Reliable Predictive Markers Research Articles

Uric acid to albumin ratio is a novel predictive marker for all-cause and cardiovascular death in diabetic patients: a prospective cohort study

BackgroundDiabetes is one of the leading causes of death with an increasing prevalence worldwide. Diabetes-related premature mortality is largely preventable and reversible if identified and managed early. Accordingly, we intend to investigate the predictive value of uric acid to albumin ratio (UAR) for all-cause and cardiovascular death in diabetic patients.MethodsUnivariate and multivariate Cox regression analyses were performed to identify risk factors for all-cause death of diabetic patients. The receiver operating characteristic (ROC) curves and nomogram model were used to evaluate the predictive ability of variables. Kaplan-Meier survival analysis was used to display the progression risks of diabetic patients.ResultsA total of 804 diabetic patients were enrolled in the study. During the 5-year follow-up, all-cause death was found in 80 participants (9.95%) and cardiovascular death was found in 24 participants (2.99%). Age, UAR, and hsCRP were independent risk factors for all-cause death in diabetic patients after adjusting for potential confounding factors. Age and UAR had good predictive value for 1-, 3-, and 5-year all-cause death in diabetic patients, and the combination of UAR and age had the highest predictive value. An easy and intuitive prognostic nomogram model with good predictive accuracy was constructed based on age and UAR. Patients in higher quantiles of age and UAR had more rapid progression to all-cause death and higher mortality risk than patients in the lower quantiles. UAR also had good predictive value for cardiovascular death in diabetic patients.ConclusionsUAR may be a simple, cost-effective, and reliable predictive marker for all-cause and cardiovascular death in U.S. diabetic patients. The clinical utility of UAR and nomogram based on age and UAR can help physicians identify individuals at higher risk and therefore promote prevention strategies.

Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Schönlein Purpura.

Henoch-Schönlein Purpura (HSP) is a common systemic vasculitis in children that often involves the gastrointestinal system (GIS). Identifying reliable predictive markers for GIS complications is crucial for early intervention and improved patient outcomes. This study aims to identify laboratory markers predictive of GIS complications in children with HSP using a machine learning approach. This retrospective study included children diagnosed with HSP and a control group from May 2020 to January 2024. Detailed demographic and laboratory data, including WBC count, lymphocyte count, neutrophil count, platelet count, hemoglobin, NLR, PLR, MPV, MPR, C-reactive protein, ESR, albumin, BUN, creatinine, sodium, potassium, calcium, IgA, PT, aPTT, and INR, were collected. GIS complications was classified based on clinical symptoms and diagnostic findings. Patients were categorized into groups without GIS complications, with mild GIS complications, and with severe GIS complications. We compared laboratory parameters across these groups to identify significant differences associated with GIS complications. Furthermore, a predictive model was developed by a Random Forest classifier to identify key markers and assess their ability to distinguish between patients with and without GIS complications. Significant differences were observed in several laboratory parameters between HSP patients and the control group, and between patients with and without GIS complications. Key predictive markers identified included neutrophil count, NLR, WBC count, PLR, and platelet count. The RandomForest model achieved an accuracy of 91% and an AUC of 0.90. Our findings highlight the importance of specific laboratory markers in predicting GIS complications in HSP. The use of machine learning models can enhance the early identification and management of high-risk patients, potentially improving clinical outcomes.

Pre-implantation Scalp EEG Can Predict VNS Efficacy in Children

Introduction. Vagal nerve stimulation (VNS) is a therapeutical option for the treatment of drug-resistant epileptic patients. The response to VNS varies from patient to patient and is difficult to predict. The proposed study is based on our previous work, identifying relative mean power in pre-implantation EEG as a reliable marker for VNS efficacy prediction in adult patients. Our study has two main tasks. Firstly, to confirm the utility of relative mean power as a feature correlating with VNS efficacy in children. The second is to validate the applicability of our prediction classifier, Pre-X-Stim, in the pediatric population. Material and Methods. We identified a group of children with drug-resistant epilepsy. We included only children in whom EEG contained photic stimulation (Task 1) or was recorded based on the defined acquisition protocol used for development Pre-X-Stim (Task 2). Relative mean powers were calculated. VNS responders and non-responders were compared based on relative mean powers’ values. In the next step, we evaluate the utility of our classifier, Pre-X-Stim, in the children population. Results: We identified 57 children treated with VNS – 17 patients were recruited for the Task 1 and 7 patients for the Task 2. When focusing on relative mean powers in EEG spectra, we observed statistically significant differences in theta range. The Pre-X-Stim algorithm was able to predict VNS efficacy correctly in 6 out of 7 patients (the accuracy 83.3%, the sensitivity 75%, the specificity 100%). Conclusions. Based on our results, it seems that children and adults share a similar pattern of EEG relative mean power changes. These changes can be used for pre-implantation prediction of VNS efficacy.

Prognostic significance of serum interleukin-6 in severe/critical COVID-19 patients treated with tocilizumab: a detailed observational study analysis

Baseline IL-6 levels have been found to be non-predictive of subsequent outcomes following tocilizumab treatment, highlighting the need for more reliable predictive markers. To address this, a retrospective analysis was conducted on the clinical profiles, diagnostic tests, and follow-up prognoses of 60 patients with severe or critical COVID-19, all of whom were identified as experiencing a cytokine storm and subsequently received tocilizumab treatment. Among the patients, the overall survival rate during follow-up was 80%, with further analysis revealing that advanced age was an independent risk factor for adverse outcomes. Following tocilizumab administration, a statistically significant increase in IL-6 and D-dimer levels was observed, while markers such as C-reactive protein (CRP), procalcitonin (PCT), and fibrinogen demonstrated reductions compared to pre-treatment values. Specifically, IL-6 levels initially surged briefly after tocilizumab intervention before gradually diminishing. To assess the prognostic utility of IL-6, the Receiver Operating Characteristic (ROC) curve was employed, which yielded an area under the curve (AUC) of 0.812, indicating strong predictive capability, with a sensitivity of 100% and a specificity of 53.49%. The optimal cut-off value for IL-6 was identified at 147.79 pg/mL. In conclusion, IL-6 levels tend to rise transiently following tocilizumab therapy, before gradually declining. These post-treatment IL-6 measurements may serve as a valuable biomarker for assessing prognosis in patients undergoing this treatment.

Molecular and Clinicopathologic Characterization of HER2-overexpressed Squamous Cell Carcinoma of the Cervix.

HER2 amplification in cervical cancer has been associated with worse clinical prognosis and a potential favorable response to HER2 inhibitors. Immunohistochemistry for the HER2 receptor is a universally accepted surrogate test for HER2 amplification, but no standardized scoring system currently exists for cervical carcinomas. In this study, we investigated HER2 overexpression in cervical squamous cell carcinoma and correlated it with HER2 amplification using fluorescence in situ hybridization (FISH) and molecular methods. Seventy-two cases of human papillomavirus-associated cervical cancer were retrospectively reviewed, and at least 2 representative tumor sections were stained for HER2. HER2 scoring was performed using the 2018 American Society of Clinical Oncology/College of American Pathologist breast cancer criteria, and cases with equivocal (2+) to positive (3+) expression were analyzed for HER2 amplification using FISH and next-generation sequencing. The average patient age was 50yrs (range: 27-85yr), with most patients being African American (73.6%) and diagnosed at FIGO stage I (65.3%). Nineteen (26.4%) had equivocal HER2 expression and 4 (5.5%) showed positive expression. Three of the 4 cases with positive expression had enough tumors for FISH, and all 3 were amplified. Three cases with equivocal expression showed HER2 polysomy on FISH, and none showed HER2 amplification. Late clinical stage, high tumor grade, and regional lymph node metastasis were significantly correlated with HER2 overexpression and HER2 amplification. Next-generation sequencing of the 3 HER2-amplified tumors showed amplification of various genes, including CD274, JAK2, BIRC3, and ERBB2, and a PIK3CA missense mutation. In summary, HER2 immunohistochemistry is a reliable predictive marker of HER2 amplification in cervical cancer.

CT assessed morphological features can predict higher mitotic index in gastric gastrointestinal stromal tumors.

To investigate the correlation of the mitotic index (MI) of 1-5 cm gastric gastrointestinal stromal tumors (gGISTs) with CT-identified morphological and first-order radiomics features, incorporating subgroup analysis based on tumor size. We enrolled 344 patients across four institutions, each pathologically diagnosed with 1-5 cm gGISTs and undergoing preoperative contrast-enhanced CT scans. Univariate and multivariate analyses were performed to investigate the independent CT morphological high-risk features of MI. Lesions were categorized into four subgroups based on their pathological LD: 1-2 cm (n = 69), 2-3 cm (n = 96), 3-4 cm (n = 107), and 4-5 cm (n = 72). CT morphological high-risk features of MI were evaluated in each subgroup. In addition, first-order radiomics features were extracted on CT images of the venous phase, and the association between these features and MI was investigated. Tumor size (p = 0.04, odds ratio, 1.41; 95% confidence interval: 1.01-1.96) and invasive margin (p < 0.01, odds ratio, 4.55; 95% confidence interval: 1.77-11.73) emerged as independent high-risk features for MI > 5 of 1-5 cm gGISTs from multivariate analysis. In the subgroup analysis, the invasive margin was correlated with MI > 5 in 3-4 cm and 4-5 cm gGISTs (p = 0.02, p = 0.03), and potentially correlated with MI > 5 in 2-3 cm gGISTs (p = 0.07). The energy was the sole first-order radiomics feature significantly correlated with gGISTs of MI > 5, displaying a strong correlation with CT-detected tumor size (Pearson's ρ = 0.85, p < 0.01). The invasive margin stands out as the sole independent CT morphological high-risk feature for 1-5 cm gGISTs after tumor size-based subgroup analysis, overshadowing intratumoral morphological characteristics and first-order radiomics features. Question How can accurate preoperative risk stratification of gGISTs be achieved to supporttreatment decision-making? Findings Invasive margins may serve as a reliable marker for risk prediction in gGISTs up to 5 cm, rather than surface ulceration, irregular shape, necrosis, or heterogeneous enhancement. Clinical relevance For gGISTs measuring up to 5 cm, preoperative prediction of the metastatic risk could help select patients who could be treated by endoscopic resection, thereby avoiding overtreatment.

Assessing Stroke Recurrence Risk by Using a Lipoprotein-Associated Phospholipase A2 and Platelet Count-Based Nomogram.

Stroke recurrence remains a critical challenge in clinical neurology, necessitating the identification of reliable predictive markers for better management and treatment strategies. This study investigates the interaction between lipoprotein-associated phospholipase A2 (Lp-PLA2) and platelets as a potential predictor for stroke recurrence, aiming to refine risk assessment and therapeutic approaches. In a retrospective cohort of 580 ischemic stroke patients, we analyzed clinical data with a focus on Lp-PLA2 and platelet levels. By using multivariable logistic regression, we identified independent predictors of stroke recurrence. These predictors were then used to develop a comprehensive nomogram. The study established diabetes mellitus, hypertension, low-density lipoprotein (LDL), Lp-PLA2 levels, and platelet counts as independent predictors of stroke recurrence. Crucially, the interaction parameter Lp-PLA2 * platelet (multiplication of Lp-PLA2 and platelet count) exhibited superior predictive power over each factor considered separately. Our nomogram incorporated diabetes mellitus, cerebral infarction causes, hypertension, LDL, and the Lp-PLA2 * platelet count interaction and demonstrated enhanced accuracy in predicting stroke recurrence compared to traditional risk models. The interaction between Lp-PLA2 and platelets emerged as a significant predictor for stroke recurrence when integrated with traditional risk factors. The developed nomogram offers a novel and practical tool in molecular neurobiology for assessing individual risks, facilitating personalized treatment strategies. This approach underscores the importance of multifactorial assessment in stroke management and opens avenues for targeted interventions to mitigate recurrence risks.

Advancements in Radiomics for Immunotherapy of Non-small Cell Lung Cancer

Lung cancer is the main cause of cancer-related deaths, with non-small cell lung cancer (NSCLC) being the predominant subtype. At present, immunotherapy represented by immune checkpoint inhibitors (ICIs) of programmed cell death receptor 1 or its ligand has been widely used in the clinical diagnosis and treatment of patients with NSCLC. However, only a few patients can benefit from it, and reliable predictive markers for immunotherapy are lacking. Radiomics is a tool that uses computer software and algorithms to extract a large amount of quantitative information from biomedical images. A large number of studies have confirmed that the radiomic model that predicts the immune efficacy of NSCLC can be used as a new type of immune efficacy predictive marker, which is expected to guide the individualized diagnosis and treatment decisions for patients with lung cancer and has a bright application prospect. This article reviews the research progress of radiomics in predicting the immune therapy response of NSCLC, identifying pseudo-progression and hyperprogression, ICIs-related pneumonia, cachexia risk, and combining with other genomics. .

The prognostic value of the combined neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-platelet ratio (NPR) in sepsis

Sepsis is a severe disease characterized by high mortality rates. Our aim was to develop an early prognostic indicator of adverse outcomes in sepsis, utilizing easily accessible routine blood tests. A retrospective analysis of sepsis patients from the MIMIC-IV database was conducted. We performed univariate and multivariate regression analyses to identify independent risk factors associated with in-hospital mortality within 28 days. Logistic regression was utilized to combine the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-platelet ratio (NPR) into a composite score, denoted as NLR_NPR. We used ROC curves to compare the prognostic performance of the models and Kaplan–Meier survival curves to assess the 28 day survival rate. Subgroup analysis was performed to evaluate the applicability of NLR_NPR in different subpopulations based on specific characteristics. This study included a total of 1263 sepsis patients, of whom 179 died within 28 days of hospitalization, while 1084 survived beyond 28 days. Multivariate regression analysis identified age, respiratory rate, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelet ratio (NPR), hypertension, and sequential organ failure assessment (SOFA) score as independent risk factors for 28 day mortality in septic patients (P < 0.05). Additionally, in the prediction model based on blood cell-related parameters, the combined NLR_NPR score exhibited the highest predictive value for 28 day mortality (AUC = 0.6666), followed by NLR (AUC = 0.6456) and NPR (AUC = 0.6284). Importantly, the performance of the NLR_NPR score was superior to that of the commonly used SOFA score (AUC = 0.5613). Subgroup analysis showed that NLR_NPR remained an independent risk factor for 28 day in-hospital mortality in the subgroups of age, respiratory rate, and SOFA, although not in the hypertension subgroup. The combined use of NLR and NPR from routine blood tests represents a readily available and reliable predictive marker for 28 day mortality in sepsis patients. These results imply that clinicians should prioritize patients with higher NLR_NPR scores for closer monitoring to reduce mortality rates.

Neutrophil CD64 – A Reliable Predictive Marker of Sepsis in Adult Intensive Care Unit

Abstract BACKGROUND: Sepsis poses a significant threat to adult patients admitted to intensive care units (ICUs), contributing to substantial morbidity and mortality. Neutrophil CD64 (nCD64) expression has been linked to inflammatory responses during infection or tissue injury, suggesting its potential as a predictive marker for sepsis. MATERIALS AND METHODS: We conducted a prospective analytical study over 2 years at a tertiary care hospital, enrolling 91 sepsis cases from the adult ICU. Alongside routine laboratory parameters, including complete blood count, prothrombin time, activated partial thromboplastin time, fibrinogen, C-reactive protein, and procalcitonin, the expression of nCD64 was analyzed using a Beckman Coulter Navios flow cytometer. Median fluorescence intensity (MFI) was calculated for these cases. Statistical analysis was performed using SPSS version 25.0 software, with a significance threshold of P &lt; 0.05. RESULTS: MFI scores demonstrated a notable increase in diagnosed sepsis cases, as determined by clinical and biochemical parameters. Moreover, changes in MFI scores on Day 4 exhibited a correlation with other clinical and biochemical parameters, strengthening the association between nCD64 expression and sepsis severity. CONCLUSION: Our findings suggest that nCD64 serves as an independent prognostic factor in adult ICU sepsis patients. It offers a promising alternative to traditional sepsis markers for predicting patient outcomes, emphasizing its potential clinical utility in guiding therapeutic interventions and improving patient care in ICU settings.

Validation of the 10-gene expression profile for prediction of response to checkpoint inhibitor in patients with advanced NSCLS

Purpose: individualization of the use of immune checkpoint inhibitors in patients with inoperable NSCLC potentially sensitive to immunotherapy based on the level of the combined expression index as a predictive marker of effectiveness.Methods: in 146 patients with inoperable NSCLC, the level of the combined expression index in a tumor tissue sample was determined using PCR, which included studies of ten genes (CCL5, CXCL9, CXCR6, HLA-DQA1, TIGIT, EOMES, CTLA4, PD-L2, GZMB, PRF1). The patients were divided into cohorts depending on the level of the combined expression index and randomized into chemotherapy or immunotherapy groups. The null hypothesis of the study was that the group with a low combined expression index would have a 6-month progression-free survival of 35%, and with a high combined expression index – 75%, with α-error probability of 0.05, a power of 90%. Results: the use immune checkpoint inhibitors in the first line of the therapy for patients with a high level of the combined expression index leads to a significant increase in the rate of objective responses from 18% to 43%, the median overall survival from 10 to 21 months, and the median time to progression from 7.3 to 14.8 months. The 6-month progression-free survival rate in the group with a high combined expression index was 79%, versus 35% in the group with a low combined expression index. Conclusion: analysis of the gene expression signature can become one of the reliable predictive markers that predict the real effectiveness of therapy with immune checkpoint inhibitors, which will improve the results of treatment of patients with unresectable or metastatic NSCLC. Key words: NSCLC, immune checkpoint inhibitors, gene expression signature, predictive markers

Utility of Initial Tumor Reduction as a Prognostic Factor in Esophageal Squamous Cell Cancer Patients Undergoing Neoadjuvant Chemotherapy Followed by Surgery : A Retrospective Cohort Study.

While a neoadjuvant chemotherapy regimen using docetaxel, cisplatin, and 5-fluorouracil (NAC-DCF) is considered the standard treatment for locally advanced esophageal cancer (EC) in Japan, a reliable marker for early prediction of treatment efficacy remains unclear. We investigated the utility of the tumor response after a first course of NAC-DCF as a post-surgery survival predictor in patients with EC. We enrolled 150 consecutive patients who underwent NAC-DCF followed by surgery for EC between September 2009 and January 2019. The initial tumor reduction (ITR), defined as the percentage decrease in the shorter diameter of the tumor after the first course of NAC-DCF, was evaluated using computed tomography. We analyzed the relationship between ITR, clinicopathological parameters, and survival. The median ITR was 21.07% (range -11.45 to 50.13%). The optimal cut-off value for ITR for predicting prognosis was 10% (hazard ratio [HR] 3.30, 95% confidence interval [CI] 1.98-5.51), based on univariate logistic regression analyses for recurrence-free survival (RFS). Compared with patients with ITR <10%, patients with ITR ≥10% showed a significantly higher proportion of ypM0 (80.0% vs. 92.5%) and responders in terms of overall clinical response (50.0% vs. 80.8%). Multivariate analysis for RFS revealed that ypN2-3 (HR 2.78, 95% CI 1.67-4.62), non-response in terms of overall clinical response (HR 1.87, 95% CI 1.10-3.18), and ITR <10% (HR 2.48, 95% CI 1.42-4.32) were independent prognostic factors. Tumor response after the first course of NAC-DCF may be a good predictor of survival in patients with EC who underwent NAC-DCF plus surgery.

Study of the different sleep disturbances during the prodromal phase of depression and mania in bipolar disorders.

One of the challenges in bipolar disorder (BD) lies in early detection of the illness and its recurrences, to improve prognosis. Sleep disturbances (SD) have been proposed as reliable predictive markers of conversion. While preliminary studies have explored the relationship between SD and the onset of mood episodes, the results remain heterogeneous and a few have specifically examined patients' perception of prodromal symptoms and their progression until the episode occurs. Identifying prodromes represents a crucial clinical challenge, as it enables early intervention, thereby reducing the severity of BD. Therefore, the objective of this study is to better characterize and evaluate the progressive nature of SD as prodromal symptoms of mood episodes, and patients' perception of it. Patients diagnosed with BD, either hospitalized or seeking treatment for a (hypo)manic or depressive episode benefited from standardized questionnaires, structured interviews, and self-report questionnaires to evaluate SD prior to the current episode, as well as sociodemographic and clinical information. Out of the 41 patients included, 59% spontaneously reported SD prior to the episode, appearing 90 days before depression and 35 days before mania (pre-indexed/spontaneous reports: 51.22% insomnia complaints, 4.88% hypersomnolence complaints, 7.32% parasomnias, 2.44% sleep movements). After inquiry about specific SD, the percentage of patients reporting prodromal SD increased significantly to 83%, appearing 210 days before depression and 112.5 days before mania (post-indexed reports: 75.61% presented with insomnia complaints appearing 150 days before depression and 20 days before mania, 46.34% had hypersomnolence complaints appearing 60 days before depression, 43.9% had parasomnias appearing 210 days before depression and 22.5 days before mania, 36.59% had sleep movements appearing 120 days before depression and 150 days before mania). Of note, bruxism appeared in 35% of patients before mania, and restless legs syndrome in 20% of patients before depression. This study highlights the very high prevalence of SD prior to a mood episode in patients with BD with differences between depressive and manic episodes. The more systematic screening of sleep alterations of the prodromal phase improved the recognition and characterization of different symptoms onset by patients. This underscores the need for precise questioning regarding sleep patterns in patients, to better identify the moment of transition toward a mood episode, referred to as "Chronos syndrome". The study emphasizes the importance of educating patients about the disorder and its sleep prodromal symptoms to facilitate early intervention and prevent recurrences.

Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non–Small-Cell Lung Cancer: A Retrospective Study

BackgroundNeoadjuvant chemotherapy has variable efficacy in patients with non–small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC. Materials and MethodsRetrospective data collection involved 130 patients with NSCLC who underwent neoadjuvant chemotherapy and surgery. Patients were randomly divided into training and independent testing sets. Nine radiomics features from prechemotherapy computed tomography (CT) images were extracted from intratumoral and peritumoral regions. An auto-encoder model was constructed, and its performance was evaluated. X-tile software classified patients into high and low-risk groups based on their predicted probabilities. survival of patients in different risk groups and the role of postoperative adjuvant chemotherapy were examined. ResultsThe model demonstrated area under the receiver operating characteristic (ROC) curve of 0.874 (training set) and 0.876 (testing set). The larger the area under curve (AUC), the better the model performance. Calibration curve and decision curve analysis indicated excellent model calibration (Hosmer–Lemeshow test, P = .763, the higher the P-value, the better the model fit) and potential clinical applicability. Survival analysis revealed significant differences in overall survival (P = .011) and disease-free survival (P = .017) between different risk groups. Adjuvant chemotherapy significantly improved survival in the low-risk group (P = .041) but not high-risk group (P = 0.56). ConclusionThis study represents the first successful prediction of pathological complete response achievement after neoadjuvant chemotherapy for NSCLC, as well as the patients’ survival, utilizing intratumoral and peritumoral radiomics features.

Abstract 6469: The association of mismatch repair gene alterations with elevated tumor mutational burden in microsatellite stable gastrointestinal cancers

Abstract Background: Benefit from single-agent immune checkpoint inhibitor has been observed in 5-10% of the patients with microsatellite stable (MSS) gastrointestinal (GI) cancers. However, a reliable predictive marker to immune checkpoint inhibitor has not been discovered in this patient population. We sought to assess the incidence of mismatch repair (MMR) gene alterations and their association with tumor mutational burden (TMB) in MSS GI cancers. Methods: This is a retrospective cohort study of patients with advanced GI cancer who had molecular profiling of the tumor using either Guardant360 blood-based or Tempus tissue next generation sequencing (NGS) platforms between 2020 and 2023. We assessed four MMR genes including MLH1, MSH2, MSH6 and PMS2 for alterations. Continuous variables were summarized as median and interquartile range (IQR) and compared using Mann-Whitney test. Categorical variables were summarized as counts and proportions and compared using Fisher’s exact test. Results: A total of 2920 patients with Guardant360 were included with a median age of 65 (IQR: 56-73) years. 870 (30%) patients had pancreas cancer; 846 (29%) patients had colorectal cancer; 688 (24%) patients had biliary tract cancers; and the rest had other GI cancers. Among patients evaluable for microsatellite instability (MSI) status, 58 patients (2%) had MSI-high GI cancers, 31 (53%) of whom had MMR gene alterations. 2445 (98%) patients had MSS GI cancers, 130 (5.3%) of whom had MMR gene alterations. Patients with MSS GI cancers bearing MMR gene alterations had a median age of 70 (IQR: 62-75) years, significantly older than those without MMR gene alterations (p&amp;lt;0.001). In addition, patients with MMR gene alterations had significantly higher median blood TMB of 11 (IQR: 7.4-16) mut/Mb compared to 7.7 (IQR: 4.8-11) mut/Mb in patients without MMR gene alterations. In MSS GI cancers, MMR gene alterations are more commonly associated with the presence of alterations in APC (p=0.03), TP53 (p=0.01), CTNNB1 (p&amp;lt;0.001) and SMAD4 (p=0.01). 1453 patients with GI cancer and Tempus tissue NGS data were included as a validation cohort. They had comparable demographics with the Guardant360 cohort. 14 (1.6%) of 896 patients with MSS GI cancers had MMR gene alterations, significantly fewer (p&amp;lt;0.001) than those found in the Guardant360 cohort. Patients with MMR gene alterations and wild type POLE had significantly higher (p=0.001) median TMB of 9.8 (IQR: 4.8-14) mut/Mb compared to 4.7 (IQR: 3.2-6.3) mut/Mb in patients without MMR gene alterations. Conclusions: MMR gene alterations can be identified by clinical NGS platforms in a small proportion of patients with MSS GI cancers. They are associated with elevated TMB, which may suggest a hypermutated profile serving as a basis for potential role of immune checkpoint inhibitor in MSS GI cancers. Citation Format: Conor D. O'Donnell, Mojun Zhu, Hao Xie. The association of mismatch repair gene alterations with elevated tumor mutational burden in microsatellite stable gastrointestinal cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6469.

The potential role of heparin-binding protein in neonatal sepsis: research progress.

Neonatal sepsis is a major global health challenge, leading to significant morbidity and mortality in newborns. The search for precise biomarkers for its early prediction in clinical settings has been ongoing, with heparin-binding protein (HBP) emerging as a promising candidate. Originating from granules in neutrophils, HBP is released into the bloodstream in response to infection and plays a pivotal role in the body's inflammatory response. Its significance extends beyond its inflammatory origins; research indicates dynamic changes in HBP levels are strongly linked to reduce in-hospital mortality, offering a prognostic advantage over existing biomarkers. Furthermore, HBP has demonstrated considerable clinical utility in the early diagnosis and stratification of neonatal sepsis, suggesting its potential as a reliable blood marker for early prediction of the disease and its severity. Its application may extend to guiding the judicious use of antibiotics in treating newborns, addressing a critical aspect of neonatal care. Despite these encouraging results, the precise clinical utility of HBP for diagnosing and treating sepsis in neonates still demands further clarification through extensive research. This review delves into the current scientific understanding of HBP's contribution to diagnosing, prognosticating, and treating neonatal sepsis, while considering its future clinical applications.

Urinary Nephrin Levels Among Pregnant Women With Preeclampsia in Lagos, Southwest Nigeria: An Analytical Cross-Sectional Study.

Hypertensive disorders in pregnancy are one of the leading causes of maternal and perinatal morbidity and mortality worldwide. The clinical utility of urinary nephrin as a diagnostic biomarker of preeclampsia is currently of research interest. However, this is yet to gain significant traction within clinical settings. We evaluated the association between maternal urinary nephrin levels and the occurrence and severity of preeclampsia among pregnant women in Lagos, Nigeria. We conducted an analytical cross-sectional study involving pregnant women diagnosed with preeclampsia as well as their age- and gestational-age-matched normotensive counterparts. We tested the association between high maternal urinary nephrin levels and the occurrence of preeclampsia without and with severe features. P < 0.05 was reported as statistically significant. The study showed that for every unit increase in urinary nephrin levels, the odds of preeclampsia increased by about ninefold (adjusted Odds ratio = 8.9, 95% confidence interval: 2.8-29.2, P < 0.001). The levels of urinary nephrin increased steadily with increasing severity of the disease: 1.9 ± 0.8 ng/mL in preeclampsia without severe features, 2.7 ± 0.7 ng/mL in preeclampsia with at least one severe feature, and 3.3 ±1.1 ng/mL in eclampsia. There was an association between elevated levels of urinary nephrin and preeclampsia and its severe variant. However, there is a need for more robust studies with a longitudinal characterization of urinary nephrin levels to establish causal relationships with preeclampsia, explore other potential risk factors of preeclampsia, and define the clinical usefulness of urinary nephrin as a potentially reliable and accurate predictive marker of preeclampsia among women in low- and middle-income countries (LMIC) settings.

Natural killer cell activity in metastatic castration resistant prostate cancer patients treated with enzalutamide

Metastatic castration resistant prostate cancer (mCRPC) is still the lethal stage for the whole spectrum of prostate cancer disease. Even though different treatment options have been introduced in the last decade with a significant survival improvement for this population, a lack of more reliable prognostic and predictive markers is still one of the main clinical challenges in management of mCRPC. The aim of this study was to investigate the correlation between Natural Killer cell activity (NKA) and both treatment effect and outcomes in patients with mCRPC treated with enzalutamide. A total of 87 patients with mCRPC treated with enzalutamide as the first line treatment were enrolled. NKA was estimated at baseline and prior to each treatment cycle. Endpoints included both treatment effect with biochemical response (BR), biochemical progression (BP) and radiological progression (RP), as well as outcome data with overall survival (OS), radiologic progression free survival (rPFS), and time to next treatment (TTT). At the time of BR, interferon-gamma (IFNγ) decreased significantly compared to levels detected at baseline (z-score = 2.33, p = 0.019). Regarding outcome data, the whole cohort was divided into four groups according to the change of IFNγ level during the first 3 cycles of enzalutamide treatment. In group 1 (n = 42) the IFNγ level remained within a normal range (≥ 250 pg/mL),while in group 2 (n = 7) it increased from an abnormal (< 250 pg/mL) to a normal level. In group 3 (n = 13) it dropped to an abnormal level, and it remained at an abnormal level during treatment in group 4 (n = 17). Patients in group 2 showed the worst prognosis with shorter both rPFS and TTT (HR 4.30, p = 0.037; and HR 6.82, p = 0.011, respectively). In this study inverse correlations between NKA and both treatment response and outcomes was observed in mCRPC patients receiving enzalutamide, suggesting an unfavourable role of NK cells in the late stage of PCa.

Thyroid Predictors of Postpartum Mood Disorders.

Postpartum mood disorders (PMD) are currently among the leading causes of maternal postpartum morbidity and mortality. PMD includethe conditions of postpartum blues (PB), postpartum depression (PPD), and postpartum psychosis. The pathogenesis of PMDs are ambiguous, and there are no reliable prenatal predictive markers despite current research efforts. Even though reliable indicators have not been found, leading ideas suggest an etiology of hormonal fluctuations. Although thyroid markers have long been linked to psychiatric disorders such as major depressive disorder (MDD), how they correlate with PMDs is still unclear. This study aimed to evaluate the pathophysiological link between thyroid function, PMDs, and the usefulness of thyroid markers as indicators of their occurrence and severity. The methodology consisted of a narrative literature review. Several inclusion and exclusion criteria were used to filter the results of literature searches in PubMed. Studies were included if they discussed any marker related to thyroid endocrinology in relation to the incidence or pathophysiology of any PMD. Both primary and secondary analyses were included. The permissive inclusion criteria were used due to the relative scarcity of research on the topic and the ambiguous pathophysiology of PMD. The results demonstrated the potential utility of thyroid autoimmunity as a predictor of late-onset PPD. Hypothyroidism, low euthyroid hormone levels, and the presence of thyroid autoantibodies were correlated with increased incidence of PPD and late postpartum depressive symptoms, past the timeline of PB. Most notably a rapid postpartum drop in cortisol level may precipitate thyroid autoimmunity in anti-thyroid peroxidase (TPO) antibody positive women, which could eventually produce a hypothyroid phase associated with depressive symptoms. There was insufficient evidence to suggest a relationship with postpartum psychosis. In conclusion, the exact pathophysiological mechanisms of PMDs remain ambiguous, but TPO-antibodies in the third trimester may be a predictor of late PPD.

Machine learning identification of patient clusters for cutaneous melanoma.

Cutaneous melanoma is a cancer arising in melanocyte skin cells and is the deadliest form of skin cancer worldwide. Although some risk factors are known, accurate prediction of disease progression and probability for metastasis are difficult to ascertain, given the complexity of the disease and the absence of reliable predictive markers. Since early detection and treatment are essential to enhance survival, this study utilizing machine learning (ML) aims to further delineate additional risk factors associated with cutaneous melanoma. A Bayesian Gaussian Mixture ML model was created with data from 2056 patients diagnosed with cutaneous melanoma and then used to group the patients into six Clusters based on a Silhouette Score analysis. A t-distributed stochastic neighbor embedding (t-SNE) model was used to visualize the six Clusters. Statistical analysis revealed that Cluster 4 showed a significantly higher rate of metastatic disease, as well as higher Breslow depth at diagnosis, compared to the other five Clusters. Compared to the other five Clusters, patients represented in Cluster 4 also had lower healthcare utilization, fewer dermatology clinic visits, fewer primary care providers, and less frequent colonoscopies and mammograms, and were more likely to smoke and less likely to have a prior diagnosis of basal cell carcinoma. This study uncovers gaps in healthcare utilization of services among patient groups with cutaneous melanoma as well as possible implications for management of disease progression. Data-driven analyses emphasize the importance of routine clinic visits to dermatologists and/or primary care physicians (PCPs) for early detection and management of cutaneous melanoma. The findings from this study demonstrate that unsupervised ML methodology may serve to define the best candidate patients to benefit from enhanced dermatology/primary care which, in turn, is expected to improve outcomes for cutaneous melanoma.