESULTS: In the catheter segmentation task, 300 X-ray images were utilized for training the model, then 48 images were tested. In the tip detection task, 154 X-ray images were used for retraining and 20 images were used in the test. Our model achieved generally better results among several popular deep learning models previously proposed. CONCLUSIONS: We proposed a multi-task deep learning model that could segment the catheter and detect the tip of PICC simultaneously from X-ray chest images. This model could help nurses to recognize the correct position of PICC, and therefore, to handle the potential complications properly.

The New Technologies Developed from Laser Shock Processing.

Laser shock processing (LSP) is an advanced material surface hardening technology that can significantly improve mechanical properties and extend service life by using the stress effect generated by laser-induced plasma shock waves, which has been increasingly applied in the processing fields of metallic materials and alloys. With the rapidly development of modern industry, many new technologies developed from LSP have emerged, which broadens the application of LSP and enriches its technical theory. In this work, the technical theory of LSP was summarized, which consists of the fundamental principle of LSP and the laser-induced plasma shock wave. The new technologies, developed from LSP, are introduced in detail from the aspect of laser shock forming (LSF), warm laser shock processing (WLSP), laser shock marking (LSM) and laser shock imprinting (LSI). The common feature of LSP and these new technologies developed from LSP is the utilization of the laser-generated stress effects rather than the laser thermal effect. LSF is utilized to modify the curvature of metal sheet through the laser-induced high dynamic loading. The material strength and the stability of residual stress and micro-structures by WLSP treatment are higher than that by LSP treatment, due to WLSP combining the advantages of LSP, dynamic strain aging (DSA) and dynamic precipitation (DP). LSM is an effective method to obtain the visualized marks on the surface of metallic materials or alloys, and its critical aspect is the preparation of the absorbing layer with a designed shape and suitable thickness. At the high strain rates induced by LSP, LSI has the ability to complete the direct imprinting over the large-scale ultrasmooth complex 3D nanostructures arrays on the surface of crystalline metals. This work has important reference value and guiding significance for researchers to further understand the LSP theory and the new technologies developed from LSP.

[Research Advances on the Biological Characteristics of Hematological Malignant Cells Immunologically Regulated by Exosome--Review].

Abstract　　The minimal residual disease (MRD) is the origin element that caused the relapse and drug resistance of hematological malignancies, the immune cells play a great role to clear MRD. A variety of immune cells have anti-tumor effects. However, tumor cells antagonize anti-tumor effects by reprogramming of constituents associated with tumor environment. Many different cell types, including immune cells, mesenchymal cells and tumor cells in tumor microenvironment release exosomes. The latest researches indicate that "cargo" and surface ligands carried by exosomes secreted by hematological malignant cells not only can affect the function of natural killer cell (migration, activation, proliferation, secretion and NKG2D expression), macrophage (migration and secretion) and dendritic cell (maturation and presentation), but also regulate the expression of PD-L1 and CCR2, CCL2 secretion and transformation of monocytes. The altered function of immune cells will eventually have effect on the progression of hematological malignancies.

Long non-coding RNA XIST predicting advanced clinical parameters in cancer: A Meta-Analysis and case series study in a single institution.

Dysregulated expression of long non-coding RNA X-inactive specific transcript (lncRNA-XIST) has been indicated in various cancer types. In the present study, a meta-analysis was conducted to evaluate the potential role of lncRNA-XIST in predicting the clinicopathological parameters of patients with cancer. Eligible studies were obtained through a systematic search of PubMed, Web of Science, Embase and the Cochrane Library, of articles published prior to January 2019. The combined odds ratio and 95% confidence interval were calculated to determine the association between lncRNA-XIST expression and patient outcome. In addition, 45 pairs of osteosarcoma (OS) tissues and adjacent healthy tissues from a single institution were analyzed for the expression of lncRNA-XIST, and its association with clinicopathological features; ultimately, a total of 1,869 cancer patients from 25 studies were assessed. The results demonstrated that high expression levels of lncRNA-XIST were significantly associated with lymphatic metastasis, larger tumor size, advanced cancer stage and distant metastasis. However, sex was not associated with lncRNA-XIST expression level. In the OS patient cohort, it was demonstrated that lncRNA-XIST was highly expressed in OS tissues, which negatively correlated with patient prognosis. The present study indicated that lncRNA-XIST may serve as a potential biomarker for advanced clinical parameters in human cancer.

Bone marrow-derived mesenchymal stromal cells promote resistance to tyrosine kinase inhibitors in chronic myeloid leukemia via the IL-7/JAK1/STAT5 pathway.

Chronic myeloid leukemia (CML) is caused by the fusion of the BCR activator of RhoGEF and GTPase activating protein (BCR) and ABL proto-oncogene, the nonreceptor tyrosine kinase (ABL) genes. Although the tyrosine kinase inhibitors (TKIs) imatinib (IM) and nilotinib (NI) have remarkable efficacy in managing CML, the malignancies in some patients become TKI-resistant. Here, we isolated bone marrow (BM)-derived mesenchymal stem cells (MSCs) from several CML patients by Ficoll-Hypaque density-gradient centrifugation for coculture with K562 and BV173 cells with or without TKIs. We used real-time quantitative PCR to assess the level of interleukin 7 (IL-7) expression in the MSCs and employed immunoblotting to monitor protein expression in the BCR/ABL, phosphatidylinositol 3-kinase (PI3K)/AKT, and JAK/STAT signaling pathways. We also used a xenograft tumor model to examine the in vivo effect of different MSCs on CML cells. MSCs from patients with IM-resistant CML protected K562 and BV173 cells against IM- or NI-induced cell death, and this protection was due to increased IL-7 secretion from the MSCs. Moreover, IL-7 levels in the BM of patients with IM-resistant CML were significantly higher than in healthy donors or IM-sensitive CML patients. IL-7 elicited IM and NI resistance via BCR/ABL-independent activation of JAK1/STAT5 signaling, but not of JAK3/STAT5 or PI3K/AKT signaling. IL-7 or JAK1 gene knockdown abrogated IL-7-mediated STAT5 phosphorylation and IM resistance in vitro and in vivo Because high IL-7 levels in the BM mediate TKI resistance via BCR/ABL-independent activation of JAK1/STAT5 signaling, combining TKIs with IL-7/JAK1/STAT5 inhibition may have significant utility for managing CML.

Urinary nerve growth factor: a biomarker for detrusor overactivity in children? A meta-analysis and trail sequential analysis.

PURPOSE: Based on, previously, a systematic review, urinary nerve growth factor (NGF) has emerged as one potentially noninvasive biomarker for detrusor overactivity (DO) in adults. We performed this systematic review to explore if NGF is a biomarker for DO in children. METHODS: A literature search was conducted in PubMed, Embase, Web of science, and Cochrane Library. Copies of all relevant articles were retrieved for quality assessment and data abstraction by two reviewers. Primary outcome was pooled standardized mean difference (SMD) for NGF/Cr (NGF normalized to urine creatinine) level between DO group and controls. RESULTS: Three case-control studies published from 2012 to 2016 were included with 74 patients and 70 controls. Children with DO had a significant higher baseline urinary NGF/Cr level compared to controls (SMD = 2.48, 95%CI = 0.85-4.10, P < 0.01). After treatment, the level of NGF/Cr decreased significantly compared to baseline level at 6th month time points (SMD = 0.94, 95%CI = 0.03-1.86, P = 0.04). We calculated the required information size to 99 patients for comparison of urinary NGF/Cr level between DO and controls by trail sequential analysis (TSA). CONCLUSION: Based on this systematic review, NGF/Cr may be a noninvasive biomarker for DO in children in the future. However, based on TSA, more original studies are needed to clarify the role of NGF/Cr in the biomarker effect.

Long-Term Diabetes Mellitus Is Associated with an Increased Risk of Pancreatic Cancer: A Meta-Analysis.

PURPOSE: Previous studies have shown a bidirectional relationship between diabetes and pancreatic cancer (PC). In particular, new-onset diabetes might be induced by PC, and people with long-term diabetes might be at increased risk for the development of PC. The purpose of our study was to examine whether long-term diabetes represented an independent risk factor for PC development. METHODOLOGY: A literature search was performed by searching electronic databases for studies published before July 1, 2014, and relative risks (RRs) and corresponding 95% confidence intervals (CIs) were calculated. Data pertaining to diabetes were recorded at both individual and study levels, with RRs calculated separately to analyze the relationship between the duration of diabetes and the development of PC. RESULTS: Forty-four studies were included in this meta-analysis, including 18 studies with a case-control design, 5 with a nested case-control design and 21 with a cohort design. The overall summary estimate for the relationship between the population with a duration of diabetes ≥2 years and PC was 1.64 (1.52-1.78). The pooled RR (95% CI) of PC for the population with a duration of diabetes ≥5 years was 1.58 (1.42-1.75). For the population with a duration of diabetes ≥10 years, the RR (95% CI) of PC was 1.50 (1.28-1.75). CONCLUSIONS: Our study suggests that long-term diabetes mellitus is associated with an increased risk of PC. However, the level of risk is negatively correlated with increasing diabetes mellitus duration.

Congenital jugular vein phlebectasia.

BACKGROUND/PURPOSE: The purpose of the present study was to elucidate the clinical presentation, diagnosis, treatment, and postoperative complications of jugular vein phlebectasia (JVP), and to recommend diagnostic methods and treatment choices. MATERIALS AND METHODS: Thirty-six cases of JVP were reviewed (right vein in 27, left in 6, and bilateral in 3). The internal jugular veins were the most commonly involved. The main complaint was the soft and compressible mass in the neck. Ultrasound or color Doppler flow imaging (CDFI) was performed on all the children. Surgical intervention was performed in 32 cases, and the other 4 cases were conservatively followed up for 4 to 6 years. RESULTS: Ultrasound or CDFI demonstrated local dilatation of the unilateral or bilateral veins in all the patients. Surgical intervention included ligation of the involved jugular vein in 31 cases and draping with medical Dacron cloth in 1 case. All of the operated children recovered uneventfully, except 3 for cases who underwent ligation of the right internal jugular vein. CONCLUSIONS: Valsalva maneuver was most important for establishing the diagnosis. Ultrasound or CDFI was the diagnostic procedure of choice to confirm the diagnosis of JVP. Surgical ligation or excision of unilateral jugular vein was recommended for cosmetic and psychological purposes. However, special attention must be paid to prevent postoperative complications in case of ligation of right internal jugular vein. Or else, treatment should be conservative (follow-up).