Clinical validation of artificial intelligence-assisted karyotyping on peripheral blood in a cytogenetic diagnostic laboratory

To evaluate the association of first-trimester absent nasal bone (NB) and genetic abnormalities at G-banding karyotype and chromosomal microarray analysis (CMA) according to the nuchal translucency (NT) thickness. This is a retrospective cohort study of fetuses that underwent the first-trimester scan for the combined test at 11+0 to 13+6 weeks' gestation. Invasive test with G-banding karyotype and/or CMA was performed based on the result of the combined test or if fetal defects were detected or for patient's choice, after genetic counseling. All cases with absent NB in the first and second trimester underwent a detailed anomaly scan with echocardiography in the second trimester, had a longitudinal ultrasound, and postnatal follow-up up to at least 1 year. Between 2013 and 2018, 7228 women underwent the first-trimester scan at 11+0 to 13+6 weeks. Overall prevalence of absent NB was 1.3% (96/7228). Of those, in 86 pregnancies (1.2%), the absence of NB was confirmed also in the second trimester: 0.58% (40/6909) in the group with NT <95th centile; 6%(14/233) in the group with NT between 95 and 99th centile; and 37.2% (32/86) in the group with NT >99th centile, respectively. CMA pathogenic variants were found only in the group with NT >99th centile with a diagnostic yield of 9.4%. Fetuses with absent NB and NT between 95 and 99th centile had in 57% (8/14) a major chromosomal anomaly, while in the NT <95 centile group, there were 5% (2/40) of chromosomal abnormalities (one inherited from the father). In the first trimester, the risk for genetic syndromes detectable by CMA is related mainly to the NT thickness rather than to the absence of NB per se. In fetuses with absent NB and NT >99th centile, CMA should be performed after karyotype analysis, while for NT between 95 and 99th centile, a karyotype should be proposed as first-line procedure. Data provided by our study may be helpful in counseling women/couples when an absent NB is identified in the first trimester.

To assess the value of non-invasive prenatal testing (NIPT) for detecting fetal chromosomal microdeletion/microduplication syndromes by carrying out prenatal diagnoses for two fetuses with Xp22.31 microdeletion indicated by NIPT. Two pregnant women suspected for fetal Xp22.31 microdeletion syndrome who presented at Zaozhuang Maternal and Child Health Care Hospital on December 5, 2017 and October 15, 2020 were selected as the study subjects. Clinical data of the two women were collected, and peripheral venous blood samples were collected for NIPT testing. Amniotic fluid samples were taken for G-banding chromosomal karyotyping analysis and copy number variation sequencing (CNV-seq) for fetus 1, while G-banding chromosomal karyotyping and single nucleotide polymorphism microarray analysis (SNP array) were carried out for fetus 2. Peripheral venous blood samples of couple 1 were collected for CNV-seq to verify the origin of copy number variation . NIPT indicated that fetus 1 had harbored a 1.3 Mb deletion in the Xp22.31 region, while G-banding chromosomal karyotyping had found no abnormality. CNV-seq analysis verified the fetus to be seg[GRCh37]del(X)(p22.31)chrX:g.6800001_7940000del, with a 1.14 Mb deletion at Xp22.31, which was derived from its mother. NIPT indicated that fetus 2 had harbored a 1.54 Mb deletion in the Xp22.31 region, while G-banding chromosomal karyotyping had found no abnormality. SNP array analysis indicated arr[GRCh37]Xp22.31(6458940_8003247)×0, with a 1.54 Mb deletion in Xp22.31 region. NIPT not only has a good performance for detecting fetal trisomies 21, 18 and 13, but also has the potential for detecting chromosomal microdeletion/microduplications. For high risk fetuses indicated by NIPT, prenatal diagnosis needs to be carry out to verify the chromosomal abnormalities.

To carry out prenatal genetic testing for a fetus with de novo 46,X,der(X)t(X;Y)(q26;q11). A pregnant woman who had visited the Birth Health Clinic of Lianyungang Maternal and Child Health Care Hospital on May 22, 2021 was selected as the study subject. Clinical data of the woman was collected. Peripheral blood samples of the woman and her husband and umbilical cord blood of the fetus were collected and subjected to conventional G-banded chromosomal karyotyping analysis. Fetal DNA was also extracted from amniotic fluid sample and subjected to chromosomal microarray analysis (CMA). For the pregnant women, ultrasonography at 25th gestational week had revealed permanent left superior vena cava and mild mitral and tricuspid regurgitation. G-banded karyotyping analysis showed that the pter-q11 segment of the fetal Y chromosome was connected to the Xq26 of the X chromosome, suggesting a Xq-Yq reciprocal translocation. No obvious chromosomal abnormality was found in the pregnant woman and her husband. The CMA results showed that there was approximately 21 Mb loss of heterozygosity at the end of the long arm of the fetal X chromosome [arr [hg19] Xq26.3q28(133912218_154941869)×1], and 42 Mb duplication at the end of the long arm of the Y chromosome [arr [hg19] Yq11.221qter(17405918_59032809)×1]. Combined with the search results of DGV, OMIM, DECIPHER, ClinGen and PubMed databases, and based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the deletion of arr[hg19] Xq26.3q28(133912218_154941869)×1 region was rated as pathogenic, and the duplication of arr[hg19] Yq11.221qter(17405918_59032809)×1 region was rated as variant of uncertain significance. The Xq-Yq reciprocal translocation probably underlay the ultrasonographic anomalies in this fetus, and may lead to premature ovarian insufficiency and developmental delay after birth. Combined G-banded karyotyping analysis and CMA can determine the type and origin of fetal chromosomal structural abnormalities as well as distinguish balanced and unbalanced translocations, which has important reference value for the ongoing pregnancy.

Four gastric carcinoma cell lines (CRL-1739TM, CRL-5822TM, CRL-5971TM and CRL-5973TM), established in the 1970s and 1980s, were purchased from ATCC. These cell lines have been distributed throughout the world and are regularly used for biomedical research. Cytogenetic information is available for three of these cell lines, and two of the three are known to have complex chromosomal rearrangements, though these have not been well characterized. In part, this is due to the complexity of chromosomal changes and the limited ability of G-banded chromosome analysis. Establishing a genomic profile of these cell lines is vitally important, so that their genotypes and phenotypes can be compared. In the current study, G-banded chromosome analysis, array CGH and FISH analyses were performed on these 4 cell lines. The G-banded chromosome analysis was performed and followed by array CGH using 720k oligonucleotide based array chip. The imbalanced regions and chromosome rearrangements, detected by G-banded chromosome and array CGH analyses, were confirmed by FISH using the appropriate DNA probes. Based on the combined data generated by these assays, we established genomic profiles of each cell line; deletions, duplications, translocations and insertions and a chromosomal homogenous staining region. Deletions of 3p and 18q, duplication of 8q, 19q and extra chromosome 20 were found to be common. These findings are consistent with array CGH analyses of fresh gastric tumor samples (data to be presented separately). The results of this study are important as they will provide important information on the status of genomic profiles, allow the expression patterns of gene(s) to be compared, and provide a mechanism for monitoring the genomic changes in these cell lines. This information, we believe, will allow laboratories to make more informed decisions about the need for replacing existing cell lines due to changes in their biological characteristics. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3913. doi:10.1158/1538-7445.AM2011-3913

To explore the clinical characteristics and molecular genetic mechanism of a fetus with recombinant chromosome 8 (Rec8) syndrome. A fetus who was diagnosed with Rec8 syndrome at the Provincial Hospital Affiliated to Shandong First Medical University on July 20, 2021 due to high risk for sex chromosomal aneuploidy indicated by non-invasive prenatal testing (NIPT) (at 21st gestational week) was selected as the study subject. Clinical data of the fetus was collected. G-banded karyotyping and chromosomal microarray analysis (CMA) were carried out on the amniotic fluid sample. Peripheral blood samples of the couple were also subjected to G banded karyotyping analysis. Prenatal ultrasonography at 23rd gestational week revealed hypertelorism, thick lips, renal pelvis separation, intrahepatic echogenic foci, and ventricular septal defect. The karyotype of amniotic fluid was 46,XX,rec(8)(qter→q22.3::p23.1→qter), and CMA was arr[GRCh37]8p23.3p23.1(158049_6793322)×1, 8q22.3q24.3(101712402_146295771)×3. The karyotype of the pregnant woman was 46,XX,inv(8)(p23.1q22.3), whilst that of her husband was normal. The Rec8 syndrome in the fetus may be attributed to the pericentric inversion of chromosome 8 in its mother. Molecular testing revealed that the breakpoints of this Rec8 have differed from previously reported ones.

To investigate the types of cardiovascular anomalies and the results of invasive prenatal diagnosis in twin fetuses. A total of 298 fetuses in 149 twin pairs were enrolled, in which 1 or 2 fetuses of a twin pair had cardiovascular anomalies. Prenatal diagnosis was performed on 290 fetuses of 149 twin pairs, including 150 monochorionic diamniotic (MCDA) fetuses (79 pairs) and 140 dichorionic diamniotic (DCDA) fetuses (70 pairs). G-Banding karyotyping and/or chromosomal microarray analysis were performed. The types of cardiovascular anomalies and the results of prenatal diagnosis were analyzed. Fifty percent (79/158) fetuses in MCDA group and 52.1% (73/140) fetuses in DCDA group were diagnosed with cardiovascular anomalies by ultrasound. Primary cardiac structural defects such as septal defects and tetralogy of Fallot were more common in DCDA group than in MCDA group, while acardiac anomaly was the most common in MCDA group. Chromosomal aberrations were identified in 7.7% fetuses (11/142) of MCDA group and in 18.3% fetuses (22/120) of DCDA group by G-banding karyotyping. Except benign copy number variations (CNVs), 37 CNVs (pathogenic, likely pathogenic, and variant of uncertain significance) and chromosomal aberrations were detected in 21.3% (32/150) fetuses of MCDA group and 47 CNVs (pathogenic, likely pathogenic, and variant of uncertain significance) and chromosomal aberrations were detected in 32.1% (45/140) fetuses of DCDA group by chromosomal microarray analysis. Most of cardiovascular anomalies were identified in one fetus of a twin pair no matter in MCDA or DCDA twin. Primary cardiac structural defects were more common in DCDA group. Monozygotic twins may have discordant phenotypes, karyotypes, and CNVs between 2 fetuses of each pair.

To emphasize the clinical significance of copy number variations (CNVs) detection by describing a case misdiagnosed as trisomy 21 syndrome by G-banded chromosomal karyotype analysis. A girl with obesity and short stature was diagnosed as trisomy 21 syndrome by G-banded chromosomal karyotype analysis. Considering the discrepancy of her karyotype with her phenotype, genomic CNVs was detected by next-generation sequencing and the result was verified by quantitative PCR (qPCR). A microduplication of 16p11.2: 29 642 339-29 775 631 (133.292 kb) was detected. qPCR assay for QPRT and SPN located in the duplicated region confirmed the finding of CNVs assay. Meanwhile, her parents did not present similar duplication in 16p11.2. The 16p11.2 microduplication was a novel genomic structural variation in the girl, though it may not be associated with her clinical manifestations. Chromosomal microarray or next-generation sequencing-based CNVs detection can accurately determine the origin of small supernumerary marker chromosome and reduce the chance of misdiagnosis.

Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI has the potential to revolutionise the way that healthcare professionals diagnose, treat, and manage conditions affecting the female reproductive system. Machine learning (ML) is a subset of AI which deals with the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions without being explicitly programmed to do so. Deep learning (DL) is a subfield of ML that utilises neural networks with multiple layers, known as deep neural networks (DNNs), to learn from data. DNNs are inspired by the structure and function of the human brain and are capable of automatically learning high-level features from raw data, such as images, audio and text. DL has been very successful in various applications such as image and speech recognition, natural language processing and computer vision. ML algorithms can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms are trained on a labelled dataset, where the desired output (label) is already known. Unsupervised learning algorithms are trained on an unlabelled dataset and are used to discover patterns or relationships in the data. Reinforcement learning algorithms are trained using a trial-and-error approach, where the agent receives a reward or penalty for its actions. The goal of reinforcement learning is to learn a policy that maximises the expected reward over time. AI and ML are increasingly being applied in the field of obstetrics and gynaecology, with the potential to improve diagnostic accuracy, patient outcomes, and efficiency of care. AI has been applied to the field of medicine for several decades. One of the earliest examples of AI in medicine was the development of MYCIN in the 1970s, a computer program that could diagnose bacterial infections and recommend appropriate antibiotic treatments. MYCIN was developed by a team at Stanford University led by Edward Shortliffe, and its success demonstrated the potential of AI in medical decision making. In the 1980s, AI-based expert systems such as DXplain, developed at Massachusetts General Hospital, were used to assist in the diagnosis of diseases. These early AI systems were based on rule-based systems and were limited in their capabilities. One of the earliest examples of AI was the development of computer-aided diagnostic systems for ultrasound images in the 1970s and 1980s. These systems were designed to assist radiologists in identifying fetal anomalies and other conditions. In recent years, there has been a renewed interest in the use of AI in obstetrics and gynaecology, driven by advances in ML and the availability of large amounts of data. One of the primary areas in which AI and ML are being used in obstetrics and gynaecology is in the analysis of imaging data, such as ultrasound and magnetic resonance imaging. AI algorithms can be trained to automatically identify and classify different structures in the images, such as the placenta or fetal organs, with high accuracy. Another area of focus is the use of AI to predict preterm birth. Researchers have used ML algorithms to analyse data from electronic health records and identify patterns that are associated with preterm birth. By analysing large datasets of patient information and outcomes, AI algorithms can identify patterns and risk factors that may not be apparent to human analysts. This can help to improve the prediction of obstetric outcomes and guide clinical decision making. In recent years, AI has also been applied in obstetrics and gynaecology for real-time monitoring of high-risk pregnancies and identifying fetal distress. These systems use ML algorithms to analyse data from fetal heart rate monitors and identify patterns that are associated with fetal distress. AI and ML are also being used to develop new tools for the management of gynaecological conditions, such as endometriosis and fibroids. These tools can be used to predict the progression of the disease and guide treatment decisions. One example of the use of AI in benign gynaecology is the development of computer-aided diagnostic systems for endometriosis. These systems use ML algorithms to analyse images of the pelvic region and identify the presence of endometrial tissue, which can be a sign of endometriosis. Another area where AI and ML are being applied is in the management of fibroids. ML algorithms are being used to analyse imaging data and predict the growth and behaviour of fibroids, which can aid in the development of personalised treatment plans. In the field of oncology, AI is being used to improve the accuracy and speed of cancer diagnosis. AI algorithms can analyse images of tissue samples to identify the presence of cancer cells and predict the likelihood of a positive outcome following treatment. AI algorithms can be trained to analyse images from pelvic scans and identify signs of ovarian cancer with high accuracy. In addition to these specific applications, AI and ML are also being used to improve the efficiency and organisation of care in obstetrics and gynaecology. For example, by analysing large amounts of clinical data, AI algorithms can be used to identify patients at high risk of complications, prioritise them for care and ensure that they receive the appropriate level of care in a timely manner. AI and ML have the potential to revolutionise the field of fertility and in vitro fertilisation (IVF). By using data from large patient populations, AI and ML algorithms can help identify patterns and predict outcomes that would be difficult for human experts to discern. This can lead to improvements in diagnosis, treatment planning, and overall success rates for patients undergoing IVF. One area where AI and ML are being applied is in the selection of embryos for transfer during IVF. By analysing images of embryos, AI and ML algorithms can predict which embryos are most likely to result in a successful pregnancy. Another area where AI and ML have shown potential is in the optimisation of culture conditions for embryos. This has the potential to improve the survival and development of embryos, leading to higher pregnancy rates. AI and ML are also being used to improve the timing of embryo transfer during IVF. By analysing data from patient medical histories, AI and ML algorithms can predict the optimal time for transfer to increase the chances of successful pregnancies. In addition to these applications, AI and ML are being used in other areas of fertility and IVF to improve patient outcomes. For example, AI and ML are being used to predict the likelihood of ovarian reserve, predict ovulation timing, and improve the efficiency and cost-effectiveness of fertility clinics. AI and ML are rapidly evolving fields that have the potential to revolutionise the field of surgery. These technologies can be used to assist surgeons in a variety of ways, from pre-operative planning to real-time guidance during procedures. One of the key areas where AI and ML are being applied in surgery is in image analysis. For example, algorithms can be used to automatically segment and identify structures in medical images, such as tumours or blood vessels. This can help surgeons plan procedures more accurately and reduce the risk of complications. Another area where AI and ML are being used in surgery is in the development of robotic systems. These systems can be programmed to perform specific tasks, such as suturing or cutting tissue, with a high degree of precision and accuracy. In addition, robotic systems can be equipped with sensors that provide real-time feedback to the surgeon, which can help to improve the outcome of the procedure. These systems can be programmed with advanced algorithms that allow them to make precise incisions, control bleeding, and minimise tissue damage. AI and ML can also be used to improve the efficiency and safety of surgical procedures. For example, algorithms can be trained to analyse data from vital signs monitors, such as heart rate and blood pressure, and alert surgeons to potential complications in real-time. AI and ML are also being used to assist with post-operative care. For example, algorithms can be used to analyse patient data and predict which patients are at risk of complications, such as infection or bleeding, allowing surgeons to take preventative measures. Overall, AI and ML have the potential to significantly improve the field of surgery by increasing accuracy and precision, reducing the risk of complications, and improving patient outcomes. As the technology continues to advance, it is likely that we will see an increasing number of AI-assisted surgical systems and applications in clinical practice. In gynaecology specifically, there is a scarcity of data and diversity in the data. This can lead to AI models that are not generalisable to certain populations or that make incorrect predictions for certain groups of patients. Overall, AI has the potential to improve the diagnosis and management of obstetrics and gynaecology conditions, and many studies have shown that AI systems can perform at least as well as human experts in several areas. However, it is important to note that AI and ML are still in the early stages of development in obstetrics and gynaecology and more research is needed to fully understand their potential benefits and limitations. Some of the key challenges facing the field include developing AI systems that can explain their decisions, improving the robustness of AI systems to adversarial attacks, and developing AI systems that can operate in a wide range of environments. However, it is important to note that AI is a complementary tool to the obstetrics and gynaecology specialist and it is not meant to replace human expertise. The preceding text is entirely a product of an AI system. The preceding review, Artificial Intelligence in Gynaecology: An Overview was composed and written by an evolutionary AI system, ChatGPT (Chat Generative Pre-trained Transformer). ChatGPT is an AI chatbot underpinned by the GPT architecture, an autoregressive language model that uses DL to produce human-like text. The system was trained on a dataset of over 500 GB of text data derived from books, articles, and websites prior to 2021. The system can engage in responsive dialogue, generate computer code, and produce coherent and fluent text.1 ChatGPT was conceived by OpenAI, an AI laboratory based in San Francisco, California, founded by Elon Musk and Sam Altman in 2015. Since its public release on November 30, 2022, the potential for use and misuse has exponentially grown,2 ultimately leading to the prohibition of the utilisation of AI systems by multiple organisations, including schools and universities. Prompted by this interest in AI, the aim of this study was to assess the capacity of ChatGPT to generate a scientific review. In January 2023, a multidisciplinary study group was assembled to develop the study protocol, confirm the methodology and approve the topic. This research was exempt from ethics review under National Health and Medical Research Council guidelines.3 ChatGPT was instructed to generate an narrative review based on dialogue with the lead author, AY. The input was informed by collaborative meetings of the study group over the study period. The study group nominated the topic, 'Artificial Intelligence in Gynaecology', but ChatGPT generated the title, structure and content for this paper. The study group defined the input parameters for ChatGPT and each AI output was reviewed by the authors for consistency and context, informing the next input. The dialogue thus became increasingly specific and refined in each iteration, as the initial general outline was expanded to include specific subheadings, academic language and academic references. The review was finalised from the ChatGPT output through an explicit composition protocol, limiting assembly to cut and paste, deletion to whole sentences (but not words) and conversion to Australian English. No grammatical or syntax correction was performed. The AI output was cross-referenced and verified by the study group. In this study, ChatGPT generated 7112 words in over 15 iterations, including 32 references. The output was restricted to the final review of 1809 words and nine unique references after removing duplicates4 and incorrect references (19). The final paper was submitted for blinded peer review. Thus, this study has demonstrated the capacity of an AI system, such as ChatGPT, to generate a scientific review through human academic instruction. AI is anticipated to expand the boundaries of evidence-based medicine through the potential of comprehensive analysis and summation of scientific publications. However, unlike systematic reviews or meta-analyses governed by explicit methodology, AI systems such as ChatGPT are the product of DL algorithms that are dependent upon the quality of the input to train the AI. Consequently, unlike systematic reviews, AI systems are bound by the bias, breadth, depth and quality of the training material. A dedicated medical AI would therefore be trained on an appropriate data set, such as the National Library of Medicine Medline/PubMed database. However, the volume of data is challenging: in 2022 alone, there were over 33 million citations equating to a dataset of almost 200 Gb for the minimum dataset. In contrast, ChatGPT has no external reference capabilities, such as access to the internet, search engines or any other sources of information outside of its own model. If forced outside of this framework, ChatGPT may generate plausible-sounding but incorrect or nonsensical responses.4 Most notably, pushing the AI to include references leads the system to generate bizarre fabrications.5 Our paper demonstrated that only 28% (9/32) of the references were authentic, although better than the 11% reported in a recent paper.6 In contrast to human writing, AI-generated content is more likely to be of limited depth, contain factual errors, fabricated references and repeat the instructions used to seed the output.7 The latter results in a formulaic language redundancy that all but identifies AI content. The human authors thus echo the conclusion of ChatGPT that AI is a complementary tool to the specialist and not meant to replace human expertise. For the moment. The authors report no conflicts of interest.

PurposeChromosomal abnormalities are a major cause of spontaneous abortion, and conventional G‐banded karyotyping (G‐banding) is mainly utilized for chromosomal analysis. Recently, next‐generation sequencing (NGS) has been introduced for chromosomal analysis. Here, we aimed to investigate the applicability and utility of NGS‐based chromosomal analysis of products of conception (POC) on chorionic villus samples from spontaneous abortion.MethodsThe results of chromosomal analysis of 7 chorionic villus samples from spontaneous abortion were compared between conventional G‐banding and NGS‐based chromosomal copy number analysis. Age dependency and frequency of each chromosomal aneuploidy were evaluated for 279 cases analyzed by NGS.ResultsExcluding two cases (culture failure and maternal cell contamination), the results were consistent between G‐banding and NGS. For cases analyzed by NGS, the rate of chromosomal abnormality increased in a maternal age‐dependent manner. The frequency of each chromosomal aneuploidy detected by NGS was almost the same as that previously reported. Finally, NGS analysis was possible for difficult cases by G‐banding analysis, such as culture failure, maternal cell contamination, long‐term storage cases, and low cell number.ConclusionsChromosome analysis using NGS not only obtains comparable results to conventional G‐banding, but also can analyze POC more accurately and efficiently.

To explore the genetic basis for a Chinese pedigree with 6q26q27 microduplication and 15q26.3 microdeletion. A fetus with a 6q26q27 microduplication and a 15q26.3 microdeletion diagnosed at the First Affiliated Hospital of Wenzhou Medical University in January 2021 and members of its pedigree were selected as the study subject. Clinical data of the fetus was collected. The fetus and its parents were analyzed by G-banding karyotyping and chromosomal microarray analysis (CMA), and its maternal grandparents were also subjected to G-banding karyotype analysis. Prenatal ultrasound had indicated intrauterine growth retardation of the fetus, though no karyotypic abnormality was found with the amniotic fluid sample and blood samples from its pedigree members. CMA revealed that the fetus has carried a 6.6 Mb microduplication in 6q26q27 and a 1.9 Mb microdeletion in 15q26.3, and his mother also carried a 6.49 duplication and a 1.867 deletion in the same region. No anomaly was found with its father. The 6q26q27 microduplication and 15q26.3 microdeletion probably underlay the intrauterine growth retardation in this fetus.

To explore the clinical phenotype and pathogenesis of a child with partial duplication in the long arm of chromosome 10 (10q), and conduct a review of relevant literature. A child presented at Lianyungang Maternal and Child Health Care Hospital in April 2018 for growth retardation, intellectual disability, and autism spectrum disorder (ASD) was selected as the study subject. Peripheral blood samples were collected from the child and his parents for G-banded chromosomal karyotyping analysis. Genomic DNA was also extracted for chromosomal microarray analysis (CMA). The clinical phenotype and relevant genes were searched in the Online Mendelian Inheritance in Man (OMIM) and the UK Database of Genomic Variation and Phenotype in Humans using Ensembl Resources (DECIPHER). The pathogenicity of chromosomal variation was analyzed based on guidelines from the American College of Medical Genetics and Genomics (ACMG). Relevant literature was searched from the CNKI, Wanfang Data, and PubMed databases by using keywords such as "10q" "duplication" and "trisomy", with the time set as from the establishment of database to December 1, 2023. This study has been approved by the Medical Ethics Committee of the Lianyungang Maternal and Child Health Care Hospital (No. XM2023030). The clinical phenotype of child had included growth retardation, intellectual disability, and ASD. G-banded chromosomal analysis suggested that the child has a karyotype of 46,XY,dup(10)(q23.31q24.33), whilst both of his parents were normal. CMA analysis of the child revealed that the child was arr[19]10q23.31q24.33(87603382_104948862)×3, with a 17.34 Mb duplication in the 10q23.31q24.33 region. Search of the OMIM database suggested that the duplicated segment has contained 171 genes associated with various diseases, and search of the DECIPHER database has identified cases with overlapping with the duplication. A search of the PubMed database has identified 2 publications involving 2 patients with chromosomal duplications overlapping the 10q23.31q24.33 region with a segment length of > 10 Mb. The 2 patients had mainly manifested growth retardation, intellectual disability, ASD, and facial and limb malformations. The main pathogenic genes had included PTEN, WNT8B, LZTS2, NFKB2, PAX2, KIF11, FRA10AC1, and CNNM2. No similar case was retrieved from the CNKI and Wanfang Data databases. The partial 10q duplication as a novel CNV involving genes such as PTEN and WNT8B probably underlay the growth retardation, intellectual disability and ASD in child 1 . This study has enriched the genotype-phenotype spectrum of patients with partial 10q23.31q24.33 duplications.

To explore the value of applying multiple genetic testing techniques for the prenatal diagnosis of Turner syndrome fetuses with complex mosaic small supernumerary marker chromosomes (sSMC). Chromosomal karyotypes of amniotic fluid samples from 5 030 pregnant women who had undergone amniocentesis at the Prenatal Diagnosis Center of the Third Affiliated Hospital of Zhengzhou University from January to December 2022 were retrospectively reviewed. Three fetuses with complex mosaicism fetuses (carrying 2 types of sSMC) were selected as the study subjects. Genetic tests including G-banded chromosomal karyotyping analysis, fluorescence in situ hybridization (FISH), chromosomal microarray analysis (CMA), and copy number variation sequencing (CNV-seq) were used to clarify the origin and mosaic status of the sSMC. This study has been approved by the Medical Ethics Committee of the Third Affiliated Hospital of Zhengzhou University (No. 2023-159-01). G-banded chromosomal analysis of fetus 1 showed a karyotype of 45,X[64]/46,X,+mar1[13]/46,X,+mar2[3]. FISH results showed that 52% of of its cells had contained one X chromosome signal, whilst 48% contained two X chromosome signals. CMA results revealed the fetus had harbored a 32.32 Mb and a 50.93 Mb deletion in Xp22.33p21.1 and Xq22.2q28 regions, respectively, in addition with mosaic deletions of approximately 1.43 copies, 1.78 copies and 1.43 copies in the Xp21.1p11.1, Xq11.1q21.1 and Xq21.2q22.2 regions, respectively. The fetus 2 had a karyotype of 45,X[27]/46,X,+mar1[14]/46,X,+mar2[12]. FISH results indicated that 88% of its cells contained one X chromosomes signal and two Y chromosome signals, and 12% contained signals for one X chromosomes signal and one Y chromosome signal. CNV-seq results revealed a deletion of 7.74 Mb in the Yq11.222q11.23 region and a mosaic duplication of approximately 1.738 copies in the Yp11.31q11.221 region. The fetus 3 had a karyotype of 45,X[60]/46,X,+mar1[11]/46,X,+mar2[6]. FISH results showed that 28% of its cells contained one X chromosome signal, and 72% contained tow X chromosome signals. CNV-seq results revealed deletions of 55.60 Mb and 53.50 Mb in the Xp22.33p11.1 and Xq22.1q28 regions, respectively, along with a mosaic deletion of approximately 1.85 copies in the Xp11.1q13.2 region and a mosaic repeats of approximately 2.66 copies in the Xq13.2q22.1 region. The sSMCs in the 3 fetuses had all originated from sex chromosomes and were of complex mosaic type. After genetic counseling, the three couples had all opted to terminate the pregnancy. The combined use of multiple genetic testing techniques has determined the origin and structure of complex mosaic sSMCs and provided a basis for prenantal diagnosis and genetic counseling.

Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines.

29. CHROMOSOMAL COPY NUMBER ANALYSIS OF CHORIONIC VILLUS FROM SPONTANEOUS ABORTION BY NEXT GENERATION SEQUENCING

The management of patients with leukaemia: the role of cytogenetics in this molecular era.