Membrane anchoring of the DIRAS3 N-terminal extension permits tumor suppressor function.

DIRAS3 is an imprinted tumor suppressor gene encoding a GTPase that has a distinctive N-terminal extension (NTE) not found in other RAS proteins. This NTE and the prenylated C-terminus are required for DIRAS3-mediated inhibition of RAS/MAP signaling and PI3K activity at the plasma membrane. In this study, we applied biochemical, biophysical, and computational methods to characterize the structure and function of the NTE. The NTE peptide recognizes phosphoinositides PI(3,4,5)P3 and PI(4,5)P2 with rapid kinetics and strong affinity. Lipid binding induces NTE structural change from disorder to amphipathic helix. Mass spectrometry identified N-myristoylation of DIRAS3. All-atom molecular dynamic simulations predict DIRAS3 could adhere to the membrane through both termini, suggesting the NTE is involved in targeting and stabilizing DIRAS3 on the membrane by double anchoring. Overall, our results are consistent with DIRAS3's function as a tumor suppressor, whereby the membrane-bound DIRAS3 can effectively target PI3K and KRAS at the membrane.

Progress of Ultrasound Techniques in the Evaluation of Carotid Vulnerable Plaque Neovascularization.

BACKGROUND: The rupture and detachment of unstable plaques in the carotid artery can cause embolism in the cerebral artery, leading to acute cerebrovascular events. Intraplaque neovascularization (IPN) is a very important contributor to carotid plaque instability, and its evolution plays a key role in determining the outcome of vulnerable plaques. Ultrasound techniques, represented by contrast-enhanced ultrasound and superb microvascular imaging, are reported to be non-invasive, rapid and effective techniques for the semi-quantitative or quantitative evaluation for IPN. Although ultrasound techniques have been widely applied in the detection of carotid plaque stability, it has been limited owing to the lack of unified IPN quantitative standards. SUMMARY: This review summarizes the application and semi-quantitative/quantitative diagnostic standards of ultrasound techniques in evaluating IPN, and looks forward to the prospects of the future research. With the development of novel techniques like artificial intelligence, ultrasound will offer appropriate selections for achieving more accuracy diagnosis. KEY MESSAGES: A large number of studies have used contrast-enhanced ultrasound and superb microvascular imaging to detect IPN and perform semi-quantitative grading to predict the occurrence of diseases such as stroke, and to accurately assess drug efficacy based on rating changes. These studies have made great progress at this stage, but more accurate and intelligent quantitative imaging methods should become the future development goal.

A review of contrast-enhanced ultrasound using SonoVue® and Sonazoid™ in non-hepatic organs.

Contrast-enhanced ultrasound (CEUS) is a dependable modality for the diagnosis of various clinical conditions. A judicious selection of ultrasound contrast agent (UCA) is imperative for optimizing imaging and improving diagnosis. Approved UCAs for imaging the majority of organs include SonoVue, a pure blood agent, and Sonazoid, which exhibits an additional Kupffer phase. Despite the fact that the two UCAs are increasingly being employed, there is a lack of comparative reviews between the two agents in different organs diseases. This review represents the first attempt to compare the two UCAs in non-hepatic organs, primarily including breast, thyroid, pancreas, and spleen diseases. Through comparative analysis, this review provides a comprehensive and objective evaluation of the performance characteristics of SonoVue and Sonazoid, with the aim of offering valuable guidance for the clinical application of CEUS. Overall, further clinical evidences are required to compare and contrast the dissimilarities between the two UCAs in non-hepatic organs, enabling clinicians to make an appropriate selection based on actual clinical applications.

Imaging biomarker for quantitative analysis of endometrial injury based on optical coherence tomography/ultrasound integrated imaging mode.

Precise evaluation of endometrial injury is significant to clinical decision-making in gynecological disease and assisted reproductive technology. However, there is a lack of assessment methods for endometrium in vivo. In this research, we intend to develop quantitative imaging markers with optical coherence tomography (OCT)/ultrasound (US) integrated imaging system through intrauterine endoscopic imaging. OCT/US integrated imaging system was established as our previous research reported. The endometrial injury model was established and after treatment, OCT/US integrated imaging and uterus biopsy was performed to evaluate the endometrial thickness, number of superficial fold, and intrauterine area. According to the results, three quantitative indexes acquired from OCT/US image and HE staining have the same trend and have a strong relationship with the severity of the endometrial injury. Accordingly, we developed three imaging markers for quantitative analysis of endometrial injury in vivo, which provided a precise mode for endometrium evaluation in clinical practice.

Artificial intelligence-aided ultrasound in renal diseases: a systematic review.

Background: The development of artificial intelligence (AI) techniques has provided a novel strategy for improving the performance of renal ultrasound. To reflect the development of AI methods in renal ultrasound, we aimed to clarify and analyze the state of AI-aided ultrasound research in renal diseases. Methods: PRISMA 2020 guidelines have been used to guide all processes and results. AI-aided renal ultrasound studies (for both image segmentation and disease diagnosis) published up to June 2022 were screened through the databases of PubMed and Web of Science. Accuracy/Dice similarity coefficient (DICE), the area under the curve (AUC), sensitivity/specificity, and other indications were applied as evaluation parameters. The PROBAST was used to assess the risk of bias in the studies screened. Results: Of 364 articles, 38 studies were analyzed, and could be divided into AI-aided diagnosis or prediction related studies (28/38) and image segmentation related studies (10/38). The output of these 28 studies involved differential diagnosis of local lesions, disease grading of, automatic diagnosis, and diseases prediction. The median values of accuracy and AUC were 0.88 and 0.96, respectively. Overall, 86% of the AI-aided diagnosis or prediction models were classified as high risk. An unclear source of data, inadequate sample size, inappropriate analysis methods, and lack of rigorous external validation were found to be the most frequent and critical risk factors in AI-aided renal ultrasound studies. Conclusions: AI is a potential technique in the ultrasound diagnosis of different types of renal diseases, but the reliability and availability need to be strengthened. The use of AI-aided ultrasound in chronic kidney disease and quantitative hydronephrosis diagnosis will be a promising possibility. The size and quality of sample data, rigorous external validation, and adherence to guidelines and standards should be considered in further studies.

An ultrasound-based deep learning radiomic model combined with clinical data to predict clinical pregnancy after frozen embryo transfer: a pilot cohort study.

RESEARCH QUESTION: Can a multi-modal fusion model based on ultrasound-based deep learning radiomics combined with clinical parameters provide personalized evaluation of endometrial receptivity and predict the occurrence of clinical pregnancy after frozen embryo transfer (FET)? DESIGN: Prospective cohort study of women (n = 326) who underwent FET between August 2019 and December 2021. Input quantitative variables and input image data for radiomic feature extraction were collected to establish a multi-modal fusion prediction model. An additional independent dataset of 453 ultrasound endometrial images was used to establish the segmentation model to determine the endometrial region on ultrasound images for analysis. The performance of different algorithms and different input data for prediction of FET outcome were compared. RESULTS: A total of 240 patients with complete data were included in the final cohort. The proposed multi-modal fusion model performed significantly better than the use of either image or quantitative variables alone to predict the occurrence of clinical pregnancy after FET (P ≤ 0.034). Its area under the curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the proposed model were 0.825, 72.5%, 96.2%, 58.3%, 72.3% and 89.5%, respectively. The Dice coefficient of the multi-task endometrial ultrasound segmentation model was 0.89. Use of endometrial segmentation features significantly improved the prediction performance of the model (P = 0.041). CONCLUSIONS: The multi-modal fusion model based on ultrasound-based deep learning radiomics combined with clinical quantitative variables offers a favourable and rapid non-invasive approach for personalized prediction of FET outcome.

Seasonal variations in CDOM characteristics and effects of environmental factors in coastal rivers, Northeast China.

Colored dissolved organic matter (CDOM) is highly spatiotemporally varied due to the effects of complex environmental factors within a catchment or system. The seasonal nutritional status and potential risks of heavy metals in the coastal rivers of the Liaohe River basin were evaluated based on 40 water samples in January, April, May, and September. Meanwhile, the effects of environmental factors on CDOM, especially human activities, were quantitatively analyzed. The trophic state index (TSI) and the potential ecological risk index (RI) of heavy metals in the Liaohe River basin exhibited significant differences. The rivers were mesotrophic in January, lightly eutrophic in May, and highly eutrophic in April and September. An extremely high RI was shown in April and May, while a high RI was exhibited in September. CDOM exhibited great seasonal characteristics and showed significant seasonal correlations with environmental factors. Based on multiple general linear model analysis, total phosphorus (TP) was the most influential factor and significantly explained 62.1% of aCDOM(440) (p < 0.01) among the water parameters, followed by total alkalinity (38.3%). The percentages of built-up area exerted significantly positive effects on aCDOM(440) (R2 = 0.44), while distance from oil extraction sites significantly negatively affected aCDOM(440) (r = - 0.328, p < 0.05). Polluting enterprises showed non-significant correlation with CDOM (r = 0.314, p = 0.178).

Evaluation of oocyte maturity using artificial intelligence quantification of follicle volume biomarker by three-dimensional ultrasound.

RESEARCH QUESTION: Can a novel deep learning-based follicle volume biomarker using three-dimensional ultrasound (3D-US) be established to aid in the assessment of oocyte maturity, timing of HCG administration and the individual prediction of ovarian hyper-response? DESIGN: A total of 515 IVF cases were enrolled, and 3D-US scanning was carried out on HCG administration day. A follicle volume biomarker established by means of a deep learning-based segmentation algorithm was used to calculate optimal leading follicle volume for predicting number of mature oocytes retrieved and optimizing HCG trigger timing. Performance of the novel biomarker cut-off value was compared with conventional two-dimensional ultrasound (2D-US) follicular diameter measurements in assessing oocyte retrieval outcome. Moreover, demographics, infertility work-up and ultrasound biomarkers were used to build models for predicting ovarian hyper-response. RESULTS: On the basis of the deep learning method, the optimal cut-off value of the follicle volume biomarker was determined to be 0.5 cm3 for predicting number of mature oocytes retrieved; its performance was significantly better than the conventional method (two-dimensional diameter measurement ≥10 mm). The cut-off value for leading follicle volume to optimize HCG trigger timing was determined to be 3.0 cm3 and was significantly associated with a higher number of mature oocytes retrieved (P = 0.01). Accuracy of the multi-layer perceptron model was better than two-dimensional diameter measurement (0.890 versus 0.785) and other multivariate classifiers in predicting ovarian hyper-response (P < 0.001). CONCLUSIONS: Deep learning segmentation methods and multivariate classifiers based on 3D-US were found to be potentially effective approaches for assessing mature oocyte retrieval outcome and individual prediction of ovarian hyper-response.

Supramolecular isomorphic dodecanuclear cobalt clusters with the same metal shell but different core ligands.

In this work, we report two supramolecular isomorphic dodecanuclear cobalt complexes, [Co12(mtz)3(L)6(NO3)2(OH)(N3)3]·(OH)3 (1) and [Co12(mtz)3(L)6(NO3)2(OH)(N3)(OAc)]·(OH)4 (2), (Hmtz = 5-methyl-1H-tetrazole, H2L = 7,7'-(ethane-1,1-diyl) diquinolin-8-ol) crystallizing in the P̄ space group with the same unit cell parameters. In 1 and 2, two pirate hat-like hexanuclear Co6(NO3)(L)3 units form the same dodecanuclear metal shell, but the ligands between the hexanuclear units as the core are distinct. The introduction of acetate anions leads to a blue shift of the absorption band in the visible region. Magnetism studies indicate an antiferromagnetic interaction between the CoII ions in the clusters.

Directed Evolution of PD-L1-Targeted Affibodies by mRNA Display.

Therapeutic monoclonal antibodies directed against PD-L1 (e.g., atezolizumab) disrupt PD-L1:PD-1 signaling and reactivate exhausted cytotoxic T-cells in the tumor compartment. Although anti-PD-L1 antibodies are successful as immune checkpoint inhibitor (ICI) therapeutics, there is still a pressing need to develop high-affinity, low-molecular-weight ligands for molecular imaging and diagnostic applications. Affibodies are small polypeptides (∼60 amino acids) that provide a stable molecular scaffold from which to evolve high-affinity ligands. Despite its proven utility in the development of imaging probes, this scaffold has never been optimized for use in mRNA display, a powerful in vitro selection platform incorporating high library diversity, unnatural amino acids, and chemical modification. In this manuscript, we describe the selection of a PD-L1-binding affibody by mRNA display. Following randomization of the 13 amino acids that define the binding interface of the well-described Her2 affibody, the resulting library was selected against recombinant human PD-L1 (hPD-L1). After four rounds, the enriched library was split and selected against either hPD-L1 or the mouse ortholog (mPD-L1). The dual target selection resulted in the identification of a human/mouse cross-reactive PD-L1 affibody (M1) with low nanomolar affinity for both targets. The M1 affibody bound with similar affinity to mPD-L1 and hPD-L1 expressed on the cell surface and inhibited signaling through the PD-L1:PD-1 axis at low micromolar concentrations in a cell-based functional assay. In vivo optical imaging with M1-Cy5 in an immune-competent mouse model of lymphoma revealed significant tumor uptake relative to a Cy5-conjugated Her2 affibody.

Early Alterations of Intra-Mural Elastic Lamellae Revealed by Synchrotron X-ray Micro-CT Exploration of Diabetic Aortas.

Diabetes is a major concern of our society as it affects one person out of 11 around the world. Elastic fiber alterations due to diabetes increase the stiffness of large arteries, but the structural effects of these alterations are poorly known. To address this issue, we used synchrotron X-ray microcomputed tomography with in-line phase contrast to image in three dimensions C57Bl6J (control) and db/db (diabetic) mice with a resolution of 650 nm/voxel and a field size of 1.3 mm3. Having previously shown in younger WT and db/db mouse cohorts that elastic lamellae contain an internal supporting lattice, here we show that in older db/db mice the elastic lamellae lose this scaffold. We coupled this label-free method with automated image analysis to demonstrate that the elastic lamellae from the arterial wall are structurally altered and become 11% smoother (286,665 measurements). This alteration suggests a link between the loss of the 3D lattice-like network and the waviness of the elastic lamellae. Therefore, waviness measurement appears to be a measurable elasticity indicator and the 3D lattice-like network appears to be at the origin of the existence of this waviness. Both could be suitable indicators of the overall elasticity of the aorta.

Efficacy and safety of immune-modulating therapy for primary sclerosing cholangitis: A systematic review and meta-analysis.

Primary sclerosing cholangitis (PSC) is a rare chronic cholestatic liver disease of unclear cause. Until now, there are no effective therapies for patients with PSC. A number of studies have evaluated the effects of immune-modulating therapies on the treatment of PSC. However, clinical benefits of these treatments in PSC patients are controversial and inconclusive. We performed a systematic review and meta-analysis to assess the efficacy and adverse effects of immunomodulators in adult patients with PSC based on prognostic markers (alkaline phosphatase (ALP) and total bilirubin), liver function marker (aspartate aminotransferase (AST)) and adverse event (AE) rates. Twenty-one studies (seven randomized controlled trials (RCT) and fourteen observational studies) involving 737 patients were included in this analysis. Immune-modulating therapies significantly reduced ALP level in PSC patients, but not to normal level. AST level was non-significantly decreased, while no effect was observed on total bilirubin level after treatments in PSC patients. In 16 studies reporting AEs, an average of 16.1% patients had severe AEs, resulting in discontinuation of therapies. Importantly, subgroup analysis further indicated that immune-modulating therapy significantly reduced ALP or AST levels in PSC patients with high baseline levels of ALP (over 420 U/L, > three times the upper limit of normal (ULN)) or AST (over 80 U/L, > two times the ULN), but had no effect in patients with low baseline levels. Compared to other immune-modulating therapies, immunosuppressants had the most significant effect on reducing ALP and AST levels in PSC patients but was associated with the highest incidence of severe AEs of 24.9%. Glucocorticoids showed a positive effect by significantly reducing ALP levels with the minimal AE rate of 6.1%. In conclusion, immune-modulating therapies could improve the prognostic marker of cholestasis (ALP level) in patients with PSC, especially in those of worse liver function. These findings suggest patients with high baseline level of ALP (>420 U/L) and AST (>80 U/L) respond better to immune-modulating therapy compared to those with low level of ALP and AST. Future RCTs would be needed to include different dosing regimens, a longer treatment duration and follow-up period, and patients stratified by liver function to obtain solid conclusion.

Relationship of Amniotic Fluid Sludge and Short Cervix With a High Rate of Preterm Birth in Women After Cervical Cerclage.

OBJECTIVE: We aims to determine the relationship of amniotic fluid sludge (AFS) and/or short cervical length (CL, ≤25 mm) with a high rate of preterm birth in women after cervical cerclage. METHODS: A retrospective cohort study was conducted among singleton pregnancies after cervical cerclage between January 2018 and December 2021. A total of 296 patients who underwent transvaginal ultrasound to evaluate CL and the presence of AFS within 2 weeks after cerclage were included. Pregnancy outcome after cerclage was analyzed in accordance with the presence of AFS and CL ≤25 mm. RESULTS: In patients with cerclage, AFS was an independent risk factor for preterm birth at <28 and <36 weeks but not for preterm birth at <32 weeks, and CL ≤25 mm was an independent risk factor for preterm birth at <28, <32, and <36 weeks. The Kaplan-Meier analysis showed that the association between the presence of AFS and short gestational age at delivery was statistically significant in women with CL ≤25 mm (log rank test, P = .000). The Cox regression analysis showed that these results remained significant after adjusting for confounding factors (P = .000). The negative linear relationships between AFS and CL (R = -0.454, P < .001) also explained the outcome. CONCLUSIONS: AFS and short cervix have a direct effect on pregnancies after cerclage. Mid-trimester AFS can become a supplementary ultrasound index for detecting preterm birth after cerclage in pregnant women with a short cervix.

Investigation of Protein-Lipid Interactions Using Native Mass Spectrometry.

Integral membrane proteins are embedded in biological membranes where various lipids modulate their structure and function. There exists a critical need to elucidate how these lipids participate in the physiological and pathological processes associated with the membrane protein dysfunction. Native mass spectrometry (MS), combined with ion mobility spectrometry (IM), is emerging as a powerful tool to probe membrane protein complexes and their interactions with ligands, lipids, and other small molecules. Unlike other biophysical approaches, native IM-MS can resolve individual ligand/lipid binding events. We have developed a novel method using native MS, coupled with a temperature-control apparatus, to determine the thermodynamic parameters of individual ligand or lipid binding events to proteins. This approach has been validated using several soluble protein-ligand systems wherein MS results are compared with those acquired from conventional biophysical techniques, such as isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR). Using these principles, it is possible to elucidate the thermodynamics of individual lipid binding to integral membrane proteins. Herein, we use the ammonia channel (AmtB) from Escherichia coli as a model membrane protein. Remarkably, distinct thermodynamic signatures for AmtB binding to lipids with different headgroups and acyl chain configurations are observed. Additionally, using a mutant form of AmtB that abolishes a specific lipid binding site, distinct changes have been discovered in the thermodynamic signatures compared with the wild-type, implying that these signatures can identify key residues involved in specific lipid binding and potentially differentiate between specific lipid binding sites. This chapter provides procedures and findings associated with temperature-controlled native MS as a novel approach to interrogate membrane proteins and their interactions with lipids and other molecules.

DIRAS3: An Imprinted Tumor Suppressor Gene that Regulates RAS and PI3K-driven Cancer Growth, Motility, Autophagy, and Tumor Dormancy.

DIRAS3 is an imprinted tumor suppressor gene that encodes a 26 kDa GTPase with 60% amino acid homology to RAS, but with a distinctive 34 amino acid N-terminal extension required to block RAS function. DIRAS3 is maternally imprinted and expressed only from the paternal allele in normal cells. Loss of expression can occur in a single "hit" through multiple mechanisms. Downregulation of DIRAS3 occurs in cancers of the ovary, breast, lung, prostate, colon, brain, and thyroid. Reexpression of DIRAS3 inhibits signaling through PI3 kinase/AKT, JAK/STAT, and RAS/MAPK, blocking malignant transformation, inhibiting cancer cell growth and motility, and preventing angiogenesis. DIRAS3 is a unique endogenous RAS inhibitor that binds directly to RAS, disrupting RAS dimers and clusters, and preventing RAS-induced transformation. DIRAS3 is essential for autophagy and triggers this process through multiple mechanisms. Reexpression of DIRAS3 induces dormancy in a nu/nu mouse xenograft model of ovarian cancer, inhibiting cancer cell growth and angiogenesis. DIRAS3-mediated induction of autophagy facilitates the survival of dormant cancer cells in a nutrient-poor environment. DIRAS3 expression in dormant, drug-resistant autophagic cancer cells can serve as a biomarker and as a target for novel therapy to eliminate the residual disease that remains after conventional therapy.

Long-term survival and postoperative complications of pre-liver transplantation transarterial chemoembolisation in hepatocellular carcinoma: A systematic review and meta-analysis.

OBJECTIVES: The aim of this meta-analysis was to conduct a contemporary systematic review of high quality non-randomised controlled trials to determine the effect of pre-liver transplantation (LT) transarterial chemoembolisation (TACE) on long-term survival and complications of hepatocellular carcinoma (HCC) patients. BACKGROUND: TACE is used as a neoadjuvant therapy to mitigate waitlist drop-out for patients with HCC awaiting LT. Previous studies have conflicting conclusions on the effect of TACE on long-term survival and complications of HCC patients undergoing LT. METHODS: CINAHL, Cochrane Controlled Register of Trials, Embase, PubMed, and Web of Science were systematically searched. Baseline characteristics included number of patients outside Milan criteria, tumour diameter, MELD score, and time on the waiting list. Primary outcomes included 3- and 5-year overall and disease-free survival. Secondary outcomes included tumour recurrence, 30-day postoperative mortality, and hepatic artery and biliary complications. RESULTS: Twenty-one high-quality NRCTs representing 8242 patients were included. Tumour diameter was significantly larger in TACE patients (3.49 cm vs 3.15 cm, P = 0.02) and time on the waiting list was significantly longer in TACE patients (4.87 months vs 3.46 months, P = 0.05), while MELD score was significantly higher in non-TACE patients (10.81 vs 12.35, P = 0.005). All primary and secondary outcomes displayed non-significant differences. CONCLUSION: Patients treated with TACE had similar survival and postoperative outcomes to non-TACE patients, however, they had worse prognostic features compared to non-TACE patients. These findings strongly support the current US and European clinical practice guidelines that neoadjuvant TACE can be used for patients with longer expected waiting list times (specifically >6 months). Randomised controlled trials would be needed to increase the quality of evidence.

Application of ultrasound markers measured at different time points of COH cycle in the prediction of ovarian response for individualised ovulation induction.

The purpose of this study was to evaluate the predictive value of ultrasound markers measured at different time points of the controlled ovarian hyperstimulation (COH) cycle on ovarian response and outcome indicators in the IVF-ET cycle. According to the oestrogen level and the number of retrieved oocytes, patients who planned for COH treatment were separated into low-response group, normal and high-response group. The ovarian stromal artery flow parameters on the day of pituitary down-regulation, day 1, day 7, day 10, and the day of hCG injection were collected prospectively. We also have collected the data of cumulus oophorus count on the day of hCG injection by transvaginal sonography. Compared with the low-response group, on the first day of the COH cycle PI, RI, and S/D were lower in the high-response group than they were in the low-response group (p < .05). PSV and EDV were significantly higher in the high-response group than they were in the low-response group (p < .01), and the PSV on the first day of the COH cycle have statistical significance in predicting the number of high-quality embryos. The number of cumulus oophorus on the day of hCG injection has statistical significance in predicting the number of oocytes retrieved and fertilised oocytes. We conclude that the ovarian stromal artery flow parameters on the first day of the COH cycle and cumulus oophorus count on hCG injection day can serve as efficient indicators for an early assessment of ovarian response and individualised ovulation induction.IMPACT STATEMENTWhat is already known on this subject? AMH, AFC, and the age of the patient are well-known effective parameters for the evaluation of ovarian response, but these are insufficient and full of individual differences. Some researchers have investigated the value of colour Doppler ultrasound and cumulus oophorus in assessing ovarian response, but no definitive conclusion has been reached.What do the results of this study add? The hemodynamic parameters of ovarian stromal artery on the first day of the COH cycle and the number of cumulus oophorus on the day of hCG injection detected by Transvaginal Colour Doppler Sonography (TV-CDS) could be used to predict the ovarian response.What are the implications of these findings for clinical practice and/or further research? Ovarian stromal artery flow parameters and cumulus oophorus detected by TV-CDS can potentially be offered as a complementary parameter for ovarian reserve.

KRAS Inhibitor that Simultaneously Inhibits Nucleotide Exchange Activity and Effector Engagement.

We describe a small molecule ligand ACA-14 (2-hydroxy-5-{[(2-phenylcyclopropyl) carbonyl] amino} benzoic acid) as an initial lead for the development of direct inhibitors of KRAS, a notoriously difficult anticancer drug target. We show that the compound binds to KRAS near the switch regions with affinities in the low micromolar range and exerts different effects on KRAS interactions with binding partners. Specifically, ACA-14 impedes the interaction of KRAS with its effector Raf and reduces both intrinsic and SOS-mediated nucleotide exchange rates. Likely as a result of these effects, ACA-14 inhibits signal transduction through the MAPK pathway in cells expressing mutant KRAS and inhibits the growth of pancreatic and colon cancer cells harboring mutant KRAS. We thus propose compound ACA-14 as a useful initial lead for the development of broad-acting inhibitors that target multiple KRAS mutants and simultaneously deplete the fraction of GTP-loaded KRAS while abrogating the effector-binding ability of the already GTP-loaded fraction.

A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules.

PURPOSE: The fully automatic AI-Sonic computer-aided design (CAD) system was employed for the detection and diagnosis of benign and malignant thyroid nodules. The aim of this study was to investigate the efficiency of the AI-Sonic CAD system with the use of a deep learning algorithm to improve the diagnostic accuracy of ultrasound-guided fine-needle aspiration (FNA). METHODS: A total of 138 thyroid nodules were collected from 124 patients and diagnosed by an expert, a novice, and the Thyroid Imaging Reporting and Data System (TI-RADS). Diagnostic efficiency and feasibility were compared among the expert, novice, and CAD system. The application of the CAD system to enhance the diagnostic efficiency of novices was assessed. Moreover, with the experience of the expert as the gold standard, the values of features detected by the CAD system were also analyzed. The efficiency of FNA was compared among the expert, novice, and CAD system to determine whether the CAD system is helpful for the management of FNA. RESULT: In total, 56 malignant and 82 benign thyroid nodules were collected from the 124 patients (mean age, 46.4 ± 12.1 years; range, 12-70 years). The diagnostic area under the curve of the CAD system, expert, and novice were 0.919, 0.891, and 0.877, respectively (p < 0.05). In regard to feature detection, there was no significant differences in the margin and composition between the benign and malignant nodules (p > 0.05), while echogenicity and the existence of echogenic foci were of great significance (p < 0.05). For the recommendation of FNA, the results showed that the CAD system had better performance than the expert and novice (p < 0.05). CONCLUSIONS: Precise diagnosis and recommendation of FNA are continuing hot topics for thyroid nodules. The CAD system based on deep learning had better accuracy and feasibility for the diagnosis of thyroid nodules, and was useful to avoid unnecessary FNA. The CAD system is potentially an effective auxiliary approach for diagnosis and asymptomatic screening, especially in developing areas.

Contrastive rendering with semi-supervised learning for ovary and follicle segmentation from 3D ultrasound.

Segmentation of ovary and follicles from 3D ultrasound (US) is the crucial technique of measurement tools for female infertility diagnosis. Since manual segmentation is time-consuming and operator-dependent, an accurate and fast segmentation method is highly demanded. However, it is challenging for current deep-learning based methods to segment ovary and follicles precisely due to ambiguous boundaries and insufficient annotations. In this paper, we propose a contrastive rendering (C-Rend) framework to segment ovary and follicles with detail-refined boundaries. Furthermore, we incorporate the proposed C-Rend with a semi-supervised learning (SSL) framework, leveraging unlabeled data for better performance. Highlights of this paper include: (1) A rendering task is performed to estimate boundary accurately via enriched feature representation learning. (2) Point-wise contrastive learning is proposed to enhance the similarity of intra-class points and contrastively decrease the similarity of inter-class points. (3) The C-Rend plays a complementary role for the SSL framework in uncertainty-aware learning, which could provide reliable supervision information and achieve superior segmentation performance. Through extensive validation on large in-house datasets with partial annotations, our method outperforms state-of-the-art methods in various evaluation metrics for both the ovary and follicles.