Androgen Deprivation Therapy-Induced Muscle Loss and Fat Gain Predict Cardiovascular Events in Prostate Cancer Patients.

Background: Androgen deprivation therapy (ADT) increases the risk of adverse cardiovascular events in patients with prostate cancer. ADT can induce body composition change; however, the association between body composition change and cardiovascular outcomes remains unclear. This study aimed to determine the association between ADT-induced body composition change and cardiovascular outcomes in patients with prostate cancer.

Enhanced personalized prediction of baseball-related upper extremity injuries through novel features and explainable artificial intelligence.

Upper extremity injuries in baseball players demand advanced prevention. Our study analyzed clinical features using machine learning techniques to provide precise and individualized injury risk assessment and prediction. We recruited 98 baseball players and collected data on glenohumeral internal/external rotation, posterior capsule thickness, supraspinatus tendon thickness, acromiohumeral distance, and occupation ratio.

Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.

Objectives: Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial intelligence to identify the threshold of muscle loss associated with survival in OCSCC.

Thresholds of Body Composition Changes Associated with Survival During Androgen Deprivation Therapy in Prostate Cancer.

Background And Objective: Androgen deprivation therapy (ADT) is associated with reduced muscle and increased fat mass in patients with prostate cancer. However, the threshold for body composition changes associated with survival during ADT remains unclear. This study aimed to identify body composition change thresholds for all-cause mortality during ADT.

SheddomeDB 2023: A Revision of an Ectodomain Shedding Database Based on a Comprehensive Literature Review and Online Resources.

Ectodomain shedding of membrane proteins is a proteolytic event involved in several biological phenomena, including inflammation, development, diseases, and cancer progression. Though ectodomain shedding is a post-translational modification that plays an important role in cellular regulation, this biological phenomenon is seriously underannotated in public protein databases. Given the importance of the shedding events, we conducted a comprehensive literature review for membrane protein shedding and constructed the database, SheddomeDB in 2017.

Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer.

Background: Skeletal muscle loss during treatment is associated with poor survival outcomes in patients with ovarian cancer. Although changes in muscle mass can be assessed on computed tomography (CT) scans, this labour-intensive process can impair its utility in clinical practice. This study aimed to develop a machine learning (ML) model to predict muscle loss based on clinical data and to interpret the ML model by applying SHapley Additive exPlanations (SHAP) method.

Association of prognostic nutritional index with muscle loss and survival in patients with ovarian cancer treated with primary debulking surgery and chemotherapy.

Purpose: Sarcopenia is prevalent in ovarian cancer and contributes to poor survival. This study is aimed at investigating the association of prognostic nutritional index (PNI) with muscle loss and survival outcomes in patients with ovarian cancer.

Methods: This retrospective study analyzed 650 patients with ovarian cancer treated with primary debulking surgery and adjuvant platinum-based chemotherapy at a tertiary center from 2010 to 2019.

Putative markers for the detection of early-stage bladder cancer selected by urine metabolomics.

Background: Early detection of bladder cancer remains challenging because patients with early-stage bladder cancer usually have no incentive to take cytology or cystoscopy tests if they are asymptomatic. Our goal is to find non-invasive marker candidates that may help us gain insight into the metabolism of early-stage bladder cancer and be examined in routine health checks.

Results: We acquired urine samples from 124 patients diagnosed with early-stage bladder cancer or hernia (63 cancer patients and 61 controls).

Morpho-physiological properties and connectivity of vasoactive intestinal polypeptide-expressing interneurons in the mouse hippocampal dentate gyrus.

The hippocampus is a key brain structure for cognitive and emotional functions. Among the hippocampal subregions, the dentate gyrus (DG) is the first station that receives multimodal sensory information from the cortex. Local-circuit inhibitory GABAergic interneurons (INs) regulate the excitation-inhibition balance in the DG principal neurons (PNs) and therefore are critical for information processing.

Association between real-world home blood pressure measurement patterns and blood pressure variability among older individuals with hypertension: A community-based blood pressure variability study.

Home blood pressure (BP) monitoring is a useful tool for hypertension management. BP variability (BPV) has been associated with an increased risk of cardiovascular events. However, little is known about the correlation between BPV and different measurement patterns of long-term home BP monitoring.

Stout camphor tree genome fills gaps in understanding of flowering plant genome evolution.

We present reference-quality genome assembly and annotation for the stout camphor tree (Cinnamomum kanehirae (Laurales, Lauraceae)), the first sequenced member of the Magnoliidae comprising four orders (Laurales, Magnoliales, Canellales and Piperales) and over 9,000 species. Phylogenomic analysis of 13 representative seed plant genomes indicates that magnoliid and eudicot lineages share more recent common ancestry than monocots. Two whole-genome duplication events were inferred within the magnoliid lineage: one before divergence of Laurales and Magnoliales and the other within the Lauraceae.

Combining membrane proteomics and computational three-way pathway analysis revealed signalling pathways preferentially regulated in human iPSCs and human ESCs.

Owing to the clinical potential of human induced pluripotent stem cells (hiPSCs) in regenerative medicine, a thorough examination of the similarities and differences between hiPSCs and human embryonic stem cells (hESCs) has become indispensable. Moreover, as the important roles of membrane proteins in biological signalling, functional analyses of membrane proteome are therefore promising. In this study, a pathway analysis by the bioinformatics tool GSEA was first performed to identify significant pathways associated with the three comparative membrane proteomics experiments: hiPSCs versus precursor human foreskin fibroblasts (HFF), hESCs versus precursor HFF, and hiPSCs versus hESCs.

A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.

Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice of using a competitive null as common approach; which fundamentally implies genes or proteins as independent units.

Exploring the expression bar code of SAA variants for gastric cancer detection.

We reported an integrated platform to explore serum protein variant pattern in cancer and its utility as a new class of biomarker panel for diagnosis. On the model study of serum amyloid A (SAA), we employed nanoprobe-based affinity mass spectrometry for enrichment, identification and quantitation of SAA variants from serum of 105 gastric cancer patients in comparison with 54 gastritis patients, 54 controls, and 120 patients from other cancer. The result revealed surprisingly heterogeneous and most comprehensive SAA bar code to date, which comprises 24 SAA variants including SAA1- and SAA2-encoded products, polymorphic isoforms, N-terminal-truncated forms, and three novel SAA oxidized isotypes, in which the variant-specific peptide sequence were also confirmed by LC-MS/MS.

Metabolite marker discovery for the detection of bladder cancer by comparative metabolomics.

Bladder cancer is one of the most common urinary tract carcinomas in the world. Urine metabolomics is a promising approach for bladder cancer detection and marker discovery since urine is in direct contact with bladder epithelia cells; metabolites released from bladder cancer cells may be enriched in urine samples. In this study, we applied ultra-performance liquid chromatography time-of-flight mass spectrometry to profile metabolite profiles of 87 samples from bladder cancer patients and 65 samples from hernia patients.

SheddomeDB: the ectodomain shedding database for membrane-bound shed markers.

Background: A number of membrane-anchored proteins are known to be released from cell surface via ectodomain shedding. The cleavage and release of membrane proteins has been shown to modulate various cellular processes and disease pathologies. Numerous studies revealed that cell membrane molecules of diverse functional groups are subjected to proteolytic cleavage, and the released soluble form of proteins may modulate various signaling processes.

Wiring Specificity and Synaptic Diversity in the Mouse Lateral Central Amygdala.

Unlabelled: The central amygdala (CeA) nucleus, a subcortical structure composed of mostly GABA-releasing (GABAergic) neurons, controls fear expression via projections to downstream targets in the hypothalamus and brainstem. The CeA consists of the lateral (CeL) and medial (CeM) subdivisions. The CeL strongly gates information transfer to the CeM, the main output station of the amygdala, but little is known about the functional organization of local circuits in this region.

Prioritization of cancer marker candidates based on the immunohistochemistry staining images deposited in the human protein atlas.

Cancer marker discovery is an emerging topic in high-throughput quantitative proteomics. However, the omics technology usually generates a long list of marker candidates that requires a labor-intensive filtering process in order to screen for potentially useful markers. Specifically, various parameters, such as the level of overexpression of the marker in the cancer type of interest, which is related to sensitivity, and the specificity of the marker among cancer groups, are the most critical considerations.

SecretePipe: a screening pipeline for secreted proteins with competence to identify potential membrane-bound shed markers.

The identification of secreted protein markers has been receiving great attention as part of the trend toward noninvasive biomarker discovery. In addition, certain cell membrane proteins are known to be released into the extracellular milieu via ectodomain shedding. As membrane proteins play an essential role in signaling pathways and because most of the cancer biomarkers approved by the FDA today are membrane shed proteins, a tool that can correctly predict these class shed proteins is valuable.

A linear-time algorithm for reconstructing zero-recombinant haplotype configuration on a pedigree.

Background: When studying genetic diseases in which genetic variations are passed on to offspring, the ability to distinguish between paternal and maternal alleles is essential. Determining haplotypes from genotype data is called haplotype inference. Most existing computational algorithms for haplotype inference have been designed to use genotype data collected from individuals in the form of a pedigree.

Multi-Q: a fully automated tool for multiplexed protein quantitation.

The iTRAQ labeling method combined with shotgun proteomic techniques represents a new dimension in multiplexed quantitation for relative protein expression measurement in different cell states. To expedite the analysis of vast amounts of spectral data, we present a fully automated software package, called Multi-Q, for multiplexed iTRAQ-based quantitation in protein profiling. Multi-Q is designed as a generic platform that can accommodate various input data formats from search engines and mass spectrometer manufacturers.

RIBRA--an error-tolerant algorithm for the NMR backbone assignment problem.

We develop an iterative relaxation algorithm called RIBRA for NMR protein backbone assignment. RIBRA applies nearest neighbor and weighted maximum independent set algorithms to solve the problem. To deal with noisy NMR spectral data, RIBRA is executed in an iterative fashion based on the quality of spectral peaks.

GANA--a genetic algorithm for NMR backbone resonance assignment.

NMR data from different experiments often contain errors; thus, automated backbone resonance assignment is a very challenging issue. In this paper, we present a method called GANA that uses a genetic algorithm to automatically perform backbone resonance assignment with a high degree of precision and recall. Precision is the number of correctly assigned residues divided by the number of assigned residues, and recall is the number of correctly assigned residues divided by the number of residues with known human curated answers.