Beacon-based sleep-wake monitoring in dogs.

The sleep-wake cycle represents a crucial physiological process essential for maintaining homeostasis and promoting individual growth. In dogs, alterations in sleep patterns associated with age and dog's correlation with temperament factors, such as nervousness, have been reported, and there is an increasing demand for precise monitoring of sleep and physical activity in dogs. The present study aims to develop an analysis method for measuring sleep-wake patterns and physical activity in dogs by utilizing an accelerometer and a smartphone. By analyzing time series data collected from the accelerometer attached to the dog's collar, a comprehensive sleep and activity analysis model was constructed. This model classified the activity level into seven classes and effectively highlighted the variations in sleep-activity patterns. Two classes with lower activity levels were considered as sleep, while other five levels were regarded as wake based on the rate of occurrence. This protocol of data acquisition and analysis provides a methodology that enables accurate and extended evaluation of both sleep and physical activity in dogs.

Predictive Modeling for Hospital Readmissions for Patients with Heart Disease: An updated review from 2012-2023.

Hospital readmissions are a major concern for healthcare leaders, policy makers, and patients, resulting in adverse health outcomes and imposing an increased burden on hospital resources. This review aims to synthesize existing literature on predictive models focused on patients diagnosed with heart disease, which is known for its high readmission rates. Seven databases (i.e., Web of Science, Scopus, PubMed, ProQuest, Ovid, Cochrane Library and EBSCO) were consulted resulting in the inclusion of 56 eligible studies. Among these, 44 focused on model development, 7 on model validation, 4 on model improvement, and 1 on model implementation. Data were extracted on readmission types, data sources, modeling methods, and predictors, while assessments were conducted to analyze the quality of the studies. Findings showed that readmission types were significantly influenced by policy decisions, data predominantly originated from hospitals, and the prevalent modeling methods used were regression and single-layer machine learning techniques. The most important clinical predictors were related to comorbidities and complications, while the key demographic predictors were age and race. The study found that, despite advancements during the last decade, several limitations exist in current research, particularly in addressing attrition bias and handling missing data. Future research should, therefore, focus on optimizing readmission types, enhancing model generalization, using interpretable models, and emphasizing model implementation.

Association between postinduction hypotension and postoperative mortality: a single-centre retrospective cohort study.

PURPOSE: We aimed to elucidate whether postinduction hypotension (PIH), defined as hypotension between anesthesia induction and skin incision, and intraoperative hypotension (IOH) are associated with postoperative mortality. METHODS: We conducted a retrospective cohort study of adult patients with an ASA Physical Status I-IV who underwent noncardiac and nonobstetric surgery under general anesthesia between 2015 and 2021 at Nagoya City University Hospital. The primary and secondary outcomes were 30-day and 90-day postoperative mortality, respectively. We calculated four hypotensive indices (with time proportion of the area under the threshold being the primary exposure variable) to evaluate the association between hypotension (defined as a mean blood pressure < 65 mm Hg) and mortality using multivariable logistic regression models. We used propensity score matching and RUSBoost (random under-sampling and boosting), a machine-learning model for imbalanced data, for sensitivity analyses. RESULTS: Postinduction hypotension and IOH were observed in 82% and 84% of patients, respectively. The 30-day and 90-day postoperative mortality rates were 0.4% (52/14,210) and 1.0% (138/13,334), respectively. Postinduction hypotension was not associated with 30-day mortality (adjusted odds ratio [aOR], 1.03; 95% confidence interval [CI], 0.93 to 1.13; P = 0.60) and 90-day mortality (aOR, 1.01; 95% CI, 0.94 to 1.07; P = 0.82). Conversely, IOH was associated with 30-day mortality (aOR, 1.19; 95% CI, 1.12 to 1.27; P < 0.001) and 90-day mortality (aOR, 1.12; 95% CI, 1.06 to 1.19; P < 0.001). Sensitivity analyses supported the association of IOH but not PIH with postoperative mortality. CONCLUSION: Despite limitations, including power and residual confounding, postoperative mortality was associated with IOH but not with PIH.

RéSUMé: OBJECTIF: Nous avons cherché à déterminer si l'hypotension post-induction (HPI), définie comme une hypotension entre l'induction de l'anesthésie et l'incision cutanée, et l'hypotension peropératoire (HPO) étaient associées à la mortalité postopératoire. MéTHODE: Nous avons mené une étude de cohorte rétrospective de patient·es adultes ayant un statut physique I-IV selon l'ASA et ayant bénéficié d'une chirurgie non cardiaque et non obstétricale sous anesthésie générale entre 2015 et 2021 à l'Hôpital universitaire de la ville de Nagoya. Les critères d'évaluation principal et secondaire étaient la mortalité postopératoire à 30 et 90 jours, respectivement. Nous avons calculé quatre indices d'hypotension (la proportion temporelle de la zone sous le seuil étant la principale variable d'exposition) pour évaluer l'association entre l'hypotension (définie comme une tension artérielle moyenne < 65 mm Hg) et la mortalité à l'aide de modèles de régression logistique multivariée. Nous avons utilisé l'appariement par score de propension et le RUSBoost (sous-échantillonnage et boosting aléatoire), un modèle d'apprentissage automatique pour les données déséquilibrées, pour les analyses de sensibilité. RéSULTATS: Une HPI et une HPO ont été observées chez 82 % et 84 % des patient·es, respectivement. Les taux de mortalité postopératoire à 30 et 90 jours étaient respectivement de 0,4 % (52/14 210) et de 1,0 % (138/13 334). L'hypotension post-induction n'était pas associée à la mortalité à 30 jours (rapport de cotes ajusté [RCa], 1,03; intervalle de confiance [IC] à 95 %, 0,93 à 1,13; P = 0,60) et à la mortalité à 90 jours (RCa, 1,01; IC 95 %, 0,94 à 1,07; P = 0,82). À l'inverse, l'HPO était associée à une mortalité à 30 jours (RCa, 1,19; IC 95 %, 1,12 à 1,27; P < 0,001) et à la mortalité à 90 jours (RCa, 1,12; IC 95 %, 1,06 à 1,19; P < 0,001). Les analyses de sensibilité ont confirmé l'association de l'HPO, mais pas de l'HPI, avec la mortalité postopératoire. CONCLUSION: Malgré les limitations, y compris la puissance et persistance de facteurs confondants, la mortalité postopératoire était associée à l'hypotension peropératoire mais pas à l'hypotension post-induction seule.

Prediction model of postoperative pain exacerbation using an intravenous patient-controlled analgesia device and a wearable electrocardiogram sensor.

There is a need to develop objective and real-time postoperative pain assessment methods in perioperative medicine. Few studies have evaluated the relationship between pain severity and temporal changes of physiological signals in actual postoperative patients. In this study, we developed a machine learning model which was trained from intravenous patient-controlled analgesia (IV-PCA) records and electrocardiogram (ECG) of postoperative patients to predict pain exacerbation. A self-attentive autoencoder (SA-AE) model achieved 54% of sensitivity and a 1.76 times/h of false positive rate.Clinical relevance- We proposed a novel method for evaluating postoperative pain in real time and demonstrated the possibility of predicting pain exacerbation. The proposed method would realize the automatic administration of analgesics and the optimization of opioid doses.

FexSplice: A LightGBM-Based Model for Predicting the Splicing Effect of a Single Nucleotide Variant Affecting the First Nucleotide G of an Exon.

Single nucleotide variants (SNVs) affecting the first nucleotide G of an exon (Fex-SNVs) identified in various diseases are mostly recognized as missense or nonsense variants. Their effect on pre-mRNA splicing has been seldom analyzed, and no curated database is available. We previously reported that Fex-SNVs affect splicing when the length of the polypyrimidine tract is short or degenerate. However, we cannot readily predict the splicing effects of Fex-SNVs. We here scrutinized the available literature and identified 106 splicing-affecting Fex-SNVs based on experimental evidence. We similarly identified 106 neutral Fex-SNVs in the dbSNP database with a global minor allele frequency (MAF) of more than 0.01 and less than 0.50. We extracted 115 features representing the strength of splicing cis-elements and developed machine-learning models with support vector machine, random forest, and gradient boosting to discriminate splicing-affecting and neutral Fex-SNVs. Gradient boosting-based LightGBM outperformed the other two models, and the length and nucleotide compositions of the polypyrimidine tract played critical roles in the discrimination. Recursive feature elimination showed that the LightGBM model using 15 features achieved the best performance with an accuracy of 0.80 ± 0.12 (mean and SD), a Matthews Correlation Coefficient (MCC) of 0.57 ± 0.15, an area under the curve of the receiver operating characteristics curve (AUROC) of 0.86 ± 0.08, and an area under the curve of the precision-recall curve (AUPRC) of 0.87 ± 0.09 using a 10-fold cross-validation. We developed a web service program, named FexSplice that accepts a genomic coordinate either on GRCh37/hg19 or GRCh38/hg38 and returns a predicted probability of aberrant splicing of A, C, and T variants.

Individual resolvin E family members work distinctly and in a coordinated manner in the resolution of inflammation.

Three main E-type resolvins (RvEs): RvE1, RvE2, and RvE3, have roles in the resolution of inflammation as anti-inflammatory activities. To investigate the roles of each RvE in the resolution of inflammation, timing of interleukin (IL)- 10 release and IL-10 receptor expressions, and phagocytosis evoked by each RvE in differentiated human monocytes, macrophage-like U937 cells were examined. Here, we show that RvEs enhance the expression of IL-10, and IL-10 receptor-mediated signaling pathways and IL-10-mediated-signaling-independent resolution of inflammatory effects by activating the phagocytotic function. Thus, RvE2 mainly evoked an IL-10-mediated anti-inflammatory function, whereas RvE3 principally activated phagocytotic activity of macrophages, which may be involved in tissue repair. On the other hand, RvE1 showed both functions, although not prominent but rather acting as a relief mediator that takes over the RvE2 function and passes over to the RvE3 function. Therefore, each RvE may act as an important role/stage-specific mediator in a coordinated manner with other RvEs in the processes of the resolution of inflammation.

Learning curve of i-gel insertion in novices using a cumulative sum analysis.

The i-gel, a popular second-generation supraglottic airway device, has been used in a variety of airway management situations, including as an alternative to tracheal intubation for general anesthesia, rescue in difficult airway settings, and out-of-hospital cardiac arrest resuscitation. We aimed to investigate the number of experiences needed to achieve a rapid, highly successful first attempt i-gel insertion in novices with a cumulative sum analysis. We also looked at how learning affected success rates, insertion time, and bleeding and reflex (limb movement, frowning face, or coughing) incidences. This prospective observational study included 15 novice residents from March 2017 to February 2018 in a tertiary teaching hospital. Finally, 13 residents with 35 [30-42] (median [interquartile range]) cases of i-gel insertion were analyzed. The cumulative sum analysis showed that 11 of 13 participants had an acceptable failure rate after 15 [8-20] cases. With increasing experience, success rate (P = 0.004), insertion time (P < 0.001), and incidence of bleeding (P = 0.006) all improved. However, the incidence of reflex did not change (P = 0.43). Based on our results, we suggest that 20 cases are preferable for novices to develop skills in using the i-gel in airway management.

Delivering mRNA to Secondary Lymphoid Tissues by Phosphatidylserine-Loaded Lipid Nanoparticles.

Lipid nanoparticles (LNPs) are one of the most successful technologies in messenger RNA (mRNA) delivery. While the liver is the most frequent target for LNP delivery of mRNA, technologies for delivering mRNA molecules to extrahepatic tissues are also important. Herein, it is reported on the development of an LNP that targets secondary lymphoid tissues. New types of alcohol-soluble phosphatidylserine (PS) derivatives are designed as materials that target immune cells and then incorporated into LNPs using a microfluidic technique with a high degree of scalability and reproducibility. The resulting LNP that contained the synthesized PS delivered mRNA to the spleen much more efficiently compared to a control LNP. A sub-organ analysis revealed that the PS-loaded LNP is extensively taken up by tissue-resident macrophages in the red pulp and the marginal zone of the spleen. Thus, the PS-loaded LNP reported in this study will be a promising strategy for clinical applications that involve delivering mRNA to the spleen.

Helix-forming aliphatic homo-δ-peptide foldamers based on the conformational restriction effects of cyclopropane.

Considerable effort has been directed toward developing artificial peptide-based foldamers. However, detailed structural analysis of δ-peptide foldamers consisting of only aliphatic δ-amino acids has not been reported. Herein, we rationally designed and stereoselectively synthesized aliphatic homo-δ-peptides forming a stable helical structure by using a chiral cyclopropane δ-amino acid as a monomer unit. Structural analysis of the homo-δ-peptides using circular dichroism, infrared, and NMR spectroscopy indicated that they form a stable 14-helical structure in solution. Furthermore, we successfully conducted X-ray crystallographic analysis of the homo-δ-peptides, demonstrating a right-handed 14-helical structure. This helical structure of the crystal was consistent with those predicted by theoretical calculations and those obtained based on NMR spectroscopy in solution. This stable helical structure is due to the effective restriction of the backbone conformation by the structural characteristics of cyclopropane. This work reports the first example of aliphatic homo-δ-peptide foldamers having a stable helical structure both in the solution and crystal states.

Oxytocin Dynamics in the Body and Brain Regulated by the Receptor for Advanced Glycation End-Products, CD38, CD157, and Nicotinamide Riboside.

Investigating the neurocircuit and synaptic sites of action of oxytocin (OT) in the brain is critical to the role of OT in social memory and behavior. To the same degree, it is important to understand how OT is transported to the brain from the peripheral circulation. To date, of these, many studies provide evidence that CD38, CD157, and receptor for advanced glycation end-products (RAGE) act as regulators of OT concentrations in the brain and blood. It has been shown that RAGE facilitates the uptake of OT in mother's milk from the digestive tract to the cell surface of intestinal epithelial cells to the body fluid and subsequently into circulation in male mice. RAGE has been shown to recruit circulatory OT into the brain from blood at the endothelial cell surface of neurovascular units. Therefore, it can be said that extracellular OT concentrations in the brain (hypothalamus) could be determined by the transport of OT by RAGE from the circulation and release of OT from oxytocinergic neurons by CD38 and CD157 in mice. In addition, it has recently been found that gavage application of a precursor of nicotinamide adenine dinucleotide, nicotinamide riboside, for 12 days can increase brain OT in mice. Here, we review the evaluation of the new concept that RAGE is involved in the regulation of OT dynamics at the interface between the brain, blood, and intestine in the living body, mainly by summarizing our recent results due to the limited number of publications on related topics. And we also review other possible routes of OT recruitment to the brain.

Design and Synthesis of Cyclopropane Congeners of Resolvin E3, an Endogenous Pro-Resolving Lipid Mediator, as Its Stable Equivalents.

Resolvins are pro-resolving lipid mediators with highly potent anti-inflammatory effects. Because of their polyunsaturated structures, however, they are unstable to oxygen as a drug prototype. To address this issue, we designed and synthesized CP-RvE3 as oxidatively stable congeners of RvE3 by replacing the cis-olefin with a cis-cyclopropane to avoid the unstable bisallylic structure. Although the oxidative stabilities of CP-RvE3 were not improved, ß-CP-RvE3 was 3.7 times more metabolically stable than RvE3. Thus, we identified ß-CP-RvE3 as a metabolically stable equivalent.

Wearable sensor device-based detection of decreased heart rate variability in Parkinson's disease.

The evidence that heart rate variability (HRV) decreases during early Parkinson's disease (PD) largely depends on electrocardiogram data. In this study, we examined HRV in PD using wearable sensors and assessed various evaluation methods for detecting disease-related alterations. We evaluated 27 patients with PD and 23 disease controls. The wearable sensors POLAR V800 HR and POLAR H10 were used for the HRV measurements. The participants wore the two sensors for approximately 24 h, and long-term HRV data were acquired. We analyzed the standard deviation of normal R-R intervals (SDNN) and coefficient of variation of R-R intervals (CVRR) for every 100 consecutive beats. Focusing on the fluctuation of SDNN and CVRR, we extracted the minimum, first decile, first quartile, and median values of SDNN and CVRR. The area under the receiver operating characteristic curve (AUC) for each HRV parameter was calculated to differentiate PD from the disease controls. The minimum values of SDNN and CVRR had the highest AUC (SDNN: AUC 0.90, 95% confidence interval [CI] 0.78-0.96; CVRR: AUC 0.90, CI 0.76-0.96) among the evaluation methods tested. The minimum values of SDNN and CVRR were significantly decreased in PD (SDNN: 9.5 ± 4.0 ms vs. 4.4 ± 2.0 ms, p < 0.0001; CVRR: 1.15 ± 0.33% vs. 0.65 ± 0.24%, p < 0.0001). We detected decreased HRV in PD using wearable sensors. Analyzing the minimum values of the HRV parameter in long-term recordings appears to be appropriate for detecting the decrease in HRV in PD.

Synthesis of γ-Aminobutyric Acid (GABA) Analogues Conformationally Restricted by Bicyclo[3.1.0]hexane/hexene or [4.1.0]Heptane/heptene Backbones as Potent Betaine/GABA Transporter Inhibitors.

Novel γ-aminobutyric acid (GABA) analogues 3-5, having a bicyclo[3.1.0]hexene, [4.1.0]heptane, or [4.1.0]heptene backbone, respectively, were designed from the bioactive form analysis of the previous inhibitor 2 with a bicyclo[3.1.0]hexane backbone. Compounds 3-5 and 2 were synthesized from a common 1,7-diene intermediate 6 using ring-closing metathesis (RCM) to construct the key bicyclo backbones. Compounds 3-5 strongly inhibit betaine/GABA transporter 1 (BGT1) uptake, but compound 4 stands out with its selective low micromolar potency.

Medical checkup data analysis method based on LiNGAM and its application to nonalcoholic fatty liver disease.

Although medical checkup data would be useful for identifying unknown factors of disease progression, a causal relationship between checkup items should be taken into account for precise analysis. Missing values in medical checkup data must be appropriately imputed because checkup items vary from person to person, and items that have not been tested include missing values. In addition, the patients with target diseases or disorders are small in comparison with the total number of persons recorded in the data, which means medical checkup data is an imbalanced data analysis. We propose a new method for analyzing the causal relationship in medical checkup data to discover disease progression factors based on a linear non-Gaussian acyclic model (LiNGAM), a machine learning technique for causal inference. In the proposed method, specific regression coefficients calculated through LiNGAM were compared to estimate the causal strength of the checkup items on disease progression, which is referred to as LiNGAM-beta. We also propose an analysis framework consisting of LiNGAM-beta, collaborative filtering (CF), and a sampling approach for causal inference of medical checkup data. CF and the sampling approach are useful for missing value imputation and balancing of the data distribution. We applied the proposed analysis framework to medical checkup data for identifying factors of Nonalcoholic fatty liver disease (NAFLD) development. The checkup items related to metabolic syndrome and age showed high causal effects on NAFLD severity. The level of blood urea nitrogen (BUN) would have a negative effect on NAFLD severity. Snoring frequency, which is associated with obstructive sleep apnea, affected NAFLD severity, particularly in the male group. Sleep duration also affected NAFLD severity in persons over fifty years old. These analysis results are consistent with previous reports about the causes of NAFLD; for example, NAFLD and metabolic syndrome are mutual and bi-directionally related, and BUN has a negative effect on NAFLD progression. Thus, our analysis result is plausible. The proposed analysis framework including LiNGAM-beta can be applied to various medical checkup data and will contribute to discovering unknown disease factors.

R-R interval-based sleep apnea screening by a recurrent neural network in a large clinical polysomnography dataset.

OBJECTIVE: Easily detecting patients with undiagnosed sleep apnea syndrome (SAS) requires a home-use SAS screening system. In this study, we validate a previously developed SAS screening methodology using a large clinical polysomnography (PSG) dataset (N = 938). METHODS: We combined R-R interval (RRI) and long short-term memory (LSTM), a type of recurrent neural networks, and created a model to discriminate respiratory conditions using the training dataset (N = 468). Its performance was validated using the validation dataset (N = 470). RESULTS: Our method screened patients with severe SAS (apnea hypopnea index; AHI ≥ 30) with an area under the curve (AUC) of 0.92, a sensitivity of 0.80, and a specificity of 0.84. In addition, the model screened patients with moderate/severe SAS (AHI ≥ 15) with an AUC of 0.89, a sensitivity of 0.75, and a specificity of 0.87. CONCLUSIONS: Our method achieved high screening performance when applied to a large clinical dataset. SIGNIFICANCE: Our method can help realize an easy-to-use SAS screening system because RRI data can be easily measured with a wearable heart rate sensor. It has been validated on a large dataset including subjects with various backgrounds and is expected to perform well in real-world clinical practice.

Prediction of GABA receptor antagonist-induced convulsion in cynomolgus monkeys by combining machine learning and heart rate variability analysis.

Drug-induced convulsion is a severe adverse event; however, no useful biomarkers for it have been discovered. We propose a new method for predicting drug-induced convulsions in monkeys based on heart rate variability (HRV) and a machine learning technique. Because autonomic nervous activities are altered around the time of a convulsion and such alterations affect HRV, they may be predicted by monitoring HRV. In the proposed method, anomalous changes in multiple HRV parameters are monitored by means of a convulsion prediction model, and convulsion alarms are issued when abnormal changes in HRV are detected. The convulsion prediction model is constructed based on multivariate statistical process control (MSPC), a well-known anomaly detection algorithm in machine learning. In this study, HRV data were collected from four cynomolgus monkeys administered with multiple doses of pentylenetetrazol (PTZ) and picrotoxin (PTX), which are GABA receptor antagonists, as convulsant agents. In addition, low doses of pilocarpine (PILO) were administered as a negative control. Twelve HRV parameters in three hours after drug administration were monitored by means of the prediction model. The number and duration of convulsion alarms from HRV increased at medium and high doses of PTZ and PTX (1/3 or 1/4 of convulsion dose). On the other hand, the frequency of convulsion alarms did not increase with PILO. Although vomiting was observed in all drugs, convulsion alarms were not associated with the vomiting. Thus, convulsion alarms can be used as a biomarker for convulsions induced by GABA receptor antagonists.

Molecular Determinants and Pharmacological Analysis for a Class of Competitive Non-transported Bicyclic Inhibitors of the Betaine/GABA Transporter BGT1.

The betaine/GABA transporter 1 (BGT1) is a member of the GABA transporter (GAT) family with still elusive function, largely due to a lack of potent and selective tool compounds. Based on modeling, we here present the design, synthesis and pharmacological evaluation of five novel conformationally restricted cyclic GABA analogs related to the previously reported highly potent and selective BGT1 inhibitor (1S,2S,5R)-5-aminobicyclo[3.1.0]hexane-2-carboxylic acid (bicyclo-GABA). Using [3H]GABA radioligand uptake assays at the four human GATs recombinantly expressed in mammalian cell lines, we identified bicyclo-GABA and its N-methylated analog (2) as the most potent and selective BGT1 inhibitors. Additional pharmacological characterization in a fluorescence-based membrane potential assay showed that bicyclo-GABA and 2 are competitive inhibitors, not substrates, at BGT1, which was validated by a Schild analysis for bicyclo-GABA (pK B value of 6.4). To further elaborate on the selectivity profile both compounds were tested at recombinant α1ß2γ2 GABAA receptors. Whereas bicyclo-GABA showed low micromolar agonistic activity, the N-methylated 2 was completely devoid of activity at GABAA receptors. To further reveal the binding mode of bicyclo-GABA and 2 binding hypotheses of the compounds were obtained from in silico-guided mutagenesis studies followed by pharmacological evaluation at selected BGT1 mutants. This identified the non-conserved BGT1 residues Q299 and E52 as the molecular determinants driving BGT1 activity and selectivity. The binding mode of bicyclo-GABA was further validated by the introduction of activity into the corresponding GAT3 mutant L314Q (38 times potency increase cf. wildtype). Altogether, our data reveal the molecular determinants for the activity of bicyclic GABA analogs, that despite their small size act as competitive inhibitors of BGT1. These compounds may serve as valuable tools to selectively and potently target BGT1 in order to decipher its elusive pharmacological role in the brain and periphery such as the liver and kidneys.

Resolvin E1 Attenuates Chronic Pain-Induced Depression-Like Behavior in Mice: Possible Involvement of Chemerin Receptor ChemR23.

The antidepressant effect of eicosapentaenoic acid-derived bioactive lipid, resolvin E1 (RvE1), was examined in a murine model of chronic pain-induced depression using a tail suspension test. Because RvE1 reportedly possesses agonistic activity on a chemerin receptor ChemR23, we also examined the antidepressant effect of chemerin. Two weeks after surgery for unilateral spared nerve injury to prepare neuropathic pain model mice, immobility time was measured in a tail suspension test. Chronic pain significantly increased immobility time, and this depression-like behavior was attenuated by intracerebroventricular injection of RvE1 (1 ng) or chemerin (500 ng). These results demonstrate that RvE1 exerts an antidepressant effect in a murine model of chronic pain-induced depression, which is likely to be via ChemR23. RvE1 and its receptor may be promising targets to develop novel antidepressants.

Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis.

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles-premature ventricular contraction (PVC) and premature atrial contraction (PAC)-which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

Sympathetic hyperactivity, hypertension, and tachycardia induced by stimulation of the ponto-medullary junction in humans.

OBJECTIVE: The purpose of this study is to investigate changes in autonomic activities and systemic circulation generated by surgical manipulation or electrical stimulation to the human brain stem. METHODS: We constructed a system that simultaneously recorded microsurgical field videos and heart rate variability (HRV) that represent autonomic activities. In 20 brain stem surgeries recorded, HRV features and sites of surgical manipulation were analyzed in 19 hypertensive epochs, defined as the periods with transient increases in the blood pressure. We analyzed the period during electrical stimulation to the ponto-medullary junction, performed for the purpose of monitoring a cranial nerve function. RESULTS: In the hypertensive epoch, HRV analysis showed that sympathetic activity predominated over the parasympathetic activity. The hypertensive epoch was more associated with surgical manipulation of the area in the caudal pons or the rostral medulla oblongata compared to controls. During the period of electrical stimulation, there were significant increases in blood pressures and heart rates, accompanied by sympathetic overdrive. CONCLUSIONS: Our results provide physiological evidence that there is an important autonomic center located adjacent to the ponto-medullary junction. SIGNIFICANCE: A large study would reveal a candidate target of neuromodulation for disorders with autonomic imbalances such as drug-resistant hypertension.