Integrative high-throughput enhancer surveying and functional verification divulges a YY2-condensed regulatory axis conferring risk for osteoporosis.

The precise roles of chromatin organization at osteoporosis risk loci remain largely elusive. Here, we combined chromatin interaction conformation (Hi-C) profiling and self-transcribing active regulatory region sequencing (STARR-seq) to qualify enhancer activities of prioritized osteoporosis-associated single-nucleotide polymorphisms (SNPs). We identified 319 SNPs with biased allelic enhancer activity effect (baaSNPs) that linked to hundreds of candidate target genes through chromatin interactions across 146 loci. Functional characterizations revealed active epigenetic enrichment for baaSNPs and prevailing osteoporosis-relevant regulatory roles for their chromatin interaction genes. Further motif enrichment and network mapping prioritized several putative, key transcription factors (TFs) controlling osteoporosis binding to baaSNPs. Specifically, we selected one top-ranked TF and deciphered that an intronic baaSNP (rs11202530) could allele-preferentially bind to YY2 to augment PAPSS2 expression through chromatin interactions and promote osteoblast differentiation. Our results underline the roles of TF-mediated enhancer-promoter contacts for osteoporosis, which may help to better understand the intricate molecular regulatory mechanisms underlying osteoporosis risk loci.

Measurement properties of assessment tools of Kinesophobia in patients with cardiovascular disease: A systematic review.

Objectives: This study aimed to evaluate the measurement properties and methodological quality of assessment tools for Kinesophobia among patients with cardiovascular disease and provide a reference for healthcare professionals in selecting high-quality assessment tools. Methods: A systematic search was performed on specific databases: Embase, the Cochrane Library, PubMed, Web of Science, China National Knowledge Infrastructure, Wanfang database, China Biological Medicine disc, CINAHL, and China Science and Technology Journal Database, from inception to April 1, 2023. The researchers retrieved studies on the measurement attributes of the exercise fear scale in patients with cardiovascular diseases. They also traced back the references of the included studies to supplement relevant literature. According to the inclusion and exclusion criteria, screening and data extraction were independently undertaken by two reviewers. Two researchers individually used the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) Risk of Bias Checklist to assess the methodological quality of the scale, applied the COSMIN criteria to evaluate the measurement properties of the scale, and used a modified Grading, Recommendations, Assessment, Development, and Evaluation system to assess the certainty of evidence. Results: Seventeen studies were identified that reported the psychometric properties of six patient reported outcome measurement tools (included different languages version) The methodological quality of content validity was adequate in only two studies, the remaining patient-reported outcome measures demonstrated doubtful content validity. Limited information on cross-cultural validity/measurement invariance, measurement error, and responsiveness was retrieved. The Swedish version and the Chinese version of the Tampa Scale for Kinesiophobia Heart were graded "A." The remaining instruments were graded "B." Conclusions: The methodological and measurement attributes of the Swedish and Chinese versions of the Tampa Scale for Kinesiophobia Heart are relatively high quality and can be tentatively recommended. The measurement properties of the remaining scales remain to be verified.

Prognostic models for patients suffering a heart failure with a preserved ejection fraction: a systematic review.

The purpose of this study was to systematically review the development, performance, and applicability of prognostic models developed for predicting poor events in patients with heart failure with preserved ejection fraction (HFpEF). Databases including Embase, PubMed, Web of Science Core Collection, the Cochrane Library, China National Knowledge Infrastructure, Wan Fang, Wei Pu, and China Biological Medicine were queried from their respective dates of inception to 1 June 2023, to examine multivariate models for prognostic prediction in HFpEF. Both forward and backward citations of all studies were included in our analysis. Two researchers individually used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to extract data and assess the quality of the models using the Predictive Mode Bias Risk Assessment Tool (PROBAST). Among the 6897 studies screened, 16 studies derived and/or validated a total of 39 prognostic models. The sample size ranges for model development, internal validation, and external validation are 119 to 5988, 152 to 1000, and 30 to 5957, respectively. The most frequently employed modelling technique was Cox proportional hazards regression. Six studies (37.50%) conducted internal validation of models; bootstrap and k-fold cross-validation were the commonly used methods for internal validation of models. Ten of these models (25.64%) were validated externally, with reported the c-statistic in the external validation set ranging from 0.70 to 0.96, while the remaining models await external validation. The MEDIA echo score and I-PRESERVE-sudden cardiac death prediction mode have been externally validated using multiple cohorts, and the results consistently show good predictive performance. The most frequently used predictors identified among the models were age, n-terminal pro-brain natriuretic peptide, ejection fraction, albumin, and hospital stay in the last 5 months owing to heart failure. All study predictor domains and outcome domains were at low risk of bias, high or unclear risk of bias of all prognostic models due to underreporting in the area of analysis. All studies did not evaluate the clinical utility of the prognostic models. Predictive models for predicting prognostic outcomes in patients with HFpEF showed good discriminatory ability but their utility and generalization remain uncertain due to the risk of bias, differences in predictors between models, and the lack of clinical application studies. Future studies should improve the methodological quality of model development and conduct external validation of models.

The Value of Percutaneous Contrast-Enhanced Ultrasound in Sentinel Lymph Node Identification, Metastatic Status and Burden Diagnosis in Early Breast Cancer.

OBJECTIVE: The aim of this study was to evaluate the value of percutaneous contrast-enhanced ultrasound (PCEUS) in the identification and characterization of sentinel lymph node (SLN). METHODS: A total of 102 breast cancer patients were collected and underwent preoperative PCEUS, which was used to identify SLN and lymphatic drainage. SLNs were classified into 4 enhancement patterns, including 6 subtypes: homogeneous (I), featured inhomogeneous (II) including inhomogeneous hypoenhancement (IIa) and annular or semi-annular enhancement (IIb), focal filling defect (III) including filling defect area < 50% (IIIa) and filling defect area ≥ 50% (IIIb), and no enhancement (IV). The enhancement patterns of SLNs were compared with the final pathological diagnosis. RESULTS: The identification rate of SLNs using PCEUS was 100% (102/102); the rate of identification of LCs was 100% (102/102), and the coincidence rate was 98.0% (100/102). Four lymphatic drainage patterns (LDPs) including 5 subtypes were found: single LC/single SLN(74.5%), multiple LCs/ single SLN (13.7%) including 2 subtypes:2 LCs/1 SLN and 3 LCs/1 SLN, single LC/multiple SLNs (7.8%), and multiple LCs/multiple SLNs (3.9%). A total of 86.3% (44/51) of patients without axillary metastasis could be safely selected for types I, IIa, and IIb, while the axillary metastasis rates of types III and IV were 74.4% and 87.5%, respectively (P < .001). Compared with grayscale US, the PCEUS significant improvement in diagnosing metastatic SLNs (.794 versus .579, P < .001). For the SLN metastatic burden, Types I, IIa, IIb, and IIIa had ≤2 SLNs metastases, with a pathological coincidence rate of (64/67, 95.5%), and types IIIb and IV had >2 SLNs metastases, with a pathological coincidence rate of (25/35, 71.4%) (P < .001). The AUC of PCEUS for the diagnosis of SLN metastatic status and burden was .794 and .879, respectively (P < .001). CONCLUSION: PCEUS has a high identification rate for SLN and has good potential for diagnosing SLN metastatic status and burden by enhancement patterns.

The value of Demetics ultrasound-assisted diagnosis system in diagnosis of breast lesions and in assessment Ki-67 status of breast cancer.

PURPOSE: This study aims to explore the diagnostic efficiency of the Demetics for breast lesions and assessment of Ki-67 status. MATERIAL: This retrospective study included 291 patients. Three combined methods (method 1: upgraded BI-RADS when Demetics classified the breast lesion as malignant; method 2: downgraded BI-RADS when Demetics classified the breast lesion as benign; method 3: BI-RADS was upgraded or downgraded according to Demetrics' diagnosis) were used to compare the diagnostic efficiency of two radiologists with different seniority before and after using Demetics. The correlation between the visual heatmap by Demetics and the Ki-67 expression level of breast cancer was explored. RESULTS: The sensitivity, specificity, and area under curve (AUC) of diagnosis by Demetics, junior radiologist and senior radiologist were 89.5%, 83.1%, 0.863; 76.9%, 82.4%, 0.797 and 81.1%, 89.9%, 0.855, respectively. Method 1 was the best for senior radiologist, which increased AUC from 0.855 to 0.884. For junior radiologist, Method 3 was the best method, improving sensitivity (88.8% vs. 76.9%) and specificity (87.2% vs. 82.4%). Demetics paid more attention to the peripheral area of breast cancer with high expression of Ki-67. CONCLUSION: Demetics has shown good diagnostic efficiency in the assisted diagnosis of breast lesions and is expected to further distinguish Ki-67 status of breast cancer.

Multi-tissue transcriptome-wide association study reveals susceptibility genes and drug targets for insulin resistance-relevant phenotypes.

AIM: Genome-wide association studies (GWAS) have identified multiple susceptibility loci associated with insulin resistance (IR)-relevant phenotypes. However, the genes responsible for these associations remain largely unknown. We aim to identify susceptibility genes for IR-relevant phenotypes via a transcriptome-wide association study. MATERIALS AND METHODS: We conducted a large-scale multi-tissue transcriptome-wide association study for IR (Insulin Sensitivity Index, homeostasis model assessment-IR, fasting insulin) and lipid-relevant traits (high-density lipoprotein cholesterol, triglycerides, low-density lipoprotein cholesterol and total cholesterol) using the largest GWAS summary statistics and precomputed gene expression weights of 49 human tissues. Conditional and joint analyses were implemented to identify significantly independent genes. Furthermore, we estimated the causal effects of independent genes by Mendelian randomization causal inference analysis. RESULTS: We identified 1190 susceptibility genes causally associated with IR-relevant phenotypes, including 58 genes that were not implicated in the original GWAS. Among them, 11 genes were further supported in differential expression analyses or a gene knockout mice database, such as KRIT1 showed both significantly differential expression and IR-related phenotypic effects in knockout mice. Meanwhile, seven proteins encoded by susceptibility genes were targeted by clinically approved drugs, and three of these genes (H6PD, CACNB2 and DRD2) have been served as drug targets for IR-related diseases/traits. Moreover, drug repurposing analysis identified four compounds with profiles opposing the expression of genes associated with IR risk. CONCLUSIONS: Our study provided new insights into IR aetiology and avenues for therapeutic development.

Predictive value for axillary lymph node metastases in early breast cancer: Based on contrast-enhanced ultrasound characteristics of the primary lesion and sentinel lymph node.

OBJECTIVE: To evaluate the value of contrast-enhanced ultrasound (CEUS) characteristics based on primary lesion combined with lymphatic contrast-enhanced ultrasound (LCEUS) patterns of SLN in predicting axillary lymph node metastasis (ALNM) with T1-2N0 breast cancer. METHODS: A retrospective study was conducted in 118 patients with clinically confirmed T1-2N0 breast cancer. Conventional ultrasound (CUS) and CEUS characteristics of the primary lesion and enhancement patterns of SLN were recorded. The risk factors associated with ALNM were selected by univariate and binary logistic regression analysis, and the receiver operating characteristic (ROC) curve was drawn for the evaluation of predictive ALNM metastasis performance. RESULTS: Univariate analysis showed that age, HER-2 status, tumor size, nutrient vessels, extended range of enhancement lesion, and the enhancement patterns of SLN were significant predictive features of ALNM. Further binary logistic regression analysis indicated that the extended range of enhancement lesion (p <  0.001) and the enhancement patterns of SLN (p <  0.001) were independent risk factors for ALNM. ROC analysis showed that the AUC of the combination of these two indicators for predicting ALNM was 0.931 (95% CI: 0.887-0.976, sensitivity: 75.0%, specificity: 99.8%). CONCLUSION: The CEUS characteristics of primary lesion combined with enhancement patterns of SLN are highly valuable in predicting ALNM and can guide clinical axillary surgery decision-making in early breast cancer.

A combined clinical and specific genes' model to predict live birth for in vitro fertilization and embryo transfer patients.

BACKGROUND: We aimed to develop an accurate model to predict live birth for patients receiving in vitro fertilization and embryo transfer (IVF-ET) treatment. METHODS: This is a prospective nested case-control study. Women aged between 18 and 38 years, whose body mass index (BMI) were between the range of 18.5-24 kg/m2, who had an endometrium of ≥ 8 mm at the thickest were enrolled from 2018/9 to 2020/8. All patients received IVF-ET treatment and were followed up until Jan. 2022 when they had reproductive outcomes. Endometrial samples during the window of implantation (LH + 6 to 9 days) were subjected to analyze specific endometrial receptivity genes' expression using real-time PCR (RT-PCR). Patients were divided into live birth group and non-live birth group based on IVF-ET outcomes. Clinical signatures relevant to live birth were collected, analyzed, and used to establish a predictive model for live birth by univariate analysis (clinical model). Specific endometrial receptivity genes' expression was analyzed, selected, and used to construct a predictive model for live birth by The Least Absolute Shrinkage and Selection Operator (LASSO) analysis (gene model). Finally, significant clinical factors and genes were used to construct a combined model for predicting live birth using multivariate logistical regression (combined model). Different models' Area Under Curve (AUC) were compared to identify the most predictive model. RESULTS: Thirty-nine patients were enrolled in the study, twenty-four patients had live births, fifteen did not. In univariate analysis, the odds of live birth for women with ovulation dysfunction was 4 times higher than that for women with other IVF-ET indications (OR = 4.0, 95% CI: 1.125 - 8.910, P = 0.018). Age, body mass index, duration of infertility, primary infertility, repeated implantation failure, antral follicle counting, ovarian sensitivity index, anti-Mullerian hormone, controlled ovarian hyperstimulation protocol and duration, total dose of FSH/hMG, number of oocytes retrieved, regiment of endometrial preparation, endometrium thickness before embryo transfer, type of embryo transferred were not associated with live birth (P > 0.05). Only ovulation dysfunction was used to construct the clinical model and its AUC was 0.688. In lasso analysis, GAST, GPX3, THBS2 were found to promote the risk of live birth. AUCs for GAST, GPX3, THBS2 reached to 0.736, 0.672, and 0.678, respectively. The gene model was established based on these three genes and its AUC was 0.772. Ovulation dysfunction, GAST, GPX3, and THBS2 were finally used to construct the combined model, reaching the highest AUC (AUC = 0.842). CONCLUSIONS: Compared to the single model, the combined model of clinical (Ovulation dysfunction) and specific genes (GAST, GPX3, THBS2) was more accurate to predict live birth for IVF-ET patients.

The associations between dietary fibers intake and systemic immune and inflammatory biomarkers, a multi-cycle study of NHANES 2015-2020.

Background: In recent years, there has been considerable growth in abnormal inflammatory reactions and immune system dysfunction, which are implicated in chronic inflammatory illnesses and a variety of other conditions. Dietary fibers have emerged as potential regulators of the human immune and inflammatory response. Therefore, this study aims to investigate the associations between dietary fibers intake and systemic immune and inflammatory biomarkers. Methods: This cross-sectional study used data from the National Health and Nutrition Examination Survey (2015-2020). Dietary fibers intake was defined as the mean of two 24-h dietary recall interviews. The systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), neutrophil-to-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), red blood cell distribution width-to-albumin ratio (RA), ferritin, high-sensitivity C-reactive protein (hs-CRP), and white blood cell (WBC) count were measured to evaluate systemic immune and inflammatory states of the body. The statistical software packages R and EmpowerStats were used to examine the associations between dietary fibers intake and systemic immune and inflammatory biomarkers. Results: Overall, 14,392 participants were included in this study. After adjusting for age, gender, race, family monthly poverty level index, alcohol consumption, smoking status, vigorous recreational activity, body mass index, hyperlipidemia, hypertension, diabetes, and dietary inflammatory index, dietary fibers intake was inversely associated with SII (ß = -2.19885, 95% CI: -3.21476 to -1.18294, p = 0.000248), SIRI (ß = -0.00642, 95% CI: -0.01021 to -0.00263, p = 0.001738), NLR (ß = -0.00803, 95% CI: -0.01179 to -0.00427, p = 0.000284), RA (ß = -0.00266, 95% CI: -0.00401 to -0.00131, p = 0.000644), ferritin (ß = -0.73086, 95% CI: -1.31385 to -0.14787, p = 0.020716), hs-CRP (ß = -0.04629, 95% CI: -0.0743 to -0.01829, p = 0.002119), WBC (ß = -0.01624, 95% CI: -0.02685 to -0.00563, p = 0.004066), neutrophils (ß = -0.01346, 95% CI: -0.01929 to -0.00764, p = 0.000064). An inverse association between dietary fibers and PLR was observed in the middle (ß = -3.11979, 95% CI: -5.74119 to -0.4984, p = 0.028014) and the highest tertile (ß = -4.48801, 95% CI: -7.92369 to -1.05234, p = 0.016881) and the trend test (ßtrend = -2.2626, 95% CI: -3.9648 to -0.5604, Ptrend = 0.0150). The observed associations between dietary fibers intake and SII, SIRI, NLR, RA, ferritin, hs-CRP, WBC, and neutrophils remained robust and consistent in the sensitivity analysis. No significant interaction by race was found. Conclusion: Dietary fibers intake is associated with the improvement of the parameters of the immune response and inflammatory biomarkers, supporting recommendations to increase dietary fibers intake for enhanced immune health.

Evaluation of Breast Cancer Tumor-Infiltrating Lymphocytes on Ultrasound Images Based on a Novel Multi-Cascade Residual U-Shaped Network.

OBJECTIVE: Breast cancer has become the leading cancer of the 21st century. Tumor-infiltrating lymphocytes (TILs) have emerged as effective biomarkers for predicting treatment response and prognosis in breast cancer. The work described here was aimed at designing a novel deep learning network to assess the levels of TILs in breast ultrasound images. METHODS: We propose the Multi-Cascade Residual U-Shaped Network (MCRUNet), which incorporates a gray feature enhancement (GFE) module for image reconstruction and normalization to achieve data synergy. Additionally, multiple residual U-shaped (RSU) modules are cascaded as the backbone network to maximize the fusion of global and local features, with a focus on the tumor's location and surrounding regions. The development of MCRUNet is based on data from two hospitals and uses a publicly available ultrasound data set for transfer learning. RESULTS: MCRUNet exhibits excellent performance in assessing TILs levels, achieving an area under the receiver operating characteristic curve of 0.8931, an accuracy of 85.71%, a sensitivity of 83.33%, a specificity of 88.64% and an F1 score of 86.54% in the test group. It outperforms six state-of-the-art networks in terms of performance. CONCLUSION: The MCRUNet network based on breast ultrasound images of breast cancer patients holds promise for non-invasively predicting TILs levels and aiding personalized treatment decisions.

High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes.

Most of the single-nucleotide polymorphisms (SNPs) associated with insulin resistance (IR)-relevant phenotypes by genome-wide association studies (GWASs) are located in noncoding regions, complicating their functional interpretation. Here, we utilized an adapted STARR-seq to evaluate the regulatory activities of 5,987 noncoding SNPs associated with IR-relevant phenotypes. We identified 876 SNPs with biased allelic enhancer activity effects (baaSNPs) across 133 loci in three IR-relevant cell lines (HepG2, preadipocyte, and A673), which showed pervasive cell specificity and significant enrichment for cell-specific open chromatin regions or enhancer-indicative markers (H3K4me1, H3K27ac). Further functional characterization suggested several transcription factors (TFs) with preferential allelic binding to baaSNPs. We also incorporated multi-omics data to prioritize 102 candidate regulatory target genes for baaSNPs and revealed prevalent long-range regulatory effects and cell-specific IR-relevant biological functional enrichment on them. Specifically, we experimentally verified the distal regulatory mechanism at IRS1 locus, in which rs952227-A reinforces IRS1 expression by long-range chromatin interaction and preferential binding to the transcription factor HOXC6 to augment the enhancer activity. Finally, based on our STARR-seq screening data, we predicted the enhancer activity of 227,343 noncoding SNPs associated with IR-relevant phenotypes (fasting insulin adjusted for BMI, HDL cholesterol, and triglycerides) from the largest available GWAS summary statistics. We further provided an open resource (http://www.bigc.online/fnSNP-IR) for better understanding genetic regulatory mechanisms of IR-relevant phenotypes.

Ultrasound features combined with tumor-infiltrating lymphocytes for prediction of pathological response to neoadjuvant chemotherapy in breast cancer.

AIM: Neoadjuvant chemotherapy (NAC) plays an important role in the treatment and prognosis of breast cancer. The early identification of patients who can truly benefit from preoperative NAC is crucial in clinical practice. The purpose of this study was to explore whether the ultrasound features and clinical characteristics combined with tumor-infiltrating lymphocyte(TIL) levels can improve the performance of predicting NAC efficacy in breast cancer patients. MATERIAL AND METHODS: In this retrospective study, 202 invasive breast cancer patients who underwent NAC followed by surgery were included. The baseline ultrasound features were reviewed by two radiologists. Miller-Payne Grading (MPG) was used to assess pathological response, and MPG 4-5 was defined as major histologic responders (MHR). Multivariable logistic regression analysis was used to evaluate independent predictors for MHR and build the prediction models. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the models. RESULTS: Of the 202 patients, 104 patients achieved MHR and 98 patients achieved non-MHR. Multivariate logistic regression analysis showed the US size (p=0.042), molecular subtypes (p=0.001), TIL levels (p<0.001), shape (p=0.030), and posterior features (p=0.018) were independent predictors for MHR. The model combined the US features, clinical characteristics, and TIL levels had a better performance with an area under the curve (AUC) of 0.811, a sensitivity of 0.663, and a specificity of 0.847. CONCLUSION: The model combined US features, clinical characteristics, and TIL levels had a better performance in predicting pathological response to NAC in breast cancer.

Comparative study of pathological response evaluation systems after neoadjuvant chemotherapy for breast cancer: developing predictive models of multimodal ultrasound features including shear wave elastography combined with puncture pathology.

Background: This study created a predictive preoperative nomogram dependent on multimodal ultrasound characteristics and primary lesion biopsy results for various pathologic response assessment systems following neoadjuvant chemotherapy (NAC). Methods: This retrospective study included 145 breast cancer patients treated at Gansu Cancer Hospital between January 2021 and June 2022 who underwent shear wave elastography (SWE) prior to completing NAC. Intra- and peritumoral SWE features, including maximum (Emax), mean (Emean), minimum (Emin), and standard deviation (Esd) elasticity, were measured individually and linked with the Miller-Payne grading system and residual cancer burden (RCB) class. Univariate analysis was used for conventional ultrasound and puncture pathology. Binary logistic regression analysis was used to screen for independent risk factors and to develop a prediction model. Results: Intratumor Emean and peritumoral Esd differed significantly from the Miller-Payne grade [intratumor Emean: r=0.129, 95% confidence interval (CI): -0.002 to 0.260; P=0.042; peritumoral Esd: r=0.126, 95% CI: -0.010 to 0.254; P=0.047], RCB class (intratumor Emean: r=-0.184, 95% CI: -0.318 to -0.047; P=0.004; peritumoral Esd: r=-0.139, 95% CI: -0.265 to 0.000; P=0.029) and RCB score components (r=-0.277 to -0.139; P=0.001-0.041). Two prediction model nomograms-pathologic complete response (pCR)/non-pCR and good responder/nonresponder-for the RCB class were developed using binary logistic regression analysis for all significant variables in SWE, conventional ultrasound, and puncture results. The area under the receiver operating characteristic curve for the pCR/non-pCR and good responder/nonresponder models was 0.855 (95% CI: 0.787-0.922) and 0.845 (95% CI: 0.780-0.910), respectively. According to the calibration curve, the nomogram had excellent internal consistency between estimated and actual values. Conclusions: The preoperative nomogram can effectively guide clinicians to predict pathological response of breast cancer after NAC and has the potential to guide individualized treatment.

Contrast-enhanced ultrasound demonstrates greater adjuvant potential: past, current status, and future applications in Hepatocellular carcinoma early diagnosis.

Hepatocellular carcinoma (HCC) has an atypical onset of clinical symptoms and a rapid tumor progression. The majority of patients with HCC are already in the late stages of the disease when they are diagnosed, limiting them to the best available treatments. Contrast-enhanced ultrasound (CEUS) has achieved significant advances in the diagnosis of HCC, including the detection of small lesions, the investigation of more beneficial contrast agents, and the use of CEUS-based radiomics. The purpose of this review is to discuss the relevant research and future challenges of CEUS in the early detection of HCC, to advise more accurate therapy.

Clinical Value of Artificial Intelligence-Based Computer-Aided Diagnosis System Versus Contrast-Enhanced Ultrasound for Differentiation of Benign From Malignant Thyroid Nodules in Different Backgrounds.

OBJECTIVES: The aim of this study was to compare the value of the AI-SONIC ultrasound-assisted diagnosis system versus contrast-enhanced ultrasound (CEUS) for differential diagnosis of thyroid nodules in diffuse and non-diffuse backgrounds. METHODS: A total of 555 thyroid nodules with pathologically confirmed diagnosis were included in this retrospective study. The diagnostic efficacies of AI-SONIC and CEUS for differentiating benign from malignant nodules in diffuse and non-diffuse backgrounds were evaluated, with pathological diagnosis as the gold standard. RESULTS: The agreement between AI-SONIC diagnosis and pathological diagnosis was moderate in diffuse backgrounds (κ = 0.417) and almost perfect in non-diffuse backgrounds (κ = 0.81). The agreement between CEUS diagnosis and pathological diagnosis was substantial in diffuse backgrounds (κ = 0.684) and moderate in non-diffuse backgrounds (κ = 0.407). In diffuse backgrounds, AI-SONIC had slightly higher sensitivity (95.7 vs 89.4%, P = .375), but CEUS had significantly higher specificity (80.0 vs 40.0%, P = .008). In non-diffuse background, AI-SONIC had significantly higher sensitivity (96.2 vs 73.4%, P < .001), specificity (82.9 vs 71.2%, P = .007), and negative predictive value (90.3 vs 53.3%, P < .001). CONCLUSION: In non-diffuse backgrounds, AI-SONIC is superior to CEUS for differentiating malignant from benign thyroid nodules. In diffuse backgrounds, AI-SONIC could be useful for screening of cases to detect suspicious nodules requiring further examination by CEUS.

Deep Learning with Transformer or Convolutional Neural Network in the Assessment of Tumor-Infiltrating Lymphocytes (TILs) in Breast Cancer Based on US Images: A Dual-Center Retrospective Study.

This study aimed to explore the feasibility of using a deep-learning (DL) approach to predict TIL levels in breast cancer (BC) from ultrasound (US) images. A total of 494 breast cancer patients with pathologically confirmed invasive BC from two hospitals were retrospectively enrolled. Of these, 396 patients from hospital 1 were divided into the training cohort (n = 298) and internal validation (IV) cohort (n = 98). Patients from hospital 2 (n = 98) were in the external validation (EV) cohort. TIL levels were confirmed by pathological results. Five different DL models were trained for predicting TIL levels in BC using US images from the training cohort and validated on the IV and EV cohorts. The overall best-performing DL model, the attention-based DenseNet121, achieved an AUC of 0.873, an accuracy of 79.5%, a sensitivity of 90.7%, a specificity of 65.9%, and an F1 score of 0.830 in the EV cohort. In addition, the stratified analysis showed that the DL models had good discrimination performance of TIL levels in each of the molecular subgroups. The DL models based on US images of BC patients hold promise for non-invasively predicting TIL levels and helping with individualized treatment decision-making.

Ultrasound contrast-enhanced patterns of sentinel lymph nodes: predictive value for nodal status and metastatic burden in early breast cancer.

Background: In the post-Z0011 era, sentinel lymph node (SLN) status and metastatic burden determine whether axillary management entails conservative sentinel lymph node biopsy (SLNB) or radical axillary lymph node dissection (ALND) in breast cancer patients. However, SLN status and metastatic burden cannot be evaluated preoperatively in clinical practice. This study explored the predictive value of contrast-enhanced ultrasound (CEUS) patterns of SLN to assess the nodal status and metastatic burden in early breast cancer patients. Methods: A retrospective study was conducted on 88 consecutive patients who were diagnosed with clinical T1-2N0 breast cancer between December 2020 and November 2021 at the Lanzhou University Second Hospital and scheduled for SLNB. Preoperative CEUS was performed to confirm the location and enhancement pattern of the SLN, and the conventional ultrasonic characteristics of the primary breast lesions and SLN were recorded. Intraoperative localized SLN and postoperative pathological results were used as the gold standard for comparison with preoperative ultrasound findings. Results: CEUS successfully identified at least 1 SLN in 88 patients, with a total of 118 SLNs identified in the entire cohort. Univariate analysis showed that lesion size, blood flow grade, SLN longitudinal diameter, cortical thickness, and enhancement pattern were significant predictive features of SLN metastasis. Further multiple regression analysis indicated that the enhancement pattern of the SLN was an independent risk factor for SLN metastasis, with a sensitivity and a specificity of 84.2% (32/38) and 80.0% (40/50), respectively. Meanwhile, the SLN enhancement pattern could predict the lymph node metastasis burden (P<0.001). In patients presenting with a type I (homogeneous enhancement) or type II (heterogeneous enhancement) SLN, 91.5% (65/71) had ≤2 positive SLNs, whereas in patients with a type III (no enhancement) SLN, 70.6% (12/17) had >2 metastatic nodes. Conclusions: The contrast-enhanced pattern of the SLN is an independent risk factor for SLN status. Patients presenting with a type I or type II SLN enhanced pattern are unlikely to have high-burden metastases detected at their final surgical treatment and omission of ALND may be appropriate.

Ultrasound-mediated mesoporous silica nanoparticles loaded with PDLIM5 siRNA inhibit gefitinib resistance in NSCLC cells by attenuating EMT.

Epidermal growth factor receptor tyrosine kinase inhibitor (EGFR TKIs) was one of the main drugs in the treatment of non-small cell lung cancer (NSCLC). Previous studies had demonstrated that PDZ and LIM Domain 5 (PDLIM5) played an important role in EGFR TKIs resistance. However, there was no feasible method to eliminate EGFR TKIs resistance by suppressing this gene. Here, we formulated a novel mesoporous silica-loaded PDLIM5 siRNA (Small interfering RNA) nanoplatforms. The results have shown that PDLIM5 siRNA could be effectively bound to the nanoplatforms and had good biocompatibility. Further exploration suggested that the nano-platform combined with ultrasonic irradiation could be very effective for siRNA delivery and ultrasound imaging. Moreover, Epithelial-mesenchymal transformation (EMT) changes occurred in PC-9 Gefitinib resistance (PC-9/GR) cells during the development of drug resistance. When PDLIM5 siRNA entered PC-9/GR cells, the sensitivity of drug-resistant cells to gefitinib could be restored through the transforming growth factor-ß (TGF-ß)/EMT pathway. Therefore, PDLIM5 may be an important reason for the resistance of NSCLC cells to gefitinib, and this nanoplatform may become a novel treatment for EGFR TKIs resistance in NSCLC patients.