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The in-hospital mortality in lung cancer patients admitted to intensive care unit (ICU) is extremely high. This study intended to adopt machine learning algorithm models to predict in-hospital mortality of critically ill lung cancer for providing relative information in clinical decision-making. Data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort and data extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database for a validation cohort. Logistic regression, random forest, decision tree, light gradient boosting machine (LightGBM), eXtreme gradient boosting (XGBoost), and an ensemble (random forest+LightGBM+XGBoost) model were used for prediction of in-hospital mortality and important feature extraction. The AUC (area under receiver operating curve), accuracy, F1 score and recall were used to evaluate the predictive performance of each model. Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature. Overall, there were 653 (24.8%) in-hospital mortality in the training cohort, and 523 (21.7%) in-hospital mortality in the validation cohort. Among the six machine learning models, the ensemble model achieved the best performance. The top 5 most influential features were the sequential organ failure assessment (SOFA) score, albumin, the oxford acute severity of illness score (OASIS) score, anion gap and bilirubin in random forest and XGBoost model. The SHAP summary plot was used to illustrate the positive or negative effects of the top 15 features attributed to the XGBoost model. The ensemble model performed best and might be applied to forecast in-hospital mortality of critically ill lung cancer patients, and the SOFA score was the most important feature in all models. These results might offer valuable and significant reference for ICU clinicians' decision-making in advance.

The in-hospital mortality in lung cancer patients admitted to intensive care unit (ICU) is extremely high. This study intended to adopt machine learning algorithm models to predict in-hospital mortality of critically ill lung cancer for providing relative information in clinical decision-making. Data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort and data extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database for a validation cohort. Logistic regression, random forest, decision tree, light gradient boosting machine (LightGBM), eXtreme gradient boosting (XGBoost), and an ensemble (random forest+LightGBM+XGBoost) model were used for prediction of in-hospital mortality and important feature extraction. The AUC (area under receiver operating curve), accuracy, F1 score and recall were used to evaluate the predictive performance of each model. Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature. Overall, there were 653 (24.8%) in-hospital mortality in the training cohort, and 523 (21.7%) in-hospital mortality in the validation cohort. Among the six machine learning models, the ensemble model achieved the best performance. The top 5 most influential features were the sequential organ failure assessment (SOFA) score, albumin, the oxford acute severity of illness score (OASIS) score, anion gap and bilirubin in random forest and XGBoost model. The SHAP summary plot was used to illustrate the positive or negative effects of the top 15 features attributed to the XGBoost model. The ensemble model performed best and might be applied to forecast in-hospital mortality of critically ill lung cancer patients, and the SOFA score was the most important feature in all models. These results might offer valuable and significant reference for ICU clinicians' decision-making in advance.

Objective This study aimed to determine whether a combination of case-based learning (CBL) and problem-based learning (PBL) methods in teaching can improve the academic performance and recruitment of medical students for neurosurgery. Methods Four classes of fourth-year medical students were randomly divided into two groups. The traditional model group received the traditional teaching method, and the CBL-PBL group received the combined teaching methods of CBL and PBL. After the courses, the differences between the two groups in self-perceived competence, satisfaction with the course, post-class test scores, and clinical practice abilities were compared, and the proportions of neurosurgery major selection in pre- and post-curriculum between the two groups were also analyzed. Results Self-perceived competence, post-class test scores, and clinical practice abilities in the CBL-PBL group were better than those in the traditional model group. The students in the CBL-PBL group showed a higher degree of satisfaction with the course than those in the traditional model group (χ2 = 12.03, P  = 0.007). At the end of the semester, the proportion of students who chose neurosurgery majors in the CBL-PBL group was 13.3%, more than the 3.4% in the traditional model group (χ2 = 3.93, P  = 0.048). Conclusion Compared with the traditional teaching method, the CBL and PBL integrated method is more effective for improving the performance of medical students and enhancing their clinical capabilities in neurosurgery teaching. The CBL-PBL method effectively improved students’ interests in neurosurgery, potentially contributing to increasing medical student recruitment into neurosurgery.

Transposable elements (TEs) contribute to gene expression regulation by acting as cis-regulatory elements that attract transcription factors and epigenetic regulators. This research aims to explore the functional and clinical implications of transposable element-related molecular events in hepatocellular carcinoma, focusing on the mechanism through which liver-specific accessible TEs (liver-TEs) regulate adjacent gene expression. Our findings reveal that the expression of HNF4A is inversely regulated by proximate liver-TEs, which facilitates liver cancer cell proliferation. Mechanistically, liver-TEs are predominantly occupied by the histone demethylase, KDM1A. KDM1A negatively influences the methylation of histone H3 Lys4 (H3K4) of liver-TEs, resulting in the epigenetic silencing of HNF4A expression. The suppression of HNF4A mediated by KDM1A promotes liver cancer cell proliferation. In conclusion, this study uncovers a liver-TE/KDM1A/HNF4A regulatory axis that promotes liver cancer growth and highlights KDM1A as a promising therapeutic target. Our findings provide insight into the transposable element-related molecular mechanisms underlying liver cancer progression. The functional role of transposable elements (TEs) in hepatocellular carcinoma (HCC) remains to be explored. Here, the authors identify a liver-TE/KDM1A/HNF4A regulatory axis that promotes HCC growth and suggest therapeutic targeting of KDM1A.

This paper describes initially the application of the Tyndall effect (TE) of metal–organic framework (MOF) materials as a colorimetric signaling strategy for the sensitive detection of pyrophosphate ion (PPi). The used MOF NH2-MIL-101(Fe) was prepared with Fe3+ ions and fluorescent ligands of 2-amino terephthalic acid (NH2-BDC). The fluorescence of NH2-BDC in MOF is quenched due to the ligand-to-metal charge transfer effect, while the NH2-MIL-101(Fe) suspension shows a strong TE. In the presence of PPi analyte, the MOFs will undergo decomposition because of the competitive binding of Fe3+ by PPi over NH2-BDC, resulting in a significant decrease in the TE signal and fluorescence restoration from the released ligands. The results demonstrate that the new method only requires a laser pointer pen (for TE creation) and a smartphone (for portable quantitative readout) to detect PPi in a linear concentration range of 1.25–800 μM, with a detection limit of ~210 nM (3σ) which is ~38 times lower than that obtained from traditional fluorescence with a spectrophotometer (linear concentration range, 50–800 µM; detection limit, 8.15 µM). Moreover, the acceptable recovery of PPi in several real samples (i.e., pond water, black tea, and human serum and urine) ranges from 97.66 to 119.15%.

Recently, copper nanoclusters (CuNCs) have attracted great research interest for their low synthesis cost, wide application, and easy functionalization. Until now, CuNCs have been developed and applied in multi-fields such as sensing, catalysis, light-emitting diode manufacturing, and cell imaging. Furthermore, the application of heavy metal ions (HMIs) detection is also regarded as a major part of fluorescence sensing and the necessity of detecting the makeup of HMIs (Ag+, Te3+, Co2+, Se6+, Hg2+, Mn2+, etc.) in organisms and the environment. This has promoted the development of CuNCs in fluorescence sensing. This paper reviews the research progress of CuNCs detection in HMIs, which can be divided into four parts. The synthesis and characterization of CuNCs are first described. Then, the synthesis methods making the types of CuNCs more varied are also summarized. Furthermore, mechanisms of fluorescence changes induced by HMIs are explained. After that, the relevant reports of CuNCs in several typical HMI detection are further listed. In addition, combined with the above content, the challenges and prospects of CuNCs in HMIs detection are also proposed.

Patients with traumatic brain injury (TBI) often experience post-injury anxiety and depression, which can persist over time. However, the relationships between anxiety and depression in TBI patients and delirium, sleep quality, self-efficacy, and serum inflammatory markers require further investigation. This study aims to explore the associations of delirium, sleep quality, self-efficacy, and serum inflammatory markers with anxiety and depression in TBI patients, and to examine potential influencing factors. We conducted a cohort study involving 127 patients with TBI. Delirium was assessed using the Confusion Assessment Method (CAM) and CAM-ICU, while anxiety, depression, sleep quality, self-efficacy, and pain were evaluated using the appropriate tools, respectively. Serum inflammatory markers (CRP, TNF-α, IL-6) were collected within 1 day post-injury. Generalized estimating equations (GEE) were used to analyze the relationships between delirium, sleep, self-efficacy, and anxiety/depression. The study identified 56 patients with delirium. Patients with delirium differed significantly from those without delirium in age, TBI classification, sleep duration, CRP levels, TNF-α levels, pain, self-efficacy, and insomnia ( < 0.05). The GEE analysis revealed that delirium, CRP levels, self-efficacy, underlying diseases, insomnia, TBI classification, age, and sleep duration were associated with anxiety symptoms in TBI patients at 6 months post-discharge ( < 0.05). Depression in TBI patients at 6 months post-discharge was not associated with delirium or insomnia but correlated with CRP levels, TBI classification, and self-efficacy ( < 0.05). TBI patients who experience delirium, insomnia, and low self-efficacy during the acute phase are likely to exhibit more anxiety at the 6-month follow-up. Depression in TBI patients is not associated with delirium or insomnia but is negatively correlated with self-efficacy. CRP levels post-TBI may serve as a biomarker to identify patients at risk of emotional symptoms and potentially accelerate patient recovery.

Sensorineural hearing loss (SNHL) is characterized by a compromised cochlear perception of sound waves. Major risk factors for SNHL include genetic mutations, exposure to noise, ototoxic medications, and the aging process. Previous research has demonstrated that inflammation, oxidative stress, apoptosis, and autophagy, which are detrimental to inner ear cells, contribute to the pathogenesis of SNHL; however, the precise mechanisms remain inadequately understood. The endoplasmic reticulum (ER) plays a key role in various cellular processes, including protein synthesis, folding, lipid synthesis, cellular calcium and redox homeostasis, and its homeostatic balance is essential to maintain normal cellular function. Accumulation of unfolded or misfolded proteins in the ER leads to endoplasmic reticulum stress (ERS) and activates the unfolded protein response (UPR) signaling pathway. The adaptive UPR has the potential to reestablish protein homeostasis, whereas the maladaptive UPR, associated with inflammation, oxidative stress, apoptosis, and autophagy, can lead to cellular damage and death. Recent evidence increasingly supports the notion that ERS-mediated cellular damage responses play a crucial role in the initiation and progression of various SNHLs. This article reviews the research advancements on ERS in SNHL, with the aim of elucidating molecular biological mechanisms underlying ERS in SNHL and providing novel insights for the treatment.

Background The attentional network test (ANT) is widely used to evaluate the performance of three attentional networks: alerting, orienting and executive attention networks. This study aimed to investigate the characteristics of attention functions in HIV-negative patients with early forms of neurosyphilis (NS) and their correlation with abnormalities in brain magnetic resonance imaging (MRI). Methods Thirty patients with early forms of NS, 31 patients with syphilis but without NS (Non-NS) and 35 healthy controls were recruited from an HIV-negative cohort between September 2020 and November 2022. The participants were evaluated with the ANT and the Mini-Mental State Examination (MMSE). Brain MRI was performed in NS and Non-NS patients. Results No significant differences were observed in the MMSE scores among the three groups. However, patients with early forms of NS showed poorer performance in orienting and alerting functions than Non-NS group (F = 6.952, P  = 0.011 and F = 8.794, P  = 0.004, respectively); No significant difference was observed in executive function between the two groups (F = 0.001, P  = 0.980). Multivariate analysis of variance using the Bonferroni post hoc test indicated that patients with NS exhibited less efficient orienting function ( P  = 0.023), and alerting function ( P  = 0.003) but not executive function ( P  = 0.99), compared to Non-NS patients. Additionally, a significant difference was found in orienting function between patients with NS and healthy controls ( P  < 0.001) compared to healthy controls. MRI scans revealed that the NS group had a higher prevalence of abnormalities in the frontal lobes and/or the temporoparietal junction compared to the Non-NS group (24/25 vs. 13/19, P  = 0.032). Conclusions The orienting and alerting functions but not executive function were significantly less efficient in early forms of NS group than in the Non-NS group ( P  < 0.01). This indicates deficits in selective attention in patients with early forms of NS. Brain MRI scans revealed abnormalities in the frontal and/or parietal lobes, as well as the temporoparietal junction, suggesting potential neuropathological correlates of these attentional deficits.

The aim of this study was to establish a prognostic risk model for patients with triple negative breast cancer (TNBC). A total of 278 specimens of human TNBC tissues were investigated by immunohistochemistry for growth‐arrest specific protein 6 expression, infiltrations of stromal natural killer cells and tumor‐associated macrophages. According to their prognostic risk scores based on the model, patients were divided into three groups (score 0, 1–2, 3). Correlations of prognostic risk scores, clinicopathologic features and overall survival (OS) were analyzed. To study the clinical value of this stratification model in early disease recurrence or metastasis, 177 patients were screened out for further analysis. Based on disease free survival (DFS), 90 patients fell within the DFS ≤3 years group and 87 patients within the DFS ≥5 years group. We analyzed the differences in prognostic risk scores between the two groups. The prognostic risk scores were negatively related to tumor size, lymph node metastasis and P53 status (P < 0.001 for all). Patients with low prognostic risk scores had longer OS (P = 0.001). Using multivariate analysis, it was determined that TNM stage (HR = 0.432, 95% confidence interval [CI] = 0.281–0.665, P = 0.003), FOXP3 positive lymphocytes (HR = 1.712, 95% CI = 1.085–2.702, P = 0.021) and prognostic risk scores (HR = 1.340, 95% CI = 1.192–1.644, P = 0.005) were independent prognostic factors for OS. Compared with the DFS ≥5 years group, the DFS ≤3 years group patients had significantly higher prognostic risk scores (P < 0.001). In conclusion, the prognostic risk score of the model was a significant indicator of prognosis for patients with TNBC. The prognostic risk model showed significant effect on overall survivals of triple negative breast cancer patients. The prognostic risk score of this model was a significant indicator of prognosis for patients with triple negative breast cancer.