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Background Although risk factors that lead to falling in Parkinson's disease (PD) have been previously studied, the established predictors are mostly non-modifiable. A novel method for fall risk assessment may provide more insight into preventable high-risk activities to reduce future falls. Objectives To explore the prediction of falling in PD patients using a machine learning-based approach. Method 305 PD patients, with or without a history of falls within the past month, were recruited. Data including clinical demographics, medications, and balance confidence, scaled by the 16-item Activities-Specific Balance Confidence Scale (ABC-16), were entered into the supervised machine learning models using XGBoost to explore the prediction of fallers/recurrent fallers in two separate models. Results 99 (32%) patients were fallers and 58 (19%) were recurrent fallers. The accuracy of the model to predict falls was 72% (p = 0.001). The most important factors were item 7 (sweeping the floor), item 5 (reaching on tiptoes), and item 12 (walking in a crowded mall) in the ABC-16 scale, followed by disease stage and duration. When recurrent falls were analysed, the models had higher accuracy (81%, p = 0.02). The strongest predictors of recurrent falls were item 12, 5, and 10 (walking across parking lot), followed by disease stage and current age. Conclusion Our machine learning-based study demonstrated that predictors of falling combined demographics of PD with environmental factors, including high-risk activities that require cognitive attention and changes in vertical and lateral orientations. This enables physicians to focus on modifiable factors and appropriately implement fall prevention strategies for individual patients.

The limitations of cisplatin, a standard chemotherapy for lung cancer, have been documented with serious adverse effects and drug resistance. To address the need for novel therapy, this study firstly reveals the potential of peptide from Lentinus squarrosulus (Mont.) as a chemotherapeutic adjuvant for cisplatin treatment. The purified peptide from L. squarrosulus aqueous extracts was obtained after eluting with 0.4 M NaCl through FPLC equipped with anion exchange column. Preincubation for 24 h with 5 µg/mL of the peptide at prior to treatment with 5 µM cisplatin significantly diminished %cell viability in various human lung cancer cells but not in human dermal papilla and proximal renal cells. Flow cytometry indicated the augmentation of cisplatin-induced apoptosis in lung cancer cells pretreated with peptide from L. squarrosulus. Preculture with the peptide dramatically inhibited colony formation in lung cancer cells derived after cisplatin treatment. Strong suppression on integrin-mediated survival was evidenced with the diminution of integrins (β1, β3, β5, α5, αV) and down-stream signals (p-FAK/FAK, p-Src/Src, p-Akt/Akt) consequence with alteration of p53, Bax, Blc-2 and Mcl-1 in cisplatin-treated lung cancer cells preincubated with peptide from L. squarrosulus. These results support the development of L. squarrosulus peptide as a novel combined chemotherapy with cisplatin for lung cancer treatment.

In this study, we aimed to develop and qualify a PBPK model for scalp application using two drugs with marked differences in physicochemical properties and PK profiles. The parameters related to scalp physiology, drug PK, and formulations were incorporated into a Multi-Phase and Multi-Layer (MPML) Mechanistic Dermal Absorption (MechDermA) model within the Simcyp® Simulator V17. The finasteride PBPK model was linked to its effect on dihydrotestosterone (DHT) levels in plasma and scalp using an indirect response model. Predicted PK (and PD for finasteride) profiles and parameters were compared against the clinically reported data and justified by visual predictive checks and two-fold error criteria for model verification. The PBPK/PD model for finasteride reasonably demonstrated an ability to predict its respective PK and PD profiles, and parameters following scalp application under various clinical scenarios. Using the same scalp physiological input parameters, the minoxidil PBPK model was then developed and satisfactorily qualified with independent clinical datasets. Collectively, these results suggested that the established PBPK model may have broader utility for other topical formulations intended for scalp application.

Genomic DNA is the best "unique identifier" for organisms. Alignment-free phylogenomic analysis, simple, fast, and efficient method to compare genome sequences, relies on looking at the distribution of small DNA sequence of a particular length, referred to as k-mer. The k-mer approach has been explored as a basis for sequence analysis applications, including assembly, phylogenetic tree inference, and classification. Although this approach is not novel, selecting the appropriate k-mer length to obtain the optimal resolution is rather arbitrary. However, it is a very important parameter for achieving the appropriate resolution for genome/sequence distances to infer biologically meaningful phylogenetic relationships. Thus, there is a need for a systematic approach to identify the appropriate k-mer from whole-genome sequences. We present K-mer-length Iterative Selection for UNbiased Ecophylogenomics (KITSUNE), a tool for assessing the empirically optimal k-mer length of any given set of genomes of interest for phylogenomic analysis via a three-step approach based on (1) cumulative relative entropy (CRE), (2) average number of common features (ACF), and (3) observed common features (OCF). Using KITSUNE, we demonstrated the feasibility and reliability of these measurements to obtain empirically optimal k-mer lengths of 11, 17, and ∼34 from large genome datasets of viruses, bacteria, and fungi, respectively. Moreover, we demonstrated a feature of KITSUNE for accurate species identification for the two de novo assembled bacterial genomes derived from error-prone long-reads sequences, and for a published yeast genome. In addition, KITSUNE was used to identify the shortest species-specific k-mer accurately identifying viruses. KITSUNE is freely available at https://github.com/natapol/kitsune.

Metabolite profiles from biological samples suffer from both technical variations and subject-specific variants. To improve the quality of metabolomics data, conventional data processing methods can be employed to remove technical variations. These methods do not consider sources of subject variation as separate factors from biological factors of interest. This can be a significant issue when performing quantitative metabolomics in clinical trials or screening for a potential biomarker in early-stage disease, because changes in metabolism or a desired-metabolite signal are small compared to the total metabolite signals. As a result, inter-individual variability can interfere subsequent statistical analyses. Here, we propose an additional data processing step using linear mixed-effects modelling to readjust an individual metabolite signal prior to multivariate analyses. Published clinical metabolomics data was used to demonstrate and evaluate the proposed method. We observed a substantial reduction in variation of each metabolite signal after model fitting. A comparison with other strategies showed that our proposed method contributed to improved classification accuracy, precision, sensitivity and specificity. Moreover, we highlight the importance of patient metadata as it contains rich information of subject characteristics, which can be used to model and normalize metabolite abundances. The proposed method is available as an R package lmm2met., Graphical abstract Unlabelled Image

Conioscypha nakagirii, a novel fungus, is described and illustrated from decaying submerged wood collected in a small stream in Pak Chong District, Nakhon Ratchasima Province, Thailand. Morphologically, it differs from known Conioscypha species and similar taxa in that it has large, turbinate to pyriform, black and smooth-walled conidia. Phylogenetic analysis of three loci (nuc 18S and nuc 28S, and RPB2) supports a placement of this taxon as a new species in Conioscypha (Conioscyphales, Hypocreomycetidae, Sordariomycetes), as a sister group to a clade including C. lignicola, C. japonica, C. varia and C. peruensis, and shares some morphological characteristics with C. fabiformis and C. taiwaniana. Both morphological and molecular evidence justifies the introduction of the new species, C. nakagirii.

Background Malaria is a parasitic disease that produces significant infection in red blood cells. The objective of this study is to investigate the relationships between factors affecting the penetration of currently available anti-malarials into red blood cells. Methods Fifteen anti-malarial drugs listed in the third edition of the World Health Organization malaria treatment guidelines were enrolled in the study. Relationship analysis began with the prioritization of the physicochemical properties of the anti-malarials to create a multivariate linear regression model that correlates the red blood cell penetration. Results It was found that protein binding was significantly correlated with red blood cell penetration, with a negative coefficient. The next step was repeated analysis to find molecular descriptors that influence protein binding. The coefficients of the number of rotating bonds and the number of aliphatic hydrocarbons are negative, as opposed to the positive coefficients of the number of hydrogen bonds and the number of aromatic hydrocarbons. The p-value was less than 0.05. Conclusions Anti-malarials with a small number of hydrogen bonds and aromatic hydrocarbons, together with a high number of rotatable bonds and aliphatic hydrocarbons, may have a higher tendency to penetrate the red blood cells.

The major genetic determinants of cutaneous melanoma risk in the general population are disruptive variants (R alleles) in the melanocortin 1 receptor (MC1R) gene. These alleles are also linked to red hair, freckling, and sun sensitivity, all of which are known melanoma phenotypic risk factors. Here we report that in melanomas and for somatic C>T mutations, a signature linked to sun exposure, the expected single-nucleotide variant count associated with the presence of an R allele is estimated to be 42% (95% CI, 15–76%) higher than that among persons without an R allele. This figure is comparable to the expected mutational burden associated with an additional 21 years of age. We also find significant and similar enrichment of non-C>T mutation classes supporting a role for additional mutagenic processes in melanoma development in individuals carrying R alleles., Deleterious germline variants in the MC1R gene are associated with red hair and freckles, and with an increased risk of developing melanoma. Here, the authors investigate melanoma samples from patients with and without these variants and find that their presence is predictive of a higher overall mutation prevalence.

Immunotherapy is an emerging approach in cancer treatment that activates the host immune system to destroy cancer cells that express unique peptide signatures (neoepitopes). Administrations of cancer-specific neoepitopes in the form of synthetic peptide vaccine have been proven effective in both mouse models and human patients. Because only a tiny fraction of cancer-specific neoepitopes actually elicit immune response, selection of potent, immunogenic neoepitopes remains a challenging step in cancer vaccine development. A basic approach for immunogenicity prediction is based on the premise that effective neoepitope should bind with the Major Histocompatibility Complex (MHC) with high affinity. In this study, we developed MHCSeqNet, an open-source deep learning model, which not only competes favorably with state-of-the-art predictors but also exhibits promising generalization to new MHC class I and class II alleles. MHCSeqNet employed neural network architectures developed for natural language processing to model amino acid sequence representations of MHC allele and epitope peptide as sentences with amino acids as individual words. This consideration allows MHCSeqNet to accept new MHC alleles as well as peptides of any length. The flexibility offered by MHCSeqNet should make it a valuable tool for screening effective neoepitopes in cancer vaccine development.Availability and implementationhttps://github.com/cmbcu/MHCSeqNet

We performed exome sequencing of 77 melanocytic specimens composed of Spitz nevi (n=29), Spitzoid melanomas (n=27), and benign melanocytic nevi (n=21), and compared the results with published melanoma sequencing data. Our study highlights the prominent similarity between Spitzoid and conventional melanomas with similar copy number changes and high and equal numbers of ultraviolet-induced coding mutations affecting similar driver genes. Mutations in MEN1, PRKAR1A, and DNMT3A in Spitzoid melanomas may indicate involvement of the protein kinase A pathway, or a role of DNA methylation in the disease. Other than activating HRAS variants, there were few additional mutations in Spitz nevi, and few copy number changes other than 11p amplification and chromosome 9 deletions. Similarly, there were no large-scale copy number alterations and few somatic alterations other than activating BRAF or NRAS mutations in conventional nevi. A presumed melanoma driver mutation (IDH1Arg132Cys) was revealed in one of the benign nevi. In conclusion, our exome data show significantly lower somatic mutation burden in both Spitz and conventional nevi compared with their malignant counterparts, and high genetic similarity between Spitzoid and conventional melanoma.

RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach. High reproducibility among biological replicates (correlation >= 0.99) and high consistency between the two platforms for analysis of gene expression levels (correlation >= 0.91) are reported. The results from differential gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays) for gene expression analysis and addresses the contribution of the different steps involved in the analysis of RNA-seq data.

Summary: In this article, we introduce a robust and efficient strategy for deriving global and allele-specific copy number alternations (CNA) from cancer whole exome sequencing data based on Log R ratios and B-allele frequencies. Applying the approach to the analysis of over 200 skin cancer samples, we demonstrate its utility for discovering distinct CNA events and for deriving ancillary information such as tumor purity. Availability and implementation: https://github.com/xfwang/CLOSE Contact: xuefeng.wang@stonybrook.edu or michael.krauthammer@yale.edu Supplementary information: Supplementary data are available at Bioinformatics online.

3 Background: Expression of inhibitory immune checkpoints (ICPs) within tumors has emerged as an important barrier for effective anti-tumor immunity. Antibody-mediated blockade of ICPs can lead to durable responses in patients. Interestingly, only a small subset of tumor infiltrating lymphocytes (TILs) express these checkpoints and there is a need to better understand the characteristics of this subset. We undertook this study to understand characteristics of TILs within melanoma. Methods: We used single cell mass cytometry, gene expression profiling of purified T cell subsets, T cell receptor (TCR) sequencing as well as functional studies to understand the characteristics of TILs in melanoma patients (n=50). We also performed exome sequencing of tumor cells in some patients. Results: We find that TILs are functionally and phenotypically distinct from circulating T cells. They express higher levels of inhibitory ICPs (PD-1, TIM-3) and secrete less IL2, IFNg and TNFa than T cells in circulation. Expression of vascular endothelial growth factor within tumors correlated with reduced T cell infiltration. Expression of ICPs (PD-1, TIM-3, PD-L1) were enriched in T cells with a phenotype and expression profile of tissue resident memory T (TRM) cells with most cells expressing multiple checkpoints. Within the myeloid compartment, ICPs were predominantly expressed on CD14+CD16+ subset. TCR sequencing revealed that individual melanoma metastases revealed that the top clones within each of the lesions have distinct TCRs. Concurrent TCR and tumor exome sequencing of individual metastases in the same patient revealed that inter-lesional diversity of TCRs exceeded differences in mutation/neoantigen load in tumor cells. Conclusions: Our findings suggest that TRM cells and CD16+ myeloid cells may be the major target of ICP blockade within tumors. The ability to activate, and retain TRM cells may be an important determinant of the T cell content of the tumor microenvironment and should be a goal for future vaccines. Importantly, our study illustrates inter-lesional diversity of TCRs within individual metastases which may differentially impact the outcome of immune therapy at each site.