Association of Congenital Heart Defects (CHD) with Factors Related to Maternal Health and Pregnancy in Newborns in Puerto Rico

Background:Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States,there is a compelling need to investigate the intricate interplay among body mass index(BMI),pregesta-tional,and gestational maternal diabetes,and their potential impact on the occurrence of congenital heart defects(CHD)during neonatal development.Methods:Using the comprehensive System of Vigilance and Surveillance of Congenital Defects in Puerto Rico,we conducted a focused analysis on neonates diagnosed with CHD between 2016 and 2020.Our assessment encompassed a range of variables,including maternal age,gestational age,BMI,pregestational diabetes,gestational diabetes,hypertension,history of abortion,and presence of preeclampsia.Results:A cohort of 673 patients was included in our study.The average maternal age was 26 years,within a range of 22 to 32 years.The mean gestational age measured 39 weeks,with a median span of 38 to 39 weeks.Of the 673 patients,274(41%)mothers gave birth to neonates diagnosed with CHD.Within this group,22 cases were linked to pre-gestational diabetes,while 202 were not;20 instances were associated with gestational diabetes,compared to 200 without;and 148 cases exhibited an overweight or obese BMI,whereas 126 displayed a normal BMI.Conclusion:We identified a statistically significant correlation between pre-gestational diabetes mellitus and the occurrence of CHD.However,our analysis did not show a statistically significant association between maternal BMI and the likelihood of CHD.These results may aid in developing effective strategies to prevent and manage CHD in neonates.

CHD2基因相关癫痫的临床特点和遗传学分析

目的研究CHD2基因变异相关癫痫患者的临床表型及基因型特点。方法对6例CHD2基因变异相关癫痫患者的临床表现、脑电图、基因特点及抗癫痫发作药物疗效等进行回顾性总结分析。结果6例患儿中男5例、女1例,癫痫起病年龄为1岁8个月至12岁。癫痫发作类型包括局灶性发作及全面性强直阵挛发作各3例次,眼睑肌阵挛伴或不伴失神发作2例次,不典型失神发作、痉挛发作、肌阵挛发作及强直发作各1例次。3例诊断为癫痫综合征,其中2例为Jeavons综合征,1例为Lennox-Gastaut综合征;2例具有光敏性。共患病中智力障碍6例,注意力缺陷多动障碍3例,孤独症谱系疾病2例,精神障碍1例。6例CHD2基因变异中4例为新发突变,2例为母源;其中无义突变3例,错义突变2例,片段缺失1例。末次随访年龄5岁至15岁10月龄,5例患儿抗癫痫药物规律治疗,4例有效,其中2例癫痫发作控制超2年,1例控制1年8月。结论癫痫发作是CHD2基因变异的常见表型,癫痫起病年龄差别较大,表型为Jeavons综合征者预后不良,丙戊酸钠对CHD2基因变异相关癫痫疗效相对较好。精神障碍为罕见临床表型。

Built defects of homogeneous junction to enhance the lithium storage capacity of niobium pentoxide materials

Niobium pentoxide(Nb_(2)O_(5))is deemed one of the promising anode materials for lithium-ion batteries(LIBs)for its outstanding intrinsic fast Li-(de)intercalation kinetics.The specific capacity,however,is still limited,because the(de)intercalation of excessive Li-ions brings the undesired stress to damage Nb_(2)O_(5) crystals.To increase the capacity of Nb_(2)O_(5) and alleviate the lattice distortion caused by stress,numerous homogeneous H-and M-phases junction interfaces were proposed to produce coercive stress within theNb_(2)O_(5)crystals.Such interfaces bring about rich oxygen vacancies with structural shrinkage tendency,which pre-generate coercive stress to resist the expansion stress caused by excessive Li-ions intercalation.Therefore,the synthesized Nb_(2)O_(5) achieves the highest lithium storage capacity of 315 mA h g−1 to date,and exhibits high-rate performance(118 mA h g^(-1) at 20 C)as well as excellent cycling stability(138 mA h g^(-1) at 10 C after 600 cycles).

Effect of Vacancy Defects on the Properties of CoS_(2) and FeS_(2)

In order to explore the effect of vacancy defects on the structural,electronic,magnetic and optical properties of CoS_(2) and FeS_(2),first-principles calculation method was used to investigate the alloys.The calculated results of materials without vacancy are consistent with those reported in the literatures,while the results of materials with vacancy defect were different from those of literatures due to the difference vacancy concentration.The Co vacancy defect hardly changes the half-metallic characteristic of CoS_(2).The Fe vacancy defect changes FeS_(2) from semiconductor to half-metal,and the bottom of the spin-down conduction band changes from the p orbital state of S to the d(t_(2g))orbital state of Fe,while the top of the valence band remains the d orbital d(eg)state of Fe.The half-metallic Co vacancy defects of CoS_(2) and Fe vacancy defects of FeS_(2) are expected to be used in spintronic devices.S vacancy defects make both CoS_(2) and FeS_(2) metallic.Both the Co and S vacancy defects lead to the decrease of the magnetic moment of CoS_(2),while both the Fe and S vacancy defects lead to the obvious magnetic property of FeS_(2).Vacancy defects enhance the absorption coefficient of infrared band and long band of visible light obviously,and produce obvious red shift phenomenon,which is expected to be used in photoelectric devices.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

SAM Era:Can It Segment Any Industrial Surface Defects?

Segment Anything Model(SAM)is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model.Due to its superior performance in general object segmentation,it quickly gained attention and interest.This makes SAM particularly attractive in industrial surface defect segmentation,especially for complex industrial scenes with limited training data.However,its segmentation ability for specific industrial scenes remains unknown.Therefore,in this work,we select three representative and complex industrial surface defect detection scenarios,namely strip steel surface defects,tile surface defects,and rail surface defects,to evaluate the segmentation performance of SAM.Our results show that although SAM has great potential in general object segmentation,it cannot achieve satisfactory performance in complex industrial scenes.Our test results are available at:https://github.com/VDT-2048/SAM-IS.

Micro defects formation and dynamic response analysis of steel plate of quasi-cracking area subjected to explosive load

As the protective component,steel plate had attracted extensive attention because of frequently threats of explosive loads.In this paper,the evolution of microstructure and the mechanism of damage in the quasi-cracking area of steel plate subjected to explosive load were discussed and the relationships between micro defects and dynamic mechanical response were revealed.After the explosion experiment,five observation points were selected equidistant from the quasi-cracking area of the section of the steel plate along the thickness direction,and the characteristics of micro defects at the observation points were analyzed by optical microscope(OM),scanning electron microscope(SEM) and electron backscattered diffraction(EBSD).The observation result shows that many slip bands(SBs) appeared,and the grain orientation changed obviously in the steel plate,the two were the main damage types of micro defects.In addition,cracks,peeling pits,grooves and other lager micro defects were appeared in the lower area of the plate.The stress parameters of the observation points were obtained through an effective numerical model.The mechanism of damage generation and crack propagation in the quasicracking area were clarified by comparing the specific impulse of each observation point with the corresponding micro defects.The result shows that the generation and expansion of micro defects are related to the stress area(i.e.the upper compression area,the neutral plane area,and the lower tension area).The micro defects gather and expand at the grain boundary,and will become macroscopic damage under the continuous action of tensile stress.Besides,the micro defects at the midpoint of the section of the steel plate in the direction away from the explosion center(i.e.the horizontal direction) were also studied.It was found that the specific impulse at these positions were much smaller than that in the thickness direction,the micro defects were only SBs and a few micro cracks,and the those decreased with the increase of the distance from the explosion center.

Customized scaffolds for large bone defects using 3D‑printed modular blocks from 2D‑medical images

Additive manufacturing(AM)has revolutionized the design and manufacturing of patient-specific,three-dimensional(3D),complex porous structures known as scaffolds for tissue engineering applications.The use of advanced image acquisition techniques,image processing,and computer-aided design methods has enabled the precise design and additive manufacturing of anatomically correct and patient-specific implants and scaffolds.However,these sophisticated techniques can be timeconsuming,labor-intensive,and expensive.Moreover,the necessary imaging and manufacturing equipment may not be readily available when urgent treatment is needed for trauma patients.In this study,a novel design and AM methods are proposed for the development of modular and customizable scaffold blocks that can be adapted to fit the bone defect area of a patient.These modular scaffold blocks can be combined to quickly form any patient-specific scaffold directly from two-dimensional(2D)medical images when the surgeon lacks access to a 3D printer or cannot wait for lengthy 3D imaging,modeling,and 3D printing during surgery.The proposed method begins with developing a bone surface-modeling algorithm that reconstructs a model of the patient’s bone from 2D medical image measurements without the need for expensive 3D medical imaging or segmentation.This algorithm can generate both patient-specific and average bone models.Additionally,a biomimetic continuous path planning method is developed for the additive manufacturing of scaffolds,allowing porous scaffold blocks with the desired biomechanical properties to be manufactured directly from 2D data or images.The algorithms are implemented,and the designed scaffold blocks are 3D printed using an extrusion-based AM process.Guidelines and instructions are also provided to assist surgeons in assembling scaffold blocks for the self-repair of patient-specific large bone defects.

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

Microscopic defects formation and dynamic mechanical response analysis of Q345 steel plate subjected to explosive load

As the basic protective element, steel plate had attracted world-wide attention because of frequent threats of explosive loads. This paper reports the relationships between microscopic defects of Q345 steel plate under the explosive load and its macroscopic dynamics simulation. Firstly, the defect characteristics of the steel plate were investigated by stereoscopic microscope(SM) and scanning electron microscope(SEM). At the macroscopic level, the defect was the formation of cave which was concentrated in the range of 0-3.0 cm from the explosion center, while at the microscopic level, the cavity and void formation were the typical damage characteristics. It also explains that the difference in defect morphology at different positions was the combining results of high temperature and high pressure. Secondly, the variation rules of mechanical properties of steel plate under explosive load were studied. The Arbitrary Lagrange-Euler(ALE) algorithm and multi-material fluid-structure coupling method were used to simulate the explosion process of steel plate. The accuracy of the method was verified by comparing the deformation of the simulation results with the experimental results, the pressure and stress at different positions on the surface of the steel plate were obtained. The simulation results indicated that the critical pressure causing the plate defects may be approximately 2.01 GPa. On this basis, it was found that the variation rules of surface pressure and microscopic defect area of the Q345 steel plate were strikingly similar, and the corresponding mathematical relationship between them was established. Compared with Monomolecular growth fitting models(MGFM) and Logistic fitting models(LFM), the relationship can be better expressed by cubic polynomial fitting model(CPFM). This paper illustrated that the explosive defect characteristics of metal plate at the microscopic level can be explored by analyzing its macroscopic dynamic mechanical response.

Molecular Dynamics Simulation of Shock Response of CL-20 Co-crystals Containing Void Defects

To investigate the effect of void defects on the shock response of hexanitrohexaazaisowurtzitane(CL-20)co-crystals,shock responses of CL-20 co-crystals with energetic materials ligands trinitrotoluene(TNT),1,3-dinitrobenzene(DNB),solvents ligands dimethyl carbonate(DMC) and gamma-butyrolactone(GBL)with void were simulated,using molecular dynamics method and reactive force field.It is found that the CL-20 co-crystals with void defects will form hot spots when impacted,significantly affecting the decomposition of molecules around the void.The degree of molecular fragmentation is relatively low under the reflection velocity of 2 km/s,and the main reactions are the formation of dimer and the shedding of nitro groups.The existence of voids reduces the safety of CL-20 co-crystals,which induced the sensitivity of energetic co-crystals CL-20/TNT and CL-20/DNB to increase more significantly.Detonation has occurred under the reflection velocity of 4 km/s,energetic co-crystals are easier to polymerize than solvent co-crystals,and are not obviously affected by voids.The results show that the energy of the wave decreases after sweeping over the void,which reduces the chemical reaction frequency downstream of the void and affects the detonation performance,especially the solvent co-crystals.

An Analysis of Specific Categories of Birth Defects and Developmental Disabilities for Children of Participants of the Air Force Health Study

Background: The Air Force Health Study collected reproductive outcomes for live-born children of male Air Force veterans of the Vietnam War. Methods: Dioxin values for participants were obtained from blood samples. Analyses were conducted of occurrence of 16 specific categories of birth defects and developmental disabilities. Children were categorized as conceived before and after the start of participants’ Vietnam War service. Children conceived before the start of Vietnam War service were treated as being conceived when their fathers had unquantifiable dioxin values. Children conceived after the start of Vietnam War service for participants with missing dioxin values were excluded from primary analyses, but were used to assess the impact of their exclusion on conclusions. Correlation between values for specific categories for multiple children fathered by the same participant was accounted for. The dose-response relationship was treated as a step function increasing for dioxin values larger than adaptively identified individual thresholds changing with the specific category. Results: For 15 of 16 specific categories, the probability of occurrence increased substantially for a sufficiently high dioxin level above identified thresholds. Exclusion of children due to missing dioxin likely did not affect these results. Conclusions: Results supported the conclusion of substantial adverse effects on a wide variety of specific categories of birth defects and developmental disabilities due to sufficiently high exposures to dioxin, a toxic contaminant of Agent Orange used for herbicide spraying in the Vietnam War. Results may hold more generally, but might also have been affected by a variety of limitations.

Interface optimization and defects suppression via Na F introduction enable efficient flexible Sb_(2)Se_(3) thin-film solar cells

Sb_(2)Se_(3) with unique one-dimensional(1D) crystal structure exhibits exceptional deformation tolerance,demonstrating great application potential in flexible devices.However,the power conversion efficiency(PCE) of flexible Sb_(2)Se_(3) photovoltaic devices is temporarily limited by the complicated intrinsic defects and the undesirable contact interfaces.Herein,a high-quality Sb_(2)Se_(3) absorber layer with large crystal grains and benign [hkl] growth orientation can be first prepared on a Mo foil substrate.Then NaF intermediate layer is introduced between Mo and Sb_(2)Se_(3),which can further optimize the growth of Sb_(2)Se_(3)thin film.Moreover,positive Na ion diffusion enables it to dramatically lower barrier height at the back contact interface and passivate harmful defects at both bulk and heterojunction.As a result,the champion substrate structured Mo-foil/Mo/NaF/Sb_(2)Se_(3)/CdS/ITO/Ag flexible thin-film solar cell delivers an obviously higher efficiency of 8.03% and a record open-circuit voltage(V_(OC)) of 0.492 V.This flexible Sb_(2)Se_(3) device also exhibits excellent stability and flexibility to stand large bending radius and multiple bending times,as well as superior weak light photo-response with derived efficiency of 12.60%.This work presents an effective strategy to enhance the flexible Sb_(2)Se_(3) device performance and expand its potential photovoltaic applications.

Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models

Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including low throughput,large computation cost,high latency,and poor flexibility,which limits the efficiency of online PCBA inspection.In this paper,a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed.In this method,the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection frame-work.To improve the performance of the model,extensive real PCBA images are collected from production lines as datasets.Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices.Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods.Our method can be integrated into a lightweight inference system and pro-mote the flexibility of AOI.The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.

High quality repair of osteochondral defects in rats using the extracellular matrix of antler stem cells

BACKGROUND Cartilage defects are some of the most common causes of arthritis.Cartilage lesions caused by inflammation,trauma or degenerative disease normally result in osteochondral defects.Previous studies have shown that decellularized extracellular matrix(ECM)derived from autologous,allogenic,or xenogeneic mesenchymal stromal cells(MSCs)can effectively restore osteochondral integrity.AIM To determine whether the decellularized ECM of antler reserve mesenchymal cells(RMCs),a xenogeneic material from antler stem cells,is superior to the currently available treatments for osteochondral defects.METHODS We isolated the RMCs from a 60-d-old sika deer antler and cultured them in vitro to 70%confluence;50 mg/mL L-ascorbic acid was then added to the medium to stimulate ECM deposition.Decellularized sheets of adipocyte-derived MSCs(aMSCs)and antlerogenic periosteal cells(another type of antler stem cells)were used as the controls.Three weeks after ascorbic acid stimulation,the ECM sheets were harvested and applied to the osteochondral defects in rat knee joints.RESULTS The defects were successfully repaired by applying the ECM-sheets.The highest quality of repair was achieved in the RMC-ECM group both in vitro(including cell attachment and proliferation),and in vivo(including the simultaneous regeneration of well-vascularized subchondral bone and avascular articular hyaline cartilage integrated with surrounding native tissues).Notably,the antler-stem-cell-derived ECM(xenogeneic)performed better than the aMSC-ECM(allogenic),while the ECM of the active antler stem cells was superior to that of the quiescent antler stem cells.CONCLUSION Decellularized xenogeneic ECM derived from the antler stem cell,particularly the active form(RMC-ECM),can achieve high quality repair/reconstruction of osteochondral defects,suggesting that selection of decellularized ECM for such repair should be focused more on bioactivity rather than kinship.

Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNNModel

Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline rupture.Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management.Aiming at the shortcomings of the BP Neural Network(BPNN)model,such as low learning efficiency,sensitivity to initial weights,and easy falling into a local optimal state,an Improved Sparrow Search Algorithm(ISSA)is adopted to optimize the initial weights and thresholds of BPNN,and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established.Taking 61 sets of pipelines blasting test data as an example,the prediction model was built and predicted by MATLAB software,and compared with the BPNN model,GA-BPNN model,and SSA-BPNN model.The results show that the MAPE of the ISSA-BPNN model is 3.4177%,and the R2 is 0.9880,both of which are superior to its comparison model.Using the ISSA-BPNN model has high prediction accuracy and stability,and can provide support for pipeline inspection and maintenance.

In-doping collaboratively controlling back interface and bulk defects to achieve efficient flexible CZTSSe solar cells

Focusing on the low open circuit voltage(V_(OC))and fill factor(FF)in flexible Cu_(2)ZnSn(S,Se)_(4)(CZTSSe)solar cells,indium(In)ions are introduced into the CZTSSe absorbers near Mo foils to modify the back interface and passivate deep level defects in CZTSSe bulk concurrently for improving the performance of flexible device.The results show that In doping effectively inhibits the formation of secondary phase(Cu(S,Se)_(2))and VSndefects.Further studies demonstrate that the barrier height at the back interface is decreased and the deep level defects(Cu_(Sn)defects)in CZTSSe bulk are passivated.Moreover,the carrier concentration is increased and the V_(OC) deficit(V_(OC,def))is decreased significantly due to In doping.Finally,the flexible CZTSSe solar cell with 10.01%power conversion efficiency(PCE)has been obtained.The synergistic strategy of interface modification and bulk defects passivation through In incorporation provides a new thought for the fabrication of efficient flexible kesterite-based solar cells.

An Approach to Detect Structural Development Defects in Object-Oriented Programs

Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.

整体护理对CHD患者冠脉内支架植入术围术期血脂水平的影响研究

探讨整体护理用于冠心病(CHD)患者冠脉内支架植入术(PCI)围术期后对机体血脂水平的影响。方法 将医院行PCI治疗的60例CHD患者为主体)2020年11月-2022年11月,运用随机分组法分成参照组(30例)与实验组(30例),分别实施常规护理、整体护理,对比两组护理情况。结果 实验组出院时的血脂水平均较参照组低,组间差异明显(P<0.05);实验组护理满意度较参照组高,组间差异明显(P<0.05);实验组并发生率较参照组低,组间差异明显(P<0.05);实验组GAD-7、PSQI评分较参照组低,组间差异明显(P<0.05)。结论 在CHD患者PCI治疗围术期运用整体护理具有显著价值,可改善机体血脂水平与情绪状态,同时还可降低并发症风险,提升护理满意度。

3例CHDFIDD患儿的分子遗传学分析及文献复习

目的分析先天性心脏缺陷、面部畸形和智力发育障碍[CHDFIDD,细胞周期蛋白依赖性激酶13(CDK13)相关疾病]患儿的临床表型及基因突变情况,探讨其遗传学病因。方法采用芯片捕获高通量测序技术对3例CHDFIDD患儿及其父母的基因组DNA进行全外显子组测序,对疑似致病突变进行Sanger测序验证和生物信息分析。以“CDK13基因”“CDK13相关疾病”为检索词,检索中国知网、万方数据库建库至2024年2月的文献;以“CDK13”“CDK13-related disorder”“CHDFIDD”为检索词,检索PubMed数据库建库至2024年2月的文献,对相关文献进行复习。结果全外显子组测序结果均提示3例患儿存在CDK13基因杂合突变,分别为c.2572C>T(p.Leu858Phe)、c.2579G>A(p.Arg860Gln)和c.2602C>T(p.Arg868Trp),Sanger测序也证实了3种突变,结合临床表型,3例患儿均被确诊为CHDFIDD。3例患儿在各自家系中表现为新发突变;但不排除患儿双亲之一为该突变的生殖系嵌合体。根据美国医学遗传学与基因组学学会的指南,3个突变位点均可能致病。文献复习检索到14篇相关文献共108例CHDFIDD病例,其中c.2572C>T突变未见文献报道。结论CDK13基因突变可能是该3例患儿的遗传学病因。本研究丰富了CDK13基因突变谱,为CHDFIDD相关疾病的诊疗提供了参考。