Deciphering the multi-functional role of Indian propolis for the management of Alzheimer's disease by integrating LC-MS/MS, network pharmacology, molecular docking, and in-vitro studies.

The conventional one-drug-one-disease theory has lost its sheen in multigenic diseases such as Alzheimer's disease (AD). Propolis, a honeybee-derived product has ethnopharmacological evidence of antioxidant, anti-inflammatory, antimicrobial and neuroprotective properties. However, the chemical composition is complex and highly variable geographically. So, to leverage the potential of propolis as an effective treatment modality, it is essential to understand the role of each phytochemical in the AD pathophysiology. Therefore, the present study was aimed at investigating the anti-Alzheimer effect of bioactive in Indian propolis (IP) by combining LC-MS/MS fingerprinting, with network-based analysis and experimental validation. First, phytoconstituents in IP extract were identified using an in-house LC-MS/MS method. The drug likeness and toxicity were assessed, followed by identification of AD targets. The constituent-target-gene network was then constructed along with protein-protein interactions, gene pathway, ontology, and enrichment analysis. LC-MS/MS analysis identified 16 known metabolites with druggable properties except for luteolin-5-methyl ether. The network pharmacology-based analysis revealed that the hit propolis constituents were majorly flavonoids, whereas the main AD-associated targets were MAOB, ESR1, BACE1, AChE, CDK5, GSK3ß, and PTGS2. A total of 18 gene pathways were identified to be associated, with the pathways related to AD among the topmost enriched. Molecular docking analysis against top AD targets resulted in suitable binding interactions at the active site of target proteins. Further, the protective role of IP in AD was confirmed with cell-line studies on PC-12, in situ AChE inhibition, and antioxidant assays.

Mammography screening using independent double reading with consensus: is there a potential benefit for computer-aided detection?

BACKGROUND: Double reading improves the cancer detection rate in mammography screening. Single reading with computer-aided detection (CAD) has been considered to be an alternative to double reading. Little is known about the potential benefit of CAD in breast cancer screening with double reading. PURPOSE: To compare prospective independent double reading of screen-film (SFM) and full-field digital (FFDM) mammography in population-based screening with retrospective standalone CAD performance on the baseline mammograms of the screen-detected cancers and subsequent cancers diagnosed during the follow-up period. MATERIAL AND METHODS: The study had ethics committee approval. A 5-point rating scale for probability of cancer was used for 23,923 (SFM = 16,983; FFDM = 6940) screening mammograms. Of 208 evaluable cancers, 104 were screen-detected and 104 were subsequent (44 interval and 60 next screening round) cancers. Baseline mammograms of subsequent cancers were retrospectively classified in consensus without information about cancer location, histology, or CAD prompting as normal, non-specific minimal signs, significant minimal signs, and false-negatives. The baseline mammograms of the screen-detected cancers and subsequent cancers were evaluated by CAD. Significant minimal signs and false-negatives were considered 'actionable' and potentially diagnosable if correctly prompted by CAD. RESULTS: CAD correctly marked 94% (98/104) of the baseline mammograms of the screen-detected cancers (SFM = 95% [61/64]; FFDM = 93% [37/40]), including 96% (23/24) of those with discordant interpretations. Considering only those baseline examinations of subsequent cancers prospectively interpreted as normal and retrospectively categorized as 'actionable', CAD input at baseline screening had the potential to increase the cancer detection rate from 0.43% to 0.51% (P = 0.13); and to increase cancer detection by 16% ([104 + 17]/104) and decrease interval cancers by 20% (from 44 to 35). CONCLUSION: CAD may have the potential to increase cancer detection by up to 16%, and to reduce the number of interval cancers by up to 20% in SFM and FFDM screening programs using independent double reading with consensus review. The influence of true- and false-positive CAD marks on decision-making can, however, only be evaluated in a prospective clinical study.

Effect of computer-aided detection on independent double reading of paired screen-film and full-field digital screening mammograms.

OBJECTIVE: The purpose of this study was to evaluate the performance and potential contribution of computer-aided detection (CAD) to independent double reading of paired screen-film and full-field digital screening mammograms. MATERIALS AND METHODS: The cases of 3,683 women who underwent both screen-film mammography and full-field digital mammography (FFDM) with independent double reading for each technique were followed for 2 years to include cancers detected in the interval between screening rounds and cancers detected at the next screening round. Fifty-five biopsy-proven cancers were diagnosed. The baseline screening mammograms of the 55 cancers were defined as having positive findings if at least one of two independent readers scored it 2 or higher on a 5-point rating scale. The baseline mammograms of interval (n = 10) or secondround (n = 16) cancers were retrospectively classified as overlooked (n = 2), minimal sign actionable (n = 8), minimal sign nonactionable (n = 5), and normal (n = 11). The baseline mammograms of these cases of cancer were evaluated with a CAD system, and the CAD results were compared (McNemar's test for paired proportions) with the findings at prospective independent double reading of mammograms obtained with each technique. RESULTS: For FFDM, CAD sensitivity was 95% (37/39) compared with 64% (25/39) for double reading (p = 0.006), and for screen-film mammography, CAD sensitivity was 85% (33/39) compared with 77% (30/39) for prospective double reading (p = 0.57) of radiographically visible lesions in baseline mammograms. CAD correctly marked five (13%) of 39 cancers on screen-film mammography and 14 (36%) of 39 cancers on FFDM not detected at prospective independent double reading. CONCLUSION: CAD showed the potential to increase the cancer detection rate for FFDM and for screen-film mammography in breast cancer screening performed with independent double reading.