Development and Validation of a Nocturnal Hypoglycaemia Risk Model for Patients With Type 2 Diabetes Mellitus.

AIM: To develop and test different machine learning algorithms for predicting nocturnal hypoglycaemia in patients with type 2 diabetes mellitus. DESIGN: A retrospective study. METHODS: We collected data from dynamic blood glucose monitoring of patients with T2DM admitted to the Department of Endocrinology and Metabolism at a hospital in Shanghai, China, from November 2020 to January 2022. Patients undergone the continuous glucose monitoring (CGM) for ≥ 24 h were included in this study. Logistic regression, random forest and light gradient boosting machine algorithms were employed, and the models were validated and compared using AUC, accuracy, specificity, recall rate, precision, F1 score and the Kolmogorov-Smirnov test. RESULTS: A total of 4015 continuous glucose-monitoring data points from 440 patients were included, and 28 variables were selected to build the risk prediction model. The 440 patients had an average age of 62.7 years. Approximately 48.2% of the patients were female and 51.8% were male. Nocturnal hypoglycaemia appeared in 573 (14.30%) of 4015 continuous glucose monitoring data. The light gradient boosting machine model demonstrated the highest predictive performances: AUC (0.869), specificity (0.802), accuracy (0.801), precision (0.409), recall rate (0.797), F1 score (0.255) and Kolmogorov (0.603). The selected predictive factors included time below the target glucose range, duration of diabetes, insulin use before bed and dynamic blood glucose monitoring parameters from the previous day. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.

Effects of fine particulate matter mass and chemical components on oxidative DNA damage in human early placenta.

The effects of chemical components of ambient fine particulate matter (PM2.5) on human early maternal-fetal interface are unknown. We estimated the associations of PM2.5 and component exposures with placental villi 8-hydroxy-2'-deoxyguanosine (8-OHdG) in 142 normal early pregnancy (NEP) and 142 early pregnancy loss (EPL) from December 2017 to December 2022. We used datasets accessed from the Tracking Air Pollution in China platform to estimate maternal daily PM2.5 and component exposures. Effect of average PM2.5 and component exposures during the post-conception period (i.e., from ovulation to villi collection) on the concentration of villi 8-OHdG were analyzed using multivariable linear regression models. Distributed lag and cumulative effects of PM2.5 and component exposures during the periovulatory period and within ten days before villi collection on villi 8-OHdG were analyzed using distributed lag non-linear models combined with multivariable linear regression models. Per interquartile range increase in average PM2.5, black carbon (BC), and organic matter (OM) exposures during the post-conception period increased villi 8-OHdG in all subjects (ß = 34.48% [95% CI: 9.33%, 65.42%], ß = 35.73% [95% CI: 9.08%, 68.89%], and ß = 54.71% [95% CI: 21.56%, 96.91%], respectively), and in EPL (ß = 63.37% [95% CI: 16.00%, 130.10%], ß = 47.43% [95% CI: 4.30%, 108.39%], and ß = 72.32% [95% CI: 18.20%, 151.21%], respectively), but not in NEP. Specific weekly lag effects of PM2.5, BC, and OM exposures during the periovulatory period increased villi 8-OHdG in all subjects. Ten-day cumulative and lag effects of PM2.5, BC, and OM increased villi 8-OHdG in all subjects and EPL, but not in NEP; and the effects of OM were robust after adjusting for BC, ammonium, nitrate, or sulfate in two-pollutant models. In conclusion, placental oxidative DNA damage in early pregnancy was associated with maternal exposure to PM2.5, especially its chemical components BC and OM.

Changes in short-chain fatty acids affect brain development in mice with early life antibiotic-induced dysbacteriosis.

Background: Early enteral nutrition and the gut microbiota profoundly influence neonatal brain development, with short-chain fatty acids (SCFAs) from the microbiota playing a pivotal role. Understanding the relationship between dysbiosis, SCFAs, and brain development is crucial. In this study, we investigated the impact of antibiotics on the concentration of SCFAs in neonatal feces. Additionally, we developed a model of gut dysbiosis in neonatal mice to examine the potential relationship between this imbalance, SCFAs production, and brain function development. Methods: We measured the SCFAs content in the feces of two groups of neonates, categorized based on whether antibiotics were used, and conducted the Neonatal Behavioral Neurological Assessment (NBNA) test on all neonates. Then we evaluated fecal SCFAs levels in neonates and neonatal mice post-antibiotic treatment using liquid chromatography-mass spectrometry (LC-MS) analysis. Morris water maze (MWM) tests assessed behavioral performance, and western blot analysis examined brain tissue-related proteins-neuron-specific enolase (NSE), ionized calcium binding adaptor molecule-1 (IBA1), and myelin basic proteins (MBP). Results: The use of antibiotics did not affect the NBNA scores of the two groups of neonates, but it did reduce the SCFAs content in their feces. Antibiotic administration induced gut dysbiosis in mice, resulting in decreased IBA1 and MBP expression. Interventions to restore gut microbiota ameliorated these effects. Mice with dysbiosis displayed cognitive deficits in the MWM test. SCFAs levels decreased during dysbiosis, and increased upon microbiota recovery. Conclusions: Neonatal dysbiosis affects the microbiota-gut-brain axis, impairing cognitive function and nervous system development. Reduced SCFAs may contribute significantly to these alterations.

Efficacy and Safety of General Anesthesia Induction with Ciprofol in Hip Fracture Surgery of Elderly Patients: A Randomized Controlled Trial.

Background: Ciprofol is a new intravenous sedative / anesthetic drug. In recent years, many clinical studies have also confirmed the sedative effect of ciprofol. However, more clinical research is still needed on its clinical application characteristics in special populations. Objective: The aim of this study was to compare the clinical effects of ciprofol and propofol in general anesthesia induction of elderly patients. Methods: 60 elderly (aged ≥ 75 years) patients underwent hip fracture surgery were randomly into two groups of a 1:1 ratio. Group C (ciprofol group): 0.3mg/kg ciprofol was infused. Group P (propofol group): 1.5mg/kg propofol was infused. The observation period was from the infusion of test drug to 5 min after endotracheal intubation. The primary outcomes included the incidence of severe hypotension and hypotension during the observation period. The secondary outcomes were as follows: the success rate of general anesthesia induction, the number of additional sedation, the time of loss of consciousness (LOC), Δ MAP, Δ HR, adverse events and the frequency of vasoactive drugs used. Results: Finally, 60 subjects completed the study. Compared with Group P, the incidence of severe hypotension in Group C was lower (26.7% vs 53.3%, P = 0.035), the incidence of hypotension was also lower (36.7% vs 63.3%, P = 0.037), Δ MAP in Group C was significantly lower (31.4 ± 11.4 vs 39.6 ± 15.7, P = 0.025), the frequency of ephedrine used and the incidence of injection pain in Group C were also significantly lower. Conclusion: Ciprofol showed similar efficacy to propofol when used for general anesthesia induction in elderly patients underwent hip fracture surgery and could maintain more stable blood pressure.

Causal relationship between rheumatoid arthritis and carpal tunnel syndrome: a bidirectional two-sample Mendelian randomization study.

BACKGROUND: Although there is considerable evidence of a robust correlation between rheumatoid arthritis (RA) and carpal tunnel syndrome (CTS) in previous research, the causal link between the two remains a topic of controversy. METHODS: We conducted a two-sample Mendelian randomization (MR) study to explore the causal impact of RA on CTS. We obtained aggregate data from genome-wide association studies (GWAS) of CTS (ebi database and GEO database) and RA (FinnGen database). This study employed five MR analysis methods, with a focus on the inverse variance-weighted (IVW) method. Sensitivity analyses were conducted to ensure the robustness of the results of this study. Additionally, we performed reverse MR analysis. RESULTS: We selected 84 and 78 single nucleotide polymorphisms (SNPs) significantly associated with RA from two databases as instrumental variables (IVs), respectively. Our results showed that RA patients have a higher risk of getting CTS regardless of whether the ebi database (IVW, OR = 1.045, 95% CI: 1.016-1.075, P = 0.002) or the GEO database (IVW, OR = 1.001, 95% CI: 1.001-1.002, P = 0.001) is selected for CTS data. However, the MR analysis showed no causal link between CTS and the increased risk of RA (ebi: IVW, OR = 1.084, 95% CI: 0.918-1.279, P = 0.341; GEO: IVW, OR = 1.968, 95% CI: 0.011-360.791, P = 0.799). CONCLUSION: The analysis revealed that RA can increase the risk of CTS, but did not support the causal relationship that CTS can increase the risk of RA.

Exploring potential mediating mechanisms between maladaptive perfectionism and athlete burnout based on multi-theory perspectives.

Background: Athletes with maladaptive perfectionism are vulnerable to experiencing a variety of psychological issues, such as burnout. Burnout in athletes can have detrimental effects on their performance and careers. The potential mechanisms by which fear of failure and self-handicapping explain the association between maladaptive perfectionism and athlete burnout remain understudied. This study examined their mediating role in the relationship between maladaptive perfectionism and athlete burnout. Methods: A total of 221 athletes were chosen to participate in a cross-sectional survey study. Data analysis was carried out using SPSS and AMOS structural equation modeling. The participants filled out self-report assessments on maladaptive perfectionism, fear of failure, self-handicapping, and athlete burnout. Results: Analyses indicated that maladaptive perfectionism positively predicts fear of failure, self-handicapping, and athlete burnout. Fear of failure positively predicts self-handicapping and athlete burnout, while self-handicapping also predicts athlete burnout. In addition to the direct pathway, we identified three mediating pathways through mediation analyses: (a) an independent mediation of fear of failure (b) an independent mediation of self-handicapping (c) a chained mediation of both. Discussion: The results of this study provide a better understanding of the underlying mechanisms between maladaptive perfectionism and athletes burnout by considering fear of failure and self-handicapping as mediating variable factors. It is shown that the relationship between maladaptive perfectionism and athlete burnout can be partially explained through the mediating role of individuals' fear of failure as well as self-handicapping behaviors. These insights offer a valuable foundation for the design of psychological interventions to address athlete burnout, enabling coaches and sport psychologists to develop more effective coping strategies for enhancing athletes' psychological well-being and performance.

Excavating regulated cell death signatures to predict prognosis, tumor microenvironment and therapeutic response in HR+/HER2- breast cancer.

Regulated cell death (RCD) has been documented to have great potentials for discovering novel biomarkers and therapeutic targets in malignancies. But its role and clinical value in HR+/HER2- breast cancer, the most common subtype of breast cancer, are obscure. In this study, we comprehensively explored 12 types of RCD patterns and found extensive mutations and dysregulations of RCD genes in HR+/HER2- breast cancer. A prognostic RCD scoring system (CDScore) based on six critical genes (LEF1, SLC7A11, SFRP1, IGFBP6, CXCL2, STXBP1) was constructed, in which a high CDScore predicts poor prognosis. The expressions and prognostic value of LEF1 and SFRP1were also validated in our tissue microarrays. The nomogram established basing on CDScore, age and TNM stage performed satisfactory in predicting overall survival, with an area under the ROC curve of 0.89, 0.82 and 0.8 in predicting 1-year, 3-year and 5-year overall survival rates, respectively. Furthermore, CDScore was identified to be correlated with tumor microenvironments and immune checkpoints by excavation of bulk and single-cell sequencing data. Patients in CDScore high group might be resistant to standard chemotherapy and target therapy. Our results underlined the potential effects and importance of RCD in HR+/HER2- breast cancer and provided novel biomarkers and therapeutic targets for HR+/HER2- breast cancer patients.

Radiomics-based model for prediction of TGF-ß1 expression in head and neck squamous cell carcinoma.

OBJECTIVE: To explore the connection between TGF-ß1 expression and the survival of patients with head and neck squamous cell carcinoma (HNSCC), as well as whether non-invasive CT-based Radiomics can predict TGF-ß1 expression in HNSCC patients. METHODS: Data on transcriptional profiling and clinical information were acquired from the TCGA database and subsequently categorized based on the TGF-ß1 expression cutoff value. Based on the completeness of enhanced arterial phase CT scans, 139 HNSCC patients were selected. The PyRadiomics package was used to extract radiomic features, and the 3D Slicer software was used for image segmentation. Using the mRMR_RFE and Repeat LASSO algorithms, the optimal features for establishing the corresponding gradient enhancement prediction models were identified. RESULTS: A survival analysis was performed on 483 patients, who were divided into two groups based on the TGF-ß1 expression cut-off. The Kaplan-Meier curve indicated that TGF-ß1 was a significant independent risk factor that reduced patient survival. To construct gradient enhancement prediction models, we used the mRMR_RFE algorithm and the Repeat_LASSO algorithm to obtain two features (glrlm and ngtdm) and three radiation features (glrlm, first order_10percentile, and gldm). In both the training and validation cohorts, the two established models demonstrated strong predictive potential. Furthermore, there was no statistically significant difference in the calibration curve, DCA diagram, or AUC values between the mRMR_RFE_GBM model and the LASSO_GBM model, suggesting that both models fit well. CONCLUSION: Based on these findings, TGF-ß1 was shown to be significantly associated with a poor prognosis and to be a potential risk factor for HNSCC. Furthermore, by employing the mRMR_RFE_GBM and Repeat_LASSO_GBM models, we were able to effectively predict TGF-ß1 expression levels in HNSCC through non-invasive CT-based Radiomics.

Estimating Per-Class Statistics for Label Noise Learning.

Real-world data may contain a considerable amount of noisily labeled examples, which usually mislead the training algorithm and result in degraded classification performance on test data. Therefore, Label Noise Learning (LNL) was proposed, of which one popular research trend focused on estimating the critical statistics (e.g., sample mean and sample covariance), to recover the clean data distribution. However, existing methods may suffer from the unreliable sample selection process or can hardly be applied to multi-class cases. Inspired by the centroid estimation theory, we propose Per-Class Statistic Estimation (PCSE), which establishes the quantitative relationship between the clean (first-order and second-order) statistics and the corresponding noisy statistics for every class. This relationship is further utilized to induce a generative classifier for model inference. Unlike existing methods, our approach does not require sample selection from the instance level. Moreover, our PCSE can serve as a general post-processing strategy applicable to various popular networks pre-trained on the noisy dataset for boosting their classification performance. Theoretically, we prove that the estimated statistics converge to their ground-truth values as the sample size increases, even if the label transition matrix is biased. Empirically, we conducted intensive experiments on various binary and multi-class datasets, and the results demonstrate that PCSE achieves more precise statistic estimation as well as higher classification accuracy when compared with state-of-the-art methods in LNL.

PPM1G promotes autophagy and progression of pancreatic cancer via upregulating HMGB1.

Pancreatic cancer remains one of the most aggressive and lethal malignancies worldwide, with a dismal 5-year relative survival rates of only 12%. Therefore, it is urgent to discover the key molecular markers to improve the therapeutic outcomes in pancreatic cancer. Herein, we first demonstrated that PPM1G is upregulated in pancreatic cancer and that PPM1G depletion decreases pancreatic cancer cell growth in vitro and in vivo. High PPM1G expression was linked to short overall survival of pancreatic cancer patients, which was further validated in the TCGA database. Moreover, by detecting Beclin 1, LC3-II, and SQSTM1/p62 expressions and observing autolysosome under transmission electron microscope, we discovered that PPM1G is a novel positive regulator of macroautophagy/autophagy. Furthermore, by using immunoprecipitation-mass spectrometry (IP-MS) analysis and following systemic molecular biology experiment, we demonstrated PPM1G promotes the autophagy and proliferation of pancreatic cancer by directly upregulating HMGB1. Additionally, patients with both high PPM1G and high HMGB1 exhibited poorer prognosis in our cohort. This study preliminarily investigated the possibility of PPM1G as a potential therapeutic target and prognostic biomarker in pancreatic cancer patients.

LMP1 enhances aerobic glycolysis in natural killer/T cell lymphoma.

Natural killer/T cell lymphoma (NKTCL) exhibits highly aggressive clinical behavior, and the outcomes for relapsed/refractory patients are still poor. Recently, the mechanism underlying the effect of Epstein-Barr virus (EBV) infection, which has not been fully defined in NKTCL, has attracted great attention. We explored how LMP1 promoted aerobic glycolysis via metabolic sequencing combined with mRNA sequencing and immunoprecipitation coupled to mass spectrometry. Experimental assays were used to determine the effects of LMP1 and its downstream pathway on the function and glucose metabolism of NKTCL cells. The correlations between LMP1 expression in patients and their clinical features, treatment response, and prognosis were analyzed. Results show that LMP1 enhances NKTCL cell proliferation in vitro and in vivo, inhibits apoptosis, and decreases gemcitabine sensitivity. In addition, LMP1 also enhances aerobic glycolysis in NKTCL cells, as indicated by increases in glucose uptake, lactate production, and extracellular acidification rate. Clinically, LMP1 expression is correlated with risk stratification, treatment response, and prognosis, and higher LMP1 expression indicates greater SUVmax for NKTCL patients. Mechanistically, LMP1 competitively binds to TRAF3 to promote cell proliferation and aerobic glycolysis by regulating the noncanonical NF-κB pathway. The application of an NF-κB pathway inhibitor or reactivation of the NF-κB pathway affects aerobic glycolysis and the biological function of NKTCL cells. In summary, this study is the first to describe and define in detail how LMP1 affects glucose metabolism in NKTCL and might provide a novel perspective for further treatment.

Rhabdomyosarcoma of the biliary tract in a child: a case report.

Pediatric rhabdomyosarcoma of the biliary tract (BRMS) is extremely rare. Here, we present a case of a 2-year-old child who was initially misdiagnosed with choledocholithiasis upon admission. The diagnosis was later confirmed as BRMS through endoscopic retrograde cholangiopancreatography (ERCP). The patient was cured through surgery followed by chemotherapy. Due to the lack of specific early symptoms and the challenges in imaging differentiation, particularly in pediatric patients, clinical awareness of this condition needs to be heightened. Our findings indicate that ERCP is currently the optimal diagnostic tool for this disease, and a combination of surgery and chemotherapy can yield better therapeutic outcomes.

[Retracted] MicroRNA­9 inhibits gastric cancer cell proliferation and migration by targeting neuropilin­1.

[This retracts the article DOI: 10.3892/etm.2019.7841.].

BRD4-specific PROTAC inhibits basal-like breast cancer partially through downregulating KLF5 expression.

Interest in the use of proteolysis-targeting chimeras (PROTACs) in cancer therapy has increased in recent years. Targeting bromodomain and extra terminal domain (BET) proteins, especially bromodomain-containing protein 4 (BRD4), has shown inhibitory effects on basal-like breast cancer (BLBC). However, the bioavailability of BRD4 PROTACs is restricted by their non-selective biodegradability and low tumor-targeting ability. We demonstrated that 6b (BRD4 PROTAC) suppresses BLBC cell growth by targeting BRD4, but not BRD2 and BRD3, for cereblon (CRBN)-mediated ubiquitination and proteasomal degradation. Compound 6b also inhibited expression of Krüppel-like factor 5 (KLF5) transcription factor, a key oncoprotein in BLBC, controlled by BRD4-mediated super-enhancers. Moreover, 6b inhibited HCC1806 tumor growth in a xenograft mouse model. The combination of 6b and KLF5 inhibitors showed additive effects on BLBC. These results suggest that BRD4-specific PROTAC can effectively inhibit BLBC by downregulating KLF5, and that 6b has potential as a novel therapeutic drug for BLBC.

The benefit of favorable venous outflow profiles is mediated through the reduced risk of intracranial hemorrhage in acute ischemic stroke patients undergoing endovascular treatment.

BACKGROUND: Favorable venous outflow (VO) has been recognized as an independent predictor of excellent clinical outcomes in acute ischemic stroke caused by anterior circulation large vessel occlusion (AIS-LVO) patients who received endovascular treatment (EVT). However, the reasons why VO affects clinical outcomes have not been fully explained. In this study, we aimed to identify the potential mediators of VO affecting prognosis. METHODS: We conducted a multicenter retrospective cohort study of consecutive patients with AIS-LVO who underwent EVT. Baseline computed tomographic angiography (CTA) was applied to assess VO by the Cortical Vein Opacification Score (COVES). The primary outcome was functional independence at 90 days (modified Rankin Scale (mRS) score of 0-2). Classifying subtypes of intracranial hemorrhage (ICH) to explore the relationship between ICH subtypes and VO. Multivariate logistic regression and causal mediation analyses were used to evaluate the relationship among VO, functional independence, and potential mediators. RESULTS: Among 860 AIS-LVO patients undergoing EVT, a total of 515 patients were included in the present study after strict screening. In multivariate logistic regression analysis, favorable VO profiles (defined as COVES 3-6) were significantly associated with a lower incidence of ICH (24.2% vs 46.9%, adjusted odds ratio (aOR) 0.48, 95% confidence interval (CI) 0.30 to 0.77, P=0.002) and a higher proportion of functional independence (58.9% vs 15.0%, aOR 4.07, 95% CI 2.41 to 6.88, P<0.001). Mediation analysis showed that favorable VO profiles significantly reduced the incidence of parencuymal hematoma (PH) 2 accounting for 8.0% (95% CI 0.9% to 19.0%) of its beneficial effect on functional independence. CONCLUSION: This study demonstrated the potential mediating effects of severe ICH for the beneficial effect of favorable VO on clinical prognosis among patients with AIS-LVO who underwent EVT.

Chronic mitral regurgitation predicts acute heart failure and worse outcomes after endovascular treatment for large vessel occlusion stroke.

BACKGROUND: Valvular diseases are widely recognized as important etiologies for large vessel occlusion stroke (LVO) but their impact on outcomes among patients with LVO receiving endovascular treatment (EVT) are less well delineated. METHODS: This study was a post hoc exploratory analysis of the RESCUE-BT trial, DEVT trial and BASILAR prospective registry. Outcome measures included the modified Rankin Scale (mRS) score at 90 days, symptomatic intracranial hemorrhage, and post-stroke early acute heart failure (EAHF). Chronic significant mitral regurgitation (csMR) was defined as a long-existing mitral regurgitation (MR) with moderate-to-severe MR grade examined by the transthoracic echocardiography. Adjusted odds ratio (aOR) and 95% confidence interval (CI) were obtained by logistic regression models. RESULTS: Among 2011 patients in these three studies, 837 individuals receiving EVT with available information for valvular status were included in this study. In all categories of chronic valvular disorders, only csMR was related to very poor outcomes (mRS 5-6, aOR 2.76 (95% CI 1.59 to 4.78), P<0.001). CsMR (aOR 7.65 (95% CI 4.33 to 13.49), P<0.001) was an independent predictor of post-stroke EAHF. Mediation analysis showed that csMR increased EAHF instead of reocclusion events or venous thrombosis mediated its effects on functional outcome (49.50% (95% CI 24.83% to 90.00%)). Identical results of csMR on clinical outcomes and post-stroke EAHF were detected in novel cohorts constructed by propensity score matching and sensitivity analysis. CONCLUSION: Our study demonstrated that csMR was a mediator of heart-brain interaction associated with poor outcomes of LVO after EVT by increasing the frequency of post-stroke EAHF. Replication of these findings in a larger cohort is warranted.

Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI.

Automated breast tumor segmentation on the basis of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) has shown great promise in clinical practice, particularly for identifying the presence of breast disease. However, accurate segmentation of breast tumor is a challenging task, often necessitating the development of complex networks. To strike an optimal tradeoff between computational costs and segmentation performance, we propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers. Specifically, the hybrid network consists of a encoder-decoder architecture by stacking convolution and deconvolution layers. Effective 3D transformer layers are then implemented after the encoder subnetworks, to capture global dependencies between the bottleneck features. To improve the efficiency of hybrid network, two parallel encoder sub-networks are designed for the decoder and the transformer layers, respectively. To further enhance the discriminative capability of hybrid network, a prototype learning guided prediction module is proposed, where the category-specified prototypical features are calculated through online clustering. All learned prototypical features are finally combined with the features from decoder for tumor mask prediction. The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network achieves superior performance than the state-of-the-art (SOTA) methods, while maintaining balance between segmentation accuracy and computation cost. Moreover, we demonstrate that automatically generated tumor masks can be effectively applied to identify HER2-positive subtype from HER2-negative subtype with the similar accuracy to the analysis based on manual tumor segmentation. The source code is available at https://github.com/ZhouL-lab/ PLHN.