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In this paper, the semi-local convergence of the Cordero's sixth-order iterative method in Banach space was proved by the method of recursion relation. In the process of proving, the auxiliary sequence and three increasing scalar functions can be derived using Lipschitz conditions on the first-order derivatives. By using the properties of auxiliary sequence and scalar function, it was proved that the iterative sequence obtained by the iterative method was a Cauchy sequence, then the convergence radius was obtained and its uniqueness was proven. Compared with Cordero's process of proving convergence, this paper does not need to ensure that $ \mathcal{G}(s) $ is continuously differentiable in higher order, and only the first-order Fréchet derivative was used to prove semi-local convergence. Finally, the numerical results showed that the recursion relationship is reasonable.

Abstract Background Our study aimed to analyze the characteristics of ultrasound images corresponding to each histological stage of primary biliary cholangitis (PBC). Methods We prospectively analyzed 75 confirmed cases of PBC and used liver biopsy as the gold standard to determine the disease stage. Results The typical ultrasound images of patients with PBC were characterized by a thickening of the portal vein wall (PVW) and periportal hypoechoic band (PHB) width with increasing histological stages, and significant increases in the left hepatic lobe diameter (LHLD) in stage II (by 64.0%) and stage III (by 69.2%). PHB width (r = 0.857, p

Abstract Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.

Abstract Background Fetal facial profile could be measured during the early pregnancy. Its abnormalities might be associated with certain congenital malformations. We aimed to study the associations between fetal facial profile measurements with crown-rump length and congenital malformations (cleft lip and palate, micrognathia, and open spina bifida) during early pregnancy. Methods We performed a prospective cross-sectional study between June 2019 and April 2022. Pregnant women at a gestational age between 11–13+ 6 weeks were enrolled. Two sonographers performed fetal facial profile measurements independently. The associations between these measurements with crown-rump length and congenital malformations were evaluated. Results There were 406 and 25 fetuses without or with congenital malformations, respectively. Two sonographers showed satisfactory inter- and intra-observer agreements and reproducibility. The maxillary gap was only observed in 7.6% of normal fetuses, whereas all cleft lip and palate fetuses had a maxillary gap ≥ 0.8 mm. The crown-rump length was negatively correlated with frontomaxillary facial angle, inferior facial angle, and profile line distance but positively correlated with maxilla-nasion-mandible angle, facial maxillary angle, frontal space distance, and palatine maxillary diameter. These measurements showed various significant changes with different congenital malformations. Conclusions Measurements of fetal facial profile in early pregnancy were feasible with satisfactory reproducibility. These measurements correlated with crown-rump length and showed significant differences with certain fetal congenital malformations.

Abstract African Americans have a significantly higher risk of developing chronic kidney disease, especially focal segmental glomerulosclerosis -, than European Americans. Two coding variants (G1 and G2) in the APOL1 gene play a major role in this disparity. While 13% of African Americans carry the high-risk recessive genotypes, only a fraction of these individuals develops FSGS or kidney failure, indicating the involvement of additional disease modifiers. Here, we show that the presence of the APOL1 p.N264K missense variant, when co-inherited with the G2 APOL1 risk allele, substantially reduces the penetrance of the G1G2 and G2G2 high-risk genotypes by rendering these genotypes low-risk. These results align with prior functional evidence showing that the p.N264K variant reduces the toxicity of the APOL1 high-risk alleles. These findings have important implications for our understanding of the mechanisms of APOL1-associated nephropathy, as well as for the clinical management of individuals with high-risk genotypes that include the G2 allele.

Abstract Congenital malformations of the central nervous system are among the most common major congenital malformations. Deep learning systems have come to the fore in prenatal diagnosis of congenital malformation, but the impact of deep learning-assisted detection of congenital intracranial malformations from fetal neurosonographic images has not been evaluated. Here we report a three-way crossover, randomized control trial (Trial Registration: ChiCTR2100048233) that assesses the efficacy of a deep learning system, the Prenatal Ultrasound Diagnosis Artificial Intelligence Conduct System (PAICS), in assisting fetal intracranial malformation detection. A total of 709 fetal neurosonographic images/videos are read interactively by 36 sonologists of different expertise levels in three reading modes: unassisted mode (without PAICS assistance), concurrent mode (using PAICS at the beginning of the assessment) and second mode (using PAICS after a fully unaided interpretation). Aided by PAICS, the average accuracy of the unassisted mode (73%) is increased by the concurrent mode (80%; P

Abstract The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations.

Immunoglobulin A protects against infectious disease and contributes to autoimmune and inflammatory disorders. Here, the authors perform a genome-wide association study for serum IgA levels, identifying 20 genome-wide significant loci, providing new insights into the genetic regulation of IgA levels.

Layered double oxides are widely employed in catalyzing the aldol condensation for producing biofuels, but its selectivity and stability need to be further improved. Herein, a novel MCM-41-supported Mg–Al-layered double oxide (LDO/MCM-41) was prepared via the in situ integration of a sol–gel process and coprecipitation, followed by calcination. This composite was first employed to catalyze the self-condensation of cyclopentanone for producing high-density cycloalkane precursors. LDO/MCM-41 possessed large specific surface area, uniform pore size distribution, abundant medium basic sites and Bronsted acid sites. Compared with the bulk LDO, LDO/MCM-41 exhibited a higher selectivity for C10 and C15 oxygenates at 150 °C (93.4% vs. 84.6%). The selectivity for C15 was especially enhanced on LDO/MCM-41, which was three times greater than that on LDO. The stability test showed that naked LDO with stronger basic strength had a rapid initial activity, while it suffered an obvious deactivation due to its poor carbon balance. LDO/MCM-41 with lower basic strength had an enhanced stability even with a lower initial activity. Under the optimum conditions (50% LDO loading, 170 °C, 7 h), the cyclopentanone conversion on LDO/MCM-41 reached 77.8%, with a 60% yield of C10 and 15.2% yield of C15.

ObjectiveTo evaluate the diagnostic performance and inter-observer agreement of the American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS) in the diagnosis of ovarian masses in children.MethodsFrom June 2012 to December 2021, 163 ovarian masses in 159 patients with pathologic results were retrospectively analyzed. Each mass was classified into an O-RADS category according to the criteria. The diagnostic performance of O-RADS for detecting malignant ovarian masses was assessed using histopathology as the reference standard. Kappa (k) statistic was used to assess inter-observer agreement between a less-experienced and a well-experienced radiologist.ResultsOut of 163 ovarian masses, 18 (11.0%) were malignant and 145 (89.0%) were benign. The malignancy rates of O-RADS 5, O-RADS 4, and O-RADS 3 masses were 72.7%, 34.6%, and 4.8%, respectively. The area under the receiver operating characteristic curve was 0.944 (95% CI, 0.908–0.981). The optimal cutoff value for predicting malignant ovarian masses was > O-RADS 3 with a sensitivity, specificity, and accuracy of 94.4%, 86.2% and 86.2% respectively. The inter-observer agreement of the O-RADS category was good (k = 0.777).ConclusionsO-RADS has a high diagnostic performance for children with ovarian masses. It provides an effective malignant risk classification for ovarian masses in children, which shows high consistency between radiologists with different levels of experience.

Abstract Introduction Currently, one of the commonly used methods for disseminating electronic health record (EHR)-based phenotype algorithms is providing a narrative description of the algorithm logic, often accompanied by flowcharts. A challenge with this mode of dissemination is the potential for under-specification in the algorithm definition, which leads to ambiguity and vagueness. Methods This study examines incidents of under-specification that occurred during the implementation of 34 narrative phenotyping algorithms in the electronic Medical Record and Genomics (eMERGE) network. We reviewed the online communication history between algorithm developers and implementers within the Phenotype Knowledge Base (PheKB) platform, where questions could be raised and answered regarding the intended implementation of a phenotype algorithm. Results We developed a taxonomy of under-specification categories via an iterative review process between two groups of annotators. Under-specifications that lead to ambiguity and vagueness were consistently found across narrative phenotype algorithms developed by all involved eMERGE sites. Discussion and conclusion Our findings highlight that under-specification is an impediment to the accuracy and efficiency of the implementation of current narrative phenotyping algorithms, and we propose approaches for mitigating these issues and improved methods for disseminating EHR phenotyping algorithms.

ObjectiveDiverticular disease (DD) is one of the most prevalent conditions encountered by gastroenterologists, affecting ~50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique.Materials and methodsWe developed a NLP-enriched phenotyping algorithm that incorporated colonoscopy or abdominal imaging reports to identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of DD in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in clinical phenotypes.ResultsOur developed algorithm showed a significant improvement in patient classification performance for DD analysis (algorithm PPVs ≥ 0.94), with up to a 3.5 fold increase in terms of the number of identified patients than the traditional method. Ancestry-stratified analyses of diverticulosis and diverticulitis of the identified subjects replicated the well-established associations between ARHGAP15 loci with DD, showing overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients. Our PheWAS analyses identified significant associations between the DD GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes.DiscussionAs the first multi-ancestry GWAS-PheWAS study, we showcased that heterogenous EHR data can be mapped through an integrative analytical pipeline and reveal significant genotype-phenotype associations with clinical interpretation.ConclusionA systematic framework to process unstructured EHR data with NLP could advance a deep and scalable phenotyping for better patient identification and facilitate etiological investigation of a disease with multilayered data.

Abstract Background Patients with primary biliary cholangitis (PBC) often have comorbid dyslipidemia, and determining the degree of hepatic steatosis can help predict the risk of cardiovascular events in PBC patients. The aim of our study was to analyze the characteristics of lipid distribution and the degree of hepatic steatosis in PBC. Methods We retrospectively analyzed 479 cases of PBC, chronic hepatitis B (CHB), chronic hepatitis C (CHC), non-alcoholic fatty liver disease (NAFLD), and healthy subjects (Normal) diagnosed by liver biopsy or definitive clinical diagnosis. Controlled attenuation parameter (CAP) values were applied to assess the degree of steatosis of the liver, and lipid levels were also compared in the five cohorts. Results We found that among the five groups of subjects, the PBC group had the lowest CAP values (P

Prenatal imaging phenotypes and genotypes were analyzed in 13 cases prenatally diagnosed with Joubert syndrome (JS), all of which underwent magnetic resonance imaging (MRI), ultrasound, and genetic testing. Prenatal MRI diagnosed 10 cases as JS with a typical molar tooth sign (MTS), while prenatal ultrasound diagnosed or suspiciously diagnosed 11 cases as JS with typical or mild MTS in 10 cases. Mutations in JS-related genes and other prenatal JS imaging phenotypes were identified in 10 cases, including OFD1 in two cases [cerebellar vermis (CV) absence, posterior fossa dilation, ventriculomegaly, polydactyly, malformations of cortical development (MCD), and persistent left superior vena cava], TMEM67 in two cases (CV absence, polydactyly, hyperechoic kidneys or polycystic kidneys, posterior fossa dilation, and ventriculomegaly), CC2D2A in two cases (CV absence, polydactyly, MCD, agenesis of the corpus callosum, encephalocele and hydrocephalus, ventriculomegaly, and posterior fossa dilation), RPGRIP1L in one case (CV absence), TCTN3 in one case (CV absence, polydactyly, MCD, and posterior fossa dilation), CEP290 in one case (CV absence and polycystic kidney), and NPHP1 in one case (CV absence). The prenatal diagnosis of JS presents a number of challenges, including the variants of unknown significance, the lack of functional assessment in prenatal imaging, unclear phenotype–genotype relationships in prenatal evaluation, and the incorrect identification of the JS hallmark, the MTS, in prenatal imaging, especially on ultrasound. Although combined MRI, ultrasound, and exome sequencing could help improve the prenatal diagnosis of JS, there still exist significant challenges.

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

Abstract Labeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians, can be applied to large datasets, and alleviate some of the main weaknesses of existing phenotyping algorithms. We show applications to phenotypic data on approximately 100,000 individuals in eMERGE, and focus on several complex diseases, including Chronic Kidney Disease, Coronary Artery Disease, Type 2 Diabetes, Heart Failure, and a few others. We demonstrate that relative to existing approaches, the proposed methods have higher prediction accuracy, can better identify phenotypic features relevant to the disease under consideration, can perform better at clinical risk stratification, and can identify undiagnosed cases based on phenotypic features available in the EHR. Using genetic data from the eMERGE-seq panel that includes sequencing data for 109 genes on 21,363 individuals from multiple ethnicities, we also show how the new quantitative disease risk scores help improve the power of genetic association studies relative to the standard use of disease phenotypes. The results demonstrate the effectiveness of quantitative disease risk scores derived from rich phenotypic EHR databases to provide a more meaningful characterization of clinical risk for diseases of interest beyond the prevalent binary (case-control) classification.

A dilated lateral ventricle is a relatively common finding on prenatal ultrasound, and the causes are complex. We aimed to explore the etiology of a fetus with a dilated lateral ventricle. Trio whole-exome sequencing was performed to detect causative variants. A de novo variant of TAOK1 (NM_020791.2: c.227A>G) was detected in the proband and evaluated for potential functional impacts using a variety of prediction tools. Droplet digital polymerase chain reaction was used to exclude the parental mosaicism and to verify the phasing of the de novo variant. Based on peripheral blood analysis, the parents did not exhibit mosaicism at this site, and the de novo variant was paternally derived. Here, we describe a fetus with a de novo likely pathogenic variant of TAOK1 who had a dilated lateral ventricle and a series of particular phenotypes. This case expands the clinical spectrum of TAOK1-associated disorders. We propose a method for solving genetic disorders in which the responsible genes have not yet gone through ClinGen curation, particularly for prenatal cases.

Abstract Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate (“A-by-G” grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.

Variations in the visceral vasculature are often encountered, but rarely cause clinical symptoms. We report a 12-year-old girl with portal hypertension caused by congenital variations in visceral vessels. The clinical manifestations included gastrointestinal hemorrhage and ascites. The common hepatic artery and splenic artery stem shared the same trunk from the aorta, and the common hepatic artery was directly connected with the main portal vein to form an arteriovenous fistula. In addition, the left hepatic artery and the left gastric artery shared a common trunk termed the “hepatic-gastric trunk” which originated from the anterior wall of the aorta, while the right hepatic artery originated from the superior mesenteric artery and supplied the right liver. The patient was treated with interventional embolization and remained in good condition throughout the follow-up and at the time of publication.

Carcinoma expleomorphic adenoma (CXPA) is a malignant carcinoma arising from a primary or recurrent benign pleomorphic adenoma. The lesion is recognized to be an aggressive malignancy with high recurrence rates and a high metastatic rate. CXPA is commonly seen in the sixth or seventh decade of life and shows a predilection for females. It accounts for approximately 3–5% of all salivary neoplasms and 12% of all salivary gland malignancies. Histologically, CXPA is characterized by the presence of both epithelial and mesenchymal components. We present an unusual case of a submandibular gland CXPA that clinically mimicked a benign tumour. The case offers an interesting perspective on the management challenges and associated prognostic significance after a follow-up evaluation of 6 years.

ObjectivesNano-drug delivery system is an interesting field in precise cancer treatment, but few study has reported the microenvironmental changes after such treatment. This study aimed to detect the hemodynamic and microenvironmental changes in a lung cancer xenograft model after treated with doxorubicin (DOX) encapsulated by a cyclic arginine-glycine-aspartic acid polypeptide modified poly-(lactic-co-glycolic acid) nanosystem (cRGD-PLGA@DOX) using functional magnetic resonance imaging.Materials and MethodsThirty-two tumor-bearing mice were randomly divided into four groups. Group A was treated with 0.9% saline, Group B with 4 mg/kg of doxorubicin, Group C with 2 mg/kg of cRGD-PLGA@DOX, and Group D with 4 mg/kg of cRGD-PLGA@DOX. Intravoxel incoherent motion diffusion-weighed imaging (IVIM-DWI) and R2∗ mapping were performed, and D∗, f, D, and R2∗ values were obtained before and1, 2, and 3 weeks after treatment. They were sacrificed for pathological examination after examinations.ResultsThe reconstructed cRGD-PLGA@DOX was homogeneous, well-dispersed, and spherical in shape, with an average size of 180 nm. Group D demonstrated the smallest tumor volume and highest tumor inhibition rate in 3 weeks. D value of Group B, C, and D manifested an upward trend in 3 weeks with the highest increase in Group D. D∗ values shared a similar increased trends with f values in Group A, B, and C in 3 weeks, except Group D. R2∗ value of Group A gradually increased in 3 weeks, but the trends were reversed in the treatment groups. D value was significantly negative with Ki-67 expression (r = −0.757, P < 0.001) but positive with TUNEL (r = 0.621, P < 0.001), and phosphate and tension homology deleted on chromosome ten (PTEN) staining (r = 0.57, P = 0.004). R2∗ value was closely correlated with HIF-1a (r = 0.721, P < 0.001).ConclusionThe nano-drug demonstrated an enhanced anti-tumor effect without the need of increased chemotherapeutic dosage. The tumor microenvironment such as cellular and perfusion changes during treatment can be non-invasively detected by two functional MRI including IVIM-DWI and R2∗ mapping.

OBJECTIVES/GOALS: There is an imperative need to initiate translational genetic studies of hidradenitis suppurativa (HS). Such work requires large cohorts and no HS registries exist. Precision medicine initiatives provide new resources and methods for efficiently constructing cohorts, but empirically informed best practice guidelines are needed. METHODS/STUDY POPULATION: Traditional methods for building cohorts rely on clinical encounters to identify patients and collect phenotype data. Precision medicine initiatives aim to decrease the time and cost of data collection by using alternative sources, including electronic health records (EHR) and remote collection of patient-reported data. The public’s use of the Internet to obtain and exchange health-related information coupled with the success of direct-to-consumer genetic companies suggests that it is feasible to remotely ascertain research participants for genetic studies. Importantly, Internet cohorts provide an opportunity to include research participants who are disconnected from healthcare, and thus remain hidden from research that relies on EHR or clinical services. RESULTS/ANTICIPATED RESULTS: First, to conduct studies in EHR we are developing an analytic pipeline for the automated extraction of an accurate HS diagnosis using natural language processing of clinical notes. In our preliminary work we are also using ICD codes to build cohorts in two EHR systems with and without linked genetic data. Second, we have developed Internet advertising campaigns for symptom-based recruitment. Informed consent and patient-reported data is collected on-line through a series of short surveys. Patients who complete the surveys and express interest in participating in genetic studies are sent saliva collection kits and return mailing material. Finally, we have established an HS biobank that has DNA from 300 participants identified through clinical services. Enrollment is on going. DISCUSSION/SIGNIFICANCE OF IMPACT: Our goal is to assemble an HS cohort that is large enough to power genetic discoveries. Our work is generating empirical evidence for precision medicine guidelines and will improve our knowledge about HS. The methods we are developing can be applied to efficiently create new cohorts for genetic studies of other diseases across different clinical areas.