Objective: Our study provides an innovative approach to exploring herbal formulas that contribute to the promotion of sustainability and biodiversity conservation. We employ data mining, integrating keyword extraction, association rules, and LSTM-based generative models to analyze classical Traditional Chinese Medicine (TCM) texts. We systematically decode classical Chinese medical literature, conduct statistical analyses, and link these historical texts with modern pharmacogenomic references to explore potential alternatives. Methods: We present a novel iterative keyword extraction approach for discerning diverse herbs in historical TCM texts from the Pu-Ji Fang copies. Utilizing association rules, we uncover previously unexplored herb pairs. To bridge classical TCM herbal pairs with modern genetic relationships, we conduct gene-herb searches in PubMed and statistically validate this genetic literature as supporting evidence. We have expanded on the present work by developing a generative language model for suggesting innovative TCM formulations based on textual herb combinations. Results: We collected associations with 7,664 PubMed cross-search entries for gene-herb and 934 for Shenqifuzheng Injection as a positive control. We analyzed 16,384 keyword combinations from Pu-Ji Fang’s 426 volumes, employing statistical methods to probe gene-herb associations, focusing on examining differences among the target genes and Pu-Ji Fang herbs. Conclusion: Analyzing Pu-Ji Fang reveals a historical focus on flavor over medicinal aspects in TCM. We extend our work on developing a generative model from classical textual keywords to rapidly produces novel herbal compositions or TCM formulations. This integrated approach enhances our comprehension of TCM by merging ancient text analysis, modern genetic research, and generative modeling.

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods––FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods––to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in Acinetobacter baumannii, Acinetobacter nosocomialis, Enterococcus faecium, and Group B Streptococci (GBS) based on MALDI-TOF MS spectra collected from two branches of a referral tertiary medical center. The ensemble method was compared with the individual methods. Random forest models built with the data preprocessed by the ensemble method outperformed individual preprocessing methods and achieved the highest accuracy, with values of 84.37% (A. baumannii), 90.96% (A. nosocomialis), 78.54% (E. faecium), and 70.12% (GBS) on independent testing datasets. Through feature selection, important peaks related to antibiotic resistance could be detected from integrated information. The prediction model can provide an opinion for clinicians. The discriminative peaks enabling better prediction performance can provide a reference for further investigation of the resistance mechanism.

Protein malonylation, a reversible post-translational modification of lysine residues, is associated with various biological functions, such as cellular regulation and pathogenesis. In proteomics, to improve our understanding of the mechanisms of malonylation at the molecular level, the identification of malonylation sites via an efficient methodology is essential. However, experimental identification of malonylated substrates via mass spectrometry is time-consuming, labor-intensive, and expensive. Although numerous methods have been developed to predict malonylation sites in mammalian proteins, the computational resource for identifying plant malonylation sites is very limited. In this study, a hybrid model incorporating multiple convolutional neural networks (CNNs) with physicochemical properties, evolutionary information, and sequenced-based features was developed for identifying protein malonylation sites in mammals. For plant malonylation, multiple CNNs and random forests were integrated into a secondary modeling phase using a support vector machine. The independent testing has demonstrated that the mammalian and plant malonylation models can yield the area under the receiver operating characteristic curves (AUC) at 0.943 and 0.772, respectively. The proposed scheme has been implemented as a web-based tool, Kmalo (https://fdblab.csie.ncu.edu.tw/kmalo/home.html), which can help facilitate the functional investigation of protein malonylation on mammals and plants.

Background: The unusual high dialysis prevalence and upper urinary tract urothelial carcinoma (UTUC) incidence in Taiwan may attribute to aristolochic acid (AA), which is nephrotoxic and carcinogenic, exposure. AA can cause a unique mutagenic pattern showing A:T to T:A transversions (mutational Signature 22) analyzed by whole exome sequencing (WES). However, a fast and cost-effective tool is still lacking for clinical practice. To address this issue, we developed an efficient and quantitative platform for the quantitation of AA and tried to link AA detection with clinical outcomes and decipher the genomic landscape of UTUC in Taiwan.Patients and Methods: We recruited 61 patients with de novo onset of UTUC after kidney transplantation who underwent radical nephroureterectomy. A liquid chromatography-tandem mass spectrometry (LC-MS/MS) platform was developed for the quantitation of AA. Pearson’s chi-square test, Kaplan–Meier method, and Cox proportional hazard model were utilized to assess the correlations among AA detection, clinicopathological characteristics, and clinical outcomes. Seven tumors and seven paired normal tissues were sequenced using WES (approximately 800x sequencing depth) and analyzed by bioinformatic tool.Results: We found that high level of 7-(deoxyadenosin-N⁶-yl)aristolactam I (dA-AL-I) detected in paired normal tissues was significantly correlated with fast UTUC initiation times after renal transplantation (p = 0.035) and with no use of sirolimus (p = 0.046). Using WES analysis, we further observed that all tumor samples were featured by Signature 22 mutations, apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC)-associated gene mutations, p53 mutations, no fibroblast growth factor receptor 3 (FGFR3) mutation, and high tumor mutation burden (TMB). Especially, mammalian target of rapamycin (mTOR) activation predominated in dA-AL-I-detected samples compared with those without dA-AL-I detection and might be associated with UTUC initiation through cell proliferation and suppression of UTUC progression via autophagy inhibition.Conclusion: Accordingly, dA-AL-I detection can provide more direct evidence to AA exposure and serve as a more specific predictive and prognostic biomarker for patients with de novo onset of UTUC after kidney transplantation.

Traditional Chinese Medicine (TCM), with its extensive knowledge base documented in ancient texts, offers a unique resource for contemporary drug discovery, particularly in combatting microbial infections. The success of antimalarial drugs like artemisinin and artesunate, derived from the TCM herb Artemisia annua L., exemplifies the potential of TCM-derived small molecules. This rich repository of natural products and intricate molecular structures could reveal novel compounds with unexplored mechanisms of action. Our study employs a multifaceted approach that combines text mining, detailed textual analysis, and modern antibacterial molecular prediction methodologies to unlock the potential of ancient TCM remedies. We use external knowledge maps, which include databases of known bioactive compounds and their targets, to identify promising TCM candidates. This approach leverages both historical texts and contemporary scientific data to explore the therapeutic potential of TCM. We discovered that herb patterns DiYu→ZeXie and Kushen→ShengJiang potentially combat both Grams-positive and Grams-negative bacteria. We utilized the AntiBac-Pred online tool to identify and analyze the chemical components of herbs, integrating data from ancient texts and TCMDB@Taiwan external knowledge graph. The DiYu→ZeXie groups showed antimicrobial potential against resistant Staphylococcus simulans, while the Kushen→ShengJiang groups exhibited dual antimicrobial effects against Bacillus subtilis. Exploring TCM’s extensive repository offers numerous opportunities for discovering therapeutically active compounds. Our synergistic approach, which combines ancient wisdom with modern science, holds significant promise for enhancing our ability to combat infectious diseases. This method could pave the way for a new era of personalized medicine, addressing the urgent need for innovative treatments against multidrug-resistant bacteria and viruses.