OBJECTIVE: To explore the application value of artificial intelligence (AI)-assisted chromosomal karyotype analysis in the diagnosis of prenatal chromosomal mosaicism. METHODS: A retrospective analysis was conducted on 172 pregnant women who underwent amniocentesis at the Department of Medical Genetics and Prenatal Diagnosis, the Third Affiliated Hospital of Zhengzhou University between January 2019 and December 2024. All cases whose fetuses were diagnosed with chromosomal mosaicism via karyotype analysis and stratified into two groups based on the analytical software employed: the conventional analysis group (n = 70), which utilized Leica analysis software for karyotype image recognition and cell counting; and the AI-assisted analysis group (n = 102), which utilized AI-assisted software for the same procedures. The clinical performance of AI-assisted karyotype analysis in diagnosing chromosomal mosaicism was comprehensively evaluated by comparing the types of mosaic karyotypes, distribution of mosaic ratios, and verification outcomes of different detection modalities between the two groups. This study was approved by the Medical Ethics Committee of the Third Affiliated Hospital of Zhengzhou University (Ethics No.: 2024-406-01). RESULTS: No statistically significant difference was observed in baseline characteristics (maternal age, gestational week, and indications for prenatal diagnosis) between the two groups. Regarding the detection efficacy for numerical and structural mosaicisms, no significant difference was found in the detection of numerical mosaicism. However, the conventional analysis group exhibited a significantly higher detection rate of autosomal structural mosaicism compared to the AI-assisted group (11.43% vs. 0.98%, P < 0.05). Numerical mosaicism cases were further verified using copy number variation sequencing (CNV-seq) and/or fluorescence in situ hybridization (FISH). The AI-assisted group demonstrated a significantly lower inconsistency rate (5.56% vs. 20.41%, P < 0.05) compared to the conventional group. For low-proportion (< 10%) chromosomal mosaicism, the AI-assisted group had a significantly lower detection rate (13.25% vs. 29.69%, P < 0.05). Subsequent validation of low-proportion mosaicism by CNV-seq and/or FISH showed a higher consistency rate in the AI-assisted group (81.82% vs. 54.55%), though the difference did not reach statistical significance (P = 0.360). CONCLUSION For the karyotyping analysis of prenatal chromosomal mosaicism, AI-assisted karyotype analysis shows high accuracy and consistency in identifying numerical chromosomal mosaicism, particularly in reducing the detection of low-proportion (< 10%) mosaicism while improving verification accuracy. AI-assisted analysis can significantly improve the detection accuracy of numerical mosaicism and mitigate the risk of misclassification for low-proportion (< 10%) mosaicism, thereby providing more precise clinical evidence for the prenatal diagnosis of chromosomal mosaicisms.
Humans ; Female ; Mosaicism ; Pregnancy ; Karyotyping/methods* ; Artificial Intelligence ; Prenatal Diagnosis/methods* ; Adult ; Retrospective Studies ; Chromosome Disorders/genetics* ; Amniocentesis

OBJECTIVE: To explore the influence of oligospermia (OS) on the detection of sperm DNA fragmentation index (DFI) by fluorescence method based on artificial intelligence (AI) recognition and flow cytometry-based sperm chromatin structure assay (SCSA). METHODS: We collected semen samples from 201 males, including 50 azoospermia (AS) patients as negative controls, 90 OS patients (sperm concentration >0×10⁶/ml and <15×10⁶/ml), and 61 normal men (sperm concentration ≥15×10⁶/ml). Then we subdivided the OS patients into a mild OS (sperm concentration ≥10×10⁶/ml and <15×10⁶/ml), a moderate OS (sperm concentration ≥5×10⁶/ml and <10×10⁶/ml) and a severe/extremely severe OS group (sperm concentration >0×10⁶/ml and <5×10⁶/ml), with 30 cases in each group, and compared the results of DFI detection between the AI fluorescence method and traditional flow cytometry. RESULTS: The DFI value detected by AI fluorescence method showed statistically significant difference from that detected by flow cytometry in the AS, moderate OS and severe/extremely severe OS groups (P<0.01), the former even lower than the latter, but not in the normal control and the mild OS groups (P > 0.05). In the AS group, a dramatically lower rate of non-0 results was achieved by AI fluorescence method than by flow cytometry (8% vs 100%, P<0.01). The DFI values detected by AI fluorescence method exhibited a good linear correlation to those obtained by flow cytometry in the normal control and mild OS groups (R2 = 0.7470; R2 = 0.7180), but a poor linear correlation in the OS full-sample, moderate OS and severe/extremely severe OS groups (R2 = 0.3092; R2 = 0.3558; R2 = 0.2147). CONCLUSION The AI fluorescence method has a higher specificity and is more suitable than flow cytometry for detection of sperm DFI in OS patients. The DFI values obtained by the two methods are consistent with sperm concentration ≥10×10⁶/ml, but the accuracy of the results of detection may be affected with sperm concentration >0×10⁶/ml and <10×10⁶/ml.
Humans ; Male ; Flow Cytometry/methods* ; Oligospermia/genetics* ; Artificial Intelligence ; Spermatozoa ; Adult ; DNA Fragmentation ; Case-Control Studies ; Fluorescence

With the rapid advancement of synthetic biology, CRISPR-Cas systems have emerged as a powerful tool for gene editing, demonstrating significant potential in various fields, including medicine, agriculture, and industrial biotechnology. This review comprehensively summarizes the significant progress in applying artificial intelligence (AI) technologies to the design, mining, and modification of CRISPR-Cas systems. AI technologies, especially machine learning, have revolutionized sgRNA design by analyzing high-throughput sequencing data, thereby improving the editing efficiency and predicting off-target effects with high accuracy. Furthermore, this paper explores the role of AI in sgRNA design and evaluation, highlighting its contributions to the annotation and mining of CRISPR arrays and Cas proteins, as well as its potential for modifying key proteins involved in gene editing. These advancements have not only improved the efficiency and precision of gene editing but also expanded the horizons of genome engineering, paving the way for intelligent and precise genome editing.
CRISPR-Cas Systems/genetics* ; Artificial Intelligence ; Gene Editing/methods* ; RNA, Guide, CRISPR-Cas Systems/genetics* ; Machine Learning ; Humans ; Genetic Engineering/methods* ; Synthetic Biology

Nucleic acid elements are essential functional sequences that play critical roles in regulating gene expression, optimizing pathways, and enabling gene editing to enhance the production of target products in biomanufacturing. Therefore, the design and optimization of these elements are crucial in constructing efficient cell factories. Artificial intelligence (AI) provides robust support for biomanufacturing by accurately predicting functional nucleic acid elements, designing and optimizing sequences with quantified functions, and elucidating the operating mechanisms of these elements. In recent years, AI has significantly accelerated the progress in biomanufacturing by reducing experimental workloads through the design and optimization of promoters, ribosome-binding sites, terminators, and their combinations. Despite these advancements, the application of AI in biomanufacturing remains limited due to the complexity of biological systems and the lack of highly quantified training data. This review summarizes the various nucleic acid elements utilized in biomanufacturing, the tools developed for predicting and designing these elements based on AI algorithms, and the case studies showcasing the applications of AI in biomanufacturing. By integrating AI with synthetic biology and high-throughput techniques, we anticipate the development of more efficient tools for designing nucleic acid elements and accelerating the application of AI in biomanufacturing.
Artificial Intelligence ; Synthetic Biology ; Nucleic Acids/genetics* ; Algorithms ; Gene Editing ; Promoter Regions, Genetic ; Biotechnology/methods*

Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. Enzymes, as biological catalysts, play a key role in biosynthetic pathways, significantly enhancing the rate and selectivity of biochemical reactions. However, the catalytic efficiency, stability, substrate specificity, and tolerance of natural enzymes often fall short of industrial production requirements. Therefore, exploring and modifying enzymes to suit specific biomanufacturing processes has become crucial. In recent years, artificial intelligence (AI) has played an increasingly important role in the discovery, evaluation, engineering, and de novo design of proteins. AI can accelerate the discovery and optimization of proteins by analyzing large amounts of bioinformatics data and predicting protein functions and characteristics by machine learning and deep learning algorithms. Moreover, AI can assist researchers in designing new protein structures by simulating and predicting their performance under different conditions, providing guidance for protein design. This paper reviews the latest research advances in protein discovery, evaluation, engineering, and de novo design for biomanufacturing and explores the hot topics, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for researchers in related fields.
Protein Engineering/methods* ; Artificial Intelligence ; Proteins/genetics* ; Computational Biology ; Machine Learning ; Data Mining ; Algorithms ; Deep Learning

Humans ; Breast Neoplasms/genetics* ; Receptor, ErbB-2/genetics* ; Female ; Reproducibility of Results ; Biomarkers, Tumor/genetics* ; Quality Control ; Artificial Intelligence

Proteins play a variety of functional roles in cellular activities and are indispensable for life. Understanding the functions of proteins is crucial in many fields such as medicine and drug development. In addition, the application of enzymes in green synthesis has been of great interest, but the high cost of obtaining specific functional enzymes as well as the variety of enzyme types and functions hamper their application. At present, the specific functions of proteins are mainly determined through tedious and time-consuming experimental characterization. With the rapid development of bioinformatics and sequencing technologies, the number of protein sequences that have been sequenced is much larger than those can be annotated, thus developing efficient methods for predicting protein functions becomes crucial. With the rapid development of computer technology, data-driven machine learning methods have become a promising solution to these challenges. This review provides an overview of protein function and its annotation methods as well as the development history and operation process of machine learning. In combination with the application of machine learning in the field of enzyme function prediction, we present an outlook on the future direction of efficient artificial intelligence-assisted protein function research.
Artificial Intelligence ; Machine Learning ; Proteins/genetics* ; Computational Biology/methods* ; Drug Development

Rapid and accurate detection technologies are crucial for disease prevention and control. In particular, the COVID-19 pandemic has posed a great threat to our society, highlighting the importance of rapid and highly sensitive detection techniques. In recent years, CRISPR/Cas-based gene editing technique has brought revolutionary advances in biotechnology. Due to its fast, accurate, sensitive, and cost-effective characteristics, the CRISPR-based nucleic acid detection technology is revolutionizing molecular diagnosis. CRISPR-based diagnostics has been applied in many fields, such as detection of infectious diseases, genetic diseases, cancer mutation, and food safety. This review summarized the advances in CRISPR-based nucleic acid detection systems and its applications. Perspectives on intelligent diagnostics with CRISPR-based nucleic acid detection and artificial intelligence were also provided.
Humans ; CRISPR-Cas Systems/genetics* ; COVID-19/genetics* ; Pandemics ; Artificial Intelligence ; Nucleic Acids

BACKGROUND: Excessive exposure to fluoride can reduce intelligence. Methylenetetrahydrofolate dehydrogenase, cyclohydrolase, and formyltetrahydrofolate synthetase 1 ( MTHFD1 ) polymorphisms have important roles in neurodevelopment. However, the association of MTHFD1 polymorphisms with children's intelligence changes in endemic fluorosis areas has been rarely explored. METHODS: A cross-sectional study was conducted in four randomly selected primary schools in Tongxu County, Henan Province, from April to May in 2017. A total of 694 children aged 8 to 12 years were included in the study with the recruitment by the cluster sampling method. Urinary fluoride (UF) and urinary creatinine were separately determined using the fluoride ion-selective electrode and creatinine assay kit. Children were classified as the high fluoride group and control group according to the median of urinary creatinine-adjusted urinary fluoride (UF Cr ) level. Four loci of MTHFD1 were genotyped, and the Combined Raven's Test was used to evaluate children's intelligence quotient (IQ). Generalized linear model and multinomial logistic regression model were performed to analyze the associations between children's UF Cr level, MTHFD1 polymorphisms, and intelligence. The general linear model was used to explore the effects of gene-environment and gene-gene interaction on intelligence. RESULTS: In the high fluoride group, children's IQ scores decreased by 2.502 when the UF Cr level increased by 1.0 mg/L (β = -2.502, 95% confidence interval [CI]:-4.411, -0.593), and the possibility for having "excellent" intelligence decreased by 46.3% (odds ratio = 0.537, 95% CI: 0.290, 0.994). Children with the GG genotype showed increased IQ scores than those with the AA genotype of rs11627387 locus in the high fluoride group ( P   <  0.05). Interactions between fluoride exposure and MTHFD1 polymorphisms on intelligence were observed (Pinteraction < 0.05). CONCLUSION Our findings suggest that excessive fluoride exposure may have adverse effects on children's intelligence, and changes in children's intelligence may be associated with the interaction between fluoride and MTHFD1 polymorphisms.
Child ; Creatinine ; Cross-Sectional Studies ; Fluorides/urine* ; Formate-Tetrahydrofolate Ligase ; Humans ; Intelligence/genetics* ; Methylenetetrahydrofolate Dehydrogenase (NADP) ; Methylenetetrahydrofolate Reductase (NADPH2)

Hepatocellular carcinoma (HCC) is the sixth most common malignancy and the fourth leading cause of cancer related death worldwide. China covers over half of cases, leading HCC to be a vital threaten to public health. Despite advances in diagnosis and treatments, high recurrence rate remains a major obstacle in HCC management. Multi-omics currently facilitates surveillance, precise diagnosis, and personalized treatment decision making in clinical setting. Non-invasive radiomics utilizes preoperative radiological imaging to reflect subtle pixel-level pattern changes that correlate to specific clinical outcomes. Radiomics has been widely used in histopathological diagnosis prediction, treatment response evaluation, and prognosis prediction. High-throughput sequencing and gene expression profiling enabled genomics and proteomics to identify distinct transcriptomic subclasses and recurrent genetic alterations in HCC, which would reveal the complex multistep process of the pathophysiology. The accumulation of big medical data and the development of artificial intelligence techniques are providing new insights for our better understanding of the mechanism of HCC via multi-omics, and show potential to convert surgical/intervention treatment into an antitumorigenic one, which would greatly advance precision medicine in HCC management.
Artificial Intelligence ; Carcinoma, Hepatocellular/therapy* ; Gene Expression Profiling ; Humans ; Liver Neoplasms/genetics* ; Prognosis