Cost-effectiveness analysis of artificial intelligence-based diabetic retinopathy screening in rural China based on the Markov model.

This study assessed the cost-effectiveness of different diabetic retinopathy (DR) screening strategies in rural regions in China by using a Markov model to make health economic evaluations. In this study, we determined the structure of a Markov model according to the research objectives, which required parameters collected through field investigation and literature retrieval. After perfecting the model with parameters and assumptions, we developed a Markov decision analytic model according to the natural history of DR in TreeAge Pro 2011. For this model, we performed Markov cohort and cost-effectiveness analyses to simulate the probabilistic distributions of different developments in DR and the cumulative cost-effectiveness of artificial intelligence (AI)-based screening and ophthalmologist screening for DR in the rural population with diabetes mellitus (DM) in China. Additionally, a model-based health economic evaluation was performed by using quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios. Last, one-way and probabilistic sensitivity analyses were performed to assess the stability of the results. From the perspective of the health system, compared with no screening, AI-based screening cost more (the incremental cost was 37,257.76 RMB (approximately 5,211.31 US dollars)), but the effect was better (the incremental utility was 0.33). Compared with AI-based screening, the cost of ophthalmologist screening was higher (the incremental cost was 14,886.76 RMB (approximately 2,070.19 US dollars)), and the effect was worse (the incremental utility was -0.31). Compared with no screening, the incremental cost-effectiveness ratio (ICER) of AI-based DR screening was 112,146.99 RMB (15,595.47 US dollars)/QALY, which was less than the threshold for the ICER (< 3 times the per capita gross domestic product (GDP), 217,341.00 RMB (30,224.03 US dollars)). Therefore, AI-based screening was cost-effective, which meant that the increased cost for each additional quality-adjusted life year was merited. Compared with no screening and ophthalmologist screening for DR, AI-based screening was the most cost-effective, which not only saved costs but also improved the quality of life of diabetes patients. Popularizing AI-based DR screening strategies in rural areas would be economically effective and feasible and can provide a scientific basis for the further formulation of early screening programs for diabetic retinopathy.

Anti-inflammatory effect of two novel peptides derived from Binglangjiang buffalo whey protein in lipopolysaccharide-stimulated RAW264.7 macrophages.

Whey protein hydrolysate from Binglangjiang buffalo, a unique genetic resource, has anti-inflammatory activity, but its anti-inflammatory composition and effects are unknown. The aim of this study was to investigate the anti-inflammatory peptides from Binglangjiang buffalo whey protein hydrolysate. A total of 1483 peptides were identified using LC-MS/MS, and 12 peptides were chosen for chemical synthesis using peptidomics, and then two novel anti-inflammatory peptides (DQPFFHYN (DN8) and YSPFSSFPR (YR9)) were screened out using LPS-stimulated RAW264.7 cells. The molecular weights of DN8 and YR9 with ß-turn conformations were 1067.458 Da and 1087.52 Da, respectively, and showed a high in-vitro safety profile and thermal stability, but were intolerant to pepsin. Furthermore, ELISA and Western blot analysis indicated that peptides DN8 and YR9 significantly suppressed the secretion of pro-inflammatory cytokines NO, TNF-α, and IL-6 and the expression of mediators iNOS, TNF-α, and IL-6 in LPS-stimulated RAW264.7 cells. The study provides insights into the development of novel food-based anti-inflammatory nutritional supplements.

Proteomic approach-based comparison of metabolic pathways and functional activities of whey proteins derived from Guishan and Saanen goat milk.

Guishan goats, a unique goat breed in Yunnan Province, have a long history and representation, but their whey protein and function remain unclear. In this study, we carried out a quantitative analysis of the Guishan and Saanen goat whey proteome using a label-free proteomic approach. A total of 500 proteins were quantified from the 2 kinds of goat whey proteins, including 463 common proteins, 37 uniquely expressed whey proteins (UEWP), and 12 differentially expressed whey proteins (DEWP). Bioinformatics analysis indicated that UEWP and DEWP were mainly involved in cellular and immune system processes, membrane, and binding. In addition, UEWP and DEWP in Guishan goats participated primarily in metabolism and immune-related pathways, whereas Saanen goat whey proteins were associated mostly with environmental information processing-related pathways. Guishan goat whey promoted the growth of RAW264.7 macrophages more than Saanen goat whey, and significantly reduced the production of nitric oxide in lipopolysaccharide-stimulated RAW264.7 cells. This study provides a reference for further understanding these 2 goat whey proteins and finding functional active substances from them.

Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China.

BACKGROUND: Although numerous studies have described the application of artificial intelligence (AI) in diabetic retinopathy (DR) screening among diabetic populations, studies among populations in rural areas are rare. The purpose of this study was to evaluate the application value of an AI-based diagnostic system for DR screening in rural areas of midwest China. METHODS: In this diagnostic accuracy study, diabetes mellitus (DM) patients in the National Basic Public Health Information Systems of Licheng County and Lucheng County of Changzhi city from July to December 2020 were selected as the target population. A total of 7824 eyes of 3933 DM patients were enrolled in this screening; the patients included 1395 males and 2401 females, with an average age of 19-87 years (63±8.735 years). All fundus photographs were collected by a professional ophthalmologist under natural pupil conditions in a darkroom using the Zhiyuan Huitu fundus image AI analysis software EyeWisdom. The AI-based diagnostic system and ophthalmologists were tasked with diagnosing the photos independently, and the consistency rate, sensitivity and specificity of the two methods in diagnosing DR were calculated and compared. RESULTS: The prevalence rates of DR according to the ophthalmologist and AI diagnoses were 22.7% and 22.5%, respectively; the consistency rate was 81.6%. The sensitivity and specificity of the AI system relative to the ophthalmologists' grades were 81.2% (95% confidence interval [CI]: 80.3% 82.1%) and 94.3% (95% CI: 93.7% 94.8%), respectively. There was no significant difference in diagnostic outcomes between the methods (χ2 = 0.329, P = 0.566, P>0.05), and the AI-based diagnostic system had high consistency with the ophthalmologists' diagnostic results (κ = 0.752). CONCLUSION: Our research demonstrated that DR patients in rural area hospitals can be screened feasibly. Compared with that of the ophthalmologists, however, the accuracy of the AI system must be improved. The results of this study might lend support to the large-scale application of AI in DR screening among different populations.