To systematically compare and rank the accuracy of AI-based intraocular lens (IOL) power calculation formulas and traditional IOL formulas in highly myopic eyes.
We screened PubMed, Web of Science, Embase, and Cochrane Library databases for studies published from inception to April 2023. The following outcome data were collected: mean absolute error (MAE), percentage of eyes with a refractive prediction error (PE) within ±0.25, ±0.50, and ±1.00 diopters (D), and median absolute error (MedAE). The network meta-analysis was conducted by R 4.3.0 and STATA 17.0.
Twelve studies involving 2,430 adult myopic eyes (with axial lengths >26.0 mm) that underwent uncomplicated cataract surgery with mono-focal IOL implantation were included. The network meta-analysis of 21 formulas showed that the top three AI-based formulas, as per the surface under the cumulative ranking curve (SUCRA) values, were XGBoost, Hill-RBF, and Kane. The three formulas had the lowest MedAE and were more accurate than traditional vergence formulas, such as SRK/T, Holladay 1, Holladay 2, Haigis, and Hoffer Q regarding MAE, percentage of eyes with PE within ±0.25, ±0.50, and ±1.00 D.
The top AI-based formulas for calculating IOL power in highly myopic eyes were XGBoost, Hill-RBF, and Kane. They were significantly more accurate than traditional vergence formulas and ranked better than formulas with Wang-Koch AL modifications or newer generations of formulas such as Barrett and Olsen.
https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022335969.

The accuracy of intraocular lens power calculation formulas based on artificial intelligence in highly myopic eyes: a systematic review and network meta-analysis.

Since the beginning of 2020 the SARS-CoV-2 virus has spread to nearly every country in the world. The mainly airborne pathogen has led to large numbers of deaths, principally in elderly and vulnerable segments of the population. Protective vaccines have recently become available, but it is not yet clear whether and when population-wide immunity will be achieved. The existence of evidence for the protective effect of masks covering the mouth and nose is a topic of public debate.
A selective literature search was carried out in PubMed. Data from the German Robert Koch Institute and the Centers for Disease Control and Prevention were also taken into account.
When talking, as many as 20 000 droplets ranging in size from 20 to 500 μM are released every second. According to PCR tests, the amount of virus exhaled is highest immediately before the onset of symptoms. No randomized trials have been conducted on the effect of masks covering the mouth and nose. A metaanalysis of 29 studies on infection with SARS-CoV-2, SARS, or MERS revealed that type N-95 masks (corresponding approximately to FFP-2), surgical masks, or similar multilayer cotton masks can greatly reduce the infection risk for the wearers (RR 0.34 [0.26; 0.45], with moderate heterogeneity [I2 = 48%]). Model experiments and case reports suggest that masks covering the mouth and nose afford considerable protection against transmission of SARS-CoV-2 and other airborne diseases by reducing release of and exposure to potentially infectious droplets; in addition, infections that do occur take a milder course. A limitation of the studies analyzed is that in most cases, this effect cannot be viewed in isolation from the protective impact of other measures (distancing, hygiene precautions).
It can plausibly be assumed that consistent use of masks covering the mouth and nose can play an important role in containing the spread of SARSCoV- 2.

Protection From COVID-19–The Efficacy of Face Masks.

Inflammatory cytokines have long been considered closely related to the development of oral lichen planus (OLP), and we further explored the causal relationship between the two by Mendelian randomization (MR) method.
We performed bidirectional MR analyses by large genome-wide association studies (GWAS). The data included a large-scale OLP dataset, as well as datasets of 41 inflammatory cytokines. All data were obtained from the University of Bristol database, which includes 41 inflammatory cytokines, and the GWAS Catalog database, which includes 91 inflammatory cytokines. OLP data were obtained from the Finngen database, which includes 6411 cases and 405770 healthy controls. We used the inverse variance weighted (IVW) method, MR-Egger method, weighted median method, simple mode method and weighted mode method to analyze the causal relationship between inflammatory cytokines and OLP, and we also combined with sensitivity analysis to further verify the robustness of the results. We performed a meta-analysis of positive or potentially positive results for the same genes to confirm the reliability of the final results.
We primarily used the IVW analysis method, corrected using the Benjamin Hochberg (BH) method. When p<0.00038 (0.05/132), the results are significantly causal; when 0.00038<p<0.05, the results are potentially causal. We found a total of 7 inflammatory cytokines with significant or potential associations with OLP (University of Bristol database: 2, GWAS Catalog database: 5). In the reverse analysis, we found that a total of 30 inflammatory cytokines were significantly or potentially associated with OLP (University of Bristol database: 5, GWAS Catalog database: 25). After sensitivity analysis and meta-analysis, we finally determined that there was a causal relationship between a total of 3 inflammatory cytokines and OLP in the forward analysis, the most significant of which was FGF21 (p=0.02954, odds ratio (OR): 1.113, 95% confidence interval (95%CI): 1.011-1.226). In the reverse analysis, 14 inflammatory cytokines were causally associated with OLP, the most significant of which was PLAU (p=0.00002, OR: 0.951, 95%CI: 0.930-0.973).
There is a causal association between OLP and some inflammatory cytokines, which may play an important role in the pathogenesis of OLP and require further attention.

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