Examination of the functions and mechanism of KCP in mediating paclitaxel resistance in cervical squamous carcinoma cells by Yue He, Jian-Qing Xu, Jing-Jing Zhang, Zhen-You Liu, Chen Ji, Yang Liu, Yun-Fan Wang, Ming Wang, Yu-Mei Wu, Yan Wang

“…Key cell functions were assessed by colony formation assay, measurement of cell proliferation by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and FACS-based detection of apoptosis. The downstream mechanism of KCP-mediated resistance to paclitaxel was then examined using human gene chip detection and IPA bioinformatics analysis, and qPCR analysis was used to validate its downstream genes.Results①Functional studies of SiHa cells showed that KCP knockout (sgRNA) inhibited colony formation and proliferation of SiHa cells in the presence of paclitaxel (p<0.05). ②Using a whole human genome microarray, a total of 491 differentially expressed genes were identified in KCP knockout versus the NC SiHa cells. …”
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Concave Magnetic-Responsive Hydrogel Discs for Enhanced Bioassays by Amin Ghaffarzadeh Bakhshayesh, Huiyan Li

“…In response to this challenge, we presented a rapid and cost-effective technique for fabricating concave magnetic-responsive hydrogel discs (CMDs) by straightforward pipetting directly onto microscope glass slides. …”
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Deep learning-based spatial analysis on tumor and immune cells of pathology images predicts MIBC prognosis. by Chao Hu, Fan Wang, Hui Xu, XiQi Dong, XiuJuan Xiong, Yun Zhang, TianCheng Zhao, YuanQiao He, LiBin Deng, XiongBing Lu

“…Lymph_inside % can be an effective biomarker for predicting MIBC prognosis. …”
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c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease. by Sherlyn Jemimah, Ferial Abuhantash, Aamna AlShehhi

“…We trained the model with blood genotyping data, microarray, and clinical features from the Alzheimer's Neuroimaging Disease Initiative (ADNI). …”
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On-Chip DNA Assembly via Dielectrophoresis by Xichuan Rui, Lin-Sheng Wu, Xin Zhao

“…While there exist several microarray-based oligo synthesis technologies, on-chip gene assembly has yet to be demonstrated. …”
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A Novel Feature Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded Approaches by Saba Bashir, Irfan Ullah Khattak, Aihab Khan, Farhan Hassan Khan, Abdullah Gani, Muhammad Shiraz

“…Feature selection is the process of identifying the most relevant features from the given data having a large feature space. Microarray datasets are comprised of high-quality features and very few samples of data. …”
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Type-2 diabetes epigenetic biomarkers: present status and future directions for global and Indigenous health by Sarah Munns, Sarah Munns, Alex Brown, Alex Brown, Sam Buckberry, Sam Buckberry

“…Recent advances in epigenomics methods, particularly in DNA methylation detection, have enabled the discovery of associations between epigenetic changes and Type-2 diabetes. …”
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Comprehensive analysis of intervention and control studies for the computational identification of dengue biomarker genes. by Jibon Kumar Paul, Mahir Azmal, Tasnim Alam, Omar Faruk Talukder, Ajit Ghosh

“…Despite significant progress in understanding Dengue fever, effective prognosis and treatment remain elusive due to the complex clinical presentations and limitations in current diagnostic methods. …”
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Genetic test results and one-year developmental outcomes of fetuses with congenital heart disease by Hui Hu, Bo Zhou, Qunda Shan, Shuangshuang Shen, Xiangdong Zhang, Penglong Chen, Jiao Liu, Xiaofang Lan

“…The detection rate of CNV was significantly lower in fetuses with isolated cardiac abnormalities (P < 0.05). …”
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A retrospective analysis of prenatal genetic results in fetal hydronephrosis. by Keqin Jin, Xiayuan Xu, Yue Qian, Liping Zhang, Min Hu, Jianfeng Luo

“…<h4>Objective</h4>To discuss the application value of technologies such as chromosome microarray analysis (CMA) in fetuses with hydronephrosis and pyelectasis.…”
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Sperm associated antigen 9 (SPAG9) a promising therapeutic target of ovarian carcinoma by Nirmala Jagadish, Rukhsar Fatima, Aditi Sharma, Sonika Devi, Vitusha Suri, Vikash Kumar, Anil Suri

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Machine learning-based single-sample molecular classifier for cancer grading by Zoia Antysheva, Nikita Kotlov, Mariia V. Guryleva, Ivan Valiev, Viktor Svekolkin, Anna Belozerova, Sheila T. Yong, Dmitry Tabakov, Alexander Bagaev, Vladimir Kushnarev

“…Next, we showed that mGrades were effective in assessing risk levels for G2 samples. …”
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Investigating Single-Molecule Molecular Inversion Probes for Medium-Scale Targeted DNA Methylation Analysis by Roy B. Simons, Hieab H. H. Adams, Manfred Kayser, Athina Vidaki

“…Considering CpGs with at least 20X coverage, we yielded robust methylation detection with levels comparable to those obtained from the gold standard EPIC microarray analysis (Pearsons’s r: 0.96). …”
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Gene co-expression module study identifies key genes and pathways underpinning hypercholesterolemia-induced atherosclerosis development by Muhammad-Redha Abdullah-Zawawi, Muhammad Irfan Abdul Jalal, Siti Aishah Sulaiman, Nur Alyaa Afifah Md Shahri, Rahman Jamal, Nor Azian Abdul Murad

“…Methods The Affymetrix microarray dataset (GSE13985), which includes samples from five healthy controls and five FH patients, was obtained from the Gene Expression Omnibus (GEO) database. …”
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Identification of Key Genes and Potential Therapeutic Targets in Sepsis-Associated Acute Kidney Injury Using Transformer and Machine Learning Approaches by Zhendong Zhai, JunZhe Peng, Wenjun Zhong, Jun Tao, Yaqi Ao, Bailin Niu, Li Zhu

“…Early diagnosis and effective therapy remain difficult despite extensive investigation. …”
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Identification of Differentially Expressed Genes and Elucidation of Pathophysiological Relevance of ABCA1 in HaCaT Cells Induced by PM2.5 by Fen Peng, Chen-Hong Xue, Xiao-Jing Yang, Jing-Yi Huang, Zhou Chen, Jian-Zhong Zhang

“…Microarray analyses were used to find out the microarray gene expression profiling; data processing included gene enrichment and pathway analysis. …”
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Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods by Mark Burton, Mads Thomassen, Qihua Tan, Torben A. Kruse

“…This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. …”
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Identification of sero-reactive antigens for the early diagnosis of Johne's disease in cattle. by Lingling Li, John P Bannantine, Joseph J Campo, Arlo Randall, Yrjo T Grohn, Robab Katani, Megan Schilling, Jessica Radzio-Basu, Vivek Kapur

“…JD has a high herd prevalence and causes serious animal health problems and significant economic loss in domesticated ruminants throughout the world. Since serological detection of MAP infected animals during the early stages of infection remains challenging due to the low sensitivity of extant assays, we screened 180 well-characterized serum samples using a whole proteome microarray from Mycobacterium tuberculosis (MTB), a close relative of MAP. …”
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The role of sialyl glycan recognition in host tissue tropism of the avian parasite Eimeria tenella. by Livia Lai, Janene Bumstead, Yan Liu, James Garnett, Maria A Campanero-Rhodes, Damer P Blake, Angelina S Palma, Wengang Chai, David J P Ferguson, Peter Simpson, Ten Feizi, Fiona M Tomley, Stephen Matthews

“…The microarray analyses also reveal a lack of recognition of glycan sequences terminating in the N-glycolyl form of sialic acid by EtMIC3. …”
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Mutation Spectrum of the ABCA4 Gene in a Greek Cohort with Stargardt Disease: Identification of Novel Mutations and Evidence of Three Prevalent Mutated Alleles by Kamakari Smaragda, Kokkinou Vassiliki, Koutsodontis George, Stamatiou Polixeni, Giatzakis Christoforos, Anastasakis Anastasios, Aslanides Ioannis Minas, Koukoula Stavrenia, Panagiotoglou Theoni, Datseris Ioannis, Tsilimbaris K. Miltiadis

“…Direct sequencing improved significantly the complete characterization rate, that is, identification of two mutations compared to the microarray analysis (93.1% versus 50%). In total, 40 distinct potentially disease-causing variants of the ABCA4 gene were detected, including six previously unreported potentially pathogenic variants. …”
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