A multicenter clinical AI system study for detection and diagnosis of focal liver lesions.

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists' F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.

A machine learning approach for preoperatively assessing pulmonary function with computed tomography in patients with lung cancer.

Background: It is clinically important to accurately assess the pulmonary function of patients with lung cancer, especially before surgery. This knowledge can help clinicians to monitor patients pre- and post-surgery, predict the impact of surgery on pulmonary function, and help to optimize postsurgical recovery. We used a deep learning approach for assessing pulmonary function on computed tomography (CT) scans in patients with lung cancer before they underwent surgery. Methods: A total of 188 patients with lung cancer whose diagnoses had been pathologically confirmed were enrolled in this study. We used a software to automatically delineate regions of interest (ROIs) throughout the airways, lobes, and the whole lungs. We then used AK software to extract radiomics features of the 3 types of ROIs. We randomly separated these cases into a training cohort and a test cohort at a ratio of 7:3. We next constructed a logistic regression model to assess pulmonary function from the radiomics features. The machine learning outcomes were compared with established clinical criteria for pulmonary function. including forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), FVC, and maximum vital capacity (VCmax) to evaluate the accuracy of the machine learning model. Results: In the ROIs of the lobes, our results showed that the machine learning model had good performance in predicting FVC and VCmax, attaining a Spearman correlation r value of 0.714 with P<0.001 for FVC and a r value of 0.687 with P<0.001 for VCmax. Using the airway ROIs, our model achieved a r of 0.603 with P=0.001 for VCmax. Using the whole lung ROIs, our model achieved a r of 0.704 with P<0.001 for FVC and a r of 0.693 with P<0.001 for VCmax. Conclusions: Preoperative CT may provide a means for evaluating pulmonary function in patients with lung cancer. With radiomics features extracted from the airway, lobes, and the whole lung region, and a properly trained machine learning model, it is possible to obtain accurate estimation for metrics used in clinical criteria and to offer clinicians imaging-based indicators for the status of pulmonary functions.

Experimental measurement of quadrature squeezing in quadripartite entanglement.

Multipartite entanglement is one of the most fundamental and important resources for quantum information processing in both discrete variable and continuous variable (CV) regimes. For its applications in the CV regime, such as the realization of quantum teleportation networks and quantum dense coding, the quadrature squeezing of multipartite entanglement plays a significant role. Here, we report the first, to the best of our knowledge, experimental measurement of the quadrature squeezing in the quadripartite entanglement generated by the two-beam pumped cascaded four-wave mixing process in a 85 R b vapor cell. Moreover, we find that the quadrature squeezing is nonexistent in each pair of beams, but exists in the whole quadripartite entanglement. Our results may find potential applications in building a multi-user quantum secret sharing network.

Toward More Accessible Fully Automated 3D Volumetric MRI Decision Trees for the Differential Diagnosis of Multiple System Atrophy, Related Disorders, and Age-Matched Healthy Subjects.

Differentiating multiple system atrophy (MSA) from related neurodegenerative movement disorders (NMD) is challenging. MRI is widely available and automated decision-tree analysis is simple, transparent, and resistant to overfitting. Using a retrospective cohort of heterogeneous clinical MRIs broadly sourced from a tertiary hospital system, we aimed to develop readily translatable and fully automated volumetric diagnostic decision-trees to facilitate early and accurate differential diagnosis of NMDs. 3DT1 MRI from 171 NMD patients (72 MSA, 49 PSP, 50 PD) and 171 matched healthy subjects were automatically segmented using Freesurfer6.0 with brainstem module. Decision trees employing substructure volumes and a novel volumetric pons-to-midbrain ratio (3D-PMR) were produced and tenfold cross-validation performed. The optimal tree separating NMD from healthy subjects selected cerebellar white matter, thalamus, putamen, striatum, and midbrain volumes as nodes. Its sensitivity was 84%, specificity 94%, accuracy 84%, and kappa 0.69 in cross-validation. The optimal tree restricted to NMD patients selected 3D-PMR, thalamus, superior cerebellar peduncle (SCP), midbrain, pons, and putamen as nodes. It yielded sensitivities/specificities of 94/84% for MSA, 72/96% for PSP, and 73/92% PD, with 79% accuracy and 0.62 kappa. There was correct classification of 16/17 MSA, 5/8 PSP, 6/8 PD autopsy-confirmed patients, and 6/8 MRIs that preceded motor symptom onset. Fully automated decision trees utilizing volumetric MRI data distinguished NMD patients from healthy subjects and MSA from other NMDs with promising accuracy, including autopsy-confirmed and pre-symptomatic subsets. Our open-source methodology is well-suited for widespread clinical translation. Assessment in even more heterogeneous retrospective and prospective cohorts is indicated.

Dysarthria Speech Detection Using Convolutional Neural Networks with Gated Recurrent Unit.

In recent years, due to the rise in the population and aging, the prevalence of neurological diseases is also increasing year by year. Among these patients with Parkinson's disease, stroke, cerebral palsy, and other neurological symptoms, dysarthria often appears. If these dysarthria patients are not quickly detected and treated, it is easy to cause difficulties in disease course management. When the symptoms worsen, they can also affect the patient's psychology and physiology. Most of the past studies on dysarthria detection used machine learning or deep learning models as classification models. This study proposes an integrated CNN-GRU model with convolutional neural networks and gated recurrent units to detect dysarthria. The experimental results show that the CNN-GRU model proposed in this study has the highest accuracy of 98.38%, which is superior to other research models.

Metabolomics study of treatment response to conbercept of patients with neovascular age-related macular degeneration and polypoidal choroidal vasculopathy.

Background: Neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) are major causes of blindness in aged people. 30% of the patients show unsatisfactory response to anti-vascular endothelial growth factor (anti-VEGF) drugs. This study aims to investigate the relationship between serum metabolome and treatment response to anti-VEGF therapy. Methods: A prospective longitudinal study was conducted between March 2017 and April 2019 in 13 clinical sites in China. The discovery group were enrolled from Shanghai General Hospital. The validation group consisted of patients from the other 12 sites. Participants received at least one intravitreal injection of 0.5 mg anti-VEGF drug, conbercept, and were divided into two groups - responders and non-responders. Serum samples of both groups were processed for UHPLC-MS/MS analysis. We constructed principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models to investigate the metabolic differences between two groups using SIMCA-P. Area under curve (AUC) was calculated to screen the biomarkers to predict treatment response. Metabolites sub-classes and enriched pathways were obtained using MetaboAnalyst5.0. Results: 219 eyes from 219 patients (nAMD = 126; PCV = 93) were enrolled. A total of 248 metabolites were detected. PCA and PLS-DA models of the discovery group demonstrated that the metabolic profiles of responders and non-responders clearly differed. Eighty-five differential metabolites were identified, including sub-classes of diacylglycerophosphocholines, lysophosphatidylcholine (LPC), fatty acids, phosphocholine, etc. Responders and non-responders differed most significantly in metabolism of LPC (p = 7.16 × 10^-19) and diacylglycerophosphocholine (p = 6.96 × 10^-17). LPC 18:0 exhibited the highest AUC, which is 0.896 with 95% confidence internal between 0.833 and 0.949, to discriminate responders. The predictive accuracy of LPC 18:0 was 72.4% in the validation group. Conclusions: This study suggests that differential metabolites may be useful for guiding treatment options for nAMD and PCV. Metabolism of LPC and diacylglycerophosphocholine were found to affect response to conbercept treatment. LPC 18:0 was a potential biomarker to discriminate responders from non-responders.

3D printed scaffold for repairing bone defects in apical periodontitis.

OBJECTIVES: To investigate the feasibility of the 3D printed scaffold for periapical bone defects. METHODS: In this study, antimicrobial peptide KSL-W-loaded PLGA sustainable-release microspheres (KSL-W@PLGA) were firstly prepared followed by assessing the drug release behavior and bacteriostatic ability against Enterococcus faecalis and Porphyromonas gingivalis. After that, we demonstrated that KSL-W@PLGA/collagen (COL)/silk fibroin (SF)/nano-hydroxyapatite (nHA) (COL/SF/nHA) scaffold via 3D-printing technique exhibited significantly good biocompatibility and osteoconductive property. The scaffold was characterized as to pore size, porosity, water absorption expansion rate and mechanical properties. Moreover, MC3T3-E1 cells were seeded into sterile scaffold materials and investigated by CCK-8, SEM and HE staining. In the animal experiment section, we constructed bone defect models of the mandible and evaluated its effect on bone formation. The Japanese white rabbits were killed at 1 and 2 months after surgery, the cone beam computerized tomography (CBCT) and micro-CT scanning, as well as HE and Masson staining analysis were performed on the samples of the operation area, respectively. Data analysis was done using ANOVA and LSD tests. (α = 0.05). RESULTS: We observed that the KSL-W@PLGA sustainable-release microspheres prepared in the experiment were uniform in morphology and could gradually release the antimicrobial peptide (KSL-W), which had a long-term antibacterial effect for at least up to 10 days. HE staining and SEM showed that the scaffold had good biocompatibility, which was conducive to the adhesion and proliferation of MC3T3-E1 cells. The porosity and water absorption of the scaffold were (81.96 ± 1.83)% and (458.29 ± 29.79)%, respectively. Histological and radiographic studies showed that the bone healing efficacy of the scaffold was satisfactory. CONCLUSIONS: The KSL-W@PLGA/COL/SF/nHA scaffold possessed good biocompatibility and bone repairing ability, and had potential applications in repairing infected bone defects. Clinical significance The 3D printed scaffold not only has an antibacterial effect, but can also promote bone tissue formation, which provides an alternative therapy option in apical periodontitis.

Development and autoregulation of kidney function in children: a retrospective study using 99mTc-MAG3 renography.

BACKGROUND: Both the development of kidney function in healthy children and autoregulation ability of kidney function in patients with asymmetric kidneys are important in clinical diagnosis and treatment of kidney-related diseases, but there are however only limited studies. This study aimed to investigate development of kidney function in normal children with healthy symmetric kidneys and autoregulation of the healthy kidney compensating the functional loss of a diseased one in children with asymmetric kidneys. METHODS: Two hundred thirty-seven children (156 male, 81 female) from 0 to 20y (average 4.6y ± 5.1) undergoing 99mTc-MAG3 renography were included, comprising 134 with healthy symmetrically functioning kidneys and 103 with asymmetric kidneys. Clearance was calculated from kidney uptakes at 1-2 min. A developmental model between MAG3 clearance (CL) and patient age in normal group was identified (CL = 84.39Age0.395 ml/min, r = 0.957, p < 0.001). The clearance autoregulation rate in abnormal group with asymmetric kidneys was defined as the ratio of the measured MAG3 clearance and the normal value predicted from the renal developmental model of normal group. RESULTS: No significant difference of MAG3 clearance (p = 0.723) was found between independent abnormal group and normal group. The autoregulation rate of kidney clearance in abnormal group was 94.2% on average, and no significant differences were found between two age groups (p = 0.49), male and female (p = 0.39), and left kidney and right kidney (p = 0.92) but two different grades of asymmetric kidneys (p = 0.02). CONCLUSIONS: The healthy kidney of two asymmetric kidneys can automatically regulate total kidney function up to 94% of two symmetric kidneys in normal children.

IMAGE COMPRESSION BASED ON IMPORTANCE USING OPTIMAL MASS TRANSPORTATION MAP.

Demand for efficient image transmission and storage is increasing rapidly because of the continuing growth of multimedia technology and VR and AR applications. In this paper, we proposed an image compression method based on the recognition of importance of regions in images. As not all the information in an image is equally useful, we can identify important regions in an image for high fidelity compression and accept a comparatively more lossy compression about less important regions of the image. First, we segment images to two parts, namely, foreground and background, where the foreground represents the more important component and the background is of less importance. Second, we apply optimal mass transportation mapping in a GAN (generative adversarial network) framework to both the foreground and background to magnify the foreground and shrink the background while keeping the shape and total image area unchanged. As a result, in the processed image, the ratio of foreground to background is larger than the corrresponding ratio in the original image. This ratio is controllable in our process, giving users the ability to control the degree of compression. The GAN-processed image is then used for compression. To restore the image, we apply a GAN model to the compressed image and recover the ratio of foreground and background using an optimal mass transportation map. Test results show that our method is highly effective in reconstructing detail of important components in compressed images while achieving a high compression ratio.

Efficient Discrete Optimal Transport Algorithm by Accelerated Gradient Descent.

Optimal transport (OT) plays an essential role in various areas like machine learning and deep learning. However, computing discrete OT for large scale problems with adequate accuracy and efficiency is highly challenging. Recently, methods based on the Sinkhorn algorithm add an entropy regularizer to the prime problem and obtain a trade off between efficiency and accuracy. In this paper, we propose a novel algorithm based on Nesterov's smoothing technique to further improve the efficiency and accuracy in computing OT. Basically, the non-smooth c-transform of the Kantorovich potential is approximated by the smooth Log-Sum-Exp function, which smooths the original non-smooth Kantorovich dual functional. The smooth Kantorovich functional can be efficiently optimized by a fast proximal gradient method, the fast iterative shrinkage thresholding algorithm (FISTA). Theoretically, the computational complexity of the proposed method is given by O(n52logn∕ϵ), which is lower than current estimation of the Sinkhorn algorithm. Experimentally, compared with the Sinkhorn algorithm, our results demonstrate that the proposed method achieves faster convergence and better accuracy with the same parameter.

A New Framework of Designing Iterative Techniques for Image Deblurring.

In this work we present a framework of designing iterative techniques for image deblurring in inverse problem. The new framework is based on two observations about existing methods. We used Landweber method as the basis to develop and present the new framework but note that the framework is applicable to other iterative techniques. First, we observed that the iterative steps of Landweber method consist of a constant term, which is a low-pass filtered version of the already blurry observation. We proposed a modification to use the observed image directly. Second, we observed that Landweber method uses an estimate of the true image as the starting point. This estimate, however, does not get updated over iterations. We proposed a modification that updates this estimate as the iterative process progresses. We integrated the two modifications into one framework of iteratively deblurring images. Finally, we tested the new method and compared its performance with several existing techniques, including Landweber method, Van Cittert method, GMRES (generalized minimal residual method), and LSQR (least square), to demonstrate its superior performance in image deblurring.

Gland context networks: A novel approach for improving prostate cancer identification.

Prostate cancer (PCa) is a pervasive condition that is manifested in a wide range of histologic patterns in biopsy samples. Given the importance of identifying abnormal prostate tissue to improve prognosis, many computerized methodologies aimed at assisting pathologists in diagnosis have been developed. It is often argued that improved diagnosis of a tissue region can be obtained by considering measurements that can take into account several properties of its surroundings, therefore providing a more robust context for the analysis. Here we propose a novel methodology that can be used for systematically defining contextual features regarding prostate glands. This is done by defining a Gland Context Network (GCN), a representation of the prostate sample containing information about the spatial relationship between glands as well as the similarity between their appearance. We show that such a network can be used for establishing contextual features at any spatial scale, therefore providing information that is not easily obtained from traditional shape and textural features. Furthermore, it is shown that even basic features derived from a GCN can lead to state-of-the-art classification performance regarding PCa. All in all, GCNs can assist in defining more effective approaches for PCa grading.

Quantification of retinal blood leakage in fundus fluorescein angiography in a retinal angiogenesis model.

Blood leakage from the vessels in the eye is the hallmark of many vascular eye diseases. One of the preclinical mouse models of retinal blood leakage, the very-low-density-lipoprotein receptor deficient mouse (Vldlr-/-), is used for drug screening and mechanistic studies. Vessel leakage is usually examined using Fundus fluorescein angiography (FFA). However, interpreting FFA images of the Vldlr-/- model is challenging as no automated and objective techniques exist for this model. A pipeline has been developed for quantifying leakage intensity and area including three tasks: (i) blood leakage identification, (ii) blood vessel segmentation, and (iii) image registration. Morphological operations followed by log-Gabor quadrature filters were used to identify leakage regions. In addition, a novel optic disk detection algorithm based on graph analysis was developed for registering the images at different timepoints. Blood leakage intensity and area measured by the methodology were compared to ground truth quantifications produced by two annotators. The relative difference between the quantifications from the method and those obtained from ground truth images was around 10% ± 6% for leakage intensity and 17% ± 8% for leakage region. The Pearson correlation coefficient between the method results and the ground truth was around 0.98 for leakage intensity and 0.94 for leakage region. Therefore, we presented a computational method for quantifying retinal vascular leakage and vessels using FFA in a preclinical angiogenesis model, the Vldlr-/- model.

A Comparative Retrospective Study of Immunotherapy RANO Versus Standard RANO Criteria in Glioblastoma Patients Receiving Immune Checkpoint Inhibitor Therapy.

OBJECTIVES: Real-time assessment of treatment response in glioblastoma (GBM) patients on immune checkpoint blockade (ICB) remains challenging because inflammatory effects of therapy may mimic progressive disease, and the temporal evolution of these inflammatory findings is poorly understood. We compare GBM patient response during ICB as assessed with the Immunotherapy Response Assessment in Neuro-Oncology (iRANO) and the standard Response Assessment in Neuro-Oncology (RANO) radiological criteria. METHODS: 49 GBM patients (seven newly diagnosed and 42 recurrent) treated with ICBs at a single institution were identified. Tumor burden was quantified on serial MR scans according to RANO criteria during ICB. Radiographic response assessment by iRANO and RANO were compared. RESULTS: 82% (40/49) of patients received anti-PD-1, 16% (8/49) received anti-PD-L1, and 2% (1/49) received anti-PD-1 and anti-CTLA4 treatment. Change in tumor burden and best overall response ranged from -100 to +557% (median: +48%). 12% (6/49) of patients were classified as concordant non-progressors by both RANO and iRANO (best response: one CR, one PR, and four SD). Another12% (6/49) had discordant assessments: 15% (6/41) of RANO grade progressive disease (PD) patients had iRANO grade of progressive disease unconfirmed (PDU). The final classification of these discordant patients was pseudoprogression (PsP) in three of six, PD in two of six, and PDU in one of six who went off study before the iRANO assessment of PDU. iRANO delayed diagnosis of PD by 42 and 93 days in the two PD patients. 76% (37/49) patients were classified as concordant PD by both RANO and iRANO. 12% (6/49) of all patients were classified as PsP, starting at a median of 12 weeks (range, 4-30 weeks) after ICB initiation. CONCLUSIONS: Standard RANO and iRANO have high concordance for assessing PD in patients within 6 months of ICB initiation. iRANO was beneficial in 6% (3/49) cases later proven to be PsP, but delayed confirmation of PD by <3 months in 4% (2/49). PsP occurred in 12% of patients, starting at up to 7 months after initiation of ICB. Further study to define the utility of modified RANO compared with iRANO in ICB GBM patients is needed.

Cortical Morphometry Analysis based on Worst Transportation Theory.

Biomarkers play an important role in early detection and intervention in Alzheimer's disease (AD). However, obtaining effective biomarkers for AD is still a big challenge. In this work, we propose to use the worst transportation cost as a univariate biomarker to index cortical morphometry for tracking AD progression. The worst transportation (WT) aims to find the least economical way to transport one measure to the other, which contrasts to the optimal transportation (OT) that finds the most economical way between measures. To compute the WT cost, we generalize the Brenier theorem for the OT map to the WT map, and show that the WT map is the gradient of a concave function satisfying the Monge-Ampere equation. We also develop an efficient algorithm to compute the WT map based on computational geometry. We apply the algorithm to analyze cortical shape difference between dementia due to AD and normal aging individuals. The experimental results reveal the effectiveness of our proposed method which yields better statistical performance than other competiting methods including the OT.

Microbial ecology in selenate-reducing biofilm communities: Rare biosphere and their interactions with abundant phylotypes.

Selenate (SeO42- ) reduction in hydrogen (H2 )-fed membrane biofilm reactors (H2 -MBfRs) was studied in combinations with other common electron acceptors. We employed H2 -MBfRs with two distinctly different conditions: R1, with ample electron-donor availability and acceptors SeO42- and sulfate (SO42- ), and R2, with electron-donor limitation and the presence of electron acceptors SeO42- , nitrate (NO3- ), and SO42- . Even though H2 was available to reduce all input SeO42- and SO42- in R1, SeO42- reduction was preferred over SO42- reduction. In R2, co-reduction of NO3- and SeO42- occurred, and SO42- reduction was mostly suppressed. Biofilms in all MBfRs had high microbial diversity that was influenced by the "rare biosphere" (RB), phylotypes with relative abundance less than 1%. While all MBfR biofilms had abundant members, such as Dechloromonas and Methyloversatilis, the bacterial communities were significantly different between R1 and R2. For R1, abundant genera were Methyloversatilis, Melioribacter, and Propionivibrio; for R2, abundant genera were Dechloromonas, Hydrogenophaga, Cystobacter, Methyloversatilis, and Thauera. Although changes in electron-acceptor or -donor loading altered the phylogenetic structure of the microbial communities, the biofilm communities were resilient in terms of SeO42- and NO3- reductions, because interacting members of the RB had the capacity of respiring these electron acceptors.

Schisandrin B Protects against Acute Ethanol-Induced Cardiac Injury by Downregulating Autophagy via the NOX4/ROS Pathway.

INTRODUCTION: Although oxidative stress has been demonstrated to mediate acute ethanol-induced changes in autophagy in the heart, the precise mechanism behind redox regulation in acute ethanol heart disease remains largely unknown. METHODS: Wild-type C57BL/6 mice were intraperitoneally injected with ethanol (3 g/kg/day) for 3 consecutive days. The effects of ethanol on cultured primary cardiomyocytes and H9c2 myoblasts were also studied in vitro. Levels of autophagic flux, cardiac apoptosis and function, reactive oxygen species (ROS) accumulation, NOX4, and NOX2 were examined. The NOX4 gene was knocked down with NOX4 siRNA. RESULTS: In this study, we demonstrated that schisandrin B inhibited acute ethanol-induced autophagy and sequent apoptosis. In addition, schisandrin B treatment improved cardiac function in ethanol-treated mice. Furthermore, NOX4 protein expression was increased during acute ethanol exposure, and the upregulation of NOX4 was significantly inhibited by schisandrin B treatment. The knockdown of NOX4 prevented ROS accumulation, cell autophagy, and apoptosis. CONCLUSION: These results highlight that NOX4 is a critical mediator of ROS and elaborate the role of the NOX4/ROS axis in the effect of schisandrin B on autophagy and autophagy-mediated apoptosis in acute ethanol exposure, which suggests a therapeutic strategy for acute alcoholic cardiomyopathy.

Cortical Surface Shape Analysis Based on Alexandrov Polyhedra.

Shape analysis has been playing an important role in early diagnosis and prognosis of neurodegenerative diseases such as Alzheimer's diseases (AD). However, obtaining effective shape representations remains challenging. This paper proposes to use the Alexandrov polyhedra as surface-based shape signatures for cortical morphometry analysis. Given a closed genus-0 surface, its Alexandrov polyhedron is a convex representation that encodes its intrinsic geometry information. We propose to compute the polyhedra via a novel spherical optimal transport (OT) computation. In our experiments, we observe that the Alexandrov polyhedra of cortical surfaces between pathology-confirmed AD and cognitively unimpaired individuals are significantly different. Moreover, we propose a visualization method by comparing local geometry differences across cortical surfaces. We show that the proposed method is effective in pinpointing regional cortical structural changes impacted by AD.

Quantitative assessment of pulmonary function in lymphangioleiomyomatosis patients using high-resolution computed tomography and pulmonary function tests.

BACKGROUND: To explore the feasibility of using quantitative high-resolution computed tomography (HRCT) to evaluate pulmonary function in patients with pulmonary lymphangioleiomyomatosis (PLAM). METHODS: Pulmonary function tests (PFTs) were performed in 30 patients with pathologically confirmed PLAM with the use of HRCT. These results were correlated with quantitative HRCT in 21 patients. RESULTS: There were significant correlations between the HRCT parameters for lung function and PFT parameters. Among these parameters, emphysema volume (EV), pulmonary volume with a pixel index less than the trigger threshold (-950 HU) to account for a proportion of total lung volume [PI-950 (%)] and forced expiratory volume in 1 second/forced vital capacity [FEV1/FVC (%)] had the strongest correlations, reaching values between -0.71 and -0.68. HRCT lung function might therefore also be helpful for predicting changes in lung function before and after treatment. CONCLUSIONS: HRCT is helpful for the assessment of pulmonary function in PLAM patients and can assist in the clinical evaluation of lung function and treatment response in patients with this disease.

Characterization of a novel compound that promotes myogenesis via Akt and transcriptional co-activator with PDZ-binding motif (TAZ) in mouse C2C12 cells.

Transcriptional co-activator with PDZ-binding motif (TAZ) plays versatile roles in the regulation of cell proliferation and differentiation. TAZ activity changes in response to the cellular environment such as mechanic and nutritional stimuli, osmolarity, and hypoxia. To understand the physiological roles of TAZ, chemical compounds that activate TAZ in cells are useful as experimental reagents. Kaempferol, TM-25659, and ethacridine are reported as TAZ activators. However, as each TAZ activator has a distinct property in cellular functions, additional TAZ activators are awaiting. We screened for TAZ activators and previously reported IB008738 as a TAZ activator that promotes myogenesis in C2C12 cells. In this study, we have characterized IBS004735 that was obtained in the same screening. IBS004735 also promotes myogenesis in C2C12 cells, but is not similar to IBS008738 in the structure. IBS004735 activates TAZ via Akt and has no effect on TAZ phosphorylation, which is the well-described key modification to regulate TAZ activity. Thus, we introduce IBS004735 as a novel TAZ activator that regulates TAZ in a yet unidentified mechanism.