Objective: To investigate the associations between serum vitamin D, calcium and uterine fibroids in a Chinese female population. Methods: In this case-control study, adult female patients with fibroids (cases) were compared with females without fibroids (controls) in terms of serum 25-hydroxyvitamin D (25OHD) and calcium levels. Results: Out of 546 total participants (mean age, 41.68 ± 5.99 years; 279 with fibroids and 267 without fibroids), only 232 had serum 25OHD levels above the sufficient threshold (>20 ng/ml). In addition, females with fibroids had lower serum 25OHD levels versus those without fibroids. The prevalence of fibroids in females with deficient (20 ng/ml) 25OHD levels. Serum calcium levels were within normal range in both groups. Conclusion: Hypovitaminosis D was highly prevalent among a population of Chinese females of reproductive-age, and serum 25OHD levels were lower in female patients with fibroids.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations amongst existing classification or prediction models including abstract data processing and complicated dataset attributes, which hinder their applications in clinical practice. Objective: Develop a Bayesian network (BN)-based classification model to classify people with knee OA. The proposed model can be treated as a prescreening tool which can provide decision support for health professionals. Methods: The proposed model’s structure is based on a three-level BN structure, and then retrained by the Bayesian Search (BS) learning algorithm. The model’s parameters are determined by the Expectation-Maximization (EM) algorithm. The utilized dataset includes backgrounds (the basic characteristics of subjects), the target disease (namely the knee OA), and predictors (the scores of physical fitness tests). The performance of the model is evaluated based on classification accuracy, area under a curve (AUC), specificity, sensitivity, positive predictive value (PPV) and negative predictive value (NPV), and is also compared with other well-known classification models. A test is also performed to explore whether physical fitness tests could improve the performance of the proposed model. Results: 249 elderly people between 60 and 80 years of age, living in the Kongjiang community (shanghai) were recruited from April to September 2007. A total of 157 instances are adopted as the dataset after data preprocessing. The experimental results show that the proposed model’s results are higher than, or equal to, the mean scores of the other classification models: 0.754 for accuracy, 0.78 for AUC, 0.78 for specificity and 0.73 for sensitivity. The proposed model can provide 0.45 for PPV and 0.92 for NPV at the prevalence of 20%. The proposed model also shows a significant improvement when compared to the traditional BN model: 6.35% increase in accuracy (from 0.709 to 0.754), 4.00% increase in AUC (from 0.75 to 0.78), 6.85% increase in specificity (from 0.73 to 0.78), 5.80% increase in sensitivity (from 0.69 to 0.73), 15.38% increase in PPV (from 0.39 to 0.45) and 2.22% increase in NPV (from 0.90 to 0.92). Furthermore, the test results show that the performance of the proposed model could be largely enhanced through physical fitness tests in three evaluation indexes: 10.56% increase in accuracy (from 0.682 to 0.754), 16.42% increase in AUC (from 0.67 to 0.78) and 30.00% increase in specificity (from 0.60 to 0.78). Conclusions: The proposed model presents a promising method to classify people with knee OA when compared to other classification models and the traditional BN model. The proposed model could be implemented in clinical practice as a prescreening tool for knee OA, which could, not only improve the quality of healthcare for elderly people, but also reduce overall medical costs.

Coordinated reaching and grasping movements may be impaired in patients with poststroke hemiplegia. Patients frequently adopt compensatory strategies, which require investigation. This pilot study used kinematic parameters to examine compensatory strategies by assessing the reach-to-grasp-pen movements in patients with stroke and unaffected participants. Twelve patients with stroke with mild impairment (45.16 ± 12.62 years, 2.41 ± 1.97 months), twelve with moderate impairment (50.41 ± 12.92 years, 3.83 ± 3.58 months), and ten healthy individuals (20.6 ± 0.69 years) performed a reach-to-grasp-pen task. Kinematics parameters of upper limb and fingers, such as movement time, number of movement units, index of curvature, spectral arc length, trunk forward transition, trunk lateral transition, elbow extension, shoulder flexion, shoulder abduction, trunk rotation, arm-plane angle, the joint angles of interphalangeal joints of the thumb, index, middle, ring, and little fingers were examined in the study. These parameters were evaluated with two Microsoft Azure Kinect and Leap Motion, which belong to markerless motion capture systems. Patients with stroke showed longer reaching movement time, less smooth movement trajectories, and more trunk rotation ( ). In patients with stroke, the metacarpophalangeal joint (MCP) and proximal interphalangeal joint (PIP) of the thumb were flexed in the starting position; the MCP and PIP joints of the index finger in the stroke group were more extended during pen grasp; the range of motion of the MCP of the middle finger and the PIP joints of the middle, ring, and little fingers became greater, suggesting a larger peak aperture ( ). The more significant extension was observed in the index finger at the end of the grasp, suggesting inadequate flexion ( ). In clinical practice, the reach-to-grasp-pen task using markless sensing technology can effectively distinguish patients with stroke from healthy individuals and evaluate the recovery and compensation strategies of upper limb and hand functions. It can potentially become an evaluation tool in hospital and community scenes. Accurate identification of abnormal trunk, arm, and finger strategies is crucial for therapists to develop targeted upper limb treatment methods and evaluate treatment effects.