Earlier investigations have revealed that tumor cells undergo metabolic reprogramming and mainly derive their cellular energy from aerobic glycolysis rather than oxidative phosphorylation even in the presence of oxygen. However, recent studies have shown that certain cancer cells display increased oxidative phosphorylation or high metabolically active phenotype. Cellular bioenergetic profiling of 13 established and 12 patient derived ovarian cancer cell lines revealed significant bioenergetics diversity. The bioenergetics phenotype of ovarian cancer cell lines correlated with functional phenotypes of doubling time and oxidative stress. Interestingly, chemosensitive cancer cell lines (A2780 and PEO1) displayed a glycolytic phenotype while their chemoresistant counterparts (C200 and PEO4) exhibited a high metabolically active phenotype with the ability to switch between oxidative phosphorylation or glycolysis. The chemosensitive cancer cells could not survive glucose deprivation, while the chemoresistant cells displayed adaptability. In the patient derived ovarian cancer cells, a similar correlation was observed between a high metabolically active phenotype and chemoresistance. Thus, ovarian cancer cells seem to display heterogeneity in using glycolysis or oxidative phosphorylation as an energy source. The flexibility in using different energy pathways may indicate a survival adaptation to achieve a higher ‘cellular fitness’ that may be also associated with chemoresistance.

Nanomedicine is a very promising field and nanomedical drugs have recently been used as therapeutic agents against cancer. In a previous study, we showed that Nanoceria (NCe), nanoparticles of cerium oxide, significantly inhibited production of reactive oxygen species, cell migration and invasion of ovarian cancer cells in vitro, without affecting cell proliferation and significantly reduced tumor growth in an ovarian cancer xenograft nude model. Increased expression of folate receptor-α, an isoform of membrane-bound folate receptors, has been described in ovarian cancer. To enable NCe to specifically target ovarian cancer cells, we conjugated nanoceria to folic acid (NCe-FA). Our aim was to investigate the pre-clinical efficacy of NCe-FA alone and in combination with Cisplatin. Ovarian cancer cell lines were treated with NCe or NCe-FA. Cell viability was assessed by MTT and colony forming units. In vivo studies were carried in A2780 generated mouse xenografts treated with 0.1 mg/Kg NCe, 0.1 mg/Kg; NCe-FA and cisplatinum, 4 mg/Kg by intra-peritoneal injections. Tumor weights and burden scores were determined. Immunohistochemistry and toxicity assays were used to evaluate treatment effects. We show that folic acid conjugation of NCe increased the cellular NCe internalization and inhibited cell proliferation. Mice treated with NCe-FA had a lower tumor burden compared to NCe, without any vital organ toxicity. Combination of NCe-FA with cisplatinum decreased the tumor burden more significantly. Moreover, NCe-FA was also effective in reducing proliferation and angiogenesis in the xenograft mouse model. Thus, specific targeting of ovarian cancer cells by NCe-FA holds great potential as an effective therapeutic alone or in combination with standard chemotherapy.

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI’s significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI’s utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI’s potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI’s versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

Omental adipocytes promote ovarian cancer by secretion of adipokines, cytokines and growth factors, and acting as fuel depots. We investigated if metformin modulates the ovarian cancer promoting effects of adipocytes. Effect of conditioned media obtained from differentiated mouse 3T3L1 preadipoctes on the proliferation and migration of a mouse ovarian surface epithelium cancer cell line (ID8) was estimated. Conditioned media from differentiated adipocytes increased the proliferation and migration of ID8 cells, which was attenuated by metformin. Metformin inhibited adipogenesis by inhibition of key adipogenesis regulating transcription factors (CEBPα, CEBPß, and SREBP1), and induced AMPK. A targeted Cancer Pathway Finder RT-PCR (real-time polymerase chain reaction) based gene array revealed 20 up-regulated and 2 down-regulated genes in ID8 cells exposed to adipocyte conditioned media, which were altered by metformin. Adipocyte conditioned media also induced bio-energetic changes in the ID8 cells by pushing them into a highly metabolically active state; these effects were reversed by metformin. Collectively, metformin treatment inhibited the adipocyte mediated ovarian cancer cell proliferation, migration, expression of cancer associated genes and bio-energetic changes. Suggesting, that metformin could be a therapeutic option for ovarian cancer at an early stage, as it not only targets ovarian cancer, but also modulates the environmental milieu.

Antimicrobial resistance (AMR) is an emerging public health problem in modern times and the current COVID-19 pandemic has further exaggerated this problem. Due to bacterial co-infection in COVID-19 cases, an irrational consumption of antibiotics has occurred during the pandemic. This study aimed to observe the COVID-19 patients hospitalized from 1 March 2019 to 31 December 2020 and to evaluate the AMR pattern of bacterial agents isolated. This was a single-center study comprising 494 bacterial isolates (blood and urine) that were obtained from patients with SARS-CoV-2 admitted to the ICU and investigated in the Department of Microbiology of a tertiary care hospital in Delhi, India. Out of the total bacterial isolates, 55.46% were gram negative and 44.53% were gram positive pathogens. Of the blood samples processed, the most common isolates were CoNS (Coagulase Negative Staphylococcus) and Staphylococcus aureus. Amongst the urinary isolates, most common pathogens were Escherichia coli and Staphylococcus aureus. A total of 60% MRSA was observed in urine and blood isolates. Up to 40% increase in AMR was observed amongst these isolates obtained during COVID-19 period compared to pre-COVID-19 times. The overuse of antibiotics gave abundant opportunity for the bacterial pathogens to gradually develop mechanisms and to acquire resistance. Since the dynamics of SARS-COV-2 are unpredictable, a compromise on hospital antibiotic policy may ultimately escalate the burden of drug resistant pathogens in hospitals. A shortage of trained staff during COVID-19 pandemic renders it impossible to maintain these records in places where the entire hospital staff is struggling to save lives. This study highlights the extensive rise in the use of antibiotics for respiratory illness due to COVID-19 compared to antibiotic use prior to COVID-19 in ICUs. The regular prescription audit followed by a constant surveillance of hospital infection control practices by the dedicated teams and training of clinicians can improve the quality of medications in the long run and help to fight the menace of AMR.