A growing environmental concern revolves around the widespread use of medicines, particularly antibiotics, which adversely impact water quality and various life forms. The unregulated production and utilization of antibiotics not only affect non-targeted organisms but also exert significant evolutionary pressures, leading to the rapid development of antimicrobial resistance (AMR) in bacterial communities. To address this issue, global studies have been conducted to assess the prevalence and quantities of antibiotics in various environmental components including freshwater, ocean, local sewage, and fish. These studies aim to establish effective analytical methods for identifying and measuring antibiotic residues in environmental matrices that might enable authorities to establish norms for the containment and disposal of antibiotics. This article offers a comprehensive overview of methods used to extract antibiotics from environmental matrices exploring purification techniques such as liquid-liquid extraction, solid-phase extraction, green extraction techniques, and concentration methods like lyophilization and rotary evaporation. It further highlights qualitative and quantitative analysis methods, high-performance liquid chromatography, ultra-high-performance liquid chromatography, and liquid chromatography-tandem along with analytical methods such as UV-Vis and tandem mass spectrometry for detecting and measuring antibiotics. Urgency is underscored for proactive strategies to curb antibiotic contamination, safeguarding the integrity of aquatic ecosystems and public health on a global scale.

Various techniques are available to illuminate geometric structures of molecular ions in gas phase, such as Förster Resonance Energy Transfer (FRET) informing on distances between two dyes covalently attached to a molecule. Typically, cationic rhodamines, which absorb and emit visible light, are used for labeling. Extensive work has revealed that the transition energy of a rhodamine is intricately linked to its nearby microenvironment, with nearby charges causing Stark-shifted emission. This occurs because the inter-dye Coulomb interaction is weaker in the excited state (S1) than in the ground state (S0) due to the increase in polarizability upon excitation. Therefore, absorption and emission spectra, along with FRET efficiencies, provide insights into structural motifs. At room temperature, multiple conformers often co-exist, leading to overlapping absorption bands among different conformers and broad spectra. To study specific conformers, it is necessary to isolate them, for example, using ion-mobility spectrometry. Another approach is to reduce temperature, which results in spectral narrowing and distinct absorption bands, allowing for the selection of specific conformers through selective excitation. Here, we describe the instrumentation used for cryogenically cold FRET experiments and discuss recent results for small model systems, as well as future directions for a technique still in its infancy.

BACKGROUND: Polymorphism rs1049434 characterizes the nonsynonymous exchange of adenosine (A) by thymidine (T) in the gene for monocarboxylate transporter 1 (MCT1). We tested whether T-allele carriers of rs1049434 demonstrate increased accumulation of markers of metabolic strain.
METHODS: Physically active, healthy, young male subjects (n = 22) conducted a power-matched one-legged cycling exercise to exhaustion. Metabolic substrates in capillary blood, selected metabolic compounds, and indices for the slow oxidative phenotype of vastus lateralis muscle were quantified in samples collected before and after exercise. The genotypes of the rs1049434 polymorphism were determined with polymerase chain reactions.
RESULTS: One-legged exercise affected the concentration of muscle metabolites entering the tricarboxylic acid cycle, such as acetyl-co-enzyme A (+448%) and acetyl-L-carnitine (+548%), muscle glycogen (-59%), and adenosine monophosphate (-39%), 30 min post-exercise. Exercise-related variability in the muscular concentration of glycogen, long-chain acyl co-enzyme As and a triglyceride, nicotinamide adenine dinucleotide (NADH), and adenosine monophosphate (AMP) interacted with rs1049434. T-allele carriers demonstrated a 39% lesser reduction in glycogen after exercise than non-carriers when NADH increased only in the non-carriers. Muscle lactate concentration was 150% higher, blood triacyl-glyceride concentration was 53% lower, and slow fiber percentage was 20% lower in T-allele carriers.
CONCLUSIONS: The observations suggest a higher anaerobic glycolytic strain during exhaustive exercise and a lowered lipid handling in T-allele non-carriers.

Mass spectroscopy (MS) is a robust technique for polymer characterization, and it can provide the chemical fingerprint of a complete sample regarding polymer distribution chains. Nevertheless, polymer chemical properties such as polydispersity (Pd), average molecular mass (Mn), weight average molecular mass (Mw) and others are not determined by MS, as they are commonly characterized by gel permeation chromatography (GPC). In order to calculate polymer properties from MS, a Python script was developed to interpret polymer properties from spectroscopic raw data. Polypy script can be considered a peak detection and area distribution method, and represents the result of combining the MS raw data filtered using Root Mean Square (RMS) calculation with molecular classification based on theoretical molar masses. Polypy filters out areas corresponding to repetitive units. This approach facilitates the identification of the polymer chains and calculates their properties. The script also integrates visualization graphic tools for data analysis. In this work, aryl resin (poly(2,2-bis(4-oxy-(2-(methyloxirane)phenyl)propan) was the study case polymer molecule, and is composed of oligomer chains distributed mainly in the range of dimers to tetramers, in some cases presenting traces of pentamers and hexamers in the distribution profile of the oligomeric chains. Epoxy resin has Mn = 607 Da, Mw = 631 Da, and polydispersity (Pd) of 1.015 (data given by GPC). With Polypy script, calculations resulted in Mn = 584.42 Da, Mw = 649.29 Da, and Pd = 1.11, which are consistent results if compared with GPC characterization. Additional information, such as the percentage of oligomer distribution, was also calculated and for this polymer matrix it was not possible to retrieve it from the GPC method. Polypy is an approach to characterizing major polymer chemical properties using only MS raw spectra, and it can be utilized with any MS raw data for any polymer matrix.

The genus Aeromonas includes well-known pathogenic species for fishes and humans that are widely distributed in the aquatic environment and foods. Nowadays, one of the main issues related to wild Aeromonas isolates is their identification at the species level, which is challenging using classical microbiological and biomolecular methods. This study aims to test MALDI-TOF MS technology in the identification of Aeromonas strains isolated from n. 60 retail sushi and sashimi boxes using an implemented version of the SARAMIS software V4.12. A total of 43 certified Aeromonas strains were used to implement the SARAMIS database by importing the spectra obtained from their identification. The original SARAMIS version (V4.12) failed to recognize 62.79% of the certified strains, while the herein-implemented version (V4.12plus) allowed the identification of all the certified strains at least to the genus level with a match of no less than 85%. Regarding the sushi and sashimi samples, Aeromonas spp. was detected in n. 18 (30%) boxes. A total of 127 colonies were identified at the species level, with A. salmonicida detected as the most prevalent species, followed by A. bestiarum and A. caviae. Based on the results of the present study, we could speculate that MALDI-TOF technology could be a useful tool both for the food industry to monitor product contamination and for clinical purposes to make diagnoses effectively and quickly.

\"Guava\" (Acca sellowiana) is an unconventional edible plant from Brazil. It is used in traditional medicine as an anti-diabetic; however, pharmacological studies on this plant are scarce. This study aimed to evaluate the chemical and safety profile of an aqueous A. sellowiana peel extract (ASPE) and its effects on endothelial EA.hy926 cells under glucose overload and in vivo (Artemia salina). An ethanolic extract from A. sellowiana peels (ASPEetOH) was also produced and characterized. Results showed that ASPE did not present in vivo toxicity, and it was found to contain high phenolic content and redox capacity. ASPE (50 µg/mL; 24 h) prevented oxidative stress and mitochondrial dysfunction, besides positively modulating Sirtuins 1 and 3, and prevented the increase of COX-2 and NF-kβ expression levels in EA.hy926 cells under glucose overload. Chromatographic fractionation, metabolite profiling, spectroscopic and bioinformatics analyses revealed the presence of phenolic acids, flavan-3-ols, flavonols, flavones, flavanones, and anthocyanidins, displaying a diversity of compounds in the crude and fractionated ASPEetOH. This study provided evidence on the safety profile, chemical composition, and pharmacological activities of A. sellowiana.

Upon implanting tissue-engineered heart valves (TEHVs), blood-derived macrophages are believed to orchestrate the remodeling process. They initiate the immune response and mediate the remodeling of the TEHV, essential for the valve\'s functionality. The exact role of another macrophage type, the tissue-resident macrophages (TRMs), has not been yet elucidated even though they maintain the homeostasis of native tissues. Here, we characterized the response of hTRM-like cells in contact with a human tissue engineered matrix (hTEM). HTEMs comprised intracellular peptides with potentially immunogenic properties in their ECM proteome. Human iPSC-derived macrophages (iMφs) could represent hTRM-like cells in vitro and circumvent the scarcity of human donor material. iMφs were derived and after stimulation they demonstrated polarization towards non-/inflammatory states. Next, they responded with increased IL-6/IL-1β secretion in separate 3/7-day cultures with longer production-time-hTEMs. We demonstrated that iMφs are a potential model for TRM-like cells for the assessment of hTEM immunocompatibility. They adopt distinct pro- and anti-inflammatory phenotypes, and both IL-6 and IL-1β secretion depends on hTEM composition. IL-6 provided the highest sensitivity to measure iMφs pro-inflammatory response. This platform could facilitate the in vitro immunocompatibility assessment of hTEMs and thereby showcase a potential way to achieve safer clinical translation of TEHVs.

Compounds of natural origin are in burgeoning demand driven by heightened awareness of their health benefits. We present the maiden study on the production of neurosporaxanthin, a carotenoid, from marine Rhodococcus ruber O16N. Analysing various physical parameters including carbon source, agitation speed, temperature, salt and pH, we found that agitation adversely affects biomass and carotenoid production. Isolate O16N grew well, when medium was supplemented with mannitol or sorbitol, CaCl2, at pH 6 and best carotenoid production was observed when sorbitol or fructose and CaCl2 was supplemented in media at pH 7 at 37 °C in static condition with the maximum carotenoid yield of 1097 mg/L, whopping 18-fold more as compared to nutrient medium. Furthermore, thorough characterisation identified the produced carotenoid as neurosporoxanthin. These findings highlight the potential of marine Rhodococcus ruber O16N as a valuable source for neurosporaxanthin production and emphasise the importance of optimising physical parameters for maximising carotenoid yield.

Artificial intelligence-based methods such as chemometrics, machine learning, and deep learning are promising tools that lead to a clearer and better understanding of data. Only with these tools, data can be used to its full extent, and the gained knowledge on processes, interactions, and characteristics of the sample is maximized. Therefore, scientists are developing data science tools mentioned above to automatically and accurately extract information from data and increase the application possibilities of the respective data in various fields. Accordingly, AI-based techniques were utilized for chemical data since the 1970s and this review paper focuses on the recent trends of chemometrics, machine learning, and deep learning for chemical and spectroscopic data in 2020. In this regard, inverse modeling, preprocessing methods, and data modeling applied to spectra and image data for various measurement techniques are discussed.

Single-cell proteomics is currently far less productive than other approaches. Still, the proteomic community is having trouble adapting to the limitation of having to examine fewer cells than they would like. Studies on a small number of cells should be carefully planned to maximize the chances of success in this situation. This study aims to determine how sample size and measurement speed (slope)/variation affect the accuracy of a protein proteome mass spectrometric determination. The determination accuracy was shown to increase, and the false positive rate was shown to decrease as the sample size increased from 7 to 100 cells and the measurement slope/variation (S/V) ratio increased from 1 to 6. Furthermore, it was discovered that the number of cells in the sample increased the accuracy of this estimate. Thus, for 100 cells, the measurement S/V ratio was typically estimated to be very close to the real-world value, with a standard deviation of 0.35. For sample sizes from 7 to 100 cells, this accuracy was seen when calculating the measurement S/V ratio. The findings can help researchers plan experiments for mass spectroscopic protein proteome determination and other research purposes.