Abstract: In recent years, the field of nucleic acid therapeutics has been flourishing, progressively establishing itself as the third generation of drug modalities following small molecules and antibody-based drugs. Artificial intelligence technology based on machine learning is advancing rapidly, which can significantly accelerate the development process of nucleic acid therapeutics. This review provides an overview of the foundational aspects of artificial intelligence algorithms, databases, and characterizations in the field of nucleic acid drug development. It elucidates the advances in the application of artificial intelligence in nucleic acid structural prediction, small nucleic acid drug design, and other research and development phases of nucleic acid therapeutics, aiming to offer some reference for the interdisciplinary development of artificial intelligence and nucleic acid drugs.

Objective: To investigate the effects of dopamine on olfactory function and inflammatory injury of olfactory bulb in mice with allergic rhinitis (AR). Methods: AR mouse model was established by using ovalbumin (OVA), and the mice were divided into two groups: olfactory dysfunction (OD) group and without OD group through buried food pellet test (BFPT). The OD mice were randomly divided into 2 groups, and OVA combined with dopamine (3, 6, 9 and 12 days, respectively) or OVA combined with an equal amount of PBS (the same treatment time) was administered nasally. The olfactory function of mice was evaluated by BFPT. The number of eosinophils and goblet cells in the nasal mucosa were detected by HE and PAS staining. Western blotting, immunohistochemistry or immunofluorescence were used to detect the expression of olfactory marker protein (OMP) in olfactory epithelium, the important rate-limiting enzyme tyrosine hydroxylase (TH) of dopamine, and the marker proteins glial fibrillary acidic protein (GFAP) and CD11b of glial cell in the olfactory bulb. TUNEL staining was used to detect the damage of the olfactory bulb. SPSS 26.0 software was used for statistical analysis. Results: AR mice with OD had AR pathological characteristics. Compared with AR mice without OD, the expression of OMP in olfactory epithelium of AR mice with OD was reduced (F=26.09, P<0.05), the expression of GFAP and CD11b in the olfactory bulb was increased (F value was 38.95 and 71.71, respectively, both P<0.05), and the expression of TH in the olfactory bulb was decreased (F=77.00, P<0.05). Nasal administration of dopamine could shorten the time of food globule detection in mice to a certain extent, down-regulate the expression of GFAP and CD11b in the olfactory bulb (F value was 6.55 and 46.11, respectively, both P<0.05), and reduce the number of apoptotic cells in the olfactory bulb (F=25.64, P<0.05). But dopamine had no significant effect on the number of eosinophils and goblet cells in nasal mucosa (F value was 36.26 and 19.38, respectively, both P>0.05), and had no significant effect on the expression of OMP in the olfactory epithelium (F=55.27, P>0.05). Conclusion: Dopamine can improve olfactory function in mice with AR to a certain extent, possibly because of inhibiting the activation of glial cells in olfactory bulb and reducing the apoptotic injury of olfactory bulb cells.
Animals ; Disease Models, Animal ; Dopamine ; Humans ; Mice ; Mice, Inbred BALB C ; Nasal Mucosa/metabolism* ; Olfactory Bulb/pathology* ; Ovalbumin ; Rhinitis, Allergic/metabolism*

Objective: To explore sex differences in 3D T1texture features in the progression of Alzheimer's disease (AD) and to predict the diagnosis of AD patients of different sex． Methods: Seventy-seven AD patients (34 males and 42 females) ，74 amnestic mild cognitive impairment ( aMCI) patients ( 35 males and 39 females) and 75 healthy controls (HC) (35 males and 40 females) were recruited and high-resolution 3-dimensional T1 structural images were collected． Brain regions closely related to AD brain damage were selected as regions of interest ( ROIs) ，texture feature extraction and feature screening were performed．Analyses were performed by sex，and the support vector machine (SVM) was used for classification and prediction. Results : In the AD vs HC，AD vs aMCI and aMCI VS HC groups by different sex，we obtained some brain regions with relatively high recognition index in different subgroups，and found that there were significant differences between female patients and male patients with high recognition index，and the recognition index of female patients ( area under the curve，accuracy，sensitivity and specificity were generally higher than that of male． Conclusion There are significant sex differences in texture features in AD process，and the classification and prediction ability of texture features in female patients is better， suggesting the importance of sex differences in AD research．This study provides some reliable biomarkers for early sex-specific identification of AD，which may be helpful for the early diagnosis and treatment of AD in the future.