3D printing, also known as additive manufacturing, is the computer-guided process of fabricating physical objects by depositing successive layers of material. It has transformed manufacturing acros... 要約積層造形法としても知られる3D印刷は、コンピュータガイド下で、原料の連続層を積み重ねることにより物理的物体を造形する手法である。3D printingはほぼすべての産業で製造工程を変容させ、その結果、研究および医学分野において驚くべき進歩がもたらされた。急速に拡大する消費者市場の中には今や、便利で手頃な価格の「デスクトップ型」3Dプリンターも含まれている。研究室ではこうした製品を使用して... 초록적층 가공으로도 알려진 3D 프린팅은 물질의 연속적인 층을 증착하여 실제 물체를 제조하는 컴퓨터 유도 방식의 프로세스입니다. 이 기술은 거의 모든 산업에 걸쳐 제조 방식의 혁신을 가져오고 연구와 의학 분야의 놀라운 발전을 이끌고 있습니다. 빠르게 성장하는 소비자 시장에 이제 편리하고 저렴한 “데스크톱” 3D 프린터가 포함되었습니다. 이들은 맞춤형 ... 摘要3D 打印，也称为增材制造，它是一种通过层层叠加材料来制造实体物品的计算机辅助过程。这种技术几乎改变了各个行业的生产制造，极大地推动了研究和医学的进步。现在，方便实惠的“桌面”3D 打印机也已进入快速发展的消费者市场。在实验室中，它们用于创造自定义 3D 打印设备，越来越多的设计师为经济实惠的开放源码创新做出了贡献，这些创新成果既可供专业人员使用，也可供爱好者使用。美国国家卫生研究院（N... 摘要3D 列印也稱為增材製造，是一種透過逐層疊加材料來製造實體物件的電腦輔助過程。此技術幾乎改變了各個行業的生產製造，極大地推動了研究和醫學的進步。現在，方便實惠的「桌面」3D 列印機也已進入快速發展的消費者市場。在實驗室中，它們被用來創造自訂 3D 列印設備，越來越多的設計師們為經濟實惠的開放源碼創新做出了貢獻，這些創新成果既可供專業人員使用，也可供愛好者使用。美國國立衛生研究院 (Nat...

Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics to combat bacterial resistance to conventional drugs. The design of de novo AMPs with high therapeutic indexes, low cost of synthesis, high resistance to proteases and high bioavailability remains a challenge. Such design requires computational modeling of antimicrobial properties. Currently, most computational methods cannot accurately calculate antimicrobial potency against particular strains of bacterial pathogens. We developed a tool for AMP prediction (Special Prediction (SP) tool) and made it available on our Web site (https://dbaasp.org/prediction). Based on this tool, a simple algorithm for the design of de novo AMPs (DSP) was created. We used DSP to design short peptides with high therapeutic indexes against gram-negative bacteria. The predicted peptides have been synthesized and tested in vitro against a panel of gram-negative bacteria, including drug resistant ones. Predicted activity against Escherichia coli ATCC 25922 was experimentally confirmed for 14 out of 15 peptides. Further improvements for designed peptides included the synthesis of D-enantiomers, which are traditionally used to increase resistance against proteases. One synthetic D-peptide (SP15D) possesses one of the lowest values of minimum inhibitory concentration (MIC) among all DBAASP database short peptides at the time of the submission of this article, while being highly stable against proteases and having a high therapeutic index. The mode of anti-bacterial action, assessed by fluorescence microscopy, shows that SP15D acts similarly to cell penetrating peptides. SP15D can be considered a promising candidate for the development of peptide antibiotics. We plan further exploratory studies with the SP tool, aiming at finding peptides which are active against other pathogenic organisms.

Tuberculosis is a major global health threat claiming millions of lives each year. While the total number of tuberculosis cases has been decreasing over the last years, the rise of drug-resistant tuberculosis has reduced the chance of controlling the disease. The purpose is to implement a timely diagnosis of drug-resistant tuberculosis, which is essential to administering adequate treatment regimens and stopping the further transmission of drug-resistant tuberculosis.

Context: Primary generalized glucocorticoid resistance is a rare genetic disorder characterized by generalized, partial, target-tissue insensitivity to glucocorticoids. The molecular basis of the condition has been ascribed to inactivating mutations in the human glucocorticoid receptor (hGR) gene. Objective: The objective of the study was to present three new cases caused by a novel mutation in the hGR gene and to delineate the molecular mechanisms through which the mutant receptor impairs glucocorticoid signal transduction. Design and Results: The index case (father) and his two daughters presented with increased urinary free cortisol excretion and resistance of the hypothalamic-pituitary-adrenal axis to dexamethasone suppression in the absence of clinical manifestations suggestive of Cushing syndrome. All subjects harbored a novel, heterozygous, point mutation (T→G) at nucleotide position 1724 of the hGR gene, which resulted in substitution of valine by glycine at amino acid 575 of the receptor. Compared with the wild-type receptor, the hGRαV575G demonstrated a significant (33%) reduction in its ability to transactivate the mouse mammary tumor virus promoter in response to dexamethasone, a 50% decrease in its affinity for the ligand, and a 2.5-fold delay in nuclear translocation. Although it did not exert a dominant negative effect on the wild-type receptor and preserved its ability to bind to DNA, hGRαV575G displayed significantly enhanced (∼80%) ability to transrepress the nuclear factor-κΒ signaling pathway. Finally, the mutant receptor hGRαV575G demonstrated impaired interaction with the LXXLL motif of the glucocorticoid receptor-interacting protein 1 coactivator in vitro and in computer-based structural simulation via its defective activation function-2 (AF-2) domain. Conclusions: The natural mutant receptor hGRαV575G causes primary generalized glucocorticoid resistance by affecting multiple steps in the glucocorticoid signaling cascade, including the affinity for the ligand, the time required for nuclear translocation, and the interaction with the glucocorticoid-interacting protein-1 coactivator.

The evolution of drug-resistant pathogenic microbial species is a major global health concern. Naturally occurring, antimicrobial peptides (AMPs) are considered promising candidates to address antibiotic resistance problems. A variety of computational methods have been developed to accurately predict AMPs. The majority of such methods are not microbial strain specific (MSS): they can predict whether a given peptide is active against some microbe, but cannot accurately calculate whether such peptide would be active against a particular MS. Due to insufficient data on most MS, only a few MSS predictive models have been developed so far. To overcome this problem, we developed a novel approach that allows to improve MSS predictive models (MSSPM), based on properties, computed for AMP sequences and characteristics of genomes, computed for target MS. New models can perform predictions of AMPs for MS that do not have data on peptides tested on them. We tested various types of feature engineering as well as different machine learning (ML) algorithms to compare the predictive abilities of resulting models. Among the ML algorithms, Random Forest and AdaBoost performed best. By using genome characteristics as additional features, the performance for all models increased relative to models relying on AMP sequence-based properties only. Our novel MSS AMP predictor is freely accessible as part of DBAASP database resource at http://dbaasp.org/prediction/genome

Availability of trained radiologists for fast processing of CXRs in regions burdened with tuberculosis always has been a challenge, affecting both timely diagnosis and patient monitoring. The paucity of annotated images of lungs of TB patients hampers attempts to apply data-oriented algorithms for research and clinical practices. The TB Portals Program database (TBPP, https://TBPortals.niaid.nih.gov) is a global collaboration curating a large collection of the most dangerous, hard-to-cure drug-resistant tuberculosis (DR-TB) patient cases. TBPP, with 1,179 (83%) DR-TB patient cases, is a unique collection that is well positioned as a testing ground for deep learning classifiers. As of January 2019, the TBPP database contains 1,538 CXRs, of which 346 (22.5%) are annotated by a radiologist and 104 (6.7%) by a pulmonologist–leaving 1,088 (70.7%) CXRs without annotations. The Qure.ai qXR artificial intelligence automated CXR interpretation tool, was blind-tested on the 346 radiologist-annotated CXRs from the TBPP database. Qure.ai qXR CXR predictions for cavity, nodule, pleural effusion, hilar lymphadenopathy was successfully matching human expert annotations. In addition, we tested the 12 Qure.ai classifiers to find whether they correlate with treatment success (information provided by treating physicians). Ten descriptors were found as significant: abnormal CXR (p = 0.0005), pleural effusion (p = 0.048), nodule (p = 0.0004), hilar lymphadenopathy (p = 0.0038), cavity (p = 0.0002), opacity (p = 0.0006), atelectasis (p = 0.0074), consolidation (p = 0.0004), indicator of TB disease (p = < .0001), and fibrosis (p = < .0001). We conclude that applying fully automated Qure.ai CXR analysis tool is useful for fast, accurate, uniform, large-scale CXR annotation assistance, as it performed well even for DR-TB cases that were not used for initial training. Testing artificial intelligence algorithms (encapsulating both machine learning and deep learning classifiers) on diverse data collections, such as TBPP, is critically important toward progressing to clinically adopted automatic assistants for medical data analysis.