OligoM-Cancer: A multidimensional information platform for deep phenotyping of heterogenous oligometastatic cancer.

Patients with oligometastatic cancer (OMC) exhibit better response to local therapeutic interventions and a more treatable tendency than those with polymetastatic cancers. However, studies on OMC are limited and lack effective integration for systematic comparison and personalized application, and the diagnosis and precise treatment of OMC remain controversial. The application of large language models in medicine remains challenging because of the requirement of high-quality medical data. Moreover, these models must be enhanced using precise domain-specific knowledge. Therefore, we developed the OligoM-Cancer platform (http://oligo.sysbio.org.cn), pioneering knowledge curation that depicts various aspects of oligometastases spectrum, including markers, diagnosis, prognosis, and therapy choices. A user-friendly website was developed using HTML, FLASK, MySQL, Bootstrap, Echarts, and JavaScript. This platform encompasses comprehensive knowledge and evidence of phenotypes and their associated factors. With 4059 items of literature retrieved, OligoM-Cancer includes 1345 valid publications and 393 OMC-associated factors. Additionally, the included clinical assistance tools enhance the interpretability and credibility of clinical translational practice. OligoM-Cancer facilitates knowledge-guided modeling for deep phenotyping of OMC and potentially assists large language models in supporting specialised oligometastasis applications, thereby enhancing their generalization and reliability.

A deep learning-based cascade algorithm for pancreatic tumor segmentation.

Pancreatic tumors are small in size, diverse in shape, and have low contrast and high texture similarity with surrounding tissue. As a result, the segmentation model is easily confused by complex and changeable background information, leading to inaccurate positioning of small targets and false positives and false negatives. Therefore, we design a cascaded pancreatic tumor segmentation algorithm. In the first stage, we use a general multi-scale U-Net to segment the pancreas, and we exploit a multi-scale segmentation network based on non-local localization and focusing modules to segment pancreatic tumors in the second stage. The non-local localization module learns channel and spatial position information, searches for the approximate area where the pancreatic tumor is located from a global perspective, and obtains the initial segmentation results. The focusing module conducts context exploration based on foreground features (or background features), detects and removes false positive (or false negative) interference, and obtains more accurate segmentation results based on the initial segmentation. In addition, we design a new loss function to alleviate the insensitivity to small targets. Experimental results show that the proposed algorithm can more accurately locate pancreatic tumors of different sizes, and the Dice coefficient outperforms the existing state-of-the-art segmentation model. The code will be available at https://github.com/HeyJGJu/Pancreatic-Tumor-SEG.

CDI-NSTSEG: A clinical diagnosis-inspired effective and efficient framework for non-salient small tumor segmentation.

To accurately segment various clinical lesions from computed tomography(CT) images is a critical task for the diagnosis and treatment of many diseases. However, current segmentation frameworks are tailored to specific diseases, and limited frameworks can detect and segment different types of lesions. Besides, it is another challenging problem for current segmentation frameworks to segment visually inconspicuous and small-scale tumors (such as small intestinal stromal tumors and pancreatic tumors). Our proposed framework, CDI-NSTSEG, efficiently segments small non-salient tumors using multi-scale visual information and non-local target mining. CDI-NSTSEG follows the diagnostic process of clinicians, including preliminary screening, localization, refinement, and segmentation. Specifically, we first explore to extract the unique features at three different scales (1×, 0.5×, and 1.5×) based on the scale space theory. Our proposed scale fusion module (SFM) hierarchically fuses features to obtain a comprehensive representation, similar to preliminary screening in clinical diagnosis. The global localization module (GLM) is designed with a non-local attention mechanism. It captures the long-range semantic dependencies of channels and spatial locations from the fused features. GLM enables us to locate the tumor from a global perspective and output the initial prediction results. Finally, we design the layer focusing module (LFM) to gradually refine the initial results. LFM mainly conducts context exploration based on foreground and background features, focuses on suspicious areas layer-by-layer, and performs element-by-element addition and subtraction to eliminate errors. Our framework achieves state-of-the-art segmentation performance on small intestinal stromal tumor and pancreatic tumor datasets. CDI-NSTSEG outperforms the best comparison segmentation method by 7.38% Dice on small intestinal stromal tumors.

DRGKB: a knowledgebase of worldwide diagnosis-related groups' practices for comparison, evaluation and knowledge-guided application.

As a prospective payment method, diagnosis-related groups (DRGs)'s implementation has varying effects on different regions and adopt different case classification systems. Our goal is to build a structured public online knowledgebase describing the worldwide practice of DRGs, which includes systematic indicators for DRGs' performance assessment. Therefore, we manually collected the qualified literature from PUBMED and constructed DRGKB website. We divided the evaluation indicators into four categories, including (i) medical service quality; (ii) medical service efficiency; (iii) profitability and sustainability; (iv) case grouping ability. Then we carried out descriptive analysis and comprehensive scoring on outcome measurements performance, improvement strategy and specialty performance. At last, the DRGKB finally contains 297 entries. It was found that DRGs generally have a considerable impact on hospital operations, including average length of stay, medical quality and use of medical resources. At the same time, the current DRGs also have many deficiencies, including insufficient reimbursement rates and the ability to classify complex cases. We analyzed these underperforming parts by domain. In conclusion, this research innovatively constructed a knowledgebase to quantify the practice effects of DRGs, analyzed and visualized the development trends and area performance from a comprehensive perspective. This study provides a data-driven research paradigm for following DRGs-related work along with a proposed DRGs evolution model. Availability and implementation: DRGKB is freely available at http://www.sysbio.org.cn/drgkb/. Database URL: http://www.sysbio.org.cn/drgkb/.

RARPKB: a knowledge-guide decision support platform for personalized robot-assisted surgery in prostate cancer.

BACKGROUND: Robot-assisted radical prostatectomy (RARP) has emerged as a pivotal surgical intervention for the treatment of prostate cancer (PCa). However, the complexity of clinical cases, heterogeneity of PCa, and limitations in physician expertise pose challenges to rational decision-making in RARP. To address these challenges, the authors aimed to organize the knowledge of previously complex cohorts and establish an online platform named the RARP knowledge base (RARPKB) to provide reference evidence for personalized treatment plans. MATERIALS AND METHODS: PubMed searches over the past two decades were conducted to identify publications describing RARP. The authors collected, classified, and structured surgical details, patient information, surgical data, and various statistical results from the literature. A knowledge-guided decision-support tool was established using MySQL, DataTable, ECharts, and JavaScript. ChatGPT-4 and two assessment scales were used to validate and compare the platform. RESULTS: The platform comprised 583 studies, 1589 cohorts, 1 911 968 patients, and 11 986 records, resulting in 54 834 data entries. The knowledge-guided decision support tool provide personalized surgical plan recommendations and potential complications on the basis of patients' baseline and surgical information. Compared with ChatGPT-4, RARPKB outperformed in authenticity (100% vs. 73%), matching (100% vs. 53%), personalized recommendations (100% vs. 20%), matching of patients (100% vs. 0%), and personalized recommendations for complications (100% vs. 20%). Postuse, the average System Usability Scale score was 88.88±15.03, and the Net Promoter Score of RARPKB was 85. The knowledge base is available at: http://rarpkb.bioinf.org.cn . CONCLUSIONS: The authors introduced the pioneering RARPKB, the first knowledge base for robot-assisted surgery, with an emphasis on PCa. RARPKB can assist in personalized and complex surgical planning for PCa to improve its efficacy. RARPKB provides a reference for the future applications of artificial intelligence in clinical practice.

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Abnormal expressions of PURPL, miR-363-3p and ADAM10 predicted poor prognosis for patients with ovarian serous cystadenocarcinoma.

Objective: This study aimed to elucidate the prognostic implications of deviant expressions of long non-coding RNA (lncRNA) p53 upregulated regulator of p53 levels (PURPL), microRNA-363-3p (miR-363-3p), and ADAM metallopeptidase domain 10 (ADAM10) in patients diagnosed with ovarian serous cystadenocarcinoma (OSC). Methods: To predict and refine the targeted miRNAs and downstream target genes for PURPL, we utilized open medical databases. Through the employment of real-time RT-PCR, we conducted tissue analysis to discern the expressions of PURPL, miR-363-3p, and ADAM10 in both OSC and control tissues. The pathological correlations in the clinic and the prognostic implications of deviant expressions of PURPL, miR-363-3p, and ADAM10 in OSC patients were analyzed independently. Results: Database inquiries revealed that PURPL might target miR-363-3p, and in turn, miR-363-3p could target ADAM10. Differential expression of PURPL, miR-363-3p, and ADAM10 was observed between OSC and paired tissues. The premature version of miR-363-3p, miR-363, correlated with overall survival (OS), while ADAM10 corresponded with progression-free survival (PFS) in ovarian cancer patients. Tissue detection displayed significantly elevated expressions of PURPL and ADAM10, and conspicuously diminished expressions of miR-363-3p in OSC tissues compared to the control tissues (P<0.05). A negative correlation was observed between the expressions of PURPL and miR-363-3p, and miR-363-3p and ADAM10, while a positive correlation was found between PURPL and ADAM10 in different ovarian tissues (P<0.05). In OSC tissues, upregulation of PURPL was associated with an advanced clinical stage, TP53 mutation, and lymph node metastasis (P<0.05), downregulation of miR-363-3p was associated with a more advanced clinical stage and lymph node metastasis (P<0.05), and overexpression of ADAM10 correlated with a more advanced FIGO stage. High expressions of PURPL and ADAM10, and low expression of miR-363-3p, were linked with poor PFS and OS in OSC patients, respectively (P<0.05). In addition, OSC patients with elevated PURPL and reduced miR-363-3p, patients with elevated PURPL and ADAM10, and patients with reduced miR-363-3p and elevated ADAM10 also demonstrated worse PFS and OS, respectively (P<0.05). Conclusions: The anomalous expressions of PURPL, miR-363-3p, and ADAM10 might contribute to the pathogenesis of OSC via up-down stream regulation, and these abnormal expressions could serve as potential prognostic indicators for OSC patients.

CTRR-ncRNA: A Knowledgebase for Cancer Therapy Resistance and Recurrence Associated Non-coding RNAs.

Cancer therapy resistance and recurrence (CTRR) are the dominant causes of death in cancer patients. Recent studies have indicated that non-coding RNAs (ncRNAs) can not only reverse the resistance to cancer therapy but also are crucial biomarkers for the evaluation and prediction of CTRR. Herein, we developed CTRR-ncRNA, a knowledgebase of CTRR-associated ncRNAs, aiming to provide an accurate and comprehensive resource for research involving the association between CTRR and ncRNAs. Compared to most of the existing cancer databases, CTRR-ncRNA is focused on the clinical characterization of cancers, including cancer subtypes, as well as survival outcomes and responses to personalized therapy of cancer patients. Information pertaining to biomarker ncRNAs has also been documented for the development of personalized CTRR prediction. A user-friendly interface and several functional modules have been incorporated into the database. Based on the preliminary analysis of genotype-phenotype relationships, universal ncRNAs have been found to be potential biomarkers for CTRR. The CTRR-ncRNA is a translation-oriented knowledgebase and it provides a valuable resource for mechanistic investigations and explainable artificial intelligence-based modeling. CTRR-ncRNA is freely available to the public at http://ctrr.bioinf.org.cn/.

[The phenotypes and genotypes of four patients with Dubin-Johnson syndrome].

OBJECTIVE: To explore the genetic etiology in four patients with hyperbilirubinemia, and discuss the correlation between clinical characteristics and molecular basis. METHODS: The data of clinical manifestation and auxiliary examinations were collected. Genomic DNA of the four patients was extracted and analyzed by next-generation sequencing using the panel including genes involved in hereditary metabolic liver diseases. Suspected variants were verified by Sanger sequencing. RESULTS: All of the four patients were males with normal liver enzymes. It was revealed that all the patients had heterozygous variants, among which c.3011C>T, c.2443C>T and c.2556del were the variants which have not been reported previously. CONCLUSION: All of the patients were diagnosed as Dubin-Johnson syndrome (DJS) caused by ABCC2 gene variants. The novel variants add to the spectrum of genetic variants of the disease. Because of the favorite prognosis, precise diagnosis can greatly reduce the psychological pressure of patients and avoid excessive treatments. At the same time, it could provide pertinent genetic counseling for the families.

A novel mutation of KCNJ1 identified in an affected child with nephrolithiasis.

Nephrolithiasis is not common in children, but the incidence is gradually increased in these years. Urinary tract malformations, urinary infection, dietary habits, geographic region and genetic factor are involved in the etiology of nephrolithiasis. For the affected child, it is especially important to elucidate the etiology, which may provide an accurate diagnosis, a personalized therapy and effective follow-up strategy. Here to seek the etiology of a ten-year-old boy incidentally found with nephrolithiasis, next generation sequencing (NGS) including a panel with 248 genes involved in hereditary kidney diseases was performed for the boy and identified two mutations of KCNJ1, c.89G > A (p.C30Y) and c.65G > T (p.R22M), and the later was a novel missense mutation originated from his father. The child was confirmed with type II Bartter syndrome (BS) caused by KCNJ1 mutations. Our study suggests that BS may be difficult to get diagnosed at an early stage based on clinical manifestations or biochemical laboratory tests, and NGS is an efficient way to determine the etiology and provide further treatment and guide fertility counseling for the affected family.

5G, Big Data, and AI for Smart City and Prevention of Virus Infection.

With the development of urbanization, artificial intelligence, communication technology, and the Internet of Things, cities have evolved a new ecology from traditional city structures, that is, smart city. Combining 5G and big data, the applications of smart cities have been extended to every aspect of residents' lives. Based on the popularization of communication equipment and sensors, the great improvement in data transmission and processing technology, the production efficiency in medical field, industrial field, and security field has been improved. This chapter introduces the current research related to smart cities, including its architecture, technologies, and equipment involved. Then it discussed the challenges and opportunities of explainable artificial intelligence (XAI), which is the next important development direction of AI, especially in the medical field, where patients and medical personnel have non-negligible needs for the interpretability of AI models. Then, taking COVID-19 as an example, it discussed how smart cities play a role during virus infection and introduced the specific applications designed so far. Finally, it discussed the shortcomings of the current situation and the aspects that can be improved in the future.

[Prenatal diagnosis and genetic counselling for a pedigree carrying a large fragment deletion of 13q].

OBJECTIVE: To carry out prenatal diagnosis for a fetus with normal ultrasonographic finding at 20 weeks' gestation but a copy number variant(CNV) of 13q indicated by non-invasive prenatal test (NIPT). METHODS: Karyotyping analysis and chromosomal CNV assay were carried out on the amniotic fluid sample. Parental peripheral blood sample was collected for chromosomal analysis. Detailed fetal ultrasound scan was carried out to rule out structural abnormalities of the fetus. RESULTS: The fetus was detected with a heterozygous 10.14 Mb deletion at 13q21.1q21.32, which has originated from the phenotypically normal mother. No apparent karyotypic abnormality was detected in the fetus and its parents. No ultrasonic abnormality was found in the fetus. CONCLUSION: Both the fetus and its mother have carried a heterozygous 10.14 Mb deletion at 13q21.1q21.32 and presented normal phenotypes.Combined with literature review, the segmental deletion was judged to be a benign variant.

Whole-exome sequencing identified novel variants in three Chinese Leigh syndrome pedigrees.

Leigh syndrome (LS), the most common mitochondrial disease in early childhood, usually manifests variable neurodegenerative symptoms and typical brain magnetic resonance imaging (MRI) lesions. To date, pathogenic variants in more than 80 genes have been identified. However, there are still many cases without molecular diagnoses, and thus more disease-causing variants need to be unveiled. Here, we presented three clinically suspected LS patients manifesting neurological symptoms including developmental delay, hypotonia, and epilepsy during the first year of age, along with symmetric brain lesions on MRI. We explored disease-associated variants in patients and their nonconsanguineous parents by whole-exome sequencing and subsequent Sanger sequencing verification. Sequencing data revealed three pairs of disease-associated compound heterozygous variants: c.1A>G (p.Met1?) and 409G>C (p.Asp137His) in SDHA, c.1253G>A (p.Arg418His) and 1300C>T (p.Leu434Phe) in NARS2, and c.5C>T (p.Ala2Val) and 773T>G (p.Leu258Trp) in ECHS1. Among them, the likely pathogenic variants c.409G>C (p.Asp137His) in SDHA, c.1300C>T (p.Leu434Phe) in NARS2, and c.773T>G (p.Leu258Trp) in ECHS1 were newly identified. Segregation analysis indicated the possible disease-causing nature of the novel variants. In silico prediction and three-dimensional protein modeling further suggested the potential pathogenicity of these variants. Our discovery of novel variants expands the gene variant spectrum of LS and provides novel evidence for genetic counseling.

CRPMKB: a knowledge base of cancer risk prediction models for systematic comparison and personalized applications.

MOTIVATION: In the era of big data and precision medicine, accurate risk assessment is a prerequisite for the implementation of risk screening and preventive treatment. A large number of studies have focused on the risk of cancer, and related risk prediction models have been constructed, but there is a lack of effective resource integration for systematic comparison and personalized applications. Therefore, the establishment and analysis of the cancer risk prediction model knowledge base (CRPMKB) is of great significance. RESULTS: The current knowledge base contains 802 model data. The model comparison indicates that the accuracy of cancer risk prediction was greatly affected by regional differences, cancer types and model types. We divided the model variables into four categories: environment, behavioral lifestyle, biological genetics and clinical examination, and found that there are differences in the distribution of various variables among different cancer types. Taking 50 genes involved in the lung cancer risk prediction models as an example to perform pathway enrichment analyses and the results showed that these genes were significantly enriched in p53 Signaling and Aryl Hydrocarbon Receptor Signaling pathways which are associated with cancer and specific diseases. In addition, we verified the biological significance of overlapping lung cancer genes via STRING database. CRPMKB was established to provide researchers an online tool for the future personalized model application and developing. This study of CRPMKB suggests that developing more targeted models based on specific demographic characteristics and cancer types will further improve the accuracy of cancer risk model predictions. AVAILABILITY AND IMPLEMENTATION: CRPMKB is freely available at http://www.sysbio.org.cn/CRPMKB/. The data underlying this article are available in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[Analysis of OTOGL gene variants in a child with moderate non-syndromic hearing loss].

OBJECTIVE: To explore the genetic basis for a child with moderate non-syndromic hearing loss. METHODS: Next generation sequencing was carried out for the child. Co-segregation of the phenotype and candidate variants was verified among his family members by Sanger sequencing. RESULTS: The child was found to harbor biallelic variants of the OTOGL gene, namely c.2773C>T (p.Arg925Ter) and c.2826C>G (p.Tyr942Ter), which were respectively inherited from his phenotypically normal father and mother. Both variants were predicted to cause premature termination of protein synthesis and be disease causing by MutationTaster software. The c.2826C>G (p.Tyr942Ter) variant has not been recorded in the Human Gene Mutation Database. Based on the guidelines of the American College of Medical Genetics and Genomics, both variants were predicted to be pathogenic (PVS1+PM2+PM4+PP3+PP5 and PVS1+PM2+PM4+PP3, respectively). CONCLUSION: The c.2773C>T (p.Arg925Ter) and c.2826C>G (p.Tyr942Ter) variants of the OTOGL gene probably underlay the hearing loss in this child.

HFBD: a biomarker knowledge database for heart failure heterogeneity and personalized applications.

MOTIVATION: Heart failure (HF) is a cardiovascular disease with a high incidence around the world. Accumulating studies have focused on the identification of biomarkers for HF precision medicine. To understand the HF heterogeneity and provide biomarker information for the personalized diagnosis and treatment of HF, a knowledge database collecting the distributed and multiple-level biomarker information is necessary. RESULTS: In this study, the HF biomarker knowledge database (HFBD) was established by manually collecting the data and knowledge from literature in PubMed. HFBD contains 2618 records and 868 HF biomarkers (731 single and 137 combined) extracted from 1237 original articles. The biomarkers were classified into proteins, RNAs, DNAs and the others at molecular, image, cellular and physiological levels. The biomarkers were annotated with biological, clinical and article information as well as the experimental methods used for the biomarker discovery. With its user-friendly interface, this knowledge database provides a unique resource for the systematic understanding of HF heterogeneity and personalized diagnosis and treatment of HF in the era of precision medicine. AVAILABILITY AND IMPLEMENTATION: The platform is openly available at http://sysbio.org.cn/HFBD/.

[Analysis of TMC1 gene variants and prenatal diagnosis in four Chinese families affected with deafness].

OBJECTIVE: To explore the genetic basis of four Chinese families affected with deafness. METHODS: All probands were subjected to next generation sequencing (NGS). Suspected variant were verified by Sanger sequencing among the family members. Prenatal diagnosis was provided for three couples through Sanger sequencing. RESULTS: All probands were found to carry pathogenic variants of the TMC1 gene, which included c.100C>T (p.R34X) and c.642+4A>C in family 1, c.582G>A (p.W194X) and c.589G>A (p.G197R) in family 2, c.1396_1398delAAC and c.1571T>C (p.F524S) in family 3, and homozygosity of c.2050G>C (p.D684H) in family 4. All parents were heterozygous carriers of the variants. The c.642+4A>C and c.1571T>C (p.F524S) were unreported previously. Prenatal diagnosis revealed that none of the fetuses were affected. Follow-up confirmed that all newborns had normal hearing. CONCLUSION: Variant of the TMC1 gene probably underlay the deafness in the four families. Above findings have enhanced our understanding of the function of the TMC1 gene and enriched its variant spectrum. The results also facilitated genetic counseling and prenatal diagnosis for the families.

[Prenatal diagnosis of a fetus with X-linked hypohidrotic ectodermal dysplasia].

OBJECTIVE: To detect variant of EDA gene in a fetus with absence of germ teeth detected by prenatal ultrasonography. METHODS: Clinical data and amniotic fluid and peripheral venous blood samples of the pregnant woman were collected for the analysis. Following extraction of genome DNA, the coding regions of the EDA gene were amplified by PCR and subjected to next-generation sequencing. Candidate variant was verified by Sanger sequencing. RESULTS: The pregnant woman was found to carry a heterozygous c.574G>A variant in the EDA gene, for which the fetus was hemizygous. Bioinformatic analysis suggested the variant to be pathogenic. CONCLUSION: Combined ultrasonographic and genetic findings suggested the fetus is affected with X-linked hypohidrotic ectodermal dysplasia due to pathogenic variant of the EDA gene.

[Ultrasonographic manifestation and genetic analysis of a fetus with nephronophthisis type 2].

OBJECTIVE: To carry out genetic analysis for a family with a fetus manifesting bilateral polycystic renal dysplasia and oligohydramnios at 16+ gestational week and a previous history for fetal renal anomaly. METHODS: Ultrasound scan was carried out to detect the morphological changes. Following genetic counselling, the parents had decided to terminate the pregnancy. Fetal kidneys were subjected to histological examination. Target capture and next generation sequencing (NGS) was applied to the abortus to detect potential variants. The results were verified by Sanger sequencing. RESULTS: Histological examination of fetal kidneys revealed cystic changes without cortex, medulla or normal renal structure. NGS has identified a heterozygous c.100+1G>A variant and deletion of exon 3 of the INVS gene, which were respectively inherited from the mother and father. CONCLUSION: Through NGS and Sanger sequencing, the fetus was diagnosed with type II nephronophthisis (NPHP2). Above result can provide guidance for further pregnancy and enforce understanding of clinical features and genetic etiologies for NPHP.