Mass testing is fundamental to face the pandemic caused by the coronavirus SARS-CoV-2 discovered at the end of 2019. To this aim, it is necessary to establish reliable, fast and cheap tools to detect viral particles in biological material so to identify the people capable to spread the infection. We demonstrate that a colorimetric biosensor based on gold nanoparticle (AuNP) interaction induced by SARS-CoV-2 lends itself as an outstanding tool for detecting viral particles in nasal and throat swabs. The extinction spectrum of a colloidal solution of multiple viral-target gold nanoparticles – AuNPs functionalized with antibodies targeting three surface proteins of SARS-CoV-2 (spike, envelope and membrane) - is redshifted in few minutes when mixed to a solution containing the viral particle. The optical density of the mixed solution measured at 560 nm was compared to the threshold cycle (C_t) of a real-time PCR (gold standard for detecting the presence of viruses) finding that the colorimetric method is able to detect very low viral load with a detection limit approaching that of the real-time PCR. Since the method is sensitive to the infecting viral particle rather than to its RNA, the achievements reported here open new perspective not only in the context of the current and possible future pandemics, but also in microbiology as the biosensor proves itself to be a powerful though simple tool for measuring the viral particle concentration.

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.

Although appreciable attempts in screening and diagnostic approaches have been achieved, prostate cancer (PCa) remains a widespread malignancy, representing the second leading cause of cancer-related death in men. Drugs currently used in PCa therapy initially show a potent anti-tumor effect, but frequently induce resistance and PCa progresses toward metastatic castration-resistant forms (mCRPC), virtually incurable. Liquid biopsy has emerged as an attractive and promising strategy complementary to invasive tissue biopsy to guide PCa diagnosis and treatment. Liquid biopsy shows the ability to represent the tumor microenvironment, allow comprehensive information and follow-up the progression of the tumor, enabling the development of different treatment strategies as well as permitting the monitoring of therapy response. Liquid biopsy, indeed, is endowed with a significant potential to modify PCa management. Several blood biomarkers could be analyzed for diagnostic, prognostic and predictive purposes, including circulating tumor cells (CTCs), extracellular vesicles (EVs), circulating tumor DNA (ctDNA) and RNA (ctRNA). In addition, several other body fluids may be adopted (i.e., urine, sperm, etc.) beyond blood. This review dissects recent advancements and future perspectives of liquid biopsies, highlighting their strength and weaknesses in PCa management.

Aims/hypothesis Type 2 diabetes and obesity are associated with increased risk of site-specific cancers. We have investigated whether metabolic alterations at the level of adipose-derived differentiating cells may affect specific phenotypes of breast cancer cells. Methods Growth profiles of breast cancer cell lines were evaluated in co-cultures with differentiated adipocytes or their precursor cells and upon treatment with adipocyte conditioned media. Production and release of cytokines and growth factors were assessed by real-time RT-PCR and multiplex-based ELISA assays. Results Co-cultures with either differentiated mouse 3T3-L1 or human mammary adipocytes increased viability of MCF-7 cells to a greater extent, when compared with their undifferentiated precursors. Adipocytes cultured in 25 mmol/l glucose were twofold more effective in promoting cell growth, compared with those grown in 5.5 mmol/l glucose, and activated mitogenic pathways in MCF-7 cells. Growth-promoting action was also enhanced when adipocytes were incubated in the presence of palmitate or oleate. Interestingly, 3T3-L1 and human adipocytes released higher amounts of keratinocyte-derived chemokine/IL-8, the protein ‘regulated upon activation, normally T expressed, and secreted’ (RANTES), and IGF-1, compared with their precursor cells. Their levels were reduced upon incubation with low glucose and enhanced by fatty acids. Moreover, both undifferentiated cells and differentiated adipocytes from obese individuals displayed about twofold higher IGF-1 release and MCF-7 cell growth induction than lean individuals. Finally, inhibition of the IGF-1 pathway almost completely prevented the growth-promoting effect of adipocytes on breast cancer cells. Conclusions/interpretation IGF-1 release by adipocytes is regulated by glucose and fatty acids and may contribute to the control of cancer cell growth in obese individuals.

Congenital hypothyroidism with thyroid dysgenesis (TD) is a frequent human condition characterized by elevated levels of TSH in response to reduced thyroid hormone levels. Congenital hypothyroidism is a genetically heterogeneous disease. In the majority of cases studied, no causative mutations have been identified and very often the disease does not show a Mendelian transmission. However, in approximately 5% of cases, it can be a consequence of mutations in genes encoding the TSH receptor or the transcription factors TITF1, FOXE1, or PAX8. We report here that in mouse models, the combination of partial deficiencies in the Titf1 and Pax8 genes results in an overt TD phenotype that is absent in either of the singly deficient, heterozygous mice. The disease is characterized by a small thyroid gland, elevated levels of TSH, reduced thyroglobulin biosynthesis, and high occurrence of hemiagenesis. The observed phenotype is strain specific, and the pattern of transmission indicates that at least two other genes, in addition to Titf1 and Pax8, are necessary to generate the condition. These results show that TD can be of multigenic origin in mice and strongly suggest that a similar pathogenic mechanism may be observed in humans.