Current Research in Biotechnology最新文献

The urgent need for new antimicrobials combats the rising threat of resistant pathogens. To optimize lead compounds, understanding their mechanisms of action is crucial for target optimization and improved pharmacokinetics. We employed microarray analysis to investigate the impact of selected MMV compounds on the S. aureus transcriptome. Differential gene expression (DEG) analysis was performed following exposure to MMV 676501, MMV 687807, and MMV 102872. MMV 676501 treatment resulted in 26 DEGs (12 upregulated, 14 downregulated). Similarly, MMV 687807 and MMV 102872 treatments yielded 34 DEGs (11 upregulated, 23 downregulated) and 31 DEGs (18 upregulated, 13 downregulated), respectively. Pathway analysis revealed that MMV 676501 targets nitrogen metabolism, while MMV 102872 downregulates genes (purS, purC, lexA) involved in purine metabolism. MMV 687807 appears to affect multiple metabolic pathways. Our transcriptomic approach demonstrates the differential impact of MMV compounds on S. aureus. These results provide mechanistic insights, highlighting specific metabolic pathways affected by each compound. This knowledge is valuable for guiding future target optimization and improving the compounds’ pharmacological properties.

Pain, whether acute or chronic, is one of the most unpleasant experiences. It can have different origins and long-term effects on the body starting from the trivial once such as physical discomfort, accompanying by emotional distress and going to the more serious like depression, anxiety, and social isolation. The removal and proper treatment of the pain is a problem highly dependent on both the source and the individual features of each organism. Herein the view is turned on investigation of activity of new analogs of natural FELL peptide as a promising alternative of the existing antipain molecules. All targeted compounds are obtained by means of conventional peptide synthesis on solid support using standard Fmoc/OtBu approach and their analgesic activity was evaluated by Paw-pressure (Randall-Selitto) test. Determination of the in vivo analgesic activity of the newly synthesized substances showed that the substitution of both Leu (BB11) with Val residues (BB8) increased PPT of the experimental animals on the 10th min, compared to the values after the nonmodified parent molecule injection. On the 20th and the 30th min, BB8 analgesic activity was comparable to BB11 and further a decrease in the PPT was observed. In addition, compared to the controls, analgesia exists until the end of the monitored period of 50 min. The other three newly synthesized substances including Nle (BB6), Ile (BB7) and triple Leu (BB5) instead of double Leu residues showed time-varying short-term analgesic activity, which did not reach that of the parent molecule BB11. Final results show that D-Phe in a first position of the molecule, combined with both Leu residues in the third and fourth positions are the best combination concerning analgesic activity. In addition, lengthening the peptide chain by adding one more hydrophobic residue has also a positive effect on the obtained analgesia. Cytotoxicity of final molecules is significantly lower than those of the positive control SLS, combined with complete hydrolytic stability, which allows their safety use in pharmacy.

By devising a novel near-infrared (NIR) in-situ online monitoring system, this study proposed a new process control method for rice protein (RP) enzymatic hydrolysis in order to address the challenge of monitoring certain chemical indices in real-time. The endpoint of the enzymatic hydrolysis reaction and the flexible switching point of dual-enzyme hydrolysis were determined through real-time spectrum collection. Correction and prediction models were constructed to predict the angiotensin I-converting enzyme (ACE) inhibitory activity of RP hydrolysis as well as the endpoint and the dual-enzyme flexible switching point at different substrate concentrations in an attempt to establish the new control system. At the RP substrate concentrations of 35, 40, and 45 g/L, the R² of predicted and measured ACE inhibitory activity values under hydrolysis with a single alcalase were 0.8852, 0.8360, and 0.8613, respectively. In addition, the ACE inhibitory activity of dual-enzyme hydrolysis showed a high growth trend before and after the flexible switching point, and the hydrolysis time was significantly shortened at the same endpoint threshold as that of single alcalase hydrolysis. The results indicated that this method is capable of accurately determining and regulating the endpoint and adaptable transition point of RP hydrolysis. Consequently, this approach holds promise for the regulation of food basic materials during large-scale processing operations.

Serious environmental concerns are associated with lithium and nanoplastics. Batteries of electronic devices use a considerable quantity of lithium, while nanoplastics (<1 μm) are found in many everyday products or come from the breakdown of large chunks of plastic waste, making them a significant hazard. These contaminants may also affect plant physiology and gene expression, affecting organisms across trophic levels. However, lithium-resistant bacteria and biochar can reduce the toxicity of emerging contaminants. An eco-corona could protect plants from emerging contaminants. Bioremediation may reduce the effects of these contaminants on plant health and the environment. This article summarizes recent research investigating the impact of lithium, nanoplastics, and other emerging contaminants on plant growth and development, along with a limited exploration of bioremediation strategies. It surveys these topics to protect the environment and promote sustainability.

The medicine and healthcare sector has been evolving and advancing very fast. The advancement has been initiated and shaped by the applications of data-driven, robust, and efficient machine learning (ML) to deep learning (DL) technologies. ML in the medical sector is developing quickly, causing rapid progress, reshaping medicine, and improving clinician and patient experiences. ML technologies evolved into data-hungry DL approaches, which are more robust and efficient in dealing with medical data. This article reviews some critical data-driven aspects of machine intelligence in the medical field. In this direction, the article illustrated the recent progress of data-driven medical science using ML to DL in two categories: firstly, the recent development of data science in medicine with the use of ML to DL and, secondly, the chabot technologies in healthcare and medicine, particularly on ChatGPT. Here, we discuss the progress of ML, DL, and the transition requirements from ML to DL. To discuss the advancement in data science, we illustrate prospective studies of medical image data, newly evolved DL interpretation data from EMR or EHR, big data in personalized medicine, and dataset shifts in artificial intelligence (AI). Simultaneously, the article illustrated recently developed DL-enabled ChatGPT technology. Finally, we summarize the broad role of ML and DL in medicine and the significant challenges for implementing recent ML to DL technologies in healthcare. The overview of the data-driven paradigm shift in medicine using ML to DL technologies in the article will benefit researchers immensely.

Background

Studies show that malnutrition is common among cancer patients, ranging from about 20% to more than 70%. It is known that malnutrition that is not managed properly worsens the course of the disease, prolongs the hospital stay, and impacts the quality of life and treatment toxicities. Malnutrition has many negative effects, therefore individualization and optimization of nutritional support is crucial for the patient's health. This study aimed to assess the impact of home enteral nutrition (HEN) planned with the use of indirect calorimetry on the nutritional status and body composition of cancer patients.

Methods

This prospective, observational study included 28 patients with cancer recruited at the Nutrition Clinic of Department of General, Gastroenterologic and Oncologic Surgery, of the Medical University of Warsaw from December 2021 to December 2022. All patients were supported with HEN setup based on individual caloric needs for 3 months. We evaluated nutritional status, body composition, and biochemical markers of malnutrition.

Results

For 67.9 % of patients, using the equations rather than indirect calorimetry would underestimate energy needs. Individualized HEN resulted in weight loss stops in 46.4 % of patients. The average change in body mass in the study group was positive and was equal to 0.46 ± 6.73 kg. No significant differences were found when comparing the mid-arm circumference (MAC) and triceps skinfold thickness (SFT) before and after inclusion in HEN, although, as in the case of body mass SFT showed an upward trend. Moreover, the percentage of patients with insufficient total protein (TP) and serum albumin concentrations decreased. This difference was statistically significant for albumin. The Nutritional Risk Index (NRI) was also significantly higher. The analysis of body composition also showed a decrease in the percentage of patients with reduced fat-free mass (FFM), body cell mass (BCM), and phase angle (PhA) value.

Conclusions

The results of our research highlight the significance of well-planned nutritional care for cancer patients. Utilizing HEN and individualized approaches can effectively preserve body mass, composition, and nutritional status while minimizing safety risks, ultimately improving quality of life. Indirect calorimetry if available can be helpful to optimize nutrition care for patients with cancer in HEN.

In recent years, metaplasmonic biosensors have emerged as a novel counterpart of well-established plasmonic biosensors based on thin metallic layers. Metaplasmonic biosensors offer high potential for sensor miniaturization, extreme sensitivity biosensing, and high multiplexing capabilities with detection methods free of coupling optical elements. These capabilities make metaplasmonic biosensors highly attractive for Point-of-Care and handled/portable devices or novel On-Chip devices; as a result, it has increased the number of prototypes and potential applications that emerged during the last years. One of the main challenges to achieving fully operative devices is the achievement of high-throughput biointerfaces for sensitive and selective biodetection in complex media. Despite the superior surface sensitivity achieved by metaplasmonic sensors compared to conventional plasmonic sensors based on metallic thin films, the main limitations to achieving high-throughput and multiplexed biosensing usually are associated with the sensitivity and selectivity of the biointerface and, as a consequence, their application to the direct analysis of real complex samples. This graphical review discusses the potential challenges and capabilities of different biofunctionalization strategies, biorecognition elements, and antifouling strategies to achieve scalable and high-throughput metaplasmonic biosensing for Point-of-Care devices and bioengineering applications like Organs-On-Chip.

Environmental (abiotic) stresses significantly threaten the worldwide crop production and food security. Rapid, drastic changes in the global climate have exacerbated such stresses for crops. Plant-associated bacteria have been shown to enhance stress resistance and cope with the negative impacts of various abiotic stresses through the induction of various mechanisms. In soil, the rhizosphere and endosphere of plants, the Bacillus genus is a predominant bacterial genus. Members of this genus, which are tremendously diverse both metabolically and genetically, survive for a long time under unfavorable environmental conditions due to their ability to form long-lived, stress-tolerant spores. Bacillus spp. secrete several metabolites that trigger plant growth and enhance plants’ tolerance to biotic and abiotic stresses. Some of the Bacillus species are available commercially as phytostimulants, biopesticides, and biofertilizers. Due to this functional versatility, the Bacillus genus is one of the most widely used in the agro-biotech industry. However, the potential of the Bacillus genus has not yet been sufficiently realized, and transferring technology related to the genus from the lab environment to real world applications in the field needs to be emphasized. A better understanding of mechanisms of action of beneficial Bacillus spp. is needed for the development of products to support green biotechnology in agriculture and industries. This report comprehensively reviews the applications of Bacillus spp. in abiotic (e.g., salinity, drought, inorganic and organic pollutant toxicity, nutritional imbalance, low–high temperatures, and waterlogging) -stressed agriculture and discusses their potentials for the development of new products of biotechnological implications, highlighting gaps that remain to be explored to improve and expand on Bacillus-based biostimulants.

Isoflavonoids are an essential source of bioactive compounds. Due to their enormous potential for human health, the demand for isoflavonoids is rising. Isoflavonoid synthase (IFS), a unique P450 monooxygenase, catalyzes the rate-limiting step in the biosynthesis of isoflavonoids. Here, IFS enzymes from five different plants are characterized for isoflavonoid biosynthesis in yeast. Although IFS generally prefers liquiritigenin as a substrate, this is the first time an IFS from Pueraria candollei has been characterised with a preference for naringenin. Co-expression of 2-hydroxyisoflavonoid dehydratase (HID) with IFS significantly improves isoflavonoids biosynthesis. However, a preference of IFS towards particular HID enzymes types was observed in this study. Additionally, the protein conformational basis of aryl ring migration mechanism has been explored for Onobrychis viciifolia IFS, P. candollei IFS, and CYP82D26 and compared with other P450 enzymes that accept the same substrates but do not produce isoflavonoids. Protein structure and docking analysis based on key amino acids, heme orientation, and IFS promiscuity has led us to suggest a new mechanism for aryl ring migration. Characterisation of IFS for efficiency, substrate preference, and understanding of aryl ring migration mechanism will help advance heterologous biosynthesis and protein engineering efforts.

Sweat contains a wealth of health-related biomarkers, which has been a promising resource for personalized real-time monitoring at molecular level. Emergence of non-enzymatic electrochemical sensor that simulates the enzyme catalysis utilizing the functional material further promotes the development of wearable sweat sensor, successfully addressing the limitations of enzyme sensing in sensitivity and stability. Thus, there is an urgent need for centering on the regulation of the nanostructure, combination and preparation method of functional materials to enhance the catalytic activity for enzyme-free detection of sweat biomarkers. This review aims to present the superiors of enzyme-free sensing on wearable sweat sensor, and provides guidance for material innovation, sensor design and system integration. Firstly, we primarily focus on the recent advances of novel functional nanomaterials in wearable non-enzymatic electrochemical sensor, and briefly describe the sensing principles for detecting biomarkers in sweat. Subsequently, the correlation between the electrochemical strategy and functional material is elaborately interpreted by coupling with the diverse molecular structures of the biomarkers and the pH changes of test environments. Finally, challenges and opportunities for wearable non-enzymatic electrochemical sensor in sweat sensing are delineated in the development of future personalized healthcare.