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This study unites six popular machine learning approaches to enhance the prediction of a molecular binding affinity between receptors (large protein molecules) and ligands (small organic molecules). Here we examine a scheme where affinity of ligands is predicted against a single receptor - human thrombin, thus, the models consider ligand features only. However, the suggested approach can be repurposed for other receptors. The methods include Support Vector Machine, Random Forest, CatBoost, feed-forward neural network, graph neural network, and Bidirectional Encoder Representations from Transformers. The first five methods use input features based on physico-chemical properties of molecules, while the last one is based on textual molecular representations. All approaches do not rely on atomic spatial coordinates, avoiding a potential bias from known structures, and are capable of generalizing for compounds with unknown conformations. Within each of the methods, we have trained two models that solve classification and regression tasks. Then, all models are grouped into a pipeline of two subsequent ensembles. The first ensemble aggregates six classification models which vote whether a ligand binds to a receptor or not. If a ligand is classified as active (i.e., binds), the second ensemble predicts its binding affinity in terms of the inhibition constant Ki.The buffalo herds in Brazil have been an alternative for increasing the economy in different biomes. For this reason, knowledge of the spatial distribution of diseases of mandatory notification in buffalo herds, such as brucellosis and tuberculosis, is essential to guarantee the quality of exported animal products, as well as assist in strategies of national control and eradication programs. In the present study, we aimed to evaluate the spatiotemporal distribution and temporal trends of brucellosis and tuberculosis in buffalo in Brazilian states between 2012-2019. During this period, 474 cases of brucellosis and 604 cases of tuberculosis were observed in buffalo in Brazil, with no significant differences between the total number of cases and incidence risk. The spatial distribution for the states was mostly heterogeneous, showing similarities of occurrences for both diseases in the south, north, and the states of Minas Gerais and Pernambuco. In the eight years evaluated, tuberculosis showed cyclical variatio heterogeneously distributed, with well-defined regions of high historical risk of infection. Among these regions, the states of Amazonas, Pará, Amapá, and Minas Gerais stand out due to the higher risk of infection for both brucellosis and tuberculosis, alerting Brazilian authorities to the need for disease control actions.Tumor protein p53 (TP53) is a tumor suppressor gene and TP53 mutations are associated with poor prognosis in non-small cell lung cancer. However, the in-depth classification of TP53 and its relationship with treatment response and prognosis in epidermal growth factor receptor (EGFR)-mutant tumors treated with EGFR tyrosine kinase inhibitors are unclear. Circulating tumor DNA was prospectively collected at baseline in advanced treatment-naïve EGFR-mutant lung adenocarcinoma patients treated with gefitinib in an open-label, single-arm, prospective, multicenter, phase 2 clinical trial (BENEFIT trial) and analyzed using next-generation sequencing. Survival was estimated using the Kaplan-Meier method. Of the 180 enrolled patients, 115 (63.9%) harbored TP53 mutations. The median progression-free survival (PFS) and overall survival (OS) of patients with TP53-wild type tumors were significantly longer than those of patients with TP53-mutant tumors. Mutations in exons 5-8 accounted for 80.9% of TP53 mutations. Mutations in TP53 exons 6 and 7 were significantly associated with inferior PFS and OS compared to wild-type TP53. TP53 mutation also influenced the prognosis of patients with different EGFR mutations. Patients with TP53 and EGFR exon 19 mutations had significantly longer PFS and OS than patients with TP53 and EGFR L858R mutations, and both groups had worse survival than patients with only EGFR mutations. Patients with TP53 mutations, especially in exons 6 and 7, had a lower response rate and shorter PFS and OS when treated with gefitinib. Moreover, TP53 exon 5 mutation divided TP53 mutations in disruptive and non-disruptive types.
Deep learning has the potential to improve diagnostic accuracy and efficiency in medical image recognition. In the current study, we developed a deep learning algorithm and assessed its performance in discriminating melanoma from nevus using whole-slide pathological images (WSIs).
The deep learning algorithm was trained and validated using a set of 781 WSIs (86 melanomas, 695 nevi) from PLA General Hospital. The diagnostic performance of the algorithm was tested on an independent test set of 104 WSIs (29 melanomas, 75 nevi) from Tianjin Chang Zheng Hospital. The same test set was also diagnostically classified by 7 expert dermatopathologists.
The deep learning algorithm receiver operating characteristic (ROC) curve achieved a sensitivity 100% at the specificity of 94.7% in the classification of melanoma and nevus on the test set. The area under ROC curve was 0.99. read more Dermatopathologists achieved a mean sensitivity and specificity of 95.1% (95% confidence interval [CI] 92.0%-98.2%) and 96.0% (95% CI 94.2%-9king efficiency of pathologists.We leverage the onset of type 1 diabetes (T1D) in childhood to estimate the impact of a childhood health shock on parental labor supply. T1D is the second most common childhood chronic physical health condition, inheritability is low, the exact cause is unknown, the onset is unpredictable, and receiving treatment is crucial to survival. Using Danish administrative registry data with both an event study and difference-in-differences analysis shows that mothers shift to part-time work, marginally shift from the private to public sector, and experience a long-term 4-5% decrease in wage income. The dynamic effects reveal large initial impacts, but the magnitudes decrease (although are not eliminated) over time. Fathers do not experience any long-term reduction in wage income. This suggests part of the motherhood penalty is likely due to mothers bearing the economic burden when their child is diagnosed with a chronic health condition.GA 2-oxidases (GA2oxs) are a class of enzymes that inhibit the biosynthesis of bioactive GAs in plants. Although GA2oxs have clear roles in the development and defence responses in Arabidopsis, rice, and wheat, their potential effects on maize remain unclear. This study identified thirteen ZmGA2ox genes in maize and further characterized them using phylogenetic, gene structure, genomic locus, expression pattern analyses and GA content determination. Phylogenetic relationship analysis clearly divided the ZmGA2ox family into three groups-seven in C19-GA2ox class I, three in C19-GA2ox class II, and three in C20-GA2ox class. Evolutionary analysis suggested that ZmGA2ox1;1 and ZmGA2ox1;2, ZmGA2ox3;1 and ZmGA2ox3;2, and ZmGA2ox7;1 and ZmGA2ox7;2 are three pairs of segmental duplicated genes. Prediction of cis-regulatory elements in promoters suggested that ZmGA2ox genes were mainly associated with growth, development, hormones, and biotic/abiotic stress. Therefore, their spatial and temporal expression patterns and responses to various stress treatments were analysed on the basis of published RNA-seq data. Moreover, the changes of ZmGA2ox expression in leaves and roots of maize seedlings was detected under salt, alkali, dehydration, and cold stresses by qRT-PCR. The ZmGA2oxs exhibited obvious expression tendencies or characteristics in various organs under different abiotic stresses. The variations in the expression of three ZmGA2ox genes in the C20-GA2ox class in maize seedling roots showed significant regularity and a clear negative correlation with bioactive GA contents under cold and drought conditions, indicating that these three genes might exert key effects on the regulation of GA synthesis and the response to drought and cold stress. Taken together, this study is useful for further dissection of the effect of ZmGA2oxs on abiotic stress responses and might provide potential targets for the genetic improvement of maize.
Breast milk is considered to offer the best nutrition to infants; however, it may be a source of exposure to environmental chemicals such as perfluoroalkyl compounds (PFAS) for breastfeeding infants. PFAS are a complex group of synthetic chemicals whose high stability has led to their ubiquitous contamination of the environment.
To assess the concentrations and profiles of PFAS in breast milk from donors to a human milk bank and explore factors potentially related to this exposure.
Pooled milk samples were collected from 82 donors to the Human Milk Bank of the Virgen de las Nieves University Hospital (Granada, Spain). Ultra-high performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) was applied to determine milk concentrations of 11 PFAS, including long-chain and short-chain compounds. A questionnaire was used to collect information on donors' socio-demographic characteristics, lifestyle, diet, and use of personal care products (PCPs). Factors related to individual and totns, highlighting the use of PCPs.
Several PFAS, including short-chain compounds, are detected in pooled donor milk samples. Breast milk may be an important pathway for the PFAS exposure of breastfed infants, including preterm infants in NICUs. Despite the reduced sample size, these data suggest that various lifestyle factors influence PFAS concentrations, highlighting the use of PCPs.Sexual assault offences are one of the most serious crimes committed against a person, typically rank only second to homicide, and represent one of the major challenges in forensic sciences. In some cases of sexual assault, there may be more than one suspect and the analysis of the biological evidence with currently available methods such as human DNA analysis may not yield results. In this study using the designed experimental model (with different experimental scenarios that can be designed), it was aimed to investigate the effectiveness of the microbiome profile for the identification of the offender by comparing the microbiome structures of the suspects' saliva samples with the mixed samples on the victim (saliva transmitted on breast skin) within the first 48 h after a sexual assault. For this purpose, a total of 44 samples was collected from four healthy females and four healthy males aged 20-50 years. Microbiome profiles of 44 samples in four groups containing saliva, breast skin and mixed samples wereng the suspects or to reduce the number of suspects. Supporting the results of this study with further studies using parameters such as different case models, different body regions, larger time periods and a higher number of participants will be beneficial to draw accurate conclusion of the judicial case.Multiphase chemistry of chlorine is coupled into a 3D regional air quality model (CMAQv5.0.1) to investigate the impacts on the atmospheric oxidation capacity, ozone (O3), as well as fine particulate matter (PM2.5) and its major components over the Yangtze River Delta (YRD) region. The developed model has significantly improved the simulated hydrochloric acid (HCl), particulate chloride (PCl), and hydroxyl (OH) and hydroperoxyl (HO2) radicals. O3 is enhanced in the high chlorine emission regions by up to 4% and depleted in the rest of the region. PM2.5 is enhanced by 2-6%, mostly due to the increases in PCl, ammonium, organic aerosols, and sulfate. Nitrate exhibits inhomogeneous variations, by up to 8% increase in Shanghai and 2-5% decrease in most of the domain. Radicals show different responses to the inclusion of the multiphase chlorine chemistry during the daytime and nighttime. Both OH and HO2 are increased throughout the day, while nitrate radicals (NO3) and organic peroxy radicals (RO2) show an opposite pattern during the daytime and nighttime.
