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Journal of Clinical Medicine Aug 2023The aim of the study was to evaluate the effectiveness of extra virgin olive (EVO) oil and fruity oil for the treatment of gingivitis.
OBJECTIVES
The aim of the study was to evaluate the effectiveness of extra virgin olive (EVO) oil and fruity oil for the treatment of gingivitis.
MATERIALS AND METHODS
A sample of 75 patients over 18 years of age with gingivitis induced by plaque bacteria was divided into three groups: study group A, with extra virgin olive oil; study group B, with fruity oil; and control group C. In the two study groups, EVO oil was administered as a mouthwash to patients with gingival inflammation. The protocol included a daily application of the product for 30 days, with three recalls 15 days apart. Clinical parameters of plaque formation and gingivitis, including plaque index (PI) and bleeding index (BI), were assessed at each recall and scored on a specific periodontal chart. The control group received no mouthwash treatment in addition to normal daily oral hygiene procedures, and the same clinical parameters as the study group were evaluated. Data were evaluated using SPSS 27.0 software for Windows (SPSS Inc., Chicago, IL, USA). Then, the pre- and post-treatment values of the groups were compared using Student's -test, setting < 0.05 as the significance level.
RESULTS
Comparison of the three groups showed that extra-virgin olive oil was an adjuvant in the treatment of gingival inflammation, improving PI and BI. In group A, the mean plaque index showed a 48% reduction, and the bleeding index showed a 64% reduction after 30 days. In group B, the mean plaque index showed a 35% reduction and a bleeding index reduction of 43% after 30 days.
CONCLUSIONS
The collected data showed significant improvements in the formation of bacterial plaque and gingivitis. The exact mechanism of such treatment is still to be elucidated. As a result of this, further studies with a different sample of patients than those used and a comparison with other products need to be addressed to verify and demonstrate the antibacterial and anti-inflammatory effects of the components of this natural product.
PubMed: 37629298
DOI: 10.3390/jcm12165256 -
The Spine Journal : Official Journal of... Dec 2023A traumatic spinal cord injury (SCI) can cause temporary or permanent motor and sensory impairment, leading to serious short and long-term consequences that can result...
BACKGROUND CONTEXT
A traumatic spinal cord injury (SCI) can cause temporary or permanent motor and sensory impairment, leading to serious short and long-term consequences that can result in significant morbidity and mortality. The cervical spine is the most commonly affected area, accounting for about 60% of all traumatic SCI cases.
PURPOSE
This study aims to employ machine learning (ML) algorithms to predict various outcomes, such as in-hospital mortality, nonhome discharges, extended length of stay (LOS), extended length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with cervical SCI (cSCI).
STUDY DESIGN
Our study was a retrospective machine learning classification study aiming to predict the outcomes of interest, which were binary categorical variables, in patients diagnosed with cSCI.
PATIENT SAMPLE
The data for this study were obtained from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database, which was queried to identify patients who suffered from cSCI between 2019 and 2021.
OUTCOME MEASURES
The outcomes of interest of our study were in-hospital mortality, nonhome discharges, prolonged LOS, prolonged ICU-LOS, and major complications. The study evaluated the models' performance using both graphical and numerical methods. The receiver operating characteristic (ROC) and precision-recall curves (PRC) were used to assess model performance graphically. Numerical evaluation metrics included AUROC, balanced accuracy, weighted area under PRC (AUPRC), weighted precision, and weighted recall.
METHODS
The study employed data from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database to identify patients with cSCI. Four ML algorithms, namely XGBoost, LightGBM, CatBoost, and Random Forest, were utilized to develop predictive models. The most effective models were then incorporated into a publicly available web application designed to forecast the outcomes of interest.
RESULTS
There were 71,661 patients included in the analysis for the outcome mortality, 67,331 for the outcome nonhome discharges, 76,782 for the outcome prolonged LOS, 26,615 for the outcome prolonged ICU-LOS, and 72,132 for the outcome major complications. The algorithms exhibited an AUROC value range of 0.78 to 0.839 for in-hospital mortality, 0.806 to 0.815 for nonhome discharges, 0.679 to 0.742 for prolonged LOS, 0.666 to 0.682 for prolonged ICU-LOS, and 0.637 to 0.704 for major complications. An open access web application was developed as part of the study, which can generate predictions for individual patients based on their characteristics.
CONCLUSIONS
Our study suggests that ML models can be valuable in assessing risk for patients with cervical cSCI and may have considerable potential for predicting outcomes during hospitalization. ML models demonstrated good predictive ability for in-hospital mortality and nonhome discharges, fair predictive ability for prolonged LOS, but poor predictive ability for prolonged ICU-LOS and major complications. Along with these promising results, the development of a user-friendly web application that facilitates the integration of these models into clinical practice is a significant contribution of this study. The product of this study may have significant implications in clinical settings to personalize care, anticipate outcomes, facilitate shared decision making and informed consent processes for cSCI patients.
Topics: Humans; Retrospective Studies; Cervical Cord; Precision Medicine; Spinal Cord Injuries; Machine Learning; Hospitals
PubMed: 37619871
DOI: 10.1016/j.spinee.2023.08.009 -
PloS One 2023Product-harm crises have detrimental effects on firm's sales, reputation, and financial value, requiring crisis managers to promptly adopt appropriate response...
Product-harm crises have detrimental effects on firm's sales, reputation, and financial value, requiring crisis managers to promptly adopt appropriate response strategies to mitigate these impacts. Situational Crisis Communication Theory (SCCT) guides managers to align responsibility attribution with response strategies. Using Chinese listed firms' product-harm crises sample from 2015 to 2021, this study analyzes the stock market's reaction to different response strategies. The event study method reveals that a passive strategy is more effective during the disclosure stage, and accept+no recall and deny+recall are conforming strategies during the initial response stage. Additionally, firms with a crisis history should assume greater responsibility when developing response strategies for product-harm crises, as crisis history amplifies negative effects. The results provide recommendations to help managers formulate appropriate strategies.
Topics: Commerce; Disclosure; Social Perception; Product Recalls and Withdrawals; Consumer Product Safety; Private Sector; Public Opinion; Truth Disclosure; China
PubMed: 37616251
DOI: 10.1371/journal.pone.0290548 -
Frontiers in Nutrition 2023Dietary assessment is important for understanding nutritional status. Traditional methods of monitoring food intake through self-report such as diet diaries, 24-hour...
INTRODUCTION
Dietary assessment is important for understanding nutritional status. Traditional methods of monitoring food intake through self-report such as diet diaries, 24-hour dietary recall, and food frequency questionnaires may be subject to errors and can be time-consuming for the user.
METHODS
This paper presents a semi-automatic dietary assessment tool we developed - a desktop application called Image to Nutrients (I2N) - to process sensor-detected eating events and images captured during these eating events by a wearable sensor. I2N has the capacity to offer multiple food and nutrient databases (e.g., USDA-SR, FNDDS, USDA Global Branded Food Products Database) for annotating eating episodes and food items. I2N estimates energy intake, nutritional content, and the amount consumed. The components of I2N are three-fold: 1) sensor-guided image review, 2) annotation of food images for nutritional analysis, and 3) access to multiple food databases. Two studies were used to evaluate the feasibility and usefulness of I2N: 1) a US-based study with 30 participants and a total of 60 days of data and 2) a Ghana-based study with 41 participants and a total of 41 days of data).
RESULTS
In both studies, a total of 314 eating episodes were annotated using at least three food databases. Using I2N's sensor-guided image review, the number of images that needed to be reviewed was reduced by 93% and 85% for the two studies, respectively, compared to reviewing all the images.
DISCUSSION
I2N is a unique tool that allows for simultaneous viewing of food images, sensor-guided image review, and access to multiple databases in one tool, making nutritional analysis of food images efficient. The tool is flexible, allowing for nutritional analysis of images if sensor signals aren't available.
PubMed: 37575335
DOI: 10.3389/fnut.2023.1191962 -
Sensors (Basel, Switzerland) Jul 2023Moving target detection (MTD) is a crucial task in computer vision applications. In this paper, we investigate the problem of detecting moving targets in infrared (IR)...
Moving target detection (MTD) is a crucial task in computer vision applications. In this paper, we investigate the problem of detecting moving targets in infrared (IR) surveillance video sequences captured using a steady camera in a maritime setting. For this purpose, we employ robust principal component analysis (RPCA), which is an improvement of principal component analysis (PCA) that separates an input matrix into the following two matrices: a low-rank matrix that is representative, in our case study, of the slowly changing background, and a sparse matrix that is representative of the foreground. RPCA is usually implemented in a non-causal batch form. To pursue a real-time application, we tested an online implementation, which, unfortunately, was affected by the presence of the target in the scene during the initialization phase. Therefore, we improved the robustness by implementing a saliency-based strategy. The advantages offered by the resulting technique, which we called "saliency-aided online moving window RPCA" (S-OMW-RPCA) are the following: RPCA is implemented online; along with the temporal features exploited by RPCA, the spatial features are also taken into consideration by using a saliency filter; the results are robust against the condition of the scene during the initialization. Finally, we compare the performance of the proposed technique in terms of precision, recall, and execution time with that of an online RPCA, thus, showing the effectiveness of the saliency-based approach.
PubMed: 37514626
DOI: 10.3390/s23146334 -
BMC Public Health Jul 2023This report describes two L. monocytogenes outbreak investigations that occurred in March and September of 2018 and that linked illness to a food premises located in an...
BACKGROUND
This report describes two L. monocytogenes outbreak investigations that occurred in March and September of 2018 and that linked illness to a food premises located in an Ontario cancer centre. The cancer centre serves patients from across the province.
METHODS
In Ontario, local public health agencies follow up with all reported laboratory-confirmed cases of listeriosis to identify possible sources of disease acquisition and to carry out investigations, including at suspected food premises. The Canadian Food Inspection Agency (CFIA) is notified of any Listeria-positive food product collected in relation to a case. The CFIA traces Listeria-positive product through the food distribution system to identify the contamination source and ensure the implicated manufacturing facility implements corrective measures.
RESULTS
Outbreaks one and two each involved three outbreak-confirmed listeriosis cases. All six cases were considered genetically related by whole genome sequencing (WGS). In both outbreaks, outbreak-confirmed cases reported consuming meals at a food premises located in a cancer centre (food premises A) before illness onset. Various open deli meat samples and, in outbreak two, environmental swabs (primarily from the meat slicer) collected from food premises A were genetically related to the outbreak-confirmed cases. Food premises A closed as a result of the investigations.
CONCLUSIONS
When procuring on-site food premises, healthcare facilities and institutions serving individuals with immuno-compromising conditions should consider the potential health risk of foods available to their patient population.
Topics: Humans; Listeria monocytogenes; Foodborne Diseases; Food Microbiology; Neoplasms; Listeriosis; Disease Outbreaks; Ontario
PubMed: 37507665
DOI: 10.1186/s12889-023-16371-7 -
European Journal of Immunology Nov 2023To obtain a better understanding of the biology behind life-threatening fungal infections caused by Candida albicans, we recently conducted an in silico screening for...
To obtain a better understanding of the biology behind life-threatening fungal infections caused by Candida albicans, we recently conducted an in silico screening for fungal and host protein interaction partners. We report here that the extracellular domain of human CD4 binds to the moonlighting protein enolase 1 (Eno1) of C. albicans as predicted bioinformatically. By using different anti-CD4 monoclonal antibodies, we determined that C. albicans Eno1 (CaEno1) primarily binds to the extracellular domain 3 of CD4. Functionally, we observed that CaEno1 binding to CD4 activated lymphocyte-specific protein tyrosine kinase (LCK), which was also the case for anti-CD4 monoclonal antibodies tested in parallel. CaEno1 binding to naïve human CD4 T cells skewed cytokine secretion toward a Th2 profile indicative of poor fungal control. Moreover, CaEno1 inhibited human memory CD4 T-cell recall responses. Therapeutically, CD4 T cells transduced with a p41/Crf1-specific T-cell receptor developed for adoptive T-cell therapy were not inhibited by CaEno1 in vitro. Together, the interaction of human CD4 T cells with CaEno1 modulated host CD4 T-cell responses in favor of the fungus. Thus, CaEno1 mediates not only immune evasion through its interference with complement regulators but also through the direct modulation of CD4 T-cell responses.
Topics: Humans; Candida albicans; T-Lymphocytes; CD4-Positive T-Lymphocytes; Phosphopyruvate Hydratase; Antibodies, Monoclonal
PubMed: 37503840
DOI: 10.1002/eji.202250284 -
HIV/AIDS (Auckland, N.Z.) 2023Poor adherence to antiretroviral therapy (ART) causes drug resistance, treatment failure and death. Studies conducted among children below 15 years were limited in...
BACKGROUND
Poor adherence to antiretroviral therapy (ART) causes drug resistance, treatment failure and death. Studies conducted among children below 15 years were limited in Ethiopia in general and in the study area. Therefore, this study aimed to assess the status of children's adherence to ART and associated factors in the study area.
METHODS
We conducted a facility-based cross-sectional study from April 1 to May 10, 2020 by including 282 children <15 years. All children who received ART for at least one month and attend ART clinic during data collection period were consecutively recruited. Face-to-face interview was conducted using a standardized questionnaire. Both bivariate and multivariate logistic regression were performed. Adherence and exposure variables (i.e., sociodemographic and reason for missing) were measured by the caregivers/children's report of a one-month recall of missed doses.
RESULTS
Among 282 caregivers included with their children, 226 (80.2%) were females (mean age = 38.6 and SD = 12.35) and half (50%) of children were females. Two hundred forty six (87.2%) children were aged between 5-14 years (mean age = 8.5 and SD = 2.64), and 87.2% were adhered (≥95%) to ART in the month prior to the interview. Children whose caregivers were residing in urban were 3.3 (95% CI: 1.17, 9.63) times more adherent to ART than their counterparties. Children whose caregivers were biological parents were 2.37 (95% CI: 1.59, 3.3) times more adherent than children with non-biological parents. Children with knowledgeable caregivers about ART were 4.5 (95% CI: 1.79, 9.8) times more adherent to ART.
CONCLUSION AND RECOMMENDATION
Children's adherence to ART in our study area was sub optimal. Biological caregivers, residing in urban and being knowledgeable about ART facilitate adherence to ART. Adherence counseling targeting non-biological parents and for those who come from rural areas were recommended.
PubMed: 37497118
DOI: 10.2147/HIV.S407105 -
North American Spine Society Journal Sep 2023Bone grafting is commonly used in spine surgery to supplement or replace the need for autografts. This is harvested, prepared, and utilized predominantly for...
BACKGROUND
Bone grafting is commonly used in spine surgery to supplement or replace the need for autografts. This is harvested, prepared, and utilized predominantly for osteoconductive properties. Anterior cervical discectomy and fusion, a procedure to decompress and fuse the spine which treats herniated discs and compressed nerves, commonly uses Polyetheretherketone (PEEK) interbody filled with allograft bone matrices to reconstruct the disc space after a discectomy is performed.
CASE DESCRIPTION
The presented case is one of a 57-year-old male patient who underwent an uneventful cervical 5-6 and cervical 6-7 discectomy and fusion using a PEEK interbody and bone allograft. The allograft had been prepared using cancellous bone particles with preserved living cells and demineralized cortical bone fibers to facilitate bone repair and healing, which is a common technique. The allograft was aseptically processed to preserve native factors that can support bone repair and prevent contamination and cross-contamination of the product. Additionally, the product was sterilized using gamma irradiation to further prevent contamination.
OUTCOME
Unfortunately, with the presented case, the State's Department of Health and The Center for Diseases Control and Prevention identified that the graft was from a source contaminated with tuberculosis. The patient being reported went on to develop disseminated tuberculosis, including lung abscesses and osteomyelitis.
CONCLUSIONS
The current case highlights that there was contamination of the donor bone sources. Tuberculosis was not screened in the tissue donor even though he had risk factors, symptoms, and signs consistent with tuberculosis. Although there are methods to screen potential organ donors for tuberculosis, there is currently no approved standard laboratory tuberculosis screening tool for bone grafts. Thus, this emphasizes the importance of proper screening among individual institutions for even the most uncommon diseases in all donated bone grafts.
PubMed: 37483264
DOI: 10.1016/j.xnsj.2023.100241 -
Research (Washington, D.C.) 2023Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing...
Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing customizability and direct control of the architecture. The trial-and-error approach currently used for developing the composition of printable inks is time- and resource-consuming due to the increasing number of variables requiring expert knowledge. Artificial intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental data. In this paper, we constructed machine learning (ML) algorithms including decision tree, random forest (RF), and deep learning (DL) to predict the printability of biomaterials. A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed, and their printability was assessed. All ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial formulations. In particular, the RF algorithm has achieved the highest accuracy (88.1%), precision (90.6%), and F1 score (87.0%), indicating the best overall performance out of the 3 algorithms, while DL has the highest recall (87.3%). Furthermore, the ML algorithms have predicted the printability window of biomaterials to guide the ink development. The printability map generated with DL has finer granularity than other algorithms. ML has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications.
PubMed: 37469394
DOI: 10.34133/research.0197