Negative Prediction Research Articles

How does depressive cognition develop? A state-dependent network model of predictive processing.

Depression is vastly heterogeneous in its symptoms, neuroimaging data, and treatment responses. As such, describing how it develops at the network level has been notoriously difficult. In an attempt to overcome this issue, a theoretical "negative prediction mechanism" is proposed. Here, eight key brain regions are connected in a transient, state-dependent, core network of pathological communication that could facilitate the development of depressive cognition. In the context of predictive processing, it is suggested that this mechanism is activated as a response to negative/adverse stimuli in the external and/or internal environment that exceed a vulnerable individual's capacity for cognitive appraisal. Specifically, repeated activation across this network is proposed to update individual's brain so that it increasingly predicts and reinforces negative experiences over time-pushing an individual at-risk for or suffering from depression deeper into mental illness. Within this, the negative prediction mechanism is poised to explain various aspects of prognostic outcome, describing how depression might ebb and flow over multiple timescales in a dynamically changing, complex environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Quantitative Structure-Activity Relationship Models to Predict Cardiac Adverse Effects.

Drug-induced cardiotoxicity represents one of the most common causes of attrition of drug candidates in preclinical and clinical development. For this reason, the evaluation of cardiac toxicity is essential during drug development and regulatory review. In the present study, drug-induced postmarket adverse event combinations from the FDA Adverse Event Reporting System were extracted for 2002 drugs using 243 cardiac toxicity-related preferred terms (PTs). These PTs were combined into 12 groups based on their clinical relevance to serve as training sets. The optimal classification scheme was determined using a combination of data sources that included drug labeling information, published literature, clinical study data, and postmarket surveillance data. Two commercial QSAR platforms were used to construct 12 models, including general cardiac toxicity, cardiac ischemia, heart failure, cardiac valve disease, myocardial disease, pericardial disease, structural heart disease, cardiac arrhythmia, Torsades de Pointes, long QT syndrome, atrial fibrillation and ventricular arrhythmia, and cardiac arrest. The cross-validated performance for the new models reached a sensitivity of up to 80% and negative predictivity of up to 80%. These new models covering a wide range of cardiac endpoints will provide fast, reliable, and comprehensive predictions of potential cardiotoxic compounds in drug discovery and regulatory safety assessment.

Robust visual question answering via polarity enhancement and contrast

The Visual Question Answering (VQA) task is an important research direction in the field of artificial intelligence, which requires a model that can simultaneously understand visual images and natural language questions, and answer questions related to images. Recent studies have shown that many Visual Question Answering models rely on statistically regular correlations between questions and answers, which in turn weakens the correlation between visual content and textual information. In this work, we propose an unbiased Visual Question Answering method to solve language priors from the perspective of strengthening the contrast between the correct answer and the positive and negative predictions. We design a new model consisting of two modules with different roles. We input the image and the question corresponding to it into the Answer Visual Attention Modules to generate positive prediction output, and then use a Dual Channels Joint Module to generate negative prediction output with great linguistic prior knowledge. Finally, we input the positive and negative predictions together with the correct answer to our newly designed loss function for training. Our method achieves high performance (61.24%) on the VQA-CP v2 dataset. In addition, most existing debiasing methods improve performance on VQA-CP v2 dataset at the cost of reducing performance on VQA v2 dataset, while our method not only does not reduce the accuracy on VQA v2 dataset. Instead, it improves performance on both datasets mentioned above.

Predicting long-term risk of sudden cardiac death with automatic computer-interpretations of electrocardiogram.

Computer-interpreted electrocardiogram (CIE) data is provided by almost all commercial software used to capture and store digital electrocardiograms. CIE is widely available, inexpensive, and accurate. We tested the potential of CIE in long-term sudden cardiac death (SCD) risk prediction. This is a retrospective of 8,568 consecutive patients treated for acute coronary syndrome. The primary endpoint was five-year occurrence of SCDs or equivalent events (SCDs aborted by successful resuscitation or adequate ICD therapy). CIE statements were extracted from summary statements and measurements made by the GE Muse 12SL algorithm from ECGs taken during admission. Three supervised machine learning algorithms (logistic regression, extreme gradient boosting, and random forest) were then used for analysis to find risk features using a random 70/30% split for discovery and validation cohorts. Five-year SCD occurrence rate was 3.3% (n = 287). Regardless of the used ML algorithm, the most significant risk ECG risk features detected by the CIE included known risk features such as QRS duration and factors associated with QRS duration, heart rate-corrected QT time (QTc), and the presence of premature ventricular contractions (PVCs). Risk score formed by using most significant CIE features associated with the risk of SCD despite adjusting for any clinical risk factor (including left ventricular ejection fraction). Sensitivity of CIE data to correctly identify patients with high risk of SCD (over 10% 5-year risk of SCD) was usually low, but specificity and negative prediction value reached up to 96.9% and 97.3% when selecting only the most significant features identified by logistic regression modeling (p-value threshold <0.01 for accepting features in the model). Overall, CIE data showed a modest overall performance for identifying high risk individuals with area under the receiver operating characteristic curve values ranging between 0.652 and 0.693 (highest for extreme gradient boosting and lowest for logistic regression). This proof-of-concept study shows that automatic interpretation of ECG identifies previously validated risk features for SCD.

Monitoring focused ultrasound ablation surgery (FUAS) using echo amplitudes of the therapeutic focused transducer

ObjectiveB-mode sonography is commonly used to monitor focused ultrasound ablation surgery (FUAS), but has limitations in sensitivity. More accurate and reliable prediction of coagulation is required. MethodsThe focused ultrasound (FUS) transducer was adapted for echo reception. Numerical simulations compared the normalized echo amplitudes from the FUS transducer and imaging probe at varying tissue depths and frequencies with a 3 mm necrosis at focus. An ex vivo experiment then evaluated echo changes from the FUS transducer and ultrasound imaging probe under different settings. Finally, coagulation prediction using FUS echo data was compared to sonography in a clinical ex vivo context. ResultsThe echo amplitudes from the FUS transducer exhibit a less pronounced decline with increasing tissue penetration depth compared to the ultrasound imaging probe. In ex vivo bovine liver experiments at depths of 2 cm and 4 cm, the FUS transducer detected normalized echo amplitudes that were significantly larger (i.e., 2∼3 folds) than those received by the ultrasound imaging probe. Moreover, multi-layered ex vivo tissue experiments that replicate clinical conditions revealed that coagulation prediction utilizing the FUS transducer's echo amplitudes achieved superior accuracy (91.2% vs. 60.3 %), sensitivity (92.1% vs. 54.5 %), and negative prediction (78.9% vs. 30.6 %), but similar specificity (88.2% vs. 84.6 %) and positive prediction (95.9% vs. 93.8 %) in comparison to sonography. ConclusionThe echo amplitude of the FUS transducer serves as a sensitive and dependable metric for monitoring the FUAS outcomes. Its utilization may augment the procedure's safety and efficacy.

Functional characterisation of human recessive DIS3 variants in premature ovarian insufficiency.

Premature ovarian insufficiency (POI) is characterised by the loss or complete absence of ovarian activity in women under the age of 40. Clinical presentation of POI varies with phenotypic severity ranging from premature loss of menses to complete gonadal dysgenesis. POI is genetically heterogeneous with >100 causative gene variants identified thus far. The aetiology of POI varies from syndromic, idiopathic, monogenic to autoimmune causes the condition. Genetic diagnoses are beneficial to those impacted by POI as it allows for improved clinical management and fertility preservation. Identifying novel variants in candidate POI genes, however, is insufficient to make clinical diagnoses. The impact of missense variants can be predicted using bioinformatic algorithms but computational approaches have limitations and can generate false positive and false negative predictions. Functional characterisation of missense variants, is therefore imperative, particularly for genes lacking a well-established genotype:phenotype correlation. Here we used whole-exome sequencing (WES) to identify the first case of a homozygous missense variant in DIS3 (c.2320C > T; p.His774Tyr) a critical component of the RNA exosome in a POI patient. This adds to the previously described compound heterozygous patient. We perform the first functional characterisation of a human POI-associated DIS3 variant. A slight defect in mitotic growth was caused by the variant in a Saccharomyces cerevisiae model. Transgenic rescue of Dis3 knockdown in Drosophila melanogaster with human DIS3 carrying the patient variant led to aberrant ovarian development and egg chamber degeneration. This supports a potential deleterious impact of the human c.2320C > T; p.His774Tyr variant.

Effect of extending the period from oral administration of 5-aminolevulinic acid hydrochloride to photodynamic diagnosis during transurethral resection for non-muscle invasive bladder cancer on diagnostic accuracy and safety: a single-arm multicenter phase III trial.

In Japan, the authorized period (2-4 h) between oral administration of 5-aminolevulinic acid hydrochloride (5-ALA) and transurethral resection for non-muscle invasive bladder cancer (NMIBC) may restrict photodynamic diagnosis (PDD) usage. Therefore, this prospective, single-arm, phase III study aimed to evaluate the diagnostic accuracy and safety of PDD at an extended administration period (4-8h). From January 2022 to May 2023, 161 patients with NMIBC were enrolled from eight hospitals. The primary endpoint was the blue light (BL) sensitivity of pathologically positive biopsies. The secondary endpoints were a comparison of the specificity and positive and negative prediction rates under BL and white light (WL) conditions. A total of 1242 specimens comprising 337 histological NMIBC specimens were analyzed. BL-sensitivity was 95.3%. Its lower limit of 95% confidence interval (92.4-97.3%) exceeded the threshold (70%) of non-inferiority to authorized usage. Sensitivity and specificity were significantly higher and lower for BL than those for WL (95.3% vs. 61.1%, P < 0.001; 52.7% vs. 95.2%, P < 0.001), respectively. The positive and negative predictive rates were significantly lower and higher for BL than those for WL (42.9% vs. 82.7%, P < 0.001; 96.8% vs. 86.8%, P < 0.001), respectively. Of the 145 patients receiving 5-ALA, 136 (93.8%) and 75 (51.7%) experienced 377 adverse events and 95 adverse reactions, respectively, most of which were grade 1 or 2. For extended period, the efficacy of PDD for NMIBC was similar to that of authorized period, in terms of higher sensitivity and lower specificity compared with WL, and the safety was acceptable.

7771 Surveillance Role in Differentiated Thyroid Cancer Through Ultrasensitive Thyroglobulin IRMA with High Negative Prediction Value

Abstract Disclosure: J. Jang: None. H. Kim: None. S. Ha: None. K. Jung: None. G. Jung: None. S. Cho: None. D. Park: None. G. Cheon: None. Y. Park: None. TSH-stimulated serum thyroglobulin (s-Tg) is a well-established marker for monitoring differentiated thyroid cancer (DTC) following remnant ablation therapy. The 2nd-generation immunoradiometric assay for Tg (ultrasensitive Tg, uTg) was developed to detect Tg with higher sensitivity compared with the 1st generation (conventional Tg, cTg). This study aimed to compare efficacy of both methods, cTg and uTg, to determine whether unstimulated Tg (us-Tg) could predict s-Tg levels. Paired serum samples were collected from 274 DTC patients before (us-Tg) and after (s-Tg) TSH stimulation for 1st radioiodine therapy after surgery (1st Tg) or both 1st therapy and 2nd therapy/diagnostic test (1st and 2nd Tg, respectively). Both cTg (Dynotest Tg-plus, BRAHMS Gmbh, Germany, LOD 0.1 ng/mL and functional sensitivity (FS) 0.2 ng/mL) and uTg (RIAKEY, Shinjin Medics, South Korea; LOD 0.01 ng/mL and FS 0.06 ng/mL) were measured. For analysis, samples with inadequate TSH stimulation (&amp;lt;30 μIU/mL) or with anti-thyroglobulin antibody levels &amp;gt; 60 U/mL were excluded. Ultimately, the included samples consisted of 296 for us-Tg and 352 for s-Tg.. Among these, paired samples for us-Tg and s-Tg were available in 260 cases (189 for 1st Tg and 71 for 2nd Tg). Comparing all Tg values, cTg and uTg levels had a correlation (Intraclass correlation coefficient r=0.752, P &amp;lt; 0.001), with no significant difference between us-Tg (r=0.803, P &amp;lt; 0.001) and s-Tg (r= 0.752, P &amp;lt; 0.001). The optimal cut-off level for predicting s-cTg at 1 ng/mL was 0.12 ng/mL in uTg or 0.16 ng/mL in cTg tests using AUC-ROC curve analysis. Considering the FS for cTg, diagnostic performance was assessed with a cut-off level of 0.2 ng/mL for cTg and 0.16 ng/mL for uTg. For s-cTg positivity as ≥ 1 ng/mL, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of uTg were 72.1%, 59.8%. 52.0%, and 78.0%, respectively, while for cTg, they were 33.3%, 95.3%, 78.4%, and 73.9%, respectively. Similar trends were observed for s-cTg ≥ 2 ng/mL (69.0%, 71.4%, 35.0%, and 91.2% for uTg, respectively; 49.0%, 93.8%, 64.9%, and 88.7% for cTg, respectively). In essence, uTg demonstrated higher sensitivity than cTg, while having lower specificity and PPV, with a similar NPV. Of the 71 patients with paired 2nd Tg levels, 7 (9.8%) had biochemical disease (BCD) by s-cTg (1 ng/mL), whereas only 1 (1.4%) showed BCD using us-cTg ≥ 0.2 ng/mL. Conversely, 40 (56.0%) were classified as BCD using us-uTg ≥ 0.16 ng/mL. During the 6.48 years of follow-up, 4 patients developed structural recurrence; all had positive us-Tg levels with uTg tests, while only 1 patient demonstrated positivity with cTg tests. Although, the ultrasensitive Tg is sensitive in predicting positive stimulated Tg and recurrence, the specificity is very low resulting high frequency of BCD cases without clinical significance. Therefore, the clinical utility of uTg appears to be limited. Presentation: 6/2/2024

A clinically applicable model more suitable for predicting malignancy or benignity of pulmonary ground glass nodules in women patients

BackgroundIn recent years, clinicians often encounter patients with multiple pulmonary nodules in their clinical practices. As most of these ground glass nodules (GGNs) are small in volume and show no spicule sign, it is difficult to use Mayo Clinic Model to make early diagnosis of lung cancer accurately, especially in large numbers of nonsmoking women who have no tumor history. Other clinical models are disadvantaged by a relatively high false-positive or false-negative rate. Therefore, there is an urgent need to establish a new model of predicting malignancy or benignity of pulmonary GGNs for the sake of making accurate and early diagnosis of lung cancer.MethodsIncluded in this study were GGNs surgically resected from patients who were admitted to Yiwu Central Hospital from January 2018 to March 2024, including both male and female patients, there is no gender specific issue. The nature of all these GGN tissues was confirmed pathologically. The case data were statistically analyzed to establish a mathematical prediction model, the prediction performance of which was verified by the pathological results.ResultsAltogether 261 GGN patients met the inclusion criteria. Using the results of logistic regression analysis, a mathematical prediction equation was established as follows: Malignant probability (mP) = ex/ (1 + ex); when mP was > 0.5, the GGN was considered as malignant, and when mP was ≤ 0.5, it was considered as benign. x= -2.46 + 1.032*gender + 1.85*mGGN + 1.40*VCS-0.0027*mean CT value of the nodule + 0.078*maximum diameter of the nodule, where e represents the natural logarithm; if the patient was a female, gender = 1 (otherwise = 0); if the pulmonary nodule was a mixed GGN, mGGN = 1 (otherwise = 0); if the pulmonary nodule had vascular convergence sign, VCS = 1 (otherwise = 0). The prediction performance of the mathematical prediction model was verified as follows: the negative prediction value was 0.97156 and the positive prediction value was 0.3800 in the model group versus 1 and 0.25 in the verification group.ConclusionIn this study, we identified female gender, mGGN, VCS, mean CT value and maximum nodule diameter as five key factors for predicting malignancy or benignity of pulmonary nodules, based on which we established a mathematical prediction model. This novel innovation may provide a useful auxiliary tool for predicting malignancy and benignity of pulmonary nodules, especially in women patients.

The expectation-updating mechanism in gratitude: A predictive coding perspective.

The fluctuations in emotions during constant help are unexplained by traditional emotion theories but may align with the predictive coding theory. This theory suggests that individuals tend to form expectations of others' help during social interactions. When outcomes exceed expectations, positive prediction errors are generated, potentially increasing gratitude. Conversely, constant help may build up expectations that surpass outcomes, resulting in negative prediction errors and reduced gratitude. Nevertheless, there is a lack of studies to examine the relationship between prediction errors and gratitude and its underlying mechanism. Here, we conducted two studies. Study 1 consistently found that higher expectations were associated with lower gratitude, when benefactors refused to help, in both reward-gaining and punishment-avoiding tasks. Moreover, prediction errors were positively and reliably linked to gratitude. Study 2 further identified that gratitude dynamically changed through an expectation-updating mechanism. A computational model incorporating predictive coding outperformed traditional theories in predicting the dynamics of gratitude. The findings support predictive coding theory, providing a temporal perspective and a mechanistic understanding of the fluctuations in gratitude, thus having implications for new interventions to improve mental health and well-being. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Understanding internal dynamics of state anxiety during COVID-19 pandemic: Seven-wave longitudinal findings via panel network analysis.

Research on state anxiety has long been dominated by the traditional psychometric approach that assumes anxiety symptoms have a common cause. Yet state anxiety can be conceptualized as a network system. In this study, we utilized data from the COVID-Dynamic dataset from waves 7 to 13, collected at three-week intervals from June 6, 2020, to October 13, 2020, and included 1,042 valid participants to characterize the internal dynamics of state anxiety. Using the Gaussian graphical model along with strength centrality, we estimated three network models of state anxiety. The between-subjects and contemporaneous network showed numerous positive relations between items and some unexpected negative relations. Three communities were identified in the between-subjects network, and two communities were identified in the contemporaneous network. The temporal network showed the coexistence of positive and negative predictions between items after three weeks. Several items exhibited significant positive autocorrelations after three weeks. These findings have implications for anxiety theory and clinical interventions at between-subjects and within-subjects levels.

Genomic correlates of the response to first-line PD-1 blockade plus chemotherapy in patients with advanced non-small-cell lung cancer.

Programmed death 1 (PD-1) blockade plus chemotherapy has become the new first-line standard of care for patients with advanced non-small-cell lung cancer (NSCLC). Yet not all NSCLC patients benefit from this regimen. This study aimed to investigate the predictors of PD-1 blockade plus chemotherapy in untreated advanced NSCLC. We integrated clinical, genomic, and survival data from 287 patients with untreated advanced NSCLC who were enrolled in one of five registered phase 3 trials and received PD-1 blockade plus chemotherapy or chemotherapy alone. We randomly assigned these patients into a discovery cohort (n=125), a validation cohort (n=82), and a control cohort (n=80). The candidate genes that could predict the response to PD-1 blockade plus chemotherapy were identified using data from the discovery cohort and their predictive values were then evaluated in the three cohorts. Immune deconvolution was conducted using transcriptome data of 1014 NSCLC patients from The Cancer Genome Atlas dataset. A genomic variation signature, in which one or more of the 15 candidate genes were altered, was correlated with significantly inferior response rates and survival outcomes in patients treated with first-line PD-1 blockade plus chemotherapy in both discovery and validation cohorts. Its predictive value held in multivariate analyses when adjusted for baseline parameters, programmed cell death ligand 1 (PD-L1) expression level, and tumor mutation burden. Moreover, applying both the 15-gene panel and PD-L1 expression level produced better performance than either alone in predicting benefit from this treatment combination. Immune landscape analyses revealed that tumors with one or more variation in the 15-gene panel were associated with few immune infiltrates, indicating an immune-desert tumor microenvironment. These findings indicate that a 15-gene panel can serve as a negative prediction biomarker for first-line PD-1 blockade plus chemotherapy in patients with advanced NSCLC.

Negative and Positive Predictors of Anastomotic Leakage in Colorectal Cancer Patients-The Case of Neutrophil-to-Lymphocyte Ratio.

Colorectal surgery for cancer is associated with a high rate of surgical complications, including anastomotic leakage. The ability to predict the risk of leakage early enough seems to be of high value, since it would facilitate the design of personalized treatment and duration of hospitalization. Although different studies present the neutrophil-to-lymphocyte ratio [NLR] as having a strong predictive value, there is a discrepancy with respect to which postoperative day is the most reliable. We evaluated a series of NLR values, from the day before surgery up to the POD7, in a cohort of 245 colorectal surgery patients in order to clarify the best predictable score for the identification of the risk of anastomotic leakage. There were 28 patients with leaks. ROC curve analysis of NLR on POD1 indicates that a cut-off point ≥ 7.4 exerts a negative prediction for leakage (AUC 0.881, sensitivity 68.7%, specificity 96.4%, PPV 28.4%, and NPV of 99.3%), thus excluding 150 patients from the risk of leakage. Furthermore, the ROC curve analysis of NLR on POD4 indicates that a cut-off point ≥ 6.5 gives a positive prediction of leakage (AUC 0.698, sensitivity 82.1%, specificity 51.6%, PPV 17.6%, and NPV of 95.6%), thus indicating 52 patients as being at high risk of leakage. Finally, NLR failed to identify five leaks out of twenty-eight. These results strongly indicate the ability of NLR on POD1 to predict patients at low risk of developing a leak and then on POD4 to predict the high-risk patients. This makes our study particularly innovative, in that it enables doctors to concentrate on potential high-risk patients from POD1.

Immune-based Machine learning Prediction of Diagnosis and Illness State in Schizophrenia and Bipolar Disorder

BackgroundSchizophrenia and bipolar disorder frequently face significant delay in diagnosis, leading to being missed or misdiagnosed in early stages. Both disorders have also been associated with trait and state immune abnormalities. Recent machine learning-based studies have shown encouraging results using diagnostic biomarkers in predictive models, but few have focused on immune-based markers. Our main objective was to develop supervised machine learning models to predict diagnosis and illness state in schizophrenia and bipolar disorder using only a panel of peripheral kynurenine metabolites and cytokines. MethodsThe cross-sectional I-GIVE cohort included hospitalized acute bipolar patients (n = 205), stable bipolar outpatients (n = 116), hospitalized acute schizophrenia patients (n = 111), stable schizophrenia outpatients (n = 75) and healthy controls (n = 185). Serum kynurenine metabolites, namely tryptophan (TRP), kynurenine (KYN), kynurenic acid (KA), quinaldic acid (QUINA), xanthurenic acid (XA), quinolinic acid (QUINO) and picolinic acid (PICO) were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS), while V-plex Human Cytokine Assays were used to measure cytokines (interleukin-6 (IL-6), IL-8, IL-17, IL-12/IL23-P40, tumor necrosis factor-alpha (TNF-ɑ), interferon-gamma (IFN-γ)). Supervised machine learning models were performed using JMP Pro 17.0.0. We compared a primary analysis using nested cross-validation to a split set as sensitivity analysis. Post-hoc, we re-ran the models using only the significant features to obtain the key markers. ResultsThe models yielded a good Area Under the Curve (AUC) (0.804, Positive Prediction Value (PPV) = 86.95; Negative Prediction Value (NPV) = 54.61) for distinguishing all patients from controls. This implies that a positive test is highly accurate in identifying the patients, but a negative test is inconclusive. Both schizophrenia patients and bipolar patients could each be separated from controls with a good accuracy (SCZ AUC 0.824; BD AUC 0.802). Overall, increased levels of IL-6, TNF-ɑ and PICO and decreased levels of IFN-γ and QUINO were predictive for an individual being classified as a patient. Classification of acute versus stable patients reached a fair AUC of 0.713. The differentiation between schizophrenia and bipolar disorder yielded a poor AUC of 0.627. ConclusionsThis study highlights the potential of using immune-based measures to build predictive classification models in schizophrenia and bipolar disorder, with IL-6, TNF-ɑ, IFN-γ, QUINO and PICO as key candidates. While machine learning models successfully distinguished schizophrenia and bipolar disorder from controls, the challenges in differentiating schizophrenic from bipolar patients likely reflect shared immunological pathways by the both disorders and confounding by a larger state-specific effect. Larger multi-centric studies and multi-domain models are needed to enhance reliability and translation into clinic.

Attachment styles and empathy in trainee nurses: the mediating and moderating roles of attitudes toward death.

A growing body of evidence has shown that attachment styles and death attitudes have a significant impact on empathy. This study aimed to explore the precise role of death attitudes in the relationship between attachment styles and empathy levels among trainee nurses. A total of 626 Chinese trainee nurses with different attachment types were enrolled, and their attachment styles, death attitudes, and empathy levels were assessed using the Revised Adult Attachment Scale, the Death Attitude Profile-Revised, and the Jefferson Scale of Empathy-Healthy Professionals, and finally, data from 566 participants were included for statistical analysis. We found that among trainee nurses with secure attachment type, fear of death, approach acceptance, escape acceptance, and neutral acceptance (-) mediated the relationships between attachment-related avoidance/anxiety and their overall empathy levels and all its dimensions; in the preoccupied type, only neutral acceptance (-) mediated the relationships between attachment-related avoidance and their overall empathy levels and compassionate care; and in the fearful type, only fear of death mediated the relationship between attachment-related avoidance and compassionate care. Furthermore, in the secure type, neutral acceptance attenuated the negative predictions of attachment-related avoidance on overall empathy level and perspective taking. Attitudes toward death played different mediating and moderating roles in the relationship between attachment styles and empathy among trainee nurses with different attachment types. In addition to acculturated empathy-specific training, targeted education related to death for trainee nurses with different attachment types is needed to prevent their compassion fatigue.

The use of weight-of-evidence approaches to characterize developmental toxicity risk for therapeutic monoclonal antibodies in humans without in vivo studies

Regulatory guidance for global drug development relies on animal studies to evaluate safety risks for humans, including risk of reproductive toxicity. Weight-of-evidence approaches (WoE) are increasingly becoming acceptable to evaluate risk. A WoE for developmental risk of monoclonal antibodies (mAbs) was evaluated for its ability to retrospectively characterize risk and to determine the need for further in vivo testing based on the remaining uncertainty. Reproductive toxicity studies of 65 mAbs were reviewed and compared to the WoE. Developmental toxicities were absent in 52/65 (80%) mAbs. Lack of toxicity was correctly predicted in 29/52 (56%) cases. False positive and equivocal predictions were made in 9/52 (17%) and 14/52 (27%) cases. For 3/65 (5%) mAbs, the findings were equivocal. Of mAbs with developmental toxicity findings (10/65, 15%), the WoE correctly anticipated pharmacology based reproductive toxicity without any false negative predictions in 9/10 (90%) cases, and in the remaining case (1/10, 10%) an in vivo study was recommended due to equivocal WoE outcome. Therefore, this WoE approach could characterize presence and absence of developmental risk without animal studies. The current WoE could have reduced the need for developmental toxicity studies by 42% without loss of important patient information in the label.

Correlating Advanced Pharmacy Practice Experience Exam Scores and Pharmacist Licensure Status in a Single Program

ObjectivePharmacist licensure exam first-time pass rates have declined for several years. Predictors of licensure exam performance, including the Pharmacy College Admission Test, are no longer required, necessitating alternative strategies. Our aim is to assess the relationship between numerical scores on advanced pharmacy practice experience (APPE) exams and pharmacist licensure exams first-time pass rates. MethodsWe conducted a single-center, retrospective, observational analysis. APPE examination results between May 2020 and November 2023 were analyzed for association with licensure information from the Texas State Board of Pharmacy. Exam scores were collected for student cohorts graduating from 2021 to 2022 as study cohort data. Correlation between exam scores and probability of successful licensure was assessed using logistic regression. A classification and regression tree analysis identified the most significant threshold. Predictive ability of the best-fit model was prospectively validated using the 2023 graduate cohort. ResultsThe overall licensure success rate was 80.6% for the study cohort (2021–2022, n = 206). Exam scores were well correlated to the likelihood of licensing success. The most significant threshold was 77.8%. The success rate was 82.5% vs 16.7% for those scoring above and below the threshold, respectively. The observed and predicted licensure success rates were 91.0% and 88.8%, respectively, in the validation cohort (2023, n = 89). The positive and negative prediction values were 94.9% and 40.0%, respectively. ConclusionThe performances on APPE exams were reasonable in predicting the first-time licensure success rate for a graduating class. Our assessment appears promising as a risk-stratification tool for students in gaining successful pharmacist licensure.

Building Dual AI Models and Nomograms Using Noninvasive Parameters for Aiding Male Bladder Outlet Obstruction Diagnosis and Minimizing the Need for Invasive Video-Urodynamic Studies: Development and Validation Study.

Diagnosing underlying causes of nonneurogenic male lower urinary tract symptoms associated with bladder outlet obstruction (BOO) is challenging. Video-urodynamic studies (VUDS) and pressure-flow studies (PFS) are both invasive diagnostic methods for BOO. VUDS can more precisely differentiate etiologies of male BOO, such as benign prostatic obstruction, primary bladder neck obstruction, and dysfunctional voiding, potentially outperforming PFS. These examinations' invasive nature highlights the need for developing noninvasive predictive models to facilitate BOO diagnosis and reduce the necessity for invasive procedures. We conducted a retrospective study with a cohort of men with medication-refractory, nonneurogenic lower urinary tract symptoms suspected of BOO who underwent VUDS from 2001 to 2022. In total, 2 BOO predictive models were developed-1 based on the International Continence Society's definition (International Continence Society-defined bladder outlet obstruction; ICS-BOO) and the other on video-urodynamic studies-diagnosed bladder outlet obstruction (VBOO). The patient cohort was randomly split into training and test sets for analysis. A total of 6 machine learning algorithms, including logistic regression, were used for model development. During model development, we first performed development validation using repeated 5-fold cross-validation on the training set and then test validation to assess the model's performance on an independent test set. Both models were implemented as paper-based nomograms and integrated into a web-based artificial intelligence prediction tool to aid clinical decision-making. Among 307 patients, 26.7% (n=82) met the ICS-BOO criteria, while 82.1% (n=252) were diagnosed with VBOO. The ICS-BOO prediction model had a mean area under the receiver operating characteristic curve (AUC) of 0.74 (SD 0.09) and mean accuracy of 0.76 (SD 0.04) in development validation and AUC and accuracy of 0.86 and 0.77, respectively, in test validation. The VBOO prediction model yielded a mean AUC of 0.71 (SD 0.06) and mean accuracy of 0.77 (SD 0.06) internally, with AUC and accuracy of 0.72 and 0.76, respectively, externally. When both models' predictions are applied to the same patient, their combined insights can significantly enhance clinical decision-making and simplify the diagnostic pathway. By the dual-model prediction approach, if both models positively predict BOO, suggesting all cases actually resulted from medication-refractory primary bladder neck obstruction or benign prostatic obstruction, surgical intervention may be considered. Thus, VUDS might be unnecessary for 100 (32.6%) patients. Conversely, when ICS-BOO predictions are negative but VBOO predictions are positive, indicating varied etiology, VUDS rather than PFS is advised for precise diagnosis and guiding subsequent therapy, accurately identifying 51.1% (47/92) of patients for VUDS. The 2 machine learning models predicting ICS-BOO and VBOO, based on 6 noninvasive clinical parameters, demonstrate commendable discrimination performance. Using the dual-model prediction approach, when both models predict positively, VUDS may be avoided, assisting in male BOO diagnosis and reducing the need for such invasive procedures.

Preincubation-dependent inhibition of organic anion transporting polypeptide 2B1

Preincubation with inhibitor in organic anion transporting polypeptide (OATP) in vitro assays may increase the inhibition potency of inhibitors compared to conventional inhibition assays with only short inhibitor coincubation with substrate. The decrease in IC50 may affect prediction of drug-drug interactions (DDI) involving these transporters and inhibitors. Only few drugs, however, have been assessed for the preincubation-dependent inhibition of the OATP2B1 transporter. Therefore, we studied the effect of preincubation on OATP2B1 inhibition with five known OATP2B1 inhibitors (atorvastatin, erlotinib, ezetimibe, ticagrelor and simeprevir) in HEK293 cells transiently overexpressing OATP2B1. IC50 values were determined with and without inhibitor preincubation for 20 min with three different OATP2B1 substrates (dibromofluorescein, DBF; 5-carboxyfluorescein, 5-CF; estrone sulfate). Atorvastatin, ezetimibe, and simeprevir displayed more than 2-fold lower IC50 values after preincubation with at least one of the tested substrates. Altogether, 4 out of 15 inhibitor/substrate combinations exhibited more than 2-fold potentiation of IC50 after inhibitor preincubation. In addition, preincubation by itself, without inhibitor present with the substrate, resulted in more than 50% inhibition of OATP2B1-mediated uptake of DBF and/or 5-CF by atorvastatin, ticagrelor and simeprevir. Thus, erlotinib was the only inhibitor with no indication of potentiation of inhibition by preincubation with any of the tested substrates. In conclusion, preincubation resulted in inhibitor- and substrate-dependent inhibition of OATP2B1. These results support the conclusion that to reduce the risk of false negative DDI prediction, preincubation should be considered also in OATP2B1 inhibition assays.

Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region.

Objective. Determination of the optimal machine learning model for the creation of software for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region. Material and Methods. The clinical material was based on 1254 patients who were examined in the centralized laboratory of the Volgograd Consultative and Diagnostic Polyclinic No. 2. Of these, 139 were subsequently operated on at the Volgograd Regional Clinical Oncology Dispensary for pancreatic malignancies. In 65 (46.7 %) cases, distal pancreatic resection was performed, and in 74 (53.3 %) cases, pancreaticoduodenectomy was performed. In 28 (20.1 %) cases, at the time of tumor detection, patients did not have clinical symptoms. Statistical processing of the data was carried out using the Python programming language. Five different classifiers were used for machine learning. Results. In the course of factor analysis, 11 parameters were selected from 62 laboratory blood parameters, the dynamics of changes in which should be specifically assessed at the stages of screening and early diagnosis of pancreatic neoplasms. A comparative assessment of machine learning techniques showed that the best option for creating the appropriate software was Hist Gradient Boosting (diagnostic accuracy 0.909, sensitivity 0.642, specificity 0.965, negative predictability 0.928, positive predictability 0.794, F1 0.828, logistic regression loss function 0.352, area under the ROC curve 0.89). Conclusion. The creation of software based on the selected algorithm will make it possible to clarify the real effectiveness of machine learning on a larger population of patients with pancreatic neoplasms.