A machine learning model to predict spontaneous vaginal delivery failure for term nulliparous women: An observational study.

OBJECTIVE: This study aims to construct and evaluate a model to predict spontaneous vaginal delivery (SVD) failure in term nulliparous women based on machine learning algorithms. METHODS: In this retrospective observational study, data on nulliparous women without contraindications for vaginal delivery with a singleton pregnancy ≥37 weeks and before the onset of labor from September 2020 to September 2021 were divided into a training set and a temporal validation set. Transperineal ultrasound was performed to collect angle of progression, head-perineum distance, subpubic arch angle, and their levator hiatal dimensions. The cervical length was measured via transvaginal ultrasound. The delivery methods were later recorded. Through LASSO regression analysis, indicators that can affect SVD failure were selected. Seven common machine learning algorithms were selected for model training, and the optimal algorithm was selected based on the area under the curve (AUC) to evaluate the effectiveness of the validation model. RESULTS: Four indicators related to SVD failure were identified through LASSO regression screening: angle of progression, cervical length, subpubic arch angle, and estimated fetal weight. The Gaussian NB algorithm was found to yield the highest AUC (0.82, 95% confidence interval [CI] 0.65-0.98) during model training, and hence it was chosen for verification with the temporal validation set, in which an AUC of 0.79 (95% CI 0.64-0.95) was obtained with accuracy, sensitivity, and specificity rates of 80.9%, 72.7%, and 75.0%, respectively. CONCLUSION: The Gaussian NB model showed good predictive effect, proving its potential as a clinical reference for predicting SVD failure of term nulliparous women before actual delivery.

Combination of serum markers with optical coherence tomography angiography for evaluating neuromyelitis optica spectrum disorders and multiple sclerosis.

BACKGROUND: Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS), autoimmune inflammatory diseases of the central nervous system, affect the optic nerve and brain. A lumbar puncture to obtain biomarkers is highly invasive. Serum biomarkers and optical coherence tomography angiography (OCTA) are more accessible and less expensive than magnetic resonance imaging and provide reliable, reproducible measures of neuroaxonal damage. This study investigated the association between serum neurofilament light chain (sNfL), serum glial fibrillary acidic protein (sGFAP), and OCTA metrics. Serum sNfL and sGFAP levels, OCTA values, and clinical characteristics were compared among 91 patients with NMOSD, 81 patients with MS, and 34 healthy controls (HCs) at baseline and 1-year follow-up. RESULTS: sNfL and sGFAP levels were higher while the sGFAP/sNfL quotients were significantly lower in NMOSD and MS patients than those in HCs. At baseline, the average thicknesses of the peripapillary retinal nerve fibre layer (pRNFL) and macular ganglion cell-inner plexiform layer (mGC-IPL) were significantly smaller in NMOSD and MS patients than those in HCs (pRNFL: MS 92.0 [80.2; 101] µm, NMOSD 80.0 [59.0; 95.8] µm, vs HC 99.0 [92.0; 104] µm, p < 0.001; mGC-IPL: MS 74.5 [64.2; 81.0] µm, NMOSD 68.0 [56.0; 81.0] µm, vs HC 83.5 [78.0; 88.0] µm, p < 0.001). The vessel density (VD) and perfusion density (PD) were increased in MS patients without optic neuritis compared to HCs (VD: MS 16.7 [15.6; 17.9] HC 15.3 [13.4; 16.9], p = 0.008; PD: MS 0.41 [0.38; 0.43], HC 0.37 [0.32; 0.41], p = 0.017). In NMOSD patients without optic neuritis, sNfL was significantly associated with PD at baseline (r = 0.329, q = 0.041). The baseline and follow-up values of the sNfL level and average pRNFL and mGC-IPL thicknesses in MS patients showed significant differences. NMOSD patients showed significant differences between baseline and follow-up sNfL and sGFAP levels but not OCTA metrics. CONCLUSION: Changes in retinal microvasculature might occur earlier than those in retinal structure and may therefore serve as a promising diagnostic marker for early NMOSD. The combination of serum markers and OCTA metrics could be used to evaluate and differentiate between MS and NMOSD.

Development and validation of machine learning based prediction model for postoperative pain risk after extraction of impacted mandibular third molars.

Background: Predicting postoperative pain risk in patients with impacted mandibular third molar extractions is helpful in guiding clinical decision-making, enhancing perioperative pain management, and improving the patients' medical experience. This study aims to develop a prediction model based on machine learning algorithms to identify patients at high risk of postoperative pain after tooth extraction. Methods: We conducted a prospective cohort study. Outpatients with impacted mandibular third molars were recruited and the outcome was defined as the NRS (Numerical Rating Scale) score of peak postoperative pain within 24 h after the operation ≥7, which is considered a high risk of postoperative pain. We compared the models built using nine different machine learning algorithms and conducted internal and time-series external validations to evaluate the model's predictive performances in terms of the area under the curve (AUC), accuracy, sensitivity, specificity, and F1-value. Results: A total of 185 patients and 202 cases of impacted mandibular third molar data were included in this study. Five modeling variables were screened out using least absolute selection and shrinkage operator regression, including physician qualification, patient self-reported maximum pain sensitivity, OHI-S-CI, BMI, and systolic blood pressure. The overall performance of the random forest model was evaluated. The AUC, sensitivity, and specificity of the prediction model built using the random forest method were 0.879 (0.861-0.891), 0.857, and 0.846, respectively, for the training set and 0.724 (0.673-0.732), 0.667, and 0.600, respectively, for the time series validation set. Conclusions: This study developed a machine learning-based postoperative pain risk prediction model for impacted mandibular third molar extraction, which is promising for providing a theoretical basis for better pain management to reduce postoperative pain after third molar extraction.

In situ construction of a hierarchical TiO2/Ti3C2 hybrid via water steam etching for high-performance potassium-ion batteries.

Potassium ion batteries (PIBs) have attracted great research interest in new-generation large-scale energy storage considering their abundant source, low cost, and suitable working potential. Herein, a hierarchical TiO2/Ti3C2 hybrid is developed via a green, facile water steam etching method for realizing an efficient and durable anode material for PIBs. In this hierarchical assembly, the TiO2 nanoparticles anchored on the Ti3C2 surface contribute a high pseudocapacitance while mitigating the restacking of the Ti3C2 MXene skeleton, which ensures mechanical robustness to accommodate large K+ ions. Benefiting from the amalgamation of structural properties and the synergistic effects stemming from the individual constituents, the optimized TiO2/Ti3C2 anode harvests remarkable performance in the potassium ion storage, including a high reversible capacity of ∼255 mA h g-1 at 0.2 A g-1 after 1300 cycles as well as an outstanding long-term cycling performance and rate capability (a high capacity of ∼230 mA h g-1 even after intensive 10 000 cycles at 2 A g-1). The excellent TiO2/Ti3C2 anode enables the assembled pouch-cell coupling PTCDA cathode to deliver a capacity of ∼173 mA h g-1 at 0.05 A g-1 and retain 120 mA h g-1 after 30 cycles. The employment of the pouch-cell in successfully powering the LED module showcases its application prospect for advanced PIBs.

Preoperative hs-CRP/HDL ratio is associated with increased risk for postoperative SIRS in elderly patients: a retrospective cohort study.

BACKGROUND: Systemic inflammatory response syndrome (SIRS) greatly affects postoperative lives of afflicted aged patients. This study aimed to determine whether preoperative high hs-CRP/HDL ratio (CHR) was associated with an increased risk of postoperative SIRS in the elderly population. METHODS: This retrospective cohort study included data on patients aged ≥ 65 years who underwent general anesthesia surgery at two clinical centers between January 2015 and September 2020. The primary exposure was preoperative CHR which was divided into two groups (≤ 12.82 and > 12.82) based on its normal range in our hospital, and the primary outcome was the incidence of postoperative SIRS. Targeted maximum likelihood estimation analyses were used to model the exposure-outcome relationship. RESULTS: The analysis included 5595 elderly patients, of whom 1410 (25.20%) developed SIRS within three postoperative days. Targeted maximum likelihood estimation analysis revealed that elderly patients with CHR > 12.82 vs. CHR ≤ 12.82 was associated with increased risk of postoperative SIRS (aOR = 1.40, 95% CI [1.33, 1.48], P < 0.001). Those results were consistent both in subgroup analyses and sensitivity analyses. Compared with patients with CHR ≤ 12.82, patients with CHR > 12.82 had a higher prevalence of postoperative SIRS (49.06% vs. 22.70%), postoperative in-hospital mortality (3.40% vs. 0.65%), a longer hospital stay after surgery [10 (IQR, 6-16) vs. 8 (IQR, 5-11) days] and higher direct medical cost [10070 (IQR, 6878-15577) vs. 7117 (IQR, 4079-10314) euros, all P < 0.001]. CONCLUSIONS: In elderly patients, preoperative CHR > 12.82 was significantly associated with a higher risk of postoperative SIRS.

Development and validation of a machine learning model to predict prognosis in HIV-negative cryptococcal meningitis patients: a multicenter study.

PURPOSE: To predict prognosis in HIV-negative cryptococcal meningitis (CM) patients by developing and validating a machine learning (ML) model. METHODS: This study involved 523 HIV-negative CM patients diagnosed between January 1, 1998, and August 31, 2022, by neurologists from 3 tertiary Chinese centers. Prognosis was evaluated at 10 weeks after the initiation of antifungal therapy. RESULTS: The final prediction model for HIV-negative CM patients comprised 8 variables: Cerebrospinal fluid (CSF) cryptococcal count, CSF white blood cell (WBC), altered mental status, hearing impairment, CSF chloride levels, CSF opening pressure (OP), aspartate aminotransferase levels at admission, and decreased rate of CSF cryptococcal count within 2 weeks after admission. The areas under the curve (AUCs) in the internal, temporal, and external validation sets were 0.87 (95% CI 0.794-0.944), 0.92 (95% CI 0.795-1.000), and 0.86 (95% CI 0.744-0.975), respectively. An artificial intelligence (AI) model was trained to detect and count cryptococci, and the mean average precision (mAP) was 0.993. CONCLUSION: A ML model for predicting prognosis in HIV-negative CM patients was built and validated, and the model might provide a reference for personalized treatment of HIV-negative CM patients. The change in the CSF cryptococcal count in the early phase of HIV-negative CM treatment can reflect the prognosis of the disease. In addition, utilizing AI to detect and count CSF cryptococci in HIV-negative CM patients can eliminate the interference of human factors in detecting cryptococci in CSF samples and reduce the workload of the examiner.

Differential diagnosis of secondary hypertension based on deep learning.

Secondary hypertension is associated with higher risks of target organ damage and cardiovascular and cerebrovascular disease events. Early aetiology identification can eliminate aetiologies and control blood pressure. However, inexperienced doctors often fail to diagnose secondary hypertension, and comprehensively screening for all causes of high blood pressure increases health care costs. To date, deep learning has rarely been involved in the differential diagnosis of secondary hypertension. Relevant machine learning methods cannot combine textual information such as chief complaints with numerical information such as the laboratory examination results in electronic health records (EHRs), and the use of all features increases health care costs. To reduce redundant examinations and accurately identify secondary hypertension, we propose a two-stage framework that follows clinical procedures. The framework carries out an initial diagnosis process in the first stage, on which basis patients are recommended for disease-related examinations, followed by differential diagnoses of different diseases based on the different characteristics observed in the second stage. We convert the numerical examination results into descriptive sentences, thus blending textual and numerical characteristics. Medical guidelines are introduced through label embedding and attention mechanisms to obtain interactive features. Our model was trained and evaluated using a cross-sectional dataset containing 11,961 patients with hypertension from January 2013 to December 2019. The F1 scores of our model were 0.912, 0.921, 0.869 and 0.894 for primary aldosteronism, thyroid disease, nephritis and nephrotic syndrome and chronic kidney disease, respectively, which are four kinds of secondary hypertension with high incidence rates. The experimental results show that our model can powerfully use the textual and numerical data contained in EHRs to provide effective decision support for the differential diagnosis of secondary hypertension.

Cerebrospinal Fluid Chloride Is Associated with Disease Activity of Relapsing-Remitting Multiple Sclerosis: A Retrospective Cohort Study.

BACKGROUND: Blood-brain barrier dysfunction in active multiple sclerosis (MS) lesions leads to pathological changes in the cerebrospinal fluid (CSF). This study aimed to investigate the possible association between routine CSF findings, especially CSF chloride, at the time of the first lumbar puncture and the relapse risk and disability progression of relapsing-remitting MS (RRMS). METHODS: This retrospective study included 77 patients with RRMS at the MS Center of our institution from January 2012 to December 2020. The Anderson and Gill (AG) model and Spearman correlation analysis were used to explore predictors of relapse and disability during follow-up. RESULTS: In the multivariate AG model, patients with elevated CSF chloride level (hazard ratio [HR], 1.1; 95% confidence interval [CI]: 1.06-1.22; p = 0.001) had a high risk of MS relapse. Using median values of CSF chloride (123.2 mmol/L) as a cut-off, patients with CSF chloride level ≥ 123.2 mmol/L had a 120% increased relapse risk compared with those with CSF chloride level < 123.2 mmol/L (HR = 2.20; 95% CI: 1.19-4.05; p = 0.012). CONCLUSIONS: Elevated CSF chloride levels might be a biologically unfavorable predictive factor for disease relapse in RRMS.

Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study.

Introduction: Postoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SIRS in older patients. Methods: Patients aged ≥65 years who underwent general anesthesia in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were included. The cohort was divided into training and validation cohorts. A simple nomogram was developed to predict the postoperative SIRS in the training cohort using two logistic regression models and the brute force algorithm. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). The external validity of the nomogram was assessed in the validation cohort. Results: A total of 5,904 patients spanning from January 2015 to December 2019 were enrolled in the training cohort and 1,105 patients from January 2020 to September 2020 comprised the temporal validation cohort, in which incidence rates of postoperative SIRS were 24.6 and 20.2%, respectively. Six feature variables were identified as valuable predictors to construct the nomogram, with high AUCs (0.800 [0.787, 0.813] and 0.822 [0.790, 0.854]) and relatively balanced sensitivity (0.718 and 0.739) as well as specificity (0.718 and 0.729) in both training and validation cohorts. An online risk calculator was established for clinical application. Conclusion: We developed a patient-specific model that may assist in predicting postoperative SIRS among the aged patients.

Multimorbidity of Allergic Conditions in Urban Citizens of Southern China: A Real-World Cross-Sectional Study.

BACKGROUND: Extensive knowledge of allergic multimorbidities is required to improve the management of allergic diseases with the industrialization of China. However, the demography and allergen distribution patterns of allergic multimorbidities in China remain unclear, despite the increasing prevalence of allergies. METHODS: This was a real-world, cross-sectional study of 1273 outpatients diagnosed with one or more allergic diseases in Guangzhou, the most populated city of southern China, with leading industrial and commercial centers, between April 2021 and March 2022. Seven allergic diseases (allergic rhinitis (AR), asthma (AS)/cough variant asthma (CVA), atopic dermatitis (AD)/eczema, food allergy (FA), allergic conjunctivitis (AC), drug allergy (DA), and anaphylaxis) were assessed. Positive rates of sensitization to different allergens were measured using an allergen detection system of the UniCAP (Pharmacia Diagnostics, Sweden) instrument platform to compare the groups of allergic multimorbidities against a single entity. RESULTS: There were 659 (51.8%) males and 614 (48.2%) females aged from 4 months to 74 years included in the analysis. The study participants who were diagnosed with allergic diseases had an average of 1.6 diagnoses. Overall, 46.5% (592 of 1273) of the patients had more than one allergic condition, and allergic rhinitis was the most common type of multimorbidity. Women were more likely to suffer from an allergic disease alone, whereas allergic multimorbidities were more likely to be diagnosed in men (p = 0.005). In addition, allergic multimorbidities were common in all age groups, with an incidence ranging from 37.1% to 57.4%, in which children and adolescents were more frequently diagnosed with allergic multimorbidities than adults (18-60 years old) (all p < 0.05). Allergic multimorbidity was observed throughout the year. A difference in the positive rate of allergens sensitization and total immunoglobulin E (tIgE) levels between different allergic multimorbidities was observed. CONCLUSIONS: Allergic multimorbidities were very commonly found in nearly half of all patients with allergies. The proportion of allergic multimorbidities varied with the type of disease, sex, age, and allergen distribution pattern. These findings may help clinicians to develop "One health" strategies for the clinical management of allergic diseases.

Study on recognition of coal and gangue based on multimode feature and image fusion.

Aiming at the problems of low accuracy of coal gangue recognition and difficult recognition of mixed gangue rate, a coal rock recognition method based on modal fusion of RGB and infrared is proposed. A fully mechanized coal gangue transportation test bed is built, RGB images are obtained by camera, and infrared images are obtained by industrial microwave heating system and infrared thermal imager. the image data of the whole coal, whole gangue, and coal gangue with different gangue mixing as training and test samples, identify the released coal gangue and its mixing rate. The AlexNet, VGG-16, ResNet-18 classification networks and their convolutional neural networks with modal feature fusion are constructed. results: The classification accuracy of ResNet networks on RGB and infrared image data is higher than AlexNet and VGG-16 networks. The early convergence network performance of ResNet is verified through the convergence of different models. The recognition rate of the network is 97.92 the confusion matrix statistics, which verifies the feasibility of the application of modal fusion method in the field of coal gangue recognition. The fusion of modal features and early models of ResNet coal gangue, which is the basic premise for realizing intelligent coal caving.

Natural Killer Cells Disrupt Nerve Fibers by Granzyme H in Atheriosclerotic Cerebral Small Vessel Disease.

Natural killer (NK) cells are enriched in the central nervous system in aging-related atheriosclerotic cerebral small vessel disease (aCSVD), but their roles and underlying mechanism remain to be elucidated. To identify potential cytotoxic molecules released by NK cells in aCSVD lesions, proteomic analysis of cerebrospinal fluid (CSF), plasma, and peripheral NK cells from patients with aCSVD were performed. We found that integrin ß2 (ITGB2), cathepsin D (CTSD), and granzyme H (GZMH) were highly expressed in NK cells. ITGB2 interacted with intercellular adhesion molecule 1 in vascular endothelial cells. As assessed by immunofluorescence and scanning electron microscopy of the blood-brain barrier model, transwell membranes covered with primary human brain microvascular endothelial cells and astrocytes, we demonstrated that the CTSD-mediated degradation of collagen in the blood-brain barrier depended on the cytotoxicity of NK cells in aCSVD. With the immunostaining in vitro and in vivo, GZMH disruption of demyelinated nerve fibers was reversed by cotreatment with the inhibitor 3,4-DCIC during white matter hyperintensity (WMH) in aCSVD. Our results indicate that NK cells contribute to CTSD-induced damage to the blood-brain barrier and GZMH-induced disruption of nerve fibers during WMH in aCSVD.

Effects of acupuncture on the outcome of tinnitus: An overview of systematic reviews.

Background: To systematically summarize the evidence for the efficacy of acupuncture in tinnitus treatment, we assessed the methodological quality, reporting quality, and evidence quality of systematic reviews/meta-analyses (SRs/MAs) of acupuncture in the treatment of tinnitus. Methods: From inception to March 2022, we conducted a detailed and comprehensive search of eight electronic databases in Chinese and English. The Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR-2), the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) were used to assess methodological quality, reporting quality and evidence quality for inclusion in SRs/MAs, respectively. Results: Fourteen published SRs/MAs met the inclusion criteria and were included in the study. Eleven studies reported that acupuncture was effective in treating tinnitus, and three studies reported that no firm conclusions could be drawn about the effectiveness of acupuncture in treating tinnitus. The results of the AMSTAR-2 assessment showed that the methodological quality of the included studies was relatively low in general, with one being moderate quality and the rest being very low quality. The PRISMA checklist evaluation results showed that no studies fully report checklists, with protocol registration and search strategies being the main reporting weaknesses. The GRADE assessment showed that no results were high-quality evidence, 17 results were moderate-quality evidence, 25 results were low-quality evidence, and 12 results were very low-quality evidence. Conclusion: Acupuncture seems to be a positive and effective treatment for tinnitus. However, the methodological quality and quality of evidence for SRs/MAs in the included studies were generally low, and this result must be viewed with caution. Therefore, more high-quality, large-scale, multi-center randomized controlled trials are needed in the future to verify the effectiveness of acupuncture in the treatment of tinnitus.

Tree-based, two-stage risk factor analysis for postoperative sepsis based on Sepsis-3 criteria in elderly patients: A retrospective cohort study.

Background: Sepsis remains the leading cause of postoperative death in elderly patients and is defined as organ dysfunction with proven or suspected infection according to Sepsis-3 criteria. To better avoid potential non-linear associations between the risk factors, we firstly used a tree-based analytic methods to explore the putative risk factors of geriatric sepsis based on the criteria in the study. Methods: Data of 7,302 surgical patients aged ≥ 65 years at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were collected. An analytic method that combined tree-based analysis with the method of Mantel-Haenszel and logistic regression was adopted to assess the association between 17 putative risk factors and postoperative sepsis defined by the Sepsis-3 guideline by controlling 16 potential confounding factors. Results: Among the 16 potential covariates, six major confounders were statistically identified by the tree-based model, including cerebrovascular diseases, preoperative infusion of red blood cells, pneumonia, age ≥ 75, malignant tumor and diabetes. Our analysis indicated that emergency surgery increases the risk of postoperative sepsis in elderly patients by more than six times. The type of surgery is also a crucial risk factor for sepsis, particularly transplantation and neurosurgery. Other risk factors were duration of surgery > 120 min, administration of steroids, hypoalbuminemia, elevated creatinine, blood urea nitrogen, hematocrit, platelets, glucose, white blood cell count, abnormal neutrophil-to-lymphocyte ratio and elevated hsCRP-to-albumin ratio. Conclusions: Our study uses an effective method to explore some risk factors for postoperative sepsis in elderly by adjusting many potential confounders and it can provide information for intervention design.

A Systematic Review of Patient Preferences, Expectations, and Values for the Management and Treatment of Hypertension.

Objective: To comprehensively summarize the evidence on the preferences, expectations, and values of hypertension management and treatment in hypertensive patients. Methods: From inception through January 2022, PubMed, Embase, Web of Science, EBSCO, and China National Knowledge Infrastructure were searched for studies with hypertension value, preference, or expectation. A qualitative retrospective analysis was performed. Results: A total of 24 studies involving 8701 participants were included. Although studies on the preferences of hypertensive patients are difficult to standardize, considerable convergence has occurred: 1) Patients preferred treatment regimens with lower incidence of comorbidities, faster response, lower cost, more frequent blood pressure monitoring, fewer side effects, and more convenient medication. 2) Patients have a crucial role in the treatment, and their physicians are expected to develop a shared patient-centered decision-making model. Conclusion: The results of this systematic review demonstrated that side effects, cost, and convenience are important factors for patients when selecting a treatment regimen for hypertension. Patients often obtain hypertension information from their physicians and prefer shared patient-centered decision-making.

Prevalence of multiple sclerosis in Guangzhou, China: A population-based case-finding prospective study.

BACKGROUND: Multiple sclerosis (MS) is rare in China, and the prevalence previously reported may be biased. Currently, few studies that have investigated the prevalence of MS in China based on the latest diagnostic criteria. METHODS: Through a population-based survey from August 8, 2021 to December 31, 2021, we calculated the prevalence of multiple sclerosis in 18,676,605 residents of Guangzhou, China. MS patients were identified through the health insurance system of the Guangzhou Health Insurance Bureau, and we surveyed 17 large tertiary hospitals using a case-finding approach. All MS patients were diagnosed according to the 2017 McDonald criteria. RESULTS: A total of 143 patients in the resident population of Guangzhou were diagnosed with MS, with a crude prevalence of 0.77 per 100,000 (95% confidence interval (CI): 0.65-0.90), and the prevalence was higher in in females (1.14/100,000) than in males (0.44/100,000). The age-adjusted prevalence was 0.92 per 100,000 (95% CI: 0.77-1.10). The prevalence peaked at the age of 25-29 years (2.86/100,000) for both males and females (1.44/100,000 and 4.42/100,000, respectively). CONCLUSIONS: This is the first study to report the prevalence of MS in Guangzhou, China, according to the criteria. Our study shows that the prevalence of MS in Guangzhou is lower than that in other cities in China.

Meconium-Stained Amniotic Fluid: Impact on Prognosis of Neonatal Bacterial Meningitis.

OBJECTIVES: Clinical data with respect to the impact of meconium on the prognosis of neonatal bacterial meningitis are scarce. Therefore, in this study, we aimed to determine whether meconium-stained amniotic fluid (MSAF) represents a risk factor for poor prognosis of neonatal bacterial meningitis in a confirmed case population. METHODS: This was a retrospective cohort study of 256 neonates diagnosed with bacterial meningitis hospitalized at one of three hospitals in Shantou, China, between October 2013 and September 2018. Clinical manifestation, laboratory test results and treatment were compared between the two groups, with outcomes dichotomized into 'good' or 'poor' prognosis. Multivariate analysis and follow-up logistic regression analysis were used to identify predictive factors of a poor outcome. RESULTS: Of the 256 neonates with BM, 95 (37.1%) had a good prognosis at discharge and 161 (62.9%) had a poor prognosis. In the poor prognosis group, 131/161 (79.4%) neonates had a permanent neurological sequelae and 19 (11.8%) had ≥2 sequelae. Of note, 11 neonates died. The rate of poor prognosis of BM was significantly higher among neonates with than without MSAF (26.1% vs. 12.6%, respectively; p < 0.05). A logistic multivariate analysis to evaluate the prognostic effect of MSAF to BM showed that neonatal with MSAF is more likely to have a worse prognosis of BM [unadjusted odds ratio (OR), 2.44, 95% confidence interval (CI), 1.24-5.10; adjusted OR, 2.31; 95% CI, 1.09-5.17]. CONCLUSION: MSAF is significantly associated with poor prognosis of neonatal bacterial meningitis. Therefore, in case of MSAF, more attention should be paid to neonatal bacterial meningitis.

G-CSF promotes the viability and angiogenesis of injured liver via direct effects on the liver cells.

BACKGROUND: Presently, liver transplantation is the only treatment strategy for liver failure (LF). Although granulocyte-colony stimulating factor (G-CSF) exhibits protective functions in LF, it is not clear whether it directly affects the liver cells. METHODS AND RESULTS: We established an injured liver cell model and observed that G-CSF treatment promoted cell viability and enhanced Ki67 and VEGF-A expression. Thereafter, human umbilical vein endothelial cells (HUVECs) were cultured in a conditioned medium collected from the G-CSF-treated injured liver cells. HUVECs' proliferation and tubule formation were promoted. Furthermore, in an injured liver mouse model, confirmed via haematoxylin-eosin staining, we evaluated serum alanine aminotransferase activity, Ki67 expression, and microvessel density (MVD). G-CSF treatment significantly relieved liver injury, upregulated Ki67 expression, and enhanced MVD in the injured mouse liver tissue. Additionally, AKT and ERK signal targets were explored, and it was demonstrated that the effects of G-CSF on injured liver cells were mediated through the AKT and ERK signalling pathways. CONCLUSIONS: G-CSF promotes injured liver viability and angiogenesis by directly affecting injured liver cells via the AKT and ERK signalling pathways. These findings improve our understanding of the role of G-CSF in recovery from LF.