Metabolic Heterogeneity and Potential Immunotherapeutic Responses Revealed by Single-Cell Transcriptomics of Breast Cancer.

BACKGROUND: Breast cancer (BC) exhibits remarkable heterogeneity. However, the transcriptomic heterogeneity of BC at the single-cell level has not been fully elucidated. METHODS: We acquired BC samples from 14 patients. Single-cell RNA sequencing (scRNA-seq), bioinformatic analyses, along with immunohistochemistry (IHC) and immunofluorescence (IF) assays were carried out. RESULTS: According to the scRNA-seq results, 10 different cell types were identified. We found that Cancer-Associated Fibroblasts (CAFs) exhibited distinct biological functions and may promote resistance to therapy. Metabolic analysis of tumor cells revealed heterogeneity in glycolysis, gluconeogenesis, and fatty acid synthetase reprogramming, which led to chemotherapy resistance. Furthermore, patients with multiple metastases and progression were predicted to benefit from immunotherapy based on a heterogeneity analysis of T cells and tumor cells. CONCLUSIONS: Our findings provide a comprehensive understanding of the heterogeneity of BC, provide comprehensive insight into the correlation between cancer metabolism and chemotherapy resistance, and enable the prediction of immunotherapy responses based on T-cell heterogeneity.

Super-resolution ultrasound and microvasculomics: a consensus statement.

This is a summary of a consensus statement on the introduction of "Ultrasound microvasculomics" produced by The Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. The evaluation of microvessels is a very important part for the assessment of diseases. Super-resolution ultrasound (SRUS) microvascular imaging surpasses traditional ultrasound imaging in the morphological and functional analysis of microcirculation. SRUS microvascular imaging relies on contrast microbubbles to gain sensitivity to microvessels and improves the spatial resolution of ultrasound blood flow imaging for a more detailed depiction of vascular structures and hemodynamics. This method has been applied in preclinical animal models and pilot clinical studies, involving areas including neurology, oncology, nephrology, and cardiology. However, the current quantitative parameters of SRUS images are not enough for precise evaluation of microvessels. Therefore, by employing omics methods, more quantification indicators can be obtained, enabling a more precise and personalized assessment of microvascular status. Ultrasound microvasculomics - a high-throughput extraction of image features from SRUS images - is one novel approach that holds great promise but needs further validation in both bench and clinical settings. CLINICAL RELEVANCE STATEMENT: Super-resolution Ultrasound (SRUS) blood flow imaging improves spatial resolution. Ultrasound microvasculomics is possible to acquire high-throughput information of features from SRUS images. It provides more precise and abundant micro-blood flow information in clinical medicine. KEY POINTS: This consensus statement reviews the development and application of super-resolution ultrasound (SRUS). The shortcomings of the current quantification indicators of SRUS and strengths of the omics methodology are addressed. "Ultrasound microvasculomics" is introduced for a high-throughput extraction of image features from SRUS images.

CCE and EODF as two distinct non-shivering thermogenesis models inducing weight loss.

Increasing energy expenditure and reducing energy intake are considered two classical methods to induce weight loss. Weight loss through physical methods instead of drugs has been a popular research topic nowadays, but how these methods function in adipose and cause weight loss in body remains unclear. In this study, we set up chronic cold exposure (CCE) and every-other-day fasting (EODF) as two distinct models in long-term treatment to induce weight loss, recording their own characteristics in changes of body temperature and metabolism. We investigated the different types of non-shivering thermogenesis induced by CCE and EODF in white and brown adipose tissue through sympathetic nervous system (SNS), creatine-driven pathway, and fibroblast growth factor 21 (FGF21)-adiponectin axis. CCE and EODF could reduce body weight, lipid composition, increase insulin sensitivity, promote the browning of white fat, and increase the expression of endogenous FGF21 in adipose tissue. CCE stimulated the SNS and increased the thermogenic function of brown fat, and EODF increased the activity of protein kinase in white fat. In this study, we further explained the thermogenic mechanism function in adipose and metabolic benefits of the stable phenotype through physical treatments used for weight loss, providing more details for the literature on weight loss models. The influence on metabolism, non-shivering thermogenesis, endogenous FGF21, and ADPN changes in the long-term treatment of distinct methods (increasing energy expenditure and decreasing energy intake) to induce weight loss.

MiR-222-3p suppresses C2C12 myoblast proliferation and differentiation via the inhibition of IRS-1/PI3K/Akt pathway.

Numerous studies have revealed the profound impact of microRNAs on regulating skeletal muscle development and regeneration. However, the biological function and regulation mechanism of miR-222-3p in skeletal muscle remains largely unknown. In this study, miR-222-3p was found to be abundantly expressed in the impaired skeletal muscles, indicating that it might have function in the development and regeneration process of the skeletal muscle. MiR-222-3p overexpression impeded C2C12 myoblast proliferation and myogenic differentiation, whereas inhibition of miR-222-3p got the opposite results. The dual-luciferase reporter assay showed that insulin receptor substrate-1 (IRS-1) was the target gene of miR-222-3p. We next found that knockdown of IRS-1 could obviously suppress C2C12 myoblast proliferation and differentiation. Additionally, miR-222-3p-induced repression of myoblast proliferation and differentiation was verified to be associated with a decrease in phosphoinositide 3-kinase (PI3K)-Akt signaling. Overall, we demonstrated that miR-222-3p inhibited C2C12 cells myogenesis via IRS-1/PI3K/Akt pathway. Therefore, miR-222-3p may be used as a therapeutic target for alleviating muscle loss caused by inherited and nonhereditary diseases.

Generalizability and Diagnostic Performance of AI Models for Thyroid US.

Background Artificial intelligence (AI) models have improved US assessment of thyroid nodules; however, the lack of generalizability limits the application of these models. Purpose To develop AI models for segmentation and classification of thyroid nodules in US using diverse data sets from nationwide hospitals and multiple vendors, and to measure the impact of the AI models on diagnostic performance. Materials and Methods This retrospective study included consecutive patients with pathologically confirmed thyroid nodules who underwent US using equipment from 12 vendors at 208 hospitals across China from November 2017 to January 2019. The detection, segmentation, and classification models were developed based on the subset or complete set of images. Model performance was evaluated by precision and recall, Dice coefficient, and area under the receiver operating characteristic curve (AUC) analyses. Three scenarios (diagnosis without AI assistance, with freestyle AI assistance, and with rule-based AI assistance) were compared with three senior and three junior radiologists to optimize incorporation of AI into clinical practice. Results A total of 10 023 patients (median age, 46 years [IQR 37-55 years]; 7669 female) were included. The detection, segmentation, and classification models had an average precision, Dice coefficient, and AUC of 0.98 (95% CI: 0.96, 0.99), 0.86 (95% CI: 0.86, 0.87), and 0.90 (95% CI: 0.88, 0.92), respectively. The segmentation model trained on the nationwide data and classification model trained on the mixed vendor data exhibited the best performance, with a Dice coefficient of 0.91 (95% CI: 0.90, 0.91) and AUC of 0.98 (95% CI: 0.97, 1.00), respectively. The AI model outperformed all senior and junior radiologists (P < .05 for all comparisons), and the diagnostic accuracies of all radiologists were improved (P < .05 for all comparisons) with rule-based AI assistance. Conclusion Thyroid US AI models developed from diverse data sets had high diagnostic performance among the Chinese population. Rule-based AI assistance improved the performance of radiologists in thyroid cancer diagnosis. © RSNA, 2023 Supplemental material is available for this article.

Safety findings after intravenous administration of sulfur hexafluoride microbubbles to 463,434 examinations at 24 centers.

OBJECTIVES: We aimed to evaluate the safety of the ultrasound contrast agent sulfur hexafluoride microbubbles in a large group of patients referred for routine contrast-enhanced ultrasound (CEUS). METHODS: A retrospective assessment was made of all patients that received sulfur hexafluoride microbubbles intravenously for CEUS at 24 centers between January 2006 and April 2019. Patient demographic details, examination type, and the dose of sulfur hexafluoride microbubbles administered were recorded with specific adverse events (AEs) documentation tools at each center. All AEs were recorded as serious or non-serious. Non-serious AEs were classified by intensity as mild, moderate, or severe according to ACR criteria. The frequencies of AEs across patient subgroups were compared using the chi-square test. RESULTS: A total of 463,434 examinations were evaluated. Overall, 157 AEs (153 [0.033%] non-serious; 4 [0.001%] serious) were reported after sulfur hexafluoride microbubbles administration, giving an AE frequency of 0.034% (157/463,434). Among the non-serious AEs, 66 (0.014%) were mild, 70 (0.015%) moderate, and 17 (0.004%) severe in intensity. The liver was the most common examination site, presenting an AE frequency of 0.026%. The highest AE frequency (0.092%) was for patients undergoing CEUS for vascular disease. There were no significant gender differences in either the total number or the severity of non-serious AEs (chi-square = 2.497, p = 0.287). The onset of AEs occurred within 30 min of sulfur hexafluoride microbubbles administration in 91% of cases. CONCLUSION: The frequency of AEs to sulfur hexafluoride microbubbles is very low and severe reactions are rare, confirming that sulfur hexafluoride microbubbles are appropriate for routine CEUS applications. KEY POINT: • The frequency of AEs to sulfur hexafluoride microbubbles is very low and severe reactions are rare.

Endurance Exercise-Induced Fgf21 Promotes Skeletal Muscle Fiber Conversion through TGF-ß1 and p38 MAPK Signaling Pathway.

Fgf21 has been identified as playing a regulatory role in muscle growth and function. Although the mechanisms through which endurance training regulates skeletal muscle have been widely studied, the contribution of Fgf21 remains poorly understood. Here, muscle size and function were measured, and markers of fiber type were evaluated using immunohistochemistry, immunoblots, or qPCR in endurance-exercise-trained wild-type and Fgf21 KO mice. We also investigated Fgf21-induced fiber conversion in C2C12 cells, which were incubated with lentivirus and/or pathway inhibitors. We found that endurance exercise training enhanced the Fgf21 levels of liver and GAS muscle and exercise capacity and decreased the distribution of skeletal muscle fiber size, and fast-twitch fibers were observed converting to slow-twitch fibers in the GAS muscle of mice. Fgf21 promoted the markers of fiber-type transition and eMyHC-positive myotubes by inhibiting the TGF-ß1 signaling axis and activating the p38 MAPK signaling pathway without apparent crosstalk. Our findings suggest that the transformation and function of skeletal muscle fiber types in response to endurance training could be mediated by Fgf21 and its downstream signaling pathways. Our results illuminate the mechanisms of Fgf21 in endurance-exercise-induced fiber-type conversion and suggest a potential use of Fgf21 in improving muscle health and combating fatigue.

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer.

OBJECTIVES: To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer. METHODS: Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status-related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration. RESULTS: SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773-0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765-0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777-0.914) for the training cohort and 0.817 (95%CI, 0.769-0.865) for the validation cohort. The tool could also discriminate between low (N + (1-2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742-0.913) in the training cohort and 0.810 (95%CI, 0.755-0.864) in the validation cohort. CONCLUSION: The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making. KEY POINTS: • Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy. • This multicentre retrospective study showed that radiomics nomogram based on shear-wave elastography provides incremental information for risk stratification. • Treatment can be given with more precision based on the model.

Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study.

OBJECTIVES: To evaluate the prediction performance of deep convolutional neural network (DCNN) based on ultrasound (US) images for the assessment of breast cancer molecular subtypes. METHODS: A dataset of 4828 US images from 1275 patients with primary breast cancer were used as the training samples. DCNN models were constructed primarily to predict the four St. Gallen molecular subtypes and secondarily to identify luminal disease from non-luminal disease based on the ground truth from immunohistochemical of whole tumor surgical specimen. US images from two other institutions were retained as independent test sets to validate the system. The models' performance was analyzed using per-class accuracy, positive predictive value (PPV), and Matthews correlation coefficient (MCC). RESULTS: The model achieved good performance in identifying the four breast cancer molecular subtypes in the two test sets, with accuracy ranging from 80.07% (95% CI, 76.49-83.23%) to 97.02% (95% CI, 95.22-98.16%) and 87.94% (95% CI, 85.08-90.31%) to 98.83% (95% CI, 97.60-99.43) for the two test cohorts for each sub-category, respectively. In terms of 4-class weighted average MCC, the model achieved 0.59 for test cohort A and 0.79 for test cohort B. Specifically, the DCNN also yielded good diagnostic performance in discriminating luminal disease from non-luminal disease, with a PPV of 93.29% (95% CI, 90.63-95.23%) and 88.21% (95% CI, 85.12-90.73%) for the two test cohorts, respectively. CONCLUSION: Using pretreatment US images of the breast cancer, deep learning model enables the assessment of molecular subtypes with high diagnostic accuracy. TRIAL REGISTRATION: Clinical trial number: ChiCTR1900027676 KEY POINTS: • Deep convolutional neural network (DCNN) helps clinicians assess tumor features with accuracy. • Multicenter retrospective study shows that DCNN derived from pretreatment ultrasound imagine improves the prediction of breast cancer molecular subtypes. • Management of patients becomes more precise based on the DCNN model.

Porcine parvovirus infection impairs progesterone production in luteal cells through mitogen-activated protein kinases, p53, and mitochondria-mediated apoptosis.

Porcine parvovirus (PPV) is a major virus that leads to fetal death in swine. However, the effects of PPV infection on sows are poorly understood. The aim of this study was to investigate the effects of PPV on porcine steroidogenic luteal cells (SLCs) survival and functions and underlying mechanisms. In vivo experiment results showed that artificial infection of PPV significantly reduced the concentration of serum progesterone and induced histopathological lesions and SLCs apoptosis in porcine corpora luteum. In in vitro cultured primary porcine SLCs, PPV could infect and replicate in SLCs and induced SLCs apoptosis through mitochondria, but not the death receptor, mediated apoptosis pathway. Meanwhile, PPV infection also decreased progesterone production in SLCs. Moreover, PPV infection could increase active p53 transcriptional activity and protein expression as well as promoting p53 translocation to nucleus. Using the p53-specific pharmacological inhibitor (pifithrin-α) and siRNA could partially attenuate PPV-induced Bax upregulation, caspase-3 activation, apoptosis, and the reduction of progesterone production in primary porcine SLCs. Furthermore, the phosphorylation of p38 mitogen-activated protein kinase (MAPK) was also increased in PPV-infected SLCs. Pretreatment with p38 MAPK inhibitor (SB203580) suppressed PPV-induced p53 accumulation and translocation, SLCs apoptosis, and progesterone production reduction. In summary, these findings indicate that PPV could induce SLCs apoptosis and a decrease of progesterone production in vivo and in vitro via p38 MAPK signaling and p53-dependent mitochondrial pathway, which provides the potential clinical therapy methods for PPV infection.

Porcine parvovirus infection induces apoptosis in PK-15 cells through activation of p53 and mitochondria-mediated pathway.

Porcine parvovirus (PPV) infection has been reported to induce the cytopathic effects (CPE) in some special host cells and contribute the occurrence of porcine parvovirus disease, but the molecular mechanisms underlying PPV-induced CPE are not clear. In this study, we investigated the morphological and molecular changes of porcine kidney cell line (PK-15 cells) infected with PPV. The results showed that PPV infection inhibited the viability of PK-15 cells in a time and concentration dependent manner. PPV infection induced typical apoptotic features including chromatin condensation, apoptotic body formation, nuclear fragmentation, and Annexin V-binding activity. Further studies showed that Bax was increased and translocated to mitochondria, whereas Bcl-2 was decreased in PPV-infected cells, which caused mitochondrial outer-membrane permeabilization, resulting in the release of mitochondrial cytochrome c, followed by caspase-9 and caspase-3 activation. However, the expression of Fas and Fas ligand (FasL) did not appear significant changes in the process of PPV-induced apoptosis. Moreover, PPV infection activated p53 signaling, which was involved in the activation of apoptotic signaling induced by PPV infection via regulation of Bax and Bcl-2. Taken together, our results demonstrated that PPV infection induced apoptosis in PK-15 cells through activation of p53 and mitochondria-mediated apoptosis pathway. This study may contribute to shed light on the molecular pathogenesis of PPV infection.

Myogenic exosome miR-140-5p modulates skeletal muscle regeneration and injury repair by regulating muscle satellite cells.

Muscle satellite cells (SCs) play a crucial role in the regeneration and repair of skeletal muscle injuries. Previous studies have shown that myogenic exosomes can enhance satellite cell proliferation, while the expression of miR-140-5p is significantly reduced during the repair process of mouse skeletal muscle injuries induced by BaCl2. This study aims to investigate the potential of myogenic exosomes carrying miR-140-5p inhibitors to activate SCs and influence the regeneration of injured muscles. Myogenic progenitor cell exosomes (MPC-Exo) and contained miR-140-5p mimics/inhibitors myogenic exosomes (MPC-Exo140+ and MPC-Exo140-) were employed to treat SCs and use the model. The results demonstrate that miR-140-5p regulates SC proliferation by targeting Pax7. Upon the addition of MPC-Exo and MPC-Exo140-, Pax7 expression in SCs significantly increased, leading to the transition of the cell cycle from G1 to S phase and an enhancement in cell proliferation. Furthermore, the therapeutic effect of MPC-Exo140- was validated in animal model, where the expression of muscle growth-related genes substantially increased in the gastrocnemius muscle. Our research demonstrates that MPC-Exo140- can effectively activate dormant muscle satellite cells, initiating their proliferation and differentiation processes, ultimately leading to the formation of new skeletal muscle cells and promoting skeletal muscle repair and remodeling.

The PCV3 Cap Virus-like Particle Vaccine with the Chimeric PCV2-Neutralizing Epitope Gene Is Effective in Mice.

Porcine circovirus type 3 (PCV3) infection can cause symptoms similar to those of porcine circovirus type 2 (PCV2) infection, and coinfections with both PCV2 and PCV3 are observed in the swine industry. Consequently, developing chimeric vaccines is essential to prevent and control porcine circovirus infections. In this study, we used both E. coli and mammalian expression systems to express PCV3 Cap (Cap3) and a chimeric gene containing the PCV2-neutralizing epitope within the PCV3 Cap (Cap3-Cap2E), which were assembled into virus-like particle (VLP) vaccines. We found that Cap3 lacking nuclear localization signal (NLS) could not form VLPs, while Cap3 with a His-tag successfully assembled into VLPs. Additionally, the chimeric of PCV2-neutralizing epitopes did not interfere with the assembly process of VLPs. Various immunization approaches revealed that pCap3-Cap2E VLP vaccines were capable of activating high PCV3 Cap-specific antibody levels and effectively neutralizing both PCV3 and PCV2. Furthermore, pCap3-Cap2E VLPs demonstrated a potent ability to activate cellular immunity, protecting against PCV3 infection and preventing lung damage in mice. In conclusion, this study successfully developed a PCV3 Cap VLP vaccine incorporating chimeric PCV2-neutralizing epitope genes, providing new perspectives for PCV3 vaccine development.

Oleanolic acid alleviates obesity-induced skeletal muscle atrophy via the PI3K/Akt signaling pathway.

Oleanolic acid (OA) is a pentacyclic triterpene with reported protective effects against various diseases, including diabetes, hepatitis, and different cancers. However, the effects of OA on obesity-induced muscle atrophy remain largely unknown. This study investigated the effects of OA on skeletal muscle production and proliferation of C2C12 cells. We report that OA significantly increased skeletal muscle mass and improved glucose intolerance and insulin resistance. OA inhibited dexamethasone (Dex)-induced muscle atrophy in C2C12 myoblasts by regulating the PI3K/Akt signaling pathway. In addition, it also inhibited expression of MuRF1 and Atrogin1 genes in skeletal muscle of obese mice suffering from muscle atrophy, and increased the activation of PI3K and Akt, thereby promoting protein synthesis, and eventually alleviating muscle atrophy. Taken together, these findings suggest OA may have potential for the prevention and treatment of muscle atrophy.

Exercise-inducible circulating extracellular vesicle irisin promotes browning and the thermogenic program in white adipose tissue.

AIM: Exercise can reduce body weight and promote white fat browning, but the underlying mechanisms remain largely unknown. This study investigated the role of fibronectin type III domain-containing protein 5 (FNDC5)/Irisin, a hormone released from exercising muscle, in the browning of white fat in circulating extracellular vesicles (EVs). METHODS: Mice were subjected to a 4 weeks of running table exercise, and fat browning was analyzed via histology, protein blotting and qPCR. Circulating EVs were extracted by ultrahigh-speed centrifugation, and ELISA was used to measure the irisin concentration in the circulating EVs. Circulating EVs that differentially expressed irisin were applied to adipocytes, and the effect of EV-irisin on adipocyte energy metabolism was analyzed by immunofluorescence, protein blotting, and cellular oxygen consumption rate analysis. RESULTS: During sustained exercise, the mice lost weight and developed fat browning. FNDC5 was induced, cleaved, and secreted into irisin, and irisin levels subsequently increased in the plasma during exercise. Interestingly, irisin was highly expressed in circulating EVs that effectively promoted adipose browning. Mechanistically, the circulating EV-irisin complex is transported intracellularly by the adipocyte membrane receptor integrin αV, which in turn activates the AMPK signaling pathway, which is dependent on mitochondrial uncoupling protein 1 to cause mitochondrial plasmonic leakage and promote heat production. After inhibition of the AMPK signaling pathway, the effects of the EV-irisin on promoting fat browning were minimal. CONCLUSION: Exercise leads to the accumulation of circulating EV-irisin, which enhances adipose energy metabolism and thermogenesis and promotes white fat browning in mice, leading to weight loss.

An MRI-based Radiomics Approach to Improve Breast Cancer Histological Grading.

RATIONALE AND OBJECTIVES: Nottingham histological grade (NHG) 2 breast cancer has an intermediate risk of recurrence, which is not informative for therapeutic decision-making. We sought to develop and independently validate an MRI-based radiomics signature (Rad-Grade) to improve prognostic re-stratification of NHG 2 tumors. MATERIALS AND METHODS: Nine hundred-eight subjects with invasive breast cancer and preoperative MRI scans were retrospectively obtained. The NHG 1 and 3 tumors were randomly split into training and independent test cohort, with the NHG 2 as the prognostic validation set. From MRI image features, a radiomics-based signature predictive of the histological grade was built by use of the LASSO logistic regression algorithm. The model was developed for identifying NHG 1 and 3 radiological patterns, followed with re-stratification of NHG 2 tumors into Rad-Grade (RG)2-low (NHG 1-like) and RG2-high (NHG 3-like) subtypes using the learned patterns, and the prognostic value was assessed in terms of recurrence-free survival (RFS). RESULTS: The Rad-Grade showed independent prognostic value for re-stratification of NHG 2 tumors, where RG2-high had an increased risk for recurrence (HR 2.20, 1.10-4.40, p = 0.026) compared with RG2-low after adjusting for established risk factors. RG2-low shared similar phenotypic characteristics and RFS outcomes with NHG 1, and RG2-high with NHG 3, revealing that the model captures radiomic features in NHG 2 that are associated with different aggressiveness. The prognostic value of Rad-Grade was further validated in the NHG2 ER+ (HR 2.53, 1.13-5.56, p = 0.023) and NHG 2 ER+LN- (HR 5.72, 1.24-26.44, p = 0.025) subgroups, and in specific treatment contexts. CONCLUSION: The radiomics-based re-stratification of NHG 2 tumors offers a cost-effective promising alternative to gene expression profiling for tumor grading and thus may improve clinical decisions.

A deep learning model for the differential diagnosis of benign and malignant salivary gland tumors based on ultrasound imaging and clinical data.

Background: The preoperative differentiation between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs) is of great significance for therapeutic decision-making. Deep learning (DL), an artificial intelligence algorithm based on neural networks, can help overcome inconsistencies in conventional ultrasonic (CUS) examination outcomes. Therefore, as an auxiliary diagnostic tool, DL can support accurate diagnosis using massive ultrasonic (US) images. This current study developed and validated a DL-based US diagnosis for the preoperative differentiation of BPGT from MPGT. Methods: A total of 266 patients, including 178 patients with BPGT and 88 patients with MPGT, were consecutively identified from a pathology database and enrolled in this study. Ultimately, considering the limitations of the DL model, 173 patients were selected from the 266 patients and divided into 2 groups: a training set, and a testing set. US images of the 173 patients were used to construct the training set (including 66 benign and 66 malignant PGTs) and testing set (consisting of 21 benign and 20 malignant PGTs). These were then preprocessed by normalizing the grayscale of each image and reducing noise. Processed images were imported into the DL model, which was then trained to predict the images from the testing set and evaluated for performance. Based on the training and validation datasets, the diagnostic performance of the 3 models was assessed and verified using receiver operating characteristic (ROC) curves. Ultimately, before and after combining the clinical data, we compared the area under the curve (AUC) and diagnostic accuracy of the DL model with the opinions of trained radiologists to evaluate the application value of the DL model in US diagnosis. Results: The DL model showed a significantly higher AUC value compared to doctor 1 + clinical data, doctor 2 + clinical data, and doctor 3 + clinical data (AUC =0.9583 vs. 0.6250, 0.7250, and 0.8025 respectively; all P<0.05). In addition, the sensitivity of the DL model was higher than the sensitivities of the doctors combined with clinical data (97.2% vs. 65%, 80%, and 90% for doctor 1 + clinical data, doctor 2 + clinical data, and doctor 3 + clinical data, respectively; all P<0.05). Conclusions: The DL-based US imaging diagnostic model has excellent performance in differentiating BPGT from MPGT, supporting its value as a diagnostic tool for the clinical decision-making process.

Bone marrow mesenchymal stem cell-derived exosomes reduce insulin resistance and obesity in mice via the PI3K/AKT signaling pathway.

Obesity is a common chronic metabolic disease that induces chronic systemic inflammation in the body, eventually leading to related complications such as insulin resistance (IR), type 2 diabetes mellitus, and metabolic syndromes such as cardiovascular disease. Exosomes transfer bioactive substances to neighboring or distal cells through autosomal, paracrine, or distant secretion, regulating the gene and protein expression levels of receptor cells. In this study, we investigated the effect of mouse bone marrow mesenchymal stem cell-derived exosomes (BMSC-Exos) on high-fat diet obese mice and mature 3T3-L1 adipocyte models of IR. BMSC-Exo treatment of obese mice promoted their metabolic homeostasis, including reduction of obesity, inhibition of M1-type proinflammatory factor expression, and improvement of insulin sensitivity. In vitro analysis revealed that BMSC-Exos improved IR and lipid droplet accumulation in mature 3T3-L1 adipocytes treated with palmitate (PA). Mechanistically, BMSC-Exos cause increased glucose uptake and improved IR in high-fat chow-fed mice and PA-acting 3T3-L1 adipocytes by activating the phosphoinositide 3-kinases/protein kinase B (PI3K/AKT) signaling pathway and upregulating glucose transporter protein 4 (GLUT4) expression. This study offers a new perspective for the development of treatments for IR in obese and diabetic patients.

Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis.

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped.

Diagnosis of rectal cancer based on the Xception-MS network.

Objective. Accurate T staging of rectal cancer based on ultrasound images is convenient for doctors to determine the appropriate treatment. To effectively solve the problems of low efficiency and accuracy of traditional methods for T staging diagnosis of rectal cancer, a deep-learning-based Xception-MS diagnostic model is proposed in this paper.Approach. The proposed diagnostic model consists of three steps. First, the model preprocesses rectal cancer images to solve the problem of data imbalance and deficiency of sample size, and reduces the risk of model overfitting. Second, a new Xception-MS network with stronger feature extraction capability, which is a combination of the Xception network and MS module, is proposed. The MS module is a new function module that can more effectively extract multi-scale information from rectal cancer images. In addition, to solve the deficiency of the small sample size of rectal cancer, the proposed network is combined with transfer learning technology. At last, the output layer of the network is modified, in which the global average pooling and a fully connected softmax layer are employed to replace the original ones, and then the rectal cancer 4 classification (T1, T2, T3, T4 staging) is output.Main results. Experiments of rectal cancer T staging are conducted on a dataset of 1078 rectal cancer images in 4 categories collected from the Department of Colorectal Surgery of the Third Affiliated Hospital of Kunming Medical University. The experimental results show that the accuracy, precision, recall andF1 values obtained by the model are 94.66%, 94.70%, 94.65%, and 94.67%, respectively.Significance. The experimental results show that our model is superior to the existing classification models, can effectively and automatically classify ultrasound images of rectal cancer, and can better assist doctors in the diagnosis of rectal cancer.