Franckhan1259

Grey literature will be identified through searching clinical trial registers and dissertation databases. Two reviewers will independently screen all citations, full-text articles and abstract data. The study methodological quality (or bias) will be appraised using the Cochrane risk of bias tool. selleck kinase inhibitor If feasible, we will conduct random effects meta-analysis where appropriate. DISCUSSION This systematic review will evaluate the evidence for pre- and post-surgical intervention with oral nutritional supplements in adults. Findings from this planned review may inform subsequent nutritional interventions for hospitalised patients who undergo surgery. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42020140954.BACKGROUND Lipodystrophies are a group of diseases which are characterized by abnormal adipose tissue deposition and are frequently associated with metabolic changes. Congenital generalized lipodystrophy is an autosomal recessive syndrome, with a prevalence less then  110 million. Acromegaly is a rare disease, secondary to the chronic hypersecretion of growth hormone and insulin-like growth factor-1, with characteristic metabolic and somatic effects. "Acromegaloidism" is a term used for patients who manifest clinical features of acromegaly, but do not present a demonstrable hormone growth hypersecretion. The extreme shortage of subcutaneous adipose tissues and muscle hypertrophy confer an acromegaloid-like appearance in these patients. CASE PRESENTATION We describe a case of a patient with the rare combination of Berardinelli-Seip congenital lipodystrophy and acromegaly; our patient is a 63-year-old white man, who was referred to an endocrinology consultation for suspected lipodystrophy. He had lipoatrophy oxpression of follicle-stimulating hormone and prolactin. A genetic study revealed an exon 3/exon 4 deletion of the AGPAT2 gene in homozygosity. CONCLUSIONS Congenital generalized lipodystrophy is a rare disease which occurs with acromegaloid features. As far as we know, we have described the first case of genetic lipodystrophy associated with true acromegaly. Although this is a rare association, the presence of congenital generalized lipodystrophy should not exclude the possibility of simultaneous acromegaly.BACKGROUND The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking. RESULTS We provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. CONCLUSIONS We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.BACKGROUND The initiation and subsequent evolution of cancer are largely driven by a relatively small number of somatic mutations with critical functional impacts, so-called driver mutations. Identifying driver mutations in a patient's tumor cells is a central task in the era of precision cancer medicine. Over the decade, many computational algorithms have been developed to predict the effects of missense single-nucleotide variants, and they are frequently employed to prioritize mutation candidates. These algorithms employ diverse molecular features to build predictive models, and while some algorithms are cancer-specific, others are not. However, the relative performance of these algorithms has not been rigorously assessed. RESULTS We construct five complementary benchmark datasets mutation clustering patterns in the protein 3D structures, literature annotation based on OncoKB, TP53 mutations based on their effects on target-gene transactivation, effects of cancer mutations on tumor formation in xenograft experiments, and functional annotation based on in vitro cell viability assays we developed including a new dataset of ~ 200 mutations. We evaluate the performance of 33 algorithms and found that CHASM, CTAT-cancer, DEOGEN2, and PrimateAI show consistently better performance than the other algorithms. Moreover, cancer-specific algorithms show much better performance than those designed for a general purpose. CONCLUSIONS Our study is a comprehensive assessment of the performance of different algorithms in predicting cancer driver mutations and provides deep insights into the best practice of computationally prioritizing cancer mutation candidates for end-users and for the future development of new algorithms.Nerves of the peripheral nervous system contain two classes of Schwann cells myelinating Schwann cells that ensheath large caliber axons and generate the myelin sheath, and Remak Schwann cells that surround smaller axons and do not myelinate. While tools exist for genetic targeting of Schwann cell precursors and myelinating Schwann cells, such reagents have been challenging to generate specifically for the Remak population, in part because many of the genes that mark this population in maturity are also robustly expressed in Schwann cell precursors. To circumvent this challenge, we utilized BAC transgenesis to generate a mouse line expressing a tamoxifen-inducible Cre under the control of a Remak-expressed gene promoter (Egr1). However, as Egr1 is also an activity dependent gene expressed by some neurons, we flanked this Cre by flippase (Flpe) recognition sites, and coinjected a BAC expressing Flpe under control of a pan-neuronal Snap25 promoter to excise the Cre transgene from these neuronal cells. Genotyping and inheritance demonstrate that the two BACs co-integrated into a single locus, facilitating maintenance of the line.