The neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) are used as prognostic biomarkers for many diseases. In this study, we aimed to explore the possibility of using ratios of NLR and MLR to predict the prognosis of viral encephalitis (VE).A total of 81 patients with an initial diagnosis of VE who were admitted to our hospital from January 2018 to January 2021 were retrospectively analyzed. A routine blood test within 24 h of admission was utilized to determine the ratios of NLR and MLR for each patient. The modified Rankin Scale (mRS) at 12 months after discharge was used to evaluate patients' clinical prognosis and the patients were divided into the group of good prognosis (mRS ≤ 1) and the group of poor prognosis (mRS ≥ 2) according to the mRS scores. Univariate and multivariable regression analyses were used to differentiate and assess independent prognostic factors for the prognosis of VE.Neutrophil-to-lymphocyte ratio and MLR of the poor prognosis group were significantly higher than that of the good prognosis group. Multivariate logistic regression analysis results showed that NLR [odds ratio (OR): 1.421, 95% confidence interval (CI): 1.105-1.827; P < 0.05] and MLR (OR: 50.423, 95% CI: 2.708-939.001; P < 0.05) were independent risk factors for the poor prognosis of VE. NLR > 4.32 and MLR > 0.44 were suggested as the cutoff threshold for the prediction of the poor prognosis of VE.Neutrophil-to-lymphocyte ratio and MLR obtained from blood tests done at hospital admission have the potential to predict poor prognosis in patients with VE.
Background Lactate, a byproduct of glucose metabolism, is primarily utilized for gluconeogenesis and numerous cellular and organismal life processes. Interestingly, many studies have demonstrated a correlation between lactate metabolism and tumor development. However, the relationship between long non-coding RNAs (lncRNAs) and lactate metabolism in papillary thyroid cancer (PTC) remains to be explored. Methods Lactate metabolism-related lncRNAs (LRLs) were obtained by differential expression and correlation analyses, and the risk model was further constructed by least absolute shrinkage and selection operator analysis (Lasso) and Cox analysis. Clinical, immune, tumor mutation, and enrichment analyses were performed based on the risk model. The expression level of six LRLs was tested using RT-PCR. Results This study found several lncRNAs linked to lactate metabolism in both The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets. Using Cox regression analysis, 303 lactate LRLs were found to be substantially associated with prognosis. Lasso was done on the TCGA cohort. Six LRLs were identified as independent predictive indicators for the development of a PTC prognostic risk model. The cohort was separated into two groups based on the median risk score (0.39717 -0.39771). Subsequently, Kaplan-Meier survival analysis and multivariate Cox regression analysis revealed that the high-risk group had a lower survival probability and that the risk score was an independent predictive factor of prognosis. In addition, a nomogram that can easily predict the 1-, 3-, and 5-year survival rates of PTC patients was established. Furthermore, the association between PTC prognostic factors and tumor microenvironment (TME), immune escape, as well as tumor somatic mutation status was investigated in high- and low-risk groups. Lastly, gene expression analysis was used to confirm the differential expression levels of the six LRLs. Conclusion In conclusion, we have constructed a prognostic model that can predict the prognosis, mutation status, and TME of PTC patients. The model may have great clinical significance in the comprehensive evaluation of PTC patients.
Abstract Introduction: Angiotensin-converting enzyme 2 (ACE2) is the receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The effects of SARS-CoV-2 on normal pituitary glands function or pituitary neuroendocrine tumors (PitNETs) have not yet been elucidated. Thus, the present study aimed to investigate the potential risks of SARS-CoV-2 infection on the impairment of pituitary glands and the development of PitNETs. Methods: PitNETs tissues were obtained from 114 patients, and normal pituitary gland tissues were obtained from the autopsy. The mRNA levels of ACE2 and angiotensin II receptor type 1 (AGTR1) were examined by quantitative real-time PCR. Immunohistochemical staining was performed for ACE2 in 69 PitNETs and 3 normal pituitary glands. The primary tumor cells and pituitary cell lines (MMQ, GH3 and AtT-20/D16v-F2) were treated with diminazene aceturate (DIZE), an ACE2 agonist, with various dose regimens. The pituitary hormones between 43 patients with SARS-CoV-2 infection were compared with 45 healthy controls. Results: Pituitary glands and the majority of PitNET tissues showed low/negative ACE2 expression at both the mRNA and protein levels, while AGTR1 showed high expression in normal pituitary and corticotroph adenomas. ACE2 agonist increased the secretion of ACTH in AtT-20/D16v-F2 cells through downregulating AGTR1. The level of serum adrenocorticotropic hormone (ACTH) was significantly increased in COVID-19 patients as compared to normal controls (p<0.001), but was dramatically decreased in critical cases as compared to non-critical patients (p=0.003). Conclusion: This study revealed a potential impact of SARS-CoV-2 infection on corticotroph cells and adenomas.
Objectives Obstructive sleep apnea (OSA) is a common sleep-disordered breathing condition linked to the accelerated onset of mild cognitive impairment (MCI). However, the prevalence of undiagnosed MCI among OSA patients is high and attributable to the complexity and specialized nature of MCI diagnosis. Timely identification and intervention for MCI can potentially prevent or delay the onset of dementia. This study aimed to develop screening models for MCI in OSA patients that will be suitable for healthcare professionals in diverse settings and can be effectively utilized without specialized neurological training. Methods A prospective observational study was conducted at a specialized sleep medicine center from April 2021 to September 2022. Three hundred and fifty consecutive patients (age: 18–60 years) suspected OSA, underwent the Montreal Cognitive Assessment (MoCA) and polysomnography overnight. Demographic and clinical data, including polysomnographic sleep parameters and additional cognitive function assessments were collected from OSA patients. The data were divided into training (70%) and validation (30%) sets, and predictors of MCI were identified using univariate and multivariate logistic regression analyses. Models were evaluated for predictive accuracy and calibration, with nomograms for application. Results Two hundred and thirty-three patients with newly diagnosed OSA were enrolled. The proportion of patients with MCI was 38.2%. Three diagnostic models, each with an accompanying nomogram, were developed. Model 1 utilized body mass index (BMI) and years of education as predictors. Model 2 incorporated N1 and the score of backward task of the digital span test (DST_B) into the base of Model 1. Model 3 expanded upon Model 1 by including the total score of digital span test (DST). Each of these models exhibited robust discriminatory power and calibration. The C-statistics for Model 1, 2, and 3 were 0.803 [95% confidence interval (CI): 0.735–0.872], 0.849 (95% CI: 0.788–0.910), and 0.83 (95% CI: 0.763–0.896), respectively. Conclusion Three straightforward diagnostic models, each requiring only two to four easily accessible parameters, were developed that demonstrated high efficacy. These models offer a convenient diagnostic tool for healthcare professionals in diverse healthcare settings, facilitating timely and necessary further evaluation and intervention for OSA patients at an increased risk of MCI.
Abstract Background Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations. Objective To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables. Methods 5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit. Results Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC = 0.95) and external cohort (AUC = 0.873). The APCU was significant for biliogenic AP (OR = 4.25 [2.08–8.72], P < 0.001), alcoholic AP (OR = 3.60 [1.67–7.72], P = 0.001), hyperlipidemic AP (OR = 2.63 [1.28–5.37], P = 0.008) and tumor AP (OR = 4.57 [2.14–9.72], P < 0.001). APCU yielded the highest clinical net benefit, comparatively. Conclusion Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP.
Aims This study aimed to investigate the distant metastasis pattern from newly diagnosed colorectal cancer (CRC) and also construct and validate a prognostic nomogram to predict both overall survival (OS) and cancer-specific survival (CSS) of CRC patients with distant metastases. Methods Primary CRC patients who were initially diagnosed from 2010 to 2016 in the SEER database were included in the analysis. The independent risk factors affecting the OS, CSS, all-cause mortality, and CRC-specific mortality of the patients were screened by the Cox regression and Fine–Gray competitive risk model. The nomogram models were constructed to predict the OS and CSS of the patients. The reliability and accuracy of the prediction model were evaluated by consistency index (C-index) and calibration curve. The gene chip GSE41258 was downloaded from the GEO database, and differentially expressed genes (DEGs) were screened by the GEO2R online tool ( p < 0.05, |logFC|>1.5). The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway and Gene Ontology (GO) annotation and String website were used for enrichment analysis and protein–protein interaction (PPI) analysis of DEGs, respectively, and Cytoscape software was used to construct PPI network and screen function modules and hub genes. Results A total of 57,835 CRC patients, including 47,823 without distant metastases and 10,012 (17.31%) with metastases, were identified. Older age, unmarried status, poorly differentiated or undifferentiated grade, right colon site, larger tumor size, N2 stage, more metastatic sites, and elevated carcinoembryonic antigen (CEA) might lead to poorer prognosis (all p < 0.01). The independent risk factors of OS and CSS were included to construct a prognosis prediction model for predicting OS and CSS in CRC patients with distant metastasis. C-index and calibration curve of the training group and validation group showed that the models had acceptable predictive performance and high calibration degree. Furthermore, by comparing CRC tissues with and without liver metastasis, 158 DEGs and top 10 hub genes were screened. Hub genes were mainly concentrated in liver function and coagulation function. Conclusion The big data in the public database were counted and transformed into a prognostic evaluation tool that could be applied to the clinic, which has certain clinical significance for the formulation of the treatment plan and prognostic evaluation of CRC patients with distant metastasis.
Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes mellitus. It leads to distressing and expensive clinical sequelae such as foot ulceration, leg amputation and neuropathic pain (painful-DPN). Unfortunately, DPN is often diagnosed late when irreversible nerve injury has occurred and its first presentation may be with a diabetic foot ulcer. Several novel diagnostic techniques are available which may supplement clinical assessment and aid the early detection of DPN. Moreover, treatments for DPN and painful-DPN are limited. Only tight glucose control in type 1 diabetes has robust evidence in reducing the risk of developing DPN. However, neither glucose control nor pathogenetic treatments are effective in painful-DPN and symptomatic treatments are often inadequate. It has recently been hypothesized that using various patient characteristics it may be possible to stratify individuals and assign them targeted therapies to produce better pain relief. We review the diagnostic techniques which may aid the early detection of DPN in the clinical and research environment, and recent advances in precision medicine techniques for the treatment of painful-DPN.
The COVID-19 outbreak in Wuhan has subsided but the world is still suffering from it. We present our experience gained during this outbreak from city and hospital perspectives which might inform others to make evidence-based decisions to tackle this devastating pandemic more effectively. We studied the counter-measures adopted by Wuhan Government and analyzed the city’s new coronavirus infected disease patient’s data obtained from the National Health Commission and one re-purposed hospital to accommodate COVID-19 patients of the counter-measures for the COVID-19 outbreak. There was a significant drop of new-patient after February 18th, 2020. Patients with disease-onset after February 4th had shorter onset to admission days than those with onset before February 4th; and also had less critical-illness and mortality rates. This was due to quicker hospitalization after February 4th. The scores of PCR results at diagnosis, the national early warning score and the time-to-death were not significantly different for critical-illness patients whose onset before vs. after February 4th. The critical and death rates can be decreased by early hospitalization and oxygen therapy for less severe novel coronavirus-infected pneumonia within 7 days after disease onset.
Background Cardiovascular autonomic neuropathy (CAN) is common in patients with type 2 diabetes mellitus (T2DM), mainly presented as decreased heart rate variability (HRV) which often leads to cardiac death. However, HRV measurement is not convenient in most clinics. Therefore, identifying high-risk patients for CAN in diabetes with easier measurements is crucial for the early intervention and prevention of catastrophic consequences. Methods In this cross-sectional study, 675 T2DM patients with normocalcemia were selected. Of these, they were divided into two groups: normal HRV group (n = 425, 100 ms≤ SDNN ≤180 ms) vs . declined HRV group (n = 250, SDNN <100 ms). All patients’ clinical data were collected and the correlation of clinical variables with HRV were analyzed by correlation and logistic regression analysis. The area below the ROC curve was used to evaluate the predictive performance of serum calcium on HRV. Results In this study, declines in HRV were present in 37.0% of T2DM patients. Significant differences in albumin-adjusted serum calcium levels (CaA) (8.86 ± 0.27 vs. 9.13 ± 0.39 mg/dl, p < 0.001) and E/A (0.78 ± 0.22 vs. 0.83 ± 0.26, p = 0.029) were observed between declined HRV and normal HRV groups. Bivariate linear correlation analysis showed that CaA and E/A were positively correlated with HRV parameters including SDNN ( p < 0.001), SDNN index ( p < 0.001), and Triangle index ( p < 0.05). The AUC in the ROC curve for the prediction of CaA on HRV was 0.730 (95% CI (0.750–0.815), p < 0.001). The cutoff value of CaA was 8.87 mg/dl (sensitivity 0.644, specificity 0.814). The T2DM patients with CaA <8.87 mg/dl had significantly lower HRV parameters (SDNN, SDNN index, rMSSD, and triangle index) than those with CaA ≥8.87 mg/dl ( p < 0.01, respectively). Multivariate logistic regression analysis showed a significantly increased risk of declined HRV in subjects with CaA level <8.87 mg/dl [OR (95% CI), 0.049 (0.024–0.099), p < 0.001]. Conclusions Declined HRV is associated with a lower CaA level and worse cardiac function. The serum calcium level can be used for risk evaluation of declined HRV in T2DM patients even within the normocalcemic range.
Objective
To confirm the risk factors of poor prognosis in patients with severe acute epiglottitis by comparing symptoms and results of laboratory tests.
Methods
A total of 698 patients with acute epiglottitis from outpatient and emergency room from 1995 to 2014 were retrospectively studied. These patients were divided into severe or mild group as per the means of treatment of airway including invasive (n=115) and non-invasive (n=583). The past history, general conditions and laboratory tests were compared between the two groups by chi-square or t test; the spearman correlation between the degree of dyspnea and epiglottis edema was analyzed, and the risk factors of poor prognosis were detected by logistic regression.
Results
The ratio of male to female was 1.366/1. There was a high prevalence of sever acute epiglottitis in spring, winter and at night, respectively. More smokers were found in the severe group compared with the mild group (χ2 = 41.957, P< 0.01). Severe dyspnea and low PaO2 (r=0.573, P< 0.01), but not the poor grading of epiglottis edema evaluated by pharyngo-fiberoscope (r=-0.024, P= 0.525), were correlated with poor prognosis. Male (OR=1.84, 95%CI: 1.41-3.22, P= 0.001), an attack at night (OR=2.61, 95%CI: 1.98-3.16), P= 0.07), smoker (OR=1.63, 95%CI: 1.05-3.39, P= 0.04) and low PaO2 (OR=2.97, 95%CI: 1.58-4.49, P= 0.02) were independent risk factors for a poor prognosis.
Conclusions
Male, an attack at night, smoker and low PaO2 were independent risk factors for a poor prognosis of acute epiglottitis. A critical care should be given to patients with those risk factors, even their epiglottis edema was not very serious.
Key words:
Acute epiglottitis; Risk factor; Prognosis
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)