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Foods (Basel, Switzerland) Feb 2023China's outbreak related to cold-chain aquatic product quality and safety in 2020 caused public panic and further led to a crisis in China's aquatic industry. This paper...
China's outbreak related to cold-chain aquatic product quality and safety in 2020 caused public panic and further led to a crisis in China's aquatic industry. This paper uses topic clustering and emotion analysis methods to text-mine the comments of netizens on Sina Weibo to study the main features of the public's views on the administration's crisis management measures and to provide experience for future imported food safety management. The findings show that for the imported food safety incident and the risk of virus infection, the public response had four types of characteristics: a higher proportion of negative emotion; a wider range of information demand; attention paid to the whole imported food industry chain; and a differentiated attitude towards control policies. Based on the online public response, countermeasures to further improve the management ability of imported food safety crises are proposed as follows: the government should pay active attention to the development trend of online public opinion; work more on exploring the content of public concern and emotion; strengthen the risk assessment of imported food and establish the classification and management measures of imported food safety events; construct the imported food safety traceability system; build a special recall mechanism for imported food safety; and improve the cooperation between government and media, enhancing the public's trust in policies.
PubMed: 36900551
DOI: 10.3390/foods12051033 -
Veterinary Immunology and... Apr 2023Lokivetmab (Cytopoint®, Zoetis) is a canine monoclonal antibody that specifically binds and neutralizes interleukin (IL)-31. Lokivetmab is approved for use in dogs for...
Lokivetmab (Cytopoint®, Zoetis) is a canine monoclonal antibody that specifically binds and neutralizes interleukin (IL)-31. Lokivetmab is approved for use in dogs for the treatment of atopic dermatitis (AD) and allergic dermatitis. The laboratory safety of lokivetmab was evaluated in 2 studies by adapting the science-based, case-by-case approach used for preclinical and early clinical safety evaluation of human biopharmaceuticals. The main objectives were to demonstrate the safety of lokivetmab in healthy laboratory Beagle dogs by using integrated clinical, morphologic, and functional evaluations. In Study 1, dogs were treated s.c. with saline or lokivetmab at 3.3 mg/kg (1X, label dose) or 10 mg/kg (3X intended dose) for 7 consecutive monthly doses, with terminal pathology and histology assessments. In Study 2, the functional immune response was demonstrated in naïve dogs using the T-cell dependent antibody response (TDAR) test with 2 different dose levels of unadjuvanted keyhole limpet hemocyanin (KLH) as the model immunogen. The primary endpoint was anti-KLH IgG antibody titer, and secondary endpoints were ex vivo IL-2 enzyme-linked immunospot (ELISpot) and peripheral blood mononuclear cell lymphoproliferation assays. Both studies included monitoring general health, periodic veterinary clinical evaluations, serial clinical pathology and toxicokinetics, and monitoring for anti-drug antibodies. In both studies, the health of dogs receiving lokivetmab was similar to controls, with no treatment-related changes uncovered. Extensive pathology evaluations of immune tissues (Study 1) revealed no lokivetmab-related morphologic changes, and in dogs treated at 10 mg/kg lokivetmab, immunization with the model antigen KLH did not impair the functional antibody or T-cell recall responses. There were no immunogenicity-related or hypersensitivity-related responses observed in either study. These studies in healthy laboratory dogs showed that lokivetmab was well-tolerated, did not produce any treatment-related effects, and had no effect on immune system morphology or its functional response. These studies also demonstrated the utility of a science-based case-by-case approach to the safety evaluation of a veterinary biopharmaceutical product.
Topics: Animals; Dogs; Humans; Antibodies, Monoclonal; Antibody Formation; Dermatitis, Atopic; Dog Diseases; Hemocyanins; Leukocytes, Mononuclear; T-Lymphocytes; Interleukins
PubMed: 36842258
DOI: 10.1016/j.vetimm.2023.110574 -
Blockchain in Healthcare Today 2022Regulating and monitoring a traditionally fragmented pharma supply chain has been a global challenge for decades. Without a trusted system and strong collaboration...
Regulating and monitoring a traditionally fragmented pharma supply chain has been a global challenge for decades. Without a trusted system and strong collaboration between stakeholders, threats such as counterfeits can easily intercept the supply chain and cause monumental disruptions. Today, the COVID-19 pandemic has accelerated the need for greater data transparency, better deployment of technology, and improved ways of connecting stakeholder information along the supply chain. There is a need for improved ways of working to help build up supply chain resilience, and one way is by implementing better end-to-end traceability using blockchain technology such as Hyperledger Fabric. This paper will explore the business value that blockchain brings to the pharma supply chain with better end-to-end traceability, using the example of an industry-grade blockchain solution called eZTracker. Through six key features, pharmaceutical manufacturers, patients, and Healthcare Practitioners (HCPs) can now participate in data sharing, with extended use cases of integrating blockchain with warehouse platforms, a patient-facing mobile application, and an interactive dashboard for real-time verification and data transparency. Beyond anti-counterfeit verification, other potential use cases include effective product recall management, cold chain monitoring, e-product information, and more. The effectiveness of a traceability solution is heavily dependent on the amount of data collected and is affected by poor adoption and scalability. Existing limitations that need to be addressed include the lack of mandated serialization in Asia and blockchain interoperability. To maximize the value of blockchain, collaboration is the key. Pharmaceutical manufacturers need to invest in new technologies, such as blockchain, to help them break out of data silos and operationalize data to build supply chain resilience. Pharmaceutical supply chain is the backbone of a US$1.27 trillion industry, but because of its highly complex and fragmented nature, it is hard to regulate and protect, and this makes it a valuable target for opportunistic parties such as counterfeiters looking to profit. As a result of the COVID-19 pandemic, there has been greater emphasis on transparency of data and connecting stakeholders along the pharma supply chain in real-time in the last few years. With the introduction of blockchain technology, companies are now able to implement solutions with more effective track and trace results, providing quality assurance to pharmaceutical manufacturers, patients, and Healthcare Practitioners (HCPs), and even improving operational efficiencies. This paper seeks to explore the positive business impact of end-to-end traceability using blockchain technology, and the effects it brings about, such as improving supply chain resilience and combating counterfeits, as seen in successful live use cases in Asia.
PubMed: 36779026
DOI: 10.30953/bhty.v5.231 -
JAMA Network Open Feb 2023
Topics: Humans; Varenicline; Tobacco Use Disorder; Nicotinic Agonists; Smoking Cessation
PubMed: 36745457
DOI: 10.1001/jamanetworkopen.2022.54655 -
Journal of Pharmaceutical Sciences Jun 2023Receptor binding domain (RBD) of SARS-CoV-2 is a prime vaccine target against which neutralizing antibody responses are directed. Purified RBD as a vaccine candidate...
Receptor binding domain (RBD) of SARS-CoV-2 is a prime vaccine target against which neutralizing antibody responses are directed. Purified RBD as a vaccine candidate warrants administration of multiple doses along with adjuvants and use of delivery systems to improve its immunogenicity. The present investigation examines the immunogenicity of RBD delivered by biodegradable polymer particles from single dose administration. Mice upon single point immunization of RBD entrapped microparticles generated improved antibody response. The polymer microparticles showed better temperature stability and could be stored at 37 degrees for one month without any considerable loss of immunogenicity. Further, immunization with microparticles could elicit memory antibody response upon challenge after four months of single dose administration. Thus, using microparticles entrapping RBD as a vaccine candidate confer improved immunogenicity, temperature stability and recall response. These thermostable microparticles seem to be a potentially cost-effective approach which can help in dose reduction, provide a wider access of vaccines and accelerate the end of global pandemic.
Topics: Animals; Mice; SARS-CoV-2; COVID-19; Immunization; Vaccination; Antibodies, Neutralizing; Polymers; Antibodies, Viral
PubMed: 36736778
DOI: 10.1016/j.xphs.2023.01.024 -
Analytical Chemistry Feb 2023Mass spectrometry is a vital tool in the analytical chemist's toolkit, commonly used to identify the presence of known compounds and elucidate unknown chemical...
Mass spectrometry is a vital tool in the analytical chemist's toolkit, commonly used to identify the presence of known compounds and elucidate unknown chemical structures. All of these applications rely on having previously measured spectra for known substances. Computational methods for predicting mass spectra from chemical structures can be used to augment existing spectral databases with predicted spectra from previously unmeasured molecules. In this paper, we present a method for prediction of electron ionization-mass spectra (EI-MS) of small molecules that combines physically plausible substructure enumeration and deep learning, which we term rapid approximate subset-based spectra prediction (RASSP). The first of our two models, , produces a probability distribution over chemical subformulae to achieve a state-of-the-art forward prediction accuracy of 92.9% weighted (Stein) dot product and database lookup recall (within top 10 ranked spectra) of 98.0% when evaluated against the NIST 2017 Mass Spectral Library. The second model, , produces a probability distribution over vertex subsets of the original molecule graph to achieve similar forward prediction accuracy and superior generalization in the high-resolution, low-data regime. Spectra predicted by our best model improve upon the previous state-of-the-art spectral database lookup error rate by a factor of 2.9×, reducing the lookup error (top 10) from 5.7 to 2.0%. Both models can train on and predict spectral data at arbitrary resolution. Source code and predicted EI-MS spectra for 73.2M small molecules from PubChem will be made freely accessible online.
PubMed: 36695638
DOI: 10.1021/acs.analchem.2c02093 -
Journal of Big Data 2023There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as...
There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram and Facebook, e-commerce websites like Amazon, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all these sources produce massive amounts of text data. The interpretation of such data can help business owners analyze the social outlook of their product, brand, or service and take necessary steps. The development of a consumer review summarization model using Natural Language Processing (NLP) techniques and Long short-term memory (LSTM) to present summarized data and help businesses obtain substantial insights into their consumers' behavior and choices is the topic of this research. A hybrid approach for analyzing sentiments is presented in this paper. The process comprises pre-processing, feature extraction, and sentiment classification. Using NLP techniques, the pre-processing stage eliminates the undesirable data from input text reviews. For extracting the features effectively, a hybrid method comprising review-related features and aspect-related features has been introduced for constructing the distinctive hybrid feature vector corresponding to each review. The sentiment classification is performed using the deep learning classifier LSTM. We experimentally evaluated the proposed model using three different research datasets. The model achieves the average precision, average recall, and average F1-score of 94.46%, 91.63%, and 92.81%, respectively.
PubMed: 36686621
DOI: 10.1186/s40537-022-00680-6 -
Frontiers in Public Health 2022Botulism outbreaks due to commercial products are extremely rare in the European Union. Here we report on the first international outbreak of foodborne botulism caused...
Botulism outbreaks due to commercial products are extremely rare in the European Union. Here we report on the first international outbreak of foodborne botulism caused by commercial salt-cured, dried roach (). Between November and December 2016, an outbreak of six foodborne botulism type E cases from five unrelated households was documented in Germany and Spain. The outbreak involved persons of Russian and Kazakh backgrounds, all consumed unheated salt-cured, dried roach-a snack particularly favored in Easter-European countries. The implicated food batches had been distributed by an international wholesaler and were recalled from Europe-wide outlets of a supermarket chain and other independent retailers. Of interest, and very unlike to other foodborne disease outbreaks which usually involves a single strain or virus variant, different strains and toxin variants could be identified even from a single patient's sample. Foodborne botulism is a rare but potentially life-threatening disease and almost exclusively involves home-made or artisan products and thus, outbreaks are limited to individual or few cases. As a consequence, international outbreaks are the absolute exception and this is the first one within the European Union. Additional cases were likely prevented by a broad product recall, underscoring the importance of timely public health action. Challenges and difficulties on the diagnostic and epidemiological level encountered in the outbreak are highlighted.
Topics: Animals; Humans; Botulism; European Union; Clostridium botulinum; Disease Outbreaks; Cyprinidae; Sodium Chloride, Dietary
PubMed: 36684858
DOI: 10.3389/fpubh.2022.1039770 -
JAMA Jan 2023Most regulated medical devices enter the US market via the 510(k) regulatory submission pathway, wherein manufacturers demonstrate that applicant devices are...
IMPORTANCE
Most regulated medical devices enter the US market via the 510(k) regulatory submission pathway, wherein manufacturers demonstrate that applicant devices are "substantially equivalent" to 1 or more "predicate" devices (legally marketed medical devices with similar intended use). Most recalled medical devices are 510(k) devices.
OBJECTIVE
To examine the association between characteristics of predicate medical devices and recall probability for 510(k) devices.
DESIGN, SETTING, AND PARTICIPANTS
In this exploratory cross-sectional analysis of medical devices cleared by the US Food and Drug Administration (FDA) between 2003 and 2018 via the 510(k) regulatory submission pathway, linear probability models were used to examine associations between a 510(k) device's recall status and characteristics of its predicate medical devices. Public documents for the 510(k) medical devices were collected using FDA databases. A text extraction algorithm was applied to identify predicate medical devices cited in 510(k) regulatory submissions. Algorithm-derived metadata were combined with 2003-2020 FDA recall data.
EXPOSURES
Citation of predicate medical devices with certain characteristics in 510(k) regulatory submissions, including the total number of predicate medical devices cited by the applicant device, the age of the predicate medical devices, the lack of similarity of the predicate medical devices to the applicant device, and the recall status of the predicate medical devices.
MAIN OUTCOMES AND MEASURES
Class I or class II recall of a 510(k) medical device between its FDA regulatory clearance date and December 31, 2020.
RESULTS
The sample included 35 176 medical devices, of which 4007 (11.4%) were recalled. The applicant devices cited a mean of 2.6 predicate medical devices, with mean ages of 3.6 years and 7.4 years for the newest and oldest, respectively, predicate medical devices. Of the applicant devices, 93.9% cited predicate medical devices with no ongoing recalls, 4.3% cited predicate medical devices with 1 ongoing class I or class II recall, 1.0% cited predicate medical devices with 2 ongoing recalls, and 0.8% cited predicate medical devices with 3 or more ongoing recalls. Applicant devices citing predicate medical devices with 3 or more ongoing recalls were significantly associated with a 9.31-percentage-point increase (95% CI, 2.84-15.77 percentage points) in recall probability compared with devices without ongoing recalls of predicate medical devices, or an 81.2% increase in recall probability relative to the mean recall probability. A 1-SD increase in the total number of predicate medical devices cited by the applicant device was significantly associated with a 1.25-percentage-point increase (95% CI, 0.62-1.87 percentage points) in recall probability, or an 11.0% increase in recall probability relative to the mean recall probability. A 1-SD increase in the newest age of a predicate medical device was significantly associated with a 0.78-percentage-point decrease (95% CI, 1.29-0.30 percentage points) in recall probability, or a 6.8% decrease in recall probability relative to the mean recall probability.
CONCLUSIONS AND RELEVANCE
This exploratory cross-sectional study of 510(k) medical devices cleared by the FDA between 2003 and 2018 demonstrated significant associations between 510(k) submission characteristics and recalls of medical devices. Further research is needed to understand the implications of these associations.
Topics: Algorithms; Cross-Sectional Studies; Databases, Factual; Device Approval; Medical Device Recalls; United States; United States Food and Drug Administration
PubMed: 36625811
DOI: 10.1001/jama.2022.22974 -
JAMA Jan 2023In the US, nearly all medical devices progress to market under the 510(k) pathway, which uses previously authorized devices (predicates) to support new authorizations....
IMPORTANCE
In the US, nearly all medical devices progress to market under the 510(k) pathway, which uses previously authorized devices (predicates) to support new authorizations. Current regulations permit manufacturers to use devices subject to a Class I recall-the FDA's most serious designation indicating a high probability of adverse health consequences or death-as predicates for new devices. The consequences for patient safety are not known.
OBJECTIVE
To determine the risk of a future Class I recall associated with using a recalled device as a predicate device in the 510(k) pathway.
DESIGN AND SETTING
In this cross-sectional study, all 510(k) devices subject to Class I recalls from January 2017 through December 2021 (index devices) were identified from the FDA's annual recall listings. Information about predicate devices was extracted from the Devices@FDA database. Devices authorized using index devices as predicates (descendants) were identified using a regulatory intelligence platform. A matched cohort of predicates was constructed to assess the future recall risk from using a predicate device with a Class I recall.
MAIN OUTCOMES AND MEASURES
Devices were characterized by their regulatory history and recall history. Risk ratios (RRs) were calculated to compare the risk of future Class I recalls between devices descended from predicates with matched controls.
RESULTS
Of 156 index devices subject to Class I recall from 2017 through 2021, 44 (28.2%) had prior Class I recalls. Predicates were identified for 127 index devices, with 56 (44.1%) using predicates with a Class I recall. One hundred four index devices were also used as predicates to support the authorization of 265 descendant devices, with 50 index devices (48.1%) authorizing a descendant with a Class I recall. Compared with matched controls, devices authorized using predicates with Class I recalls had a higher risk of subsequent Class I recall (6.40 [95% CI, 3.59-11.40]; P<.001).
CONCLUSIONS AND RELEVANCE
Many 510(k) devices subjected to Class I recalls in the US use predicates with a known history of Class I recalls. These devices have substantially higher risk of a subsequent Class I recall. Safeguards for the 510(k) pathway are needed to prevent problematic predicate selection and ensure patient safety.
Topics: Humans; Cross-Sectional Studies; Databases, Factual; Device Approval; Medical Device Recalls; United States; United States Food and Drug Administration
PubMed: 36625810
DOI: 10.1001/jama.2022.23279