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Computational prediction and conformation of relationships among microbes, drugs and diseases
Abstract Complex and diverse microbial communities are closely linked to human health, and their study plays a vital role in advancing medicine, particularly personalized healthcare. Identifying potential microbe–disease–drug relationships is useful for drug discovery and clinical treatment, and it also improves our understanding of microbial mechanisms. Due to the complexity and cost of biological experiments, computational methods provide a rapid and efficient way to predict potential interactions between microbes, drugs, and diseases. In this article, we predict relationships between microbes, drugs, and diseases using existing similarity and interaction data through Cytoscape software. Some of the potential relationships were confirmed by the available information, while the others require further clinical investigation. Due to the critical role of the microbiome in disease and medicine, more research and information are needed in this field. In the future, the various interactions between drugs, microbes, and diseases may improve the understanding of personalized medicine, promote early diagnosis, and provide potential treatments for a wide range of diseases.
Nutritional modulation of host physiology, behavior, and gut microbiome in the captive rodent Octodon degus
Combining citation and productivity metrics through harmonic mean enhances researcher ranking accuracy
Abstract Addressing the challenge of predicting scientific impact and ranking researchers is a complex yet critical task, drawing significant attention from scholars across diverse fields. This effort plays a key role in improving research productivity, supporting decision-making processes, and advancing methodologies for scientific evaluation. Over time, various metrics such as citation counts, total publications, hybrid methods, the h index, and h-type indicators have been introduced to identify influential researchers. Despite these efforts, no single metric has been universally accepted as the best approach, as different metrics serve varying purposes and contexts. This study presents a novel index developed through comprehensive analysis of a dataset comprising 1060 Neuroscience researchers, including both awardees and non-awardees. The initial phase of the research involved evaluating specific metrics to determine their ability to place awardees among the top 100 researchers, leading to the identification of the five parameters most frequently associated with awardee inclusion. Advanced deep learning techniques were then applied to refine the selection, pinpointing the top five influential parameters and assessing the disjointness in their outputs. To further enhance the findings, seven statistical models were examined for their ability to combine the most disjoint parameter pair while retaining their individual strengths. Selecting the most disjoint pair ensures that the ranking process integrates diverse evaluation criteria rather than relying on redundant or highly correlated parameters. This approach captures a broader spectrum of researcher impact, reducing bias and increasing the robustness of the final ranking index. Among these models, the h2 upper and k indices exhibited the highest disjointness ratio at 0.97. Additionally, the Harmonic Mean approach demonstrated superior performance, achieving an average impact score of 0.76, and excelled at preserving the unique features of the selected parameter pair. Based on these results, a new index was formulated using the Harmonic Mean (HM) of the most disjoint pair. This index showed significantly improved performance compared to existing metrics, offering a robust solution for ranking researchers effectively.
Chemometric-assisted spectrophotometric approach for stability assessment of safinamide and its synthetic precursor in antiparkinsonian formulation with sustainability profiling
Abstract Parkinson’s disease (PD) is a significant health issue that affects older individuals. Safinamide mesylate (SAF) is a recently developed adjunct therapy increasingly employed in the management of PD. Ensuring the stability of new drug formulations and establishing suitable stability-indicating methodologies is crucial for pharmaceutical analysis. The current study has developed three new, user-friendly, and robust mathematical methods using multivariate spectrophotometric analysis to quantify SAF, its synthetic precursor impurity (4-HBD), and stress-induced degradation products. The multivariate algorithms used include principal component regression (PCR), partial least squares (PLS), and synergy intervals partial least squares (siPLS). The ranges of the proposed methods were 3.00–23.00, 1.00–5.00, 2.50–6.50, and 5.00–13.00 µg/mL for SAF, 4-HBD, SAF hydrolytic degradation products, and SAF oxidative degradation products, respectively. When analyzed using the applied methods, the commercially available tablet preparation showed stability with no impurities or interference from tablet additives. In terms of accuracy and precision, the statistical analysis also revealed no significant differences in comparison to the reported methods. The developed multivariate models were validated using internal and external validation sets. The results demonstrated that the siPLS model outperformed PCR and PLS based on the root mean square error of prediction (RMSEP) and the correlation coefficient values (r). Furthermore, these methods can be used as a substitute for HPLC in quality control laboratories when multiple samples need to be analyzed within a short timeframe. Finally, various sustainability assessment tools were utilized to evaluate and measure the environmental background of the established methods.
The toxicological effects of low-density polyethylene microplastic particles (LDPE-MPs) on the growth and metabolic activities of the marine diatom Chaetoceros muellerii
Abstract The aim of the current study is to examine the response of the marine diatom Chaetoceros muellerii upon exposure to LDPE-MPs. The toxic effects of LDPE-MP treatment on C. muellerii cultures were dependent on its concentration, particle size, and exposure time. The highest percentage of growth inhibition (60.87%) was observed in cultures treated with a dose of 100 mg L⁻ 1 and a particle size of 100 µm of LDPE-MP after 6 days of exposure. A notable reduction was also recorded for the chlorophyll, carotenoids, carbohydrate, and protein contents of the exposed C. muellerii cultures compared to the control. In contrast, exposure to LDPE-MPs promoted the lipid content by 47.78 and 51.78% over control at 100 and 250 µm particle sizes, respectively. Also, enhanced the antioxidant activities of CAT (by 41.76 and 33.33%) and SOD (by 57.26 and 44.87%) of C. muellerii cultures at both tested particle size, respectively. As a defense mechanism, C. muellerii cells secreted exopolysaccharides (EPS) which reached 12.75 and 19.98 folds over control in cultures of 10 mg L⁻ 1 LDPE-MPs at both tested particle size, respectively. The EPS triggered the adsorption of LDPE-MPs on C. muellerii surfaces forming hetero-aggregate clusters, obviously shown in the Scanning Electron Microscopy (SEM) images. The Diffraction Scanning Calorimetric (DSC) technique and combustion techniques were applied for quantifying the adsorbed LDPE-MPs on the surfaces of C. muellerii cells. The accumulated LDPE-MPs on C. muellerii cells at 100 mg L⁻ 1 recorded 0.334 and 0.167 g g −1 DW at 100 and 250µm treatment, respectively. To our knowledge, this is the first work to applying both techniques for quantifying MPs accumulated on the microalgal cells, which could be adopted in future studies.
An integrated facial recognition system for classroom resource optimization using MobileNet and SSA-SVM
Age-specific patterns of breast cancer in Nigerian women unraveled through histological analysis
Abstract Sub-Saharan African women face a high burden of breast cancer, influenced by genetic and lifestyle factors. However, the lack of comprehensive, age-stratified data hinders the identification of risk factors and the development of effective, population-specific interventions. This study aimed to assess age-related variations in breast cancer prevalence among Nigerian women, providing insight into associated risk factors and disease trends. A retrospective review of 3,263 breast histopathology records (9.46% of total from 2015 to 2023) was conducted. Lesions—benign and malignant—were analyzed across five age groups: children and adolescents (0–19), young adults (20–39), middle-aged (40–59), higher-aged (60–79), and elderly (≥ 80), using MS Excel and GraphPad Prism 8.0. Statistical comparisons were performed by age and lesion type. Most cases were in young adults (45.97%) and middle-aged women (33.83%). The left breast was more commonly affected (46.86%) and had higher malignancy rates than the right (44.41%) or bilateral lesions (7.20%). Benign lesions were predominant (56.76%), especially among young adults (57.34%). Malignancy incidence increased with age, peaking in middle-aged women (53.30%). Fibroadenoma was the most frequent benign lesion in children and adolescents and young adults, while fibrosis predominated in middle age. Invasive ductal carcinoma (IDC) was the leading malignant subtype, with a sharp rise by 2023—particularly among middle-aged (172 cases) and young adult women (71 cases). Among 339 immunohistochemically profiled cases, triple-negative breast cancer (TNBC; 42.77%) and ER+/PR+ tumors (36.87%) were most common. TNBC was the only subtype detected in children and adolescents. Middle-aged women bore the highest burden of all subtypes, with a marked increase in TNBC and ER+/PR+ cases in 2023. The rising incidence of aggressive subtypes, particularly TNBC, highlights the need for enhanced molecular diagnostics and personalized therapies. Age-specific trends reinforce the urgency for targeted screening, especially for young and middle-aged Nigerian women.
Simethicone dosages with minimal normal saline solution volume to enhance mucosal visualization in upper endoscopy: a multicenter, randomized, double-blind, placebo-controlled trial
SHLP2 restores pre-osteoblastic cells against oxidative stress-induced inflammaging
Learning transformer network for effective radar chaff jamming suppression
Machine learning algorithms and artificial neural networks for predicting schizophrenia using orbital parameters
A high efficiency slot array antenna with single layered gap waveguide feeding system for long-range wireless systems
Predicting drug solubility in binary solvent mixtures using graph convolutional networks: a comprehensive deep learning approach
Mapping the physiological landscape of body movements during nocturnal sleep and wakefulness and their cardiovascular correlates with a wearable multi-sensor array
Abstract Spontaneous motor activity is a physiological feature of sleep and is enhanced in sleep-related movement disorders. We aimed to develop a measurement and analysis approach to movements during nocturnal sleep and wakefulness, as well as their cardiovascular correlates, entirely based on a wearable multi-sensor array, and to test its feasibility and internal consistency. Twelve healthy participants slept overnight at home wearing an array of seven wearable sensors: five accelerometers, a photoplethysmograph, and an electrocardiograph. Sleep-wake states were determined from wrist actigraphy and sleep diaries. We developed an algorithm for detecting movements based on body segment acceleration and validated it against visual annotations of accelerometer tracings. The F1 scores ranged from 90.6% to 94.4% for different segments. Our algorithm indicated that movements during sleep were significantly fewer than during wakefulness (21.4 ± 1.5 vs. 90.4 ± 6.2 per hour, p < 0.001), with relatively more segmental (42.8% vs. 15.0%) and lower-body (15.3% vs. 5.7%) movements and relatively fewer global movements (23.7% vs. 66.1%). Heart rate started to increase and pulse wave amplitude started to decrease at, or up to 6 s before, movement onset, depending on movement type and wake-sleep state, consistent with cardiac activation and peripheral vasoconstriction due to central autonomic commands. The magnitude of these cardiovascular responses positively correlated with intensity particularly of global movements ( p < 0.001). These results demonstrate the feasibility and internal consistency of a new approach, entirely based on wearables sensors, to map the physiological landscape of body movements and their cardiovascular correlates during nocturnal sleep and wakefulness.
Repurposing the Knutsford-1 borehole as a deep borehole heat exchanger with consideration of palaeoclimate corrections to heat flow in the Cheshire Basin
Abstract Subsurface thermal data from UK boreholes typically lack palaeoclimatic corrections, leading to underestimations in heat flow. This can significantly affect predicted geothermal resources and system performance, creating a false perception of energy limitations. This study evaluates the impact of palaeoclimate corrections on geothermal performance in an onshore setting; focusing on the potential for a well to be re-entered and repurposed as a deep borehole heat exchanger (DBHE). Using the Knutsford-1 borehole, this re-evaluation for palaeoclimatic impacts on heat flow shows that corrected heat flows (52 mW/m 2 ) exceed uncorrected values (46 mW/m 2 ). In a steady state conductive model, temperature predictions based on the corrected heat flow align more closely to the recorded temperature data. Moreover, transient DBHE simulations using OpenGeoSys software over 25 years reveal a minimum 17 kW increase in thermal yield, highlighting the operational implications and benefits of these corrections. With thousands of legacy boreholes worldwide, integrating palaeoclimate corrections into geothermal assessments could reveal substantial untapped energy potential. By unlocking previously overlooked geothermal potential, this research highlights how accurate subsurface (re)assessments can transform legacy infrastructure into a cost-effective, sustainable energy source – demonstrating that with better data and a more holistic approach, existing wells can support low-carbon heat production.
Automated wave runup monitoring using coastal CCTV cameras for tsunami detection
Abstract Coastal closed-circuit television (CCTV) cameras are ubiquitous yet rarely exploited quantitatively for tsunami detection. To address this gap and the scarcity of automated methods, we propose a technique that converts CCTV footage into a time-series of wave runup heights—instantaneous shoreline elevations corresponding to each incoming wave. The workflow has two main steps: (i) compute a luminance-variation (SIGMA) image in which the runup edge appears as a bright curve, and (ii) apply a color-based land–water mask to suppress dynamic noise on land. Preliminary tests under varied lighting, wave, and obstacle conditions confirmed the method’s stability. Application to footage from seven CCTV cameras during the 1 January 2024 Mw 7.5 Noto Peninsula tsunami revealed one of the tsunami’s dominant 300–500 s energy band and yielded root-mean-square errors of 0.094 m and 0.191 m at two sites after removing short-period (< 180 s) components—while processing ran faster than real time on a standard computer. In the cases studied, the extracted runup time-series demonstrated the potential to complement sparse offshore gauges for real-time tsunami detection and post-event analysis. Future work will target a broader range of recording conditions, refine signal separation, and validate the method on additional tsunami events with characteristics different from the Noto case.
Synthesis, antimicrobial evaluation, and computational investigation of new triazine-based compounds via DFT and molecular docking
Abstract A green synthesis protocol produced triazine derivatives such as imidazo-triazine, pyrimido-triazine, pyrazolotriazine, and triazolotriazine, which were subsequently evaluated for their antimicrobial activity.