A self-supervised fusion network for carotid plaque ultrasound image classification.

Carotid plaque classification from ultrasound images is crucial for predicting ischemic stroke risk. While deep learning has shown effectiveness, it heavily relies on substantial labeled datasets. Achieving high performance with limited labeled images is essential for clinical use. Self-supervised learning (SSL) offers a potential solution; however, the existing works mainly focus on constructing the SSL tasks, neglecting the use of multiple tasks for pretraining. To overcome these limitations, this study proposed a self-supervised fusion network (Fusion-SSL) for carotid plaque ultrasound image classification with limited labeled data. Fusion-SSL consists of two SSL tasks: classifying image block order (Ordering) and predicting image rotation angle (Rotating). A dual-branch residual neural network was developed to fuse feature presentations learned by the two tasks, which can extract richer visual boundary shape and contour information than a single task. In this experiment, 1270 carotid plaque ultrasound images were collected from 844 patients at Zhongnan Hospital (Wuhan, China). The results showed that Fusion-SSL outperforms single SSL methods across different percentages of labeled training data, ranging from 10 to 100%. Moreover, with only 40% labeled training data, Fusion-SSL achieved comparable results to a single SSL method (predicting image rotation angle) with 100% labeled data. These results indicate that Fusion-SSL could be beneficial for the classification of carotid plaques and the early warning of a stroke in clinical practice.

Uncovering potential distinctive acoustic features of healing music.

Background: Music therapy is a promising complementary intervention for addressing various mental health conditions. Despite evidence of the beneficial effects of music, the acoustic features that make music effective in therapeutic contexts remain elusive. Aims: This study aimed to identify and validate distinctive acoustic features of healing music. Methods: We constructed a healing music dataset (HMD) based on nominations from related professionals and extracted 370 acoustic features. Healing-distinctive acoustic features were identified as those that were (1) independent from genre within the HMD, (2) significantly different from music pieces in a classical music dataset (CMD) and (3) similar to pieces in a five-element music dataset (FEMD). We validated the identified features by comparing jazz pieces in the HMD with a jazz music dataset (JMD). We also examined the emotional properties of the features in a Chinese affective music system (CAMS). Results: The HMD comprised 165 pieces. Among all the acoustic features, 74.59% shared commonalities across genres, and 26.22% significantly differed between the HMD classical pieces and the CMD. The equivalence test showed that the HMD and FEMD did not differ significantly in 9.46% of the features. The potential healing-distinctive acoustic features were identified as the standard deviation of the roughness, mean and period entropy of the third coefficient of the mel-frequency cepstral coefficients. In a three-dimensional space defined by these features, HMD's jazz pieces could be distinguished from those of the JMD. These three features could significantly predict both subjective valence and arousal ratings in the CAMS. Conclusions: The distinctive acoustic features of healing music that have been identified and validated in this study have implications for the development of artificial intelligence models for identifying therapeutic music, particularly in contexts where access to professional expertise may be limited. This study contributes to the growing body of research exploring the potential of digital technologies for healthcare interventions.

Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images.

Atherosclerotic carotid plaques have been shown to be closely associated with the risk of stroke. Since patients with symptomatic carotid plaques have a greater risk for stroke, stroke risk stratification based on the classification of carotid plaques into symptomatic or asymptomatic types is crucial in diagnosis, treatment planning, and medical treatment monitoring. A deep learning technique would be a good choice for implementing classification. Usually, to acquire a high-accuracy classification, a specific network architecture needs to be designed for a given classification task. In this study, we propose an object-specific four-path network (OSFP-Net) for stroke risk assessment by integrating ultrasound carotid plaques in both transverse and longitudinal sections of the bilateral carotid arteries. Each path of the OSFP-Net comprises of a feature extraction subnetwork (FE) and a feature downsampling subnetwork (FD). The FEs in the four paths use the same network structure to automatically extract features from ultrasound images of carotid plaques. The FDs use different object-specific pooling strategies for feature downsampling based on the observation that the sizes and shapes in the feature maps obtained from FEs should be different. The object-specific pooling strategies enable the network to accept arbitrarily sized carotid plaques as input and to capture a more informative context for improving the classification accuracy. Extensive experimental studies on a clinical dataset consisting of 333 subjects with 1332 carotid plaques show the superiority of our OSFP-Net against several state-of-the-art deep learning-based methods. The experimental results demonstrate better clinical agreement between the ground truth and the prediction, which indicates its great potential for use as a risk stratification and as a monitoring tool in the management of patients at risk for stroke.

Multilevel Strip Pooling-Based Convolutional Neural Network for the Classification of Carotid Plaque Echogenicity.

Carotid plaque echogenicity in ultrasound images has been found to be closely correlated with the risk of stroke in atherosclerotic patients. The automatic and accurate classification of carotid plaque echogenicity is of great significance for clinically estimating the stability of carotid plaques and predicting cardiovascular events. Existing convolutional neural networks (CNNs) can provide an automatic carotid plaque echogenicity classification; however, they require a fixed-size input image, while the carotid plaques are of varying sizes. Although cropping and scaling the input carotid plaque images is promising, it will cause content loss or distortion and hence reduce the classification accuracy. In this study, we redesign the spatial pyramid pooling (SPP) and propose multilevel strip pooling (MSP) for the automatic and accurate classification of carotid plaque echogenicity in the longitudinal section. The proposed MSP module can accept arbitrarily sized carotid plaques as input and capture a long-range informative context to improve the accuracy of classification. In our experiments, we implement an MSP-based CNN by using the visual geometry group (VGG) network as the backbone. A total of 1463 carotid plaques (335 echo-rich plaques, 405 intermediate plaques, and 723 echolucent plaques) were collected from Zhongnan Hospital of Wuhan University. The 5-fold cross-validation results show that the proposed MSP-based VGGNet achieves a sensitivity of 92.1%, specificity of 95.6%, accuracy of 92.1%, and F1-score of 92.1%. These results demonstrate that our approach provides a way to enhance the applicability of CNN by enabling the acceptance of arbitrary input sizes and improving the classification accuracy of carotid plaque echogenicity, which has a great potential for an efficient and objective risk assessment of carotid plaques in the clinic.

Deep Learning-Based Carotid Plaque Segmentation from B-Mode Ultrasound Images.

Carotid ultrasound measurement of total plaque area (TPA) provides a method for quantifying carotid plaque burden and monitoring changes in carotid atherosclerosis in response to medical treatment. Plaque boundary segmentation is required to generate the TPA measurement; however, training of observers and manual delineation are time consuming. Thus, our objective was to develop an automated plaque segmentation method to generate TPA from longitudinal carotid ultrasound images. In this study, a deep learning-based method, modified U-Net, was used to train the segmentation model and generate TPA measurement. A total of 510 plaques from 144 patients were used in our study, where the Monte Carlo cross-validation was used by randomly splitting the data set into 2/3 and 1/3 for training and testing. Two observers were trained to manually delineate the 510 plaques separately, which were used as the ground-truth references. Two U-Net models (M1 and M2) were trained using the two different ground-truth data sets from the two observers to evaluate the accuracy, variability and sensitivity on the ground-truth data sets used for training our method. The results of the algorithm segmentations of the two models yielded strong agreement with the two manual segmentations with the Pearson correlation coefficient r = 0.989 (p < 0.0001) and r = 0.987 (p < 0.0001). Comparison of the U-Net and manual segmentations resulted in mean TPA differences of 0.05 ± 7.13 mm2 (95% confidence interval: 14.02-13.02 mm2) and 0.8 ± 8.7 mm2 (17.85-16.25 mm2) for the two models, which are small compared with the TPA range in our data set from 4.7 to 312.8 mm2. Furthermore, the mean time to segment a plaque was only 8.3 ± 3.1 ms. The presented deep learning-based method described has sufficient accuracy with a short computation time and exhibits high agreement between the algorithm and manual TPA measurements, suggesting that the method could be used to measure TPA and to monitor the progression and regression of carotid atherosclerosis.

Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images.

Measurement of total-plaque-area (TPA) is important for determining long term risk for stroke and monitoring carotid plaque progression. Since delineation of carotid plaques is required, a deep learning method can provide automatic plaque segmentations and TPA measurements; however, it requires large datasets and manual annotations for training with unknown performance on new datasets. A UNet++ ensemble algorithm was proposed to segment plaques from 2D carotid ultrasound images, trained on three small datasets (n = 33, 33, 34 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The ensemble was also trained on the entire SPARC dataset and tested with a different dataset (n = 497, Zhongnan Hospital, China). Algorithm and manual segmentations were compared using Dice-similarity-coefficient (DSC), and TPAs were compared using the difference ( ∆TPA), Pearson correlation coefficient (r) and Bland-Altman analyses. Segmentation variability was determined using the intra-class correlation coefficient (ICC) and coefficient-of-variation (CoV). For 44 SPARC subjects, algorithm DSC was 83.3-85.7%, and algorithm TPAs were strongly correlated (r = 0.985-0.988; p < 0.001) with manual results with marginal biases (0.73-6.75) mm 2 using the three training datasets. Algorithm ICC for TPAs (ICC = 0.996) was similar to intra- and inter-observer manual results (ICC = 0.977, 0.995). Algorithm CoV = 6.98% for plaque areas was smaller than the inter-observer manual CoV (7.54%). For the Zhongnan dataset, DSC was 88.6% algorithm and manual TPAs were strongly correlated (r = 0.972, p < 0.001) with ∆TPA = -0.44 ±4.05 mm 2 and ICC = 0.985. The proposed algorithm trained on small datasets and segmented a different dataset without retraining with accuracy and precision that may be useful clinically and for research.

Correlation between carotid intima-media roughness and cardiovascular risk factors.

Arterial intima-media roughness (IMR) may indicate an early manifestation of atherosclerosis. To date, few studies have been performed to quantitatively evaluate carotid IMR by ultrasonography (US). The aim of the present study was to analyze the effect of cardiovascular risk factors on carotid IMR. A total of 185 subjects were enrolled for US examination of carotid arteries. Carotid intima-media thickness (IMT) and IMR were measured in US images by a novel automatic software. According to the number of combined high-risk factors for coronary heart disease, subjects were assigned to four groups (risk groups 0, 1, 2 and 3+). IMR was lowest in risk group 0 (32.9±2.7 µm), higher in risk group 1 (64.5±6.9 µm; P<0.01 vs. risk group 0) and highest in risk groups 2 and 3+ (89.1±7.4 and 92.0±6.7 µm, respectively; P<0.01 vs. risk groups 0 and 1). According to a multivariate regression analysis, age, systolic blood pressure, smoking status and the triglyceride/high-density lipoprotein cholesterol ratio were significant predictors of IMR. There was a progressive increase in carotid artery plaque with increasing tertiles of IMR (4.9, 33.9 and 53.2% in tertiles 1, 2 and 3, respectively; P<0.05). Compared with that of subjects in the lowest tertile of IMR, those in the highest tertile had a significantly elevated risk of the presence of plaque in the carotid tree (odds ratio=10.61, 95%CI: 2.15-52.49, P=0.004). Quantification of carotid IMR from US images with this software is feasible, and carotid IMR, which may help estimate the extent of atherosclerosis, may be used as a complementary factor to stratify patients with cardiovascular risk factors.

Carotid Intima-Media Roughness and Elasticity in Hypertensive Patients With Normal Carotid Intima-Media Thickness.

OBJECTIVES: To investigate carotid intima-media roughness (IMR) in hypertensive patients with normal carotid intima-media thickness (IMT) using automatic identification software and the correlation between carotid IMR and risk factors. METHODS: This case-control study comprised 61 hypertensive patients with normal carotid IMT and 51 control participants. Carotid IMR, carotid IMT, pulsed wave velocity (PWV), stiffness (ß), and arterial compliance were determined by carotid ultrasound and image postprocessing using an automatic identification program and echo-tracking analysis software. RESULTS: Carotid IMR, mean carotid IMT, maximum carotid IMT, ß, and PWV in the hypertension group were higher than those in the control group (58.24 versus 34.61 µm, 641.17 versus 576.48 µm, 746.82 versus 640.55 µm, 9.42 versus 7.35, and 7.10 versus 5.86 m/s, respectively; P < .05), and arterial compliance was lower than that in the control group (0.70 versus 0.95 mm2 /kPa; P < .05). Intima-media roughness was correlated with maximum IMT, mean IMT, PWV, ß, age, diagnosis of hypertension for greater than 1 year, and pulse pressure. Multivariate logistic regression showed that age, diagnosis of hypertension for greater than 1 year, and pulse pressure were influential factors for IMR in hypertensive patients, with odds ratios of 6.719 (95% confidence interval, 1.658-27.221; P = .008), 4.726 (95% confidence interval, 1.174-19.022; P = .029), and 3.998 (95% confidence interval, 1.033-15.466; P = .045), respectively. CONCLUSIONS: Carotid IMR and the elasticity index have important clinical importance in evaluating the risk of early atherosclerosis in hypertensive populations.

Novel compound heterozygous mutations in low density lipoprotein receptor gene causes a severe phenotype in a Chinese hypercholesterolemia family.

Mutations in the low density lipoprotein receptor (LDLR) gene serve a causative role in the pathophysiology of familial hypercholesterolemia (FH), a common autosomal inherited disorder characterized by abnormal lipid metabolism. The aim of the present study was to investigate genetic defects in a Chinese family with FH. Clinical features and family histories were collected, as were the results of various laboratory tests, including determinations of serum lipid concentrations, ultrasonography and angiography results. Potential mutations in LDLR were screened using direct polymerase chain reaction (PCR) sequencing. Multiple sequence alignments, structure and hydrophobicity predictions were performed in silico. Novel compound heterozygote mutations in LDLR of the proband were identified, with a Trp577Term-bearing maternal allele and a Pro685Leu-bearing paternal allele. The proband, a 27-year-old male, had severe and diffuse coronary stenosis and non-ST segment elevation myocardial infarction, as well as multiple skin xanthomas and high serum lipid levels. The allele-dosage-dependent clinical features, including hypercholesterolemia and peripheral arterial atherosclerosis, were observed in the proband and the other heterozygous patients. Therefore, the coexistence of Pro685Leu and Trp577Term mutations in LDLR is a novel compound heterozygosis in Chinese patients and may lead to a severe FH phenotype. The explanation for the existence of compound heterozygous mutations instead of homozygous mutations in this particular family requires further study.

Exogenous BMP-7 Facilitates the Recovery of Cardiac Function after Acute Myocardial Infarction through Counteracting TGF-ß1 Signaling Pathway.

Myocardial fibrosis after acute myocardial infarction (AMI) is one of the main causes of myocardial remodeling and heart function abnormalities. Bone morphogenetic protein-7 (BMP-7) has been reported to play essential roles in anti-fibrosis. In this study, we demonstrated the role of exogenous BMP-7 on myocardial fibrosis and heart function recovery after AMI. A rat model of AMI was established via ligation of the left anterior descending coronary artery (LAD). Twenty rats were grouped into sham group which underwent chest open operation, but did not receive LAD ligation. Another 40 rats underwent LAD ligation were randomly grouped into saline-treated group (n = 20) and BMP-7-treated group (n = 20) which received saline treatment or exogenous BMP-7 treatment for 14 days, respectively. Two weeks after LAD ligation, the survival rate of BMP-7-treated AMI group was significantly improved compared to the saline group. Moreover, the cardiac function was preserved as shown by echocardiography examination, and the infarcted size was limited upon BMP-7 treatment. In addition, we investigated the role of TGF-ß1 signaling pathway in BMP-7-mediated cardioprotective effects by analyzing the expression levels of TGF-ß1, Smad 2 and Smad 3 in the infarct zone, border zone, and non-infarct zone. Western blot and quantitative PCR results suggested that BMP-7 attenuated myocardial fibrosis through counteracting TGF-ß1 signaling pathway, thereby exerting cardioprotective effects. In conclusion, our data provide a potential therapeutic direction for preserving cardiac function and improving prognosis of AMI patients.

Dynamic programming in parallel boundary detection with application to ultrasound intima-media segmentation.

Segmentation of carotid artery intima-media in longitudinal ultrasound images for measuring its thickness to predict cardiovascular diseases can be simplified as detecting two nearly parallel boundaries within a certain distance range, when plaque with irregular shapes is not considered. In this paper, we improve the implementation of two dynamic programming (DP) based approaches to parallel boundary detection, dual dynamic programming (DDP) and piecewise linear dual dynamic programming (PL-DDP). Then, a novel DP based approach, dual line detection (DLD), which translates the original 2-D curve position to a 4-D parameter space representing two line segments in a local image segment, is proposed to solve the problem while maintaining efficiency and rotation invariance. To apply the DLD to ultrasound intima-media segmentation, it is imbedded in a framework that employs an edge map obtained from multiplication of the responses of two edge detectors with different scales and a coupled snake model that simultaneously deforms the two contours for maintaining parallelism. The experimental results on synthetic images and carotid arteries of clinical ultrasound images indicate improved performance of the proposed DLD compared to DDP and PL-DDP, with respect to accuracy and efficiency.

[The phenotypes of a hypercholesterolemia family with low density lipoprotein receptor exon 13 A606T mutation].

OBJECTIVE: To investigate the clinical phenotypes of familial hypercholesterolemia (FH) caused by exon 13 A606T mutation in low density lipoprotein receptor. METHODS: Clinical data of the suffered family were collected and analyzed, as well as measurement of perivascular intima-medial thickness and follow-mediated-dilation function by ultrasonography. RESULTS: There were totally 11 sufferers including 4 males and 9 females, aged 8-90 years, with 2 homozygotes and 9 heterozygotes. Among them, one homozygote showed angina pectoris and hematuria, both homozygotes had skin xanthomata. TC, TG, LDL-C and HDL-C were (7.39 ± 1.30) mmol/L, (0.93 ± 0.36) mmol/L, (11.76 ± 1.10) mmol/L and (1.22 ± 0.17) mmol/L, respectively. The left/right sided intima-medial thickness of the common, internal, external and bulb carotid artery were (1.15 ± 0.45) mm/(1.30 ± 0.60) mm, (0.82 ± 0.30) mm/(1.00 ± 0.66) mm, (0.77 ± 0.28) mm/(0.78 ± 0.30) mm and (1.40 ± 0.59) mm/(1.46 ± 0.71) mm respectively. The brachial artery flow mediated dilation rate was (4.85 ± 4.80)%. Echocardiography revealed 2 patients with cardiac valvular disease and 3 with atrium septum aneurysm. CONCLUSION: FH patients show a variety of phenotypes including extraordinary hypercholesterolemia, subcutaneous xanthomata and premature coronary heart disease.

Intima-medial thickness homogeneity in the common carotid artery: measurement method and preliminary clinical study.

BACKGROUND: To propose and test in a preliminary clinical study a novel method for calculating intima-medial thickness (IMT) homogeneity (IMTH). METHODS: IMT was measured off-line on every horizontal pixel line along the far wall of the common carotid artery, with previously validated software. IMTH was assessed by the SD, coefficient of variation, and interval distribution of obtained IMT values. This method was applied to 129 individuals (age, 40-60 years), including 49 healthy control subjects, 44 subjects at high risk of atherosclerosis, and 36 subjects with known atherosclerosis. Differences with a p value <0.05 were considered statistically significant. RESULTS: SD and coefficient of variation were higher in the high-risk than in the control group, as well as in high-risk and control subgroups with maximal IMT = 0.8 mm or mean IMT = 0.55-0.65 mm. There were 85.7, 62.8, and 36% of IMT values in the 0.4- to 0.6-mm range and 0.89, 13.8, and 21.2% of IMT values in the 0.8- to 1.2-mm range in the control, high-risk, and atherosclerosis groups, respectively. CONCLUSIONS: IMTH is a promising approach for the assessment of atherosclerosis, in addition to conventional IMT measurement. Further clinical studies are needed to assess its clinical usefulness.

Ultrasound intima-media segmentation using Hough transform and dual snake model.

Common carotid artery intima-media thickness (IMT), which is usually measured upon ultrasound images, is an important indicator to cardiovascular diseases. This paper proposes a snake model based segmentation method to automatically detect the boundary of intima-media for IMT measurement. In the proposed method, two contours are initialized from line segments generated by Hough transform and then evolved simultaneously by dual snake model for boundary detection. Experimental results show that the proposed method has strong robustness against ultrasound artifacts, gives better results than traditional snake model and dynamic programming based methods, and achieves similar clinical parameters to ground truth data.

Cell-cell interaction-dependent regulation of N-acetylglucosaminyltransferase III and the bisected N-glycans in GE11 epithelial cells. Involvement of E-cadherin-mediated cell adhesion.

Changes in oligosaccharide structures are associated with numerous physiological and pathological events. In this study, the effects of cell-cell interactions on N-linked oligosaccharides (N-glycans) were investigated in GE11 epithelial cells. N-glycans were purified from whole cell lysates by hydrazinolysis and then detected by high performance liquid chromatography and mass spectrometry. Interestingly, the population of the bisecting GlcNAc-containing N-glycans, the formation of which is catalyzed by N-acetylglucosaminyltransferase III (GnT-III), was substantially increased in cells cultured under dense conditions compared with those cultured under sparse conditions. The expression levels and activities of GnT-III but not other glycosyltransferases, such as GnT-V and alpha1,6-fucosyltransferase, were also consistently increased in these cells. However, this was not observed in mouse embryonic fibroblasts or MDA-MB231 cells, in which E-cadherin is deficient. In contrast, perturbation of E-cadherin-mediated adhesion by treatment with EDTA or a neutralizing anti-E-cadherin antibody abolished the up-regulation of expression of GnT-III. Furthermore, we observed the significant increase in GnT-III activity under dense growth conditions after restoration of the expression of E-cadherin in MDA-MB231 cells. Our data together indicate that a E-cadherin-dependent pathway plays a critical role in regulation of GnT-III expression. Given the importance of GnT-III and the dynamic regulation of cell-cell interaction during tissue development and homeostasis, the changes in GnT-III expression presumably contribute to intracellular signaling transduction during such processes.

Acrolein induces Hsp72 via both PKCdelta/JNK and calcium signaling pathways in human umbilical vein endothelial cells.

Acrolein is a highly electrophilic alpha,beta-unsaturated aldehydes to which humans are exposed in a variety of environment situations and is also a product of lipid peroxidation. Increased levels of unsaturated aldehydes play an important role in the pathogenesis of a number of human diseases such as Alzheimer's disease, atherosclerosis and diabetes. A number of studies have reported that acrolein evokes downstream signaling via an elevation in cellular oxidative stress. Here, we report that low concentrations of acrolein induce Hsp72 in human umbilical vein endothelial cells (HUVEC) and that both the PKCdelta/JNK pathway and calcium pathway were involved in the induction. The findings confirm that the production of reactive oxygen species (ROS) is not directly involved in the pathway. The induction of Hsp72 was not observed in other cells such as smooth muscle cells (SMC) or COS-1 cells. The results suggest that HUVEC have a unique defense system against cell damage by acrolein in which Hsp72 is induced via activation of both the PKCd/JNK and the calcium pathway.

Production of a recombinant single-chain variable-fragment (scFv) antibody against sulfoglycolipid.

Mammalian sulfoglycolipids are comprised of two major classes of compounds, sulfatide (SO(3)-3Gal-ceramide) and seminolipid (SO(3)-3Gal-alkylacylglycerol). Sulfatide is present in relatively high levels in myelin, and seminolipid is present in testis. The sulfation of these sulfoglycolipids is catalyzed by a common enzyme, cerebroside sulfotransferase (CST). Disruption of the Cst gene in mice revealed that sulfatide and seminolipid are essential for, respectively, myelin formation and spermatogenesis. The present study describes the generation of a recombinant single-chain variable fragment (scFv) antibody against sulfoglycolipid, for use in the functional analysis of sulfoglycolipids in living cells. A positive hybridoma producing anti-sulfoglycolipid IgG3, referred to as DI8, was initially obtained by immunizing CST-null mice with an isolated sulfatide. The DI8 monoclonal antibody was found to bind specifically to sulfoglycolipids with the terminal 3-O-sulfated galactose structure, as evidenced by ELISA and thin-layer chromatogram-immunostaining. The antibody stained seminolipid on the cell surface of spermatogenic cells of wild-type testis, but it did not react with any cells in the seminiferous tubules of CST-null testis. Total RNA was extracted from this hybridoma, and cDNAs that encode the variable regions of the heavy and light chains of IgG3 were obtained by RT-PCR. These DNA fragments were linked through a DNA linker coding (Gly(4)Ser)(3), and the recombinant scFv fragment was then inserted into a phagemid vector pCANTAB 5E. The scFv antibody that was displayed at the tip of the M13 phage in the form of a g3p fusion protein bound to sulfatide. Furthermore, a soluble form of the scFv antibody was also found to bind to the sulfoglycolipids in ELISA.

Testis-specific sulfoglycolipid, seminolipid, is essential for germ cell function in spermatogenesis.

More than 90% of the glycolipid in mammalian testis consists of a unique sulfated glyceroglycolipid, seminolipid. The sulfation of the molecule is catalyzed by a Golgi membrane-associated sulfotransferase, cerebroside sulfotransferase (CST). Disruption of the Cst gene in mice results in male infertility due to the arrest of spermatogenesis prior to the metaphase of the first meiosis. However, the issue of which side of the cell function-germ cells or Sertoli cells-is deteriorated in this mutant mouse remains unknown. Our findings show that the defect is in the germ cell side, as evidenced by a transplantation analysis, in which wild-type spermatogonia expressing the green fluorescent protein were injected into the seminiferous tubules of CST-null testis. The transplanted GFP-positive cells generated colonies and spermatogenesis proceeded over meiosis in the mutant testis. The findings also clearly show that the seminolipid is expressed on the plasma membranes of spermatogonia, spermatocytes, spermatids, and spermatozoa, as evidenced by the immunostaining of wild-type testes using an anti-sulfogalactolipid antibody, Sulph-1 in comparison with CST-null testes as a negative control, and that seminolipid appears as early as day 8 of age, when Type B spermatogonia emerge.