Open Surgical Implantation of a Viable Intact Cryopreserved Human Placental Membrane for the Treatment of Recalcitrant Plantar Fasciitis: Case Report With Greater than 2-Year Follow-Up Duration.

Plantar fasciitis is one of the most common conditions encountered by a podiatric physician. Although most individuals respond well to traditional conservative and surgical remedies, a portion of patients will exhaust all available treatment options and will experience ongoing pain that can ultimately affect their quality of life. There has been an increase in scientific and clinical research surrounding the medical use of human placental membranes (HPMs) and many of these point-of-care allografts are now commercially available. We present the case of a 53-year-old female with chronic plantar fasciitis for whom both conservative therapies and surgical treatments of 1 year's duration had previously failed. After open revision with implantation of viable intact cryopreserved human placental membrane (vCPM; Grafix®, Osiris Therapeutics, Inc., Columbia, MD), the patient was able to resume her full-work duty with minimal symptoms at the 12- and 24-month follow-up examinations. This case report highlights the use of HPMs as an adjunct approach in the treatment of recalcitrant plantar fasciitis and the need for continued research.

Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer.

Objective: To investigate the effect of transrectal multimodal ultrasound combined with serum prostate-specific antigen (PSA)-related indicators and machine learning for the diagnosis of clinically significant prostate cancer. Methods: Based on Gleason score of postoperative pathological results, the subjects were divided into clinically significant prostate cancer groups(GS>6)and non-clinically significant prostate cancer groups(GS ≤ 6). The independent risk factors were obtained by univariate logistic analysis. Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) machine learning models were combined with clinically significant prostate cancer risk factors to establish the machine learning model, calculate the model evaluation indicators, construct the receiver operating characteristic curve (ROC), and calculate the area under the curve (AUC). Results: Independent risk factor items (P< 0.05) were entered into the machine learning model. A comparison of the evaluation indicators of the model and the area under the ROC curve showed the ANN model to be best at predicting clinically significant prostate cancer, with a sensitivity of 80%, specificity of 88.6%, F1 score of 0.897, and the AUC was 0.855. Conclusion: Establishing a machine learning model by rectal multimodal ultrasound and combining it with PSA-related indicators has definite application value in predicting clinically significant prostate cancer.

Comparison of machine learning models based on multi-parametric magnetic resonance imaging and ultrasound videos for the prediction of prostate cancer.

Objective: To establish machine learning (ML) prediction models for prostate cancer (PCa) using transrectal ultrasound videos and multi-parametric magnetic resonance imaging (mpMRI) and compare their diagnostic performance. Materials and methods: We systematically collated the data of 383 patients, including 187 with PCa and 196 with benign lesions. Of them, 307 patients (150 with PCa and 157 with benign lesions) were randomly selected to train and validate the ML models, 76 patients were used as test set. B-Ultrasound videos (BUS), mpMRI T2 sequence (T2), and ADC sequence (ADC) were obtained from all patients. We extracted 851 features of each patient in the BUS, T2, and ADC groups and used a t-test, the Mann-Whitney U test, and LASSO regression to screen the features. Support vector machine (SVM), random forest (RF), adaptive boosting (ADB), and gradient boosting machine (GBM) models were used to establish radiomics models. In addition, we fused the features screened via LASSO regression from three groups as new features and rebuilt ML models. The performance of the ML models in diagnosing PCa in the BUS, T2, ADC, and fusion groups was compared using the area under the ROC curve (AUC), sensitivity, specificity, and accuracy. Results: In the test cohort, the AUC of each model in the ADC group was higher than that of in the.BUS and T2 groups. Among the models, the RF model had the best diagnostic performance, with an AUC of 0.85, sensitivity of 0.78 (0.61-0.89), specificity of 0.84 (0.69-0.94), and accuracy of 0.83 (0.66-0.93). The SVM model in both the BUS and T2 groups performed best. Based on the features screened in the BUS, T2, and ADC groups fused to construct the models, the SVM model was found to perform best, with an AUC of 0.87, sensitivity of 0.73 (0.56-0.86), specificity of 0.79 (0.63-0.90), and accuracy of 0.77 (0.59-0.89). The difference in the results was statistically significant (p<0.05). Conclusion: The ML prediction models had a good diagnostic ability for PCa. Among them, the SVM model in the fusion group showed the best performance in diagnosing PCa. These prediction models can help radiologists make better diagnoses.

Grading Characteristics of Texaco Gasification Fine Slag: Quality Distinction and Selective Distribution of Trace Elements.

Aiming at hard-to-reuse gasification fine slag, a new process of treating gasification fine slag by classification was presented. The screening treatment was carried out based on ensuring the original particle size composition of fine slag, and it was divided into six particle size ranges as follows: +0.5, 0.3-0.5, 0.125-0.3, 0.074-0.125, 0.045-0.074, and -0.045 mm. The physical properties of different size range samples were examined by elemental analysis, X-ray diffraction, X-ray fluorescence, cold field emission scanning electron microscopy, and energy-dispersive spectrometry. The results showed that the carbon content of the median section (0.125-0.3 mm) fine slag had significant improvement compared with the other section fine slag. The carbon distribution of the +0.125 mm fine slag was concentrated, while the carbon distribution of -0.125 mm was dispersed and closely mixed with minerals. The content of trace elements Cr, Mn, Ni, V, Cd, Pb, and Mo was determined by inductively coupled plasma-mass spectrometry, and the correlation between minerals and trace elements of different particle size-graded fine slag was evaluated by Pearson correlation analysis. The results suggested that high vaporization temperature and metallic oxide forms of trace elements had a strong correlation with feldspar.