Effects of telehealth interventions on the caregiver burden and mental health for caregivers of people with dementia: a systematic review and meta-analysis.

OBJECTIVES: To systematically evaluate the effects of telehealth interventions on the caregiver burden and mental health of caregivers for people with dementia (PWD). METHOD: Relevant randomized controlled trials (RCTs) of telehealth interventions on caregivers were extracted from nine electronic databases (PubMed, The Cochrane Library, Web of Science, Embase, CINAHL, SinoMed, CNKI, WanFang, and VIP). The retrieval time was from inception to 26 July 2023. RESULTS: Twenty-two articles with 2132 subjects were included in the final analysis. The meta-analysis demonstrated that telehealth interventions exerted a significant effect in reducing caregiver burden (SMD: -0.14, 95 % CI: -0.25, -0.02, p = 0.02), depression (SMD = -0.17; 95%CI: -0.27, -0.07, p < 0.001) and stress (SMD = -0.20, 95%CI: -0.37, -0.04, p = 0.01). However, no statistically significant effect was observed on anxiety (SMD = -0.12, 95%CI: -0.27, 0.03, p = 0.12). Moreover, subgroup analysis showed that tailored interventions were associated with more evident reductions in depression (SMD = -0.26; 95%CI: -0.40, -0.13, p < 0.001) than standardized interventions (SMD = -0.08; 95%CI: -0.22, 0.06, p = 0.25). In addition, telehealth was effective in relieving depression in Internet-based (SMD = -0.17, 95%CI: -0.30, -0.03, p = 0.01) and Telephone-based group (SMD = -0.18, 95%CI: -0.34, -0.02, p = 0.03), while there was no significant difference in the Internet and Telephone-based group (SMD = -0.18, 95%CI: -0.54, 0.18, p = 0.32). CONCLUSION: Telehealth could effectively reduce the burden and relieve the depression and stress of caregivers of PWD, while its effect on anxiety requires further research. Overall, telehealth has potential benefits in dementia care.

PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference.

A citywide traffic crash risk map is of great significance for preventing future traffic crashes. However, the fine-grained geographic traffic crash risk inference is still a challenging task, mainly due to the complex road network structure, human behavior and high data requirements. In this work, we propose a deep-learning framework PL-TARMI, which leverages easily accessible data to achieve accurate fine-grained traffic crash risk map inference. Specifically, we integrate the satellite image and road network image, combine with other accessible data (e.g., point of interest distribution, human mobility data, traffic data, etc.) as input, and finally obtain the pixel-level traffic crash risk map, which could provide more reasonable traffic crash prevention guidance with a lower cost. Extensive experiments on real-world datasets demonstrate the effectiveness of PL-TARMI.

Alzheimer's Disease Classification Based on Image Transformation and Features Fusion.

It has become an inevitable trend for medical personnel to analyze and diagnose Alzheimer's disease (AD) in different stages by combining functional magnetic resonance imaging (fMRI) and artificial intelligence technologies such as deep learning in the future. In this paper, a classification method was proposed for AD based on two different transformation images of fMRI and improved the 3DPCANet model and canonical correlation analysis (CCA). The main ideas include that, firstly, fMRI images were preprocessed, and subsequently, mean regional homogeneity (mReHo) and mean amplitude of low-frequency amplitude (mALFF) transformation were performed for the preprocessed images. Then, mReHo and mALFF images were extracted features using the improved 3DPCANet, and these two kinds of the extracted features were fused by CCA. Finally, the support vector machine (SVM) was used to classify AD patients with different stages. Experimental results showed that the proposed approach was robust and effective. Classification accuracy for significant memory concern (SMC) vs. mild cognitive impairment (MCI), normal control (NC) vs. AD, and NC vs. SMC, respectively, reached 95.00%, 92.00%, and 91.30%, which adequately proved the feasibility and effectiveness of the proposed method.

Identification of critical links in a large-scale road network considering the traffic flow betweenness index.

The traditional full-scan method is commonly used for identifying critical links in road networks. This method simulates each link to be closed iteratively and measures its impact on the efficiency of the whole network. It can accurately identify critical links. However, in this method, traffic assignments are conducted under all scenarios of link disruption, making this process prohibitively time-consuming for large-scale road networks. This paper proposes an approach considering the traffic flow betweenness index (TFBI) to identify critical links, which can significantly reduce the computational burden compared with the traditional full-scan method. The TFBI consists of two parts: traffic flow betweenness and endpoint origin-destination (OD) demand (rerouted travel demand). There is a weight coefficient between these two parts. Traffic flow betweenness is established by considering the shortest travel-time path betweenness, link traffic flow and total OD demand. The proposed approach consists of the following main steps. First, a sample road network is selected to calibrate the weight coefficient between traffic flow betweenness and endpoint OD demand in the TFBI using the network robustness index. This index calculates changes in the whole-system travel time due to each link's closure under the traditional full-scan method. Then, candidate critical links are pre-selected according to the TFBI value of each link. Finally, a given number of real critical links are identified from the candidate critical links using the traditional full-scan method. The applicability and computational efficiency of the TFBI-based approach are demonstrated for the road network in Changchun, China.

A vacuum impregnation method for synthesizing octahedral Pt2CuNi nanoparticles on mesoporous carbon support and the oxygen reduction reaction electrocatalytic properties.

Mesoporous carbon (MPC) nanomaterials, with large specific surface area, excellent conductivity and stability, and effective mass transfer are beneficial for use as catalyst support in electrochemical oxygen reduction reaction (ORR) for fuel cell applications. However, MPC utilization was limited by difficulties in loading catalyst nanoparticles within the MPC pores while simultaneously controlling critical particle parameters such as size and distribution. In this study we report a new vacuum impregnation method combined with solid-state chemistry synthesis for preparing highly active ORR catalyst nanoparticles on MPC supports. We confirm the effectiveness of this method by synthesizing octahedral Pt2CuNi nanoparticles on hydrophilic MPC with an even particle distribution in the MPC pores. We also demonstrate the capability of this method in controlling the particle size and morphology by adjusting the synthesis parameters. The synthesized catalysts exhibited excellent ORR activity and promising durability, which proves the goodness of using MPC support in ORR electrocatalysis. The findings offer a new methodology for synthesizing nanoparticles in MPC pores with parameter control and provide an intriguing strategy to develop new ORR catalysts using MPC support structure.

A high-performance oxygen evolution catalyst in neutral-pH for sunlight-driven CO2 reduction.

The efficiency of sunlight-driven reduction of carbon dioxide (CO2), a process mimicking the photosynthesis in nature that integrates the light harvester and electrolysis cell to convert CO2 into valuable chemicals, is greatly limited by the sluggish kinetics of oxygen evolution in pH-neutral conditions. Current non-noble metal oxide catalysts developed to drive oxygen evolution in alkaline solution have poor performance in neutral solutions. Here we report a highly active and stable oxygen evolution catalyst in neutral pH, Brownmillerite Sr2GaCoO5, with the specific activity about one order of magnitude higher than that of widely used iridium oxide catalyst. Using Sr2GaCoO5 to catalyze oxygen evolution, the integrated CO2 reduction achieves the average solar-to-CO efficiency of 13.9% with no appreciable performance degradation in 19 h of operation. Our results not only set a record for the efficiency in sunlight-driven CO2 reduction, but open new opportunities towards the realization of practical CO2 reduction systems.

Dual-Site Cascade Oxygen Reduction Mechanism on SnO x/Pt-Cu-Ni for Promoting Reaction Kinetics.

Designing highly active oxygen reduction reaction (ORR) catalysts is crucial to boost the fuel cell economy. Previous research has mainly focused on Pt-based alloy catalysts in which surface Pt is the solely active site and the activity improvement was challenged by the discovered scaling relationship. Herein we report a new concept of utilizing dual active sites for the ORR and demonstrate its effectiveness by synthesizing a SnO x/Pt-Cu-Ni heterojunctioned catalyst. A maximum of 40% enhancement in the apparent specific activity, which corresponds to 10-fold enhancement on interface sites, is measured compared with pure Pt-Cu-Ni. Detailed investigations suggest an altered dual-site cascade mechanism wherein the first two steps occur on SnO x sites and the remaining steps occur on adjacent Pt sites, allowing a significant decrease in the energy barrier. This study with the suggested dual-site cascade mechanism shows the potential to overcome the ORR energy barrier bottleneck to develop highly active catalysts.

The temporal geographically-explicit network of public transport in Changchun City, Northeast China.

The vehicle trajectory data is a feasible way for us to understand and reveal urban traffic conditions and human mobility. Therefore, it is extremely valuable to have a fine-grained picture of large-scale vehicle trajectory data, particularly in two different modes, taxis and buses, over the same period at an urban scale. This paper integrates the trajectory data of approximately 7,000 taxis and 1,500 buses in Changchun City, China and accesses the temporal geographically-explicit network of public transport via sequential snapshots of vehicle trajectory data every 30 seconds of the first week in March 2018. In order to reveal urban traffic conditions and human mobility, we construct two-layer urban traffic network (UTN) between these two different transport modes, take crossings as nodes and roads as edges weighted by the volume or average speed of vehicles in each hour. We released this temporal geographically-explicit network of public transport and the dynamics, weighted and directed UTN in simple formats for easy access.

Selective CO2 reduction on a polycrystalline Ag electrode enhanced by anodization treatment.

Electrochemical reduction of CO2 to CO on polycrystalline silver (Ag) was greatly improved by a simple anodization treatment. A CO faradaic efficiency of 92.8% was achieved at an overpotential of 0.50 V in an aqueous electrolyte. This study suggests that the enhanced performance is due to a preferred (220) orientation and a thin silver oxide layer formed by anodization.

Activity and stability trends of perovskite oxides for oxygen evolution catalysis at neutral pH.

Perovskite oxides (ABO3) have been studied extensively to promote the kinetics of the oxygen evolution reaction (OER) in alkaline electrolytes. However, developing highly active catalysts for OER at near-neutral pH is desirable for many photoelectrochemical/electrochemical devices. In this paper, we systematically studied the activity and stability of well-known perovskite oxides for OER at pH 7. Previous activity descriptors established for perovskite oxides at pH 13, such as having an eg occupancy close to unity or having an O p-band center close to Fermi level, were shown to scale with OER activity at pH 7. Stability was a greater challenge at pH 7 than at pH 13, where two different modes of instability were identified from combined transmission electron microscopy and density functional theory analyses. Perovskites with O p-band close to Fermi level showed leaching of A-site atoms and surface amorphization under all overpotentials examined at pH 7, while those with O p-band far from Fermi level were stable under low OER current/potential but became unstable at high current/potential accompanied by leaching of B-site atoms. Therefore, efforts are needed to enhance the activity and stability of perovskites against A-site or B-site loss if used at neutral pH.

Quantum Chemical Design of Doped Ca2MnAlO(5+δ) as Oxygen Storage Media.

Brownmillerite Ca2MnAlO5 has an exceptional capability to robustly adsorb half-molecules of oxygen and form Ca2MnAlO5.5. To utilize this unique property to regulate oxygen-involved reactions, it is crucial to match the oxygen release-intake equilibrium with targeted reaction conditions. Here we perform a comprehensive investigation of the strategy of tuning the oxygen storage property of Ca2MnAlO5 through chemical doping. For undoped Ca2MnAlO5+δ, our first-principles calculation predicts that the equilibrium temperature at a pressure of 1 atm of O2 is 848 K, which is in excellent agreement with experimental results. Furthermore, the doping of alkaline earth ions at the Ca site, trivalent ions at the Al site, and 3d transition metal ions at the Mn site is analyzed. By the doping of 12.5% of Ga, V, and Ti, the equilibrium temperature shifts to high values by approximately 110-270 K, while by the doping of 12.5% of Fe, Sr, and Ba, the equilibrium temperature is lowered by approximately 20-210 K. The doping of these elements is thermodynamically stable, and doping other elements including Mg, Sc, Y, Cr, Co, and Ni generates metastable compounds. The doping of a higher content of Fe, however, lowers the oxygen storage capacity. Finally, on the basis of our calculated data, we prove that the formation energetics of nondilute interacting oxygen vacancy in doped Ca2MnAlO5.5 scale linearly with a simple descriptor, the oxygen p-band position relative to the Fermi level. The higher-oxygen p-band position leads to a lower vacancy formation energy and thus a lower oxygen release temperature. Understanding such a relationship between fundamental quantum chemical properties and macroscopic properties paves the road to the design and optimization of novel functional oxides.

Combined EUS and CT for evaluating gastrointestinal submucosal tumors before endoscopic resection.

GOALS: The aim of this study was to evaluate the combination of endoscopic ultrasonography (EUS) and computed tomography (CT) in predicting the maneuvers for therapeutic endoscopy for gastrointestinal submucosal tumors (SMTs). METHODS: Patients with SMTs, who were scheduled for endoscopic resection, were randomized to preoperative performance of both EUS and CT (group A) or EUS only (group B). The following data were collected: therapeutic maneuvers, duration of procedure, dose of propofol, resected lesion size, and complications. RESULTS: A total of 36 patients were included in group A and 36 patients were included in group B. Endoscopic submucosal excavation was performed in 43 patients, endoscopic full-thickness resection in 18 patients, and submucosal tunneling endoscopic resection in 11 patients. No significant differences were observed between the two groups (P>0.05). The coincidence rate between the preoperative program and the actual endoscopic procedures in group A was higher than that in group B (83.3 vs. 61.1%, P<0.05). The procedural time in group A was less than that in group B (39.36±17.83 vs. 48.06±12.03 min, P<0.05), and the dose of propofol in group A was less than that in group B (249.18±125.12 vs. 304.16±102.61 mg, P<0.05). The mean resected lesion size was 2.32±1.46 cm in group A and 2.12±0.75 cm in group B, without differences (P>0.05). A total of 14 cardiopulmonary complications and seven endoscopic complications occurred, without significant differences between the two groups (P>0.05). CONCLUSION: EUS combined with CT can better evaluate SMTs compared with EUS only in predicting the maneuvers for therapeutic endoscopy.

Enzyme-enabled responsive surfaces for anti-contamination materials.

Many real-life stains have origins from biological matters including proteins, lipids, and carbohydrates that act as gluing agents binding along with other particulates or microbes to exposed surfaces of automobiles, furniture, and fabrics. Mimicking naturally occurring self-defensive processes, we demonstrate in this work that a solid surface carrying partially exposed enzyme granules protected the surface in situ from contamination by biological stains and fingerprints. Attributed to the activities of enzymes which can be made compatible with a wide range of materials, such anti-contamination and self-cleaning functionalities are highly selective and efficient toward sticky chemicals. This observation promises a new mechanism in developing smart materials with desired anti-microbial, self-reporting, self-cleaning, or self-healing functions.

Poly(ethylene glycol) conjugated enzyme with enhanced hydrophobic compatibility for self-cleaning coatings.

Enzyme-based smart materials constitute a rapidly growing group of functional materials. Often the natively evolved enzymes are not compatible with hydrophobic synthetic materials, thus significantly limiting the performance of enzymes. This work investigates the use of a polyethylene glycol (PEG)-conjugated detergent enzyme for self-cleaning coatings. As a result, PEG conjugated α-amylase demonstrated a much more homogeneous distribution in polyurethane coatings than the parent native enzyme as detected by both fluorescent microscopy and scanning electron microscopy (SEM) equipped with energy-dispersive X-ray spectroscopy (SEM-EDX). Additionally, the conjugated enzyme showed enhanced retention in the coating and much improved thermal stability with a halflife of 20 days detected at 80 °C and over 350 days under room temperature. Such coating-incorporated enzyme afforded interesting self-cleaning functionality against starch-based stains as examined through a slipping drop test.

Thermodynamics and kinetics of CO2, CO, and H+ binding to the metal centre of CO2 reduction catalysts.

In our developing world, carbon dioxide has become one of the most abundant greenhouse gases in the atmosphere. It is a stable, inert, small molecule that continues to present significant challenges toward its chemical activation as a useful carbon end product. This tutorial review describes one approach to the reduction of carbon dioxide to carbon fuels, using cobalt and nickel molecular catalysts, with particular focus on studying the thermodynamics and kinetics of CO(2) binding to metal catalytic sites.

Enzyme-carrying electrospun nanofibers.

Compared to other nanomaterials as supports for enzyme immobilization, nanofibers provide a promising configuration in balancing the key factors governing the catalytic performance of the immobilized enzymes including surface area-to-volume ratio, mass transfer resistance, effective loading, and the easiness to recycle. Synthetic and natural polymers can be fabricated into nanofibers via a physical process called electrospinning. The process requires only simple apparatus to operate, yet has proved to be very flexible in the selection of feedstock materials and also effective to control and manipulate the properties of the resulting nanofibers such as size and surface morphology, which are typically important parameters for enzyme immobilization supports. This chapter describes a protocol for the preparation of nanofibrous enzyme, involving the synthesis and end-group functionalization of polystyrene, production of electrospun nanofibers, and surface immobilization of enzyme via covalent attachment.

Highly stable enzyme precipitate coatings and their electrochemical applications.

This paper describes highly stable enzyme precipitate coatings (EPCs) on electrospun polymer nanofibers and carbon nanotubes (CNTs), and their potential applications in the development of highly sensitive biosensors and high-powered biofuel cells. EPCs of glucose oxidase (GOx) were prepared by precipitating GOx molecules in the presence of ammonium sulfate, then cross-linking the precipitated GOx aggregates on covalently attached enzyme molecules on the surface of nanomaterials. EPCs-GOx not only improved enzyme loading, but also retained high enzyme stability. For example, EPC-GOx on CNTs showed a 50 times higher activity per unit weight of CNTs than the conventional approach of covalent attachment, and its initial activity was maintained with negligible loss for 200 days. EPC-GOx on CNTs was entrapped by Nafion to prepare enzyme electrodes for glucose sensors and biofuel cells. The EPC-GOx electrode showed a higher sensitivity and a lower detection limit than an electrode prepared with covalently attached GOx (CA-GOx). The CA-GOx electrode showed an 80% drop in sensitivity after thermal treatment at 50°C for 4 h, while the EPC-GOx electrode maintained its high sensitivity with negligible decrease under the same conditions. The use of EPC-GOx as the anode of a biofuel cell improved the power density, which was also stable even after thermal treatment of the enzyme anode at 50°C. The excellent stability of the EPC-GOx electrode together with its high current output create new potential for the practical applications of enzyme-based glucose sensors and biofuel cells.

Kinetic limitations of a bioelectrochemical electrode using carbon nanotube-attached glucose oxidase for biofuel cells.

Carbon nanotubes (CNTs) have been used for various bioelectrochemical applications, presumably for substantial improvement in performance. However, often only moderate results observed, with many governing factors have been considered and suggested yet without much systematic evaluation and verification. In this study, CNT-supported glucose oxidase (CNT-GOx) was examined in the presence of 1,4-benzoquinone (BQ). The intrinsic Michaelis parameters of the reaction catalyzed by CNT-GOx were found very close to those of native GOx. However, the Nafion entrapment of CNT-GOx for an electrode resulted in a much lower activity due to the limited availability of the embedded enzyme. Interestingly, kinetic studies revealed that the biofuel cell employing such an enzyme electrode only generated a power density equivalent to <40% of the reaction capability of the enzyme on electrode. It appeared to us that factors such as electron and proton transfer resistances can be more overwhelming than the heterogeneous reaction kinetics in limiting the power generation of such biofuel cells.

Enzymic thin film coatings for bioactive materials.

Bioactive materials have been explored for a broad range of applications including biocatalysts, biosensors, antifouling membranes and other functional and smart materials. We report herein a unique method for preparation of bioactive materials through a spin coating process. Specifically, we investigated the preparation of protease Subtilisin Carlsberg-coated plastic films and examined their activities for hydrolysis of chicken egg albumin (CEA). The process generated enzymic coatings with a typical loading of 13 microg/cm2, retaining 46% of the enzyme activity for hydrolysis of CEA in aqueous solutions. Interestingly, the surface-coated protease thin film not only catalyzed the hydrolysis of CEA in aqueous solutions, but also showed good activity for solid-state CEA that was coated on top of the enzyme thin film.

Enzymatic synthesis of galactosyl lactic ethyl ester and its polymer for use as biomaterials.

Lactate-based chemicals and polymers including poly(lactic acid) (PLA) are highly valuable materials for biomedical, food and general-purpose applications. Chemical synthesis, albeit the high reaction velocities achieved with it, often leaves chemical residues that are subject to health and safety concerns. Alternative biosynthesis is preferred in order to overcome these problems. Herein we report a novel enzymatic synthesis for the preparation of beta-d-galactosyl-l-lactic acid ethyl ester (GLAEE). Such a product, which may find applications in food and personal care products, is generally difficult to synthesize via traditional chemical routes because the reactions have to be highly selective due to the multiple hydroxyl groups of sugars. We further explore the enzymatic polymerization of GLAEE to form a unique biopolymer, poly(beta-d-galactoside-co-l-lactic acid) (PGLA). Novozyme 435 was found efficient in catalyzing the polymerization reaction in acetone with a conversion yield of 60% within 100 h. The molecular weight of the polymer product ranged from about 800-2000 as analyzed by using ESI-MS. It is expected that a variety of sugar-hydroxyl acids copolymers can be prepared through the same approach and a new class of biomaterials can thus be developed.