The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients’ health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient’s condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.

We report an automated microfluidic platform for ‘digitally’ screening the composition space of droplets containing cocktails of small molecules and demonstrate the features of this system by studying epistatic interactions between antibiotics and Escherichia coli ATCC 25922. This system has several key characteristics: (i) it uses small (<100 μL) samples of liquids and suspensions of bacteria that are introduced directly into the chip; (ii) it generates a sequence of droplets with compositions, including reagents and bacterial cell suspensions that are programmed by the user; (iii) it exports the sequence of droplets to an external segment of tubing that is subsequently disconnected for incubation and storage; and (iv) after incubation of bacteria in droplets, the droplets are injected into a second device equipped with an in-line fiber optic spectrophotometer that measures cell growth. The system generates and fuses droplets with precise (<1% in standard deviation) control of liquid volumes and of the concentrations of input substrates. We demonstrate the application of this technology in determining the minimum inhibitory concentration and pair-wise interactions of ampicillin, tetracycline, and chloramphenicol against E. coli. The experiments consumed small volumes of reagents and required minutes to create the droplets and several hours for their incubation and analysis.

Hypoxia‐inducible factors (HIFs) 1 and 2 are dimeric α/β transcription factors that regulate cellular responses to low oxygen. HIF‐1 is induced first, whereas HIF‐2 is associated with chronic hypoxia. To determine how HIF1A mRNA, the inducible subunit of HIF‐1, is regulated during hypoxia, we followed HIF1A mRNA levels in primary HUVECs over 24 hours using quantitative PCR. HIF1A and VEGF A ( VEGFA ) mRNA, a transcriptional target of HIF‐1, increased ~2.5‐ and 8‐fold at 2‐4 hours, respectively. To determine how the mRNAs were regulated, we identified a microRNA (miRNA), miR‐429, that destabilized HIF1A message and decreased VEGFA mRNA by inhibiting HIF1A. Target protector analysis, which interferes with miRNA‐mRNA complex formation, confirmed that miR‐429 targeted HIF1A message. Desferoxamine treatment, which inhibits the hydroxylases that promote HIF‐1α protein degradation, stabilized HIF‐1 activity during normoxic conditions and elevated miR‐429 levels, demonstrating that HIF‐1 promotes miR‐429 expression. RNA‐sequencing‐based transcriptome analysis indicated that inhibition of miRNA‐429 in HUVECs up‐regulated 209 mRNAs, a number of which regulate angiogenesis. The results demonstrate that HIF‐1 is in a negative regulatory loop with miR‐429, that miR‐429 attenuates HIF‐1 activity by decreasing HIF1A message during the early stages of hypoxia before HIF‐2 is activated, and this regulatory network helps explain the HIF‐1 transition to HIF‐2 during chronic hypoxia in endothelial cells.—Bartoszewska, S., Kochan, K., Piotrowski, A., Kamysz, W., Ochocka, R. J., Collawn, J. F., Bartoszewski, R. The hypoxia‐inducible miR‐429 regulates hypoxia hypoxia‐inducible factor‐1α expression in human endothelial cells through a negative feedback loop. FASEB J. 29, 1467‐1479 (2015). www.fasebj.org