Human viruses and viruses from animals can cause illnesses in humans after the consumption of contaminated food or water. Contamination may occur during preparation by infected food handlers, during food production because of unsuitably controlled working conditions, or following the consumption of animal-based foods contaminated by a zoonotic virus. This review discussed the recent information available on the general and clinical characteristics of viruses, viral foodborne outbreaks and control strategies to prevent the viral contamination of food products and water. Viruses are responsible for the greatest number of illnesses from outbreaks caused by food, and risk assessment experts regard them as a high food safety priority. This concern is well founded, since a significant increase in viral foodborne outbreaks has occurred over the past 20 years. Norovirus, hepatitis A and E viruses, rotavirus, astrovirus, adenovirus, and sapovirus are the major common viruses associated with water or foodborne illness outbreaks. It is also suspected that many human viruses including Aichi virus, Nipah virus, tick-borne encephalitis virus, H5N1 avian influenza viruses, and coronaviruses (SARS-CoV-1, SARS-CoV-2 and MERS-CoV) also have the potential to be transmitted via food products. It is evident that the adoption of strict hygienic food processing measures from farm to table is required to prevent viruses from contaminating our food.

Patients with viral illness are at higher risk of secondary infections—whether bacterial, viral, or parasitic—that usually lead to a worse prognosis. In the setting of Corona Virus Disease 2019 (COVID-19), the Severe Acute Respiratory Syndrome Coronavirus-type 2 (SARS-CoV-2) infection may be preceded by a prior microbial infection or has a concurrent or superinfection. Previous reports documented a significantly higher risk of microbial coinfection in SARS-CoV-2-positive patients. Initial results from the United States (U.S.) and Europe found a significantly higher risk of mortality and severe illness among hospitalized patients with SARS-CoV-2 and bacterial coinfection. However, later studies found contradictory results concerning the impact of coinfection on the outcomes of COVID-19. Thus, we conducted the present literature review to provide updated evidence regarding the prevalence of coinfection and superinfection amongst patients with SARS-CoV-2, possible mechanisms underlying the higher risk of coinfection and superinfection in SARS-CoV-2 patients, and the impact of coinfection and superinfection on the outcomes of patients with COVID-19.

Background/Objectives: Combining Whole Slide Imaging (WSI) and Artificial Intelligence (AI) in digital pathology (DP) is accelerating the field of diagnostic pathology by improving analysis metrics accuracy, reproducibility, and speed. AI applications in pathology include automated image capture, assessment and analysis, risk stratification, and prognostic prediction. This integration introduces significant challenges, including data quality, high computational demands, the ability to generalize across different settings, and a range of ethical considerations. This review provides an end-to-end roadmap covering WSI acquisition, preprocessing, and deep learning (DL) channels through tumor recognition, biomarker prediction, and evolving computational methods such as original models and combined learning, highlighting the specific challenges and opportunities of WSI-attached AI in pathology. Methods: This review provides a WSI-centric analysis that examines AI and DL applications specifically as they overlap with the acquisition, processing, and computational analysis of WSI. Therefore, this review aims to comprehensively examine the challenges and pitfalls associated with the use of WSI in AI-Based Digital Pathology. Results: Pre-analytical factors like how the tissue is prepared, staining, and scanning artifacts affect AI and contain possible post-analytical barriers such as the range of colors used, color standardization, and algorithm transparency. Furthermore, there may be bias found in the training datasets that can blur the ethical and legal boundaries alongside regulatory uncertainty. Conclusions: Even though there is an array of challenges, AI applied in DP can enhance the accuracy of medical diagnosis, encourage workflow efficiency, facilitate cross-collaboration for pediatric research, and enable research into rare diseases. Further development on the topic needs to focus on defining standard operating procedures and guidelines alongside dependable datasets through teamwork from various scientific fields.

Biomedical and Pharmacology Journal (BPJ) is an international, open access, peer reviewed, quarterly research journal.

The outcomes of clinical studies utilizing Photobiomodulation on tendon disorders are not homogeneous and since its design depends on preclinical studies utilizing animal models as a prerequisite, this review investigates the literature to help extrapolate preclinical evidence-based outcomes (safety and efficacy) into clinical treatment options. PubMed and Embase databases were searched for tendinopathy, Photobiomodulation and preclinical studies with inclusion criteria were restricted to in vivo animal studies using rat's models subjected to partially injured tendons. Data extracted were methodology quality and design, light parameters, study methods, and outcomes. Twenty two articles were included, half of them were partially transected, while the other half were partially traumatized. The most common wavelengths used were 660, 830, and 904 nm with energy density range between 1 and 17 J/cm2 and applied mostly daily. Fourteen studies (64%) relied merely on a single assessment. Histological measurements were the most common method of evaluation (75%) and 35% of studies used chemical measures. Mechanical outcomes were reported in four, and two studies demonstrate significant effects. Photobiomodulation has the potential to enhance tendon structure and function, however, inevitable embedded heterogeneity related to study design and quality of methods, light parameters delivered, and evaluations methods renders the ability to establish unified treatment parameters difficult. Adjustments for variability within preclinical studies might lead to better designing of clinical studies. It is recommended to embrace a protocol for designing and performing preclinical studies to improve its quality. Moreover, it is recommended to prove efficacy using at least two evaluation methods and apply Photobiomodulation as a combined varying energy/wavelength protocol (several energies with several wavelengths). Clinical relevance: Establishing a proof of concept for Photobiomodulation that may lead to further high-quality clinical studies, in which the applicable parameters can be determined.