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Purpose: Severe combined immunodeficiency (SCID) is a set of rare monogenic inherited diseases that together represent the most severe form of the primary immunodeficiency disease phenotype. Preimplantation genetic testing for monogenic defects (PGT-M) is an effective reproductive technology strategy to prevent disease-causing gene mutations from being transmitted to offspring. The aim of this study was to report the use of PGT-M strategy based on karyomapping in four families to avoid the birth of SCID children.

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Purpose: The Coronavirus Disease 2019 (COVID-19) pandemic delayed elective procedures such as total joint arthroplasty. As surgical volumes return to prepandemic levels, understanding the implications of COVID-19 becomes imperative. This study explored the effects of COVID-19 on the short-term outcomes of hip arthroplasty.

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Background: Transient receptor potential cation channel subfamily V member 2 (TRPV2) functions as a stretch-sensitive calcium channel, with overexpression in the sarcolemma of skeletal and cardiac myocytes leading to detrimental calcium influx and triggering muscle degeneration. In our previous pilot study, we showed that tranilast, a TRPV2 inhibitor, reduced brain natriuretic peptide levels in two patients with muscular dystrophy and advanced heart failure. Building on this, we performed a single-arm, open-label, multicenter study herein to evaluate the safety and efficacy of tranilast in the treatment of advanced heart failure in patients with muscular dystrophy.

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Background: Global re-emergence of the zoonotic viral disease, Mpox (Monkeypox) has drawn global attention, leading to its declaration as a Public Health Emergency of International Concern (PHEIC) by World Health Organisation (WHO) in July 2022. Nigeria is a spotlight identified for the viral disease outbreak, with attention drawn on its transmission to non-endemic nations. With the country's healthcare challenges, care seeking practices particularly amongst low-income urban informal settlement populations are diverse - presenting challenges to both case identification and management during an outbreak.

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Background: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-diem payment is a common internationally implemented payment method for medical insurance of inpatients with mental disorders, necessitating the exploration of advanced machine learning methods for predicting the average daily hospitalization costs (ADHC) based on the characteristics of inpatients with mental disorders.

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