Analysis of Acupoint Selection Rules for Electroacupuncture Treatment of Osteonecrosis of the Femoral Head Based on Data Mining Technology

Objective: Analysis was focused on the data mining technology of electroacupuncture (EA) of osteonecrosis of the femoral head (ONFH) of the rules and characteristics of point selection in the clinical treatment, to provide a basis for clinical electroacupuncture treatment of ONFH. Methods: The Chinese and English literatures obtained from the CNKI and PubMed database on the treatment of ONFH by electroacupuncture, the Endnote database for the treatment of osteonecrosis of the femoral head by electroacupuncture was established, and the rule of point selection was analyzed by data mining and statistical software Excel ,SPSS,SPSS Modeler. Results: A total of 17 articles were included, and 44 acupoints were selected with a total frequency of 169 times. The most frequently used acupoints in turn are Juliao(GB29),Shenshu (BL23), Biguan (ST31);The selected acupoints mainly belonged to bladder meridian; The acupoints are mainly distributed in the lower limbs, the five shu points are used mostly in the special points, among them, the he-sea points is the most widely used; the dense-sparse waves is mostly used in the electroacupuncture waveform. Cluster analysis can be divided into three categories. The result of correlation analysis showed that "Shenshu(BL23)→Ashi Point" had the highest support degree. Conclusion: electroacupuncture treatment of acupoint selection of Osteonecrosis of the femoral head is centered on Juliao (GB29), Shenshu(BL23), Biguan(ST31), Huantiao(GB30) and Ashi acupoints,with emphasis on selection of acupoints along local points and dialectical matching

Re-conceptualization of the “Chinese Expert Guidelines for the Prevention of Stroke Associated with Patent Foramen Ovale” for the Management of Perioperative Stroke in Patients with Lung Cancer

The Chinese Heart Journal published the “Chinese Expert Guidelines for the Prevention of Stroke Associated with Patent Foramen Ovale” (hereafter referred to as “the Guidelines”) in 2021. The Guidelines were initiated by Professor Yushun Zhang of the No.1 Affiliated Hospital of Xi’an Jiaotong University, and 55 domestic experts participated in their discussion and formulation. The Guidelines focus on eight key issues in the prevention of stroke associated with patent foramen ovale (PFO), including definition and epidemiology, anatomical features, ultrasound diagnosis, clinical screening, and prevention and treatment of PFO-associated stroke. The prevention and treatment of PFO-associated stroke include pharmacological prevention, prevention of PFO with transcatheter occlusion and transcatheter occlusion of PFO. Patients with PFO are at elevated risk of perioperative stroke. In China, lung cancer ranks first in incidence among malignant tumors. The number of lung cancer surgeries is increasing each year, and the incidence of PFO in the population is approximately 25%. Although perioperative stroke in patients with lung cancer due to the presence of PFO has rarely been reported, given the high disability rate of stroke, incidence of PFO, and incidence of lung cancer, herein, we consider the Guidelines for the management of perioperative stroke in lung cancer. Our aim is to provide further perspectives in decreasing the risk of perioperative stroke in patients with lung cancer and PFO, to improve their quality of life and increase the safety of surgery

Bi-Objective simplified swarm optimization for fog computing task scheduling

In the face of burgeoning data volumes, latency issues present a formidable challenge to cloud computing. This problem has been strategically tackled through the advent of fog computing, shifting computations from central cloud data centers to local fog devices. This process minimizes data transmission to distant servers, resulting in significant cost savings and instantaneous responses for users. Despite the urgency of many fog computing applications, existing research falls short in providing time-effective and tailored algorithms for fog computing task scheduling. To bridge this gap, we introduce a unique local search mechanism, Card Sorting Local Search (CSLS), that augments the non-dominated solutions found by the Bi-objective Simplified Swarm Optimization (BSSO). We further propose Fast Elite Selecting (FES), a ground-breaking one-front non-dominated sorting method that curtails the time complexity of non-dominated sorting processes. By integrating BSSO, CSLS, and FES, we are unveiling a novel algorithm, Elite Swarm Simplified Optimization (EliteSSO), specifically developed to conquer time-efficiency and non-dominated solution issues, predominantly in large-scale fog computing task scheduling conundrums. Computational evidence reveals that our proposed algorithm is both highly efficient in terms of time and exceedingly effective, outstripping other algorithms on a significant scale