Curve Analysis Research Articles

Development of a whole-cell SELEX process to select species-specific aptamers against Aspergillus niger

BackgroundSpores produced by the filamentous fungus Aspergillus niger are abundant in a variety of environments. The proliferation of this fungus in indoor environments has been associated to health risks and its conidia can cause allergic reaction and severe invasive disease in animals and humans. Therefore, the detection and monitoring of Aspergillus conidia is of utmost importance to prevent serious fungal infections and contaminations. Among others, aptamers could serve as biosensors for the specific detection of fungal spores.ResultsIn this study, DNA aptamers specific to conidia of A. niger were developed by optimizing a whole-cell SELEX approach. Three whole-cells SELEX experiments were performed in parallel with similar conditions. Quantification of recovered ssDNA and melting curve analyses were applied to monitor the ongoing SELEX process. Next-generation sequencing was performed on selected recovered ssDNA pools, allowing the identification of DNA aptamers which bind with high affinity to the target cells. The developed aptamers were shown to be species-specific, being able to bind to A. niger but not to A. tubingensis or to A. nidulans. The binding affinity of two aptamers (AN01-R9-006 and AN02-R9-185) was measured to be 58.97 nM and 138.71 nM, respectively, which is in the range of previously developed aptamers.ConclusionsThis study demonstrates that species-specific aptamers can be successfully developed via whole-cell SELEX to distinguish different Aspergillus species and opens up new opportunities in the field of diagnostics of fungal infections.

Study on the predictive model of delirium risk after surgery for elderly hip fractures based on meta-analysis.

To develop and validate a risk prediction model for postoperative delirium in elderly patients with hip fractures, aiming to identify high-risk patients and implement preventive measures. A systematic search of five authoritative medical databases was conducted, retrieving a total of 1368 relevant articles. After screening, 44 high-quality studies were included in the meta-analysis, analyzing 13 potential risk factors, such as age, gender, diabetes, and history of stroke. A risk prediction model was constructed and validated in a cohort of 189 elderly hip fracture patients. The model's predictive performance was evaluated using ROC curves, with calibration assessed through the Hosmer-Lemeshow test, and clinical utility examined via Decision Curve Analysis (DCA) and Clinical Impact Curves (CIC). The meta-analysis identified the following as independent risk factors for postoperative delirium: age (≥ 70years), male gender, diabetes, history of stroke, preoperative comorbidities (≥ 2), previous delirium, preoperative cognitive impairment, low preoperative albumin levels (≤ 40g/L), prolonged preoperative waiting time (≥ 48h), anemia (≤ 100g/L), ASA classification (≥ 3), use of general anesthesia, and prolonged surgery duration (≥ 2h). The prediction model demonstrated strong efficiency in the validation cohort, with an AUC of 0.956, sensitivity of 87.3%, specificity of 94.8%, and a Brier score of 0.144, indicating high predictive accuracy and calibration. DCA and CIC analyses showed the model to have strong clinical decision-making value and impact across most thresholds. The risk prediction model developed in this study shows high predictive accuracy and clinical utility, making it valuable for identifying high-risk patients and implementing preventive measures in clinical practice. However, the study has limitations, such as potential retrospective bias, and further validation in larger, multicenter prospective studies is needed to confirm the model's broader applicability and stability.

Critical risk factors analysis of infection-associated hemophagocytic lymphohistiocytosis in children.

To analyze the critical factors of infection-associated Hemophagocytic lymphohistiocytosis (HLH) in children, so as to provide theoretical basis for clinicians to evaluate the disease condition, formulate treatment plan and improve prognosis.This study is a retrospective analysis. 60 cases of children with infection-associated HLH were divided into critical and non-critical groups based on the presence of multiple organ dysfunction syndrome (MODS), and the clinical characteristics and laboratory data of the two groups of children were analyzed. A multifactor logistic regression analysis model was used to assess the independent risk factors affecting critical illness in children with infection-associated HLH, and the Receiver Operating Characteristic (ROC) curve analysis was used to evaluate the predictive value of risk factors for critical illness in children with infection-associated HLH. Children in the critical group with HLH had a younger age at onset. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT), D-dimer (DD), and triglycerides (TG) were significantly higher in the critical group, while albumin (ALB) was significantly lower, showing statistical significance (P < 0.05). Multifactorial logistic regression analysis of age, ALB, and TG showed that younger age and lower ALB were associated with a higher risk of MODS in children with infection-associated HLH, with age and ALB being independent risk factors for critical illness. ALB predicted the ROC area under the curve for critical children with infection-associated HLH was 0.765 (95% CI: 0.643-0.888, P = 0.011), with the optimal cut-off value being 32.50g/L (sensitivity = 68.3%, specificity = 84.2%); age predicted the ROC area under the curve for critical children with infection-associated HLH was 0.711 (95% CI: 0.570-0.851, P = 0.009), with the optimal cut-off value being 1.50 years (sensitivity = 70.7%, specificity = 68.4%). This study suggests that younger patients and those with hypoalbuminemia among infection-related HLH patients are more likely to develop MODS. In the future, verification will be required through large-scale, multi-center studies.

Predictive value of bone metabolism markers in the progression of diabetic kidney disease: a cross-sectional study

ObjectiveThis study aimed to investigate the relationship between bone metabolism markers, including serum klotho, fibroblast growth factor 23 (FGF23), 25(OH)D3, iPTH, calcium (Ca), and PHOS and the progression of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). Additionally, the predictive value of these markers for DKD progression was evaluated.MethodsThis study involved 126 patients with T2DM between May 2021 and March 2023. DKD staging was assessed based on urinary protein excretion rates and estimated glomerular filtration rate (eGFR). The study evaluated serum concentrations of klotho, FGF23, 25(OH)D3, iPTH, Ca and PHOS across various stages and examined their relationships with clinical parameters. Receiver operating characteristic (ROC) curve analysis was utilized to determine the predictive accuracy of these bone metabolism markers for DKD. Multivariate linear and logistic regression analyses identified risk factors linked to DKD severity.ResultsAmong the 126 participants, 30 had non-DKD with normal proteinuria, while 96 had DKD, categorized as 31 with stage III DKD (microproteinuria), 34 with stage IV DKD, and 31 with stage V DKD (massive proteinuria). With advancing DKD from stage III to V, levels of klotho, 25(OH)D3, and Ca decreased significantly, whereas FGF23, iPTH and PHOS levels increased markedly. Klotho is significantly positively correlated with eGFR (r = 0.285, P = 0.001.) and negative correlations with serum creatinine (Scr) and UACR (r = -0.255, P = 0.004; r = -0.260, P = 0.011). FGF23 was positively related to systolic blood pressure (SBP) (r = 0.224, P = 0.012), but negatively with eGFR (r = -0.294, P = 0.001). Additionally, 25(OH)D3 exhibited significant negative correlations with several adverse clinical biomarkers, and both iPTH, Ca and PHOS were strongly associated with DKD progression (P&amp;lt;0.05). ROC analysis showed high predictive accuracy for DKD using these bone metabolism markers, with a combined area under the curve (AUC) of 0.846. Multivariate logistic regression analysis reinforced the significance of these markers in DKD progression.ConclusionBone metabolism markers, such as klotho, FGF23, 25(OH)D3, iPTH, Ca and PHOS are intricately linked to DKD progression and may function as valuable predictive biomarkers.

The maximum transverse diameter: an effective indicator for predicting peroral en bloc retrieval rate of mesenchymal tumors after endoscopic resection.

Gastrointestinal mesenchymal tumors (GIMTs) are being increasingly resected under endoscopy. Large GIMTs cannot be completely retrieved through the mouth, but the cut-off diameter of peroral en bloc retrieval (PEBR) for GIMTs completely resected is still unknown. This study aimed to investigate the ability of maximum transverse diameter (MTD) to predict the PEBR rate of GIMTs after endoscopic resection (ER). We retrospectively reviewed all patients who underwent ER for upper GIMTs from January 2009 to August 2023. The MTD was measured according to the maximum transverse diameter of specimen immediately retrieved after ER. For the PEBR rate, the independent predictors and optimal cut-off value of MTD were determined by logistic regression analysis and the receiver operating characteristic (ROC) curve analysis. The potential significance of preoperative CT for the evaluation of MTD was also clarified. A total of 2032 patients were diagnosed with upper GIMTs after en bloc resection under endoscopy. The overall PEBR rate was 98.72% (2006/2032). The PEBR rate was 100% for 1943 GIMTs with MTD < 2.5cm, 85.71% (60/70) for GIMTs with 2.5cm but ≤ 3.0cm, and 15.79% (3/19) for GIMTs with MTD > 3.0cm, and these rates were significantly different (P < 0.01). In terms of the PEBR rate of GIMTs, the ROC curve revealed that the optimal cut-off MTD value was 3.0cm, and logistic regression analysis revealed that MTD > 3.0cm was an independent predictive factor (OR 71.07, 95% CI 9.14-552.43; P < 0.001). The MTD of CT was related to that of the resected specimen (r = 0.7149, P < 0.01), and CT underestimated the mean MTD of upper GIMTs by 0.17cm (95% CI 0.09-0.24, P < 0.01). MTD is an effective indicator for predicting the PEBR rate of GIMTs after ER. Resected specimens with MTD > 3.0cm could not be routinely retrieved en bloc. Preoperative CT is suitable for evaluating the MTD of GIMTs, but underestimates the mean MTD of upper GIMTs by 0.17cm.

QRISK3 and SCORE2 are equally reliable predictors of carotid plaques in patients with systemic lupus erythematosus.

Cardiovascular (CV) disease is the leading cause of death among patients with systemic lupus erythematosus (SLE). This study aimed to compare the performance of QRESEARCH-risk estimator version 3 (QRISK3) and the Systematic Coronary Risk Evaluation-2 (SCORE2) scores to identify SLE patients at high risk of CV events, as indicated by the presence of carotid plaques. Subclinical atherosclerosis was evaluated using carotid ultrasound in 365 SLE patients. QRISK3 and SCORE2 were calculated. The relationship between these scores and the presence of carotid plaques was analyzed by treating the scores as continuous and categorical variables, and separately and in combination. Logistic regression and area under the receiver operating characteristic curve (AUC) analyses were used to assess their predictive accuracy. The discriminatory capacity of QRISK3, with an AUC of 0.770 (95%CI : 0.720, 0.821), and SCORE2, with an AUC of 0.800 (95%CI : 0.753, 0.843), for carotid plaque was similar, with no statistically significant difference (p= 0.070). However, when examining the association of both calculators considered continuously and together with their interaction, the discriminatory capacity of this combination was significantly greater than that of QRISK3 alone (p= 0.034) but did not differ from SCORE2 (p= 0.71). QRISK3 and SCORE2 are equally reliable predictors of carotid plaques in SLE patients. The combination of both calculators offers significantly better discrimination than QRISK3 alone but shows no significant difference when compared with SCORE2 alone. Therefore, SCORE2 alone, without the need for additional tools, can be used to identify patients with SLE who are at high risk of CV events.

Added value in low-risk pregnancies of longitudinal changes in uterine Doppler and circulating angiogenic factors during the third trimester in predicting term preeclampsia.

To assess the relationship between longitudinal changes in the uterine Doppler velocimetry and the maternal profile of angiogenic factors in the third trimester of pregnancy and to assess their ability to predict term preeclampsia. A cohort of low-risk pregnant women was constructed at second trimester routine scan and scheduled for a uterine Doppler evaluation and measurement of the circulating levels of angiogenic factors at ~30 and ~36 weeks. The performance of both parameters at both time-points and their change over time from the first to the second measurement in predicting term preeclampsia was evaluated by logistic regression and receiver operating characteristic curve (ROC) analyses. A total of 1172 women were analyzed, of which 28 (2.4%) women developed term preeclampsia. At ~30 weeks, a model including the ratio sFlt-1/PlGF (fms-like tyrosine kinase-1/placental growth factor) and the uterine Doppler explained 16.2% of the uncertainty of developing term preeclampsia, while at ~36 weeks the same variables explained 25.2% [p<0.001]. A model including the longitudinal changes of both predictors had an R2 of 26.8%, which was not significantly different from that of the ~36 weeks evaluation [p=0.45]. The area under the curve (AUC) of the ~36 weeks sFlt-1/PlGF ratio was significantly higher than at ~30 weeks (0.86 [0.77-0.94] vs. 0.81 [0.73-0.9]; p=0.043). The AUC of the 30-to-36 week change of the sFlt-1/PlGF ratio (0.85 [0.77-0.94]) did not significantly differ from that of at ~36 weeks (p=0.82). At ~36 weeks, for a 10% of false positives, the sFlt-1/PlGF ratio had a detection rate of 71.4%, with positive and negative likelihood ratios of 7.3 and 0.32, respectively. A cross-sectional measurement of the sFlt-1/PlGF ratio outperforms uterine Doppler in predicting term preeclampsia. The combination of both markers does not improve such prediction, nor the evaluation of the longitudinal changes between ~30 and ~36 weeks.

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features. We developed a gradient boosting ML model (Neo-GB) that utilizes clinical features and QEEG to forecast time-dependent seizure risk. Clinical variables included cord blood gas values, Apgar scores, gestational age at birth, postmenstrual age (PMA), postnatal age, and birth weight. QEEG features included statistical moments, spectral power, and recurrence quantification analysis (RQA) features. We trained and evaluated Neo-GB on a University of California, San Francisco (UCSF) neonatal HIE dataset, augmenting training with publicly available neonatal electroencephalogram (EEG) datasets from Cork University and Helsinki University Hospitals. We assessed the performance of Neo-GB at providing dynamic and static forecasts with diagnostic performance metrics and incident/dynamic area under the receiver operating characteristic curve (iAUC) analyses. Model explanations were performed to assess contributions of QEEG features and channels to model predictions. The UCSF dataset included 60 neonates with HIE (30 with seizures). In subject-level static forecasting at 30 min after EEG initiation, baseline Neo-GB without time-dependent features had an area under the receiver operating characteristic curve (AUROC) of .76 and Neo-GB with time-dependent features had an AUROC of .89. In time-dependent evaluation of the initial seizure onset within a 24-h seizure occurrence period, dynamic forecast with Neo-GB demonstrated median iAUC = .79 (interquartile range [IQR] .75-.82) and concordance index (C-index) = .82, whereas baseline static forecast at 30 min demonstrated median iAUC = .75 (IQR .72-.76) and C-index = .69. Model explanation analysis revealed that spectral power, PMA, RQA, and cord blood gas values made the strongest contributions in driving Neo-GB predictions. Within the most influential EEG channels, as the preictal period advanced toward eventual seizure, there was an upward trend in broadband spectral power. This study demonstrates an ML model that combines QEEG with clinical features to forecast time-dependent risk of initial seizure onset in neonatal HIE. Spectral power evolution is an early EEG marker of seizure risk in neonatal HIE.

A nomogram of inflammatory indexes for preoperatively predicting the risk of lymph node metastasis in colorectal cancer

PurposeTo investigate the independent risk factors associated with the development of lymph node metastasis (LNM) in patients with colorectal cancer (CRC), focusing on preoperative systemic inflammatory indicators, and to construct a corresponding risk predictive model.Materials and MethodsThe clinical data of 241 patients with CRC who underwent surgery after the first diagnosis between January 2012 and December 2017 at our hospital were reviewed. A best logistic regression model was constructed by Lasso regression for multivariate analysis, from which a Nomogram was derived. Using bootstrap to conduct internal validation. The model’s predictive performance and clinical practicability were evaluated using the receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis (DCA). External validation was conducted using retrospective data from 170 patients who underwent surgery between January 2020 and May 2022 at another hospital.ResultsCross-validation indicated smoking history, neutrophil–lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), fibrinogen-albumin ratio (FAR), and fecal occult blood (FOB) as variables with non-zero coefficients. These factors were included in the logistic regression, and multivariate analysis confirmed that smoking history, NLR, LMR, FAR, and FOB were independent risk factors (P < 0.05). The ROC and calibration curve of the original model and external validation indicated strong predictive power of the model. DCA suggested the model’s favorable clinical utility.ConclusionsThe model constructed in this study has robust predictive performance and clinical utility for the preoperative determination of CRC LMN, offering significant for clinical decision-making in patients with CRC.

Construction of a Wilms tumor risk model based on machine learning and identification of cuproptosis-related clusters

BackgroundCuproptosis, a recently identified type of programmed cell death triggered by copper, has mechanisms in Wilms tumor (WT) that are not yet fully understood. This research focuses on examining the link between WT and Cuproptosis-related genes (CRGs), with the goal of developing a predictive model for WT.MethodsFour gene expression datasets related to WT were sourced from the GEO database. Subsequently, expression profiles of CRGs were extracted for differential analysis and immune infiltration studies. Utilizing 105 WT samples, clusters related to Cuproptosis were identified. This involved analyzing associated immune cell infiltration and conducting functional enrichment analysis. Disease-characteristic genes were pinpointed using weighted gene co-expression network analysis. Finally, the WT risk prediction model was constructed by four machine learning methods: random forest, support vector machine (SVM), generalized linear and extreme gradient strength model. The best-performing machine learning model was chosen, and a nomogram was created. The effectiveness of this predictive model was validated using methods such as the calibration curve, decision curve analysis, and by appiying it to the TARGET-GTEx dataset.ResultsThirteen differentially expressed Cuproptosis-related genes were identified. The infiltration level of CD8 + T cells in WT children was lower than that in Normal tissue (NT) children, and the level of M0 infiltration of macrophages and T follicular helper cells was higher than that in NT children. In addition, two clusters of cuproptosis-related WT were identified. Enrichment analysis results indicated that genes in cluster 2 were primarily involved in cell division, nuclear division regulation, DNA biosynthesis process, ubiquitin-mediated proteolysis. The SVM model was judged to be the optimal model using 5 genes. Its accuracy was confirmed through a calibration curve and decision curve analysis, demonstrating satisfactory performance on the TARGET-GTEx validation dataset. Additional analysis revealed that these five genes exhibited high expression in both the TARGET-GTEx validation dataset and sequencing data.ConclusionThis research established a link between WT and Cuproptosis. It developed a predictive model for assessing the risk of WT and pinpointed five key genes associated with the disease.

On the use of receiver operating characteristic area under the curve in eyewitness memory research

AbstractPurposeEyewitness memory research has reformed police practices and policy and is sometimes relied upon in legal proceedings. Due to the practical implications derived from this research, it is imperative to evaluate how practical recommendations are postulated. To assess the practical relevance of research, effect sizes and their interpretation play a pivotal role.MethodsWe examined how the frequently used effect size Area Under the Curve (AUC) obtained via Receiver Operating Characteristic (ROC) curves are used and interpreted in eyewitness memory research. We identified 157 eyewitness memory related articles that conducted ROC curve analyses resulting in 1580 AUCs.ResultsApproximately 90% of the AUCs were only interpreted via statistical significance. The majority of studies did not report 95%CIs for their AUCs. Finally, power analyses were frequently not conducted or not reproducible.ConclusionsTo improve the practical inferences of eyewitness memory research, we recommend establishing a smallest effect size of interest, focusing on 95%CIs, and conducting reproducible power analyses.

ELF1 serves as a potential biomarker for the disease activity and renal involvement in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune disease that affects multiple organs, yet its underlying mechanisms remain unclear, and precise biomarkers are lacking. In this study, we employed Mendelian randomization and HEIDI tools to comprehensively analyze large-scale Genome-Wide Association Study (GWAS) and expression Quantitative Trait Loci (eQTL) data, leading to the identification of seven novel potential functional genes associated with SLE, including BLK, ELF1, STIM1, B3GALT6, APOLD1, INPP5B, and FHL3. Subsequent investigations revealed a significant downregulation of ELF1 gene expression in CD4+ T cells of SLE patients compared to healthy controls. Moreover, within various SLE subgroups, such as those with decreased serum complement C3 levels, positive urinary protein, new-onset skin rashes, and SLE Disease Activity Index (SLEDAI) scores ≥ 5, ELF1 expression displayed a consistent decreasing trend. Notably, ROC curve analysis highlighted the diagnostic potential of ELF1 expression in SLE (AUC = 0.9493), as well as its value in assessing disease activity (AUC = 0.6852) and renal involvement (AUC = 0.7363). In conclusion, this study underscores the potential of ELF1 as a SLE biomarker for diagnosis and evaluation, offering insights into the underlying mechanisms of SLE and paving the way for future therapeutic research.

The proteinuria selectivity index value predicts the remission of IgA nephropathy: a retrospective cohort study

IgA nephropathy (IgAN) is the most common type of primary glomerulonephritis worldwide and leads to end-stage kidney disease. The proteinuria selectivity index (PSI) has been used to assess the prognosis in nephrotic syndrome, but its predictive value in patients with IgAN remains unclear. This single-center retrospective cohort study included patients who diagnosed with IgAN between March 2012 and March 2020. The PSI was calculated at the time of kidney biopsy. Patients were followed up from the time of kidney biopsy to kidney replacement therapy, death, transfer to another facility, or study completion. Ninety-four patients with a median age of 51 years were enrolled and divided according to the cutoff value of PSI determined by the receiver operating characteristic curve analysis into low-PSI (PSI <0.243, n = 39) and high-PSI groups (PSI ≥0.243, n = 55). The median follow-up duration was 70 months. Rates of remission of proteinuria and survival without a two-fold increase in serum creatinine were significantly better in the low-PSI group (both p < 0.01, log-rank test). Cox regression analysis showed that a low PSI was significantly associated with an increased likelihood of remission of proteinuria and hematuria (hazard ratio [HR] 1.96; 95% confidence interval [CI] 1.02–3.85 and HR 1.75; 95% CI 1.01–3.13, respectively), and a decreased risk of a two-fold increase in serum creatinine (HR 0.10; 95% CI 0.01–0.81). In conclusion, The PSI could have the potential to support the assessment of the prognosis of IgAN, in addition to established prognostic markers, by reflecting the overall glomerular permeability.

Prognostic significance of creatine kinase in resected pancreatic cancer.

Creatine kinase (CK) levels decrease with cancer progression and muscle wasting, but its association with pancreatic ductal adenocarcinoma (PDAC) remains unclear. The aim of this study was to investigate CK as a prognostic biomarker and surrogate marker for muscle mass in patients with PDAC. A retrospective analysis of 476 patients with PDAC was conducted. CK levels were categorized into low and high groups using receiver-operating characteristic (ROC) curve analysis. Among the 476 patients, 200 (42.0%) and 276 (58.0%) were classified into the low and high CK groups, respectively. The low CK group had significantly poorer overall survival (p < .001) and recurrence-free survival (p < .001) compared to the high CK group. Multivariate analysis identified low CK as an independent poor prognostic factor (p < .001). The low CK group had significantly lower skeletal muscle index (p = .048) than the high CK group; however, the difference was slight and not significantly associated with sarcopenia. Additionally, combined risk assessment incorporating CK and resectability facilitated a more nuanced prognostic stratification. CK served as a reliable prognostic marker independent from resectability but was less effective as a marker for sarcopenia in PDAC.

Inhibitory effects of N-trans-cinnamoyltyramine on growth of invasive weeds and weedy rice.

N-trans-cinnamoyltyramine (NTCT) has been identified from an allelopathic Vietnamese rice accession OM 5930. This study employed bioassays to analyze NTCT's effects on shoot and root growth of multiple test and weed species. NTCT demonstrated potent inhibitory effects on cress, lettuce, canola, palmer amaranth, timothy, barnyardgrass, red sprangletop, and weedy rice, with increasing concentrations leading to substantial reductions in growth in all species. Linear regression analysis of dose response curves revealed ED50 values for NTCT, providing critical insights into the concentration required for 50% growth inhibition in each species. They revealed high sensitivity of the test species cress and lettuce, intermediate sensitivities of barnyardgrass, red sprangletop, timothy, and amaranth, and comparatively lower sensitivity of two weedy rice accessions. The findings underscore NTCT's efficacy in suppressing the growth of a wide range of weeds, including both grasses and broadleaf species. As such, NTCT may hold promise as a tool for sustainable weed management, particularly in addressing herbicide-resistant weeds in diverse ecological settings.

Development of a nomogram for predicting postoperative recurrence of cervical intraepithelial neoplasia using immunohistochemical and clinical parameters

ABSTRACT Background We aimed to develop a nomogram to predict abnormal follow-up results of co-testing for cytology and human papillomavirus (HPV) in cervical intraepithelial neoplasia (CIN) patients after conization. Research design and methods Two hundred sixty-three patients initially diagnosed as CIN2+ were recruited. Data on immunohistochemical (IHC) staining scores, along with demographic and clinical information were collected. Using least absolute shrinkage and selection operator (LASSO) regression analysis, variables were identified for inclusion. A predict model and nomogram were developed through multi-factor logistic regression. The goodness-of-fit test was applied across different cohorts to construct the calibration curve of the model, and the predictive effect was evaluated by the receiver operating characteristic curve. Decision curve analysis was performed to determine the net benefit. Results Five predictor variables, including protein expression score, vaginal infection, HPV coinfection, and cone height were screened and plotted as a nomogram. The calibration curves showed a good fit. The area under the curve of the model was 0.835 for the training cohort and 0.728 for the internal test cohort. The decision curve analysis indicated that the nomogram provides significant net advantages for clinical use. Conclusion A practical nomogram predict model was developed to predict abnormal follow-up outcomes in CINs after conization.

A Proton Pump Inhibitor Independently Elevates Gastrin Levels as a Marker for Metachronous Gastric Cancer After Endoscopic Submucosal Dissection

Background and Objective: Serum markers such as gastrin and pepsinogen are useful for stratifying gastric cancer risk. However, their utility in predicting metachronous gastric cancer after endoscopic submucosal dissection (ESD) in patients with gastric cancer after Helicobacter pylori eradication (GCAE) is unclear. This study aimed to clarify predictive factors for metachronous gastric cancer after ESD with a focus on serum markers. Methods: A retrospective analysis was conducted on 197 patients with 224 GCAE lesions who underwent ESD at Hiroshima University Hospital between April 2010 and December 2019. In total, 63 patients with 74 differentiated-type lesions were classified into metachronous gastric cancer (MG) and non-metachronous gastric cancer (NMG) groups, excluding proton pump inhibitor (PPI) users, female patients, and undifferentiated-type cases. The predictive value of serum markers was assessed using ROC curve analysis, and their association with carcinogenesis was evaluated using multiple logistic regression. Furthermore, the incidence of MG was compared between long-term PPI users and non-users. Results: ROC analysis revealed that serum gastrin had the highest discriminative ability for MG (AUC 0.77, cut-off 99 pg/mL, sensitivity 61.6%, and specificity 80.0%). Severe mucosal atrophy and high gastrin levels were significantly more common in the MG group and were independent predictors (p &lt; 0.01). Although serum gastrin levels were significantly elevated in PPI users, no increased risk of MG was observed. Conclusions: In addition to severe mucosal atrophy, PPI-independent elevated serum gastrin levels may be associated with an increased risk of MG after ESD. Serum gastrin may serve as a valuable marker for post-ESD cancer surveillance in GCAE patients.

Leukocytes telomere length as a biomarker of adverse drug reactions induced by Osimertinib in advanced non-small cell lung cancer

This study aimed to measure relative telomere length (RTL) in blood leukocytes of advanced-stage NSCLC patients either with or without Osimertinib-induced ADRs and determine whether RTL could serve as a biomarker of Osimertinib-induced ADRs. Blood leukocytes RTL were measured in 63 advanced-stage NSCLC patients and 62 age-matched healthy controls using real-time polymerase chain reaction. In patients with advanced-stage NSCLC, RTL was significantly shorter than that in healthy controls (P < 0.001). Compared to patients without ADRs and those with mild/moderate ADRs, patients with severe ADRs exhibited significantly decreased RTL (P < 0.001, P < 0.001, respectively). ROC curve analysis uncovered a diagnostic value of RTL as a biomarker of Osimertinib-induced ADRs (AUC = 1.000, P < 0.001). Kaplan-Meier analysis revealed a significant association between shorter RTL and increased cumulative incidence of Osimertinib-induced ADRs in patients with advanced-stage NSCLC (P < 0.001). Shorter RTL in blood leukocytes would reflect the occurrence of Osimertinib-induced ADRs and might emerge as a promising biomarker for identifying advanced-stage NSCLC patients who are at risk of experiencing Osimertinib-induced ADRs, particularly those with severe ADRs.

Effects of Licorice Functional Components Intakes on Blood Pressure: A Systematic Review with Meta-Analysis and NETWORK Toxicology

Objective: To investigate the effects of licorice functional ingredient intake on blood pressure, explore its potential mechanisms of action, and provide safety information for personalized nutritional interventions in special populations and for the application of licorice-derived functional foods. Methods: PubMed, Cochrane Library, Medline, Embase, EBSCO, ScienceDirect, and Web of Science databases were searched from inception to 31 August 2024. Randomized controlled trials (RCTs) investigating the intake of licorice or its functional components were included. The range of continuous variables was assessed using the weighted mean difference (WMD) with 95% confidence intervals. Genes associated with hypertension were screened using an online database. Machine learning, receiver operating characteristic(ROC) curve analysis, molecular docking, and gene set enrichment analysis (GSEA) were employed to explore the potential mechanisms underlying licorice-induced blood pressure fluctuations. Results: Eight RCTs (541 participants) were included in the meta-analysis, which indicated interventions containing glycyrrhizic acid (GA) as the main component increased systolic blood pressure (SBP) and diastolic blood pressure (DBP) (SBP: WMD [95% CI] = 3.48 [2.74, 4.21], p &lt; 0.001; DBP: WMD [95% CI] = 1.27 [0.76, 1.78], p &lt; 0.001). However, interventions dominated by licorice flavonoids(LF) had no significant effect on SBP or DBP (SBP: WMD [95% CI] = 0.58 [−1.15, 2.31], p = 0.511; DBP: WMD [95% CI] = 0.17 [−1.53, 1.88], p = 0.843). Three machine learning algorithms identified five biomarkers associated with hypertension: calmodulin 3 (CALM3), cluster of differentiation 9 (CD9), growth factor independence 1B transcriptional repressor (GFI1B), myosin light chain kinase (MYLK), and Ras suppressor-1 (RSU1). After removing biomarkers with lower validity and reliability, GFI1B, MYLK, and RSU1 were selected for subsequent analysis. The network toxicology results suggested that GA and its metabolite glycyrrhetinic acid may act on GFI1B, MYLK, and RSU1, influencing blood pressure fluctuations by modulating nitrogen metabolism signaling pathways. Conclusions: There were distinct differences in the effects of licorice functional components on blood pressure. Functional constituents dominated by GA were shown to increase both SBP and DBP, whereas those dominated by LF did not exhibit significant effects on blood pressure. The hypertensive mechanism of GA may involve the modulation of GFI1B, MYLK, and RSU1 to regulate nitrogen metabolic pathways.

Pharmacogenetic assessment of cytostatic therapy efficacy depending on the polymorphism of T1 and M1 glutathione-S-transferase genes in breast cancer patients

Aim. To study the effect of deletion polymorphism of the GST genes (GSTT1, GSTM1) on chemotherapy (CT) efficacy in women diagnosed with breast cancer (BC) in the Primorsky Krai. Materials and methods. The study involved 132 women with breast cancer aged 23 to 79 years (mean age 48 ± 13 years) who received chemotherapy treatment. The detection of deletion (null) genotypes of GSTM1 and GSTT1 was carried out using multiplex PCR followed by an analysis of the melting curves of the reaction products. Results. Relapse-free survival (RFS) of BC patients with the “null” GSTT1 genotype was statistically significantly higher (116.7 months (9.6 years) versus 75.8 months (6.2 years)) than in patients with the “normal” GSTT genotype. Thus, in the case of the GSTT1“null” genotype, the risk of disease relapse decreased by 2.4 times (HR = 0.418, CI = 0.191–0.915, p = 0.024). Similar to GSTT1, the DFS of patients with the GSTM1 null genotype increased from 72.7 months (6 years) to 76.1 months (6.3 years). At the same time, the risk of disease relapse in carriers of the “null” GSTM1 genotype decreased by 1.7 times (HR = 0.596, CI = 0.369–0.964, p = 0.033) compared with that in carriers of the “normal” GSTM1 genotype. Conclusions. Deletion polymorphism of the GSTT1 and GSTM1 genes has a significant impact on chemotherapy efficacy in breast cancer patients. Carriers of “null” genotypes demonstrated a higher RFS and a lower risk of developing disease relapse. Further research in this direction and validation of the data obtained may contribute to individualizing the treatment of breast cancer patients, optimizing chemotherapy regimens, and reducing the number of adverse events.