Effect of platelet-rich fibrin on periapical healing and resolution of clinical symptoms in patients following periapical surgery: A systematic review and meta-analysis.

Context: Adjuvant use of platelet-rich fibrin (PRF) in many areas of dentistry is well documented. However, its role in periapical surgery remains contested which requires further clarification by a higher level of evidence. Aim: The objective of this systematic review was to evaluate the effect of PRF on periapical surgery using meta-analysis. Materials and Methods: A comprehensive literature search was conducted in PUBMED, Cochrane Central Register of Controlled Trials, SCIENCE DIRECT, and GOOGLE SCHOLAR for randomized controlled trials (RCT) published until May 2021. Meta-analysis was performed for comparisons of baseline (pretreatment) versus posttreatment values for different measurement parameters (postoperative pain, peri apical healing both qualitatively and quantitatively). The risk of bias in all the included trials was assessed after the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions. Results: Among the 356 eligible articles found in the initial search, 10 RCTs from 2011 through 2021 were included. Qualitative analysis of all the included studies showed that PRF may play a positive role in bone healing, reduction in periapical lesions, and enhancing quality of life using different imaging modalities. The results of the meta-analysis indicated a significant reduction in postoperative pain when PRF was used (standard mean difference [SMD] = 0.515; 95% confidence interval [CI] = 0.061- 0.969;P = 0.026; I 2 = 0%). However, there was no statistically significant association observed while evaluating peri apical bone healing both qualitatively (odds ratio [OR] = 1.427; 95% CI = 0.309-6.584; P = 0.648) and quantitatively measured by Cone beam computed tomography (SMD = -0.264; 95% CI = -0.974-0.447;P = 0.454) between PRF and control group. Conclusions: Considering the notable benefits demonstrated by use of PRF, it may be considered as a valuable adjunct in periapical surgery. However, more high-quality trials are necessary to assess the exact role of PRF.

ICOR: improving codon optimization with recurrent neural networks.

BACKGROUND: In protein sequences-as there are 61 sense codons but only 20 standard amino acids-most amino acids are encoded by more than one codon. Although such synonymous codons do not alter the encoded amino acid sequence, their selection can dramatically affect the expression of the resulting protein. Codon optimization of synthetic DNA sequences is important for heterologous expression. However, existing solutions are primarily based on choosing high-frequency codons only, neglecting the important effects of rare codons. In this paper, we propose a novel recurrent-neural-network based codon optimization tool, ICOR, that aims to learn codon usage bias on a genomic dataset of Escherichia coli. We compile a dataset of over 7,000 non-redundant, high-expression, robust genes which are used for deep learning. The model uses a bidirectional long short-term memory-based architecture, allowing for the sequential context of codon usage in genes to be learned. Our tool can predict synonymous codons for synthetic genes toward optimal expression in Escherichia coli. RESULTS: We demonstrate that sequential context achieved via RNN may yield codon selection that is more similar to the host genome. Based on computational metrics that predict protein expression, ICOR theoretically optimizes protein expression more than frequency-based approaches. ICOR is evaluated on 1,481 Escherichia coli genes as well as a benchmark set of 40 select DNA sequences whose heterologous expression has been previously characterized. ICOR's performance is measured across five metrics: the Codon Adaptation Index, GC-content, negative repeat elements, negative cis-regulatory elements, and codon frequency distribution. CONCLUSIONS: The results, based on in silico metrics, indicate that ICOR codon optimization is theoretically more effective in enhancing recombinant expression of proteins over other established codon optimization techniques. Our tool is provided as an open-source software package that includes the benchmark set of sequences used in this study.

Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer.

Background/Purpose: Sarcopenia is a prognostic factor in patients with head and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) calculated from cervical neck skeletal muscle (SM) segmentations. However, SM segmentation requires manual input, which is time-consuming and variable. Therefore, we developed a fully-automated approach to segment cervical vertebra SM. Materials/Methods: 390 HNC patients with contrast-enhanced CT scans were utilized (300-training, 90-testing). Ground-truth single-slice SM segmentations at the C3 vertebra were manually generated. A multi-stage deep learning pipeline was developed, where a 3D ResUNet auto-segmented the C3 section (33 mm window), the middle slice of the section was auto-selected, and a 2D ResUNet auto-segmented the auto-selected slice. Both the 3D and 2D approaches trained five sub-models (5-fold cross-validation) and combined sub-model predictions on the test set using majority vote ensembling. Model performance was primarily determined using the Dice similarity coefficient (DSC). Predicted SMI was calculated using the auto-segmented SM cross-sectional area. Finally, using established SMI cutoffs, we performed a Kaplan-Meier analysis to determine associations with overall survival. Results: Mean test set DSC of the 3D and 2D models were 0.96 and 0.95, respectively. Predicted SMI had high correlation to the ground-truth SMI in males and females (r>0.96). Predicted SMI stratified patients for overall survival in males (log-rank p = 0.01) but not females (log-rank p = 0.07), consistent with ground-truth SMI. Conclusion: We developed a high-performance, multi-stage, fully-automated approach to segment cervical vertebra SM. Our study is an essential step towards fully-automated sarcopenia-related decision-making in patients with HNC.

Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer.

The accurate determination of sarcopenia is critical for disease management in patients with head and neck cancer (HNC). Quantitative determination of sarcopenia is currently dependent on manually-generated segmentations of skeletal muscle derived from computed tomography (CT) cross-sectional imaging. This has prompted the increasing utilization of machine learning models for automated sarcopenia determination. However, extant datasets currently do not provide the necessary manually-generated skeletal muscle segmentations at the C3 vertebral level needed for building these models. In this data descriptor, a set of 394 HNC patients were selected from The Cancer Imaging Archive, and their skeletal muscle and adipose tissue was manually segmented at the C3 vertebral level using sliceOmatic. Subsequently, using publicly disseminated Python scripts, we generated corresponding segmentations files in Neuroimaging Informatics Technology Initiative format. In addition to segmentation data, additional clinical demographic data germane to body composition analysis have been retrospectively collected for these patients. These data are a valuable resource for studying sarcopenia and body composition analysis in patients with HNC.

Visible spectrophotometric method for the determination of aripiprazole in tablets.

A simple, accurate and economic spectrophotometric method for the determination of aripiprazole in tablet formulation is proposed. In the present method acidic solution of the aripiprazole formed colored ion-association complexes with bromocresol green, soluble in chloroform. Yellowish orange chromogen showed λ(max) at 414 nm and obeyed Beer's law in the concentration range of 10-60 µg/ml. Statistical analysis and recovery studies validated the method. The proposed method is rapid, precise and accurate and can be applied for the routine estimation of aripiprazole in the laboratory.