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There are more than 260 million people of Slavic descent worldwide, who reside mainly in Eastern Europe but also represent a noticeable share of the population in the USA and Canada. Slavic populations, particularly Eastern Slavs and some Western Slavs, demonstrate a surprisingly high degree of genetic homogeneity, and, consequently, remarkable contribution of recurrent alleles associated with hereditary diseases. Along with pan-European pathogenic variants with clearly elevated occurrence in Slavic people (e.g., ATP7B c.3207C>A and PAH c.1222C>T), there are at least 52 pan-Slavic germ-line mutations (e.g., NBN c.657_661del and BRCA1 c.5266dupC) as well as several disease-predisposing alleles characteristic of the particular Slavic communities (e.g., Polish SDHD c.33C>A and Russian ARSB c.1562G>A variants). From a clinical standpoint, Slavs have some features of a huge founder population, thus providing a unique opportunity for efficient genetic studies.

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From Mendel's discovery of the basic laws of genetics in 1865 to the widespread application of genomics in medicine today, medical genetics has made enormous progress, and the concept of genetic diseases has also been evolved. In 1972, the World Health Organization (WHO) expert group began to use "Genetic Disease" to define hereditary diseases, while early Chinese genetics textbooks used "inferior inheritance", and later introduced terms such as "Genetic Disease" and "Inherited Disease". In the early days, it was generally believed that genetic diseases were inherited from ancestors. However, research in recent years has found that genetic diseases are not necessarily inherited, and some diseases are actually caused by de novo mutations in the offspring. Although the occurrence of this type of genetic disease is related to genetic factors, it is not inherited from ancestors. If we still use "Inherited Disease" or "Hereditary Disease" to describe it, it is not accurate enough. In order to further standardize the translation and use of the concept of "Genetic Disease", this article briefly reviews its development process in both English and Chinese literature, discusses the difference between different Chinese translations, and provides guidance and suggestions for scientifically and accurately describing genetic diseases in Chinese, with a view to promote efficient exchange and cooperation in the field of medical genetics.

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Geographic and sociodemographic aspects may influence the natural history and epidemiology of mucopolysaccharidoses (MPS). The main objective in this work was to evaluate the clinical, molecular, and geographic profile of MPS in a population from Ceará (Northeast Brazil). For this, we have performed a descriptive cross-sectional study based on clinical evaluation, interviews with patients and/or family members, and review of medical records of 76 MPS patients. MPS II was the most common type, with the most affected individuals presenting missense pathogenic variants. Patients with MPS I proved to be the most severe clinical phenotype, presenting the first symptoms (mean: 7.1 months; SD = 4.5) and being diagnosed earlier (2.2 years; SD = 2.1) in comparison with the other types. In addition, we have shown that 13 individuals with MPS VI were born of consanguineous marriages in small, nearby cities, in a place where geographical isolation, consanguinity, and clusters of genetic diseases were previously reported. Ten of these individuals (at least, seven different families) presented a rare pathogenic variant in the ARSB gene, c.1143-8T > G in homozygosity, previously reported only among Iberian and South American patients. The results presented here provide a comprehensive picture of MPS in an important state of the Brazilian Northeast, a region that concentrates many risk factors for rare genetic diseases, such as endogamy, inbreeding, and reproductive isolation. We discuss the possible evolutionary processes and biosocial dynamics that can help to explain this finding in terms of population medical genetics and public health.

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Large language models (LLMs) are generating interest in medical settings. For example, LLMs can respond coherently to medical queries by providing plausible differential diagnoses based on clinical notes. However, there are many questions to explore, such as evaluating differences between open- and closed-source LLMs as well as LLM performance on queries from both medical and non-medical users. In this study, we assessed multiple LLMs, including Llama-2-chat, Vicuna, Medllama2, Bard/Gemini, Claude, ChatGPT3.5, and ChatGPT-4, as well as non-LLM approaches (Google search and Phenomizer) regarding their ability to identify genetic conditions from textbook-like clinician questions and their corresponding layperson translations related to 63 genetic conditions. For open-source LLMs, larger models were more accurate than smaller LLMs: 7b, 13b, and larger than 33b parameter models obtained accuracy ranges from 21%-49%, 41%-51%, and 54%-68%, respectively. Closed-source LLMs outperformed open-source LLMs, with ChatGPT-4 performing best (89%-90%). Three of 11 LLMs and Google search had significant performance gaps between clinician and layperson prompts. We also evaluated how in-context prompting and keyword removal affected open-source LLM performance. Models were provided with 2 types of in-context prompts: list-type prompts, which improved LLM performance, and definition-type prompts, which did not. We further analyzed removal of rare terms from descriptions, which decreased accuracy for 5 of 7 evaluated LLMs. Finally, we observed much lower performance with real individuals' descriptions; LLMs answered these questions with a maximum 21% accuracy.

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This manuscript reviewed the state of the art about the teaching and training of human resources for genetics and genomics in Brazil. We presented the national scenario of teaching genetics in medical undergraduate and other health courses. We discussed the training of medical geneticists through medical residency and addressed the training in genetics of physicians from specialties other than genetics. We examined the training of health professionals specializing in genetics through lato sensu and stricto sensu postgraduate programs and presented the proposals for multi-professional residency in genetic counseling and genetics and genomics that are currently the subject of discussion in the country. Finally, we highlighted the importance of training primary health care professionals concerning genetics and genomics for the effective establishment of a line of care for individuals with genetic disorders in the Brazilian Unified Health System. Therefore, we provided a thorough overview of how genetics is (or is not) incorporated into professional training in a comprehensive public healthcare system such as the Brazilian.

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Brazil is a continent-size country with 203 million inhabitants, classified as a developing upper-middle-income country, although inequities remain significant. Most of the population is assisted by the public Unified Health System (SUS), along with a thriving private health sector. Congenital malformations are the second leading cause of infant mortality and chronic/genetic disorders and a significant burden in hospital admissions. The past two decades have been crucial for formalizing medical genetics as a recognized medical specialty in the SUS, as well as for implementing a new health policy by the Ministry of Health for comprehensive care for rare diseases. These public health policies had the broad support of the Brazilian Society of Medical Genetics and Genomics and patient organizations. Most comprehensive genetic services are concentrated in large urban centers in the South and Southeast regions of Brazil; with this new policy, new services throughout the country are progressively being integrated. The number of medical geneticists increased by 103% in a decade. Details on the policy and an overview of the availability of services, testing, human resources, newborn screening, research projects, patient organizations, and relevant issues regarding medical genetics in this vast and diverse country are presented.

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BACKGROUND: Understanding genetics is crucial for medical students, particularly in Saudi Arabia, where genetic disorders are prevalent owing to high rates of consanguineous marriages. This knowledge is essential for the early detection, prevention, and management of genetic disorders, and for incorporating medical genetics and genomics into patient care. This study aimed to assess the current state of genetics knowledge among medical students and interns across Saudi Arabia and to identify knowledge gaps in genetics. METHOD: A cross-sectional study was conducted between August and September 2023 involving 732 medical students from all regions of Saudi Arabia. The participants completed a validated questionnaire assessing their knowledge of basic genetics, genetic inheritance, genetic testing, and clinical genetics. RESULT: Over 60% of medical students and interns reported that they considered themselves to have only slight knowledge in all areas of genetics. The results revealed a general lack of medical genetic understanding among students and interns, particularly regarding genetic inheritance and testing. For genetic inheritance, slight knowledge was found in 65.2% of pre-clinical, 60.1% of clinical, and 53.2% of interns, with significant differences between groups (p < 0.001). In genetic testing, 75.4% of pre-clinical, 83.9% of clinical, and 90.6% of interns showed slight knowledge, with significant differences across stages (p = 0.021). This study also found that lectures, genetics laboratories, and problem-solving sessions were the preferred resources for learning genetics. CONCLUSION: The current study revealed a notable deficiency in the understanding of medical genetics among medical students and interns in Saudi Arabia, particularly regarding genetic inheritance and testing. This is consistent with previous research highlighting the widespread lack of genetics knowledge among medical students. Integrating more comprehensive genetics education, especially during the clinical years, could improve students' preparedness and confidence in managing genetic disorders. These findings highlight the critical need for curriculum development to equip future physicians with the essential skills for managing genetic disorders.

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Children with severe inflammatory diseases are challenging to diagnose and treat, and the etiology of disease often remains unexplained. Here we present DIAPH1 deficiency as an unexpected genetic finding in a child with fatal inflammatory bowel disease who also displayed complex neurological and developmental phenotypes. Bi-allelic mutations of DIAPH1 were first described in patients with a severe neurological phenotype including microcephaly, intellectual disability, seizures, and blindness. Recent findings have expanded the clinical phenotype of DIAPH1 deficiency to include severe susceptibility to infections, placing this monogenic disease amongst the etiologies of inborn errors of immunity. Immune phenotypes in DIAPH1 deficiency are largely driven aberrant lymphocyte activation, particularly the failure to form an effective immune synapse in T cells. We present the case of a child with a novel homozygous deletion in DIAPH1, leading to a premature truncation in the Lasso domain of the protein. Unlike other cases of DIAPH1 deficiency, this patient did not have seizures or lung infections. Her major immune-related clinical symptoms were inflammation and enteropathy, diarrhea and failure to thrive. This patient did not show T or B cell lymphopenia but did have dramatically reduced naïve CD4+ and CD8+ T cells, expanded CD4-CD8- T cells, and elevated IgE. Similar to other cases of DIAPH1 deficiency, this patient had non-hematological phenotypes including microcephaly, developmental delay, and impaired vision. This patient's symptSoms of immune dysregulation were not successfully controlled and were ultimately fatal. This case expands the clinical spectrum of DIAPH1 deficiency and reveals that autoimmune or inflammatory enteropathy may be the most prominent immunological manifestation of disease.

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BACKGROUND: Serine residues in the protein backbone of heavily glycosylated proteoglycans are bound to glycosaminoglycans through a tetrasaccharide linker. UXS1 encodes UDP-glucuronate decarboxylase 1, which catalyzes synthesis of UDP-xylose, the donor of the first building block in the linker. Defects in other enzymes involved in formation of the tetrasaccharide linker cause so-called linkeropathies, characterized by short stature, radio-ulnar synostosis, decreased bone density, congenital contractures, dislocations, and more. METHODS: Whole exome sequencing was performed in a father and son who presented with a mild skeletal dysplasia, as well as the father's unaffected parents. Wild-type and mutant UXS1 were recombinantly expressed in Escherichia coli and purified. Enzyme activity was evaluated by LC-MS/MS. In vivo effects were studied using HeparinRed assay and metabolomics. RESULTS: The son had short long bones, normal epiphysis, and subtle metaphyseal changes especially in his legs. The likely pathogenic heterozygous variant NM_001253875.1(UXS1):c.557T>A p.(Ile186Asn) detected in the son was de novo in the father. Purified Ile186Asn-UXS1, in contrast to the wild-type, was not able to convert UDP-glucuronic acid to UDP-xylose. Plasma glycosaminoglycan levels were decreased in both son and father. CONCLUSION: This is the first report linking UXS1 to short-limbed short stature in humans.

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Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

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With the rapid development of multimedia technology, the student centered flipped classroom model (FCM) and massive open online courses (MOOCs) have been increasingly introduced and implemented in higher medical education. However, comparative analyses of the offline face-to-face FCM and completely online FCM have been rarely reported. In this study, we focused specifically on a set of flipped classrooms in which prerecorded videos were provided before class. Using the Zhihuishu platform as the major online course platform, our team built a MOOC and evaluated the teaching effectiveness of the FCM in both the offline face-to-face class and the online electronic live class for medical genetics education. Questionnaires, paper-based and oral exams were used to collect data on the teaching effects of the different teaching methods. We found that student satisfaction and overall student performance in the offline FCM group was significantly higher than that in the completely online teaching group. Although online FCM allowed students to play back and review anywhere and anytime after class, students taught in offline FCM had a significantly higher degree of knowledge mastery, had a deeper understanding of theoretical knowledge, and were better at knowledge comprehensive application. The effects of their training on genetic disease clinical diagnosis and treatment skills were significantly better, and their capacity for scientific research was also significantly improved. Our research discussed the advantages of the online courses and the problems brought about by using these technologies, and it provided insight into online teaching practices in the era of internet-based medical education.

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There is a shortage of clinical geneticists, even with concerted recruitment efforts. Previously, no data had been collected about why young career geneticists chose this specialty. To investigate this question, we carried out a survey of current and recent medical genetics and genomics residents. The goal of this survey was to understand their reasons for pursuing medical genetics and genomics as a specialty. Results demonstrate that, for most, interest in genetics begins in medical school and was largely influenced by mentorship. This suggests that placing greater focus on introducing medical genetics as a clinical specialty and fostering robust mentorship of students in preclinical years may increase recruitment into medical genetics residencies.

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Population-based genomic screening may help diagnose individuals with disease-risk variants. Here, we perform a genome-first evaluation for nine disorders in 29,039 participants with linked exome sequences and electronic health records (EHRs). We identify 614 individuals with 303 pathogenic/likely pathogenic or predicted loss-of-function (P/LP/LoF) variants, yielding 644 observations; 487 observations (76%) lack a corresponding clinical diagnosis in the EHR. Upon further investigation, 75 clinically undiagnosed observations (15%) have evidence of symptomatic untreated disease, including familial hypercholesterolemia (3 of 6 [50%] undiagnosed observations with disease evidence) and breast cancer (23 of 106 [22%]). These genetic findings enable targeted phenotyping that reveals new diagnoses in previously undiagnosed individuals. Disease yield is greater with variants in penetrant genes for which disease is observed in carriers in an independent cohort. The prevalence of P/LP/LoF variants exceeds that of clinical diagnoses, and some clinically undiagnosed carriers are discovered to have disease. These results highlight the potential of population-based genomic screening.

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This case report sheds light on the complex management of hemihyperplasia (HHP), highlighting the difficulties associated with diagnosis and the critical importance of a multimodal approach to treatment. The story of Acharya Vinoba Bhave Rural Hospital's (AVBRH) successful resolution following a misdiagnosis at another clinic emphasizes the value of expert care. The successful outcome resulted from the fusion of surgical innovation, genetic insights, and psychosocial factors through genetic testing, liposuction, and postoperative rehabilitation. This example emphasizes the need to treat congenital illnesses holistically and the transforming power of individualized, multidisciplinary treatment to improve the functional and esthetic elements of life for patients with HHP.

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BACKGROUND: While characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism. OBJECTIVE: We aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt. METHODS: Using a retrospective sample of 4796 images of patients with 486 different genetic syndromes (London Medical Database, GestaltMatcher Database, and literature images) and 323 inconspicuous control images, we determined the clinical use of D-Score, GestaltMatcher, and DeepGestalt, evaluating sensitivity; specificity; accuracy; the number of supported diagnoses; and potential biases such as age, sex, and ethnicity. RESULTS: DeepGestalt suggested 340 distinct syndromes and GestaltMatcher suggested 1128 syndromes. The top-30 sensitivity was higher for DeepGestalt (88%, SD 18%) than for GestaltMatcher (76%, SD 26%). DeepGestalt generally assigned lower scores but provided higher scores for patient images than for inconspicuous control images, thus allowing the 2 cohorts to be separated with an area under the receiver operating characteristic curve (AUROC) of 0.73. GestaltMatcher could not separate the 2 classes (AUROC 0.55). Trained for this purpose, D-Score achieved the highest discriminatory power (AUROC 0.86). D-Score's levels increased with the age of the depicted individuals. Male individuals yielded higher D-scores than female individuals. Ethnicity did not appear to influence D-scores. CONCLUSIONS: If used with caution, algorithms such as D-score could help clinicians with constrained resources or limited experience in syndromology to decide whether a patient needs further genetic evaluation. Algorithms such as DeepGestalt could support diagnosing rather common genetic syndromes with facial abnormalities, whereas algorithms such as GestaltMatcher could suggest rare diagnoses that are unknown to the clinician in patients with a characteristic, dysmorphic face.

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Genetics has become a critical component of medicine over the past five to six decades. Alongside genetics, a relatively new discipline, dysmorphology, has also begun to play an important role in providing critically important diagnoses to individuals and families. Both have become indispensable to unraveling rare diseases. Almost every medical specialty relies on individuals experienced in these specialties to provide diagnoses for patients who present themselves to other doctors. Additionally, both specialties have become reliant on molecular geneticists to identify genes associated with human disorders. Many of the medical geneticists, dysmorphologists, and molecular geneticists traveled a circuitous route before arriving at the position they occupied. The purpose of collecting the memoirs contained in this article was to convey to the reader that many of the individuals who contributed to the advancement of genetics and dysmorphology since the late 1960s/early 1970s traveled along a journey based on many chances taken, replying to the necessities they faced along the way before finding full enjoyment in the practice of medical and human genetics or dysmorphology. Additionally, and of equal importance, all exhibited an ability to evolve with their field of expertise as human genetics became human genomics with the development of novel technologies.

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OBJECTIVE: Aggregating evidence convincingly establishes the predominant genetic basis underlying congenital heart defects (CHD), though the heritable determinants contributing to CHD in the majority of cases remain elusive. In the current investigation, BMP10 was selected as a prime candidate gene for human CHD mainly due to cardiovascular developmental abnormalities in Bmp10-knockout animals. The objective of this retrospective study was to identify a new BMP10 mutation responsible for CHD and characterize the functional effect of the identified CHD-causing BMP10 mutation. METHODS: Sequencing assay of BMP10 was fulfilled in a cohort of 276 probands with various CHD and a total of 288 non-CHD volunteers. The available family members from the proband harboring an identified BMP10 mutation were also BMP10-genotyped. The effect of the identified CHD-causative BMP10 mutation on the transactivation of TBX20 and NKX2.5 by BMP10 was quantitatively analyzed in maintained HeLa cells utilizing a dual-luciferase reporter assay system. RESULTS: A novel heterozygous BMP10 mutation, NM_014482.3:c.247G>T;p.(Glu83*), was identified in one proband with patent ductus arteriosus (PDA), which was confirmed to co-segregate with the PDA phenotype in the mutation carrier's family. The nonsense mutation was not observed in 288 non-CHD volunteers. Functional analysis unveiled that Glu83*-mutant BMP10 had no transactivation on its two representative target genes TBX20 and NKX2.5, which were both reported to cause CHD. CONCLUSION: These findings provide strong evidence indicating that genetically compromised BMP10 predisposes human beings to CHD, which sheds light on the new molecular mechanism that underlies CHD and allows for antenatal genetic counseling and individualized precise management of CHD.

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Medical genetics is a basic medical course that discusses the diagnosis, prevention and treatment of diseases in relation with genetic factors. This course requires students who have abilities of strong logical thinking, independent thinking, problem analyzing and solving. Single "cramming" teaching is difficult to mobilize students' autonomous learning, and hardly achieves teaching effect of medical genetics. Teaching of case-based discussion breaks passive teaching mode in traditional class. The teacher throws out typically clinical cases. The students prepare materials around relevant problems of cases, and carry out class discussion. Then, key and difficult points of the course are integrated in teaching and learning interaction, which reaches a remarkable effect of teaching. Since 2013, the teaching and research group has carried out teaching of case-based discussion in undergraduates majoring in clinical medicine. In this paper, we screen and sort clinical cases on the basis of course teaching plan and case-based discussion in the teaching of medical genetics. The cases are summarized into 8 chapters in teaching case base, which basically cover the teaching of disease genetics and clinical genetics.The construction of teaching case base in medical genetics has realized the deep integration of clinical cases and teaching. Students can understand and master important and difficult points of teaching in a more intuitive way, which is helpful to stimulate students' innovative thinking, improve students' learning interest and class participation.

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Turnpenny-Fry Syndrome (TPFS) is a rare genetic disorder characterized by a severe developmental delay and a distinctive facial gestalt. It is caused by mutations in the Polycomb Group Ring Finger Protein 2 (PCGF2) gene, which is also known to play a role in numerous tumor types. Up to date, there have been no published case reports of patients with TPFS and concomitant malignancies. The present case describes the clinical evaluation and follow-up of a male infant with severe global developmental delay (GDD) and a distinctive phenotype. At 4 years of age, clinical exome sequencing confirmed the diagnosis of TPFS. Posteriorly, at 5 years of age, the patient was also diagnosed with T-cell acute lymphoblastic leukemia (ALL). Given the scarce literature regarding this syndrome, the authors expect that this case report will provide valuable information that could improve the follow-up of patients with TPFS. Furthermore, this case highlights the necessity for the appropriate diagnosis of developmental disorders, to ensure adequate care, surveillance of comorbidities and proper genetic counselling.