Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system.

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.

Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos.

BACKGROUND: In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. METHODS: We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on days or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for aneuploidy prediction and consequent live-birth outcomes. RESULTS: These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709-0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. CONCLUSIONS: Our study underscores the AI model's ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.

Accurate prediction of myopic progression and high myopia by machine learning.

Background: Myopia is a leading cause of visual impairment in Asia and worldwide. However, accurately predicting the progression of myopia and the high risk of myopia remains a challenge. This study aims to develop a predictive model for the development of myopia. Methods: We first retrospectively gathered 612 530 medical records from five independent cohorts, encompassing 227 543 patients ranging from infants to young adults. Subsequently, we developed a multivariate linear regression algorithm model to predict the progression of myopia and the risk of high myopia. Result: The model to predict the progression of myopia achieved an R2 value of 0.964 vs a mean absolute error (MAE) of 0.119D [95% confidence interval (CI): 0.119, 1.146] in the internal validation set. It demonstrated strong generalizability, maintaining consistent performance across external validation sets: R2 = 0.950 vs MAE = 0.119D (95% CI: 0.119, 1.136) in validation study 1, R2 = 0.950 vs MAE = 0.121D (95% CI: 0.121, 1.144) in validation study 2, and R2 = 0.806 vs MAE = -0.066D (95% CI: -0.066, 0.569) in the Shanghai Children Myopia Study. In the Beijing Children Eye Study, the model achieved an R2 of 0.749 vs a MAE of 0.178D (95% CI: 0.178, 1.557). The model to predict the risk of high myopia achieved an area under the curve (AUC) of 0.99 in the internal validation set and consistently high area under the curve values of 0.99, 0.99, 0.96 and 0.99 in the respective external validation sets. Conclusion: Our study demonstrates accurate prediction of myopia progression and risk of high myopia providing valuable insights for tailoring strategies to personalize and optimize the clinical management of myopia in children.

Pold4 is dispensable for mouse development, DNA replication and DNA repair.

The DNA polymerase delta (Pol δ), a heterotetramer of four subunits (Pol δ4), plays a pivotal role in DNA replication, as well as in DNA damage repair. Pold4, as the smallest subunit of Pol δ, is degraded in response to DNA damage or when entering into S-phase. This leads to the conversion of Pol δ4 to the trimeric complex Pol δ3. However, the contribution of Pold4 has not been fully elucidated in mammals. Cdm1, the Pold4 ortholog in Schizosaccharomyces pombe, is dispensable for cell growth and DNA damage repair, and there are no Pold4 orthologs in Saccharomyces cerevisiae. We previously generated a knockout mouse model of Pold3 and revealed its essential role in genome stability. Unexpectedly, we here found that Pold4 knockout mice are viable and fertile. In addition, Pold4 knockout mice do not exhibit any pathologic changes in the lung and spleen, tissues with the most abundant expression of Pold4. Moreover, Pold4 knockout mouse tail tip fibroblasts (TTF) exhibited normal cell growth, cell cycle, DNA replication, DNA damage and DNA repair capacity. These results suggested that Pol δ3 but not Pol δ4 may be responsible for these processes in normal cells. Interestingly, 19-month-old wild-type (WT) mice had tumors in the liver, while Pold4 knockout mice did not, and Pold4 knockout mice showed increased longevity. In further, this provided evidence suggested that Pold4 could be a potential novel target for lung carcinoma because its depletion does not affect normal cells but does affect cancer cells.

Seasonal variation of oxygen-18 in precipitation and surface water of the Poyang Lake Basin, China.

Based on the monthly δ(18)O value measured over a hydrology period in precipitation, runoff of five tributaries and the main lake of the Poyang Lake Basin, combined with hydrological and meteorological data, the characteristics of δ(18)O in precipitation (δ(18)OPPT) and runoff (δ(18)OSUR) are discussed. The δ(18)OPPT and δ(18)OSUR values range from-2.75 to-14.12 ‰ (annual mean value=-7.13 ‰ ) and from-2.30 to-8.56 ‰, respectively. The seasonal variation of δ(18)OPPT is controlled by the air mass circulation in this region, which is dominated by the Asian summer monsoon and the Siberian High during winter. The correlation between the wet seasonal averages of δ(18)OSUR in runoff of the rivers and δ(18)OPPT of precipitation at the corresponding stations shows that in the Poyang Lake catchment area the river water consists of 23% direct runoff (precipitation) and 77% base flow (shallow groundwater). This high proportion of groundwater in the river runoff points to the prevalence of wetland conditions in the Poyang Lake catchment during rainy season. Considering the oxygen isotopic composition of the main body of Poyang Lake, no isotopic enrichment relative to river inflow was found during the rainy season with maximum expansion of the lake. Thus, evaporation causing isotopic enrichment is a minor component of the lake water balance in the rainy period. During dry season, a slight isotopic enrichment has been observed, which suggests a certain evaporative loss of lake water in that period.