Design and validation of a high-speed hyperspectral laparoscopic imaging system.

Significance: Minimally invasive surgery (MIS) has shown vast improvement over open surgery by reducing post-operative stays, intraoperative blood loss, and infection rates. However, in spite of these improvements, there are still prevalent issues surrounding MIS that may be addressed through hyperspectral imaging (HSI). We present a laparoscopic HSI system to further advance the field of MIS. Aim: We present an imaging system that integrates high-speed HSI technology with a clinical laparoscopic setup and validate the system's accuracy and functionality. Different configurations that cover the visible (VIS) to near-infrared (NIR) range of electromagnetism are assessed by gauging the spectral fidelity and spatial resolution of each hyperspectral camera. Approach: Standard Spectralon reflectance tiles were used to provide ground truth spectral footprints to compare with those acquired by our system using the root mean squared error (RMSE). Demosaicing techniques were investigated and used to measure and improve spatial resolution, which was assessed with a USAF resolution test target. A perception-based image quality evaluator was used to assess the demosaicing techniques we developed. Two configurations of the system were developed for evaluation. The functionality of the system was investigated in a phantom study and by imaging ex vivo tissues. Results: Multiple configurations of our system were tested, each covering different spectral ranges, including VIS (460 to 600 nm), red/NIR (RNIR) (610 to 850 nm), and NIR (665 to 950 nm). Each configuration is capable of achieving real-time imaging speeds of up to 20 frames per second. RMSE values of 3.51 ± 2.03 % , 3.43 ± 0.84 % , and 3.47% were achieved for the VIS, RNIR, and NIR systems, respectively. We obtained sub-millimeter resolution using our demosaicing techniques. Conclusions: We developed and validated a high-speed hyperspectral laparoscopic imaging system. The HSI system can be used as an intraoperative imaging tool for tissue classification during laparoscopic surgery.

3D-Mapping and Manipulation of Photocurrent in an Optoelectronic Diamond Device.

Establishing connections between material impurities and charge transport properties in emerging electronic and quantum materials, such as wide-bandgap semiconductors, demands new diagnostic methods tailored to these unique systems. Many such materials host optically-active defect centers which offer a powerful in situ characterization system, but one that typically relies on the weak spin-electric field coupling to measure electronic phenomena. In this work, charge-state sensitive optical microscopy is combined with photoelectric detection of an array of nitrogen-vacancy (NV) centers to directly image the flow of charge carriers inside a diamond optoelectronic device, in 3D and with temporal resolution. Optical control is used to change the charge state of background impurities inside the diamond on-demand, resulting in drastically different current flow such as filamentary channels nucleating from specific, defective regions of the device. Conducting channels that control carrier flow, key steps toward optically reconfigurable, wide-bandgap optoelectronics are then engineered using light. This work might be extended to probe other wide-bandgap semiconductors (SiC, GaN) relevant to present and emerging electronic and quantum technologies.

Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections.

Tumors may contain billions of cells, including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that are consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.

Sequential emergence and contraction of epithelial subtypes in the prenatal human choroid plexus revealed by a stem cell model.

Despite the major roles of choroid plexus epithelial cells (CPECs) in brain homeostasis and repair, their developmental lineage and diversity remain undefined. In simplified differentiations from human pluripotent stem cells, derived CPECs (dCPECs) displayed canonical properties and dynamic multiciliated phenotypes that interacted with Aß uptake. Single dCPEC transcriptomes over time correlated well with human organoid and fetal CPECs, while pseudotemporal and cell cycle analyses highlighted the direct CPEC origin from neuroepithelial cells. In addition, time series analyses defined metabolic (type 1) and ciliogenic dCPECs (type 2) at early timepoints, followed by type 1 diversification into anabolic-secretory (type 1a) and catabolic-absorptive subtypes (type 1b) as type 2 cells contracted. These temporal patterns were then confirmed in independent derivations and mapped to prenatal stages using human tissues. In addition to defining the prenatal lineage of human CPECs, these findings suggest new dynamic models of ChP support for the developing human brain.

Harnessing the melanocortin system in the control of food intake and glucose homeostasis.

The central and peripheral melanocortin system, comprising of five receptors and their endogenous ligands, is responsible for a wide array of physiological functions such as skin pigmentation, sexual function and development, and inflammation. A growing body of both clinical and pre-clinical research is demonstrating the relevance of this system in metabolic health. Disruption of hypothalamic melanocortin signalling is the most common cause of monogenic obesity in humans. Setmelanotide, an FDA-approved analogue of alpha-melanocyte stimulating hormone (α-MSH) that functions by restoring central melanocortin signalling, has proven to be a potent pharmacological tool in the treatment of syndromic obesity. As the first effective therapy targeting the melanocortin system to treat metabolic disorders, its approval has sparked research to further harness the links between these melanocortin receptors and metabolic processes. Here, we outline the structure of the central and peripheral melanocortin system, discuss its critical role in the regulation of food intake, and review promising targets that may hold potential to treat metabolic disorders in humans.

Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve.

Accurate segmentation of retinal layers in optical coherence tomography (OCT) images is critical for assessing diseases that affect the optic nerve, but existing automated algorithms often fail when pathology causes irregular layer topology, such as extreme thinning of the ganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid approach that combines the strengths of deep learning and 3D graph search to overcome their limitations, was developed to improve the accuracy, robustness and generalizability of retinal layer segmentation. The method was trained on 124 OCT volumes from both eyes of 31 non-arteritic anterior ischemic optic neuropathy (NAION) patients and tested on three cross-sectional datasets with available reference tracings: Test-NAION (40 volumes from both eyes of 20 NAION subjects), Test-G (29 volumes from 29 glaucoma subjects/eyes), and Test-JHU (35 volumes from 21 multiple sclerosis and 14 control subjects/eyes) and one longitudinal dataset without reference tracings: Test-G-L (155 volumes from 15 glaucoma patients/eyes). In the three test datasets with reference tracings (Test-NAION, Test-G, and Test-JHU), Deep LOGISMOS achieved very high Dice similarity coefficients (%) on GCIPL: 89.97±3.59, 90.63±2.56, and 94.06±1.76, respectively. In the same context, Deep LOGISMOS outperformed the Iowa reference algorithms by improving the Dice score by 17.5, 5.4, and 7.5, and also surpassed the deep learning framework nnU-Net with improvements of 4.4, 3.7, and 1.0. For the 15 severe glaucoma eyes with marked GCIPL thinning (Test-G-L), it demonstrated reliable regional GCIPL thickness measurement over five years. The proposed Deep LOGISMOS approach has potential to enhance precise quantification of retinal structures, aiding diagnosis and treatment management of optic nerve diseases.

Study protocol of a systematic review and metaanalysis of stress and burnout among general surgery residents.

Introduction: Chronic workplace stress and burnout are impediments to physicians' professional fulfillment, healthcare organizations' efficiency, and patient care quality/safety. General surgery residents are especially at risk due to the complexity of their training. We report the protocol of a metaanalysis of chronic stress and burnout among Accreditation Council for Graduate Medical Education (ACGME)-affiliated general surgery residents in the era after duty-hour reforms, plus downstream effects on their health and clinical performance. Methods: The proposed systematic review and metaanalysis (PROSPERO registration CRD42021277626) will synthesize/pool data from studies of chronic stress and burnout among general surgery residents at ACGME-affiliated programs. The timeframe under review is subdivided into three intervals: (a) after the 2003 duty-hour restrictions but before 2011 reforms, (b) after the 2011 reforms but before the coronavirus pandemic, and (c) the first 3 years after the pandemic's outbreak. Only studies reporting outcomes based on validated instruments will be included. Qualitative studies, commentaries/editorials, narrative reviews, and studies not published in English will be excluded. Multivariable analyses will adjust for sample characteristics and the methodological quality of included studies. Conclusions: The metaanalysis will yield evidence reflecting experiences of North American-based general surgery residents in the years after ACGME-mandated duty-hour restructuring.

An augmented reality and high-speed optical tracking system for laparoscopic surgery.

While minimally invasive laparoscopic surgery can help reduce blood loss, reduce hospital time, and shorten recovery time compared to open surgery, it has the disadvantages of limited field of view and difficulty in locating subsurface targets. Our proposed solution applies an augmented reality (AR) system to overlay pre-operative images, such as those from magnetic resonance imaging (MRI), onto the target organ in the user's real-world environment. Our system can provide critical information regarding the location of subsurface lesions to guide surgical procedures in real time. An infrared motion tracking camera system was employed to obtain real-time position data of the patient and surgical instruments. To perform hologram registration, fiducial markers were used to track and map virtual coordinates to the real world. In this study, phantom models of each organ were constructed to test the reliability and accuracy of the AR-guided laparoscopic system. Root mean square error (RMSE) was used to evaluate the targeting accuracy of the laparoscopic interventional procedure. Our results demonstrated a registration error of 2.42 ± 0.79 mm and a procedural targeting error of 4.17 ± 1.63 mm using our AR-guided laparoscopic system that will be further refined for potential clinical procedures.

Dense surface reconstruction using a learning-based monocular vSLAM model for laparoscopic surgery.

Augmented reality (AR) has seen increased interest and attention for its application in surgical procedures. AR-guided surgical systems can overlay segmented anatomy from pre-operative imaging onto the user's environment to delineate hard-to-see structures and subsurface lesions intraoperatively. While previous works have utilized pre-operative imaging such as computed tomography or magnetic resonance images, registration methods still lack the ability to accurately register deformable anatomical structures without fiducial markers across modalities and dimensionalities. This is especially true of minimally invasive abdominal surgical techniques, which often employ a monocular laparoscope, due to inherent limitations. Surgical scene reconstruction is a critical component towards accurate registrations needed for AR-guided surgery and other downstream AR applications such as remote assistance or surgical simulation. In this work, we utilize a state-of-the-art (SOTA) deep-learning-based visual simultaneous localization and mapping (vSLAM) algorithm to generate a dense 3D reconstruction with camera pose estimations and depth maps from video obtained with a monocular laparoscope. The proposed method can robustly reconstruct surgical scenes using real-time data and provide camera pose estimations without stereo or additional sensors, which increases its usability and is less intrusive. We also demonstrate a framework to evaluate current vSLAM algorithms on non-Lambertian, low-texture surfaces and explore using its outputs on downstream tasks. We expect these evaluation methods can be utilized for the continual refinement of newer algorithms for AR-guided surgery.

Bariatric surgery for spontaneous ovulation in women living with polycystic ovary syndrome: the BAMBINI multicentre, open-label, randomised controlled trial.

BACKGROUND: Polycystic ovary syndrome (PCOS) is the most common cause of anovulatory infertility. Obesity exacerbates the reproductive complications of PCOS; however, the management of obesity in women with PCOS remains a large unmet clinical need. Observational studies have indicated that bariatric surgery could improve the rates of ovulatory cycles and prospects of fertility; however, the efficacy of surgery on ovulation rates has not yet been compared with behavioural modifications and medical therapy in a randomised trial. The aim of this study was to compare the safety and efficacy of bariatric surgery versus medical care on ovulation rates in women with PCOS, obesity, and oligomenorrhoea or amenorrhoea. METHODS: In this multicentre, open-label, randomised controlled trial, 80 women older than 18 years, with a diagnosis of PCOS based on the 2018 international evidence-based guidelines for assessing and managing PCOS, and a BMI of 35 kg/m2 or higher, were recruited from two specialist obesity management centres and via social media. Participants were randomly assigned at a 1:1 ratio to either vertical sleeve gastrectomy or behavioural interventions and medical therapy using a computer-generated random sequence (PLAN procedure in SAS) by an independent researcher not involved with any other aspect of the clinical trial. The median age of the entire cohort was 31 years and 79% of participants were White. The primary outcome was the number of biochemically confirmed ovulatory events over 52 weeks, and was assessed using weekly serum progesterone measurements. The primary endpoint included the intention-to-treat population and safety analyses were per-protocol population. This study is registered with the ISRCTN registry (ISRCTN16668711). FINDINGS: Participants were recruited from Feb 20, 2020 to Feb 1, 2021. 40 participants were assigned to each group and there were seven dropouts in the medical group and ten dropouts in the surgical group. The median number of ovulations was 6 (IQR 3·5-10·0) in the surgical group and 2 (0·0-4·0) in the medical group. Women in the surgical group had 2.5 times more spontaneous ovulations compared with the medical group (incidence rate ratio 2·5 [95% CI 1·5-4·2], p<0·0007). There were more complications in the surgical group than the medical group, although without long-term sequelae. There were 24 (66·7%) adverse events in the surgical group and 12 (30·0%) in the medical group. There were no treatment-related deaths. INTERPRETATION: Bariatric surgery was more effective than medical care for the induction of spontaneous ovulation in women with PCOS, obesity, and oligomenorrhoea or amenorrhoea. Bariatric surgery could, therefore, enhance the prospects of spontaneous fertility in this group of women. FUNDING: The Jon Moulton Charity Trust.

A dual-camera hyperspectral laparoscopic imaging system.

Minimally invasive surgery (MIS) has expanded broadly in the field of abdominal and pelvic surgery. However, there are still prevalent issues surrounding intracorporeal surgery, such as iatrogenic injury, anastomotic leakage, or the presence of positive tumor margins after resection. Current approaches to address these issues and advance laparoscopic imaging techniques often involve fluorescence imaging agents, such as indocyanine green (ICG), to improve visualization, but these have drawbacks. Hyperspectral imaging (HSI) is an emerging optical imaging modality that takes advantage of spectral characteristics of different tissues. Various applications include tissue classification and digital pathology. In this study, we developed a dual-camera system for high-speed hyperspectral imaging. This includes the development of a custom application interface and corresponding hardware setup. Characterization of the system was performed, including spectral accuracy and spatial resolution, showing little sacrifice in speed for the approximate doubling of the covered spectral range, with our system acquiring 29 spectral images from 460-850 nm. Reference color tiles with various reflectance profiles were imaged and a RMSE of 3.56 ± 1.36% was achieved. Sub-millimeter resolution was shown at 7 cm working distance for both hyperspectral cameras. Finally, we image ex vivo tissues, including porcine stomach, liver, intestine, and kidney with our system and use a high-resolution, radiometrically calibrated spectrometer for comparison and evaluation of spectral fidelity. The dual-camera hyperspectral laparoscopic imaging system can have immediate applications in various surgeries.

Feature Extraction of Ultrasound Radiofrequency Data for the Classification of the Peripheral Zone of Human Prostate.

Prostate cancer ranks among the most prevalent types of cancer in males, prompting a demand for early detection and noninvasive diagnostic techniques. This paper explores the potential of ultrasound radiofrequency (RF) data to study different anatomic zones of the prostate. The study leverages RF data's capacity to capture nuanced acoustic information from clinical transducers. The research focuses on the peripheral zone due to its high susceptibility to cancer. The feasibility of utilizing RF data for classification is evaluated using ex-vivo whole prostate specimens from human patients. Ultrasound data, acquired using a phased array transducer, is processed, and correlated with B-mode images. A range filter is applied to highlight the peripheral zone's distinct features, observed in both RF data and 3D plots. Radiomic features were extracted from RF data to enhance tissue characterization and segmentation. The study demonstrated RF data's ability to differentiate tissue structures and emphasizes its potential for prostate tissue classification, addressing the current limitations of ultrasound imaging for prostate management. These findings advocate for the integration of RF data into ultrasound diagnostics, potentially transforming prostate cancer diagnosis and management in the future.

An augmented reality-guided biopsy system using a high-speed motion tracking and real-time registration platform.

Biopsies play a crucial role in diagnosis of various diseases including cancers. In this study, we developed an augmented reality (AR) system to improve biopsy procedures and increase targeting accuracy. Our AR-guided biopsy system uses a high-speed motion tracking technology and an AR headset to display a holographic representation of the organ, lesions, and other structures of interest superimposed on real physical objects. The first application of our AR system is prostate biopsy. By incorporating preoperative scans, such as computed tomography (CT) or magnetic resonance imaging (MRI), into real-time ultrasound-guided procedures, this innovative AR-guided system enables clinicians to see the lesion as well as the organs in real time. With the enhanced visualization of the prostate, lesion, and surrounding organs, surgeons can perform prostate biopsies with an increased accuracy. Our AR-guided biopsy system yielded an average targeting accuracy of 2.94 ± 1.04 mm and can be applied for real-time guidance of prostate biopsy as well as other biopsy procedures.

Latched detection of zeptojoule spin echoes with a kinetic inductance parametric oscillator.

When strongly pumped at twice their resonant frequency, nonlinear resonators develop a high-amplitude intracavity field, a phenomenon known as parametric self-oscillations. The boundary over which this instability occurs can be extremely sharp and thereby presents an opportunity for realizing a detector. Here, we operate such a device based on a superconducting microwave resonator whose nonlinearity is engineered from kinetic inductance. The device indicates the absorption of low-power microwave wavepackets by transitioning to a self-oscillating state. Using calibrated pulses, we measure the detection efficiency to zeptojoule energy wavepackets. We then apply it to measurements of electron spin resonance, using an ensemble of 209Bi donors in silicon that are inductively coupled to the resonator. We achieve a latched readout of the spin signal with an amplitude that is five hundred times greater than the underlying spin echoes.

Photoionisation detection of a single Er3+ ion with sub-100-ns time resolution.

Efficient detection of single optical centres in solids is essential for quantum information processing, sensing and single-photon generation applications. In this work, we use radio-frequency (RF) reflectometry to electrically detect the photoionisation induced by a single Er3+ ion in Si. The high bandwidth and sensitivity of the RF reflectometry provide sub-100-ns time resolution for the photoionisation detection. With this technique, the optically excited state lifetime of a single Er3+ ion in a Si nano-transistor is measured for the first time to be [Formula: see text]s. Our results demonstrate an efficient approach for detecting a charge state change induced by Er excitation and relaxation. This approach could be used for fast readout of other single optical centres in solids and is attractive for large-scale integrated optical quantum systems thanks to the multi-channel RF reflectometry demonstrated with frequency multiplexing techniques.

Navigating the 16-dimensional Hilbert space of a high-spin donor qudit with electric and magnetic fields.

Efficient scaling and flexible control are key aspects of useful quantum computing hardware. Spins in semiconductors combine quantum information processing with electrons, holes or nuclei, control with electric or magnetic fields, and scalable coupling via exchange or dipole interaction. However, accessing large Hilbert space dimensions has remained challenging, due to the short-distance nature of the interactions. Here, we present an atom-based semiconductor platform where a 16-dimensional Hilbert space is built by the combined electron-nuclear states of a single antimony donor in silicon. We demonstrate the ability to navigate this large Hilbert space using both electric and magnetic fields, with gate fidelity exceeding 99.8% on the nuclear spin, and unveil fine details of the system Hamiltonian and its susceptibility to control and noise fields. These results establish high-spin donors as a rich platform for practical quantum information and to explore quantum foundations.

Visualization of Optic Nerve Structural Patterns in Papilledema Using Deep Learning Variational Autoencoders.

Purpose: To visualize and quantify structural patterns of optic nerve edema encountered in papilledema during treatment. Methods: A novel bi-channel deep-learning variational autoencoder (biVAE) model was trained using 1498 optical coherence tomography (OCT) scans of 125 subjects over time from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) and 791 OCT scans of 96 control subjects from the University of Iowa. An independent test dataset of 70 eyes from 70 papilledema subjects was used to evaluate the ability of the biVAE model to quantify and reconstruct the papilledema spatial patterns from input OCT scans using only two variables. Results: The montage color maps of the retinal nerve fiber layer (RNFL) and total retinal thickness (TRT) produced by the biVAE model provided an organized visualization of the variety of morphological patterns of optic disc edema (including differing patterns at similar thickness levels). Treatment effects of acetazolamide versus placebo in the IIHTT were also demonstrated in the latent space. In image reconstruction, the mean signed peripapillary retinal nerve fiber layer thickness (pRNFLT) difference ± SD was -0.12 ± 17.34 µm, the absolute pRNFLT difference was 13.68 ± 10.65 µm, and the RNFL structural similarity index reached 0.91 ± 0.05. Conclusions: A wide array of structural patterns of papilledema, integrating the magnitude of disc edema with underlying disc and retinal morphology, can be quantified by just two latent variables. Translational Relevance: A biVAE model encodes structural patterns, as well as the correlation between channels, and may be applied to visualize individuals or populations with papilledema throughout treatment.

A lack of genetic diversity and minimal adaptive evolutionary divergence in introduced Mysis shrimp after 50 years.

The successes of introduced populations in novel habitats often provide powerful examples of evolution and adaptation. In the 1950s, opossum shrimp (Mysis diluviana) individuals from Clearwater Lake in Minnesota, USA were transported and introduced to Twin Lakes in Colorado, USA by fisheries managers to supplement food sources for trout. Mysis were subsequently introduced from Twin Lakes into numerous lakes throughout Colorado. Because managers kept detailed records of the timing of the introductions, we had the opportunity to test for evolutionary divergence within a known time interval. Here, we used reduced representation genomic data to investigate patterns of genetic diversity, test for genetic divergence between populations, and for evidence of adaptive evolution within the introduced populations in Colorado. We found very low levels of genetic diversity across all populations, with evidence for some genetic divergence between the Minnesota source population and the introduced populations in Colorado. There was little differentiation among the Colorado populations, consistent with the known provenance of a single founding population, with the exception of the population from Gross Reservoir, Colorado. Demographic modeling suggests that at least one undocumented introduction from an unknown source population hybridized with the population in Gross Reservoir. Despite the overall low genetic diversity we observed, F ST outlier and environmental association analyses identified multiple loci exhibiting signatures of selection and adaptive variation related to elevation and lake depth. The success of introduced species is thought to be limited by genetic variation, but our results imply that populations with limited genetic variation can become established in a wide range of novel environments. From an applied perspective, the observed patterns of divergence between populations suggest that genetic analysis can be a useful forensic tool to determine likely sources of invasive species.