Risk Curves Research Articles

Predicted Cancer Risk Probability using Linear and Nonlinear Regression Models with Medical Condition Inputs from Chronic Diseases and Lifestyle Details Collected Data of a Type 2 Diabetes Patient Based on GH-Method: Math- Physical Medicine (No. 550)

The author has spent approximately 40,000 hours over the past 12 years self- studying and researching internal medicine branches, with a focus on endocrinology and diabetes. Since 2018, he has expanded his interest, learning and research work into other medical branches related to lifestyle, metabolism and immunity. Currently, his type 2 diabetes (T2D) is well under control, where the HbA1C level decreased from 10% in 2010 down to 5.8% in 2021 without medication intervention. Naturally, he is concerned about other life-threatening diseases of the elderly population, specifically cancers and dementia. Over the past decade, he has written and published more than 540 medical papers in various medical journals. In total, he applied about 30 different research methodologies based on his developed GH-method: math-physical medicine, including physics theories, engineering modeling, mathematical equations, computer science tools of big data analytics and artificial intelligence (AI), as well as some traditional statistical approaches to explore and interpret various biomarkers and their biophysical phenomena. However, the majority of published medical research papers he has read to date are primarily based on statistics (~90% of his total reading volume of ~2,000 papers). In this particular article, he decides to follow the majority of other medical scientists’ footsteps, to use the traditional statistical regression model with linear and various nonlinear formulas involving multiple independent variables to investigate his overall risk probability of developing cancer versus 4 categories of metabolic disorder induced chronic diseases (obesity, diabetes, hypertension, and hyperlipidemia) and 6 categories of lifestyle details (food, water, exercise, sleep, stress, and daily life routines). In this paper, he will not repeat the detailed introduction of the regression analysis models in the Methods section because it is available in many statistics text books. It should be noted that in regression analysis, the correlation coefficient R should be > 0.5 or 50% to indicate a strong inter-connectivity and the p-value should be < 0.05 or 5% to be considered as statistically significant. The author recently studied a consensus report published jointly by the American Diabetes Association (ADA) and the American Cancer Society (ACS) in 2010 regarding relationships between cancers and diabetes. Based on the information from this report plus ~3 million collected data of his own overall metabolism situation, including medical conditions and lifestyle details, he decided to conduct a research study regarding the estimation of his overall and relative risk probability of having cancer over two time periods: the longer 12-year period from 2010 to 2021 and the shorter 9-year period from 2013 to 2021. The reason for using two time-periods is due to his insufficient data gathering and guesstimateddata from 2010 and 2012. He would like to investigate the prediction difference. In summary, from the time-domain analysis, the two cancer risk waveforms, metabolism index (MI) based cancer risk curve and regression predicted cancer risk curve, are similar to each other in terms of shape similarity. They have an extremely high of 92% - 98% correlation between the curves and with a 100% prediction accuracy of averaged cancer risks.

Quantitative multi-hazard risk assessment to buildings in the Jiuzhaigou valley, a world natural heritage site in Western China

Mountain villages in earthquake-prone areas are likely to be exposed to multiple geological hazards. In this study, a comprehensive method is proposed to calculate the risk of buildings in two residential villages threatened by rockfalls and debris flows in the Jiuzhaigou Valley. The risk is a function of the probability of the hazard occurrence, the probability of the spatial impact, vulnerability and the economic value of the buildings. The probability of occurrence was estimated via geomorphological, geotechnical and historical record analyses; Run-out maps were obtained by simulation and used to assess the probability of spatial impact, and the vulnerability of individual buildings was calculated by both hazard intensity and building resistance. Risk curves were generated for debris flows and rockfalls for risk comparison. Uncertainties were assessed by an error propagation technique and expressed as maximum and minimum risk curves of the two hazards. Single risk maps were summed up as multi-hazard risk maps. The multi-hazard risk maps indicate that the highest risk for buildings is due to debris flows, while rockfalls pose relatively low risks to buildings. The risk result can be used as a reference for administrators and decision makers. Mitigation measures should be implemented the high-risk areas.

Assessment of potential beach erosion risk and impact of coastal zone development: a case study on Bongpo–Cheonjin Beach

Abstract. In many parts, coastal erosion is severe due to human-induced coastal zone development and storm impacts, in addition to climate change. In this study, the beach erosion risk was defined, followed by a quantitative assessment of potential beach erosion risk based on three components associated with the watershed, coastal zone development, and episodic storms. On an embayed beach, the background erosion due to development in the watershed affects sediment supply from rivers to the beach, while alongshore redistribution of sediment transport caused by construction of a harbor induces shoreline reshaping, for which the parabolic-type equilibrium bay shape model is adopted. To evaluate beach erosion during storms, the return period (frequency) of a storm occurrence was evaluated from long-term beach survey data conducted four times per year. Beach erosion risk was defined, and assessment was carried out for each component, from which the results were combined to construct a combined potential erosion risk curve to be used in the environmental impact assessment. Finally, the proposed method was applied to Bongpo–Cheonjin Beach in Gangwon-do, South Korea, with the support of a series of aerial photographs taken from 1972 to 2017 and beach survey data obtained from the period commencing in 2010. The satisfactory outcomes derived from this study are expected to benefit eroding beaches elsewhere.

Association of Systolic Blood Pressure With Dementia Risk and the Role of Age, U-Shaped Associations, and Mortality

The optimal systolic blood pressure (SBP) to minimize the risk of dementia in older age is unknown. To investigate whether the association between SBP and dementia risk is U-shaped and whether age and comorbidity play a role in this association. This cohort study used an individual participant data approach to analyze 7 prospective, observational, population-based cohort studies that were designed to evaluate incident dementia in older adults. These studies started between 1987 and 2006 in Europe and the US. Participants had no dementia diagnosis and had SBP and/or diastolic blood pressure (BP) data at baseline and incident dementia status during follow-up. Data analysis was conducted from November 7, 2019, to October 3, 2021. Baseline systolic BP. All-cause dementia (defined using Diagnostic and Statistical Manual of Mental Disorders [Third Edition Revised] or Diagnostic and Statistical Manual of Mental Disorders [Fourth Edition] and established at follow-up measurements or in clinical practice), mortality, and combined dementia and mortality were the outcomes. Covariates included baseline antihypertensive medication use, sex, educational level, body mass index, smoking status, diabetes, stroke history, myocardial infarction history, and polypharmacy. Cox proportional hazards regression models were used, and nonlinear associations were explored using natural splines. The study analyzed 7 cohort studies with a total of 17 286 participants, among whom 10 393 were women (60.1%) and the mean (SD) baseline age was 74.5 (7.3) years. Overall, dementia risk was lower for individuals with higher SBP, with the lowest risk associated with an SBP of approximately 185 mm Hg (95% CI, 161-230 mm Hg; P = .001). Stratified by overlapping 10-year baseline age groups, the lowest dementia risk was observed at somewhat lower systolic BP levels in those older than 75 years (158 [95% CI, 152-178] mm Hg to 170 [95% CI, 160-260] mm Hg). For mortality, there was a clear U-shaped association, with the lowest risk at 160 mm Hg (95% CI, 154-181 mm Hg; P < .001). This U-shape occurred across all age groups, with the lowest dementia risk associated with an SBP of 134 mm Hg (95% CI, 102-149 mm Hg; P = .03) in those aged 60 to 70 years and increasing to between 155 mm Hg (95% CI, 150-166 mm Hg; P < .001) and 166 mm Hg (95% CI, 154-260 mm Hg; P = .02) for age groups between 70 and 95 years. Combined dementia and mortality risk curves closely resembled those for mortality. Associations of diastolic BP with dementia risk were generally similar but were less distinct. This cohort study found that dementia risk was lower for older individuals with higher SBP levels and that more distinctly U-shaped associations appeared for those older than 75 years, but these associations cannot be explained by SBP-associated changes in mortality risk. The findings may warrant future trials on tailored BP management in older age groups that take life expectancy and health context into consideration.

Identification and validation of xenobiotic metabolism-associated prognostic signature based on five genes to evaluate immune microenvironment in colon cancer.

Xenobiotic metabolism plays an important role in the progression of colon cancer; however, little is known about its related biomarkers. This study sought to construct a prognostic model related to xenobiotic metabolism in colon cancer, and further reveal the characteristics of tumor immune microenvironment based on the prognostic model. Transcriptome data of 41 normal colon tissues and 473 colon tumor tissues and the clinical features of 452 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Data on xenobiotic metabolism genes (XMGs) were obtained from the hallmark xenobiotic metabolism set of the Molecular Signatures Database (MSigDB) and articles. Additionally, data on differential XMGs in colon cancer were acquired for a functional enrichment analysis by R software. An XMG prognostic model was constructed by a Cox regression analysis, and evaluated using Kaplan-Meier survival curves, risk curves, receiver operating characteristic (ROC) curves, and an independent prognostic analysis in a training cohort and validation cohort. Moreover, tumor immune infiltration and negative regulatory immune genes of cancer-immunity cycle (CIC), including immune checkpoints and immune cytokines, were further analyzed between low- and high-risk groups in both the training and validation cohorts. Differences with P value <0.05 were interpreted as statistically significant. A total of 126 differential XMGs were distinguished in the colon cancer data set, which were mainly enriched in the metabolism pathways of drugs and nutrients. There were 5 optimized genes (i.e., CYP2W1, GSTM1, TGFB2, MPP2, and ACOX1) used to construct the prognosis model, which effectively predicted prognosis and had good ROC curves. Between low- and high-risk groups, there were significant differences in abundance for T cells CD4 memory resting and T cells regulatory (Tregs), and expression of PDCD1, LAG3, NOS3, TGFB1, and ICAM1 in the training cohort and validation cohort. The XMGs in the prognostic model have a good prediction effect on the prognosis of colon cancer patients. The T cells CD4 memory resting, and Tregs, immune checkpoints PDCD1 and LAG3, and CIC negative regulatory immune cytokines NOS3, TGFB1, and ICAM1 are closely associated with xenobiotic metabolism.

Tracking the National Early Warning Score 2 from prehospital care to the emergency department: A prospective, ambulance-based, observational study

Aim of the study: To assess the prognostic ability of the National Early Warning Score 2 (NEWS2) at three time points of care -at the emergency scene (NEWS2-1), just before starting the transfer by ambulance to the hospital (NEWS2- 2), and at the hospital triage box (NEWS2-3)- to estimate in-hospital mortality after two days since the index event. Methods: Prospective, multicenter, ambulance-based, cohort ongoing study in adults (>18 years) consecutively attended by advanced life support (ALS) and evacuated with high-priority to the emergency departments (ED) between October 2018 and May 2021. Vital sign measures were used to calculate the NEWS2 score at each time point, then this score was entered in a logistic regression model as the single predictor. Two outcomes were considered: first, all-cause mortality of the patients within 2 days of presentation to EMS, and second, unplanned ICU admission. The calibration and scores comparison was performed by representing the predicted vs the observed risk curves according to NEWS score value. Results: 4943 patients were enrolled. Median age was 69 years (interquartile range 53- 81). The NEWS2-3 presented the better performance for all-cause two-day in-hospital mortality with an AUC of 0.941 (95% CI: 0.917-0.964), showing statistical differences with both the NEWS2-1 (0.872 (95% CI: 0.833-0.911); p < 0.003) and with the NEWS2- 2 (0.895 (95% CI: 0.866-0.925; p < 0.05). The calibration and scores comparison results showed that the NEWS2-3 was the best predictive score followed by the NEWS2-2 and the NEWS2-1, respectively. Conclusions: The NEWS2 has an excellent predictive performance. The score showed a very consistent response over time with the difference between “at the emergency scene” and “pre-evacuation” presenting the sharpest change with decreased threshold values, thus displaying a drop in the risk of acute clinical impairment.

Biological Functions and Prognostic Value of Ferroptosis-Related Genes in Bladder Cancer.

Background: Every year, nearly 170,000 people die from bladder cancer worldwide. A major problem after transurethral resection of bladder tumor is that 40–80% of the tumors recur. Ferroptosis is a type of regulatory necrosis mediated by iron-catalyzed, excessive oxidation of polyunsaturated fatty acids. Increasing the sensitivity of tumor cells to ferroptosis is a potential treatment option for cancer. Establishing a diagnostic and prognostic model based on ferroptosis-related genes may provide guidance for the precise treatment of bladder cancer. Methods: We downloaded mRNA data in Bladder Cancer from The Cancer Genome Atlas and analyzed differentially expressed genes based on and extract ferroptosis-related genes. We identified relevant pathways and annotate the functions of ferroptosis-related DEGs using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Ontology functions. On the website of Search Tool for Retrieving Interacting Genes database (STRING), we downloaded the protein-protein interactions of DEGs, which were drawn by the Cytoscape software. Then the Cox regression analysis were performed so that the prognostic value of ferroptosis-related genes and survival time are combined to identify survival- and ferroptosis-related genes and establish a prognostic formula. Survival analysis and receiver operating characteristic curvevalidation were then performed. Risk curves and nomograms were generated for both groups to predict survival. Finally, RT-qPCR was applied to analyze gene expression. Results: Eight ferroptosis-related genes with prognostic value (ISCU, NFE2L2, MAFG, ZEB1, VDAC2, TXNIP, SCD, and JDP2) were identified. With clinical data, we established a prognostic model to provide promising diagnostic and prognostic information of bladder cancer based on the eight ferroptosis-related genes. RT-qPCR revealed the genes that were differentially expressed between normal and cancer tissues. Conclusion: This study found that the ferroptosis-related genes is associated with bladder cancer, which may serve as new target for the treatment of bladder cancer.

Flood risk assessment and application of risk curves for design of mitigation strategies

A transportation network is a critical infrastructure system that forms the backbone for economy, serves the everyday life, and is critical to national safety. Holistic design of such a system is required to ensure smooth operation under both operational and emergency conditions. However, with ever-changing climate, transportation systems are exposed to significant weather-related hazards. For example, flood events are proven to be a dominant hazard in the U.S. due to their frequency and intensity. While flood events affect state agencies by requiring direct tax-dollar investments to repair damages, they also adversely influence communities by producing substantial indirect losses. This motivates state agencies, asset owners, and planners to developcost-effective mitigation strategies. However, the uncertainty of flooding and the interdependency between assets (such as roads and bridges) within the transportation system and traffic users on one hand and limited budgetary resources on the other hand, challenge the design of a cost-effective risk mitigation strategy. This is exacerbated by the fact that the estimation of indirect losses associated with closures resulting from damaged assets is difficult to assess. To address such gaps, this paper develops an integrated risk assessment method that synthesizes various inputs, including hazards, geographic features, spatial distribution of assets, and traffic, to simulate the flood-induced risk to a real-life transportation system. This framework is capable of estimating actual physical infrastructure damages as well as quantitatively evaluating the indirect losses of traffic users such as traffic delays and opportunity costs closely associated with flood risk. The developed flood risk curves can be used by decision-makers to implement proper pre-event mitigation or post-event response plans.

Human pelvis injury risk curves from underbody blast impact

IntroductionUnderbody blast loading can result in injuries to the pelvis and the lumbosacral spine. The purpose of this study was to determine human tolerance in this region based on survival...

Deflection-based parametric survival analysis side impact chest injury risk curves AIS 2015

Objectives The objective of this study was to reanalyze lateral postmortem human surrogate (PMHS) sled test chestband data to construct updated lateral thoracic injury risk curves (IRCs) using survival analysis. Methods Chestband and injury data were gathered from 16 previously conducted PMHS sled tests. Briefly, 2 chestbands were wrapped around the thorax’s circumference at the levels of ribs 4 and 8. Tests were conducted at 6.7 m/s on a rigid and padded load wall fixed to the top of a rebound sled. The injuries were reclassified using the Abbreviated Injury Scale (AIS) 2015 coding scheme. Chestband signals were combined with pretest specimen measurements to calculate the chest deflection contour time history. Deflections were determined using updated processing techniques calculating the change in length of every point on the contour from the impacted side using the thorax’s midpoint as the origin. Four candidate metrics were selected: the deflection from rib 4, the deflection from rib 8, the greater of the deflections from ribs 4 and 8, and the average of the deflections from ribs 4 and 8. AIS 3+ IRCs were developed considering outcomes of AIS ≥3 injuries. All injury data were uncensored, and noninjury data were right-censored. Three specimen mass-based IRCs were determined using the IRC with the lowest Brier score metric (BSM): The first corresponded to the 5th percentile female mass (49 kg), the second to the 50th percentile male mass (77 kg), and the third to the average mass of the PMHS ensemble (65 kg). Results Sixteen PMHS were used in the current study. Six specimens were right-censored, and 10 were uncensored. The average metric had the lowest BSM, and mass was a significant covariate with 50% risk of AIS3+ injury at 72mm of chest deflection. The 50% risk deflection magnitudes for the 5th percentile female (49 kg), 50th percentile male (77 kg), and PMHS ensemble (PMHS-E) (65 kg) were 59, 81, and 71 mm. IRCs for the 4 metrics and the 3 occupant masses are given. Conclusions IRCs were developed using survival analysis, and the average of the peak deflections was found to best represent the thoracic chest deflection response. Mass-based side impact IRCs were calculated for occupants representing the WorldSID 5th percentile female and 50th percentile male anthropomorphic test device.

Comprehensive Analysis of Inflammatory Response–Related Genes, and Prognosis and Immune Infiltration in Patients With Low-Grade Glioma

Background: Although low-grade glioma (LGG) has a good prognosis, it is prone to malignant transformation into high-grade glioma. It has been confirmed that the characteristics of inflammatory factors and immune microenvironment are closely related to the occurrence and development of tumors. It is necessary to clarify the role of inflammatory genes and immune infiltration in LGG. Methods: We downloaded the transcriptome gene expression data and corresponding clinical data of LGG patients from the TCGA and GTEX databases to screen prognosis-related differentially expressed inflammatory genes with the difference analysis and single-factor Cox regression analysis. The prognostic risk model was constructed by LASSO Cox regression analysis, which enables us to compare the overall survival rate of high- and low-risk groups in the model by Kaplan–Meier analysis and subsequently draw the risk curve and survival status diagram. We analyzed the accuracy of the prediction model via ROC curves and performed GSEA enrichment analysis. The ssGSEA algorithm was used to calculate the score of immune cell infiltration and the activity of immune-related pathways. The CellMiner database was used to study drug sensitivity. Results: In this study, 3 genes (CALCRL, MMP14, and SELL) were selected from 9 prognosis-related differential inflammation genes through LASSO Cox regression analysis to construct a prognostic risk model. Further analysis showed that the risk score was negatively correlated with the prognosis, and the ROC curve showed that the accuracy of the model was better. The age, grade, and risk score can be used as independent prognostic factors (p < 0.001). GSEA analysis confirmed that 6 immune-related pathways were enriched in the high-risk group. We found that the degree of infiltration of 12 immune cell subpopulations and the scores of 13 immune functions and pathways in the high-risk group were significantly increased by applying the ssGSEA method (p < 0.05). Finally, we explored the relationship between the genes in the model and the susceptibility of drugs. Conclusion: This study analyzed the correlation between the inflammation-related risk model and the immune microenvironment. It is expected to provide a reference for the screening of LGG prognostic markers and the evaluation of immune response.

Process‐Based Flood Risk Assessment for Germany

AbstractLarge‐scale flood risk assessments are crucial for decision making, especially with respect to new flood defense schemes, adaptation planning and estimating insurance premiums. We apply the process‐based Regional Flood Model (RFM) to simulate a 5000‐year flood event catalog for all major catchments in Germany and derive risk curves based on the losses per economic sector. The RFM uses a continuous process simulation including a multisite, multivariate weather generator, a hydrological model considering heterogeneous catchment processes, a coupled 1D–2D hydrodynamic model considering dike overtopping and hinterland storage, spatially explicit sector‐wise exposure data and empirical multi‐variable loss models calibrated for Germany. For all components, uncertainties in the data and models are estimated. We estimate the median Expected Annual Damage (EAD) and Value at Risk at 99.5% confidence for Germany to be €0.529 bn and €8.865 bn, respectively. The commercial sector dominates by making about 60% of the total risk, followed by the residential sector. The agriculture sector gets affected by small return period floods and only contributes to less than 3% to the total risk. The overall EAD is comparable to other large‐scale estimates. However, the estimation of losses for specific return periods is substantially improved. The spatial consistency of the risk estimates avoids the large overestimation of losses for rare events that is common in other large‐scale assessments with homogeneous return periods. Thus, the process‐based, spatially consistent flood risk assessment by RFM is an important step forward and will serve as a benchmark for future German‐wide flood risk assessments.

Seismic probabilistic risk assessment of weir structures considering the earthquake hazard in the Korean Peninsula

Seismic safety evaluation of weir structure is significant considering the catastrophic economical consequence of operational disruption. In recent years, the seismic probabilistic risk assessment (SPRA) has been issued as a key area of research for the hydraulic system to mitigate and manage the risk. The aim of this paper is to assess the seismic probabilistic risk of weir structures employing the seismic hazard and the structural fragility in Korea. At the first stage, probabilistic seismic hazard analysis (PSHA) approach is performed to extract the hazard curve at the weir site using the seismic and geological data. Thereafter, the seismic fragility that defines the probability of structural collapse is evaluated by using the incremental dynamic analysis (IDA) method in accordance with the four different design limit states as failure identification criteria. Consequently, by combining the seismic hazard and fragility results, the seismic risk curves are developed that contain helpful information for risk management of hydraulic structures. The tensile stress of the mass concrete is found to be more vulnerable than other design criteria. The hazard deaggregation illustrates that moderate size and far source earthquakes are the most likely scenario for the site. In addition, the annual loss curves for two different hazard source models corresponding to design limit states are extracted.

Risky Corporate Bonds in 2021: A Bubble, or Rational Underwriting in a Low-Rate Environment?

In response to the pandemic, the Federal Reserve and US Treasury aggressively supported the corporate debt markets in 2020, both through easy monetary policy and direct participation. As the economy recovered, the Fed maintained an easy monetary policy in pursuit of more robust employment, gross domestic product growth, and inflation targets. Many observers have asserted that these policy actions and other factors have driven a bubble in risk assets, such as equities, special purpose acquisition companies, real estate, commodities, cryptocurrencies, nonfungible tokens, and risky bonds. Others believe that the valuations for at least some of these assets reflect investors’ rational incorporation of the current interest rate environment and economic outlook into their underwriting assumptions. Here, the authors analyze this question with regard to one of the riskiest classes of debt securities—the CCC-rated portion of the corporate high-yield debt market—to draw broader conclusions about the leveraged credit markets in the United States. Using historical market metrics, this portion of the market at its recent peak offered almost no excess return to compensate investors for the risk taken relative to low-risk alternatives. From this, the authors conclude that investors are underwriting significantly more optimistic outcomes than those reflected in historical averages. Although the authors believe that this segment of the market falls short of a true bubble, as they define it, they warn that current conditions pose key risks to investors and to the broader economy. <b>Key Findings</b> ▪ The US high-yield bond market, like other asset classes in the wake of the current easy monetary environment, has reached peak levels. ▪ Consistent with past bubbles, investors have moved up the risk curve, disproportionately bidding up the riskiest portion of this market. ▪ Although this reflects excessive risk appetite on the part of investors, it falls short of a true bubble as we define it. ▪ That said, these conditions pose key risks to investors and to the broader economy.

Determinants of financial performance: An evidence from Internet finance sector

Financial performance is one of the most important factors for the decision making in the operation of Internet finance sector. The purpose of this study is to examine the impacts of credit risk, liquidity, profitability, capital structure, and growth on the financial performance. The empirical research is carried out on the sample of 24 listed companies steadily engaged in Internet finance services from 2013 to September 2020. The originality of the paper is the industrial credit risk curve and the relationships between the variables, which are expected to provide insights for the healthy development of both entire industry and individual enterprises.

Brain injury severity due to direct head contact from near-side motor vehicle collisions

Objective The objective of this study was to generate functional forms of brain injury risk curves using the National Automotive Sample System Crashworthiness Data System’s (NASS-CDS) database for the years of 2001–2015. The population of interest was near-side occupants who experienced a direct head impact with an injury source located lateral to a typical seated position. Methods Brain injuries were restricted to Abbreviated Injury Scale (AIS) 2005 Update 2008 defined concussions and internal organ injuries of the head. Near-side occupants comprised two major groups, both of which were required to have evidence of head contact (i.e., a head injury with DIRINJ = 1 and SOUCON = 1 or 2): brain injured occupants (MAIS1, MAIS2, MAIS3+) and non-brain injured occupants with some other direct contact head injury (MAIS0). Analyzed cases were required to have an indication of a reasonable crash reconstruction. Injury sources allowed within the final sample consisted of A-pillars, B-pillars, roof/roof rails, impacting vehicles/exterior objects, other components of the vehicle’s side interior, and other occupants or otherwise unspecified interior objects. Risk curves for occupants with brain injury severities of MAIS0, MAIS1+, MAIS2+, and MAIS3+ were generated using multivariate stepwise logistic regressions. Investigated predictors involved vehicle change in velocity, seat belt use, principal direction of force (PDOF), and injury source type (B-pillar and side window). Results Multivariate stepwise logistic regressions identified significant predictors of lateral change in velocity (dvlat) for all injury severity categories, and side window injury source (INJSOU = 56, 57, 58, 106, and 107) for MAIS0 and MAIS1+ risk curves. Although model sensitivity decreased for more severe injury predictions, risk curves dependent on only dvlat yielded accuracies of 70% for all presented models. Conclusions Real world crashes are often complex and lack the benefit of real time monitoring; however, NASS-CDS post-crash investigations provide data useful for injury risk prediction. Further analysis is needed to determine the effect of data confidence, injury source, and accident sequence restrictions on NASS-CDS sampling biases. The presented models likely favor a more conservative risk prediction due to the limitations of NASS-CDS data collection, AIS code conversion, and unweighted sample analysis.

Determination of a six-gene prognostic model for cervical cancer based on WGCNA combined with LASSO and Cox-PH analysis

AimThis study aimed to establish a risk model of hub genes to evaluate the prognosis of patients with cervical cancer.MethodsBased on TCGA and GTEx databases, the differentially expressed genes (DEGs) were screened and then analyzed using GO and KEGG analyses. The weighted gene co-expression network (WGCNA) was then used to perform modular analysis of DEGs. Univariate Cox regression analysis combined with LASSO and Cox-pH was used to select the prognostic genes. Then, multivariate Cox regression analysis was used to screen the hub genes. The risk model was established based on hub genes and evaluated by risk curve, survival state, Kaplan-Meier curve, and receiver operating characteristic (ROC) curve.ResultsWe screened 1265 DEGs between cervical cancer and normal samples, of which 620 were downregulated and 645 were upregulated. GO and KEGG analyses revealed that most of the upregulated genes were related to the metastasis of cancer cells, while the downregulated genes mostly acted on the cell cycle. Then, WGCNA mined six modules (red, blue, green, brown, yellow, and gray), and the brown module with the most DEGs and related to multiple cancers was selected for the follow-up study. Eight genes were identified by univariate Cox regression analysis combined with the LASSO Cox-pH model. Then, six hub genes (SLC25A5, ENO1, ANLN, RIBC2, PTTG1, and MCM5) were screened by multivariate Cox regression analysis, and SLC25A5, ANLN, RIBC2, and PTTG1 could be used as independent prognostic factors. Finally, we determined that the risk model established by the six hub genes was effective and stable.ConclusionsThis study supplies the prognostic value of the risk model and the new promising targets for the cervical cancer treatment, and their biological functions need to be further explored.

Comparison of refracture risk between sandwich vertebrae and ordinary adjacent vertebrae

To compare the refracture risk between sandwich vertebrae and ordinary adjacent vertebrae, and to explore the risk factors related to refracture. Retrospective analysis was performed on the data of patients who received percutaneous vertebral augmentation (PVA) and formed sandwich vertebrae between April 2015 and October 2019. Of them, 115 patients were enrolled in the study. There were 27 males and 88 females with an average age of 73.9 years (range, 53-89 years). Univariate analysis was performed to analyzed the patients' general data, vertebral augmentation related indexes, and sandwich vertebrae related indexes. Survival analysis was performed for all untreated vertebrae at T 4-L 5 of the included patients at the vertebra-specific level, and risk curves of refracture probability of untreated vertebrae between sandwich vertebrae and ordinary adjacent vertebrae were compared. Cox's proportional hazards regression model was used to analyze risk factors for refracture. The 115 patients were followed up 12.6-65.9 months (mean, 36.2 months). Thirty-seven refractures involving 51 vertebral bodies occurred in 31 patients. The refracture rate of 27.0% (31/115) in patients with sandwich vertebrae was significantly higher than that of 15.2% (187/1228) in all patients who received PVA during the same period ( χ 2=10.638, P=0.001). Univariate analysis results showed that there was a significant difference in the number of augmented vertebrae between patients with and without refractures ( Z=0.870, P=0.004). However, there was no significant difference in gender, age, body mass index, whether had clear causes of fracture, whether had dual energy X-ray absorptiometry testing, whether the sandwich vertebra generated through the same PVA, puncture method, method of PVA, number of PVA procedures, number of vertebrae with old fracture, whether complicated with spinal deformity, bone cement distribution, and kyphosis angle of sandwich vertebral area ( P>0.05). Among the 1 293 untreated vertebrae, there were 136 sandwich vertebrae and 286 ordinary adjacent vertebrae. The refracture rate of sandwich vertebrae was 11.3% which was higher than that of ordinary adjacent vertebrae (6.3%)( χ 2=4.668, P=0.031). The 1- and 5-year fracture-free probabilities were 0.90 and 0.87 for the sandwich vertebrae, and 0.95 and 0.93 for the ordinary adjacent vertebrae, respectively. There was a significant difference between the two risk curves of refracture ( χ 2=4.823, P=0.028). Cox's proportional hazards regression model analysis results showed that the sandwich vertebrae, thoracolumbar location, the number of the augmented vertebrae, and the unilateral puncture were significant risk factors for refracture ( P<0.05). The sandwich vertebrae has a higher risk of refracture when compared with the ordinary adjacent vertebrae, and its 1- and 5-year fracture-free probabilities are lower than those of the ordinary adjacent vertebrae. However, the 5-year fracture-free probability of sandwich vertebrae is still 0.87, so prophylactic enhancement is not recommended for all sandwich vertebrae. In addition, the sandwich vertebrae, thoracolumbar location, the number of the augmented vertebrae, and the unilateral puncture were important risk factors for refracture.

Travel speed and the risk of serious injury in vehicle crashes

While there is a large quantity of prior research on speed and road safety, no previous studies have quantified the absolute risk of serious injury in a crash relative to travel speed. This study aimed to produce risk curves that relate travel speed to the risk of serious injury in light vehicle impacts in order to contribute to the process of selecting acceptable travel speeds. Serious injury was defined in this study as any injury having a maximum abbreviated injury scale (MAIS) of three or greater, or a fatal injury (MAIS3+F). In the context of a crash, travel speed is defined as the vehicle's speed before the driver reacts to the crash situation. Travel speed was determined by selecting the highest pre-impact speed recorded by an Event Data Recorder (EDR) in the seconds before the crash. A total of 1,618 light vehicle impacts were analysed using logistic regression. Individual risk curves were produced for front, head on, side, rear and single vehicle impacts. The analysis found significant positive relationships between the risk of serious injury and travel speed for all of these impact types. The travel speeds at which the risk of serious injury reached one per cent were 63 km/h across all impacts, 17 km/h for head on impacts, 48 km/h for single vehicle impacts, 58 km/h for side impacts, 81 km/h for front impacts and 96 km/h for rear impacts. These results have implications for the setting of speed limits and other measures that influence the speed at which vehicles travel.

Construction and Analysis of a Colorectal Cancer Prognostic Model Based on N6-Methyladenosine-Related lncRNAs.

Given the relatively poor understanding of the expression and functional effects of the N6-methyladenosine (m6A) RNA methylation on colorectal cancer (CRC), we attempted to measure its prognostic value and clinical significance. We comprehensively screened 37 m6A-related prognostic long non-coding RNAs (lncRNAs) with significant differences in expression based on 21 acknowledged regulators of m6A modification and data on 473 colorectal cancer tissues and 41 para-cancer tissues obtained from the TCGA database. Accordingly, we classified 473 CRC patients into two clusters by consensus clustering on the basis of significantly different survival outcomes. We also found a potential correlation between m6A-related prognostic lncRNAs and BRAF-KRAS expression, as well as immune cell infiltration. Then, we established a prognostic model by selecting 16 m6A-related prognostic lncRNAs via LASSO Cox analysis and grouped the CRC patients into low- and high-risk groups to calculate risk scores. Then, we performed stratified sampling to validate and confirm our model by categorising the 473 samples into a training group (N = 208) and a testing group (N = 205) in a 1:1 ratio. The survival curve showed a distinct clinical outcome in the low- and high-risk subgroups. We reconfirmed the reliability and independence of the prognostic model through various measures: risk curve, heat map and univariate and multivariate Cox analyses. To ensure that the outcomes were applicable to clinical settings, we performed stratified analyses on different clinical features, such as age, lymph node status and clinical stage. CRC patients with downregulated m6A-related gene expression, lower immune score, distant metastasis, lymph node metastasis or more advanced clinical staging had higher risk scores, indicating less-desirable outcomes. Moreover, we explored the immunology of colorectal cancer cells. The risk score showed positive correlations with eosinophils, M2 macrophages and neutrophils. In summary, our effort revealed the significance of m6A RNA methylation regulators in colorectal cancer, and the prognostic model we constructed may be used as an essential reference for predicting the outcome of CRC patients.