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Early identification of human pathogens is crucial for the effective treatment of bloodstream infections to prevent sepsis. Since pathogens that are present in small numbers are usually difficult to detect directly, we hypothesize that the behavior of the immune cells that are present in large numbers may provide indirect evidence about the causative pathogen of the infection. We previously applied time-lapse microscopy to observe that neutrophils isolated from human whole-blood samples, which had been infected with the human-pathogenic fungus Candida albicans or C. glabrata, indeed exhibited a characteristic morphodynamic behavior. Tracking the neutrophil movement and shape dynamics over time, combined with machine learning approach, the accuracy for the differentiation between the two Candida species was about 75%. In this study, the focus is on improving the classification accuracy of the Candida species using advanced deep learning methods. We implemented (i) gated recurrent unit (GRU) networks and transformer-based networks for video data, and (ii) convolutional neural networks (CNNs) for individual frames of the time-lapse microscopy data. While the GRU and transformer-based approaches yielded promising results with 96% and 100% accuracy, respectively, the classification based on videos proved to be very time-consuming and required several hours. In contrast, the CNN model for individual microscopy frames yielded results within minutes, and, utilizing a majority-vote technique, achieved 100% accuracy both in identifying the pathogen-free blood samples and in distinguishing between the Candida species. The applied CNN demonstrates the potential for automatically differentiating bloodstream Candida infections with high accuracy and efficiency. We further analysed the results of the CNN using explainable artificial intelligence (XAI) techniques to understand the critical features and patterns, thereby shedding light on potential key morphodynamic characteristics of neutrophils in response to different Candida species. This approach could provide new insights into host-pathogen interactions and may facilitate the development of rapid, automated diagnostic tools for differentiating fungal species in blood samples.

In p-MFCs living plants photosynthesize within a bio-electrochemical circuit. The plant exudes organic waste material from the roots. In the rhizosphere, bacteria consume these wastes by oxidizing them in contrast to the atmosphere that reduces it. This redox reaction along with photosynthesis can be harnessed as bioelectricity. In this work, the plant Withania somnifera (L.) Dunal was used for generating bioelectricity from the root exudates and organic matter available in the soil. An open circuit voltage of 930±21 mV was achieved between multiple cycles of operation. The cell voltage further increased to 1260±140 mV with enrichment in the form of discards from vegetable matter. The peak recorded voltage was 1400 mV. Graphite fibre felt electrodes ensured uniform microbial growth with power densities that were achieved at 57 mW/m2 and 84 mW/m2 with and without enrichment respectively. ATR-FTIR demonstrated complete degradation of specific compounds attached to the carbon matrix in the soil along with the polysaccharide content from the enrichments. Additionally, this work also monitored the changes in soil pH and its homogeneity, the impact of photosynthetically active radiation, humidity, and the presence of CO2 in the air, and how it affects plant growth and ultimately the microbes at the rhizosphere which accounted for the bioremediation and the resultant bioelectricity production. SEM imaging provided additional evidence that the presence of electrochemically active soil bacteria, an anaerobic environment, and electrode characteristics are crucial for the development of conductive biofilms.

Rapid identification of pathogens is required for early diagnosis and treatment of life-threatening bloodstream infections in humans. This requirement is driving the current developments of molecular diagnostic tools identifying pathogens from human whole blood after successful isolation and cultivation. An alternative approach is to determine pathogen-specific signatures from human host immune cells that have been exposed to pathogens. We hypothesise that activated immune cells, such as neutrophils, may exhibit a characteristic behaviour — for instance in terms of their speed, dynamic cell morphology — that allows (i) identifying the type of pathogen indirectly and (ii) providing information on therapeutic efficacy. In this feasibility study, we propose a method for the quantitative assessment of static and morphodynamic features of neutrophils based on label-free time-lapse imaging data. We investigate neutrophil activation phenotypes after confrontation with fungal pathogens and isolation from a human whole-blood assay. In particular, we applied a machine learning supported approach to time-lapse microscopy data from different infection scenarios and were able to distinguish between Candida albicans and C. glabrata infection scenarios with test accuracies well above 75%, and to identify pathogen-free samples with accuracy reaching 100%. These results significantly exceed the test accuracies achieved using state-of-the-art deep neural networks to classify neutrophils by their morphodynamics.

We present the case of an 83-year-old man with metastatic prostate cancer who had testosterone levels reading above castration range despite appropriate medical and surgical castration. Mass spectrometry was performed to confirm presence of testosterone, but no testosterone was detected. The elevated testosterone as measured by standard immunoassay was postulated to be secondary to heterophile antibodies in the patient's serum. This report highlights the need for a high index of suspicion for interference in testosterone immunoassays when levels remain mildly elevated. Mass spectrometry may provide a more reliable method by which to detect testosterone concentration prior to escalation of care.

Summary: To understand how neural-immune-associated genes and pathways contribute to neurodegenerative disease pathophysiology, we performed a systematic functional genomic analysis in purified microglia and bulk tissue from mouse and human AD, FTD, and PSP. We uncover a complex temporal trajectory of microglial-immune pathways involving the type 1 interferon response associated with tau pathology in the early stages, followed by later signatures of partial immune suppression and, subsequently, the type 2 interferon response. We find that genetic risk for dementias shows disease-specific patterns of pathway enrichment. We identify drivers of two gene co-expression modules conserved from mouse to human, representing competing arms of microglial-immune activation (NAct) and suppression (NSupp) in neurodegeneration. We validate our findings by using chemogenetics, experimental perturbation data, and single-cell sequencing in post-mortem brains. Our results refine the understanding of stage- and disease-specific microglial responses, implicate microglial viral defense pathways in dementia pathophysiology, and highlight therapeutic windows.