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PubMed1978

Precise anticoagulation for routine hemodialysis.

A pharmacokinetic model for minimal dose heparinization for chornic hemodialysis patients was recently described by Gotch and Keen. The model requires the determination of two parameters: dose sensitivity (computed for a given heparin dose from increase in WBPTT above a baseline value) and the heparin elimination constant. This study describes the extension of this model to the more precise control of anticoagulation during routine dialysis. S and K were measured in 30 stable chronic dialysis patients and were found to differ markedly (0.015 less than or equal to S 0.08 sec/unit; 0.04 less than or equal to K less than or equal to 1.7 hr-1). The mean S value was 0.041 +/- 0.002 sec/unit (N = 30) and the mean K value was 0.90 +/- 0.06 hr-1 (N = 30). In addition, the mean t 1/2 of heparin obtained in the group of 30 patients from individual rate constants was 0.86 +/- 0.06 hr, in excellent agreement with values obtained in normal subjects given similar doses of the drug. Variations in sensitivity during dialysis were minimal, but variations in elimination rate of up to 50% were encountered during modeling. However, the large variations in K did not affect the applicability of the model of control clotting times during dialysis when infusion requirements were based on mean values of S and K taken over four to five dialyses. In an initial group of five patients, whose heparin requirements were reduced by an average of 38% +/- 20 (range 13% to 65%), there was no significant change in the degree of dialyzer clotting in comparing premodeling and postmodeling heparin therapy. In most of the remaining patients (N = 22) the pattern was similar: a reduction in total heparin administration without increased dialyzer clotting. In three patients (10%) over-all heparin dose had to be moderately increased (3% to 13%). Heparin modeling has been successfully applied to routine anticoahe technique cannot be extended to other clinical procedures involving either intermittent or continuous infusion of heparin. To assist in the application of heparin modeling, nomograms have been developed.

Blood Coagulation Tests↗
PubMed2025

Construction and accuracy assessment of an efferocytosis-related prognostic model for ovarian cancer: A diagnostic accuracy study.

The study aimed to investigate the prognostic significance of efferocytosis-related genes in ovarian cancer (OC) with regard to cancer development, progression, invasion, and metastasis. OC cohorts were assembled from bioinformatics repositories. Utilizing consensus clustering analysis, distinct clusters were delineated based on the intersection of OC-related genes and efferocytosis-related genes. A prognostic signature specific to efferocytosis in OC was developed using data from The Cancer Genome Atlas, validated against the gene expression omnibus database, and subjected to independent prognostic analysis. Subsequently, a nomogram model was formulated. Moreover, investigations encompassed the immune microenvironment, immunotherapy, mutation profiling, drug sensitivity assessments, drug prediction models, and molecular docking analyses. Finally, quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays were employed to ascertain the mRNA expression levels of key genes. Five key genes, FCGBP, BTN3A3, WDR91, SLC25A45, and BTNL3, were identified as significantly associated with OC. Both datasets and qRT-PCR demonstrated elevated expression levels of FCGBP and WDR91 in OC. Notably, AFLATOXIN B1 exhibited strong binding affinity to SLC25A45, ciclopirox to BTN3A3, and irinotecan to WDR91. The risk score, age, and stage were identified as independent prognostic factors, with the nomogram displaying efficacy in predicting OC patient survival. Variations in the immune cell infiltration profiles, including naive B cells, and expression levels of 6 immune checkpoint genes, such as CTLA4, were notable. High tumor mutation burden scores were associated with improved survival outcomes. Additionally, significant differences in the IC50 values of 123 anticancer drugs were observed between the 2 risk groups. This findings of this study highlight the efficacy of the efferocytosis-associated risk model in predicting the survival outcomes of OC patients, thus providing a novel reference for prognostic prediction in OC patients.

Humans↗
PubMed2025

Identification of multicohort-based predictive signature for NMIBC recurrence reveals SDCBP as a novel oncogene in bladder cancer.

BACKGROUND: Despite surgical and intravesical chemotherapy interventions, non-muscle invasive bladder cancer (NMIBC) poses a high risk of recurrence, which significantly impacts patient survival. Traditional clinical characteristics alone are inadequate for accurately assessing the risk of NMIBC recurrence, necessitating the development of novel predictive tools. METHODS: We analyzed microarray data of NMIBC samples obtained from the ArrayExpress and GEO databases. LASSO regression was utilized to develop the predictive signature. We combined gene signature and clinicopathological factors to construct a clinical nomogram for estimating NMIBC recurrence in a local cohort. Finally. the biological functions and potential mechanisms of SDCBP in bladder cancer were investigated experimentally in vitro and in vivo. RESULTS: An 8-gene signature was developed, and its efficiency for predicting NMIBC recurrence was evaluated using Kaplan-Meier and time-dependent ROC curves in both training and validation datasets. Immunohistochemical testing revealed elevated levels of ACTN4 and SDCBP in recurrent NMIBC tissues. We integrated the two proteins with clinical factors to develop a nomogram model, which showed superior accuracy compared to individual parameters. Gene Set Variation Analysis and Gene Set Enrichment Analysis unveiled SDCBP exerted cancer-promoting biological processes, such as angiogenesis, EMT, metastasis and proliferation. Experimental procedures demonstrated that silencing SDCBP attenuated cell growth, glucose metabolism and extracellular acidification rate, accompanied by decreased expression of p-AKT, p-ERK1/2, LDHA and Vimentin. CONCLUSIONS: The established 8-gene signature holds promise as a tool for predicting NMIBC recurrence, while targeting SDCBP may represent a potential strategy for delaying disease relapse.

Urinary Bladder Neoplasms↗
PubMed2025

Inflammatory pathways and immune dysregulation in pediatric postoperative septic shock: A study integrating transcriptomics, machine learning and molecular docking.

This study elucidates the molecular and immune regulatory mechanisms of pediatric postoperative septic shock. Transcriptomic data were obtained from the Gene Expression Omnibus database. Differentially expressed genes were identified using the limma package, and gene co-expression modules were constructed using Weighted Gene Co-expression Network Analysis. Functional enrichment was performed via gene set enrichment analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses. Immune cell infiltration was assessed using ESTIMATE and CIBERSORT. Mendelian randomization was applied to explore causal relationships between gene expression and septic shock. Feature genes were selected using machine learning algorithms, and a diagnostic nomogram model was constructed. Finally, molecular docking analysis was performed to screen and evaluate the binding affinity of traditional Chinese medicine monomers to core target proteins. A total of 1331 differentially expressed genes were identified, and the turquoise module was strongly correlated with septic shock. Enrichment analysis revealed significant activation of IL-6/JAK/STAT3, TNF-α/NF-κB, and PI3K/Akt/mTOR pathways. Immune infiltration analysis indicated suppressed immune scores and imbalances in neutrophils, macrophages, T cells, and B cells. Mendelian randomization confirmed causal associations for 6 genes, including PIM3. The predictive model based on feature genes demonstrated high diagnostic performance. Molecular docking suggested that quercetin and astramembrannin I could stably bind PIM3. This study systematically identified core genes, dysregulated immune pathways, and candidate small-molecule interventions in pediatric septic shock, providing novel insights for early diagnosis and targeted therapy.

Humans↗
PubMed2026

PSMB8: an immune-related prognostic marker for low-grade gliomas.

BACKGROUND: Glioma is the most common primary intracranial tumor in adults. As a subunit of immune proteasome, proteasome subunit beta type-8 (PSMB8) may regulate the progression of glioma via participating in degradation and presentation of tumor antigenic peptides, but its prognostic and clinical applicant usage is under investigation. Therefore, this study aimed to comprehensively evaluate the prognostic significance of PSMB8 in low-grade glioma (LGG) and to elucidate its association with the tumor immune microenvironment and potential as a predictor for immunotherapy response. METHODS: Transcriptome data were downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Gene Expression Omnibus (GEO) repositories. The correlations between PSMB8 expression and the clinicopathological features of LGG were investigated in our study, and the prognostic role of PSMB8 in LGGs was assessed fully and comprehensively. Furthermore, we evaluated the correlation between PSMB8 expression and LGG immune environment via the experiments and bioinformatic analysis. RESULTS: Our results indicated that, PSMB8 were highly expressed in most tumor tissues, including LGG. Lower expression of PSMB8 was significantly correlated with lower World Health Organization (WHO) grade and isocitrate dehydrogenase (IDH) mutation status. Moreover, PSMB8 showed a promising prognostic ability for LGG patients via nomogram model and receiver operating characteristic (ROC) curves. Association analysis showed that PSMB8 expression was associated with immune cell infiltration in a variety of tumors, including LGG. Our experiments validated the positive correlation between PSMB8 expression and M2-macrophage infiltration level in clinical LGG tissues and invasive ability of LGG cell. CONCLUSIONS: PSMB8 could be used as one of the prognostic indicators of LGG and it could regulate the LGG cell migratory and invasive ability. Besides, PSMB8 is expected to be a promising biomarker of cancer immunotherapy.

Low-grade glioma (LGG)↗
PubMed1992

Sample size determination for bioequivalence assessment by means of confidence intervals.

The statistical analysis of bioequivalence assessment has been consolidated in recent years through the work of Schuirmann [1987], Westlake [1988] and Hauschke et al. [1990], and this has been reflected in the CPMP Note for Guidance on Bioavailability and Bioequivalence and in the joint recommendations of the APV (International Association for Pharmaceutical Technology) and ZL (Central Laboratories of German Pharmacists) during a recent workshop in support of EC-Guidelines [Blume et al. 1990]. Since the decision procedure based on the inclusion of the shortest 90%-confidence interval in the bioequivalence range is the procedure of choice, and as this is equivalent to the two one-sided tests procedure, the sample size determination is based on the power of the latter. Following the approach of Phillips [1990] for the additive model, corresponding nomograms for the more relevant multiplicative model are given in this paper for various ratios of the expected means for test and reference and various coefficients of variation.

Confidence Intervals↗
PubMed1991

Sample size determination for bioequivalence assessment by means of confidence intervals.

The statistical analysis of bioequivalence assessment has been consolidated in recent years through the work of Schuirmann [1987], Westlake [1988] and Hauschke et al. [1990], and this has been reflected in the CPMP Note for Guidance on Bioavailability and Bioequivalence and in the joint recommendations of the APV (International Association for Pharmaceutical Technology) and ZL (Central Laboratories of German Pharmacists) during a recent workshop in support of EC-Guidelines [Blume et al. 1990]. Since the decision procedure based on the inclusion of the shortest 90%-confidence interval in the bioequivalence range is the procedure of choice, and as this is equivalent to the two one-sided tests procedure, the sample size determination is based on the power of the latter. Following the approach of Phillips [1990] for the additive model, corresponding nomograms for the more relevant multiplicative model are given in this paper for various ratios of the expected means for test and reference and various coefficients of variation.

Humans↗
PubMed2026

Development and Validation of a Predictive Model for Identification of Cognitive Impairment Risk in Older Adults with Subjective Cognitive Decline：A Longitudinal Study.

BACKGROUND: Subjective cognitive decline (SCD) is a transitional state between objective cognitive impairment and cognitively intact mental status, providing a critical window for implementing preventive interventions to delay objective cognitive decline. AIMS: We aimed to develop a predictive model for SCD progression in older adults with mild cognitive impairment (MCI). This model will facilitate the identification of risk factors and establishment of targeted interventions for community-based SCD management. METHODS: Data from the China Health and Retirement Longitudinal Study (CHARLS) was utilized in this study, extracting 18 indicators. Potential predictors selected through univariate Cox regression and LASSO regression analyses were sequentially incorporated into a multivariable Cox regression model. A nomogram was constructed to establish a predictive model. Model validation encompassed Area Under Curve (AUC) metrics for discriminative capacity, complemented by quantitative assessments using calibration curve analysis for precision verification and decision curve analysis (DCA) for clinical utility evaluation. RESULTS: A total of 1099 older adults with SCD were included in the final analysis, of whom 114 (10.3%) developed MCI. Multivariable Cox regression identified residence, marital status, educational level, social participation, gait speed, and baseline cognitive function. The model demonstrated time-dependent AUC values of 0.885, 0.830, 0.839, and 0.836 in the training set when evaluating discriminative capacity at 2-, 4-, 7-, and 9-year, respectively. The predictive model showed excellent predictive ability according to AUC, calibration curve, and DCA. CONCLUSIONS: A predictive model was created to estimate the risk of developing MCI in older individuals with SCD, offering clinician-actionable intervention benchmarks for preventive care.

Humans↗
PubMed2025

Predicting telomerase reverse transcriptase promoter mutation status in glioblastoma by whole-tumor multi-sequence magnetic resonance texture analysis.

OBJECTIVE: This study aimed to determine the feasibility of preoperative multi-sequence magnetic resonance texture analysis (MRTA) for predicting TERT promoter mutation status in IDH-wildtype glioblastoma (IDHwt GB). METHODS: The clinical and imaging data of 111 patients with IDHwt GB at our hospital between November 2018 and June 2023 were retrospectively analyzed as the training set, and those of 23 patients with IDHwt GB between July 2023 and November 2023 were interpreted as the validation set. We used molecular sequencing results to classify the training set into TERT promoter mutation and wildtype groups. Textural features of the whole-tumor volume were extracted, including T2-weighted imaging (T2WI), T2-fluid-attenuated inversion recovery, apparent diffusion coefficient (ADC) map, and contrast-enhanced T1-weighted imaging (CE-T1). All textural features were obtained using open-source pyradiomics. After feature selection, logistic regression was used to build prediction models, and a nomogram was generated. Finally, the model was validated using validation cohort. RESULTS: The CE-T1_Model (AUC 0.704) had a better predictive ability than the T2_Model (AUC 0.684) and ADC_Model (AUC 0.624). The MRI_Combined_Model (CE-T1, T2, and ADC texture features) (AUC 0.780) had a better predictive ability than the Clinical_Model (AUC 0.758). The Combined_Model (CE-T1, T2, ADC texture features, and clinical features) had the best predictive performance (AUC 0.871), with a sensitivity, specificity, and accuracy of 82.60 %, 83.30 %, and 80.18 %, respectively. The AUC, sensitivity, specificity, and accuracy in the validation cohort were 0.775, 86.70 %, 75.00 %, and 69.57 %, respectively. CONCLUSIONS: Whole-tumor multi-sequence MRTA can be used as non-invasive quantitative parameters to assist in the preoperative clinical prediction of TERT promoter mutation status in IDHwt GB.

Humans↗
PubMed2026

Metabolism pathway-based subtyping in pancreatic adenocarcinoma: an integrated study by bulk RNA-sequence and machine learning algorithms.

BACKGROUND: Pancreatic adenocarcinoma (PAAD) is highly aggressive, and its tumor microenvironment has significant metabolic and immune microenvironment complexity and genomic instability. In this study, by integrating the metabolic pathway activity score and clinical data, we constructed a novel risk assessment model to reveal the unique biological behavior and clinical significance behind different PAAD subtypes. METHODS: In this study, the transcriptome and clinical data of TCGA and GSE57495 databases were integrated to explore the interaction between metabolic pathways. Based on unsupervised clustering analysis of pathway activity and survival prognosis, patients with PAAD were classified into metabolic subtypes with significant prognostic differences. Subsequently, we assessed the heterogeneity of these subtypes in terms of clinical outcomes, genomic characteristics, and immune microenvironment composition. Based on the differentially expressed genes (DEGs) among metabolic subtypes, a clinical prognostic risk model and nomogram were constructed, which were double-validated by GSE57495-independent cohort and GSE57495 + TCGA-PAAD combined cohort. Finally, the correlations between risk scores (RSs) and signaling pathway activity and tumor immune microenvironment characteristics were evaluated. RESULTS: Based on metabolic pathway correlation and prognostic information, 240 patients in the TCGA-PAAD and GSE57495 datasets were divided into three subgroups. There were significant differences between subgroups in gene expression, pathway activity, clinical prognosis, and immune infiltration characteristics among the subtypes. Using machine learning algorithms, an RS model was constructed from DEGs among the subgroups, with the random forest method showing the best performance. A nomogram integrating the RS and clinical indicators demonstrated excellent predictive accuracy for 1-, 3-, and 5-year survival rates, confirming the RS as an independent prognostic factor. High- and low-risk groups exhibited significant differences in immune infiltration, pathway activity, and gene mutations. Drug sensitivity analysis showed that the high-risk group was more sensitive to AZD6244, ABT737, and other drugs. CONCLUSION: This study stratified patients with PAAD into three subgroups based on metabolic pathways and prognostic information, revealing significant differences in clinical outcomes, immune characteristics, and genetic mutations. The robust RS model developed from these findings demonstrated strong predictive power for patient survival and identified promising therapeutic strategies, providing valuable insights for advancing precision medicine in PAAD.

immune microenvironment↗
PubMed2025

A clinically applicable method for early interstitial lung disease detection in incident rheumatoid arthritis cases: integration of protein biomarkers and clinical factors.

BACKGROUND: This study aimed to develop an early diagnostic method integrating proteomic biomarkers and clinical parameters for screening interstitial lung disease (ILD) in patients with newly diagnosed rheumatoid arthritis (RA) through a multi-phase research strategy. METHODS: A three-phase study was conducted: (1) Discovery: Tandem mass tag (TMT)-labeled quantitative proteomics with liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyzed serum protein profiles in 5 RA-ILD and 5 RA-non-ILD patients, identifying candidates via bioinformatics. (2) Verification: Enzyme-linked immunosorbent assay (ELISA) validated candidates in an independent cohort (13 RA-ILD vs 14 RA-non-ILD). (3) Application: Biomarkers combined with clinical indicators (Krebs von den Lungen-6 [KL-6], age, sex) were evaluated in 110 patients (51 RA-ILD vs 59 RA-non-ILD) to build a predictive model. RESULTS: Proteomic analysis identified matrix metalloproteinase-3 (MMP3), von Willebrand factor (VWF), and other significantly differentially expressed proteins. ELISA validation confirmed that serum MMP3 and VWF levels were significantly higher in the RA-ILD group than in the RA-non-ILD group (p = 0.025 and 0.027, respectively). Expanded validation demonstrated superior diagnostic performance when combining MMP3 and VWF with KL-6 (area under the curve [AUC] = 0.90). The nomogram prediction model based on univariate analysis exhibited excellent discrimination (AUC = 0.89) and calibration. CONCLUSION: This systematic study from discovery to validation identified MMP3 and VWF as potential biomarkers for RA-ILD. The integrated predictive model combining these biomarkers with clinical parameters (KL-6, age, sex) provides a potential tool for early ILD screening in RA patients, offering novel strategies for early diagnosis and intervention of RA-ILD.

Humans↗
PubMed2025

Identification and validation of prognostic genes associated with mitochondrial nuclear genes in gastric cancer.

Mitochondrial-related nuclear genes (MNGs) have shown great importance in cancer diagnosis and prognosis, but their role in gastric cancer (GC) remains unclear. GC-related transcriptome data from the gene expression omnibus and cancer genome atlas databases were analyzed to identify differentially expressed MNGs. A prognostic risk model was constructed through univariate Cox and least absolute shrinkage and selection operator regression, validated by Kaplan-Meier (K-M) survival curve and receiver operating characteristic curve. This was followed by immune infiltration analysis, independent prognostic analysis, functional enrichment analysis, drug sensitivity analysis, drug prediction, molecular docking and construction of regulatory networks. Three prognostic genes (ATP8A2, COX15 and TARS2) were identified. The expression of TARS2 and COX15 was positively correlated with CNV, while ATP8A2 was unaffected. The risk model and nomogram, integrating risk score and clinicopathological factors, exhibited excellent predictive performance. A significant correlation was observed between prognostic genes and differential immune cells, such as T cells, B cells, and NK cells. BMS-754807, Gefitinib, JQ1, Lapatinib, and Sapitinib exhibited significant differences in sensitivity between the high-risk group and the low-risk group. The results of molecular docking showed TP8A2 has stable binding ability with cytosine, COX15 with indomethacin, and TARS2 with bisacodyl. RT-qPCR revealed downregulation of ATP8A2 and upregulation of COX15 and TARS2 in GC samples. MNGs, including ATP8A2, COX15, and TARS2, demonstrated significant associations with immune infiltration, CNV, and prognostic outcomes of GC.

Humans↗
PubMed2026

Genetic insights into lung squamous cell carcinoma: how TP53 and CSMD3 co-mutations shape prognosis and immune response.

BACKGROUND: Lung squamous cell carcinoma (LUSC) accounts for a significant proportion of lung cancer cases and is often associated with smoking and various environmental factors. The prognostic and immunologic implications of TP53 and CSMD3 co-mutations in LUSC remain poorly understood. This study aimed to investigate the role of TP53/CSMD3 co-mutations in LUSC using comprehensive bioinformatics analyses. METHODS: Data from 487 LUSC patients were obtained from The Cancer Genome Atlas (TCGA) database, with external validation performed using the combined cohort. Patients were stratified into TP53/CSMD3 co-mutation, single-mutation, and wild-type (WT) groups. Prognostic analysis was conducted using Kaplan-Meier survival curves. Tumor mutational burden (TMB) was calculated, and immune cell infiltration was assessed using multiple algorithms. Differentially expressed genes (DEGs) between co-mutated and WT groups were identified, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A nomogram incorporating mutation status, gender, age, and tumor stage (T stage) was developed for individualized prognostic prediction. RESULTS: The TP53/CSMD3 co-mutated group exhibited significantly better overall survival (OS) compared to single-mutation and WT groups. TMB scores were markedly higher in co-mutated patients, suggesting potential sensitivity to immune checkpoint inhibitors. Immune infiltration analysis revealed distinct profiles, including elevated CD8 T cells and reduced immunosuppressive components, in the co-mutation group. A total of 403 DEGs were identified between co-mutated and WT groups, with significant enrichment in immune-related pathways. Mechanistically, the co-mutation was associated with distinct downregulation of complement negative regulators (CFH/CFI), indicating complement hyperactivation independent of TMB. The constructed nomogram provided accurate individualized prognostic assessments. CONCLUSIONS: The co-mutation of TP53 and CSMD3 identifies a distinct LUSC subtype with favorable survival, marked by high TMB and an immune-activated microenvironment. Beyond TMB-driven neoantigen generation, the significant downregulation of complement negative regulators (CFH/CFI) reveals an independent complement hyperactivation pathway associated with CSMD3 loss. The constructed nomogram provides accurate individualized survival prediction. These findings establish TP53/CSMD3 co-mutation as a promising prognostic biomarker and offer mechanistic insights for personalized immunotherapy strategies. Future prospective cohorts are warranted to validate its predictive value.

Lung squamous cell carcinoma (LUSC)↗
PubMed1990

Pharmacokinetic and pharmacodynamic studies on recombinant human erythropoietin.

In order to optimize the treatment of anemia in uremic patients the pharmacokinetic and pharmacodynamic properties of recombinant erythropoietin (r-Epo) were studied after i.v., s.c. and i.p. administration. Both in healthy volunteers and in patient with chronic renal failure, the half-life of r-Epo after i.v. administration was short (about 6 h) in comparison with the commonly used dosing interval of 3 to 7 days. Hence, the minimum serum concentration during a dosing interval is expected to be less than 1% of the peak concentration. Absorption following a s.c. dose was slow, resulting in a markedly different concentration-time profile in comparison to i.v. dosing. The half-life was about 25 h and only approximately 25% of the given dose reached the systemic circulation. As a result of differences in concentration-time profiles, higher trough concentrations during s.c. dosing intervals may be expected in comparison to those occurring after i.v. dosing. When r-Epo was given i.p. (diluted in dialysate) the extent of systemic absorption depended on the dwell time in the peritoneal cavity. A long administration time was required to absorb an amount of r-Epo predicted from s.c. studies to be adequate to achieve the desired clinical effect. In spite of reduced bioavailability, s.c. treatment did not require higher r-Epo doses than i.v. treatment to maintain the desired hemoglobin concentration. On the contrary, a trend to a requirement for lower doses was detected. The pharmacodynamic and pharmacokinetic results strongly indicate a more efficacious concentration-time profile following s.c. administration. Since s.c. dosing also allows self-administration the use of this administration route is recommended. To simplify the treatment of anemic patients with r-Epo, a model was developed to predict the required weekly s.c. dose. To facilitate the use of this model a nomogram was constructed.

Adult↗
PubMed1989

Predictors of excessive blood use after coronary artery bypass grafting. A multivariate analysis.

One hundred fifty-nine consecutive patients who underwent coronary artery bypass grafting were studied to determine clinical and laboratory predictors of excessive postoperative packed red blood cell transfusion. Consideration of the distribution of packed red blood cells administered revealed that the patients could be divided into two groups: those patients who received 5 units of red blood cells or less (group I, n = 139) and those patients who received more than 5 units of packed red blood cells (group II, n = 20). The Mann-Whitney test or Fisher's exact test was used whenever appropriate to test differences between these two groups with respect to twelve patient variables. Patients in group II were found to have a significantly longer preoperative template bleeding time and decreased preoperative packed red blood cell volume (p less than 0.0008 for both variables). In addition, group II patients were significantly older (p = 0.026), were more likely to have had preoperative heparin therapy (p = 0.049), and contained a greater proportion of women (p = 0.0048). Of interest, variables that did not achieve statistical significance between groups were partial thromboplastin time, prothrombin time, platelet count, preoperative hematocrit level, urgency of operation, recent ingestion of aspirin, and recent heparin administration. All of the measured variables were used in a stepwise logistic regression analysis to identify the best predictors of the need for more than 5 units of packed red blood cells after operation. Of the variables examined, bleeding time (p less than 0.001; chi 2 improvement = 15.1) and red blood cell volume (p = 0.009; chi 2 improvement = 6.8) were the best predictors of excessive postoperative packed red blood cell use. On the basis of a 50% logistic probability level, the specificity and sensitivity of these two variables in predicting greater than a 5-unit transfusion requirement were 85% and 99%, respectively. A clinically useful nomogram based on this logistic model is presented. This nomogram suggests that a ratio of bleeding time to red blood cell volume of 0.0071 or greater is associated with a greater than 70% chance of requiring more than 5 units of packed red blood cells. We conclude that preoperative bleeding time and red blood cell volume are useful predictors of excessive postoperative blood transfusion. These results suggest that factors other than aspirin therapy may be associated with bleeding time prolongation leading to excessive postoperative transfusion.

Aged↗
PubMed2026

Construction of a new predictive model in head and neck squamous cell carcinoma based on the investigation of extracellular matrix-associated genes.

A key aspect influencing immune cell infiltration is the composition of the extracellular matrix (ECM). Therefore, investigating the association between ECM-associated proteins and immune cell infiltration is key for the identification of new biomarkers to distinguish 'immune-hot' solid tumors and predict patient prognosis. A total of 513 head and neck squamous cell carcinoma (HNSCC) cases as training samples from The Cancer Genome Atlas and an additional 270 as testing samples from the Gene Expression Omnibus were obtained for use in the present study. Using a single-sample Gene Set Enrichment Analysis method, the 513 training samples were divided into Cluster 1 and Cluster 2. Subsequently, the present analysis uncovered 1,573 differentially expressed genes distinguishing the two clusters. After performing an intersection analysis with 751 ECM-associated genes, 103 differentially expressed ECM-associated genes were identified. Least absolute shrinkage and selection operator-Cox and multivariate Cox regression analyses were employed to identify candidate ECM risk genes (P<0.05) and to construct a predictive model. Finally, a nomogram and a three gene (cerebellin 2, galectin-10 and cathepsin G) predictive model were developed. Therefore, the present prognostic risk score model can evaluate the immune infiltration, predict the prognosis of HNSCC, and potentially guide more personalized immunotherapy interventions.

extracellular matrix↗
PubMed2026

A Dynamic Nomogram to Predict Metabolic Dysfunction-Associated Fatty Liver Disease in Patients with Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) involves multiple metabolic disorders. This study aimed to identify high-risk populations for metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with MetS and to establish a dynamic predictive nomogram. METHODS: A total of 627 patients with MetS from six regions in Zhejiang Province were enrolled and categorized into MAFLD and non-MAFLD groups, then randomly assigned to training and validation sets at a ratio of 7:3. Independent predictors of MAFLD were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses. These predictors were then used to construct a dynamic nomogram. RESULTS: A total of 627 patients with MetS were included in the final analysis, of whom 77.0% (483/627) were diagnosed with MAFLD. Multivariable logistic regression analysis identified body mass index (BMI), waist circumference (WC), total cholesterol (TC), alanine aminotransferase (ALT), MetS-defined dysglycemia, and education level as independent risk factors for MAFLD. MetS-defined dysglycemia showed the highest odds ratio (OR) for MAFLD development [OR = 1.87, 95% confidence interval (CI): 1.07-3.29]. Although the number of MetS components and the metabolic syndrome score were significantly associated with MAFLD in univariate analysis, they were not independently associated with MAFLD in the multivariate model. A dynamic nomogram for predicting MAFLD risk in patients with MetS was developed and internally validated. The area under the receiver operating characteristic curve was 0.834 (95% CI: 0.787-0.880) in the training set and 0.839 (95% CI: 0.771-0.899) in the validation set, indicating strong predictive performance. Bootstrap internal validation demonstrated good agreement between predicted and observed outcomes in calibration curves. Decision curve analysis further indicated favorable clinical applicability of the nomogram. CONCLUSION: BMI, WC, TC, ALT, MetS-defined dysglycemia, and education level are independent risk factors for MAFLD. A dynamic nomogram for predicting MAFLD risk in patients with MetS was successfully developed and validated.

Humans↗