bluuebunny/crossref_metadata_2025_split huggingface dataset:
The ventromedial hypothalamic nucleus: watchdog of whole-body glucose homeostasis
Authors: Tu Longlong , Fukuda Makoto , Tong Qingchun , Xu Yong 
Published: May 2022

AbstractThe brain, particularly the ventromedial hypothalamic nucleus (VMH), has been long known for its involvement in glucose sensing and whole-body glucose homeostasis. However, it is still not fully understood how the brain detects and responds to the changes in the circulating glucose levels, as well as brain-body coordinated control of glucose homeostasis. In this review, we address the growing evidence implicating the brain in glucose homeostasis, especially in the contexts of hypoglycemia and diabetes. In addition to neurons, we emphasize the potential roles played by non-neuronal cells, as well as extracellular matrix in the hypothalamus in whole-body glucose homeostasis. Further, we review the ionic mechanisms by which glucose-sensing neurons sense fluctuations of ambient glucose levels. We also introduce the significant implications of heterogeneous neurons in the VMH upon glucose sensing and whole-body glucose homeostasis, in which sex difference is also addressed. Meanwhile, research gaps have also been identified, which necessities further mechanistic studies in future.
Application and prospect of targeting innate immune sensors in the treatment of autoimmune diseases
Authors: Liu Jun , Zhang Hui , Su Yanhong , Zhang Baojun 
Published: May 2022

AbstractDysregulation of auto-reactive T cells and autoantibody-producing B cells and excessive inflammation are responsible for the occurrence and development of autoimmune diseases. The suppression of autoreactive T cell activation and autoantibody production, as well as inhibition of inflammatory cytokine production have been utilized to ameliorate autoimmune disease symptoms. However, the existing treatment strategies are not sufficient to cure autoimmune diseases since patients can quickly suffer a relapse following the end of treatments. Pattern recognition receptors (PRRs), including Toll-like receptors (TLRs), Nod-like receptors (NLRs), RIG-I like receptors (RLRs), C-type lectin receptors (CLRs) and various nucleic acid sensors, are expressed in both innate and adaptive immune cells and are involved in the development of autoimmune diseases. Here, we have summarized advances of PRRs signaling pathways, association between PRRs and autoimmune diseases, application of inhibitors targeting PRRs and the corresponding signaling molecules relevant to strategies targeting autoimmune diseases. This review emphasizes the roles of different PRRs in activating both innate and adaptive immunity, which can coordinate to trigger autoimmune responses. The review may also prompt the formulation of novel ideas for developing therapeutic strategies against autoimmune diseases by targeting PRRs-related signals.
DQN-based mobile edge computing for smart Internet of vehicle
Authors: Zhang Lianhong , Zhou Wenqi , Xia Junjuan , Gao Chongzhi , Zhu Fusheng , Fan Chengyuan , Ou Jiangtao 
Published: May 2022

AbstractIn this paper, we investigate a multiuser mobile edge computing (MEC)-aided smart Internet of vehicle (IoV) network, where one edge server can help accomplish the intensive calculating tasks from the vehicular users. For the MEC networks, most existing works mainly focus on minimizing the system latency to guarantee the user’s quality of service (QoS) through designing some offloading strategies, which, however, fail to consider the pricing from the server and hence fail to take into account the budget constraint from the users. To address this issue, we jointly incorporate the budget constraint into the system design of the MEC-based IoV networks and then propose a joint deep reinforcement learning (DRL) approach combined with the convex optimization algorithm. Specifically, a deep Q-network (DQN) is firstly used to make the offloading decision, and then, the Lagrange multiplier method is employed to allocate the calculating capability of the server to multiple users. Simulations are finally presented to demonstrate that the proposed schemes outperform the conventional ones. In particular, the proposed scheme can effectively reduce the system latency by up to 56% compared to the conventional schemes.
External validation of 5A score model for predicting in-hospital mortality among the accidental hypothermia patients: JAAM-Hypothermia study 2018–2019 secondary analysis
Authors: Okada Yohei , Matsuyama Tasuku , Hayashida Kei , Takauji Shuhei , Kanda Jun , Yokobori Shoji 
Published: May 2022

AbstractBackgroundThe 5A score including five components “Age, Activities of daily living, Arrest, Acidemia and Albumin” was developed as an easy-to-use screening tool for predicting in-hospital mortality among patients with accidental hypothermia. However, the external validity of the 5A score has not yet been evaluated. We aimed to perform an external validation of the 5A score model.MethodThis secondary analysis of the multicenter, prospective cohort Japanese Association for Acute Medicine-Hypothermia Study (2018–2019), which was conducted at 87 and 89 institutions throughout Japan, collected data from December 2018 to February 2019 and from December 2019 to February 2020. Adult accidental hypothermia patients whose body temperature was 35 °C or less were included in this analysis. The probability of in-hospital mortality was calculated using a logistic regression model of the 5A score. The albumin was not recorded in this database; thus, it was imputed by estimation. Predictions were compared with actual observations to evaluate the calibration of the model. Furthermore, decision-curve analysis was used to evaluate the clinical usefulness.ResultsOf the 1363 patients registered in the database, data of 1139 accidental hypothermia patients were included for analysis. The median [interquartile range] age was 79 [68–87] years, and there were 625 men (54.9%) in the study cohort. The predicted probability and actual observation by risk groups produced the following results: low 7% (5.4–8.6), mild 19.1% (17.4–20.8), moderate 33.2% (29.9–36.5), and high 61.9% (55.9–67.9) predicted risks, and the low 12.4% (60/483), mild 17.7% (59/334), moderate 32.6% (63/193), and high 69% (89/129) observed mortality. These results indicated that the model was well calibrated. Decision-curve analysis visually indicated the clinical utility of the 5A score model.ConclusionThis study indicated that the 5A score model using estimated albumin value has external validity in a completely different dataset from that used for the 5A model development. The 5A score is potentially helpful to predict the mortality risk and may be one of the valuable information for discussing the treatment strategy with patients and their family members.
A description of self-medication with cannabis among adults with legal access to cannabis in Quebec, Canada
Authors: Asselin Antoine , Lamarre Olivier Beauparlant , Chamberland Richard , McNeil Sarah-Jeanne , Demers Eric , Zongo Arsène 
Published: May 2022

Abstract
Objective
Cannabis is increasingly used for medical purposes, particularly in countries like Canada where cannabis was recently legalized for recreational use. We aimed to assess self-medication with cannabis post-cannabis legalization among adults in the Canadian province of Quebec.

Methods
This is a cross-sectional online survey of a self-selected convenience sample conducted in Quebec, Canada, from November 2020 to January 2021. Individuals aged ≥ 21 years who endorsed using cannabis bought in legal recreational cannabis stores to self-medicate a health condition were included. Data were analyzed using descriptive statistics and stratified according to sex, age, and the type of cannabis use (exclusively medical versus medical and recreational use).

Results
Four hundred eighty-nine participants were included. The median age was 34 years, and 48% were women. About 25% reported exclusive medical use of cannabis. Treated conditions included anxiety (70%), insomnia (56%), pain (53%), depression (37%), and many others. Reasons for not consulting in cannabis clinics included lack of information (52%), the complexity of the process (39%), accessibility of cannabis clinics (23%), and others.
Tetrahydrocannabinol (THC) dosage > 20% was reported by 32%. Smoking was the main route of use (81%). Possession of prescribed drugs was reported by 56%. Professionals consulted for information on cannabis included recreational cannabis store agents (36%), physicians (29%), and others.
Overall, significant differences were observed for many of the comparisons according to sex, age, and the type of cannabis use.

Conclusions
Many conditions are self-medicated with cannabis. The use of high doses of cannabis, smoking as a preferred method of use, and concurrent use of other medications may pose some risks to individuals. Addressing the reported barriers to medical access to cannabis is urgently needed.
Neonatal orally administered curcumin: impact on the metabolic response and renal histology of Sprague-Dawley rats fed a high-fructose diet until adolescence
Authors: Ibrahim Kasimu Ghandi , Chivandi Eliton , Nkomozepi Pilani , Erlwanger Kennedy Honey 
Published: May 2022

Abstract
Background
Fructose-rich diets are linked to the epidemic of metabolic syndrome and co-morbidities including nephropathy. Phytochemicals are increasingly being used for medicinal purposes due to their perceived safety compared to conventional drugs. Although previously shown to beneficially programme metabolism, these phytochemicals might have a negative impact on metabolic health when introduced early in life. We investigated whether neonatal administration of curcumin to rats would impact their response in adolescence to a high-fructose diet. Sprague-Dawley pups (n = 128) were administered either 0.5% dimethyl sulphoxide, curcumin (500 mg kg−1), fructose (20%, w/v) or a combination of curcumin and fructose from post-natal day 6 to 21. Each group was then subdivided into two; one had tap water, while the other had fructose (20%, w/v) to drink for 6 weeks.

Results
There were no differences (p > 0.05) in the fasting blood glucose, triglycerides, cholesterol, plasma insulin and adiponectin concentrations across the groups. The renal corpuscular, glomerular tuft and Bowman capsular areas were similar (p > 0.05) across the treatment groups, in both sexes. Post-weaning fructose alone induced tubular secretions and mesangial proliferation in the kidneys which were prevented by curcumin.

Conclusions
Despite the observed benefits in adolescence, the impact of curcumin on renal health beyond adolescence needs to be explored.
Targeted isolation of biocontrol agents from plants through phytopathogen co-culture and pathogen enrichment
Authors: Wang Bozhen , Li Li , Lin Yuheng , Shen Danyu , Shao Xiaolong , Zhong Caihong , Qian Guoliang 
Published: May 2022

AbstractIn a long-term symbiotic relationship between plants and pathogens, plants have evolved to harbor beneficially endophytic microbiomes, thereby conferring them the ability to resist infection by pathogens. This prompted us to establish a phytopathogen-based co-culture platform for the targeted isolation of potential biocontrol agents from plants via specific pathogen enrichment. In this study, we investigated three different phytopathogenic systems, including kiwifruit, turfgrass, and rice, and their infectious bacterial and/or fungal pathogens. By using the developed mono- or co-enrichment platform, we efficiently isolated three antimicrobial agents, including Bacillus safensis ZK-1 against Pseudomonas syringae pv. actinidiae that causes kiwifruit canker, Pseudomonas alcaligenes ZK-2 against Clarireedia paspali that causes dollar spot disease in turfgrass, and Bacillus velezensis ZK-3 against rice bacterial blight pathogen Xanthomonas oryzae pv. oryzae and rice blast fungus Magnaporthe oryzae. We believe that the phytopathogenic co-culture and pathogen enrichment platform developed here is versatile and effective for the isolation of potential biocontrol agents with specific or broad-spectrum antimicrobial activities from plants of interest in a targeted and large-scale manner.
The exploration and utilization of functional substances in edible insects: a review
Authors: Qian Lei , Deng Pan , Chen Fajun , Cao Ye , Sun Hongwu , Liao Huaijian 
Published: May 2022

AbstractEdible insects as food have been considered as a core future protein source. Not only are edible insects abundant in nutrient value, but also have numbers of functional substances, which could provide a multiple valuable benefits for humans. This review examines and summarizes the functional substances in edible insects and their special effects for people, as well as the use and prospects for these functional substances. There are many functional substances in edible insects, such as antimicrobial peptides, interferon, sex attractant hormones, cordycepin, active polysaccharides, microelement, chitin/chitosan, steroidal material, vitamin, lecithin, etc., which could provide multiple benefits for human beings. These special functional substances could help people enhance immunity, inhibit tumor, regulate intestinal function, relieve fatigue, anti oxidation, protect against colds, improve sleeping, promote the growth and development, as well as reducing blood sugar and blood pressure, among others. According to these valuable efficacies, the functional substances extracted from edible insects can be exploited mainly in three ways: edible insects are used to discover new medicines or health care products for people; and edible insects are applied in chemical, agricultural, food technology, pollutant treatment and textile sectors; as well as being consumed as animal feedstuff for livestock and other animals, which indirectly meet the different requirements for humans. Currently, edible insects have generated global interest to be developed as different forms of products and has promoted more research and development. However, pupae and larvae are still the main consumption ways in the market, which is unacceptable to most people. It is expected that the edible insect industry would be prosperous and sustainable by integrating the power of beneficial policies and regulations, huge consumer demand and proper strategies of promotion and production.
Graphical Abstract
PPP-RTK considering the ionosphere uncertainty with cross-validation
Authors: Li Pan , Cui Bobin , Hu Jiahuan , Liu Xuexi , Zhang Xiaohong , Ge Maorong , Schuh Harald 
Published: May 2022

AbstractWith the high-precision products of satellite orbit and clock, uncalibrated phase delay, and the atmosphere delay corrections, Precise Point Positioning (PPP) based on a Real-Time Kinematic (RTK) network is possible to rapidly achieve centimeter-level positioning accuracy. In the ionosphere-weighted PPP–RTK model, not only the a priori value of ionosphere but also its precision affect the convergence and accuracy of positioning. This study proposes a method to determine the precision of the interpolated slant ionospheric delay by cross-validation. The new method takes the high temporal and spatial variation into consideration. A distance-dependent function is built to represent the stochastic model of the slant ionospheric delay derived from each reference station, and an error model is built for each reference station on a five-minute piecewise basis. The user can interpolate ionospheric delay correction and the corresponding precision with an error function related to the distance and time of each reference station. With the European Reference Frame (EUREF) Permanent GNSS (Global Navigation Satellite Systems) network (EPN), and SONEL (Système d'Observation du Niveau des Eaux Littorales) GNSS stations covering most of Europe, the effectiveness of our wide-area ionosphere constraint method for PPP-RTK is validated, compared with the method with a fixed ionosphere precision threshold. It is shown that although the Root Mean Square (RMS) of the interpolated ionosphere error is within 5 cm in most of the areas, it exceeds 10 cm for some areas with sparse reference stations during some periods of time. The convergence time of the 90th percentile is 4.0 and 20.5 min for horizontal and vertical directions using Global Positioning System (GPS) kinematic solution, respectively, with the proposed method. This convergence is faster than those with the fixed ionosphere precision values of 1, 8, and 30 cm. The improvement with respect to the latter three solutions ranges from 10 to 60%. After integrating the Galileo navigation satellite system (Galileo), the convergence time of the 90th percentile for combined kinematic solutions is 2.0 and 9.0 min, with an improvement of 50.0% and 56.1% for horizontal and vertical directions, respectively, compared with the GPS-only solution. The average convergence time of GPS PPP-RTK for horizontal and vertical directions are 2.0 and 5.0 min, and those of GPS + Galileo PPP-RTK are 1.4 and 3.0 min, respectively.
The application of rapid test paper technology for pesticide detection in horticulture crops: a comprehensive review
Authors: Ghosh Soumya , AlKafaas Samar Sami , Bornman Charné , Apollon Wilgince , Hussien Aya Misbah , Badawy Ahmed Emad , Amer Mohamed Hussein , Kamel Manar Bakr , Mekawy Eman Ahmed , Bedair Heba 
Published: May 2022

Abstract
Background
The ever increasing pests and diseases occurring during vegetable crop production is a challenge for agronomists and farmers. One of the practices to avoid or control the attack of the causal agents is the use of pesticides, including herbicides, insecticides nematicides, and molluscicides. However, the use of these products can result in the presence of harmful residues in horticultural crops, which cause several human diseases such as weakened immunity, splenomegaly, renal failure, hepatitis, respiratory diseases, and cancer. Therefore, it was necessary to find safe and effective techniques to detect these residues in horticultural crops and to monitor food security.

Main body
The review discusses the use of conventional methods to detect pesticide residues on horticultural crops, explain the sensitivity of nanoparticle markers to detect a variety of pesticides, discuss the different methods of rapid test paper technology and highlight recent research on rapid test paper detection of pesticides.

Conclusions
The methodologies discussed in the current review can be used in a certain situation, and the variety of methods enable detection of different types of pesticides in the environment. Notably, the highly sensitive immunoassay, which offers the advantages of being low cost, highly specific and sensitive, allows it to be integrated into many detection fields to accurately detect pesticides.
Deep transfer learning–based fully automated detection and classification of Alzheimer’s disease on brain MRI
Authors: Ghaffari Hamed , Tavakoli Hassan , Pirzad Jahromi Gila 
Published: May 2022

Objectives:
To employ different automated convolutional neural network (CNN)-based transfer learning (TL) methods for both binary and multiclass classification of Alzheimer’s disease (AD) using brain MRI.

Methods:
Herein, we applied three popular pre-trained CNN models (ResNet101, Xception, and InceptionV3) using a fine-tuned approach of TL on 3D T 1-weighted brain MRI from a subset of ADNI dataset (n = 305 subjects). To evaluate power of TL, the aforementioned networks were also trained from scratch for performance comparison. Initially, Unet network segmentedthe MRI scans into characteristic components of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The proposed networks were trained and tested over the pre-processed and augmented segmented and whole images for both binary (NC/AD + progressive mild cognitive impairment (pMCI)+stable MCI (sMCI)) and 4-class (AD/pMCI/sMCI/NC) classification. Also, two independent test sets from the OASIS (n = 30) and AIBL (n = 60) datasets were used to externally assess the performance of the proposed algorithms.

Results:
The proposed TL-based CNN models achieved better performance compared to the training CNN models from scratch. On the ADNI test set, InceptionV3-TL achieved the highest accuracy of 93.75% and AUC of 92.0% for binary classification, as well as the highest accuracy of 93.75% and AUC of 96.0% for multiclass classification of AD on the whole images. On the OASIS test set, InceptionV3-TL outperformed two other models by achieving 93.33% accuracy with 93.0% AUC in binary classification of AD on the whole images. On the AIBL test set, InceptionV3-TL also outperformed two other models in both binary and multiclass classification tasks on the whole MR images and achieved accuracy/AUC of 93.33%/95.0% and 90.0%/93.0%, respectively. The GM segment as input provided the highest performance in both binary and multiclass classification of AD, as compared to the WM and CSF segments.

Conclusion:
This study demonstrates the potential of applying deep TL approach for automated detection and classification of AD using brain MRI with high accuracy and robustness across internal and external test data, suggesting that these models can possibly be used as a supportive tool to assist clinicians in creating objective opinion and correct diagnosis.

Advances in knowledge:
We used CNN-based TL approaches and the augmentation techniques to overcome the insufficient data problem. Our study provides evidence that deep TL algorithms can be used for both binary and multiclass classification of AD with high accuracy.
Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium
Authors: Shuaib Haris , Barker Gareth J , Sasieni Peter , De Vita Enrico , Chelliah Alysha , Andrei Roman , Ashkan Keyoumars , Beaumont Erica , Brazil Lucy , Rowland-Hill Chris , Lau Yue Hui , Luis Aysha , Powell James , Swampillai Angela , Tenant Sean , Thust Stefanie C , Wastling Stephen , Young Tom , Booth Thomas C ,   ZGBM consortium, Brock Juliet , Currie Stuart , Fatani Kavi , Foweraker Karen , Glendenning Jennifer , Hoggard Nigel , Kanodia Avinash K , Krishnan Anant , Thurston Mark DV , Lewis Joanne , Linares Christian , Mathew Ryan K , Ramalingam Satheesh , Sawlani Vijay , Welsh Liam , Williams Matt 
Published: May 2022

Objective:
To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep-learning models.

Methods:
MR imaging data were analysed from a random sample of five patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules.

Results:
All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T 1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site.

Conclusion:
The imaging protocol and scheduling varies across the UK, making it challenging to develop machine-learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres.

Advances in knowledge:
Successful translation of deep-learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.