Factors Associated With the Uptake of Cervical Cancer Screening Among Family Medicine Physicians, Compared With Women of the Community in Riyadh, Saudi Arabia.

BACKGROUND: Cervical cancer is the ninth diagnosed cancer among Saudi women. The majority of cervical cancer cases occur in women who did not undergo screening. However, the screening rates in several countries, including Saudi Arabia, remain suboptimal. It is important to identify the factors associated with the uptake of screening and predictors of screening in order to increase the uptake rate. AIM: To determine the factors associated with the uptake of cervical cancer screening among family medicine physicians (FMPs), compared with women of the community. METHODS: This was a cross-sectional study conducted in the central region (Riyadh), Kingdom of Saudi Arabia from February 2021 for 12 months on female physicians and women of the community. An electronic questionnaire was used to investigate the demographics of women and variables related to the uptake of screening. RESULTS: A total of 126 FMP and 127 women from the community were included. The factors affecting screening among FMP included age (P=0.013), health insurance (P=0.002), availability of Pap smear (P˂0.001), and physician encouragement (P˂0.001). The factors affecting the screening of community women included the availability of Pap smears (P˂0.001) and physician encouragement (P˂0.001). Multivariate analysis revealed that physician encouragement of Pap smear was a significant predictor of screening among FMP (OR=8.26, P˂0.001) and community women (OR=6.67, P˂0.001). The perceived benefit was the only predictor for screening among FMP (OR=0.75, P=0.004). CONCLUSION: The uptake of cervical cancer screening was higher in the community women. The factors linked to the uptake differed among the two groups, but the support of doctors played a significant role in the likelihood of uptake, regardless of the group of women. It is recommended to enhance the guidance of medical personnel in recommending screening during clinic visits for the specific target group. Additionally, there should be increased education on the significance of screening and efforts to educate the community about cervical cancer and screening.

Ambient levels of BTEX at roadside in northern Iraq and its relationship with traffic volume.

Among the mono-aromatic volatile organic compounds, benzene, toluene, ethylbenzene and xylene (BTEX) have occupied a large area in air pollution studies due to their carcinogenic and non-carcinogenic effect. In this study, a station was used to monitor BTEX concentrations at roadside in urban area at Mosul city along a year, with traffic volume and meteorological factors measurement. The annual mean of benzene was 12 µg/m3, which is more than twofolds of the standard European Union level of 5 µg/m3. In addition, 87.4% of the measured values in summer was higher than the standard level at roadside. Benzene was dominant in spring and summer among BTEX species, while the dominance changed to ethylbenzene in autumn and winter. Besides, benzene, toluene, ethylbenzene and o-xylene showed significant seasonal variation. BTEX and benzene concentrations increased as the number of vehicles on gasoline and diesel increased. In contrast, toluene and ethylbenzene were more affected with number of vehicles on diesel. On the other hand, the weak significant correlations among BTEX species and high T/B ratio indicate the difference in fuel types used and the existence of additional sources of BTEX emission with the vehicular exhausts. These results can be utilized in determining the control strategy in air quality management in Mosul city.

Colorectal Cancer in Saudi Arabia: The Way Forward.

Colorectal cancer is the most common cancer in Saudi males and the second most common cancer in Saudi females with increasing incidence throughout the last four decades. Although the disease incidence is on the rise, still there is no systemic screening for colorectal cancer in the Saudi population. Early onset colorectal cancer is common in the Saudi population and up to 50% in Saudi patients diagnosed at late stages with regional and distal metastasis. Therefore, more efforts are required to control the disease in the Kingdom of Saudi Arabia. In this regard,  systematic work at national level is highly required to make  colorectal cancer screening for population at risk part of the routine primary health care activities. This paper highlights the current situation of colorectal cancer in the Kingdom of Saudi Arabia with relation to incidence, mortality and morbidity in addition to the disease control efforts going on. Finally, some recommendations are provided to strengthen the control program of colorectal cancer.

Energy-Efficient Fuzzy-Logic-Based Clustering Technique for Hierarchical Routing Protocols in Wireless Sensor Networks.

In wireless sensor networks, the energy source is limited to the capacity of the sensor node's battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms' energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.

Data Randomization and Cluster-Based Partitioning for Botnet Intrusion Detection.

Botnets, which consist of remotely controlled compromised machines called bots, provide a distributed platform for several threats against cyber world entities and enterprises. Intrusion detection system (IDS) provides an efficient countermeasure against botnets. It continually monitors and analyzes network traffic for potential vulnerabilities and possible existence of active attacks. A payload-inspection-based IDS (PI-IDS) identifies active intrusion attempts by inspecting transmission control protocol and user datagram protocol packet's payload and comparing it with previously seen attacks signatures. However, the PI-IDS abilities to detect intrusions might be incapacitated by packet encryption. Traffic-based IDS (T-IDS) alleviates the shortcomings of PI-IDS, as it does not inspect packet payload; however, it analyzes packet header to identify intrusions. As the network's traffic grows rapidly, not only the detection-rate is critical, but also the efficiency and the scalability of IDS become more significant. In this paper, we propose a state-of-the-art T-IDS built on a novel randomized data partitioned learning model (RDPLM), relying on a compact network feature set and feature selection techniques, simplified subspacing and a multiple randomized meta-learning technique. The proposed model has achieved 99.984% accuracy and 21.38 s training time on a well-known benchmark botnet dataset. Experiment results demonstrate that the proposed methodology outperforms other well-known machine-learning models used in the same detection task, namely, sequential minimal optimization, deep neural network, C4.5, reduced error pruning tree, and randomTree.

Assessment of maternal knowledge about infantile colic in Saudi Arabia.

INTRODUCTION: The infantile colic is a difficult experience met by parents in the first few months of an infant's life. This may lead to increased discomfort for infants as well as psychological distress for mothers. This study aimed at assessing the baseline knowledge of mothers in Saudi Arabia about infantile colic mainly in regard to the etiology and management. METHODS: In this cross-sectional study, a questionnaire was distributed among mothers in six primary healthcare centers (PHCC) in Riyadh, Saudi Arabia, during their visit for immunization clinics in 2016. The questionnaire consisted of two domains for determining the sociodemography characteristics and the maternal knowledge of participants about infantile colic. SPSS version 20 and chi-square test were used for data analysis. RESULTS: A total of 230 mothers completed the survey questionnaire. Of these, 208 participants were Saudis. The majority of the participants were in the age group of 18-29 years (42.6%). The average age of the infants in this study was found to be 5.75±4.26 months. Eighty percent replied that they did not have any previous knowledge of infantile colic; 42.6% mothers believed that the causes of infantile colic were unclear and might involve several factors; 36% of the participants perceived milk allergy as the attributing cause for infantile colic. The source of knowledge about infantile colic was mainly through experiences of dealing with previous siblings who have the same issue (34.4%); 27.4% of mothers prefer the use of herbal medicines to treat this condition. CONCLUSIONS: It is recommended that health education needs to be provided to mothers at outpatient clinics during their antenatal hospital visits. This reduces the discomfort of infant and distress in mothers.

Randomized Subspace Learning for Proline Cis-Trans Isomerization Prediction.

Proline residues are common source of kinetic complications during folding. The X-Pro peptide bond is the only peptide bond for which the stability of the cis and trans conformations is comparable. The cis-trans isomerization (CTI) of X-Pro peptide bonds is a widely recognized rate-limiting factor, which can not only induces additional slow phases in protein folding but also modifies the millisecond and sub-millisecond dynamics of the protein. An accurate computational prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. In our earlier work, we successfully developed a biophysically motivated proline CTI predictor utilizing a novel tree-based consensus model with a powerful metalearning technique and achieved 86.58 percent Q2 accuracy and 0.74 Mcc, which is a better result than the results (70-73 percent Q2 accuracies) reported in the literature on the well-referenced benchmark dataset. In this paper, we describe experiments with novel randomized subspace learning and bootstrap seeding techniques as an extension to our earlier work, the consensus models as well as entropy-based learning methods, to obtain better accuracy through a precise and robust learning scheme for proline CTI prediction.

Protein contact map prediction using multi-stage hybrid intelligence inference systems.

Proteins are one of the most important molecules in organisms. Protein function can be inferred from its 3D structure. The gap between the number of discovered protein sequences and the number of structures determined by the experimental methods is increasing. Accurate prediction of protein contact map is an important step toward the reconstruction of the protein's 3D structure. In spite of continuous progress in developing contact map predictors, highly accurate prediction is still unresolved problem. In this paper, we introduce a new predictor, JUSTcon, which consists of multiple parallel stages that are based on adaptive neuro-fuzzy inference System (ANFIS) and K nearest neighbors (KNNs) classifier. A smart filtering operation is performed on the final outputs to ensure normal connectivity behaviors of amino acids pairs. The window size of the filter is selected by a simple expert system. The dataset was divided into testing dataset of 50 proteins and training dataset of 450 proteins. The system produced an average accuracy of 45.2% for the sequence separation of six amino acids. In addition, JUSTcon outperformed SVMcon and PROFcon predictors in the cases of large separation distances. JUSTcon produced an average accuracy of 15% for the sequence separation of 24 amino acids after applying it on CASP9 targets.

Quantitative assessment of liver fibrosis: a novel automated image analysis method.

BACKGROUND: Semiquantitative staging of liver fibrosis is a highly subjective procedure and may lead to an uncertainty in judgment regarding the degree of severity and hence the progression of the disease. AIM: In this work, we present an automated quantification system (AQS) for evaluating the degree of severity of fibrosis in liver biopsies based on Ishak et al.'s classification. Accordingly, liver fibrosis is classified into six classes depending on its severity and progression. The described system is of special value in accurately assessing the prognosis of chronic liver disease. METHODS: In our method, we tried to approximate the architecture of the fibrosis in the subject sample using texture features and shape representation of the fibrosis structural expansion with an overall accuracy of about 98%. RESULTS AND CONCLUSION: The presented AQS is considered to be a novel approach in the domain of automatic liver fibrosis quantification. It is a true quantification and intelligent approach that attempts to utilize the current semiquantitative methods of liver fibrosis assessment to turn them into real quantitative ones with significant reduction in variability and subjectivity. We propose that our method can be adopted by a panel of expert liver pathologists and software to be developed and used on a wide scale.

Municipal solid waste landfill siting using intelligent system.

Historically, landfills have been the dominant alternative for the ultimate disposal of municipal solid waste. This paper addresses the problem of siting a new landfill using an intelligent system based on fuzzy inference. The proposed system can accommodate new information on the landfill site selection by updating its knowledge base. Several factors are considered in the siting process including topography and geology, natural resources, socio-cultural aspects, and economy and safety. The system will rank sites on a scale of 0-100%, with 100% being the most appropriate one. A weighting system is used for all of the considered factors. The results from testing the system using different sites show the effectiveness of the system in the selection process.