Comparative toxicology of algal cell extracts and pure cyanotoxins: insights into toxic effects and mechanisms of harmful cyanobacteria Raphidiopsis raciborskii.

Ongoing research on cyanotoxins, driven by the socioeconomic impact of harmful algal blooms, emphasizes the critical necessity of elucidating the toxicological profiles of algal cell extracts and pure toxins. This study comprehensively compares Raphidiopsis raciborskii dissolved extract (RDE) and cylindrospermopsin (CYN) based on Daphnia magna assays. Both RDE and CYN target vital organs and disrupt reproduction, development, and digestion, thereby causing acute and chronic toxicity. Disturbances in locomotion, reduced behavioral activity, and weakened swimming capability in D. magna have also been reported for both RDE and CYN, indicating the insufficiency of conventional toxicity evaluation parameters for distinguishing between the toxic effects of algal extracts and pure cyanotoxins. Additionally, chemical profiling revealed the presence of highly active tryptophan-, humic acid-, and fulvic acid-like fluorescence compounds in the RDE, along with the active constituents of CYN, within a 15-day period, demonstrating the chemical complexity and dynamics of the RDE. Transcriptomics was used to further elucidate the distinct molecular mechanisms of RDE and CYN. They act diversely in terms of cytotoxicity, involving oxidative stress and response, protein content, and energy metabolism, and demonstrate distinct modes of action in neurofunctions. In essence, this study underscores the distinct toxicity mechanisms of RDE and CYN and emphasizes the necessity for context- and objective-specific toxicity assessments, advocating nuanced approaches to evaluate the ecological and health implications of cyanotoxins, thereby contributing to the precision of environmental risk assessments.

Cross-Modality Reference and Feature Mutual-Projection for 3D Brain MRI Image Super-Resolution.

High-resolution (HR) magnetic resonance imaging (MRI) can reveal rich anatomical structures for clinical diagnoses. However, due to hardware and signal-to-noise ratio limitations, MRI images are often collected with low resolution (LR) which is not conducive to diagnosing and analyzing clinical diseases. Recently, deep learning super-resolution (SR) methods have demonstrated great potential in enhancing the resolution of MRI images; however, most of them did not take the cross-modality and internal priors of MR seriously, which hinders the SR performance. In this paper, we propose a cross-modality reference and feature mutual-projection (CRFM) method to enhance the spatial resolution of brain MRI images. Specifically, we feed the gradients of HR MRI images from referenced imaging modality into the SR network to transform true clear textures to LR feature maps. Meanwhile, we design a plug-in feature mutual-projection (FMP) method to capture the cross-scale dependency and cross-modality similarity details of MRI images. Finally, we fuse all feature maps with parallel attentions to produce and refine the HR features adaptively. Extensive experiments on MRI images in the image domain and k-space show that our CRFM method outperforms existing state-of-the-art MRI SR methods.

Patient-Controlled Subcutaneous Analgesia with Hydromorphone versus Oral Oxycontin for Opioid Titration of Cancer Pain: A Prospective Multicenter Randomized Trial.

Background: Studies have shown that oral oxycontin tablets can be used for opioid titration. The European Society for Medical Oncology (ESMO) guidelines for adult cancer pain recommend opioid titration through the parenteral route, usually the intravenous or subcutaneous route. Patient-controlled subcutaneous analgesia (PCSA) with hydromorphone needs further evaluation for opioid titration. This prospective multicenter study was designed to compare the efficacy and safety of hydromorphone PCSA with oral oxycontin tablets for opioid titration of cancer pain. Patients and Methods: Eligible patients with cancer pain were randomly assigned in a 1:1 ratio to the PCSA group or the oxycontin group for dose titration. Different titration methods were given in both groups depending on whether the patient had an opioid tolerance. The primary endpoint of this study was time to successful titration (TST). Results: A total of 256 patients completed this study. The PCSA group had a significantly lower TST compared with the oxycontin group (median [95% confidence interval (CI)], 5.5[95% CI:2.5-11.5] hours vs.16.0 [95% CI:11.5-22.5] hours; p<0.001). The frequency (median; interquartile) of breakthrough pain (Btp) over 24 hours was significantly lower in the PCSA group (2.5;2.0-3.5) than in the oxycontin group.(3.0; 2.5-4.5) (p=0.04). The pain was evaluated by numeric rating scale (NRS) score at 12 hours after the start of titration. The pain score (median; interquartile) was significantly lower in the PCSA versus the oxycontin group (2.5;1.5-3.0) vs 4.5;3.0-6.0) (p=0.02). The equivalent dose of oral morphine (EDOM) for a successful titration was similar in both groups (p=0.29), but there was a significant improvement in quality of life (QoL) in both groups (p=0.03). No between-group difference in the incidence of opioid-related adverse effects was observed (p=0.32). Conclusion: Compared with oral oxycontin tablet, the use of PCSA with hydromorphone achieved a shorter titration duration for patients with cancer pain (p<0.001), without significantly increasing adverse events (p=0.32).

The augmentation of cytotoxic immune cell functionality through physical exertion bolsters the potency of chemotherapy in models of mammary carcinoma.

BACKGROUND: Mammary carcinoma, a pervasive and potentially lethal affliction, is conjectured to be profoundly influenced by physical exercise, both in prophylaxis and therapeutic contexts. This study endeavors to explore the repercussions of exercise training on the progression of mammary carcinoma, particularly the mechanisms by which the amalgamation of an exercise regimen and doxorubicin induces tumor cell apoptosis. METHODS: Female BALB/c mice were categorized into four distinct groups: A sedentary group (SED), an exercise group (Ex), a doxorubicin group (Dox, 5 mg/kg), and a combined treatment group (Dox + Ex). The exercise training lasted for 21 days and included aerobic rotarod exercise and resistance training. The impact of exercise training on tumor growth, immune cell proportions, inflammatory factor levels, and cell apoptosis pathway was assessed. RESULTS: Exercise training significantly curtailed tumor growth in a mouse model of breast cancer. Both the Ex and Dox groups exhibited significant reductions in tumor volume and weight (p < 0.01) in comparison to the SED group, while the Dox + Ex group had a significantly lower tumor volume and weight than the Dox group (p < 0.01). Exercise training also significantly increased the proportion of NK and T cells in various parts of the body and tumor tissue, while decreasing tumor blood vessels density. Exercise training also increased IL-6 and IL-15 levels in the blood and altered the expression of apoptosis-related proteins in tumor tissue, with the combined treatment group showing even more significant changes. CONCLUSIONS: Physical training improves the effectiveness of doxorubicin in treating breast cancer by activating cytotoxic immune cells, releasing tumor suppressor factors, and initiating mt-apoptosis, all while mitigating the adverse effects of chemotherapy.

Lifelong Text-Audio Sentiment Analysis learning.

Sentiment analysis refers to the mining of textual context, which is conducted with the aim of identifying and extracting subjective opinions in textual materials. However, most existing methods neglect other important modalities, e.g., the audio modality, which can provide intrinsic complementary knowledge for sentiment analysis. Furthermore, much work on sentiment analysis cannot continuously learn new sentiment analysis tasks or discover potential correlations among distinct modalities. To address these concerns, we propose a novel Lifelong Text-Audio Sentiment Analysis (LTASA) model to continuously learn text-audio sentiment analysis tasks, which effectively explores intrinsic semantic relationships from both intra-modality and inter-modality perspectives. More specifically, a modality-specific knowledge dictionary is developed for each modality to obtain shared intra-modality representations among various text-audio sentiment analysis tasks. Additionally, based on information dependence between text and audio knowledge dictionaries, a complementarity-aware subspace is developed to capture the latent nonlinear inter-modality complementary knowledge. To sequentially learn text-audio sentiment analysis tasks, a new online multi-task optimization pipeline is designed. Finally, we verify our model on three common datasets to show its superiority. Compared with some baseline representative methods, the capability of the LTASA model is significantly boosted in terms of five measurement indicators.

Combined methods elucidate the multi-organ toxicity of cylindrospermopsin (CYN) on Daphnia magna.

Global water bodies are now at risk from inevitable cyanobacterial blooms and their production of multiple cyanotoxins, in particular cylindrospermopsin (CYN). However, research on the CYN toxicity and its molecular mechanisms is still limited, whilst the responses of aquatic species against CYN are uncovered. By integrating behavioral observations, chemical detections and transcriptome analysis, this study demonstrated that CYN exerted multi-organ toxicity to model species, Daphnia magna. The present study confirmed that CYN could cause protein inhibition by undermining total protein contents, and altered the gene expression related to proteolysis. Meantime, CYN induced oxidative stress by increasing reactive oxygen species (ROS) level, decreasing the glutathione (GSH) concentration, and interfered with protoheme formation process molecularly. Neurotoxicity led by CYN was solidly determined by abnormal swimming patterns, reduced acetylcholinesterase (AChE), and downward expression of muscarinic acetylcholine receptor (CHRM). Importantly, for the first time, this research determined CYN directly interfered with energy metabolism in cladocerans. CYN distinctively reduced filtration and ingestion rate by targeting on heart and thoracic limbs, which declined the energy intake, and could be further displayed by the reduction of motional strength and the trypsin concentration. These phenotypic alterations were supported by transcriptomic profile, including the down-regulation of oxidative phosphorylation and ATP synthesis. Moreover, CYN was speculated to trigger the self-defense responses of D. magna, known as "abandon-ship" by moderating lipid metabolism and distribution. This study, overall, comprehensively demonstrated the CYN toxicity and the responses of D. magna against it, which is of great significance to the advancements of CYN toxicity knowledge.

Identification of hub genes and construction of an mRNA-miRNA-lncRNA network of gastric carcinoma using integrated bioinformatics analysis.

BACKGROUND: Gastric carcinoma (GC) is one of the most common cancer globally. Despite its worldwide decline in incidence and mortality over the past decades, gastric cancer still has a poor prognosis. However, the key regulators driving this process and their exact mechanisms have not been thoroughly studied. This study aimed to identify hub genes to improve the prognostic prediction of GC and construct a messenger RNA-microRNA-long non-coding RNA(mRNA-miRNA-lncRNA) regulatory network. METHODS: The GSE66229 dataset, from the Gene Expression Omnibus (GEO) database, and The Cancer Genome Atlas (TCGA) database were used for the bioinformatic analysis. Differential gene expression analysis methods and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify a common set of differentially co-expressed genes in GC. The genes were validated using samples from TCGA database and further validation using the online tools GEPIA database and Kaplan-Meier(KM) plotter database. Gene set enrichment analysis(GSEA) was used to identify hub genes related to signaling pathways in GC. The RNAInter database and Cytoscape software were used to construct an mRNA-miRNA-lncRNA network. RESULTS: A total of 12 genes were identified as the common set of differentially co-expressed genes in GC. After verification of these genes, 3 hub genes, namely CTHRC1, FNDC1, and INHBA, were found to be upregulated in tumor and associated with poor GC patient survival. In addition, an mRNA-miRNA-lncRNA regulatory network was established, which included 12 lncRNAs, 5 miRNAs, and the 3 hub genes. CONCLUSIONS: In summary, the identification of these hub genes and the establishment of the mRNA-miRNA-lncRNA regulatory network provide new insights into the underlying mechanisms of gastric carcinogenesis. In addition, the identified hub genes, CTHRC1, FNDC1, and INHBA, may serve as novel prognostic biomarkers and therapeutic targets.

3D dense convolutional neural network for fast and accurate single MR image super-resolution.

Super-resolution (SR) MR image reconstruction has shown to be a very promising direction to improve the spatial resolution of low-resolution (LR) MR images. In this paper, we presented a novel MR image SR method based on a dense convolutional neural network (DDSR), and its enhanced version called EDDSR. There are three major innovations: first, we re-designed dense modules to extract hierarchical features directly from LR images and propagate the extracted feature maps through dense connections. Therefore, unlike other CNN-based SR MR techniques that upsample LR patches in the initial phase, our methods take the original LR images or patches as input. This effectively reduces computational complexity and speeds up SR reconstruction. Second, a final deconvolution filter in our model automatically learns filters to fuse and upscale all hierarchical feature maps to generate HR MR images. Using this, EDDSR can perform SR reconstructions at different upscale factors using a single model with one stride fixed deconvolution operation. Third, to further improve SR reconstruction accuracy, we exploited a geometric self-ensemble strategy. Experimental results on three benchmark datasets demonstrate that our methods, DDSR and EDDSR, achieved superior performance compared to state-of-the-art MR image SR methods with less computational load and memory usage.

3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads.

The existing deep convolutional neural networks (DCNNs) based methods have achieved significant progress regarding automatic glioma segmentation in magnetic resonance imaging (MRI) data. However, there are two main problems affecting the performance of traditional DCNNs constructed by simply stacking convolutional layers, namely, exploding/vanishing gradients and limitations to the feature computations. To address these challenges, we propose a novel framework to automatically segment brain tumors. First, a three-dimensional (3D) dense connectivity architecture is used to build the backbone for feature reuse. Second, we design a new feature pyramid module using 3D atrous convolutional layers and add this module to the end of the backbone to fuse multiscale contexts. Finally, a 3D deep supervision mechanism is equipped with the network to promote training. On the multimodal brain tumor image segmentation benchmark (BRATS) datasets, our method achieves Dice similarity coefficient values of 0.87, 0.72, and 0.70 on the BRATS 2013 Challenge, 0.84, 0.70, and 0.61 on the BRATS 2013 LeaderBoard, 0.83, 0.70, and 0.62 on the BRATS 2015 Testing, 0.8642, 0.7738, and 0.7525 on the BRATS 2018 Validation in terms of whole tumors, tumor cores, and enhancing cores, respectively. Compared to the published state-of-the-art methods, the proposed method achieves promising accuracy and fast processing, demonstrating good potential for clinical medicine.

Effects of different doses of X-ray irradiation on cell apoptosis, cell cycle, DNA damage repair and glycolysis in HeLa cells.

The present study examined the radiation biological response of cancer cells to different fractional irradiation doses and investigates the optimal fractional irradiation dose with improved biological effects. Radiobiological studies were performed at the molecular and cellular levels to provide insights into DNA damage and repair, and the apoptosis mechanism of cells that were exposed to different doses of X-ray irradiation (0, 2, 4, 6, 8, 10, 12.5, 15 and 20 Gy). Evidence of increased reactive oxygen species (ROS), DNA double strand breaks (DSB), cellular apoptosis, G2/M phase proportion and inhibition of cell proliferation were observed following irradiation. Differences in the ROS amount and apoptotic percentages of cells between the 2 and 4 Gy groups were insignificant. Compared with 0 Gy, the expression of the apoptosis suppression protein B-cell lymphoma-2 was decreased following at increased irradiation doses. However, apoptosis-associated protein Bcl-2-associated X (Bax), caspase-9 and BH3 interacting domain death agonist (Bid) were elevated following irradiation, compared with the control group (0 Gy). Furthermore, the expression levels of Bax in the 6, 8, 10 and 12.5 Gy groups were significantly increased, compared with the other groups. Caspase-9 expression with 2, 4, 6 and 8 Gy were increased compared with other groups, and the Bid levels with 6 and 8 Gy were also increased compared with other groups. G2/M phase arrest was associated with the increase of checkpoint kinase 1 and reduction of cyclin dependent kinase 1. DNA damage repair was associated with the protein Ku70 in the 2, 8, 10, 12.5, 15 and 20 Gy groups were less than other group. Compared with other group, Ku80 levels were reduced in the 6 and 8 Gy groups, and Rad51 levels were reduced in the 2, 8 and 10 Gy groups. The expression of hypoxia inducible factor-1α, c-Myc and glucose transporter 1 (GLUT1) demonstrated an increasing trend following irradiation in a dose-dependent manner, but the expression of pyruvate kinase M2, in the 2-10 Gy irradiation groups, and GLUT1, in the 12.5, 15 and 20 Gy irradiation groups, were reduced, compared with the other groups. Considering the DNA damage repair and apoptosis mechanisms at molecular and cellular levels, it was concluded that 2, 6, 8 and 10 Gy may be the optimal fractional dose that can promote cell apoptosis, and inhibit DNA damage repair and glycolysis.

Identification of two novel biomarkers of rectal carcinoma progression and prognosis via co-expression network analysis.

mRNA expression profiles provide important insights on a diversity of biological processes involved in rectal carcinoma (RC). Our aim was to comprehensively map complex interactions between the mRNA expression patterns and the clinical traits of RC. We employed the integrated analysis of five microarray datasets and The Cancer Genome Atlas rectal adenocarcinoma database to identify 2118 consensual differentially expressed genes (DEGs) in RC and adjacent normal tissue samples, and then applied weighted gene co-expression network analysis to parse DEGs and eight clinical traits in 66 eligible RC samples. A total of 16 co-expressed gene modules were identified. The green-yellow and salmon modules were most appropriate to the pathological stage (R = 0.36) and the overall survival (HR =13.534, P = 0.014), respectively. A diagnostic model of the five pathological stage hub genes (SCG3, SYP, CDK5R2, AP3B2, and RUNDC3A) provided a powerful classification accuracy between localized RC and non-localized RC. We also found increased Secretogranin III (SCG3) expression with higher pathological stage and poorer prognosis in the test and validation set. The increased Homer scaffolding protein 2 (HOMER2) expression with the favorable survival prediction efficiency significantly correlated with the markedly reduced overall survival of RC patients and the higher pathological stage during the test and validation set. Our findings indicate that the SCG3 and HOMER2 mRNA levels should be further evaluated as predictors of pathological stage and survival in patients with RC.

Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis.

Although papillary renal cell carcinoma (PRCC) accounts for 10%-15% of renal cell carcinoma (RCC), no predictive molecular biomarker is currently applicable to guiding disease stage of PRCC patients. The mRNASeq data of PRCC and adjacent normal tissue in The Cancer Genome Atlas was analyzed to identify 1148 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 11 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = 0.45) by Pearson's correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on nuclear division, cell cycle phase, and spindle (all P < 1e-10). All 40 hub genes in blue module can distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) PRCC (P < 0.01). A good molecular biomarker for pathological stage of RCC must be a prognostic gene in clinical practice. Survival analysis was performed to reversely validate if hub genes were associated with pathological stage. Survival analysis unveiled that all hub genes were associated with patient prognosis (P < 0.01).The validation cohort GSE2748 verified that 30 hub genes can differentiate localized from non-localized PRCC (P < 0.01), and 18 hub genes are prognosis-associated (P < 0.01).ROC curve indicated that the 17 hub genes exhibited excellent diagnostic efficiency for localized and non-localized PRCC (AUC > 0.7). These hub genes may serve as a biomarker and help to distinguish different pathological stages for PRCC patients.

A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans.

In magnetic resonance (MR), hardware limitations, scan time constraints, and patient movement often result in the acquisition of anisotropic 3-D MR images with limited spatial resolution in the out-of-plane views. Our goal is to construct an isotropic high-resolution (HR) 3-D MR image through upsampling and fusion of orthogonal anisotropic input scans. We propose a multiframe super-resolution (SR) reconstruction technique based on sparse representation of MR images. Our proposed algorithm exploits the correspondence between the HR slices and the low-resolution (LR) sections of the orthogonal input scans as well as the self-similarity of each input scan to train pairs of overcomplete dictionaries that are used in a sparse-land local model to upsample the input scans. The upsampled images are then combined using wavelet fusion and error backprojection to reconstruct an image. Features are learned from the data and no extra training set is needed. Qualitative and quantitative analyses were conducted to evaluate the proposed algorithm using simulated and clinical MR scans. Experimental results show that the proposed algorithm achieves promising results in terms of peak signal-to-noise ratio, structural similarity image index, intensity profiles, and visualization of small structures obscured in the LR imaging process due to partial volume effects. Our novel SR algorithm outperforms the nonlocal means (NLM) method using self-similarity, NLM method using self-similarity and image prior, self-training dictionary learning-based SR method, averaging of upsampled scans, and the wavelet fusion method. Our SR algorithm can reduce through-plane partial volume artifact by combining multiple orthogonal MR scans, and thus can potentially improve medical image analysis, research, and clinical diagnosis.

UBE2D3 gene overexpression increases radiosensitivity of EC109 esophageal cancer cells in vitro and in vivo.

Ubiquitin-conjugating enzyme E2D3 (UBE2D3), a key component in ubiquitin (Ub) proteasome system, plays a crucial role in tumorigenesis. We previously found that it is bound to hTERT, and UBE2D3 could attenuate radiosensitivity of human breast cancer cells. Here we investigated a contributing role of UBE2D3 in radiosensitivity of esophageal squamous carcinoma. We demonstrated that the overexpression of UBE2D3 in esophageal squamous carcinoma cells (EC109) resulted in prolonged G1 phase and shortened G2/M phase after irradiation. UBE2D3 overexpression also decreased length of telomere and activity of telomerase. In addition, the overexpression of UBE2D3 increased mRNA expression but decreased protein levels of hTERT in both vitro and vivo systems. Compared with untreated cells, the treatment of UBE2D3 overexpressing cells with the specific proteasome inhibitor (MG132) could up-regulate hTERT. MG132 treatment of UBE2D3 overexpressed cells caused a clear and dramatic increase in the amount of ubiquitinated hTERT species. These findings indicate that UBE2D3 enhances radiosensitivity of EC109 cells by degradating hTERT through the ubiquitin proteolysis pathway.

Single Anisotropic 3-D MR Image Upsampling via Overcomplete Dictionary Trained From In-Plane High Resolution Slices.

In magnetic resonance (MR), hardware limitation, scanning time, and patient comfort often result in the acquisition of anisotropic 3-D MR images. Enhancing image resolution is desired but has been very challenging in medical image processing. Super resolution reconstruction based on sparse representation and overcomplete dictionary has been lately employed to address this problem; however, these methods require extra training sets, which may not be always available. This paper proposes a novel single anisotropic 3-D MR image upsampling method via sparse representation and overcomplete dictionary that is trained from in-plane high resolution slices to upsample in the out-of-plane dimensions. The proposed method, therefore, does not require extra training sets. Abundant experiments, conducted on simulated and clinical brain MR images, show that the proposed method is more accurate than classical interpolation. When compared to a recent upsampling method based on the nonlocal means approach, the proposed method did not show improved results at low upsampling factors with simulated images, but generated comparable results with much better computational efficiency in clinical cases. Therefore, the proposed approach can be efficiently implemented and routinely used to upsample MR images in the out-of-planes views for radiologic assessment and postacquisition processing.

A Novel Approach to Multiple Sequence Alignment Using Multiobjective Evolutionary Algorithm Based on Decomposition.

Multiple sequence alignment (MSA) is a fundamental and key step for implementing other tasks in bioinformatics, such as phylogenetic analyses, identification of conserved motifs and domains, structure prediction, etc. Despite the fact that there are many methods to implement MSA, biologically perfect alignment approaches are not found hitherto. This paper proposes a novel idea to perform MSA, where MSA is treated as a multiobjective optimization problem. A famous multiobjective evolutionary algorithm framework based on decomposition is applied for solving MSA, named MOMSA. In the MOMSA algorithm, we develop a new population initialization method and a novel mutation operator. We compare the performance of MOMSA with several alignment methods based on evolutionary algorithms, including VDGA, GAPAM, and IMSA, and also with state-of-the-art progressive alignment approaches, such as MSAprobs, Probalign, MAFFT, Procons, Clustal omega, T-Coffee, Kalign2, MUSCLE, FSA, Dialign, PRANK, and CLUSTALW. These alignment algorithms are tested on benchmark datasets BAliBASE 2.0 and BAliBASE 3.0. Experimental results show that MOMSA can obtain the significantly better alignments than VDGA, GAPAM on the most of test cases by statistical analyses, produce better alignments than IMSA in terms of TC scores, and also indicate that MOMSA is comparable with the leading progressive alignment approaches in terms of quality of alignments.

LDA-SVM-based EGFR mutation model for NSCLC brain metastases: an observational study.

Epidermal growth factor receptor (EGFR) activating mutations are a predictor of tyrosine kinase inhibitor effectiveness in the treatment of non-small-cell lung cancer (NSCLC). The objective of this study is to build a model for predicting the EGFR mutation status of brain metastasis in patients with NSCLC. Observation and model set-up. This study was conducted between January 2003 and December 2011 in 6 medical centers in Southwest China. The study included 31 NSCLC patients with brain metastases. Eligibility requirements were histological proof of NSCLC, as well as sufficient quantity of paraffin-embedded lung and brain metastases specimens for EGFR mutation detection. The linear discriminant analysis (LDA) method was used for analyzing the dimensional reduction of clinical features, and a support vector machine (SVM) algorithm was employed to generate an EGFR mutation model for NSCLC brain metastases. Training-testing-validation (3 : 1 : 1) processes were applied to find the best fit in 12 patients (validation test set) with NSCLC and brain metastases treated with a tyrosine kinase inhibitor and whole-brain radiotherapy. Primary and secondary outcome measures: EGFR mutation analysis in patients with NSCLC and brain metastases and the development of a LDA-SVM-based EGFR mutation model for NSCLC brain metastases patients. EGFR mutation discordance between the primary lung tumor and brain metastases was found in 5 patients. Using LDA, 13 clinical features were transformed into 9 characteristics, and 3 were selected as primary vectors. The EGFR mutation model constructed with SVM algorithms had an accuracy, sensitivity, and specificity for determining the mutation status of brain metastases of 0.879, 0.886, and 0.875, respectively. Furthermore, the replicability of our model was confirmed by testing 100 random combinations of input values. The LDA-SVM-based model developed in this study could predict the EGFR status of brain metastases in this small cohort of patients with NSCLC. Further studies with larger cohorts should be carried out to validate our findings in the clinical setting.

Constrained optimization via artificial immune system.

An artificial immune system inspired by the fundamental principle of the vertebrate immune system, for solving constrained optimization problems, is proposed. The analogy between the mechanism of biological immune response and constrained optimization formulation is drawn. Individuals in population are classified into feasible and infeasible groups according to their constraint violations that closely match with the two states, inactivated and activated, of B-cells in the immune response. Feasible group focuses on exploitation in the feasible areas through clonal selection, recombination, and hypermutation, while infeasible group facilitates exploration along the feasibility boundary via location update. Direction information is extracted to promote the interactions between these two groups. This approach is validated by the benchmark functions proposed most recently and compared with those of the state of the art from various branches of evolutionary computation paradigms. The performance achieved is considered fairly competitive and promising.

The interaction between different types of activated RAW 264.7 cells and macrophage inflammatory protein-1 alpha.

BACKGROUND: Two major ways of macrophage (MΦ) activation can occur in radiation-induced pulmonary injury (RPI): classical and alternative MΦ activation, which play important roles in the pathogenesis of RPI. MΦ can produce chemokine MΦ inflammatory protein-1α (MIP-1α), while MIP-1α can recruit MΦ. The difference in the chemotactic ability of MIP-1α toward distinct activated MΦ is unclear. We speculated that there has been important interaction of MIP-1α with different activated MΦ, which might contribute to the pathogenesis of RPI. METHODS: Classically and alternatively activated MΦ were produced by stimulating murine MΦ cell line RAW 264.7 cells with three different stimuli (LPS, IL-4 and IL-13); Then we used recombinant MIP-1α to attract two types of activated MΦ. In addition, we measured the ability of two types of activated MΦ to produce MIP-1α at the protein or mRNA level. RESULTS: Chemotactic ability of recombinant MIP-1α toward IL-13-treated MΦ was the strongest, was moderate for IL-4-treated MΦ, and was weakest for LPS-stimulated MΦ (p<0.01). The ability of LPS-stimulated MΦ to secrete MIP-1α was significantly stronger than that of IL-4-treated or IL-13-treated MΦ (p<0.01). The ability of LPS-stimulated MΦ to express MIP-1α mRNA also was stronger than that of IL-4- or IL-13-stimulated MΦ (p<0.01). CONCLUSIONS: The chemotactic ability of MIP-1α toward alternatively activated MΦ (M2) was significantly greater than that for classically activated MΦ (M1). Meanwhile, both at the mRNA and protein level, the capacity of M1 to produce MIP-1α is better than that of M2. Thus, chemokine MIP-1α may play an important role in modulating the transition from radiation pneumonitis to pulmonary fibrosis in vivo, through the different chemotactic affinity for M1 and M2.

Supersecondary structure prediction using Chou's pseudo amino acid composition.

Supersecondary structures (SSSs) are the building blocks of protein 3D structures. Accurate prediction of SSSs can be one important step toward building a tertiary structure from the specified secondary structure. How to improve the accuracy of prediction of SSSs by effectively incorporating the sequence order effects is an important and challenging problem. Based on a different form of Chou's pseudo amino acid composition, a novel approach for feature representation of SSSs is proposed. Amino acid basic compositions, dipeptide components, and amino acid composition distribution are incorporated to represent the compositional features of proteins. Each supersecondary structural motif is characterized as a vector of 36 dimensions. In addition, we propose a novel prediction system by using SVM and IDQD algorithm as classifiers. Our method is trained and tested on ArchDB40 dataset containing 3088 proteins. The highest overall accuracy for the training dataset and the independent testing dataset are 77.7 and 69.4%, respectively.