Frontiers in Medicine最新文献

{"title":"5-aminolevulinic acid induced photodynamic reactions in diagnosis and therapy for female lower genital tract diseases","authors":"Yuqing Chen, Peng Guo, Lihong Chen, Dalin He","doi":"10.3389/fmed.2024.1370396","DOIUrl":"https://doi.org/10.3389/fmed.2024.1370396","url":null,"abstract":"Since the patients suffering from female lower genital tract diseases are getting younger and younger and the human papilloma virus (HPV) infection is becoming more widespread, the novel non-invasive precise modalities of diagnosis and therapy are required to remain structures of the organ and tissue, and fertility as well, by which the less damage to normal tissue and fewer adverse effects are able to be achieved. In all nucleated mammalian cells, 5-Aminolevulinic acid (5-ALA) is an amino acid that occurs spontaneously, which further synthesizes in the heme biosynthetic pathway into protoporphyrin IX (PpIX) as a porphyrin precursor and photosensitizing agent. Exogenous 5-ALA avoids the rate-limiting step in the process, causing PpIX buildup in tumor tissues. This tumor-selective PpIX distribution after 5-ALA application has been used successfully for tumor photodynamic diagnosis (PDD) and photodynamic therapy (PDT). Several ALA-based drugs have been used for ALA-PDD and ALA-PDT in treating many (pre)cancerous diseases, including the female lower genital tract diseases, yet the ALA-induced fluorescent theranostics is needed to be explored further. In this paper, we are going to review the studies of the mechanisms and applications mainly on ALA-mediated photodynamic reactions and its effectiveness in treating female lower genital tract diseases.","PeriodicalId":502302,"journal":{"name":"Frontiers in Medicine","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141647812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Congenital anomalies of the kidney and urinary tract","authors":"Anfal Hussain Mahmoud, Iman M. Talaat, Abdelaziz Tlili, R. Hamoudi","doi":"10.3389/fmed.2024.1384676","DOIUrl":"https://doi.org/10.3389/fmed.2024.1384676","url":null,"abstract":"Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) refer to a range of conditions that affect the kidney and urinary tract. These anomalies can be severe, such as kidney agenesis, or milder, such as vesicoureteral reflux. CAKUT affects over 1% of live births and accounts for 40–50% of cases of chronic kidney failure in children. The pathogenesis of CAKUT is caused by various environmental, genetic, and epigenetic factors that disrupt normal nephrogenesis. Environmental factors that can lead to CAKUT include maternal diabetes, obesity, malnutrition, alcohol consumption, or medications affecting kidneys development. Genetic factors can cause an imbalance in the metanephros and the ureteric bud interaction. Defects in specific genes such as PAX2, TBX18, NRIP1, REX, SIX2, BMP4, and chromosome 17 cause CAKUT. Over 50 genes have been identified as the root cause of this condition, with monogenetic variants causing up to 20% of all cases. CAKUTs can be diagnosed through fetal ultrasonography, but some anomalies may remain undetected. GWASs, Next Generation Sequencing for targeted and whole exome DNA sequencing may provide additional diagnostic methods. This review article highlights some the leading factors that cause CAKUT, which adversely affects kidney development and urinary tract function.","PeriodicalId":502302,"journal":{"name":"Frontiers in Medicine","volume":"114 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141646776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Spotlight on the relationship between visual experience and myopia","authors":"Manrong Yu, Liqin Jiang, Jinhui Dai, Maria Liu","doi":"10.3389/fmed.2024.1440572","DOIUrl":"https://doi.org/10.3389/fmed.2024.1440572","url":null,"abstract":"","PeriodicalId":502302,"journal":{"name":"Frontiers in Medicine","volume":"53 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Case reports in PET imaging 2023","authors":"Carmelo Caldarella, M. Bauckneht, Ramin Sadeghi","doi":"10.3389/fmed.2024.1455227","DOIUrl":"https://doi.org/10.3389/fmed.2024.1455227","url":null,"abstract":"","PeriodicalId":502302,"journal":{"name":"Frontiers in Medicine","volume":"36 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141661887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Insights in healthcare professions education: 2023","authors":"L. Monrouxe, Jacqueline Bloomfield","doi":"10.3389/fmed.2024.1438116","DOIUrl":"https://doi.org/10.3389/fmed.2024.1438116","url":null,"abstract":"","PeriodicalId":502302,"journal":{"name":"Frontiers in Medicine","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141665190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum: Artificial intelligence based data curation: enabling a patient-centric European health data space","authors":"","doi":"10.3389/fmed.2024.1455319","DOIUrl":"https://doi.org/10.3389/fmed.2024.1455319","url":null,"abstract":"","PeriodicalId":502302,"journal":{"name":"Frontiers in Medicine","volume":"78 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141664693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal relationship between type 1 diabetes mellitus and mycoses: a Mendelian randomization study","authors":"Xiaolan Chen, Chen Chen, Mingyan Wu, Shanmei Wang, Hongbin Jiang, Zhe Li, Yuetian Yu, Bing Li","doi":"10.3389/fmed.2024.1408297","DOIUrl":"https://doi.org/10.3389/fmed.2024.1408297","url":null,"abstract":"Type 1 diabetes mellitus (T1DM) is frequently associated with various infections, including mycoses; however, the direct link between T1DM and fungal infections remains under-researched. This study utilizes a Mendelian randomization (MR) approach to investigate the potential causal relationship between T1DM and mycoses.Genetic variants associated with T1DM were sourced from the European Bioinformatics Institute database, while those related to fungal infections such as candidiasis, pneumocystosis, and aspergillosis were obtained from the Finngen database, focusing on European populations. The primary analysis was conducted using the inverse variance weighted (IVW) method, with additional insight from Mendelian randomization Egger regression (MR-Egger). Extensive sensitivity analyses assessed the robustness, diversity, and potential horizontal pleiotropy of our findings. Multivariable Mendelian randomization (MVMR) was employed to adjust for confounders, using both MVMR-IVW and MVMR-Egger to evaluate heterogeneity and pleiotropy.Genetically, the odds of developing candidiasis increased by 5% in individuals with T1DM, as determined by the IVW method (OR = 1.05; 95% CI 1.02–1.07, p = 0.0001), with a Bonferroni-adjusted p-value of 0.008. Sensitivity analyses indicated no significant issues with heterogeneity or pleiotropy. Adjustments for confounders such as body mass index, glycated hemoglobin levels, and white blood cell counts further supported these findings (OR = 1.08; 95% CI:1.03–1.13, p = 0.0006). Additional adjustments for immune cell counts, including CD4 and CD8 T cells and natural killer cells, also demonstrated significant results (OR = 1.04; 95% CI: 1.02–1.06, p = 0.0002). No causal associations were found between T1DM and other fungal infections like aspergillosis or pneumocystosis.This MR study suggests a genetic predisposition for increased susceptibility to candidiasis in individuals with T1DM. However, no causal links were established between T1DM and other mycoses, including aspergillosis and pneumocystosis.","PeriodicalId":502302,"journal":{"name":"Frontiers in Medicine","volume":"108 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141342439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advancements in machine learning for bone marrow cell morphology analysis","authors":"Yifei Lin, Qingquan Chen, Tebin Chen","doi":"10.3389/fmed.2024.1402768","DOIUrl":"https://doi.org/10.3389/fmed.2024.1402768","url":null,"abstract":"As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition, classification, and visualization. Within the domain of bone marrow cell morphology analysis, deep learning offers substantial benefits due to its robustness, ability for automatic feature learning, and strong image characterization capabilities. Deep neural networks are a machine learning paradigm specifically tailored for image processing applications. Artificial intelligence serves as a potent tool in supporting the diagnostic process of clinical bone marrow cell morphology. Despite the potential of artificial intelligence to augment clinical diagnostics in this domain, manual analysis of bone marrow cell morphology remains the gold standard and an indispensable tool for identifying, diagnosing, and assessing the efficacy of hematologic disorders. However, the traditional manual approach is not without limitations and shortcomings, necessitating, the exploration of automated solutions for examining and analyzing bone marrow cytomorphology. This review provides a multidimensional account of six bone marrow cell morphology processes: automated bone marrow cell morphology detection, automated bone marrow cell morphology segmentation, automated bone marrow cell morphology identification, automated bone marrow cell morphology classification, automated bone marrow cell morphology enumeration, and automated bone marrow cell morphology diagnosis. Highlighting the attractiveness and potential of machine learning systems based on bone marrow cell morphology, the review synthesizes current research and recent advances in the application of machine learning in this field. The objective of this review is to offer recommendations to hematologists for selecting the most suitable machine learning algorithms to automate bone marrow cell morphology examinations, enabling swift and precise analysis of bone marrow cytopathic trends for early disease identification and diagnosis. Furthermore, the review endeavors to delineate potential future research avenues for machine learning-based applications in bone marrow cell morphology analysis.","PeriodicalId":502302,"journal":{"name":"Frontiers in Medicine","volume":"55 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term prognosis in patients with thymoma combined with myasthenia gravis: a propensity score-matching analysis","authors":"Kai Zhao, Yiming Liu, M. Jing, Wenhan Cai, Jiamei Jin, Zirui Zhu, Leilei Shen, Jiaxin Wen, Zhiqiang Xue","doi":"10.3389/fmed.2024.1407830","DOIUrl":"https://doi.org/10.3389/fmed.2024.1407830","url":null,"abstract":"We aimed to assess the impact of myasthenia gravis (MG) on the long-term prognosis in patients with thymoma after surgery and identify related prognostic factors or predictors.This retrospective observational study included 509 patients with thymoma (thymoma combined with MG [MG group] and thymoma alone [non-MG group]). Propensity score matching was performed to obtain comparable subsets of 96 patients in each group. A comparative analysis was conducted on various parameters.Before matching, the 10-year survival and recurrence-free survival rates in both groups were 93.8 and 98.4%, and 85.9 and 93.4%, respectively, with no statistically significant difference observed in the survival curves between the groups (p > 0.05). After propensity score matching, 96 matched pairs of patients from both groups were created. The 10-year survival and recurrence-free survival rates in these matched pairs were 96.9 and 97.7%, and 86.9 and 91.1%, respectively, with no statistical significance in the survival curves between the groups (p > 0.05). Univariate analysis of patients with thymoma postoperatively revealed that the World Health Organization histopathological classification, Masaoka–Koga stage, Tumor Node Metastasis stage, resection status, and postoperative adjuvant therapy were potentially associated with tumor recurrence after thymoma surgery. Multivariate analysis demonstrated that the Masaoka–Koga stage and postoperative adjuvant therapy independently predicted the risk of recurrence in patients with thymoma after surgery.There was no difference in prognosis in patients with thymoma with or without MG. The Masaoka–Koga stage has emerged as an independent prognostic factor affecting recurrence-free survival in patients with thymoma, while postoperative adjuvant therapy represents a poor prognostic factor.","PeriodicalId":502302,"journal":{"name":"Frontiers in Medicine","volume":"111 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141342491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanisms and therapeutic research progress in intestinal fibrosis","authors":"Yanjiang Liu, Tao Zhang, Kejian Pan, He Wei","doi":"10.3389/fmed.2024.1368977","DOIUrl":"https://doi.org/10.3389/fmed.2024.1368977","url":null,"abstract":"Intestinal fibrosis is a common complication of chronic intestinal diseases with the characteristics of fibroblast proliferation and extracellular matrix deposition after chronic inflammation, leading to lumen narrowing, structural and functional damage to the intestines, and life inconvenience for the patients. However, anti-inflammatory drugs are currently generally not effective in overcoming intestinal fibrosis making surgery the main treatment method. The development of intestinal fibrosis is a slow process and its onset may be the result of the combined action of inflammatory cells, local cytokines, and intestinal stromal cells. The aim of this study is to elucidate the pathogenesis [e.g., extracellular matrix (ECM), cytokines and chemokines, epithelial-mesenchymal transition (EMT), differentiation of fibroblast to myofibroblast and intestinal microbiota] underlying the development of intestinal fibrosis and to explore therapeutic advances (such as regulating ECM, cytokines, chemokines, EMT, differentiation of fibroblast to myofibroblast and targeting TGF-β) based on the pathogenesis in order to gain new insights into the prevention and treatment of intestinal fibrosis.","PeriodicalId":502302,"journal":{"name":"Frontiers in Medicine","volume":"28 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141342583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}