A nomogram based on neuron-specific enolase and substantia nigra hyperechogenicity for identifying cognitive impairment in Parkinson's disease.

Background: One in four individuals with Parkinson's disease (PD) experience cognitive impairment (CI). However, few practical models integrating clinical and neuroimaging biomarkers have been developed to address CI in PD. This study aimed to evaluate the correlation between circulating neuron-specific enolase (NSE) levels, substantia nigra hyperechogenicity (SNH), and cognitive function in PD and to develop a nomogram based on clinical and neuroimaging biomarkers for predicting CI in patients with PD. Methods: A total of 385 patients with PD who underwent transcranial sonography (TCS) from January 2021 to December 2022 at Beijing Tiantan Hospital, Capital Medical University, were recruited as the training cohort. For validation, 165 patients with PD treated from January 2023 to December 2023 were enrolled. Data for SNH, plasma NSE, and other clinical measures were collected, and cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Logistic regression analysis was employed to select potential risk factors and establish a nomogram. The receiver operating characteristic curve and calibration curve were generated to evaluate the performance of the nomogram. Results: Patients with PD exhibiting CI displayed advanced age, elevated Unified PD Rating Scale-III (UPDRS-III) score, an increased percentage of SNH, higher levels of plasma NSE and homocysteine (Hcy), a larger SNH area, and lower education levels compared to PD patients without CI. Gender [odds ratio (OR) =0.561, 95% confidence interval (CI): 0.330-0.954, P=0.03], age (OR =1.039; 95% CI: 1.011-1.066; P=0.005), education level (OR =0.892; 95% CI: 0.842-0.954; P<0.001), UPDRS-III scores (OR =1.026; 95% CI: 1.009-1.043; P=0.003), plasma NSE concentration (OR =1.562; 95% CI: 1.374-1.776; P<0.001), and SNH (OR =0.545; 95% CI: 0.330-0.902; P=0.02) were independent predictors of CI in patients with PD. A nomogram developed using these six factors yielded a moderate discrimination performance with an area under the curve (AUC) of 0.823 (95% CI 0.781-0.864; P<0.001). The calibration curve demonstrated acceptable agreement between predicted outcomes and actual values. Validation further confirmed the reliability of the nomogram, with an AUC of 0.864 (95% CI: 0.805-0.922; P<0.001). Conclusions: The level of NSE in plasma and the SNH assessed by TCS are associated with CI in patients with PD. The proposed nomogram has the potential to facilitate the detection of cognitive decline in individuals with PD.

Development and Validation of a Risk Nomogram Model for Predicting Constipation in Patients with Type 2 Diabetes Mellitus.

Purpose: Constipation is a common complication of diabetic patients, which has a negative impact on their own health. This study aims to establish and internally validate the risk nomogram of constipation in patients with type 2 diabetes mellitus (T2DM) and to test its predictive ability. Patients and Methods: This retrospective study included 746 patients with T2DM at two medical centers. Among the 746 patients with T2DM, 382 and 163 patients in the Beilun branch of the First Affiliated Hospital of Zhejiang University were enrolled in the training cohort and the validation cohort, respectively. A total of 201 patients in the First Affiliated Hospital of Nanchang University were enrolled in external validation cohorts. The nomogram was established by optimizing the predictive factors through univariate and multivariable logistic regression analysis. The prediction performance of the nomogram was measured by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and the decision curve analysis (DCA). Furthermore, its applicability was internally and independently validated. Results: Among the 16 clinicopathological features, five variables were selected to develop the prediction nomogram, including age, glycated hemoglobin (HbA1c), calcium, anxiety, and regular exercise. The nomogram revealed good discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.908 (95% CI = 0.865-0.950) in the training cohort, and 0.867 (95% CI = 0.790-0.944) and 0.816 (95% CI = 0.751-0.881) in the internal and external validation cohorts, respectively. The calibration curve presented a good agreement between the prediction by the nomogram and the actual observation. The DCA revealed that the nomogram had a high clinical application value. Conclusion: In this study, the nomogram for pretreatment risk management of constipation in patients with T2DM was developed which could help in making timely personalized clinical decisions for different risk populations.

In Situ Synthesis of Carbon Nanotube-Steel Slag Composite for Pb(II) and Cu(II) Removal from Aqueous Solution.

Methods and materials that effectively remove heavy metals, such as lead and copper, from wastewater are urgently needed. In this study, steel slag, a low-cost byproduct of steel manufacturing, was utilized as a substrate material for carbon nanotube (CNT) growth by chemical vapor deposition (CVD) to produce a new kind of efficient and low-cost absorbent without any pretreatment. The synthesis parameters of the developed CNT-steel slag composite (SS@CNTs) were optimized, and its adsorption capacities for Pb(II) and Cu(II) were evaluated. The results showed that the optimal growth time, synthesis temperature and acetylene flow rate were 45 min, 600 °C and 200 sccm (standard cubic centimeter per minute), respectively. The SS@CNTs composite had a high adsorption capacity with a maximum removal amount of 427.26 mg·g-1 for Pb(II) and 132.79 mg·g-1 for Cu(II). The adsorption proceeded rapidly during the first 15 min of adsorption and reached equilibrium at approximately 90 min. The adsorption processes were in accordance with the isotherms of the Langmuir model and the pseudo-second-order model, while the adsorption thermodynamics results indicated that the removal for both metals was an endothermic and spontaneous process. This study showed that compared with other adsorbent materials, the SS@CNTs composite is an efficient and low-cost adsorbent for heavy metals such as lead and copper.

Nomogram Model for Prediction of Portal Vein Thrombosis in Patients with Liver Cirrhosis After Splenectomy: A Retrospective Analysis of 2 Independent Cohorts.

BACKGROUND The aim of this study was to establish and validate an easy-to-use nomogram to predict portal vein thrombosis (PVT) in patients with cirrhosis after splenectomy and to test its predictive ability. MATERIAL AND METHODS This retrospective study included 315 patients with cirrhosis who underwent splenectomy at 2 high-volume medical centers. The least absolute shrinkage and selection operator (LASSO) regression method was used to select the predictors in the training cohort, and multivariable logistic regression analysis was performed to establish the predictive nomogram model. We determined the prediction value of the nomogram by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and decision curve analysis. Finally, the applicability of the nomogram was internally and independently validated. RESULTS The predictors of PVT included portal vein diameter, splenic vein diameter, body mass index, and platelet count. Based on the clinical and radiomic models, the nomogram had good predictive efficiency for predicting PVT in patients with cirrhosis after splenectomy, with an AUROC of 0.887 (0.856 in internal validation and 0.796 in independent validation). The decision curve analysis revealed that the nomogram had good clinical application value. CONCLUSIONS We successfully developed an easy-to-use nomogram to predict the probability of PVT in patients with cirrhosis after splenectomy. The nomogram can help clinicians make timely, individualized clinical decisions for PVT in patients with cirrhosis after splenectomy.

Investigation of the Match Relation between Steel Fiber and High-Strength Concrete Matrix in Reactive Powder Concrete.

This study investigated the strength and toughness of reactive powder concrete (RPC) made with various steel fiber lengths and concrete strengths. The results indicated that among RPC samples with strength of 150 MPa, RPC reinforced with long steel fibers had the highest compressive strength, peak strength, and toughness. Among the RPC samples with strength of 270 MPa, RPC reinforced with short steel fibers had the highest compressive strength, and peak strength, while RPC reinforced with medium-length steel fibers had the highest toughness. As a result of the higher bond adhesion between fibers and ultra-high-strength RPC matrix, long steel fibers were more effective for the reinforcement of RPC with strength of 150 MPa, while short steel fibers were more effective for the reinforcement of RPC with strength of 270 MPa.

Synthesis, Characterization and Hexavalent Chromium Adsorption Characteristics of Aluminum- and Sucrose-Incorporated Tobermorite.

Tobermorites were synthesized from the lime-quartz slurries with incorporations of aluminum and sucrose under hydrothermal conditions, and then used for adsorption of Cr(VI). The chemical components, and structural and morphological properties of tobermorite were characterized by X-ray diffraction (XRD), thermogravimetric-differential scanning calorimetry (TG-DSC), Fourier transform infrared spectroscopy (FT-IR), nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), X-ray photoelectron spectroscopic (XPS) and N2 adsorption-desorption measurements. The formation and crystallinity of tobermorite could be largely enhanced by adding 2.3 wt.% aluminum hydroxide or 13.3 wt.% sucrose. Sucrose also played a significantly positive role in increasing the surface area. The adsorption performances for Cr(VI) were tested using a batch method taking into account the effects of pH, the adsorption kinetics, and the adsorption isotherms. The adsorption capacities of the aluminum- and sucrose-incorporated tobermorites reached up to 31.65 mg/g and 28.92 mg/g, respectively. Thus, the synthesized tobermorites showed good adsorption properties for removal of Cr(VI), making this material a promising candidate for efficient bulk wastewater treatment.