Templated Laser-Induced-Graphene-Based Tactile Sensors Enable Wearable Health Monitoring and Texture Recognition via Deep Neural Network

Flexible tactile sensors show great potential for portable healthcare and environmental monitoring applications. However, challenges persist in scaling up the manufacturing of stable tactile sensors with real-time feedback. This work demonstrates a robust approach to fabricating templated laser-induced graphene (TLIG)-based tactile sensors via laser scribing, elastomer hot-pressing transfer, and 3D printing of the Ag electrode. With different mesh sandpapers as templates, TLIG sensors with adjustable sensing properties were achieved. The tactile sensor obtains excellent sensitivity (52260.2 kPa–1 at a range of 0–7 kPa), a broad detection range (up to 1000 kPa), a low limit of detection (65 Pa), a rapid response (response/recovery time of 12/46 ms), and excellent working stability (10000 cycles). Benefiting from TLIG’s high performance and waterproofness, TLIG sensors can be used as health monitors and even in underwater scenarios. TLIG sensors can also be integrated into arrays acting as receptors of the soft robotic gripper. Furthermore, a deep neural network based on the convolutional neural network was employed for texture recognition via a soft TLIG tactile sensing array, achieving an overall classification rate of 94.51% on objects with varying surface roughness, thus offering high accuracy in real-time practical scenarios

Templated Laser-Induced-Graphene-Based Tactile Sensors Enable Wearable Health Monitoring and Texture Recognition via Deep Neural Network

Flexible tactile sensors show great potential for portable healthcare and environmental monitoring applications. However, challenges persist in scaling up the manufacturing of stable tactile sensors with real-time feedback. This work demonstrates a robust approach to fabricating templated laser-induced graphene (TLIG)-based tactile sensors via laser scribing, elastomer hot-pressing transfer, and 3D printing of the Ag electrode. With different mesh sandpapers as templates, TLIG sensors with adjustable sensing properties were achieved. The tactile sensor obtains excellent sensitivity (52260.2 kPa–1 at a range of 0–7 kPa), a broad detection range (up to 1000 kPa), a low limit of detection (65 Pa), a rapid response (response/recovery time of 12/46 ms), and excellent working stability (10000 cycles). Benefiting from TLIG’s high performance and waterproofness, TLIG sensors can be used as health monitors and even in underwater scenarios. TLIG sensors can also be integrated into arrays acting as receptors of the soft robotic gripper. Furthermore, a deep neural network based on the convolutional neural network was employed for texture recognition via a soft TLIG tactile sensing array, achieving an overall classification rate of 94.51% on objects with varying surface roughness, thus offering high accuracy in real-time practical scenarios

Templated Laser-Induced-Graphene-Based Tactile Sensors Enable Wearable Health Monitoring and Texture Recognition via Deep Neural Network

Flexible tactile sensors show great potential for portable healthcare and environmental monitoring applications. However, challenges persist in scaling up the manufacturing of stable tactile sensors with real-time feedback. This work demonstrates a robust approach to fabricating templated laser-induced graphene (TLIG)-based tactile sensors via laser scribing, elastomer hot-pressing transfer, and 3D printing of the Ag electrode. With different mesh sandpapers as templates, TLIG sensors with adjustable sensing properties were achieved. The tactile sensor obtains excellent sensitivity (52260.2 kPa–1 at a range of 0–7 kPa), a broad detection range (up to 1000 kPa), a low limit of detection (65 Pa), a rapid response (response/recovery time of 12/46 ms), and excellent working stability (10000 cycles). Benefiting from TLIG’s high performance and waterproofness, TLIG sensors can be used as health monitors and even in underwater scenarios. TLIG sensors can also be integrated into arrays acting as receptors of the soft robotic gripper. Furthermore, a deep neural network based on the convolutional neural network was employed for texture recognition via a soft TLIG tactile sensing array, achieving an overall classification rate of 94.51% on objects with varying surface roughness, thus offering high accuracy in real-time practical scenarios

Data_Sheet_1_Advanced age is associated with increased adverse outcomes in patients undergoing middle cerebral artery stenting.docx

PurposeThis study tried to evaluate whether advanced age has an increased incidence of major complications in patients undergoing MCA stenting.MethodsA total of 348 patients who underwent MCA stenting were reviewed from a prospectively maintained database. Ninety-day ischemic stroke, intracerebral hemorrhage, and death outcomes were compared among the young (≤40 years old), middle (41–60 years old) and old (≥61 years old) groups. Univariate analysis and multivariable logistic regression analysis were used to investigate different variables associated with 90-day major adverse events. Kaplan–Meier analysis was performed to determine long-term outcomes during follow-up.ResultsThe incidence of 90-day ischemic stroke was 9.26% in the old group, 2.86% in the middle group, and 0% in the young group (P = 0.024). The incidence of all 90-day major adverse events was 3.33% in patients ≤40 years old, 19.90% in patients 41–60 years old, and 24.07% in patients ≥61 years old, with statistical significance (P = 0.04). Advanced age was associated with increased 90-day ischemic stroke (OR = 1.074, 95% CI: 1.019–1.132, P = 0.007; adjusted OR: 1.071, 95% CI: 1.008–1.138, P = 0.026) and 90-day death (OR = 1.072, 95% CI: 1.012–1.135, P = 0.018; adjusted OR: 1.095, 95% CI: 1.015–1.182, P = 0.018). Meanwhile, advanced age was also associated with decreased long-term survival and ischemic stroke-free survival during follow-up.ConclusionOur data indicated that MCA stenting in elderly patients is associated with a high risk of adverse events and should be cautiously considered.</p

The Effect of Pre-Condition Cerebella Fastigial Nucleus Electrical Stimulation within and beyond the Time Window of Thrombolytic on Ischemic Stroke in the Rats

<div><p>Objective</p><p>To investigate the effect of neurogenic neuroprotection conferred by cerebellar fastigial nucleus stimulation (FNS) and the role of PPARγ- mediated inflammation in a rat model of cerebral ischemia reperfusion.</p><p>Methods</p><p>After a continuous 1 hour fastigial nucleus electric stimulation, the male Sprague Dawley (SD) rats were given middle cerebral artery occlusion (MCAO) for 1, 3, 6, 9, 12 and 15 hours undergoing reperfusion with intravenous recombinant tissue plasminogen activator (rt-PA), while the control group received without FNS. After 72h of reperfusion, the neurological deficits, infarct volume and brain edema were evaluated. The brain tissue in ischemic penumbra was determined the myeloperoxidase (MPO) activity by a spectrophotometer and expression of PPARγ was measured by Rt-PCR and Western blotting.</p><p>Results</p><p>Our findings showed that FNS group had significantly reduced infarct volume and brain edema, and improved neurological deficits compared with the control group, especially in 6h and 9h reperfusion subgroups(p<0.05). The expression levels of PPARγ increased gradually and the peak may be before and after 9h reperfusion, the 3h, 6h, 9h, 12h and 15h reperfusion subgroups were higher than each control group(p<0.05). The MPO activity of 6h, 12h and 15h reperfusion subgroups were higher than each control group(p<0.05).</p><p>Conclusions</p><p>The neuroprotective effects of FNS have been shown to prolong the therapeutic window in cerebral ischemia/reperfusion, which might be related to the PPARγ mediated-inflammation in penumbral region.</p></div

The mRNA levels of PPARγ in cerebral ischemic penumbra.

<p>Results are expressed as the mean ± SD. *p < 0.05, vs the control group, n = 5.</p

The mortality of two groups.

<p>Every group has 10 animals to be survival.</p

The protein expressions of PPARγ in cerebral ischemic penumbra.

<p>Results are expressed as the mean ± SD. *p < 0.05, vs the control group, n = 5.</p

Brain water content of two groups.

<p>(A) The percentage of ischemic lesion area was represented as the ratio of the infarction area to the whole slice area. (B)Water content was calculated by (wet weight −dry weight) /wet weight × 100%. Results are expressed as the mean ± SD. *p < 0.05, vs the control group, n = 5.</p

The scores of two groups.

<p>FNS group were significantly decreased compared to the control group. Results are expressed as the mean ± SD. *p < 0.05, vs the control group, n = 10.</p