Brobergvargas7764

Repeating results with multiple distinct chronic stressors in male and female mice combined with increased usage of translationally relevant behavior tasks will help to advance the understanding of how chronic stress can precipitate mood disorders.With the growth in accessibility of 3D printing, there has been a growing application of and interest in additive manufacturing processes in chemical laboratories and chemical education. Building on the long and successful history of physical modeling of molecular systems, we present select models along with a protocol to facilitate 3D printing of molecular structures that are able to do more than represent shape and connectivity. Models assembled as described incorporate dynamic aspects and degrees of freedom into saturated hydrocarbon structures. As a representative example, cyclohexane was assembled from parts printed and finished using different thermoplastics, and the resulting models retain their functionality at a variety of scales. #link# The resulting structures show configurational space accessibility consistent with calculations and literature, and versions of these structures can be used as aids to illustrate concepts that are difficult to convey in other ways. This exercise enables us to evaluate successful printing protocols, make practical recommendations for assembly, and outline design principles for physical modeling of molecular systems. The provided structures, procedures, and results provide a foundation for individual manufacture and exploration of molecular structure and dynamics with 3D printing.Neonates are at an increased risk of bacterial sepsis due to the unique immune profile they display in the first months of life. We have established a protocol for studying the pathogenesis of E. coli O1K1H7, a serotype responsible for high mortality rates in neonates. Our method utilizes intravital imaging of neonatal pups at different time points during the progression of infection. This imaging, paralleled by measurement of bacteria in the blood, inflammatory profiling, and tissue histopathology, signifies a rigorous approach to understanding infection dynamics during sepsis. In the current report, we model two infectious inoculums for comparison of bacterial burdens and severity of disease. ML141 find that subscapular infection leads to disseminated infection by 10 h post-infection. By 24 h, infection of luminescent E. coli was abundant in the blood, lungs, and other peripheral tissues. Expression of inflammatory cytokines in the lungs is significant at 24 h, and this is followed by cellular infiltration and evidence of tissue damage that increases with infectious dose. Intravital imaging does have some limitations. This includes a luminescent signal threshold and some complications that can arise with neonates during anesthesia. Despite some limitations, we find that our infection model offers an insight for understanding longitudinal infection dynamics during neonatal murine sepsis, that has not been thoroughly examined to date. We expect this model can also be adapted to study other critical bacterial infections during early life.Edhazardia aedis is a microsporidian parasite of Aedes aegypti mosquitoes, a disease vector that transmits multiple arboviruses which cause millions of disease cases each year. E. aedis causes mortality and reduced reproductive fitness in the mosquito vector and has been explored for its potential as a biocontrol agent. The protocol we present for culturing E. aedis is based on its natural infection cycle, which involves both horizontal and vertical transmission at different life stages of the mosquito host. Ae. aegypti mosquitoes are exposed to spores in the larval stage. These infected larvae then mature into adults and transmit the parasite vertically to their offspring. Infected offspring are then used as a source of spores for future horizontal transmission. Culturing E. aedis can be challenging to the uninitiated given the complexities of the parasite's life cycle, and this protocol provides detailed guidance and visual aids for clarification.Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, depending on the cancer type. To reduce the brain metastatic tumor burden, gaps in basic and translational knowledge need to be addressed. Major challenges include a paucity of reproducible preclinical models and associated tools. Three-dimensional models of brain metastasis can yield the relevant molecular and phenotypic data used to address these needs when combined with dedicated analysis tools. Moreover, compared to murine models, organ-on-a-chip models of patient tumor cells traversing the blood brain barrier into the brain microenvironment generate results rapidly and are more interpretable with quantitative methods, thus amenable to high throughput testing. Here we describe and demonstrate the use of a novel 3D microfluidic blood brain niche (µmBBN) platform where multiple elements of the niche can be cultured for an extended period (several days), fluorescently imaged by confocal microscopy, and the images reconstructed using an innovative confocal tomography technique; all aimed to understand the development of micro-metastasis and changes to the tumor micro-environment (TME) in a repeatable and quantitative manner. We demonstrate how to fabricate, seed, image, and analyze the cancer cells and TME cellular and humoral components, using this platform. link2 Moreover, we show how artificial intelligence (AI) is used to identify the intrinsic phenotypic differences of cancer cells that are capable of transit through a model µmBBN and to assign them an objective index of brain metastatic potential. The data sets generated by this method can be used to answer basic and translational questions about metastasis, the efficacy of therapeutic strategies, and the role of the TME in both.Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy exceeding its constituents. This protocol describes the development of an ML algorithm to predict the growth of breast cancer bone macrometastases in a rat model before any abnormalities are observable with standard imaging methods. Such an algorithm can facilitate the detection of early metastatic disease (i.e., micrometastasis) that is regularly missed during staging examinations. The applied metastasis model is site-specific, meaning that the rats develop metastases exclusively in their right hind leg. The model's tumor-take rate is 60%-80%, with macrometastases becoming visible in magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a subset of animals 30 days after induction, whereas a second subset of animals exhibit no tumor growth. Starting from image examinations acquired at an earlier time point, this protocol describes the extraction of features that indicate tissue vascularization detected by MRI, glucose metabolism by PET/CT, and the subsequent determination of the most relevant features for the prediction of macrometastatic disease. These features are then fed into a model-averaged neural network (avNNet) to classify the animals into one of two groups one that will develop metastases and the other that will not develop any tumors. The protocol also describes the calculation of standard diagnostic parameters, such as overall accuracy, sensitivity, specificity, negative/positive predictive values, likelihood ratios, and the development of a receiver operating characteristic. An advantage of the proposed protocol is its flexibility, as it can be easily adapted to train a plethora of different ML algorithms with adjustable combinations of an unlimited number of features. Moreover, it can be used to analyze different problems in oncology, infection, and inflammation.Techniques available for micro- and nano-scale mechanical characterization have exploded in the last few decades. From further development of the scanning and transmission electron microscope, to the invention of atomic force microscopy, and advances in fluorescent imaging, there have been substantial gains in technologies that enable the study of small materials. Conpokal is a portmanteau that combines confocal microscopy with atomic force microscopy (AFM), where a probe "pokes" the surface. Although each technique is extremely effective for the qualitative and/or quantitative image collection on their own, Conpokal provides the capability to test with blended fluorescence imaging and mechanical characterization. Designed for near simultaneous confocal imaging and atomic force probing, Conpokal facilitates experimentation on live microbiological samples. The added insight from paired instrumentation provides co-localization of measured mechanical properties (e.g., elastic modulus, adhesion, surface roughness) by AFM with subcellular components or activity observable through confocal microscopy. This work provides a step by step protocol for the operation of laser scanning confocal and atomic force microscopy, simultaneously, to achieve same cell, same region, confocal imaging, and mechanical characterization.Ex vivo perfusion is an important physiological tool to study the function of isolated organs (e.g. liver, kidneys). At the same time, due to the small size of mouse organs, ex vivo perfusion of bone, bladder, skin, prostate, and reproductive organs is challenging or not feasible. Here, we report for the first time an in situ lower body perfusion circuit in mice that includes the above tissues, but bypasses the main clearance organs (kidney, liver, and spleen). The circuit is established by cannulating the abdominal aorta and inferior vena cava above the iliac artery and vein and cauterizing peripheral blood vessels. Perfusion is performed via a peristaltic pump with perfusate flow maintained for up to 2 h. In situ staining with fluorescent lectin and Hoechst solution confirmed that the microvasculature was successfully perfused. This mouse model can be a very useful tool for studying pathological processes as well as mechanisms of drug delivery, migration/metastasis of circulating tumor cells into/from the tumor, and interactions of immune system with perfused organs and tissues.In the United States, 35% of the total carbon dioxide (CO2) emissions come from the electrical power industry, of which 30% represent natural gas electricity generation. Microalgae can biofix CO2 10 to 15 times faster than plants and convert algal biomass to products of interest, such as biofuels. Thus, this study presents a protocol that demonstrates the potential synergies of microalgae cultivation with a natural gas power plant situated in the southwestern United States in a hot semi-arid climate. State-of-the-art technologies are used to enhance carbon capture and utilization via the green algal species Chlorella sorokiniana, which can be further processed into biofuel. We describe a protocol involving a semi-automated open raceway pond and discuss the results of its performance when it was tested at the Tucson Electric Power plant, in Tucson, Arizona. Flue gas was used as the main carbon source to control pH, and Chlorella sorokiniana was cultivated. An optimized medium was used to grow the algae. link3 The amount of CO2 added to the system as a function of time was closely monitored.