Exosomes-mediated delivery of miR-486-3p alleviates neuroinflammation via SIRT2-mediated inhibition of mitophagy after subarachnoid hemorrhage.

BACKGROUND: Neuroinflammation participates in the pathogenesis of subarachnoid haemorrhage (SAH); however, no effective treatments exist. MicroRNAs regulate several aspects of neuronal dysfunction. In a previous study, we found that exosomal miR-486-3p is involved in the pathophysiology of SAH. Targeted delivery of miR-486-3p without blood-brain barrier (BBB) restriction to alleviate SAH is a promising neuroinflammation approach. METHODS: In this study, we modified exosomes (Exo) to form an RVG-miR-486-3p-Exo (Exo/miR) to achieve targeted delivery of miR-486-3p to the brain. Neurological scores, brain water content, BBB damage, flow cytometry and FJC staining were used to determine the effect of miR-486-3p on SAH. Western blot analysis, ELISA and RT-qPCR were used to measure relevant protein and mRNA levels. Immunofluorescence staining and laser confocal detection were used to measure the expression of mitochondria, lysosomes and autophagosomes, and transmission electron microscopy was used to observe the level of mitophagy in the brain tissue of mice after SAH. RESULTS: Tail vein injection of Exo/miR improved targeting of miR-486-3p to the brains of SAH mice. The injection reduced levels of neuroinflammation-related factors by changing the phenotype switching of microglia, inhibiting the expression of sirtuin 2 (SIRT2) and enhancing mitophagy. miR-486-3p treatment alleviated neurobehavioral disorders, brain oedema, BBB damage and neurodegeneration. Further research found that the mechanism was achieved by regulating the acetylation level of peroxisome proliferator-activated receptor γ coactivator l alpha (PGC-1α) after SIRT2 enters the nucleus. CONCLUSION: Exo/miR treatment attenuates neuroinflammation after SAH by inhibiting SIRT2 expression and stimulating mitophagy, suggesting potential clinical applications.

Father support breastfeeding self-efficacy positively affects exclusive breastfeeding at 6 weeks postpartum and its influencing factors in Southeast China: a multi-centre, cross-sectional study.

BACKGROUND: The exclusive breastfeeding condition in China is not optimism now. Maternal breastfeeding self-efficacy stands as a pivotal factor influencing exclusive breastfeeding. Interestingly, studies have suggested that father support breastfeeding self-efficacy is a pivotal mediator in infant breastfeeding. Thus, the current research aimed to investigate the association between father support breastfeeding self-efficacy and exclusive breastfeeding at six weeks postpartum, and the influencing factors of father support breastfeeding self-efficacy. METHODS: This research was structured as a multi-centre cross-sectional study, involving 328 fathers, whose partners were six weeks postpartum, and recruited from two public hospitals in Southeast China. Self-designed demographic questionnaires, namely, Father Support Breastfeeding Self-Efficacy Scale-Short Form, Breastfeeding Knowledge Questionnaire, Positive Affect Scale and the 14-item Fatigue Scale, were applied. Descriptive statistics, Chi-square test, logistic regression univariate analysis and multiple linear regression were used to analyse data. RESULTS: Results indicate a significant difference between the infant feeding methods at six weeks postpartum and fathers with different levels of support breastfeeding self-efficacy (p < 0.05). Particularly, father support breastfeeding self-efficacy positively affected exclusive breastfeeding at six weeks postpartum after adjusting all the demographic characteristics of fathers (OR: 2.407; 95% CI: 1.017-4.121). Moreover, results show that the significant influencing factors of father support breastfeeding self-efficacy include breastfeeding knowledge, fatigue, positive affect, successfully experienced helping mothers to breastfeed, spousal relationships and companionship time. CONCLUSIONS: High-level father support breastfeeding self-efficacy effectively increased exclusive breastfeeding rate at six weeks postpartum. To enhance the exclusive breastfeeding rate, nurses or midwives can endeavour to design educational programmes or take supportive interventions customised for fathers, such as enhancing their breastfeeding knowledge education, reducing fatigue and mobilising positive emotions, thereby bolstering paternal self-efficacy in breastfeeding.

Hemiarthroplasty vs. internal fixation for nondisplaced femoral neck fracture in mainland China: a cost-effectiveness analysis.

Objective: Nondisplaced femoral neck fractures constitute a substantial portion of these injuries. The optimal treatment strategy between internal fixation (IF) and hemiarthroplasty (HA) remains debated, particularly concerning cost-effectiveness. Methods: We conducted a cost-effectiveness analysis using a Markov decision model to compare HA and IF in treating nondisplaced femoral neck fractures in elderly patients in China. The analysis was performed from a payer perspective with a 5-year time horizon. Costs were measured in 2020 USD, and effectiveness was measured in quality-adjusted life-years (QALYs). Sensitivity analyses, including one-way and probabilistic analyses, were conducted to assess the robustness of the results. The willingness-to-pay threshold for incremental cost-effectiveness ratio (ICER) was set at $11,083/QALY following the Chinese gross domestic product in 2020. Results: HA demonstrated higher cumulative QALYs (2.94) compared to IF (2.75) but at a higher total cost ($13,324 vs. $12,167), resulting in an ICER of $6,128.52/QALY. The one-way sensitivity analysis identified the costs of HA and IF as the most influential factors. Probabilistic sensitivity analysis indicated that HA was more effective in 69.3% of simulations, with an ICER below the willingness-to-pay threshold of $11,083 in 58.8% of simulations. Conclusions: HA is a cost-effective alternative to IF for treating nondisplaced femoral neck fractures in elderly patients in mainland China.

S2P-Matching: Self-supervised Patch-based Matching Using Transformer for Capsule Endoscopic Images Stitching.

The Magnetically Controlled Capsule Endoscopy (MCCE) has a limited shooting range, resulting in capturing numerous fragmented images and an inability to precisely locate and examine the region of interest (ROI) as traditional endoscopy can. Addressing this issue, image stitching around the ROI can be employed to aid in the diagnosis of gastrointestinal (GI) tract conditions. However, MCCE images possess unique characteristics, such as weak texture, close-up shooting, and large angle rotation, presenting challenges to current image-matching methods. In this context, a method named S2P-Matching is proposed for self-supervised patch-based matching in MCCE image stitching. The method involves augmenting the raw data by simulating the capsule endoscopic camera's behavior around the GI tract's ROI. Subsequently, an improved contrast learning encoder is utilized to extract local features, represented as deep feature descriptors. This encoder comprises two branches that extract distinct scale features, which are combined over the channel without manual labeling. The data-driven descriptors are then input into a Transformer model to obtain patch-level matches by learning the globally consented matching priors in the pseudo-ground-truth match pairs. Finally, the patch-level matching is refined and filtered to the pixel-level. The experimental results on real-world MCCE images demonstrate that S2P-Matching provides enhanced accuracy in addressing challenging issues in the GI tract environment with image parallax. The performance improvement can reach up to 203 and 55.8% in terms of NCM (Number of Correct Matches) and SR (Success Rate), respectively. This approach is expected to facilitate the wide adoption of MCCE-based gastrointestinal screening.

A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms.

Importance: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a variety of health care settings. Objectives: To evaluate DL algorithms for MM classification and segmentation and compare their performance with that of ophthalmologists. Design, Setting, and Participants: The Myopic Maculopathy Analysis Challenge (MMAC) was an international competition to develop automated solutions for 3 tasks: (1) MM classification, (2) segmentation of MM plus lesions, and (3) spherical equivalent (SE) prediction. Participants were provided 3 subdatasets containing 2306, 294, and 2003 fundus images, respectively, with which to build algorithms. A group of 5 ophthalmologists evaluated the same test sets for tasks 1 and 2 to ascertain performance. Results from model ensembles, which combined outcomes from multiple algorithms submitted by MMAC participants, were compared with each individual submitted algorithm. This study was conducted from March 1, 2023, to March 30, 2024, and data were analyzed from January 15, 2024, to March 30, 2024. Exposure: DL algorithms submitted as part of the MMAC competition or ophthalmologist interpretation. Main Outcomes and Measures: MM classification was evaluated by quadratic-weighted κ (QWK), F1 score, sensitivity, and specificity. MM plus lesions segmentation was evaluated by dice similarity coefficient (DSC), and SE prediction was evaluated by R2 and mean absolute error (MAE). Results: The 3 tasks were completed by 7, 4, and 4 teams, respectively. MM classification algorithms achieved a QWK range of 0.866 to 0.901, an F1 score range of 0.675 to 0.781, a sensitivity range of 0.667 to 0.778, and a specificity range of 0.931 to 0.945. MM plus lesions segmentation algorithms achieved a DSC range of 0.664 to 0.687 for lacquer cracks (LC), 0.579 to 0.673 for choroidal neovascularization, and 0.768 to 0.841 for Fuchs spot (FS). SE prediction algorithms achieved an R2 range of 0.791 to 0.874 and an MAE range of 0.708 to 0.943. Model ensemble results achieved the best performance compared to each submitted algorithms, and the model ensemble outperformed ophthalmologists at MM classification in sensitivity (0.801; 95% CI, 0.764-0.840 vs 0.727; 95% CI, 0.684-0.768; P = .006) and specificity (0.946; 95% CI, 0.939-0.954 vs 0.933; 95% CI, 0.925-0.941; P = .009), LC segmentation (DSC, 0.698; 95% CI, 0.649-0.745 vs DSC, 0.570; 95% CI, 0.515-0.625; P < .001), and FS segmentation (DSC, 0.863; 95% CI, 0.831-0.888 vs DSC, 0.790; 95% CI, 0.742-0.830; P < .001). Conclusions and Relevance: In this diagnostic study, 15 AI models for MM classification and segmentation on a public dataset made available for the MMAC competition were validated and evaluated, with some models achieving better diagnostic performance than ophthalmologists.

MSEmbGAN: Multi-Stitch Embroidery Synthesis via Region-Aware Texture Generation.

Convolutional neural networks (CNNs) are widely used for embroidery feature synthesis from images. However, they are still unable to predict diverse stitch types, which makes it difficult for the CNNs to effectively extract stitch features. In this paper, we propose a multi-stitch embroidery generative adversarial network (MSEmbGAN) that uses a region-aware texture generation sub-network to predict diverse embroidery features from images. To the best of our knowledge, our work is the first CNN-based generative adversarial network to succeed in this task. Our region-aware texture generation sub-network detects multiple regions in the input image using a stitchclassifierandgeneratesastitchtextureforeachregionbasedonitsshapefeatures.Wealsoproposeacolorizationnetworkwitha color feature extractor, which helps achieve full image color consistency by requiring the color attributes of the output to closely resemble the input image. Because of the current lack of labeled embroidery image datasets, we provide a new multi-stitch embroidery dataset that is annotated with three single-stitch types and one multi-stitch type. Our dataset, which includes more than 30K high-quality multistitch embroidery images, more than 13K aligned content-embroidered images, and more than 17K unaligned images, is currently the largest embroidery dataset accessible, as far as we know. Quantitative and qualitative experimental results, including a qualitative user study, show that our MSEmbGAN outperforms current state-of-the-artembroiderysynthesisandstyle-transfermethodsonallevaluation indicators. Our demo and dataset sample can be found on the website https://csai.wtu.edu.cn/TVCG01/index.html.

Highly Sensitive and Flexible Capacitive Pressure Sensors Combined with Porous Structure and Hole Array Using Sacrificial Templates and Laser Ablation.

Flexible, wearable pressure sensors offer numerous benefits, including superior sensing capabilities, a lightweight and compact design, and exceptional conformal properties, making them highly sought after in various applications including medical monitoring, human-computer interactions, and electronic skins. Because of their excellent characteristics, such as simple fabrication, low power consumption, and short response time, capacitive pressure sensors have received widespread attention. As a flexible polymer material, polydimethylsiloxane (PDMS) is widely used in the preparation of dielectric layers for capacitive pressure sensors. The Young's modulus of the flexible polymer can be effectively decreased through the synergistic application of sacrificial template and laser ablation techniques, thereby improving the functionality of capacitive pressure sensors. In this study, a novel sensor was introduced. Its dielectric layer was developed through a series of processes, including the use of a sacrificial template method using NaCl microparticles and subsequent CO2 laser ablation. This porous PDMS dielectric layer, featuring an array of holes, was then sandwiched between two flexible electrodes to create a capacitive pressure sensor. The sensor demonstrates a sensitivity of 0.694 kPa-1 within the pressure range of 0-1 kPa and can effectively detect pressures ranging from 3 Pa to 200 kPa. The sensor demonstrates stability for up to 500 cycles, with a rapid response time of 96 ms and a recovery time of 118 ms, coupled with a low hysteresis of 6.8%. Furthermore, our testing indicates that the sensor possesses limitless potential for use in detecting human physiological activities and delivering signals.

Paeoniflorin suppresses ferroptosis after traumatic brain injury by antagonizing P53 acetylation.

BACKGROUND: Traumatic brain injury (TBI) could induce multiple forms of cell death, ferroptosis, a novel form of cell death distinct from apoptosis and autophagy, plays an important role in disease progression in TBI. Therapies targeting ferroptosis are beneficial for recovery from TBI. Paeoniflorin (Pae) is a water-soluble monoterpene glycoside and the active ingredient of Paeonia lactiflora pall. It has been shown to exert anti-inflammatory and antioxidant effects. However The effects and mechanisms of paeoniflorin on secondary injury after TBI are unknown. PURPOSE: To investigate the mechanism by which Pae regulates ferroptosis after TBI. METHODS: The TBI mouse model and cortical primary neurons were utilized to study the protective effect of paeoniflorin on the brain tissue after TBI. The neuronal cell ferroptosis model was established by treating cortical primary neurons with erastin. Liproxstatin-1(Lip-1) was used as a positive control drug. Immunofluorescence staining, Nissl staining, biochemical analyses, pharmacological analyses, and western blot were used to evaluate the effects of paeoniflorin on TBI. RESULTS: Pae significantly ameliorated neuronal damage after TBI, inhibited mitochondrial damage, increased glutathione peroxidase 4 (GPX4) activity, decreased malondialdehyde (MDA) production, restored neurological function and inhibited cerebral edema. Pae promotes the degradation of P53 in the form of proteasome, promotes its ubiquitination, and reduces the stability of P53 by inhibiting its acetylation, thus alleviating the P53-mediated inhibition of cystine/glutamate antiporter solute carrier family 7 member 11 (SLC7A11) by P53. CONCLUSION: Pae inhibits ferroptosis by promoting P53 ubiquitination out of the nucleus, inhibiting P53 acetylation, and modulating the SLC7A11-GPX4 pathway.

A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children.

The increasing prevalence of myopia worldwide presents a significant public health challenge. A key strategy to combat myopia is with early detection and prediction in children as such examination allows for effective intervention using readily accessible imaging technique. To this end, we introduced DeepMyopia, an artificial intelligence (AI)-enabled decision support system to detect and predict myopia onset and facilitate targeted interventions for children at risk using routine retinal fundus images. Based on deep learning architecture, DeepMyopia had been trained and internally validated on a large cohort of retinal fundus images (n = 1,638,315) and then externally tested on datasets from seven sites in China (n = 22,060). Our results demonstrated robustness of DeepMyopia, with AUCs of 0.908, 0.813, and 0.810 for 1-, 2-, and 3-year myopia onset prediction with the internal test set, and AUCs of 0.796, 0.808, and 0.767 with the external test set. DeepMyopia also effectively stratified children into low- and high-risk groups (p < 0.001) in both test sets. In an emulated randomized controlled trial (eRCT) on the Shanghai outdoor cohort (n = 3303) where DeepMyopia showed effectiveness in myopia prevention compared to NonCyc-based model, with an adjusted relative reduction (ARR) of -17.8%, 95% CI: -29.4%, -6.4%. DeepMyopia-assisted interventions attained quality-adjusted life years (QALYs) of 0.75 (95% CI: 0.53, 1.04) per person and avoided blindness years of 13.54 (95% CI: 9.57, 18.83) per 1 million persons compared to natural lifestyle with no active intervention. Our findings demonstrated DeepMyopia as a reliable and efficient AI-based decision support system for intervention guidance for children.

DDE: Deep Dynamic Epidemiological Modeling for Infectious Illness Development Forecasting in Multi-level Geographic Entities.

Understanding and addressing the dynamics of infectious diseases, such as coronavirus disease 2019, are essential for effectively managing the current situation and developing intervention strategies. Epidemiologists commonly use mathematical models, known as epidemiological equations (EE), to simulate disease spread. However, accurately estimating the parameters of these models can be challenging due to factors like variations in social distancing policies and intervention strategies. In this study, we propose a novel method called deep dynamic epidemiological modeling (DDE) to address these challenges. The DDE method combines the strengths of EE with the capabilities of deep neural networks to improve the accuracy of fitting real-world data. In DDE, we apply neural ordinary differential equations to solve variant-specific equations, ensuring a more precise fit for disease progression in different geographic regions. In the experiment, we tested the performance of the DDE method and other state-of-the-art methods using real-world data from five diverse geographic entities: the USA, Colombia, South Africa, Wuhan in China, and Piedmont in Italy. Compared to the state-of-the-art method, DDE significantly improved accuracy, with an average fitting Pearson coefficient exceeding 0.97 across the five geographic entities. In summary, the DDE method enhances the accuracy of parameter fitting in epidemiological models and provides a foundation for constructing simpler models adaptable to different geographic areas.

Multi-Label Chest X-Ray Image Classification with Single Positive Labels.

Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such datasets with full annotations is costly, time-consuming, and prone to noisy labels. Therefore, we introduce a weakly supervised learning problem called Single Positive Multi-label Learning (SPML) into CXR images classification (abbreviated as SPML-CXR), in which only one positive label is annotated per image. A simple solution to SPML-CXR problem is to assume that all the unannotated pathological labels are negative, however, it might introduce false negative labels and decrease the model performance. To this end, we present a Multi-level Pseudo-label Consistency (MPC) framework for SPML-CXR. First, inspired by the pseudo-labeling and consistency regularization in semi-supervised learning, we construct a weak-to-strong consistency framework, where the model prediction on weakly-augmented image is treated as the pseudo label for supervising the model prediction on a strongly-augmented version of the same image, and define an Image-level Perturbation-based Consistency (IPC) regularization to recover the potential mislabeled positive labels. Besides, we incorporate Random Elastic Deformation (RED) as an additional strong augmentation to enhance the perturbation. Second, aiming to expand the perturbation space, we design a perturbation stream to the consistency framework at the feature-level and introduce a Feature-level Perturbation-based Consistency (FPC) regularization as a supplement. Third, we design a Transformer-based encoder module to explore the sample relationship within each mini-batch by a Batch-level Transformer-based Correlation (BTC) regularization. Extensive experiments on the CheXpert and MIMIC-CXR datasets have shown the effectiveness of our MPC framework for solving the SPML-CXR problem.

[Effect of chronic periodontitis on quality of life and severity in patients with bronchiectasis].

PURPOSE: To investigate the effects of chronic periodontitis on the quality of life and severity of the disease in patients with bronchiectasis. METHODS: A total of 80 bronchiectasis patients admitted to The Fourth Hospital of Changsha between April 2021 and April 2023 were randomly selected. Patients were divided into two groups according to whether they had moderate to severe chronic periodontitis: bronchiectasis with periodontitis group (n=45) and bronchiectasis without periodontitis group (n=35). The Qualify of Life Questionnaire for Bronchiectasi(QoL-B) was used to assess patients' quality. The severity of the disease was assessed using the Bronchiectasis Severity Index (BSI), and serum levels of hypersensitive C-reactive protein (hsCRP), tumor necrosis factor alpha(TNF-alpha), and interleukin-6(IL-6) were detected. SPSS 20.0 software package was used for data analysis. RESULTS: The QoL-B score of bronchiectasis with periodontitis group was significantly lower than that of bronchiectasis without periodontitis group, and the BSI score was significantly higher than that of bronchiectasis without periodontitis group(P＜0.05). The levels of hs-CRP, TNF-alpha and IL-6 in bronchiectasis with periodontitis were significantly higher than those in bronchiectasis without periodontitis group(P＜0.05). CONCLUSIONS: Chronic periodontitis shows significant adverse effects on both quality of life and disease severity in patients with bronchiectasis, which may be related to the common mechanism of inflammatory response between the two kinds of diseases.

Artificial intelligence for diabetes care: current and future prospects.

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.

SSM-Net: Semi-supervised multi-task network for joint lesion segmentation and classification from pancreatic EUS images.

Pancreatic cancer does not show specific symptoms, which makes the diagnosis of early stages difficult with established image-based screening methods and therefore has the worst prognosis among all cancers. Although endoscopic ultrasonography (EUS) has a key role in diagnostic algorithms for pancreatic diseases, B-mode imaging of the pancreas can be affected by confounders such as chronic pancreatitis, which can make both pancreatic lesion segmentation and classification laborious and highly specialized. To address these challenges, this work proposes a semi-supervised multi-task network (SSM-Net) to leverage unlabeled and labeled EUS images for joint pancreatic lesion classification and segmentation. Specifically, we first devise a saliency-aware representation learning module (SRLM) on a large number of unlabeled images to train a feature extraction encoder network for labeled images by computing a contrastive loss with a semantic saliency map, which is obtained by our spectral residual module (SRM). Moreover, for labeled EUS images, we devise channel attention blocks (CABs) to refine the features extracted from the pre-trained encoder on unlabeled images for segmenting lesions, and then devise a merged global attention module (MGAM) and a feature similarity loss (FSL) for obtaining a lesion classification result. We collect a large-scale EUS-based pancreas image dataset (LS-EUSPI) consisting of 9,555 pathologically proven labeled EUS images (499 patients from four categories) and 15,500 unlabeled EUS images. Experimental results on the LS-EUSPI dataset and a public thyroid gland lesion dataset show that our SSM-Net clearly outperforms state-of-the-art methods.

[Intraoperative slide rail CT assistance in percutaneous sacroiliac joint screws for the treatment of pelvic posterior ring injury].

OBJECTIVE: To compare the clinical efficacy of intraoperative slide rail CT combined with C-arm X-ray assistance and just C-arm for percutaneous screw in the treatment of pelvic posterior ring injury. METHODS: A retrospective analysis was performed on the patient data of 76 patients with posterior pelvic ring injury admitted to the Department of Orthopedic Trauma from December 2018 to February 2022. Among them, 39 patients in the CT group were treated with C-arm combined with slide rail CT-assisted inline fixation including 23 males and 16 females with an average age of (44.98±7.33) years old;and the other 37 patients in the C-arm group were treated with intraline fixation treatment under only C-arm fluoroscopy including 24 males and 13 females with an average age of (44.37±10.82) years old. Among them, 42 patients with anterior ring fractures were treated with percutaneous inferior iliac spines with internal fixation (INFIX) or suprapubic support screws to fix the anterior pelvic ring. Postoperative follow-up time, operation time, complications of the two groups were compared. Results of Matta reduction criteria, Majed efficacy evaluation, the CT grading and the rate of secondary surgical revision were compared. RESULTS: The nailing time of (32.63±7.33) min in CT group was shorter than that of (52.95±10.64) min in C-arm group (t=-9.739, P<0.05). The follow-up time between CT group (11.97±1.86) months and C-arm group (12.03±1.71) months were not statistically significant(P>0.05). The postoperative complication rates between two groups were not statistically significant (χ2=0.159, P>0.05). Results of Matta reduction criteria (Z=2.79, P<0.05), Majeed efficacy evaluation(Z=2.79, P<0.05), CT grading (Z=2.83, P<0.05) in CT group were better than those in C-arm group(P<0.05); the secondary surgical revision rate in the CT group was significantly lower than that in the C-arm group (χ2=5.641, P<0.05). CONCLUSION: Compared with traditional C-arm fluoroscopy, intraoperative slide rail CT combined with C-arm assisted percutaneous sacroiliac joint screw placement surgery has the characteristics of short operation time, high accuracy and safety, and significant decrease in postoperative secondary revision rate, and is one of the effective methods for re-establishing the stability of the posterior ring of pelvic fracture.

Linear Capacitive Pressure Sensor with Gradient Architecture through Laser Ablation on MWCNT/Ecoflex Film.

The practical application of flexible pressure sensors, including electronic skins, wearable devices, human-machine interaction, etc., has attracted widespread attention. However, the linear response range of pressure sensors remains an issue. Ecoflex, as a silicone rubber, is a common material for flexible pressure sensors. Herein, we have innovatively designed and fabricated a pressure sensor with a gradient micro-cone architecture generated by CO2 laser ablation of MWCNT/Ecoflex dielectric layer film. In cooperation with the gradient micro-cone architecture and a dielectric layer of MWCNT/Ecoflex with a variable high dielectric constant under pressure, the pressure sensor exhibits linearity (R2 = 0.990) within the pressure range of 0-60 kPa, boasting a sensitivity of 0.75 kPa-1. Secondly, the sensor exhibits a rapid response time of 95 ms, a recovery time of 129 ms, hysteresis of 6.6%, and stability over 500 cycles. Moreover, the sensor effectively exhibited comprehensive detection of physiological signals, airflow detection, and Morse code communication, thereby demonstrating the potential for various applications.

A Highly Sensitive Strain Sensor with Wide Linear Sensing Range Prepared on a Hybrid-Structured CNT/Ecoflex Film via Local Regulation of Strain Distribution.

With the development of information technology, high-performance wearable strain sensors with high sensitivity and stretchability have played a significant role in motion detection. However, many high-sensitivity and outstanding-stretchability strain sensors possess a limited linear sensing range, which limits the enhancement of the flexible strain sensors' performance. Herein, we develop a hybrid-structured carbon nanotube (CNT)/Ecoflex strain sensor with laser-engraved grooves along with punched circular holes in a composite CNT/Ecoflex film by vacuum filtration and permeation. By optimizing the distribution of grooves and circular holes, the strain in the sensing layer can be locally regulated, which alters the morphology of cracks under strain and allows the hybrid-structured CNT/Ecoflex strain sensor to simultaneously exhibit high sensitivity (GF = 43.8) as well as a wide linear sensing range (200%). On the basis of excellent performance, the hybrid-structured CNT/Ecoflex strain sensor is capable of detecting movements in various parts of the human body, including movements of larynx and joint bending.