Activity

regnancy. Future studies need to be designed to address the methodological challenges noted here to determine what facets of early childhood education most effectively prevent teenage pregnancy. Randomized control trials, while challenging to implement, are best suited to determine the true causal effect of early childhood education for preventing teenage pregnancy.

To compare non-pharmacological interventions in their ability to prevent delirium in critically ill patients, and find the optimal regimen for treatment.

Literature searches were conducted using PubMed, Embase, CINAHL, and Cochrane Library databases until the end of June 2019. We estimated the risk ratios (RRs) for the incidence of delirium and in-hospital mortality and found the mean difference (MD) for delirium duration and the length of ICU stay. The probabilities of interventions were ranked based on clinical outcomes. The study was registered on PROSPERO (CRD42020160757).

Twenty-six eligible studies were included in the network meta-analysis. Studies were grouped into seven intervention types physical environment intervention (PEI), sedation reducing (SR), family participation (FP), exercise program (EP), cerebral hemodynamics improving (CHI), multi-component studies (MLT) and usual care (UC). In term of reducing the incidence of delirium, the two most effective interventions were FP (risk ratio (Rptimal intervention techniques for preventing delirium and reducing ICU length of stay in critically ill patients by way of utilizing several interventions simultaneously. Additionally, family participation as a method of patient-centered care resulted in better outcomes for reducing the incidence of delirium.

Whether tumor mutation burden (TMB) affects prognosis and immune infiltration of tumor patients is controversial. We designed and conducted a multi-omics study with the aim of investigating the prognostic value of TMB and the relationship between TMB and immune infiltration in head and neck squamous cell carcinoma (HNSCC).

TMB scores were calculated from the mutation data of 506 HNSCC samples from The Cancer Genome Atlas (TCGA), and the patients were divided into low- and high-TMB groups according to the TMB score quartiles. Differentially expressed genes (DEGs) between the low-TMB and high-TMB groups were identified. Immune cell infiltration and survival analyses were conducted between groups.

High TMB in HNSCC patients was associated with a poor prognosis, large primary tumor size, advanced clinical stage and a human papillomavirus (HPV)-negative status. A total of 576 DEGs were identified, and gene set enrichment analysis (GSEA) revealed that the DEGs in the low-TMB group were enriched in immune-related pathways. Four hub genes were significantly associated with prognosis, and mutations in these genes affected immune infiltration. The estimated fractions of B memory cells and CD4+ memory resting cells were higher in the low-TMB group than in the high-TMB group, and B cell and CD4+T cell infiltration was positively correlated with prognosis in HNSCC patients.

HNSCC patients with low TMB have better prognoses than those with high TMB, and TMB might affect B cell and CD4+T cell infiltration.

HNSCC patients with low TMB have better prognoses than those with high TMB, and TMB might affect B cell and CD4+T cell infiltration.

The prognostic nutritional index (PNI) is an index reflecting the nutritional and inflammatory status of patients and is explored for prognosis in nasopharyngeal carcinoma (NPC). click here However, the data are conflicting. In the current study, a meta-analysis was performed to comprehensively clarify the association between PNI and prognosis of NPC.

PubMed, Web of Science, the Cochrane library, China National Knowledge Infrastructure (CNKI), and Wanfang database were searched up to July 25, 2020. Hazard ratio (HR) and with 95% confidence interval (CI) were calculated to assess the impact of PNI on the survival outcomes of patients with NPC.

A total of 10 studies containing with 4511 patients were identified. The pooled results showed that NPC patients with a low PNI would have a worse overall survival (OS) (HR=1.89, 95%CI=1.59-2.25, p<0.001), distant metastasis-free survival (DMFS) (HR=2.01. 95%CI=1.66-2.43, p<0.001), progression-free survival (PFS) (HR=1.59, 95%CI=1.32-1.91, p<0.001), and locoregional recurrence-free survival (LRRFS) (HR=1.51, 95%CI=1.04-2.21, p=0.032). Subgroup analysis showed that the low PNI was still a significant prognostic factor for OS and DMFS.

Our meta-analysis demonstrated that a low PNI was significantly correlated to poor OS, DMFS, PFS, and LRRFS in NPC. Therefore, we suggest PNI applied as an indicator for prediction of the short- and long- term survival outcomes in patients with NPC.

Our meta-analysis demonstrated that a low PNI was significantly correlated to poor OS, DMFS, PFS, and LRRFS in NPC. Therefore, we suggest PNI applied as an indicator for prediction of the short- and long- term survival outcomes in patients with NPC.

In spite of the prevalence of occupational neck disorders as a result of work force fluctuating from industry to sedentary office work, most cervical spine computational models are not capable of simulating occupational and daily living activities whereas majority of cervical spine models specialized to simulate crash and impact scenarios. Therefore, estimating spine tissue loads accurately to quantify the risk of neck disorders in occupational environments within those models is not possible due to the lack of muscle models, dynamic simulation and passive spine structures.

In this effort the structure, logic, and validation process of an electromyography-assisted cervical biomechanical model that is capable of estimating neck loading under three-dimensional complex motions is described. The developed model was designed to simulate complex dynamic motions similar to work place exposure. Curved muscle geometry, personalized muscle force parameters, and separate passive and (electromyography-driven) active muscle force components are implemented in this model.

Calibration algorithms were able to reverse-engineer personalized muscle properties to calculate active and passive muscle forces of each individual.

This electromyography-assisted cervical spine model with curved muscle model is capable to accurately predict spinal tissue loads during isometric and dynamic head and neck activities. Personalized active and passive muscle force algorithms will help to more robustly investigate person-specific muscle forces and spinal tissue loads.

This electromyography-assisted cervical spine model with curved muscle model is capable to accurately predict spinal tissue loads during isometric and dynamic head and neck activities. Personalized active and passive muscle force algorithms will help to more robustly investigate person-specific muscle forces and spinal tissue loads.