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It is generally believed that bioavailability (F) calculated based on systemic concentration area under the curve (AUC) measurements cannot exceed 1.0, yet some published studies report this inconsistency. We teach and believe, based on differential equation derivations, that rate of absorption has no influence on measured systemic clearance following an oral dose, i.e., determined as available dose divided by AUC. Previously, it was thought that any difference in calculating F from urine data versus that from systemic concentration AUC data was due to the inability to accurately measure urine data. A PubMed literature search for drugs exhibiting F > 1.0 and studies for which F was measured using both AUC and urinary excretion dose-corrected analyses yielded data for 35 drugs. We show and explain, using Kirchhoff's Laws, that these universally held concepts concerning bioavailability may not be valid in all situations. Bioavailability, determined using systemic concentration measurements, for many drugs may be overestimated since AUC reflects not only systemic elimination but also absorption rate characteristics, which is most easily seen for renal clearance measures. Clearance of drug from the absorption site must be significantly greater than clearance following an iv bolus dose for F(AUC) to correctly correspond with F(urine). The primary purpose of this paper is to demonstrate that studies resulting in F > 1.0 and/or greater systemic vs urine bioavailability predictions may be accurate. Importantly, these explications have no significant impact on current regulatory guidance for bioequivalence testing, nor on the use of exposure (AUC) measures in making drug dosing decisions.

Perfusion index (PI) and CRIB (Clinical risk index of babies) Score II both are minimally invasive tools for prediction of mortality and morbidity in sick neonates. This study aims to know the predictive values of both PI and CRIB Score II in assessment of severity of illness in preterm neonates of 28 to 32 weeks and their correlation.PI and CRIB II Score of 125 sick preterm neonates admitted in NICU were assessed within 12 hours of admission. Severity noted in form of outcome. Correlation between PI and CRIB Score II were observed.Receiver operating characteristic (ROC) curve were plotted for PI and CIRB Score II with outcome (discharged vs expired). Area under curve for perfusion index was 0.776 at 95% confidence interval. Optimum cutoff point based on ROC curve was 1.65 with sensitivity of 84%., specificity of 37.2% with positive predictive value of 70% and negative predictive value of 57%. Area under curve for CRIB Score II was 0.622 (p value < 0.028), optimum cut off point based on ROC curve for CRIB II score was 3.5 with sensitivity 79.10% of specificity of 40% positive predictive value is 43.03% and negative predictive value of 76.9%. Spearman’s correlation coefficient between PI and CRIB II score was –0.272 and p value is 0.002.This study concluded that perfusion index (p < 0.001) is better for predicting the severity compared to CRIB II score (p < 0.028). A weak negative correlation exists between PI and CRIB II score (r = –0.272). [ABSTRACT FROM AUTHOR]

Objective: To elucidate the impact and predictive value of the Triglyceride Glucose Index (TyG) and the ratio of Triglycerides to High-Density Lipoprotein Cholesterol (TG/HDL-C) in identifying the risk of diabetes progression in Chinese individuals with prediabetes. Methods: This longitudinal study enrolled 15,012 prediabetic adults from the Rich Healthcare Group between 2010 and 2016. Diabetes was defined as self-reported diabetes or a fasting glucose level ≥ 7.0 mmol/L. The Cox proportional hazards models was utilized to assess the relationship between the two indices and the risk of developing diabetes. The predictive efficacy of the two markers was gauged by the area under the curve (AUC). Results: Over a median follow-up period of 2.87 years, 1,730 (11.5%) prediabetic participants developed diabetes. The adjusted hazard ratios for the top quartile of the TyG index and the TG/HDL-C ratio were 2.03 (95% confidence interval [CI]: 1.71–2.40) and 2.59 (95% CI: 2.20–3.05), respectively, compared to the lowest quartile. A significant trend of increasing diabetes risk with higher quartiles of both indices was observed. The AUC for the adjusted prediction model for prediabetes-to-diabetes transition was 0.726 for the TyG index and 0.710 for the TG/HDL-C ratio. The difference in AUCs was statistically significant (P = 0.03). Conclusions: The baseline TyG index or TG/HDL-C ratio was significantly associated with an increased risk of diabetes in prediabetic individuals. The TyG index demonstrated superior predictive accuracy, underscoring its importance in preventing diabetes in prediabetic individuals. [ABSTRACT FROM AUTHOR]

Abstract Objective To elucidate the impact and predictive value of the Triglyceride Glucose Index (TyG) and the ratio of Triglycerides to High-Density Lipoprotein Cholesterol (TG/HDL-C) in identifying the risk of diabetes progression in Chinese individuals with prediabetes. Methods This longitudinal study enrolled 15,012 prediabetic adults from the Rich Healthcare Group between 2010 and 2016. Diabetes was defined as self-reported diabetes or a fasting glucose level ≥ 7.0 mmol/L. The Cox proportional hazards models was utilized to assess the relationship between the two indices and the risk of developing diabetes. The predictive efficacy of the two markers was gauged by the area under the curve (AUC). Results Over a median follow-up period of 2.87 years, 1,730 (11.5%) prediabetic participants developed diabetes. The adjusted hazard ratios for the top quartile of the TyG index and the TG/HDL-C ratio were 2.03 (95% confidence interval [CI]: 1.71–2.40) and 2.59 (95% CI: 2.20–3.05), respectively, compared to the lowest quartile. A significant trend of increasing diabetes risk with higher quartiles of both indices was observed. The AUC for the adjusted prediction model for prediabetes-to-diabetes transition was 0.726 for the TyG index and 0.710 for the TG/HDL-C ratio. The difference in AUCs was statistically significant (P = 0.03). Conclusions The baseline TyG index or TG/HDL-C ratio was significantly associated with an increased risk of diabetes in prediabetic individuals. The TyG index demonstrated superior predictive accuracy, underscoring its importance in preventing diabetes in prediabetic individuals.

Abstract Background Early detection of cognitive impairment is among the top research priorities aimed at reducing the global burden of dementia. Currently used screening tools have high sensitivity but lack specificity at their original cut-off, while decreasing the cut-off was repeatedly shown to improve specificity, but at the cost of lower sensitivity. In 2012, a new screening tool was introduced that aims to overcome these limitations – the Quick mild cognitive impairment screen (Qmci). The original English Qmci has been rigorously validated and demonstrated high diagnostic accuracy with both good sensitivity and specificity. We aimed to determine the optimal cut-off value for the German Qmci, and evaluate its diagnostic accuracy, reliability (internal consistency) and construct validity. Methods We retrospectively analyzed data from healthy older adults (HOA; n = 43) and individuals who have a clinical diagnosis of ‘mild neurocognitive disorder’ (mNCD; n = 37) with a biomarker supported characterization of the etiology of mNCD of three studies of the ‘Brain-IT’ project. Using Youden’s Index, we calculated the optimal cut-off score to distinguish between HOA and mNCD. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic accuracy based on the area under the curve (AUC). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Reliability (internal consistency) was analyzed by calculating Cronbach’s α. Construct validity was assessed by analyzing convergent validity between Qmci-G subdomain scores and reference assessments measuring the same neurocognitive domain. Results The optimal cut-off score for the Qmci-G was ≤ 67 (AUC = 0.96). This provided a sensitivity of 91.9% and a specificity of 90.7%. The PPV and NPV were 89.5% and 92.9%, respectively. Cronbach’s α of the Qmci-G was 0.71 (CI95% [0.65 to 0.78]). The Qmci-G demonstrated good construct validity for subtests measuring learning and memory. Subtests that measure executive functioning and/or visuo-spatial skills showed mixed findings and/or did not correlate as strongly as expected with reference assessments. Conclusion Our findings corroborate the existing evidence of the Qmci’s good diagnostic accuracy, reliability, and construct validity. Additionally, the Qmci shows potential in resolving the limitations of commonly used screening tools, such as the Montreal Cognitive Assessment. To verify these findings for the Qmci-G, testing in clinical environments and/or primary health care and direct comparisons with standard screening tools utilized in these settings are warranted.

Zubia Jamil,1 Samreen Khalid,1 Hafiz Muhammad Manan Haider Khan,2 Ikram Waheed,1,3 Amna Ehsan,1,3 Mohammed Alissa,4 Khalid Muhammad,5 Nayla Munawar,6 Yasir Waheed7,8 1Department of Medicine, Foundation University School of Health Sciences, Foundation University, Islamabad, 44000, Pakistan; 2Department of Rehabilitation Sciences, Shifa Tameer-E-Millat University, Islamabad, 44000, Pakistan; 3Department of Medicine, Fauji Foundation Hospital, Rawalpindi, 43600, Pakistan; 4Department of Medical Laboratory, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; 5Department of Biology, College of Science, UAE University, Al Ain, 15551, United Arab Emirates; 6Department of Chemistry, College of Science, UAE University, Al Ain, 15551, United Arab Emirates; 7Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Byblos, 1401, Lebanon; 8MEU Research Unit, Middle East University, Amman, 11831, JordanCorrespondence: Yasir Waheed, Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Byblos, 1401, Lebanon, Email yasir_waheed_199@hotmail.com Nayla Munawar, Department of Chemistry, College of Science, UAE University, Al Ain, 15551, United Arab Emirates, Email nmunawar@uaeu.ac.aeIntroduction: The occurrence of dengue fever presents a considerable burden for public health care in developing countries. This study aims to validate APRI as predictor score for severity of dengue fever so that catastrophic events could be prevented, and early triage can save lives.Methods: The retrospective cross-sectional study was done on dengue positive patients from August to November 2023. APRI score was calculated for every patient at the time of admission. The primary end-point was non-complicated disease (Simple dengue fever) vs complicated disease (dengue hemorrhagic fever and dengue shock syndrome). ROC curve was used to identify the role of APRI in prediction of dengue complication. Youden index was used to find the cut-off value of APRI along with sensitivity, specificity, positive and negative likelihood ratios. To further evaluate the role of APRI score, patients were divided into two groups, patients with APRI score greater and lesser than cut-off value. The qualitative variables among two groups were compared by chi-square testing. The predictors of complicated dengue were first determined by univariate regression analysis and then confirmed by multivariate regression analysis.Results: The mean APRI score of 135 patients was 20.06 ± 6.31. AUC for APRI score was 0.93 (p < 0.0001) indicating that APRI score calculated at the time of admission is an excellent marker in determining the complicated dengue. The cut-off value for APRI score was 9.04 (sensitivity 84.91%, specificity 89.02%, p < 0.0001). The patients with APRI < 9.04 mostly developed simple dengue fever (54.1%) vs DHF (4.4%) and DSS (1.5%), while patients with APRI > 9.04 had more DHF (20.7%) and DSS (12.6%) vs simple dengue fever (6.7%). None of the patient died with APRI < 9.04 while the mortality rate was 3.7% in patients with APRI > 9.04.Conclusion: The APRI score, calculated at the time of admission, is an excellent marker in determining the severe dengue.Keywords: dengue, severe dengue, dengue hemorrhagic fever, aspartate aminotransferase, ROC curve, area under curve

Background: Early detection of cognitive impairment is among the top research priorities aimed at reducing the global burden of dementia. Currently used screening tools have high sensitivity but lack specificity at their original cut-off, while decreasing the cut-off was repeatedly shown to improve specificity, but at the cost of lower sensitivity. In 2012, a new screening tool was introduced that aims to overcome these limitations – the Quick mild cognitive impairment screen (Qmci). The original English Qmci has been rigorously validated and demonstrated high diagnostic accuracy with both good sensitivity and specificity. We aimed to determine the optimal cut-off value for the German Qmci, and evaluate its diagnostic accuracy, reliability (internal consistency) and construct validity. Methods: We retrospectively analyzed data from healthy older adults (HOA; n = 43) and individuals who have a clinical diagnosis of 'mild neurocognitive disorder' (mNCD; n = 37) with a biomarker supported characterization of the etiology of mNCD of three studies of the 'Brain-IT' project. Using Youden's Index, we calculated the optimal cut-off score to distinguish between HOA and mNCD. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic accuracy based on the area under the curve (AUC). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Reliability (internal consistency) was analyzed by calculating Cronbach's α. Construct validity was assessed by analyzing convergent validity between Qmci-G subdomain scores and reference assessments measuring the same neurocognitive domain. Results: The optimal cut-off score for the Qmci-G was ≤ 67 (AUC = 0.96). This provided a sensitivity of 91.9% and a specificity of 90.7%. The PPV and NPV were 89.5% and 92.9%, respectively. Cronbach's α of the Qmci-G was 0.71 (CI95% [0.65 to 0.78]). The Qmci-G demonstrated good construct validity for subtests measuring learning and memory. Subtests that measure executive functioning and/or visuo-spatial skills showed mixed findings and/or did not correlate as strongly as expected with reference assessments. Conclusion: Our findings corroborate the existing evidence of the Qmci's good diagnostic accuracy, reliability, and construct validity. Additionally, the Qmci shows potential in resolving the limitations of commonly used screening tools, such as the Montreal Cognitive Assessment. To verify these findings for the Qmci-G, testing in clinical environments and/or primary health care and direct comparisons with standard screening tools utilized in these settings are warranted. [ABSTRACT FROM AUTHOR]

Background: Binary classification models are frequently used to predict clinical deterioration, however they ignore information on the timing of events. An alternative is to apply time-to-event models, augmenting clinical workflows by ranking patients by predicted risks. This study examines how and why time-to-event modelling of vital signs data can help prioritise deterioration assessments using lift curves, and develops a prediction model to stratify acute care inpatients by risk of clinical deterioration. Methods: We developed and validated a Cox regression for time to in-hospital mortality. The model used time-varying covariates to estimate the risk of clinical deterioration. Adult inpatient medical records from 5 Australian hospitals between 1 January 2019 and 31 December 2020 were used for model development and validation. Model discrimination and calibration were assessed using internal–external cross validation. A discrete-time logistic regression model predicting death within 24 h with the same covariates was used as a comparator to the Cox regression model to estimate differences in predictive performance between the binary and time-to-event outcome modelling approaches. Results: Our data contained 150,342 admissions and 1016 deaths. Model discrimination was higher for Cox regression than for discrete-time logistic regression, with cross-validated AUCs of 0.96 and 0.93, respectively, for mortality predictions within 24 h, declining to 0.93 and 0.88, respectively, for mortality predictions within 1 week. Calibration plots showed that calibration varied by hospital, but this can be mitigated by ranking patients by predicted risks. Conclusion: Time-varying covariate Cox models can be powerful tools for triaging patients, which may lead to more efficient and effective care in time-poor environments when the times between observations are highly variable. [ABSTRACT FROM AUTHOR]

Background: The importance of diffusion in prostate cancer (PCa) diagnosis has been widely proven. Several studies investigated diffusion models in PCa diagnosis. Materials and Methods: This systematic review and meta-analysis study was performed to evaluate the ability of three diffusion models to diagnose PCa from the scientific electronic databases Embase, PubMed, Scopus, and Web of Science (ISI) for the period up to March 2022 to identify all relevant articles. Results: Eighteen studies were included in the systematic review section (7 diffusion kurtosis imaging [DKI] studies, 4 diffusion tensor imaging [DTI] studies, 4 intravoxel incoherent motion [IVIM] studies, and 3 IVIM-DKI studies). Pooled sensitivity, specificity, accuracy, and summary area under each diffusion model's curve (AUC) and 95% confidence intervals (CIs) were calculated. The pooled accuracy and 95% CI on detection (differentiation of tumor from normal tissue and benign prostatic hyperplasia/prostatitis) were obtained for apparent diffusion coefficient (ADC) at 87.97% (84.56%-91.38%) for DKI parameters (Gaussian diffusion [DK] 87.94% [78.71%-97.16%] and deviation from Gaussian diffusion [K] 86.84% [81.83%-91.85%]) and IVIM parameters (true molecular diffusion [DIVIM] 81.73% [72.54%-90.91%], perfusion-related diffusion [D*] 65% [48.47%-81.53%] and perfusion fraction [f] 80.36% [64.23%-96.48%]). The AUC values and 95% CI in the detection of PCa were obtained for ADC at 0.95 (0.92-0.97), for DKI parameters (DK 0.94 [0.89-0.99] and K 0.93 [0.90-0.96]) and for IVIM parameters (DIVIM 0.85 [0.80-0.91], D* 0.60 [0.43-0.77] and f 0.73 [0.63-0.84]). Two studies showed that the DTI accuracy values were 97.34% and 85%. For IVIM--kurtosis model in PCa detection, two studies stated that the DIVIM-K and KIVIM-K accuracy values were 85% and 84.44% (the pooled accuracy; 84.64% with 95% CI 75.78%-93.50%), and 72.50% and 71.11% (the pooled accuracy, 72.10% with 95% CI 64.73%-79.48%), respectively. Conclusion: Our findings showed that among the DKI, IVIM, and ADC parameters, it seems that ADC, Dk, DIVIM, and K are the most important, which can be used as an indicator to distinguish PCa from normal tissue. The DKI model probably has a higher ability to detect PCa from normal tissue than the IVIM model. DKI probably has the same diagnostic performance in PCa detection and grading compared to diffusion-weighted imaging and ADC. [ABSTRACT FROM AUTHOR]

BACKGROUND: It is difficult to differentiate between chronic obstructive pulmonary disease (COPD)-peripheral bronchogenic carcinoma (COPD-PBC) and inflammatory masses. OBJECTIVE: This study aims to predict COPD-PBC based on clinical data and preoperative Habitat-based enhanced CT radiomics (HECT radiomics) modeling. METHODS: A retrospective analysis was conducted on clinical imaging data of 232 cases of postoperative pathological confirmed PBC or inflammatory masses. The PBC group consisted of 82 cases, while the non-PBC group consisted of 150 cases. A training set and a testing set were established using a 7:3 ratio and a time cutoff point. In the training set, multiple models were established using clinical data and radiomics texture changes within different enhanced areas of the CT mass (HECT radiomics). The AUC values of each model were compared using Delong's test, and the clinical net benefit of the models was tested using decision curve analysis (DCA). The models were then externally validated in the testing set, and a nomogram of predicting COPD-PBC was created. RESULTS: Univariate analysis confirmed that female gender, tumor morphology, CEA, Cyfra21-1, CT enhancement pattern, and Habitat-Radscore B/C were predictive factors for COPD-PBC (P < 0.05). The combination model based on these factors had significantly higher predictive performance [AUC: 0.894, 95% CI (0.836–0.936)] than the clinical data model [AUC: 0.758, 95% CI (0.685–0.822)] and radiomics model [AUC: 0.828, 95% CI (0.761–0.882)]. DCA also confirmed the higher clinical net benefit of the combination model, which was validated in the testing set. The nomogram developed based on the combination model helped predict COPD-PBC. CONCLUSION: The combination model based on clinical data and Habitat-based enhanced CT radiomics can help differentiate COPD-PBC, providing a new non-invasive and efficient method for its diagnosis, treatment, and clinical decision-making. [ABSTRACT FROM AUTHOR]

Rawa Aopa National Park has experienced a severe wildfire. These fires are affected by several factors, including topography, meteorology, vegetation, and source of fire. This study uses a Machine Learning approach based on re-sampling methods (e.g. crossvalidation, bootstrap, and random subsampling) to evaluate, and improve the performance of twelve basic Machine Learning algorithms: Generalized Linear Model, Support Vector Machine, Random Forest, Boosted Regression Trees, Classification And Regression Tree, Multivariate Adaptive Regression Splines, Mixture Discriminate Analysis, Flexible Discriminant Analysis, Maximum Entropy, Maximum Likelihood, Radial Basis Function, and Multi-Layer Perceptron, analyze the causes of wildfires, and the correlation between variables. The model is evaluated by Area Under Curve, Correlation, True Skill Statistics, and Deviance. The evaluation results show that Bt-RF has a good performance in predicting wildfire susceptibility in TNRAW with AUC=0.98, COR=0.96, TSS=0.97, and Deviance=0.15. An area of 644.88 km2 or the equivalent of 59.82% of the area is a wildfire susceptibility area with the concentration of fires occurring in the savanna ecosystem which is around 245.12 km2 or the equivalent of 88.95% of the jungle zone. Among the 17 parameters that cause fires, this area is strongly influenced by Maximum Temperature, Land Use Land Cover, and Distance from Road. There is a strong correlation between soil and distance from settlements = 0.96. [ABSTRACT FROM AUTHOR]

Purpose Vancomycin treats methicillin-resistant Staphylococcus aureus infections in hospitalized patients, yet nephrotoxicity is a major risk. Dosing based on the ratio of vancomycin 24-hour area under the curve to minimum inhibitory concentration (AUC/MIC) is preferred over a trough-only vancomycin dosing approach to minimize the risk of acute kidney injury (AKI). This study compares the safety of AUC/MIC-guided and trough-only vancomycin dosing at a 255-bed hospital. Methods A retrospective cohort study of adult patients with stable renal function who received at least 3 days of intravenous vancomycin via either AUC/MIC or trough-only dosing was conducted. The primary outcome was AKI occurrence during treatment. Secondary outcomes included the frequencies of therapeutic, subtherapeutic, and supratherapeutic vancomycin troughs. Relative risk calculations were performed for all outcomes. Results 600 patients from the trough-only group and 561 patients from the AUC/MIC group were included. 121 patients from the trough-only group and 87 patients from the AUC/MIC group experienced AKI during treatment (relative risk [RR], 0.769; 95% CI, 0.599-0.988; P = 0.0397). Compared with the trough-only group, the AUC/MIC group was significantly less likely to have supratherapeutic troughs (RR, 0.703; 95% CI, 0.611-0.809; P < 0.0001) and significantly more likely to have therapeutic troughs (RR, 1.14; 95% CI, 1.069-1.211; P < 0.0001), with no significant between-group difference in subtherapeutic troughs (RR, 1.03; 95% CI, 0.854-1.25; P = 0.74). Conclusion AUC/MIC dosing was associated with significantly lower risk of AKI, a lower risk of supratherapeutic trough levels, and a higher risk of therapeutic trough levels, with no significant difference in subtherapeutic troughs when compared to trough-only dosing. Limitations of this study included its retrospective nature and reliance on manual chart review. [ABSTRACT FROM AUTHOR]

The application of deep learning techniques in the medical field, specifically for Atrial Fibrillation (AFib) detection through Electrocardiogram (ECG) signals, has witnessed significant interest. Accurate and timely diagnosis increases the patient's chances of recovery. However, issues like overfitting and inconsistent accuracy across datasets remain challenges. In a quest to address these challenges, a study presents two prominent deep learning architectures, ResNet-50 andDenseNet-121, to evaluate their effectiveness in AFib detection. The aim was to create a robust detection mechanism that consistently performs well. Metrics such as loss, accuracy, precision, sensitivity, and Area Under the Curve (AUC) were utilized for evaluation. The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories. It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77% and 98.88%, precision of 98.78% and 98.89% and sensitivity of 98.76% and 98.86% for training and validation, hinting at its advanced capability for AFib detection. These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals. The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection. Moreover, the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies, for more accurate and early detection of AFib, thereby fostering improved patient care and outcomes. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to compare some nonparametric methods used to estimate the confidence intervals of the area under the receiver operating characteristic (ROC) curve (AUC) in terms of the width of the confidence intervals, and to investigate the effects of different sample sizes on AUC confidence intervals. Material and Methods: Eight different commonly used nonparametric methods were used to establish the confidence interval of the AUC in the presence of different sample sizes. A simulation study was carried out to compare these methods, and data was derived for 35 different n values in the 10 ≤ n ≤ 1.000 range using the Phyton-random library. Results: Considering the width of confidence intervals, the methods considered in the study can be listed from best to worst as Logit Transformation Based Mann-Whitney, Mann-Whitney, Agresti-Coull, Wilson, Frequency Approach, Wilson with Continuity Correction, Adapted Wald and Adapted Wald method with Continuity Correction. In terms of the min-max values of the lower/upper limits remaining in the range of 0-1, this order is Logit Transformation Based Mann-Whitney, Wilson, Frequency Approach, Continuity Corrected Wilson, Agresti-Coull, Adapted Wald, Continuity Corrected Adapted Wald and Mann-Whitney. For n ≥ 175, most methods provide similar confidence intervals in that confidence intervals tend to encompass AUC values. Therefore, there is no preferred approach. In case of n < 175, it was concluded that Logit Transformation Based Mann-Whitney and non-transformational Mann-Whitney methods should be used to determine AUC confidence intervals. Depending on whether the lower and upper limit values are < 0 or > 1, it has been determined that the Logit Transformation Based Mann-Whitney, Wilson and Frequency Approach has values in the range of 0 ≤ lower and upper limit ≤ 1 compared to other methods. Conclusion: Among the compared methods, it can be said that the Logit Transformation Based Mann-Whitney method is the best method, considering both the coverage tendency and the criteria for the border values to be in the range of 0-1. [ABSTRACT FROM AUTHOR]

Introduction Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14–56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop a machine learning (ML) model for POD prediction in older patients, in close cooperation with the PAWEL (patient safety, cost-effectiveness and quality of life in elective surgery) project. Methods The model was trained on the PAWEL study's dataset of 878 patients (no intervention, age ≥ 70, 209 with POD). Presence of POD was determined by the Confusion Assessment Method and a chart review. We selected 15 features based on domain knowledge, ethical considerations and a recursive feature elimination. A logistic regression and a linear support vector machine (SVM) were trained, and evaluated using receiver operator characteristics (ROC). Results The selected features were American Society of Anesthesiologists score, multimorbidity, cut-to-suture time, estimated glomerular filtration rate, polypharmacy, use of cardio-pulmonary bypass, the Montreal cognitive assessment subscores 'memory', 'orientation' and 'verbal fluency', pre-existing dementia, clinical frailty scale, age, recent falls, post-operative isolation and pre-operative benzodiazepines. The linear SVM performed best, with an ROC area under the curve of 0.82 [95% CI 0.78–0.85] in the training set, 0.81 [95% CI 0.71–0.88] in the test set and 0.76 [95% CI 0.71–0.79] in a cross-centre validation. Conclusion We present a clinically useful and explainable ML model for POD prediction. The model will be deployed in the Supporting SURgery with GEriatric Co-Management and AI project. [ABSTRACT FROM AUTHOR]

The present study attempts to explore the efficacy of machine learning models in landslide predictions caused by rainfall events along highway from Bandipora to Gurez, J&K, India. Random forest (RF) and logistic regression (LR) models were employed to find the optimal parameters for targeted feature, i.e., landslide prediction. These models were evaluated for accuracy using the receiver operating characteristics, area under the curve (ROC-AUC) and false-negative rate (FNR). The results reveal a positive correlation between antecedent rainfall and landslide occurrence rather than between single-day landslide and rainfall events. Comparing the two models, LR model's performance is well within the acceptable limits of FNR and, therefore, could be preferred for landslide prediction over RF. LR model's incorrect prediction rate is 8.48% without including antecedent precipitation data and 5.84% including antecedent precipitation data. Our study calls for wider use of machinery learning models for developing early warning systems of landslides. [ABSTRACT FROM AUTHOR]

Introduction: Torsade de pointes (TdP) is a deadly complication from drug-induced QT prolongation. Each of the 12 lead of an electrocardiogram (ECG) has a different length of QT interval, and thus might have a different performance in TdP prediction. This study aimed to determine the best ECG lead or set of leads in this regard. Methods: This is a comparative prognostic accuracy study using a two-gate data gathering design. The population in this study was from two sources, a case group (Patients who had drug-induced TdP, which were identified through a systematic Medline search) and a control group (those who overdosed on QT-prolonging drugs, which included patients who were under the consultation of Medical Toxicology Services). The areas under the receiver operating characteristic curve (AUROC) of heart rate-corrected QT (QTc) in each single ECG lead and of a mean/median QTc from a set of ECG leads (17 index test) in predicting the risk of TdP were calculated and compared with each other, trying to find the best lead for this propose. QTc Interval measurements were done by four investigators (Interrater reliabilities 0.95). Results: Finally, we included 136 and 148 ECGs from TdP cases and controls, respectively. V3 lead had the highest frequency of longest QTc interval, among the leads. The lead having the longest QTc yielded the greatest AUROC in predicting TdP regardless of QT correction formulas (QTcFRA=0.9915, QTcRTH=0.9893, QTcBZT=0.9904). The mean QTc of 3 leads (lead II, plus any two of leads V2-V4), and a median QTc of 6 leads (I, II, aVF, V2, V4, V6) provided similar overall performance for TdP prediction (regardless of the type of QTc formula). Conclusion: The longest QTc provided the greatest AUROC in predicting drug-induced TdP, however, the longest QTc is not located in a fixed individual lead in any patient. A less time-consuming method with comparable performance to that of the longest QTc was to use a mean QTc from 3 leads (lead II, plus any two of leads V2-V4). The potential clinical impact of this finding needs to be verified in a prospective cohort study.

Cyclophosphamide (CP) is an alkylating agent commonly included in multi-drug treatment protocols for canine cancer. As a prodrug, CP requires hepatic metabolism for activation to the intermediate compound 4-hydroxycyclophosphamide (4-OHCP) which then spontaneously forms alkylating phosphoramide mustard. CP is frequently administered in a fractionated manner, with the total dose given over multiple days. CP is reported to cause auto-induction of metabolism in humans, with faster CP clearance and relatively increased 4-OHCP formation following fractionated versus bolus dosing, however canine pharmacokinetic studies of CP dose fractionation are lacking. The study objective was to evaluate the pharmacokinetics of fractionated oral CP dosing at a dose of 200-250 mg/m2 over 3 to 4 days in a prospectively identified population of cancer-bearing dogs. Plasma concentrations of CP and 4-OHCP were measured by ultra-high performance liquid chromatography tandem-mass spectrometry in eight dogs following the first and last doses to assess for auto-induction of CP metabolism. No significant difference in the rate of CP elimination between first and last doses were detected (0.73 ± 0.46 vs. 1.22 ± 0.5 h-1 ; p = .125). Additionally, no significant difference in dose-normalized 4-OHCP exposure was identified between first and last doses (5.9 ± 2.1 vs. 7.9 ± 6.4 h × ng/ml; p = .936). These results suggest that fractionated dosing may not increase exposure to the active metabolite of CP in dogs as it does in humans. As such, standard bolus dosing and fractionated dosing may be equivalent in terms of bio-activation of CP in dogs administered a dose of 200-250 mg/m2 .

ObjectiveThe pharmacokinetics of oral diazepam are affected by food, but food-effect studies have not been conducted for diazepam nasal spray because it is believed that most absorption occurs via the nasal mucosa. However, gastrointestinal side effects reported with nasal diazepam suggest that at least a portion of the drug may be absorbed enterally and thus subject to food effects. The objective of this study was to evaluate the possible effects of food on the pharmacokinetics of diazepam nasal spray in healthy adults.MethodsThis randomized, open-label crossover study compared equal doses of diazepam nasal spray after an overnight fast and after a standardized high-fat, high-calorie breakfast. Each participant served as their own control, and there was a washout period of at least 21 days between treatments.ResultsTwenty-four healthy adults enrolled in this study. Two participants withdrew consent, and two had pre-dose diazepam concentrations that exceeded the protocol-defined minimum after the washout period and were excluded from the final analysis population of 20 participants. Under fed conditions, the mean maximum plasma diazepam concentration was decreased by 48% (p

ObjectiveThis study was undertaken to describe the safety, tolerability, pharmacokinetics, and immunogenicity of elezanumab (ABT-555), a fully human monoclonal antibody (mAb) directed against repulsive guidance molecule A (RGMa), in healthy and multiple sclerosis (MS) study participants.MethodsThe single-center, first-in-human, single ascending dose (SAD) study evaluated elezanumab (50-1,600mg intravenous [IV] and 150mg subcutaneous) in 47 healthy men and women. The multicenter multiple ascending dose (MAD; NCT02601885) study evaluated elezanumab (150mg, 600mg, and 1,800mg) in 20 adult men and women with MS, receiving either maintenance or no immunomodulatory treatment.ResultsNo pattern of study drug-related adverse events was identified for either the SAD or MAD elezanumab regimens. Across both studies, the Tmax occurred within 4 hours of elezanumab IV infusion, and the harmonic mean of t1/2 ranged between 18.6 and 67.7 days. Following multiple dosing, elezanumab Cmax , area under the curve, and Ctrough increased dose-proportionally and resulted in dose-dependent increases in elezanumab cerebrospinal fluid (CSF) concentrations. Elezanumab CSF penetration was 0.1% to 0.4% across both studies, with CSF levels of free RGMa decreased by >40%. Changes in CSF interleukin-10 (IL-10) and free RGMa demonstrated dose/exposure-dependence.InterpretationThe elezanumab pharmacokinetic profile supports monthly, or bimonthly, administration of up to 1,800mg with the option of a loading dose of 3,600mg. Elezanumab partitioning into CSF is within the range expected for mAbs. Reduced CSF levels of free RGMa demonstrate central nervous system target binding of elezanumab with an apparent maximal effect at 1,800mg IV. Exposure-associated increases in CSF IL-10, an anti-inflammatory cytokine with neuroprotective/neurorestorative properties, support potential pathway modulation in MS participants. ANN NEUROL 2023;93:285-296.

BackgroundPer- and polyfluoroalkyl substances (PFAS) are persistent synthetic chemicals and are commonly found in everyday items. PFAS have been linked to disrupting glucose homeostasis, however, whether they are associated with gestational diabetes mellitus (GDM) risk remains inconclusive. We examined prospective associations of PFAS concentrations measured twice in pregnancy with GDM risk.MethodsIn the PETALS pregnancy cohort, a nested case-control study which included 41 GDM cases and 87 controls was conducted. PFAS analytes were measured in blood serum collected in both early and mid-pregnancy (mean [SD]: 13.9 [2.2] and 20.2 [2.2] gestational weeks, respectively), with cumulative exposure calculated by the area-under-the-curve (AUC) to integrate both the PFAS concentration and the timing of the exposure. Individual adjusted weighted unconditional logistic regression models examined seven PFAS in association with GDM risk. P-values were corrected using the false-discovery-rate (FDR). Mixture models were analyzed with Bayesian kernel machine regression (BKMR).ResultsPFDA, PFNA and PFOA were individually associated with higher GDM risk per interquartile range (IQR) in early pregnancy (OR [95% CI]: 1.23 [1.09, 1.38]), 1.40 [1.24, 1.58]), and 1.15 [1.04, 1.27], respectively), mid-pregnancy (1.28 [1.15, 1.43], 1.16 [1.05, 1.28], and 1.20 [1.09, 1.33], respectively), and with cumulative exposure (1.23 [1.09, 1.38], 1.21 [1.07, 1.37], and 1.19 [1.09, 1.31], respectively). PFOS in mid-pregnancy and with cumulative exposure was associated with increased GDM risk (1.41 [1.17, 1.71] and 1.33 [1.06, 1.58], respectively). PFUnDA in early pregnancy was associated with lower GDM risk (0.79 [0.64, 0.98]), whereas mid-pregnancy levels were associated with higher risk (1.49 [1.18, 1.89]). PFHxS was associated with decreased GDM risk in early and mid-pregnancy (0.48 [0.38, 0.60] and 0.48 [0.37, 0.63], respectively) and with cumulative exposure (0.49 [0.38,0.63]). PFPeA was not associated with GDM. Similar conclusions were observed in BKMR models; however, overall associations in these models were not statistically significant.ConclusionsHigher risk of GDM was consistently observed in association with PFDA, PFNA, and PFOA exposure in both early and mid-pregnancy. Results should be corroborated in larger population-based cohorts and individuals of reproductive age should potentially avoid known sources of PFAS.

Chest pain is a common clinical complaint for which myocardial injury is the primary concern and is associated with significant morbidity and mortality. To aid providers' decision-making, we aimed to analyze the electrocardiogram (ECG) using a deep convolutional neural network (CNN) to predict serum troponin I (TnI) from ECGs. We developed a CNN using 64,728 ECGs from 32,479 patients who underwent ECG within 2 h prior to a serum TnI laboratory result at the University of California, San Francisco (UCSF). In our primary analysis, we classified patients into groups of TnI

Objective To study changes of white blood cell (WBC) and platelet parameters in peripheral blood of Brucellosis patients so as to explore the clinical significance. Methods A total of 90 patients with confirmed Brucellosis were included, including 59 in the acute stage and 31 in the chronic stage. In addition, 40 healthy people during the same period were selected as the healthy control group. Basic data such as clinical symptoms were collected. Total WBC count, lymphocyte count, neutrophil count, monocyte count, platelet count, mean platelet volume (MPV) and platelet distribution width (PDW) in peripheral blood were detected by automatic blood analyzer. The counts of CD3+ T, CD4+ T and CD8+ T cells in peripheral blood were detected by flow cytometry. Neutrophil-lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) of patients were calculated. Changes of each cell parameter were analyzed and the clinical diagnostic value of each parameter for Brucellosis were analyzed. Results The proportion of arthralgia and spondylitis were higher in the chronic stage group than those in the acute stage group (P＜0.05). Compared with the healthy control group, the total number of WBC, neutrophil count, platelet count and NLR were decreased in the acute stage group, and MPV and PDW were decreased in the chronic stage group, and CD3+ T cell count and monocyte count were increased (P＜0.05). White blood count, lymphocyte count, CD3+ T cell count, and CD8+ T cell count were lower in patients with acute Brucellosis with arthritic symptoms than those of patients without arthritis, while PLR was increased (P＜0.05). Receiver operating characteristic (ROC) curve analysis showed that the area under the curve of neutrophil count and NLR level for acute Brucellosis was 0.780 and 0.717, respectively, while the area under the ROC curve of CD3+ T cell count for chronic Brucellosis was 0.748. In acute stage patients, the area under ROC curve of CD3+ T, CD8+ T cell count and PLR level for the diagnosis of arthritis was 0.779, 0.768 and 0.722, respectively. Conclusion The indexes of WBC and platelet of peripheral blood in patients with acute and chronic human Brucellosis are changed abnormally, which has a certain reference value in diagnosis of human Brucellosis and arthritis in acute Brucellosis patients. [ABSTRACT FROM AUTHOR]

Objectives: To develop a multitask deep learning (DL) algorithm to automatically classify mammography imaging findings and predict the existence of extensive intraductal component (EIC) in invasive breast cancer. Methods: Mammograms with invasive breast cancers from 2010 to 2019 were downloaded for two radiologists performing image segmentation and imaging findings annotation. Images were randomly split into training, validation, and test datasets. A multitask approach was performed on the EfficientNet-B0 neural network mainly to predict EIC and classify imaging findings. Three more models were trained for comparison, including a single-task model (predicting EIC), a two-task model (predicting EIC and cell receptor status), and a three-task model (combining the abovementioned tasks). Additionally, these models were trained in a subgroup of invasive ductal carcinoma. The DeLong test was used to examine the difference in model performance. Results: This study enrolled 1459 breast cancers on 3076 images. The EIC-positive rate was 29.0%. The three-task model was the best DL model with an area under the curve (AUC) of EIC prediction of 0.758 and 0.775 at the image and breast (patient) levels, respectively. Mass was the most accurately classified imaging finding (AUC = 0.915), followed by calcifications and mass with calcifications (AUC = 0.878 and 0.824, respectively). Cell receptor status prediction was less accurate (AUC = 0.625–0.653). The multitask approach improves the model training compared to the single-task model, but without significant effects. Conclusions: A mammography-based multitask DL model can perform simultaneous imaging finding classification and EIC prediction. Clinical relevance statement: The study results demonstrated the potential of deep learning to extract more information from mammography for clinical decision-making. Key Points: • Extensive intraductal component (EIC) is an independent risk factor of local tumor recurrence after breast-conserving surgery. • A mammography-based deep learning model was trained to predict extensive intraductal component close to radiologists' reading. • The developed multitask deep learning model could perform simultaneous imaging finding classification and extensive intraductal component prediction. [ABSTRACT FROM AUTHOR]

Background: Timely and accurate screening of malnutrition at the community level is essential to identifying malnourished children. The World Health Organization (WHO) guidelines classify non-oedematous acute malnutrition among children using mid-upper arm circumference (MUAC) or weight-for-height Z-score (WHZ). Study Design: A cross-sectional study. Methods: This study was conducted among children aged 6-60 months. After necessary exclusions, 433 participants were selected using a multi-stage simple random sampling method. Using WHO guidelines for global acute malnutrition (GAM) [WHZ < -2, MUAC < 12.5 cm], the sensitivity (Se), specificity (Sp), predictive values, likelihood ratios, Youden index, and receiver operating characteristic (ROC) curve were calculated for MUAC using WHZ as the criterion. Results: Out of 433 participants, 30% were diagnosed with GAM using WHZ, while 17.6% were found malnourished using MUAC measurements. As per WHO cut-offs, the Se, Sp, positive predictive value (PPV), negative predictive value (NPV), Youden index, positive likelihood ratio (LR +), and negative likelihood ratio (LR-) of MUAC were 48%, 96%, 83%, 81%, 0.44, 12, and 0.54, respectively. The ROC curve displayed an area under the curve of 0.86 (95% confidence interval = 0.83, 0.90) for MUAC < 12.5 cm. Bivariate Pearson correlation also demonstrated a positive linear relationship (R² = 0.302) between the WHZ and MUAC variables. Conclusion: Based on the findings, 48% of the children were correctly identified by the MUAC with an 83% probability of GAM (PPV = 0.83). Moreover, there was 96% Sp in non-malnourished children, with only 4% false positives. Therefore, personnel at the grassroots level can use MUAC for timely and accurate screening of children in Anganwadi centers (AWCs) due to its ease of use and simplicity. [ABSTRACT FROM AUTHOR]

Background: Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma. Methods: Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non‐case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non‐case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta‐analyzed using inverse‐variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with atopy (n = 207), asthma without atopy (n = 554), atopy without asthma (n = 147), compared to neither (n = 948)). Results: Meta‐analysis of 4860 proteins identified 115 significantly (FDR<0.05) associated with asthma. Multiple signaling pathways related to airway inflammation and pulmonary injury were enriched (FDR<0.05) among these proteins. A proteomic score generated using machine learning provided predictive value for asthma (AUC = 0.77, 95% CI = 0.75–0.79 in training set; AUC = 0.72, 95% CI = 0.69–0.75 in validation set). Twenty proteins are targeted by approved or investigational drugs for asthma or other conditions, suggesting potential drug repurposing. The combined asthma‐atopy phenotype showed significant associations with 20 proteins, including five not identified in the overall asthma analysis. Conclusion: This first large‐scale proteomics study identified over 100 plasma proteins associated with current asthma in adults. In addition to validating previous associations, we identified many novel proteins that could inform development of diagnostic biomarkers and therapeutic targets in asthma management. [ABSTRACT FROM AUTHOR]

Grapiprant is a prostaglandin E2 receptor antagonist that has been found to be an effective anti-inflammatory in dogs and that is devoid of some of the adverse effects associated with traditional NSAIDs that elicit their effects through inhibition of PGE2 production. Previously published reports have described the pharmacokinetics of this drug in horses when administered at 2 mg/kg; however, pharmacodynamic effects in this species have yet to be described. The objective of the current study was to describe the pharmacokinetics and pharmacodynamics of grapiprant at a higher dose. Eight horses received a single oral administration of 15 mg/kg. Plasma concentrations were determined for 96 h using liquid chromatography-tandem mass spectrometry. Non-compartmental analysis was used to determine pharmacokinetic parameters. Pharmacodynamic effects were assessed ex vivo by stimulating blood samples with PGE2 and determining TNF-ɑ concentrations. Maximum concentration, time to maximum concentration and area under the curve were 327.5 (188.4-663.0) ng/ml, 1 (0.75-2.0) hour and 831.8 (512.6-1421.6) h*ng/ml, respectively. The terminal half-life was 11.1 (8.27-21.2) hr. Significant stimulation of TNF alpha was noted for 2-4 h post-drug administration. Results of this study suggest a short duration of EP4 receptor engagement when administered at a dose of 15 mg/kg.

Landslide susceptibility projections that rely on independent models produce biased results. This situation will worsen class balance if working with a small population. This study proposes a landslide susceptibility prediction model based on resampling, cross-validation, bootstrap, and random subsampling approaches, which is integrated with the machine learning model, generalized linear model, support vector machine, random forest, boosted regression trees, classification and regression tree, multivariate adaptive regression splines, mixture discriminate analysis, flexible discriminant analysis, maximum entropy, and maximum likelihood. This methodology was applied in Kendari City, an urban area which faced destructive erosion. Area under the ROC curve (AUC), true skill statistics (TSS), correlation coefficient (COR), normalized mutual information (NMI), and correct classification rate (CCR) were used to evaluate the predictive accuracy of the proposed model. The results show that the resampling algorithm improves the performance of the standalone model. Results also revealed that standalone models had better performance with the CV algorithm compared to the Bt and RS algorithms. The Bt-RF model excels in statistical measures (AUC = 0.97, TSS = 0.97, COR = 0.99, NMI = 0.50, and CCR = 0.93). Given the admirable performance of the proposed models in a moderate scale area, promising results can be expected from these models for other regions. HIGHLIGHTS The resampling technique is often neglected in the pre-processing process.; The cross-validation technique is superior to other techniques (i.e. RS and Bt).; Distance from fault and rainfall factors correlate with the occurrence of landslides in the study area of this research.;

Background: Sepsis is a common cause of morbidity and mortality among geriatrics. Aims and Objectives: The objective of study was to find the clinicomicrobiological profile of elderly inpatients (≥60 years) with sepsis and to evaluate the diagnostic accuracy of urine albumin to creatinine ratio (ACR) or microalbuminuria in predicting mortality. Materials and Methods: A prospective observational study was conducted among 50 geriatric patients admitted with sepsis in the ward or intensive therapy unit under medicine department of a tertiary care center for a period of 1 year. Baseline clinicomicrobiological profile was noted. Urine ACR values were measured at admission, day 4, and day 7 and geriatrics were followed up for 28 days to see the outcome. Receiver operator characteristic curve analysis was performed to evaluate the area under the curve for microalbuminuria on the 3 days and to determine the optimum cut-off points to predict mortality. Results: The mortality rate within 28 days was 64%. Urine microalbuminuria on day 1, day 4, and day 7 of admission was significantly higher among the non-survivors compared to survivors (P≤0.001, P≤0.001, and P=0.006, respectively). Microalbuminuria levels (in mcg/mg) of 143.0, 60.05, and 42.6 on day 1, 4, and 7 of admission, respectively, can be used as the cut-off to predict the death of geriatric sepsis inpatients with overall diagnostic accuracy of 83.0%, 83.4% and 86.1%, respectively, sensitivity as 93.75%, 87.50%, and 100.0%, respectively, and specificity as 44.44%, 72.22%, and 72.22%, respectively. Conclusion: Microalbuminuria can be a potential biomarker to predict death among geriatric septic inpatients, which can further help in improving the treatment strategy.

Background and aim: Considering the importance of early detection of breast cancer and the use of non-invasive methods, the present study was conducted to evaluate the role of exosome-derived miRNA in breast cancer diagnosis. Material and methods: In the present systematic review and meta-analysis, information about exosome-derived miRNA in breast cancer diagnosis in all articles published up until the end of July 2023 was collected by two trained researchers individually using keywords and combinations of keywords from databases PubMed, Scopus, Science Direct, ISI, Web of Knowledge, Elsevier, Wiley, and Embase and Google Scholar. STATA (version 17) was used to analyze data using the fixed effects model in meta-analysis; p values less than 0.05 were considered significant. Results: A total of six studies were included in the meta-analysis process. The sensitivity and specificity of plasma-based exosomal miRNAs were 68% (ES, 95% CI: 0.39, 0.97) and 82% (ES, 95% CI: 0.52, 1.11), respectively. The sensitivity and specificity of serum-based exosomal miRNAs were 67% (ES, 95% CI: 0.33, 1.01) and 88% (ES, 95% CI: 0.54, 1.22), respectively. Conclusions: Exosomal miRNAs have high sensitivity and specificity in breast cancer diagnosis and can be considered a valuable and promising tool for early detection of breast cancer non-invasively.

BackgroundIsavuconazole is a triazole antifungal drug that has shown good efficacy in human patients. Absorption and pharmacokinetics have not been evaluated in cats.ObjectivesTo determine the pharmacokinetics of isavuconazole in cats given a single IV or PO dose.AnimalsEight healthy, adult research cats.MethodsFour cats received 100 mg capsules of isavuconazole PO. Four cats received 5 mg/kg isavuconazole solution IV. Serum was collected at predetermined intervals for analysis using ultra-high performance liquid chromatography-tandem mass spectrometry. Data were analyzed using a 2-compartment uniform weighting pharmacokinetic analysis with lag time for PO administration and a 2 compartment, 1/y2 weighting for IV administration. Predicted 24 and 48-hour dosing intervals of 100 mg isavuconazole administered PO were modeled and in vitro plasma protein binding was assessed.ResultsBoth PO and IV drug administration resulted in high serum concentrations. Intravenous and PO formulations of isavuconazole appear to be able to be used interchangeably. Peak serum isavuconazole concentrations occurred 5 ± 3.8 hours after PO administration with an elimination rate half-life of 66.2 ± 55.3 hours. Intersubject variability was apparent in both the PO and IV groups. Two cats vomited 6 to 8 hours after PO administration. No adverse effects were observed in the IV group. Oral bioavailability was estimated to be approximately 88%. Serum protein binding was calculated to be approximately 99.0% ± 0.03%.Conclusions and clinical importanceIsavuconazole might prove to be useful in cats with fungal disease given its favorable pharmacokinetics. Additional studies on safety, efficacy, and tolerability of long-term isavuconazole use are needed.

Objective: The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). Methods: Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). [ABSTRACT FROM AUTHOR]

The drug phenobarbital (PHB) was determined in bulk and tablet dosage form by a simple and sensitive spectrophotometric method. This method is based on calculating the area under the curve in the region between 230-246 nm. The Linearity of the method was 10-45 ppm and R2 was 0.9998. The percentage recovery rate was 100.10204% and the relative standard deviation coefficient was in the range of 0.19140-0.12386%. Limit detection was 0.15746 ppm, limit quantification was 0.52489 ppm, the Molar absorption coefficient was 18.66888 × 103 l mol-1 cm-1, and the Sandell's index was 0.01243 µg/cm2. The method was successfully applied to determination of PHB in its pharmaceutical preparations. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate posterior glenohumeral capsule edema compared to other MRI findings in adhesive capsulitis (AC). Methods: This study was approved by the local Institutional Review Board and it is HIPAA compliant. A retrospective search identified subjects who received fluoroscopically guided intra-articular corticosteroid injections for AC and had an MRI within 6 months prior to injection. The study group was compared with an age-, sex-, and side-matched control group who underwent the same procedures but did not have AC. MRIs were evaluated for edema of posterior capsule, anterior capsule, axillary pouch, coracohumeral ligament (CHL) and rotator interval (RI), thickness of axillary pouch and CHL, thickness of anterior capsule, RI and subcoracoid fat replacement, and teres minor atrophy and edema. Multivariable analysis was performed. Results: A total of 57 subjects with AC and 57 matched controls were studied: mean age 52 ± 7 (range 31–71) years, 37 female and 20 male, 22 right and 35 left. Posterior capsule edema was more common in the AC group vs. control group (66.7 vs 17.5%, p < 0.001). Multivariable analysis showed posterior capsule edema, CHL edema, and axillary pouch (glenoid) thickness (optimum cutoff = 4 mm) were significant independent predictors of AC. Simplified analysis using these three variables had an area under the curve of 0.860 (95%CI: 0.792–0.928). With all three variables present, the sensitivity and specificity for AC were 32% and 98%, respectively. Conclusions: Posterior joint capsule edema may be helpful to confirm AC. Posterior capsule edema, CHL edema, and axillary pouch (glenoid) thickness produce a strong model for distinguishing AC from controls. Clinical relevance statement: Edema involving the posterior shoulder joint capsule is an imaging marker of capsulitis and is useful in differentiating patients with adhesive capsulitis from those without in conjunction with other proven MRI findings. Key Points: • Posterior capsule edema has a sensitivity of 66.7% and a specificity of 82.5% for the detection of adhesive capsulitis. • Posterior capsule edema, coracohumeral ligament (CHL) edema, and axillary pouch (glenoid) thickness were significant independent predictors of adhesive capsulitis, and combining these variables together produces a very strong model for distinguishing cases from controls (AUC = 0.860). • Optimal cutoff values for CHL, axillary pouch (humeral), axillary pouch (glenoid), and axillary pouch (total) thickness were 2.5, 2.6, 4, and 6.3 mm, respectively. [ABSTRACT FROM AUTHOR]

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Background: Sepsis is a common cause of morbidity and mortality among geriatrics. Aims and Objectives: The objective of study was to find the clinicomicrobiological profile of elderly inpatients (=60 years) with sepsis and to evaluate the diagnostic accuracy of urine albumin to creatinine ratio (ACR) or microalbuminuria in predicting mortality. Materials and Methods: A prospective observational study was conducted among 50 geriatric patients admitted with sepsis in the ward or intensive therapy unit under medicine department of a tertiary care center for a period of 1 year. Baseline clinicomicrobiological profile was noted. Urine ACR values were measured at admission, day 4, and day 7 and geriatrics were followed up for 28 days to see the outcome. Receiver operator characteristic curve analysis was performed to evaluate the area under the curve for microalbuminuria on the 3 days and to determine the optimum cut-off points to predict mortality. Results: The mortality rate within 28 days was 64%. Urine microalbuminuria on day 1, day 4, and day 7 of admission was significantly higher among the non-survivors compared to survivors (P=0.001, P=0.001, and P=0.006, respectively). Microalbuminuria levels (in mcg/mg) of 143.0, 60.05, and 42.6 on day 1, 4, and 7 of admission, respectively, can be used as the cut-off to predict the death of geriatric sepsis inpatients with overall diagnostic accuracy of 83.0%, 83.4% and 86.1%, respectively, sensitivity as 93.75%, 87.50%, and 100.0%, respectively, and specificity as 44.44%, 72.22%, and 72.22%, respectively. Conclusion: Microalbuminuria can be a potential biomarker to predict death among geriatric septic inpatients, which can further help in improving the treatment strategy. [ABSTRACT FROM AUTHOR]

ObjectiveImprove methodology for equitable suicide death prediction when using sensitive predictors, such as race/ethnicity, for machine learning and statistical methods.MethodsTrain predictive models, logistic regression, naive Bayes, gradient boosting (XGBoost) and random forests, using three resampling techniques (Blind, Separate, Equity) on emergency department (ED) administrative patient records. The Blind method resamples without considering racial/ethnic group. Comparatively, the Separate method trains disjoint models for each group and the Equity method builds a training set that is balanced both by racial/ethnic group and by class.ResultsUsing the Blind method, performance range of the models' sensitivity for predicting suicide death between racial/ethnic groups (a measure of prediction inequity) was 0.47 for logistic regression, 0.37 for naive Bayes, 0.56 for XGBoost and 0.58 for random forest. By building separate models for different racial/ethnic groups or using the equity method on the training set, we decreased the range in performance to 0.16, 0.13, 0.19, 0.20 with Separate method, and 0.14, 0.12, 0.24, 0.13 for Equity method, respectively. XGBoost had the highest overall area under the curve (AUC), ranging from 0.69 to 0.79.DiscussionWe increased performance equity between different racial/ethnic groups and show that imbalanced training sets lead to models with poor predictive equity. These methods have comparable AUC scores to other work in the field, using only single ED administrative record data.ConclusionWe propose two methods to improve equity of suicide death prediction among different racial/ethnic groups. These methods may be applied to other sensitive characteristics to improve equity in machine learning with healthcare applications.

BackgroundPrevious latent class analysis of adults with acute respiratory distress syndrome (ARDS) identified two phenotypes, distinguished by the degree of inflammation. We aimed to identify phenotypes in children with ARDS in whom developmental differences might be important, using a latent class analysis approach similar to that used in adults.MethodsThis study was a secondary analysis of data aggregated from the Randomized Evaluation of Sedation Titration for Respiratory Failure (RESTORE) clinical trial and the Genetic Variation and Biomarkers in Children with Acute Lung Injury (BALI) ancillary study. We used latent class analysis, which included demographic, clinical, and plasma biomarker variables, to identify paediatric ARDS (PARDS) phenotypes within a cohort of children included in the RESTORE and BALI studies. The association of phenotypes with clinically relevant outcomes and the performance of paediatric data in adult ARDS classification algorithms were also assessed.Findings304 children with PARDS were included in this secondary analysis. Using latent class analysis, a two-class model was a better fit for the cohort than a one-class model (p

PurposeTo develop and evaluate an automated, portable algorithm to differentiate active corneal ulcers from healed scars using only external photographs.DesignA convolutional neural network was trained and tested using photographs of corneal ulcers and scars.ParticipantsDe-identified photographs of corneal ulcers were obtained from the Steroids for Corneal Ulcers Trial (SCUT), Mycotic Ulcer Treatment Trial (MUTT), and Byers Eye Institute at Stanford University.MethodsPhotographs of corneal ulcers (n = 1313) and scars (n = 1132) from the SCUT and MUTT were used to train a convolutional neural network (CNN). The CNN was tested on 2 different patient populations from eye clinics in India (n = 200) and the Byers Eye Institute at Stanford University (n = 101). Accuracy was evaluated against gold standard clinical classifications. Feature importances for the trained model were visualized using gradient-weighted class activation mapping.Main outcome measuresAccuracy of the CNN was assessed via F1 score. The area under the receiver operating characteristic (ROC) curve (AUC) was used to measure the precision-recall trade-off.ResultsThe CNN correctly classified 115 of 123 active ulcers and 65 of 77 scars in patients with corneal ulcer from India (F1 score, 92.0% [95% confidence interval (CI), 88.2%-95.8%]; sensitivity, 93.5% [95% CI, 89.1%-97.9%]; specificity, 84.42% [95% CI, 79.42%-89.42%]; ROC: AUC, 0.9731). The CNN correctly classified 43 of 55 active ulcers and 42 of 46 scars in patients with corneal ulcers from Northern California (F1 score, 84.3% [95% CI, 77.2%-91.4%]; sensitivity, 78.2% [95% CI, 67.3%-89.1%]; specificity, 91.3% [95% CI, 85.8%-96.8%]; ROC: AUC, 0.9474). The CNN visualizations correlated with clinically relevant features such as corneal infiltrate, hypopyon, and conjunctival injection.ConclusionsThe CNN classified corneal ulcers and scars with high accuracy and generalized to patient populations outside of its training data. The CNN focused on clinically relevant features when it made a diagnosis. The CNN demonstrated potential as an inexpensive diagnostic approach that may aid triage in communities with limited access to eye care.

This study quantifies the performance of an international cohort of cornea specialists in image-based differentiation of bacterial and fungal keratitis, identifying significant regional variation and establishing a reference standard for comparison against machine learning models.

Objective To investigate the expression levels of serum exosomal miR-223 and nucleotide-binding oligome-rization domain-like receptor protein 3 (NLRP3) in patients with Alzheimer’s disease (AD) and their association with the development and progression of AD. Methods A total of 65 elderly patients with AD who attended Department of Geriatrics in our hospital from March 2018 to December 2019 were enrolled as study group and were divided into mild cognitive impairment (MCI) subgroup with 41 patients and dementia of Alzheimer type (DAT) subgroup with 24 patients, and 60 elderly individuals with normal neurological function were enrolled as control group. Quantitative real-time PCR and ELISA were used to measure the expression levels of serum exosomal miR-223 and NLRP3. The Pearson and Spearman tests were used to analyze the correlation of serum exosomal miR-223 and NLRP3 with Mini-Mental State Examination (MMSE) score. The receiver operating characteristic (ROC) curve was plotted to calculate the area under the ROC curve (AUC) of serum exosomal miR-223 and NLRP3 in the diagnosis of MCI in AD patients or the differential diagnosis of DAT. Results Compared with the control group, the study group had a significantly lower expression level of serum exosomal miR-223 and a significantly higher expression level of NLRP3 (t=6.623,Z=-9.451,P

Abstract Background Clinical prediction models are widely used in health and medical research. The area under the receiver operating characteristic curve (AUC) is a frequently used estimate to describe the discriminatory ability of a clinical prediction model. The AUC is often interpreted relative to thresholds, with “good” or “excellent” models defined at 0.7, 0.8 or 0.9. These thresholds may create targets that result in “hacking”, where researchers are motivated to re-analyse their data until they achieve a “good” result. Methods We extracted AUC values from PubMed abstracts to look for evidence of hacking. We used histograms of the AUC values in bins of size 0.01 and compared the observed distribution to a smooth distribution from a spline. Results The distribution of 306,888 AUC values showed clear excesses above the thresholds of 0.7, 0.8 and 0.9 and shortfalls below the thresholds. Conclusions The AUCs for some models are over-inflated, which risks exposing patients to sub-optimal clinical decision-making. Greater modelling transparency is needed, including published protocols, and data and code sharing.

Study Design Retrospective study. Purpose The purpose of this study was to see how well the Tomita score, revised Tokuhashi score, modified Bauer score, Van der Linden score, classic Skeletal Oncology Research Group (SORG) algorithm, SORG nomogram, and New England Spinal Metastasis Score (NESMS) predicted 3-month, 6-month, and 1-year survival of non-surgical lung cancer spinal metastases. Overview of Literature There has been no study assessing the performance of prognostic scores for non-surgical lung cancer spinal metastases. Methods Data analysis was carried out to identify the variables that had a significant impact on survival. For all patients with spinal metastasis from lung cancer who received non-surgical treatment, the Tomita score, revised Tokuhashi score, modified Bauer score, Van der Linden score, classic SORG algorithm, SORG nomogram, and NESMS were calculated. The performance of the scoring systems was assessed by using receiver operating characteristic (ROC) curves at 3 months, 6 months, and 12 months. The predictive accuracy of the scoring systems was quantified using the area under the ROC curve (AUC). Results A total of 127 patients are included in the present study. The median survival of the population study was 5.3 months (95% confidence interval [CI], 3.7–9.6 months). Low hemoglobin was associated with shorter survival (hazard ratio [HR], 1.49; 95% CI, 1.00–2.23; p=0.049), while targeted therapy after spinal metastasis was associated with longer survival (HR, 0.34; 95% CI, 0.21–0.51; p

ObjectiveTo establish the pharmacokinetics of a single 2-mg oral dose of chlorambucil in cats with indolent lymphoproliferative malignancies.Animals24 client-owned cats.ProceduresCats were assigned to 1 of 4 groups, with each group having a total of 3 sample collection time points over 12 hours after receiving a single 2-mg oral dose of chlorambucil. Each time point combined to generate 6 full patient plasma chlorambucil concentration-time curves from the 24 cats. Chlorambucil treatment was continued every other day and a single, variably timed sample collection was obtained on day 14. Population parameter estimates were obtained by nonlinear mixed-effects modeling. Covariates investigated included age, sex, baseline serum cobalamin, study location, weight, and body condition score.ResultsChlorambucil administered orally to cats was found to have a peak plasma concentration of approximately 170 ng/mL (SE, 31.1 ng/mL), percent coefficient of variation (%CV) of 18.4% within 15 minutes, and a terminal half-life of 1.8 hours (SE, 0.21 hour; %CV, 12.4). At the 4-hour mark, a smaller secondary peak in plasma chlorambucil was found. Day 14 samples were similar to those of the initial dose. No covariates showed a significant effect in the population model.Clinical relevanceIn these cats, chlorambucil at a 2-mg dose administered every other day undergoes rapid gastrointestinal absorption and plasma clearance with no drug accumulation between doses. These data are critical to inform future work investigating the association of chlorambucil drug exposure with adverse events and outcome of cats with lymphoproliferative diseases.

ObjectiveTo create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing.DesignRetrospective cohort study.SettingOne US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21.Participants33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19.Main outcome measuresAn ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early.Results9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge.ConclusionA model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.

A simple approach for analyzing longitudinally measured biomarkers is to calculate summary measures such as the area under the curve (AUC) for each individual and then compare the mean AUC between treatment groups using methods such as t test. This two-step approach is difficult to implement when there are missing data since the AUC cannot be directly calculated for individuals with missing measurements. Simple methods for dealing with missing data include the complete case analysis and imputation. A recent study showed that the estimated mean AUC difference between treatment groups based on the linear mixed model (LMM), rather than on individually calculated AUCs by simple imputation, has negligible bias under random missing assumptions and only small bias when missing is not at random. However, this model assumes the outcome to be normally distributed, which is often violated in biomarker data. In this paper, we propose to use a LMM on log-transformed biomarkers, based on which statistical inference for the ratio, rather than difference, of AUC between treatment groups is provided. The proposed method can not only handle the potential baseline imbalance in a randomized trail but also circumvent the estimation of the nuisance variance parameters in the log-normal model. The proposed model is applied to a recently completed large randomized trial studying the effect of nicotine reduction on biomarker exposure of smokers.

Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85  %   detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8  %   accuracy. To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l'Université de Montréal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Québec-Université Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4  %   (+8  %  ), +7  %   (+9  %  ), +2  %   (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2  %   (+1.7  %  ), +4.5  %   (+3.6  %  ), +0  %   (+0  %  ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0  %   (-2  %  ), +0  %   (-3  %  ), +2  %   (-2  %  ), +4 (+3)], the AUC was improved in both testing sets. Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.

ObjectiveTo assess whether adding prostate volume to the kallikrein panel improves discrimination for ISUP Grade Group 2 or higher (GG2+) disease, as some men may have volume measurements available at the time of blood draw. While prostate volume predicts biopsy outcome, it requires an imaging procedure for measurement. The four kallikrein panel - commercially available as the 4Kscore - predicts risk of GG2+ disease and requires only a blood draw.Materials and methodsA total of 9131 patients with available prostate volume and total PSA ≤25 ng/ml from 5 historical (sextant biopsy, pre-ISUP 2005 grading) and 4 contemporary cohorts (10+ cores, ISUP 2005 grading). Previously published kallikrein panel models were used to predict risk of GG2+. Volume was added to the model in each cohort and change in discrimination was meta-analyzed.ResultsIncreased prostate volume was associated with decreased risk of GG2+ disease after controlling for the kallikrein panel in 7/9 cohorts. However, kallikrein panel discrimination (0.817, 95% CI 0.802, 0.831) was not improved after including volume (AUC difference 0.002, 95% CI -0.003, 0.006). Heterogeneity (P

Background: The importance of diffusion in prostate cancer (PCa) diagnosis has been widely proven. Several studies investigated diffusion models in PCa diagnosis. Materials and Methods: This systematic review and meta-analysis study was performed to evaluate the ability of three diffusion models to diagnose PCa from the scientific electronic databases Embase, PubMed, Scopus, and Web of Science (ISI) for the period up to March 2022 to identify all relevant articles. Results: Eighteen studies were included in the systematic review section (7 diffusion kurtosis imaging [DKI] studies, 4 diffusion tensor imaging [DTI] studies, 4 intravoxel incoherent motion [IVIM] studies, and 3 IVIM-DKI studies). Pooled sensitivity, specificity, accuracy, and summary area under each diffusion model’s curve (AUC) and 95% confidence intervals (CIs) were calculated. The pooled accuracy and 95% CI on detection (differentiation of tumor from normal tissue and benign prostatic hyperplasia/prostatitis) were obtained for apparent diffusion coefficient (ADC) at 87.97% (84.56%–91.38%) for DKI parameters (Gaussian diffusion [DK] 87.94% [78.71%–97.16%] and deviation from Gaussian diffusion [K] 86.84% [81.83%–91.85%]) and IVIM parameters (true molecular diffusion [DIVIM] 81.73% [72.54%–90.91%], perfusion-related diffusion [D*] 65% [48.47%–81.53%] and perfusion fraction [f] 80.36% [64.23%–96.48%]). The AUC values and 95% CI in the detection of PCa were obtained for ADC at 0.95 (0.92–0.97), for DKI parameters (DK 0.94 [0.89–0.99] and K 0.93 [0.90–0.96]) and for IVIM parameters (DIVIM 0.85 [0.80–0.91], D* 0.60 [0.43–0.77] and f 0.73 [0.63–0.84]). Two studies showed that the DTI accuracy values were 97.34% and 85%. For IVIM–kurtosis model in PCa detection, two studies stated that the DIVIM-K and KIVIM-K accuracy values were 85% and 84.44% (the pooled accuracy; 84.64% with 95% CI 75.78%–93.50%), and 72.50% and 71.11% (the pooled accuracy, 72.10% with 95% CI 64.73%–79.48%), respectively. Conclusion: Our findings showed that among the DKI, IVIM, and ADC parameters, it seems that ADC, Dk, DIVIM, and K are the most important, which can be used as an indicator to distinguish PCa from normal tissue. The DKI model probably has a higher ability to detect PCa from normal tissue than the IVIM model. DKI probably has the same diagnostic performance in PCa detection and grading compared to diffusion-weighted imaging and ADC.

What is known and objectiveTenofovir exposure is increased in non-pregnant adults when tenofovir disoproxil fumarate is coadministered with lopinavir/ritonavir. In pregnant women, tenofovir exposure is decreased. Our objective is to describe the effect of lopinavir/ritonavir on tenofovir pharmacokinetics during pregnancy.MethodsData were collected through the International Maternal Pediatric and Adolescent AIDS Clinical Trials (IMPAACT) Network P1026s protocol. This was a nonrandomized, open-label, parallel-group and multicentre phase-IV prospective study in pregnant women with HIV. Intensive steady-state 24-h pharmacokinetic profiles were collected during the third trimester of pregnancy and postpartum. Tenofovir was measured in plasma using validated liquid chromatography-mass spectrometry method (quantification limit: 10 ng/ml). Statistical tests compared paired and between group pharmacokinetic data.Results and discussionIn women not receiving lopinavir/ritonavir (n = 28), tenofovir AUC0-24 was 27% lower (2.2 mcg·h/ml vs 2.8 mcg·h/ml, p = 0.002) and oral clearance was 27% higher (61 L/h vs 48 L/h, p = 0.001) during the third trimester compared to paired postpartum data. In women receiving lopinavir/ritonavir (n = 10), tenofovir AUC0-24 and oral clearance were not different antepartum compared to postpartum. Women with and women without concomitant lopinavir/ritonavir displayed no significant differences in postpartum tenofovir pharmacokinetics.What is new and conclusionTenofovir exposure during the third trimester was reduced compared to postpartum in pregnant women not receiving lopinavir/ritonavir, but not in pregnant women also receiving lopinavir/ritonavir. Our findings suggest that pregnancy confounds the expected decrease in tenofovir exposure with concomitant lopinavir/ritonavir in non-pregnant adults. These findings illustrate the need for drug-drug interaction studies in pregnant women as drug disposition differs significantly in pregnant women compared to non-pregnant adults.