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Abstract Background Assessing the methodological quality of case reports and case series is challenging due to human judgment variability and time constraints. We evaluated the agreement in judgments between human reviewers and GPT-4 when applying a standard methodological quality assessment tool designed for case reports and series. Methods We searched Scopus for systematic reviews published in 2023–2024 that cited the appraisal tool by Murad et al. A GPT-4 based agent was developed to assess the methodological quality using the 8 signaling questions of the tool. Observed agreement and agreement coefficient were estimated comparing published judgments of human reviewers to GPT-4 assessment. Results We included 797 case reports and series. The observed agreement ranged between 41.91% and 80.93% across the eight questions (agreement coefficient ranged from 25.39 to 79.72%). The lowest agreement was noted in the first signaling question about selection of cases. The agreement was similar in articles published in journals with impact factor

Abstract In the realm of intelligent healthcare, there is a growing ambition to reshape medical services through the integration of artificial intelligence (AI). However, conventional machine learning faces inherent challenges such as privacy issues, delayed updates, and protracted training times, particularly due to the hesitance of medical institutions to directly share sensitive data, with possible noises. In response to these concerns, a Quantum-Assisted Federated Intelligent Diagnosis Algorithm ( $$\beta $$ β -QuAFIDA) is proposed, applied into real medical data. Leveraging the capabilities of the 5G mobile network, this approach works the connection between Internet of Medical Things (IoMT) devices through the 5G, synchronizing training and updating the server model without disrupting their real-world applications. In our quest to safeguard patient data and enhance training efficiency, our study employs an innovative heuristic approach marked by a nested loop structure. Specifically, the inner loop is dedicated to training the beta-variational quantum eigensolver ( $$\beta $$ β -VQE) to approximate the expectation values of the proposed algorithm; the outer loop trains the $$\beta $$ β -QuAFIDA to reduce the relative entropy towards the target. This approach involves a balance between privacy considerations and the urgency of training. Results demonstrate that representations with low-rank attained through $$\beta $$ β -QuAFIDA offer an effective approach for acquiring low-rank states. This research signifies a step forward in the synergy between AI and 5G technologies, presenting a novel avenue for the advancement of intelligent healthcare.

Background: Autism spectrum disorder (ASD) exerts significant impacts on family life. Elevated levels of anxiety, stress, isolation, and uncertainty among parents and family members of children with ASD are common consequences. Objectives: This study aimed to examine the mediating role of parental self-efficacy in the relationship between social loneliness and family quality of life among mothers of children with ASD in Mashhad, Iran. Methods: This cross-sectional study employed structural equation modeling. The population consisted of all mothers with children aged 4 to 12 years with ASD in Mashhad in 2023. A convenience sample of 372 mothers was selected from rehabilitation, educational, and therapeutic centers for autism in the city. Data were collected using the Family Quality of Life Scale, the UCLA Loneliness Scale, and the Parental Self-Efficacy Scale. Pearson correlation and structural equation modeling were conducted using SPSS and AMOS version 24. Results: Results indicated significant correlations between social loneliness and family quality of life (r=-0.53, P

Extended Abstract Background: Human activities, climate variability, and environmental stress have strongly affected forest ecosystems worldwide. Forest fires are among the major factors of global ecosystem destruction. Fires in the forest, whether of human or natural origin, have been raised as a serious crisis in recent years. Hence, fire risk assessment plays an important role in forest fire management because knowing where the highest risk is essential to minimize threats to resources, lives, and property. Integration of spatial information from different sources using statistical analysis in the GIS environment is a suitable tool for managing and spreading forest fires, which is one of the main natural hazards in northern Iran. Therefore, it is necessary to prepare a fire risk assessment map for the planning and protection of forests. Methods: The current practical research concerning its nature is a combination of documentary, descriptive, and quantitative model-based methods regarding the research method. In this study, fuzzy and hierarchical (AHP) logic models were combined to investigate the risk of forest fire in Mazandaran province in five classes, very high, high, medium, low, and very low, respectively, using four main criteria and nine sub-criteria, namely topography (height, slope, direction, and rivers), climatic factors (peak temperature and precipitation), human factors (residential areas and the network of communication roads), and biological factors (vegetation). To obtain the net vegetation cover, the Normalized Difference Vegetation Index (NDVI) was applied to the Sentinel-2 satellite image set in a 5-year period (2017-2022) in the GEE web system. The height, slope, and slope direction maps of the study area were prepared from the digital elevation model (DEM) of 12.5 m from the ALOS AVNIR-2 dataset. The distance from rivers, residential areas, and the road network was calculated using the Euclidean distance tool in ArcMAP software. The geographic location of meteorological synoptic stations was obtained from the Meteorological Organization, and its information was used as meteorological input data. In the ArcMap environment, a map of average annual precipitation and maximum temperature was prepared from synoptic stations through interpolation for the period from 2007 to 2021. Based on this modeling method, experts' opinions were used for the relative importance and priority of criteria and sub-criteria in the risk of forest fire in the study area to obtain the fuzzy weight of criteria and sub-criteria. Based on the weighting coefficients applied in the present plan, the final weights of the criteria and sub-criteria affecting forest fire from the highest to the lowest weights belong to the topographical, biological, climatic, and human criteria. Among the sub-criteria, the highest and lowest weights belong to vegetation and slope, respectively. The consistency rate (CR) for the matrices of the affecting factors is equal to 6.25%, which is less than 10%, actually indicating that the weight of the criteria is proportionate and reliable. The highest weights were obtained for the vegetation cover and the slope direction, and the lowest weights belonged to the distance from the river and the slope. Finally, the fire risk assessment map was prepared by combining the fuzzy maps of the sub-criteria in GIS. Results: Overall, medium to very high fire risk potential was found in 72% of the studied area. From a total area of about 2373189 hectares, very low (8.4%), low (18.3%), medium (23.66%), high (25.62), and very high (24%) vulnerability rates were identified in Mazandaran province. Higher fire potential was detected in the East and Southeast parts than in other parts of the study area. The aforementioned fuzzy layers clearly show that the height, slope, and amount of precipitation are low and the density of residential areas and the network of communication roads are high in these parts, with high temperatures. In fact, these factors have increased the risk of fire in these areas. In the present study, the highest fire potential was observed at low altitudes, which could have resulted from the concentration of human activities at low altitudes. Moreover, most fires occurred on low slopes in the studied area. The distance layer from waterways also plays a dual role in the occurrence of fire. The results of the model show an inverse correlation between the distance from roads and fire potential. Based on the results of the fuzzy AHP model, the probability of fire increased with the decrease in precipitation and the increase in annual temperature. A decrease in the amount of precipitation causes a decrease in soil moisture and vegetation, elevating the possibility of fire. On the other hand, the increase in temperature causes the drying of vegetation and reduces humidity, thereby increasing the possibility of fire. Conclusion: It can be concluded that preparing a fire risk assessment map can help managers and planners in identifying areas with high potential and in crisis management in vulnerable areas. The obtained fire risk assessment map can be used as a decision-making support system to predict future fires in the study area.

Migration of intrauterine devices (IUD) is uncommon; however, it can lead to increasingly severe complications, ranging from simple pelvic pain to potentially lethal septic shock. Therefore, diagnostic and therapeutic approaches combining endoscopic paraclinical examinations with surgical intervention need further investigation by clinicians to ensure better management. This study presents 2 rare cases where a migrating intrauterine device at the rectal level was removed via hysteroscopic approach and a second one was removed via laparoscopy.

Cedrus atlantica, a species of coniferous tree in the Pinaceae family, is endemic to Algeria and Morocco. It originated in the Atlas Mountains of North Africa, hence the name Atlas Cedar. Cedar forests are home to a diverse range of flora and fauna. Yet despite their importance as a rich reservoir of fauna, very few faunistic studies have been carried out in Algeria in this type of ecosystem, particularly for the Araneae. Théniet El Had National Park (34.25 km2) is a unique national park in the western part of Algeria with centuries-old cedars. The present work is the first of its kind, consisting of establishing a preliminary list of Lyniphiidae in this typical ecosystem.A preliminary checklist of Linyphiidae of the National Park is provided, following a sampling period between January and December 2019. A total of 24 species were collected belonging to 15 genera of which Pelecopsis is the richest genus with six species. Pelecopsis oujda Bosmans & Abrous, 1992 and Walckenaeria heimbergi Bosmans, 2007, only known from Morocco so far, were collected for the first time in Algeria. Their distribution area is, thus, more extensive than present.

This research aimed to explore multifaceted academic practices for gifted students. A descriptive methodology was employed, utilizing a questionnaire for data collection, administered to a random sample of 300 male and female students. The sample was stratified based on educational level (middle school/secondary school), gender (male/female), type of education (public/private), and caregiver status (both parents / fathers only / mother only). The results indicated that the sample respondents' overall response to the questionnaire items was moderate. Furthermore, statistically significant differences were found at the 0.05 level between the means of the two research groups according to the educational level (middle vs. secondary) in response to the four questionnaire items collectively, with higher means favoring the middle school group. The study also confirmed the absence of statistically significant differences at 0.05 level between the means of the two research groups (males and females) in response to the four questionnaire items collectively. However, statistically significant differences were found at 0.05 level between the means of the two research groups according to the type of education (public vs. private) in response to the four questionnaire items collectively, with higher means favoring public education. Finally, the results indicated statistically significant differences in the sample respondents' responses based on caregiver status (both parents / fathers only / mother only) for the total questionnaire score, with higher means favoring those cared for by both parents compared to those cared for by either the father or the mother alone.

The escalating integration of artificial intelligence (AI) technologies, particularly the widespread use of ChatGPT in higher education, necessitates a profound exploration of effective communication strategies. This paper addresses the critical role of prompt development as a skill essential for university instructors engaging with ChatGPT. While emphasizing the practical implications for higher education, the study introduces a novel two-layered AI prompt formula, considering both components and elements. In methodology, the research synthesizes insights from existing models and proposes a tailored approach for ChatGPT, addressing its unique characteristics and the contextual elements within higher education. The results highlight the formula's flexibility and potential applications in diverse fields, from syllabus planning to assessment. Moreover, the study identifies limitations inherent in ChatGPT, emphasizing the need for instructors to exercise caution in its usage. In conclusion, the paper underscores the evolving landscape of AI in education, envisaging specialized versions of ChatGPT for academic settings and advocating for the proactive adoption of ethical frameworks in the use of AI in higher education. This study serves as a foundational contribution to the discourse on effective AI communication in educational settings.

A cataract is an eye disease that causes visual distortion and the late stage of this disease can lead to blindness. It is considered a silent disease that can occur without the appearance of symptoms. Therefore, the most effective way to detect cataracts is through accurate and timely detection to prevent hurting, expensive operations, and to stop blindness. The purpose of this paper is to propose an automated system based on the wavelet scattering network which categorizes the patients into four classes: early cataract, intermediate cataract, late cataract, and non-cataract conditions using 512 images of the ODIR dataset (212 Cataract and 300 of Normal). The first step in this technique is the preprocessing step for the retinal image was a mean filter, which was utilized to reduce the image's noise. The limited contrast adaptive histogram equalization (CLAHE) method was then employed to improve the image's contract level. Then, Low-variance characteristics can be extracted from image data using a wavelet scattering network for use in deep learning applications. In this network, lowpass scaling filters and predefined wavelets are employed. The average accuracy of the suggested method was 100% for four classes (Normal, Early, Moderate, and Severe). The results are promising compared with other similar works.

The US is the world leader in imprisoning immigrants. Its mass immigration detention system emerged as an extension of mass incarceration, rooted in a legacy of racist US immigration and criminal laws. Immigration policy is a structural determinant of health that negatively affects the health of imprisoned immigrants, their families, and their communities. The systemic harms of detention facilities, which we refer to as immigration prisons, have been extensively documented, yet incrementalist reforms have failed to result in improved outcomes for immigrants. We argue that ending the practice of immigrant imprisonment is the most effective solution to mitigating its harms. Community-based programs are safer and less expensive than imprisonment, while also being effective at ensuring compliance with government requirements. We identify several priorities for researchers and policy makers to tackle the health inequities resulting from this structurally racist system. These include applying a critical, intersectional lens to studying the policies and practices that drive imprisonment, engaging affected communities in research and policy development, and creating an accountable and transparent system of data collection and release to inform health interventions. The reliance of the US on immigrant imprisonment is a policy choice with immense social and economic costs; dismantling it is critical to advancing health equity.