Mussel-inspired resilient hydrogels with strong skin adhesion and high-sensitivity for wearable device.

Stretchable and self-adhesive conductive hydrogels hold significant importance across a wide spectrum of applications, including human-machine interfaces, wearable devices, and soft robotics. However, integrating multiple properties, such as high stretchability, strong interfacial adhesion, self-healing capability, and sensitivity, into a single material poses significant technical challenges. Herein, we present a multifunctional conductive hydrogel based on poly(acrylic acid) (PAA), dopamine-functionalized pectin (PT-DA), polydopamine-coated reduction graphene oxide (rGO-PDA), and Fe3+ as an ionic cross-linker. This hydrogel exhibits a combination of high stretchability (2000%), rapid self-healing (~ 94% recovery in 5 s), and robust self-adhesion to various substrates. Notably, the hydrogel demonstrates a remarkable skin adhesion strength of 85 kPa, surpassing previous skin adhesive hydrogels. Furthermore, incorporating rGO within the hydrogel network creates electric pathways, ensuring excellent conductivity (0.56 S m-1). Consequently, these conductive hydrogels exhibit strain-sensing properties with a significant increase in gauge factor (GF) of 14.6, covering an extensive detection range of ~ 1000%, fast response (198 ms) and exceptional cycle stability. These multifunctional hydrogels can be seamlessly integrated into motion detection sensors capable of distinguishing between various strong or subtle movements of the human body.

Control of meiotic crossover interference by a proteolytic chaperone network.

Meiosis is a specialized eukaryotic division that produces genetically diverse gametes for sexual reproduction. During meiosis, homologous chromosomes pair and undergo reciprocal exchanges, called crossovers, which recombine genetic variation. Meiotic crossovers are stringently controlled with at least one obligate exchange forming per chromosome pair, while closely spaced crossovers are inhibited by interference. In Arabidopsis, crossover positions can be explained by a diffusion-mediated coarsening model, in which large, approximately evenly spaced foci of the pro-crossover E3 ligase HEI10 grow at the expense of smaller, closely spaced clusters. However, the mechanisms that control HEI10 dynamics during meiosis remain unclear. Here, through a forward genetic screen in Arabidopsis, we identified high crossover rate3 (hcr3), a dominant-negative mutant that reduces crossover interference and increases crossovers genome-wide. HCR3 encodes J3, a co-chaperone related to HSP40, which acts to target protein aggregates and biomolecular condensates to the disassembly chaperone HSP70, thereby promoting proteasomal degradation. Consistently, we show that a network of HCR3 and HSP70 chaperones facilitates proteolysis of HEI10, thereby regulating interference and the recombination landscape. These results reveal a new role for the HSP40/J3-HSP70 chaperones in regulating chromosome-wide dynamics of recombination via control of HEI10 proteolysis.

Sargachromenol Attenuates Inflammatory Responses by Regulating NF-κB and Nrf2 Pathways in RAW 264.7 Cells and LPS-treated Mice.

This study aims to explore the anti-inflammatory mechanisms of sargachromenol in both RAW 264.7 cells and lipopolysaccharide (LPS)-treated mice, as previous reports have suggested that sargachromenol possesses anti-aging, anti-inflammatory, antioxidant, and neuroprotective properties. Although the precise mechanism behind its anti-inflammatory activity remains unclear, pretreatment with sargachromenol effectively reduced the production of nitric oxide, prostaglandin E2, and interleukin (IL)-1ß in LPS-stimulated RAW 264.7 cells by inhibiting cyclooxygenase-2. Moreover, sargachromenol inhibited the activation of nuclear factor-κB (NF-κB) by preventing the degradation of the inhibitor of κB-α (IκB-α) and inhibiting protein kinase B (Akt) phosphorylation in LPS-stimulated cells. We also found that sargachromenol induced the production of heme oxygenase-1 (HO-1) by activating the nuclear transcription factor erythroid-2-related factor 2 (Nrf2). In LPS-treated mice, oral administration of sargachromenol effectively reduced the levels of IL-1ß, IL-6, and tumor necrosis factor-α (TNF-α) in the serum, suggesting its ability to suppress the production of inflammatory mediators by inhibiting the Akt/NF-κB pathway and upregulating the Nrf2/HO-1 pathway.

A risk prediction model for hepatocellular carcinoma in non-alcoholic fatty liver disease without cirrhosis: HCC prediction for non-cirrhotic NAFLD.

BACKGROUND & AIMS: Although non-alcoholic fatty liver disease (NAFLD) is becoming a leading cause of hepatocellular carcinoma (HCC), HCC risk in non-cirrhotic NAFLD received little attention. We aimed to develop and validate an HCC risk prediction model for non-cirrhotic NAFLD. METHODS: A nationwide cohort of non-cirrhotic NAFLD patients in Korea was recruited to develop a risk prediction model and validate it internally (n = 409 088). A model using a simplified point system was developed by Cox proportional hazard model. K-fold cross-validation assessed the accuracy, discrimination and calibration. The model was validated externally using a hospital cohort from Asan Medical Center (n = 8721). RESULTS: An 11-point HCC risk prediction model for non-cirrhotic NAFLD was developed using six independent factors of age, sex, diabetes, obesity, serum alanine aminotransferase level and gamma-glutamyl transferase level (c-index 0.75). The average area under receiver operating curves (AUROCs) of the model was 0.72 at 5 years and 0.75 at 10 years. In the external validation cohort, the AUROCs were 0.79 [95% confidence interval [CI], 0.59-0.95] at 5 years and 0.84 (95% CI, 0.73-0.94) at 10 years. The calibration plots showed the expected risks corresponded well with the observed risks. Risk stratification categorized patients into the low (score 0-6), moderate (7, 8) and high (9-11; estimated incidence rate >0.2%/year) risk groups. CONCLUSIONS: A novel HCC risk prediction model for non-cirrhotic NAFLD patients was developed and validated with fair performance. The model is expected to serve as a simple and reliable tool to assess HCC risk and assist precision screening of HCC.

Sulfated Polysaccharides from Seaweeds: A Promising Strategy for Combatting Viral Diseases-A Review.

The limited availability of treatments for many infectious diseases highlights the need for new treatments, particularly for viral infections. Natural compounds from seaweed are attracting increasing attention for the treatment of various viral diseases, and thousands of novel compounds have been isolated for the development of pharmaceutical products. Seaweed is a rich source of natural bioactive compounds, including polysaccharides. The discovery of algal polysaccharides with antiviral activity has significantly increased in the past few decades. Furthermore, unique polysaccharides isolated from seaweeds, such as carrageenan, alginates, fucoidans, galactans, laminarians, and ulvans, have been shown to act against viral infections. The antiviral mechanisms of these agents are based on their inhibition of DNA or RNA synthesis, viral entry, and viral replication. In this article, we review and provide an inclusive description of the antiviral activities of algal polysaccharides. Additionally, we discuss the challenges and opportunities for developing polysaccharide-based antiviral therapies, including issues related to drug delivery and formulation. Finally, this review highlights the need for further research for fully understanding the potential of seaweed polysaccharides as a source of antiviral agents and for developing effective treatments for viral diseases.

Marine Algal Polyphenols as Skin Protective Agents: Current Status and Future Prospectives.

The skin is the outermost anatomical barrier, which plays a vital role in the maintenance of internal homeostasis and protection against physical, chemical, and biological detractors. Direct contact with various stimuli leads to several physiological changes that are ultimately important for the growth of the cosmetic industry. Due to the consequences of using synthetic compounds in skincare and cosmeceutical-related industries, the pharmaceutical and scientific communities have recently shifted their focus to natural ingredients. The nutrient-rich value of algae, which are some of the most interesting organisms in marine ecosystems, has attracted attention. Secondary metabolites isolated from seaweeds are potential candidates for a wide range of economic applications, including food, pharmaceuticals, and cosmetics. An increasing number of studies have focused on polyphenol compounds owing to their promising biological activities against oxidation, inflammation, allergies, cancers, melanogenesis, aging, and wrinkles. This review summarizes the potential evidence of the beneficial properties and future perspectives of using marine macroalgae-derived polyphenolic compounds for advancing the cosmetic industry.

Superlubricity with Graphitization in Ti-Doped DLC/Steel Tribopair: Response on Humidity and Temperature.

The anti-friction of diamond-like carbon (DLC) is achieved by a well-developed carbonaceous transfer layer, and Ti-doped DLC is developed into a robustly built-up carbonaceous transfer layer. The friction performance of DLC depends on the operating environment, e.g., ambient gas, humidity, temperature, lubricants, and mating material. In this study, we aimed to reveal the environmental sensitivities of Ti-DLC on friction characteristics. To this end, a Ti-DLC was rubbed against a steel ball, and friction behaviors were evaluated with different gas compositions, humidity, and temperature. Finally, we identified that fractional coverage of water on surfaces affected the anti-graphitization on Ti-DLC, leading to avoiding friction reduction.

A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography.

In the medical field, it is delicate to anticipate good performance in using deep learning due to the lack of large-scale training data and class imbalance. In particular, ultrasound, which is a key breast cancer diagnosis method, is delicate to diagnose accurately as the quality and interpretation of images can vary depending on the operator's experience and proficiency. Therefore, computer-aided diagnosis technology can facilitate diagnosis by visualizing abnormal information such as tumors and masses in ultrasound images. In this study, we implemented deep learning-based anomaly detection methods for breast ultrasound images and validated their effectiveness in detecting abnormal regions. Herein, we specifically compared the sliced-Wasserstein autoencoder with two representative unsupervised learning models autoencoder and variational autoencoder. The anomalous region detection performance is estimated with the normal region labels. Our experimental results showed that the sliced-Wasserstein autoencoder model outperformed the anomaly detection performance of others. However, anomaly detection using the reconstruction-based approach may not be effective because of the occurrence of numerous false-positive values. In the following studies, reducing these false positives becomes an important challenge.

3D Printable Self-Adhesive and Self-Healing Ionotronic Hydrogels for Wearable Healthcare Devices.

Ionotronic hydrogels have attracted significant attention in emerging fields such as wearable devices, flexible electronics, and energy devices. To date, the design of multifunctional ionotronic hydrogels with strong interfacial adhesion, rapid self-healing, three-dimensional (3D) printing processability, and high conductivity are key requirements for future wearable devices. Herein, we report the rational design and facile synthesis of 3D printable, self-adhesive, self-healing, and conductive ionotronic hydrogels based on the synergistic dual reversible interactions of poly(vinyl alcohol), borax, pectin, and tannic acid. Multifunctional ionotronic hydrogels exhibit strong adhesion to various substrates with different roughness and chemical components, including porcine skin, glass, nitrile gloves, and plastics (normal adhesion strength of 55 kPa on the skin). In addition, the ionotronic hydrogels exhibit intrinsic ionic conductivity imparting strain-sensing properties with a gauge factor of 2.5 up to a wide detection range of approximately 2000%, as well as improved self-healing behavior. Based on these multifunctional properties, we further demonstrate the use of ionotronic hydrogels in the 3D printing process for implementing complex patterns as wearable strain sensors for human motion detection. This study is expected to provide a new avenue for the design of multifunctional ionotronic hydrogels, enabling their potential applications in wearable healthcare devices.

CT-Loc: Cross-domain visual localization with a channel-wise transformer.

We tackle the cross-domain visual localization problem of estimating camera position and orientation from real images without three-dimensional (3D) spatial mapping or modeling. Recent studies have shown suboptimal performance in this task owing to the photometric and geometric differences between synthetic and real images. In this study, we present a deep learning approach that uses a channel-wise transformer localization (CT-Loc) framework. Inspired by the human behavior of looking for structural landmarks to estimate one's location, CT-Loc encodes the most salient features of task-relevant objects in target scenes. To evaluate the efficacy of the proposed method in a real-world application, we built a complex and large-scale dataset of the interior of the mechanical room during operations and conducted extensive performance comparisons with the publicly available state-of-the-art University of Melbourne Corridor and Virtual KITTI 2 datasets. Compared with the otherwise best-performing BIM-PoseNet indoor camera localization model, our method significantly reduces position and orientation errors through the application of attention weights and saliency maps while also learning only the visual structural patterns (e.g., floors and doors) that are most relevant to localization tasks. Our model successfully ignores uninformative objects. This approach yields higher-level robust camera-pose regression localization results without requiring prebuilt maps. The code is available at https://github.com/kdaeho27/CT-Loc.

Distinct cerebral cortical perfusion patterns in idiopathic normal-pressure hydrocephalus.

The aims of the study are to evaluate idiopathic normal-pressure hydrocephalus (INPH)-related cerebral blood flow (CBF) abnormalities and to investigate their relation to cortical thickness in INPH patients. We investigated cortical CBF utilizing surface-based early-phase 18 F-florbetaben (E-FBB) PET analysis in two groups: INPH patients and healthy controls. All 39 INPH patients and 20 healthy controls were imaged with MRI, including three-dimensional volumetric images, for automated surface-based cortical thickness analysis across the entire brain. A subgroup with 37 participants (22 INPH patients and 15 healthy controls) that also underwent 18 F-fluorodeoxyglucose (FDG) PET imaging was further analyzed. Compared with age- and gender-matched healthy controls, INPH patients showed statistically significant hyperperfusion in the high convexity of the frontal and parietal cortical regions. Importantly, within the INPH group, increased perfusion correlated with cortical thickening in these regions. Additionally, significant hypoperfusion mainly in the ventrolateral frontal cortex, supramarginal gyrus, and temporal cortical regions was observed in the INPH group relative to the control group. However, this hypoperfusion was not associated with cortical thinning. A subgroup analysis of participants that also underwent FDG PET imaging showed that increased (or decreased) cerebral perfusion was associated with increased (or decreased) glucose metabolism in INPH. A distinctive regional relationship between cerebral cortical perfusion and cortical thickness was shown in INPH patients. Our findings suggest distinct pathophysiologic mechanisms of hyperperfusion and hypoperfusion in INPH patients.

Marine Biological Macromolecules and Chemically Modified Macromolecules; Potential Anticoagulants.

Coagulation is a potential defense mechanism that involves activating a series of zymogens to convert soluble fibrinogen to insoluble fibrin clots to prevent bleeding and hemorrhagic complications. To prevent the extra formation and diffusion of clots, the counterbalance inhibitory mechanism is activated at levels of the coagulation pathway. Contrariwise, this system can evade normal control due to either inherited or acquired defects or aging which leads to unusual clots formation. The abnormal formations and deposition of excess fibrin trigger serious arterial and cardiovascular diseases. Although heparin and heparin-based anticoagulants are a widely prescribed class of anticoagulants, the clinical use of heparin has limitations due to the unpredictable anticoagulation, risk of bleeding, and other complications. Hence, significant interest has been established over the years to investigate alternative therapeutic anticoagulants from natural sources, especially from marine sources with good safety and potency due to their unique chemical structure and biological activity. This review summarizes the coagulation cascade and potential macromolecular anticoagulants derived from marine flora and fauna.

Differentiated function and localisation of SPO11-1 and PRD3 on the chromosome axis during meiotic DSB formation in Arabidopsis thaliana.

During meiosis, DNA double-strand breaks (DSBs) occur throughout the genome, a subset of which are repaired to form reciprocal crossovers between chromosomes. Crossovers are essential to ensure balanced chromosome segregation and to create new combinations of genetic variation. Meiotic DSBs are formed by a topoisomerase-VI-like complex, containing catalytic (e.g. SPO11) proteins and auxiliary (e.g. PRD3) proteins. Meiotic DSBs are formed in chromatin loops tethered to a linear chromosome axis, but the interrelationship between DSB-promoting factors and the axis is not fully understood. Here, we study the localisation of SPO11-1 and PRD3 during meiosis, and investigate their respective functions in relation to the chromosome axis. Using immunocytogenetics, we observed that the localisation of SPO11-1 overlaps relatively weakly with the chromosome axis and RAD51, a marker of meiotic DSBs, and that SPO11-1 recruitment to chromatin is genetically independent of the axis. In contrast, PRD3 localisation correlates more strongly with RAD51 and the chromosome axis. This indicates that PRD3 likely forms a functional link between SPO11-1 and the chromosome axis to promote meiotic DSB formation. We also uncovered a new function of SPO11-1 in the nucleation of the synaptonemal complex protein ZYP1. We demonstrate that chromosome co-alignment associated with ZYP1 deposition can occur in the absence of DSBs, and is dependent on SPO11-1, but not PRD3. Lastly, we show that the progression of meiosis is influenced by the presence of aberrant chromosomal connections, but not by the absence of DSBs or synapsis. Altogether, our study provides mechanistic insights into the control of meiotic DSB formation and reveals diverse functional interactions between SPO11-1, PRD3 and the chromosome axis.

Maclurin Exhibits Antioxidant and Anti-Tyrosinase Activities, Suppressing Melanogenesis.

Maclurin is rich in some edible fruits such as Morus alba (white mulberry) and Garcinia mangostana. Although maclurin showed anti-cancer and antioxidant effects, its roles in ultraviolet (UV)-induced melanogenesis have not been studied. Here, we investigated the effects of maclurin in melanogenesis using skin cells and a three-dimensional human skin model. When the cytotoxicity of maclurin was examined in B16F10 cells, no cytotoxicity was found up to 20 µM. Maclurin suppressed UVB-mediated tyrosinase activation and melanin accumulation in B16F10 cells without changes in mRNA levels of melanogenesis-related genes including tyrosinase, TRP1, TRP2, CREB, and MITF. Moreover, maclurin reduced melanin contents in melan-a cells, a cell line for normal melanocytes. When applied to a human skin model consisting of the epidermis and melanocytes, maclurin significantly reduced UVB-induced melanin accumulation (~47%) in a concentration-dependent manner based on microscopic observation and Fontana-Masson staining. Protein-ligand docking simulation followed by binding residue analysis showed that maclurin may bind to inactivate tyrosinase by forming multiple hydrogen bonds and hydrophobic and aromatic interactions with the residues of tyrosinase. Together, our study suggests that maclurin may be applied as an anti-melanogenic agent.

Prediction of Implant Size Based on Breast Volume Using Mammography with Fully Automated Measurements and Breast MRI.

BACKGROUND: Determination of implant size is crucial for patients with breast cancer undergoing one-stage breast reconstruction. The purpose of this study is to predict the implant size based on the breast volume measured by mammography (MG) with a fully automated method, and by breast magnetic resonance imaging (MRI) with a semi-automated method, in breast cancer patients with direct-to-implant reconstruction. PATIENTS AND METHODS: This retrospective study included 84 patients with breast cancer who underwent direct-to-implant reconstruction after nipple-sparing or skin-sparing mastectomy and preoperative MG and MRI between April 2015 and April 2019. Breast volume was measured using (a) MG with a fully automated commercial software and (b) MRI with an in-house semi-automated software program. Multivariable regression analyses including breast volume and patient weight (P < 0.05 in univariable analysis) were conducted to predict implant size. RESULTS: MG and MRI breast volume was highly correlated with both implant size (correlation coefficient 0.862 and 0.867, respectively; P values < 0.001) and specimen weight (correlation coefficient 0.802 and 0.852, respectively; P values < 0.001). Mean absolute difference between the MR breast volume and implant size was 160 cc, which was significantly higher than that between the MG breast volume and implant size of 118 cc (P < 0.001). On multivariable analyses, only breast volume measured by both MG and MRI was significantly associated with implant size in any implant type (all P values < 0.001). CONCLUSION: Breast volume measured by MG and MRI can be used to predict appropriate implant size in breast cancer patients undergoing direct-to-implant reconstruction in an efficient and objective manner.

Arabidopsis HEAT SHOCK FACTOR BINDING PROTEIN is required to limit meiotic crossovers and HEI10 transcription.

The number of meiotic crossovers is tightly controlled and most depend on pro-crossover ZMM proteins, such as the E3 ligase HEI10. Despite the importance of HEI10 dosage for crossover formation, how HEI10 transcription is controlled remains unexplored. In a forward genetic screen using a fluorescent crossover reporter in Arabidopsis thaliana, we identify heat shock factor binding protein (HSBP) as a repressor of HEI10 transcription and crossover numbers. Using genome-wide crossover mapping and cytogenetics, we show that hsbp mutations or meiotic HSBP knockdowns increase ZMM-dependent crossovers toward the telomeres, mirroring the effects of HEI10 overexpression. Through RNA sequencing, DNA methylome, and chromatin immunoprecipitation analysis, we reveal that HSBP is required to repress HEI10 transcription by binding with heat shock factors (HSFs) at the HEI10 promoter and maintaining DNA methylation over the HEI10 5' untranslated region. Our findings provide insights into how the temperature response regulator HSBP restricts meiotic HEI10 transcription and crossover number by attenuating HSF activity.

Spatiotemporal neural network with attention mechanism for El Niño forecasts.

To learn spatiotemporal representations and anomaly predictions from geophysical data, we propose STANet, a spatiotemporal neural network with a trainable attention mechanism, and apply it to El Niño predictions for long-lead forecasts. The STANet makes two critical architectural improvements: it learns spatial features globally by expanding the network's receptive field and encodes long-term sequential features with visual attention using a stateful long-short term memory network. The STANet conducts multitask learning of Nino3.4 index prediction and calendar month classification for predicted indices. In a comparison of the proposed STANet performance with the state-of-the-art model, the accuracy of the 12-month forecast lead correlation coefficient was improved by 5.8% and 13% for Nino3.4 index prediction and corresponding temporal classification, respectively. Furthermore, the spatially attentive regions for the strong El Niño events displayed spatial relationships consistent with the revealed precursor for El Niño occurrence, indicating that the proposed STANet provides good understanding of the spatiotemporal behavior of global sea surface temperature and oceanic heat content for El Niño evolution.