Correlated physical and mental health composite scores for the RAND-36 and RAND-12 health surveys: can we keep them simple?

Background: The RAND-36 and RAND-12 (equivalent to versions 1 of the SF-36 Health Survey and SF-12 Health Survey, respectively) are widely used measures of health-related quality of life. However, there are diverging views regarding how to create the physical health and mental health composite scores of these questionnaires. We present a simple approach using an unweighted linear combination of subscale scores for constructing composite scores for physical and mental health that assumes these scores should be free to correlate. The aim of this study was to investigate the criterion validity and convergent validity of these scores. Methods: We investigated oblique and unweighted RAND-36/12 composite scores from a random sample of the general Norwegian population (N = 2107). Criterion validity was tested by examining the correlation between unweighted composite scores and weighted scores derived from oblique principal component analysis. Convergent validity was examined by analysing the associations between the different composite scores, age, gender, body mass index, physical activity, rheumatic disease, and depression. Results: The correlations between the composite scores derived by the two methods were substantial (r = 0.97 to 0.99) for both the RAND-36 and RAND-12. The effect sizes of the associations between the oblique versus the unweighted composite scores and other variables had comparable magnitudes. Conclusion: The unweighted RAND-36 and RAND-12 composite scores demonstrated satisfactory criterion validity and convergent validity. This suggests that if the physical and mental composite scores are free to be correlated, the calculation of these composite scores can be kept simple.publishedVersio

VISEM-Tracking, a human spermatozoa tracking dataset

A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-assisted sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet sperm preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa

Sperm motility assessed by deep convolutional neural networks into WHO categories

Abstract Semen analysis is central in infertility investigation. Manual assessment of sperm motility according to the WHO recommendations is the golden standard, and extensive training is a requirement for accurate and reproducible results. Deep convolutional neural networks (DCNN) are especially suitable for image classification. In this study, we evaluated the performance of the DCNN ResNet-50 in predicting the proportion of sperm in the WHO motility categories. Two models were evaluated using tenfold cross-validation with 65 video recordings of wet semen preparations from an external quality assessment programme for semen analysis. The corresponding manually assessed data was obtained from several of the reference laboratories, and the mean values were used for training of the DCNN models. One model was trained to predict the three categories progressive motility, non-progressive motility, and immotile spermatozoa. Another model was used in predicting four categories, where progressive motility was differentiated into rapid and slow. The resulting average mean absolute error (MAE) was 0.05 and 0.07, and the average ZeroR baseline was 0.09 and 0.10 for the three-category and the four-category model, respectively. Manual and DCNN-predicted motility was compared by Pearson’s correlation coefficient and by difference plots. The strongest correlation between the mean manually assessed values and DCNN-predicted motility was observed for % progressively motile spermatozoa (Pearson’s r = 0.88, p < 0.001) and % immotile spermatozoa (r = 0.89, p < 0.001). For rapid progressive motility, the correlation was moderate (Pearson’s r = 0.673, p < 0.001). The median difference between manual and predicted progressive motility was 0 and 2 for immotile spermatozoa. The largest bias was observed at high and low percentages of progressive and immotile spermatozoa. The DCNN-predicted value was within the range of the interlaboratory variation of the results for most of the samples. In conclusion, DCNN models were able to predict the proportion of spermatozoa into the WHO motility categories with significantly lower error than the baseline. The best correlation between the manual and the DCNN-predicted motility values was found for the categories progressive and immotile. Of note, there was considerable variation between the mean motility values obtained for each category by the reference laboratories, especially for rapid progressive motility, which impacts the training of the DCNN models

Characteristics of reproductive hormones according to BMI group, and associations between BMI and serum hormone levels by multiple linear regression.

<p>T, testosterone; FAI, free androgen index; SHBG, sex hormone binding globulin; FSH, follicle stimulating hormone; LH, luteinizing hormone; AMH, anti-Müllerian hormone; B, regression coefficient; CI, confidence interval.</p><p>Associations tested by multiple linear regression were adjusted for age. All variables in the regression analyses were continuous variables.</p><p>^a, log transformed data.</p><p>Characteristics of reproductive hormones according to BMI group, and associations between BMI and serum hormone levels by multiple linear regression.</p

Characteristics of the participants according to BMI groups.

<p>BMI, body mass index.</p><p>Characteristics of the participants according to BMI groups.</p

Characteristics of semen parameters according to BMI groups, comparison between group 1 and group 4, and associations between BMI and semen parameters by multiple linear regression.

<p>BMI, body mass index; DFI, DNA fragmentation index; B, regression coefficient; CI, confidence interval.</p><p>P-values for differences between group 1 and group 4 were calculated by Mann-Whitney U test. Associations tested by multiple linear regression were adjusted for age and time of abstinence. All variables in the regression analyses were continuous.</p><p>^a, log transformed data</p><p>^b, square root transformed data</p><p>Characteristics of semen parameters according to BMI groups, comparison between group 1 and group 4, and associations between BMI and semen parameters by multiple linear regression.</p

Characteristics of reproductive hormones according to BMI group, and associations between BMI and serum hormone levels by multiple linear regression.

<p>T, testosterone; FAI, free androgen index; SHBG, sex hormone binding globulin; FSH, follicle stimulating hormone; LH, luteinizing hormone; AMH, anti-Müllerian hormone; B, regression coefficient; CI, confidence interval.</p><p>Associations tested by multiple linear regression were adjusted for age. All variables in the regression analyses were continuous variables.</p><p>^a, log transformed data.</p><p>Characteristics of reproductive hormones according to BMI group, and associations between BMI and serum hormone levels by multiple linear regression.</p

Proportions of normal weight and severely obese men with semen parameters below the WHO lower reference limits [38].

<p>χ², Chi-square value; df, degrees of freedom.</p><p>n (%), number (percentage) of participants with sperm characteristics below WHO lower reference limit/ group total.</p><p>Associations were tested by Chi-square test with Yates’ correction for continuity.</p><p>Proportions of normal weight and severely obese men with semen parameters below the WHO lower reference limits [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130210#pone.0130210.ref038" target="_blank">38</a>].</p

Body Mass Index Is Associated with Impaired Semen Characteristics and Reduced Levels of Anti-Müllerian Hormone across a Wide Weight Range

There is still controversy as to how body mass index (BMI) affects male reproduction. We investigated how BMI is associated with semen quality and reproductive hormones in 166 men, including 38 severely obese men. Standard semen analysis and sperm DNA integrity analysis were performed, and blood samples were analysed for reproductive hormones. Adjusted for age and time of abstinence, BMI was negatively associated with sperm concentration (B = -0.088, P = 0.009), total sperm count (B = -0.223, P = 0.001), progressive sperm motility (B = -0.675, P = 0.007), normal sperm morphology (B = -0.078, P = 0.001), and percentage of vital spermatozoa (B = -0.006, P = 0.027). A negative relationship was observed between BMI and total testosterone (B = -0.378, P < 0.001), sex hormone binding globulin (B = -0.572, P < 0.001), inhibin B (B = -3.120, P < 0.001) and anti-Müllerian hormone (AMH) (B = -0.009, P < 0.001). Our findings suggest that high BMI is negatively associated with semen characteristics and serum levels of AMH