OBJECTIVE

To determine the predictive values of the TIRADS and Bethesda Scoring Systems in diagnosing thyroid malignancy at the Quezon City General Hospital.

Design: Retrospective Review of Records

Setting: Tertiary Government Training Hospital

Participants: Records of patients aged 18 years old and above who were admitted and underwent thyroidectomy under the Department of Otorhinolaryngology-Head and Neck Surgery from 2018 to 2023

A total of 47 patient records were included, 16 had thyroid malignancy while 31 had benign histopathology results. The Bethesda system showed 60% sensitivity, 94% specificity, 82% positive predictive value and 83% accuracy while the TIRADS system showed 53% sensitivity, 50% specificity, 33% positive predictive value,and 51% accuracy. Using Chi-Square tests, the Bethesda system had a strong association (p value < .0001), while the TIRADS system had no significant association (p value 1.000) with thyroid malignancy [odds ratios 24.2 and 1.00] respectively.

Our study showed that the Bethesda classification had good specificity, positive predictive value, accuracy and fair sensitivity while the TIRADS system had poor predictive value

This study employed bioinformatics to screen the feature genes related to efferocytosis in diabetic kidney disease(DKD) and explores traditional Chinese medicine(TCM) regulating these feature genes. The GSE96804 and GSE30528 datasets were integrated as the training set, and the intersection of differentially expressed genes and efferocytosis-related genes(ERGs) was identified as DKD-ERGs. Subsequently, correlation analysis, protein-protein interaction(PPI) network construction, enrichment analysis, and immune infiltration analysis were performed. Consensus clustering was conducted on DKD patients based on the expression levels of DKD-ERGs, and the expression levels, immune infiltration characteristics, and gene set variations between different subtypes were explored. Eight machine learning models were constructed and their prediction performance was evaluated. The best-performing model was evaluated by nomograms, calibration curves, and external datasets, followed by the identification of efferocytosis-related feature genes associated with DKD. Finally, potential TCMs that can regulate these feature genes were predicted. The results showed that the training set contained 640 differentially expressed genes, and after intersecting with ERGs, 12 DKD-ERGs were obtained, which demonstrated mutual regulation and immune modulation effects. Consensus clustering divided DKD into two subtypes, C1 and C2. The support vector machine(SVM) model had the best performance, predicting that growth arrest-specific protein 6(GAS6), S100 calcium-binding protein A9(S100A9), C-X3-C motif chemokine ligand 1(CX3CL1), 5'-nucleotidase(NT5E), and interleukin 33(IL33) were the feature genes of DKD. Potential TCMs with therapeutic effects included Astragali Radix, Trionycis Carapax, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma, which mainly function to clear heat, replenish deficiency, activate blood, resolve stasis, and promote urination and drain dampness. Molecular docking revealed that the key components of these TCMs, including β-sitosterol, quercetin, and sitosterol, exhibited good binding activity with the five target genes. These results indicated that efferocytosis played a crucial role in the development and progression of DKD. The feature genes closely related to both DKD and efferocytosis, such as GAS6, S100A9, CX3CL1, NT5E, and IL33, were identified. TCMs such as Astragali Radix, Trionycis Carapa, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma may provide a new therapeutic strategy for DKD by regulating efferocytosis.
Humans ; Computational Biology ; Diabetic Nephropathies/physiopathology* ; Protein Interaction Maps ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal ; Phagocytosis/genetics* ; Efferocytosis

Chronic cerebral hypoperfusion (CCH) plays an important role in the occurrence and development of vascular dementia (VD). Recent studies have indicated that multiple stages of immune-inflammatory response are involved in the process of cerebral ischemia, drawing increasing attention to immune therapies for cerebral ischemia. This study aims to identify potential immune therapeutic targets for CCH using bioinformatics methods from an immunological perspective. We identified a total of 823 differentially expressed genes associated with CCH, and further screened for 9 core immune-related genes, namely RASGRP1, FGF12, SEMA7A, PAK6, EDN3, BPHL, FCGRT, HSPA1B and MLNR. Gene enrichment analysis showed that core genes were mainly involved in biological functions such as cell growth, neural projection extension, and mesenchymal stem cell migration. Biological signaling pathway analysis indicated that core genes were mainly involved in the regulation of T cell receptor, Ras and MAPK signaling pathways. Through LASSO regression, we identified RASGRP1 and BPHL as key immune-related core genes. Additionally, by integrating differential miRNAs and the miRwalk database, we identified miR-216b-5p as a key immune-related miRNA that regulates RASGRP1. In summary, the predicted miR-216b-5p/ RASGRP1 signaling pathway plays a significant role in immune regulation during CCH, which may provide new targets for immune therapy in CCH.
Humans ; Computational Biology/methods* ; Brain Ischemia/therapy* ; Immunotherapy ; MicroRNAs/genetics* ; Signal Transduction ; Dementia, Vascular/genetics* ; Chronic Disease

Ankylosing spondylitis (AS) is linked to an increased prevalence of myocardial infarction (MI). However, research dedicated to elucidating the pathogenesis of AS-MI is lacking. In this study, we explored the biomarkers for enhancing the diagnostic and therapeutic efficiency of AS-MI. Datasets were obtained from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and machine learning models to screen hub genes. A receiver operating characteristic curve and a nomogram were designed to assess diagnostic accuracy. Gene set enrichment analysis was conducted to reveal the potential function of hub genes. Immune infiltration analysis indicated the correlation between hub genes and the immune landscape. Subsequently, we performed single-cell analysis to identify the expression and subcellular localization of hub genes. We further constructed a transcription factor (TF)-microRNA (miRNA) regulatory network. Finally, drug prediction and molecular docking were performed. S100A12 and MCEMP1 were identified as hub genes, which were correlated with immune-related biological processes. They exhibited high diagnostic value and were predominantly expressed in myeloid cells. Furthermore, 24 TFs and 9 miRNA were associated with these hub genes. Enzastaurin, meglitinide, and nifedipine were predicted as potential therapeutic agents. Our study indicates that S100A12 and MCEMP1 exhibit significant potential as biomarkers and therapeutic targets for AS-MI, offering novel insights into the underlying etiology of this condition.
Humans ; Spondylitis, Ankylosing/complications* ; Systems Biology/methods* ; Myocardial Infarction/diagnosis* ; Biomarkers/metabolism* ; MicroRNAs/genetics* ; Gene Regulatory Networks ; Gene Expression Profiling ; Machine Learning

Sepsis is a life-threatening organ dysfunction syndrome caused by a dysregulated host response to infection. Due to different infection sources, pathogens and basic conditions of patients, there is significant heterogeneity in clinical manifestations, response to treatment and prognosis of patients with sepsis. Accurate classification and individualized treatment of sepsis will help to further improve the prognosis of patients with sepsis. In recent years, the integration of artificial intelligence and bioinformatics has brought new opportunities for the research of sepsis classification. This review systematically introduces a variety of sepsis classification methods and their clinical application value. The clinical data in the electronic medical record, such as the dynamic changes of vital signs such as body temperature, can be used as the basis for sepsis classification. Different subtypes of body temperature trajectories have differences in physiological characteristics and prognosis, which contributes to predict the prognosis of patients and guide fluid management strategies. Biomarker classification can more comprehensively reflect the pathophysiological state of patients. Immune index classification is helpful to identify immunocompromised patients so as to carry out targeted immunotherapy. Transcriptome data and genotyping reveal the heterogeneity of sepsis at the molecular level and provide a new perspective for precision medicine. In addition, a detailed systematic review of sepsis-related organ function damage, such as acute respiratory distress syndrome (ARDS), acute kidney injury (AKI), and acute liver injury, has also been conducted, which is helpful to develop targeted organ protection and treatment strategies. These typing methods have shown good application prospects in clinical practice. However, there are still limitations in the current research, such as typing stability and biomarker selection, which need to be further explored. Future research should focus on the development of stable and efficient typing tools to achieve precise treatment of sepsis and improve the prognosis of patients.
Humans ; Sepsis/classification* ; Multiple Organ Failure/classification* ; Prognosis ; Artificial Intelligence ; Biomarkers ; Computational Biology ; Respiratory Distress Syndrome

OBJECTIVE: Circadian rhythm disruption (CRD) is a risk factor that correlates with poor prognosis across multiple tumor types, including hepatocellular carcinoma (HCC). However, its mechanism remains unclear. This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity. METHODS: To quantify CRD, the HCC CRD score (HCCcrds) was developed. Using machine learning algorithms, we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort (n = 369), and the robustness of this method was validated. Furthermore, we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes. RESULTS: We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis. The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis, higher pathological grade, and advanced clinical stages, while the CRD-related subtype with low HCCcrds had better clinical outcomes. We also identified novel biomarkers for each subtype, such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1. CONCLUSION We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers. Within these groups greater HCCcrds was associated with worse prognosis. This approach has the potential to improve prediction of an individual's prognosis, guide precision treatments, and assist clinical decision making for HCC patients. Please cite this article as: Li XJ, Chang L, Mi Y, Zhang G, Zhu SS, Zhang YX, et al. Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm. J Integr Med. 2025; 23(4): 445-456.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Circadian Rhythm/genetics* ; Prognosis ; Male ; Female ; Biomarkers, Tumor/genetics* ; Middle Aged ; Machine Learning ; Computational Biology

OBJECTIVES: This study aimed to explore the biological functions and clinical value of mothers against decapentaplegic homolog (SMAD) 7 in head and neck squamous cell carcinoma (HNSCC) through bioinformatics analysis and basic experiments. METHODS: The expression of SMAD7 in HNSCC in public databases was studied. Western blot was used to detect the expression of SMAD7 in HNSCC cell lines and normal epithelial cells. The SMAD7 highly expressed HNSCC cell line HSC-4 was silenced, and CCK-8, Transwell assays, and cell scratch experiments were conducted to study the effect of SMAD7 on the biological functions of HSC-4 cells. HNSCC expression profile data were obtained from UCSC xena, and genes related to SMAD7 were selected for gene ontology and Kyoto encyclopedia of genes and genomes gene enrichment analysis, construction of a co-expression gene interaction network, and screening of related cell signaling pathways. Western blot was used to detect the expression changes of proteins in the related cell signaling pathways in HNSCC cells with silenced SMAD7. cBioPortal was utilized to analyze the mutation rate of the SMAD7 gene, and the MethSurv database was used to analyze the methylation level of the SMAD7 gene and its correlation with prognosis. The receiver operating characteristic curve was used to assess the diagnostic value of SMAD7 for HNSCC. TIMER2.0 was used to analyze the correlation between SMAD7 expression and immune cell infiltration. RESULTS: SMAD7 was highly expressed in HNSCC tumor tissues and some cell lines. Silencing the expression of SMAD7 can significantly inhibit the proliferation, migration, and invasion of cancer cells. Silencing SMAD7 can induce the downregulation of vascular cell adhesion molecule 1 (VCAM-1). The bioinformatics analysis showed that the mutation rate of the SMAD7 gene and the methylation level were significantly correlated with the prognosis of patients with HNSCC. The expression of SMAD7 was related to the level of immune cell infiltration in HNSCC. CONCLUSIONS SMAD7 promotes the proliferation, migration, and invasion of HNSCC cells by regulating the expression of VCAM-1. It may be a potential tumor biomarker and therapeutic target for HNSCC.
Humans ; Smad7 Protein/metabolism* ; Prognosis ; Squamous Cell Carcinoma of Head and Neck ; Head and Neck Neoplasms/pathology* ; Cell Line, Tumor ; Cell Movement ; Cell Proliferation ; Signal Transduction ; Gene Expression Regulation, Neoplastic ; Gene Silencing ; Computational Biology

Gene synthesis is an enabling technology that supports the development of synthetic biology. The existing approaches for de novo gene synthesis generally have tedious operation, low efficiency, high error rates, and limited product lengths, being difficult to support the huge demand of synthetic biology. The assembly and error correction are the keys in gene synthesis. This study first designed the oligonucleotide sequences by reasonably splitting the virus genome of approximately 10 kb by balancing the parameters of sequence design software ability, PCR amplification ability, and assembly enzyme assembly ability. Then, two-step PCR was performed with high-fidelity polymerase to complete the de novo synthesis of 3.0 kb DNA fragments, and error correction reactions were performed with T7 endonuclease Ⅰ for the products from different stages of PCR. Finally, the virus genome was assembled by 3.0 kb DNA fragments from de novo synthesis and error correction and then sequenced. The experimental results showed that the proposed method successfully produced the DNA fragment of about 10 kb and reduced the probability of large fragment mutations during the assembly process, with the lowest error rate reaching 0.36 errors/kb. In summary, this study developed an efficient de novo method for synthesizing a viral genome of about 10 kb with T7 endonuclease Ⅰ-mediated error correction. This method enabled the synthesis of a 10 kb viral genome in one day and the correct plasmid of the viral genome in five days. This study optimized the de novo gene synthesis process, reduced the error rate, simplified the synthesis and assembly steps, and reduced the cost of viral genome assembly.
Genome, Viral/genetics* ; Polymerase Chain Reaction/methods* ; DNA, Viral/genetics* ; Bacteriophage T7/enzymology* ; Synthetic Biology/methods*

Biomanufacturing is an advanced manufacturing method that integrates biology, chemistry, and engineering. It utilizes renewable biomass and biological organisms as production media to scale up the production of target products through fermentation. Compared with petrochemical routes, biomanufacturing offers significant advantages in reducing CO₂ emissions, lowering energy consumption, and cutting costs. With the development of systems biology and synthetic biology and the accumulation of bioinformatics data, the integration of information technologies such as artificial intelligence, large models, and high-performance computing with biotechnology is propelling biomanufacturing into a data-driven era. This paper reviews the latest research progress on databases, knowledge bases, and large language models for biomanufacturing. It explores the development directions, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for scientific research in related areas.
Biotechnology/methods* ; Knowledge Bases ; Synthetic Biology ; Databases, Factual ; Artificial Intelligence ; Systems Biology ; Computational Biology ; Fermentation

Computational modeling is an invaluable tool for mechanism analysis, directed engineering, and rational design of biological parts, metabolic networks, and even cellular systems. It can provide new technological solutions to address biological challenges at different levels and has become a central focus of research in biomanufacturing. In the computational modeling of proteins, which are the key parts in biological systems, the traditional physics-based methods (computer software and mathematical model) have been widely used to study the physical and chemical processes in the functioning of proteins, and have thus been recognized as a powerful tool for understanding complex biological systems and guiding experimental designs. As the scale of computational modeling continues to expand, traditional modeling techniques face difficulties in balancing computational accuracy and speed. In recent years, the explosive growth of biological data has made it possible to construct high-performance artificial intelligence (AI) models, which brings new opportunities to the computational modeling of proteins, and the AI-enhanced physics-based computational modeling technologies have emerged. This combined strategy not only incorporates the chemical knowledge and established physical principles but also is powerful in data processing and pattern recognition, which greatly improves the computational efficiency and prediction accuracy, as well as possesses stronger interpretation ability, transferability, and robustness. The AI-enhanced physics-based computational modeling technologies have already shown great potential and value in biocatalysis, paving a new way for the future development of biomanufacturing.
Artificial Intelligence ; Proteins/chemistry* ; Computer Simulation ; Software ; Computational Biology/methods*