The World Health Organization (WHO) declared in a May 2023 announcement that the COVID-19 illness is no longer categorized as a Public Health Emergency of International Concern (PHEIC); nevertheless, it is still considered an actual threat to world health, social welfare and economic stability. Consequently, the development of a convenient, reliable and affordable approach for detecting and identifying SARS-CoV-2 and its emerging new variants is crucial. The fingerprint and signal amplification characteristics of surface-enhanced Raman spectroscopy (SERS) could serve as an assay scheme for SARS-CoV-2. Here, we report a machine learning-based label-free SERS technique for the rapid and accurate detection and identification of SARS-CoV-2. The SERS spectra collected from samples of four types of coronaviruses on gold nanoparticles film, fabricated using a Langmuir–Blodgett self-assembly, can provide more spectroscopic signatures of the viruses and exhibit low limits of detection (<100 TCID₅₀/mL or even <10 TCID₅₀/mL). Furthermore, the key Raman bands of the SERS spectra were systematically captured by principal component analysis (PCA), which effectively distinguished SARS-CoV-2 and its variant from other coronaviruses. These results demonstrate that the combined use of SERS technology and PCA analysis has great potential for the rapid analysis and discrimination of multiple viruses and even newly emerging viruses without the need for a virus-specific probe.

Indirect transmission of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been investigated but it is still not completely understood. The present study aimed to compare the persistence and viability of the lineage B.1 and omicron BA.1 subvariant in five daily-use materials to evaluate the role of fomites as a possible source of infection. Artificial contamination was performed in the first set of materials, ethylene vinyl acetate (EVA), cardboard, polystyrene, aluminium, and plastic. Further surfaces using BA.1 (glass, plexiglass, cotton, polyester, and tetrapak) were conducted. The persistence, viability of Vero E6 cell cultures and the residual infectivity of the two lineages were evaluated over 5 days. The results showed different stabilities between the tested matrices. In cotton and polyester, the RNA was undetectable in 24 and 48h post-contamination (p.c.), respectively, and the virus was not viable within 30 min, while in the other surfaces, both lineages, RNA was detectable until 120h p.c. A rapid decay of the viral load was revealed on cardboard, mostly for the omicron variant. Furthermore, on all the materials, longer stability of BA.1 was demonstrated, but showing a less intense CPE than the wild-type. EVA was the material that was able to better sustain virus stability as the virus developed CPE up to 72h p.c. In conclusion, the potential spread of SARS-CoV-2 through fomites is conceivable, albeit it is difficult to establish the real capacity to infect people. Nevertheless, thise information is fundamental to adopting the appropriate measures to mitigate the spread of SARS-CoV-2 and its variants.

Climate changes and human-related activities are identified as major factors responsible for the increasing distribution and abundance of vectors worldwide and, consequently, of vector-borne diseases (VBDs). Farmed animals, during grazing or in establishments with the absence of biosecurity measures, can easily be exposed to wildlife showing high-risk of contagion of several infectious diseases, including VBDs. Furthermore, livestock represents an interface between wildlife and humans, and thus, promoting the transmission pathway of VBDs. Little is known about the presence and prevalence of VBDs in livestock in Southern Italy; therefore, the present study evaluated the circulation of zoonotic VBDs in livestock and potential risk of exposure. A total of 621 whole blood samples belonging to cattle and buffaloes (n = 345) and small ruminants (n = 276) were examined by molecular examinations for the detection of tick-borne pathogens (TBPs). High prevalence (66.3%) for at least one agent was observed. Moreover, the risk of exposure related to environmental features was assessed, as follows: presence of humid areas, high-density of animals, and sample collection during May. These results show a high circulation of TBPs among livestock and underline the need for surveillance in high-risk habitats for public health.

We investigated the occurrence of eight potential zoonotic viruses in 91 exotic companion mammals from pet shops in southern Italy via real-time PCR and end-point PCR. The animals were screened for aichivirus, sapovirus, astrovirus, hepatitis A, noroviruses (GI and GII), rotavirus, circovirus, and SARS-CoV-2. Among the nine species of exotic pets studied, only one rat tested positive for aichivirus. The high sequence similarity to a murine kobuvirus-1 strain previously identified in China suggests that the virus may have been introduced into Italy through the importation of animals from Asia. Since exotic companion mammals live in close contact with humans, continuous sanitary monitoring is crucial to prevent the spread of new pathogens among domestic animals and humans. Further investigations on detecting and typing zoonotic viruses are needed to identify emerging and re-emerging viruses to safeguard public health.

Rotavirus is one of the leading causes of acute gastroenteritis worldwide, and its surveillance in wastewater represents a valuable tool for early outbreak detection and public health monitoring. In this work, we address the need for a rapid, sensitive, and cost-effective sensing platform capable of detecting Rotavirus in complex wastewater matrices without the use of time-consuming molecular amplification techniques. To this aim, we developed a surface-enhanced Raman scattering (SERS) biosensor based on large-area plasmonic substrates fabricated via liquid-liquid self-assembly of monodisperse gold nanoparticles driven by the Marangoni effect. The substrates were functionalized with monoclonal anti-Rotavirus antibodies, and SERS spectra were acquired from both controlled samples and real wastewater matrices. Spectral data were subsequently processed and classified using a supervised machine learning approach based on a support vector machine (SVM). The SERS substrates exhibited high uniformity, reproducibility, long-term stability, and low fabrication cost. The antibody-functionalized biosensor enabled sensitive and selective detection of Rotavirus, with a limit of detection of 3.9 TCID₅₀/mL in phosphate-buffered saline and a clear dose-dependent response. When applied to wastewater matrices, the trained SERS-SVM classification pipeline achieved an average accuracy of 0.89 and a Cohen's κ of 0.79, supporting the feasibility of discriminating between Rotavirus-positive and negative samples. These results highlight the proof-of-concept value of the proposed SERS-machine-learning platform as a screening tool for wastewater-based epidemiology.