Knee osteoarthritis (KOA) is a major health problem especially in the aging population. There is a need for safe treatment that restores the cartilage and reduces the symptoms. The use of stem cells is emerging as a possible option for the moderate and severe cases. This study aimed at testing the safety of autologous bone marrow mesenchymal stem cells (BM-MSCs) expanded in vitro when given intra-articularly to patients with stage II and III KOA. As a secondary end point, the study tested the ability of these cells to relieve symptoms and restore the knee cartilage in these patients as judged by normalized knee injury and Osteoarthritis Outcome Score (KOOS) and by magnetic resonance imaging (MRI). Thirteen patients with a mean age of 50 years suffering from KOA stages II and III were given two doses of BM-MSCs 1 month apart totaling 61 × 106 ± 0.6 × 106 by intra-articular injection in a phase I prospective clinical trial. Each patient was followed for a minimum of 24 months for any adverse events and for clinical outcome using normalized KOOS. Cartilage thickness was assessed by quantitative MRI T2 at 12 months of follow-up. No severe adverse events were reported up to 24 months follow-up. Normalized KOOS improved significantly. Mean knee cartilage thickness measured by MRI improved significantly. BM-MSCs given intra-articularly are safe in knee osteoarthrosis. Despite the limited number of patients in this study, the procedure described significantly improved the KOOS and knee cartilage thickness, indicating that they may enhance the functional outcome as well as the structural component. ClinicalTrials.gov, NCT02118519

This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neural network model, namely the VGG19 model, at six different extraction levels. The deep features extracted at each level are analyzed using a features selection algorithm to identify the deep feature combination that achieves the highest classification performance. Furthermore, the extracted deep features are combined with handcrafted texture and morphological features and processed using features selection to investigate the possibility of improving the classification performance. The cross-validation analysis, which is performed using 380 breast ultrasound images, shows that the best combination of deep features is obtained using a feature set, denoted by $CONV$ features that include convolution features extracted from all convolution blocks of the VGG19 model. In particular, the $CONV$ features achieved mean accuracy, sensitivity, and specificity values of $94.2\%$, $93.3\%$, and $94.9\%$, respectively. The analysis also shows that the performance of the $CONV$ features degrades substantially when the features selection algorithm is not applied. The classification performance of the $CONV$ features is improved by combining these features with handcrafted morphological features to achieve mean accuracy, sensitivity, and specificity values of $96.1\%$, $95.7\%$, and $96.3\%$, respectively. Furthermore, the cross-validation analysis demonstrates that the $CONV$ features and the combined $CONV$ and morphological features outperform the handcrafted texture and morphological features as well as the fine-tuned VGG19 model. The generalization performance of the $CONV$ features and the combined $CONV$ and morphological features is demonstrated by performing the training using the 380 breast ultrasound images and the testing using another dataset that includes 163 images. The results suggest that the combined $CONV$ and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.

Objective: To describe variable mullerian duct anomalies using magnetic resonance imaging (MRI) and to classify these anomalies according to the available classification systems, namely the American Fertility Society (AFS) system, the European Society of Human Reproduction and Embryology (ESHRE) system, and the new American Society for Reproductive Medicine (ASRM) system. Design: Retrospective chart review. Subjects: The pelvic MRI studies and the clinical records of 64 females with mullerian congenital anomalies were retrospectively reviewed between January 2010 and December 2021. The mean age was 22 years (age range 2– 63 years). Main Outcome Measures: Detailed imaging findings were recorded, and the resulting mullerian anomalies were then classified according to the three classification systems of interest. Results: Variable mullerian anomalies were found among patients with multiple frequencies. Mullerian agenesis and hypoplasia were found in 12 patients (19%) and 16 patients (25%), respectively. Uterus didelphys was found in 5 patients (8%). Twelve (19%) patients had septate uterus, while 8 (12.5%) had a bicornuate anomaly. Unicornuate uterus was present in 7 patients (11%). Isolated vaginal anomaly was diagnosed in 4 patients (6%). Renal/urinary tract imaging was available for 27 (42%) patients, and accompanying urinary tract anomalies were noted in 10 of them (37%). Few ovarian and other extra-renal anomalies were observed. Conclusion: MRI could efficiently delineate the mullerian anomalies regardless of their complexity. Most of these anomalies were more efficaciously categorized by the ESHRE and the new ASRM systems, compared to the originally widely used AFS system. The new ASRM classification was found to be more practical as it is a modification of the original AFS system, using drawings with clear descriptions instead of symbols. This is particularly helpful in the radiological era, saving time and effort.

The incidence of breast cancer among females is increasing and early detection greatly improves patient prognosis and reduces mortality rates. This study aims at evaluating the awareness of these screening measures among adult females in Jordan. A questionnaire was administered to 400 adult women October to December 2018. The majority of women (64.5%) were within the age range 18 to 40 years. Almost all women had ever heard about breast cancer (99.50%) and 86.51% wanted to know more about it. Family history was the most frequently perceived risk factor for breast cancer (45.75%), with lack of awareness regarding other factors. Most participants were aware about breast self-examination (90.50%), but only half of them practiced it (52.75%). Regarding clinical breast examination, 76.0% of the participants have heard about it and a similar proportion recognized it as a method for early detection of breast cancer. However, only 26.33% were aware of the correct frequency of this examination. Screening mammography was acknowledged by 73.75% of participants but was performed by only 14.50%. Most of participants (70%) got their information about breast cancer from media, while only 8.34% got their information from the health centers. Awareness about breast cancer month in Jordan was significantly less among women with less than a university education, married women, and housewives (p < .043, <.007, and <.019, respectively). In conclusion, suboptimal awareness of breast cancer and its screening methods was found among the Jordanian women, despite an over 11-year-old national screening program. This should prompt more innovative strategies to disseminate more effective awareness across the country.