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Glucose-6 phosphate dehydrogenase (G6PD) deficiency is an X-linked blood disease that affects 400 million people globally and is especially prevalent in malaria-endemic regions. A significant portion of carriers are asymptomatic and undiagnosed posing complications in the eradication of malaria as it restricts the types of drugs used for malaria treatment. A simple and accurate diagnosis of the deficiency is vital in the eradication of malaria. In this study, we investigate the potential of attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) as a diagnostic technique for G6PD deficiency. Venous blood samples were collected in lithium heparin anticoagulant tubes from G6PD partial and fully deficient volunteers, n = 17, and normal volunteers, n = 59, in Khon Kaen, Thailand. Spectra of aqueous and dry samples were acquired of whole blood, plasma, and red blood cells, and modeled using partial least squares discriminant analysis (PLS-DA). PLS-DA modeling resulted in a sensitivity of 0.800 and specificity of 0.800 correctly classifying fully deficient participants as well as a majority of partially deficient females who are often misdiagnosed as normal by current screening methods. The viability of utilizing aqueous samples has always been hindered by the variability of hydration in the sample, but by employing multicurve curve resolution-alternating least squares to subtract water from each sample we are able to produce high-quality spectra with minimized water contributions. The approach shows proof of principle that ATR FT-IR combined with multivariate data analysis could become a frontline screening tool for G6PD deficiency by improving tailored drug treatments and ultimately saving lives.

The prevalence of neglected tropical diseases (NTDs) are advancing at an alarming rate. The NTD Leishmaniasis is now endemic in over 90 tropical and sub-tropical low socioeconomic countries. Current diagnosis for this disease involves serological assessment of infected tissue by either light microscopy, anti-body tests or culturing via in vitro inoculation or in vivo animal inoculation. Furthermore, co-infection by other pathogens can make it difficult to accurately determine Leishmania infection with light microscopy. Herein, for the first time, we demonstrate the potential of combining synchrotron FTIR microspectroscopy with powerful discrimination tools such as partial least squares – discriminant analysis (PLS-DA), support vector machine – discriminant analysis (SVM-DA), and k-nearest neighbors (KNN), to characterize the parasitic forms of Leishmania major both isolated and within infected macrophages. For measurements performed on functional infected and uninfected macrophages in physiological solutions the sensitivity from PLS-DA, SVM-DA, KNN classification methods was found to be 0.923, 0.981, and 0.989, while the specificity was 0.897, 1.00, and 0.975, respectively. Cross-validated PLS-DA models on live amastigotes and promastigotes showed a sensitivity and specificity of 0.98 in the lipid region, whilst a specificity and sensitivity of 1.00 was achieved in the fingerprint region. The study demonstrates the potential of the FTIR technique to identify unique diagnostic bands and utilize them to generate machine learning models to predict leishmania infection.

Oxidative treatment of human red blood cells (RBCs) prior to freeze-drying appears to stabilize the RBCs to withstand dried storage at room temperature.

Shear stresses generated by a silicon resonator embedded inside a microfluidic channel can rapidly and effectively lyse cells and homogenise whole blood. This significantly improves sensitivity of ATR-FTIR spectroscopy based diagnostic tests.

Visible microspectroscopy combined with machine learning is able to detect and quantify functional malaria infected erythrocytes at different stages of the P. falciparum erythrocytic life cycle.

Spectral histopathology has shown promise for the classification and diagnosis of tumors with defined morphology, but application in tumors with variable or diffuse morphologies is yet to be investigated. To address this gap, we evaluated the application of Fourier transform infrared (FTIR) imaging as an accessory diagnostic tool for canine hemangiosarcoma (HSA), a vascular endothelial cell cancer that is difficult to diagnose. To preserve the delicate vascular tumor tissue structure, and potential classification of single endothelial cells, paraffin removal was not performed, and a partial least square discrimination analysis (PLSDA) and Random Forest (RF) models to classify different tissue types at individual pixel level were established using a calibration set (24 FTIR images from 13 spleen specimens). Next, the prediction capability of the PLSDA model was tested with an independent test set (n = 11), resulting in 74% correct classification of different tissue types at an individual pixel level. Finally, the performance of the FTIR spectropathology and chemometric algorithm for diagnosis of HSA was established in a blinded set of tissue samples (n = 24), with sensitivity and specificity of 80 and 81%, respectively. Taken together, these results show that FTIR imaging without paraffin removal can be applied to tumors with diffuse morphology, and this technique is a promising tool to assist in canine splenic HSA differential diagnosis.

The global fight against mosquito-borne viral diseases has in recent years been bolstered by the introduction of the endosymbiotic bacteria Wolbachia to vector populations, which in host mosquitoes suppresses the transmissibility of several viruses. Researchers engaged on this front of the battle often need to know the Wolbachia infection status of individual mosquitoes, as the intervention progresses and the mosquitoes become established in the target population. Previously, we successfully applied attenuated total reflection Fourier transform infrared spectroscopy to the detection of Wolbachia in adult Aedes aegypti mosquitoes; here we apply the same principles to Aedes eggs, with sensitivity and selectivity > 0.95. Further, we successfully distinguish between infections in eggs of the wMel and wMelPop strains of Wolbachia pipientis, with a classification error of 3% . The disruption of host lipid profile by Wolbachia is found to be a key driver in spectral differences between these sample classes.

Malaria is considered to be one of the most catastrophic health issues in the whole world. Vibrational spectroscopy is a rapid, robust, label-free, inexpensive, highly sensitive, nonperturbative, and nondestructive technique with high diagnostic potential for the early detection of disease agents. In particular, the fingerprinting capability of attenuated total reflection spectroscopy is promising as a point-of-care diagnostic tool in resource-limited areas. However, improvements are required to expedite the measurements of biofluids, including the drying procedure and subsequent cleaning of the internal reflection element to enable high throughput successive measurements. As an alternative, we propose using an inexpensive coverslip to reduce the sample preparation time by enabling multiple samples to be collectively dried together under the same temperature and conditions. In conjunction with partial least squares regression, attenuated total reflection spectroscopy was able to detect and quantify the parasitemia with root mean square error of cross-validation and R2 values of 0.177 and 0.985, respectively. Here, we characterize an inexpensive, disposable coverslip for the high throughput screening of malaria parasitic infections and thus demonstrate an alternative approach to direct deposition of the sample onto the internal reflection element.

New point-of-care diagnostic approaches for malaria that are sensitive to low parasitemia, easy to use in a field setting, and affordable are urgently required to meet the World Health Organization's objective of reducing malaria cases and related life losses by 90% globally on or before 2030. In this study, an inexpensive "matchbox size" near-infrared (NIR) spectrophotometer was used for the first time to detect and quantify malaria infection in vitro from isolated dried red blood cells using a fingerpick volume of blood. This the first study to apply a miniaturized NIR device to diagnose a parasitic infection and identify marker bands indicative of malaria infection in the NIR region. An NIR device has many advantages including wavelength accuracy and repeatability, speed, resolution, and a greatly improved signal-to-noise ratio compared to existing spectroscopic options. Using multivariate data analysis, we discriminated control red blood cells from infected cells and established the limit of detection of the technique. Principal component analysis displayed a good separation between the infected and uninfected RBCs, while partial least-squares regression analysis yielded a robust parasitemia prediction with root-mean-square error of prediction values of 0.446 and 0.001% for the higher and lower parasitemia models, respectively. The R2 values of the higher and lower parasitemia models were 0.947 and 0.931, respectively. Finally, an estimated parasitemia detection limit of 0.00001% and a qunatification limit of 0.001% was achieved; to ascertain the true efficacy of the technique for point-of-care screening, clinical studies using large patient numbers are required, which is the subject of future studies.

The magnitude of infectious diseases in the twenty-first century created an urgent need for point-of-care diagnostics. Critical shortages in reagents and testing kits have had a large impact on the ability to test patients with a suspected parasitic, bacteria, fungal, and viral infections. New point-of-care tests need to be highly sensitive, specific, and easy to use and provide results in rapid time. Infrared spectroscopy, coupled to multivariate and machine learning algorithms, has the potential to meet this unmet demand requiring minimal sample preparation to detect both pathogenic infectious agents and chronic disease markers in blood. This focal point article will highlight the application of Fourier transform infrared spectroscopy to detect disease markers in blood focusing principally on parasites, bacteria, viruses, cancer markers, and important analytes indicative of disease. Methodologies and state-of-the-art approaches will be reported and potential confounding variables in blood analysis identified. The article provides an up to date review of the literature on blood diagnosis using infrared spectroscopy highlighting the recent advances in this burgeoning field.

Blood screening is a fundamental part of disease diagnosis and monitoring health. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy offers an innovative solution to streamlining the process, especially for multianalyte detection in aqueous samples. However, samples always undergo a storage phase before they are processed for testing and blood transfusion. In this study, we investigated the effect of standard storage procedures on the macromolecular composition of whole blood, and plasma collected in blood tubes for diagnostic purposes and initial screening of blood products. Periphery blood samples were collected from 10 volunteers and then stored for 14 days at 4 °C. Samples were stored as isolated plasma and whole blood to provide three different datasets, namely: (1) plasma stored independently, (2) plasma stored with other blood components and (3) whole blood. ATR-FTIR spectra of aqueous blood were acquired every 24 h from the time of collection on a portable ATR-FTIR spectrophotometer to monitor the evolution of the macromolecular composition in each blood component. Principal component analysis (PCA), partial least squares regression (PLS-R) and multi-curve resolution alternate least squares (MCR-ALS) models were built to study changes in the spectra with the storage time and identify the key bands. Isolated plasma stored without red blood cells (RBCs) showed no changes over the 14 day period indicating limited degradation. By contrast, plasma stored with the other blood components showed visual and spectroscopic signs of degradation including increasing lipid bands and the amide I and II bands from haemoglobin (Hb). Ideally, for the application of IR spectroscopy in blood diagnostics and for initial screening of blood products, whole blood and isolated red blood cells can be stored for a maximum of 4 days at 4 °C in lithium-heparin anticoagulant tubes prior to spectral analysis before any signs of degradation. Isolated plasma, on the other hand, can be stored for much longer periods and shows no evidence of degradation in the spectra after 14 days.

Cholangiocarcinoma (CCA) is a bile duct cancer that originates in the bile duct epithelium. Northeastern Thailand has the highest incidence of CCA, and there is a direct correlation with liver fluke (Opisthorchis viverrini) infection. The high mortality rate of CCA is a consequence of delayed diagnosis. Fourier transform infrared (FTIR) spectroscopy is a powerful technique that detects the absorbance of molecular vibrations and is perfectly suited for the interrogation of biological samples. In this study, we applied synchrotron radiation-FTIR (SR-FTIR) microspectroscopy and focal plane array (FPA-FTIR) microspectroscopy to characterize periductal fibrosis and bile duct cells progressing to CCA induced by inoculating O. viverrini metacercariae into hamsters. SR-FTIR and FPA-FTIR measurements were performed in liver sections harvested from 1-, 2-, 3-, and 6-month post-infected hamsters compared to uninfected liver tissues. Principal component analysis (PCA) of the tissue samples showed a clear discrimination among uninfected and early-stage (1 and 2 months) and cancerous-stage (3 and 6 months) tissues. The discrimination is based on intensity changes in the phosphodiester band (1081 cm-1), amino acid residue (∼1396 cm-1), and C═O stretching carboxylic esters (1745 cm-1). Infected tissues also show definitive bands at ∼1280, 1234, and 1201 cm-1 characteristic of the collagen triplet and indicative of fibrosis. Hierarchical cluster analysis (HCA) was performed on the FPA data and showed a classification into specific cell types. Hepatocyte, fibrotic lesion, and bile duct (cancer) were classified and HCA mapping showed similar cellular distribution pattern compared to Sirius red staining. This study was also extended to less invasive sample analysis using attenuated total reflectance-FTIR (ATR-FTIR) spectroscopy. Sera from O. viverrini-infected and uninfected hamsters were analyzed using multivariate analysis, including principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA). PCA was able to classify spectra of normal, early-stage CCA, and CCA, while the PLS-DA gave 100% accuracy for the validation. The model was established from 17 samples (11 normal, 6 cancer) in the calibration set and 9 samples in the validation set (4 normal, 2 cancer, 3 precancerous). These results indicate that FTIR-based technology is a potential tool to detect the progression of CCA, especially in the early stages of the disease.

AIMS Attenuated Total Reflection Fourier Transform Infrared (ATR-FT-IR) Spectroscopy and chemometric modelling, including soft independent modelling by class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM), were applied to attempt to discriminate 60 clinical isolates of Enterococcus faecium and Enterococcus faecalis and hence evaluate the performance of the spectroscopic approach in identifying enterococci infections. METHODS AND RESULTS The bacterial samples were identified by polymerize chain reaction (PCR) amplification and their ATR-FT-IR spectra acquired. Spectra were processed to the second derivative using the Savitzky-Golay algorithm and normalized using extended multiplicative signal correction employing the UnscramblerX (CAMO, Norway) software package. Multivariate classification models and their performance were evaluated using Cohen's Kappa coefficient. Principal component analysis (PCA) score plots showed separate clusters of spectra related to membership to E. faecium and E. faecalis, with this explained by bands assigned to PO2 (1230 cm-1 ), P-O-C (1114 cm-1 ), monosubstituted alkene (997, 987 cm-1 ) and C-O (1070, 1055, 1036 cm-1 ) corresponding to teichoic acids, polysaccharides and peptidoglycan from the cell wall in PCA and PLS-DA loading plots. The best classification model for E. faecium and E. faecalis is SVM, indicating via highest Kappa score. The classification coefficient between SIMCA, PLS-DA, SVM and PCR as reference method were 0·59, 0·9 and 1, respectively, shown as the Kappa scores. CONCLUSIONS The main spectral differences observed between the two clinically relevant enterococci species were associated with changes in the teichoic acid content of cell walls. With regard to the binary classification method, SVM was found to be the best performing classification model, providing the highest correlation with the PCR results. SIGNIFICANCE AND IMPACT OF THE STUDY The study shows that ATR-FT-IR spectroscopy in combination with chemometric modelling can be applied for the phenotypic identification and discrimination of clinically relevant and similar enterococcal species.

The presence of low amounts of specific proteins in urine can be an indicator of diagnosis and prognosis of several diseases including renal failure and cancer. Hence, there is an urgent need for Point-of-care (PoC) methods, which can quantify microproteinuria levels (30-300 ppm) and identify the major proteins associated with the microproteinuria. In this study, we coupled ultracentrifugation with attenuated total reflectance-Fourier transform infrared (ATR-FTIR) to identify and quantify proteins in urine at low parts per million levels. The process involves the preconcentration of proteins from 500 μL of urine using an ultrafiltration device. After several washings, the isolated proteins are dried onto the ATR crystal forming a thin film. Imaging studies showed that the absorbance of the protein bands was linear with the amount of mass deposited on the crystal. The methodology was first evaluated with artificial urine spiked with 30-300 ppm of albumin. The calibration showed acceptable linearity (R2 = 0.97) and a limit of detection of 6.7 ppm. Linear relationships were also observed from urine of healthy subjects spiked with microproteinuria concentrations of albumin, immunoglobulin, and hemoglobin, giving a prediction error of the spiked concentration of 23 ppm. When multiple proteins were spiked into the real urine, multivariate analysis was able to decompose the data set into the different proteins, but the multicomponent evaluation was challenging for proteins at low levels. Although the introduction of a preprocessing step reduces the PoC capability of the method, it largely increases its performance, showing great potential as a tool for the diagnosis and prognosis of several illnesses affecting urine proteic composition.

Application of modern and innovative spectroscopic and microscopic approaches to biomedical analysis opens new horizons and sheds new light on many unexplored scientific territories. In this review, we critically summarize up-to-date Raman-based methodologies for red blood cells (RBCs) analysis used in biology and medicine, and compare them with both classical, as well as other spectroscopic and microscopic approaches. The main emphasis is put on advantages, disadvantages and capabilities of each technique for detection of RBC deteriorations and RBC-related diseases. Although currently used classical techniques of medical analytsis serve as a gold standard for clinicians in diagnosis of erythropathies, they provide insufficient insight into RBC alterations at the molecular level. In addition, there is a demand for non-destructive and label-free analytical techniques for rapid detection and diagnosis of erythropathies. Their recognition often requires multimodal methodology comprising application of methods including sophisticated spectroscopy-based techniques, where Raman-based approaches play an important role.

The development of antimicrobial resistance (AMR) resulting from widespread antibiotic usage is occurring at an alarming pace, much faster than our understanding of the mechanisms behind resistance. Knowledge about resistance-related phenotypic and genotypic changes is critical for the development of new drugs. Here, we identify changes in the chemical composition of Staphylococcus aureus associated with the development of resistance to last resort drugs, vancomycin and daptomycin, using a novel, single cell, nanoscale technique, atomic force microscopy-infrared spectroscopy (AFM-IR), combined with chemometric analysis. We utilized paired clinical isolates, with the parent (susceptible) strain isolated prior to treatment and the daughter (resistant) strain obtained from the same patient after drug admission and clinical failure. We observed an increase in the amount of nonintracellular carbohydrates, indicating thickening or changes in the packing of the cell wall, as well as changes in the phospholipid content in relation to vancomycin resistance and daptomycin nonsusceptibility, respectively.

Infrared Spectroscopy (IR) enables the direct and rapid characterization of cells at the molecular level. Achieving a rapid and consistent cell preparation is critical for the development of Point-of-Care diagnostics for cell analysis. Here we introduce an open-source, 3D printed device for integrating the isolation, preconcentration, and measurement of attenuated total reflectance IR spectra of cells from biofluids. The tool comprises a disposable card for cytocentrifugation, equipped with magnets, which allows reproducible integration into the pathlength of the IR spectrophotometer. Preliminary results using cell media containing A459 cells indicate that this system enables a qualitative and quantitative characterization of cells down to 10 cells µL-1 by using a single and cost-effective device and within a few minutes.

Introduction: Fibrosis is significantly associated with nearly all forms of heart and kidney disease. Clinical diagnosis of fibrosis is currently reliant on conventional methods that do not have the sensitivity and specificity required for effective diagnosis. Hypothesis: An handheld portable near-infrared (NIR) spectrometer, usable intraoperatively coupled to machine learning algorithms can discriminate between fibrotic and healthy cardiac and renal tissue. We sought to validate this in an animal model. Methods: 10 Male Sprague Dawley rats (SDR) with either induced cardiac (SDR-H) and renal (SDR-K) fibrosis (n=5) compare to normal controls (n=5). Hearts from all rats were used as tissue screening in model validation as they contain a high amount of collagen. Multiple tissue samples were harvested from SDR-K ( n fibrosis = 12, n control = 4) and SDR-H ( n fibrosis = 12, n control = 4) groups. NIR spectra (1350 - 2500 nm) were acquired from all tissue sections. Results: Stained sections showed insignificant differences between the fibrotic SDR-H and their corresponding controls as collagen fibrils dominated both groups. SDR-K showed distinguishable features between examined groups. NIR absorption at 1509, 1725, 2055, and 2306 nm were found to be highly indicative of fibrosis (Figure1). PCA (57% explained variance) and PLS-DA (sensitivity: 96%) showed excellent discrimination for SDR-K groups while the heart shows no meaningful discrimination for SDR-H groups. SVM and LR analysis corroborated these results by achieving a 98% classification accuracy for SDR-K and no discrimination for SDR-H. All machine learning models were cross-validated with outcomes of histological staining to establish a robust interpretation and underpin their pathological meanings. Conclusions: NIR accurately diagnoses cardiac and renal fibrosis in rats model. There is potential for this technology to be translated into an intraoperative instrument for tissue diagnosis

Simple Summary Cholangiocarcinoma is a form of liver cancer that is found, predominantly, in Thailand. Due to the non-specific symptoms and laboratory investigation, it is difficult to rule out cholangiocarcinoma from other liver conditions. Here, we demonstrate the development of a diagnostic tool for cholangiocarcinoma, based on the ATR-FTIR analyses of sera, coupled with multivariate analyses and machine learning tools to obtain a better specificity. The innovative approach that shows highly promising results for this otherwise difficult to diagnose cancer. Abstract Cholangiocarcinoma (CCA) is a malignancy of the bile duct epithelium. Opisthorchis viverrini infection is a known high-risk factor for CCA and in found, predominantly, in Northeast Thailand. The silent disease development and ineffective diagnosis have led to late-stage detection and reduction in the survival rate. Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) is currently being explored as a diagnostic tool in medicine. In this study, we apply ATR-FTIR to discriminate CCA sera from hepatocellular carcinoma (HCC), biliary disease (BD) and healthy donors using a multivariate analysis. Spectral markers differing from healthy ones are observed in the collagen band at 1284, 1339 and 1035 cm−1, the phosphate band (vsPO2−) at 1073 cm−1, the polysaccharides band at 1152 cm−1 and 1747 cm−1 of lipid ester carbonyl. A Principal Component Analysis (PCA) shows discrimination between CCA and healthy sera using the 1400–1000 cm−1 region and the combined 1800—1700 + 1400–1000 cm−1 region. Partial Least Square-Discriminant Analysis (PLS-DA) scores plots in four of five regions investigated, namely, the 1400–1000 cm−1, 1800–1000 cm−1, 3000–2800 + 1800–1000 cm−1 and 1800–1700 + 1400–1000 cm−1 regions, show discrimination between sera from CCA and healthy volunteers. It was not possible to separate CCA from HCC and BD by PCA and PLS-DA. CCA spectral modelling is established using the PLS-DA, Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN). The best model is the NN, which achieved a sensitivity of 80–100% and a specificity between 83 and 100% for CCA, depending on the spectral window used to model the spectra. This study demonstrates the potential of ATR-FTIR spectroscopy and spectral modelling as an additional tool to discriminate CCA from other conditions.

The scourge of malaria infection continues to strike hardest against pregnant women and children in Africa and South East Asia. For global elimination, testing methods that are ultrasensitive to low-level ring-staged parasitemia are urgently required. In this study, we used a novel approach for diagnosis of malaria infection by combining both electronic ultraviolet-visible (UV/vis) spectroscopy and near infrared (NIR) spectroscopy to detect and quantify low-level (1-0.000001%) ring-staged malaria-infected whole blood under physiological conditions uisng Multiclass classification using logistic regression, which showed that the best results were achieved using the extended wavelength range, providing an accuracy of 100% for most parasitemia classes. Likewise, partial least-squares regression (PLS-R) analysis showed a higher quantification sensitivity (R2 = 0.898) for the extended spectral region compared to UV/vis and NIR (R2 = 0.806 and 0.556, respectively). For quantifying different-stage blood parasites, the extended wavelength range was able to detect and quantify all thePlasmodium falciparum accurately compared to testing each spectral component separately. These results demonstrate the potential of a combined UV/vis-NIR spectroscopy to accurately diagnose malaria-infected patients without the need for elaborate sample preparation associated with the existing mid-IR approaches.

Saturated fatty acid-containing lipids, such as milkfat, may protect long chain polyunsaturated fatty acids in fish oil when blended together into solid lipid particles (SLPs). One of the main challenges of SLPs is structural polymorphism, which can lead to expulsion of the protected component during prolonged storage. To investigate this phenomenon, the change in thermal and crystalline behaviours, and fatty acid distribution, were analysed in SLPs of fish oil and milkfat during storage at different temperatures for up to 28 days. X-ray diffraction analysis showed changes in molten and crystalline states occurred even at -22 °C. Room temperature (21 °C) storage led to more than 45% molten state but SLPs retained their initial shape. Confocal Raman Spectroscopy of the SLPs showed the distribution of fatty acids was not uniform, with 10 μm outermost layer of predominantly saturated fatty acids likely responsible for the intact SLP shape and stability of the core.

Filamentous cable bacteria display long-range electron transport, generating electrical currents over centimeter distances through a highly ordered network of fibers embedded in their cell envelope. The conductivity of these periplasmic wires is exceptionally high for a biological material, but their chemical structure and underlying electron transport mechanism remain unresolved. Here, we combine high-resolution microscopy, spectroscopy, and chemical imaging on individual cable bacterium filaments to demonstrate that the periplasmic wires consist of a conductive protein core surrounded by an insulating protein shell layer. The core proteins contain a sulfur-ligated nickel cofactor, and conductivity decreases when nickel is oxidized or selectively removed. The involvement of nickel as the active metal in biological conduction is remarkable, and suggests a hitherto unknown form of electron transport that enables efficient conduction in centimeter-long protein structures., Filamentous cable bacteria conduct electrical currents over centimeter distances through fibers embedded in their cell envelope. Here, Boschker et al. show that the fibers consist of a conductive core containing nickel proteins that is surrounded by an insulating protein shell.

Extracellular vesicles (EVs) are membranous nanoparticles naturally released from living cells which can be found in all types of body fluids. Recent studies found that cancer cells secreted EVs containing the unique set of biomolecules, which give rise to a distinctive absorbance spectrum representing its cancer type. In this study, we aimed to detect the medium EVs (200–300 nm) from the urine of prostate cancer patients using Fourier transform infrared (FTIR) spectroscopy and determine their association with cancer progression. EVs extracted from 53 urine samples from patients suspected of prostate cancer were analyzed and their FTIR spectra were preprocessed for analysis. Characterization of morphology, particle size and marker proteins confirmed that EVs were successfully isolated from urine samples. Principal component analysis (PCA) of the EV’s spectra showed the model could discriminate prostate cancer with a sensitivity of 59% and a specificity of 81%. The area under curve (AUC) of FTIR PCA model for prostate cancer detection in the cases with 4–20 ng/mL PSA was 0.7, while the AUC for PSA alone was 0.437, suggesting the analysis of urinary EVs described in this study may offer a novel strategy for the development of a noninvasive additional test for prostate cancer screening.

The global fight against mosquito-borne viral diseases has in recent years been bolstered by the introduction of the endosymbiotic bacteria

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented need for diagnostic testing that is critical in controlling the spread of COVID-19. We propose a portable infrared spectrometer with purpose-built transflection accessory for rapid point-of-care detection of COVID-19 markers in saliva. Initially, purified virion particles were characterized with Raman spectroscopy, synchrotron infrared (IR) and AFM-IR. A data set comprising 171 transflection infrared spectra from 29 subjects testing positive for SARS-CoV-2 by RT-qPCR and 28 testing negative, was modeled using Monte Carlo Double Cross Validation with 50 randomized test and model sets. The testing sensitivity was 93 % (27/29) with a specificity of 82 % (23/28) that included positive samples on the limit of detection for RT-qPCR. Herein, we demonstrate a proof-of-concept high throughput infrared COVID-19 test that is rapid, inexpensive, portable and utilizes sample self-collection thus minimizing the risk to healthcare workers and ideally suited to mass screening.