Peripheral mitochondrial DNA as a neuroinflammatory biomarker for major depressive disorder.

In the pathogenesis of major depressive disorder, chronic stress-related neuroinflammation hinders favorable prognosis and antidepressant response. Mitochondrial DNA may be an inflammatory trigger, after its release from stress-induced dysfunctional central nervous system mitochondria into peripheral circulation. This evidence supports the potential use of peripheral mitochondrial DNA as a neuroinflammatory biomarker for the diagnosis and treatment of major depressive disorder. Herein, we critically review the neuroinflammation theory in major depressive disorder, providing compelling evidence that mitochondrial DNA release acts as a critical biological substrate, and that it constitutes the neuroinflammatory disease pathway. After its release, mitochondrial DNA can be carried in the exosomes and transported to extracellular spaces in the central nervous system and peripheral circulation. Detectable exosomes render encaged mitochondrial DNA relatively stable. This mitochondrial DNA in peripheral circulation can thus be directly detected in clinical practice. These characteristics illustrate the potential for mitochondrial DNA to serve as an innovative clinical biomarker and molecular treatment target for major depressive disorder. This review also highlights the future potential value of clinical applications combining mitochondrial DNA with a panel of other biomarkers, to improve diagnostic precision in major depressive disorder.

Comprehensive comparison of water quality risk and microbial ecology between new and old cast iron pipe distribution systems.

The effects of cast iron pipe corrosion on water quality risk and microbial ecology in drinking water distribution systems (DWDSs) were investigated. It was found that trihalomethane (THMs) concentration and antibiotic resistance genes (ARGs) increased sharply in the old DWDSs. Under the same residual chlorine concentration conditions, the adenosine triphosphate concentration in the effluent of old DWDSs (Eff-old) was significantly higher than that in the effluent of new DWDSs. Moreover, stronger bioflocculation ability and weaker hydrophobicity coexisted in the extracellular polymeric substances of Eff-old, meanwhile, iron particles could be well inserted into the structure of the biofilms to enhance the mechanical strength and stability of the biofilms, hence enhancing the formation of THMs. Old DWDSs significantly influenced the microbial community of bulk water and triggered stronger microbial antioxidant systems response, resulting in higher ARGs abundance. Corroded cast iron pipes induced a unique interaction system of biofilms, chlorine, and corrosion products. Therefore, as the age of cast iron pipes increases, the fluctuation of water quality and microbial ecology should be paid more attention to maintain the safety of tap water.

High-Speed and High-Responsivity Blue Light Photodetector with an InGaN NR/PEDOT:PSS Heterojunction Decorated with Ag NWs.

InGaN nanorods possessing larger and wavelength selective absorption by regulating In component based visible light photodetectors (PDs) as one of the key components in the field of visible light communication have received widespread attention. Currently, the weak photoelectric conversion efficiency and slow photoresponse speed of InGaN nanorod (NR) based PDs due to high surface states of InGaN NRs impede the actualization of high-responsivity and high-speed blue light PDs. Here, we have demonstrated high-performance InGaN NR/PEDOT:PSS@Ag nanowire (NW) heterojunction blue light photodetectors utilizing surface passivation and a localized surface plasmon resonance effect. The dark current is significantly reduced by passivating the InGaN NR surface states using PEDOT:PSS. The photoelectric conversion efficiency is significantly increased by increasing light absorption due to the electromagnetic field oscillation of Ag NWs. The responsivity, external quantum efficiency, detectivity, and fall/off time of the InGaN NR/PEDOT:PSS@Ag NW PDs are up to 2.9 A/W, 856%, 6.64 × 1010 Jones, and 439/725 µs, respectively, under 1 V bias and 420 nm illumination. The proposed device design presents a novel approach toward the development of low-cost, high-responsivity, high-speed blue light photodetectors for applications involving visible light communication.

An improved lightweight object detection algorithm for YOLOv5.

Object detection based on deep learning has made great progress in the past decade and has been widely used in various fields of daily life. Model lightweighting is the core of deploying target detection models on mobile or edge devices. Lightweight models have fewer parameters and lower computational costs, but are often accompanied by lower detection accuracy. Based on YOLOv5s, this article proposes an improved lightweight target detection model, which can achieve higher detection accuracy with smaller parameters. Firstly, utilizing the lightweight feature of the Ghost module, we integrated it into the C3 structure and replaced some of the C3 modules after the upsample layer on the neck network, thereby reducing the number of model parameters and expediting the model's inference process. Secondly, the coordinate attention (CA) mechanism was added to the neck to enhance the model's ability to pay attention to relevant information and improved detection accuracy. Finally, a more efficient Simplified Spatial Pyramid Pooling-Fast (SimSPPF) module was designed to enhance the stability of the model and shorten the training time of the model. In order to verify the effectiveness of the improved model, experiments were conducted using three datasets with different features. Experimental results show that the number of parameters of our model is significantly reduced by 28% compared with the original model, and mean average precision (mAP) is increased by 3.1%, 1.1% and 1.8% respectively. The model also performs better in terms of accuracy compared to existing lightweight state-of-the-art models. On three datasets with different features, mAP of the proposed model achieved 87.2%, 77.8% and 92.3%, which is better than YOLOv7tiny (81.4%, 77.7%, 90.3%), YOLOv8n (84.7%, 77.7%, 90.6%) and other advanced models. When achieving the decreased number of parameters, the improved model can successfully increase mAP, providing great reference for deploying the model on mobile or edge devices.