Elucidating the Molecular Pathways and Therapeutic Interventions of Gaseous Mediators in the Context of Fibrosis.

Fibrosis, a pathological alteration of the repair response, involves continuous organ damage, scar formation, and eventual functional failure in various chronic inflammatory disorders. Unfortunately, clinical practice offers limited treatment strategies, leading to high mortality rates in chronic diseases. As part of investigations into gaseous mediators, or gasotransmitters, including nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H2S), numerous studies have confirmed their beneficial roles in attenuating fibrosis. Their therapeutic mechanisms, which involve inhibiting oxidative stress, inflammation, apoptosis, and proliferation, have been increasingly elucidated. Additionally, novel gasotransmitters like hydrogen (H2) and sulfur dioxide (SO2) have emerged as promising options for fibrosis treatment. In this review, we primarily demonstrate and summarize the protective and therapeutic effects of gaseous mediators in the process of fibrosis, with a focus on elucidating the underlying molecular mechanisms involved in combating fibrosis.

Impact of propofol and sevoflurane anesthesia on cognition and emotion in gastric cancer patients undergoing radical resection.

BACKGROUND: Propofol and sevoflurane are commonly used anesthetic agents for maintenance anesthesia during radical resection of gastric cancer. However, there is a debate concerning their differential effects on cognitive function, anxiety, and depression in patients undergoing this procedure. AIM: To compare the effects of propofol and sevoflurane anesthesia on postoperative cognitive function, anxiety, depression, and organ function in patients undergoing radical resection of gastric cancer. METHODS: A total of 80 patients were involved in this research. The subjects were divided into two groups: Propofol group and sevoflurane group. The evaluation scale for cognitive function was the Loewenstein occupational therapy cognitive assessment (LOTCA), and anxiety and depression were assessed with the aid of the self-rating anxiety scale (SAS) and self-rating depression scale (SDS). Hemodynamic indicators, oxidative stress levels, and pulmonary function were also measured. RESULTS: The LOTCA score at 1 d after surgery was significantly lower in the propofol group than in the sevoflurane group. Additionally, the SAS and SDS scores of the sevoflurane group were significantly lower than those of the propofol group. The sevoflurane group showed greater stability in heart rate as well as the mean arterial pressure compared to the propofol group. Moreover, the sevoflurane group displayed better pulmonary function and less lung injury than the propofol group. CONCLUSION: Both propofol and sevoflurane could be utilized as maintenance anesthesia during radical resection of gastric cancer. Propofol anesthesia has a minimal effect on patients' pulmonary function, consequently enhancing their postoperative recovery. Sevoflurane anesthesia causes less impairment on patients' cognitive function and mitigates negative emotions, leading to an improved postoperative mental state. Therefore, the selection of anesthetic agents should be based on the individual patient's specific circumstances.

Unraveling the allosteric inhibition mechanism of PARP-1 CAT and the D766/770A mutation effects via Gaussian accelerated molecular dynamics and Markov state model.

PARP-1 (Poly (ADP-ribose) polymerase 1) is a nuclear enzyme and plays a key role in many cellular functions, such as DNA repair, modulation of chromatin structure, and recombination. Developing the PARP-1 inhibitors has emerged as an effective therapeutic strategy for a growing list of cancers. The catalytic structural domain (CAT) of PARP-1 upon binding the inhibitor allosterically regulates the conformational changes of helix domain (HD), affecting its identification with the damaged DNA. The typical type I (EB47) and III (veliparib) inhibitors were able to lengthening or shortening the retention time of this enzyme on DNA damage and thus regulating the cytotoxicity. Nonetheless, the basis underlying allosteric inhibition is unclear, which limits the development of novel PARP-1 inhibitors. Here, to investigate the distinct allosteric changes of EB47 and veliparib against PARP-1 CAT, each complex was simulated via classical and Gaussian accelerated molecular dynamics (cMD and GaMD). To study the reverse allosteric basis and mutation effects, the complexes PARP-1 with UKTT15 and PARP-1 D766/770A mutant with EB47 were also simulated. Importantly, the markov state models were built to identify the transition pathways of crucial substates of allosteric communication and the induction basis of PARP-1 reverse allostery. The conformational change differences of PARP-1 CAT regulated by allosteric inhibitors were concerned with to their interaction at the active site. Energy calculations suggested the energy advantage of EB47 in inhibiting the wild-type PARP-1, compared with D766/770A PARP-1. Secondary structure results showed the change of two key loops (αB-αD and αE-αF) in different systems. This work reported the basis of PARP-1 allostery from both thermodynamic and kinetic views, providing the guidance for the discovery and design of more innovative PARP-1 allosteric inhibitors.

Endothelial discoidin domain receptor 1 senses flow to modulate YAP activation.

Mechanotransduction in endothelial cells is critical to maintain vascular homeostasis and can contribute to disease development, yet the molecules responsible for sensing flow remain largely unknown. Here, we demonstrate that the discoidin domain receptor 1 (DDR1) tyrosine kinase is a direct mechanosensor and is essential for connecting the force imposed by shear to the endothelial responses. We identify the flow-induced activation of endothelial DDR1 to be atherogenic. Shear force likely causes conformational changes of DDR1 ectodomain by unfolding its DS-like domain to expose the buried cysteine-287, whose exposure facilitates force-induced receptor oligomerization and phase separation. Upon shearing, DDR1 forms liquid-like biomolecular condensates and co-condenses with YWHAE, leading to nuclear translocation of YAP. Our findings establish a previously uncharacterized role of DDR1 in directly sensing flow, propose a conceptual framework for understanding upstream regulation of the YAP signaling, and offer a mechanism by which endothelial activation of DDR1 promotes atherosclerosis.

An Intelligent Multi-Local Model Bearing Fault Diagnosis Method Using Small Sample Fusion.

It is essential to accurately diagnose bearing faults to avoid property losses or casualties in the industry caused by motor failures. Recently, the methods of fault diagnosis for bearings using deep learning methods have improved the safety of motor operations in a reliable and intelligent way. However, most of the work is mainly suitable for situations where there is sufficient monitoring data of the bearings. In industrial systems, only a small amount of monitoring data can be collected by the bearing sensors due to the harsh monitoring conditions and the short time of the signals of some special motor bearings. To solve the issue above, this paper introduces a transfer learning strategy by focusing on the multi-local model bearing fault based on small sample fusion. The algorithm mainly includes the following steps: (1) constructing a parallel Bi-LSTM sub-network to extract features from bearing vibration and current signals of industrial motor bearings, serially fusing the extracted vibration and current signal features for fault classification, and using them as a source domain fault diagnosis model; (2) measuring the distribution difference between the source domain bearing data and the target bearing data using the maximum mean difference algorithm; (3) based on the distribution differences between the source domain and the target domain, transferring the network parameters of the source domain fault diagnosis model, fine-tuning the network structure of the source domain fault diagnosis model, and obtaining the target domain fault diagnosis model. A performance evaluation reveals that a higher fault diagnosis accuracy under small sample fusion can be maintained by the proposed method compared to other methods. In addition, the early training time of the fault diagnosis model can be reduced, and its generalization ability can be improved to a great extent. Specifically, the fault diagnosis accuracy can be improved to higher than 80% while the training time can be reduced to 15.3% by using the proposed method.

Improved cycling performance of silicon-based nanocomposites with pyrolytic polyacrylonitrile for lithium-ion batteries.

The Si/FeSi2@C composite material offers several advantages due to its unique design. It effectively combines the high capacity and safety features of the Si negative electrode with FeSi2's stabilizing properties. By incorporating a homogeneous carbon layer, the composite material enhances electrical conductivity and provides structural support, thereby mitigating the detrimental effects of significant volume expansion resulting from repeated insertion and extraction of lithium ions. Furthermore, the composite material contributes to stabilizing the solid-electrolyte interphase (SEI) film, which is a critical factor in battery performance. The improved SEI film stability, combined with the overall enhancement in electronic conductivity, significantly enhances the performance of the negative electrode. Test results demonstrate that the composite, consisting of pyrolyzed polyacrylonitrile and Si/FeSi2nanoparticles, exhibits excellent electrochemical properties. During the first charging cycle, the composite material achieves a specific capacity of 1280 mAh g-1. Impressively, after 200 cycles, the specific capacity of the composite doubles compared to that of the raw material, indicating a remarkable improvement in cycling stability. These findings highlight the positive impact of rational material design on the performance of the Si/FeSi2@C composites.

Combinatorial MAB-Based Joint Channel and Spreading Factor Selection for LoRa Devices.

Long-Range (LoRa) devices have been deployed in many Internet of Things (IoT) applications due to their ability to communicate over long distances with low power consumption. The scalability and communication performance of the LoRa systems are highly dependent on the spreading factor (SF) and channel allocations. In particular, it is important to set the SF appropriately according to the distance between the LoRa device and the gateway since the signal reception sensitivity and bit rate depend on the used SF, which are in a trade-off relationship. In addition, considering the surge in the number of LoRa devices recently, the scalability of LoRa systems is also greatly affected by the channels that the LoRa devices use for communications. It was demonstrated that the lightweight decentralized learning-based joint channel and SF-selection methods can make appropriate decisions with low computational complexity and power consumption in our previous study. However, the effect of the location situation of the LoRa devices on the communication performance in a practical larger-scale LoRa system has not been studied. Hence, to clarify the effect of the location situation of the LoRa devices on the communication performance in LoRa systems, in this paper, we implemented and evaluated the learning-based joint channel and SF-selection methods in a practical LoRa system. In the learning-based methods, the channel and SF are decided only based on the ACKnowledge information. The learning methods evaluated in this paper were the Tug of War dynamics, Upper Confidence Bound 1, and ϵ-greedy algorithms. Moreover, to consider the relevance of the channel and SF, we propose a combinational multi-armed bandit-based joint channel and SF-selection method. Compared with the independent methods, the combinations of the channel and SF are set as arms. Conversely, the SF and channel are set as independent arms in the independent methods that are evaluated in our previous work. From the experimental results, we can see the following points. First, the combinatorial methods can achieve a higher frame success rate and fairness than the independent methods. In addition, the FSR can be improved by joint channel and SF selection compared to SF selection only. Moreover, the channel and SF selection dependents on the location situation to a great extent.

Gasotransmitters in non-alcoholic fatty liver disease: just the tip of the iceberg.

Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by fatty lesions and fat accumulation in hepatic parenchymal cells, which is in the absence of excessive alcohol consumption or definite liver damage factors. The exact pathogenesis of NAFLD is not fully understood, but it is now recognized that oxidative stress, insulin resistance, and inflammation are essential mechanisms involved in the development and treatment of NAFLD. NAFLD therapy aims to stop, delay or reverse disease progressions, as well as improve the quality of life and clinical outcomes of patients with NAFLD. Gasotransmitters are produced by enzymatic reactions, regulated through metabolic pathways in vivo, which can freely penetrate cell membranes with specific physiological functions and targets. Three gasotransmitters, nitric oxide, carbon monoxide, and hydrogen sulfide have been discovered. Gasotransmitters exhibit the effects of anti-inflammatory, anti-oxidant, vasodilatory, and cardioprotective agents. Gasotransmitters and their donors can be used as new gas-derived drugs and provide new approaches to the clinical treatment of NAFLD. Gasotransmitters can modulate inflammation, oxidative stress, and numerous signaling pathways to protect against NAFLD. In this paper, we mainly review the status of gasotransmitters research on NAFLD. It provides clinical applications for the future use of exogenous and endogenous gasotransmitters for the treatment of NAFLD.

Therapeutic modulation of V Set and Ig domain-containing 4 (VSIG4) signaling in immune and inflammatory diseases.

Inflammation is the result of acute and chronic stresses, caused by emotional or physical trauma, or nutritional or environmental pollutants, and brings serious harm to human life and health. As an important cellular component of the innate immune barrier, the macrophage plays a key role in maintaining tissue homeostasis and promoting tissue repair by controlling infection and resolving inflammation. Several studies suggest that V Set and Ig domain-containing 4 is specifically expressed in tissue macrophages and is associated with a variety of inflammatory diseases. In this paper, we mainly summarize the recent research on V Set and Ig domain-containing 4 structures, functions, function and roles in acute and chronic inflammatory diseases, and provide a novel therapeutic avenue for the treatment of inflammatory diseases, including nervous system, urinary, respiratory and metabolic diseases.

Pairing Optimization via Statistics: Algebraic Structure in Pairing Problems and Its Application to Performance Enhancement.

Fully pairing all elements of a set while attempting to maximize the total benefit is a combinatorically difficult problem. Such pairing problems naturally appear in various situations in science, technology, economics, and other fields. In our previous study, we proposed an efficient method to infer the underlying compatibilities among the entities, under the constraint that only the total compatibility is observable. Furthermore, by transforming the pairing problem into a traveling salesman problem with a multi-layer architecture, a pairing optimization algorithm was successfully demonstrated to derive a high-total-compatibility pairing. However, there is substantial room for further performance enhancement by further exploiting the underlying mathematical properties. In this study, we prove the existence of algebraic structures in the pairing problem. We transform the initially estimated compatibility information into an equivalent form where the variance of the individual compatibilities is minimized. We then demonstrate that the total compatibility obtained when using the heuristic pairing algorithm on the transformed problem is significantly higher compared to the previous method. With this improved perspective on the pairing problem using fundamental mathematical properties, we can contribute to practical applications such as wireless communications beyond 5G, where efficient pairing is of critical importance. As the pairing problem is a special case of the maximum weighted matching problem, our findings may also have implications for other algorithms on fully connected graphs.

Ropivacaine Induces Cell Cycle Arrest in the G0/G1 Phase and Apoptosis of PC12 Cells via Inhibiting Mitochondrial STAT3 Translocation.

STAT3 has neuroprotective effect via non-canonical activation and mitochondrial translocation, but its effect on ropivacaine-induced neurotoxicity remains unclear. Our previous study revealed that apoptosis was an important mechanism of ropivacaine-induced neurotoxicity; this study is to illustrate the relationship between STAT3 with ropivacaine-induced apoptosis. Those results showed that ropivacaine treatment decreased cell viability, induced cell cycle arrest in the G0/G1 phase, apoptosis, oxidative stress, and mitochondrial dysfunction in PC12 cells. Moreover, ropivacaine decreased the phosphorylated levels of STAT3 at Ser727 and downregulated the expression of STAT3 upstream gene IL-6. The mitochondrial translocation of STAT3 was also hindered by ropivacaine. To further illustrate the connection of STAT3 protein structure with ropivacaine, the autodock-vina was used to examine the interaction between STAT3 and ropivacaine, and the results showed that ropivacaine could bind to STAT3's proline site and other sites. In addition, the activator and inhibitor of mitoSTAT3 translocation were used to demonstrate it was involved in ropivacaine-induced apoptosis; the results showed that enhancing the mitochondrial STAT3 translocation could prevent ropivacaine-induced apoptosis. Finally, the expression of p-STAT3 and the levels of apoptosis in the spinal cord were also detected; the results were consistent with the cell experiment; ropivacaine decreased the expression of p-STAT3 protein and increased the levels of apoptosis in the spinal cord. We demonstrated that ropivacaine induced apoptosis by inhibiting the phosphorylation of STAT3 at Ser727 and the mitochondrial STAT3 translocation. This effect was reversed by the activation of the mitochondrial STAT3 translocation.

A Sensitive Secondary Users Selection Algorithm for Cognitive Radio Ad Hoc Networks.

Secondary Users (SUs) are allowed to use the temporarily unused licensed spectrum without disturbing Primary Users (PUs) in Cognitive Radio Ad Hoc Networks (CRAHNs). Existing architectures for CRAHNs impose energy-consuming Cognitive Radios (CRs) on SUs. However, the advanced CRs will increase energy cost for their cognitive functionalities, which is undesirable for the battery powered devices. A new architecture referred to as spectral Requirement-based CRAHN (RCRAHN) is proposed to enhance energy efficiency for CRAHNs in this paper. In RCRAHNs, only parts of SUs are equipped with CRs. SUs equipped with CRs are referred to as Cognitive Radio Users (CRUs). To further enhance energy efficiency of CRAHNs, we aim to select minimum CRUs to sense available spectrum. A non-linear programming problem is mathematically formulated under the constraints of energy efficiency and real-time. Considering the NP-hardness of the problem, a framework of a heuristic algorithm referred to as Sensitive Secondary Users Selection (SSUS) was designed to compute the near-optimal solutions. The simulation results demonstrate that SSUS not only improves the energy efficiency, but also achieves satisfied performances in end-to-end delay and communication reliability.