Laisheng Li

Objective: Microvascular invasion (MVI) has been identified as a risk factor for the prognosis of patients with hepatocellular carcinoma (HCC). However, it can only be diagnosed pathologically, and thus no widely applicable preoperative MVI risk prediction model has been established. The aim of this study was to develop a preoperative predictive model for MVI. Design: The model derivation set was derived from a global meta‐analysis involving 39,253 patients. Model validation was performed at the First Affiliated Hospital of Nanjing Medical University (Nanjing set), which included 538 participants, and the First Affiliated Hospital of Sun Yat‐sen University (Guangzhou set), which included 111 participants. The model's discriminative ability, assessed by the concordance index ( C ‐statistic), and calibration curves for MVI probability were evaluated. Results: Through meta‐analysis, we identified 40 MVI risk factors and utilized the three most widely used preoperative indicators (alpha‐fetoprotein [AFP], tumor size, and tumor margin) to establish an MVI risk prediction model. The C ‐statistics of the scoring model were 0.805 (95% CI 0.765–0.844) in the Nanjing set and 0.808 (95% CI 0.729–0.887) in the Guangzhou set. Calibration of the established preoperative MVI risk prediction model was adequate (all χ ² values < 20). Conclusion: The preoperative MVI risk prediction model developed via global meta‐analysis exhibits good discriminative ability in different Chinese validation cohorts. Preoperative assessment of MVI status can guide the selection of hepatectomy type, surgical margin width, and neoadjuvant therapy administration, thereby ultimately improving patient prognosis.

Background: Sepsis remains a lethal global health crisis with persistently high mortality, exacerbated by diagnostic challenges stemming from its heterogeneous clinical presentation and limitations of current diagnostic tools like SOFA score. While biomarkers such as Presepsin or human epididymis protein 4 (HE4) show promise, single-marker approaches exhibit insufficient accuracy for reliable early detection and prognosis. Objective: We evaluated the diagnostic and prognostic utility of Presepsin, HE4, Oxygenation Index (OI), and their combined panel (PHO) in critically ill patients with or without sepsis. Methods: This single-center study analyzed 411 ICU patients (165 non-sepsis, 246 sepsis or septic shock). Clinical parameters—including SOFA, and OI—alongside laboratory parameters (hematological indices, hepatic/renal function markers, inflammatory biomarkers, blood culture results) were extracted from the hospital’s Laboratory Information System. Residual admission samples were used to quantify Presepsin and HE4. Receiver operating characteristic (ROC) curve analysis, Spearman’s rank correlation, and gradient boosting machine learning models were employed to evaluate the diagnostic and prognostic performance. Results: Presepsin, HE4, and OI were significantly elevated in septic patients compared to controls (all P < 0.05) and correlated strongly with 30-day mortality. The gradient boosting algorithm identified these three markers as the most significant predictors. Importantly, the combined biomarker panel PHO demonstrated superior performance in both diagnosis and prognosis. For sepsis diagnosis, PHO achieved an outstanding AUC of 0.892 (95% CI: 0.860– 0.924), significantly outperforming individual biomarkers (Presepsin: 0.821; HE4: 0.803; OI: 0.752) and conventional inflammatory markers (all P< 0.05). For mortality prediction, PHO maintained the highest prognostic accuracy (AUC 0.706, 95% CI: 0.641– 0.772), with improved sensitivity and specificity compared to single biomarkers. Conclusion: The combined Presepsin, HE4 and OI biomarker panel significantly outperforms individual markers in sepsis diagnosis and prognosis. This machine learning-validated composite indicator enables early risk stratification and may guide timely interventions to improve outcomes.

Febrile neutropenia (FN) is a prevalent infectious complication in paediatric cancer patients undergoing anticancer treatment. Presepsin is a reliable biomarker for sepsis; however, its clinical value in the context of paediatric FN remains unclear. This study aimed to evaluate the efficacy of presepsin in the entire course of paediatric patients with FN. In this prospective study, 108 paediatric patients with haematological tumours complicated with neutropenia were recruited, of whom 23 had febrile neutropenia. Blood samples were collected daily, and four inflammation biomarkers, presepsin, procalcitonin (PCT), interleukin-6 (IL-6) and C-reactive protein (CRP), were analyzed. Clinical and laboratory parameters were collected, and the diagnostic and prognostic value of biomarkers for FN was assessed. The concentrations of presepsin, PCT, IL-6 and CRP were prospectively measured in 57 FN episodes in 23 cases of malignant haematological diseases in children. The concentrations of presepsin, PCT and CRP were significantly elevated 72 h before onset compared to those in the control group. Only the concentration of presepsin at FN onset was significantly higher than 72 h before FN onset. The area under the curve (AUC) of presepsin, PCT, IL-6 and CRP for FN diagnosis 24 h before FN onset were 0.9118, 0.8214, 0.9052 and 0.8286, respectively. For prognosis, the levels of presepsin were consistently and significantly higher at every time point in patients with unfavourable outcomes than in those with favourable outcomes. Presepsin demonstrated the best performance in both the early diagnosis and prognosis assessment of FN, indicating that it is a reliable biomarker for managing the entire FN process. Close monitoring of presepsin may enable earlier intervention for FN, reducing the occurrence of adverse events caused by infection.

Background: Neonatal sepsis (NS) is an important cause of mortality and morbidity in newborn infants. However, early diagnosis of proven sepsis (culture-positive sepsis) is difficult. We aimed to define the best combination of biomarkers to diagnose the onset of neonatal sepsis, distinguish culture-positive neonatal sepsis and predict the time of confirmation of neonatal sepsis. Methods: This retrospective cohort study was conducted from January 2016 to December 2020. Clinical characteristics and laboratory results were collected from the electronic medical records. Hematology profiles and biochemical indices were obtained upon hospital admission. Multivariate logistic regression analysis was used to evaluate the risk factors and construct a nomogram. The performance of the nomogram was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Multivariable linear regression was used to identify the association between admission-to-diagnosis interval (ADI) and correlated variables. Results: Overall, 148 infants with neonatal sepsis (67 culture positive sepsis and 81 culture negative sepsis) and 150 controls were included. C-reactive protein (CRP) (p< 0.001), platelets (PLT) (p=0.011), urea nitrogen (BUN) (p=0.001) and conjugated bilirubin (BC) (p=0.007) were independent risk factors for neonatal sepsis. The diagnostic nomogram based on CRP, PLT, BUN and BC showed excellent diagnostic accuracy for neonatal sepsis (AUC=0.928). The nomogram based on red blood cell distribution width (RDW) and mean platelet volume (MPV) was efficient in distinguishing proven neonatal sepsis from clinical sepsis, with an AUC of 0.700 in the training group and 0.689 in the validation group. Decision curve analysis (DCA) showed that the nomogram had good clinical utility. Multivariable analysis revealed gestational age, CRP, and MPV were significantly associated with admission-to-diagnosis interval in culture-positive sepsis (p < 0.001). Conclusion: Different combinations biomarkers were performant to diagnose the onset of neonatal sepsis, distinguish culture-positive neonatal sepsis, predict the time of confirmation, and aid in individual therapy.

Background Presepsin is a soluble CD14 subtype that has been considered as a novel marker for patients with sepsis. This study explored the clinical value of presepsin for sepsis in Southern China, and further established models for diagnosis and prognosis of sepsis through using machine learning (ML), by combining presepsin and other laboratory parameters. Methods 269 subjects (105 infected patients, 164 sepsis and septic shock) and 198 healthy controls were enrolled. Laboratory parameters (hematological parameters, coagulation parameters, liver function indices, renal function indices, and inflammatory markers) were collected. Plasma presepsin was tested by chemiluminescence enzyme immunoassay. ML of DxAI™ Research platform was used to establish diagnostic and prognostic models. Sensitivity, specificity, and other performance indicators were used to evaluate the performance of each model. Results The level of presepsin was obviously increased in sepsis and sepsis shock, compared with that of infected and healthy group (all P < 0.0001). Presepsin concentration was positively correlated with positive blood culture and 30-day mortality in sepsis and septic shock patients. Through ROC curve analysis, Hb, UREA, APTT, CRP, PCT, and presepsin were incorporated into machine learning to construct diagnosis models. Ada Boost model had the best diagnostic efficiency (AUC: 0.94 (95% CI 0.919–0.968) in the training set and AUC: 0.86 (95% CI 0.813–0.900) in validation set). Furthermore, AST, APTT, UREA, PCT, and presepsin were included in the prognosis ML models, and the Bernoulli NB model had greater predictive ability for 30-day mortality risk of sepsis (AUC: 0.706), which was higher than that of PCT (AUC: 0.617) and presepsin (AUC: 0.634) alone. Conclusion Machine-learning model based on presepsin and routinely laboratory parameters showed good performance of diagnostic and prognostic ability for sepsis patients.

Aims: Our study focused on exploring the feasible prognostic laboratory parameters of HCC and establishing a score model to estimate individualized overall survival (OS) in HCC after resection.Methods: Four hundred and sixty‐one patients with HCC who underwent hepatectomy between January 2010 and December 2017 was enrolled in this investigation. Cox proportional hazards model was conducted to analyze the prognostic value of laboratory parameters. The score model construction was based on the forest plot results. Overall survival was evaluated by Kaplan‐Meier method and the log‐rank test. The novel score model was validated in an external validation cohort from a different medical institution.Results: We identified that alpha fetoprotein (AFP), total bilirubin (TB), fibrinogen (FIB), albumin (ALB), and lymphocyte (LY) were independent prognostic factors. High AFP, TB, FIB (HR > 1, p < 0.05), and low ALB, LY (HR < 1, p < 0.05) were associated with the survival of HCC. The novel score model of OS based on these five independent prognostic factors achieved high C‐index of 0.773 (95% confidence interval [CI]: 0.738–0.808), which was significantly higher than those of the single five independent factors (0.572–0.738). The score model was validated in the external cohort whose C‐index was 0.7268 (95% CI: 0.6744–0.7792).Conclusion: The novel score model we established was an easy‐to‐use tool which could enable individualized estimation of OS in patients with HCC who underwent curative hepatectomy.

Background: The aim of the present study was to develop an improved diagnostic and prognostic model for HBV-associated HCC by combining AFP with PIVKA-II and other potential serum/plasma protein biomarkers. Methods: A total of 578 patients, including 352 patients with HBV-related HCC, 102 patients with HBV-associated liver cirrhosis (LC), 124 patients with chronic HBV, and 127 healthy subjects (HS), were enrolled in the study. The serum levels of AFP, PIVKA-II, and other laboratory parameters were collected. Univariate and multivariate logistic regression and Cox regression analyses were performed to identify independent diagnostic and prognostic factors, respectively. The diagnostic efficacy of the nomogram was evaluated using receiver operator curve (ROC) analysis and the prognostic performance was measured by Harrell’s concordance index (C-index). Results: AFP and PIVKA-II levels were significantly increased in HBV-related HCC, compared with those in HBV-associated LC and chronic HBV participants (p < 0.05 and p < 0.001, respectively). The diagnostic nomogram, which included age, gender, AFP, PIVKA-II, prothrombin time (PT), and total protein (TP), discriminated patients with HBV-HCC from those with HBV-LC or chronic HBV with an AUC of 0.970. In addition, based on the univariate and multivariate Cox regression analysis, PIVKA-II, γ-glutamyl transpeptidase, and albumin were found to be significantly associated with the prognosis of HBV-related HCC and were incorporated into a nomogram. The C-index of the nomogram for predicting 3-year survival in the training and validation groups was 0.75 and 0.78, respectively. The calibration curves for the probability of 3-year OS showed good agreement between the nomogram prediction and the actual observation in the training and the validation groups. Furthermore, the nomogram had a higher C-index (0.74) than that of the Child−Pugh grade (0.62), the albumin−bilirubin (ALBI) score (0.64), and Barcelona Clinic Liver Cancer (0.56) in all follow-up cases. Conclusion: Our study suggests that the nomograms based on AFP, PIVKA-II, and potential serum protein biomarkers showed a better performance in the diagnosis and prognosis of HCC, which may help to guide therapeutic strategies and assess the prognosis of HCC.

The balance between inflammatory T helper type 17 (Th17) and immunosuppressive regulatory T (Treg) cells is critical for maintaining immune homeostasis in the human body and is tightly regulated under healthy conditions. An increasing number of studies have reported that deubiquitinases (DUBs) play a vital role in regulating Th17- and Treg-cell differentiation. However, the biological functions of only a small fraction of DUBs in Th17- and Treg-cell differentiation are well defined. In this study, we identified ubiquitin-specific peptidase 1 (USP1) as a vital regulator of CD4⁺ T-cell differentiation. USP1 promoted Th17-cell differentiation but attenuated Treg-cell differentiation, thereby promoting the development of inflammatory diseases. Mechanistically, USP1 in CD4⁺ T cells enhanced the activity of RORγt but promoted the proteasomal degradation of Foxp3 through deubiquitination and stabilization of TAZ in vitro and in vivo. Notably, ML323, a specific inhibitor of the USP1/UAF1 deubiquitinase complex, inhibited Th17-cell differentiation and promoted Treg-cell differentiation in vitro and in vivo, indicating that ML323 might be a promising candidate for the treatment of diseases associated with an imbalance between Th17 and Treg cells. Our study highlights the critical role of USP1 in regulating adaptive immune responses and suggests that USP1 might be a drug target for the treatment of diseases associated with an imbalance between Th17 and Treg cells.

Background: The authors investigated a panel of novel biomarkers for diagnosis and prognosis assessment of sepsis using machine learning (ML) methods. Methods: Hematological parameters, liver funct...