A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study.

BACKGROUND: Recently, patients with type 2 diabetes mellitus (T2DM) have experienced a higher incidence and severer degree of vascular calcification (VC), which leads to an increase in the incidence and mortality of vascular complications in patients with T2DM. HYPOTHESIS: To construct and validate prediction models for the risk of VC in patients with T2DM. METHODS: Twenty-three baseline demographic and clinical characteristics were extracted from the electronic medical record system. Ten clinical features were screened with least absolute shrinkage and selection operator method and were used to develop prediction models based on eight machine learning (ML) algorithms (k-nearest neighbor [k-NN], light gradient boosting machine, logistic regression [LR], multilayer perception [(MLP], Naive Bayes [NB], random forest [RF], support vector machine [SVM], XGBoost [XGB]). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and precision. RESULTS: A total of 1407 and 352 patients were retrospectively collected in the training and test sets, respectively. Among the eight models, the AUC value in the NB model was higher than the other models (NB: 0.753, LGB: 0.719, LR: 0.749, MLP: 0.715, RF: 0.722, SVM: 0.689, XGB:0.707, p < .05 for all). The k-NN model achieved the highest sensitivity of 0.75 (95% confidence interval [CI]: 0.633-0.857), the MLP model achieved the highest accuracy of 0.81 (95% CI: 0.767-0.852) and specificity of 0.875 (95% CI: 0.836-0.912). CONCLUSIONS: This study developed a predictive model of VC based on ML and clinical features in type 2 diabetic patients. The NB model is a tool with potential to facilitate clinicians in identifying VC in high-risk patients.

[Value of CT Quantitative Parameters in Prediction of Pathological Types
of Lung Ground Glass Nodules].

BACKGROUND: The pathological types of lung ground glass nodules (GGNs) show great significance to the clinical treatment. This study was aimed to predict pathological types of GGNs based on computed tomography (CT) quantitative parameters. METHODS: 389 GGNs confirmed by postoperative pathology were selected, including 138 cases of precursor glandular lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], 109 cases of microinvasive adenocarcinoma (MIA) and 142 cases of invasive adenocarcinoma (IAC). The morphological characteristics of nodules were evaluated subjectively by radiologist, as well as artificial intelligence (AI). RESULTS: In the subjective CT signs, the maximum diameter of nodule and the frequency of spiculation, lobulation and pleural traction increased from AAH+AIS, MIA to IAC. In the AI quantitative parameters, parameters related to size and CT value, proportion of solid component, energy and entropy increased from AAH+AIS, MIA to IAC. There was no significant difference between AI quantitative parameters and the subjective CT signs for distinguishing the pathological types of GGNs. CONCLUSIONS: AI quantitative parameters were valuable in distinguishing the pathological types of GGNs.

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.

Predicting the efficacy of non-steroidal anti-inflammatory drugs in migraine using deep learning and three-dimensional T1-weighted images.

Deep learning (DL) models based on individual images could contribute to tailored therapies and personalized treatment strategies. We aimed to construct a DL model using individual 3D structural images for predicting the efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in migraine. A 3D convolutional neural network model was constructed, with ResNet18 as the classification backbone, to link structural images to predict the efficacy of NSAIDs. In total, 111 patients were included and allocated to the training and testing sets in a 4:1 ratio. The prediction accuracies of the ResNet34, ResNet50, ResNeXt50, DenseNet121, and 3D ResNet18 models were 0.65, 0.74, 0.65, 0.70, and 0.78, respectively. This model, based on individual 3D structural images, demonstrated better predictive performance in comparison to conventional models. Our study highlights the feasibility of the DL algorithm based on brain structural images and suggests that it can be applied to predict the efficacy of NSAIDs in migraine treatment.

An MRI-based machine learning radiomics can predict short-term response to neoadjuvant chemotherapy in patients with cervical squamous cell carcinoma: A multicenter study.

BACKGROUND AND PURPOSE: Neoadjuvant chemotherapy (NACT) has become an essential component of the comprehensive treatment of cervical squamous cell carcinoma (CSCC). However, not all patients respond to chemotherapy due to individual differences in sensitivity and tolerance to chemotherapy drugs. Therefore, accurately predicting the sensitivity of CSCC patients to NACT was vital for individual chemotherapy. This study aims to construct a machine learning radiomics model based on magnetic resonance imaging (MRI) to assess its efficacy in predicting NACT susceptibility among CSCC patients. METHODS: This study included 234 patients with CSCC from two hospitals, who were divided into a training set (n = 180), a testing set (n = 20), and an external validation set (n = 34). Manual radiomic features were extracted from transverse section MRI images, and feature selection was performed using the recursive feature elimination (RFE) method. A prediction model was then generated using three machine learning algorithms, namely logistic regression, random forest, and support vector machines (SVM), for predicting NACT susceptibility. The model's performance was assessed based on the area under the receiver operating characteristic curve (AUC), accuracy, and sensitivity. RESULTS: The SVM approach achieves the highest scores on both the testing set and the external validation set. In the testing set and external validation set, the AUC of the model was 0.88 and 0.764, and the accuracy was 0.90 and 0.853, the sensitivity was 0.93 and 0.962, respectively. CONCLUSIONS: Machine learning radiomics models based on MRI images have achieved satisfactory performance in predicting the sensitivity of NACT in CSCC patients with high accuracy and robustness, which has great significance for the treatment and personalized medicine of CSCC patients.

Facile preparation of PEGylated polyethylenimine polymers as vaccine carriers with reduced cytotoxicity and enhanced Interleukin-2 (IL-2) production.

Developing low-cost, easy-to-prepare, biocompatible and highly efficient vaccine carriers is a promising approach to realize practical cancer immunotherapy. In this study, through facile modification of mPEG5k-4-toluenesulfonate (mPEG5k-OTs) on PEI25k under mild conditions, a series of "stealth" mPEG5k-PEI25k polymers (PP1, PP2 and PP3) were prepared, their structures and physicochemical properties were characterized and theoretically analyzed. The polymers could bind/load ovalbumin (OVA) to form mPEG5k-PEI25k/OVA complexes as negatively charged nanoparticles with small hydrodynamic particle size (80-210 nm) and narrow size distribution. Compared to PEI25k/OVA, lower cytotoxicity could be achieved on mPEG5k-PEI25k/OVA complexes in dendritic cells (DCs). In DCs-RF 33.70 T-cells co-culture system, the mPEG5k-PEI25k/OVA complexes could bring about higher IL-2 production /secretion than that of PEI25k/OVA, notably, the optimum IL-2 secretion could reach 9.3-folds of the PEI25k/OVA under serum condition (10% FBS). Moreover, the cell biological features could be optimized by selecting suitable mPEG5k-grafting ratios and/or mPEG5k-PEI25k/OVA weight ratios. Intracellular imaging results showed that the mPEG5k-PEI25k(PP3)/Rhodamine-OVA complexes mainly localized inside lysosomes. Taken together, this work provided a facile method to prepare "stealth" PEGylated-PEI25k polymers with reduced cytotoxicity, promoted OVA cross-presentation efficiency and improved serum compatibility towards cancer immunotherapy.

Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters.

Radiomics has been a promising imaging biomarker for many malignant diseases. We developed a novel radiomics strategy that incorporating radiomics features extracted from dual-view mammograms and clinical parameters for identifying benign and malignant breast lesions, and validated whether the radiomics assessment could improve the accurate diagnosis of breast cancer. A total of 380 patients (mean age, 52 ± 7 years) with 621 breast lesions utilizing mammograms on craniocaudal (CC) and mediolateral oblique (MLO) views were randomly allocated into the training (n = 486) and testing (n = 135) sets in this retrospective study. A total of 1184 and 2368 radiomics features were extracted from single-position region of interest (ROI) and position-paired ROI, separately. Clinical parameters were then combined for better prediction. Recursive feature elimination and least absolute shrinkage and selection operator methods were applied to select optimal predictive features. Random forest was used to conduct the predictive model. Intraclass correlation coefficient test was used to assess repeatability and reproducibility of features. After preprocessing, 467 radiomics features and clinical parameters remained in the single-view and dual-view models. The performance and significance of models were quantified by the area under the curve (AUC), sensitivity, specificity, and accuracy. The correlation analysis between variables was evaluated using the correlation ratio and Pearson correlation coefficient. The model using a combination of dual-view radiomics and clinical parameters achieved a favorable performance (AUC: 0.804, 95% CI: 0.668-0.916), outperformed single-view model and model without clinical parameters. Incorporating with radiomics features of dual-view (CC&MLO) mammogram, age, breast density, and type of suspicious lesions can provide a noninvasive approach to evaluate the malignancy of breast lesions and facilitate clinical decision-making.

Polydatin administration attenuates the severe sublesional bone loss in mice with chronic spinal cord injury.

BACKGROUND: Spinal cord injury (SCI) is often accompanied by rapid and extensive bone mineral loss below the lesion level, and there is currently no gold standard for treatment. Evidence suggests that polydatin (PLD) may help promote osteogenic differentiation and exhibit anti-osteoporotic activity. However, whether PLD could reverse substantial bone loss in SCI patients, especially those with protracted injury, and the underlying regulatory mechanism have not been investigated. STUDY DESIGN: Male C57BL/6J mice were subjected to either contusion SCI or laminectomy at the T8-9 level. Eight weeks after SCI, PLD (40 mg/kg/day) or vehicle was administrated to the mice via the intragastric route for consecutive eight weeks. Blood was collected after the treatment regimen, and the tibiae and femora were removed. Bone marrow stromal cells were isolated from the long bones for ex vivo osteoblastogenesis and osteoclastogenesis assays. RESULTS: Chronic SCI led to a rapid and significant decrease in bone mineral density (BMD) of the distal femur and proximal tibia, resulting in structural deterioration of the bone tissues. Treatment with PLD largely restored BMD and bone structure. In addition, static histo-morphometric analysis revealed that PLD enhanced bone formation and inhibited bone resorption in vivo. PLD also promoted osteoblastogenesis and inhibited osteoclastogenesis ex vivo, which was accompanied by increased OPG/RANKL ratio, and reduced expression levels of CTR, TRAP, NFATc1 and c-Fos. However, PLD had no marked effect on serum 25(OH)D levels and VDR protein expression, although it did significantly lower serum and femoral malondialdehyde levels, inhibited expression level of matrix metallopeptidase 9 (MMP9), upregulated skeletal Wnt3a, Lrp5 and ctnnb1 mRNAs, and increased ß-catenin protein expression. CONCLUSIONS: PLD protected mice with chronic SCI against sublesional bone loss by modulating genes involved the differentiation and activity of osteoclasts and osteoblasts, abating oxidative stress and MMP activity, and restoring the Wnt/ß-catenin signaling pathway.

A Single-Phase High-Impedance Ground Faulty Feeder Detection Method for Small Resistance to Ground Systems Based on Current-Voltage Phase Difference.

At present, the small resistance to ground system (SRGS) is mainly protected by fixed-time zero-sequence overcurrent protection, but its ability to detect transition resistance is only about 100 Ω, which is unable to detect single-phase high resistance grounding fault (SPHIF). This paper analyzes the zero-sequence characteristics of SPHIF for SRGS and proposes a SPHIF feeder detection method that uses the current-voltage phase difference. The proposed method is as follows: first, the zero-sequence current phase of each feeder is calculated. Second, the phase voltage root mean square (RMS) value is used to determine the fault phase and obtain its initial phase as the reference value. The introduction of the initial phase of the fault phase voltage can highlight the fault characteristics and improve the sensitivity and reliability of feeder detection, and then CVPD is the difference between each feeder ZSC phase and the reference value. Finally, the magnitude of CVPD is judged. If the CVPD of a particular feeder meets the condition, the feeder is detected as the faulted feeder. Combining the theoretical and practical constraints, the specific adjustment principle and feeder detection logic are given. A large number of simulations show that the proposed method can be successfully detected under the conditions of 5000 Ω transition resistance, -1 dB noise interference, and 40% data missing. Compared with existing methods, the proposed method uses phase voltages that are easy to measure to construct SPHIF feeder detection criteria, without adding additional measurement and communication devices, and can quickly achieve local isolation of SPHIF with better sensitivity, reliability, and immunity to interference.

Polydatin Attenuates OGD/R-Induced Neuronal Injury and Spinal Cord Ischemia/Reperfusion Injury by Protecting Mitochondrial Function via Nrf2/ARE Signaling Pathway.

Spinal cord ischemia/reperfusion injury (SCII) is a devastating complication of spinal or thoracic surgical procedures and can lead to paraplegia or quadriplegia. Neuronal cell damage involving mitochondrial dysfunction plays an important role in the pathogenesis of SCII. Despite the availability of various treatment options, there are currently no mitochondria-targeting drugs that have proven effective against SCII. Polydatin (PD), a glucoside of resveratrol, is known to preserve mitochondrial function in central nervous system (CNS) diseases. The aim of the present study was to explore the neuro- and mito-protective functions of PD and its underlying mechanisms. An in vitro model of SCII was established by exposing spinal cord motor neurons (SMNs) to oxygen-glucose-deprivation/reperfusion (OGD/R), and the cells were treated with different dosages of PD for varying durations. PD improved neuronal viability and protected against OGD/R-induced apoptosis and mitochondrial injury in a dose-dependent manner. In addition, PD restored the activity of neuronal mitochondria in terms of mitochondrial membrane potential (MMP), intracellular calcium levels, mitochondrial permeability transition pore (mPTP) opening, generation of reactive oxygen species (ROS), and adenosine triphosphate (ATP) levels. Mechanistically, PD downregulated Keap1 and upregulated Nrf2, NQO-1, and HO-1 in the OGD/R-treated SMNs. Likewise, PD treatment also reversed the neuronal and mitochondrial damage induced by SCII in a mouse model. Furthermore, the protective effects of PD were partially blocked by the Nrf2 inhibitor. Taken together, PD relieves mitochondrial dysfunction-induced neuronal cell damage by activating the Nrf2/ARE pathway and is a suitable therapeutic option for SCII.