Digital Customized Titanium Mesh for Bone Regeneration of Vertical, Horizontal and Combined Defects: A Case Series.

Background and Objective: Guided bone regeneration allows new bone formation in anatomical sites showing defects preventing implant rehabilitation. Material and Methods: The present case series reported the outcomes of five patients treated with customized titanium meshes manufactured with a digital workflow for achieving bone regeneration at future implant sites. A significant gain in both width and thickness was achieved for all patients. Results: From a radiographic point of view (CBTC), satisfactory results were reached both in horizontal and vertical defects. An average horizontal gain of 3.6 ± 0.8 mm and a vertical gain of 5.2 ± 1.1 mm. Conclusions: The findings from this study suggest that customized titanium meshes represent a valid method to pursue guided bone regeneration in horizontal, vertical or combined defects. Particular attention must be paid by the surgeon in the packaging of the flap according to a correct method called the "poncho" technique in order to reduce the most frequent complication that is the exposure of the mesh even if a partial exposure of one mesh does not compromise the final outcome of both the reconstruction and the healing of the implants.

Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment.

Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed at inferring relationships between variables. The benefits of ML comprise flexibility and scalability compared with conventional statistical approaches, which makes it deployable for several tasks, such as diagnosis and classification, and survival predictions. However, much of ML-based analysis remains scattered, lacking a cohesive structure. There is a need to evaluate and compare the performance of well-developed conventional statistical methods and ML on patient outcomes, such as survival, response to treatment, and patient-reported outcomes (PROs). In this article, we compare the usefulness and limitations of traditional statistical methods and ML, when applied to the medical field. Traditional statistical methods seem to be more useful when the number of cases largely exceeds the number of variables under study and a priori knowledge on the topic under study is substantial such as in public health. ML could be more suited in highly innovative fields with a huge bulk of data, such as omics, radiodiagnostics, drug development, and personalized treatment. Integration of the two approaches should be preferred over a unidirectional choice of either approach.

In Vitro Identification of New Transcriptomic and miRNomic Profiles Associated with Pulmonary Fibrosis Induced by High Doses Everolimus: Looking for New Pathogenetic Markers and Therapeutic Targets.

The administration of Everolimus (EVE), a mTOR inhibitor used in transplantation and cancer, is often associated with adverse effects including pulmonary fibrosis. Although the underlying mechanism is not fully clarified, this condition could be in part caused by epithelial to mesenchymal transition (EMT) of airway cells. To improve our knowledge, primary bronchial epithelial cells (BE63/3) were treated with EVE (5 and 100 nM) for 24 h. EMT markers (α-SMA, vimentin, fibronectin) were measured by RT-PCR. Transepithelial resistance was measured by Millicell-ERS ohmmeter. mRNA and microRNA profiling were performed by Illumina and Agilent kit, respectively. Only high dose EVE increased EMT markers and reduced the transepithelial resistance of BE63/3. Bioinformatics showed 125 de-regulated genes that, according to enrichment analysis, were implicated in collagen synthesis/metabolism. Connective tissue growth factor (CTGF) was one of the higher up-regulated mRNA. Five nM EVE was ineffective on the pro-fibrotic machinery. Additionally, 3 miRNAs resulted hyper-expressed after 100 nM EVE and able to regulate 31 of the genes selected by the transcriptomic analysis (including CTGF). RT-PCR and western blot for MMP12 and CTGF validated high-throughput results. Our results revealed a complex biological network implicated in EVE-related pulmonary fibrosis and underlined new potential disease biomarkers and therapeutic targets.

Proteomic-based research strategy identified laminin subunit alpha 2 as a potential urinary-specific biomarker for the medullary sponge kidney disease.

Medullary sponge kidney (MSK) disease, a rare kidney malformation featuring recurrent renal stones and nephrocalcinosis, continues to be diagnosed using expensive and time-consuming clinical/instrumental tests (mainly urography). Currently, no molecular diagnostic biomarkers are available. To identify such we employed a proteomic-based research strategy utilizing urine from 22 patients with MSK and 22 patients affected by idiopathic calcium nephrolithiasis (ICN) as controls. Notably, two patients with ICN presented cysts. In the discovery phase, the urine of 11 MSK and 10 controls, were randomly selected, processed, and analyzed by mass spectrometry. Subsequently, several statistical algorithms were undertaken to select the most discriminative proteins between the two study groups. ELISA, performed on the entire patients' cohort, was used to validate the proteomic results. After an initial statistical analysis, 249 and 396 proteins were identified exclusive for ICN and MSK, respectively. A Volcano plot and ROC analysis, performed to restrict the number of MSK-associated proteins, indicated that 328 and 44 proteins, respectively, were specific for MSK. Interestingly, 119 proteins were found to differentiate patients with cysts (all patients with MSK and the two ICN with renal cysts) from ICN without cysts. Eventually, 16 proteins were found to be common to three statistical methods with laminin subunit alpha 2 (LAMA-2) reaching the higher rank by a Support Vector Machine, a binary classification/prediction scheme. ELISA for LAMA-2 validated proteomic results. Thus, using high-throughput technology, our study identified a candidate MSK biomarker possibly employable in future for the early diagnosis of this disease.

Proteomic Analysis of Urinary Microvesicles and Exosomes in Medullary Sponge Kidney Disease and Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND AND OBJECTIVES: Microvesicles and exosomes are involved in the pathogenesis of autosomal dominant polycystic kidney disease. However, it is unclear whether they also contribute to medullary sponge kidney, a sporadic kidney malformation featuring cysts, nephrocalcinosis, and recurrent kidney stones. We addressed this knowledge gap by comparative proteomic analysis. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The protein content of microvesicles and exosomes isolated from the urine of 15 patients with medullary sponge kidney and 15 patients with autosomal dominant polycystic kidney disease was determined by mass spectrometry followed by weighted gene coexpression network analysis, support vector machine learning, and partial least squares discriminant analysis to compare the profiles and select the most discriminative proteins. The proteomic data were verified by ELISA. RESULTS: A total of 2950 proteins were isolated from microvesicles and exosomes, including 1579 (54%) identified in all samples but only 178 (6%) and 88 (3%) specific for medullary sponge kidney microvesicles and exosomes, and 183 (6%) and 98 (3%) specific for autosomal dominant polycystic kidney disease microvesicles and exosomes, respectively. The weighted gene coexpression network analysis revealed ten modules comprising proteins with similar expression profiles. Support vector machine learning and partial least squares discriminant analysis identified 34 proteins that were highly discriminative between the diseases. Among these, CD133 was upregulated in exosomes from autosomal dominant polycystic kidney disease and validated by ELISA. CONCLUSIONS: Our data indicate a different proteomic profile of urinary microvesicles and exosomes in patients with medullary sponge kidney compared with patients with autosomal dominant polycystic kidney disease. The urine proteomic profile of patients with autosomal dominant polycystic kidney disease was enriched of proteins involved in cell proliferation and matrix remodeling. Instead, proteins identified in patients with medullary sponge kidney were associated with parenchymal calcium deposition/nephrolithiasis and systemic metabolic derangements associated with stones formation and bone mineralization defects. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2019_04_24_CJASNPodcast_19_06_.mp3.