Relationship between Permanent Catheter Patency and Nutrient Score in Patients Aged >75 Years Requiring Renal Replacement Therapy.

Background: Malnutrition is common in patients undergoing hemodialysis and is a powerful predictor of morbidity and mortality. This study aimed to investigate the effect of nutritional status on permanent catheter patency in elderly patients aged >75 years of age undergoing dialysis using tunneled dialysis catheters; Methods: Records of 383 patients whose nutritional factors and body cell mass (BCM) were measured simultaneously at the start of dialysis between 14 January 2020 and 30 September 2023, at Chungnam National University Hospital, were retrospectively reviewed. The relationships between permanent catheter patency at 180 days and BCM parameters and clinical parameters were studied using Kaplan-Meier survival curves and multivariate Cox proportional hazards analysis. Results: Age and sexual differences were significant (p ≤ 0.05), and most of the BCM parameters and BCM were not significant (p ≤ 0.05), except for intracellular water. Permanent catheter patency was superior at low controlling nutritional status (CONUT) scores (p < 0.05). After adjustment for covariates, the CONUT score remained an independent factor associated with permanent catheter-patency survival; Conclusions: CONUT scores measured before the start of dialysis are expected to play an important role in predicting the prognosis of permanent catheter-patency survival in patients aged >75 years.

Differential Gene Regulatory Network Analysis between Azacitidine-Sensitive and -Resistant Cell Lines.

Azacitidine, a DNA methylation inhibitor, is employed for the treatment of acute myeloid leukemia (AML). However, drug resistance remains a major challenge for effective azacitidine chemotherapy, though several studies have attempted to uncover the mechanisms of azacitidine resistance. With the aim to identify the mechanisms underlying acquired azacitidine resistance in cancer cell lines, we developed a computational strategy that can identify differentially regulated gene networks between drug-sensitive and -resistant cell lines by extending the existing method, differentially coexpressed gene sets (DiffCoEx). The technique specifically focuses on cell line-specific gene network analysis. We applied our method to gene networks specific to azacitidine sensitivity and identified differentially regulated gene networks between azacitidine-sensitive and -resistant cell lines. The molecular interplay between the metallothionein gene family, C19orf33, ELF3, GRB7, IL18, NRN1, and RBM47 were identified as differentially regulated gene network in drug resistant cell lines. The biological mechanisms associated with azacitidine and AML for the markers in the identified networks were verified through the literature. Our results suggest that controlling the identified genes (e.g., the metallothionein gene family) and "cellular response"-related pathways ("cellular response to zinc ion", "cellular response to copper ion", and "cellular response to cadmium ion", where the enriched functional-related genes are MT2A, MT1F, MT1G, and MT1E) may provide crucial clues to address azacitidine resistance in patients with AML. We expect that our strategy will be a useful tool to uncover patient-specific molecular interplay that provides crucial clues for precision medicine in not only gastric cancer but also complex diseases.

Unveiling Gene Regulatory Networks That Characterize Difference of Molecular Interplays Between Gastric Cancer Drug Sensitive and Resistance Cell Lines.

Gastric cancer is a leading cause of cancer-related deaths globally and chemotherapy is widely accepted as the standard treatment for gastric cancer. However, drug resistance in cancer cells poses a significant obstacle to the success of chemotherapy, limiting its effectiveness in treating gastric cancer. Although many studies have been conducted to unravel the mechanisms of acquired drug resistance, the existing studies were based on abnormalities of a single gene, that is, differential gene expression (DGE) analysis. Single gene-based analysis alone is insufficient to comprehensively understand the mechanisms of drug resistance in cancer cells, because the underlying processes of the mechanism involve perturbations of the molecular interactions. To uncover the mechanism of acquired gastric cancer drug resistance, we perform for identification of differentially regulated gene networks between drug-sensitive and drug-resistant cell lines. We develop a computational strategy for identifying phenotype-specific gene networks by extending the existing method, CIdrgn, that quantifies the dissimilarity of gene networks based on comprehensive information of network structure, that is, regulatory effect between genes, structure of edge, and expression levels of genes. To enhance the efficiency of identifying differentially regulated gene networks and improve the biological relevance of our findings, we integrate additional information and incorporate knowledge of network biology, such as hubness of genes and weighted adjacency matrices. The outstanding capabilities of the developed strategy are validated through Monte Carlo simulations. By using our strategy, we uncover gene regulatory networks that specifically capture the molecular interplays distinguishing drug-sensitive and drug-resistant profiles in gastric cancer. The reliability and significance of the identified drug-sensitive and resistance-specific gene networks, as well as their related markers, are verified through literature. Our analysis for differentially regulated gene network identification has the capacity to characterize the drug-sensitive and resistance-specific molecular interplays related to mechanisms of acquired drug resistance that cannot be revealed by analysis based solely on abnormalities of a single gene, for example, DGE analysis. Through our analysis and comprehensive examination of relevant literature, we suggest that targeting the suppressors of the identified drug-resistant markers, such as the Melanoma Antigen (MAGE) family, Trefoil Factor (TFF) family, and Ras-Associated Binding 25 (RAB25), while enhancing the expression of inducers of the drug sensitivity markers [e.g., Serum Amyloid A (SAA) family], could potentially reduce drug resistance and enhance the effectiveness of chemotherapy for gastric cancer. We expect that the developed strategy will serve as a useful tool for uncovering cancer-related phenotype-specific gene regulatory networks that provide essential clues for uncovering not only drug resistance mechanisms but also complex biological systems of cancer.

RNA-binding motif protein 10 inactivates c-Myc by partnering with ribosomal proteins uL18 and uL5.

RNA-binding motif protein 10 (RBM10) is a frequently mutated tumor suppressor in lung adenocarcinoma (LUAD). Yet, it remains unknown whether cancer-derived mutant RBM10 compromises its tumor suppression function and, if so, the molecular insight of the underlying mechanisms. Here, we show that wild-type RBM10 suppresses lung cancer cell growth and proliferation by inactivating c-Myc that is essential for cancer cell survival. RBM10 directly binds to c-Myc and promotes c-Myc's ubiquitin-dependent degradation, while RBM10 knockdown leads to the induction of c-Myc level and activity. This negative action on c-Myc is further boosted by ribosomal proteins (RPs) uL18 (RPL5) and uL5 (RPL11) via their direct binding to RBM10. Cancer-derived mutant RBM10-I316F fails to bind to uL18 and uL5 and to inactivate c-Myc, thus incapable of suppressing tumorigenesis. Our findings uncover RBM10 as a pivotal c-Myc repressor by cooperating with uL18 and uL5 in lung cancer cells, as its failure to do so upon mutation favors tumorigenesis.

Comprehensive Physical Work Capacity Evaluations for Korean Farmers Assessed in Healthy Volunteers.

OBJECTIVE: To establish the lower limits of normative values of the physical work capacity for Korean farmers in healthy working individual. METHODS: We developed a comprehensive set of physical work capacity evaluation items that encompass common farming tasks. These items include measurements of trunk flexion/extension angles, strength (hand grip, trunk flexion/extension, leg/back lifting, and pushing/pulling), and positional tolerances. We calculated the normative values for the items and defined the normal range in 124 healthy volunteers aged 20-79 years. We calculated the intraclass correlation coefficient (ICC) to validate the test-retest reliability of the measurements protocol. RESULTS: The normal values for each measurement item were as follows: trunk flexion and extension angle (65.3°±11.6° and 29.6°±6.6°), dominant hand grip strength (32.2±10.5 kgf), trunk flexion and extension strength (288.4±119.0 N and 297.3±129.9 N), leg and back lifting strength (452.9±233.5 N and 349.2±166.7 N), pushing and pulling strength (214.7±75.1 N and 221.7±63.3 N), and positional tolerance time (squat: 76.8±9.0 seconds, front: 73.8±7.7 seconds, twist: 82.2±8.8 seconds, upward: 71.9±11.3 seconds). Regarding test-retest reliability, all strength measurements demonstrated excellent absolute agreement (ICC, 0.91-0.96). However, positional tolerance showed poor-to-moderate absolute agreement (ICC, 0.37-0.58). CONCLUSION: We conducted measurements of muscle strength and positional tolerance in healthy participants of various ages, focusing on tasks commonly performed by Korean farmers. The outcomes hold significant value as they offer a pertinent instrument for assessing the appropriateness of workers, thereby carrying implications for rehabilitation objectives, legal evaluations, and work capacity assessments within the agricultural domain.

Back Extensor Strength as a Potential Marker of Frailty Using Propensity Score Matching and Machine Learning.

This study assessed the potential of back extensor strength as an alternative marker of frailty. A total of 560 farmers were included. Computed tomography scans measured fat and muscle mass volumes at the mid-L4 vertebral level. Back extensor strength was measured in a seated posture. Multivariate linear regression was used to analyze the associations between back extensor strength and trunk muscle/fat compositions. The participants were divided into two groups based on back extensor strength. Propensity score matching, multivariate logistic regression, and Extreme Gradient Boosting (XGBoost) were employed to evaluate the relationship between Fried's frailty criteria and back extensor strength. Back extensor strength exhibited positive associations with abdominal muscle volume (r = 1.12) as well as back muscle volume (r = 0.89) (p < 0.05). Back extensor strength was linked to more frail status, such as reduced grip strength, walking speed, and frequent self-reported exhaustion. Multivariate logistic regression indicated that back extensor strength was associated with higher frail status (OR = 0.990), and XGBoost analysis identified back extensor strength as the most important predictor (gain = 0.502) for frailty. The prediction models using grip strength produced similar results (OR = 0.869, gain = 0.482). These findings suggested the potential of back extensor strength as an alternative frailty marker.

Unraveling the Molecular Puzzle: Exploring Gene Networks across Diverse EMT Status of Cell Lines.

Understanding complex disease mechanisms requires a comprehensive understanding of the gene regulatory networks, as complex diseases are often characterized by the dysregulation and dysfunction of molecular networks, rather than abnormalities in single genes. Specifically, the exploration of cell line-specific gene networks can provide essential clues for precision medicine, as this methodology can uncover molecular interplays specific to particular cell line statuses, such as drug sensitivity, cancer progression, etc. In this article, we provide a comprehensive review of computational strategies for cell line-specific gene network analysis: (1) cell line-specific gene regulatory network estimation and analysis of gene networks under varying epithelial-mesenchymal transition (EMT) statuses of cell lines; and (2) an explainable artificial intelligence approach for interpreting the estimated massive multiple EMT-status-specific gene networks. The objective of this review is to help readers grasp the concept of computational network biology, which holds significant implications for precision medicine by offering crucial clues.

Ratio of Extracellular to Intracellular Water Is Associated with Permanent Catheter Patency Survival in Patients Receiving Maintenance Hemodialysis.

Patients undergoing dialysis through a permanent catheter often experience infection or malfunction. However, few studies have clarified the predictors of permanent catheter patency survival in patients undergoing hemodialysis. We assessed the relationship between the parameters of body composition monitoring (BCM), determined before the initiation of dialysis, and the patency survival of the permanent catheters inserted in 179 patients who commenced hemodialysis between 14 January 2020 and 31 August 2021. The relationships between permanent catheter patency at 6 weeks and BCM parameters, laboratory tests, age, sex, comorbidities, and medications at baseline were studied using Kaplan-Meier survival curves. Permanent catheter patency was observed to be superior at high extracellular-to-intracellular (ECW/ICW) ratio (p < 0.005). After adjustment for covariates, the ECW/ICW ratio remained an independent factor associated with permanent catheter patency survival. When patients with non-patent catheters were subdivided into infection and malfunction groups, and the associations of BCM parameters were evaluated in those groups, the ECW/ICW ratio was not significantly associated with permanent catheter patency survival in the infection group (p = 0.327); instead, a significant association was found for the lean tissue index (p < 0.001). In the malfunction group, the ECW/ICW ratio remained significantly associated with permanent catheter patency survival (p < 0.001).

Comprehensive information-based differential gene regulatory networks analysis (CIdrgn): Application to gastric cancer and chemotherapy-responsive gene network identification.

Biological condition-responsive gene network analysis has attracted considerable research attention because of its ability to identify pathways or gene modules involved in the underlying mechanisms of diseases. Although many condition-specific gene network identification methods have been developed, they are based on partial or incomplete gene regulatory network information, with most studies only considering the differential expression levels or correlations among genes. However, a single gene-based analysis cannot effectively identify the molecular interactions involved in the mechanisms underlying diseases, which reflect perturbations in specific molecular network functions rather than disorders of a single gene. To comprehensively identify differentially regulated gene networks, we propose a novel computational strategy called comprehensive analysis of differential gene regulatory networks (CIdrgn). Our strategy incorporates comprehensive information on the networks between genes, including the expression levels, edge structures and regulatory effects, to measure the dissimilarity among networks. We extended the proposed CIdrgn to cell line characteristic-specific gene network analysis. Monte Carlo simulations showed the effectiveness of CIdrgn for identifying differentially regulated gene networks with different network structures and scales. Moreover, condition-responsive network identification in cell line characteristic-specific gene network analyses was verified. We applied CIdrgn to identify gastric cancer and itsf chemotherapy (capecitabine and oxaliplatin) -responsive network based on the Cancer Dependency Map. The CXC family of chemokines and cadherin gene family networks were identified as gastric cancer-specific gene regulatory networks, which was verified through a literature survey. The networks of the olfactory receptor family with the ASCL1/FOS family were identified as capecitabine- and oxaliplatin sensitive -specific gene networks. We expect that the proposed CIdrgn method will be a useful tool for identifying crucial molecular interactions involved in the specific biological conditions of cancer cell lines, such as the cancer stage or acquired anticancer drug resistance.

Tumor Microenvironment-Responsive 6-Mercaptopurine-Releasing Injectable Hydrogel for Colon Cancer Treatment.

Colon cancer is a significant health concern. The development of effective drug delivery systems is critical for improving treatment outcomes. In this study, we developed a drug delivery system for colon cancer treatment by embedding 6-mercaptopurine (6-MP), an anticancer drug, in a thiolated gelatin/polyethylene glycol diacrylate hydrogel (6MP-GPGel). The 6MP-GPGel continuously released 6-MP, the anticancer drug. The release rate of 6-MP was further accelerated in an acidic or glutathione environment that mimicked a tumor microenvironment. In addition, when pure 6-MP was used for treatment, cancer cells proliferated again from day 5, whereas a continuous supply of 6-MP from the 6MP-GPGel continuously suppressed the survival rate of cancer cells. In conclusion, our study demonstrates that embedding 6-MP in a hydrogel formulation can improve the efficacy of colon cancer treatment and may serve as a promising minimally invasive and localized drug delivery system for future development.

Association Between Ergonomic Burden Assessed Using 20-Item Agricultural Work-Related Ergonomic Risk Questionnaire and Shoulder, Low Back, and Leg Pain in Korean Farmers.

OBJECTIVES: This study aimed to determine the agricultural ergonomic burden in Korean farmers and to analyze its correlation with musculoskeletal pain. METHODS: In total, 1001 farmers (525 females and 476 males; mean age, 59.6±7.5years) who owned or rented a farm and belonged to an agricultural cooperative unit were recruited. Ergonomic burdens were assessed using a 20-item Agricultural Work-related Ergonomic Risk Questionnaire (20 agricultural works). The presence of musculoskeletal pain (shoulder, low back, and leg/foot), Farm Stressor Inventory, subjective stress index, and agricultural workload (low, moderate, somewhat hard, or hard) were collected using structured questionnaires. RESULTS: Factor analysis of the Agricultural Work-related Ergonomic Risk Questionnaire revealed a four-factor solution: neck and upper limb, trunk and push - pull, machine and heavy lifting, and repetitive trauma. Cronbach's alpha was greater than 0.65. For 18 of the 20 items, there was a significant association with the Farm Stressor Inventory, subjective stress index, and agricultural workload. The most frequent ergonomic burdens were squatting (51.2%), highly repetitive wrist movements (53.5%), shoulder flexion at 45-90° (51.2%), and trunk flexion or twisting at≥45° (48.8%). Ergonomic burdens were significantly different in 13 items between sexes. The musculoskeletal pain was associated with increased agricultural burdens in 10 items in male farmers and 14 items in female farmers. CONCLUSION: Increased agricultural ergonomic burdens were associated with musculoskeletal pain. Ergonomic burden showed different patterns between male and female farmers, with female farmers appearing to be more affected by ergonomic burden than male farmers.

Gene Regulatory Network-Classifier: Gene Regulatory Network-Based Classifier and Its Applications to Gastric Cancer Drug (5-Fluorouracil) Marker Identification.

The complex mechanisms of diseases involve the disturbance of the molecular network, rather than disorder in a single gene, implying that single gene-based analysis is insufficient to understand these mechanisms. Gene regulatory networks (GRNs) have attracted a lot of interest and various approaches have been developed for their statistical inference and gene network-based analysis. Although various computational methods have been developed, relatively little attention has been paid to incorporation of biological knowledge into the computational approaches. Furthermore, existing studies on network-based analysis perform prediction/classification of status of cell lines based on preconstructed GRNs, implying that we cannot extract prediction/classification-specific gene networks, leading to difficulty in interpretation of biological mechanisms and marker identification related to the status of cancer cell lines. We developed a novel strategy to build a GRN-based classifier, called a GRN-classifier. The proposed GRN-classifier estimates GRNs and classifies cell lines simultaneously, where the gene network is estimated to minimize error in gene network estimation and the negative log-likelihood for classifying cell lines. Thus, we can identify biological status-specific gene regulatory systems, enabling us to achieve biologically reliable interpretation of the classification. We also propose an algorithm to implement the GRN-classifier based on coordinate descent update. Monte Carlo simulations were conducted to examine performance of the GRN-classifier. Results: Our strategy provides effective results in feature selection in the classification model and edge selection in gene network estimation. The GRN-classifier also shows outstanding classification accuracy. We apply the GRN-classifier to classify cancer cell lines into anticancer drug-related status, that is, 5-fluorouracil (5-FU)-sensitive/resistant and 5-FU target/nontarget cancer cell lines. We then identified 5-FU markers based on 5-FU-related status classification-specific gene networks. The mechanisms of the identified markers were verified through literature survey. Our results suggest that the molecular interplay between MYOF and AHNAK2 may play a crucial role in drug resistance and can provide information on the chemotherapy efficiency of 5-FU. It is also suggested that suppression of the identified 5-FU markers, including MYOF/AHNAK2 and AKR1C1/AKR1C3 may improve 5-FU resistance of cancer cell lines.

Hitting them where it hurts: Pantothenate kinase targeting in pathogenic fungi.

In this issue of Structure, Gihaz and colleagues develop antifungal compounds targeting fungal pantothenate kinase (PanK). Through utilization of high-throughput chemical screening, along with biochemical and functional analyses and newly solved fungal PanK crystal structures, high-affinity inhibitors are produced, and invaluable insights into the mechanisms of fungal PanK are gained.

Carbon-based nanostructures for cancer therapy and drug delivery applications.

Synthesis, design, characterization, and application of carbon-based nanostructures (CBNSs) as drug carriers have attracted a great deal of interest over the past half of the century because of their promising chemical, thermal, physical, optical, mechanical, and electrical properties and their structural diversity. CBNSs are well-known in drug delivery applications due to their unique features such as easy cellular uptake, high drug loading ability, and thermal ablation. CBNSs, including carbon nanotubes, fullerenes, nanodiamond, graphene, and carbon quantum dots have been quite broadly examined for drug delivery systems. This review not only summarizes the most recent studies on developing carbon-based nanostructures for drug delivery (e.g. delivery carrier, cancer therapy and bioimaging), but also tries to deal with the challenges and opportunities resulting from the expansion in use of these materials in the realm of drug delivery. This class of nanomaterials requires advanced techniques for synthesis and surface modifications, yet a lot of critical questions such as their toxicity, biodistribution, pharmacokinetics, and fate of CBNSs in biological systems must be answered.

Computational Tactics for Precision Cancer Network Biology.

Network biology has garnered tremendous attention in understanding complex systems of cancer, because the mechanisms underlying cancer involve the perturbations in the specific function of molecular networks, rather than a disorder of a single gene. In this article, we review the various computational tactics for gene regulatory network analysis, focused especially on personalized anti-cancer therapy. This paper covers three major topics: (1) cell line's (or patient's) cancer characteristics specific gene regulatory network estimation, which enables us to reveal molecular interplays under varying conditions of cancer characteristics of cell lines (or patient); (2) computational approaches to interpret the multitudinous and massive networks; (3) network-based application to uncover molecular mechanisms of cancer and related marker identification. We expect that this review will help readers understand personalized computational network biology that plays a significant role in precision cancer medicine.

A non-traditional crystal-based compound screening method targeting the ATP binding site of Plasmodium falciparum GRP78 for identification of novel nucleoside analogues.

Drug resistance to front-line malarial treatments represents an ongoing threat to control malaria, a vector borne infectious disease. The malarial parasite, Plasmodium falciparum has developed genetic variants, conferring resistance to the current standard therapeutic artemisinin and its derivatives commonly referred to as artemisinin-combination therapies (ACTs). Emergence of multi-drug resistance parasite genotypes is a warning of potential treatment failure, reaffirming the urgent and critical need to find and validate alternate drug targets to prevent the spread of disease. An attractive and novel drug target includes glucose-regulated protein 78 kDa (GRP78, or BiP), an essential molecular chaperone protein involved in the unfolded protein response that is upregulated in ACT treated P. falciparum parasites. We have shown that both sequence and structure are closely related to human GRP78 (hGRP78), a chaperone belonging to the HSP70 class of ATPase proteins, which is often upregulated in cellular stress responses and cancer. By screening a library of nucleoside analogues, we identified eight 'hit' compounds binding at the active site of the ATP binding domain of P. falciparum GRP78 using a high-throughput ligand soaking screen using x-ray crystallography. These compounds were further evaluated using protein thermal shift assays to assess target binding activity. The nucleoside analogues identified from our screen provide a starting point for the development of more potent and selective antimalarial inhibitors. In addition, we have established a well-defined, high-throughput crystal-based screening approach that can be applied to many crystallizable P. falciparum proteins for generating anti-Plasmodium specific compounds.

PredictiveNetwork: predictive gene network estimation with application to gastric cancer drug response-predictive network analysis.

BACKGROUND: Gene regulatory networks have garnered a large amount of attention to understand disease mechanisms caused by complex molecular network interactions. These networks have been applied to predict specific clinical characteristics, e.g., cancer, pathogenicity, and anti-cancer drug sensitivity. However, in most previous studies using network-based prediction, the gene networks were estimated first, and predicted clinical characteristics based on pre-estimated networks. Thus, the estimated networks cannot describe clinical characteristic-specific gene regulatory systems. Furthermore, existing computational methods were developed from algorithmic and mathematics viewpoints, without considering network biology. RESULTS: To effectively predict clinical characteristics and estimate gene networks that provide critical insights into understanding the biological mechanisms involved in a clinical characteristic, we propose a novel strategy for predictive gene network estimation. The proposed strategy simultaneously performs gene network estimation and prediction of the clinical characteristic. In this strategy, the gene network is estimated with minimal network estimation and prediction errors. We incorporate network biology by assuming that neighboring genes in a network have similar biological functions, while hub genes play key roles in biological processes. Thus, the proposed method provides interpretable prediction results and enables us to uncover biologically reliable marker identification. Monte Carlo simulations shows the effectiveness of our method for feature selection in gene estimation and prediction with excellent prediction accuracy. We applied the proposed strategy to construct gastric cancer drug-responsive networks. CONCLUSION: We identified gastric drug response predictive markers and drug sensitivity/resistance-specific markers, AKR1B10, AKR1C3, ANXA10, and ZNF165, based on GDSC data analysis. Our results for identifying drug sensitive and resistant specific molecular interplay are strongly supported by previous studies. We expect that the proposed strategy will be a useful tool for uncovering crucial molecular interactions involved a specific biological mechanism, such as cancer progression or acquired drug resistance.

SARS-CoV-2 Epitopes following Infection and Vaccination Overlap Known Neutralizing Antibody Sites.

Identification of epitopes targeted following virus infection or vaccination can guide vaccine design and development of therapeutic interventions targeting functional sites, but can be laborious. Herein, we employed peptide microarrays to map linear peptide epitopes (LPEs) recognized following SARS-CoV-2 infection and vaccination. LPEs detected by nonhuman primate (NHP) and patient IgMs after SARS-CoV-2 infection extensively overlapped, localized to functionally important virus regions, and aligned with reported neutralizing antibody binding sites. Similar LPE overlap occurred after infection and vaccination, with LPE clusters specific to each stimulus, where strong and conserved LPEs mapping to sites known or likely to inhibit spike protein function. Vaccine-specific LPEs tended to map to sites known or likely to be affected by structural changes induced by the proline substitutions in the mRNA vaccine's S protein. Mapping LPEs to regions of known functional importance in this manner may accelerate vaccine evaluation and discovery of targets for site-specific therapeutic interventions.

Associations Between Trunk Muscle/Fat Composition, Narrowing Lumbar Disc Space, and Low Back Pain in Middle-Aged Farmers: A Cross-Sectional Study.

OBJECTIVE: To investigate the association of trunk fat and muscle composition, lumbar disc space narrowing, and low back pain in middle-aged farmers. METHODS: Fat and muscle areas were identified using standard Hounsfield unit ranges for adipose tissue and skeletal muscle with computed tomography images at the mid-L4 vertebral level. Trunk fat mass, muscle mass, and fat/muscle mass ratio were calculated. Low back pain was assessed using the Oswestry Disability Index (ODI). The L4/5-disc space and low back pain were also assessed. RESULTS: Male had a higher total trunk, back, psoas, and abdominal muscle mass, and visceral fat; female had a higher subcutaneous fat mass and fat/muscle ratio. Pearson correlation coefficients with ODI for waist circumference, total fat mass, visceral fat mass, and fat/muscle ratio were all significant in female; only the fat/muscle ratio was significant in male. Pearson correlation coefficients with L4/5-disc space narrowing grades for visceral fat mass, total, back, and psoas muscle mass, and fat/muscle ratio, were all significant in female; total and back muscle mass, and fat/muscle ratio in male. CONCLUSION: There were significant relationships between: fat indicators with low back pain; trunk muscle mass with lumbar disc degeneration; and fat/muscle ratio with both lumbar disc degeneration and low back pain. The fat/muscle ratio may be a useful index for low back pain.

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks.

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.