Utility of CD123 immunohistochemistry in differentiating lupus erythematosus from cutaneous T cell lymphoma

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149293/1/his13817_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149293/2/his13817.pd

Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration

Health insurers often use algorithms to identify members who would benefit from care and condition management programs, which provide personalized, high-touch clinical support. Timely, accurate, and seamless integration between algorithmic identification and clinical intervention depends on effective collaboration between the system designers and nurse care managers. We focus on a high-risk pregnancy (HRP) program designed to reduce the likelihood of adverse prenatal, perinatal, and postnatal events and describe how we overcome three challenges of HRP programs as articulated by nurse care managers; (1) early detection of pregnancy, (2) accurate identification of impactable high-risk members, and (3) provision of explainable indicators to supplement predictions. We propose a novel algorithm for pregnancy identification that identifies pregnancies 57 days earlier than previous code-based models in a retrospective study. We then build a model to predict impactable pregnancy complications that achieves an AUROC of 0.760. Models for pregnancy identification and complications are then integrated into a proposed user interface. In a set of user studies, we collected quantitative and qualitative feedback from nurses on the utility of the predictions combined with clinical information driving the predictions on triaging members for the HRP program

A novel α-type carbonic anhydrase associated with the thylakoid membrane in Chlamydomonas reinhardtii is required for growth at ambient CO\u3csub\u3e2\u3c/sub\u3e

A 29.5 kDa intracellular α-type carbonic anhydrase, designated Cah3, from the unicellular green alga Chlamydomonas reinhardtii is the first of this type discovered inside a photosynthetic eukaryote cell. We describe the cloning of a cDNA which encodes the protein. Immunoblot studies with specific antibodies raised against Cah3 demonstrate that the polypeptide is associated exclusively with the thylakoid membrane. The putative transit peptide suggests that Cah3 is directed to the thylakoid lumen, which is confirmed further by the presence of mature sized Cah3 after thermolysin treatment of intact thylakoids. Complementation of the high inorganic carbon concentration-requiring mutant, cia3, with a subcloned cosmid containing the cah3 gene yielded transformants that grew on atmospheric levels of CO2 (0.035%) and contained an active 29.5 kDa α-type carbonic anhydrase. Although, cia3 has reduced internal carbonic anhydrase activity, unexpectedly the level of Cah3 was similar to that of the wild-type, suggesting that the mutant accumulates an inactive Cah3 polypeptide. Genomic sequence analysis of the mutant revealed two amino acid changes in the transit peptide. Results from photosynthesis and chlorophyll a fluorescence parameter measurements show that the cia3 mutant is photosynthetically impaired. Our results indicate that the carbonic anhydrase, extrinsically located within the chloroplast thylakoid lumen, is essential for growth of C.reinhardtii at ambient levels of CO2, and that at these CO2 concentrations the enzyme is required for optimal photosystem II photochemistry

Analysis of retinal cell development in chick embryo by immunohistochemistry and in ovo electroporation techniques

<p>Abstract</p> <p>Background</p> <p>Retinal cell development has been extensively investigated; however, the current knowledge of dynamic morphological and molecular changes is not yet complete.</p> <p>Results</p> <p>This study was aimed at revealing the dynamic morphological and molecular changes in retinal cell development during the embryonic stages using a new method of targeted retinal injection, <it>in ovo </it>electroporation, and immunohistochemistry techniques. A plasmid DNA that expresses the green fluorescent protein (GFP) as a marker was delivered into the sub-retinal space to transfect the chick retinal stem/progenitor cells at embryonic day 3 (E3) or E4 with the aid of pulses of electric current. The transfected retinal tissues were analyzed at various stages during chick development from near the start of neurogenesis at E4 to near the end of neurogenesis at E18. The expression of GFP allowed for clear visualization of cell morphologies and retinal laminar locations for the indication of retinal cell identity. Immunohistochemistry using cell type-specific markers (e.g., Visinin, Xap-1, Lim1+2, Pkcα, NeuN, Pax6, Brn3a, Vimentin, etc.) allowed further confirmation of retinal cell types. The composition of retinal cell types was then determined over time by counting the number of GFP-expressing cells observed with morphological characteristics specific to the various retinal cell types.</p> <p>Conclusion</p> <p>The new method of retinal injection and electroporation at E3 - E4 allows the visualization of all retinal cell types, including the late-born neurons, e.g., bipolar cells at a level of single cells, which has been difficult with a conventional method with injection and electroporation at E1.5. Based on data collected from analyses of cell morphology, laminar locations in the retina, immunohistochemistry, and cell counts of GFP-expressing cells, the time-line and dynamic morphological and molecular changes of retinal cell development were determined. These data provide more complete information on retinal cell development, and they can serve as a reference for the investigations in normal retinal development and diseases.</p

Design of small molecule-responsive microRNAs based on structural requirements for Drosha processing

MicroRNAs (miRNAs) are prevalent regulatory RNAs that mediate gene silencing and play key roles in diverse cellular processes. While synthetic RNA-based regulatory systems that integrate regulatory and sensing functions have been demonstrated, the lack of detail on miRNA structure–function relationships has limited the development of integrated control systems based on miRNA silencing. Using an elucidated relationship between Drosha processing and the single-stranded nature of the miRNA basal segments, we developed a strategy for designing ligand-responsive miRNAs. We demonstrate that ligand binding to an aptamer integrated into the miRNA basal segments inhibits Drosha processing, resulting in titratable control over gene silencing. The generality of this control strategy was shown for three aptamer–small molecule ligand pairs. The platform can be extended to the design of synthetic miRNAs clusters, cis-acting miRNAs and self-targeting miRNAs that act both in cis and trans, enabling fine-tuning of the regulatory strength and dynamics. The ability of our ligand-responsive miRNA platform to respond to user-defined inputs, undergo regulatory performance tuning and display scalable combinatorial control schemes will help advance applications in biological research and applied medicine

Zwitterionic PEG-PC hydrogels modulate the foreign body response in a modulus-dependent manner

Reducing the foreign body response (FBR) to implanted biomaterials will enhance their performance in tissue engineering. Poly(ethylene glycol) (PEG) hydrogels are increasingly popular for this application due to their low cost, ease of use, and the ability to tune their compliance via molecular weight and crosslinking densities. PEG hydrogels can elicit chronic inflammation in vivo, but recent evidence has suggested that extremely hydrophilic, zwitterionic materials and particles can evade the immune system. To combine the advantages of PEG-based hydrogels with the hydrophilicity of zwitterions, we synthesized hydrogels with co-monomers PEG and the zwitterion phosphorylcholine (PC). Recent evidence suggests that stiff hydrogels elicit increased immune cell adhesion to hydrogels, which we attempted to reduce by increasing hydrogel hydrophilicity. Surprisingly, hydrogels with the highest amount of zwitterionic co-monomer elicited the highest FBR we observed. Lowering the hydrogel modulus (165 kPa to 3 kPa), or PC content (20 wt% to 0 wt%), mitigated this effect. A high density of macrophages was found at the surface of implants associated with a high FBR, and mass spectrometry analysis of the proteins adsorbed to these gels implicated extracellular matrix, immune response, and cell adhesion protein categories as drivers of macrophage recruitment to these hydrogels. Overall, we show that modulus regulates macrophage adhesion to zwitterionic-PEG hydrogels, and demonstrate that chemical modifications to hydrogels should be studied in parallel with their physical properties to optimize implant design

Tell me why! Explanations support learning relational and causal structure

Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of explanations--plays a considerable role in overcoming this challenge. Here, we show that language can play a similar role for deep RL agents in complex environments. While agents typically struggle to acquire relational and causal knowledge, augmenting their experience by training them to predict language descriptions and explanations can overcome these limitations. We show that language can help agents learn challenging relational tasks, and examine which aspects of language contribute to its benefits. We then show that explanations can help agents to infer not only relational but also causal structure. Language can shape the way that agents to generalize out-of-distribution from ambiguous, causally-confounded training, and explanations even allow agents to learn to perform experimental interventions to identify causal relationships. Our results suggest that language description and explanation may be powerful tools for improving agent learning and generalization.Comment: ICML 2022; 23 page

Visual Genome: Connecting language and vision using crowdsourced dense image annotations

Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked “What vehicle is the person riding?”, computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that “the person is riding a horse-drawn carriage.” In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of (Formula presented.) objects, (Formula presented.) attributes, and (Formula presented.) pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs