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.

Background: Tendinopathy is a challenging condition associated with high treatment costs, prolonged dysfunction, and lower quality of life. Current treatment strategies aim to accelerate healing by modulating the healing phases. Phototherapy and growth factor-based modalities have shown promising outcomes in promoting tendon healing. A two-factor experimental design investigates the therapeutic efficacy of conditioning a partially tenotomized rat Achilles tendon model with low concentrations of collagenase, followed by platelet-rich plasma and/or light-emitting diode treatments. Methods: Forty-six adult male Wistar rats (284.8g ± 6.8) were randomly assigned to nine groups (G1 (n = 6), G2–G9; n = 5 per group) based on the treatment applied upon a partially incised rat’s hind-limb Achilles tendon model for three weeks. On day 21, blood samples were collected for hematological and biochemical analyses and tendon explants were harvested and subjected to histology. Results: Observational findings support the safety and validity of the model with insignificant weight gain. Hematological measures revealed no significant differences, except WBC, which was affected by phototherapy (p = 0.037). Blood biochemical measures of creatinine and AST levels were significantly affected by collagenase, while both treatments significantly influence CPK levels (p < 0.001). Histological scores revealed no significant main or interaction effect of both treatment modalities. Effect size estimates for biochemical variables were strong effects while hematological and histological variables demonstrated weak effects. Conclusions: Preconditioning a partially incised tendon with low collagenase and combined with PRP and/or LED therapy may offer therapeutic benefits by enhancing the remodeling phase of tendon repair. Study results validated the rat model, which could be a reliable model for future research to refine treatment as well as the investigational tools protocols.