Search

Objective: Perihematomal edema (PHE) is one of the significant secondary cerebral damages, with the blood–brain barrier's integrity playing a pivotal role in its progression. Strengthening tight junction (TJ) proteins enhances blood–brain barrier integrity, yet the complex genetics behind brain edema remain not fully understood. Our research endeavors to uncover pivotal genes and their roles in brain edema following cerebral hemorrhage, and to investigate potential treatment strategies. Methods: By analyzing intracerebral hemorrhage (ICH) and control samples using the GSE216607 and GSE206971 datasets, we identified differentially expressed genes. Cross-referencing with the KEGG database, we aligned these genes with those related to tight junctions. Extensive enrichment analysis and protein interactions were performed to examine the expression and clinical significance of the identified genes. Our study employed the C57BL/6J mouse ICH model and qRT-PCR for key gene validation. Results: Notably, VASP, HCLS1, MSN, and EZR, critical for tight junctions, showed increased expression post-ICH, emphasizing their significance in BBB upkeep and PHE progression. Drug validation indicated potential therapeutic effects of Testosterone enanthate, SELENIUM, and LY 294002 on tight junction-related genes. Conclusion: This study sheds light on the potential involvement of these genes in brain edema progression post-ICH, offering promising therapeutic targets. Further research is needed for deeper understanding.

Abstract Purpose To evaluate the predictive capabilities of MRI-based radiomics for detecting lymphovascular space invasion (LVSI) in patients diagnosed with endometrial carcinoma (EC). Materials and methods A retrospective analysis was conducted on 160 female patients diagnosed with EC. The radiomics model including T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) images was established. Additionally, a conventional MRI model, which incorporated MRI-reported FIGO stage, deep myometrial infiltration (DMI), adnexal involvement, and vaginal/parametrial involvement, was established. Finally, a combined model was created by integrating the radiomics signature and conventional MRI characteristics. The predictive performance was validated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. A stratified analysis was conducted to compare the differences between the three models by Delong test. Results In predicting LVSI, the radiomics model outperformed the clinical model in the training cohort (AUC: 0.899 vs. 0.8862) but not in the test cohort (AUC: 0.812 vs. 0.8758). The combined model demonstrated superior performance in both the training and test cohorts (training cohort: AUC = 0.934, 95% CI: 0.8807–0.9873; testing cohort: AUC = 0.905, 95% CI: 0.7679-1). Conclusions The combined model exhibited utility in preoperatively predicting LVSI in patients with EC, offering potential benefits for clinical decision-making.

Abstract Objective To develop an ensemble machine learning (eML) model using multiphase computed tomography (MPCT) for distinguishing between gastric ectopic pancreas (GEP) and gastric stromal tumors (GIST) in lesions

Abstract Background Proliferative hepatocellular carcinomas (HCCs) is a class of aggressive tumors with poor prognosis. We aimed to construct a computed tomography (CT)-based radiomics nomogram to predict proliferative HCC, stratify clinical outcomes and explore the tumor microenvironment. Methods Patients with pathologically diagnosed HCC following a hepatectomy were retrospectively collected from two medical centers. A CT-based radiomics nomogram incorporating radiomics model and clinicoradiological features to predict proliferative HCC was constructed using the training cohort (n = 184), and validated using an internal test cohort (n = 80) and an external test cohort (n = 89). The predictive performance of the nomogram for clinical outcomes was evaluated for HCC patients who underwent surgery (n = 201) or received transarterial chemoembolization (TACE, n = 104). RNA sequencing data and histological tissue slides from The Cancer Imaging Archive database were used to perform transcriptomics and pathomics analysis. Results The areas under the receiver operating characteristic curve of the radiomics nomogram to predict proliferative HCC were 0.84, 0.87, and 0.85 in the training, internal test, and external test cohorts, respectively. The radiomics nomogram could stratify early recurrence-free survivals in the surgery outcome cohort (hazard ratio [HR] = 2.25; P

Abstract Objectives Exploring the value of adding correlation analysis (radiomic features (RFs) of pelvic metastatic lymph nodes and primary lesions) to screen RFs of primary lesions in the feature selection process of establishing prediction model. Methods A total of 394 prostate cancer (PCa) patients (263 in the training group, 74 in the internal validation group and 57 in the external validation group) from two tertiary hospitals were included in the study. The cases with pelvic lymph node metastasis (PLNM) positive in the training group were diagnosed by biopsy or MRI with a short-axis diameter ≥ 1.5 cm, PLNM-negative cases in the training group and all cases in validation group were underwent both radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). The RFs of PLNM-negative lesion and PLNM-positive tissues including primary lesions and their metastatic lymph nodes (MLNs) in the training group were extracted from T2WI and apparent diffusion coefficient (ADC) map to build the following two models by fivefold cross-validation: the lesion model, established according to the primary lesion RFs selected by t tests and absolute shrinkage and selection operator (LASSO); the lesion-correlation model, established according to the primary lesion RFs selected by Pearson correlation analysis (RFs of primary lesions and their MLNs, correlation coefficient > 0.9), t test and LASSO. Finally, we compared the performance of these two models in predicting PLNM. Results The AUC and the DeLong test of AUC in the lesion model and lesion-correlation model were as follows: training groups (0.8053, 0.8466, p = 0.0002), internal validation group (0.7321, 0.8268, p = 0.0429), and external validation group (0.6445, 0.7874, p = 0.0431), respectively. Conclusion The lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.

Objective: It is important to discriminate different headaches in clinical practice, and neurocognitive biomarkers may serve as objective tools. Several reports have suggested potential cognitive impairment for primary headaches, whereas cognitions within specific domains remain elusive, e.g., emotional processing. In this study, we aimed to characterize processing of facial expressions in migraine and tension-type headache (TTH) by analyzing expression-related visual mismatch negativity (EMMN) and explored whether their processing patterns were distinct. Methods: Altogether, 73 headache patients (20 migraine with aura (MA), 28 migraine without aura (MwoA), 25 TTH) and 27 age-matched healthy controls were recruited. After a battery of mood/neuropsychological evaluations, an expression-related oddball paradigm containing multiple models of neutral, happy and sad faces was used to investigate automatic emotional processing. Results: We observed cognitive impairment in all headache patients, especially in attention/execution subdomains, but no discrepancy existed among different headaches. Although analyses of P1/N170 did not reach significant levels, amplitude of early and late EMMN was markedly diminished in MA and MwoA compared with controls and TTH, regardless of happy or sad expression. Moreover, sad EMMN was larger (more negative) than happy EMMN only in controls, while not in all headache groups. Conclusions: Our findings implied that migraine, rather than TTH, might lead to more severe impairment of automatic emotional processing, which was manifested as no observable EMMN elicitation and disappearance of negative bias effect. The EMMN component could assist in discrimination of migraine from TTH and diagnosis of undefined headaches, and its availability needed further validations.

Abstract Background The causal impact of lipid-lowering drugs on ovarian cancer (OC) and cervical cancer (CC) has received considerable attention, but its causal relationship is still a subject of debate. Hence, the objective of this study is to evaluate the impact of lipid-lowering medications on the occurrence risk of OC and CC through Mendelian randomization (MR) analysis of drug targets. Methods This investigation concentrated on the primary targets of lipid-lowering medications, specifically, 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) and proprotein convertase kexin 9 (PCSK9). Genetic variations associated with HMGCR and PCSK9 were derived from published genome-wide association study (GWAS) findings to serve as substitutes for HMGCR and PCSK9 inhibitors. Employing a MR approach, an analysis was conducted to scrutinize the impact of inhibitors targeting HMGCR and PCSK9 on the occurrence of OC and CC. Coronary heart disease (CHD) risk was utilized as a positive control, and the primary outcomes encompassed OC and CC. Results The findings of the study suggest a notable elevation in the risk of OC among patients treated with HMGCR inhibitors (OR [95%CI] = 1.815 [1.316, 2.315], p = 0.019). In contrast, no significant correlation was observed between PCSK9 inhibitors and the occurrence of OC. Additionally, the analysis did not reveal any noteworthy connection between HMGCR inhibitors, PCSK9 inhibitors, and CC. Conclusion HMGCR inhibitors significantly elevate the risk of OC in patients, but their mechanism needs further investigation, and no influence of PCSK9 inhibitors on OC has been observed. There is no significant relationship between HMGCR inhibitors, PCSK9 inhibitors, and CC.

Abstract Background The effect of nonalcoholic fatty liver disease (NAFLD) on major adverse cardiovascular events (MACEs) can be influenced by the degree of coronary artery stenosis. However, the association between the severity of NAFLD and MACEs in patients who underwent coronary computed tomography angiography (CCTA) is unclear. Methods A total of 341 NAFLD patients who underwent CCTA were enrolled. The severity of NAFLD was divided into mild NAFLD and moderate-severe NAFLD by abdominal CT results. The degree of coronary artery stenosis was evaluated by using Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Cox regression analysis and Kaplan–Meier analysis were used to assess poor prognosis. Results During the follow-up period, 45 of 341 NAFLD patients (13.20%) who underwent CCTA occurred MACEs. The severity of NAFLD (hazard ratio [HR] = 2.95[1.54–5.66]; p = 0.001) and CAD-RADS categories 3–5 (HR = 16.31[6.34–41.92]; p

Abstract Background We investigated the value of three-dimensional amide proton transfer-weighted imaging (3D-APTWI) in the diagnosis of early-stage breast cancer (BC) and its correlation with the immunohistochemical characteristics of malignant lesions. Methods Seventy-eight women underwent APTWI and dynamic contrast-enhanced (DCE)-MRI. Pathological results were categorized as either benign (n = 43) or malignant (n = 37) lesions. The parameters of APTWI and DCE-MRI were compared between the benign and malignant groups. The diagnostic value of 3D-APTWI was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) to establish a diagnostic threshold. Pearson’s correlation was used to analyze the correlation between the magnetization transfer asymmetry (MTRasym) and immunohistochemical characteristics. Results The MTRasym and time-to-peak of malignancies were significantly lower than those of benign lesions (all p < 0.010). The volume transfer constant, rate constant, and wash-in and wash-out rates of malignancies were all significantly greater than those of benign lesions (all p < 0.010). ROC-AUCs of 3D-APTWI, DCE-MRI, and 3D-APTWI+DCE to differential diagnosis between early-stage BC and benign lesions were 0.816, 0.745, and 0.858, respectively. Only the difference between AUCAPT+DCE and AUCDCE was significant (p < 0.010). When a threshold of MTRasym for malignancy for 2.42%, the sensitivity and specificity of 3D-APTWI for BC diagnosis were 86.5% and 67.6%, respectively; MTRasym was modestly positively correlated with pathological grade (r = 0.476, p = 0.003) and Ki-67 (r = 0.419, p = 0.020). Conclusions 3D-APTWI may be used as a supplementary method for patients with contraindications of DCE-MRI. MTRasym can imply the proliferation activities of early-stage BC. Relevance statement 3D-APTWI can be an alternative diagnostic method for patients with early-stage BC who are not suitable for contrast injection. Key points • 3D-APTWI reflects the changes in the microenvironment of early-stage breast cancer. • Combined 3D-APTWI is superior to DCE-MRI alone for early-stage breast cancer diagnosis. • 3D-APTWI improves the diagnostic accuracy of early-stage breast cancer. Graphical Abstract

Abstract Purpose To develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment. Material and methods A total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis. Results The radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p

Abstract Background The detection of local recurrence for prostate cancer (PCa) patients following radical prostatectomy (RP) is challenging and can influence the treatment plan. Our aim was to construct and verify machine learning models with three different algorithms based on post-operative mpMRI for predicting local recurrence of PCa after RP and explore their potential clinical value compared with the Prostate Imaging for Recurrence Reporting (PI-RR) score of expert-level radiologists. Methods A total of 176 patients were retrospectively enrolled and randomly divided into training (n = 123) and testing (n = 53) sets. The PI-RR assessments were performed by two expert-level radiologists with access to the operative histopathological and pre-surgical clinical results. The radiomics models to predict local recurrence were built by utilizing three different algorithms (i.e., support vector machine [SVM], linear discriminant analysis [LDA], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]). The combined model integrating radiomics features and PI-RR score was developed using the most effective classifier. The classification performances of the proposed models were assessed by receiver operating characteristic (ROC) curve analysis. Results There were no significant differences between the training and testing sets concerning age, prostate-specific antigen (PSA), Gleason score, T-stage, seminal vesicle invasion (SVI), perineural invasion (PNI), and positive surgical margins (PSM). The radiomics model based on LR-LASSO exhibited superior performance than other radiomics models, with an AUC of 0.858 in the testing set; the PI-RR yielded an AUC of 0.833, and there was no significant difference between the best radiomics model and the PI-RR score. The combined model achieved the best predictive performance with an AUC of 0.924, and a significant difference was observed between the combined model and PI-RR score. Conclusions Our radiomics model is an effective tool to predict PCa local recurrence after RP. By integrating radiomics features with the PI-RR score, our combined model exhibited significantly better predictive performance of local recurrence than expert-level radiologists’ PI-RR assessment.

Abstract Background To investigate whether CT-based radiomics can effectively differentiate between heterotopic pancreas (HP) and gastrointestinal stromal tumor (GIST), and whether different resampling methods can affect the model’s performance. Methods Multi-phase CT radiological data were retrospectively collected from 94 patients. Of these, 40 with HP and 54 with GISTs were enrolled between April 2017 and November 2021. One experienced radiologist manually delineated the volume of interest and then resampled the voxel size of the images to 0.5 × 0.5 × 0.5 mm3, 1 × 1 × 1 mm3, and 2 × 2 × 2 mm3, respectively. Radiomics features were extracted using PyRadiomics, resulting in 1218 features from each phase image. The datasets were randomly divided into training set (n = 66) and validation set (n = 28) at a 7:3 ratio. After applying multiple feature selection methods, the optimal features were screened. Radial basis kernel function-based support vector machine (RBF-SVM) was used as the classifier, and model performance was evaluated using the area under the receiver operating curve (AUC) analysis, as well as accuracy, sensitivity, and specificity. Results The combined phase model performed better than the other phase models, and the resampling method of 0.5 × 0.5 × 0.5 mm3 achieved the highest performance with an AUC of 0.953 (0.881-1), accuracy of 0.929, sensitivity of 0.938, and specificity of 0.917 in the validation set. The Delong test showed no significant difference in AUCs among the three resampling methods, with p > 0.05. Conclusions Radiomics can effectively differentiate between HP and GISTs on CT images, and the diagnostic performance of radiomics is minimally affected by different resampling methods.

Recently, the combination of convolutional neural network (CNN) and long short-term memory (LSTM) exhibits better performance than single network architecture. Most of these studies connect LSTM networks behind CNNs. When operating on hardware, the current design of CNN-LSTM is similar to a pipeline architecture. However, the classic structure lead to a feature loss when data is sent to LSTM since CNN is not good at extracting temporal features. At the same time, as the depth and scale increases, it will bring a huge amount of computation, which makes hardware implementation difficult. Based on that, a parallel CNN-LSTM architecture is proposed, in which two networks extract features from the input data synchronously, being proven to be more effective than classical CNN-LSTM. This paper designs a parallel CNN-LSTM computing device based on FPGA. The device is divided into control unit and operation unit. Control stream and data stream transport between the two units, ensuring the proper running of the device. A highly parallel multi-channel convolution layer and pooling layer are designed to improve the calculation efficiency. A 4-stage pipeline structure is adopted to implement the LSTM part. This paper makes full use of on-chip BRAM to design a look-up table for activation function approximation, reducing the resource consumption by 95% compared with the traditional polynomial approximation. Finally, we verify our device under cooperative spectrum sensing (CSS) and handwritten classification scenarios. Proposed device reaches higher accuracy in two scenarios compared with classic CNN-LSTM structure as well as faster calculating speed, and the overall project power is limited below 2W. The scalability and limitation of this computing device are also discussed.

Xanthoceras sorbifolium Bunge oil exhibits favorable characteristics such as excellent low temperature fluidity, high flash point, and degradability. However, it also suffers from drawbacks including low kinematic viscosity at low temperatures and significant loss of kinematic viscosity at high temperatures. In view of these characteristics of X. sorbifolium oil, it was modified in this work with a combination of ethyl cellulose (EC), montmorillonite (MMT), and cetyltrimethylammonium chloride (CTAB). The effects of temperature, time, and additive content on the rheological properties of lubricating oil were studied. The kinematic viscosity of the prepared lubricating oil reached up to 433 mm2/s at 40 °C and 51.1 mm2/s at 100 °C. The coagulation point could be reduced to -25 °C, the friction coefficient was 0.026, and the average wear spot diameter was 0.81 mm, The anti-friction performance was enhanced, and the anti-wear performance decreased somewhat. The prepared lubricating oil had the characteristics of high kinematic viscosity at high temperature and excellent rheological properties at low temperature. It also met the energy and environmental application requirements of a green, environmentally friendly, and biodegradable sustainable development strategy. This study greatly broadens the application range of lubricating oil.

The distorted spots induced by atmospheric turbulence significantly degrade the spot position detection accuracy of the quadrant detector (QD). In this paper, we utilize angular measurement and homogenization characteristics of non-imaging microlens array (NI-MLA) systems, effectively reducing the distortion degree of the spots received on the QD target surface, thereby significantly enhancing the spot detection accuracy of the QD. First, based on the principles of geometric optics and Fourier optics, it is proved that the NI-MLA system possesses the angular measurement characteristic (AMC) within the paraxial region while deriving and verifying the focal length of the system. Then, the QD computation curve characteristics of the system under non-turbulence are explored. This study further elucidates the mathematical principle of the NI-MLA system for mitigating the spot position detection random error of QD (SPDRE-QD) and discusses in depth the relationship between the NI-MLA system’s capability to mitigate the SPDRE-QD and the system’s parameters under various turbulence intensities. Finally, it is experimentally verified that the root-mean-square error (RMSE) of the QD computation values using the NI-MLA system are reduced by a significant improvement of at least 2.44 times and up to 17.36 times compared with that of the conventional optical system of QD (COS-QD) under turbulence conditions ranging from weak to strong.

Background: Coronary artery disease (CAD) in type 2 diabetes mellitus (T2DM) patients often presents diffuse lesions, with extensive calcification, and it is time-consuming to measure coronary artery calcium score (CACS). Objectives: To explore the predictive ability of deep learning (DL)-based CACS for obstructive CAD and hemodynamically significant CAD in T2DM. Methods: 469 T2DM patients suspected of CAD who accepted CACS scan and coronary CT angiography between January 2013 and December 2020 were enrolled. Obstructive CAD was defined as diameter stenosis ≥50%. Hemodynamically significant CAD was defined as CT-derived fractional flow reserve ≤0.8. CACS was calculated with a fully automated method based on DL algorithm. Logistic regression was applied to determine the independent predictors. The predictive performance was evaluated with area under receiver operating characteristic curve (AUC). Results: DL-CACS (adjusted odds ratio (OR): 1.005; 95% CI: 1.003–1.006; P ＜ 0.001) was significantly associated with obstructive CAD. DL-CACS (adjusted OR:1.003; 95% CI: 1.002–1.004; P ＜ 0.001) was also an independent predictor for hemodynamically significant CAD. The AUCs, sensitivities, specificities, positive predictive values and negative predictive values of DL-CACS for obstructive CAD and hemodynamically significant CAD were 0.753 (95% CI: 0.712–0.792), 75.9%, 66.5%, 74.8%, 67.8% and 0.769 (95% CI: 0.728–0.806), 80.7%, 62.1%, 59.6% and 82.3% respectively. It took 1.17 min to perform automated measurement of DL-CACS in total, which was significantly less than manual measurement of 1.73 min (P ＜ 0.001). Conclusions: DL-CACS, with less time-consuming, can accurately and effectively predict obstructive CAD and hemodynamically significant CAD in T2DM.

Summary: The increasing need for improved energy storage devices renders it particularly important that inexpensive electrodes with high capacitance, excellent cycling stability, and environment-friendly characteristics are developed. In this study, a wood-derived carbon@reduced graphene (WRG) conductive precursor with an average conductivity of 15.38 S/m was firstly synthesized. The binder-free WRG-MnO2 electrode was successfully constructed by growing MnO2 onto a WRG under hydrothermal conditions. The asymmetric supercapacitor assembled with the WRG-20MnO2 cathode exhibited excellent electrochemical capacitive behavior with a voltage window of 0–2 V, maximum energy density of 52.3 Wh kg−1, and maximum power density of 1642.7 W kg−1, which is mainly due to the distinctive icicle-shaped structure of the MnO2. Thus, a facile strategy for developing high-performance hierarchical porous carbon electrodes that can be used in supercapacitors was developed herein, which may provide new opportunities to improve the high added value of poplar wood.

Abstract Purpose This study aims to establish and validate a new diagnosis model called P.Z.A. score for clinically significant prostate cancer (csPCa). Methods The demographic and clinical characteristics of 956 patients were recorded. Age, prostate-specific antigen (PSA), free/total PSA (f/tPSA), PSA density (PSAD), peripheral zone volume ratio (PZ-ratio), and adjusted PSAD of PZ (aPSADPZ) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The nomogram was established, and discrimination abilities of the new nomogram were verified with a calibration curve and area under the ROC curve (AUC). The clinical benefits of P.Z.A. score were evaluated by decision curve analysis and clinical impact curves. External validation of the model using the validation set was also performed. Results The AUCs of aPSADPZ, age, PSA, f/tPSA, PSAD and PZ-ratio were 0.824, 0.672, 0.684, 0.715, 0.792 and 0.717, respectively. The optimal threshold of P.Z.A. score was 0.41. The nomogram displayed excellent net benefit and better overall calibration for predicting the occurrence of csPCa. In addition, the number of patients with csPCa predicted by P.Z.A. score was in good agreement with the actual number of patients with csPCa in the high-risk threshold. The validation set provided better validation of the model. Conclusion P.Z.A. score (including PIRADS(P), aPSADPZ(Z) and age(A)) can increase the detection rate of csPCa, which may decrease the risk of misdiagnosis and reduce the number of unnecessary biopsies. P.Z.A. score contains data that is easy to obtain and is worthy of clinical replication.

Objective: Ureteral lesions caused by impacted ureteral stones are likely to result in postoperative ureteral stricture. On this basis, the study aimed to investigate if dual-energy spectral computed tomography can predict ureteral hardening caused by impacted stones and to explore the relationship between different types of ureteral lesions and the risk of ureteral stricture. Methods: This prospective study collected data of 93 patients with impacted stones from hospital automation system during January 2018 to October 2019. They underwent an abdominal scan on a dual-energy spectral computed tomography. During surgery, the operator used ureteroscopy to identify ureteral lesions, which were classified into four categories: edema, polyps, pallor, and hardening. Seven months later, 90 patients were reviewed for the degree of hydronephrosis. Results: Endoscopic observations revealed 38 (41%) cases of ureteral edema, 20 (22%) cases of polyps, 13 (14%) cases of pallor, and 22 (24%) cases of hardening. There were significant differences in hydronephrosis, the period of impaction, the calcium concentration of the ureter, and the slope of the spectral Hounsfield unit curve between the four groups. After that, we evaluated the factors associated with ureteral hardening and found that the calcium concentration of the ureter and hydronephrosis remained independent predictors of ureteral hardening. Receiver operating characteristic curve analysis showed that 5.3 mg/cm³ calcium concentration of the ureter is an optimal cut-off value to predict ureteral hardening. The result of follow-up showed that 80 patients had complete remission of hydronephrosis, with a complete remission rate of 61.9% (13/21) in the hardening group and 97.1% (67/69) in the non-hardening group (p

Abstract Aldehyde dehydrogenase 3A1 (ALDH3A1) is an NAD+-dependent enzyme that is closely related to tumor development. However, its role in non-small-cell lung cancer (NSCLC) has not been elucidated. This study aimed to clarify the mechanism of ALDH3A1 and identify potential therapeutic targets for NSCLC. Here, for the first time, we found that ALDH3A1 expression could be induced by a hypoxic environment in NSCLC. ALDH3A1 was highly expressed in NSCLC tissue, especially in some late-stage patients, and was associated with a poor prognosis. In mechanistic terms, ALDH3A1 enhances glycolysis and suppresses oxidative phosphorylation (OXPHOS) to promote cell proliferation by activating the HIF-1α/LDHA pathway in NSCLC. In addition, the results showed that ALDH3A1 was a target of β-elemene. ALDH3A1 can be downregulated by β-elemene to inhibit glycolysis and enhance OXPHOS, thus suppressing NSCLC proliferation in vitro and in vivo. In conclusion, hypoxia-induced ALDH3A1 is related to the energy metabolic status of tumors and the efficacy of β-elemene, providing a new theoretical basis for better clinical applications in NSCLC.

This study aimed to investigate the effect of the transverse sinus (TS) stenosis (TSS) position caused by arachnoid granulation on patients with venous pulsatile tinnitus (VPT) and to further identify the types of TSS that are of therapeutic significance for patients. Multiphysics interaction models of six patients with moderate TSS caused by arachnoid granulation and virtual stent placement in TSS were reconstructed, including three patients with TSS located in the middle segment of the TS (group 1) and three patients with TTS in the middle and proximal involvement segment of the TS (group 2). The transient multiphysics interaction simulation method was applied to elucidate the differences in biomechanical and acoustic parameters between the two groups. The results revealed that the blood flow pattern at the TS and sigmoid sinus junction was significantly changed depending on the stenosis position. Preoperative patients had increased blood flow in the TSS region and TSS downstream where the blood flow impacted the vessel wall. In group 1, the postoperative blood flow pattern, average wall pressure, vessel wall vibration, and sound pressure level of the three patients were comparable to the preoperative state. However, the postoperative blood flow velocity decreased in group 2. The postoperative average wall pressure, vessel wall vibration, and sound pressure level of the three patients were significantly improved compared with the preoperative state. Intravascular intervention therapy should be considered for patients with moderate TSS caused by arachnoid granulations in the middle and proximal involvement segment of the TS. TSS might not be considered the cause of VPT symptoms in patients with moderate TSS caused by arachnoid granulation in the middle segment of the TS.

Abstract Background Differentiating between low-grade glioma and brain glial cell hyperplasia is crucial for the customized clinical treatment of patients. Objective Based on multiparametric MRI imaging and clinical risk factors, a radiomics-clinical model and nomogram were constructed for the distinction of brain glial cell hyperplasia from low-grade glioma. Methods Patients with brain glial cell hyperplasia and low-grade glioma who underwent surgery at the First Affiliated Hospital of Soochow University from March 2016 to March 2022 were retrospectively included. In this study, A total of 41 patients of brain glial cell hyperplasia and 87 patients of low-grade glioma were divided into training group and validation group randomly at a ratio of 7:3. Radiomics features were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1-enhanced). Then, LASSO, SVM, and RF models were created in order to choose a model with a greater level of efficiency for calculating each patient’s Rad-score (radiomics score). The independent risk factors were identified via univariate and multivariate logistic regression analysis to filter the Rad-score and clinical risk variables in turn. A radiomics-clinical model was next built of which effectiveness was assessed. Results Brain glial cell hyperplasia and low-grade gliomas from the 128 cases were randomly divided into 10 groups, of which 7 served as training group and 3 as validation group. The mass effect and Rad-score were two independent risk variables used in the construction of the radiomics-clinical model, and their respective AUCs for the training group and validation group were 0.847 and 0.858. The diagnostic accuracy, sensitivity, and specificity of the validation group were 0.821, 0.750, and 0.852 respectively. Conclusion Combining with radiomics constructed by multiparametric MRI images and clinical features, the radiomics-clinical model and nomogram that were developed to distinguish between brain glial cell hyperplasia and low-grade glioma had a good performance.

Abstract Background Spinal metastasis and multiple myeloma share many overlapping conventional radiographic imaging characteristics, thus, their differentiation may be challenging. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the differentiation of spinal metastasis and multiple myeloma. Materials and methods A total of 312 patients (training set: n = 146, validation set: n = 65, our center; external test set: n = 101, two other centers) with spinal metastasis (n = 196) and multiple myeloma (n = 116) were retrospectively enrolled. Demographics and MRI findings were assessed to build a clinical factor model. Radiomics features were extracted from MRI images. A radiomics model was constructed by the least absolute shrinkage and selection operator method. A radiomics nomogram combining the radiomics signature and independent clinical factors was constructed. And, one experienced radiologist reviewed the MRI images for all case. The diagnostic performance of the different models was evaluated by receiver operating characteristic curves. Results A clinical factors model was built based on heterogeneous appearance and shape. Twenty-one features were used to build the radiomics signature. The area under the curve (AUC) values of the radiomics nomogram (0.853 and 0.762, respectively) were significantly higher than that of the clinical factor model (0.692 and 0.540, respectively) in both validation (p = 0.048) and external test (p

Breast cancer is one of the most prevalent cancers worldwide, and early diagnosis and screening are vital to its successful treatment. Although medical imaging methods can assist in the early detection of breast cancer, imaging methods that are currently used for clinical diagnosis have drawbacks, such as low sensitivity and accuracy. Contrast agents are often used in diagnostic imaging to address these drawbacks. Nanocontrast agents have attracted considerable attention in recent years due to their unique physicochemical characteristics. Among these agents, inorganic nanoprobes have been substantially developed through improvements in synthesis techniques and pairings with other organic molecules. This paper mainly summarizes the specific applications of inorganic nanoprobes in the magnetic resonance imaging, fluorescence imaging, radionuclide imaging, and bimodal/multimodal imaging of breast cancer.

ObjectThe aim of our study was to examine the alterations in microstructure in patients with coronary artery disease (CAD) and cognitive impairment (CI) using diffusion kurtosis imaging (DKI). Additionally, we aimed to investigate the potential correlation between DKI parameters and cognitive function.Materials and methodsA total of 28 CAD patients and 30 healthy controls (HC) were prospectively enrolled in our study. All participants underwent routine and diffusion sequences of head imaging. DKE software was utilized to generate various diffusion kurtosis imaging parameters (DKI), including kurtosis fractional anisotropy (KFA), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), fractional anisotropy (FA), and mean diffusivity (MD). Nonparametric tests were conducted using tract-based spatial statistics (TBSS) to compare the parameter values between the two groups. The parameter values of the significantly different fiber tracts were extracted and correlated with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores.ResultsCompared to the HC group, patients with coronary artery disease exhibited significant reductions in FA values in the bilateral Superior corona radiata, bilateral Anterior corona radiata, bilateral Posterior corona radiata, corpus callosum, left Posterior thalamic radiation, right Posterior limb of internal capsule, Anterior limb of internal capsule, and Cerebral peduncle, as well as in the left Superior longitudinal fasciculus. Additionally, KFA values decreased in the bilateral Anterior corona radiata, bilateral Anterior limb of internal capsule, and Genu of the corpus callosum. The MK values decreased in the right Posterior corona radiata, Retrolenticular part of the internal capsule, Posterior thalamic radiation (including optic radiation), Superior longitudinal fasciculus, and left Posterior thalamic radiation (including optic radiation). Moreover, the RK values decreased in the bilateral Retrolenticular part of the internal capsule, right Posterior thalamic radiation (including optic radiation), and Superior longitudinal fasciculus, as well as in the left Superior longitudinal fasciculus and Posterior thalamic radiation (including optic radiation) (p 0.01, TFCE corrected). The FA values of the right posterior limb of the internal capsule (r = 0.610, p = 0.001) and the right cerebral peduncle (r = 0.622, p

ObjectiveThe purpose of this study is to evaluate the efficacy and safety of the local application of silver nitrate (LASN) as an adjuvant treatment before deep lamellar keratoplasty (DLKP) for fungal keratitis responding poorly to medical treatment.MethodsA total of 12 patients (12 eyes) with fungal keratitis responding poorly to medical treatment (for at least 2 weeks) were included. LASN was performed using 2% silver nitrate, the ulcer was cleaned and debrided, and then, the silver nitrate cotton stick was applied to the surface of the ulcer for a few seconds. The effect of LASN was recorded. The number of hyphae before and after treatment was determined by confocal microscope. After the condition of the ulcer improved, DLKP was performed. Fungal recurrence, best-corrected visual acuity (BCVA), loose sutures, and endothelial cell density (ECD) were recorded in detail.ResultsClinical resolution of corneal infiltration and edema was observed, and the ulcer boundary became clear in all 12 patients after 7–9 days of LASN. Confocal microscopy showed that the number of hyphae was significantly reduced. Ocular pain peaked on days 1 and 2 after treatment, and 9 patients (75%, day 1) and 1 patient (8.3%, day 2) required oral pain medication. During the follow-up period after DLKP, no fungal recurrence and loose sutures were observed. After the operation, the BCVA of all patients improved. The mean corneal ECD was 2,166.83 ± 119.75 cells/mm2.ConclusionThe LASN was safe and effective and can be well tolerated by patients. Eye pain can be relieved quickly. LASN as an adjuvant treatment before DLKP might be a promising therapeutic strategy.

Hydroxypropyl starch (HPS) nano antibacterial films incorporating Ethylene Diamine Tetraacetic Acid (EDTA) and lysozyme (LY) were fabricated via solvent casting method. The synergistic effects of EDTA and LY on the microstructure, component interactions, color, optical, mechanical, barrier and antibacterial properties of HPS nano antibacterial films were evaluated. The results indicated that EDTA and LY were well dispersed in the matrix of the HPS nano antibacterial films, the film-forming substrates have good compatibility, resulting in a dense multi-layer structure of the HPS nano antibacterial films. The addition of EDTA and LY increased the color parameters (L*, a*, b* and △E*) of the HPS nano antibacterial films. The synergistic effects of EDTA and LY significantly decreased the light transmission of the HPS nano antibacterial films. The presence of EDTA and LY increased the tensile strength (TS) and the elongation at break (EAB) of the HPS nano antibacterial films. The TS and EAB of E2.5L1 reached the highest values of 6.329 MPa and 50.24 %, respectively. The incorporation of EDTA and LY had positive effects on the improvement of water vapor permeability (WVP) and oxygen permeability (OP). The WVP and OP of E2.5L1 reached the highest values of 0.9350 × 10−12 g cm/cm2•s•Pa and 0.297 × 10 −2 g m/m2 •d, respectively. In addition, EDTA and LY had significant synergistic effects on the antibacterial activity against S. aureus (Gram-positive bacteria) and E. coli (Gram-negative bacteria). E2.5L1 exhibited the highest antibacterial activity and the inhibition zone diameters of S. aureus and E. coli were 3.69 mm and 4.28 mm, respectively. The HPS nano antibacterial films incorporating EDTA and LY are potential functional packaging materials.

Abstract Background Respiratory motion during PET acquisition may result in image blurring and resolution loss, reduced measurement of radiotracer uptake, and consequently, inaccurate lesion quantification and description. With the introduction of the total-body PET system, short-time PET acquisition is feasible due to its high sensitivity and spatial resolution. The purpose of this study was to evaluate the additional value of 20-s breath-hold (BH) lung PET in patients with stage IA pulmonary adenocarcinoma. Methods Forty-seven patients with confirmed stage IA pulmonary adenocarcinoma were enrolled in this retrospective study. All patients underwent a 300-s FB whole-body PET, followed by a BH lung PET. The SUVmax, TBR of the lesions and the percentage difference in nodule SUVmax (%ΔSUVmax) and TBR (%ΔTBR) between the two acquisitions was also calculated. The lesions were further divided by distance from pleura for subgroup analysis. The lesion detectability on PET images was the percentage of FDG-positive lesions. Results Among 47 patients, the BH lung PET images identified all lung nodules, and there was a significant difference in overall nodule SUVmax and TBR between BH PET and FB PET (both p

Abstract. Background:. Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. Methods:. In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. Results:. A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94–0.99), 0.93 (95% CI: 0.85–0.98), and 0.94 (95% CI: 0.87–0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63–0.83), 0.72 (95% CI: 0.62–0.82), and 0.73 (95% CI: 0.62–0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model (P

Abstract Purpose To develop machine learning-based radiomics models derive from different MRI sequences for distinction between benign and malignant PI-RADS 3 lesions before intervention, and to cross-institution validate the generalization ability of the models. Methods The pre-biopsy MRI datas of 463 patients classified as PI-RADS 3 lesions were collected from 4 medical institutions retrospectively. 2347 radiomics features were extracted from the VOI of T2WI, DWI and ADC images. The ANOVA feature ranking method and support vector machine classifier were used to construct 3 single-sequence models and 1 integrated model combined with the features of three sequences. All the models were established in the training set and independently verified in the internal test and external validation set. The AUC was used to compared the predictive performance of PSAD with each model. Hosmer–lemeshow test was used to evaluate the degree of fitting between prediction probability and pathological results. Non-inferiority test was used to check generalization performance of the integrated model. Results The difference of PSAD between PCa and benign lesions was statistically significant (P = 0.006), with the mean AUC of 0.701 for predicting clinically significant prostate cancer (internal test AUC = 0.709 vs. external validation AUC = 0.692, P = 0.013) and 0.630 for predicting all cancer (internal test AUC = 0.637 vs. external validation AUC = 0.623, P = 0.036). T2WI-model with the mean AUC of 0.717 for predicting csPCa (internal test AUC = 0.738 vs. external validation AUC = 0.695, P = 0.264) and 0.634 for predicting all cancer (internal test AUC = 0.678 vs. external validation AUC = 0.589, P = 0.547). DWI-model with the mean AUC of 0.658 for predicting csPCa (internal test AUC = 0.635 vs. external validation AUC = 0.681, P = 0.086) and 0.655 for predicting all cancer (internal test AUC = 0.712 vs. external validation AUC = 0.598, P = 0.437). ADC-model with the mean AUC of 0.746 for predicting csPCa (internal test AUC = 0.767 vs. external validation AUC = 0.724, P = 0.269) and 0.645 for predicting all cancer (internal test AUC = 0.650 vs. external validation AUC = 0.640, P = 0.848). Integrated model with the mean AUC of 0.803 for predicting csPCa (internal test AUC = 0.804 vs. external validation AUC = 0.801, P = 0.019) and 0.778 for predicting all cancer (internal test AUC = 0.801 vs. external validation AUC = 0.754, P = 0.047). Conclusions The radiomics model based on machine learning has the potential to be a non-invasive tool to distinguish cancerous, noncancerous and csPCa in PI-RADS 3 lesions, and has relatively high generalization ability between different date set.

Heat treatment effectively inhibits the water absorption recovery of compressed wood. To elucidate this phenomenon, we prepared compressed pine and thermally compressed pine (heartwood and sapwood) using the hot pressing method at 160 °C, 180 °C, 200 °C, and 220 °C. The effects of chemical components, swelling stresses, and monosaccharides on modified wood recovery were investigated using regression analyses. Notably, the recovery of both compressed heartwood and sapwood during water absorption declined from 18.89% to 2.66% and from 58.40% to 1.60%, respectively, after heat treatment. Similarly, the swelling stresses of the compressed heartwood and sapwood at 220 °C, respectively, ranged from 0.693 MPa to 0.275 MPa and from 0.783 MPa to 0.330 MPa. These were close to the values of untreated heartwood (0.175 MPa) and sapwood (0.225 MPa). Regression functions indicated that the recovery of compressed wood is chemically dependent on hemicellulose and mechanically related to swelling stress. For monosaccharides, regression functions indicated that modified heartwood recovery primarily relied on mannose, whereas modified sapwood recovery was remarkably affected by mannose and xylose. This confirmed that the pyrolytic monosaccharides in hemicellulose promoted stress relaxation, which induced the deformation fixation of thermally compressed wood.

Background Coronary artery disease (CAD) and cognitive impairment (CI) have become significant global disease and medical burdens. There have been several reports documenting the alterations in regional brain function and their correlation with CI in CAD patients. However, there is limited research on the changes in brain network connectivity in CAD patients. To investigate the resting-state connectivity and further understand the effective connectivity strength and directionality in patients with CAD, we utilized degree centrality (DC) and spectral dynamic causal modeling (spDCM) to detect functional hubs in the whole brain network, followed by an analysis of directional connections. Using the aforementioned approaches, it is possible to investigate the hub regions and aberrant connections underlying the altered brain function in CAD patients, providing neuroimaging evidence for the cognitive decline in patients with coronary artery disease. Materials and Methods This study was prospectively conducted involving 24 patients diagnosed with CAD and 24 healthy controls (HC) who were matched in terms of age, gender, and education. Functional MRI (fMRI) scans were utilized to investigate brain activity in these individuals. Neuropsychological examinations were performed on all participants. DC analysis and spDCM were employed to investigate abnormal brain networks in patients with CAD. Additionally, the association between effective connectivity strength and cognitive function in patients with CAD was examined based on the aforementioned results. Results By assessing cognitive functions, we discovered that patients with CAD exhibited notably lower cognitive function compared to the HC group. By utilizing DC analysis and spDCM, we observed significant reductions in DC values within the left parahippocampal cortex (PHC) and the left medial temporal gyrus (MTG) in CAD patients when compared to the control group. In terms of effective connectivity, we observed the absence of positive connectivity between the right superior frontal gyrus (SFG) and PHC in CAD patients. Moreover, there was an increase in negative connectivity from PHC and MTG to SFG, along with a decrease in the strength of positive connectivity between PHC and MTG. Furthermore, we identified a noteworthy positive correlation (r = 0.491, p = 0.015) between the strength of connectivity between the PHC and the MTG and cognitive function in CAD patients. Conclusions These research findings suggest that alterations in the connectivity of the brain networks involving SFG, PHC, and MTG in CAD patients may mediate changes in cognitive function.

Introduction Pneumonic-type primary pulmonary lymphoma (PPL) is often misdiagnosed as pneumonia in clinical practice. However, this disease requires different treatments, which calls for a correct diagnosis.Materials and methods A total of 227 patients with pneumonic-type PPL (n=72) and pneumonia (n=155) from 7 institutions were retrospectively enrolled between January 2017 and January 2022. Clinical features (age, sex, cough, sputum, fever, haemoptysis, chest pain, smoking, weight loss and laboratory results (haemoglobin, white blood cell count, C reactive protein level and erythrocyte sedimentation rate)) and CT imaging characteristics (air bronchogram, bronchiectasis, halo sign, pleural traction, pleural effusion, lymphadenopathy, lesion maximum diameter and CT attenuation value) were analysed. Receiver operating characteristic curve analysis was performed for model construction based on independent predictors in identifying pneumonic-type PPL. In addition, we used a calibration curve and decision curve analysis to estimate the diagnostic efficiency of the model.Results The patients with pneumonia showed a higher prevalence of sputum, fever, leucocytosis and elevation of C reactive protein level than those with pneumonic-type PPL (p=0.002, p

Rationale and introductionIt is of significance to assess the severity and predict the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system.Materials and methodsPatients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor model was built by demographics and CT features, and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis.ResultsA total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (CI): 0.827–0.940]}, the internal validation cohort [AUC, 0.885 (95% CI: 0.816–0.922)], and the external validation cohort [AUC, 0.85 (95% CI: 0.720–0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical factor model and radiomics signature in terms of clinical usefulness.ConclusionThe CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages.

We conducted environmental surveillance to detect avian influenza viruses circulating at live poultry markets (LPMs) and poultry farms in Guangxi Autonomous Region, China, where near the China-Vietnam border. From November through April 2017–2018 and 2018–2019, we collected environmental samples from 14 LPMs, 4 poultry farms, and 5 households with backyard poultry in two counties of Guangxi and tested for avian influenza A, H5, H7, and H9 by real-time reverse transcription-polymerase chain reaction (rRT-PCR). In addition, we conducted four cross-sectional questionnaire surveys among stall owners on biosecurity practices in LPMs of two study sites. Among 16,713 environmental specimens collected and tested, the median weekly positive rate for avian influenza A was 53.6% (range = 33.5% − 66.0%), including 25.2% for H9, 4.9% for H5, and 21.2% for other avian influenza viruses A subtypes, whereas a total of two H7 positive samples were detected. Among the 189 LPM stalls investigated, most stall owners (73.0%) sold chickens and ducks. Therefore, continued surveillance of the avian influenza virus is necessary for detecting and responding to emerging trends in avian influenza virus epidemiology.

Abstract Background Differentiating between solitary spinal metastasis (SSM) and solitary primary spinal tumor (SPST) is essential for treatment decisions and prognosis. The aim of this study was to develop and validate an MRI-based radiomics nomogram for discriminating SSM from SPST. Methods One hundred and thirty-five patients with solitary spinal tumors were retrospectively studied and the data set was divided into two groups: a training set (n = 98) and a validation set (n = 37). Demographics and MRI characteristic features were evaluated to build a clinical factors model. Radiomics features were extracted from sagittal T1-weighted and fat-saturated T2-weighted images, and a radiomics signature model was constructed. A radiomics nomogram was established by combining radiomics features and significant clinical factors. The diagnostic performance of the three models was evaluated using receiver operator characteristic (ROC) curves on the training and validation sets. The Hosmer–Lemeshow test was performed to assess the calibration capability of radiomics nomogram, and we used decision curve analysis (DCA) to estimate the clinical usefulness. Results The age, signal, and boundaries were used to construct the clinical factors model. Twenty-six features from MR images were used to build the radiomics signature. The radiomics nomogram achieved good performance for differentiating SSM from SPST with an area under the curve (AUC) of 0.980 in the training set and an AUC of 0.924 in the validation set. The Hosmer–Lemeshow test and decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model. Conclusions A radiomics nomogram as a noninvasive diagnostic method, which combines radiomics features and clinical factors, is helpful in distinguishing between SSM and SPST.

PURPOSEThe T2 mapping of magnetic resonance imaging (MRI) in endometrial cancer (EC), benign endometrial lesions (BELs), and normal endometrium (NE) has rarely been reported. This study aimed to determine the T2 values of MRI in EC, BELs, and NE to investigate whether the T2 values can differentiate them and to assess the aggressiveness of EC.METHODSIn total, 73 patients [EC, 51 (age, 57.4 ± 5.4 years); BELs, 22 (age, 57.8 ± 11.8 years)] and 23 normal volunteers (age, 56.1 ± 6.6 years) were included. The T2 values of MRI of the EC (type I and II), BEL, and NE groups were described and compared. The relationships between the T2 values of MRI in EC and the pathological characteristics [International Federation of Gynecology and Obstetrics (FIGO) stage and grade] were analyzed.RESULTSThe median T2 values of NE, BEL, and EC were 197.5 (142.9–324.0) ms, 131.1 (103.2–247.9) ms, and 103.0 (71.6–243.5) ms (P < 0.001), respectively. The median T2 values of type I and type II EC were 100.8 (71.62–130.44) ms and 125.7 (119.7–243.5) ms, respectively. There were significant differences in the T2 values among the NE, BEL, type I EC, and type II EC groups (P < 0.001) except for between the type II EC and BEL groups (P = 0.938). The T2 value of MRI in type I EC was significantly lower than that in type II EC (P = 0.001). There were no significant differences in patients with type I EC having different FIGO stages (P = 0.273) or tumor grades (P = 0.686).CONCLUSIONT2 mapping of MRI has the potential to quantitatively differentiate between EC, BELs, and NE as well as between type I and type II EC.

Abstract Background To establish and verify a radiomics nomogram for differentiating isolated micronodular adrenal hyperplasia (iMAD) from lipid-poor adenoma (LPA) based on computed tomography (CT)-extracted radiomic features. Methods A total of 148 patients with iMAD or LPA were divided into three cohorts: a training cohort (n = 72; 37 iMAD and 35 LPA), a validation cohort (n = 36; 22 iMAD and 14 LPA), and an external validation cohort (n = 40; 20 iMAD and 20 LPA). Radiomics features were extracted from contrast-enhanced and non-contrast CT images. The least absolute shrinkage and selection operator (LASSO) method was applied to develop a triphasic radiomics model and unenhanced radiomics model using reproducible radiomics features. A clinical model was constructed using certain laboratory variables and CT findings. Radiomics nomogram was established by selected radiomics signature and clinical factors. Nomogram performance was assessed by calibration curve, the areas under receiver operating characteristic curves (AUC), and decision curve analysis (DCA). Results Eleven and eight extracted features were finally selected to construct an unenhanced radiomics model and a triphasic radiomics model, respectively. There was no significant difference in AUC between the two models in the external validation cohort (0.838 vs. 0.843, p = 0.949). The radiomics nomogram inclusive of the unenhanced model, maximum diameter, and aldosterone showed the AUC of 0.951, 0.938, and 0.893 for the training, validation, and external validation cohorts, respectively. The nomogram showed good calibration, and the DCA demonstrated the superiority of the nomogram compared with the clinical factors model alone in terms of clinical usefulness. Conclusions A radiomics nomogram based on unenhanced CT images and clinical variables showed favorable performance for distinguishing iMAD from LPA. In addition, an efficient unenhanced model can help avoid extra contrast-enhanced scanning and radiation risk.

Dynamic transmission requirements and the limited cache space bring great challenges to wireless data transmission in the malicious jamming environment.Aiming at the above problems, a collaborative anti-jamming channel selection and data scheduling joint decision method for distributed internet of things was studied from the perspective of frequency domain and time domain.A data transmission model based on multi-user Markov decision process was constructed and a collaborativeanti-jamming joint-channel-and-data decision algorithm based on multi-agent deep reinforcement learning was proposed.Simulation results show that the proposed algorithm can effectively avoid the malicious jamming and the co-channel interference.Compared with the comparison algorithm, the network throughput is significantly improved, and the number of packet dropout is significantly reduced.

Extraction of oil from walnut kernel as raw material was explored using the method of pressing. Soxhlet and ultrasonic-assisted extractions of walnut oil, its evaluation index, the extraction process optimization by single factor experiment, the physical and chemical properties of oil, fatty acid composition, element composition, and the main functional group analysis were studied. The results showed that walnut kernel could be extracted effectively with petroleum ether. The optimal extraction conditions of walnut oil by ultrasonic assisted extraction were as follows: solid/liquid ratio of 1:11 (g/mL), ultrasonic power 180 W, extraction time 60 min, and the extraction efficiency of walnut oil was 59.13%. The optimal extraction conditions of walnut oil by Soxhlet were solid to liquid ratio of 1:7, temperature of 76 °C, time of 4 h, and the extraction efficiency of walnut oil was 58.3%. The extraction efficiency of pressing was 43.2%. Walnut oil mainly contains phosphorus, zinc, magnesium, potassium, calcium, and other elements. The fatty acids of walnut oil mainly consist of palmitic acid, palmitoleic acid, cis-linoleic acid, cis,cis,cis-9,12,15-octadecanotrienoic acid and other fatty acids. The cis linoleic acid accounted for the highest proportion in GC content, and the content of both was more than 90%.

Key points Distinguishing carotid plaques with intraplaque hemorrhage is difficult by conventional imaging. A radiomics nomogram facilitates differentiation of carotid plaques with intraplaque hemorrhage. CT-based nomogram may be a non-invasive tool to identify carotid plaque vulnerability.

Abstract Background Pneumonic-type invasive mucinous adenocarcinoma (IMA) was often misdiagnosed as pneumonia in clinic. However, the treatment of these two diseases is different. Methods A total of 341 patients with pneumonic-type IMA (n = 134) and infectious pneumonia (n = 207) were retrospectively enrolled from January 2017 to January 2022 at six centers. Detailed clinical and CT imaging characteristics of two groups were analyzed and the characteristics between the two groups were compared by χ2 test and Student’s t test. The multivariate logistic regression analysis was performed to identify independent predictors. Receiver operating characteristic curve analysis was used to determine the diagnostic performance of different variables. Results A significant difference was found in age, fever, no symptoms, elevation of white blood cell count and C-reactive protein level, family history of cancer, air bronchogram, interlobular fissure bulging, satellite lesions, and CT attenuation value (all p

In this study, Arundo donax Linnaeus was utilized as the biomass and a TH/DS (Tetra-n-butylammonium hydroxide/Dimethyl sulfoxide, C16H37NO/C2H6OS) system was employed to dissolve biomass cellulose. The optimal process for the preparation of Arundo donax L. biomass regenerated cellulose fiber was determined through process optimization. The physical properties and antimicrobial performance of the resulting products were analyzed. The results demonstrated that the physical indicators of biomass regenerated cellulose fiber, prepared from Arundo donax L. cellulose, met the requirements of the standard for Viscose Filament (Dry breaking strength ≥ 1.65 CN/dtex, Elongation at dry breaking 15.5–26.0%, and Dry elongation CV value ≤ 10.0%). Additionally, excellent antimicrobial properties were exhibited by the biomass regenerated cellulose fiber developed in this study, with antibacterial rates against Staphylococcus aureus and other three strain indexes meeting the Viscose Filament standards. Furthermore, high antiviral activity of 99.99% against H1N1 and H3N2 strains of influenza A virus was observed in the experimental samples, indicating a remarkable antiviral effect. Valuable references for the comprehensive utilization of Arundo donax L. biomass resources are provided by this research.

Abstract Background We studied the magnetic resonance spectroscopy (MRS) associations with clinicopathologic features of estrogen-dependent endometrial cancer (type I EC). Methods Totally 45 patients with type I EC who underwent preoperative multi-voxel MRS at 3.0 T were enrolled. The mean ratio of the Cho peak integral to the unsuppressed water peak integral (Cho/water) of the tumor was calculated. The Cho/water and apparent diffusion coefficient (ADC) of type I EC with and without local invasion, as well as with different levels of Ki-67 staining index (SI) (≤ 40% and > 40%), were compared. Correlation test was used to examine the relationship of Cho/water, as well as mean ADC, with Ki-67 SI, tumor stage, and tumor grade. Results The mean Cho/water of EC with Ki-67 SI ≤ 40% (2.28 ± 0.93) × 10−3 was lower than that with Ki-67 SI > 40% (4.08 ± 1.00) × 10−3 (P 0.05). Conclusion MRS is helpful for preoperative assessment of clinicopathological features of type I EC.

Both heart failure (HF) and cognitive impairment (CI) have a significant negative impact on the health of the elderly individuals. Magnetic resonance imaging (MRI) can non-invasively detect functional and structural variations in the heart and brain, making it easier to explore the connection between the heart and brain. According to neuroimaging studies, HF patients have a higher chance of developing CI because they have a variety of different types of brain injuries. To examine how HF and CI are influenced by one another, English-language literature was searched in the Web of Science, PubMed EMBASE (OVID), PsycInfo, and Scopus databases. The search terms included “high-frequency,” “brain function,” “brain injury,” “cognition,” “cognitive impairment,” and “magnetic resonance imaging.” Normal brain function is typically impaired by HF in the form of decreased cerebral perfusion pressure, inflammation, oxidative stress, and damage to the BBB, resulting in CI and subsequent HF. Early pathophysiological alterations in patients’ brains have been widely detected using a range of novel MRI techniques, opening up new avenues for investigating the connection between HF and CI. This review aims to describe the pathogenesis of HF with CI and the early diagnostic role of MRI in the heart-brain domain.

Fallopian tube blockage-related diseases are the main cause of female infertility. Diagnosis of tubal patency and accurate determination of tubal obstruction lesions is of great value for infertility treatment. Conventionally, the common used hysterosalpingogram (HSG) has the disadvantages of radioactive hazard and high false positive rate. Thus, the development of a safe and accurate method which can replace traditional HSG for tubal patency diagnosis is urgently needed. Recently, fluorescence imaging in the second near-infrared region (NIR-II) was widely used in in-vivo imaging research due to its excellent tissue penetration and signal-to noise ratio. With the aid of fluorescence agents, NIR-II imaging has the potential to diagnose tubal patency, which, unfortunately, has not been explored. Herein, a kind of rare-earth Erbium-based nanoprobe (Er-RENPs) with outstanding NIR-II fluorescence property was synthesized. Based on the Er-RENPs, the potential of NIR-II fluorescence imaging was explored. Results show that the outline of fallopian tubes can be clearly visualized with the help of Er-RENPs. And the stenotic, occlusive fallopian tubes and hydrosalpinx can be diagnosed. Additionally, the Er-RENPs exhibit good biocompatibility. In this study, we reported that NIR-II rare-earth fluorescent nanoprobe can serve as a promising fluorescent agent, and exhibits considerable potential application for tubal diagnosis.

The modulation accuracy of Multi-Plane Light Conversion (MPLC) mainly depends on the positioning accuracy of the phase mask on the Spatial Light Modulator (SLM). To improve positioning accuracy, the impact of phase mask shift on modulation accuracy is investigated, and a position method is proposed. In order to investigate the influence of phase mask offset on the input light conversion effect, a convolution transmission model for the adjustable multi-pass cavity is established. Then, the positioning process for the phase masks is analyzed and simulated, and a method of positioning the phase masks is presented. This method reduces the positioning time and increases the positioning accuracy to 8 μm. Finally, experiments are performed to verify the feasibility of the method. Experimental results show that the similarity of the adjustable multi-pass cavity positioned by this method can reach 93.44%.

ObjectiveTo investigate the diagnostic value of quantitative parameters of spectral computed tomography (CT) in ischaemic stroke areas.MethodsThe medical records of 57 patients with acute ischaemic stroke (AIS) who underwent plain computed tomography (CT) head scans, CT angiography (CTA), and CT perfusion (CTP) were retrospectively reviewed. The ischaemic areas (including the core infarct area and penumbra) and non-ischaemic areas in each patient were quantitatively analyzed using F-STROKE software. Two independent readers measured the corresponding values of the spectroscopic quantitative parameters (effective atomic number [Zeff value], iodine density value, and iodine–no-water value) in the ischaemic area and contralateral normal area alone. The differences in spectroscopic quantitative parameters between the two groups were compared, and their diagnostic efficacy was obtained.ResultsThe Zeff, iodine–no-water value, and iodine density value of the ischaemic area all showed significant lower than those of non-ischaemic tissue (P < 0.001). For differentiating the ischaemic area from non-ischaemic tissue, the area under the curve (AUC) of the Zeff value reached 0.869 (cut-off value: 7.385; sensitivity: 93.0%; specificity: 70.2%), the AUC of the iodine density value reached 0.932 (cut-off value: 0.235; sensitivity: 91.2%; specificity: 82.5%), and the AUC of the iodine–no-water value reached 0.922 (cut-off value: 0.205; sensitivity: 96.5%; specificity: 78.9%).ConclusionThe study showed the spectral CT would be a potential novel rapid method for identifying AIS. The spectral CT quantitative parameters (Zeff, iodine density values, and iodine–no-water values) can effectively differentiate the ischaemic area from non-ischaemic tissue in stroke patients.

The objective of this study was to evaluate symptoms of depression and anxiety as well as changes in spontaneous neuronal activity in college students studying abroad during the coronavirus 2019 (COVID-19) pandemic. We examined functional brain changes using resting-state functional magnetic resonance imaging (fMRI), the amplitude of low-frequency fluctuations (ALFF), and regional homogeneity (ReHo) in overseas students with enforced isolation due to the COVID-19 pandemic. Additionally, emotional assessments were administered to determine the severity of depression and anxiety. The questionnaire results showed that anxiety and depressive symptoms differed between overseas students (i.e., those attending an overseas college virtually) and local students (i.e., those attending a local college in person). The fMRI data revealed higher ALFF values in the bilateral superior medial frontal gyrus, bilateral pre-central gyrus, left insula, and left superior temporal gyrus as well as lower ALFF values in the bilateral paracentral lobule (supplementary motor area) in overseas students. Moreover, ReHo analysis also revealed significant differences between overseas students and local students. Compared with local students, overseas students showed significantly increased ReHo in the right inferior frontal and superior temporal gyri and decreased ReHo in the bilateral paracentral lobule, bilateral superior medial frontal gyrus (supplementary motor area), and bilateral pre-central gyrus. In addition, in overseas students, altered ReHo in the cluster including the left superior and medial frontal gyri, pre-central gyrus, and paracentral lobule was significantly positively correlated with Self-Rating Depression Scale scores. Thus, spontaneous brain activity in overseas students changed during the COVID-19 pandemic. This change in brain function might be related to depression and anxiety symptoms. These results suggest that mental health services are needed to decrease the risk of anxiety and depression among college students studying abroad during the COVID-19 pandemic.