Explore the vast range of titles available.

It has been suggested that stress responses induced by fasting have analgesic effects on nociception by elevating the levels of stress-related hormones, while there is limited understanding of pain control mechanisms. Here, we investigated whether acute or intermittent fasting alleviates formalin-induced pain in mice and whether spinal orexin A (OXA) plays a role in this process. 6, 12, or 24 h acute fasting (AF) and 12 or 24 h intermittent fasting (IF) decreased the second phase of pain after intraplantar formalin administration. There was no difference in walking time in the rota-rod test and distance traveld in the open field test in all groups. Plasma corticosterone level and immobility time in the forced swim test were increased after 12 h AF, but not after 12 h IF. 12 h AF and IF increased not only the activation of OXA neurons in the lateral hypothalamus but also the expression of OXA in the lateral hypothalamus and spinal cord. Blockade of spinal orexin 1 receptor with SB334867 restored formalin-induced pain and spinal c-Fos immunoreactivity that were decreased after 12 h IF. These results suggest that 12 h IF produces antinociceptive effects on formalin-induced pain not by corticosterone elevation but by OXA-mediated pathway.

Although bipolar electrograms (Bi-egms) are commonly used for catheter mapping and ablation of cardiac arrhythmias, the accuracy and reproducibility of Bi-egms have not been evaluated. We aimed to clarify the influence of the catheter orientation (CO), catheter contact angle (CA), local conduction velocity (CV), scar size, and catheter type on the Bi-egm morphology using an in silico 3-dimensional realistic model of atrial fibrillation. We constructed a 3-dimensional, realistic, in silico left atrial model with activation wave propagation including bipolar catheter models. Bi-egms were obtained by computing the extracellular potentials from the distal and proximal electrodes. The amplitude and width were measured on virtual Bi-egms obtained under different conditions created by changing the CO according to the wave direction, catheter-atrial wall CA, local CV, size of the non-conductive area, and catheter type. Bipolar voltages were also compared between virtual and clinically acquired Bi-egms. Bi-egm amplitudes were lower for a perpendicular than parallel CO relative to the wave direction (p<0.001), lower for a 90° than 0° CA (p<0.001), and lower for a CV of 0.13m/s than 0.48m/s (p<0.001). Larger sized non-conductive areas were associated with a decreased bipolar amplitude (p<0.001) and increased bipolar width (p<0.001). Among three commercially available catheters (Orion, Pentaray, and Thermocool), those with more narrowly spaced and smaller electrodes produced higher voltages on the virtual Bi-egms (p<0.001). Multiple factors including the CO, CA, CV, and catheter design significantly influence the Bi-egm morphology. Universal voltage cut-off values may not be appropriate for bipolar voltage-guided substrate mapping.

(1) Background: The government of South Korea has established a nationwide web- and mobile-based emergency teleconsultation network by designating urban and rural hospitals. The purpose of this study is to analyze the characteristics and effectiveness of the tele-emergency system in South Korea. (2) Methods: Tele-emergency consultation cases from May 2015 to December 2018 were analyzed in the present study. The definition of a tele-emergency in the present study is an emergency consultation between doctors in rural and urban hospitals via a web- and mobile-based remote emergency consultation system (RECS). Consultations through an RECS are grouped into three categories: medical procedure or treatment guidance, image interpretation, and transportation requests. The present study analyzed the characteristics of the tele-emergency system and the reduction in unnecessary transportation (RUT). (3) Results: A total of 2604 cases were analyzed in the present study from 2985 tele-emergency consultation cases. A total of 381 cases were excluded for missing data. Consultations for image interpretation were the most common in trauma cases (71.3%), while transfer requests were the most common in non-trauma cases (50.3%). Trauma patients were more frequently admitted to rural hospitals or discharged and followed up with at rural hospitals (20.3% vs. 40.5%) after consultations. In terms of disease severity, non-severe cases were statistically higher in trauma cases (80.6% vs. 59.4%; p < 0.001). The RUT was statistically highly associated with trauma cases (60.8% vs. 42.8%; p < 0.001). In an analysis that categorized cases by region, a statistically higher proportion of transportation was used in island regions (69.9% vs. 49.5%; p < 0.003). More RUT was associated with non-island regions (30.1% vs. 50.5%; p = 0.001). (4) Conclusions: The tele-emergency system had a great role in reducing unnecessary patient transportation in non-severe trauma cases and non-island rural area emergency cases. Further research is needed for a cost/benefit analysis and clinical outcomes.

Common friend and place recommendation services in Location-based Social Network (LBSN) is based on user’s location tracking. However, since each user can do different activities even in the same place, location data is not enough to provide accurate recommendation for LSBN. To address this problem, Activity-based friend and place Recommendation System (ARS) is proposed. ARS considers two additional factors to improve recommendation accuracy: time and activity. ARS collects the time-related activity and location data from users through the developed scheduler application and then performs the recommendation for users based on the calculated similarity among them. Performance evaluation shows that ARS can provide accurate recommendation between users who have similar activity and location patterns according to time.

Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server where the classification is performed based on its computing resource. However, real-time ECG data transmission continuously consumes battery and network resources, which are expensive and limited. To mitigate this problem, this paper proposes a tiny machine learning (TinyML)-based classification (i.e., TinyCES), where the ECG monitoring device performs the classification by itself based on the machine-learning model, which can reduce the memory and the network resource usages for the classification. To demonstrate the feasibility, after we configure the convolutional neural networks (CNN)-based model using ECG data from the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt (PTB) diagnostic ECG databases, TinyCES is validated using the TinyML-supported Arduino prototype. The performance results show that TinyCES can have an approximately 97% detection ratio, which means that it has great potential to be a lightweight and resource-efficient ECG monitoring system.

Hollow fiber membrane contactors (HFMCs) provide a large specific surface area. Thus, their significantly reduced volume provides an advantage compared to the conventional gas–liquid contactor. In this study, the selective removal efficiency of flue gas, in which sulfur oxide (SO2) and carbon dioxide (CO2) coexist, was measured using a polypropylene (PP) HFMC with such advantages. To increase the selective removal efficiency of SO2, experiments were conducted using various alkaline absorbents. As a result, with 0.05 M ammonia solution, the removal efficiency of 95% or more was exhibited with continuous operation for 100 h or more. We confirmed that the absorbent saturated by the once-through mode was aqueous ammonium sulfate ((NH4)2SO4) solution and could be used as a fertilizer without additional processing.

Accurate diagnosis is essential for effective cancer treatment, particularly in peritoneal surface malignancies, where failure to detect metastatic lesions can mislead the treatment plan. This study assessed the diagnostic accuracy of staging laparoscopy using the integration of artificial intelligence (AI)‐guided photodynamic diagnosis (PDD) with the photosensitizer Phonozen, activated at 405 nm in a rabbit model. To create peritoneal carcinomatosis, VX2 cells were inoculated laparoscopically into the peritoneum of female white New Zealand rabbits. Conventional and PDD‐guided laparoscopy utilized a customized light source that emitted broad‐spectrum white light or 405‐nm blue light, respectively. The surgical procedure comprised a tripartite approach: exploration and labeling of suspected nodules under white‐light visualization, identification of additional metastatic tumors under blue‐excitation fluorescent light, and confirmatory open laparotomy to locate overlooked nodules by palpation. Our results showed that the initial experimental data from 371 nodules in 14 rabbits, comparing conventional diagnostic laparoscopy and PDD, showed increased detection sensitivity from 67% ± 1.9% (conventional) to 98% ± 0.7% (PDD) in the small‐size nodule. In the second experimental data set from 265 nodules in 10 rabbits, the addition of a real‐time AI algorithm further increased the sensitivity to 100% ± 0.0%. Combining PDD with AI enhances the detection of peritoneal cancer metastasis in staging laparoscopy. This study demonstrates the potential of combining photodynamic diagnosis (PDD) with artificial intelligence (AI) to significantly improve the detection of peritoneal metastases in a rabbit model. The PDD‐AI approach has substantial clinical implications, particularly in the early detection of peritoneal carcinomatosis, which is crucial for effective surgical planning, optimizing treatment strategies, and reducing cancer recurrence. The findings suggest that PDD‐AI could become a valuable tool in managing peritoneal surface malignancies, such as advanced gastric cancer, leading to improved patient outcomes.

At livestock farms, the most important thing is to control odors released from manure. In this study, four commercially available microbial additives designed to control odor and NH 3 emissions were applied to swine slurries stored in containers, and their effectiveness in odor reduction was statistically evaluated. Seventeen different odorous compounds in the headspace of each container were analyzed to calculate an overall odor index for slurries treated with different microbial additives over time. Of the four microbial additives tested in this study, only two were effective in reducing the odors from the swine slurry. After a 80-day storage period, the odor indexes of the slurries could be reduced by over 70 % with 50 % reduction in volatile fatty acids. In addition, a significant five orders of magnitude reduction in Escherichia coli could be achieved within 60 days. The other two microbial additives did not affect the odor characteristics of swine slurries under storage; their time profiles were statistically identical with that of the control. Results of this study imply that farmers considering applying microbial additives for controlling odors from swine manure should be careful in choosing an additive.

Municipal Solid Waste (MSW) planning requires accurate estimation of waste amounts from different material types. However, the scarcity of reliable data about material-specific waste quantities prohibits the development of robust models. We leverage a dataset containing publicly available demographic, economic, and spatial features along with waste sampling reports from several municipalities across the US to help fill this gap. We focus on enhancing the prediction of MSW composition (i.e., the percentages of specific material types in the waste stream), which represents the first key phase of a recently proposed two-phase prediction strategy (the second phase estimates the overall MSW quantity). To this end, we develop a more accurate model based on Random Forests (RF) and deploy it to predict MSW composition across 43 material categories at the county scale. We pair the model with SHAP (SHapely Additive exPlanations) value analysis to identify 24 significant features from a total of 231 demographic, economic, and spatial predictors. Notably, the proposed approach achieves an R2 value of 0.747. Altogether, the study illustrates an effective application of machine learning for strategic waste management planning.