iLABMED最新文献

{"title":"Construction of Reporter Phage T4::Nluc and Its Application in the Detection of Escherichia coli in Urinary Tract Infections","authors":"Zhiyun Hao,&nbsp;Minwei Li,&nbsp;Qiang Zhao,&nbsp;Liye Wang,&nbsp;Ting Liu,&nbsp;Chi Wang,&nbsp;Chengbin Wang","doi":"10.1002/ila2.70007","DOIUrl":"https://doi.org/10.1002/ila2.70007","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Urinary tract infections (UTIs) are one of the most common infectious diseases worldwide, predominantly caused by <i>Escherichia coli</i>. We constructed a reporter phage T4::<i>Nluc</i> to achieve rapid, sensitive, and specific detection of <i>Escherichia coli</i> in UTIs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>T4::<i>Nluc</i> was constructed using the CRISPR/Cas9 system combined with homologous recombination and was confirmed through Sanger sequencing. The biological properties of T4 and T4::<i>Nluc</i> were compared. Time-luminescence curves were detected to investigate the limit of detection (LOD) and the influence of urine. Additionally, the specificity of T4::<i>Nluc</i> was examined by co-culturing it with other pathogens. In total, 104 urinary <i>Escherichia coli</i> isolates were collected to assess detection coverage. Finally, 698 urine samples were collected for clinical validation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>T4::<i>Nluc</i> was confirmed to be correct. The one-step growth curves of T4 and T4::<i>Nluc</i> were similar, but the optimal multiplicity of infection for T4 was 1, and that for T4::<i>Nluc</i> was 0.1, indicating that genetic modification had some effect. The LOD was 10⁴ colony-forming unit/mL detected at 220 min. Urine did not affect detection and T4::<i>Nluc</i> did not cross-react with other pathogens. T4::<i>Nluc</i> could detect 38.46% of clinical strains, demonstrating higher sensitivity than the double-layer overlay assay (25.96%). In clinical urine samples, its detection sensitivity was 36.59%, and the specificity was 100%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>T4::<i>Nluc</i> was successfully constructed and could detect <i>Escherichia coli</i> with superior sensitivity and specificity compared with traditional diagnostics, fulfilling the diagnostic criteria for UTIs while significantly reducing the detection time. This presented a novel approach for rapid and accurate detection of <i>E</i>. <i>coli</i> in UTIs.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100656,"journal":{"name":"iLABMED","volume":"3 2","pages":"158-170"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ila2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Trend of Laboratory Medicine Based on Big Data","authors":"Chengcheng Bi,&nbsp;Shitao Zhou,&nbsp;Xiaobing Zhao,&nbsp;Zhen Wang,&nbsp;Feng Tian","doi":"10.1002/ila2.70009","DOIUrl":"https://doi.org/10.1002/ila2.70009","url":null,"abstract":"<p>Laboratory medicine, a fundamental component of clinical medicine, is undergoing an unprecedented transformation with the emergence of the big data era. This review primarily elucidates the developmental trends and significance of big data technology within the domain of laboratory medicine. Furthermore, the applications of big data technology in this field are explored in depth across several dimensions, including quality control management, the automated review of test results, support for clinical decision-making, real-world research, and biomedical scientific inquiries. Such advancements promise to deliver more efficient and accurate diagnostic methods and personalized treatment plans, further driving innovation and growth in laboratory medicine. Ultimately, the review underscores that, despite challenges, the advent of big data technology is a remarkable leap forward for laboratory medicine, offers invaluable insights for research and practice, and signals a trajectory toward a more efficient, personalized, and predictive future.</p>","PeriodicalId":100656,"journal":{"name":"iLABMED","volume":"3 3","pages":"312-321"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ila2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Antimicrobial Susceptibility Profile of Eggerthella lenta Isolated From Bloodstream and Abdominal Fluid Infections","authors":"Yuan Gao,&nbsp;Longfeng Qu,&nbsp;Shu Zhang,&nbsp;Cheng Cheng,&nbsp;Bin Tang","doi":"10.1002/ila2.70010","DOIUrl":"https://doi.org/10.1002/ila2.70010","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p><i>Eggerthella lenta</i> is an anaerobic gram-positive bacillus associated with severe bloodstream infections and high mortality rates. However, limited antimicrobial susceptibility data in China hinder effective clinical treatment. We aimed to address this by analyzing the antimicrobial susceptibility profiles of <i>E. lenta</i> strains isolated from bloodstream and abdominal fluid infections to provide evidence-based guidance for empirical treatment in clinical practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study reviewed 36 cases of <i>E. lenta</i> isolated and cultured from bloodstream and abdominal fluids between 2018 and 2024. The isolates were identified using various methods, including the VITEK 2 ANC card, MALDI-TOF mass spectrometry, and a 16S rRNA gene sequencing assay. Antimicrobial drug susceptibility testing of the 36 <i>E. lenta</i> isolates was conducted using the agar dilution method. A minimum inhibitory concentration (MIC) fold analysis was performed with reference to the CLSI and EUCAST guidelines.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The identification of <i>E. lenta</i> was performed using VITEK-2, MALDI-TOF MS, and 16S rRNA sequencing. All methods showed high consistency, with 16S rRNA sequencing confirming the species classification of the isolates. <i>Eggerthella lenta</i> exhibited varying degrees of resistance to penicillin, ampicillin, ceftriaxone, levofloxacin, clindamycin, ceftazidime, imipenem, piperacillin-tazobactam, and amikacin. Conversely, the bacterium was sensitive to amoxicillin-clavulanic acid, moxifloxacin, chloramphenicol, metronidazole, and vancomycin.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These findings indicate that metronidazole, amoxicillin-clavulanic acid, moxifloxacin, and vancomycin are the preferred empirical treatments for <i>E. lenta</i>. Furthermore, there is an urgent need to optimize anti-<i>E. lenta</i> treatment guidelines and the MIC threshold values for piperacillin-tazobactam to ensure a close correlation with clinical data and to accurately guide the effective management of invasive <i>E. lenta</i> infections.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100656,"journal":{"name":"iLABMED","volume":"3 2","pages":"141-147"},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ila2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progress of Cell Sorting in Flow Cytometry","authors":"Baijun Cai,&nbsp;Jiaxuan Ding,&nbsp;Yaxiaer Yalikun,&nbsp;Dahai Ren","doi":"10.1002/ila2.70000","DOIUrl":"https://doi.org/10.1002/ila2.70000","url":null,"abstract":"<p>Flow cytometry is a powerful technique for rapidly analyzing the physical and biological characteristics of small biological particles, such as cells, as well as sorting specific targets. This capability has made it indispensable in a range of biological fields, including cell sequencing, drug development, medical diagnosis, and environmental monitoring. Over the past few decades, these areas have been revolutionized by significant advancements in flow cytometry, facilitated by the expansion of bio-particle sorting applications. In particular, various innovative sorting technologies with improved detection accuracy have emerged. This paper reviews the principles and current development of conventional and microfluidic sorting approaches in flow cytometry and further introduces the diverse applications of flow cytometry in fields such as oncology and immunology. Despite notable progress, further research is essential to improve the accuracy of sorting and applicability of flow cytometry.</p>","PeriodicalId":100656,"journal":{"name":"iLABMED","volume":"3 1","pages":"96-105"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ila2.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Immune-Related Gene CD48 Is a Prognostic Biomarker Associated With the Breast Cancer Tumor Microenvironment","authors":"Yi Zhao,&nbsp;Hengheng Zhang,&nbsp;Wenwen Wang,&nbsp;Guoshuang Shen,&nbsp;Miaozhou Wang,&nbsp;Zhen Liu,&nbsp;Jiuda Zhao,&nbsp;Jinming Li","doi":"10.1002/ila2.70005","DOIUrl":"https://doi.org/10.1002/ila2.70005","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>While tumor cells can affect the biological behavior of malignant tumors, the tumor microenvironment (TME) also plays an important role in the occurrence, development, and metastasis of tumors. The dynamic changes in the immune and stromal components of the TME and their correlations with breast cancer (BCa) patient prognosis may help guide clinical practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this study, transcriptomic data and clinical information of BCa samples were obtained from The Cancer Genome Atlas database. The immune score and matrix score were calculated using the ESTIMATE algorithm. Examining the differentially expressed genes (DEGs), protein–protein interaction network development, and univariate Cox analysis helped identify CD48 as a key BCa-related gene.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The DEG and survival analysis results showed that CD48 was significantly upregulated in BCa samples compared with normal samples, potentially affecting patient prognosis. Gene set enrichment analysis showed that high CD48 expression was mainly associated with immune-related pathways, suggesting that CD48 may be an important factor for maintaining an immune-dominant TME in BCa. Analysis of tumor-infiltrating immune cell types showed that high expression of CD48 could inhibit the infiltration of M2 macrophages and promote the entry of CD8+ T cells, CD4+ T cells, and M1 macrophages into the TME to exert anti-tumor effects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>CD48 may serve as an effective biomarker for predicting BCa patient prognosis and a potential immune-related therapeutic target.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100656,"journal":{"name":"iLABMED","volume":"3 1","pages":"51-63"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ila2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Significance and Diagnostic Potential of CEA and FIB in Colorectal Cancer","authors":"Lin Zhu,&nbsp;Jie Feng,&nbsp;Xu Zhang,&nbsp;Xuemei Wei,&nbsp;Cuiling Ming,&nbsp;Yanhong Gao","doi":"10.1002/ila2.70003","DOIUrl":"https://doi.org/10.1002/ila2.70003","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Colorectal cancer (CRC) is the third most common cancer worldwide; most cases are diagnosed at an advanced stage. This study explored the value of carcinoembryonic antigen (CEA) and fibrinogen (FIB) in the differential diagnosis of colorectal polyps and CRC.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Clinical data of 466 CRC patients and 231 patients with colorectal polyps treated at the Chinese PLA General Hospital from October 2021 to February 2024 were retrospectively analyzed. The efficacy of tumor markers in diagnosing CRC was assessed using receiver operating characteristic curves using the binary logistic regression model. Bioinformatics analysis of FIB-related differentially expressed genes related to CRC was performed using the String, LinkedOmics, and Gene Expression Omnibus databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses were performed using the Metascape database.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The CRC group was older and had higher proportions of male patients, smokers, and drinkers than the colorectal polyp group (&lt;i&gt;p&lt;/i&gt; &lt; 0.05). Compared with the colorectal polyp group, the CRC group had higher levels of CEA, carbohydrate antigen 19–9 (CA19-9), cytokeratin 19 fragment (CYFRA21-1), activated partial thromboplastin time (APTT), prothrombin time (PT), and FIB (&lt;i&gt;p&lt;/i&gt; &lt; 0.01). CEA and FIB levels were significantly different between patients with different Tumor-Node-Metastasis staging (&lt;i&gt;p&lt;/i&gt; &lt; 0.01). The combination of CEA and FIB showed better ability to discriminate CRC from colorectal polyps (sensitivity: 76.5%, specificity: 80.2%, area under the curve: 0.85). Protein-protein interaction network analysis showed that the fibrinogen alpha (FGA) gene had the strongest correlation with albumin (ALB), alpha-2-Heremans Schmid glycoprotein (AHSG), and serpin peptidase inhibitor, clade D, member 1 (SERPIND1). Gene Ontology functional analysis in CRC showed that FGA and related genes were enriched in biological processes including biosynthesis of ribonucleoprotein complex and non-coding ribonucleic acid metabolic process; in cellular components, they were primarily enriched in pre-ribosomes; and in molecular functions, they were mainly enriched in binding of unfolded protein. Kyoto Encyclopedia of Genes and Genomes enrichment indicated that differential genes were mainly involved in pathways such as the Wnt signaling pathway.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The combination of CEA and FIB may be u","PeriodicalId":100656,"journal":{"name":"iLABMED","volume":"3 1","pages":"42-50"},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ila2.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Artificial Intelligence and Clinical Laboratory Evidence to Advance Mobile Health Applications in Ophthalmology: Taking the Ocular Surface Disease as a Case Study","authors":"Mini Han Wang,&nbsp;Yi Pan,&nbsp;Xudong Jiang,&nbsp;Zhiyuan Lin,&nbsp;Haoyang Liu,&nbsp;Yunxiao Liu,&nbsp;Jiazheng Cui,&nbsp;Jiaxiang Tan,&nbsp;Chengqi Gong,&nbsp;Guanghui Hou,&nbsp;Xiaoxiao Fang,&nbsp;Yang Yu,&nbsp;Moawiya Haddad,&nbsp;Marion Schindler,&nbsp;José Lopes Camilo Da Costa Alves,&nbsp;Junbin Fang,&nbsp;Xiangrong Yu,&nbsp;Kelvin Kam-Lung Chong","doi":"10.1002/ila2.70001","DOIUrl":"https://doi.org/10.1002/ila2.70001","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The advent of mobile health (mHealth) applications has fundamentally transformed the healthcare landscape, particularly within the field of ophthalmology, by providing unprecedented opportunities for remote diagnosis, monitoring, and treatment. Ocular surface diseases, including dry eye disease (DED), are the most common eye diseases that can be detected by mHealth applications. However, most remote artificial intelligence (AI) systems for ocular surface disease detection are predominantly based on self-reported data collected through interviews, which lack the rigor of clinical evidence. These constraints underscore the need to develop robust, evidence-based AI frameworks that incorporate objective health indicators to improve the reliability and clinical utility of remote health applications.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Two novel deep learning (DL) models, YoloTR and YoloMBTR, were developed to detect key ocular surface indicators (OSIs), including tear meniscus height (TMH), non-invasive Keratograph break-up time (NIKBUT), ocular redness, lipid layer, and trichiasis. Additionally, back propagation neural networks (BPNN) and universal network for image segmentation (U-Net) were employed for image classification and segmentation of meibomian gland images to predict Demodex mite infections. These models were trained on a large dataset from high-resolution devices, including Keratograph 5M and various mobile platforms (Huawei, Apple, and Xiaomi).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The proposed DL models of YoloMBTR and YoloTR outperformed baseline you only look once (YOLO) models (Yolov5n, Yolov6n, and Yolov8n) across multiple performance metrics, including test average precision (AP), validation AP, and overall accuracy. These two models also exhibit superior performance compared to machine plug-in models in KG5M when benchmarked against the gold standard. Using Python's Matplotlib for visualization and SPSS for statistical analysis, this study introduces an innovative proof-of-concept framework leveraging quantitative AI analysis to address critical challenges in ophthalmology. By integrating advanced DL models, the framework offers a robust approach for detecting and quantifying OSIs with a high degree of precision. This methodological advancement bridges the gap between AI-driven diagnostics and clinical ophthalmology by translating complex ocular data into actionable insights.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Integrating AI with clinical laboratory data holds ","PeriodicalId":100656,"journal":{"name":"iLABMED","volume":"3 1","pages":"64-85"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ila2.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Unveiling Process of Recurrent Thrombosis in an Older Patient: A Case Report","authors":"Ting Zhang,&nbsp;Jie Feng,&nbsp;Yutao Guo,&nbsp;Yanhong Gao","doi":"10.1002/ila2.70004","DOIUrl":"https://doi.org/10.1002/ila2.70004","url":null,"abstract":"<p>A 59-year-old male patient had long suffered from chest tightness and shortness of breath because of coronary artery disease. Ultimately, the diagnosis of “gastric adenocarcinoma with multiple metastases” was confirmed through the analysis of pericardial effusion cytopathology. This article aims to explore the potential link between the patient's recurrent coronary artery thromboses and tumor growth.</p>","PeriodicalId":100656,"journal":{"name":"iLABMED","volume":"3 1","pages":"86-89"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ila2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advancements in Microfluidic Biosensors for Clinical Applications","authors":"Haiyan Wang,&nbsp;Wenjuan Wu","doi":"10.1002/ila2.70002","DOIUrl":"https://doi.org/10.1002/ila2.70002","url":null,"abstract":"<p>The combination of low-consumption microfluidic chips and high-sensitivity biosensors enables rapid and accurate detection of complex target analytes. This integrated system holds significant potential for applications in disease diagnosis, health monitoring, and treatment management. Advances in novel biomaterials have led to device integration into wearable and implantable systems for point-of-care testing. Here, we review recent advances in microfluidic biosensors for clinical applications in detecting nucleic acids, proteins, metabolites, pathogens, and cellular components. We outline the prospects of integrated devices based on microfluidic biosensors for the analysis of biofluids such as sweat and discuss the remaining challenges facing the clinical application of microfluidic biosensors.</p>","PeriodicalId":100656,"journal":{"name":"iLABMED","volume":"3 2","pages":"130-140"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ila2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Fatty Liver Index Based on Real-World Data","authors":"Wenfeng Ding,&nbsp;Lanlan Liu,&nbsp;Wenjing Lu,&nbsp;Yingli Li,&nbsp;Jing Zhang,&nbsp;Cui Zhang,&nbsp;Yufei Wang,&nbsp;Xueping Ma,&nbsp;Xiaoli Yang","doi":"10.1002/ila2.75","DOIUrl":"https://doi.org/10.1002/ila2.75","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The aim of this study was to develop and validate a fatty liver index based on laboratory data (FLI-L) and a fatty liver index based on both physical examination and laboratory data (FLI-PL) in the hope of providing a more convenient, accurate, and quantitative method for the diagnosis of fatty liver disease.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The study included data for 12,391 patients obtained from the Third Medical Center of Chinese PLA General Hospital. FLI-L and FLI-PL were developed using binary logistic regression analysis. The diagnostic performance of the FLI-L and FLI-PL was evaluated using the area under the receiver-operating characteristic curve (AUC-ROC) with sensitivity, specificity, and positive and negative likelihood ratios. FLI-L and FLI-PL were subsequently validated in 3170 patients from the same hospital.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The AUC-ROC for FLI-L was 0.876 with a cut-off value of 55.03. Sensitivity was 81.35 and specificity was 78.28, with an accuracy of 79.99% for discriminating between patients with and without fatty liver disease. The AUC-ROC for FLI-PL was 0.902 with a cut-off value of 20.51. Sensitivity was 85.10 and specificity was 79.64. FLI-PL classified 91.65% of patients correctly.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>FLI-L and FLI-PL is used for simple and accurate quantitative diagnosis of fatty liver disease. This study provides evidence to support the use of this index in clinical management.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100656,"journal":{"name":"iLABMED","volume":"3 1","pages":"33-41"},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ila2.75","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}