Deep learning to estimate gestational age from fly-to cineloop videos: A novel approach to ultrasound quality control.

OBJECTIVE: Low-cost devices have made obstetric sonography possible in settings where it was previously unfeasible, but ensuring quality and consistency at scale remains a challenge. In the present study, we sought to create a tool to reduce substandard fetal biometry measurement while minimizing care disruption. METHODS: We developed a deep learning artificial intelligence (AI) model to estimate gestational age (GA) in the second and third trimester from fly-to cineloops-brief videos acquired during routine ultrasound biometry-and evaluated its performance in comparison to expert sonographer measurement. We then introduced random error into fetal biometry measurements and analyzed the ability of the AI model to flag grossly inaccurate measurements such as those that might be obtained by a novice. RESULTS: The mean absolute error (MAE) of our model (±standard error) was 3.87 ± 0.07 days, compared to 4.80 ± 0.10 days for expert biometry (difference -0.92 days; 95% CI: -1.10 to -0.76). Based on simulated novice biometry with average absolute error of 7.5%, our model reliably detected cases where novice biometry differed from expert biometry by 10 days or more, with an area under the receiver operating characteristics curve of 0.93 (95% CI: 0.92, 0.95), sensitivity of 81.0% (95% CI: 77.9, 83.8), and specificity of 89.9% (95% CI: 88.1, 91.5). These results held across a range of sensitivity analyses, including where the model was provided suboptimal truncated fly-to cineloops. CONCLUSIONS: Our AI model estimated GA more accurately than expert biometry. Because fly-to cineloop videos can be obtained without any change to sonographer workflow, the model represents a no-cost guardrail that could be incorporated into both low-cost and commercial ultrasound devices to prevent reporting of most gross GA estimation errors.

Statistical Analysis of Methotrexate Degradation by UV-C Photolysis and UV-C/TiO2 Photocatalysis.

Methotrexate (MTX) is a folic acid analog and has been used to treat a wide variety of malignant and non-malignant diseases. The wide use of these substances has led to the continuous discharge of the parent compound and its metabolites in wastewater. In conventional wastewater treatment plants, the removal or degradation of drugs is not complete. In order to study the MTX degradation by photolysis and photocatalysis processes, two reactors were used with TiO2 as a catalyst and UV-C lamps as a radiation source. H2O2 addition was also studied (absence and 3 mM/L), and different initial pHs (3.5, 7, and 9.5) were tested to define the best degradation parameters. Results were analyzed by means of ANOVA and the Tukey test. Results show that photolysis in acidic conditions with 3 mM of H2O2 added is the best condition for MTX degradation in these reactors, with a kinetic constant of 0.028 min-1. According to the ANOVA test, all considered factors (process, pH, H2O2 addition, and experimentation time) caused statistically significant differences in the MTX degradation results.

Assessment of systemic AAV-microdystrophin gene therapy in the GRMD model of Duchenne muscular dystrophy.

Duchenne muscular dystrophy (DMD) is a progressive muscle wasting disease caused by the absence of dystrophin, a membrane-stabilizing protein encoded by the DMD gene. Although mouse models of DMD provide insight into the potential of a corrective therapy, data from genetically homologous large animals, such as the dystrophin-deficient golden retriever muscular dystrophy (GRMD) model, may more readily translate to humans. To evaluate the clinical translatability of an adeno-associated virus serotype 9 vector (AAV9)-microdystrophin (µDys5) construct, we performed a blinded, placebo-controlled study in which 12 GRMD dogs were divided among four dose groups [control, 1 × 1013 vector genomes per kilogram (vg/kg), 1 × 1014 vg/kg, and 2 × 1014 vg/kg; n = 3 each], treated intravenously at 3 months of age with a canine codon-optimized microdystrophin construct, rAAV9-CK8e-c-µDys5, and followed for 90 days after dosing. All dogs received prednisone (1 milligram/kilogram) for a total of 5 weeks from day -7 through day 28. We observed dose-dependent increases in tissue vector genome copy numbers; µDys5 protein in multiple appendicular muscles, the diaphragm, and heart; limb and respiratory muscle functional improvement; and reduction of histopathologic lesions. As expected, given that a truncated dystrophin protein was generated, phenotypic test results and histopathologic lesions did not fully normalize. All administrations were well tolerated, and adverse events were not seen. These data suggest that systemically administered AAV-microdystrophin may be dosed safely and could provide therapeutic benefit for patients with DMD.

Effect of goal-directed haemodynamic therapy guided by non-invasive monitoring on perioperative complications in elderly hip fracture patients within an enhanced recovery pathway.

BACKGROUND: Goal-directed haemodynamic therapy (GDHT) has been shown to reduce morbidity and mortality in high-risk surgical patients. However, there is little evidence of its efficacy in patients undergoing hip fracture surgery. This study aims to evaluate the effect of GDHT guided by non-invasive haemodynamic monitoring on perioperative complications in patients undergoing hip fracture surgery. METHODS: Patients > 64 years undergoing hip fracture surgery within an enhanced recovery pathway (ERP) were enrolled in this single-centre, non-randomized, intervention study with a historical control group and 12-month follow-up. Exclusion criteria were patients with pathological fractures, traffic-related fractures and refractures. Control group (CG) patients received standard care treatment. Intervention group (IG) patients received a GDHT protocol based on achieving an optimal stroke volume, in addition to a systolic blood pressure > 90 mmHg and an individualized cardiac index. No changes were made between groups in the ERP during the study period. Primary outcome was percentage of patients who developed intraoperative haemodynamic instability. Secondary outcomes were intraoperative arrhythmias, postoperative complications (cardiovascular, respiratory, infectious and renal complications), administered fluids, vasopressor requirements, perioperative transfusion, length of hospital stay, readmission and 1-year survival. RESULTS: In total, 551 patients (CG=272; IG=279) were included. Intraoperative haemodynamic instability was lower in the IG (37.5% vs 28.0%; p=0.017). GDHT patients had fewer postoperative cardiovascular (18.8% vs 7.2%; p < 0.001), respiratory (15.1% vs 3.6%; p<0.001) and infectious complications (21% vs 3.9%; p<0.001) but not renal (12.1% vs 33.7%; p<0.001). IG patients had less vasopressor requirements (25.5% vs 39.7%; p<0.001) and received less fluids [2.600 ml (IQR 1700 to 2700) vs 850 ml (IQR 750 to 1050); p=0.001] than control group. Fewer patients required transfusion in GDHT group (73.5% vs 44.4%; p<0.001). For IG patients, median length of hospital stay was shorter [11 days (IQR 8 to 16) vs 8 days; (IQR 6 to 11) p < 0.001] and 1-year survival higher [73.4% (95%CI 67.7 to 78.3 vs 83.8% (95%CI 78.8 to 87.7) p<0.003]. CONCLUSIONS: The use of GDHT decreases intraoperative complications and postoperative cardiovascular, respiratory and infectious but not postoperative renal complications. This strategy was associated with a shorter hospital stay and increased 1-year survival. TRIAL REGISTRATION: ClinicalTrials.gov NCT02479321 .

CONTINUITY: CONnectivity Tool with INtegration of sUbcortical regions, regIstration and visualization of TractographY.

In this work, we present CONTINUITY, a novel, open-source interactive computation and visualization tool for brain connectome data. The connectome processing pipeline performs surface based processing as the main mode of operation. The automated processing includes structural-to-diffusion image co-registration, surface reconstruction for subcortical structures, as well as fiber tractography. The tool supports 3 different probabilistic methods of tractography offered by the tractography frameworks in FSL, MRtrix and DIPY. All methods employ brain and subcortical surfaces as seeds to initialize the tractography algorithms. CONTINUITY implements a friendly Graphical User Interface (GUI) to make the workflow accessible for nontechnical users. Additionally, it offers the possibility to visualize the results of the brain connectome in several interactive plot types such as a hierarchical edge bundling circle plot and over 2D/3D brain templates. This visualization tool can also be applied to connectome matrices computed with other tools and pipelines.

Tumorigenic transformation of human prostatic epithelial cell line RWPE-1 by growth hormone-releasing hormone (GHRH).

BACKGROUND: Growth hormone-releasing hormone (GHRH) and its receptors have been implicated in the progression of various tumors. In this study, we analyzed the carcinogenetic potential of exposure to GHRH of a nontumor human prostate epithelial cell line (RWPE-1) as well as its transforming effect in a xenograft model. METHODS: We performed cell viability, cell proliferation, adhesion and migration assays. In addition, metalloprotease (MMP)-2 activity by means gelatin zymography, GHRH-R subcellular location using confocal immunofluorescence microscopy and vascular endothelial growth factor (VEGF) levels by enzyme-linked immunoassay were assessed. Besides, we developed an in vivo model in order vivo model to determine the role of GHRH on tumorigenic transformation of RWPE-1 cells. RESULTS: In cell cultures, we observed development of a migratory phenotype consistent with the gelatinolytic activity of MMP-2, expression of VEGF, as well as E-cadherin-mediated cell-cell adhesion and increased cell motility. Treatment with 0.1 µM GHRH for 24 h significantly increased cell viability and cell proliferation. Similar effects of GHRH were seen in RWPE-1 tumors developed by subcutaneous injection of GHRH-treated cells in athymic nude mice, 49 days after inoculation. CONCLUSIONS: Thus, GHRH appears to act as a cytokine in the transformation of RWPE-1 cells by mechanisms that likely involve epithelial-mesenchymal transition, thus reinforcing the role of GHRH in tumorigenesis of prostate.

AI Estimation of Gestational Age from Blind Ultrasound Sweeps in Low-Resource Settings.

BACKGROUND: Ultrasound is indispensable to gestational age estimation and thus to quality obstetrical care, yet high equipment cost and the need for trained sonographers limit its use in low-resource settings. METHODS: From September 2018 through June 2021, we recruited 4695 pregnant volunteers in North Carolina and Zambia and obtained blind ultrasound sweeps (cineloop videos) of the gravid abdomen alongside standard fetal biometry. We trained a neural network to estimate gestational age from the sweeps and, in three test data sets, assessed the performance of the artificial intelligence (AI) model and biometry against previously established gestational age. RESULTS: In our main test set, the mean absolute error (MAE) (±SE) was 3.9±0.12 days for the model versus 4.7±0.15 days for biometry (difference, -0.8 days; 95% confidence interval [CI], -1.1 to -0.5; P<0.001). The results were similar in North Carolina (difference, -0.6 days; 95% CI, -0.9 to -0.2) and Zambia (-1.0 days; 95% CI, -1.5 to -0.5). Findings were supported in the test set of women who conceived by in vitro fertilization (MAE of 2.8±0.28 vs. 3.6±0.53 days for the model vs. biometry; difference, -0.8 days; 95% CI, -1.7 to 0.2) and in the set of women from whom sweeps were collected by untrained users with low-cost, battery-powered devices (MAE of 4.9±0.29 vs. 5.4±0.28 days for the model vs. biometry; difference, -0.6; 95% CI, -1.3 to 0.1). CONCLUSIONS: When provided blindly obtained ultrasound sweeps of the gravid abdomen, our AI model estimated gestational age with accuracy similar to that of trained sonographers conducting standard fetal biometry. Model performance appears to extend to blind sweeps collected by untrained providers in Zambia using low-cost devices. (Funded by the Bill and Melinda Gates Foundation.).

An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation.

Accurate assessment of fetal gestational age (GA) is critical to the clinical management of pregnancy. Industrialized countries rely upon obstetric ultrasound (US) to make this estimate. In low- and middle- income countries, automatic measurement of fetal structures using a low-cost obstetric US may assist in establishing GA without the need for skilled sonographers. In this report, we leverage a large database of obstetric US images acquired, stored and annotated by expert sonographers to train algorithms to classify, segment, and measure several fetal structures: biparietal diameter (BPD), head circumference (HC), crown rump length (CRL), abdominal circumference (AC), and femur length (FL). We present a technique for generating raw images suitable for model training by removing caliper and text annotation and describe a fully automated pipeline for image classification, segmentation, and structure measurement to estimate the GA. The resulting framework achieves an average accuracy of 93% in classification tasks, a mean Intersection over Union accuracy of 0.91 during segmentation tasks, and a mean measurement error of 1.89 centimeters, finally leading to a 1.4 day mean average error in the predicted GA compared to expert sonographer GA estimate using the Hadlock equation.

FlyBy CNN: A 3D surface segmentation framework.

In this paper, we present FlyBy CNN, a novel deep learning based approach for 3D shape segmentation. FlyByCNN consists of sampling the surface of the 3D object from different view points and extracting surface features such as the normal vectors. The generated 2D images are then analyzed via 2D convolutional neural networks such as RUNETs. We test our framework in a dental application for segmentation of intra-oral surfaces. The RUNET is trained for the segmentation task using image pairs of surface features and image labels as ground truth. The resulting labels from each segmented image are put back into the surface thanks to our sampling approach that generates 1-1 correspondence of image pixels and triangles in the surface model. The segmentation task achieved an accuracy of 0.9.

Performance Enhanced Ultrasound Probe Tracking With a Hemispherical Marker Rigid Body.

Among tracking techniques applied in the 3-D freehand ultrasound (US), the camera-based tracking method is relatively mature and reliable. However, constrained by manufactured marker rigid bodies, the US probe is usually limited to operate within a narrow rotational range before occlusion issues affect accurate and robust tracking performance. Thus, this study proposed a hemispherical marker rigid body to hold passive noncoplanar markers so that the markers could be identified by the camera, mitigating self-occlusion. The enlarged rotational range provides greater freedom for sonographers while performing examinations. The single-axis rotational and translational tracking performances of the system, equipped with the newly designed marker rigid body, were investigated and evaluated. Tracking with the designed marker rigid body achieved high tracking accuracy with 0.57° for the single-axis rotation and 0.01 mm for the single-axis translation for sensor distance between 1.5 and 2 m. In addition to maintaining high accuracy, the system also possessed an enhanced ability to capture over 99.76% of the motion data in the experiments. The results demonstrated that with the designed marker rigid body, the missing data were remarkably reduced from over 15% to less than 0.5%, which enables interpolation in the data postprocessing. An imaging test was further conducted, and the volume reconstruction of a four-month fetal phantom was demonstrated using the motion data obtained from the tracking system.

Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study.

BACKGROUND AND RATIONALE: Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. METHODS: Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. RESULTS: In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings. CONCLUSION: Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings.

Immunodeficiency in a Patient with 22q11.2 Distal Deletion Syndrome and a p.Ala7dup Variant in the MAPK1 Gene.

The genetic basis for sporadic immunodeficiency in patients with 22q11.2 distal deletion syndrome is unknown. We report an adult with a type 1 (D-F) 22q11.2 distal deletion syndrome and recurrent severe infections due to herpes zoster virus, presenting mild T cell lymphopenia and diminished frequency of naive CD4+ T cells, but increased frequencies of central, effector, and terminally differentiated memory T cells. Antigen-specific CD4+ and CD8+ T cells to influenza, rotavirus, and SEB were conserved in the patient, but responses to tetanus toxoid were temporarily undetectable. Exomic sequencing identified the c.20_22dupCGG (NM_002745.4) variant in the remaining MAPK1 gene of the patient, which adds 1 alanine to the polyalanine amino-terminal tract of the protein (p.Ala7dup). The mother, unlike the father, was heterozygote for the variant. Western blot analysis with the patient's activated PBMCs showed a 91% reduction in the MAPK1 protein. Further studies will be necessary to determine whether or not the variant present in the remaining MAPK1 gene of the patient is pathogenic.

Stimulation of neuroendocrine differentiation in prostate cancer cells by GHRH and its blockade by GHRH antagonists.

Prostate cancer is the second leading cause of cancer-related deaths among men in developed countries. Neuroendocrine prostate cancer, in particular, is associated with an aggressive phenotype and a poor prognosis. Neuroendocrine cells produce and secrete peptide hormones and growth factors in a paracrine/autocrine manner which promote the progression of the disease. Recent studies have demonstrated that extracellular vesicles or exosomes are released by prostate cancer cells, supporting the spread of prostate cancer. Hence, the aim of this study was to investigate the effect of growth hormone-releasing hormone (GHRH) on neuroendocrine differentiation (NED) in the androgen-dependent prostate cancer cell line LNCaP and the molecular mechanisms underlying these effects. GHRH induced an increase in the percentage of neurite-bearing cells and in the protein levels of Neuron-Specific Enolase. Both effects were blocked by the GHRH receptor antagonist MIA-690. In addition, pretreatment of these cells with the calcium chelator BAPTA, the EGFR inhibitor AG-1478 or the HER2 inhibitor AG-825 reduced the effect of GHRH, suggesting that the GHRH-induced stimulation of NED involves calcium channel activation and EGFR/HER2 transactivation. Finally, PC3-derived exosomes led to an increase in NED, cell proliferation and cell adhesion. Altogether, these findings suggest that GHRH antagonists should be considered for in the management of neuroendocrine prostate cancer.

Multiseg pipeline: automatic tissue segmentation of brain MR images with subject-specific atlases.

Automated segmentation and labeling of individual brain anatomical regions is challenging due to individual structural variability. Although, atlas-based segmentation has shown its potential for both tissue and structure segmentation, the inherent natural variability as well as disease-related changes in MR appearance is often inappropriately represented by a single atlas image. In order to have a more accurate representation, several atlases may be used for the segmentation task in a given neuroimaging study. In this paper, we present the MultisegPipeline, it uses multiple atlases that have been visually inspected and capture the expected variability in a neonatal population. The MultisegPipeline transfers the labeled regions from each atlas to the target image using deformable registration (ANTs1 or QuickSilver2 is available for this task). Additionally, the set of labels are merged using a label fusion technique that reduces the errors produced by the registration. The final output is a single label map that combines the results produced by all atlases into a consensus solution. In our study, the MultisegPipeline is used to segment brain MR images from 31 infants, a leave-one-out strategy was used to test our framework. The average dice score coefficient was 0.89.

Alirocumab in Patients With Polyvascular Disease and Recent Acute Coronary Syndrome: ODYSSEY OUTCOMES Trial.

BACKGROUND: Patients with acute coronary syndrome (ACS) and concomitant noncoronary atherosclerosis have a high risk of major adverse cardiovascular events (MACEs) and death. The impact of lipid lowering by proprotein convertase subtilisin-kexin type 9 inhibition in such patients is undetermined. OBJECTIVES: This pre-specified analysis from ODYSSEY OUTCOMES (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab) determined whether polyvascular disease influenced risks of MACEs and death and their modification by alirocumab in patients with recent ACS and dyslipidemia despite intensive statin therapy. METHODS: Patients were randomized to alirocumab or placebo 1 to 12 months after ACS. The primary MACEs endpoint was the composite of coronary heart disease death, nonfatal myocardial infarction, fatal or nonfatal ischemic stroke, or unstable angina requiring hospitalization. All-cause death was a secondary endpoint. RESULTS: Median follow-up was 2.8 years. Of 18,924 patients, 17,370 had monovascular (coronary) disease, 1,405 had polyvascular disease in 2 beds (coronary and peripheral artery or cerebrovascular), and 149 had polyvascular disease in 3 beds (coronary, peripheral artery, cerebrovascular). With placebo, the incidence of MACEs by respective vascular categories was 10.0%, 22.2%, and 39.7%. With alirocumab, the corresponding absolute risk reduction was 1.4% (95% confidence interval [CI]: 0.6% to 2.3%), 1.9% (95% CI: -2.4% to 6.2%), and 13.0% (95% CI: -2.0% to 28.0%). With placebo, the incidence of death by respective vascular categories was 3.5%, 10.0%, and 21.8%; the absolute risk reduction with alirocumab was 0.4% (95% CI: -0.1% to 1.0%), 1.3% (95% CI: -1.8% to 4.3%), and 16.2% (95% CI: 5.5% to 26.8%). CONCLUSIONS: In patients with recent ACS and dyslipidemia despite intensive statin therapy, polyvascular disease is associated with high risks of MACEs and death. The large absolute reductions in those risks with alirocumab are a potential benefit for these patients. (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab [ODYSSEY OUTCOMES]: NCT01663402).

White matter connectomes at birth accurately predict cognitive abilities at age 2.

Cognitive ability is an important predictor of mental health outcomes that is influenced by neurodevelopment. Evidence suggests that the foundational wiring of the human brain is in place by birth, and that the white matter (WM) connectome supports developing brain function. It is unknown, however, how the WM connectome at birth supports emergent cognition. In this study, a deep learning model was trained using cross-validation to classify full-term infants (n = 75) as scoring above or below the median at age 2 using WM connectomes generated from diffusion weighted magnetic resonance images at birth. Results from this model were used to predict individual cognitive scores. We additionally identified WM connections important for classification. The model was also evaluated in a separate set of preterm infants (n = 37) scanned at term-age equivalent. Findings revealed that WM connectomes at birth predicted 2-year cognitive score group with high accuracy in both full-term (89.5%) and preterm (83.8%) infants. Scores predicted by the model were strongly correlated with actual scores (r = 0.98 for full-term and r = 0.96 for preterm). Connections within the frontal lobe, and between the frontal lobe and other brain areas were found to be important for classification. This work suggests that WM connectomes at birth can accurately predict a child's 2-year cognitive group and individual score in full-term and preterm infants. The WM connectome at birth appears to be a useful neuroimaging biomarker of subsequent cognitive development that deserves further study.

Synergism between gabapentin-tramadol in experimental diabetic neuropathic pain.

Neuropathic pain is associated with several conditions such as surgery, cancer, and diabetes and can be induced experimentally. Among the drugs used as monotherapy are gabapentin and tramadol. The purpose of this study was to evaluate the coadministration of gabapentin and tramadol, by isobolographic analysis, in three different algesiometric assays in experimental diabetic neuropathic pain induced by streptozocin in mice. In all the behavioral tests, gabapentin or tramadol produced a dose-dependent antinociception and their coadministration resulted in a positive interaction. This effect can be explained by principles of multimodal analgesia, whereby the different mechanisms of action of each drug contribute to the combined effect in a supra-additive manner. The findings of the present study suggest that the combination of gabapentin and tramadol could be a useful strategy for the treatment of pain induced by diabetic neuropathy.

TRAFIC: Fiber Tract Classification Using Deep Learning.

We present TRAFIC, a fully automated tool for the labeling and classification of brain fiber tracts. TRAFIC classifies new fibers using a neural network trained using shape features computed from previously traced and manually corrected fiber tracts. It is independent from a DTI Atlas as it is applied to already traced fibers. This work is motivated by medical applications where the process of extracting fibers from a DTI atlas, or classifying fibers manually is time consuming and requires knowledge about brain anatomy. With this new approach we were able to classify traced fiber tracts obtaining encouraging results. In this report we will present in detail the methods used and the results achieved with our approach.

SVA: Shape variation analyzer.

Temporo-mandibular osteo arthritis (TMJ OA) is characterized by progressive cartilage degradation and subchondral bone remodeling. The causes of this pathology remain unclear. Current research efforts are concentrated in finding new biomarkers that will help us understand disease progression and ultimately improve the treatment of the disease. In this work, we present Shape Variation Analyzer (SVA), the goal is to develop a noninvasive technique to provide information about shape changes in TMJ OA. SVA uses neural networks to classify morphological variations of 3D models of the mandibular condyle. The shape features used for training include normal vectors, curvature and distances to average models of the condyles. The selected features are purely geometric and are shown to favor the classification task into 6 groups generated by consensus between two clinician experts. With this new approach, we were able to accurately classify 3D models of condyles. In this paper, we present the methods used and the results obtained with this new tool.

Growth hormone-releasing hormone receptor antagonists modify molecular machinery in the progression of prostate cancer.

BACKGROUND: Therapeutic strategies should be designed to transform aggressive prostate cancer phenotypes to a chronic situation. To evaluate the effects of the new growth hormone-releasing hormone receptor (GHRH-R) antagonists: MIA-602, MIA-606, and MIA-690 on processes associated with cancer progression as cell proliferation, adhesion, migration, and angiogenesis. METHODS: We used three human prostate cell lines (RWPE-1, LNCaP, and PC3). We analyzed several molecules such as E-cadherin, ß-catenin, Bcl2, Bax, p53, MMP2, MMP9, PCNA, and VEGF and signaling mechanisms that are involved on effects exerted by GHRH-R antagonists. RESULTS: GHRH-R antagonists decreased cell viability and provoked a reduction in proliferation in LNCaP and PC3 cells. Moreover, GHRH-R antagonists caused a time-dependent increase of cell adhesion in all three cell lines and retarded the wound closure with the highest value with MIA-690 in PC3 cells. GHRH-R antagonists also provoked a large number of cells in SubG0 phase revealing an increase in apoptotic cells in PC3 cell line. CONCLUSIONS: Taken all together, GHRH-R antagonists of the MIAMI series appear to be inhibitors of tumor progression in prostate cancer and should be considered for use in future therapeutic strategies on this malignancy.