Detecting Bladder Biomarkers for Closed-Loop Neuromodulation: A Technological Review

Neuromodulation was introduced for patients with poor outcomes from the existing traditional treatment approaches. It is well-established as an alternative, novel treatment option for voiding dysfunction. The current system of neuromodulation uses an open-loop system that only delivers continuous stimulation without considering the patient’s state changes. Though the conventional open-loop system has shown positive clinical results, it can cause problems such as decreased efficacy over time due to neural habituation, higher risk of tissue damage, and lower battery life. Therefore, there is a need for a closed-loop system to overcome the disadvantages of existing systems. The closed-loop neuromodulation includes a system to monitor and stimulate micturition reflex pathways from the lower urinary tract, as well as the central nervous system. In this paper, we reviewed the current technological status to measure biomarker for closed-loop neuromodulation systems for voiding dysfunction

Analysis of Nociceptive Information Encoded in the Temporal Discharge Patterns of Cutaneous C-Fibers

The generation of pain signals from primary afferent neurons is explained by a labeled-line code. However, this notion cannot apply in a simple way to cutaneous C-fibers, which carry signals from a variety of receptors that respond to various stimuli including agonist chemicals. To represent the discharge patterns of C-fibers according to different agonist chemicals, we have developed a quantitative approach using three consecutive spikes. By using this method, the generation of pain in response to chemical stimuli is shown to be dependent on the temporal aspect of the spike trains. Furthermore, under pathological conditions, gamma-aminobutyric acid resulted in pain behavior without change of spike number but with an altered discharge pattern. Our results suggest that information about the agonist chemicals may be encoded in specific temporal patterns of signals in C-fibers, and nociceptive sensation may be influenced by the extent of temporal summation originating from the temporal patterns.open0

A data-driven artificial intelligence model for remote triage in the prehospital environment.

In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical personnel. However, when relying on a small number of medical personnel, the ability to increase survivability is limited. Therefore, developing a classification model for survival prediction that can quickly and precisely triage via wearable devices without medical personnel is important. In this study, we designed a consciousness index to substitute the factor by manpower and improved the classification accuracy by applying a machine learning algorithm. First, logistic regression analysis using vital signs and a consciousness index capable of remote monitoring through wearable devices confirmed the high efficiency of the consciousness index. We then developed a classification model with high accuracy which corresponds to existing injury severity scoring systems through the machine learning algorithms. We extracted 460,865 cases which met our criteria for developing the survival prediction from the national sample project in the national trauma databank which contains 408,316 cases of blunt injury and 52,549 cases of penetrating injury. Among the dataset, 17,918 (3.9%) cases died while the other survived. The AUCs with 95% confidence intervals (CIs) for the different models with the proposed simplified consciousness score as follows: RTS (as baseline), 0.78 (95% CI = 0.775 to 0.785); logistic regression, 0.87 (95% CI = 0.862 to 0.870); random forest, 0.87 (95% CI = 0.862 to 0.872); deep neural network, 0.89 (95% CI = 0.882 to 0.890). As a result, we confirmed the possibility of remote triage using a wearable device. It is expected that the time required for triage can be effectively reduced by using the developed classification model of survival prediction

Production and characterization of lentivirus vector-based SARS-CoV-2 pseudoviruses with dual reporters: Evaluation of anti-SARS-CoV-2 viral effect of Korean Red Ginseng

Background: Pseudotyped virus systems that incorporate viral proteins have been widely employed for the rapid determination of the effectiveness and neutralizing activity of drug and vaccine candidates in biosafety level 2 facilities. We report an efficient method for producing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudovirus with dual luciferase and fluorescent protein reporters. Moreover, using the established method, we also aimed to investigate whether Korean Red Ginseng (KRG), a valuable Korean herbal medicine, can attenuate infectivity of the pseudotyped virus. Methods: A pseudovirus of SARS-CoV-2 (SARS-2pv) was constructed and efficiently produced using lentivirus vector systems available in the public domain by the introduction of critical mutations in the cytoplasmic tail of the spike protein. KRG extract was dose-dependently treated to Calu-3 cells during SARS2-pv treatment to evaluate the protective activity against SARS-CoV-2. Results: The use of Calu-3 cells or the expression of angiotensin-converting enzyme 2 (ACE2) in HEK293T cells enabled SARS-2pv infection of host cells. Coexpression of transmembrane protease serine subtype 2 (TMPRSS2), which is the activator of spike protein, with ACE2 dramatically elevated luciferase activity, confirming the importance of the TMPRSS2-mediated pathway during SARS-CoV-2 entry. Our pseudovirus assay also revealed that KRG elicited resistance to SARS-CoV-2 infection in lung cells, suggesting its beneficial health effect. Conclusion: The method demonstrated the production of SARS-2pv for the analysis of vaccine or drug candidates. When KRG was assessed by the method, it protected host cells from coronavirus infection. Further studies will be followed for demonstrating this potential benefit

Analysis of temporal firing patterns of primary afferent C-fibers for different sensations in mice

Some people with amputated limbs can benefit from neural prosthetics to restore tactile sensation through electrical stimulation of the afferent nerve. The temporal spike train pattern generated in healthy subject's nerve by various types of somatosensation could provide key information to closely mimic natural sensations using electrical stimulation. However, the temporal firing patterns of peripheral sensory fibers have not been well understood yet. To interpret somatosensory spike trains, we performed ex vivo singlefiber recordings from the saphenous nerve in isolated skin-nerve preparations from mice. Some mechanically sensitive primary afferent C-fibers could also be activated by hot, cold, and itching stimuli, and we observed stimulus-specific firing patterns. These temporal patterns of the C-fibers for chemical stimuli were analyzed using a computational model based on quadruplets of spikes, which we classified into three groups of responses, i.e., capsaicin (hot), allyl-isothiocyanate (cold), and alpha-methyl-serotonin (itching). Each group of responses to the chemical stimuli was different from that evoked by mechanical stimuli. Therefore, these findings indicate that nontactile somatosensation can be decoded and used as input to a computerized system. Our quadruplet approach to the temporal patterns of spike trains contributes valuable insight to the identification of temporal profiles of other biological conditions