Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis (Mtb), remains one of the world’s biggest health problems. Indeed, TB is a leading cause of death in infectious diseases, second only to COVID-19. Furthermore, drug-resistant strains of Mtb against first line and second-line anti-TB drugs have become more prominent and are left undiagnosed due to lack of rapid and accurate drug susceptibility testing (DST). Therefore, we aim to develop a fast, simple, and inexpensive DST using only a 96-well plate with novel solvatochromic probes, 4-N,N-dimethylamino-1,8-naphthalimide (DMN) and 3-hydroxychromone (3HC), conjugated to a trehalose analog that are highly specific to live Mtb. Due to its specificity to live Mtb, we can analyze the susceptibility of Mtb to anti-TB drugs. Mycobacterium smegmatis (Msmeg), a nonpathogenic surrogate for Mtb, treated with first-line TB treatment drugs Rifampicin, Isoniazid, Pyrazinamide, Ethambutol (RIPE) drugs, are grown and labeled with solvatochromic probes. Susceptibility to RIPE were quantified via four methods: fluorescent plate reader, flow cytometry, optical density, and colony-forming units. Our initial results showed a decrease in fluorescence with increasing RIPE drug concentration from both plate reader and flow cytometry. These results indicate that the plate reader may enable inexpensive high-throughput readout of Mtb DST of frontline TB drugs in high-burden countries.

Despite the commonality of digestive diseases, we have little understanding of the intestinal muscularis cells (IMC) in such bowel disorders. One potential mechanism controlling bowel motility is mechanosensing, allowing each segment to sense and respond to stretch in a coordinated manner. Such cells include interstitial cells of Cajal (ICC), the pacemaker cells of the bowel. ICC generates slow waves and interacts closely with smooth muscle cells (SMC) and Platelet-Derived Growth Factor Receptor-𝛼 (PDGFR𝛼) cells comprising the SIP syncytium. SIP cells respond to stretch and express the ion channels Piezo1 and 2. We hypothesized that Piezo is essential for coordinating bowel motility in SMC and potentially ICC and PDGFR𝛼 cells. In a pilot study to test this hypothesis, we generated Myh11-creERT2/+; Piezo1fl/fl (Piezo1ΔSMC) mice and discovered that they have growth failure and a delay in intestinal transit compared to control (Piezo1WT) mice. We also found that Piezo1 is not located in the plasma membrane of SMCs but in the endoplasmic reticulum (ER) membrane. The ER stores Ca+2, ER membrane channels, Inositol trisphosphate (InsP3), and Ryanodine channels worked cooperatively to release Ca+2 to the cytoplasm. We are interested in determining the cooperative role of Piezo1 in SMCs. The long-term goal is to identify and characterize the role of Piezo1 & 2 in cells of the SIP syncytium using a myriad of novel in vivo, ex vivo, and in vitro approaches. This study provides insights into the role of Piezo1 and its potential as a therapeutic target for bowel motility disorders.

Huntington’s disease (HD) is an inherited neurodegenerative disease characterized by progressive motor and psychiatric symptoms. HD is caused by a CAG repeat expansion in the huntingtin (HTT) gene, which is translated into an elongated polyglutamate (polyQ) repeat in the N-terminus of the resulting protein. Patients with HD demonstrate pathological hallmarks such as degeneration of the striatum, and the cortex at a later stage. Additionally, mutant HTT (mHTT) can aggregate in both the nucleus and the neuronal processes of affected neurons. Including HTT, nine polyQ proteins are known to be associated with diseases, but there are over 4,000 other human proteins that also contain the polyQ tracts. Emerging evidence indicates polyQ is a motif to modulate protein–protein interactions, we therefore hypothesize that mutant HTT, especially in its aggregate form, interacts with other polyQ proteins and thereby impairs their normal functions. By immunohistochemistry in the Q140 knock-in HD mouse model, we test whether the mHTT aggregates in the striatum colocalize with several polyQ proteins that are known to be important for striatal gene expression: Foxp1, Foxp2 and Med15. Foxp1 and Foxp2, two members in the Forkhead family of transcription factors, are neuroprotective and have been shown to be reduced in HD patients. Med15 is a general transcriptional cofactor in the mediator and can potentially form amyloids and propagate by itself. Testing colocalization of these proteins with mHTT aggregates may provide insight into their association with transcriptionopathy and other deficits in these mice.

Given the importance of fundraising to the sustainability of nonprofits, organizations are always looking for additional revenue sources. As one way of collecting funds, organizations utilize online fundraising websites, which commonly include an interface of default suggested donation amounts. We model donor behavior by assuming that each donor has a unique ideal donation amount, and the addition of suggested defaults can effectively attract donors whose ideal amount is close. We first demonstrate how to use data from a single experiment to derive maximum likelihood estimates of model parameters. Given those parameters, we construct an efficient algorithm that leverages dynamic programming to maximize donation gains by selecting the optimal default menu. Due to the potential cost of experimentation, this model can be utilized as a mechanism for augmenting income for nonprofits operating under limited resources. Our work opens the door to future research involving the integration of this tool within an organization’s infrastructure.

Given the importance of fundraising to the sustainability of nonprofits, organizations are always looking for additional revenue sources. As one way of collecting funds, organizations utilize online fundraising websites, which commonly include an interface of default suggested donation amounts. We model donor behavior by assuming that each donor has a unique ideal donation amount, and the addition of suggested defaults can effectively attract donors whose ideal amount is close. We first demonstrate how to use data from a single experiment to derive maximum likelihood estimates of model parameters. Given those parameters, we construct an efficient algorithm that leverages dynamic programming to maximize donation gains by selecting the optimal default menu. Due to the potential cost of experimentation, this model can be utilized as a mechanism for augmenting income for nonprofits operating under limited resources. Our work opens the door to future research involving the integration of this tool within an organization’s infrastructure.