Intravenous fluids are among the most commonly prescribed drugs. Many preparations classified by composition as crystalloids or colloids, of natural or synthetic origin, are available in clinical practice. Guidelines favor crystalloids as first-choice fluids in most clinical situations, mainly because of the lack of advantage of using colloids in reducing mortality and organ complications and generating higher treatment costs. This review focuses on the evidence for the use of colloids in selected clinical conditions.

Traditional fluorescent proteins, based on genetic encoding and expression, have introduced biocompatible fluorescent labeling analysis methods at the cellular and organismal levels, becoming an essential research tool in life sciences. In contrast, organic small-molecule fluorescent dyes offer greater flexibility in modification and tunability of wavelengths. The integration of the advantages of fluorescent proteins and organic dyes is a key focus for further enriching fluorescence analysis technologies in the understanding of life. In this work, we utilized the spontaneous and covalent modification of fluorescent dyes with human serum albumin to construct a series of complexes with different fluorescent activities, which we named fluoroalbumin (FLA). FLAs exhibit high fluorescence brightness, excellent photostability, and biocompatibility. Interestingly, the natural drug-binding sites of albumin are preserved, and through allosteric effects, they regulate the fluorescence signals of FLAs, thereby enabling the fluorescence analysis of clinical drugs such as ibuprofen.

The neutrophil-percentage-to-albumin ratio (NPAR) has emerged as a concise and effective biomarker for assessing systemic inflammatory status, with established prognostic value for mortality risk across various disease populations. This study aimed to elucidate the association between NPAR and both all-cause and cardiovascular mortality in patients with diabetes and prediabetes. A cohort of 8560 patients with diabetes and prediabetes was recruited from the National Health and Nutrition Examination Survey (NHANES), with mortality outcomes tracked through the National Death Index up to December 31, 2019. A weighted Cox regression model was used to evaluate the association of NPAR with all-cause and cardiovascular mortality. The restricted cubic spline (RCS) curve was used to assess the nonlinear relationship between NPAR and mortality outcomes. Subgroup analysis was conducted to evaluate the differences in the predictive performance of various NPAR features across different subgroups. Cox regression analysis indicated that participants in the highest quartile of NPAR exhibited a substantially increased risk of all-cause mortality (hazard ratio [HR] 1.54; 95% confidence interval [CI] 1.28-1.87) and cardiovascular death (HR 2.04; 95% CI 1.47-2.84) compared to the reference group. The RCS curve further illustrated a significant nonlinear correlation between NPAR and outcomes in patients with diabetes and prediabetes. NPAR can serve as a robust nonlinear predictor for both all-cause and cardiovascular mortality in individuals diagnosed with diabetes or prediabetes.

Human serum albumin (HSA) is a versatile, biodegradable, biocompatible, non-toxic, and non-immunogenic protein nanocarrier, making it an ideal platform for developing advanced drug delivery systems. These properties have garnered significant attention in utilizing HSA nanoparticles for the safe and efficient delivery of chemotherapeutic agents. HSA-based nanoparticles can be surface-modified with various ligands to enable tumor-targeted drug delivery, enhancing therapeutic specificity and efficacy. Furthermore, the multifunctionality of HSA nanoparticles offers promising strategies to overcome challenges in cancer therapy, including poor bioavailability, off-target toxicity, and drug resistance. This review highlights the structural features of HSA, explores its diverse modifications to improve drug-binding affinity and targeting ability, and discusses its potential as a multifunctional carrier in oncology. By summarizing the latest advances in HSA modification techniques and applications, this review provides a comprehensive perspective on the future of protein-based drug delivery systems in tumor therapy.

Neonatal sepsis significantly contributes to infant mortality rates. The absence of predictive markers has hindered timely clinical intervention, leading to elevated mortality. This study aimed to assess the prognostic relevance of the lactate dehydrogenase (LDH)-to-albumin ratio (LAR) in neonatal sepsis. A retrospective examination was conducted on a cohort of 130 neonates diagnosed with sepsis. Admission laboratory data were gathered. The optimal threshold for LAR was established using receiver operating characteristic curve analysis. Both univariate and multivariate analyses were performed to gauge the predictive efficacy of LAR. A statistical disparity in LAR was noted between survivors and non-survivors (p < 0.001). Multivariate analysis confirmed that LAR serves as an independent risk determinant for neonatal sepsis (Hazard ratio [HR] 11.236, 95% Confidence interval [CI] 3.311-38.462, p < 0.001). ROC analysis indicated that the area under the curve for LAR was 0.806 across the entire cohort, 0.842 for early-onset sepsis, and 0.737 for late-onset sepsis. Moreover, with a cutoff value set at 23.72, LAR exhibited a prediction specificity of 88.2% and sensitivity of 70.0%. Our research indicates that elevated admission LAR is a negative prognostic indicator in neonatal sepsis, suggesting its potential as a valuable biomarker in clinical settings.

This study examines the association between serum albumin (sALB) levels and periodontitis severity, focusing on subgroup differences and nonlinear relationships. It extends previous findings, which were limited to chronic kidney disease (CKD) patients.

This cross-sectional study utilized data from 8352 participants in the NHANES 2009 to 2014 survey cycles. sALB and periodontitis were the exposure and outcome variables. Logistic regression models and restricted cubic spline curves were used to investigate the relationship between the two. Additionally, subgroup and interaction analyses were conducted to assess the stability of the findings. All statistical analyses considered the complex survey design.

A significant negative association was observed between sALB and periodontitis status (aOR 0.94, 95% confidence intervals: 0.93-0.96, P value <.001). The strength of this association may be influenced by participants' gender, CKD status, and hypertension status. Among participants with sALB levels below 35 g/L (defined as hypoalbuminemia), no significant association with periodontitis was observed, even in those with CKD. Restricted cubic spline analysis demonstrated an inverted U-shaped relationship between sALB levels and periodontitis, with a threshold effect at 38 g/L. Above this inflection point, higher sALB levels were significantly associated with a lower prevalence of periodontitis (P < .001).

ALB levels were inversely associated with moderate and severe periodontitis, with an inverted U-shaped relationship observed in this study. The differences among subgroups warrant further research.

Maintaining appropriate sALB levels may be beneficial for periodontal health. Further research is needed to confirm its role in periodontitis prevention and treatment.

An increasing number of studies suggests an association between systemic inflammation, nutritional status, and cancer. However, the relationship between the prevalence of breast cancer (BC) and the neutrophil-percentage-to-albumin ratio (NPAR), a recently identified biomarker of inflammation, is not well established. Therefore, this study aims to investigate the relationship between BC risk and the NPAR.

This study included 18,726 participants from the National Health and Nutrition Examination Survey (NHANES) conducted between 2001 and 2018. The NPAR was used to assess inflammation and nutritional status. Statistical methods such as multivariate logistic regression, subgroup analysis, and restricted cubic spline (RCS) analysis were conducted to investigate the influence of NPAR on the prevalence of BC. In addition, propensity score matching was employed to further validate the findings.

The logistic regression results showed that the prevalence of breast cancer is significantly associated with the NPAR (OR = 1.05; 95% CI = 1.02-1.09, p = 0.003). In comparison to participants in the lowest quartile, Q1, the prevalence of breast cancer increased by 5% for those in Q2 (p = 0.745), 3% for those in Q3 (p = 0.032), and 38% for those in Q4 (p = 0.018) with a higher NPAR. In addition, subgroup and RCS analyses showed that the NPAR and BC prevalence were positively correlated. Furthermore, a significant association was observed between the NPAR and marital status. The significance of traits was assessed using mean decrease accuracy (MDA) and mean decrease impurity (MDI). These measures of random forest modeling showed that NPAR is one of the major factors affecting the prevalence of BC. Furthermore, linear analysis demonstrated a correlation between a high NPAR and increased total testosterone and sex hormone-binding globulin (SHBG) levels.

A significant association was observed between a high NPAR and a higher prevalence of breast cancer, which could be attributable to sex hormone levels. This finding suggests that the NPAR may serve as a biomarker for BC in adult women in the US.

Our objective was to identify distinct clinical subtypes among critically ill patients with cirrhosis and analyze the clinical features and prognosis of each subtype.

We extracted routine clinical data within 24 h of ICU admission from the MIMIC-IV database. To determine the number of clinical subtypes, we employed the "elbow method," "cumulative distribution function (CDF) plot," and "consensus matrix." Consensus k-means, k-means, and SOM methods were used to identify different clinical subtypes of critically ill cirrhosis. We validated our findings using patients from the eICU database. The SHapley Additive exPlanations (SHAP) method was used to explore the features of each clinical subtype, and 28-day Kaplan-Meier curves were generated. Survival differences among the clinical subtypes were assessed using the log-rank test.

Our study included 2,586 patients from the MIMIC-IV database and 1,670 patients from the eICU database. Based on the clinical routine variables, we identified three clinical subtypes among patients in the MIMIC-IV database. Subtype A (N = 1424, 55.07 %) was labeled the "common subtype" and exhibited the lowest mortality. Subtype B (N = 703, 27.18 %) was classified as the "hyperinflammatory response subtype" and had a relatively high mortality. Subtype C (N = 459, 17.75 %) was identified as the "liver dysfunction subtype" and had the highest mortality. These findings were consistent with the results obtained from both the internal validation set (MIMIC-IV database) and the external validation set (eICU database).

Our study presents a novel and clinically applicable approach for subtyping critically ill cirrhosis.

The neutrophil percentage-to-albumin ratio (NPAR) had been suggested as a potential prognostic biomarker in various health outcomes. However, its association with mortality in cancer survivors remains unclear.

A total of 3,022 cancer survivors from the National Health and Nutrition Examination Survey (NHANES) 2005-2018 were linked to mortality outcomes from the National Death Index (NDI). Weighted Cox proportional hazards models was conducted to investigate the association between NPAR and all-cause, cancer and cardiovascular disease (CVD) mortality and the hazard ratio (HR) with 95% confidence interval (CI) were calculated. Restricted cubic spline (RCS) was used to clarify the non-linear association. Additionally, analyses for stratification and sensitivity were performed.

During a median follow-up of 75 months, 790 all-cause deaths occurred, including 244 from cancer and 209 from CVD. After adjustment for covariates, higher NPAR was independently associated with increased risk of all-cause mortality (HR = 1.09, 95% CI = 1.06-1.13), cancer mortality (HR = 1.05, 95% CI = 0.99-1.12), and CVD mortality (HR = 1.13, 95% CI = 1.06-1.21). The RCS revealed a U-shaped relationship for all-cause and cancer mortality, with thresholds of 12.76 and 13.60, respectively. Below the threshold, higher NPAR was associated with a reduced risk of mortality (HR = 0.90, 95% CI = 0.82-0.99; HR = 0.87, 95% CI = 0.76-0.99), whereas above the threshold, the risk of mortality increased significantly (HR = 1.14, 95% CI = 1.09-1.18; HR = 1.15, 95% CI = 1.07-1.24). Subgroup and sensitivity analyses confirmed these findings.

The U-shaped association with all-cause and cancer mortality, along with the linear association with CVD mortality, underscores the potential of NPAR as a valuable prognostic marker in cancer survivors.

Ischemic stroke (IS) is associated with high disability and mortality. This study aimed to identify the prognostic predictors and develop a nomogram for a prediction model for ischemic stroke patients after thrombolysis.

We retrospectively analyzed data from 359 IS patients who underwent thrombolysis. Clinical characteristics, laboratory parameters, and prognosis data were collected. One-third of the subjects were randomly selected as a validation set (n = 108) for internal validation. Logistic regression analysis was used to derive independent risk indicators. A nomogram was constructed using these indicators, and the performance of the nomogram was assessed by the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). The agreement of the model predictions with actual observations was assessed via calibration curves, and the clinical utility of the nomogram was assessed via decision curve analysis.

Multivariate logistic regression analysis showed that age, leukocytes, Lactate Dehydrogenase to Albumin Ratio (LAR) and NIHSS were independent predictors of three-month post-thrombolysis prognosis in IS patients. We created a nomogram based on the weighting coefficients of these factors. The AUC curves showed that our model including age, leukocytes, LAR and NIHSS was more accurate in predicting prognosis than a single factor. The calibration curves showed a good fit between actual and predicted probabilities in both the training and validation groups.

LAR has a good predictive power for the prognosis of IS patients 3 months after thrombolytic therapy and can be used as a new clinical indicator to establish a practical nomogram.

Background/Objectives: Current approaches to monitoring obstructive sleep apnea (OSA) risk primarily focus on structural or functional abnormalities, often neglecting systemic metabolic and physiological factors. Resource-intensive methods, such as polysomnography (PSG), limit their routine applicability. This study aimed to evaluate composite nutritional-inflammatory indices derived from routine blood markers to identify feasible indices for OSA management and explore their association with OSA risk. Methods: Data from 9622 adults in the NHANES (2015-2020) and GWAS datasets were analyzed using logistic regression, restricted cubic splines, machine learning, and Mendelian randomization (MR). These techniques were employed to identify nutritional-inflammatory indices associated with OSA risk. Random forest modeling identified body mass index (BMI) and albumin (ALB) as key components of the advanced lung cancer inflammation index (ALI). Causal relationships between ALI components and OSA were validated using MR. Results: ALI was significantly associated with OSA, with individuals in the highest ALI tertile exhibiting a 59% higher likelihood of OSA (OR = 1.59, 95% CI: 1.38-1.84; p < 0.001). BMI and ALB were identified as key contributors to ALI and confirmed as causal risk factors for OSA (BMI: OR = 1.91, 95% CI: 1.80-2.02; ALB: OR = 1.11, 95% CI: 1.04-1.19). Age, gender, and the neutrophil-to-lymphocyte ratio (NLR) were also significant predictors. Conclusions: This study identifies ALI as a potential composite index for assessing OSA risk. Integrating statistical modeling, machine learning, and causal inference techniques highlights the utility of nutritional-inflammatory indices in improving OSA monitoring and management in clinical practice.

Hypoalbuminemia is a risk factor for mortality in patients with end-stage liver disease (ESLD) and in those undergoing orthotopic liver transplantation (OLT), since it represents a biomarker of post-operative delayed functional recovery of the graft. Despite albumin infusion during and after OLT is frequently adopted in recipients with hypoalbuminemia, it remains unclear whether this procedure could improve post OLT clinical outcomes. Observational studies indicated that treatment with albumin after OLT might be beneficial in reducing ascites and acute kidney injury (AKI) development. However, considering potential complications and the cost of albumin therapy, the decision to use albumin after OLT should be based on careful consideration of patient's individual needs and risks. In addition, the threshold plasma value of albumin below which it could be clinically useful to infuse albumin has not been clearly defined. This systematic review, prepared in accordance with the PRISMA 2020 guidelines, aimed to assess the efficacy of albumin infusion in patients undergoing OLT, in the prevention or treatment of ascites, AKI, and ischemia reperfusion syndrome, as well as its potential impact on patient survival. Furthermore, this review aimed to illustrate the pathophysiological bases justifying the use of albumin infusion in a subset of patients receiving OLT.

Blood glucose and serum albumin can be biomarkers at admission since they are easily accessible and demonstrate correlations with cardiovascular diseases. The predictive ability of the admission blood glucose to albumin ratio (AAR) for long-term prognosis in patients with acute coronary syndrome (ACS) and its potential to elevate the predictive value of the Global Registry of Acute Coronary Events (GRACE) risk score in ACS patients post-percutaneous coronary intervention (PCI) remains unknown. Hence, this study aimed to investigate the incremental prognostic value of the AAR in patients with ACS undergoing PCI.

A rigorous development-validation approach was implemented to optimize the GRACE risk score, utilizing the AAR parameter in 1498 patients suffering from ACS after PCI at the Third People's Hospital of Chengdu, Sichuan, China.

Over a median of 31.25 (27.53, 35.10) months, the incidence of major adverse cardiac events (MACEs), defined as a composite outcome encompassing all-cause death, cardiac death, nonfatal myocardial infarction, nonfatal stroke, and unplanned repeat revascularization, was higher in individuals with higher AARs. Thus, the AAR was an independent predictor of long-term prognosis in ACS patients undergoing PCI (HR, 1.145; 95% CI: 1.045-1.255; p = 0.004). The integration of the AAR score with the GRACE risk score increased the C statistic from 0.717 (95% CI: 0.694-0.740) to 0.733 (95% CI: 0.690-0.776) (p < 0.01).

The AAR is an independent predictor of prognosis in ACS patients and significantly increased the predictive value of the GRACE risk score.

To investigate the association of red blood cell distribution width (RDW) to albumin ratio (RAR) with mortality in chronic obstructive pulmonary disease (COPD) patients.

We selected 1,652 patients with COPD from the National Health and Nutrition Examination Survey (NHANES) 1999-2018, who were categorized into four groups according to the RAR quartiles. Kaplan-Meier curves, restricted cubic splines and the Cox proportional hazard model were used to evaluate the associations between RAR and all-cause mortality and chronic lower respiratory disease (CLRD) mortality in the COPD patients. Subgroup analyses were performed to check the interaction of the different characteristics.

There were 640 deaths during follow-up, of which, 145 were from CLRD. Kaplan-Meier curves indicated COPD patients with higher RAR had significantly increased all-cause mortality and CLRD mortality. Multivariate Cox regression analyses showed HR of Q4 RAR was 2.88 (95% CI 2.18 - 3.81, p < 0.0001) for all cause-mortality and 3.39 (95% CI 1.76 - 6.53, p < 0.001) for CLRD mortality, compared with Q1 RAR. Restricted cubic splines analysis indicated a dose-response between RAR and risk of all-cause and CLRD mortality (p for non-linearity < 0.001).

RAR had an independent association with all-cause mortality, especially CLRD mortality, in COPD patients. RAR has potential as a novel and promising predictor to identify COPD individuals with high mortality risk.

The prevalence and incidence of metabolic dysfunction-associated steatotic liver disease (MASLD) are significantly increasing globally, but the index of non-invasive disease is limited. Platelet-albumin ratio(PAR) is a non-invasive biomarker of inflammation, the aim of this study was to evaluate the relationship between PAR and MASLD. This population-based cross-sectional retrospective study analyzed data extracted from the National Health and Nutrition Survey (NHANES) database from 2017 to 2018. Multivariate logistic regression analysis was used to evaluate the correlation between PAR and MASLD in different models. Model I was unadjusted, model II adjusted for race, sex and age, and model III was adjusted based on model II plus smoking status, hypertension, and diabetes. Further subgroup analysis was carried out according to sex, age, hypertension and diabetes status. The study involved 3287 participants, of whom 873 (26.5%) were diagnosed with MASLD. The PAR level in MASLD group was significantly higher than non-MASLD group (P < 0.05). Multivariate logistic regression revealed that high PAR level was an independent risk factor for MASLD (OR = 2.58, 95%CI: 1.26-5.27, P = 0.03), which adjusted for sex, age, race, smoking status, hypertension, and diabetes.The same results were observed in multiple subgroups of further subgroup analysis, and it can effectively predict the risk of MASLD (AUC = 0.842, 95% CI: 0.826-0.859). In conclusion, the new biomarker PAR shows a positive correlation with the risk of MASLD in the population, and can be used as a biomarker of MASLD to help clinicians identify people at high risk of MASLD.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a prevalent chronic liver disease globally, with inflammation and nutrition playing key roles in its progression. The Advanced Lung Cancer Inflammation Index (ALI) is a novel biomarker reflecting nutritional and inflammatory status. This study aims to explore the association between ALI and the risk of liver fibrosis and prognosis in MAFLD patients.

This cross-sectional study analyzed NHANES data from the 1999-2018 on adult participants in the US. Weighted logistic regression assessed the association between ALI and liver fibrosis risk. Mortality outcomes, including all-cause, cardiovascular disease (CVD), and cancer mortality, analyzed using weighted Kaplan-Meier and Cox proportional hazards models. Restricted cubic splines (RCS) and threshold effect analyses were uesd to explore non-linear relationships. Receiver operating characteristic (ROC) curve evaluated the prognostic value of ALI, and stratified analyses examined subgroup differences.

A total of 6,858 MAFLD patients (mean age 51.38 ± 17.22 years, 54% male) were included. A non-linear relationship was found between ALI and liver fibrosis risk, with a threshold at 5.68, beyond which the risk increased significantly (OR = 2.35, 95% CI: 1.89-2.95). Stronger associations were observed in subgroups with central obesity and prediabetes (P for interaction < 0.05). ALI was inversely associated with all-cause mortality (HR = 0.64, 95% CI: 0.56-0.72) and CVD mortality (HR = 0.57, 95% CI: 0.46-0.65), but not cancer mortality. RCS analysis showed an L-shaped non-linear relationship with all-cause mortality (threshold at 5.36) and a linear relationship with CVD mortality. Low HDL cholesterol and excessive alcohol consumption influenced the association between ALI and all-cause mortality (P for interaction < 0.05). ALI demonstrated the highest predictive accuracy for CVD mortality.

ALI is associated with an increased risk of liver fibrosis and reduced all-cause and CVD mortality, highlighting its potential value in assessing MAFLD prognosis, particularly CVD-related mortality.

Background/Objectives: Renal cell carcinoma (RCC) accounts for 2-3% of all cancers, with approximately 25% of patients being detected at the metastatic stage. This study aimed to determine the prognostic significance of co-evaluating two risk parameters: one, the HALP score based on haemoglobin, albumin, lymphocyte, and platelet counts, and the other, albumin-to-alkaline phosphatase ratio (AAPR) in patients with metastatic RCC treated with targeted therapy. Methods: This retrospective cohort study included 147 patients with metastatic RCC. The HALP score and AAPR values were calculated from pre-treatment blood test results, and followingly, the cut-off value was determined by using ROC analysis. Patients were categorised into three groups with a low, moderate or high combined risk score based on this cut-off value. The effect of these risk groups on survival was evaluated. Results: The mean age of patients was 64.1 ± 11.9. The median follow-up time was 38.3 months, and the mortality rate was 53.7% in all groups. Kaplan-Meier survival analysis showed a statistically significant difference between the combined scores of the risk groups: the median survival time was 51.6 months in the low-risk group, 20.7 months in the medium-risk group, and 10.4 months in the high-risk group (p < 0.001), with 5-year survival rates being 38.8% in the low-risk group, 30% in the intermediate-risk group, and 19% in the high-risk group. When compared to the low-risk group, Cox regression analysis revealed that the mortality risk, i.e., HR (hazard ratio), was 2.42 times higher in the intermediate-risk group and 3.76 times higher in the high-risk group. A nephrectomy operation decreased the mortality risk (HR = 0.24) by 4.16 times. Conclusions: This new combined risk scoring, obtained from co-evaluating the HALP score and AAPR, was found to be an independent prognostic factor in metastatic RCC patients. This combined risk scoring is expected to help clinicians in treatment decisions.

Acute-on-chronic liver failure (ACLF) is a syndrome that develops in approximately 30% of patients hospitalised with cirrhosis and is characterised by an acute decompensation of liver function associated with extra-hepatic organ failures and a high short-term mortality. At present, no specific therapies are available for ACLF, and current management is limited to treatment of the precipitating event and organ support. Given the high prevalence and high mortality of this severe liver disease, there is an urgent need for targeted treatments. There is increasing evidence of the important role played by systemic inflammation and immune dysfunction in the pathophysiology of ACLF and a better understanding of these immune processes is resulting in new therapeutic targets. The aim of this review is to present an overview of ongoing studies of potentially promising therapies and how they could be utilised in the management of ACLF.

Background Liver cirrhosis (LC) is characterized by the development of fibrosis and nodules within the liver, leading to progressive liver dysfunction. The mortality rate associated with LC has consistently remained high over the years. Prognostic tools such as the Child-Turcotte-Pugh (CTP) score and the Model for End-Stage Liver Disease (MELD) score are commonly used to predict mortality and assess the severity of LC. Both scoring systems rely on laboratory parameters, including serum albumin, total bilirubin, and international normalized ratio (INR) levels. However, the CTP score interpretation can be variable, and INR testing is not routinely performed in many clinical settings, which may limit its utility. This study aims to assess the MELD and CTP scores, along with a new predictive tool, in estimating 30-day mortality for patients with LC. Methodology This retrospective cohort study focuses on patients diagnosed with LC at Ngoerah Central General Hospital. Physical examination data and laboratory ratios, including neutrophil-to-lymphocyte ratio (NLR), aspartate transaminase (AST) to alanine transaminase (ALT) ratio (de Ritis), neutrophil-to-lymphocyte-to-albumin (NLA) ratio, albumin bilirubin index (ALBI), and blood urea nitrogen-to-albumin ratio (BAR), were collected from medical records. Optimal cutoff values were established using receiver operating characteristic (ROC) curves. Survival analysis was performed using the Kaplan-Meier method, and multivariate Cox regression was employed to determine the hazard ratio (HR) for each variable that was statistically significant as a predictor of 30-day mortality. Results In this study, a total of 140 samples were analyzed. Kaplan-Meier analysis revealed that hepatic encephalopathy (HE) met the criteria, while interaction analysis testing was required for other variables. Results from the multivariate Cox regression interaction model showed that ALBI-HE (HR = 1.743, 95% confidence interval [CI] 1.102-2.759, P = 0.018), BAR-HE (HR = 0.577, 95% CI 0.367-0.905, P = 0.017), and NLA-HE (HR = 0.332, 95% CI 0.195-0.563, P < 0.001) were significant independent predictors of 30-day mortality in LC. CTP, MELD, NLR, and de Ritis did not demonstrate statistical significance. ALBI-HE emerged as the strongest predictor based on its HR. Conclusions ALBI-HE, BAR-HE, and NLA-HE have emerged as novel predictors for assessing 30-day mortality in LC. ALBI-HE is the strongest predictor of 30-day mortality in LC.

Post-stroke myocardial injury is a potentially preventable complication after acute ischemic stroke. Therefore, identifying modifiable variables, such as nutritional status, is crucial for reducing the risk of post-stroke myocardial injury. This study aimed to investigate the association between malnutrition risk on admission, as evaluated by the Geriatric Nutritional Risk Index (GNRI), and post-stroke myocardial injury in elderly patients with first‑ever ischemic stroke.

We conducted this study using the GNRI score to evaluate the nutritional status of older patients with first‑ever ischemic stroke. The primary outcome of interest was post-stroke myocardial injury. Restricted cubic spline (RCS) was executed to assess the dose-effect relationship between the GNRI score and post-stroke myocardial injury. The correlation of malnutrition risk identified by GNRI score for post-stroke myocardial injury was examined using multivariate logistic regression analysis. To balance the potential confounders and verify the robustness of the results, propensity score matching (PSM) was further conducted.

Based on the GNRI score, 30.8% of patients were at moderate to severe risk of malnutrition. The overall incidence of post-stroke myocardial injury was 33.2%. The adjusted RCS analysis revealed a negative dose-response relationship between the GNRI score and post-stroke myocardial injury (P for non-linearity = 0.536). After adjusting for confounders, moderate to severe malnutrition risk, as evaluated by the GNRI score, was substantially associated with an increased risk of post-stroke myocardial injury (OR: 3.25; 95% CI: 1.93-5.48; P < 0.001). Following PSM adjustment, the association between the GNRI score and post-stroke myocardial injury remained significantly robust (OR: 4.28; 95% CI: 2.34-7.83; P < 0.001).

Malnutrition risk on admission is associated with higher risk of post-stroke myocardial injury among elderly patients with first‑ever ischemic stroke. Early screening for malnutrition risk is crucial in the management of patients with first‑ever ischemic stroke.

The risk of microbial infections is increased in cirrhosis and other forms of advanced liver disease such as alcohol-associated hepatitis. Such infections may precipitate new or further decompensation and death, especially in patients with clinical features of acute-on-chronic liver failure. The severe immune dysfunction or "immune paralysis" caused by advanced liver disease is associated with high short-term mortality. However, the pathogenic mechanisms underlying immune dysfunction and immunodeficiency are incompletely understood. Evidence to date suggests a complex, dynamic process that perturbs the physiological roles of the liver as a master regulator of systemic immunity and protector against noxious effects of exogenous molecules in the portal vein flowing from the gut. Thus, in cirrhosis and severe alcohol-associated hepatitis, the ability of hepatocytes and intrahepatic immune cells to balance normal context-dependent dichotomous responses of tolerance vs immune activation is lost. Contributing factors include loss of the gut barrier with translocation of microbial products through the portal vein, culminating in development of functional defects in innate and adaptive immune cells, and generation of immune-regulatory myeloid cells that permit microbial colonization and infection. This review addresses key evidence supporting the paradigm of immune dysfunction as a risk for microbial infections and identifies potential therapeutic targets for intervention. The primary focus is on cirrhosis-associated immune dysfunction and alcohol-associated liver disease, because the bulk of available data are from these 2 conditions.

Hepatitis B-associated cirrhosis (HBC) is associated with severe complications and adverse clinical outcomes. This study aimed to develop and validate a predictive model for the occurrence of multiple complications (three or more) in patients with HBC and to explore the effects of multiple complications on HBC prognosis.

In this retrospective cohort study, data from 121 HBC patients treated at Nanjing Second Hospital from February 2009 to November 2019 were analysed. The maximum follow-up period was 10.75 years, with a median of 5.75 years. Eight machine learning techniques were employed to construct predictive models, including C5.0, linear discriminant analysis (LDA), least absolute shrinkage and selection operator (LASSO), k-nearest neighbour (KNN), gradient boosting decision tree (GBDT), support vector machine (SVM), generalised linear model (GLM) and naive Bayes (NB), utilising variables such as medical history, demographics, clinical signs, and laboratory test results. Model performance was evaluated via receiver operating characteristic (ROC) curve analysis, residual analysis, calibration curve analysis, and decision curve analysis (DCA). The influence of multiple complications on HBC survival time was assessed via Kaplan‒Meier curve analysis. Furthermore, LASSO and univariable and multivariable Cox regression analyses were conducted to identify independent prognostic factors for overall survival (OS) in patients with HBC, followed by ROC, C-index, calibration curve, and DCA curve analyses of the constructed prognostic nomogram model. This study utilized bootstrap resampling for internal validation and employed the Medical Information Mart for Intensive Care IV (MIMIC-IV) database for external validation.

The GBDT model exhibited the highest area under the curve (AUC) and emerged as the optimal model for predicting the occurrence of multiple complications. The key predictive factors included posthospitalisation fever (PHF), body mass index (BMI), retinol binding protein (RBP), total bilirubin (TB) levels, and eosinophils (EOS). Kaplan-Meier analysis revealed that patients with multiple complications had significantly worse OS than those with fewer complications. Additionally, multivariable Cox regression analysis, informed by least absolute shrinkage and LASSO selection, identified hepatocellular carcinoma (HCC), multiple complications, and lactate dehydrogenase (LDH) levels as independent prognostic factors for OS. The prognostic model demonstrated 1-year, 3-year, and 5-year OS ROC AUCs of 0.802, 0.793, and 0.817, respectively. For the internal validation cohort, the corresponding AUC values were 0.797, 0.832, and 0.835. In contrast, the external validation cohort yielded a 1-year ROC AUC of 0.707. Calibration curves indicated good consistency of the model, and DCA demonstrated the model's clinical utility, showing high net benefits within certain threshold ranges. Compared with the univariable models, the multivariable ROC curves indicated higher AUC values for this prognostic model, and the model also possessed the best c-index.

The GBDT prediction model provides a reliable tool for the early identification of high-risk HBC patients prone to developing multiple complications. The concurrent occurrence of multiple complications is an independent prognostic factor for OS in patients with HBC. The constructed prognostic model demonstrated remarkable predictive performance and clinical applicability, indicating its crucial role in enhancing patient outcomes through timely and targeted interventions.

Identifying patients with a particularly high risk of refeeding syndrome (RFS) is essential for taking preventive measures. To guide the development of clinical decision-making and risk prediction models or other screening tools for RFS, increased knowledge of risk factors is needed. Therefore, we conducted a systematic review to identify risk factors for the development of RFS. PubMed, EMBASE, Cochrane Library, and Web of Science were searched from January 1990 until March 2023. Studies investigating demographic, clinical, drug use, laboratory, and/or nutrition factors for RFS were considered. The Newcastle-Ottawa Scale was used to appraise the methodological quality of included studies. Of 1589 identified records, 30 studies were included. Thirty-three factors associated with increased risk of RFS after multivariable adjustments were identified. The following factors were reported by two or more studies, with 0-1 study reporting null findings: a previous history of alcohol misuse, cancer, comorbid hypertension, high Acute Physiology and Chronic Health Evaluation II score, high Sequential Organ Failure Assessment score, low Glasgow coma scale score, the use of diuretics before refeeding, low baseline serum prealbumin level, high baseline level of creatinine, and enteral nutrition. The majority of the studies (20, 66.7%) were of high methodological quality. In conclusion, this systematic review informs on several risk factors for RFS in patients. To improve risk stratification and guide development of risk prediction models or other screening tools, further confirmation is needed because there were a small number of studies and a low number of high-quality studies on each factor.

Intrahepatic cholangiocarcinoma (iCCA) is aggressive with limited treatment and poor prognosis. Preoperative nutritional status assessment is crucial for predicting outcomes in patients. This study aimed to compare the predictive capabilities of preoperative blood like albumin-bilirubin (ALBI), controlling nutritional status (CONUT), prognostic nutritional index (PNI) and CT-imaging nutritional indicators like skeletal muscle index (SMI), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), visceral to subcutaneous adipose tissue ratio (VSR) in iCCA patients undergoing curative hepatectomy.

290 iCCA patients from two centers were studied. Preoperative blood and CT-imaging nutritional indicators were evaluated. Short-term outcomes like complications, early recurrence (ER) and very early recurrence (VER), and overall survival (OS) as long-term outcome were assessed. Six machine learning (ML) models, including Gradient Boosting (GB) survival analysis, were developed to predict OS.

Preoperative blood nutritional indicators significantly associated with postoperative complications. CT-imaging nutritional indicators show insignificant associations with short-term outcomes. All preoperative nutritional indicators were not effective in predicting early tumor recurrence. For long-term outcomes, ALBI, CONUT, PNI, SMI, and VSR were significantly associated with OS. Six ML survival models demonstrated strong and stable performance. GB model showed the best predictive performance (C-index: 0.755 in training cohorts, 0.714 in validation cohorts). Time-dependent ROC, calibration, and decision curve analysis confirmed its clinical value.

Preoperative ALBI, CONUT, and PNI scores significantly correlated with complications but not ER. Four Image Nutritional Indicators were ineffective in evaluating short-term outcomes. Six ML models were developed based on nutritional and clinicopathological variables to predict iCCA prognosis.

There are insufficient data to support a link between serum albumin-to-globulin ratio (AGR) and mortality in individuals with diabetes.

This prospective study sought to investigate the relationship between serum AGR and all-cause and cause-specific mortality in adult diabetics.

This study included 8508 adults with diabetes from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. Death outcomes were ascertained by linkage to National Death Index records through December 31, 2019. Hazard ratios (HR) and 95% CIs for mortality from all causes, cardiovascular disease (CVD), and cancer were estimated using weighted Cox proportional-hazards models.

A total of 2415 all-cause deaths, including 688 CV deaths and 413 cancer deaths, were recorded over an average of 9.61 years of follow-up. After multivariate adjustment, there was a significant and linear relationship between higher serum AGR levels and reduced all-cause and cause-specific mortality in a dose-response manner. The multivariate-adjusted HR and 95% CI for all-cause mortality (Ptrend < .0001), cardiovascular mortality (Ptrend < .001), and cancer mortality (Ptrend < .01) were 0.51 (0.42-0.60), 0.62 (0.46-0.83), and 0.57 (0.39-0.85), respectively, for individuals in the highest AGR quartile. There was a 73% decreased risk of all-cause death per 1-unit rise in natural log-transformed serum AGR, as well as a 60% and 63% decreased risk of mortality from CVD and cancer, respectively (all P < .001). Both the stratified analysis and the sensitivity analyses revealed the same relationships.

AGR is a promising biomarker in risk predictions for long-term mortality in diabetic individuals, particularly in those younger than 60 years and heavy drinkers.

Podophyllotoxin is a compound with clinical effects, such as anticancer and antiacromegaly effects, but its systemic toxicity has led to extremely limited clinical application.

Using the toxicological evidence chain (TEC) as a research method, our team constructed, for the first time, a rat model in which podophyllotoxin caused ovarian damage and investigated the mechanism of the toxic effects of podophyllotoxin on the ovaries.

The rats presented different degrees of diarrhoea, body surface bruising, and petechiae, and the serum biochemical results revealed significant changes in the activities of the oxidative stress indicators SOD and MDA and the levels of the inflammatory indicators TNF-α and IL-1β. The pathological results suggested that the rat ovaries were significantly damaged, and the histological results revealed Th17 cell differentiation, necroptosis, Hspa9 expression, and other pathways or targets related to inflammation, necroptosis/apoptosis or autophagy.

Podophyllotoxin exerts toxic effects by altering autophagy through the AMPK/TSC1/mTOR/ULK1 signalling pathway. This study provides new insights into the mechanism of the toxic effects of podophyllotoxin and new ideas for the clinical application of podophyllotoxin.

Chronic obstructive pulmonary disease (COPD) is a widespread condition that significantly endangers human health. The C-reactive protein to albumin ratio (CAR) has been utilized as an important marker for the occurrence and development of various diseases. Nevertheless, the association between CAR and COPD remains inadequately explored. This research aimed to examine the link between the CAR and COPD.

This cross-sectional study examined data from adult participants in the National Health and Nutrition Examination Survey between 1999 and 2010. The identification of COPD relied on data obtained directly from self-reported information. Logistic regression analysis was used to estimate the odds ratio (OR) and 95% confidence interval (CI) for the association between CAR and COPD. Additionally, subgroup and sensitivity analyses were performed to evaluate the stability of the observed relationship.

The study comprised 24,627 participants, among whom 1,809 had COPD. After adjusting for potential confounders, CAR was found to be associated with COPD (OR 1.37; 95% CI, 1.12-1.69; P = 0.003). In the analysis comparing individuals in the lowest CAR tertile (T1) with those in the highest, the adjusted OR for COPD was 1.39 (95% CI, 1.17-1.65; P < 0.001). Validation through subgroup and sensitivity analyses further strengthened the stability and consistency of the study's findings.

Elevated levels of the CAR were correlated with an increased risk of COPD.

not applicable.

Background Overwhelming splanchnic and systemic vasodilatation and low mean arterial pressure (MAP) pose significant challenges in mobilizing ascites in patients with decompensated cirrhosis. We aimed to evaluate the efficacy and survival benefits of oral vasoconstrictor and weekly albumin therapy in patients with diuretic intractable and recurrent ascites in cirrhosis. Materials and methods A total of 113 cirrhotic patients with diuretic intractable (n=45) and recurrent ascites (n=68) with MAP ≤ 82 mmHg were included. Of the 113 patients, 85 patients received midodrine (12.5±2.5 mg thrice daily) and weekly albumin 20-40 g/day to achieve a serum albumin level of 4 g/dL with standard medical therapy (SMT), whereas 28 patients received SMT alone. On follow-up, at three months, we evaluated and compared the control of ascites, urinary sodium, systemic vascular resistance (SVR), and renal arterial resistive index (RARI) between arm 1 and arm 2. Results Among 113 patients, mean Child-Turcotte-Pugh (CTP) and Model for End-stage Liver Disease-sodium (MELD-Na) scores were comparable in both arms (CTP 9.52±1.16 and 9±1.18 and MELD 21.33±4.17 and 22.36±4.2 in arm 1 and arm 2, respectively). After three months of therapy in arm 1, a significant increase was seen in urinary sodium excretion (25.99±15.73 and 114.38±71.33 meq/24 hours), MAP (78.91±3.11 and 84.3±3.13 mmHg), SVR (1,059.4±23.09 and 1,178.3±12.39 dynes/s/cm⁵), and decrease in RARI (0.71±0.054 and 0.67±0.039) (p <0.05). The median overall survival and ascites control were better in arm 1 (p <0.001) at the end of follow-up (12 months). Conclusion Response-guided midodrine and weekly albumin therapy, along with SMT, have better overall survival and ascites control.

Chronic kidney disease (CKD) poses a major global health challenge, often foreshadowing poor patient outcomes. The C-reactive protein to albumin ratio (CAR) serves as a pivotal biomarker, demonstrating a strong correlation with adverse outcomes in cardiovascular disease (CVD). This study sought to examine the correlation between CAR and the risk of all-cause and cardiovascular mortality in patients with CKD stages 3-5.

This study utilized data of CKD patients from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010, with follow-up to December 31, 2019. The optimal CAR cutoff value was identified utilizing the method of maximally selected rank statistics. Multivariable Cox proportional hazards regression model, restricted cubic splines (RCS) model, and subgroup analysis were employed to assess the association between CAR and mortality among CKD patients.

During a median (with interquartile range) follow-up period of 115 (112,117) months among 2,841 CKD individuals, 1,893 deaths were observed, including 692 deaths due to CVD events. Based on the RCS analysis, a non-linear correlation was observed between CAR and mortality. Using 0.3 as the optimal CAR cutoff value, the cohort was divided into high and low groups. In the fully adjusted model, CKD patients with high CAR values exhibited an elevated risk of all-cause mortality (hazard ratio [HR] 1.53, 95% confidence interval [CI] 1.28-1.83, P < 0.001) and cardiovascular mortality (HR 1.48, 95% CI 1.08-2.02, P = 0.014). Compared to the population aged >65 years (HR 1.32, 95% CI 0.99-1.76, P = 0.064), the risk of cardiovascular mortality was significantly higher in those aged ≤65 years (HR 2.19, 95% CI 1.18-4.09, P = 0.014) with elevated CAR levels.

A notable correlation exists between the elevation of CAR and increased all-cause and cardiovascular mortality, suggesting its potential as an independent indicator for evaluating the prognosis of patients with CKD stages 3-5.

The role of inflammation and hypoproteinemia in influencing outcomes of critically ill patients has been widely recognized. However, there is a paucity of research on the prognostic value of the platelet-to-albumin ratio (PAR) in critically ill patients. Therefore, the aim of this study is to assess the prognostic significance of PAR in this population.

Patients diagnosed with critical illnesses from January 2020 to October 2022 were retrospectively enrolled in our study. Baseline demographic and clinical data were collected for each participant. Platelet counts and albumin levels were measured at baseline, and the PAR was calculated. The area under the receiver operating characteristic curve, Kaplan-Meier survival analysis, and multivariate Cox regression analysis were used to predict 30-day mortality.

Three hundred and seventy-eight patients diagnosed with critical illness were categorized into two groups: survivors (n = 299) and non-survivors (n = 79). Analysis of the 30-day outcome revealed that the area under the curve (AUC) for the PAR (AUC: 0.705; 95% CI 0.639-0.771; p < 0.001) was significantly higher than that for albumin (AUC: 0.673; 95% CI 0.609-0.736; p < 0.001), but slightly lower than that for the APACHE II score (AUC: 0.713; 95% CI 0.650-0.777; p < 0.001). In addition, the Kaplan-Meier survival analysis demonstrated a significantly higher 30-day mortality in the high-PAR group. Furthermore, Cox regression analysis identified albumin (HR: 0.936; 95% CI 0.895-0.978; p = 0.003), APACHE II score (HR: 1.225; 95% CI 1.149-1.305; p < 0.001), and high PAR (HR: 1.237; 95% CI 1.130-1.353; p < 0.001) as independent risk factors for the prognosis of critically ill patients.

The PAR has emerged as a significant prognostic indicator in critically ill patients, with an elevated ratio being associated with poorer clinical outcomes.

This study aimed to investigate the value of the inflammatory prognostic score (IPS) system for predicting the risk of all-cause major adverse cardiovascular events (MACEs) and cardiac-related MACEs in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI).

Overall, 1384 patients with ACS who underwent PCI between January 2016 and December 2018 were consecutively enrolled. Demographic characteristics and related laboratory results for 11 inflammatory markers were collected. Least absolute shrinkage and selection operator (LASSO)-COX regression, Kaplan- Meier, restricted cubic spline (RCS), receiver operator characteristic curve (ROC), time-dependent ROC, and Cox hazard proportional regression were applied to explore the values of individual and IPS parameters.

Based on the LASSO analysis, albumin (ALB) and monocyte-to-lymphocyte ratio (MLR) were included in the construction of the IPS system. A higher IPS was associated with a significantly worse long-term prognosis in the Kaplan-Meier analysis (log-rank p < 0.001). The Cox proportional hazards model demonstrated that the IPS was an independent risk factor for patients with ACS undergoing PCI. In addition, the IPS remained an independent prognosticator compared to the lowest tertiles. The time-dependent ROC showed satisfactory values for the long-term prognosis of different MACEs. Additionally, RCS showed a linear association with IPS, all-cause MACEs, and cardiac-related MACEs.

A higher IPS level was associated with an increased risk in patients with ACS undergoing PCI, suggesting that the IPS may be a useful method for risk stratification in the assessment of the long-term prognosis of ACS.

Chronic inflammation significantly contributes to human malignancies. We investigated the prognostic significance of the preoperative modified systemic inflammation score (mSIS) in patients with primary oral cavity squamous cell carcinoma (OCSCC).

We retrospectively reviewed data from 320 OCSCC patients who underwent curative surgery between 2007 and 2017. Based on preoperative lymphocyte-to-monocyte ratio (LMR) and serum albumin levels, patients were classified into three groups: mSIS = 2 (LMR < 3.4 and albumin < 4.0 g/dL), mSIS = 1 (LMR < 3.4 or albumin < 4.0 g/dL), and mSIS = 0 (LMR ≥ 3.4 and albumin ≥ 4.0 g/dL). We explored the associations between the preoperative mSIS and overall survival (OS) and disease-free survival (DFS). We developed a nomogram based on mSIS for OS prediction.

The distribution was mSIS = 0 (n = 197, 61.6%), mSIS = 1 (n = 99, 30.9%), and mSIS = 2 (n = 24, 7.5%). Kaplan-Meier estimated OS and DFS for the mSIS = 0, mSIS = 1, and mSIS = 2 groups demonstrated a sequential decrease (both p < 0.001). The prognostic significance of mSIS was consistent across subgroup analyses. Multivariable analysis revealed that mSIS = 1 and mSIS = 2 were independent negative prognostic indicators. The mSIS-based nomogram effectively predicted OS (concordance index: 0.755).

The mSIS reliably predicts OS and DFS in OCSCC patients undergoing surgery, with the nomogram providing individualized OS estimates, enhancing mSIS's clinical utility.

To develop and validate a deep learning-based automatic segmentation model and combine with radiomics to predict post-TACE liver failure (PTLF) in hepatocellular carcinoma (HCC) patients.

This was a retrospective study enrolled 210 TACE-trated HCC patients. Automatic segmentation model based on nnU-Net neural network was developed to segment medical images and assessed by the Dice similarity coefficient (DSC). The screened clinical and radiomics variables were separately used to developed clinical and radiomics predictive model, and were combined through multivariate logistic regression analysis to develop a combined predictive model. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were applied to compare the performance of the three predictive models.

The automatic segmentation model showed satisfactory segmentation performance with an average DSC of 83.05% for tumor segmentation and 92.72% for non-tumoral liver parenchyma segmentation. The international normalized ratio (INR) and albumin (ALB) was identified as clinically independent predictors for PTLF and used to develop clinical predictive model. Ten most valuable radiomics features, including 8 from non-tumoral liver parenchyma and 2 from tumor, were selected to develop radiomics predictive model and to calculate Radscore. The combined predictive model achieved the best and significantly improved predictive performance (AUC: 0.878) compared to the clinical predictive model (AUC: 0.785) and the radiomics predictive model (AUC: 0.815).

This reliable combined predictive model can accurately predict PTLF in HCC patients, which can be a valuable reference for doctors in making suitable treatment plan.

The development of tumor is closely linked to inflammation. Therefore, targeting molecules involved in inflammation may be effective in predicting cancer prognosis. Transarterial chemoembolization (TACE) holds significant therapeutic significance in addressing hepatocellular carcinoma (HCC). At present, no studies have evaluated the predictive value of γ-glutamyl transferase to albumin ratio (GAR) on the prognosis of HCC undergoing TACE.

To explore the potential prognostic significance of the GAR in individuals undergoing TACE for HCC.

A total of 1231 patients from seven hospitals in China were randomized into a training cohort (n = 862) and a validation cohort (n = 369). To establish independent prognostic factors for overall survival (OS), we utilized multivariate and univariate Cox regression models. The best cut-off value of the GAR was determined with the X-tile software, with OS as the basis. Validations were performed using dual therapy cohort and triple therapy cohort.

X-tile software revealed a GAR threshold of 4.75 as optimal. Both pre- and post-propensity score matching analyses demonstrated that the median OS in the low-GAR group (< 4.75) was notably longer compared to the high-GAR group (≥ 4.75), showing results of 26.9 vs 9.8 months (P < 0.001) initially, and 18.1 vs 11.3 months (P < 0.001) after match. Furthermore, multivariate analysis identified GAR ≥ 4.75 as an independent prognostic factor (P < 0.001). The receiver operating characteristic curves for the nomogram showed area under receiver operating characteristic curves of 0.741, 0.747, and 0.708 for predicting 1-, 2-, and 3-year survival, respectively. Consistent findings were reiterated in the two cohorts involving TACE in combination with targeted therapy and TACE in combination with targeted therapy and immunotherapy. Calibration curve and decision curve analyses substantiated the model's relatively robust predictive capabilities.

Our study validates the effective prognostic capacity of the GAR-based nomogram for HCC patients undergoing TACE or TACE in combination with systemic therapy.

In patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI), a low serum albumin-to-creatinine ratio (sACR) is associated with elevated risk of poor short- and long-term outcomes. However, the relationship between sACR and pulmonary infection during hospitalization in patients with STEMI undergoing PCI remains unclear.

A total of 4,507 patients with STEMI undergoing PCI were enrolled and divided into three groups according to sACR tertile. The primary outcome was pulmonary infection during hospitalization, and the secondary outcome was in-hospital major adverse cardiovascular events (MACE) including stroke, in-hospital mortality, target vessel revascularization, recurrent myocardial infarction, and all-cause mortality during follow-up.

Overall, 522 (11.6%) patients developed pulmonary infections, and 223 (4.9%) patients developed in-hospital MACE. Cubic spline models indicated a non-linear, L-shaped relationship between sACR and pulmonary infection (P=0.039). Receiver operating characteristic curve analysis indicated that sACR had good predictive value for both pulmonary infection (area under the ROC curve [AUC]=0.73, 95% CI=0.70-0.75, P＜0.001) and in-hospital MACE (AUC=0.72, 95% CI=0.69-0.76, P＜0.001). Kaplan-Meier survival analysis indicated that higher sACR tertiles were associated with a greater cumulative survival rate (P＜0.001). Cox regression analysis identified lower sACR as an independent predictor of long-term all-cause mortality (hazard ratio [HR]=0.96, 95% CI=0.95-0.98, P＜0.001).

A low sACR was significantly associated with elevated risk of pulmonary infection and MACE during hospitalization, as well as all-cause mortality during follow-up among patients with STEMI undergoing PCI. These findings highlighted sACR as an important prognostic marker in this patient population.

Hyponatremia can worsen the outcomes of patients with liver cirrhosis. However, it remains unclear about how to predict the risk of death in cirrhotic patients with hyponatremia. Patients with liver cirrhosis and hyponatremia were screened. Eligible patients were randomly divided into the training (n = 472) and validation (n = 471) cohorts. In the training cohort, the independent predictors for in-hospital death were identified by logistic regression analyses. Odds ratios (ORs) were calculated. An artificial neural network (ANN) model was established in the training cohort. Areas under curve (AUCs) of ANN model, Child-Pugh, model for end-stage liver disease (MELD), and MELD-Na scores were calculated by receiver operating characteristic curve analyses. In multivariate logistic regression analyses, ascites (OR = 2.705, P = 0.042), total bilirubin (OR = 1.004, P = 0.003), serum creatinine (OR = 1.004, P = 0.017), and international normalized ratio (OR = 1.457, P = 0.005) were independently associated with in-hospital death. Based on the four variables, an ANN model was established. Its AUC was 0.865 and 0.810 in the training and validation cohorts, respectively, which was significantly larger than those of Child-Pugh (AUC = 0.757), MELD (AUC = 0.765), and MELD-Na (AUC = 0.769) scores. An ANN model has been developed and validated for the prediction of in-hospital death in patients with liver cirrhosis and hyponatremia.

The relationship between the alkaline phosphatase-to-albumin ratio (APAR) and mortality remains unclear. This research looked into the association between APAR levels and cause-specific mortality in US adults. A cohort of 7561 participants from National Health and Nutrition Examination Survey (2005-2016) was analyzed, with mortality outcomes collected from National Death Index records. Cox proportional hazards models and restricted cubic spline (RCS) analysis were utilized to determine hazard ratio (HR) and reveal the nonlinear relationship between APAR levels and mortality. Inflection points were calculated using a recursive algorithm. Followed for an average 99.41 months, a total of 1048 deaths occurred, including 200 cancer deaths and 348 cardiovascular disease-related deaths. Following multivariate adjustment, significant associations were observed between APAR levels and increased all-cause (HR 1.50, 95% CI 1.28-1.75, P < .001) and cardiovascular disease (HR 1.39, 95% CI 1.06-1.82, P = .018) mortality. Furthermore, nonlinear correlations between APAR levels and all-cause and cancer mortality were revealed, characterized by an L-shaped pattern, with mortality rates stabilizing at 1.289 and 2.167, respectively. Participants with APAR levels above the inflection point exhibited a 29.2% increase in all-cause mortality risk per unit increase in APAR levels (HR 1.292, 95% CI 1.217-1.372, P < .001), and a 38.3% increase in cancer mortality risk (HR 1.383, 95% CI 1.199-1.596, P < .001). This study demonstrated nonlinear associations between APAR levels and all-cause and cancer mortality. Thresholds of 1.289 and 2.167 might serve as potential targets for APAR to reduce all-cause and cancer mortality, respectively. Our findings suggest that APAR can be a valuable prognostic tool for clinical mortality risk assessments, helping to identify individuals at higher risk. Nevertheless, these findings necessitate validation through large-scale clinical trials for further substantiation.

Ascites is the most common complication in patients with decompensated cirrhosis. This condition results in a severely impaired quality of life, excessive healthcare use, recurrent hospitalizations and significant morbidity and mortality. While loop diuretics and mineralocorticoid receptor antagonists are commonly employed for symptom relief, our understanding of their impact on survival remains limited. A comprehensive understanding of the underlying pathophysiological mechanism of ascites is crucial for its optimal management. The renin-angiotensin-aldosterone system (RAAS) is increasingly believed to play a pivotal role in the formation of cirrhotic ascites, as RAAS overactivation leads to a reduction in urine sodium excretion then a decrease in the ability of the kidneys to excrete water. In this review, the authors provide an overview of the pathogenesis of cirrhotic ascites, the challenges associated with current pharmacologic treatments, and the previous attempts to modulate the RAAS, followed by a description of some emerging targeted RAAS agents with the potential to be used to treat ascites.

Interferon-alpha1b (IFN-α1b) has shown remarkable therapeutic potential as adjuvant therapy for melanoma. This study aimed to develop five machine learning models to evaluate the efficacy of postoperative IFN-α1b in patients with advanced melanoma.

We retrospectively analyzed 113 patients with the American Joint Committee on Cancer (AJCC) stage III-IV melanoma who received postoperative IFN-α1b therapy between July 2009 and February 2024. Recurrence-free survival (RFS) and overall survival (OS) were assessed using Kaplan-Meier analysis. Five machine learning models (Decision Tree, Cox Proportional Hazards, Random Forest, Support Vector Machine, and LASSO regression) were developed and compared for their capacity to predict the outcomes of patients. Model performance was evaluated using concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis.

The 1-year, 2-year, and 3-year RFS rates were 71.10%, 43.10%, and 31.10%, respectively. For OS, the 1-year, 2-year, and 3-year OS rates were 99.10%, 82.30%, and 75.00%, respectively. The Decision Tree (DT) model demonstrated superior predictive performance with the highest C-index of 0.792. Time-dependent ROC analysis for predicting 1-, 2-, and 3-year RFS based on the DT model is 0.77, 0.79 and 0.76, respectively. Serum albumin emerged as the important predictor of RFS.

Our study demonstrates the considerable efficacy DT model for predicting the efficacy of adjuvant IFN-α1b in patients with advanced melanoma. Serum albumin was identified as a key predictive factor of the treatment efficacy.