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Hydrogels, with their distinctive three-dimensional networks of hydrophilic polymers, drive innovations across various biomedical applications. The ability of hydrogels to absorb and retain significant volumes of water, coupled with their structural integrity and responsiveness to environmental stimuli, renders them ideal for drug delivery, tissue engineering, and wound healing. This review delves into the classification of hydrogels based on cross-linking methods, providing insights into their synthesis, properties, and applications. We further discuss the recent advancements in hydrogel-based drug delivery systems, including oral, injectable, topical, and ocular approaches, highlighting their significance in enhancing therapeutic outcomes. Additionally, we address the challenges faced in the clinical translation of hydrogels and propose future directions for leveraging their potential in personalized medicine and regenerative healthcare solutions.

Mitochondria play important roles in maintaining cellular homeostasis and skeletal muscle health, and damage to mitochondria can lead to a series of pathophysiological changes. Mitochondrial dysfunction can lead to skeletal muscle atrophy, and its molecular mechanism leading to skeletal muscle atrophy is complex. Understanding the pathogenesis of mitochondrial dysfunction is useful for the prevention and treatment of skeletal muscle atrophy, and finding drugs and methods to target and modulate mitochondrial function are urgent tasks in the prevention and treatment of skeletal muscle atrophy. In this review, we first discussed the roles of normal mitochondria in skeletal muscle. Importantly, we described the effect of mitochondrial dysfunction on skeletal muscle atrophy and the molecular mechanisms involved. Furthermore, the regulatory roles of different signaling pathways (AMPK-SIRT1-PGC-1α, IGF-1-PI3K-Akt-mTOR, FoxOs, JAK-STAT3, TGF-β-Smad2/3 and NF-κB pathways, etc.) and the roles of mitochondrial factors were investigated in mitochondrial dysfunction. Next, we analyzed the manifestations of mitochondrial dysfunction in muscle atrophy caused by different diseases. Finally, we summarized the preventive and therapeutic effects of targeted regulation of mitochondrial function on skeletal muscle atrophy, including drug therapy, exercise and diet, gene therapy, stem cell therapy and physical therapy. This review is of great significance for the holistic understanding of the important role of mitochondria in skeletal muscle, which is helpful for researchers to further understanding the molecular regulatory mechanism of skeletal muscle atrophy, and has an important inspiring role for the development of therapeutic strategies for muscle atrophy targeting mitochondria in the future.

Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirements for screening procedures due to high cost and limited availability. In this work, we took the initiative to evaluate the retina, especially the retinal vasculature, as an alternative for conducting screenings for dementia patients caused by Alzheimer's disease. Highly modular machine learning techniques were employed throughout the whole pipeline. Utilizing data from the UK Biobank, the pipeline achieved an average classification accuracy of 82.44%. Besides the high classification accuracy, we also added a saliency analysis to strengthen this pipeline's interpretability. The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer's diseases, which aligns with related studies.

A radar is an important part of an air defense and combat system. It is of great significance to military defense to improve the effectiveness of radar state monitoring and the accuracy of fault diagnosis during operation. However, the complexity of radar equipment’s structure and the uncertainty of the operating environment greatly increase the difficulty of fault diagnosis in real life situations. Therefore, a Bayesian network diagnosis method based on multi-source information fusion technology is proposed to solve the fault diagnosis problems caused by uncertain factors such as the high integration and complexity of the system during the process of fault diagnosis. Taking a fault of a radar receiver as an example, we study 2 typical fault phenomena and 21 fault points. After acquiring and processing multi-source information, establishing a Bayesian network model, determining conditional probability tables (CPTs), and finally outputting the diagnosis results. The results are convincing and consistent with reality, which verifies the effectiveness of this method for fault diagnosis in radar receivers. It realizes device-level fault diagnosis, which shortens the maintenance time for radars and improves the reliability and maintainability of radars. Our results have significance as a guide for judging the fault location of radars and predicting the vulnerable components of radars.

The vehicular ad hoc network (VANET) constitutes a key technology for realizing intelligent transportation services. However, VANET is characterized by diverse message types, complex security attributes of communication nodes, and rapid network topology changes. In this case, how to ensure safe, efficient, convenient, and comfortable message services for users has become a challenge that should not be ignored. To improve the flexibility of routing matching multiple message types in VANET, this paper proposes a secure intelligent message forwarding strategy based on deep reinforcement learning (DRL). The key supporting elements of the model in the strategy are reasonably designed in combination with the scenario, and sufficient training of the model is carried out by deep Q networks (DQN). In the strategy, the state space is composed of the distance between candidate and destination nodes, the security attribute of candidate nodes and the type of message to be sent. The node can adaptively select the routing scheme according to the complex state space. Simulation and analysis show that the proposed strategy has the advantages of fast convergence, well generalization ability, high transmission security, and low network delay. The strategy has flexible and rich service patterns and provides flexible security for VANET message services.

DDIAS has been recognized as an oncogene in various cancers, but its role in endometrial cancer remains unexplored. The expression of DDIAS in normal endometrium and endometrial cancer samples was analysed via the The Cancer Genome Atlas (TCGA) database, and prognostic analysis was performed. The differential expression of DDIAS between endometrial cancer and normal endometrium tissues was analyzed using quantitative polymerase chain reaction (qPCR). To study the expression of DDIAS in endometrial cancer, immunohistochemistry was performed on endometrial cancer, atypical endometrial hyperplasia and normal endometrial tissue. The association between DDIAS expression and clinicopathology was analysed. The expression of DDIAS in endometrial cancer cell lines was studied via Western blot (WB) analysis. DDIAS was knocked down in endometrial cancer cell lines via small interfering RNA (siRNA), and the effects of DDIAS knockdown on endometrial cancer cell biology and its related regulatory mechanisms were investigated via Cell Counting Kit-8(CCK8), Colony formation assay, scratch test, Transwell, and WB assays. Finally, the relevant regulatory mechanisms were verified using rescue experiments. According to the public database analysis, High DDIAS expression correlates with endometrial cancer and predicts unfavorable prognosis..The qPCR confirmed higher expression of DDIAS in tumor samples.We found that DDIAS was highly expressed in endometrial cancer and atypical endometrial hyperplasia, and that the upregulation of DDIAS expression predicted poor prognosis. In endometrial cancer, higher DDIAS expression was associated with increased tumor grade and advanced FIGO stage. In terms of cellular function, knocking down DDIAS suppressed the proliferation, migration and invasion capabilities of endometrial cancer cells. In the mechanistic pathway, reducing DDIAS expression led to the inhibition of β-catenin and its downstream targets, including c-Myc, cyclin D1, and survivin, while also suppressing epithelial-mesenchymal transition (EMT).However, these changes were rescued by the upregulation of β-catenin. DDIAS regulated EMT in endometrial cancer cell migration and invasion through the β-catenin pathway, demonstrating that DDIAS is a potential target for the treatment of endometrial cancer.

Cerebral ischemia-reperfusion injury (Cerebral I/R injury) is a critical pathological process following ischemic stroke, closely associated with multiple mechanisms including oxidative stress, neuroinflammation, and neuronal apoptosis. It also involves the alteration and regulation of numerous key genes and non-coding RNAs. Due to its complex regulatory mechanisms, there are currently no Food and Drug Administration (FDA)-approved drugs specifically targeting Cerebral I/R injury. Developing effective therapeutic strategies for Cerebral I/R injury remains a significant challenge in medical research. This review summarizes current treatment approaches for Cerebral I/R injury, which include traditional drugs, antioxidants, neuroprotective agents, exosomes, noncoding RNA therapeutics and combined intervention therapy. Pharmacotherapies exert positive effects on Cerebral I/R injury through antioxidative, anti-inflammatory, and neuroprotective mechanisms. Exosomes and noncoding RNA therapeutics can mitigate brain cell damage and promote neural function recovery by regulating the expression of downstream key genes via miRNAs, demonstrating potential as novel therapeutic options. Emerging evidence indicates that combined therapeutic strategies incorporating nanoparticle-mediated targeting demonstrate efficacy in treating cerebral I/R injury. By exploring the mechanisms of action and clinical application prospects of these different treatment strategies, this review aims to provide new insights and methods for the clinical management of Cerebral I/R injury.

Background Patient satisfaction is a powerful predictor of an individual's mental health, according to previous research. However, there has not been a thorough study on the relationship between depression and overall medical service satisfaction (OMSS) in middle-aged and older adults. Moreover, little is known about how different Internet-using patterns affect this relationship. Methods We selected 4,523 participants from the China Family Panel Studies (CFPS) 2020 dataset who were aged 45 and older. The relationship between OMSS and depression was examined using logistic regression analysis, distinct Internet-using patterns were investigated using latent class analysis, and the moderating effects of these patterns were examined using SPSS PROCESS macro analysis. Results The results showed OMSS was negatively related to depression in middle-aged and older adults ( β  = -0.181, p  < 0.001). For skilled Internet users, there was a significant positive moderating effect ( β  = -0.272, SE = 0.096, p  < 0.01), for unskilled users, there was a significant negative moderating effect ( β  = 0.497, SE = 0.156, p  < 0.01). Yet, there is no moderating effect of a controlled Internet-using pattern on the correlation between OMSS and depression. Conclusions This study highlights the potential value of improving medical service satisfaction in reducing depressive symptoms in middle-aged and older adults. Additionally, in order to maximize the benefits of healthcare for mental health, the study suggests that Internet-using patterns could be a significant area for intervention.

In this paper, an in-depth study of interactive visual communication of network topology through non-line-of-sight congestion control algorithms is conducted to address the real-time routing problem of adapting to dynamic topologies, and a delay-constrained stochastic routing algorithm is proposed to enable packets to reach GB within the delay threshold in the absence of end-to-end delay information while improving network throughput and reducing network resource consumption. The algorithm requires each sending node to select an available relay set based on the location of its neighbor nodes and channel state and computes transfer probabilities for each node in the relay set combining the remaining delay of the packet with the distance from the relay node to GB. Based on the obtained transfer probability and local channel state, the sending node passes the packet to the relay node. The convergence of the algorithm is proved and its performance is verified by simulation. The first part of the algorithm is based on the greedy algorithm to deploy and locate the network flying platform nodes with the goal of efficient coverage of the network flying platform nodes, considering the ground base station services. As the delay on each link varies due to the change of channel state, the source and relay nodes asynchronously update the data generation rate and the pairwise parameters based on the received local information and use the obtained optimal values to pass the packets to GB.

Background Glucose transporters play an important role in the fermentation of citric acid. In this study, a high-affinity glucose transporter (HGT1) was identified and overexpressed in the industrial strain A. niger CGMCC 10142. HGT1-overexpressing strains using the P gla A and P aox1 promoters were constructed to verify the glucose transporter functions. Result As hypothesized, the HGT1-overexpressing strains showed higher citric acid production and lower residual sugar contents. The best-performing strain A. niger 20-15 exhibited a reduction of the total sugar content and residual reducing sugars by 16.5 and 44.7%, while the final citric acid production was significantly increased to 174.1 g/L, representing a 7.3% increase compared to A. niger CGMCC 10142. Measurement of the mRNA expression levels of relevant genes at different time-points during the fermentation indicated that in addition to HGT1, citrate synthase and glucokinase were also expressed at higher levels in the overexpression strains. Conclusion The results indicate that HGT1 overexpression resolved the metabolic bottleneck caused by insufficient sugar transport and thereby improved the sugar utilization rate. This study demonstrates the usefulness of the high-affinity glucose transporter HGT1 for improving the citric acid fermentation process of Aspergillus niger CGMCC 10142.