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Abstract The antinuclear antibody (ANA) test has high sensitivity in diagnosing and classifying systemic lupus erythematosus (SLE). Objectives To describe the immunological pattern of SLE patients through investigating specific antinuclear autoantibodies by enzyme dot immunoassay and studying their frequency in both positive and negative ANA indirect immunofluorescence assay (IIF) cases. Methods In a cross-sectional study, blood samples from 393 newly diagnosed SLE patients were analyzed using (IIF) on HEp-2 cells and ANA dot immunoassay by automated enzyme immunoassay (EIA) to detect 19 antibodies. Results Ninety-one percent of the patients are females; their mean age was 37 ± 12.28. Antinuclear antibody (ANA) was detected by IIF in 82.4% of cases, with 181 (46.1%) speckled and 167 (42.4%) homogeneous ANA patterns. The majority of patients (96%) demonstrated autoantibodies via EIA. Among the ANA-IIF-negative patients, 97.2% demonstrated autoantibodies. There was a significant difference in the frequency of certain autoantibodies between SLE patients with negative and positive ANA-IIF (1.44 0.73, 3.12 2.09, p = 0.00) respectively. Conclusion The results of analyzing 19 autoantibodies with the ANA staining pattern increased the significance of analyzing the immune profile even if IIF is negative when clinical symptoms strongly suggest SLE diagnosis. Certain autoantibodies may evade staining by the IFA approach while they are present in the patient’s serum, and they may not be detected by the ANA EIA profile if it does not contain that antigenic substrate. Key Points• Indirect immunofluorescence on Hep-2 is the conventional method for ANA detection and is regarded as the “gold standard” for testing in clinical practice for SLE.• In our study, ANA profile dot enzyme immunoassay (EIA)-based test was performed to evaluate 19 autoantibodies in SLE patients either positive or negative for ANA-IIF.• The presence of anti-dsDNA with ANA-IIF-negative serum in 32.4% of SLE patients provides evidence that not all anti-dsDNA antibodies are identified on standard HEp-2 substrates.• certain autoantibodies can evade staining by the ANA-IIF method despite being present in the SLE patient’s blood; this supports the ANA profile enzyme dot immunoassay as a more sensitive test.

Intelligent healthcare has made significant progress using artificial intelligence (AI) methods. Since the COVID-19 pandemic, healthcare service has gained more attention, particularly remote and automated healthcare consultation. Medical bots are becoming increasingly popular for obtaining medical advice and support. Medical bots offer numerous benefits, making them an attractive option for patients and healthcare providers. They provide 24/7 access to medical advice, reduce appointment wait times by providing quick answers to common questions or concerns, and cost savings associated with fewer visits or tests required for diagnosis and treatment plans. The outcome of the medical bot depends upon the learning quality. Thereby depends on the appropriate corpus within the domain of interest. Arabic is one of the most commonly used languages for sharing users' internet content. Developing a medical bot on Arabic faces many challenges: including the morphological composition of the language, the diversity of dialects, and the need for appropriate corpora in the domain. Thus, this paper's main contribution is introducing the largest Arabic Healthcare Q&A dataset, called (MAQA). The dataset consists of more than 430k questions distributed into 20 medical specializations. Also, the paper adopts three deep learning models, LSTM, Bi-LSTM, and transformers, for experimenting and benchmarking the proposed corpus MAQA. The experimental results indicate that the recent Transformer model, with an average cosine similarity of 80.81% and BLeU score of 58%, outperforms the traditional deep learning models.

This study aimed to report the dynamic profile of IgG-specific antibodies to SARS-CoV-2 infection for 6 months after infection. We conducted a prospective study, recruited 33 recently confirmed covid -19 patients and collected 6 samples from each patient. The first samples were collected one month from the start of symptoms and subsequent samples collected at 30 days interval. We measured the IgG by chemiluminescent immunoassay (CLIA). According to the disease severity, patients were categorized as asymptomatic 4 (12.1%), mild 14 (42,4%), moderate 9 (27.3%), and severe 6 (18.2%). Patients were 12 (35.3%) females and 21 (64.7%) males. The mean IgG levels maintained a high level till the second month (92.81 ± 110.15 AU/ml) from the onset of symptoms followed by a gradual decrease till the sixth month after infection (17.42 ± 22.61 AU/ml). The patients with severe symptoms significantly exhibited the highest IgG levels, reached the highest level (mean=237.44 ± 164.13 AU/ml) at the second month. While the lowest levels were detected among the asymptomatic patients (mean= 3.04 ± 2.94 AU/ml) at the second month. Older age correlated with higher IgG antibody level (r= 0.350 p=0.046); however, sex was not related to IgG level. In conclusion, Symptomatic COVID-19 disease is followed by protective immunity for more than 6 months. Immunity in asymptomatic patients is low and fades rapidly than symptomatic cases. Patients with severe disease had significantly higher IgG levels compared to mild, moderate, or asymptomatic patients.

Relative positions are recent solutions to overcome the limited accuracy of GPS in urban environment. Vehicle positions obtained using V2I communication are more accurate because the known roadside unit (RSU) locations help predict errors in measurements over time. The accuracy of vehicle positions depends more on the number of RSUs; however, the high installation cost limits the use of this approach. It also depends on nonlinear localization nature. They were neglected in several research papers. In these studies, the accumulated errors increased with time due to the linearity localization problem. In the present study, a cooperative localization method based on V2I communication and distance information in vehicular networks is proposed for improving the estimates of vehicles’ initial positions. This method assumes that the virtual RSUs based on mobility measurements help reduce installation costs and facilitate in handling fault environments. The extended Kalman filter algorithm is a well-known estimator in nonlinear problem, but it requires well initial vehicle position vector and adaptive noise in measurements. Using the proposed method, vehicles’ initial positions can be estimated accurately. The experimental results confirm that the proposed method has superior accuracy than existing methods, giving a root mean square error of approximately 1 m. In addition, it is shown that virtual RSUs can assist in estimating initial positions in fault environments.

Objective: The present study aims to evaluate coronavirus shedding in the tears of asymptomatic pediatric COVID-19 positive patients. Methods: A prospective interventional study that included a total of 145 pediatric asymptomatic COVID-19 patients hospitalized from 17th May 2020 to 16th July 2020 in Sohag Tropical Hospital. On admission, all of them were COVID-19 positive detected through nasopharyngeal swab. They were in intimate contact with positive symptomatic COVID-19 patients before testing and admission. Reverse Transcriptase Polymerase chain reaction (RT-PCR) was done for tears samples at an interval of 5 days after admission and twice before discharge. Results: Of the 145 asymptomatic pediatric COVID-19 positive patients, no one showed ocular or systemic manifestations. They were silent carriers. Ten were positive for tears sample on admission. They became negative for nasopharyngeal and tear samples before discharge. Conclusion: Pediatric positive COVID-19 patients can shed coronavirus through their tears. Even among asymptomatic patients, transmission through tears is possible.

The expansion of the Internet of Things has resulted in new creative solutions, such as smart cities, that have made our lives more productive, convenient, and intelligent. The core of smart cities is the Intelligent Transportation System (ITS) which has been integrated into several smart city applications that improve transportation and mobility. ITS aims to resolve traffic issues, especially traffic congestion. Recently, new traffic flow prediction models and frameworks have been rapidly developed in tandem with the introduction of Artificial Intelligence (AI) approaches to improve the accuracy of traffic flow prediction. Traffic forecasting is a crucial duty in the transportation industry. It can significantly affect the design of road constructions and projects in addition to its importance for route planning and traffic rules. Furthermore, traffic congestion is a critical issue in urban areas and overcrowded cities. Therefore, it must be accurately evaluated and forecasted. Hence, a reliable and efficient method for predicting traffic is essential. The main objectives of this study are: First, present a comprehensive review of the most popular machine learning and deep learning techniques applied in traffic prediction. Second, identifying inherent obstacles to applying machine learning and deep learning in the domain of traffic prediction.

Aim This paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction, as well as identify inherent obstacles to applying these approaches in the healthcare prediction domain. Background Healthcare prediction has been a significant factor in saving human lives in recent years. In the domain of healthcare, there is a rapid development of intelligent systems for analyzing complicated relationships among data and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry. Thus comes the role of systems depending on machine learning as well as deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Methods The studies discussed in this paper have been presented in journals published by IEEE, Springer, and Elsevier. Machine learning, deep learning, healthcare, surgery, cardiology, radiology, hepatology, and nephrology are some of the terms used to search for these studies. The studies chosen for this survey are concerned with the use of machine learning as well as deep learning algorithms in healthcare prediction. Results A total of 40 working papers were selected and the methodology for each paper was clarified. Conclusion This paper presents a comprehensive survey as well as the current challenges in healthcare prediction. studies have shown that artificial intelligence plays a significant role in diseases diagnosing.

Recently, most companies market their products on the web to recognize their customers’ requirements and to improve their services’ quality according to the customers’ feedback and opinions. A huge amount of reviews and opinions are posted daily on products. Obtaining and quickly analyzing these opinions become a difficult task. These opinions might lead to a tendency or disinclination to a specific point of view. To represent the products’ opinions from customers’ perspectives, opinion retrieval becomes a demanding and essential task for automatically extracting, analyzing, and summarizing customers’ reviews. Usually, online products are offered by several suppliers in e-commerce. Therefore, to keep up the competitiveness among suppliers, the need for innovative requirements is required. This paper proposed an enhanced opinion retrieval approach depending on the explicit feature based opinion mining. The proposed approach expands the initial products’ requirements using extended heuristics and linguistic patterns of the Arabic opinions. Besides the relevant score, several factors, like features’ weight, the opinion importance, and the sentiment polarity are used to rank the retrieved results. The experimental results show the capability of the proposed approach to automatically extract more innovative features compared to the conditional random field (CRF) results.

The problem of finding relevant data while searching the internet represents a big challenge for web users due to the enormous amounts of available information on the web. These difficulties are related to the well-known problem of information overload. In this work, we propose an online web assistant called OWNA. We developed a fully integrated framework for making recommendations in real-time based on web usage mining techniques. Our work starts with preparing raw data, then extracting useful information that helps build a knowledge base as well as assigns a specific weight for certain factors. The experiments show the advantages of the proposed model against alternative approaches.

Determining the resource requirements at airports especially in-ground services companies is essential to successful planning in the future, which is represented in the resources demand curve according to the future flight schedule, through which staff schedules are created at the airport to cover the workload with ensuring the highest possible quality service provided. Given in the presence of variety service level agreements used on flight service vary according to many flight features, the resources assumption method makes planning difficult. For instance, flight position is not included in future flight schedule but it's efficacious in the identification of flight resources. In this regard, based on machine learning, we propose a model for building a resource demand curve for future flight schedules. It is divided into two phases, the first is the use of machine learning to predict resources of the service level agreement required on future flight schedules, and the second is the use of implement a resource allocation algorithm to build a demand curve based on predicted resources. This proposal could be applicable to airports that will provide efficient and realistic for the resources demand curve to ensure the resource planning does not deviate from the real-time resource requirements. the model has proven good accuracy when using one day of flights to measuring deviation between the proposed model predict demand curve when flights did not include the location feature and the actual demand curve when flights include location.

Background Lupus nephritis can be seen in up to 60% of all SLE patients with 10–15% of nephritis patients progressing to end-stage renal disease; late diagnosis of lupus nephritis is correlated with a higher frequency of renal insufficiency. The study aim is determination of the value of urinary human epidermal growth factor (urinary EGF) as an early biomarker of lupus nephritis in SLE patients and its relevance to disease activity and renal histopathology. Results The study included 58 SLE patients and 30 healthy controls; a significant difference was noticed between SLE and controls in urinary protein, creatinine, protein/creatinine ratio, and urinary EGF. The mean level of urinary EGF was less in classes IV and V renal nephritis than in classes I, II, and III. There is a significant difference in urinary EGF (33±29, 27±16, P = 0.04) between class II and class III lupus nephritis, with no significant differences in urinary protein, creatinine, protein/creatinine ratio, and SLEDAI. On the other hand, the comparison between classes II and IV showed significant difference not only in urinary EGF (33±29, 11.7±4.9 m, P=0.003), but also in SLEDAI (37.4±8, 70.5±27, P= 0.007), and protein/creatinine ratio (0.98±0.62, 3±1.8, P=0.006). Conclusion This study raises the attention to test the sensitivity of urinary EGF in detecting the early and the subsequent changes in renal pathology of SLE patients as an easy, non-invasive, accurate, cheap marker that could help in following up the nephritis progression and adjusting the plan of treatment; also, it can be used to guide the time of biopsy or as an alternative in cases where renal biopsy is contraindicated.