Difference in Perception of Nasal Aesthetic Appearance Between Surgeons and Rhinoplasty Patients: A Single-Center Perspective.

BACKGROUND: Patient satisfaction is an essential outcome measure after a rhinoplasty. Yet it is not known whether the opinions of rhinoplasty patients and surgeons on nasal aesthetic appearance differ. OBJECTIVES: The aim of this study was to determine the differences between patients and surgeons in their perception of nasal aesthetic appearance. METHODS: A retrospective cohort of 300 patients seen in consultation for cosmetic, functional, or combined cosmetic and functional rhinoplasty at a single tertiary care center from June 2017 to June 2020 was studied. Based on preoperative patient images, 6 surgeons with varying levels of expertise assessed nasal aesthetics utilizing a modified Standardized Cosmesis and Health Nasal Outcomes Survey for nasal cosmesis (SCHNOS-C). These scores were then compared to the patient-reported SCHNOS-C scores. RESULTS: The cosmetic, functional, and combined subgroups consisted of 100 patients each. The mean [standard deviation] age was 35.4 [13.7] years and 64% were women. The modified SCHNOS-C scores were well-correlated among the 6 surgeons but showed only weak correlations of 0.07 to 0.20 between patient-reported scores and scores assessed by the surgeons. Compared with the surgeon's scores, patients in the cosmetic subgroup perceived their nasal aesthetic problems to be more severe whereas the those in the functional subgroup perceived their nasal aesthetic problems to be milder compared with the surgeons' assessment. CONCLUSIONS: Our findings suggest that patients and surgeons perceive nasal cosmesis differently. This difference should be considered carefully when planning rhinoplasty or assessing its outcome.

Cyber-Attack Prediction Based on Network Intrusion Detection Systems for Alert Correlation Techniques: A Survey.

Network Intrusion Detection Systems (NIDS) are designed to safeguard the security needs of enterprise networks against cyber-attacks. However, NIDS networks suffer from several limitations, such as generating a high volume of low-quality alerts. Moreover, 99% of the alerts produced by NIDSs are false positives. As well, the prediction of future actions of an attacker is one of the most important goals here. The study has reviewed the state-of-the-art cyber-attack prediction based on NIDS Intrusion Alert, its models, and limitations. The taxonomy of intrusion alert correlation (AC) is introduced, which includes similarity-based, statistical-based, knowledge-based, and hybrid-based approaches. Moreover, the classification of alert correlation components was also introduced. Alert Correlation Datasets and future research directions are highlighted. The AC receives raw alerts to identify the association between different alerts, linking each alert to its related contextual information and predicting a forthcoming alert/attack. It provides a timely, concise, and high-level view of the network security situation. This review can serve as a benchmark for researchers and industries for Network Intrusion Detection Systems' future progress and development.

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts.

Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%.

Forest Fire Detection Using New Routing Protocol.

The Mobile Ad-Hoc Network (MANET) has received significant interest from researchers for several applications. In spite of developing and proposing numerous routing protocols for MANET, there are still routing protocols that are too inefficient in terms of sending data and energy consumption, which limits the lifetime of the network for forest fire monitoring. Therefore, this paper presents the development of a Location Aided Routing (LAR) protocol in forest fire detection. The new routing protocol is named the LAR-Based Reliable Routing Protocol (LARRR), which is used to detect a forest fire based on three criteria: the route length between nodes, the temperature sensing, and the number of packets within node buffers (i.e., route busyness). The performance of the LARRR protocol is evaluated by using widely known evaluation measurements, which are the Packet Delivery Ratio (PDR), Energy Consumption (EC), End-to-End Delay (E2E Delay), and Routing Overhead (RO). The simulation results show that the proposed LARRR protocol achieves 70% PDR, 403 joules of EC, 2.733 s of E2E delay, and 43.04 RO. In addition, the performance of the proposed LARRR protocol outperforms its competitors and is able to detect forest fires efficiently.

Covid-19 fake news sentiment analysis.

'Fake news' refers to the misinformation presented about issues or events, such as COVID-19. Meanwhile, social media giants claimed to take COVID-19 related misinformation seriously, however, they have been ineffectual. This research uses Information Fusion to obtain real news data from News Broadcasting, Health, and Government websites, while fake news data are collected from social media sites. 39 features were created from multimedia texts and used to detect fake news regarding COVID-19 using state-of-the-art deep learning models. Our model's fake news feature extraction improved accuracy from 59.20% to 86.12%. Overall high precision is 85% using the Recurrent Neural Network (RNN) model; our best recall and F1-Measure for fake news were 83% using the Gated Recurrent Units (GRU) model. Similarly, precision, recall, and F1-Measure for real news are 88%, 90%, and 88% using the GRU, RNN, and Long short-term memory (LSTM) model, respectively. Our model outperformed standard machine learning algorithms.

Association of AFF3 Gene Polymorphism rs10865035 with Rheumatoid Arthritis: A Population-Based Case-Control Study on a Pakistani Cohort.

Rheumatoid arthritis (RA) is one of the complex diseases with the involvement of the genetic as well as environmental factors in its onset and severity. Different genome-wide association and candidate gene studies have shown the role of several genetic variants in multiple loci/genes with ethnical and geographical variations. This study was designed to detect the association of a single-nucleotide polymorphism (SNP) rs10865035 in the AFF3 gene with the genetic background of rheumatoid arthritis (RA) in the Pakistani cohort. A total of 703 individuals, including 409 RA patients and 294 healthy controls, were genotyped using TaqMan assay and Tri primer ARMS-PCR (amplification-refractory mutation system-polymerase chain reaction) methods. The association of rs10865035 with the RA was statistically determined using different models. Interestingly, besides the homozygous recessive model (G/G vs. A/G + A/A) (OR = 1.693(1.06-2.648); P = 0.025), all other models, which included the codominant (χ 2 = 5.169; P = 0.075), homozygous dominant (A/A vs. G/G + A/G) (OR = 0.867 (0.636-1.187); P = 0.41), heterozygous (A/G vs. A/A + GG) (OR = 0.491 (0.667-1.215); P = 0.49), and additive model (OR = 0.826 (0.665-1.027); P = 0.08) showed insignificant distribution of the genotypes among the cases and controls. These findings suggest that the AFF3 gene (rs10865035) has no significant role in the onset of RA in the Pakistani population.

Early National Landscape of Surgical Stabilization of Sternal Fractures.

BACKGROUND: Operative management of chest wall injuries aims to restore respiratory mechanics and mitigate pulmonary complications. Extensive studies support surgical stabilization of rib fractures (SSRF) for select patients, but role for surgical stabilization of sternal fractures (SSSF) remains unclear. We aimed to understand national prevalence of SSSF and compare outcomes after surgical stabilization and non-operative management of sternal fractures. METHODS: We retrospectively analyzed adult patients (age ≥ 18 years) admitted with sternal fractures after blunt trauma using the 2016 National Trauma Data Bank. We compared odds of inpatient mortality, pneumonia, and respiratory failure for propensity score matched patients (4:1) who underwent non-operative management vs SSSF. We characterized subgroup of patients with concurrent rib and sternal fractures who underwent concomitant SSRF-SSSF. RESULTS: We identified 14,760 encounters of adults admitted with sternal fractures; 270 (1.8%) underwent SSSF. Compared to matched patients who underwent non-operative management, patients who underwent SSSF had lower odds of mortality (OR [95%CI]: 0.19 [0.06-0.62], p = 0.006). Adjusted for trauma center level, Mantel-Haenszel mortality odds remained lower for patients who underwent SSSF. Odds of pneumonia and respiratory failure were similar between matched groups. Among 46% of patients who had concomitant rib fractures, 0.3% (n = 18) underwent concurrent SSRF-SSSF and these patients survived hospitalization without pneumonia or respiratory failure. CONCLUSION: A vast majority of patients who suffer sternal fractures undergo non-operative management. Potential mortality benefit of SSSF and concurrent SSRF-SSSF's role for commonly concomitant rib and sternal fractures deserve further study. Our preliminary findings call for delineating heterogeneity of sternal fractures and establishing consensus SSSF indications.

Development of Low-Cost Wireless Sensing System for Smart Ultra-High Performance Concrete.

This study proposes the development of a wireless sensor system integrated with smart ultra-high performance concrete (UHPC) for sensing and transmitting changes in stress and damage occurrence in real-time. The smart UHPC, which has the self-sensing ability, comprises steel fibers, fine steel slag aggregates (FSSAs), and multiwall carbon nanotubes (MWCNTs) as functional fillers. The proposed wireless sensing system used a low-cost microcontroller unit (MCU) and two-probe resistance sensing circuit to capture change in electrical resistance of self-sensing UHPC due to external stress. For wireless transmission, the developed wireless sensing system used Bluetooth low energy (BLE) beacon for low-power and multi-channel data transmission. For experimental validation of the proposed smart UHPC, two types of specimens for tensile and compression tests were fabricated. In the laboratory test, using a universal testing machine, the change in electrical resistivity was measured and compared with a reference DC resistance meter. The proposed wireless sensing system showed decreased electrical resistance under compressive and tensile load. The fractional change in resistivity (FCR) was monitored at 39.2% under the maximum compressive stress and 12.35% per crack under the maximum compressive stress tension. The electrical resistance changes in both compression and tension showed similar behavior, measured by a DC meter and validated the developed integration of wireless sensing system and smart UHPC.

SSVM: An Ultra-Low-Power Strain Sensing and Visualization Module for Long-Term Structural Health Monitoring.

Structural health monitoring (SHM) is crucial for quantitative behavioral analysis of structural members such as fatigue, buckling, and crack propagation identification. However, formerly developed approaches cannot be implemented effectively for long-term infrastructure monitoring, owing to power inefficiency and data management challenges. This study presents the development of a high-fidelity and ultra-low-power strain sensing and visualization module (SSVM), along with an effective data management technique. Deployment of 24-bit resolution analog to a digital converter and precise half-bridge circuit for strain sensing are two significant factors for efficient strain measurement and power management circuit incorporating a low-power microcontroller unit (MCU), and electronic-paper display (EPD) enabled long-term operation. A prototype for SSVM was developed that performs strain sensing and encodes the strain response in a QR code for visualization on the EPD. For efficient power management, SSVM only activated when the trigger-signal was generated and stayed in power-saving mode consuming 18 mA and 337.9 µA, respectively. The trigger-signal was designed to be generated either periodically by a timer or intentionally by a push-button. A smartphone application and cloud database were developed for efficient data acquisition and management. A lab-scale experiment was carried out to validate the proposed system with a reference strain sensing system. A cantilever beam was deflected by increasing load at its free end, and the resultant strain response of SSVM was compared with the reference. The proposed system was successfully validated to use for long-term static strain measurement.

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems.

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition.

Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.

Elafin Treatment Rescues EGFR-Klf4 Signaling and Lung Cell Survival in Ventilated Newborn Mice.

Mechanical ventilation with O2-rich gas (MV-O2) inhibits alveologenesis and lung growth. We previously showed that MV-O2 increased elastase activity and apoptosis in lungs of newborn mice, whereas elastase inhibition by elafin suppressed apoptosis and enabled lung growth. Pilot studies suggested that MV-O2 reduces lung expression of prosurvival factors phosphorylated epidermal growth factor receptor (pEGFR) and Krüppel-like factor 4 (Klf4). Here, we sought to determine whether apoptosis and lung growth arrest evoked by MV-O2 reflect disrupted pEGFR-Klf4 signaling, which elafin treatment preserves, and to assess potential biomarkers of bronchopulmonary dysplasia (BPD). Five-day-old mice underwent MV with air or 40% O2 for 8-24 hours with or without elafin treatment. Unventilated pups served as controls. Immunoblots were used to assess lung pEGFR and Klf4 proteins. Cultured MLE-12 cells were exposed to AG1478 (EGFR inhibitor), Klf4 siRNA, or vehicle to assess effects on proliferation, apoptosis, and EGFR regulation of Klf4. Plasma elastase and elafin levels were measured in extremely premature infants. In newborn mice, MV with air or 40% O2 inhibited EGFR phosphorylation and suppressed Klf4 protein content in lungs (vs. unventilated controls), yielding increased apoptosis. Elafin treatment inhibited elastase, preserved lung pEGFR and Klf4, and attenuated the apoptosis observed in lungs of vehicle-treated mice. In MLE-12 studies, pharmacological inhibition of EGFR and siRNA suppression of Klf4 increased apoptosis and reduced proliferation, and EGFR inhibition decreased Klf4. Plasma elastase levels were more than twofold higher, without a compensating increase of plasma elafin, in infants with BPD, compared to infants without BPD. These findings indicate that pEGFR-Klf4 is a novel prosurvival signaling pathway in lung epithelium that MV disrupts. Elafin preserves pEGFR-Klf4 signaling and inhibits apoptosis, thereby enabling lung growth during MV. Together, our animal and human data raise the question: would elastase inhibition prevent BPD in high-risk infants exposed to MV-O2?

Lung matrix and vascular remodeling in mechanically ventilated elastin haploinsufficient newborn mice.

Elastin plays a pivotal role in lung development. We therefore queried if elastin haploinsufficient newborn mice (Eln(+/-)) would exhibit abnormal lung structure and function related to modified extracellular matrix (ECM) composition. Because mechanical ventilation (MV) has been linked to dysregulated elastic fiber formation in the newborn lung, we also asked if elastin haploinsufficiency would accentuate lung growth arrest seen after prolonged MV of neonatal mice. We studied 5-day-old wild-type (Eln(+/+)) and Eln(+/-) littermates at baseline and after MV with air for 8-24 h. Lungs of unventilated Eln(+/-) mice contained ∼50% less elastin and ∼100% more collagen-1 and lysyl oxidase compared with Eln(+/+) pups. Eln(+/-) lungs contained fewer capillaries than Eln(+/+) lungs, without discernible differences in alveolar structure. In response to MV, lung tropoelastin and elastase activity increased in Eln(+/+) neonates, whereas tropoelastin decreased and elastase activity was unchanged in Eln(+/-) mice. Fibrillin-1 protein increased in lungs of both groups during MV, more in Eln(+/-) than in Eln(+/+) pups. In both groups, MV caused capillary loss, with larger and fewer alveoli compared with unventilated controls. Respiratory system elastance, which was less in unventilated Eln(+/-) compared with Eln(+/+) mice, was similar in both groups after MV. These results suggest that elastin haploinsufficiency adversely impacts pulmonary angiogenesis and that MV dysregulates elastic fiber integrity, with further loss of lung capillaries, lung growth arrest, and impaired respiratory function in both Eln(+/+) and Eln(+/-) mice. Paucity of lung capillaries in Eln(+/-) newborns might help explain subsequent development of pulmonary hypertension previously reported in adult Eln(+/-) mice.

A comprehensive review on adaptability of network forensics frameworks for mobile cloud computing.

Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC.

Comparative Evaluation of Antimicrobial Activity and Minimum Inhibitory Concentration of Commercially Available Pediatric Dentifrices: An In Vitro Study.

Aim: The aim of this study was to evaluate the antimicrobial efficacy and minimum inhibitory concentration (MIC) of commercially available pediatric dentifrices containing different compositions against Streptococcus mutans and Lactobacillus activity. Materials and methods: Four different commercially available brands of pediatric dentifrices, designated as sample I-fluoride, sample II-herbal, sample III-xylitol with nanosilver particles, and sample IV-xylitol with fluoride, along with two control groups (a positive control-ciprofloxacin and a negative control-distilled water), were tested for their antibacterial activity by measuring the zone of inhibition, followed by MIC against two dental bacterial pathogens, S. mutans strain and Lactobacillus acidophilus (LB) strain, at five different twofold dilutions of 100, 50, 25%, 12.5, and 6.25% concentrations. Result: All four dentifrices were found to have wide variations in their effectiveness against the two tested microorganisms at 100% (pure) and 50% concentrations, with sample I having the highest activity, followed by sample IV and sample II. At 25% concentration, only sample I and sample IV showed antibacterial activity, while at 12.5 and 6.25% concentrations, none of the tested toothpastes exhibited any antibacterial activity. Sample III failed to show antibacterial activity even in pure form against the two microorganisms. Conclusion: In our present study, the fluoride-containing pediatric dentifrice with a lower fluoride concentration (458 ppm) exhibited the highest zone of inhibition, followed by the xylitol with fluoride dentifrice and the herbal dentifrice. No zone of inhibition was observed in the nanosilver with xylitol dentifrice. How to cite this article: Dureha R, Navit S, Khan SA, et al. Comparative Evaluation of Antimicrobial Activity and Minimum Inhibitory Concentration of Commercially Available Pediatric Dentifrices: An In Vitro Study. Int J Clin Pediatr Dent 2024;17(8):938-944.

Asymptomatic Salmonella Myocarditis: A Case Report of a Rare Entity.

Enteric fever typically displays symptoms like high fever, abdominal pain, constipation, and headaches, primarily affecting the digestive system. While it is commonly seen as a gastrointestinal infection, it can also lead to rare but significant cardiovascular issues. There have been only a few reported cases of enteric fever causing heart manifestations. We present a case of a young male with enteric fever-induced myocarditis, which, due to its rarity, can be challenging to diagnose and is essentially a diagnosis of exclusion. Cardiac MRI (CMR) is crucial for diagnosis, supported by ECG, echocardiograms, and troponin levels. The treatment involves standard approaches for cardiomyopathy, including angiotensin-converting enzyme (ACE) inhibitors, beta-blockers, and diuretics. However, our patient presented as a case of asymptomatic myocarditis and fully recovered with treatment without any long-lasting heart problems. Our study aims to contribute to the limited body of knowledge on heart-related complications of enteric fever, raising awareness among clinicians of such presentations in enteric fever cases.

Assessment of Liver Transplant Eligibility in Chronic Liver Disease Patients: A Cross-Sectional Study From a Tertiary Care Hospital of Pakistan.

INTRODUCTION: The burden of chronic liver disease (CLD) is increasing globally and the ultimate treatment is a liver transplant. As Pakistan is a developing country, liver transplantation is not easily available due to limited resources. This study aims to assess the patients with CLD for liver transplantation and to find the frequency of eligible candidates for liver transplantation. METHODS: A cross-sectional observational study was conducted on patients with CLD from June 2022 to December 2022. Total bilirubin, serum creatinine complete blood count, serum electrolytes, and international normalised ratio (INR) were done. The Model for End-Stage Liver Disease (MELD) score was calculated and the frequency of eligible patients for liver transplant was determined. Data was entered and analyzed using Statistical Package for Social Sciences (SPSS) version 22 (IBM Corp., Armonk, NY, USA). RESULTS: In our study, 149 patients were enrolled with a mean age of 46.81±15.7 years. There were 58.7% male and 41.6% female patients. The mean duration of liver cirrhosis was 18.22±11.7 months. The mean MELD score was 20.71±5.2. The common liver cirrhosis stages were stage II and stage II was found in 32.2% of each. Hepatocellular carcinoma (HCC) was present in 15.4% of patients. There were 25.5% of patients eligible for liver transplants. CONCLUSION: In our study, we found that significant numbers of patients with CLD were eligible for liver transplantation.

Leiomyomatous Hamartoma of Incisive Papilla with High Frenal Attachment: A Case Report.

Aim: This case report aims to describe a rare congenital lesion of the incisive papilla with a high labial frenulum attachment, clinically mimicking congenital epulis but histopathologically diagnosed as an oral leiomyomatous hamartoma. Background: Oral leiomyomatous hamartoma is a very rare congenital lesion, mainly appearing on the median anterior maxilla/incisive papilla and tongue. Case description: This clinical paper is about a rare lesion in a 6-year-old female child whose parents reported to the department with the complaint of slow-growing soft tissue overgrowth between the front teeth of the upper jaw, present since birth. The soft tissue growth is now causing difficulty in biting food and is visible during smiling and speaking, causing an esthetically unpleasing appearance. The clinical examination also revealed a high labial frenulum attached to the lesion. The lesion was provisionally diagnosed as congenital epulis based on the clinical picture. However, after excisional biopsy and histologic evaluation with special stains, the lesion was finally diagnosed as leiomyomatous hamartoma. Conclusion: Surgical excision of the lesion followed by frenectomy was performed with no postoperative complications. Clinical significance: Owing to the rare occurrence and nature of mimicking congenital epulis, it is important for a dental practitioner to have knowledge about these types of lesions. The final diagnosis of such lesions can only be made after histopathological evaluation using special stains. This case report describes the clinical and histopathological features of a rare leiomyomatous hamartoma of the incisive papilla, along with high frenulum attachment and its management. How to cite this article: Pal SS, Khan SA, Navit S, et al. Leiomyomatous Hamartoma of Incisive Papilla with High Frenal Attachment: A Case Report. Int J Clin Pediatr Dent 2024;17(6):717-722.

Enhancing mucoadhesion: Exploring rheological parameters and texture profile in starch solutions, with emphasis on micro-nanofiber influence.

This comprehensive analysis explores the rheological parameters and texture profile analysis (TPA) to effect starch solutions for mucoadhesion and assess the impact of micro-nanofibers (MNFs) on these parameters. The surface chemistry of all six samples was examined through the Fourier transform infrared (FTIR) technique. The spectrum of FTIR was recorded in the range of 500-4000 cm-1. The viscosity of different pHs (2-11) and temperatures (20-70 °C) of verious starches, potato, corn, and rice, decreased with the increasing of shear rate, exhibiting shear thinning behavior, which conformed to pseudoplastic fluid.The combination of chitosan and collagen MNFs significantly changed rheological properties, and the sample with the addtion of 1500 µL CC-MNF exhibited a greater viscosity of 59.8 mPa·s at a shear rate of 1.49 s-1. Potato starch emerged as a strong candidate for mucoadhesion due to its low hardness (4.62 ± 0.31 N), high adhesion (0.0322 ± 0.0053 mJ), cohesiveness (0.37 ± 0.03 Ratio), low chewiness (0.66 ± 0.12 mJ), and gumminess (1.69 ± 0.23 N). The inclusion of MNFs, especially collagen/chitosan MNFs showed the potential to further enhance adhesion.