Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

Proteína C-reativa não é um marcador útil de infecção em unidade de terapia intensiva cirúrgica

CONTEXT AND OBJECTIVE: C-reactive protein (CRP) is commonly used as a marker for inflammatory states and for early identification of infection. This study aimed to investigate CRP as a marker for infection in patients with postoperative septic shock. DESIGN AND SETTING: Prospective, single-center study, developed in a surgical intensive care unit at Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo. METHODS: This study evaluated 54 patients in the postoperative period, of whom 29 had septic shock (SS group) and 25 had systemic inflammatory response syndrome (SIRS group). All of the patients were monitored over a seven-day period using the Sequential Organ Failure Assessment (SOFA) score and daily CRP and lactate measurements. RESULTS: The daily CRP measurements did not differ between the groups. There was no correlation between CRP and lactate levels and the SOFA score in the groups. We observed that the plasma CRP concentrations were high in almost all of the patients. The patients presented an inflammatory state postoperatively in response to surgical aggression. This could explain the elevated CRP measurements, regardless of whether the patient was infected or not. CONCLUSIONS: This study did not show any correlation between CRP and infection among patients with SIRS and septic shock during the early postoperative period.CONTEXTO E OBJETIVO: A proteína C reativa (PCR) é muito usada como marcador de estados inflamatórios e na identificação precoce de infecção. Este estudo teve como proposta investigar a PCR como marcadora de infecção em pacientes em choque séptico no período pós-operatório. TIPO DE ESTUDO E LOCAL: Estudo prospectivo, monocêntrico, desenvolvido numa unidade de terapia intensiva pós-operatória do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo. MÉTODOS: Foram avaliados 54 pacientes no pós-operatório, sendo 29 deles com choque séptico (grupo SS) e 25 com síndrome da resposta inflamatória sistêmica (grupo SI). Todos os pacientes foram acompanhados durante sete dias pelo escore SOFA (Sequential Organ Failure Assessment) e com dosagens diárias de PCR e lactato. RESULTADOS: As dosagens de PCR não diferiram entre os grupos. Não foi observada correlação entre dosagem de PCR e lactato ou escore SOFA nos grupos estudados. Observamos que as concentrações plasmáticas de PCR estavam elevadas em quase todos os pacientes avaliados. Os pacientes no pós-operatório apresentam estado inflamatório em resposta à agressão cirúrgica, sendo este fato capaz de explicar as dosagens de PCR elevadas, independentemente de o paciente estar ou não infectado. CONCLUSÕES: Este estudo não evidenciou correlação entre PCR e infecção nos pacientes com síndrome da resposta inflamatória sistêmica e choque séptico no período pós-operatório precoce

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

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

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