Ratiometric electrochemical aptasensor based on split aptamer and Au-rGO for detection of aflatoxin M1.

A novel ratiometric electrochemical aptasensor based on split aptamer and Au-reduced graphene oxide (Au-rGO) nanomaterials was proposed to detect aflatoxin M1 (AFM1). In this work, Au-rGO nanomaterials were coated on the electrode through the electrodeposition method to increase the aptamer enrichment. We split the aptamer of AFM1 into 2 sequences (S1 and S2), where S1 was immobilized on the electrode due to the Au-S bond, and S2 was tagged with methylene blue (MB) and acted as a response signal. A complementary strand to S1 (CS1) labeled with ferrocene (Fc) was introduced as another reporter. In the presence of AFM1, CS1 was released from the electrode surface due to the formation of the S1-AFM1-S2 complex, leading to a decrease in Fc and an increase in the MB signal. The developed ratiometric aptasensor exhibited a linear range of 0.03 µg L-1 to 2.00 µg L-1, with a detection limit of 0.015 µg L-1 for AFM1 detection. The ratiometric aptasensor also showed a linear relationship from 0.2 µg L-1 to 1.00 µg L-1, with a detection limit of 0.05 µg L-1 in natural milk after sample pretreatment, indicating the successful application of the developed ratiometric aptasensor. Our proposed strategy provides a new way to construct aptasensors with high sensitivity and selectivity.

Cryo-electron tomography of intact cardiac muscle reveals myosin binding protein-C linking myosin and actin filaments.

Myosin binding protein C (MyBP-C) is an accessory protein of the thick filament in vertebrate cardiac muscle arranged over 9 stripes of intervals of 430 Å in each half of the A-band in the region called the C-zone. Mutations in cardiac MyBP-C are a leading cause of hypertrophic cardiomyopathy the mechanism of which is unknown. It is a rod-shaped protein composed of 10 or 11 immunoglobulin- or fibronectin-like domains labelled C0 to C10 which binds to the thick filament via its C-terminal region. MyBP-C regulates contraction in a phosphorylation dependent fashion that may be through binding of its N-terminal domains with myosin or actin. Understanding the 3D organisation of MyBP-C in the sarcomere environment may provide new light on its function. We report here the fine structure of MyBP-C in relaxed rat cardiac muscle by cryo-electron tomography and subtomogram averaging of refrozen Tokuyasu cryosections. We find that on average MyBP-C connects via its distal end to actin across a disc perpendicular to the thick filament. The path of MyBP-C suggests that the central domains may interact with myosin heads. Surprisingly MyBP-C at Stripe 4 is different; it has weaker density than the other stripes which could result from a mainly axial or wavy path. Given that the same feature at Stripe 4 can also be found in several mammalian cardiac muscles and in some skeletal muscles, our finding may have broader implication and significance. In the D-zone, we show the first demonstration of myosin crowns arranged on a uniform 143 Å repeat.

Dietary lysine level affects digestive enzyme, amino acid transport and hepatic intermediary metabolism in turbot (Scophthalmus maximus).

Lysine is one of the most important essential amino acids in fish, especially in the feed formulated with high levels of plant ingredients. Lysine restriction always led to growth inhibition and poor feed utilization. However, little information was available on its effects on digestion, absorption, and metabolism response in fish. In the present study, three experimental diets were formulated with three lysine levels, 1.69% (LL group), 3.32% (ML group), and 4.90% (HL group). A 10-week feeding trial was carried out to explore the effects of dietary lysine levels on the digestive enzymes, amino acid transporters, and hepatic intermediary metabolism in turbot (Scophthalmus maximus). As the results showed, the activities of lipase and trypsin in ML group were higher than in other groups. Lysine restriction inhibited the expression levels of peptides and amino acid transporters such as PpeT1, y+LAT2, b0,+AT, and rBAT but significantly induced the expression of CAT1. Meanwhile, lysine deficiency elevated the content of T-CHO and LDL-C in plasma, while a higher HDL-C/LDL-C ratio was observed in ML group. For hepatic intermediary metabolism, the increase of lysine level induced the mRNA expression of G6Pase1 and FBPase, but no differences were observed in the expression of the key regulators in glycolysis pathway, such as GK and PK. Furthermore, an appropriate increase in the level of lysine promoted the genes involved in lipolysis, including PPARα, ACOX1, CPT1A, and LPL. However, no differences were observed in the expression of PPARγ, FAS, SREBP1, and LXR, which were important genes related to lipid synthesis. These results provide clues on the metabolic responses on dietary lysine in teleost.

OGR1 negatively regulates ß-casein and triglyceride synthesis and cell proliferation via the PI3K/AKT/mTOR signaling pathway in goat mammary epithelial cells.

Goat milk in some cases is less allergenic than cow milk, therefore, more people drink goat milk in the world, so it is necessary for us to improve the yield and quality of goat milk. Previous studies have shown that some genes are closely related to lactation. Ovarian cancer G protein-coupled 1 (OGR1) is a G protein-coupled receptor discovered recently. OGR1 is widely found in various tissues of organisms and is involved in cell skeleton reorganization, carcinogenesis, cell proliferation, and apoptosis by regulating multiple signaling pathways in cells. However, the modulating effect of OGR1 in lactation is still unknown. Therefore, the objective of this study is to investigate the function of OGR1 in goat mammary epithelial cells (GMECs). Flow cytometry, CCK8, EDU, enzyme-linked immunosorbent assay, and triglyceride test kit assays were performed and we found that OGR1 regulated Bcl-2/Bax ratio, Fas protein expression as well as the phosphorylation of AKT and mammalian target of rapamycin (mTOR). si-OGR1 could enhance the proliferation of GMECs by promoting G1/S phase progression and the synthesis of ß-casein and triglyceride. By contrast, OGR1 repressed GMECs proliferation and down-regulated the synthesis of ß-casein and triglyceride by blocking the PI3K/AKT/mTOR signaling pathway in GMECs.

Exponential subtraction in 3D ultrashort echo time imaging to visualize short T2 tissues in tibia.

BACKGROUND: Short T2 tissues can be directly visualized by dual-echo ultrashort echo time imaging with weighted subtraction. As a type of post-processing method, exponential subtraction of ultrashort echo time images with an optimal exponential factor is expected to provide improved positive short T2 contrast. PURPOSE: To test the feasibility and effectiveness of exponential subtraction in three-dimensional ultrashort echo time imaging and to determine the optimal exponential factor. MATERIAL AND METHODS: A dual-echo three-dimensional ultrashort echo time sequence was implemented on a 3-T MRI system. Exponential subtraction was performed on dual three-dimensional ultrashort echo time images of the tibia of seven healthy volunteers with exponential factors in the range of 1.00-3.00 in increments of 0.01. The regions of interest, including cortical bone, marrow, and muscle, were depicted on subtracted images of different exponential factors. Contrast-to-noise ratio values were calculated from these regions of interest and then used to assess the optimal exponential factor. To determine intra-observer agreement regarding region of interest selection, paired intra-observer measurements of regions of interest in all direct subtraction images were conducted with a one-week interval and the paired measurements were assessed using Bland-Altman analysis and paired-samples t-test. RESULTS: Cortical bone can be better visualized by using exponential subtraction in three-dimensional ultrashort echo time imaging; the suggested optimal exponential factor is 1.99-2.03 in the tibia. Paired measurements showed excellent intra-observer agreement. CONCLUSION: It is feasible to visualize cortical bone of the tibia using exponential subtraction in three-dimensional ultrashort echo time imaging. Compared with weighted subtraction images, exponential subtraction images with an optimal exponential factor provide enhanced visualization of short T2 tissues.

Characterization of influenza virus PR8 strain cultured in embryonated eggs by cryo-electron tomography.

Influenza A viruses, as causative agents of seasonal epidemics and periodic worldwide pandemics, cause enormous mortality loss globally. The PR8 strain cultured in chicken eggs is widely used for scientific research and the production of influenza vaccines. Here, based on Cryo-electron Tomography (CET), we analyzed the morphological and structural characteristics of the influenza virus PR8 strain at different pHs. We found that a large number of defective virions were propagated in embryonated eggs. By comparing virions with/without the matrix layer, it was revealed that the matrix layer played an essential role in the structural integrity of virions and RNPs encapsulation during the influenza virus life cycle. We also utilized hemagglutinin receptor-containing liposomes to mimic the membrane fusion process. Several potential intermediates of HA during membrane fusion were observed at acidic pH. Our observations afford insight into the architecture and function of influenza virus.

Analysis of Acupoint Selection Rules for Guasha Treatment of Primary Headaches Based on Data Mining.

Objective: We aimed to understand the commonly used acupoints and the acupoint combination rules in Guasha therapy for primary headaches using data mining technology, providing a reference for the clinical application of Guasha therapy for primary headaches. Methods: Literature related to Guasha therapy for primary headaches in PubMed, Web of Science, Chinese National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, and Chinese Biomedical Literature Database were searched, up until May 12, 2023. A database of acupoints for Guasha therapy for primary headaches was established in Excel. The frequency of the acupoints used for Guasha in therapy of primary headaches were calculated by SPSS 25.0. The association rules between the acupoints were further described using SPSS Modeler 18.0. Results: A total of 67 papers were included, involving 51 acupoints for Guasha against primary headaches. The most commonly used acupoints were Fengchi, Baihui, Taiyang, Shuaigu, Tianzhu, and Hegu. The common acupoint combinations for Guasha therapy for primary headaches were Fengchi-Taiyang, Fengchi-Baihui, Fengchi-Taiyang-Baihui, Fengchi-Tianzhu-Baihui, and Fengchi-Shuaigu-Taiyang-Baihui. Conclusion: Data mining can effectively analyze the commonly used acupoints and the acupoint combination rules in Guasha therapy for primary headaches, providing a reliable basis for clinical acupoint selection.

Curcumin-loaded keratin-chitosan hydrogels for enhanced peripheral nerve regeneration.

Peripheral nerve injury often leads to symptoms of motor and sensory impairment, and slow recovery of nerves after injury and limited treatment methods will aggravate symptoms or even lead to lifelong disability. Curcumin can promote peripheral nerve regeneration, but how to accurately deliver the appropriate concentration of curcumin in the local peripheral nerve remains to be solved. In this study, we designed a human hair keratin/chitosan (C/K) hydrogel with sodium tripolyphosphate ions crosslinked to deliver curcumin topically. Chitosan improves the mechanical properties of hydrogels and keratin improves the biocompatibility of hydrogels. C/K hydrogel showed good cytocompatibility, histocompatibility and degradability. In vitro experiments showed that hydrogels can continuously release curcumin for up to 10 days. In addition, a comprehensive analysis of behavioral, electrophysiological, histology, and target organ recovery results in animal experiments showed that locally delivered curcumin can enhance nerve regeneration in addition to hydrogels. In short, we provide a new method that combines the advantages of human hair keratin, chitosan, and curcumin for nerve damage repair.

A self-supervised spatio-temporal attention network for video-based 3D infant pose estimation.

General movement and pose assessment of infants is crucial for the early detection of cerebral palsy (CP). Nevertheless, most human pose estimation methods, in 2D or 3D, focus on adults due to the lack of large datasets and pose annotations on infants. To solve these problems, here we present a model known as YOLO-infantPose, which has been fine-tuned, for infant pose estimation in 2D. We further propose a self-supervised model called STAPose3D for 3D infant pose estimation based on videos. We employ multi-view video data during the training process as a strategy to address the challenge posed by the absence of 3D pose annotations. STAPose3D combines temporal convolution, temporal attention, and graph attention to jointly learn spatio-temporal features of infant pose. Our methods are summarized into two stages: applying YOLO-infantPose on input videos, followed by lifting these 2D poses along with respective confidences for every joint to 3D. The employment of the best-performing 2D detector in the first stage significantly improves the precision of 3D pose estimation. We reveal that fine-tuned YOLO-infantPose outperforms other models tested on our clinical dataset as well as two public datasets MINI-RGBD and YouTube-Infant dataset. Results from our infant movement video dataset demonstrate that STAPose3D effectively comprehends the spatio-temporal features among different views and significantly improves the performance of 3D infant pose estimation in videos. Finally, we explore the clinical application of our method for general movement assessment (GMA) in a clinical dataset annotated as normal writhing movements or abnormal monotonic movements according to the GMA standards. We show that the 3D pose estimation results produced by our STAPose3D model significantly boost the GMA prediction performance than 2D pose estimation. Our code is available at github.com/wwYinYin/STAPose3D.

ELK4 Promotes Cell Cycle Progression and Stem Cell-like Characteristics in HPV-associated Cervical Cancer by Regulating the FBXO22/PTEN Axis

Background: Cervical cancer (CC) is a prevalent gynecological carcinoma, and patients infected with human papillomavirus (HPV) have a higher morbidity rate. Aims: To explore the effects of ETS-like transcription factor 4 (ELK4) in patients with HPV+ CC. Study design: In vitro cell lines and human-sample study. Methods: The ELK4 levels in human tissue (65 HPV+ CC tissue and 25 HPV− normal cervical tissue) and cell lines (human cervical epithelial immortalized cell line H8 and CC cell lines HeLa [HPV18], CaSki [HPV16], and SiHa [HPV−]) were quantified using qRT-PCR and western blot assay. ELK4 knockdown transfection was effective and confirmed by western blotting. The MTT and EDU assays were used to evaluate cell viability and proliferation, respectively. Flow cytometry was used to detect the CC cell cycle stage. Stem cell markers, such as cluster of differentiation 133 (CD133), CD44, and aldehyde dehydrogenase 1, and the cervicospheres formed were measured. ChIP-qPCR and luciferase activity experiments were used to assess the bond between ELK4 and F-box protein 22 (FBXO22). Results: ELK4 was highly expressed in the HPV+ CC tissue. CC cells with ELK4 knockdown had lower viability and proliferation than the control cells. ELK4 knockdown blocked the progression of the cell cycle from G1 to S phase. ELK4 knockdown suppressed the stem cell-like characteristics of the HPV+ CC cells. ELK4 bonded with the FBXO22 promoter, inhibiting the levels of phosphatase and tensin homolog (PTEN). Conclusion: ELK4 facilitated cell cycle progression and stem cell-like characteristics by regulating the FBXO22/PTEN axis. Thus, ELK4 could be a potential therapeutic target to arrest the progress of HPV-associated CC.

Enhancing length of stay prediction by learning similarity-aware representations for hospitalized patients.

This paper focuses on predicting the length of stay for patients on the first day of admission and propose a predictive model named DGLoS. In order to capture the influence of various complex factors on the length of stay as well as the dependencies among various factors, DGLoS uses a deep neural network to model both the patient information and diagnostic information. Targeting at different attribution types, we utilize different coding methods to convert raw data to the input features. Besides, we find that similar patients have closer lengths of stay. Therefore, we further design a module based on graph representation learning to generate patients' similarity-aware representations, capturing the similarity between patients and therefore enhancing predictions. These similarity-aware representations are incorporated into the output of the deep neural network to jointly perform the prediction. We have conducted comprehensive experiments on a real-world hospitalization dataset. The performance comparison shows that our proposed DGLoS model improves predictive performance and the significance test demonstrates the improvement is significant. The ablation study verifies the effectiveness of each of the proposed components and the hyper-parameter investigation shows the robustness of the proposed model.

Hippo Signaling Regulates Blastema Formation During Limb Regeneration in Chinese Mitten Crab (Eriocheir sinensis).

Limb autotomy and regeneration are specific adaptations of crustaceans in response to external stress and attacks, which make them a suitable model to investigate the mechanism of organ regeneration in invertebrates. In this study, the Hippo gene of Eriocheir sinensis (EsHPO) was identified, and the effects of Hippo signaling on limb regeneration were evaluated. The expression of EsHPO and other key components of Hippo signaling was down-regulated during the basal growth phase in response to limb autotomy stress and then up-regulated during the proecdysial growth phase. The descending expression patterns of Hippo signal components were correlated with transcriptional activation of YKI and downstream target genes during the blastema formation stage, which suggested that Hippo signaling plays a key role during limb regeneration in E. sinensis. To further test the hypothesis, the transcription factor YKI was blocked via verteporfin injection after autotomy, which disrupted limb regeneration by repressing wound healing and preventing blastema emergence. Furthermore, our experiments revealed that the proliferation of blastema cells was blocked by verteporfin. In addition, the expression of genes related to ECM remodeling, cell cycle progression, and apoptosis resistance was down-regulated following the injection of verteporfin. Our findings therefore indicate that Hippo signaling is essential for successful wound healing and limb regeneration in E. sinensis by inducing ECM remodeling, as well as promoting the proliferation and repressing the apoptosis of blastema cells.

Real-Time Image Processing Toolbox for All-Optical Closed-Loop Control of Neuronal Activities.

The development of in vivo imaging and optogenetic tools makes it possible to control neural circuit activities in an all-optical, closed-loop manner, but such applications are limited by the lack of software for online analysis of neuronal imaging data. We developed an analysis software ORCA (Online Real-time activity and offline Cross-session Analysis), which performs image registration, neuron segmentation, and activity extraction at over 100 frames per second, fast enough to support real-time detection and readout of neural activity. Our active neuron detection algorithm is purely statistical, achieving a much higher speed than previous methods. We demonstrated closed-loop control of neurons that were identified on the fly, without prior recording or image processing. ORCA also includes a cross-session alignment module that efficiently tracks neurons across multiple sessions. In summary, ORCA is a powerful toolbox for fast imaging data analysis and provides a solution for all-optical closed-loop control of neuronal activity.

Fusing hand-crafted and deep-learning features in a convolutional neural network model to identify prostate cancer in pathology images.

Prostate cancer can be diagnosed by prostate biopsy using transectal ultrasound guidance. The high number of pathology images from biopsy tissues is a burden on pathologists, and analysis is subjective and susceptible to inter-rater variability. The use of machine learning techniques could make prostate histopathology diagnostics more precise, consistent, and efficient overall. This paper presents a new classification fusion network model that was created by fusing eight advanced image features: seven hand-crafted features and one deep-learning feature. These features are the scale-invariant feature transform (SIFT), speeded up robust feature (SURF), oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) (ORB) of local features, shape and texture features of the cell nuclei, the histogram of oriented gradients (HOG) feature of the cavities, a color feature, and a convolution deep-learning feature. Matching, integrated, and fusion networks are the three essential components of the proposed deep-learning network. The integrated network consists of both a backbone and an additional network. When classifying 1100 prostate pathology images using this fusion network with different backbones (ResNet-18/50, VGG-11/16, and DenseNet-121/201), we discovered that the proposed model with the ResNet-18 backbone achieved the best performance in terms of the accuracy (95.54%), specificity (93.64%), and sensitivity (97.27%) as well as the area under the receiver operating characteristic curve (98.34%). However, each of the assessment criteria for these separate features had a value lower than 90%, which demonstrates that the suggested model combines differently derived characteristics in an effective manner. Moreover, a Grad-CAM++ heatmap was used to observe the differences between the proposed model and ResNet-18 in terms of the regions of interest. This map showed that the proposed model was better at focusing on cancerous cells than ResNet-18. Hence, the proposed classification fusion network, which combines hand-crafted features and a deep-learning feature, is useful for computer-aided diagnoses based on pathology images of prostate cancer. Because of the similarities in the feature engineering and deep learning for different types of pathology images, the proposed method could be used for other pathology images, such as those of breast, thyroid cancer.

Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function.

Background: Magnetic resonance imaging (MRI) images synthesized from computed tomography (CT) data can provide more detailed information on pathological structures than that of CT data alone; thus, the synthesis of MRI has received increased attention especially in medical scenarios where only CT images are available. A novel convolutional neural network (CNN) combined with a contextual loss function was proposed for synthesis of T1- and T2-weighted images (T1WI and T2WI) from CT data. Methods: A total of 5,053 and 5,081 slices of T1WI and T2WI, respectively were selected for the dataset of CT and MRI image pairs. Affine registration, image denoising, and contrast enhancement were done on the aforementioned multi-modality medical image dataset comprising T1WI, T2WI, and CT images of the brain. A deep CNN was then proposed by modifying the ResNet structure to constitute the encoder and decoder of U-Net, called double ResNet-U-Net (DRUNet). Three different loss functions were utilized to optimize the parameters of the proposed models: mean squared error (MSE) loss, binary crossentropy (BCE) loss, and contextual loss. Statistical analysis of the independent-sample t-test was conducted by comparing DRUNets with different loss functions and different network layers. Results: DRUNet-101 with contextual loss yielded higher values of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Tenengrad function (i.e., 34.25±2.06, 0.97±0.03, and 17.03±2.75 for T1WI and 33.50±1.08, 0.98±0.05, and 19.76±3.54 for T2WI respectively). The results were statistically significant at P<0.001 with a narrow confidence interval of difference, indicating the superiority of DRUNet-101 with contextual loss. In addition, both image zooming and difference maps presented for the final synthetic MR images visually reflected the robustness of DRUNet-101 with contextual loss. The visualization of convolution filters and feature maps showed that the proposed model can generate synthetic MR images with high-frequency information. Conclusions: The results demonstrated that DRUNet-101 with contextual loss function provided better high-frequency information in synthetic MR images compared with the other two functions. The proposed DRUNet model has a distinct advantage over previous models in terms of PSNR, SSIM, and Tenengrad score. Overall, DRUNet-101 with contextual loss is recommended for synthesizing MR images from CT scans.

Highly Stretchable Hydrogels as Wearable and Implantable Sensors for Recording Physiological and Brain Neural Signals.

Recording electrophysiological information such as brain neural signals is of great importance in health monitoring and disease diagnosis. However, foreign body response and performance loss over time are major challenges stemming from the chemomechanical mismatch between sensors and tissues. Herein, microgels are utilized as large crosslinking centers in hydrogel networks to modulate the tradeoff between modulus and fatigue resistance/stretchability for producing hydrogels that closely match chemomechanical properties of neural tissues. The hydrogels exhibit notably different characteristics compared to nanoparticles reinforced hydrogels. The hydrogels exhibit relatively low modulus, good stretchability, and outstanding fatigue resistance. It is demonstrated that the hydrogels are well suited for fashioning into wearable and implantable sensors that can obtain physiological pressure signals, record the local field potentials in rat brains, and transmit signals through the injured peripheral nerves of rats. The hydrogels exhibit good chemomechanical match to tissues, negligible foreign body response, and minimal signal attenuation over an extended time, and as such is successfully demonstrated for use as long-term implantable sensory devices. This work facilitates a deeper understanding of biohybrid interfaces, while also advancing the technical design concepts for implantable neural probes that efficiently obtain physiological information.

The Drainage of Interstitial Fluid in the Deep Brain is Controlled by the Integrity of Myelination.

In searching for the drainage route of the interstitial fluid (ISF) in the deep brain, we discovered a regionalized ISF drainage system as well as a new function of myelin in regulating the drainage. The traced ISF from the caudate nucleus drained to the ipsilateral cortex along myelin fiber tracts, while in the opposite direction, its movement to the adjacent thalamus was completely impeded by a barrier structure, which was identified as the converged, compact myelin fascicle. The regulating and the barrier effects of myelin were unchanged in AQP4-knockout rats but were impaired as the integrity of boundary structure of drainage system was destroyed in a demyelinated rat model. We thus proposed that the brain homeostasis was maintained within each ISF drainage division locally, rather than across the brain as a whole. A new brain division system and a new pathogenic mechanism of demyelination are therefore proposed.

Exploring an optimal wavelet-based filter for cryo-ET imaging.

Cryo-electron tomography (cryo-ET) is one of the most advanced technologies for the in situ visualization of molecular machines by producing three-dimensional (3D) biological structures. However, cryo-ET imaging has two serious disadvantages-low dose and low image contrast-which result in high-resolution information being obscured by noise and image quality being degraded, and this causes errors in biological interpretation. The purpose of this research is to explore an optimal wavelet denoising technique to reduce noise in cryo-ET images. We perform tests using simulation data and design a filter using the optimum selected wavelet parameters (three-level decomposition, level-1 zeroed out, subband-dependent threshold, a soft-thresholding and spline-based discrete dyadic wavelet transform (DDWT)), which we call a modified wavelet shrinkage filter; this filter is suitable for noisy cryo-ET data. When testing using real cryo-ET experiment data, higher quality images and more accurate measures of a biological structure can be obtained with the modified wavelet shrinkage filter processing compared with conventional processing. Because the proposed method provides an inherent advantage when dealing with cryo-ET images, it can therefore extend the current state-of-the-art technology in assisting all aspects of cryo-ET studies: visualization, reconstruction, structural analysis, and interpretation.

Applying a Modified Wavelet Shrinkage Filter to Improve Cryo-Electron Microscopy Imaging.

Cryo-electron microscopy (Cryo-EM) imaging has the unique potential to bridge the gap between cellular and molecular biology by revealing the structures of large macromolecular assemblies and cellular complexes. Therefore, cryo-EM three-dimensional (3D) reconstruction has been rapidly developed in recent several years and applied widely in life science research; however, it suffers from reduced contrast and low signal-to-noise ratios with a high degree of noise under low electron dose conditions, resulting in failures of many conventional filters. In this article, we explored a modified wavelet shrinkage filter (with optimal wavelet parameters: three-level decomposition, level-1 zeroed out, subband-dependent threshold, soft thresholding, and spline-based discrete dyadic wavelet transform) and extended its application in the cryo-EM field in two aspects: single-particle analysis and cryo-electron tomography. Its performance was assessed with simulation data and real cryo-EM experimental data. Compared with the undenoised results and conventional denoising techniques (e.g., Gaussian, median, and bilateral filters), the modified wavelet shrinkage filter maintained the resolution and contrast but reduced the noise, leading to higher quality images and more accurate measures of the biological structure. We expect that our study can provide benefits to cryo-EM applications: 3D reconstruction, visualization, structural analysis, and interpretation. All these data and programs are available.

The relationship between S-adenosylhomocysteine and coronary artery lesions: A case control study.

The role of homocysteine (Hcy) in the pathogenesis of coronary artery disease (CAD) is controversial, as decreased Hcy levels have not demonstrated consistent clinical benefits. Recent studies propose that S-adenosylhomocysteine (SAH), and not Hcy, plays a role in cardiovascular disease (CVD). We aimed to assess the relationship between plasma SAH and coronary artery lesions. Participants (n=160; aged 40-80years) with chest pain and suspected CAD underwent coronary angiography (CAG) for assessment of coronary artery stenosis, and were assigned to either the atherosclerosis (AS) or CAD group. Plasma SAH and S-adenosylmethionine (SAM) concentrations were measured and the association between coronary artery lesions and SAH was assessed. SAH levels were significantly higher in the CAD group (23.09±2.4nmol/L) than in the AS group (19.2±1.5nmol/L). While the AS group had higher values for SAM/SAH (5.1±0.7 vs. 4.1±1.1), levels of SAM, Hcy, folate, and vitamin B12 were similar in the two groups. Coronary artery lesions were associated with SAH (ß=11.8 [95% CI: 5.88, 17.7, P<0.05]. Plasma SAH concentrations are independently associated with coronary artery lesions among patients undergoing coronary angiography. Plasma SAH might be a novel biomarker for the early clinical identification of CVD.