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In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31(3):606-660. 2017) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly referred to as a ‘bake off’, identified that only nine algorithms performed significantly better than the Dynamic Time Warping (DTW) and Rotation Forest benchmarks that were used. The study categorised each algorithm by the type of feature they extract from time series data, forming a taxonomy of five main algorithm types. This categorisation of algorithms alongside the provision of code and accessible results for reproducibility has helped fuel an increase in popularity of the TSC field. Over six years have passed since this bake off, the UCR archive has expanded to 112 datasets and there have been a large number of new algorithms proposed. We revisit the bake off, seeing how each of the proposed categories have advanced since the original publication, and evaluate the performance of newer algorithms against the previous best-of-category using an expanded UCR archive. We extend the taxonomy to include three new categories to reflect recent developments. Alongside the originally proposed distance, interval, shapelet, dictionary and hybrid based algorithms, we compare newer convolution and feature based algorithms as well as deep learning approaches. We introduce 30 classification datasets either recently donated to the archive or reformatted to the TSC format, and use these to further evaluate the best performing algorithm from each category. Overall, we find that two recently proposed algorithms, MultiROCKET+Hydra (Dempster et al. 2022) and HIVE-COTEv2 (Middlehurst et al. Mach Learn 110:3211-3243. 2021), perform significantly better than other approaches on both the current and new TSC problems.

Lung adenocarcinoma progression is closely linked to ferroptosis suppression. Emerging studies have found that the expression of its related gene SLC7A11 may be regulated by LncRNA. However, the mechanism of LncRNA in affecting the development of SLC7A11-mediated lung adenocarcinoma remains unclear. Here, we identified a Uc.339/miR-339/SLC7A11 axis that involves LncRNA T-UCR Uc.339-mediated repression of miR-339 and affects the expression of SLC7A11 to participate in tumor metastasis and development. In this study, we identified Uc.339 as upregulated in patients with lung adenocarcinoma. RAP-qPCR proved that LncRNA Uc.339 competitively binds to pri-miR-339 and inhibits the production of mature miR-339. The interaction between miR-339 and SCL7A11 was confirmed by luciferase reporter assay. The Uc.339/miR-339/SLC7A11 axis regulated the proliferation, migration and invasion of A549 and H1299cells by affecting ferroptosis. Finally, in mouse xenograft models, knocking down Uc.339 in LLC cells was able to inhibits tumor growth by blocking the axis of Uc.339/miR-339/SLC7A11 i , but miR-339 inhibitors could reverse this inhibition. Taken together, our results uncovered a Uc.339/miR-339/SLC7A11 axis that leads to defects in the ferroptosis in lung cancer, and constitutes a potential mechanism that drives the metastasis of lung adenocarcinoma.

Lung cancer is the most prevalent and deadliest cancer worldwide. A significant part of lung cancer studies is dedicated to the expression alterations of non-coding RNAs. The non-coding RNAs are transcripts that cannot be translated into proteins. While the study of microRNAs and siRNAs in lung cancer received a lot of attention over the last decade, highly efficient therapeutic option or the diagnostic methods based on non-coding RNAs are still lacking. Because of this, it is of utmost importance to direct future research on lung cancer towards analyzing other RNA types for which the currently available data indicates that are essential at modulating lung tumorigenesis. Through our review of studies on this subject, we identify the following non-coding RNAs as tumor suppressors: ts-46, ts-47, ts-101, ts-53, ts-3676, ts-4521 (tRNA fragments), SNORD116-26, HBII-420, SNORD15A, SNORA42 (snoRNAs), piRNA-like-163, piR-35127, the piR-46545 (piRNAs), CHIAP2, LOC100420907, RPL13AP17 (pseudogenes), and uc.454 (T-UCR). We also found non-coding RNAs with tumor-promoting function: tRF-Leu-CAG, tRNA-Leu, tRNA-Val (tRNA fragments), circ-RAD23B, circRNA 100146, circPVT1, circFGFR3, circ_0004015, circPUM1, circFLI1, circABCB10, circHIPK3 (circRNAs), SNORA42, SNORA3, SNORD46, SNORA21, SNORD28, SNORA47, SNORD66, SNORA68, SNORA78 (snoRNAs), piR-65, piR-34871, piR-52200, piR651 (piRNAs), hY4 5’ fragments (YRNAs), FAM83A-AS1, WRAP53, NKX2-1-AS1 (NATs), DUXAP8, SFTA1P (pseudogene transcripts), uc.338, uc.339 (T-UCRs), and hTERC.

Background Severe protein catabolism is a major aspect of critical illness and leads to pronounced muscle wasting and, consequently, extended intensive care unit (ICU) stay and increased mortality. The urea-to-creatinine ratio (UCR) has emerged as a promising biomarker for assessing protein catabolism in critical illness, which is currently lacking. This review aims to elucidate the role of UCR in the context of critical illness. Methods This scoping review adhered to the PRISMA Extension for Scoping Reviews guidelines. A comprehensive literature search was conducted on the 3rd of September 2024, across Embase, PubMed, ScienceDirect, and Cochrane Library to identify studies related to (1) critically ill adult patients and (2) reporting at least a single UCR value. A meta-analysis was conducted for ≥ 5 studies with identical outcome parameters. Results Out of 1,450 studies retrieved, 47 were included in this review, focusing on UCR's relation to protein catabolism and persistent critical illness (10 studies), mortality (16 studies), dietary protein interventions (2 studies), and other outcomes (19 studies), such as delirium, and neurological and cardiac adverse events. UCR is inversely correlated to muscle cross-sectional area over time and associated to length of ICU stay, emphasising its potential role in identifying patients with ongoing protein catabolism. A UCR (BUN-to-creatinine in mg/dL) of ≥ 20 (equivalent to a urea-to-creatinine in mmol/L of approximately 80) upon ICU admission, in comparison with a value < 20, was associated with a relative risk of 1.60 (95% CI 1.27–2.00) and an adjusted hazard ratio of 1.29 (95% CI 0.89–1.86) for in-hospital mortality. Discussion UCR elevations during critical illness potentially indicate muscle protein catabolism and the progression to persistent critical illness, and high levels at ICU admission could be associated with mortality. UCR increments during ICU stay may also indicate excessive exogenous dietary protein intake, overwhelming the body's ability to use it for whole-body or muscle protein synthesis. Dehydration, gastrointestinal bleeding, kidney and liver dysfunction, and renal replacement therapy may also influence UCR and are considered potential pitfalls when assessing catabolic phases of critical illness by UCR. Patient group-specific cut-off values are warranted to ensure its validity and application in clinical practice.

The assessment method of iodine nutrition for pregnant women lacks strong evidence-based medicine. The prevalence of iodine deficiency in pregnant women may be overestimated using urinary iodine concentration (UIC). The reference intervals of UIC-to-urinary creatinine concentration ratio (UIC/UCr) were established using a self-sequential longitudinal study of pregnant women with singleton gestation who were recruited using the criteria of the National Academy of Clinical Biochemistry in Dandong City, which is a long-term iodine-replete area. Nine thousand one hundred sixty-four pregnant women in the first trimester from Dalian City, Dandong City, and Shenyang City were included to verify our proposed reference intervals. UIC and concentrations of urinary creatinine, serum iodine, TSH, FT4, TPOAb, and TgAb were measured. The reference intervals of UIC/UCr were 38.63–489.46 μg/g for the first trimester, 58.48–644.03 μg/g for the second trimester, and 56.27–644.93 μg/g for the third trimester. The prevalence of iodine deficiency was 49.50% using UIC as the indicator (< 150 μg/L), while the prevalence was 3.28% using UIC/UCr (< 38.63 μg/g). The prevalence of iodine excess was 3.21% using UIC as the indicator (> 500 μg/L) while the prevalence was 1.45% using UIC/UCr (> 489.46 μg/g). The highest prevalence of overt hypothyroidism and positive thyroid antibodies was in the group with UIC/UCr < 38.63 μg/g. In contrast to the place of residence and age, BMI was an influencing factor for UIC/UCr. The reference intervals of UIC/UCr were established. UIC/UCr may eliminate the effect of urine volume and reflect the actual prevalence of iodine deficiency in pregnant women.

PurposeAmong homicides in the United States, intimate partners kill almost 50% of female and 10% of male victims. Intimate partner violence (IPV) also contributes to an estimated 6% of suicides. These trends suggest that opportunities for IPV interventions prior to the fatalities may have been missed. Thus, researchers must investigate the context and circumstances of IPV-related fatalities to inform effective prevention strategy development. There are two primary national fatality databases that can be used to examine such factors: the National Violent Death Reporting System (NVDRS, homicide and suicides); and the Uniform Crime Reporting-Supplementary Homicide Reports (UCR-SHR, homicides). These datasets include data on many IPV-related violent deaths but are limited by variations in data quality.MethodThis critical review summarizes opportunities and challenges when examining IPV-related fatalities using these national datasets. To document how the current literature is conceptualizing IPV, a rapid review on IPV-related homicide and suicide articles was performed (2019–2022). Missingness analyses were conducted to describe limitations in key dataset variables.ResultsThese datasets enable tracking IPV-related fatalities nationally over time. However, issues with the operationalization of variables that record IPV circumstances, particularly in the UCR-SHR, and high levels of missingness represent significant barriers to research. Novel methodologies can optimize the use of these datasets.ConclusionNational-level datasets enable researchers to examine IPV-related fatalities, evaluate policy differences between states, and monitor trends and disparities. This research can inform key recommendations for interventions to prevent IPV-related fatalities.

Background: Pregnant women with late-onset fetal growth restriction (LFGR) are at high risk of perinatal morbidity and mortality. However, it is difficult to identify patients with a higher risk of adverse perinatal outcomes at the time of diagnosing FGR. The aim of this study is whether amniotic-umbilical-to-cerebral ratio (AUCR) is a better predictor than cerebroplacental ratio (CPR) and umblicocerebral ratio (UCR) in detecting short and long-term adverse perinatal outcomes (APO) in late-onset fetal growth restriction. Methods: Retrospective cohort study, Doppler examinations were performed between 35–37 weeks on pregnant women who were followed up in the obstetrics and gynecology outpatient clinic of Nisa Hospital between April 1st, 2012, and April 1st, 2022, and were considered to have delayed growth according to the Delphi consensus criteria. Sensitivity and specificity of measurements of UCR, CPR, and AUCR for predicting a negative intrapartum or postpartum outcome (fetal distress, Apgar score<7 at 5 minutes, umbilical arterial pH< 7.1, admission of the newborn to the neonatal intensive care unit, intrauterine death) were evaluated. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were compared for UCR, CPR, and AUCR. Results: In this study, 185 pregnant women were evaluated. It was determined that 56 women had negative intrapartum or postpartum outcomes. UCR values were statistically significantly higher in the group with APO (p < 0.001), and the CPR (p < 0.001) and AUCR (p = 0.001) values were significantly lower in this group. The AUC values for CPR, UCR, and AUCR were 0.70 [95% confidence interval (CI): 0.62–0.79], 0.70 (95% CI: 0.62–0.79), and 0.66 (95% CI: 0.58–0.75), respectively. In the multivariate Logistic regression analysis of UCR, CPR, and AUCR values, there was no statistically significant correlation between CPR, UCR, and AUCR Doppler parameters in fetuses with LFGR in terms of detecting APO (p >0.05). Conclusions: A low AUCR and CPR, and a high UCR were significantly associated with APO in fetuses with LFGR. There was no difference in the diagnostic performance between AUCR, CPR, and UCR in predicting adverse outcomes.

When the NIBRS data were introduced by the FBI, they were welcomed as representing a potential revolution in U.S. crime statistics. More than two decades later, however, participation in NIBRS remains limited. In the coming months and years, however, the FBI will transition from the UCR Summary Reporting System (SRS) to NIBRS. Once completed successfully, the ability for researchers and practitioners to use the data will be unprecedented. But the NIBRS data are incredibly useful for both agencies and researchers regardless of these changes. Within this context, the current article explores the past contributions and future promise of the NIBRS data.

Persistent critical illness (PCI) is a syndrome in which the acute presenting problem has been stabilized, but the patient's clinical state does not allow ICU discharge. The burden associated with PCI is substantial. The most obvious marker of PCI is prolonged ICU length of stay (LOS), usually greater than 10 days. Urea to Creatinine ratio (UCr) has been suggested as an early marker of PCI development. A single-center retrospective study. Data of patients admitted to a general mixed medical-surgical ICU during Jan 1st 2018 till Dec 31st 2022 was extracted, including demographic data, baseline characteristics, daily urea and creatinine results, renal replacement therapy (RRT) provided, and outcome measures – length of stay, and mortality (ICU, and 90 days). Patients were defined as PCI patients if their LOS was >10 days. We used Fisher exact test or Chi-square to compare PCI and non-PCI patients. The association between UCr with PCI development was assessed by repeated measures linear model. Multivariate Cox regression was used for 1 year mortality assessment. 2098 patients were included in the analysis. Patients who suffered from PCI were older, with higher admission prognostic scores. Their 90-day mortality was significantly higher than non-PCI patients (34.58% vs 12.18%, p < 0.0001). A significant difference in UCr was found only on the first admission day among all patients. This was not found when examining separately surgical, trauma, or transplantation patients. We did not find a difference in UCr in different KDIGO (Kidney Disease Improving Global Outcomes) stages. Elevated UCr and PCI were found to be significantly associated with 1 year mortality. In this single center retrospective cohort study, UCr was not found to be associated with PCI development. •Urea to creatinine ratio was not found associated with persistent critical illness•Urea to creatinine ratio was found associated with 1 year mortality

Sarcopenia and osteoporosis are two closely related degenerative diseases, and the urea-to-creatinine ratio (UCR) and creatinine-to-cystatin C ratio (CCR) have been reported as recognized muscle markers. Therefore, this study aims to investigate the relationship between muscle indicators (UCR and CCR) and bone mineral density (BMD), and osteoporosis prevalence. This cross-sectional study included 1,146 participants, of whom 363 were diagnosed with osteoporosis. Through regression and smooth curve fitting analysis, after adjusting for covariates, it was revealed that UCR was positively correlated with the risk of osteoporosis (OR = 1.020, 95% CI 1.011–1.029), and negatively correlated with BMD of lumbar spine (β = − 0.002, 95% CI − 0.002 to − 0.001), total hip (β = − 0.001, 95% CI − 0.002 to − 0.001), and femoral neck (β = − 0.001, 95% CI − 0.002 to − 0.001) (all P  < 0.001). In contrast, CCR was negatively correlated with the prevalence of osteoporosis (OR = 0.839, 95% CI 0.762–0.923) and positively correlated with BMD at the lumbar spine (β = 0.016, 95% CI 0.010–0.022), total hip (β = 0.014, 95% CI 0.009–0.019), and femoral neck (β = 0.012, 95% CI 0.008–0.017) (all P  < 0.001). In subgroup analyses, muscle markers were significantly associated with osteoporosis in women, normal-weight, and overweight patients. After categorizing UCR and CCR according to quartiles, their associations with osteoporosis risk and BMD remained significant. These results helped to understand the correlation between muscle markers (UCR and CCR) and BMD, providing new ideas for the evaluation and screening of osteoporosis.