High-Throughput/High Content Imaging Screen Identifies Novel Small Molecule Inhibitors and Immunoproteasomes as Therapeutic Targets for Chordoma.

Chordomas account for approximately 1-4% of all malignant bone tumors and 20% of primary tumors of the spinal column. It is a rare disease, with an incidence estimated to be approximately 1 per 1,000,000 people. The underlying causative mechanism of chordoma is unknown, which makes it challenging to treat. Chordomas have been linked to the T-box transcription factor T (TBXT) gene located on chromosome 6. The TBXT gene encodes a protein transcription factor TBXT, or brachyury homolog. Currently, there is no approved targeted therapy for chordoma. Here, we performed a small molecule screening to identify small chemical molecules and therapeutic targets for treating chordoma. We screened 3730 unique compounds and selected 50 potential hits. The top three hits were Ribociclib, Ingenol-3-angelate, and Duvelisib. Among the top 10 hits, we found a novel class of small molecules, including proteasomal inhibitors, as promising molecules that reduce the proliferation of human chordoma cells. Furthermore, we discovered that proteasomal subunits PSMB5 and PSMB8 are increased in human chordoma cell lines U-CH1 and U-CH2, confirming that the proteasome may serve as a molecular target whose specific inhibition may lead to better therapeutic strategies for chordoma.

Necessary conditions for sustainable water and sanitation service delivery in schools: A systematic review.

Access to water, sanitation, and hygiene (WASH) services confers significant health and economic benefits, especially for children, but only if those services can be delivered on a consistent basis. The challenge of sustainable, school-based WASH service delivery has been widely documented, particularly in resource-constrained contexts. We conducted a systematic review of published research that identifies drivers of, or tests solutions to, this challenge within low- and middle-income countries (PROSPERO 2020 CRD42020199163). Authors in the first group employ cross-sectional research designs and interrogate previously implemented school WASH interventions. Most conclude that dysfunctional accountability and information sharing mechanisms drive school WASH service delivery failures. By contrast, most of the interventions developed and tested experimentally by authors in the second group focus on increasing the financial and material resources available to schools for WASH service delivery. Overall, these authors find negligible impact of such infusions of cash, infrastructure, and supplies across a variety of sustainability outcome metrics. Taken together, the evidence suggests that sustainable service delivery depends on three simultaneously necessary components: resources, information, and accountability. Drawing upon theory and evidence from social psychology, public management, and political science, we identify priority knowledge gaps that can meaningfully improve the design of effective interventions. We also highlight the importance of both interdisciplinary collaboration and local expertise in designing WASH programming that aligns with sociocultural and institutional norms, and is thus more likely to generate sustainable impact.

Non-Invasive Monitoring for Rejection in Kidney Transplant Recipients After SARS-CoV-2 mRNA Vaccination.

Introduction: Studies have shown reduced antiviral responses in kidney transplant recipients (KTRs) following SARS-CoV-2 mRNA vaccination, but data on post-vaccination alloimmune responses and antiviral responses against the Delta (B.1.617.2) variant are limited. Materials and methods: To address this issue, we conducted a prospective, multi-center study of 58 adult KTRs receiving mRNA-BNT162b2 or mRNA-1273 vaccines. We used multiple complementary non-invasive biomarkers for rejection monitoring including serum creatinine, proteinuria, donor-derived cell-free DNA, peripheral blood gene expression profile (PBGEP), urinary CXCL9 mRNA and de novo donor-specific antibodies (DSA). Secondary outcomes included development of anti-viral immune responses against the wild-type and Delta variant of SARS-CoV-2. Results: At a median of 85 days, no KTRs developed de novo DSAs and only one patient developed acute rejection following recent conversion to belatacept, which was associated with increased creatinine and urinary CXCL9 levels. During follow-up, there were no significant changes in proteinuria, donor-derived cell-free DNA levels or PBGEP. 36% of KTRs in our cohort developed anti-wild-type spike antibodies, 75% and 55% of whom had neutralizing responses against wild-type and Delta variants respectively. A cellular response against wild-type S1, measured by interferon-γ-ELISpot assay, developed in 38% of KTRs. Cellular responses did not differ in KTRs with or without antibody responses. Conclusions: SARS-CoV-2 mRNA vaccination in KTRs did not elicit a significant alloimmune response. About half of KTRs who develop anti-wild-type spike antibodies after two mRNA vaccine doses have neutralizing responses against the Delta variant. There was no association between anti-viral humoral and cellular responses.

Solar radiation and solar energy estimation using ANN and Fuzzy logic concept: A comprehensive and systematic study.

To overcome the need of the world for energy consumption, we have to find some better and stable alternate ways of renewable energy with advanced technology. The most readily available source of energy is solar energy but solar energy has nonlinear nature due to the random nature of climate conditions. So, one way to solve is solar radiation prediction and solar energy prediction using more accurate techniques. Also, energy business and power system control units require more accuracy along with very short to large duration prediction in advance. So, to complete the requirement many prediction techniques are used and among them, Artificial Neural Network (ANN) and Fuzzy are more accurate and reliable techniques. In this paper basically, a literature study for solar radiation and energy prediction using ANN and Fuzzy logic techniques has been carried out. Many studies are reviewed and then selected some most accurate, reliable, and relevant studies for further study. ANN models with different algorithms such as feed-forward back-propagation-based ANN, Multi-layer feed-forward-based ANN model, Linear regression with ANN model, GNN-based model are reviewed in the study. ANN models with different input parameters combinations and the different number of neurons were also reviewed. Fuzzy logic-based and Adaptive Neuro-Fuzzy interface (ANFIS)-based different models have been reviewed and observed that the ANFIS technique performs better. From the study, it has been noted that ANN and Fuzzy logic employed models are most effective for estimation than any other empirical models. It is found that solar radiation and energy prediction models are dependent on input parameters more. At last, highlighted some possible research opportunities and areas for better efficiency of the results.