A Study on the Prediction of Cancer Using Whole-Genome Data and Deep Learning.

The number of patients diagnosed with cancer continues to increasingly rise, and has nearly doubled in 20 years. Therefore, predicting cancer occurrence has a significant impact on reducing medical costs, and preventing cancer early can increase survival rates. In the data preprocessing step, since individual genome data are used as input data, they are classified as individual genome data. Subsequently, data embedding is performed in character units, so that it can be used in deep learning. In the deep learning network schema, using preprocessed data, a character-based deep learning network learns the correlation between individual feature data and predicts cancer occurrence. To evaluate the objective reliability of the method proposed in this study, various networks published in other studies were compared and evaluated using the TCGA dataset. As a result of comparing various networks published in other studies using the same data, excellent results were obtained in terms of accuracy, sensitivity, and specificity. Thus, the superiority of the effectiveness of deep learning networks in predicting cancer occurrence using individual whole-genome data was demonstrated. From the results of the confusion matrix, the validity of the model for predicting the cancer using an individual's whole-genome data and the deep learning proposed in this study was proven. In addition, the AUC, which is the area under the ROC curve, which judges the efficiency of diagnosis as a performance evaluation index of the model, was found to be 90% or more, good classification results were derived. The objectives of this study were to use individual genome data for 12 cancers as input data to analyze the whole genome pattern, and to not separately use reference genome sequence data of normal individuals. In addition, several mutation types, including SNV, DEL, and INS, were applied.

Ethnicity, Progressive Keratoconus, and Outcomes after Corneal Cross-Linking in Southern Israel.

PURPOSE: To assess clinical outcomes of corneal cross-linking (CXL) intervention in a population diagnosed with progressive keratoconus. METHODS: This single-center retrospective cohort study included consecutive patients who underwent standard CXL or accelerated CXL for progressive keratoconus at a major teaching hospital in southern Israel between January 2015 and December 2019. Patients' medical files were reviewed, and pre-operative and post-operative data regarding demographics and clinical and tomographic characteristics were extracted and analyzed. RESULTS: This study included 166 patients (representing 198 eyes), out of which 98 patients (123 eyes) were ethnically Bedouin, and 68 patients (75 eyes) were ethnically Jewish. Overall, 126 patients (144 eyes) had a follow-up of at least 12 months (16.84 ± 5.76). The mean patient age was 20.62 ± 7.1 years old. There were significant baseline differences between the two ethnic groups in best-corrected visual acuity (BCVA; p < 0.001), uncorrected visual acuity (UCVA; p < 0.001), mean keratometry (p = 0.028), and corneal thickness (p < 0.001). Significant changes in BCVA, UCVA, and pachymetry parameters within each group were found after 12 months. Negative binomial regression analysis showed a maximal keratometry below 55D (RR = 1.247, p < 0.001), and a standard CXL procedure (RR = 1.147, p = 0.041) are significantly related to the stability of KC after 12 months. However, the effect size of the origin of patients is negligible (RR = 1.047, p = 0.47). CONCLUSIONS: In this study, the Bedouin population suffered from more progressive keratoconus when compared to the Jewish population. CXL was significantly effective in improving BCVA and UCVA in both groups after 12 months of follow-up. The effect size of the origin of patients on the stability of KC was found to be negligible.

Role of Corneal Epithelial Measurements in Differentiating Eyes with Stable Keratoconus from Eyes that Are Progressing.

Purpose: To evaluate measures of corneal epithelium in eyes that showed documented signs of keratoconus (KC) progression and compare with stable eyes and healthy controls. Also, to determine the correlation of these epithelial parameters with maximum keratometry (K max) and pachymetry. Design: Prospective, observational, comparative study. Participants: One-hundred and fifty eyes from 150 patients. The study included 50 eyes from patients with documented KC progression, 50 eyes with stable KC, and 50 clinically normal eyes to serve as controls. Methods: A spectral-domain (SD)-OCT imaging was obtained in all eyes, and mean values were compared between the groups. The correlation of epithelial parameters with K max and thinnest pachymetry was also investigated. Main Outcome Measures: For the purposes of this study, the epithelial measures maximum, minimum, superior, and inferior values as well as the difference between the minimum and maximum (min-max) and epithelial standard deviation were considered, obtained from SD-OCT and compared between groups. Measurements of the thinnest point and min-max in pachymetry were also recorded. Results: The only epithelial parameter that presented a statistically significant difference between stable and progressive KC was epithelium min-max. Although stable KC presented epithelium min-max mean values of -18.2 ± 6.6, progressive KC eyes presented mean values of -23.4 ± 10.3 (P < 0.0001). Epithelial maximum (P = 0.16), minimum (P = 0.25), superior (P = 0.28), inferior (P = 0.23), and standard deviation (P = 0.25) values were not significantly different between stable and progressive eyes. Difference min-max pachymetry points in stable (-108.3 ± 33.5) and progressive KC (-115.2 ± 56.0) were not significantly different (P = 0.723). There was no significant correlation between epithelium min-max with corneal thinning (P = 0.39) or K max (P = 0.09) regardless of disease progression. Conclusions: Epithelial measures are useful to identify KC eyes that are progressing; the parameters that measure the difference between min-max epithelium points were significantly different between stable and progressive groups, unlike this difference in pachymetry. Finally, this epithelial parameter seems to be independent of corneal thinning and K max. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Comparison of Efficacy and Safety Between Standard, Accelerated Epithelium-Off and Transepithelial Corneal Collagen Crosslinking in Pediatric Keratoconus: A Meta-Analysis.

Purpose: The purpose of the study is to compare the efficacy of standard epithelium-off CXL (SCXL), accelerated epithelium-off CXL (ACXL), and transepithelial crosslinking CXL (TECXL) for pediatric keratoconus. Methods: A literature search on the efficacy of SCXL, ACXL, and TECXL [including accelerated TECXL (A-TECXL)] for keratoconus patients younger than 18 years was conducted using PubMed, Cochrane Library, ClinicalTrials.gov, and EMBASE up to 2021. Primary outcomes were changes in uncorrected visual acuity (UCVA) and maximum keratometry (Kmax) after CXL. Secondary outcomes were changes in best-corrected visual acuity (BCVA), mean refractive spherical equivalent (MRSE), and central corneal thickness (CCT). Estimations were analyzed by weighted mean difference (WMD) and 95% confidence interval (CI). Results: A number of eleven identified studies enrolled 888 eyes (SCXL: 407 eyes; ACXL: 297 eyes; TECXL: 28 eyes; A-TECXL: 156 eyes). For pediatric keratoconus, except for a significant greater improvement in BCVA at 24-month follow-up in SCXL (WMD = -0.08, 95%CI: -0.14 to -0.01, p = 0.03, I2 = 71%), no significant difference was observed in other outcomes between the SCXL and ACXL groups. SCXL seems to provide greater changes in UCVA (WMD = -0.24, 95% CI: -0.34 to -0.13, p < 0.00001, I2 = 89%), BCVA (WMD = -0.09, 95% CI: -0.15 to -0.04, p = 0.0008, I2 = 94%), and Kmax (WMD = -1.93, 95% CI: -3.02 to -0.85, p = 0.0005, I2 = 0%) than A-TECXL, with higher incidence of adverse events. Conclusion: For pediatric keratoconus, both SCXL and ACXL appear to be comparable in the efficacy of visual effects and keratometric outcomes; SCXL seems to provide greater changes in visual and pachymetric outcomes than A-TECXL.

Modelling survival of Salmonella Enteritidis during storage of yoghurt at different temperatures.

The aim of this study was to evaluate the behaviour of Salmonella Enteritidis during the storage of yoghurt at different temperatures (4, 12, 20, and 25 °C), and to develop mathematical models to predict the behaviour of this bacterium as a function of storage temperature. Results indicated that Salmonella was able to survive longer during storage when temperature was low (e.g. 304 h at 4 °C, 60 h at 25 °C). The Geeraerd model with log-decrease and tailing was selected as the most suitable model to describe survival. To evaluate the effect of storage temperature on kinetic parameters such as death rate (kmax) secondary models were developed. The kmax was maximum at 25 °C and minimum at 4 °C with kmax = 0.28 and 0.039 h-1, respectively. The residual population (Nres) ranged 0.5 and 1.8 log CFU/g but there was no temperature dependency of this parameter. A probabilistic example was conduced based on the developed model to assess the exposure to Salmonella by consumption of traditional Turkish yoghurt.