Development and Assessment of an E-Learning Course on Breast Imaging for Radiographers: A Stratified Randomized Controlled Trial

Background: Mammography is considered the best imaging technique for breast cancer screening, and the radiographer plays an important role in its performance. Therefore, continuing education is critical to improving the performance of these professionals and thus providing better health care services. Objective: Our goal was to develop an e-learning course on breast imaging for radiographers, assessing its efficacy , effectiveness, and user satisfaction. Methods: A stratified randomized controlled trial was performed with radiographers and radiology students who already had mammography training, using pre- and post-knowledge tests, and satisfaction questionnaires. The primary outcome was the improvement in test results (percentage of correct answers), using intention-to-treat and per-protocol analysis. Results: A total of 54 participants were assigned to the intervention (20 students plus 34 radiographers) with 53 controls (19+34). The intervention was completed by 40 participants (11+29), with 4 (2+2) discontinued interventions, and 10 (7+3) lost to follow-up. Differences in the primary outcome were found between intervention and control: 21 versus 4 percentage points (pp), P<.001. Stratified analysis showed effect in radiographers (23 pp vs 4 pp; P=.004) but was unclear in students (18 pp vs 5 pp; P=.098). Nonetheless, differences in students’ posttest results were found (88% vs 63%; P=.003), which were absent in pretest (63% vs 63%; P=.106). The per-protocol analysis showed a higher effect (26 pp vs 2 pp; P<.001), both in students (25 pp vs 3 pp; P=.004) and radiographers (27 pp vs 2 pp; P<.001). Overall, 85% were satisfied with the course, and 88% considered it successful. Conclusions: This e-learning course is effective, especially for radiographers, which highlights the need for continuing education.

Short- and medium-term effects of manual therapy on cervical active range of motion and pressure pain sensitivity in latent myofascial pain of the upper trapezius muscle: a randomized controlled trial

Objective: The purpose of this study was to investigate effects of different manual techniques on cervical ranges of 17 motion and pressure pain sensitivity in subjects with latent trigger point of the upper trapezius muscle. 18 Methods: One hundred seventeen volunteers, with a unilateral latent trigger point on upper trapezius due to computer 19 work, were randomly divided into 5 groups: ischemic compression (IC) group (n = 24); passive stretching group (n = 20 23); muscle energy technique group (n = 23); and 2 control groups, wait-and-see group (n = 25) and placebo group 21 (n = 22). Cervical spine range of movement was measured using a cervical range of motion instrument as well as 22 pressure pain sensitivity by means of an algometer and a visual analog scale. Outcomes were assessed pretreatment, 23 immediately, and 24 hours after the intervention and 1 week later by a blind researcher. A 4 × 5 mixed repeated- 24 measures analysis of variance was used to examine the effects of the intervention and Cohen d coefficient was used. 25 Results: A group-by-time interaction was detected in all variables (P b .01), except contralateral rotation. The 26 immediate effect sizes of the contralateral flexion, ipsilateral rotation, and pressure pain threshold were large for 3 27 experimental groups. Nevertheless, after 24 hours and 1 week, only IC group maintained the effect size. 28 Conclusions: Manual techniques on upper trapezius with latent trigger point seemed to improve the cervical range of 29 motion and the pressure pain sensitivity. These effects persist after 1 week in the IC group. (J Manipulative Physiol 301 Ther 2013;xx:1-10)

Effects of microcurrents and physical exercise on the abdominal fat: a randomized controlled trial in patients with coronary artery disease

Objectives: Coronary artery disease are associated with decreased levels of physical activity, contributing to increases in abdominal fat and consequently the metabolic risk. The use of microcurrents is an innovative and effective method to increase lipolytic rate of abdominal adipocytes. This study aims to investigate the effects of microcurrents with a homebased exercise program on total, subcutaneous and visceral abdominal adipose tissue in subjects with coronary artery disease. Methods: This controlled trial included 44 subjects with myocardial infarction, randomly divided into Intervention Group 1 (IG1; n = 16), Intervention Group 2 (IG2; n = 12) and Control Group (CG; n = 16). IG1 performed a specific exercise program at home during 8 weeks, and IG2 additionally used microcurrents on the abdominal region before the exercise program. All groups were subjected to health education sessions. Computed Tomography was used to evaluate abdominal, subcutaneous and visceral fat, accelerometers to measure habitual physical activity and the semiquantitative Food Frequency Questionnaire for dietary intake. Results: After 8 weeks, IG2 showed a significantly decreased in subcutaneous fat (p ≤ 0.05) when compared to CG. Concerning visceral fat, both intervention groups showed a significant decrease in comparison to the CG (p ≤ 0.05). No significant changes were found between groups on dietary intake and habitual physical activity, except for sedentary activity that decreased significantly in IG2 in comparison with CG (p ≤ 0.05). Conclusions: This specific exercise program showed improvements in visceral fat in individuals with coronary artery disease. Microcurrent therapy associated with a home-based exercise program suggested a decreased in subcutaneous abdominal fat.

Effects of self-administered exercises based on Tuina techniques on musculoskeletal disorders of professional orchestra musicians: a randomized controlled trial

Background Musicians are frequently affected by playing-related musculoskeletal disorders (PRMD). Common solutions used by Western medicine to treat musculoskeletal pain include rehabilitation programs and drugs, but their results are sometimes disappointing. Objective To study the effects of self-administered exercises based on Tuina techniques on the pain intensity caused by PRMD of professional orchestra musicians, using numeric visual scale (NVS). Design, setting, participants and interventions We performed a prospective, controlled, single-blinded, randomized study with musicians suffering from PRMD. Participating musicians were randomly distributed into the experimental (n = 39) and the control (n = 30) groups. After an individual diagnostic assessment, specific Tuina self-administered exercises were developed and taught to the participants. Musicians were instructed to repeat the exercises every day for 3 weeks. Main outcome measures Pain intensity was measured by NVS before the intervention and after 1, 3, 5, 10, 15 and 20 d of treatment. The procedure was the same for the control group, however the Tuina exercises were executed in points away from the commonly-used acupuncture points. Results In the treatment group, but not the control group, pain intensity was significantly reduced on days 1, 3, 5, 10, 15 and 20. Conclusion The results obtained are consistent with the hypothesis that self-administered exercises based on Tuina techniques could help professional musicians controlling the pain caused by PRMD. Although our results are very promising, further studies are needed employing a larger sample size and double blinding designs.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

A novel antibody-like material for breast cancer antigen CA15-3, used to track breast cancer by potentiometric transduction

This work presents the development of a low cost sensor device for the diagnosis of breast cancer in point-of-care, made with new synthetic biomimetic materials inside plasticized poly(vinyl chloride), PVC, membranes, for subsequent potentiometric detection. This concept was applied to target a conventional biomarker in breast cancer: Breast Cancer Antigen (CA15-3). The new biomimetic material was obtained by molecularly-imprinted technology. In this, a plastic antibody was obtained by polymerizing around the biomarker that acted as an obstacle to the growth of the polymeric matrix. The imprinted polymer was specifically synthetized by electropolymerization on an FTO conductive glass, by using cyclic voltammetry, including 40 cycles within -0.2 and 1.0 V. The reaction used for the polymerization included monomer (pyrrol, 5.0×10-3 mol/L) and protein (CA15-3, 100U/mL), all prepared in phosphate buffer saline (PBS), with a pH of 7.2 and 1% of ethylene glycol. The biomarker was removed from the imprinted sites by proteolytic action of proteinase K. The biomimetic material was employed in the construction of potentiometric sensors and tested with regard to its affinity and selectivity for binding CA15-3, by checking the analytical performance of the obtained electrodes. For this purpose, the biomimetic material was dispersed in plasticized PVC membranes, including or not a lipophilic ionic additive, and applied on a solid conductive support of graphite. The analytical behaviour was evaluated in buffer and in synthetic serum, with regard to linear range, limit of detection, repeatability, and reproducibility. This antibody-like material was tested in synthetic serum, and good results were obtained. The best devices were able to detect 5 times less CA15-3 than that required in clinical use. Selectivity assays were also performed, showing that the various serum components did not interfere with this biomarker. Overall, the potentiometric-based methods showed several advantages compared to other methods reported in the literature. The analytical process was simple, providing fast responses for a reduced amount of analyte, with low cost and feasible miniaturization. It also allowed the detection of a wide range of concentrations, diminishing the required efforts in previous sample pre-treating stages.

Longitudinal analysis of tumor marker CEA of breast cancer patients from Braga's hospital

Allied to an epidemiological study of population of the Senology Unit of Braga’s Hospital that have been diagnosed with malignant breast cancer, we describe the progression in time of repeated measurements of tumor marker Carcinoembryonic antigen (CEA). Our main purpose is to describe the progression of this tumor marker as a function of possible risk factors and, hence, to understand how these risk factors influences that progression. The response variable, values of CEA, was analyzed making use of longitudinal models, testing for different correlation structures. The same covariates used in a previous survival analysis were considered in the longitudinal model. The reference time used was time from diagnose until death from breast cancer. For diagnostic of the models fitted we have used empirical and theoretical variograms. To evaluate the fixed term of the longitudinal model we have tested for a changing point on the effect of time on the tumor marker progression. A longitudinal model was also fitted only to the subset of patients that died from breast cancer, using the reference time as time from date of death until blood test.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.