"What's yours is mine": Partners' everyday emotional experiences and cortisol in older adult couples.

The existing literature consistently finds that emotional experiences and cortisol secretion are linked at the within-person level. Further, relationship partners tend to covary in emotional experience, and in cortisol secretion. However, we are only beginning to understand whether and how an individuals' emotions are linked to their relationship partners' cortisol secretion. In this project, we harmonized data from three intensive measurement studies originating from Canada and Germany to investigate the daily dynamics of emotions and cortisol within 321 older adult couples (age range=56-87 years). Three-level multilevel models accounted for the nested structure of the data (repeated assessments within individuals within couples). Actor-Partner Interdependence Models were used to examine the effect of own emotional experiences (actor effects) and partner emotional experiences (partner effects) on momentary and daily cortisol secretion. Adjusting for age, sex, education, comorbidities, assay version, diurnal cortisol rhythm, time spent together, medication, and time-varying behaviors that may increase cortisol secretion, results suggest that higher relationship partner's positive emotions are linked with lower momentary cortisol and total daily cortisol. Further, this association was stronger for older participants and those who reported higher relationship satisfaction. We did not find within-couple links between negative emotions and cortisol. Overall, our results suggest that one's relationship partner's positive emotional experience may be a protective factor for their physiological responding, and that these more fleeting and day-to-day fluctuations may accumulate over time, contributing to overall relationship satisfaction.

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.

Characterizing Day 1 Area Under the Curve Following Vancomycin Loading Dose Administration in Adult Hospitalized Patients Using Non-Trapezoidal Linear Pharmacokinetic Equations: A Retrospective Observational Study.

INTRODUCTION: Methicillin-resistant Staphylococcus aureus (MRSA) infections are a serious threat to public health. Vancomycin (VAN) remains the primary treatment for these infections, and achieving the recommended area under the curve (AUC) target has been linked to improved clinical outcomes. The current VAN therapeutic monitoring guidelines recommend a loading dose (LD) of 20-35 mg/kg to rapidly attain targeted VAN exposures within 24 h of therapy. However, there is a paucity of data describing the impact of VAN LD on day 1 area under the curve (AUC0-24). This study aims to employ pharmacokinetic (PK) equations to calculate and describe the AUC0-24 following a VAN LD of 20 mg/kg. METHODS: This was a retrospective study of adult patients who were loaded with VAN 20 mg/kg, received ≥ 48 h of treatment, and had two consecutive serum VAN levels collected within 24 h. Linear, non-trapezoidal PK equations and two post-infusion VAN levels were used to calculate AUC0-24. Therapeutic AUC0-24 was defined as 400-600 mg/l*h. RESULTS: Among 123 included patients, the median age was 46 years (IQR 36, 62), 54% (67/123) of the patients had a body mass index (BMI) ≥ 30 kg/m2 and 27% (33/123) were admitted to the intensive care unit (ICU). Following a LD of 20 mg/kg, 50% (61/123) of the patients met the therapeutic AUC0-24, while 22% (27/123) of the patients were subtherapeutic, and 28% (35/123) were supratherapeutic. Compared with patients who achieved therapeutic AUC0-24, patients with subtherapeutic AUC0-24 were more likely to be younger (44 vs. 37 years old) and have a BMI ≥ 30 kg/m2 (67 vs. 52%). In contrast, patients with supratherapeutic AUC0-24 were more likely to be older (64 vs. 44 years old) and to have chronic kidney disease diagnosis (23 vs. 7%) when compared to patients who achieved a therapeutic AUC0-24. CONCLUSIONS: Only 50% of patients achieve the target AUC0-24 following a VAN 20 mg/kg LD, with younger, heavier patients underexposed and older patients with renal impairment overexposed, suggesting that different dosing strategies are needed for these populations.

Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost.

Introduction: Regulatory agencies generate a vast amount of textual data in the review process. For example, drug labeling serves as a valuable resource for regulatory agencies, such as U.S. Food and Drug Administration (FDA) and Europe Medical Agency (EMA), to communicate drug safety and effectiveness information to healthcare professionals and patients. Drug labeling also serves as a resource for pharmacovigilance and drug safety research. Automated text classification would significantly improve the analysis of drug labeling documents and conserve reviewer resources. Methods: We utilized artificial intelligence in this study to classify drug-induced liver injury (DILI)-related content from drug labeling documents based on FDA's DILIrank dataset. We employed text mining and XGBoost models and utilized the Preferred Terms of Medical queries for adverse event standards to simplify the elimination of common words and phrases while retaining medical standard terms for FDA and EMA drug label datasets. Then, we constructed a document term matrix using weights computed by Term Frequency-Inverse Document Frequency (TF-IDF) for each included word/term/token. Results: The automatic text classification model exhibited robust performance in predicting DILI, achieving cross-validation AUC scores exceeding 0.90 for both drug labels from FDA and EMA and literature abstracts from the Critical Assessment of Massive Data Analysis (CAMDA). Discussion: Moreover, the text mining and XGBoost functions demonstrated in this study can be applied to other text processing and classification tasks.

Automatic detection of facial expressions during the Cyberball paradigm in Borderline Personality Disorder: a pilot study.

Borderline Personality Disorder (BPD) symptoms include inappropriate control of anger and severe emotional dysregulation after rejection in daily life. Nevertheless, when using the Cyberball paradigm, a tossing game to simulate social exclusion, the seven basic emotions (happiness, sadness, anger, surprise, fear, disgust, and contempt) have not been exhaustively tracked out. It was hypothesized that these patients would show anger, contempt, and disgust during the condition of exclusion versus the condition of inclusion. When facial emotions are automatically detected by Artificial Intelligence, "blending", -or a mixture of at least two emotions- and "masking", -or showing happiness while expressing negative emotions- may be most easily traced expecting higher percentages during exclusion rather than inclusion. Therefore, face videos of fourteen patients diagnosed with BPD (26 ± 6 years old), recorded while playing the tossing game, were analyzed by the FaceReader software. The comparison of conditions highlighted an interaction for anger: it increased during inclusion and decreased during exclusion. During exclusion, the masking of surprise; i.e., displaying happiness while feeling surprised, was significantly more expressed. Furthermore, disgust and contempt were inversely correlated with greater difficulties in emotion regulation and symptomatology, respectively. Therefore, the automatic detection of emotional expressions during both conditions could be useful in rendering diagnostic guidelines in clinical scenarios.

Construction of an in vitro simulated one compartment extravascular administration model and its comparison with classic in vitro administration model in copper chloride induced HepG2 cell death.

In this study, we designed an in vitro administration device based on compartment model theory and utilized it to construct an in vitro simulated one compartment extravascular administration model of copper chloride. Within the Cmax range of 3.91-1000.00 µM, the measured concentration-time curves of the simulated one compartment extravascular administration model almost coincide with the corresponding theoretical curves. The measured values of toxicokinetic parameters, including ke, T1/2, ka, T1/2a, Tmax, Cmax, CL, and AUC0-∞ are close to the corresponding theoretical values. The fitting coefficients are >0.9990. In simulated one compartment extravascular administration and classic in vitro administration, copper chloride dose-dependently induced HepG2 cell death. When Cmax/administration concentration is equal, classic in vitro administration induces a higher cell death rate than simulated one compartment extravascular administration. However, there is no significant difference in inducing cell death between the two administration models when area under the curve is equal. In conclusion, the device designed in this study can be used to in vitro simulate one compartment extravascular administration, making in vitro toxicity testing more similar to in vivo scenarios. There are differences in copper chloride induced HepG2 cell death between simulated one compartment extravascular administration and classic in vitro administration.

Avapritinib Carries the Risk of Drug Interaction via Inhibition of UDP-Glucuronosyltransferase (UGT) 1A1.

BACKGROUND: Avapritinib is the only drug for adult patients with PDGFRA exon 18 mutated unresectable or metastatic gastrointestinal stromal tumor (GIST). Although avapritinib has been approved by the FDA for four years, little is known about the risk of drug-drug interactions (DDIs) via UDP-glucuronyltransferases (UGTs) inhibition. OBJECTIVE: The aim of the present study was to systematically evaluate the inhibitory effects of avapritinib against UGTs and to quantitatively estimate its potential DDIs risk in vivo. METHODS: Recombinant human UGTs were employed to catalyze the glucuronidation of substrates in a range of concentrations of avapritinib. The kinetics analysis was performed to evaluate the inhibition types of avapritinib against UGTs. The quantitative prediction of DDIs was done using in vitro-in vivo extrapolation (IVIVE). RESULTS: Avapritinib had a potent competitive inhibitory effect on UGT1A1. Quantitative prediction results showed that avapritinib administered at clinical doses might result in a 14.85% in-crease in area under the curve (AUC) of drugs primarily cleared by UGT1A1. Moreover, the Rgut value was calculated to be 18.44. CONCLUSION: Avapritinib has the potential to cause intestinal DDIs via the inhibition of UGT1A1. Additional attention should be paid when avapritinib is coadministered with UGT1A1 substrates.

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank.

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Value of turbo spin echo-based diffusion-weighted imaging in the differential diagnosis of benign and malignant solitary pulmonary lesions.

To quantitatively assess the diagnostic efficacy of multiple parameters derived from multi-b-value diffusion-weighted imaging (DWI) using turbo spin echo (TSE)-based acquisition techniques in patients with solitary pulmonary lesions (SPLs). A total of 105 patients with SPLs underwent lung DWI using single-shot TSE-based acquisition techniques and multiple b values. The apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) parameters, and lesion-to-spinal cord signal intensity ratio (LSR), were analyzed to compare the benign and malignant groups using the Mann-Whitney U test and receiver operating characteristic analysis. The Dstar values observed in lung cancer were slightly lower than those observed in pulmonary benign lesions (28.164 ± 31.950 versus 32.917 ± 34.184; Z = -2.239, p = 0.025). The LSR values were significantly higher in lung cancer than in benign lesions (1.137 ± 0.581 versus 0.614 ± 0.442; Z = - 4.522, p < 0.001). Additionally, the ADC800, ADCtotal, and D values were all significantly lower in lung cancer than in the benign lesions (Z = - 5.054, -5.370, and -6.047, respectively, all p < 0.001), whereas the f values did not exhibit any statistically significant difference between the two groups. D had the highest area under the curve (AUC = 0.887), followed by ADCtotal (AUC = 0.844), ADC800 (AUC = 0.824), and LSR (AUC = 0.789). The LSR, ADC800, ADCtotal, and D values did not differ statistically significantly in diagnostic effectiveness. Lung DWI using TSE is feasible for differentiating SPLs. The LSR method, conventional DWI, and IVIM have comparable diagnostic efficacy for assessing SPLs.

Gut microbiota predicts the diagnosis of ulcerative colitis in Saudi children.

BACKGROUND: Ulcerative colitis (UC) is an immune-mediated chronic inflammatory condition with a worldwide distribution. Although the etiology of this disease is still unknown, the understanding of the role of the microbiota is becoming increasingly strong. AIM: To investigate the predictive power of the gut microbiota for the diagnosis of UC in a cohort of newly diagnosed treatment-naïve Saudi children with UC. METHODS: The study population included 20 children with a confirmed diagnosis of UC and 20 healthy controls. Microbial DNA was extracted and sequenced, and shotgun metagenomic analysis was performed for bacteria and bacteriophages. Biostatistics and bioinformatics demonstrated significant dysbiosis in the form of reduced alpha diversity, beta diversity, and significant difference of abundance of taxa between children with UC and control groups. The receiver operating characteristic curve, a probability curve, was used to determine the difference between the UC and control groups. The area under the curve (AUC) represents the degree of separability between the UC group and the control group. The AUC was calculated for all identified bacterial species and for bacterial species identified by the random forest classification algorithm as important potential biomarkers of UC. A similar method of AUC calculation for all bacteriophages and important species was used. RESULTS: The median age and range were 14 (0.5-21) and 12.9 (6.8-16.3) years for children with UC and controls, respectively, and 40% and 35% were male for children with UC and controls, respectively. The AUC for all identified bacterial species was 89.5%. However, when using the bacterial species identified as important by random forest classification algorithm analysis, the accuracy increased to 97.6%. Similarly, the AUC for all the identified bacteriophages was 87.4%, but this value increased to 94.5% when the important bacteriophage biomarkers were used. CONCLUSION: The very high to excellent AUCs of fecal bacterial and viral species suggest the potential use of noninvasive microbiota-based tests for the diagnosis of unusual cases of UC in children. In addition, the identification of important bacteria and bacteriophages whose abundance is reduced in children with UC suggests the potential of preventive and adjuvant microbial therapy for UC.

Effect of liposomal bupivacaine for preoperative erector spinae plane block on postoperative pain following video-assisted thoracoscopic lung surgery: a protocol for a multicenter, randomized, double-blind, clinical trial.

Background: There is still a controversy about the superiority of liposomal bupivacaine (LB) over traditional local anesthetics in postoperative analgesia after thoracic surgery. This study aims to determine the effect of LB versus bupivacaine hydrochloride (HCl) for preoperative ultrasound-guided erector spinae plane block (ESPB) on postoperative acute and chronic pain in patients undergoing video-assisted thoracoscopic lung surgery. Methods: This multicenter, randomized, double-blind, controlled trial will include 272 adult patients scheduled for elective video-assisted thoracoscopic lung surgery. Patients will be randomly assigned, 1:1 and stratified by site, to the liposomal bupivacaine (LB) group or the bupivacaine (BUPI) HCl group. All patients will receive ultrasound-guided ESPB with either LB or bupivacaine HCl before surgery and patient-controlled intravenous analgesia (PCIA) as rescue analgesia after surgery. The numeric rating scale (NRS) score will be assessed after surgery. The primary outcome is the area under the curve of pain scores at rest for 0-72 h postoperatively. The secondary outcomes include the total amount of opioid rescue analgesics through 0-72 h postoperatively, time to the first press on the PCIA device as rescue analgesia, the area under the curve of pain scores on activity for 0-72 h postoperatively, NRS scores at rest and on activity at different time points during the 0-72 h postoperative period, Quality of Recovery 15 scores at 72 h after surgery, and NRS scores on activity on postsurgical day 14 and postsurgical 3 months. Adverse events after the surgery are followed up to the postsurgical day 7, including postoperative nausea and vomiting, fever, constipation, dizziness, headache, insomnia, itching, prolonged chest tube leakage, new-onset atrial fibrillation, severe ventricular arrhythmia, deep venous thrombosis, pulmonary embolism, pulmonary atelectasis, cardiac arrest, ileus, urinary retention, chylothorax, pneumothorax, and organ failure. Analyzes will be performed first according to the intention to treat principle and second with the per-protocol analysis. Discussion: We hypothesize that LB for preoperative ultrasound-guided ESPB would be more effective than bupivacaine HCl in reducing postoperative pain in video-assisted thoracoscopic lung surgery. Our results will contribute to the optimization of postoperative analgesia regimens for patients undergoing video-assisted thoracoscopic lung surgery.Clinical trial registration:http://www.chictr.org.cn, identifier ChiCTR2300074852.

ROC curve analysis: a useful statistic multi-tool in the research of nephrology.

In the past decade, scientific research in the area of Nephrology has focused on evaluating the clinical utility and performance of various biomarkers for diagnosis, risk stratification and prognosis. Before implementing a biomarker in everyday clinical practice for screening a specific disease context, specific statistic measures are necessary to evaluate the diagnostic accuracy and performance of this biomarker. Receiver Operating Characteristic (ROC) Curve analysis is an important statistical method used to estimate the discriminatory performance of a novel diagnostic test, identify the optimal cut-off value for a test that maximizes sensitivity and specificity, and evaluate the predictive value of a certain biomarker or risk, prediction score. Herein, through practical examples, we aim to present a simple methodological approach to explain in detail the principles and applications of ROC curve analysis in the field of nephrology pertaining diagnosis and prognosis.

A novel approach to model cumulative stress: Area under the s-factor curve.

OBJECTIVE: Using a large longitudinal sample of adults from the Midlife in the United States (MIDUS) study, the present study extended a recently developed hierarchical model to determine how best to model the accumulation of stressors, and to determine whether the rate of change in stressors or traditional composite scores of stressors are stronger predictors of health outcomes. METHOD: We used factor analysis to estimate a stress-factor score and then, to operationalize the accumulation of stressors we examined five approaches to aggregating information about repeated exposures to multiple stressors. The predictive validity of these approaches was then assessed in relation to different health outcomes. RESULTS: The prediction of chronic conditions, body mass index, difficulty with activities of daily living, executive function, and episodic memory later in life was strongest when the accumulation of stressors was modeled using total area under the curve (AUC) of estimated factor scores, compared to composite scores that have traditionally been used in studies of cumulative stress, as well as linear rates of change. CONCLUSIONS: Like endogenous, biological markers of stress reactivity, AUC for individual trajectories of self-reported stressors shows promise as a data reduction technique to model the accumulation of stressors in longitudinal studies. Overall, our results indicate that considering different quantitative models is critical to understanding the sequelae and predictive power of psychosocial stressors from midlife to late adulthood.

Correlation between trough concentration and AUC for elexacaftor, tezacaftor and ivacaftor.

Therapeutic drug monitoring (TDM) of elexacaftor, tezacaftor, ivacaftor (ETI) could be a useful tool to increase efficacy and decrease the risk of adverse effects in people with Cystic Fibrosis (pwCF). It is however unclear whether drug exposure should be monitored by assessment of trough (Cmin) levels or determination of the area under the curve (AUC). Hence, in this study the correlation between measured Cmin concentration and AUC was evaluated. Serial plasma samples, including Cmin, were drawn after administration of ETI in order to calculate the AUC and assess the correlation between the two parameters. A linear correlation between Cmin and AUC0-24h was found, with Pearson's r correlation coefficients of 0.963, 0.908 and 0.860 for elexacaftor, tezacaftor and ivacaftor, respectively. Exposure of ETI may be monitored by assessment of Cmin levels.

ANN multi-layer perceptron for prediction of blood-brain barrier permeable compounds for central nervous system therapeutics.

Endothelial cells produce a semipermeable barrier known as the blood-brain barrier (BBB) to keep undesired chemicals out of the central nervous system (CNS). However, this barrier also restricts the exploration of potential new medications due to insufficient exposure. To address this challenge, machine learning (ML) algorithms can be useful to predict the BBB permeability of chemical compounds. Support vector machines, continuous neural networks, and deep learning approaches have been used to identify compounds that can penetrate the BBB. However, predicting BBB permeability based solely on chemical structure can be difficult. In the current research, we developed an ML model using a large dataset to predict BBB permeability, which could be used for early-stage drug screening of potential CNS medications. Our artificial neural network ANN algorithm exhibited an accuracy of 0.94, specificity of 0.83, sensitivity of 0.97, AUC of 0.96, and MCC of 0.83. These metrics suggest that our model has a high accuracy rate in predicting BBB permeability and therefore has the potential to advance drug discovery efforts in the CNS. This study's outcomes demonstrate the potential for ML models to predict BBB permeability accurately, aiding in the identification of new CNS therapeutic options.Communicated by Ramaswamy H. Sarma.

Predictive Performance of an Updated Polygenic Risk Score for Age-Related Macular Degeneration.

PURPOSE: A recent genome-wide association study of age-related macular degeneration (AMD) identified new AMD-associated risk variants. These variants now can be incorporated into an updated polygenic risk score (PRS). This study aimed to assess the performance of an updated PRS, PRS2023, in an independent cohort of older individuals with retinal imaging data and to compare performance with an older PRS, PRS2016. DESIGN: Cross-sectional study. PARTICIPANTS: A total of 4175 participants of European ancestry, 70 years of age or older, with genotype and retinal imaging data. METHODS: We used logistic regression models and area under the receiver operating characteristic curve (AUC) to assess the performance of PRS2023 compared with PRS2016. AMD status and severity were graded using color fundus photography. MAIN OUTCOME MEASURES: Association of PRS2023 and PRS2016 with AMD risk at baseline. RESULTS: At enrollment among 4175 participants, 2605 participants (62.4%) had no AMD and 853 participants (20.4%), 671 participants (16.1%), and 46 participants (1.1%) had early, intermediate, and late-stage AMD, respectively. More than 27% of the participants with a high PRS2023 (top quartile) had intermediate or late-stage AMD, compared with < 15% for those in the middle 2 quartiles and less than 13% for those in the lowest quartile. Both PRS2023 and PRS2016 were associated significantly with AMD after adjustment for age, sex, smoking status, and lipid levels, with increasing odds ratios (ORs) for worsening AMD grades. PRS2023 outperformed PRS2016 (P = 0.03 for all AMD and P = 0.03 for late AMD, DeLong test comparing AUC). PRS2023 was associated with late-stage AMD with an adjusted OR of 5.05 (95% confidence interval [CI], 3.41-7.47) per standard deviation. The AUC of a model containing conventional or nongenetic risk factors and PRS2023 was 91% (95% CI, 87%-95%) for predicting late-stage AMD, which improved 12% over the model without the PRS (AUC, 79%; P < 0.001 for difference). CONCLUSIONS: A new PRS, PRS2023, for AMD outperforms a previous PRS and predicts increasing risk for late-stage AMD (with stronger association for more severe imaging-confirmed AMD grades). Our findings have clinical implications for the improved prediction and risk stratification of AMD. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Click SP/AP Area Ratio Vesrus Tone Burst SP Amplitude to Diagnose Ménière's Disease Using Electrocochleography.

OBJECTIVES: To evaluate the sensitivity and the specificity of summating potential (SP)/action potential (AP) area under the curve (AUC) ratio by a transtympanic electrode and a click stimulus (TT-CS), SP/AP AUC ratio by an extratympanic electrode and a click stimulus (ET-CS) and SP amplitude value by a transtympanic electrode and tone burst stimulus (TT-TBS) in regard of Ménière's disease (MD) diagnosis. This is the first study that compares SP amplitude value performed by a TT-TBS and the SP/AP AUC ratio performed by a TT-CS. STUDY DESIGN: Retrospective comparative study. SETTINGS: Ninety-five patients met the inclusion criteria for electrocochleography (ECochG) testing in a tertiary care center. METHODS: The sensitivity and specificity of our different ECochG protocols were calculated in regard of the diagnosis of MD. RESULTS: The patients' mean age was 54 years old (female predominance). The sensitivity and the specificity of SP/AP area ratio by a TT-CS were 88.5% and 70.0%, respectively. On the other hand, the sensitivity and specificity for the SP amplitude value by a TT-TBS were 60.0% and 55.6%, respectively. SP/AP area ratio by TT-CS was statistically better than SP amplitude value by TT-TBS to detect MD disease (P = .016). However, no difference was identified between SP/AP area ratio by ET-CS and SP amplitude value by a TT-TBS (P = .573). CONCLUSION: SP/AP area ratio by click stimulation has higher sensitivity and specificity to detect MD compared to SP amplitude value by tone burst stimulation. ECochG would be extremely useful in the diagnosis of MD if we use the SP/AP area ratio (sensitivity: 88.5%); therefore, it changes the bad reputation of ECochG sensitivity using SP/AP amplitude ratio (sensitivity: 51.7%) for the diagnosis of MD.

Evaluation of interleukins (IL-1α, IL-1Ra, IL-12, IL-17A, IL-31, and IL-33) and chemokines (CXCL10 and CXCL16) in the serum of male patients with ankylosing spondylitis.

BACKGROUND: A case-control study was performed to explore eight pro-inflammatory and anti-inflammatory cytokines, namely interleukin (IL)-1α, IL-1Ra (IL-1 receptor antagonist), IL-12, IL-17A, IL-31, IL-33, CXCL10 (C-X-C motif chemokine ligand 10), and CXCL16, with the aim to understand their role in ankylosing spondylitis (AS) pathogenesis and evaluate their utility as markers to differentiate between diseased and healthy individuals. Among these cytokines, IL-31 and CXCL16 have not been well studied in AS. PATIENTS AND METHODS: The study included 94 male patients with AS and 91 age-matched control males. Interleukin and chemokine levels were measured using ELISA kits. RESULTS: Serum levels of IL-17A, CXCL10, and CXCL16 were significantly elevated in patients compared to controls, while IL-31 levels were significantly decreased in patients. IL-17A, CXCL10, and CXCL16 were associated with an increased risk of AS, while IL-31 was associated with a decreased risk of disease (odds ratio = 1.22, 1.78, 1.14, and 0.89, respectively). As indicated by the area under the curve (AUC), IL-17A, IL-31, CXCL10, and CXCL16 were potential markers to differentiate between AS patients and controls (AUC = 0.877, 0.735, 0.8, and 0.7, respectively). IL-1α, IL-1Ra, IL-12, and IL-33 levels showed no significant variations between patients and controls. CONCLUSIONS: Among the eight cytokines examined, IL-17A, CXCL10, and CXCL16 were up-regulated in the serum of AS patients, while IL-31 was down-regulated. The levels of IL-1α, IL-1Ra, IL-12, and IL-33 showed no significant differences between patients and controls. Serum levels of all cytokines were not affected by disease duration, HLA-B27 positivity, or disease activity.

Comparison of Diagnostic Performance between Classic and Modified Abbreviated Breast MRI and the MRI Features Affecting Their Diagnostic Performance.

Abbreviated breast magnetic resonance imaging (AB-MRI) has emerged as a supplementary screening tool, though protocols have not been standardized. The purpose of this study was to compare the diagnostic performance of modified and classic AB-MRI and determine MRI features affecting their diagnostic performance. Classic AB-MRI included one pre- and two post-contrast T1-weighted imaging (T1WI) scans, while modified AB-MRI included a delayed post-contrast axial T1WI scan and an axial T2-weighted interpolated scan obtained between the second and third post-contrast T1WI scans. Four radiologists (two specialists and two non-specialists) independently categorized the lesions. The MRI features investigated were lesion size, lesion type, and background parenchymal enhancement (BPE). The Wilcoxon rank-sum test, Fisher's exact test, and bootstrap-based test were used for statistical analysis. The average area under the curve (AUC) for modified AB-MRI was significantly greater than that for classic AB-MRI (0.76 vs. 0.70, p = 0.010) in all reader evaluations, with a similar trend in specialist evaluations (0.83 vs. 0.76, p = 0.004). Modified AB-MRI demonstrated increased AUCs and better diagnostic performance than classic AB-MRI, especially for lesion size > 10 mm (p = 0.018) and mass lesion type (p = 0.014) in specialist evaluations and lesion size > 10 mm (p = 0.003) and mild (p = 0.026) or moderate BPE (p = 0.010) in non-specialist evaluations.

The value of intratumoral and peritumoral radiomics features in differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes.

Background: The identification of different subtypes of early-stage lung invasive adenocarcinoma before surgery contributes to the precision treatment. Radiomics could be one of the effective and noninvasive identification methods. The value of peritumoral radiomics in predicting the subtypes of early-stage lung invasive adenocarcinoma perhaps clinically useful. Methods: This retrospective study included 937 lung adenocarcinomas which were randomly divided into the training set (n=655) and testing set (n=282) with a ratio of 7:3. This study used the univariate and multivariate analysis to choose independent clinical predictors. Radiomics features were extracted from 18 regions of interest (1 intratumoral region and 17 peritumoral regions). Independent and conjoint prediction models were constructed based on radiomics and clinical features. The performance of the models was evaluated using receiver operating characteristic (ROC) curves, accuracy (ACC), sensitivity (SEN), and specificity (SPE). Significant differences between areas under the ROC (AUCs) were estimated using in the Delong test. Results: Patient age, smoking history, carcinoembryonic antigen (CEA), lesion location, length, width and clinic behavior were the independent predictors of differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes. The highest AUC value among the 19 independent models was obtained for the PTV0~+3 radiomics model with 0.849 for the training set and 0.854 for the testing set. As the peritumoral distance increased, the predictive power of the models decreased. The radiomics-clinical conjoint model was statistically significantly different from the other models in the Delong test (P<0.05). Conclusions: The intratumoral and peritumoral regions contained a wealth of clinical information. The diagnostic efficacy of intra-peritumoral radiomics combined clinical model was further improved, which was particularly important for preoperative staging and treatment decision-making.