Dehnbowden0532

890. The SHAP values showed that Glasgow Coma Scale (GCS), blood urea nitrogen, cumulative urine output on Day 1 and age were the top 4 most important variables contributing to the XGBoost model. The LIME algorithm was used to explain the individualized predictions.

Machine-learning models based on clinical features were developed and validated with great performance for the early prediction of a high risk of death in patients with AKI.

Machine-learning models based on clinical features were developed and validated with great performance for the early prediction of a high risk of death in patients with AKI.Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the Escherichia coli periplasm. Two sets of XGBoost classifiers were employed in the first stage to classify the expression levels of the target protein as high (>50 mg/L), medium (between 0.5 and 50 mg/L), or low ( less then 0.5 mg/L). mTOR inhibitor therapy The second-stage framework consisted of three regression models involving support vector machines and random forest to predict the expression yields corresponding to each expression-level-class. Independent tests showed that the predictor achieved an overall average accuracy of 75% and a Pearson coefficient correlation of 0.91 for the correctly classified instances. Therefore, our model offers a reliable substitution of numerous trial-and-error experiments to identify the optimal fermentation conditions and yield for RPP. It is also implemented as an open-access webserver, PERISCOPE-Opt (http//periscope-opt.erc.monash.edu).Non-coding RNAs (ncRNAs), including miRNAs, lncRNAs, and circRNAs, emerge as crucial components for gene regulation. Nelumbo nucifera (lotus), a horticulturally important plant, differentiates into a temperate ecotype of enlarged rhizomes and a tropical ecotype of thin rhizomes. Nevertheless, whether and how ncRNAs can be rewired in expression and differentially methylated contributing to adaptive divergence of this storage organ in lotus ecotypes is unclear. Herein, we study the expression behaviors and DNA methylation patterns of ncRNAs in temperate and tropical lotus rhizomes. By whole transcriptome sequencing, we found both mRNAs and lncRNAs have divergent expression patterns between ecotypes, whereas miRNAs and circRNAs tended to be accession-specific or noisier in expression. The differentially expressed ncRNAs are involved in phenotypic differentiation of lotus rhizome between ecotypes, as the genes that interacted with them in the competing endogenous RNA network are enriched in functions including carbohydrate metabolism and plant hormone signaling, being critical to rhizome enlargement. Intriguingly, ncRNA-targeted genes are less prone to show positive selection or differential expression during ecotypic divergence due to constraints from ncRNA-mRNA interactions. The methylation levels of ncRNAs generally tend to be higher in temperate lotus than in tropical lotus, and differential methylation of lncRNAs also tends to have expression changes. Overall, our study of ncRNAs and their targets highlights the role of ncRNAs in rhizome growth variation between lotus ecotypes through expression rewiring and methylation modification.

In vitro cell line models provide a valuable resource to investigate compounds useful in the systemic chemotherapy of cancer. However, the due to the dispersal of the data into several different databases, the utilization of these resources is limited. Here, our aim was to establish a platform enabling the validation of chemoresistance-associated genes and the ranking of available cell line models.

We processed four independent databases, DepMap, GDSC1, GDSC2, and CTRP. The gene expression data was quantile normalized and HUGO gene names were assigned to have unambiguous identification of the genes. Resistance values were exported for all agents. The correlation between gene expression and therapy resistance is computed using ROC test.

We combined four datasets with chemosensitivity data of 1562 agents and transcriptome-level gene expression of 1250 cancer cell lines. We have set up an online tool utilizing this database to correlate available cell line sensitivity data and treatment response in a unifoments.

Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely unknown.

Using OCLR machine learning algorithm, weighted gene co-expression network analysis, consistent unsupervised clustering, survival analysis and multivariate cox regression etc. to identify biomarkers and molecular subtypes related to tumor stemness in HCC.

We firstly demonstrate that the high mRNAsi is significantly associated with the poor survival and high disease grades in HCC. Secondly, we identify 212 mRNAsi-related genes that can divide HCC into three molecular subtypes low cancer stemness cell phenotype (CSCP-L), moderate cancer stemness cell phenotype (CSCP-M) and high cancer stemness cell phenotype (CSCP-H), especially over-activated ribosomes, spliceosomes and nucleotide metabolism lead to the worst prognosis for the CSCP-H subtype patients, while activated amino acids, fatty acids and complement systems result in the best prognosis for the CSCP-L subtype. Thirdly, we find that three CSCP subtypes have different mutation characteristics, immune microenvironment and immune checkpoint expression, which may cause the differential prognosis for three subtypes. Finally, we identify 10 robust mRNAsi-related biomarkers that can effectively predict the survival of HCC patients.

These novel cancer stemness-related CSCP subtypes and biomarkers in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy of HCC patients.

These novel cancer stemness-related CSCP subtypes and biomarkers in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy of HCC patients.The circadian rhythm is one of the most general and important rhythms in biological organisms. In this study, continuous 24-h video recordings showed that the cumulative movement distance and duration of the abalone, Haliotis discus hannai, reached their maximum values between 2000-0000, but both were significantly lower between 0800-1200 than at any other time of day or night (P 0.05). Following the injection of three different concentrations of neostigmine methylsulfate, as an AchE inhibitor, the concentration of Ach in the hemolymph, and the expression levels of nAchR in the cerebral ganglia increased significantly (P less then 0.05). Four hours after drug injection, the cumulative movement distance and duration of abalones were significantly higher than those in the uninjected control group, and the group injected with saline (P less then 0.05). The expression levels of the core diurnal clock Bmal1 over a 24-h period also tended to be high during the day and low at night. First, a co-immunoprecipitation assay demonstrated the binding between Bmal1 and AchE or nAchR. A dual-luciferase gene test and electrophoretic mobility shift assay showed that Bmal1 bound to the promoter regions of AchE and nAchR. Twenty-four hours after silencing the Bmal1 gene, the expression levels of AchE and nAchR decreased significantly compared to those of the dsEGFP and PBS control groups, further showing that Bmal1 mediates the cholinergic system to regulate the behavioral rhythm of abalone. These findings shed light on the endocrine mechanism regulating the rhythmic behavior of abalone, and provide a reference for understanding the life history adaptation strategies of nocturnal organisms and the proliferation and protection of bottom dwelling economically important organisms.Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis. This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.The COVID-19 pandemic has brought the entire world to a standstill. Wearing of mask and time-to-time sanitization have become a customary daily practice. Additionally, as the outdoor activities and movements have been curtailed, concept of work from home is being widely adopted. Hence, the screen exposure time has considerably increased. All these conditions have directly or indirectly impacted the health of eye. This article emphasizes on the repercussions of this pandemic on eye health. It also focuses on the precautions that may be taken to prevent them as well as some solutions to manage them.

To identify the psychological impact of coronavirus disease on ophthalmologists practicing in Iran between August and December 2020.

In this cross-sectional online survey, a standard Patient Health Questionnaire- 9 (PHQ- 9) was completed by 228 ophthalmologists who were practicing in Iran. The PHQ- 9 questionnaire was revised by adding two additional questions specifically applicable for the assessment of the psychological impact of coronavirus disease on the Iranian ophthalmologists. An organized classification regarding the assessment of different depression severities identified as no (0-4), mild (5-9), moderate (10-14), or severe (15-21) was then considered for data analysis.

The mean age of our participants was 49.0





±



15.61 years and the majority of them (67.1%) were male. Depression was discovered in 73.68% (

= 168) with different severities ranging from mild (

= 61, 26.75%), moderate (

= 63, 27.63%), and severe (

= 44, 19.3%). It was found that participants with depression wious concerns with respect to their training/profession and income. It is recommended that the health policymakers of Iran pay more attention to the ophthalmologists who experience the aforementioned factors.

To evaluate the ray tracing method's accuracy employing Okulix ray tracing software and thin-lens formulas to calculate intraocular lens (IOL) power using a swept-source optical coherence tomography (SS-OCT) biometer (OA2000).

A total of 188 eyes from 180 patients were included in this study. An OA-2000 optical biometer was used to collect biometric data. The predicted postoperative refraction based on thin-lens formulas including SRK/T, Hoffer Q, Holladay 1, and Haigis formulas and the ray tracing method utilizing the OKULIX software was determined for each patient. To compare the accuracy of approaches, the prediction error and the absolute prediction error were determined.

The mean axial length (AL) was 23.66 mm (range 19-35). In subgroup analysis based on AL, in all ranges of ALs the ray tracing method had the lowest mean absolute error (0.56), the lowest standard deviation (SD; 0.55), and the greatest proportion of patients within 1 diopter of predicted refraction (87.43%) and the lowest absolute prediction error compared to the other formulas (except to SRK/T) in the AL range between 22 and 24 mm (all







<



0.