Article
Biochemistry & Molecular Biology
Li Ke, Han Gao, Chang Hu, Jiahao Zhang, Qiuyue Zhao, Zhongyi Sun, Zhiyong Peng
Summary: This study identified four potential immune-related gene markers for the diagnosis and prognosis of sepsis using DEGs and machine learning methods. ROC curves and survival analysis were used to evaluate their diagnostic and prognostic abilities. GSEA and ssGSEA analysis further elucidated the molecular mechanisms and immune-related processes. Validation in a septic mouse model confirmed the expression patterns of these potential biomarkers.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Anesthesiology
Han She, Yuanlin Du, Yunxia Du, Lei Tan, Shunxin Yang, Xi Luo, Qinghui Li, Xinming Xiang, Haibin Lu, Yi Hu, Liangming Liu, Tao Li
Summary: This study employed metabolomics and machine learning algorithms to identify differential metabolites associated with the diagnosis and prognosis of sepsis. Unraveling the relationship between metabolic characteristics and sepsis provides new insights into the underlying biological mechanisms, which could potentially assist in the diagnosis and treatment of sepsis.
BMC ANESTHESIOLOGY
(2023)
Review
Oncology
Andrea Romano, Tea Lanisnik Rizner, Henrica Maria Johanna Werner, Andrzej Semczuk, Camille Lowy, Christoph Schroeder, Anne Griesbeck, Jerzy Adamski, Dmytro Fishman, Janina Tokarz
Summary: Endometrial cancer, the most common gynaecological malignancy, has a high incidence and mortality rate in developed countries. There is a need for non/minimally invasive tools for diagnosis and prognosis to manage the increasing number of women at risk. Biomarkers from proteomics and metabolomics can be used to develop such tools, and this review explores the current research in this field. A systematic review of published studies using proteomics and/or metabolomics for biomarker discovery in endometrial cancer is provided, along with recommendations for future studies.
FRONTIERS IN ONCOLOGY
(2023)
Article
Multidisciplinary Sciences
Abha Umesh Sardesai, Ambalika Sanjeev Tanak, Subramaniam Krishnan, Deborah A. Striegel, Kevin L. Schully, Danielle Clark, Sriram Muthukumar, Shalini Prasad
Summary: This research demonstrates the potential feasibility of predicting sepsis host-immune response through a data-driven model combining biomarker measurements. Supervised machine learning algorithms showed good accuracy, indicating the proposed AI approach could be valuable for promoting clinical decision-making.
SCIENTIFIC REPORTS
(2021)
Article
Endocrinology & Metabolism
Hongdong Han, Yanrong Chen, Hao Yang, Wei Cheng, Sijing Zhang, Yunting Liu, Qiuhong Liu, Dongfang Liu, Gangyi Yang, Ke Li
Summary: Two robust genes were identified as potential diagnostic biomarkers for diabetic nephropathy (DN) and may serve as potential therapeutic targets. These genes are closely associated with immune cells, indicating the presence of abnormal immune status in patients with DN.
FRONTIERS IN ENDOCRINOLOGY
(2022)
Article
Cell Biology
Juehui Wu, Xiaoxia Zhan, Songzi Wang, Xuanren Liao, Laisheng Li, Jinmei Luo
Summary: This study examined the clinical value of presepsin in patients with sepsis in Southern China and established diagnostic and prognostic models for sepsis using machine learning and other laboratory parameters.
INFLAMMATION RESEARCH
(2023)
Article
Cell Biology
Juehui Wu, Xiaoxia Zhan, Songzi Wang, Xuanren Liao, Laisheng Li, Jinmei Luo
Summary: The study explored the clinical value of presepsin for sepsis and established diagnosis and prognosis models using machine learning. The models showed good performance in diagnosing and predicting sepsis.
INFLAMMATION RESEARCH
(2023)
Article
Environmental Sciences
Jin Zhang, Lu Ma, Boyan Li, Xiong Chen, Dapeng Wang, Aihua Zhang
Summary: This study identified metabolic biomarkers associated with arsenicosis using untargeted metabolomics and machine learning algorithms. A total of 143 metabolic biomarkers, mainly organic acids, were found to be closely associated with arsenicosis. The disrupted metabolisms of beta-alanine and arginine were the most significant in arsenicosis patients with different symptom severity. Metabolic biomarkers combined with machine learning algorithms can be efficient for risk assessment and early identification of arsenicosis.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Endocrinology & Metabolism
Kuipeng Yu, Shan Li, Chunjie Wang, Yimeng Zhang, Luyao Li, Xin Fan, Lin Fang, Haiyun Li, Huimin Yang, Jintang Sun, Xiangdong Yang
Summary: Diabetic nephropathy is a major cause of end-stage renal disease and lacks effective diagnostic markers. This study identifies APOC1 as a potential novel diagnostic biomarker for diabetic nephropathy, with potential for intervention targeting.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Review
Immunology
Jiyun Hu, Shucai Xie, Wenchao Li, Lina Zhang
Summary: This systematic review and meta-analysis aimed to explore the diagnostic and prognostic value of serum S100 calcium-binding protein B (S100B) in Sepsis-associated encephalopathy (SAE) patients. The results showed that higher serum S100B levels are moderately associated with SAE and unfavorable outcomes, suggesting that serum S100B level may serve as a useful diagnostic and prognostic biomarker for SAE.
FRONTIERS IN IMMUNOLOGY
(2023)
Article
Public, Environmental & Occupational Health
Jun Li, Mi Zhou, Jia-Qi Feng, Soon-Min Hong, Shao-Ying Yang, Lang-Xian Zhi, Wan-Yi Lin, Cheng Zhu, Yue-Tian Yu, Liang-Jing Lu
Summary: This study identified candidate genes closely related to sepsis and macrophages, screened out 17 prognostic genes, and found BCL2A1 as a potential diagnostic biomarker for sepsis.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Genetics & Heredity
Fangfang Duan, Weisen Wang, Wenyu Zhai, Junye Wang, Zerui Zhao, Lie Zheng, Bingyu Rao, Yuheng Zhou, Hao Long, Yaobin Lin
Summary: This study investigated the expression of costimulatory molecules in lung squamous carcinoma (LUSC) and identified diagnostic biomarkers for immunotherapy response. Five CMGs were considered as diagnostic markers and a diagnostic nomogram was developed to predict individual tumor immune microenvironment subclasses. The nomogram showed satisfactory predictive accuracy in three datasets.
FRONTIERS IN GENETICS
(2022)
Article
Oncology
Alireza Asadnia, Elham Nazari, Ladan Goshayeshi, Nima Zafari, Mehrdad Moetamani-Ahmadi, Lena Goshayeshi, Haneih Azari, Ghazaleh Pourali, Ghazaleh Khalili-Tanha, Mohammad Reza Abbaszadegan, Fatemeh Khojasteh-Leylakoohi, Mohammadjavad Bazyari, Mir Salar Kahaei, Elnaz Ghorbani, Majid Khazaei, Seyed Mahdi Hassanian, Ibrahim Saeed Gataa, Mohammad Ali Kiani, Godefridus J. Peters, Gordon A. Ferns, Jyotsna Batra, Alfred King-yin Lam, Elisa Giovannetti, Amir Avan
Summary: This study identified genetic variants and differentially expressed genes in colorectal cancer patients using genome-wide DNA and RNA sequencing. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers, and two novel genetic variants were found to potentially regulate gene expression. These findings provide a proof of concept for the evaluation of emerging biomarkers in colorectal cancer.
Article
Medicine, General & Internal
Zeynep Kucukakcali, Cemil Colak, Harika Gozde Gozukara Bag, Ipek Balikci Cicek, Onural Ozhan, Azibe Yildiz, Nefsun Danis, Ahmet Koc, Hakan Parlakpinar, Sami Akbulut
Summary: This study aimed to perform bioinformatic analysis of lncRNAs from liver tissue samples of rats with cisplatin hepatotoxicity and without pathology. It also aimed to identify possible biomarkers for hepatotoxicity diagnosis through ensemble learning methods. The results showed that certain lncRNAs can be used as predictive biomarker candidates for hepatotoxicity.
Article
Oncology
Qing-nan Zhou, Rong-e Lei, Yun-xiao Liang, Si-qi Li, Xian-wen Guo, Bang-li Hu
Summary: This study aimed to identify lncRNAs related to oxaliplatin sensitivity and predict the prognosis of CRC patients underwent oxaliplatin-based chemotherapy. Four machine learning algorithms were applied to identify the key lncRNAs, and predictive and prognostic models were established based on these lncRNAs. Results showed that certain lncRNAs were associated with oxaliplatin sensitivity and predicted the response to oxaliplatin treatment, as well as the prognosis of patients given oxaliplatin-based chemotherapy.
CANCER CELL INTERNATIONAL
(2023)
Article
Statistics & Probability
Sihai Dave Zhao, William Biscarri
Summary: This article proposes an alternative paradigm based on regression modeling to address the challenge of incorporating structural information in simultaneous estimation of multiple parameters. The approach is able to effectively handle denoising gene expression data.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2022)
Review
Biochemistry & Molecular Biology
Rashid Ahmed, Robin Augustine, Enrique Valera, Anurup Ganguli, Nasrin Mesaeli, Irfan S. Ahmad, Rashid Bashir, Anwarul Hasan
Summary: Spatial mapping of gene expression in cancer tissues can enhance our understanding of cancers and improve cancer detection accuracy. Recent advancements in OMICS technologies have the potential to map biopsy tissue samples and their molecular profiles at a single-cell level. However, further developments are needed for the widespread and effective application of these technologies.
BIOCHIMICA ET BIOPHYSICA ACTA-REVIEWS ON CANCER
(2022)
Review
Materials Science, Multidisciplinary
Nantao Li, Bin Zhao, Robert Stavins, Ana Sol Peinetti, Neha Chauhan, Rashid Bashir, Brian T. Cunningham, William P. King, Yi Lu, Xing Wang, Enrique Valera
Summary: The COVID-19 pandemic has highlighted limitations in current infectious disease diagnosis models, but efforts are underway to develop more effective rapid tests and address cost and time barriers for new test kits. Nanomaterials and nanochemistry show promise in enabling simpler workflows, high sensitivity, and scalable manufacturing for improved virus detection.
CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE
(2022)
Article
Mathematical & Computational Biology
Ruixuan Rachel Zhou, Sihai Dave Zhao, Layla Parast
Summary: In clinical studies, finding a surrogate marker to evaluate treatment effects is important. This article proposes a method for estimating the proportion of treatment effect explained by high-dimensional surrogate markers, and evaluates its performance through simulation studies.
STATISTICS IN MEDICINE
(2022)
Article
Engineering, Biomedical
Sanjay S. Timilsina, Nolan Durr, Mohamed Yafia, Hani Sallum, Pawan Jolly, Donald E. Ingber
Summary: This study presents a simple and ultrafast method for coating electrochemical sensors with an antifouling layer. The coated sensors exhibit unprecedented sensitivity and selectivity, and maintain high electrode conductivity for up to 9 weeks in unprocessed biological samples. The method is used to develop a multiplexed platform for detecting clinically relevant biomarkers, such as myocardial infarction and traumatic brain injury.
ADVANCED HEALTHCARE MATERIALS
(2022)
Article
Biology
Huiqin Xin, Sihai Dave Zhao
Summary: This paper studies a new approach to estimating high-dimensional covariance matrices and frames it as a compound decision problem. By using a nonparametric empirical Bayes g-modeling approach, the optimal rule in the class is estimated. Experimental results show that this method can achieve comparable or better performance in gene network inference.
Article
Mathematics, Interdisciplinary Applications
Wenjing Yin, Sihai Dave Zhao, Feng Liang
Summary: This paper proposes a variable selection method for high dimensional survival data by extending the Buckley-James method and using a penalized L-2 loss function with a penalty function induced from a bivariate spike-and-slab prior specification. Empirical studies show that the proposed method outperforms alternative procedures for both univariate and bivariate survival data.
LIFETIME DATA ANALYSIS
(2022)
Article
Multidisciplinary Sciences
Hailun Zhu, Sihai Dave Zhao, Alokananda Ray, Yu Zhang, Xin Li
Summary: In this study, the authors used single-cell RNA sequencing to analyze the gene expression patterns in Drosophila neural progenitors and identified a gene regulatory network that controls the sequential generation of different neural types. They discovered previously unknown temporal transcription factors and characterized their roles in temporal patterning and neuronal specification. The study provides insights into the mechanisms involved in the temporal patterning of neural progenitors.
NATURE COMMUNICATIONS
(2022)
Article
Nanoscience & Nanotechnology
Roxana M. Calderon-Olvera, Encarnacion Arroyo, Aaron M. Jankelow, Rashid Bashir, Enrique Valera, Manuel Ocana, Ana Isabel Becerro
Summary: This study synthesized highly uniform Mn-doped Zn2GeO4 nanoparticles using a one-pot, microwave-assisted hydrothermal method with polyacrylic acid as an additive. The nanoparticles showed good dispersibility, high chemical stability, and surface functionalization. The presence of carboxylate groups allows for the conjugation of biomolecules and the development of a persistent luminescence-based sandwich immunoassay for the detection of interleukin-6 in human serum and plasma samples.
ACS APPLIED MATERIALS & INTERFACES
(2023)
Article
Biophysics
Sanjay S. Timilsina, Nolan Durr, Pawan Jolly, Donald E. Ingber
Summary: This article presents a rapid electrochemical device that can detect SARS-CoV-2 antibodies in less than 10 minutes using a small patient sample. The device has 100% sensitivity and specificity, making it convenient for evaluating immune responses to vaccination or infection in populations.
BIOSENSORS & BIOELECTRONICS
(2023)
Review
Chemistry, Physical
Enrique Valera, Victoria Kindratenko, Aaron M. Jankelow, John Heredia, Alicia Y. Kim, Thomas W. Cowell, Chih-Lin Chen, Karen White, Hee-Sun Han, Rashid Bashir
Summary: Sepsis is a life-threatening disease caused by a dysregulated immune system during an infectious process. It is a major cause of hospital mortality and readmissions in the United States. Early diagnosis and treatment are crucial for reducing mortality, and electrochemical-based point-of-care detection platforms offer potential solutions due to their high sensitivity, fast response, miniaturization capabilities, and low cost. In this review, we discuss the current state, limitations, and future directions of these platforms for sepsis diagnosis and monitoring.
CURRENT OPINION IN ELECTROCHEMISTRY
(2023)
Article
Chemistry, Multidisciplinary
Jongwon Lim, Shuaizhen Zhou, Janice Baek, Alicia Yeaeun Kim, Enrique Valera, Jonathan Sweedler, Rashid Bashir
Summary: The presence of inhibitors in blood makes their use in nucleic acid amplification techniques difficult. A biphasic method is developed to address this issue, which involves drying blood to physically trap inhibitors and allow for DNA amplification. This method significantly improves detection limits for bacteria and has the potential to be used as a diagnostic platform for pathogen detection in blood.
Review
Food Science & Technology
Alexander J. Taylor, Eduardo Cardenas-Torres, Michael J. Miller, Sihai Dave Zhao, Nicki J. Engeseth
Summary: This study investigates spontaneous cacao fermentations (SCFs) in the production of chocolate, aiming to identify SCF microbes, their interrelationships, and other key fermentation parameters. The analysis of over 1700 microbes across 60 articles reveals the major gaps in knowledge about the cacao microbiome. By understanding the cacao microbiome, researchers can optimize fermentation by identifying key microbes and fermentation parameters.
CURRENT RESEARCH IN FOOD SCIENCE
(2022)
Article
Biochemical Research Methods
Hamed Tavakoli, Elisabeth Hirth, Man Luo, Sanjay Sharma Timilsina, Maowei Dou, Delfina C. Dominguez, XiuJun Li
Summary: In this research, a microfluidic fully paper-based analytical device (mu FPAD) integrated with loop-mediated isothermal amplification (LAMP) and ssDNA-functionalized graphene oxide (GO) nano-biosensors was developed for the first time for simple, rapid, low-cost, and quantitative detection of Neisseria meningitidis (N. meningitidis). The presented mu FPAD offers versatile functions and is capable of a simple, highly sensitive, and specific diagnosis of N. meningitidis. Furthermore, this microfluidic approach has great potential in the rapid detection of a wide variety of different other pathogens in low-resource settings.
Article
Biochemical Research Methods
Jongwon Lim, Robert Stavins, Victoria Kindratenko, Janice Baek, Leyi Wang, Karen White, James Kumar, Enrique Valera, William Paul King, Rashid Bashir
Summary: This study presents a microfluidic assay and device for rapid detection and differentiation of the Alpha variant and early strains of the SARS-CoV-2 virus in saliva samples. The detection assay utilizes isothermal RT-LAMP amplification and takes advantage of the S-gene target failure to distinguish between different variants using a binary detection system. Clinical sample testing confirms the high sensitivity and specificity of the developed point-of-care device.