Objective:To explore the factors influencing diarrhea after nasal feeding in patients with severe neurological illness, construct a nomogram model and verify its predictive performance.Methods:A retrospective study was conducted to select 797 nasal feeding patients admitted to the Neurological Intensive Care Unit of the Tianjin First Central Hospital from May 2020 to November 2023 as study subjects. Patients were divided into a modeling set ( n=558) and a validation set ( n=239) in a 7∶3 ratio. Influencing factors were screened based on univariate analysis, correlations between variables were detected by multicollinearity diagnostics, and then risk factors were identified using binomial Logistic regression, and a nomogram model was created. The predictive capacity of the model was assessed using the area under the receiver operating characteristic curve ( AUC), the degree of fit of the model was evaluated using the Hosmer-Lemeshow test and calibration curves were plotted, and the practical application value of the model in clinical practice was appraised by means of clinical decision curve analysis. Results:Binomial Logistic regression analysis showed that Glasgow Coma Scale score ( OR=0.891), Acute Physiology and Chronic Health Evaluation Ⅱ score ( OR=1.063), sedative-analgesic ( OR=0.326), albumin level ( OR=0.856), days of antibiotic use ( OR=3.338), and acid suppressants ( OR=3.260 ) were the factors influencing diarrhea after nasal feeding in patients with severe neurological illness ( P<0.05). In the validation set, the AUC for the nomogram model of the risk of diarrhea after nasal feeding in patients with severe neurological illness was 0.82 [95% CI (0.77, 0.87) ], with a sensitivity of 0.72 and a specificity of 0.72. Clinical decision curve showed nomogram model had high application value. Conclusions:The constructed nomogram model has good predictive performance and can help healthcare professionals in neurocritical care specialties to identify patients at high risk of diarrhea at an early stage, thus informing the development of individualized prevention strategies in clinical practice.

Objective:To explore the factors influencing diarrhea after nasal feeding in patients with severe neurological illness, construct a nomogram model and verify its predictive performance.Methods:A retrospective study was conducted to select 797 nasal feeding patients admitted to the Neurological Intensive Care Unit of the Tianjin First Central Hospital from May 2020 to November 2023 as study subjects. Patients were divided into a modeling set ( n=558) and a validation set ( n=239) in a 7∶3 ratio. Influencing factors were screened based on univariate analysis, correlations between variables were detected by multicollinearity diagnostics, and then risk factors were identified using binomial Logistic regression, and a nomogram model was created. The predictive capacity of the model was assessed using the area under the receiver operating characteristic curve ( AUC), the degree of fit of the model was evaluated using the Hosmer-Lemeshow test and calibration curves were plotted, and the practical application value of the model in clinical practice was appraised by means of clinical decision curve analysis. Results:Binomial Logistic regression analysis showed that Glasgow Coma Scale score ( OR=0.891), Acute Physiology and Chronic Health Evaluation Ⅱ score ( OR=1.063), sedative-analgesic ( OR=0.326), albumin level ( OR=0.856), days of antibiotic use ( OR=3.338), and acid suppressants ( OR=3.260 ) were the factors influencing diarrhea after nasal feeding in patients with severe neurological illness ( P<0.05). In the validation set, the AUC for the nomogram model of the risk of diarrhea after nasal feeding in patients with severe neurological illness was 0.82 [95% CI (0.77, 0.87) ], with a sensitivity of 0.72 and a specificity of 0.72. Clinical decision curve showed nomogram model had high application value. Conclusions:The constructed nomogram model has good predictive performance and can help healthcare professionals in neurocritical care specialties to identify patients at high risk of diarrhea at an early stage, thus informing the development of individualized prevention strategies in clinical practice.

OBJECTIVES: To investigate the expression of cartilage acidic protein 1 (CRTAC1) in gastric cancer (GC) and its effect on biological behaviors and immune cell infiltration of GC. METHODS: Transcriptomic, GO and KEGG analyses were conducted to investigate the association of CRTAC1 expression with prognosis of GC patients and its involvement in cell function and signaling pathways. ESTIMATE algorithm was used to analyze the effect of CRTAC1 expression on the tumor microenvironment and the tumor mutation load. In two GC cell clines (HGC-27 and MKN-74), CCK8, EdU and clone formation assays, flow cytometry, and Hoechst staining were used to examine the effects of CRTAC1 knockdown on cell proliferation, cell cycle changes and apoptosis. Wound healing assay, Transwell assay, and Western blotting were performed to analyze the effect of CRTAC1 knockdown on GC cell migration and the underlying mechanism. RESULTS: Bioinformatics analysis showed significantly higher expression of CRTAC1 in GC tissues than in adjacent tissues (P<0.05). Age and tumor stage were both prognostic risk factors in GC patients with high CRTAC1 expression (P<0.001). Analysis using ESTIMATE algorithm showed that CRTAC1 expression increased immune cell infiltration and decreased tumor mutational load in GC (P<0.001). In HGC-27 and MKN-74 cells, CRTAC1 knockdown significantly inhibited cell proliferation and migration and promoted cell apoptosis. Western blotting demonstrated that CRTAC1 knockdown significantly increased E-cadherin expression and reduced the expression levels of vimentin, p-PI3K, AKT2, p-AKT and p-mTOR in GC cells. CONCLUSIONS High expression of CRTAC1 in GC tissues affects immunotherapeutic efficacy and prognosis of the patients, possibly by promoting epithelial-mesenchymal transition via modulating tumor mutational load, tumor microenvironment, and the PI3K/AKT signaling pathway.
Stomach Neoplasms/metabolism* ; Humans ; Cell Proliferation ; Proto-Oncogene Proteins c-akt/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Signal Transduction ; Cell Movement ; Cell Line, Tumor ; Prognosis ; Apoptosis ; Tumor Microenvironment ; Female ; Male ; Epithelial-Mesenchymal Transition/genetics*

Objective:To explore and optimize the management process of bladder irrigation for patients after greenlight photoselective vaporization (PVP) , reduce the occurrence of postoperative complications, and improve the satisfaction of patients and nurses for bladder irrigation.Methods:Totally 315 patients undergoing PVP in the Department of Urinary Surgery, the Second Hospital of Hebei Medical University from May 2017 to April 2019 were selected using purposive sampling. A total of 153 patients admitted from May 2017 to April 2018 were divided into the control group, who received routine bladder irrigation management, while 162 patients admitted from May 2018 to April 2019 were divided into the observation group, who received bladder irrigation management by the failure mode and effect analysis. The incidence of complications such as bladder spasm and urinary tract obstruction, the duration of continuous bladder irrigation, and patient satisfaction were compared between the two groups, and the nurse satisfaction before and after the process optimization was compared.Results:After the process optimization, the incidence of bladder spasm decreased from 17.0% (26/153) to 8.0% (13/162) , the incidence of urinary tract obstruction decreased from 11.8% (18/153) to 3.7% (6/162) , and the differences were statistically significant (χ 2=5.835, 7.264; P<0.05) . The duration of bladder irrigation was shortened from (25.311±2.295) h to (22.422±1.481) h, and the difference was statistically significant ( t=13.348, P<0.05) . After optimizing the bladder irrigation management process, the nurse satisfaction score was (34.221±5.124) , and the patient satisfaction score was (37.113±6.108) , both higher than those before the optimization, and the differences were statistically significant ( t=19.817, 14.546; P<0.05) . Conclusions:Optimizing the bladder irrigation management process for patients after PVP using the failure mode and effect analysis can reduce the incidence of postoperative bladder irrigation complications, shorten the duration of bladder irrigation, and improve patient and nurse satisfaction.

Objective To investigate the prevalence of Mycoplasma pneumoniae ( Mp) infection in children in Hengyang from 2013 to 2016 and to analyze the p1 genotypes of the isolated Mp strains by using polymerase chain reaction-restriction fragment length polymorphism ( PCR-RFLP) , nested polymerase chain reaction (nPCR) and rapid-cycle polymerase chain reaction (Rapid-Cycle PCR).Methods Throat swab samples of children with acute respiratory tract infection were collected from four hospitals in Hengyang , Hu-nan Province from 2013 to 2016.Mp strains in these samples were identified by PCR amplification of the 16S rRNA gene.PCR-RFLP, nPCR and Rapid-Cycle PCR were performed for Mp p1 genotyping in order to fur-ther analyze the genotypes of Mp strains circulating in Hengyang .Results A total of 109 clinical strains of Mp were identified from the 984 throat swab samples .The sensitivities of PCR-RFLP and nPCR for genoty-ping MP strains were both 100％, while that of rapid-Cycle PCR was 98 .17％.All of the three methods showed 100％specificity for genotyping.Of all isolated Mp strains, 78.90％ were p1 gene type Ⅰ and 21.10％were p1 gene typeⅡ(t=93.239, P=0.01).From 2013 to 2016, the annual isolation rates of p1 gene type Ⅰ and type Ⅱ strains were 93.10％, 87.5％, 76.92％, 65.79％ and 6.90％, 12.5％, 23.08％, 34.21％, respectively.The rate of Mp p1 gene type Ⅰinfection decreased over year , while that of p1 gene type Ⅱinfection increased gradually .Conclusion PCR-RFLP, nPCR and rapid-Cycle PCR are reliable for genotyping of Mp p1 gene.The predominant genotype of Mp strains circulating in Hengyang is p 1 gene type Ⅰ, but the incidence of p 1 gene type Ⅱinfection gradually increases from 2013 to 2016 .