Identification and validation of ferroptosis-related hub genes and immune infiltration in liver ischemia-reperfusion injury.

Ischemia-reperfusion injury (IRI) is a cumulation of pathophysiological processes that involves cell and organelle damage upon blood flow constraint and subsequent restoration. However, studies on overall immune infiltration and ferroptosis in liver ischemia-reperfusion injury (LIRI) are limited. This study explored immune cell infiltration and ferroptosis in LIRI using bioinformatics and experimental validation. The GSE151648 dataset, including 40 matched pairs of pre- and post- transplant liver samples was downloaded for bioinformatic analysis. Eleven hub genes were identified by overlapping differentially expressed genes (DEGs), iron genes, and genes identified through weighted gene co-expression network analysis (WGCNA). Subsequently, the pathway enrichment, transcription factor-target, microRNA-mRNA and protein-protein interaction networks were investigated. The diagnostic model was established by logistic regression, which was validated in the GSE23649 and GSE100155 datasets and verified using cytological experiments. Moreover, several drugs targeting these genes were found in DrugBank, providing a more effective treatment for LIRI. In addition, the expression of 11 hub genes was validated using quantitative real-time polymerase chain reaction (qRT-PCR) in liver transplantation samples and animal models. The expression of the 11 hub genes increased in LIRI compared with the control. Five genes were significantly enriched in six biological process terms, six genes showed high enrichment for LIRI-related signaling pathways. There were 56 relevant transcriptional factors and two central modules in the protein-protein interaction network. Further immune infiltration analysis indicated that immune cells including neutrophils and natural killer cells were differentially accumulated in the pre- and post-transplant groups, and this was accompanied by changes in immune-related factors. Finally, 10 targeted drugs were screened. Through bioinformatics and further experimental verification, we identified hub genes related to ferroptosis that could be used as potential targets to alleviate LIRI.

Identification of Blood Biomarkers in Ischemic Stroke by Integrated Analysis of Metabolomics and Proteomics.

We aimed to uncover the pathological mechanism of ischemic stroke (IS) using a combined analysis of untargeted metabolomics and proteomics. The serum samples from a discovery set of 44 IS patients and 44 matched controls were analyzed using a specific detection method. The same method was then used to validate metabolites and proteins in the two validation sets: one with 30 IS patients and 30 matched controls, and the other with 50 IS patients and 50 matched controls. A total of 105 and 221 differentially expressed metabolites or proteins were identified, and the association between the two omics was determined in the discovery set. Enrichment analysis of the top 25 metabolites and 25 proteins in the two-way orthogonal partial least-squares with discriminant analysis, which was employed to identify highly correlated biomarkers, highlighted 15 pathways relevant to the pathological process. One metabolite and seven proteins exhibited differences between groups in the validation set. The binary logistic regression model, which included metabolite 2-hydroxyhippuric acid and proteins APOM_O95445, MASP2_O00187, and PRTN3_D6CHE9, achieved an area under the curve of 0.985 (95% CI: 0.966-1) in the discovery set. This study elucidated alterations and potential coregulatory influences of metabolites and proteins in the blood of IS patients.

Targeting HOXA11-AS to mitigate prostate cancer via the glycolytic metabolism: In vitro and in vivo.

As oncogenes or oncogene suppressors, long-stranded non-coding RNAs are essential for the formation and progression of human tumours. However, the mechanisms behind the regulatory role of RNA HOXA11-AS in prostate cancer (PCa) are unclear. PCa is a common malignant tumour worldwide, and an increasing number of studies have focused on its metabolic profile. Studies have shown that the long non-coding RNA (lncRNA) HOXA11-AS is aberrantly expressed in many tumours. However, the role of HOXA11-AS in PCa is unclear. This work aimed to determine how HOXA11-AS regulated PCa in vitro and in vivo. We first explored the clinical role of HOXA11-AS in PCa using bioinformatics methods, including single sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO)-logistics systematically. In this study, PCa cell lines were selected to assess the PCa regulatory role of HOXA11-AS overexpression versus silencing in vitro, and tumour xenografts were performed in nude mice to assess tumour suppression by HOXA11-AS silencing in vivo. HOXA11-AS expression was significantly correlated with clinicopathological factors, epithelial-mesenchymal transition (EMT) and glycolysis. Moreover, key genes downstream of HOXA11-AS exhibited good clinical diagnostic properties for PCa. Furthermore, we studied both in vitro and in vivo effects of HOXA11-AS expression on PCa. Overexpression of HOXA11-AS increased PCa cell proliferation, migration and EMT, while silencing HOXA11-AS had the opposite effect on PCa cells. In addition, multiple metabolites were downregulated by silencing HOXA11-AS via the glycolytic pathway. HOXA11-AS silencing significantly inhibited tumour development in vivo. In summary, silencing HOXA11-AS can inhibit PCa by regulating glucose metabolism and may provide a future guidance for the treatment of PCa.

Machine Learning-Based Models for Advanced Fibrosis and Cirrhosis Diagnosis in Chronic Hepatitis B Patients With Hepatic Steatosis.

BACKGROUND AND AIMS: The global rise of chronic hepatitis B (CHB) superimposed on hepatic steatosis (HS) warrants noninvasive, precise tools for assessing fibrosis progression. This study leveraged machine learning (ML) to develop diagnostic models for advanced fibrosis and cirrhosis in this patient population. METHODS: Treatment-naive CHB patients with concurrent HS who underwent liver biopsy in 10 medical centers were enrolled as a training cohort and an independent external validation cohort (NCT05766449). Six ML models were implemented to predict advanced fibrosis and cirrhosis. The final models, derived from SHAP (Shapley Additive exPlanations), were compared with Fibrosis-4 Index, nonalcoholic fatty liver disease Fibrosis Score, and aspartate aminotransferase-to-platelet ratio index using the area under receiver-operating characteristic curve (AUROC) and decision curve analysis (DCA). RESULTS: Of 1,198 eligible patients, the random forest model achieved AUROCs of 0.778 (95% confidence interval [CI], 0.749-0.807) for diagnosing advanced fibrosis (random forest advanced fibrosis model) and 0.777 (95% CI, 0.748-0.806) for diagnosing cirrhosis (random forest cirrhosis model) in the training cohort, and maintained high AUROCs in the validation cohort. In the training cohort, the random forest advanced fibrosis model obtained an AUROC of 0.825 (95% CI, 0.787-0.862) in patients with hepatitis B virus DNA ≥105 IU/mL, and the random forest cirrhosis model had an AUROC of 0.828 (95% CI, 0.774-0.883) in female patients. The 2 models outperformed Fibrosis-4 Index, nonalcoholic fatty liver disease Fibrosis Score, and aspartate aminotransferase-to-platelet ratio index in the training cohort, and also performed well in the validation cohort. CONCLUSIONS: The random forest models provide reliable, noninvasive tools for identifying advanced fibrosis and cirrhosis in CHB patients with concurrent HS, offering a significant advancement in the comanagement of the 2 diseases. CLINICALTRIALS: gov, Number: NCT05766449.

A deep learning model for differentiating paediatric intracranial germ cell tumour subtypes and predicting survival with MRI: a multicentre prospective study.

BACKGROUND: The pretherapeutic differentiation of subtypes of primary intracranial germ cell tumours (iGCTs), including germinomas (GEs) and nongerminomatous germ cell tumours (NGGCTs), is essential for clinical practice because of distinct treatment strategies and prognostic profiles of these diseases. This study aimed to develop a deep learning model, iGNet, to assist in the differentiation and prognostication of iGCT subtypes by employing pretherapeutic MR T2-weighted imaging. METHODS: The iGNet model, which is based on the nnUNet architecture, was developed using a retrospective dataset of 280 pathologically confirmed iGCT patients. The training dataset included 83 GEs and 117 NGGCTs, while the retrospective internal test dataset included 31 GEs and 49 NGGCTs. The model's diagnostic performance was then assessed with the area under the receiver operating characteristic curve (AUC) in a prospective internal dataset (n = 22) and two external datasets (n = 22 and 20). Next, we compared the diagnostic performance of six neuroradiologists with or without the assistance of iGNet. Finally, the predictive ability of the output of iGNet for progression-free and overall survival was assessed and compared to that of the pathological diagnosis. RESULTS: iGNet achieved high diagnostic performance, with AUCs between 0.869 and 0.950 across the four test datasets. With the assistance of iGNet, the six neuroradiologists' diagnostic AUCs (averages of the four test datasets) increased by 9.22% to 17.90%. There was no significant difference between the output of iGNet and the results of pathological diagnosis in predicting progression-free and overall survival (P = .889). CONCLUSIONS: By leveraging pretherapeutic MR imaging data, iGNet accurately differentiates iGCT subtypes, facilitating prognostic evaluation and increasing the potential for tailored treatment.

Application of a single-cell-RNA-based biological-inspired graph neural network in diagnosis of primary liver tumors.

Single-cell technology depicts integrated tumor profiles including both tumor cells and tumor microenvironments, which theoretically enables more robust diagnosis than traditional diagnostic standards based on only pathology. However, the inherent challenges of single-cell RNA sequencing (scRNA-seq) data, such as high dimensionality, low signal-to-noise ratio (SNR), sparse and non-Euclidean nature, pose significant obstacles for traditional diagnostic approaches. The diagnostic value of single-cell technology has been largely unexplored despite the potential advantages. Here, we present a graph neural network-based framework tailored for molecular diagnosis of primary liver tumors using scRNA-seq data. Our approach capitalizes on the biological plausibility inherent in the intercellular communication networks within tumor samples. By integrating pathway activation features within cell clusters and modeling unidirectional inter-cellular communication, we achieve robust discrimination between malignant tumors (including hepatocellular carcinoma, HCC, and intrahepatic cholangiocarcinoma, iCCA) and benign tumors (focal nodular hyperplasia, FNH) by scRNA data of all tissue cells and immunocytes only. The efficacy to distinguish iCCA from HCC was further validated on public datasets. Through extending the application of high-throughput scRNA-seq data into diagnosis approaches focusing on integrated tumor microenvironment profiles rather than a few tumor markers, this framework also sheds light on minimal-invasive diagnostic methods based on migrating/circulating immunocytes.

Identification of novel protein biomarkers from the blood and urine for the early diagnosis of bladder cancer via proximity extension analysis.

BACKGROUND: Bladder cancer (BC) is a very common urinary tract malignancy that has a high incidence and lethality. In this study, we identified BC biomarkers and described a new noninvasive detection method using serum and urine samples for the early detection of BC. METHODS: Serum and urine samples were retrospectively collected from patients with BC (n = 99) and healthy controls (HC) (n = 50), and the expression levels of 92 inflammation-related proteins were examined via the proximity extension analysis (PEA) technique. Differential protein expression was then evaluated by univariate analysis (p < 0.05). The expression of the selected potential marker was further verified in BC and adjacent tissues by immunohistochemistry (IHC) and single-cell sequencing. A model was constructed to differentiate BC from HC by LASSO regression and compared to the detection capability of FISH. RESULTS: The univariate analysis revealed significant differences in the expression levels of 40 proteins in the serum (p < 0.05) and 17 proteins in the urine (p < 0.05) between BC patients and HC. Six proteins (AREG, RET, WFDC2, FGFBP1, ESM-1, and PVRL4) were selected as potential BC biomarkers, and their expression was evaluated at the protein and transcriptome levels by IHC and single-cell sequencing, respectively. A diagnostic model (a signature) consisting of 14 protein markers (11 in serum and three in urine) was also established using LASSO regression to distinguish between BC patients and HC (area under the curve = 0.91, PPV = 0.91, sensitivity = 0.87, and specificity = 0.82). Our model showed better diagnostic efficacy than FISH, especially for early-stage, small, and low-grade BC. CONCLUSION: Using the PEA method, we identified a panel of potential protein markers in the serum and urine of BC patients. These proteins are associated with the development of BC. A total of 14 of these proteins can be used to detect early-stage, small, low-grade BC. Thus, these markers are promising for clinical translation to improve the prognosis of BC patients.

Urine and serum metabolomic analysis of endometrial cancer diagnosis and classification based on ultra-performance liquid chromatography mass spectrometry.

OBJECTIVE: This study aimed to reveal the urinary and serum metabolic pattern of endometrial cancer (EC) and establish diagnostic models to identify EC from controls, high-risk from low-risk EC, and type II from type I EC. METHOD: This study included 146 EC patients (comprising 79 low-risk and 67 high-risk patients, including 124 type I and 22 type II) and 59 controls. The serum and urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry. Analysis was used to elucidate the distinct metabolites and altered metabolic pathways. Receiver operating characteristic (ROC) analyses were employed to discover and validate the potential biomarker models. RESULTS: Serum and urine metabolomes displayed significant differences between EC and controls, with metabolites related to amino acid and nicotinamide metabolisms. The serum and urine panels distinguished these two groups with Area Under the Curve (AUC) of 0.821 and 0.902, respectively. The panel consisting of serum and urine metabolites demonstrated the best predictive ability (AUC = 0.953 and 0.976 in discovering and validation group). In comparing high-risk and low risk EC, differential metabolites were enriched in purine and glutamine metabolism. The AUC values for serum and urine panels were 0.818, and 0.843, respectively. The combined panel exhibited better predictive accuracy (0.881 in discovering group and 0.936 in external validation). In the comparison between type I and type II group, altered folic acid metabolism was identified. The serum, urine and combined panels discriminated these two groups with the AUC of 0.829, 0.913 and 0.922, respectively. CONCLUSION: The combined urine and serum metabolome effectively revealed the metabolic patterns in EC patients, offering valuable diagnostic models for EC diagnosis and classification.

Exploring the diagnostic and immune infiltration roles of disulfidptosis related genes in pulmonary hypertension.

BACKGROUND: Pulmonary hypertension (PH) is marked by elevated pulmonary artery pressures due to various causes, impacting right heart function and survival. Disulfidptosis, a newly recognized cell death mechanism, may play a role in PH, but its associated genes (DiGs) are not well understood in this context. This study aims to define the diagnostic relevance of DiGs in PH. METHODS: Using GSE11726 data, we analyzed DiGs and their immune characteristics to identify core genes influencing PH progression. Various machine learning models, including RF, SVM, GLM, and XGB, were compared to determine the most effective diagnostic model. Validation used datasets GSE57345 and GSE48166. Additionally, a CeRNA network was established, and a hypoxia-induced PH rat model was used for experimental validation with Western blot analysis. RESULTS: 12 DiGs significantly associated with PH were identified. The XGB model excelled in diagnostic accuracy (AUC = 0.958), identifying core genes DSTN, NDUFS1, RPN1, TLN1, and MYH10. Validation datasets confirmed the model's effectiveness. A CeRNA network involving these genes, 40 miRNAs, and 115 lncRNAs was constructed. Drug prediction suggested therapeutic potential for folic acid, supported by strong molecular docking results. Experimental validation in a rat model aligned with these findings. CONCLUSION: We uncovered the distinct expression patterns of DiGs in PH, identified core genes utilizing an XGB machine-learning model, and established a CeRNA network. Drugs targeting the core genes were predicted and subjected to molecular docking. Experimental validation was also conducted for these core genes.

Vaginal and endometrial microbiome dysbiosis associated with adverse embryo transfer outcomes.

BACKGROUND: Assisted reproductive technology (ART) is the most effective method to treat infertility and the pathogenesis of implantation failure after in vitro fertilization-embryo transfer (IVF-ET) is a challenging filed in infertility. Microbes in the female reproductive tract are considered to be associated with gynecological and obstetric diseases. However, its effects on embryo implantation failure are unsured. PURPOSE: This study aimed to investigate reproductive tract dysbiosis, identify different bacteria in reproductive tract as potential biomarkers of embryo implantation failure and demonstrate the pathogenesis through metabolites analysis. METHODS: We compared the data from 16S rRNA gene and metagenome in reproductive tracts through QIIME2 and HUMAnN2 by the times of embryo implantation failure on 239 infertile patients and 17 healthy women. RESULTS: Our study revealed a strong positive correlation between Lactobacillus abundance and embryo implantation success (IS) after IVF-ET. The microbial community composition and structure in reproductive tract showed substantially difference between the embryo implantation failure (IF) and healthy control. Moreover, we established a diagnostic model through receiver operating characteristic (ROC) with 0.913 area under curve (AUC) in IS and multiple implantation failures (MIF), verified its effectiveness with an AUC = 0.784 demonstrating microbial community alterations could efficiently discriminate MIF patients. Metagenome functional analyses of vaginal samples from another independent infertile patients after IVF-ET revealed the L-lysine synthesis pathway enriched in IF patients, along with ascended vaginal pH and decreased Lactobacillus abundance. CONCLUSIONS: This study clarifies several independent relationships of bacteria in vagina and endometrial fluid on embryo implantation failure and undoubtedly broadens the understanding about female reproductive health.

Humoral immune response to tumor-associated antigen Ubiquilin 1 (UBQLN1) and its tumor-promoting potential in lung cancer.

BACKGROUND: This study aims to investigate the expression of UBQLN1 in lung cancer (LC) tissue and the diagnostic capability of autoantibody to UBQLN1 (anti-UBQLN1) in the detection of LC and the discrimination of pulmonary nodules (PNs). METHODS: Sera from 798 participants were used to discover and validate the level of autoantibodies via HuProt microarray and Enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis was applied to establish model. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic potential. Immunohistochemistry was performed to detect UBQLN1 expression in 88 LC tissues and 88 para-tumor tissues. qRT-PCR and western blotting were performed to detect the expression of UBQLN1 at the mRNA and protein levels, respectively. Trans-well assay and cell counting kit-8 (CCK-8) was used to investigate the function of UBQLN1. RESULTS: Anti-UBQLN1 was identified with the highest fold change by protein microarray. The level of anti-UBQLN1 in LC patients was obviously higher than that in NC or patients with benign lung disease of validation cohort 1 (P<0.05). The area under the curve (AUC) of anti-UBQLN1 was 0.610 (95%CI: 0.508-0.713) while reached at 0.822 (95%CI: 0.784-0.897) when combining anti-UBQLN1 with CEA, CYFRA21-1, CA125 and three CT indicators (vascular notch sign, lobulation sign and mediastinal lymph node enlargement) in the discrimination of PNs. UBQLN1 protein was overexpressed in lung adenocarcinoma (LUAD) tissues compared to para-tumor tissues. UBQLN1 knockdown remarkably inhibited the migration, invasion and proliferation of LUAD cell lines. CONCLUSIONS: Anti-UBQLN1 might be a potential biomarker for the diagnosis of LC and the discrimination of PNs.

Differentiated metabolomic profiling reveals plasma amino acid signatures for primary glomerular disease.

Primary glomerular disease (PGD) is an idiopathic cause of renal glomerular lesions that is characterized by proteinuria or hematuria and is the leading cause of chronic kidney disease (CKD). The identification of circulating biomarkers for the diagnosis of PGD requires a thorough understanding of the metabolic defects involved. In this study, ultra-high performance liquid chromatography-tandem mass spectrometry was performed to characterize the amino acid (AA) profiles of patients with pathologically diagnosed PGD, including minimal change disease (MCD), focal segmental glomerular sclerosis (FSGS), membranous nephropathy, and immunoglobulin A nephropathy. The plasma concentrations of asparagine and ornithine were low, and that of aspartic acid was high, in patients with all the pathologic types of PGD, compared to healthy controls. Two distinct diagnostic models were generated using the differential plasma AA profiles using logistic regression and receiver operating characteristic analyses, with areas under the curves of 1.000 and accuracies up to 100.0% in patients with MCD and FSGS. In conclusion, the progression of PGD is associated with alterations in AA profiles, The present findings provide a theoretical basis for the use of AAs as a non-invasive, real-time, rapid, and simple biomarker for the diagnosis of various pathologic types of PGD.

Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning.

BACKGROUND: T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been implicated in numerous pathological conditions, but its role in acute myocardial Infarction (AMI) remains largely unexplored. This research aims to identify and characterize all TEX-related genes for AMI diagnosis. METHODS: By integrating gene expression profiles, differential expression analysis, gene set enrichment analysis, protein-protein interaction networks, and machine learning algorithms, we were able to decipher the molecular mechanisms underlying TEX and its significant association with AMI. In addition, we investigated the diagnostic validity of the leading TEX-related genes and their interactions with immune cell profiles. Different types of candidate small molecule compounds were ultimately matched with TEX-featured genes in the "DrugBank" database to serve as potential therapeutic medications for future TEX-AMI basic research. RESULTS: We screened 1725 differentially expressed genes (DEGs) from 80 AMI samples and 71 control samples, identifying 39 differential TEX-related transcripts in total. Functional enrichment analysis identified potential biological functions and signaling pathways associated with the aforementioned genes. We constructed a TEX signature containing five hub genes with favorable prognostic performance using machine learning algorithms. In addition, the prognostic performance of the nomogram of these five hub genes was adequate (AUC between 0.815 and 0.995). Several dysregulated immune cells were also observed. Finally, six small molecule compounds which could be the future therapeutic for TEX in AMI were discovered. CONCLUSION: Five TEX diagnostic feature genes, CD48, CD247, FCER1G, TNFAIP3, and FCGRA, were screened in AMI. Combining these genes may aid in the early diagnosis and risk prediction of AMI, as well as the evaluation of immune cell infiltration and the discovery of new therapeutics.

Risk assessment and prediction of occult uterine sarcoma in patients with presumed uterine fibroids before high-intensity focused ultrasound treatment.

OBJECTIVE: To develop a diagnostic model for predicting occult uterine sarcoma in patients with presumed uterine fibroids. MATERIALS AND METHODS: We retrospectively reviewed 41631 patients with presumed uterine fibroids who presented for HIFU treatment in 13 hospitals between November 2008 and October 2023. Of these patients, 27 with occult uterine sarcoma and 54 with uterine fibroids were enrolled. Univariate analysis and multivariate logistics regression analysis were used to determine the independent risk factors for the diagnosis of occult uterine sarcoma. A prediction model was constructed based on the coefficients of the risk factors. RESULTS: The multivariate analysis revealed abnormal vaginal bleeding, ill-defined boundary of tumor, hyperintensity on T2WI, and central unenhanced areas as independent risk factors. A scoring system was created to assess for occult uterine sarcoma risk. The score for abnormal vaginal bleeding was 56. The score for ill-defined lesion boundary was 90. The scores for lesions with hypointensity, isointensity signal/heterogeneous signal intensity, and hyperintensity on T2WI were 0, 42, and 93, respectively. The scores for lesions without enhancement on the mass margin, uniform enhancement of tumor, and no enhancement in the center of tumor were 0, 20, and 100, respectively. Patients with a higher total score implied a higher likelihood of a diagnosis of occult uterine sarcoma than that of patients with a lower score. The established model showed good predictive efficacy. CONCLUSIONS: Our results demonstrated that the diagnostic prediction model can be used to evaluate the risk of uterine sarcoma in patients with presumed uterine fibroids.

Single-cell and bulk RNA sequencing reveal heterogeneity and diagnostic markers in papillary thyroid carcinoma lymph-node metastasis.

PURPOSE: Papillary thyroid carcinoma (PTC) is characterized by lymph-node metastasis (LNM), which affects recurrence and prognosis. This study analyzed PTC LNM by single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) to find diagnostic markers and therapeutic targets. METHODS: ScRNA-seq data were clustered and malignant cells were identified. Differentially expressed genes (DEGs) were identified in malignant cells of scRNA-seq and bulk RNA-seq, respectively. PTC LNM diagnostic model was constructed based on intersecting DEGs using glmnet package. Next, PTC samples from 66 patients were used to validate the two most significant genes in the diagnostic model, S100A2 and type 2 deiodinase (DIO2) by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and immunohistochemical (IHC). Further, the inhibitory effect of DIO2 on PTC cells was verified by cell biology behavior, western blot, cell cycle analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, and xenograft tumors. RESULTS: Heterogeneity of PTC LNM was demonstrated by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. A total of 19 differential genes were used to construct the diagnostic model. S100A2 and DIO2 differ significantly at the RNA (p < 0.01) and protein level in LNM patient tissues (p < 0.001). And differed in PTC tissues with different pathologic typing (p < 0.001). Further, EdU (p < 0.001) and cell biology behavior revealed that PTC cells overexpressed DIO2 had reduced proliferative capacity. Cell cycle proteins were reduced and cells are more likely to be stuck in G2/M phase (p < 0.001). CONCLUSIONS: This study explored the heterogeneity of PTC LNM using scRNA-seq. By combining with bulk RNA-seq data, diagnostic markers were explored and the model was established. Clinical diagnostic efficacy of S100A2 and DIO2 was validated and the treatment potential of DIO2 was discovered.

Establishment of diagnostic model and identification of diagnostic markers between liver cancer and cirrhosis based on multi-chip and machine learning.

OBJECTIVE: Most cases of hepatocellular carcinoma (HCC) arise as a consequence of cirrhosis. In this study, our objective is to construct a comprehensive diagnostic model that investigates the diagnostic markers distinguishing between cirrhosis and HCC. METHODS: Based on multiple GEO datasets containing cirrhosis and HCC samples, we used lasso regression, random forest (RF)-recursive feature elimination (RFE) and receiver operator characteristic analysis to screen for characteristic genes. Subsequently, we integrated these genes into a multivariable logistic regression model and validated the linear prediction scores in both training and validation cohorts. The ssGSEA algorithm was used to estimate the fraction of infiltrating immune cells in the samples. Finally, molecular typing for patients with cirrhosis was performed using the CCP algorithm. RESULTS: The study identified 137 differentially expressed genes (DEGs) and selected five significant genes (CXCL14, CAP2, FCN2, CCBE1 and UBE2C) to construct a diagnostic model. In both the training and validation cohorts, the model exhibited an area under the curve (AUC) greater than 0.9 and a kappa value of approximately 0.9. Additionally, the calibration curve demonstrated excellent concordance between observed and predicted incidence rates. Comparatively, HCC displayed overall downregulation of infiltrating immune cells compared to cirrhosis. Notably, CCBE1 showed strong correlations with the tumour immune microenvironment as well as genes associated with cell death and cellular ageing processes. Furthermore, cirrhosis subtypes with high linear predictive scores were enriched in multiple cancer-related pathways. CONCLUSION: In conclusion, we successfully identified diagnostic markers distinguishing between cirrhosis and hepatocellular carcinoma and developed a novel diagnostic model for discriminating the two conditions. CCBE1 might exert a pivotal role in regulating the tumour microenvironment, cell death and senescence.

Enhanced morphological assessment based on interocular asymmetry analysis for keratoconus detection.

PURPOSE: To clarify the interocular asymmetry of corneal morphological descriptors and evaluate its discriminant ability of keratoconus (KC). METHODS: This retrospective study recruited 344 normal participants and 290 KC patients, randomized to training and validation datasets. Interocular correlation and agreement were evaluated on 44 corneal morphological descriptors derived from Schiempflug tomography. Logistic regression models were constructed using binocular data and of which diagnostic performance was evaluated using the area under receiver operating characteristics curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). RESULTS: Interocular agreement of corneal descriptors is better in the normal than in KC except for dimensions of cornea and anterior chamber. The interocular asymmetry increases along with the severity of KC. Interocular asymmetry in maximum anterior keratometry, mean anterior keratometry and higher-order aberrations of anterior surface show high AUC above 0.950. Binocular logistic regression index reaches an AUC of 0.963 with high specificity (95.2%) and brings gain to monocular parameters in distinguishing the normal eyes from KC (NRI = 0.080 (0.042 ~ 0.118), P < 0.001) and IDI = 0.071 (0.049 ~ 0.092), P < 0.001). Interocular asymmetry benefits even more in subclinical keratoconus (SKC) detection reflected by NRI (0.4784 (0.2703-0.6865), P < 0.001) and IDI (0.2680 (0.1495-0.3866), P < 0.001) measures. CONCLUSION: Interocular asymmetry is a well-characterized feature of KC and related to the severity. It is feasible to apply the interocular asymmetry in diagnosis of KC and SKC as a replenishment of monocular parameters and in progression tracking.

Diagnosis of forme fruste keratoconus with scheimpflug photography in Ghanaian patients.

AIM: This study aimed to differentiate moderate to high myopic astigmatism from forme fruste keratoconus using Pentacam parameters and develop a predictive model for early keratoconus detection. METHODS: We retrospectively analysed 196 eyes from 105 patients and compared Pentacam variables between myopic astigmatism (156 eyes) and forme fruste keratoconus (40 eyes) groups. Receiver operating characteristic curve analysis was used to determine the optimal cut-off values, and a logistic regression model was used to refine the diagnostic accuracy. RESULTS: Statistically significant differences were observed in most Pentacam variables between the groups (p < 0.05). Parameters such as the Index of Surface Variance (ISV), Keratoconus Index (KI), Belin/Ambrosio Deviation Display (BAD_D) and Back Elevation of the Thinnest Corneal Locale (B.Ele.Th) demonstrated promising discriminatory abilities, with BAD_D exhibiting the highest Area under the Curve. The logistic regression model achieved high sensitivity (92.5%), specificity (96.8%), accuracy (95.9%), and positive predictive value (88.1%). CONCLUSION: The simultaneous evaluation of BAD_D, ISV, B.Ele.Th, and KI aids in identifying forme fruste keratoconus cases. Optimal cut-off points demonstrate acceptable sensitivity and specificity, emphasizing their clinical utility pending further refinement and validation across diverse demographics.

Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method.

BACKGROUND: Pneumocystis jirovecii pneumonia (PJP) is an interstitial pneumonia caused by pneumocystis jirovecii (PJ). The diagnosis of PJP primarily relies on the detection of the pathogen from lower respiratory tract specimens. However, it faces challenges such as difficulty in obtaining specimens and low detection rates. In the clinical diagnosis process, it is necessary to combine clinical symptoms, serological test results, chest Computed tomography (CT) images, molecular biology techniques, and metagenomics next-generation sequencing (mNGS) for comprehensive analysis. PURPOSE: This study aims to overcome the limitations of traditional PJP diagnosis methods and develop a non-invasive, efficient, and accurate diagnostic approach for PJP. By using this method, patients can receive early diagnosis and treatment, effectively improving their prognosis. METHODS: We constructed an intelligent diagnostic model for PJP based on the different Convolutional Neural Networks. Firstly, we used the Convolutional Neural Network to extract CT image features from patients. Then, we fused the CT image features with clinical information features using a feature fusion function. Finally, the fused features were input into the classification network to obtain the patient's diagnosis result. RESULTS: In this study, for the diagnosis of PJP, the accuracy of the traditional PCR diagnostic method is 77.58%, while the mean accuracy of the optimal diagnostic model based on convolutional neural networks is 88.90%. CONCLUSION: The accuracy of the diagnostic method proposed in this paper is 11.32% higher than that of the traditional PCR diagnostic method. The method proposed in this paper is an efficient, accurate, and non-invasive early diagnosis approach for PJP.

Relationship of dietary nutrients with early childhood caries and caries activity among children aged 3-5 years-a cross-sectional study.

BACKGROUND: Early childhood caries (ECC) is a challenge for pediatric dentists all over the world, and dietary factor is an important factor affecting the occurrence of ECC. Currently, there is limited research on the impact of dietary nutrient intake from Chinese diets on ECC. The purpose of this study is to explore the correlation of dietary nutrients intake with ECC and caries activity (CA) among children aged 3-5 years, and to provide dietary guidance to slow down the occurrence and development of ECC. METHODS: A cross-sectional study was conducted in 2022. A total of 155 children were divided into three groups: caries-free group, ECC group and Severe early childhood caries (SECC) group according to the caries statues. And according to the caries activity test (CAT) value, they were also divided into three group: low CA group (L-CA), middle CA group (M-CA) and high CA group (H-CA). The 24-hour dietary intake information was collected by mobile phone application (APP). The intake of children's daily dietary nutrients were calculated referring to "China Food Composition Tables". RESULTS: In this study, 17, 39,and 99 children were diagnosed with caries-free, ECC, and SECC. There were 33, 36, and 86 children diagnosed with L-CA, M-CA, and H-CA. The risk of ECC was increased with the intake of cholesterol(OR = 1.005) and magnesium (OR = 1.026) and decreased with the intake of iron (OR = 0.770). The risk of SECC was increased with the intake of cholesterol (OR = 1.003). The risk of high CA was increased with the intake of cholesterol (OR = 1.002). The combined application of dietary total calories, carbohydrate, cholesterol, sodium, magnesium and selenium in the diagnosis of ECC had an area under ROC curve of 0.741. CONCLUSIONS: The increased dietary cholesterol intake may be a common risk factor for ECC and high CA in children aged 3-5. The combined application of dietary intake of total calories, carbohydrate, cholesterol, sodium, magnesium and selenium has a higher predictive value for the occurrence of ECC.