Causality between sarcopenia and diabetic neuropathy.

Background: Past studies have demonstrated that diabetic neuropathy is related to sarcopenia, but the further causal relation is still unclear. We sought to investigate the causal relationship by combining data from cross-sectional and Mendelian randomization (MR) studies. Methods: The genome-wide association studies data were collected from the UK Biobank and the European Working Group on Sarcopenia to conduct a bi-directional two-sample MR study to explore the causality between diabetic neuropathy and relevant clinical traits of sarcopenia, including appendicular lean mass (ALM), walking speed and low hand grip strength. The inverse-variance weighted and various sensitivity analyses were used to obtain MR estimates. We also enrolled a total of 196 Type 2 diabetes patients from April 2021 to April 2024 and divided them into the Distal peripheral neuropathy (DPN) group (n=51) and non-DPN group (n=145) via vibration perception threshold (VPT) and neuropathy deficit score. Logistic regression and ROC curve analysis were used to investigate the relationship between DPN and relevant sarcopenia clinical features. Results: According to a forward MR analysis, decreased walking speed (OR: 0.04, 95% confidence interval (CI): 0.01-0.16; P<0.001) and increased ALM (1.25 [1.05-1.50], P=0.012) had a causal effect on developing diabetic neuropathy. According to reverse MR results, developing diabetic neuropathy had a causal effect on decreased walking speed (0.99 [0.99-1.00], P=0.007) and low grip strength (1.05 [1.02-1.08], P<0.001). The cross-sectional study showed that 5-time stand time (P=0.002) and 6-meter walking speed (P=0.009) had an inverse association with DPN. Additionally, we discovered that ASMI (P=0.030) and 5-time stand time (P=0.013) were separate risk factors for DPN.ConclusionThe MR study suggested that diabetic neuropathy may have a causality with relevant clinical traits of sarcopenia, and our cross-sectional study further proved that sarcopenia indexes are predictors of diabetic neuropathy.

Ultrasound-based Radiomics Analysis for Assessing Risk Factors Associated With Early Recurrence Following Surgical Resection of Hepatocellular Carcinoma.

OBJECTIVE: The aim of this study was to explore the value of ultrasound-based radiomics analysis for early recurrence after surgical resection of hepatocellular carcinoma (HCC). METHODS: This retrospective study included 127 patients who underwent primary surgical resection for HCC between October 2019 and November 2021. The patients were subsequently divided into training and validation sets (7:3 ratio). All patients received preoperative routine ultrasound and contrast-enhanced ultrasound examination, with postoperative pathological confirmation of HCC. Radiomics features were extracted from maximum section of a two-dimensional ultrasound image. The least absolute shrinkage and selection operation logistic regression algorithm with 10-fold cross-validation was used to establish ultrasonic radiomics features. Logistic regression modelling was used to build models based on clinical and ultrasonic features (model 1, clinical-ultrasonic model), radiomics signature (model 2, ultrasonic radiomics model), and the combination (model 3, clinical-ultrasonic-radiomics model). Then, a nomogram model was established to predict the risk of early recurrence, and the application value of nomogram through internal verification was evaluated. RESULTS: Model 3 showed optimal diagnostic performance in both training set (area under the curve [AUC], 0.907) and validation set (AUC, 0.925), followed by the model 1 in training set (AUC, 0.846) and validation set (AUC, 0.855), both above two models performed better than model 2 in training set (AUC, 0.751) and validation set (AUC, 0.702) (p < 0.05). In the training set and validation set of model 3, the sensitivity were 83.3%, 77.8%, the specificity ware 95.8%, 100.0% and the C-index were 0.791, 0.778. CONCLUSION: The preoperative clinical-ultrasonic-radiomics model is anticipated to be a reliable tool for predicting the early recurrence of surgical resection of HCC.

A practical risk stratification system based on ultrasonography and clinical characteristics for predicting the malignancy of soft tissue masses.

OBJECTIVE: To establish a practical risk stratification system (RSS) based on ultrasonography (US) and clinical characteristics for predicting soft tissue masses (STMs) malignancy. METHODS: This retrospective multicenter study included patients with STMs who underwent US and pathological examinations between April 2018 and April 2023. Chi-square tests and multivariable logistic regression analyses were performed to assess the association of US and clinical characteristics with the malignancy of STMs in the training set. The RSS was constructed based on the scores of risk factors and validated externally. RESULTS: The training and validation sets included 1027 STMs (mean age, 50.90 ± 16.64, 442 benign and 585 malignant) and 120 STMs (mean age, 51.93 ± 17.90, 69 benign and 51 malignant), respectively. The RSS was constructed based on three clinical characteristics (age, duration, and history of malignancy) and six US characteristics (size, shape, margin, echogenicity, bone invasion, and vascularity). STMs were assigned to six categories in the RSS, including no abnormal findings, benign, probably benign (fitted probabilities [FP] for malignancy: 0.001-0.008), low suspicion (FP: 0.008-0.365), moderate suspicion (FP: 0.189-0.911), and high suspicion (FP: 0.798-0.999) for malignancy. The RSS displayed good diagnostic performance in the training and validation sets with area under the receiver operating characteristic curve (AUC) values of 0.883 and 0.849, respectively. CONCLUSION: The practical RSS based on US and clinical characteristics could be useful for predicting STM malignancy, thereby providing the benefit of timely treatment strategy management to STM patients. CRITICAL RELEVANCE STATEMENT: With the help of the RSS, better communication between radiologists and clinicians can be realized, thus facilitating tumor management. KEY POINTS: There is no recognized grading system for STM management. A stratification system based on US and clinical features was built. The system realized great communication between radiologists and clinicians in tumor management.

Risk Factors for Early Recurrence After Radical Resection of Hepatocellular Carcinoma Based on Preoperative Contrast-Enhanced Ultrasound and Clinical Features.

OBJECTIVES: To investigate risk factors for the early recurrence (ER) of hepatocellular carcinoma (HCC) after radical resection based on preoperative contrast-enhanced ultrasound (CEUS) and clinical features to provide guidance for clinical treatment. METHODS: The retrospective analysis selected 130 HCC patients who underwent radical tumor resection from October 2019 to November 2021. All patients underwent preoperative routine ultrasound examination and CEUS, and the pathology was confirmed as HCC after surgery. The patients were divided into two groups based on whether there is an ER, namely the ER group and the non-ER group. The general clinical, routine and CEUS data of patients were collected, and the factors were selected by using the least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was used to screen the independent influencing factors of ER. Then a nomogram model was established to predict the risk of ER, and the application value of nomogram through internal validation was evaluated. RESULTS: Multivariate logistic regression identified several independent factors influencing ER after radical HCC resection. Significant factors included early wash-out phase (95%CI = 0.003-0.206, P = 0.001), liver cirrhosis (95%CI = 2.835-221.224, P = 0.004), incomplete envelope (95%CI = 5.247-1056.130,P = 0.001), multiple lesions (95%CI = 1.110-135.424,P = 0.041), Albumin <40 g/L (95%CI = 2.496-127.223,P = 0.004), and Golgi Protein 73 (GP73) ≥ 85 ng/mL (95%CI = 1.594-30.002, P = 0.010), with all P-values <0.05. The nomogram prediction model constructed based on the results of multivariate logistic regression, demonstrated a ROC curve AUC of 0.879, a sensitivity of 93.5%, a specificity of 66.7%, and a C-index of 0.602, indicating superior diagnostic efficiency compared to independent influencing factors. The ER nomogram prediction model confirmed good discrimination and calibration in internal validation. CONCLUSION: The CEUS-Clinical combined model effectively monitors the risk of ER in high-risk populations following radical resection of HCC, timely interventions to improve patient prognosis.

Application of ultrasound elastography and radiomic for predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma.

Objective: This study aims to combine ultrasound (US) elastography (USE) and radiomic to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). Methods: A total of 204 patients with 204 thyroid nodules who were confirmed with PTMC and treated in our hospital were enrolled and randomly assigned to the training set (n = 142) and the validation set (n = 62). US features, USE (gender, shape, echogenic foci, thyroid imaging reporting and data system (TIRADS) category, and elasticity score), and radiomic signature were employed to build three models. A nomogram was plotted for the combined model, and decision curve analysis was applied for clinical use. Results: The combined model (USE and radiomic) showed optimal diagnostic performance in both training (AUC = 0.868) and validation sets (AUC = 0.857), outperforming other models. Conclusion: The combined model based on USE and radiomic showed a superior performance in the prediction of CLNM of patients with PTMC, covering the shortage of low specificity of conventional US in detecting CLNM.

Analysis of early response to chemotherapy for non-Hodgkin's lymphoma by quantitative contrast-enhanced ultrasound: A prospective case-control crossectional study.

OBJECTIVE: To investigate the value of quantitative contrast-enhanced ultrasonography (CEUS) in assessing and predicting early therapy response of non-Hodgkin's lymphoma (NHL). METHODS: Fifty-six cases of NHL were studied using CEUS before and after three cycles of R-CHOP / CHOP. Quantitative parameters such as arrival time (ATM), time to peak (TTP), △T = TTP-ATM, area under the gamma curve (Area), curve gradient (Grad), wash-out time (WT), base intensity (BI), peak intensity (PI) and ΔI = PI-BI were compared between the lymphoma and normal lymph nodes before and at mid-treatment, respectively. Changes in quantitative CEUS parameters were also compared between complete response (CR) and incomplete response(non-CR) groups. Besides, the correlation analysis was performed between pretreatment PI and changes in quantitative parameters. RESULTS: After three cycles of R-CHOP/CHOP, S/L (P < 0.001), PI (P = 0.002), ΔI (P < 0.001), Grad (P < 0.001), and Area (P < 0.001) of NHL were significantly decreased. The CR group and non-CR group only differed in ATM before treatment. In contrast, there was no statistical difference in any of the parameters between the two groups at mid-treatment. Finally, a significant correlation was observed between pre-treatment PI and PI△% (r = 0.736, P < 0.001). CONCLUSIONS: CEUS is promising for the assessment of response of NHL to R-CHOP/CHOP. Intra-lesion perfusion changes take precedence over morphological changes suggesting treatment efficacy. Pre-treatment ATM values may help to suggest efficacy outcomes and pre-treatment PI values may be a valid predictor of lymphoma perfusion response.

A lung disease diagnosis algorithm based on 2D spectral features of ultrasound RF signals.

Lung diseases are commonly diagnosed based on clinical pathological indications criteria and radiological imaging tools (e.g., X-rays and CT). During a pandemic like COVID-19, the use of ultrasound imaging devices has broadened for emergency examinations by taking their unique advantages such as portability, real-time detection, easy operation and no radiation. This provides a rapid, safe, and cost-effective imaging modality for screening lung diseases. However, the current pulmonary ultrasound diagnosis mainly relies on the subjective assessments of sonographers, which has high requirements for the operator's professional ability and clinical experience. In this study, we proposed an objective and quantifiable algorithm for the diagnosis of lung diseases that utilizes two-dimensional (2D) spectral features of ultrasound radiofrequency (RF) signals. The ultrasound data samples consisted of a set of RF signal frames, which were collected by professional sonographers. In each case, a region of interest of uniform size was delineated along the pleural line. The standard deviation curve of the 2D spatial spectrum was calculated and smoothed. A linear fit was applied to the high-frequency segment of the processed data curve, and the slope of the fitted line was defined as the frequency spectrum standard deviation slope (FSSDS). Based on the current data, the method exhibited a superior diagnostic sensitivity of 98% and an accuracy of 91% for the identification of lung diseases. The area under the curve obtained by the current method exceeded the results obtained that interpreted by professional sonographers, which indicated that the current method could provide strong support for the clinical ultrasound diagnosis of lung diseases.

Self-supplying Cu2+ and H2O2 synergistically enhancing disulfiram-mediated melanoma chemotherapy.

Disulfiram (DSF) can target and kill cancer cells by disrupting cellular degradation of extruded proteins and has therefore received particular attention for its tumor chemotherapeutic potential. However, the uncontrollable Cu2+/DSF ratio reduces the efficacy of DSF-mediated chemotherapy. Herein, self-supplying Cu2+ and oxidative stress synergistically enhanced DSF-mediated chemotherapy is proposed for melanoma-based on PVP-coated CuO2 nanodots (CPNDs). Once ingested, DSF is broken down to diethyldithiocarbamate (DTC), which is delivered into a tumor via the circulation. Under the acidic tumor microenvironment, CPNDs produce sufficient Cu2+ and H2O2. DTC readily chelates Cu2+ ions to generate CuET, which shows antitumor efficacy. CuET-mediated chemotherapy can be enhanced by H2O2. Sufficient Cu2+ generation can guarantee the maximum efficacy of DSF-mediated chemotherapy. Furthermore, released Cu2+ can be reduced to Cu+ by glutathione (GSH) and O2- in tumor cells, and Cu+ can react with H2O2 to generate toxic hydroxyl radicals (·OH) via a Fenton-like reaction, promoting the efficacy of CuET. Therefore, this study hypothesizes that employing CPNDs instead of Cu2+ ions could enhance DSF-mediated melanoma chemotherapy, providing a simple but efficient strategy for achieving chemotherapeutic efficacy.

Predicting the malignancy of extremity soft-tissue tumors by an ultrasound-based radiomics signature.

BACKGROUND: Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning. PURPOSE: To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy. MATERIAL AND METHODS: A dataset of US images from 108 ESTTs were retrospectively enrolled and divided into the training cohort (78 ESTTs) and validation cohort (30 ESTTs). A total of 1037 radiomics features were extracted from each US image. The most useful predictive radiomics features were selected by the maximum relevance and minimum redundancy method, least absolute shrinkage, and selection operator algorithm in the training cohort. A US-based radiomics signature was built based on these selected radiomics features. In addition, a conventional radiologic model based on the US features from the interpretation of two experienced radiologists was developed by a multivariate logistic regression algorithm. The diagnostic performances of the selected radiomics features, the US-based radiomics signature, and the conventional radiologic model for differentiating ESTTs were evaluated and compared in the validation cohort. RESULTS: In the validation cohort, the area under the curve (AUC), sensitivity, and specificity of the US-based radiomics signature for predicting ESTTs malignancy were 0.866, 84.2%, and 81.8%, respectively. The US-based radiomics signature had better diagnostic predictability for predicting ESTT malignancy than the best single radiomics feature and the conventional radiologic model (AUC = 0.866 vs. 0.719 vs. 0.681 for the validation cohort, all P <0.05). CONCLUSION: The US-based radiomics signature could provide a potential imaging biomarker to accurately predict ESTT malignancy.

Predictors of High Cardiovascular Risk Among Nonobese Patients with Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease in a Chinese Population.

Purpose: This study aims to investigate cardiovascular risk factors in nonobese patients with type 2 diabetes (T2DM) and non-alcoholic fatty liver disease (NAFLD) and to determine whether they might be used to predict high-risk individuals effectively. Patients and Methods: This cross-sectional study included 245 nonobese patients with T2DM who underwent FibroTouch in the National Metabolic Management Center of the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University from January 2021 to December 2022. All individuals were divided into NAFLD and non-NAFLD groups. Patients with NAFLD were further grouped by UAP tertiles (T1, T2 and T3). We created a Cardiovascular Score (total scale: 0-5 points; ≥3 points was defined as high-risk individual) based on baPWV, carotid ultrasound, and urinary microalbumin creatinine ratio (UA/CR) to assess the risk of cardiovascular disease in non-obese T2DM patients with NAFLD. Risk factors were evaluated using univariate and multivariate analysis. The performance of risk factors was compared according to the area under the receiver operating characteristic (ROC) curve. Results: Atherogenic index of plasma (AIP), atherosclerosis index (AI), prevalence of hypertension, body mass index (BMI) and homeostatic model assessment of insulin resistance (HOMA-IR) were higher in the NAFLD group compared to the non-NAFLD group. In T3 group, AIP, AI, BMI and HOMA-IR were higher than those of T1 group. Multivariate logistic regression showed that age, systolic blood pressure, low-density lipoprotein-cholesterol (LDL-C) and AIP were risk factors for cardiovascular disease among nonobese patients with T2DM and NAFLD. The area under the ROC curve for age, systolic blood pressure, LDL-C and AIP were 0.705, 0.688, 0.738 and 0.642, respectively. The area under the ROC curve was 0.895 when combining them. Conclusion: Age, systolic blood pressure, AIP and LDL-C are all independent risk factors for cardiovascular disease in non-obese individuals with T2DM and NAFLD, which can be combined to identify high-risk populations and carry out intervention.