[Regression modeling strategies]

The mediating effect of social connectedness between internet gaming disorder and somatic symptoms in adolescents: a large sample cross-sectional study

BACKGROUND

Previous studies have indicated that social connectedness can serve as a protective buffer against negative outcomes associated with online victimization. However, the role of social connectedness between Internet gaming disorder and somatic symptoms is still unclear. This study aims to examine the mediating effect of social connectedness on the association between Internet gaming disorder and somatic symptoms.

METHODS

A cross-sectional design was utilized, using questionnaires for data collection and multi-stage stratified cluster sampling. The general demographic questionnaire, Nine-Item Internet Gaming Disorder Scale-Short Form, Social Connectedness Scale-Revised and Patient Health Questionnaire Physical Symptoms were used to collect data. We adopted Pearson's correlation analysis and the PROCESS Macro Model in regression analysis to explore the relationships among Internet gaming disorder, social connectedness and somatic symptoms.

RESULTS

Internet gaming disorder was positively correlated with somatic symptoms (r = 0.20, P < 0.001), while network (r=-0.08, P < 0.001) and real-life social connectedness (r=-0.31, P < 0.001) negatively affected somatic symptoms. The network social connectedness and the real-life social connectedness played a chain mediating role in the development of Internet gaming disorder to somatic symptoms [95%CI: 0.073, 0.088], explaining 45.25% of the total effect value. The difference of real-life social connectedness and network social connectedness played a partial mediating role between Internet gaming disorder and somatic symptoms [95% CI:0.050, 0.062], accounting for 31.28% of the total effect value.

CONCLUSIONS

Real-life social connectedness, network social connectedness, and their disparity all mediated the relationship between Internet gaming disorder and somatic symptoms. Real-life social connectedness acted as a protective factor, while network social connectedness served as a risk factor. Encouraging offline activities and guiding teenagers to use the internet responsibly may help prevent and reduce physical symptoms linked to Internet gaming disorder.

The synergistic effects of anoikis-related genes and EMT-related genes in the prognostic prediction of Wilms tumor

Wilms tumor (WT) is the most common type of malignant abdominal tumor in children; it exhibits a high degree of malignancy, grow rapidly, and is prone to metastasis. This study aimed to construct a prognosis model based on anoikis-related genes (ARGs) and epithelial-mesenchymal transition (EMT)-related genes (ERGs) for WT patients; we assessed the characteristics of the tumor microenvironment and treatment efficacy, as well as identifying potential therapeutic targets. To this end, we downloaded transcriptome sequencing data and clinical data for WT and normal renal cortices and used R to construct and validate the prognostic model based on ARGs and ERGs. Additionally, we performed clinical feature analysis, nomogram construction, mutation analysis, drug sensitivity analysis, Connectivity Map (cMAP) analysis, functional enrichment analysis, and immune infiltration analysis. Finally, we screened the hub gene using the STRING database and validated it via experiments. In this way, we constructed a model with good accuracy and robustness, which was composed of seven anoikis- and EMT-related genes. Paclitaxel and mesna were selected as potential chemotherapeutic drugs and adjuvant chemotherapeutic drugs for the WT high-risk group by using the Genomics of Drug Sensitivity in Cancer (GDSC) and cMAP compound libraries, respectively. We proved the existence of a strong correlation between invasive immune cells and prognostic genes and risk scores. Next, we selected NTRK2 as the hub gene, and in vitro experiments confirmed that its inhibition can significantly inhibit the proliferation and migration of tumor cells and promote late apoptosis. In summary, we screened out the potential biomarkers and chemotherapeutic drugs that can improve the prognosis of patients with WT.

Unraveling the regulatory cell death pathways in gastric cancer: a multi-omics study

Gastric cancer (GC) is a prevalent form of cancer worldwide and has a high death rate, with less than 40% of patients surviving for 5 years. GC demonstrates a vital characteristic of evading regulatory cell death (RCD). However, the extent to which RCD patterns are clinically significant in GC has not been well investigated. The study created a regulatory cell death index (RCDI) signature by employing 101 machine-learning algorithms. These algorithms were based on the expression files of 1292 GC patients from 6 multicenter cohorts. RCDI is a reliable and robust determinant of the likelihood of surviving in general. Furthermore, the precision of RCDI surpasses that of the 20 signatures that have been previously disclosed. The presence of RCDI signature is closely linked to immunological characteristics, such as the infiltration of immune cells, the presence of immunotherapy markers, and the activation of immune-related functions. This suggests that there is a higher level of immune activity in cases with RCDI signature. Collectively, the use of RCDI has the potential to be a strong and encouraging method for enhancing the clinical results of individual individuals with GC.

Correlation between serum vitamin D level and acute invasive enteritis in children

BACKGROUND

Diarrhea is a leading cause of death in young children worldwide. Vitamin D deficiency impairs the body's ability to clear pathogens, reduces tight junction protein expression in intestinal epithelial cells, and enhances Th1-mediated intestinal inflammation. This study aimed to investigate the effects of serum vitamin D levels on acute invasive enteritis in children.

METHODS

This prospective cohort study included 82 children aged 1-3 years with clinically diagnosed acute invasive enteritis at Sichuan Maternal and Child Health Hospital from February 2021 to February 2022, alongside a control group of 80 healthy children. Fecal specimens were collected for routine tests and occult blood analysis, while blood samples were taken for routine tests, C-reactive protein, and 25-OHD levels. Comparative analyses were performed between groups, and multifactorial logistic regression was used to identify factors influencing invasive enteritis.

RESULTS

The study group showed significantly lower serum 25-OHD levels (27.95 ± 9.91 ng/mL) compared to controls (32.76 ± 10.23 ng/mL, p < .01). Among the study group, 19.5% (16/82) had levels <20 ng/mL, versus 12.5% (10/80) in controls. Regular vitamin D supplementation was lower in the study group (58.5% vs. 77.5%, p < .05). Outdoor activity duration was also reduced (2.57 ± 0.98 h vs. 3.04 ± 0.88 h, p < .01). Multivariate analysis identified that exclusive breastfeeding, greater outdoor activity time and regular vitamin D supplementation were all associated with reduced risk of invasive enteritis (p < .05).

CONCLUSION

The findings indicate an association between low serum 25-OHD levels and acute invasive enteritis in children aged 1-3 years, suggesting that consistent vitamin D supplementation and sufficient outdoor activity may protect against this condition.

Construction of an immune-related gene prognostic model for obese endometrial cancer patients based on bioinformatics analysis

Background

The tumor microenvironment (TME) affected the prognosis of tumors. However, its effect on the outcomes of obese endometrial cancer (EC) patients had not been reported.

Methods

This research performed a retrospective analysis of the transcriptome profiles and medical data of 503 EC patients. Immune scores were assessed by estimation algorithms. Cox and LASSO regression analyses were utilized to pinpoint key genes linked to prognosis, and the RPS was created to forecast the outcomes of obese EC patients. The relationship among genetic mutations and RPS was examined using CNV and somatic mutation information. ssGSEA and GSVA were employed to detect immune infiltration and immune pathway enrichment associated with key genes. The TIDE algorithm and GDSC database were utilized to forecast patients' responses of patients to immunotherapy and chemotherapy, respectively. Finally, we employed the 'rms' R software package to construct the nomogram.

Results

The prognosis of obese EC patients was associated with immune scores. Three key genes (EYA4, MBOAT2 and SCGB2A1) were identified. The risk prognosis score (RPS) for obese EC patients was established by risk stratification and prognostic prediction using prognostic genes. The higher the RPS, the worse the prognosis, and the more malignant the genomic alterations. The high RPS group had a significantly reduced proportion of most immune cells in comparison to the low RPS group. The high RPS group was linked to G2M, MYC and E2F related pathways such as cell proliferation, cell cycle and cell death. Cisplatin, tamoxifen and topotecan had a greater effect on the low RPS group. Notably, the nomogram had a good predictive ability.

Conclusion

Our study designed a reliable RPS for obese EC patients to forecast their prognosis, immune aggressiveness, and responses to immunotherapy and drug treatments.

Optimizing first-line TKI treatment efficacy in PD-L1-positive EGFR-mutated NSCLC: the impact of antiangiogenic agents

Background

In individuals receiving treatment with epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs), those exhibiting positive PD-L1 expression might experience reduced progression-free survival (PFS). However, the effects on overall survival (OS) and the determination of efficacious treatment approaches are still not well-defined.

Methods

In our retrospective study, we examined data from 193 NSCLC patients with advanced EGFR mutations who received first-line TKI treatments, treated at two centers of Shaw Hospital in Zhejiang, China. This analysis covered a period from 1 January 2016 to 30 April 2023.

Results

Patients with PD-L1 positivity exhibited a markedly shorter average PFS (9.5 months versus 17.8 months, P < 0.001) and OS (44.4 months versus 65.7 months, P = 0.016) relative to those without PD-L1 expression. This difference in both PFS and OS remained statistically significant even after adjusting for multiple factors (P < 0.001 for PFS and P = 0.028 for OS). In the PD-L1-positive cohort, introducing combination antiangiogenic significantly extended both PFS (from 9.1 to 25.7 months, P = 0.026) and OS (from 42 to 53.5 months, P = 0.03). Post-first-line TKI therapy, 39.3% of PD-L1-positive patients and 54.5% of PD-L1-negative patients developed the T790M mutation (P = 0.212), with no notable difference in PFS from second-line TKI treatments between the groups. Additionally, subsequent combination therapy with immunotherapy markedly prolonged OS in the PD-L1-positive group. However, for PD-L1-negative patients, neither combination antiangiogenic therapy nor later-line immunotherapy demonstrated significant benefits in PFS or OS.

Conclusion

For PD-L1-positive patients, combined antiangiogenic treatments and immunotherapy can significantly improve survival outcomes. In contrast, PD-L1-negative patients show less benefit from these therapies, highlighting the greater efficacy of these treatments in PD-L1-positive individuals.

Binge drinking at time of bariatric surgery is associated with liver disease, suicides, and increases long-term mortality

BACKGROUND AND AIMS

Alcohol use disorder has been reported in patients undergoing bariatric procedures, but the pattern of alcohol consumption has not been evaluated. We investigated the prevalence, risk factors, and impact of binge drinking (BD) at the time of surgery and during follow-up.

METHODS

A prospective, longitudinal study of subjects undergoing bariatric surgery was included in the LABS-2 registry between 2006 and 2009. Participants with AUDIT questionnaire at the time of surgery and a minimum of 12 months follow-up were included. BD was defined as consuming ≥5 drinks on at least 1 occasion in the previous month. Liver biopsies were obtained during bariatric procedures in not all cases. Survival analysis was performed with the adjusted Cox regression model and competing risk.

RESULTS

A total of 2257 subjects were included, with a median follow-up of 79 months. The prevalence of BD at time of surgery was 12%, and it raised up to 23% during follow-up. Patients with BD predominantly had a binge eating disorder (OR=1.35 [95% CI: 1.04-1.76]), regularly consumed fast food [OR=1.4 (95% CI: 1.07-1.85)] and used other drugs (OR=2.65 [95% CI: 1.74-4.04]). Within liver biopsies evaluation, BD showed higher hepatic iron deposits (OR=3.00 [95% CI: 1.25-7.21]). BD at the time of surgery was associated with a higher risk of BD during follow-up (OR=10.49 [95% CI: 7.86-14.00]) and long-term mortality (HR: 3.21 [95% CI: 1.67-6.18]). Specific causes of death in these patients with BD were liver disease (p=0.020), suicide (p=0.015), neoplasms (p=0.034), and respiratory (p=0.025).

CONCLUSIONS

The prevalence of BD in patients undergoing bariatric surgery is high and increases the risk of postoperative liver disease, suicides, and long-term mortality.

Cross-Combination Analyses of Random Forest Feature Selection and Decision Tree Model for Predicting Intraoperative Hypothermia in Total Joint Arthroplasty

BACKGROUND

In total joint arthroplasty patients, intraoperative hypothermia (IOH) is associated with perioperative complications and an increased economic burden. Previous models have some limitations and mainly focus on regression modeling. Random forest (RF) algorithms and decision tree modeling are effective for eliminating irrelevant features and making predictions that aid in accelerating modeling and reducing application difficulty.

METHODS

We conducted this prospective observational study using convenience sampling and collected data from 327 total joint arthroplasty patients in a tertiary hospital from March 4, 2023, to September 11, 2023. Of those, 229 patients were assigned to the training and 98 to the testing sets. The Chi-square, Mann-Whitney U, and t-tests were used for baseline analyses. The feature variables selection used the RF algorithms, and the decision tree model was trained on 299 examples and validated on 98. The sensitivity, specificity, recall, F1 score, and area under the curve were used to test the model's performance.

RESULTS

The RF algorithms identified the preheating time, the volume of flushing fluids, the intraoperative infusion volume, the anesthesia time, the surgical time, and the core temperature after intubation as risk factors for IOH. The decision tree was grown to 5 levels with 9 terminal nodes. The overall incidence of IOH was 42.13%. The sensitivity, specificity, recall, F1 score, and area under the curve were 0.651, 0.907, 0.916, 0.761, and 0.810, respectively. The model indicated strong internal consistency and predictive ability.

CONCLUSIONS

The preheating time, the volume of flushing fluids, the intraoperative infusion volume, the anesthesia time, the surgical time, and the core temperature after intubation could accurately predict IOH in total joint arthroplasty patients. By monitoring these factors, the clinical staff could achieve early detection and intervention of IOH in total joint arthroplasty patients.

Comparative assessment of the capability of machine learning-based radiomic models for predicting omental metastasis in locally advanced gastric cancer

The study aims to investigate the predictive capability of machine learning algorithms for omental metastasis in locally advanced gastric cancer (LAGC) and to compare the performance metrics of various machine learning predictive models. A retrospective collection of 478 pathologically confirmed LAGC patients was undertaken, encompassing both clinical features and arterial phase computed tomography images. Radiomic features were extracted using 3D Slicer software. Clinical and radiomic features were further filtered through lasso regression. Selected clinical and radiomic features were used to construct omental metastasis predictive models using support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), and logistic regression (LR). The models' performance metrics included accuracy, area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In the training cohort, the RF predictive model surpassed LR, SVM, DT, and KNN in terms of accuracy, AUC, sensitivity, specificity, PPV, and NPV. Compared to the other four predictive models, the RF model significantly improved PPV. In the test cohort, all five machine learning predictive models exhibited lower PPVs. The DT model demonstrated the most significant variation in performance metrics relative to the other models, with a sensitivity of 0.231 and specificity of 0.990. The LR-based predictive model had the lowest PPV at 0.210, compared to the other four models. In the external validation cohort, the performance metrics of the predictive models were generally consistent with those in the test cohort. The LR-based model for predicting omental metastasis exhibited a lower PPV. Among the machine learning algorithms, the RF predictive model demonstrated higher accuracy and improved PPV relative to LR, SVM, KNN, and DT models.

A novel nomogram model for lung adenocarcinoma subtypes based on RNA-modification regulatory genes

Background

In non-small cell lung cancer (NSCLC), lung adenocarcinoma (LUAD) is the most common subtype. RNA modification has become the frontier and hotspot of current tumor research.

Results

In this study, 109 genes that regulate RNA modifications were identified according to The Cancer Genome Atlas (TCGA). A differential gene expression analysis identified 46 differentially expressed RNA modification regulatory genes (DERRGs). LUAD samples were stratified into two distinct clusters based on the expression of these DERRGs. A significant correlation was observed between these clusters and patient survival rates, as well as clinical features. Furthermore, a four-DERRG signature (EIF3B, HNRNPC, IGF2BP1, and METTL3) developed using LASSO regression. According to the calculated risk scores from this signature, LUAD patients were categorized into high-risk and low-risk groups. Patients in the low-risk group exhibited a more favorable prognosis. A prognostic nomogram was crafted, integrating the four-DERRGs signature with clinical parameters. The nomogram was revealed that OS, age, clinical stage, immune cell infiltration, and immune checkpoint molecule expression were significantly linked to the OS of LUAD. GSEA analysis found that the DERRGs were primarily regulated immune pathways.

Conclusions

This study developed four DERRGs signatures and formulated a nomogram model for precise prognosis estimation in LUAD patients. The study's insights are instrumental for advancing diagnosis, prognosis, and therapeutic strategies for LUAD.

Association between oxidative balance score, systemic inflammatory response index, and cardiovascular disease risk: a cross-sectional analysis based on NHANES 2007-2018 data

Background

There is limited research on the relationship between Systemic Oxidative Stress (SOS) status and inflammatory indices. Adding onto existing literature, this study aimed to examine the association between dietary Oxidative Balance Score (OBS) and lifestyle OBS (which make up the overall OBS), and Cardiovascular Disease (CVD) prevalence at different Systemic Immune Inflammation Index (SII) and Systemic Inflammatory Response Index (SIRI) levels.

Methods

This study involved 9,451 subjects selected from the National Health and Nutrition Examination Survey (NHANES) 2007-2018. The OBS comprised 20 dietary and lifestyle factors. Statistical methods included Weighted Linear Regression Analysis (WLRA), Logistic Regression Analysis (LRA), Sensitivity Analysis (SA), and Restricted Cubic Spline (RCS) analysis.

Results

The multivariate WLRA revealed that OBS was significantly negatively correlated with both SII (β = -5.36, p < 0.001) and SIRI (β = -0.013, p < 0.001) levels. In SA, removing any single OBS component had no significant effect on the WLRA results of SII and SIRI. Further subgroup analyses revealed that OBS was more impactful in lowering SII in women than in men. Additionally, OBS was more significantly negatively correlated with SII and SIRI in the low-age group than in the high-age group. Moreover, RCS analysis confirmed this linear relationship. Compared to dietary OBS, lifestyle OBS exerted a more significant effect on Coronary Artery Disease (CAD) (OR: 0.794, p = 0.002), hypertension (OR: 0.738, p < 0.001), Congestive Heart Failure (CHF) (OR: 0.736, p = 0.005), Myocardial Infarction (MI) (OR: 0.785, p = 0.002), and stroke (OR: 0.807, p = 0.029) prevalence. Furthermore, SIRI exhibited a significant interaction in the relationship between overall OBS, dietary OBS, and CHF (P for interaction < 0.001). On the other hand, SII had a significant interaction in the relationship between overall OBS, dietary OBS, and MI (P for interaction < 0.05).

Conclusion

OBS, including lifestyle and dietary OBS, were significantly negatively associated with SII and SIRI. Higher lifestyle OBS was associated with reduced risks of CAD, hypertension, CHF, MI, and stroke.

Machine learning-based integration develops an immunogenic cell death-derived lncRNA signature for predicting prognosis and immunotherapy response in lung adenocarcinoma

Accumulating evidence demonstrates that lncRNAs are involved in the regulation of the immune microenvironment and early tumor development. Immunogenic cell death occurs mainly through the release or increase of tumor-associated antigen and tumor-specific antigen, exposing "danger signals" to stimulate the body's immune response. Given the recent development of immunotherapy in lung adenocarcinoma, we explored the role of tumor immunogenic cell death-related lncRNAs in lung adenocarcinoma for prognosis and immunotherapy benefit, which has never been uncovered yet. Based on the lung adenocarcinoma cohorts from the TCGA database and GEO database, the study developed the immunogenic cell death index signature by several machine learning algorithms and then validated the signature for prognosis and immunotherapy benefit of lung adenocarcinoma patients, which had a more stable performance compared with published signatures in predicting the prognosis, and demonstrated predictive value for benefiting from immunotherapy in multiple cohorts of multiple cancers, and also guided the utilization of chemotherapy drugs.

Deciphering the heterogeneity dominated by tumor-associated macrophages for survival prognostication and prediction of immunotherapy response in lung adenocarcinoma

Tumor-associated macrophages (TAMs) are a specific subset of macrophages that reside inside the tumor microenvironment. The dynamic interplay between TAMs and tumor cells plays a crucial role in the treatment response and prognosis of lung adenocarcinoma (LUAD). The study aimed to examine the association between TAMs and LUAD to advance the development of targeted strategies and immunotherapeutic approaches for treating this type of lung cancer. The study employed single-cell mRNA sequencing data to characterize the immune cell composition of LUAD and delineate distinct subpopulations of TAMs. The "BayesPrism" and "Seurat" R packages were employed to examine the association between these subgroups and immunotherapy and clinical features to identify novel immunotherapy biomarkers. Furthermore, a predictive signature was generated to forecast patient prognosis by examining the gene expression profile of immunotherapy-associated TAMs subsets and using 104 machine-learning techniques. A comprehensive investigation has shown the existence of a hitherto unidentified subgroup of TAMs known as RGS1 + TAMs, which has been found to have a strong correlation with the efficacy of immunotherapy and the occurrence of tumor metastasis in LUAD patients. CD83 was identified CD83 as a distinct biomarker for the expression of RGS1 + TAMs, showcasing its potential utility as an indicator for immunotherapeutic interventions. Furthermore, the prognostic capacity of the RTMscore signature, encompassing three specific mRNA (NR4A2, MMP14, and NPC2), demonstrated enhanced robustness when contrasted against the comprehensive collection of 104 features outlined in the published study. CD83 has potential as an immunotherapeutic biomarker. Meanwhile, The RTMscore signature established in the present study might be beneficial for survival prognostication.

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration.

Methods

We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis.

Results

Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis.

Conclusion

RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.

Identifying a survival-associated cell type based on multi-level transcriptome analysis in idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a progressive disease with a five-year survival rate of less than 40%. There is significant variability in survival time among IPF patients, but the underlying mechanisms for this are not clear yet.

METHODS AND RESULTS

We collected single-cell RNA sequence data of 13,223 epithelial cells taken from 32 IPF patients and bulk RNA sequence data from 456 IPF patients in GEO. Based on unsupervised clustering analysis at the single-cell level and deconvolution algorithm at bulk RNA sequence data, we discovered a special alveolar type 2 cell subtype characterized by high expression of CCL20 (referred to as ATII-CCL20), and found that IPF patients with a higher proportion of ATII-CCL20 had worse prognoses. Furthermore, we uncovered the upregulation of immune cell infiltration and metabolic functions in IPF patients with a higher proportion of ATII-CCL20. Finally, the comprehensive decision tree and nomogram were constructed to optimize the risk stratification of IPF patients and provide a reference for accurate prognosis evaluation.

CONCLUSIONS

Our study by integrating single-cell and bulk RNA sequence data from IPF patients identified a special subtype of ATII cells, ATII-CCL20, which was found to be a risk cell subtype associated with poor prognosis in IPF patients. More importantly, the ATII-CCL20 cell subtype was linked with metabolic functions and immune infiltration.

Environmental factors affecting the risk of generalization for ocular-onset myasthenia gravis: a nationwide cohort study

BACKGROUND

The environmental effects on the prognosis of ocular myasthenia gravis (OMG) remain largely unexplored.

AIM

To investigate the association between specific environmental factors and the generalization of OMG.

DESIGN

The cohort study was conducted in China based on a nationwide multicenter database.

METHODS

Adult patients with OMG at onset, who were followed up for at least 2 years until May 2022, were included. We collected data on demographic and clinical factors, as well as environmental factors, including latitude, socioeconomic status (per capita disposable income [PDI] at provincial level and education) and smoking. The study outcome was the time to the development of generalized myasthenia gravis (GMG). Cox models were employed to examine the association between environmental exposures and generalization. Restricted cubic spline was used to model the association of latitude with generalization risk.

RESULTS

A total of 1396 participants were included. During a median follow-up of 5.15 (interquartile range [IQR] 3.37-9.03) years, 735 patients developed GMG within a median of 5.69 (IQR 1.10-15.66) years. Latitude of 20-50°N showed a U-shaped relation with generalization risk, with the lowest risk at around 30°N; both higher and lower latitudes were associated with the increased risk (P for non-linearity <0.001). Living in areas with lower PDI had 1.28-2.11 times higher risk of generalization. No significant association was observed with education or smoking.

CONCLUSIONS

Latitude and provincial-level PDI were associated with the generalization of OMG in China. Further studies are warranted to validate our findings and investigate their potential applications in clinical practice and health policy.

Diagnostic value of sphingolipid metabolism-related genes CD37 and CXCL9 in nonalcoholic fatty liver disease

The development of nonalcoholic fatty liver disease (NAFLD) has been reported to be caused by sphingolipid family inducing insulin resistance, mitochondrial dysfunction, and inflammation, which can be regulated by multiple sphingolipid metabolic pathways. This study aimed to explore the molecular mechanism of crucial sphingolipid metabolism related genes (SMRGs) in NAFLD. Firstly, the datasets (GSE48452, GSE126848, and GSE63067) from the Gene Expression Omnibus database and sphingolipid metabolism genes (SMGs) from previous research were collected for this study. The differentially expressed genes (DEGs) between different NAFLD and controls were acquired through "limma," and the SMRGs were authenticated via weighted gene co-expression network analysis (WGCNA). After overlapping the DEGs and SMRGs, the causality between the intersection genes (DE-SMRGs) and NAFLD was explored to sort out the candidate biomarkers by Mendelian randomization (MR) study. The receiver operating characteristic (ROC) curves of candidate biomarkers in GSE48452 and GSE126848 were yielded to determine the biomarkers, followed by the nomogram construction and enrichment analysis. Finally, the immune infiltration analysis, the prediction of transcription factors (TFs) and drugs targeting biomarkers were put into effect. A total of 23 DE-SMRGs were acquired based on the differential analysis and weighted gene co-expression network analysis (WGCNA), of which 3 DE-SMRGs (CD37, CXCL9 and IL7R) were picked out for follow-up analysis through univariate and multivariate MR analysis. The values of area under ROC curve of CD37 and CXCL9 were >0.7 in GSE48452 and GSE126848, thereby being regarded as biomarkers, which were mainly enriched in amino acid metabolism. With respect to the Spearman analysis between immune cells and biomarkers, CD37 and CXCL9 were significantly positively associated with M1 macrophages (P < .001), whose proportion was observably higher in NAFLD patients compared with controls. At last, TFs (ZNF460 and ZNF384) of CD37 and CXCL9 and a total of 79 chemical drugs targeting CD37 and CXCL9 were predicted. This study mined the pivotal SMRGs, CD37 and CXCL9, and systematically explored the mechanism of action of both biomarkers based on the public databases, which could tender a fresh reference for the clinical diagnosis and therapy of NAFLD.

Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea Severity in Children

Purpose

The clinical presentation of Obstructive Sleep Apnea (OSA) in children is insidious and harmful. Early identification of children with OSA, particularly those at a higher risk for severe symptoms, is essential for making informed clinical decisions and improving long-term outcomes. Therefore, we developed and validated a risk prediction model for severity in Chinese children with OSA to effectively identify children with moderate-to-severe OSA in a clinical setting.

Patients and Methods

From June 2023 to September 2023, we retrospectively analyzed the medical records of 367 Children diagnosed with OSA through portable bedside polysomnography (PSG). Predictor variables were screened using the least absolute shrinkage and selection operator (LASSO) and logistic regression techniques to construct nomogram to predict the severity of OSA. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to determine the discrimination, calibration, and clinical usefulness of the nomogram.

Results

A total of 367 children with a median age of 84 months were included in this study. Neck circumference, ANB, gender, learning problem, and level of obstruction were identified as independent risk factors for moderate-severe OSA. The consistency indices of the nomogram in the training and validation cohorts were 0.841 and 0.75, respectively. The nomogram demonstrated a strong concordance between the predicted probabilities and the observed probabilities for children diagnosed with moderate-severe OSA. With threshold probabilities ranging from 0.1 to 1.0, the predictive model demonstrated strong predictive efficacy and yielded improved net benefit for clinical decision-making. ROC analysis was employed to classify the children into high and low-risk groups, utilizing the Optimal Cutoff value of 0.39.

Conclusion

A predictive model using LASSO regression was developed and validated for children with varying levels of OSA. This model identifies children at risk of developing OSA at an early stage.

Establishment and external validation of prognosis prediction nomogram for patients with distant metastatic intrahepatic cholangiocarcinoma: based on a large population

BACKGROUND

Most patients with intrahepatic cholangiocarcinoma (ICC) have developed distant metastasis at the time of diagnosis, while there is rear related nomogram to predict the prognosis.

METHODS

Clinical data of patients pathologically diagnosed of ICC with distant metastasis were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database during 2005 to 2019. Finally, patients diagnosed as ICC in the Second Affiliated Hospital of Nanchang University from 2014 to 2019 were collected for external verification. All data were divided into training cohort and validation cohort in a ratio of 7:3. The nomogram was established based on independent prognostic factors using Cox univariate and multivariate analyses. The area under the receiver operating characteristic (ROC) curves (AUC), the calibration curve and the decision curve analysis (DCA) were used to determine the prediction accuracy of the nomogram.

RESULTS

This study finally included 572 ICC with distant metastasis patients, another 32 patients collected by the author's hospital were used as external verification. Results showed that age, surgery, radiotherapy and chemotherapy were independent prognostic factors, and nomogram was established. The AUC of predicting 3, 6, 9-month overall survival were 0.866, 0.841 and 0.786. The ROC curves and calibration curves showed that the nomogram had good predictive accuracy, and DCA showed that the nomogram had good clinical applicability.

CONCLUSIONS

The nomogram has good accuracy in predicting prognosis of DM-ICC patients, which would be of good significance to improve the prognosis of these patients.

Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma

OBJECTIVE

To screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it.

METHODS

Clinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group.

RESULTS

The patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model.

CONCLUSION

The study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate.

Association of elastic power in mechanical ventilation with the severity of acute respiratory distress syndrome: a retrospective study

BACKGROUND

Mechanical power (MP) is the total energy released into the entire respiratory system per minute which mainly comprises three components: elastic static power, Elastic dynamic power and resistive power. However, the energy to overcome resistance to the gas flow is not the key factor in causing lung injury, but the elastic power (EP) which generates the baseline stretch of the lung fibers and overcomes respiratory system elastance may be closely related to the ARDS severity. Thus, this study aimed to investigate whether EP is superior to other ventilator variables for predicting the severity of lung injury in ARDS patients.

METHODS

We retrieved patient data from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The retrieved data involved adults (≥ 18 years) diagnosed with ARDS and subjected to invasive mechanical ventilation for ≥ 48 h. We employed univariate and multivariate logistic regression analyses to investigate the correlation between EP and development of moderate-severe ARDS. Furthermore, we utilized restricted cubic spline models to assess whether there is a linear association between EP and incidence of moderate-severe ARDS. In addition, we employed a stratified linear regression model and likelihood ratio test in subgroups to identify potential modifications and interactions.

RESULTS

Moderate-severe ARDS occurred in 73.4% (296/403) of the patients analyzed. EP and MP were significantly associated with moderate-severe ARDS (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.15-1.28, p < 0.001; and OR 1.15, 95%CI 1.11-1.20, p < 0.001; respectively), but EP showed a higher area-under-curve (95%CI 0.72-0.82, p < 0.001) than plateau pressure, driving pressure, and static lung compliance in predicting ARDS severity. The optimal cutoff value for EP was 14.6 J/min with a sensitivity of 75% and specificity of 66%. Quartile analysis revealed that the relationship between EP and ARDS severity remained robust and reliable in subgroup analysis.

CONCLUSION

EP is a good ventilator variable associated with ARDS severity and can be used for grading ARDS severity. Close monitoring of EP is advised in patients undergoing mechanical ventilation. Additional experimental trials are needed to investigate whether adjusting ventilator variables according to EP can yield significant improvements in clinical outcomes.

Clinical characteristics, treatment, and blood pressure control in patients with hypertension seen by primary care physicians in Spain: the IBERICAN study

Objectives

To determine the clinical profile, according to the history of hypertension, the risk of developing hypertension, current antihypertensive treatment and BP control rates in patients with hypertension from the IBERICAN cohort.

Methods

IBERICAN is an ongoing prospective cohort study, whose primary objective is to determine the frequency, incidence, and distribution of CVRF in the adult Spanish population seen in primary care settings. This analysis shows the baseline clinical characteristics of patients with hypertension. Adequate BP control was defined as BP <140/90 mmHg according to 2013 ESH/ESC guidelines.

Results

A total of 8,066 patients were consecutively included, of whom 3,860 (48.0%) had hypertension. These patients were older (65.8 ± 10.9 vs. 51.6 ± 14.7 years; p < 0.001), had more cardiovascular risk factors, target organ damage and cardiovascular disease (CVD) in comparison with those without hypertension. The risk of hypertension increased with the presence of associated CV risk factors and comorbidities, particularly diabetes, obesity and the metabolic syndrome, and decreased with the intensity of physical activity. Regarding antihypertensive treatments, 6.1% of patients did not take any medication, 38.8% were taking one antihypertensive drug, 35.5% two drugs, and 19.6% three or more antihypertensive drugs. Overall, 58.3% achieved BP goals <140/90 mmHg. A greater probability of BP control was observed with increasing age of patients and the greater number of antihypertensive drugs. Blood pressure control was lower in hypertensive patients with diabetes, obesity, the metabolic syndrome, increased urinary albumin excretion, higher pulse pressure, and lack of antihypertensive treatment.

Conclusions

About half of patients attended in primary care settings have hypertension in Spain. Patients with hypertension have a worse CV clinical profile than non-hypertensive patients, with greater association of CVRF and CVD. Around four out of ten patients do not achieve the recommended BP goals, and higher use of combination therapies is associated with a better BP control.

Identification of Immune-Related Genes as Biomarkers for Uremia

Purpose

Uremia, which is characterized by immunodeficiency, is associated with the deterioration of kidney function. Immune-related genes (IRGs) are crucial for uremia progression.

Methods

The co-expression network was constructed to identify key modular genes associated with uremia. IRGs were intersected with differentially expressed genes (DEGs) between uremia and control groups and key modular genes to obtain differentially expressed IRGs (DEIRGs). DEIRGs were subjected to functional enrichment analysis. The protein-protein interaction (PPI) network was constructed. The candidate genes were identified using the cytoHubba tool. The biomarkers were identified using various machine learning algorithms. The diagnostic value of the biomarkers was evaluated using receiver operating characteristic (ROC) analysis. The immune infiltration analysis was implemented. The biological pathways of biomarkers were identified using gene set enrichment analysis and ingenuity pathway analysis. The mRNA expression of biomarkers was validated using blood samples of patients with uremia and healthy subjects with quantitative real-time polymerase chain reaction (qRT-PCR).

Results

In total, four biomarkers (PDCD1, NGF, PDGFRB, and ZAP70) were identified by machine learning methods. ROC analysis demonstrated that the area under the curve values of individual biomarkers were > 0.9, indicating good diagnostic power. The nomogram model of biomarkers exhibited good predictive power. The proportions of six immune cells significantly varied between the uremia and control groups. ZAP70 expression was positively correlated with the proportions of resting natural killer (NK) cells, naïve B cells, and regulatory T cells. Functional enrichment analysis revealed that the biomarkers were mainly associated with translational function and neuroactive ligand-receptor interaction. ZAP70 regulated NK cell signaling. The PDCD1 and NGF expression levels determined using qRT-PCR were consistent with those determined using bioinformatics analysis.

Conclusion

PDCD1, NGF, PDGFRB, and ZAP70 were identified as biomarkers for uremia, providing a theoretical foundation for uremia diagnosis.

Insulin-like growth factor binding protein 2: a core biomarker of left ventricular dysfunction in dilated cardiomyopathy

BACKGROUND

RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined.

METHODS

In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis.

RESULTS

A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10-5). Single-cell analysis revealed that this gene was mainly expressed in endothelial cells.

CONCLUSION

In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.

Prehospital stratification and prioritisation of non-ST-segment elevation acute coronary syndrome patients (NSTEACS): the MARIACHI scale

OBJECTIVE

The objective of this study was to develop and validate a risk scale (MARIACHI) for patients classified as non-ST-segment elevation acute coronary syndrome (NSTEACS) in a prehospital setting with the ability to identify patients at an increased risk of mortality at an early stage.

METHODS

A retrospective observational study conducted in Catalonia over two periods: 2015-2017 (development and internal validation cohort) and Aug 2018-Jan 2019 (external validation cohort). We included patients classified as prehospital NSTEACS, assisted by an advanced life support unit and requiring hospital admission. The primary outcome was in-hospital mortality. Cohorts were compared using logistic regression and a predictive model was created using bootstrapping techniques.

RESULTS

The development and internal validation cohort included 519 patients. The model is composed of five variables associated with hospital mortality: age, systolic blood pressure, heart rate > 95 bpm, Killip-Kimball III-IV and ST depression ≥ 0.5 mm. The model showed good overall performance (Brier = 0.043) and consistency in discrimination (AUC 0.88, 95% CI 0.83-0.92) and calibration (slope = 0.91; 95% CI 0.89-0.93). We included 1316 patients for the external validation sample. There was no difference in discrimination (AUC 0.83, 95% CI 0.78-0.87; DeLong Test p = 0.071), but there was in calibration (p < 0.001), so it was recalibrated. The finally model obtained was stratified and scored into three groups according to the predicted risk of patient in-hospital mortality: low risk: < 1% (-8 to 0 points), moderate risk: 1-5% (+ 1 to + 5 points) and high risk: > 5% (6-12 points).

CONCLUSION

The MARIACHI scale showed correct discrimination and calibration to predict high-risk NSTEACS. Identification of high-risk patients may help with treatment and low referral decisions at the prehospital level.

Depressive and Anxious Symptoms Increase with Problematic Technologies Use Among Adults: The Effects of Personal Factors Related to Health Behavior

Background

Depression and anxiety disorders are a significant and growing health problem that has a significant impact on psychosocial functioning and quality of life. The onset and severity of mental health problems have been related to various biological, psychosocial, and behavioral variables.

Purpose

The purpose of this study was to explore the association among the severity of depression and anxiety, problematic information and communications technology (ICT) use, and some related personal factors with health behavior among adults. It also analyzes the moderating role of personal factors in the relationship between the problematic use of ICT and anxiety and depression.

Patients and Methods

Descriptive, bivariate, multivariate and moderation analyzes of data from 391 participants of 35-74 years old in primary health care centers located in Aragón (Spain) were performed between July 2021 and July 2022. The primary outcome was the severity of depressive and anxious symptoms as continuous variable.

Results

Low sense of coherence (β = -0.058; p = 0.043), low self-esteem (β = -0.171; p=0.002), and low self-efficacy (β = -0.122; p= 0.001), are predictors of having more severe depressive symptoms. Furthermore, low self-esteem (β = -0.120; p= 0.012), low self-efficacy (β = -0.092; p=0.004), and high problematic use of ICT (β = 0.169; p = 0.001), are predictors of having more severe anxiety symptoms. Moderation analyzes were significant in the effect of self-efficacy (b = -0.040, p=0.001) and resilience (b = -0.024, p=0.033) on the relationship between problematic ICT use and anxiety.

Conclusion

The problematic use of ICT and personal factors are related to depressive and anxiety symptoms. The interrelationship between problematic ICT use, personal factors, and depression needs to be further explored.

Covid-19: did higher trust societies fare better?

Social trust-between governments and people and between individuals-and trust in science were proposed as prerequisites for tackling covid. Others suggested less democratic societies were more able to impose strict rules stopping the virus. These propositions were tested for a group of mainly advanced countries. The dependent variable is cumulated deaths from covid. Findings are broken down between (a) OECD member countries, (b) these and countries having cooperation agreements with it, and (c) all these plus China. They are also broken down by time-between (a) the period before the appearance of "new variants" in late 2020 and (b) the period from then until end September 2021. The best, most parsimonious, models explain nearly half of the changes in the level of deaths. Trust in government improves outcomes, as does interpersonal trust. Vaccine antipathy does not play a role. Also, there is little indication that authoritarian regimes performed better than higher trust societies. In the first period, increasing wealth inequality-indicating a more divided society-is related to higher death rates. Hospital bed availability is important then, but not thereafter. Furthermore, as the pandemic persisted, the importance of pre-existing levels of social trust declined. The paper warns that institutions and cultures cannot easily be transferred from one country to another. Nor would all transfers be desired. It also suggests that some other lessons of what contributed to better outcomes under covid might be relevant for the monkeypox virus-its successor public health emergency.

Clinical features and factors related to lifetime suicidal ideation and suicide attempts in patients who have had substance-induced psychosis across their lifetime

Suicidal behaviors are common among patients with substance use disorders (SUD). However, the prevalence and clinical factors related to suicide behaviors among patients who have had substance-induced psychosis (SIP) is unknown. This study aims to explore the prevalence, clinical features, and factors related to lifetime suicidal ideation (SI) and suicide attempts (SA) in patients who have had SIP across their lifetime. A cross-sectional study was conducted in an outpatient center for addiction treatment between 01/01/2010 and 12/31/2021. 601 patients were evaluated with validated scales and questionnaires (79.03% males; M age 38.11±10.11 years). The prevalence of SI and SA was 55.4% and 33.6%, respectively. SI was independently related to any type of lifetime abuse, depressive spectrum disorders, benzodiazepine use disorder, borderline personality disorder, and the level of depressive symptoms. SA was independently associated with lifetime physical abuse, benzodiazepine use disorder, the number of psychotic symptoms, borderline personality disorder, and the level of depressive symptoms. The main factors related to SI and SA in those patients should be evaluated in daily clinical practice and considered in any approach to clinical practice as well as in health policies targeting suicide prevention.

The association of macronutrient quality and its interactions with energy intake with survival among patients with ovarian cancer: results from a prospective cohort study

BACKGROUND

Emerging evidence supports shifting the focus from the quantity of macronutrients to quality to obtain greater benefits for the prognosis of ovarian cancer (OC). Additionally, despite the high relevance between macronutrient quality and quantity, the interaction of these parameters on OC survival remains unknown.

OBJECTIVE

A multidimensional macronutrient quality index (MQI) was applied to investigate the association between overall macronutrient quality and the survival of patients with OC.

METHODS

A prospective cohort study was conducted with 701 females diagnosed with OC who were enrolled from 2015 to 2020. Dietary intake information was obtained from a validated food frequency questionnaire. The MQI was calculated based on 3 quality indices: carbohydrate quality index (CQI), fat quality index (FQI), and protein quality index (PQI). Cox proportional hazards regression was conducted to calculate HRs and 95% CIs. Furthermore, we evaluated whether energy intake status (total energy intake and energy balance) modified the association between MQI and OC survival.

RESULTS

During a median follow-up period of 38 (interquartile: 35-40) mo, 130 deaths occurred. The prediagnosis high MQI scores were associated with substantially improved survival among females with OC (HR_{tertile 3 vs. tertile 1} = 0.50, 95% CI: 0.33, 0.77). For sub-indices of the MQI, higher CQI (HR = 0.60, 95% CI: 0.36, 0.99), higher FQI (HR = 0.55, 95% CI: 0.34, 0.87), and higher PQI (HR = 0.58, 95% CI: 0.35, 0.94) scores were all associated with better survival. Notably, significant interactions were observed for the MQI score with total energy intake and energy balance as well as the quantity and quality of carbohydrates on survival.

CONCLUSIONS

Intake of high-quality macronutrients before diagnosis was associated with improved survival among females with OC, especially for those with energy imbalance. Am J Clin Nutr 2023;xxx:xx-xx.

Association of time spent in outdoor light and genetic risk with the incidence of depression

Depression is the consequence of both environment and genes working together. Genetic factors increase depression risk, but it is unclear whether this association can be offset by time spent in outdoor light. The study was undertaken to investigate the optimal time spent in outdoor light for lowering the risk of depression and the joint association of time spent in outdoor light and depression genetic risk. In UK Biobank, 380,976 depression-free individuals were included in this study. Polygenic risk score (PRS) was categorized into three groups in terms of tertiles. Time spent in outdoor light on a typical day in summer or winter originated from the questionnaire survey. Depression was defined as hospital admission. The potential dose-response relationship between time spent in outdoor light and depression risk was shown by a restricted cubic spline. Data were analyzed using Cox regressions and Laplace regression. After the median follow-up of 12.6 years, 13,636 individuals suffered from depression in the end. A nonlinear (J-shaped relationship) trend was observed between time spent in outdoor light and depression risk. On average, 1.5 h/day of outdoor light was related to the minimum risk of depression. Individuals below and above this optimal time both had elevated depression risk (below, HR = 1.09, 95% CI: 1.02-1.16; above, HR = 1.13, 95% CI: 1.07-1.20), and the time to incident depression were both shortened by 0.46 years (50th percentile differences [PD] = -0.46, 95% CI: -0.78, -0.14) and 0.63 years (50th PD = -0.63, 95% CI: -0.90, -0.35) years, respectively. In a comparison of individuals with the lowest tertile of PRS and average 1.5 h/day outdoor light, the HRs and 95% CIs of depression were 1.36 (1.21-1.53) and 1.43 (1.29-1.58) in those with the highest tertile of PRS and below/above this reference value, respectively. Significant multiplicative interactions were observed between intermediate genetic risks and longer time spent in outdoor light. We found that an average of 1.5 h/day spent in outdoor light was associated with a lower depression risk whatever the degree of depression genetic predisposition. Moderate time spent in outdoor light may contribute to a decreased depression risk even among people with a higher genetic risk of depression.

MARCKS is a New Prognostic Biomarker in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common type of cancers, but there is still a lack of known biomarkers for the effective diagnosis or prognosis of HCC. Myristoylated alanine-rich C-kinase substrate (MARCKS) is a substrate of protein kinase C, which was located in the cell plasma membrane. The purpose of our study was to evaluate the role of MARCKS in HCC.

Methods

The role of MARCKS in HCC was explored by bioinformatics and experiment.

Results

We demonstrated that MARCKS expression was significantly elevated in HCC datasets of TCGA. MARCKS was up-regulated in tumor sample in HCC. Functional enrichment indicated that MARCKS-related differentially expressed genes (DEGs) were mainly enriched in cell junction tissue, response to growth factors and cell population proliferation. Tumor and ECM-receptor interactions related pathways were enriched by the KEGG. MARCKS expression in HCC patients was higher in females, younger individuals, and those at worse clinical stages. Cox regression analysis showed that MARCKS expression was a risk factor for overall survival and disease-specific survival of patients.

Conclusion

MARCKS was up-regulated in HCC, may play a crucial role in HCCs, and has prognostic value for clinical outcomes.

Infections and nutrient deficiencies during infancy predict impaired growth at 5 years: Findings from the MAL-ED study in Pakistan

Background

Socio-economic, nutritional, and infectious factors have been associated with impaired infant growth, but how the presence of these factors during infancy affects growth around 5 years is not well understood.

Methods

This secondary analysis of the MAL-ED cohort included 277 children from Pakistan for whom socio-demographic, breastfeeding, complementary foods, illness, nutritional biomarkers, stool pathogens and environmental enteropathy indicators between 0 and 11 months were recorded. We used linear regression models to analyze associations of these indicators with height-for-age (HAZ), weight-for-age (WAZ) and weight-for-height (WLZ) at 54-66 months (~5 years), and Poisson regression with robust standard errors to estimate risk ratios for stunting and underweight ~5 years, controlling for gender, first available weight, and income.

Results

Among the 237 infants followed longitudinally and evaluated at about 5 years of age, exclusive breastfeeding was short (median = 14 days). Complementary feeding started before 6 months with rice, bread, noodles, or sugary foods. Roots, dairy products, fruits/vegetables, and animal-source foods were provided later than recommended (9-12 months). Anemia (70.9%), deficiencies in iron (22.0%), zinc (80.0%), vitamin A (53.4%) and iodine (13.3%) were common. Most infants (>90%) presented with diarrhea and respiratory infections in their first year. At ~5 years, low WAZ (mean-1.91 ± 0.06) and LAZ (-2.11 ± 0.06) resulted in high prevalence of stunting (55.5%) and underweight (44.4%) but a relatively low rate of wasting (5.5%). While 3.4% had concurrent stunting and wasting ~5 years, 37.8% of children had coexisting stunting and underweight. A higher income and receiving formula or dairy products during infancy were associated with a higher LAZ ~5 years, but infant's history of hospitalizations and more respiratory infections were associated with lower LAZ and higher risk of stunting ~5 years. Infants' intake of commercial baby foods and higher serum-transferrin receptors were associated with higher WAZ and lower risk of underweight ~5 years. Presence of Campylobacter and fecal neopterin >6.8 nmol/L in the first year were associated with increased risk of underweight ~5 years.

Conclusion

Growth indicators ~5 years were associated with poverty, inappropriate complementary feeding, and infections during the first year of life, which supports the early start of public health interventions for preventing growth delay ~5 years.

Risk prediction model for early postoperative death in patients with hepatocellular carcinoma: a retrospective study based on random forest algorithm and logistic regression

BACKGROUND

At present, little is known about the risk factors of early postoperative death in patients with hepatocellular carcinoma (HCC).

METHODS

We collected the data of patients who were diagnosed with primary liver cancer between 2010 and 2015 in the Surveillance, Epidemiology, and End Results database and further allocated them to the training set and validation set. Univariate and multivariate logistic regression analysis was used to determine the independent influencing factors of early postoperative death of HCC patients. Random forest and Least absolute shrinkage and selection operator regression analysis were used to screen out vital variables for the construction of the nomogram. It was evaluated by receiver operating characteristic curve, calibration curve and decision curve analysis.

RESULTS

A total of 4154 patients were selected in this process, including 2647 patients with postoperative early death (outcome1) and 1507 patients with liver cancer-specific postoperative early death (outcome2). Surgery method, age category, marital status and tumor grade were the risk factors for early postoperative death. As for the liver cancer-specific early postoperative death, AJCC, surgery method, chemotherapy and tumor grade were independent prognostic factors. Early death and liver cancer-specific early death nomograms have an area under curves of 0.643 and 0.679 in the training set, respectively, and 0.617 and 0.688 in the validation set. The calibration curve and decision curve analysis shows that the nomograms have good performance.

CONCLUSION

This model provides an intuitive and practical tool for future studies based on large-scale cohorts by exploring the risk factors of early death in patients with HCCs undergoing surgery.

Epidemiological Study of the Occurrence of Typhus Group Rickettsia Natural Infection in Domiciliated Dogs from a Rural Community in South-Eastern Mexico

Simple Summary

Rickettsioses are relevant emergent and reemergent zoonoses in the Americas, including Mexico. Murine typhus caused by typhus group (TG) Rickettsia is prevalent in humans and their companion animals, such as dogs. This study found that 23.9% of the dogs (34/142) were infected by TG Rickettsia in Maxcanú, Yucatan (southeastern Mexico). Statistical analyses showed that reduced outdoor activities, age, sex and previous antiparasitic treatment are associated factors with less risk of TG Rickettsia infection in the sampled dogs. Monitoring and controlling these factors could help to restrict the enzootic transmission risk and prevent the potential zoonotic transmission.

Abstract

The aim is to describe the Typhus group (TG) Rickettsia infection in dogs and to identify factors associated with this infection. We collected blood samples and gathered exposure and clinical data of 142 dogs from a rural community of Yucatan. The Rickettsia group was determined by semi-nested PCR. Generalized linear models with binomial error distribution were used to model the associated factors from the dog sample for risk ratio (RR) estimation. Thirty-four dogs (23.9%) showed molecular evidence of TG Rickettsia DNA. The multivariate model showed that mixed-breed dogs (RR = 0.06) and dogs that had received antiparasitic treatment (RR = 0.049) had a lower risk of getting infected, taking as reference the purebred group and the non-treated dogs, respectively. Looking at variable interactions, adult dogs without outdoor activities had a lower infection risk than puppies (RR = 0.26). Among dogs with antiparasitic treatment, females had a higher infection risk than male dogs (RR = 26.2). The results showed enzootic TG Rickettsia circulation in dogs of a rural community. The factors outdoor activities, age and previous antiparasitic treatment, as well as the clinical variables signs of hemorrhages and epistaxis, were associated with a less chance of natural infection in the studied dogs. Prevention and control of the enzootic transmission risk of TG Rickettsia should help to reduce the potential zoonotic transmission of this pathogen.

Potential biomarkers and immune characteristics of small bowel adenocarcinoma

Small bowel adenocarcinoma (SBA) is a gastrointestinal malignancy with low incidence but poor prognosis, and its pathogenesis is still unclear. This study aimed to explore potential disease-causing biomarkers of SBA. The gene expression datasets of SBA and normal samples were downloaded from the Gene Expression Omnibus database. First, differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed. Common genes (CGs) were obtained by intersection of differentially expressed genes (DEGs) and optimal modal genes of WGCNA. Subsequently, a protein‒protein interaction network was established to screen hub genes, and target genes were obtained by Lasso regression analysis of hub genes. An SBA risk prediction model was established based on target genes. The prediction accuracy of the model was evaluated by the area under the receiver operating characteristic curve (AUC). The levels of immune cell infiltration and activation of immune pathways were compared between SBA and normal samples using the "ggpubr" and "reshape2" packages. A total of 1058 DEGs were identified. WGCNA showed that the signature gene in the brown module was significantly associated with SBA (p = 7E−17), and 469 CGs were obtained. Four target genes (APOA4, APOB, COL1A2, FN1) were identified and showed excellent prediction of SBA risk (AUC = 0.965). In addition, active dendritic cells and macrophages showed higher infiltration levels in SBA. Meanwhile, the APC_co_stimulation pathway and parainflammation pathway were strongly active in SBA. Four target genes (APOA4, APOB, COL1A2, FN1) may be involved in the pathogenesis of small bowel adenocarcinoma.

Associations between Psychological Variables, Knowledge, Attitudes, Risk Perceptions and Health Behaviours towards COVID-19 among Adolescents

There is currently little scientific evidence available that allows us to understand patterns of knowledge, risk perception, attitudes, and behaviours among adolescents in relation to COVID-19. This study aims to analyse the relationship between knowledge about COVID-19, risk perception, and psychological variables and the adherence to preventive measures among the adolescent population. It is a descriptive cross-sectional study, which included adolescents between the ages of 12 and 18 (n = 354). The questionnaire was sent to several secondary schools chosen by convenience sampling and following a non-probabilistic snowball sampling. Descriptive, univariate, and multivariate analyses were carried out in order to determine whether knowledge about COVID-19, risk perception, tolerance of frustration, planning and decision-making, family functionality, self-efficacy, self-esteem, and social skills are related to preventive measures. The adoption among adolescents of behaviours which protect them against COVID-19 depends on knowledge about the disease, the perception of the risk it poses to them, as well as their tolerance of frustration and planning and decision-making abilities. The relationship between the individual variables among adolescents with the adoption of behaviours which protect them against COVID-19 has been confirmed. The development of intervention and communication strategies that take the psychosocial situation of adolescents into account will help to increase the adoption of protective health behaviours in the context of a pandemic.

A tumor microenvironment gene set–Based prognostic signature for non-small-cell lung cancer

Background: The tumor microenvironment (TME) is involved in the development and progression of lung carcinomas. A deeper understanding of TME landscape would offer insight into prognostic biomarkers and potential therapeutic targets investigation. To this end, we aimed to identify the TME components of lung cancer and develop a prognostic signature to predict overall survival (OS).

Methods: Expression data was retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed TME-related genes were calculated between tumor and normal tissues. Then nonnegative matrix factorization (NMF) clustering was used to identify two distinct subtypes.

Results: Our analysis yielded a gene panel consisting of seven TME-related genes as candidate signature set. With this panel, our model showed that the high-risk group experienced a shorter survival time. This model was further validated by an independent cohort with data from Gene Expression Omnibus (GEO) database (GSE50081 and GSE13213). Additionally, we integrated the clinical factors and risk score to construct a nomogram for predicting prognosis. Our data suggested less immune cells infiltration but more fibroblasts were found in tumor tissues derived from patients at high-risk and those patients exhibited a worse immunotherapy response.

Conclusion: The signature set proposed in this work could be an effective model for estimating OS in lung cancer patients. Hopefully analysis of the TME could have the potential to provide novel diagnostic, prognostic and therapeutic opportunities.

Performance of PRECISE-DAPT and Age–Bleeding–Organ Dysfunction Score for Predicting Bleeding Complication During Dual Antiplatelet Therapy in Chinese Elderly Patients

Background

Recently, the Age–Bleeding–Organ Dysfunction (ABO) algorithm was recommended by the Asian Pacific Society of Cardiology Consensus as a binary approach to evaluate bleeding risk. This analysis made comparison of the predictive performances between the PRECISE-DAPT and ABO bleeding score in identifying the risk of 12-months major bleeding in Chinese elderly patients over 65 years old patients who underwent percutaneous coronary intervention (PCI) during dual-antiplatelet therapy period.

Methods

A total of 2,037 elderly coronary artery disease (CAD) patients (≥65 years) receiving dual antiplatelet therapy (DAPT) after PCI were enrolled in the study. The predictive accuracy of the two bleeding risk scores (PRECISE-DAPT and ABO) was compared for identifying the risk of bleeding during the dual-antiplatelet therapy in patients who underwent PCI. Major clinically relevant bleeding events were defined according to the Bleeding Academic Research Consortium (BARC) criteria.

Results

The PRECISE-DAPT score in the no bleeding, BARC = 1 bleeding, BARC ≥ 2 bleeding patients was 23.55 ± 10.46, 23.23 ± 10.03, and 33.54 ± 14.33 (p < 0.001), respectively. Meanwhile, the ABO score in the three groups was 0.72 ± 0.80, 0.69 ± 0.81, and 1.49 ± 0.99 (p < 0.001), respectively. The C-statistic of the PRECISE-DAPT model for prediction of BARC ≥ 2 bleeding in overall patients was 0.717 (95% CI, 0.656–0.777) and 0.720 (95% CI, 0.656–0.784) in acute coronary syndrome (ACS) patients. Similar discriminatory capacity was demonstrated in the ABO risk score [overall, patients, AUC: 0.712 (95% CI, 0.650–0.774); ACS patients, AUC: 0.703 (95% CI, 0.634–0.772)]. No differences were observed when the ABO model was in comparison with the PRECISE-DAPT model, regardless in overall patients (z = −0.199, p = 0.842) or ACS patients (z = −0.605, p = 0.545). The calibration for BARC ≥ 2 bleeding of the PRECISE-DAPT and ABO score were acceptable, regardless in overall patients [goodness-of-fit (GOF) Chi-square = 0.432 and 0.001, respectively; p-value = 0.806 and 0.999, respectively] or ACS patients (GOF Chi-square = 0.008 and 0.580, respectively; p-value = 0.996 and 0.748, respectively).

Conclusion

No matter of clinical presentation in Asian 65-years older patients with DAPT, the PRECISE-DAPT, and ABO scores had the similar discriminative ability for 12-months BARC ≥ 2 bleeding. Considering the simplicity and reliability, the PRECISE-DAPT score might be more clinically applicable in the overall population and ACS patients in bleeding prediction.

Untargeted Metabolomics Reveal Parenteral Nutrition-Associated Alterations in Pediatric Patients with Short Bowel Syndrome

Short bowel syndrome (SBS) is a major cause of intestinal failure (IF) that may require long-term parenteral nutrition (PN) support. However, long-term PN is accompanied by severe complications such as catheter-related blood stream infection (CRBSI) and intestinal failure-associated liver disease (IFALD), and it is associated with high healthcare costs. In this study, we characterized the plasma metabolomic profile and investigated the role of metabolism in predicting long-term PN in pediatric patients with SBS. Untargeted metabolomics was performed in plasma samples from 20 SBS patients with PN support: 6 patients had IFALD and 14 patients had no liver disease. As controls, 18 subjects without liver or intestinal diseases were included for the analysis. SBS patients had distinct plasma metabolomic signatures compared to controls, and several pathways associated with amino acid metabolism and cell death were significantly changed. The presence of IFALD in SBS was associated with alterations of metabolites mainly classified as “amino acids, peptides, and analogues” and “benzene and derivatives”. Serum direct bilirubin levels were negatively correlated with levels of uridine, skatole, and glabrol. Importantly, SBS patients with long-term PN showed significantly increased levels of glutamine compared to those in the short-term PN group. Finally, using multivariate logistic regression analysis, we developed a prediction model including glutamine and creatinine to identify pediatric SBS patients who need long-term PN support. These findings underscore the potential key role of the metabolome in SBS with IF and suggest that metabolomic profiles could be used in long-term PN assessment.

[Translated article] Prognostic Significance of Findings on CTPA Supporting an Alternative Diagnosis to PE Among Patients Hospitalized for an Exacerbation of COPD: Predefined Subanalysis of the SLICE Trial

BACKGROUND

Among patients hospitalized for an exacerbation of chronic obstructive pulmonary disease (COPD), the SLICE trial showed that the addition of an active diagnostic strategy for pulmonary embolism (PE) to usual care compared with usual care alone did not improve a composite set of health outcomes. The objective of this subanalysis was to determine the frequency and prognostic significance of findings on computed tomography pulmonary angiogram (CTPA) supporting an alternative diagnosis to PE.

METHODS

We analyzed all patients randomized to the intervention in the SLICE trial who received a CTPA that did not show PE. We used multivariable logistic regression to assess the independent association between findings supporting an alternative diagnosis to PE and a composite of readmission for COPD or death within 90 days after randomization.

RESULTS

Among the 746 patients who were randomized, this subanalysis included 175 patients in the intervention group who received a CTPA that did not show PE. Eighty-four (48.0%) patients had acute bronchial infection, 13 (7.4%) had lung cancer, 10 (5.7%) had congestive heart failure, 8 (4.6%), 18 (10.3%) had other diagnoses, and 42 (24.0%) had a normal CTPA. In multivariable analysis, findings supporting an alternative diagnosis to PE were not significantly associated with the primary outcome (odds ratio: 0.64; 95% confidence interval: 0.30-1.38; P=0.26).

CONCLUSIONS

Among patients hospitalized for an exacerbation of COPD, CTPA identified an alternative diagnosis in 76% of the patients. However, specific management of these patients was not associated with improved outcomes within 90 days after randomization.

Anthropometric Equations to Determine Maximum Height in Adults ≥ 60 Years: A Systematic Review

Although it is common to measure bone lengths for study, methodological errors in data measurement and processing often invalidate their clinical and scientific usefulness. This manuscript reviews the validity of several published equations used to determine the maximum height in older adults, since height is an anthropometric parameter widely employed in health sciences. A systematic review of original articles published in the English, Spanish, or Portuguese languages was performed in PubMed, ScienceDirect, EBSCO, Springer Link, and two institutional publisher integrators (UACJ and CONRICYT). The search terms were included in the metasearch engines in a combined way and text form using the Boolean connectors AND and OR {(Determination OR Estimation OR Equation) AND Height AND (Elderly OR "Older adults")}. Eleven manuscripts were selected from 1935 records identified through database searching after applying the following criteria: (1) original articles that designed and validated equations for the determination of height by anthropometric methods in adults 60 years of age and older and (2) manuscripts that presented robust evidence of validation of the proposed regression models. The validity of the reported linear regression models was assessed throughout a manuscript review process called multi-objective optimization that considered the collection of the models, the prediction errors, and the adjustment values (i.e., R², standard error of estimation, and pure error). A total of 64 equations were designed and validated in 45,449 participants (57.1% women) from four continents: America (85.3%, with 46 equations), Asia (8.1%, with 10), Europe (4.6%, with 7), and Africa (2.0%, with 1); the Hispanic American ethnic group was the most numerous in participants and equations (69.0%, with 28). Due to various omissions and methodological errors, this study did not find any valid and reliable equations to assess the maximum height in older adults by anthropometric methods. It is proposed to adjust allometric mathematical models that can be interpreted in the light of ontogenetic processes.

A Novel Risk Score to Predict In-Hospital Mortality in Patients With Acute Myocardial Infarction: Results From a Prospective Observational Cohort

Objectives

The aim of this study was to develop and validate a novel risk score to predict in-hospital mortality in patients with acute myocardial infarction (AMI) using the Heart Failure after Acute Myocardial Infarction with Optimal Treatment (HAMIOT) cohort in China.

Methods

The HAMIOT cohort was a multicenter, prospective, observational cohort of consecutive patients with AMI in China. All participants were enrolled between December 2017 and December 2019. The cohort was randomly assigned (at a proportion of 7:3) to the training and validation cohorts. Logistic regression model was used to develop and validate a predictive model of in-hospital mortality. The performance of discrimination and calibration was evaluated using the Harrell’s c-statistic and the Hosmer-Lemeshow goodness-of-fit test, respectively. The new simplified risk score was validated in an external cohort that included independent patients with AMI between October 2019 and March 2021.

Results

A total of 12,179 patients with AMI participated in the HAMIOT cohort, and 136 patients were excluded. In-hospital mortality was 166 (1.38%). Ten predictors were found to be independently associated with in-hospital mortality: age, sex, history of percutaneous coronary intervention (PCI), history of stroke, presentation with ST-segment elevation, heart rate, systolic blood pressure, initial serum creatinine level, initial N-terminal pro-B-type natriuretic peptide level, and PCI treatment. The c-statistic of the novel simplified HAMIOT risk score was 0.88, with good calibration (Hosmer–Lemeshow test: P = 0.35). Compared with the Global Registry of Acute Coronary Events risk score, the HAMIOT score had better discrimination ability in the training (0.88 vs. 0.81) and validation (0.82 vs. 0.72) cohorts. The total simplified HAMIOT risk score ranged from 0 to 121. The observed mortality in the HAMIOT cohort increased across different risk groups, with 0.35% in the low risk group (score ≤ 50), 3.09% in the intermediate risk group (50 < score ≤ 74), and 14.29% in the high risk group (score > 74).

Conclusion

The novel HAMIOT risk score could predict in-hospital mortality and be a valid tool for prospective risk stratification of patients with AMI.

Clinical Trial Registration

[https://clinicaltrials.gov], Identifier: [NCT03297164].

Associations Between Severity of Depression, Lifestyle Patterns, and Personal Factors Related to Health Behavior: Secondary Data Analysis From a Randomized Controlled Trial

Background

Depression is a prevalent condition that has a significant impact on psychosocial functioning and quality of life. The onset and persistence of depression have been linked to a variety of biological and psychosocial variables. Many of these variables are associated with specific lifestyle characteristics, such as physical activity, diet, and sleep patterns. Some psychosocial determinants have an impact on people’ health-related behavior change. These include personal factors such as sense of coherence, patient activation, health literacy, self-efficacy, and procrastination. This study aims to analyze the association between the severity of depression, lifestyle patterns, and personal factors related to health behavior. It also aims to analyze whether personal factors moderate the relationship between lifestyles and depression.

Methods

This study is a secondary data analysis (SDA) of baseline data collected at the start of a randomized controlled trial (RCT). A sample of 226 patients with subclinical, mild, or moderate depression from primary healthcare centers in two sites in Spain (Zaragoza and Mallorca) was used, and descriptive, bivariate, multivariate, and moderation analyses were performed. Depression was the primary outcome, measured by Beck II Self-Applied Depression Inventory. Lifestyle variables such as physical exercise, adherence to Mediterranean diet and sleep quality, social support, and personal factors such as self-efficacy, patient activation in their own health, sense of coherence, health literacy, and procrastination were considered secondary outcomes.

Results

Low sense of coherence (β = −0.172; p < 0.001), poor sleep quality (β = 0.179; p = 0.008), low patient activation (β = −0.119; p = 0.019), and sedentarism (more minutes seated per day; β = 0.003; p = 0.025) are predictors of having more depressive symptoms. Moderation analyses were not significant.

Discussion

Lifestyle and personal factors are related to depressive symptomatology. Our findings reveal that sense of coherence, patient’s activation level, sedentarism, and sleep quality are associated with depression. Further research is needed regarding adherence to Mediterranean diet, minutes walking per week and the interrelationship between lifestyles, personal factors, and depression.

Significado pronóstico de los diagnósticos alternativos a la TEP hallados en la tomografía computarizada de tórax de pacientes ingresados por agudización de EPOC: subanálisis predefinido del ensayo SLICE

BACKGROUND

Among patients hospitalized for an exacerbation of chronic obstructive pulmonary disease (COPD), the SLICE trial showed that the addition of an active diagnostic strategy for pulmonary embolism (PE) to usual care compared with usual care alone did not improve a composite set of health outcomes. The objective of this subanalysis was to determine the frequency and prognostic significance of findings on computed tomography pulmonary angiogram (CTPA) supporting an alternative diagnosis to PE.

METHODS

We analyzed all patients randomized to the intervention in the SLICE trial who received a CTPA that did not show PE. We used multivariable logistic regression to assess the independent association between findings supporting an alternative diagnosis to PE and a composite of readmission for COPD or death within 90 days after randomization.

RESULTS

Among the 746 patients who were randomized, this subanalysis included 175 patients in the intervention group who received a CTPA that did not show PE. Eighty-four (48.0%) patients had acute bronchial infection, 13 (7.4%) had lung cancer, 10 (5.7%) had congestive heart failure, 8 (4.6%), 18 (10.3%) had other diagnoses, and 42 (24.0%) had a normal CTPA. In multivariable analysis, findings supporting an alternative diagnosis to PE were not significantly associated with the primary outcome (odds ratio: 0.64; 95% confidence interval: 0.30-1.38; P=0.26).

CONCLUSIONS

Among patients hospitalized for an exacerbation of COPD, CTPA identified an alternative diagnosis in 76% of the patients. However, specific management of these patients was not associated with improved outcomes within 90 days after randomization.

Field cancerization profile-based prognosis signatures lead to more robust risk evaluation in hepatocellular carcinoma

The development of reliable biomarkers has been an urgent issue as well as a hot spot of research on the diagnosis, treatment, and prognostic evaluation of hepatocellular carcinoma (HCC). Here, we established and validated two field cancerization profile-based prognostic signatures (gene expression score [GES] and immune score [IS]) for HCC. Our study confirmed that field cancerization profile-based models outperform conventional models on risk evaluation, offering insights for further studies on prognostic model construction. The nomogram constructed by combining GES, IS, and TNM stage was proved effective in improving the individualized prediction of the overall risk of patients. Distinct peritumoral characteristics were observed in several immune cells (e.g., CD8 T cells and dendritic cells), which might explain the diversified prognosis and clinical benefit of immunotherapy. Moreover, a series of drug targets, prognosis-associated genes, and pathways were identified, which may contribute to molecular mechanism studies as well as therapeutic target development of HCC.

•

Two field cancerization feature-based prognostic signatures for HCC were developed

•

Joint nomogram is effective in improving individualized risk prediction

•

Different peritumor signatures were observed in several immune cells

•

Several peritumoral drug targets, prognostic genes, and pathways were identified

Time-varying association between body mass index and all-cause mortality in patients with hypertension

BACKGROUND

Relationship between BMI and all-cause mortality in patients with hypertension remains controversial. This study aimed to evaluate the time-varying association between BMI in patients with hypertension and all-cause mortality.

METHODS

This population-based cohort study included 212,394 Chinese adults with hypertension from 2007 to 2015 and was followed up until death, loss-to-follow-up, or December 31, 2018. According to the World Health Organization criteria for Asians, BMI was categorized into five groups: underweight (BMI < 18.5 kg/m²), normal weight (18.5-22.9 kg/m²), overweight (23-24.9 kg/m²), class I obesity (25-29.9 kg/m²) and class II obesity (BMI ≥ 30 kg/m²). Cox model was used to estimate the time-varying association of BMI on the risk of mortality by including the interaction term between BMI and time using restricted cubic spline.

RESULTS

Compared with normal weight, underweight and class II obesity were associated with higher mortality (Hazard ratio [HRs] at 1 and 10 years of follow-up: 1.51 [95% CI: 1.39-1.65], and 1.27 (1.15-1.41) for underweight, respectively; 1.08 (0.96-1.21), and 1.16 (1.03-1.30) for class II obesity, respectively). However, overweight and class I obesity were associated with lower mortality, although the protective effects gradually attenuated over time (HRs at 1 and 10 years of follow-up: 0.85 (0.81-0.90), and 0.96 (0.91-1.02) for overweight, respectively; 0.80 (0.76-0.84), and 1.04 (0.99-1.10) for class I obesity, respectively).

CONCLUSIONS

We found increased mortality among hypertensive patients with underweight and class II obesity while decreased mortality with overweight and class I obesity was observed during the first 5 years of follow-up. Management efforts for hypertension may target controlling body weight in a reasonable range for patients, and probably more attention should be given to underweight patients.

The Importance of Selected Coordination Motor Skills for an Individual Football Player's Effectiveness in a Game

The appropriate level of coordination motor skills (CMS) in a football player is one of the factors determining the effectiveness of their actions. Adaptability and complex reaction time are of particular importance in models of coordination requirements in football. The lead aim of this study is to determine the relationship between two selected coordination motor skills and the offensive, defensive and comprehensive effectiveness of an individual player's actions. The study was conducted on a group of 91 Polish male football players aged 20 to 31 years, all in the senior age category. The research tools included: a test assessing motor adaptation (research by dribbling the ball with the dominant leg), psychomotor test of complex reaction time (tested with an S-10.2 measuring device) and a test of the effectiveness of an individual player's actions (one-on-one simulation game). The conducted research indicated that adaptability and complex reaction time are both important abilities for success when attacking in an individual game, and in the assessment of a comprehensive index of individual competences in a one-on-one football game. However, the most significant factor influencing the effectiveness of a player's defensive action is solely the complex reaction time.

Construction of a Ferroptosis-Related Gene Signature for Head and Neck Squamous Cell Carcinoma Prognosis Prediction

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignant cancers, and few studies have demonstrated the value of ferroptosis-related genes in prognosis.

Methods

The original counts of RNA sequencing data and clinicopathological data were obtained from TCGA and GSE65858 datasets. Common ferroptosis-related genes related to prognosis were identified from the training set and were included in LASSO to determine the best prognosis. To evaluate the efficacy, time-dependent ROC and Kaplan–Meier (KM) survival analyses were applied. Moreover, univariate and multivariate Cox regression analyses were used to screen independent parameters of prognosis and build a nomogram. Eventually, possible biological pathways were proposed based on GSEA.

Results

Among 242 ferroptosis-related genes, we identified that the FLT3, IL6, Keap1, NQO1, SOCS1 and TRIB3 genes were significantly connected with HNSCC patient prognosis as a six-gene signature. After, the patients were divided into high- and low-risk groups based on the six-gene signature. The KM survival curves demonstrated that the high-risk group had worse OS (p < 0.0001) and higher AUC values (0.654, 0.735, and 0.679 for 1-, 3-, and 5-year survival, respectively) for the prognostic signature of the six genes compared with other genes, which were also validated in the GSE65858 dataset. Moreover, GSEA suggested that the epithelial mesenchymal transition pathway was abundant and that the mesenchymal status in the high-risk group was substantially higher than that in the low-risk group. Finally, the immune microenvironment and differences in the content of immune cell types were demonstrated.

Conclusion

We established a six-ferroptosis-related-gene model crossing clinical prognostic parameters that can predict HNSCC patient prognosis and provide a reliable prognostic evaluation tool to assist clinical treatment decisions.

Prediction and prognosis of adverse maternal and foetal/neonatal outcomes in pulmonary hypertension: an observational study and nomogram construction

BACKGROUND

Pregnant women with pulmonary hypertension (PH) have higher mortality rates and poor foetal/neonatal outcomes. Tools to assess these risk factors are not well established.

METHODS

Predictive and prognostic nomograms were constructed using data from a "Development" cohort of 420 pregnant patients with PH, recorded between January 2009 and December 2018. Logistic regression analysis established models to predict the probability of adverse maternal and foetal/neonatal events and overall survival by Cox analysis. An independent "Validation" cohort comprised data of 273 consecutive patients assessed from January 2019 until May 2022. Nomogram performance was evaluated internally and implemented with online software to increase the ease of use.

RESULTS

Type I respiratory failure, New York Heart Association functional class, N-terminal pro-brain natriuretic peptide [Formula: see text] 1400 ng/L, arrhythmia, and eclampsia with pre-existing hypertension were independent risk factors for maternal mortality or heart failure. Type I respiratory failure, arrhythmia, general anaesthesia for caesarean section, New York Heart Association functional class, and N-terminal pro-brain natriuretic peptide [Formula: see text] 1400 ng/L were independent predictors of pulmonary hypertension survival during pregnancy. For foetal/neonatal adverse clinical events, type I respiratory failure, arrhythmia, general anaesthesia for caesarean section, parity, platelet count, fibrinogen, and left ventricular systolic diameter were important predictors. Nomogram application for the Development and Validation cohorts showed good discrimination and calibration; decision curve analysis demonstrated their clinical utility.

CONCLUSIONS

The nomogram and its online software can be used to analyse individual mortality, heart failure risk, overall survival prediction, and adverse foetal/neonatal clinical events, which may be useful to facilitate early intervention and better survival rates.

Preliminary Pharmacogenomic-Based Predictive Models of Tamoxifen Response in Hormone-dependent Chilean Breast Cancer Patients

Tamoxifen (TAM), a selective oestrogen receptor modulator, is one of the most used treatments in oestrogen receptor-positive (ER+) early and metastatic breast cancer (BC) patients. The response to TAM has a high degree of inter-individual variability. This is mainly due to genetic variants in CYP2D6 gene, as well as other genes encoding proteins involved in the TAM pharmacokinetic and/or pharmacodynamic. Therefore, prediction of the TAM response using these genetic factors together with other non-genetic variables may be relevant to improve breast cancer treatment. Thus, in this work, we used genetic polymorphisms and clinical variables for TAM response modelling. One hundred sixty-two ER + BC patients with 2 years of TAM treatment were retrospectively recruited, and the genetic polymorphisms CYP2D6*4, CYP3A4*1B (CYP3A4*1.001), CYP3A5*3, UGT2B7*2, UGT2B15*2, SULT1A1*2, and ESRA V364E were analyzed by PCR-RFLP. Concomitantly, the therapeutic response was obtained from clinical records for association with genotypes using univariate and multivariate biostatistical models. Our results show that UGT2B15*1/*2 genotype protects against relapse (OR = 0.09; p = 0.02), CYP3A5*3/*3 genotype avoids endometrial hyperplasia (OR = 0.07; p = 0.01), SULT1A1*1/*2 genotype avoids vaginal bleeding (OR = 0.09; p = 0.03) and ESRA 364E/364E genotype increases the probability of vaginal bleeding (OR = 5.68; p = 0.02). Logistic regression models, including genomic and non-genomic variables, allowed us to obtain preliminary predictive models to explain relapse (p = 0.010), endometrial hyperplasia (p = 0.002) and vaginal bleeding (p = 0.014). Our results suggest that the response to TAM treatment in ER + BC patients might be associated with the presence of the studied genetic variants in UGT2B15, CYP3A5, SULT1A1 and ESRA genes. After clinical validation protocols, these models might be used to help to predict a percentage of BC relapse and adverse reactions, improving the individual response to TAM-based treatment.