The data mining technique is adopted to analyze characteristics and rules of acupoint and meridian selection of acupuncture-moxibustion for treatment of vertigo at different time periods in the ancient. The data is collected from literature regarding acupuncture-moxibustion from the pre-Qin period to the end of Qing Dynasty, so as to establish a clinical literature database of ancient acupuncture-moxibustion for treatment of vertigo. Data mining method is applied to analyze the commonly used meridians, acupoints and special acupoints in different dynasties, also possible rules are explored. Totally 82 pieces of prescription of acupuncture-moxibustion for treatment of vertigo are included. In the history the leading selection of acupoitns are Fengchi (GB 20), Hegu (LI 4), Shangxing (GV 23) and Jiexi (ST 41) while that of meridians are mainly three yang meridians of foot and the Governor Vessel, especially the acupoints on the Bladder Meridian of foot yangming had the highest utilization rate, accounting for 23.04%. The acupoint selection is characterized by special acupoint, accounting for 80.6%, among which the crossing points are the most common choice. Distal-proximal acupoints combination is the most frequent method. The results indicate that the ancient acupuncture-moxibustion for treatment of vertigo focused on acupoints in the yang meridians, and the specific acupoints play an essential role in prescription; also the principle of syndrome differentiation and selecting acupoints along the meridians could be seen.
Acupuncture Points ; Acupuncture Therapy ; history ; Data Mining ; History, 18th Century ; History, 19th Century ; History, 20th Century ; Humans ; Medicine in Literature ; Moxibustion ; history ; Vertigo ; history ; therapy

The present article summarized development history and domestic and foreign research condition of intelli‐gent diagnostic technique , focused on its development and application in ECG diagnosis , narrated methods and di‐agnostic process of ECG intelligent diagnosis , and made an investigation and prospect on development of ECG intel‐ligent diagnostic technique .

Objective To investigate whether elevated baseline levels of high sensitivity C-Reactive Protein (hsCRP) and neutrophil (NE) are associated with an increased risk of breast cancer in Kailuan female cohort. Methods Females from Kailuan cohort (2006-2007) were included in this study. Information on check-up, hsCRP and NE were collected at baseline for all subjects. Multivariable Cox proportional hazards regression models were used to calculate hazard ratios (HR) and 95% confidence intervals (95%CI) of association between baseline hsCRP and NE values and breast cancer risk. Results By December 31, 2015, a total of 18 866 participants were enrolled in this study. During the follow-up, 183 new cases of breast cancer were observed. All participants were divided into three groups according to the level of hsCRP (<1 mg/L, 1-3 mg/L and >3 mg/L). The cumulative incidence of breast cancer were 829/105, 1 211/105 and 1 495/105 in these 3 groups, respectively ( 2=12.08， P=0.002). Compared with participants with lower hsCRP levels (<1 mg/L), individuals with the highest hsCRP (>3 mg/L) levels had significantly increased risk of breast cancer (HR=1.71,95%CI: 1.18-2.47, P=0.005), howerver, we didn’t find the statistically significant association between NE level (<3.70×109/Lvs. ≥3.70×109/L) and the risk of brease cancer (P>0.05). Conclusions Elevated levels of hsCRP at baseline might increase the risk of breast cancer in females.

Objective: Several COVID-19 patients have overlapping comorbidities. The independent role of each component contributing to the risk of COVID-19 is unknown, and how some non-cardiometabolic comorbidities affect the risk of COVID-19 remains unclear. Methods: A retrospective follow-up design was adopted. A total of 1,160 laboratory-confirmed patients were enrolled from nine provinces in China. Data on comorbidities were obtained from the patients' medical records. Multivariable logistic regression models were used to estimate the odds ratio ( Results: Overall, 158 (13.6%) patients were diagnosed with severe illness and 32 (2.7%) had unfavorable outcomes. Hypertension (2.87, 1.30-6.32), type 2 diabetes (T2DM) (3.57, 2.32-5.49), cardiovascular disease (CVD) (3.78, 1.81-7.89), fatty liver disease (7.53, 1.96-28.96), hyperlipidemia (2.15, 1.26-3.67), other lung diseases (6.00, 3.01-11.96), and electrolyte imbalance (10.40, 3.00-26.10) were independently linked to increased odds of being severely ill. T2DM (6.07, 2.89-12.75), CVD (8.47, 6.03-11.89), and electrolyte imbalance (19.44, 11.47-32.96) were also strong predictors of unfavorable outcomes. Women with comorbidities were more likely to have severe disease on admission (5.46, 3.25-9.19), while men with comorbidities were more likely to have unfavorable treatment outcomes (6.58, 1.46-29.64) within two weeks. Conclusion Besides hypertension, diabetes, and CVD, fatty liver disease, hyperlipidemia, other lung diseases, and electrolyte imbalance were independent risk factors for COVID-19 severity and poor treatment outcome. Women with comorbidities were more likely to have severe disease, while men with comorbidities were more likely to have unfavorable treatment outcomes.
Adult ; Aged ; COVID-19/virology* ; China/epidemiology* ; Comorbidity ; Female ; Humans ; Male ; Middle Aged ; Retrospective Studies ; Severity of Illness Index ; Treatment Outcome