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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 -
Toxins Sep 2023The farming of shellfish plays an important role in providing sustainable economic growth in coastal, rural communities in Scotland and acts as an anchor industry,...
The farming of shellfish plays an important role in providing sustainable economic growth in coastal, rural communities in Scotland and acts as an anchor industry, supporting a range of ancillary jobs in the processing, distribution and exporting industries. The Scottish Government is encouraging shellfish farmers to double their economic contribution by 2030. These farmers face numerous challenges to reach this goal, among which is the problem caused by toxin-producing microplankton that can contaminate their shellfish, leading to harvesting site closure and the recall of product. Food Standards Scotland, a non-ministerial department of the Scottish Government, carries out a monitoring programme for both the toxin-producing microplankton and the toxins in shellfish flesh, with farms being closed when official thresholds for any toxin are breached. The farm remains closed until testing for the problematic toxin alone, often diarrhetic shellfish toxin (DST), shows the site to have dropped below the regulatory threshold. While this programme has proved to be robust, questions remain regarding the other toxins that may be present at a closed site. In this study, we tested archival material collected during site closures but only tested for DSTs as part of the official control monitoring. We found the presence of amnesic shellfish toxin (AST) in low concentrations in the majority of sites tested. In one case, the level of AST breached the official threshold. This finding has implications for AST monitoring programmes around Europe.
Topics: Marine Toxins; Shellfish; Diatoms; Seafood; Aquaculture
PubMed: 37755980
DOI: 10.3390/toxins15090554 -
Analytical Chemistry Oct 2023A "chemical linearization" approach was applied to synthetic peptide macrocycles to enable their de novo sequencing from mixtures using nanoliquid chromatography-tandem...
A "chemical linearization" approach was applied to synthetic peptide macrocycles to enable their de novo sequencing from mixtures using nanoliquid chromatography-tandem mass spectrometry (nLC-MS/MS). This approach─previously applied to individual macrocycles but not to mixtures─involves cleavage of the peptide backbone at a defined position to give a product capable of generating sequence-determining fragment ions. Here, we first established the compatibility of "chemical linearization" by Edman degradation with a prominent macrocycle scaffold based on -Cys peptides cross-linked with the -xylene linker, which are of major significance in therapeutics discovery. Then, using macrocycle libraries of known sequence composition, the ability to recover accurate de novo assignments to linearized products was critically tested using performance metrics unique to mixtures. Significantly, we show that linearized macrocycles can be sequenced with lower recall compared to linear peptides but with similar accuracy, which establishes the potential of using "chemical linearization" with synthetic libraries and selection procedures that yield compound mixtures. Sodiated precursor ions were identified as a significant source of high-scoring but inaccurate assignments, with potential implications for improving automated de novo sequencing more generally.
PubMed: 37724843
DOI: 10.1021/acs.analchem.3c01742 -
Journal of Medical Internet Research Sep 2023The innovative method of sentiment analysis based on an emotional lexicon shows prominent advantages in capturing emotional information, such as individual attitudes,...
BACKGROUND
The innovative method of sentiment analysis based on an emotional lexicon shows prominent advantages in capturing emotional information, such as individual attitudes, experiences, and needs, which provides a new perspective and method for emotion recognition and management for patients with breast cancer (BC). However, at present, sentiment analysis in the field of BC is limited, and there is no emotional lexicon for this field. Therefore, it is necessary to construct an emotional lexicon that conforms to the characteristics of patients with BC so as to provide a new tool for accurate identification and analysis of the patients' emotions and a new method for their personalized emotion management.
OBJECTIVE
This study aimed to construct an emotional lexicon of patients with BC.
METHODS
Emotional words were obtained by merging the words in 2 general sentiment lexicons, the Chinese Linguistic Inquiry and Word Count (C-LIWC) and HowNet, and the words in text corpora acquired from patients with BC via Weibo, semistructured interviews, and expressive writing. The lexicon was constructed using manual annotation and classification under the guidance of Russell's valence-arousal space. Ekman's basic emotional categories, Lazarus' cognitive appraisal theory of emotion, and a qualitative text analysis based on the text corpora of patients with BC were combined to determine the fine-grained emotional categories of the lexicon we constructed. Precision, recall, and the F1-score were used to evaluate the lexicon's performance.
RESULTS
The text corpora collected from patients in different stages of BC included 150 written materials, 17 interviews, and 6689 original posts and comments from Weibo, with a total of 1,923,593 Chinese characters. The emotional lexicon of patients with BC contained 9357 words and covered 8 fine-grained emotional categories: joy, anger, sadness, fear, disgust, surprise, somatic symptoms, and BC terminology. Experimental results showed that precision, recall, and the F1-score of positive emotional words were 98.42%, 99.73%, and 99.07%, respectively, and those of negative emotional words were 99.73%, 98.38%, and 99.05%, respectively, which all significantly outperformed the C-LIWC and HowNet.
CONCLUSIONS
The emotional lexicon with fine-grained emotional categories conforms to the characteristics of patients with BC. Its performance related to identifying and classifying domain-specific emotional words in BC is better compared to the C-LIWC and HowNet. This lexicon not only provides a new tool for sentiment analysis in the field of BC but also provides a new perspective for recognizing the specific emotional state and needs of patients with BC and formulating tailored emotional management plans.
Topics: Humans; Female; Breast Neoplasms; Sentiment Analysis; Emotions; Fear; Sadness
PubMed: 37698914
DOI: 10.2196/44897 -
Journal of Chromatography. A Oct 2023Control of N-nitrosamines has been in the focus of health authorities in recent years because many of these compounds are probable human carcinogens. In July 2018 the...
Control of N-nitrosamines has been in the focus of health authorities in recent years because many of these compounds are probable human carcinogens. In July 2018 the U.S. Food and Drug Administration (FDA) announced a recall for valsartan-containing medicines due to contamination with the carcinogenic low molecular weight nitrosamine, N-nitrosodimethylamine (NDMA). It has become clear that the problem can not only exist in the case of sartans, but in any active pharmaceutical ingredient (API)/drug product in which secondary or tertiary amines are present (as API or as impurities) and a nitrosating agent is available. The decision was made by regulators, according to which manufacturers of pharmaceutical products are obliged to perform a risk assessment for the potential presence of nitrosamines in active pharmaceutical ingredients and drug products. This resulted in a high demand for validated analytical methods that are able to quantify N-nitrosamines at low ppb levels in pharmaceutical products. In this work we have developed and validated a generic fast GC-MS method suitable for the quantitative determination of a wide range of low molecular weight nitrosamines, which include N-nitrosodiethylamine (NDEA), N-nitrosodimethylamine (NDMA), N-nitroso-diphenylamine (NDPh), N-nitrosodipropylamine (NDPA), N-nitrosomethylethylamine (NMEA), N-nitrosomorpholine (NMOR), N-nitrosopiperidine (NPIP), N-nitroso-ethylisopropylamine (EIPNA), N-nitroso-diisopropylamine (DIPNA), N-nitroso-N-methylaniline (NMPA), 1-Methyl-4-nitrosopiperazine (MeNP) and N-nitroso-pyrrolidine (NPYR). The advantage of the method is that it is possible to screen low molecular weight nitrosamines in low concentrations with a short analysis time in a wide range of APIs and drug products.
Topics: United States; Humans; Pharmaceutical Preparations; Dimethylnitrosamine; Gas Chromatography-Mass Spectrometry; Molecular Weight; Nitrosamines
PubMed: 37696123
DOI: 10.1016/j.chroma.2023.464323 -
Irish Journal of Medical Science Apr 2024Patient information leaflets (PILs) are documents that are standardized in nature and provide guidance for patients or caregivers on the safe and effective use of...
BACKGROUND
Patient information leaflets (PILs) are documents that are standardized in nature and provide guidance for patients or caregivers on the safe and effective use of medicines. Previous evidence suggests that written information is linked to enhancing the amount of information remembered. Currently, patients have become more involved in digital searches for information. However, there is variability in the quality and reliability of information obtained from the web. According to Saudi Food and Drug Authority regulations, pharmaceutical manufacturers are required to supplement each pharmaceutical product entering the Saudi market with a digital leaflet in addition to a paper leaflet. This research aimed to evaluate patients' attitudes and practices towards PILs.
METHODS
A cross-sectional study design using an anonymous online self-administered questionnaire was adopted. The study took place in Saudi Arabia between October and December 2022. A convenience sampling strategy was used to recruit the study participants. The questionnaire was adapted from previous research that investigated patient attitudes and practices towards PILs.
RESULTS
A total of 463 participants agreed to take part in the study and completed the questionnaire. Physicians were the top utilized source for getting medicine information (92.7%), followed by pharmacists (84.7%), PILs (67.4%), searching the Internet (53.6%), and consulting family and friends (31.7%). About 78% of the participants reported often or always reading PILs for new drugs (78.2%), but this percentage was lower (45.4%) for repeat prescriptions. A positive perception towards PILs was observed among the study participants. While 54.6% of the participants indicated a preference for having both paper and digital information leaflets, 33.3% reported a preference for paper leaflets, and 12% indicated a preference for digital formats.
CONCLUSION
Although patients had positive perceptions towards PILs, physicians were the top-consulted source for medicine information. Pharmacists should educate patients about the importance of referring to PILs which can also be accessed electronically in the case of a preference for a digital format, as the quality and reliability of the information obtained from the web cannot be confirmed.
Topics: Humans; Cross-Sectional Studies; Pamphlets; Reproducibility of Results; Publications; Mental Recall
PubMed: 37676583
DOI: 10.1007/s11845-023-03515-2 -
Computers in Biology and Medicine Oct 2023LncRNA-protein interactionplays an important regulatory role in biological processes. In this paper, the proposed RPIPCM based on a novel deep network model uses the...
LncRNA-protein interactionplays an important regulatory role in biological processes. In this paper, the proposed RPIPCM based on a novel deep network model uses the sequence feature encoding of both RNA and protein to predict lncRNA-protein interactions (LPIs). A negative sampling of sliding window method is proposed for solving the problem of unbalanced between positive and negative samples. The proposed negative sampling method is effective and helpful to solve the problem of data imbalance in the existing LPIs research by comparative experiments. Experimental results also show that the proposed sequence feature encoding method has good performance in predicting LPIs for different datasets of different sizes and types. In the RPI488 dataset related to animal, compared with the direct original sequence encoding model, the accuracy of sequence feature encoding model increased by 1.02%, the recall increased by 4.08%, and the value of MCC increased by 1.67%. In the case of the plant dataset ATH948, the sequence feature-based encoding demonstrated a 1.58% higher accuracy, a 1.53% higher recall, a 1.62% higher specificity, a 1.62% higher precision, and a 3.16% higher value of MCC compared to the direct original sequence-based encoding. Compared with the latest prediction work in the ZEA22133 dataset, RPIPCM is shown to be more effective with the accuracy increased by 2.23%, the recall increased by 1.78%, the specificity increased by 2.67%, the precision increased by 2.52%, and the value of MCC increased by 4.43%, which also proves the effectiveness and robustness of RPIPCM. In conclusion, RPIPCM of deep network model based on sequence feature encoding can automatically mine the hidden feature information of the sequence in the lncRNA-protein interaction without relying on external features or prior biomedical knowledge, and its low cost and high efficiency can provide a reference for biomedical researchers.
Topics: Animals; RNA, Long Noncoding; Computational Biology
PubMed: 37633089
DOI: 10.1016/j.compbiomed.2023.107366