Association between arteriosclerosis index and lumbar bone mineral density in U.S adults: a cross-sectional study from the NHANES 2011–2018

BackgroundThe arteriosclerosis index, defined as the ratio of non-high density lipoprotein cholesterol to high density lipoprotein cholesterol (NHHR), has emerged as a novel biomarker for various diseases. The relationship between NHHR and lumbar bone mineral density (BMD) has not been previously examined.MethodsThis cross-sectional study analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2011–2018. NHHR was calculated as (total cholesterol—high-density lipoprotein cholesterol)/high-density lipoprotein cholesterol. Lumbar BMD was calculated to Z scores. Weighted multivariate linear regression, subgroup analysis, interaction analysis, generalized additive model, and two-piecewise linear regression were used.ResultsA total of 8,602 participants were included. The negative association between NHHR and lumbar BMD was consistent and significant (Model 1: β = −0.039, 95% CI: −0.055, −0.023, p < 0.001; Model 2: β = −0.045, 95% CI: −0.062, −0.027, p < 0.001; Model 3: β = −0.042, 95% CI: −0.061, −0.023, p < 0.001). The linear relationship between NHHR and lumbar BMD was significantly influenced by body mass index (p for interaction = 0.012) and hypertension (p for interaction = 0.047). Non-linear associations between NHHR and lumbar BMD Z scores were observed in specific populations, including U-shaped, reverse U-shaped, L-shaped, reverse L-shaped, and U-shaped relationships among menopausal females, underweight participants, those with impaired glucose tolerance, those with diabetes mellitus and those taking anti-hyperlipidemic drugs, respectively.ConclusionsNHHR exhibited a negative association with lumbar BMD, but varying across specific populations. These findings suggest that NHHR should be tailored to individual levels to mitigate bone loss through a personalized approach. Individuals at heightened risk of cardiovascular disease should focus on their bone health

Relationship between systemic immune-inflammation index and osteoarthritis: a cross-sectional study from the NHANES 2005–2018

ObjectiveThe study aimed to explore the relationship between systemic inflammatory response index (SIRI) levels and osteoarthritis (OA) using cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) database from 2005 to 2018.MethodsUsing cross-sectional data from the NHANES database from 2005 to 2018, we included 11,381 study participants divided into OA (n = 1,437) and non-OA (n = 9,944) groups. Weighted multivariable regression models and subgroup analyses were employed to investigate the relationship between SIRI and OA. Additionally, restricted cubic spline models were used to explore nonlinear relationships.ResultsThis study enrolled 11,381 participants aged ≥20 years, including 1,437 (14%) with OA. Weighted multivariable regression analysis in the fully adjusted Model 3 indicated a correlation between higher levels of SIRI (log2-transformed) and an increased OA risk (odds ratio: 1.150; 95% confidence interval: 1.000–1.323, p < 0.05). Interaction tests showed that the variables did not significantly affect this correlation (p for interaction all >0.05). Additionally, a restricted cubic spline model revealed a nonlinear relationship between log2(SIRI) and OA risk, with a threshold effect showing 4.757 as the critical value of SIRI. SIRI <4.757 showed almost unchanged OA risk, whereas SIRI >4.757 showed rapidly increasing OA risk.ConclusionThe positive correlation between SIRI and OA risk, with a critical value of 4.757, holds clinical value in practical applications. Additionally, our study indicates that SIRI is a novel, clinically valuable, and convenient inflammatory biomarker that can be used to predict OA risk in adults

SC-Track: a robust cell tracking algorithm for generating accurate single-cell lineages from diverse cell segmentations

Computational analysis of fluorescent timelapse microscopy images at the single-cell level is a powerful approach to study cellular changes that dictate important cell fate decisions. Core to this approach is the need to generate reliable cell segmentations and classifications necessary for accurate quantitative analysis. Deep learning-based convolutional neural networks (CNNs) have emerged as a promising solution to these challenges. However, current CNNs are prone to produce noisy cell segmentations and classifications, which is a significant barrier to constructing accurate single-cell lineages. To address this, we developed a novel algorithm called Single Cell Track (SC-Track), which employs a hierarchical probabilistic cache cascade model based on biological observations of cell division and movement dynamics. Our results show that SC-Track performs better than a panel of publicly available cell trackers on a diverse set of cell segmentation types. This cell-tracking performance was achieved without any parameter adjustments, making SC-Track an excellent generalised algorithm that can maintain robust cell-tracking performance in varying cell segmentation qualities, cell morphological appearances and imaging conditions. Furthermore, SC-Track is equipped with a cell class correction function to improve the accuracy of cell classifications in multi-class cell segmentation time series. These features together make SC-Track a robust cell-tracking algorithm that works well with noisy cell instance segmentation and classification predictions from CNNs to generate accurate single-cell lineages and classifications

SSyncOA: Self-synchronizing Object-aligned Watermarking to Resist Cropping-paste Attacks

Modern image processing tools have made it easy for attackers to crop the region or object of interest in images and paste it into other images. The challenge this cropping-paste attack poses to the watermarking technology is that it breaks the synchronization of the image watermark, introducing multiple superimposed desynchronization distortions, such as rotation, scaling, and translation. However, current watermarking methods can only resist a single type of desynchronization and cannot be applied to protect the object's copyright under the cropping-paste attack. With the finding that the key to resisting the cropping-paste attack lies in robust features of the object to protect, this paper proposes a self-synchronizing object-aligned watermarking method, called SSyncOA. Specifically, we first constrain the watermarked region to be aligned with the protected object, and then synchronize the watermark's translation, rotation, and scaling distortions by normalizing the object invariant features, i.e., its centroid, principal orientation, and minimum bounding square, respectively. To make the watermark embedded in the protected object, we introduce the object-aligned watermarking model, which incorporates the real cropping-paste attack into the encoder-noise layer-decoder pipeline and is optimized end-to-end. Besides, we illustrate the effect of different desynchronization distortions on the watermark training, which confirms the necessity of the self-synchronization process. Extensive experiments demonstrate the superiority of our method over other SOTAs.Comment: 7 pages, 5 figures (Have been accepted by ICME 2024

DBDH: A Dual-Branch Dual-Head Neural Network for Invisible Embedded Regions Localization

Embedding invisible hyperlinks or hidden codes in images to replace QR codes has become a hot topic recently. This technology requires first localizing the embedded region in the captured photos before decoding. Existing methods that train models to find the invisible embedded region struggle to obtain accurate localization results, leading to degraded decoding accuracy. This limitation is primarily because the CNN network is sensitive to low-frequency signals, while the embedded signal is typically in the high-frequency form. Based on this, this paper proposes a Dual-Branch Dual-Head (DBDH) neural network tailored for the precise localization of invisible embedded regions. Specifically, DBDH uses a low-level texture branch containing 62 high-pass filters to capture the high-frequency signals induced by embedding. A high-level context branch is used to extract discriminative features between the embedded and normal regions. DBDH employs a detection head to directly detect the four vertices of the embedding region. In addition, we introduce an extra segmentation head to segment the mask of the embedding region during training. The segmentation head provides pixel-level supervision for model learning, facilitating better learning of the embedded signals. Based on two state-of-the-art invisible offline-to-online messaging methods, we construct two datasets and augmentation strategies for training and testing localization models. Extensive experiments demonstrate the superior performance of the proposed DBDH over existing methods.Comment: 7 pages, 6 figures (Have been accepted by IJCNN 2024

Efficacy and safety of tranexamic acid in cervical spine surgery: a systematic review and meta-analysis

BackgroundTranexamic acid (TXA) is an antifibrinolytic drug associated with reduced blood loss in a range of surgical specialties. This meta-analysis aimed to compare the efficacy and safety of TXA in cervical surgery, focusing on its effects on intraoperative blood loss and related outcomes.MethodsWe searched the PubMed, EMBASE, Medline, and Cochrane Library databases to identify all literature related to TXA used in cervical spinal surgery. Intraoperative blood loss, postoperative drainage volume, total blood loss, postoperative hematological variables, and complications were analyzed.ResultsEight trials met the inclusion criteria. The pooled results showed that intraoperative blood loss, total blood loss, and postoperative drainage volume were significantly lower in the TXA group than in the control group. The hemoglobin and hematocrit on postoperative day 1 was significantly higher in the TXA group than in the control group. There was no significant difference in complications between the two groups.ConclusionThe available evidence indicates that TXA effectively reduces blood loss in cervical spinal surgery while maintaining a favorable safety profile, without increasing associated risks.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023459652

3D porous reduced graphene cathode and non-corrosive electrolyte for long-life rechargeable aluminum batteries

Owing to their high volumetric capacity, low cost and high safety, rechargeable aluminum batteries have become promising candidates for energy applications. However, the high charge density of Al3+ leads to strong coulombic interactions between anions and the cathode, resulting in sluggish diffusion kinetics and irreversible collapse of the cathode structure. Furthermore, AlCl3-based ionic liquids, which are commonly used as electrolytes in such batteries, corrode battery components and are prone to side reactions. The above problems lead to low capacity and poor cycling stability. Herein, we propose a reduced graphene oxide (rGO) cathode with a three-dimensional porous structure prepared using a simple and scalable method. The lamellar edges and oxygen-containing group defects of rGO synergistically provide abundant ion storage sites and enhance ion transfer kinetics. We matched the prepared rGO cathode with noncorrosive electrolyte 0.5 mol·L−1 Al(OTF)3/[BMIM]OTF and Al metal to construct a high-performance battery, Al||rGO-150, with good cycling stability for 2700 cycles. Quasi-in-situ physicochemical characterization results show that the ion storage mechanism is codominated by diffusion and capacitance. The capacity consists of the insertion of Al-based species cations as well as synergistic adsorption of Al(OTF)x(3−x)+ (x < 3) and [BMIM]+. The present study promotes the fundamental and applied research on rechargeable aluminum batteries

Activation of the hedgehog pathway in advanced prostate cancer

BACKGROUND: The hedgehog pathway plays a critical role in the development of prostate. However, the role of the hedgehog pathway in prostate cancer is not clear. Prostate cancer is the second most prevalent cause of cancer death in American men. Therefore, identification of novel therapeutic targets for prostate cancer has significant clinical implications. RESULTS: Here we report that activation of the hedgehog pathway occurs frequently in advanced human prostate cancer. We find that high levels of hedgehog target genes, PTCH1 and hedgehog-interacting protein (HIP), are detected in over 70% of prostate tumors with Gleason scores 8–10, but in only 22% of tumors with Gleason scores 3–6. Furthermore, four available metastatic tumors all have high expression of PTCH1 and HIP. To identify the mechanism of the hedgehog signaling activation, we examine expression of Su(Fu) protein, a negative regulator of the hedgehog pathway. We find that Su(Fu) protein is undetectable in 11 of 27 PTCH1 positive tumors, two of them contain somatic loss-of-function mutations of Su(Fu). Furthermore, expression of sonic hedgehog protein is detected in majority of PTCH1 positive tumors (24 out of 27). High levels of hedgehog target genes are also detected in four prostate cancer cell lines (TSU, DU145, LN-Cap and PC3). We demonstrate that inhibition of hedgehog signaling by smoothened antagonist, cyclopamine, suppresses hedgehog signaling, down-regulates cell invasiveness and induces apoptosis. In addition, cancer cells expressing Gli1 under the CMV promoter are resistant to cyclopamine-mediated apoptosis. All these data suggest a significant role of the hedgehog pathway for cellular functions of prostate cancer cells. CONCLUSION: Our data indicate that activation of the hedgehog pathway, through loss of Su(Fu) or overexpression of sonic hedgehog, may involve tumor progression and metastases of prostate cancer. Thus, targeted inhibition of hedgehog signaling may have significant implications of prostate cancer therapeutics

Isolating hydrogen in hexagonal boron nitride bubbles by a plasma treatment

Atomically thin hexagonal boron nitride (h-BN) is often regarded as an elastic film that is impermeable to gases. The high stabilities in thermal and chemical properties allow h-BN to serve as a gas barrier under extreme conditions.In this work, we demonstrate the isolation of hydrogen in bubbles of h-BN via plasma treatment.Detailed characterizations reveal that the substrates do not show chemical change after treatment. The bubbles are found to withstand thermal treatment in air,even at 800 degree celsius. Scanning transmission electron microscopy investigation shows that the h-BN multilayer has a unique aligned porous stacking nature, which is essential for the character of being transparent to atomic hydrogen but impermeable to hydrogen molecules. We successfully demonstrated the extraction of hydrogen gases from gaseous compounds or mixtures containing hydrogen element. The successful production of hydrogen bubbles on h-BN flakes has potential for further application in nano/micro-electromechanical systems and hydrogen storage.Comment: 55 pages, 33figure