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Background Artificial intelligence (AI) tools are designed to create or generate content from their trained parameters using an online conversational interface. AI has opened new avenues in redefining the role boundaries of teachers and learners and has the potential to impact the teaching-learning process. Methods In this descriptive proof-of- concept cross-sectional study we have explored the application of three generative AI tools on drug treatment of hypertension theme to generate: (1) specific learning outcomes (SLOs); (2) test items (MCQs- A type and case cluster; SAQs; OSPE); (3) test standard-setting parameters for medical students. Results Analysis of AI-generated output showed profound homology but divergence in quality and responsiveness to refining search queries. The SLOs identified key domains of antihypertensive pharmacology and therapeutics relevant to stages of the medical program, stated with appropriate action verbs as per Bloom’s taxonomy. Test items often had clinical vignettes aligned with the key domain stated in search queries. Some test items related to A-type MCQs had construction defects, multiple correct answers, and dubious appropriateness to the learner’s stage. ChatGPT generated explanations for test items, this enhancing usefulness to support self-study by learners. Integrated case-cluster items had focused clinical case description vignettes, integration across disciplines, and targeted higher levels of competencies. The response of AI tools on standard-setting varied. Individual questions for each SAQ clinical scenario were mostly open-ended. The AI-generated OSPE test items were appropriate for the learner’s stage and identified relevant pharmacotherapeutic issues. The model answers supplied for both SAQs and OSPEs can aid course instructors in planning classroom lessons, identifying suitable instructional methods, establishing rubrics for grading, and for learners as a study guide. Key lessons learnt for improving AI-generated test item quality are outlined. Conclusions AI tools are useful adjuncts to plan instructional methods, identify themes for test blueprinting, generate test items, and guide test standard-setting appropriate to learners’ stage in the medical program. However, experts need to review the content validity of AI-generated output. We expect AIs to influence the medical education landscape to empower learners, and to align competencies with curriculum implementation. AI literacy is an essential competency for health professionals.

Background Smith-Lemli-Opitz syndrome (SLOS) is a rare autosomal recessive disorder of cholesterol biosynthesis. SLOS leads to increased levels of 7-dehydrocholesterol (7-DHC) and decreased levels of total cholesterol (TC). Dietary therapy usually involves supplementation with cholesterol in an oil-based or, less commonly, an aqueous cholesterol suspension. The limited solubility of cholesterol can result in uneven distribution, sedimentation and clumping. Methods In seven patients (6 m, 1 f, 1–12 years) the previously administered dose of cholesterol was replaced by a newly developed emulsion and primary parameters TC, 7-DHC, HDL, LDL, vitamin D (25; 1–25), height and weight were determined. In addition, a personal structured interview was conducted with the caregivers of five participants to determine their satisfaction with the product, the care, and the effects on behaviour and health. Results One patient was excluded due to non-compliance ( N  = 6). Before the intervention, the mean TC level was 42 ± 9 mg/dl (min = 29, max = 52; n  = 5) and increased by at least 95% and at most 299% (163 ± 93%). 7-DHC levels showed a decrease of -28% to -96% (-63 ± 29%). No effect on anthropometric parameters was observed. Overall, the families were satisfied with the care and the effect of the emulsion was predominantly described as successful. The emulsion and its application were well tolerated with few side effects. Conclusions Overall, there was an improved effect on TC and 7-DHC levels compared to standard therapy, with high patient satisfaction and low side effects.

Technology enhancement learning is a process that leads to deep point of learning and adds knowledge of technologies. Various studies shed light on technology development and its effect in educational sector. The aim of this integrative review is to examine the current evidence of the impact of technology learning on student learning and academic performance in courses requiring collaborative or activities. The authors searched electronic databases for relevant articles, with different learning techniques. 24 articles met the requirement of paper, it’s collected from (2011-2017). Three themes of techniques used for student learning outcomes, includes technology enhanced learning, assessment method and faculty experience on academic performance in universities with technology use. The final results of this paper show the relationship between what has been done and the factors used by the authors. Also the future work needs more use of technologies in different phases of learning process.

Cellular lipids are speculated to act as key intermediates in Hedgehog signal transduction, but their precise identity and function remain enigmatic. In an effort to identify such lipids, we pursued a Hedgehog pathway inhibitory activity that is particularly abundant in flagellar lipids of Chlamydomonas reinhardtii, resulting in the purification and identification of ergosterol endoperoxide, a B-ring oxysterol. A mammalian analog of ergosterol, 7-dehydrocholesterol (7-DHC), accumulates in Smith–Lemli–Opitz syndrome, a human genetic disease that phenocopies deficient Hedgehog signaling and is caused by genetic loss of 7-DHC reductase. We found that depleting endogenous 7-DHC with methyl-β-cyclodextrin treatment enhances Hedgehog activation by a pathway agonist. Conversely, exogenous addition of 3β,5α-dihydroxycholest-7-en-6-one, a naturally occurring B-ring oxysterol derived from 7-DHC that also accumulates in Smith–Lemli–Opitz syndrome, blocked Hedgehog signaling by inhibiting activation of the essential transduction component Smoothened, through a mechanism distinct from Smoothened modulation by other lipids.

The mouse optic nerve crush (ONC) model has been widely used to study optic neuropathies and central nervous system (CNS) axon injury and repair. Previous histological studies of retinal ganglion cell (RGC) somata in retina and axons in ON demonstrate significant neurodegeneration after ONC, but longitudinal morphological and functional assessment of RGCs in living animals is lacking. It is essential to establish these assays to provide more clinically relevant information for early detection and monitoring the progression of CNS neurodegeneration. Here, we present data gathered by scanning laser ophthalmoscopy (SLO), optical coherence tomography (OCT), and pattern electroretinogram (PERG) at different time points after ONC in mouse eyes and corresponding histological quantification of the RGC somata and axons. Not surprisingly, direct visualization of RGCs by SLO fundus imaging correlated best with histological quantification of RGC somata and axons. Unexpectedly, OCT did not detect obvious retinal thinning until late time points (14 and 28-days post ONC) and instead detected significant retinal swelling at early time points (1-5 days post-ONC), indicating a characteristic initial retinal response to ON injury. PERG also demonstrated an early RGC functional deficit in response to ONC, before significant RGC death, suggesting that it is highly sensitive to ONC. However, the limited progression of PERG deficits diminished its usefulness as a reliable indicator of RGC degeneration.

Outcome-Based Education (OBE) is increasingly recognized as a powerful tool for aligning teaching with intended learning outcomes. In this work, a web-based platform is presented that integrates OBE principles with Q-Learning model which is a reinforcement learning technique to verify and enhance student learning. The motivation for this system came from observing the challenge that educators face in adapting instruction to meet diverse learner needs in real-time. This platform enables educators to define the learning objectives and monitor the student performance against them. What makes this approach distinct is the adaptive learning engine powered by Q-Learning model. This analyses student data and dynamically adjusts the content delivery for personalized learning. The proposed work translates the OBE principles into a web-based platform which not only assesses the learning outcomes but also actively contributes to improving them. Early experiments conducted in controlled classroom settings showed measurable gains in student comprehension and retention. The accessibility and adaptability of the proposed web-based platform make it a promising tool for institutions aiming to scale personalized learning without compromising on quality. The web-based OBE platform offers a hands-on-approach to implement OBE digitally, which gives educators a real time insight and ability with adjusting their teaching methodology with respect to the student needs.

Background Smith-Lemli-Opitz Syndrome (SLOS) is an autosomal recessive disorder of cholesterol biosynthesis caused by biallelic pathogenic variants in DHCR7 , which encodes the enzyme 7-dehydrocholesterol reductase (DHCR7). SLOS is a multisystemic disorder affecting various aspects of health, including growth, development, behavior, and quality of life, underscoring the need for safe, efficacious interventions that limit disease burden. DHCR7 enzyme deficiency leads to a “metabolic block” resulting in decreased cholesterol production and accumulation of its precursor 7-dehydrocholesterol and the secondary isomer 8-dehydrocholesterol. Reduced cholesterol synthesis, in turn, leads to decreased levels of cholic acid (CA), an endogenous bile acid synthesized from cholesterol and essential for cholesterol absorption. Dietary cholesterol supplementation is standard therapy. Bile acid supplementation with CA has been shown to improve dietary cholesterol absorption and raise plasma cholesterol levels. However, there is a paucity of patient-level data regarding the utility of CA as a treatment for SLOS. The purpose of this case series is to address the lack of comprehensive patient data through documentation of the outcomes of a company-sponsored CA patient experience program. A retrospective chart review was conducted for these individuals while on CA plus cholesterol supplementation. Data for demographics and key clinical/laboratory parameters were captured with a standardized data collection tool. Results Eight genetically confirmed individuals with SLOS (age range 1 to 20 years) with median plasma cholesterol levels at baseline ≤ 125 mg/dL were treated with CA at 10–15 mg/kg/d for 30 to 450 days. Exogenous CA administration improved cholesterol levels in the majority of patients. Growth improved after CA initiation and trended toward age-appropriate growth percentiles. Reports from the patient, parent/caregiver, and/or healthcare professional noted positive behavioral changes leading to increased social interaction, cognitive engagement, and improved communication skills. Improvements in biochemical parameters and quality of life were also observed in several patients after CA treatment. CA supplementation was well tolerated with minimal adverse events. Conclusions The cumulative experiences of eight patients provide a compelling narrative supporting the potential utility of CA treatment in SLOS while underscoring the safety of CA in this patient population. Larger longitudinal studies of CA in patients with SLOS are warranted.

Internet of Things (IoT) agents that trigger network enforcement actions must be both well-calibrated (for safe triage) and tail-latency predictable (for service level objectives, SLOs). We present Confidence-Calibrated HP-FedGAT-Trust-IBN, a federated, graph-attention architecture that closes the loop from IoMT sensing to SDN enforcement via parameter-efficient (LoRA/PEFT) updates ( MB/round), trust-weighted secure aggregation, and intent verification (IBN) triage. Evaluation follows a two-plane protocol: a learning plane with simulated clients under a matched comparator harness (Graph-FL and uncertainty-aware FL baselines), and a serving plane that replays exported checkpoints on real edge devices (Raspberry Pi 5, Jetson Orin Nano, Intel NUC 11) and validates SLOs using hardware ECDFs and empirical . The model achieves high discrimination (ROC-AUC/PR-AUC – ) with improved calibration (low ECE) under the matched harness, while the serving loop satisfies the ms requirement by device-measured (e.g., enforcement ms, vs.  ms for an efficient-UQ baseline) and explicit compliance . The latency decomposition includes all calibration costs and Monte-Carlo expectations ( , with measured MC share reported), and security modes are quantified end-to-end: CKKS + SMPC adds device-measured and crypto-attributable Joules (e.g., ms and J/round on Raspberry Pi 5). Energy/round is measured on identical hardware and mapped to CO 2 e for carbon-aware selection of operating points.

Currently, linguistics (LING) courses are underrepresented in general education at most US universities. As general education requirements undergo reform in higher education, the field of linguistics has an opportunity to assume a more central role. We argue that linguistics courses aligned with the key competencies of critical thinking, information literacy, and inquiry and analysis are well positioned to augment general education curricula, particularly at institutions that utilize common student learning objectives. An innovative The Language of Now core course and its signature assignment, a learner-centered research project on the use of the text-messaging discourse marker lol , illustrate how the methods used in linguistic inquiry are amenable to a range of standards that support general education goals.

The emerging serverless computing has become a captivating paradigm for deploying cloud applications, alleviating developers’ concerns about infrastructure resource management by configuring necessary parameters such as latency and memory constraints. Existing resource configuration solutions for cloud-based serverless applications can be broadly classified into modeling based on historical data or a combination of sparse measurements and interpolation/modeling. In pursuit of service response and conserving network bandwidth, platforms have progressively expanded from the traditional cloud to the edge. Compared to cloud platforms, serverless edge platforms often lead to more running overhead due to their limited resources, resulting in undesirable financial costs for developers when using the existing solutions. Meanwhile, it is extremely challenging to handle the heterogeneity of edge platforms, characterized by distinct pricing owing to their varying resource preferences. To tackle these challenges, we propose an adaptive and efficient approach called FireFace, consisting of prediction and decision modules. The prediction module extracts the internal features of all functions within the serverless application and uses this information to predict the execution time of the functions under specific configuration schemes. Based on the prediction module, the decision module analyzes the environment information and uses the Adaptive Particle Swarm Optimization algorithm and Genetic Algorithm Operator (APSO-GA) algorithm to select the most suitable configuration plan for each function, including CPU, memory, and edge platforms. In this way, it is possible to effectively minimize the financial overhead while fulfilling the Service Level Objectives (SLOs). Extensive experimental results show that our prediction model obtains optimal results under all three metrics, and the prediction error rate for real-world serverless applications is in the range of 4.25∼9.51%. Our approach can find the optimal resource configuration scheme for each application, which saves 7.2∼44.8% on average compared to other classic algorithms. Moreover, FireFace exhibits rapid adaptability, efficiently adjusting resource allocation schemes in response to dynamic environments.