Analytical‐Based Methodologies for Examining the In Vitro Absorption,Distribution, Metabolism,and Elimination (ADME) of Silver Nanoparticles

The clinical applications of silver nanoparticles (AgNPs) remain limited due to the lack of well‐established methodologies for studying their nanokinetics. Hereby, the primary goal is to adapt a suite of analytical‐based methodologies for examining the in vitro absorption, distribution, metabolism, and elimination of AgNPs. Vero 76 and HEK 293 cells are exposed to ≈10‐nm spherical AgNPs⁺ and AgNPs^? at relevant concentrations (0–300 µg mL^?1) and times (4–48 h). Absorption: Inductively coupled plasma optical emission spectroscopy (ICP‐OES) demonstrates that the two AgNP formulations are not bioequivalent. For example, different bioavailabilities (C _maximum < 20.7 ± 4% and 6.82 ± 0.4%), absorption times (T _maximum > 48 and ≈24 h), and absorption rate laws (first‐ and zeroth‐order at 300 µg mL^?1) are determined in Vero 76 for AgNPs⁺ and AgNPs^?, respectively. Distribution: Raman and CytoViva hyperspectral imaging show different cellular localizations for AgNPs⁺ and AgNPs^?. Metabolism: Cloud point extraction (CPE)‐tangential flow filtration (TFF) reveal that ≤ 11% ± 4% of the administered, sublethal AgNPs release Ag⁺ and contribute to the observed cytotoxicity. Elimination: ICP‐OES‐CPE suggests that AgNPs are cleared via exocytosis.     

Hydrogen-rich syngas fermentation for bioethanol production using Sacharomyces cerevisiea

Bioethanol is an eco-friendly biofuel due to its merit that makes it a top-tier fuel. The present study emphasized on bioethanol production from hydrogen-rich syngas through fermentation using Sacharomyces cerevisiea. Syngas fermentation was performed in a tar free fermenter using a syngas mixture of 13.05% H₂, 22.92% CO, 7.9% CO₂, and 1.13% CH₄, by volume. In the fermentation process, effects of various parameters including syngas impurity, temperature, pH, colony forming unit, total organic carbon and syngas composition were investigated. The yield of bioethanol was identified by Gas chromatography-Mass spectrometry analysis and further, it was confirmed by Nuclear magnetic resonance (¹H) analysis. From GC-MS results, it is revealed that the concentration of bioethanol using Saccharomyces cerevisiae was 30.56 mmol from 1 L of syngas. Thus, hydrogen-rich syngas is suited for bioethanol production through syngas fermentation using Saccharomyces cerevisiae. This research may contribute to affordable and environment-friendly bioethanol-based energy to decrease the dependency on fossil fuels.     

Profiling of the Bacterial Microbiota along the Murine Alimentary Tract

Here, the spatial distribution of the bacterial flora along the murine alimentary tract was evaluated using high throughput sequencing in wild-type and Tff3-deficient (Tff3^KO) animals. Loss of Tff3 was linked to increased dextran sodium sulfate-induced colitis. This systematic study shows the results of 13 different regions from the esophagus to the rectum. The number of bacterial species (richness) increased from the esophagus to the rectum, from 50 to 200, respectively. Additionally, the bacterial community structure changed continuously; the highest changes were between the upper/middle and lower gastrointestinal compartments when comparing adjacent regions. Lactobacillus was the major colonizer in the upper/middle gastrointestinal tract, especially in the esophagus and stomach. From the caecum, a drastic diminution of Lactobacillus occurred, while members of Lachnospiraceae significantly increased. A significant change occurred in the bacterial community between the ascending and the transverse colon with Bacteroidetes being the major colonizers with relative constant abundance until the rectum. Interestingly, wild-type and Tff3^KO animals did not show significant differences in their bacterial communities, suggesting that Tff3 is not involved in alterations of intraluminal or adhesive microbiota but is obviously important for mucosal protection, e.g., of the sensitive stem cells in the colonic crypts probably by a mucus plume.     

一种超宽带CMOS除二电路及其优化方法

本文提出了一种新型的基于轮换触发的除二电路及其基于大信号分析的优化方法。通过减小跟随相输出节点的RC常数,增大锁存相输出节点的RC常数,减小内部信号摆幅和补偿锁存相输出节点漏电流的损失等电路技术,大大拓宽了其工作频带。本论文在SMIC 0.13μm RF CMOS工艺条件下设计了一款原型电路,其后仿工作频率可以达到320MHz到29.6GHz。此外,这款除二电路还应用于两款整数分频锁相环芯片中,分别对频率为4224MHz和10GHz的信号进行分频。测试结果表明,这款除二电路可以对其进行正确分频,而且整体锁相环的带内相噪分别为-94dBC/Hz@10kHz和-84dBc/Hz@10kHz.     

玻璃衬底上MIUC Poly-Si TFT显示驱动电路

以高性能的金属诱导单一方向横向晶化多晶硅薄膜晶体管(MIUC poly-Si TFT)为基础,研制出性能能满足AM-LCD和AM-OLED要求、版图和象素尺寸适配、制备工艺和象素电路兼容的多晶硅TFT行扫描和列驱动电路.该行扫描电路工作电压为3.5-10V;当工作电压为5V、负载电容为22pf时,下降沿约为150ns,上升沿约为205ns,最高工作频率在1MHz以上;列驱动电路工作电压为3.5-8V;当工作电压为5V、负载电容为22pf时,上升沿约为200ns,信号衰减率为15%(64μs扫描周期),最高工作频率达到4MHz.将该MIUC poly-Si TFT多晶硅行扫描、列驱动电路和有源选址电路集成到同一基板上,制备出象素数为80×RGB×60、动态显示效果良好的全集成型LCD屏样品.     

Design and optimization of an ultra-wide frequency range CMOS divide-by-two circuit

A novel toggled flip-flop(TFF) divide-by-two circuit(DTC) and its optimization method based on a large-signal analysis approach are proposed.By reducing the output RC constant in tracking mode and making it large in latching mode,compressing the internal signal swing as well as compensating the current leaked in the latching mode, the operating frequency range is greatly expanded.Implemented in a SMIC 0.13μm RF CMOS process with a 1.2 V power supply,it can work under an ultra-wide frequency band ranging ...     

Purification of PEGylated Nanoparticles Using Tangential Flow Filtration (TFF)

A tangential flow filtration system was evaluated to purify PEGylated nanoparticles. Two widely used surfactants, PVA and sodium cholate were efficiently removed from an empty nanoparticles suspension using the proposed system. During drug loading, surfactant (PVA) was observed to be entrapped within the core of the nanoparticle to a higher extent, hence was purified at a comparatively slower rate. The presence of dextran sulfate enhanced the drug loading but also resulted in reduced purification rate; this was described by the hypothesis of PVA inclusion within the core of the nanoparticles. Practically, it was possible to correlate the slow purification rate of PVA to its reduced filtration flow during the purification of the empty and loaded nanoparticles containing dextran sulfate. Indirectly, this system was capable of revealing the influence of an excipient and drug on the nanoparticle surface.     

Neuropeptide Trefoil Factor 3 Reverses Depressive-Like Behaviors by Activation of BDNF-ERK-CREB Signaling in Olfactory Bulbectomized Rats

The trefoil factors (TFFs) are a family of three polypeptides, among which TFF1 and TFF3 are widely distributed in the central nervous system. Our previous study indicated that TFF3 was a potential rapid-onset antidepressant as it reversed the depressive-like behaviors induced by acute or chronic mild stress. In order to further identify the antidepressant-like effect of TFF3, we applied an olfactory bulbectomy (OB), a classic animal model of depression, in the present study. To elucidate the mechanism underlying the antidepressant-like activity of TFF3, we tested the role of brain-derived neurotrophic factor (BDNF)-extracellular signal-related kinase (ERK)-cyclic adenosine monophosphate response element binding protein (CREB) signaling in the hippocampus in the process. Chronic systemic administration of TFF3 (0.1 mg/kg, i.p.) for seven days not only produced a significant antidepressant-like efficacy in the OB paradigm, but also restored the expression of BDNF, pERK, and pCREB in the hippocampal CA3. Inhibition of BDNF or extracellular signal-related kinase (ERK) signaling in CA3 blocked the antidepressant-like activity of TFF3 in OB rats. Our findings further confirmed the therapeutic effect of TFF3 against depression and suggested that the normalization of the BDNF-ERK-CREB pathway was involved in the behavioral response of TFF3 for the treatment of depression.     

An adaptive gravitational search optimization (AGSO)-based convolutional neural network—long short-term memory (CNN-LSTM) approach for face recognition and classification

Face recognition and classification have gained increasing attraction in the recent decades due to their widespread adoption in real time application systems. Most of the conventional research efforts focused on developing face recognition frameworks using enhanced optimization-based classification methods, they are hampered by issues such as computational complexity, increased overhead, limited capacity to handle large datasets, and lengthy processing time. The novel contribution of this paper is to develop a highly competent and precise face recognition methodology through an innovative mechanism. In this framework, the initial step involves face detection from input images using an analytical face parts detection methodology. Subsequently, the tutor face filtering (TFF) technique is applied to preprocess the face image, enhancing its quality and filtering out noise content. Following this preprocessing step, features are extracted from the processed image using the direction-based pattern extraction (DBPE) model. To improve classifier accuracy, a novel adaptive gravitational search optimization (AGSO) technique is employed to select the optimal features during model training. Finally, an integrated deep learning model, referred to as convolutional neural network — long short-term memory (LSTM), is utilized for accurate face image recognition based on the selected optimal features. To assess and compare the system's performance, various metrics are employed in the results analysis to demonstrate the superiority of the proposed approach.