Organellophagy regulates cell death:A potential therapeutic target for inflammatory diseases.

BACKGROUND: Dysregulated alterations in organelle structure and function have a significant connection with cell death, as well as the occurrence and development of inflammatory diseases. Maintaining cell viability and inhibiting the release of inflammatory cytokines are essential measures to treat inflammatory diseases. Recently, many studies have showed that autophagy selectively targets dysfunctional organelles, thereby sustaining the functional stability of organelles, alleviating the release of multiple cytokines, and maintaining organismal homeostasis. Organellophagy dysfunction is critically engaged in different kinds of cell death and inflammatory diseases. AIM OF REVIEW: We summarized the current knowledge of organellophagy (e.g., mitophagy, reticulophagy, golgiphagy, lysophagy, pexophagy, nucleophagy, and ribophagy) and the underlying mechanisms by which organellophagy regulates cell death. KEY SCIENTIFIC CONCEPTS OF REVIEW: We outlined the potential role of organellophagy in the modulation of cell fate during the inflammatory response to develop an intervention strategy for the organelle quality control in inflammatory diseases.

miR-153 promotes neural differentiation by activating the cell adhesion/Ca2+ signaling pathway and targeting ion channel activity in HT-22 cells by bioinformatic analysis.

MicroRNAs have been studied extensively in neurodegenerative diseases. In a previous study, miR-153 promoted neural differentiation and projection formation in mouse hippocampal HT-22 cells. However, the pathways and molecular mechanism underlying miR-153-induced neural differentiation remain unclear. To explore the molecular mechanism of miR-153 on neural differentiation, we performed RNA sequencing on miR-153-overexpressed HT-22 cells. Based on RNA sequencing, differentially expressed genes (DEGs) and pathways in miR-153-overexpressed cells were identified. The Database for Annotation, Visualization and Integrated Discovery and Gene Set Enrichment Analysis were used to perform functional annotation and enrichment analysis of DEGs. Targetscan predicted the targets of miR-153. The Search Tool for the Retrieval of Interacting Genes and Cytoscape, were used to construct protein-protein interaction networks and identify hub genes. Q-PCR was used to detect mRNA expression of the identified genes. The expression profiles of the identified genes were compared between embryonic days 9.5 (E9.5) and E11.5 in the embryotic mouse brain of the GDS3442 dataset. Cell Counting Kit-8 assay was used to determine cell proliferation and cellular susceptibility to amyloid ß-protein (Aß) toxicity in miR-153-overexpressed cells. The results indicated that miR-153 increased cell adhesion/Ca2+ (Cdh5, Nrcam, and P2rx4) and Bdnf/Ntrk2 neurotrophic signaling pathway, and decreased ion channel activity (Kcnc3, Kcna4, Clcn5, and Scn5a). The changes in the expression of the identified genes in miR-153-overexpressed cells were consistent with the expression profile of GDS3442 during neural differentiation. In addition, miR-153 overexpression decreased cellular susceptibility to Aß toxicity in HT-22 cells. In conclusion, miR-153 overexpression may promote neural differentiation by inducing cell adhesion and the Bdnf/Ntrk2 pathway, and regulating electrophysiological maturity by targeting ion channels. MiR-153 may play an important role in neural differentiation; the findings provide a useful therapeutic direction for neurodegenerative diseases.

Intravoxel incoherent motion diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting parametrial invasion in cervical cancer.

PURPOSE: This study aimed to assess the predictive efficacy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in parametrial invasion (PMI) in cervical cancer patients. METHODS: A total of 83 cervical cancer patients (32 PMI-positive and 51 PMI-negative) retrospectively underwent pretreatment IVIM-DWI and DCE-MRI scans. IVIM-DWI parameters included apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and perfusion fraction (f). DCE-MRI parameters included volume transfer constant (Ktrans), flux rate constant (Kep), and fractional extravascular extracellular space volume (Ve). Logistic regression analyses were conducted to identify independent variables associated with PMI. Receiver operating characteristic curves were generated to assess the predictive performance of significant parameters. RESULTS: Multivariable analysis revealed that the MRI parameters D (odds ratio [OR]: 7.05; 95% CI 1.78-27.88; P = 0.005), D* (OR 6.58; 95% CI 1.49-29.10; P = 0.01), f (OR 5.12; 95% CI 1.23-21.37; P = 0.03), Ktrans (OR 4.60; 95% CI 1.19-17.81; P = 0.03), and Kep (OR 4.90; 95% CI 1.25-19.18; P = 0.02) were independent predictors of PMI in cervical cancer patients. The combined parameter incorporating these parameters demonstrated the highest performance in predicting PMI, yielding an area under the curve of 0.906, sensitivity of 84.4%, and specificity of 86.3%. CONCLUSION: The proposed combined parameter exhibited favorable performance in identifying PMI in cervical cancer patients.

Enzyme-Driven LC-HRMS Approach for Specific Recognition of 12α-Hydroxy Bile Acids.

The synthesis of 12α-hydroxylated bile acids (12HBAs) and non-12α-hydroxylated bile acids (non-12HBAs) occurs via classical and alternative pathways, respectively. The composition of these BAs is a crucial index for pathophysiologic assessment. However, accurately differentiating 12HBAs and non-12HBAs is highly challenging due to the limited standard substances. Here, we innovatively introduce 12α-hydroxysteroid dehydrogenase (12α-HSDH) as an enzymatic probe synthesized by heterologous expression in Escherichia coli, which can specifically and efficiently convert 12HBAs in vitro under mild conditions. Coupled to the conversion rate determined by liquid chromatography-high resolution mass spectrometry (LC-HRMS), this enzymatic probe allows for the straightforward distinguishing of 210 12HBAs and 312 non-12HBAs from complex biological matrices, resulting in a BAs profile with a well-defined hydroxyl feature at the C12 site. Notably, this enzyme-driven LC-HRMS approach can be extended to any molecule with explicit knowledge of enzymatic transformation. We demonstrate the practicality of this BAs profile in terms of both revealing cross-species BAs heterogeneity and monitoring the alterations of 12HBAs and non-12HBAs under asthma disease. We envisage that this work will provide a novel pattern to recognize the shift of BA metabolism from classical to alternative synthesis pathways in different pathophysiological states, thereby offering valuable insights into the management of related diseases.

Porphyrin-based covalent organic frameworks from design, synthesis to biological applications.

Covalent organic frameworks (COFs) constitute a class of highly functional porous materials composed of lightweight elements interconnected by covalent bonds, characterized by structural order, high crystallinity, and large specific surface area. The integration of naturally occurring porphyrin molecules, renowned for their inherent rigidity and conjugate planarity, as building blocks in COFs has garnered significant attention. This strategic incorporation addresses the limitations associated with free-standing porphyrins, resulting in the creation of well-organized porous crystal structures with molecular-level directional arrangements. The unique optical, electrical, and biochemical properties inherent to porphyrin molecules endow these COFs with diversified applications, particularly in the realm of biology. This review comprehensively explores the synthesis and modulation strategies employed in the development of porphyrin-based COFs and delves into their multifaceted applications in biological contexts. A chronological depiction of the evolution from design to application is presented, accompanied by an analysis of the existing challenges. Furthermore, this review offers directional guidance for the structural design of porphyrin-based COFs and underscores their promising prospects in the field of biology.

Synaptic Properties of a PbHfO3 Ferroelectric Memristor for Neuromorphic Computing.

The conventional von Neumann architecture has proven to be inadequate in keeping up with the rapid progress in artificial intelligence. Memristors have become the favored devices for simulating synaptic behavior and enabling neuromorphic computations to address challenges. An artificial synapse utilizing the perovskite structure PbHfO3 (PHO) has been created to tackle these concerns. By employing the sol-gel technique, a ferroelectric film composed of Au/PHO/FTO was created on FTO/glass for the purpose of this endeavor. The artificial synapse is composed of Au/PHO/FTO and exhibits learning and memory characteristics that are similar to those observed in biological neurons. The recognition accuracy for both MNIST and Fashion-MNIST data sets saw an increase, reaching 92.93% and 76.75%, respectively. This enhancement resulted from employing a convolutional neural network architecture and implementing an improved stochastic adaptive algorithm. The presented findings showcase a viable approach to achieve neuromorphic computation by employing artificial synapses fabricated with PHO.

Neuromorphic Computing of Optoelectronic Artificial BFCO/AZO Heterostructure Memristors Synapses.

Compared with purely electrical neuromorphic devices, those stimulated by optical signals have gained increasing attention due to their realistic sensory simulation. In this work, an optoelectronic neuromorphic device based on a photoelectric memristor with a Bi2FeCrO6/Al-doped ZnO (BFCO/AZO) heterostructure is fabricated that can respond to both electrical and optical signals and successfully simulate a variety of synaptic behaviors, such as STP, LTP, and PPF. In addition, the photomemory mechanism was identified by analyzing the energy band structures of AZO and BFCO. A convolutional neural network (CNN) architecture for pattern classification at the Mixed National Institute of Standards and Technology (MNIST) was used and improved the recognition accuracy of the MNIST and Fashion-MNIST datasets to 95.21% and 74.19%, respectively, by implementing an improved stochastic adaptive algorithm. These results provide a feasible approach for future implementation of optoelectronic synapses.

An adjustable multistage resistance switching behavior of a photoelectric artificial synaptic device with a ferroelectric diode effect for neuromorphic computing.

Neuromorphic computing, which mimics biological neural networks, is widely regarded as the optimal solution for addressing the limitations of traditional von Neumann computing architecture. In this work, an adjustable multistage resistance switching ferroelectric Bi2FeCrO6 diode artificial synaptic device was fabricated using a sol-gel method with a simple process. The device exhibits nonlinearity in its electrical characteristics, demonstrating tunable multistage resistance switching behavior and a strong ferroelectric diode effect through the manipulation of ferroelectric polarization. One of its salient advantages resides in its capacity to dynamically regulate its polarization state in response to an external electric field, thereby facilitating the fine-tuning of synaptic connection strength while maintaining synaptic stability. The device is capable of accurately simulating the fundamental properties of biological synapses, including long/short-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity. Additionally, the device exhibits a distinctive photoelectric response and is capable of inducing synaptic plasticity by light signal activation. The utilization of a femtosecond laser for the scrutiny of carrier transport mechanisms imparts profound insights into the intricate dynamics governing the optical memory effect. Furthermore, utilizing a convolutional neural network (CNN) architecture, the recognition accuracy of the MNIST and fashion MNIST datasets was improved to 95.6% and 78%, respectively, through the implementation of improved random adaptive algorithms. These findings present a new opportunity for utilizing Bi2FeCrO6 materials in the development of artificial synapses for neuromorphic computation.

Emerging ferroelectric materials ScAlN: applications and prospects in memristors.

The research found that after doping with rare earth elements, a large number of electrons and holes will be produced on the surface of AlN, which makes the material have the characteristics of spontaneous polarization. A new type of ferroelectric material has made a new breakthrough in the application of nitride-materials in the field of integrated devices. In this paper, the application prospects and development trends of ferroelectric material ScAlN in memristors are reviewed. Firstly, various fabrication processes and structures of the current ScAlN thin films are described in detail to explore the implementation of their applications in synaptic devices. Secondly, a series of electrical properties of ScAlN films, such as the current switching ratio and long-term cycle durability, were tested to explore whether their electrical properties could meet the basic needs of memristor device materials. Finally, a series of summaries on the current research studies of ScAlN thin films in the synaptic simulation are made, and the working state of ScAlN thin films as a synaptic device is observed. The results show that the ScAlN ferroelectric material has high residual polarization, no wake-up function, excellent stability and obvious STDP behavior, which indicates that the modified material has wide application prospects in the research and development of memristors.

Development of target-based cell membrane affinity ultrafiltration technology for a simplified approach to discovering potential bioactive compounds in natural products.

Target-based drug discovery technology based on cell membrane targets has gained significant traction and has been steadily advancing. However, current methods still face certain limitations that need to be addressed. One of the challenges is the laborious preparation process of screening materials, which can be time-consuming and resource-intensive. Additionally, there is a potential issue of non-specific adsorption caused by carrier materials, which can result in false-positive results and compromise the accuracy of the screening process. To address these challenges, this paper proposes a target-based cell membrane affinity ultrafiltration technology for active ingredient discovery in natural products. In this technique, the cell membranes of human lung adenocarcinoma epithelial cells (A549) with a high expression of epidermal growth factor receptor (EGFR) were incubated with candidate drugs and then transferred to an ultrafiltration tube. Through centrifugation, components that interacted with EGFR were retained in the ultrafiltration tube as "EGFR-ligand" complex, while the components that did not interact with EGFR were separated. After thorough washing and eluting, the components interacting with EGFR were dissociated and further identified using LC-MS, enabling the discovery of bioactive compounds. Moreover, the target-based cell membrane affinity ultrafiltration technology exhibited commendable binding capacity and selectivity. Ultimately, this technology successfully screened and identified two major components from the Curcumae Rhizoma-Sparganii Rhizoma (CS) herb pair extracts, which were further validated for their potential anti-tumor activity through pharmacological experiments. By eliminating the need for laborious preparation of screening materials and the potential non-specific adsorption caused by carriers, the development of target-based cell membrane affinity ultrafiltration technology provides a simplified approach and method for bioactive compounds discovery in natural sources.

FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks.

Metabolomics has emerged as a powerful tool in biomedical research to understand the pathophysiological processes and metabolic biomarkers of diseases. Nevertheless, it is a significant challenge in metabolomics to identify the reliable core metabolites that are closely associated with the occurrence or progression of diseases. Here, we proposed a new research framework by integrating detection-based metabolomics with computational network biology for function-guided and network-based identification of core metabolites, namely, FNICM. The proposed FNICM methodology is successfully utilized to uncover ulcerative colitis (UC)-related core metabolites based on the significantly perturbed metabolic subnetwork. First, seed metabolites were screened out using prior biological knowledge and targeted metabolomics. Second, by leveraging network topology, the perturbations of the detected seed metabolites were propagated to other undetected ones. Ultimately, 35 core metabolites were identified by controllability analysis and were further hierarchized into six levels based on confidence level and their potential significance. The specificity and generalizability of the discovered core metabolites, used as UC's diagnostic markers, were further validated using published data sets of UC patients. More importantly, we demonstrated the broad applicability and practicality of the FNICM framework in different contexts by applying it to multiple clinical data sets, including inflammatory bowel disease, colorectal cancer, and acute coronary syndrome. In addition, FNICM was also demonstrated as a practicality methodology to identify core metabolites correlated with the therapeutic effects of Clematis saponins. Overall, the FNICM methodology is a new framework for identifying reliable core metabolites for disease diagnosis and drug treatment from a systemic and a holistic perspective.

Perspective into ion storage of pristine metal-organic frameworks in capacitive deionization.

Metal-organic frameworks (MOFs), featuring tunable conductivity, tailored pore/structure and high surface area, have emerged as promising electrode nanomaterials for ion storage in capacitive deionization (CDI) and garnered tremendous attention in recent years. Despite the many advantages, the perspective from which MOFs should be designed and prepared for use as CDI electrode materials still faces various challenges that hinder their practical application. This summary proposes design principles for the pore size, pore environment, structure and dimensions of MOFs to precisely tailor the surface area, selectivity, conductivity, and Faradaic activity of electrode materials based on the ion storage mechanism in the CDI process. The account provides a new perspective to deepen the understanding of the fundamental issues of MOFs electrode materials to further meet the practical applications of CDI.

Value of Non-Contrast-Enhanced Vessel Wall MR Imaging in Assessing Vascular Invasion of Retroperitoneal Tumors.

BACKGROUND: Due to their location and growth patterns, retroperitoneal tumors often involve the surrounding blood vessels. Clinical decisions on a proper treatment depend on the information on this condition. Evaluation of blood vessels using non-contrast-enhanced vessel wall MRI may provide noninvasive assessment of the extent of tumor invasion to assist clinical decision-making. PURPOSE: To investigate the performance and potential of non-contrast-enhanced vessel wall MRI in evaluating the degree of vessel wall invasion of retroperitoneal tumors. STUDY TYPE: Prospective. POPULATION: Thirty-seven participants (mean age: 60.59 ± 11.77 years, 59% male) with retroperitoneal tumors close to vessels based on their diagnostic computer tomography. FIELD STRENGTH/SEQUENCES: 3 T; vessel wall MRI sequences: two-dimensional T2-weighted MultiVane XD turbo spin-echo (2D-T2-MVXD-TSE) and three-dimensional T1-weighted motion sensitized driven equilibrium fat suppression turbo spin-echo (3D-T1-MSDE-TSE) sequences; conventional MRI sequences: T2-weighted fat suppression turbo spin-echo (T2-FS-TSE), T2-weighted turbo spin-echo (T2-TSE), modified Dixon T1-weighted fast field echo (T1-mDixon-FFE), and diffusion-weighted echo planar imaging (DWI-EPI) sequences. ASSESSMENT: All patients underwent preoperative imaging using both non-contrast conventional and vessel wall MRI sequences. Images obtained from conventional and vessel wall MRI sequences were evaluated independently by three junior radiologists (3 and 2 years of experience in reading MRI) and reviewed by one senior radiologist (25 years of experience in reading MRI) to assess the degree of vessel wall invasion. MRI were validated results from the clinical standard diagnosis based on surgical confirmation or histopathological reports. Interobserver agreement was determined based on the reports from three readers with similar years of experiences. Intraobserver variability was assessed based on categorizing and recategorizing the vessels of 37 patients 1 month apart. STATISTICAL TESTS: Intra-class correlation efficient (ICC), Chi-square test, McNemar test, area under the receiver-operating characteristic curve (AUC), Delong test, P < 0.05 was considered significant. RESULTS: The accuracy of vessel wall MRI (91.96%, 95% CI: 85.43-95.71; 103 of 112) in detecting the degree of vessel wall invasion was significantly higher than that of conventional MRI (75%, 95% CI: 66.24-82.10; 84 of 112). The interobserver variability or reproducibility in categorization of the degree of vascular wall invasion was good in evaluating images from conventional and vessel wall MRI sequences (ICC = 0.821, 95% CI: 0.765-0.867 and ICC = 0.881, 95% CI: 0.842-0.913, respectively). DATA CONCLUSION: Diagnosis of vessel wall invasion of retroperitoneal tumors and assessment of its severity can be improved by using non-contrast-enhanced vessel wall MRI. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 3.

Highly Sensitive Ratiometric Fluorescent Flexible Sensor Based on the RhB@ZIF-8@PVDF Mixed-Matrix Membrane for Broad-Spectrum Antibiotic Detection.

Antibiotics play an essential role in the treatment of various diseases. However, the overuse of antibiotics has led to the pollution of water bodies and food safety, affecting human health. Herein, we report a dual-emission MOF-based flexible sensor for the detection of antibiotics in water, which was prepared by first encapsulating rhodamine B (RhB) by a zeolite imidazolium ester skeleton (ZIF-8) and then blending it with polyvinylidene difluoride (PVDF). The luminescent properties, structural tunability, and flexible porosity of the MOF-based composites were combined with the processability and flexibility of polymers to prepare luminescent membranes. The sensor is capable of dual-emission ratiometric fluorescence sensing of nitrofurantoin (NFT) and oxytetracycline (OTC), exhibiting sensitive detection of fluorescence burst and fluorescence enhancement, respectively, with detection limits of 0.012 µM and 8.9 nM. With the advantages of visual detection, high sensitivity, short detection time, and simplicity, the highly sensitive ratiometric fluorescent flexible sensor has great potential for detecting antibiotics in an aqueous environment. It will further stimulate interest in luminescent MOF-based mixed matrix membranes and their sensing applications.

Soft-hard interface design in super-elastic conductive polymer hydrogel containing Prussian blue analogues to enable highly efficient electrochemical deionization.

The poor cycling stability of faradaic materials owing to volume expansion and stress concentration during faradaic processes limits their use in large-scale electrochemical deionization (ECDI) applications. Herein, we developed a "soft-hard" interface by introducing conducting polymer hydrogels (CPHs), that is, polyvinyl alcohol/polypyrrole (PVA/PPy), to support the uniform distribution of Prussian blue analogues (e.g., copper hexacyanoferrate (CuHCF)). In this design, the soft buffer layer of the hydrogel effectively alleviates the stress concentration of CuHCF during the ion-intercalation process, and the conductive skeleton of the hydrogel provides charge-transfer pathways for the electrochemical process. Notably, the engineered CuHCF@PVA/PPy demonstrates an excellent salt-adsorption capacity of 22.7 mg g-1 at 10 mA g-1, fast salt-removal rate of 1.68 mg g-1 min-1 at 100 mA g-1, and low energy consumption of 0.49 kW h kg-1. More importantly, the material could maintain cycling stability with 90% capacity retention after 100 cycles, which is in good agreement with in situ X-ray diffraction tests and finite element simulations. This study provides a simple strategy to construct three-dimensional conductive polymer hydrogel structures to improve the desalination capacity and cycling stability of faradaic materials with universality and scalability, which promotes the development of high-performance electrodes for ECDI.

Pseudotargeted metabolomics-based random forest model for tracking plant species from herbal products.

BACKGROUND: The "one-to-multiple" phenomenon is prevalent in medicinal herbs. Accurate species identification is critical to ensure the safety and efficacy of herbal products but is extremely challenging due to their complex matrices and diverse compositions. PURPOSE: This study aimed to identify the determinable chemicalome of herbs and develop a reasonable strategy to track their relevant species from herbal products. METHODS: Take Astragali Radix-the typical "one to multiple" herb, as a case. An in-house database-driven identification of the potentially bioactive chemicalome (saponins and flavonoids) in AR was performed. Furthermore, a pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data. Then based on the data matrix, the random forest algorithm was trained to predict Astragali Radix species from commercial products. RESULTS: The pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data (including 56 saponins and 49 flavonoids) from 26 batches of AR. Then the random forest algorithm was well-trained by importing the valid data matrix and showed high performance in predicting Astragalus species from ten commercial products. CONCLUSION: This strategy could learn species-special combination features for accurate herbal species tracing and could be expected to promote the traceability of herbal materials in herbal products, contributing to manufacturing standardization.

Skin-Inspired Highly Sensitive Tactile Sensors with Ultrahigh Resolution over a Broad Sensing Range.

Flexible tactile sensors with high sensitivity, a broad pressure detection range, and high resolution are highly desired for the applications of health monitoring, robots, and the human-machine interface. However, it is still challenging to realize a tactile sensor with high sensitivity and resolution over a wide detection range. Herein, to solve the abovementioned problem, we demonstrate a universal route to develop a highly sensitive tactile sensor with high resolution and a wide pressure range. The tactile sensor is composed of two layers of microstructured flexible electrodes with high modulus and conductive cotton fabric with low modulus. By optimizing the sensing films, the fabricated tactile sensor shows a high sensitivity of 8.9 × 104 kPa-1 from 2 Pa to 250 kPa because of the high structural compressibility and stress adaptation of the multilayered composite films. Meanwhile, a fast response speed of 18 ms, an ultrahigh resolution of 100 Pa over 100 kPa, and excellent durability over 20 000 loading/unloading cycles are demonstrated. Moreover, a 6 × 6 tactile sensor array is fabricated and shows promising potential application in electronic skin (e-skin). Therefore, employing multilayered composite films for tactile sensors is a novel strategy to achieve high-performance tactile perception in real-time health monitoring and artificial intelligence.

Recent Advances in Polymer Composites for Flexible Pressure Sensors.

Pressure sensors show significant potential applications in health monitoring, bio-sensing, electronic skin, and tactile perception. Consequently, tremendous research interest has been devoted to the development of high-performance pressure sensors. In this paper, recent progress on the polymer composite-based flexible pressure sensor is reviewed. The parameters of pressure sensors, including sensitivity, linear response range, detection limit, response speed, and reliability, are first introduced. Secondly, representative types of pressure sensors and relevant working principles are introduced and discussed. After that, the applications in human physiology monitoring, health monitoring, artificial skin, and self-powered smart system are listed and discussed in detail. Finally, the remaining challenges and outlook of polymer composite-based flexible sensors are summarized at the end of this review paper. This work should have some impact on the development of high-performance flexible pressure sensors.

Assessment of nucleus accumbens impairment in a rat model of postoperative cognitive dysfunction using diffusion tensor imaging.

BACKGROUND: Postoperative cognitive dysfunction (POCD) often occurs in elderly patients, causing depression and other symptoms. Nucleus accumbens (NAc) is involved in depression. We investigate the diffusion tensor imaging (DTI) parameters of NAc in a POCD model of depression. METHODS: Twenty-month-old male Sprague-Dawley (SD) rats were randomly divided into the POCD and Sham groups. The POCD group underwent exploratory laparotomy to establish a POCD depression model, while the Sham group underwent a sham operation. The fractional anisotropy (FA) and mean diffusivity (MD) values of the bilateral NAc, behavioural changes of forced swimming test and sucrose preference rate, and pathological changes of glial fibrillary acidic protein (GFAP) fluorescent intensity were observed at 15 days (D15 ) and 30 days (D30 ) after the operation. RESULTS: The FA value of the bilateral NAc area in the POCD group was lower than that in the Sham group at the two time periods after the operation (P < 0.05). However, the MD value at D30 was higher in the POCD group than in the Sham group (P < 0.05). The FA value in the POCD group was lower at D30 than at D15 (P < 0.05). The floating time was prolonged while the sucrose preference rate was decreased in the POCD group compared with the Sham group (P < 0.05). The floating time in the POCD group was longer at D30 than at D15 . However, the sucrose preference rate in the POCD was lower at D30 than at D15 . The GFAP fluorescent intensity in the bilateral NAc region in the POCD group was higher than in the Sham group (P < 0.05). CONCLUSION: Microstructural changes of the NAc area are associated with POCD related depression. In addition, FA and MD were demonstrated to be effective in diagnosing and monitoring postoperative depression and its severity.

Low-cost composite film triboelectric nanogenerators for a self-powered touch sensor.

In the last two decades, nanogenerators have been studied inorder to solve the power supply problems of electronic devices. Triboelectric nanogenerators (TENGs) have gained widespread attention due to their excellent properties and wide range of applications. However, previous studies frequently ignored numerous application-related issues and even wasted some of the benefits of the TENG itself in favor of enhanced performance. Here, we propose a TENG based on BaTiO3 (BTO)-polydimethylsiloxane (PDMS) composite films with low cost and simple preparation, where its maximum output performance is obtained when the mass proportion of BTO to PDMS is 40%. In addition, we demonstrate how the single-electrode TENG may be used as a self-powered touch sensor that can communicate with a microcontroller unit (MCU) to turn LED lights on and off. This practical example will provide a valuable reference for the application of low-cost self-powered sensors in wearable devices, Internet of Things, human-machine interactions and other fields. Furthermore, we discovered a number of issues that were rarely or never addressed in previous studies and provide some solutions, such as a signal processing method for a TENG-based self-powered sensor. It serves as a foundation for future investigations on the performance assessment and application of TENGs.