Assessing pre-eclampsia awareness among pregnant women in Syria: a cross-sectional study on knowledge and perceptions.

BACKGROUND: Pre-eclampsia (PE) is a major contributor to morbidity and mortality in mothers worldwide. Adequate understanding of this condition improves treatment, control, and prevention. This study evaluated preeclampsia awareness among pregnant women in Syria, and the characteristics related to awareness adequacy. METHODS: This national cross-sectional study was conducted in Syria between 25 October and November 19, 2022. We included pregnant females of all age groups from all Syrian governorates. The questionnaire consisted of sociodemographic characteristics and knowledge of pre-eclampsia and its associated factors, symptoms, and complications. RESULTS: Overall, 706 participants were involved in this research, with a mean age of 38.22. Only 52.1% of them reported that they had heard of preeclampsia. Among the participants, 56.5% stated that they would not terminate a pregnancy if they were determined to be likely to develop preeclampsia, while nearly 55.2% agreed to continue the pregnancy rather than deliver prematurely even if their where a potential risk on their health risks. Participants who reported a family history of PE or had already experienced PE were more likely to have appropriate preeclampsia knowledge than those who did not (OR = 2.27, OR = 3.18, respectively). Respondents aged 25 to 35 years had the highest knowledge scores, and participants living in cities scored higher knowledge than rural residents. CONCLUSION: According to our findings, pregnant women in Syria have a awareness gaps regarding the PE topic. This highlights the need to enhance women's preeclampsia understanding for better pregnancy outcomes. Education through organizations, the media, and national programs is a significant aspect that promotes an adequate understanding of preeclampsia.

Osteoporosis and vitamin D consumption: knowledge and practice in different Arab countries.

Despite the high prevalence of osteoporosis and vitamin D deficiency, the knowledge about osteoporosis and vitamin D-related practices were moderate in some countries in the Middle East and North Africa (MENA) region. Improving knowledge through awareness campaign and screening programs is essential to enhance vitamin D-related practices. PURPOSE: Osteoporosis is the most common skeletal disease, which usually remains silent until fractures occur. Vitamin D deficiency impairs bone mineralization and increases the risk of osteoporosis. Despite being relatively sunny, the Middle East and North Africa (MENA) region has a high prevalence of osteoporosis and hypovitaminosis D. This study aims to assess the knowledge about osteoporosis and vitamin D-related practices and to determine the correlation between them in some countries of the MENA region. METHODS: A cross-sectional study was performed in Lebanon, Syria, Egypt, Palestine, Iraq, Jordan, and Saudi Arabia. From each country, 600 participants were enrolled. The survey included four sections: sociodemographic information, past medical history, Osteoporosis Knowledge Assessment Tool to assess the knowledge about osteoporosis, and Practice Towards Vitamin D scale to assess vitamin D-related practices. RESULTS: Our study found that 67.14% of respondents had moderate knowledge about osteoporosis and 42.31% had moderate vitamin D-related practices. Higher knowledge level was reported in the young, females, Syrians, singles, postgraduates, and healthcare employees (p < 0.05). Better vitamin D-related practices were detected in the elderly, males, Egyptians, married, and high school or below educational level (p < 0.05). The Internet was the most listed source of information. Adequate osteoporosis knowledge was associated with better vitamin D-related practices (p < 0.001). CONCLUSION: Most participants, representing some countries of the MENA region, displayed moderate knowledge regarding osteoporosis and moderate vitamin D-related practices. Adequate knowledge about osteoporosis is essential to improve practices, so awareness campaigns and screening programs should be more frequently implemented.

Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments.

LoRaWAN is a popular internet of things (IoT) solution over the unlicensed radio band. It sustains low-cost, durable, and long range IoT wireless communications. Nonetheless, with over 24 billion connected IoT devices being expected by the end of the year, and over 50 billion by 2025, the concurrent and legacy approaches to spreading factor and channel assignment in LoRaWAN networks can no longer keep up. This is exacerbated with the growing densification of IoT device deployments and, with the increasing requirements for better throughput and packet delivery ratios. In this paper, we propose a proportional fair-based joint optimal formulation for spreading factor and channel assignment in multi-operator LoRaWAN deployments. The objective of this problem is to maximize the total sum of the logarithmic normalized throughput. We split the problem into two subproblems, and propose a game theoretic approach to solving them. We prove that our games converge towards a pure Nash equilibrium and, afterwards, solve the optimization problems using both semi-distributed and completely distributed algorithms. Via simulations, we show that our algorithms greatly improve the total normalized throughput for LoRaWAN as well as the packet success rate, in comparison to the legacy approaches.

Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks.

PURPOSE: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees' surgical skills in order to improve surgical practice. METHODS: In this paper, we designed a convolutional neural network (CNN) to classify surgical skills by extracting latent patterns in the trainees' motions performed during robotic surgery. The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression. RESULTS: Our results show that deep neural networks constitute robust machine learning models that are able to reach new competitive state-of-the-art performance on the JIGSAWS dataset. While we leveraged from CNNs' efficiency, we were able to minimize its black-box effect using the class activation map technique. CONCLUSIONS: This characteristic allowed our method to automatically pinpoint which parts of the surgery influenced the skill evaluation the most, thus allowing us to explain a surgical skill classification and provide surgeons with a novel personalized feedback technique. We believe this type of interpretable machine learning model could integrate within "Operation Room 2.0" and support novice surgeons in improving their skills to eventually become experts.

Surgical motion analysis using discriminative interpretable patterns.

OBJECTIVE: The analysis of surgical motion has received a growing interest with the development of devices allowing their automatic capture. In this context, the use of advanced surgical training systems makes an automated assessment of surgical trainee possible. Automatic and quantitative evaluation of surgical skills is a very important step in improving surgical patient care. MATERIAL AND METHOD: In this paper, we present an approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions. A pattern is defined as a series of actions or events in the kinematic data that together are distinctive of a specific gesture or skill level. Our approach is based on the decomposition of continuous kinematic data into a set of overlapping gestures represented by strings (bag of words) for which we compute comparative numerical statistic (tf-idf) enabling the discriminative gesture discovery via its relative occurrence frequency. RESULTS: We carried out experiments on three surgical motion datasets. The results show that the patterns identified by the proposed method can be used to accurately classify individual gestures, skill levels and surgical interfaces. We also present how the patterns provide a detailed feedback on the trainee skill assessment. CONCLUSIONS: The proposed approach is an interesting addition to existing learning tools for surgery as it provides a way to obtain a feedback on which parts of an exercise have been used to classify the attempt as correct or incorrect.

Extraovarian Primary Peritoneal Carcinomatosis: A Case Report.

BACKGROUND Extraovarian primary peritoneal carcinoma (EOPPC) is a rare malignant epithelial tumor with an age-adjusted incidence rate of 6.78 per million, which arises from the peritoneal lining with minimal or no ovarian involvement. EOPPC is a diagnosis of exclusion with the absence of other identifiable primary sites and after surgical assessment and consideration of the histological report to evaluate the extent of ovarian invasion. CASE REPORT A 66-year-old woman complained of mild postprandial epigastric pain. Physical exam revealed positive bowel sounds over all 4 quadrants, with a soft, non-tender abdomen. Distension and dullness to percussion were noted. A CT scan revealed peritoneal fluid, and evaluation of the peritoneal fluid showed an elevated white blood cell count, while fulfilling criteria for exudate. There was no bacterial growth from the peritoneal fluid. Serum tumor markers CEA were significantly elevated, suggestive of an underlying malignancy. An exploratory laparoscopy confirmed diffuse carcinomatosis in the omentum, the coloparietal region bilaterally, as well as surrounding small bowel loops, the transverse and sigmoid colon, and the rectum. A bilateral oophorectomy was performed and 2 biopsies from the thickened peritoneum were taken. The histomorphological features from the pathology examination concluded the patient had peritoneal serous carcinoma with uninvolved ovaries. CONCLUSIONS EOPPC may be misdiagnosed as epithelial ovarian cancer, and should be considered in patients with peritoneal carcinomatosis, normal-sized ovaries, and no identifiable primary lesion. Since the common therapeutic strategies achieve comparable survival rates, failed recognition may be without consequences. However, to further improve survival, optimal cytoreductive surgery is essential and should be made a priority in disease management.