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(1)【用户论文】 PDF全文


ABSTRACT: OBJECTIVE: Congenital adrenal hyperplasia (CAH) can be complicated by central precocious puberty (CPP) in children, which may compromise final height. We aimed to evaluate the effect of gonadotropin-releasing hormone analog (GnRHa) therapy on growth in children with CAH. DESIGN: Twelve children with CAH were enrolled in a follow-up study. Eight patients underwent the GnRH stimulation test. GnRHa-treatment was administered at 3.75 mg every 4 weeks; the dose had to be increased to 7.5 mg in three patients. Bone age, growth velocities and body mass index of the patients were monitored during treatment. RESULTS: Median chronologic age and bone age at diagnosis were 6.8 (3.5) years and 11 (1.2) years, respectively. Median follow-up was 4.4 (4.9) years. A significant difference was found in the median ratio of bone age to chronological age between diagnosis and last visit (p=0.005) and between the beginning of GnRHa treatment and last visit (p=0.004). Median growth vel
词汇表:
key words: childhood, congenital adrenal hyperplasia, leuprolide acetate, precocious puberty patients; diagnosis; height; hormone; therapy; children; velocity; complicated; chronologic; analog; mutation; levels;
关键词:儿童,先天性肾上腺皮质增生症、醋酸亮丙瑞林,性早熟患者;诊断;高度;激素;治疗;儿童;速度;复杂;年代学;模拟;突变;水平;rn

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doi:10.1038/nature23270
In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target
词汇表:
key word: tumour; immunotherapy; melanoma; immune; antigen; blockade; indicated; presentation; resistance;
关键词:肿瘤;免疫治疗;黑色素瘤;免疫;抗原;封锁;表示;表示;耐药

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doi:10.1038/nature23003
Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer.
T cells directed against mutant neo-epitopes drive cancer immunity. However, spontaneous immune recognition of mutations is inefficient. We recently introduced the concept of individualized mutanome vaccines and implemented an RNA-based poly-neoepitope approach to mobilize immunity against a spectrum of cancer mutations1,2. Here we report the first-in-human application of this concept in melanoma. We set up a process comprising comprehensive identification of individual mutations, computational prediction of neo-epitopes, and design and manufacturing of a vaccine unique for each patient. All patients developed T cell responses against multiple vaccine neo-epitopes at up to high single-digit percentages. Vaccine-induced T cell infiltration and neo-epitope-specific killing of autologous tumour cells were shown in post-vaccination resected metastases from two patients. The cumulative rate of metastatic events was highly significantly reduced after the start of vaccination, resulting in a sustained progression-free survival. Two of the five patients with metastatic disease experienced vaccine-related objective responses. One of these patients had a late relapse owing to outgrowth of 2-microglobulin-deficient melanoma cells as an acquired resistance mechanism. A third patient developed a complete response to vaccination in combination with PD-1 blockade therapy. Our study demonstrates that individual mutations can be exploited, thereby opening a path to personalized immunotherapy for patients with cancer.

词汇表:
epitope: 抗原表位
melanoma: 黑色素瘤


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doi:10.1038/nature22991
An immunogenic personal neoantigen vaccine for patients with melanoma.
Effective anti-tumour immunity in humans has been associated with the presence of T cells directed at cancer neoantigens1, a class of HLA-bound peptides that arise from tumour-specific mutations. They are highly immunogenic because they are not present in normal tissues and hence bypass central thymic tolerance. Although neoantigens were long-envisioned as optimal targets for an anti-tumour immune response2, their systematic discovery and evaluation only became feasible with the recent availability of massively parallel sequencing for detection of all coding mutations within tumours, and of machine learning approaches to reliably predict those mutated peptides with highaffinity binding of autologous human leukocyte antigen (HLA) molecules. We hypothesized that vaccination with neoantigens can both expand pre-existing neoantigen-specific T-cell populations and induce a broader repertoire of new T-cell specificities in cancer patients, tipping the intra-tumoural balance in favour of enhanced tumour control. Here we demonstrate the feasibility, safety, and immunogenicity of a vaccine that targets up to 20 predicted personal tumour neoantigens. Vaccine-induced polyfunctional CD4+ and CD8+ T cells targeted 58 (60%) and 15 (16%) of the 97 unique neoantigens used across patients, respectively. These T cells discriminated mutated from wild-type antigens, and in some cases directly recognized autologous tumour. Of six vaccinated patients, four had no recurrence at 25 months after vaccination, while two with recurrent disease were subsequently treated with anti-PD-1 (anti- programmed cell death-1) therapy and experienced complete tumour regression, with expansion of the repertoire of neoantigen-specific T cells. These data provide a strong rationale for further development of this approach, alone and in combination with checkpoint blockade or other immunotherapies.

词汇表:
neoantigen:肿瘤抗原
vaccination: 接种
melanoma: 黑色素瘤
peptide: 肽


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Artificial Intelligence and the Pathologist: Future Frenemies
The manuscript titled AlphaGo, deep learning, and the future of the human microscopist in this months issue of the Archives of Pathology Laboratory Medicine1 describes the triumph of Googles (Mountain View, California) artificial intelligence (AI) program, AlphaGo, which beat the 18-time world champion of Go, an ancient Chinese board game far more complex than chess.
词汇表:
Frenemies:亦敌亦友 Friend--enemy
artificial intelligence (AI):人工智能
pathologist:病理学家,病理科医生
algorithm: 算法,程序算法
microscopist: 显微技术人员
non–small cell lung carcinoma: 非小细胞肺癌
hypothesis: 假说
diagnosis : 诊断


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AlphaGo, Deep Learning, and the Future of the Human Microscopist
In March of last year, Googles (Menlo Park, California) artificial intelligence (AI) computer program AlphaGo beat the best Go player in the world, 18-time champion Lee Se-dol, in a tournament, winning 4 of 5 games.1 At first glance this news would seem of little interest to a pathologist, or to anyone else for that matter. After all, many will remember that IBMs (Armonk, New York) computer program Deep Blue beat Garry Kasparov—at the time the greatest chess player in the world—and that was 19 years ago. So, whats so significant about a computer winning another board game?
词汇表:
artificial intelligence (AI):人工智能
pathologist:病理学家,病理科医生
algorithm: 算法,程序算法
microscopist: 显微技术人员
non–small cell lung carcinoma: 非小细胞肺癌
hypothesis: 假说
diagnosis : 诊断