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The impacts and threats posed by wildfires are dramatically increasing due to climate change. In recent years, the wildfire community has attempted to estimate wildfire occurrence with machine learning models. However, to fully exploit the potential of these models, it is of paramount importance to make their predictions interpretable and intelligible. This study is a first attempt to provide an eXplainable artificial intelligence (XAI) framework for estimating wildfire occurrence using a Random Forest model with Shapley values for interpretation. Our findings accurately detected regions with a high presence of wildfires (area under the curve 81.3%) and outlined the drivers empowering occurrence, such as the Fire Weather Index and Normalized Difference Vegetation Index. Furthermore, our analysis suggests the presence of anomalous hotspots. In contexts where human and natural spheres constantly intermingle and interact, the XAI framework, suitably integrated into decision support systems, could support forest managers to prevent and mitigate future wildfire disasters and develop strategies for effective fire management, response, recovery, and resilience.

Advanced oxidation processes represent a viable alternative in water reclamation for potable reuse. Sensing methods of hydrogen peroxide are, therefore, needed to test both process progress and final quality of the produced water. Several bio-based assays have been developed so far, mainly relying on peroxidase enzymes, which have the advantage of being fast, efficient, reusable, and environmentally safe. However, their production/purification and, most of all, batch-to-batch consistency may inherently prevent their standardization. Here, we provide evidence that a synthetic de novo miniaturized designed heme-enzyme, namely Mimochrome VI*a, can be proficiently used in hydrogen peroxide assays. Furthermore, a fast and automated assay has been developed by using a lab-bench microplate reader. Under the best working conditions, the assay showed a linear response in the 10.0–120 μM range, together with a second linearity range between 120 and 500 μM for higher hydrogen peroxide concentrations. The detection limit was 4.6 μM and quantitation limits for the two datasets were 15.5 and 186 μM, respectively. In perspective, Mimochrome VI*a could be used as an active biological sensing unit in different sensor configurations.

Restenosis, defined as the re-narrowing of an arterial lumen after revascularization, represents an increasingly important issue in clinical practice. Indeed, as the number of stent placements has risen to an estimate that exceeds 3 million annually worldwide, revascularization procedures have become much more common. Several investigators have demonstrated that vessels in patients with diabetes mellitus have an increased risk restenosis. Here we present a systematic overview of the effects of diabetes on in-stent restenosis. Current classification and updated epidemiology of restenosis are discussed, alongside the main mechanisms underlying the pathophysiology of this event. Then, we summarize the clinical presentation of restenosis, emphasizing the importance of glycemic control in diabetic patients. Indeed, in diabetic patients who underwent revascularization procedures a proper glycemic control remains imperative.

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7−64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson’s correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.

Substantial evidence indicates that microRNA-155 (miR-155) plays a crucial role in the pathogenesis of diabetes mellitus (DM) and its complications. A number of clinical studies reported low serum levels of miR-155 in patients with type 2 diabetes (T2D). Preclinical studies revealed that miR-155 partakes in the phenotypic switch of cells within the islets of Langerhans under metabolic stress. Moreover, miR-155 was shown to regulate insulin sensitivity in liver, adipose tissue, and skeletal muscle. Dysregulation of miR-155 expression was also shown to predict the development of nephropathy, neuropathy, and retinopathy in DM. Here, we systematically describe the reports investigating the role of miR-155 in DM and its complications. We also discuss the recent results from in vivo and in vitro models of type 1 diabetes (T1D) and T2D, discussing the differences between clinical and preclinical studies and shedding light on the molecular pathways mediated by miR-155 in different tissues affected by DM.

Designing metal sites into de novo proteins has significantly improved, recently. However, identifying the minimal coordination spheres, able to encompass the necessary information for metal binding and activity, still represents a great challenge, today. Here, we test our understanding with a benchmark, nevertheless difficult, case. We assemble into a miniature 28-residue protein, the quintessential elements required to fold properly around a FeCys 4 redox center, and to function efficiently in electron-transfer. This study addresses a challenge in de novo protein design, as it reports the crystal structure of a designed tetra-thiolate metal-binding protein in sub-Å agreement with the intended design. This allows us to well correlate structure to spectroscopic and electrochemical properties. Given its high reduction potential compared to natural and designed FeCys 4 -containing proteins, we exploit it as terminal electron acceptor of a fully artificial chain triggered by visible light. Living organisms regulate their energy demand by managing electron trafficking in complex transport chains. Here, the authors pioneer a fully artificial electron chain triggered by visible light using designed proteins, unlocking possibilities in bioengineering.

Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker.

Pentose phosphate pathway (PPP) is a major glucose metabolism pathway, which has a fundamental role in cancer growth and metastasis. Even though PPP blockade has been pointed out as a very promising strategy against cancer, effective anti-PPP agents are not still available in the clinical setting. Here we demonstrate that the natural molecule polydatin inhibits glucose-6-phosphate dehydrogenase (G6PD), the key enzyme of PPP. Polydatin blocks G6PD causing accumulation of reactive oxygen species and strong increase of endoplasmic reticulum stress. These effects are followed by cell cycle block in S phase, an about 50% of apoptosis, and 60% inhibition of invasion in vitro. Accordingly, in an orthotopic metastatic model of tongue cancer, 100 mg/kg polydatin induced an about 30% tumor size reduction with an about 80% inhibition of lymph node metastases and 50% reduction of lymph node size ( p  < 0.005). Polydatin is not toxic in animals up to a dose of 200 mg/kg and a phase II clinical trial shows that it is also well tolerated in humans (40 mg twice a day for 90 days). Thus, polydatin may be used as a reliable tool to limit human cancer growth and metastatic spread.

Background While the augmented incidence of diabetes after COVID-19 has been widely confirmed, controversial results are available on the risk of developing hypertension during the COVID-19 pandemic. Methods We designed a longitudinal cohort study to analyze a closed cohort followed up over a 7-year period, i.e., 3 years before and 3 years during the COVID-19 pandemic, and during 2023, when the pandemic was declared to be over. We analyzed medical records of more than 200,000 adults obtained from a cooperative of primary physicians from January 1, 2017, to December 31, 2023. The main outcome was the new diagnosis of hypertension. Results We evaluated 202,163 individuals in the pre-pandemic years and 190,743 in the pandemic years, totaling 206,857 when including 2023 data. The incidence rate of new hypertension was 2.11 (95% C.I. 2.08–2.15) per 100 person-years in the years 2017–2019, increasing to 5.20 (95% C.I. 5.14–5.26) in the period 2020–2022 (RR = 2.46), and to 6.76 (95% C.I. 6.64–6.88) in 2023. The marked difference in trends between the first and the two successive observation periods was substantiated by the fitted regression lines of two Poisson models conducted on the monthly log-incidence of hypertension. Conclusions We detected a significant increase in new-onset hypertension during the COVID-19 pandemic, which at the end of the observation period affected ~ 20% of the studied cohort, a percentage higher than the diagnosis of COVID-19 infection within the same time frame. This observation suggests that increased attention to hypertension screening should not be limited to individuals who are aware of having contracted the infection but should be extended to the entire population.

The microRNA(miRNA)-34a is a key regulator of tumor suppression. It controls the expression of a plethora of target proteins involved in cell cycle, differentiation and apoptosis, and antagonizes processes that are necessary for basic cancer cell viability as well as cancer stemness, metastasis, and chemoresistance. In this review, we focus on the molecular mechanisms of miR-34a-mediated tumor suppression, giving emphasis on the main miR-34a targets, as well as on the principal regulators involved in the modulation of this miRNA. Moreover, we shed light on the miR-34a role in modulating responsiveness to chemotherapy and on the phytonutrients-mediated regulation of miR-34a expression and activity in cancer cells. Given the broad anti-oncogenic activity of miR-34a, we also discuss the substantial benefits of a new therapeutic concept based on nanotechnology delivery of miRNA mimics. In fact, the replacement of oncosuppressor miRNAs provides an effective strategy against tumor heterogeneity and the selective RNA-based delivery systems seems to be an excellent platform for a safe and effective targeting of the tumor.