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Data curation has significant research implications irrespective of application areas. As most curated studies rely on databases for data extraction, the availability of data resources is extremely important. Taking a perspective from pharmacology, extracted data contribute to improved drug treatment outcomes and well-being but with some challenges. Considering available pharmacology literature, it is necessary to review articles and other scientific documents carefully. A typical method of accessing articles on journal websites is through long-established searches. In addition to being labor-intensive, this conventional approach often leads to incomplete-content downloads. This paper presents a new methodology with user-friendly models to accept search keywords according to the investigators' research fields for metadata and full-text articles. To accomplish this, scientifically published records on the pharmacokinetics of drugs were extracted from several sources using our navigating tool called the Web Crawler for Pharmacokinetics (WCPK). The results of metadata extraction provided 74,867 publications for four drug classes. Full-text extractions performed with WCPK revealed that the system is highly competent, extracting over 97% of records. This model helps establish keyword-based article repositories, contributing to comprehensive databases for article curation projects. This paper also explains the procedures adopted to build the proposed customizable-live WCPK, from system design and development to deployment phases.

AIM: To characterize the distribution of meibomian gland (MG) area loss (MGL) and its relationship with demographic characteristics, mites, and symptoms. METHODS: This retrospective observational study included patients who visited the Dry Eye Clinic of Shenzhen Eye Hospital between June 2020 and August 2021. General patient characteristics, ocular symptoms, Demodex test results of the eyelid edges, and the results of a comprehensive ocular surface analysis were collected. MGL was analyzed using Image J software. RESULTS: This study enrolled 1204 outpatients aged 20-80 (40.70±13.44)y, including 357 males (29.65%) and 847 females (70.35%). The patients were classified into mild (n=155; 12.87%), moderate (n=795; 66.03%), severe (n=206; 17.11%), and extremely severe (n=48; 3.99%) MGL groups. MGL was significantly larger in female than in male (P=0.006). The degree of MGL also significantly differed in age (P

Abstract In the current study, we utilized molecular modeling and simulation approaches to define putative potential molecular targets for Burdock Inulin, including inflammatory proteins such as iNOS, COX-2, TNF-alpha, IL-6, and IL-1β. Molecular docking results revealed potential interactions and good binding affinity for these targets; however, IL-1β, COX-2, and iNOS were identified as the best targets for Inulin. Molecular simulation-based stability assessment demonstrated that inulin could primarily target iNOS and may also supplementarily target COX-2 and IL-1β during DSS-induced colitis to reduce the role of these inflammatory mechanisms. Furthermore, residual flexibility, hydrogen bonding, and structural packing were reported with uniform trajectories, showing no significant perturbation throughout the simulation. The protein motions within the simulation trajectories were clustered using principal component analysis (PCA). The IL-1β–Inulin complex, approximately 70% of the total motion was attributed to the first three eigenvectors, while the remaining motion was contributed by the remaining eigenvectors. In contrast, for the COX2–Inulin complex, 75% of the total motion was attributed to the eigenvectors. Furthermore, in the iNOS–Inulin complex, the first three eigenvectors contributed to 60% of the total motion. Furthermore, the iNOS–Inulin complex contributed 60% to the total motion through the first three eigenvectors. To explore thermodynamically favorable changes upon mutation, motion mode analysis was carried out. The Free Energy Landscape (FEL) results demonstrated that the IL-1β–Inulin achieved a single conformation with the lowest energy, while COX2–Inulin and iNOS–Inulin exhibited two lowest-energy conformations each. IL-1β–Inulin and COX2–Inulin displayed total binding free energies of − 27.76 kcal/mol and − 37.78 kcal/mol, respectively, while iNOS–Inulin demonstrated the best binding free energy results at − 45.89 kcal/mol. This indicates a stronger pharmacological potential of iNOS than the other two complexes. Thus, further experiments are needed to use inulin to target iNOS and reduce DSS-induced colitis and other autoimmune diseases.

Background Type 2 diabetes mellitus (T2DM) has become a public health burden facing the whole world, with the rapid development of Internet and digital technology, intelligent service model plays an important role in the management of patients with T2DM. Objective To explore the effectiveness of wearable devices combined with health coaching in patients with T2DM. Methods A total of 315 patients with T2DM who were hospitalized in the Second Affiliated Hospital of Nanchang University from June 2020 to June 2021 were selected as the study subjects, and divided into the control group, the intervention A group and the intervention B group based on the method of randomized numerical table, the control group implemented the routine management measures based on capillary blood glucose monitoring, the intervention A group adopted the wearable devices for routine management, the intervention B group was managed by wearable devices combined with health coaching. The differences in glycemic and lipid control status and self-management behaviors among the three groups before and 3, 6, 9 and 12 months after intervention were compared. Results A total of 15 lost visits were made during the implementation of the study, and 300 study subjects were finally included in the analysis, with 100 in each of the three groups. There was an interaction between intervention method and time in the three groups on 2-hour postprandial blood glucose (2 hPG) , glycated hemoglobin (HbA1c) , number of hypoglycemic episodes, low-density lipoprotein cholesterol (LDL-C) , self-management behaviors of diabetes mellitus (2-DSCS) , and self-management behaviors of diabetic patients (SDSCA) scores (P