Computational prediction of limiting activity coefficients is of great relevance for process design. For highly nonideal mixtures including molecules with directed interactions, methods that maintain the molecular character of the solvent are most promising. Computational expense and force-field deficiencies are the main limiting factors that prevent the use of highthroughput molecular dynamics (MD) simulations in a predictive setup. The combination of MD simulations and machine learning used in this work accounts for both issues. Comparison to published data including free-energy simulations, COSMO-RS and UNIFAC models, reveals competitive prediction accuracy.
Brain metastases are the most severe tumorous spread during breast cancer disease. They are associated with a limited quality of life and a very poor overall survival. A subtype of extracellular vesicles, exosomes, are sequestered by all kinds of cells, including tumor cells, and play a role in cell-cell communication. Exosomes contain, among others, microRNAs (miRs). Exosomes can be taken up by other cells in the body, and their active molecules can affect the cellular process in target cells. Tumor-secreted exosomes can affect the integrity of the blood-brain barrier (BBB) and have an impact on brain metastases forming. Serum samples from healthy donors, breast cancer patients with primary tumors, or with brain, bone, or visceral metastases were used to isolate exosomes and exosomal miRs. Exosomes expressed exosomal markers CD63 and CD9, and their amount did not vary significantly between groups, as shown by Western blot and ELISA. The selected 48 miRs were detected using real-time PCR. Area under the receiver-operating characteristic curve (AUC) was used to evaluate the diagnostic accuracy. We identified two miRs with the potential to serve as prognostic markers for brain metastases. Hsa-miR-576-3p was significantly upregulated, and hsa-miR-130a-3p was significantly downregulated in exosomes from breast cancer patients with cerebral metastases with AUC: 0.705 and 0.699, respectively. Furthermore, correlation of miR levels with tumor markers revealed that hsa-miR-340-5p levels were significantly correlated with the percentage of Ki67-positive tumor cells, while hsa-miR-342-3p levels were inversely correlated with tumor staging. Analysis of the expression levels of miRs in serum exosomes from breast cancer patients has the potential to identify new, non-invasive, blood-borne prognostic molecular markers to predict the potential for brain metastasis in breast cancer. Additional functional analyzes and careful validation of the identified markers are required before their potential future diagnostic use.
Background and Objectives: Skills-lab training is crucial for the development of advanced laparoscopic skills. In this study, we examined whether a systematic deconstructive and comprehensive tutoring approach improves training results in laparoscopic suturing and intracorporeal knot tying. Methods: Sixteen residents in obstetrics and gynecology participating in structured skills-lab laparoscopy training were randomized in 2 equal-sized groups receiving 1-on-1 tutoring either in the traditional method or according to the Peyton's 4-step approach, involving an additional training step, with the trainees instructing the tutor to perform the exercises. A validated assessment tool (revised Objective Structured Assessment of Technical Skills) and the number of completed square knots per training session and the mean time per knot were used to assess the efficacy of training in both groups. Results: Trainees in Peyton's group achieved significantly higher revised Objective Structured Assessment of Technical Skills scores (28.6 vs 23.9 points; P = .05) and were able to improve their scores during autonomous training repetitions, in contrast to the trainees not in Peyton's group (difference +4.75 vs –4.29 points, P = .02). Additionally, they seemed to be able to perform a greater number of successful knots during the exercise and to complete each knot quicker with the later observations failing to reach the threshold of statistical significance. Conclusion: Peyton's 4-step approach seemed to be superior for teaching laparoscopic skills to obstetrics and gynecology residents in the skills-lab setting and can be therefore proposed for training curricula.
Background Pelvic palpation is a core component of every Gynecologic examination. It requires vigorous training, which is difficult due to its intimate nature, leading to a need of simulation. Up until now, there are mainly models available for mere palpation which do not offer adequate visualization of the concerning anatomical structures. In this study we present a 3D printed model of the female pelvis. It can improve both the practical teaching of gynecological pelvic examination for health care professionals and the spatial understanding of the relevant anatomy. Methods We developed a virtual, simplified model showing selected parts of the female pelvis. 3D printing was used to create a physical model. Results The life-size 3D printed model has the ability of being physically assembled step by step by its users. Consequently, it improves teaching especially when combining it with commercial phantoms, which are built solely for palpation training. This is achieved by correlating haptic and visual sensations with the resulting feedback received. Conclusion The presented 3D printed model of the female pelvis can be of aid for visualizing and teaching pelvic anatomy and examination to medical staff. 3D printing provides the possibility of creating, multiplying, adapting and sharing such data worldwide with little investment of resources. Thus, an important contribution to the international medical community can be made for training this challenging examination.
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