Objective

We aim to study to what extent conventional and deep-learning-based T₂ relaxometry patterns are able to distinguish between knees with and without radiographic osteoarthritis (OA).

Methods

T₂ relaxation time maps were analyzed for 4,384 subjects from the baseline Osteoarthritis Initiative (OAI) Dataset. Voxel Based Relaxometry (VBR) was used for automatic quantification and voxel-based analysis of the differences in T₂ between subjects with and without radiographic OA. A Densely Connected Convolutional Neural Network (DenseNet) was trained to diagnose OA from T₂ data. For comparison, more classical feature extraction techniques and shallow classifiers were used to benchmark the performance of our algorithm's results. Deep and shallow models were evaluated with and without the inclusion of risk factors. Sensitivity and Specificity values and McNemar test were used to compare the performance of the different classifiers.

Results

The best shallow model was obtained when the first ten Principal Components, demographics and pain score were included as features (AUC = 77.77%, Sensitivity = 67.01%, Specificity = 71.79%). In comparison, DenseNet trained on raw T₂ data obtained AUC = 83.44%, Sensitivity = 76.99%, Specificity = 77.94%. McNemar test on two misclassified proportions form the shallow and deep model showed that the boost in performance was statistically significant (McNemar's chi-squared = 10.33, degree of freedom (DF) = 1, P-value = 0.0013).

Conclusion

In this study, we presented a Magnetic Resonance Imaging (MRI)-based data-driven platform using T₂ measurements to characterize radiographic OA. Our results showed that feature learning from T₂ maps has potential in uncovering information that can potentially better diagnose OA than simple averages or linear patterns decomposition.

Background

Previous research suggests that females may be more motivated to smoke for negative reinforcement (NR) than males. However, it remains unclear whether gender differences in smoking outcome expectancies for negative smoking reinforcement-an important theoretical and clinical target defined as beliefs that smoking alleviates negative affect-exist above and beyond gender differences in depression and/or other outcome expectancies.

Methods

Relations between gender and negative smoking reinforcement expectancies were examined in two independent samples. Sample 1 consisted of non-treatment seeking daily smokers (Male n = 188; Female n = 91) recruited from Southern California (49.5% Black, 32.2% Caucasian, and 18.3% other race/ethnicity). Sample 2 consisted of treatment seeking daily smokers (Male n = 257; Female n = 237) in Northern Florida and Vermont (10.7% Black, 82.9% Caucasian, and 6.4% other).

Results

Females (vs. males) reported stronger NR smoking expectancies with and without statistically controlling for nicotine dependence, other smoking expectancies, and anxiety and depression in both samples (βs = .06 to .14, ps = .06 to < .001).

Conclusions

Beliefs that smoking alleviates negative affect may reflect a gender-specific etiological process disproportionately prominent in women. Enhancing ability to cope with negative affect without smoking or challenge NR expectancies may be particularly important for cessation treatment in women.

In an exploratory trial treating "long COVID" with the CCR5-binding antibody leronlimab, we observed significantly increased blood cell surface CCR5 in treated symptomatic responders but not in nonresponders or placebo-treated participants. These findings suggest an unexpected mechanism of abnormal immune downmodulation in some persons that is normalized by leronlimab. Clinical Trials Registration. NCT04678830.

Drug use outcome expectancies are a central construct to psychosocial theories of addictive disorders. In tobacco literature, the Smoking Consequences Questionnaire (SCQ; Brandon & Baker, 1991) is a tool used to assess this construct. Despite its common use, the SCQ has received little psychometric evaluation. In the current report, samples from 2 studies were used to examine the assumed SCQ structure, develop a novel truncated scale, and evaluate the psychometric properties of the novel scale. In Study 1, the 4-factor SCQ structure was examined using data from 343 (32.4% female; M_age = 43.7; SD = 10.8) adult nontreatment-seeking smokers. Results from Study 1 indicated that the 4-factor SCQ structure did not adequately explain covariance between items. Instead, results provided evidence for a 5-factor structure that tapped into outcome expectancies related to (a) immediate negative consequences (IC), (b) long-term negative consequences (LTC), (c) sensory satisfaction (SS), (d) negative affect reduction, and (e) appetite-weight control (AW). In Study 2, the 5-factor structure of the SCQ was confirmed and the construct validity was evaluated in 582 (48.2% female; M_age = 36.9; SD = 13.5) treatment-seeking adult smokers. Study 2 found evidence for measurement invariance across sex and overtime of the 5-factor structure as well as substantial construct validity. Results from 2 independent samples challenge the traditional 4-factor model of the SCQ, and instead, provide evidence for a novel 5-factor SCQ structure with strong validity and reliability. Alternate scoring algorithms for the SCQ, including a 5-subscale scheme, warrant consideration to ensure optimal measurement precision and construct differentiation. (PsycINFO Database Record