Browse Articles

The application of machine learning approaches to classify and predict fertility rate in Ethiopia 

Population-level shape variation and otolith asymmetry in Diplodus annularis

Multi-branch LSTM encoded latent features with CNN-LSTM for Youtube popularity prediction

Seasonal forecasting of East African short rains

Proximal tubule-on-chip as a model for predicting cation transport and drug transporter dynamics

Abstract Deciphering the sources of variability in drug responses requires to understand the processes modulating drug pharmacokinetics. However, pharmacological research suffers from poor reproducibility across clinical, animal, and experimental models. Predictivity can be improved by using Organs-on-Chips, which are more physiological, human-oriented, micro-engineered devices that include microfluidics. OoC are particularly relevant at the fundamental and preclinical stages of drug development by providing more accurate assessment of key pharmacokinetic events. We have developed a proximal tubule-on-a-chip model combining commercial microfluidic and chip technologies. Using the RPTEC/TERT1 cell line, we set up a dual-flow system with antiparallel flows to mimic the dynamics of blood and urine. We assessed transporters mRNA expression, cellular polarization and protein expression via immunofluorescence, and monitored the transcellular transport of prototypic xenobiotics by determining their efflux ratios. Our results show that flow exposure significantly modulate mRNA expression of drug membrane transporters. Dynamic conditions also enhance cell polarization, as evidenced by preferential basal and apical expressions of Na + /K + -ATPase, P-gp, OCT2, and MATE1 , as well as the cellular secretory profile. We demonstrated unidirectional transcellular transport of metformin with a higher efflux than influx ratio, inhibited with OCT2 inhibitor, thus confirming the relevance of our proximal tubule-on-a-chip set up for cation transport investigations. Our proximal tubule-on-a-chip can also be used to explore the interactions between transporters, xenobiotics, and endogenous metabolites, possibly involved in the variability of individual drug responses. This study provides additional evidence that OoC can help bridge the gaps between systemic and local pharmacokinetics.

Association between cognitive restraint, emotional eating, uncontrolled eating, and body mass index among health care professionals

Enhancing black mulberry storage with sodium caseinate and gum tragacanth edible films

Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy

Multi-objective optimization of hybrid energy systems using gravitational search algorithm

Transcriptome analysis of liver injury of fatty liver disease induced by ALDH2 deficiency

Echocardiography evaluation of iatrogenic atrial septal defect after combined procedure of catheter ablation and left atrial appendage closure for atrial fibrillation

Cost-effectiveness analysis of durvalumab with chemotherapy and maintenance durvalumab with or without olaparib for advanced endometrial cancer

Real-time trajectory imaging of alpha particles emitted from actinium-225 and its daughter radionuclides

Abstract In targeted alpha-particle therapy, actinium-225 (Ac-225) has emerged as a radionuclide of potential, driving extensive efforts to develop innovative radiopharmaceuticals. High-resolution imaging of alpha particles is required for precisely detecting alpha-emitting radionuclides in cellular environments and small organs. Here, we report real-time trajectory imaging of alpha particles emitted by Ac-225 and its daughter radionuclides, utilizing an alpha particle trajectory imaging system. This system incorporates a magnification unit, a cooled electron-multiplying charge-coupled device (EM-CCD) camera, and a Ce-doped Gd3Al2Ga3O12 (GAGG) scintillator. Alpha particles were projected onto the GAGG scintillator, producing magnified images that were captured at 100 ms intervals. We successfully tracked particle trajectories with varying lengths and intensities for 4 different alpha particles emitted from Ac-225 and its daughter radionuclides with a spatial resolution of 1.0 μm. Notably, we achieved the imaging of sequentially emitted trajectories from Fr-221 and its decay product At-217, characterized by short decay intervals, along with the extended trajectories of high-energy alpha particles emitted by Po-213. These results demonstrate that high-resolution trajectory imaging, integrated with temporal and energy information, offers profound insights into the real-time behavior of Ac-225 and its daughter radionuclides within living cells or tissue sections, thereby driving advancements in targeted alpha-particle therapy.

Quantifying intratumoral biomarker heterogeneity in tubo-ovarian high-grade serous carcinoma to optimize clinical translation

Characterization of gut microbiota on gender and age groups bias in Thai patients with autism spectrum disorder

Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates

Abstract Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. Six input combinations were created, utilizing Tmax, Tmin, RHmean, Rs, and U2, with the PM-FAO56 model serving as the benchmark. Model performance was assessed using four statistical indicators: Kling-Gupta efficiency index (KGE), coefficient of determination (R2), mean squared error (RMSE), and relative root squared error (RRSE). Results indicate that the Valiantzas 2013 (VAL2013b) model outperformed other empirical models across all stations, achieving high KGE and R2 values (0.95–0.97) and low RMSE (0.32–0.35 mm/day) and RRSE (8.14–10.30%). The XGBoost-LightGBM and RF-LightGBM hybrid models exhibited the highest accuracy (average RMSE of 0.015–0.097 mm/day), underscoring the potential of hybrid ML models for RET estimation in subhumid and semi-arid regions, thereby enhancing water resource management and irrigation scheduling.

Generation and propagation of high fecundity gene edited fine wool sheep by CRISPR/Cas9

Comparative response of fennel, ajwain, and anise in terms of osmolytes accumulation, ion imbalance, photosynthetic and growth functions under salinity

Electro-tactile modulation of muscle activation and intermuscular coordination in the human upper extremity

Exploring the utility of different bulking agents for speeding up the composting process of household kitchen waste

Abstract Household kitchen waste (HKW) is produced in large quantity and its management is difficult due to high moisture content and complex organic matter. Aerobic composting of HKW is an easy, efficient, cost-effective and eco-friendly method. This study is designed to achieve a zero-waste concept and to convert HKW. We optimized the type and size of three different bulking agents to speed up the composting process. The tested bulking agents were fallen leaves, sawdust and fly ash. The results showed a higher and longer thermophilic phase (55oC) for 11 days in C2. Higher moisture content (69%) and higher organic matter degradation (38.4%) were also observed in C2. The pH range in all compost treatments was 7-8.5, Electrical conductivity range was 1.8–3.55 mS/cm, C/N ratio range was 15.4–18.1, water holding capacity range was 3.25–4.3 g water/g dry sample, total potassium range was 1.52–1.61%, total phosphorous range was 0.83–1.14%. The highest germination index (119.1%) was also obtained in C2. The highest chili height (16.7 cm), greater number of leaves (20), greater shoot fresh weight (4.75 g) and root fresh weight (1.2 g) was obtained in the presence of C2. Similarly, greater water WHC (2.8 g water/g DW), higher porosity (55.49%) and higher aggregate stability (54.14%) of soil was also obtained by C2. This research effectively reduced the maturation time to 32 days and converted kitchen waste into compost (resource). This is a very practical idea for home composting and kitchen gardening to combat food security issues in developing countries.