Calibration Chart Research Articles

A prognostic signature of six necroptosis-related miRNAs for predicting the overall survival of breast cancer patients

Breast cancer (BRCA) is the most frequent malignant disease and cause of death in females. Recent studies have uncovered the crucial roles of necroptosis-related miRNAs in diverse cancers, including BRCA. However, the significance of necroptosis-related miRNA in predicting the prognosis of BRCA remains largely undefined. This study aimed at constructing a miRNA risk signature related to necroptosis and a nomogram for estimating the prognosis of BRCA. The miRNA expression data and related clinical information were downloaded from the BRCA cohort (containing tissue samples from BRCA patients and normal para-cancer patients) of The Cancer Genome Atlas (TCGA) database. We analyzed the miRNA expression profile and screened the differentially expressed necroptosis-related miRNAs between BRCA and non-tumor samples. Then, a risk signature for BRCA patients was developed based on prognostic necroptosis-related miRNAs. The prognostic value of the risk signature was determined by Cox regression analysis. We also constructed a prognostic nomogram based on risk signature and clinicopathological characteristics, and evaluated its clinical potential using a calibration chart. Lastly, to identify potential therapeutic target for BRCA. Six necroptosis-related miRNAs (miR-141-3p, miR-148a-3p, miR-200a-5p, miR-223-3p, miR-425-5p, miR-7-5p) were differentially expressed between normal and BRCA tissues, including five up-regulated miRNAs and one down-regulated miRNA. They were used to construct the risk signature. Receiver Operating Characteristic (ROC) curve analysis indicated that the risk signature had good sensitivity and specificity (2-year area under curve (AUC): 0.627; 3-year AUC: 0.647) and was an independent prognostic factor (univariate Cox regression: hazard ratio (HR) = 1.8066, 95% confidence interval (CI) (1.2867–2.5365), p < 0.05; multivariate Cox regression: HR = 1.5246, 95% CI (1.0830–2.1462), p < 0.05). Calibration chart showed that the nomogram had good accuracy for predicting the prognosis of BRCA patients. We also identified miR-223a-3p as a potential therapeutic target for BRCA. This study identified 6 promising necroptosis-related miRNAs, which were used to construct a signature model to predict BRCA patients’ prognosis. Further, a nomogram based on risk signatures and clinicopathological characteristics was constructed and showed promising potential as an effective and individualized diagnostic tool.

Fluorescence determination of lactate dehydrogenase activity based on silicon quantum dots

Silicon quantum dots (SiQDs) synthesized based on 3-aminopropyltrimethoxysilane (ATPMS) as silicon source were used to detect the activity of lactate dehydrogenase (LDH) through changes of fluorescence intensity of SiQDs. In this system, the fluorescence of SiQDs was first quenched by nicotinamide adenine dinucleotide (NADH), and then recovered with the addition of LDH, as NADH was consumed by catalytic reaction of LDH. A linear calibration chart of LDH is obtained in the range of 0.77–385 U/mL. The assay displays high selectivity towards LDH detection, and was successfully applied to the analysis of LDH in human serum samples. This assay has great prospects for the diagnosis and prognosis of various diseases, especially melanoma.

Identification of Immune-Related Genes Associated With Bladder Cancer Based on Immunological Characteristics and Their Correlation With the Prognosis.

Background:To identify the immune-related genes of bladder cancer (BLCA) based on immunological characteristics and explore their correlation with the prognosis. Methods:We downloaded the gene and clinical data of BLCA from the Cancer Genome Atlas (TCGA) as the training group, and obtained immune-related genes from the Immport database. We downloaded GSE31684 and GSE39281 from the Gene Expression Omnibus (GEO) as the external validation group. R (version 4.0.5) and Perl were used to analyze all data. Result:Univariate Cox regression analysis and Lasso regression analysis revealed that 9 prognosis-related immunity genes (PIMGs) of differentially expressed immune genes (DEIGs) were significantly associated with the survival of BLCA patients (p < 0.01), of which 5 genes, including NPR2, PDGFRA, VIM, RBP1, RBP1 and TNC, increased the risk of the prognosis, while the rest, including CD3D, GNLY, LCK, and ZAP70, decreased the risk of the prognosis. Then, we used these genes to establish a prognostic model. We drew receiver operator characteristic (ROC) curves in the training group, and estimated the area under the curve (AUC) of 1-, 3- and 5-year survival for this model, which were 0.688, 0.719, and 0.706, respectively. The accuracy of the prognostic model was verified by the calibration chart. Combining clinical factors, we established a nomogram. The ROC curve in the external validation group showed that the nomogram had a good predictive ability for the survival rate, with a high accuracy, and the AUC values of 1-, 3-, and 5-year survival were 0.744, 0.770, and 0.782, respectively. The calibration chart indicated that the nomogram performed similarly with the ideal model. Conclusion:We had identified nine genes, including PDGFRA, VIM, RBP1, RBP1, TNC, CD3D, GNLY, LCK, and ZAP70, which played important roles in the occurrence and development of BLCA. The prognostic model based on these genes had good accuracy in predicting the OS of patients and might be promising candidates of therapeutic targets. This study may provide a new insight for the diagnosis, treatment and prognosis of BLCA from the perspective of immunology. However, further experimental studies are necessary to reveal the underlying mechanisms by which these genes mediate the progression of BLCA.

Prognostic Nomogram for Overall Survival of Patients Aged 50 Years or Older with Cervical Cancer.

ObjectiveThe prognostic factors of cervical cancer in elderly patients have not been researched systematically. We aimed to investigate the clinicopathological characteristics of patients with cervical cancer aged ≥50 years and establish a nomogram for evaluating their prognoses for overall survival.MethodsFrom the Surveillance, Epidemiology, and End Results database, we obtained data of 8538 patients with pathology-confirmed cervical cancer between 2004 and 2015. Patients were divided into training (n = 5941) and validation (n = 2597) cohorts. A nomogram was constructed to evaluate the prognostic prediction value for disease progression. The concordance index, receiver operating characteristic curve, and calibration chart were used to evaluate the model’s prediction accuracy and discriminative ability. Survival condition was analyzed using the Kaplan–Meier method.ResultsIn the training cohort, age at diagnosis, race, histology, grade, stage, tumor size, number of examined lymph nodes, and treatment significantly correlated with outcome and were used to develop the nomogram. The calibration curve for survival probability showed an excellent agreement between the nomogram-predicted and actual survival in the training cohort.ConclusionOur nomogram has less bias and gives better accuracy than the International Federation of Gynecology and Obstetrics staging system and can help set up a more individualized feasible follow-up plan.

Establishment and Verification of Scoring System for Colorectal Adenoma Recurrence.

ObjectiveThe purpose of this study was to establish and verify a risk-scoring system for colorectal adenoma recurrence.MethodsA total of 359 patients with colorectal adenoma who underwent polypectomy from October 2017 to December 2018 were included in this retrospective study. Information including taking traditional Chinese medicine, demographic characteristics, adenoma characteristics were collected. The patients will review the colonoscopy one year after surgery. The patients were divided into a modeling cohort (216 cases) and a model validation cohort (143 cases) according to the ratio of 6:4. Modeling and model verification were performed by logistic regression, ROC curve, nomogram (calibration chart) and other methods.ResultsAfter adjusting for confounding factors by logistic regression, it was found that taking Chinese medicine, the number, size, site, pathological type and morphology of adenoma were independent influencing factors for the recurrence of colorectal adenoma. The area under the ROC curve (AUC) in the model validation cohort of established risk scoring system was 0.771 (95% CI: 0.694–0.847), indicating that there was good consistency.ConclusionThe established risk prediction model of colorectal adenoma recurrence and its risk scoring system performed well and had high predictive value.

Transient Electromagnetic (TEM) Surveys as a First Approach for Characterizing a Regional Aquifer: The Case of the Saint-Narcisse Moraine, Quebec, Canada

Geological contexts that lack minimal stratigraphic and piezometric information can be challenging to produce an initial hydrogeological map in remote territories. This study proposes an approach to characterize a regional aquifer using transient electromagnetic (TEM) surveys. Given the presence of randomly dispersed boreholes, the Saint-Narcisse moraine in the Mauricie region of Quebec (Canada) is an appropriate site for collecting the required geophysical data, correlating the stratigraphic and piezometric information, and characterizing regional granular aquifers in terms of stratigraphy, geometry, thickness, and extent. In order to use all TEM results (i.e., 47 stations) acquired in the moraine area, we also correlated 13 TEM stations, 7 boreholes, and 6 stratigraphic cross-sections to derive an empirical and local petrophysical relationship and to establish a calibration chart of the sediments. Our TEM data, combined with piezometric mapping and the sedimentary records from boreholes and stratigraphic cross-sections, revealed the compartmentalization of a multi-kilometer morainic system and indicated the presence of two large unconfined granular aquifers overlying the bedrock. These aquifers extend more than 12 km east to west across the study area and are between 25 and &gt;94 m thick. The TEM method provides critical information on groundwater at a regional scale by acquiring information from multiple stations within a short time span to a degree not possible with other existing methodologies.

Nomograms for Predicting Cancer-Specific Survival of Patients with Gingiva Squamous Cell Carcinoma: A Population-Based Study.

The use of the traditional American Joint Committee on Cancer (AJCC) staging system alone has limitations in predicting the survival of gingiva squamous cell carcinoma (GSCC) patients. We aimed to establish a comprehensive prognostic nomogram with a prognostic value similar to the AJCC system. Patients were identified from SEER database. Variables were selected by a backward stepwise selection method in a Cox regression model. A nomogram was used to predict cancer-specific survival rates for 3, 5 and 10 years in patients with GSCC. Several basic features of model validation were used to evaluate the performance of the survival model: consistency index (C-index), receiver operating characteristic (ROC) curve, calibration chart, net weight classification improvement (NRI), comprehensive discriminant improvement (IDI) and decision curve analysis (DCA). Multivariate analyses revealed that age, race, marital status, insurance, AJCC stage, pathology grade and surgery were risk factors for survival. In particular, the C-index, the area under the ROC curve (AUC) and the calibration plots showed good performance of the nomogram. Compared to the AJCC system, NRI and IDI showed that the nomogram has improved performance. Finally, the nomogram's 3-year and 5-year and 10-year DCA curves yield net benefits higher than traditional AJCC, whether training set or a validation set. We developed and validated the first GSCC prognosis nomogram, which has a better prognostic value than the separate AJCC staging system. Overall, the nomogram of this study is a valuable tool for clinical practice to consult patients and understand their risk for the next 3, 5 and 10 years.

Clinical prognostic analysis of 124 adult patients with hemophagocytic lymphohistiocytosis: a multicenter retrospective study of the Huaihai Lymphoma Working Group

目的基于多中心数据，分析成人噬血细胞综合征（HLH）预后的影响因素。方法收集2014年3月至2020年7月淮海淋巴瘤协作组8个医疗中心确诊的124例成人HLH患者的临床资料。基于Maxstat算法、X-Tile软件和限制立方样条获取连续变量的最佳截断值。采用Cox比例风险回归模型构建成人HLH风险预测模型并通过列线图实现模型的可视化，采用Bootstrap重抽样的方法进行验证，使用一致性指数（C-index）和校正曲线验证列线图，检查预测精度。采用Kaplan-Meier法分析计算生存率并绘制生存曲线，组间比较使用Log-rank检验。结果124例患者的中位年龄为55（18～84）岁，男性61例（49.19％）。最常见的病因是感染，血清铁蛋白增高者110例（88.71％），肝脾肿大者57例（45.97％）。124例患者中77例（62.10％）死亡，患者的中位生存期为7.07个月，单因素分析结果表明成人HLH的预后受性别、年龄、纤维蛋白原、血肌酐、ALT和白蛋白的影响（P<0.05）。多因素分析结果表明性别、PLT、白蛋白、ALT和治疗方案是预后的独立影响因素，基于以上5个危险因素建立列线图预测模型，模型的C-index为0.739，校准图显示HLH的观测值和预测值之间有较好的一致性。结论成人HLH的预后受多方面因素影响，性别、PLT、白蛋白、ALT和治疗方案是独立危险因素，基于上述危险因素建立的列线图为临床医师评估成人HLH预后提供了一个可视化工具。

Development of Embedded System-Based Lens-Less Microscopy System and Testing on Tissue Samples

In this study, a low cost and portable general-purpose lens-less microscopy system suitable for clinical conditions was designed and its performance was increased with the help of image processing algorithms without using numerical approaches. In order to avoid the noises caused by the light source or the imaging sensor, the flat field correction method was applied to the images in real time and then interpolation was applied to the images. The microscopy system is integrated into the embedded system and it is aimed to display in RGB color space. By applying flat field correction and interpolation to the obtained images in real time, the SNR and contrast value of the images were improved. The success of the imaging system was examined with a standard calibration chart. The improvement obtained by calculating the pixel intensity values for pre-processing and post-processing is shown. Various tissue samples were examined with the system and its success on medical samples was demonstrated. It has been shown on tissue samples that the system reaches 2.5 µm resolution in imaging tissue samples. It has been shown that the proposed system can be used for imaging pathological tissues in intraoperative procedures and can reduce the examination time. In addition, the obtained system has the potential to be used in the examination of other micro-sized medical samples.

Establishment of an Autophagy-Related Clinical Prognosis Model for Predicting the Overall Survival of Osteosarcoma.

Purpose Osteosarcoma is the most common primary and highly invasive bone tumor in children and adolescents. The purpose of this study is to construct a multi-gene expression feature related to autophagy, which can be used to predict the prognosis of patients with osteosarcoma. Materials and methods. The clinical and gene expression data of patients with osteosarcoma were obtained from the target database. Enrichment analysis of autophagy-related genes related to overall survival (OS-related ARGs) screened by univariate Cox regression was used to determine OS-related ARGs function and signal pathway. In addition, the selected OS-related ARGs were incorporated into multivariate Cox regression to construct prognostic signature for the overall survival (OS) of osteosarcoma. Use the dataset obtained from the GEO database to verify the signature. Besides, gene set enrichment analysis (GSEA) were applied to further elucidate the molecular mechanisms. Finally, the nomogram is established by combining the risk signature with the clinical characteristics. Results Our study eventually included 85 patients. Survival analysis showed that patients with low riskScore had better OS. In addition, 16 genes were included in OS-related ARGs. We also generate a prognosis signature based on two OS-related ARGs. The signature can significantly divide patients into low-risk groups and high-risk groups, and has been verified in the data set of GEO. Subsequently, the riskScore, primary tumor site and metastasis status were identified as independent prognostic factors for OS and a nomogram were generated. The C-index of nomogram is 0.789 (95% CI: 0.703~0.875), ROC curve and calibration chart shows that nomogram has a good consistency between prediction and observation of patients. Conclusions ARGs was related to the prognosis of osteosarcoma and can be used as a biomarker of prognosis in patients with osteosarcoma. Nomogram can be used to predict OS of patients and improve treatment strategies.

STABILITY INDICATING AND COST-EFFECTIVE ANALYTICAL METHOD DEVELOPMENT AND VALIDATION OF SOTORASIB BY USING RP-HPLC

Objective: The current investigation was pointed at developing and progressively validating novel, simple, responsive and stable RP-HPLC method for the measurement of active pharmaceutical ingredient of Sotorasib. Methods: A simple, selective, validated and well-defined stability that shows isocratic RP-HPLC methodology for the quantitative determination of Sotorasib. The chromatographic strategy utilized symmetry C18 column of dimensions 150x4.6 mm, 3.5 µ, using isocratic elution with a mobile phase of acetonitrile and 0.1% orthophosphoric acid (70:30). A flow rate of 1 ml/min and a detector wavelength of 221 nm utilizing the PDA detector were given in the instrumental settings. Validation of the proposed method was carried out according to an international conference on harmonization (ICH) guidelines. Results: LOD and LOQ for the active ingredient were established with respect to test concentration. The calibration chart plotted was linear with a regression coefficient of R2&gt;0.999, means the linearity was within the limit. Recovery, specificity, linearity, accuracy, robustness, ruggedness were determined as a part of method validation and the results were found to be within the acceptable range. Conclusion: The proposed method to be fast, simple, feasible and affordable in assay condition. During stability tests, it can be used for routine analysis of the selected drug.

Identification of a Novel Glycolysis-Related LncRNA Signature for Predicting Overall Survival in Patients With Bladder Cancer.

BackgroundBoth lncRNAs and glycolysis are considered to be key influencing factors in the progression of bladder cancer (BCa). Studies have shown that glycolysis-related lncRNAs are an important factor affecting the overall survival and prognosis of patients with bladder cancer. In this study, a prognostic model of BCa patients was constructed based on glycolysis-related lncRNAs to provide a point of reference for clinical diagnosis and treatment decisions.MethodsThe transcriptome, clinical data, and glycolysis-related pathway gene sets of BCa patients were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Set Enrichment Analysis (GSEA) official website. Next, differentially expressed glycolysis-related lncRNAs were screened out, glycolysis-related lncRNAs with prognostic significance were identified through LASSO regression analysis, and a risk scoring model was constructed through multivariate Cox regression analysis. Then, based on the median of the risk scores, all BCa patients were divided into either a high-risk or low-risk group. Kaplan-Meier (KM) survival analysis and the receiver operating characteristic (ROC) curve were used to evaluate the predictive power of the model. A nomogram prognostic model was then constructed based on clinical indicators and risk scores. A calibration chart, clinical decision curve, and ROC curve analysis were used to evaluate the predictive performance of the model, and the risk score of the prognostic model was verified using the TCGA data set. Finally, Gene Set Enrichment Analysis (GSEA) was performed on glycolysis-related lncRNAs.ResultsA total of 59 differentially expressed glycolysis-related lncRNAs were obtained from 411 bladder tumor tissues and 19 pericarcinomatous tissues, and 9 of those glycolysis-related lncRNAs (AC099850.3, AL589843.1, MAFG-DT, AC011503.2, NR2F1-AS1, AC078778.1, ZNF667-AS1, MNX1-AS1, and AC105942.1) were found to have prognostic significance. A signature was then constructed for predicting survival in BCa based on those 9 glycolysis-related lncRNAs. ROC curve analysis and a nomogram verified the accuracy of the signature.ConclusionThrough this study, a novel prognostic prediction model for BCa was established based on 9 glycolysis-related lncRNAs that could effectively distinguish high-risk and low-risk BCa patients, and also provide a new point of reference for clinicians to make individualized treatment and review plans for patients with different levels of risk.

How to Identify Patients at High Risk of Developing Nasal-Type, Extranodal Nature Killer/T-Cell Lymphoma-Associated Hemophagocytic Syndrome.

Nasal-type, extranodal nature killer (NK)/T-cell lymphoma-associated hemophagocytic syndrome (NK/T-LAHS) is a rare and life-threatening disease, requiring investigation of risk stratification. We conducted a retrospective study and proposed nomograms to predict NK/T-LAHS. The discriminative ability and calibration of the nomograms for prediction were tested using C statistics and calibration plots. We analyzed 533 patients with extranodal NK/T-cell lymphoma (ENKTL), out of which 71 were diagnosed with hemophagocytic syndrome (HPS), with a cumulative incidence of 13.3%. Significant difference for 2-year survival was found between patients with and without HPS (14.7% vs. 77.5%). Analyses showed that Eastern Cooperative Oncology Group (ECOG) performance status (PS) ≥2, B symptoms, and bone marrow (BM) invasion were significantly associated with NK/T-LAHS. We used these data as the basis to establish a nomogram of risk index for ENKTL (RINK). In 335 patients with available data for Epstein-Barr virus DNA (EBV-DNA), we found high viral copies (≥4,450 copies/ml) were correlated with NK/T-LAHS. When these data were added to RINK, we developed another nomogram that included EBV-DNA data (RINK-E). The nomograms displayed good accuracy in predicting NK/T-LAHS with a C-statistics of 0.919 for RINK and a C-statistics of 0.946 for RINK-E, respectively. The calibration chart also showed an excellent consistency between the predicted and observed probabilities. The proposed nomograms provided individualized risk estimate of HPS in patients with ENKTL.

Development and validation of prognostic nomogram in ependymoma: A retrospective analysis of the SEER database.

BackgroundThe prognostic factors for survival in patients with ependymoma (EPN) remain controversial. The aim of this study was to establish a prognostic model for 5‐ and 10‐year survival probability nomograms for patients with EPN.MethodsClinical data from the Surveillance, Epidemiology, and End Results (SEER) database were used for patients diagnosed with ependymoma between 2000 and 2018 and were randomized 7:3 into a development set and a validation set. Factors significantly associated with prognosis were screened out using the least absolute shrinkage and selection operator (LASSO) regression. The calibration chart and consistency index (C‐index) are used to evaluate the discrimination and consistency of the prediction model. Decision curve analysis (DCA) was used to further evaluate the established model. Finally, prognostic factors selected by LASSO regression were evaluated using Kaplan–Meier (KM) survival curves.ResultsA total of 3820 patients were included in the prognostic model. Seven survival predictors were obtained by LASSO regression screening, including age, gender, morphology, location, size, laterality, and resection. The prognostic model of the nomogram showed moderate discriminative ability in the development group and the validation group, with a C‐index of 0.642 and 0.615, respectively. In the development set and validation set survival curves, the prognosis index of high risk was less effective than low risk (p < 0.001).ConclusionsOur nomograms may play an important role in predicting 5 and 10‐year outcomes for patients with ependymoma. This will help assist clinicians in personalized medicine.

Analysis of High-Risk Factors Associated with the Progression of Subaneurysmal Aorta to Abdominal Aortic Aneurysm in Rural Area in China.

ObjectiveTo determine the risk factors associated with the progress of subaneurysmal aorta (SAA) to abdominal aortic aneurysm (AAA) and provide a reference for the prevention of AAA in rural areas.MethodsA total of 747 SAA patients screened by the Health Management Center of the Second Hospital of Lanzhou University from January 2015 to January 2016 were recruited. The ratio of SAA progressing to AAA was observed through 5 years of follow-up. Logistic stepwise regression analysis was performed to analyze the high-risk factors. The relevant clinical prediction model score table (Nom) was made and the C-index and calibration chart were used to verify the prediction ability of the model.ResultsOf the 747 patients diagnosed with SAA, 260 developed to AAA, with an incidence of 34.8%. Univariate analysis showed that age (62–65 years old), abdominal aorta diameter greater than 2.7 cm, smoking after 30 years old, moderate to severe hypertension, and blood pressure variability were the important high-risk factors of SAA progressing to AAA. Logistic regression analysis showed that these factors were statistically significant. The nomogram of clinical prediction model score showed that when 50–60% of SAA developed to AAA, the score was 189–201 and the C-index was 0.883, verifying the moderate predictive ability of this model.ConclusionAge, smoking habit, degree of hypertension, and control situation were high-risk factors associated with the progression of SAA to AAA. The control of the above high-risk factors was imperative for the prevention of AAA in rural areas without sufficient medical resources.

Bioinformatics analysis based on immune-autophagy-related lncRNAs combined with immune infiltration in bladder cancer.

BackgroundTo construct a prognostic model based on immune-autophagy-related long noncoding RNA (IArlncRNAs), mainly to predict the overall survival rate (OS) of bladder cancer patients and investigate its possible mechanisms.MethodsTranscriptome and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We identified the IArlncRNA by co-expression analysis, differential expression analysis, and Venn analysis. Then, we identified the independent prognostic IArlncRNAs by univariate Cox regression, LASSO regression, and multivariate Cox regression analysis. Moreover, we constructed the prognostic model based on the independent prognostic IArlncRNAs and clinical features. The proportion of 22 immune cell subtypes was analyzed by the CIBERSORT algorithm. Besides, we identified the differential proportion of 22 immune cell subtypes between the high- and low-risk groups. In addition, we identified the correlation between immune-infiltrating cells (screened by univariate Cox regression and multivariate Cox regression analysis) and IArlncRNAs by Pearson correlation analysis. Finally, we estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients with bladder cancer based on the pRRophetic algorithm.ResultsFour IArlncRNAs were identified as independent prognostic factors, including AL136084.3, AC006270.1, Z84484.1, and AL513218.1. The OS of patients in the high-risk group was significantly worse compared to the low-risk group. The nomogram showed an excellent predictive effect with the C-index of 0.64. The calibration chart showed a good actual vs. predicted probability. B cells naïve, T cells CD8, T cells CD4 memory resting, T cells follicular helper, macrophages M1, dendritic resting and activated cells had higher infiltrations in the low-risk group and lower infiltration of macrophages M2. The fraction of macrophages M2 was positively associated with AL136084.3. The fraction of T cells CD8 was positively associated with Z84484.1. The fraction of M + macrophages M0 was negatively associated with Z84484.1. Further, we identified the differential IC50 of 24 chemotherapeutic drugs between the high- and low-risk groups.ConclusionsThe prognostic model based on 4 IArlncRNAs showed an excellent predictive effect. Furthermore, we reasonably speculated that IArlncRNAs are directly or indirectly involved in the immune regulation of the tumor microenvironment (TME), as well as autophagy.

Prognostic signature composed of transcription factors accurately predicts the prognosis of gastric cancer patients

BackgroundTranscription factors (TFs) are involved in important molecular biological processes of tumor cells and play an essential role in the occurrence and development of gastric cancer (GC).MethodsCombined The Cancer Genome Atlas Program and Genotype-Tissue Expression database to extract the expression of TFs in GC, analyzed the differences, and weighted gene co-expression network analysis to extract TFs related to GC. The cohort including the training and validation cohort. Univariate Cox, least absolute contraction and selection operator (LASSO) regression, and multivariate Cox analysis was used for screening hub TFs to construct the prognostic signature in the training cohort. The Kaplan–Meier (K–M) and the receiver operating characteristic curve (ROC) was drawn to evaluate the predictive ability of the prognostic signature. A nomogram combining clinical information and prognostic signatures of TFs was constructed and its prediction accuracy was evaluated through various methods. The target genes of the hub TFs was predicted and enrichment analysis was performed to understand its molecular biological mechanism. Clinical samples and public data of GC was collected to verify its expression and prognosis. 5-Ethynyl-2′-deoxyuridine and Acridine Orange/Ethidium Bromide staining, flow cytometry and Western-Blot detection were used to analyze the effects of hub-TF ELK3 on the proliferation and apoptosis of gastric cancer in vitro.ResultsA total of 511 misaligned TFs were obtained and 200 GC-related TFs were exposed from them. After systematic analysis, a prognostic signature composed of 4 TFs (ZNF300, ELK3, SP6, MEF2B) were constructed. The KM and ROC curves demonstrated the good predictive ability in training, verification, and complete cohort. The areas under the ROC curve are respectively 0.737, 0.705, 0.700. The calibration chart verified that the predictive ability of the nomogram constructed by combining the prognostic signature of TFs and clinical information was accurate, with a C-index of 0.714. Enriching the target genes of hub TFs showed that it plays an vital role in tumor progression, and its expression and prognostic verification were consistent with the previous analysis. Among them, ELK3 was proved in vitro, and downregulation of its expression inhibited the proliferation of gastric cancer cells, induced proliferation, and exerted anti-tumor effects.ConclusionsThe 4-TFs prognostic signature accurately predicted the overall survival of GC, and ELK3 may be potential therapeutic targets for GC

LABORATORY CALIBRATION OF EARTH PRESSURE CELLS

Strain-gauge type soil pressure transducers are widely used in laboratory and field investigation on the performance, and health monitoring of geo-structures to accurately measure the soil pressure. Even though these pressure plates are sold with the factory-measured calibration factors, these sensors should be re-calibrated in the laboratory before using them because the calibration can be affected by the data-logging system and the length of the cable used. Therefore, in this study, a laboratory calibration procedure for strain-gauge type soil pressure plates was proposed. The soil pressure transducer was embedded in a uniform-fine- sand medium in a specially designed pressure cylinder, and the pneumatic pressure was applied into the system as gradual increments. After that, the calibration chart of the pressure gauge was developed based on the sensor outputs for different pressures applied. Then the calibrated soil pressure transducer was used in the laboratory pavement model test to measure soil pressure at the base-subgrade interface under a surface loading area. The measured soil pressure values were compared with the estimated vertical stresses from elastic theories to validate the pressure measurements and the calibration process of the soil pressure transducer. Test results revealed that there is a satisfactory agreement between the pressure measured by the soil pressure transducer and theoretical estimations. Thus, the calibration process of the soil pressure transducer and its outputs are proven to be accurate.

Establishment and validation of a predictive model for mortality within 30 days in patients with sepsis-induced blood pressure drop: A retrospective analysis.

ObjectivesTo establish and validate an individualized nomogram to predict the probability of death within 30 days in patients with sepsis-induced blood pressure drop would help clinical physicians to pay attention to those with higher risk of death after admission to wards.MethodsA total of 1023 patients who were admitted to the Dongyang People’s Hospital, China, enrolled in this study. They were divided into model group (717 patients) and validation group (306 patients). The study included 13 variables. The independent risk factors leading to death within 30 days were screened by univariate analyses and multivariate logistic regression analyses and used for Nomogram. The discrimination and correction of the prediction model were assessed by the area under the Receiver Operating Characteristic (ROC) curve and the calibration chart. The clinical effectiveness of the prediction model was assessed by the Decision Curve Analysis (DCA).ResultsSeven variables were independent risk factors, included peritonitis, respiratory failure, cardiac insufficiency, consciousness disturbance, tumor history, albumin level, and creatinine level at the time of admission. The area under the ROC curve of the model group and validation group was 0.834 and 0.836. The P value of the two sets of calibration charts was 0.702 and 0.866. The DCA curves of the model group and validation group were above the two extreme (insignificant) curves.ConclusionsThe model described in this study could effectively predict the death of patients with sepsis-induced blood pressure drop.

Use Machine Learning Methods to Explore the Associated Risk Factors of Osteoporosis or Bone Loss in Chinese People With Type 2 Diabetes: A Clinical Prediction Model

Objective: This study aimed to use machine learning (ML) methods to explore the risk factors associated with OP and bone loss in the Chinese T2DM population, so as to construct useful clinical prediction models. Methods: This was a two-center, retrospective study. The data came from a chronic disease epidemiological investigation database conducted in Ningde City and Wuyishan City, Fujian Province, China from March 2011 to December 2014. Finally, 798 T2DM patients who met the enrollment criteria were included in the final analysis. In order to control gender as a confounding factor that affects the results, we constructed two clinical prediction models based on different genders. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter relevant feature variables. The selected characteristic variables were modeled by logistic regression (LR), and clinical nomograms were used for more intuitive expression. The stability, clinical applicability and recognition of the model were evaluated by C-index, receiver operating characteristic (ROC) curve, calibration chart and decision curve analysis (DCA). Internal verification was achieved through bootstrapping validation. Results: In exploring the related risk factors of OP or bone loss in female T2DM patients. There were a total of 9 related predictors, namely age, marital status, glutamyl transpeptidase, fracture, coronary heart disease, fruit-flavored drinks, moderate-intensity exercise, menopause and nap time were determined by LASSO analysis from a total of 69 variables. The model we constructed using these 9 related predictors showed medium prediction ability (C-index value: 0.738, 95%CI[0.692, 0.784]), the C-index in bootstrapping validation was 0.714, and the area under the ROC curve (AUC) was 0.738. The DCA showed that if the risk threshold was between 4% and 100%, the nomogram could be used clinically. In exploring the related risk factors of OP and bone loss in male T2DM patients. A total of 12 related predictors were identified from 65 variables through LASSO analysis, including age, marital status, fasting serum insulin, alanine aminotransferase, coronary heart disease, respiratory diseases, diabetic retinopathy, seafood, desserts, fruit-flavored beverages, coffee, high-intensity exercise. The model we constructed using these 12 related predictors showed medium prediction ability (C-index value: 0.751, 95%CI[0.694–0.808]), the C-index in bootstrapping validation was 0.704, and the AUC value was 0.751. The DCA showed that if the risk threshold was between 3% and 68%, the nomogram could be used clinically. Conclusion: We explored the associated risk factors of osteoporosis or bone Loss in Chinese people with type 2 diabetes, and developed a risk nomogram with moderate predictive power. The nomogram can help clinicians and patients make joint decisions before treatment.