Development and validation of an intensive care unit acquired weakness prediction model: A cohort study.

Background: At present, intensive care unit acquired weakness (ICU-AW) has become an important health care issue. The aim of this study was to develop and validate an ICU-AW prediction model for adult patients in intensive care unit (ICU) to provide a practical tool for early clinical diagnosis. Methods: An observational cohort study was conducted including 400 adult patients admitted from September 2021 to June 2022 at an ICU with four ward at a medical university affiliated hospital in China. The Medical Research Council (MRC) scale was used to assess bedside muscle strength in ICU patients as a diagnostic basis for ICUAW. Patients were divided into the ICU-AW group and the no ICU-AW group and the clinical data of the two groups were statistically analyzed. A risk prediction model was then developed using binary logistic regression. Sensitivity, specificity, and the area under the curve (AUC) were used to evaluate the predictive ability of the model. The Hosmer-Lemeshow test was used to assess the model fit. The bootstrap method was used for internal verification of the model. In addition, the data of 120 patients in the validation group were selected for external validation of the model. Results: The prediction model contained five risk factors: gender (OR: 4.31, 95% CI: 1.682-11.042), shock (OR: 3.473, 95% CI: 1.191-10.122), mechanical ventilation time (OR: 1.592, 95% CI: 1.317-1.925), length of ICU stay (OR: 1.085, 95% CI: 1.018-1.156) and age (OR: 1.075, 95% CI: 1.036-1.115). The AUC of this model was 0.904 (95% CI: 0.847-0.961), with sensitivity of 87.5%, specificity of 85.8%, and Youden index of 0.733. The AUC of the model after resampling is 0.889. The model verification results showed that the sensitivity, specificity and accuracy were 71.4, 92.9, and 92.9%, respectively. Conclusion: An accurate, and readily implementable, risk prediction model for ICU-AW has been developed. This model uses readily obtained variables to predict patient ICU-AW risk. This model provides a tool for early clinical screening for ICU-AW.

Machine learning-based patient classification system for adult patients in intensive care units: A cross-sectional study.

AIM: This study aimed to develop a patient classification system that stratifies patients admitted to the intensive care unit based on their disease severity and care needs. BACKGROUND: Classifying patients into homogenous groups based on clinical characteristics can optimize nursing care. However, an objective method for determining such groups remains unclear. METHODS: Predictors representing disease severity and nursing workload were considered. Patients were clustered into subgroups with different characteristics based on the results of a clustering algorithm. A patient classification system was developed using a partial least squares regression model. RESULTS: Data of 300 patients were analysed. Cluster analysis identified three subgroups of critically patients with different levels of clinical trajectories. Except for blood potassium levels (p = .29), the subgroups were significantly different according to disease severity and nursing workload. The predicted value ranges of the regression model for Classes A, B and C were <1.44, 1.44-2.03 and >2.03. The model was shown to have good fit and satisfactory prediction efficiency using 200 permutation tests. CONCLUSIONS: Classifying patients based on disease severity and care needs enables the development of tailored nursing management for each subgroup. IMPLICATIONS FOR NURSING MANAGEMENT: The patient classification system can help nurse managers identify homogeneous patient groups and further improve the management of critically ill patients.

Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study.

OBJECTIVES: We aimed to develop and validate a postoperative delirium (POD) prediction model for patients admitted to the intensive care unit (ICU). DESIGN: A prospective study was conducted. SETTING: The study was conducted in the surgical, cardiovascular surgical and trauma surgical ICUs of an affiliated hospital of a medical university in Heilongjiang Province, China. PARTICIPANTS: This study included 400 patients (≥18 years old) admitted to the ICU after surgery. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome measure was POD assessment during ICU stay. RESULTS: The model was developed using 300 consecutive ICU patients and was validated using 100 patients from the same ICUs. The model was based on five risk factors: Physiological and Operative Severity Score for the enumeration of Mortality and morbidity; acid-base disturbance and history of coma, diabetes or hypertension. The model had an area under the receiver operating characteristics curve of 0.852 (95% CI 0.802 to 0.902), Youden index of 0.5789, sensitivity of 70.73% and specificity of 87.16%. The Hosmer-Lemeshow goodness of fit was 5.203 (p=0.736). At a cutoff value of 24.5%, the sensitivity and specificity were 71% and 69%, respectively. CONCLUSIONS: The model, which used readily available data, exhibited high predictive value regarding risk of ICU-POD at admission. Use of this model may facilitate better implementation of preventive treatments and nursing measures.

A novel antisense oligonucleotide anchored on the intronic splicing enhancer of hTERT pre-mRNA inhibits telomerase activity and induces apoptosis in glioma cells.

INTRODUCTION: Alternative splicing of hTERT pre-mRNA is an important step in the regulation of telomerase activity, but the regulation mechanisms and functions remain unclear. METHODS: RT-PCR analysis was used to detect hTERT splicing in glioma cell lines and brain tissues. TRAP assay was used to detect the telomerase activity. Then, we designed and synthesized 2'-O-methyl-RNA phosphorothioate AONs and transfected them into glioma cells to detect the changes in telomerase activity. MTT assay, plate colony formation assay, western blotting and Annexin V/PI assay were used to detect cell proliferation and apoptosis. At last, bioinformatics analyses were used to predict the expression and function of splicing protein SRSF2 in gliomas. RESULTS: hTERT splicing occurs both in glioma cell lines and glioma patients' tissues. The telomerase activity was related to the expression level of the full-length hTERT, rather than the total hTERT transcript level. AON-Ex726 was complementary to the sequence of the intronic splicing enhancer (ISE) in intron six, and significantly altered the splicing pattern of hTERT pre-mRNA, reducing the expression level of the full-length hTERT mRNA and increasing the expression level of the -ß hTERT mRNA. After transfection with AON-Ex726, the level of apoptosis was increased, while telomerase activity and cell proliferation were significantly decreased. By bioinformatic predictions, we found the AON-Ex726 anchoring sequence in ISE overlaps the binding site of SRSF2 protein, which is up-regulated during the development of gliomas. CONCLUSIONS: Our findings provided new targets and important clues for the gene therapy of gliomas by regulating the alternative splicing pattern of hTERT pre-mRNA.

Alternative Splicing of hTERT Pre-mRNA: A Potential Strategy for the Regulation of Telomerase Activity.

The activation of telomerase is one of the key events in the malignant transition of cells, and the expression of human telomerase reverse transcriptase (hTERT) is indispensable in the process of activating telomerase. The pre-mRNA alternative splicing of hTERT at the post-transcriptional level is one of the mechanisms for the regulation of telomerase activity. Shifts in splicing patterns occur in the development, tumorigenesis, and response to diverse stimuli in a tissue-specific and cell type-specific manner. Despite the regulation of telomerase activity, the alternative splicing of hTERT pre-mRNA may play a role in other cellular functions. Modulating the mode of hTERT pre-mRNA splicing is providing a new precept of therapy for cancer and aging-related diseases. This review focuses on the patterns of hTERT pre-mRNA alternative splicing and their biological functions, describes the potential association between the alternative splicing of hTERT pre-mRNA and telomerase activity, and discusses the possible significance of the alternative splicing of the hTERT pre-mRNA in the diagnosis, therapy, and prognosis of cancer and aging-related diseases.

Comprehensive Analysis of Interaction Networks of Telomerase Reverse Transcriptase with Multiple Bioinformatic Approaches: Deep Mining the Potential Functions of Telomere and Telomerase.

Telomerase reverse transcriptase (TERT) is the protein component of telomerase complex. Evidence has accumulated showing that the nontelomeric functions of TERT are independent of telomere elongation. However, the mechanisms governing the interaction between TERT and its target genes are not clearly revealed. The biological functions of TERT are not fully elucidated and have thus far been underestimated. To further explore these functions, we investigated TERT interaction networks using multiple bioinformatic databases, including BioGRID, STRING, DAVID, GeneCards, GeneMANIA, PANTHER, miRWalk, mirTarBase, miRNet, miRDB, and TargetScan. In addition, network diagrams were built using Cytoscape software. As competing endogenous RNAs (ceRNAs) are endogenous transcripts that compete for the binding of microRNAs (miRNAs) by using shared miRNA recognition elements, they are involved in creating widespread regulatory networks. Therefore, the ceRNA regulatory networks of TERT were also investigated in this study. Interestingly, we found that the three genes PABPC1, SLC7A11, and TP53 were present in both TERT interaction networks and ceRNAs target genes. It was predicted that TERT might play nontelomeric roles in the generation or development of some rare diseases, such as Rift Valley fever and dyscalculia. Thus, our data will help to decipher the interaction networks of TERT and reveal the unknown functions of telomerase in cancer and aging-related diseases.

The neuroprotection of repetitive transcranial magnetic stimulation pre-treatment in vascular dementia rats.

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive technique that could interfere cortical excitability though brief electric currents induced by alternating magnetic fields from the inductive coil. Currently, it has been applied in many fields of basic and clinical neuro-research. The aims of the present study are to investigate the effect of rTMS pre-treatment on cognitive function in vascular dementia (VaD) rats and further explore the molecular mechanism of rTMS neuroprotection on VaD. We found that rTMS pre-treated VaD rats showed significantly better memory and learning abilities in Morris water maze test compared to the untreated group. Moreover, the mRNA and protein expression levels of BDNF, TrkB, and SYN were significantly higher in the rTMS pre-treated group, indicating that rTMS pre-treatment has neuroprotective effect for VaD, which may have resulted from the increased level of BDNF, TrkB, and SYN in the hippocampal CA1 area.

NGF and TERT co-transfected BMSCs improve the restoration of cognitive impairment in vascular dementia rats.

Vascular dementia (VaD) is a mental disorder caused by brain damage due to cerebrovascular disease, and incidence of VaD is rising. To date, there is no known effective cure for VaD, so effort in developing an effective treatment for VaD is of great importance. The differentiation plasticity of BMSCs, in conjunction with its weak immunogenicity, makes manipulated BMSCs an attractive strategy for disease treatment. However, BMSCs often display disabled differentiation, premature aging, and unstable proliferation, reducing their neuroprotective function. These problems may be caused by the lack of telomerase activity in BMSCs. Our results show that NGF-TERT co-transfected BMSCs have a better therapeutic effect than BMSCs lacking NGF and TERT expression, demonstrated by significant improvements in learning and memory in VaD rats. The underlying mechanism might be increased expression of NGF, TrkA and SYN in the hippocampal CA1 area, which has potential implication in advancing therapeutics for VaD.