Engineering Escherichia coli for renewable production of the 5-carbon polyamide building-blocks 5-aminovalerate and glutarate.

Through metabolic pathway engineering, novel microbial biocatalysts can be engineered to convert renewable resources into useful chemicals, including monomer building-blocks for bioplastics production. Here we describe the systematic engineering of Escherichia coli to produce, as individual products, two 5-carbon polyamide building blocks, namely 5-aminovalerate (AMV) and glutarate. The modular pathways were derived using "parts" from the natural lysine degradation pathway of Pseudomonas putida KT2440. Endogenous over-production of the required precursor, lysine, was first achieved through metabolic deregulation of its biosynthesis pathway by introducing feedback resistant mutants of aspartate kinase III and dihydrodipicolinate synthase. Further disruption of native lysine decarboxylase activity (by deleting cadA and ldcC) limited cadaverine by-product formation, enabling lysine production to 2.25 g/L at a glucose yield of 138 mmol/mol (18% of theoretical). Co-expression of lysine monooxygenase and 5-aminovaleramide amidohydrolase (encoded by davBA) then resulted in the production of 0.86 g/L AMV in 48 h. Finally, the additional co-expression of glutaric semialdehyde dehydrogenase and 5-aminovalerate aminotransferase (encoded by davDT) led to the production of 0.82 g/L glutarate under the same conditions. At this output, yields on glucose were 71 and 68 mmol/mol for AMV and glutarate (9.5 and 9.1% of theoretical), respectively. These findings further expand the number and diversity of polyamide monomers that can be derived directly from renewable resources.

Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training.

BACKGROUND: Missing occult cancer lesions accounts for the most diagnostic errors in retrospective radiology reviews as early cancer can be small or subtle, making the lesions difficult to detect. Second-observer is the most effective technique for reducing these events and can be economically implemented with the advent of artificial intelligence (AI). AIM: To achieve appropriate AI model training, a large annotated dataset is necessary to train the AI models. Our goal in this research is to compare two methods for decreasing the annotation time to establish ground truth: Skip-slice annotation and AI-initiated annotation. METHODS: We developed a 2D U-Net as an AI second observer for detecting colorectal cancer (CRC) and an ensemble of 5 differently initiated 2D U-Net for ensemble technique. Each model was trained with 51 cases of annotated CRC computed tomography of the abdomen and pelvis, tested with 7 cases, and validated with 20 cases from The Cancer Imaging Archive cases. The sensitivity, false positives per case, and estimated Dice coefficient were obtained for each method of training. We compared the two methods of annotations and the time reduction associated with the technique. The time differences were tested using Friedman's two-way analysis of variance. RESULTS: Sparse annotation significantly reduces the time for annotation particularly skipping 2 slices at a time (P < 0.001). Reduction of up to 2/3 of the annotation does not reduce AI model sensitivity or false positives per case. Although initializing human annotation with AI reduces the annotation time, the reduction is minimal, even when using an ensemble AI to decrease false positives. CONCLUSION: Our data support the sparse annotation technique as an efficient technique for reducing the time needed to establish the ground truth.

Delayed bolus-tracking trigger at CT correlates with cardiac dysfunction and suboptimal portovenous contrast phase.

OBJECTIVE: To assess whether delayed trigger during bolus-tracking for CT correlates with reduced heart function and suboptimal portovenous contrast phase. METHODS AND MATERIALS: Patients who underwent portovenous abdominal CT using bolus-tracking and echocardiography within 2 weeks were included and excluded if there was a non-standard contrast injection. The bolus trigger time (BTT) at 100 Hounsfield units in the abdominal aorta, patient age, congestive heart failure (CHF) history, and ejection fraction were recorded. Two radiologists scored the liver contrast phase (1-5, 5 being an optimal portovenous phase). When applicable, the BTT and contrast score of the most recent comparison examination with equivalent technical parameters were also recorded. Simple linear regression (univariate) was used to test for associations with trigger time. RESULTS: 114 patients with a mean age of 61 ± 15 years fulfilled criteria. The mean trigger time was 18 ± 6 s (range: 6-38 s) and the mean ejection fraction was 52 ± 12% (range: 19-69%). A longer bolus trigger had a significant correlation with reduced ejection fraction (P = 0.0018), lower hepatic contrast score (P < 0.0001), history of CHF (P = 0.0212), and older age (P = 0.0223). Contrast score differences between the study exam and available prior exams revealed score differences of 0 (n = 73), 1 (n = 15) and 2 (n = 5); these were associated, respectively, with a mean bolus trigger time difference between exams of 2 s (range, 0-6 s), 6 s (range, 1-15 s), and 11 s (range, 5-13). The P-value comparing bolus trigger time and contrast score differences was less than 0.0001. A lower ejection fraction also significantly correlated with suboptimal PV contrast phase (P < 0.0001). CONCLUSION: Delayed time to trigger during bolus-tracking for CT can indicate cardiac dysfunction and may not adequately adjust to provide an optimal portovenous contrast phase.

Diffuse Bone Metastases in Pancreatic Neuroendocrine Tumor Shown on Octreoscan.

A 32-year-old man with a pancreatic neuroendocrine tumor and known liver metastasis presented with diffuse back pain. Workup octreoscan showed newly developed diffuse bone metastases, in addition to the persistent liver metastases. The bone metastases were later confirmed on MRI spine images.

Engineering microbial chemical factories to produce renewable "biomonomers".

By applying metabolic engineering tools and strategies to engineer synthetic enzyme pathways, the number and diversity of commodity and specialty chemicals that can be derived directly from renewable feedstocks is rapidly and continually expanding. This of course includes a number of monomer building-block chemicals that can be used to produce replacements to many conventional plastic materials. This review aims to highlight numerous recent and important advancements in the microbial production of these so-called "biomonomers." Relative to naturally-occurring renewable bioplastics, biomonomers offer several important advantages, including improved control over the final polymer structure and purity, the ability to synthesize non-natural copolymers, and allowing products to be excreted from cells which ultimately streamlines downstream recovery and purification. To highlight these features, a handful of biomonomers have been selected as illustrative examples of recent works, including polyamide monomers, styrenic vinyls, hydroxyacids, and diols. Where appropriate, examples of their industrial penetration to date and end-product uses are also highlighted. Novel biomonomers such as these are ultimately paving the way toward new classes of renewable bioplastics that possess a broader diversity of properties than ever before possible.