Enhancing Colorectal Cancer Screening with Droplet Digital PCR (ddPCR) Analysis of Fusobacterium nucleatum in Fecal Immunochemical Test (FIT) Samples.

Fecal immunochemical test (FIT) followed by colonoscopy in positive cases is commonly used for population-based colorectal cancer (CRC) screening. However, specificity of FIT for CRC is not ideal, and has poor performance for advanced adenoma detection. Fecal Fusobacterium nucleatum (Fn) detection has been proposed as a potential non-invasive biomarker for CRC and advanced adenoma detection. We aimed to evaluate the diagnostic performance of Fn detection using droplet digital PCR (ddPCR) in FIT samples from individuals enrolled in a CRC screening program with colorectal adenoma or cancer. We evaluated Fn presence in DNA isolated from FIT leftover material of 300 participants in a CRC Screening Program using ddPCR. The Fn DNA amount was classified as Fn-low/negative and Fn-high, and the association with patients clinicopathological features and accuracy measurements was calculated. Fn high levels were more prevalent in FIT-positive (47.2%n=34 of72) than FIT-negative samples (28.9%, n=66 of 228) (p<0.04). Among FIT-positive samples, high Fn levels were significantly more frequent in cancer patients (CA, n=8) when compared to normal (NT, n=16) (p=0.02), non-advanced adenomas (NAA, n=36) (p=0.01), and advanced adenomas (AA, n=12) (p=0.01). Performance analysis of Fn in FIT-positive samples for colorectal cancer detection yielded an AUC of 0.8203 (CI: 0.6464-0.9942), with high sensitivity (100%) and specificity of 50%%. Concluding, we showed the feasibility of detecting Fn in FIT leftovers using the ultrasensitive ddPCR technique. Furthermore, we highlighted the potential use of Fn levels in fecal samples to ameliorate CRC detection.

Evaluation of a Phytogenic Compound with Minerals as a Possible Alternative to Ractopamine for Finishing Pigs.

The objective of this work was to evaluate the effect of a phytogenic compound for pigs in the growing and finishing phases as a possible substitute for ractopamine. A total of 140 pigs with an average initial weight of 48.8 kg ± 5.9 kg were used, distributed in a randomized block design, in a 3 × 2 factorial scheme (control diet (CONT), diet with inclusion of 2.5 kg per ton of a phytogenic compound (PC), and diet with 10 ppm of ractopamine (RAC), and two sexes: gilts and barrows), distributed in eight pens per treatment. The performance parameters were measured, and, at the end of the experimental period, the animals were slaughtered for carcass characteristics and pork quality analysis. The animals consuming RAC showed a better feed conversion, 4% improvement in relation to the group with the PC (p < 0.05). For daily weight gain, the animals supplemented with the PC showed 4.46% lower gain compared to RAC, and 3% greater gain compared to the CONT (p < 0.05). The animals that consumed the PC showed 5.6% lower shear force of the pork (p < 0.05) in relation to the CONT group and 29% lower in relation to the RAC group. The TBARS value presented a significant difference (p < 0.05), the CONTT group was 29% higher than the RAC, and the PC was 15.5% higher than the RAC. For chroma, the pork of the RAC group was 14% lower than the CONTT group and 10.3% less than the PC. There was no significant difference for the carcass parameters. It was concluded that the pigs in the ractopamine group presented the best performance; however, the phytogenic compound can be used against ractopamine's restriction because it improves daily weight gain and promotes a softer and less pale meat when compared with ractopamine.

Transcripts Expressed during Germination Sensu Stricto Are Associated with Vigor in Soybean Seeds.

The rapid and uniform establishment of crop plants in the field underpins food security through uniform mechanical crop harvesting. In order to achieve this, seeds with greater vigor should be used. Vigor is a component of physiological quality related to seed resilience. Despite this importance, there is little knowledge of the association between events at the molecular level and seed vigor. In this study, we investigated the relationship between gene expression during germination and seed vigor in soybean. The expression level of twenty genes related to growth at the beginning of the germination process was correlated with vigor. In this paper, vigor was evaluated by different tests. Then we reported the identification of the genes Expansin-like A1, Xyloglucan endotransglucosylase/hydrolase 22, 65-kDa microtubule-associated protein, Xyloglucan endotransglucosylase/hydrolase 2, N-glycosylase/DNA lyase OGG1 and Cellulose synthase A catalytic subunit 2, which are expressed during germination, that correlated with several vigor tests commonly used in routine analysis of soybean seed quality. The identification of these transcripts provides tools to study vigor in soybean seeds at the molecular level.

An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality.

Seeds of high physiological quality are defined by their superior germination capacity and uniform seedling establishment. Here, it was investigated whether multispectral images combined with machine learning models can efficiently categorize the quality of peanut seedlots. The seed quality from seven lots was assessed traditionally (seed weight, water content, germination, and vigor) and by multispectral images (area, length, width, brightness, chlorophyll fluorescence, anthocyanin, and reflectance: 365 to 970 nm). Seedlings from the seeds of each lot were evaluated for their photosynthetic capacity (fluorescence and chlorophyll index, F0, Fm, and Fv/Fm) and stress indices (anthocyanin and NDVI). Artificial intelligence features (QDA method) applied to the data extracted from the seed images categorized lots with high and low quality. Higher levels of anthocyanin were found in the leaves of seedlings from low quality seeds. Therefore, this information is promising since the initial behavior of the seedlings reflected the quality of the seeds. The existence of new markers that effectively screen peanut seed quality was confirmed. The combination of physical properties (area, length, width, and coat brightness), pigments (chlorophyll fluorescence and anthocyanin), and light reflectance (660, 690, and 780 nm), is highly efficient to identify peanut seedlots with superior quality (98% accuracy).

Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases.

Light-based methods are being further developed to meet the growing demands for food in the agricultural industry. Optical imaging is a rapid, non-destructive, and accurate technology that can produce consistent measurements of product quality compared to conventional techniques. In this research, a novel approach for seed quality prediction is presented. In the proposed approach two advanced optical imaging techniques based on chlorophyll fluorescence and chemometric-based multispectral imaging were employed. The chemometrics encompassed principal component analysis (PCA) and quadratic discrimination analysis (QDA). Among plants that are relevant as both crops and scientific models, tomato, and carrot were selected for the experiment. We compared the optical imaging techniques to the traditional analytical methods used for quality characterization of commercial seedlots. Results showed that chlorophyll fluorescence-based technology is feasible to discriminate cultivars and to identify seedlots with lower physiological potential. The exploratory analysis of multispectral imaging data using a non-supervised approach (two-component PCA) allowed the characterization of differences between carrot cultivars, but not for tomato cultivars. A Random Forest (RF) classifier based on Gini importance was applied to multispectral data and it revealed the most meaningful bandwidths from 19 wavelengths for seed quality characterization. In order to validate the RF model, we selected the five most important wavelengths to be applied in a QDA-based model, and the model reached high accuracy to classify lots with high-and low-vigor seeds, with a correct classification from 86 to 95% in tomato and from 88 to 97% in carrot for validation set. Further analysis showed that low quality seeds resulted in seedlings with altered photosynthetic capacity and chlorophyll content. In conclusion, both chlorophyll fluorescence and chemometrics-based multispectral imaging can be applied as reliable proxies of the physiological potential in tomato and carrot seeds. From the practical point of view, such techniques/methodologies can be potentially used for screening low quality seeds in food and agricultural industries.