A Machine Learning Approach for Predicting Clinical Trial Patient Enrollment in Drug Development Portfolio Demand Planning

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded vs. unblinded, sponsor CRO selection, enrollment quarter, and enrollment country values to predict patient enrollment characteristics in clinical trials. The model was tested using a dataset consisting of 5,000 data points and yielded a high level of accuracy. This development in patient enrollment prediction will optimize portfolio demand planning and help avoid costs associated with inaccurate patient enrollment forecasting

Clinical Evaluation of Denture Retention by Multi-suction Cup and Denture Adhesive

AIM: The aim of the study was to compare the retention of two modalities: Multi-suction cup denture, and denture adhesive and to evaluate the change of retention by different time intervals. PATIENTS AND METHODS: Twelve completely edentulous patients were selected. The patients received two dentures: One conventional denture, and the other with multi-suction cups. The retention was measured by a universal testing machine at insertion, 15 min, 30 min, 1 h, 2 h, and 4 h. All values were recorded in Newtons. Statistical analysis was carried out using two-way analysis of variance with post hoc Tukey’s test. RESULTS: Retention was higher in denture adhesive than multi-suction cup, and the change of retention was not statistically significant by time. CONCLUSION: Denture adhesive showed better retention clinically and simplified laboratory procedures than multi-suction denture

Polydimethylsiloxane (PDMS)/Carbon Nanofiber Nanocomposite with Piezoresistive Sensing Functions

Flexible material that can be deployed for sensing a wide range of pressure and strain is an active research area due to potential applications in engineering and biomedical devices. Current load sensing materials such as metals, semiconductor, and piezo ceramics have limitations in certain applications, due to their heavy density and small maximum measurable strain. In order to overcome those issues, this thesis delves into an alternative material class based on polydimethyl siloxane (PDMS) and carbon nanofiber (CNF) nanocomposites. Although silica and carbon nanoparticles have been traditionally used to reinforce mechanical properties in PDMS matrix nanocomposites, this study focuses on novel sensing systems with high sensitivity and wide load ranges. A series of nanocomposites with different CNF and silica concentrations were synthesized and characterized to understand their thermal, electrical, and sensing capabilities. The thermal properties, such as thermal stability and thermal diffusivity, of the developed nanocomposites were studied using thermogravimetirc and laser flash techniques, respectively. The electrical volume conductivity of each type of nanocomposite was measured using the four-probe method to eliminate the effects of contact electrical resistance during measurement. Scanning electron microscopy (SEM) was used at different length scales which showed uniform dispersion. Experimental results showed that both CNFs and silica were able to impact on the overall properties of the synthesized PDMS/CNF nanocomposites. The pressure sensing functions were achieved by correlating the piezoresistance variations of the materials to the applied load on the sensing area. Due to the conductive network formed by carbon nanofibers (CNFs) and the tunneling effect between neighboring CNFs, the experimental results showed a clear correlation between piezoresistance and the loading conditions. The proposed nanocomposite based sensor materials were experimentally characterized under both quasi-static and cyclic tensile and compressive loading conditions. The optimal nanocomposite formulation was identified by choosing materials with the highest sensing gauge factors under the required load ranges. The ideal material were employed to sense strain as high as 30% and pressures up to 50, 100, and 150 psi, which was a significant improvement compared to current off-the-shelf similar sensors. The sensing capability and sensitivity of the identified nanocomposites were further optimized using advanced optimization algorithms and finite element analysis method. Three different shapes including cylinder, conical, and truncated pyramid shaped sensing units were designed, fabricated, and characterized. Cyclic compression tests verified that the optimized sensor units enhanced the sensing capability by obtaining higher gauge factors. Finally, optimized sensing units were assembly in array forms for the continuous monitoring of pressure in a large area. The prototypes of sensor arrays successfully demonstrated their sensing capability under both static and cyclic pressure conditions in the desired pressure range

Moir\'e Engineering in 2D Heterostructures with Process-Induced Strain

We report deterministic control over moir\'e superlattice geometry in twisted bilayer graphene by implementing designable device-level heterostrain with process-induced strain engineering. We quantify strain and moir\'e interference with Raman spectroscopy through in-plane and moir\'e-activated phonon mode shifts. Results support systematic C$_{3}$ rotational symmetry breaking and tunable periodicity in moir\'e superlattices under the application of uniaxial or biaxial heterostrain, confirmed with density functional theory based first principles calculations. This provides a method to not only tune moir\'e interference without additional twisting, but also allows for a systematic pathway to explore new van der Waals based moir\'e superlattice symmetries by deterministic design

COVID-19 impact on poultry production and distribution networks in Bangladesh

The COVID-19 pandemic has severely affected numerous economic sectors across the world, including livestock production. This study investigates how the pandemic has impacted the poultry production and distribution network (PDN), analyses stakeholders' changing circumstances, and provides recommendations for rapid and long-term resilience. This is based on a literature review, social media monitoring, and key informant interviews (n = 36) from across the poultry sector in Bangladesh. These included key informants from breeder farms and hatcheries, pharmaceutical suppliers, feed companies, dealers, farmers, middlemen, and vendors. We show that the poultry sector was damaged by the COVID-19 pandemic, partly as a result of the lockdown and also by rumors that poultry and their products could transmit the disease. This research shows that hardly any stakeholder escaped hardship. Disrupted production and transportation, declining consumer demand and volatile markets brought huge financial difficulties, even leading to the permanent closure of many farms. We show that the extent of the damage experienced during the first months of COVID-19 was a consequence of how interconnected stakeholders and businesses are across the poultry sector. For example, a shift in consumer demand in live bird markets has ripple effects that impact the price of goods and puts pressure on traders, middlemen, farmers, and input suppliers alike. We show how this interconnectedness across all levels of the poultry industry in Bangladesh makes it fragile and that this fragility is not a consequence of COVID-19 but has been revealed by it. This warrants long-term consideration beyond the immediate concerns surrounding the COVID-19 pandemic

Thymoquinone Inhibits Bone Metastasis of Breast Cancer Cells Through Abrogation of the CXCR4 Signaling Axis

Overexpression of chemokine receptor type 4 (CXCR4) has been found to be associated with increased cell proliferation, metastasis and also act as an indicator of poor prognosis in patients with breast cancer. Therefore, new agents that can abrogate CXCR4 expression have potential against breast cancer metastasis. In this study, we examined the potential effect of thymoquinone (TQ), derived from the seeds of Nigella sativa, on the expression and regulation of CXCR4 in breast cancer cells. TQ was found to inhibit the expression of CXCR4 in MDA-MB-231 triple negative breast cancer (TNBC) cells in a dose- and time-dependent manner. It was noted that suppression of CXCR4 by TQ was possibly transcriptionally regulated, as treatment with this drug caused down-regulation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) activation and suppression of NF-kB binding to the CXCR4 promoter. Pretreatment with a proteasome inhibitor and/or lysosomal stabilization did not affect TQ induced suppression of CXCR4. Down-regulation of CXCR4 was further correlated with the inhibition of CXCL12-mediated migration and invasion of MDA-MB-231 cells. Interestingly, it was observed that the deletion of p65 could reverse the observed antiinvasive/ anti-migratory effects of TQ in breast cancer cells. TQ also dose-dependently inhibited MDA-MB-231 tumor growth and tumor vascularity in a chick chorioallantoic membrane assay model. We also observed TQ (2 and 4 mg/kg) treatment significantly suppressed multiple lung, brain, and bone metastases in a dose-dependent manner in a metastasis breast cancer mouse model. Interestingly, H&E and immunohistochemical analysis of bone isolated from TQ treated mice indicated a reduction in number of osteolytic lesions and the expression of metastatic biomarkers. In conclusion, the results indicate that TQ primarily exerts its anti-metastatic effects by down-regulation of NF-kB regulated CXCR4 expression and thus has potential for the treatment of breast cancer

Thymoquinone Induces Telomere Shortening, DNA Damage and Apoptosis in Human Glioblastoma Cells

Background: A major concern of cancer chemotherapy is the side effects caused by the non-specific targeting of both normal and cancerous cells by therapeutic drugs. Much emphasis has been placed on discovering new compounds that target tumour cells more efficiently and selectively with minimal toxic effects on normal cells. Methodology/Principal Findings: The cytotoxic effect of thymoquinone, a component derived from the plant Nigella sativa, was tested on human glioblastoma and normal cells. Our findings demonstrated that glioblastoma cells were more sensitive to thymoquinone-induced antiproliferative effects. Thymoquinone induced DNA damage, cell cycle arrest and apoptosis in the glioblastoma cells. It was also observed that thymoquinone facilitated telomere attrition by inhibiting the activity of telomerase. In addition to these, we investigated the role of DNA-PKcs on thymoquinone mediated changes in telomere length. Telomeres in glioblastoma cells with DNA-PKcs were more sensitive to thymoquinone mediated effects as compared to those cells deficient in DNA-PKcs. Conclusions/Significance: Our results indicate that thymoquinone induces DNA damage, telomere attrition by inhibiting telomerase and cell death in glioblastoma cells. Telomere shortening was found to be dependent on the status of DNA-PKcs. Collectively, these data suggest that thymoquinone could be useful as a potential chemotherapeutic agent in th