Differential impacts of clinical, anatomical, and procedural factors on early and late mortality following open thoracoabdominal aortic repair: a retrospective observational study.

BACKGROUND: The operative outcomes of thoracoabdominal aortic aneurysms (TAAAs) are challenged by high operative mortality and disabling complications. This study aimed to explore the baseline clinical, anatomical, and procedural risk factors that impact early and late outcomes following open repair of TAAAs. METHODS: We reviewed the medical records of 290 patients who underwent open repair of TAAAs between 1992 and 2020 at a tertiary referral center. Determinants of early mortality (within 30 days or in hospital) were analyzed using multivariable logistic regression models, while those of overall follow-up mortality were explored using multivariable Cox proportional hazards models and landmark analyses. RESULTS: The rates of early mortality and spinal cord deficits were 13.1% and 11.0%, respectively, with Crawford extent II showing the highest rates. In the logistic regression models, older age (P < 0.001), high cardiopulmonary bypass (CPB) time (P < 0.001), and low surgical volume of the surgeon (P < 0.001) emerged as independent factors significantly associated with early mortality. During follow-up (median, 5.0 years; interquartile range, 1.1-7.6 years), 82 late deaths occurred (5.7%/patient-year). Cox proportional hazards models demonstrated that older age (P < 0.001) and low hemoglobin level (P = 0.032) were significant risk factors of overall mortality, while the landmark analyses revealed that the significant impacts of low surgical volume (P = 0.017), high CPB time (P = 0.002), and Crawford extent II (P = 0.017) on mortality only remained in the early postoperative period, without significant late impacts (all P > 0.05). CONCLUSION: There were differential temporal impacts of perioperative risk variables on mortality in open repair of TAAAs, with older age and low hemoglobin level having significant impacts throughout the postoperative period, and low surgical volume, high CPB time, and Crawford extent II having impacts in the early postoperative phase.

Epidemiology and Outcomes of Severe Traumatic Brain Injury: Regional Trauma Center in Incheon, Korea, 2018-2022.

Objective: This study aims to explore the epidemiology and outcomes of severe traumatic brain injury (TBI) in Incheon, focusing on regional characteristics using data from a local trauma center. Methods: From January 2018 to December 2022, 559 patients with severe TBI were studied. We analyzed factors related to demography, prehospitalization, surgery, complications, and clinical outcomes, including intensive care unit stay, ventilator use, hospital stay, mortality, and Glasgow outcome scale (GOS) scores at discharge and after 6 months. Results: In this study, most severe TBI patients were in the 60-79 age range, constituting 37.4% of cases. Most patients (74.1%) used public emergency medical services for transportation, and 75.3% arrived directly at the hospital, a significantly higher proportion compared to transferred patients. Timewise, 40.0% reached the hospital within an hour of injury. Complication rates stood at 16.1%, with pneumonia being the most common. The mortality rate was 44.0%, and at discharge, 81.2% of patients had unfavorable outcomes (GOS 1-3), reducing to 70.1% at 6 months. Conclusion: As a pioneering study at Incheon's trauma center, this research provides insights into severe TBI outcomes, enhancing understanding by contrasting local and national data.

Automated Segmentation and Diagnostic Measurement for the Evaluation of Cervical Spine Injuries Using X-Rays.

Accurate assessment of cervical spine X-ray images through diagnostic metrics plays a crucial role in determining appropriate treatment strategies for cervical injuries and evaluating surgical outcomes. Such assessment can be facilitated through the use of automatic methods such as machine learning and computer vision algorithms. A total of 852 cervical X-rays obtained from Gachon Medical Center were used for multiclass segmentation of the craniofacial bones (hard palate, basion, opisthion) and cervical spine (C1-C7), incorporating architectures such as EfficientNetB4, DenseNet201, and InceptionResNetV2. Diagnostic metrics automatically measured using computer vision algorithms were compared with manually measured metrics through Pearson's correlation coefficient and paired t-tests. The three models demonstrated high average dice coefficient values for the cervical spine (C1, 0.93; C2, 0.96; C3, 0.96; C4, 0.96; C5, 0.96; C6, 0.96; C7, 0.95) and lower values for the craniofacial bones (hard palate, 0.69; basion, 0.81; opisthion, 0.71). Comparison of manually measured metrics and automatically measured metrics showed high Pearson's correlation coefficients in McGregor's line (r = 0.89), space available cord (r = 0.94), cervical sagittal vertical axis (r = 0.99), cervical lordosis (r = 0.88), lower correlations in basion-dens interval (r = 0.65), basion-axial interval (r = 0.72), and Powers ratio (r = 0.62). No metric showed adjusted significant differences at P < 0.05 between manual and automatic metric measuring methods. These findings demonstrate the potential of multiclass segmentation in automating the measurement of diagnostic metrics for cervical spine injuries and showcase the clinical potential for diagnosing cervical spine injuries and evaluating cervical surgical outcomes.

AXL+SIGLEC6+ dendritic cells in cerebrospinal fluid and brain tissues of patients with autoimmune inflammatory demyelinating disease of CNS.

Inflammatory demyelinating disease of the CNS (IDD) is a heterogeneous group of autoimmune diseases, and multiple sclerosis is the most common type. Dendritic cells (DCs), major antigen-presenting cells, have been proposed to play a central role in the pathogenesis of IDD. The AXL+SIGLEC6+ DC (ASDC) has been only recently identified in humans and has a high capability of T cell activation. Nevertheless, its contribution to CNS autoimmunity remains still obscure. Here, we aimed to identify the ASDC in diverse sample types from IDD patients and experimental autoimmune encephalomyelitis (EAE). A detailed analysis of DC subpopulations using single-cell transcriptomics for the paired cerebrospinal fluid (CSF) and blood samples of IDD patients (total n = 9) revealed that three subtypes of DCs (ASDCs, ACY3+ DCs, and LAMP3+ DCs) were overrepresented in CSF compared with their paired blood. Among these DCs, ASDCs were also more abundant in CSF of IDD patients than in controls, manifesting poly-adhesional and stimulatory characteristics. In the brain biopsied tissues of IDD patients, obtained at the acute attack of disease, ASDC were also frequently found in close contact with T cells. Lastly, the frequency of ASDC was found to be temporally more abundant in acute attack of disease both in CSF samples of IDD patients and in tissues of EAE, an animal model for CNS autoimmunity. Our analysis suggests that the ASDC might be involved in the pathogenesis of CNS autoimmunity.

Machine learning model for predicting age in healthy individuals using age-related gut microbes and urine metabolites.

Age-related gut microbes and urine metabolites were investigated in 568 healthy individuals using metataxonomics and metabolomics. The richness and evenness of the fecal microbiota significantly increased with age, and the abundance of 16 genera differed between the young and old groups. Additionally, 17 urine metabolites contributed to the differences between the young and old groups. Among the microbes that differed by age, Bacteroides and Prevotella 9 were confirmed to be correlated with some urine metabolites. The machine learning algorithm eXtreme gradient boosting (XGBoost) was shown to produce the best performing age predictors, with a mean absolute error of 5.48 years. The accuracy of the model improved to 4.93 years with the inclusion of urine metabolite data. This study shows that the gut microbiota and urine metabolic profiles can be used to predict the age of healthy individuals with relatively good accuracy.

Automatic Spine Segmentation and Parameter Measurement for Radiological Analysis of Whole-Spine Lateral Radiographs Using Deep Learning and Computer Vision.

Radiographic examination is essential for diagnosing spinal disorders, and the measurement of spino-pelvic parameters provides important information for the diagnosis and treatment planning of spinal sagittal deformities. While manual measurement methods are the golden standard for measuring parameters, they can be time consuming, inefficient, and rater dependent. Previous studies that have used automatic measurement methods to alleviate the downsides of manual measurements showed low accuracy or could not be applied to general films. We propose a pipeline for automated measurement of spinal parameters by combining a Mask R-CNN model for spine segmentation with computer vision algorithms. This pipeline can be incorporated into clinical workflows to provide clinical utility in diagnosis and treatment planning. A total of 1807 lateral radiographs were used for the training (n = 1607) and validation (n = 200) of the spine segmentation model. An additional 200 radiographs, which were also used for validation, were examined by three surgeons to evaluate the performance of the pipeline. Parameters automatically measured by the algorithm in the test set were statistically compared to parameters measured manually by the three surgeons. The Mask R-CNN model achieved an average precision at 50% intersection over union (AP50) of 96.2% and a Dice score of 92.6% for the spine segmentation task in the test set. The mean absolute error values of the spino-pelvic parameters measurement results were within the range of 0.4° (pelvic tilt) to 3.0° (lumbar lordosis, pelvic incidence), and the standard error of estimate was within the range of 0.5° (pelvic tilt) to 4.0° (pelvic incidence). The intraclass correlation coefficient values ranged from 0.86 (sacral slope) to 0.99 (pelvic tilt, sagittal vertical axis).

Comparison of Long-Term Follow-Up Outcomes Between Minimally Invasive and Open Surgery for Single-Level Lumbar Fusion.

AIM: To evaluate, and to compare the clinical outcomes of minimally invasive surgery (MIS), and open surgery for single-level lumbar fusion over a minimum of 10-year follow-up. MATERIAL AND METHODS: We included 87 patients who underwent spinal fusion at the L4 - L5 level between January 2004 and December 2010. Based on the surgical method, the patients were divided into the open surgery (n=44) and MIS groups (n=43). We evaluated baseline characteristics, perioperative comparisons, postoperative complications, radiologic findings, and patientreported outcomes. RESULTS: The mean follow-up period was > 10 years in both groups (open surgery, 10.50 years; MIS, 10.16 years). The operative time was longer in the MIS group (4.37 h) than that in the open surgery group (3.34 h) (p=0.001). Estimated blood loss was lower in the MIS group (281.40 mL) than in the open surgery group (440.23 mL) (p < 0.001). Postoperative complications, including surgical site infection, adjacent segment disease, and pseudoarthrosis, did not differ between the groups. Plain radiographic findings of the lumbar spine did not differ between the two groups. Visual scores for back/leg pain and the Oswestry disability index did not differ between the two groups, preoperatively and at 6 months, 1, 5, and 10 years after surgery. CONCLUSION: After a minimum of the 10-year follow-up, postoperative complications and clinical outcomes did not differ significantly between patients who underwent open fusion and MIS fusion at the L4 - L5 level.

Evaluation of U-Net models in automated cervical spine and cranial bone segmentation using X-ray images for traumatic atlanto-occipital dislocation diagnosis.

Segmentation of the cervical spine in tandem with three cranial bones, hard palate, basion, and opisthion using X-ray images is crucial for measuring metrics used to diagnose traumatic atlanto-occipital dislocation (TAOD). Previous studies utilizing automated segmentation methods have been limited to segmenting parts of the cervical spine (C3 ~ C7), due to difficulties in defining the boundaries of C1 and C2 bones. Additionally, there has yet to be a study that includes cranial bone segmentations necessary for determining TAOD diagnosing metrics, which are usually defined by measuring the distance between certain cervical (C1 ~ C7) and cranial (hard palate, basion, opisthion) bones. For this study, we trained a U-Net model on 513 sagittal X-ray images with segmentations of both cervical and cranial bones for an automated solution to segmenting important features for diagnosing TAOD. Additionally, we tested U-Net derivatives, recurrent residual U-Net, attention U-Net, and attention recurrent residual U-Net to observe any notable differences in segmentation behavior. The accuracy of U-Net models ranged from 99.07 to 99.12%, and dice coefficient values ranged from 88.55 to 89.41%. Results showed that all 4 tested U-Net models were capable of segmenting bones used in measuring TAOD metrics with high accuracy.

Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia.

PURPOSE: To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia. METHODS: This retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017-March 2018, n = 596) and internal validation test set (April 2018-June 2018, n = 204). RESULTS: Optimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively. CONCLUSION: Deep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.

Diagnostic yield of TOF-MRA for detecting incidental vascular lesions in patients with cognitive impairment: An observational cohort study.

Objectives: The role of three-dimensional (3D) TOF-MRA in patients with cognitive impairment is not well established. We evaluated the diagnostic yield of 3D TOF-MRA for detecting incidental extra- or intracranial artery stenosis and intracranial aneurysm in this patient group. Methods: This retrospective study included patients with cognitive impairment undergoing our brain MRI protocol from January 2013 to February 2020. The diagnostic yield of TOF-MRA for detecting incidental vascular lesions was calculated. Patients with positive TOF-MRA results were reviewed to find whether additional treatment was performed. Logistic regression analysis was conducted to identify the clinical risk factors for positive TOF-MRA findings. Results: In total, 1,753 patients (mean age, 70.2 ± 10.6 years; 1,044 women) were included; 199 intracranial aneurysms were detected among 162 patients (9.2%, 162/1,753). A 3D TOF-MRA revealed significant artery stenoses (>50% stenosis) in 162 patients (9.2%, 162/1,753). The overall diagnostic yield of TOF-MRA was 16.8% (294/1,753). Among them, 92 patients (31.3%, 92/294) underwent either medical therapy, endovascular intervention, or surgery. In total, eighty-one patients with stenosis were prescribed with either antiplatelet medications or lipid-lowering agent. In total, fifteen patients (aneurysm: 11 patients, stenosis: 4 patients) were further treated with endovascular intervention or surgery. Thus, the "number needed to scan" was 19 for identifying one patient requiring treatment. Multivariate logistic regression analysis showed that being female (odds ratio [OR] 2.05) and old age (OR 1.04) were the independent risk factors for intracranial aneurysm; being male (OR 1.52), old age (OR 1.06), hypertension (OR 1.78), and ischemic heart disease history (OR 2.65) were the independent risk factors for significant artery stenosis. Conclusions: Our study demonstrated the potential benefit of 3D TOF-MRA, given that it showed high diagnostic yield for detecting vascular lesions in patients with cognitive impairment and the considerable number of these lesions required further treatment. A 3D TOF-MRA may be included in the routine MR protocol for the work-up of this patient population, especially in older patients and patients with vascular risk factors.

The Effect of the Heating Rate during Carbonization on the Porosity, Strength, and Electrical Resistivity of Graphite Blocks Using Phenolic Resin as a Binder.

In the present study, graphite blocks were fabricated using synthetic graphite scrap and phenolic resin, and the effect of the heating rate during carbonization on their mechanical and electrical characteristics was examined. While varying the heating rate from 1, 3, 5, and 7 to 9 °C/min, the microstructure, density, porosity, flexural strength, compressive strength, and electrical resistivity of the fabricated graphite blocks were measured. As the heating rate increased, the pores in the graphite blocks increased in size, and the shape of the gas release paths became more irregular. Overall, it was found that increases in the heating rate led to the degradation of the graphite blocks' mechanical and electrical properties.

Optogenetic Targeting of Mouse Vagal Afferents Using an Organ-specific, Scalable, Wireless Optoelectronic Device.

Optogenetics has the potential to transform the study of the peripheral nervous system (PNS), but the complex anatomy of the PNS poses unique challenges for the focused delivery of light to specific tissues. This protocol describes the fabrication of a wireless telemetry system for studying peripheral sensory pathways. Unlike existing wireless approaches, the low-power wireless telemetry offers organ specificity via a sandwiched pre-curved tether, and enables high-throughput analysis of behavioral experiments with a channel isolation strategy. We describe the technical procedures for the construction of these devices, the wireless power transmission (TX) system with antenna coils, and their implementation for in vivo experimental applications. In total, the timeline of the procedure, including device fabrication, implantation, and preparation to begin in vivo experimentation can be completed in ~2-4 weeks. Implementation of these devices allows for chronic (>1 month) wireless optogenetic manipulation of peripheral neural pathways in freely behaving animals navigating homecage environments (up to 8).

AI-enabled, implantable, multichannel wireless telemetry for photodynamic therapy.

Photodynamic therapy (PDT) offers several advantages for treating cancers, but its efficacy is highly dependent on light delivery to activate a photosensitizer. Advances in wireless technologies enable remote delivery of light to tumors, but suffer from key limitations, including low levels of tissue penetration and photosensitizer activation. Here, we introduce DeepLabCut (DLC)-informed low-power wireless telemetry with an integrated thermal/light simulation platform that overcomes the above constraints. The simulator produces an optimized combination of wavelengths and light sources, and DLC-assisted wireless telemetry uses the parameters from the simulator to enable adequate illumination of tumors through high-throughput (<20 mice) and multi-wavelength operation. Together, they establish a range of guidelines for effective PDT regimen design. In vivo Hypericin and Foscan mediated PDT, using cancer xenograft models, demonstrates substantial suppression of tumor growth, warranting further investigation in research and/or clinical settings.

Prognostic value of diffusion-weighted imaging in patients with newly diagnosed sporadic Creutzfeldt-Jakob disease.

OBJECTIVES: To evaluate clinico-radiologic markers that predict poor overall survival (OS) in sporadic Creutzfeldt-Jakob disease (sCJD) and to develop a prognostic model. MATERIALS AND METHODS: Patients with newly diagnosed sCJD were included who underwent diffusion-weighted imaging (DWI) from February 2000 to July 2020. The impact of 9 clinico-radiologic features on OS was analyzed using univariable and multivariable Cox proportional hazards regression model. The DWI prognostic score model was generated. The weighted kappa was calculated for interobserver agreement. RESULTS: Sixty patients (mean age ± SD, 61.0 ± 9.7 years, 32 women) were included. Univariable analysis showed positive associations between poor OS and patient age (p = 0.003), extent of involved cortical lobes (p = 0.11), involvement of caudate nucleus (p = 0.07), and putamen (p = 0.04). Multivariable analysis demonstrated two independent prognostic factors: age ≥ 60 (HR 2.65, 95% CI, 1.41-4.98), and diffusion restriction in caudate nucleus and putamen (HR 2.24, 95% CI, 1.15-4.37). Based on these features, the DWI prognostic score model was generated: low-risk (0-1 point), intermediate-risk (2-3 points), and high-risk (4-5 points) groups. Median OS in high-risk group was 1.7 months, which was significantly shorter than those in the intermediate-risk (14.2 months) and low-risk (26.5 months) groups (p < 0.001). Interobserver agreements were excellent (κ = 0.91-0.92). CONCLUSIONS: Our study demonstrated that age and diffusion restriction in caudate nucleus and putamen were the independent prognostic factors of poor overall survival in sporadic Creutzfeldt-Jakob disease. Our DWI prognostic score model may be useful in clinical settings for disease stratification. KEY POINTS: • Age ≥ 60, and diffusion restriction in caudate nucleus and putamen were the independent prognostic factors of poor overall survival in sCJD. • Based on our DWI prognostic score model, median overall survival in high-risk group was 1.7 months, which was significantly shorter than those in the intermediate-risk group (14.2 months) and low-risk group (26.5 months) (p < 0.001). • The proposed DWI prognostic score model may be useful in clinical settings for disease stratification.

Robust, wireless gastric optogenetic implants for the study of peripheral pathways and applications in obesity.

Optogenetics has the potential to transform the study of organ functions in the peripheral nervous system via relatively easy access to the nerves and a direct link between the brain and organ systems. Implementation typically requires a static skeletal feature for the securement of a fiber. Unfortunately, the soft nature of peripheral nervous systems makes the wired fiber-optic approach less ideal for the study of the peripheral nervous system. Existing wireless approaches could bypass some constraints associated with optical fibers and thereby offer organ specificity. However, they suffer from durability loss due to considerable biological strains and unable to perform longitudinal experiments. Here, we propose a new class of wireless gastric optogenetic implant for identifying signaling pathways, in particular viscerosensory pathways, that can regulate food intake to treat obesity. Robust, wireless gastric optogenetic implants with a tubing-assisted U-shaped tether directly interface with nerve endings in the stomach with chronic stability in operation (> 100 kilocycles) and allows for optogenetic stimulations of vagus nerves in a freely behaving animal. We demonstrated utilities of the proposed wireless device in in vivo experiments. Results suggest the potential for identifying interventions for the treatment of obesity.Clinical Relevance - Identification of the roles of subpopulations in viscerosensory pathways would provide the platform for the development of better therapeutics for the treatment of obesity.

Multimodal Sensing Capabilities for the Detection of Shunt Failure.

Hydrocephalus is a medical condition characterized by the abnormal accumulation of cerebrospinal fluid (CSF) within the cavities of the brain called ventricles. It frequently follows pediatric and adult congenital malformations, stroke, meningitis, aneurysmal rupture, brain tumors, and traumatic brain injury. CSF diversion devices, or shunts, have become the primary therapy for hydrocephalus treatment for nearly 60 years. However, routine treatment complications associated with a shunt device are infection, obstruction, and over drainage. Although some (regrettably, the minority) patients with shunts can go for years without complications, even those lucky few may potentially experience one shunt malfunction; a shunt complication can require emergency intervention. Here, we present a soft, wireless device that monitors distal terminal fluid flow and transmits measurements to a smartphone via a low-power Bluetooth communication when requested. The proposed multimodal sensing device enabled by flow sensors, for measurements of flow rate and electrodes for measurements of resistance in a fluidic chamber, allows precision measurement of CSF flow rate over a long time and under any circumstances caused by unexpected or abnormal events. A universal design compatible with any modern commercial spinal fluid shunt system would enable the widespread use of this technology.

Learning curve of trans-sacral epiduroscopic laser decompression in herniated lumbar disc disease.

BACKGROUND: Trans-sacral epiduroscopic laser decompression (SELD) using slender epiduroscope and a holmium YAG laser is one of the minimally invasive surgical options for lumbar disc herniation. However, the learning curve of SELD and the effect of surgical proficiency on clinical outcome have not yet been established. We investigated patients with lumbar disc herniation undergoing SELD to report the clinical outcome and learning curve. METHODS: Retrospective analysis of clinical outcome and learning curve were performed at a single center from clinical data collected from November 2015 to November 2018. A total of 82 patients who underwent single-level SELD for lumbar disc herniation with a minimum follow-up of 6.0 months were enrolled. Based on the findings that the cut-off of familiarity was 20 cases according to the cumulative study of operation time, patients were allocated to two groups: early group (n = 20) and late group (n = 62). The surgical, clinical, and radiological outcomes were retrospectively evaluated between the two groups to analyze the learning curve of SELD. RESULTS: According to linear and log regression analyses, the operation time was obtained by the formula: operation time = 58.825-(0.181 × [case number]) (p < 0.001). The mean operation time was significantly different between the two groups (mean 56.95 min; 95% confidence interval [CI], 49.12-64.78 in the early group versus mean 45.34 min; 95% CI, 42.45-48.22 in the late group; p = 0.008, non-parametric Mann-Whitney U test). Baseline characteristics, including demographic data, clinical factors, and findings of preoperative magnetic resonance imaging, did not differ between the two groups. Also, there was no significant difference in terms of surgical outcomes, including complication and failure rates, as well as clinical and radiological outcomes between the two groups. CONCLUSION: The learning curve of SELD was not as steep as that of other minimally invasive spinal surgery techniques, and the experience of surgery was not an influencing factor for outcome variation.

Organ-specific, multimodal, wireless optoelectronics for high-throughput phenotyping of peripheral neural pathways.

The vagus nerve supports diverse autonomic functions and behaviors important for health and survival. To understand how specific components of the vagus contribute to behaviors and long-term physiological effects, it is critical to modulate their activity with anatomical specificity in awake, freely behaving conditions using reliable methods. Here, we introduce an organ-specific scalable, multimodal, wireless optoelectronic device for precise and chronic optogenetic manipulations in vivo. When combined with an advanced, coil-antenna system and a multiplexing strategy for powering 8 individual homecages using a single RF transmitter, the proposed wireless telemetry enables low cost, high-throughput, and precise functional mapping of peripheral neural circuits, including long-term behavioral and physiological measurements. Deployment of these technologies reveals an unexpected role for stomach, non-stretch vagal sensory fibers in suppressing appetite and demonstrates the durability of the miniature wireless device inside harsh gastric conditions.

Fully Implantable Low-Power High Frequency Range Optoelectronic Devices for Dual-Channel Modulation in the Brain.

Wireless optoelectronic devices can deliver light to targeted regions in the brain and modulate discrete circuits in an animal that is awake. Here, we propose a miniaturized fully implantable low-power optoelectronic device that allows for advanced operational modes and the stimulation/inhibition of deep brain circuits in a freely-behaving animal. The combination of low power control logic circuits, including a reed switch and dual-coil wireless power transfer platform, provides powerful capabilities for the dissection of discrete brain circuits in wide spatial coverage for mouse activity. The actuating mechanism enabled by a reed switch results in a simplified, low-power wireless operation and systematic experimental studies that are required for a range of logical operating conditions. In this study, we suggest two different actuating mechanisms by (1) a magnet or (2) a radio-frequency signal that consumes only under 300 µA for switching or channel selection, which is a several ten-folds reduction in power consumption when compared with any other existing systems such as embedded microcontrollers, near field communication, and Bluetooth. With the efficient dual-coil transmission antenna, the proposed platform leads to more advantageous power budgets that offer improved volumetric and angular coverage in a cage while minimizing the secondary effects associated with a corresponding increase in transmitted power.