Cinnamic acid (CA) has many beneficial effects on human health. However, its poor water solubility (0.23 g/L, at 25 °C) is responsible for its poor bioavailability. This drawback prevents its clinical use. To overcome the solubility limits of this extraordinary natural compound, in this study, we developed a highly water-soluble inclusion complex of CA with randomly methylated-β-cyclodextrin (RAMEB). The host-guest interaction was explored in liquid and solid states by UV-Vis titration, phase solubility analysis, FT-IR spectroscopy, and ¹H-NMR. Additionally, molecular modeling studies were carried out. Both experimental and theoretical studies revealed a 1:1 CA/RAMEB inclusion complex, with a high apparent stability constant equal to 15,169.53 M⁻¹. The inclusion complex increases the water solubility of CA by about 250-fold and dissolves within 5 min. Molecular modeling demonstrated that CA inserts its phenyl ring into the RAMEB cavity with its propyl-2-enoic acid tail leaning from the wide rim. Finally, a biological in vitro study of the inclusion complex, compared to the free components, was performed on the neuroblastoma SH-SY5Y cell line. None of them showed cytotoxic effects at the assayed concentrations. Of note, the pretreatment of SH-SY5Y cells with CA/RAMEB at 10, 30, and 125 µM doses significantly counteracted the effect of the neurotoxin MPP⁺, whilst CA and RAMEB alone did not show any neuroprotection. Overall, our data demonstrated that inclusion complexes overcome CA solubility problems, supporting their use for clinical applications.

Background: Non‐obstructive azoospermia (NOA) affects approximately 10% of infertile men and represents a major challenge in assisted reproductive technology (ART). A model that includes histological variants could be helpful in predicting sperm retrieval rate (SRR) after microdissection testicular sperm extraction (mTESE) in patients affected by NOA and without genetic abnormalities Objectives: To develop and validate a predictive nomogram integrating clinical and histopathological variables to estimate SRR in NOA patients undergoing microTESE. Material and Methods: A multi‐center retrospective/prospective cohort study was conducted between 2022 and 2024, enrolling 333 men diagnosed with NOA across six academic centers. Preoperative data, including age, body mass index (BMI), hormonal profile (follicle‐stimulating hormone [FSH], luteinizing hormone [LH], testosterone), genetic analysis, and ultrasound‐measured testicular volume, were collected. MicroTESE was performed under microscopic guidance with histopathological evaluation. Predictive factors for successful sperm retrieval were analyzed through logistic regression, and a predictive nomogram was constructed and internally validated using bootstrapping techniques. Multi‐variate logistic regression was performed to identify independent variables associated with SRR in patients with available final pathology. Therefore, a predictive nomogram was developed and validated using 1000 bootstrap samples. Results: The overall SRR was 52.55%. Multi‐variate analysis identified FSH levels (odds ratio [OR]: 0.97; p = 0.049), maturation arrest (OR: 0.04; p < 0.01), and Sertoli cell‐only syndrome (SCOS; OR: 0.03; p < 0.01) as independent predictors of SRR. The predictive nomogram demonstrated good accuracy, with a C ‐index of 0.75, sensitivity of 73%, specificity of 82%, and overall accuracy of 77% at a cutoff of 0.33. Discussion: Using preoperative and histology data, we developed a nomogram to predict SR outcomes in patients with NOA undergoing mTESE. Our findings demonstrate that integrating hormonal and histopathological data enhances predictive accuracy of the model, thus providing a valuable tool for preoperative counseling and clinical decision‐making in couples presenting with infertility. Conclusion: This validated nomogram effectively predicts sperm retrieval outcomes in NOA patients undergoing microTESE, facilitating improved patient counseling, informed clinical decisions, and optimized patient selection. Prospective external validation and further model refinement are recommended to enhance generalizability and clinical applicability.