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Magnetic hyperthermia (MHT) has been shown as a promising alternative therapy for glioblastoma (GBM) treatment. This study consists of three parts: The first part evaluates the heating potential of aminosilane-coated superparamagnetic iron oxide nanoparticles (SPIONa). The second and third parts comprise the evaluation of MHT multiple applications in GBM model, either in vitro or in vivo. The obtained heating curves of SPIONa (100 nm, +20 mV) and their specific absorption rates (SAR) stablished the best therapeutic conditions for frequencies (309 kHz and 557 kHz) and magnetic field (300 Gauss), which were stablished based on three in vitro MHT application in C6 GBM cell line. The bioluminescence (BLI) signal decayed in all applications and parameters tested and 309 kHz with 300 Gauss have shown to provide the best therapeutic effect. These parameters were also established for three MHT applications in vivo, in which the decay of BLI signal correlates with reduced tumor and also with decreased tumor glucose uptake assessed by positron emission tomography (PET) images. The behavior assessment showed a slight improvement after each MHT therapy, but after three applications the motor function displayed a relevant and progressive improvement until the latest evaluation. Thus, MHT multiple applications allowed an almost total regression of the GBM tumor in vivo. However, futher evaluations after the therapy acute phase are necessary to follow the evolution or tumor total regression. BLI, positron emission tomography (PET), and spontaneous locomotion evaluation techniques were effective in longitudinally monitoring the therapeutic effects of the MHT technique.

The thermocoagulation model, which consists of focal cerebral ischemia with craniectomy, is helpful in studying permanent ischemic brain lesions and has good reproducibility and low mortality. This study analyzed the best conditions for inducing a focal ischemic lesion by thermocoagulation. We investigated parameters such as temperature and thermal dissipation in the brain tissue during induction and analyzed real-time blood perfusion, histological changes, magnetic resonance imaging (MRI), and motor behavior in a permanent ischemic stroke model. We used three-month-old male Wistar rats, weighing 300-350 g. In the first experiment, the animals were divided into four groups (n = 5 each): one sham surgery group and three ischemic lesion groups having thermocoagulation induction (TCI) temperatures of 200°C, 300°C, and 400°C, respectively, with blood perfusion (basal and 30 min after TCI) and 2,3,5-Triphenyl-tetrazolium chloride (TTC) evaluation at 2 h after TCI. In the second experiment, five groups (n = 5 each) were analyzed by MRI (basal and 24 h after TCI) and behavioral tests (basal and seven days after TCI) with the control group added for the surgical effects. The MRI and TTC analyses revealed that ischemic brain lesions expressively evolved, especially at TCI temperatures of 300°C and 400°C, and significant motor deficits were observed as the animals showed a decrease frequency of movement and an asymmetric pattern. We conclude that a TCI temperature of 400°C causes permanent ischemic stroke and motor deficit.

What are the main findings? Maximum tillering (∼79 DAS) ranked among the top three UAV windows for CH4, N2O and CO2, with GLCM textures carrying the gain; PlanetScope instead peaked at Midand Late-boot (103–107 DAS), where vegetation indices alone explained the flux. Nested Leave-One-Plot-Out validation reached RCV2 between +0.57 and +0.69 for four of the six platform × gas combinations, and the UAV thermal mean (Tmean) was decisive for N2O (ΔR2=+0.48). What is the implication of the main findings? Phenological windows can be matched to what each sensor actually resolves (texture-rich tillering for the UAV, boot-stage canopy for PlanetScope), so remote GHG monitoring of AWD rice on the Peruvian north coast does not need to be spread evenly across the season. UAV–CH4 and PlanetScope–N2O remained weak, so the workflow needs multi-site and multi-season testing before it is used to report fluxes operationally under AWD irrigation. Alternate wetting and drying (AWD) irrigation reduces CH4 emissions from flooded rice but amplifies N2O pulses; identifying candidate phenological windows for the remote screening of greenhouse gas (GHG) fluxes remains challenging with small datasets. In a single-site, single-season exploratory study at INIA Vista Florida (Lambayeque, Peru), eight UAV flights were paired with eight PlanetScope SuperDove scenes (|Δ|≤1 d) and closed-chamber CH4, N2O and CO2 fluxes under four water regimes (CF, AWD5, AWD10, AWD20; 96 sub-plot × date observations). Multivariate explanatory power was assessed by bootstrap Ridge regression on each sensor’s native predictors (VI + GLCM + Tmean for the UAV, VI for PlanetScope). Maximum tillering (79 DAS) emerged as a candidate UAV window, ranking in the top three for all gases through GLCM textures, whereas PlanetScope peaked at Mid-boot and Late-boot (103–107 DAS), with median R2˜UAV at 0.34–0.71 and R2˜Planet at 0.20–0.60. Nested Leave-One-Plot-Out (LOPO) validation gave RCV2 between +0.57 and +0.69 for four of six platform × gas combinations (UAV-CH4 and Planet-N2O stayed weak), and Tmean was decisive for N2O on the UAV (ΔR2=+0.48). Repeating the stage selection inside every LOPO fold preserved the leading combinations and their ranking. These exploratory windows and sensor-native descriptors need multi-site, multi-season validation before operational use.

Despite being a leading cause of acquired seizures in endemic regions, the pathological mechanisms of neurocysticercosis are still poorly understood. This study aims to investigate the impact of anthelmintic treatment on neuropathological features in a rat model of neurocysticercosis. Rats were intracranially infected with Taenia solium oncospheres and treated with albendazole + praziquantel (ABZ), oxfendazole + praziquantel (OXF), or untreated placebo (UT) for 7 days. Following the last dose of treatment, brain tissues were evaluated at 24 h and 2 months. We performed neuropathological assessment for cyst damage, perilesional brain inflammation, presence of axonal spheroids, and spongy changes. Both treatments showed comparable efficacy in cyst damage and inflammation. The presence of spongy change correlated with spheroids counts and were not affected by anthelmintic treatment. Compared to white matter, gray matter showed greater spongy change (91.7% vs. 21.4%, p < 0.0001), higher spheroids count (45.2 vs. 0.2, p = 0.0001), and increased inflammation (72.0% vs. 21.4%, p = 0.003). In this rat model, anthelmintic treatment destroyed brain parasitic cysts at the cost of local inflammation similar to what is described in human neurocysticercosis. Axonal spheroids and spongy changes as markers of damage were topographically correlated, and not affected by anthelmintic treatment. Grey and white matter pathological differences in neurocysticercosis. Grey matter showing severe spongy change (arrows), inflammatory infiltrate (arrowhead), and abundant axonal spheroids (arrows), while white matter showing slight spongy change (arrow), inflammatory infiltrate (arrowhead), and small varicosities (arrows).

Ultrasound is considered a physical technology that can be applied at various stages of food processing to optimise resources and reduce production time. In this study, the influence of ultrasonic treatments at different power levels (0 W, 100 W, 200 W, and 300 W) on three Isaño genotypes (yellow, yellow with purple eyes, and purple) during alcoholic fermentation was investigated. The main parameters assessed were yeast growth kinetics, ethanol production, and substrate consumption in an ultrasonic bath operating at a frequency of 50 kHz. The findings demonstrated enhanced yeast growth, accelerated substrate consumption, and increased ethanol production compared to the control (untreated sample). However, the impact of ultrasound on fermentation decreased as the power level increased. Notably, an ultrasonic power of 100 W applied over 84 h of fermentation resulted in the highest ethanol yield (10.36% v/v) in the purple Isaño genotype. In conclusion, ultrasonic treatment is a promising approach to improve the fermentation process of Isaño, potentially enabling its development as a functional beverage with both nutritional and therapeutic properties.

High-altitude environments impose unique physiological challenges due to reduced barometric pressure and lower inspired oxygen partial pressure, raising concerns about the safety of face mask use in individuals with chronic obstructive pulmonary disease. While evidence at sea level shows that surgical and N95 masks modestly impair gas exchange-slightly reducing oxygen uptake, oxygen saturation-they consistently increase perceived exertion, dyspnea, and fatigue. Limited studies conducted under hypobaric conditions-typically involving small sample sizes, brief mask-wearing periods, and healthy participants. However, data at higher altitudes and in patients with COPD remain scarce. Emerging epidemiological evidence suggests that COPD prevalence may decrease with increasing altitude, yet high-altitude residence can alter pathophysiology and could increase vulnerability to hypoxemia and hypercapnia during mask use in advanced disease. Existing studies in COPD at sea level report generally modest physiological effects with short-term mask use, but their small sample sizes and short exposure durations limit generalizability. This perspective highlights the urgent need for well-designed studies evaluating both acute and chronic physiological responses to prolonged mask use in COPD patients at high altitudes to inform evidence-based public health recommendations.

Accurate crop yield prediction is crucial for efficient agricultural and socio-economic management. Remote sensing, using satellite imagery and unmanned aerial vehicles (UAVs), provides an effective approach for yield prediction and crop monitoring, allowing time series of vegetation indices such as NDVI to be obtained and facilitating detailed analysis of crop phenology. In this study, multi-seasonality in rice yield prediction is analysed using NDVI time series obtained from Sentinel-2 (S2) and UAVs in the Lambayeque region, Peru. NDVI from S2 was extracted by applying scene classification map (SCM) masks to remove clouds and shadows. A total of 7 and 11 UAV flights were conducted during the growing season for 2022 and 2023, and yield was collected mechanically in 35 rice-producing plots. The results showed an overestimation of NDVI values obtained by UAV compared to Sentinel-2 values, as well as a significant difference in yield prediction between 2022 and 2023. In 2022, by integrating S2 and UAV NDVI series, a coefficient of determination (R2) of 0.66 was obtained for the combination of UAV and S2, higher value than those obtained with UAV or S2 independently, with a root mean square error (RMSE) of 1.096 t/ha and a %RMSE of 10.36. In 2023, a R2 of 0.32, a RMSE of 0.85 and a %RMSE of 9.21 were achieved. This difference is interpreted as a consequence of the cyclone Yaku, which caused rainfall and damage to the irrigation infrastructure, and fungi disease, leading to water stress and a decrease in yield, highlighting the importance of considering the meteorological conditions in the development of yield predictions based on NDVI series metrics obtained along the campaign.

Here we describe multimodal iron oxide nanoparticles conjugated to Rhodamine-B (MION-Rh), their stability in culture medium, and subsequent validation of an in vitro protocol to label mesenchymal stem cells from umbilical cord blood (UC-MSC) with MION-Rh. These cells showed robust labeling in vitro without impairment of their functional properties, the viability of which were evaluated by proliferation kinetic and ultrastructural analyzes. Thus, labeled cells were infused into striatum of adult male rats of animal model that mimic late onset of Parkinson's disease and, after 15 days, it was observed that cells migrated along the medial forebrain bundle to the substantia nigra as hypointense spots in T2 magnetic resonance imaging. These data were supported by short-term magnetic resonance imaging. Studies were performed in vivo, which showed that about 5 × 10(5) cells could be efficiently detected in the short term following infusion. Our results indicate that these labeled cells can be efficiently tracked in a neurodegenerative disease model.

Accurate rice yield prediction is essential for optimizing water management and supporting decision-making in agricultural systems, particularly in arid environments where irrigation efficiency is critical. This study assessed five machine learning algorithms—Multiple Linear Regression (MLR), Support Vector Regression (SVR, linear and RBF), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—for plot-scale rice yield estimation using Sentinel-2 vegetation indices (VIs) during the 2022 and 2023 seasons in the Chancay–Lambayeque Valley, Peru. VIs sensitive to canopy vigor, water status, and structure were derived in Google Earth Engine and optimized via Sequential Forward Selection to identify the most relevant predictors per phenological stage. Models were trained and validated against field yields using leave-one-out cross-validation (LOOCV). Intermediate stages (Flowering, Milk, Dough) yielded the strongest relationships, with water-sensitive indices (NDMI, MSI) consistently ranked as key predictors. MLR and PLSR achieved the highest generalization (R2_CV up to 0.68; RMSE_CV ≈ 1.3 t ha−1), while RF and XGBoost showed high training accuracy but lower validation performance, indicating overfitting. Model accuracy decreased in 2023 due to climatic variability and limited satellite observations. Findings confirm that Sentinel-2–based VI modeling offers a cost-effective, scalable alternative to UAV data for operational rice yield monitoring, supporting water resource management and decision-making in data-scarce agricultural regions.

In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem.