We study the avalanche statistics observed in a minimal random growth model. The growth is governed by a reproduction rate obeying a probability distribution with finite mean $\overline{a}$ and variance $v_a$. These two control parameters determine if the avalanche size tends to a stationary distribution (finite scale statistics with finite mean and variance, or power-law tailed statistics with exponent $\in (1,3]$), or instead to a nonstationary regime with log-normal statistics. Numerical results and their statistical analysis are presented for a uniformly distributed growth rate, which are corroborated and generalized by mathematical results. The latter show that the numerically observed avalanche regimes exist for a wide family of growth rate distributions, and they provide a precise definition of the boundaries between the three regimes.

Although Instagram is widely used among young people, it has only recently caught researchers’ attention. Following the perspective of the I-PACE model, the present study examined the relationship between neuroticism and problematic Instagram use (PIU) in Italian university students who had an Instagram account. We assumed that the relationship between neuroticism and PIU could be explained by direct and indirect paths, passing through Fear of Missing Out (FoMO) and motives for Instagram use. 362 Instagram users (72.1% female; M_age = 25.35, SD = 4.25) filled out a questionnaire assessing demographics, Instagram use features, Neuroticism, Problematic Instagram use, FoMO, and Motivations for Using Instagram. Results showed that neuroticism affected PIU only indirectly through FoMO, escapism and self-expression motives. Additionally, FoMO promoted higher levels of escapism and self-expression motives, increasing participants’ risk of PIU. This is the first study testing the mediating role of motives for using Instagram between personality predisposing factors and PIU. The findings provide new insights into the relationship between the factors that may promote PIU and have important clinical implications. These include the possibility for practitioners to prevent PIU more easily by acting on users’ motivations and cognitions, such as escapism, self-expession and FoMO, rather than working on predisposing personality factors, that are usually more stable over time.

Single-cell transcriptomics has redefined our understanding of cancer by exposing the complexity of tumor ecosystems and their therapeutic vulnerabilities. scRNA-seq studies have identified lineage hierarchies, immune evasion programs, and resistance-associated states across solid and liquid tumors, informing biomarker development and drug discovery. Advanced computational frameworks integrate these data with longitudinal profiling, RNA velocity, and network diffusion to prioritize targets and predict therapeutic response. Emerging multi-omics approaches further expand the scope of precision oncology by linking genetic alterations, protein-level markers, and spatial context to functional states. This narrative review aims to synthesize current applications of single-cell transcriptomics for target discovery, highlight computational frameworks that translate high-dimensional data into actionable insights, and explore how multi-omics integration is shaping future directions. By bridging molecular complexity with target prioritization, these approaches hold promise for translating single-cell insights into clinically actionable biomarkers and therapeutic strategies for personalized cancer treatment and rational drug development.

Pupillometry is a promising technique for the potential diagnosis of several neurological pathologies. However, its potential is not fully explored yet, especially for prediction purposes and results interpretation. In this work, we analyzed 100 pupillometric curves obtained by 12 subjects, applying both advanced signal processing techniques and physics methods to extract typically collected features and newly proposed ones. We used machine learning techniques for the classification of Optic Neuritis (ON) vs. Healthy subjects, controlling for overfitting and ranking the features by random permutation, following their importance in prediction. All the extracted features, except one, turned out to have significant importance for prediction, with an average accuracy of 76%, showing the complexity of the processes involved in the pupillary light response. Furthermore, we provided a possible neurological interpretation of this new set of pupillometry features in relation to ON vs. Healthy classification.

We introduce the Random Walk Approximation (RWA), a new method to approximate the stationary solution of master equations describing stochastic processes taking place on graphs. Our approximation can be used for all processes governed by non-linear master equations without long-range interactions and with a conserved number of entities, which are typical in biological systems, such as gene regulatory or chemical reaction networks, where no exact solution exists. For linear systems, the RWA becomes the exact result obtained from the maximum entropy principle. The RWA allows having a simple analytical, even though approximated, form of the solution, which is global and easier to deal with than the standard System Size Expansion (SSE). Here, we give some theoretically sufficient conditions for the validity of the RWA and estimate the order of error calculated by the approximation with respect to the number of particles. We compare RWA with SSE for two examples, a toy model and the more realistic dual phosphorylation cycle, governed by the same underlying process. Both approximations are compared with the exact integration of the master equation, showing for the RWA good performances of the same order or better than the SSE, even in regions where sufficient conditions are not met.

Introduction: Impulsivity induced by dopaminergic agents, like pramipexole and aripiprazole, can lead to behavioral addictions impacting social functioning and quality of life of patients and families (e.g., resulting in unemployment, marital problems, anxiety). These secondary effects, interconnected in networks of signs and symptoms, are usually overlooked by clinical trials, not reported in package inserts, and neglected in clinical practice. Objective: This study explores the syndromic burden of impulsivity induced by pramipexole and aripiprazole, pinpointing key symptoms for targeted mitigation. Methods: An event-event Information Component (IC) on the FDA Adverse Event Reporting System (January 2004 – March 2022) identified the syndrome of events disproportionally co-reported with impulsivity, separately for pramipexole and aripiprazole. A greedy-modularity clustering on composite network analyses (PPMI, Ising, Φ) identified subsyndromes. Bayesian network modeling highlighted possible precipitating events. Results: Suspected drug-induced impulsivity was documented in 7.49% pramipexole and 4.50% aripiprazole recipients. The highest IC concerned obsessive-compulsive disorder (reporting rate = 26.77%; IC median = 3.47, 95%CI = 3.33-3.57) and emotional distress (21.35%; 3.42, 3.26-3.54) for pramipexole, bankruptcy (10.58%; 4.43, 4.26-4.55) and divorce (7.59%; 4.38, 4.19-4.53) for aripiprazole. The network analysis identified delusional jealousy and dopamine dysregulation subsyndromes for pramipexole, obesity-hypoventilation and social issues for aripiprazole. The Bayesian network highlighted anxiety and economic problems as potentially precipitating events. Conclusion: The under-explored consequences of drug-induced impulsivity significantly burden patients and families. Network analyses, exploring syndromic reactions and potential precipitating events, complement traditional techniques and clinical judgment. Characterizing the secondary impact of reactions will support informed patient-centered decision-making. Highlights: Drug-induced impulsivity significantly impacts patients’ lives. Network analyses help characterize reactions as syndromes. We explore the impulsivity syndrome and subsyndromes resulting from pramipexole and aripiprazole. The manifestation of drug-induced impulsivity was different for the two drugs. Anxiety and economic problems bridge between other symptoms and could be important therapeutical targets.