Receiver Operating Characteristic Curve Research Articles

A nomogram for predicting nutritional risk before gastric cancer surgery.

Gastric cancer (GC) is the fourth leading cause of cancer death worldwide. Patients with GC have higher nutritional risk. This study aimed to construct a nomogram model for predicting preoperative nutritional risk in patients with GC in order to assess preoperative nutritional risk in patients more precisely. Patients diagnosed with GC and undergoing surgical treatment were included in this study. Data was collected through clinical information, laboratory testing, and radiomics-derived characteristics. Least absolute shrinkage selection operator (LASSO) regression analysis and multi-variable logistic regression were employed to construct a clinical prediction model, which takes the form of a logistic nomogram. The effectiveness of the nomogram model was evaluated using receiver operat-ing characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). A total of three predictors, namely body mass index (BMI), hemoglobin (Hb) and radiomics characteristic score (Radscore) were identified by LASSO regression analysis from a total of 21 variables studied. The model constructed using these three predictors displayed medium prediction ability. The area under the ROC curve was 0.895 (95% CI 0.844-0.945) in the training set, with a cutoff value of 0.651, precision of 0.957, and sensitivity of 0.718. In the validation set, it was 0.880 (95% CI 0.806-0.954), with a cutoff value of 0.655, precision of 0.930, and sensitivity of 0.698. DCA also confirmed the clinical benefit of the combined model. This simple and dependable nomogram model for clinical prediction can assist physicians in assessing preoperative nutritional risk in GC patients in a time-efficient and accurate manner to facilitate early identification and diagnosis.

Preexisting chronic pain is not associated with moderate-to-severe acute pain after laparoscopic cholecystectomy: a prospective cohort study.

We aimed to evaluate the relationship between preexisting chronic pain and acute postoperative pain after laparoscopic cholecystectomy (LC) and to identify predictors of acute postsurgery pain. In this prospective cohort study, patients undergoing LC with general anesthesia were enrolled. The primary outcome was the incidence of moderate-to-severe pain during movement in the first 24 hours after surgery. Logistic regression analysed the association of perioperative risk factors with acute postoperative pain. Model performance was assessed using receiver operating characteristic curves (ROC), calibration belt plots, and 10-fold cross-validation. Of the 193 patients analysed, 49.74% experienced moderate-severe acute pain. Univariable analysis indicated that preoperative chronic pain increased the likelihood of acute postoperative pain (odds ratio [OR], 2.53; 95% confidence interval [CI], 1.40-4.55). Risk factors identified in the multivariable analysis were higher preoperative pain intensity (OR, 1.28; 95% CI, 1-1.65), sleep disturbances (OR, 1.42; 95% CI, 1.24-1.61), intraoperative fentanyl supplementation (OR, 3.68; 95% CI, 1.48-9.12), incision extension for gallbladder retrieval (OR, 7.27; 95% CI, 1.58-33.39), and abdominal drain placement (OR, 6.09; 95% CI, 1.08-34.34). Dexamethasone use was associated with reduced pain (OR, 0.05; 95% CI, 0.004-0.74). The model demonstrated strong discrimination (ROC = 0.909) and good calibration (test statistic = 0.02; P = 0.89). Cross-validation showed slightly lower accuracy (ROC = 0.896). Preexisting chronic pain was not associated with acute postoperative pain following laparoscopic cholecystectomy. Significant predictors for acute pain included higher preoperative pain intensity, sleep disturbances, intraoperative fentanyl, incision extension, and abdominal drain placement, while dexamethasone use was associated with reduced pain.

Reevaluation of prognostic and severity indicators for COVID-19 patients in the emergency department.

This study aimed to re-evaluate whether the scoring systems, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were effective in predicting prognosis and severity of COVID-19 patients in the emergency department (ED). COVID-19 patients enrolled in this retrospective study divided into the death (DEA) and survival (SUR) groups, the severe/critical (SC) and non-severe/critical (non-SC) groups. The Acute Physiology and Chronic Health Evaluation (APACHE) II, Sequential Organ Failure Assessment (SOFA), National Early Warning Score (NEWS) and CCEDRRN COVID-19 Mortality Score were calculated. The neutrophil, lymphocyte and platelet counts were extracted from the first routine blood examination, and NLR and PLR were calculated accordingly. Receiver Operating Characteristic (ROC) curve and logistic regression were performed. All the scoring systems, as well as NLR and PLR, significantly increased in both the DEA and SC groups. The ROC curve showed that the CCEDRRN COVID-19 Mortality Score had the highest predictive value for mortality and severity (AUC 0.779, 0.850, respectively), which outperformed the APACHE II, SOFA and NEWS. NLR presented better predictive ability for severity (AUC 0.741) than death (AUC 0.702). The APACHE II, NEWS and CCEDRRN COVID-19 Mortality Score were positively correlated with both prognosis and severity, whereas NLR only with severity. The NEWS and CCEDRRN COVID-19 Mortality Score were reconfirmed for early and rapid predicting the poor prognosis and severity of COVID-19 patients in ED, especially the CCEDRRN COVID-19 Mortality Score with the highest discrimination capacity, and NLR was more appropriate for predicting the severity.

Machine Learning Algorithms Identify Target Genes and the Molecular Mechanism of Matrine against Diffuse Large B-cell Lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease. The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL. Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms. A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway. Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.

A universal method for seizure onset zone localization in focal epilepsy using standard deviation of spike amplitude

BackgroundPrecisely localizing the seizure onset zone (SOZ) is critical for focal epilepsy surgery. Existing methods mainly focus on high-frequency activities in stereo-electroencephalography, but often fail when seizures are not driven by high-frequency activities. Recognized as biomarkers of epileptic seizures, ictal spikes in SOZ induce epileptiform discharges in other brain regions. Based on this understanding, we aim to develop a universal algorithm to localize SOZ and investigate how ictal spikes within the SOZ induce seizures. MethodsWe proposed a novel metric called standard deviation of spike amplitude (SDSA) and utilized channel-averaged SDSA to describe seizure processes and detect seizures. By integrating SDSA values in specific intervals, the score for each channel located within SOZ was calculated. Channels with high SOZ scores were clustered as SOZ. The localization accuracy was asserted using area under the receiver operating characteristic (ROC) curve. Further, we analyzed early ictal signals from SOZ channels and investigated factors influencing their duration to reveal the seizure inducing conditions. ResultsWe analyzed data from 15 patients with focal epilepsy. The channel-averaged SDSA successfully detected all 28 seizures without false alarms. Using SDSA integration, we achieved precise SOZ localization with an average area under ROC curve (AUC) of 0.96, significantly outperforming previous methods based on high-frequency activities. Further, we discovered that energy of ictal spikes in SOZ was concentrated at a specific frequency distributed in [6, 12Hz]. Additionally, we found that the higher the energy per second in this frequency band, the faster ictal spikes could induce seizures. ConclusionThe SDSA metric offered precise SOZ localization with robustness and low computational cost, making it suitable for clinical practice. By studying the propagation patterns of ictal spikes between the SOZ and non-SOZ, we suggest that ictal spikes from SOZ need to accumulate energy at a specific central frequency to induce epileptic spikes in non-SOZ, which may have significant implications for understanding the seizure onset pattern.

Analysis and identification of ferroptosis-related diagnostic markers in rheumatoid arthritis.

Rheumatoid arthritis (RA) is an autoimmune, inflammatory joint disease. There is growing evidence that ferroptosis is involved in the pathogenesis of RA. This study aimed to search for diagnostic markers of ferroptosis in RA and to analyse the potential mechanisms and clinical value. RA-associated datasets were used from the publicly available GEO database. Three methods of machine learning were applied to screen biomarkers. The diagnostic efficacy of the results was also verified by receiver operating characteristic (ROC) curve, external dataset, qRT-PCR and Western blot. Enrichment analysis was performed in this process, while protein-protein interaction (PPI) analysis and immune infiltration correlation analysis were performed using biomarkers, and competing endogenous RNA (ceRNA) networks were constructed to search for prospective therapeutic targets. MMP13 and GABARAPL1 can be used as ferroptosis diagnostic genes in RA. The ROC curve and PPI result demonstrated that MMP13 and GABARAPL1 had an excellent diagnostic value. The results of signature genes in the external dataset, qRT-PCR and Western blot further confirm our findings. The enrichment analysis showed that p53, MAPK and NOD-like receptor signalling pathways may be involved in the process of ferroptosis in RA. In addition, two ferroptosis diagnostic genes in RA participate in the occurrence of ferroptosis in RA via oxidative stress, metabolism and immune response. Immune infiltration analysis showed that RA extensively infiltrated B cells, T cells, macrophages and other immune cells. Persistent immune activation may be an essential reason for the progression of ferroptosis in RA. We also obtained five potential therapeutic agents for RA and some long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) regulating ferroptosis diagnostic genes. Our study suggests that MMP13 and GABARAPL1, which are closely linked with oxidative stress and immunological modulation, can be used as ferroptosis-related potential diagnostic markers in RA and provide new clues regarding the diagnostic and therapeutic targets of ferroptosis in RA.

A Novel Prognostic Risk Score Model Based on RNA Editing Level in Lower-Grade Glioma

BackgroundLower-grade glioma (LGG) refers to WHO grade 2 and 3 gliomas. Surgery combined with radiotherapy and chemotherapy can significantly improve the prognosis of LGG patients, but tumor progression is still unavoidable. As a form of posttranscriptional regulation, RNA editing (RE) has been reported to be involved in tumorigenesis and progression and has been intensively studied recently. MethodsSurvival data and RE data were subjected to univariate and multivariate Cox regression analysis and lasso regression analysis to establish an RE risk score model. A nomogram combining the risk score and clinicopathological features was built to predict the 1-, 3-, and 5-year survival probability of patients. The relationship among ADAR1, SOD2 and SOAT1 was verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) ResultsA risk model associated with RE was constructed and patients were divided into different risk groups based on risk scores. The model demonstrated strong prognostic capability, with the area under the ROC curve (AUC) values of 0.882, 0.938, and 0.947 for 1-, 3-, and 5-year survival predictions, respectively. Through receiver operating characteristic curve (ROC) curves and calibration curves, it was verified that the constructed nomogram had better performance than age, grade, and risk score in predicting patient survival probability. Apart from this functional analysis, the results of correlation analyses between risk differentially expressed genes (RDEGs) and RE help us to understand the underlying mechanism of RE in LGG. ADAR may regulate the expression of SOD2 and SOAT1 through gene editing. ConclusionIn conclusion, this study establishes a novel and accurate 17-RE model and a nomogram for predicting the survival probability of LGG patients. ADAR may affect the prognosis of glioma patients by influencing gene expression.

Association between serum albumin with geriatric nutritional risk index and osteopenia in Chinese elderly men: a nested case-control study.

Malnutrition is associated with a higher risk of osteoporosis. We aim to assess the relationship between serum albumin with geriatric nutritional risk index and osteopenia in Chinese elderly men. This is a nested case-control study from a prospective cohort enrolled 1109 individuals who were followed for seven years. Demographic data, medical history, signs and symptoms, and laboratory parameters were collected and analysed. Nutritional status and Geriatric Nutritional Risk Index (GNRI) were assessed. The nutrition-related indexes predictive value for osteopenia development was analyzed through multivariate Cox regression analysis and by creating a receiver operating characteristic curve (ROC), calculating the area under the curve (AUC). Kaplan-Meier (K-M) method was further used to find the nutritional status level in the elderly men. The ALB and GNRI correlated with the risk of osteopenia in Chinese elderly men. After adjusting for all covariates, people with higher ALB level (HR: 0.821; 95% CI: 0.790-0.852) and higher GNRI score (HR: 0.889; 95% CI: 0.869-0.908) had a smaller risk of osteopenia. ROC analysis showed that the AUC for ALB was 0.729 (p<0.05) and for the GNRI score was 0.731 (p<0.05). K-M curve indicated a significant difference in ALB level (p<0.001) and GNRI score (p<0.001) in the respective subgroups. This study found that lower ALB level and lower GNRI score are associated with a higher prevalence of osteopenia among elderly men in China.

MRI-based vector radiomics for predicting breast cancer HER2 status and its changes after neoadjuvant therapy

Purpose: To develop a novel MRI-based vector radiomic approach to predict breast cancer (BC) human epidermal growth factor receptor 2 (HER2) status (zero, low, and positive; task 1) and its changes after neoadjuvant therapy (NAT) (positive-to-positive, positive-to-negative, and positive-to-pathologic complete response; task 2). Materials and Methods: Both dynamic contrast-enhanced (DCE) MRI data and multi-b-value (MBV) diffusion-weighted imaging (DWI) data were acquired in BC patients at two centers. Vector-radiomic and conventional-radiomic features were extracted from both DCE-MRI and MBV-DWI. After feature selection, the following models were built using the retained features and logistic regression: vector model, conventional model, and combined model that integrates the vector-radiomic and conventional-radiomic features. The models’ performances were quantified by the area under the receiver-operating characteristic curve (AUC). Results:The training/external test set (center 1/2) included 483/361 women. For task 1, the vector model (AUCs=0.73∼0.86) was superior to (p<.05) the conventional model (AUCs=0.68∼0.81), and the addition of vector-radiomic features to conventional-radiomic features yielded an incremental predictive value (AUCs=0.80∼0.90, p<.05). For task 2, the combined MBV-DWI model (AUCs=0.85∼0.89) performed better than (p<.05) the conventional MBV-DWI model (AUCs=0.73∼0.82). In addition, for the combined DCE-MRI model and the combined MBV-DWI model, the former (AUCs=0.85∼0.90) outperformed (p<.05) the latter (AUCs=0.80∼0.85) in task 1, whereas the latter (AUCs=0.85∼0.89) outperformed (p<.05) the former (AUCs=0.76∼0.81) in task 2. The above results are true for the training and external test sets. Conclusions:MRI-based vector radiomics may predict BC HER2 status and its changes after NAT and provide significant incremental prediction over and above conventional radiomics.

Role and clinical value of serum hsa_tsr011468 in lung adenocarcinoma.

Transfer RNA‑derived small RNAs (tsRNAs) are novel non‑coding RNAs that are associated with the pathogenesis of various diseases. However, their association with lung adenocarcinoma (LUAD) has not been studied comprehensively. Therefore, the present study aimed to explore the diagnostic value of a tsRNA, hsa_tsr011468, in LUAD. The OncotRF database was used to screen tsRNAs and reverse transcription‑quantitative PCR (RT‑qPCR) was performed to detect the expression levels of hsa_tsr011468 in various samples. Subsequently, the diagnostic and prognostic values of hsa_tsr011468 for LUAD were determined via receiver operating characteristic (ROC) curve and survival curve analyses, and by assessing clinicopathological parameters. In addition, both nuclear and cytoplasmic RNA were extracted to assess the location of hsa_tsr011468. The OncotRF database identified high expression of hsa_tsr011468 in LUAD. In addition, the results of RT‑qPCR showed that the relative expression levels of hsa_tsr011468 in the serum and tissues of patients with LUAD were higher than those in normal controls. Furthermore, its expression was lower in postoperative serum samples than in preoperative serum samples from patients with LUAD. ROC and survival curves indicated that hsa_tsr011468 had good diagnostic and prognostic value. Furthermore, the clinicopathological analysis revealed that hsa_tsr011468 was associated with tumor size. In addition, hsa_tsr011468 was mainly localized in the cytoplasm of LUAD cells. The present study indicated that hsa_tsr011468 has good diagnostic value and, therefore, could be employed as a serum marker for LUAD.

The diagnostic value of combining preoperative serum CA19-9, ALBI score, and 18 F-FDG PET/CT imaging in preoperative resectability of pancreatic cancer.

Pancreatic cancer is an increasing cause of cancer-related mortality, with persistently low survival rates. We investigated the clinical diagnostic value of the combination of preoperative serum carbohydrate antigen 19-9 (CA19-9), albumin-bilirubin (ALBI) score, and 18 F-fluoro-2-deoxy- d -glucose PET integrated with computed tomography ( 18 F-FDG PET/CT) imaging in pancreatic cancer preoperative resectability. This study included 143 pancreatic cancer patients, including 68 preoperative resectable and 75 preoperative unresectable pancreatic cancer patients. Meanwhile, 67 patients with non-pancreatic cancer were included as the control group. The clinical data were collected. Serum CA19-9 level was measured by ELISA. The levels of total bilirubin and albumin were determined using a biochemical analyzer, with the ALBI score calculated. All patients underwent 18 F-FDG PET/CT imaging. The consistency of the diagnosis was evaluated by the Kappa test. Logistic univariate and multivariate regression analyses were performed. The diagnostic efficacy of these parameters was evaluated using receiver operating characteristic (ROC) curves, and the optimal ROC curve thresholds were obtained using the Youden index. The preoperative serum CA19-9 and ALBI score of patients with preoperative resectable pancreatic cancer were increased, which helped diagnose preoperative resectable pancreatic cancer. 18 F-FDG PET/CT imaging had diagnostic value for preoperative resectable pancreatic cancer. Preoperative serum CA19-9, ALBI score, and 18 F-FDG PET/CT imaging were independent influencing factors for pancreatic cancer preoperative resectability, and their combination had higher diagnostic value for preoperative resectable pancreatic cancer than any single of these indexes. The combination of preoperative serum CA19-9, ALBI score, and 18 F-FDG PET/CT imaging had high diagnostic value for pancreatic cancer preoperative resectability.

Clinical Value of Circ-PNPT1 on Adverse Pregnancy Outcomes of Patients with Gestational Diabetes Mellitus.

Several circular RNAs are associated with important pathophysiological characteristics of gestational diabetes mellitus (GDM). This study intended to measure the expression of circ-PNPT1 in sera of GDM patients and to expound on its values on pregnancy outcomes. Totally 104 GDM patients and 71 healthy controls were recruited. The expression pattern of serum circ-PNPT1 was measured by reverse transcription-quantitative polymerase chain reaction. The diagnostic efficacy of circ-PNPT1 and fasting blood glucose (FBG) on GDM was evaluated by receiver operating characteristic (ROC) analysis. Parameters of glycolipid metabolism were determined using automatic biochemical analyzers. The correlation between circ-PNPT1 and glycolipid metabolism parameters was analyzed using Pearson analysis. GDM patients were divided into a high expression group and a low expression group based on the median value of circ-PNPT1 expression. Curves of adverse neonatal outcomes were drawn by Log Rank analysis. GDM patients exhibited higher circ-PNPT1 expression than healthy controls. The area under the ROC curve of circ-PNPT1 diagnosing GDM was 0.9184 and the cut-off value was 1.435 (90.38% sensitivity, 85.92% specificity). Serum circ-PNPT1 expression was positively correlated with FBG, total cholesterol, and triglyceride in GDM patients. Neonates born to GDM patients with high circ- PNPT1 expression were prone to adverse outcomes. Circ-PNPT1 was highly-expressed in the sera of GDM patients. Circ-PNPT1 affected glycolipid metabolism and its expression had certain reference values on adverse pregnancy outcomes.

Dose-averaged linear energy transfer within the gross tumor volume of non-small-cell lung cancer affects the local control in carbon-ion radiotherapy

Background and purposeHigh linear energy transfer (LET) radiation exhibits stronger tumor-killing effect. However, the correlation between LET and the therapeutic efficacy in Carbon-ion radiotherapy (CIRT) for locally advanced non-small-cell lung cancer (LA-NSCLC) is currently not clear. This study aimed to investigate the relationship between the dose-averaged LET (LETd) distribution within tumor and local recurrence for LA-NSCLC treated with CIRT. Methods and materialsAn analysis of 62 consecutive patients with LA-NSCLC who underwent CIRT from 2018 to 2022 was conducted. The LETd distribution was calculated based on their treated plans, and the correlation between local recurrence and LETd, relative biological effectiveness (RBE)-weighted doses (DRBE) and clinical factors was investigated. Receiver operating characteristic (ROC) curve, log-rank test, and Cox regression analysis were performed based on that. Results16 patients were defined as local recurrence. Overall survival (OS) and local control (LC) at 24 months were 76.9 % and 73.2 %, respectively. The mean LETd in internal gross tumor volume (iGTV) in the local recurrence group was 48.7 keV/µm, significantly lower than the mean LETd of 53.2 keV/µm in the local control group (p = 0.016). No significant difference was observed in DRBE between the local recurrence and local control groups. ROC curve analysis indicated that a percentage of 88 % of volume in iGTV receiving at least 40 keV/µm (V40keV/μm) is the optimal threshold for predicting local recurrence (Area under curve (AUC) = 0.7636). The log-rank test and Cox regression analysis revealed that the LETd value covering 98 % volume of iGTV (LETd98%) was a significant risk factor for LC (p = 0.020). ConclusionsOur study revealed an association between LETd distribution and local recurrence in patients with LA-NSCLC. These findings suggest that lower LETd may increase the probability of local recurrence. We suggest that LETd distribution within iGTV should be routinely assessed in CIRT for lung cancer.

Study on the Expression and Potential Function of LncRNA in Peripheral Blood of Patients with Ankylosing Spondylitis.

Ankylosing spondylitis (AS) is an autoimmune disease that has the characteristics of difficult early diagnosis and a high disability rate. The objective of this study was to further explore the possible mechanism and potential function of lncRNA in AS. We used lncRNA microarray technology to detect the expression of lncRNA and mRNA in patients with active AS, stable patients, and healthy controls (HC). Afterward, bioinformatics analysis was conducted on differentially expressed genes. Seven differentially expressed lncRNAs were screened out for real-time fluorescent quantitative PCR (RT-qPCR), combined with various clinical indicators for correlation analysis, and the receiver operating characteristic (ROC) curve was used to analyze the potential of lncRNA as a diagnostic marker for AS. The results showed that the expression levels of NR-037662 and ENST00000599316 in the AS subgroups were significantly higher than those in the HC group, while the expression levels of ENST00000577914 and ENST00000579003 were lower than those in the HC group. The expression levels of NR-003542 and ENST00000512051 in the ASA group were significantly higher than those in the ASS and HC groups, while NR-026756 was just the opposite. Spearman's correlation analysis showed that the expression level of NR-003542 was positively correlated with Bath Ankylosing Spondylitis Functional Index (BASFI), Erythrocyte Sedimentation Rate (ESR), and high sensitivity C-Reactive Protein (hsCRP). The expression level of NR-026756 was negatively correlated with the Bath Ankylosing Spine Inflammatory Disease Activity Index (BASDAI), BASFI, ESR, hsCRP, and globulin (GLOB). In addition, it was also found that the ROC curve analysis of the 4 lncRNAs between the AS group (ASA group and ASS group) and the HC group were statistically significant, and the area under the curve (AUC) of NR-037662, ENST00000599316, ENST00000577914, and ENST00000579003 was 0.804, 0.812, 0.706, and 0.698, respectively. It was found that these differentially expressed lncRNAs of AS may be involved in the occurrence and development of the disease. Among them, NR-037662, ENST00000599316, ENST00000577914, and ENST00000579003 might have the potential to become AS diagnostic molecular markers. Moreover, NR -003542, ENST00000512051, and NR-026756 might have the potential to be indicators of disease activity.

Can the lung ultrasound score predict pulmonary complications after non-thoracic surgery in patients with blunt thoracic trauma: A single-center observational study

Study objectivePatients with blunt thoracic trauma have a high risk of developing postoperative pulmonary complications (PPCs). In this study, we aimed to identify the risk factors for PPCs after non-thoracic surgery in patients with blunt thoracic trauma and investigate the efficacy of perioperative lung ultrasound scores in predicting PPC occurrence. DesignProspective observational study. SettingA Chinese tertiary orthopedic hospital. ParticipantsThis observational study included 369 patients with blunt thoracic trauma who underwent surgery for pelvic and upper or lower extremity fractures. InterventionsLung ultrasonography was performed pre- and postoperatively. MeasurementsPatients were followed up for 1 week to assess PPCs and assigned to the PPC or non-PPC groups. We identified risk factors for PPCs using univariate and multivariate logistic regression analyses. The predictive value of these risk factors was evaluated using receiver operating characteristic (ROC) curves. Main resultsPPCs incidence was 36.58 %. The two groups differed significantly in Injury Severity Scores, American Society of Anesthesiologists (ASA) classification, chronic obstructive pulmonary disease, hydrothorax, preoperative hypoxemia, lung ultrasound score, preoperative hemoglobin level, intraoperative infusion volume, and Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) scores (P < 0.05). Independent risk factors for PPCs included ASA classification III, higher postoperative lung ultrasound scores, preoperative anemia, higher intraoperative infusion volume, and higher ARISCAT scores. ROC curve analysis revealed that postoperative lung ultrasound score (area under the curve [AUC]: 0.810, cutoff: 10), preoperative hemoglobin level (AUC: 0.627, cutoff: 97), intraoperative infusion volume (AUC: 0.701, cutoff: 886.51 ml/h) and ARISCAT score (AUC: 0.718, cutoff: 33) predicted postoperative outcomes effectively. ConclusionsPostoperative lung ultrasound scores reliably predicted pulmonary complications after non-thoracic surgery in patients with blunt thoracic trauma. ASA classification III, preoperative anemia, excessive intraoperative fluid infusion, higher ARISCAT, and postoperative lung ultrasound scores were significant risk factors associated with PPCs.Trial registration: Clinical Trial Number: 1900023408. Registered on May 26, 2019.

Bioinformatic Analysis of Neuropeptide Related Genes in Patients Diagnosed with Invasive Breast Carcinoma

PurposeNeuropeptide receptors are expressed in many malignancies. Effectors involved in the action mechanisms of HCRTR1, HCRTR2, NPY4R (PPYR1) may be related to breast cancer (BC). Genes encoding these receptors and PPY and PTPN11 genes were aimed to examine via bioinformatics tools in the BRCA cohort. To our knowledge, this is the first study in which these receptor genes and PP, which have not found much research in BC, are examined together with PTPN11 and analyzed comprehensively in large patient cohorts from public databases. MethodscBioPortal was used for gene alteration analyses, GeneMania for association analyses with other genes, Kaplan-Meier Plotter for Overall Survival (OS) and Relapse Free Survival (RFS) analyses, UALCAN for methylation analyses, TIMER2.0 for expression analyses, The Human Protein Atlas database for expression validations, TIMER for immune infiltration analyses, WEKA 3.8.6 for diagnostic classification performances of the genes based on Random Forest Classifier and Enrichr-KG for Gene Ontology (GO) Biological Process (BP) and KEGG analysis. Results19 (1.9%) nucleotide changes were found in 996 cases. Missense mutation is most common. Decreased expression levels of the HCRTR1 gene were associated with shorter OS and RFS, but decreased expression levels of the PTPN11 gene were associated with longer OS and RFS. Decreased expression levels NPY4R (PPYR1) gene were associated with shorter RFS. Increased expression levels of HCRTR2 and PPY genes were associated with longer RFS. HCRTR1 and NPY4R (PPYR1) genes were statistically hypermethylated; conversely HCRTR2 and PPY genes were hypomethylated. There was no significant change in PTPN11 gene promoter methylation level. HCRTR1, NPY4R (PPYR1) and PTPN11 gene expressions were downregulated; conversely, HCRTR2 and PPY gene expressions upregulated. Weak correlations were observed between NPY4R (PPYR1) gene expression and CD4+ T Cell, Neutrophil, Dendritic Cell and between PTPN11 gene expression and CD8+ T Cell, Macrophage, Neutrophil, Dendritic Cell infiltrations. Area under the receiver operating characteristics curve values of the 10-fold cross-validation and by splitting the dataset in a ratio of 80:20 models were 0.930 and 0.963 respectively. HCRTR2 and HCRTR1 belong to regulation of cytosolic calcium ion concentration, cellular calcium ion homeostasis GO BPs. ConclusionIn BC patients, increases in HCRTR2 and PPY gene expressions could be considered as positive prognostic factors. Decreases in HCRTR1 and NPY4R (PPYR1) gene expressions could be considered as negative prognostic factors. Decreased expression of PTPN11 gene may have a positive prognostic factor. Changes in existing genes are likely to be both a biomarker and therapeutic target for BC. However, experimental and clinical studies are needed to elucidate the mechanisms underlying these neuropeptide receptors in terms of breast carcinogenesis.

The value of differential diagnosis of post-reflux swallow-induced peristaltic wave index between esophageal diseases of gut-brain interaction and gastroesophageal reflux disease

Objective: To analyze the value of differential diagnosis of post-reflux swallow-induced peristaltic wave index (PSPWI) between esophageal diseases of gut-brain interaction (E-DGBI) and gastroesophageal reflux disease (GERD). Methods: The patients diagnosed as E-DGBI and GERD in the Department of Gastroenterology, Beijing Tsinghua Changguang Hospital from June 2016 to June 2018 were retrospectively included. The medical records, gastroscopy and 24-hour pH-intraluminal impedance monitoring reports were collected. According to clinical diagnosis, the patients were divided into E-DGBI group and GERD group. PSPWI, acid exposure time (AET), DeMeester score, total reflux times and mean nocturnal baseline impedance (MNBI) were compared between the both group. The receiver operating characteristic curve (ROC) of PSPWI, AET, total reflux times and MNBI was drawn to identify the sensitivity and specificity of E-DGBI and GERD. The cut-off values were calculated with Youden index and the area under the curve (AUC) was compared. Results: A total of 73 patients were included,including 36 males and 37 females, whose age [M (Q1, Q3)] was 54.0 (42.6, 63.0) years. There were 39 cases in E-DGBI group and 34 cases in GERD group. The 24-h AET [0.5 (0.1, 1.6) vs 6.0 (3.6, 11.9)], DeMeester score [2.8 (1.1, 5.7) vs 20.4 (14.7, 47.4) score] and total reflux times [32.0 (19.0, 53.0) vs 66.0 (34.3, 96.0) times] were significantly higher in E-DGBI group than those in GERD group (all P<0.001);MNBI in E-DGBI group was higher than that in GERD group[2 375.0 (1 726.0, 2 978.0)Ω vs 1 361.0 (814.0, 2 273.5)Ω, P<0.001]. The PSPWI in E-DGBI group is higher than that in GERD (0.56±0.19 vs 0.25±0.15, P<0.001). According to ROC, with PSPWI>0.46 as the cut-off value, the AUC was 0.895 (95%CI: 0.827-0.963) in differentiating E-DGBI and GERD. The sensitivity was 94.1% and the specificity was 69.2%. There were no significant differences of AUC between PSPWI and AET or total reflux events (all P>0.05). But the AUC of PSPWI was higher than that of MNBI (AUC=0.749,95%CI:0.632-0.866,P=0.031). Conclusion: PSPWI can better distinguish E-DGBI from GERD.

Comparative Analysis of ML Models with Selection Methods for Early Predictive Analytics of Sepsis in ICU

Objectives: This study aims to make early prediction of Sepsis using ML algorithms and provide comparative analysis of different feature selection techniques. Methods: In this study, the Physionet website dataset has been used. Sepsis categorization is done on the basis of Sepsis-3 criteria. The dataset used is highly imbalanced, with only 2% of the data belonging to Sepsis. The SmoteTomek technique is used to handle an imbalanced dataset. Various filter, embedded, and wrapper feature selection techniques, like tree-based feature selection technique, recursive feature elimination (RFE), information gain, Bhattacharya distance, lasso, etc., have been applied for the top-performing classification ML models. These selection techniques were applied to RandomForest, K Nearest Neighbors (KNN), and Decision Tree models. We compared the impact of these selection techniques on the aforesaid machine learning models. Findings: After applying the RFE technique, the Area Under the Receiver Operating Characteristic curve (AUROC) score of the RandomForest model has slightly increased from 0.996 to 0.9974. KNN model with a tree-based feature selection technique showed the highest sensitivity of 0.934. which is slightly higher than the sensitivity of 0.922, which was without applying any feature selection technique. Along with the AUROC score, the highest performance, in terms of specificity (0.9976), accuracy (0.9959), and f-measure score (0.9062), is achieved when RFE is applied to the RandomForest model. The best selection algorithm for decision trees and KNN is the tree-based selection technique. RFE is the best selection technique for RandomForest. Novelty: In this research, the AUROC score is slightly increased to 0.9974, which has not been achieved yet. Instead of 40, the number of features chosen is 20. This research also provides a comparison of different feature selection techniques like tree-based feature selection, information gain, Bhattacharya, and RFE. It also analyses their impact on the performance of models, which has not been done yet with the same set of selection techniques. Keywords: Sepsis, Machine Learning, Feature selection, Early prediction, Predictive Analytics

Corneal subepithelial nerve fibers in type 2 diabetes: potential biomarker of diabetic neuropathy

AIM: To observe the changes in corneal subepithelial nerve fibers (CNFs) and Langerhans cells (LCs) in patients with type 2 diabetes using corneal laser confocal microscopy (CLCM). METHODS: A total of 60 patients (64 eyes), including 40 patients with type 2 diabetes (DM group) and 20 subjects without diabetes (control group) were included with CLCM. Neuron J plugin of Image J software were used for quantitative analysis of CNF length (CNFL), CNF density (CNFD), corneal nerve branch fiber density (CNBD), main branch length density, branch length density, corneal nerve fiber tortuosity (NT) score, and LCs density. An independent samples t-test to analyze the variability between the two groups was performed, and Pearson correlation analysis was used to analyze the relationships between CNF and multiple biochemical indicators in the DM group. The predictive power of CNF for type 2 diabetes was assessed using the receiver operating characteristic (ROC) curve. RESULTS: There were significant differences in the CNFL, CNFD, and main branch length density between two groups. The results of Pearson correlation analysis showed a significant negative correlation between CNFD and the duration of diabetes as well as triglyceride levels and total cholesterol, and a significant positive correlation between CNFD and serum albumin. In addition, the NT score showed a positive correlation and urea nitrogen, similar to the positive correlation observed between LC density and glycosylated hemoglobin (HbA1c) levels. CNFD showed the highest area under the curve (AUC of ROC) value, followed by main branch length density and CNFL. The AUC of the ROC curve under the logistic regression model also demonstrated good predictive values. The cut-off values of CNFD, CNFL, and main branch length density for diabetes showed 31.25, 18.85, and 12.56, respectively. CONCLUSION: In patients with type 2 diabetes, there is a notable reduction in both CNFL and CNFD. These measurements can be influenced by various blood biochemical factors. However, the compromised nerve fibers can serve as valuable indicators for predicting the onset of type 2 diabetes and also as biomarkers for detecting diabetic neuropathy and its related complications.

Development and validation of a predictive risk tool for VTE in women with breast cancer under chemotherapy: a cohort study in China.

The incidence of venous thromboembolism (VTE) is significantly elevated in breast cancer patients, with a three-to-fourfold increase, and further escalates to sixfold in those undergoing chemotherapy. This study aims to identify the risk factors for VTE and develop a Nomogram risk prediction model distinct from the traditional Khorana score. Univariate Cox regression analysis assessed the impact of each variable on the occurrence of VTE, while stepwise multivariate Cox regression analysis identified independent predictors. Based on these results, we constructed a Nomogram model. The model's performance was validated using the C-index, receiver-operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Comparisons were made with the Khorana score to evaluate the practical application value. Out of the 903 patients, 108 (11.96%) developed VTE. Cox regression analysis identified that Stage, undergoing surgery, age, white blood cells (WBC), D-dimer, and carcinoembryonic antigen (CEA) were significant risk factors for VTE (P < 0.05). The Nomogram model's C-index was 0.77 (95% CI 0.72-0.83) in the training set and 0.76 (95% CI 0.69-0.84) in the validation set. The model demonstrated excellent predictive accuracy and generalizability on the receiver-operating characteristic (ROC) curves and calibration curves. Compared to the traditional Khorana score, the Nomogram model showed superior predictive accuracy and greater clinical benefit. This study established a VTE risk prediction model for breast cancer patients undergoing chemotherapy. The model is characterized by its intuitive and straightforward application, making it highly suitable for rapid VTE risk assessment in clinical practice.