-
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 -
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 -
BMC Medical Informatics and Decision... Jul 2023Real-world evidence (RWE)-based on information obtained from sources such as electronic health records (EHRs), claims and billing databases, product and disease...
BACKGROUND
Real-world evidence (RWE)-based on information obtained from sources such as electronic health records (EHRs), claims and billing databases, product and disease registries, and personal devices and health applications-is increasingly used to support healthcare decision making. There is variability in the collection of EHR data, which includes "structured data" in predefined fields (e.g., problem list, open claims, medication list, etc.) and "unstructured data" as free text or narrative. Healthcare providers are likely to provide more complete information as free text, but extracting meaning from these fields requires newer technologies and a rigorous methodology to generate higher-quality evidence. Herein, an approach to identify concepts associated with the presence and progression of migraine was developed and validated using the complete patient record in EHR data, including both the structured and unstructured portions.
METHODS
"Traditional RWE" approaches (i.e., capture from structured EHR fields and extraction using structured queries) and "Advanced RWE" approaches (i.e., capture from unstructured EHR data and processing by artificial intelligence [AI] technology, including natural language processing and AI-based inference) were evaluated against a manual chart abstraction reference standard for data collected from a tertiary care setting. The primary endpoint was recall; differences were compared using chi square.
RESULTS
Compared with manual chart abstraction, recall for migraine and headache were 66.6% and 29.6%, respectively, for Traditional RWE, and 96.8% and 92.9% for Advanced RWE; differences were statistically significant (absolute differences, 30.2% and 63.3%; P < 0.001). Recall of 6 migraine-associated symptoms favored Advanced RWE over Traditional RWE to a greater extent (absolute differences, 71.5-88.8%; P < 0.001). The difference between traditional and advanced techniques for recall of migraine medications was less pronounced, approximately 80% for Traditional RWE and ≥ 98% for Advanced RWE (P < 0.001).
CONCLUSION
Unstructured EHR data, processed using AI technologies, provides a more credible approach to enable RWE in migraine than using structured EHR and claims data alone. An algorithm was developed that could be used to further study and validate the use of RWE to support diagnosis and management of patients with migraine.
Topics: Humans; Electronic Health Records; Artificial Intelligence; Algorithms; Natural Language Processing; Migraine Disorders
PubMed: 37452338
DOI: 10.1186/s12911-023-02190-8 -
Scientific Reports Jul 2023E-commerce is a field that changed how consumers purchase and interact with products. Although, inherent limitations such as the difficulty of testing the products...
E-commerce is a field that changed how consumers purchase and interact with products. Although, inherent limitations such as the difficulty of testing the products "first-hand" before a purchase can compromise consumers' trust in online purchases. Virtual Reality (VR) has been investigated as a tool to solve limitations in several fields and how we can harness its potential to improve the overall user experience. This study analysed how immersive VR (IVR) could solve these limitations by allowing consumers to test products beforehand. We have studied how the Novelty Factor (evaluated by the users' past VR experience) and Immersive Tendencies correlate with the users' Purchase Intention and Memory (how well they remember the product's characteristics). We have analysed a sample of 38 participants (21 males) from 18 to 28 years old. Participants experienced a refrigerator with an interactive touchscreen in an IVR setup and were guided through its functionalities. Results indicated that memory of the product's characteristics was positively correlated with how recently they experienced VR. No correlations were found in the female sample. A negative correlation between Purchase Intention and Memory of the product's characteristics was found in the male sample. We concluded that IVR applications could become helpful for both consumers and online shops in an e-commerce context regardless of the Novelty Factor and Immersive Tendencies of consumers. However, differences between genders should be further investigated.
Topics: Humans; Male; Female; Adolescent; Young Adult; Adult; Intention; Virtual Reality; Commerce; Consumer Behavior; Mental Recall
PubMed: 37452064
DOI: 10.1038/s41598-023-36557-8 -
Soft Computing Jun 2023Online reviews play a critical role in modern word-of-mouth communication, influencing consumers' shopping preferences and purchase decisions, and directly affecting a...
Online reviews play a critical role in modern word-of-mouth communication, influencing consumers' shopping preferences and purchase decisions, and directly affecting a company's reputation and profitability. However, the credibility and authenticity of these reviews are often questioned due to the prevalence of fake online reviews that can mislead customers and harm e-commerce's credibility. These fake reviews are often difficult to identify and can lead to erroneous conclusions in user feedback analysis. This paper proposes a new approach to detect fake online reviews by combining convolutional neural network (CNN) and adaptive particle swarm optimization with natural language processing techniques. The approach uses datasets from popular online review platforms like Ott, Amazon, Yelp, TripAdvisor, and IMDb and applies feature selection techniques to select the most informative features. The paper suggests using attention mechanisms like bidirectional encoder representations from transformers and generative pre-trained transformer, as well as other techniques like Deep contextualized word representation, word2vec, GloVe, and fast Text, for feature extraction from online review datasets. The proposed method uses a multimodal approach based on a CNN architecture that combines text data to achieve a high accuracy rate of 99.4%. This outperforms traditional machine learning classifiers in terms of accuracy, recall, and F measure. The proposed approach has practical implications for consumers, manufacturers, and sellers in making informed product choices and decision-making processes, helping maintain the credibility of online consumer reviews. The proposed model shows excellent generalization abilities and outperforms conventional discrete and existing neural network benchmark models across multiple datasets. Moreover, it reduces the time complexity for both training and testing.
PubMed: 37362263
DOI: 10.1007/s00500-023-08507-z -
Pharmaceutical Research Sep 2023The goal of pharmacovigilance (PV) is to prevent adverse events (AEs) associated with drugs and vaccines. Current PV programs are of a reactive nature and rest entirely...
The goal of pharmacovigilance (PV) is to prevent adverse events (AEs) associated with drugs and vaccines. Current PV programs are of a reactive nature and rest entirely on data science, i.e., detecting and analyzing AE data from provider/patient reports, health records and even social media. The ensuing preventive actions are too late for people who have experienced AEs and often overly broad, as responses include entire product withdrawals, batch recalls, or contraindications of subpopulations. To prevent AEs in a timely and precise manner, it is necessary to go beyond data science and incorporate measurement science into PV efforts through person-level patient screening and dose-level product surveillance. Measurement-based PV may be called 'preventive pharmacovigilance', the goal of which is to identify susceptible individuals and defective doses to prevent AEs. A comprehensive PV program should contain both reactive and preventive components by integrating data science and measurement science.
Topics: Humans; Pharmacovigilance; Vaccines
PubMed: 37349651
DOI: 10.1007/s11095-023-03548-3 -
PeerJ. Computer Science 2023In the modern era, Internet-based e-commerce world, consumers express their thoughts on the product or service through ranking and reviews. Sentiment analysis uncovers...
BACKGROUND
In the modern era, Internet-based e-commerce world, consumers express their thoughts on the product or service through ranking and reviews. Sentiment analysis uncovers contextual inferences in user sentiment, assisting the commercial industry and end users in understanding the perception of the product or service. Variations in textual arrangement, complex logic, and sequence length are some of the challenges to accurately forecast the sentiment score of user reviews. Therefore, a novel improvised local search whale optimization improved long short-term memory (LSTM) for feature-level sentiment analysis of online product reviews is proposed in this study.
METHODS
The proposed feature-level sentiment analysis method includes 'data collection', 'pre-processing', 'feature extraction', 'feature selection', and finally 'sentiment classification'. First, the product reviews given from different customers are acquired, and then the retrieved data is pre-processed. These pre-processed data go through a feature extraction procedure using a modified inverse class frequency algorithm (LFMI) based on log term frequency. Then the feature is selected levy flight-based mayfly optimization algorithm (LFMO). At last, the selected data is transformed to the improvised local search whale optimization boosted long short-term memory (ILW-LSTM) model, which categorizes the sentiment of the customer reviews as 'positive', 'negative', 'very positive', 'very negative', and 'neutral'. The 'Prompt Cloud dataset' is used for the performance study of the suggested classifiers. Our suggested ILW-LSTM model is put to the test using standard performance evaluation. The primary metrics used to assess our suggested model are 'accuracy', 'recall', 'precision', and 'F1-score'.
RESULTS AND CONCLUSION
The proposed ILW-LSTM method provides an accuracy of 97%. In comparison to other leading algorithms, the outcome reveals that the ILW-LSTM model outperformed well in feature-level sentiment classification.
PubMed: 37346605
DOI: 10.7717/peerj-cs.1336 -
Frontiers in Veterinary Science 2023Foot-and-mouth disease (FMD) is a highly contagious viral disease that is endemic in East Africa. FMD virus infection incurs significant control costs and reduces animal...
INTRODUCTION
Foot-and-mouth disease (FMD) is a highly contagious viral disease that is endemic in East Africa. FMD virus infection incurs significant control costs and reduces animal productivity through weight loss, lowered milk yield, and potentially death but how household's respond to these losses may differentially affect household income and food consumption.
METHODOLOGY
To address this, we use unique data from a FMD outbreak to assess how household production and consumption activities change from before to during the outbreak. Data came from a 2018 survey of 254 households in selected Tanzanian wards and sub-counties in Uganda. The data includes household recall of before and during an outbreak in the past year on livestock and livestock product sales, milk and beef consumption, as well as related changes in market prices. We apply both difference-in-difference and change in difference ordinary least squares regressions with fixed effects to evaluate the impact of FMD on household production and consumption.
RESULTS AND DISCUSSION
We find that households reported the largest reductions in livestock and livestock product sales, followed by reduced milk consumption and animal market prices. The changes in household income from livestock sales appears to be driven by FMD virus infection within the household herd while changes in market prices of substitute protein sources are primary associated with changes in milk and beef consumption. The role of widespread market price effects across both infected and uninfected herds and countries, tends to suggest that stabilizing prices will likely have a large impact on household nutritional security and income generation. We also propose that promoting diversity in market activity may mitigate differing impacts on households in FMD endemic regions.
PubMed: 37342624
DOI: 10.3389/fvets.2023.1156458