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Mean platelet volume (MPV) is an indicator of platelet activation and has been proposed as a diagnostic marker for several kinds of cancers. We investigated the value of MPV as a diagnostic marker for prostate cancer (PCa) and examined whether MPV in combination with prostate-specific antigen (PSA) could increase the sensitivity or specificity of PSA for PCa diagnosis. For this study, 107 pathologically confirmed PCa and 177 non-PCa patients who underwent prostate biopsy were retrospectively studied. Clinical data and pre-biopsy hematological parameters were collected. The above parameters were compared between PCa and non-PCa patients. The correlation between MPV and clinical characteristics was analyzed. Receiver operating characteristic (ROC) analysis was used to explore the diagnostic value of MPV for PCa. Among all parameters analyzed, the difference was only found in MPV, platelet distribution width (PDW), and PSA between PCa and non-PCa patients. MPV was significantly decreased and PDW increased in PCa than that of non-PCa among men. ROC analysis identified MPV ≤ 9.05 fl as a cut-off value for potential PCa with area under the ROC curve (AUC) = 0.783, 95% CI = 0.733–0.833, sensitivity = 0.746, and specificity = 0.708. AUC and the sensitivity of MPV were comparable with total PSA (TPSA) or free PSA (FPSA). However, the specificity of MPV was larger than that of TPSA (0.461) or FPSA (0.561). Furthermore, MPV combined with TPSA or FPSA further enhanced the specificity of TPSA (0.844) or FPSA (0.927), but PDW did not. These findings suggested that MPV could have a predictive value for the diagnosis of PCa. MPV in combination with TPSA or FPSA could enhance the specificity of PSA and may reduce the rate of unnecessary biopsy for patients with high levels of PSA.

BackgroundThe main objective of the current study was to gain insight into the heterogeneity and profiles of depressive symptoms in Chinese individuals aged 45 and over and to determine the optimal cut-off point for the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10) to provide a reference for future practical application.MethodsThe participants were 16,997 Chinese community-dwelling adults aged 45 years or older who completed survey interviews for the 2018 China Health and Retirement Longitudinal Study. The current study utilised latent profile analysis (LPA) to identify distinct profiles based on participants’ responses to CES-D-10 items, and receiver operating characteristic (ROC) curve analyses were applied to determine the optimal cut-off point for the CES-D-10 scale.ResultsA three-profile solution was suggested as the optimum and included a “minimal depression” group (63.1%), “mild depression” group (23.4%) and “moderate-severe depression” group (13.5%); 36.9% (95% CI: 36.2 ∼ 37.6%) were considered at risk for probable depression. The “minimal depression” group was viewed as “non-cases,” and the remaining were viewed as “cases” that served as the reference standard for the ROC analysis, which obtained an AUC value of 97.8% (95% CI: 97.7–98.0%) and identified an optimal cut-off point of 10 (sensitivity:91.93%, specificity: 92.76%, and accuracy: 92.45).ConclusionThe identification of these distinct profiles underscores the heterogeneity in depressive symptoms among Chinese middle-aged and older adults. The CES-D-10 scale was demonstrated to have acceptable psychometric properties, with a cut-off point of 10 recommended for future research and practical application.

Objective: To compare sensitivity, specificity, receiver operating characteristic (ROC), and area under the curve (AUC) values using the modified Frailty Index 11 (mFI-11), EuroSCORE II, and combined mFI-11 and EuroSCORE II to predict in-hospital mortality and composite morbidities., Design: Retrospective cohort study SETTING: Songklanagarind Hospital, a tertiary care center in southern Thailand., Participants: Elderly patients age ≥60 years who underwent elective open-heart surgical procedures on a pump between January 2017 and December 2022 were included., Interventions: ROC curves were constructed to evaluate the discriminatory power of EuroSCORE II and mFI-11 for predicting in-hospital mortality and postoperative complications., Measurements and Main Results: The actual in-hospital mortality was 2.5% for all patients. The discriminative accuracy of mFI-11, EuroSCORE II, and combined mFI-11 with EuroSCORE II for predicting in-hospital mortality was good, with respective AUC values of 0.733 (95% confidence interval [CI], 0.6157-0.8499), 0.793 (95% CI, 0.6826-0.9026), and 0.78 (95% CI, 0.6686-0.893). The AUC of mFI-11 for predicting postoperative cardiac, respiratory, neurologic, and renal complications was 0.558 (95% CI, 0.5101-0.6063), 0.606 (95% CI, 0.5542-0.6581), 0.543 (95% CI, 0.4533-0.6337), and 0.652 (95% CI, 0.5859-0.7179), respectively, and that of EuroSCORE II was 0.553 (95% CI, 0.5038-0.6013), 0.631 (95% CI, 0.578-0.6836), 0.619 (95% CI, 0.5306-0.7076), and 0.702 (95% CI, 0.6378-0.7657), respectively., Conclusions: The mFI-11 and EuroSCORE II demonstrated good discrimination in ROC analysis, with EuroSCORE II showing superior predictive accuracy for in-hospital mortality in elderly elective cardiac surgery patients. However, neither score independently predicted mortality in multiple logistic regression, nor did combining them enhance predictive power significantly. Furthermore, both scores were less effective in predicting postoperative complications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

The Glasgow Coma Scale (GCS) is the most commonly used consciousness rating scale worldwide. Although it is a sensitive and accurate way of assessing a patient's level of consciousness, it is time-consuming and requires training. We designed the Simple Coma Scale (SCS) as a simplified version of the GCS. In this study, we examined whether the SCS could predict favorable neurogenic outcomes at discharge, survival, and GCS scores in patients with traumatic brain injury (TBI). We analyzed the data of 1,230 patients registered in the Japan Neurotrauma Data Bank (Project 2015) between April 2015 and March 2017. In the SCS, eye, verbal, and motor scores are given based on a 3-point scoring system, with similar wording ("Normal," "Something Wrong," and "None") used for all scores. The SCS is based on a 7-point scale. The Glasgow Outcome Scale was used to assess the outcomes. For the receiver operating characteristic (ROC) curves with the objective variable of good prognosis at discharge in the SCS and GCS, the area under the curve (AUC) for the SCS was 0.740 (95% confidence interval [CI]: 0.711-0.769), and that of the GCS was 0.757 (95% CI: 0.729-0.786). For ROC curves with survival as the objective variable, the AUC of the SCS was 0.751 (95% CI: 0.724-0.778), and that of the GCS was 0.764 (95% CI: 0.737-0.791). The SCS, similar to the GCS, may predict good prognosis and survival at discharge. Further analyses will continue to examine the usefulness and practicality of the SCS., (© The Author(s) 2024. Published by Mary Ann Liebert, Inc.)

Background: Cardiopulmonary exercise testing (CPET) is a unique diagnostic tool that assesses the functional capacity of the heart, lungs, and peripheral oxidative system in an integrated manner. However, the clinical utility of CPET for evaluating interstitial lung disease (ILD) remains uncertain. The objective of this study was to determine the predictive value of CPET for mortality in subjects with ILD., Methods: We prospectively enrolled subjects with ILD who underwent CPET at a tertiary medical center in Taiwan and followed up their survival status for 12 months. Mortality prediction was based on comparing CPET parameters between subjects who survived and those who died. We further analyzed CPET parameters that showed significant differences using receiver operating characteristic curves to identify their optimal cutoff values., Results: A total of 106 newly diagnosed subjects with ILD underwent CPET, and the 1-y mortality rate was 7.5%. Six CPET variables were found to be significant predictors of mortality: peak oxygen consumption, oxygen pulse, end-tidal partial pressure of carbon dioxide, heart rate recovery 1 min after CPET, minute ventilation to carbon dioxide output slope, and functional aerobic impairment. We calculated a summed score by adding the number of CPET variables that exceeded their cutoff values. Subjects with a summed score of 6 had a 1-y survival rate of only 25%, whereas subjects with scores of 0-5 had a survival rate of 98%., Conclusions: In conclusion, the summed score represents a useful tool for screening patients with ILD who can undergo a CPET to determine their prognosis., Competing Interests: The authors have disclosed no conflicts of interest., (Copyright © 2024 by Daedalus Enterprises.)

ObjectiveTo explore the value of quantitative parameters derived from diffusion spectrum imaging (DSI) in preoperatively predicting human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer.MethodsIn this prospective study, 114 and 56 female patients with invasive ductal carcinoma were consecutively included in a derivation cohort and an independent validation cohort, respectively. Each patient was categorized into HER2-positive or HER2-negative groups based on the pathologic result. All patients underwent DSI and conventional MRI including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). The tumor size, type of the time-signal intensity curve (TIC) from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, and quantitative parameters derived from DSI, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) of primary tumors, were measured and compared between the HER2-positive and HER2-negative groups in the derivation cohort. Univariable and multivariable logistic regression analyses were used to determine the potential independent predictors of HER2 status. The discriminative ability of quantitative parameters was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort.ResultsIn the derivation cohort, the tumor size, TIC type, and ADC values did not differ between the HER2-positive and HER2-negative groups (p = 0.126–0.961). DSI quantitative parameters including axial kurtosis of DKI (DKI_AK), non-Gaussianity (MAP_NG), axial non-Gaussianity (MAP_NGAx), radial non-Gaussianity (MAP_NGRad), return-to-origin probability (MAP_RTOP), return-to-axis probability of MAP (MAP_RTAP), and intracellular volume fraction of NODDI (NODDI_ICVF) were lower in the HER2-positive group than in the HER2-negative group (p ≤ 0.001–0.035). DSI quantitative parameters including radial diffusivity (DTI_RD), mean diffusivity of DTI (DTI_MD), mean squared diffusion (MAP_MSD), and q-space inverse variance of MAP (MAP_QIV) were higher in the HER2-positive group than in the HER2-negative group (p = 0.016–0.049). The ROC analysis showed that the area under the curve (AUC) of ADC was 0.632 and 0.568, respectively, in the derivation and validation cohorts. The AUC values of DSI quantitative parameters ranged from 0.628 to 0.700 and from 0.673 to 0.721, respectively, in the derivation and validation cohorts. Logistic regression analysis showed that only NODDI_ICVF was an independent predictor of HER2 status (p = 0.001), with an AUC of 0.700 and 0.721, respectively, in the derivation and validation cohorts.ConclusionsDSI could be helpful for preoperative prediction of HER2, but DSI alone may not be sufficient in predicting HER2 status preoperatively in patients with breast cancer.

Receiver operating characteristic (ROC) curves are useful statistical tools for medical diagnostic testing. It has been proved its capability to assess diagnostic marker’s ability to distinguish between healthy and diseased subjects and to compare different diagnostic markers. In this paper we introduce non parametric ROC summary statistics to assess a ROC curve across the entire range of F P F s ∈ (0, 1) as well as over a restricted range of F P F s and compare them with some existing ones through a simulation study and through some real data examples. We also show their capability to compare two diagnostic markers.

Background: Invasive intracranial pressure (ICP) can result in complications, pain, or even aggravate intracranial hypotension (IH) or headache in patients with IH. Objective: To investigate whether ultrasonographic measurements of optic nerve sheath diameter (ONSD) could serve as a noninvasive IH marker. Methods: Ultrasonographic ONSD was measured prior to lumbar puncture (LP) and ONSD measurements compared to LP's opening pressure. We analyzed correlations between ONSD and ICP and determined the optimal ONSD cut-off point for IH. According to their LP on admission, patients were divided into three groups: IH group, normal ICP group, and elevated ICP group. Correlations between ONSD and ICP were analyzed using Pearson's correlation. A receiver operating characteristic (ROC) curve was generated to determine the optimal cut-off point for IH. Results: In total, 136 subjects (75 men, 55.1% men) were included, and 1088 ONSDs were measured. The ONSD of the IH group (2.96 ± 0.15 mm) was significantly lower than that of the normal (3.59 ± 0.33 mm) and elevated ICP groups (4.90 ± 0.42 mm, p < 0.001). There was a significant difference in ONSD within all groups (p < 0.001), and the differences between the two groups were also statistically significant. ONSD and ICP values were strongly correlated, with an r = 0.952 (95% confidence interval [CI]: 0.924–0.969; p < 0.001). After adjusting for age, diastolic blood pressure, systolic blood pressure, body mass index, waistline, and head circumference, ONSD was positively associated with ICP. ROC curve analysis revealed an area under the curve of 0.990 (95% CI: 0.975–1.000). The ONSD cut-off point for identifying decreased opening pressure on LP was 3.15 mm, with 98.3% sensitivity and 91.7% specificity. Conclusions: Ultrasonographic ONSD may be a noninvasive, valuable, and easy-to-perform bedside technology for evaluating IH. [ABSTRACT FROM AUTHOR]

Objective: To explore the value of quantitative parameters derived from diffusion spectrum imaging (DSI) in preoperatively predicting human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer. Methods: In this prospective study, 114 and 56 female patients with invasive ductal carcinoma were consecutively included in a derivation cohort and an independent validation cohort, respectively. Each patient was categorized into HER2-positive or HER2-negative groups based on the pathologic result. All patients underwent DSI and conventional MRI including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). The tumor size, type of the time-signal intensity curve (TIC) from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, and quantitative parameters derived from DSI, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) of primary tumors, were measured and compared between the HER2-positive and HER2-negative groups in the derivation cohort. Univariable and multivariable logistic regression analyses were used to determine the potential independent predictors of HER2 status. The discriminative ability of quantitative parameters was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort. Results: In the derivation cohort, the tumor size, TIC type, and ADC values did not differ between the HER2-positive and HER2-negative groups (p = 0.126–0.961). DSI quantitative parameters including axial kurtosis of DKI (DKI_AK), non-Gaussianity (MAP_NG), axial non-Gaussianity (MAP_NGAx), radial non-Gaussianity (MAP_NGRad), return-to-origin probability (MAP_RTOP), return-to-axis probability of MAP (MAP_RTAP), and intracellular volume fraction of NODDI (NODDI_ICVF) were lower in the HER2-positive group than in the HER2-negative group (p ≤ 0.001–0.035). DSI quantitative parameters including radial diffusivity (DTI_RD), mean diffusivity of DTI (DTI_MD), mean squared diffusion (MAP_MSD), and q-space inverse variance of MAP (MAP_QIV) were higher in the HER2-positive group than in the HER2-negative group (p = 0.016–0.049). The ROC analysis showed that the area under the curve (AUC) of ADC was 0.632 and 0.568, respectively, in the derivation and validation cohorts. The AUC values of DSI quantitative parameters ranged from 0.628 to 0.700 and from 0.673 to 0.721, respectively, in the derivation and validation cohorts. Logistic regression analysis showed that only NODDI_ICVF was an independent predictor of HER2 status (p = 0.001), with an AUC of 0.700 and 0.721, respectively, in the derivation and validation cohorts. Conclusions: DSI could be helpful for preoperative prediction of HER2, but DSI alone may not be sufficient in predicting HER2 status preoperatively in patients with breast cancer. [ABSTRACT FROM AUTHOR]

Summary: Study of emerging sleep–wake patterns in neonates is important for promptly identifying and treating abnormal sleep behaviours to ensure healthy infant development and neurobehavioral outcomes. Current methods to assess sleep are costly, labour intensive, and particularly difficult to implement in fragile, hospitalised infants requiring intensive medical care. The aim of the present study was to assess the validity of actigraphy as a tool for detecting sleep in preterm infants, using polysomnography (PSG) as the "gold standard". A total of 10 neonates (mean [SD] 35.8 [1.2] weeks post‐menstrual age; five female) hospitalised since birth for prematurity each participated in one 8–10 hr session during which PSG and actigraphy were recorded simultaneously. Inter‐feed minute‐by‐minute PSG Sleep–Wake scores were compared to concurrent actigraph epochs categorised as either "Sleep" or "Wake" using three separate movement‐per‐minute thresholds (≤20, ≤40, ≤80). Tool validity was assessed using five metrics. A key finding was that for each of the movement thresholds there was high agreement rate, sensitivity, and predictive value of sleep (85.2%–97.2%), whereas specificity and predictive value of wake remained low (12%–46%). Receiver operating characteristic curve analysis also revealed low discriminatory power of actigraphy for estimating sleep (area under the curve = 0.636; Youden's Index J = 0.2173). Lack of sufficient minutes of autonomous wake periods among infants was identified as a key limitation in actigraphy. Findings from the present study suggest actigraphy cannot be validated for Sleep/Wake discrimination in preterm infants and that proper validation requires sufficient data from periods of both Sleep and Wake. [ABSTRACT FROM AUTHOR]

Study Design: An expert-panel consensus-based content validation and case-based clinical validation study.Objective: To develop a novel scoring system for diagnosing instability in tuberculosis (TB) spine using an expert-panel consensus followed by clinical validation for validating the content.Summary Of Background Data: Currently, diagnosis of instability is primarily experience-based which may lead to considerable variability and misdiagnosis in the hands of a relatively in-experienced spine surgeon. Considering the potential complications this entity entails, a universally accepted scoring criteria is very important for accurate and uniform diagnosis of instability in TB spine.Methods: The development of TB spine instability score (TSIS) followed a two-step process, one designing the instrument and the other obtaining judgemental evidence. For judgemental evidence a panel of experts was appointed to make appropriate modifications and content validation for finalizing the scoring instrument. This score was applied on 30 patients of TB spine and receiver operating characteristic (ROC) curves were drawn for sensitivity and specificity analysis.Results: The comprehensive scoring criteria to diagnose instability in TB spine was approved after three rounds of expert panel discussions with an index of content validation more than 0.75 after final round of panel discussion. On case-based validation after plotting ROC curves, sensitivity and specificity for diagnosing stable and potentially unstable lesions at a cut-off score of 6 was 92.9% and 86.8% respectively whereas for diagnosing potentially unstable and unstable lesions at a cut-off score of 10 was 94.3% and 81.9%, respectively.Conclusion: TSIS is a comprehensive scoring system integrating demographic, anatomical, clinical, and radiological factors aimed at diagnosing instability in TB spine. The classification determines indications for surgical stabilization in patients with TB spine, with no or little neurological deficit.Level of Evidence: 4. [ABSTRACT FROM AUTHOR]

A real time scenario of dynamic primary user (PU) is considered in additive Laplacian noise. Two transitions or status changes of PU in the fixed sensing time are considered. The last status change point (LSCP) is estimated with maximum likelihood estimation by using dynamic programming. We consider Cumulative Sum (CuSum) based weighted samples for detection. We consider three detection schemes such as sample mean detector, energy detection and improved absolute value cumulation detection. We derive closed form expressions of detection probability (P D) and false alarm probability (P F) for all the three schemes. We present our results with receiver operating characteristic (ROC) for the considered schemes. We also present simulation results, which are closely matching with their analytical counterparts. We compare the ROC of the considered system with the ROC of conventional techniques. In the conventional techniques, all the samples in the sensing time are used for detection without LSCP estimation and weight. It is found that the considered system outperforms the conventional schemes. [ABSTRACT FROM AUTHOR]

Background: Invasive intracranial pressure (ICP) can result in complications, pain, or even aggravate intracranial hypotension (IH) or headache in patients with IH. Objective: To investigate whether ultrasonographic measurements of optic nerve sheath diameter (ONSD) could serve as a noninvasive IH marker. Methods: Ultrasonographic ONSD was measured prior to lumbar puncture (LP) and ONSD measurements compared to LP’s opening pressure. We analyzed correlations between ONSD and ICP and determined the optimal ONSD cut-off point for IH. According to their LP on admission, patients were divided into three groups: IH group, normal ICP group, and elevated ICP group. Correlations between ONSD and ICP were analyzed using Pearson’s correlation. A receiver operating characteristic (ROC) curve was generated to determine the optimal cut-off point for IH. Results: In total, 136 subjects (75 men, 55.1% men) were included, and 1088 ONSDs were measured. The ONSD of the IH group (2.96±0.15 mm) was significantly lower than that of the normal (3.59±0.33 mm) and elevated ICP groups (4.90±0.42 mm, p<0.001). There was a significant difference in ONSD within all groups (p<0.001), and the differences between the two groups were also statistically significant. ONSD and ICP values were strongly correlated, with an r=0.952 (95% confidence interval [CI]: 0.924–0.969; p<0.001). After adjusting for age, diastolic blood pressure, systolic blood pressure, body mass index, waistline, and head circumference, ONSD was positively associated with ICP. ROC curve analysis revealed an area under the curve of 0.990 (95% CI: 0.975–1.000). The ONSD cut-off point for identifying decreased opening pressure on LP was 3.15 mm, with 98.3% sensitivity and 91.7% specificity. Conclusions: Ultrasonographic ONSD may be a noninvasive, valuable, and easy-to-perform bedside technology for evaluating IH. [ABSTRACT FROM AUTHOR]

This contribution proposes a spatially weighted factor analysis (SWFA) to recognize effectively the underlying mineralization-related feature(s) in geochemical signals. The 3D spatial properties of the sampled surficial earth materials provide the opportunity to orient the results toward the potential sources through defining the proper localization functions. Perceiving the hydrothermal alterations as mineralization-indicating vectors in geochemical systems, a weighting function integrates the distance to prospective alteration zones and the geometry of productive geo-objects into a monolith formulation to achieve the source-oriented results. A covariance matrix tuned by the system localization function reformulates the standard factor analysis (FA) model to manifest source-oriented mineralization factor(s). The established mathematical setting was adapted to the compositional nature of multi-elemental signals and implemented via a combination of programming in MATLAB platform and R packages. An experiment on a porphyry Cu deposit was subjected to the outlined procedure for performance appraisal and comparison with the FA. The results indicated that the use of a weighting function configures the permutation of eigenvalues in such a way as to reflect spatial zoning from proximal to distal signals while providing clearly interpretable eigenvectors for ore-forming elements. By amplifying the signal of interest and reducing the signal of uncalled-for geo-processes, SWFA modulates the frequency distribution and spatial continuity of the feature of interest in such a way that the continuous-value mineralization landscape is allowed to be more consistent with the subsurface metallogenic reality in the survey area. A receiver operating characteristic analysis was adopted to evaluate quantitatively the factorized signal in predicting the mineralized ground to narrow down on the target areas. The results revealed a significant spatial coincidence between the source-oriented metallogenic pattern and mineralization evidence, implying superiority over the model derived by standard FA. The suggested scheme holds potential to information as a more efficient basis for follow-up exploration. [ABSTRACT FROM AUTHOR]

Mycobacterium tuberculosis complex (MTBC) infection is an important public health concern in Taiwan. In addition to pulmonary tuberculosis (PTB), MTBC can also cause genitourinary tuberculosis (GUTB). This study aimed to examine the role of laboratory data and the values that can be calculated from them for the early detection of GUTB. Patients admitted from 2011 to 2020 were retrospectively recruited to analyze their associated clinical data. Statistical significance was analyzed using the chi-square test and univariate analysis for different variables. A receiver operating characteristic (ROC) curve analysis was used to evaluate the performances of the examined laboratory data and their calculated items, including the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), neutrophil-to-monocyte-plus-lymphocyte ratio (NMLR), and platelet-to-lymphocyte ratio (PLR), in diagnosing PTB or GUTB. A p-value of p = 0.024, 0.008, and 0.033, respectively). The discriminative power of monocytes and the MLR was within the acceptable level (AUC = 0.782 and 0.778; p = 0.008 and 0.010, respectively). Meanwhile, the neutrophil and lymphocyte counts, NLR, NMLR, and PLR had good discriminative power (AUC = 0.916, 0.896, 0.898, 0.920, and 0.800; p < 0.001,

Background: To date, radiographic sign clusters of multidrug-resistant pulmonary tuberculosis (MDR-TB) patients have not been reported. We conducted a study to investigate the classification and prognosis of sign clusters in pulmonary Computed Tomography (CT) images from patients with MDR-TB for the first time by using principal component analysis (PCA).Methods: The clinical data and pulmonary CT findings of 108 patients with MDR-TB in the Liupanshui Third Hospital were collected (from January 2018 to December 2020). PCA was used to analyze the sign clusters on pulmonary CT, and receiver operating characteristic (ROC) analysis was used to analyze the predictive value of the treatment outcome of MDR-TB patients.Results: Six cluster signs of MDR-TB were determined by PCA: nodules, infiltration, consolidation, cavities, destroyed lung and non-active lesions. Nine months after treatment, the area under the ROC curve (AUC) of MDR-TB patients with a cavity sign cluster was 0.818 (95% CI: 0.733–0.886), and the positive predictive value (PPV) and negative predictive value (NPV) of the treatment outcome were 79.6% (95% CI: 65.7–89.8%) and 72.9% (95% CI: 59.7–83.6%), respectively.Conclusion: PCA plays an important role in the classification of sign groups on pulmonary CT images of MDR-TB patients, and the sign clusters obtained from PCA are of great significance in predicting the treatment outcome.

Early diagnosis and treatment of colorectal cancer (CRC) significantly improves the survival rate and quality of life. Here we screened for differences in glycoproteins associated with tumor-derived exosomes and validated their clinical value to serve as liquid biopsy biomarkers to diagnosed early CRC. Exosomes were extracted from paracancerous tissues, cancer tissues, and plasma. LC-MS/MS proteomic and glycoproteomics analyses were performed using an LTQ-Orbitrap Elite mass spectrometer. The differences in glycoproteins associated with exosomes of paracancerous tissues and cancer tissue were determined, and their levels in plasma exosomes were determined. Statistical analysis was performed to evaluate the diagnostic efficacy of exosome-associated glycoproteins for CRC. We found that the levels of fibrinogen beta chain (FGB) and beta-2-glycoprotein 1 (β2-GP1) in the exosome of CRC tissue were significantly higher compared with those of paracancerous tissues exosome. The areas under the receiver operating characteristic (ROC) curves of plasma exosomal FGB and β2-GP1 as biomarkers for CRC were 0.871 (95% CI = 0.786–0.914) and 0.834 (95% CI = 0.734–0.901), respectively, compared with those of the concentrations of carcinoembryonic antigen concentration [0.723 (95% CI = 0.679–0.853)] and carbohydrate antigen19-9 concentration [0.614 (95% CI = 0.543–0.715)]. Comprehensive proteomics analyses of plasma exosomal biomarkers in CRC identified biomarkers with significant diagnostic efficacy for early CRC, which can be measured using relatively non-invasive techniques.

Aim: To define the optimal cutoff point for determining methylation status of O6-methylguanine-DNA methyltransferase (MGMT) by pyrosequencing in glioblastoma. Patients & methods: A retrospective study of 109 glioblastoma patients was performed to determine the optimal cutoff point for MGMT methylation status. Results: Receiver operating characteristic (ROC) analysis revealed 21% as the optimal cutoff (sensitivity: 68%; specificity: 59%) for MGMT methylation corresponding with the highest likelihood ratio of 1.66 and accuracy of 0.65. Methylation status (hazard ratio: 0.453; 95% CI: 0.279–0.735; p = 0.001) was associated with better overall survival. The crude model indicated linearity between methylation percent and survival rate; an increase of 10% of methylation resulted in a reduction of risk of death by 20% (p = 0.004). Conclusion: ROC analysis determined 21% as the optimal cutoff point for MGMT methylation status by pyrosequencing.

Early diagnosis and treatment of colorectal cancer (CRC) significantly improves the survival rate and quality of life. Here we screened for differences in glycoproteins associated with tumor-derived exosomes and validated their clinical value to serve as liquid biopsy biomarkers to diagnosed early CRC. Exosomes were extracted from paracancerous tissues, cancer tissues, and plasma. LC-MS/MS proteomic and glycoproteomics analyses were performed using an LTQ-Orbitrap Elite mass spectrometer. The differences in glycoproteins associated with exosomes of paracancerous tissues and cancer tissue were determined, and their levels in plasma exosomes were determined. Statistical analysis was performed to evaluate the diagnostic efficacy of exosome-associated glycoproteins for CRC. We found that the levels of fibrinogen beta chain (FGB) and beta-2-glycoprotein 1 (β2-GP1) in the exosome of CRC tissue were significantly higher compared with those of paracancerous tissues exosome. The areas under the receiver operating characteristic (ROC) curves of plasma exosomal FGB and β2-GP1 as biomarkers for CRC were 0.871 (95% CI = 0.786–0.914) and 0.834 (95% CI = 0.734–0.901), respectively, compared with those of the concentrations of carcinoembryonic antigen concentration [0.723 (95% CI = 0.679–0.853)] and carbohydrate antigen19-9 concentration [0.614 (95% CI = 0.543–0.715)]. Comprehensive proteomics analyses of plasma exosomal biomarkers in CRC identified biomarkers with significant diagnostic efficacy for early CRC, which can be measured using relatively non-invasive techniques. [ABSTRACT FROM AUTHOR]

The research addresses the problem of analytical description of energy detection procedure being one of the most favourable mechanism of white-space detection for cognitive radio and Internet-of-Things as well as unknown signal detection in radars and Unmanned Ground/Aerial Vehicle applications. A novel closed-form analytic solutions for average quality metrics (average receiver operating characteristic and area under the AROC curve) are derived for a generic shadowed fading κ − μ channel model. Numerical simulations verify the computational effectiveness of the derived expressions compared to straightforward numerical integration and analytical treatment demonstrated their consistency with the several specific simplified cases widely used in engineering practice. The results enable one to estimate signal detection quality on the stage of wireless communications' system design. [ABSTRACT FROM AUTHOR]

Background: Conicity index, body-shape index, lipid accumulation product (LAP), waist circumference (WC), triglyceride, triglyceride-glucose (TyG) index, hepatic steatosis index (HSI), waist-to-height ratio (WHtR), TyG index-related parameters (TyG-WHtR, TyG-BMI, TyG-WC), body mass index (BMI), visceral adiposity index, triglyceride to high-density lipoprotein cholesterol ratio and body roundness index have been reported as reliable markers of non-alcoholic fatty liver disease (NAFLD). However, there is debate about which of the above obesity and lipid-related indices has the best predictive performance for NAFLD risk. Methods: This study included 6870 female and 7411 male subjects, and 15 obesity and lipid-related indices were measured and calculated. NAFLD was diagnosed by abdominal ultrasound. The area under the curve (AUC) of 15 obesity and lipid-related indices were calculated by receiver operating characteristic (ROC) analysis. Results: Among the 15 obesity and lipid-related indices, the TyG index-related parameters had the strongest association with NAFLD. ROC analysis showed that except for ABSI, the other 14 parameters had high predictive value in identifying NAFLD, especially in female and young subjects. Most notably, TyG index-related parameters performed better than other parameters in predicting NAFLD in most populations. In the female population, the AUC of TyG-WC for predicting NAFLD was 0.9045, TyG-BMI was 0.9084, and TyG-WHtR was 0.9071. In the male population, the AUC of TyG-WC was 0.8356, TyG-BMI was 0.8428, and TyG-WHtR was 0.8372. In addition, BMI showed good NAFLD prediction performance in most subgroups (AUC>0.8). Conclusions: Our data suggest that TyG index-related parameters, LAP, HSI, BMI, and WC appear to be good predictors of NAFLD. Of these parameters, TyG index-related parameters showed the best predictive potential. [ABSTRACT FROM AUTHOR]

Background: To date, radiographic sign clusters of multidrug-resistant pulmonary tuberculosis (MDR-TB) patients have not been reported. We conducted a study to investigate the classification and prognosis of sign clusters in pulmonary Computed Tomography (CT) images from patients with MDR-TB for the first time by using principal component analysis (PCA). Methods: The clinical data and pulmonary CT findings of 108 patients with MDR-TB in the Liupanshui Third Hospital were collected (from January 2018 to December 2020). PCA was used to analyze the sign clusters on pulmonary CT, and receiver operating characteristic (ROC) analysis was used to analyze the predictive value of the treatment outcome of MDR-TB patients. Results: Six cluster signs of MDR-TB were determined by PCA: nodules, infiltration, consolidation, cavities, destroyed lung and non-active lesions. Nine months after treatment, the area under the ROC curve (AUC) of MDR-TB patients with a cavity sign cluster was 0.818 (95% CI: 0.733–0.886), and the positive predictive value (PPV) and negative predictive value (NPV) of the treatment outcome were 79.6% (95% CI: 65.7–89.8%) and 72.9% (95% CI: 59.7–83.6%), respectively. Conclusion: PCA plays an important role in the classification of sign groups on pulmonary CT images of MDR-TB patients, and the sign clusters obtained from PCA are of great significance in predicting the treatment outcome. [ABSTRACT FROM AUTHOR]

Soluble HLA-G (sHLA-G) molecules are considered potent immunomodulators, and their dysregulated expression has been implicated in several pathological conditions, including coronavirus disease 19 (COVID-19). Therefore, a case-control study (103 COVID-19 patients and 105 controls) was performed to determine sHLA-G role in severity of COVID-19. Results revealed that median levels of sHLA-G were significantly increased in serum of patients compared to controls (19.3 vs. 12.7 ng/mL; p <0.001). When patients and controls were stratified by age group, gender, body mass index, chronic disease, or ABO and Rh blood groups, the sHLA-G level did not show a significant difference in each stratum. Logistic regression analysis demonstrated that the up-regulated expression of sHLA-G was associated with an elevated risk of developing COVID-19. Receiver operating characteristic curve analysis showed that sHLA-G was a very good predictor of COVID-19, and at a cut-off value of 15.4 ng/mL, the sensitivity and specificity of sHLA-G were 79.6 and 79.0%, respectively. Spearman rank correlation analysis revealed that sHLA-G was positively correlated with age, erythrocyte sedimentation rate, white blood cell count, and random blood glucose, while a negative correlation was recorded with vitamin D. In conclusion, up-regulated expression of sHLA-G was indicated in patients with severe COVID-19. [ABSTRACT FROM AUTHOR]

Objectives: To explore the predictive effect of abnormal glucose metabolism and fetal hemodynamic parameters on adverse pregnancy outcome. Methods: One hundred and nine pregnant women with abnormal glucose metabolism during pregnancy from June 2016 to October 2018 were selected and divided into poor prognosis group (34 cases) and good prognosis group (75 cases). The hemodynamic parameters of fetal cerebral artery (MCA), umbilical artery (UA) and uterine artery of pregnancy (UT-A), including peak systolic velocity (s / D), resistance index (RI) and plasticity index (PI), were measured by color Doppler ultrasound. The receiver operating characteristic (ROC) curve of adverse pregnancy outcomes was drawn and the best threshold index was determined. Results: MCA-PI poor prognosis group, MCA-RI, RI ratio (MCA/UA) are lower than the good prognosis group, Ut-A-PI is higher than the good prognosis group (P<0.05,). ROC curve analysis results show that when the MCA-PI 1.56, the sensitivity of the predicted adverse outcomes of pregnancy, the highest specificity<, was 91.18%, 80.00%, respectively. Logistic regression analysis of risk factors shows poor pregnancy outcomes include: pregnant women, older age, body mass index ≥24.0kg/m2 and a family history of diabetes. Protective factors include exercise during pregnancy, MCA-PI≥1.56, MCA-RI≥0.63 and RI The ratio (MCA/UA) ≥0.84. Conclusion: Color Doppler ultrasound measured MCA-PI<1.56 the most important indicators of poor pregnancy outcomes as abnormal glucose metabolism during pregnancy and predict the exact cutoff. Pregnant women, older age, body mass index ≥24.0kg/m2 and a family history of diabetes and abnormal glucose metabolism during pregnancy risk factors for adverse outcomes of pregnancy. [ABSTRACT FROM AUTHOR]

The individual and combined effects of PM 2.5 constituents on cardiometabolic risk factors are sparsely investigated. Besides, the key cardiometabolic risk factor that PM 2.5 constituents targeted and the biological mechanisms remain unclear. A multistage, stratified cluster sampling survey was conducted in two typically air-polluted Chinese cities. The PM 2.5 and its constituents including sulfate, nitrate, ammonium, organic matter, and black carbon were predicted using a machine learning model. Twenty biomarkers in three category were simultaneously adopted as cardiometabolic risk factors. We explored the individual and mixture association of long-term PM 2.5 constituents with these markers using generalized additive model and quantile-based g-computation, respectively. To minimize potential confounding effects, we accounted for covariates including demographic, lifestyle, meteorological, temporal trends, and disease-related information. We further used ROC curve and mediation analysis to identify the key subclinical indicators and explore whether inflammatory mediators mediate such association, respectively. PM 2.5 constituents was positively correlated with HOMA-B, TC, TG, LDL-C and LCI, and negatively correlated with PP and RC. Further, PM 2.5 constituent mixture was positive associated with DBP, MAP, HbA1c, HOMA-B, AC, CRI-1 and CRI-2, and negative associated with PP and HDL-C. The ROC analysis further reveals that multiple cardiometabolic risk factors can collectively discriminate exposure to PM 2.5 constituents (AUC>0.9), among which PP and CRI-2 as individual indicators exhibit better identifiable performance for nitrate and ammonium (AUC>0.75). We also found that multiple blood lipid indicators may be affected by PM 2.5 and its constituents, possibly mediated through complement C3 or hsCRP. Our study suggested associations of individual and combined PM 2.5 constituents exposure with cardiometabolic risk factors. PP and CRI-2 were the targeted markers of long-term exposure to nitrate and ammonium. Inflammation may serve as a mediating factor between PM 2.5 constituents and dyslipidemia, which enhance current understanding of potential pathways for PM 2.5 -induced preclinical cardiovascular responses. [Display omitted] • Long-term exposure to PM 2.5 constituents was positively correlated with HOMA-B, TC, TG, LDL-C and LCI, and negatively correlated with PP and RC. • PM 2.5 component mixture was positive associated with DBP, MAP, HbA1c, HOMA-B, AC, CRI-1 and CRI-2, and negative associated with PP and HDL-C. • The ROC analysis reveals that multiple cardiometabolic risk factors can collectively discriminate exposure to PM 2.5 constituents (AUC>0.9), among which PP and CRI-2 as individual indicators exhibit better predictive performance for nitrate and ammonium (AUC>0.75). • In medication analysis, we found that multiple blood lipid indicators such as TC, TG, and LDL-C may be affected by PM 2.5 and its constituents, possibly mediated through complement C3 or hsCRP. [ABSTRACT FROM AUTHOR]

Abstract Background Tuberculosis pleural effusion (TPE) and malignant pleural effusion (MPE) are very common clinical complications. Considering the totally different prognosis and clinical treatment of TPE and MPE, the accurate and non-invasive diagnosis are very critical for patients with pleural effusion to initiate efficient management and treatment. However, effective clinical biomarkers were rarely explored to distinguish benign from MPE. The purpose of this study is to identify potential miRNAs which can probably be used to differentiate malignant pleural effusion from TPE. Results A total of 23 significantly differentially expressed miRNAs were identified in MPE, with 18 up-expressed and 5 down-expressed. And the target genes of the miRNAs mainly involved in the biology process of nervous system, cancer, immune system and metabolic process etc. Three high confident target genes, AGO4, FGF9 and LEF1 can be regulated by miR-195-5p, miR-182-5p and miR-34a-5p respectively. And these genes participate in the canonical pathway of regulation of the Epithelial-Mesenchymal and the biological functions of apoptosis, growth of tumor and cell proliferation of tumor cell lines. Further, RT-PCR validation results based on 64 collected individuals showed that the expression levels of the three miRNAs were 2–5 times higher in MPE samples, which were consistent with the microarray results. In addition, ROC curve analysis demonstrated that the combination of the three miRNAs can achieve higher AUC of 0.93 (p-value

Abstract Background In classification and diagnostic testing, the receiver-operator characteristic (ROC) plot and the area under the ROC curve (AUC) describe how an adjustable threshold causes changes in two types of error: false positives and false negatives. Only part of the ROC curve and AUC are informative however when they are used with imbalanced data. Hence, alternatives to the AUC have been proposed, such as the partial AUC and the area under the precision-recall curve. However, these alternatives cannot be as fully interpreted as the AUC, in part because they ignore some information about actual negatives. Methods We derive and propose a new concordant partial AUC and a new partial c statistic for ROC data—as foundational measures and methods to help understand and explain parts of the ROC plot and AUC. Our partial measures are continuous and discrete versions of the same measure, are derived from the AUC and c statistic respectively, are validated as equal to each other, and validated as equal in summation to whole measures where expected. Our partial measures are tested for validity on a classic ROC example from Fawcett, a variation thereof, and two real-life benchmark data sets in breast cancer: the Wisconsin and Ljubljana data sets. Interpretation of an example is then provided. Results Results show the expected equalities between our new partial measures and the existing whole measures. The example interpretation illustrates the need for our newly derived partial measures. Conclusions The concordant partial area under the ROC curve was proposed and unlike previous partial measure alternatives, it maintains the characteristics of the AUC. The first partial c statistic for ROC plots was also proposed as an unbiased interpretation for part of an ROC curve. The expected equalities among and between our newly derived partial measures and their existing full measure counterparts are confirmed. These measures may be used with any data set but this paper focuses on imbalanced data with low prevalence. Future work Future work with our proposed measures may: demonstrate their value for imbalanced data with high prevalence, compare them to other measures not based on areas; and combine them with other ROC measures and techniques.

Background: The diagnosis of acute appendicitis (AA) sometimes is illusive and the accompanying clinical and laboratory manifestations cannot be used for definitive diagnosis. Objective: This study aimed to evaluate the diagnostic value of neutrophil/lymphocyte ratio (NLR) in detection of AA. Materials and Methods: This is a cross-sectional study that included a total of 80 adult patients with AA and 62 age- and gender-matched patients with abdominal pain due to causes other than AA. Three milliliter of peripheral blood were collected from each participant. The NLR was calculated by dividing the absolute neutrophil count by the absolute lymphocyte count. Receiver operating characteristic curve was used to assess the diagnostic value of NLR in detection of AA cases. Results: Mean NLR in AA patients was 7.18 ± 2.11 compared with 2.68 ± 1.08 in patients with abdominal pain due to causes other than AA with a highly significant difference. The area under the curve was 0.916 (95%confidence interval = 0.842–0.989), P < 0.001. The sensitivity and specificity of the test at NLR = 4.45 were 90% and 83%, respectively. Conclusions: NLR is an easy, inexpensive test that can be used for AA detection. This test is more sensitive and more specific than either total white blood cell or absolute neutrophil count.

Abstract Background Tumor necrosis factor-alpha-induced protein 8-like 2 (TIPE2) is a novel target and molecule in the negative regulation of immune homeostasis. The present study aimed to investigate the dynamic expression of TIPE2 mRNA during the progression of chronic hepatitis B virus (HBV) infection. Methods A total of 193 patients with chronic HBV infection were retrospectively recruited into this cross-sectional study, including 97 patients with chronic hepatitis B (CHB), 55 with liver cirrhosis and 41 with HBV-related hepatocellular carcinoma (HCC). TIPE2 mRNA was determined using real-time quantitative polymerase chain reaction. Results The expression of TIPE2 levels in patients with HCC was significantly decreased compared with expression in patients with liver cirrhosis, CHB and healthy controls (P

Abstract Background The ratio of triglycerides to HDL cholesterol (TG/HDL-C ratio) is known as a good predictor for cardiovascular disease. The purpose of this study was to compare discrimination for cardiovascular risk by different cut-off values of the TG/HDL-C ratio. Methods Receiver operating characteristic (ROC) analysis was performed for the relationship between TG/HDL-C ratio and accumulation of cardio-metabolic risk factors including visceral obesity, hypertension and diabetes. Logistic regression analysis was performed for the relationships of TG/HDL-C ratio with cardio-metabolic risk factors using the cut-off values obtained by ROC analysis and conventional cut-off values (men, 3.75; women, 3.00). Results In ROC analysis, the optimal cut-off values for TG/HDL-C ratio were 2.967 in men and 2.237 in women, which were much smaller than the conventional cut-of values. Odds ratios for multiple cardio-metabolic risk factors of subjects with vs. subjects without a high TG/HDL-C ratio in men and women were 5.75 (4.43–7.46) and 18.76 (10.32–34.13), respectively, by using the new cut-off values and they were 5.03 (3.96–6.39) and 16.11 (9.20–28.20), respectively, by using the conventional cut-off values. The odds ratios for visceral obesity, hypertension and diabetes were comparable when using these two different cut-off values. Conclusion Cut-off values should be ideally calculated by ROC analysis. However, the discrimination power of cut-off values for the TG/HDL-C ratio calculated by ROC analysis for cardio-metabolic risk was similar to those by using the conventional cut-off values. Further studies using cardiovascular events as outcomes in the analysis may be needed to determine more suitable cut-off values of the TG/HDL-C ratio.

Background: Accurate diagnosis and classification of ovarian hyperstimulation syndrome (OHSS) is important for its management. We employed a new high-sensitivity chemiluminescence immunoassay to detect the thrombin-antithrombin complex (TAT), plasmin alpha2-plasmin inhibitor complex (PIC), soluble thrombomodulin (sTM), and tissue plasminogen activator-inhibitor complex (TPAI-C), and evaluated their diagnostic and classification performance for OHSS.Methods: A total of 106 women were enrolled, including 51 patients with OHSS (25 mild or moderate OHSS, 26 severe OHSS), and 55 without OHSS (control group). TAT, PIC, sTM, and TPAI-C levels were measured using the Sysmex HISCL5000 automated analyzer.Results: Compared to the control group, TAT, PIC, and TPAI-C levels were significantly higher (P < 0.001, P < 0.001, P < 0.001, respectively), whereas the sTM level was significantly lower (P < 0.001) in the patients with OHSS. The receiver operating characteristic was used to evaluate the diagnostic efficiency. For the diagnosis of OHSS, the area under the curves (AUCs) for TAT, PIC, sTM, and TPAI-C were 0.991, 0.973, 0.809, and 0.722, respectively. In particular, the sensitivity, specificity, positive predictive value, and negative predictive value for TAT and PIC were all above 90%. For the differential diagnosis of mild–moderate and severe OHSS, the AUCs for TAT, PIC, and TPAI-C were 0.736, 0.735, and 0.818, respectively. The cutoff values of TAT, PIC, and TPAI-C for the differential diagnosis of mild–moderate and severe OHSS were 11.5 ng/mL, 2.4 μg/mL, and 5.8 ng/mL, respectively. Based on these cutoff values, eight cases of mild–moderate OHSS exceeded the cutoff values, two of which developed to severe OHSS in the following days. However, of the remaining 17 cases of mild–moderate OHSS patients with negative biomarkers, none subsequently developed severe OHSS.Conclusions: TAT, PIC, sTM, and TPAI-C can be used as sensitive biomarkers in the diagnosis of OHSS. Meanwhile, TAT, PIC, and TPAI-C also displayed remarkable potential in the classification of OHSS. In addition, the levels of TAT, PIC, and TPAI-C above the cutoff values in patients with mild–moderate OHSS might predict a high risk of developing severe OHSS.

Aims: Currently, there is no reliable method to effectively predict and diagnose early-onset preeclampsia (EOPE). microRNAs (miRs) are promising biomarkers for EOPE. This study investigated the role of miR-320a in EOPE.Methods: Expressions of miR-320a and insulin-like growth factor-1 receptor (IGF-1R) in serum of EOPE patients and normal pregnant women were detected. The clinical diagnostic efficacy of miR-320a and IGF-1R for EOPE was analyzed using receiver operating characteristic curve. The correlation between miR-320a expression and EOPE clinical indicators [mean arterial pressure (MAP), 24-h urinary protein excretion, serum creatinine (SCR), uric acid (UA), albumin (ALB) and platelet count] was analyzed. The correlation and binding relationship between miR-320a and IGF-1R was predicted and verified.Results: miR-320a was upregulated, and IGF-1R was downregulated in EOPE patients with their differential expressions more obvious in severe EOPE than mild EOPE. miR-320a and IGF-1R possessed potent clinical diagnostic efficacy for EOPE. miR-320a expression showed a positive correlation with MAP, 24-h urinary protein excretion, UA and SCR levels, and a negative correlation with ALB level and platelet count in EOPE patients. Moreover, miR-320a targeted IGF-1R.Conclusion: We demonstrated that miR-320a was aberrantly elevated in EOPE and showed powerful clinical diagnostic efficacy for EOPE, which may be achieved by directly targeting IGF-1R. This study provided great reference values for EOPE early diagnosis and novel targets for EOPE treatment. [ABSTRACT FROM AUTHOR]

Background: The Global Leadership Initiative on Malnutrition (GLIM) provided a new approach for the diagnosis of malnutrition based on a number of phenotypic and etiologic criteria. However, different diagnostic criteria could potentially lead to heterogeneity in the diagnosis. We identified different subsets of criteria to define malnutrition in a cohort of elder nursing‐home residents and investigated their clinical utility in terms of 1‐year survival. Methods: Our study included all residents (n = 485) from 3 nursing homes. We proposed 12 GLIM models based on different phenotypic and etiologic criteria. The main outcome was 1‐year all‐cause mortality. We also assessed the sensitivity and specificity for different phenotypic criteria using time‐dependent receiver operating characteristic curves, and cutoff values were calculated. Results: During 1 year of follow‐ups, 94 deaths occurred. The prevalence of malnutrition was 13.5% for models based on reduced food intake (RFI) and 10.45% for models based on inflammation associated with acute disease (IAD). Unadjusted Cox regression analyses showed that malnutrition was associated with a 2.31‐ to 4.64‐fold increase in mortality when diagnosed using IAD‐dependent models or with a 1.37‐ to 1.78‐fold increase in mortality using RFI‐dependent models. Cutoffs associated with mortality for the phenotypic criteria were lower than those recommended in the GLIM criteria. Conclusion: This study describes the association of several variations of the GLIM model with 1‐year mortality in nursing‐home residents. However, our data suggest a high heterogeneity to fulfill the GLIM criteria and the necessity of finding specific, tailored cutoff points for the studied populations. [ABSTRACT FROM AUTHOR]

Background: D‐dimer is generally considered positive above 0.5 mg/L irrespective of sex. However, women have been shown to be more likely to have a positive D‐dimer after controlling for other factors. Thus, differences may exist between males and females for using D‐dimer as a marker of venous thromboembolic (VTE) disease. We hypothesized that the accuracy of D‐dimer tests may be enhanced by using appropriate cutoff values that reflect sex‐related differences in D‐dimer levels. Methods: This research is a secondary analysis of a multicenter, international, prospective, observational study of adult (18+ years) patients suspected of VTE, with low‐to‐intermediate pretest probability based on Wells criteria ≤ 6 for pulmonary embolism (PE) and ≤ 2 for deep vein thrombosis (DVT). VTE diagnoses were based on computed tomography, ventilation perfusion scanning, or venous ultrasound. D‐dimer levels were tested for statistical difference across groups stratified by sex and diagnosis. Multivariable regression was used to investigate sex as a predictor of diagnosis. Sex‐specific optimal D‐dimer thresholds for PE and DVT were calculated from receiver operating characteristic analyses. A Youden threshold (D‐dimer level coinciding with the maximum of sensitivity plus specificity) and a cutoff corresponding to 95% sensitivity were calculated. Statistical difference for cutoffs was tested via 95% confidence intervals from 2,000 bootstrapped samples. Results: We included 3,586 subjects for analysis, of whom 61% were female. Race demographics were 63% White, 27% Black/African American, and 6% Hispanic. In the suspected PE cohort, 6% were diagnosed with PE, while in the suspected DVT cohort, 11% were diagnosed with DVT. D‐dimer levels were significantly higher in males than females for the PE‐positive group and the DVT‐negative group, but males had significantly lower D‐dimer levels than females in the PE‐negative group. Regression models showed male sex as a significant positive predictor of DVT diagnosis, controlling for D‐dimer levels. The Youden thresholds for PE patients were 0.97 (95% CI = 0.64 to 1.79) mg/L and 1.45 (95% CI = 1.36 to 1.95) mg/L for females and males, respectively; 95% sensitivity cutoffs for this group were 0.64 (95% CI = 0.20 to 0.89) and 0.55 (95% CI = 0.29 to 1.61). For DVT, the Youden thresholds were 0.98 (95% CI = 0.84 to 1.56) mg/L for females and 1.25 (95% CI = 0.65 to 3.33) mg/L for males with 95% sensitivity cutoffs of 0.33 (95% CI = 0.2 to 0.61) and 0.32 (95% CI = 0.18 to 0.7), respectively. Conclusion: Differences in D‐dimer levels between males and females are diagnosis specific; however, there was no significant difference in optimal cutoff values for excluding PE and DVT between the sexes. [ABSTRACT FROM AUTHOR]

The survival of forest tree species within certain areas of their distributions is in question due to the increasing occurrence of disturbances and degradation processes in forest ecosystems due to climate change. The aim of this paper is to predict changes in the spatial distribution of the ten most important tree species in Serbia (European beech, Pedunculate oak, Austrian oak, Hungarian oak, Sessile oak, Narrow-leafed ash, Silver fir, Norway spruce, Black and Scots pine) using climate indices (Forestry Aridity Index, FAI and Ellenberg Quotient, EQ) with up-to-date climate observations (E-OBS, covering the time period 1990-2019) and projections of future climate conditions (RCP 4.5 and RCP 8.5 scenarios, split into two time periods 2041-2070, 2071-2100). The computation of the area under the receiver operating characteristic (ROC) curves has shown that both FAI and EQ have a “fair” to “excellent” ability to predict the occurrence of five out of ten species (European beech, Silver fir, Norway spruce, Black and Scots pine), with EQ having a slightly better predicting ability. EQ-based projections from mid (2041-2070) to late 21st century (2071-2100) under RCP 4.5 predict that reduction rates will not exceed 25%. Similar reduction rates are given by FAI-based projections until 2070, while rates increase to approximately 35% towards the end of this century. For RCP 8.5, FAI-based projections are significantly worse than EQ-based projections. Irrespective of the used index, projections until 2070 suggest that 55- 75% of existing habitats will remain intact. Towards the end of the century, however, our analysis indicate that 75-85% (EQ) to 90-100% (FAI) of the five analyzed species habitats, will be found outside of their current climate space. [ABSTRACT FROM AUTHOR]

Background: The prevalence of parkinsonism in developing countries is largely unknown due to difficulty in ascertainment because access to neurologists is often limited.Objective: Develop and validate a parkinsonism screening tool using objective motor task-based tests that can be administered by non-clinicians.Methods: In a cross-sectional population-based sample from South Africa, we evaluated 315 adults, age >40, from an Mn-exposed (smelter) community, using the Unified Parkinson Disease Rating Scale motor subsection 3 (UPDRS3), Purdue grooved pegboard, and kinematic-UPDRS3-based motor tasks. In 275 participants (training dataset), we constructed a linear regression model to predict UPDRS3. We selected motor task summary measures independently associated with UPDRS3 (p < 0.05). We validated the model internally in the remaining 40 participants from the manganese-exposed community (test dataset) using the area under the receiver operating characteristic curve (AUC), and externally in another population-based sample of 90 participants from another South African community with only background levels of environmental Mn exposure.Results: The mean UPDRS3 score in participants from the Mn-exposed community was 9.1 in both the training and test datasets (standard deviation = 6.4 and 6.1, respectively). Together, 57 (18.1%) participants in this community had a UPDRS3 ≥ 15, including three with Parkinson's disease. In the non-exposed community, the mean UPDRS3 was 3.9 (standard deviation = 4.3). Three (3.3%) had a UPDRS3 ≥ 15. Grooved pegboard time and mean velocity for hand rotation and finger tapping tasks were strongly associated with UPDRS3. Using these motor task summary measures and age, the UPDRS3 predictive model performed very well. In the test dataset, AUCs were 0.81 (95% CI 0.68, 0.94) and 0.91 (95% CI 0.81, 1.00) for cut points for neurologist-assessed UPDRS3 ≥ 10 and UPDRS3 ≥ 15, respectively. In the external validation dataset, the AUC was 0.85 (95% CI 0.73, 0.97) for UPDRS3 ≥ 10. AUCs were 0.76–0.82 when excluding age.Conclusion: A predictive model based on a series of objective motor tasks performs very well in assessing severity of parkinsonism in both Mn-exposed and non-exposed population-based cohorts.

MicroRNAs (miRNAs), endogenous non‐coding RNA regulators, post‐transcriptionally inhibit the expression of their target genes. Several tools have been developed for predicting annotated known miRNAs, but there is no consensus about how to select the most suitable method for any given species. In this study, eight miRNA prediction tools (mirnovo, miRPlant, miRDeep‐P2, miRExpress, miRkwood, miRDeep2, miR‐PREFeR, and sRNAbench) were selected for evaluation. High‐throughput small RNA sequencing data from four plant species (including C3 and C4 species, and both monocots and dicots, i.e., Arabidopsis thaliana, Oryza sativa, Triticum aestivum, and Zea mays) were used for the analysis. The sensitivity, accuracy, area under the curve, consistency, duration, and RAM usage of the known miRNA predictions were evaluated for each tool. The miRNA annotations were obtained using miRBase and sRNAanno. Algorithms, such as random forest, BLAST, and receiver operating characteristic curves, were used to evaluate accuracy. Of the tools evaluated, sRNAbench was found to be the most accurate, miRDeep‐P2 was the most sensitive, miRDeep‐P2 was the fastest, and miRkwood had the highest memory usage. Due to its large genome size, only three tools were able to successfully predict known miRNAs in wheat (Triticum aestivum). Our results enable us to recommend the tool best suited to a variety of researcher needs, which we hope will reduce confusion and enhance future work.

ObjectivesDistinguishing flares from bacterial infections in systemic lupus erythematosus (SLE) patients remains a challenge. This study aimed to build a model, using multiple blood cells and plasma indicators, to improve the identification of bacterial infections in SLE.DesignBuilding PLS-DA/OPLS-DA models and a bioscore system to distinguish bacterial infections from lupus flares in SLE.SettingDepartment of Rheumatology of the Second Hospital of Shanxi Medical University.ParticipantsSLE patients with flares (n = 142) or bacterial infections (n = 106) were recruited in this retrospective study.OutcomeThe peripheral blood of these patients was collected by the experimenter to measure the levels of routine examination indicators, immune cells, and cytokines. PLS-DA/OPLS-DA models and a bioscore system were established.ResultsBoth PLS-DA (R2Y = 0.953, Q2 = 0.931) and OPLS-DA (R2Y = 0.953, Q2 = 0.942) models could clearly identify bacterial infections in SLE. The white blood cell (WBC), neutrophile granulocyte (NEUT), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), IL-10, interferon-γ (IFN-γ), and tumor necrosis factor α (TNF-α) levels were significantly higher in bacteria-infected patients, while regulatory T (Treg) cells obviously decreased. A multivariate analysis using the above 10 dichotomized indicators, based on the cut-off value of their respective ROC curve, was established to screen out the independent predictors and calculate their weights to build a bioscore system, which exhibited a strong diagnosis ability (AUC = 0.842, 95% CI 0.794–0.891). The bioscore system showed that 0 and 100% of SLE patients with scores of 0 and 8–10, respectively, were infected with bacteria. The higher the score, the greater the likelihood of bacterial infections in SLE.ConclusionsThe PLS-DA/OPLS-DA models, including the above biomarkers, showed a strong predictive ability for bacterial infections in SLE. Combining WBC, NEUT, CRP, PCT, IL-6, and IFN-γ in a bioscore system may result in faster prediction of bacterial infections in SLE and may guide toward a more appropriate, timely treatment for SLE.

Background: The prevalence of parkinsonism in developing countries is largely unknown due to difficulty in ascertainment because access to neurologists is often limited. Objective: Develop and validate a parkinsonism screening tool using objective motor task-based tests that can be administered by non-clinicians. Methods: In a cross-sectional population-based sample from South Africa, we evaluated 315 adults, age >40, from an Mn-exposed (smelter) community, using the Unified Parkinson Disease Rating Scale motor subsection 3 (UPDRS3), Purdue grooved pegboard, and kinematic-UPDRS3-based motor tasks. In 275 participants (training dataset), we constructed a linear regression model to predict UPDRS3. We selected motor task summary measures independently associated with UPDRS3 (p < 0.05). We validated the model internally in the remaining 40 participants from the manganese-exposed community (test dataset) using the area under the receiver operating characteristic curve (AUC), and externally in another population-based sample of 90 participants from another South African community with only background levels of environmental Mn exposure. Results: The mean UPDRS3 score in participants from the Mn-exposed community was 9.1 in both the training and test datasets (standard deviation = 6.4 and 6.1, respectively). Together, 57 (18.1%) participants in this community had a UPDRS3 ≥ 15, including three with Parkinson's disease. In the non-exposed community, the mean UPDRS3 was 3.9 (standard deviation = 4.3). Three (3.3%) had a UPDRS3 ≥ 15. Grooved pegboard time and mean velocity for hand rotation and finger tapping tasks were strongly associated with UPDRS3. Using these motor task summary measures and age, the UPDRS3 predictive model performed very well. In the test dataset, AUCs were 0.81 (95% CI 0.68, 0.94) and 0.91 (95% CI 0.81, 1.00) for cut points for neurologist-assessed UPDRS3 ≥ 10 and UPDRS3 ≥ 15, respectively. In the external validation dataset, the AUC was 0.85 (95% CI 0.73, 0.97) for UPDRS3 ≥ 10. AUCs were 0.76–0.82 when excluding age. Conclusion: A predictive model based on a series of objective motor tasks performs very well in assessing severity of parkinsonism in both Mn-exposed and non-exposed population-based cohorts. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs), endogenous non‐coding RNA regulators, post‐transcriptionally inhibit the expression of their target genes. Several tools have been developed for predicting annotated known miRNAs, but there is no consensus about how to select the most suitable method for any given species. In this study, eight miRNA prediction tools (mirnovo, miRPlant, miRDeep‐P2, miRExpress, miRkwood, miRDeep2, miR‐PREFeR, and sRNAbench) were selected for evaluation. High‐throughput small RNA sequencing data from four plant species (including C3 and C4 species, and both monocots and dicots, i.e., Arabidopsis thaliana, Oryza sativa, Triticum aestivum, and Zea mays) were used for the analysis. The sensitivity, accuracy, area under the curve, consistency, duration, and RAM usage of the known miRNA predictions were evaluated for each tool. The miRNA annotations were obtained using miRBase and sRNAanno. Algorithms, such as random forest, BLAST, and receiver operating characteristic curves, were used to evaluate accuracy. Of the tools evaluated, sRNAbench was found to be the most accurate, miRDeep‐P2 was the most sensitive, miRDeep‐P2 was the fastest, and miRkwood had the highest memory usage. Due to its large genome size, only three tools were able to successfully predict known miRNAs in wheat (Triticum aestivum). Our results enable us to recommend the tool best suited to a variety of researcher needs, which we hope will reduce confusion and enhance future work. [ABSTRACT FROM AUTHOR]

Objectives: Distinguishing flares from bacterial infections in systemic lupus erythematosus (SLE) patients remains a challenge. This study aimed to build a model, using multiple blood cells and plasma indicators, to improve the identification of bacterial infections in SLE. Design: Building PLS-DA/OPLS-DA models and a bioscore system to distinguish bacterial infections from lupus flares in SLE. Setting: Department of Rheumatology of the Second Hospital of Shanxi Medical University. Participants: SLE patients with flares (n = 142) or bacterial infections (n = 106) were recruited in this retrospective study. Outcome: The peripheral blood of these patients was collected by the experimenter to measure the levels of routine examination indicators, immune cells, and cytokines. PLS-DA/OPLS-DA models and a bioscore system were established. Results: Both PLS-DA (R2Y = 0.953, Q2 = 0.931) and OPLS-DA (R2Y = 0.953, Q2 = 0.942) models could clearly identify bacterial infections in SLE. The white blood cell (WBC), neutrophile granulocyte (NEUT), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), IL-10, interferon-γ (IFN-γ), and tumor necrosis factor α (TNF-α) levels were significantly higher in bacteria-infected patients, while regulatory T (Treg) cells obviously decreased. A multivariate analysis using the above 10 dichotomized indicators, based on the cut-off value of their respective ROC curve, was established to screen out the independent predictors and calculate their weights to build a bioscore system, which exhibited a strong diagnosis ability (AUC = 0.842, 95% CI 0.794–0.891). The bioscore system showed that 0 and 100% of SLE patients with scores of 0 and 8–10, respectively, were infected with bacteria. The higher the score, the greater the likelihood of bacterial infections in SLE. Conclusions: The PLS-DA/OPLS-DA models, including the above biomarkers, showed a strong predictive ability for bacterial infections in SLE. Combining WBC, NEUT, CRP, PCT, IL-6, and IFN-γ in a bioscore system may result in faster prediction of bacterial infections in SLE and may guide toward a more appropriate, timely treatment for SLE. [ABSTRACT FROM AUTHOR]

Background: Deep venous thrombosis (DVT) after spinal surgery has recently attracted increasing attention. Patients with spinal metastases who undergo decompression with fixation are at a high risk of developing DVT. D-dimer levels indicate the risk of DVT, and the purpose of our study was to investigate D-dimer levels as a predictor of DVT perioperatively., Methods: We prospectively evaluated 100 patients with spinal metastases. D-dimer tests were performed twice: once before surgery and one day postoperatively. DVT was diagnosed by duplex ultrasonographic assessment of both lower extremities. Pulmonary embolisms (PEs) were diagnosed using multidetector computed tomography and pulmonary angiography. Perioperative serum D-dimer levels were compared between the DVT (+) and DVT (-) groups. The cutoff value of the D-dimer level was calculated using receiver operating characteristic analysis., Results: Preoperative and postoperative DVT prevalences were 8.0% (8/100) and 6.6% (6/91), respectively, and none of the patients developed PE. Before surgery, there was no significant differences in D-dimer levels between the pre-DVT (+) and pre-DVT (-) groups. After surgery, the D-dimer level one-day postoperatively for the post-DVT (+) group (17.6 ± 11.8 mg/L) was significantly higher than that of the post-DVT (-) group (5.0 ± 4.7 mg/L). The cutoff value of the postoperative D-dimer level was 9.51(mg/L), and the sensitivity and specificity for the optimum threshold were 83.3% and 89.4%, respectively., Conclusions: Our findings suggest that preoperative D-dimer level may not be a predictor of DVT. Preoperative ultrasound examinations should be routinely performed in patients with spinal metastases. Postoperative D-dimer levels greater than 9.51(mg/L) are a predictive factor for the early diagnosis of DVT after spine surgery., Trial Registration: Our study was registered on Chinese Clinical Trial Registry (No.ChiCTR2000029737). Registered 11 February 2020 - Retrospectively registered, https://www.chictr.org.cn/index.aspx., (© 2024. The Author(s).)

Proliferative diabetic retinopathy (PDR) is a vision-threatening complication of diabetes mellitus (DM). Anterior chamber (AC) flare and intraocular cytokines are potent biomarkers reflecting the intraocular immune status in PDR. This study aimed to elucidate the complex interrelationship between AC flare and intraocular cytokines in PDR eyes. A retrospective observational study was conducted on 19 PDR eyes of 19 patients with type 2 DM, and on 19 eyes of 19 patients with idiopathic macular hole or epiretinal membrane as controls. AC flare was measured before pars plana vitrectomy (PPV). Aqueous humor (AH) and vitreous fluid (VF) samples were collected at the time of PPV, and the quantities of 27 cytokines in both intraocular fluids were analyzed. In the PDR and control groups, Spearman's rank correlation analysis revealed a positive correlation between AC flare and IL-8 level in both AH and VF. Additionally, IL-8 levels in AH correlated positively with IL-8 levels in VF. In the PDR group, receiver operating characteristic curve analysis identified IL-8 level in AH as a significant predictor for both diabetic macular edema (DME) and vitreous hemorrhage (VH) complications. The cut-off values of IL-8 were established at ≥26.6 pg/mL for DME and ≥7.96 pg/mL for VH. Given the positive correlation between AC flare and AH IL-8 level, the present findings suggest that AC flare value may potentially be a non-invasive biomarker for predicting DME.

Purpose: Acoustic lie detection, prized for its covert nature and capability for remote processing, has spurred growing interest in acoustic features that can reliably aid in lie detection. In this study, the aim was to construct an acoustic polygraph based on a variety of phonetic and acoustic features rather than on electrodermal, cardiovascular, and respiratory values., Methods: Sixty-two participants from the University of Science and Technology of China, aged 18-30 years old, were involved in the mock crime experiment and were randomly assigned to the innocent and guilty groups. We collected 31 deceptive and truthful audios to analyze the performance of voice onset time (VOT) in lie detection., Results: Our findings revealed that VOT performed well in lie detection. Both the average sensitivity and specificity of the area under the curve are 0.888, and its lower and upper confidence limit are up to 0.803 and 0.973 respectively at the 95% confidence level. Although the other acoustic features had a lower reference value, they also provided a general trend in the judgment of lie detection., Conclusions: Our results suggested that some acoustic features can be effectively used as aids to lie detection. Through a similar approach, we will explore more acoustic and phonetic features that contribute to detecting lies in the future., Competing Interests: Declaration of Competing Interest We declare that all coauthors of the paper have no real or potential conflicts of interest, including financial, personal, or other links with other organizations or enterprises that might unduly impact or influence the study and interpretation of the findings presented in our paper., (Copyright © 2024 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)