Construction of a 3-year risk prediction model for developing diabetes in patients with pre-diabetes

IntroductionTo analyze the influencing factors for progression from newly diagnosed prediabetes (PreDM) to diabetes within 3 years and establish a prediction model to assess the 3-year risk of developing diabetes in patients with PreDM.MethodsSubjects who were diagnosed with new-onset PreDM at the Physical Examination Center of the First Affiliated Hospital of Soochow University from October 1, 2015 to May 31, 2023 and completed the 3-year follow-up were selected as the study population. Data on gender, age, body mass index (BMI), waist circumference, etc. were collected. After 3 years of follow-up, subjects were divided into a diabetes group and a non-diabetes group. Baseline data between the two groups were compared. A prediction model based on logistic regression was established with nomogram drawn. The calibration was also depicted.ResultsComparison between diabetes group and non-diabetes group: Differences in 24 indicators including gender, age, history of hypertension, fatty liver, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, fasting blood glucose, HbA1c, etc. were statistically significant between the two groups (P<0.05). Differences in smoking, creatinine and platelet count were not statistically significant between the two groups (P>0.05). Logistic regression analysis showed that ageing, elevated BMI, male gender, high fasting blood glucose, increased LDL-C, fatty liver, liver dysfunction were risk factors for progression from PreDM to diabetes within 3 years (P<0.05), while HDL-C was a protective factor (P<0.05). The derived formula was: In(p/1-p)=0.181×age (40-54 years old)/0.973×age (55-74 years old)/1.868×age (≥75 years old)-0.192×gender (male)+0.151×blood glucose-0.538×BMI (24-28)-0.538×BMI (≥28)-0.109×HDL-C+0.021×LDL-C+0.365×fatty liver (yes)+0.444×liver dysfunction (yes)-10.038. The AUC of the model for predicting progression from PreDM to diabetes within 3 years was 0.787, indicating good predictive ability of the model.ConclusionsThe risk prediction model for developing diabetes within 3 years in patients with PreDM constructed based on 8 influencing factors including age, BMI, gender, fasting blood glucose, LDL-C, HDL-C, fatty liver and liver dysfunction showed good discrimination and calibration

The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method

Congestive heart failure (CHF) is a cardiovascular disease associated with autonomic dysfunction, where sympathovagal imbalance was reported in many studies using heart rate variability (HRV). To learn more about the dynamic interaction in the autonomic nervous system (ANS), we explored the directed interaction between the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS) with the help of transfer entropy (TE). This article included 24-h RR interval signals of 54 healthy subjects (31 males and 23 females, 61.38 ± 11.63 years old) and 44 CHF subjects (8 males and 2 females, 19 subjects’ gender were unknown, 55.51 ± 11.44 years old, 4 in class I, 8 in class II and 32 in class III~IV, according to the New York Heart Association Function Classification), obtained from the PhysioNet database and then segmented into 5-min non-overlapping epochs using cubic spline interpolation. For each segment in the normal group and CHF group, frequency-domain features included low-frequency (LF) power, high-frequency (HF) power and LF/HF ratio were extracted as classical estimators of autonomic activity. In the nonlinear domain, TE between LF and HF were calculated to quantify the information exchanging between SNS and PNS. Compared with the normal group, an extreme decrease in LF/HF ratio (p = 0.000) and extreme increases in both TE(LF→HF) (p = 0.000) and TE(HF→LF) (p = 0.000) in the CHF group were observed. Moreover, both in normal and CHF groups, TE(LF→HF) was a lot greater than TE(HF→LF) (p = 0.000), revealing that TE was able to distinguish the difference in the amount of directed information transfer among ANS. Extracted features were further applied in discriminating CHF using IBM SPSS Statistics discriminant analysis. The combination of the LF/HF ratio, TE(LF→HF) and TE(HF→LF) reached the highest screening accuracy (83.7%). Our results suggested that TE could serve as a complement to traditional index LF/HF in CHF screening

A SVM-Based Text Classification System for Knowledge Organization Method of Crop Cultivation

Part 1: Decision Support Systems, Intelligent Systems and Artificial Intelligence ApplicationsInternational audienceThe organization of crop cultivation practices is still far from completion, and Web Resources are not used adequately. This paper proposed a method, based on SVM, to organize the knowledge of crop cultivation practices efficiently from Web Resources. The knowledge organization method of crop cultivation was proposed with Good Agricultural Practices (GAP) in the application of the crop cultivation practices. It is that how to organize the existing crop cultivation knowledge, according to the requirements of crop cultivation practices. It mainly includes a text classification method and a search strategy on the knowledge of crop cultivation. For the text classification method, it used a text classification method based on SVM Decision Tree; for the search strategy, it used a strategy, organized by Ontology and custom knowledge bases. The experiment shows that performance of the proposed text classification method and the knowledge organization method with wheat, is workable and feasible

Use of Mutual Information and Transfer Entropy to Assess Interaction between Parasympathetic and Sympathetic Activities of Nervous System from HRV

Obstructive sleep apnea (OSA) is a common sleep disorder that often associates with reduced heart rate variability (HRV) indicating autonomic dysfunction. HRV is mainly composed of high frequency components attributed to parasympathetic activity and low frequency components attributed to sympathetic activity. Although, time domain and frequency domain features of HRV have been used to sleep studies, the complex interaction between nonlinear independent frequency components with OSA is less known. This study included 30 electrocardiogram recordings (20 OSA patient recording and 10 healthy subjects) with apnea or normal label in 1-min segment. All segments were divided into three groups: N-N group (normal segments of normal subjects), P-N group (normal segments of OSA subjects) and P-OSA group (apnea segments of OSA subjects). Frequency domain indices and interaction indices were extracted from segmented RR intervals. Frequency domain indices included nuLF, nuHF, and LF/HF ratio; interaction indices included mutual information (MI) and transfer entropy (TE (H→L) and TE (L→H)). Our results demonstrated that LF/HF ratio was significant higher in P-OSA group than N-N group and P-N group. MI was significantly larger in P-OSA group than P-N group. TE (H→L) and TE (L→H) showed a significant decrease in P-OSA group, compared to P-N group and N-N group. TE (H→L) were significantly negative correlation with LF/HF ratio in P-N group (r = −0.789, p = 0.000) and P-OSA group (r = −0.661, p = 0.002). Our results indicated that MI and TE is powerful tools to evaluate sympathovagal modulation in OSA. Moreover, sympathovagal modulation is more imbalance in OSA patients while suffering from apnea event compared to free event

The carbon emissions risk evolution and low-carbon optimization in a typical mountainous region on the western edge of the Sichuan Basin, China

Land use and cover change (LUCC) is a major driver of this rapid increase in atmospheric carbon. To reasonably plan various types of land use areas and mitigate the rate of growth of carbon emissions, this study takes Yaan, Sichuan province, as a case study. First, the calculation of Yaan's land use carbon emissions (LUCE) was approached by taking land use structure into account. Subsequently, the spatiotemporal distribution of LUCE was evaluated by employing the carbon emission risk index and the Moran index. Finally, the multi-objective linear programming (MOP), the Markov chain, and the PLUS model were used to predict the spatial distribution of LUCC in 2030, including natural development scenarios (NDS) and low-carbon optimization development scenarios (LODS). According to the findings, the impervious surface is identified as the principal contributor to LUCE, while the forest is recognized as the principal absorbers of carbon. The carbon emissions in typical mountainous areas are distributed in cities and generally concentrate towards the plains in the northeast direction. Under the LODS, LUCE decrease significantly. For both NDS and LODS, the overall trend of land development direction in Yaan is “northeast-southwest” from 2020 to 2030. These results could provide some suggestions for low-carbon land use in cities like Yaan

Achieving an excellent combination of strength and ductility in a single-phase metastable medium-entropy alloy

The development of robust alloys capable of maintaining high strength and ductility at cryogenic temperatures has been a long-sought goal, particularly for load-bearing applications in extremely low-temperature environments. In this study, we reported a newly developed face-centered-cubic (FCC) metastable (Ni0·3Co0·4Cr0.3)94Mo6 medium-entropy alloy (Mo-MEA) with an excellent synergy of strength and ductility at 77 K, surpassing the toughest equiatomic NiCoCr MEA. Compared to the equiatomic NiCoCr MEA, the Mo-MEA exhibited a substantial increase in yield strength by 68 % (from 407 to 685 MPa) and a decent enhancement of the ultimate tensile strength by 10 % (from 1289 to 1415 MPa), along with a marginal increase in elongation from 75 % to 78.45 %. Electron backscatter diffraction and transmission electron microscopy revealed the activation of multimodal deformation mechanisms, including dislocations, stacking faults, twinning, and FCC-to-hexagonal-close-packed phase transformation, during the tensile deformation at 77 K