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Objective. The aim of this study was to evaluate the overall effects of herbal mouthwashes as supplements to daily oral hygiene on plaque and inflammation control compared with placebos and chlorhexidine (CHX) mouthwashes in the treatment of gingivitis. Methods. PubMed, EMBASE, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and grey literature databases were searched. Only randomised controlled trials (RCTs) comparing herbal mouthwashes with placebos or CHX in the daily oral hygiene of patient with gingivitis were included to compare the effect of different mouthwashes on plaque and inflammation control. Results. A total of 13 studies satisfied the eligibility criteria, and 11 studies were included in meta-analyses. Significant differences were observed in favour of herbal mouthwashes compared with placebos in both plaque- and inflammation-related indices (Quigley-Hein Plaque Index, QHPI: WMD = −0.61, 95% CI (−0.80, −0.42), P<0.001; Gingival Index, GI: −0.28 (−0.51, −0.06), P=0.01; Modified Gingival Index, MGI: −0.59 (−1.08, −0.11), P=0.02; Gingival Bleeding Index, GBI: −0.06 (−0.09, −0.04), P<0.001). No significant difference was found between herbal and CHX mouthwashes. Conclusions. Herbal mouthwashes have potential benefits in plaque and inflammation control as supplements to the daily oral hygiene of patients with gingivitis. Although no difference was observed between herbal and CHX mouthwashes in the selected studies, further high-quality RCTs are needed for more firm support before advising patients with gingivitis about whether they can use herbal mouthwashes to substitute for CHX mouthwashes or not (PROSPERO registration number: CRD42019122841).

Introduction Severe undernutrition among under-5 children is usually assessed using single or conventional indicators (i.e., severe stunting, severe wasting, and/or severe underweight). But these conventional indicators partly overlap, thus not providing a comprehensive estimate of the proportion of malnourished children in the population. Incorporating all these conventional nutritional indicators, the Composite Index of Severe Anthropometric Failure (CSIAF) provides six different undernutrition measurements and estimates the overall burden of severe undernutrition with a more comprehensive view. This study applied the CISAF indicators to investigate the prevalence of severe under-5 child undernutrition in Bangladesh and its associated socioeconomic factors in the rural-urban context. Methods This study extracted the children dataset from the 2017–18 Bangladesh Demographic Health Survey (BDHS), and the data of 7661 children aged under-5 were used for further analyses. CISAF was used to define severe undernutrition by aggregating conventional nutritional indicators. Bivariate analysis was applied to examine the proportional differences of variables between non-severe undernutrition and severe undernutrition group. The potential associated socioeconomic factors for severe undernutrition were identified using the adjusted model of logistic regression analysis. Results The overall prevalence of severe undernutrition measured by CISAF among the children under-5 was 11.0% in Bangladesh (rural 11.5% vs urban 9.6%). The significant associated socioeconomic factors of severe undernutrition in rural areas were children born with small birth weight (AOR: 2.84), children from poorest households (AOR: 2.44), and children aged < 36 months, and children of uneducated mothers (AOR: 2.15). Similarly, in urban areas, factors like- children with small birth weight (AOR: 3.99), children of uneducated parents (AOR: 2.34), poorest households (APR: 2.40), underweight mothers (AOR: 1.58), mothers without postnatal care (AOR: 2.13), and children’s birth order ≥4 (AOR: 1.75), showed positive and significant association with severe under-5 undernutrition. Conclusion Severe undernutrition among the under-5 children dominates in Bangladesh, especially in rural areas and the poorest urban families. More research should be conducted using such composite indices (like- CISAF) to depict the comprehensive scenario of severe undernutrition among the under-5 children and to address multi-sectoral intervening programs for eradicating severe child undernutrition.

MANET is a hot research subject. Its qualities, including no infrastructure, fast network setup, and no centralized management, have led to its popularity and widespread use in numerous sectors. A major part of the network is security. Intrusion Discovery Scheme (IDS) is a network security strategy. In this paper, we implemented effective intrusion detection and efficient clustering with cluster head selection. For these two stages of implementation, we are not integrating any logic combinations to make decisions. Machine learning models are filled up that place of work in the proposed optimal MANET design. At first, the IDS is performed in the network using Adaptive Ensemble Tree Learning (AETL) based classification of typical nodes and malicious intrusions. Once the attacker nodes are identified, the nodes will be recovered to mold the network for the next sequence process of clustering. In the disemboweled network, the proposed model of second stage Hybrid Dual Optimization of Machine Learning Model (HDOMLM) is applied to elect the leading agent node in the formed clusters. Particle Swarm Optimization (PSO) is defined for the initial clustering of nodes and immediately the O-MLM is performed to detect the leading agent nodes in each cluster with the selection features of node degree, node mobility, energy, distance and delay. Experiment validations are accomplished to analyze the results of the proposed method using the MATLAB simulation tool and quantitative evaluations done for different Key Performance Indices (KPI) of network lifetime, residual energy, packet delivery and transmission delay with earlier works of the same. From the simulation performances, our proposed AETL with HDOMLM attained the peak results than other algorithms with the metrics of 71% of energy saving and 50.12% enlarging the network lifetime.

Describe the exercise fidelity and utilisation fidelity of the Knee Control injury prevention exercise programme (IPEP) in youth floorball alongside an intervention RCT. Observation study 20 floorball team training groups (12 male, 8 female, age 12–17 years) from the intervention arm of an RCT were included. The Knee Control IPEP was implemented at the beginning of the season. A research team member attended a team training session twice in the season (first and second half of 26 week season) with a total 31 training sessions observed. An IPEP specific exercise fidelity checklist was used to assess how the programme was used. Of 535 individual Knee Control exercises observed (76% of observations in males), 58% were performed correctly. Exercise fidelity was higher in females than in males (71% vs 54%, proportion difference 16%, 95% CI 7–25%, P=0.001). The full Knee Control IPEP (7 exercises x 3 sets) was completed only during 4 of 31 (13%) training sessions observed. The utilisation fidelity did not differ between sexes, and the mean number of completed exercises performed during the observations was 11 (SD 5). The exercise fidelity to an IPEP in youth floorball players was low, with only three out of five exercises performed according to instructions. Furthermore, only half of the IPEP exercises were executed on average. To make IPEPs effective in youth floorball and other similar team-ball sports, more work is needed to understand the reasons for low exercise and utilisation fidelity.

Nowadays, there is a growing demand for information security and security rules all across the world. Intrusion detection (ID) is a critical technique for detecting dangers in a network during data transmission. Artificial Intelligence (AI) methods support the Internet of Things (IoT) and smart cities by creating gadgets replicating intelligent behavior and enabling decision making with little or no human intervention. This research proposes novel technique for secure data transmission and detecting an intruder in a biometric authentication system by feature extraction with classification. Here, an intruder is detected by collecting the biometric database of the smart building based on the IoT. These biometric data are processed for noise removal, smoothening, and normalization. The processed data features are extracted using the kernel-based principal component analysis (KPCA). Then, the processed features are classified using the convolutional VGG−16 Net architecture. Then, the entire network is secured using a deterministic trust transfer protocol (DTTP). The suggested technique’s performance was calculated utilizing several measures, such as the accuracy, f-score, precision, recall, and RMSE. The simulation results revealed that the proposed method provides better intrusion detection outcomes.

To 1) investigate the incidence, prevalence, burden and characteristics of injuries; and 2) explore the frequency of physiotherapy and medical servicing for elite sports academy athletes over a 12-month season. Prospective cohort study. Medical attention and time-loss injuries were prospectively recorded by Physiotherapy and Medical (Sports Physician) staff for 94 athletes (72.3% females). The number of linked physiotherapy and medical servicing appointments was also recorded. Injury incidence rates (IIR), point and period prevalence, and injury burden were calculated and compared by athlete gender, sport, and categorisation (performance level) using incidence rate ratios (IRR). The number of injuries reported was 193 in 71 (75.5%) athletes. The IIR was 2.1 (95%CI: 1.8 to 2.4) injuries per 365 days, with no gender difference observed (IRR: 1.1, 0.8 to 1.4). The injury burden was 43.5 (95%CI: 37.8 to 50.1) days absent per 365 days. More than one-quarter (point prevalence, 26.6%) of athletes commenced the season with an injury. In-season injury risk was 2.5 fold greater in athletes who started the season with an injury compared to athletes who started the season without an injury (IRR: 2.5, 1.9 to 3.4). The majority (81.2%) of the 1164 appointments recorded were physiotherapy, with an overall 4.3:1.0 physiotherapy to medical appointment ratio. One in four athletes began the elite pathway season with a pre-existing injury, while also demonstrating a 2.5 fold greater risk of subsequent injury in the scholarship period. Sports should not assume their athletes are uninjured at the beginning of their scholarship. Injury profiles, and physiotherapy and medical servicing varied across sports. To reduce health as a barrier in the successful transition of talented young athletes to elite athletes, injury management strategies at the commencement of recruitment and throughout the scholarship should be prioritised in the development pathway.

The human blood includes Red blood Corpuscles (RBC), White Blood Corpuscles (WBC), platelets and plasma. The status of one’s health is determined by a complete blood count, therefore segmentation and identification of blood cells are critical. A Complete Blood Count (CBC) is a test that counts all of the cells in the body to assess a person’s health. The RBC and WBC count are vital in diagnosing disorders such as anaemia, leukaemia, tissue damage, and so forth. This paper focuses mainly on RBC counting and the detection of abnormality, anaemia based on the count of RBCs from a peripheral blood smear using digital image processing techniques. Anemia is an indicator, and the most important one at that, for many other diseases. Therefore, basic screening of anemia is very important, especially in regions prone to poverty. Malnutrition due to poverty is the major cause for anemia. The paper presents an algorithm to automatically count the RBCs present in the blood of a person. The count of RBC, in 1 microlitre if blood is considered and it is observed how the count varies in normal blood smears and the anemic blood smears. In remote places, where a lot of people are to be screened, using cell counters and hemocytometer is not feasible. A faster method of counting is one of major demand. Therefore, to reduce the computation time, an algorithm in digital image processing is developed to compute the number of red blood cells. Although anemia is a vast subject and there are various different characteristics to consider, this is a humble approach to automate the counting of the RBCs which would be useful for future research purposes.

When it comes to diabetic retinopathy, exudates are the most common sign; alarms for early screening and diagnosis are suggested. The images taken by cameras and high-definition ophthalmoscopes are riddled with flaws and noise. Overcoming noise difficulties and pursuing automated/computer-aided diagnosis is always a challenge. The major objective of this approach is to obtain a better prediction rate of diabetic retinopathy analysis. The accuracy, sensitivity, specificity, and prediction rate improvement are focused on the objective view. The images are separated into relevant patches of various sizes and stacked for use as inputs to CNN, which is then trained, tested, and validated. The article presents a mathematical approach to determine the prevalence, shape in precise, color, and density in the populations among image patches to operate and discover the fact the image collection consists of symptoms of exudates and methods to comprehend the diagnosis and suggest risks of early hospital treatment. The experimental result analysis of malignant quality shows the accuracy, sensitivity, specificity, and predictive value. Here, 78% of accuracy, 78.8% of sensitivity, and 78.3% of specificity are obtained, and both positive and negative predictive values are obtained.

To develop and assess the test-retest reliability of a survey exploring high-performance athletes’ perceptions and experiences during and post-pregnancy. Cross-sectional mixed-methods survey. A three-phase approach was employed to develop the Mum-Alete survey. Relevant domains and questions were identified through a review of the literature and gap analysis (Phase 1). The face and content validity were assessed during Phase 2. The survey was modified, and the final survey included 113 questions. The test-retest reliability was assessed during Phase 3. Seven athletes aged ≥18 years who were currently pregnant and/or given birth since 1 July 2016 were recruited. The survey was administered via Qualtrics and completed on two occasions. Intraclass correlation coefficient (ICC) were determined to assess test-retest reliability (excellent, good, moderate, and poor). The average ICC of all items was 0.962 (95% CI 0.957–0.966) demonstrating excellent test-retest reliability. The test-retest reliability was excellent for the demographic and general questions domain (ICC = 0.967 95% CI 0.955–0.977) and good for the exercise (ICC 0.762 95% CI 0.707–0.811), physical health (ICC 0.841 95% CI 0.810–0.868) and well-being (ICC 0.827 95% CI 0.784–0.865) domains. The high test-retest reliability of the survey indicates excellent consistency of measures between the two time-points. •Active involvement of athletes in research promotes relevance and quality.•First survey to collect data among Australian high-performance athletes.•Test-retest reliability of the Mum-Alete survey was high.•Excellent consistency of measures between the two time points.•Accurate data on athlete experiences can be collected.

Background Coronavirus disease 2019 (COVID-19) is a highly infectious respiratory tract disease. The most common clinical manifestation of severe COVID-19 is acute respiratory failure. Respiratory rehabilitation can be a crucial part of treatment, but data lack for patients with COVID-19. This study investigates the effects of short-term respiratory rehabilitation (i.e., breathing exercises) on respiratory recovery among non-ICU hospitalised patients with COVID-19. Methods This was a quasi-experimental, pre-and post-test study. The study recruited 173 patients hospitalised with moderate to severe COVID-19. All the patients received standardised care for COVID-19, and 94 patients in the intervention group also received the intervention of breathing exercises, which included breathing control, followed by diaphragmatic breathing, deep breathing, or thoracic expansion exercise, and huffing (forced expiratory technique) and coughing. Data on the mean values of peripheral oxygen saturation (SpO 2 ), need for oxygen therapy (litre/min), respiratory rate (breaths/minute), and heart rate (beats/minute) and were collected at baseline, 4 days, and 7 days after the baseline assessment. Analysis of variance on repeated measures was applied to compare the mean value of outcome measures of all the time points. Results The mean (± SD) age of the intervention (69.6% men) and control group (62.1% men) were 50.1 (10.5) and 51.5 (10.4) years, respectively. At 4-day of follow-up, SpO2 (96.6% ± 1.9 vs. 90.7% ± 1.8, P  < 0.001), need for oxygen therapy (0.8 ± 2.6 vs. 2.3 ± 2.9, P  < 0.001), respiratory rate (20.5 ± 2.3 vs. 22.3 ± 2.5, P  < 0.001), and heart rate (81.2 ± 9.5 vs. 89.2 ± 8.9, P  < 0.001) improved in the intervention group compared to the control group. At 7-day follow-up, differences remained significant concerning the oxygen saturation and the need for oxygen therapy ( P  < 0.001) between the groups. Conclusions Our results indicate that breathing exercise, even for a short period, effectively improves specific respiratory parameters in moderate to severe COVID-19 patients. As a non-invasive and cost-effective respiratory rehabilitation intervention, breathing exercise can be a valuable tool for a health care system overwhelmed by the COVID-19 pandemic. These results should be considered preliminary until they are replicated in larger samples in different settings.