Risk assessment scales to predict risk of lower extremity deep vein thrombosis among multiple trauma patients: a prospective cohort study.

BACKGROUND: Deep vein thrombosis (DVT) is a common complication in orthopedic patients. Previous studies have focused on major orthopedic surgery.There are few studies with multiple trauma. We aimed to describe the prevalence of DVT and compare the predictive power of the different risk assessment scales in patients with multiple trauma. METHODS: This prospective cohort study involved multiple trauma patients admitted to our hospital between October 2021 and December 2022. Data were prospectively collected for thrombotic risk assessments using the Risk Assessment Profile for thromboembolism(RAPT), the DVT risk assessment score (DRAS), and the Trauma Embolic Scoring System (TESS), respectively. The receiver operation characteristic (ROC) curve and the area under the curve (AUC) were evaluated to compare the predictive power. The whole leg duplex ultrasound of both lower extremities Doppler ultrasound was used to determine DVT incidence. RESULTS: A total of 210 patients were included, and the incidence of DVT was 26.19%. Distal DVT accounted for 87.27%; postoperative DVT, 72.73%; and bilateral lower extremity thrombosis, 30.91%. There were significant differences in age, education degree, pelvic fracture, surgery, ISS, D-dimer level, length of hospital stay and ICU stay between the thrombosis group and the non-thrombosis group. The AUCs for RAPT, DRAS, and TESS were 0.737, 0.710, and 0.683, respectively. There were no significant differences between the three ROC curves. CONCLUSIONS: The incidence of DVT was relatively high during hospitalization. We prospectively validated the tests to predict risk of DVT among patients with multiple trauma to help trauma surgeons in the clinical administration of DVT prophylaxis.

Distributed genotyping and clustering of Neisseria strains reveal continual emergence of epidemic meningococcus over a century.

Core genome multilocus sequence typing (cgMLST) is commonly used to classify bacterial strains into different types, for taxonomical and epidemiological applications. However, cgMLST schemes require central databases for the nomenclature of new alleles and sequence types, which must be synchronized worldwide and involve increasingly intensive calculation and storage demands. Here, we describe a distributed cgMLST (dcgMLST) scheme that does not require a central database of allelic sequences and apply it to study evolutionary patterns of epidemic and endemic strains of the genus Neisseria. We classify 69,994 worldwide Neisseria strains into multi-level clusters that assign species, lineages, and local disease outbreaks. We divide Neisseria meningitidis into 168 endemic lineages and three epidemic lineages responsible for at least 9 epidemics in the past century. According to our analyses, the epidemic and endemic lineages experienced very different population dynamics in the past 100 years. Epidemic lineages repetitively emerged from endemic lineages, disseminated worldwide, and apparently disappeared rapidly afterward. We propose a stepwise model for the evolutionary trajectory of epidemic lineages in Neisseria, and expect that the development of similar dcgMLST schemes will facilitate epidemiological studies of other bacterial pathogens.

Clinical implication and immunological landscape analyses of ANLN in pan-cancer: A new target for cancer research.

BACKGROUND: Anillin is a F-actin binding protein (ANLN) mainly involved in the process of cytokinesis and known to be dysregulated in diverse cancers. However, the role of ANLN in pan-cancer prognosis and tumor immunity remains unclear. METHODS: Gene expression profiles of 31 solid tumors were downloaded from The Cancer Genome Atlas (TCGA) database. ANLN mRNA and protein expression were quantified using quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC). Protein expression of ANLN was further confirmed in Human Protein Atlas (HPA) database. Cox regression and Kaplan-Meier analysis were utilized to assess the prognostic value of ANLN in pan-cancer. The correlation between ANLN and different immune gene markers and infiltration cells was analyzed via ESTIMATE and CIBERSORT. A BLCA immunotherapy cohort: IMvigor (210) was used to confirm the role of ANLN in immune response. RESULTS: ANLN upregulation was detected in 21 types of cancers and was associated with poor overall survival (OS), disease-free interval (DFI), and progression-free interval (PFI) in most cancers except in THYM (Thymoma). Additionally, correlation analysis revealed a significantly positive association between ANLN expression and tumor mutation burden (TMB), microsatellite instability (MSI), immune cells infiltration. and immune checkpoint genes in various cancers. The BLCA immunotherapy cohort confirmed that patients with higher ANLN level had better immune responses and longer OS. CONCLUSION: ANLN may serve as a prognostic biomarker for pan-cancer. ANLN upregulation is associated with higher TMB, MSI, and immune cell infiltration in multiple types of tumors, shedding new light for cancer treatment.

A cross-sectional study of mental health status and self-psychological adjustment in nurses who supported Wuhan for fighting against the COVID-19.

AIMS AND OBJECTIVES: To evaluate the mental health status, stressors and self-adjustment of nurses in isolation wards at different periods in Wuhan, China. BACKGROUND: Mental health issues easily occurred among the frontline medical workers of a major epidemic. However, the stressors and psychological adjustments experienced by nurses have not been well described. This is crucial to improving clinical quality and nursing safety and ensuring nurses' physical and psychological health. METHODS: We performed a cross-sectional prospective study using the Self Reporting Questionnaire-20, stressor and self-adjustment questionnaire administered to frontline nurses in Wuhan at two time points: after they had worked in isolation wards for 7-10 days (T1 ) and 2 months (T2 ). This paper complies with the STROBE reporting guideline for cross-sectional studies. RESULTS: T1 has 92 respondents, and T2 has 86. The positive rates of mental health problems were 26.09% and 9.30%, respectively, showing significantly different in the two periods. The main factors influenced mental health were self-perceived stress and only child status. The most common stressors were as follows: a large infected population, high infectivity; concerned about family's health status; high mortality if not treated in time (T1 ); and long duration of the epidemic, separate from family for a long time (T2 ). In terms of self-adjustment, 97.83%(T1 )/88.04%(T2 ) of nurses thought it was necessary, but 9(T1 ) /5(T2 ) chose to avoid addressing it, and 8(T1 ) /5(T2 ) utilised a professional psychological counselling hotline. CONCLUSIONS: Mental health problems among frontline nurses fighting COVID-19 need special attention, so administrators should offer timely counselling and strengthen effective psychosocial support to improve their mental resilience. RELEVANCE TO CLINICAL PRACTICE: This study surveyed the mental problems and self-adjustment status among nurses working Wuhan during the outbreak of COVID-19, to provide administrators with a scientific basis to effectively intervene.

A risk score system based on DNA methylation levels and a nomogram survival model for lung squamous cell carcinoma.

Lung squamous cell carcinoma (LSCC) is one of the primary types of non­small cell lung carcinoma, and patients with recurrent LSCC usually have a poor prognosis. The present study was conducted to build a risk score (RS) system for LSCC. Methylation data on LSCC (training set) and on head and neck squamous cell carcinoma (validation set 2) were obtained from The Cancer Genome Atlas database, and GSE39279 (validation set 1) was retrieved from the Gene Expression Omnibus database. Differentially methylated protein­coding genes (DMGs)/long non­coding RNAs (DM­lncRNAs) between recurrence­associated samples and nonrecurrence samples were screened out using the limma package, and their correlation analysis was conducted using the cor.test() function. Following identification of the optimal combinations of DMGs or DM­lncRNAs using the penalized package in R, RS systems were built, and the system with optimal performance was selected. Using the rms package, a nomogram survival model was then constructed. For the differentially expressed genes (DEGs) between the high­ and low­risk groups, pathway enrichment analysis was performed by Gene Set Enrichment Analysis. There were 335 DMGs and DM­lncRNAs in total. Following screening out of the top 10 genes (aldehyde dehydrogenase 7 family member A1, chromosome 8 open reading frame 48, cytokine­like 1, heat shock protein 90 alpha family class A member 1, isovaleryl­CoA dehydrogenase, phosphodiesterase 3A, PNMA family member 2, SAM domain, SH3 domain and nuclear localization signals 1, thyroid hormone receptor interactor 13 and zinc finger protein 878) and 6 top lncRNAs, RS systems were constructed. According to Kaplan­Meier analysis, the DNA methylation level­based RS system exhibited the best performance. In combination with independent clinical prognostic factors, a nomogram survival model was built and successfully predicted patient survival. Furthermore, 820 DEGs between the high­ and low­risk groups were identified, and 3 pathways were identified to be enriched in this gene set. The 10­DMG methylation level­based RS system and the nomogram survival model may be applied for predicting the outcomes of patients with LSCC.

The distribution and density of monocarboxylate transporter 2 in cerebral cortex, hippocampus and cerebellum of wild-type mice.

Monocarboxylates cannot cross the blood-brain barrier freely to participate in brain energy metabolism. Specific monocarboxylate transporters (MCTs) are needed to cross cellular membranes. Monocarboxylate transporter 2 (MCT2) is a major monocarboxylate transporter encoded by the SLC16A7 gene. Recent studies reported that neurodegenerative diseases of the CNS, such as Alzheimer's disease (AD) and Parkinson's disease (PD), were related to energy metabolic impairment. MCT2 also plays an important role in energy metabolism in the CNS. To provide experimental evidence for future research on the role of MCT2 in the pathological process of CNS degenerative diseases, the distribution and density of MCT2 in different subregions of wild-type mouse brain was examined using immunohistochemistry, western blot and immunogold post-embedding electron microscopic techniques. The amount of MCT2 was higher in cerebellum than in cortex and hippocampus on western blots, and there was no statistical difference between cortex and hippocampus. Immunohistochemistry assay revealed the highest density of MCT2 in the CA3 of the hippocampus. The granular cell layer of the cerebellum contained more MCT2 than the molecular layer. The MCT2 density on the end feet of astrocytes of molecular layer was lower than in hippocampus, but the postsynaptic densities (PSDs) of asymmetric synapses in the molecular layer exhibited a high density using immunogold post-embedding electron microscopic techniques.

[Recognition of fatigue status of pilots based on deep contractive auto-encoding network].

We proposed a new deep learning model by analyzing electroencephalogram signals to reduce the complexity of feature extraction and improve the accuracy of recognition of fatigue status of pilots. For one thing, we applied wavelet packet transform to decompose electroencephalogram signals of pilots to extract the δ wave (0.4-3 Hz), θ wave (4-7 Hz), α wave (8-13 Hz) and ß wave (14-30 Hz), and the combination of them was used as de-nosing electroencephalogram signals. For another, we proposed a deep contractive auto-encoding network-Softmax model for identifying pilots' fatigue status. Its recognition results were also compared with other models. The experimental results showed that the proposed deep learning model had a nice recognition, and the accuracy of recognition was up to 91.67%. Therefore, recognition of fatigue status of pilots based on deep contractive auto-encoding network is of great significance.