Identifying Putative Biomarkers of Foodborne Pathogens Using a Metabolomic Approach.

Metabolomics is the study of low molecular weight biochemical molecules (typically <1500 Da) in a defined biological organism or system. In case of food systems, the term "food metabolomics" is often used. Food metabolomics has been widely explored and applied in various fields including food analysis, food intake, food traceability, and food safety. Food safety applications focusing on the identification of pathogen-specific biomarkers have been promising. This chapter describes a nontargeted metabolite profiling workflow using gas chromatography coupled with mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for the extraction of polar metabolites from media, the analysis of the extracts using GC-MS, and finally chemometric data analysis using univariate and multivariate statistical tools to identify potential pathogen-specific biomarkers.

A limited sampling model to estimate the area under the curve of mycophenolic acid in hematopoietic stem cell transplantation recipients.

Relationship between the areas under the curve (AUC) of mycophenolic acid (MPA) and the likelihood of rejection is well-established in solid organ transplantation recipients. In hematopoietic stem cell transplantation (HSCT), MPA AUC is also linked to graft versus host disease. This study aimed to develop a simplified method to estimate MPA AUC0-12 in Chinese patients undergoing allogeneic HSCT (allo-HSCT). Intensive sampling was conducted in 22 patients who were orally administered mycophenolate mofetil. Plasma concentrations of total MPA were measured, and a model predicting AUC0-12 using data from these 22 patients was constructed through regression analysis. The accuracy of the most suitable model was assessed in an additional 20 patients. None of the individual MPA concentrations showed a strong correlation with AUC0-12 (r2 < 0.7). Models utilizing 4 or more concentrations were found to effectively estimate MPA AUC0-12 (r2 > 0.87). The most operationally feasible model demonstrated good predictive performance with a mean absolute percentage error (APE%) < 20%. Single MPA concentrations showed poor correlation with MPA AUC0-12. A model utilizing 4 oral concentrations (0, 0.5, 1, and 4 h postdose) over a 12-h period could effectively estimate MPA AUC0-12 with precise results and minimal bias.

In vivo pharmacokinetic study of carmustine in rats after giving single-dose of carmustine API solution, flexible liposomes, in situ nasal gel, optimized flexible liposomes embedded in situ nasal gel, and marketed formulation.

BACKGROUND: Carmustine is used in the treatment of glioblastoma (GBM). GBM is a well-known life-threatening type of cancerous tumor. GBM covers 60.00% among all primary brain tumors, with an occurrence of 74,000 cases across the globe. Management for GBM is still very difficult because most of the medicines are unable to cross the blood-brain barrier (BBB). The present work observed that flexible liposomes embedded in situ nasal gel of carmustine is the best brain-targeted medicine delivery system for the management of GBM through the nasal route. AIM: To evaluate in vivo pharmacokinetic parameters of carmustine formulations administered through nasal routes in Wistar rats. METHODS: In this work, different pharmacokinetic parameters were determined for carmustine formulations viz. carmustine API (Active Pharmaceutical Ingredient) solution, flexible liposomes, in situ thermoreversible intranasal gel, optimized flexible liposomes embedded in situ thermoreversible intranasal gel via intranasal administration in rats, and compared with marketed intravenous injection of carmustine administered through intravenous route. Carmustine was estimated with the help of a validated high-performance liquid chromatography (HPLC) approach. Three to four-months-old normal Wistar rats of either sex, having a weight of 200-250 grams were used in this study. RESULTS: Intranasal administration of optimized flexible liposomes embedded in situ nasal gel showed greater Cmax (∼two-fold), AUC0→t (∼three-fold), AUC0→∞ (∼six-fold), and decreased Tmax (1h) data in the brain, than commercial intravenous injection of carmustine. The plasma concentration of carmustine administered through nasal route was found to be comparatively lower than intravenous administration, indicating lower systemic exposure to carmustine via the nasal route. CONCLUSION: In vivo pharmacokinetics results revealed that the optimized flexible liposomes embedded in situ nasal gel of carmustine can effectively deliver carmustine to brain by nasal drug delivery system in Wistar rats.

A microRNA diagnostic biomarker for amyotrophic lateral sclerosis.

Blood-based diagnostic biomarkers for amyotrophic lateral sclerosis will improve patient outcomes and positively impact novel drug development. Critical to the development of such biomarkers is robust method validation, optimization and replication with adequate sample sizes and neurological disease comparative blood samples. We sought to test an amyotrophic lateral sclerosis biomarker derived from diverse samples to determine if it is disease specific. Extracellular vesicles were extracted from blood plasma obtained from individuals diagnosed with amyotrophic lateral sclerosis, primary lateral sclerosis, Parkinson's disease and healthy controls. Immunoaffinity purification was used to create a neural-enriched extracellular vesicle fraction. MicroRNAs were measured across sample cohorts using real-time polymerase chain reaction. A Kruskal-Wallis test was used to assess differences in plasma microRNAs followed by post hoc Mann-Whitney tests to compare disease groups. Diagnostic accuracy was determined using a machine learning algorithm and a logistic regression model. We identified an eight-microRNA diagnostic signature for blood samples from amyotrophic lateral sclerosis patients with high sensitivity and specificity and an area under the curve calculation of 98% with clear statistical separation from neurological controls. The eight identified microRNAs represent disease-related biological processes consistent with amyotrophic lateral sclerosis. The direction and magnitude of gene fold regulation are consistent across four separate patient cohorts with real-time polymerase chain reaction analyses conducted in two laboratories from diverse samples and sample collection procedures. We propose that this diagnostic signature could be an aid to neurologists to supplement current clinical metrics used to diagnose amyotrophic lateral sclerosis.

Capturing biomarkers associated with Alzheimer disease subtypes using data distribution characteristics.

Late-onset Alzheimer disease (AD) is a highly complex disease with multiple subtypes, as demonstrated by its disparate risk factors, pathological manifestations, and clinical traits. Discovery of biomarkers to diagnose specific AD subtypes is a key step towards understanding biological mechanisms underlying this enigmatic disease, generating candidate drug targets, and selecting participants for drug trials. Popular statistical methods for evaluating candidate biomarkers, fold change (FC) and area under the receiver operating characteristic curve (AUC), were designed for homogeneous data and we demonstrate the inherent weaknesses of these approaches when used to evaluate subtypes representing less than half of the diseased cases. We introduce a unique evaluation metric that is based on the distribution of the values, rather than the magnitude of the values, to identify analytes that are associated with a subset of the diseased cases, thereby revealing potential biomarkers for subtypes. Our approach, Bimodality Coefficient Difference (BCD), computes the difference between the degrees of bimodality for the cases and controls. We demonstrate the effectiveness of our approach with large-scale synthetic data trials containing nearly perfect subtypes. In order to reveal novel AD biomarkers for heterogeneous subtypes, we applied BCD to gene expression data for 8,650 genes for 176 AD cases and 187 controls. Our results confirm the utility of BCD for identifying subtypes of heterogeneous diseases.

Acquisition Time for Resting-State HbO/Hb Coupling Measured by Functional Near-Infrared Spectroscopy in Assessing Autism.

Functional near-infrared spectroscopy was used to record spontaneous hemodynamic fluctuations form the bilateral temporal lobes in 25 children with autism spectrum disorder (ASD) and 22 typically developing (TD) children. The coupling between oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) was calculated by Pearson correlation coefficient, showing significant difference between ASD and TD, thus the coupling could be a characteristic feature for ASD. To evaluate the discrimination ability of the feature obtained in different acquisition times, the receiver operating characteristic curve (ROC) was constructed and the area under curve (AUC) was calculated. The results showed AUC > 0.8 when the time duration was longer than 1.5 min, but longer than 4 min, AUC value (~0.87) hardly varied, implying the maximal discrimination ability reached. This study demonstrated the coupling could be one of characteristic features for ASD even acquired in a short measurement time.

Incidence of Acute Kidney Injury in Trough and AUC/MIC Vancomycin Dosing Strategies in a Large Tertiary Care Center: A Retrospective Cohort.

Acute kidney injury (AKI) is a complication associated with vancomycin use. There is evidence that this was related to the presence of supratherapeutic vancomycin levels rather than the drug itself. The area under the curve over 24 h to minimum inhibitory concentration (AUC/MIC) dosing for vancomycin has replaced trough-based dosing, but the impact of this change on AKI rates remains unclear. A retrospective cohort study was conducted in a tertiary care teaching hospital. Patients from the trough cohort were recruited from January 1, 2019, to June 30, 2019, and the AUC/MIC cohort from July 1, 2021, to January 1, 2022. Sociodemographics, clinical characteristics, and concomitant medications were obtained. AKI was defined by The Kidney Disease Improving Global Outcomes. A total of 1056 patients were included, 509 in the trough cohort and 547 in the AUC/MIC cohort. The baseline rates of chronic kidney disease were 15.4% and 9.9%, respectively. The AKI rates were 15.9% and 11.9% for trough and AUC/MIC cohorts, respectively (P-value .045). The most frequent nephrotoxins were piperacillin/tazobactam (TZP), diuretics, and IV contrast for both groups. The rates of supratherapeutic levels were higher in the trough cohort (20.7%) than in the AUC/MIC cohort (6.6%). The multivariate logistic regression analysis showed that trough dosing was not associated with increased rates of AKI (OR = 0.96 CI 0.64-1.44). Supratherapeutic levels (OR = 4.64), diuretics (OR = 1.62), TZP (OR = 2.01), and ICU admission (OR = 1.72) were associated with AKI. Vancomycin AUC/MIC dosing strategy was associated with decreased rates of supratherapeutic levels of this drug compared to trough dosing, with a trend toward lower rates of AKI.

Unlocking Hidden Risks: Harnessing Artificial Intelligence (AI) to Detect Subclinical Conditions from an Electrocardiogram (ECG).

Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancements in diagnosis, prediction, treatment, and outcomes. This article aims to provide a basic understanding of AI enabled ECG technology. Specific conditions and findings will be discussed, followed by reviewing associated terminology and methodology. In the appendix, definitions of AUC versus accuracy are explained. The application of deep learning models enables detecting diseases from normal electrocardiograms at accuracy not previously achieved by technology or human experts. Results with AI enabled ECG are encouraging as they considerably exceeded current screening models for specific conditions (i.e., atrial fibrillation, left ventricular dysfunction, aortic stenosis, and hypertrophic cardiomyopathy). This could potentially lead to a revitalization of the utilization of the ECG in the insurance domain. While we are embracing the findings with this rapidly evolving technology, but cautious optimism is still necessary at this point.

Clinical pharmacokinetics and pharmacodynamics of nicardipine; a systematic review.

INTRODUCTION: Nicardipine is a type of calcium channel blocker that is commonly used in the treatment of angina pectoris, hypertension, and related cardiovascular disorders. This systematic review assesses the reported pharmacokinetic (PK) and associated pharmacodynamic (PD) parameters of nicardipine in humans. AREAS COVERED: An exhaustive literature search using four internet databases was conducted up to 5 October 2023, which yielded 871 papers, of which 32 fulfilled the eligibility requirements by including human PK and related PD data. The area under the plasma concentration vs. time curve from zero to infinity (AUC0-∞) and maximum plasma concentration (Cmax) of nicardipine rise proportionately with increasing dosage. One study revealed that AUC0-∞ of nicardipine was increased by 5-fold in hepatic cirrhosis patients compared to the control subjects. Moreover, related PD data in renal-impaired hypertensive patients revealed that a notable reduction in blood pressure was associated with nicardipine administration. EXPERT OPINION: This review covers comprehensive data on clinical PK, drug-drug interaction studies, effects of dosage form on ADME, and associated PD parameters of nicardipine using all relevant published studies. The present study will also aid in the development and evaluation of PK models for suggesting model-informed dosing regimens. PROSPERO NUMBER: CRD42024533051.

Clinical pharmacokinetics of glipizide: a systematic review.

INTRODUCTION: Glipizide is an oral antidiabetic drug widely used to treat non-insulin-dependent type II diabetes mellitus (NIDDM). This systematic review extensively examines all reported pharmacokinetic (PK) parameters of glipizide in healthy and diseased populations. AREAS COVERED: A total of 31 articles were retrieved after screening various databases, i.e. Google Scholar, PubMed, Science Direct, and Cochrane, regarding the PK parameters of glipizide in healthy, diseased, drug-drug, and drug-food interaction studies. The Cmax was 35% higher in healthy Koreans than in Caucasian Americans. In type II diabetes patients, the AUC0-∞ increases ~2-fold after multiple dosage regimen in comparison with a single dose. Furthermore, the Cmax increased in fasting conditions compared to the non-fasting state in diabetic individuals i.e. 1338.28 ± 125.18 ng/mL and 1297.29 ± 47.22 ng/mL, respectively. EXPERT OPINION: The presented data has depicted that glipizide exposure varies between single and multiple dosing and its Cmax also changes between different demographic populations. Since it has a shorter half-life, the development of its new extended-release formulations may assist practitioners in improving adherence among diabetic patients. PROSPERO REGISTRATION NO: CRD42024538428.

Emerging insights into traditional Chinese medicine associated with neurodegenerative diseases: A bibliometric analysis.

ETHNOPHARMACOLOGICAL RELEVANCE: Research suggests that traditional Chinese medicine (TCM) holds promise in offering innovative approaches to tackle neurodegenerative disorders. In our endeavor, we conducted a comprehensive bibliometric analysis to delve into the landscape of TCM research within the realm of neurodegenerative diseases, aiming to uncover the present scenario, breadth, and trends in this field. This analysis presents potentially valuable insights for the clinical application of traditional Chinese medicine and provides compelling evidence supporting its efficacy in the treatment of neurodegenerative conditions. AIM OF THE STUDY: The incidence of neurodegenerative diseases is on the rise, yet effective treatments are still lacking. Research indicates that TCM could offer novel perspectives for addressing neurodegenerative conditions. Nonetheless, the literature on this topic is intricate and multifaceted, with existing reviews offering only limited coverage. To gain a thorough understanding of TCM research in neurodegenerative diseases, we undertook a bibliometric analysis to explore the current status, scope, and trends in this area. MATERIALS AND METHODS: A literature search was carried out on April 1, 2024, utilizing the Web of Science Core Collection (WoSCC). Visualization and quantitative analyses were then performed with the assistance of CiteSpace, VOSviewer, and R software. RESULTS: A total of 6856 articles were retrieved in the search. Research on TCM for neurodegenerative diseases commenced in 1989 and has exhibited a notable overall growth since then. Main research contributors include East Asian countries like China, as well as the United States. Through our analysis, we identified 15 highly productive authors, 10 top-tier journals, 13 citation clusters, 11 influential articles, and observed a progression in keyword evolution across 4 distinct categories. In 2020, there was a significant upsurge in the knowledge base, collaboration efforts, and publication output within the field. This field is interdisciplinary: network pharmacology emerges as the cutting-edge paradigm in TCM research, while Alzheimer's disease remains a prominent focus among neurodegenerative conditions due to its evolving etiology. A burst detection analysis unveils that in 2024, the focal points of research convergence between TCM and neurodegenerative diseases lie in two key biological processes or mechanisms: autophagy and microbiota. CONCLUSIONS: For the first time, this study quantitatively and visually captures the evolution of TCM in addressing neurodegenerative diseases, showcasing a notable acceleration in recent years. Our findings underscore the pivotal role of interdisciplinary collaboration and the necessity for increased global partnerships. Network pharmacology, leveraging the advancements of the big data era, embraces a holistic and systematic approach as a novel paradigm in exploring traditional Chinese medicine and unraveling their fundamental mechanisms. Three ethnomedical plants-Tianma, Renshen, and Wuweizi-demonstrate the promise of their bioactive compounds in treating neurodegenerative disorders, bolstered by their extensive historical usage for such ailments. Moreover, our intricate analysis of the evolutionary trajectories of key themes such as targets and biomarkers substantially enriches our comprehension of the underlying mechanisms involved.

Single point insulin sensitivity estimator index for identifying metabolic syndrome in US adults: NHANES 2017-march 2020.

PURPOSE: Single Point Insulin Sensitivity Estimator (SPISE) index was recently introduced as a reliable indirect indicator of insulin resistance, applicable to large population-based research. Here, we aimed to 1) examine racial/ethnic differences in SPISE index among US adults, 2) compare predictive power of SPISE index for metabolic syndrome (MetSyn) by race/ethnicity, and 3) evaluate its predictive power for MetSyn against other well-known IR indices including Triglyceride/HDL-C, Triglyceride-glucose index, homeostatic model assessment for insulin resistance, and inverse fasting insulin. METHODS: A total of 2168 adults (814 white, 690 black, and 664 Hispanic) from NHANES 2017-March 2020 Pre-Pandemic Data was analyzed in this study. MetSyn was defined by the AHA/NHLBI criteria. SPISE index and insulin resistance indices were calculated by using physical and cardiometabolic parameters. RESULTS: SPISE index was lowest in Hispanic, followed by black and white, with no difference between white vs. black. The area under the curve of receiver operating characteristics of SPISE index for predicting MetSyn was highest in white (88 %), followed by Hispanic (86 %) and black (82 %) (P < 0.05 vs. black), with optimal cutoffs of 5.03, 4.84, and 4.89, respectively. In the total cohort, the predictive power of the SPISE index for MetSyn was 85 %, higher than the other insulin resistance indices (all P < 0.05). CONCLUSIONS: SPISE index outperforms various insulin resistance indices for predicting MetSyn in US adults, signifying its potential in large-scale observational studies. Race/ethnicity should be stratified when using the SPISE index as its predictive power and cutoffs for predicting MetSyn vary by race/ethnicity.

Comparison of Day-Specific Serum LH, Estradiol, and Progesterone with MiraTM Monitor Urinary LH, Estrone-3-glucuronide, and Pregnanediol-3-glucuronide Levels in Ovulatory Cycles.

Background and Objectives: Fertility tracking apps and devices are now currently available, but urinary hormone levels lack accuracy and sensitivity in timing the start of the 6-day fertile window and the precise 24 h interval of transition from ovulation to the luteal phase. We hypothesized the serum hormones estradiol (E2) and progesterone (P) might be better biomarkers for these major ovulatory cycle events, using appropriate mathematical tools. Materials and Methods: Four women provided daily blood samples for serum E2, P, and LH (luteinizing hormone) levels throughout their entire ovulatory cycles, which were indexed to the first day of dominant follicle (DF) collapse (defined as Day 0) determined by transvaginal sonography; therefore, ovulation occurred in the 24 h interval of Day -1 (last day of maximum diameter DF) to Day 0. For comparison, a MiraTM fertility monitor was used to measure daily morning urinary LH (ULH), estrone-3-glucuronide (E3G), and pregnanediol-3-glucuronide (PDG) levels in three of these cycles. Results: There were more fluctuations in the MiraTM hormone levels compared to the serum levels. Previously described methods, the Fertility Indicator Equation (FIE) and Area Under the Curve (AUC) algorithm, were tested for identifying the start of the fertile window and the ovulation/luteal transition point using the day-specific hormone levels. The FIE with E2 levels predicted the start of the 6-day fertile window on Day -7 (two cycles) and Day -5 (two cycles), whereas no identifying signal was found with E3G. However, both pairs of (E2, P) and (E3G, PDG) levels with the AUC algorithm signaled the Day -1 to Day 0 ovulation/luteal transition interval in all cycles. Conclusions: serum E2 and (E2, P) were better biomarkers for signaling the start of the 6-day fertile window, but both MiraTM and serum hormone levels were successful in timing the [Day -1, Day 0] ovulatory/luteal transition interval. These results can presently be applied to urinary hormone monitors for fertility tracking and have implications for the direction of future fertility tracking technology.

A Dunnett-Type Test and Its Sample Size Calculation for Comparing K ROC Curves with a Control.

Diagnostic biomarkers are key components of diagnostics. In this paper, we consider diagnostic biomarkers taking continuous values that are associated with a dichotomous disease status, called malignant or benign. The performance of such a biomarker is evaluated by the area under the curve (AUC) of its receiver operating characteristic curve. We assume that, together with the disease status, one control and multiple experimental biomarkers are collected from each subject to test if any of the experimental biomarkers have a larger AUC than the control. In this case, each experimental biomarker will be compared with the control so that a multiple testing issue is involved in the comparisons. In this paper, we propose a simple non-parametric statistical testing procedure to compare K(≥2) experimental biomarkers with a control, adjusting for the multiplicity and its sample size calculation method. Our sample size formula requires the specification of the AUC values (or the standardized effect size of each biomarker between the benign and malignant groups) together with the correlation coefficients between the biomarkers, the prevalence of the malignant group in the study population, the type I error rate, and the power. Through simulations, we show that the statistical test controls the overall type I error rate accurately and the proposed sample size closely maintains the specified statistical power.

Pharmacokinetic Simulation and Area under the Curve Estimation of Drugs Subject to Enterohepatic Circulation.

Enterohepatic circulation (EHC) is a complex process where drugs undergo secretion and reabsorption from the intestinal lumen multiple times, resulting in pharmacokinetic profiles with multiple peaks. The impact of EHC on area under the curve (AUC) has been a topic of extensive debate, questioning the suitability of conventional AUC estimation methods. Moreover, a universal model for accurately estimating AUC in EHC scenarios is lacking. To address this gap, we conducted a simulation study evaluating five empirical models under various sampling strategies to assess their performance in AUC estimation. Our results identify the most suitable model for EHC scenarios and underscore the critical role of meal-based sampling strategies in accurate AUC estimation. Additionally, we demonstrate that while the trapezoidal method performs comparably to other models with a large number of samples, alternative models are essential when sample numbers are limited. These findings not only illuminate how EHC influences AUC but also pave the way for the application of empirical models in real-world drug studies.

Enhanced Hand Gesture Recognition with Surface Electromyogram and Machine Learning.

This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human-computer interaction technologies.

In-Depth Comparison of Adeno-Associated Virus Containing Fractions after CsCl Ultracentrifugation Gradient Separation.

Recombinant adeno-associated viruses (rAAVs) play a pivotal role in the treatment of genetic diseases. However, current production and purification processes yield AAV-based preparations that often contain unwanted empty, partially filled or damaged viral particles and impurities, including residual host cell DNA and proteins, plasmid DNA, and viral aggregates. To precisely understand the composition of AAV preparations, we systematically compared four different single-stranded AAV (ssAAV) and self-complementary (scAAV) fractions extracted from the CsCl ultracentrifugation gradient using established methods (transduction efficiency, analytical ultracentrifugation (AUC), quantitative and digital droplet PCR (qPCR and ddPCR), transmission electron microscopy (TEM) and enzyme-linked immunosorbent assay (ELISA)) alongside newer techniques (multiplex ddPCR, multi-angle light-scattering coupled to size-exclusion chromatography (SEC-MALS), multi-angle dynamic light scattering (MADLS), and high-throughput sequencing (HTS)). Suboptimal particle separation within the fractions resulted in unexpectedly similar infectivity levels. No single technique could simultaneously provide comprehensive insights in the presence of both bioactive particles and contaminants. Notably, multiplex ddPCR revealed distinct vector genome fragmentation patterns, differing between ssAAV and scAAV. This highlights the urgent need for innovative analytical and production approaches to optimize AAV vector production and enhance therapeutic outcomes.

Impact of the First Twenty-Four-Hour Area Under the Concentration-Time Curve/Minimum Inhibitory Concentration of Vancomycin on Treatment Outcomes in Patients With Methicillin-Resistant Staphylococcus aureus Bacteremia.

Background: Vancomycin regimens are designed to achieve an area under the concentration-time curve/minimum inhibitory concentration (AUC/MIC) ratio ranging between 400 and 600 µg·h/mL in the steady state. However, in cases of critical infections such as bacteremia requiring an early treatment approach, the clinical course may be affected by the AUC/MIC before reaching the steady state, that is, the AUC/MIC values 24 h after the first dose (first 24-h AUC/MIC). This study evaluated the relationship between the first 24-h AUC/MIC and the clinical course of methicillin-resistant Staphylococcus aureus (MRSA) infection. Methods: We retrospectively reviewed the records of patients with MRSA bacteremia in a university hospital between 2015 and 2022. The first 24-h AUC/MIC cutoff was set at 300 µg·h/mL based on the results of early response, and eligible patients were divided into groups with a first 24-h AUC/MIC either < 300 µg·h/mL (< 300 group, n = 32) or ≥ 300 µg·h/mL (≥ 300 group, n = 38). The primary endpoint was the rate of treatment efficacy, and the secondary endpoints were time to clinical and bacteriological improvement and 30-day survival rate. Results: Treatment efficacy and 30-day survival rates were not significantly different between the two groups (78.1% vs. 79.0%, P = 0.933 and 83.9% vs. 87.2%, P = 0.674, respectively). Among patients who showed treatment efficacy, the median time to clinical and bacteriological improvement was 11.5 days and 8.0 days in the < 300 and ≥ 300 groups, respectively; compared to the ≥ 300 group, the < 300 group had a significantly longer time to improvement (P = 0.001). Conclusions: The first 24-h AUC/MIC had no effect on the treatment efficacy and 30-day survival rates. However, the time to clinical and bacteriological improvement was significantly prolonged in the < 300 group, indicating that the first 24-h AUC/MIC does not affect the rate of therapeutic efficacy but may affect the treatment period.

Application of peripheral blood routine parameters in the diagnosis of influenza and Mycoplasma pneumoniae.

OBJECTIVES: Influenza and Mycoplasma pneumoniae infections often present concurrent and overlapping symptoms in clinical manifestations, making it crucial to accurately differentiate between the two in clinical practice. Therefore, this study aims to explore the potential of using peripheral blood routine parameters to effectively distinguish between influenza and Mycoplasma pneumoniae infections. METHODS: This study selected 209 influenza patients (IV group) and 214 Mycoplasma pneumoniae patients (MP group) from September 2023 to January 2024 at Nansha Division, the First Affiliated Hospital of Sun Yat-sen University. We conducted a routine blood-related index test on all research subjects to develop a diagnostic model. For normally distributed parameters, we used the T-test, and for non-normally distributed parameters, we used the Wilcoxon test. RESULTS: Based on an area under the curve (AUC) threshold of ≥ 0.7, we selected indices such as Lym# (lymphocyte count), Eos# (eosinophil percentage), Mon% (monocyte percentage), PLT (platelet count), HFC# (high fluorescent cell count), and PLR (platelet to lymphocyte ratio) to construct the model. Based on these indicators, we constructed a diagnostic algorithm named IV@MP using the random forest method. CONCLUSIONS: The diagnostic algorithm demonstrated excellent diagnostic performance and was validated in a new population, with an AUC of 0.845. In addition, we developed a web tool to facilitate the diagnosis of influenza and Mycoplasma pneumoniae infections. The results of this study provide an effective tool for clinical practice, enabling physicians to accurately diagnose and differentiate between influenza and Mycoplasma pneumoniae infection, thereby offering patients more precise treatment plans.

Analyzing heterogeneity in biomarker discriminative performance through partial time-dependent receiver operating characteristic curve modeling.

This study investigates the heterogeneity of a biomarker's discriminative performance for predicting subsequent time-to-event outcomes across different patient subgroups. While the area under the curve (AUC) for the time-dependent receiver operating characteristic curve is commonly used to assess biomarker performance, the partial time-dependent AUC (PAUC) provides insights that are often more pertinent for population screening and diagnostic testing. To achieve this objective, we propose a regression model tailored for PAUC and develop two distinct estimation procedures for discrete and continuous covariates, employing a pseudo-partial likelihood method. Simulation studies are conducted to assess the performance of these procedures across various scenarios. We apply our model and inference procedure to the Alzheimer's Disease Neuroimaging Initiative data set to evaluate potential heterogeneities in the discriminative performance of biomarkers for early Alzheimer's disease diagnosis based on patients' characteristics.