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Journal of Family Medicine and Primary... Sep 2023Chronic gastritis is one of the most prevalent disorders affecting individuals, which affects hundreds of millions of people in different ways around the world. The...
INTRODUCTION
Chronic gastritis is one of the most prevalent disorders affecting individuals, which affects hundreds of millions of people in different ways around the world. The significant incidence of poor dietary quality and diet-related illnesses may be addressed by orienting customers toward healthier food options. The objective of the study was to estimate the knowledge of front-of-package labels (FoPLs) and food group-based dietary intake of nutrients among patients with chronic gastritis.
MATERIALS AND METHODS
The study design was a hospital-based cross-sectional study that was done in Guntur district of Andhra Pradesh. The study population included 208 chronic gastritis patients between 20 to 60 years of age selected by systematic sampling. Detailed information on sociodemographic and lifestyle factors was collected using a questionnaire and 24-h dietary recall was done. The objective assessment of Knowledge of FoPLs was assessed mock package images representing a fictional brand to prevent other factors from interfering with product evaluation.
RESULTS
A total of 208 patients were studied with a near-equal proportion of males and females. Among participants, more than half (57.2%) can interpret FoPL, more than three-fourths (77.4%) have a belief that they eat a healthy diet mostly and only half (52.4%) of participants are somewhat knowledgeable about nutrition, and finally almost half (46.6%) of participants are not seeing the FoP label during food purchase. The mean score of knowledge of FoP labeling was 0.92 ± 1.135. Knowledge of FOPL was positively associated with the age of study participants OR 0.178 (95% CI: 0.178 to 0.856) with P value = 0.02. Grains have the maximum intake among all the food groups with a mean intake of 123.21 g/day.
CONCLUSION
The majority of participants do not know the food labeling, thus methods of education that focus on dietary interventions are urgently needed to raise awareness among the people.
PubMed: 38024917
DOI: 10.4103/jfmpc.jfmpc_322_23 -
International Journal of Applied Earth... Sep 2023Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are...
Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.
PubMed: 37975073
DOI: 10.1016/j.jag.2023.103469 -
International Journal of Language &... 2024Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a...
BACKGROUND
Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early detection of dementia can significantly reduce the effort and resources needed for caretaking.
AIMS
This research proposes an approach for assessing cognitive decline by analysing speech data, specifically focusing on speech relevance as a crucial indicator for memory recall.
METHODS & PROCEDURES
This is a cross-sectional, online, self-administered. The proposed method used deep learning architecture based on transformers, with BERT (Bidirectional Encoder Representations from Transformers) and Sentence-Transformer to derive encoded representations of speech transcripts. These representations provide contextually descriptive information that is used to analyse the relevance of sentences in their respective contexts. The encoded information is then compared using cosine similarity metrics to measure the relevance of uttered sequences of sentences. The study uses the Pitt Corpus Dementia dataset for experimentation, which consists of speech data from individuals with and without dementia. The accuracy of the proposed multi-QA-MPNet (Multi-Query Maximum Inner Product Search Pretraining) model is compared with other pretrained transformer models of Sentence-Transformer.
OUTCOMES & RESULTS
The results show that the proposed approach outperforms the other models in capturing context level information, particularly semantic memory. Additionally, the study explores the suitability of different similarity measures to evaluate the relevance of uttered sequences of sentences. The experimentation reveals that cosine similarity is the most appropriate measure for this task.
CONCLUSIONS & IMPLICATIONS
This finding has significant implications for the early warning signs of dementia, as it suggests that cosine similarity metrics can effectively capture the semantic relevance of spoken language. The persistent cognitive decline over time acts as one of the indicators for prevalence of dementia. Additionally early dementia could be recognised by analysis on other modalities like speech and brain images.
WHAT THIS PAPER ADDS
What is already known on this subject It is already known that speech- and language-based detection methods can be useful for dementia diagnosis, as language difficulties are often early signs of the disease. Additionally, deep learning algorithms have shown promise in detecting and diagnosing dementia through analysing large datasets, particularly in speech- and language-based detection methods. However, further research is needed to validate the performance of these algorithms on larger and more diverse datasets and to address potential biases and limitations. What this paper adds to existing knowledge This study presents a unique and effective approach for cognitive decline assessment through analysing speech data. The study provides valuable insights into the importance of context and semantic memory in accurately detecting the potential in dementia and demonstrates the applicability of deep learning models for this purpose. The findings of this study have important clinical implications and can inform future research and development in the field of dementia detection and care. What are the potential or actual clinical implications of this work? The proposed approach for cognitive decline assessment using speech data and deep learning models has significant clinical implications. It has the potential to improve the accuracy and efficiency of dementia diagnosis, leading to earlier detection and more effective treatments, which can improve patient outcomes and quality of life.
Topics: Humans; Deep Learning; Cognitive Dysfunction; Cross-Sectional Studies; Semantics; Aged; Female; Male; Aged, 80 and over; Dementia
PubMed: 37971395
DOI: 10.1111/1460-6984.12973 -
Nucleic Acids Research Jan 2024One challenge in the development of novel drugs is their interaction with potential off-targets, which can cause unintended side-effects, that can lead to the subsequent...
One challenge in the development of novel drugs is their interaction with potential off-targets, which can cause unintended side-effects, that can lead to the subsequent withdrawal of approved drugs. At the same time, these off-targets may also present a chance for the repositioning of withdrawn drugs for new indications, which are potentially rare or more severe than the original indication and where certain adverse reactions may be avoidable or tolerable. To enable further insights into this topic, we updated our database Withdrawn by adding pharmacovigilance data from the FDA Adverse Event Reporting System (FAERS), as well as mechanism of action and human disease pathway prediction features for drugs that are or were temporarily withdrawn or discontinued in at least one country. As withdrawal data are still spread over dozens of national websites, we are continuously updating our lists of discontinued or withdrawn drugs and related (off-)targets. Furthermore, new systematic entry points for browsing the data, such as an ATC tree, were added, increasing the accessibility of the database in a user-friendly way. Withdrawn 2.0 is publicly available without the need for registration or login at https://bioinformatics.charite.de/withdrawn_3/index.php.
Topics: Humans; Drug-Related Side Effects and Adverse Reactions; Pharmacovigilance; Safety-Based Drug Withdrawals; Databases, Pharmaceutical
PubMed: 37971295
DOI: 10.1093/nar/gkad1017 -
World Journal of Microbiology &... Nov 2023Staphylococcus aureus is an important and leading cause of foodborne diseases worldwide. Prompt detection and recall of contaminated foods are crucial to prevent...
Staphylococcus aureus is an important and leading cause of foodborne diseases worldwide. Prompt detection and recall of contaminated foods are crucial to prevent untoward health consequences caused by S. aureus. Helix loop-mediated isothermal amplification (HAMP) is an exciting recent addition to the array of available isothermal-based nucleic acid amplification techniques. This study aimed to develop and evaluate a HAMP assay for detecting S. aureus in milk and milk products. The assay is completed in 75 minutes of isothermal temperature incubation (64 ˚C) and dye-based visual interpretation of results based on colour change. The specificity of the developed assay was ascertained using 27 S. aureus and 17 non S. aureus bacterial strains. The analytical sensitivity of the developed HAMP assay was 9.7 fg/µL of pure S. aureus DNA. The detection limit of the HAMP assay in milk (86 CFU/mL) was 1000x greater than the routinely used endpoint PCR (86 × 10 CFU/mL). The practicality of applying the HAMP assay was also assessed by analysing milk and milk product samples (n = 95) obtained from different dairy farms and retail outlets. The developed test is a more rapid, sensitive, and user-friendly method for the high-throughput screening of S. aureus in food samples and may therefore be suitable for field laboratories. To our knowledge, this is the first study to develop and evaluate the HAMP platform for detecting S. aureus.
Topics: Humans; Animals; Milk; Staphylococcus aureus; Colorimetry; Nucleic Acid Amplification Techniques; Staphylococcal Infections; Hepcidins
PubMed: 37966568
DOI: 10.1007/s11274-023-03838-3 -
Nutrients Sep 2023The range of gluten-free food products available to consumers is steadily expanding. In recent years, recalls of food products have highlighted the importance of...
The range of gluten-free food products available to consumers is steadily expanding. In recent years, recalls of food products have highlighted the importance of accurate labeling of food products for the presence of wheat, other gluten-containing cereals, or gluten itself as refined ingredient. The purpose of this study was to gain more insights into recent food recalls related to undeclared gluten/wheat contamination and consumer experiences with these recalls. Recalls of products triggered by gluten contamination are relatively scarce and are not often triggered by a consumer complaint. The impact of these recalls on consumer trust was evaluated through an online survey that was distributed among supporters of Celiac Canada (CCA) and covered (i) strategies to adhere to a gluten-free diet, (ii) experiences with gluten-free recalls and their impact on consumer trust, and (iii) demographic information. Consumer concern regarding gluten-free product recalls is significant, but the concern regarding recalls is not heightened after experiencing a recall. Companies pursuing transparency in the process, identification of the source of contamination, and mitigation strategies going forward are likely to retain consumer trust in their product and brand. Based on the survey results, further efforts focusing on consumer education regarding interpreting nutrient labels, identifying sources of information on product recalls, and understanding procedures to follow upon suspected gluten contamination of a gluten-free product are recommended.
Topics: Humans; Diet, Gluten-Free; Food Labeling; Trust; Glutens; Product Recalls and Withdrawals; Celiac Disease
PubMed: 37836454
DOI: 10.3390/nu15194170 -
Signal Transduction and Targeted Therapy Oct 2023Long-term humoral immunity to SARS-CoV-2 is essential for preventing reinfection. The production of neutralizing antibody (nAb) and B cell differentiation are tightly...
Long-term humoral immunity to SARS-CoV-2 is essential for preventing reinfection. The production of neutralizing antibody (nAb) and B cell differentiation are tightly regulated by T follicular help (T) cells. However, the longevity and functional role of T cell subsets in COVID-19 convalescents and vaccine recipients remain poorly defined. Here, we show that SARS-CoV-2 infection and inactivated vaccine elicited both spike-specific CXCR3 T cell and CXCR3 T cell responses, which showed distinct response patterns. Spike-specific CXCR3 T cells exhibit a dominant and more durable response than CXCR3 T cells that positively correlated with antibody responses. A third booster dose preferentially expands the spike-specific CXCR3 T cell subset induced by two doses of inactivated vaccine, contributing to antibody maturation and potency. Functionally, spike-specific CXCR3 T cells have a greater ability to induce spike-specific antibody secreting cells (ASCs) differentiation compared to spike-specific CXCR3 T cells. In conclusion, the persistent and functional role of spike-specific CXCR3 T cells following SARS-CoV-2 infection and vaccination may play an important role in antibody maintenance and recall response, thereby conferring long-term protection. The findings from this study will inform the development of SARS-CoV-2 vaccines aiming to induce long-term protective immune memory.
Topics: Humans; SARS-CoV-2; COVID-19; COVID-19 Vaccines; Antibodies, Neutralizing; Vaccines, Inactivated
PubMed: 37802996
DOI: 10.1038/s41392-023-01650-x -
Drug Safety Oct 2023Substandard medicines can lead to serious safety issues affecting public health; however, the nature of such issues can be widely heterogeneous. Health product...
BACKGROUND AND OBJECTIVE
Substandard medicines can lead to serious safety issues affecting public health; however, the nature of such issues can be widely heterogeneous. Health product regulators seek to prioritise critical product quality defects for review to ensure that prompt risk mitigation measures are taken. This study aims to classify the nature of issues for substandard medicines using machine learning to augment a risk-based and timely review of cases.
METHODS
A combined machine learning algorithm with a keyword-based model was developed to classify quality issues using text relating to substandard medicines (CISTERM). The nature of issues for product defect cases were classified based on Medical Dictionary for Regulatory Activities-Health Sciences Authority (MedDRA-HSA) lowest-level terms.
RESULTS
Product defect cases received from January 2010 to December 2021 were used for training (n = 11,082) and for testing (n = 2771). The machine learning model achieved a good recall (precision) of 92% (96%) for 'Product adulterated and/or contains prohibited substance', 86% (90%) for 'Out of specification or out of trend test result' and 90% (91%) for 'Manufacturing non-compliance'.
CONCLUSION
Post-market surveillance of substandard medicines remains a key activity for drug regulatory authorities. A combined machine learning algorithm with keyword-based model can help to prioritise the review of product quality defect issues in a timely manner.
Topics: Humans; Substandard Drugs; Machine Learning; Algorithms; Drug Contamination; Public Health
PubMed: 37776421
DOI: 10.1007/s40264-023-01339-8 -
Developmental Psychology Oct 2023Children's drawings of common object categories become dramatically more recognizable across childhood. What are the major factors that drive developmental changes in...
Children's drawings of common object categories become dramatically more recognizable across childhood. What are the major factors that drive developmental changes in children's drawings? To what degree are children's drawings a product of their changing internal category representations versus limited by their visuomotor abilities or their ability to recall the relevant visual information? To explore these questions, we examined the degree to which developmental changes in drawing recognizability vary across different drawing tasks that vary in memory demands (i.e., drawing from verbal vs. picture cues) and with children's shape-tracing abilities across two geographical locations (San Jose, United States, and Beijing, China). We collected digital shape tracings and drawings of common object categories (e.g., cat, airplane) from 4- to 9-year-olds (N = 253). The developmental trajectory of drawing recognizability was remarkably similar when children were asked to draw from pictures versus verbal cues and across these two geographical locations. In addition, our Beijing sample produced more recognizable drawings but showed similar tracing abilities to children from San Jose. Overall, this work suggests that the developmental trajectory of children's drawings is remarkably consistent and not easily explainable by changes in visuomotor control or working memory; instead, changes in children's drawings over development may at least partly reflect changes in the internal representations of object categories. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Topics: Child; Humans; China; Cues; East Asian People; Memory, Short-Term; United States; Asian
PubMed: 37768614
DOI: 10.1037/dev0001600 -
Transfusion Medicine and Hemotherapy :... Aug 2023An increasing shortage of donor blood is expected, considering the demographic change in Germany. Due to the short shelf life and varying daily fluctuations in...
INTRODUCTION
An increasing shortage of donor blood is expected, considering the demographic change in Germany. Due to the short shelf life and varying daily fluctuations in consumption, the storage of platelet concentrates (PCs) becomes challenging. This emphasizes the need for reliable prediction of needed PCs for the blood bank inventories. Therefore, the objective of this study was to evaluate multimodal data from multiple source systems within a hospital to predict the number of platelet transfusions in 3 days on a per-patient level.
METHODS
Data were collected from 25,190 (42% female and 58% male) patients between 2017 and 2021. For each patient, the number of received PCs, platelet count blood tests, drugs causing thrombocytopenia, acute platelet diseases, procedures, age, gender, and the period of a patient's hospital stay were collected. Two models were trained on samples using a sliding window of 7 days as input and a day 3 target. The model predicts whether a patient will be transfused 3 days in the future. The model was trained with an excessive hyperparameter search using patient-level repeated 5-fold cross-validation to optimize the average macro F2-score.
RESULTS
The trained models were tested on 5,022 unique patients. The best-performing model has a specificity of 0.99, a sensitivity of 0.37, an area under the precision-recall curve score of 0.45, an MCC score of 0.43, and an F1-score of 0.43. However, the model does not generalize well for cases when the need for a platelet transfusion is recognized.
CONCLUSION
A patient AI-based platelet forecast could improve logistics management and reduce blood product waste. In this study, we build the first model to predict patient individual platelet demand. To the best of our knowledge, we are the first to introduce this approach. Our model predicts the need for platelet units for 3 days in the future. While sensitivity underperforms, specificity performs reliably. The model may be of clinical use as a pretest for potential patients needing a platelet transfusion within the next 3 days. As sensitivity needs to be improved, further studies should introduce deep learning and wider patient characterization to the methodological multimodal, multisource data approach. Furthermore, a hospital-wide consumption of PCs could be derived from individual predictions.
PubMed: 37767277
DOI: 10.1159/000528428