Methods: We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher's exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher's exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters.
Results: The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.
METHODS: Point prevalence survey (PPS) of HAIs in the children's wards of 19 public sector secondary- and tertiary-care hospitals of Pakistan and associated key drivers.
RESULTS: A total of 1147 children were included in the PPS. 35.7% were neonates with 32.8% aged >1-5 years. 35.2% were admitted to the intensive care units (ICUs). Peripheral, central venous and urinary catheters were present in 48%, 2.9% and 5.6% of the patients, respectively. A total of 161 HAIs from various pathogens were observed in 153 cases, giving a prevalence of 13.3%. The majority of HAIs were caused by Staphylococcus aureus (31.7%) followed by Klebsiella pneumoniae (22.9%) and Escherichia coli (17.4%). Bloodstream infections were identified in 42 cases followed by lower-respiratory-tract infections in 35. Increased length of hospital stays and being admitted to the ICU, 'rapidly fatal' patients under the McCabe and Jackson criteria, central and peripheral catheterization, and invasive mechanical ventilation were, associated with higher HAIs (P<0.001). 99.7% of HAI patients fully recovered and were discharged from the hospital.
CONCLUSION: There is a high prevalence of HAIs among neonates and children admitted to health facilities in Pakistan. Infection prevention and control measures should be implemented to help prevent future HAIs.
METHODS: This is a retrospective cross-sectional study that included patients with AIS admitted to Hospital Sultanah Nur Zahirah, Malaysia from 2017 to 2020. SAP was defined as infection with pneumonia during the first seven days after IS. HG was defined as a blood glucose level > 7.8 mmol/L within 72 h after admission. Patients with SAP were divided into two groups according to HG status. Multivariate logistic regression analysis was performed using SPSS software, version 22 (IBM Corp., Armonk, NY) to identify SAP predictors among patients with HG. Kaplan-Meier log-rank test was used to compare the survival rate from unfavourable functional outcomes between hyperglycaemic patients with and without SAP.
RESULTS: Among 412 patients with AIS, 69 (16.74%) had SAP. The prevalence of SAP among patients with HG and normoglycemia during AIS was 20.98%, and 10.65%, respectively. Age above 60 years, leucocytosis, and National Institute of Health Stroke Scale (NIHSS) > 14 on admission were independent predictors of SAP with aOR of 2.08 (95% CI;1.01-4.30), 2.83 (95% CI; 1.41-5.67), and 3.67 (95% CI; 1.53-8.80), respectively. No significant difference in unfavourable functional outcomes survival was found among patients with and without SAP (p = 0.653).
CONCLUSION: This study demonstrated the prevalence of SAP was higher among patients with HG compared to normoglycemia during AIS. The patient being old, leucocytosis and severe stroke upon admission predict the occurrence of SAP among patients with HG during AIS.