Dynamic changes and diagnostic and prognostic significance of serum PCT, hs-CRP and s-100 protein in central nervous system infection

Comparative effects of insulin pump and injection on gestational diabetes mellitus pregnancy outcomes and serum biomarkers

BACKGROUND

Insulin injection is the basic daily drug treatment for diabetic patients.

AIM

To evaluate the comparative impacts of continuous subcutaneous insulin infusion (CSII).

METHODS

Based on the treatment modality received, the patients were allocated into two cohorts: The CSII group and the multiple daily injections (MDI) group, with each cohort comprising 210 patients. Comparative assessments were made regarding serum levels of serum-secreted frizzled-related protein 5, homocysteine, and C1q/TNF-related protein 9. Furthermore, outcomes such as fasting plasma glucose, 2-hour postprandial glucose levels, pain assessment scores, and the incidence of complications were evaluated post-treatment.

RESULTS

The CSII group displayed notably lower fasting plasma glucose and 2-h postprandial glucose levels in comparison to the MDI group (P < 0.05). Subsequent analysis post-treatment unveiled a significantly higher percentage of patients reporting no pain in the CSII group (60.00%) in contrast to the MDI group (36.19%) (P < 0.05). Additionally, the CSII group exhibited a markedly reduced occurrence of fetal distress and premature rupture of membranes compared to the MDI group (P < 0.05). However, there were no significant variances observed in other pregnancy outcomes between the two groups (P > 0.05). A statistical analysis revealed a significant difference in the incidence of complications between the groups (χ 2 = 11.631, P = 0.001).

CONCLUSION

The utilization of CSII via an insulin pump, as opposed to MDI, can significantly enhance the management of insulin administration in patients with GDM by diversifying the sites of insulin delivery. This approach not only promotes optimal glycemic control but also regulates metabolic factors linked to blood sugar, reducing the likelihood of adverse pregnancy outcomes and complications. The clinical relevance and importance of CSII in GDM management highlight its wide-ranging clinical usefulness.

Ubiquity of Synthetic Phenolic Antioxidants in Children's Cerebrospinal Fluid from South China: First Evidence for Their Penetration across the Blood-Cerebrospinal Fluid Barrier

Synthetic phenolic antioxidants (SPAs) and relevant transformation products (TPs) are potentially neurotoxic pollutants to which humans are widely exposed. However, their penetration behavior across the brain barrier and associated exposure to the central nervous system (CNS) remain unknown. This study is the first to investigate a wide range of 30 SPAs and TPs, including emerging SPAs, in matched serum and cerebrospinal fluid (CSF) samples from children in Guangzhou, China. Sixty-two children of either sex aged <14 years with nonbloody CSF and complete clinical information were included. The findings demonstrated the ubiquitous occurrence of many SPAs and TPs, particularly BHT, 2,4-di-tert-butylphenol (DBP), AO 1010, AO 1076, BHT-Q, and BHT-quinol, not only in serum but also in the CSF. Median total concentrations of SPAs and TPs were up to 22.0 and 2.63 ng/mL in serum and 14.5 and 2.11 ng/mL in CSF, respectively. On calculating the penetration efficiencies across the blood-CSF barrier (BCSFB) (RCSF/serum, CCSF/Cserum) for selected SPAs and TPs, their RCSF/serum values (median 0.52-1.41) were highly related to their physicochemical properties, indicating that passive diffusion may be the potential mechanism of BCSFB penetration. In addition, the RCSF/serum values were positively correlated with the barrier permeability index RAlb (AlbuminCSF/Albuminserum), indicating that barrier integrity is an important determinant of BCSFB penetration. Overall, these results will improve our perception of human internal exposure to SPAs and lay a solid foundation for assessing the risk of CNS exposure to various SPAs.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00521-023-08606-w.

Assessment of mechanisms of infectious pneumonia based on expression of fibrinogen, procalcitonin, high-sensitivity C-reactive protein expression, T helper 17 cells, regulatory T cells interleukin-10, and interleukin-17

BACKGROUND

Infectious pneumonia is one of the important causes of neonatal death that can lead to the imbalance of T helper 17 cells (Th17) and T regulatory T cells (Treg) cells. The correlation between plasma fibrinogen (FIB), procalcitonin (PCT), C-reactive protein (CRP) and Th17/Treg-IL-10/IL-17 axis balance and their specific role in the occurrence and development of infectious pneumonia are not completely clear.

METHODS

Thirty specific-pathogen free Sprague Dawley (SD) rats were randomly divided into a control group for comparison and IPN model group. After the establishment of infectious pneumonia model, levels of FIB, PCT, hs-CRP, IL-10, and IL-17 in the serum of the two groups were measured using enzyme linked immunosorbent assay (ELISA), the pathological changes of lung tissue were observed using hematoxylin and eosin (HE) staining, the number of Treg and Th17 cells and the ratio of Th17/Treg in serum were detected using flow cytometry, and the levels of retinoic acid-related orphan receptor γt and forkhead box P3 (FOXP3) in lung tissue were detected using reverse transcription polymerase chain reaction (RT-PCR) and western blot.

RESULTS

The results showed that the serum levels of FIB, PCT, hs-CRP, Th17 cell number, Th17/Treg ratio, left lung dry and wet weight, lung tissue wet/dry ratio, lung pathology score and IL-17 level in the model group were significantly higher than those in the control group, while the number of Treg cells and the level of IL-10 in the model group were significantly lower than those in the control group. In addition, the expression of Foxp3mRNA and protein in lung tissue of model group decreased significantly, while the expression level of ROR- γ t mRNA and protein increased.

CONCLUSIONS

In infectious pneumonia, the expression levels of FIB, PCT and hs-CRP are up-regulated, and Th17 cells are activated, Treg cells are inhibited, proinflammatory cytokine IL-17 expression is up-regulated, anti-inflammatory cytokine IL-10 expression is down-regulated, resulting in increased inflammatory response, thus promoting the occurrence and development of infectious pneumonia.

Gut Microbiota as Early Predictor of Infectious Complications before Cardiac Surgery: A Prospective Pilot Study

Cardiac surgery remains a field of medicine with a high percentage of postoperative complications, including infectious ones. Modern data indicate a close relationship of infectious disorders with pathological changes in the composition of the gut microbiome; however, the extent of such changes in cardiac surgery patients is not fully clarified. In this prospective, observational, single center, pilot study, 72 patients were included, 12 among them with the infectious complications. We analyzed the features of the fecal microbiota before and in the early postoperative period, as one of the markers for predicting the occurrence of bacterial infection. We also discovered the significant change in microbial composition in the group of patients with infectious complications compared to the non-infectious group before and after cardiac surgery, despite the intra-individual variation in composition of gut microbiome. Our study demonstrated that the group of patients that had a bacterial infection in the early postoperative period already had an altered microbial composition even before the surgery. Further studies will evaluate the clinical significance of the identified proportions of individual taxa of the intestinal microbiota and consider the microbiota as a novel target for reducing the risk of infectious complications.

Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.

Combining C reactive protein and serum albumin to predict 90-day mortality in systemic lupus erythematosus with serious community-acquired infections

Objective

Serious infections in SLE are common and have emerged as the major cause of death. However, effective methods to identify poor prognosis are still lacking. Therefore, we aimed to determine the predictive value of C reactive protein (CRP) plus albumin (ALB) in SLE with serious infections.

Methods

From May 2015 to December 2018, consecutive patients with SLE presenting with serious infections in our emergency department were prospectively recruited. Serum CRP and ALB were measured within 24 hours of admission. The outcome was defined as mortality rate at 90 days. A CRP plus ALB score (2–6) was assigned based on the CRP and ALB concentrations. We performed univariate and multivariate regression analyses to detect the independent effects of CRP plus ALB on 90-day mortality (all-cause and infection-related). Subgroup analyses were used to show the effects stratified by lupus nephritis.

Results

A total of 150 patients were included, and the all-cause 90-day mortality rate was 38% (n=57), 41 of which was infection-related. The predominant infection sites were pulmonary (79.3%) and bloodstream infection (20.7%). Serum CRP and ALB levels were significantly different in non-surviving patients compared with those in surviving patients (p=0.002 and p<0.001, respectively). In the fully adjusted logistic regression model, the CRP plus ALB score was associated with decreased 90-day survival (adjusted OR 1.52; 95% CI 1.08 to 2.13; p=0.017).

Conclusions

CRP plus ALB was associated with the risk of all-cause and infection-related 90-day mortality in SLE with serious infections. Although this finding requires further verification, the two parameters may be useful for predicting poor outcomes in such patients.

An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning

COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on a dataset made public by Yan et al. in [1] of 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high-sensitivity C-reactive protein, and age (LNLCA)-acquired at hospital admission-were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate-, and high-risk groups using LNLCA cutoff values of 10.4 and 12.65 with the death probability less than 5%, 5-50%, and above 50%, respectively. The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.

Characterization of a new human astrocytoma cell line SHG140: cell proliferation, cell phenotype, karyotype, STR markers and tumorigenicity analysis

Background: Primary tumor Cell was an important tool for tumor research. Here, a new astrocytoma cell line SHG-140 was established and its proliferation, phenotype, karyotype, STR authentication, pathological characteristics, and characteristics of the cells' intrancranial xenografts of nude mice were studied.

Methods: Primary SHG-140 culture was performed in DMEM/F12 medium with 10% FBS. Cell proliferation, karyotype analysis, cell immunofluorescence and STR authentication of SHG140 cells were performed. HE staining and immunohistochemistry, Whole oncogene high flux sequencing of the patient sample were carried out. SHG140 cells were injected into the brain of nude mice, HE staining and immunohistochemistry of intracranial xenograft tumor were detected.

Results: Cell immunofluorescence demonstrated that SHG140 cells were positive for A2B5 (Glial precursors ganglioside), GFAP (Glial fibrillary acidic protein), Nestin, S-100 (Acid calcium bingding protein), Olig2 (Oligodendrocyte transcription factor 2) and Ki67 (Nuclear-associated antigen), cells negatively stained for Vimentin. Cell proliferation curve revealed that SHG140 proliferated slightly within 48 h, which then significantly proliferated to the fourth day. Karyotype analysis demonstrated its total number of chromosomes was 55, having trisomy of chromosome 6, 7, 8, 9 and X, and tetrad of chromosome 1 and 21, chromosomal deletion and rearrangement were observed. STR markers analysis showed the cells were derived from human male. SHG140 cells had tumorigenic properties - the intracranial injection of these cells into nude mice gave rise to growing tumors. We found that the glioma tissue was diffusively positive for GFAP, Nestin, slightly positive for Olig2, S-100; the positive rate of Ki-67 was 65% and negative for Vimentin. SHG140 cells were tumorigenic, GFAP, Nestin, S-100 Olig-2, the proliferation marker Ki-67 were expressed in its intracranial xenograft, Vimentin was negative expressed. Whole oncogene high flux sequencing of the patient tissue showed TP53, PTEN, IDH1 and PTCH1 mutation were existed.

Conclusions: Our study showed that SHG140 was an astrocytoma glioma continuous cell line derived from a human adult male, having a strong tumorigenicity in nude mice, which made it wound be a useful model for the study of human glioblastoma multiforme.

Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters

The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (<=5%), moderate (>5% and <=50%), and high (>50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.

Definition and review on a category of long non-coding RNA: Atherosclerosis-associated circulating lncRNA (ASCLncRNA)

Atherosclerosis (AS) is one of the most common cardiovascular system diseases which seriously affects public health in modern society. Finding potential biomarkers in the complicated pathological progression of AS is of great significance for the prevention and treatment of AS. Studies have shown that long noncoding RNAs (lncRNAs) can be widely involved in the regulation of many physiological processes, and have important roles in different stages of AS formation. LncRNAs can be secreted into the circulatory system through exosomes, microvesicles, and apoptotic bodies. Recently, increasing studies have been focused on the relationships between circulating lncRNAs and AS development. The lncRNAs in circulating blood are expected to be new non-invasive diagnostic markers for monitoring the progression of AS. We briefly reviewed the previously reported lncRNA transcripts which related to AS development and detectable in circulating blood, including ANRIL, SENCR, CoroMarker, LIPCAR, HIF1α-AS1, LncRNA H19, APPAT, KCNQ1OT1, LncPPARδ, LincRNA-p21, MALAT1, MIAT, and UCA1. Further researches and a definition of atherosclerosis-associated circulating lncRNA (ASCLncRNA) were also discussed.

ANDC: an early warning score to predict mortality risk for patients with Coronavirus Disease 2019

Background

Patients with severe Coronavirus Disease 2019 (COVID-19) will progress rapidly to acute respiratory failure or death. We aimed to develop a quantitative tool for early predicting mortality risk of patients with COVID-19.

Methods

301 patients with confirmed COVID-19 admitted to Main District and Tumor Center of the Union Hospital of Huazhong University of Science and Technology (Wuhan, China) between January 1, 2020 to February 15, 2020 were enrolled in this retrospective two-centers study. Data on patient demographic characteristics, laboratory findings and clinical outcomes was analyzed. A nomogram was constructed to predict the death probability of COVID-19 patients.

Results

Age, neutrophil-to-lymphocyte ratio, d-dimer and C-reactive protein obtained on admission were identified as predictors of mortality for COVID-19 patients by LASSO. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.921 and 0.975 for the derivation and validation cohort, respectively. An integrated score (named ANDC) with its corresponding death probability was derived. Using ANDC cut-off values of 59 and 101, COVID-19 patients were classified into three subgroups. The death probability of low risk group (ANDC < 59) was less than 5%, moderate risk group (59 ≤ ANDC ≤ 101) was 5% to 50%, and high risk group (ANDC > 101) was more than 50%, respectively.

Conclusion

The prognostic nomogram exhibited good discrimination power in early identification of COVID-19 patients with high mortality risk, and ANDC score may help physicians to optimize patient stratification management.

Laryngoscope-assisted and cotton ball wiping methods in prevention of oral and pulmonary infection in patients receiving mechanical ventilation and the influence on hypersensitive C-reactive protein and procalcitonin

Effects of laryngoscope-assisted and cotton ball wiping methods on the prevention of oral and pulmonary infection in patients receiving mechanical ventilation were compared to explore the influence of the two methods on high-sensitivity C-reactive protein (hs-CRP) and procalcitonin (PCT). In total, 152 patients who underwent mechanical ventilation in the ICU of Beijing Jishuitan Hospital from May 2005 to January 2018 were assigned and equally divided into two groups: 76 patients who had their oropharynxes scrubbed and rinsed by an electric toothbrush under direct vision by the use of a laryngoscope were selected as the laryngoscope group, and 76 patients who received the conventional cotton ball wiping method and the flushing method for oropharyngeal cleaning were assigned in the cotton ball group. Detection of serum hs-CRP and PCT levels in a 2-ml sample of fasting venous blood was performed on both groups of patients before hospitalization, and on the 5th and 10th day of hospitalization. The incidence rate of oral infection and ventilator-associated pneumonia, as well as the length of the cleaning time were recorded. The incidence rate of oral infection and ventilator-associated pneumonia in the laryngoscope group was statistically much lower than that in the cotton ball group (P<0.05). Before the experiment, there was no significant difference in the hs-CRP and PCT levels between the two groups (P>0.050), whereas the laryngoscope group had significantly lower hs-CRP and PCT levels at the 5th and 10th day of hospitalization than those in the cotton ball group (P<0.05). The hs-CRP and PCT levels at the three time-points in the same group were statistically different (P<0.05). In conclusion, oropharyngeal scrub and rinse by an electric toothbrush assisted by a laryngoscope, can not only better prevent oral infection and reduce the incidence of ventilator-associated pneumonia, but it also has shorter cleaning time and results in lower levels of inflammatory factors, which make this method beneficial in the clinic.