Side Effects of Opioids Are Ameliorated by Regulating TRPV1 Receptors

Humans have used opioids to suppress moderate to severe pain for thousands of years. However, the long-term use of opioids has several adverse effects, such as opioid tolerance, opioid-induced hyperalgesia, and addiction. In addition, the low efficiency of opioids in controlling neuropathic pain limits their clinical applications. Combining nonopioid analgesics with opioids to target multiple sites along the nociceptive pathway may alleviate the side effects of opioids. This study reviews the feasibility of reducing opioid side effects by regulating the transient receptor potential vanilloid 1 (TRPV1) receptors and summarizes the possible underlying mechanisms. Blocking and activating TRPV1 receptors can improve the therapeutic profile of opioids in different manners. TRPV1 and μ-opioid receptors are bidirectionally regulated by β-arrestin2. Thus, drug combinations or developing dual-acting drugs simultaneously targeting μ-opioid and TRPV1 receptors may mitigate opioid tolerance and opioid-induced hyperalgesia. In addition, TRPV1 receptors, especially expressed in the dorsal striatum and nucleus accumbens, participate in mediating opioid reward, and its regulation can reduce the risk of opioid-induced addiction. Finally, co-administration of TRPV1 antagonists and opioids in the primary action sites of the periphery can significantly relieve neuropathic pain. In general, the regulation of TRPV1 may potentially ameliorate the side effects of opioids and enhance their analgesic efficacy in neuropathic pain.</jats:p

Side Effects of Opioids Are Ameliorated by Regulating TRPV1 Receptors

Humans have used opioids to suppress moderate to severe pain for thousands of years. However, the long-term use of opioids has several adverse effects, such as opioid tolerance, opioid-induced hyperalgesia, and addiction. In addition, the low efficiency of opioids in controlling neuropathic pain limits their clinical applications. Combining nonopioid analgesics with opioids to target multiple sites along the nociceptive pathway may alleviate the side effects of opioids. This study reviews the feasibility of reducing opioid side effects by regulating the transient receptor potential vanilloid 1 (TRPV1) receptors and summarizes the possible underlying mechanisms. Blocking and activating TRPV1 receptors can improve the therapeutic profile of opioids in different manners. TRPV1 and mu-opioid receptors are bidirectionally regulated by beta-arrestin2. Thus, drug combinations or developing dual-acting drugs simultaneously targeting mu-opioid and TRPV1 receptors may mitigate opioid tolerance and opioid-induced hyperalgesia. In addition, TRPV1 receptors, especially expressed in the dorsal striatum and nucleus accumbens, participate in mediating opioid reward, and its regulation can reduce the risk of opioid-induced addiction. Finally, co-administration of TRPV1 antagonists and opioids in the primary action sites of the periphery can significantly relieve neuropathic pain. In general, the regulation of TRPV1 may potentially ameliorate the side effects of opioids and enhance their analgesic efficacy in neuropathic pain.</p

Federated Learning Incentive Mechanism Setting in UAV-Assisted Space&ndash;Terrestrial Integration Networks

The UAV-assisted space&ndash;terrestrial integrated network provides extensive coverage and high flexibility in communication services. UAVs and ground terminals collaborate to train models and provide services. In order to protect data privacy, federated learning is widely used. However, the participation of UAVs and ground terminals is not gratuitous, and reasonable incentives for federated learning need to be set up to encourage their participation. To address the above issues, this paper proposes a federated reliable incentive mechanism based on hierarchical reinforcement learning. The mechanism allocates inter-round incentives at the upper level to ensure the maximisation of the server&rsquo;s utility, and performs inter-client incentive allocation at the lower level to ensure the minimisation of each round&rsquo;s latency. The reasonable incentive allocation enables the central server to achieve higher model training accuracy under the limited incentive budget, which reduces the cost of model training. At the same time, an attack detection mechanism is implemented to identify malicious clients participating in federated learning, preventing their involvement in aggregation and revoking their incentives. This better ensures the security of model training. Finally, we conducted experiments on Fmnist, and the results indicate that this method effectively improves the accuracy and security of model training

A Fairness-Enhanced Federated Learning Scheduling Mechanism for UAV-Assisted Emergency Communication

As the frequency of natural disasters increases, the study of emergency communication becomes increasingly important. The use of federated learning (FL) in this scenario can facilitate communication collaboration between devices while protecting privacy, greatly improving system performance. Considering the complex geographic environment, the flexible mobility and large communication radius of unmanned aerial vehicles (UAVs) make them ideal auxiliary devices for wireless communication. Using the UAV as a mobile base station can better provide stable communication signals. However, the number of ground-based IoT terminals is large and closely distributed, so if all of them transmit data to the UAV, the UAV will not be able to take on all of the computation and communication tasks because of its limited energy. In addition, there is competition for spectrum resources among many terrestrial devices, and all devices transmitting data will bring about an extreme shortage of resources, which will lead to the degradation of model performance. This will bring indelible damage to the rescue of the disaster area and greatly threaten the life safety of the vulnerable and injured. Therefore, we use user scheduling to select some terrestrial devices to participate in the FL process. In order to avoid the resource waste generated by the terrestrial device resource prediction, we use the multi-armed bandit (MAB) algorithm for equipment evaluation. Considering the fairness issue of selection, we try to replace the single criterion with multiple criteria, using model freshness and energy consumption weighting as reward functions. The state of the art of our approach is demonstrated by simulations on the datasets

How to improve new product performance through customer relationship management and product development management: evidence from China

PurposeThis paper aims to investigate how to improve new product performance in turbulent circumstances of emerging economies.Design/methodology/approachThis paper used regression analysis to examine the performance impact of customer relationship management (CRM) and product development management (PDM) concentration strategy in new product development (NPD). A detailed contingent analysis of the market and institutional environments in emerging economies is also conducted based on a survey of 114 Chinese high-tech manufacturers.FindingsThe research findings show that PDM has a stronger positive effect on new product performance than CRM in emerging economies and that the contingent effects of the market and institutional environment vary. More specifically, technological turbulence and enforcement inefficiency can positively moderate the relationship between CRM and new product performance, whereas the moderating effect of market turbulence on CRM is negative. Meanwhile, enforcement inefficiency negatively moderates the effect of PDM on new product performance, while the moderating effect of market turbulence on PDM is positive.Research limitations/implicationsThis paper is limited to a survey of high-tech manufacturing enterprises in China. Further research should continues to explore and document the strategic issue about NPD in emerging economies by longitudinal study.Originality/valueThis paper contributed to theoretical and practical initiatives on the strategic issue of NPD and provided firms a further understanding of how to select the right NPD strategy in emerging economies to improve new product performance.</jats:sec

Neuronal mechanisms of nociceptive-evoked gamma-band oscillations in rodents

Gamma-band oscillations (GBOs) in the primary somatosensory cortex (S1) play key roles in nociceptive processing. Yet, one crucial question remains unaddressed: what neuronal mechanisms underlie nociceptiveevoked GBOs? Here, we addressed this question using a range of somatosensory stimuli (nociceptive and non-nociceptive), neural recording techniques (electroencephalography in humans and silicon probes and calcium imaging in rodents), and optogenetics (alone or simultaneously with electrophysiology in mice). We found that (1) GBOs encoded pain intensity independent of stimulus intensity in humans, (2) GBOs in S1 encoded pain intensity and were triggered by spiking of S1 interneurons, (3) parvalbumin (PV)-positive interneurons preferentially tracked pain intensity, and critically, (4) PV S1 interneurons causally modulated GBOs and pain-related behaviors for both thermal and mechanical pain. These findings provide causal evidence that nociceptive-evoked GBOs preferentially encoding pain intensity are generated by PV interneurons in S1, thereby laying a solid foundation for developing GBO-based targeted pain therapies.</p