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Abstract Background Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. Methods This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. Results A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. Conclusions In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.

Abstract Background Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years. Methods This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE (n = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness. Results The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit. Conclusion The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.

PurposeTo develop a predictive model using machine learning for levothyroxine (L-T4) dose selection in patients with differentiated thyroid cancer (DTC) after resection and radioactive iodine (RAI) therapy and to prospectively validate the accuracy of the model in two institutions.MethodsA total of 266 DTC patients who received RAI therapy after thyroidectomy and achieved target thyroid stimulating hormone (TSH) level were included in this retrospective study. Sixteen clinical and biochemical characteristics that could potentially influence the L-T4 dose were collected; Significant features correlated with L-T4 dose were selected using machine learning random forest method, and a total of eight regression models were established to assess their performance in prediction of L-T4 dose after RAI therapy; The optimal model was validated through a two-center prospective study (n=263).ResultsSix significant clinical and biochemical features were selected, including body surface area (BSA), weight, hemoglobin (HB), height, body mass index (BMI), and age. Cross-validation showed that the support vector regression (SVR) model was with the highest accuracy (53.4%) for prediction of L-T4 dose among the established eight models. In the two-center prospective validation study, a total of 263 patients were included. The TSH targeting rate based on constructed SVR model were dramatically higher than that based on empirical administration (Rate 1 (first rate): 52.09% (137/263) vs 10.53% (28/266); Rate 2 (cumulative rate): 85.55% (225/263) vs 53.38% (142/266)). Furthermore, the model significantly shortens the time (days) to achieve target TSH level (62.61 ± 58.78 vs 115.50 ± 71.40).ConclusionsThe constructed SVR model can effectively predict the L-T4 dose for postoperative DTC after RAI therapy, thus shortening the time to achieve TSH target level and improving the quality of life for DTC patients.

In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.

Abstract Background The purpose of this study was to explore the relationship between quantitative epicardial adipose tissue (EAT) based on coronary computed tomography angiography (CCTA) and coronary slow flow (CSF). Methods A total of 85 patients with 0.05). Multivariate logistic regression analysis showed that EAT volume was independently related to CSF [odds ratio (OR) = 4.82, 95% confidence interval (CI): 3.06–7.27, P

Abstract Background Studies on coronary slow flow are receiving increasing attention, but objective evaluations are still lacking. The purpose of this study was to visualize the current status and research hotspots of coronary slow flow through bibliometric analysis. Methods All relevant publications on coronary slow flow from 2003 to 2022 were extracted from the Web of Science Core Collection database and analyzed by VOSviewer and CiteSpace visualization software. Year of publication, journal, country/region, institution, and first author of each paper, as well as research hotspots were identified. Results A total of 913 publications were retrieved. The journal with the most publications was Coronary Artery Disease. The country/region with the most publications was Turkey, followed by China and the United States. The institution with the largest publication volume was Turkey Specialized Higher Education Research Hospital. The author with the largest publication volume was Chun-Yan Ma from China. Keyword analysis indicated that “treatment and prognosis”, “pathogenesis and risk factors” and “diagnosis” were the clustering centers of coronary slow flow, and the research hotspots gradually changed with time, from pathogenesis to treatment and prognosis. Conclusion Future research will focus on the search for effective and non-invasive detection indicators and treatments of coronary slow flow. Collaboration needs to be enhanced between different institutions or countries/regions, which would improve clinical outcomes for patients with coronary slow flow.

Under the background of the increasing demand for germanium in high-tech, this paper finds out the mechanism of silicon germanium polymerization precipitation by simulating the leaching process of zinc oxide dust containing germanium. By comparing and analyzing the simulation experiments under ultrasonic and conventional conditions, the mechanism of efficient dispersion and depolymerization of germanium silicon precipitation induced by ultrasonic was found out. The results show that in the leaching process of germanium-containing zinc oxide dust, due to the increase of local pH, some silicon ions in the solution will hydrolyze and polymerize to form silicic acid colloid to adsorb germanium in the solution, causing the loss of germanium 14.81%, resulting in low recovery rate of germanium industry. Ultrasonic wave was introduced in the leaching process, and the shock wave and micro-jet released by ultrasonic cavitation continuously impacted the surface of polysilicate colloid. The germanium adsorbed on the surface of the silicic acid colloid is desorbed, and the large particles of silica gel are depolymerized into small particles of silicic acid, which reduces the adsorption of silica gel on germanium. The loss of germanium under ultrasonic conditions can be reduced by 59.35%. This study can effectively realize the efficient recovery of germanium in zinc oxide dust, and provide process and theoretical basis for the sustainable development of germanium industry.

In recent years, lithium-ion batteries using nickel-cobalt-manganese ternary materials as cathode materials have advantages of good electrical performance, high specific energy, green environmental protection, low cost, and high discharge stability. These have been widely used in new energy vehicles and portable electronics product areas. As a new generation of rechargeable batteries, lithium batteries have a certain service life, which is generally 3–5 years. Therefore, the rapid development of lithium-ion batteries has caused a blowout increase in the number of used lithium-ion batteries. Waste ternary lithium-ion batteries are very harmful to the environment and humans, but valuable metals such as lithium, nickel, cobalt, and manganese have a high recycling value. In terms of resource recycling and environmental protection, used lithium-ion batteries have a high recycling value. At present, the recycling of waste lithium-ion batteries is facing some problems. For example, the diversity of electrode materials makes their separation and purification difficult, and the high cost can also cause some problems such as secondary pollution. Therefore, it is necessary to find green and low-cost methods for the recycling of waste lithium-ion batteries. Microwave metallurgy has outstanding advantages in this respect. Therefore, this paper used the mechanically crushed ternary lithium battery as the raw material for studying the dielectric properties of the apparent density of the positive electrode material at room temperature and the microwave dielectric properties and absorption that change with temperature. Results show that at room temperature, the cathode material has the best dielectric performance at an apparent density of 1.484 g·cm–3. During the heating process, the cathode material has good microwave absorption performance at 25–700 ℃. At 400 ℃, the dielectric constant \begin{document}$ {\textit{ε}}_{\rm{r}}^{'} $\end{document} reaches the maximum value of 11.96 F·M–1. With the increase in the microwave power, the time for the positive electrode powder to rise to 700 °C is significantly shortened, and the maximum heating rate is in the range of 320–450 °C. The changing trend of the dielectric properties is consistent with the changing trend of the microwave heating characteristics.

Abstract Background The ideal treatment strategy for stable three-vessel coronary artery disease (CAD) patients are difficult to determine and for patients undergoing conservative treatment, imaging evidence of coronary atherosclerotic severity progression remains limited. Epicardial fat volume (EFV) on coronary CT angiography (CCTA) has been considered to be associated with coronary atherosclerosis. Therefore, this study aims to evaluate the relationship between EFV level and coronary atherosclerosis severity in three-vessel CAD. Methods This retrospective study enrolled 252 consecutive patients with three-vessel CAD and 252 normal control group participants who underwent CCTA between January 2018 and December 2019. A semi-automatic method was developed for EFV quantification on CCTA images, standardized by body surface area. Coronary atherosclerosis severity was evaluated and scored by the number of coronary arteries with ≥ 50% stenosis on coronary angiography. Patients were subdivided into groups on the basis of lesion severity: mild (score = 3 vessels, n = 85), moderate (3.5 vessels ≤ score

In order to reduce postoperative complications, it is required that the puncture needle should not pass through the lung lobe without tumor as far as possible in lung biopsy surgery. Therefore, it is necessary to accurately segment the lung lobe on the lung CT images. This paper proposed an automatic lung lobe segmentation method on lung CT images. Considering the boundary of the lung lobe is difficult to be identified, our lung lobe segmentation network is designed to be a multi-stage cascade network based on edge enhancement. In the first stage, the anatomical features of the lung lobe are extracted based on the generative adversarial network (GAN), and the lung lobe boundary is Gaussian smoothed to generate the boundary response map. In the second stage, the CT images and the boundary response map are used as input, and the dense connection blocks are used to realize deep feature extraction, and finally five lung lobes are segmented. The experiments indicated that the average value of Dice coefficient is 0.9741, which meets the clinical needs.

The corrosion failure of steel is one of the key problems to shorten the service life of power facilities in some strong corrosive environments， such as marine and chemical plants.To address this problem， the proposition of substituting steel in power transmission and transformation equipment with high-strength 7xxx series aluminum alloys was introduced.Accelerated indoor simulation tests were conducted， using a 5%NaCl salt spray corrosion environment to emulate marine atmospheric conditions.A comparative analysis was performed on the mechanical property degradation and change law of surface corrosion morphology of Q345 steel for substation facilities and 7075 aluminum alloy after varying corrosion durations (7，21，42 d) by integrating tensile tests and microscopic structural observations.Further， employing corrosion weight loss methods and computer simulation techniques， the mechanical properties of the two materials under identical corrosion conditions and loads were comparatively studied.Results indicated that the mechanical properties of both materials tended to decline with the extension of salt spray corrosion time under 5%NaCl salt spray corrosion environment， moreover， the highest tensile strength attenuation rate of Q345 steel (24.58%) was 8 times that of 7075 aluminum alloy (3.00%).Both materials displayed a corrosion rate trend that initially increased and then decreased， with the average corrosion rate of Q345 steel [0.03 g/(m2·d)] being approximately 15 times that of the 7075 aluminum alloy [0.002 g/(m2·d)].At the same corrosion cycle， the corrosion pits in Q345 steel were larger and deeper than those in the 7075 aluminum alloy， resulting in a significantly higher degree of stress concentration caused by corrosion pits in Q345 steel under the same external load compared to 7075 aluminum alloy.Consequently， in theory，7075 aluminum alloy could replace Q345 steel as the base material for power facility components.

Objective: This study aimed to investigate the influence of degree of polymerization of amylose on the digestibility characteristics of its ternary complexes. Methods: Ternary complexes of seedless breadfruit starch, prepared by separately mixing amyloses extracted from five cultivars (GJS, FSS, SMS, SS and CS) with amylopectin extracted from GJS, having the highest degree of polymerization among amylopectins from these cultviars, at a 1:1 ratio were prepared with lauric acid (LA) and β-lactoglobulin (β-LG) and characterized for supramolecular structure and digestion kinetics by high-performance size-exclusion chromatography (HPSEC), X-ray diffraction (XRD), small-angle X-ray scattering (SAXS), differential scanning calorimetry (DSC), atomic force microscopy (AFM), and Fourier transform infrared (FTIR) spectroscopy. All five complex samples displayed obvious characteristic peaks of ternary complexes, V-type crystalline structure and rodlike molecular configuration. As the degree of polymerization of amylose increased, the complexing index (CI) (from 68.81% to 83.55%), relative crystallinity (from 21.10% to 28.35%), short-range order (from 0.44 to 0.63), molecular mass (from 2.90 × 106 to 8.02 × 106 Da), molecular density index (from 3.50 to 23.07 g/(mol·nm3), gelation temperature (from 98.52 to 103.71 ℃), and gelation enthalpy (from 13.95 to 18.73 J/g), second-order kinetic rate constant (from 1.00 × 10-2 to 2.92 × 10-2 min-1) increased, while the semicrystalline lamellae thickness (from 3.20 to 4.82 nm), mass fractal dimension (from 1.28 to 1.81), characteristic length of crystallite unit (from 6.33 to 7.01 Å), radius of gyration (from 70.31 to 93.94 nm), and nanosurface “blocklet” size (from 7.05 to 15.03 nm) dropped, which led to a decrease of in vitro glycemic index (from 78.83 to 85.71). Statistical analysis showed that the degree of polymerization of amylose could remarkably affect the multiscale superstructure of the ternary complexes, which in turn could change its digestion kinetics. The results provide new theoretical guidance for the production of starch-based multicomponent foods.