Biological Products
These products play a crucial role in various fields, such as medicine, biotechnology, and agriculture.
Biological Products offer unique properties and functionalities that make them valuable for research, diagnostics, and therapeutic applications.
Researchers can utilize PubCompare.ai to easily locate and compare protocols related to Biological Products from published literature, preprints, and patents, helping to identify the most effeicient and effective approaches for their research needs.
By leveraging the power of PubCompare.ai, scientists can take the guesswork out of their experiments and maximize the effeciency of their biological research invovling Biological Products.
Most cited protocols related to «Biological Products»
FAERS (FDA Adverse Event Reporting System) for drugs and selected biological products.5–7
SPL (Structured Product Labeling) for drugs and selected biological products.8 ,9
RES (Recall Enterprise System), primarily recall notices, and also market withdrawals and safety alerts, for drugs, selected biological products, devices, and foods.10–12
MAUDE (Manufacturer and User Device Experience), adverse event reports for medical devices.13
FAERS is an important source of post-marketing safety surveillance for all approved drug and therapeutic biologic products in the US, and it consists of more than 20 million reports up to May 2018. Moreover, even though FAERS is an US database, it has worldwide coverage, receiving serious reports from EU and other non-US countries. Therefore, the size and worldwide coverage of this database makes it particularly robust for the conduction of spontaneous reporting data analysis.
The database contains anonymized reports submitted by healthcare professionals, consumers, manufacturers and other sources (FDA Adverse Event Reporting System (FAERS)-FDA Adverse Event Reporting System (FAERS): Latest Quarterly Data Files). The data set of FAERS consists of seven data tables, in which demographic information about the patient (e.g., age, sex, weight), source and type of the report, country of the reports, drugs with dates of start and end (when available), doses and routes of administration, AEs and their outcomes, and indications of use were contained.
In FAERS, AEs are coded using event-related information according to the Medical Dictionary for Regulatory Activities (MedDRA). MedDRA is a hierarchical dictionary that could be used to code diagnoses, symptoms and signs, investigations, surgical and medical procedures, as well as therapeutic indications, and medical/social history [32 (link),33 (link)].
In order to select the CV events of potential interest for this study, the SMQs available were screened (MedDRA Maintenance and Support Services Organization. Introductory Guide for Standardised MedDRA Queries (SMQs) Version 15.0. Chantilly (VA): International Federation of Pharmaceutical Manufacturers and associations; 2012). SMQs group all the terms representing signs, symptoms, investigations or diagnoses likely to be relevant to a defined medical condition or area of interest.
Most SMQs have two different types of search—the narrow search is composed of terms that are without any reasonable doubt related to a selected event; the broad search includes terms of the narrow one and terms that could be related to an event of interest, but for which there exists a degree of uncertainty. Thus, the narrow search is intended to be more specific, while the broad one is intended to be more sensitive. A previous work in the French Pharmacovigilance database showed that the narrow version of four different SMQs is equivalent to the broad version in terms of sensitivity, but have globally better performances in terms of positive predictive value of case identification [34 (link)]. Thus, for this study narrow version of the selected SMQs was used.
PubMed searches were performed for each drug (“drug search”) using an ontology of drug name synonyms in ChEMBL (38 ) and the National Center for Biotechnology Information (NCBI) Query Translation. PubMed searches for molecular targets (“target searches”) were performed using Boolean search terms and NCBI Query Translation. The PubMed Identifier (PMID) was recorded for each publication identified in the search.
Data associating publications with specific NIH-funded projects were obtained from the RePORTER/ExPORTER format files catalog (39 ). The “Link Tables for Project to Publication Associations” (hereafter, “Link Table”) associates PMIDs from 1980–present with projects that provided research funding and the PMID year. Each PMID was associated with a funding year corresponding to the project number and year in the Link Table. The Project Data Table provides the fiscal year cost for each project (2000–present). Costs were assigned for each funding year corresponding to the program cost in the year associated with the PMID in the Link Table. For publications with dates 1–4 y after the end of the project, costs for the final year of the project were used. The activity code associated with the core project number indicates the grant type.
Redundant identification of PMIDs and funding years occurred when a publication was identified in different drug or target searches or was cited in more than one supporting project. Consequently, each analysis required two steps, first identifying all PMIDs or project years with the specific properties being characterized and then eliminating duplicates within that subset.
Funding years were categorized as “drug” if one or more of the PMIDs associated with that project were identified in a drug search. Funding years were categorized as “target only” if every PMID associated with that project was identified through target searches. The process is illustrated in a schematic (
Data analysis and visualization were performed in PostgreSQL, Excel, and Tableau. All costs are given in constant dollars inflation-adjusted to 2016 using the US Bureau of Labor Statistics’ consumer price index (CPI) (40 ). A more detailed description of the analytical methods is provided in
Most recents protocols related to «Biological Products»
Amoxicillin, kanamycin, florfenicol, and tetracycline were purchased from China National Institute for Drug and Biological Products Control (Beijing, China). Polymyxin B sulfate (PMB) purchased from Beijing Solarbio Science & Technology co., Ltd (Beijing, China).
The main challenge virus. The CA6 strain of D3 genotype (coded CA6-WH-15, 7.7 lgCCID50/ml) was kindly provided by Wuhan Institute of Biological Products Co., Ltd. and distributed by NIFDC as the main challenge strain for neutralizing antibody.
Other virus strains. If participants routinely use other genotype strains in the laboratory, they were encouraged to also evaluate the panel of sera against other CA6 virus strains as well. Thus, in addition to the main CA6 challenge strain, Lab 1-3 also used other four detecting strains, including CA6-Gdula (Genbank no. AY421764.1, 7.8 lgCCID50/ml) of A genotype, CA6-XM (Genbank no. KR706309.1, 8.1 lgCCID50/ml) of D1 genotype, CA6-17-155 (7.4 lgCCID50/ml) of D3 genotype, and CA6-YN129 (7.0 lgCCID50/ml) of D3 genotype. Among them, Lab 1 used other two detecting strains.
For histopathology, tissue samples were harvested and fixed in 4% paraformaldehyde, processed according to standard procedures for dehydration, paraffin embedding, section cutting, and deparaffinization. The sections were stained with hematoxylin–eosin (Baso, Zhuhai, China) and observed under a light microscope (Olympus, Japan). Total inflammatory scores were assessed based on the following parameters according to previous literature:28 (link) neutrophil infiltration (0, none; 1, slight increase; 2, marked increase), fibrin deposition, submucosal neutrophil margination, submucosal edema, epithelial necrosis, epithelial ulceration (0, absent; 1, present). The percentage of pathological lesions was counted on a total scale of 0–20.
For bacterial burden measurement, spleen, and liver were harvested and then immersed in 100 µg/ml of amikacin for 1 h. Tissues were homogenized in PBS containing 0.3% Triton (Sigma, USA) for 30 min. CFU values were quantified by plating lysates with appropriate dilutions onto Salmonella-Shigella agar (Hangwei, China), followed by incubation overnight.
For immunofluorescence analysis, ceca samples were fixed with 4% paraformaldehyde at 4°C overnight, processed for paraffin embedding. Tissues were cut into 6-μm transverse sections, followed by deparaffinizing and rehydrating for antigen retrieval. The sections were blocked with 3% BSA for 30 min and then incubated with diluted primary antibodies at 4°C overnight. The antibody against EpCAM (#GB11274) was purchased from Servicebio (Wuhan, China; 1:3000 dilution). The antibody against S. Typhimurium O antigen (#S10820100) was purchased from the Lanzhou Institute of Biological Products Co., Ltd. (Lanzhou, China; 1:200 dilution). Next, the slides were incubated with Cy3 (#GB21303; 1:300 dilution) or Alexa Fluor 488 (#GB25303; 1:400 dilution) conjugated goat anti-rabbit immunofluorescent secondary antibodies purchased from Servicebio (Wuhan, China). The nuclei were stained with DAPI (#G1012) purchased from Servicebio (Wuhan, China). Slides were mounted in Anti-fade mounting medium (#G1401; Servicebio). Images were photographed using a Nikon microscope (ECLIPSE, Ts2R-FL, Tokyo, Japan).
Sample processing for transmission electron microscopy (TEM) was carried out in the School of Biology and Basic Medical Science, Medical College of Soochow University. Ceca samples were fixed in ice-cold 2.5% glutaraldehyde for at least 4 h. Samples were washed twice using 0.1 M phosphate buffer for 15 min at room temperature. Subsequently, the samples were post-fixed in 1% OsO4 for 1 h, dehydrated through an acetone series and embedded in epoxy resin. Ultra-thin sections were stained and observed using a 120 kV Transmission electron microscope (HT7700, Hitachi, Japan).
Top products related to «Biological Products»
More about "Biological Products"
These biomolecules, also known as biopharmaceuticals or biologics, play a crucial role in various fields, such as medicine, biotechnology, and agriculture.
Biological Products offer unique properties and functionalities that make them invaluable for research, diagnostics, and therapeutic applications.
Researchers can leverage the power of PubCompare.ai, an AI-driven platform, to easily locate and compare protocols related to Biological Products from published literature, preprints, and patents.
This helps identify the most efficient and effective approaches for their research needs, whether it involves cell culture media like Fetal Bovine Serum (FBS), Dulbecco's Modified Eagle's Medium (DMEM), or RPMI 1640, or commonly used solvents and reagents like Dimethyl Sulfoxide (DMSO), Acetonitrile, Methanol, and Formic Acid.
By using PubCompare.ai, scientists can take the guesswork out of their experiments and maximize the effeciency of their biological research involving Biological Products, such as proteins, enzymes, monoclonal antibodies, and vaccines.
With its intelligent protocol comparison capabilities, PubCompare.ai empowers researchers to make informed decisions and optimize their research workflows, ultimately advancing the field of biotechnology and delivering innovative solutions for medicine, agriculture, and beyond.