Hasil untuk "Biotechnology"

Johnathan Daniel Maxey, Neil D. Hartstein, Dane Dickinson et al.

Abstract Aquaculture’s contribution to global N2O emissions is poorly constrained and often reliant on supply chain/industrial emissions/life-cycle analyses which generalise system responses to farm-derived inputs and contain few examples of direct measurements made in situ. Among the studies that do report aquaculture associated N2O emissions the focus has been on pond culture and wetlands systems rather than open marine systems. Our study examined the effects of open system aquaculture culture on water column N2O cycling in two hydrodynamically contrasting southern hemisphere systems: the heavily stratified Macquarie Harbour, Tasmania, Australia and the semi-enclosed but well-mixed Big Glory Bay, New Zealand. Significant, but localised, N2O undersaturation was observed under the active salmon farm in the heavily stratified Macquarie Harbour during the peak feeding season, but not under fallowed salmon farms or the non-farmed areas. This was observed in a low-oxygen but not anoxic water column. Water column N2O was either in equilibrium with the atmosphere or supersaturated in all other instances. In Big Glory Bay N2O undersaturation was observed during winter and spring sampling surveys that generally persisted across the bay and resulted in removal of atmospheric N2O. The specific mechanisms of N2O loss are still uncertain but is likely driven by a combination of particle associated denitrification activity in farm waste plumes, denitrification/DNRA in sediments and on the detritus covered mussel shells and lines. Overall, this study demonstrates that industry impacts to N2O cycling can include loss dynamics which have previously been unreported. Therefore, global estimates of N2O emissions from aquaculture may be significantly overestimated.

Mahmoud M. Omran, Mohamed Emam, Mariam Gamaleldin et al.

Abstract Background Breast cancer (BC) is a critical cause of cancer-related death globally. The heterogeneity of BC subtypes poses challenges in understanding molecular mechanisms, early diagnosis, and disease management. Recent studies suggest that integrating multi-omics layers can significantly enhance BC subtype identification. However, evaluating different multi-omics integration methods for BC subtyping remains ambiguous. Methods In this study, we conducted a multi-omics integration analysis on 960 BC patient samples, incorporating three omics layers: Host transcriptomics, epigenomics, and shotgun microbiome. We compared two integration approaches the statistical-based approach (MOFA+) and a deep learning-based approach (MOGCN) for this integration. We evaluated both methods using complementary evaluation criteria. First, we assessed the ability of selected features to discriminate between BC subtypes using both linear and nonlinear classification models. Second, we analyzed the biological relevance of the selected features to key BC pathways, focusing on transcriptomics-driven insights. Results Our results showed that MOFA+ outperformed MOGCN in feature selection, achieving the highest F1 score (0.75) in the nonlinear classification model, with MOFA+ also identifying 121 relevant pathways compared to 100 from MOGCN. Notably, one of the key pathways Fc gamma R-mediated phagocytosis and the SNARE pathway was implicated, offering insights into immune responses and tumor progression. Conclusion These findings suggest that MOFA+ is a more effective unsupervised tool for feature selection in BC subtyping. Our study underscores the potential of multi-omics integration to improve BC subtype prediction and provides critical insights for advancing personalized medicine in BC.

STEVEN J. ZWEIG

Xinyue Li, Zhankun Xiong, Wen Zhang et al.

Abstract The prediction of drug‐drug interactions (DDIs) is a crucial task for drug safety research, and identifying potential DDIs helps us to explore the mechanism behind combinatorial therapy. Traditional wet chemical experiments for DDI are cumbersome and time‐consuming, and are too small in scale, limiting the efficiency of DDI predictions. Therefore, it is particularly crucial to develop improved computational methods for detecting drug interactions. With the development of deep learning, several computational models based on deep learning have been proposed for DDI prediction. In this review, we summarized the high‐quality DDI prediction methods based on deep learning in recent years, and divided them into four categories: neural network‐based methods, graph neural network‐based methods, knowledge graph‐based methods, and multimodal‐based methods. Furthermore, we discuss the challenges of existing methods and future potential perspectives. This review reveals that deep learning can significantly improve DDI prediction performance compared to traditional machine learning. Deep learning models can scale to large‐scale datasets and accept multiple data types as input, thus making DDI predictions more efficient and accurate.

NI Xingting, SUN Xizhen, LIU Huaichen, CHEN Jie, HUANG Liya, XIONG Yaqing, LI Qiang, ZHANG Fan, ZHANG Xueyuan

In order to analyze the off-flavor-bran flavor substances in strong-flavor (Nongxiangxing) Baijiu, the sensory evaluation of normal and bran flavor raw liquor was conducted according to the relevant national standards, and the volatile flavor substances in normal and bran flavor raw liquor were detected by gas chromatography-mass spectrometry (GC-MS) and gas chromatography-olfactory-mass spectrometry (GC-O-MS) technology, and using partial least square-discriminant analysis (PLS-DA) to establish model. Important aroma compounds and important potential markers for the difference between normal and bran flavor raw liquor were screened using odor activity value (OAV) and variable importance in the projection (VIP) values, and the bran flavor substances were identified by aroma addition and omission tests. The results showed that the aroma of bran flavor in bran raw liquor were stronger compared with normal raw liquor, but the pit aroma, grains aroma, aging aroma and sour aroma were weaker. A total of 135 volatile flavor substances were detected in normal and bran raw liquor, including 64 important aroma compounds (OAV>1), and 32 important potential markers (VIP>1) causing the difference between normal and bran flavor of strong-flavor Baijiu were screened out by PLS-DA. The results of aroma addition and omission tests showed that the main cause of bran flavor of strong-flavor Baijiu was the interaction of soil flavin, diethyl succinate and ethyl benzoate.

Habtamu Hawaz, Mestawet Taye, Diriba Muleta

Food safety remains the main health concern in the developing countries. Thus, the major purpose of the present study was to characterize and determine antibiotic susceptibility patterns of Listeria monocytogenes from raw milk samples collected from southern Ethiopia. Two hundred and forty raw cow milk samples were collected from dairy farms and smallholder dairy producers using a simple random sampling technique and analyzed by cultural and multiplex PCR methods. The antimicrobial susceptibility profile of L. monocytogenes was evaluated using the standard disk diffusion method. Over 28% of the samples were found positive for Listeria spp., of which 17 (7.08%) isolates were identified as L. monocytogenes after morphological and biochemical confirmation. The prevalence of L. monocytogenes was 6.02% in Hawassa city, 5.56% in Dale district, and 9.41% in Arsi Negele district. L. monocytogenes was higher in the wet season (9.32%) than in the dry season (4.92%). The gene for Listeria specific 16S rRNA was detected in all the 17 examined isolates, while hlyA and iapA were only found in 11 of them. Furthermore, no isolate was identified to have the prfA, actA, or plcA genes. Antimicrobial resistance profiling revealed that all the L. monocytogenes isolates were resistant to nalidixic acid (100%), followed by erythromycin (88.24%). However, all the L. monocytogenes isolates were sensitive to vancomycin, gentamicin, and sulfamethoxazole. Raw cow milk is a potential source of L. monocytogenes and it poses a threat to human and animal health. Therefore, it is crucial that dairy producers and vendors of raw milk in the study areas should take considerable precautions to prevent Listeria species from contaminating raw fresh milk.

Muhamad-Firus Bin Noor-Hassim, Chuen L. NG, Han M. Teo et al.

As the global human population continues to grow, the demand for food rises accordingly. Unfortunately, anthropogenic activities, climate change, and the release of gases from the utilization of synthetic fertilizers and pesticides are causing detrimental effects on sustainable food production and agroecosystems. Despite these challenges, there remain underutilized opportunities for sustainable food production. This review discusses the advantages and benefits of utilizing microbes in food production. Microbes can be used as alternative food sources to directly supply nutrients for both humans and livestock. Additionally, microbes offer higher flexibility and diversity in facilitating crop productivity and agri-food production. Microbes function as natural nitrogen fixators, mineral solubilizers, nano-mineral synthesizers, and plant growth regulator inducers, all of which promote plant growth. They are also active organisms in degrading organic materials and remediating heavy metals and pollution in soils, as well as soil-water binders. In addition, microbes that occupy the plant rhizosphere release biochemicals that have nontoxic effects on the host and the environment. These biochemicals could act as biocides in controlling agricultural pests, pathogens, and diseases. Therefore, it is important to consider the use of microbes for sustainable food production.

Luca Mazzoni, Franco Capocasa, Maria Teresa Ariza Fernández

Consumer awareness regarding the significance of a well-balanced diet in preventing chronic diseases has increased significantly in recent years [...]

Steven J. Zweig

Steven J. Zweig

Guangze Yang, Yun Liu, Jisi Teng et al.

Fluorescence labelling is often used for tracking nanoparticles, providing a convenient assay for monitoring nanoparticle drug delivery. However, it is difficult to be quantitative, as many factors affect the fluorescence intensity. Förster resonance energy transfer (FRET), taking advantage of the energy transfer from a donor fluorophore to an acceptor fluorophore, provides a distance ruler to probe NP drug delivery. This article provides a review of different FRET approaches for the ratiometric monitoring of the self-assembly and formation of nanoparticles, their in vivo fate, integrity and drug release. We anticipate that the fundamental understanding gained from these ratiometric studies will offer new insights into the design of new nanoparticles with improved and better-controlled properties.

Jun-Fa Liang, Cheng Peng, Peiyu Li et al.

Antibiotics, as veterinary drugs, have made extremely important contributions to disease prevention and treatment in the animal breeding industry. However, the accumulation of antibiotics in animal food due to their overuse during animal feeding is a frequent occurrence, which in turn would cause serious harm to public health when they are consumed by humans. Antibiotic residues in food have become one of the central issues in global food safety. As a safety measure, rapid and effective analytical approaches for detecting these residues must be implemented to prevent contaminated products from reaching the consumers. Traditional analytical methods, such as liquid chromatography, liquid chromatography mass spectrometry, and capillary electrophoresis, involve time-consuming sample preparation and complicated operation and require expensive instrumentation. By comparison, surface-enhanced Raman spectroscopy (SERS) has excellent sensitivity and remarkably enhanced target recognition. Thus, SERS has become a promising alternative analytical method for detecting antibiotic residues, as it can provide an ultrasensitive fingerprint spectrum for the rapid and noninvasive detection of trace analytes. In this study, we comprehensively review the recent progress and advances that have been achieved in the use of SERS in antibiotic residue detection. We introduce and discuss the basic principles of SERS. We then present the prospects and challenges in the use of SERS in the detection of antibiotics in food. Finally, we summarize and discuss the current problems and future trends in the detection of antibiotics in food.

Raife Dilek Turan, Cihan Tastan, Derya Dilek Kancagi et al.

Abstract The SARS-CoV-2 virus caused the most severe pandemic around the world, and vaccine development for urgent use became a crucial issue. Inactivated virus formulated vaccines such as Hepatitis A and smallpox proved to be reliable approaches for immunization for prolonged periods. In this study, a gamma-irradiated inactivated virus vaccine does not require an extra purification process, unlike the chemically inactivated vaccines. Hence, the novelty of our vaccine candidate (OZG-38.61.3) is that it is a non-adjuvant added, gamma-irradiated, and intradermally applied inactive viral vaccine. Efficiency and safety dose (either 1013 or 1014 viral RNA copy per dose) of OZG-38.61.3 was initially determined in BALB/c mice. This was followed by testing the immunogenicity and protective efficacy of the vaccine. Human ACE2-encoding transgenic mice were immunized and then infected with the SARS-CoV-2 virus for the challenge test. This study shows that vaccinated mice have lowered SARS-CoV-2 viral RNA copy numbers both in oropharyngeal specimens and in the histological analysis of the lung tissues along with humoral and cellular immune responses, including the neutralizing antibodies similar to those shown in BALB/c mice without substantial toxicity. Subsequently, plans are being made for the commencement of Phase 1 clinical trial of the OZG-38.61.3 vaccine for the COVID-19 pandemic.