Gaussian Modelling Characteristics of Peripheral Arterial Pulse: Difference between Measurements from the Three Trimesters of Healthy Pregnancy

Blood Glucose Prediction from Nutrition Analytics in Type 1 Diabetes: A Review

INTRODUCTION

Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components.

METHOD

A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy.

RESULTS

The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31-60 min in 34%, 61-90 min in 11%, 91-120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%).

CONCLUSION

The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance.

Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy

Hypertensive disorder in pregnancy (HDP) remains a major health burden, and it is associated with systemic cardiovascular adaptation. The pulse wave is an important basis for evaluating the status of the human cardiovascular system. This research aims to evaluate the application value of pulse waves in the diagnosis of hypertensive disorder in pregnancy.This research a retrospective study of pregnant women who attended prenatal care and labored at Beijing Haidian District Maternal and Child Health Hospital. We extracted maternal hemodynamic factors and measured the pulse wave of the pregnant women. We developed an HDP predictive model by using support vector machine algorithms at five-gestational-week stages.At five-gestational-week stages, the area under the receiver operating characteristic curve (AUC) of the predictive model with pulse wave parameters was higher than that of the predictive model with hemodynamic factors. The AUC values of the predictive model with pulse wave parameters were 0.77 (95% CI 0.64 to 0.9), 0.83 (95% CI 0.77 to 0.9), 0.85 (95% CI 0.81 to 0.9), 0.93 (95% CI 0.9 to 0.96) and 0.88 (95% CI 0.8 to 0.95) at five-gestational-week stages, respectively. Compared to the predictive models with hemodynamic factors, the predictive model with pulse wave parameters had better prediction effects on HDP.Pulse waves had good predictive effects for HDP and provided appropriate guidance and a basis for non-invasive detection of HDP.

Entropy Information of Pulse Dynamics in Three Stages of Pregnancy

The aim of the present study is to use entropy to explore the change of pulse generated by normal pregnant women with gestational. Firstly, the subjects were divided into early (E), middle (M), and late (L) three stages according to gestational age. Then, pulse signals of the Chi position of 90 pregnant women at different gestational ages were collected. Secondly, the four entropies, namely fuzzy entropy (FuEn), approximate entropy (ApEn), sample entropy (SamEn), and permutation entropy (PerEn), were applied to the analysis of the long-term pulse changes of the pregnancy. Finally, the related information about pulse in different stages of pregnancy is given by the analysis of four kinds of entropy. Furthermore, the statistical tests are conducted for further comparison, and the descriptive statistics and the results are presented. In addition, boxplots are employed to show the distribution of four entropies of pregnancy. This work has studied the changes in pulse during pregnancy from quantitative and qualitative aspects. Our results show that entropy improves the diagnostic value of pulse analysis during pregnancy and could be applied to facilitate noninvasive diagnosis of pregnant women's physiological signals in the future.

Avicenna and clinical experiences in Canon of Medicine

Avicenna used his medical knowledge and experience of scientists from different nations to create a new style in medicine. For this reason, his textbook, Canon of Medicine, has been considered a medical reference in all universities worldwide for centuries. In this article, some valuable and interesting diagnostic and therapeutic clinical experiences mentioned in the Canon of Medicine are described in five sections. This research was conducted to review Avicenna’s specific clinical observations and interventions in PubMed, Google Scholar, and Scopus databases using the keywords “Avicenna” and “Canon of Medicine”. In this article, we presented several examples of diagnostic and therapeutic clinical experiences mentioned in the Canon of Medicine in 5 areas, including semiology, therapeutic strategy, urology, neurology, obstetrics, and gynecology. Canon of Medicine, as a complete medical series containing the medical experiences from different nations and Iranian medical scientists, has influenced the world’s medical knowledge for several centuries. Some of Avicenna’s clinical and experimental views can be useful from both a historical point of view and new research.

CHANGES IN GAUSSIAN MODELING PARAMETERS OF PPG PULSE DURING HEALTHY PREGNANCY

We used Gaussian modeling to depict the changes in finger photoplethysmographic (PPG) pulse during pregnancy in healthy women. We enrolled 70 healthy pregnant women and recorded their PPG pulses in 11–13 gestational weeks, 20–22 gestational weeks, and 37–39 gestational weeks. Three independent positive Gaussian functions were utilized to decompose the pulses, and each Gaussian function extracted three key parameters: the peak amplitude ([Formula: see text]), the peak position ([Formula: see text]), and the half-width ([Formula: see text]). The method of ANOVA and post-hoc multiple comparisons of mathematical statistics were utilized to study the differences of these parameters between the three trimesters. We found that in the first trimester [Formula: see text] increased significantly ([Formula: see text]: [Formula: see text] versus [Formula: see text], [Formula: see text]). [Formula: see text] and [Formula: see text] increased in the first trimester ([Formula: see text]: [Formula: see text] versus [Formula: see text], [Formula: see text]; [Formula: see text]: [Formula: see text] versus [Formula: see text], [Formula: see text]), then decreased significantly ([Formula: see text]: [Formula: see text] versus [Formula: see text], [Formula: see text]: [Formula: see text] versus [Formula: see text], [Formula: see text]). [Formula: see text] is associated with cardiac output, and [Formula: see text] and [Formula: see text] are associated with peripheral vascular resistance. The results of this study were consistent with the conclusion that healthy pregnant women exhibited high flow state of the cardiovascular system and their peripheral vascular resistance decreased first and then gradually recovered during pregnancy. This study indicated that PPG pulse could also reflect the changes in the maternal cardiovascular system during pregnancy.