The on-column hydrolytic degradation of metamizole to impurity C in aqueous mobile phases represents a major source of analytical errors and false-positive results. Accurate and precise determination of impurity C is critical because this analyte is a direct precursor to a monitored genotoxic nitrosamine impurity. To resolve this issue, a novel isocratic non-aqueous hydrophilic interaction liquid chromatography method was developed on a triazole-bonded stationary phase. The separation was performed on Cosmosil HILIC column column (150 × 3.0 mm, 2.5 µm). The mobile phase was composed of 10 mM ammonium formate and 5 mM formic acid in a mixture of methanol and acetonitrile (3:97, v/v). An isocratic flow rate of 0.8 mL/min was utilized at a column temperature of 10 °C. A short analysis time of 3 min was achieved under optimized conditions. Impurity C exhibited minimal retention (k = 0.4), whereas metamizole was strongly retained on the column (k > 100). Due to this distinct selectivity, on-column artifact formation was completely prevented throughout the chromatographic run. To manage metamizole accumulation on the stationary phase, a periodic column regeneration step was introduced after every 30 sample injections. The method was coupled with mass spectrometric detection in positive electrospray ionization mode. Quadrupole mass analyser was employed in selected ion monitoring mode using a target ion at m/z = 218 as [M+H][+]. A low limit of quantification of 2 ng/mL (2 ppm) and excellent linearity (r = 0.9991) were obtained. The calculated back-fit bias remained between -2.6% and 2.3% across the entire calibrated range. Critical verification parameters demonstrated full compliance with International Council for Harmonisation guidelines. The practical utility of the procedure was confirmed through the analysis of two production batches of metamizole.

Demographic bias in AI-driven clinical decision support systems (CDSS) represents one of the most consequential and least-addressed risks in healthcare AI. Large Language Models (LLMs) applied to emergency triage can silently perpetuate or amplify existing health disparities across thousands of patient decisions. This paper introduces FairGuard, a continuous bias detection and governance framework that embeds demographic fairness auditing directly into the LLM inference pipeline using four integrated mechanisms: (1) an equity-enforcing consent gate that applies demographic-blind access control; (2) a RAG corpus bias analyzer that traces class-directional misclassification to retrieval layer composition; (3) per-subgroup confusion matrix stratification computing ΔF1 across demographic groups; and (4) a blockchain-anchored continuous monitoring layer that enforces a ΔF1 ≤ 0.05 governance threshold across every inference batch. Evaluated on the MIMIC-IV Full Emergency dataset (N=300-1,000), FairGuard achieves a gender ΔF1 of 0.020, well within threshold, while identifying RAG corpus composition as the root cause of conservative triage bias (50.9% Urgent → Non-Urgent misclassification), establishing that the observed bias is class-directional rather than demographically concentrated. FairGuard provides the first continuous, blockchain-enforced fairness governance mechanism for LLM-based emergency triage CDSS.

The stability of key Orbitrap analyzer parameters-specifically, the absence of parasitic spectral signals and the maintenance of constant ultra-high vacuum (UHV) pressure-is critically dependent on the quality of its quartz insulators. However, results from testing over 500 production units in 2025 indicate that the occurrence of disqualifying anomalies (spectral spikes and UHV pressure jumps) is not merely a function of general quartz quality but is deterministically linked to specific, localized defects introduced during assembly. A clear differentiation of root causes was established: Spectral spikes showed a statistically significant correlation (p < .001) with defects (micro-cracks, chips) on the quartz insulators of the external injection (IE) and detection (DE) electrodes, as well as on the quartz spacer ring. In contrast, UHV pressure jumps were almost exclusively (97% of cases) caused by mechanical damage and contamination on the quartz insulators of the central electrode power contact (Buchse-ZE). Critical defects on the latter included contamination and scoring from the mounting bolt, indentation marks from the spring contact, and other manufacturing micro-damage. These defects act as potent local outgassing sources. The implementation of targeted 100% optical inspection of these critical zones, supported by photographic documentation, along with modified assembly procedures, led to the practical elimination of these anomalies in a subsequent validation batch of 100 units.

Coal is an abundant source of energy; however, the use of Pakistani coal is limited due to its high sulfur content, which causes environmental and health issues. Hence, pre-combustion desulfurization of coal is imperative for quality enhancement. The present study investigates the microbial desulfurization of coal from the Balochistan mine by using local bacterial strains, Bacillus pumilus and Bacillus licheniformis, isolated from the same coal mine, for their sustainability, adaptability, and local availability. Batch incubation experiments with a coal particle size of 75 and 150 µm and incubation periods of 7 and 14 days were conducted to evaluate the effect of particle size and incubation period on the sulfur removal efficiency. Proximate analysis was conducted in accordance with ASTM D3173, ASTM D3174, and ASTM D3175 to determine the moisture content (MC), ash content (AC), and volatile matter (VM), while ultimate analysis was performed using a CHNS analyzer. Additionally, XRD was used to determine changes in composition and structure before and after treatment. The results showed that microbial treatment increased calorific value, specifically at smaller particle sizes and longer incubation periods. The maximum sulfur removal achieved by B. licheniformis was 32.73% and 41.06%, whereas B. pumilus exhibited a removal of 20.75% and 33.48% at a particle size of 75 µm after 7 and 14 days, respectively. These findings demonstrate that bio-desulfurization using indigenous microorganisms is an effective and environmentally sustainable approach for upgrading low-grade Pakistani coal for cleaner energy production.

RNAanalyzer3 ("RNA analyzer cubic"; https://rnaanalyzer.bioapps.biozentrum.uni-wuerzburg.de) substitutes the frequently consulted current RNAanalyzer webserver (https://rnaanalyzer-old.bioapps.biozentrum.uni-wuerzburg.de). RNAanalyzer3 is free/open via the secure HTTPS protocol, with example data, help and tutorial, web-link to results, and rich data output. We combine a general detailed structure analysis with motif analyses. It accepts either a single plain-text nucleotide sequence or batch submission in FASTA format, which can be pasted or uploaded as a FASTA file. Our tool (i) has up-to-date software and operating systems, (ii) combines diverse RNA motif analyses with RNA structure prediction, (iii) puts found motifs into structural context, and (iv) offers dedicated tools for probing RNA-protein binding interactions. RNAanalyzer3 links motif searches to Rfam and miRNA search to miRbase. It focuses on structural features first, looks for stem-loops, hairpins, and specific enrichment regions such as stem-GG pairs, plus AU-rich regions with their locations for easier identification, while providing structural context and interactive RNA structure visualization. A tabulated overview shows all RNA features including structure details, coding potential, untranslated regions (UTRs, including Shine-Dalgarno sequences, Kozak sequences, and polyadenylation signals), transfer RNA (tRNA), microRNA (miRNA), long noncoding RNA (lncRNA), trans-splicing motifs, iron response elements (IRE), riboswitches, small nuclear ribonucleoprotein (snRNP) motifs, and spliceosomal Sm-sites.

Accurate characterisation of dissolution behaviour is critical for understanding and optimising the performance of inhalable pharmaceutical powders, yet existing in vitro dissolution methods are often labour-intensive and limited in their ability to capture transient dissolution dynamics under physiologically relevant conditions. In this study, an optical coherence tomography (OCT)-based refractometric approach is developed and validated as a quantitative, time-resolved method for monitoring dissolution in situ without liquid sampling. A custom dissolution cell incorporating a dedicated optical interrogation region was integrated with an OCT system to continuously measure dissolution-induced optical path length changes, from which local refractive index changes were estimated. The method was applied to fine and coarse lactose grades (with different median particle sizes), commercial mannitol, and in-house lactose-salbutamol sulphate blends. The obtained dissolution profiles were validated against standard analytical methods, demonstrating strong agreement in both temporal trends (Pearson r = 0.92; Spearman ρ = 0.93) and excellent linearity between OCT-measured refractive index and known concentration (linear regression R[2] > 0.99) for lactose powder. Differences in dissolution behaviour arising from powder properties (e.g., particle size) and hydrodynamic conditions (e.g., agitation rate) were resolved across the investigated formulations, demonstrating sensitivity to powder properties and hydrodynamic effects. Overall, the proposed method provides a robust, non-destructive framework for real-time analysis of transient and equilibrium dissolution behaviour, well suited to rapid batch-to-batch screening and dissolution studies.

Effective use of agricultural wastes has become one of the promising fields of wastewater treatment technology due to their cost effectiveness as well as their eco-friendly nature. Unmodified Cola acuminata (UCA), phosphoric acid modified Cola acuminata (PMCA) and thermally modified Cola acuminata (TMCA) fruit husk were used in this study as a low cost adsorbents for the removal of Pb (II) ion from aqueous solution. Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM), energy dispersive X-ray spectrometer (EDX), and a Brunauer-Emmett-Teller (BET) surface area analyzer were used to characterize the adsorbents. The impact of pH, metal ion concentration, adsorbent dose, contact time, and temperature on bisorption was evaluated using the batch adsorption approach. The data obtained were then applied to suitable isotherm and kinetic models. The percentage adsorption of Pb (II) decreased with increase in metal ion concentration while for pH, adsorbent dose, contact time and temperature there was an increase in percentage adsorption as their parameters increases while the optimum was at pH of 6. The percentage adsorption of Pb (II) increased from 15.86 to 73.14, 28.32 to 84.00, and 26.15 to 79.12%, for UCA, PMCA, and TMCA respectively. The trend of metal adsorption on the adsorbents was in the order PMCA > TMCA > UCA. The adsorption data were fitted to the Langmuir, Freundlich, Temkin and Dubinin-Radushkevich (D-R) with the Langmuir isotherm providing the best fit. The FTIR showed that the O-H, C≡C and C = O groups were the major sites for the adsorbent material. The energy dispersive X-ray spectrum displayed the principal elements, such as nitrogen, silicon, carbon, oxygen, calcium, potassium, and magnesium, in different percentages, while the scanning electron microscope (SEM) image demonstrated its high porosity and numerous pore cavities that served as adsorption sites. Only pseudo-second order and physisorptions were found to offer the best fit to the experimental data of Pb (II) ions, according to the kinetics and thermodynamic analyses. TMCA showed the highest desorption 86.92% after fourth cycles of desorption experiment. These results show that the three adsorbents have the potential to be used as low-cost and efficient adsorbents for the removal of lethal heavy metals from aqueous solutions.

Protein and nucleic acid mutational studies are central to understanding biomolecular structure, function, and interactions, yet existing computational tools often lack user-friendly interfaces for high-throughput and systematic mutagenesis. To address this limitation, we present BioMutation, a graphical user interface (GUI) that enables automated, batch-wise introduction of substitution-based mutations in proteins, nucleic acids, and their complexes facilitated via UCSF ChimeraX functionalities. BioMutation supports user-defined and class-wise amino acid substitutions in proteins, as well as user-specified and combinatorial substitutions in DNA and RNA. The tool automatically generates libraries of mutated structures in standard formats (PDB, MOL2, and mmCIF) suitable for downstream computational studies, including molecular docking and molecular dynamics simulations. In addition, BioMutation includes a Google Colab-based Structure Analyzer module for comparative assessment of physicochemical features before and after mutation. By integrating automation, flexibility, and accessibility within a single platform, BioMutation facilitates efficient in silico mutagenesis for applications in structural biology, biomolecular engineering, and drug discovery. The GUI is freely available at https://github.com/Computational-biolab/BioMutation.

Agarwood is a highly precious heartwood containing aromatic resin, which can be used both as medicine and incense. The consumption centers of agarwood are distributed worldwide, and many countries place great emphasis on the quality evaluation of agarwood. Throughout history, the appearance and physical characteristics of agarwood, such as sinkability and color, have often been used as indicators for quality assessment and commercial grading. However, the scientific basis of grading agarwood purely on appearance warrants additional exploration. In this study, 24 batches of agarwood were subjected to traditional sinkability tests and density measurements. The results revealed that samples floating wholly on the sea surface had densities ranging from 0.4465 to 0.7135, whereas samples partially or completely submerged had densities ranging from 0.5494 to 0.9075. Samples that sank completely to the bottom had densities above 1.0, confirming the consistency between sinkability and density. Using an intelligent visual analyzer, the appearance color of agarwood was digitally characterized. Combined with density results, it was found that as density increased, the overall L*, b* and E*ab values of agarwood gradually decreased, while the a* values were consistently positive but relatively small. This indicates that agarwood tends to be dark red, and higher density correlates with darker color. UPLC-MS was employed to comprehensively analyze the chemical components of agarwood, leading to the identification of 89 compounds. Among them, 19 differential components were identified between agarwood with densities below 0.7 and those above 0.7. It was observed that agarwood with densities above 0.7 contained more chromone dimers, while those with densities below 0.7 contained more chromone monomers. This study validates the long-held idea that agarwood, which sinks in water and exhibits a darker color is of superior quality, revealing that such dense, sinking-grade agarwood is enriched with chromone dimers, which provide a scientific basis for its quality assessment.

In the current work, the use of carbide-derived carbon (CDC) as a novel adsorbent to remove lead (Pb2+ ions) from water is presented for the first time. The effect of different adsorption factors, including CDC dose, adsorption contact time, and lead concentration, was studied. The CDC adsorbent was characterized by using advanced characterization techniques such as scanning electron microscopy (SEM), energy-dispersive spectrometer (EDS), transmission electron microscopy (TEM), x-ray photoelectron spectroscopy (XPS), Brunauer–Emmett–Teller (BET) surface area analyzer and zeta potential measurement. It was observed that CDC showed a BET surface area of 1597.15 m2/g and showed a point-of-zero charge (PZC) at pH 6. The batch adsorption experiments showed that CDC has a high removal efficiency of 98.9% for Pb2+ ions at CDC dose of 6.25 mg/L. It was shown that adsorption kinetic is very fast and 98% of lead removal was reached just in five minutes adsorption time. The adsorption data were fitted to the Langmuir, Freundlich, Temkin, and Sips isotherms, which showed that the adsorption process follows the Langmuir isotherm with a regression value of 0.9302. The adsorption of Pb2+ ions onto CDC showed a maximum adsorption capacity of 89.16 mg/g. Moreover, the experimental adsorption data were fitted to pseudo-first order, pseudo-second order, the Elovich model, intraparticle diffusion, and liquid film diffusion, kinetic models and the experimental adsorption results were consistent with the pseudo-second order model, with a regression value of 0.9999. The thermodynamic study indicated that the adsorption is spontaneous at temperatures of 25, 35, and 45 ℃. The ionic strength experiment showed that increasing the ionic strength of the solution causes a decrease in adsorption capacity due to decreasing electrostatic interactions between negatively charged CDC surface and lead ions; however, a removal efficiency > 99% was achieved even at 0.1 M NaCl feed solution. Moreover, the mechanisms of adsorption of lead towards CDC were discussed. The treatment of real groundwater spiked with 500 µg/L of lead showed a complete removal of lead at CDC dose of 1 g/L. The study showed the possibility of efficient regeneration of lead-saturated adsorbent for further reuse. These findings support the potential application of CDC for lead removal from water.

For many decades, there have been numerous reported cases of falsified liquid medical products, including vaccine and insulin preparations worldwide, but to date, there has been a lack of affordable and accessible analytical methods for biological medicines and vaccine authenticity testing. A conventional clinical chemistry analyser (Abbott Architect c16000) was used to determine the concentrations of analytes in genuine liquid biological products (vaccines and insulin) and falsified vaccine surrogates. Eight analytes were measured for each sample: sodium, potassium, chloride, calcium, magnesium, phosphate, glucose and protein. Each genuine liquid product had unique concentrations of analytes when tested using the eight methods applied, allowing clear differentiation from the falsified surrogates. In a blinded study, reproducibility was significantly high when the samples were run intra- and inter-batch up to 9 times over 9 different days, and it was possible to identify most of the samples by analyte presence alone. Imprecision was < 1.0 CV% for ion-selective electrode methods and typically < 5 CV% for spectrophotometric methods. A decision tree was created which was able to identify all samples. We demonstrate for the first time that a conventional clinical chemistry analyser provides a low-cost method to accurately differentiate genuine products from falsified surrogate liquid medicines and vaccines. This novel method has the potential to be used globally due to widespread use of clinical chemistry analysers in hospitals across the world, including in low- and middle-income countries where many cases of falsified medicines have been identified.

Microwave heating of certain foods (e.g., popcorn) can produce an expanded, puffed structure. During certain baking conditions, some cheeses (e.g., mozzarella) can form expanded structures (e.g., blisters on pizza). However, Cheddar cheese usually melts during heating instead of expanding. Our hypothesis is that by altering the proportions of moisture, protein, and fat content in cheese by drying, perhaps we can achieve a cheese composition or texture more favorable to expansion (i.e., less meltable) during microwave heating. A licensed Wisconsin cheesemaker made 4 batches (n = 4) of Cheddar cheese over a 90-d period. Cylinders (9 mm in height × 13 mm in diameter) were cut from the cheese at 7, 14, 30, 90, and 180 d of age during ripening at 4°C. These cylinders were dried for various periods in a Parmesan drying room (15.5°C, 70% relative humidity, ~10 m[3]/min air flow) to make partially dried cheeses with target moisture contents of 20%, 16%, and 12% (and thus, protein contents of 31.2%, 32.9%, and 34.3%, respectively). During heating, none of the partially dried cheese samples melted (loss tangent values <1) when measured with small-strain oscillatory rheology. The partially dried cheeses were heated in a homestyle 900-W microwave oven (35-75 s at 100% power) to attain expanded puffs with ~1.4% moisture and ~0.2 water activity. A split-plot design was used to analyze the impact of the age of the cheese, composition, and their interactions on the properties of the puffs. At 7 d of cheese age, puffs made from cheeses dried to 20% moisture expanded the most, and puffs made from cheeses dried to 12% moisture expanded the least. For cheeses dried to 20% moisture, puff expansion (%) decreased as the age of the cheese (before expansion) increased. Puff texture was assessed by trained sensory panelists (n = 8-10) using a 15-point quantitative descriptive analysis scale and a texture analyzer equipped with an acoustic detector. Sensory hardness and crunchiness of the puffs increased as the moisture content of the dried cheese decreased from 20% to 12%, whereas the quantity of force and sound peaks decreased. Puffs made from cheese dried to 12% moisture achieved higher acoustic peaks (~90 dB) than puffs made from cheese dried to 20% moisture (~80 dB). Scanning electron micrographs of the puffed cheeses indicated well-defined strands of protein were observed in puffs made from cheeses dried to 16% and 12% moisture. All partially dried cheeses expanded during microwave heating. We have demonstrated a novel way of making puffed snacks from Cheddar cheese. In our system, increasing the ripening time of cheese and decreasing the moisture content of the dried cheese (which caused a concomitant increase in the protein content) decreased the expansion of the puffs.

This study introduces an innovative approach for the flexible monitoring of bioprocesses using Raman spectroscopy coupled with automated transfer learning. Traditional Raman spectroscopy requires extensive process-specific calibration, limiting its transferability across different conditions. To address this, we developed an automated method that utilizes the dynamic orthogonal projection (DOP) algorithm to use pre-existing chemometric models built from one process ("Input Process") to monitor a distinct process ("Target Process") which lacked its own Raman models. These new processes conditions varied in terms of culture mode, cell line, media, analyzer, and acquisition settings. This method used spectral data from Target Process to modify the existing spectral data from Input Process, aligning them with the new conditions. The approach was validated on Chinese Hamster Ovary cell cultures, targeting critical metabolic parameters such as glucose, lactate, glutamine, and viable cell density. The results showed that with only one batch used for the transfer, the average relative errors compared to offline values were around 10% for glucose and lactate but remained high for VCD and glutamine. After a second batch to perform the transfer on two batches, the relative errors were further reduced below 10% for most parameters. By effectively transferring models across different processes, this approach minimizes the need for extensive recalibrations, enhancing the efficiency and applicability of Raman spectroscopy in diverse bioprocess environments.