Intravesical Ty21a vaccine promotes dendritic cells and T cell-mediated tumor regression in the MB49 bladder cancer model

Preclinical data shows that intravesical instillation of Ty21a/Vivotif\uae, a commercial vaccine against typhoid fever, is an effective alternative option to standard Bacillus-Calmette-Gu\ue9rin (BCG) immunotherapy for nonmuscle-invasive bladder cancer (NMIBC). Here we characterized the inflammatory effects of Ty21a on the bladder and investigated the immune mechanisms underlying tumor-regression towards the use of this bacterial vaccine in NMIBC patients. MB49 bladder tumor-bearing mice had significantly improved survival after intravesical instillations of Ty21a doses of 106 to 108 colony-forming units. By immunohistochemistry and morphology, both BCG and Ty21a instillations were associated with bladder inflammation, which was decreased with the use of low, but effective, doses of Ty21a. Flow cytometry analysis showed a significant infiltration of T cells, natural killer (NK) cells, and myeloid cells, compared with controls, after a single dose of Ty21a, whereas this was only observed after multiple doses of BCG. The induced myeloid cells were predominantly neutrophils and Ly6C+CD103+ dendritic cells (DC), the latter being significantly more numerous after instillation of Ty21a than BCG. Ex vivo infection of human leukocytes with Ty21a, but not BCG, similarly significantly increased DC frequency. CD4+ and CD8+ T cells, but not NK cells nor neutrophils, were required for effective Ty21a bladder tumor responses. Thus, the generation of antitumor adaptive immunity was identified as a key process underlying Ty21a-mediated treatment efficacy. Altogether, these results demonstrate mechanisms of intravesical Ty21a therapy and suggest its potential as a safe and effective treatment for NMIBC patients

Stability of core/shell quantum dots-role of pH and small organic ligands

The improvement of knowledge about the toxicity and even processability, and stability of quantum dots (QD) requires the understanding of the relationship between the QD binding head group, surface structure, and interligand interaction. The scanned stripping chronopotentiometry and absence of gradients and Nernstian equilibrium stripping techniques were used to determine the concentration of Cd dissolved from a polyacrylate-stabilized CdTe/CdS QD. The effects of various concentrations of small organic ligands such as citric acid, glycine, and histidine and the roles of pH (4.5–8.5) and exposure time (0–48 h) were evaluated. The highest QD dissolution was obtained at the more acidic pH in absence of the ligands (52 %) a result of the CdS shell solubility. At pH 8.5 the largest PAA ability to complex the dissolved Cd leads to a further QD solubility until the equilibrium is reached (24 % of dissolved Cd vs.4 % at pH 6.0). The citric acid presence resulted in greater QD dissolution, whereas glycine, an amino acid, acts against QD dissolution. Surprisingly, the presence of histidine, an amino acid with an imidazole functional group, leads to the formation of much strong Cd complexes over time, which may be non-labile, inducing variations in the local environment of the QD surface

Predicting physical properties of woven fabrics via automated machine learning and textile design and finishing features

This paper presents a novel Machine Learning (ML) approach to support the creation of woven fabrics. Using data from a textile company, two CRoss-Industry Standard Process for Data Mining (CRISP-DM) iterations were executed, aiming to compare three input feature representation strategies related with fabric design and finishing processes. During the modeling stage of CRISP-DM, an Automated ML (AutoML) procedure was used to select the best regression model among six distinct state-of-the-art ML algorithms. A total of nine textile physical properties were modeled (e.g., abrasion, elasticity, pilling). Overall, the simpler yarn representation strategy obtained better predictive results. Moreover, for eight fabric properties (e.g., elasticity, pilling) the addition of finishing features improved the quality of the predictions. The best ML models obtained low predictive errors (from 2% to 7%) and are potentially valuable for the textile company, since they can be used to reduce the number of production attempts (saving time and costs).This work was carried out within the project “TexBoost: less Commodities moreSpecialities” reference POCI-01-0247-FEDER-024523, co-funded byFundo Eu-ropeu de Desenvolvimento Regional(FEDER), through Portugal 2020 (P2020)

Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

<p>Abstract</p> <p>Background</p> <p>Tuberculosis is a contagious disease caused by <it>Mycobacterium tuberculosis </it>(Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.</p> <p>Results</p> <p>We utilized physicochemical properties of compounds to train four supervised classifiers (Naïve Bayes, Random Forest, J48 and SMO) on three publicly available bioassay screens of Mtb inhibitors and validated the robustness of the predictive models using various statistical measures.</p> <p>Conclusions</p> <p>This study is a comprehensive analysis of high-throughput bioassay data for anti-tubercular activity and the application of machine learning approaches to create target-agnostic predictive models for anti-tubercular agents.</p

Evidence for a Novel Marine Harmful Algal Bloom: Cyanotoxin (Microcystin) Transfer from Land to Sea Otters

“Super-blooms” of cyanobacteria that produce potent and environmentally persistent biotoxins (microcystins) are an emerging global health issue in freshwater habitats. Monitoring of the marine environment for secondary impacts has been minimal, although microcystin-contaminated freshwater is known to be entering marine ecosystems. Here we confirm deaths of marine mammals from microcystin intoxication and provide evidence implicating land-sea flow with trophic transfer through marine invertebrates as the most likely route of exposure. This hypothesis was evaluated through environmental detection of potential freshwater and marine microcystin sources, sea otter necropsy with biochemical analysis of tissues and evaluation of bioaccumulation of freshwater microcystins by marine invertebrates. Ocean discharge of freshwater microcystins was confirmed for three nutrient-impaired rivers flowing into the Monterey Bay National Marine Sanctuary, and microcystin concentrations up to 2,900 ppm (2.9 million ppb) were detected in a freshwater lake and downstream tributaries to within 1 km of the ocean. Deaths of 21 southern sea otters, a federally listed threatened species, were linked to microcystin intoxication. Finally, farmed and free-living marine clams, mussels and oysters of species that are often consumed by sea otters and humans exhibited significant biomagnification (to 107 times ambient water levels) and slow depuration of freshwater cyanotoxins, suggesting a potentially serious environmental and public health threat that extends from the lowest trophic levels of nutrient-impaired freshwater habitat to apex marine predators. Microcystin-poisoned sea otters were commonly recovered near river mouths and harbors and contaminated marine bivalves were implicated as the most likely source of this potent hepatotoxin for wild otters. This is the first report of deaths of marine mammals due to cyanotoxins and confirms the existence of a novel class of marine “harmful algal bloom” in the Pacific coastal environment; that of hepatotoxic shellfish poisoning (HSP), suggesting that animals and humans are at risk from microcystin poisoning when consuming shellfish harvested at the land-sea interface

Expression profile of genes associated with mastitis in dairy cattle

In order to characterize the expression of genes associated with immune response mechanisms to mastitis, we quantified the relative expression of the IL-2, IL-4, IL-6, IL-8, IL-10, IFN-γ and TNF- α genes in milk cells of healthy cows and cows with clinical mastitis. Total RNA was extracted from milk cells of six Black and White Holstein (BW) cows and six Gyr cows, including three animals with and three without mastitis per breed. Gene expression was analyzed by real-time PCR. IL-10 gene expression was higher in the group of BW and Gyr cows with mastitis compared to animals free of infection from both breeds (p < 0.05). It was also higher in BW Holstein animals with clinical mastitis (p < 0.001), but it was not significant when Gyr cows with and without mastitis were compared (0.05 < p < 0.10). Among healthy cows, BW Holstein animals tended to present a higher expression of all genes studied, with a significant difference for the IL-2 and IFN- γ genes (p < 0.001). For animals with mastitis no significant difference in gene expression was observed between the two breeds. These findings suggest that animals with mastitis develop a preferentially cell-mediated immune response. Further studies including larger samples are necessary to better characterize the gene expression profile in cows with mastitis