Portable, handheld, and affordable blood perfusion imager for screening of subsurface cancer in resource-limited settings

Precise information on localized variations in blood circulation holds the key for noninvasive diagnostics and therapeutic assessment of various forms of cancer. While thermal imaging by itself may provide significant insights on the combined implications of the relevant physiological parameters, viz. local blood perfusion and metabolic balance due to active tumors as well as the ambient conditions, knowledge of the tissue surface temperature alone may be somewhat inadequate in distinguishing between some ambiguous manifestations of precancer and cancerous lesions, resulting in compromise of the selectivity in detection. This, along with the lack of availability of a user-friendly and inexpensive portable device for thermal-image acquisition, blood perfusion mapping, and data integration acts as a deterrent against the emergence of an inexpensive, contact-free, and accurate in situ screening and diagnostic approach for cancer detection and management. Circumventing these constraints, here we report a portable noninvasive blood perfusion imager augmented with machine learning–based quantitative analytics for screening precancerous and cancerous traits in oral lesions, by probing the localized alterations in microcirculation. With a proven overall sensitivity >96.66% and specificity of 100% as compared to gold-standard biopsy-based tests, the method successfully classified oral cancer and precancer in a resource-limited clinical setting in a double-blinded patient trial and exhibited favorable predictive capabilities considering other complementary modes of medical image analysis as well. The method holds further potential to achieve contrast-free, accurate, and low-cost diagnosis of abnormal microvascular physiology and other clinically vulnerable conditions, when interpreted along with complementary clinically evidenced decision-making perspectives.

Detection of Disease-Specific Volatile Organic Compounds Using Infrared Spectroscopy

Many life-threatening diseases at an early stage remain unrecognized due to a lack of pronounced symptoms. It is also accepted that the early detection of disease is a key ingredient for saving many lives. Unfortunately, in most of the cases, diagnostics implies an invasive sample collection, being problematic at the asymptomatic stage. Infrared spectroscopy of breath offers reliable noninvasive diagnostics at every stage and has already been tested for several diseases. This approach offers not only the detection of specific metabolites, but also the analysis of their imbalance and transportation. In this article, the power of infrared spectroscopy is demonstrated for diabetes, cerebral palsy, acute gastritis caused by bacterial infection, and prostate cancer.

Ultrasensitive multispecies spectroscopic breath analysis for real-time health monitoring and diagnostics

Breath analysis enables rapid, noninvasive diagnostics, as well as long-term monitoring of human health, through the identification and quantification of exhaled biomarkers. Here, we demonstrate the remarkable capabilities of mid-infrared (mid-IR) cavity-enhanced direct-frequency comb spectroscopy (CE-DFCS) applied to breath analysis. We simultaneously detect and monitor as a function of time four breath biomarkers—CH3OH, CH4, H2O, and HDO—as well as illustrate the feasibility of detecting at least six more (H2CO, C2H6, OCS, C2H4, CS2, and NH3) without modifications to the experimental apparatus. We achieve ultrahigh detection sensitivity at the parts-per-trillion level. This is made possible by the combination of the broadband spectral coverage of a frequency comb, the high spectral resolution afforded by the individual comb teeth, and the sensitivity enhancement resulting from a high-finesse cavity. Exploiting recent advances in frequency comb, optical coating, and photodetector technologies, we can access a large variety of biomarkers with strong carbon–hydrogen-bond spectral signatures in the mid-IR.

Towards reliable diagnostics of prostate cancer via breath

Early detection of cancer is a key ingredient for saving many lives. Unfortunately, cancers of the urogenital system are difficult to detect at early stage. The existing noninvasive diagnostics of prostate cancer (PCa) suffer from low accuracy (< 70%) even at advanced stages. In an attempt to improve the accuracy, a small breath study of 63 volunteers representing three groups: (1) of 19 healthy, (2) 28 with PCa, (3) with 8 kidney cancer (KC) and 8 bladder cancer (BC) was performed. Ultrabroadband mid-infrared Fourier absorption spectroscopy revealed eight spectral ranges (SRs) that differentiate the groups. The resulting accuracies of supervised analyses exceeded 95% for four SRs in distinguishing (1) vs (2), three for (1) vs (3) and four SRs for (1) vs (2) + (3). The SRs were then attributed to volatile metabolites. Their origin and involvement in urogenital carcinogenesis are discussed.

Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning

To harness the full potential of the ultrafast electron diffraction (UED) and microscopy (UEM), we must know accurately the electron beam properties, such as emittance, energy spread, spatial-pointing jitter, and shot-to-shot energy fluctuation. Owing to the inherent fluctuations in UED/UEM instruments, obtaining such detailed knowledge requires real-time characterization of the beam properties for each electron bunch. While diagnostics of these properties exist, they are often invasive, and many of them cannot operate at a high repetition rate. Here, we present a technique to overcome such limitations. Employing a machine learning (ML) strategy, we can accurately predict electron beam properties for every shot using only parameters that are easily recorded at high repetition rate by the detector while the experiments are ongoing, by training a model on a small set of fully diagnosed bunches. Applying ML as real-time noninvasive diagnostics could enable some new capabilities, e.g., online optimization of the long-term stability and fine single-shot quality of the electron beam, filtering the events and making online corrections of the data for time-resolved UED, otherwise impossible. This opens the possibility of fully realizing the potential of high repetition rate UED and UEM for life science and condensed matter physics applications.

Analysis of recurrently protected genomic regions in cell-free DNA found in urine

Cell-free DNA (cfDNA) in urine is a promising analyte for noninvasive diagnostics. However, urine cfDNA is highly fragmented. Whether characteristics of these fragments reflect underlying genomic architecture is unknown. Here, we characterized fragmentation patterns in urine cfDNA using whole-genome sequencing. Size distribution of urine cfDNA fragments showed multiple strong peaks between 40 and 120 base pairs (bp) with a modal size of 81- and sharp 10-bp periodicity, suggesting transient protection from complete degradation. These properties were robust to preanalytical perturbations, such as at-home collection and delay in processing. Genome-wide sequencing coverage of urine cfDNA fragments revealed recurrently protected regions (RPRs) conserved across individuals, with partial overlap with nucleosome positioning maps inferred from plasma cfDNA. The ends of cfDNA fragments clustered upstream and downstream of RPRs, and nucleotide frequencies of fragment ends indicated enzymatic digestion of urine cfDNA. Compared to plasma, fragmentation patterns in urine cfDNA showed greater correlation with gene expression and chromatin accessibility in epithelial cells of the urinary tract. We determined that tumor-derived urine cfDNA exhibits a higher frequency of aberrant fragments that end within RPRs. By comparing the fraction of aberrant fragments and nucleotide frequencies of fragment ends, we identified urine samples from cancer patients with an area under the curve of 0.89. Our results revealed nonrandom genomic positioning of urine cfDNA fragments and suggested that analysis of fragmentation patterns across recurrently protected genomic loci may serve as a cancer diagnostic.

Action potentials induce biomagnetic fields in carnivorous Venus flytrap plants

Upon stimulation, plants elicit electrical signals that can travel within a cellular network analogous to the animal nervous system. It is well-known that in the human brain, voltage changes in certain regions result from concerted electrical activity which, in the form of action potentials (APs), travels within nerve-cell arrays. Electro- and magnetophysiological techniques like electroencephalography, magnetoencephalography, and magnetic resonance imaging are used to record this activity and to diagnose disorders. Here we demonstrate that APs in a multicellular plant system produce measurable magnetic fields. Using atomic optically pumped magnetometers, biomagnetism associated with electrical activity in the carnivorous Venus flytrap, Dionaea muscipula, was recorded. Action potentials were induced by heat stimulation and detected both electrically and magnetically. Furthermore, the thermal properties of ion channels underlying the AP were studied. Beyond proof of principle, our findings pave the way to understanding the molecular basis of biomagnetism in living plants. In the future, magnetometry may be used to study long-distance electrical signaling in a variety of plant species, and to develop noninvasive diagnostics of plant stress and disease.