How can excess residual cement be reduced in implant-supported restorations?: An in vitro study

Objective

The aim of this study was to investigate the effect of different cements and cementation techniques used in implant-supported restorations and the application of various vent modifications and extraoral replica techniques on the amount of overflowing cement in cemented systems.

Materials and Methods

In this study, three different abutment designs were used as fully closed, occlusal vented, and occlusal + proximal vented. An extraoral replica was produced by milling the CAD/CAM ceramic block. The number of groups with and without replicas was determined as six (n = 10). For the cementation procedures, three different cements were tested: dual-cure resin, eugenol-free zinc oxide, and polycarboxylate cements. Cobalt–chromium superstructures to be cemented to the implant analog–abutment complex were produced by direct metal laser sintering method. Twenty-four hours after the cementation process, residual cement were measered with Micro-CT. In comparisons between groups, ANOVA test was used for normally distributed variables and Kruskall–Wallis H test was used for non-normally distributed variables at a significance level of p < 0.05.

Results

The difference in residual cement volumes between the groups in terms of both cementation techniques (whether or not to use an extraoral replica and different vent desings) and cement types was found to be statistically significant (p < 0.05). There was significantly less residual cement in all groups that used extraoral replicas than those that did not. As for the cement types, the most residual cement occurred in the resin cement.

Conclusion

The use of extraoral replicas and vent designs on the abutment significantly reduces the amount of residual cement. Regardless of the cementation technique, the type of cement used affects the amount of excess cement.

Clinical Relevance

To reduce residual cement, both the type of cement and the cementation technique used must be considered.     

Tracing topics and trends in drug-resistant epilepsy research using a natural language processing–based topic modeling approach

Epilepsy is a common neurological disorder affecting over 70 million people worldwide. Although many patients achieve seizure control with anti-epileptic drugs (AEDs), 30%–40% develop drug-resistant epilepsy (DRE), where seizures persist despite adequate trials of AEDs. DRE is associated with reduced quality of life, increased mortality and morbidity, and greater socioeconomic challenges. The continued intractability of DRE has fueled exponential growth in research that aims to understand and treat this serious condition. However, synthesizing this vast and continuously expanding DRE literature to derive insights poses considerable difficulties for investigators and clinicians. Conventional review methods are often prolonged, hampering the timely application of findings. More-efficient approaches to analyze the voluminous research are needed. In this study, we utilize a natural language processing (NLP)–based topic modeling approach to examine the DRE publication landscape, uncovering key topics and trends. Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic. This technique employs transformer models like BERT (Bidirectional Encoder Representations from Transformers) for contextual understanding, thereby enabling accurate topic categorization. Analysis revealed 18 distinct topics spanning various DRE research areas. The 10 most common topics, including “AEDs,” “Neuromodulation Therapy,” and “Genomics,” were examined further. “Cannabidiol,” “Functional Brain Mapping,” and “Autoimmune Encephalitis” emerged as the hottest topics of the current decade, and were examined further. This NLP methodology provided valuable insights into the evolving DRE research landscape, revealing shifting priorities and declining interests. Moreover, we demonstrate an efficient approach to synthesizing and visualizing patterns within extensive literature that could be applied to other research fields.     

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.     

Maternal serum concentrations of s-Endoglin and IL-6 in pregnancy complicated by preterm premature membrane rupture

Objective: This study aimed to investigate maternal serum concentrations of s-Endoglin and compare s-Endoglin with other inflammatory markers in prediction of time to delivery, in pregnancies complicated by preterm premature rupture of membranes (PPROM).

Materials and methods: Fifty five patients complicated by PPROM whose gestational age were between 2433 weeks and 44 matched healthy pregnant women were included in present study. Maternal concentrations of s-Endoglin concentrations were measured by an enzyme-linked immunosorbent assay (ELISA) and compared with maternal inflammatory markers including interleukin-6 (IL-6), white blood cell (WBC) count and serum C-reactive protein (CRP). The best variable for prediction of preterm birth was computed.

Results: Mean s-Endoglin levels in PPROM were lower than control groups (0.24?±?0.12?pg/ml and 0.69?±?0.25?pg/ml, respectively, p?<?0.01). Besides IL-6 (p?<?0.01), WBC (p?=?0.016) and CRP (p?=?0.010) levels were higher in PPROM group. In PPROM group, ROC analysis results of s-Endoglin for prediction of preterm delivery <48 h, <7 days, <32 weeks were not different (p?>?0.05). For predicting preterm birth before 48 h and 7 days, only IL-6 at cut off value >0.70 (pg/ml) and >0.55 (pg/ml) had area under curve (AUC); 0.871 (0.7750.965), p?<?0.01, AUC; 0.925 (0.8560.993), p?<?0.001, respectively.

Conclusion: s-Endoglin as an anti-angiogenic marker seemed to have a role in pathogenesis but results of present study showed that, unlike IL-6, it was unsatisfactory for estimating time to delivery in PPROM.