[Clinical characteristics and prognosis of children with perianal fistulizing Crohn's disease]. / å„¿ç«¥è‚›å‘¨ç˜˜ç®¡åž‹å ‹ç½—æ©ç— çš„ä¸´åºŠç‰¹å¾åŠé¢„åŽ.

OBJECTIVES: To investigate the clinical characteristics, treatment, and prognosis of children with perianal fistulizing Crohn's disease (pfCD). METHODS: A retrospective analysis was conducted on the children, aged 6-17 years, who were diagnosed with Crohn's disease (CD) from April 2015 to April 2023. According to the presence or absence of perianal fistulizing lesions, they were divided into two groups: pfCD (n=60) and non-pfCD (n=82). The two groups were compared in terms of clinical characteristics, treatment, and prognosis. RESULTS: The incidence of pfCD was 42.3% (60/142). The proportion of males in the pfCD group was higher than that in the non-pfCD group. Compared with the non-pfCD group, the pfCD group had a significantly higher proportion of children with involvement of the colon and small intestine or those with upper gastrointestinal lesions (P<0.05). Compared with the non-pfCD group, the pfCD group had a significantly higher rate of use of infliximab during both induction and maintenance treatment (P<0.05). In the pfCD group, the children with complex anal fistula accounted for 62% (37/60), among whom the children receiving non-cutting suspended line drainage accounted for 62% (23/37), which was significantly higher than the proportion among the children with simple anal fistula patients (4%, 1/23) (P<0.05). There were no significant differences between the two groups in mucosal healing rate and clinical remission rate at week 54 of treatment (P>0.05). The pfCD group achieved a fistula healing rate of 57% (34/60) at week 54, and the children with simple anal fistula had a significantly higher rate than those with complex anal fistula (P<0.05). CONCLUSIONS: There is a high incidence rate of pfCD in children with CD, and among the children with pfCD, there is a high proportion of children with the use of biological agents. There is a high proportion of children receiving non-cutting suspended line drainage among the children with complex anal fistula. The occurrence of pfCD should be closely monitored during the follow-up in children with CD.

On-Site Diagnostic Ability of CEUS/CT/MRI for Hepatocellular Carcinoma (2019-2022): A Multicenter Study.

OBJECTIVES: To compare the on-site diagnostic performance of contrast-enhanced ultrasound (CEUS), computed tomography (CECT), and magnetic resonance imaging (CEMRI) for hepatocellular carcinoma (HCC) across diverse practice settings. METHODS: Between May 2019 and April 2022, a total of 2085 patients with 2320 pathologically confirmed focal liver lesions (FLLs) were enrolled. Imaging reports were compared with results from pathology analysis. Diagnostic performance was analyzed in defined size, high-risk factors for HCC, and hospital volume categories. RESULTS: Three images achieved similar diagnostic performance in classifying HCC from 16 types of FLLs, including HCC ≤2.0 cm. For HCC diagnosis at low-volume hospitals and HCC with high-risk factors, the accuracy and specificity of CEUS were comparable to CECT and CEMRI, while the sensitivity of CEUS (77.4 and 89.5%, respectively) was inferior to CEMRI (87.0 and 92.8%, respectively). The diagnostic accuracy of CEUS + CEMRI and CEUS + CECT increased by 7.8 and 6.2% for HCC ≤2.0 cm, 8.0 and 5.0% for HCC with high-risk factors, and 7.4 and 5.5% for HCC at low-volume hospitals, respectively, compared with CEMRI/CECT alone. CONCLUSIONS: Compared with CECT and CEMRI, CEUS provides adequate diagnostic performance in clinical first-line applications at high-volume hospitals. Moreover, a higher diagnostic performance for HCC is achieved by combining CEUS with CECT/CEMRI compared with any single imaging technique.

Development and validation of a transformer-based CAD model for improving the consistency of BI-RADS category 3-5 nodule classification among radiologists: a multiple center study.

Background: Significant differences exist in the classification outcomes for radiologists using ultrasonography-based Breast Imaging Reporting and Data Systems for diagnosing category 3-5 (BI-RADS 3-5) breast nodules, due to a lack of clear and distinguishing image features. Consequently, this retrospective study investigated the improvement of BI-RADS 3-5 classification consistency using a transformer-based computer-aided diagnosis (CAD) model. Methods: Independently, 5 radiologists performed BI-RADS annotations on 21,332 breast ultrasonographic images collected from 3,978 female patients from 20 clinical centers in China. All images were divided into training, validation, testing, and sampling sets. The trained transformer-based CAD model was then used to classify test images, for which sensitivity (SEN), specificity (SPE), accuracy (ACC), area under the curve (AUC), and calibration curve were evaluated. Variations in these metrics among the 5 radiologists were analyzed by referencing BI-RADS classification results for the sampling test set provided by CAD to determine whether classification consistency (the k value), SEN, SPE, and ACC could be improved. Results: After the training set (11,238 images) and validation set (2,996 images) were learned by the CAD model, the classification ACC of the CAD model applied to the test set (7,098 images) was 94.89% in category 3, 96.90% in category 4A, 95.49% in category 4B, 92.28% in category 4C, and 95.45% in category 5 nodules. Based on pathological results, the AUC of the CAD model was 0.924 and the predicted probability of CAD was a little higher than the actual probability in the calibration curve. After referencing BI-RADS classification results, the adjustments were made to 1,583 nodules, of which 905 were classified to a lower category and 678 to a higher category in the sampling test set. As a result, the ACC (72.41-82.65%), SEN (32.73-56.98%), and SPE (82.46-89.26%) of the classification by each radiologist were significantly improved on average, with the consistency (k values) in almost all of them increasing to >0.6. Conclusions: The radiologist's classification consistency was markedly improved with almost all the k values increasing by a value greater than 0.6, and the diagnostic efficiency was also improved by approximately 24% (32.73% to 56.98%) and 7% (82.46% to 89.26%) for SEN and SPE, respectively, of the total classification on average. The transformer-based CAD model can help to improve the radiologist's diagnostic efficacy and consistency with others in the classification of BI-RADS 3-5 nodules.

Atomic vacancies-engineered ultrathin trimetallic nanozyme with anti-inflammation and antitumor performances for intestinal disease treatment.

Colitis-associated colorectal cancer, which represents a highly aggressive subtypes of colorectal cancer, requires concurrent antitumor and anti-inflammation therapies in clinic. Herein, we successfully engineered Ru38Pd34Ni28 ultrathin trimetallic nanosheets (TMNSs) by introducing diverse transition metal atoms into the structure of RuPd nanosheets. Density functional theory (DFT) calculations reveal that the elaborate introduction of transition metal Ru and Ni facilitates the formation of Ru-O and Ni-O bonds on the surface of TMNSs for efficient reactive oxygen species (ROS) and reactive nitrogen species (RNS) scavenging, respectively. Moreover, the engineered abundant atomic vacancies on their surface conspicuously improve the performance in eliminating reactive oxygen and nitrogen species (RONS). The designed TMNSs act as a multi-metallic nanocatalyst with RONS elimination performance for chronic colitis treatment by relieving inflammation, as well as photothermal conversion capability for colon cancer therapy by inducing hyperthermia effect. Profiting from the excellent RONS scavenging activities, TMNSs can down-regulate the expression levels of the pro-inflammatory factors, thereby leading to prominent therapeutic efficacy against dextran sulfate sodium-induced colitis. Benefiting from the high photothermal performance, TMNSs cause significant suppression of CT-26 tumors without obvious recurrence. This work provides a distinct paradigm to design multi-metallic nanozymes for colon disease treatment by elaborate introduction of transition metal atoms and engineering of atomic vacancies.

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.

Markedly hypoechoic: a new definition improves the diagnostic performance of thyroid ultrasound.

OBJECTIVES: To determine the contribution of a modified definition of markedly hypoechoic in the differential diagnosis of thyroid nodules. METHODS: A total of 1031 thyroid nodules were included in this retrospective multicenter study. All of the nodules were examined with US before surgery. The US features of the nodules were evaluated, in particular, the classical markedly hypoechoic and modified markedly hypoechoic (decreased or similar echogenicity relative to the adjacent strap muscles). The sensitivity, specificity, and AUC of classical/modified markedly hypoechoic and the corresponding ACR-TIRADS, EU-TIRADS, and C-TIRADS categories were calculated and compared. The inter- and intraobserver variability in the evaluation of the main US features of the nodules was assessed. RESULTS: There were 264 malignant nodules and 767 benign nodules. Compared with classical markedly hypoechoic as a diagnostic criterion for malignancy, using modified markedly hypoechoic as the criterion resulted in a significant increase in sensitivity (28.03% vs. 63.26%) and AUC (0.598 vs. 0.741), despite a significant decrease in specificity (91.53% vs. 84.88%) (p < 0.001 for all). Compared to the AUC of the C-TIRADS with the classical markedly hypoechoic, the AUC of the C-TIRADS with the modified markedly hypoechoic increased from 0.878 to 0.888 (p = 0.01); however, the AUCs of the ACR-TIRADS and EU-TIRADS did not change significantly (p > 0.05 for both). There was substantial interobserver agreement (κ = 0.624) and perfect intraobserver agreement (κ = 0.828) for the modified markedly hypoechoic. CONCLUSION: The modified definition of markedly hypoechoic resulted in a significantly improved diagnostic efficacy in determining malignant thyroid nodules and may improve the diagnostic performance of the C-TIRADS. CLINICAL RELEVANCE STATEMENT: Our study found that, compared with the original definition, modified markedly hypoechoic significantly improved the diagnostic performance in differentiating malignant from benign thyroid nodules and the predictive efficacy of the risk stratification systems. KEY POINTS: • Compared with the classical markedly hypoechoic as a diagnostic criterion for malignancy, the modified markedly hypoechoic resulted in a significant increase in sensitivity and AUC. • The C-TIRADS with the modified markedly hypoechoic achieved higher AUC and specificity than that with the classical markedly hypoechoic (p = 0.01 and < 0.001, respectively).

Development and Validation of a Combined MRI Radiomics, Imaging and Clinical Parameter-Based Machine Learning Model for Identifying Idiopathic Central Precocious Puberty in Girls.

BACKGROUND: Idiopathic central precocious puberty (ICPP) impairs child development, without early intervention. The current reference standard, the gonadotropin-releasing hormone stimulation test, is invasive which may hinder diagnosis and intervention. PURPOSE: To develop a model for accurate diagnosis of ICPP, by integrating pituitary MRI, carpal bone age, gonadal ultrasound, and basic clinical data. STUDY TYPE: Retrospective. POPULATION: A total of 492 girls with PP (185 with ICPP and 307 peripheral precocious puberty [PPP]) were randomly divided by reference standard into training (75%) and internal validation (25%) data. Fifty-one subjects (16 with ICPP, 35 with PPP) provided by another hospital as external validation. FIELD STRENGTH/SEQUENCE: T1-weighted (spin echo [SE], fast SE, cube) and T2-weighted (fast SE-fat suppression) imaging at 3.0 T or 1.5 T. ASSESSMENT: Radiomics features were extracted from pituitary MRI after manual segmentation. Carpal bone age, ovarian, follicle and uterine volumes and endometrium presence were assessed from radiographs and gonadal ultrasound. Four machine learning methods were developed: a pituitary MRI radiomics model, an integrated image model (with pituitary MRI, gonadal ultrasound and bone age), a basic clinical model (with age and sex hormone data), and an integrated multimodal model combining all features. STATISTICAL TESTS: Intraclass correlation coefficients were used to assess consistency of segmentation. Receiver operating characteristic (ROC) curves and the Delong tests were used to assess and compare the diagnostic performance of models. P < 0.05 was considered statistically significant. RESULTS: The area under of the ROC curve (AUC) of the pituitary MRI radiomics model, integrated image model, basic clinical model, and integrated multimodal model in the training data was 0.668, 0.809, 0.792, and 0.860. The integrated multimodal model had higher diagnostic efficacy (AUC of 0.862 and 0.866 for internal and external validation). CONCLUSION: The integrated multimodal model may have potential as an alternative clinical approach to diagnose ICPP. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

A Ganoderma lucidum polysaccharide F31 alleviates hyperglycemia through kidney protection and adipocyte apoptosis.

In this paper, we reported an excellent hypoglycemic effect of a Ganoderma lucidium polysaccharide F31 with efficacies between 45 and 54 %, approaching to that of liraglutide (52 %). Significantly, F31 reduced the body weight gains and food intakes. F31 decreased 4 key compounds, consisting of adenosine, adenosine, galactitol and glycerophosphocholine and elevated 8 key compounds, including arginine, proline, arachidonic acid, creatine, aspartic acid, leucine, phenylalanine and ornithine, which protected kidney function. Also, apoptosis was promoted by F31 in epididymal fat through increasing Caspase-3, Caspase-6 and Bax and decreasing Bcl-2. On 3 T3-L1 preadipocyte cells, F31 induced early apoptosis through reducing mitochondrial membrane potential. Finally, a molecular docking was performed to reveal a plausible cross-talk between kidney and epididymal fat through glycerophosphorylcholine-Bax axis. Overall, F31 alleviated hyperglycemia through kidney protection and adipocyte apoptosis in db/db mice. This work may provide novel insights into the hypoglycemic activity of polysaccharides.

Novel Human Artificial Intelligence Hybrid Framework Pinpoints Thyroid Nodule Malignancy and Identifies Overlooked Second-Order Ultrasonographic Features.

We present a Human Artificial Intelligence Hybrid (HAIbrid) integrating framework that reweights Thyroid Imaging Reporting and Data System (TIRADS) features and the malignancy score predicted by a convolutional neural network (CNN) for nodule malignancy stratification and diagnosis. We defined extra ultrasonographical features from color Doppler images to explore malignancy-relevant features. We proposed Gated Attentional Factorization Machine (GAFM) to identify second-order interacting features trained via a 10 fold distribution-balanced stratified cross-validation scheme on ultrasound images of 3002 nodules all finally characterized by postoperative pathology (1270 malignant ones), retrospectively collected from 131 hospitals. Our GAFM-HAIbrid model demonstrated significant improvements in Area Under the Curve (AUC) value (p-value < 10−5), reaching about 0.92 over the standalone CNN (~0.87) and senior radiologists (~0.86), and identified a second-order vascularity localization and morphological pattern which was overlooked if only first-order features were considered. We validated the advantages of the integration framework on an already-trained commercial CNN system and our findings using an extra set of ultrasound images of 500 nodules. Our HAIbrid framework allows natural integration to clinical workflow for thyroid nodule malignancy risk stratification and diagnosis, and the proposed GAFM-HAIbrid model may help identify novel diagnosis-relevant second-order features beyond ultrasonography.

Two-dimensional silicene photodynamic tumor-targeting nanomedicine.

Since the innovative development of photosensitizers (PSs) is pivotal prerequisite for the successful clinical translation of photodynamic therapy (PDT), the unresolved challenges of classical PSs such as photobleaching, poor bioavailability, lack of tumor selectivity encourage the exploitation of new-generation PSs. In this work, we develop silicene nanosheets with unparalleled physiochemical nature and intriguing biocompatibility as the distinct two-dimensional (2D) nanoscale photosensitizer based on the mechanism of wet-chemical synthetic approach to achieve effective PDT-based tumor nanotherapy. The generation capacities of singlet oxygen (1O2) in different atmospheres have been systematically explored in depth. Furthermore, the conjunction of c (RGDyC) onto 2D silicene nanosheets (denoted as SRGD) endows the SRGD nanomedicines with specific targeting properties to α v ß 3 integrin-overexpressed cancer cells, accomplishing in vivo and in vitro potent tumor growth inhibition efficiency. More notably, the excellent light absorption capacity of 2D silicene enables the tumor-specific photoacoustic imaging for potentiating the therapeutic guidance and monitoring. This paradigm can inspire the future design and applications of 2D silicene-based cancer-theranostic nanoplatform in biology and medicine.

Identification of Prognostic Factors in Patients With Streptococcus Bloodstream Infection.

Aim: The purpose of this study was to explore prognostic factors of bloodstream infections (BSIs), a common severe infection and a major cause of mortality worldwide, so as to construct a prognosis model of patients with BSI. Materials and Methods: Clinical and biochemical test data were obtained retrospectively from the medical records of 562 patients with BSI who had been treated at a single center; the end point was 60 days of all-cause death. The chi-square test was used to compare the mortality of patients grouped by the types of antibiotic treatment. The logistic regression analysis was adopted to identify prognostic factors; the Kaplan-Meier survival curve and log-rank test were conducted to compare the survival rate of patients with different prognostic factors; the receiver operating characteristic (ROC) curve was used to estimate the predictive value of different prognostic factors. Results: Of the 562 patients, 455 survived (80.96%), and 107 died (19.04%). The mortality rate of patients treated with a combination of antibiotics (25.40%) was higher than that treated with a single antibiotic (15.82%). Univariate analysis identified 19 prognostic factors for patients with BSI, including gender, age, diabetes, malignant tumor (non-blood system), total hospitalization time, alanine aminotransferase, aspartate aminotransferase, total protein, albumin, total bilirubin, direct bilirubin, creatinine, ratio of granulocytes, fibrinogen, D-dimer, platelet, C-reactive protein, shock, and respiratory failure (P < 0.05). Multivariate analysis indicated that albumin (odds ratio [OR] = 0.94, 95% confidence interval [CI]: 0.89-0.99), fibrinogen (OR = 0.61, 95%CI: 0.46-0.82), shock (OR = 16.61, 95%CI: 7.00-39.41), and respiratory failure (OR = 47.53, 95%CI: 19.93-133.64) were independent factors. The combination of four indicators demonstrated a favorable predictive value for the 60-day outcome of patients with BSI, with an area under the ROC of 0.96 (95%CI: 0.94-0.99), sensitivity of 90.65%, specificity of 94.95%, and accuracy of 94.13%. Conclusions: Shock, respiratory failure, albumin, and fibrinogen are potential independent prognostic factors for 60-day mortality.

Comparison between spleen and liver stiffness measurements by sound touch elastography for diagnosing cirrhosis at different aminotransferase levels: a prospective multicenter study.

OBJECTIVES: To compare the performance of spleen stiffness measurement (SSM) and liver stiffness measurement (LSM) by sound touch elastography (STE) for the diagnosis of cirrhosis at different alanine aminotransferase (ALT) levels, and to compare the applicability and repeatability of SSM with LSM performed by STE, a new two-dimensional shear wave elastography technology. METHODS: This prospective multicenter study included 25 centers and recruited chronic hepatitis B (CHB) patients with liver biopsy between May 2018 and November 2019. All patients underwent LSM and SSM by STE. Success and reliability rates were calculated and compared. Intra-observer agreement was assessed using intraclass correlation coefficients (ICCs). Differences between areas under the receiver operating characteristic curves (AUCs) of LSMs and SSMs at different ALT levels were compared using the Delong test. RESULTS: Among 603 CHB patients, the success and reliability rates of SSM were 94.53% (570/603) and 85.74% (517/603), respectively, which were similar to those of LSM (p > 0.05), respectively. The ICC for intra-observer agreements of SSM was 0.964 (p < 0.001). In the total cohort, ALT ≤ 2 × upper limit of normal (ULN) group, and A0-1 group, the AUCs of SSMs were significantly lower than those of LSMs for the diagnosis of cirrhosis (p < 0.001). In the ALT > 2 × ULN group and A2-3 group, the AUC of SSM improved and was not significantly different from that of LSM (p = 0.342, p = 0.510, respectively). CONCLUSIONS: SSM by STE achieved applicability and repeatability equivalent to those of LSM. SSM might be a good substitute to LSM in patients with high ALT levels. KEY POINTS: • Spleen stiffness measurement performed by sound touch elastography was proven to have similar applicability and repeatability to liver stiffness measurement in this prospective multicenter study. • Spleen stiffness measurement demonstrated a poorer diagnostic performance for cirrhosis compared with liver stiffness measurement in the total cohort and low ALT level group, yet it showed a similar diagnostic performance to liver stiffness measurement in patients with high ALT levels.

Usefulness of New Shear Wave Elastography Technique for Noninvasive Assessment of Liver Fibrosis in Patients with Chronic Hepatitis B: A Prospective Multicenter Study.

PURPOSE: To explore the usefulness of liver stiffness measurements (LSMs) by sound touch elastography (STE) and sound touch quantification (STQ) in chronic hepatitis B (CHB) patients for staging fibrosis. METHODS: This prospective multicenter study recruited normal volunteers and CHB patients between May 2018 and October 2019. The volunteers underwent LSM by STE and supersonic shear imaging (SSI) or by STQ and acoustic radiation force impulse imaging (ARFI). CHB patients underwent liver biopsy and LSM by both STE/STQ. The areas under the receiver operating characteristic curves (AUCs) for staging fibrosis were calculated. RESULTS: Overall, 97 volunteers and 524 CHB patients were finally eligible for the study. The successful STE and STQ measurement rates were both 100 % in volunteers and 99.4 % in CHB patients. The intraclass correlation coefficients (ICCs) for the intra-observer stability of STE and STQ (0.94; 0.90) were similar to those of SSI and ARFI (0.95; 0.87), respectively. STE and STQ showed better accuracy than the aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 index (FIB-4) (AUC: 0.87 vs 0.86 vs 0.73 vs 0.77) in staging cirrhosis. However, both STE and STQ were not superior to APRI and FIB-4 in staging significant fibrosis (AUC: 0.76 vs 0.73 vs 0.70 vs 0.71, all P-values > 0.05). CONCLUSION: STE and STQ are convenient techniques with a reliable LSM value. They have a similar diagnostic performance and are superior to serum biomarkers in staging cirrhosis in CHB patients.

Engineering Electronic Band Structure of Binary Thermoelectric Nanocatalysts for Augmented Pyrocatalytic Tumor Nanotherapy.

Photothermal therapy (PTT) has emerged as a distinct therapeutic modality owing to its noninvasiveness and spatiotemporal selectivity. However, heat-shock proteins (HSPs) endow tumor cells with resistance to heat-induced apoptosis, severely lowering the therapeutic efficacy of PTT. Here, a high-performance pyroelectric nanocatalyst, Bi13 S18 I2 nanorods (NRs), with prominent pyroelectric conversion and photothermal conversion performance for augmented pyrocatalytic tumor nanotherapy, is developed. Canonical binary compounds are reconstructed by inserting a third biocompatible agent, thus facilitating the formation of Bi13 S18 I2 NRs with enhanced pyrocatalytic conversion efficiency. Under 808 nm laser irradiation, Bi13 S18 I2 NRs induce a conspicuous temperature elevation for photonic hyperthermia. In particular, Bi13 S18 I2 NRs harvest pyrocatalytic energy from the heating and cooling alterations to produce abundant reactive oxygen species, which results in the depletion of HSPs and hence the reduction of thermoresistance of tumor cells, thereby significantly augmenting the therapeutic efficacy of photothermal tumor hyperthermia. By synergizing the pyroelectric dynamic therapy with PTT, tumor suppression with a significant tumor inhibition rate of 97.2% is achieved after intravenous administration of Bi13 S18 I2 NRs and subsequent exposure to an 808 nm laser. This work opens an avenue for the design of high-performance pyroelectric nanocatalysts by reconstructing canonical binary compounds for therapeutic applications in biocatalytic nanomedicine.

Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study.

BACKGROUND: The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images. METHODS: Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR-), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists. RESULTS: The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87-0.91, 0.89-0.92, 0.87-0.91, and 0.86-0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%). CONCLUSIONS: The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists. TRIAL REGISTRATION: Chictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.

A C-type lectin (OmCTL) in Onychostoma macrolepis: Binding ability to LPS, PGN and agglutinating activity against bacteria.

C-type lectins (CTLs) are calcium-dependent carbohydrate-binding proteins that mainly bind to carbohydrate-based or other ligands to mediate cell adhesion, recognize pathogens, and play important roles in the immune system. In the present study, a novel C-type lectin (OmCTL) isolated from Onychostoma macrolepis was investigated. The open reading frame of OmCTL comprises 468 bp, encoding a 155 amino acid polypeptide with an 18 amino acid putative signaling peptide. The predicted primary OmCTL structure contains a signal peptide, a single carbohydrate recognition domain (CRD) and an EPN/WND motif required for carbohydrate-binding specificity. Using tissue expression pattern analysis, OmCTL has been shownto be highly expressed in the liver, and is also detected in other tissues. OmCTL was significantly upregulated in the liver and spleen following infection with Aeromonas hydrophila, suggesting its involvement in immune response. The recombinant OmCTL protein (rOmCTL) agglutinated two gram-negative bacteria, Escherichia coli and A. hydrophila, in vitro in the presence of Ca2+, showing that it is a typical Ca2+-dependent carbohydrate-binding protein.Furthermore, rOmCTL purified from E. coli BL21 (DE3) strongly bound to LPS and PGN, as well as all tested bacteria in a Ca2+-independent manner. These results indicate that OmCTL plays a central role in the innate immune response and as a pattern recognition receptor that recognizes diverse pathogens among O. macrolepis.

Thyroid imaging reporting and data system (TIRADS) for ultrasound features of nodules: multicentric retrospective study in China.

PURPOSE: To establish a practical and simplified Chinese thyroid imaging reporting and data system (C-TIRADS) based on the Chinese patient database. METHODS: A total of 2141 thyroid nodules that were neither cystic nor spongy were used in the current study. These specimens were derived from 2141 patients in 131 alliance hospitals of the Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. The ultrasound features, including location, orientation, margin, halo, composition, echogenicity, echotexture, echogenic foci and posterior features were assessed. Univariate and multivariate analyses were performed to investigate the association between ultrasound features and malignancy. The regression equation, the weighting, and the counting methods were used to determine the malignant risk of the thyroid nodules. The areas under the receiver operating characteristic curve (Az values) were calculated. RESULTS: Of the 2141 thyroid nodules, 1572 were benign, 565 were malignant, and 4 were borderline. Vertical orientation, ill-defined, or irregular margin (including extrathyroidal extension), microcalcifications, solid, and markedly hypoechoic were positively associated with malignancy, while comet-tail artifacts were negatively associated with malignancy. The logistic regression equation yielded the highest Az value of 0.913, which was significantly higher than that obtained using the weighting method (0.893) and the counting method (0.890); however, no significant difference was found between the latter two. The C-TIRADS, based on the counting method, was designed following the principle of balancing the diagnostic performance and sensitivity of the risk stratification with the ease of use. CONCLUSIONS: A relatively simple C-TIRADS was established using the counting value of positive and negative ultrasound features.

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A.

BACKGROUND: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. METHODS: A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. RESULTS: Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. CONCLUSIONS: Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.