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Abstract Background The prevalence and severity of pediatric Mycoplasma pneumoniae pneumonia (MPP) poses a significant threat to the health and lives of children. In this study, we aim to systematically evaluate the value of routine blood parameters in predicting MPP and develop a robust and generalizable ensemble artificial intelligence (AI) model to assist in identifying patients with MPP. Methods We collected 27 features, including routine blood parameters and hs-CRP levels, from patients admitted to The Affiliated Dazu’s Hospital of Chongqing Medical University with or without MPP between January, 2023 and January, 2024. A classification model was built using seven machine learning (ML) algorithms to develop an integrated prediction tool for diagnosing MPP. It was evaluated on both an internal validation set (982 individuals) and an external validation set (195 individuals). The primary outcome measured the accuracy of the model in predicting MPP. Results The GBDT is state-of-the-art based on 27 features. Following inter-laboratory cohort testing, the GBDT demonstrated an AUC, accuracy, specificity, sensitivity, PPV, NPV, and F1-score of 0.980 (0.938–0.995), 0.928 (0.796–0.970), 0.929 (0.717-1.000), 0.926 (0.889–0.956), 0.922 (0.727-1.000), 0.937 (0.884–0.963), and 0.923 (0.800-0.966) in stratified 10-fold cross-validation. A GBDT-based AI Lab was developed to facilitate the healthcare providers in remote and impoverished areas. Conclusions The GBDT-based AI Lab tool, with high sensitivity and specificity, could help discriminate between pediatric MPP infection and non-MPP infection based on routine blood parameters. Moreover, a user-friendly webpage tool for AI Lab could facilitate healthcare providers in remote and impoverished areas where advanced technologies are not accessible.

The aim of this study was to address the problems of water scarcity and low water and fertilizer use efficiency in vineyards in the Guanzhong Plain of Shaanxi, China. In the experiment, drip fertigation technology was used, including three irrigation levels (W3 (100% M, and M as the irrigation quota), W2 (75% M) and W1 (50% M)) and four fertilization levels (F3 (648 kg/hm2), F2 (486 kg/hm2), F1 (324 kg/hm2) and F0 (0 kg/hm2)). Traditional fertilization (CG) and a rainfed (CK) treatments were used as control treatments. Field experiments were conducted in Yangling, Shaanxi, China from 2019 to 2021 (wet years in 2019 and 2020, and dry years in 2021) to determine irrigation and fertilization intervals corresponding to higher crop yields and water and fertilizer utilization efficiency in different precipitation patterns. The results showed that, compared with the CK and CG treatments, the drip irrigation and fertilization treatment increased grape yield and significantly improved water productivity and fertilizer agronomic use efficiency. The highest grape yields were obtained in the F2 treatment under drip fertigation, and the highest grape yields were 16044 kg/hm2 and 12728 kg/hm2 in wet years and dry years obtained at W1F2 and W2F2, respectively. With the same fertilization level (F2), the grape yields of the W1, W2 and W3 treatments in wet years increased by 37.95%, 23.73% and 23.50% compared with those in dry years, respectively. The optimal irrigation and fertilization intervals determined by the response surface method were: the irrigation amount ranged from 8 to 11 mm and 32–38 mm, nitrogen application ranged from 182 to 190 kg/hm2 and 162–164 kg/hm2, phosphorus application ranged from 116 to 131 kg/hm2 and 98–100 kg/hm2, and potassium application ranged from 216 to 225 kg/hm2 and 194–201 kg/hm2 in wet years and dry years, respectively. This provides a guideline for the precision management of irrigation and fertilization of grape under different precipitation year types in Guanzhong Plain of Shaanxi Province.

Repairing defects in alveolar bone is essential for regenerating periodontal tissue, but it is a formidable challenge. One promising therapeutic approach involves using a strategy that specifically recruits periodontal ligament cells (PDLCs) with high regenerative potential to achieve in situ regeneration of alveolar bone. In this study, we have created a new type of microsphere conjugated with an antibody to target p75 neurotrophin receptor (p75NTR), which is made of nano-hydroxyapatite (nHA) and chitosan (CS). The goal of this design is to attract p75NTR+hPDLCs selectively and promote osteogenesis. In vitro experiments demonstrated that the antibody-conjugated microspheres attracted significantly more PDLCs compared to non-conjugated microspheres. Incorporating nHA not only enhances cell adhesion and proliferation on the surface of the microsphere but also augments its osteoinductive properties. Microspheres effectively recruited p75NTR+ cells at bone defect sites in SD rats, as observed through immunofluorescent staining of p75NTR antibodies. This p75NTR antibody-conjugated nHA/CS microsphere presents a promising approach for selectively recruiting cells and repairing bone defects.

The circadian clock plays a critical role in dentomaxillofacial development. Tooth biomineralization is characterized by the circadian clock; however, the mechanisms underlying the coordination of circadian rhythms with tooth development and biomineralization remain unclear. The p75 neurotrophin receptor (p75NTR) is a clock factor that regulates the oscillatory components of the circadian rhythm. This study aims to investigate the impact of p75NTR on the rhythmic mineralization of teeth and elucidate its underlying molecular mechanisms. We generated p75NTR knockout mice to examine the effects of p75NTR deficiency on tooth mineralization. Ectomesenchymal stem cells (EMSCs), derived from mouse tooth germs, were used for in vitro experiments. Results showed a reduction in tooth mineral density and daily mineralization rate in p75NTR knockout mice. Deletion of p75NTR decreased the expression of DMP1, DSPP, RUNX2, and ALP in tooth germ. Odontogenic differentiation and mineralization of EMSCs were activated by p75NTR. Histological results demonstrated predominant detection of p75NTR protein in odontoblasts and stratum intermedium cells during rapid formation phases of dental hard tissue. The mRNA expression of p75NTR exhibited circadian variations in tooth germs and EMSCs, consistent with the expression patterns of the core clock genes Bmal1 and Clock. The upregulation of BMAL1/CLOCK expression by p75NTR positively regulated the mineralization ability of EMSCs, whereas BMAL1 and CLOCK exerted a negative feedback regulation on p75NTR by inhibiting its promoter activity. Our findings suggest that p75NTR is necessary to maintain normal tooth biomineralization. Odontogenic differentiation and mineralization of EMSCs is regulated by the p75NTR-BMAL1/CLOCK signaling axis. These findings offer valuable insights into the associations between circadian rhythms, tooth development, and biomineralization.

As the main expenditure item in water balance, evapotranspiration has an important impact on the surface ecosystem. Assessing the impact of changes in meteorological elements on evapotranspiration is essential to identify the spatiotemporal heterogeneity of hydrographic responses to climate changes. Based on the actual evapotranspiration (ETa) product (GPR-ET) generated by Gaussian process regression (GPR), as well as temperature and precipitation datasets, our study employed various statistical analysis methods, including geographic detector, the center of gravity migration model, spatial variation coefficients, and partial differential models, to investigate the spatiotemporal variation in ETa in China from 2000 to 2018. The analysis covered future trends in ETa changes and the contribution of meteorological factors. Our results showed that the ETa in northwest China had stronger spatial heterogeneity and the mean value was generally lower than that in the southeast. But the center of gravity of ETa was shifting towards the northwest. In most areas, the future trend was expected to be inconsistent with the current stage. ETa in the regions of north and west was mainly driven by precipitation, while its increase in southeast China was largely attributed to temperature. In addition to spatial variations, the joint enhancement effect of temperature and precipitation on ETa exists. According to the contribution analysis, precipitation contributed more to the change in ETa than temperature. These findings have enhanced our comprehension of the contribution of climate variability to ETa changes, providing scientific proof for the optimization apportion of future water resources.

The rapid and accurate acquisition of nitrogen, phosphorus and potassium nutrient contents in grape leaves is critical for improving grape yields and quality and for industrial development. In this study, crop growth was non-destructively monitored based on unmanned aerial vehicle (UAV) remote sensing technology. Three irrigation levels (W1, W2 and W3) and four fertilization levels (F3, F2, F1 and F0) were set in this study, and drip irrigation fertilization treatments adopted a complete block design. A correlation analysis was conducted using UAV multispectral image data obtained from 2019 to 2021 and the field-measured leaf nitrogen content (LNC), leaf potassium content (LKC) and leaf phosphorus content (LPC) values; from the results, the vegetation indices (VIs) that were sensitive to LNC, LKC and LPC were determined. By combining spectral indices with partial least squares (PLS), random forest (RF), support vector machine (SVM) and extreme learning machine (ELM) machine-learning algorithms, prediction models were established. Finally, the optimal combinations of spectral variables and machine learning models for predicting LNC, LPC and LKC in each grape growth period were determined. The results showed that: (1) there were high demands for nitrogen during the new shoot growth and flowering periods, potassium was the main nutrient absorbed in the fruit expansion period, and phosphorus was the main nutrient absorbed in the veraison and maturity periods; (2) combining multiple spectral variables with the RF, SVM and ELM models could result in improved LNC, LPC and LKC predictions. The optimal prediction model determination coefficient (R2) derived during the new shoot growth period was above 0.65, and that obtained during the other growth periods was above 0.75. The relative root mean square error (RRMSE) of the above models was below 0.20, and the Willmott consistency index (WIA) was above 0.88. In conclusion, UAV multispectral images have good application effects when predicting nutrient contents in grape leaves. This study can provide technical support for accurate vineyard nutrient management using UAV platforms.

Understanding variations in sap flow rates and the environmental factors that influence sap flow is important for exploring grape water consumption patterns and developing reasonable greenhouse irrigation schedules. Three irrigation levels were established in this study: adequate irrigation (W1), moderate deficit irrigation (W2) and deficit irrigation (W3). Grape sap flow estimation models were constructed using partial least squares (PLS) and random forest (RF) algorithms, and the simulation accuracy and stability of these models were evaluated. The results showed that the daily mean sap flow rates in the W2 and W3 treatments were 14.65 and 46.94% lower, respectively, than those in the W1 treatment, indicating that the average daily sap flow rate increased gradually with an increase in the irrigation amount within a certain range. Based on model error and uncertainty analyses, the RF model had better simulation results in the different grape growth stages than the PLS model did. The coefficient of determination and Willmott’s index of agreement for RF model exceeded 0.78 and 0.90, respectively, and this model had smaller root mean square error and d-factor (evaluation index of model uncertainty) values than the PLS model did, indicating that the RF model had higher prediction accuracy and was more stable. The relative importance of the model predictors was determined. Moreover, the RF model more comprehensively reflected the influence of meteorological factors and the moisture content in different soil layers on the sap flow rate than the PLS model did. In summary, the RF model accurately simulated sap flow rates, which is important for greenhouse grape irrigation.