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Background: Chronic uncontrolled hyperglycemia, a precursor to chronic low-grade inflammation, is a leading cause of coronary artery disease (CAD) due to plaque buildup in type-1 diabetes (T1D) patients. We evaluated levels of 22 inflammatory markers in cross-sectional serum samples from 1222 subjects to evaluate their potential as risk factors for CAD in T1D patients., Hypothesis: Circulating levels of markers of inflammation may be the risk factors for incident CAD., Methods: The T1D subjects were divided into two groups: those without CAD (n = 1107) and with CAD (n = 115). Serum levels of proteins were assayed using multiplex immunoassays on a Luminex Platform. Differences between the two groups were made by univariate analysis. Multivariate logistic regression was used to ascertain the potential of proteins as risk factors for CAD. Influence of age, duration of diabetes, sex, hypertension, and dyslipidemia was determined in a stepwise manner. Serum levels of 22 proteins were combined into a composite score using Ridge regression for risk-based stratification., Results: Mean levels of CRP, IGFBP1, IGFBP2, insulin-like growth factors binding protein-6 (IGFBP6), MMP1, SAA, sTNFRI, and sTNFRII were elevated in CAD patients (n = 115) compared to T1D patients without CAD (nCAD, n = 1107). After adjusting for age, duration of diabetes, sex, hypertension, and dyslipidemia, higher levels of sTNFRI (odds ratio [OR] = 2.18, 1.1 × 10 -3 ), sTNFRII (OR = 1.52, 1 × 10 -2 ), and IGFBP6 (OR = 3.62, 1.8 × 10 -3 ) were significantly associated with CAD. The composite score based on Ridge regression, was able to stratify CAD patients into low, medium, and high-risk groups., Conclusions: The results show activation of the TNF pathway in CAD patients. Evaluating these markers in serum can be a potential tool for identifying high-risk T1D patients for intensive anti-inflammatory therapeutic interventions., (© 2023 The Authors. Clinical Cardiology published by Wiley Periodicals LLC.)

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations ( SETD2 , PBRM1 , BAP1 , and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

Type 1 diabetes (T1D) is an autoimmune disease, characterized by the presence of autoantibodies to protein and non-protein antigens. Here we report the identification of specific anti-carbohydrate antibodies (ACAs) that are associated with pathogenesis and progression to T1D. We compare circulatory levels of ACAs against 202 glycans in a cross-sectional cohort of T1D patients (n = 278) and healthy controls (n = 298), as well as in a longitudinal cohort (n = 112). We identify 11 clusters of ACAs associated with glycan function class. Clusters enriched for aminoglycosides, blood group A and B antigens, glycolipids, ganglio-series, and O-linked glycans are associated with progression to T1D. ACAs against gentamicin and its related structures, G418 and sisomicin, are also associated with islet autoimmunity. ACAs improve discrimination of T1D status of individuals over a model with only clinical variables and are potential biomarkers for T1D., (© 2022. The Author(s).)

Malignant chromophobe renal cancer (chRCC) and benign oncocytoma (RO) are two renal tumor types difficult to differentiate using histology and immunohistochemistry-based methods because of their similarity in appearance. We previously developed a transcriptomics-based classification pipeline with "Chromophobe-Oncocytoma Gene Signature" (COGS) on a single-molecule counting platform. Renal cancer patients ( n = 32, chRCC = 17, RO = 15) were recruited from Augusta University Medical Center (AUMC). Formalin-fixed paraffin-embedded (FFPE) blocks from their excised tumors were collected. We created a custom single-molecule counting code set for COGS to assay RNA from FFPE blocks. Utilizing hematoxylin-eosin stain, pathologists were able to correctly classify these tumor types (91.8%). Our unsupervised learning with UMAP (Uniform manifold approximation and projection, accuracy = 0.97) and hierarchical clustering (accuracy = 1.0) identified two clusters congruent with their histology. We next developed and compared four supervised models (random forest, support vector machine, generalized linear model with L2 regularization, and supervised UMAP). Supervised UMAP has shown to classify all the cases correctly (sensitivity = 1, specificity = 1, accuracy = 1) followed by random forest models (sensitivity = 0.84, specificity = 1, accuracy = 1). This pipeline can be used as a clinical tool by pathologists to differentiate chRCC from RO.

Publicly available gene expression datasets were analyzed to develop a chromophobe and oncocytoma related gene signature (COGS) to distinguish chRCC from RO. The datasets GSE11151, GSE19982, GSE2109, GSE8271 and GSE11024 were combined into a discovery dataset. The transcriptomic differences were identified with unsupervised learning in the discovery dataset (97.8% accuracy) with density based UMAP (DBU). The top 30 genes were identified by univariate gene expression analysis and ROC analysis, to create a gene signature called COGS. COGS, combined with DBU, was able to differentiate chRCC from RO in the discovery dataset with an accuracy of 97.8%. The classification accuracy of COGS was validated in an independent meta-dataset consisting of TCGA-KICH and GSE12090, where COGS could differentiate chRCC from RO with 100% accuracy. The differentially expressed genes were involved in carbohydrate metabolism, transcriptomic regulation by TP53, beta-catenin-dependent Wnt signaling, and cytokine (IL-4 and IL-13) signaling highly active in cancer cells. Using multiple datasets and machine learning, we constructed and validated COGS as a tool that can differentiate chRCC from RO and complement histology in routine clinical practice to distinguish these two tumors.

Development of complications in type 1 diabetes patients can be reduced by modifying risk factors. We used a cross-sectional cohort of 1646 patients diagnosed with type 1 diabetes (T1D) to develop a clinical risk score for diabetic peripheral neuropathy (DPN), autonomic neuropathy (AN), retinopathy (DR), and nephropathy (DN). Of these patients, 199 (12.1%) had DPN, 63 (3.8%) had AN, 244 (14.9%) had DR, and 88 (5.4%) had DN. We selected five variables to include in each of the four microvascular complications risk models: age, age of T1D diagnosis, duration of T1D, and average systolic blood pressure and HbA1C over the last three clinic visits. These variables were selected for their strong evidence of association with diabetic complications in the literature and because they are modifiable risk factors. We found the optimism-corrected R2 and Harrell's C statistic were 0.39 and 0.87 for DPN, 0.24 and 0.86 for AN, 0.49 and 0.91 for DR, and 0.22 and 0.83 for DN, respectively. This tool was built to help inform patients of their current risk of microvascular complications and to motivate patients to control their HbA1c and systolic blood pressure in order to reduce their risk of these complications.

Chronic low-grade inflammation is involved in the pathogenesis of type-1 diabetes (T1D) and its complications. In this cross-section study design, we investigated association between serum levels of soluble cytokine receptors with presence of peripheral neuropathy in 694 type-1 diabetes patients. Sex, age, blood pressure, smoking, alcohol intake, HbA1c and lipid profile, presence of DPN (peripheral and autonomic), retinopathy and nephropathy was obtained from patient's chart. Measurement of soluble cytokine receptors, markers of systemic and vascular inflammation was done using multiplex immunoassays. Serum levels were elevated in in DPN patients, independent of gender, age and duration of diabetes. Crude odds ratios were significantly associated with presence of DPN for 15/22 proteins. The Odds ratio (OR) remained unchanged for sTNFRI (1.72, p=0.00001), sTNFRII (1.45, p=0.0027), sIL2Rα (1.40, p=0.0023), IGFBP6 (1.51, p=0.0032) and CRP (1.47, p=0.0046) after adjusting for confounding variables, HbA1C, hypertension and dyslipidemia. Further we showed risk of DPN is associated with increase in serum levels of sTNFRI (OR=11.2, p<10), sIL2Rα (8.69, p<10 -15 ), sNTFRII (4.8, p<10 -8 ) and MMP2 (4.5, p<10 -5 ). We combined the serum concentration using ridge regression, into a composite score, which can stratify the DPN patients into low, medium and high-risk groups. Our results here show activation of inflammatory pathway in DPN patients, and could be a potential clinical tool to identify T1D patients for therapeutic intervention of anti-inflammatory therapies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Purohit, Tran, Tran, Satter, He, Zhi, Bai, Hopkins, Gardiner, Wakade, Bryant, Bernard, Morgan, Bode, Reed and She.)

Gene expression profiling has been shown to be comparable to other molecular methods for glioma classification. We sought to validate a gene-expression based glioma classification method. Formalin-fixed paraffin embedded tissue and flash frozen tissue collected at the Augusta University (AU) Pathology Department between 2000-2019 were identified and 2 mm cores were taken. The RNA was extracted from these cores after deparaffinization and bead homogenization. One hundred sixty-eight genes were evaluated in the RNA samples on the nCounter instrument. Forty-eight gliomas were classified using a supervised learning algorithm trained by using data from The Cancer Genome Atlas. An ensemble of 1000 linear support vector models classified 30 glioma samples into TP1 with classification confidence of 0.99. Glioma patients in TP1 group have a poorer survival (HR (95% CI) = 4.5 (1.3-15.4), p = 0.005) with median survival time of 12.1 months, compared to non-TP1 groups. Network analysis revealed that cell cycle genes play an important role in distinguishing TP1 from non-TP1 cases and that these genes may play an important role in glioma survival. This could be a good clinical pipeline for molecular classification of gliomas.

Campylobacter jejuni CsrA is an mRNA-binding, post-transcriptional regulator that controls many metabolic- and virulence-related characteristics of this important pathogen. In contrast to E. coli CsrA, whose activity is modulated by binding to small non-coding RNAs (sRNAs), C. jejuni CsrA activity is controlled by binding to the CsrA antagonist FliW. In this study, we identified the FliW binding site on CsrA. Deletion of the C-terminus of C. jejuni CsrA, which is extended relative to sRNA-binding CsrA proteins, abrogated FliW binding. Bacterial two-hybrid experiments were used to assess the interaction of FliW with wild-type CsrA and mutants thereof, in which every amino acid was individually mutated. Two CsrA mutations (V51A and N55A) resulted in a significant decrease in FliW binding. The V51A and N55A mutants also showed a decrease in CsrA-FliW complex formation, as assessed by size-exclusion chromatography and surface plasmon resonance. These residues were highly conserved in bacterial species containing CsrA orthologs whose activities are predicted to be regulated by FliW. The location of FliW binding was immediately adjacent to the two RNA-binding sites of the CsrA homodimer, suggesting the model that FliW binding to CsrA modulates its ability to bind to its mRNA targets either by steric hindrance, electrostatic repulsion, or by altering the overall structure of the RNA-binding sites., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Bogacz, El Abbar, Cox, Li, Fiedler, Tran, Tran, Daugherty, Blake, Wang, Azadi and Thompson.)

Gliomas are currently classified through integration of histology and mutation information, with new developments in DNA methylation classification. However, discrepancies exist amongst the major classification methods. This study sought to compare transcriptome-based classification to the established methods. RNAseq and microarray data were obtained for 1032 gliomas from the TCGA and 395 gliomas from REMBRANDT. Data were analyzed using unsupervised and supervised learning and other statistical methods. Global transcriptomic profiles defined four transcriptomic glioma subgroups with 91.4% concordance with the WHO-defined mutation subtypes. Using these subgroups, 168 genes were selected for the development of 1000 linear support vector classifiers (LSVC). Based on plurality voting of 1000 LSVC, the final ensemble classifier confidently classified all but 17 TCGA gliomas to one of the four transcriptomic profile (TP) groups. The classifier was validated using a gene expression microarray dataset. TP1 cases include IDHwt, glioblastoma high immune infiltration and cellular proliferation and poor survival prognosis. TP2a is characterized as IDHmut-codel, oligodendrogliomas with high tumor purity. TP2b tissue is mostly composed of neurons and few infiltrating malignant cells. TP3 exhibit increased NOTCH signaling, are astrocytoma and IDHmut-non-codel. TP groups are highly concordant with both WHO integrated histology and mutation classification as well as methylation-based classification of gliomas. Transcriptomic profiling provides a robust and objective method to classify gliomas with high agreement to the current WHO guidelines and may provide additional survival prediction to the current methods.

Molecular biomarkers that can predict survival and therapeutic outcome are still lacking for cervical cancer. Here we measured a panel of 19 serum proteins in sera from 565 patients with stage II or III cervical cancer and identified 10 proteins that have an impact on disease specific survival (DSS) (Hazzard's ratio; HR = 1.51-2.1). Surprisingly, all ten proteins are implicated in senescence-associated secreted phenotype (SASP), a hallmark of cellular senescence. Machine learning using Ridge regression of these SASP proteins can robustly stratify patients with high SASP, which is associated with poor survival, and patients with low SASP associated with good survival (HR = 3.09-4.52). Furthermore, brachytherapy, an effective therapy for cervical cancer, greatly improves survival in SASP-high patients (HR = 3.3, p < 5 × 10 -5 ) but has little impact on survival of SASP-low patients (HR = 1.5, p = 0.31). These results demonstrate that cellular senescence is a major determining factor for survival and therapeutic response in cervical cancer and suggest that senescence reduction therapy may be an efficacious strategy to improve the therapeutic outcome of cervical cancer.

Objectives: To develop a transcriptomic signature capable of predicting overall survival (OS) for uterine serous carcinoma (USC)., Methods: RNAseq data for 58 USC patients were obtained from TCGA. Expression of 73 candidate genes was measured for 67 Augusta University (AU) samples using NanoString technology., Results: Analysis of the TCGA RNAseq data identified 73 genes that individually predict prognosis for USC patients and an elastic net model with all 73 genes (USC73) distinguishes a good OS group with low USC73 score from a poor OS group with high USC73 score (5-year OS = 83.3% and 13.3% respectively, HR = 40.1; p = 3 × 10 -8 ). This finding was validated in the independent AU cohort (HR = 4.3; p = 0.0004). The poor prognosis group with high USC73 score consists of 37.9% and 32.8% of patients in the TCGA and AU cohort respectively. USC73 score and pathologic stage independently contribute to OS and together provide the best prognostic value. Early stage, low USC73 patients have the best prognosis (5-year OS = 85.1% in the combined dataset), while advanced stage, high USC73 patients have the worst prognosis (5-year OS = 6.4%, HR = 30.5, p = 1.2 × 10 -12 ). Consistent with the observed poor survival, primary cell cultures from high USC73 patients had higher proliferation rate and cell cycle progression; and high USC73 patients had lower rates of complete response to standard therapy., Conclusions: The USC73 transcriptomic signature and stage independently predict OS of USC patients and the best prediction is achieved using USC73 and stage. USC73 may also serve as a therapeutic biomarker to guide patient care., Competing Interests: Declaration of competing interest The authors declare no potential conflicts of interest., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objective: To measure anti-glycan antibodies (AGA) in cervical cancer (CC) patient sera and assess their effect on therapeutic outcome., Patients and Methods: Serum AGA was measured in 276 stage II and 292 stage III Peruvian CC patients using a high content and throughput Luminex multiplex glycan array (LMGA) containing 177 glycans. Association with disease-specific survival (DSS) and progression free survival (PFS) were analyzed using Cox regression., Results: AGAs were detected against 50 (28.3%) of the 177 glycans assayed. Of the 568 patients, 84.5% received external beam radiation therapy (EBRT) plus brachytherapy (BT), while 15.5% only received EBRT. For stage-matched patients (Stage III), receiving EBRT alone was significantly associated with worse survival (HR 6.4, p < 0.001). Stage III patients have significantly worse survival than Stage II patients after matching for treatment (HR = 2.8 in EBRT+BT treatment group). Furthermore, better PFS and DSS were observed in patients positive for AGA against multiple glycans belonging to the blood group H, Lewis, Ganglio, Isoglobo, lacto and sialylated tetrarose antigens (best HR = 0.49, best p = 0.0008)., Conclusions: Better PFS and DSS are observed in cervical cancer patients that are positive for specific antiglycan antibodies and received brachytherapy., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objective: Determine the utility of a clinical calculator to predict the benefit of chemotherapy in stage IA uterine papillary serous cancer (UPSC)., Patients and Methods: Data were collected from NCDB from years 2010-2014. Based on demographic and surgical characteristics, a clinical score was developed using the random survival forest machine learning algorithm., Results: Of 1,751 patients with stage IA UPSC, 1,012 (58%) received chemotherapy and 739 (42%) did not. Older age (HR 1.06), comorbidities (HR 1.31), larger tumor size (HR 1.27), lymphovascular invasion (HR 1.86), positive peritoneal cytology (HR 2.62), no pelvic lymph node dissection (HR 1.51), and no chemotherapy (HR 2.16) were associated with poorer prognosis. Compared to no chemotherapy, patients who underwent chemotherapy had a 5-year overall survival of 80% vs. 67%. To better delineate those who may derive more benefit from chemotherapy, we designed a clinical calculator capable of dividing patients into low, moderate, and high-risk groups with associated 5-year OS of 86%, 73%, and 53%, respectively. Using the calculator to assess the relative benefit of chemotherapy in each risk group, chemotherapy improved the 5-year OS in the high (42% to 64%; p < 0.001) and moderate risk group (66% to 79%; p < 0.001) but did not benefit the low risk group (84% to 87%; p = 0.29)., Conclusion: Our results suggest a clinical calculator is useful for counseling and personalizing chemotherapy for stage IA UPSC., (Published by Elsevier Inc.)

Clear cell renal cell carcinoma (ccRCC) represents the most common subtype of renal tumor. Despite recent advances in identifying novel target molecules, the prognosis of patients with ccRCC continues to be poor, mainly due to the lack of sensitivity to chemo- and radiotherapy and because of one-third of renal cell carcinoma patients displays metastatic disease at diagnosis. Thus, identifying new molecules for early detection and for developing effective targeted therapies is mandatory. In this work, we focused on paraoxonase-2 (PON2), an intracellular membrane-bound enzyme ubiquitously expressed in human tissues, whose upregulation has been reported in a variety of malignancies, thus suggesting its possible role in cancer cell survival and proliferation. To investigate PON2 involvement in tumor cell metabolism, human ccRCC cell lines were transfected with plasmid vectors coding short harpin RNAs targeting PON2 transcript and the impact of PON2 silencing on cell viability, migration, and response to chemotherapeutic treatment was then explored. Our results showed that PON2 downregulation was able to trigger a decrease in proliferation and migration of ccRCC cells, as well as an enhancement of cell sensitivity to chemotherapy. Thus, taken together, data reported in this study suggest that the enzyme may represent an interesting therapeutic target for ccRCC., (© 2024 Wiley Periodicals LLC.)

We present a case of the T2-FLAIR mismatch sign in glioblastoma, isocitrate dehydrogenase (IDH)-wild type. The T2-FLAIR mismatch sign is known as a highly specific imaging finding of astrocytoma, IDH -mutant. Meanwhile, IDH -wildtype diffuse astrocytic gliomas with telomerase reverse transcriptase (TERT) promoter mutation in adults are defined as glioblastoma in the 2021 World Health Organization Classification of Tumors of the Central Nervous System, fifth edition (2021 WHO classification), which underscores the importance of molecular information in central nervous system tumors. This indicates even glioblastoma, IDH -wild type may be masquerading as lower-grade glioma in histology. The reasons for the discrepancy between tumors with less aggressive histology and poor prognosis caused by telomerase reverse transcriptase promoter mutation of IDH -wildtype diffuse glioma remain unclear. However, glioblastoma, IDH -wildtype should be considered as a potential differential diagnosis even in patients with the T2-FLAIR mismatch sign in diffuse gliomas. [ABSTRACT FROM AUTHOR]

Renal cell carcinoma (RCC) is the second lethal urogenital malignancy with the increasing incidence and mortality in the world. Clear cell renal cell carcinoma (ccRCC) is one major subtype of RCC, which accounts for about 70 to 80% of all RCC cases. Although many innovative therapeutic options have emerged during the last few decades, the efficacy of these treatments for ccRCC patients is very limited. To date, the prognosis of patients with advanced or metastatic ccRCC is still poor. The 5-year survival rate of these patients remains less than 10%, which mainly attributes to the complexity and heterogeneity of the tumor microenvironment (TME). It has been demonstrated that long non-coding RNAs (lncRNAs) perform an indispensable role in the initiation and progression of various tumors. They mostly function as sponges for microRNAs (miRNAs) to regulate the expression of target genes, finally influence the growth, metastasis, apoptosis, drug resistance and TME of tumor cells. However, the role of lncRNA/miRNA/mRNA axis in the TME of ccRCC remains poorly understood. In this review, we summarized the biological function of lncRNA/miRNA/mRNA axis in the pathogenesis of ccRCC, then discussed how lncRNA/miRNA/mRNA axis regulate the TME, finally highlighted their potential application as novel biomarkers and therapeutic targets for ccRCC. [ABSTRACT FROM AUTHOR]

This study was aimed at developing a clinical risk score for cardiovascular autonomic neuropathy (CAN) for type 1 and type 2 diabetes. In a retrospective cross‐sectional one‐centre study in an unselected population, 115 participants with type 1 diabetes (age 41.1 ± 12.2 years) and 161 with type 2 diabetes (age 63.1 ± 8.9 years), well‐characterized for clinical variables, underwent standard cardiovascular reflex tests (CARTs). Strength of associations of confirmed CAN (based on 2 abnormal CARTs) with clinical variables was used to build a CAN risk score. CAN risk score was based on resting heart rate, HbA1c, retinopathy, nephropathy, cardiovascular disease in both type 1 and type 2 diabetes, and on HDL cholesterol, systolic blood pressure, and smoking in type 1 diabetes or insulin treatment and physical activity in type 2 diabetes (range 0‐10). In type 1 diabetes, CAN risk score showed an area under the ROC curve (AUC) of 0.890 ± 0.034, and at cut‐off of 4 sensitivity of 88%, specificity of 74.4%, and negative predictive value (NPV) of 95.7% for confirmed CAN. In type 2 diabetes, CAN risk score showed an AUC of 0.830 ± 0.051 and at the cut‐off of 4 sensitivity and specificity of 78.6% and 73.5%, respectively, and NPV of 97.3% for confirmed CAN. These newly developed CAN risk scores are accessible in clinical practice and, if confirmed in a validation study, they might identify asymptomatic individuals with diabetes at greater risk of CAN to be referred to CARTs, thus limiting the burden of a universal screening. [ABSTRACT FROM AUTHOR]

Accurate and effective prediction of mutation-induced protein energy change remains a great challenge and of great interest in computational biology. However, high resource consumption and insufficient structural information of proteins severely limit the experimental techniques and structure-based prediction methods. Here, we design a structure-independent protocol to accurately and effectively predict the mutation-induced protein folding free energy change with only sequence, physicochemical and evolutionary features. The proposed clustered tree regression protocol is capable of effectively exploiting the inherent data patterns by integrating unsupervised feature clustering by K-means and supervised tree regression using XGBoost, and thus enabling fast and accurate protein predictions with different mutations, with an average Pearson correlation coefficient of 0.83 and an average root-mean-square error of 0.94kcal/mol. The proposed sequence-based method not only eliminates the dependence on protein structures, but also has potential applications in protein predictions with rare structural information. [ABSTRACT FROM AUTHOR]

Purpose: To establish an accurate risk prediction model of diabetic retinopathy (DR) using cost effective and easily available patients' characteristics and clinical biomarkers. Patients and Methods: Totally 18,904 cases diagnosed type 2 diabetes mellitus (T2DM) were collected, among which 13,980 cases were selected after quality screening. The least absolute shrinkage and selection operator (LASSO) regression models were used for univariate analysis and factors selection, and the multi-factor logistic regression analysis was used to establish the prediction model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using AUC/ Harrell's C statistic, calibration plot, and decision curve analysis. Both the development group and validation group were assessed. Results: Candidate variables were selected by Lasso regression and multivariate logistic regression analysis. Finally, the candidate predictive variables were included diabetic peripheral neuropathy (DPN), age, neutrophilic granulocyte (NE), high-density lipoprotein (HDL), hemoglobin A1c (HbA1C), duration of T2DM, and glycosylated serum protein (GSP) were used to establish a nomogram model for predicting the risk of DR. In the development group, the area under the receiver operating characteristic curve (AUC) was 0.882 (95% CI, 0.875– 0.888). In the validation group, the AUC was 0.870 (95% CI, 0.856– 0.881). Meanwhile, the optimism-corrected Harrell's C statistic were 0.878 and 0.867 in the development group and the validation group, respectively. Decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion: We constructed and verified nomograms that could accurately predict the risk of DR in T2DM patients, which could be used to predict the personalized risk of DR patients in Xinjiang, China. [ABSTRACT FROM AUTHOR]

Cervical cancer has become the most frequent female malignancy and presents as a general health challenge in many countries undergoing economic development. Various human papillomaviruses (HPV) types have appeared as one of the most critically identifiable causes of widespread cervical cancers. Conventional cervical cytological inspection has limitations of variable sensitivity according to cervical cytology. Glycobiology has been fundamental in related exploration in various gynecologic and reproductive fields and has contributed to our understanding of cervical cancer. It is associated with altered expression of N-linked glycan as well as abnormal expression of terminal glycan structures. The analytical approaches available to determine serum and tissue glycosylation, as well as potential underlying molecular mechanisms involved in the cellular glycosylation alterations, are monitored. Moreover, cellular glycosylation influences various aspects of cervical cancer biology, ranging from cell surface expressions, cell-cell adhesion, cancer signaling, cancer diagnosis, and management. In general, discoveries in glycan profiling make it technically reproducible and affordable to perform serum glycoproteomic analyses and build on previous work exploring an expanded variety of glycosylation markers in the majority of cervical cancer patients. [ABSTRACT FROM AUTHOR]

Transcriptional profiling of pre- and post-malignant colorectal cancer (CRC) lesions enable temporal monitoring of molecular events underlying neoplastic progression. However, the most widely used transcriptomic dataset for CRC, TCGA-COAD, is devoid of adenoma samples, which increases reliance on an assortment of disparate microarray studies and hinders consensus building. To address this, we developed a microarray meta-dataset comprising 231 healthy, 132 adenoma, and 342 CRC tissue samples from twelve independent studies. Utilizing a stringent analytic framework, select datasets were downloaded from the Gene Expression Omnibus, normalized by frozen robust multiarray averaging and subsequently merged. Batch effects were then identified and removed by empirical Bayes estimation (ComBat). Finally, the meta-dataset was filtered for low variant probes, enabling downstream differential expression as well as quantitative and functional validation through cross-platform correlation and enrichment analyses, respectively. Overall, our meta-dataset provides a robust tool for investigating colorectal adenoma formation and malignant transformation at the transcriptional level with a pipeline that is modular and readily adaptable for similar analyses in other cancer types. Measurement(s) transcriptome • colorectal cancer Technology Type(s) microarray Factor Type(s) gene expression Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14589006 [ABSTRACT FROM AUTHOR]

Background: Diabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. Therefore, it is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM.Methods: Aiming at the problem of high-dimensional feature space and high feature redundancy of medical data, as well as the problem of data imbalance often faced. This study explored different supervised classifiers, combined with SVM-SMOTE and two feature dimensionality reduction methods (Logistic stepwise regression and LAASO) to classify the diabetes survey sample data with unbalanced categories and complex related factors. Analysis and discussion of the classification results of 4 supervised classifiers based on 4 data processing methods. Five indicators including Accuracy, Precision, Recall, F1-Score and AUC are selected as the key indicators to evaluate the performance of the classification model.Results: According to the result, Random Forest Classifier combining SVM-SMOTE resampling technology and LASSO feature screening method (Accuracy = 0.890, Precision = 0.869, Recall = 0.919, F1-Score = 0.893, AUC = 0.948) proved the best way to tell those at high risk of DM. Besides, the combined algorithm helps enhance the classification performance for prediction of high-risk people of DM. Also, age, region, heart rate, hypertension, hyperlipidemia and BMI are the top six most critical characteristic variables affecting diabetes.Conclusions: The Random Forest Classifier combining with SVM-SMOTE and LASSO feature reduction method perform best in identifying high-risk people of DM from individuals. And the combined method proposed in the study would be a good tool for early screening of DM. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate predictive models based on Ki‐67 index, radiomics, and Ki‐67 index combined with radiomics for survival analysis of patients with clear cell renal cell carcinoma. Methods: This study enrolled 148 patients who were pathologically diagnosed as ccRCC between March 2010 and December 2018 at our institute. All tissue sections were collected and immunohistochemical staining was performed to calculate Ki‐67 index. All patients were randomly divided into the training and validation sets in a 7:3 ratio. Regions of interests (ROIs) were segmented manually. Radiomics features were selected from ROIs in unenhanced, corticomedullary, and nephrographic phases. Multivariate Cox models based on the Ki‐67 index and radiomics and univariate Cox models based on the Ki‐67 index or radiomics alone were built; the predictive power was evaluated by the concordance (C)‐index, integrated area under the curve, and integrated Brier Score. Results: Five features were selected to establish the prediction models of radiomics and combined model. The C‐indexes of Ki‐67 index model, radiomics model, and combined model were 0.741, 0.718, and 0.782 for disease‐free survival (DFS); 0.941, 0.866, and 0.963 for overall survival, respectively. The predictive power of combined model was the best in both training and validation sets. Conclusion: The survival prediction performance of combined model was better than Ki‐67 model or radiomics model. The combined model is a promising tool for predicting the prognosis of patients with ccRCC in the future. Advances in knowledge: Both Ki‐67 and radiomics have showed giant potential in prognosis prediction. There are few studies to investigate the predictive ability of Ki‐67 combined with radiomics. This study intended to build a combined model and provide a reliable prognosis for ccRCC in clinical practice. [ABSTRACT FROM AUTHOR]