Deep learning radiomics based on ultrasound images for the assisted diagnosis of chronic kidney disease.

AIM: This study aimed to explore the value of ultrasound (US) images in chronic kidney disease (CKD) screening by constructing a CKD screening model based on grey-scale US images. METHODS: According to the CKD diagnostic criteria, 1049 patients from Tongde Hospital of Zhejiang Province were retrospectively enrolled in the study. A total of 4365 renal US images were collected from these patients. Convolutional neural networks were used for feature extractions and a screening model was constructed by fusing ResNet34 and texture features to identify CKD and its stage. A comparative analysis was performed to compare the diagnosis results of the model with physicians. RESULTS: When diagnosing CKD or non-CKD, the receiver operating characteristic curve (AUC) of our model was 0.918 and that of the senior physician group was 0.869 (p < .05). For the diagnosis of CKD stage, the AUC of our model for CKD G1-G3 was 0.781, 0.880, and 0.905, respectively, while the AUC of the senior physician group for CKD G1-G3 was 0.506, 0.586, and 0.796, respectively; all differences were statistically significant (p < .05). The diagnostic efficiency of our model for CKD G4 and G5 reached the level of the senior physicians group. Specifically, the AUC of our model for CKD G4-G5 was 0.867 and 0.931, respectively, while the AUC of the senior physician group for CKD G4-G5 was 0.838 and 0.963, respectively (all p > .05). CONCLUSIONS: Our deep learning radiomics model is more effective than senior physicians in the diagnosis of early CKD.

Multiple benign metastasizing leiomyoma in the abdominal wall: a case report and literature review.

Benign metastasizing leiomyoma (BML) is a rare disease that results from metastasis of uterine leiomyoma to distant sites with benign pathologic features. The lung is the most common metastatic site for BML. This report describes the case of a 49-year-old woman who presented with a mass in the abdominal wall with a surgical history of uterine myomectomy. Ultrasound and Magnetic resonance imaging (MRI) revealed multiple mass lesions. The histopathology of the mass specimen indicated BML. The imaging and clinical features of BML are discussed based on the characteristics of this case and related literature reports.

Analysis of flow cytometry data from ultrasound-guided lymph node biopsies with two types of needles.

BACKGROUND: In this study, we combined two techniques, ultrasound-guided needle biopsy and flow cytometry (FCM), to explore their value in patients with enlarged lymph nodes. METHODS: We compared the results of 198 needle biopsies on FCM and pathology. Forty-two were done by (fine needle aspiration, FNA), and the remaining 156 with (core needle biopsy, CNB), in 36 of 156 patients, a FNA was performed in the same lymph node after completion of the CNB. Except for five types of pathological entities, the rest were differentiated only detected or undetected tumours as the outcome distinction. RESULTS: Among the 198 needle biopsies, 13 were inadequate specimens, while the remaining 185 had pathological findings, including 47 benign and 138 neoplastic findings. Thirty-six patients underwent puncture with both FNA and CNB, both needles produced identical results by FCM, but more cells were obtained by FNA. Among the pathologically positive results, there were 23 missed diagnoses in FCM, in contrast, evidence of tumours was observed in the FCM images of 15 needle biopsies that reported benign or findings that were inconsistent with pathology, and the final diagnosis was consistent with the FCM in 10 cases. FCM detected haematolymphoid tumours with a sensitivity of 87.8% and a specificity of 91.9%. CONCLUSION: The combination of FCM and ultrasound-guided lymph node needle biopsy can quickly provide guidance for clinical decision-making. We recommend that all lymph node needle biopsies be sent for FCM, the specimen can be obtained by the last puncture with FNA.

A viral movement protein targets host catalases for 26S proteasome-mediated degradation to facilitate viral infection and aphid transmission in wheat.

The infection of host plants by many different viruses causes reactive oxygen species (ROS) accumulation and yellowing symptoms, but the mechanisms through which plant viruses counteract ROS-mediated immunity to facilitate infection and symptom development have not been fully elucidated. Most plant viruses are transmitted by insect vectors in the field, but the molecular mechanisms underlying virus‒host-insect interactions are unclear. In this study, we investigated the interactions among wheat, barley yellow dwarf virus (BYDV), and its aphid vector and found that the BYDV movement protein (MP) interacts with both wheat catalases (CATs) and the 26S proteasome ubiquitin receptor non-ATPase regulatory subunit 2 homolog (PSMD2) to facilitate the 26S proteasome-mediated degradation of CATs, promoting viral infection, disease symptom development, and aphid transmission. Overexpression of the BYDV MP gene in wheat enhanced the degradation of CATs, which leading to increased accumulation of ROS and thereby enhanced viral infection. Interestingly, transgenic wheat lines overexpressing BYDV MP showed significantly reduced proliferation of wingless aphids and an increased number of winged aphids. Consistent with this observation, silencing of CAT genes also enhanced viral accumulation and reduced the proliferation of wingless aphids but increased the occurrence of winged aphids. In contrast, transgenic wheat plants overexpressing TaCAT1 exhibited the opposite changes and showed increases in grain size and weight upon infection with BYDV. Biochemical assays demonstrated that BYDV MP interacts with PSMD2 and promotes 26S proteasome-mediated degradation of TaCAT1 likely in a ubiquitination-independent manner. Collectively, our study reveals a molecular mechanism by which a plant virus manipulates the ROS production system of host plants to facilitate viral infection and transmission, shedding new light on the sophisticated interactions among viruses, host plants, and insect vectors.

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study.

BACKGROUND: To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer. METHODS: Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms. RESULTS: Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians. CONCLUSION: The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.

Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer.

OBJECTIVES: To investigate the feasibility and efficacy of a nomogram that combines clinical and radiomic features of magnetic resonance imaging (MRI) for preoperative perirectal fat invasion (PFI) prediction in rectal cancer. METHODS: This was a retrospective study. A total of 363 patients from two centers were included in the study. Patients in the first center were randomly divided into training cohort (n = 212) and internal validation cohort (n = 91) at the ratio of 7:3. Patients in the second center were allocated to the external validation cohort (n = 60). Among the training cohort, the numbers of patients who were PFI positive and PFI negative were 108 and 104, respectively. The radiomics features of preoperative T2-weighted images, diffusion-weighted images and enhanced T1-weighted images were extracted, and the total Radscore of each patient was obtained. We created Clinic model and Radscore model, respectively, according to clinical data or Radscore only. And that, we assembled the combined model using the clinical data and Radscore. We used DeLong's test, receiver operating characteristic, calibration and decision curve analysis to assess the models' performance. RESULTS: The three models had good performance. Clinic model and Radscore model showed equivalent performance with AUCs of 0.85, 0.82 (accuracy of 81%, 81%) in the training cohort, AUCs of 0.78, 0.86 (accuracy of 74%, 84%) in the internal cohort, and 0.84, 0.84 (accuracy of 80%, 82%) in the external cohort without statistical difference (DeLong's test, p > 0.05). AUCs and accuracy of Combined model were 0.89 and 87%, 0.90 and 88%, and 0.90 and 88% in the three cohorts, respectively, which were higher than that of Clinic model and Radscore model, but only in the training cohort with a statistical difference (DeLong's test, p < 0.05). The calibration curves of the nomogram exhibited acceptable consistency, and the decision curve analysis indicated higher net benefit in clinical practice. CONCLUSION: A nomogram combining clinical and radiomic features of MRI to compute the probability of PFI in rectal cancer was developed and validated. It has the potential to serve as a preoperative biomarker for predicting pathological PFI of rectal cancer.

Chirality-driven self-assembly: application toward renewable/exchangeable resin-immobilized catalysts.

Resin-immobilized catalysts were prepared through chirality-driven self-assembly. The method allows the resin-immobilized catalyst to be regenerated under mild conditions and in situ catalyst exchange to be carried out quantitatively. The uniqueness of the methodology was demonstrated by the preparation of a catalyst for TEMPO oxidation as well as a two-step sequential TEMPO oxidation/aldol condensation sequence enabled by facile catalyst exchange.

Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis.

BACKGROUND: Accurate and non-invasive diagnosis of pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP) can avoid unnecessary puncture and surgery. This study aimed to develop a deep learning radiomics (DLR) model based on contrast-enhanced ultrasound (CEUS) images to assist radiologists in identifying PDAC and CP. METHODS: Patients with PDAC or CP were retrospectively enrolled from three hospitals. Detailed clinicopathological data were collected for each patient. Diagnoses were confirmed pathologically using biopsy or surgery in all patients. We developed an end-to-end DLR model for diagnosing PDAC and CP using CEUS images. To verify the clinical application value of the DLR model, two rounds of reader studies were performed. RESULTS: A total of 558 patients with pancreatic lesions were enrolled and were split into the training cohort (n=351), internal validation cohort (n=109), and external validation cohorts 1 (n=50) and 2 (n=48). The DLR model achieved an area under curve (AUC) of 0.986 (95% CI 0.975-0.994), 0.978 (95% CI 0.950-0.996), 0.967 (95% CI 0.917-1.000), and 0.953 (95% CI 0.877-1.000) in the training, internal validation, and external validation cohorts 1 and 2, respectively. The sensitivity and specificity of the DLR model were higher than or comparable to the diagnoses of the five radiologists in the three validation cohorts. With the aid of the DLR model, the diagnostic sensitivity of all radiologists was further improved at the expense of a small or no decrease in specificity in the three validation cohorts. CONCLUSIONS: The findings of this study suggest that our DLR model can be used as an effective tool to assist radiologists in the diagnosis of PDAC and CP.

Levosimendan Versus Dobutamine in Myocardial Injury Patients with Septic Shock: A Randomized Controlled Trial.

BACKGROUND We aimed to investigate the effect of levosimendan on biomarkers of myocardial injury and systemic hemodynamics in patients with septic shock. MATERIAL AND METHODS After achieving normovolemia and a mean arterial pressure of at least 65 mmHg, 38 septic shock patients with low cardiac output (left ventricular ejective fraction), LEVF £45%) were randomly divided into two groups: levosimendan dobutamine. Patients in the levosimendan and dobutamine groups were maintained with intravenous infusion of levosimendan (0.2 µg/kg/minute) and dobutamine (5 µg/kg/minute) for 24 hours respectively. During treatment we monitored hemodynamics and LVEF, and measured levels of heart-type fatty acid binding protein (HFABP), troponin I (TNI), and brain natriuretic peptide(BNP). In addition, the length of mechanical ventilation, intensive care unit (ICU) stay, hospital stay, and 28-day mortality were compared between the two groups. RESULTS The levosimendan group and the dobutamine group were well matched with respect to age (years, 55.4 ± 1 7.5 versus 50.2 ± 13.6) and gender (males, 68.4% versus 57.9%). Levosimendan-treated patients had higher stroke volume index (SVI), cardiac index (CI), LVEF, and left ventricular stroke work index (LVSWI), and lower extravascular lung water index (EVLWI) compared to dobutamine-treated patients (p<0.05). HFABP, TNI, and BNP in the levosimendan group were less than in the dobutamine group (p<0.05). There was no difference in the mechanical ventilation time, length of stay in ICU and hospital, and 28-day mortality between the two groups. CONCLUSIONS Compared with dobutamine, levosimendan reduces biomarkers of myocardial injury and improves systemic hemodynamics in patients with septic shock. However, it does not reduce the days on mechanical ventilation, length of stay in ICU and hospital, or 28-day mortality.