Objective To explore the effects of air pressure, humidity, wind, and sunshine on the incidence of cardiovascular and cerebrovascular diseases (CVD) in Guiyang, and to provide reference for the prevention of CVD. Methods Using CVD incidence data from September 2021 to August 2022 in Guiyang City and meteorological data including average air pressure, average humidity, wind, and sunshine during the same period, the effects of meteorological factors on CVD incidence were explored and the importance of each factor was analyzed. Results When air pressure was below 868 hPa, above 887 hPa, or between 877 and 883 hPa, and when air pressure dropped less than 5.3 hPa within 24 hours, there was a higher risk of CVD. When the humidity was above 81%, the wind speed was small (<1.2 m/s) or high (>4m/s), and there was less sunlight (less than 3 hours), the risk of CVD was higher. Low humidity (<60%) was not conducive to the onset of CVD. There were highest risks at lag 5~10 days and 4-25 days for high pressure and low sunlight, respectively. When the relative humidity was saturated, there was an immediate effect. When the wind speed was low and high, the immediate effect and hysteresis effects were significant. Among the above meteorological factors, the impact of 24-hour variation of pressure and high or low atmospheric pressure on the incidence of CVD was the most significant, while the impact of sunlight and humidity was the weakest. The impact of diurnal variations in wind and atmospheric pressure was not clear. Conclusion The impact of air pressure on the incidence of CVD does not exhibit a simple linear relationship. The risk of CVD is high in high humidity, low light, and moderate or strong winds. It is necessary to fully consider changes in meteorological factors for CVD prevention and control.

OBJECTIVE: To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction. METHODS: Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial. RESULTS: The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation. CONCLUSION The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
Humans ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Adult ; Male ; Female

OBJECTIVE: This study aimed to reexplore minimum iodine excretion and to build a dietary iodine recommendation for Chinese adults using the obligatory iodine loss hypothesis. METHODS: Data from 171 Chinese adults (19-21 years old) were collected and analyzed based on three balance studies in Shenzhen, Yinchuan, and Changzhi. The single exponential equation was accordingly used to simulate the trajectory of 24 h urinary iodine excretion as the low iodine experimental diets offered (iodine intake: 11-26 μg/day) and to further deduce the dietary reference intakes (DRIs) for iodine, including estimated average requirement (EAR) and recommended nutrient intake (RNI). RESULTS: The minimum iodine excretion was estimated as 57, 58, and 51 μg/day in three balance studies, respectively. Moreover, it was further suggested as 57, 58, and 51 μg/day for iodine EAR, and 80, 81, and 71 μg/day for iodine RNI or expressed as 1.42, 1.41, and 1.20 μg/(day·kg) of body weight. CONCLUSION The iodine DRIs for Chinese adults were established based on the obligatory iodine loss hypothesis, which provides scientific support for the amendment of nutrient requirements.
Humans ; Iodine/administration & dosage* ; Male ; Female ; China ; Young Adult ; Diet ; Adult ; Nutritional Requirements ; East Asian People

Objective To observe the value of 18F-FDG PET/CT semi-quantitative parameters for predicting spread through air spaces(STAS)of clinical stage Ⅰa—Ⅲa lung adenocarcinoma.Methods Data of 85 patients with clinical stage Ⅰa—Ⅲ a lung adenocarcinoma who underwent preoperative 18F-FDG PET/CT were retrospectively analyzed.The patients were divided into positive group(n=23)or negative group(n=62)according to whether pathology showed STAS or not.Clinical and PET/CT data were compared between groups,and logistic analysis was performed to explore the efficacy of each parameter for predicting STAS.Results Significant differences of gender,carcinoma embryonic antigen,clinical stage,pathological grade,micropapillary growth and proportion were found between groups(all P＜0.05).The maximum,the mean,the peak standard uptake value(SUVmax,SUVmean,SUVpeak),as well as the maximum,the mean and the peak standard uptake value normalized by lean body mass(SULmax,SULmean,SULpeak),also the total lesion glycolysis(TLG)in positive group were all significantly higher than those in negative group(all P＜0.05).Patients'gender,proportion of micropapillary growth,SUVmax and SULmax were all independent risk factors of STAS of clinical stage Ⅰa—Ⅲa lung adenocarcinoma.The area under the curve(AUC)of the above parameters for predicting STAS was 0.666,0.912,0.839 and 0.842,respectively,and of the combination was 0.957.Conclusion 18 F-FDG PET/CT semi-quantitative parameters SUVmax and SULmax were helpful for predicting STAS of clinical stage Ⅰa—Ⅲ a lung adenocarcinoma,and further combination of gender and proportion of micropapillary growth could improve diagnostic efficacy.

Objective:To construct an analytic method aimed at the characteristics of the commonly method of supplementing and pouring of acupuncture based on the analysis and modeling of ultrasound images around acupoint region in the process of acupuncture.Methods:A total of 7 healthy subjects who underwent physical examination in Beijing Zhongguancun Hospital from June,2022 to June,2023 were selected,and their Kongzui acupoints were acupunctured by 10 acupuncturists with associate senior title as 4 kinds of acupuncture manipulations included reinforcing by twisting and rotating(RFTR),reducing by twisting and rotating(RDTR),reinforcing by lifting and thrusting(RFLT),and reducing by lifting and thrusting(RDLT).The B-ultrasound diagnostic device was used to collect the images of muscle and fascial tissue below the acupoint,so as to construct the model of images.The definition of virtual acupuncture point was adopted to study the regulation of perturbation of subcutaneous tissue that was caused after the skin was acupunctured by needle.The change regulation of the virtual acupuncture point of muscle bundle below skin at Zuikong acupoint of subjects was analyzed.Results:The difference value of average absolution value between peak and trough of the trajectory of virtual acupuncture point of twisting and rotating was 0.066±0.045,and the average value of amplitude of this method was less than that(0.428±0.276)of lifting and thrusting method,and the twisting and rotating method was uniform and symmetrical,and there was difference between two kinds of acupuncture methods.The characteristics of computer graphics was used to qualify the work effect of lifting and thrusting,and reinforcing and reducing,which showed the heavy insertion and light lifting of RFLT,and showed heavy lifting and light insertion of RDLT,thus distinguished the two methods[(RFLT)and(RDLT)].Conclusions:The ultrasound imaging and computer graphics can be used to analyze the regularity of the common"reinforcing and reducing"method of acupuncture and moxibustion.

Objective To explore the clinical characteristics and treatment scheme of patients with spinal infection caused by Prevotella intermedia(P.intermedia).Methods Clinical diagnosis and treatment processes of a patient with spinal infection caused by P.intermedia admitted to the spinal surgery department of a hospital were summa-rized,and relevant literature was retrieved from database for reviewing.Results The patient,a 50 year old male,was admitted to the hospital due to"lumbago pain complicated with pain in double lower extremities for 2 months".The lesion tissue was taken for metagenomic next-generation sequencing(mNGS)detection,which detected P.in-termedia,and the patient was diagnosed with P.intermedia spondylitis.After treatments with open lesion clea-rance,tube rinsing+autologous bone transplantation fusion internal fixation,intravenous drip of ceftriaxone sodium and metronidazole,as well as metronidazole rinsing,infection was under control.A total of 16 available papers were retrieved,together with this case,a total of 17 patients were included,with 7 males and 10 females.The main risk factors were diabetes and history of corticosteroid use(35.3％).The most common invasion sites were lumbar ver-tebra(n=12)and thoracic vertebra(n=6).13 cases were positive for pathogen culture,3 cases were positive for molecular detection,and 1 case was positive for staining microscopy.17 patients received anti-anaerobic bacteria treatment,with 14 cases receiving combined surgical treatment.One case died,with a mortality of 5.9％;5 cases had partial neurological impairment,with a disability rate of 29.4％.The survival rate of patients who received treatment of anti-anaerobic bacteria combined with surgery was 92.8％,3 patients only with anti-anaerobic bacteria treatment but without surgery were all cured.Conclusion P.intermedia is an opportunistic pathogeanic bacteria which often causes infection in immunocomprised individuals and is prone to be misdiagnosed.It is recommended to perform mNGS detection to identify the pathogen as early as possible and seize the opportunity for treatment to reduce mortality.

Objective To study the factors influencing the blood concentration of caspofungin(CPFG),construct a prediction model,and validate the predictive effect of the model,so as to provide reference for the individualised dosing of patients with fungal infections in haematology.Methods Seventy-five patients admitted to the Department of Haematology,General Hospital of Tianjin Medical University,who were treated with CPFG for antifungal therapy during the period of March 2021 to June 2022 were selected as the study subjects,and CPFG blood concentration monitoring was carried out to explore the influencing factors of CPFG blood concentration and to construct a prediction model accordingly.Hosmer-Lemeshow(H-L)was used to test the goodness-of-fit of the model,and another 30 patients were selected as the verification group,and the predictive effect of the model was verified by the receiver's operating characteristics(ROC)curve.Results The mean blood concentrations of the patients at 0.5,9 and 24 h were(12.54±4.38),(6.80±2.76),(4.13±2.16)μg·mL-1,and the mean AUC0-24h were(152.05±57.60)μg·mL-1·h.AUC0-24h was lower than the reference value(98 μg·mL-1·h)in two patients.The results of correlation analysis showed that gender showed a correlation with 0.5 h blood concentration(P＜0.05),and there was no correlation with the rest of the two time points blood concentration and AUC0-24h(P＞0.05).Body weight and albumin(Alb)concentration showed correlation with 0.5,9,24 h blood drug concentration and AUC0-24 h(P＜0.05),and the rest of the indicators showed no correlation with blood drug concentration and AUC0_24h at each time point(P＞0.05).The results of multifactorial analysis showed that the factors influencing the patients'0.5 h blood concentration were gender,Alb concentration and body weight,and the factors influencing the 9 and 24 h blood concentration and AUC0-24h were Alb concentration and body weight(P＜0.05).Correlation analysis showed that the daily dose was positively correlated with the plasma concentration of CPFG at 0.5,9 and 24 h and AUC0-24h(P＜0.05).The results of multivariate analysis showed that the daily dose was also one of the influencing factors of the plasma concentration of CPFG(P＜0.05).ROC curve shows that the model has good prediction ability.Conclusion Body weight and Alb are significantly associated with CPFG blood concentrations and area under the drug-time curve,which can be used as a basis for preventive risk avoidance.

To investigate the associations of serum gamma-glutamyl transferase (GGT) levels with the risk of cardiovascular disease (CVD) and its subtypes in patients with type 2 diabetes mellitus (T2DM) in Jiangsu Province.Methods:The participants were enrolled in the Comprehensive Research project regarding 'Prevention and Control of Diabetes' in Jiangsu Province. The baseline survey was conducted from 2013 to 2014, and follow-up until December 31, 2021. After excluding the participants who self-reported with chronic liver disease/stroke/coronary heart disease at baseline survey and those with incomplete information on GGT, a total of 16 147 T2DM patients were included in the final analysis. Cox proportional hazard regression models were used to calculate the hazard ratio ( HR) and their 95% CI of GGT for CVD, myocardial infarction, and stroke. Restricted cubic spline models were applied to analyze the dose-response relationship between GGT and the risk of CVD and its subtypes. Results:During the median follow-up time of 8.02 years, 2 860 CVD cases were registered, including 196 cases of myocardial infarction and 2 730 cases of stroke. Multivariate Cox proportional risk regression model indicated that compared to the lowest serum GGT level group, the highest GGT level group had a 24% increased risk of CVD ( HR=1.24, 95% CI: 1.09-1.41) and a 23% increased risk of stroke ( HR=1.23, 95% CI: 1.08-1.40). The restricted cubic spline model showed a nonlinear dose-response relationship between GGT and the risk of CVD, myocardial infarction, and stroke in T2DM patients. Conclusions:High levels of GGT may be associated with an increased risk of cardiovascular disease in T2DM patients, which needs further exploration and validation in future clinical practice.

Objective To establish an early prediction model for the diagnosis of severe acute pancreatitis based on the improved machine learning models,and to analyze its clinical value.Methods A case-control study was conducted on 352 patients with acute pancreatitis admitted to the Gastroenterology and Hepatobiliary Surgery Departments of the Army Medical Center of PLA and Emergency and Critical Care Medicine Department of No.945 Hospital of Joint Logistics Support Force of PLA from January 2014 to August 2023.According to the severity of the disease,the patients were divided into the severe group(n=88)and the non-severe group(n=264).The RUSBoost model and improved Archimead optimization algorithm was used to analyze 39 routine laboratory biochemical indicators within 48 h after admission to construct an early diagnosis and prediction model for severe acute pancreatitis.The task of feature screening and hyperparameter optimization was completed simultaneously.The ReliefF algorithm feature importance rank and multivariate logistic analysis were used to analyze the value of the selected features.Results In the training set,the area under curve(AUC)of the improved machine learning model was 0.922.In the testing set,the AUC of the improved machine learning model reached 0.888.The 4 key features of predicting severe acute pancreatitis based on the improved Archimedes optimization algorithm were C-reactive protein,blood chlorine,blood magnesium and fibrinogen level,which were consistent with the results of ReliefF algorithm feature importance ranking and multivariate logistic analysis.Conclusion The application of improved machine learning model analyzing the laboratory examination results can help to early predict the occurrence of severe acute pancreatitis.