Exploring the Oral Manifestations and Hazards to Dental Professionals During the COVID-19 Pandemic: A Scoping Review.

The novel coronavirus (COVID-19) has inundated the whole world by causing severe acute respiratory syndrome in humans, thus posing serious public health concerns. The oral and olfactory systems are significantly impacted by the COVID-19 condition, in addition to the respiratory and digestive systems. Oral symptoms of COVID-19 can be caused by a loss in oral hygiene, immune system suppression, a decline in general health, and candidiasis due to prolonged antibiotic use. Because many aerosols and droplets are produced during various dental operations, dental professionals and dentists are at a significant risk of contracting the COVID-19 infection. Few precautionary measures include performing a pre-appointment health screening for all patients, performing aerosol-producing procedures in a designated space or operatory, using proper personal protective equipment and disinfecting the operatory between patients, and limiting the number of dental procedures at one time. Against this background, the purposes of this article are to explore the oral presentations of COVID-19 infection and to emphasize the hazards to dental professionals during COVID-19 pandemic. Additionally, few preventive measures are also highlighted.

SinoNasal Microbiota Transfer to treat recalcitrant chronic rhinosinusitis: A case series.

KEY POINTS: SinoNasal Microbiota Transfer (SNMT) was safe with immediate benefit in all recipients, with sustained improvement in two of three recipients for up to 180 days. The addition of antimicrobial photodynamic therapy worsened chronic rhinosinusitis. These promising SNMT results warrant further study of safety and efficacy.

Retraction: Amyand Hernia With Acute Appendicitis: A Rare Type of Hernia.

[This retracts the article DOI: 10.7759/cureus.18720.].

Awareness and Attitudes Toward Childhood Mental Disorders Among Primary School Teachers in Taif, Saudi Arabia.

Background and aim Early detection and intervention can improve the treatment outcome of childhood mental disorders, and primary school teachers may play an important role in referring suspected cases to mental health facilities if they have good awareness and attitudes toward these disorders. The aim of this study is to assess the awareness and attitudes of primary school teachers toward childhood mental disorders in Taif, Saudi Arabia. Methods This is a cross-sectional study conducted among classroom teachers in primary schools in Taif, Kingdom of Saudi Arabia. It was conducted during the period from 2022 to 2023 in both public and private schools. An anonymous, self-administered, online questionnaire was used to assess participants' awareness and attitudes toward pupils with mental health issues. The collected data were analyzed using the chi-square test to examine the associations between various categories and the ANOVA test to compare means. Results The study included 417 teachers, 63.5% of whom were males, the mean of their ages was 39.59 years (SD±8.66), and the mean of their work experience was 12.8 years (SD±8.02) in different teaching specialties. Among participants, 60.2% claimed that no pupils had mental health problems in their classes, 80.1% had not referred any pupils to mental health facilities, and 88.5% did not receive any training related to childhood mental health problems. A humble percent (12.2%) of the participants claimed a good awareness of the signs and symptoms of childhood disorders. Only 54% of teachers advise visiting a psychiatric clinic in case of psychiatric problems, and a similar percentage of teachers believe that psychiatric drugs cause addiction. The male gender, being specialized in humanitarian subjects, having relatives or friends with childhood mental disorders, and receiving training related to childhood mental health were significantly associated with teachers' better awareness. Conclusion Primary school teachers generally lack awareness of childhood mental health and have underestimation and poor recognition of cases of mental disorders. There are many teachers who also have unfavorable attitudes toward psychiatric disorders, patients, and treatments, which requires much effort to improve their awareness and attitudes toward childhood mental disorders.

Demographic Data, Clinical Characteristics, and Outcomes of Pediatric Patients Who Received Palliative Care in King Abdullah Specialized Children's Hospital, Riyadh, Kingdom of Saudi Arabia.

Background Palliative care is defined as a comprehensive care approach that improves the quality of life of patients and their families facing the problems associated with life-threatening illnesses by alleviating the pain by different means. The death of children receiving palliative care is mainly due to congenital anomalies (26% of infants) and cancer (17% of children). This study aims to identify the demographic data, clinical characteristics, and outcomes of patients who received specialized pediatric palliative care (PPC) services in a tertiary care center in Saudi Arabia. Method This five-year retrospective chart review examines all children who received specialized palliative care services at King Abdullah Specialized Children's Hospital (KASCH), Riyadh, Saudi Arabia, from 2016 to 2021. The data include the sociodemographic and clinical characteristics, as well as the referral process information and the used PPC interventions. Results A total of 138 patients were included in this study. The gender distribution was 50.40% male and 49.60% female. Children aged 1-10 years accounted for 52.9% (n=73) of the sample size. Malignancy was the predominant diagnosis. Tube feeding was the most common intervention provided (28%, n=39). The most common symptom was chronic pain (61.6%, n=85). Morphine was used in more than half of the patients (53%, n=73). Conclusion In our study, children between 1 and 10 years of age comprised 52.9% (n=73) of the total sample. Malignancy was the most common diagnosis. The most common reason for consulting the PPC unit was symptomatic treatment (87.7%, n=121). The symptom reported most commonly by children was chronic pain (61.6%, n=85). The medical technology most commonly used was tube feeding (28%, n=39). The most common medication given to patients was morphine (53%, n=73). To sum up, identifying the demographics and clinical characteristics of children who previously required PPC would help healthcare professionals identify future cases in need of PPC.

Emerging Technology-Driven Hybrid Models for Preventing and Monitoring Infectious Diseases: A Comprehensive Review and Conceptual Framework.

The emergence of the infectious diseases, such as the novel coronavirus, as a significant global health threat has emphasized the urgent need for effective treatments and vaccines. As infectious diseases become more common around the world, it is important to have strategies in place to prevent and monitor them. This study reviews hybrid models that incorporate emerging technologies for preventing and monitoring infectious diseases. It also presents a comprehensive review of the hybrid models employed for preventing and monitoring infectious diseases since the outbreak of COVID-19. The review encompasses models that integrate emerging and innovative technologies, such as blockchain, Internet of Things (IoT), big data, and artificial intelligence (AI). By harnessing these technologies, the hybrid system enables secure contact tracing and source isolation. Based on the review, a hybrid conceptual framework model proposes a hybrid model that incorporates emerging technologies. The proposed hybrid model enables effective contact tracing, secure source isolation using blockchain technology, IoT sensors, and big data collection. A hybrid model that incorporates emerging technologies is proposed as a comprehensive approach to preventing and monitoring infectious diseases. With continued research on and the development of the proposed model, the global efforts to effectively combat infectious diseases and safeguard public health will continue.

Specification, validation, and adherence of quality indicators to optimize the safe use of nonsteroidal anti-inflammatory drugs for knee osteoarthritis pain in the primary care setting.

BACKGROUND: Nonsteroidal anti-inflammatory drugs (NSAIDs) used for osteoarthritis (OA) in primary care may cause gastrointestinal or renal injury. This study estimated adherence to two quality indicators (QIs) to optimize NSAID safety: add proton pump inhibitors (PPI) to NSAIDs for patients with gastrointestinal (GI) risk (QI #1 NSAID-PPI) and avoid oral NSAIDs in chronic kidney disease (CKD) stage G4 or G5 (QI #2 NSAID-CKD). METHODS: This retrospective study included index primary care clinic visits for knee OA at our health system in 2019. The validation cohort consisted of a random sample of 60 patients. The remainder were included in the expanded cohort. Analysis of structured data extracts was validated against chart review of clinic visit notes (validation cohort) and estimated QI adherence (expanded cohort). RESULTS: Among 60 patients in the validation cohort, analysis of data extracts was validated against chart review for QI #1 NSAID-PPI (100% sensitivity and 91% specificity) and QI #2 NSAID-CKD (100% accuracy). Among 335 patients in the expanded cohort, 44% used NSAIDs, 27% used PPIs, 73% had elevated GI risk, and only 2% had CKD stage 4 or 5. Twenty-one percent used NSAIDs and had elevated GI risk but were not using PPIs. Therefore, adherence to QI #1 NSAID-PPI was 79% (95% CI, 74-83%). No patients with CKD stage 4 or 5 used NSAIDs. Therefore, adherence to QI #2 NSAID-CKD was 100%. CONCLUSION: A substantial proportion of knee OA patients with GI risk factors did not receive PPI with NSAID therapy during primary care visits.

Syndrome of Inappropriate Antidiuretic Hormone Release Secondary to Ectopic Olfactory Neuroblastoma: Case Report and Review of Literature.

Ectopic olfactory neuroblastoma (ONB) is a rare neuroendocrine sinonasal malignancy which arises from sinonasal regions where olfactory neuroepithelium does not exist. Presentation of syndrome of inappropriate antidiuretic hormone release (SIADH) in patients with ectopic ONB is extremely rare. We report a case of a 22-year-old patient with ectopic ONB and paraneoplastic SIADH that was managed at our center. The ONB was arising from the left lamina papyracea and extending into the maxillary and ethmoid sinuses and filling the nasal cavity. Correction of sodium levels occurred within 24 hours of endoscopic resection via a combined trans-nasal transorbital approach. To avoid negative sequela secondary to rapid sodium correction, the patient was managed by desmopressin to obtain gradual sodium correction, which was achieved on the fourth postoperative day. Ectopic presentation of ONB with paraneoplastic SIADH is extremely rare with only 7 cases reported in the literature to date. Management via surgical resection and/or radiotherapy for these patients requires the managing clinician to be vigilant of serum sodium levels. A multidisciplinary approach is essential for optimal outcomes.

Bridged-U-Net-ASPP-EVO and Deep Learning Optimization for Brain Tumor Segmentation.

Brain tumor segmentation from Magnetic Resonance Images (MRI) is considered a big challenge due to the complexity of brain tumor tissues, and segmenting these tissues from the healthy tissues is an even more tedious challenge when manual segmentation is undertaken by radiologists. In this paper, we have presented an experimental approach to emphasize the impact and effectiveness of deep learning elements like optimizers and loss functions towards a deep learning optimal solution for brain tumor segmentation. We evaluated our performance results on the most popular brain tumor datasets (MICCAI BraTS 2020 and RSNA-ASNR-MICCAI BraTS 2021). Furthermore, a new Bridged U-Net-ASPP-EVO was introduced that exploits Atrous Spatial Pyramid Pooling to enhance capturing multi-scale information to help in segmenting different tumor sizes, Evolving Normalization layers, squeeze and excitation residual blocks, and the max-average pooling for down sampling. Two variants of this architecture were constructed (Bridged U-Net_ASPP_EVO v1 and Bridged U-Net_ASPP_EVO v2). The best results were achieved using these two models when compared with other state-of-the-art models; we have achieved average segmentation dice scores of 0.84, 0.85, and 0.91 from variant1, and 0.83, 0.86, and 0.92 from v2 for the Enhanced Tumor (ET), Tumor Core (TC), and Whole Tumor (WT) tumor sub-regions, respectively, in the BraTS 2021validation dataset.

Studying the Association Between Knowledge of Professionalism and Demographic Characteristics in King Saud University Medical Students.

Objectives To study the association between the level of knowledge regarding professionalism and demographic characteristics among medical students from years three to five at King Saud University, Riyadh, Saudi Arabia. Methods Data for this quantitative observational cross-sectional study were collected using stratified random sampling. The participants included male and female students from years three to five studying at the College of Medicine, King Saud University. Data were collected using a self-administered questionnaire and analyzed using the Student's t-test and a one-way ANOVA test. Results The study comprised 112 female (52%) and 103 male (48%) students who completed 215 questionnaires. The mean percentage values of correct answers by females and males were 59.99% and 59.31%, respectively. Statistical analysis revealed no significant difference between the mean percentage of correct answers given by males and females (p=0.684). The mean percentage of correct answers among the 3rd, 4th, and 5th-year medical students was 59.27%, 56.56%, and 62.72%, respectively. Statistical analysis revealed significant differences between the academic year groups (p<0.05), and the grade point average (GPA) groups showed significant differences (p<0.05). Conclusion A highly significant association was found between knowledge of professionalism and both academic level and performance among medical students. This suggests that professional perception evolves parallel to acquiring basic science and clinical knowledge.

Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture.

Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.

U-Net-Based Models towards Optimal MR Brain Image Segmentation.

Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture's performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization.

Nanoparticle pre-treatment for enhancing the survival and activation of pulmonary macrophage transplant.

Despite recent clinical successes of chimeric antigen receptor T cell therapies in treating liquid cancers, many lingering challenges stand in the way of therapeutic translation to broader types of malignancies. Macrophages have been proposed as alternatives to T cells given macrophages' advantages in promoting tumor infiltration, acquiring diverse antigens, and possessing the ability to continuously stimulate adaptive responses. However, the poor survival of macrophages upon transplantation in addition to transient anti-tumor phenotypical states have been major obstacles standing in the way of macrophage-based cell therapies. Given recent discoveries of nanoparticle strategies in improving macrophage survival and promoting phenotype retention, we herein report the ability to extend the survival and phenotype of macrophage transplants in murine lungs via pre-treatment with nanoparticles of varying degradation rates. Macrophages pre-treated with 100 µg/ml dose of poly(ethylene glycol) diacrylate nanoparticle formulations improve pulmonary macrophage transplant survival over untreated cells beyond 7 days, where degradable nanoparticle formulations result in over a 50% increase in retention of transplanted cell counts relative to untreated cells. Furthermore, pre-treated macrophages more efficiently retain an imposed pro-inflammatory-like polarization state following transplantation out to 7 days compared to macrophages pre-treated with a classical pro-inflammatory stimulus, interferon-gamma, where CD86 costimulatory molecule expression is greater than 150% higher in pre-treated macrophage transplants compared to untreated counterparts. These findings provide an avenue for a major improvement in the lifespan and efficacy of macrophage-based cell therapies and have broader implications to other phagocyte-based cellular therapeutics and administration routes.

DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms.

The aedes mosquito-borne dengue viruses cause dengue fever, an arboviral disease (DENVs). In 2019, the World Health Organization forecasts a yearly occurrence of infections from 100 million to 400 million, the maximum number of dengue cases ever testified worldwide, prompting WHO to label the virus one of the world's top ten public health risks. Dengue hemorrhagic fever can progress into dengue shock syndrome, which can be fatal. Dengue hemorrhagic fever can also advance into dengue shock syndrome. To provide accessible and timely supportive care and therapy, it is necessary to have indispensable practical instruments that accurately differentiate Dengue and its subcategories in the early stages of illness development. Dengue fever can be predicted in advance, saving one's life by warning them to seek proper diagnosis and treatment. Predicting infectious diseases such as dengue is difficult, and most forecast systems are still in their primary stages. In developing dengue predictive models, data from microarrays and RNA-Seq have been used significantly. Bayesian inferences and support vector machine algorithms are two examples of statistical methods that can mine opinions and analyze sentiment from text. In general, these methods are not very strong semantically, and they only work effectively when the text passage inputs are at the level of the page or the paragraph; they are poor miners of sentiment at the level of the sentence or the phrase. In this research, we propose to construct a machine learning method to forecast dengue fever.

The Effects of Nasal Surgery on Pulmonary Function: A Systematic Review and Meta-Analysis.

OBJECTIVE: A deviated nasal septum (DNS) can result in an anatomical obstruction and impact lung function through prolonged suboptimal inspiration. Given the improvements in respiration reported by patients following septoplasty or septorhinoplasty (with or without inferior turbinate reduction), our study investigated the effect of these procedures on pulmonary function through a systematic review and meta-analysis. DATA SOURCES: Medline, Embase, Cochrane Databases, Web of Science, and Google Scholar. REVIEW METHODS: The review was registered with PROSPERO [CRD42022316309]. The study population was composed of adult patients (18-65) who were symptomatic with confirmed DNS. Extracted outcomes (pre-operative versus postoperative) included the six-minute walk test (6MWT) and pulmonary function tests (FEV1, FVC, FEV1/FVC, FEF25-75, PEF). Meta-analyses were performed using a random-effects model. RESULTS: Three studies included measures of the 6MWT in meters and all three found a statistically significant increase in the distance walked after surgery with a mean difference of 62.40 m (95% CI 24.79-100.00). Statistically significant improvements in PFT outcomes were observed with a standard mean difference of 0.72 for FEV1 (95% CI 0.31-1.13), 0.63 for FVC (95% CI 0.26-1.00), and 0.64 for PEF (95% CI 0.47-0.82). Of the twelve studies which measured PFT outcomes, six showed statistically significant improvements, three studies showed mixed results, and three studies found no difference in PFT outcomes between pre-and post-surgery testing. CONCLUSIONS: The present study suggests that pulmonary function does improve after nasal surgery for DNS, but the high heterogeneity observed in the meta-analyses indicates that the evidence supporting this conclusion is low. Laryngoscope, 133:2837-2845, 2023.

Impact of COVID-19 Pandemic Lockdown on the Prognosis, Morbidity, and Mortality of Patients Undergoing Elective and Emergency Abdominal Surgery: A Retrospective Cohort Study in a Tertiary Center, Saudi Arabia.

The SARS-CoV-2 pandemic's main concerns are limiting the spread of infectious diseases and upgrading the delivery of health services, infrastructure, and therapeutic provision. The goal of this retrospective cohort study was to evaluate the emergency experience and delay of elective abdominal surgical intervention at King Abdul-Aziz University Hospital from October 2019 to October 2020, with a focus on post-operative morbidity and mortality before and during the COVID-19 pandemic. This study compares two groups of patients with emergent and elective abdominal surgical procedures between two different periods; the population was divided into two groups: the control group, which included 403 surgical patients, and the lockdown group, which included 253 surgical patients. During the lockdown, surgical activity was reduced by 37.2% (p = 0.014), and patients were more likely to require reoperations and blood transfusions during or after surgery (p= 0.002, 0.021, and 0.018, respectively). During the lockdown period, the average length of stay increased from 3.43 to 5.83 days (p = 0.002), and the patients who developed complications (53.9%) were more than those in the control period (46.1%) (p = 0.001). Our tertiary teaching hospital observed a significant decline in the overall number of surgeries performed during the COVID-19 pandemic and lockdown period. During the lockdown, abdominal surgery was performed only on four patients; they were positive for COVID-19. Three of them underwent exploratory laparotomy; two of the three developed shock post-operative; one patient had colon cancer (ASA score 3), one had colon disease (ASA score 2), and two had perforated bowels (ASA scores 2 and 4, respectively). Two out of four deaths occurred after surgery. Our results showed the impact of the COVID-19 lockdown on surgical care as both 30-day mortality and total morbidity have risen considerably.

Allergic Fungal Rhinosinusitis: The Role and Expectations of Biologics.

Allergic fungal rhinosinusitis (AFRS) is a noninvasive subtype of chronic rhinosinusitis with nasal polyps (CRSwNP) that usually develops in immunocompetent atopic individuals and is more common in geographic regions characterized by warm temperatures and high humidity, conducive to higher environmental fungal presence. Allergic fungal rhinosinusitis usually presents with unique computed tomography findings and significant polyp burden, yet patients often report minimal sinus symptoms. Patients with AFRS often have extremely elevated serum total and fungal-specific IgE levels. Treatment almost always requires surgery, in which adjuvant medical therapy is critical to success. However, until recently the choice of adjuvant therapy has consisted primarily of either oral and/or topical steroids. Although oral corticosteroids decrease recurrence after surgery, data for the effectiveness of other adjunctive pharmacologic agents, including topical and oral antifungal agents and immunotherapy, have remained unclear and hence are not recommended in recent guidelines including the International Consensus of Allergy and Rhinology. Three biologics, omalizumab, dupilumab, and mepolizumab, have recently been approved for treating CRSwNP in general, but clinical trials to date with these biologics did not involve AFRS patients. Recently published case reports and smaller prospective studies have shown good efficacy of these biologics on the AFRS subgroup of patients. This article provides an overview of the understanding of the pathophysiology of AFRS, implications of this understanding on the possible role of biologics, and clinical reports on the use of biologics in treating AFRS. Because biologics are indicated for treating CRSwNP, follow up real-world evidence studies are needed for AFRS.

Destructive fibrotic teamwork: how both microenvironment stiffness and profibrotic interleukin 13 impair alveolar macrophage phenotype and function.

The pulmonary fibrotic microenvironment is characterized by increased stiffness of lung tissue and enhanced secretion of profibrotic soluble cues contributing to a feedback loop that leads to dysregulated wound healing and lung failure. Pinpointing the individual and tandem effects of profibrotic stimuli in impairing immune cell response remains difficult and is needed for improved therapeutic strategies. We utilized a statistical design of experiment (DOE) to investigate how microenvironment stiffness and interleukin 13 (IL13), a profibrotic soluble factor linked with disease severity, contribute to the impaired macrophage response commonly observed in pulmonary fibrosis. We used engineered bioinspired hydrogels of different stiffness, ranging from healthy to fibrotic lung tissue, and cultured murine alveolar macrophages (MH-S cells) with or without IL13 to quantify cell response and analyze independent and synergistic effects. We found that, while both stiffness and IL13 independently influence macrophage morphology, phenotype, phagocytosis and efferocytosis, these factors work synergistically to exacerbate impaired macrophage phenotype and efferocytosis. These unique findings provide insights into how macrophages in fibrotic conditions are not as effective in clearing debris, contributing to fibrosis initiation/progression, and more broadly inform how underlying drivers of fibrosis modulate immune cell response to facilitate therapeutic strategies.

Nanoparticle Internalization Promotes the Survival of Primary Macrophages.

Macrophages, a class of tissue resident innate immune cells, are responsible for sequestering foreign objects through the process of phagocytosis, making them a promising target for immune-modulation via particulate engineering. Here, we report that nanoparticle (NP) dosing and cellular internalization via phagocytosis significantly enhances survival of ex vivo cultures of primary bone marrow-derived, alveolar, and peritoneal macrophages over particle-free controls. The enhanced survival is attributed to suppression of caspase-dependent apoptosis and is linked to phagocytosis and lysosomal signaling. Uniquely, poly(ethylene glycol)-based NP treatment extended cell viability in the absence of macrophage polarization and enhanced expression of pro-survival B cell lymphoma-2 (Bcl-2) protein in macrophages following multiple routes of in vivo administration. The enhanced survival phenomenon is also applicable to NPs of alternative chemistries, indicating the potential universality of this phenomenon with relevant drug delivery particles. These findings provide a framework for extending the lifespan of primary macrophages ex vivo for drug screening, vaccine studies, and cell therapies and has implications for any in vivo particulate immune-engineering applications.