The main aim of this research was to find and develop a new spectrophotometric method for Visible analysis of Metformin hydrochloride in a pharmaceutical. The method was based on the quantitative reaction of metformin hydrochloride with 0.1% beta-naphthol alkaline solution in the presence of 5% sodium nitrite and 15% hydrochloric acid by resting in cold conditions for 25 minutes. The following was observed: the quantitative formation of an intense yellow azo dye with an absorption maximum at λ = 408 nm, which was subsequently dosed in relation to double distilled water as a blank. Method was linear over the studied concentration range between 0,80 μg/mL – 8,00 μg/mL. Linear regression coefficient was R² = 0,9994 , R² ≥ 0,9990 and correlation coefficient R = 0.9997, R > 0.9990; both fit perfectly within the normal limits. Limit of Detection (LOD) = 0,17496 μg/ mL, and limit of quantitation (LOQ) = 0,5832 μg/ mL, LD < 1 and LQ < 1 . Standard Error of the regression line was SE = 0.0065902, SE << 1, which had a very small value that was statistically accepted. Pure Metformin hydrochloride content found was 998.9858 mg in tablet, very close to the official stated amount of 1000 mg pure Metformin hydrochloride on extended-release tablet. The relative percentage deviation of the calculated amount 998.9858 mg was 0.1014% below the official reference value of 1000 mg and fell below the maximum average percentage deviation allowed (± 5%) imposed by the Romanian European Pharmacopoeias Rules.

Unsaturated hydraulic conductivity (USHC) is a key property that governs water flow in partially saturated soils. This study examines the USHC of lateritic soil treated with Bacillus megaterium using three soil–water characteristic curve (SWCC) models: van Genuchten (VG), Brooks–Corey (BC), and Fredlund–Xing (FX). Conductivity was estimated as the product of relative hydraulic conductivity, derived from model fitting parameters, and the saturated hydraulic conductivity (k$_{sat}$) measured through falling head tests. Specimens were compacted at different moulding water contents (MWC), microbial suspension densities, and energy levels to evaluate their combined influence on USHC. Results revealed that USHC generally decreased with increasing MWC. Higher values were obtained for specimens compacted on the dry side of optimum, due to the dominance of larger pores, whereas wet-side compaction resulted in lower conductivity because of finer pore structures. Variation in microbial suspension density produced only slight reductions in USHC, attributed to calcite precipitation from microbial urease activity, which partially obstructed pores. With respect to compaction, the VG model predicted rising USHC with matric suction, while BC and FX indicated decreasing patterns. These variations were linked to pore structure, calcite distribution, and model parameter sensitivity. The findings highlight the interactive effects of water content, microbial treatment, and compaction effort on USHC and confirm the usefulness of SWCC-based models in predicting hydraulic performance of bio-stabilized lateritic soils.

The Sogamoso River basin, a major tributary of the Magdalena River in Colombia, is influenced by agricultural, industrial, and energy-related activities that have promoted the accumulation of trace metals, posing a significant environmental risk. While the presence of these metals in soils and sediments has been documented, their dynamics in water and suspended particulate matter (SPM) are critical to understanding their transport and ecological impact. This study evaluated concentrations of Cd, Pb, and Hg in SPM and their relationship with partitioning mechanisms associated with organic carbon and nitrogen content. Water samples collected at 10 sites along the basin were filtered through 0.45 μm membranes. Cadmium and lead were determined by graphite furnace atomic absorption, while total mercury was measured following EPA Method 7473 using a RA-915LAB Direct Mercury Analyzer. Organic carbon and nitrogen data were incorporated to examine associations with trace metals in SPM. Concentrations ranged from 4.48–63.31 ng/mg SPM for Cd, 31.77–271.66 ng/mg SPM for Pb, and 0.06–0.74 ng/mg SPM for Hg. Results showed a common trend: initial low concentrations in the upper catchment, a rise in areas dominated by cattle ranching and fruit crops, followed by continuous decline. Positive correlations with organic carbon highlight that SPM with higher organic content serves as the main carrier of these contaminants. Findings suggest that trace metals in the Sogamoso River are predominantly bound to fine suspended fractions, favoring downstream transport to the Magdalena River and enhancing ecotoxicological risk.

Serine protease inhibitor (AMPI) was extracted from Aegle marmelos fruit pulp to explore its antimicrobial potential. Extraction was performed using a phosphate buffer with protease inhibitors, followed by ammonium sulfate precipitation and chromatographic purification using DEAE-cellulose ion-exchange and Sephadex G-75 and G-50 gel filtration columns. Biochemical characterization of AMPI showed that it retained inhibitory activity over a wide temperature range, with optimal performance at 30°C. Activity decreased sharply above 50°C, indicating thermal sensitivity beyond physiological limits. pH profiling revealed maximal stability between pH 6 and 8, with a notable decline under strongly acidic or alkaline conditions. AMPI maintained its function in the presence of non-ionic surfactants, but its activity was diminished by certain metal ions, particularly Fe³⁺ and Cu²⁺, suggesting potential oxidative vulnerability. AMPI was tested against S. aureus strains for its ability to inhibit and eradicate biofilms. At higher concentrations, the inhibitor demonstrated significant anti-biofilm effects, with MSSA showing the highest response. Biofilm eradication at 8× MIC levels exceeded 80% in certain strains, supporting AMPI's potential as a biofilm-targeting agent. Membrane permeabilization studies using standard fluorescence-based methods showed that AMPI induced gradual damage to both the outer and inner membranes in a time-dependent manner. To evaluate the regulatory impact of AMPI, sRNA expression profiling was conducted using quantitative real-time PCR. At 12 hours post treatment, early upregulation of RNAIII, Teg41, and Teg49 was observed in MSSA, possibly reflecting a transient stress response. By 24 hours, however, the expression of all tested sRNAs was markedly downregulated across MSSA, MRSA, and MDR-SA.

Cuffed endotracheal tubes (ETTs) are widely used in critical care settings to provide life-saving mechanical ventilation to patients undergoing surgery or experiencing respiratory distress. However, improper inflation and inadequate monitoring of ETT cuff pressure can lead to postoperative complications such as sore throat, tracheal mucosal injury, and ventilator-associated pneumonia. The recommended cuff pressure range to reduce the chances of any complications is 20-30 cmH₂O. This study aims to characterize the inflation characteristics of different sizes of commercially available ETT to improve our understanding of their cuff compliance dynamics. Two benchtop tests were performed: (1) unrestricted inflation to measure intracuff pressure, inflation volume, and cuff diameter of the ETT, and (2) restricted inflation within a rigid tracheal analog to capture the relationship between intracuff pressure and tracheal wall contact pressure. Using volume and pressure data from these tests, cuff compliance was calculated to measure each cuff’s inflation characteristics. In comparing data from both tests, it was found that the cuff compliance is higher for high-volume cuffs than medium-volume cuffs in the unrestricted tests, but is found to be higher for medium-volume cuffs in the restricted tests. Unrestricted data also revealed significant manufacturing differences across cuff sizes, with no uniform pattern between sizes of either cuff type. These findings highlight critical differences in inflation characteristics between ETT types and sizes, underscoring the dangers of generalizing ETT behavior and emphasizing the importance of continuous intracuff pressure monitoring to reduce the risk of complications and improve patient care.

Climate change is one of the most important issues facing the world today; precise climate modelling is crucial for forecasting its effects, directing adaptation plans, and influencing policies. Despite their scientific rigour, traditional climate models frequently have drawbacks, such as high computational costs, coarse temporal and spatial resolutions, and difficulties accurately representing complex nonlinear processes. By facilitating the identification of hidden patterns, increasing predictive accuracy, and accelerating simulation processes, artificial intelligence (AI) has recently surfaced as a promising tool to supplement and extend traditional approaches. This manuscript reviews the current use of AI for climate change modelling, focusing primarily on machine learning, deep learning, and hybrid AI approaches for diverse tasks. Several previous studies indicate that AI-driven models can improve seasonal forecasting, enhance extreme weather event projections, and optimise resources and energy management in light of climate change. Despite these advancements, numerous challenges remain. Limited data availability and quality frequently characterise climate change-related studies, significantly impacting the reliability of AI models. Furthermore, issues with computational scalability, uncertainty quantification, and integration with physical climate models continue to constrain widespread adoption. Future work must concentrate on improving surrogate modelling techniques, building strong AI-driven early warning systems for climate-related disasters, and creating hybrid frameworks that combine AI and physical models to fill these gaps.

Intensifying climate change, characterized by aridification and diminishing freshwater reserves, necessitates sustainable desalination to achieve Algeria’s 2030 Water Strategy goal of 2.1 billion m³/year. Conventional seawater reverse osmosis (SWRO), with energy demands of 3–4 kWh/m³ and brine discharge of 1–1.5 times product water, poses sustainability challenges. This study introduces a novel framework integrating renewable energy, artificial intelligence (AI), and circular economy principles to enhance water security. The framework combines solar-powered SWRO, AI-driven optimization using Artificial Neural Networks and Genetic Algorithms, MCDM for site selection, and brine valorization for lithium and magnesium recovery. Solar photovoltaic systems leverage Algeria’s high solar irradiance (>5 kWh/m²/day), while AI optimizes operational efficiency. MCDM balances energy, environmental, and public health criteria, and brine valorization targets economic sustainability. Solar-powered SWRO reduces greenhouse gas emissions by 85–95% compared to fossil fuel-based systems and 70–80% versus thermal desalination. AI optimization lowers costs by 15–35%, and brine valorization offsets 10–20% of expenses, potentially yielding $50–100 million annually. The framework projects a CO₂ reduction of 2.1 million tons/year by 2030, aligning with SDGs 6, 7, 9, and 13. This framework transforms desalination into a sustainable, circular economy-driven solution, offering a replicable model for arid coastal regions globally. Strategic site selection and smart grid integration minimize ecological and health risks, ensuring climate-resilient water security.

Powered wheelchair maneuvering skill is essential for independence and safety in people with mobility impairment. However, real-time monitoring of maneuver compliance under training or assessment is challenging for therapists, affecting intervention effectiveness and assessment objectiveness. To overcome this and digitalize users' performance, rehabilitation engineers at the Hospital Authority Community Rehabilitation Service Support Centre (CRSSC) created a Boundary Detection System (BDS); this study assesses the effectiveness of the BDS, comparing system and manual counting.

BDS utilized wheelchair-clamped webcams recording real-time video of wheels and boundaries. Through computer vision algorithms, wheels (grey) and boundaries (yellow) were separated through color filtering, with contours identified using binary masking. Boundary violations were registered when wheel contours intersected a dilated boundary contour during indoor training within a training area. Subjects were asked to complete clockwise and counterclockwise circles for three laps without cues. Human ethics approval was acquired from the Central Institutional Review Board (Ref. No. KC/KE-23-0216/ER-1).

In total, 13 male and 13 female (mean age=67.3±10.2) wheelchair users were recruited. The system-detected boundary violations (8.74 ± 7.25) differ significantly (p < 0.001) from the manual counting method (7.22 ± 6.86), representing the high sensitivity of the proposed system. Overall, an average of 2.64 ± 2.48 boundary violations that lasted less than 0.5 seconds wascaptured by the system and validated by video inspection, which shows the system’s ability to eliminate human error.

This pilot study illustrates the viability of the BDS as a computer vision-based, scalable solution for objective wheelchair training monitoring. Future research will advance algorithmic accuracy and investigate integrations with clinical rehabilitation practice to modernize intervention effectiveness and objectiveness.

Membrane capacitive deionization (MCD) technology offers numerous opportunities for water, wastewater treatment, desalination, nutrient removal and recovery and high value-added chemical production. While MCD technology has been explored very well, there are many design-, material- and process-related challenges that pose barriers to their large-scale applications. In this research, we study design parameters such as the distance between the electrodes and membranes, the volume of the anode and cathode compartments and the type of electrode materials that may influence the performance of the MCD process. This study investigates the effect of electrode–membrane distance, novel carbon electrode materials and the type of ion exchange membranes on the efficiency of the MCD process. We test the novel MCD process for the application of desalination across varying concentrations of saline water. We analyze the performance of the system using different initial salt concentrations to assess its ion removal capacity and energy consumption at various electric loads and different process conditions. Key performance indicators such as salt removal efficiency, charge efficiency, and energy per ion removed were evaluated. In addition, we also evaluate the nutrient recovery potential of this novel MCD process. Nitrogen and phosphorous recovery potentials from used hydroponic nutrient water are evaluated using this process. Similar parameters such as the nutrient recovery efficiency, charge efficiency and specific energy consumption of the process are evaluated. The results demonstrate that optimal design of anode and cathode compartments, electrode–membrane distance and sustainable electrode materials may enhance resource efficiency and cost savings.

Introduction: Gallbladder cancer (GBC) is the most common and aggressive malignancy of biliary tract and has a poor prognosis. Radical surgery is only choice of curative treatment but most of the patients diagnosed with GBC are unresectable. Thus, there is an urgent need of developing the diagnostic biomarkers and therapeutic targets in GBC. Here, we aimed to discover and validate novel regulatory signatures for early diagnosis of patients with GBC.

Methods: Next-generation sequencing (NGS) was performed to identify the dysregulated plasma microRNAs in a discovery phase of patients with GBC (n=17), chronic cholecystitis (CC, n=9) and healthy controls (HC, n=4). The significantly distinct expression of two miRNAs on DESeq2 was validated using real-time PCR in 58 GBC, 20 CC and 10 HC.

Results: Using DESeq2 of high-confidence microRNA data between GBC vs. HC, 312 microRNAs were identified. Of 312 microRNAs, 25 microRNAs were significantly upregulated in GBC than HC. The expression of only a single microRNA hsa-miR-378a-3p was found to be 1.8-fold higher in patients with GBC than CC (p=0.012). During the validation phase, hsa-miR-378a-3p was found to be significantly higher in GBC than CC (P=0.001) and HC (P<0.001), respectively. The second microRNA hsa-miR-423-3p was also upregulated in GBC than HC (P=0.002); its expression was comparable in GBC to CC.

Conclusions: NGS results showed that dysregulated microRNAs profiling were found to be significantly different among patients with GBC than CC and HC. Serum hsa-miR-378a-3p and hsa-miR-423-3p may act as potential non-invasive diagnostic biomarkers for GBC.