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Abstract Background Off-targeted distribution of chemotherapeutic drugs causes severe side effects, further leading to poor prognosis and patient compliance. Ligand/receptor-mediated targeted drug delivery can improve drug accumulation in the tumor but it always attenuated by protein corona barriers. Results To address these problems, a radically different strategy is proposed that can leave the off-targeted drugs inactive but activate the tumor-distributed drugs for cancer-targeting therapy in a tumor microenvironment-independent manner. The feasibility and effectiveness of this strategy is demonstrated by developing an ultrasound (US)-activated prodrug-loaded liposome (CPBSN38L) comprising the sonosensitizer chlorin e6 (Ce6)-modified lipids and the prodrug of pinacol boronic ester-conjugated SN38 (PBSN38). Once CPBSN38L is accumulated in the tumor and internalized into the cancer cells, under US irradiation, the sonosensitizer Ce6 rapidly induces extensive production of intracellular reactive oxygen species (ROS), thereby initiating a cascade amplified ROS-responsive activation of PBSN38 to release the active SN38 for inducing cell apoptosis. If some of the injected CPBSN38L is distributed into normal tissues, the inactive PBSN38 exerts no pharmacological activity on normal cells. CPBSN38L exhibited strong anticancer activity in multiple murine tumor models of colon adenocarcinoma and hepatocellular carcinoma with no chemotherapy-induced side effects, compared with the standard first-line anticancer drugs irinotecan and topotecan. Conclusions This study established a side-effect-evitable, universal, and feasible strategy for cancer-targeting therapy. Graphical Abstract

Infections caused by Pseudomonas aeruginosa biofilm significantly endanger human health worldwide. Biofilms are closely associated with antibiotic resistance because biofilms significantly undermine the efficacy of antibiotics. A novel ultrasound-launched targeted nanoparticle was developed to universally destroy biofilm, target bacteria, deliver antibiotics, and efficiently kill bacteria via ultrasonic cavitation and antibacterial sonodynamic therapy. The nanoparticle consisted of poly (lactic-co-glycolic acid) loading ciprofloxacin and perfluoropentane with a bacteria-targeted antibody installed on the nanoparticle for binding to specific bacteria. The nanoparticle exhibited a sensitive response to ultrasound and the rapid liquid-gas phase transition of perfluoropentane resulted in a cavitation effect that destroyed the extracellular polymeric substances of the biofilm and allowed deep penetration of the antibiotics. In addition, ciprofloxacin induced additional reactive oxygen species production under ultrasound stimulation, leading to an enhanced bactericidal effect and potent anti-infective activity in vivo. This study presents an effective strategy to tackle the extracellular polymeric substance barriers for overcoming antibiotic resistance and removing a biofilm.

The blood–brain barrier (BBB) maintains brain homeostasis, regulates influx and efflux transport, and provides protection to the brain tissue. Ultrasound (US) and microbubble (MB)-mediated blood–brain barrier opening is an effective and safe technique for drug delivery in-vitro and in-vivo. However, the exact mechanism underlying this technique is still not fully elucidated. The aim of the study is to explore the contribution of transcytosis in the BBB transient opening using an in-vitro model of BBB. Utilizing a diverse set of techniques, including Ca2+ imaging, electron microscopy, and electrophysiological recordings, our results showed that the combined use of US and MBs triggers membrane deformation within the endothelial cell membrane, a phenomenon primarily observed in the US + MBs group. This deformation facilitates the vesicles transportation of 500 kDa fluorescent Dextran via dynamin-/caveolae-/clathrin- mediated transcytosis pathway. Simultaneously, we observed increase of cytosolic Ca2+ concentration, which is related with increased permeability of the 500 kDa fluorescent Dextran in-vitro. This was found to be associated with the Ca2+-protein kinase C (PKC) signaling pathway. The insights provided by the acoustically-mediated interaction between the microbubbles and the cells delineate potential mechanisms for macromolecular substance permeability.

Abstract In order to provide clinical references for the RFA procedure and to study the pivotal factors affecting the recovery time of radiofrequency ablation (RFA) in patients with papillary thyroid microcarcinoma (PTMC), 176 patients with low-risk intrathyroidal PTMC were included in this research. We randomly divided the whole cohort into training and test groups at a ratio of 7:3. The two-sample t-test was used to detect differences between the two groups. Least absolute shrinkage and selection operator (LASSO) regression was used to select the best predictor variables for predicting the status of RFA zone. Multiple test methods were used to ensure the scientific nature and accuracy of the Cox proportional hazards model. We tested the performance for the parameters and revealed the best cut-off value of each variable by the ROC curve and log-rank tests. The results showed patients aged above 49 years old, with RFA energy above 2800 J, the average diameter of the original tumour above 0.6 cm, or the average diameter of ablation zone at 1 month after RFA above 1.1 cm are risk factors for RFA zone delayed healing.

Abstract Background Sonodynamic therapy (SDT) induces immunogenic cell death (ICD) in tumors and promises to play an assistive role in immunotherapy in pancreatic cancer. However, the short half-life and limited diffusion distance of reactive oxygen species (ROS) impair ICD induction, especially in tumors with relatively poor blood perfusion and dense stroma. Results To address this problem, we fabricated cavitation-assisted endoplasmic reticulum (ER) targeted sonodynamic nanodroplets (PMPS NDs, 329 nm). The good sonodynamic effect and precise endoplasmic reticulum target effect was verified. After intravenous injection, the cRGD peptide modified nanodroplets initially aggregated around the tumor vascular endothelial cells. Stimulated by ultrasound, the liquid-to-gas bubbles began to oscillate and cavitate. This acoustic droplet evaporation strategy facilitated transport of the nanoparticle across the vessel, with deep penetration. This loosened the tumor stroma and facilitated accumulation and penetration of loaded sonosensitizer after 6 h. The modified sonosensitizer can selectively accumulate in the ER to generate a large amount of ROS in situ, inducing potent ER stress, amplified ICD and dendritic cell maturation in vitro and in vivo. Furthermore, the elevated antitumor effect of SDT plus anti-PD-L1 immunotherapy was verified using an orthotopic tumor model. Conclusions This study reports a cavitation assisted ER targeted sonodynamic therapy that can enhance the effect of anti-PD-L1 immunotherapy effectively in orthotopic and distant pancreatic cancer. Graphical Abstract

Antimicrobial resistance is an aggravating public health problem worldwide, with more than 700 000 deaths attributable to infections caused by antibiotic‐resistant bacteria annually. To tackle this challenge, it is important to design appropriate regimens based on data regarding the species identity of bacterial pathogen concerned, as well as their antimicrobial‐resistance genotypes and phenotypes. Herein, a novel method that utilizes artificial intelligence to analyze Raman spectra to identify microbes and their susceptibility to commonly used antibiotics at both genotype and phenotype level is developed. A total of 130 strains of Enterococcus spp. and Staphylococcus capitis with known minimum inhibitory concentrations (MICs) of commonly used antimicrobial agents are included in this study. After the models are configured and trained, long short‐term memory (LSTM) based Raman platform is developed and is found to be able to offer an accuracy of 89.9 ± 1.1%, 82.4 ± 0.6%, and 60.4–89.2% in bacterial species classification, identification of antimicrobial‐resistance genes (ARGs), and prediction of resistance phenotypes, respectively. This novel method exhibits higher level of accuracy than those using the machine learning algorithms. The results indicate that Raman spectroscopy combined with LSTM analysis can be used for rapid bacterial species identification, detection of ARGs, and assessment of drug‐resistance phenotypes.

BackgroundConventional ultrasound (CUS) is the first choice for discrimination benign and malignant lymphadenectasis in supraclavicular lymph nodes (SCLNs), which is important for the further treatment. Radiomics provide more comprehensive and richer information than radiographic images, which are imperceptible to human eyes.ObjectiveThis study aimed to explore the clinical value of CUS-based radiomics analysis in preoperative differentiation of malignant from benign lymphadenectasis in CUS suspected SCLNs.MethodsThe characteristics of CUS images of 189 SCLNs were retrospectively analyzed, including 139 pathologically confirmed benign SCLNs and 50 malignant SCLNs. The data were randomly divided (7:3) into a training set (n=131) and a validation set (n=58). A total of 744 radiomics features were extracted from CUS images, radiomics score (Rad-score) built were using least absolute shrinkage and selection operator (LASSO) logistic regression. Rad-score model, CUS model, radiomics-CUS (Rad-score + CUS) model, clinic-radiomics (Clin + Rad-score) model, and combined CUS-clinic-radiomics (Clin + CUS + Rad-score) model were built using logistic regression. Diagnostic accuracy was assessed by receiver operating characteristic (ROC) curve analysis.ResultsA total of 20 radiomics features were selected from 744 radiomics features and calculated to construct Rad-score. The AUCs of Rad-score model, CUS model, Clin + Rad-score model, Rad-score + CUS model, and Clin + CUS + Rad-score model were 0.80, 0.72, 0.85, 0.83, 0.86 in the training set and 0.77, 0.80, 0.82, 0.81, 0.85 in the validation set. There was no statistical significance among the AUC of all models in the training and validation set. The calibration curve also indicated the good predictive performance of the proposed nomogram.ConclusionsThe Rad-score model, derived from supraclavicular ultrasound images, showed good predictive effect in differentiating benign from malignant lesions in patients with suspected supraclavicular lymphadenectasis.

Summary Klebsiella pneumoniae has become the number one bacterial pathogen that causes high mortality in clinical settings worldwide. Clinical K. pneumoniae strains with carbapenem resistance and/or hypervirulent phenotypes cause higher mortality comparing with classical K. pneumoniae strains. Rapid differentiation of clinical K. pneumoniae with high resistance/hypervirulence from classical K. pneumoniae would allow us to develop rational and timely treatment plans. In this study, we developed a convolution neural network (CNN) as a prediction method using Raman spectra raw data for rapid identification of ARGs, hypervirulence‐encoding factors and resistance phenotypes from K. pneumoniae strains. A total of 71 K. pneumoniae strains were included in this study. The minimum inhibitory concentrations (MICs) of 15 commonly used antimicrobial agents on K. pneumoniae strains were determined. Seven thousand four hundred fifty‐five spectra were obtained using the InVia Reflex confocal Raman microscope and used for deep learning‐based and machine learning (ML) algorithms analyses. The quality of predictors was estimated in an independent data set. The results of antibiotic resistance and virulence‐encoding factors identification showed that the CNN model not only simplified the classification system for Raman spectroscopy but also provided significantly higher accuracy to identify K. pneumoniae with high resistance and virulence when compared with the support vector machine (SVM) and logistic regression (LR) models. By back‐testing the Raman‐CNN platform on 71 K. pneumoniae strains, we found that Raman spectroscopy allows for highly accurate and rationally designed treatment plans against bacterial infections within hours. More importantly, this method could reduce healthcare costs and antibiotics misuse, limiting the development of antimicrobial resistance and improving patient outcomes.

Abstract The aim of this study was to identify the applicability of an ultrasound-guided attenuation parameter (UGAP) for the noninvasive assessment of hepatic steatosis in clinical practice and to compare its correlation with B-mode ultrasound (US). From May to July 2021, 63 subjects with different body mass index (BMI) grades were included in the prospective study. All of them performed UGAP measurements, under different breathing manipulations, positions, diet statuses, and operators. After that, the UGAP values were compared with the visual grades of hepatic steatosis on B-mode US using a 4-point scale method. The intraclass correlation (ICC) of the UGAP values between the two radiologists was 0.862 (p 0.05), positions (p > 0.05), or diet statuses (p = 0.300). The UGAP values in the fasting (supine position, segment V, 1) condition among the lean (BMI

BackgroundHashimoto thyroiditis (HT) is the most common autoimmune thyroid disease and is considered an independent risk factor for papillary thyroid carcinoma (PTC), with a higher incidence of PTC in patients with HT.ObjectiveTo build an integrated nomogram using clinical information and ultrasound-based radiomics features in patients with papillary thyroid carcinoma (PTC) with Hashimoto thyroiditis (HT) to predict central lymph node metastasis (CLNM).MethodsIn total, 235 patients with PTC with HT were enrolled in this study, including 101 with CLNM and 134 without CLNM. They were divided randomly into training and validation datasets with a 7:3 ratio for developing and evaluating clinical features plus conventional ultrasound features (Clin-CUS) model and clinical features plus radiomics scores (Clin-RS) model, respectively. In the Clin-RS model, the Pyradiomics package (V1.3.0) was used to extract radiomics variables, and LASSO regression was used to select features and construct radiomics scores (RS). The Clin-CUS and Clin-RS nomogram models were built using logistic regression analysis.ResultsTwenty-seven CLNM-associated radiomics features were selected using univariate analysis and LASSO regression from 1488 radiomics features and were calculated to construct the RS. The integrated model (Clin-RS) had better diagnostic performance than the Clin-CUS model for differentiating CLNM in the training dataset (AUC: 0.845 vs. 0.778) and the validation dataset (AUC: 0.808 vs. 0.751), respectively.ConclusionOur findings suggest that applying an ultrasound-based radiomics approach can effectively predict CLNM in patients with PTC with HT. By incorporating clinical information and RS, the Clin-RS model can achieve a high diagnostic performance in diagnosing CLNM in patients with PTC with HT.

Cancer is an intractable disease and has ability to escape immunological recognition. Cancer immunotherapy to enhance the autogenous immune response to cancer tissue is reported to be the most promising method for cancer treatment. After the release of damage-associated molecular patterns, dendritic cells come mature and then recruit activated T cells to induce immune response. To trigger the release of cancer associated antigens, cancer acoustics-based therapy has various prominent advantages and has been reported in various research. In this review, we classified the acoustics-based therapy into sonopyrolysis-, sonoporation-, and sonoluminescence-based therapy. Then, detailed mechanisms of these therapies are discussed to show the status of cancer immunotherapy induced by acoustics-based therapy in quo. Finally, we express some future prospects in this research field and make some predictions of its development direction.

BackgroundAn increasing proportion of patients with diabetic kidney disease (DKD) has been observed among incident hemodialysis patients in large cities, which is consistent with the continuous growth of diabetes in the past 20 years.PurposeIn this multicenter retrospective study, we developed a deep learning (DL)-based automatic segmentation and radiomics technology to stratify patients with DKD and evaluate the possibility of clinical application across centers.Materials and MethodsThe research participants were enrolled retrospectively and separated into three parts: training, validation, and independent test datasets for further analysis. DeepLabV3+ network, PyRadiomics package, and least absolute shrinkage and selection operator were used for segmentation, extraction of radiomics variables, and regression, respectively.ResultsA total of 499 patients from three centers were enrolled in this study including 246 patients with type II diabetes mellitus (T2DM) and 253 patients with DKD. The mean intersection-over-union (Miou) and mean pixel accuracy (mPA) of automatic segmentation of the data from the three medical centers were 0.812 ± 0.003, 0.781 ± 0.009, 0.805 ± 0.020 and 0.890 ± 0.004, 0.870 ± 0.002, 0.893 ± 0.007, respectively. The variables from the renal parenchyma and sinus provided different information for the diagnosis and follow-up of DKD. The area under the curve (AUC) of the radiomics model for differentiating between DKD and T2DM patients was 0.674 ± 0.074 and for differentiating between the high and low stages of DKD was 0.803 ± 0.037.ConclusionIn this study, we developed a DL-based automatic segmentation, radiomics technology to stratify patients with DKD. The DL technology was proposed to achieve fast and accurate anatomical-level segmentation in the kidney, and an ultrasound-based radiomics model can achieve high diagnostic performance in the diagnosis and follow-up of patients with DKD.

Medical diagnostic imaging is essential for the differential diagnosis of cervical lymphadenopathy. Here we develop an ultrasound radiomics method for accurately differentiating cervical lymph node tuberculosis (LNTB), cervical lymphoma, reactive lymph node hyperplasia, and metastatic lymph nodes especially in the multi-operator, cross-machine, multicenter context. The inter-observer and intra-observer consistency of radiomics parameters from the region of interest were 0.8245 and 0.9228, respectively. The radiomics model showed good and repeatable diagnostic performance for multiple classification diagnosis of cervical lymphadenopathy, especially in LNTB (area under the curve, AUC: 0.673, 0.662, and 0.626) and cervical lymphoma (AUC: 0.623, 0.644, and 0.602) in the whole set, training set, and test set, respectively. However, the diagnostic performance of lymphadenopathy among skilled radiologists was varied (Kappa coefficient: 0.108, *p < 0.001). The diagnostic performance of radiomics is comparable and more reproducible compared with those of skilled radiologists. Our study offers a more comprehensive method for differentiating LNTB, cervical lymphoma, reactive lymph node hyperplasia, and metastatic LN.

Background and aim: Diagnosing nonalcoholic steatohepatitis (NASH) is challenging. This study intended to explore the diagnostic value of multiple technical acoustic measurements in the diagnosis of NASH, and to establish a diagnostic model combining technical acoustic measurements with clinical parameters to improve the diagnostic efficacy of NASH. Methods: We consecutively enrolled 75 patients with clinically suspected nonalcoholic fatty liver disease (NAFLD) who underwent percutaneous liver biopsy in our hospital from June 2020 to December 2021. All cases underwent multiple advanced acoustic measurements for liver such as shear wave dispersion (SWD), shear wave speed (SWS), attenuation imaging (ATI), normalized local variance (NLV), and liver–kidney intensity ratio (Ratio) examination before liver biopsies. A nomogram prediction model combining the technical acoustic measurements and clinical parameters was established and the model is proposed to improve the diagnostic performance of NASH. Results: A total of 75 cases were included in this study. The classification of pathological grade for NASH was as follows: normal liver, (n = 15, 20%), nonalcoholic fatty liver (NAFL), (n = 44, 58.7%), and NASH, (n = 16, 21.3%). There were statistically significant differences in SWS (p = 0.002), acoustic coefficient (AC) (p = 0.018), NLV (p = 0.033), age (p = 0.013) and fasting blood glucose (Glu) (p = 0.049) between NASH and non-NASH. A nomogram model which includes SWS, AC, NLV, age and Glu was built to predict NASH, and the calibration curves showed good calibrations in both training and validation sets. The AUCs of the combined nomogram model for the training set and validation set were 0.8597 and 0.7794, respectively. Conclusion: There were statistically significant differences in SWS, AC, NLV, age and Glu between NASH and non-NASH. A nomogram model which includes SWS, AC, NLV, age and Glu was built to predict NASH. The predictive model has a higher diagnostic performance than a single factor model in the diagnosis of NASH and has good clinical application prospects.

Summary: Ultrasound (US), as a safe and non-invasive tool, has drawn researchers' attention to treat pancreatic ductal adenocarcinoma (PDAC). Piezo1, a mechanosensitive channel, can be activated by various mechanical stimuli. In this study, we tested the expression of Piezo1 in PDAC cell lines and tissues, and cell apoptosis in vitro and in vivo with siRNA, a lentivirus system, and a subcutaneous xenograft tumor-bearing model under the condition of US with microbubbles (MBs). We found that Piezo1 was highly expressed in PDAC cells; it was activated by US with MBs and was closely related to the apoptosis of PDAC cell lines and tumors. This study highlighted the idea of utilizing the high expression of Piezo1 in PDAC and US with MBs to provide a non-invasive strategy for the treatment of PDAC from the aspect of mechanotransduction.

For disposal of scrap tyres, there have been a lot of studies done by previous researchers, but it yet remains a longstanding challenge in practice. To find a solution for this problem, the feasibility of a proposed approach in a laboratory scale was tested. The pyrolysis of scrap tyres pretreated by waste coal tar (noted as “as-pretreated” tyres) was performed in a batch reactor at atmospheric pressure. Waste coal tar was used to modify the scrap tyres to improve the pyrolysis efficiency of scrap tyres input and the quality of the product tar. The swelling behavior of scrap tyres soaked in waste coal tar at 10 °C, effect of pyrolysis temperature on the product yields, and the synergies during pyrolysis of the as-pretreated tyres and co-pyrolysis of scrap tyres/waste coal tar mixture were all investigated in detail. The results observed found that by comparison of the calculated yields and the observed experimental yields for pyrolysis products, the synergy from co-pyrolysis of scrap tyres/waste coal tar mixture boosted the gas yield without increasing the tar yield. In contrast, the synergy during pyrolysis of the as-pretreated tyres could enhance the tar yield, but reduced the gas and char yields. According to the distillation range analyses of the tar, more light fractions were found in the tar from pyrolysis of the as-pretreated tyres, in which the gasoline fraction, diesel fraction and kerosene fraction were 38.4 wt%, 46.8 wt% and 50.2 wt%, respectively. This work is expected to be helpful in further use of scrap tyres. Keywords: Scrap tyres, Waste coal tar, Pyrolysis, Synergy, Distillation range analyses