Laboratory Investigation最新文献

{"title":"Acid sensor ASIC1a promotes malignant cell proliferation through the TCF7/c-Myc pathway in liver cancer.","authors":"Lina Liu, Zhuoyan Zai, Xuewen Qian, Yuemin Tao, Huifang Lv, Meng Wang, Chuanzhu Zhang, Juan Wang, Yinci Zhang, Yihao Zhang","doi":"10.1016/j.labinv.2025.104212","DOIUrl":"https://doi.org/10.1016/j.labinv.2025.104212","url":null,"abstract":"<p><p>The extracellular acidic microenvironment plays a pivotal role in driving tumor initiation and sustaining its malignant progression. Therefore, understanding its regulatory mechanism is crucial for the treatment of liver cancer. Acid sensing ion channel 1a (ASIC1a) serves as the primary acid sensor, transmitting the extracellular low pH signal into the cell to initiate downstream signaling pathways. In this study, we have investigated the pathogenic role of ASIC1a in liver cancer and elucidated its molecular mechanism. Our findings suggest that ASIC1a facilitates the proliferation of liver cancer cells and enhances their malignant characteristics in the acidic microenvironment. Relevantly, this deleterious characteristic was notably repressed upon the inhibition of ASIC1a activity. Transcription factor 7 (TCF7) acts as a crucial mediator, transmitting the activation signals from ASIC1a to upregulate c-Myc expression, thereby promoting the proliferation of liver cancer cells. In conclusion, our results provide new insights into how the extracellular acidic microenvironment contributes to the advancement of liver cancer.</p>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":" ","pages":"104212"},"PeriodicalIF":5.1,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144567615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinicopathological, Cellular and Molecular Analyses of Pulmonary Neuroendocrine Carcinoma with High Expression of Hepatocyte Nuclear Factor 4 Alpha.","authors":"Kei Asayama, Ryota Matsuoka, Suzuka Tachi, Aya Shiba-Ishii, Yoshihiko Murata, Tomoki Nakagawa, Yosuke Furuhashi, Hitomi Kawai, Ayako Suzuki, Yutaka Suzuki, Naohiro Kobayashi, Yukio Sato, Nobuyuki Hizawa, Yoshinori Murakami, Toshiro Niki, Daisuke Matsubara","doi":"10.1016/j.labinv.2025.104210","DOIUrl":"https://doi.org/10.1016/j.labinv.2025.104210","url":null,"abstract":"<p><p>Pulmonary neuroendocrine carcinoma (NEC), including small cell carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC), is highly aggressive and have a poor prognosis. The molecular subtyping of NECs has recently attracted attention, and we identified a new NEC subtype, the hepatocyte nuclear factor 4α (HNF4α) subtype. HNF4α, a transcription factor associated with gastrointestinal differentiation, and TTF-1 are mutually and exclusively expressed in lung adenocarcinomas; however, the characteristics of HNF4α-high NEC and TTF-1-high NEC have yet to be compared. We immunohistochemically examined the characteristics of HNF4α-high NEC in 83 surgically resected specimens (37 SCLCs and 46 LCNECs) and revealed that HNF4α-high and TTF-1-high NEC accounted for 15% (12/83) and 47% (39/83), respectively. In SCLCs, HNF4α-high cases (n=3) and TTF-1-high cases (n=20) were almost confined to the neuroendocrine phenotype with high ASCL1 expression, and the expressions of HNF4α, TTF-1, and POU2F3 were mutually exclusive. Similar results were obtained for LCNECs; however, some HNF4α-high cases were positive for TTF-1 or YAP1, possibly due to the heterogeneity of LCNEC. Therefore, we investigated the heterogeneity of LCNEC and performed a spatial transcriptome analysis of one HNF4α-high LCNEC case, which revealed a mutually exclusive mixture of different subgroups characterized by HNF4A and NKX2-1 (TTF-1) expression. A whole-genome analysis of 10 LCNECs showed that NFE2L2/KEAP1 mutations were characteristic of HNF4α-positive LCNECs. A prognostic analysis revealed a significantly worse prognosis in HNF4α-high LCNECs than in HNF4α-low LCNECs. A cell line analysis showed that TTF-1-high-expressing (Lu139/H889/H510A) and HNF4α-high-expressing (VMRC-LCD/H810) lines were consistent with ASCL1-high-expressing lines. HNF4α knockdown/knock-in experiments in VMRC-LCD and SBC5 (HNF4α-negative) revealed that HNF4α promoted cell proliferation by inhibiting apoptosis. The HNF4α-subtype of pulmonary NEC is a unique subtype, characterized by a neuroendocrine phenotype with high ASCL1 expression and mutual exclusivity with the TTF-1/POU2F3 subtypes. NFE2L2/KEAP1 mutations and HNF4α itself are potential therapeutic targets for this subtype.</p>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":" ","pages":"104210"},"PeriodicalIF":5.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144564870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover","authors":"","doi":"10.1016/S0023-6837(25)00117-5","DOIUrl":"10.1016/S0023-6837(25)00117-5","url":null,"abstract":"","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 7","pages":"Article 104207"},"PeriodicalIF":5.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning can accurately predict the prognosis of gynecologic smooth muscle tumors of uncertain malignant potential: a multicenter pilot study.","authors":"João Costa, Van-Linh Le, Antonio De Leo, Caterina Ravaioli, Valérie Velasco, Ben Davidson, Tone Skeie-Jensen, Mojgan Devouassoux-Shisheboran, Alexis Trecourt, Carla Bartosch, Elisabete Rios, Catherine Genestie, Patricia Pautier, Coriolan Lebreton, Frédéric Guyon, Guillaume Babin, Jean-Michel Coindre, Francois Le Loarer, Olivier Saut, Sabrina Croce","doi":"10.1016/j.labinv.2025.104211","DOIUrl":"https://doi.org/10.1016/j.labinv.2025.104211","url":null,"abstract":"<p><p>Smooth muscle tumors of uncertain malignant potential of the gynecologic tract (STUMP) are a heterogeneous group of tumors, with ambiguous or worrisome features, whose biological behavior is difficult to predict. Several ancillary techniques have been used to try to predict their prognosis, with limited success. The aim of this study is to explore whether deep learning (DL) based features can be used to predict progression-free survival (PFS) in STUMP and identify high-risk patients, directly from histological slides. A cohort of 95 STUMP was collected from 7 academic centers (79 for training and 16 for external validation). Non overlapping tiles were extracted from the tumor area and used to train a DL model to predict PFS. Python's scikit-learn library and the R software environment were used for data analysis. After 4-fold cross-validation, mean C-indexes of 0.7052 (95%CI: 0.4951-0.9152) and 1.0 (95%CI: 1.0-1.0) were achieved, in the training and external validation cohorts, respectively. The predicted PFS probabilities were used to classify the patients into low-risk and high-risk groups, based on the thresholds of the median and the first quartile of predicted PFS probabilities. Significant differences between both groups were observed, at 10 years, with both thresholds. Cox regression analysis showed that the output of the DL model was associated with a worse prognosis (p = 0.0356). Both STUMP groups were compared with a cohort of leiomyomas (n = 160) and leiomyosarcomas (n = 58). The lowest hazard ratio was observed in leiomyomas, followed, consecutively, by low-risk STUMP, high-risk STUMP and leiomyosarcomas. The Cox model showed good discriminatory potential between the four groups (all pairwise comparisons were statistically significant). These findings suggest that DL-based features can be used for outcome prediction of STUMP. Additional work is needed to establish whether this \"high-risk\" group can be identified via molecular markers and used to tailor patient surveillance.</p>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":" ","pages":"104211"},"PeriodicalIF":5.1,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential for clinical management of pancreatic cancer through whole exome profiling of site-specific metastases and matched primary tumors.","authors":"Martin Loveček, Ondřej Strouhal, Lenka Červenková, Simona Šůsová, Viktor Hlaváč, Magdalena Chottova Dvorakova, Petr Holý, Václav Liška, Pavel Skalický, Daniela Skanderová, Aleš Langer, Beatrice Mohelníková-Duchoňová, Pavel Souček","doi":"10.1016/j.labinv.2025.104205","DOIUrl":"10.1016/j.labinv.2025.104205","url":null,"abstract":"<p><p>Considering the lack of molecular background of the metastatic process in pancreatic ductal adenocarcinoma (PDAC) and fact that the location of the metastasis may carry prognostic information and potential therapeutic opportunities, we aimed to explore genomic profiles of metastases from diverse loci and their value for the patients' therapeutic management. DNA samples from paired primary and metastatic tissue of 20 patients were microdissected and sequenced using whole exome target enrichment. Somatic genetic variability, copy number variations (CNVs), and mutational signatures were assessed for associations with clinical data of patients. KRAS (78% in primary tumors-74% in metastases), TP53 (67-68%), CDKN2A (28-37%), and SMAD4 (22-26%) were the most commonly mutated oncodrivers in primary tumors and metastases. Other frequently mutated genes were CCDC187 (50%-58%), MUC5AC (50%-53%), EPPK1 (39%-63%), SYN2 (39%-26%), MUC19 (33%-47%), MUC3A (33%-26%), DNAH12 (28%-37%), ZBED3 (22%-26%), PKHD1L1 (28%-16%), and GTPBP6 (11%-32%). Lung metastases differed from other metastatic sites (liver, stomach, and locoregional) in a higher frequency of nonsense mutations in the MH2 domain of SMAD4, oncodriver co-mutations, gains on chromosomes 2 and 20, CNV counts, and share of signature SBS5. Somatic alterations of KRAS in metastases (p=0.041) and MUC3A in both loci (p=0.041 and p=0.011, respectively) and CNVs count and size in metastases (p=0.024 and p=0.011) associated with response to systemic chemotherapy. Patients with mutated KRAS (p=0.045), high mutational load (p=0.004), and frequent CNVs (p=0.004) in metastatic loci had shortened survival after metastasis resection. Interestingly, the personalized-treatment targetable alterations, such as microsatellite instability and mismatch repair or homologous recombination deficiencies did not differ between the primary tumors and paired metastases or between the metastases from different secondary sites and had no prognostic value. The results suggest a potential prognostic role of KRAS mutations, mutation load, and CNVs in PDAC patients after metastasectomy and encourage further molecular profiling for personalized treatment of PDAC patients with different metastasis localization.</p>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":" ","pages":"104205"},"PeriodicalIF":5.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144368969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Subtypes of Balanopreputial and Urethral Male Genital Lichen Sclerosus: Distinct Transcriptomic and Clinicopathological Profiles","authors":"Xianjie Xiu ,&nbsp;Zhenwei Yu ,&nbsp;Georgios Kravvas ,&nbsp;Christopher B. Bunker ,&nbsp;Liang Cheng ,&nbsp;Guangyu Mao ,&nbsp;Juan Tang ,&nbsp;Ruihang Zhang ,&nbsp;Tianzheng Hao ,&nbsp;Lichun Yang ,&nbsp;Zeyu Wang ,&nbsp;Weidong Zhu ,&nbsp;Wei Yuan ,&nbsp;Zuojing Yin ,&nbsp;Lujie Song","doi":"10.1016/j.labinv.2025.104206","DOIUrl":"10.1016/j.labinv.2025.104206","url":null,"abstract":"<div><div>Male genital lichen sclerosus (MGLSc) is a heterogeneous and aggressive disease characterized by varying severities of balanopreputial and urethral disease (MGLSc-US) and outcomes, including stricture. This study aims to elucidate the transcriptomic heterogeneity of MGLSc and explore its associations with histological and clinical features. We collected 40 preputial samples and 14 urethral tissue samples from patients with MGLSc-US, non-MGLSc urethral strictures, and redundant prepuce. Bulk RNA sequencing was performed to comprehensively profile the transcriptome. Molecular subtypes, functional features, and gene signatures were identified in MGLSc prepuce and urethral lesions. Additionally, we examined the histological and clinical features specific to each subtype. Two distinct transcriptomic subtypes in preputial lesions were identified. Subtype 1 was characterized by the upregulation of immune pathways and increased lymphocytic stromal infiltration. Subtype 2 showed an upregulation of epithelial cell proliferation and cellular stress response pathways. Both subtypes demonstrated features of hyperkeratosis; however, atrophy was specifically associated with subtype 1, whereas subtype 2 showed significant downregulation of extracellular matrix organization pathways and milder dermal sclerosis. <em>PLEK</em>, <em>PIK3AP1</em>, <em>NCF1</em>, <em>CTSS</em>, and <em>SELL</em> and <em>EVPL</em>, <em>RAPGEFL1</em>, and <em>TMEM79</em> were identified as 2 subtype gene signatures across preputial and urethral lesion cohorts. Clinically, subtype 2 was significantly associated with longer US segments compared with subtype 1. This study provides the first detailed transcriptomic characterization of MGLSc, identifying 2 distinct molecular subtypes with stratified markers. These findings offer a foundation for clinical and molecular classification of MGLSc and may guide management strategies and novel therapeutic developments for this challenging condition.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 10","pages":"Article 104206"},"PeriodicalIF":5.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale Fusion Models With Genomic, Topological, and Pathomic Features to Predict Response to Radiation Therapy for Non–Small Cell Lung Cancer Patients","authors":"Yu Jin ,&nbsp;Hidetaka Arimura ,&nbsp;Takeshi Iwasaki ,&nbsp;Takumi Kodama ,&nbsp;Noriaki Yamamoto ,&nbsp;Yunhao Cui ,&nbsp;Yoshinao Oda","doi":"10.1016/j.labinv.2025.104204","DOIUrl":"10.1016/j.labinv.2025.104204","url":null,"abstract":"<div><div>Artificial intelligence models with biomarkers to predict treatment responses to radiation would be necessary to maximize the treatment outcomes of individual patients, especially with histopathology images routinely obtained before treatment. We hypothesized that multiscale features, such as genomic (GM), pathomic (PM), and topological (TP) features, could be associated with the radiation response. We investigated fusion models with multiscale features in histopathology images to predict response to radiation therapy for patients (responders) with non–small cell lung cancer. Ten radiosensitivity-related (radiosensitive and radioresistant) genes were deployed as GM features. PM features were extracted from histopathology images by conventional PM analyses. TP features represent the intrinsic properties of tumor cells using Betti numbers, which are mathematical invariants. We analyzed non–small cell lung cancer patients from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium who received radiotherapy and established 3 base models with GM, TP, and PM features, respectively, and 3 fusion models. The TP model showed a higher area under the receiver operating characteristic curve of 0.707 (<em>P</em> = .026, log-rank test in overall survival analysis) in the internal test data set and 0.720 (<em>P</em> = .136) in the external test data set. The results indicated that the TP models achieved better classification and prognostic prediction powers than the other base models. The inner-cell TP structure may have the ability to reveal the cell radiosensitivity-related information. Furthermore, the best fusion model with GM, TP, and PM features achieved the highest area under the receiver operating characteristic curve of 0.846 (<em>P</em> = .019) and 0.731 (<em>P</em> = .043) in predicting the treatment response and prognoses in the internal and external test data sets, respectively. This study demonstrated the predictive power of the multiscale fusion model for histopathology images, which may assist clinical physicians in the selection of responders to radiation for personalized radiation therapy and would be substantially beneficial for patients with cancer.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 10","pages":"Article 104204"},"PeriodicalIF":5.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144293991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “386 Targeted NGS for TP53 and BRCA1, BRCA2, Gene Mutations and Comparison with Immune Cell Profiling and Immune Checkpoint Inhibitor Expression in Ascitic Fluid with Metastases from High Grade Serous Ovarian Carcinoma” [Laboratory Investigation 105 (2025) 102613]","authors":"Radhika Srinivasan ,&nbsp;Megha Sharma ,&nbsp;Amit Sharma ,&nbsp;Bhavneet Kaur ,&nbsp;Bhavana Rai ,&nbsp;Man Updesh Sachdeva ,&nbsp;Parikshaa Gupta ,&nbsp;Upasana Gautam ,&nbsp;Rashmi Bagga","doi":"10.1016/j.labinv.2025.104173","DOIUrl":"10.1016/j.labinv.2025.104173","url":null,"abstract":"","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 7","pages":"Article 104173"},"PeriodicalIF":5.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Siglec Ligand Immunohistochemistry Reveals Association With Immune Exclusion and Survival","authors":"Jeremy R. Ellis ,&nbsp;Elizabeth Will ,&nbsp;Aleksandra Ogurtsova ,&nbsp;Logan L. Engle ,&nbsp;Janis M. Taube ,&nbsp;Joel C. Sunshine","doi":"10.1016/j.labinv.2025.104203","DOIUrl":"10.1016/j.labinv.2025.104203","url":null,"abstract":"<div><div>Sialic acids are overexpressed in many cancers, and binding of sialic acid via sialic acid binding immunoglobulin-like lectins (Siglecs) may contribute significantly to immune evasion and cancer progression. This important resistance mechanism in the tumor immune microenvironment has been understudied, partially due to the lack of useful reagents. Here, we developed and optimized an immunohistochemistry staining protocol for novel reagents that detect 3 types of Siglec-engaging sialoglycans (HYDRA-3, -7, and -9, which detect sialoglycans recognized by Siglec-3, -7, and -9, respectively) in the tumor immune microenvironment. We evaluated HYDRA staining across 10 different cancer types across whole slides, finding that HYDRA-9 exhibited the highest overall staining range, with HYDRA-3 and -7 showing lower to moderate staining across all tested tumor types. To correlate HYDRA staining patterns and immune infiltration in melanoma, we stained melanoma tissue microarrays with the 3 HYDRA reagents and compared HYDRA staining profiles with a 6-plex multiplex immunofluorescence panel targeting CD8, CD163, FoxP3, PD1, PDL1, and Sox10/S100. Siglec-3 and -9 sialoglycan ligand expression negatively correlated with CD8 T cell infiltration (r = −0.28/<em>P</em> = .002 and r = −0.29/<em>P</em> = .001, respectively), particularly at the tumor-stromal interface (r = −0.37/<em>P</em> &lt; .001 and r = −0.44/<em>P</em> &lt; .001, respectively). Additionally, a high ratio of Siglec-3 and -9 ligand expression at the tumor-stromal interface versus the tumor core was associated with reduced overall survival (Hazard’s ratio: 2.60 and 2.11, respectively), whereas CD8 infiltration was not associated with survival outcomes in our cohort. Taken together, the expression levels and spatial distribution of Siglec-engaging sialoglycans may play a role in patient prognosis, potentially representing a biomarker of survival that is independent of conventional metrics of an inflamed tumor microenvironment. This study highlights the need for further investigation of Siglec ligand expression as a predictive and prognostic biomarker of treatment response and resistance.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 10","pages":"Article 104203"},"PeriodicalIF":5.1,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144275277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover","authors":"","doi":"10.1016/S0023-6837(25)00104-7","DOIUrl":"10.1016/S0023-6837(25)00104-7","url":null,"abstract":"","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 6","pages":"Article 104194"},"PeriodicalIF":5.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}