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A Hospital Performance Assessment Design While using the IPOCC Method.
Acrodysostosis refers to a rare heterogeneous group of bone dysplasias that share skeletal features, hormone resistance, and intellectual disability. Two genes have been associated with acrodysostosis with or without hormone resistance (PRKAR1A and PDE4D). Severe intellectual disability has been reported with acrodysostosis but brain malformations and ichthyosis have not been reported in these syndromes. Here we describe a female patient with acrodysostosis, intellectual disability, cerebellar hypoplasia, and lamellar ichthyosis. The patient has an evolving distinctive facial phenotype and childhood onset ataxia. see more X-rays showed generalized osteopenia, shortening of middle and distal phalanges, and abnormal distal epiphysis of the ulna and radius. Brain magnetic resonance imaging showed cerebellar atrophy without other brainstem abnormalities. Genetic workup included nondiagnostic chromosomal microarray and skeletal dysplasia molecular panels. These clinical findings are different from any recognized form of acrodysostosis syndrome. Whole exome sequencing did not identify rare or predicted pathogenic variants in genes associated with known acrodysostosis, lamellar ichthyosis, and other overlapping disorders. A broader search for rare alleles absent in healthy population databases and controls identified two heterozygous truncating alleles in FBNL7 and PPM1M genes, and one missense allele in the NPEPPS gene. Identification of additional patients is required to delineate the mechanism of this unique disorder.Natural products and secondary metabolites comprise an indispensable resource from living organisms that have transformed areas of medicine, agriculture, and biotechnology. Recent advances in high-throughput DNA sequencing and computational analysis suggest that the vast majority of natural products remain undiscovered. To accelerate the natural product discovery pipeline, cell-free metabolic engineering approaches used to develop robust catalytic networks are being repurposed to access new chemical scaffolds, and new enzymes capable of performing diverse chemistries. Such enzymes could serve as flexible biocatalytic tools to further expand the unique chemical space of natural products and secondary metabolites, and provide a more sustainable route to manufacture these molecules. Herein, we highlight select examples of natural product biosynthesis using cell-free systems and propose how cell-free technologies could facilitate our ability to access and modify these structures to transform synthetic and chemical biology.Short-rib polydactyly syndromes are a heterogeneous group of disorders characterized by narrow thorax with short ribs, polydactyly and often other visceral and skeletal malformations. To date there have only been six reported patients with homozygous and compound heterozygous variants in IFT81, causing a short-rib thoracic dysplasia, with, or without, polydactyly (SRTD19 OMIM 617895). IFT81 is a protein integral to the core of the intraflagellar transport complex B (IFT-B), which is involved in anterograde transport in the cilium. We describe the case of a male infant with compound heterozygous variants in IFT81, who presented with short long bones, a narrow thorax, polydactyly, and multiple malformations. Three novel clinical features are reported including complete situs inversus, micropenis, and rectal atresia, which have not previously been associated with variants in IFT81. We reviewed the literature and identified the most consistent clinical features associated with this rare ciliopathy syndrome. We postulate that dolichocephaly and sagittal craniosynostosis may be associated with this condition, and provide a clue to considering IFT81 as the causative gene when deciphering complex ciliopathies.
The hairline is an essential component of the human face. Disfigurement of the hairline may cause physical and psychological problems. Standard guidelines do not exist for female hairline designs, average values of infratemporal portion, hairline classifications, and preferences.

We aimed to study hairline characteristics in Thai females and further compare the values with previous studies in different populations. Furthermore, we aimed to introduce a comprehensive hairline classification system that allowed an easy and detailed phenotypic characterization of female hairline.

Healthy Thai females aged over 18years were included in the study. Collection of demographic data, infratemporal measurements, and standard photography was performed. All collected data were analyzed to determine the standard values of each hairline dimension for comparison to previous studies.

Two hundred and twenty-nine females, with a mean age of 32.4±8.6years, participated in this study. The mean mid-frontal line was 6.45±0.8d no statistically significant differences between Korean and Turkish participants. The hairline classification system introduced in our study was comprehensive and would be easily applicable in clinical practice. Female hairline dimensions and patterns reported in our study could provide reference values for hairline design in hairline restoration surgery as well as assist in accurate diagnosis of hair disorders.The generation and maintenance of genome edited zebrafish lines is typically labor intensive due to the lack of an easy visual read-out for the modification. To facilitate this process, we have developed a novel method that relies on the inclusion of an artificial intron with a transgenic marker (InTraM) within the knock-in sequence of interest, which upon splicing produces a transcript with a precise and seamless modification. We have demonstrated this technology by replacing the stop codon of the zebrafish fli1a gene with a transcriptional activator KALTA4, using an InTraM that enables red fluorescent protein expression in the heart.Although deep learning has been explored extensively for computer-aided medical imaging diagnosis in human medicine, very little has been done in veterinary medicine. The goal of this retrospective, pilot project was to apply the deep learning artificial intelligence technique using thoracic radiographs for detection of canine left atrial enlargement and compare results with those of veterinary radiologist interpretations. Seven hundred ninety-two right lateral radiographs from canine patients with thoracic radiographs and contemporaneous echocardiograms were used to train, validate, and test a convolutional neural network algorithm. The accuracy, sensitivity, and specificity for determination of left atrial enlargement were then compared with those of board-certified veterinary radiologists as recorded on radiology reports. The accuracy, sensitivity, and specificity were 82.71%, 68.42%, and 87.09%, respectively, using an accuracy driven variant of the convolutional neural network algorithm and 79.01%, 73.68%, and 80.
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