Bone regeneration at extraction sockets filled with leukocyte-platelet-rich fibrin:an experimental pre-clinical study

We aimed to histomorphometrically evaluate the effects of Leucocyte-Platelet-Rich Fibrin (L-PRF), with and without the combination of a bone grafting material, for alveolar ridge preservation using an in vivo canine model. Seven dogs (Female Beagles, ~18-month-old) were acquired for the study. L-PRF was prepared from each individual animal by drawing venous blood and spinning them through a centrifuge at 408 RCF-clot (IntrasSpin, Intra-Lock, Boca Raton, FL). L-PRF membranes were obtained from XPression fabrication kit (Biohorizons Implant Systems, Inc., AL, USA). A split mouth approach was adopted with the first molar mesial and distal socket defects treated in an interpolated fashion of the following study groups: 1) Empty socket (negative control); 2) OSS filled defect 3) L-PRF membrane; and 4) Mix of Bio-Oss® with L-PRF. After six weeks, samples were harvested, histologically processed, and evaluated for bone area fraction occupancy (BAFO), vertical/horizontal ridge dimensions (VRD and HRD, respectively), and area of coronal soft tissue infiltration. BAFO was statistically lower for the control group in comparison to all treatment groups. Defects treated with Bio-Oss® were not statistically different then defects treated solely with L-PRF. Collapsed across all groups, L-PRF exhibited higher degrees of BAFO than groups without L-PRF. Defects filled with Bio-Oss® and Bio-Oss® with L-PRF demonstrated greater maintenance of VRD relative to the control group. Collapsed across all groups, Bio-Oss® maintained the VRD and resulted in less area of coronal soft tissue infiltration compared to the empty defect. Soft tissue infiltration observed at the coronal area was not statistically different among defects filled with L-PRF, Bio-Oss®, and Bio-Oss® with L-PRF. Inclusion of L-PRF to particulate xenograft did not promote additional bone heading at 6 weeks in vivo. However, we noted that L-PRF alone promoted alveolar socket regeneration to levels comparable to particulate xenografts, suggesting its potential utilization for socket preservation

Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods

This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels generated during operation by transformers in a real environment. The spectra frequency interval and its resolution are automatically optimized for the selected machine learning algorithm. Various machine learning algorithms, optimization techniques, and transformer types were researched: two indoor type transformers from Schneider Electric and two overhead type transformers manufactured by ABB. As a result, a method was proposed that provides a way in which inspections of working transformers (from background) and their type can be performed with an accuracy of over 97%, based on the generated low-frequency noise. The application of the proposed preprocessing stage increased the accuracy of this method by 10%. Additionally, machine learning algorithms were selected which offer robust solutions (with the highest accuracy) for noise classification

Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm

The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task

Application of Selected Machine Learning Techniques for Identification of Basic Classes of Partial Discharges Occurring in Paper-Oil Insulation Measured by Acoustic Emission Technique

The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established

Use of magnetic resonance imaging lymphangiography for preoperative planning in lymphedema surgery: A systematic review

BackgroundIn recent years, magnetic resonance imaging lymphangiography (MRL) has emerged as a way to predict if patients are candidates for lymphedema surgery, particularly lymphovenous anastomosis (LVA). Our goal was to conduct a systematic review of the literature on the use of MRL for preoperative planning in lymphedema surgery. We hypothesized that MRL could add valuable information to the standard preoperative evaluation of lymphedema patients.MethodsOn February 17, 2020, we conducted a systematic review of the PubMed/MEDLINE, Cochrane Clinical Answers, and Embase databases, without time frame or language limitations, to identify articles on the use of MRL for preoperative planning of lymphedema surgery. We excluded studies that investigated other applications of magnetic resonance imaging, such as lymphedema diagnosis and treatment evaluation. The primary outcome was the examination capacity to identify lymphatic anatomy and the secondary outcome was the presence of adverse effects.ResultsOf 372 potential articles identified with the search, nine studies fulfilled the eligibility criteria. A total of 334 lymphedema patients were enrolled in these studies. Two studies compared MRL findings with those of other standard examinations (indocyanine green lymphography [ICG‐L] or lymphoscintigraphy). No adverse effects due to MRL were reported. A study shown that MRL had higher sensitivity to detect lymphatic vessel abnormalities compared with lymphoscintigraphy and a statistically higher chance of successful LVA was observed when the results of MRL agreed with those of ICG‐L (p < .001).ConclusionsMRL could be useful for preoperative planning in lymphedema surgery. The scientific evidence has been limited, so further studies with greater numbers of patients and cost analysis are necessary to justify the addition of MRL to current preoperative protocols.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167841/1/micr30731_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167841/2/micr30731.pd

Local Pro- and Anti-Coagulation Therapy in the Plastic Surgical Patient: A Literature Review of the Evidence and Clinical Applications

The risks of systemic anti-coagulation or its reversal are well known but accepted as necessary under certain circumstances. However, particularly in the plastic surgical patient, systemic alteration to hemostasis is often unnecessary when local therapy could provide the needed adjustments. The aim of this review was to provide a summarized overview of the clinical applications of topical anti- and pro-coagulant therapy in plastic and reconstructive surgery. While not a robust field as of yet, local tranexamic acid (TXA) has shown promise in achieving hemostasis under various circumstances, hemostats are widely used to halt bleeding, and local anticoagulants such as heparin can improve flap survival. The main challenge to the advancement of local therapy is drug delivery. However, with increasingly promising innovations underway, the field will hopefully expand to the betterment of patient care

Factors that Influence Chemotherapy Treatment Rate in Patients With Upper Limb Osteosarcoma

Background/Aim: Chemotherapy is the mainstay treatment of osteosarcoma. The purpose of this study was to elucidate the factors that affect the rate of chemotherapy treatment of osteosarcoma patients. Materials and Methods: We queried the National Cancer Database for bone cancer patients. We included patients diagnosed with osteosarcoma of the upper extremities regardless of age and sex. With bivariate and multivariate models, we analyzed the demographic, facility, and tumor-specific characteristics, comparing the group that received chemotherapy with those that did not. Results: Female patients (OR=0.567; 95% CI=0.337-0.955), non-White patients (OR=0.485; 95% CI=0.25-0.939), and patients with government insurance (OR=0.506; 95% CI=0.285-0.9) had lower odds of receiving chemotherapy treatment than male, white, and privately insured patients. Patients with stages II (OR=4.817; 95% CI=2.594-8.946) and IV disease (OR=0.457; 95% CI=1.931-10.286) had higher odds of receiving chemotherapy than those with stage I disease. Conclusion: Age, sex, race and insurance affected the rate of chemotherapy treatment in patients with upper limb osteosarcoma