Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

After breast cancer surgery, women might develop musculoskeletal impairments that affect movements of the upper limbs and reduce the quality of life. The objective of this research was to analyze the effect of the unilateral breast cancer surgery on the kinematics of the shoulder and the electrical activity of the upper trapezius, middle deltoid and pectoralis major muscles. Eight right-handed female participants, mean age 46.5 ± 5.45 years and mean body mass 71.21 ± 13.33 kg, with unilateral breast cancer surgery, without breast reconstruction and without lymphedema symptoms were included in the research. Flexion-extension and abduction-adduction movements of the shoulder were evaluated with infrared cameras and the electrical activity was measured using surface electromyography. The statistical analysis of the direction angles showed a significant reduction of the flexion-extension and abduction-adduction movements in the affected side in most of the participants (p<0.05). The muscle electrical activity did not present a significant difference between the two sides for the flexion-extension and abduction-adduction movements (p>0.05). The results suggest that the surgical procedure could compromise the range of motion of the affected side. Furthermore, this research contributes to clarify the effect of the surgical procedure in the range of motion of the upper limbs.

This project's purpose was to evaluate the healing effects of chitosan (CS) hydrogels loaded with extracts from Aloe vera (CS+AV) and Calendula officinalis (CS+CO) on wounds of diabetic and non-diabetic Wistar rats. A total of 24 rats were used; animals were randomly divided into three diabetic and three non-diabetic groups (one control and two treated groups) and monitored for 13 days. A biopsy on the wound site was recovered to assess the collagen and n-acetyl glucosamine content. The wound area ratio was reduced since day 1 on both non-diabetic treated groups. A similar effect was observed on the diabetic group treated with CS+AV, while the diabetic group treated with CS+CO showed a reduction in wound area compared to the diabetic control until day 11 after being wounded. Collagen and n-acetyl glucosamine content were higher in every treated group. Further studies are needed to clarify the underlying mechanisms through which they promote wound healing. These results suggest that the hydrogels prepared are potential material to be used as wound dressings.

The novel coronavirus (COVID-19) is a disease that mainly affects the lung tissue. The detection of lesions caused by this disease can help to provide an adequate treatment and monitoring its evolution. This research focuses on the binary classification of lung lesions caused by COVID-19 in images of computed tomography (CT) using deep learning. The database used in the experiments comes from two independent repositories, which contains tomographic scans of patients with a positive diagnosis of COVID-19. The output layers of four pre-trained convolutional networks were adapted to the proposed task and re-trained using the fine-tuning technique. The models were validated with test images from the two database’s repositories. The model VGG19, considering one of the repositories, showed the best performance with 88% and 90.2% of accuracy and recall, respectively. The model combination using the soft voting technique presented the highest accuracy (84.4%), with a recall of 94.4% employing the data from the other repository. The area under the receiver operating characteristic curve was 0.92 at best. The proposed method based on deep learning represents a valuable tool to automatically classify COVID-19 lesions on CT images and could also be used to assess the extent of lung infection.

The accelerated aging process is incorporated into the design and development of intravascular catheters to assess their reliability assuring that this medical device is safe and effective for the intended use during their shelf life. The accelerated aging process is based on a common approach that assumes that the rate of aging increases by a factor of  , where  is the temperature increment. However, with the life data obtained from this empirical method is difficult to do inferences about reliability. This paper presents an accelerated destructive degradation test using thermal stress to obtain degradation data directly relates reliability to critical performance characteristic, which is the tensile strength in the intravascular catheter tip considered as a critical concern in patients’ safety. The degradation data model is given by a stochastic Wiener process with the drift parameter being represented as Arrhenius function. The parameters of the Wiener process and Arrhenius function are estimated using maximum likelihood; these parameters are used to estimate the first-passage time (time to failure) distribution when the intravascular catheters degradation path reaches a tensile strength critical value in each thermal stress level. Based on this, a complete product reliability assessment is performed and presented.

This work analyzes the electrical impedance (EI) measurement of cervical mucus (CM) using a device to determine the fertile window. In this prospective and longitudinal study, fourteen healthy women aged 18 to 44 were enrolled to evaluate three menstrual cycles. EI was measured through a medical device inserted into the vagina for two minutes daily. Patients were monitored by urine luteinizing hormone (LH) strip, blood collection, and vaginal ultrasound to visualize the dominant follicle. Finally, the predictive EI capacity was validated by the receiver operating characteristic (ROC) of anovulatory vs. ovulatory impedances. The peak of LH was 35.7 (±4.5) mUI/ml and the dominant follicle size was 15.45 mm (±0.559). There were statistical differences in EI measurements between the follicular and luteal phases vs. the ovulation phase (p<0.0361 and p<0.0160). After data normalization, an area under the ROC curve (AUC) of 0.713 (P value= 0.0253), a Youden J index of 0.4545Ω, a sensitivity of 63.6%, and a specificity of 81.8% were found. Low EI in the ovulatory period belongs to the LH ovulatory peak and follicular release. EI can be used for ovulation monitoring, birth control, or promoting pregnancy as a safe and innocuous method.

Spirometry is a test for the diagnosis of chronic obstructive pulmonary disease. It is a technique that can be intolerant due to the essential use of a mouthpiece and a clamp. This study proposes the use of electrical impedance tomography to measure respiratory parameters. Patients underwent spirometry and three respiratory exercises. The impedance signals were convolved, and the resultant was analyzed by fast Fourier transform. The frequency spectrum was divided into seven segments (R1 to R7). Each segment was represented in terms of quartiles (Q25%, Q50%, Q75%). Each quartile of each segment was correlated with the spirometric parameters to obtain a fitting equation. FVC was correlated 70% with the 3 quartiles of R7, 3 equations were obtained with a fit of 60%. FEV1 correlated 70% with the Q50% of R7, obtaining an equation with a fit of 40%. FEV1/FVC correlated 69% with Q75% of R2, obtaining an equation with a fit of 60%. Spirometric parameters can be estimated from the implied carrier frequency components of the ventilatory impedance signal.

This study presents a methodology for identifying the color space that provides the best performance in an image processing application. When measurements are performed without selecting the appropriate color model, the accuracy of the results is considerably altered. It is significant in computation, mainly when a diagnostic is based on stained cell microscopy images. This work shows how the proper selection of the color model provides better characterization in two types of cancer, acute lymphoid leukemia, and multiple myeloma. The methodology uses images from a public database. First, the nuclei are segmented, and then statistical moments are calculated for class identification. After, a principal component analysis is performed to reduce the extracted features and identify the most significant ones. At last, the predictive model is evaluated using the k-nearest neighbor algorithm and a confusion matrix. For the images used, the results showed that the CIE L*a*b color space best characterized the analyzed cancer types with an average accuracy of 95.52%. With an accuracy of 91.81%, RGB and CMY spaces followed. HSI and HSV spaces had an accuracy of 87.86% and 89.39%, respectively, and the worst performer was grayscale with an accuracy of 55.56%.

Deep learning (DL) techniques achieve high performance in the detection of illnesses in retina images, but the majority of models are trained with different databases for solving one specific task. Consequently, there are currently no solutions that can be used for the detection/segmentation of a variety of illnesses in the retina in a single model. This research uses Transfer Learning (TL) to take advantage of previous knowledge generated during model training of illness detection to segment lesions with encoder-decoder Convolutional Neural Networks (CNN), where the encoders are classical models like VGG-16 and ResNet50 or variants with attention modules. This shows that it is possible to use a general methodology using a single fundus image database for the detection/segmentation of a variety of retinal diseases achieving state-of-the-art results. This model could be in practice more valuable since it can be trained with a more realistic database containing a broad spectrum of diseases to detect/segment illnesses without sacrificing performance. TL can help achieve fast convergence if the samples in the main task (Classification) and sub-tasks (Segmentation) are similar. If this requirement is not fulfilled, the parameters start from scratch.

Medical image security is acquiring its importance to preserve the integrity and confidentiality of information (medical data) from malicious users given its importance in timely and successful diagnosis. In this context, several techniques have been developed to protect medical images, such as encryption, data hiding, image tagging, application of Hash algorithms, etc. This paper proposes a technique to cipher medical images by adding the metadata inside a cover image, based on extended visual cryptography as well as the inclusion of a Hash-like function to verify the integrity of the image and the metadata once they are recovered. The method proposed in this work is implemented using medical images with a grayscale resolution of [0,4095] that is a depth of 12 bits/pixel and color images with 24 bits/pixel depth. Experimental results prove the effectiveness of the proposed method in the task of secure exchange of medical images by allowing higher hiding capability, lower distortion in the visual quality of the image with the hidden medical data, as well as a means to verify the integrity of the sent data, compared to state-of-the-art.

The purpose of tissue engineering (regenerative medicine) is to develop materials that replace human tissue, having as main characteristics' biodegradability, biocompatibility, no toxicity, osteoconductivity, which lead to cell maturation and proliferation. Due to the importance of the development of this type of materials, several researchers have used biopolymers and calcium phosphate salts (hydroxyapatite) as composites to be used in this area as drug releases, scaffolds, implants, among others. Different biopolymers can be suitable for this type of application, in this work we have described the most widely used biopolymers for biomedical purposes, such as alginate, collagen, gellan gum, chitosan, and polylactic acid, in addition to a detailed description of hydroxyapatite, biopolymers, as well as biopolymer/hydroxyapatite composites, to highlight their potential and the most relevant characteristics of these materials.