Search Results (26)

Tanshinones are a class of lipophilic diterpenoid quinones extracted from Salvia miltiorrhiza (Dan shen), a widely used herb in traditional Chinese medicine. These compounds, particularly tanshinone IIA (T-IIA) and sodium tanshinone sulfonate (STS), have been acknowledged for their broad spectrum of biological activities, including anti-inflammatory, antioxidant, anti-tumor, antiresorptive, and antimicrobial effects. Recent studies have highlighted the potential of tanshinones in the treatment of osteolytic diseases, characterized by excessive bone resorption, such as osteoporosis, rheumatoid arthritis, and periodontitis. The therapeutic effects of tanshinones in these diseases are primarily attributed to their ability to inhibit osteoclast differentiation and activity, suppress inflammatory cytokine production (e.g., tumor necrosis factor alpha (TNF-α), interleukin (IL)-1β, and IL-6), and modulate critical signaling pathways, including NF-kB, MAPK, PI3K/Akt, and the RANKL/RANK/OPG axis. Additionally, tanshinones promote osteoblast differentiation and mineralization by enhancing the expression of osteogenic markers such as Runx2, ALP, and OCN. Preclinical models have demonstrated that T-IIA and STS can significantly reduce bone destruction and inflammatory cell infiltration in arthritic joints and periodontal tissues while also enhancing bone microarchitecture in osteoporotic conditions. This review aims to provide a comprehensive overview of the pharmacological actions of tanshinones in osteolytic diseases, summarizing current experimental findings, elucidating underlying molecular mechanisms, and discussing the challenges and future directions for their clinical application as novel therapeutic agents in bone-related disorders, especially periodontitis. Despite promising in vitro and in vivo findings, clinical evidence remains limited, and further investigations are necessary to validate the efficacy, safety, and pharmacokinetics of tanshinones in human populations. Full article

The increase in biogas production has caused a simultaneous increase in the production of digestate, which is a valuable carrier of nutrients in crop plant production. Digestate-derived nitrogen ensures the optimal nutritional status of winter oilseed plants at critical stages of yield formation. This hypothesis was verified in field experiments with winter oilseed rape (WOSR) conducted in the 2015/2016, 2016/2017, and 2017/2018 growing seasons. The experiment consisted of three nitrogen fertilization systems (FSs)—mineral ammonium nitrate (AN) (AN-FS), digestate-based (D-FS), and ²/₃ digestate + ¹/₃ AN (DAN-FS)—and five N_f doses: 0, 80, 120, 160, and 240 kg N ha⁻¹. Plants fertilized with digestate had higher yields than those fertilized with AN. The highest seed yield (SY) was recorded in the DAN-FS, which was 0.56 t ha⁻¹ higher than that in the M-FS. The nitrogen fertilizer replacement value (NFRV), averaged over N doses, was 104% for the D-FS and reached 111% for the mixed DAN-FS system. The N content in WOSR leaves, which was within the range of 41–48 g kg⁻¹ DM at the rosette stage and within 34–44 g kg⁻¹ DM at the beginning of flowering, ensured optimal plant growth and seed yield. In WOSR plants fertilized with digestate, the nitrogen (N) content was significantly lower compared to that in plants fertilized with AN, but this difference did not have a negative impact on the seed yield (SY). The observed positive effect of the digestate on plant growth in the pre-flowering period of WOSR growth and on SY resulted from the impact of Mg, which effectively controlled Ca, especially in the third growing season (which was dry). Mg had a significant effect on the biomass of rosettes and on SY, but only when its content in leaves exceeded 2.0 g kg⁻¹ DM. It is necessary to emphasize the specific role of the digestate, which significantly reduced the Ca content in the indicator WOSR organs. Increased Ca content during the vegetative period of WOSR growth reduced leaf N and Zn contents, which ultimately led to a decrease in SY. Therefore, the rosette phase of WOSR growth should be considered a reliable diagnostic phase for both the correction of plants’ nutritional status and the prediction of SY. It can be concluded that the fertilization value of digestate-derived N was the same as that of ammonium nitrate. This means that the mineral fertilizer can be replaced by digestate in WOSR production. Full article

This study deals with the extraction of active compounds for a formula (Angelica gigas, Cornus officinalis, Ganoderma lucidum, Thymus vulgaris, and Asparagus cochinchinensis) and the evaluation of its skin anti-aging properties. This formulation was inspired by the oriental medicine Gongjin-dan (Angelica gigas, Cornus officinalis, deer antler, and musk), which has been used as a restorative drug for longevity. Enzyme-based extraction and chemical purification were used to obtain a mixed fraction (GEF) enriched in glycopeptides and glycoproteins from the five herbal materials. The chemical characteristics of GEF, including the carbohydrate groups attached to the peptides and proteins, the total carbohydrate and protein contents, and the composition of monosaccharides and amino acids were determined. The chemical characteristics that were significantly different from those of the extract, generally prepared in the same ratio, were the abundance of glycopeptides and glycoproteins and the high proportions of conditionally essential amino acids (51.0%) and acidic/basic amino acids (67.7%). These are necessary components for strengthening the skin layers against aging. The in vitro skin anti-aging properties of GEF on human fibroblasts (HS68), keratinocytes (HaCaT), and adipose-derived mesenchymal stem cells (ADMSCs) were evaluated. It was found that MMP-1 gene expression was inhibited (18–28%) and fibrillin-1 protein (23–37%) was restored contrary to the effect of UV irradiation. COL1A1 and COL4A1 gene expression (25–35%), HAS2 gene expression (22–213%), and adipogenesis (15%) were facilitated. These results demonstrate the potential of GEF as a raw material for skin anti-aging and reinforce the scientific evidence supporting a traditional medicine with a long history. Full article

Functional differences in key brain networks, including the dorsal attention network (DAN), control network (CN), and default mode network (DMN), have been identified in individuals with obsessive–compulsive disorder (OCD). However, the precise nature of these differences remains unclear. In this study, we further explored these differences and validated previous findings using a novel edge functional connectivity (eFC) approach, which enables a more refined analysis of brain network interaction. By employing this advanced method, we sought to gain deeper insights into FC alterations that may underlie the pathology of OCD. We collected data during movie watching from 44 patients with OCD and 33 healthy controls (HCs). The two-sample t test was used to assess differences in entropy between the DAN, CN, and DMN between groups. The analysis was performed with control for potentially confounding variables to ensure the robustness of the findings. Significant differences in network entropy were found between the OCD and HC groups. Relative to HCs, patients with OCD showed significantly reduced entropy in the DAN and increased entropy in the CN and DMN. The decreased entropy in the DAN and increased entropy in the CN and DMN observed in this study may be related to the core symptoms of OCD, such as attention deficit, impaired cognitive control, and self-referential thinking. These results provide valuable insights into the neurobiological mechanisms of OCD and highlight the potential of network entropy as a biomarker for the disorder. Future research should further explore the relationship between these network changes and the severity of OCD symptoms, as well as assess their implications for the development of treatment strategies. Full article

Background: Vitamin K is a cofactor necessary for the biological activity of proteins like Matrix Gla Protein (MGP), which reduce calcification and help preserve lung function. This study aims to determine, first, whether low vitamin K status is associated with chronic obstructive pulmonary disease (COPD), and secondary, whether the level of vitamin K is associated with COPD severity, smoking exposure, or mortality. Methods: The plasma concentration of dephosphorylated uncarboxylated (dp-uc) MGP was used as an inverse biomarker for vitamin K in 98 COPD patients from the CODEX-P COPD study and 986 controls from the DanFunD study. Low vitamin K status was defined as the upper quartile of dp-ucMGP (>589 pmol/L). Using a logistic regression model, we examined whether low vs. high/moderate vitamin K status increased the odds ratio (OR) of having COPD. Secondary analyses, in the COPD cohort only, examined the association between low vitamin K status and COPD severity, smoking exposure in packyears and all-cause mortality, using a Welch’s t-test and log-rank test, respectively. Results: Low vitamin K status was associated with increased odds of having COPD, OR 9.7 (95% CI [5.5 to 17.5], p < 0.001). We found no associations between low vitamin K and COPD severity (est. −0.03, p = 0.7; 95% CI [−0.2 to 0.1]), smoking exposure (p = 0.7), or all-cause mortality (p = 0.5). Conclusions: Low vitamin K status was associated with substantially higher odds of having COPD compared to high/moderate vitamin K status. No association was found between low vitamin K status and COPD severity, smoking exposure, or all-cause mortality. Further studies are needed to determine if vitamin K plays a role in the pathophysiology of COPD and whether supplement therapy is indicated. Full article

Background: Diabetic autonomic neuropathy (DAN) is a severe complication of diabetes that affects the autonomic nervous system, impacting cardiovascular, gastrointestinal, genitourinary, and other systems. This study examines the levels of three potential biomarkers—DEFA1, progranulin, and NRG4—to assess their diagnostic and prognostic value in DAN patients. Methods: This observational, single-center study included 80 patients with type 2 diabetes. Clinical data and laboratory results were collected, and serum levels of DEFA1, progranulin, and NRG4 were measured using ELISA. The presence of DAN was assessed using Ewing’s tests. Statistical analyses included t-tests, Pearson’s correlations, and ROC analysis to explore associations and the predictive values of the biomarkers. Results: Progranulin levels were significantly elevated in patients with DAN compared to those without (p < 0.05), showing a positive correlation with diabetes duration (r = 0.375; p = 0.01) and a significant predictive value for DAN (AUC = 0.666; p = 0.013). DEFA1 and NRG4 levels did not differ significantly between the groups. Progranulin was also higher in patients who were treated with sulfonylureas and GLP-1 receptor agonists and in those with coronary artery disease. Conclusions: Progranulin emerges as a potential biomarker for the presence and severity of DAN, correlating with disease duration and autonomic dysfunction. While DEFA1 and NRG4 showed no significant association, the findings underscore the importance of further exploring the inflammatory pathways in DAN. Progranulin measurement could enhance early diagnosis and personalized management of autonomic neuropathy in diabetes. Full article

For rapid deployments of various IoT application systems, we have developed Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) as an integrated server platform. It is equipped with rich functions for collecting, analyzing, and visualizing various data. Unfortunately, the proper configuration of SEMAR with a variety of IoT devices can be complex and challenging for novice users, since it often requires technical expertise. The assistance of Generative AI can be helpful to solve this drawback. In this paper, we present an implementation of a sensor input setup assistance service for SEMAR using prompt engineering techniques and Generative AI. A user needs to define the requirement specifications and environments of the IoT application system for sensor inputs, and give them to the service. Then, the service provides step-by-step guidance on sensor connections, communicating board configurations, network connections, and communication protocols to the user, which can help the user easily set up the configuration to connect the relevant devices to SEMAR. For evaluations, we applied the proposal to the input sensor setup processes of three practical IoT application systems with SEMAR, namely, a smart light, water heater, and room temperature monitoring system. In addition, we applied it to the setup process of an IoT application system for a course for undergraduate students at the Insitut Bisnis dan Teknologi (INSTIKI), Indonesia. The results demonstrate the effectiveness of the proposed service for SEMAR. Full article

This study investigated the psycho-physiological impact of a black belt examination. Older brown-belt judoka (Tori, F = 2, M = 4; age = 75.6 ± 4.5 yrs) and their 2nd–5th Dan black-belt coaches (Uke; M = 6; age = 36.5 ± 10.8 yr) were evaluated during a simulated and official examination and a resting day. Participants’ trait anxiety (STAI-Y2) was recorded prior to the study. State anxiety (STAI-Y1), ratings of perceived exertion (RPE), enjoyment (ENJ), and fear of falling (FoF) were collected 15 min before and after the experimental conditions. Saliva samplings at awakening (T0), PRE (T1), and POST (T2) exercise and during the recovery (15 min-T3, 30 min-T4, 60 min-T5) were collected for cortisol (sC), testosterone (sT), and alpha-amylase (sAA). Participants showed normal age-reference population trait anxiety. A difference (p ≤ 0.05) for role emerged for ENJ and sT only. For STAI-Y1, higher PRE values with respect to POST ones emerged (p = 0.005), and the highest values (p = 0.007) for PRE of the examination were with respect to the simulation. For sAA, differences for sampling were found in the examination conditions only, with peak values at T2 (370.3 ± 78.6 U/mL, p = 0.001). For sC, a significant peak value (0.51 ± 0.09 μg/dL, p = 0.012) emerged at T2 in the examination condition. With respect to Tori, Uke showed higher mean sT values in all conditions (p ≤ 0.05) and the highest T2 during examination (712.5 ± 57.2 pg/mL). Findings suggest the relevance of monitoring psycho-physiological stress-related responses in judo for optimizing both coaching effectiveness and sport performance, especially in older judo practitioners. Full article

The prompt acquisition of precise land cover categorization data is indispensable for the strategic development of contemporary farming practices, especially within the realm of forestry oversight and preservation. Forests are complex ecosystems that require precise monitoring to assess their health, biodiversity, and response to environmental changes. The existing methods for classifying remotely sensed imagery often encounter challenges due to the intricate spacing of feature classes, intraclass diversity, and interclass similarity, which can lead to weak perceptual ability, insufficient feature expression, and a lack of distinction when classifying forested areas at various scales. In this study, we introduce the DASR-Net algorithm, which integrates a dual attention network (DAN) in parallel with the Residual Network (ResNet) to enhance land cover classification, specifically focusing on improving the classification of forested regions. The dual attention mechanism within DASR-Net is designed to address the complexities inherent in forested landscapes by effectively capturing multiscale semantic information. This is achieved through multiscale null attention, which allows for the detailed examination of forest structures across different scales, and channel attention, which assigns weights to each channel to enhance feature expression using an improved BSE-ResNet bilinear approach. The two-channel parallel architecture of DASR-Net is particularly adept at resolving structural differences within forested areas, thereby avoiding information loss and the excessive fusion of features that can occur with traditional methods. This results in a more discriminative classification of remote sensing imagery, which is essential for accurate forest monitoring and management. To assess the efficacy of DASR-Net, we carried out tests with 10m Sentinel-2 multispectral remote sensing images over the Heshan District, which is renowned for its varied forestry. The findings reveal that the DASR-Net algorithm attains an accuracy rate of 96.36%, outperforming classical neural network models and the transformer (ViT) model. This demonstrates the scientific robustness and promise of the DASR-Net model in assisting with automatic object recognition for precise forest classification. Furthermore, we emphasize the relevance of our proposed model to hyperspectral datasets, which are frequently utilized in agricultural and forest classification tasks. DASR-Net’s enhanced feature extraction and classification capabilities are particularly advantageous for hyperspectral data, where the rich spectral information can be effectively harnessed to differentiate between various forest types and conditions. By doing so, DASR-Net contributes to advancing remote sensing applications in forest monitoring, supporting sustainable forestry practices and environmental conservation efforts. The findings of this study have significant practical implications for urban forestry management. The DASR-Net algorithm can enhance the accuracy of forest cover classification, aiding urban planners in better understanding and monitoring the status of urban forests. This, in turn, facilitates the development of effective forest conservation and restoration strategies, promoting the sustainable development of the urban ecological environment. Full article

Scientific knowledge evolves in small steps, with occasional backsteps to correct inaccuracies, all occurring within a competitive environment. This perspective for the first time looks at the history of latency-related RNA (LR-RNA) that was later renamed latency-associated transcript (LAT). At the 1986 International Herpesvirus Workshop (IHW) meeting in Leeds, England, Daniel L Rock and Anthony B Nesburn first reported the discovery of human herpes virus 1 (HSV-1) latency-related (LR) RNA that is antisense to ICP0. Less than a month after the IHW meeting, a paper was submitted to Science magazine and 8 months later appeared in print thanking “D. Rock for suggesting RNA complementary to the ICP0 message may be present in latently infected cells”. This perspective is not a review of the LAT literature but intends to clarify the timeline of LAT discovery and subsequent breakthroughs such as reactivation, apoptosis, CD8⁺ T cell exhaustion, and LAT expression in different cell types detected during latency. While many review articles have been written about LAT since 1987, the most comprehensive and balanced review about LAT was written by Dr. David Bloom’s group. In this overview, I will discuss our original collaboration with Dr. Dan Rock and subsequent work that our group performed, which is still ongoing. Finally, I will discuss the controversies associated with LAT from its inception to current times. Full article

Background: Major Depressive Disorder (MDD) is a significant challenge in modern medicine due to its unclear underlying causes. Brain network dysfunction is believed to play a key role in its pathophysiology. Resting-state functional MRI (rs-fMRI), a neuroimaging technique, enables the in vivo assessment of functional connectivity (FC) between brain regions, offering insights into these network dysfunctions. The aim of this study was to evaluate abnormalities in FC within major brain networks in patients with drug-resistant MDD. Methods: The study group consisted of 26 patients with drug-resistant MDD and an age-matched control group (CG) of 26 healthy subjects. The rs-fMRI studies were performed on a 3T MR scanner (Philips, Ingenia) using a 32-channel head and neck coil. Imaging data were statistically analyzed, focusing on the intra- and inter-network FC of the following networks: default mode (DMN), sensorimotor (SMN), visual (VN), salience (SN), cerebellar (CN), dorsal attention (DAN), language (LN), and frontoparietal (FPN). Results: In patients with MDD, the intra-network analysis showed significantly decreased FC between nodes within VN compared to CG. In contrast, the inter-network analysis showed significantly increased FC between nodes from VN and SN or VN and DAN compared to CG. Decreased FC was found between SN and CN or SN and FPN as well as VN and DAN nodes compared to CG. Conclusions: Patients with MDD showed significant abnormalities in resting-state cortical activity, mainly regarding inter-network functional connectivity. These results contribute to the knowledge on the pathomechanism of MDD and may also be useful for developing new treatments. Full article

Object detection is becoming increasingly critical in autonomous driving. However, the accuracy and effectiveness of object detectors are often constrained by the obscuration of object features and details in adverse weather conditions. Therefore, this paper presented the DAN-YOLO vehicle object detector specifically designed for driving conditions in adverse weather. Building on the YOLOv7-Tiny network, SPP was replaced with SPPF, resulting in the SPPFCSPC structure, which enhances processing speed. The concept of Hybrid Dilated Convolution (HDC) was also introduced to improve the SPPFCSPC and ELAN-T structures, expanding the network’s receptive field (RF) while maintaining a lightweight design. Furthermore, an efficient multi-scale attention (EMA) mechanism was introduced to enhance the effectiveness of feature fusion. Finally, the Wise-IoUv1 loss function was employed as a replacement for CIoU to enhance the localization accuracy of the bounding box (bbox) and the convergence speed of the model. With an input size of 640 × 640, the DAN-YOLO algorithm proposed in this study achieved an increase in mAP0.5 values of 3.4% and 6.3% compared to the YOLOv7-Tiny algorithm in the BDD100K and DAWN benchmark tests, respectively, while achieving real-time detection (142.86 FPS). When compared with other state-of-the-art detectors, it reports better trade-off in terms of detection accuracy and speed under adverse driving conditions, indicating the suitability for autonomous driving applications. Full article

Ameloblastoma (AM), periapical cyst (PC), and chronic suppurative osteomyelitis (CSO) are prevalent maxillofacial diseases with similar imaging characteristics but different treatments, thus making preoperative differential diagnosis crucial. Existing deep learning methods for diagnosis often require manual delineation in tagging the regions of interest (ROIs), which triggers some challenges in practical application. We propose a new model of Wavelet Extraction and Fusion Module with Vision Transformer (WaveletFusion-ViT) for automatic diagnosis using CBCT panoramic images. In this study, 539 samples containing healthy (n = 154), AM (n = 181), PC (n = 102), and CSO (n = 102) were acquired by CBCT for classification, with an additional 2000 healthy samples for pre-training the domain-adaptive network (DAN). The WaveletFusion-ViT model was initialized with pre-trained weights obtained from the DAN and further trained using semi-supervised learning (SSL) methods. After five-fold cross-validation, the model achieved average sensitivity, specificity, accuracy, and AUC scores of 79.60%, 94.48%, 91.47%, and 0.942, respectively. Remarkably, our method achieved 91.47% accuracy using less than 20% labeled samples, surpassing the fully supervised approach’s accuracy of 89.05%. Despite these promising results, this study’s limitations include a low number of CSO cases and a relatively lower accuracy for this condition, which should be addressed in future research. This research is regarded as an innovative approach as it deviates from the fully supervised learning paradigm typically employed in previous studies. The WaveletFusion-ViT model effectively combines SSL methods to effectively diagnose three types of CBCT panoramic images using only a small portion of labeled data. Full article

The current trend in renovating existing buildings is to perform retrofits on a case-by-case basis without a systematic assessment, using static tools with broad assumptions and generic inputs. As a result, only about 1% of the building stock undergoes energy renovations each year. To address this issue, new approaches and modern tools are necessary to enhance and expedite energy retrofits in Danish buildings. While there were a few initiatives and projects exploring the implementation of digital twins in building applications, the focus is primarily on newly constructed, highly energy-efficient buildings with integrated building information models (BIM). Conversely, existing and older buildings often lack any form of digital modeling, making it challenging to implement digital twins in those contexts. This paper presents an innovative digital twin solution, ‘DanRETwin’, which will provide decision-making support, retro-commissioning, and data-driven performance optimization for non-residential existing buildings. The proposed solution will utilize building operational data, employing machine learning and artificial intelligence techniques to develop scalable data-driven models of building energy. Additionally, clamp-on IoT sensors will be used for data collection, enabling a fully automated and flexible solution. By utilizing DanRETwin, building owners will enjoy higher energy efficiency and improved comfort in their retrofitted buildings; facility managers will have an advanced monitoring solution that enables systematic retro-commissioning of their newly retrofitted buildings, eliminating faults and reducing losses; consultants will have a potential solution to retrofit, enhance, and optimize their clients’ building performance, allowing them to make informed, data-driven decisions and interventions; and city planners will have an effective, scalable, and adaptable tool to expand retrofit efforts and evaluate various scenarios. Full article

The search for disease-modifying agents targeted against Parkinson’s disease led us to rationally design a small array of six Anle138b-centered PROTACs, 7a,b, 8a,b and 9a,b, targeting αSynuclein (αSyn) aggregates for binding, polyubiquitination by the E3 ligase Cereblon (CRBN), and proteasomal degradation. Lenalidomide and thalidomide were used as CRBN ligands and coupled with amino- and azido Anle138b derivatives through flexible linkers and coupling reactions (amidation, ‘click’ chemistry). Four Anle138b-PROTACs, 8a,b and 9a,b, were characterized against in vitro αSyn aggregation, monitoring them in a Thioflavin T (ThT) fluorescence assay and in dopaminergic neurons derived from a set of isogenic pluripotent stem cell (iPSC) lines with SNCA multiplications. Native and seeded αSyn aggregation was determined with a new biosensor, and a partial correlation between αSyn aggregation, cellular dysfunctions, and neuronal survival was obtained. Anle138b-PROTAC 8a was characterized as the most promising αSyn aggregation inhibitor/degradation inducer, with potential usefulness against synucleinopathies and cancer. Full article