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Abstract Background Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. Methods In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. Results We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. Discussion This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. Conclusion The customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

Abstract Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The growing research on ACPs as therapeutic agent is increasing due to its minimal side effects. However, identifying novel ACPs using wet-lab experiments are generally time-consuming, labor-intensive, and expensive. Leveraging computational methods for fast and accurate prediction of ACPs would harness the drug discovery process. Herein, a machine learning-based predictor, called PLMACPred, is developed for identifying ACPs from peptide sequence only. PLMACPred adopted a set of encoding schemes representing evolutionary-property, composition-property, and protein language model (PLM), i.e., evolutionary scale modeling (ESM-2)- and ProtT5-based embedding to encode peptides. Then, two-dimensional (2D) wavelet denoising (WD) was employed to remove the noise from extracted features. Finally, ensemble-based cascade deep forest (CDF) model was developed to identify ACP. PLMACPred model attained superior performance on all three benchmark datasets, namely, ACPmain, ACPAlter, and ACP740 over tenfold cross validation and independent dataset. PLMACPred outperformed the existing models and improved the prediction accuracy by 18.53%, 2.4%, 7.59% on ACPmain, ACPalter, ACP740 dataset, respectively. We showed that embedding from ProtT5 and ESM-2 was capable of capturing better contextual information from the entire sequence than the other encoding schemes for ACP prediction. For the explainability of proposed model, SHAP (SHapley Additive exPlanations) method was used to analyze the feature effect on the ACP prediction. A list of novel sequence motifs was proposed from the ACP sequence using MEME suites. We believe, PLMACPred will support in accelerating the discovery of novel ACPs as well as other activities of microbial peptides.

Abstract Background This study’s purposes were to evaluate the impact of biological therapies on outcomes in patients with severe asthma (SA) and chronic rhinosinusitis (CRS) and to compare these effects among those with NP (CRSwNP) versus those without NP (CRSsNP) in the “real-world” setting in Saudi Arabian patients. Methods From March to September 2022, a retrospective observational cohort study was undertaken at the severe asthma clinics of the Armed Forces Hospital—Southern Region (AFHSR) and King Khalid University Hospital, Abha, Saudi Arabia, to delineate the effects of dupilumab therapy. Outcomes were assessed, including clinical outcomes, FEV1, and laboratory findings before and one year after dupilumab. Post-therapy effects were compared between CRSwNP and CRSsNP. Results Fifty subjects were enrolled, with a mean age of 46.56. There were 27 (54%) females and 23(46%) males. Significant improvements in clinical parameters (frequency of asthma exacerbations and hospitalizations, the use of OCs, anosmia, SNOTT-22, and the ACT), FEV1, and laboratory ones (serum IgE and eosinophilic count) were observed 6 and 12 months after using dupilumab (p

This study suggests a digital assessment tool - Smart Equation Exam System (SEED) - as a substitute for traditional multiple-choice and paper and pen exams. SEED includes a question bank, an answer platform, mathematical equations, and physics formulae. The novelty of SEED is that it allows all students with or without physical disabilities via creation of an equal and non-discriminatory platform to solve the physics questions step by step and submit all the solution steps as well as the final answer. The study population was composed of students enrolled in the General Physics (II) course at university level. The sample consisted of fifty students who were selected randomly. The results showed that more than eighty presents of the participants had a positive perception of SEED compared to traditional exams, and they found it more flexible in reviewing solution steps, and getting feedback, especially for the student with handwriting disabilities.

With the outbreak of the COVID-19 pandemic, new words appeared in English. EFL learners need to learn the new words. Learners depend on many strategies to help them learn new terms. Investigating this topic is significant because it will contribute to the knowledge of teaching and learning new words that appeared rapidly and suddenly. This study aims to investigate the acquisition of word-formation processes related to COVID-19 terms. The research seeks to answer two questions: (1) Are Saudi female EFL learners familiar with English word-formation processes? (2) Do word-formation processes aid Saudi female EFL students in learning COVID-19 pandemic terms? The study used a quantitative method. The data was collected from 71 Saudi female students via a multiple-choice electronic questionnaire. The results revealed that the percentages of incorrect responses were higher than the percentages of correct ones, thus suggesting that the students were not familiar with word-formation processes. The findings showed that they had similar results in words formed via compounding and blending, indicating they failed to distinguish between the two processes. The findings show that the Saudi female students did not prefer to use a word-formation process over the other. Further, results showed that even though some of the common COVID-19 terms frequently were used on a daily basis, the students failed to predict and use the correct word-formation processes for those terms. The findings as a whole reveal that Saudi learners had difficulty in learning word-formation processes deriving COVID-19 terms. The study proposes some recommendations for further research.

The research investigates the hypothesis that teachers' attitudes toward inclusive education may be related to their value priorities. Specifically, it is hypothesized that some teachers' personal values correlate positively with their attitudes toward inclusive education. The study, conducted during a pre-service teacher training course, involved a sample of 647 student teachers. The Portrait Values Questionnaire (PVQ-RR) (Schwartz, 2012) and the Multidimensional Attitudes Toward Inclusive Education Scale (MATIES) (Mahat, 2008). A bivariate linear regression analysis was performed to detect the correlation between the two variables examined statistically. The bivariate statistical analysis confirms a significant positive relationship between the values of Self-transcendence and Openness to Change with the attitudes of pre-service teachers toward inclusive education. It emerged that the more a teacher approves of these values, the more she tends to favor of attitudes related to inclusive education. The results of this study suggest that values should be considered in teacher training programs to improve the implementation of inclusive education policies.

A field experiment was carried out at the research station of the College of Agriculture, University of Anbar, during the spring season of 2021-2022 to study the effect of adding organic fertilizer and spraying with zinc sulfate on two cultivars of Solanum tuberosum L. The study was conducted as a factorial experiment within the design of completely randomized sectors (RCBD). The first factor included ten composting treatments (control treatment, chemical fertilization treatment, organic fertilization poultry treatment 50 g/plant, organic fertilization treatment 100 g/plant, spraying treatment 1 g zinc sulfate/plant, spraying treatment 3 g zinc sulfate/plant, interaction treatment 50 g organic fertilizer/plant + 1 g zinc sulfate, interaction treatment 50 g organic fertilizer/plant + 3 g zinc sulfate, interaction treatment 100 g organic fertilizer/plant + 1 g zinc sulfate, interaction treatment 100 g organic fertilizer/plant + 3 g zinc sulfate). The second factor included the potato cultivars Naima and Challenger rank Elite. The total number of treatments amounted to 20 and was repeated three times, bringing the number of experimental units to 60. The averages were compared at a probability level of 5%. The results of the experiment can be summarized as follows: Fertilization treatments affected shoot characteristics such as the number of leaves, shoot dry weight, and chlorophyll content in the leaves. The interaction treatments (100 g organic fertilizer + 3 g zinc sulfate) and (50 g organic fertilizer + 1 g zinc sulfate) excelled by achieving the highest shoot dry weight and chlorophyll content, amounting to 349.900 g. plant-1 and 38.430 gm-1, respectively, compared to the lowest value achieved by the control treatment, which amounted to 73.800 gm. plant-1 and 21.130 mg.100 g-1.

Abstract Background Diabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan. Methods In this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. ML models were utilized to distinguish diabetes groups from non-diabetes controls. Recursive feature elimination (RFE) was leveraged to identify a subset of features to improve the performance of model. SHAP based analysis was used for the importance of features and support the explainability of the proposed model. Results Ensemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which improved the model accuracy to 87.2%. From a clinical standpoint, higher HDL-Cholesterol and Neutrophil levels were observed in the diabetic group, along with lower vitamin B12 and testosterone levels. Lower sodium levels were found in diabetics, potentially stemming from clinical factors including specific medications, hormonal imbalances, unmanaged diabetes. We believe Dapagliflozin prescriptions in Qatar were associated with decreased Gamma Glutamyltransferase and Aspartate Aminotransferase enzyme levels, confirming prior research. We observed that bone area, bone mineral content, and bone mineral density were slightly lower in the Diabetes group across almost all body parts, but the difference against the control group was not statistically significant except in T12, troch and trunk area. No significant negative impact of diabetes progression on bone health was observed over a period of 5-15 yrs in the cohort. Conclusion This study recommends the inclusion of ML model which combines both DXA and clinical data for the early diagnosis of diabetes.

Abstract Background Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor’s performance is still not satisfactory. Methods In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF. Results The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew’s-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process. Availability The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .

Abstract Aim To determine the occurrence of occupational stress among Palestinian nurses, and their associated sources and risk factors. Design A cross‐sectional descriptive design. Methods A total of 250 registered nurses from eight governmental hospitals, using a convenience sampling method. Data collection were conducted using the 30‐items self‐reported Occupational Stress Scale from December 2022 to March 2023. Descriptive statistics, independent sample t‐test, one‐way ANOVA, and multiple linear regression analysis were applied to analysis data. Data analysis included descriptive statistics, independent sample t‐test, one‐way ANOVA, and multiple linear regression analysis. Results The prevalence of high occupational stress levels was 64.8% (Mean = 3.9 out of 5). The main sources of stress are too much responsibility and work, understaffing, lack of promotion and recognition, inadequate pay, time pressure, and management style. The results regression analysis demonstrated that male nurses with a Masters or PhD degree and those working in fixed shifts experienced higher occupational stress. Moreover, participants who worked overtime hours were more susceptible to stress. Conclusions The research indicates that occupational stress presents a notable challenge for nurses in the Gaza Strip, Palestine. It suggests that in order to alleviate this stress, decision‐makers in healthcare policy and hospital management should prioritize the execution of strategies aimed at addressing the primary stressors and risk factors identified. Reporting Method This study adhered to the STROBE guidelines. Public Contribution A total of 250 registered nurses were taken part in this study by answering a self‐administered study survey.

Key Clinical Message Down syndrome (DS), characterized by trisomy 21, significantly increases susceptibility to leukemia, although the occurrence of multiple myeloma (MM) in DS is exceedingly rare. This report details the case of a 45‐year‐old female with DS who was diagnosed with MM, highlighting diagnostic and therapeutic complexities. It emphasizes the importance of tailored therapeutic strategies for treating MM in individuals with DS and the need for specialized approaches in these cases.

هدف الدراسة الى الكشف عن تأثير تمرينات نقص الاوكسجين على بعض المؤشرات الوظيفية والبدنية والمهارية والتي ادت الى تحسن متغيرات البحث وبنسب متفاوتة واستعمل الباحث المنهج التجريبي بالتصميم المجموعة الواحدة بالقياس القبلي والبعدي لملائمته لطبيعة ومشكلة البحث وطبقت الدراسة على عينة من لاعبي نادي هيت الرياضي الدرجة الاولى بكرة السلة للموسم الرياضي (2023- 2024) وبلغ حجم العينة الاساسية (12) لاعب يمثلون المجموعة التجريبية ويشكلون نسبة (75%) من مجتمع البحث وبلغ عدد العينة الاستطلاعية (4) لاعبين من خارج العينة الاساسية وكانت اهم النتائج وجود تحست في المتغيرات الوظيفية والبدنية مما انعكس هذا الى تحسن الاداء المهاري حيث كان للتدريبات تأثيرا ايجابيا على متغيرات البحث الاساسية ويوصي الباحث بضرورة استخدام تدريبات نقص الاوكسجين على لاعبي كرة السلة لما لها من تأثير ملحوظ لرفع مستوى الاداء البدني والمهاري والخططي وعلى جميع انواع الانشطة المختلفة.

A 53-year-old post-menopausal Indian female presented with invasive ductal carcinoma, treated with neoadjuvant chemotherapy and pembrolizumab due to a PD-L1 combined positive score of 5. Following a right mastectomy and axillary dissection, she received a breast expander and AlloDerm™ graft. After resuming pembrolizumab and paclitaxel postoperatively, she developed severe breast redness and high-grade fever, necessitating expander removal due to suspected pembrolizumab-induced complications. This case underscores the unique and severe adverse effects of pembrolizumab on breast reconstruction, highlighting the need for careful monitoring and management in patients undergoing similar treatments.

In the rapidly evolving landscape of urbanisation and transportation infrastructure in Bangladesh, it is critical to address the disparities in public transportation access that disproportionately affect vulnerable people such as older adults and people with disabilities. This scoping review aims to synthesise the available evidence and highlight research gaps on unequal access to public transportation and how these inequalities lead to the social exclusion of older adults and people with disabilities in Bangladesh. We followed the Preferred Reporting Items for Systematic Review and Meta-Analyses, Scoping Review Extension (PRISMA-ScR) guidelines and employed thematic analysis to interpret the findings. Four databases Scopus, Embase, PubMed and Web of Science, and online sources such as Google Scholar and TRID were searched from January 2002 to July 2022. The findings show that the existing modes of transportation are inadequate and non-inclusive and pose both physical (including built environments such as roads, ramps, and footpaths) and social barriers (such as low income, low employment opportunities, and negative social attitude) which reduced the accessibility to workplaces, healthcare, and social networks for these two vulnerable groups. An inclusive and sustainable urban transport infrastructure, older adult-specific and disabled-friendly transportation policies, and behaviour changes are significant steps to improve safety and social inclusion. The transport authorities have ignored the transport necessities of these vulnerable groups and (in)advertently pushed them towards marginalised sections. This study theoretically contributes to the age-friendly city framework for older adults and people with disabilities by adopting an inclusive urban mobility approach for all.

Key Clinical Message Myeloid sarcoma (MS) is a rare extramedullary infiltration of acute myeloid leukemia (AML). We present a case of 19‐year‐old male with AML‐M2 who relapse with AML sarcoma in brain and optic nerve. MS as AML extramedullary relapse had a poor prognosis.

Abstract Background The mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost. Methods In this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an in silico strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER). Results The proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection. Availability We have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP .

In the present work, manganese–copper co-infused nickel oxide nanoparticles (MnCu co-doped NiO NPs) were formulated via a green process using Carica papaya extract. The MnCu co-doped NiO NPs were characterized by X-ray diffraction (XRD), UV–Vis, Fourier transform infrared, field emission scanning electron microscope, energy dispersive X-ray analysis, and photoluminescence (PL) spectrum. The XRD pattern demonstrated that synthesized MnCu co-doped NiO NPs exhibit cubic structure. On the PL spectrum, various surface defects were identified. MnCu co-doped NiO NPs exhibited ferromagnetic properties at 37°C. The antimicrobial activity of green synthesis MnCu co-doped NiO NPs against human pathogens (Escherichia coli, Streptococcus pneumoniae, Bacillus megaterium, Bacillus subtilis, Shigella dysenteriae, Pseudomonas aeruginosa) and Candida albicans as fungal strains were demonstrated. The MnCu co-doped NiO NPs treatment considerably reduced MDA-MB-231 cell viability while not disturbing HBL-100 cell viability. Different fluorescent staining analyses revealed that MnCu co-doped NiO NPs induced nuclear and mitochondrial damage to improve free radical production, altering mitochondrial membrane protein potential, which led to apoptotic cell death in MDA-MB-231 cells. The MnCu co-doped NiO NP treatment enhanced pro-apoptotic protein expression and inhibited the cell cycle at the S phase in MDA-MB-231 cells. This makes it easy, cheap, and environmentally friendly to make MnCu co-doped NiO NPs using C. papaya extract, which has excellent antimicrobial properties.

Abstract Background Periodontal phenotype is regarded to be one of the key factors influencing the efficacy of restorative therapies in dental practice. The objective of the systematic review was to explore the importance of thin and thick periodontal phenotypes and how they affect the outcome of periodontal and restorative therapies by looking at a number of academic publications from various online databases. Methods Following the PRISMA guidelines (Preferred Reporting Items for Systematic Review standards), relevant data will be searched and retrieved from three significant scientific databases, including PubMed, EBSCO, and Scopus. The articles with full texts that matched the keywords and published in English between 2018 and 2023 were taken into consideration. Results The majorities of these articles were based on the type of periodontal phenotype and their impact on periodontal and restorative treatment outcomes were selected. The initial search yielded a total of 530 articles. Only 273 were relevant to the review’s objectives, and these were considered for determining eligibility. Only 20 publications were eligible for analysis. Conclusion Understanding these anatomical aspects of periodontal phenotype is crucial to both periodontology and restorative dentistry. The clinical outcome of restorative, prosthetic, orthodontic, surgical, and periodontal therapies is determined in large part by the periodontal phenotype, which also plays a significant role in clinical failure or success in dental treatments. Trial registration This study protocol registered with the International Prospective Register of Systematic Reviews (PROSPERO) dated 16th June 2023 with the registration ID CRD42023432568.

AbstractTransdermal drug delivery systems (TDDS) for antibiotics have seen significant advances in recent years that aimed to improve the efficacy and safety of these drugs. TDDS offer many advantages over other conventional delivery systems such as non-invasiveness, controlled-release pattern, avoidance of first-pass metabolism. The objective of this review is to provide an overview on the recent advances in the TDDS of different groups of antibiotics including β-lactams, tetracyclines, macrolides, and lincosamides, utilized for their effective delivery through the skin and to explore the challenges associated with this field. The majority of antibiotics do not have favorable properties for passive transdermal delivery. Thus, novel strategies have been employed to improve the delivery of antibiotics through the skin, such as the use of nanotechnology (nanoparticles, solid-lipid nanoparticles, nanoemulsions, vesicular carriers, and liposomes) or the physical enhancement techniques like microneedles and ultrasound. In conclusion, the transdermal delivery systems could be a promising method for delivering antibiotics that have the potential to improve patient outcomes and enhance the efficacy of drugs. Further research and development are still needed to explore the potential of delivering more antibiotic drugs by using various transdermal drug delivery approaches.

Employing Landau theory and the Landau–Khalatnikov (L–K) equation of motion, we investigate the phase transitions in individual layers of antiferroelectric lead zirconate (PbZrO3), ferroelectric lead zirconate titanate (PbZrTiO3), and an antiferroelectric/ferroelectric PbZrO3/PbZr(0.21)Ti(0.79)O3 bilayer bulk structure. We examine the dielectric hysteresis loop behavior of the three systems, with a specific focus on the PbZrO3/PbZr(0.21)Ti(0.79)O3 bilayer under different stabilities of the PbZrO3 layer. In addition, we explore cases where the coercive field of the bilayer structure is lower than that of the PbZrTiO3 individual layer. The recoverable electric energy for the PbZrO3/PbZr(0.21)Ti(0.79)O3 bilayer increases significantly to 118 J/cm3 at an applied field of 7.5 × 108 V/m at 20 °C. In comparison, the PbZr(0.21)Ti(0.79)O3 layer reaches 71.8 J/cm3 under the same field and temperature conditions. This is much higher than those predicted experimentally by previous studies on thin film single and bilayer structures (15.6 and 28.2 J/Cm3 respectively), indicating that the antiferroelectric/ferroelectric PbZrO3/PbZr(0.21)Ti(0.79)O3 bilayer bulk structure could be used to target specific large-scale, long-term energy storage applications. Upon increasing the value of the coupling coefficient, the transition temperatures of the PbZrO3 layer and the PbZrO3/PbZr(0.21)Ti(0.79)O3 bilayer are increased up to the transition temperature of the PbZr(0.21)Ti(0.79)O3 individual layer (450 °C). This increment in the transition temperature in the bilayer system contributes to its stability in storing energy at higher temperatures. Furthermore, the recoverable energy density of the PbZrO3/PbZr(0.21)Ti(0.79)O3 bilayer increases further with temperature from 20 to 440 °C correlated with the rise in the difference between the spontaneous and the remanent polarizations (Ps − Pr). The significant stored energy observed over a wide temperature range highlights the promise of this bilayer structure for creating high-power capacitors where stability at different temperatures is crucial and possesses greater energy storage capacity.

This study sought to investigate the acquisition of English compounds among Saudi EFL learners. The focus was on the comprehension of particular compound types, specifically subordinative, attributive, and coordinative compounds, by considering the concepts of endocentricity and exocentricity. The sample involved 236 Saudi female students. The data was gathered by using a questionnaire in which the participants were instructed to provide the meaning of 24 compounds without relying on a dictionary. The results reveal that 75% of the responses were accurate. This implies that Saudi EFL learners may have a sufficient grasp of English compounds. The findings indicate that the learners performed better in endocentric compounds compared to exocentric compounds. The findings showed that the learners had a higher level of mastery of subordinative and attributive endocentric compounds, followed by coordinative compounds. This is attributed to the transparency of meaning for endocentric compounds.

OverviewThis study evaluates the diagnostic accuracy of a multimodal large language model (LLM), ChatGPT-4, in recognizing glaucoma using color fundus photographs (CFPs) with a benchmark dataset and without prior training or fine tuning.MethodsThe publicly accessible Retinal Fundus Glaucoma Challenge “REFUGE” dataset was utilized for analyses. The input data consisted of the entire 400 image testing set. The task involved classifying fundus images into either ‘Likely Glaucomatous’ or ‘Likely Non-Glaucomatous’. We constructed a confusion matrix to visualize the results of predictions from ChatGPT-4, focusing on accuracy of binary classifications (glaucoma vs non-glaucoma).ResultsChatGPT-4 demonstrated an accuracy of 90% with a 95% confidence interval (CI) of 87.06%-92.94%. The sensitivity was found to be 50% (95% CI: 34.51%-65.49%), while the specificity was 94.44% (95% CI: 92.08%-96.81%). The precision was recorded at 50% (95% CI: 34.51%-65.49%), and the F1 Score was 0.50.ConclusionChatGPT-4 achieved relatively high diagnostic accuracy without prior fine tuning on CFPs. Considering the scarcity of data in specialized medical fields, including ophthalmology, the use of advanced AI techniques, such as LLMs, might require less data for training compared to other forms of AI with potential savings in time and financial resources. It may also pave the way for the development of innovative tools to support specialized medical care, particularly those dependent on multimodal data for diagnosis and follow-up, irrespective of resource constraints.

Objective: Breakthrough COVID-19 infections are common following immunisation with various types of vaccines. The patterns of infections have not been well established. We aimed to analyse the signs and symptoms of post vaccination infections in addition to the need for hospital admission, ER visit and supplemental oxygen in relation to age and gender. Methods: A cross-sectional cohort study was conducted in JUH from March 2021 to August 2022, we interviewed 1479 individuals who are >15 years of age and got a breakthrough infection. The statistical analysis was performed using STATA statistical software. Results: Out of the 1479 cases, 50.2 % and 69.4 % were females and less than 45 years of age respectively. Symptoms of cough, fever and headache were reported by nearly 50 % of the patients, while one-third complained of dyspnoea. We found that participants older than 45 years had worse clinical outcomes (P-value

Introduction: Primary Upper tract urothelial carcinoma (UTUC) is a rare subtype of urothelial carcinoma and has an unknown incidence and prevalence in Yemen. Radical nephroureterectomy (RNU) with bladder cuff removal is the standard treatment for UTUC. Case presentation: We present a 67-year-old male patient who developed grade II vesicoureteral reflux (VUR) on the left side of the urinary tract after undergoing right-sided RNU for non-invasive UTUC. Follow-up examinations at one-, three-, and six-month post-surgery revealed no evidence of kidney diseases. The patient's recovery has been satisfactory, and ongoing regular follow-ups are being maintained. Conclusion: Vigilant monitoring of VUR presence and effective management following RNU is crucial to minimize complications and preserve renal function. The underlying mechanisms linking VUR development and RNU remain unclear, necessitating further research.

Key Clinical Message Extramedullary involvement of the central nervous system (CNS) in Chronic Myeloid Leukemia (CML) is an uncommon relapse. In this case, we present a unique instance of a 43‐year‐old male diagnosed with CML experiencing a CNS blast crisis.