MicroRNA: function, detection, and bioanalysis.

Organ printing can be defined as layer-by-layer additive robotic biofabrication of three-dimensional functional living macrotissues and organ constructs using tissue spheroids as building blocks. The microtissues and tissue spheroids are living materials with certain measurable, evolving and potentially controllable composition, material and biological properties. Closely placed tissue spheroids undergo tissue fusion - a process that represents a fundamental biological and biophysical principle of developmental biology-inspired directed tissue self-assembly. It is possible to engineer small segments of an intraorgan branched vascular tree by using solid and lumenized vascular tissue spheroids. Organ printing could dramatically enhance and transform the field of tissue engineering by enabling large-scale industrial robotic biofabrication of living human organ constructs with "built-in" perfusable intraorgan branched vascular tree. Thus, organ printing is a new emerging enabling technology paradigm which represents a developmental biology-inspired alternative to classic biodegradable solid scaffold-based approaches in tissue engineering.

Organ Printing: Tissue Spheroids as Building Blocks

The amyloid-β peptide (Aβ) is a key protein in Alzheimer’s disease (AD) pathology. We previously reported in vitro evidence suggesting that Aβ is an antimicrobial peptide. We present in vivo data showing that Aβ expression protects against fungal and bacterial infections in mouse, nematode, and cell culture models of AD. We show that Aβ oligomerization, a behavior traditionally viewed as intrinsically pathological, may be necessary for the antimicrobial activities of the peptide. Collectively, our data are consistent with a model in which soluble Aβ oligomers first bind to microbial cell wall carbohydrates via a heparin-binding domain. Developing protofibrils inhibited pathogen adhesion to host cells. Propagating β-amyloid fibrils mediate agglutination and eventual entrapment of unatttached microbes. Consistent with our model, Salmonella Typhimurium bacterial infection of the brains of transgenic 5XFAD mice resulted in rapid seeding and accelerated β-amyloid deposition, which closely colocalized with the invading bacteria. Our findings raise the intriguing possibility that β-amyloid may play a protective role in innate immunity and infectious or sterile inflammatory stimuli may drive amyloidosis. These data suggest a dual protective/damaging role for Aβ, as has been described for other antimicrobial peptides.

Amyloid-β peptide protects against microbial infection in mouse and worm models of Alzheimer’s disease

Amyloid precursor protein (APP) gives rise to the amyloid-β peptide and thus has a key role in the pathogenesis of Alzheimer disease. By contrast, the physiological functions of APP and the closely related APP-like proteins (APLPs) remain less well understood. Studying these physiological functions has been challenging and has required a careful long-term strategy, including the analysis of different App-knockout and Aplp-knockout mice. In this Review, we summarize these findings, focusing on the in vivo roles of APP family members and their processing products for CNS development, synapse formation and function, brain injury and neuroprotection, as well as ageing. In addition, we discuss the implications of APP physiology for therapeutic approaches.

Not just amyloid: physiological functions of the amyloid precursor protein family

The prokaryote-derived CRISPR–Cas genome editing technology has altered plant molecular biology beyond all expectations. Characterized by robustness and high target specificity and programmability, CRISPR–Cas allows precise genetic manipulation of crop species, which provides the opportunity to create germplasms with beneficial traits and to develop novel, more sustainable agricultural systems. Furthermore, the numerous emerging biotechnologies based on CRISPR–Cas platforms have expanded the toolbox of fundamental research and plant synthetic biology. In this Review, we first briefly describe gene editing by CRISPR–Cas, focusing on the newest, precise gene editing technologies such as base editing and prime editing. We then discuss the most important applications of CRISPR–Cas in increasing plant yield, quality, disease resistance and herbicide resistance, breeding and accelerated domestication. We also highlight the most recent breakthroughs in CRISPR–Cas-related plant biotechnologies, including CRISPR–Cas reagent delivery, gene regulation, multiplexed gene editing and mutagenesis and directed evolution technologies. Finally, we discuss prospective applications of this game-changing technology. The newest CRISPR–Cas genome editing technologies enable precise and simplified formation of crops with increased yield, quality, disease resistance and herbicide resistance, as well as accelerated domestication. Recent breakthroughs in CRISPR–Cas plant biotechnologies improve reagent delivery, gene regulation, multiplexed gene editing and directed evolution.

Applications of CRISPR–Cas in agriculture and plant biotechnology

Zygotic Genome Activation in Vertebrates.

Innovations in biomaterials and stem cell technology have allowed for the emergence of novel three-dimensional (3D) tissue-like structures known as organoids and spheroids. As a result, compared to conventional 2D cell culture and animal models, these complex 3D structures have improved the accuracy and facilitated in vitro investigations of human diseases, human development, and personalized medical treatment. Due to the rapid progress of this field, numerous spheroid and organoid production methodologies have been published. However, many of the current spheroid and organoid production techniques are limited by complexity, throughput, and reproducibility. Microfabricated and microscale platforms (e.g., microfluidics and microprinting) have shown promise to address some of the current limitations in both organoid and spheroid generation. Microfabricated and microfluidic devices have been shown to improve nutrient delivery and exchange and have allowed for the arrayed production of size-controlled culture areas that yield more uniform organoids and spheroids for a higher throughput at a lower cost. In this review, we discuss the most recent production methods, challenges currently faced in organoid and spheroid production, and microfabricated and microfluidic applications for improving spheroid and organoid generation. Specifically, we focus on how microfabrication methods and devices such as lithography, microcontact printing, and microfluidic delivery systems can advance organoid and spheroid applications in medicine. Microtechnology-based approaches could overcome the limitations of current three-dimensional cell and tissue culture processes. Complex 3D cultures provide deeper insights into human biology and pathology than standard 2D cultures, but their high complexity brings issues like low reproducibility and low throughput. In this paper, Rahim Esfandyarpour, PhD, Assistant Professor of Electrical Engineering, & Biomedical Engineering, and his collaborators from the University of California, Irvine, Santa Cruz, and Stanford University introduce the benefits of microscale technologies. The team describe how microcontact printing of cell-scaffold proteins onto culture mediums allows for higher throughput. The team also describe the construction of an “organ-on-a-chip,” where cultures are constrained between microfluidic nutrient exchange channels, driving the development of complex and accurate tissue structures. Organ-on-a-chip devices are highly economical and offer a platform that is yet to be fully exploited.

/pdf/microtechnology-based-methods-for-organoid-models-4mc5k6pc0p.pdf

Microtechnology-based methods for organoid models.

Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we successfully implemented our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.

/pdf/covid-classifier-an-automated-machine-learning-model-to-2w1ugjdotr.pdf

COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images.

https://febs.onlinelibrary.wiley.com/doi/pdf/10.1016/j.febslet.2013.05.026

Redundancy and divergence in the amyloid precursor protein family

Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections make the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we were able to successfully implement our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.

/pdf/covid-classifier-an-automated-machine-learning-model-to-3qkl6q37ci.pdf

S. Ali M. Shariati

Papers

Zygotic Genome Activation in Vertebrates.

Microtechnology-based methods for organoid models.

COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images.

Redundancy and divergence in the amyloid precursor protein family

COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images.