De-icing performance evolution with increasing hydrophobicity by regulating surface topography.

It is of great significance to grasp the role of surface topography in de-icing, which however remains unclear yet. Herein, four textured surfaces are developed by regulating surface topography while keeping surface chemistry and material constituents same. Specifically, nano-textures are maintained and micro-textures are gradually enlarged. The resultant ice adhesion strength is proportional to a topography parameter, i.e. areal fraction of the micro-textures, owing to the localized bonding strengthening, which is verified by ice detachment simulation using finite element method. Moreover, the decisive topography parameter is demonstrated to be determined by the interfacial strength distribution between ice and test surface. Such parameters vary from paper to paper due to different interfacial strength distributions corresponding to respective situations. Furthermore, since hydrophobic and de-icing performance may rely on different topography parameters, there is no certain relationship between hydrophobicity and de-icing.

The role of surface topography in de-icing is verified to be determined by the interfacial strength distribution between ice and surface experimentally and numerically, unveiling the relationship between hydrophobicity and de-icing.

PixRevive: Latent Feature Diffusion Model for Compressed Video Quality Enhancement.

In recent years, the rapid prevalence of high-definition video in Internet of Things (IoT) systems has been directly facilitated by advances in imaging sensor technology. To adapt to limited uplink bandwidth, most media platforms opt to compress videos to bitrate streams for transmission. However, this compression often leads to significant texture loss and artifacts, which severely degrade the Quality of Experience (QoE). We propose a latent feature diffusion model (LFDM) for compressed video quality enhancement, which comprises a compact edge latent feature prior network (ELPN) and a conditional noise prediction network (CNPN). Specifically, we first pre-train ELPNet to construct a latent feature space that captures rich detail information for representing sharpness latent variables. Second, we incorporate these latent variables into the prediction network to iteratively guide the generation direction, thus resolving the problem that the direct application of diffusion models to temporal prediction disrupts inter-frame dependencies, thereby completing the modeling of temporal correlations. Lastly, we innovatively develop a Grouped Domain Fusion module that effectively addresses the challenges of diffusion distortion caused by naive cross-domain information fusion. Comparative experiments on the MFQEv2 benchmark validate our algorithm's superior performance in terms of both objective and subjective metrics. By integrating with codecs and image sensors, our method can provide higher video quality.

Calcipotriol abrogates cancer-associated fibroblast-derived IL-8-mediated oxaliplatin resistance in gastric cancer cells via blocking PI3K/Akt signaling.

Activation of vitamin D receptor (VDR) in cancer-associated fibroblasts (CAFs) has been implicated in hesitating tumor progression and chemoresistance of several human malignancies. Yet, the role of VDR in CAF-induced chemotherapy resistance of gastric cancer (GC) cells remains elusive. In this study we first conducted immunohistochemistry analysis on tissue microarrays including 88 pairs of GC and normal mucosa samples, and provided clinical evidence that VDR was mainly expressed in gastric mucous cells but almost invisible in CAFs, and VDR expression was negatively correlated with malignant clinical phenotype and advanced stages, low VDR expression confers to poor overall survival rate of patients with GC. In a co-culture system of primary CAFs and cancer cells, we showed that treatment of HGC-27 and AGS GC cells with VDR ligand calcipotriol (Cal, 500 nM) significantly inhibited CAF-induced oxaliplatin resistance. By using RNA-sequencing and Human Cytokine Antibody Array, we demonstrated that IL-8 secretion from CAFs induced oxaliplatin resistance via activating the PI3K/AKT pathway in GC, whereas Cal treatment greatly attenuated the tumor-supportive effect of CAF-derived IL-8 on GC cells. Taken together, this study verifies the specific localization of VDR in GC tissues and demonstrates that activation of VDR abrogates CAF-derived IL-8-mediated oxaliplatin resistance in GC via blocking PI3K/Akt signaling, suggesting vitamin D supplementation as a potential strategy of enhancing the anti-tumor effect of chemotherapy in GC.

Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study.

BACKGROUND: Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work. OBJECTIVE: In this study, we aimed to develop a novel approach called "probability in work mode," which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours. METHODS: To capture human-smartphone interactions and GPS locations, we used the "Staff Hours" app, developed by our team, to passively and continuously record participants' screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work. RESULTS: Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day; P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes. CONCLUSIONS: Our novel approach, the probability in work mode, harnessed human-smartphone interaction patterns and machine learning models to enhance the precision and accuracy of work hour investigation. By integrating human-smartphone interactions and GPS data, our method provides valuable insights into work patterns, including remote work and breaks, offering potential applications in optimizing work productivity and well-being.

Development of a Cloud-Based Image Processing Health Checkup System for Multi-Item Urine Analysis.

With the busy pace of modern life, an increasing number of people are afflicted by lifestyle diseases. Going directly to the hospital for medical checks is not only time-consuming but also costly. Fortunately, the emergence of rapid tests has alleviated this burden. Accurately interpreting test results is extremely important; misinterpreting the results of rapid tests could lead to delayed medical treatment. Given that URS-10 serve as a rapid test capable of detecting 10 distinct parameters in urine samples, the results of assessing these parameters can offer insights into the subject's physiological condition. These parameters encompass aspects such as metabolism, renal function, diabetes, urinary tract disorders, hemolytic diseases, and acid-base balance, among others. Although the operational procedure is straightforward, the variegated color changes exhibited in the outcomes of individual parameters render it challenging for lay users to deduce causal factors solely from color variations. Moreover, potential misinterpretations could arise due to visual discrepancies. In this study, we successfully developed a cloud-based health checkup system that can be used in an indoor environment. The system is used by placing a URS-10 test strip on a colorimetric board developed for this study, then using a smartphone application to take images which are uploaded to a server for cloud computing. Finally, the interpretation results are stored in the cloud and sent back to the smartphone to be checked by the user. Furthermore, to confirm whether the color calibration technology can eliminate color differences between different cameras, and also whether the colorimetric board and the urine test strips can perform color comparisons correctly in different light intensity environments, indoor environments that could simulate a specific light intensity were established for testing purposes. When comparing the experimental results to real test strips, only two groups failed to reach an identification success rate of 100%, and in both of these cases the success rate reached 95%. The experimental results confirmed that the system developed in this study was able to eliminate color differences between camera devices and could be used without special technical requirements or training.

Twin-Boundary Reduced Surface Diffusion on Electrically Stressed Copper Nanowires.

Surface diffusion is intimately correlated with crystal orientation and surface structure. Fast surface diffusion accelerates phase transformation and structural evolution of materials. Here, through in situ transmission electron microscopy observation, we show that a copper nanowire with dense nanoscale coherent twin-boundary (CTB) defects evolves into a zigzag configuration under electric-current driven surface diffusion. The hindrance at the CTB-intercepted concave triple junctions decreases the effective surface diffusivity by almost 1 order of magnitude. The energy barriers for atomic migration at the concave junctions and different faceted surfaces are computed using density functional theory. We proposed that such a stable zigzag surface is shaped not only by the high-diffusivity facets but also by the stalled atomic diffusion at the concave junctions. This finding provides a defect-engineering route to develop robust interconnect materials against electromigration-induced failures for nanoelectronic devices.

Extending Ag Nanoparticles as Colorimetric Sensor to Industrial Zinc Electrolyte for Cobalt Ion Detection.

The direct and rapid determination of trace cobalt ion (Co2+) in the electrolyte of zinc smelting plants is urgently needed but is impeded by the severe interference of extremely high-concentration zinc ions in the solution. Herein, colorimetric detection of Co2+ by the polyvinylpyrrolidone functionalized silver nanoparticles (PVP-AgNPs) is realized in solutions with the Zn/Co ratio being high, up to (0.8-5) × 104, which is located within the ratio range in industrial solution. The high concentration of Zn2+ induces a strong attenuation of Co2+-related signals in ultraviolet-visible (UV-vis) extinction spectra; nevertheless, a good linear range for detecting 1-6 mg/L Co2+ in 50 g/L Zn2+ solution is still acquired. The strong anti-interference toward other metal ions and the mechanism understanding for trace Co2+ detection in such a high-concentration Zn2+ solution are also revealed by systematic analysis techniques. The results extend the AgNPs as colorimetric sensors to industrial solutions, providing a new strategy for detecting trace-metal ions in industrial plants.

Sourcing decisions with loss aversion under yield and demand randomness.

Yield and demand randomness are common in the industry, and loss aversion has been regarded as an inherent behavior for decision-makers. We combine these two factors and investigate a retailer's ordering decisions under both yield and demand randomness with loss aversion. Before the selling season, the demand is unknown and the loss-averse retailer places an order from an unreliable supplier with an uncertain yield rate. After the demand and supply from the unreliable supplier are known, the retailer can carry out emergency replenishment from a spot market during the selling season. We characterize the retailer's optimal ordering decisions in three scenarios: (1) the retailer is risk-neutral; (2) the retailer is loss-averse and has a zero reference profit; (3) the retailer is loss-averse and has a nonzero fixed reference profit (FRP). We compare the retailer's order quantities in the three scenarios and find that the order quantity of the loss-averse retailer with a zero reference profit is always lower than that of the risk-neutral retailer. However, the order quantity of the loss-averse retailer with a nonzero fixed reference profit is higher than that of the risk-neutral retailer when the salvage value is sufficiently large. Interestingly, we find that the loss-averse retailer's optimal order quantity decreases with the reference profit and increases with the loss-averse degree and the maximum fulfillment rate from the unreliable supplier under some conditions. We investigate these conditions under a uniform demand distribution. We further study the ordering quantity of the retailer with a prospect-dependent reference point (PRP) and compare the results under FRP and PRP.

Evolution of and Disparity among Biomimetic Superhydrophobic Surfaces with Gecko, Petal, and Lotus Effect.

It is desirable to turn one kind of superhydrophobic (SHPO) surfaces into another by changing surface topography alone and attaining solid surfaces with tunable properties. Herein, gecko-, petal-, and lotus-like SHPO surfaces, composed of ZnO tetrapods and polydimethylsiloxane, are realized by adjusting the roughness factor and length scale of roughness, while keeping the surface chemistry the same. Afterward, water droplet sliding and impacting are investigated. The surfaces behave similarly in spreading but deviate from each other in sliding, receding, jetting, and rebounding due to their different adhesive properties. Moreover, the disparity between surfaces with petal and lotus effects is well explained by Furmidge's and Young-Dupre equations. On the other hand, these formulas fail to elucidate the surface with gecko effect because of its inside sealed air that produces negative pressure upon droplet motion. This paper provides a facile topography evolution path and a manifest correlation between topography and performance in water droplet dynamics for SHPO surfaces with gecko, petal, and lotus effects.

The impact of serum thyroid-stimulation hormone levels on the outcome of hepatitis B virus related acute-on-chronic liver failure: an observational study.

BACKGROUND: Thyroid dysfunction has been reported in severe liver diseases. The aim of this study was to analyze the impact of serum thyroid-stimulation hormone (TSH) levels on the prognosis of patients with hepatitis B virus (HBV)-related acute-on-chronic liver failure (ACLF). METHODS: This retrospective cohort study included 1,862 patients with HBV-related ACLF. Risk factors associated with 30-day and 90-day survival, hazard ratios (HRs), and 95% confidence intervals (CIs) for TSH were estimated using Cox proportional hazards regression. The Area Under the ROC curve (AUROC) analysis was carried out, and the cut-off values were calculated. After grouping by the cut-off value, survival was compared between the groups using the log-rank test. This study data is from the "Survival Cohort Study (SCS)", which has been registered at ClinicalTrials.gov (NCT03992898). RESULTS: Multivariate analysis indicated that an elevated TSH level was a highly significant predictor for 30-day survival (HR = 0.743, 95% CI: 0.629-0.878, P < 0.001) and 90-day survival (HR = 0.807, 95% CI: 0.717-0.909, P < 0.001). The AUROC of TSH level for 30-day and 90-day mortality were 0.655 and 0.620, respectively, with the same best cut-off values of 0.261 µIU/mL. Log-rank test showed that the group with higher TSH level had higher 30-day (78.5%, 95% CI: 76.1%-80.9% vs. 56.9%, 95% CI: 53.4%-60.4%; P < 0.001) and 90-day survival rate (61.5%, 95% CI: 58.6%-64.4% vs. 42.8%, 95% CI: 39.3%-46.3%; P < 0.001). Similar findings were observed in subgroups analysis. After adjusting for age and other risk factors, the higher level of TSH remained associated with 30-day survival (HR = 0.602, 95% CI: 0.502-0.721, P < 0.001) and 90-day survival (HR = 0.704, 95% CI, 0.609-0.814, P < 0.001). CONCLUSIONS: Serum TSH level significantly correlate with HBV-related ACLF patients' survival and may be of value for predicting 30-day and 90-day survival of patients with HBV-related ACLF.

Risk factors for underlying comorbidities and complications in patients with hepatitis B virus-related acute-on-chronic liver failure.

Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a severe and life-threatening complication, characterised by multi-organ failure and high short-term mortality. However, there is limited information on the impact of various comorbidities on HBV-ACLF in a large population. This study aimed to investigate the relationship between comorbidities, complications and mortality. In this retrospective observational study, we identified 2166 cases of HBV-ACLF hospitalised from January 2010 to March 2018. Demographic data from the patients, medical history, treatment, laboratory indices, comorbidities and complications were collected. The mortality rate in our study group was 47.37%. Type 2 diabetes mellitus was the most common comorbidity, followed by alcoholic liver disease. Spontaneous bacterial peritonitis, pneumonia and hepatic encephalopathy (HE) were common in these patients. Diabetes mellitus and hyperthyroidism are risk factors for death within 90 days, together with gastrointestinal bleeding and HE at admission, HE and hepatorenal syndrome during hospitalisation. Knowledge of risk factors can help identify HBV-ACLF patients with a poor prognosis for HBV-ACLF with comorbidities and complications.

Factors allowing small monovalent Li+ to displace Ca2+ in proteins.

Because Li+ and Ca2+ differ in both charge and size, the possibility that monovalent Li+ could dislodge the bulkier, divalent Ca2+ in Ca2+ proteins had not been considered. However, our recent density functional theory/continuum dielectric calculations predicted that Li+ could displace the native Ca2+ from the C2 domain of cytosolic PKCα/γ. This would reduce electrostatic interactions between the Li+-bound C2 domain and the membrane, consistent with experimental studies showing that Li+ can inhibit the translocation of cytoplasmic PKC to membranes. Besides the trinuclear Ca2+-site in the PKCα/γ C2 domain, it is not known whether other Ca2+-sites in human proteins may be susceptible to Li+ substitution. Furthermore, it is unclear what factors determine the outcome of the competition between divalent Ca2+ and monovalent Li+. Here we show that the net charge of residues in the first and second coordination shell is a key determinant of the selectivity for divalent Ca2+ over monovalent Li+ in proteins: neutral/anionic Ca2+-carboxylate sites are protected against Li+ attack. They are further protected by outer-shell Asp-/Glu- and the protein matrix rigidifying the Ca2+-site or limiting water entry. In contrast, buried, cationic Ca2+-sites surrounded by Arg+/Lys+, which are found in the C2 domains of PKCα/γ, as well as certain synaptotagmins, are prone to Li+ attack.

Radium-223 for metastatic, castration-resistant prostate cancer: A retrospective chart review study of real-world use in a tertiary hospital in Taiwan.

BACKGROUND: Radium-223 is an alpha-emitting, bone-targeting radiopharmaceutical that confers a survival benefit in patients with confirmed bone-metastatic, castration-resistant prostate cancer with no visceral metastases. We studied real-world use of radium-223 in eligible patients from a tertiary hospital. METHODS: We retrospectively collected clinical data of patients treated with radium-223 in Tungs' Taichung MetroHarbor Hospital by chart review. Data included biochemical parameters, pain scores, other prostate cancer treatments received and adverse events (AEs). RESULTS: Of 36 patients included in the study, 12 patients received radium-223 as first-line therapy, 11 as second-line treatment and 13 as third-line treatment. Prostate-specific antigen significantly increased from baseline in patients who received radium-223 as third-line treatment and in patients who received radium-223 post-chemotherapy. Pain scores significantly decreased when radium-223 was given as second-line and as third-line treatment. In the patients who were naive to novel anti-hormone (NAH) therapy and chemotherapy, mean alkaline phosphatase level significantly decreased from baseline. The most common AE was anemia, found in 16.7% of patients. CONCLUSION: Radium-223 had early biochemical benefits, while in the later stages of the disease, it reduced bone pain in this real-world cohort of chemotherapy-naive, NAH-naive patients, and patients with prior therapy, from a tertiary institution in Taiwan.

Smart fibers for energy conversion and storage.

Fibers have played a critical role in the long history of human development. They are the basic building blocks of textiles. Synthetic fibers not only make clothes stronger and more durable, but are also customizable and cheaper. The growth of miniature and wearable electronics has promoted the development of smart and multifunctional fibers. Particularly, the incorporation of functional semiconductors and electroactive materials in fibers has opened up the field of fiber electronics. The energy supply system is the key branch for fiber electronics. Herein, after a brief introduction on the history of smart and functional fibers, we review the current state of advanced functional fibers for their application in energy conversion and storage, focusing on nanogenerators, solar cells, supercapacitors and batteries. Subsequently, the importance of the integration of fiber-shaped energy conversion and storage devices via smart structure design is discussed. Finally, the challenges and future direction in this field are highlighted. Through this review, we hope to inspire scientists with different research backgrounds to enter this multi-disciplinary field to promote its prosperity and development and usher in a truly new era of smart fibers.

Development of a Visual Perception System on a Dual-Arm Mobile Robot for Human-Robot Interaction.

This paper presents the development of a visual-perception system on a dual-arm mobile robot for human-robot interaction. This visual system integrates three subsystems. Hand gesture recognition is utilized to trigger human-robot interaction. Engagement and intention of the participants are detected and quantified through a cognitive system. Visual servoing uses YOLO to identify the object to be tracked and hybrid, model-based tracking to follow the object's geometry. The proposed visual-perception system is implemented in the developed dual-arm mobile robot, and experiments are conducted to validate the proposed method's effects on human-robot interaction applications.

Enterprise Data Sharing with Privacy-Preserved Based on Hyperledger Fabric Blockchain in IIOT's Application.

Internet of Things (IoT) technology is now widely used in energy, healthcare, services, transportation, and other fields. With the increase in industrial equipment (e.g., smart mobile terminals, sensors, and other embedded devices) in the Internet of Things and the advent of Industry 4.0, there has been an explosion of data generated that is characterized by a high volume but small size. How to manage and protect sensitive private data in data sharing has become an urgent issue for enterprises. Traditional data sharing and storage relies on trusted third-party platforms or distributed cloud storage, but these approaches run the risk of single-node failure, and third parties and cloud storage providers can be vulnerable to attacks that can lead to data theft. To solve these problems, this paper proposes a Hyperledger Fabric blockchain-based secure data transfer scheme for enterprises in the Industrial Internet of Things (IIOT). We store raw data in the IIoT in the InterPlanetary File System (IPFS) network after encryption and store the Keyword-index table we designed in Hyperledger Fabric blockchain, and enterprises share the data by querying the Keyword-index table. We use Fabric's channel mechanism combined with our designed Chaincode to achieve privacy protection and efficient data transmission while using the Elliptic Curve Digital Signature Algorithm (ECDSA) to ensure data integrity. Finally, we performed security analysis and experiments on the proposed scheme, and the results show that overall the data transfer performance in the IPFS network is generally better than the traditional network, In the case of transferring 5 MB file size data, the transmission speed and latency of IPFS are 19.23 mb/s and 0.26 s, respectively, and the IPFS network is almost 4 times faster than the TCP/IP network while taking only a quarter of the time, which is more advantageous when transferring small files, such as data in the IIOT. In addition, our scheme outperforms the blockchain systems mainly used today in terms of both throughput, latency, and system overhead. The average throughput of our solution can reach 110 tps (transactions are executed per second), and the minimum throughput in experimental tests can reach 101 tps.

A Traceable Vaccine Supply Management System.

Everyone should be vaccinated, but the eligibility and safety of the vaccine are always overlooked by most people. The outbreak of COVID-19 has led many countries to intensify the development and production of the COVID-19 vaccine. and some countries have even required universal vaccination against this epidemic. However, such popularization of vaccination has also exposed various flaws in vaccine management that existed in the past, and vaccinators have become more concerned about the effectiveness of their vaccinations. In this paper, we propose a blockchain-based traceable vaccine management system. First, the system uses smart contracts to store the records generated during the whole process, from vaccine production to vaccination. Second, the proposed scheme uses the Edwards-curve digital signature algorithm (EdDSA) to guarantee the security and integrity of these data. Third, the system participants can access the corresponding data according to their authority to ensure the transparency of the whole system operation process. Finally, this paper will also conduct a security analysis of the whole system to ensure that the system can resist potential attacks by criminals.

Flexible Sulfide Electrolyte Thin Membrane with Ultrahigh Ionic Conductivity for All-Solid-State Lithium Batteries.

All-solid-state lithium batteries (ASSLBs) employing Li-metal anode, sulfide solid electrolyte (SE) can deliver high energy density with high safety. The thick SE separator and its low ionic conductivity are two major challenges. Herein, a 30 µm sulfide SE membrane with ultrahigh room temperature conductivity of 8.4 mS cm-1 is realized by mechanized manufacturing technologies using highly conductive Li5.4PS4.4Cl1.6 SE powder. Moreover, a 400 nm magnetron sputtered Al2O3 interlayer is introduced into the SE/Li interface to improve the anodic stability, which suppresses the short circuit in Li/Li symmetric cells. Combining these merits, ASSLBs with LiNi0.5Co0.2Mn0.3O2 as the cathode exhibit a stable cyclic performance, delivering a discharge specific capacity of 135.3 mAh g-1 (1.4 mAh cm-2) with a retention of 80.2% after 150 cycles and an average Coulombic efficiency over 99.5%. The high ionic conductivity SE membrane and interface design principle show promising feasible strategies for practical high performance ASSLBs.

Integrated analyses identify miR-34c-3p/MAGI3 axis for the Warburg metabolism in hepatocellular carcinoma.

Activated oncogenes and loss of tumor suppressors contribute to reprogrammed energy metabolism and induce aerobic glycolysis, also known as Warburg effect. MicroRNAs are profoundly implicated in human malignancies by inhibiting translation of multiple mRNA targets. Using hepatocellular carcinoma (HCC) molecular profiles from The Cancer Genome Atlas (TCGA), we identified a handful of dysregulated microRNA in HCC glycolysis, especially miR-34c-3p. Antagonization of miR-34c-3p inhibited the lactate production, glucose consumption, extracellular acidification rate (ECAR), and aggressive proliferation in HCC cells. Hijacking glycolysis by 2-deoxy-d-glucose or galactose largely abrogated the suppressive effects of miR-34c-3p inhibition in HCC. Membrane associated guanylate kinase, WW, and PDZ domain containing 3 (MAGI3) is then identified as a direct functional target of miR-34c-3p in regulating HCC glycolysis and oncogenic activities. Mechanistically, MAGI3 physically interacted with ß-catenin to regulate its transcriptional activity and c-Myc expression, which further facilitates the Warburg effect by increasing expression of glycolytic genes including HK2, PFKL, and LDHA. Moreover, overexpressed miR-34c-3p and reduced MAGI3 predicted poor clinical outcome and was closely associated with the maximum standard uptake value (SUVmax) in HCC patients who received preoperative 18 F-FDG PET/CT. Our findings elucidate critical several microRNAs implicated in HCC glycolysis and reveal a novel function of miR-34c-3p/MAGI3 axis in Warburg effect through regulating ß-catenin activity.

Long non-coding RNA DANCR modulates osteogenic differentiation by regulating the miR-1301-3p/PROX1 axis.

The balance of osteoblasts and marrow adipocytes from bone marrow mesenchymal stem cells (BM-MSCs) maintains bone health. Under aging or other pathological stimuli, BM-MSCs will preferentially differentiate into marrow adipocytes and reduce osteoblasts, leading to osteoporosis. Long non-coding RNA differentiation antagonizing non-protein coding RNA (DANCR) participates in the osteogenic differentiation of human BM-MSCs, but the mechanism by which DANCR regulates the osteogenic differentiation of human BM-MSCs has not been fully explained. We observed that DANCR and prospero homeobox 1 (PROX1) were downregulated during osteogenic differentiation of human BM-MSCs, while miR-1301-3p had an opposite trend. DANCR overexpression decreased the levels of alkaline phosphatase, RUNX2, osteocalcin, Osterix in BM-MSCs after osteogenic induction, but DANCR silencing had the opposite result. Moreover, DANCR sponged miR-1301-3p to regulate PROX1 expression. miR-1301-3p overexpression reversed the suppressive role of DANCR elevation on the osteogenic differentiation of human BM-MSCs. Also, PROX1 elevation abolished the promoting role of miR-1301-3p overexpression on the osteogenic differentiation of human BM-MSCs. In conclusion, DANCR suppressed the osteogenic differentiation of human BM-MSCs through the miR-1301-3p/PROX1 axis, offering a novel mechanism by which DANCR is responsible for the osteogenic differentiation of human BM-MSCs.