This study aims at merging the therapeutic effects associated to the inhibition of Carbonic Anhydrase IX (CAIX), an essential enzyme overexpressed by cancer cells including mesothelioma and breast cancer, with those ones brought by the application of Boron Neutron Capture Therapy (BNCT). This task was pursued by designing a sulfonamido-functionalised-carborane (CA-SF) that acts simultaneously as CAIX inhibitor and boron delivery agent. The CAIX expression, measured by Western blot analysis, resulted high in both mesothelioma and breast tumours. This finding was exploited for the delivery of a therapeutic dose of boron (> 20 μg/g) to the cancer cells. The synergic cytotoxic effects operated by the enzymatic inhibition and neutron irradiation was evaluated in vitro on ZL34, AB22 and MCF7 cancer cells. Next, an in vivo model was prepared by subcutaneous injection of AB22 cells in Balb/c mice and CA-SF was administered as inclusion complex with a β-cyclodextrin oligomer. After irradiation with thermal neutrons tumour growth was evaluated for 25 days by MRI. The obtained results appear very promising as the tumour growth was definitively markedly lower in comparison to controls and the CAIX inhibitor alone. This approach appears promising and it call consideration for the design of new therapeutic routes to cure patients affected by this disease.The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https//github.com/pmartR/FIRM-AVP with an accompanying web application at https//msc-viz.emsl.pnnl.gov/AVPR .The insertion of porous metal media inside the pipes and channels has already shown a significant heat transfer enhancement by experimental and numerical studies. Porous media could make a mixing flow and small-scale eddies. Therefore, the turbulence parameters are attractive in such cases. The computational fluid dynamics (CFD) approach can predict the turbulence parameters using the turbulence models. However, the CFD is unable to find the relation of the turbulence parameters to the boundary conditions. The artificial intelligence (AI) has shown potential in combination with the CFD to build high-performance predictive models. This study is aimed to establish a new AI algorithm to capture the patterns of the CFD results by changing the system's boundary conditions. The ant colony optimization-based fuzzy inference system (ACOFIS) method is used for the first time to reduce time and computational effort needed in the CFD simulation. This investigation is done on turbulent forced convection of water through an aluminum metal foam tube under constant wall heat flux. The ANSYS-FLUENT CFD software is used for the simulations. The x and y of the fluid nodal locations, inlet temperature, velocity, and turbulent kinetic energy (TKE) are the inputs of the ACOFIS to predict turbulence eddy dissipation (TED) as the output. The results revealed that for the best intelligence of the ACOFIS, the number of inputs, the number of ants, the number of membership functions (MFs) and the rule are 5, 10, 93 and 93, respectively. Further comparison is made with the adaptive network-based fuzzy inference system (ANFIS). The coefficient of determination for both methods was close to 1. The ANFIS showed more learning and prediction times (785 s and 10 s, respectively) than the ACOFIS (556 s and 3 s, respectively). Finding the member function versus the inputs, the value of TED is calculated without the CFD modeling. So, solving the complicated equations by the CFD is replaced with a simple correlation.Clinical reports have found that with the improvement of treatment, most septic patients are able to survive the severe systemic inflammatory response and to enter the immunoparalysis stage. Considering that immunoparalysis leads to numerous deaths of clinical sepsis patients, alleviation of the occurrence and development of immunoparalysis has become a top priority in the treatment of sepsis. In our study, we investigate the effects of oroxylin A on sepsis in cecal ligation and puncture (CLP) mice.  https://www.selleckchem.com/products/10-dab-10-deacetylbaccatin.html  We find that the 60 h + 84 h (30 mg/kg) injection scheme of oroxylin A induce the production of pro-inflammatory factors, and further significantly improves the survival of CLP mice during the middle or late stages of sepsis. Mechanistically, C/EBP-homologous protein (CHOP) is upregulated and plays anti-inflammatory roles to facilitate the development of immunoparalysis in CLP mice. Oroxylin A induces the transcription of E3 ligase F-box only protein 15 gene (fbxo15), and activated FBXO15 protein binds to CHOP and further mediates the degradation of CHOP through the proteasome pathway, which eventually relieves the immunoparalysis of CLP mice. Taken together, these findings suggest oroxylin A relieves the immunoparalysis of CLP mice by degrading CHOP through interacting with FBXO15.The recorded clinical cases of S. mansoni at primary health facility level contain an excessive number of zero records. This could mean that no S. mansoni infection occurred (a true zero) in the health facility service area but it could also that at least one infection occurred but none were reported or diagnosed (a false zero). Standard statistical analysis, using exploratory or confirmatory spatial regression, fail to account for this type of data insufficiency. This study developed a zero-inflated Poisson model to explore the spatiotemporal variation in schistosomiasis risk at a fine spatial scale. We used environmental data generated at primary health facility service area level as explanatory variables affecting transmission risk. Identified risk factors were subsequently used to project the spatial variability of S. mansoni infection risk for 2050. The zero-inflated Poisson model shows a considerable increase of relative risk of the schistosomiasis over one decade. Furthermore, the changes between the risk in 2009 and forecasted risk by 2050 indicated both persistent and emerging areas with high relative risk of schistosomiasis infection.