Effects of exercise training on glucose metabolism indicators and inflammatory markers in obese children and adolescents: A meta-analysis.

BACKGROUND: Obesity in children and adolescents is a serious problem, and the efficacy of exercise therapy for these patients is controversial. AIM: To assess the efficacy of exercise training on overweight and obese children based on glucose metabolism indicators and inflammatory markers. METHODS: The PubMed, Web of Science, and Embase databases were searched for randomized controlled trials related to exercise training and obese children until October 2023. The meta-analysis was conducted using RevMan 5.3 software to evaluate the efficacy of exercise therapy on glucose metabolism indicators and inflammatory markers in obese children. RESULTS: In total, 1010 patients from 28 studies were included. Exercise therapy reduced the levels of fasting blood glucose (FBG) [standardized mean difference (SMD): -0.78; 95% confidence interval (CI): -1.24 to -0.32, P = 0.0008], fasting insulin (FINS) (SMD: -1.55; 95%CI: -2.12 to -0.98, P < 0.00001), homeostatic model assessment for insulin resistance (HOMA-IR) (SMD: -1.58; 95%CI: -2.20 to -0.97, P < 0.00001), interleukin-6 (IL-6) (SMD: -1.31; 95%CI: -2.07 to -0.55, P = 0.0007), C-reactive protein (CRP) (SMD: -0.64; 95%CI: -1.21 to -0.08, P = 0.03), and leptin (SMD: -3.43; 95%CI: -5.82 to -1.05, P = 0.005) in overweight and obese children. Exercise training increased adiponectin levels (SMD: 1.24; 95%CI: 0.30 to 2.18, P = 0.01) but did not improve tumor necrosis factor-alpha (TNF-α) levels (SMD: -0.80; 95%CI: -1.77 to 0.18, P = 0.11). CONCLUSION: In summary, exercise therapy improves glucose metabolism by reducing levels of FBG, FINS, HOMA-IR, as well as improves inflammatory status by reducing levels of IL-6, CRP, leptin, and increasing levels of adiponectin in overweight and obese children. There was no statistically significant effect between exercise training and levels of TNF-α. Additional long-term trials should be conducted to explore this therapeutic perspective and confirm these results.

Construction and Validation of a Nomogram to Identify the Risk of Cavitation in Pulmonary Tuberculosis.

Background: The present study aimed to construct and validate a nomogram based on clinical metrics to identify CPTB. Patients and Methods: The present study retrospectively recruited pulmonary tuberculosis (PTB) patients admitted to Jiashan County First People's Hospital in China from November 2018 to September 2023. PTB patients were classified into the CPTB group and the non-CPTB group based on chest computed tomography findings, and were randomly allocated to the training set (70%) and the validation cohort (30%). The training set and validation set were used to establish and validate nomogram, respectively. Multivariate logistic regression analysis (MLSA) was used to identify the independent risk factors for CPTB in patients with PTB. Statistically significant variables in the MLSA were then used to construct a nomogram predicting CPTB in patients with PTB. The receiver operating characteristic (ROC) curve, calibration curve analysis (CCA), and decision curve analysis (DCA) were used for the evaluation of the nomogram. Results: A total of 293 PTB patients, including 208 in the training set (85 CPTB) and 85 in the validation set (33 CPTB\), were included in this study. Stepwise MLSA showed that sputum smear (≥2+), smoking(yes), glycosylated hemoglobin A1c(HbA1c), hemoglobin (HB), and systemic inflammatory response index (SIRI) were independent risk factors for the development of cavitation in patients with PTB. The nomogram identifying the high-risk CPTB patients was successfully established and showed a strong predictive capacity, with area under the curves (AUCs) of 0.875 (95% CI:0.806-0.909) and 0.848 (95% CI:0.751-0.946) in the training set and validation set respectively. In addition, the CCA and DCA corroborated the nomogram's high level of accuracy and clinical applicability within both the training and validation sets. Conclusion: The constructed nomogram, consisting of sputum smear positivity, smoking, HbA1C, HB, and SIRI, serves as a practical and effective tool for early identification and personalized management of CPTB.

Serotonin signalling in cancer: Emerging mechanisms and therapeutic opportunities.

BACKGROUND: Serotonin (5-hydroxytryptamine) is a multifunctional bioamine serving as a neurotransmitter, peripheral hormone and mitogen in the vertebrate system. It has pleiotropic activities in central nervous system and gastrointestinal function via an orchestrated action of serotonergic elements, particularly serotonin receptor-mediated signalling cascades. The mitogenic properties of serotonin have garnered recognition for years and have been exploited for repurposing serotonergic-targeted drugs in cancer therapy. However, emerging conflicting findings necessitate a more comprehensive elucidation of serotonin's role in cancer pathogenesis. MAIN BODY AND CONCLUSION: Here, we provide an overview of the biosynthesis, metabolism and action modes of serotonin. We summarise our current knowledge regarding the effects of the peripheral serotonergic system on tumourigenesis, with a specific emphasis on its immunomodulatory activities in human cancers. We also discuss the dual roles of serotonin in tumour pathogenesis and elucidate the potential of serotonergic drugs, some of which display favourable safety profiles and impressive efficacy in clinical trials, as a promising avenue in cancer treatment. KEY POINTS: Primary synthesis and metabolic routes of peripheral 5-hydroxytryptamine in the gastrointestinal tract. Advanced research has established a strong association between the serotonergic components and carcinogenic mechanisms. The interplay between serotonergic signalling and the immune system within the tumour microenvironment orchestrates antitumour immune responses. Serotonergic-targeted drugs offer valuable clinical options for cancer therapy.

TL1A Promotes Fibrogenesis in Colonic Fibroblasts via the TGF-ß1/Smad3 Signaling Pathway.

OBJECTIVE: Intestinal fibrosis is a refractory complication of inflammatory bowel disease (IBD). Tumor necrosis factor ligand-related molecule-1A (TL1A) is important for IBD-related intestinal fibrosis in a dextran sodium sulfate (DSS)-induced experimental colitis model. This study aimed to explore the effects of TL1A on human colonic fibroblasts. METHODS: A trinitrobenzene sulfonic acid (TNBS)-induced experimental colitis model of LCK-CD2-TL1A-GFP transgenic (Tg) or wild-type (WT) mice was established to determine the effect and mechanism of TL1A on intestinal fibrosis. The human colonic fibroblast CCD-18Co cell line was treated concurrently with TL1A and human peripheral blood mononuclear cell (PBMC) supernatant. The proliferation and activation of CCD-18Co cells were detected by BrdU assays, flow cytometry, immunocytochemistry and Western blotting. Collagen metabolism was tested by Western blotting and real-time quantitative polymerase chain reaction (RT-qPCR). RESULTS: The level of collagen metabolism in the TNBS+ethyl alcohol (EtOH)/Tg group was greater than that in the TNBS+EtOH/WT group. Transforming growth factor-ß1 (TGF-ß1) and p-Smad3 in the TNBS+EtOH/Tg group were upregulated as compared with those in the TNBS+EtOH/WT group. The proliferation of CCD-18Co cells was promoted by the addition of human PBMC supernatant supplemented with 20 ng/mL TL1A, and the addition of human PBMC supernatant and TL1A increased CCD-18Co proliferation by 24.4% at 24 h. TL1A promoted cell activation and increased the levels of COL1A2, COL3A1, and TIMP-1 in CCD-18Co cells. Treatment of CCD-18Co cells with TL1A increased the expression of TGF-ß1 and p-Smad3. CONCLUSION: TL1A promotes TGF-ß1-mediated intestinal fibroblast activation, proliferation, and collagen deposition and is likely related to an increase in the TGF-ß1/Smad3 signaling pathway.

Deep learning-assisted diagnosis of benign and malignant parotid tumors based on ultrasound: a retrospective study.

BACKGROUND: To develop a deep learning(DL) model utilizing ultrasound images, and evaluate its efficacy in distinguishing between benign and malignant parotid tumors (PTs), as well as its practicality in assisting clinicians with accurate diagnosis. METHODS: A total of 2211 ultrasound images of 980 pathologically confirmed PTs (Training set: n = 721; Validation set: n = 82; Internal-test set: n = 89; External-test set: n = 88) from 907 patients were retrospectively included in this study. The optimal model was selected and the diagnostic performance evaluation is conducted by utilizing the area under curve (AUC) of the receiver-operating characteristic(ROC) based on five different DL networks constructed at varying depths. Furthermore, a comparison of different seniority radiologists was made in the presence of the optimal auxiliary diagnosis model. Additionally, the diagnostic confusion matrix of the optimal model was calculated, and an analysis and summary of misjudged cases' characteristics were conducted. RESULTS: The Resnet18 demonstrated superior diagnostic performance, with an AUC value of 0.947, accuracy of 88.5%, sensitivity of 78.2%, and specificity of 92.7% in internal-test set, and with an AUC value of 0.925, accuracy of 89.8%, sensitivity of 83.3%, and specificity of 90.6% in external-test set. The PTs were subjectively assessed twice by six radiologists, both with and without the assisted of the model. With the assisted of the model, both junior and senior radiologists demonstrated enhanced diagnostic performance. In the internal-test set, there was an increase in AUC values by 0.062 and 0.082 for junior radiologists respectively, while senior radiologists experienced an improvement of 0.066 and 0.106 in their respective AUC values. CONCLUSIONS: The DL model based on ultrasound images demonstrates exceptional capability in distinguishing between benign and malignant PTs, thereby assisting radiologists of varying expertise levels to achieve heightened diagnostic performance, and serve as a noninvasive imaging adjunct diagnostic method for clinical purposes.

Method for the extraction of circulating nucleic acids based on MOF reveals cell-free RNA signatures in liver cancer.

Cell-free RNA (cfRNA) allows assessment of health, status, and phenotype of a variety of human organs and is a potential biomarker to non-invasively diagnose numerous diseases. Nevertheless, there is a lack of highly efficient and bias-free cfRNA isolation technologies due to the low abundance and instability of cfRNA. Here, we developed a reproducible and high-efficiency isolation technology for different types of cell-free nucleic acids (containing cfRNA and viral RNA) in serum/plasma based on the inclusion of nucleic acids by metal-organic framework (MOF) materials, which greatly improved the isolation efficiency and was able to preserve RNA integrity compared with the most widely used research kit method. Importantly, the quality of cfRNA extracted by the MOF method is about 10-fold that of the kit method, and the MOF method isolates more than three times as many different RNA types as the kit method. The whole transcriptome mapping characteristics of cfRNA in serum from patients with liver cancer was described and a cfRNA signature with six cfRNAs was identified to diagnose liver cancer with high diagnostic efficiency (area under curve = 0.905 in the independent validation cohort) using this MOF method. Thus, this new MOF isolation technique will advance the field of liquid biopsy, with the potential to diagnose liver cancer.

Altered bile metabolome and its diagnostic potential for biliopancreatic malignancies.

BACKGROUND: Due to the difficulty of pathological sampling, the clinical differentiation between benign and malignant biliopancreatic diseases remains challenging. Endoscopic retrograde cholangiopancreatography (ERCP) is used to investigate biliary diseases, enabling the collection of bile. This study assessed potential metabolic alterations in biliopancreatic malignancies by exploring changes in the bile metabolome and the diagnostic potential of bile metabolome analysis. METHODS: A total of 264 bile samples were collected from patients who were divided into a discovery cohort (n = 85) and a validation cohort (n = 179). Untargeted metabolomic analysis was used in the discovery cohort, while targeted metabolomic analysis was used in the validation cohort for further investigation of the differentially abundant metabolites. RESULTS: The untargeted metabolomic analysis revealed that the metabolic changes associated with biliopancreatic malignancies occurred mainly in lipid metabolites, among which fatty acid metabolism was most significantly altered, and differentially abundant metabolites identified in the discovery cohort were mainly enriched in unsaturated fatty acid synthesis and linolenic acid synthesis pathways. Analysis of free fatty acid (FFA) metabolism in the validation cohort revealed that the FFA levels and related indicators verified the abnormal fatty acid metabolism associated with biliopancreatic malignancies. The combined model for biliopancreatic malignancies based on the fatty acid indexes and clinical test results improved the diagnostic performance of current clinical level. Then, we used machine learning to define three different FFA metabolic clusters of biliopancreatic malignancies, and survival analysis showed significant differences in prognostic outcomes among the three clusters. CONCLUSIONS: This study found metabolic alterations in biliopancreatic malignancies based on bile samples, which may provide new insights for the clinical diagnosis and prognostic assessment of biliopancreatic malignancies.

Targeting UBR5 inhibits postsurgical breast cancer lung metastases by inducing CDC73 and p53 mediated apoptosis.

UBR5 is a HECT domain E3 ubiquitin ligase that is frequently amplified in breast, ovarian and prostate cancers. Heightened UBR5 expression plays a profound role in tumor growth through immune-dependent mechanisms; however, its mode of action in driving tumor metastasis has not been definitively delineated. Herein, we used a tetracycline (Tet)-inducible RNAi-mediated expression silencing cell system to investigate how UBR5 enables postsurgical mammary tumor metastatic growth in mouse lungs without the continuous influence of the primary lesion. In vitro, Ubr5 knockdown induces morphological and molecular changes characteristic of epithelial-mesenchymal transition (EMT). In vivo, UBR5 promotes lung metastasis in an E3 ubiquitin ligase-dependent manner. Moreover, doxycycline-induced UBR5 expression knockdown in metastatic cells in the lungs, following removing the primary tumors, resulted in increased apoptosis, decreased proliferation and prolonged survival, whereas silencing the expression of cell division cycle 73 (CDC73), a tumor suppressor and E3 ligase substrate of UBR5, reversed these effects. Transcriptome analyses revealed a prominent role of the p53 pathway in dovitinib-induced apoptosis of tumor cells differentially regulated by UBR5 and CDC73. In human triple-negative breast cancer (TNBC) patient specimens, a strong inverse correlation was observed between UBR5 and CDC73 protein levels, with reduced CDC73 expression at metastatic sites compared to primary lesions. Furthermore, a xenograft model of human TNBC recapitulated the metastatic properties and characteristics of the unique UBR5-CDC73 functional antagonism. This study reveals the novel and critical roles and intricate relationships of UBR5, CDC73 and p53 in postsurgical breast cancer metastasis and indicates the potential of targeting this pathway in cancer therapy.

Co-metabolic degradation and metabolite detection of hexabromocyclododecane by Shewanella oneidensis MR-1.

Hexabromocyclododecane (HBCD) is a widely used brominated flame retardant; however, it is a persistent organic pollutant as well as affects the human thyroid hormones and causes cancer. However, the degradation of HBCD has received little attention from researchers. Due to its bioaccumulative and hazardous properties, an appropriate strategy for its remediation is required. In this study, we investigated the biodegradation of HBCD using Shewanella oneidensis MR-1 under optimized conditions. The Box-Behnken design (BBD) was implemented for the optimization of the physical degradation parameters of HBCD. S. oneidensis MR-1 showed the best degradation performance at a temperature of 30 °C, pH 7, and agitation speed of 115 rpm, with an HBCD concentration of 1125 µg/L in mineral salt medium (MSM). The strain tolerated up to 2000 µg/L HBCD. Gas chromatography-mass spectrometry analysis identified three intermediates, including 2-bromo dodecane, 2,7,10-trimethyldodecane, and 4-methyl-1-decene. The results provide an insightful understanding of the biodegradation of HBCD by S. oneidensis MR-1 under optimized conditions and could pave the way for further eco-friendly applications. KEY POINTS: • HBCD biodegradation by Shewanella oneidensis • Optimization of HBCD biodegradation by the Box-Behnken analysis • Identification of useful metabolites from HBCD degradation.

Novel insights into anticancer mechanisms of elesclomol: More than a prooxidant drug.

As an essential micronutrient for humans, the metabolism of copper is fine-tuned by evolutionarily conserved homeostatic mechanisms. Copper toxicity occurs when its concentration exceeds a certain threshold, which has been exploited in the development of copper ionophores, such as elesclomol, for anticancer treatment. Elesclomol has garnered recognition as a potent anticancer drug and has been evaluated in numerous clinical trials. However, the mechanisms underlying elesclomol-induced cell death remain obscure. The discovery of cuproptosis, a novel form of cell death triggered by the targeted accumulation of copper in mitochondria, redefines the significance of elesclomol in cancer therapy. Here, we provide an overview of copper homeostasis and its associated pathological disorders, especially copper metabolism in carcinogenesis. We summarize our current knowledge of the tumor suppressive mechanisms of elesclomol, with emphasis on cuproptosis. Finally, we discuss the strategies that may contribute to better application of elesclomol in cancer therapy.

Human umbilical cord mesenchymal stem cells ameliorate colon inflammation via modulation of gut microbiota-SCFAs-immune axis.

BACKGROUND: Inflammatory bowel disease (IBD) is a global health problem in which gut microbiota dysbiosis plays a pivotal pathogenic role. Mesenchymal stem cells (MSCs) therapy has emerged as a prospective novel tool for managing IBD, and which can also regulate the composition of gut microbiota. However, the functional significance of MSCs-induced changes in gut microbiome is poorly understood. METHODS: Here, we investigated for the first time the role of gut microbiota in mediating the protective effect of human umbilical cord MSCs (HUMSCs) on DSS-induced colitis. Gut microbiota alteration and short-chain fatty acids (SCFAs) production were analyzed through 16S rRNA sequencing and targeted metabolomics. Spectrum antibiotic cocktail (ABX), fecal microbiota transplantation (FMT) and sterile fecal filtrate (SFF) were employed to evaluate the protective effect of intestinal flora and its metabolites. Cytokine microarray, Enzyme-linked immunosorbent assay (ELISA), and flow cytometry were conducted to assess the effect on CD4+T homeostasis. RESULTS: Here, we investigated for the first time the role of gut microbiota in mediating the protective effect of MSCs on DSS-induced colitis. By performing gut microbiota depletion and fecal microbiota transplantation (FMT) experiments, we revealed that MSCs derived from human umbilical cord ameliorated colon inflammation and reshaped T-cells immune homeostasis via remodeling the composition and diversity of gut flora, especially up-regulated SCFAs-producing bacterial abundance, such as Akkermansia, Faecalibaculum, and Clostridia_UCG_014. Consistently, targeted metabolomics manifested the increased SCFAs production with MSCs administration, and there was also a significant positive correlation between differential bacteria and SCFAs. Meanwhile, combined with sterile fecal filtrate (SFF) gavage experiments, the underlying protective mechanism was further associated with the improved Treg/Th2/Th17 balance in intestinal mucosa mediated via the increased microbiota-derived SCFAs production. CONCLUSION: The present study advances understanding of MSCs in the protective effects on colitis, providing evidence for the new role of the microbiome-metabolite-immune axis in the recovery of colitis by MSCs.

Antibody-Free Fluorine-Assisted Metabolic Sequencing of RNA N4-Acetylcytidine.

N4-Acetylcytidine (ac4C) has been found to affect a variety of cellular and biological processes. For a mechanistic understanding of the roles of ac4C in biology and disease, we present an antibody-free, fluorine-assisted metabolic sequencing method to detect RNA ac4C, called "FAM-seq". We successfully applied FAM-seq to profile ac4C landscapes in human 293T, HeLa, and MDA cell lines in parallel with the reported acRIP-seq method. By comparison with the classic ac4C antibody sequencing method, we found that FAM-seq is a convenient and reliable method for transcriptome-wide mapping of ac4C. Because this method holds promise for detecting nascent RNA ac4C modifications, we further investigated the role of ac4C in regulating chemotherapy drug resistance in chronic myeloid leukemia. The results indicated that drug development or combination therapy could be enhanced by appreciating the key role of ac4C modification in cancer therapy.

Effect of night shift work on metabolic syndrome in adults who suffered from earthquake stress in early life.

Objective: To examine the role of night shift work on the risk of metabolic syndrome (MetS) in adults suffered from earthquakes prenatally or as infants and to analyse the effect of stress on factors that influence MetS in this population. Methods: We included 870 subjects from 2014 to 2015. All subjects work as miners for the Kailuan Mining Group and were born were living in Tangshan. Participants were classified into two groups on basis of their work schedules: day shift and night shift. They were further classified into the prenatal exposure group, the infancy exposure group, and the control group based on their age during the Tangshan earthquake. This study was conducted 38 years after the earthquake. Participants' general demographic data, smoking and drinking habits, as well as work schedules were collected. All participants' sleep status was assessed with the Pittsburgh Sleep Quality Index. The measurement of all subjects' waist circumference and blood pressure was made, and triglycerides, fasting blood glucose, high-density lipoproteins, and low-density lipoproteins were measured by collecting blood samples. The definition of MetS was made after the guidelines for preventing and controlling type 2 diabetes in China (2017 Edition). Results: A total of 187 (21.5%) workers were determined to have MetS. The incidence of MetS was greatly higher in night shift workers who were exposed to an earthquake during infancy than in day shift workers (χ2 = 8.053, p = 0.005). A multivariate logistic regression analysis displayed male participants had a higher risk develop MetS than female participants (p = 0.042, OR = 0.368, 95% CI = 0.140, 0.965). Current smokers (p = 0.030, OR = 1.520, 95%CI = 1.042, 2.218) and participants who sleep fewer than 7 h per night (p = 0.015, OR = 1.638, 95%CI = 1.101, 2.437) had a higher risk of MetS. Prenatal earthquake stress was also a risk element for MetS (p = 0.012, OR = 1.644, 95%CI = 1.115, 2.423). Conclusion: The risk of MetS is significantly higher in night shift workers exposed to earthquake stress during infancy than day shift workers. Earthquake exposure during pregnancy is an independent risk factor for MetS. Smoking and sleeping less than 7 h have a higher risk of MetS than the control group.

Cuproptosis-related signature for clinical prognosis and immunotherapy sensitivity in hepatocellular carcinoma.

BACKGROUND: Copper homeostasis imbalance has been implicated in tumor progression, aggressiveness, and treatment response. However, the precise roles of cuproptosis-related genes (CRGs) in hepatocellular carcinoma (HCC) remain poorly understood. METHODS: In this study, we employed a consensus clustering algorithm to identify distinct molecular subtypes. We then performed Kaplan-Meier analysis and univariate Cox regression analysis to identify prognostic differentially expressed genes. The expression of these genes was subsequently validated using qPCR on fresh-frozen tissues obtained from HCC patients. Moreover, leveraging the TCGA-HCC cohort, we constructed a CRGs-related risk prediction model using the LASSO and multivariate Cox regression analysis. RESULTS: By analyzing the data, we successfully established a CRGs risk prognostic model for HCC patients, comprising five differential genes (CAD, SGCB, TXNRD1, KDR, and MTND4P20). Cox regression analysis revealed that the CRGs risk score could serve as an independent prognostic factor for overall survival (hazard ratio [HR] = 1.308, 95% confidence interval [CI] = 1.200 - 1.426, P < 0.001). The area under the curve (AUC) values of the CRGs-score for predicting 1-year, 3-year, and 5-year survival rates were 0.785, 0.724, and 0.723, respectively. Notably, the expression levels of immune checkpoints (including PD-1, PD-L1, and CTLA4) significantly differed between the low- and high-risk score groups. Furthermore, the low-risk score group displayed increased sensitivity to sorafenib, cisplatin, cyclopamine, nilotinib, salubrinal, and gemcitabine, whereas the high-risk score group exhibited heightened sensitivity to lapatinib, erlotinib, and gefitinib. CONCLUSIONS: Our findings highlight the potential of the CRGs risk score as an independent and promising biomarker for clinical prognosis and immunotherapy sensitivity in HCC patients.

Emerging insights into mechanisms of trastuzumab resistance in HER2-positive cancers.

HER2 is an established therapeutic target in breast, gastric, and gastroesophageal junction carcinomas with HER2 overexpression or genomic alterations. The humanized monoclonal antibody trastuzumab targeting HER2 has substantially improved the clinical outcomes of HER2-positive patients, yet the inevitable intrinsic or acquired resistance to trastuzumab limits its clinical benefit, necessitating the elucidation of resistance mechanisms to develop alternate therapeutic strategies. This review presents an overview of trastuzumab resistance mechanisms involving signaling pathways, cellular metabolism, cell plasticity, and tumor microenvironment, particularly discussing the prospects of developing rational combinations to improve patient outcomes.

Serum Lipoprotein(a) and High-Density Lipoprotein Cholesterol Associate with Diabetic Nephropathy: Evidence from Machine Learning Perspectives.

Purpose: Diabetic nephropathy (DN) is a common complication of type 2 diabetes mellitus (T2DM) that significantly impacts the quality of life for affected patients. Dyslipidemia is a known risk factor for developing cardiovascular complications in T2DM patients. However, the association between serum lipoprotein(a) (Lp(a)) and high-density lipoprotein cholesterol (HDL-C) with DN requires further investigation. Patients and Methods: For this cross-sectional study, we randomly selected T2DM patients with nephropathy (DN, n = 211) and T2DM patients without nephropathy (T2DM, n = 217) from a cohort of 142,611 patients based on predefined inclusion and exclusion criteria. We collected clinical data from the patients to identify potential risk factors for DN using binary logistic regression and machine learning. After obtaining the feature importance score of clinical indicators by building a random forest classifier, we examined the correlations between Lp(a), HDL-C and the top 10 indicators. Finally, we trained decision tree models with top 10 features using training data and evaluated their performance with independent testing data. Results: Compared to the T2DM group, the DN group had significantly higher serum levels of Lp(a) (p < 0.001) and lower levels of HDL-C (p = 0.028). Lp(a) was identified as a risk factor for DN, while HDL-C was found to be protective. We identified the top 10 indicators that were associated with Lp(a) and/or HDL-C, including urinary albumin (uALB), uALB to creatinine ratio (uACR), cystatin C, creatinine, urinary ɑ1-microglobulin, estimated glomerular filtration rate (eGFR), urinary ß2-microglobulin, urea nitrogen, superoxide dismutase and fibrinogen. The decision tree models trained using the top 10 features and with uALB at a cut-off value of 31.1 mg/L showed an average area under the receiver operating characteristic curve (AUC) of 0.874, with an AUC range of 0.870 to 0.890. Conclusion: Our findings indicate that serum Lp(a) and HDL-C are associated with DN and we have provided a decision tree model with uALB as a predictor for DN.

[Risk factors of recurrence and canceration for premalignant vocal fold lesions after surgery].

Objective:To analyze the risk factors of recurrence and canceration for premalignant vocal fold lesions after surgery, and to provide a reasonable basis for preoperative evaluation and postoperative follow-up. Methods:This study retrospective analyzed the relationship between clinicopathological factors and clinical outcome（recurrence, canceration, recurrence-free survival, and canceration-free survival） in 148 patients undergoing surgical treatment in Chongqing General Hospital from 2014 to 2017. Results:The five-year overall recurrence rate was 14.86% and the overall recurrence rate was 8.78%. Univariate analysis showed that smoking index, laryngopharyngeal reflux and lesion range were significantly associated with recurrence（P<0.05）, and smoking index and lesion range were significantly associated with canceration（P<0.05）. Multivariate logistic regression analysis showed that smoking index ≥600 and laryngopharyngeal reflux were independent risk factors for recurrence（P<0.05）, and smoking index ≥600 and lesion range ≥1/2 vocal cord were independent risk factors for canceration（P<0.05）. The mean carcinogenesis interval for the postoperative smoking cessation group was significantly longer（P<0.05）. Conclusion:Excessive smoking, laryngopharyngeal reflux and a wide range of lesions may be related to postoperative recurrence or malignant progression of precancerous lesions in the vocal cord, and further large-scale multi-center prospective randomized controlled studies are needed to clarify the effects of the above factors on recurrence and malignant changes in the future.

The construction and validation of a frailty risk prediction model for older adults with lung cancer: A cross-sectional study.

OBJECTIVE: We aimed to construct and internally validate a frailty risk prediction model in older adults with lung cancer. METHOD: In total, 538 patients were recruited in a grade A tertiary cancer hospital in Tianjin, and patients were randomly divided into the training group (n = 377) and the testing group (n = 166) at a ratio of 7:3. The Frailty Phenotype scale was used to identify frailty and logistic regression analysis was used to identify the risk factors and establish a frailty risk prediction model. RESULTS: In the training group, logistic regression showed that age, fatigue-related symptom cluster, depression, nutritional status, D-dimer level, albumin level, presence of comorbidities, and disease course were independent risk factors for frailty. The areas under the curve (AUCs) of the training and testing groups were 0.921 and 0.872, respectively. A calibration curve of P = 0.447 validated model calibration. The decision curve analysis demonstrated greater clinical benefit when the threshold probability was >20%. CONCLUSION: The prediction model had a favorable prediction power for determining the risk of frailty, contributing to the prevention and screening of frailty. Patients with a frailty risk score of more than 0.374 should be regularly monitored for frailty and receive personalized preventive interventions.

Long Noncoding RNA LINC01614 is a Diagnostic and Prognostic Marker for Breast Cancer.

BACKGROUND: The long intergenic non-coding RNA 01614 (LINC01614) is aberrantly expressed in various malignancies, suggesting its role in oncogenesis. However, it has not been well studied in breast cancer. METHODS: The cancer genome atlas databases (TCGA) and public database of breast cancer gene-expression miner (bc-GenExMiner) were utilized to analyze the prognostic role of LINC01614 in breast cancer. Kaplan-Meier, and Cox regression analyses were conducted for survival analysis. Nomograms were built to predict survival. We used deconvolution-based methods, such as TIMER (Tumor Immune Estimation Resource) and CIBERSORT (cell-type identification by estimating relative subsets of RNA transcripts), to explore the relationship between LINC01614 and immune cell characteristics. RESULTS: The very abnormal expression of LINC01614 was found in 14 types of malignancy, including breast cancer. The LINC01614 was significantly overexpressed in human epidermal growth factor receptor 2 (HER2)+, estrogen receptor (ER)+, progesterone receptor (PR)+, and non-triple negative breast cancer (non-TNBC). According to survival analysis, the higher expression of LINC01614 was related with poor survival. The co-expressed genes analysis exhibited that LINC01614 was closely associated with the collagen-associated process and phosphoinositide 3-kinases-protein kinase B (PI3K-Akt) signaling pathway. Moreover, this study has explored the association among LINC01614 expression, tumor-infiltrating immune cells, and the efficacy of chemotherapeutics. CONCLUSIONS: Our data reveal the expression pattern of LINC01614 in breast carcinoma with different molecular subtypes. The results also indicated that the LINC01614 could be a novel diagnostic and prognostic marker for breast carcinoma.

A new xAI framework with feature explainability for tumors decision-making in Ultrasound data: comparing with Grad-CAM.

BACKGROUND AND OBJECTIVE: The value of implementing artificial intelligence (AI) on ultrasound screening for thyroid cancer has been acknowledged, with numerous early studies confirming AI might help physicians acquire more accurate diagnoses. However, the black box nature of AI's decision-making process makes it difficult for users to grasp the foundation of AI's predictions. Furthermore, explainability is not only related to AI performance, but also responsibility and risk in medical diagnosis. In this paper, we offer Explainer, an intrinsically explainable framework that can categorize images and create heatmaps highlighting the regions on which its prediction is based. METHODS: A dataset of 19341 thyroid ultrasound images with pathological results and physician-annotated TI-RADS features is used to train and test the robustness of the proposed framework. Then we conducted a benign-malignant classification study to determine whether physicians perform better with the assistance of an explainer than they do alone or with Gradient-weighted Class Activation Mapping (Grad-CAM). RESULTS: Reader studies show that the Explainer can achieve a more accurate diagnosis while explaining heatmaps, and that physicians' performances are improved when assisted by the Explainer. Case study results confirm that the Explainer is capable of locating more reasonable and feature-related regions than the Grad-CAM. CONCLUSIONS: The Explainer offers physicians a tool to understand the basis of AI predictions and evaluate their reliability, which has the potential to unbox the "black box" of medical imaging AI.