Maxillary nerve block: A comparison between the greater palatine canal and high tuberosity approaches.

Aim: Analgesia and anxiolysis during dental procedures are important for dental care and patient compliance. This study aims to compare two classical maxillary nerve block (MNB) techniques: the greater palatine canal (GPC) and the high tuberosity (HT) approaches, seldom used in routine dental practice. Methods: The study was conducted on 30 patients, scheduled for sinus lift surgery, who were randomly divided into 2 groups: the GPC approach to the MNB was used in 15 and the HT one in the other 15 patients. Anxiolysis was also used, depending on the results of the pre- preoperative assessment. Patients\u2019 sensations/pain during the procedure, details about anesthesia, and the dentist\u2019s considerations were all recorded. Data are expressed as mean \ub1SD. Statistical tests including ANOVA, \u3c72 following Yates correction and linear regression analysis were carried out. A < 0.05 p value was considered significant. Results: Study results showed that the anesthesia was effective and constant in the molar and premolar area. Additional infiltrations of local anesthetics were necessary for vestibular and palatal areas in the anterior oral cavity, respectively, in the GPC and HT groups. The two techniques were equally difficult to carry out in the dentist\u2019s opinion. There were no differences in pain or unpleasant sensations between the two groups, nor were any anesthesia-related complications reported. Conclusion: The GPC approach ensures effective anesthesia in the posterior maxillary region as far as both the dental pulp and the palatal/vestibular mucous membranes are concerned; the HT approach did not guarantee adequate anesthesia of the pterygopalatine branch of the maxillary nerve. These regional anesthesia techniques were characterized by a low incidence of intra and postoperative pain, no noteworthy complications, and high patient satisfaction

Oral malodor in Special Care Patients: current knowledge

Epidemiological studies report that about 50% of the population may have oral malodor with a strong social and psychological impact in their daily life. When intra-oral causes are excluded, referral to an appropriate medical specialist is paramount for management and treatment of extra-oral causes. The intra-oral causes of halitosis are highly common, and the dentist is the central clinician to diagnose and treat them. Pseudohalitosis or halitophobia may occur and an early identification of these conditions by the dentist is important in order to avoid unnecessary dental treatments for patients who need psychological or psychiatric therapy. The organoleptic technique is still considered the most reliable examination method to diagnose genuine halitosis. Special needs patients are more prone than others to have oral malodor because of concurrent systemic or metabolic diseases, and medications. The present report reviews halitosis, its implications, and the management in special care dentistry

From Theory to Practice: Plug and Play with Succinct Data Structures

Engineering efficient implementations of compact and succinct structures is a time-consuming and challenging task, since there is no standard library of easy-to- use, highly optimized, and composable components. One consequence is that measuring the practical impact of new theoretical proposals is a difficult task, since older base- line implementations may not rely on the same basic components, and reimplementing from scratch can be very time-consuming. In this paper we present a framework for experimentation with succinct data structures, providing a large set of configurable components, together with tests, benchmarks, and tools to analyze resource requirements. We demonstrate the functionality of the framework by recomposing succinct solutions for document retrieval.Comment: 10 pages, 4 figures, 3 table

Speeding up FastICA by Mixture Random Pruning

We study and derive a method to speed up kurtosis-based FastICA in presence of information redundancy, i.e., for large samples. It consists in randomly decimating the data set as more as possible while preserving the quality of the reconstructed signals. By performing an analysis of the kurtosis estimator, we find the maximum reduction rate which guarantees a narrow confidence interval of such estimator with high confidence level. Such a rate depends on a parameter \u3b2 easily computed a priori combining together the fourth and the eighth norms of the observations. Extensive simulations have been done on different sets of real world signals. They show that actually the sample size reduction is very high, preserves the quality of the decomposition and impressively speeds up FastICA. On the other hand, the simulations also show that, decimating data more than the rate fixed by \u3b2, the decomposition ability of FastICA is compromised, thus validating the reliability of the parameter \u3b2. We are confident that our method will follow to better approach real time applications

Compressed Data Structures for Dynamic Sequences

We consider the problem of storing a dynamic string $S$ over an alphabet $\Sigma=\{\,1,\ldots,\sigma\,\}$ in compressed form. Our representation supports insertions and deletions of symbols and answers three fundamental queries: $\mathrm{access}(i,S)$ returns the $i$-th symbol in $S$, $\mathrm{rank}_a(i,S)$ counts how many times a symbol $a$ occurs among the first $i$ positions in $S$, and $\mathrm{select}_a(i,S)$ finds the position where a symbol $a$ occurs for the $i$-th time. We present the first fully-dynamic data structure for arbitrarily large alphabets that achieves optimal query times for all three operations and supports updates with worst-case time guarantees. Ours is also the first fully-dynamic data structure that needs only $nH_k+o(n\log\sigma)$ bits, where $H_k$ is the $k$-th order entropy and $n$ is the string length. Moreover our representation supports extraction of a substring $S[i..i+\ell]$ in optimal $O(\log n/\log\log n + \ell/\log_{\sigma}n)$ time

Cholesterol depletion inhibits synaptic transmission and synaptic plasticity in rat hippocampus.

Several neurodegenerative disorders are associated with impaired cholesterol homeostasis in the nervous system where cholesterol is known to play a role in modulating synaptic activity and stabilizing membrane microdomains. In the present report, we investigated the effects of methyl-beta-cyclodextrin-induced cholesterol depletion on synaptic transmission and on the expression of 1) paired-pulse facilitation (PPF); 2) paired-pulse inhibition (PPI) and 3) long-term potentiation (LTP) in the CA1 hippocampal region. Results demonstrated that cyclodextrin strongly reduced synaptic transmission and blocked the expression of LTP, but did not affect PPF and PPI. The role of glutamatergic and GABAergic receptors in these cholesterol depletion-mediated effects was evaluated pharmacologically. Data indicate that, in cholesterol depleted neurons, modulation of synaptic transmission and synaptic plasticity phenomena are sustained by AMPA-, kainate-and NMDA-receptors but not by GABA-receptors. The involvement of AMPA-and kainate-receptors was confirmed by fluorimetric analysis of intracellular calcium concentrations in hippocampal cell cultures. These data suggest that modulation of receptor activity by manipulation of membrane lipids is a possible therapeutic strategy in neurodegenerative disease

Liquid biopsy in non-small cell lung cancer: highlights and challenges

Non-small cell lung cancer is one leading cause of death worldwide, and patients would greatly benefit from an early diagnosis. Since targeted and immunotherapies have emerged as novel approaches for more tailored treatments, repeated assessments of the tumor biology have become pivotal to drive clinical decisions. Currently, tumor tissue biopsy is the gold standard to investigate potentially actionable biomarkers, but this procedure is invasive and may prove inadequate to represent the whole malignancy. In this regard, liquid biopsy represents a minimally invasive and more comprehensive option for early detection and investigation of this tumor. Today, cell-free DNA is the only approved circulating marker to select patients for a targeted therapy. Conversely, the other tumor-derived markers (i.e., circulating tumor cells, miRNAs, exosomes, and tumor educated platelets) are still at a pre-clinical phase, although they show promising results for their application in screening programs or as prognostic/predictive biomarkers. The main challenges for their clinical translation are the lack of reliable cutoffs and, especially for miRNAs, the great variability among the studies. Moreover, no established tool has been approved for circulating tumor cells and exosome isolation. Finally, large prospective clinical trials are mandatory to provide evidence of their clinical utility

Human primary dermal fibroblasts interacting with 3-dimensional matrices for surgical application show specific growth and gene expression programs

Several types of 3-dimensional (3D) biological matrices are employed for clinical and surgical applications, but few indications are available to guide surgeons in the choice among these materials. Here we compare the in vitro growth of human primary fibroblasts on different biological matrices commonly used for clinical and surgical applications and the activation of specific molecular pathways over 30 days of growth. Morphological analyses by Scanning Electron Microscopy and proliferation curves showed that fibroblasts have different ability to attach and proliferate on the different biological matrices. They activated similar gene expression programs, reducing the expression of collagen genes and myofibroblast differentiation markers compared to fibroblasts grown in 2D. However, differences among 3D matrices were observed in the expression of specific metalloproteinases and interleukin-6. Indeed, cell proliferation and expression of matrix degrading enzymes occur in the initial steps of interaction between fibroblast and the investigated meshes, whereas collagen and interleukin-6 expression appear to start later. The data reported here highlight features of fibroblasts grown on different 3D biological matrices and warrant further studies to understand how these findings may be used to help the clinicians choose the correct material for specific applications

The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible