Critical Value Research Articles

Using Time in Tight Glucose Range as a Health-Promoting Strategy in Preschoolers With Type 1 Diabetes.

Children who develop diabetes in their first years of life risk being exposed to many decades of hyperglycemia, hence having a high risk of early complications and premature death. An additional age-dependent risk is that dysglycemia, especially hyperglycemia, negatively affects the developing brain. In evaluating the outcome of insulin treatment at an individual and group level, cutoff thresholds for glucose values are needed. Time in tight range (TITR) was defined as a measurement of time spent in a state of normoglycemia. The International Society of Pediatric and Adolescent Diabetes recommended that for preschoolers with type 1 diabetes (T1D), either >70% of time with glucose in range 70-180 mg/dL (3.9-10 mmol/L) or >50% of time in a tighter range 70-140 mg/dL (TITR) can be used as continuous glucose monitoring targets. In Sweden, over the past two decades, pediatric diabetes teams set glycemic targets to 70-140 mg/dL (3.9-7.8 mmol/L). Swedish registry data show that >50% of children <7 years old have >50% TITR. The purpose of this review is to share and discuss international knowledge and experiences of working with TITR as a health-promoting strategy in preschoolers with T1D on a structural and individual level. We conclude that as insulin treatment improves, a reasonable goal is to strive for as much time in a state of normoglycemia as possible, and this can easily be explained to families of children with diabetes. For children with access to an experienced health care team and diabetes technologies a currently realistic target can be at least half of the time in normoglycemic range, i.e., TITR >50%.

Identification of novel PAD2 inhibitors using pharmacophore-based virtual screening, molecular docking, and MD simulation studies.

In the realm of epigenetic regulation, Protein arginine deiminase 2 (PAD2) stands out as a therapeutic target due to its significant role in neurological disorders, rheumatoid arthritis (RA), multiple sclerosis (MS), and various cancers. To date, no in silico studies have focused on PAD2 for lead compound identification. Therefore, we conducted structure-based pharmacophore modeling, virtual screening, molecular docking, molecular dynamics (MD) simulations, and essential dynamics studies using PCA and free energy landscape analyses to identify repurposed drugs and selective inhibitors against PAD2. The best pharmacophore model, 'Pharm_01,' had a selectivity score of 10.485 and an excellent ROC curve quality of 0.972. Pharm1 consisted of three hydrogen bond donors (HBD) and two hydrophobic (Hy) features (DDDHH). A virtual screening of about 9.2million compounds yielded 2575 hits using a fit value threshold of 2.5 and drug-likeness criteria. Molecular docking identified the top ten molecules, which were verified using MD simulations. Stability was verified using MM-PBSA studies, whereas conformational differences were investigated using PCA and free energy landscape analyses. Two hits (Leads 1 and 2) from the DrugBank dataset showed promise for repurposing as PAD2 inhibitors, while one hit compound (Lead 8) from the ZINC database emerged as a novel PAD2 inhibitor. These findings indicate that the discovered compounds may be potent PAD2 inhibitors, necessitating additional preclinical and clinical research to produce viable treatments for cancer and neurological disorders.

Finite love

ABSTRACT It seems like a problem to say that love can be merited – its value is located in its transcendence of comparative judgments. However, we commonly make judgments about who is and is not worth loving. We deem certain romantic partners, friends, and family members as worth or not worth our loving time, attention, and effort. In this paper, I argue that love can be merited, and it is merited because of a person’s intrinsic valuable qualities. However, it is not the fact that such qualities can be compared that is relevant. What is relevant is the fact that such qualities are gradable, and what it means for someone to merit love is that their qualities meet a threshold or standard of value for love. Moreover, love requires some amount of practical wisdom in discerning these thresholds and when someone meets or fails to meet them. This is important to recognize since we are creatures of finite love. We do not have the capacity to love everyone who is both worthy and unworthy of love. Given our finite capacities, I argue that making wise and discerning judgments about whom to love should be kept in mind when philosophizing about love.

Ratio of red blood cell distribution width to albumin level and risk of mortality in sarcopenic obesity.

The aim of this study was to investigate the relationship between red blood cell distribution width and albumin ratio (RAR) levels and mortality in adult patients with sarcopenic obesity in the United States. The study included 1,361 adult patients with sarcopenic obesity from the National Health and Nutrition Examination Survey (1999-2006). The X-tile was used to determine the optimal subgroup thresholds for RAR values, and propensity score matching (PSM) was employed to reduce baseline bias. Cox regression analysis, Kaplan-Meier survival curves, and restricted cubic spline analysis were utilized to assess the relationship between RAR levels and all-cause and cardiovascular mortality. Subgroup analysis and the Subpopulation Treatment Effect Pattern Plot were employed to determine survival advantages across different subgroups. Time-dependent ROC analysis to evaluate the accuracy of RAR level in predicting survival outcomes at different time points. Post-PSM multifactorial Cox regression analyses revealed that RAR was a significant independent predictor of all-cause mortality (HR 1.487, 95% CI: 1.259-1.756) and an independent risk factor for cardiovascular mortality (HR 1.487, 95% CI: 1.260-1.758) in patients with sarcopenic obesity. The survival advantage was consistent across subgroups. Restricted cubic spline analysis indicated an approximate S-shaped association between RAR levels and mortality. Time-dependent ROC curves demonstrate that the areas under the all-cause mortality curves at the RAR level for 1-year, 3-year, 5-year, and 10-year are 0.79, 0.66, 0.64, and 0.63, respectively. The areas under the cardiovascular mortality curve are 0.80, 0.70, 0.66, and 0.61, respectively. Moreover, in comparison to the baseline model lacking covariates, the AUC values of the joint model exhibited heightened levels at various time points. Therefore, We demonstrated that the RAR level is an independent prognostic factor for mortality risk in the American population with sarcopenic obesity, and it is reasonable to consider the RAR level as a simple and effective risk prediction tool.

Wavelet-based vibration denoising for structural health monitoring

In the context of civil engineering applications, vibration responses are complex, exhibiting variations in time and space and often containing nonlinearity and uncertainties not considered during data collection. These responses can also be contaminated by various sources, impacting damage identification processes. A significant challenge is how to effectively remove noise from these data to obtain reliable damage indicators that are unresponsive to noise and environmental factors.This study proposes a new denoising algorithm based on discrete wavelet transform (DWT) that addresses this issue. The suggested method offers a strategy for denoising using distinct thresholds for positive and negative coefficient values at each band and applying denoising process to both detail and trend components. The results prove the effectiveness of the technique and show that Bayes thresholding performs better than the other techniques in terms of the evaluated metrics. This suggests that Bayes thresholding is a more accurate and robust technique for thresholding compared to other common techniques.

Optimizing VAT threshold: The key to improving VAT performance

The low tax ratio in Indonesia is an issue often associated with suboptimal tax revenue performance. This study examines the role of the threshold for Value Added Tax (VAT), which is suspected to significantly influence the optimization of VAT revenue, while also exploring the effect of VAT rate and Gross Domestic Product (GDP) per capita. The study analyze data from member countries of the Organization for Economic Co-operation and Development (OECD) in 2022 using multiple linear regression methods. The results show that increases in the VAT threshold and VAT rate positively impact VAT revenue performance, while an increase in per capita GDP has a negative effect. A higher VAT threshold allows the country to focus on larger taxpayers, while the Micro, Small, and Medium Enterprises (MSMEs) benefit from reduced administrative burdens. These findings can serve as a basis for government evaluation in determining the appropriate threshold and VAT rates to enhance tax revenue performance optimally.

Empirical Inferences Under Bayesian Framework to Identify Cellwise Outliers

Outliers are typically identified using frequentist methods. The data are classified as “outliers” or “not outliers” based on a test statistic that measures the magnitude of the difference between a value and the majority part of the data. The threshold for a data value to be an outlier is typically defined by the user. However, a subjective choice of the threshold increases the uncertainty associated with outlier status for each data value. A cellwise outlier detection algorithm named FuzzyHRT is used to automate the editing process in repeated surveys. This algorithm uses Bienaymé–Chebyshev’s inequality and fuzzy logic to detect four different types of outliers resulting from format inconsistencies, historical, tail, and relational anomalies. However, fuzzy logic is not suited for probabilistic reasoning behind the identification of anomalous cells. Bayesian methods are well suited for quantifying the uncertainty associated with the identification of outliers. Although, as suggested by the literature, there exist well-developed Bayesian methods for record-level outlier detection, Bayesian methods for identifying outliers within individual records (i.e., at the cell level) remain unexplored. This paper presents two approaches from the Bayesian perspective to study the uncertainty associated with identifying outliers. A Bayesian bootstrap approach is explored to study the uncertainty associated with the output scores from the FuzzyHRT algorithm. Empirical likelihoods in a Bayesian setting are also considered for probabilistic reasoning behind the identification of anomalous cells. NASS survey data for livestock and major crop yield (such as corn) are considered for comparing the performances of the two proposed approaches with recent cellwise outlier methods.

Deal Value Threshold and M&amp;A: A Competition Law Analysis

In 2023, the Indian Parliament introduced amendments to the Competition Act, 2002. The primary focus of these amendments is to provide regulatory certainty, ensure faster market correction, and provide a trust-based business environment in India. These amendments also aim to give the Competition Commission of India (CCI) adequate power to ensure that no anti-competitive practices are undertaken in India. A plethora of amendments have been enacted, and one of the key amendments impacting M&amp;A is the introduction of the Deal Value Threshold (DVT) while retaining the asset and turnover test. The rationale for this amendment is to ensure digital markets are covered within the ambit of DVT and to ensure that no anti-competitive M&amp;A deals are undertaken in the digital market space. The impact of the new additional threshold in India has been analyzed, and the loopholes in the DVT framework have been highlighted. The impact of DVT on M&amp;A deals undertaken in India has been showcased. In addition, solutions have been provided that can be adopted by the CCI in order to plug the loopholes in this new framework to make it comprehensive.

Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in Oncology.

The primary results of phase III oncology trials may be challenging to interpret, given that results are generally based on P value thresholds. The probability of whether a treatment is beneficial, although more intuitive, is not usually provided. Here, we developed and released a user-friendly tool that calculates the probability of treatment benefit using trial summary statistics. We curated 415 phase III randomized trials enrolling 338,600 patients published between 2004 and 2020. A phase III prior probability distribution for the treatment effect was developed on the basis of a three-component zero-mean mixture distribution of the observed z-scores. Using this prior, we computed the probability of clinically meaningful benefit (hazard ratio [HR] <0.8). The distribution of signal-to-noise ratios and power of phase III oncology trials were compared with that of 23,551 randomized trials from the Cochrane Database. The signal-to-noise ratios of phase III oncology trials tended to be much larger than randomized trials from the Cochrane Database. Still, the median power of phase III oncology trials was only 49% (IQR, 14%-95%), and the power was <80% in 65% of trials. Using the phase III oncology-specific prior, only 53% of trials claiming superiority (114 of 216) had a ≥90% probability of clinically meaningful benefits. Conversely, the probability that the experimental arm was superior to the control arm (HR <1) exceeded 90% in 17% of trials interpreted as having no benefit (34 of 199). By enabling computation of contextual probabilities for the treatment effect from summary statistics, our robust, highly practical tool, now posted on a user-friendly webpage, can aid the wider oncology community in the interpretation of phase III trials.

Preliminary Evaluation of the Value of a Small-Molecule Probe Targeting DNMT1 in Detecting the Methylation of PAX1 in Cervical Cancer.

To investigate the screening value of a small-molecule probe to assess the methylation of PAX1 in cervical cancer. The diagnostic threshold of the grayscale values for cervical lesions was assessed by plotting the Receiver Operating Characteristic (ROC) curve of subjects. Grayscale values were significantly different among the four groups (p<0.05). Compared with the LSIL and cervicitis groups, a considerably higher grayscale value was found in the CA and high-grade squamous intraepithelial lesion (HSIL) groups (both p<0.05). The differential ROC curves of the grayscale values showed that the diagnostic Area Under Curve of the probe for cervicitis and low-grade squamous intraepithelial lesion (LSIL) was 0.8724 (95% CI=0.7762-0.9685, p<0.0001), for cervicitis and CA was 1.0000 (p<0.0001), for the LSIL and HSIL was 0.5484 (95% CI = 0.3826-0.7142, p=0.5755), and for the LSIL and CA was 0.7724 (95% CI = 0.6016-0.9432, p = 0.0138). The small molecular probe has certain application value in differentiating the type of cervical lesions and has better efficacy in distinguishing cervical inflammatory and precancerous lesions from carcinogenesis, but less efficacy in determining the type of precancerous lesions.

Maternal serum ischemia-modified albumin as an oxidative stress biomarker in preterm pre-labor rupture of membranes.

To evaluate the maternal serum ischemia-modified albumin (IMA) concentration as an oxidative stress biomarker in pregnancies complicated by preterm pre-labor rupture of membranes (PPROM) without maternal clinical infection and compare these results with healthy pregnancies. The present cohort study included 40 pregnancies complicated by PPROM and 49 similar gestational age healthy pregnancies in the third trimester of gestation. Maternal venous blood specimens were obtained at the day of first diagnosis. Maternal serum IMA level was assayed with an Albumin Cobalt Binding test. The subjects were followed up until delivery and perinatal outcomes were recorded. The maternal serum IMA concentrations were significantly higher in the study group (0.56 ± 0.05 absorbance units) as compared to controls (0.54 ± 0.03 absorbance units) (p = 0.020). The maternal serum IMA concentrations were not significantly correlated with the initial maternal white blood cell count (r: 0.118, p = 0.269) and C-reactive protein levels (r: 0.066, p = 0.541). The maternal serum IMA concentrations were negatively correlated with gestational age at delivery (r: -0.248, p = 0.019), birthweight (r: -0.247, p = 0.020) and Apgar scores (r: -0.200, p = 0.049; r: -0.245, p = 0.020). The threshold value of maternal serum IMA concentration above 0.55 absorbance units indicated the pregnancy complicated by PPROM by 57.5% sensitivity and 57.1% specificity (Area under curve 0.613, confidence interval 0.50-0.73). The current study supported for the first time that there is an association between increased maternal serum IMA levels and the development of PPROM in the third trimester of gestation without maternal clinical infection. Elevated maternal serum IMA levels may alert the obstetrician about poor ongoing perinatal outcomes in the early phase of PPROM before increased maternal C-reactive protein and white blood cell count.

Effects of Mixed Metal Exposure on MRI Metrics in Basal Ganglia.

Welding fumes contain various metals. Past studies, however, mainly focused on Manganese (Mn)-related neurotoxicity. This study investigated welding-related mixed metal exposure effects on MRI metrics in the basal ganglia (BG) and their dose-response relationship. Subjects with (N = 23) and without (N = 24) a welding exposure history were examined. Metal exposure was estimated with exposure history questionnaire and whole blood metal levels. T1 (weighted-intensity and relaxation time; estimates of brain Mn accumulation), diffusion tensor imaging [Axial (AD), mean (MD), radial diffusivity (RD), and fractional anisotropy (FA); estimates of microstructural differences] metrics in BG [caudate nucleus, putamen, and globus pallidus (GP)] and voxel-based morphometry (for volume) were examined and related with metal exposure measures. Compared to controls, welders showed higher GP R1 (1/T1; p = 0.034) but no differences in blood metal and T1-weighted (T1W) values in any ROIs (p's > 0.120). They also had higher AD and MD values in the GP (p's < 0.033) but lower FA values in the putamen (p = 0.039) with no morphologic differences. In welders, higher blood Mn and Vanadium (V) levels predicted higher BG R1 and T1W values (p's < 0.015). There also were significant overall metal mixture effects on GP T1W and R1 values. Moreover, GP AD and MD values showed non-linear associations with BG T1W values: They increased with increasing T1W values only above certain threshold of T1 values. The current findings suggest that Mn and V individually but also metal mixtures jointly predict GP T1 signals that may in turn contribute to altered DTI metrics in the BG after certain exposure threshold levels.

Associations of "Weekend Warrior" Physical Activity With Incident Disease and Cardiometabolic Health.

Achievement of guideline-recommended levels of physical activity (≥150 minutes of moderate-to-vigorous physical activity per week) is associated with lower risk of adverse cardiovascular events and represents an important public health priority. Although physical activity commonly follows a "weekend warrior" pattern, in which most moderate-to-vigorous physical activity is concentrated in 1 or 2 days rather than spread more evenly across the week (regular), the effects of physical activity pattern across a range of incident diseases, including cardiometabolic conditions, are unknown. We tested associations between physical activity pattern and incidence of 678 conditions in 89 573 participants (62±8 years of age; 56% women) of the UK Biobank prospective cohort study who wore an accelerometer for 1 week between June 2013 and December 2015. Models were adjusted for multiple baseline clinical factors, and P value thresholds were corrected for multiplicity. When compared to inactive (<150 minutes moderate-to-vigorous physical activity/week), both weekend warrior (267 total associations; 264 [99%] with lower disease risk; hazard ratio [HR] range, 0.35-0.89) and regular activity (209 associations; 205 [98%] with lower disease risk; HR range, 0.41-0.88) were broadly associated with lower risk of incident disease. The strongest associations were observed for cardiometabolic conditions such as incident hypertension (weekend warrior: HR, 0.77 [95% CI, 0.73-0.80]; P=1.2×10-27; regular: HR, 0.72 [95% CI, 0.68-0.77]; P=4.5×10-28), diabetes (weekend warrior: HR, 0.57 [95% CI, 0.51-0.62]; P=3.9×10-32; regular: HR, 0.54 [95% CI, 0.48-0.60]; P=8.7×10-26), obesity (weekend warrior: HR, 0.55 [95% CI, 0.50-0.60]; P=2.4×10-43, regular: HR, 0.44 [95% CI, 0.40-0.50]; P=9.6×10-47), and sleep apnea (weekend warrior: HR, 0.57 [95% CI, 0.48-0.69]; P=1.6×10-9; regular: HR, 0.49 [95% CI, 0.39-0.62]; P=7.4×10-10). When weekend warrior and regular activity were compared directly, there were no conditions for which effects differed significantly. Observations were similar when activity was thresholded at the sample median (≥230.4 minutes of moderate-to-vigorous physical activity/week). Achievement of measured physical activity volumes consistent with guideline recommendations is associated with lower risk for >200 diseases, with prominent effects on cardiometabolic conditions. Associations appear similar whether physical activity follows a weekend warrior pattern or is spread more evenly throughout the week.

Alcohol, smoking, and brain structure: common or substance specific associations.

Alcohol use and smoking are common substance-use behaviors with well-established negative health effects, including decreased brain health. We examined whether alcohol use and smoking were associated with the same neuroimaging-derived brain measures. We further explored whether the effects of alcohol use and smoking on the brain were additive or interactive. We leveraged a cohort of 36,309 participants with neuroimaging data from the UK Biobank. We used linear regression to determine the association between 354 neuroimaging-derived brain measures and alcohol use defined as drinks per week, pack years of smoking, and drinks per week x pack years smoking interaction. To assess whether the brain associations with alcohol are broadly similar or different from the associations with smoking, we calculated the correlation between z-scores of association for drinks per week and pack years smoking. Results indicated overall moderate positive correlation in the associations across measures representing brain structure, magnetic susceptibility, and white matter tract microstructure, indicating greater similarity than difference in the brain measures associated with alcohol use and smoking. The only evidence of an interaction between drinks per week and pack years smoking was seen in measures representing magnetic susceptibility in subcortical structures. The effects of alcohol use and smoking on brain health appeared to be additive rather than multiplicative for all other brain measures studied. 97% (224/230) of associations with alcohol and 100% (167/167) of the associations with smoking that surpassed a p value threshold are in a direction that can be interpreted to reflect reduced brain health. Our results underscore the similarity of the adverse associations between use of these substances and neuroimaging derived brain measures.

Psychiatric evidence in immigration and asylum cases – a tale of two experts

SUMMARY In an article in this issue of BJPsych Advances a courageous psychiatrist describes judicial criticism of his expert testimony in a case before the UK's Upper Tribunal (Immigration and Asylum Chamber). This commentary reflects on the value of criticism and feedback on expert witness work, contrasting the psychiatrist's positive response to the judge's words with the reaction of an expert witness in clinical negligence case, who rejected criticism of his evidence.

Exploring thermal flow dynamics in pressurized water reactors using hybrid graphene nanoplatelet coolants

AbstractThis study investigates the impact of hybrid nanoparticles on the temperature of nuclear reactor coolant, with a focus on graphene nanoplatelet (GNP)‐based hybrid nanoparticles. Sixteen different hybrid nanofluids were analyzed, and their performance was compared with a standard water‐based coolant. The criticality values were obtained through MCNP modeling, revealing that higher nanoparticle ratios led to increased criticality, with the highest value of 1.3239 observed in GNP‐Fe3O4 + Al2O3 nanofluids (0.05 wt%) and the lowest value of 1.2935 in GNP–Fe3O4 + SiO2 nanofluids (0.001 wt%). Temperature variations showed that increasing nanoparticle concentrations resulted in slightly higher temperatures, with a maximum of 611.97 K for 0.05 vol.% GNP nanoparticles. Additionally, the departure from nucleate boiling ratio values were consistently above the safety threshold of 2.08, with the lowest value of 3.657 for GNP–Fe3O4 + SiO2 nanofluids (0.05 vol.%). These findings suggest that hybrid nanofluids, particularly those with higher nanoparticle ratios, can enhance the thermal performance and safety margins of nuclear reactor coolants, offering a promising avenue for future research and application.

Practical non-stationary extreme value analysis of peaks over threshold using the generalised Pareto distribution: Estimating uncertainties in return values

Choice of tuning parameters influences the performance of non-stationary extreme value modelling for peaks over threshold using the generalised Pareto (GP) distribution. We examine the effect of tuning parameter choice on maximum roughness-penalised likelihood estimation of GP models, the shape and scale parameters of which are assumed to vary smoothly on a one-dimensional “directional” covariate domain, under a B-spline representation. We examine the effect of (a) extreme value model parameterisation, (b) relative roughness penalty of GP parameters as a function of covariate, (c) cross-validation strategy for roughness parameter tuning, and (d) estimator for return value, on the estimation of return values corresponding to return periods 1000× the period of a sample of size 1000. Bootstrap resampling is used for thorough uncertainty quantification. We also compare results with those from stationary inference.Results from a large simulation study of 16 cases broadly representative of North Sea conditions for significant wave height with direction, indicate that (i) multiple two-group cross-validation yields lower return value estimates that ten-group cross-validation (leading to negative bias on average, for the case studies considered), (ii) the quantile of the bootstrap predictive estimator yields larger values than the mean over bootstraps of the quantile estimate (leading to reduced omni-directional bias for the case studies considered). Further, (iii) the use of stationary models for non-stationary tails is only reasonable when a high extreme value threshold is set for the stationary analysis. However, (iv) the relative performance of different modelling strategies is sensitive to the specific characteristics of the case study.

Integration of interpretable machine learning and environmental magnetism elucidates reduction mechanism of bioavailable potentially toxic elements in lakes after monsoon

Little information is available on the influence of substantial precipitation and particulate matter entering during the monsoon process on the release of potentially toxic elements (PTEs) into lake sediments. Sediments from a typical subtropical lake across three periods, pre-monsoon, monsoon, and post-monsoon, were collected to determine the chemical forms of 12 PTEs (As, Cd, Co, Cr, Cu, Fe, Hg, Pb, Mn, Ni, Sb, and Zn), magnetic properties, and physicochemical indicators. Feature importance, Shapley additive explanations, and partial dependence plots were used to explore the factors influencing bioavailable PTEs. The proportion of bioavailable forms of PTEs decreased from 3.85 % (Cd) to 87.84 % (Hg) after the monsoon. Gradient extreme boosting demonstrated robust fitting accuracy for the prediction of the bioavailable forms of the 12 PTEs (R2 > 0.84). Shapley additive explanations identified that the bioavailable forms were influenced by the total PTE concentrations, wind, shortwave radiation, and particle inputs (25.1 %–88.5 % for total importance), either individually or in combination. The partial dependence plots highlighted the influence thresholds of background values and anthropogenic factors on the bioavailable forms of PTEs. Changes in environmental properties could indicate the process of external sediment influx into lakes. The optimized model combined with magnetic parameters showed strong performance in other cases (coefficient of determination>0.58), confirming the ubiquitous decrease in bioavailable forms of PTEs in sediments across subtropical lakes after monsoons.

The diagnostic utility of CT attenuation values in detecting calcification within costal cartilage

BackgroundTo establish and verify diagnostic criteria for the identification of costal cartilage calcification based on CT attenuation value. Methods360 chest CT slices of 120 patients were reviewed and annotated retrospectively and ROC curve was used to evaluate the diagnostic ability of CT attenuation value. Another 20 slices containing calcification were randomly selected and annotated by 4 doctors for further validation. H&E and COLX staining was performed on the residual costal cartilage. ResultsIn total 355129 voxels were detected and 187.5 was confirmed as the optimal CT attenuation value threshold, with a sensitivity of 98.6% and a specificity of 99.7%, for costal cartilage calcification diagnosis. Threshold-based identification of calcification demonstrated a similarity of nearly 80% with specialists' assessments, and exhibited advantages in the identification of subtle calcifications in the further validation. We also observed that CT attenuation values among males demonstrated a centralized distribution, whereas those among females exhibited a bimodal distribution. Threshold-based identified calcification showed a positivity of COLX. ConclusionsCT attenuation value could validly and reliably diagnose calcification within costal cartilage. Further investigations involving larger cohorts of patients are required to elucidate the risk factors and underlying mechanisms of costal cartilage calcification.

Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.

Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes. This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites. During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes. Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions. Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).