Accurate streamflow forecasting at fine temporal and spatial scales is essential to manage the diverse hydrological behaviors of individual catchments, particularly in rapidly responding mountainous regions. This study compares three forecasting models ARIMAX, LSTM, and HEC-HMS applied to the Posina River basin in northern Italy, using 13 years of hourly hydrological data. While recent literature promotes multi-basin LSTM training for generalization, we show that a well-configured single-basin LSTM, combined with a rolling forecast strategy, can achieve comparable accuracy under high-frequency, data-constrained conditions. The physically based HEC-HMS model, calibrated for continuous simulation, provides robust peak flow prediction but requires extensive parameter tuning. ARIMAX captures baseflows but underestimates sharp hydrological events. Evaluation through NSE, KGE, and MAE shows that both LSTM and HEC-HMS outperform ARIMAX, with LSTM offering a compelling balance between accuracy and ease of implementation. This study enhances our understanding of streamflow model behavior in small basins and demonstrates that LSTM networks, despite their simplified configuration, can be reliable tools for flood forecasting in localized Alpine catchments, where physical modeling is resource-intensive and regional data for multi-basin training are often unavailable.

Background: Heart failure remains a leading cause of morbidity and mortality, and current therapies rarely repair established myocardial damage. Nanoparticle-based interventions have been investigated across heterogeneous models of myocardial injury, remodeling, cardiomyopathy, and heart failure, but the distribution and translational maturity of this evidence remain unclear. Methods: A systematic search of PubMed, Embase, Scopus, and Web of Science was conducted from database inception to June 2024. Eligible reports were mapped according to disease model, experimental system, carrier-level nanoparticle platform, payload, route, comparator, outcomes, biodistribution, safety assessment, and translational characteristics. Reports of non-therapeutic nanoparticle exposure were retained in a separate contextual safety/toxicology stratum and were not included in the therapeutic evidence-density map. Risk of bias was evaluated using design-appropriate tools. Results: Of 2640 records screened, 157 independent studies met the criteria: 140 in the main therapeutic/platform evidence map and 17 in a separate contextual safety/toxicology stratum. Within the main corpus, polymeric systems were the largest platform class (n = 50), followed by inorganic/mineral (n = 35), biological/biomimetic (n = 24), lipid-based (n = 23), carbon-based (n = 5), and hybrid/multicomponent systems (n = 3). Evidence was concentrated in acute myocardial injury (n = 76), while direct same-agent comparisons, long-term safety assessment, repeated dosing, quantitative biodistribution, and clinically aligned heart-failure models remained limited. Conclusions: The field demonstrates substantial formulation diversity and biological activity, but translation is constrained by fragmented characterization, sparse comparative evidence, and incomplete assessment of biological fate and safety.

This study evaluates global practices for managing explanted hearts, with a focus on tissue collection and biobanking protocols. A survey conducted through the International Society for Heart and Lung Transplantation (ISHLT) assessed responses from centers across Europe, North America, and Other Countries. Results demonstrated significant variability in tissue sampling, grossing protocols, and storage practices. While 78.8% of centers had grossing protocols, fewer (73.1%) adapted sampling based on pathology. Fresh tissue collection was prevalent in 63.5% of centers, but volumes varied: North America led with higher sampling rates (10-25 samples per heart), while Europe and Other Countries collected fewer samples. Coronary artery sampling also showed regional differences. Fresh tissues enable advanced molecular studies, while fixed tissues remain fundamental for histopathology. Standardized global protocols for sampling, storage, and reporting could enhance the clinical and research value of explanted hearts, optimizing post-transplant care and driving innovation in cardiac medicine.

Accurate streamflow forecasting is essential for effective water resource management, flood mitigation, and environmental conservation. We present a comparative analysis of three streamflow forecasting models applied to an Italian natural catchment, the Posina River basin. The ARIMAX model extends the traditional ARIMA approach by incorporating exogenous variables, while the LSTM model, a type of recurrent neural network, is designed to capture complex temporal dependencies in time series data. The physical-based continuous model was developed with HEC-HMS software and is based on the Soil Moisture Accounting model, combined with the Clark Unit Hydrograph Model for runoff transformation and a Linear Reservoir Model for baseflow recessions. We utilized a dataset of hourly hydrological data from the Posina River basin, covering nearly 13 years. The models' performances were evaluated using Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency, and Mean Absolute Error as metrics. Results indicate that LSTM and traditional physical-based models outperform the ARIMAX model in predicting streamflow, particularly in capturing peak flows and overall trends. However, the LSTM model tends to smooth out rapid changes in streamflow, while the traditional physical-based model occasionally overestimates peak values. This research highlights the strengths and limitations of each model in hourly streamflow forecasting. We suggest that the choice of model should be tailored to the specific needs of the forecasting task. The LSTM recurrent neural network, more than the ARIMAX model, represents a convenient approach for streamflow forecasting as an alternative to the traditional physical-based hydrological model since it guarantees excellent results with an easier calibration process.

Background and Objective: The analysis of high-dimensional omics data is severely hampered when only a small number of labelled samples are available, a condition common in rare or resource-intensive clinical settings. Current approaches poorly accommodate this regime: methods achieving strong predictive performance require training-set sizes that such cohorts cannot provide, whereas strategies tailored to small samples seldom couple robust classification with the interpretable, reproducible feature selection needed to derive biologically verifiable biomarkers. Methods: We present a machine-learning pipeline for binary miRNA-based phenotyping under data-scarcity constraints. Class-conditional Gaussian data augmentation, applied within training folds only to prevent leakage, addresses limited sample size and class imbalance; L1-regularised logistic regression within a nested cross-validation performs stability-based selection of miRNAs; and a kernel support vector machine classifies on the selected features. Robustness under controlled data scarcity was first assessed on a public lung cancer microRNA dataset of over 2,000 samples, training on subsets as small as 10 samples per class and testing on the remaining cohort across three binary classification tasks. As a clinical proof-of-concept, it was then applied to endomyocardial biopsies from heart transplant recipients to discriminate acute cellular rejection from cytomegalovirus infection. Results: In the lung cancer experiment, the pipeline achieved 70–88% accuracy across the three tasks, with consistent performance between cross-validation and large independent hold-out sets at the smallest training size. In the transplant proof-of-concept, on a necessarily limited cohort, it achieved 91% accuracy, outperforming a baseline logistic regression (75%) and an augmented variant (82%). Pathway enrichment of the selected microRNAs revealed biologically meaningful associations: hsa-miR-514a-3p and hsa-miR-198 with the extracellular matrix receptor interaction pathway, and hsa-miR-490-5p with the glutamatergic synapse pathway. Conclusions: The pipeline provides an interpretable framework for high-dimensional omics classification under data-scarcity constraints, designed to be platform-agnostic. The transplant findings remain a proof-of-concept given the limited cohort size, but consistent performance from minimal training sets to large independent cohorts positions it as a promising tool for translational bioinformatics and biomarker discovery where sample availability is inherently limited. Prospective validation on larger cohorts and further platforms will be conducted to translate these findings towards clinical practice.