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Proceedings. IEEE International... Dec 2023Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This...
Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This methodology is a cornerstone driving the advancement of proteomics. In recent years, substantial strides have been made in Data-Independent Acquisition (DIA) strategies, facilitating impartial and non-targeted fragmentation of precursor ions. The DIA-generated MS/MS spectra present a formidable obstacle due to their inherent high multiplexing nature. Each spectrum encapsulates fragmented product ions originating from multiple precursor peptides. This intricacy poses a particularly acute challenge in de novo peptide/protein sequencing, where current methods are ill-equipped to address the multiplexing conundrum. In this paper, we introduce Casanovo-DIA, a deep-learning model based on transformer architecture. It deciphers peptide sequences from DIA mass spectrometry data. Our results show significant improvements over existing STOA methods, including DeepNovo-DIA and PepNet. Casanovo-DIA enhances precision by 15.14% to 34.8%, recall by 11.62% to 31.94% at the amino acid level, and boosts precision by 59% to 81.36% at the peptide level. Integrating DIA data and our Casanovo-DIA model holds considerable promise to uncover novel peptides and more comprehensive profiling of biological samples. Casanovo-DIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/Casanovo-DIA.
PubMed: 38665266
DOI: 10.1109/bibe60311.2023.00013 -
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 -
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 -
Annals of Work Exposures and Health Feb 2024Cleaning product use has been associated with adverse respiratory health effects such as asthma in cleaning staff and healthcare workers. Research in health effects from...
BACKGROUND
Cleaning product use has been associated with adverse respiratory health effects such as asthma in cleaning staff and healthcare workers. Research in health effects from cleaning products has largely depended upon collecting exposure information by questionnaires which has limitations such as recall bias and underestimation of exposure. The aim of this study was to develop a Cleaning and Hazardous Products Exposure Logging (CHaPEL) app with a barcode scanner and to test the feasibility of this app with university cleaners.
METHODS
The CHaPEL app was developed to collect information on demographics, individual product information, and exposure information. It also included an ease-of-use survey. A pilot study with university cleaning workers was undertaken in which cleaning workers scanned each product after use and answered the survey. Respiratory hazards of cleaning substances in the scanned cleaning products were screened by safety data sheets, a Quantitative Structure-Activity Relationship model and an asthmagen list established by an expert group in the US.
RESULTS
Eighteen university cleaners participated in this study over a period of 5 weeks. In total, 77 survey responses and 6 cleaning products were collected and all reported that using the app was easy. The most frequently used product was a multi-surface cleaner followed by a disinfectant. Out of 14 substances in cleaning products, ethanolamine and Alkyl (C12-16) dimethyl benzyl ammonium chloride were found as respiratory hazardous substances.
CONCLUSION
The CHaPEL app is a user-friendly immediate way to successfully collect exposure information using the barcodes of cleaning products. This tool could be useful for future epidemiological studies focused on exposure assessment with less interruption to the workers.
Topics: Humans; Occupational Exposure; Pilot Projects; Mobile Applications; Hazardous Substances; Asthma
PubMed: 38142412
DOI: 10.1093/annweh/wxad082 -
Tobacco Control Sep 2023Tobacco companies have used below-the-line marketing in novel ways to promote their brands to youth in low/middle-income countries in Southeast Asia. This study explores...
INTRODUCTION
Tobacco companies have used below-the-line marketing in novel ways to promote their brands to youth in low/middle-income countries in Southeast Asia. This study explores how young male smokers in Cambodia experience below-the-line marketing strategies.
METHODS
Convenience sampling was used to recruit 147 young male smokers (18-24 years) in Cambodia in early 2020. Local research assistants conducted mixed-methods interviews with participants in Khmer or English. Participants recalled exposure to below-the-line marketing strategies and provided in-depth descriptions about their experiences with individual sales promotions. Quantitative data were analysed using descriptive statistics and qualitative data were analysed using thematic analysis.
RESULTS
54% of participants recalled exposure to at least one below-the-line marketing strategy, including point-of-sale promotions (32.7%), individual sales promotions (27.9%) and online advertising (14.3%). Participants described individual sales promotions in public settings, and recalled that promoters were mostly female, attractive and targeted young males. Tactics used to encourage young people to accept promotional offers included free cigarettes and sample packets, swapping current cigarettes for new brands and collecting consumer details after interviewing. The brands and product features of cigarettes being promoted were readily described by participants.
CONCLUSION
This study provides evidence that illegal below-the-line marketing is still occurring in Cambodia, and increased monitoring and enforcement of advertising restrictions is needed.
Topics: Adolescent; Male; Humans; Female; Advertising; Nicotiana; Smokers; Cambodia; Smoking; Tobacco Industry; Marketing; Tobacco Products
PubMed: 35177539
DOI: 10.1136/tobaccocontrol-2021-057063 -
Journal of Oncology Pharmacy Practice :... Apr 2024Increasing use of expensive oral anticancer medicines comes with the downside of a financial and environmental burden, partially caused by unused medication. Returned...
INTRODUCTION
Increasing use of expensive oral anticancer medicines comes with the downside of a financial and environmental burden, partially caused by unused medication. Returned oral anticancer medicine to the pharmacy could be considered for redispensing providing guaranteed quality. This study aimed to identify and implement quality aspects and criteria for redispensing oral anticancer medicine in daily pharmacy practice.
METHODS
A systematic analysis was conducted to determine the eligibility of oral anticancer medicine for redispensing. Over a one-year period, the number of returned oral anticancer medicine accepted for redispensing was quantified, and the reduction in financial waste and environmental burden calculated based on this assessment.
RESULTS
Four categories of quality aspects were identified for determining the eligibility of oral anticancer medicine for redispensing: Product presentation suitability (stability characteristics, storage requirements), physical condition (unopened or opened secondary or primary packaging, visual appearance), authentication (Falsified Medicines Directive, confirmation of initial dispense, recall), and additional aspects (remaining shelf life, period of storage in uncontrolled conditions). A standardized procedure for redispensing was implemented in daily pharmacy practice. During the study period, 10,415 oral anticancer medicine dose units out of 13,210 returns (79%) were accepted for redispensing. The total value of oral anticancer medicine accepted for redispensing was €483,301, accounting for 0.9% of the total value dispensed during this period. Furthermore, the potential reduction in environmental burden was estimated at 1132.1 g of potent active pharmaceutical ingredient.
CONCLUSIONS
By implementing strict procedures considering all relevant quality aspects, redispensing of oral anticancer medicine can be successfully implemented into daily pharmacy practice, resulting in a significant reduction in financial waste and environmental burden.
Topics: Antineoplastic Agents; Humans; Administration, Oral; Drug Costs; Drug Storage
PubMed: 37192749
DOI: 10.1177/10781552231176199 -
Analytical and Bioanalytical Chemistry Jul 2023Bear bile powder (BBP) is a valuable animal-derived product with a huge adulteration problem on market. It is a crucially important task to identify BBP and its...
Bear bile powder (BBP) is a valuable animal-derived product with a huge adulteration problem on market. It is a crucially important task to identify BBP and its counterfeit. Electronic sensory technologies are the inheritance and development of traditional empirical identification. Considering that each drug has its own specific odor and taste characteristics, electronic tongue (E-tongue), electronic nose (E-nose) and GC-MS were used to evaluate the aroma and taste of BBP and its common counterfeit. Two active components of BBP, namely tauroursodeoxycholic acid (TUDCA) and taurochenodeoxycholic acid (TCDCA) were measured and linked with the electronic sensory data. The results showed that bitterness was the main flavor of TUDCA in BBP, saltiness and umami were the main flavor of TCDCA. The volatiles detected by E-nose and GC-MS were mainly aldehydes, ketones, alcohols, hydrocarbons, carboxylic acids, heterocyclic, lipids, and amines, mainly earthy, musty, coffee, bitter almond, burnt, pungent odor descriptions. Four different machine learning algorithms (backpropagation neural network, support vector machine, K-nearest neighbor, and random forest) were used to identify BBP and its counterfeit, and the regression performance of these four algorithms was also evaluated. For qualitative identification, the algorithm of random forest has shown the best performance, with 100% accuracy, precision, recall and F1-score. Also, the random forest algorithm has the best R and the lowest RMSE in terms of quantitative prediction.
Topics: Animals; Electronic Nose; Ursidae; Powders; Bile; Tongue
PubMed: 37199792
DOI: 10.1007/s00216-023-04740-5 -
Heart Rhythm Jun 2024
Review
Topics: Humans; Defibrillators, Implantable; Medical Device Recalls; Pacemaker, Artificial; Equipment Failure; Cardiac Electrophysiology
PubMed: 38403233
DOI: 10.1016/j.hrthm.2024.02.039 -
F1000Research 2023Enteric coating films in acidic labile tablets protect the drug molecule from the acidic environment of the stomach. However, variations in the excipients used in the...
Enteric coating films in acidic labile tablets protect the drug molecule from the acidic environment of the stomach. However, variations in the excipients used in the coating formulation may affect their ability to provide adequate protection. This study is the first to investigate the potential effects of coating materials on the protective functionality of enteric coating films for pantoprazole (PNZ) generic tablets after their recall from the market. A comparative analysis was conducted between generic and branded PNZ products, using pure drug powder for identification. The release of the drug was evaluated in different pH media. The study also utilized various analytical and thermal techniques, including differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared (FTIR), and confocal Raman microscopy. The assessment results revealed significant variations in the release profile for the generic product in acidic media at 120 min. DSC and TGA thermal profile analyses showed slight variation between the two products. XRD analysis exhibited a noticeable difference in peak intensity for the generic sample, while SEM revealed smaller particle sizes in the generic product. The obtained spectra profile for the generic product displayed significant variation in peaks and band intensity, possibly due to impurities. These findings suggest that the excipients used in the enteric coating film of the generic product may have affected its protective functionality, leading to premature drug release in acidic media. Additionally, the presence of polysorbate 80 (P-80) in the brand product might improve the properties of the enteric coating film due to its multi-functionality. : In conclusion, the excipients used in the brand product demonstrated superior functionality in effectively protecting the drug molecule from acidic media through the enteric coating film, as compared to the generic version.
Topics: Pantoprazole; Drug Liberation; Excipients; Solubility; Tablets; Stomach
PubMed: 38596002
DOI: 10.12688/f1000research.140607.1 -
Critical Reviews in Food Science and... 2024Ensuring the safety of food products is critical to food production and processing. In food processing and production, several standard guidelines are implemented to... (Review)
Review
Ensuring the safety of food products is critical to food production and processing. In food processing and production, several standard guidelines are implemented to achieve acceptable food quality and safety. This notwithstanding, due to human limitations, processed foods are often contaminated either with microorganisms, microbial byproducts, or chemical agents, resulting in the compromise of product quality with far-reaching consequences including foodborne diseases, food intoxication, and food recall. Transitioning from manual food processing to automation-aided food processing (smart food processing) which is guided by artificial intelligence will guarantee the safety and quality of food. However, this will require huge investments in terms of resources, technologies, and expertise. This study reviews the potential of artificial intelligence in food processing. In addition, it presents the technologies and methods with potential applications in implementing automated technology-aided processing. A conceptual design for an automated food processing line comprised of various operational layers and processes targeted at enhancing the microbial safety and quality assurance of liquid foods such as milk and beverages is elaborated.
Topics: Animals; Humans; Artificial Intelligence; Automation; Beverages; Food Handling; Food Microbiology; Food Safety; Milk; Technology
PubMed: 36066463
DOI: 10.1080/10408398.2022.2118660