Severe mpox requiring colostomy in a patient with advanced HIV disease: A case report and literature review.

This severe monkeypox case described a 23-year-old male with advanced HIV-1 disease presenting perirectal abscess, extensive anal ulcerative lesions requiring colostomy, and tecovirimat resistance. Radiologically non-liquefied perirectal abscess presented diagnostic challenges highlighting the complexity of aggressive monkeypox manifestations in immunocompromised individuals.

Accurate estimation of the inhibition zone of antibiotics based on laser speckle imaging and multiple random speckle illumination.

The antimicrobial susceptibility test (AST) plays a crucial role in selecting appropriate antibiotics for the treatment of bacterial infections in patients. The diffusion disk method is widely adopted AST method due to its simplicity, cost-effectiveness, and flexibility. It assesses antibiotic efficacy by measuring the size of the inhibition zone where bacterial growth is suppressed. Quantification of the zone diameter is typically achieved using tools such as rulers, calipers, or automated zone readers, as the inhibition zone is visually discernible. However, challenges arise due to inaccuracies stemming from human errors or image processing of intensity-based images. Here, we proposed a bacterial activity-based AST using laser speckle imaging (LSI) with multiple speckle illumination. LSI measures a speckle pattern produced by interferences of scattered light from the sample; therefore, LSI enables the detection of variation or movement within the sample such as bacterial activity. We found that LSI with multiple speckle illuminations provides consistent and uniform analysis of measured time-varying speckle images. Furthermore, our proposed method effectively identified the boundary of the inhibition zone using the k-means clustering algorithm, exploiting a result of speckle pattern analysis as features. Collectively, the proposed method offers a versatile analytical tool in the diffusion disk method.

Targeted multispectral filter array design for the optimization of endoscopic cancer detection in the gastrointestinal tract.

Significance: Color differences between healthy and diseased tissue in the gastrointestinal (GI) tract are detected visually by clinicians during white light endoscopy; however, the earliest signs of cancer are often just a slightly different shade of pink compared to healthy tissue making it hard to detect. Improving contrast in endoscopy is important for early detection of disease in the GI tract during routine screening and surveillance. Aim: We aim to target alternative colors for imaging to improve contrast using custom multispectral filter arrays (MSFAs) that could be deployed in an endoscopic "chip-on-tip" configuration. Approach: Using an open-source toolbox, Opti-MSFA, we examined the optimal design of MSFAs for early cancer detection in the GI tract. The toolbox was first extended to use additional classification models (k-nearest neighbor, support vector machine, and spectral angle mapper). Using input spectral data from published clinical trials examining the esophagus and colon, we optimized the design of MSFAs with three to nine different bands. Results: We examined the variation of the spectral and spatial classification accuracies as a function of the number of bands. The MSFA configurations tested showed good classification accuracies when compared to the full hyperspectral data available from the clinical spectra used in these studies. Conclusion: The ability to retain good classification accuracies with a reduced number of spectral bands could enable the future deployment of multispectral imaging in an endoscopic chip-on-tip configuration using simplified MSFA hardware. Further studies using an expanded clinical dataset are needed to confirm these findings.

Assessment of angle-dependent spectral distortion to develop accurate hyperspectral endoscopy.

Hyperspectral endoscopy has shown its potential to improve disease diagnosis in gastrointestinal tracts. Recent approaches in developing hyperspectral endoscopy are mainly focusing on enhancing image speed and quality of spectral information under a clinical environment, but there are many issues in obtaining consistent spectral information due to complicated imaging conditions, including imaging angle, non-uniform illumination, working distance, and low reflected signal. We quantitatively investigated the effect of imaging angle on the distortion of spectral information by exploiting a bifurcated fiber, spectrometer, and tissue-mimicking phantom. Spectral distortion becomes severe as increasing the angle of the imaging fiber or shortening camera exposure time for fast image acquisition. Moreover, spectral ranges from 450 to 550 nm are more susceptible to the angle-dependent spectral distortion than longer spectral ranges. Therefore, imaging angles close to normal and longer target spectral ranges with enough detector exposure time could minimize spectral distortion in hyperspectral endoscopy. These findings will help implement clinical HSI endoscopy for the robust and accurate measurement of spectral information from patients in vivo.

First experience in clinical application of hyperspectral endoscopy for evaluation of colonic polyps.

Early detection and resection of adenomatous polyps prevents their progression to colorectal cancer (CRC), significantly improving patient outcomes. Polyps are typically identified and removed during white-light colonoscopy. Unfortunately, the rate of interval cancers that arise between CRC screening events remains high, linked to poor visualization of polyps during screening and incomplete polyp removal. Here, we sought to evaluate the potential of a hyperspectral endoscope (HySE) to enhance polyp discrimination for detection and resection. We designed, built and tested a new compact HySE in a proof-of-concept clinical study. We successfully collected spectra from three tissue types in seven patients undergoing routine colonoscopy screening. The acquired spectral data from normal tissue and polyps, both pre- and post- resection, were subjected to quantitative analysis using spectral angle mapping and machine learning, which discriminated the data by tissue type, meriting further investigation of HySE as a clinical tool.

Impact of an Acute Care Surgery Model on the Management of Acute Appendicitis in South Korea: A Retrospective Cohort Study.

BACKGROUND: The acute care surgery (ACS) system is a new model for the prompt management of diseases that require rapid treatment in patients with acute abdomen. This study compared the outcomes and characteristics of the ACS system and traditional on-call system (TROS) for acute appendicitis in South Korea. METHODS: This single-center, retrospective study included all patients (aged ≥18 years) who underwent surgery for acute appendicitis in 2016 and 2018. The TROS and ACS system were used for the 2016 and 2018 groups, respectively. We retrospectively obtained data on each patient from the electrical medical records. The independent samples t-test and Mann-Whitney U-test were used for continuous and nonnormally distributed data, respectively. RESULTS: In total, 126 patients were included. The time taken to get from the emergency room admission to the operating room, operation times, and postoperative complication rates were similar between both groups. However, the length of the hospital stay was shorter in the ACS group than in the TROS group (4.3 ± 3.2 days vs. 7.2 ± 9.6 days, p=0.039). CONCLUSIONS: Since the introduction of the ACS system, the length of hospital stay for surgical patients has decreased. This may be due to the application of an integrated medical procedure, such as a new clinical pathway, rather than differences in the surgical techniques.

A background correction method to compensate illumination variation in hyperspectral imaging.

Hyperspectral imaging (HSI) can measure both spatial (morphological) and spectral (biochemical) information from biological tissues. While HSI appears promising for biomedical applications, interpretation of hyperspectral images can be challenging when data is acquired in complex biological environments. Variations in surface topology or optical power distribution at the sample, encountered for example during endoscopy, can lead to errors in post-processing of the HSI data, compromising disease diagnostic capabilities. Here, we propose a background correction method to compensate for such variations, which estimates the optical properties of illumination at the target based on the normalised spectral profile of the light source and the measured HSI intensity values at a fixed wavelength where the absorption characteristics of the sample are relatively low (in this case, 800 nm). We demonstrate the feasibility of the proposed method by imaging blood samples, tissue-mimicking phantoms, and ex vivo chicken tissue. Moreover, using synthetic HSI data composed from experimentally measured spectra, we show the proposed method would improve statistical analysis of HSI data. The proposed method could help the implementation of HSI techniques in practical clinical applications, where controlling the illumination pattern and power is difficult.

Deep learning applied to hyperspectral endoscopy for online spectral classification.

Hyperspectral imaging (HSI) is being explored in endoscopy as a tool to extract biochemical information that may improve contrast for early cancer detection in the gastrointestinal tract. Motion artefacts during medical endoscopy have traditionally limited HSI application, however, recent developments in the field have led to real-time HSI deployments. Unfortunately, traditional HSI analysis methods remain unable to rapidly process the volume of hyperspectral data in order to provide real-time feedback to the operator. Here, a convolutional neural network (CNN) is proposed to enable online classification of data obtained during HSI endoscopy. A five-layered CNN was trained and fine-tuned on a dataset of 300 hyperspectral endoscopy images acquired from a planar Macbeth ColorChecker chart and was able to distinguish between its 18 constituent colors with an average accuracy of 94.3% achieved at 8.8 fps. Performance was then tested on a set of images simulating an endoscopy environment, consisting of color charts warped inside a rigid tube mimicking a lumen. The algorithm proved robust to such variations, with classification accuracies over 90% being obtained despite the variations, with an average drop in accuracy of 2.4% being registered at the points of longest working distance and most inclination. For further validation of the color-based classification system, ex vivo videos of a methylene blue dyed pig esophagus and images of different disease stages in the human esophagus were analyzed, showing spatially distinct color classifications. These results suggest that the CNN has potential to provide color-based classification during real-time HSI in endoscopy.

A clinically translatable hyperspectral endoscopy (HySE) system for imaging the gastrointestinal tract.

Hyperspectral imaging (HSI) enables visualisation of morphological and biochemical information, which could improve disease diagnostic accuracy. Unfortunately, the wide range of image distortions that arise during flexible endoscopy in the clinic have made integration of HSI challenging. To address this challenge, we demonstrate a hyperspectral endoscope (HySE) that simultaneously records intrinsically co-registered hyperspectral and standard-of-care white light images, which allows image distortions to be compensated computationally and an accurate hyperspectral data cube to be reconstructed as the endoscope moves in the lumen. Evaluation of HySE performance shows excellent spatial, spectral and temporal resolution and high colour fidelity. Application of HySE enables: quantification of blood oxygenation levels in tissue mimicking phantoms; differentiation of spectral profiles from normal and pathological ex vivo human tissues; and recording of hyperspectral data under freehand motion within an intact ex vivo pig oesophagus model. HySE therefore shows potential for enabling HSI in clinical endoscopy.

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning.

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.

Quantitative evaluation of comb-structure correction methods for multispectral fibrescopic imaging.

Removing the comb artifact introduced by imaging fibre bundles, or 'fibrescopes', for example in medical endoscopy, is essential to provide high quality images to the observer. Multispectral imaging (MSI) is an emerging method that combines morphological (spatial) and chemical (spectral) information in a single data 'cube'. When a fibrescope is coupled to a spectrally resolved detector array (SRDA) to perform MSI, comb removal is complicated by the demosaicking step required to reconstruct the multispectral data cube. To understand the potential for using SRDAs as multispectral imaging sensors in medical endoscopy, we assessed five comb correction methods with respect to five performance metrics relevant to biomedical imaging applications: processing time, resolution, smoothness, signal and the accuracy of spectral reconstruction. By assigning weights to each metric, which are determined by the particular imaging application, our results can be used to select the correction method to achieve best overall performance. In most cases, interpolation gave the best compromise between the different performance metrics when imaging using an SRDA.

Bimodal reflectance and fluorescence multispectral endoscopy based on spectrally resolving detector arrays.

Emerging clinical interest in combining standard white light endoscopy with targeted near-infrared (NIR) fluorescent contrast agents for improved early cancer detection has created demand for multimodal imaging endoscopes. We used two spectrally resolving detector arrays (SRDAs) to realize a bimodal endoscope capable of simultaneous reflectance-based imaging in the visible spectral region and multiplexed fluorescence-based imaging in the NIR. The visible SRDA was composed of 16 spectral bands, with peak wavelengths in the range of 463 to 648 nm and full-width at half-maximum (FWHM) between 9 and 26 nm. The NIR SRDA was composed of 25 spectral bands, with peak wavelengths in the range 659 to 891 nm and FWHM 7 to 15 nm. The spectral endoscope design was based on a "babyscope" model using a commercially available imaging fiber bundle. We developed a spectral transmission model to select optical components and provide reference endmembers for linear spectral unmixing of the recorded image data. The technical characterization of the spectral endoscope is presented, including evaluation of the angular field-of-view, barrel distortion, spatial resolution and spectral fidelity, which showed encouraging performance. An agarose phantom containing oxygenated and deoxygenated blood with three fluorescent dyes was then imaged. After spectral unmixing, the different chemical components of the phantom could be successfully identified via majority decision with high signal-to-background ratio (>3). Imaging performance was further assessed in an ex vivo porcine esophagus model. Our preliminary imaging results demonstrate the capability to simultaneously resolve multiple biological components using a compact spectral endoscopy system.

White-light quantitative phase imaging unit: erratum.

We found an error in Fig. 1 of our article "White-light Quantitative Phase Imaging Unit." Here we publish the revised figure.

Holographic deep learning for rapid optical screening of anthrax spores.

Establishing early warning systems for anthrax attacks is crucial in biodefense. Despite numerous studies for decades, the limited sensitivity of conventional biochemical methods essentially requires preprocessing steps and thus has limitations to be used in realistic settings of biological warfare. We present an optical method for rapid and label-free screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and subgenus specificity. The unique "representation learning" capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate point-of-care diagnosis of pathogens.

Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning.

Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.

Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson's disease.

Parkinson's disease (PD) is a common neurodegenerative disease. However, therapeutic methods of PD are still limited due to complex pathophysiology in PD. Here, optical measurements of individual neurons from in vitro PD model using optical diffraction tomography (ODT) are presented. By measuring 3D refractive index distribution of neurons, morphological and biophysical alterations in in-vitro PD model are quantitatively investigated. It was found that neurons show apoptotic features in early PD progression. The present approach will open up new opportunities for quantitative investigation of the pathophysiology of various neurodegenerative diseases. © 2017 International Society for Advancement of Cytometry.

Antibacterial Activities of Graphene Oxide-Molybdenum Disulfide Nanocomposite Films.

Two-dimensional (2D) nanomaterials, such as graphene-based materials and transition metal dichalcogenide (TMD) nanosheets, are promising materials for biomedical applications owing to their remarkable cytocompatibility and physicochemical properties. On the basis of their potent antibacterial properties, 2D materials have potential as antibacterial films, wherein the 2D nanosheets are immobilized on the surface and the bacteria may contact with the basal planes of 2D nanosheets dominantly rather than contact with the sharp edges of nanosheets. To address these points, in this study, we prepared an effective antibacterial surface consisting of representative 2D materials, i.e., graphene oxide (GO) and molybdenum disulfide (MoS2), formed into nanosheets on a transparent substrate for real device applications. The antimicrobial properties of the GO-MoS2 nanocomposite surface toward the Gram-negative bacteria Escherichia coli were investigated, and the GO-MoS2 nanocomposite exhibited enhanced antimicrobial effects with increased glutathione oxidation capacity and partial conductivity. Furthermore, direct imaging of continuous morphological destruction in the individual bacterial cells having contacts with the GO-MoS2 nanocomposite surface was characterized by holotomographic (HT) microscopy, which could be used to detect the refractive index (RI) distribution of each voxel in bacterial cell and reconstruct the three-dimensional (3D) mapping images of bacteria. In this regard, the decreases in both the volume (67.2%) and the dry mass (78.8%) of bacterial cells that came in contact with the surface for 80 min were quantitatively measured, and releasing of intracellular components mediated by membrane and oxidative stress was observed. Our findings provided new insights into the antibacterial properties of 2D nanocomposite film with label-free tracing of bacterial cell which improve our understanding of antimicrobial activities and opened a window for the 2D nanocomposite as a practical antibacterial film in biomedical applications.

Induction of neuronal activation by femtosecond-pulsed laser irradiation and its potential application for amyloid-ß-induced toxicity assessment.

Manipulating neural activity is crucial for studying the neural connectivity and the pathophysiology of neurodegenerative disease. Among various techniques for neural activation, direct optical stimulation method with femtosecond-pulsed laser is simple and can be specifically applied on a single neuron. Brief irradiation of femtosecond laser pulses on a neuron elevates intracellular calcium, and it propagates to adjacent neurons. However, the mechanisms of laser-induced neural activation are still unclear. In this report, we have elucidated the mechanism of laser-induced neural activation which could be mediated by superoxide, specifically blocked by diphenyleneiodonium chloride, and depletion in intracellular calcium storage. Furthermore, we also showed that the propagation of calcium initiated by laser stimulation is dependent on the presence of extracellular calcium as well as electrical and chemical synapses. We verified the applicability of such mechanism for the assessment of neuronal functionality, by measuring calcium elevation, intracellular calcium propagation, ROS increase, and performing cell death assay in vehicle and Aß-treated neurons. This work suggests promising applications of the potential for implementing such laser-induced neural activation for rapid and reliable drug screening.

Three-dimensional label-free imaging and quantification of lipid droplets in live hepatocytes.

Lipid droplets (LDs) are subcellular organelles with important roles in lipid storage and metabolism and involved in various diseases including cancer, obesity, and diabetes. Conventional methods, however, have limited ability to provide quantitative information on individual LDs and have limited capability for three-dimensional (3-D) imaging of LDs in live cells especially for fast acquisition of 3-D dynamics. Here, we present an optical method based on 3-D quantitative phase imaging to measure the 3-D structural distribution and biochemical parameters (concentration and dry mass) of individual LDs in live cells without using exogenous labelling agents. The biochemical change of LDs under oleic acid treatment was quantitatively investigated, and 4-D tracking of the fast dynamics of LDs revealed the intracellular transport of LDs in live cells.

Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging.

Sickle cell disease (SCD) is common across Sub-Saharan Africa. However, the investigation of SCD in this area has been significantly limited mainly due to the lack of research facilities and skilled personnel. Here, we present optical measurements of individual red blood cells from healthy individuals and individuals with SCD and sickle cell trait in Tanzania using the quantitative phase imaging technique. By employing a quantitative phase imaging unit, an existing microscope in a clinic is transformed into a powerful quantitative phase microscope providing measurements on the morphological, biochemical, and biomechanical properties of individual cells. The present approach will open up new opportunities for cost-effective investigation and diagnosis of several diseases in low resource environments.