Mutations in SAMD7 cause autosomal-recessive macular dystrophy with or without cone dysfunction.

Sterile alpha motif domain containing 7 (SAMD7) is a component of the Polycomb repressive complex 1, which inhibits transcription of many genes, including those activated by the transcription factor Cone-Rod Homeobox (CRX). Here we report bi-allelic mutations in SAMD7 as a cause of autosomal-recessive macular dystrophy with or without cone dysfunction. Four of these mutations affect splicing, while another mutation is a missense variant that alters the repressive effect of SAMD7 on CRX-dependent promoter activity, as shown by in vitro assays. Immunostaining of human retinal sections revealed that SAMD7 is localized in the nuclei of both rods and cones, as well as in those of cells belonging to the inner nuclear layer. These results place SAMD7 as a gene crucial for human retinal function and demonstrate a significant difference in the role of SAMD7 between the human and the mouse retina.

Non-syndromic retinal dystrophy associated with biallelic variation of SUMF1 and reduced leukocyte sulfatase activity.

Biallelic variants in SUMF1 are associated with multiple sulfatase deficiency (MSD), a rare lysosomal storage disorder typically diagnosed in early infancy or childhood, marked by severe neurodegeneration and early mortality. We present clinical and molecular characterisation of three unrelated patients aged 13 to 58 years with milder clinical manifestations due to SUMF1 disease variants, including two adult patients presenting with apparent non-syndromic retinal dystrophy. Whole genome sequencing identified biallelic SUMF1 variants in all three patients; Patient 1 homozygous for a complex allele c.[290G>T;293T>A]; p.[(Gly97Val);(Val98Glu)], Patient 2 homozygous for c.866A>G; p.(Tyr289Cys), and Patient 3 compound heterozygous for c.726-1G>C and p.(Tyr289Cys). Electroretinography indicated a rod-cone dystrophy with additional possible inner retinal dysfunction in all three patients. Biochemical studies confirmed reduced, but not absent, sulfatase enzyme activity in the absence of extra-ocular disease (Patient 1) or only mild systemic disease (Patients 2, 3). These cases are suggestive that non-null SUMF1 genotypes can cause an attenuated clinical phenotype, including retinal dystrophy without systemic complications, in adulthood.

Inferring the genetic relationship between brain imaging-derived phenotypes and risk of complex diseases by Mendelian randomization and genome-wide colocalization.

Observational studies consistently disclose brain imaging-derived phenotypes (IDPs) as critical markers for early diagnosis of both brain disorders and cardiovascular diseases. However, it remains unclear about the shared genetic landscape between brain IDPs and the risk of brain disorders and cardiovascular diseases, restricting the applications of potential diagnostic techniques through brain IDPs. Here, we reported genetic correlations and putative causal relationships between 921 brain IDPs, 20 brain disorders and six cardiovascular diseases by leveraging their large-scale genome-wide association study (GWAS) summary statistics. Applications of Mendelian randomization (MR) identified significant putative causal effects of multiple region-specific brain IDPs in relation to the increased risks for amyotrophic lateral sclerosis (ALS), major depressive disorder (MDD), autism spectrum disorder (ASD) and schizophrenia (SCZ). We also found brain IDPs specifically from temporal lobe as a putatively causal consequence of hypertension. The genome-wide colocalization analysis identified three genomic regions in which MDD, ASD and SCZ colocalized with the brain IDPs, and two novel SNPs to be associated with ASD, SCZ, and multiple brain IDPs. Furthermore, we identified a list of candidate genes involved in the shared genetics underlying pairs of brain IDPs and MDD, ASD, SCZ, ALS and hypertension. Our results provide novel insights into the genetic relationships between brain disorders and cardiovascular diseases and brain IDP, which may server as clues for using brain IDPs to predict risks of diseases.

Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.

OBJECTIVES: Recent studies have revealed the change of molecular subtypes in breast cancer (BC) after neoadjuvant therapy (NAT). This study aims to construct a non-invasive model for predicting molecular subtype alteration in breast cancer after NAT. METHODS: Eighty-two estrogen receptor (ER)-negative/ human epidermal growth factor receptor 2 (HER2)-negative or ER-low-positive/HER2-negative breast cancer patients who underwent NAT and completed baseline MRI were retrospectively recruited between July 2010 and November 2020. Subtype alteration was observed in 21 cases after NAT. A 2D-DenseUNet machine-learning model was built to perform automatic segmentation of breast cancer. 851 radiomic features were extracted from each MRI sequence (T2-weighted imaging, ADC, DCE, and contrast-enhanced T1-weighted imaging), both in the manual and auto-segmentation masks. All samples were divided into a training set (n = 66) and a test set (n = 16). XGBoost model with 5-fold cross-validation was performed to predict molecular subtype alterations in breast cancer patients after NAT. The predictive ability of these models was subsequently evaluated by the AUC of the ROC curve, sensitivity, and specificity. RESULTS: A model consisting of three radiomics features from the manual segmentation of multi-sequence MRI achieved favorable predictive efficacy in identifying molecular subtype alteration in BC after NAT (cross-validation set: AUC = 0.908, independent test set: AUC = 0.864); whereas an automatic segmentation approach of BC lesions on the DCE sequence produced good segmentation results (Dice similarity coefficient = 0.720). CONCLUSIONS: A machine learning model based on baseline MRI is proven useful for predicting molecular subtype alterations in breast cancer after NAT. KEY POINTS: • Machine learning models using MRI-based radiomics signature have the ability to predict molecular subtype alterations in breast cancer after neoadjuvant therapy, which subsequently affect treatment protocols. • The application of deep learning in the automatic segmentation of breast cancer lesions from MRI images shows the potential to replace manual segmentation..

Uncovering the burden of hidden ciliopathies in the 100 000 Genomes Project: a reverse phenotyping approach.

BACKGROUND: The 100 000 Genomes Project (100K) recruited National Health Service patients with eligible rare diseases and cancer between 2016 and 2018. PanelApp virtual gene panels were applied to whole genome sequencing data according to Human Phenotyping Ontology (HPO) terms entered by recruiting clinicians to guide focused analysis. METHODS: We developed a reverse phenotyping strategy to identify 100K participants with pathogenic variants in nine prioritised disease genes (BBS1, BBS10, ALMS1, OFD1, DYNC2H1, WDR34, NPHP1, TMEM67, CEP290), representative of the full phenotypic spectrum of multisystemic primary ciliopathies. We mapped genotype data 'backwards' onto available clinical data to assess potential matches against phenotypes. Participants with novel molecular diagnoses and key clinical features compatible with the identified disease gene were reported to recruiting clinicians. RESULTS: We identified 62 reportable molecular diagnoses with variants in these nine ciliopathy genes. Forty-four have been reported by 100K, 5 were previously unreported and 13 are new diagnoses. We identified 11 participants with unreportable, novel molecular diagnoses, who lacked key clinical features to justify reporting to recruiting clinicians. Two participants had likely pathogenic structural variants and one a deep intronic predicted splice variant. These variants would not be prioritised for review by standard 100K diagnostic pipelines. CONCLUSION: Reverse phenotyping improves the rate of successful molecular diagnosis for unsolved 100K participants with primary ciliopathies. Previous analyses likely missed these diagnoses because incomplete HPO term entry led to incorrect gene panel choice, meaning that pathogenic variants were not prioritised. Better phenotyping data are therefore essential for accurate variant interpretation and improved patient benefit.

Elucidating the clinical spectrum and molecular basis of HYAL2 deficiency.

PURPOSE: We previously defined biallelic HYAL2 variants causing a novel disorder in 2 families, involving orofacial clefting, facial dysmorphism, congenital heart disease, and ocular abnormalities, with Hyal2 knockout mice displaying similar phenotypes. In this study, we better define the phenotype and pathologic disease mechanism. METHODS: Clinical and genomic investigations were undertaken alongside molecular studies, including immunoblotting and immunofluorescence analyses of variant/wild-type human HYAL2 expressed in mouse fibroblasts, and in silico modeling of putative pathogenic variants. RESULTS: Ten newly identified individuals with this condition were investigated, and they were associated with 9 novel pathogenic variants. Clinical studies defined genotype-phenotype correlations and confirmed a recognizable craniofacial phenotype in addition to myopia, cleft lip/palate, and congenital cardiac anomalies as the most consistent manifestations of the condition. In silico modeling of missense variants identified likely deleterious effects on protein folding. Consistent with this, functional studies indicated that these variants cause protein instability and a concomitant cell surface absence of HYAL2 protein. CONCLUSION: These studies confirm an association between HYAL2 alterations and syndromic cleft lip/palate, provide experimental evidence for the pathogenicity of missense alleles, enable further insights into the pathomolecular basis of the disease, and delineate the core and variable clinical outcomes of the condition.

Mutation in the intracellular chloride channel CLCC1 associated with autosomal recessive retinitis pigmentosa.

We identified a homozygous missense alteration (c.75C>A, p.D25E) in CLCC1, encoding a presumptive intracellular chloride channel highly expressed in the retina, associated with autosomal recessive retinitis pigmentosa (arRP) in eight consanguineous families of Pakistani descent. The p.D25E alteration decreased CLCC1 channel function accompanied by accumulation of mutant protein in granules within the ER lumen, while siRNA knockdown of CLCC1 mRNA induced apoptosis in cultured ARPE-19 cells. TALEN KO in zebrafish was lethal 11 days post fertilization. The depressed electroretinogram (ERG) cone response and cone spectral sensitivity of 5 dpf KO zebrafish and reduced eye size, retinal thickness, and expression of rod and cone opsins could be rescued by injection of wild type CLCC1 mRNA. Clcc1+/- KO mice showed decreased ERGs and photoreceptor number. Together these results strongly suggest that intracellular chloride transport by CLCC1 is a critical process in maintaining retinal integrity, and CLCC1 is crucial for survival and function of retinal cells.

BBS5 and INPP5E mutations associated with ciliopathy disorders in families from Pakistan.

Ciliopathies are a clinically and genetically heterogeneous group of disorders often exhibiting phenotypic overlap and caused by abnormalities in the structure or function of cellular cilia. As such, a precise molecular diagnosis is important for guiding clinical management and genetic counseling. In the present study, two Pakistani families comprising individuals with overlapping clinical features suggestive of a ciliopathy syndrome, including intellectual disability, obesity, congenital retinal dystrophy, and hypogonadism (in males), were investigated clinically and genetically. Whole-exome sequencing identified the likely causes of disease as a novel homozygous frameshift mutation (NM_152384.2: c.196delA; p.(Arg66Glufs*12); family 1) in BBS5, and a nonsense mutation (NM_019892.5:c.1879C>T; p.Gln627*; family 2) in INPP5E, previously reported in an extended Pakistani family with MORM syndrome. Our findings expand the molecular spectrum associated with BBS5 mutations in Pakistan and provide further supportive evidence that the INPP5E mutation is a common cause of ciliopathy in Northern Pakistan, likely representing a regional founder mutation. This study also highlights the value of genomic studies in Pakistan for families affected by rare heterogeneous developmental disorders and where clinical phenotyping may be limited by geographical and financial constraints. The identification of the spectrum and frequency of disease-causing variants within this setting enables the development of population-specific genetic testing strategies targeting variants common to the local population and improving health care outcomes.

Altering the speed of a DNA packaging motor from bacteriophage T4.

The speed at which a molecular motor operates is critically important for the survival of a virus or an organism but very little is known about the underlying mechanisms. Tailed bacteriophage T4 employs one of the fastest and most powerful packaging motors, a pentamer of gp17 that translocates DNA at a rate of up to ∼2000-bp/s. We hypothesize, guided by structural and genetic analyses, that a unique hydrophobic environment in the catalytic space of gp17-adenosine triphosphatase (ATPase) determines the rate at which the 'lytic water' molecule is activated and OH- nucleophile is generated, in turn determining the speed of the motor. We tested this hypothesis by identifying two hydrophobic amino acids, M195 and F259, in the catalytic space of gp17-ATPase that are in a position to modulate motor speed. Combinatorial mutagenesis demonstrated that hydrophobic substitutions were tolerated but polar or charged substitutions resulted in null or cold-sensitive/small-plaque phenotypes. Quantitative biochemical and single-molecule analyses showed that the mutant motors exhibited 1.8- to 2.5-fold lower rate of ATP hydrolysis, 2.5- to 4.5-fold lower DNA packaging velocity, and required an activator protein, gp16 for rapid firing of ATPases. These studies uncover a speed control mechanism that might allow selection of motors with optimal performance for organisms' survival.

Novel mutations in ALDH1A3 associated with autosomal recessive anophthalmia/microphthalmia, and review of the literature.

BACKGROUND: Autosomal recessive anophthalmia and microphthalmia are rare developmental eye defects occurring during early fetal development. Syndromic and non-syndromic forms of anophthalmia and microphthalmia demonstrate extensive genetic and allelic heterogeneity. To date, disease mutations have been identified in 29 causative genes associated with anophthalmia and microphthalmia, with autosomal dominant, autosomal recessive and X-linked inheritance patterns described. Biallelic ALDH1A3 gene variants are the leading genetic causes of autosomal recessive anophthalmia and microphthalmia in countries with frequent parental consanguinity. METHODS: This study describes genetic investigations in two consanguineous Pakistani families with a total of seven affected individuals with bilateral non-syndromic clinical anophthalmia. RESULTS: Using whole exome and Sanger sequencing, we identified two novel homozygous ALDH1A3 sequence variants as likely responsible for the condition in each family; missense mutation [NM_000693.3:c.1240G > C, p.Gly414Arg; Chr15:101447332G > C (GRCh37)] in exon 11 (family 1), and, a frameshift mutation [NM_000693.3:c.172dup, p.Glu58Glyfs*5; Chr15:101425544dup (GRCh37)] in exon 2 predicted to result in protein truncation (family 2). CONCLUSIONS: This study expands the molecular spectrum of pathogenic ALDH1A3 variants associated with anophthalmia and microphthalmia, and provides further insight of the key role of the ALDH1A3 in human eye development.

Selective Sparse Sampling of Water Droplets in Oil with Acoustic Tweezers.

In microfluidics, water droplets are often used as independent biochemical microreactor units, enabling the implementation of massively parallel screening assays where only a few of the reacting water droplets yield a positive result. However, sampling the product of these few successful reactions is an unsolved challenge. One possible solution is to use acoustic tweezers, which are lab-free, easily miniaturized, and biocompatible manipulation tools, and existing acoustic tweezers manipulating particles or cells, and water droplet manipulation in oil with an acoustic tweezer is absent. The first challenge in attempting to recover a few water droplets from a large batch is the selective manipulation of water droplets in an oil system. In this paper, we trap and manipulate single water droplets in oil using integrated single-beam (focused beam/vortex beam) acoustic tweezers for the first time. We find that water droplets with a diameter smaller than half a wavelength are trapped by acoustic vortices, while larger ones are better captured by focused acoustic beams. It is the first step to extract the target water droplet microreactors (positive ones) in an oil system and analyze their content. Compared to previous techniques, such as fluorescence-activated cell sorting (FACS), our technique is sparse, meaning that the sampling time is proportional to the number of droplets required and very insensitive to the total number of microreactors, making it well suited for large-scale screening assays.

Applying image features of proximal paracancerous tissues in predicting prognosis of patients with hepatocellular carcinoma.

BACKGROUND: Most of the methods using digital pathological image for predicting Hepatocellular carcinoma (HCC) prognosis have not considered paracancerous tissue microenvironment (PTME), which are potentially important for tumour initiation and metastasis. This study aimed to identify roles of image features of PTME in predicting prognosis and tumour recurrence of HCC patients. METHODS: We collected whole slide images (WSIs) of 146 HCC patients from Sun Yat-sen Memorial Hospital (SYSM dataset). For each WSI, five types of regions of interests (ROIs) in PTME and tumours were manually annotated. These ROIs were used to construct a Lasso Cox survival model for predicting the prognosis of HCC patients. To make the model broadly useful, we established a deep learning method to automatically segment WSIs, and further used it to construct a prognosis prediction model. This model was tested by the samples of 225 HCC patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). RESULTS: In predicting prognosis of the HCC patients, using the image features of manually annotated ROIs in PTME achieved C-index 0.668 in the SYSM testing dataset, which is higher than the C-index 0.648 reached by the model only using image features of tumours. Integrating ROIs of PTME and tumours achieved C-index 0.693 in the SYSM testing dataset. The model using automatically segmented ROIs of PTME and tumours achieved C-index of 0.665 (95% CI: 0.556-0.774) in the TCGA-LIHC samples, which is better than the widely used methods, WSISA (0.567), DeepGraphSurv (0.593), and SeTranSurv (0.642). Finally, we found the Texture SumAverage Skew HV on immune cell infiltration and Texture related features on desmoplastic reaction are the most important features of PTME in predicting HCC prognosis. We additionally used the model in prediction HCC recurrence for patients from SYSM-training, SYSM-testing, and TCGA-LIHC datasets, indicating the important roles of PTME in the prediction. CONCLUSIONS: Our results indicate image features of PTME is critical for improving the prognosis prediction of HCC. Moreover, the image features related with immune cell infiltration and desmoplastic reaction of PTME are the most important factors associated with prognosis of HCC.

Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics.

INTRODUCTION: Frailty Index (FI) is a common measure of frailty, which has been advocated as a routine clinical test by many guidelines. The genetic and phenotypic relationships of FI with cardiovascular indicators (CIs) and behavioral characteristics (BCs) are unclear, which has hampered ability to monitor FI using easily collected data. OBJECTIVES: This study is designed to investigate the genetic and phenotypic associations of frailty with CIs and BCs, and further to construct a model to predict FI. METHOD: Genetic relationships of FI with 288 CIs and 90 BCs were assessed by the cross-trait LD score regression (LDSC) and Mendelian randomization (MR). The phenotypic data of these CIs and BCs were integrated with a machine-learning model to predict FI of individuals in UK-biobank. The relationships of the predicted FI with risks of type 2 diabetes (T2D) and neurodegenerative diseases were tested by the Kaplan-Meier estimator and Cox proportional hazards model. RESULTS: MR revealed putative causal effects of seven CIs and eight BCs on FI. These CIs and BCs were integrated to establish a model for predicting FI. The predicted FI is significantly correlated with the observed FI (Pearson correlation coefficient = 0.660, P-value = 4.96 × 10-62). The prediction model indicated "usual walking pace" contributes the most to prediction. Patients who were predicted with high FI are in significantly higher risk of T2D (HR = 2.635, P < 2 × 10-16) and neurodegenerative diseases (HR = 2.307, P = 1.62 × 10-3) than other patients. CONCLUSION: This study supports associations of FI with CIs and BCs from genetic and phenotypic perspectives. The model that is developed by integrating easily collected CIs and BCs data in predicting FI has the potential to monitor disease risk.

Optimizing robotic thyroid surgery: lessons learned from an retrospective analysis of 104 cases.

Background: Robotic assistance in thyroidectomy is a developing field that promises enhanced surgical precision and improved patient outcomes. This study investigates the impact of the da Vinci Surgical System on operative efficiency, learning curve, and postoperative outcomes in thyroid surgery. Methods: We conducted a retrospective cohort study of 104 patients who underwent robotic thyroidectomy between March 2018 and January 2022. We evaluated the learning curve using the Cumulative Sum (CUSUM) analysis and analyzed operative times, complication rates, and postoperative recovery metrics. Results: The cohort had a mean age of 36 years, predominantly female (68.3%). The average body mass index (BMI) was within the normal range. A significant reduction in operative times was observed as the series progressed, with no permanent hypoparathyroidism or recurrent laryngeal nerve injuries reported. The learning curve plateaued after the 37th case. Postoperative recovery was consistent, with no significant difference in hospital stay duration. Complications were minimal, with a noted decrease in transient vocal cord palsy as experience with the robotic system increased. Conclusion: Robotic thyroidectomy using the da Vinci system has demonstrated a significant improvement in operative efficiency without compromising safety. The learning curve is steep but manageable, and once overcome, it leads to improved surgical outcomes and high patient satisfaction. Further research with larger datasets and longer follow-up is necessary to establish the long-term benefits of robotic thyroidectomy.

Sex Distributions in Non-ABCA4 Autosomal Macular Dystrophies.

Purpose: We sought to explore whether sex imbalances are discernible in several autosomally inherited macular dystrophies. Methods: We searched the electronic patient records of our large inherited retinal disease cohort, quantifying numbers of males and females with the more common (non-ABCA4) inherited macular dystrophies (associated with BEST1, EFEMP1, PROM1, PRPH2, RP1L1, and TIMP3). BEST1 cases were subdivided into typical autosomal dominant and recessive disease. For PRPH2, only patients with variants at codons 172 or 142 were included. Recessive PROM1 and recessive RP1L1 cases were excluded because these variants give a more widespread or peripheral degeneration. The proportion of females was calculated for each condition; two-tailed binomial testing was performed. Where a significant imbalance was found, previously published cohorts were also explored. Results: Of 325 patients included, numbers for BEST1, EFEMP1, PROM1, PRPH2, RP1L1, and TIMP3 were 152, 35, 30, 50, 14, and 44, respectively. For autosomal dominant Best disease (n = 115), there were fewer females (38%; 95% confidence interval [CI], 29-48%; P = 0.015). For EFEMP1-associated disease (n = 35), there were significantly more females (77%; 95% CI, 60%-90%; P = 0.0019). No significant imbalances were seen for the other genes. When pooling our cohort with previous large dominant Best disease cohorts, the proportion of females was 37% (95% CI, 31%-43%; P = 1.2 × 10-5). Pooling previously published EFEMP1-cases with ours yielded an overall female proportion of 62% (95% CI, 54%-69%; P = 0.0023). Conclusions: This exploratory study found significant sex imbalances in two autosomal macular dystrophies, suggesting that sex could be a modifier. Our findings invite replication in further cohorts and the investigation of potential mechanisms.

Intraoperative AI-assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery.

BACKGROUND: The preservation of parathyroid glands is crucial in endoscopic thyroid surgery to prevent hypocalcemia and related complications. However, current methods for identifying and protecting these glands have limitations. We propose a novel technique that has the potential to improve the safety and efficacy of endoscopic thyroid surgery. PURPOSE: Our study aims to develop a deep learning model called PTAIR 2.0 (Parathyroid gland Artificial Intelligence Recognition) to enhance parathyroid gland recognition during endoscopic thyroidectomy. We compare its performance against traditional surgeon-based identification methods. MATERIALS AND METHODS: Parathyroid tissues were annotated in 32 428 images extracted from 838 endoscopic thyroidectomy videos, forming the internal training cohort. An external validation cohort comprised 54 full-length videos. Six candidate algorithms were evaluated to select the optimal one. We assessed the model's performance in terms of initial recognition time, identification duration, and recognition rate and compared it with the performance of surgeons. RESULTS: Utilizing the YOLOX algorithm, we developed PTAIR 2.0, which demonstrated superior performance with an AP50 score of 92.1%. The YOLOX algorithm achieved a frame rate of 25.14 Hz, meeting real-time requirements. In the internal training cohort, PTAIR 2.0 achieved AP50 values of 94.1%, 98.9%, and 92.1% for parathyroid gland early prediction, identification, and ischemia alert, respectively. Additionally, in the external validation cohort, PTAIR outperformed both junior and senior surgeons in identifying and tracking parathyroid glands (p < 0.001). CONCLUSION: The AI-driven PTAIR 2.0 model significantly outperforms both senior and junior surgeons in parathyroid gland identification and ischemia alert during endoscopic thyroid surgery, offering potential for enhanced surgical precision and patient outcomes.

Accurate preoperative prediction of nodal metastasis in papillary thyroid microcarcinoma: Towards optimal management of patients.

OBJECTIVE: To enhance the accuracy in predicting lymph node metastasis (LNM) preoperatively in patients with papillary thyroid microcarcinoma (PTMC), refining the "low-risk" classification for tailored treatment strategies. METHODS: This study involves the development and validation of a predictive model using a cohort of 1004 patients with PTMC undergoing thyroidectomy along with central neck dissection. The data was divided into a training cohort (n = 702) and a validation cohort (n = 302). Multivariate logistic regression identified independent LNM predictors in PTMC, leading to the construction of a predictive nomogram model. The model's performance was assessed through ROC analysis, calibration curve analysis, and decision curve analysis. RESULTS: Identified LNM predictors in PTMC included age, tumor maximum diameter, nodule-capsule distance, capsular contact length, bilateral suspicious lesions, absence of the lymphatic hilum, microcalcification, and sex. Especially, tumors larger than 7 mm, nodules closer to the capsule (less than 3 mm), and longer capsular contact lengths (more than 1 mm) showed higher LNM rates. The model exhibited AUCs of 0.733 and 0.771 in the training and validation cohorts respectively, alongside superior calibration and clinical utility. CONCLUSION: This study proposes and substantiates a preoperative predictive model for LNM in patients with PTMC, honing the precision of "low-risk" categorization. This model furnishes clinicians with an invaluable tool for individualized treatment approach, ensuring better management of patients who might be proposed observation or ablative options in the absence of such predictive information.

Spectrum of Genetic Variants in the Most Common Genes Causing Inherited Retinal Disease in a Large Molecularly Characterized United Kingdom Cohort.

PURPOSE: Inherited retinal disease (IRD) is a leading cause of blindness. Recent advances in gene-directed therapies highlight the importance of understanding the genetic basis of these disorders. This study details the molecular spectrum in a large United Kingdom (UK) IRD patient cohort. DESIGN: Retrospective study of electronic patient records. PARTICIPANTS: Patients with IRD who attended the Genetics Service at Moorfields Eye Hospital between 2003 and July 2020, in whom a molecular diagnosis was identified. METHODS: Genetic testing was undertaken via a combination of single-gene testing, gene panel testing, whole exome sequencing, and more recently, whole genome sequencing. Likely disease-causing variants were identified from entries within the genetics module of the hospital electronic patient record (OpenEyes Electronic Medical Record). Analysis was restricted to only genes listed in the Genomics England PanelApp R32 Retinal Disorders panel (version 3.24), which includes 412 genes associated with IRD. Manual curation ensured consistent variant annotation and included only plausible disease-associated variants. MAIN OUTCOME MEASURES: Detailed analysis was performed for variants in the 5 most frequent genes (ABCA4, USH2A, RPGR, PRPH2, and BEST1), as well as for the most common variants encountered in the IRD study cohort. RESULTS: We identified 4415 individuals from 3953 families with molecularly diagnosed IRD (variants in 166 genes). Of the families, 42.7% had variants in 1 of the 5 most common IRD genes. Complex disease alleles contributed to disease in 16.9% of affected families with ABCA4-associated retinopathy. USH2A exon 13 variants were identified in 43% of affected individuals with USH2A-associated IRD. Of the RPGR variants, 71% were clustered in the ORF15 region. PRPH2 and BEST1 variants were associated with a range of dominant and recessive IRD phenotypes. Of the 20 most prevalent variants identified, 5 were not in the most common genes; these included founder variants in CNGB3, BBS1, TIMP3, EFEMP1, and RP1. CONCLUSIONS: We describe the most common pathogenic IRD alleles in a large single-center multiethnic UK cohort and the burden of disease, in terms of families affected, attributable to these variants. Our findings will inform IRD diagnoses in future patients and help delineate the cohort of patients eligible for gene-directed therapies under development. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.