Glucose Sensing Optionally in Optical and Optoelectrical Modes Based on Au-TiO2 Schottky Nanojunctions

Abstract: In recent years, metallic nanostructures have been extensively researched in the field of plasmonic for optical and optoelectronic applications such as biochemical sensing. However, an additional optoelectronic converter or spectrometer is usually required for the sensing application. Herein, the orderly-patterned Au-TiO2 Schottky junction with an Al film that we coupled, which simultaneously works as an optical reflector and conducting layer, can achieve optical sensing of glucose by exciting surface plasmon resonance associated with the environment, and meanwhile can realize glucose detection with direct electrical-signal readout by collecting the photogenerated carriers inside the Au nanostructures and TiO2 film. When used in optical mode, the designed sensor shows a sensing sensitivity of up to 1200.0 nmRIU-1 in numerical calculation, and the measured value is 346.1 nmRIU-1. When used in optoelectrical mode, the glucose sensor under one-sun illumination obtains a sensitivity of 70.0 µAM-1cm-2 in the concentration range of 0–10 mM, with a detection limit of 0.05 µM (Signal/Noise=3). Simulation and experimental results demonstrated that the Al-film-coupled Au-TiO2 Schottky nanojunction can monitor glucose concentration optionally in optical and optoelectrical modes, which presents an alternative route to the miniaturized, portable, and multi-functioned sensors

3D-GPT: Procedural 3D Modeling with Large Language Models

In the pursuit of efficient automated content creation, procedural generation, leveraging modifiable parameters and rule-based systems, emerges as a promising approach. Nonetheless, it could be a demanding endeavor, given its intricate nature necessitating a deep understanding of rules, algorithms, and parameters. To reduce workload, we introduce 3D-GPT, a framework utilizing large language models~(LLMs) for instruction-driven 3D modeling. 3D-GPT positions LLMs as proficient problem solvers, dissecting the procedural 3D modeling tasks into accessible segments and appointing the apt agent for each task. 3D-GPT integrates three core agents: the task dispatch agent, the conceptualization agent, and the modeling agent. They collaboratively achieve two objectives. First, it enhances concise initial scene descriptions, evolving them into detailed forms while dynamically adapting the text based on subsequent instructions. Second, it integrates procedural generation, extracting parameter values from enriched text to effortlessly interface with 3D software for asset creation. Our empirical investigations confirm that 3D-GPT not only interprets and executes instructions, delivering reliable results but also collaborates effectively with human designers. Furthermore, it seamlessly integrates with Blender, unlocking expanded manipulation possibilities. Our work highlights the potential of LLMs in 3D modeling, offering a basic framework for future advancements in scene generation and animation.Comment: Project page: https://chuny1.github.io/3DGPT/3dgpt.htm

Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning

In this paper, we focus on automatic disease diagnosis with reinforcement learning (RL) methods in task-oriented dialogues setting. Different from conventional RL tasks, the action space for disease diagnosis (i.e., symptoms) is inevitably large, especially when the number of diseases increases. However, existing approaches to this problem employ a flat RL policy, which typically works well in simple tasks but has significant challenges in complex scenarios like disease diagnosis. Towards this end, we propose to integrate a hierarchical policy of two levels into the dialogue policy learning. The high level policy consists of a model named master that is responsible for triggering a model in low level, the low level policy consists of several symptom checkers and a disease classifier. Experimental results on both self-constructed real-world and synthetic datasets demonstrate that our hierarchical framework achieves higher accuracy in disease diagnosis compared with existing systems. Besides, the datasets (http://www.sdspeople.fudan.edu.cn/zywei/data/Fudan-Medical-Dialogue2.0) and codes (https://github.com/nnbay/MeicalChatbot-HRL) are all available now

Ginseng extract improves pancreatic islet injury and promotes β-cell regeneration in T2DM mice

IntroductionPanax ginseng C. A. Mey. (Araliaceae; Ginseng Radix et Rhizoma), a traditional plant commonly utilized in Eastern Asia, has demonstrated efficacy in treating neuro-damaging diseases and diabetes mellitus. However, its precise roles and mechanism in alleviating type 2 diabetes mellitus (T2DM) need further study. The objective of this study is to explore the pharmacological effects of ginseng extract and elucidate its potential mechanisms in protecting islets and promoting β-cell regeneration.MethodsThe T2DM mouse model was induced through streptozotocin combined with a high-fat diet. Two batches of mice were sacrificed on the 7th and 28th days following ginseng extract administration. Body weight, fasting blood glucose levels, and glucose tolerance were detected. Morphological changes in the pancreatic islets were examined via H & E staining. Levels of serum insulin, glucagon, GLP-1, and inflammatory factors were measured using ELISA. The ability of ginseng extract to promote pancreatic islet β-cell regeneration was evaluated through insulin & PCNA double immunofluorescence staining. Furthermore, the mechanism behind β-cells regeneration was explored through insulin & glucagon double immunofluorescence staining, accompanied by immunohistochemical staining and western blot analyses.Results and DiscussionThe present research revealed that ginseng extract alleviates symptoms of T2DM in mice, including decreased blood glucose levels and improved glucose tolerance. Serum levels of insulin, GLP-1, and IL-10 increased following the administration of ginseng extract, while levels of glucagon, TNF-α, and IL-1β decreased. Ginseng extract preserved normal islet morphology, increased nascent β-cell population, and inhibited inflammatory infiltration within the islets, moreover, it decreased α-cell proportion while increasing β-cell proportion. Mechanistically, ginseng extract might inhibit ARX and MAFB expressions, increase MAFA level to aid in α-cell to β-cell transformation, and activate AKT-FOXM1/cyclin D2 to enhance β-cell proliferation. Our study suggests that ginseng extract may be a promising therapy in treating T2DM, especially in those with islet injury

Decoupled Land and Ocean Temperature Trends in the Early-Middle Pleistocene

Record of long-term land temperature changes remains ephemeral, discontinuous, and isolated, thus leaving the common view that Pleistocene land temperature evolution should have followed ocean temperatures unconfirmed. Here, we present a continuous land surface temperature reconstruction in the Asian monsoon region over the past 3.0 Myr based on the distribution of soil bacterial lipids from the Chinese Loess Plateau. The land temperature record indicates an unexpected warming trend over the Pleistocene, which is opposite to the cooling trend in Pleistocene ocean temperatures, resulting in increased land-sea thermal contrast. We propose that the previously unrecognized increase of land-sea thermal contrast during much of the Pleistocene is a regional climate phenomenon that provides a likely mechanism in favor of the long-term enhancement of the Pleistocene East Asian summer monsoon

The Application of Lateral Flow Immunoassay in Point of Care Testing: A Review

Point-of-care testing (POCT) is essential for providing a rapid diagnostic result in a prompt on-site diagnosis and treatment. A quick analysis time and a high sensitivity, with a sample-to-answer format, are the most important features for current POCT diagnostic systems. This review covers recent advances in POCT technologies with an emphasis on demonstrated and commercially available POCT diagnostic systems with laboratory quality using lateral flow immunoassay (LFIA). The system includes the integration of nanoparticles (NPs) in lateral flow test strips (LFTSs) and the mechanism through which particles improve the analytical performance of the fabricated strips. Several examples of NP-based LFTSs were selected to illustrate novel concepts or devices with promising applications as screening tools and superior alternatives to existing conventional strategies in clinical analysis, food safety, and environmental monitoring. In each analyte category, detection methods, configuration of LFIA modules, and advantages of POCT systems are reviewed and discussed along with future prospects. This review also discusses novel signal-enhancement strategies, optimal reader systems, and multiplex design prototypes, which have been employed for highly sensitive multiplex assay of LFTSs

Smartphone-Based Fluorescent Diagnostic System for Immunochromatographic Chip

In order to achieve fast and quantitative detection of fluorescence immunochromatographic chip, a rapid detection system based on smartphone has been developed. In this system, fluorescent signal from quantum dots (QDs) on lateral flow test strips (LFTSs) can be accurately extracted, and the system also can calculate the concentration of the analyte. The method of extraction and recognition of fluorescence signal intensity can be applied to different fluorescent chip detection systems. Based on the fluorescence tomography chip image, a specific program is used for image acquisition, processing and data handling. The Sobel operator algorithm was used in the software，which improved greatly the ability of distinguishing between the test area and the background boundary information. Extracting the components from the red format of the fluorescent strips，the high-signal intensity and sensitivity were achieved. The simulation results show that the proposed method can be applied to the detection system of fluorescence immunochromatographic chip. The experimental results show that the signal intensity has a good correlation with the concentration of immunoassay, which indicates the detection system can extract the intensity of fluorescence signal of the chip

Unbiased and Signal-Weakening Photoelectrochemical Hexavalent Chromium Sensing via a CuO Film Photocathode

Photoelectrochemical (PEC) sensors show great potential for the detection of heavy metal ions because of their low background noise, high sensitivity, and ease of integration. However, the detection limit is relatively high for hexavalent chromium (Cr(VI)) monitoring in addition to the requirement of an external bias. Herein, a CuO film is readily synthesized as the photoactive material via reactive sputtering and thermal annealing in the construction of a PEC sensing photocathode for Cr(VI) monitoring. A different mechanism (i.e., Signal-Weakening PEC sensing) is confirmed by examining the electrochemical impedance and photocurrent response of different CuO film photoelectrodes prepared with the same conditions in contact with various solutions containing concentration-varying Cr(VI) for different durations. The detection of Cr(VI) is successfully achieved with the Signal-Weakening PEC response; a drop of photocathode signal with an increasing Cr(VI) concentration from the steric hindrance effect of the in situ formed Cr(OH)3 precipitates. The photocurrent of the optimized CuO film photocathode linearly declines as the concentration of Cr(VI) increases from 0.08 to 20 µM, with a detection limit down to 2.8 nM (Signal/Noise = 3) and a fitted sensitivity of 4.22 µA·μM−1. Moreover, this proposed sensing route shows operation simplicity, satisfactory selectivity, and reproducibility

Smartphone-Based Dual-Modality Imaging System for Quantitative Detection of Color or Fluorescent Lateral Flow Immunochromatographic Strips

Abstract Nowadays, lateral flow immunochromatographic assays are increasingly popular as a diagnostic tool for point-of-care (POC) test based on their simplicity, specificity, and sensitivity. Hence, quantitative detection and pluralistic popular application are urgently needed in medical examination. In this study, a smartphone-based dual-modality imaging system was developed for quantitative detection of color or fluorescent lateral flow test strips, which can be operated anywhere at any time. In this system, the white and ultra-violet (UV) light of optical device was designed, which was tunable with different strips, and the Sobel operator algorithm was used in the software, which could enhance the identification ability to recognize the test area from the background boundary information. Moreover, this technology based on extraction of the components from RGB format (red, green, and blue) of color strips or only red format of the fluorescent strips can obviously improve the high-signal intensity and sensitivity. Fifty samples were used to evaluate the accuracy of this system, and the ideal detection limit was calculated separately from detection of human chorionic gonadotropin (HCG) and carcinoembryonic antigen (CEA). The results indicated that smartphone-controlled dual-modality imaging system could provide various POC diagnoses, which becomes a potential technology for developing the next-generation of portable system in the near future