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Background/AimsPrematurely born infants undergo costly, stressful eye examinations to uncover the small fraction with retinopathy of prematurity (ROP) that needs treatment to prevent blindness. The aim was to develop a prediction tool (DIGIROP-Screen) with 100% sensitivity and high specificity to safely reduce screening of those infants not needing treatment. DIGIROP-Screen was compared with four other ROP models based on longitudinal weights.MethodsData, including infants born at 24–30 weeks of gestational age (GA), for DIGIROP-Screen development (DevGroup, N=6991) originate from the Swedish National Registry for ROP. Three international cohorts comprised the external validation groups (ValGroups, N=1241). Multivariable logistic regressions, over postnatal ages (PNAs) 6–14 weeks, were validated. Predictors were birth characteristics, status and age at first diagnosed ROP and essential interactions.ResultsROP treatment was required in 287 (4.1%)/6991 infants in DevGroup and 49 (3.9%)/1241 in ValGroups. To allow 100% sensitivity in DevGroup, specificity at birth was 53.1% and cumulatively 60.5% at PNA 8 weeks. Applying the same cut-offs in ValGroups, specificities were similar (46.3% and 53.5%). One infant with severe malformations in ValGroups was incorrectly classified as not needing screening. For all other infants, at PNA 6–14 weeks, sensitivity was 100%. In other published models, sensitivity ranged from 88.5% to 100% and specificity ranged from 9.6% to 45.2%.ConclusionsDIGIROP-Screen, a clinical decision support tool using readily available birth and ROP screening data for infants born GA 24–30 weeks, in the European and North American populations tested can safely identify infants not needing ROP screening. DIGIROP-Screen had equal or higher sensitivity and specificity compared with other models. DIGIROP-Screen should be tested in any new cohort for validation and if not validated it can be modified using the same statistical approaches applied to a specific clinical setting.

Plants and humans cannot easily acquire iron from their nutrient sources although it is abundant in nature. Thus, iron deficiency is one of the major limiting factors affecting crop yields, food quality and human nutrition. Therefore, approaches need to be developed to increase Fe uptake by roots, transfer to edible plant portions and absorption by humans from plant food sources. Integrated strategies for soil and crop management are attractive not only for improving growing conditions for crops but also for exploiting a plant's potential for Fe mobilization and utilization. Recent research progress in soil and crop management has provided the means to resolve complex plant Fe nutritional problems through manipulating the rhizosphere (e.g., rhizosphere fertilization and water regulation), and crop management (includes managing cropping systems and screening for Fe efficient species and varieties). Some simple and effective soil management practices, termed ‘rhizosphere fertilization' (such as root feeding and bag fertilization) have been developed and widely used by local farmers in China to improve the Fe nutrition of fruit plants. Production practices for rice cultivation are shifting from paddy-rice to aerobic rice to make more efficient use of irrigation water. This shift has brought about increases in Fe deficiency in rice, a new challenge depressing iron availability in rice and reducing Fe supplies to humans. Current crop management strategies addressing Fe deficiency include Fe foliar application, trunk injection, plant breeding for enriched Fe crop species and varieties, and selection of cropping systems. Managing cropping systems, such as intercropping strategies may have numerous advantages in terms of increasing Fe availability to plants. Studies of intercropping systems on peanut/maize, wheat/chickpea and guava/sorghum or -maize increased Fe content of crops and their seed, which suggests that a reasonable intercropping system of iron-efficient species could prevent or mitigate Fe deficiency in Fe-inefficient plants. This review provides a comprehensive comparison of the strategies that have been developed to address Fe deficiency and discusses the most recent advance in soil and crop management to improve the Fe nutrition of crops. These proofs of concept studies will serve as the basis for future Fe research and for integrated and optimized management strategies to alleviate Fe deficiency in farmers' fields.

The accuracy of the intelligent diagnosis of rolling bearings depends on the quality of its vibration data and the accuracy of the state identification model constructed accordingly. Aiming at the problem of “poor quality” of data and “difficult to select” structural parameters of the identification model, a method is proposed to integrate data cleaning in order to select effective learning samples and optimize the selection of the structural parameters of the deep belief network (DBN) model. First, by calculating the relative energy fluctuation value of the finite number of intrinsic function components using the variational modal decomposition of the rolling bearing vibration data, the proportion of each component containing the fault component is characterized. Then, high-quality learning samples are obtained through screening and reconstruction to achieve the effective cleaning of vibration data. Second, the improved particle swarm algorithm (IPSO) is used to optimize the number of nodes in each hidden layer of the DBN model in order to obtain the optimal structural parameters of the intelligent diagnosis model. Finally, the high-quality learning samples obtained from data cleaning are used as input to construct an intelligent identification model for rolling bearing faults. The results showed that the proposed method not only screens out the intrinsic mode function components that contain the fault effective components in the rolling bearing vibration data, but also finds the optimal solution for the number of nodes in the DBN hidden layer, which improves bearing state identification accuracy by 3%.

Background Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide and has poor prognosis. Specially, patients with HCC usually have poor tolerance of systemic chemotherapy, because HCCs develop from chronically damaged tissue that contains considerable inflammation, fibrosis, and cirrhosis. Since HCC exhibits highly heterogeneous molecular characteristics, a proper in vitro system is required for the study of HCC pathogenesis. To this end, we have established two new hepatitis B virus (HBV) DNA-secreting HCC cell lines from infected patients. Methods Based on these two new HCC cell lines, we have developed chemosensitivity assays for patient-derived multicellular tumor spheroids (MCTSs) in order to select optimized anti-cancer drugs to provide more informative data for clinical drug application. To monitor the effect of the interaction of cancer cells and stromal cells in MCTS, we used a 3D co-culture model with patient-derived HCC cells and stromal cells from human hepatic stellate cells, human fibroblasts, and human umbilical vein endothelial cells to facilitate screening for optimized cancer therapy. Results To validate our system, we performed a comparison of chemosensitivity of the three culture systems, which are monolayer culture system, tumor spheroids, and MCTSs of patient-derived cells, to sorafenib, 5-fluorouracil, and cisplatin, as these compounds are typically standard therapy for advanced HCC in South Korea. Conclusion In summary, these findings suggest that the MCTS culture system is the best methodology for screening for optimized treatment for each patients with HCC, because tumor spheroids not only mirror the 3D cellular context of the tumors but also exhibit therapeutically relevant pathophysiological gradients and heterogeneity of in vivo tumors.

A specific phenotypic variant of obesity is metabolically healthy (MHO), which is characterized by normal blood pressure and lipid and glucose profiles, in contrast to the metabolically unhealthy variant (MUO). The genetic causes underlying the differences between these phenotypes are not yet clear. This study aims to explore the differences between MHO and MUO and the contribution of genetic factors (single nucleotide polymorphisms—SNPs) in 398 Hungarian adults (81 MHO and 317 MUO). For this investigation, an optimized genetic risk score (oGRS) was calculated using 67 SNPs (related to obesity and to lipid and glucose metabolism). Nineteen SNPs were identified whose combined effect was strongly associated with an increased risk of MUO (OR = 1.77, p < 0.001). Four of them (rs10838687 in MADD, rs693 in APOB, rs1111875 in HHEX, and rs2000813 in LIPG) significantly increased the risk of MUO (OR = 1.76, p < 0.001). Genetic risk groups based on oGRS were significantly associated with the risk of developing MUO at a younger age. We have identified a cluster of SNPs that contribute to the development of the metabolically unhealthy phenotype among Hungarian adults suffering from obesity. Our findings emphasize the significance of considering the combined effect(s) of multiple genes and SNPs in ascertaining cardiometabolic risk in obesity in future genetic screening programs.

PurposeBreast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features.Design/methodology/approachBreast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features.FindingsThe performance analysis was done for both segmentation and classification. From the analysis, the accuracy of the proposed IAP-CSA-based fuzzy was 41.9% improved than the fuzzy classifier, 2.80% improved than PSO, WOA, and CSA, and 2.32% improved than GWO-based fuzzy classifiers. Additionally, the accuracy of the developed IAP-CSA-fuzzy was 9.54% better than NN, 35.8% better than SVM, and 41.9% better than the existing fuzzy classifier. Hence, it is concluded that the implemented breast cancer detection model was efficient in determining the normal, benign and malignant images.Originality/valueThis paper adopts the latest Improved Awareness Probability-based Crow Search Algorithm (IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images, and this is the first work that utilizes this method.

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the “fitness” of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle (“BonMOLière”).

Imbalanced potassium (K) fertilization in agricultural fields has led to considerable negative impacts and remains to be the foremost challenge for maize production in India-Gangetic region. Plant growth-promoting rhizobacteria, particularly potassium solubilizing rhizobacteria (KSR), could serve as inoculants and a promising strategy for enhancement of plant absorption of K hence reducing dependency on chemical fertilizers. Maize seeds were microbiolized for 30 min with KSR suspensions. In the present study, the use of chemical fertilizers along with Agrobacterium tumefaciens strain OPVS10 showed pronounced beneficial effect on growth and yield attributes in maize. There was a significant difference among different parameters studied when varying doses of K and KSR strains were applied. Results showed that the combined application of KSR strain OPVS10 with 100% RDK (recommended dose of K) was most effective in modulating growth, physio-biochemical, and yield attributes in maize thus could be regarded as a promising alternative to mineral K-fertilization. Principal component analysis (PCA) revealed that 100-grain weight and grain yield were the most important properties to improve the sustainable growth of maize. Therefore, these KSR strains have different mechanisms for modulating various activities in maize plants. Results suggested that the synergistic application of KSR strain OPVS10 with 100% RDK can be used for optimized breeding, screening, and nutrient assimilation in maize crop. Hence, this eco-friendly approach may be one of the efficient methods for reducing dependency on chemicals, which pose adverse effects on human health directly and indirectly.

CRISPR/Cas9-mediated genome editing technology consists of a single-guide RNA (sgRNA), and the Cas9 endonuclease has the potential to treat genetic diseases in most tissues and organisms. In this system, the Cas9 protein can be directed to target genomic DNA sequences as “molecular scissors” with the guidance of sgRNAs. However, the target-specific activities of different sgRNAs are highly variable; thus, it is crucial to search for a simple, quick and economical method to screen for optimized sgRNAs with high target specificity. We have adopted and verified a newly developed white-to-blue colony formation assay to quickly screen for sgRNAs optimized for the EphA2 gene, which is highly expressed in hormone-resistant prostate cancer (PC-3) cells. This assay promises to screen for optimized sgRNAs more simply, rapidly, and efficiently. Our results suggest that the white-to-blue colony formation assay might be a useful screening strategy to quickly select for optimized sgRNAs.

Expression in Escherichia coli represents the simplest and most cost effective means for the production of recombinant proteins. This is a routine task in structural biology and biochemistry where milligrams of the target protein are required in high purity and monodispersity. To achieve these criteria, the user often needs to screen several constructs in different expression and purification conditions in parallel. We describe a pipeline, implemented in the Center for Optimized Structural Studies, that enables the systematic screening of expression and purification conditions for recombinant proteins and relies on a series of logical decisions. We first use bioinformatics tools to design a series of protein fragments, which we clone in parallel, and subsequently screen in small scale for optimal expression and purification conditions. Based on a scoring system that assesses soluble expression, we then select the top ranking targets for large-scale purification. In the establishment of our pipeline, emphasis was put on streamlining the processes such that it can be easily but not necessarily automatized. In a typical run of about 2 weeks, we are able to prepare and perform small-scale expression screens for 20–100 different constructs followed by large-scale purification of at least 4–6 proteins. The major advantage of our approach is its flexibility, which allows for easy adoption, either partially or entirely, by any average hypothesis driven laboratory in a manual or robot-assisted manner.