Revolutionizing Neurocare: Biomimetic Nanodelivery Via Cell Membranes.

Brain disorders represent a significant challenge in medical science due to the formidable blood-brain barrier (BBB), which severely limits the penetration of conventional therapeutics, hindering effective treatment strategies. This review delves into the innovative realm of biomimetic nanodelivery systems, including stem cell-derived nanoghosts, tumor cell membrane-coated nanoparticles, and erythrocyte membrane-based carriers, highlighting their potential to circumvent the BBB's restrictions. By mimicking native cell properties, these nanocarriers emerge as a promising solution for enhancing drug delivery to the brain, offering a strategic advantage in overcoming the barrier's selective permeability. The unique benefits of leveraging cell membranes from various sources is evaluated and advanced technologies for fabricating cell membrane-encapsulated nanoparticles capable of masquerading as endogenous cells are examined. This enables the targeted delivery of a broad spectrum of therapeutic agents, ranging from small molecule drugs to proteins, thereby providing an innovative approach to neurocare. Further, the review contrasts the capabilities and limitations of these biomimetic nanocarriers with traditional delivery methods, underlining their potential to enable targeted, sustained, and minimally invasive treatment modalities. This review is concluded with a perspective on the clinical translation of these biomimetic systems, underscoring their transformative impact on the therapeutic landscape for intractable brain diseases.

Deep Learning-Aided Modulation Recognition for Non-Orthogonal Signals.

Automatic Modulation Recognition (AMR) can obtain the modulation mode of the received signal for subsequent processing without the assistance of the transmitter. Although the existing AMR methods have been mature for the orthogonal signals, these methods face challenges when deployed in non-orthogonal transmission systems due to the superimposed signals. In this paper, we aim to develop efficient AMR methods for both downlink and uplink non-orthogonal transmission signals using deep learning-based data-driven classification methodology. Specifically, for downlink non-orthogonal signals, we propose a Bi-directional Long Short-Term Memory (BiLSTM)-based AMR method that exploits long-term data dependence to automatically learn irregular signal constellation shapes. Transfer learning is further incorporated to improve recognition accuracy and robustness under varying transmission conditions. For uplink non-orthogonal signals, the combinatorial number of classification types explodes exponentially with the number of signal layers, which becomes the major obstacle to AMR. We develop a spatio-temporal fusion network based on the attention mechanism to efficiently extract spatio-temporal features, and network details are optimized according to the superposition characteristics of non-orthogonal signals. Experiments show that the proposed deep learning-based methods outperform their conventional counterparts in both downlink and uplink non-orthogonal systems. In a typical uplink scenario with three non-orthogonal signal layers, the recognition accuracy can approach 96.6% in the Gaussian channel, which is 19% higher than the vanilla Convolution Neural Network.

A Cell-Based Fast Memetic Algorithm for Automated Convolutional Neural Architecture Design.

Neural architecture search (NAS) has attracted much attention in recent years. It automates the neural network construction for different tasks, which is traditionally addressed manually. In the literature, evolutionary optimization (EO) has been proposed for NAS due to its strong global search capability. However, despite the success enjoyed by EO, it is worth noting that existing EO algorithms for NAS are often very computationally expensive, which makes these algorithms unpractical in reality. Keeping this in mind, in this article, we propose an efficient memetic algorithm (MA) for automated convolutional neural network (CNN) architecture search. In contrast to existing EO algorithms for CNN architecture design, a new cell-based architecture search space, and new global and local search operators are proposed for CNN architecture search. To further improve the efficiency of our proposed algorithm, we develop a one-epoch-based performance estimation strategy without any pretrained models to evaluate each found architecture on the training datasets. To investigate the performance of the proposed method, comprehensive empirical studies are conducted against 34 state-of-the-art peer algorithms, including manual algorithms, reinforcement learning (RL) algorithms, gradient-based algorithms, and evolutionary algorithms (EAs), on widely used CIFAR10 and CIFAR100 datasets. The obtained results confirmed the efficacy of the proposed approach for automated CNN architecture design.

A neoteric dual-channel spectral sensor for wide-range pH detection based on variables in UV-vis peak and intensity.

A water-soluble 2',4',6'-trihydroxy phenylazo luminol dye (THPL) with a D-π-A conjugated structure was developed for the first time for the dual-channel spectral sensing of a wide-range of pH values in aqueous solutions, using phloroglucinol as the electron-donating group, heterocycle amide luminol as the electron-accepting one and -NN- as the π-conjugated bridge. Its UV-vis absorption spectrum, especially its spectral response to pH, was investigated in detail. The results confirmed that the proposed THPL exhibited superior UV-vis spectral properties, a strong molar absorption coefficient (εmax = 3.35 × 104 L mol-1 cm-1) and a small half-band width of ca. 126 nm. In particular, either the absorption intensity (Abs) or peak (λmax) changed linearly with pH values from 3.0 to 10.0 under the optimized conditions. Notably, THPL expressed an exclusive dual-channel spectral response to pH, i.e., Abs (a.u.) = 0.1316 + 0.0278 pH (R2 = 0.9921), λmax = 441.5 - 6.64 pH (R2 = 0.9968, pH = 3.0-6.0) and λmax = 359.6 + 7.38 pH (R2 = 0.9879, pH = 6.0-10.0). Under the optimized conditions, THPL was applied to detect pH values in some aqueous and fruit juice samples with satisfactory results. The reversible recognition mechanism was deduced by UV-vis titration, 1H NMR titration and theoretical calculation.

Effects of aluminum oxide nanoparticles on the performance, extracellular polymeric substances, microbial community and enzymatic activity of sequencing batch reactor.

The performance, pollutant removal rate, microbial community and enzymatic activity of a sequencing batch reactor (SBR) were investigated under oxide nanoparticles (Al2O3 NPs) stress. Al2O3 NPs at 0-50 mg/L showed no evident impact on the COD and NH4 + removals of SBR. The oxygen-uptake rate, nitrifying rate and nitrite-reducing rate slightly diminished with the increase of Al2O3 NPs concentration. Compared with 0 mg/L Al2O3 NPs, the dehydrogenase activity declined by 23.52% at 50 mg/L Al2O3 NPs. The activities of ammonia monooxygenase, nitrite oxidoreductase and nitrite reductase decreased with the increase of Al2O3 NPs concentration from 0 to 50 mg/L Al2O3 NPs. However, the nitrate reductase (NR) activity slightly increased at 5 and 15 mg/L Al2O3 NPs and declined at 30 and 50 mg/L Al2O3 NPs. The microbial reactive oxygen species (ROS) production and lactate dehydrogenase (LDH) release merely raised 14.80% and 20.72% at 50 mg/L Al2O3 NPs by contrast with 0 mg/L Al2O3 NPs, respectively. Al2O3 NPs enhanced the production, protein content and polysaccharide content of extracellular polymeric substances owing to preventing the microbes from Al2O3 NPs biotoxicity. The existence of Al2O3 NPs led to the variations of microbial richness and diversity in the SBR due to their biotoxicity.

A self-healing supramolecular hydrogel with temperature-responsive fluorescence based on an AIE luminogen.

In this work, an AIE luminogen-based hydrogel with temperature-responsive fluorescence was designed and synthesized. The polymeric hydrogel consisted of a supramolecular network through coordination and ionic interactions. When the temperature was decreased, due to the motion restriction of the polyacrylic acid macromolecular segments and the enhancement in ionic interaction, the hydrogel exhibited a blue-shift in the fluorescence emission peak and increase in the fluorescence intensity, resulting in the visualization of fluorescence changes. The hydrogel network benefitted from non-covalent crosslinking and thus possessed self-healing properties at room temperature with good toughness and resiliency. Therefore, this fluorescent supramolecular hydrogel might be used as a temperature-responsive material.

Impacts of silver nanoparticles on performance and microbial community and enzymatic activity of a sequencing batch reactor.

The performance, microbial community and enzymatic activity of a sequencing batch reactor (SBR) were evaluated under silver nanoparticles (Ag NPs) stress. Over 5 mg/L Ag NPs inhibited the COD and phosphorus removals, whereas the NH4+ removal kept stable during the whole operational period. The organic matter, nitrogen and phosphorus removal rates were obviously inhibited under Ag NPs stress, which showed similar varying trends with the corresponding microbial enzymatic activities. The change of Ag content in the activated sludge indicated that some Ag NPs were absorbed by the sludge. The presence of Ag NPs promoted the increase of reactive oxygen species (ROS) and lactate dehydrogenase (LDH) of microorganism due to the microbial response to the Ag NPs toxicity, which could impact on the microbial morphology and physiological functions. The presence of Ag NPs could produce some evident changes in the microbial community.

Effect of di-n-butyl phthalate (DBP) on the fruit quality of cucumber and the health risk.

Di-n-butyl phthalate (DBP) widely used as plastic films' plasticizer, can cause agricultural pollution which is of increasing concern because of the food safety issues. Cucumber ( Cucumis sativus Linn.), commonly cultured in greenhouse, was exposed to DBP stress to gain more information about the ecological risk of DBP in this study. Changes of DBP residues and fruit quality of cucumber at different DBP concentrations (0, 5, 10, 20, 40 mg/kg of dry soil) were investigated in pot experiments using an agricultural soil under greenhouse condition, respectively. DBP residue in cucumber fruits ranged from 0.5326 to 1.8938 mg/kg, and the quality of cucumber fruits (organic acids, vitamin C, soluble protein, and soluble sugar) were influenced by DBP stress. Moreover, the health risk assessment was evaluated by estimate daily intakes (EDI) and the target hazard quotient (THQ) was analyzed. Under 40 mg/kg DBP condition, the highest value of EDI was 2.49 µg/kg bw/day and the THQ ranged from 0.000700 to 0.0249. Although the risk of DBP in cucumber fruits was lower than the threshold limit value of risk, the potential health risk was not a negligible issue.