D-MAINS: A Deep-Learning Model for the Label-Free Detection of Mitosis, Apoptosis, Interphase, Necrosis, and Senescence in Cancer Cells.

BACKGROUND: Identifying cells engaged in fundamental cellular processes, such as proliferation or living/death statuses, is pivotal across numerous research fields. However, prevailing methods relying on molecular biomarkers are constrained by high costs, limited specificity, protracted sample preparation, and reliance on fluorescence imaging. METHODS: Based on cellular morphology in phase contrast images, we developed a deep-learning model named Detector of Mitosis, Apoptosis, Interphase, Necrosis, and Senescence (D-MAINS). RESULTS: D-MAINS utilizes machine learning and image processing techniques, enabling swift and label-free categorization of cell death, division, and senescence at a single-cell resolution. Impressively, D-MAINS achieved an accuracy of 96.4 ± 0.5% and was validated with established molecular biomarkers. D-MAINS underwent rigorous testing under varied conditions not initially present in the training dataset. It demonstrated proficiency across diverse scenarios, encompassing additional cell lines, drug treatments, and distinct microscopes with different objective lenses and magnifications, affirming the robustness and adaptability of D-MAINS across multiple experimental setups. CONCLUSIONS: D-MAINS is an example showcasing the feasibility of a low-cost, rapid, and label-free methodology for distinguishing various cellular states. Its versatility makes it a promising tool applicable across a broad spectrum of biomedical research contexts, particularly in cell death and oncology studies.

Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.

Despite recent advances in cancer treatment, refining therapeutic agents remains a critical task for oncologists. Precise evaluation of drug effectiveness necessitates the use of 3D cell culture instead of traditional 2D monolayers. Microfluidic platforms have enabled high-throughput drug screening with 3D models, but current viability assays for 3D cancer spheroids have limitations in reliability and cytotoxicity. This study introduces a deep learning model for non-destructive, label-free viability estimation based on phase-contrast images, providing a cost-effective, high-throughput solution for continuous spheroid monitoring in microfluidics. Microfluidic technology facilitated the creation of a high-throughput cancer spheroid platform with approximately 12 000 spheroids per chip for drug screening. Validation involved tests with eight conventional chemotherapeutic drugs, revealing a strong correlation between viability assessed via LIVE/DEAD staining and phase-contrast morphology. Extending the model's application to novel compounds and cell lines not in the training dataset yielded promising results, implying the potential for a universal viability estimation model. Experiments with an alternative microscopy setup supported the model's transferability across different laboratories. Using this method, we also tracked the dynamic changes in spheroid viability during the course of drug administration. In summary, this research integrates a robust platform with high-throughput microfluidic cancer spheroid assays and deep learning-based viability estimation, with broad applicability to various cell lines, compounds, and research settings.

Association between adult-onset still's disease and COVID-19: A report of two cases and brief review.

Adult-onset still's disease is a rare multisystemic autoinflammatory disorder with an estimated annual incidence of 0.16-0.62 per 100,000 individuals worldwide. It is typically considered a diagnosis of exclusion. SARS-CoV-2 is a positive-strand RNA virus that causes the acute respiratory infection known as COVID-19. Although COVID-19 predominantly affects the respiratory system, it has also been proposed as a trigger for autoimmune diseases, like adult-onset still's disease, as both share considerable pathophysiological similarities. We report two cases of patients with adult-onset still's disease, where COVID-19 was the most likely cause for a flare-up in the first case and the most likely trigger for adult-onset still's disease in the second case. Although the exact mechanism is not entirely understood, the similarities between adult-onset still's disease and COVID-19 could indicate a shared underlying mechanism explaining why COVID-19 can lead to adult-onset still's disease or worsen its symptoms. Further research is necessary to fully comprehend the intricate connections between the two conditions and their immunological effects.

Acute presentation of congenital diaphragmatic hernia requiring damage control laparotomy in an adult patient.

Congenital diaphragmatic hernias constitute an infrequent but potentially serious presentation of bowel obstruction in the adult patient. Herein, we present a rare case of an adult patient with strangulation of colon within a Morgagni's hernia where timely recognition and intervention were life-saving. An 18-year-old female presented with an acute abdomen, respiratory failure, and shock secondary to a strangulated, previously undiagnosed Morgagni hernia requiring emergency laparotomy, reduction of hernia contents and resection of non-viable colon. The patient underwent repair of the hernia with restoration of bowel continuity and reconstruction of her abdominal wall in sequential fashion. Although congenital diaphragmatic hernias have been previously described in the adult population, there are few if any reports of such pathology presenting in such an acute, life-threatening fashion. This case highlights the importance of a high index of suspicion, early recognition, and timely surgical intervention for this rare, potentially fatal condition.