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Colorectal cancer (CRC) represents approximately 10% of all cancers and is the second most common cause of cancer deaths. Initial clinical presentation as metastatic CRC (mCRC) occurs in approximately 20% of patients. Moreover, up to 50% of patients with localized disease eventually develop metastases. Appropriate clinical management of these patients is still a challenging medical issue. Major ef-forts have been made to unveil the molecular landscape of mCRC. This has resulted in the identification of several druggable tumor molecular targets with the aim of developing personalized treatments for each patient. This review summarizes the im-provements in the clinical management of patients with mCRC in the emerging era of precision medicine. In fact, molecular stratification, on which the current treatment algorithm for mCRC is based, although it does not completely represent the complex-ity of this disease, has been the first significant step toward clinically informative genetic profiling for implementing more effective therapeutic approaches. This has resulted in a clinically relevant increase in mCRC disease control and patient survival. The next steps in the clinical management of mCRC will be to integrate the compre-hensive knowledge of tumor gene alterations, of tumor and microenvironment gene and protein expression profiling, of host immune competence as well as the applica-tion of the resulting dynamic changes to a precision medicine- based continuum of care for each patient. This approach could result in the identification of individual prognostic and predictive parameters, which could help the clinician in choosing the most appropriate therapeutic program(s) throughout the entire disease journey for each patient with mCRC.

Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges included in the 2030 Agenda for Sustainable Development. Near real-time data on the food insecurity situation collected by international organizations such as the World Food Programme can be crucial to monitor and forecast time trends of insufficient food consumption levels in countries at risk. Here, using food consumption observations in combination with secondary data on conflict, extreme weather events and economic shocks, we build a forecasting model based on gradient boosted regression trees to create predictions on the evolution of insufficient food consumption trends up to 30 days in to the future in 6 countries (Burkina Faso, Cameroon, Mali, Nigeria, Syria and Yemen). Results show that the number of available historical observations is a key element for the forecasting model performance. Among the 6 countries studied in this work, for those with the longest food insecurity time series, that is Syria and Yemen, the proposed forecasting model allows to forecast the prevalence of people with insufficient food consumption up to 30 days into the future with higher accuracy than a naive approach based on the last measured prevalence only. The framework developed in this work could provide decision makers with a tool to assess how the food insecurity situation will evolve in the near future in countries at risk. Results clearly point to the added value of continuous near real-time data collection at sub-national level.

In the tumor microenvironment, cancer cells experience hypoxia resulting in the accumulation of misfolded/unfolded proteins largely in the endoplasmic reticulum (ER). Consequently, ER proteotoxicity elicits unfolded protein response (UPR) as an adaptive mechanism to resolve ER stress. In addition to canonical UPR, proteotoxicity also stimulates the selective, autophagy-dependent, removal of discrete ER domains loaded with misfolded proteins to further alleviate ER stress. These mechanisms can favor cancer cell growth, metastasis, and long-term survival. Our investigations reveal that during hypoxia-induced ER stress, the ER-phagy receptor FAM134B targets damaged portions of ER into autophagosomes to restore ER homeostasis in cancer cells. Loss of FAM134B in breast cancer cells results in increased ER stress and reduced cell proliferation. Mechanistically, upon sensing hypoxia-induced proteotoxic stress, the ER chaperone BiP forms a complex with FAM134B and promotes ER-phagy. To prove the translational implication of our mechanistic findings, we identified vitexin as a pharmacological agent that disrupts FAM134B-BiP complex, inhibits ER-phagy, and potently suppresses breast cancer progression in vivo.

As climate change accelerates, human societies face growing exposure to disasters and stress, highlighting the urgent need for effective early warning systems (EWS). These systems monitor, assess, and communicate risks to support resilience and sustainable development, but challenges remain in hazard forecasting, risk communication, and decision-making. This perspective explores the transformative potential of integrated Artificial Intelligence (AI) modeling. We highlight the role of AI in developing multi-hazard EWSs that integrate Meteorological and Geospatial foundation models (FMs) for impact prediction. A user-centric approach with intuitive interfaces and community feedback is emphasized to improve crisis management. To address climate risk complexity, we advocate for causal AI models to avoid spurious predictions and stress the need for responsible AI practices. We highlight the FATES (Fairness, Accountability, Transparency, Ethics, and Sustainability) principles as essential for equitable and trustworthy AI-based Early Warning Systems for all. We further advocate for decadal EWSs, leveraging climate ensembles and generative methods to enable long-term, spatially resolved forecasts for proactive climate adaptation. In the era of climate change, human societies face growing exposure to disasters and complex climate risks. This perspective explores the transformative potential of integrated Artificial Intelligence in developing multi-hazard Early Warning Systems for all.

Identifying populations at risk of inadequate micronutrient intake is necessary for governments and development partners in low- and middle-income countries to make informed and timely decisions on nutrition-relevant policies and programmes. In this study, we propose a machine learning methodological approach using data on household dietary diversity, socioeconomic status, and climate indicators to predict the risk of inadequate micronutrient intake. Using case studies from Ethiopia and Nigeria, we demonstrate that the models effectively predict risk, with key predictors showing consistency in terms of importance and direction. We also illustrate the feasibility of transferring models between countries, offering a short-term, practical solution for contexts lacking nationally representative micronutrient data. Our results show that this machine learning methodological approach can generate geographically and socioeconomically disaggregated risk estimates that reflect expected patterns of nutritional vulnerability, supporting more targeted and data-driven nutrition interventions.

Abstract Background Coronavirus disease 2019 (COVID-19) has triggered the disruption of health care on a global scale. With Italy tangled up in the pandemic response, oncology care has been largely diverted and cancer screenings suspended. Our multicenter Italian study aimed to evaluate whether COVID-19 has impacted access to diagnosis, staging, and treatment for patients newly diagnosed with colorectal cancer (CRC), compared with pre-pandemic time. Methods All consecutive new CRC patients referred to 8 Italian oncology institutions between March and December 2020 were included. Access rate and temporal intervals between date of symptoms onset, radiological and cytohistological diagnosis, treatment start and first radiological evaluation were analyzed and compared with the same months of 2019. Results A reduction (29%) in newly diagnosed CRC cases was seen when compared with 2019 (360 vs 506). New CRC patients in 2020 were less likely to be diagnosed with early stage (stages I-II-III) CRC (63% vs 78%, P < .01). Gender and sidedness were similar regardless of the year. The percentage of tumors with any mutation among BRAF, NRAS, and KRAS genes were significantly different between the 2 years (61% in 2020 vs 50% in 2019, P = .04). Timing of access to cancer diagnosis, staging, and treatment for patients with CRC has not been negatively affected by the pandemic. Significantly shorter temporal intervals were observed between symptom onset and first oncological appointment (69 vs 79 days, P = .01) and between histological diagnosis and first oncological appointment (34 vs 42 days, P < .01) during 2020 compared with 2019. Fewer CRC cases were discussed in multidisciplinary meetings during 2020 (38% vs 50%, P = .01). Conclusions Our data highlight a significant drop in CRC diagnosis after COVID-19, especially for early stage disease. The study also reveals a remarkable setback in the multidisciplinary management of patients with CRC. Despite this, Italian oncologists were able to ensure diagnostic–therapeutic pathways proper operation after March 2020. This article evaluates whether COVID-19 has affected access to diagnosis, staging, and treatment for patients with colorectal cancer.

Osimertinib is a third-generation tyrosine kinase inhibitor clinically approved for first-line treatment of EGFR-mutant non-small cell lung cancer (NSCLC) patients. Although an impressive drug response is initially observed, in most of tumors, resistance occurs after different time and an alternative therapeutic strategy to induce regression disease is currently lacking. The hyperactivation of MEK/MAPKs, is one the most common event identified in osimertinib-resistant (OR) NSCLC cells. However, in response to selective drug pressure, the occurrence of multiple mechanisms of resistance may contribute to treatment failure. In particular, the epithelial-to-mesenchymal transition (EMT) and the impaired DNA damage repair (DDR) pathways are recognized as additional cause of resistance in NSCLC thus promoting tumor progression. Here we showed that concurrent upregulation of ITGB1 and DDR family proteins may be associated with an increase of EMT pathways and linked to both osimertinib and MEK inhibitor resistance to cell death. Furthermore, this study demonstrated the existence of an interplay between ITGB1 and DDR and highlighted, for the first time, that combined treatment of MEK inhibitor with DDRi may be relevant to downregulate ITGB1 levels and increase cell death in OR NSCLC cells.

Colorectal cancer (CRC) is a heterogeneous disease representing a therapeutic challenge, which is further complicated by the common occurrence of several molecular alterations that confer resistance to standard chemotherapy and targeted agents. Mechanisms of resistance have been identified at multiple levels in the epidermal growth factor receptor (EGFR) pathway, including mutations in KRAS, NRAS, and BRAFV600E, and in the HER2 and MET receptors. These alterations represent oncogenic drivers that may co-exist in the same tumor with other primary and acquired alterations via a clonal selection process. Other molecular alterations include DNA damage repair mechanisms and rare kinase fusions, potentially offering a rationale for new therapeutic strategies. In recent years, genomic analysis has been expanded by a more complex study of epigenomic, transcriptomic, and microenvironment features. The Consensus Molecular Subtype (CMS) classification describes four CRC subtypes with distinct biological characteristics that show prognostic and potential predictive value in the clinical setting. Here, we review the panorama of actionable targets in CRC, and the developments in more recent molecular tests, such as liquid biopsy analysis, which are increasingly offering clinicians a means of ensuring optimal tailored treatments for patients with metastatic CRC according to their evolving molecular profile and treatment history.

Background Previous studies showed that the combination of an anti-Epidermal growth factor (EGFR) and a MEK-inhibitor is able to prevent the onset of resistance to anti-EGFR monoclonal antibodies in KRAS-wild type colorectal cancer (CRC), while the same combination reverts anti-EGFR primary resistance in KRAS mutated CRC cell lines. However, rapid onset of resistance is a limit to combination therapies in KRAS mutated CRC. Methods We generated four different KRAS mutated CRC cell lines resistant to a combination of cetuximab (an anti-EGFR antibody) and refametinib (a selective MEK-inhibitor) after continuous exposure to increasing concentration of the drugs. We characterized these resistant cell lines by evaluating the expression and activation status of a panel of receptor tyrosine kinases (RTKs) and intracellular transducers by immunoblot and qRT-PCR. Oncomine comprehensive assay and microarray analysis were carried out to investigate new acquired mutations or transcriptomic adaptation, respectively, in the resistant cell lines. Immunofluorescence assay was used to show the localization of RTKs in resistant and parental clones. Results We found that PI3K-AKT pathway activation acts as an escape mechanism in cell lines with acquired resistance to combined inhibition of EGFR and MEK. AKT pathway activation is coupled to the activation of multiple RTKs such as HER2, HER3 and IGF1R, though its pharmacological inhibition is not sufficient to revert the resistant phenotype. PI3K pathway activation is mediated by autocrine loops and by heterodimerization of multiple receptors. Conclusions PI3K activation plays a central role in the acquired resistance to the combination of anti-EGFR and MEK-inhibitor in KRAS mutated colorectal cancer cell lines. PI3K activation is cooperatively achieved through the activation of multiple RTKs such as HER2, HER3 and IGF1R.

Early warning systems are an essential tool for effective humanitarian action. Advance warnings on impending disasters facilitate timely and targeted response which help save lives and livelihoods. In this work we present a quantitative methodology to forecast levels of food consumption for 60 consecutive days, at the sub-national level, in four countries: Mali, Nigeria, Syria, and Yemen. The methodology is built on publicly available data from the World Food Programme’s global hunger monitoring system which collects, processes, and displays daily updates on key food security metrics, conflict, weather events, and other drivers of food insecurity. In this study we assessed the performance of various models including Autoregressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM) Network, Convolutional Neural Network (CNN), and Reservoir Computing (RC), by comparing their Root Mean Squared Error (RMSE) metrics. Our findings highlight Reservoir Computing as a particularly well-suited model in the field of food security given both its notable resistance to over-fitting on limited data samples and its efficient training capabilities. The methodology we introduce establishes the groundwork for a global, data-driven early warning system designed to anticipate and detect food insecurity. Levels of food consumption for the next 60 consecutive days can be forecast for Mali, Nigeria, Syria, and Yemen, using a machine-learning methodology that combines publicly available ecological, social-economic, and conflict-related data.