Effect of electroacupuncture at "Neiguan" (PC 6) and "Zusanli" (ST 36) on gastrointestinal hormone in the antral tissue of rats with functional dyspepsia.

Objectives: To observe the effects of electroacupuncture (EA) at "Neiguan" (PC 6) and "Zusanli"(ST 36) on the gastric emptying rate, the level of serotonin (5-HT) and the protein expression of motilin (MTL), ghrelin, substance P (SP) and vasoactive intestinal peptide (VIP) in the antral tissue of the rats with functional dyspepsia (FD) and explore the effect mechanism of EA in treatment of FD.

Methods: A total of 21 SPF male SD rat pups were randomly divided into a normal group, a model group and an EA group, with 7 rats in each group. In the model group and the EA group, FD model was prepared by the gavage with 0.

Lobelia chinensis Lour inhibits the progression of hepatocellular carcinoma via the regulation of the PTEN/AKT signaling pathway in vivo and in vitro.

Ethnopharmacological Relevance: Lobelia chinensis Lour. (LCL) is a common herb used for clearing heat and detoxifying, and it has antitumor activity. Quercetin is one of its important components, which may play an important role in the treatment of hepatocellular carcinoma (HCC).

Heterogeneous Collaborative Learning for Personalized Healthcare Analytics via Messenger Distillation.

The Healthcare Internet-of-Things (IoT) framework aims to provide personalized medical services with edge devices. Due to the inevitable data sparsity on an individual device, cross-device collaboration is introduced to enhance the power of distributed artificial intelligence. Conventional collaborative learning protocols (e.

Time Interval-Enhanced Graph Neural Network for Shared-Account Cross-Domain Sequential Recommendation.

Shared-account cross-domain sequential recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains. It is gaining immense research attention as more and more users tend to sign up on different platforms and share accounts with others to access domain-specific services. Existing works on SCSR mainly rely on mining sequential patterns via recurrent neural network (RNN)-based models, which suffer from the following limitations: 1) RNN-based methods overwhelmingly target discovering sequential dependencies in single-user behaviors and they are not expressive enough to capture the relationships among multiple entities in SCSR; 2) all existing methods bridge two domains via knowledge transfer in the latent space and ignore the explicit cross-domain graph structure; and 3) none existing studies consider the time interval information among items, which is essential in the sequential recommendation for characterizing different items and learning discriminative representations for them.

Stunting among kindergarten children in China in the context of COVID-19: A cross-sectional study.

Background: The impact of COVID-19 has most likely increased the prevalence of stunting. The study aimed to determine the prevalence of stunting among kindergarten children in the context of coronavirus disease 2019 (COVID-19) in Longgang District, Shenzhen, China, and its risk factors.

Methods: A cross-sectional study was conducted to identify children from 11 sub districts of 481 kindergartens in the Longgang District of Shenzhen City from May to July 2021.

[Comparison of stimulation-induced local changes of expression of local Meckel cell-related proteins and neuropeptides and number of mast cells among acupuncture and moxibustion alone or in combination in chronic atrophic gastritis rats].

Objective: To compare the effects of acupuncture and moxibustion alone or in combination on the number of mast cells and expression levels of cytoketatin 18 (CK18) and CK19 (marker of Meckel cells), and calcitonin gene-related peptide (CGRP), neuropeptide-Y (NPY) and bradykinin (BK) in the local acupoint area of rats with chronic atrophic gastritis (CAG).

Methods: Fifty male SD rats were randomly divided into normal, CAG model, moxibustion, acupuncture and acupuncture+moxi-bustion groups (10 rats in each group). The CAG model was established by gavage of 1-methyl-3-nitro-1-nitrosoguanidine (170 μg/mL,1 mL/100 g, once a week) and 40% ethanol solution (twice a week) for 12 consecutive weeks.

An Integrated First Principal and Deep Learning Approach for Modeling Nitrous Oxide Emissions from Wastewater Treatment Plants.

Mathematical modeling plays a critical role toward the mitigation of nitrous oxide (NO) emissions from wastewater treatment plants (WWTPs). In this work, we proposed a novel hybrid modeling approach by integrating the first principal model with deep learning techniques to predict NO emissions. The hybrid model was successfully implemented and validated with the NO emission data from a full-scale WWTP.

Personalized On-Device E-Health Analytics With Decentralized Block Coordinate Descent.

Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating. Nowadays, enhancement on medical diagnosis via machine learning models has been highly effective in many aspects of e-health analytics. Nevertheless, in the classic cloud-based/centralized e-health paradigms, all the data will be centrally stored on the server to facilitate model training, which inevitably incurs privacy concerns and high time delay.

Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modeling.

Due to the sparsity of available features in web-scale predictive analytics, combinatorial features become a crucial means for deriving accurate predictions. As a well-established approach, a factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering. With the prominent development of deep neural networks (DNNs), there is a recent and ongoing trend of enhancing the expressiveness of FM-based models with DNNs.

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection.

Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the costly treatment for a single detection. Recently, many single-sensor alternatives have been proposed to improve the cost efficiency and convenience.

Disease Prediction via Graph Neural Networks.

With the increasingly available electronic medical records (EMRs), disease prediction has recently gained immense research attention, where an accurate classifier needs to be trained to map the input prediction signals (e.g., symptoms, patient demographics, etc.

Few-Shot Deep Adversarial Learning for Video-based Person Re-identification.

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras.

MCP: A multi-component learning machine to predict protein secondary structure.

The Gene or DNA sequence in every cell does not control genetic properties on its own; Rather, this is done through the translation of DNA into protein and subsequent formation of a certain 3D structure. The biological function of a protein is tightly connected to its specific 3D structure. Prediction of the protein secondary structure is a crucial intermediate step towards elucidating its 3D structure and function.

[Impact of ecological protection construction on schistosomiasis transmission of Qionghai Lake wetland in Xichang City].

Objective: To understand the impact of Qionghai Lake wetland ecological protection construction on the prevalence of schistosomiasis, so as to provide the evidence for formulating the strategies for schistosomiasis control and prevention.

Methods: A retrospective survey of the construction of Qionghai Lake wetland was performed, and eleven villages around the wetland were surveyed for schistosomiasis endemic situation. The influence of the wetland project on the schistosomiasis prevalence and Oncomelania hupensis snail status were investigated.

[Water exposure modes and times of different populations in mountainous schistosomiasis endemic areas near Qionghai lake].

Objective: To investigate water exposure modes and times of different populations in mountainous schistosomiasis endemic areas and to inform about the control strategies.

Methods: All 1054 residents from populations around Qionghai Lake were randomly sampled according to occupation for a retrospective questionnaire survey in November 2001. Each individual was interviewed for his/her mode, frequency, and duration of water exposure occurring between April and October 2001.