Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning.

BACKGROUND: Grading of cancer histopathology slides requires more pathologists and expert clinicians as well as it is time consuming to look manually into whole-slide images. Hence, an automated classification of histopathological breast cancer sub-type is useful for clinical diagnosis and therapeutic responses. Recent deep learning methods for medical image analysis suggest the utility of automated radiologic imaging classification for relating disease characteristics or diagnosis and patient stratification. METHODS: To develop a hybrid model using the convolutional neural network (CNN) and the long short-term memory recurrent neural network (LSTM RNN) to classify four benign and four malignant breast cancer subtypes. The proposed CNN-LSTM leveraging on ImageNet uses a transfer learning approach in classifying and predicting four subtypes of each. The proposed model was evaluated on the BreakHis dataset comprises 2480 benign and 5429 malignant cancer images acquired at magnifications of 40×, 100×, 200× and 400×. RESULTS: The proposed hybrid CNN-LSTM model was compared with the existing CNN models used for breast histopathological image classification such as VGG-16, ResNet50, and Inception models. All the models were built using three different optimizers such as adaptive moment estimator (Adam), root mean square propagation (RMSProp), and stochastic gradient descent (SGD) optimizers by varying numbers of epochs. From the results, we noticed that the Adam optimizer was the best optimizer with maximum accuracy and minimum model loss for both the training and validation sets. The proposed hybrid CNN-LSTM model showed the highest overall accuracy of 99% for binary classification of benign and malignant cancer, and, whereas, 92.5% for multi-class classifier of benign and malignant cancer subtypes, respectively. CONCLUSION: To conclude, the proposed transfer learning approach outperformed the state-of-the-art machine and deep learning models in classifying benign and malignant cancer subtypes. The proposed method is feasible in classification of other cancers as well as diseases.

Anti-Atopic Dermatitis Effect of TPS240, a Novel Therapeutic Peptide, via Suppression of NF-κB and STAT3 Activation.

Atopic dermatitis (AD) is a relapsing skin disease with persistent inflammation as a causal factor for symptoms and disease progression. Current therapies provide only temporary relief and require long-term usage accompanied by side effects due to persistent relapses. A short peptide, TPS240, has been tested for its potential to subside AD. In this study, we confirmed the anti-atopic effect of TPS240 in vivo and in vitro using a DNCB-induced AD mouse model and TNF-α/IFN-γ-stimulated HaCaT cells. In the AD mouse model, topical treatment with TPS240 diminished AD-like skin lesions and symptoms such as epidermal thickening and mast cell infiltration induced by DNCB, similar to the existing treatment, dexamethasone (Dex). Furthermore, skin atrophy, weight loss, and abnormal organ weight changes observed in the Dex-treated group were not detected in the TPS240-treated group. In TNF-α/IFN-γ-stimulated HaCaT cells, TPS240 reduced the expression of the inflammatory chemokines CCL17 and CCL22 and the pruritic cytokines TSLP and IL-31 by inhibiting NF-κB and STAT3 activation. These results suggest that TPS240 has an anti-atopic effect through immunomodulation of AD-specific cytokines and chemokines and can be used as a candidate drug for the prevention and treatment of AD that can solve the safety problems of existing treatments.

Kernel-Based Independence Tests for Causal Structure Learning on Functional Data.

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert-Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert-Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data.

Comparison of Muscle Activity and Muscle Thickness According to Knee Flexion Angle during Supine Bridge Exercises using the Abdominal Drawing-in Maneuver on an Unstable Surface.

This study evaluated changes in deep trunk muscle thickness and lower extremity muscle activities during bridge exercises with the abdominal drawing-in maneuver. Bridge exercises were conducted on an unstable surface at different knee flexion angles (60º, 90º and 120º), with the aim of identifying more effective angles for bridge exercises. This study included 21 healthy adults, aged 20-27 years. Biceps femoris (BF), rectus abdominis, and rectus femoris activity was measured using surface electromyography. The thicknesses of the transverse abdominis (TrA), external oblique (EO) and internal oblique (IO) muscles were measured. BF (p = 0.000, partial η2 = 0.670) activity increased considerably as the knee flexion angle decreased. TrA (p = 0.000, partial η2 = 0.883) and IO (p = 0.000, partial η2 = 0.892) thickness significantly increased, while EO (p = 0.000, partial η2 = 0.893) thickness decreased as the knee flexion angle decreased. When performing bridge exercises using the abdominal drawing-in maneuver on an unstable surface, the knee flexion angles should be at 120º and 60º to increase trunk stability and lower extremity muscle activity, respectively.

Nonparametric estimation of recursive point processes with application to mumps in Pennsylvania.

The self-exciting Hawkes point process model (Hawkes, 1971) has been used to describe and forecast communicable diseases. A variant of the Hawkes model, called the recursive model, was proposed by Schoenberg et al. (2019) and has been shown to fit well to various epidemic disease datasets. Unlike the Hawkes model, the recursive model allows the productivity to vary as the overall rate of incidence of the disease varies. Here, we extend the data-driven nonparametric expectation-maximization method of Marsan and Lengliné (2008) in order to fit the recursive model without assuming a particular functional form for the productivity. The nonparametric recursive model is trained to fit to weekly reported cases of mumps in Pennsylvania during the January 1970-September 1990 time frame and then assessed using one week forecasts for the October 1990-December 2001 time period. Both its training and predictive ability are evaluated compared to that of other candidate models, such as Hawkes and SVEILR (susceptible, vaccinated, exposed, infected, lightly infected, recovered) compartmental models.

A Study on the Prediction of Cancer Using Whole-Genome Data and Deep Learning.

The number of patients diagnosed with cancer continues to increasingly rise, and has nearly doubled in 20 years. Therefore, predicting cancer occurrence has a significant impact on reducing medical costs, and preventing cancer early can increase survival rates. In the data preprocessing step, since individual genome data are used as input data, they are classified as individual genome data. Subsequently, data embedding is performed in character units, so that it can be used in deep learning. In the deep learning network schema, using preprocessed data, a character-based deep learning network learns the correlation between individual feature data and predicts cancer occurrence. To evaluate the objective reliability of the method proposed in this study, various networks published in other studies were compared and evaluated using the TCGA dataset. As a result of comparing various networks published in other studies using the same data, excellent results were obtained in terms of accuracy, sensitivity, and specificity. Thus, the superiority of the effectiveness of deep learning networks in predicting cancer occurrence using individual whole-genome data was demonstrated. From the results of the confusion matrix, the validity of the model for predicting the cancer using an individual's whole-genome data and the deep learning proposed in this study was proven. In addition, the AUC, which is the area under the ROC curve, which judges the efficiency of diagnosis as a performance evaluation index of the model, was found to be 90% or more, good classification results were derived. The objectives of this study were to use individual genome data for 12 cancers as input data to analyze the whole genome pattern, and to not separately use reference genome sequence data of normal individuals. In addition, several mutation types, including SNV, DEL, and INS, were applied.

Developmental Transcriptome Analysis of Red-Spotted Apollo Butterfly, Parnassius bremeri.

Parnassius bremeri (P. bremeri), a member of the genus Snow Apollo in the swallowtail family (Papilionidae), is a high alpine butterfly that lives in Russia, Korea, and China. It is an endangered wildlife (Class I) in South Korea and is a globally endangered species. The lack of transcriptomic and genomic resources of P. bremeri significantly hinders the study of its population genetics and conservation. The detailed information of the developmental stage-specific gene expression patterns of P. bremeri is of great demand for its conservation. However, the molecular mechanism underlying the metamorphic development of P. bremeri is still unknown. In the present study, the differentially expressed genes (DEGs) across the metamorphic developmental stages were compared using high-throughput transcriptome sequencing. We identified a total of 72,161 DEGs from eight comparisons. GO enrichment analysis showed that a range of DEGs were responsible for cuticle development and the melanin biosynthetic pathway during larval development. Pathway analysis suggested that the signaling pathways, such as the Wnt signaling pathway, hedgehog signaling pathway and Notch signaling pathway, are regulated during the developmental stages of P. bremeri. Furthermore, sensory receptors were also activated, especially during the larval to adult transition stage. Collectively, the results of this study provide a preliminary foundation and understanding of the molecular mechanism in their transcriptomes for further research on the metamorphic development of P. bremeri.

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies.

Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein-ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.

Cerebrovascular reactivity measurements using simultaneous 15O-water PET and ASL MRI: Impacts of arterial transit time, labeling efficiency, and hematocrit.

Cerebrovascular reactivity (CVR) reflects the capacity of the brain to meet changing physiological demands and can predict the risk of cerebrovascular diseases. CVR can be obtained by measuring the change in cerebral blood flow (CBF) during a brain stress test where CBF is altered by a vasodilator such as acetazolamide. Although the gold standard to quantify CBF is PET imaging, the procedure is invasive and inaccessible to most patients. Arterial spin labeling (ASL) is a non-invasive and quantitative MRI method to measure CBF, and a consensus guideline has been published for the clinical application of ASL. Despite single post labeling delay (PLD) pseudo-continuous ASL (PCASL) being the recommended ASL technique for CBF quantification, it is sensitive to variations to the arterial transit time (ATT) and labeling efficiency induced by the vasodilator in CVR studies. Multi-PLD ASL controls for the changes in ATT, and velocity selective ASL is in theory insensitive to both ATT and labeling efficiency. Here we investigate CVR using simultaneous 15O-water PET and ASL MRI data from 19 healthy subjects. CVR and CBF measured by the ASL techniques were compared using PET as the reference technique. The impacts of blood T1 and labeling efficiency on ASL were assessed using individual measurements of hematocrit and flow velocity data of the carotid and vertebral arteries measured using phase-contrast MRI. We found that multi-PLD PCASL is the ASL technique most consistent with PET for CVR quantification (group mean CVR of the whole brain = 42±19% and 40±18% respectively). Single-PLD ASL underestimated the CVR of the whole brain significantly by 15±10% compared with PET (p<0.01, paired t-test). Changes in ATT pre- and post-acetazolamide was the principal factor affecting ASL-based CVR quantification. Variations in labeling efficiency and blood T1 had negligible effects.

Screening of ß1- and ß2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches.

Cardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. ß-Adrenergic receptors (ß1-AR and ß2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning (ML), and a ligand-based similarity search were conducted for the PubChem database against both ß1- and ß2-AR. Initially, all docked molecules were screened using the threshold binding energy value. Molecules with a better binding affinity were further used for segregation as active and inactive through ML. The pharmacokinetic assessment was carried out on molecules retained in the above step. Further, similarity searching of the ChEMBL and DrugBank databases was performed. From detailed analysis of the above data, four compounds for each of ß1- and ß2-AR were found to be promising in nature. A number of critical ligand-binding amino acids formed potential hydrogen bonds and hydrophobic interactions. Finally, a molecular dynamics (MD) simulation study of each molecule bound with the respective target was performed. A number of parameters obtained from the MD simulation trajectories were calculated and substantiated the stability between the protein-ligand complex. Hence, it can be postulated that the final molecules might be crucial for CDs subjected to experimental validation.

Comparative Analysis of Functional and Structural Decline in Retinitis Pigmentosas.

Retinitis pigmentosa (RP) is a category of inherited retinal dystrophies that is best prognosticated using electroretinography (ERG). In this retrospective cohort study of 25 patients with RP, we evaluated the correlation between 30 Hz flicker ERG and structural parameters in the retina. Internationally standardized 30 Hz flicker ERG recordings, short-wavelength autofluorescence (SW-AF), and spectral domain-optical coherence tomography (SD-OCT) were acquired at two visits at least one year apart. Vertical and horizontal hyperautofluorescent ring diameter measurements with SW-AF, as well as ellipsoid zone (EZ) line width measurements with SD-OCT, were used as structural parameters of disease progression. The 30 Hz flicker ERG amplitude decreased by 2.2 ± 0.8 µV/year (p = 0.011), while implicit times remained unchanged. For SD-OCT, the EZ line decreased by 204.1 ± 34.7 µm/year (p < 0.001). Horizontal and vertical hyperautofluorescent ring diameters decreased by 161.9 ± 25.6 µm/year and 146.9 ± 34.6 µm/year, respectively (p = 0.001), with SW-AF. A correlation was found between the progression rates of the 30 Hz flicker amplitude recorded with Burian-Allen electrodes and both the horizontal ring diameter (p = 0.020) and EZ line (p = 0.044). SW-AF and SD-OCT, two readily available imaging techniques, may be used to prognosticate disease progression because of the reliability of their measurements and correlation with functional outcome.

Transcriptome analysis of olive flounder (Paralichthys olivaceus) head kidney infected with moderate and high virulent strains of infectious viral hemorrhagic septicaemia virus (VHSV).

Olive flounder (Paralichthys olivaceus) is one of the most valuable marine aquatic species in South Korea and faces tremendous exposure to the viral hemorrhagic septicemia virus (VHSV). Given the growing importance of flounder, it is therefore essential to understand the host defense of P. olivaceus against VHSV infection, but studies on its immune mechanism are hindered by the lack of genomic resources. In this study, the P. olivaceus was infected with disease-causing VHSV isolates, ADC-VHS2012-11 and ADC-VHS2014-5 which showed moderate virulent (20% mortality) and high virulent (65% mortality), in order to investigate the effect of difference in pathogenicity in head kidney during 1, 3, 7 days of post-infection using Illumina sequencing. After removing low-quality sequences, we obtained 144,933,160 high quality reads from thirty-six libraries which were further assembled into 53,384 unigenes with an average length of 563 bp with a range of 200 to 9605 bp. Transcriptome annotation revealed that 30,475 unigenes with a cut-off e-value of 10-5 were functionally annotated. In total, 10,046 unigenes were clustered into 26 functional categories by searching against the eggNOG database, and 22,233 unigenes to 52 GO terms. In addition, 12,985 unigenes were grouped into 387 KEGG pathways. Among the 13,270 differently expressed genes, 6578 and 6692 were differentially expressed only in moderate and high virulent, respectively. Based on our sequence analysis, many candidate genes with fundamental roles in innate immune system including, pattern recognition receptors (TLRs & RLRs), Mx, complement proteins, lectins, and cytokines (chemokines, IFN, IRF, IL, TRF) were differentially expressed. Furthermore, GO enrichment analysis for these genes revealed gene response to defense response to virus, apoptotic process and transcription factor activity. In summary, this study identifies several putative immune pathways and candidate genes deserving further investigation in the context of novel gene discovery, gene expression and regulation studies and lays the foundation for fish immunology especially in P. olivaceus against VHSV.

Stage-Wise Identification and Analysis of miRNA from Root-Knot Nematode Meloidogyne incognita.

In this study, we investigated global changes in miRNAs of Meloidogyne incognita throughout its life cycle. Small RNA sequencing resulted in approximately 62, 38, 38, 35, and 39 Mb reads in the egg, J2, J3, J4, and female stages, respectively. Overall, we identified 2724 known and 383 novel miRNAs (read count > 10) from all stages, of which 169 known and 13 novel miRNA were common to all the five stages. Among the stage-specific miRNAs, miR-286 was highly expressed in eggs, miR-2401 in J2, miR-8 and miR-187 in J3, miR-6736 in J4, and miR-17 in the female stages. These miRNAs are reported to be involved in embryo and neural development, muscular function, and control of apoptosis. Cluster analysis indicated the presence of 91 miRNA clusters, of which 36 clusters were novel and identified in this study. Comparison of miRNA families with other nematodes showed 17 families to be commonly absent in animal parasitic nematodes and M. incognita. Validation of 43 predicted common and stage-specific miRNA by quantitative PCR (qPCR) indicated their expression in the nematode. Stage-wise exploration of M. incognita miRNAs has not been carried out before and this work presents information on common and stage-specific miRNAs of the root-knot nematode.

Discovery of Gene Sources for Economic Traits in Hanwoo by Whole-genome Resequencing.

Hanwoo, a Korean native cattle (Bos taurus coreana), has great economic value due to high meat quality. Also, the breed has genetic variations that are associated with production traits such as health, disease resistance, reproduction, growth as well as carcass quality. In this study, next generation sequencing technologies and the availability of an appropriate reference genome were applied to discover a large amount of single nucleotide polymorphisms (SNPs) in ten Hanwoo bulls. Analysis of whole-genome resequencing generated a total of 26.5 Gb data, of which 594,716,859 and 592,990,750 reads covered 98.73% and 93.79% of the bovine reference genomes of UMD 3.1 and Btau 4.6.1, respectively. In total, 2,473,884 and 2,402,997 putative SNPs were discovered, of which 1,095,922 (44.3%) and 982,674 (40.9%) novel SNPs were discovered against UMD3.1 and Btau 4.6.1, respectively. Among the SNPs, the 46,301 (UMD 3.1) and 28,613 SNPs (Btau 4.6.1) that were identified as Hanwoo-specific SNPs were included in the functional genes that may be involved in the mechanisms of milk production, tenderness, juiciness, marbling of Hanwoo beef and yellow hair. Most of the Hanwoo-specific SNPs were identified in the promoter region, suggesting that the SNPs influence differential expression of the regulated genes relative to the relevant traits. In particular, the non-synonymous (ns) SNPs found in CORIN, which is a negative regulator of Agouti, might be a causal variant to determine yellow hair of Hanwoo. Our results will provide abundant genetic sources of variation to characterize Hanwoo genetics and for subsequent breeding.

The effect of co-stabilizer muscle activation on knee joint position sense: a single group pre-post test.

[Purpose] The purpose of this study was to investigate the effect of co-stabilizer muscle activation on knee joint position sense. [Subjects and Methods] This study was a pre-post, single-blinded randomly controlled trial (angle sequence randomly selected) design. Seven healthy adults with no orthopaedic or neurological problems participated in this study. Knee joint position sense was measured by a target matching test at target angles of 30°, 45° and 80° of knee flexion a using digital inclinometer under two conditions: erect sitting, which is known to highly activate co-stabilizer muscle and slump sitting, which is known to little activate the co-stabilizer muscle. [Results] A significant difference in joint position matching error at the knee flexion angle of 45° was founded between two conditions erect sitting: (3.83 ± 1.47) and slump sitting: (1.00 ± 0.63). There were no significant differences in joint position matching error at the other target angles. [Conclusion] Knee joint position sense at 45° is likely to be affected by activation of co-stabilizer muscle, and this value is suitable for facilitation of joint position sense with skilled movement.

Single nucleotide polymorphism analysis of Korean native chickens using next generation sequencing data.

There are five native chicken lines in Korea, which are mainly classified by plumage colors (black, white, red, yellow, gray). These five lines are very important genetic resources in the Korean poultry industry. Based on a next generation sequencing technology, whole genome sequence and reference assemblies were performed using Gallus_gallus_4.0 (NCBI) with whole genome sequences from these lines to identify common and novel single nucleotide polymorphisms (SNPs). We obtained 36,660,731,136 ± 1,257,159,120 bp of raw sequence and average 26.6-fold of 25-29 billion reference assembly sequences representing 97.288 % coverage. Also, 4,006,068 ± 97,534 SNPs were observed from 29 autosomes and the Z chromosome and, of these, 752,309 SNPs are the common SNPs across lines. Among the identified SNPs, the number of novel- and known-location assigned SNPs was 1,047,951 ± 14,956 and 2,948,648 ± 81,414, respectively. The number of unassigned known SNPs was 1,181 ± 150 and unassigned novel SNPs was 8,238 ± 1,019. Synonymous SNPs, non-synonymous SNPs, and SNPs having character changes were 26,266 ± 1,456, 11,467 ± 604, 8,180 ± 458, respectively. Overall, 443,048 ± 26,389 SNPs in each bird were identified by comparing with dbSNP in NCBI. The presently obtained genome sequence and SNP information in Korean native chickens have wide applications for further genome studies such as genetic diversity studies to detect causative mutations for economic and disease related traits.

Antimicrobial peptides in the centipede Scolopendra subspinipes mutilans.

The centipede Scolopendra subspinipes mutilans is an environmentally beneficial and medically important arthropod species. Although this species is increasingly applied as a reliable source of new antimicrobial peptides, the transcriptome of this species is a prerequisite for more rational selection of antimicrobial peptides. In this report, we isolated total RNA from the whole body of adult centipedes, S. subspinipes mutilans, that were nonimmunized and immunized against Escherichia coli, and we generated a total of 77,063 pooled contigs and singletons using high-throughput sequencing. To screen putative antimicrobial peptides, in silico analyses of the S. subspinipes mutilans transcriptome were performed based on the physicochemical evidence of length, charge, isoelectric point, and in vitro and in vivo aggregation scores together with the existence of continuous antimicrobial peptide stretches. Moreover, we excluded some transcripts that showed similarity with both previously known antimicrobial peptides and the human proteome, had a proteolytic cleavage site, and had downregulated expression compared with the nonimmunized sample. As a result, we selected 17 transcripts and tested their antimicrobial activity with a radial diffusion assay. Among them, ten synthetic peptides experimentally showed antimicrobial activity against microbes and no toxicity to mouse erythrocytes. Our results provide not only a useful set of antimicrobial peptide candidates and an efficient strategy for novel antimicrobial peptide development but also the transcriptome data of a big centipede as a valuable resource.

Applications of next-generation sequencing to unravelling the evolutionary history of algae.

First-generation Sanger DNA sequencing revolutionized science over the past three decades and the current next-generation sequencing (NGS) technology has opened the doors to the next phase in the sequencing revolution. Using NGS, scientists are able to sequence entire genomes and to generate extensive transcriptome data from diverse photosynthetic eukaryotes in a timely and cost-effective manner. Genome data in particular shed light on the complicated evolutionary history of algae that form the basis of the food chain in many environments. In the Eukaryotic Tree of Life, the fact that photosynthetic lineages are positioned in four supergroups has important evolutionary consequences. We now know that the story of eukaryotic photosynthesis unfolds with a primary endosymbiosis between an ancestral heterotrophic protist and a captured cyanobacterium that gave rise to the glaucophytes, red algae and Viridiplantae (green algae and land plants). These primary plastids were then transferred to other eukaryotic groups through secondary endosymbiosis. A red alga was captured by the ancestor(s) of the stramenopiles, alveolates (dinoflagellates, apicomplexa, chromeridae), cryptophytes and haptophytes, whereas green algae were captured independently by the common ancestors of the euglenophytes and chlorarachniophytes. A separate case of primary endosymbiosis is found in the filose amoeba Paulinella chromatophora, which has at least nine heterotrophic sister species. Paulinella genome data provide detailed insights into the early stages of plastid establishment. Therefore, genome data produced by NGS have provided many novel insights into the taxonomy, phylogeny and evolutionary history of photosynthetic eukaryotes.

Lysine-Rich Polypeptide Modulates Forkhead Box O3 and Phosphoinositide 3-Kinase-Protein Kinase B Pathway To Induce Apoptosis in Breast Cancer.

The PI3K/AKT/FOXO3 pathway is one of the most frequently involved signaling pathways in cancer, including breast cancer. Therefore, we synthesized a novel lysine-rich polypeptide (Lys-PP) using de novo assembly method and evaluated its anticancer effect. We characterized the structural and physicochemical properties of Lys-PP using various techniques. Later, we used integrated approaches such as in silico, in vitro, and in vivo analysis to confirm the anticancer and therapeutic effect of Lys-PP. First, RNA sequencing suggests Lys-PP disrupted the central carbon metabolic pathway through the modulation of prolactin signaling. Additionally, docking analysis also confirmed the significant association of PI3K/AKT and FOXO3 pathway to induce an apoptotic effect on cancer. Second, Lys-PP exhibited a significant cytotoxicity effect against MDA-MB-231 but no cytotoxic effects on RAW 264.7 and HEK-293, respectively. The cytotoxic effect of Lys-PP-induced apoptosis by an increase in FOXO3a protein expression and a decrease in PI3K/AKT pathway was confirmed by quantitative real-time polymerase chain reaction, immunoblotting, and fluorescent microscopy. Later, immunohistochemistry and hematoxylin and eosin staining on MDA-MD-231 showed increased FOXO3a expression and cell death in the xenograft mice model. Further, liver function, metabolic health, or lipid profile upon Lys-PP showed the absence of significant modulation in the biomarkers except for kidney-related biomarkers. Overall, our comprehensive study provides the first evidence of Lys-PP antibreast cancer action, which could serve as a potential treatment in an alternative or complementary medicine practice.

Microbiota Association and Profiling of Gingival Sulci and Root Canals of Teeth with Primary or Secondary/Persistent Endodontic Infections.

INTRODUCTION: Microbiota associated with primary endodontic infection (PEI) and secondary/persistent endodontic infection (SPEI) must be characterized to elucidate pathogenesis in apical periodontitis and bacterial biomarkers identified for diagnostic and therapeutic applications. METHODS: This study analyzed the microbial community profiles of root canals and gingival sulci (sulcus-E) for teeth with PEI (n = 10) or SPEI (n = 10), using the Illumina MiSeq platform. Bacterial samples from gingival sulci (sulcus-C) of healthy contralateral teeth served as controls. RESULTS: There were 15 phyla, 177 genera, and 340 species identified. The number and diversity of bacteria in root canals did not differ significantly between PEI and SPEI. Proteobacteria, Firmicutes, Fusobacteria, Bacteroidetes, and Actinobacteria were the dominant phyla in both groups. At the genus level, Lancefieldella, Bifidobacterium, Stomatobaculum, and Schaalia were enriched in root canals with SPEI. Of significance, Lancefieldella was observed in both root canals and sulcus-E of teeth with SPEI. At the species level, Neisseria macacae, Streptococcus gordonii, Bifidobacterium dentium, Stomatobaculum longum, and Schaalia odontolytica were increased significantly in root canals with SPEI compared to PEI. Oribacterium species, Streptococcus salivarius, Lancefieldella parvula, Prevotella denticola, and Oribacterium asaccharolyticum were more abundant in sulcus-E of teeth with SPEI compared to PEI. CONCLUSIONS: There were distinctive and differing predominant bacterial species associated with the root canals and gingival sulci between teeth with PEI and SPEI. Specific bacteria identified in sulcus-E and root canals of teeth with SPEI could serve as noninvasive diagnostic biomarkers for detecting SPEI.