Background: Ventilator weaning is one of the most significant challenges in the intensive care unit (ICU). Approximately 30% of patients fail to wean, resulting in prolonged use of ventilators and increased mortality. There are numerous high-performance prediction models available today, but they require a large number of parameters to predict and are thus impractical in clinical practice. Objectives: This study aims to create an artificial intelligence (AI) model for predicting weaning time and to identify the most simplified key predictors that will allow the model to achieve adequate accuracy with as few parameters as possible. Methods: This is a retrospective study of to-be-weaned patients (n = 1439) hospitalized in the cardiac ICU of Cheng Hsin General Hospital’s Department of Cardiac Surgery from November 2018 to August 2020. The patients were divided into two groups based on whether they could be weaned within 24 h (i.e., “patients weaned within 24 h” (n = 1042) and “patients not weaned within 24 h” (n = 397)). Twenty-eight variables were collected including demographic characteristics, arterial blood gas readings, and ventilation set parameters. We created a prediction model using logistic regression and compared it to other machine learning techniques such as decision tree, random forest, support vector machine (SVM), extreme gradient boosting, and artificial neural network. Forward, backward, and stepwise selection methods were used to identify significant variables, and the receiver operating characteristic curve was used to assess the accuracy of each AI model. Results: The SVM [receiver operating characteristic curve (ROC-AUC) = 88%], logistic regression (ROC-AUC = 86%), and XGBoost (ROC-AUC = 85%) models outperformed the other five machine learning models in predicting weaning time. The accuracies in predicting patient weaning within 24 h using seven variables (i.e., expiratory minute ventilation, expiratory tidal volume, ventilation rate set, heart rate, peak pressure, pH, and age) were close to those using 28 variables. Conclusions: The model developed in this research successfully predicted the weaning success of ICU patients using a few and easily accessible parameters such as age. Therefore, it can be used in clinical practice to identify difficult-to-wean patients to improve their treatment.

Objectives: This paper studies the segmentation and detection of small metastatic brain tumors. This study aims to evaluate the feasibility of training a deep neural network for the segmentation and detection of metastatic brain tumors in MRI using a very small dataset of 33 cases, by leveraging large public datasets of primary tumors; Methods: This study explores various methods, including supervised learning, two transfer learning approaches, and self-supervised learning, utilizing U-net and Swin UNETR models; Results: The self-supervised learning approach utilizing the Swin UNETR model yielded the best performance. The Dice score for small brain tumors was approximately 0.19. Sensitivity reached 100%, while specificity was 54.5%. When excluding subjects with hyperintensities, the specificity improved to 80.0%; Conclusions: It is feasible to train a model using self-supervised learning and a small dataset for the segmentation and detection of small brain tumors.

A chronic inflammatory state is a prominent feature in patients with end-stage renal disease (ESRD). Nuclear factor-kappa B (NF-κB) is a transcription factor that regulates the expression of genes involved in inflammation. Some genetic studies have demonstrated that the NF-κB genetic mutation could cause kidney injury and kidney disease progression. However, the association of a gene polymorphism in the transcription factor binding site of NF-κB with kidney disease is not clear. We used the Taiwan Biobank database, the University of California, Santa Cruz, reference genome, and a chromatin immunoprecipitation sequencing database to find single nucleotide polymorphisms (SNPs) at potential binding sites of NF-κB. In addition, we performed a case–control study and genotyped 847 patients with ESRD and 846 healthy controls at Tri-Service General Hospital from 2015 to 2016. Furthermore, we used the ChIP assay to identify the binding activity of different genotypes and used Luciferase reporter assay to examine the function of the rs9395890 polymorphism. The results of biometric screening in the databases revealed 15 SNPs with the potential binding site of NF-κB. Genotype distributions of rs9395890 were significantly different in ESRD cases and healthy controls (P = 0.049). The ChIP assay revealed an approximately 1.49-fold enrichment of NF-κB of the variant type TT when compared to that of the wild-type GG in rs9395890 (P = 0.027; TT = 3.20 ± 0.16, GT = 2.81 ± 0.20, GG = 1.71 ± 0.18). The luciferase reporter assay showed that the NF-κB binding site activity in T allele was slightly higher than that in G allele, though it is not significant. Our findings indicate that rs9395890 is associated with susceptibility to ESRD in Taiwan population.

Background: Previous meta-analyses have explored the association between the C677T polymorphism ofmethyltetrahydrofolate reductase(MTHFR)and chronic kidney disease (CKD) but there were no studies with a decisive conclusion. Furthermore, the high heterogeneity among different populations is not yet interpreted.Objectives: This study used trial sequential analysis (TSA) to evaluate whether the nowadays conclusion supported by current cumulative samples. We also applied case-weighted meta-regression to explore the potential gene–environment interactions.Methods: For the first stage of this study we conducted a case–control study involving 847 dialysis patients from 7 hemodialysis centers in Taipei during 2015 to 2018 and 755 normal controls from a health center in the Tri-Service General Hospital. The second stage combined the results from the first stage with previous studies. The previous studies were collected from PubMed, EMBASE, and Web of Science databases before January 2018.Results: From the case–control study, the T allele ofMTHFRC677T appeared to have a protective effect on end-stage renal disease compared with the C allele [odds ratio (OR): 0.80, 95% CI (confidence interval) = 0.69–0.93]. However, the meta-analysis contradicted the results in Asian (OR = 1.12, 95% CI = 0.96–1.30). The same analysis was also applied in Caucasian and presented similar results from Asian (OR = 1.18, 95% CI = 0.98–1.42). The TSA showed our case–control study to be the decisive sample leading to a null association among Asian population. The high heterogeneity (I² = 75%) could explain the contradictory results between the case–control study and the meta-analysis. However, further case-weighted meta-regression did not find any significant interaction between measured factors andMTHFRC677T on CKD.Conclusions: High heterogeneities were found in both Caucasian and Asian, which caused the null relationship in meta-analysis while there were significant effects in individual studies. Future studies should further explore the high heterogeneity that might be hidden in unmeasured gene–environment interactions, to explain the diverse findings among different populations.

Background: So far, numerous meta-analyses have been published regarding the correlation between peroxisome proliferator-activated receptor gamma (PPARG) proline 12 alanine (Pro12Ala) gene polymorphism and chronic kidney disease (CKD); however, the results appear to be contradictory. Hence, this study is formulated with the objective of using existing meta-analysis data together with our research population to study the correlation between PPARG Pro12Ala gene polymorphism and CKD and evaluate whether an accurate result can be obtained. Methods: First, literature related to CKD and PPARG Pro12Ala available on the PubMed and EMBASE databases up to December 2016 was gathered from 20 publications. Then, the gathered results were combined with our case-control study of 1693 enrolled subjects and a trial sequential analysis (TSA) was performed to verify existing evidence and determine whether a firm conclusion can be drawn. Results: The TSA results showed that the cumulative sample size for the Asian sample was 6078 and was sufficient to support a definite result. The results of this study confirmed that there is no obvious correlation between PPARG Pro12Ala and CKD for Asians (OR = 0.82 (95% CI = 0.66–1.02), I² = 63.1%), but this was not confirmed for Caucasians. Furthermore, the case-control sample in our study was shown to be the key for reaching this conclusion. Conclusions: The meta-analysis results of this study suggest no significant correlation between PPARG Pro12Ala gene polymorphism and CKD for Asians after adding our samples, but not for Caucasian.