Reliable Predictive Value Research Articles

LncRNA ZFHX4-AS1 as a novel biomarker in adrenocortical carcinoma.

Adrenocortical carcinoma (ACC) is a rare and highly aggressive malignant tumor. Currently, there is a lack of reliable prognostic markers in clinical practice. Extensive research has shown that long non-coding RNA (lncRNA) are critical factors in the initiation and progression of cancer, closely associated with early diagnosis and prognosis. Previous studies have identified that ZFHX4 antisense RNA 1 (ZFHX4-AS1) is aberrantly expressed in various cancers and is associated with poor outcomes. This study investigates whether ZFHX4-AS1 affects the prognosis of ACC patients and, if so, the potential mechanisms involved. In this study, utilizing four multi-center cohorts from The Cancer Genome Atlas (TCGA) program and Gene Expression Omnibus (GEO), we validated the prognostic capability of ZFHX4-AS1 in ACC patients through Kaplan-Meier survival analysis, cox regression models, and nomograms. Then, we explored the biological functions of ZFHX4-AS1 using gene set enrichment analysis (GSEA), competing endogenous RNA (ceRNA) networks, and analyses of somatic mutations and copy number variation (CNV). Finally, in vitro experiments were conducted to further validate the impact of ZFHX4-AS1 on proliferation and migration capabilities of ACC cell lines. Survival analysis indicated that patients in the high ZFHX4-AS1 expression group of ACC had worse prognosis. Cox regression analyses suggested that ZFHX4-AS1 levels were independent risk factors for prognosis. Subsequently, we constructed nomograms based on clinical features and ZFHX4-AS1 levels, demonstrating good predictive performance under the time-dependent receiver operating characteristic (ROC) curve. Analysis based on somatic mutations and CNV revealed that CTNNB1 and 9p21.3-Del drove the expression of ZFHX4-AS1. Cell Counting Kit-8 (CCK-8), colony formation, and Transwell assays confirmed that knockdown of ZFHX4-AS1 inhibited proliferation and migration of ACC cells. This study demonstrates that ZFHX4-AS1 has a reliable predictive value for the prognosis of ACC patients and is a promising biomarker.

Confirmation and pathogenicity of small copy number variations incidentally detected via a targeted next-generation sequencing–based preimplantation genetic testing for aneuploidy platform

ObjectiveTo evaluate the technical accuracy, inheritance, and pathogenicity of small copy number variants (CNVs) detected by a targeted next-generation sequencing (NGS)-based PGT-A platform. DesignRetrospective observational study performed between 2020-2022. Subjects12,157 patients who underwent clinical PGT-A performed by targeted NGS for whole chromosome and large segmental aneuploidies. ExposureAn incidental finding was reported when a CNV gain/loss of at least three consecutive amplicons appeared in at least two embryos from the same IVF cycle. Main Outcome MeasuresThe primary outcome measures were the specificity, incidence, inheritance, and pathogenicity of small CNVs detected by the PGT-A platform. Accuracy of the PGT-A platform CNV calls was assessed via concordance with the CNV calls (size and genomic location) on chromosomal microarray of the gamete provider(s). Parental origin of the CNV and pathogenicity classifications were also reported. ResultsIn 75 of 12,157 unique PGT-A patients (0.62%;95%CI:0.5-0.8%), an incidental finding that met reporting criteria was identified. Chromosomal microarray follow-up was requested for all cases and results were received for one or both members of 65 reproductive couples. In all cases, one of the gamete providers was confirmed to have the CNV identified in the embryos (100.0%: N=65/65 95%CI:94.5-100). The identified CNV was of maternal origin in 34 cases (52.3%) and of paternal origin in 31 cases (47.7%). A significant correlation was identified between PGT-A-predicted CNV sizes and chromosomal microarray detected sizes (r=0.81) and genomic coordinates on parental DNA. Twenty-six (40%) of the CNVs were classified as benign/likely benign, 30 (46.2%) as a variant of uncertain significance (VUS), and 9 (13.8%) as pathogenic/likely pathogenic. ConclusionCertain PGT-A platforms may enable the detection of inherited, small CNVs with extremely high specificity without prior knowledge of parental status. The majority of CNVs in this data set were confirmed to be benign/likely benign or a VUS; however, pathogenic/likely pathogenic CNVs associated with a broad range of phenotypic features may also be detected, although a reliable negative predictive value for small CNVs with current PGT-A technologies is unknown due to the many technical challenges.

Digital Pathology in the Detection of Infectious Microorganisms: An Evaluation of Its Strengths and Weaknesses Across a Panel of Immunohistochemical and Histochemical Stains Routinely Used in Diagnostic Surgical Pathology.

The diagnosis of some infectious diseases requires their identification in tissue specimens. As institutions adopt digital pathology for primary diagnosis, the limits of microorganism detection from digital images must be delineated. To assess the reliability of microorganism detection from digitized images of histochemical and immunohistochemical stains commonly used in pathology. Original glass slides from 620 surgical pathology cases evaluated for the presence of infectious microorganisms were digitized. Immunohistochemical stains included those for herpes simplex virus (n = 100), cytomegalovirus (n = 100), Helicobacter pylori (n = 100), and spirochetes (n = 80). Histochemical stains included mucicarmine for Cryptococcus spp (n = 20), Grocott methenamine silver for fungi (n = 100), Giemsa for H pylori (n = 100), and Ziehl-Neelsen for acid-fast bacilli (n = 20). The original diagnosis based on the glass slides was regarded as the reference standard. Six pathologists reviewed the digital images. Digital review was generally associated with high (ie, ≥90%) specificity and positive predictive value owing to a low percentage of false positive reads, whereas a high percentage of false negatives contributed to low sensitivity and negative predictive value for many stains. Fleiss κ showed substantial interobserver agreement in the interpretation of Grocott methenamine silver and immunostains for herpes simplex virus, H pylori, and cytomegalovirus; moderate agreement for spirochete, Ziehl-Neelsen, and mucicarmine; and poor agreement for Giemsa. Digital immunohistochemistry generally outperforms histochemical stains for microorganism detection. Digital interpretation of Ziehl-Neelsen and mucicarmine stains is associated with low scores for interrater reliability, accuracy, sensitivity, and negative predictive value such that it should not substitute for conventional review of glass slides.

The Ratio of Red Blood Cell Distribution Width to Albumin as a Predictor for Rehospitalization Risk and Rehospitalization All-Cause Mortality in Middle-Aged and Elderly Survivors with Sepsis: An Ambispective ICU Cohort Study.

To explore the relationship between red blood cell distribution width to albumin (RDW/ALB) ratio (RAR) and the risk of rehospitalization and rehospitalization all-cause mortality in middle-aged and elderly survivors with sepsis based on an ambispective longitudinal cohort from the Intensive Care Unit (ICU). Between 2017 and 2022, 455 adults who survived the first-episode severe sepsis without recurrence for at least 3 months were included in this study. All participants were followed up every 4 weeks for 12 months. According to the tertiles of RAR, participants were divided into three groups: low-level (≤0.36, n = 152), moderate-level (0.37-0.44, n = 152), and high-level (≥0.45, n = 151). The relationship between RAR and the risk of rehospitalization and rehospitalization all-cause mortality was evaluated. Out of 455 participants, 156 experienced rehospitalization (34.3%), of which 44 (28.2%) died. Receiver operating characteristic (ROC) analysis showed that the RAR cut-off values for rehospitalization and rehospitalization all-cause mortality were 0.4251 and 0.4743, respectively. Multivariate Cox regression analysis indicated that the RAR was positively associated with rehospitalization (P = 0.011) and all-cause mortality (P = 0.006). Compared with the low-level, the high-level RAR presented a higher dose-dependent rehospitalization risk (P = 0.02) and rehospitalization all-cause mortality (P = 0.044). The stratified analysis displayed that compared to the low-level, with the RAR increasing by 1.0, the risk for rehospitalization increased 3.602-fold in aged <65 patients (P = 0.002) and 1.721-fold in female patients (P = 0.014). Kaplan-Meier survival analysis implied a significant positive association between the RAR and the cumulative incidence of rehospitalization and rehospitalization all-cause mortality (log-rank, all P < 0.001). RAR has a reliable predictive value for the risk of rehospitalization and rehospitalization all-cause mortality in patients with sepsis. Consequently, monitoring RAR for at least 1 year after surviving sepsis in female patients aged <65 in clinical practice is critical.

Correlation Between Shock Index and Mortality in the Prehospital and Level 1 Rural Trauma Center Emergency Department Settings

Background: The shock index is a valid tool used to detect impending circulatory collapse in the pre-hospital setting. As validation of the shock index requires high sample sizes, the majority of retrospective studies have been performed at urban level 1 trauma centers. We hypothesized that the shock index would accurately predict mortality in a rural level 1 trauma center. Objective - Determine if the shock index continues to be a reliable predictive value in trauma patients for morbidity and mortality. Setting - This retrospective study was performed at a state-designated level 1 trauma center in Johnson City, Tennessee. Patients were excluded from the study if they were under the age of 18, not immediately transported to the trauma center or if insufficient data was available. The shock index was calculated as heart rate/systolic blood pressure. Both the prehospital and emergency department shock indexes were calculated, with the emergency department vital signs being the first upon arrival. Patients were divided into three categories: SI ≤ 0.7, 0.71-0.89, and ≥ 0.9. We assessed the relationship between SI, blood product usage, and outcome variables using Pearson correlation coefficients and logistic regression. Chi-square analysis was used to show the difference in mortality between the groups. Results - A higher shock index score after arrival to the emergency department experienced longer hospital, intensive care unit, and mechanical ventilation days, injury severity scores, packed red blood cells, plasma, platelets, and total blood product usage. Mortality was higher in the groups with an SI of ≥ 0.9 at the scene and arrival to the emergency department. Conclusion - Patients with a shock index &gt; 0.71-0.89 in both prehospital and emergency departments had higher mortality rates and need for transfusion. The shock index continues to be a reliable predictive value in trauma patients for morbidity and mortality.

Establishment of Prediction Model for Psoriatic Arthritis Based on Logistic Regression

Background: Psoriatic arthritis (PsA) is an inflammatory musculoskeletal disease associated with psoriasis (PsO) that appears approximately 7-8 years after psoriasis and remains undiagnosed in most of patients, which is disabled and difficult to treat. So early risk assessment is particularly important. However, at present, the risk assessment of the status quo is not optimistic. The aim of this study was to establish and validate a prediction model for assessing the risk of PsA in psoriasis patients. Methods: Ambispective cohort study was conducted. Demographic and clinical records were collected and followed up and blindly reviewed. Single and multiple factors analysis were used to analyze the factors influencing PsA, and Logistic regression equation models were developed to verify their predictive value. Results: Among 1141 Pso patients, 221 patients had PsA. Logistic regression equation model showed that Arthralgia (OR= 438.079; 95% CI: 175.154-1095.682), nail dystrophy (OR= 7.453; 95% CI: 4.116-13.498), scalp lesions (OR= 3.047; 95% CI: 1.297-7.159), Inverse (intertriginous) lesions (OR= 2.45; 95% CI: 1.382-4.342) and BMI (OR= 0.909; 95% CI: 0.846-0.977) were identified as potential predictors affecting the risk of transition from PsO to PsA. And the receiver operating characteristic (ROC) curve of PsA was drawn, the area under the curve (AUC) was 0.974 (95% CI 0.963 - 0.985), the prediction sensitivity was 96.40%, the specificity was 92.10%, the calibration curve showed that the predicted results was in good agreement with the observed results. Conclusion: The independent predictors of PsA involve Arthralgia, nail dystrophy, scalp lesions, Inverse (intertriginous) lesions, BMI. The Logistic prediction model based on these predictors has reliable predictive value and can help early clinical assessment and treatment.

Relevance of the correlation between tomography findings and laboratory test results in the accuracy of the diagnosis of pulmonary tuberculosis.

To evaluate the correlation between multidetector computed tomography (MDCT) findings and laboratory test results in patients with pulmonary tuberculosis (PTB). A total of 57 patients were evaluated. Patients with suspected PTB were divided into groups according to the final diagnosis (confirmed or excluded), and the groups were compared in terms of sociodemographic variables, clinical symptoms, tomography findings, and laboratory test results. Among the patients with a confirmed diagnosis of PTB, small pulmonary nodules with a peribronchovascular distribution were significantly more common in the patients with a positive sputum smear microscopy result (47.4% vs. 8.3%; p = 0.046), as were a miliary pattern (36.8% vs. 0.0%; p = 0.026), septal thickening (84.2% vs. 41.7%; p = 0.021), and lymph node enlargement (52.6% vs. 8.3%; p = 0.020). Small pulmonary nodules with a centrilobular distribution were significantly more common among the culture-positive patients (75.0% vs. 35.7%; p = 0.045), as was a tree-in-bud pattern (91.7% vs. 42.9%; p = 0.014). A tree-in-bud pattern, one of the main tomography findings characteristic of PTB, had a sensitivity, specificity, positive predictive value, and negative predictive value of 71.0%, 73.1%, 75.9%, and 67.9%, respectively. MDCT presented reliable predictive values for the main tomography findings in the diagnosis of PTB, being a safe tool for the diagnosis of PTB in patients with clinical suspicion of the disease. It also appears to be a suitable tool for the selection of patients who are candidates for more complex, invasive examinations from among those with high clinical suspicion of PTB and a negative sputum smear microscopy result.

Risk Factors, Outcomes, and Predictions of Extensively Drug-Resistant Acinetobacter baumannii Nosocomial Infections in Patients with Nervous System Diseases.

Acinetobacter baumannii has evolved to become a major pathogen of nosocomial infections, resulting in increased morbidity and mortality. This study aimed to investigate the risk factors, outcomes, and predictions of extensively drug-resistant (XDR)-A. baumannii nosocomial infections in patients with nervous system diseases (NSDs). A retrospective study of patients infected with XDR-A. baumannii admitted to the Affiliated Hospital of Southwest Medical University (Luzhou, China) from January 2021 to December 2022 was conducted. Three multivariate regression models were used to assess the risk factors and predictive value for specific diagnostic and prognostic subgroups. A total of 190 patients were included, of which 84 were diagnosed with NSDs and 80% of those were due to stroke. The overall rates of all-cause mortality for XDR-A. baumannii nosocomial infections and those in NSDs were 38.9% and 40.5%, respectively. Firstly, hypertension, indwelling gastric tube, tracheotomy, deep puncture, bladder irrigation, and pulmonary infections were independent risk factors for XDR-A. baumannii nosocomial infections in patients with NSDs. Moreover, pulmonary infections, the aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio, and the neutrophil-to-lymphocyte ratio (NLR) were significantly associated with increased mortality rates in patients with nosocomial infections caused by XDR-A. baumannii. Thirdly, NLR and cardiovascular diseases accounted for a high risk of mortality for XDR-A. baumannii nosocomial infections in patients with NSDs. The area under the curves of results from each multivariate regression model were 0.827, 0.811, and 0.853, respectively. This study reveals the risk factors of XDR-A. baumannii nosocomial infections in patients with NSDs, and proves their reliable predictive value. Early recognition of patients at high risk, sterilizing medical tools, and regular blood monitoring are all critical aspects for minimizing the nosocomial spread and mortality of A. baumannii infections.

Comparison of the Ct-values for genomic and subgenomic SARS-CoV-2 RNA reveals limited predictive value for the presence of replication competent virus.

SARS-CoV-2 is the causative agent of the acute respiratory disease COVID-19. In addition to the full length positive-sensed, single-stranded genomic RNA (gRNA), viral subgenomic RNAs (sgRNAs) that are required for expression of the 3' region of the genome are synthesized in virus-infected cells. However, whether these sgRNA-species might be used as a measure of active virus replication and to predict infectivity is still under debate. The commonly used methods to monitor and quantitate SARS-CoV-2 infections are based on RT-qPCR analysis and the detection of gRNA. The infectivity of a sample obtained from nasopharyngeal or throat swabs is associated with the viral load and inversely correlates with Ct-values, however, a cut-off value predicting the infectivity highly depends on the performance of the assay. Furthermore, gRNA derived Ct-values result from nucleic acid detection and do not necessarily correspond to active replicating virus. We established a multiplex RT-qPCR assay on the cobas 6800 omni utility channel concomitantly detecting SARS-CoV-2 gRNAOrf1a/b, sgRNAE,7a,N, and human RNaseP-mRNA used as human input control. We compared the target specific Ct-values with the viral culture frequency and performed ROC curve analysis to determine the assay sensitivity and specificity. We found no advantage in the prediction of viral culture when using sgRNA detection compared to gRNA only, since Ct-values for gRNA and sgRNA were highly correlated and gRNA offered a slightly more reliable predictive value. Single Ct-values alone only provide a very limited prediction for the presence of replication competent virus. Hence, careful consideration of the medical history including symptom onset has to be considered for risk stratification.

Multiomics data analyses to identify SLC25A17 as a novel biomarker to predict the prognosis and immune microenvironment in head and neck squamous cell carcinoma

ObjectiveThis study aims to explore the predictive value of SLC25A17 in the prognosis and tumor microenvironment (TME) of patients with head and neck squamous cell carcinoma (HNSCC) and to provide ideas for individual clinical treatment.MethodsA pancancer analysis of the differential expression of SLC25A17 among different tumors was first conducted via the TIMER 2.0 database. Subsequently, the expression of SLC25A17 and related clinical information of HNSCC patients were obtained from the TCGA database, and patients were divided into two groups according to the median value of SLC25A17 expression. K‒M survival analysis was conducted to compare the overall survival (OS) and progression-free survival (PFS) between the groups. The Wilcoxon test was used to compare the distribution of SLC25A17 in different clinical characteristics, and univariate Cox and multivariate Cox analyses were performed to analyze independent prognostic factors to establish a predictive nomogram. Calibration curves were generated to verify the reliability of predicting 1-year, 3-year and 5-year survival rates and another cohort (GSE65858) was used for external validation. Gene set enrichment analysis was conducted to compare the enriched pathways, and the immune microenvironment was assessed using the CIBERSORT and estimate packages. Furthermore, the expression levels of SLC25A17 in immune cells were also analyzed with single-cell RNA-seq via the TISCH. Moreover, the immunotherapeutic response and chemotherapy drug sensitivity were compared between the two groups to guide precise treatment. The TIDE database was applied to predict the possibility of immune escape in the TCGA-HNSC cohort.ResultsCompared with normal samples, the expression of SLC25A17 was much higher in HNSCC tumor samples. For patients with high SLC25A17 expression, the OS and PFS were shorter than those with low SLC25A17 expression, indicating a worse prognosis. The expression of SLC25A17 varied in different clinical features. Univariate Cox and multivariate COX analyses showed that SLC25A17, age, and lymph node metastasis are independent prognostic risk factors for HNSCC, and the survival prediction model based on these factors had reliable predictive value. Patients in the low-expression group exhibited more immune cell infiltration, higher TME scores, higher IPS scores and lower TIDE scores than those in the high-expression groups, suggesting better immunotherapeutic response with lower SLC25A17 expression. Moreover, patients in the high-expression group were more sensitive to chemotherapy.ConclusionsSLC25A17 can effectively predict the prognosis of HNSCC patients and could be a precise individual-targeted indicator for the treatment of HNSCC patients.

Interpretable deep learning survival predictive tool for small cell lung cancer.

Small cell lung cancer (SCLC) is an aggressive and almost universally lethal neoplasm. There is no accurate predictive method for its prognosis. Artificial intelligence deep learning may bring new hope. By searching the Surveillance, Epidemiology, and End Results database (SEER), 21,093 patients' clinical data were eventually included. Data were then divided into two groups (train dataset/test dataset). The train dataset (diagnosed in 2010-2014, N = 17,296) was utilized to conduct a deep learning survival model, validated by itself and the test dataset (diagnosed in 2015, N = 3,797) in parallel. According to clinical experience, age, sex, tumor site, T, N, M stage (7th American Joint Committee on Cancer TNM stage), tumor size, surgery, chemotherapy, radiotherapy, and history of malignancy were chosen as predictive clinical features. The C-index was the main indicator to evaluate model performance. The predictive model had a 0.7181 C-index (95% confidence intervals, CIs, 0.7174-0.7187) in the train dataset and a 0.7208 C-index (95% CIs, 0.7202-0.7215) in the test dataset. These indicated that it had a reliable predictive value on OS for SCLC, so it was then packaged as a Windows software which is free for doctors, researchers, and patients to use. The interpretable deep learning survival predictive tool for small cell lung cancer developed by this study had a reliable predictive value on their overall survival. More biomarkers may help improve the prognostic predictive performance of small cell lung cancer.

Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, which seriously threatens people's physical and mental health. Coagulation is closely related to the occurrence and development of HCC. Whether coagulation-related genes (CRGs) can be used as prognostic markers for HCC remains to be investigated.MethodsFirstly, we identified differentially expressed coagulation-related genes of HCC and control samples in the datasets GSE54236, GSE102079, TCGA-LIHC, and Genecards database. Then, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were used to determine the key CRGs and establish the coagulation-related risk score (CRRS) prognostic model in the TCGA-LIHC dataset. The predictive capability of the CRRS model was evaluated by Kaplan–Meier survival analysis and ROC analysis. External validation was performed in the ICGC-LIRI-JP dataset. Besides, combining risk score and age, gender, grade, and stage, a nomogram was constructed to quantify the survival probability. We further analyzed the correlation between risk score and functional enrichment, pathway, and tumor immune microenvironment.ResultsWe identified 5 key CRGs (FLVCR1, CENPE, LCAT, CYP2C9, and NQO1) and constructed the CRRS prognostic model. The overall survival (OS) of the high-risk group was shorter than that of the low-risk group. The AUC values for 1 -, 3 -, and 5-year OS in the TCGA dataset were 0.769, 0.691, and 0.674, respectively. The Cox analysis showed that CRRS was an independent prognostic factor for HCC. A nomogram established with risk score, age, gender, grade, and stage, has a better prognostic value for HCC patients. In the high-risk group, CD4+T cells memory resting, NK cells activated, and B cells naive were significantly lower. The expression levels of immune checkpoint genes in the high-risk group were generally higher than that in the low-risk group.ConclusionsThe CRRS model has reliable predictive value for the prognosis of HCC patients.

Validity &amp; Reliability of a Korean-version of Eating Assessment Tool (K-EAT-10): Predicting the Risk of Aspiration in Stroke Patients

Objectives: This study aims to investigate the reliability and validity of a Korean version of the Eating Assessment Tool (EAT-10) as a clinical evaluation tool for dysphagia with stroke patients and determine its clinical usefulness as a screening tool. Methods: A total of 50 patients with stroke (15 without dysphagia, and 35 with dysphagia, identified through a videofluoroscopic swallowing study (VFSS) participated in this study. The internal consistency, test-retest reliability, and concurrent validity of the adapted Korean version of EAT-10(K-EAT-10) were evaluated. In addition, the K- EAT-10 scores were compared between two groups according to demographic characteristics, post-stroke conditions, and swallowing function. Moreover, K-EAT-10 scores were compared with the VFSS findings to estimate the sensitivity, specificity, and positive and negative predictions of aspiration. Results: The internal consistency of the K-EAT-10 questionnaire showed a Cronbach α coefficient of .091, and the correlation coefficient of the test- retest reliability was .988. In addition, when correlation analysis was performed between the total score of K-EAT-10 and the K-DHI, the correlation coefficient was .904. In the receiver operating characteristic (ROC) curve analysis of K-EAT-10, a cut-off point for the K-EAT-10 questionnaire was set at 10 points with a sensitivity of 86.7% and specificity of 77.1%, and the area under the curve was shown to be 0.86. In addition, the positive predictive value (PPV) for the K-EAT-10 was 91%, and the negative predictive value (NPV) was 60%. Conclusion: Korean version of EAT-10 revealed excellent internal consistency, test-retest reliability, concurrent validity and positive predictive value for aspiration. The results suggest that the Korean adaptation of EAT-10 can be useful as a clinical screening test for dysphagia, especially predicting aspiration risk in patients with stroke.

Elevated serum AST and LDH levels are associated with infant death in premature babies with neonatal leukemoid reaction: a cohort study.

Neonatal leukemoid reaction (NLR) is often accompanied by infectious or non-infectious diseases, a low birth weight, sepsis, prematurity, ventricular hemorrhage, and bronchial dysplasia. It has an incidence rate of 1.3-15% and a mortality rate of about 41.4%. Previous studies on NLR have largely focused on its pathogenesis and clinical cases, but little is known about its prognostic laboratory indicators. We found that some of the NLR exhibited obviously elevation in liver function tests like aspartate transaminase (AST) and lactate dehydrogenase (LDH) which were not took by all the LR infants. The necessity for liver function tests for the prognosis of NLR was still unclear. A total of 39 premature infants with NLR at the First Hospital of Jilin University between March 2016 and March 2017 were included in this retrospective cohort study. The infants were divided into death and cured group based on the clinical outcomes. Premature infants with LR and death were defined as the case group (n=14), while infants without death were defined as the control group (n=25). Confounding factors such as age and gender between the two groups were controlled. Blood routine tests, including the white blood cell (WBC) count and subtypes, and liver function, and clinical features were recorded and analyzed. T tests were used to examine the differences in the laboratory indicators between the NLR and control groups. Receiver operating characteristic curves (ROCs) and areas under the curve (AUCs) were used to examine laboratory indicators for prognosis. For predicting clinical outcomes, the ROC curves showed that the cut-off values for AST and LDH were 279 and 1,412 U/L, respectively. The sensitivity and specificity for AST were 92% and 71.43%, respectively, with an AUC of 0.894, while the sensitivity and specificity for LDH were 88% and 78.57%, respectively, with an AUC of 0.911. This innovative study investigated the NLR prognosis depending on laboratory tests. We found that serum AST and LDH levels had reliable predictive value in determining adverse outcomes of NLR.

Developing a Danish version of the LASSI-L test – reliability and predictive value in patients with mild cognitive impairment, mild dementia due to AD and subjective cognitive decline

ABSTRACT Tests measuring proactive semantic interference as The Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L), has shown promising diagnostic properties for the diagnosis of Mild Cognitive Impairment (MCI) and dementia. LASSI-L may also be efficient in predicting cognitive decline in at-risk individuals. There is an unmet need to examine the diagnostic properties of the LASSI-L in a Danish context where traditional neuropsychological tests are typically applied when diagnosing possible dementia disorders. To investigate the reliability, convergent validity, and predictive value of the new Danish LASSI-L version in aMCI and mild dementia due to Alzheimer’s disease (AD). From a memory clinic we included 17 aMCI patients, 15 patients with mild dementia (AD), 17 patients with Subjective Cognitive Decline (SCD), and 30 healthy controls. Neuropsychological assessment was applied in all patients, and biomarker analyses were performed for patients with aMCI and mild AD. Cronbach’s alpha was 0.94. Patients with aMCI and mild dementia differed significantly from healthy controls on all LASSI-L measures. ROC analyses showed a very high AUC value for both patients with aMCI [0.85–0.97] and mild dementia [0.93–0.99]. SCD patients generally did not differ from controls, except for significantly lower scores on one item (Cued Recall A1) LASSI-L had high reliability and promising predictive value in the diagnosis of aMCI and mild AD due to AD. SCD patients diagnosed in a memory clinic did not differ significantly from healthy on the LASSI-L.

Identification and validation of autophagy-related gene expression for predicting prognosis in patients with idiopathic pulmonary fibrosis.

BackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fatal fibrotic pulmonary disease with unknow etiology. Owing to lack of reliable prognostic biomarkers and effective treatment measures, patients with IPF usually exhibit poor prognosis. The aim of this study is to establish a risk score prognostic model for predicting the prognosis of patients with IPF based on autophagy-related genes.MethodsThe GSE70866 dataset was obtained from the gene expression omnibus (GEO) database. The autophagy-related genes were collected from the Molecular Signatures Database (MSigDB). Gene enrichment analysis for differentially expressed genes (DEGs) was performed to explore the function of DEGs. Univariate, least absolute shrinkage and selection operator (LASSO), as well as multivariate Cox regression analyses were conducted to identify a multi-gene prognostic model. Receiver operating characteristic (ROC) curve was applied to assess the prediction accuracy of the model. The expression of genes screened from the prognostic model was validated in clinical samples and human lung fibroblasts by qPCR and western blot assays.ResultsAmong the 514 autophagy-related genes, a total of 165 genes were identified as DEGs. These DEGs were enriched in autophagy-related processes and pathways. Based on the univariate, LASSO, and multivariate Cox regression analyses, two genes (MET and SH3BP4) were included for establishing the risk score prognostic model. According to the median value of the risk score, patients with IPF were stratified into high-risk and low-risk groups. Patients in high-risk group had shorter overall survival (OS) than low-risk group in both training and test cohorts. Multivariate regression analysis indicated that prognostic model can act as an independent prognostic indicator for IPF. ROC curve analysis confirmed the reliable predictive value of prognostic model. In the validation experiments, upregulated MET expression and downregulated SH3BP4 expression were observed in IPF lung tissues and TGF-β1-activated human lung fibroblasts, which is consistent with results from microarray data analysis.ConclusionThese findings indicated that the risk score prognostic model based on two autophagy-related genes can effectively predict the prognosis of patients with IPF.

Molecular characteristics, clinical significance, and cancer immune interactions of cuproptosis and ferroptosis-associated genes in colorectal cancer.

ObjectiveTo systematically analyze the expression of cuproptosis and ferroptosis genes and their impact on the development, prognosis, tumor microenvironment (TME), and treatment response in colorectal cancer (CRC) patientsMethodsWe systematically evaluated 33 cuproptosis and ferroptosis-related genes and comprehensively identified the correlations between cuproptosis and ferroptosis-related genes and transcriptional patterns, prognosis, and clinical features. Three distinct subgroups were identified in CRC using the TCGA database and the GEO database. We next assessed the relationship between the molecular features, prognostic significance, and clinical indicators of the prognostic genes in the cuproptosis and ferroptosis-related gene clusters. In addition, a PAC_score, which accurately predicted the prognosis of CRC patients and the efficacy of immunomodulatory mAbs, was obtained.ResultsPatients in the low expression group (low expression of cuproptosis and ferroptosis-related genes) had a longer survival compared to the high expression group. We identified two distinct prognosis-associated molecular subtypes and observed an association between clinical information and prognosis. The enrichment analysis of differential genes associated with prognosis showed that the main enrichment was related to biological processes such as metastasis and metabolism. Next, the PCA_score for predicting overall survival (OS) was established and its reliable predictive value in CRC patients was confirmed. Furthermore, highly reliable nomogram was created to facilitate the clinical feasibility of the PCA_score. It was found that the immunomodulatory mAbs, PD-L1 and CTLA4 were highly expressed in the low PCA_score score group with statistically significance.ConclusionOverall, the PCA scores of prognostic differential genes in the cuproptosis and ferroptosis-related gene clusters were strongly associated with clinical characteristics, prognosis, and immunotherapy in CRC patients. This data may promote further exploration of more effective immunotherapy strategies for CRC.

Prognostic signature for hepatocellular carcinoma based on 4 pyroptosis-related genes

BackgroundHepatocellular carcinoma (HCC) is a cancer with a poor prognosis. Many recent studies have suggested that pyroptosis is important in tumour progression. However, the role of pyroptosis-related genes (PRGs) in HCC remains unclear.Materials and methodsWe identified differentially expressed PRGs in tumours versus normal tissues. Through univariate, LASSO, and multivariate Cox regression analyses, a prognostic PRG signature was established. The signature effectiveness was evaluated by time-dependent receiver operating characteristic (t-ROC) curve and Kaplan–Meier (KM) survival analysis. The signature was validated in the ICGC (LIRI-JP) cohort. In addition, single-sample gene enrichment analysis (ssGSEA) showed the infiltration of major immune cell types and the activity of common immune pathways in different subgroups.ResultsTwenty-nine pyroptosis-related DEGs from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset were detected, and four genes (CTSV, CXCL8, MKI67 and PRF1) among them were selected to construct a prognostic signature. Then, the patients were divided into high- and low-risk groups. The pyroptosis-related signature was significantly associated with overall survival (OS). In addition, the patients in the high-risk group had lower levels of immune infiltration.ConclusionThe prognostic signature for HCC based on 4 pyroptosis-related genes has reliable prognostic and predictive value for HCC patients.

Critically ill COVID-19 patients in northeast Brazil: mortality predictors during the first and second waves including SAPS 3.

BackgroundThe Simplified Acute Physiology Score (SAPS) 3 is a reliable score to predict mortality. This study aims to investigate the predictive values of SAPS 3 and other clinical parameters for death in critically ill coronavirus disease 2019 (COVID-19) patients.MethodsThis is a prospective study in a tertiary hospital for patients who required intensive care due to COVID-19 infection in northeast Brazil. Two distinct groups were constructed according to the epidemiological data: first wave and second wave. The severity of patients admitted was estimated using the SAPS 3 score.ResultsA total of 767 patients were included: 290 were enrolled in the first wave and 477 in the second wave. Patients in the first wave had more comorbidities, were put on mechanical ventilation and required dialysis and vasopressors more frequently (p<0.05). During the second wave, non-invasive ventilation was more often required (p<0.05). In both periods, older patients and higher SAPS 3 scores on admission were associated with death (p<0.05). Non-invasive ventilation use showed a negative association with death only in the second wave period. In the first wave, the SAPS 3 score was more useful (area under the curve [AUC] 0.897) in predicting death in critically ill COVID-19 patients than in the second wave (AUC 0.810).ConclusionThe SAPS 3 showed very reliable predictive values for death during the waves of the COVID-19 pandemic, mostly together with kidney and pulmonary dysfunction.

MO905: Calcification of Branches is Superior to Abdominal Aortic Calcification in Predicting Mortality of Hemodialysis Patients

Abstract BACKGROUND AND AIMS Vascular calcification (VC) is considered to be an important initiating risk factor of cardiovascular events, which contributes to the leading cause of death in maintenance hemodialysis (MHD) patients. VC has a high incidence and has been reported to predict the mortality of MHD patients. Inspired by an extreme example of VC in dialysis patients, calciphylaxis (which involves extensive calcification of small- and medium- arteries with poor prognosis), we hypothesized that VC of branches was a better predictor of mortality than abdominal aortic calcification. Therefore, a cohort study based on MHD patients was constructed to investigate the distribution characteristics of VC, compare the predictive value of VC on mortality among different arteries, and explore a comprehensive model predicting mortality. METHOD A total of 1583 patients in our Blood Purification Center from January 2015 to September 2017 were screened, and 237 eligible patients were finally included. Abdominal-pelvic computed tomography images were collected and an open-source software 3D Slicer (version 4.11) was used to calculate the calcification volume in abdominal aorta, celiac trunk, superior and inferior mesenteric artery, renal artery, common iliac artery and internal iliac artery. Demographic and clinical data including medical history and some laboratory indexes were collected. Patients were followed-up until all-cause death or September 2020. Cause of death and survival time were recorded in detail. The area under the curve (AUC) of Time-dependent receiver operating curves (ROC) was calculated to evaluate the predictive value of arterial calcification. 80% of cases were randomly assigned to the training dataset and the remaining were assigned to the validation dataset. Lasso regression was performed to screen variables. Cox proportional hazards regression was performed to construct the predicting model and nomogram. C-index, calibration and decision curve analysis were used to evaluate the comprehensive model. SPSS software (version 26.0) and R software (version 4.1.2) perform statistical analysis. RESULTS The prevalence of total VC is 95.36%. Among the abdominal arteries, abdominal aortic calcification is the most prevalent (88.61%), followed by calcification of internal iliac artery (85.65%) and common iliac artery (80.17%). Median survival time was 37.00 (1.00–70.00) months. The 1-year survival rate, 3-year survival rate and 5-year survival rate were 73.42%, 50.01% and 35.43%, respectively. Cardiovascular disease accounts for 54.01% of the death cause. AUC of Time-dependent ROC by internal iliac artery and mesenteric artery calcification for predicting mortality is significantly higher than that of other arterial calcification. Internal iliac artery calcification (IIAC) was screened out as the most representative predictor for all-cause death among the calcification of arterial calcification by lasso regression. When analyzing IIAC and other clinical data, age (hazard ratio 1.03, P &amp;lt; 0.001), serum albumin level (hazard ratio 0.919, P &amp;lt; 0.001) and IIAC (hazard ratio 1.36, P = 0.003) were independent factor of mortality in HD patients. The nomogram based on Cox proportional hazards model is shown in Figure 1. C-index of the model was 74.09%. Decision curves of Cox proportional hazards model from training dataset and validation dataset showed the reliable predictive value of Cox model (Figure 2). CONCLUSION This is the first study evaluating the calcification volume of abdominal aortic branches and comparing their predictive values. In conclusion, calcification of abdominal aortic branches, especially internal iliac artery, is a better predictor for mortality of HD patients than abdominal aortic calcification. Our concern with calcification of large arteries in MHD patients needs to be shifted to smaller branch arteries.