Theoretical investigation of selective inhibitory activity of chromone-based compounds against monoamine oxidase (MAO)-A and -B

Monoamine oxidase (MAO) is crucial for the breakdown of monoamine neurotransmitters, making it a promising target for treating neurodegenerative disorders, such as depression, Alzheimer’s disease, and Parkinson’s disease. In this study, we investigated the selective inhibitory activity of chromone-based compounds against MAO-A and MAO-B for neurodegenerative disease treatment. In literary sources, thirty chromone derivatives have been identified as potential ligands for MAO-A and MAO-B inhibitors. We utilized molecular docking to evaluate how the most active compound interacted with the targeted MAO-A and MAO-B. Compound 2 g , the most active for MAO-A, demonstrated a lower CDOCKER energy compared to the co-crystallized ligand. Meanwhile, compound 2f , the most active for MAO-B, showed a CDOCKER energy similar to the co-crystallized ligand and exhibited similar binding patterns. Furthermore, we constructed a quantitative structure-activity relationship (QSAR) model to predict the properties and estimate IC 50 values for 30 chromone derivatives functioning as MAO-A and MAO-B inhibitors. The model predictions were validated against experimental measurements. Our 2D QSAR model demonstrated robustness, with a statistically significant non-cross-validated coefficient (r 2 < 0.9), cross-validated correlation coefficient (q 2 < 0.6), and predictive squared correlation coefficient (r 2 pred < 0.8). Additionally, MD simulations confirmed the stable binding of compounds 2 g and 2f with MAO-A and MAO-B, respectively, displaying substantial binding energy. The most effective pharmacophore model identified key features, such as hydrogen bond acceptors and hydrophobic interactions, that contribute significantly to inhibitory potency. This study offers valuable insight into the selection of compounds with improved selectivity for MAO inhibition.

Identification of potential 3CLpro inhibitors-modulators for human norovirus infections through an advanced virtual screening approach

The present study aimed to screen small molecular compounds such as human noroviruses (HuNoV) inhibitors/modulators that could potentially be responsible for exhibiting some magnitude of inhibitory/modulatory activity against HuNoV 3CLPro. The structural similarity-based screening against the ChEMBL database is performed against known chemical entities that are presently under pre-clinical trial. After the similarity search, remaining molecules were considered for molecular docking using SCORCH and PLANTS. On detailed analyses and comparisons with the control molecule, three hits (CHEMBL393820, CHEMBL2028556, and CHEMBL3747799) were found to have the potential for HuNoV 3CLpro inhibition/modulation. The binding interaction analysis revealed several critical amino acids responsible to hold the molecules tightly at the close proximity site of the catalytic residues of HuNoV 3CLpro. Further, MD simulation study was performed in triplicate to understand the binding stability and potentiality of the proposed molecule toward HuNov 3CLpro. The binding free energy based on MM-GBSA has revealed their strong interaction affinity with 3CLpro.

Investigation of serum orexin-A levels in women with polycystic ovary syndrome

Orexin-A (OXA) is a hypothalamic neuropeptide implicated in glucose regulation and insulin sensitivity. Given the central role of insulin resistance in polycystic ovary syndrome (PCOS), this study evaluated serum OXA levels in women with PCOS. This cross-sectional case-control study included 88 women with PCOS and 88 age- and body mass index-matched controls. Serum OXA levels were quantified using a commercial ELISA kit. Serum OXA levels were significantly lower in women with PCOS than in controls (321.53 ng/L vs. 385.06 ng/L, p  = 0.012). A significant negative correlation was observed between the modified Ferriman-Gallwey score and serum OXA level. In the ROC analysis, a serum OXA level of 369.55 ng/L yielded a sensitivity of 55.7% and a specificity of 55.7% (AUC: 0.610) for detecting PCOS. Serum OXA concentrations were reduced in women with PCOS. Whether this reduction contributes to PCOS pathogenesis or reflects a secondary alteration remains uncertain.

Early treatment responses in childhood Graves’ disease: factors affecting the normalization period of thyroid hormones

Herein, we aimed to reveal the factors affecting the early treatment responses and the normalization process of thyroid hormone levels in childhood Graves’ disease.. This retrospective study included 46 pediatric patients who were diagnosed with Graves’ disease and started treatment during a period of 10 years, and whose at least one of their free T3 (fT3) or free T4 (fT4) levels decreased to the reference range during follow-up. The normalization time of fT3 and fT4 were 5 (3–8) and 3.5 (2–7) weeks after the treatment initiation, respectively. Gender, age, pubertal status, presence of goiter, ophthalmopathy or tachycardia, basal thyroid hormone levels, initial dose of methimazole, and addition of beta-blockers did not show an effect on the normalization time of either fT3 or fT4 ( p  > 0.05). In those under the age of 12 ( n  = 15) with a high TRAb titer (>10 IU/L), the time for normalization of fT3 was longer ( p  = 0.024), and in those with ophthalmopathy, this process was longer for fT4 ( p  = 0.017). By these results, we showed that early treatment response and the normalization period of thyroid hormones were not related to the initial treatment dose.

Assessing changes in academic motivation across medical training stages: a longitudinal study in Malaysia

Sustained academic motivation is essential for the success and well-being of medical students. As students progress from pre-clinical to clinical training, the transition in learning environments is theorized to facilitate motivation internalisation, consistent with Self-Determination Theory (SDT). Unlike cross-sectional studies, a longitudinal approach allows tracking of individual changes over time, offering deeper insights into developmental trends. This study examined changes in academic motivation at the start of medical school, after pre-clinical training, and following two years of clinical training. The Academic Motivation Scale (AMS) was administered to 292 students from a five-year undergraduate medical programme in Malaysia across three stages: entry, post pre-clinical, and after two years of clinical training. Three cohorts (2016–2018) were followed longitudinally over seven years (2016–2022). Analysis involved confirmatory factor analysis (CFA) to validate AMS and assess reliability using composite reliability (CR), followed by repeated measures ANOVA to examine motivational changes. CFA confirmed the AMS as valid and reliable. At entry, students showed moderate to high extrinsic and intrinsic motivation with low amotivation. Amotivation rose from Year 1 ( M  = 6.64) to Year 3 ( M  = 8.51) and Year 5 ( M  = 10.27). Identified regulation remained high in Year 1 ( M  = 23.37) and Year 3 ( M  = 23.57) before declining in Year 5 ( M  = 22.47). External and introjected regulation peaked in Year 3 ( M  = 18.92, 19.42) then dropped or stabilized in Year 5. Intrinsic motivation declined steadily across all domains from Year 1 to Year 5 (all p < .05). The decline in intrinsic motivation and rise in amotivation highlight challenges in sustaining motivation through medical training. These trends may impact academic performance, mental health, and professional growth, underscoring the need for curriculum adaptations, mentorship, and stress-reduction initiatives to better support students.

Letter regarding ‘early intervention with rescue ERCP and pancreatic stenting for unanticipated post-ERCP pancreatitis: a comparative study’

Evaluating the effectiveness of chatbots and traditional resources in patient education on dry eye disease

Artificial intelligence chatbots demonstrate potential as valuable educational resources for patients with dry eye disease, offering complementary information to established medical platforms. The increasing prevalence of dry eye disease necessitates reliable and comprehensible patient information resources. This study evaluates and compares the quality of information provided by contemporary AI chatbots with established ophthalmological sources. Three leading AI chatbots (ChatGPT-3.5, Gemini, and Llama) and the American Academy of Ophthalmology (AAO) website were systematically evaluated using 20 common patient questions about dry eye disease. Responses were assessed for accuracy using the Structure of Observed Learning Outcome (SOLO) taxonomy, understandability and actionability using the Patient Education Materials Assessment Tool (PEMAT), and linguistic accessibility using Flesch-Kincaid readability metrics. Gemini demonstrated superior understandability with a mean PEMAT-U score of 73.4 ± 11.4, significantly higher than ChatGPT (65.4 ± 10.6), Llama (63.4 ± 10.3), and AAO (52.5 ± 19.3) ( p  < 0.001). No significant differences were observed in actionability scores ( p  = 0.120). The AAO website exhibited the highest reading ease score (50.4 ± 17.9, p  = 0.015). For accuracy assessment, ChatGPT achieved the highest mean SOLO score (3.4 ± 0.7), followed closely by Gemini (3.3 ± 0.8), with no significant performance differences detected among chatbots ( p  = 0.574). No instances of incorrect or potentially harmful information were identified across any evaluated source. While AI chatbots demonstrate promising capabilities for patient education in dry eye disease, particularly in providing comprehensive and understandable information, their higher linguistic complexity presents a potential accessibility barrier. Future development should focus on enhancing readability while maintaining comprehensive content, positioning chatbots as valuable complements to – rather than replacements for – professional medical consultation.

Sepsis Circulating Extracellular Vesicles: Associated Cytokine Profile, Cellular Proliferation, and Cytoprotective Activity Against Lipopolysaccharide-Induced Cytotoxicity

Extracellular vesicles (EVs) have been linked with cytokine transportation and shown promising therapeutic and diagnostic potential in sepsis. Therefore, this study investigates the levels of cytokines associated with sepsis EVs and explores their therapeutic and diagnostic values. Twenty sepsis patients and ten healthy controls were included in this study. Plasma samples were used to isolate EVs using the Invitrogen Total Exosome Isolation Kit. EVs were fully characterized, and the cytoprotective activity and cytokine profile were determined using MTT and multiplex assays. Sepsis significantly increased the release, size, poly dispersion index, cellular proliferative and cytoprotective effects of isolated EVs, as well as altering the contents of their cytokines/chemokines and growth factors. Seven proteins were detected in EVs isolated from sepsis, but not healthy control patients. Two factors, IL-10 and basic FGF, were detected at significantly different levels in EV samples from sepsis vs. healthy control group. These findings indicate that the enrichment of IL-15, IL-10 and G-CSF in sepsis circulating EVs represents a biologically active protein signature with potential to both modulate the immune response and serve as a novel diagnostic biomarker.

Teprotumumab Use in Thyroid Eye Disease: Clinical Outcomes in the United Arab Emirates– a First Regional Case Series

Thyroid eye disease (TED), an autoimmune disorder frequently associated with Graves’ disease, manifests as orbital inflammation, proptosis, diplopia, and vision impairment, significantly diminishing quality of life. Traditional treatments, such as corticosteroids and rituximab, exhibit variable efficacy, while targeted therapies like teprotumumab, an insulin-like growth factor-1 receptor (IGF-1 R) inhibitor, have shown promise, particularly in the United States. However, data from the Middle East, including the United Arab Emirates (UAE), remain scarce, highlighting the need for regional studies. To evaluate the clinical efficacy and safety of teprotumumab in TED patients in the UAE, presenting the first case series from the Gulf region. This retrospective case series analysed 14 patients (25 eyes) with moderate-to-severe TED who completed eight teprotumumab cycles. Outcomes—proptosis reduction (primary), Clinical Activity Score (CAS), double vision, quality of life (TED-QOL), stability, and adverse effects—were assessed at 24 weeks and 6 months post-treatment using standardized measures. Teprotumumab reduced proptosis by 2.64 mm at 24 weeks ( p  < .0001) and 2.32 mm at 6 months ( p  < .0001), with 68% of eyes achieving ≥ 2 mm reduction. CAS dropped from 5.21 to 0.35 ( p  < .0001), double vision improved in 85.7% of cases, and QoL enhanced across all domains ( p  < .001). Stability persisted at 6 months, with tolerable adverse effects. Teprotumumab is effective and safe for TED in the Gulf region, offering sustained benefits. Larger, longer-term studies are needed to confirm efficacy, recurrence, and safety.

SPINE: Segmentation-guided Processing and Integration of Multimodal Spinal MRI for Natural-Language Enhanced Report Generation

Automated radiology report generation remains a key challenge in clinical AI, requiring alignment between visual, anatomical, and linguistic representations. This study introduces SPINE – a Segmentation-guided Processing and Integration framework for spinal MRI report generation – designed to integrate multimodal (T1-, T2-weighted, and segmentation) inputs for anatomically informed narrative synthesis. Using axial and sagittal MRI datasets comprising 515 and 190 patients, respectively, SPINE was evaluated across multiple multimodal and segmentation-aware configurations. Results show that incorporating segmentation maps substantially improves report accuracy, coherence, and clinical relevance, with the T1 + T2 + Seg model achieving the most balanced performance across all metrics. Models trained on LLM-standardized reports outperformed those trained on human-written text, demonstrating the importance of linguistic consistency. Comparative analysis also revealed that GPT-4o generated more fluent and clinically detailed reports than Grok-3. These findings highlight the potential of combining multimodal imaging with language-model-driven structuring to enhance both the factual accuracy and clinical reliability of automated radiology reporting, paving the way for more interpretable and trustworthy AI-assisted diagnostics.