A Systematic Review of Lung Autopsy Findings in Elderly Patients after SARS-CoV-2 Infection

Immune-Mediated Ocular Surface Disease in Diabetes Mellitus-Clinical Perspectives and Treatment: A Narrative Review

Diabetes mellitus (DM) is a chronic metabolic disorder marked by hyperglycemia due to defects in insulin secretion, action, or both, with a global prevalence that has tripled in recent decades. This condition poses significant public health challenges, affecting individuals, healthcare systems, and economies worldwide. Among its numerous complications, ocular surface disease (OSD) is a significant concern, yet understanding its pathophysiology, diagnosis, and management remains challenging. This review aims to explore the epidemiology, pathophysiology, clinical manifestations, diagnostic approaches, and management strategies of diabetes-related OSD. The ocular surface, including the cornea, conjunctiva, and associated structures, is vital for maintaining eye health, with the lacrimal functional unit (LFU) playing a crucial role in tear film regulation. In DM, changes in glycosaminoglycan metabolism, collagen synthesis, oxygen consumption, and LFU dysfunction contribute to ocular complications. Persistent hyperglycemia leads to the expression of cytokines, chemokines, and cell adhesion molecules, resulting in neuropathy, tear film abnormalities, and epithelial lesions. Recent advances in molecular research and therapeutic modalities, such as gene and stem cell therapies, show promise for managing diabetic ocular complications. Future research should focus on pathogenetically oriented therapies for diabetic neuropathy and keratopathy, transitioning from animal models to clinical trials to improve patient outcomes.

Virtual histological staining of unlabeled autopsy tissue

Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.

COVID-19 among infants: key clinical features and remaining controversies

Infants aged <1 year represent a seemingly more susceptible pediatric subset for infections. Despite this, coronavirus disease 2019 (COVID-19) infection has not been proven as more serious in this age group (outside the very early neonatal period) than in others. Indeed, a considerable number of asymptomatic infections have been recorded, and the symptoms and morbidity associated with COVID- 19 differ minimally from those of other respiratory viral infections. Whether due to an abundance of caution or truly reduced susceptibility, infections in infants have not raised the same profile as those in other age groups. In addition to direct severe acute respiratory syndrome coronavirus 2 diagnostic tests, laboratory markers that differentiate COVID-19 from other viral infections lack specificity in infants. Gastrointestinal presentations are common, and the neurological complications of infection mirror those of other respiratory viral infections. There have been relatively few reports of infant deaths. Under appropriate precautions, breastfeeding in the context of maternal infections has been associated with tangible but infrequent complications. Vaccination during pregnancy provides protection against infection in infants, at least in the early months of life. Multi-inflammatory syndrome in children and multi-inflammatory syndrome in neonates are commonly cited as variants of COVID-19; however, their clinical definitions remain controversial. Similarly, reliable definitions of long COVID in the infant group are controversial. This narrative review examines the key clinical and laboratory features of COVID-19 in infants and identifies several areas of science awaiting further clarification.

Altered sphingolipid pathway in SARS-CoV-2 infected human lung tissue

Introduction

The SARS-CoV-2 mediated COVID-19 pandemic has impacted millions worldwide. Hyper-inflammatory processes, including cytokine storm, contribute to long-standing tissue injury and damage in COVID-19. The metabolism of sphingolipids as regulators of cell survival, differentiation, and proliferation has been implicated in inflammatory signaling and cytokine responses. Sphingosine-kinase-1 (SK1) and ceramide-synthase-2 (CERS2) generate metabolites that regulate the anti- and pro-apoptotic processes, respectively. Alterations in SK1 and CERS2 expression may contribute to the inflammation and tissue damage during COVID-19. The central objective of this study is to evaluate structural changes in the lung post-SARS-CoV-2 infection and to investigate whether the sphingolipid rheostat is altered in response to SARS-CoV-2 infection.

Methods

Central and peripheral lung tissues from COVID-19+ or control autopsies and resected lung tissue from COVID-19 convalescents were subjected to histologic evaluation of airspace and collagen deposisiton, and immunohistochemical evaluation of SK1 and CERS2.

Results

Here, we report significant reduction in air space and increase in collagen deposition in lung autopsy tissues from patients who died from COVID-19 (COVID-19+) and COVID-19 convalescent individuals. SK1 expression increased in the lungs of COVID-19+ autopsies and COVID-19 convalescent lung tissue compared to controls and was mostly associated with Type II pneumocytes and alveolar macrophages. No significant difference in CERS2 expression was noted. SARS-CoV-2 infection upregulates SK1 and increases the ratio of SK1 to CERS2 expression in lung tissues of COVID-19 autopsies and COVID-19 convalescents.

Discussion

These data suggest an alteration in the sphingolipid rheostat in lung tissue during COVID-19, suggesting a potential contribution to the inflammation and tissue damage associated with viral infection.

Association of SARS-CoV-2 viral load with abnormal laboratory characteristics and clinical outcomes in hospitalised COVID-19 patients

We conducted a retrospective, analytical cross-sectional and single-centre study that included 190 hospitalised COVID-19 patients in the Fujian Provincial Hospital South Branch between December 2022 and January 2023 to analyse the correlation of viral loads of throat swabs with clinical progression and outcomes. To normalise the Ct value as quantification of viral loads, we used RNase P gene as internal control gene and subtracted the Ct value of SARS-CoV-2 N gene from the Ct value of RNase P gene, termed △Ct. Most patients were discharged (84.2%), and only 10 (5.6%) individuals who had a lower △Ct value died. The initial △Ct value of participants was also significantly correlated with some abnormal laboratory characteristics, and the duration time of SARS-CoV-2 was longer in patients with severe symptoms and a lower △Ct value at admission. Our study suggested that the △Ct value may be used as a predictor of disease progression and outcomes in hospitalised COVID-19 patients.

Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in lung cancer detection, identify the ML architectures currently in use, and evaluate the clinical relevance of these diagnostic imaging methods. A systematic search of PubMed, Web of Science, Cochrane, and Scopus databases was conducted in February 2023, encompassing the literature published up until December 2022. The review included nine studies, comprising five case-control studies, three retrospective cohort studies, and one prospective cohort study. Various ML architectures were analyzed, including artificial neural network (ANN), entropy degradation method (EDM), probabilistic neural network (PNN), support vector machine (SVM), partially observable Markov decision process (POMDP), and random forest neural network (RFNN). The ML architectures demonstrated promising results in detecting and classifying lung cancer across different lesion types. The sensitivity of the ML algorithms ranged from 0.81 to 0.99, while the specificity varied from 0.46 to 1.00. The accuracy of the ML algorithms ranged from 77.8% to 100%. The AI architectures were successful in differentiating between malignant and benign lesions and detecting small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This systematic review highlights the potential of ML AI architectures in the detection and classification of lung cancer, with varying levels of diagnostic accuracy. Further studies are needed to optimize and validate these AI algorithms, as well as to determine their clinical relevance and applicability in routine practice.