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Abstract Background A possible relationship between periodontitis (PD) and COVID-19 and its adverse outcomes has been suggested. Hence, the present systematic review and meta-analysis aimed to investigate the available evidence regarding the potential association between periodontitis (PD) and COVID-19 and its adverse outcomes. Materials and methods PubMed, Scopus, Web of Science, and Google Scholar were searched for relevant studies published up to April 15th, 2023. Studies that evaluated the association between PD and COVID-19 were included. Risk of bias was evaluated by two reviewers, and meta-analyses were performed using RevMan 5.3 software. Results A total of 22 studies involving 92,535 patients from USA, Europe, Asia, the Middle East and South America were included; of these, 12 were pooled into the meta-analysis. Most of the studies (19 studies) reported a significant association between PD and COVID-19. The pooled data found a significant association between PD and COVID-19 outcomes: more severe symptoms (OR = 6.95, P = 0.0008), ICU admissions (OR = 3.15, P = 0.0001), and mortality (OR = 1.92, P = 0.21). Additionally, compared to mild PD, severe PD was significantly associated with higher risks of severe COVID-19 outcomes: severe symptoms (P = 0.02); ICU admission (P = 0.0001); and higher mortality rates (P = 0.0001). The results also revealed 58% higher risk for COVID-19 infection in patients with PD (P = 0.00001). Conclusions The present findings suggest a possible association between poor periodontal health and the risk of poor COVID-19 outcomes. However, owing to the observed methodological heterogeneity across the included studies, further prospective cohort studies with standardized methodologies are warranted to further unravel the potential association between periodontal disease and COVID-19 and its adverse outcomes.

Migraine is a neurologic illness that produces intense throbbing pain on one side of the head and affects roughly 1 billion people worldwide. Recent research indicates a relationship between periodontitis and chronic migraines. This study aimed to review the association between chronic migraines and periodontitis through a systematic literature review.Four research databases (Google Scholar, PubMed, ProQuest, and SpringerLink) were searched according to PRISMA guidelines to retrieve the studies included in this review. A search strategy was developed to answer the study question with appropriate inclusion and exclusion criteria. Out of 34 published studies, 8 studies were included in this review. Three of the studies were cross-sectional, 3 were case-control, and 2 were clinical report and medical hypothesis papers. Seven of the 8 included studies showed that there is an association between periodontal disease and chronic migraine. The elevated blood levels of some biomarkers such as leptins, ProCalcitonin (proCT), calcitonin gene-related peptides (CGRPs), Pentraxin 3 (PTX3), and Soluble Tumor Necrosis Factor-like Weak Inducer Of Apoptosis (sTWEAK) play a significant role in this association. The limitations include a small sample size, the influence of anti-inflammatory drugs, and a self-reported headache measure that is subject to misclassification bias.This systematic review reveals a supposed correlation between periodontal disease and chronic migraine, as evidenced by various biomarkers and inflammatory mediators. This suggests that periodontal disease could potentially contribute to the development of chronic migraine. However, to further assess the potential benefits of periodontal treatment in patients with chronic migraine, additional longitudinal studies with larger sample sizes and interventional studies are needed.

Alzheimer's disease is a common type of dementia. It is a progressive neurodegenerative disease that starts with mild memory loss and may progress to loss of ability to convey a conversation and respond to the surrounding. Early detection can help in the management of this disease. In this review, we attempted to find the most accurate method for salivary biomarkers detection, which can aid in the early diagnosis of Alzheimer's disease. For this scoping review; PubMed, Scopus, and Google Scholar online databases were searched to find studies and articles that test the salivary biomarkers for the diagnosis of Alzheimer's disease. Eleven studies were eligible for our research. Ten were case–control, and one was a cohort study. Seven of the studies used the enzyme-linked immunosorbent assay (ELISA) method, five of which showed diagnostic potential. Two studies used Luminex assay and photomultiplier tube, showing similar results to ELISA. AbsoluteIDQ®p150 Kit and faster ultra-performance liquid chromatography-mass spectrometry analysis and pattern recognition techniques tested different metabolites with potential diagnostic significance. Furthermore, another study using an extended gate ion sensitive field effect transistor sensor related salivary trehalose sugar to Alzheimer's disease. Different reliable biomarkers were detected using ELISA kit and appear to be effective in disease detection. Further studies and clinical trials on a large scale are required to determine the most accurate method of detecting Alzheimer's disease.

Background: Khat (Catha edulis Forsk) leaves are chewed by people in certain regions of East Africa and the Middle East for their stimulating amphetamine-like effects. The purpose of this scoping review is to systematically map the current in vitro publications that investigated the toxicological potential effects of khat on cultured human or animal cells in terms of cellular viability and activity. Methods: A comprehensive electronic database search was undertaken up to December 2020 without starting date or language restrictions in accordance with the PRISMA extension for scoping review guideline and methodological quality evaluation based on the guidelines for reporting pre-clinical in vitro studies on dental materials. All in vitro studies that investigated the effect of khat plant extract (Catha Edulis) on the cultured human or animal cells were included. Results: The initial search yielded 599 articles and 16 articles were finally selected to be included. The treatment of cells with khat produced different degrees of cellular changes, including decreased cellular survival, induction of apoptosis, increased ROS production, alteration of cell phenotype, and of arrest cell cycle. In this contest, khat-exposed cells expressed higher levels of pro-apoptotic protein Bax and lower levels of anti-apoptotic Bcl-2, up-regulated p38, p53, p16, and p21 proteins, as well as premature expression of differentiation markers. Conclusion: Based on the current scoping review, khat induced apoptosis and cytotoxicity in cultured human cells, including oral cells.

Background: Antibiotic resistance has become life-threatening. A combination of metronidazole and amoxicillin is commonly used as adjunct to periodontal therapy. The aim of this study was to utilize an in vitro biofilm model to test the effect of replacing the broad-spectrum amoxicillin with penicillin V in the combination with metronidazole. Methods: An in vitro biofilm model, consisting of Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis and Fusobacterium nucleatum was developed. Biofilms were exposed to chlorhexidine digluconate (CHX) 0.2%, as positive control, and either to metronidazole/amoxicillin or metronidazole/penicillin V in two different concentrations. Statistical significance was achieved if p < 0.05 with paired t-test. Results: There was no growth of any bacterial species after 2h exposure to 0.2% CHX. Together, the two combinations of antibiotics were effective against F. nucleatum and P. gingivalis, but less effective against A. actinomycetemcomitans. No significant difference was found between the two combinations of antibiotics to combat A. actinomycetemcomitans neither in high (p = 0.95) nor in low concentration (p = 0.17). Conclusion: A combination of metronidazole with penicillin V had similar inhibiting effect as metronidazole combined with amoxicillin on an in vitro three-species biofilm model. Use of a narrow spectrum antibiotic may be indicated in combination with metronidazole.

Background: Biofilms are organized communities of microorganisms embedded in a self-produced extracellular polymeric matrix (EPM), often with great phylogenetic variety. Bacteria in the subgingival biofilm are key factors that cause periodontal diseases; among these are the Gram-negative bacteria Fusobacterium nucleatum and Porphyromonas gingivalis. The objectives of this study were to characterize the major components of the EPM and to test the effect of deoxyribonuclease I (DNase I) and proteinase K. Methods: F. nucleatum and P. gingivalis bacterial cells were grown in dynamic and static biofilm models. The effects of DNase I and proteinase K enzymes on the major components of the EPM were tested during biofilm formation and on mature biofilm. Confocal laser scanning microscopy was used in observing biofilm structure. Results: Proteins and carbohydrates were the major components of the biofilm matrix, and extracellular DNA (eDNA) was also present. DNase I and proteinase K enzymes had little effect on biofilms in the conditions used. In the flow cell, F. nucleatum was able to grow in partially oxygenated conditions while P. gingivalis failed to form biofilm alone in similar conditions. F. nucleatum supported the growth of P. gingivalis when they were grown together as dual species biofilm. Conclusion: DNase I and proteinase K had little effect on the biofilm matrix in the conditions used. F. nucleatum formed biofilm easily and supported the growth of P. gingivalis, which preferred anaerobic conditions.

Mission critical Machine-type Communication (mcMTC), also referred to as Ultra-reliable Low Latency Communication (URLLC), has become a research hotspot. It is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected devices. While the reduction in physical (PHY) layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability, the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper layers. The mcMTC has a significant technical impact on the design of all layers of the communication protocol stack. In this paper, an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a standard bearing in mind short packet transmission system. To achieve this aim, we analyzed and compared the channel coding performance of convolutional codes (CCs), low-density parity-check (LDPC) codes, and polar codes in wireless network on the condition of short data packet transmission. The Viterbi decoding algorithm (VA), logarithmic belief propagation (Log-BP) algorithm, and cyclic redundancy check (CRC) successive cancellation list (SCL) (CRC-SCL) decoding algorithm were adopted to CC, LDPC codes, and polar codes, respectively. Consequently, a new PHY layer for mcMTC has been proposed. The reliability of the proposed approach has been validated by simulation in terms of Bit error rate (BER) and packet error rate (PER) vs.signal-to-noise ratio (SNR). The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER = 10-5 or even better with the implementation of polar codes. The results also show that the general-purpose wireless networks are prominent in providing short packet mcMTC with the modification needed. [ABSTRACT FROM AUTHOR]

A green cellular technology is proposed to optimize the energy and spectrum resources. Such optimization will require perfect channel state information at the transmitting base station. However, reporting the channel status of the entire bandwidth requires huge undesirable feedback overhead. Therefore, the aim of this paper is to optimize the energy and bandwidth resources while maintaining quality-of-service at the downlink when a partial feedback is considered. In this paper, a modified downlink scheduler based on a Packet Prediction Mechanism (PPM) is conducted at the eNodeB to optimize the energy and spectrum resources. On the user equipment side, a partial channel feedback scheme based on an adaptive feedback threshold is developed. A primary concern of this feedback scheme is to reduce the uplink signaling overhead without a substantial loss in downlink performances. Finally, the downlink packet scheduling and the partial feedback are jointly evaluated to further enhance the system performance. Based on a system-level simulation results, the proposed energy-efficient scheduling with partial feedback has achieved an improvement in EE of up to 79% compared to the PPM scheduler. Besides, it minimizes the degradation caused by the partial channel quality indicator feedback. Thus, the proposed two-sided algorithm gives the best tradeoff between uplink and downlink performances. [ABSTRACT FROM AUTHOR]

The human action classification task is a widely researched topic and is still an open problem. Many state-ofthe- arts approaches involve the usage of bag-of-video-words with spatio-temporal local features to construct characterizations for human actions. In order to improve beyond this standard approach, we investigate the usage of co-occurrences between local features. We propose the usage of co-occurrences information to characterize human actions. A trade-off factor is used to define an optimal trade-off between vocabulary size and classification rate. Next, a spatio-temporal co-occurrence technique is applied to extract co-occurrence information between labeled local features. Novel characterizations for human actions are then constructed. These include a vector quantized correlogram-elements vector, a highly discriminative PCA (Principal Components Analysis) co-occurrence vector and a Haralick texture vector. Multi-channel kernel SVM (support vector machine) is utilized for classification. For evaluation, the well known KTH as well as the UCF-Sports action datasets are used. We obtained state-of-the-arts classification performance. We also demonstrated that we are able to fully utilize co-occurrence information, and improve the standard bag-of-video-words approach. [ABSTRACT FROM AUTHOR]