Physical activity is important for a healthy lifestyle but may have side effects, especially when performed in an excessive manner. This includes effects on the immune system but can also result in a higher need for certain micronutrients such as the trace elements selenium, zinc, copper, and iron. This study aimed to characterize short-term effects of a standardized 1-minute sit-to-stand-test (STST) on serum concentrations of these trace elements. 20 healthy individuals performed the STST. Capillary blood samples were drawn 10 min before, immediately after, 30, and 60 min post-exercise. Lactate, glucose, and blood cell counts were determined together with the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI) and neutrophil-to-lymphocyte ratio (NLR). The trace element concentrations and the biomarkers free zinc (fZn), and selenoprotein P (SELENOP) were analysed. All values except for fZn and NLR were adjusted for hemoconcentration. Immediately after the STST, the levels of copper and selenium decreased significantly, which subsequently returned to baseline values. SELENOP levels showed a similar pattern to that of total selenium concentrations but only increased significantly after 60 min, compared to immediately after exercise. In contrast, no changes were observed for total zinc concentrations. FZn increased significantly immediately after the STST, before decreasing. Iron levels were higher immediately after the STST and rose significantly after 60 min. The cellular inflammatory markers NLR and SII decreased significantly after exercise and then increased compared to pre-test values. However, this was only significant for NLR. In contrast, SIRI increased continuously after physical activity, reaching significantly higher values after 60 min. Short-term acute physical exercise modulates serum trace element concentrations together with inflammatory parameters. Accordingly, these might be connected to each other which should be analyzed after more extensive, long-term or repeated exercise.

Objective: Inflammation is reported to play an important role in the etiology of autism spectrum disorder. The purpose of this study was to investigate hemoglobin-to-red blood cell distribution width ratio (HRR) and systemic inflammatory response index (SIRI) levels, which may indicate immunological mechanisms involved in the etiopathogenesis of autism spectrum disorder. Methods: One hundred forty-eight participants (74 diagnosed with autism spectrum disorder and 74 healthy controls) aged 24-72 months were included in the study. The Childhood Autism Rating Scale was applied to measure disease severity. The Denver Developmental Screening Test-2 was applied to the children in the autism spectrum disorder group. Results: Hemoglobin-to-red blood cell distribution width ratio and lymphocyte values were significantly low in the children with autism spectrum disorder group compared to the healthy controls (P = .031 and P = .003, respectively). No significant difference was determined between the healthy children and those with autism spectrum disorder in terms of systemic inflammatory response index. Low HRR and maternal age were determined to predict autism spectrum disorder (B = −4.963, P = .003 and B = 0.176, P = .011, respectively). Receiver-operating curve analysis showed that HRR had limited discriminative ability for autism spectrum disorder (AUC = 0.602). Conclusion: Although HRR values were statistically lower in children with autism spectrum disorder, the observed difference was small. The small statistical difference observed in HRR may represent a preliminary finding that requires cautious interpretation and validation in larger, prospective studies. Cite this article as: Kocaman O, Kuru T, Kara T, Avşar PA. The hemoglobin-to-red cell distribution width ratio and systemic inflammatory response index in children with autism spectrum disorder. Neuropsychiatr Invest. 2026, 64, 0065, doi: 10.5152/NeuropsychiatricInvest.2026.25065.

Background: Sepsis-associated acute kidney injury (S-AKI) is a common complication of sepsis, and early identification can improve patient prognosis. This study incorporated novel inflammatory markers as features to construct a model and employed 6 machine learning methods to predict the occurrence of S-AKI. Methods: A total of 3613 patients with sepsis were included in this study. Novel inflammatory markers, including neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, systemic inflammatory response index, systemic immune-inflammation index, systemic inflammatory aggregate index, lactate-to-albumin ratio, and prognostic nutritional index, along with demographic characteristics, clinical conditions, and routine laboratory results, were used to construct the model. The machine learning methods employed included logistic regression, support vector machine, random forest (RF), extreme gradient boosting (XGBoost), and ensemble methods (RF+XGBoost). Model performance and stability were evaluated using 5-fold cross-validation. Model performance was assessed using the area under the receiver-operating characteristic curve, sensitivity, specificity, accuracy, precision, recall, and F1 score. Additionally, SHapley Additive exPlanations values were used to interpret the predictive model. Results: In the final algorithm group, the ensemble model of RF and XGBoost (0.843; 95% confidence interval: 0.820–0.866) was higher than those of other models. Among the single models, the XGBoost model exhibited the highest sensitivity (0.856) and F1 score (0.780), indicating its stronger ability to identify patients who will develop S-AKI, albeit at the expense of lower specificity (0.667). The 4 most influential features for XGBoost were mechanical ventilation, mean arterial pressure, blood urea nitrogen level, and sequential organ failure assessment score. Among the 3 novel inflammatory markers, lactate-to-albumin ratio showed the greatest effect. Conclusion: We successfully developed machine learning methods to predict S-AKI, highlighting the importance of novel inflammatory markers in model construction. This breakthrough offers novel perspectives for feature selection in the future development of related predictive models.

PurposeThis study aimed to investigate the association between the Systemic Inflammatory Response Index (SIRI) and acute kidney injury (AKI) following cardiac surgery using the Medical Information Mart for Intensive Care (MIMIC) database, and to evaluate whether SIRI could serve as a potential risk marker associated with post-cardiac surgery AKI.MethodsWe conducted a retrospective cohort study of 2,884 cardiac surgery patients from the MIMIC-IV database. SIRI was calculated as (neutrophil count × monocyte count)/ lymphocyte count. The primary outcome was AKI occurrence within seven days post-surgery. Logistic regression models and restricted cubic spline (RCS) analysis were used to assess the association between SIRI and AKI risk. Subgroup analyses were performed to evaluate potential effect modifiers.ResultsHigher SIRI levels were significantly associated with increased AKI risk, even after adjusting for potential confounders (OR for highest vs. lowest quartile: 1.35, 95% CI: 1.04–1.77). A dose-response relationship was observed between SIRI and AKI severity (P for trend < 0.001). The association between SIRI and AKI risk was more pronounced in patients with a history of myocardial infarction (OR: 1.261, 95% CI: 1.084–1.467) and those not using loop diuretics (OR: 2.306, 95% CI: 1.200–4.434).ConclusionSIRI showed a modest but significant association with AKI following cardiac surgery. Its integration of multiple inflammatory cell types provided a comprehensive assessment of inflammatory status. The varying strength of association across different patient subgroups suggested the need for individualized risk assessment strategies. Further research is warranted to validate these findings and explore the underlying mechanisms.

To explore sex-specific heterogeneity in the prognostic discrimination of inflammatory markers for mortality across different glycaemic states. This prospective cohort study included 450 438 participants from the UK Biobank (median follow-up: 15.3 years), stratified by sex and glycaemic status. Cox models were applied to evaluate associations between eight inflammatory markers-CRP, WBC, neutrophil-to-lymphocyte ratio (NLR), CRP-to-lymphocyte ratio (CLR), inflammatory burden index (IBI), systemic immune-inflammation index (SII), pan-immune-inflammation value (PIV), and systemic inflammation response index (SIRI)-and all-cause and cardiovascular mortality, with markers analysed in parallel using independent models. Dose-response associations and discriminative performance were assessed using restricted cubic splines (RCS) and time-dependent receiver operating characteristic (ROC) analyses, respectively. Spearman correlation analyses were conducted to contextualize relationships between inflammatory markers and cardiometabolic phenotypes. Deteriorating glycaemic status was associated with progressively higher all-cause and cardiovascular mortality in both sexes, together with sex-specific differences in inflammatory marker trajectories. As glycaemia worsened, the discriminative performance of inflammatory markers for mortality tended to attenuate in women but remained generally more stable in men. Time-dependent ROC analyses suggested stage- and sex-specific heterogeneity. In diabetes, NLR showed stronger early discrimination in women, whereas SIRI showed more stable discrimination in men and at later follow-up in women. Inflammatory biomarkers show sex- and glycaemia-specific patterns in mortality discrimination, with NLR and SIRI showing comparatively stable discrimination, particularly in diabetes.

Chronic spontaneous urticaria (CSU) is mediated not only by mast cells but also by eosinophils and basophils. We evaluated whether complete blood collection based systemic inflammatory indices- including the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), aggregate Index of Systemic Inflammation (AISI), systemic inflammation modulation index (SIMI), neutrophil-to-lymphocyte ratio (NLR), eosinophil-to-lymphocyte ratio (ELR), platelet-to-lymphocyte ratio (PLR), basophil-to-lymphocyte ratio (BLR)-and platelet parameters (mean platelet volume [MPV], platelet distribution width [PDW], platelet large cell ratio [PLCR]) reflect CSU severity or treatment response. A retrospective study of 190 CSU patients and 570 matched controls was performed, with sensitivity analyses using propensity-score matching (PSM) and inverse-probability-of-treatment weighting (IPTW). Subgroup analyses examined UAS7, antihistamine response and allergy history. As a result, CSU patients exhibited lower SII/SIRI, white blood cells (WBC), neutrophils, lymphocytes, and NLR, alongside higher MPV/PLCR and reduced PDW. NLR showed a weak correlation with UAS7, and systemic indices did not reliably differentiate standard-dosed and updosed antihistamine response. Patients with allergy history demonstrated lower eosinophils and ELR. CSU is characterized by reduced systemic inflammatory indices and enhanced platelet activation. Among these, NLR may serve as a cost-effective supplementary tool for assessing systemic inflammation trends of CSU.

Systemic Inflammatory Response Index as A predictor of Severity in Patients with Obstructive Sleep Apnea in Damietta Governorate

Aim: Despite advances in diagnostic strategies, easily accessible biomarkers that can reliably predict pulmonary embolism (PE) in patients with deep vein thrombosis (DVT) are lacking. Recently, systemic inflammatory indices, such as the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and aggregate index of systemic inflammation (AISI), have been investigated as potential prognostic tools. This study aimed to evaluate the predictive value and clinical applicability of these markers in identifying PE in patients with lower extremity DVT. Material and Methods: A retrospective propensity score–matched cohort study was conducted in patients diagnosed with acute proximal lower extremity DVT. Two groups were compared: patients with isolated DVT and those with concurrent DVT and PE were compared. Propensity score matching yielded 290 patients in each group. Novel systemic inflammatory indices derived from hematological parameters (SII, SIRI, and AISI) were evaluated together with neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and traditional biomarkers (C-reactive protein (CRP) and D-dimer). Comparative analyses, logistic regression, and receiver operating characteristic (ROC) curve assessments were performed. Results: Inflammatory indices (NLR, PLR, SII, and SIRI) and conventional biomarkers (CRP and D-dimer) were higher in the DVT and PE group compared to the isolated DVT group (all p&lt;0.001). Multivariate analysis identified NLR, CRP, and D-dimer as independent predictors of PE. Receiver operating characteristic (ROC) analysis demonstrated the strongest diagnostic accuracy for CRP (AUC 0.811), followed by D-dimer (AUC 0.746) and NLR (AUC 0.735) levels. Conclusion: Easily measurable and low-cost biomarkers, particularly NLR, along with CRP and Ddimer, provide significant predictive value for PE in patients with DVT. These parameters may support early risk stratification and assist in clinical decision-making when advanced imaging is not readily available.

To assess the predictive value of hematologic and biochemical inflammatory indices for methotrexate (MTX) treatment outcomes in tubal ectopic pregnancy (TEP) and to develop machine learning (ML) models for individualized risk stratification. This retrospective cohort included 293 hemodynamically stable TEP patients who were treated with a single dose of MTX between January 2019 and December 2023. Demographic, clinical, ultrasonographic, and laboratory data were analyzed. Inflammatory indices-including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, systemic immune-inflammation index, systemic inflammation response index (SIRI), aggregate index of systemic inflammation (AISI), and fibrinogen-to-albumin ratio (FAR)-were calculated. Outcomes were categorized as single-dose MTX success, requirement for additional MTX, or surgery. Predictive accuracy of five supervised ML algorithms was evaluated using receiver operating characteristic analysis. Single-dose MTX was successful in 65.5% of patients; 18.4% required an additional dose, and 16.0% underwent surgery. AISI had the highest predictive accuracy for surgery [area under the curve (AUC)=0.929], followed by SIRI (AUC=0.899) and FAR (AUC=0.847). NLR best predicted the need for additional MTX (AUC=0.675). Naïve Bayes achieved the highest performance for surgical prediction (accuracy=98.3%, AUC=0.998), while random forest and gradient boosting were most effective in predicting the need for additional MTX (accuracy=83.1%, AUC=0.884-0.896). Feature importance analyses consistently ranked AISI, SIRI, and FAR as top predictors. AISI, SIRI, and FAR are strong predictors of MTX failure and surgical intervention in TEP. Combining these biomarkers with ML models markedly improves predictive performance and supports a personalized approach to TEP management. Multicenter prospective validation is needed before clinical application.

Background/Objectives: Calprotectin (CLP), a calcium-binding protein complex released predominantly from neutrophils and monocytes, plays a key role in the inflammatory response. Increased levels of CLP have been reported in various inflammatory and malignant conditions. This study aimed to evaluate serum CLP concentrations and their associations with hematological and biochemical parameters in patients with lung cancer. Methods: This prospective observational study included newly diagnosed lung cancer patients and a healthy control group. Demographic data, routine laboratory parameters, CLP levels, and inflammatory indices including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune-inflammation value (PIV) were recorded. Comparisons were made between groups and across tumor molecular profile, cancer stages, and metastasis status. Correlation and ROC analyses were performed. Results: Serum CLP levels were significantly higher in the lung cancer group compared with healthy controls (p < 0.001). Among molecular subgroups, patients with positive molecular testing had significantly elevated CLP levels compared with negative and untested groups (p = 0.025). CLP did not differ significantly across cancer stages or metastasis status (p > 0.05). CLP showed a positive correlation with the SIRI (r = 0.323; p = 0.004) and PIV (r = 0.395; p < 0.001). ROC analysis revealed that CLP demonstrated good diagnostic performance for lung cancer, with an AUC of 0.930 (95% CI: 0.849-0.976), sensitivity of 79.5%, and specificity of 92.3%. Among inflammatory indices, PIV (AUC = 0.863) and SIRI (AUC = 0.810) also showed high diagnostic accuracy. Conclusions: CLP levels are significantly elevated in lung cancer and show strong discriminative ability, outperforming commonly used inflammatory indices. Although CLP is not specific to lung cancer, it may serve as a supportive, noninvasive biomarker reflecting inflammatory burden when interpreted alongside clinical evaluation, imaging findings, and other laboratory parameters.

Real-world evidence regarding biologic therapy in geriatric psoriasis is limited, particularly concerning systemic inflammatory burden and infection-related safety. This study evaluates the clinical efficacy of biologic therapy and its impact on systemic inflammatory indices while emphasizing safety related to hepatitis B virus (HBV) serology and tuberculosis screening. We conducted a retrospective analysis of eighty biologic-naïve patients aged 65 years and older with plaque psoriasis undergoing biologic therapy. Patients were categorized by biologic class: tumor necrosis factor-α (TNF-α) inhibitors, interleukin-17 inhibitors, an interleukin-12/23 inhibitor, and interleukin-23 inhibitors.The primary outcomes included changes in Psoriasis Area and Severity Index (PASI) scores and blood count-derived inflammatory indices over time (baseline and 6 months). Secondary outcomes encompassed changes in HBV serologic status and QuantiFERON-TB (QFT) results. Data analysis utilized longitudinal mixed-effects models for repeated measures. Blood count-derived inflammatory indices, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI), were assessed at baseline and 6 months, alongside HBV serology and QFT results.PASI scores demonstrated significant improvement over time (p<0.001), with no notable differences among biologic classes after adjusting for baseline covariates. Significant time effects were observed for all inflammatory indices (all p< 0.001), with significant group × time interactions noted for SII and NLR (both p< 0.05). Variability in HBV serologic markers and QFT results was observed during follow-up; however, no cases of active tuberculosis or clinically overt hepatitis were identified. In conclusion, biologic therapy led to substantial clinical improvement in geriatric psoriasis, accompanied by reductions in systemic inflammatory indices over a 6-month period, without evidence of clinically overt hepatitis or active tuberculosis during follow-up.

BACKGROUND Delayed graft function remains a common and clinically relevant complication following kidney transplantation, yet reliable early prediction tools are limited. This small-scale, retrospective, exploratory study investigated whether routine hematologic inflammatory indices, such as the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and prognostic inflammatory value (PIV), when considered alongside donor-recipient demographic characteristics, predict the need for early posttransplant dialysis. MATERIAL AND METHODS Retrospective analysis of 33 cadaveric kidney transplant recipients was performed. Catorigization into dialysis (n=14) and non-dialysis (n=19) groups was based on early posttransplant dialysis requirement. SII, SIRI, and PIV were calculated from pretransplant laboratory parameters. Given the exploratory study design, analyses were primarily descriptive, supported by univariate comparisons and logistic regression with bootstrap resampling. RESULTS Recipients requiring dialysis tended to receive kidneys from older donors, although this did not reach statistical significance. Monocyte count was significantly lower in the dialysis group (P=0.032). Inflammatory indices, including SII, SIRI, and PIV, showed no significant differences (P>0.05). Logistic regression analyses did not identify SII, SIRI, or PIV as independent dialysis predictors. However, bootstrap resampling showed consistent, although non-significant, directional trends, suggesting higher donor age and inflammatory burden among recipients requiring dialysis. CONCLUSIONS This study provides preliminary insights into the potential role of combined hematologic inflammatory indices in the context of delayed graft function, underscoring the need for larger, prospective studies to clarify whether inflammatory burden and donor-related factors can be integrated into clinically useful prediction models for early dialysis after kidney transplantation.

Introduction: Chronic inflammation plays a pivotal role in the pathogenesis and prognosis of myelofibrosis (MF), contributing to fibrosis, immune dysregulation, and cardiovascular comorbidities. Traditional prognostic tools, such as the Dynamic International Prognostic Scoring System (DIPSS), do not consider inflammatory status, despite evidence significance of linking of inflammatory indices—including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and systemic inflammation response index (SIRI) – with disease progression and survival. This study aimed to assess the prognostic relevance of these indices in MF patients treated with ruxolitinib and to develop a new inflammation-integrated prognostic model.Methods: We retrospectively analyzed 166 MF patients (98 PMF and 68 SMF) followed at the Hematology Units of Pisa and Catania (Italy) between 2013 and 2023. All patients received ruxolitinib and met WHO 2008/2016 diagnostic criteria. Inflammatory parameters (NLR, MLR, PLR, WLR, SII, SIRI) were calculated at diagnosis. Survival analyses were performed using Kaplan–Meier curves and Cox regression models. Variables significant in univariate analysis were included in multivariate models. Based on these results, a new prognostic score (DiMoL) combining DIPSS and MLR was proposed.Results: Median patient age was 68 years; (M:F=1,2). JAK2V617F mutation was detected in 76%, CALR in 16%, and MPL in 1%. According to DIPSS, 11% were low-risk, 36% intermediate-1, 40% intermediate-2, and 13% high-risk. Median overall survival (OS) was 84 months, with a 6-year OS of 54%. In univariate analysis, OS was significantly affected by DIPSS category, age &gt;65 years, and symptom burden, but not by gender, splenomegaly, or driver mutation. Among inflammatory indices, elevated MLR (p &lt; 0.01) and high SIRI (p &lt; 0.008) were associated with poorer OS, although only MLR maintained significance in multivariate analysis (HR 1.80, p = 0.03). The DiMoL score, integrating DIPSS and MLR, identified three risk categories with distinct survival outcomes: 6-year OS of 90% (low), 52% (intermediate), and 22% (high), offering improved stratification compared with DIPSS alone by reallocating part of the intermediate-risk group.Conclusions: Inflammation, as reflected by MLR, is an independent prognostic factor in MF patients treated with ruxolitinib. The novel DiMoL score effectively refines risk stratification, identifying more homogeneous subgroups than DIPSS and highlighting the clinical value of easily measurable inflammatory parameters. Incorporating inflammation into prognostic models may improve individualized management, support early therapeutic intervention in high-risk patients, and encourage the evaluation of anti-inflammatory strategies—such as statins or interferon—in future prospective studies.

Introduction: Gestational Diabetes Mellitus (GDM) is diagnosed when pregnant women develop hyperglycaemia. GDM during pregnancy causes many complications in the mother and the foetus. Until now, GDM is diagnosed by an Oral Glucose Tolerance Test (OGTT) that becomes positive in the second trimester of pregnancy. Widespread inflammation is present in GDM. Inflammatory markers could help diagnose GDM. Aim: To evaluate the levels of inflammatory markers in women with GDM. Materials and Methods: The present case-control study was conducted at the Department of Biochemistry, SRIHER, Chennai, Tamil Nadu, India. The data were collected from medical records from January 2022 to December 2023. Data on plasma glucose and Complete Blood Count (CBC) were collected. Inflammatory indices such as Neutrophil Lymphocyte Ratio (NLR), Monocyte Lymphocyte Ratio (MLR), Platelet Lymphocyte Ratio (PLR), Systemic Immune‑inflammation Index (SII), Systemic Inflammation Response Index (SIRI) were calculated. Pregnant women between the ages of 20 and 40 years without diabetes (n=119) and pregnant women with GDM (n=118) were included. Pregnant women with pre-existing diabetes mellitus, gestational hypertension, and inflammatory disorders were excluded. The obtained data were subjected to the normality of distribution. Student’s t-test and Chi-square test were used. The Pearson correlation coefficient was used to compare the variables. The p-value ≤0.05 was considered statistically significant. Results: The mean age of women in non-diabetic group was 32.5±7.51 years and in diabetic group was 30.9±8.9 years (p=0.13). Among the White Blood Cells (WBC), only monocyte count (%) showed a statistically significant difference (p=0.03) between the groups. All the derived variables showed statistically significant differences between the groups NLR (p=0.007), MLR (p=0.007), PLR (p=0.03), SII (p=0.03), and SIRI (p=0.02). Fasting plasma glucose, 1-hr OGTT, and 2-hr OGTT were positively correlated with RBC, PLR, and SII, which were statistically significant. Conclusion: All the derived variables, such as NLR, MLR, PLR, SII, and SIRI, showed higher values in GDM individuals than nondiabetic pregnant women. Plasma glucose was correlated with the systemic immune inflammation index and platelet-to-lymphocyte ratio. Thus, inflammatory markers (NLR, MLR, PLR, SII, and SIRI) could serve as potential diagnostic markers of GDM.

Association between serum trimethylamine N-oxide levels and sleep quality in mid-pregnancy women in Guangzhou: The mediating role of hematological inflammatory markers.

The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) have been associated with poor cancer outcomes; however, no comprehensive evaluation across various cancer types has been conducted. To assess the impact of SII/SIRI on survival outcomes in cancer patients. Meta-analyses up to May 10, 2025 were retrieved. Pooled hazard ratios were calculated using random-effects models, and evidence quality was evaluated with AMSTAR 2 and Ioannidis criteria. Seventy-four meta-analyses were included. Elevated SIRI was significantly associated with worse overall survival (HR=1.98), supported by Class I evidence. High SII was also linked to poor prognosis across multiple cancers, with consistent subgroup results. SII and SIRI are reliable prognostic biomarkers across cancers and may aid clinical decision-making.

Postmenopausal bleeding (PMB) and increased endometrial thickness are key clinical indicators that may suggest underlying malignancies. While endometrial biopsy remains the diagnostic gold standard, its invasiveness underscores the need for alternative, noninvasive biomarkers. This study evaluates the potential of clinical, laboratory, and peripheral blood inflammatory indices (PBII) in distinguishing malignant from benign endometrial pathologies. This retrospective study included 162 patients who underwent endometrial biopsy due to PMB and/or increased endometrial thickness between January 2023 and January 2024. Patients were categorized into benign (n = 134) and malignant (n = 28) groups. Demographic, clinical, and laboratory parameters were collected, PBII parameters were calculated, and comparisons were performed. Logistic regression analyses were conducted to identify independent predictors of malignancy. Malignant cases were significantly associated with older age (p < 0.001), longer postmenopausal duration (p = 0.002), higher body mass index (BMI) (p = 0.018), and greater endometrial thickness (p = 0.042) compared to benign cases. Hemoglobin levels were significantly lower (p = 0.022), while neutrophil (p < 0.001) and monocyte (p = 0.042) counts were notably higher in malignant cases. Among PBII parameters, neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), pan-immune-inflammation value (PIV), and systemic inflammation response index (SIRI) were significantly elevated (p < 0.001 for all). Multivariate analysis identified older age (p < 0.001), lower hemoglobin (p = 0.016), higher neutrophil count (p = 0.030), and increased PIV (p = 0.022) as independent predictors of malignancy. Integrating clinical and laboratory parameters with PBII, particularly PIV, may be a valuable, noninvasive tool for the early detection and risk stratification of endometrial malignancies. This approach could enhance diagnostic accuracy, reduce the need for invasive biopsies, and improve patient management.

BackgroundSepsis-associated acute kidney injury (SA-AKI) is a serious condition with a high mortality rate. Whole blood-derived inflammatory markers like the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI), are emerging prognostic indicators for various diseases. This study endeavors to unravel the link of these markers to all-cause mortality(ACM) in the SA-AKI population utilizing the MIMIC-IV database.MethodsA retrospective cohort study was conducted on SA-AKI patients meeting the Sepsis-3 and KDIGO criteria. Cox regression analysis was performed to evaluate the association between inflammatory markers and mortality. Restricted cubic spline (RCS) analysis was employed to unveil the potential nonlinear relation of inflammatory markers to mortality. Survival differences across varying levels of inflammation were compared via Kaplan-Meier (KM) survival curves. Subgroup analyses were executed to examine the robustness of the relation and possible interactions between variables. The predictive performance of inflammatory markers was evaluated via receiver operating characteristic (ROC) curves, and the clinical utility of these markers was assessed through clinical decision curve analysis(DCA).Results3429 SA-AKI patients were encompassed (2785 survivors at 30 days and 644 non-survivors). Cox regression analysis revealed a significant link between risen NLR, PLR, MLR, SII, and SIRI to elevated ACM. KM survival analysis demonstrated that patients with higher levels of inflammatory markers had notably higher 30-day death rates. Subgroup analysis indicated an interaction between coronary artery disease (CHD) and inflammation in influencing mortality risk. Among the markers assessed, NLR exhibited the highest forecasting accuracy for 30-day death (AUC = 0.624). Propensity score matching (PSM) confirmed the robustness of these findings.ConclusionWhole blood-derived inflammatory markers, particularly NLR, are closely linked to mortality in patients with SA-AKI. These markers may serve as valuable prognostic tools for identifying high-risk patients and improving clinical outcomes.

Background: After a failed endoscopic retrograde cholangiopancreatography (ERCP) for malignant biliary obstruction (MBO), second-line drainage is performed with endoscopic ultrasound-guided biliary drainage (EUS-BD) or percutaneous transhepatic biliary drainage (PTBD). We compared their effectiveness, safety, and short-term survival. Methods: We conducted a single-center retrospective cohort of 101 adults with MBO after they had experienced a failed ERCP (EUS-BD n = 37; PTBD n = 64). Allocation was non-randomized and driven by operational availability. Baseline laboratory tests (complete blood count, platelets, and C-reactive protein) and derived indices (neutrophil-to-lymphocyte ratio [NLR], platelet-to-lymphocyte ratio [PLR], lymphocyte-to-monocyte ratio [LMR], systemic immune-inflammation index [SII], systemic inflammation response index [SIRI], neutrophil-to-platelet score [NPS], and lymphocyte-to-CRP ratio [LCR]) were compared. Outcomes that were a technical success include: an early biochemical response (bilirubin reduction), complications (Clavien-Dindo), length of stay (LOS), and overall survival (OS). Between-group comparisons used the two-sided Mann-Whitney U test (continuous) and Fisher's exact (binary) test. Survival was assessed by the Kaplan-Meier estimator using log-rank testing. To address later adoption of EUS-BD, we also estimated a restricted mean survival time of 180 days (RMST_0-180) with 95% confidence intervals (CIs). Results: Baseline inflammatory markers and composite indices were similar; baseline total bilirubin was higher in PTBD. The technical success was 100% in both groups. Early biochemical response was 86.5% after EUS-BD vs. 78.1% after PTBD (p = 0.43). Any complication occurred in 29.7% vs. 12.5% (p = 0.04); major complications (Clavien-Dindo ≥ III) occurred in 10.8% vs. 0% (p = 0.02), respectively; and the LOS did not differ (p = 0.21). OS favored EUS-BD (median 143 vs. 54 days and log-rank p = 0.012). RMST_0-180 was 111.1 days for EUS-BD vs. 71.4 days for PTBD (difference + 39.6 days; 95% CI 11.3-65.9). Conclusions: After a failed ERCP for MBO, EUS-BD and PTBD achieved universal technical success and similar early biochemical responses, but EUS-BD was associated with higher complication rates and a significantly longer six-month survival. These findings support the individualized selection balancing procedural risk with the anticipated survival benefit and highlight the need for prospective comparative studies.

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor, with a poor prognosis despite standardized multimodal treatment. Recent research has explored the prognostic value of systemic inflammatory markers, which could provide accessible and cost-effective indicators of patient outcomes. This retrospective single-center study analyzed patients with GBM treated between 2014 and 2024. Eligible patients underwent radiotherapy with concurrent and adjuvant temozolomide. Hematological parameters, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and systemic inflammatory response index (SIRI), were assessed for prognostic significance. Overall survival (OS) was analyzed using Kaplan-Meier curves and Cox regression models. A total of 74 newly diagnosed patients with GBM were included, with a median follow-up of 18 months. ROC curve analysis identified significant cutoff values for NLR, MLR, SII, and SIRI, which were associated with OS. Patients with elevated inflammatory markers had significantly worse OS. Median OS was significantly shorter in patients with elevated NLR (14 months vs. 21 months, p=0.01), MLR (13 months vs. 16 months, p=0.006), SII (14 months vs. 20 months, p=0.04) and SIRI (13 months vs. 20 months, p=0.001). No statistically significant correlation was found between inflammatory markers and MGMT promoter methylation status. This study confirms the prognostic relevance of systemic inflammatory markers, particularly NLR, MLR, SII, and SIRI, in patients with GBM. These easily obtainable parameters could complement molecular profiling in risk stratification and treatment planning, pending validation in prospective studies, and contribute to more personalized patient management.