Bladder cancer has been increasing globally. Urinary cytology is considered a major screening method for bladder cancer, but it has poor sensitivity. This study aimed to utilize clinical laboratory data and machine learning methods to build predictive models of bladder cancer. A total of 1336 patients with cystitis, bladder cancer, kidney cancer, uterus cancer, and prostate cancer were enrolled in this study. Two-step feature selection combined with WEKA and forward selection was performed. Furthermore, five machine learning models, including decision tree, random forest, support vector machine, extreme gradient boosting (XGBoost), and light gradient boosting machine (GBM) were applied. Features, including calcium, alkaline phosphatase (ALP), albumin, urine ketone, urine occult blood, creatinine, alanine aminotransferase (ALT), and diabetes were selected. The lightGBM model obtained an accuracy of 84.8% to 86.9%, a sensitivity 84% to 87.8%, a specificity of 82.9% to 86.7%, and an area under the curve (AUC) of 0.88 to 0.92 in discriminating bladder cancer from cystitis and other cancers. Our study provides a demonstration of utilizing clinical laboratory data to predict bladder cancer.

Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality in the world. U.S. Food and Drug Administration-approved circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125 were used as prognostic biomarkers of CRC that attributed to low sensitivity in diagnosis of CRC. Therefore, our purpose is to develop a novel strategy for novel clinical biomarkers for early CRC diagnosis. We used mass spectrometry (MS) methods such as nanoLC-MS/MS, targeted LC-MS/MS, and stable isotope-labeled multiple reaction monitoring (MRM) MS coupled to test machine learning algorithms and logistic regression to analyze plasma samples from patients with early-stage CRC, late-stage CRC, and healthy controls (HCs). On the basis of our methods, 356 peptides were identified, 6 differential expressed peptides were verified, and finally three peptides corresponding wheat germ agglutinin (WGA)-captured proteins were semi-quantitated in 286 plasma samples (80 HCs and 206 CRCs). The novel peptide biomarkers combination of PF4^54–62, ITIH4^429–438, and APOE^198–207 achieved sensitivity 84.5%, specificity 97.5% and an AUC of 0.96 in CRC diagnosis. In conclusion, our study demonstrated that WGA-captured plasma PF4^54–62, ITIH4^429–438, and APOE^198–207 levels in combination may serve as highly effective early diagnostic biomarkers for patients with CRC.

Machine learning (ML) algorithms have been applied to predicting coronary artery disease (CAD). Our purpose was to utilize autoantibody isotypes against four different unmodified and malondialdehyde (MDA)-modified peptides among Taiwanese with CAD and healthy controls (HCs) for CAD prediction. In this study, levels of MDA, MDA-modified protein (MDA-protein) adducts, and autoantibody isotypes against unmodified peptides and MDA-modified peptides were measured with enzyme-linked immunosorbent assay (ELISA). To improve the performance of ML, we used decision tree (DT), random forest (RF), and support vector machine (SVM) coupled with five-fold cross validation and parameters optimization. Levels of plasma MDA and MDA-protein adducts were higher in CAD patients than in HCs. IgM anti-IGKC^76–99 MDA and IgM anti-A1AT^284–298 MDA decreased the most in patients with CAD compared to HCs. In the experimental results of CAD prediction, the decision tree classifier achieved an area under the curve (AUC) of 0.81; the random forest classifier achieved an AUC of 0.94; the support vector machine achieved an AUC of 0.65 for differentiating between CAD patients with stenosis rates of 70% and HCs. In this study, we demonstrated that autoantibody isotypes imported into machine learning algorithms can lead to accurate models for clinical use.

Autoantibodies against apolipoprotein A-I (ApoA-I) are associated with cardiovascular disease risks. We aimed to examine the 4-hydroxy-2-nonenal (HNE) modification of ApoA-I in coronary artery disease (CAD) and evaluate the potential risk of autoantibodies against their unmodified and HNE-modified peptides. We assessed plasma levels of ApoA-I, HNE-protein adducts, and autoantibodies against unmodified and HNE-peptide adducts, and significant correlations and odds ratios (ORs) were examined. Two novel CAD-specific HNE-peptide adducts, ApoA-I^251–262 and ApoA-I^70–83, were identified. Notably, immunoglobulin G (IgG) anti-ApoA-I^251–262 HNE, IgM anti-ApoA-I^70–83 HNE, IgG anti-ApoA-I^251–262, IgG anti-ApoA-I^70–83, and HNE-protein adducts were significantly correlated with triglycerides, creatinine, or high-density lipoprotein in CAD with various degrees of stenosis (70%). The HNE-protein adduct (OR = 2.208-fold, p = 0.020) and IgM anti-ApoA-I^251–262 HNE (2.046-fold, p = 0.035) showed an increased risk of progression from >30% stenosis in CAD. HNE-protein adducts and IgM anti-ApoA-I^251–262 HNE may increase the severity of CAD at high and low levels, respectively.

Theileriosis is considered an economically important disease that may decrease productivity and cause a high mortality rate in livestock. Only a few studies have reported Theileria spp., such as T. sergenti and T. buffeli, in recent decades in Taiwan. In the present study, 401 ticks have been collected on Orchid Island in June 2022 and April 2023. Our environmental investigation for SFTSV unintentionally discovered T. luwenshuni in Haemaphysalis mageshimaensis on Orchid Island via PCR. The PCR products were sequenced, and the detected 18S rRNA gene sequences shared a 99.65–99.93% identity with T. luwenshuni sequences from ticks and ruminants in Myanmar and China. Despite the difficulty in clarifying the source of T. luwenshuni within neighboring regions, our findings provide an updated distribution of T. luwenshuni in Asia. This is not only the first time that T. luwenshuni was found in H. mageshimaensis but also the first report of T. luwenshuni on Orchid Island, Taiwan. Our study indicates that ruminants may be at risk of infection. Therefore, further investigations are needed to determine the distribution of T. luwenshuni among ruminants on Orchid Island and in Taiwan.

Coronary artery disease (CAD) is one of the most common subtypes of cardiovascular disease. The progression of CAD initiates from the plaque of atherosclerosis and coronary artery stenosis, and eventually turns into acute myocardial infarction (AMI) or stable CAD. Alpha-1-antichymotrypsin (AACT) has been highly associated with cardiac events. In this study, we proposed incorporating clinical data on AACT levels to establish a model for estimating the severity of CAD. Thirty-six healthy controls (HCs) and 162 CAD patients with stenosis rates of 70% were included in this study. Plasma concentration of AACT was determined by enzyme-linked immunosorbent assay (ELISA). The receiver operating characteristic (ROC) curve analysis and associations were conducted. Further, five machine learning models, including decision tree, random forest, support vector machine, XGBoost, and lightGBM were implemented. The lightGBM model obtained a sensitivity of 81.4%, a specificity of 67.3%, and an area under the curve (AUC) of 0.822 for identifying CAD patients with a stenosis rate of 30%. In this study, we provided a demonstration of a monitoring model with clinical data and AACT.