Real-time analgesic efficacy and factors determining drug requirements of combined spinal-epidural analgesia for labor: a prospective cohort study.

PURPOSE: Combined spinal-epidural analgesia (CSEA) is effective but not sufficient for labor pain. This study was conducted to assess the real-time analgesic efficacy, side effects of anesthetic drug dosage, and maternal satisfaction in labor to provide reference for the optimization of labor analgesia. METHODS: This was a prospective, cohort, single-center study that included 3020 women who received CSEA for labor analgesia. The visual analogue scale (VAS) for labor pain, real-time anesthetic drug dosage, side effects, adverse labor outcomes, factors influencing average drug dosage, and maternal satisfaction with CSEA were assessed. RESULTS: Overall, the VAS labor pain score was lowest at the first hour after the anesthesia was given. After 4 h for primiparas and 3 h for multiparas, the VAS score was greater than 3 but the anesthetic drug dosage did not reach the maximum allowed dosage at the same time. The average anesthetic drug dosage was positively correlated with fever, urinary retention, uterine atony, prolonged active phase, prolonged second stage, assisted vaginal delivery, and postpartum hemorrhage. The average anesthetic drug dosage was the highest in women ≤ 20 years old, those with a body mass index (BMI) ≥ 24.9 kg/m2, and those with a primary or secondary education level. CONCLUSION: Appropriate age guidance and emphasis on education of labor analgesia, weight management during pregnancy, and real-time anesthetic dosage adjustment during labor based on VAS pain score may have positive effects on the satisfaction of labor analgesia. CLINICAL TRIAL NUMBER AND REGISTRY: Clinicaltrials.gov (ChiCTR2100051809).

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.

Lymphatic Node Metastasis Risk Scoring System: A Novel Instrument for Predicting Lymph Node Metastasis After Thymic Epithelial Tumor Resection.

BACKGROUND: The authors aimed to create a novel model to predict lymphatic metastasis in thymic epithelial tumors. METHODS: Data of 1018 patients were collected from the Surveillance, Epidemiology, and End Results database from 2004 to 2015. To construct a nomogram, the least absolute shrinkage and selection operator (LASSO) regression model was used to select candidate features of the training cohort from 2004 to 2013. A simple model called the Lymphatic Node Metastasis Risk Scoring System (LNMRS) was constructed to predict lymphatic metastasis. Using patients from 2014 to 2015 as the validation cohort, the predictive performance of the model was determined by receiver operating characteristic (ROC) curves. RESULTS: The LASSO regression model showed that age, extension, and histology type were significantly associated with lymph node metastasis, which were used to construct the nomogram. Through analysis of the area under the curve (AUC), the nomogram achieved a AUC value of 0.80 (95 % confidence interval [Cl] 0.75-0.85) in the training cohort and 0.82 (95 % Cl 0.70-0.93) in the validation cohort, and had closed calibration curves. Based on the nomogram, the authors constructed the LNMRS model, which had an AUC of 0.80 (95 % Cl 0.75-0.85) in the training cohort and 0.82 (95% Cl 0.70-0.93) in the validation cohort. The ROC curves indicated that the LNMRS had excellent predictive performance for lymph node metastasis. CONCLUSION: This study established a nomogram for predicting lymph node metastasis. The LNMRS model, constructed to predict lymphatic involvement of patients, was more convenient than the nomogram.

Serum NfL associated with anti-NMDA receptor encephalitis.

INTRODUCTION: Neurofilament light chains (NfL) have been reported as potential markers for neuronal-axonal injury in neuroinflammatory diseases. In the current study, we describe serum NfL levels as a prognostic marker for anti-N-methyl-D-aspartate receptor encephalitis (NMDARE). METHODS: Serum levels of NfL of 64 patients with anti-NMDARE and 84 healthy controls were measured by Simoa. The anti-NMDAR Encephalitis One-Year Functional Status (NEOS) score, Modified Rankin Scale (mRS) scores, and clinical and cerebrospinal fluid parameters were evaluated in patients with anti-NMDARE. Meanwhile, we performed a receiver-operator characteristic analysis to assess the power of the serum NFL in predicting the 1-year functional status. RESULTS: Serum NfL levels were significantly elevated in patients with anti-NMDARE compared to healthy controls (p < 0.001, padjusted < 0.001), especially in patients with severe impairments (mRS > 3 vs ≤ 3, p = 0.035) or with limited response to treatment (vs. favorable outcome, p = 0.011). Serum NFL was positively associated with the initial admission mRS (r = 0.23, p = 0.072) and 1-year mRS (r = 0.29, p = 0.018). The AUC of serum NfL and NEOS score for 1-year poor functional status was 0.697 (95% CI 0.527-0.866, p = 0.011), 0.753 (95% CI 0.616-0.890, p = 0.001), respectively. Furthermore, AUC of the combination of serum NfL and NEOS was 0.815 (95% CI 0.680-0.950, p < 0.001). CONCLUSION: Our findings show that serum NfL levels evaluated in anti-NMDAR encephalitis may be a good predictor of the risk of 1-year poor functional status.

Basal metabolic rate and risk of multiple sclerosis: a Mendelian randomization study.

To determine the relationship between basal metabolic rate (BMR) and multiple sclerosis (MS) susceptibility, we analyzed genome-wide association study (GWAS) summary statistics data from the International Multiple Sclerosis Genetics Consortium on a total of 115,803 participants of European descent, including 47,429 patients with MS and 68,374 controls. We selected 378 independent genetic variants strongly associated with BMR in a GWAS involving 454,874 participants as instrumental variables to examine a potential causal relationship between BMR and MS. A genetically predicted higher BMR was associated with a greater risk of MS (odds ratio [OR]: 1.283 per one standard deviation increase in BMR, 95% confidence interval [CI]: 1.108-1.486, P = 0.001). Moreover, we used the lasso method to eliminate heterogeneity (Q statistic = 384.58, P = 0.370). There was no pleiotropy in our study and no bias was found in the sensitivity analysis using the leave-one-out test. We provide novel evidence that a higher BMR is an independent causal risk factor in the development of MS. Further work is warranted to elucidate the potential mechanisms.

Association of serum Gasdermin D with anti-N-methyl-D-aspartate receptor encephalitis.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is an autoimmune disease of the central nervous system. Gasdermin D (GSDMD) is associated with autoimmune disorders and neuroinflammatory disorders, but its role in anti-NMDAR encephalitis is unclear. In this study, we measured serum levels of GSDMD in 42 patients with anti-NMDAR encephalitis and 25 healthy controls. Of the 42 patients, 17 had follow-up evaluation of GSDMD levels and modified Rankin scale (mRS) scores at 3 months. Association of GSDMD with anti-NMDAR encephalitis and its clinical parameters were evaluated. Serum GSDMD levels were significantly higher in patients with anti-NMDAR encephalitis than in healthy controls (p = 0.002, padjusted  = 0.009), especially in males (p = 0.001, padjusted  = 0.022). This was also evident in patients with severe impairment (mRS >3 vs mRS ≤3; p < 0.001). Serum GSDMD was associated with mRS before and after adjustment for age and gender (r = 0.440 and 0.430, p = 0.004 and 0.006, respectively) as well as serum CH50 (r = -0.419 and -0.426, p = 0.011 and 0.012, respectively). Furthermore, 3-month follow-up evaluation revealed that after treatment, anti-NMDAR encephalitis patients had significantly decreased serum GSDMD levels (p = 0.007) and significantly decreased mRS scores (p = 0.002) compared with before treatment. Furthermore, the changes in mRS scores were negatively associated with changes in GSDMD levels, although the associations were not significant (r = -0.222, p = 0.393). Our findings show that serum GSDMD levels are elevated in anti-NMDAR encephalitis and are associated with disease prognosis.

Serum neurofilament light chain and glial fibrillary acidic protein in AQP4-IgG-seropositive neuromyelitis optica spectrum disorders and multiple sclerosis: A cohort study.

We investigated the serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) levels in a cohort of Chinese patients with neuromyelitis optica spectrum disorders (NMOSD) and multiple sclerosis (MS) in relation to clinical disease course and treatment. sNfL and sGFAP levels were determined by ultrasensitive single molecule array (Simoa) assay in patients with NMOSD (n = 102) and MS (n = 98) and healthy controls (HCs; n = 84). Notably, 13 patients with NMOSD and 27 patients with MS were enrolled in the 1-year follow-up cohort. Levels were compared with data such as clinical course, disease duration, Expanded Disability Status Scale (EDSS) score, and lesions on MRI. Higher levels of sNfL and sGFAP were found in subjects with NMOSD and MS than in HCs (sNfL, median 12.11, 17.5 vs. 8.88 pg/ml, p < .05; sGFAP, median 130.2, 160.4 vs. 80.01 pg/ml, p < .05). Moreover, sNfL levels were higher in the relapse phase of MS than in the relapse phase of NMOSD (30.02 vs. 14.57 pg/ml, p < .05); sGFAP levels were higher in the remission phase of MS than in the remission phase of NMOSD (159.8 vs. 124.5 pg/ml, p < .01). A higher sGFAP/sNfL quotient at relapse differentiated NMOSD from MS. Multivariate analyses indicated that sGFAP levels were associated with the EDSS score in NMOSD (p < .05). At the 1-year follow-up, sNfL and sGFAP levels were both decreased in NMOSD patients in remission, while only sNfL levels were decreased in MS patients in remission. sGFAP and sNfL are potential blood biomarkers for diagnosing and monitoring NMOSD and MS.

Metabolic phenotyping to monitor chronic enteritis canceration.

INTRODUCTION: Colorectal cancer (CRC) remains an incurable disease. Previous metabolomic studies show that metabolic signatures in plasma distinguish CRC patients from healthy controls. Chronic enteritis (CE) represents a risk factor for CRC, with a 20 fold greater incidence than in healthy individuals. However, no studies have performed metabolomic profiling to investigate CRC biomarkers in CE. OBJECTIVE: Our aims were to identify metabolomic signatures in CRC and CE and to search for blood-derived metabolite biomarkers distinguishing CRC from CE, especially early-stage biomarkers. METHODS: In this case-control study, 612 subjects were prospectively recruited between May 2015 and May 2016, and including 539 CRC patients (stage I, 102 cases; stage II, 259 cases; stage III, 178 cases) and 73 CE patients. Untargeted metabolomics was performed to identify CRC-related metabolic signatures in CE. RESULTS: Five pathways were significantly enriched based on 153 differential metabolites between CRC and CE. 16 biomarkers were identified for diagnosis of CRC from CE and for guiding CRC staging. The AUC value for CRC diagnosis in the external validation set was 0.85. Good diagnostic performances were also achieved for early-stage CRC (stage I and stage II), with an AUC value of 0.84. The biomarker panel could also stage CRC patients, with an AUC of 0.72 distinguishing stage I from stage II CRC and AUC of 0.74 distinguishing stage II from stage III CRC. CONCLUSIONS: The identified metabolic biomarkers exhibit promising properties for CRC monitoring in CE patients and are superior to commonly used clinical biomarkers (CEA and CA19-9).

An immunophenotyping of renal clear cell carcinoma with characteristics and a potential therapeutic target for patients insensitive to immune checkpoint blockade.

Renal clear cell carcinoma (RCC) patients who do not achieve optimal control of progression with immune checkpoint blockade (ICB) should be further studied. Unsupervised consensus clustering was used to group 525 RCC patients based on two typical ICB pathways, CTLA-4 and pogrammed death 1 (PD-1)/programmed death-ligand 1 (PD-L1), as well as two new discovered regulators, CMTM6 and CMTM4. Three immune molecular subtypes (IMMSs) with different clinical and immunological characteristics were identified (type I, II, and III), among which there were more stage I and low-grade tumors in type I RCC than in type II and III. The proportion of males was highest in type II RCC. Overall survival of type II and III was similar (5.2 and 6 years) and statistically shorter than that of type I (7.6 years) before and after adjusting for age and gender. When conducting stratified analysis, our IMMSs were able to identify high-risk patients among middle-aged patients, males, and stage IV patients. Among the differentially expressed genes, approximately 84% were highly expressed in type II and III RCC. Genes related to ICB (CTLA-4, CD274, and PDCD1LG2) and cytotoxic lymphocytes (CD8A, GZMA, and PRF1) were all highly expressed in type II and III RCC. These results documented that patients with type II and III cancer may be more sensitive to anti-CTLA-4 therapy, anti-PD-1/PD-L1 therapy, and a combination of immunotherapies. High expression of CMTM4 in type I RCC (69%) and a statistically significant interaction of CD274 and CMTM6 indicated that CMTM4/6 might be new therapy targets for type I, who are resistant to ICB.

Joint bioinformatics analysis of underlying potential functions of hsa-let-7b-5p and core genes in human glioma.

BACKGROUND: Glioma accounts for a large proportion of cancer, and an effective treatment for this disease is still lacking because of the absence of specific driver molecules. Current challenges in the treatment of glioma are the accurate and timely diagnosis of brain glioma and targeted treatment plans. To investigate the diagnostic biomarkers and prospective role of miRNAs in the tumorigenesis and progression of glioma, we analyzed the expression of miRNAs and key genes in glioma based on The Cancer Genome Atlas database. METHODS: Of the 701 cases that were downloaded, five were normal and 696 were glioma. Then, 1626 differentially expressed genes were identified, and 173 aberrantly expressed miRNAs were calculated by edgeR. GO and KEGG pathway enrichment analyses were performed using Cytoscape software. A coexpression network was built by weighted correlation network analysis (WGCNA). A cell scratch test and transwell, cell apoptosis and cell cycle assays were performed to validate the function of hsa-let-7b-5p. RESULTS: Based on crosstalk genes in the KEGG, PPI network, and WGCNA analyses, PLK1, CCNA2, cyclin B2 (CCNB2), and AURKA were screened as candidate diagnostic marker genes. The survival analysis revealed that high mRNA expression of PLK1, CCNA2, and AURKA was significantly associated with poor overall survival. Furthermore, hsa-let-7b-5p was identified as a core miRNA in the regulation of candidate genes involved in glioma development. We confirmed that hsa-let-7b-5p could inhibit the migration, invasion, and cell cycle of glioma cells. CONCLUSIONS: This study provides four potential biomarkers for the diagnosis of glioma, offers a potential explanation of its pathogenesis, and proposes hsa-let-7b-5p as a therapeutic target.

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.

Causal association between genetically predicted circulating immune cell counts and frailty: a two-sample Mendelian randomization study.

Background: Despite the recognized link between immune responses and frailty, the association between immune cell counts and frailty based on previous observational studies remains disputed, with uncertain causal nexus. This study aimed to elucidate causal association between genetically predicted circulating immune cell counts and frailty. Methods: We conducted the two-sample Mendelian randomization (MR) study with independent genetic variants associated with six immune cell subtype counts from genome-wide association studies in 563,946 European individuals. Frailty summary data, assessed via frailty index (FI), was obtained from study comprising 175,226 subjects. Univariate MR, reverse MR and multivariate MR were conducted to comprehensive investigate the association between immune cell counts and FI, with two-step MR analysis for mediation analysis. Results: Univariate MR evidence indicated that among six leukocyte subtype counts, only elevated eosinophil count was significantly correlated with higher FI (ß = 0.059, 95% confidence interval [CI], 0.042-0.078, P=5.63E-11), with no reverse causal relationship identified in reverse MR. In multivariate MR, the causal effect of eosinophil count retained statistical significance (ß = 0.063, 95% CI, 0.021-0.104, P = 0.003). Ultimately, the two-step MR analysis demonstrated two mediators in this causal pathway: asthma (ß= 0.019, 95% CI, 0.013-0.025, P = 35.84E-10, mediated proportion, 31.732%) and rheumatoid arthritis (ß= 0.004, 95% CI, 0.001-0.006, P=1.75E-03, mediated proportion, 6.411%). Conclusions: Within immune cell subtypes, MR evidence indicated only genetically predicted circulating eosinophil count had irreversible and independent causal effect on frailty, with asthma and rheumatoid arthritis possibly serving as partial mediators. The finding stressed the need for further exploring physiological functions of eosinophils in order to develop effective strategies against frailty.

Cardiopulmonary Coupling Spectrogram as an Ambulatory Method for Assessing Sleep Disorders in Patients With Autoimmune Encephalitis.

BACKGROUND AND OBJECTIVES: Sleep disorders are a common and important clinical feature in patients with autoimmune encephalitis (AE); however, they are poorly understood. We aimed to evaluate whether cardiopulmonary coupling (CPC), an electrocardiogram-based portable sleep monitoring technology, can be used to assess sleep disorders in patients with AE. METHODS: Patients fulfilling the diagnostic criteria of AE were age- and sex-matched with recruited healthy control subjects. All patients and subjects received CPC testing between August 2020 and December 2022. Demographic data, clinical information, and Pittsburgh Sleep Quality Index (PSQI) scores were collected from the medical records. Data analysis was performed using R language programming software. RESULTS: There were 60 patients with AE (age 26.0 [19.8-37.5] years, male 55%) and 66 healthy control subjects (age 30.0 [25.8-32.0] years, male 53%) included in this study. Compared with healthy subjects, patients with AE had higher PSQI scores (7.00 [6.00-8.00] vs 3.00 [2.00-4.00], p < 0.001), lower sleep efficiency (SE 80% [71%-87%] vs 92% [84%-95%], p < 0.001), lower percentage of high-frequency coupling (25% [14%-43%] vs 45% [38%-53%], p < 0.001), higher percentage of REM sleep (19% ± 9% vs 15% ± 7%, p < 0.001), higher percentage of wakefulness (W% 16% [11%-25%] vs 8% [5%-16%], p = 0.074), higher low-frequency to high-frequency ratio (LF/HF 1.29 [0.82-2.40] vs 0.91 [0.67-1.29], p = 0.001), and a higher CPC-derived respiratory disturbance index (9.78 [0.50-22.2] vs 2.95 [0.40-6.53], p < 0.001). Follow-up evaluation of 14 patients showed a decrease in the PSQI score (8.00 [6.00-9.00] vs 6.00 [5.00-7.00], p = 0.008), an increased SE (79% [69%-86%] vs 89% [76%-91%], p = 0.030), and a decreased W% (20% [11%-30%] vs 11% [8%-24], p = 0.035). Multiple linear regression indicated that SE (-7.49 [-9.77 to -5.21], p < 0.001) and LF/HF ratio (0.37 [0.13-0.6], p = 0.004) were independent factors affecting PSQI scores in patients with AE. DISCUSSION: Sleep disorders with autonomic dysfunction are common in patients with AE. Improvements in the PSQI score and SE precede the restoration of sleep microstructural disruption in the remission stage. CPC parameters may be useful in predicting sleep disorders in patients with AE.

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.

Novel highly selective fluorescence sensing strategy for Mercury(â ¡) in water based on nitrogen-doped carbon quantum dots.

In this work, a fluorescent signal-closing probe of nitrogen-doped carbon quantum dots (NCQDs) was developed for quantitative detection of mercury ions (Hg2+). In this detection system, the NCQDs with high quantum yield (QY, 63.80 %) were synthesized via simple hydrothermal method with Methyl Glycine Diacetic acid Trisodium Salt (MGDA) and m-phenylenediamine (MPD) as carbon and nitrogen sources. The NCQDs have a typical surface structure and exceptional fluorescence stability, and their fluorescence zones are centered on excitation wavelengths of 440 nm and emission wavelengths of 510 nm. Under optimal conditions, the NCQDs have outstanding anti-interference ability to various ions and high selectivity to mercury ions. The fluorescence intensity of the detection system is weakened due to the generation of non-fluorescent groups caused by the static quenching effect. The fluorescence quenching efficiency shows a fascinating linear relationship with Hg2+ ions at 0-100 µM (y = 0.0051x-0.015, R2 = 0.9943), and the detection limit is 0.9 µM. Acute toxicity test shows that NCQDs have low toxicity and little harm to environment. The detection system can be used for the quantification of mercury ions in environmental water samples, and the recovery rate is between 99.64 % and 103.43 %, indicating that it is a simple and economical fluorescence detection method.

Nitrogen and sulfur co-doped carbon quantum dots as "on-off-on" fluorescence probes to detect Hg2+ and MnO4- and improving the photostability of Rhodamine B.

BACKGROUND: Carbon quantum dot (CQDs) are zero-dimensional carbon nanomaterials with a size of less than 10 nm CQDs are widely used in the field of ion detection by virtue of their fluorescence characteristics such as strong fluorescence intensity, good optical stability and tunable emission wavelength. Although the traditional atomic absorption method, electrochemical method and other metal ion detection methods are highly sensitive, the operation is complex, expensive and limited by the site. Therefore, we prepared the N, S-CQDs capable of detecting Hg2+ and MnO4- in water with the advantages of simple operation, low cost, and direct visual signal. RESULTS: N, S-CQDs with high-quantum yield (77.68%), uniform particle size (0.4 nm-2.6 nm) and green fluorescence were created utilizing a one-pot hydrothermal process with the precursors ASDA-Na4 and m-phenylenediamine. N, S-CQDs has good optical properties such as high fluorescence intensity, wavelength independence, up-conversion luminescence and fluorescence stability. We examined 27 common ions in water and found that the fluorescence of N, S-CQDs could be selectively quenched by Hg2+ and MnO4-, and the detection limits are 0.41 µM and 1.2 µM, respectively. The mechanism of quenching is further investigated. The fluorescence of N, S-CQDs-Hg2+ system can be restored by halogen ions (Cl-, Br-, I-), while the fluorescence of N, S-CQDs-MnO4- system can be partially restored by Fe2+. This forms an "on-off-on" mode of fluorescent probes. In addition, we also studied that trace amounts of N, S-CQDs can improve the photostability of RhB. SIGNIFICANCE: The N, S-CQDs are fluorescent probes in an "on-off-on" mode. N, S-CQDs with green fluorescence (on) can be quenched by Hg2+ and MnO4- (off). The fluorescence quenched by Hg2+ can be restored by halogen ions again, while the fluorescence quenched by MnO4- can partially be restored (on). This ion detection method can be used to visually detect the two ions in the field, with the advantages of low cost, simple operation and visual intuition.

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.