Notes

Proton push inhibitor therapy turns around endoscopic options that come with fibrosis in eosinophilic esophagitis.
Bluetongue (BT) is an infectious, noncontagious, vector-borne viral disease that affects wild and domestic ruminants transmitted by Culicoides spp. A cross-sectional study was carried out during the period 2016-2017 in Gadarif state. A total of 276 sera samples were collected from camels in six localities of Gadarif state, eastern Sudan, to investigate bluetongue virus (BTV) seroprevalence and associated risk factors of BTV infection including age, sex, breed, locality, and ecology of the region. Enzyme-linked immunosorbent assay (ELISA) was used for estimation of BTV seroprevalence rate. The overall BTV seroprevalence rate was 96.7% in the study area ranging from 93.5% to 100% in six screened localities with no significant differences. The findings revealed similar BTV seroprevalence rates in both males and females, but high rates were found in age group of less than one year and two to three years with estimated 100%. However, the lowest seroprevalence was found in the age group of five to four years with estimated BTV to be 92.3%. BTV seropositivity was not found to be statistically associated with examined different camel breeds which revealed 93%, 94.4%, 97.6%, and 97.8% seroprevalence in Bushari, Rashide, Arabi, and Anafi, breeds, respectively. Epidemiology of BTV assessment according to the ecology of the area showed high BTV seroprevalence in desert and savanna with estimated 100% and lower BTV seroprevalence in arid and rich savanna with estimated 94.8% and 95.7%, respectively. There was no significant association between BTV ELISA positivity and sex, breed, and ecology of the area.Colibacillosis in Indonesia until now still appears frequently, so the case of colibacillosis laying hens cannot reach the peak of egg production; the egg production period is delayed and easily infected with other diseases. The purpose of this research is that the acidifier-dextrose combination is expected to be able to suppress the development of Avian Pathogenic Escherichia coli (APEC) bacteria in laying hens so that, in the end, the case of colibacillosis can be controlled in Indonesia. A total of 240 heads of laying hens were divided into 6 treatments and each consisted of 40 replications. The results of this research state that a combination of acidifier-dextrose can increase Hen Day Production (p less then 0.05) and decrease Feed Conversion Ratio (p less then 0.05) in laying hens infected with APEC. The Hen Day Production results of the treatment group infected with APEC showed the lowest results, amounting to 65.75% whereas the other treatments are still above 90%. Furthermore, the highest Feed Conversion Ratio results were on treatments infected with APEC, which amounted to 2.17 while other treatments of the Feed Conversion Ratio results are still below 1.80. In general, the use of a combination of acidifier and dextrose with the lowest dose, that is, 1 g/3.75 liters of drinking water can still give good results to Hen Day Production and Feed Conversion Ratio for laying hens infected with APEC. Giving combination of acidifier-dextrose can increase Hen Day Production and decrease Feed Conversion Ratio in laying hens infected with APEC. The recommended dosage of acidifier-dextrose combination in laying hens based on this research is 1 g/3.75 liters of drinking water.The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. selleck chemicals The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.In the beginning of 2020, the coronavirus (COVID-19) pandemic started to spread globally, causing panic to the lives of people around the world; many countries executed lockdown of cities or even total lockdown of the entire countries. The coronavirus disease (COVID-19) is transmitted via air droplets. In medical environments that use traditional hermetic ventilation systems, medical personnel who come in contact with patients are more susceptible to infection compared to regular staff; therefore, the air flow and air quality of hermetic negative pressure isolation wards are highly critical. For this purpose, the study proposes a full-outer-air-intake natural air-conditioning system for negative pressure isolation wards. This innovative system draws in large amounts of fresh external air to greatly improve the air exchange rate in wards; negative pressure environments can be implemented depending on requirements to solve the issue of nosocomial infections in traditional negative pressure isolation wards that draw air from within the hospital. This greatly reduces the probability of nosocomial infection and infection via air droplets; furthermore, the system's intake and exhaust paths are completely isolated, solving the issue of air cross-contamination. Based on the results from the experiment site, this innovative system was designed and implemented based on the guidelines of hospital facilities and achieved air exchange per hour in excess of 12 times/hour, reaching a maximum of 54.5 times/hour. Indoor CO2 concentration was 576 ppm, negative pressure was -14 Pa, indoor temperature was 23.3°C, indoor humidity was 54.1%, and sensible heat exchange efficiency (ηs) was 105.88% which effectively reduced ventilation load. Therefore, this innovative full-outer-air-intake natural air-conditioning system can provide medical staff and patients with a safe and healthy environment that prevents cross-infection.In recent years, the Internet of Things technology has flourished, and there have been corresponding practical results in various fields. In medical care, the introduction of Internet of Things technology must also be a new trend in the development of hospital informatization, and it is the development stage of the digital medical process. The traditional infusion system shows that the infusion bottle is not replaced in time, the infusion waiting time is too long, the infusion efficiency is too low, and the existing medical staff is far from meeting the needs of the huge infusion population. Therefore, this article proposes a technology based on the Internet of Things application of the infusion control system in joint orthopedics nursing work to improve the efficiency of infusion in nursing work. This article deeply learns and uses the Internet of Things technology to build a new infusion management and control system, which is applied to joint orthopedics nursing treatment. This paper designs the application research experiment of the infusion control system. Through the Internet of Things technology, the relevant data in the infusion process are uploaded and sent to the network center of the hospital. Nursing staff can directly see the infusion situation directly through the computer console. This article compares and analyzes two different infusion systems and draws conclusions. The infusion ringing rate of the control group was 81.3%, and the infusion ringing rate of the IoT group was 29.8%; the time for timely replacement of the infusion bottle after IoT data control was 13.89 min, compared to 19.76 min before. A variety of data results show that the infusion management and control system based on the Internet of Things technology has played a great role in joint orthopedics care, which can greatly improve the efficiency of infusion, replace the infusion or deal with failures in time for patients, and improve patient satisfaction.
The aim of this study was to investigate the association between single nucleotide polymorphism (SNP) rs9891119 of the signal transducer and activator of the transcription 3 (STAT3) gene and genetic susceptibility to type 2 diabetes in Chinese Han population from the Guangdong province.

The aim of the present study was to explore the relationship between single nucleotide polymorphism rs9891119 of STAT3 gene and type 2 diabetes mellitus (T2DM), which provides a basis for molecular genetic research on the pathogenesis of T2DM in Chinese Han population.

In our case-control study, the SNP rs9891119 was picked out from the STAT3 gene and the SNP genotyping was performed by using the SNPscan
kit in 1092 patients with type 2 diabetes as cases and 1092 normal persons as controls. The distributions of genotype and allele frequencies in two groups were analyzed by SPSS 20.0 software.

Our results showed that the alleles of A and C of rs9891119 of the STAT3 gene were 54.3 and 45.7% in patients with type 2 diabetes, while 55.5% and 44.5% in the normal persons, which have no statistical significance (
> 0.05). There were also no significant differences in AA, AC, and CC genotype frequencies between type 2 diabetes patients and normal persons. There were no significant differences in codominant, dominant, recessive, and overdominant genetic models of SNP rs9891119 before and after adjusting the covariant factors (
> 0.05).

Therefore, genetic susceptibility to type 2 diabetes may be not associated with SNP rs9891119 of the STAT3 gene in Chinese Han population from the Guangdong province.
Therefore, genetic susceptibility to type 2 diabetes may be not associated with SNP rs9891119 of the STAT3 gene in Chinese Han population from the Guangdong province.Knowledge graph can effectively analyze and construct the essential characteristics of data. At present, scholars have proposed many knowledge graph models from different perspectives, especially in the medical field, but there are still relatively few studies on stroke diseases using medical knowledge graphs. Therefore, this paper will build a medical knowledge graph model for stroke. Firstly, a stroke disease dictionary and an ontology database are built through the international standard medical term sets and semiautomatic extraction-based crowdsourcing website data. Secondly, the external data are linked to the nodes of the existing knowledge graph via the entity similarity measures and the knowledge representation is performed by the knowledge graph embedded model. Thirdly, the structure of the established knowledge graph is modified continuously through iterative updating. Finally, in the experimental part, the proposed stroke medical knowledge graph is applied to the real stroke data and the performance of the proposed knowledge graph approach on the series of Trans ∗ models is compared.
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