Application of MET Technique after Upper Limb Dysfunction after Breast Cancer Surgery

Object: Explore the application and actual effect of MET (Muscle Energy) technology after breast cancer surgery with upper limb dysfunction. Methods: Taking 40 female breast cancer patients who underwent surgical treatment in our hospital from September 2017 to June 2019 as the research objects, all of them successfully completed modified radical mastectomy for breast cancer. According to different nursing methods, the patients were randomly divided into two groups. The experiment There were 20 cases in each group and the control group. The control group was given routine functional recovery exercise intervention after the operation, and the experimental group added MET technology to the base of the control group. One month after the operation, the functional recovery of the affected limbs of the two groups of patients was effectively assessed. The upper limb dysfunction of the two groups was compared by statistical methods, and the shoulder joint range of motion (ROM) was used for performance. Results: Through early functional recovery training and MET technology, 19 cases of ROM in the experimental group showed compliance (95%), compared with only 14 cases (70%) in the control group. The difference in upper limb dysfunction between the two groups is very obvious with statistical significance (P<0.05). Conclusions: Early functional recovery training combined with muscle energy technology can promote the recovery of upper limb dysfunction after breast cancer surgery faster and better, which is conducive to the recovery of patients as soon as possible and improve the quality of life

Blockchain for Finance: A Survey

As an innovative technology for enhancing authenticity, security, and risk management, blockchain is being widely adopted in trade and finance systems. The unique capabilities of blockchain, such as immutability and transparency, enable new business models of distributed data storage, point-to-point transactions, and decentralized autonomous organizations. In this paper, we focus on blockchain-based securities trading, in which blockchain technology plays a vital role in financial services as it ultimately lifts trust and frees the need for third-party verification by using consensus-based verification. We investigate the 12 most popular blockchain platforms and elaborate on 6 platforms that are related to finance, seeking to provide a panorama of securities trading practices. Meanwhile, this survey provides a comprehensive summary of blockchain-based securities trading applications. We gather numerous practical applications of blockchain-based securities trading and categorize them into four distinct categories. For each category, we introduce a typical example and explain how blockchain contributes to solving the key problems faced by FinTech companies and researchers. Finally, we provide interesting observations ranging from mainstream blockchain-based financial institutions to security issues of decentralized finance applications, aiming to picture the current blockchain ecosystem in finance

Modeling the High-Pressure Solid and Liquid Phases of Tin from Deep Potentials with ab initio Accuracy

Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-based deep potentials for Sn with pressures ranging from 0 to 50 GPa and temperatures ranging from 0 to 2000 K. In particular, we find the deep potential, which is obtained by training the ab initio data from density functional theory calculations with the state-of-the-art SCAN exchange-correlation functional, is suitable to characterize high-pressure phases of Sn. We systematically validate several structural and elastic properties of the {\alpha} (diamond structure), {\beta}, bct, and bcc structures of Sn, as well as the structural and dynamic properties of liquid Sn. The thermodynamics integration method is further utilized to compute the free energies of the {\alpha}, {\beta}, bct, and liquid phases, from which the deep potential successfully predicts the phase diagram of Sn including the existence of the triple-point that qualitatively agrees with the experiment

Transcriptional regulation by the estrogen receptor of antioxidative stress enzymes and its functional implications

We previously reported that antiestrogen-liganded estrogen receptor b (ERb) transcriptionally activates the major detoxifying enzyme quinone reductase (QR) (NAD(P)H:-quinone oxidoreductase). Our studies also indicate that upregulation of QR, either by overexpression or induction by tamoxifen, can protect breast cells against oxidative DNA damage caused by estrogen metabolites. We now report on the upregulation of glutathione S-transferases Pi (GST-Pi) and gamma-glutamylcysteine synthetase heavy subunit (GCSh) expression by antiestrogens. Studies indicate the regulation of GST-Pi and GCSh transcriptional activity by ER. While ER regulation is mediated by an electrophile response element (EpRE), we identified mechanistic differences in the involvement of other transcription factors. Regardless of these differences, ERb-mediated regulation of GST-Pi and GCSh point towards an important role for ERb in cellular protection against oxidative stress. A protective role is supported by our observation of inhibition of estrogeninduced DNA damage upon upregulation of GST-Pi and GCSh expression

The dependence of new particle formation rates on the interaction between cluster growth, evaporation, and condensation sink

New particle formation (NPF) is one of the major contributors to atmospheric aerosol number concentrations. The initial step of NPF includes the formation and growth of small clusters, their evaporation and loss to pre-existing particles (characterized by the condensation sink, CS). In the polluted atmospheric boundary layer, the high environmental CS suppresses NPF and it can work synergistically with evaporation to further reduce the NPF rates. In this study, to quantitatively include CS into NPF analysis, we make simplifications to the cluster balance equations and develop approximate equations for the NPF rates in the presence of pre-existing particles, which are applicable to nucleation mechanisms that can be represented by a nonbranched nucleation pathway. The developed equations show that the proportion of clusters that finally lead to new particle formation is given by the cluster-specific ratio of growth rate/CS | evaporation rate | growth rate. As a result, the cumulative product of this ratio for all clusters in the nucleation pathway determines the NPF rates. By comparing with benchmark cluster dynamics simulations of sulfuric acid-dimethylamine and sulfuric acid-ammonia nucleation systems, the developed equations were confirmed to give good estimates of the NPF rates and approximately capture the dependency of NPF rates on CS and nucleating vapor concentrations. The CS dependency predicted by the developed equations shows larger deviations from the simulations when the cluster evaporation rates are high, i.e., when the underlying assumptions of the equations are not satisfied. The equations were also found to be in good agreement with atmospheric NPF rates measured in long-term field observations in urban Beijing.Peer reviewe

Pathogens and drug resistance in active surveillance of foodborne diseases from 2017 to 2019 in Chenzhou City

ObjectiveTo provide a scientific basis for the prevention and control of foodborne diseases in Chenzhou， the etiological characteristics and epidemiological patterns of foodborne diseases were analyzed.MethodsThe Case information and stool and anal swab samples were collected from two sentinel hospitals in Chenzhou in 2017 and 2019. According to the methods described in “National Manual of Foodborne Disease Surveillance”， the samples were tested for pathogens， pathogen typing， and drug sensitivity.ResultsA total of 825 samples of diarrhea cases were collected， and the total detection rate of pathogens was 30.18% （249/825）， including 16.24% （134/825） Salmonella， 11.76% （97/825） Norovirus， 3.52% （29/825） diarrheal Escherichia coli， 0.73% （6/825） Vibrio parahaemolyticus， and 0.12% （1/825） Shigella. Bacterial detection rates were higher in the second and third quarters than in other quarters， and viral detection rates were higher in the first and fourth quarters than in other quarters. The highest pathogen detection rate was 40.79% （31/76） in the 2-6-year-old group. Milk and dairy products， grains and their products， and fruits and their products were suspected foods. The highest detection rate in Salmonella was in Salmonella enterica subsp.（74.63%， 100/134）， the highest detection rate in diarrheal Escherichia coli was in intestinal adhesion type and enterotoxin type （34.48%， 10/29）， and the norovirus was mainly GII type （85.57%， 83/97）. The highest resistance rate of Salmonella to tetracycline was 88.71% （110/124）， and the multidrug resistance rate of Salmonella was 85.48% （106/124）. The resistance rate of diarrheal Escherichia coli to ampicillin was significant （79.31%， 23/29）， and the multidrug resistance rate of diarrheal Escherichia coli was 62.07% （18/29）.ConclusionThe main pathogens of diarrheal cases of foodborne diseases were Salmonella and Norovirus in Chenzhou. Salmonella and diarrheal Escherichia coli are highly resistant to antibiotics. Therefore， it is necessary to conduct targeted food safety supervision， strengthen antibiotic resistance monitoring， and strictly prevent antibiotic abuse