The transcription factor BBX regulates phosphate homeostasis through the modulation of FGF23.

Fibroblast growth factor 23 (FGF23) plays an important role in phosphate homeostasis, and increased FGF23 levels result in hypophosphatemia; however, the molecular mechanism underlying increased FGF23 expression has not been fully elucidated. In this study, we found that mice lacking the bobby sox homolog (Bbx-/-) presented increased FGF23 expression and low phosphate levels in the serum and skeletal abnormalities such as a low bone mineral density (BMD) and bone volume (BV), as well as short and weak bones associated with low bone formation. Osteocyte-specific deletion of Bbx using Dmp-1-Cre resulted in similar skeletal abnormalities, elevated serum FGF23 levels, and reduced serum phosphate levels. In Bbx-/- mice, the expression of sodium phosphate cotransporter 2a (Npt2a) and Npt2c in the kidney and Npt2b in the small intestine, which are negatively regulated by FGF23, was downregulated, leading to phosphate excretion/wasting and malabsorption. An in vitro Fgf23 promoter analysis revealed that 1,25-dihydroxyvitamin D3 (1,25(OH)2D3)-induced transactivation of the Fgf23 promoter was significantly inhibited by BBX overexpression, whereas it was increased following Bbx knockdown. Interestingly, 1,25(OH)2D3 induced an interaction of the 1,25(OH)2D3 receptor (VDR) with BBX and downregulated BBX protein levels. Cycloheximide (CHX) only partially downregulated BBX protein levels, indicating that 1,25(OH)2D3 regulates BBX protein stability. Furthermore, the ubiquitination of BBX followed by proteasomal degradation was required for the increase in Fgf23 expression induced by 1,25(OH)2D3. Collectively, our data demonstrate that BBX negatively regulates Fgf23 expression, and consequently, the ubiquitin-dependent proteasomal degradation of BBX is required for FGF23 expression, thereby regulating phosphate homeostasis and bone development in mice.

Stacked-gait: A human gait recognition scheme based on stacked autoencoders.

Human gait recognition (HGR) is the mechanism of biometrics that authors extensively employ to recognize an individuals based on their walking traits. HGR has been prominent for the past few years due to its surveillance capability. In HGR, an individual's walking attributes are utilized for identification. HGR is considered a very effective technique for recognition but faces different problematic factors that degrade its performance. The major factors are variations in clothing, carrying, walking, etc. In this paper, a new hybrid method for the classification of HGR is designed called Stacked-Gait. The system is based on six major steps; initially, image resizing is performed to overcome computation problems. In the second step, these images are converted into grey-scale to extract better features. After that, the dataset division is performed into train and test set. In the next step, the training of the autoencoders and feature extraction of the dataset are performed using training data. In the next step, the stacking of two autoencoders is also performed. After that, the stacked encoders are employed to extract features from the test data. Finally, the feature vectors are given as input to various machine learning classifiers for final classification. The method assessment is performed using the CASIA-B dataset and achieved the accuracy of 99.90, 98.10, 97.20, 97.20, 96.70, and 100 percent on 000, 180, 360, 540, 720, and 900 angles. It is pragmatic that the system gives promising results compared to recent schemes.

Ank-mediated pyrophosphate regulates shear stress-induced small extracellular vesicle production in 3D-cultured osteocytes.

Osteocytes are located in the lacunae of fluid-filled bone and communicate with neighboring or distant cells by secreting small extracellular vesicles (sEVs) and growth factors as well as via dendrite-dendrite direct connections. However, the mechanism regulating sEV production in osteocytes is yet to be elucidated. In this study, we investigated sEV production and its underlying mechanism in osteocytes cultured on a three dimensional (3D) scaffold. We employed a perfusion system to apply shear stress stimulation to MLO-Y4 cells cultured on a 3D biphasic calcium phosphate (BCP) scaffold and analyzed sEV production and gene expression using RNA sequencing. We found that the expression of genes associated with sEV biogenesis and the secretory pathway were enhanced by fluid shear stress in MLO-Y4 cells cultured on a 3D BCP scaffold. In particular, fluid shear stress induced the expression of Ank, a pyrophosphate transporter, in 3D-cultured MLO-Y4 cells. The role of Ank in sEV production was further examined. Probenecid, an Ank inhibitor, significantly suppressed shear stress-induced sEV production, whereas Ank cDNA overexpression stimulated it. The inhibition of shear stress-induced sEV production by probenecid was recovered by the exogenous addition of pyrophosphate to MLO-Y4 cells. These findings suggest that shear stress-mediated sEV production in 3D-cultured osteocytes is regulated by extracellular pyrophosphate transported by Ank.

Beyond the mix: Advancements in simultaneous detection and quantification of human, dog, and cat DNA.

Animal-related crimes have increased with an increase in the number of pets worldwide, underscoring the importance of animal-related biological evidence at crime scenes. Evidence obtained in cases involving dogs and cats often includes a mixture of human and animal DNA. In this study, we developed a method using droplet digital polymerase chain reaction (ddPCR) to simultaneously identify and quantitatively detect human, dog, and cat DNA in mixed samples. HLA-DRA was chosen as a human-specific marker, OR6D7 as a dog-specific marker, and FLAI-K as a cat-specific marker. The species specificity of each target was confirmed using 14 control DNA samples from 11 mammals and 3 birds. Sensitivity tests determined the limit of detection to be 0.0008 ng/µL for human DNA and 0.00061 ng/µL for dog and cat DNA. In the mixture test, each DNA sample was independently and accurately detected in samples containing trace amounts of all three types of DNA. This study demonstrated the effectiveness of applying ddPCR to forensic case samples from dog- and cat-related incidents. We have presented a reliable method for the accurate identification and quantification of human, dog, and cat DNA simultaneously, offering possibilities for advancements in forensic DNA analysis and related fields.

An inherently interpretable deep learning model for local explanations using visual concepts.

Over the past decade, deep learning has become the leading approach for various computer vision tasks and decision support systems. However, the opaque nature of deep learning models raises significant concerns about their fairness, reliability, and the underlying inferences they make. Many existing methods attempt to approximate the relationship between low-level input features and outcomes. However, humans tend to understand and reason based on high-level concepts rather than low-level input features. To bridge this gap, several concept-based interpretable methods have been developed. Most of these methods compute the importance of each discovered concept for a specific class. However, they often fail to provide local explanations. Additionally, these approaches typically rely on labeled concepts or learn directly from datasets, leading to the extraction of irrelevant concepts. They also tend to overlook the potential of these concepts to interpret model predictions effectively. This research proposes a two-stream model called the Cross-Attentional Fast/Slow Thinking Network (CA-SoftNet) to address these issues. The model is inspired by dual-process theory and integrates two key components: a shallow convolutional neural network (sCNN) as System-I for rapid, implicit pattern recognition and a cross-attentional concept memory network as System-II for transparent, controllable, and logical reasoning. Our evaluation across diverse datasets demonstrates the model's competitive accuracy, achieving 85.6%, 83.7%, 93.6%, and 90.3% on CUB 200-2011, Stanford Cars, ISIC 2016, and ISIC 2017, respectively. This performance outperforms existing interpretable models and is comparable to non-interpretable counterparts. Furthermore, our novel concept extraction method facilitates identifying and selecting salient concepts. These concepts are then used to generate concept-based local explanations that align with human thinking. Additionally, the model's ability to share similar concepts across distinct classes, such as in fine-grained classification, enhances its scalability for large datasets. This feature also induces human-like cognition and reasoning within the proposed framework.

Effects of Rheum palmatum Root on In Vitro and In Vivo Methane Production and Rumen Fermentation Characteristics.

This study investigated the impact of Rheum palmatum root (RP) for reducing methane and its impact on rumen fermentation and blood metabolites in cattle. Rumen fluid was collected from three cannulated steers (736 ± 15 kg) and mixed with buffer (1:3 ratio) for the in vitro trial. Treatments were divided into control and RP supplement groups (1%, 3%, and 5% of substrates), with each sample incubated at 39 °C for 24 and 48 hours. Methane was measured after incubation, showing a dose-dependent linear decrease after 48 hours. Quadratic changes were observed in total volatile fatty acids, acetate, and butyrate. Additionally, in vitro dry matter digestibility decreased linearly with RP inclusion. In vivo trials involved four Korean steers in a 2 × 2 crossover design over 3 weeks, with treatments including a control group and a group with 3% RP addition. Dry matter intake (DMI) tended to decrease in the RP group compared to the control. Methane emissions (g/kg DMI) were not affected by RP addition. Blood metabolites indicated higher lipase concentrations in the RP group. In conclusion, RP reduced methane production in the in vitro trial but had no effect in the in vivo trial, likely due to adaptation of ruminal bacteria to RP.

Comparison of the accuracy of implant placement using a simple guide device and freehand surgery.

Background/purpose: In clinical settings, there may be a need for a guide device that is simple and enhances the positioning accuracy of prosthetics. This study aimed to compare the accuracy of implant positioning using two methods: implant placement with a simple guide device (SGD) and freehand surgery. Materials and methods: A total of 103 patients were randomly assigned to the control or study group. In the control group, implant placement was performed using the freehand technique. In the study group, implant placement was conducted with an SGD. Implant positioning accuracy was assessed by measuring how much the central position, fixture angulation, and fixture position differed from the ideal implant position based on periapical radiographs and cone-beam computed tomography images. In patients with double implants, parallelism between the two fixtures was also measured. Results: There were 124 subjects, with 84 having single implants (42 in the control group and 42 in the study group) and 40 having double implants (20 in the control group and 20 in the study group). Utilization of the SGD for both single and double implant placement improved the accuracy of the central position, fixture angulation, and fixture position (P < 0.05). Additionally, in double implantation cases, it significantly enhanced parallelism between the two fixtures (P < 0.001). Conclusion: These findings suggest that use of an SGD leads to more accurate implant placement compared with freehand surgery taking into account the final prosthetic restoration.

Mesenchymal Stem Cell Therapy in Alopecia Areata: Visual and Molecular Evidence from a Mouse Model.

Recent studies have highlighted the potential of Mesenchymal Stem Cells (MSCs) as an alternative treatment for Alopecia Areata (AA) due to their immunosuppressive properties. While MSCs have shown promise in cell experiments, their effectiveness in vivo remains uncertain. This study aims to validate local administration of MSC therapy's efficacy in AA treatment through animal experiments. AA was induced through Interferon-gamma (IFN-γ) administration in mice, and MSC treatment (MSCT)'s effects were assessed visually and through tissue analysis. The MSC-treated group showed more hair regrowth compared to the control (CTL) group. MSCT notably reduced local inflammatory cytokines (JAK1, JAK2, STAT1, STAT3, IFN-γR, IL-1ß, IL-16, IL-17α, and IL-18) in AA-induced mice's skin, but systemic cytokine levels remained unchanged. Furthermore, MSC treatment normalized the expression of Wnt/ß-catenin signaling pathway genes (LEF1 and ß-catenin) and growth factors (FGF7 and FGF2), which are crucial for hair cycle regulation. This study lays the groundwork for further exploring MSCs as a potential treatment for AA, but more research is needed to fully understand their therapeutic potential.

A winged-helix DNA-binding protein is essential for self-fertility during sexual development of the homothallic fungus Fusarium graminearum.

Sexual reproduction is crucial for increasing the genetic diversity of populations and providing overwintering structures, such as perithecia and associated tissue, in the destructive plant pathogenic fungus Fusarium graminearum. While mating-type genes serve as master regulators in fungal sexual reproduction, the molecular mechanisms underlying this process remain elusive. Winged-helix DNA-binding proteins are key regulators of embryogenesis and cell differentiation in higher eukaryotes. These proteins are implicated in the morphogenesis and development of several fungal species. However, their involvement in sexual reproduction remains largely unexplored in F. graminearum. Here, we investigated the function of winged-helix DNA-binding proteins in vegetative growth, conidiation, and sexual reproduction, with a specific focus on the FgWING27, which is highly conserved among Fusarium species. Deletion of FgWING27 resulted in an abnormal pattern characterized by a gradual increase in the expression of mating-type genes during sexual development, indicating its crucial role in the stage-specific genetic regulation of MAT genes in the late stages of sexual development. Furthermore, using chromatin immunoprecipitation followed by sequencing analysis, we identified Fg17056 as a downstream gene of Fgwing27, which is essential for sexual reproduction. These findings underscore the significance of winged-helix DNA-binding proteins in fungal development and reproduction in F. graminearum, and highlight the pivotal role of Fgwing27 as a core genetic factor in the intricate genetic regulatory network governing sexual reproduction.IMPORTANCEFusarium graminearum is a devastating plant pathogenic fungus causing significant economic losses due to reduced crop yields. In Fusarium Head Blight epidemics, spores produced through sexual and asexual reproduction serve as inoculum, making it essential to understand the fungal reproduction process. Here, we focus on winged-helix DNA-binding proteins, which have been reported to play crucial roles in cell cycle regulation and differentiation, and address their requirement in the sexual reproduction of F. graminearum. Furthermore, we identified a highly conserved protein in Fusarium as a key factor in self-fertility, along with the discovery of its direct downstream genes. This provides crucial information for constructing the complex genetic regulatory network of sexual reproduction and significantly contribute to further research on sexual reproduction in Fusarium species.

Correlates and Health Issues among Older Korean Immigrants Living Alone in the United States: A Scoping Review.

Older Korean immigrants are one of the most understudied and marginalized Asian ethnic groups in the United States, despite their rapid population growth. Many older Korean immigrants encounter distinct challenges in assimilating into their new country as first-generation immigrants, including cultural conflict, language barriers, low economic status, and a lack of social support. These issues may be compounded for those who live alone, which is considered a negative factor in their mental and physical health. However, little is known about the correlates and health issues of older Korean immigrants living alone. This study's objective was to explore correlates and health issues among older Korean immigrants living alone. Based on established scoping review methodology five databases, CINAHL, PubMed, MEDLINE, SocINDEX, and Health Source Nursing/Academic Edition, were used to find relevant studies. Twelve articles were reviewed, and four major themes were identified as correlates and health issues among older Korean immigrants living alone in the United States: depression, changed family relationships, social interactions, and factors on general health and well-being. The findings have significant implications for healthcare professionals for understanding the unique culture, situation, and physical and psychosocial vulnerability of older Korean immigrants living alone.

S100P binds to RAGE and activates ERK/NF-κB signaling to promote osteoclast differentiation and activity.

S100 calcium-binding protein P (S100P) is a secretory protein that is expressed in various healthy tissues and tumors. Megakaryocyte-secreted S100P promotes osteoclast differentiation and function; however, its receptor and cellular signaling in osteoclasts remain unclear. Receptor for advanced glycation end products (RAGE), which is the receptor for S100P on cancer cells, was expressed in osteoclast precursors, and S100P-RAGE binding was confirmed through co-immunoprecipitation. Additionally, the phosphorylation of ERK and NF-κB was increased in S100P-stimulated osteoclast precursors but was inhibited by addition of the RAGE antagonistic peptide (RAP). S100P-induced osteoclast differentiation and excessive bone resorption activity were also reduced by the addition of RAP. This study demonstrates that S100P, upon binding with RAGE, activates the ERK and NF-κB signaling pathways in osteoclasts, leading to increased cell differentiation and bone resorption activity.

The plasma membrane inner leaflet PI(4,5)P2 is essential for the activation of proton-activated chloride channels.

Proton-activated chloride (PAC) channels, ubiquitously expressed in tissues, regulate intracellular Cl- levels and cell death following acidosis. However, molecular mechanisms and signaling pathways involved in PAC channel modulation are largely unknown. Herein, we determine that phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2] of the plasma membrane inner leaflet is essential for the proton activation of PAC channels. PI(4,5)P2 depletion by activating phosphatidylinositol 5-phosphatases or Gq protein-coupled muscarinic receptors substantially inhibits human PAC currents. In excised inside-out patches, PI(4,5)P2 application to the cytoplasmic side increases the currents. Structural simulation reveals that the putative PI(4,5)P2-binding site is localized within the cytosol in resting state but shifts to the cell membrane's inner surface in an activated state and interacts with inner leaflet PI(4,5)P2. Alanine neutralization of basic residues near the membrane-cytosol interface of the transmembrane helice 2 significantly attenuates PAC currents. Overall, our study uncovers a modulatory mechanism of PAC channel through inner membrane PI(4,5)P2.

Causal association between serum bilirubin and ischemic stroke: multivariable Mendelian randomization.

Objectives: Previous research has predominantly focused on total bilirubin levels without clearly distinguishing between direct and indirect bilirubin. In this study, the differences between these forms were examined, and their potential causal relationships with ischemic stroke were investigated. Methods: Two-sample multivariable Mendelian randomization (MVMR) analysis was employed, extracting summary data on bilirubin from the Korean Cancer Prevention Study-II (KCPS-II; n=159,844) and the Korean Genome and Epidemiology Study (KoGES; n=72,299). Data on ischemic stroke were obtained from BioBank Japan (BBJ; n=201,800). Colocalization analysis was performed, focusing on the UGT1A1, SLCO1B1, and SLCO1B3 genes, which are the primary loci associated with serum bilirubin levels. Results: Crude 2-sample Mendelian randomization analysis revealed a significant negative association between total bilirubin levels and ischemic stroke. However, in MVMR analyses, only indirect bilirubin demonstrated a significant negative association with ischemic stroke (odds ratio, 0.76; 95% confidence interval, 0.59 to 0.98). Colocalization analysis did not identify a shared causal variant between the 3 genetic loci related to indirect bilirubin and the risk of ischemic stroke. Conclusion: Our study establishes a causal association between higher genetically determined levels of serum indirect bilirubin and reduced risk of ischemic stroke in an Asian population. Future research should include more in-depth analysis of shared genetic variants between indirect bilirubin and ischemic stroke.

Effects of Endocrine-Disrupting Chemicals on Bone Health.

This comprehensive review critically examines the detrimental impacts of endocrine-disrupting chemicals (EDCs) on bone health, with a specific focus on substances such as bisphenol A (BPA), per- and polyfluoroalkyl substances (PFASs), phthalates, and dioxins. These EDCs, by interfering with the endocrine system's normal functioning, pose a significant risk to bone metabolism, potentially leading to a heightened susceptibility to bone-related disorders and diseases. Notably, BPA has been shown to inhibit the differentiation of osteoblasts and promote the apoptosis of osteoblasts, which results in altered bone turnover status. PFASs, known for their environmental persistence and ability to bioaccumulate in the human body, have been linked to an increased osteoporosis risk. Similarly, phthalates, which are widely used in the production of plastics, have been associated with adverse bone health outcomes, showing an inverse relationship between phthalate exposure and bone mineral density. Dioxins present a more complex picture, with research findings suggesting both potential benefits and adverse effects on bone structure and density, depending on factors such as the timing and level of exposure. This review underscores the urgent need for further research to better understand the specific pathways through which EDCs affect bone health and to develop targeted strategies for mitigating their potentially harmful impacts.

Effect of apigetrin in pseudo-SARS-CoV-2-induced inflammatory and pulmonary fibrosis in vitro model.

SARS-CoV-2 has become a global public health problem. Acute respiratory distress syndrome (ARDS) is the leading cause of death due to the SARS-CoV-2 infection. Pulmonary fibrosis (PF) is a severe and frequently reported COVID-19 sequela. In this study, an in vitro model of ARDS and PF caused by SARS-CoV-2 was established in MH-S, THP-1, and MRC-5 cells using pseudo-SARS-CoV-2 (PSCV). Expression of proinflammatory cytokines (IL-6, IL-1ß, and TNF-α) and HIF-1α was increased in PSCV-infected MH-S and THP-1 cells, ARDS model, consistent with other profiling data in SARS-CoV-2-infected patients have been reported. Hypoxia-inducible factor-1 alpha (HIF-1α) siRNA and cobalt chloride were tested using this in vitro model. HIF-1α knockdown reduces inflammation caused by PSCV infection in MH-S and THP-1 cells and lowers elevated levels of CTGF, COLA1, and α-SMA in MRC-5 cells exposed to CPMSCV. Furthermore, apigetrin, a glycoside bioactive dietary flavonoid derived from several plants, including Crataegus pinnatifida, which is reported to be a HIF-1α inhibitor, was tested in this in vitro model. Apigetrin significantly reduced the increased inflammatory cytokine (IL-6, IL-1ß, and TNF-α) expression and secretion by PSCV in MH-S and THP-1 cells. Apigetrin inhibited the binding of the SARS-CoV-2 spike protein RBD to the ACE2 protein. An in vitro model of PF induced by SARS-CoV-2 was produced using a conditioned medium of THP-1 and MH-S cells that were PSCV-infected (CMPSCV) into MRC-5 cells. In a PF model, CMPSCV treatment of THP-1 and MH-S cells increased cell growth, migration, and collagen synthesis in MRC-5 cells. In contrast, apigetrin suppressed the increase in cell growth, migration, and collagen synthesis induced by CMPSCV in THP-1 and MH-S MRC-5 cells. Also, compared to control, fibrosis-related proteins (CTGF, COLA1, α-SMA, and HIF-1α) levels were over two-fold higher in CMPSV-treated MRC-5 cells. Apigetrin decreased protein levels in CMPSCV-treated MRC-5 cells. Thus, our data suggest that hypoxia-inducible factor-1 alpha (HIF-1α) might be a novel target for SARS-CoV-2 sequela therapies and apigetrin, representative of HIF-1alpha inhibitor, exerts anti-inflammatory and PF effects in PSCV-treated MH-S, THP-1, and CMPVSC-treated MRC-5 cells. These findings indicate that HIF-1α inhibition and apigetrin would have a potential value in controlling SARS-CoV-2-related diseases.

Bone Loss after Solid Organ Transplantation: A Review of Organ-Specific Considerations.

This review article investigates solid organ transplantation-induced osteoporosis, a critical yet often overlooked issue, emphasizing its significance in post-transplant care. The initial sections provide a comprehensive understanding of the prevalence and multifactorial pathogenesis of transplantation osteoporosis, including factors such as deteriorating post-transplantation health, hormonal changes, and the impact of immunosuppressive medications. Furthermore, the review is dedicated to organ-specific considerations in transplantation osteoporosis, with separate analyses for kidney, liver, heart, and lung transplantations. Each section elucidates the unique challenges and management strategies pertinent to transplantation osteoporosis in relation to each organ type, highlighting the necessity of an organ-specific approach to fully understand the diverse manifestations and implications of transplantation osteoporosis. This review underscores the importance of this topic in transplant medicine, aiming to enhance awareness and knowledge among clinicians and researchers. By comprehensively examining transplantation osteoporosis, this study contributes to the development of improved management and care strategies, ultimately leading to improved patient outcomes in this vulnerable group. This detailed review serves as an essential resource for those involved in the complex multidisciplinary care of transplant recipients.

PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection.

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

Unique cartilage matrix-associated protein inhibits osteoclast differentiation by alleviating RANKL-induced reactive oxygen species.

Unique cartilage matrix-associated protein (UCMA) is a γ-carboxyglutamic acid-rich secretory protein primarily expressed in adult cartilage. UCMA promotes osteoblast differentiation and reduces high glucose-induced reactive oxygen species (ROS) production in osteoblasts; however, its role in osteoclasts remains unclear. Since Ucma is not expressed in osteoclasts, treatment with recombinant UCMA protein (rUCMA) was employed to investigate the effect of UCMA on osteoclasts. The rUCMA-treated osteoclasts exhibited significantly reduced osteoclast differentiation, resorption activity, and osteoclast-specific gene expression. Moreover, rUCMA treatment reduced RANKL-induced ROS production and increased the expression of antioxidant genes in osteoclasts. This study demonstrates that UCMA effectively inhibits RANKL-stimulated osteoclast differentiation and oxidative stress.

Synthetic ShK-like Peptide from the Jellyfish Nemopilema nomurai Has Human Voltage-Gated Potassium-Channel-Blocking Activity.

We identified a new human voltage-gated potassium channel blocker, NnK-1, in the jellyfish Nemopilema nomurai based on its genomic information. The gene sequence encoding NnK-1 contains 5408 base pairs, with five introns and six exons. The coding sequence of the NnK-1 precursor is 894 nucleotides long and encodes 297 amino acids containing five presumptive ShK-like peptides. An electrophysiological assay demonstrated that the fifth peptide, NnK-1, which was chemically synthesized, is an effective blocker of hKv1.3, hKv1.4, and hKv1.5. Multiple-sequence alignment with cnidarian Shk-like peptides, which have Kv1.3-blocking activity, revealed that three residues (3Asp, 25Lys, and 34Thr) of NnK-1, together with six cysteine residues, were conserved. Therefore, we hypothesized that these three residues are crucial for the binding of the toxin to voltage-gated potassium channels. This notion was confirmed by an electrophysiological assay with a synthetic peptide (NnK-1 mu) where these three peptides were substituted with 3Glu, 25Arg, and 34Met. In conclusion, we successfully identified and characterized a new voltage-gated potassium channel blocker in jellyfish that interacts with three different voltage-gated potassium channels. A peptide that interacts with multiple voltage-gated potassium channels has many therapeutic applications in various physiological and pathophysiological contexts.

Fuzzy ensemble of fined tuned BERT models for domain-specific sentiment analysis of software engineering dataset.

Software engineers post their opinions about various topics on social media that can be collectively mined using Sentiment Analysis. Analyzing this opinion is useful because it can provide insight into developers' feedback about various tools and topics. General-purpose sentiment analysis tools do not work well in the software domain because most of these tools are trained on movies and review datasets. Therefore, efforts are underway to develop domain-specific sentiment analysis tools for the Software Engineering (SE) domain. However, existing domain-specific tools for SE struggle to compute negative and neutral sentiments and can not be used on all SE datasets. This work uses a hybrid technique based on deep learning and a fine-tuned BERT model, i.e., Bert-Base, Bert-Large, Bert-LSTM, Bert-GRU, and Bert-CNN presented that is adapted as a domain-specific sentiment analysis tool for Community Question Answering datasets (named as Fuzzy Ensemble). Five different variants of fine-tuned BERT on the SE dataset are developed, and an ensemble of these fine-tuned models is taken using fuzzy logic. The trained model is evaluated on four publicly available benchmark datasets, i.e., Stack Overflow, JavaLib, Jira, and Code Review, using various evaluation metrics. The fuzzy Ensemble model is also compared with the state-of-the-art sentiment analysis tools for the software engineering domain, i.e., SentiStrength-SE, Senti4SD, SentiCR, and Generative Pre-Training Transformer (GPT). GPT mode is fine-tuned by the authors for domain-specific sentiment analysis. The Fuzzy Ensemble model covers the limitation of existing tools and improve accuracy to predict neutral sentiments even on diverse dataset. The fuzzy Ensemble model performs superior to state-of-the-art tools by achieving a maximum F1-score of 0.883.