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Abstract Biological invasions have substantial and rising social‐ecological impacts threatening human livelihoods and communities and hampering progress towards a just and equitable world. Currently, biological invasions are not adequately recognised and included in the UN Agenda 2030. Using a literature review conducted in Web of Science, we highlight the bias in available literature of biological invasions related to the UN Agenda 2030 and its Sustainable Development Goals. We find abundant scientific literature towards environmental and biodiversity related sustainability targets while other especially provisioning targets are less well represented. Subsequently, we discuss the risks of neglecting biological invasions within sustainable development and how invasive alien species can have changing and adverse effects through time counteracting the intended benefits at the time of introduction. Finally, we provide key recommendations for action at the international scale to ensure that biological invasions are adequately considered in sustainable development. Those recommendations include (1) acknowledgement of biological invasions as a key threat to sustainable development, (2) a call for stronger multilateral exchange under the umbrella of an adequately financed coordinating body and (3) appropriate implementation and resource provisioning for international monitoring, data infrastructure, data exchange and use of adequate indicators of biological invasions to streamline decision making based on a solid evidence base. Read the free Plain Language Summary for this article on the Journal blog.

Abstract Background: Diabetes mellitus is a major noncommunicable disease worldwide, and its prevalence is rapidly increasing. The Finish score helps in the prediction of the risk of future diabetes development, as well as in the identification of undiagnosed diabetes. The current study was conducted to identify people at risk of developing type II diabetes mellitus in River Nile State, Sudan. Methods: This cross-sectional community-based study was conducted in River Nile state between 2019 October and 2020 March. Data were collected using a questionnaire that included the Finnish Diabetes Risk Score variables from 400 participants after an informed consent. Chi-square test was used to test the associations, with the P-value considered significant when < 0.05. Results: The majority of participants (257 [64.3%]) were < 45 years old, and 229 (57.3%) were male. The risk of type II diabetes mellitus was found to be low in 187 (46.8%) people and high in 213 (53.2%). Moreover, 128 (32%) had a body mass index (BMI) between 25and 30 kg/m2, while 46 (11.5%) had > 30 kg/m2. Waist circumference of < 94 cm was found in 147 (36.8%) males, while only 63 females (15.8%) had a waist circumference < 80 cm. Age, gender, BMI, daily activity, history of hypertension, history of hyperglycemia, and family history of diabetes were all significantly associated with the risk of developing diabetes mellitus (P < 0.001). Conclusion: The Finnish Diabetes Risk Score was found to be useful in facilitating wider access to the risk of type II diabetes among the study population. More than half of the study population were at risk of developing diabetes mellitus.

The rise in stable coronary artery disease (CAD) due to improved survival rates and population growth has increased patient numbers, straining healthcare systems. Machine learning (ML) models are being developed to predict and identify individual risk factors for early treatment, reducing harm to individuals and families. These models can predict hospitalizations, enable close monitoring of high-risk patients, and optimize medical care. Researchers are developing robust models based on ML algorithms and real-world clinical data to aid in early detection, contributing to AI research in healthcare. Advanced ML models analyze medical imaging, genetic markers, lifestyle, and environmental factors to accurately predict coronary heart disease (CHD) start and progression. Our research introduces four novel models based on two-class Logistic Regression (two-class LR), two-class Neural Network (two-class NN), two-class Decision Jungle (two-class DJ), and two-class Boosted DT (two-class BDT). Our comparative analysis reveals that the two-class Boosted DT model is the most effective, achieving an AUC score of 0.991. This model excels in real-time monitoring by predicting minor changes in patient’s health markers, allowing for timely adjustments in treatment plans. It optimizes medication selection, dosing, and intervention timing based on patient characteristics, improving therapeutic efficacy and reducing side effects. The study reveals the transformative potential of these advanced ML models in CAD prediction and management. By focusing on feature selection, algorithm improvement, and integration, our models analyze medical imaging, genetic markers, lifestyle, and environmental factors to accurately predict the onset and progression of CHD. This research proposes valuable insights into the capabilities of these models to revolutionize disease detection and management, ensuring reliable and timely healthcare interventions across various datasets.

In this era, plenty of wireless devices are being used with the support of WI-FI (Wireless Fidelity) and need to be maintained and authorized. Wireless Sensor Networks (WSN), a cornerstone of modern wireless technology, offer cost-efficient solutions for diverse monitoring tasks but are exposed to many security threats, including unauthorized access, attacks, and suspicious activities. These vulnerabilities can significantly degrade the performance and reliability of WSNs, making the early detection and mitigation of such threats imperative. Intrusion Detection Systems (IDS) are crucial tools in safeguarding WSNs against these challenges. Numerous studies focus on enhanced Intrusion Detection model accuracy and decrease in loss with higher Detection Rate and lower False Alarm Rate, because of this, eliminating the repetitive feature of the dataset is exhibited. This study introduces a sophisticated Network Intrusion Detection System (NIDS) to safeguard Wi-Fi-based WSNs from prevalent cyber threats, such as impersonation, flooding, and injection attacks. At the heart of our approach is a meticulous feature selection process that enhances the dataset’s utility by eliminating null values, substituting unknown entries with a placeholder (‘NONE’), and refining the feature set to include only the most relevant indicators of potential security breaches. Initially, from a pool of 154 features, a subset of 76 is selected, further narrowed down to 13 pivotal features, ensuring a focused and efficient analysis. Employing standard scaler function for feature scaling and preprocessing, this research train proposed a Convolutional Neural Network (CNN) based approach aiming for optimal intrusion detection and prevention across multiclass classifications within WSN environments. The study aims to enhance detection accuracy, reduce loss values, and decrease false alarm rates, comparing it to CNN, Deep Neural Network (DNN) (5), DNN (3), and (Long Short-Term Memory) LSTM networks. The model’s performance is evaluated using various metrics, including precision, recall, support, F1 score, and macro-average. The culmination of our research efforts is evidenced by the exceptional performance of the CNN model, achieving an impressive accuracy rate of 97% and a loss metric of 0.14, all while maintaining a minimal False Alarm Rate. This study significantly advances IDS accuracy while simultaneously reducing false alarms, thus fortifying the security posture of WSNs in the face of evolving cyber threats.

Digital image collections are becoming increasingly popular due to their ease of use. Still, the need for adequate indexing information makes it difficult for users to find the specific images they need. With the vast number of digital images generated daily, these databases have become enormous, making accurate image retrieval challenging. One of the most challenging tasks in computer vision and multimedia research is image annotation, where keywords are assigned to an image. Unlike humans, computers can measure colors, textures, and shapes of images but fail to interpret them semantically, known as the semantic gap. This makes image annotation complex. For semantic-level concepts generation the raw image pixels provide not enough Unmistakable information. Which mean for of “words” or “sentences” there is no clear definition with the semantics of an image unlike text annotation. Therefore, this study aims to bridge the semantic gap between low-level computer features and human interpretation of images. The proposed enhanced automatic image annotation system maps multiple labels or into single image, providing an in-depth understanding of the visual content’s meaning. This is achieved by combining Convolutional Neural Networks-based multiple features (Y is the green component of the color, Cb and Cr is the blue component and red component called YCbCr color space and Gaussian–Laplacian Pyramid) and neighbors to recall and balance precision. The image annotation (IA) scheme uses a Global Vectors for Word Representation (GloVe) model with CNN-Gaussian–Laplacian Pyramid and learning representation to predict image annotation (IA) accurately. The proposed image annotation (IA) system was execution on three public datasets and showed excellent flexibility of annotation, improved accuracy, and reduced computational costs compared to existing state-of-the-art methods. The image annotation (IA) framework can provide immense benefits in accurately selecting and extracting image features, minimizing computational complexity and facilitating annotation.

Abstract Background: Attention deficit hyperactivity disorder, which is a prevalent neurodevelopmental condition, commonly manifests during early childhood and has the potential to adversely affect an individual's social, academic, and occupational performance in multiple settings. Students with ADHD may struggle with attention, focus, listening, and completing schoolwork. Additionally, they may exhibit restless or disruptive behavior in class and may have learning disabilities that affect their academic performance. The aim of this study was to explore the perspectives of primary school teachers on ADHD in Alkadrow, Khartoum, Sudan. Methods: A descriptive cross-sectional study was conducted in Alkadrow-Bahri locality, Sudan, over a period of three to six months in 2022. The study population included primary school teachers who had taught for at least one year and encountered at least one student with ADHD. A convenience sampling technique was used to select a minimum of 59 participants, and data were collected using a self-administered questionnaire with closed-ended questions. SPSS version 23 was used to analyze the data, including descriptive statistics and inferential statistics such as chi-square tests and logistic regression analysis. Results: The study had 59 participants, with the majority being female and in the age group of 41–45 years. The participants were mostly married and had obtained psychology courses, with a bachelor's degree being the most common level of education. Many participants had over 20 years of teaching experience. Regarding the attitude toward attention deficit hyperactivity, most participants strongly agreed that they did have a negative/positive attitude toward it, and a majority agreed or were neutral toward ADHD. However, in the case of attention deficit hyperactivity, a significant percentage of participants disagreed or strongly disagreed with the statement. Conclusion: The study found that most participants had a negative attitude toward student referrals for medical care and believed that most symptoms of ADHD can be lowered by aging. Additionally, more than half strongly disagreed that punishment has a positive effect on ADHD.

The metabolic needs of the human body and preventing infections require a diet with sufficient amounts of essential nutrients. This study aimed to investigate the importance of Baobab (Adansonia digitata L.) dried leaves as a healthy food source by determining the content of macro and trace elements in different habitats and regions. This study was conducted in Sudan and covered three different habitats, wetland (W), plainland (P), and mountain (M), in two regions (Blue Nile and Kordofan). The dry matter (DM) of Baobab leaves was considered for analyzed menials, and the results showed that the mean values were significantly affected by habitats where Baobab trees grew. The highest contents of potassium K (1653 ± 34 mg/100 g) and sodium (Na) 7.67 ± 1.18 mg/100 g were found in the W zone, whereas the highest contents of calcium (Ca) 2903 ± 187 mg/100 g and magnesium (Mg) 529 ± 101 mg/100 g were detected in the M and P zones, respectively. In addition, the two regions showed significant differences in trace and macro elements, i.e., higher levels of iron (Fe) 17.17 ± 2.76 mg/100 g and magnesium (556 ± 55 mg/100 g) were found in the Kordofan region while higher levels of zinc (Zn) 2.548 ± 0.55 mg/100 g and calcium (2689 ± 305 mg/100) were in the Blue Nile region. These varying amounts of elements can be used in our daily diets because of their potentially healthy effects, especially in areas where access to nutrient-rich foods is limited.

This retrospective study aimed to analyze antibiotic utilization and trends in urinary tract infection (UTI) patients without comorbidities at a Provincial Public Hospital in Indonesia. The data of 183 eligible patients who received antibiotics for UTI treatment from 2017 to 2021 were analyzed using the anatomical therapeutic chemical (ATC) classification system. Antibiotic utilization was measured in Defined Daily Dose (DDD) per 100 patient-days and Drug Utilization 90% (DU 90%) index. The study revealed fluctuating utilization, with 2018 (51.32 DDD/100 patient-days) and 2017 (37.22 DDD/100 patient-days) showing the highest and lowest antibiotic utilization, respectively. The most frequently prescribed antibiotics were ceftriaxone injection, cefixime oral, and levofloxacin injection, while ampicillin and amoxicillin oral were the least utilized. These findings provide valuable insights into antibiotic prescribing patterns for UTIs, highlighting fluctuating antibiotic utilization and the need for appropriate antibiotic stewardship strategies in primary care settings.

In this quick study, we estimated the Weibull distribution’s parameters using wind data collected between March 2017 and January 2018 using a twelve-meter mast meteorological station on the grounds of the National Energy Research Center in Khartoum. In order to quantify these descriptors, we relied on analytical and stochastic methods, subsequently enabling specialists from researchers, engineers, decision-makers, and policymakers to apprehend the wind characteristics in the vicinity. Hence, the computed scale and shape parameters were provided, in which the Firefly algorithm (FA) resulted in the most accuracy in terms of the coefficient of determination, which equaled 0.999, which we considered logical due to the observed nonlinearity in the wind speed numbers. On the contrary, the energy pattern factor method had the worst prediction capability depending on several goodness-of-fit metrics. This concise work is unique because it is the first to use data from Sudan to forecast local wind speeds using artificial intelligence algorithms, particularly the FA technique, which is widely used in solar photovoltaic modeling. Additionally, since classic estimating approaches act differently spatially, evaluating their efficacy becomes innovative, which was accomplished here. On a similar note, a weighted-average wind speed was found to equal 4.98 m/s and the FA average wind speed was 3.73 m/s, while the rose diagram indicated that most winds with potential energy equivalent to 3 m/s or more blow from the north.

In today’s world, services are improved and advanced in every field of life. Especially in the health sector, information technology (IT) plays a vigorous role in electronic health (e-health). To achieve benefits from e-health, its cloud-based implementation is necessary. With this environment’s multiple benefits, privacy and security loopholes exist. As the number of users grows, the Electronic Healthcare System’s (EHS) response time becomes slower. This study presented a trust mechanism for access control (AC) known as role-based access control (RBAC) to address this issue. This method observes the user’s behavior and assigns roles based on it. The AC module has been implemented using SQL Server, and an administrator develops controls for users with roles and access to multiple EHS modules. To validate the user’s trust value, A.net-based framework has been introduced. The framework of e-health proposed in this research ensures that users can protect their data from intruders and other security threats.

About 1.5 million women are diagnosed with breast cancer every year, making it the most frequent disease among women. In Pakistan, one woman in every nine has a lifetime chance of being diagnosed with breast cancer, making it the country with the highest incidence rate of breast cancer in Asia. The mortality rate from breast cancer in Pakistan was 22.7% in 2020. A lack of resources, such as competent pathologists, causes a delay in diagnosis and inadequate therapy planning, all of which contribute to a dismal survival rate. End-to-end solutions that may be implemented into computer-aided diagnostic (CAD) systems have been developed by medical professionals and researchers using domain-specific artificial intelligence (AI) technologies, most notably deep learning models, to address this critical issue. By increasing the amount of work for pathologists, these AI models may help in breast cancer detection and diagnosis. The goal of this research was to compare and contrast the effectiveness of many recent convolutional neural network (CNN) designs. Five pre-trained and fine-tuned deep CNN architectures, InceptionV3, ResNet152V2, MobileNetV2, VGG-16, and DenseNet-121, are tested to determine the best-performing model. The goal is to discover which models are preferable in terms of accuracy and effectiveness. Notably, the pre-trained InceptionV3 model outperforms the basic CNN model by 9%, with a high accuracy level of 94%. ResNet152V2 got 95% accuracy, and MobileNetV2 got 97% accuracy. The VGG-16 model outperforms the competition with a remarkable 98% accuracy rate. Following suit, the DenseNet-121 model achieves a remarkable 99% accuracy. These findings highlight the utility of deep learning models in the diagnosis of breast cancer as well as the range of model precision.

Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise medical image segmentation improves diagnosis and decision-making, aiding intelligent medical services for better disease management and recovery. Due to the unique nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positives, and false negatives. In view of these problems, researchers primarily improve the network structure but rarely improve from the unstructured aspect. The paper tackles these challenges, accentuating the limitations of deep convolutional neural network-based methods and proposing solutions to reduce annotation costs, particularly in complex images, and introduces the improvement strategies to solve the problems of sample imbalance, edge blur, false positives, and false negatives. Additionally, the article introduces the latest deep learning-based applications in medical image analysis, covering segmentation, image acquisition, enhancement, registration, and classification. Moreover, the article provides an overview of four cutting-edge deep learning models, namely convolutional neural network (CNN), deep belief network (DBN), stacked autoencoder (SAE), and recurrent neural network (RNN). The study selection involved searching benchmark academic databases, collecting relevant literature and appropriate indicator for analysis, emphasizing DL-based segmentation and classification approaches, and evaluating performance metrics. The research highlights clinicians’ and scholars’ obstacles in developing an efficient and accurate malignancy prognostic framework based on state-of-the-art deep-learning algorithms. Furthermore, future perspectives are explored to overcome challenges and advance the field of medical image analysis.

Capsid protein is a pathogenic protein that needs to be examined because it helps in the virus’s proliferation and mutation. Due to this protein, the virus can replicate and reproduce itself. The virus’s outer boundary is made of capsid protein. Capsid protein analysis and prediction are essential. Several approaches, including mass spectrometry, have been developed to detect and predict Capsid protein. However, these methods are time-consuming and expensive and require highly skilled human resources. Therefore, this study proposed an efficient and robust classification approach for Capsid protein. The proposed model employs several machine learning, data science, and pattern recognition strategies to measure statistical moments based on obtained data. The experimental analysis reveals that the proposed model has achieved an overall 99% accuracy. These marks indicate that the suggested method outperformed the cutting-edge methods for classifying Capsid and non-Capsid proteins.

Ischemic Cardiovascular diseases are one of the deadliest diseases in the world. However, the mortality rate can be significantly reduced if we can detect the disease precisely and effectively. Machine Learning (ML) models offer substantial assistance to individuals requiring early treatment and disease detection in the realm of cardiovascular health. In response to this critical need, this study developed a robust system to predict ischemic disease accurately using ML-based algorithms. The dataset obtained from Kaggle encompasses a comprehensive collection of over 918 observations, encompassing 12 essential features crucial for predicting ischemic disease. In contrast, much-existing research relies primarily on datasets comprising only 303 instances from the UCI repository. Six ML-based algorithms, including K Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), and Decision Trees (DT), are trained on the ischemic heart data. The effectiveness of the proposed methodologies is meticulously evaluated and benchmarked against cutting-edge techniques, employing a range of performance criteria. The empirical findings manifest that the KNN classifier produced optimized results with 91.8% accuracy, 91.4% recall, 91.9% F1 score, 92.5% precision, and AUC of 90.27%.

Men’s fertility depends on their bodies making normal sperm and delivering them. Semen analysis has been the test of choice for assessing the male partner in an infertile couple using a single threshold value to distinguish ‘abnormal’ and ‘normal’ parameters. In the semen analysis process, rA©gime issues might affect the semen morphology, quality, and spermatozoa, and also reduce the risks of fertility due to food regimen including glycemic content and limited intake of nutrients. Determination of the connotation between adjustable rA©gime and semen morphology is a complex task to determine fertility. The goal is this study is the prediction of men’s fertility rate to analyze the connection between spermatozoa and the level of lifeblood. Impaired semen parameters alone cannot be used to predict fertility more accurately. Some factors might affect the spermatozoa, like impairing sperm function, morphology, and sustainability, and can reduce the men’s fertility rate. In this article, deep learning on convolutional neural network (DLNN) is used to predict the men’s fertility rate more quickly, accurately, and consistently from different age spam of men between 18–50 years old. The convolutional neural network performs the segmentation of sperm heads, while the deep learning algorithm allows us to calculate the movement speed of sperm heads. After the application of DLNN, we have achieved semen prediction 80.952% and sperm concentration 85.714% accuracy of sperm head detection on human spermatozoa sperm samples. The results of the experiments presented below will show the applicability of the proposed method to be used in automated artificial insemination workflow.

يُعَالِجُ البَحثُ بَعْضَ أَمْثلةِ ابنِ مالك في ألفيَّتهِ المُتَضمِّنَة القواعد الأساسيَّة للأبواب النَّحويَّة الواردة فيها؛ لإثبات رؤوس القواعد النَّحويَّة ودراستها دلاليًّا، رابطًا ذلك بِمَا ورد في الكُتبِ النَّحويَّة، مع بيانِ إفادةِ شُرَّاح الألفيَّة من هذه الأمثلة في شُرُوْحِهِم، وقد أتتْ أهميَّةُ هذا الموضوع في كونهِ يُلقي الضّوء على موضوعٍ مُهِمّ، وهو: دراسة أمثلة ابن مالك في الألفيَّة نحويًّا ودلاليًّا، ومن أهدافِ البَحثِ التي سَعَى إلى تحقيقها: الوقوف على أمثلة ابن مالك في الألفيَّة لإثبات القاعدة النَّحويَّة، ولطبيعة البحث اُنتهج فيه المنهج الوصفي، وأُخذ من أدواته التَّحليل، وقد جُعِلَتْ حدوده الأمثلة المَالكيَّة في خلاصَتِهِ النَّحويَّة، ودراستها نَحويًّا، ودلاليًّا؛ وقد قًسِّمَ البحث إلى محورين، اهتمَّ الأوَّلُ منهما بالتَّعريف بابن مالك وألفيته، واختصَّ الآخر بالحديث عن مفهوم الأمثلة، ودراسة بعض أمثلة ابن مالك في ألفيَّته دراسة نحوية دلاليَّة، ثُمَّ خاتمة ذُكِرَ فيها أهمّ ما تَوَصَّلَ إليه البَحثُ من نتائج وتوصيات.

Abstract Background The currently used malaria vaccine, RTS,S, is designed based on the Plasmodium falciparum circumsporozoite protein (PfCSP). The pfcsp gene, besides having different polymorphic patterns, can vary between P. falciparum isolates due to geographical origin and host immune response. Such aspects are essential when considering the deployment of the RTS,S vaccine in a certain region. Therefore, this study assessed the genetic diversity of P. falciparum in Sudan based on the pfcsp gene by investigating the diversity at the N-terminal, central repeat, and the C-terminal regions. Methods A cross-sectional molecular study was conducted; P. falciparum isolates were collected from different health centres in Khartoum State between January and December 2019. During the study period, a total of 261 febrile patients were recruited. Malaria diagnosis was made by expert microscopists using Giemsa-stained thick and thin blood films. DNA samples were examined by the semi-nested polymerase chain reaction (PCR). Single clonal infection of the confirmed P. falciparum cases, were used to amplify the pfcsp gene. The amplified amplicons of pfcsp have been sequenced using the Sanger dideoxy method. The obtained sequences of pfcsp nucleotide diversity parameters including the numbers of haplotypes (Hap), haplotypes diversity (Hapd), the average number of nucleotide differences between two sequences (p), and the numbers of segregating sites (S) were obtained. The haplotype networks were constructed using the online tcsBU software. Natural selection theory was also tested on pfcsp using Fuand Li’s D, Fuand Li’s F statistics, and Tajima’s D test using DnaSP. Results In comparison with the different pfcsp reference strains, the Sudanese isolates showed high similarity with other African isolates. The results of the N-terminal region showed the presence of 2 different haplotypes with a Hapd of 0.425 ± 0.00727. The presence of the unique insertion of NNNGDNGREGKDEDKRDGNN was reported. The KLKQP motif was conserved in all the studied isolates. At the central repeat region, 11 haplotypes were seen with a Hapd of 0.779 ± 0.00097. The analysis of the genetic diversity in the C-terminal region showed the presence of 10 haplotypes with a Hapd of 0.457 ± 0.073. Several non-synonymous amino acids changes were also seen at the Th2R and the Th3R T-cell epitope regions including T317K, E317K, Q318E, K321N, I322K, T322K, R322K, K324Q, I327L, G352N, S354P, R355K, N356D, Q357E, and E361A. Conclusions In this study, the results indicated a high conservation at the pfcsp gene. This may further contribute in understanding the genetic polymorphisms of P. falciparum prior to the deployment of the RTS,S vaccine in Sudan.

Background: Cardiac catheterization is the common procedure undertaken to assess and treat coronary heart diseases. After the procedure, the patient remains on bed rest.for at least 6-24 hours in order to reduce the chance of vascular complications at the groin site. Objectives: to investigate the effect of noncompliance and compliance patients on the outcome of Diagnostic Coronary Angiography in terms of hematoma or bleeding, and pain. Methodology: It was a descriptive prospective cross-sectional hospital-based study conducted at Ahmed Gasim Cardiac Center in Sudan. Data collected among 151 Sudanese adult patients presenting for diagnostic coronary angiography, Data relevant to the study were collected by questioners and observational checklist and analyzed using (SPSS). Result: Fortunately, the majority of the participants, 76.2%, were compliant with the routine bed rest post-procedure and only 23.8% were non-compliant, the compliance of patients was not shown to have an effect on patients 94.4% up to 100% did not experience back pain. Conclusion: altering a patient's position by elevating the head of the patient's bed and side-to-side positioning was safe. This will enhance patient comfort, reduce back pain, and enable them to meet self-care needs. No difference in the incidence of vascular complications. [ABSTRACT FROM AUTHOR]

Background: Cancer incidence increases with age, so some clinical guidelines include patient age as one of the criteria used to decide whether a patient should be referred through the urgent suspected cancer (USC) pathway. Little is known about how strictly GPs adhere to these age criteria and what factors might influence their referral decisions for younger patients. Aim: To understand GPs' clinical decision making for younger patients with concerning symptoms who do not meet the age criteria for USC referral. Design and setting: Qualitative study using in-depth, semi-structured interviews with GPs working in surgeries across England. Method: Participants (n = 23) were asked to recall consultations with younger patients with cancer symptoms, describe factors influencing their clinical decisions, and discuss their overall attitude to age thresholds in cancer referral guidelines. A thematic analysis guided by the Framework approach was used to identify recurring themes. Results: GPs' decision making regarding younger patients was influenced by several factors, including personal experiences, patients' views and behaviour, level of clinical concern, and ability to bypass system constraints. GPs weighted potential benefits and harms of a referral outside guidelines both on the patient and the health system. If clinical concern was high, GPs used their knowledge of local systems to ensure patients were investigated promptly even when not meeting the age criteria. Conclusion: While most GPs interpret age criteria flexibly and follow their own judgement and experience when making clinical decisions regarding younger patients, system constraints may be a barrier to timely investigation. [ABSTRACT FROM AUTHOR]

Chinese herbal medicine is widely used globally. In many instances, it is associated with severe adverse outcomes. We report case of a Chinese herbal nephropathy occurring in a 43-year-old woman showing renal impairment, metabolic acidosis, Stokes - Adams syndrome, hypernatremia, and hypokalemia, characteristics not usually encountered in published cases.

Abstract Background Malaria caused by Plasmodium falciparum parasite is still known to be one of the most significant public health problems in sub-Saharan Africa. Genetic diversity of the Sudanese P. falciparum based on the diversity in the circumsporozoite surface protein (PfCSP) has not been previously studied. Therefore, this study aimed to investigate the genetic diversity of the N-terminal region of the pfcsp gene. Methods A cross-sectional molecular study was conducted; 50 blood samples have been analysed from different regions in Sudan. Patients were recruited from the health facilities of Khartoum, New Halfa, Red Sea, White Nile, Al Qadarif, Gezira, River Nile, and Ad Damazin during malaria transmission seasons between June to October and December to February 2017–2018. Microscopic and nested PCR was performed for detection of P. falciparum. Merozoite surface protein-1 was performed to differentiate single and multiple clonal infections. The N-terminal of the pfcsp gene has been sequenced using PCR-Sanger dideoxy method and analysed to sequences polymorphism including the numbers of haplotypes (H), segregating sites (S), haplotypes diversity (Hd) and the average number of nucleotide differences between two sequences (Pi) were obtained using the software DnaSP v5.10. As well as neutrality testing, Tajima’s D test, Fu and Li’s D and F statistics. Results PCR amplification resulted in 1200 bp of the pfcsp gene. Only 21 PCR products were successfully sequenced while 29 were presenting multiple clonal P. falciparum parasite were not sequenced. The analysis of the N-terminal region of the PfCSP amino acids sequence compared to the reference strains showed five different haplotypes. H1 consisted of 3D7, NF54, HB3 and 13 isolates of the Sudanese pfcsp. H2 comprised of 7G8, Dd2, MAD20, RO33, Wellcome strain, and 5 isolates of the Sudanese pfcsp. H3, H4, and H5 were found in 3 distinct isolates. Hd was 0.594 ± 0.065, and S was 12. The most common polymorphic site was A98G; other sites were D82Y, N83H, N83M, K85L, L86F, R87L, R87F, and A98S. Fu and Li’s D* test value was − 2.70818, Fu and Li’s F* test value was − 2.83907, indicating a role of negative balancing selection in the pfcsp N-terminal region. Analysis with the global pfcsp N-terminal regions showed the presence of 13 haplotypes. Haplotypes frequencies were 79.4%, 17.0%, 1.6% and 1.0% for H1, H2, H3 and H4, respectively. Remaining haplotypes frequency was 0.1% for each. Hd was 0.340 ± 0.017 with a Pi of 0.00485, S was 18 sites, and Pi was 0.00030. Amino acid polymorphisms identified in the N-terminal region of global pfcsp were present at eight positions (D82Y, N83H/M, K85L/T/N, L86F, R87L/F, A98G/V/S, D99G, and G100D). Conclusions Sudanese pfcsp N-terminal region was well-conserved with only a few polymorphic sites. Geographical distribution of genetic diversity showed high similarity to the African isolates, and this will help and contribute in the deployment of RTS,S, a PfCSP-based vaccine, in Sudan.

Feature construction (FC) refers to a process that uses the original features to construct new features with better discrimination ability. Particle Swarm Optimisation (PSO) is an effective search technique that has been successfully utilised in FC. However, the application of PSO for feature construction using high dimensional data has been a challenge due to its large search space and high computational cost. Moreover, unnecessary features that were irrelevant, redundant and contained noise were constructed when PSO was applied to the whole feature. The feature clustering methods were used to aggregate similar features into clusters, whereby the dimensionality of the data was lowered by choosing representative features from every cluster to form the final feature subset. The clustering of each features are proven to be accurate in feature selection (FS), however, only one study investigated its application in FC for classification. The study identified some limitations, such as the implementation of only two binary classes and the decreasing accuracy of the data. This paper proposes a cluster based PSO feature construction approach called ClusPSOFC. The Redundancy-Based Feature Clustering (RFC) algorithm was applied to choose the most informative features from the original data, while PSO was used to construct new features from those selected by RFC. Experimental results were obtained by using six UCI data sets and six high-dimensional data to demonstrate the efficiency of the proposed method when compared to the original full features, other PSO based FC methods, and standard genetic programming based feature construction (GPFC). Hence, the ClusPSOFC method is effective for feature construction in the classification of high dimensional data.

Abstract Background Nutcracker syndrome is an easily missed cause of hematuria in children. It is characterized by left renal vein entrapment between the abdominal aorta and the superior mesenteric artery causing renal venous hypertension. Intermittent hematuria and orthostatic proteinuria with or without abdominal or flank pain are the common clinical manifestations. Presence of variable non-specific symptoms and non-significant physical findings results in a delayed diagnosis. Case presentation We present a ten -year -old girl with four episodes of painless gross hematuria and recurrent microscopic hematuria since the age of two years. Doppler ultrasound showed left renal vein compression while 3 D computerized tomography angiography confirmed the diagnosis of an anterior nutcracker. The patient was conservatively treated with nutritional support (pediasure complete formula and high calorie food), iron supplements and followed up, monitored for anemia, hypertension and renal insufficiency. Conclusion Nutcracker syndrome is a rare cause of recurrent gross hematuria in children. A high index of suspicion and proper imaging is needed to reach a proper diagnosis and avoid the psychological and financial stress on the family.

يتناول البحثُ الإحالةَ في التأليف الصّرفي وأثرها في تماسك النَّص ووضوح القاعدة الصرفيَّة في شرح ابن عقيل على ألفية ابن مالك؛ وقد أتت أهمية البحث في أنَّه يُلقي الضوء على مسألة مهمة، وهي توضيح معنى الإحالة في التأليف الصَّرفي، وأثرها في وضوح القاعدة لدى دارسِ الصَّرف العربي وتداخل قضاياه، وقد هدف البحث إلى معرفة معنى الإحالة وبيان أدواتها، وتوضيح العلاقة بين القاعدة المحيلة والمحال إليها في شرح ابن عقيل، وقد اتبعَ البحث المنهج الوصفي التحليلي لملاءمته طبيعة الدِّراسة.

Objectives: Rheumatoid arthritis is a chronic inflammatory autoimmune disease. This study aimed to determine the association of interleukin-17A-197G/A polymorphism with rheumatoid arthritis in Sudanese patients. Methods: A case–control study was conducted between March and December 2018. Clinical and demographic data of the study participants were collected and analyzed. Polymerase chain reaction restriction fragment length polymorphism molecular technique was done to investigate interleukin-17A-197G/A polymorphisms. All statistical tests were considered statistically significant when p

The article focuses on evaluating the safety and efficacy of SGLT-2 inhibitors (SGLT-2i) in kidney transplant recipients with pre-existing diabetes or post-transplant diabetes mellitus (PTDM). Topics include the benefits of SGLT-2i in preserving graft function, reducing glycemic burden, and potentially lowering cardiovascular risk in this patient group.

Abstract Serological screening of 199 serum samples from Dromedary camels—from different cities in Saudi Arabia—was performed using enzyme-linked immunosorbent assay for detecting antibodies against two cyst-forming coccidian parasites, namely Toxoplasma gondii and Neospora caninum. Antibodies against T. gondii were detected in 68 (34.2%) samples, while those against N. caninum were present in 33 (16.6%) samples. The highest seroprevalence of T. gondii antibodies was reported in samples from Taif (51.2%), while the lowest seroprevalence was reported in samples from Riyadh and Hofuf (15.1%). The highest seroprevalence of N. caninum antibodies was reported in samples from Jizan (35.9%) while the lowest was reported in samples from Taif (2.4%). A total of 47 male and 21 female camels exhibited antibodies against T. gondii , while 19 male and 14 female camels showed antibodies against N. caninum . Concurrent detection of both T. gondii and N. caninum antibodies was observed in 18 camels. It has been demonstrated that T. gondii and N. caninum antibodies are prevalent in camels from different cities of the Kingdom of Saudi Arabia.