Soil salinization is a global problem that limits agricultural productivity and sustainable development. As waste‐derived soil amendments, biochar and organic fertilizer have garnered considerable attention for their ability to improve soil physicochemical properties and contribution to agricultural waste resource recovery. However, comparable data on the effects of biochar and organic fertilizers on the physicochemical properties of saline‐alkali soils are lacking. Therefore, we applied biochar (B1: 5 t ha−1 year−1; B2: 10 t ha−1 year−1; and B3: 20 t ha−1 year−1) and organic fertilizer (OF1: 7.5 t ha−1 year−1 and OF2: 10 t ha−1 year−1) to saline‐alkali soil in the Yellow River Delta (YRD), China, continuously for 3 years. Because of the influence of their application on soil fertility and water‐salt status, maize yield increased by 55.01–62.51% and 15.01–26.67% for the biochar and organic fertilizer treated soils, respectively. Biochar and organic fertilizer increased soil water content, Ca2+, Mg2+, total phosphorus, available phosphorus, total nitrogen,NO3−‐N,NH4+‐N, organic matter, and microbial biomass carbon and nitrogen, while decreasing the sodium adsorption ratio and soil pH. Compared with CK, Na+ and soil salt content were reduced by 3.83–8.16% and 2.45–12.08%, respectively, under biochar treatments and increased by 2.19–5.34% and 12.95–20.02%, respectively, under organic fertilizer treatments. Principal component analysis showed that biochar was more effective than organic fertilizer in increasing SWC and reducing salinity and Na+. Based on the evidence of this study, biochar presents an eco‐friendly agricultural strategy for improving saline‐alkali soils and increasing maize yield in the YRD.
BackgroundThis prospective study aimed to compare clinical effects of intramedullary nailing guided by digital and conventional technologies in treatment of tibial fractures.Material/MethodsThirty-two patients (mean age 43 years, 18 males and 14 females) who were treated for tibial fractures from October 2010 to October 2012 were enrolled. They were sequentially randomized to receive intramedullary nailing guided by either digital technology (digital group, n=16) or conventional technology (conventional group, n=16). The operation time, fluoroscopy times, fracture healing time, distance between the actual and planned insertion point, postoperative lower limb alignment, and functional recovery were recorded for all patients.ResultsThe mean operation time in the digital group was 43.1±6.2 min compared with 48.7±8.3 min for the conventional technology (P=0.039). The fluoroscopy times and distance between the actual and planned insertion point were significantly lower in the digital group than in the conventional group (both P<0.001). The accuracy rate of the insertion point was 99.12% by digital technology. No difference was found in fracture healing time and good postoperative lower limb alignment between the digital and conventional groups (P=0.083 and P=0.310), as well as the effective rate (100% vs. 87.50%, P=0.144).ConclusionsIntramedullary nailing guided by digital technology has many advantages in treatment of tibial fractures compared to conventional technology, including shorter operation time, reduced fluoroscopy times, and decreased distance between the actual and planned insertion point of the intramedullary nail.
Abstract. The side-to-side difference in bone mineral content and soft tissue composition of extremities and their associations have been observed in patients with stroke and the results are inconsistent. The aim of the present study was to investigate the interaction between bone mineral content (BMC), lean mass (LM) and fat mass (FM) in the paretic extremities in patients following stroke and to determine the effectiveness of electrical muscle stimulation (EMS) following sciatic neurectomy (SN) in rats. BMC, LM and FM were measured by dual-energy X-ray absorptiometry in 61 hemiplegic patients following stroke. In the rat model study, groups of 10 Sprague-Dawley rats were divided into EMS and non-EMS subgroups. Myostatin expression and tetracycline interlabel width were measured. There were significant decreases in BMC, LM and FM in paretic limbs compared to non-paretic limbs. Compared to non-EMS, downregulated myostatin mRNA, and upregulated mechano growth factor (MGF) and insulin-like growth factor 1 (IGF-1) mRNA expression levels were observed in the EMS subgroup (P<0.05). In conclusion, muscle may have an important role in maintaining BMC. EMS-induced muscle contraction effectively downregulated myostatin mRNA, upregulated MGF and IGF-1 mRNA expression in muscle fiber, and mitigated amyotrophy and cortical bone loss from SN. IntroductionThere is a higher risk of bone fracture in patients with stroke compared to the healthy controls. A significant reason for this is the decreased use of paretic extremities, which induced disuse amyotrophy and bone loss (1). In a few previous studies the associations of bone mineral content (BMC) and soft tissue composition of the extremities were observed in patients with stroke and the results were inconsistent (2,3). One of the reasons was that different methodologies for rehabilitation were used.There is a hypothesis that skeletal muscle should not be only treated as a locomotorium, but also as an endocrine organ (4,5). Increasing research over the past 20 years has demonstrated that muscle is a source of myokines that can influence muscle and bone growth in positive or negative ways. Myostatin, insulin-like growth factor 1 (IGF-1) and mechano growth factor (MGF) are three significant myokines, which are produced, expressed and released by muscle fibers following external stimulation, and have their own roles in mediating bone and muscle metabolism by exerting either paracrine or endocrine effects (6-9).The present study was divided to two sections; the clinical research and animal experiment. The clinical research was designed to investigate the association between BMC, lean mass (LM) and fat mass (FM) in the hemiplegic extremities in patients following a stroke, while the animal experiment was designed to determine the effectiveness of electrical muscle stimulation (EMS) to the hindlimbs following sciatic neurectomy (SN) in attenuating disuse amyotrophy and cortical bone loss in Sprague-Dawley female rats by regulating the myostatin, IGF-1, and MGF mRNA or protein expre...
Background The absolute number of femoral neck fractures (FNFs) is increasing; however, the prediction of traumatic femoral head necrosis remains difficult. Machine learning algorithms have the potential to be superior to traditional prediction methods for the prediction of traumatic femoral head necrosis. Objective The aim of this study is to use machine learning to construct a model for the analysis of risk factors and prediction of osteonecrosis of the femoral head (ONFH) in patients with FNF after internal fixation. Methods We retrospectively collected preoperative, intraoperative, and postoperative clinical data of patients with FNF in 4 hospitals in Shanghai and followed up the patients for more than 2.5 years. A total of 259 patients with 43 variables were included in the study. The data were randomly divided into a training set (181/259, 69.8%) and a validation set (78/259, 30.1%). External data (n=376) were obtained from a retrospective cohort study of patients with FNF in 3 other hospitals. Least absolute shrinkage and selection operator regression and the support vector machine algorithm were used for variable selection. Logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost) were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model, and the external data were used to compare and evaluate the model performance. We compared the accuracy, discrimination, and calibration of the models to identify the best machine learning algorithm for predicting ONFH. Shapley additive explanations and local interpretable model-agnostic explanations were used to determine the interpretability of the black box model. Results A total of 11 variables were selected for the models. The XGBoost model performed best on the validation set and external data. The accuracy, sensitivity, and area under the receiver operating characteristic curve of the model on the validation set were 0.987, 0.929, and 0.992, respectively. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the model on the external data were 0.907, 0.807, 0.935, and 0.933, respectively, and the log-loss was 0.279. The calibration curve demonstrated good agreement between the predicted probability and actual risk. The interpretability of the features and individual predictions were realized using the Shapley additive explanations and local interpretable model-agnostic explanations algorithms. In addition, the XGBoost model was translated into a self-made web-based risk calculator to estimate an individual’s probability of ONFH. Conclusions Machine learning performs well in predicting ONFH after internal fixation of FNF. The 6-variable XGBoost model predicted the risk of ONFH well and had good generalization ability on the external data, which can be used for the clinical prediction of ONFH after internal fixation of FNF.
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