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L-PRF (leukocyte- and platelet-rich fibrin) is one of the four families of platelet concentrates for surgical use and is widely used in oral and maxillofacial regenerative therapies. The first objective of this article was to evaluate the mechanical vibrations appearing during centrifugation in four models of commercially available table-top centrifuges used to produce L-PRF and the impact of the centrifuge characteristics on the cell and fibrin architecture of a L-PRF clot and membrane. The second objective of this article was to evaluate how changing some parameters of the L-PRF protocol may influence its biological signature, independently from the characteristics of the centrifuge. In the first part, four different commercially available centrifuges were used to produce L-PRF, following the original L-PRF production method (glass-coated plastic tubes, 400 g force, 12 minutes). The tested systems were the original L-PRF centrifuge (Intra-Spin, Intra-Lock, the only CE and FDA cleared system for the preparation of L-PRF) and three other laboratory centrifuges (not CE/FDA cleared for L-PRF): A-PRF 12 (Advanced PRF, Process), LW-UPD8 (LW Scientific) and Salvin 1310 (Salvin Dental). Each centrifuge was opened for inspection, two accelerometers were installed (one radial, one vertical), and data were collected with a spectrum analyzer in two configurations (full-load or half load). All clots and membranes were collected into a sterile surgical box (Xpression kit, Intra-Lock). The exact macroscopic (weights, sizes) and microscopic (photonic and scanning electron microscopy SEM) characteristics of the L-PRF produced with these four different machines were evaluated. In the second part, venous blood was taken in two groups, respectively, Intra-Spin 9 ml glass-coated plastic tubes (Intra-Lock) and A-PRF 10 ml glass tubes (Process). Tubes were immediately centrifuged at 2700 rpm (around 400 g) during 12 minutes to produce L-PRF or at 1500 rpm during 14 minutes to produce A-PRF. All centrifugations were done using the original L-PRF centrifuge (Intra-Spin), as recommended by the two manufacturers. Half of the membranes were placed individually in culture media and transferred in a new tube at seven experimental times (up to 7 days). The releases of transforming growth factor β-1 (TGFβ-1), platelet derived growth factor AB (PDGF-AB), vascular endothelial growth factor (VEGF) and bone morphogenetic protein 2 (BMP-2) were quantified using ELISA kits at these seven experimental times. The remaining membranes were used to evaluate the initial quantity of growth factors of the L-PRF and A-PRF membranes, through forcible extraction. Very significant differences in the level of vibrations at each rotational speed were observed between the four tested centrifuges. The original L-PRF centrifuge (Intra-Spin) was by far the most stable machine in all configurations and always remained under the threshold of resonance, unlike the three other tested machines. At the classical speed of production of L-PRF, the level of undesirable vibra...

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Colposcopy is widely used to detect cervical cancers, but experienced physicians who are needed for an accurate diagnosis are lacking in developing countries. Artificial intelligence (AI) has been recently used in computer-aided diagnosis showing remarkable promise. In this study, we developed and validated deep learning models to automatically classify cervical neoplasms on colposcopic photographs. Pre-trained convolutional neural networks were fine-tuned for two grading systems: the cervical intraepithelial neoplasia (CIN) system and the lower anogenital squamous terminology (LAST) system. The multi-class classification accuracies of the networks for the CIN system in the test dataset were 48.6 ± 1.3% by Inception-Resnet-v2 and 51.7 ± 5.2% by Resnet-152. The accuracies for the LAST system were 71.8 ± 1.8% and 74.7 ± 1.8%, respectively. The area under the curve (AUC) for discriminating high-risk lesions from low-risk lesions by Resnet-152 was 0.781 ± 0.020 for the CIN system and 0.708 ± 0.024 for the LAST system. The lesions requiring biopsy were also detected efficiently (AUC, 0.947 ± 0.030 by Resnet-152), and presented meaningfully on attention maps. These results may indicate the potential of the application of AI for automated reading of colposcopic photographs. Cervical cancer is the fourth most common cancer in women worldwide, and the second most common cancer among females in developing countries 1. Screening is the principal prevention method aimed at reducing mortality rates. Screening includes certain steps, including population-based Papanicolaou (Pap) testing, colposcopydirected biopsy of suspicious lesions, and the treatment of confirmed pre-cancer lesions 2,3. In women with low-grade intraepithelial lesions (LSIL) or high-grade intraepithelial lesions (HSIL), the risk of pre-cancer is medium to high, and immediate referral for colposcopy is necessary. However, referring all women with atypical squamous cells of undetermined significance (ASC-US) is considered inefficient, as the risk of such cases being pre-cancerous is lower 4. Screening programs have been successful in the developed countries, leading to an approximately 80% decrease in the cervical cancer incidence over the past 4 decades. In contrast, the increase in cervical cancer incidence reported in developing countries 5 has been attributed to the unsuccessful implementation of screening programs. This, has been attributed to logistics in health systems, infrastructural inadequacies, and the lack of expert physicians capable of introducing screening programs and follow-up 6 .