Deep Learning-Assisted Two-Dimensional Transperineal Ultrasound for Analyzing Bladder Neck Motion in Women With Stress Urinary Incontinence.

BACKGROUND: No universally recognized transperineal ultrasound parameters are available for evaluating stress urinary incontinence. The information captured by commonly used perineal ultrasound parameters is limited and insufficient for a comprehensive assessment of stress urinary incontinence. Although bladder neck motion plays a major role in stress urinary incontinence, objective and visual methods to evaluate its impact on stress urinary incontinence remain lacking. OBJECTIVE: To use a deep learning-based system to evaluate bladder neck motion using two-dimensional transperineal ultrasound videos, exploring motion parameters for diagnosing and evaluating stress urinary incontinence. We hypothesized that bladder neck motion parameters are associated with stress urinary incontinence and are useful for stress urinary incontinence diagnosis and evaluation. STUDY DESIGN: This retrospective study including 217 women involved the following parameters: maximum and average speeds of bladder neck descent, ß angle, urethral rotation angle, and duration of the Valsalva maneuver. The fitted curves were derived to visualize bladder neck motion trajectories. Comparative analyses were conducted to assess these parameters between stress urinary incontinence and control groups. Logistic regression and receiver operating characteristic curve analyses were employed to evaluate the diagnostic performance of each motion parameter and their combinations for stress urinary incontinence. RESULTS: Overall, 173 women were enrolled in this study (82, stress urinary incontinence group; 91, control group). No significant differences were observed in the maximum and average speeds of bladder neck descent and in the speed variance of bladder neck descent. The maximum and average speed of the ß and urethral rotation angles were faster in the stress urinary incontinence group than in the control group (151.2 vs 109.0 mm/s, P=0.001; 6.0 vs 3.1 mm/s, P <0.001; 105.5 vs 69.6 mm/s, P <0.001; 10.1 vs 7.9 mm/s, P=0.011, respectively). The speed variance of the ß and urethral rotation angles were higher in the stress urinary incontinence group (844.8 vs 336.4, P <0.001; 347.6 vs 131.1, P <0.001, respectively). The combination of the average speed of the ß angle, maximum speed of the urethral rotation angle, and duration of the Valsalva maneuver demonstrated a strong diagnostic performance (area under the curve, 0.87). When 0.481*ß anglea + 0.013*URAm + 0.483*Dval = 7.405, the diagnostic sensitivity was 70% and specificity was 92%, highlighting the significant role of bladder neck motion in stress urinary incontinence, particularly changes in the speed of the ß and urethral rotation angles. CONCLUSIONS: A system utilizing deep learning can describe the motion of the bladder neck in women with stress urinary incontinence during the Valsalva maneuver, making it possible to visualize and quantify bladder neck motion on transperineal ultrasound. The speeds of the ß and urethral rotation angles and duration of the Valsalva maneuver were relatively reliable diagnostic parameters.

Artificial intelligence assistance for fetal development: evaluation of an automated software for biometry measurements in the mid-trimester.

BACKGROUND: This study presents CUPID, an advanced automated measurement software based on Artificial Intelligence (AI), designed to evaluate nine fetal biometric parameters in the mid-trimester. Our primary objective was to assess and compare the CUPID performance of experienced senior and junior radiologists. MATERIALS AND METHODS: This prospective cross-sectional study was conducted at Shenzhen University General Hospital between September 2022 and June 2023, and focused on mid-trimester fetuses. All ultrasound images of the six standard planes, that enabled the evaluation of nine biometric measurements, were included to compare the performance of CUPID through subjective and objective assessments. RESULTS: There were 642 fetuses with a mean (±SD) age of 22 ± 2.82 weeks at enrollment. In the subjective quality assessment, out of 642 images representing nine biometric measurements, 617-635 images (90.65-96.11%) of CUPID caliper placements were determined to be accurately placed and did not require any adjustments. Whereas, for the junior category, 447-691 images (69.63-92.06%) were determined to be accurately placed and did not require any adjustments. In the objective measurement indicators, across all nine biometric parameters and estimated fetal weight (EFW), the intra-class correlation coefficients (ICC) (0.843-0.990) and Pearson correlation coefficients (PCC) (0.765-0.978) between the senior radiologist and CUPID reflected good reliability compared with the ICC (0.306-0.937) and PCC (0.566-0.947) between the senior and junior radiologists. Additionally, the mean absolute error (MAE), percentage error (PE), and average error in days of gestation were lower between the senior and CUPID compared to the difference between the senior and junior radiologists. The specific differences are as follows: MAE (0.36-2.53 mm, 14.67 g) compared to (0.64- 8.13 mm, 38.05 g), PE (0.94-9.38%) compared to (1.58-16.04%), and average error in days (3.99-7.92 days) compared to (4.35-11.06 days). In the time-consuming task, CUPID only takes 0.05-0.07 s to measure nine biometric parameters, while senior and junior radiologists require 4.79-11.68 s and 4.95-13.44 s, respectively. CONCLUSIONS: CUPID has proven to be highly accurate and efficient software for automatically measuring fetal biometry, gestational age, and fetal weight, providing a precise and fast tool for assessing fetal growth and development.

Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data.

Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches-Audio Branch, Video Branch, and Text Branch-each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks-reading and interviewing-implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection.

Efficacy and safety of inhibiting the NLRP3/IL-1ß/IL-6 pathway in patients with ST-elevation myocardial infarction: A meta-analysis.

BACKGROUND: The NLRP3/IL-1ß/IL-6 pathway plays a key role in mediating inflammatory responses after ST-elevation myocardial infarction (STEMI). However, the clinical benefits of inhibiting this pathway in STEMI are uncertain. We aimed to evaluate the efficacy and safety of inhibiting the NLRP3/IL-1ß/IL-6 pathway in STEMI patients. METHODS: This study followed PRISMA guidelines. PubMed, Embase, CENTRAL and ClinicalTrials.gov databases were searched for randomized controlled trials (RCTs) of inhibiting the NLRP3/IL-1ß/IL-6 pathway in STEMI patients within 7 days of symptom onset. The efficacy outcomes included all-cause death, cardiovascular death, recurrent MI, new-onset or worsening heart failure (HF) and stroke. The safety outcomes were serious infection, gastrointestinal adverse events and injection site reactions. RESULTS: Of 316 screened records, nine trials with 1211 patients were included in the meta-analysis. Colchicine reduced the risk of recurrent MI (RR 0.28, 95% CI 0.10-0.74; I2 = 0.0%). Anakinra was associated with reduced risk of new-onset or worsening HF (RR 0.32, 95% CI 0.13-0.77; I2 = 0.0%) and decreased C-reactive protein levels (SMD -1.34, 95% CI -2.04 to -0.65; I2 = 0.0%). Colchicine and anakinra increased the risk of gastrointestinal adverse events (RR 4.43, 95% CI 2.75-7.13; I2 = 38.1%) and injection site reactions (RR 4.52, 95% CI 1.32-15.49; I2 = 0.8%), respectively. None of the three medications affected the risks of all-cause death, cardiovascular death, stroke and serious infection. CONCLUSIONS: There is still no large-scale RCT evidence on the efficacy and safety of inhibiting the NLRP3/IL-1ß/IL-6 pathway for the treatment of STEMI. Preliminary results from the available RCTs suggest colchicine and anakinra may respectively reduce the risks of recurrent MI and new-onset or worsening HF. The available RCTs in this meta-analysis lack power to determine any differences on mortality.

Deep learning based on carotid transverse B-mode scan videos for the diagnosis of carotid plaque: a prospective multicenter study.

OBJECTIVES: Accurate detection of carotid plaque using ultrasound (US) is essential for preventing stroke. However, the diagnostic performance of junior radiologists (with approximately 1 year of experience in carotid US evaluation) is relatively poor. We thus aim to develop a deep learning (DL) model based on US videos to improve junior radiologists' performance in plaque detection. METHODS: This multicenter prospective study was conducted at five hospitals. CaroNet-Dynamic automatically detected carotid plaque from carotid transverse US videos allowing clinical detection. Model performance was evaluated using expert annotations (with more than 10 years of experience in carotid US evaluation) as the ground truth. Model robustness was investigated on different plaque characteristics and US scanning systems. Furthermore, its clinical applicability was evaluated by comparing the junior radiologists' diagnoses with and without DL-model assistance. RESULTS: A total of 1647 videos from 825 patients were evaluated. The DL model yielded high performance with sensitivities of 87.03% and 94.17%, specificities of 82.07% and 74.04%, and areas under the receiver operating characteristic curve of 0.845 and 0.841 on the internal and multicenter external test sets, respectively. Moreover, no significant difference in performance was noted among different plaque characteristics and scanning systems. Using the DL model, the performance of the junior radiologists improved significantly, especially in terms of sensitivity (largest increase from 46.3 to 94.44%). CONCLUSIONS: The DL model based on US videos corresponding to real examinations showed robust performance for plaque detection and significantly improved the diagnostic performance of junior radiologists. KEY POINTS: • The deep learning model based on US videos conforming to real examinations showed robust performance for plaque detection. • Computer-aided diagnosis can significantly improve the diagnostic performance of junior radiologists in clinical practice.

A novel artificial intelligence model for fetal facial profile marker measurement during the first trimester.

BACKGROUND: To study the validity of an artificial intelligence (AI) model for measuring fetal facial profile markers, and to evaluate the clinical value of the AI model for identifying fetal abnormalities during the first trimester. METHODS: This retrospective study used two-dimensional mid-sagittal fetal profile images taken during singleton pregnancies at 11-13+ 6 weeks of gestation. We measured the facial profile markers, including inferior facial angle (IFA), maxilla-nasion-mandible (MNM) angle, facial-maxillary angle (FMA), frontal space (FS) distance, and profile line (PL) distance using AI and manual measurements. Semantic segmentation and landmark localization were used to develop an AI model to measure the selected markers and evaluate the diagnostic value for fetal abnormalities. The consistency between AI and manual measurements was compared using intraclass correlation coefficients (ICC). The diagnostic value of facial markers measured using the AI model during fetal abnormality screening was evaluated using receiver operating characteristic (ROC) curves. RESULTS: A total of 2372 normal fetuses and 37 with abnormalities were observed, including 18 with trisomy 21, 7 with trisomy 18, and 12 with CLP. Among them, 1872 normal fetuses were used for AI model training and validation, and the remaining 500 normal fetuses and all fetuses with abnormalities were used for clinical testing. The ICCs (95%CI) of the IFA, MNM angle, FMA, FS distance, and PL distance between the AI and manual measurement for the 500 normal fetuses were 0.812 (0.780-0.840), 0.760 (0.720-0.795), 0.766 (0.727-0.800), 0.807 (0.775-0.836), and 0.798 (0.764-0.828), respectively. IFA clinically significantly identified trisomy 21 and trisomy 18, with areas under the ROC curve (AUC) of 0.686 (95%CI, 0.585-0.788) and 0.729 (95%CI, 0.621-0.837), respectively. FMA effectively predicted trisomy 18, with an AUC of 0.904 (95%CI, 0.842-0.966). MNM angle and FS distance exhibited good predictive value in CLP, with AUCs of 0.738 (95%CI, 0.573-0.902) and 0.677 (95%CI, 0.494-0.859), respectively. CONCLUSIONS: The consistency of fetal facial profile marker measurements between the AI and manual measurement was good during the first trimester. The AI model is a convenient and effective tool for the early screen for fetal trisomy 21, trisomy 18, and CLP, which can be generalized to first-trimester scanning (FTS).

Artificial Intelligence-Assisted Ultrasound Diagnosis on Infant Developmental Dysplasia of the Hip Under Constrained Computational Resources.

OBJECTIVES: Ultrasound (US) is important for diagnosing infant developmental dysplasia of the hip (DDH). However, the accuracy of the diagnosis depends heavily on expertise. We aimed to develop a novel automatic system (DDHnet) for accurate, fast, and robust diagnosis of DDH. METHODS: An automatic system, DDHnet, was proposed to diagnose DDH by analyzing static ultrasound images. A five-fold cross-validation experiment was conducted using a dataset containing 881 patients to verify the performance of DDHnet. In addition, a blind test was conducted on 209 patients (158 normal and 51 abnormal cases). The feasibility and performance of DDHnet were investigated by embedding it into ultrasound machines at low computational cost. RESULTS: DDHnet obtained reliable measurements and accurate diagnosis predictions. It reported an intra-class correlation coefficient (ICC) on α angle of 0.96 (95% CI: 0.93-0.97), ß angle of 0.97 (95% CI: 0.95-0.98), FHC of 0.98 (95% CI: 0.96-0.99) and PFD of 0.94 (95% CI: 0.90-0.96) in abnormal cases. DDHnet achieved a sensitivity of 90.56%, specificity of 100%, accuracy of 98.64%, positive predictive value (PPV) of 100%, and negative predictive value (NPV) of 98.44% for the diagnosis of DDH. For the measurement task on the US device, DDHnet took only 1.1 seconds to operate and complete, whereas the experienced senior expert required an average 41.4 seconds. CONCLUSIONS: The proposed DDHnet demonstrate state-of-the-art performance for all four indicators of DDH diagnosis. Fast and highly accurate DDH diagnosis is achievable through DDHnet, and is accessible under constrained computational resources.

Enhanced denitrification of the AO-MBBR system used for expressway service area sewage treatment: A new perspective on decentralized wastewater treatment.

Decentralized wastewater treatment warrants considerable development in numerous countries and regions. Owing to the unique characteristics of high ammonia nitrogen concentrations and low carbon/nitrogen ratio, nitrogen removal is a key challenge in treating expressway service area sewage. In this study, an anoxic/oxic-moving bed biofilm reactor (A/O-MBBR) and a traditional A/O bioreactor were continuously operated for 115 days and their outcomes were compared to investigate the enhancement effect of carriers on the total nitrogen removal (TN) for expressway service area sewage. Results revealed that A/O-MBBR required lower dissolved oxygen, exhibited higher tolerance toward harsh conditions, and demonstrated better shock load resistance than traditional A/O bioreactor. The TN removal load of A/O-MBBR reached 181.5 g‧N/(m3‧d), which was 15.24% higher than that of the A/O bioreactor. Furthermore, under load shock resistance, the TN removal load of A/O-MBBR still reached 327.0 g‧N/(m3‧d), with a TN removal efficiency of above 80%. Moreover, kinetics demonstrated that the denitrification rate of the A/O-MBBR was 121.9% higher than that of the A/O bioreactor, with the anoxic tank biofilm contributing 60.9% of the total denitrification rate. Community analysis results revealed that the genera OLB8, uncultured_f_Saprospiraceae and OLB12 were the dominant in biofilm loaded on carriers, and OLB8 was the key for enhanced denitrification. FAPROTAX and PICRUSt2 analyses confirmed that more bacteria associated with nitrogen metabolism were enriched by the A/O-MBBR carriers through full denitrification metabolic pathway and dissimilatory nitrate reduction pathway. This study offers a perspective into the development of cost-effective and high-efficiency treatment solutions for expressway service area sewage.

A biological strategy for sulfide control in sewers: Removing sulfide by sulfur-oxidizing bacteria.

Sulfide produced from sewers is considered one of the dominant threats to public health and sewer lifespan due to its toxicity and corrosiveness. In this study, we developed an environmentally friendly strategy for gaseous sulfide control by enriching indigenous sulfur-oxidizing bacteria (SOB) from sewer sediment. Ceramics acted as bio-carriers for immobilizing SOB for practical use in a lab-scale sewer reactor. 16 S rRNA gene sequences revealed that the SOB consortium was successfully enriched, with Thiobacillus, Pseudomonas, and Alcaligenes occupying a dominant abundance of 64.7% in the microbial community. Metabolic pathway analysis in different acclimatization stages indicates that microorganisms could convert thiosulfate and sulfide into elemental sulfur after enrichment and immobilization. A continuous experiment in lab-scale sewer reactors confirmed an efficient result for sulfide removal with hydrogen sulfide reduction of 43.9% and 85.1% under high-sulfur load and low-sulfur load conditions, respectively. This study shed light on the promising application for sewer sulfide control by biological sulfur oxidation strategy.

Multimodal data analysis reveals that pancreatobiliary-type ampullary adenocarcinoma resembles pancreatic adenocarcinoma and differs from cholangiocarcinoma.

BACKGROUND: Ampullary adenocarcinoma (AAC) arises from the ampulla of Vater where the pancreatic duct and bile duct join and empty into the duodenum. It can be classified into intestinal and pancreatobiliary types based on histopathology or immunohistochemistry. However, there are no biomarkers for further classification of pancreatobiliary-type AAC which has important implications for its treatment. We aimed to identify the tumor origin of pancreatobiliary-type AAC by systematically analyzing whole-slide images (WSIs), survival data, and genome sequencing data collected from multiple centers. METHODS: This study involved three experiments. First, we extracted quantitative and highly interpretable features from the tumor region in WSIs and constructed a histologic classifier to differentiate between pancreatic adenocarcinoma (PAC) and cholangiocarcinoma. The histologic classifier was then applied to patients with pancreatobiliary-type AAC to infer the tumor origin. Secondly, we compared the overall survival of patients with pancreatobiliary-type AAC stratified by the adjuvant chemotherapy regimens designed for PAC or cholangiocarcinoma. Finally, we compared the mutation landscape of pancreatobiliary-type AAC with those of PAC and cholangiocarcinoma. RESULTS: The histologic classifier accurately classified PAC and cholangiocarcinoma in both the internal and external validation sets (AUC > 0.99). All pancreatobiliary-type AACs (n = 45) were classified as PAC. The patients with pancreatobiliary-type AAC receiving regimens designed for PAC showed more favorable overall survival than those receiving regimens designed for cholangiocarcinoma in a multivariable Cox regression (hazard ratio = 7.24, 95% confidence interval: 1.28-40.78, P = 0.025). The results of mutation analysis showed that the mutation landscape of AAC was very similar to that of PAC but distinct from that of cholangiocarcinoma. CONCLUSIONS: This multi-center study provides compelling evidence that pancreatobiliary-type AAC resembles PAC instead of cholangiocarcinoma in different aspects, which can guide the treatment selection and clinical trials planning for pancreatobiliary-type AAC.

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.

Spiroarborin, an ent-Clerodane Homodimer from Callicarpa arborea as an Inhibitor of the Eleven-Nineteen Leukemia (ENL) Protein by Targeting the YEATS Domain.

A spiro ent-clerodane homodimer with a rare 6/6/6/6/6-fused pentacyclic scaffold, spiroarborin (1), together with four new monomeric analogues (2-5), were isolated from Callicarpa arborea. Their structures were elucidated by comprehensive spectroscopic data analysis, quantum-chemical calculations, and X-ray diffraction. A plausible biosynthetic pathway of 1 was proposed, and a biomimetic synthesis of its derivative was accomplished. Compound 1 showed a potent inhibitory effect by directly binding to the YEATS domain of the 11-19 leukemia (ENL) protein with an IC50 value of 7.3 µM. This gave a KD value of 5.0 µM, as recorded by a surface plasmon resonance binding assay.

Leronlimab, a humanized monoclonal antibody to CCR5, blocks breast cancer cellular metastasis and enhances cell death induced by DNA damaging chemotherapy.

BACKGROUND: Triple-negative breast cancer (BCa) (TNBC) is a deadly form of human BCa with limited treatment options and poor prognosis. In our prior analysis of over 2200 breast cancer samples, the G protein-coupled receptor CCR5 was expressed in > 95% of TNBC samples. A humanized monoclonal antibody to CCR5 (leronlimab), used in the treatment of HIV-infected patients, has shown minimal side effects in large patient populations. METHODS: A humanized monoclonal antibody to CCR5, leronlimab, was used for the first time in tissue culture and in mice to determine binding characteristics to human breast cancer cells, intracellular signaling, and impact on (i) metastasis prevention and (ii) impact on established metastasis. RESULTS: Herein, leronlimab was shown to bind CCR5 in multiple breast cancer cell lines. Binding of leronlimab to CCR5 reduced ligand-induced Ca+ 2 signaling, invasion of TNBC into Matrigel, and transwell migration. Leronlimab enhanced the BCa cell killing of the BCa chemotherapy reagent, doxorubicin. In xenografts conducted with Nu/Nu mice, leronlimab reduced lung metastasis of the TNBC cell line, MB-MDA-231, by > 98% at 6 weeks. Treatment with leronlimab reduced the metastatic tumor burden of established TNBC lung metastasis. CONCLUSIONS: The safety profile of leronlimab, together with strong preclinical evidence to both prevent and reduce established breast cancer metastasis herein, suggests studies of clinical efficacy may be warranted.

Antigen-Presenting Hybrid Colloidal Crystal Clusters for Promoting T cells Expansion.

T cell based-immunotherapy has been a powerful strategy to eradicate tumor cells in clinical trials. Effectively expanding the therapeutic T cells for clinical demand is still a challenge. Here, artificial antigen-presenting scaffolds are created for T cell ex vivo expansion. The antigen-presenting hybrid colloidal crystal clusters (HCCCs) with multiple stimuli are generated by internal encapsulation with prosurvival cytokines and surface decoration with activating antibodies to CD3ε and CD28, respectively. With the large loading capacity endowed by their abundant nanoporous structures, the antigen-presenting HCCCs can constantly release prosurvival cytokine IL-2. It is found that following the direct and multiple stimulations, the antigen-presenting HCCCs can effectively promote the expansion of T cells, which exhibits robust antitumor activity in vitro. Thus, the antigen-presenting HCCCs provide a novel expansion platform for clinical manufacturing of T cells.

Synthesis of nigranoic acid and manwuweizic acid derivatives as HDAC inhibitors and anti-inflammatory agents.

As a successful anti-tumor drug target, the family of histone deacetylases (HDACs) is also a critical player in immune response, making the research of anti-inflammatory HDAC inhibitors an attractive new focus. In this report, triterpenoids nigranoic acid (NA) and manwuweizic acid (MA) were identified as HDAC inhibitors through docking-based virtual screening and enzymatic activity assay. A series of derivatives of NA and MA were synthesized and assessed for their biological effects. As a result, hydroxamic acid derivatives of NA and MA showed moderately increased activity for HDAC1/2/4/6 inhibition (the lowest IC50 against HDAC1 is 1.14 µM), with no activity against HDAC8. In J774A.1 macrophage, compound 1-3, 13 and 17-19 demonstrated inhibitory activity against lactate dehydrogenase (LDH) and IL-1ß production, without affecting cell viability. Compound 19 increased the histone acetylation level in J774A.1 cells, as well as inhibited IL-1ß maturation and caspase-1 cleavage. These results indicated that compound 19 blocks the activation of NLRP3 inflammasome, probably related to HDAC inhibition. This work provided a natural scaffold for developing low-cytotoxic and anti-inflammatory HDAC inhibitors, as well as a class of tool molecules for studying the relationship between HDACs and NLRP3 activation.

IMPDH1/YB-1 Positive Feedback Loop Assembles Cytoophidia and Represents a Therapeutic Target in Metastatic Tumors.

Recently, cytoophidium, a nonmembrane-bound intracellular polymeric structure, has been shown to exist in various organisms, including tumor tissues, but its function and mechanism have not yet been examined. Examination of cytoophidia-assembled gene inosine monophosphate dehydrogenase (IMPDH) and cytidine triphosphate synthetase (CTPS) mRNA levels showed that only IMPDH1 levels were significantly higher in the clear cell renal cell carcinoma (ccRCC). IMPDH1 was positively correlated with the metastasis-related gene Y-box binding protein 1 (YB-1) and served as an independent prognostic factor in ccRCC. Kaplan-Meier analysis indicated that patients with tumors that expressed high IMPDH1 levels had a shorter overall survival (OS) and disease-free survival (DFS). Furthermore, detection of cytoophidia by immunofluorescence staining in ccRCC tissues showed that IMPDH1-assembled cytoophidia are positively associated with tumor metastasis. Mechanistically, IMPDH1 and YB-1 formed an autoregulatory positive feedback loop: IMPDH1 maintained YB-1 protein stabilization; YB-1 induced IMPDH1 expression by binding to the IMPDH1 promoter motif. Functionally, IMPDH1-assembled cytoophidia physically interacted with YB-1 and translocated YB-1 into the cell nucleus, thus correlating with ccRCC metastasis. Our findings provide the first solid theoretical rationale for targeting the IMPDH1/YB-1 axis to improve metastatic renal cancer treatment.

Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer.

Tumors are heterogeneous tissues with different types of cells such as cancer cells, fibroblasts, and lymphocytes. Although the morphological features of tumors are critical for cancer diagnosis and prognosis, the underlying molecular events and genes for tumor morphology are far from being clear. With the advancement in computational pathology and accumulation of large amount of cancer samples with matched molecular and histopathology data, researchers can carry out integrative analysis to investigate this issue. In this study, we systematically examine the relationships between morphological features and various molecular data in breast cancers. Specifically, we identified 73 breast cancer patients from the TCGA and CPTAC projects matched whole slide images, RNA-seq, and proteomic data. By calculating 100 different morphological features and correlating them with the transcriptomic and proteomic data, we inferred four major biological processes associated with various interpretable morphological features. These processes include metabolism, cell cycle, immune response, and extracellular matrix development, which are all hallmarks of cancers and the associated morphological features are related to area, density, and shapes of epithelial cells, fibroblasts, and lymphocytes. In addition, protein specific biological processes were inferred solely from proteomic data, suggesting the importance of proteomic data in obtaining a holistic understanding of the molecular basis for tumor tissue morphology. Furthermore, survival analysis yielded specific morphological features related to patient prognosis, which have a strong association with important molecular events based on our analysis. Overall, our study demonstrated the power for integrating multiple types of biological data for cancer samples in generating new hypothesis as well as identifying potential biomarkers predicting patient outcome. Future work includes causal analysis to identify key regulators for cancer tissue development and validating the findings using more independent data sets.

Design, synthesis and anti-HIV evaluation of 5-alkyl- 6-(benzo[d][1,3]dioxol-5-alkyl)-2-mercaptopyrimidin-4(3H)-ones as potent HIV-1 NNRTIs.

In order to discover and develop the new HIV-1 NNRTIs, a series of 5-alkyl-6-(benzo[d][1,3]dioxol-5-ylalkyl)-2-mercaptopyrimidin-4(3H)-ones was synthesized and screened for their in vitro cytotoxicity against HIV-1. Most of the compounds we synthetized showed high activity against wild-type HIV-1 strain (IIIB) while IC50 values are in the range of 0.06-12.95 µM. Among them, the most active HIV-1 inhibitor was compound 6-(benzo[d][1,3]dioxol-5-ylmethyl)-5-ethyl-2-((2-(4-hydroxyphenyl)-2-oxoethyl)thio)pyrimidin-4(3H)-one (5b), which exhibited similar HIV-1 inhibitory potency (IC50 = 0.06 µM, CC50 = 96.23 µM) compared with nevirapine (IC50 = 0.04 µM, CC50 >200 µM) and most of compounds exhibited submicromolar IC50 values indicating they were specific RT inhibitors. The compounds 5b, 6-(benzo[d] [1,3]dioxol-5-yl)-5-ethyl-2-((2-(4-hydroxyphenyl)-2-oxoethyl)thio)pyrimidin-4(3H)-one (5c) and 4-(2-((4-(benzo[d][1,3]dioxol-5-ylmethyl)-5-ethyl-6-oxo-1,6-dihydropyrimidin-2-yl)thio)acetyl)phenylbenzo[d][1,3]dioxole-5-carboxylate (5r) were selected for further study. It was found that all of them had little toxicity to peripheral blood mononuclear cell (PBMC), and had a good inhibitory effect on the replication of HIV-1 protease inhibitor resistant strains, fusion inhibitor resistant strains and nucleosides reverse transcriptase inhibitor resistant strains, as well as on clinical isolates. Besides, compound 5b and 5c showed inhibition of HIV-1 RT RNA-dependent DNA polymerization activity and DNA-dependent DNA polymerization activity, while compound 5r only showed inhibition of HIV DNA-dependent DNA polymerization activity, which was different from classical reverse transcriptase inhibitors. Our study which offered the preliminary structure-activity relationships and modeling studies of these new compounds has provided the valuable avenues for future molecular optimization.

Establishment of a miRNA-mRNA regulatory network in metastatic renal cell carcinoma and screening of potential therapeutic targets.

This study aimed to screen effective diagnosis or treatment biomarkers for renal cell carcinoma, especially for metastatic renal cell carcinoma (mRCC) based on microRNA (miRNA) and messenger RNA (mRNA) genechip, and their regulatory network. The differential expressions of miRNAs and mRNAs were examined by miRNA and mRNA gene-chip analyses, respectively, in patients with either localized renal cell carcinoma (lRCC) or mRCC, and a miRNA-mRNA regulatory network was established. Subsequently, the regulation of selected mRNAs by miRNAs was validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and dual-luciferase reporter gene assay. Thirty-one up-regulated miRNAs, 196 down-regulated miRNAs, 214 up-regulated mRNAs, and 156 down-regulated mRNAs were identified in patients with mRCC. In total, 1315 miRNA-mRNA pairs, involving 34 miRNAs and 225 mRNAs, were established. The expression profiles of four up-regulated miRNAs, hsa-miR-139-5p, hsa-miR-140-3p, hsa-miR-151a-3p, and hsa-miR-204-5p, and four down-regulated miRNAs, hsa-miR-409-3p, hsa-miR-671-3p, hsa-miR-1203, and hsa-miR-1290, were consistent with the results from the miRNA gene-chip analysis. The expression profiles of NEU2, MASP1, MCL1, ARHGAP11A, HOXA1, and CLDN8 were consistent with the results from the mRNA gene-chip analysis. In vitro, hsa-miR-140-3p bound to the 3' untranslated region (3'-UTR) of the MASP1 mRNA and down-regulated its expression. Similarly, hsa-miR-151a-3p, hsa-miR-671-3p, and hsa-miR-1290 bound to the 3'-UTRs of the MCL1, HOXA1, and HOXA1 mRNAs, respectively, and down-regulated their expressions. However, binding by hsa-miR-140-3p, hsa-miR-671-3p, or hsa-miR-1290 did not down-regulate the expressions of NEU2, ARHGAP11A, and CLDN8, respectively. This study provides a significant reference of investigating the pathogenesis of mRCC and the subsequent screening of potential therapeutic targets.

Spatiotemporal patterns of cortical fiber density in developing infants, and their relationship with cortical thickness.

The intrinsic relationship between the convoluted cortical folding and the underlying complex whiter matter fiber connections has received increasing attention in current neuroscience studies. Recently, the axonal pushing hypothesis of cortical folding has been proposed to explain the finding that the axonal fibers (derived from diffusion tensor images) connecting to gyri are significantly denser than those connecting to sulci in both adult human and non-human primate brains. However, it is still unclear about the spatiotemporal patterns of the fiber density on the cortical surface of the developing infant brains from birth to 2 years of age, which is the most dynamic phase of postnatal brain development. In this paper, for the first time, we systemically characterized the spatial distributions and longitudinal developmental trajectories of the cortical fiber density in the first 2 postnatal years, via joint analysis of longitudinal structural and diffusion tensor imaging from 33 healthy infants. We found that the cortical fiber density increases dramatically in the first year and then keeps relatively stable in the second year. Moreover, we revealed that the cortical fiber density on gyral regions was significantly higher at 0, 1, and 2 years of age than that on sulcal regions in the frontal, temporal, and parietal lobes. Meanwhile, the cortical fiber density was strongly positively correlated with cortical thickness at several three-hinge junction regions of gyri. These results significantly advanced our understanding of the intrinsic relationship between the cortical folding, cortical thickness and axonal wiring during early postnatal stages.