Ginkgo biloba preparation prevents and treats senile dementia by inhibiting neuro-inflammatory responses

Purpose: To study the effect of Ginkgo biloba preparation on senile dementia and the mechanism of its action.Methods: Sprague Dawley (SD) rat model of chronic cerebral hypoperfusion was produced by vascular occlusion (2-VO). The rats were administered Ginkgo biloba preparation via intra-gastric route 4 h after the operation, and then at 7:30 am every day at a dose of 100 mg/kg per day (0.5 mL/100 g, per day) for 28 days. One group of untreated model rats, and a reference group served as controls. Garcia composite score was used to evaluate the recovery of neurological function in the rats after operation. Morris water maze test was conducted to assess learning and memory abilities. Western blot was used to measure protein expressions of BACE1, TRP1 and RAGE. Serum levels of inflammatory factors IL1, IL6 and TNF were assayed by ELISA.Results: Garcia composite score showed that the neurological function of the SD rats was significantly impaired by the blockage of blood flow to the bilateral common carotid artery. However, neurological function was gradually recovered by treatment with Ginkgo biloba preparation. The escape latency and swimming distance of the rats were significantly shortened, and the number of platform crossings was gradually increased by the Ginkgo biloba preparation. With time, there was no significant difference in swimming speed between the groups. Western blot data showed that expression of BACE1, TRP1 and RAGE gradually decreased. Results from ELISA indicate that with time, Ginkgo biloba preparation decreased the expressions of IL-1, IL-6, and TNF in the reference group.Conclusion: The preventive and therapeutic effects of Ginkgo biloba preparation extract on senile dementia may be related to its inhibition of neuro-inflammatory reaction.Keywords: Alzheimer disease, Neuro-inflammatory reactions, Ginkgo biloba preparatio

Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices. However, the issues of incomplete data analysis, high training costs, and the two-stage pipeline in Med-DANet require further improvement. To this end, this paper further explores a unified formulation of the dynamic inference framework from the perspective of both the data itself and the model structure. For each slice of the input volume, our proposed method dynamically selects an important foreground region for segmentation based on the policy generated by our Decision Network and Crop Position Network. Besides, we propose to insert a stage-wise quantization selector to the employed segmentation model (e.g. U-Net) for dynamic architecture adapting. Extensive experiments on BraTS 2019 and 2020 show that our method achieves comparable or better performance than previous state-of-the-art methods with much less model complexity. Compared with previous methods Med-DANet and TransBTS with dynamic and static architecture respectively, our framework improves the model efficiency by up to nearly 4.1 and 17.3 times with comparable segmentation results on BraTS 2019.Comment: Accepted by WACV 202

Structural Optimization and Thermal Management with PCM-Honeycomb Combination for Photovoltaic-Battery Integrated System

© 2022 Xinxi Li et al. This is an open access article distributed under the Creative Commons Attribution License, https://creativecommons.org/licenses/by/4.0/Power lithium–ion batteries retired from the electric vehicles (EVs) are confronting many problems such as environment pollution and energy dissipation. Traditional photovoltaic (PV) battery systems are exhibiting many issues such as being bulky and expensive, high working temperature, and short service span. In order to address these problems, in this study, a novel PV–battery device integrating PV controllers and battery module into an independent device is proposed. Phase change material (PCM) as the energy storage material has been utilized in battery module, and the aluminum honeycomb is combined with PCM to improve the heat conductivity under natural convection conditions. Three types of PV battery systems including the general PV–battery integrated system (G–PBIS), honeycomb PV–battery integrated system (H–PBIS), and honeycomb–paraffin PV–battery integrated system (HP–PBIS) have been investigated in detail. The results reveal that the maximum temperature of the HP–PBIS coupling with the double–layer 10×165×75 mm3 PCM was reduced to 53.72°C, exhibiting an optimum cooling effect among various PV battery systems. Thus, it can be concluded that the aluminum honeycomb provides the structural reliability and good thermal conductivity, and the PCM surrounding battery module can control the temperature rising and balance the temperature uniformly. Besides, the optimum PV–battery integrated system performs a promising future in energy storage fields.Peer reviewedFinal Published versio

Med-Tuning:A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation

The “pre-training then fine-tuning (FT)” paradigm is widely adopted to boost the model performance of deep learning-based methods for medical volumetric segmentation. However, conventional full FT incurs high computational and memory costs. Thus, it is of increasing importance to fine-tune pre-trained models for medical volumetric segmentation tasks in a both effective and parameter-efficient manner. In this paper, we introduce a new framework named Med-Tuning to realize parameter-efficient tuning (PET) for medical volumetric segmentation task and an efficient plug-and-play module named Med-Adapter for task-specific feature extraction. With a small number of tuned parameters, our framework enhances the 2D baselines’s precision on segmentation tasks, which are pre-trained on natural images. Extensive experiments on three benchmark datasets (CT and MRI modalities) show that our method achieves better results than previous PET methods on volumetric segmentation tasks. Compared to full FT, Med-Tuning reduces the fine-tuned model parameters by up to 4×, with even better segmentation performance. Our project webpage is at https://rubics-xuan.github.io/Med-Tuning/.</p

Med-Tuning:A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation

The “pre-training then fine-tuning (FT)” paradigm is widely adopted to boost the model performance of deep learning-based methods for medical volumetric segmentation. However, conventional full FT incurs high computational and memory costs. Thus, it is of increasing importance to fine-tune pre-trained models for medical volumetric segmentation tasks in a both effective and parameter-efficient manner. In this paper, we introduce a new framework named Med-Tuning to realize parameter-efficient tuning (PET) for medical volumetric segmentation task and an efficient plug-and-play module named Med-Adapter for task-specific feature extraction. With a small number of tuned parameters, our framework enhances the 2D baselines’s precision on segmentation tasks, which are pre-trained on natural images. Extensive experiments on three benchmark datasets (CT and MRI modalities) show that our method achieves better results than previous PET methods on volumetric segmentation tasks. Compared to full FT, Med-Tuning reduces the fine-tuned model parameters by up to 4×, with even better segmentation performance. Our project webpage is at https://rubics-xuan.github.io/Med-Tuning/.</p

Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery

Crop disease is widely considered as one of the most pressing challenges for food crops, and therefore an accurate crop disease detection algorithm is highly desirable for its sustainable management. The recent use of remote sensing and deep learning is drawing increasing research interests in wheat yellow rust disease detection. However, current solutions on yellow rust detection are generally addressed by RGB images and the basic semantic segmentation algorithms (e.g., UNet), which do not consider the irregular and blurred boundary problems of yellow rust area therein, restricting the disease segmentation performance. Therefore, this work aims to develop an automatic yellow rust disease detection algorithm to cope with these boundary problems. An improved algorithm entitled Ir-UNet by embedding irregular encoder module (IEM), irregular decoder module (IDM) and content-aware channel re-weight module (CCRM) is proposed and compared against the basic UNet while with various input features. The recently collected dataset by DJI M100 UAV equipped with RedEdge multispectral camera is used to evaluate the algorithm performance. Comparative results show that the Ir-UNet with five raw bands outperforms the basic UNet, achieving the highest overall accuracy (OA) score (97.13%) among various inputs. Moreover, the use of three selected bands, Red-NIR-RE, in the proposed Ir-UNet can obtain a comparable result (OA: 96.83%) while with fewer spectral bands and less computation load. It is anticipated that this study by seamlessly integrating the Ir-UNet network and UAV multispectral images can pave the way for automated yellow rust detection at farmland scales

Research progress on rheumatoid arthritis-associated depression

Depression is an independent mood disorder and one of the most common comorbidities of rheumatoid arthritis (RA). Growing evidence suggests that there is two-way regulation between RA and depression, resulting in a vicious cycle of RA, depression, poor outcomes, and disease burden. The rising prevalence of RA-associated depression warrants a re-examination of the relationships between them. Here we provide an overview of the etiology and pathological mechanisms of RA-associated depression, and recent advances in treatment with biologics, which will facilitate the development of new and effective prevention and treatment strategies

Comparative analysis of electrochemical properties and thermal behaviors of sodium ion and lithium ion batteries

Sodium-ion batteries (SIBs), characterized by their abundant raw material sources and cost-effective manufacturing processes, have emerged as one of the most promising battery technologies. However, the existing literature on the electrochemical and thermal generation characteristics of SIBs remains limited. This dissertation conducts a comparative investigation of the electrical-thermal properties of 18650-type SIBs and Lithium-ion batteries (LIBs) from both macroscopic and microscopic perspectives. The initial phase of the study involved conducting experiments under standard operating conditions, with variations in ambient temperatures and discharge rates. Furthermore, investigations into overcharging and thermal runaway (TR) were conducted under extreme conditions, with concurrent studies on heat generation and electrochemical analyses. The underlying mechanisms responsible for macroscopic performance variations were elucidated through microstructural characterization. The experimental findings reveal that at an ambient temperature of 0°C, the State of Charge (SOC) of Sodium-Ion Batteries (SIBs) exceeds that of Lithium-Ion Batteries (LIBs) by 13.93%. Under standard operating conditions, LIBs demonstrate enhanced cyclic capacity retention relative to SIBs, albeit with higher thermal generation. Under abusive conditions, the performance of SIBs markedly deteriorates, accompanied by a substantial increase in heat generation, surpassing that of LIBs. Following abuse, SIBs experience thermal runaway, attaining a peak temperature of 273.9°C. The performance degradation is primarily attributed to severe sodium deposition on the anode and the subsequent detachment of active materials. These findings furnish essential experimental data and theoretical underpinnings for the industrial deployment of SIBs, while providing critical insights for optimizing their production processes and improving thermal safety performance

The impact of interactions between heavy metals and smoking exposures on the formation of oral microbial communities

IntroductionThe primary objective of our investigation was to assess the repercussions of prolonged exposure to heavy metals and smoking on the microbiome of the oral buccal mucosa. Concurrently, we aimed to elucidate the intricate interplay between external environmental exposures and the composition of the oral microbial ecosystem, thereby discerning its potential implications for human health.MethodsOur study cohort was stratified into four distinct groups: MS (characterized by concurrent exposure to heavy metals and smoking), M (exposed solely to heavy metals), S (exposed solely to smoking), and C (comprising individuals serving as a control group). Specimens of buccal mucosa and blood were systematically acquired from the participants, facilitating subsequent microbial diversity analysis across the four oral buccal mucosa sample cohorts through 16S rRNA gene sequencing techniques. Simultaneously, blood samples were tested for heavy metal concentrations. In addition, we performed topological analyses by constructing microbial networks.ResultsOur findings notably indicate that co-exposure to heavy metals and smoking yielded a more pronounced alteration in the diversity of oral microflora when compared to singular exposures to either heavy metals or smoking. By comparing the oral bacterial communities and functional pathways between the four groups, we found significant differences in bacterial communities and functional pathways between the groups. Notably, the impact of heavy metal exposure overshadowed that of smoking, with concurrent exposure to heavy metals and smoking eliciting marginally greater effects than exposure to heavy metals alone. In addition, our analysis of the correlation between microbiota and blood heavy metal concentrations showed that the heavy metal cadmium (Cd) had a significantly greater effect on oral microbiota than other heavy metals.DiscussionChronic exposure to heavy metals and smoking disrupts the normal bacterial communities in the oral mucosa of residents of contaminated areas. This exposure reduces the complexity and stability of microbial networks and increases the risk of various diseases reduces the complexity and stability