Pemberdayaan Pasien dan Keluarga Pasien dalam Pencegahan Amputasi Penderita Diabetes di Kecamatan Mulyorejo Kota Surabaya, Jawa Timur

EMPOWERMENT OF PATIENTS AND PATIENT FAMILIES IN PREVENTION OF AMPUTATION OF DIABETES IN MULYOREJO DISTRICT, SURABAYA CITY, EAST JAVA PROVINCE. The purpose of implementing this community service program is to empower the community, especially patients and families of diabetes patients, to be able to take steps to prevent disability in diabetes due to amputation. The number of people with diabetes who have to undergo amputation is often influenced by the poor knowledge of diabetes, the low adherence to taking medication, and the poor knowledge and ability of patients and their families in performing wound care for diabetics. In addition, the lack of understanding of diabetes drugs causes the patient to experience side effects that can lead to withdrawal or the patient to experience side effects of hypoglycemia which can put the patient in critical condition. The solution to this problem is to empower diabetes patients and their families. They were given education about diabetes, the importance of taking the medication regularly according to doctor's recommendations, and wound care. In addition, they will be trained on how to do proper wound care for diabetes patients. A medication control post-program will be implemented to ensure that patients will take their medication according to the prescribed guidelines. It is hoped that with this program, the community will understand more about diabetes and its complications to avoiding the disabilities due to amputation by diabetics and patients can avoid unwanted side effects

Automatic 3D Cranial Landmark Positioning based on Surface Curvature Feature using Machine Learning

Cranial anthropometric reference points (landmarks) play an important role in craniofacial reconstruction and identification. Knowledge to detect the position of landmarks is critical. This work aims to locate landmarks automatically. Landmarks positioning using Surface Curvature Feature (SCF) is inspired by conventional methods of finding landmarks based on morphometrical features. Each cranial landmark has a unique shape. With the appropriate 3D descriptors, the computer can draw associations between shapes and landmarks using machine learning. The challenge in classification and detection in three-dimensional space is to determine the model and data representation. Using three-dimensional raw data in machine learning is a serious volumetric issue. This work uses the Surface Curvature Feature as a three-dimensional descriptor. It extracts the local surface curvature shape into a projection sequential value (depth). A machine learning method is developed to determine the position of landmarks based on local surface shape characteristics. Classification is carried out from the top-n prediction probabilities for each landmark class, from a set of predictions, then filtered to get pinpoint accuracy. The landmark prediction points are hypothetically clustered in a particular area, so a cluster-based filter is appropriate to isolate them. The learning model successfully detected the landmarks, with the average distance between the prediction points and the ground truth being 0.0326 normalized units. The cluster-based filter is implemented to increase accuracy compared to the ground truth. Thus, SCF is suitable as a 3D descriptor of cranial landmarks

Epileptic EEG signal classification using convolutional neural network based on multi-segment of EEG signal

High performance in the epileptic electroencephalogram (EEG) signal classification is an important step in diagnosing epilepsy. Furthermore, this classification is carried out to determine whether the EEG signal from a person's examination results is categorized as an epileptic signal or not (healthy). Several automated techniques have been proposed to assist neurologists in classifying these signals. In general, these techniques have yielded a high average accuracy in classification, but the performance still needs to be improved. Therefore, we propose a convolutional neural network based on multi-segment of EEG signals to classify epileptic EEG signals. This method is built to overcome data limitations in the convolutional neural network training process and add the ensemble combination process. The multi-segment of EEG signal is formed by splitting the signal without overlapping each channel and converting it into the spectrogram image based on the short-time Fourier transform value. The spectrogram image is then used as input for the convolutional neural network in in-depth training and testing. The convolutional neural network model of the training results is used to classify each EEG signal segment on each test channel before entering the ensemble combination stage for the final classification. To evaluate the performance of our proposed method, we used the Bonn EEG dataset. The dataset consists of five EEG records labelled as A, B, C, D, and E. The experiments on several datasets (AB-C, AB-D, AB-E, AB-CD, AB-CDE, and AB-CD-E) which were arranged from the dataset showed that our proposed method (with segment) performs better than without segment. Our proposed method yielded the best average of classification accuracy which is 99.33%, 100%, 100%, 99.5%, 99.8%, and 99.4% for the AB-C, AB-D, AB-E, AB-CD, AB-CDE, and AB-CD-E.By these results, the proposed method can outperform several other methods on the same dataset

Phytoconstituent Analysis and Antibacterial Potential of Epicarp Extracts from Mature Fruits of Persea americana Mill

Background: World Health Organization (WHO) has reported the antimicrobial resistance as one among the ten threats to global health in 2019. The development of plant-derived antibiotics is currently considered as a modern medicine’s greatest success. Persea americana is a plant with high medicinal profile which allow its different parts to be used for therapeutic purposes. This study is aimed to determine the antibacterial potential of ethanol and chloroform extracts from epicarp of mature fruits of P. americana Mill against human pathogens.Materials and Methods: The epicarps of avocado were dried in oven and ground into powder using porcelain mortar and pestle. The powdered plant materials were extracted with both 96% ethanol and chloroform. Extracts were qualitatively screened to examine their bioactive contents and agar well diffusion method was used to analyze the antibacterial activity of extracts against both Gram-positive and Gram-negative bacteria.Results: Both solvents showed the ability to dissolve the secondary metabolites from avocado epicarps. Phytochemical screening disclosed the presence of alkaloids, proteins, terpenoids, tannins, flavonoids, steroids and phenolic compounds in ethanolic extracts and absence of flavonoids and tannins in chloroform extracts. The extracts showed the inhibition zones ranging from 14±4.5 mm to 26±2.1 mm while streptomycin demonstrated high inhibition zones ranging from 20±3.1 mm to 30 mm. The minimum inhibitory concentration (MIC) values of extracts ranges from 0.3125 mg/mL to 20 mg/mL while the MIC values for streptomycin vary from 0.25 mg/mL to 1.25 mg/mL.Conclusion: The ethanol and chloroform extracts proved to be potentially effective as natural alternative preventives to fight against various disease-causing bacteria.Keywords: antibacterial activity, ethanol extract, chloroform extract, Persea americana, Rwand

Pengetahuan, Gangguan Psikologis, dan Burnout Dokter Umum di Era Pandemi Covid-19

KNOWLEDGE, PSYCHOLOGICAL DISORDERS, AND BURNOUT OF GENERAL PRACTITIONERS IN THE COVID-19 PANDEMIC ERA. High demand in work during the Covid-19 pandemic will cause psychological problems for general practitioners. These psychological disorders can cause burnout. The incidence of burnout is exacerbated by the lack of knowledge of general practitioners about the current condition. This study aims to determine the level of knowledge, psychological disorders, and burnout of general practitioners during the Covid-19 pandemic. The research design using quasi-experimental with one group pre posttest design without a control group. The population in this study was all participants that join the online seminar. Samples taken were 111 respondents with the total sampling technique. The data collection technique was done by using a questionnaire. The data obtained were analyzed using the Wilcoxon test and Kendall's tau-c test. The difference test before and after being given the seminar material shows a p-value of 0.001. The statistical analysis of the relationship between knowledge and burnout shows a p-value of 0.048. The statistical analysis of the relationship between stress and burnout shows a p-value of 0.026. The statistical analysis of the relationship between anxiety and burnout shows a p-value of 0.001. The statistical analysis of the relationship between depression and burnout showed a p-value of 0.002. There are differences in the knowledge of respondents before and after being given the seminar material. There is an association between knowledge with burnout, stress with burnout, anxiety with burnout, and depression with burnout

Brain Tumor Classification in MRI Images Using En-CNN

Brain tumors are among the most common diseases of the central nervous system and are harmful. Early diagnosis is essential for patient proper treatment. Radiologists need an automated system to identify brain tumor images successfully. The identification process is often a tedious and error-prone task. Furthermore, brain tumor binary classification is often characterized by malignant and benign because it involves multi-sequence MRI (T1, T2, T1CE, and FLAIR), making radiologist's work quite challenging. Recently, several classification methods based on deep learning are being used to classify brain tumors. Each model's performance is highly dependent on the CNN architecture used. Due to the complexity of the existing CNN architecture, hyperparameter tuning becomes a problem in its application. We propose a CNN method called en-CNN to overcome this problem. This method is based on VGG-16 that consists of seven convolutional networks, four ReLU, and four max-pooling. The proposed method is used to facilitate the hyperparameter tuning. We also proposed a new approach in which the classification of brain tumors is done directly without priorly doing the segmentation process. The new approach consists of the following stages: preprocessing, image augmentation, and applying the en-CNN method. Our new approach is also doing the classification using four MRI sequences of T1, T1CE, T2, and FLAIR. The proposed method delivers accuracy on the MRI multi-sequence BraTS 2018 dataset with an accuracy of 95.5% for T1, 95.5% for T1CE, 94% for T2, and 97% for FLAIR with mini-batch size 128 and epoch 200 using ADAM optimizer. The accuracy was 4% higher than previous research in the same dataset

Head MRI imaging profile of meningioma according to WHO 2016 grading

Magnetic resonance imaging (MRI) of meningiomas is essential in predicting their histopathological grade. Meningiomas are the second most common central nervous system neoplasm in adults, usually benign, originating from arachnoid cap cells and categorized according to the WHO classification as benign (grade I), atypical (grade II), and anaplastic (grade III). The study aims to determine the head MRI imaging profile in preoperative meningioma patients confirmed by pathologic results according to WHO 2016 grading. A retrospective study was conducted from January 2017 to March 2022 with 30 samples of meningioma patients who underwent surgery. Preoperative head MRI and pathologic examination were done and tumor location, border, edge, and size were confirmed with pathology results according to WHO 2016 grading. Most patients were 41-50 years old, female (100%), mostly WHO grade I histopathology with transitional type. The most common locations were convexity meningiomas, with the most MRI characteristics of well-defined borders, regular edges, size 0-3 cm, with hypointense T1, hyperintense T2, hyperintense T2 FLAIR, restricted diffusion on DWI, type 2 DCE, homogeneous enhancement pattern, and the most feeding arteries originated from the meningeal artery

Radiological findings of partial expression pentalogy of Cantrell and other multiple congenital anomalies: A rare case report

Pentalogy of Cantrell is a rare syndrome of anomalous malformation. In the present case, the syndrome was initially diagnosed as a complete pentad, including a supra-umbilical abdominal wall defect, a sternal defect, pericardial defects, an anterior diaphragmatic defect, and heart malformation. Diagnosis required several imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). In this case report, we present an 8-month-old female patient with a thoracic wall defect with ectopia cordis and a bilateral cleft lip and palate. In addition, a head CT scan showed craniosynostosis, hypogenesis of the corpus callosum, and tonsillar cerebellar herniation. Thoracoabdominal CT revealed herniation of the transverse colon up to the subcutaneous layer, diaphragmatic hernia, atrial septal defects (ASD), ventral septal defects (VSD), and a persistent left superior vena cava (PLSVC). A multidisciplinary approach is required for the optimal management of this syndrome. We describe a female infant who presented with pentalogy of Cantrell syndrome and include the findings from postnatal CT imaging

Pancoast tumor mimicking lung tuberculosis, a case report

It is well-recognized that tuberculosis (TB) can mimic several clinical diseases, particularly cancer. On several occasions, lung TB can be misdiagnosed as cancer, particularly in developed countries with a rare case of TB and high incidence of lung cancer, and vice versa— in which Indonesia, with a high incidence of TB, lung cancer may be mistakenly identified as TB, delaying the initiation of definitive therapy and causing unnecessary diagnostic and treatment procedures. We reported a 59-year-old male who complained of right upper chest pain, accompanied by chronic cough and weight loss, with a history of 6-month treatment with a TB regimen without resolution of his symptoms. Core biopsy CT guiding pathology anatomy revealed atypical adenocarcinoma. All patients seeking medical attention must be treated carefully, avoiding diagnostic procedures that can result in a delay in definitive therapy

Improvement of chest X-ray image segmentation accuracy based on FCA-Net

AbstractMedical image segmentation is a crucial stage in computer vision and image processing to help the later-stage diagnosis process become more accurate. Because medical image segmentation, such as X-ray, can extract tissue, organs, and pathological structures. However, medical image processing, primarily in the segmentation process, has significant challenges regarding feature representation. Because medical images have different characteristics than other images related to contrast, blur, and noise. This study proposes the use of lung segmentation on chest X-ray images based on deep learning with the FCA-Net (Fully Convolutional Attention Network) architecture. In addition, attention modules, namely spatial attention and channel attention, are added to the Res2Net encoder so that it is expected to be able to represent features better. This research was conducted on chest X-ray images from Qatar University contained in the Kaggle repository. A chest x-ray image measuring 256 × 256 pixels and as many as 1500 images were then divided into 10% testing data and 90% training data. The training data will then be processed in K-Fold Cross validation from K = 2 until K = 10. The experiment was conducted with scenarios that used spatial attention, channel attention, and a combination of spatial and channel attention. The best test results in this study were using a variety of spatial attention and channel attention in the division of K-Fold with a value of K = 5 with a DSC (Dice Similarity Coefficient) value in the testing data of 97.24% and IoU (Intersection over Union) in the testing data of 94.66%. This accuracy result is better than the UNet++, DeepLabV3+, and SegNet architectures