Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Materials Containing Single-, Di-, Tri-, and Multi-Metal Atoms Bonded to C, N, S, P, B, and O Species as Advanced Catalysts for Energy, Sensor, and Biomedical Applications.

Modifying the coordination or local environments of single-, di-, tri-, and multi-metal atom (SMA/DMA/TMA/MMA)-based materials is one of the best strategies for increasing the catalytic activities, selectivity, and long-term durability of these materials. Advanced sheet materials supported by metal atom-based materials have become a critical topic in the fields of renewable energy conversion systems, storage devices, sensors, and biomedicine owing to the maximum atom utilization efficiency, precisely located metal centers, specific electron configurations, unique reactivity, and precise chemical tunability. Several sheet materials offer excellent support for metal atom-based materials and are attractive for applications in energy, sensors, and medical research, such as in oxygen reduction, oxygen production, hydrogen generation, fuel production, selective chemical detection, and enzymatic reactions. The strong metal-metal and metal-carbon with metal-heteroatom (i.e., N, S, P, B, and O) bonds stabilize and optimize the electronic structures of the metal atoms due to strong interfacial interactions, yielding excellent catalytic activities. These materials provide excellent models for understanding the fundamental problems with multistep chemical reactions. This review summarizes the substrate structure-activity relationship of metal atom-based materials with different active sites based on experimental and theoretical data. Additionally, the new synthesis procedures, physicochemical characterizations, and energy and biomedical applications are discussed. Finally, the remaining challenges in developing efficient SMA/DMA/TMA/MMA-based materials are presented.

Endocrine Entropy.

This communication defines and describes the novel concept of endocrine entropy. The authors share insights regarding the various facets of entropy in endocrine epidemiology, physiology, clinical presentation and management. The discussion opens up a new way of approaching endocrinology. Recent advances in artificial intelligence, assessment and addressal of entropy may become integral part of endocrine diagnostics and therapeutics.

Endocrine Entropy.

This communication defines and describes the novel concept of endocrine entropy. The authors share insights regarding the various facets of entropy in endocrine epidemiology, physiology, clinical presentation and management. The discussion opens up a new way of approaching endocrinology. Recent advances in artificial intelligence, assessment and addressal of entropy may become integral part of endocrine diagnostics and therapeutics.

Testing personality-pace-of-life associations via artificial selection: insights from selected lines of rainbow trout on the context-dependence of correlations.

More than a decade of study since the personality pace-of-life syndrome (POLS) hypotheses were first proposed, there is little support for it within species. Lack of experimental control, insufficient sampling in the face of highly labile behavioural and metabolic traits, and context dependency of trait correlations are suggested as reasons. Here, I argue that artificial selection and/or use of existing selected lines represents a powerful but under-used approach to furthering our understanding of the POLS. To illustrate this potential, I conducted a focussed review of studies that compared the behaviour, metabolism, growth and survival of an artificially selected fast-growing rainbow trout relative to wild unselected strains, under varying food and risk conditions in the laboratory and field. Resting metabolic rate, food intake, and behaviours that enhance feeding but increase energy expenditure (activity, aggression, boldness), were all higher in the fast strain in paired contrasts, under all food and risk conditions, both in the laboratory and the field. Fast-strain fish grew faster in almost every food and risk situation except where food was highly limited (or absent), had higher survival under low or zero predation risk, but had lower survival under high risk. Several other traits rarely considered in POLS studies were also higher in the fast strain, including maximum swimming speed, and hormones (growth hormone (GH), thyroid hormone (T3) and insulin-like growth factor (IGF-1)). I conclude: (i) assumptions and predictions of the POLS hypothesis are well supported, and (ii) context-dependency was largely absent, but when present revealed trade-offs between food acquisition and predation risk. This focused review highlights the potential of artificial selection in testing POLS ideas, and will hopefully motivate further studies using other animals.

Bioinformatics and machine learning to support nanomaterial grouping.

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

Highly Efficient Far-Red Emitting Mg21Ca4Na4(PO4)18: Ce3+, Eu3+ Luminescent Glasses: A Novel Approach Towards Plant Cultivation Technologies.

One of the exciting developments in contemporary luminescence research is the development of rare earth triggered luminescent glasses, which are a type of lanthanide activated luminous material. For the first time, Ce3+, Eu3+ activated/co-activated Mg21Ca4Na4(PO4)18 orthophosphate glasses have been synthesized using the proposed work's melt quenching technique. The proposed glass sample's XRD pattern has an amorphous character, although its most prominent peak matches data from the Mg21Ca4Na4(PO4)18 standard ICSD database. FT-IR analysis was used to analyze the proposed glass sample's vibrational characteristics. Co-activated Mg21Ca4Na4(PO4)18 glass exhibits large emission peaks under UV excitations that cover the far red area during a photoluminescence examination. These outcomes demonstrate the proposed sample's value in applications such as WLEDs and plant cultivation.

Comparison of joint kinematics between upright front squat exercise and horizontal squat exercise performed on a short arm human centrifugation.

This study compared the joint kinematics between the front squat (FS) conducted in the upright (natural gravity) position and in the supine position on a short arm human centrifuge (SAHC). Male participants (N = 12) with no prior experience exercising on a centrifuge completed a FS in the upright position before (PRE) and after (POST) a FS exercise conducted on the SAHC while exposed to artificial gravity (AG). Participants completed, in randomized order, three sets of six repetitions with a load equal to body weight or 1.25 × body weight for upright squats, and 1 g and 1.25 g at the center of gravity (COG) for AG. During the terrestrial squats, the load was applied with a barbell. Knee (left/right) and hip (left/right) flexion angles were recorded with a set of inertial measurement units. AG decreased the maximum flexion angle (MAX) of knees and hips as well as the range of motion (ROM), both at 1 and 1.25 g. Minor adaptation was observed between the first and the last repetition performed in AG. AG affects the ability to FS in naïve participants by reducing MAX, MIN and ROM of the knees and hip.

Evaluation of deep learning algorithms in detecting moyamoya disease: a systematic review and single-arm meta-analysis.

The diagnosis of Moyamoya disease (MMD) relies heavily on imaging, which could benefit from standardized machine learning tools. This study aims to evaluate the diagnostic efficacy of deep learning (DL) algorithms for MMD by analyzing sensitivity, specificity, and the area under the curve (AUC) compared to expert consensus. We conducted a systematic search of PubMed, Embase, and Web of Science for articles published from inception to February 2024. Eligible studies were required to report diagnostic accuracy metrics such as sensitivity, specificity, and AUC, excluding those not in English or using traditional machine learning methods. Seven studies were included, comprising a sample of 4,416 patients, of whom 1,358 had MMD. The pooled sensitivity for common and random effects models was 0.89 (95% CI: 0.85 to 0.92) and 0.92 (95% CI: 0.85 to 0.96), respectively. The pooled specificity was 0.89 (95% CI: 0.86 to 0.91) in the common effects model and 0.91 (95% CI: 0.75 to 0.97) in the random effects model. Two studies reported the AUC alongside their confidence intervals. A meta-analysis synthesizing these findings aggregated a mean AUC of 0.94 (95% CI: 0.92 to 0.96) for common effects and 0.89 (95% CI: 0.76 to 1.02) for random effects models. Deep learning models significantly enhance the diagnosis of MMD by efficiently extracting and identifying complex image patterns with high sensitivity and specificity. Trial registration: CRD42024524998 https://www.crd.york.ac.uk/prospero/displayrecord.php?RecordID=524998.

KeratoEL: Detection of keratoconus using corneal parameters with ensemble learning.

Background and Aims: Keratoconus is a progressive eye condition in which the normally round cornea thins and bulges outwards into a cone shape. This irregular shape causes light to scatter in multiple directions as it enters the eye, leading to distorted vision, increased sensitivity to light and frequent changes in the prescription of glasses or contact lenses. Detecting keratoconus at an early stage is not only difficult but also challenging. Methods: The study has proposed an ensemble-based machine learning (ML) technique named KeratoEL to detect keratoconus at an early stage. The proposed KeratoEL model combines the basic machine learning algorithms, namely support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN). Before employing the ML model for keratoconus detection, the data set is first preprocessed manually by eliminating some features that don't contribute any significant value to predict the exact class. Moreover, the output features are labelled into three different classes and Extra Trees Classifier is used to find out the important features. Then, the features are sorted in descending order and top 45, 30, and 15 features are taken as input datasets against the output. Finally, different machine learning models are tested using the input datasets and performance metrics are measured. Results: The proposed model obtains 98.0%, 98.9% and 99.8% accuracy for top 45, 30, and 15 number of features respectively. Overall experimental results show that the proposed ensemble model outperforms the existing machine learning models. Conclusion: The proposed KeratoEL model effectively detects keratoconus at an early stage by combining SVM, DT, RF, and ANN algorithms, demonstrating superior performance over existing models. These results underscore the potential of the KeratoEL ensemble approach in enhancing early detection and treatment of keratoconus.

A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer.

The Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prostatectomy (RP) and to assess the correlation of AI-estimated proportions of different Gleason patterns with biochemical recurrence-free survival (RFS), metastasis-free survival (MFS), and overall survival (OS). Training and validation of algorithms for cancer detection and grading were completed with three large datasets containing a total of 580 whole-mount prostate slides from 191 RP patients at two centers and 6218 annotated needle biopsy slides from the publicly available Prostate Cancer Grading Assessment dataset. A cancer detection model was trained using MobileNetV3 on 0.5 mm × 0.5 mm cancer areas (tiles) captured at 10× magnification. For cancer grading, a Gleason pattern detector was trained on tiles using a ResNet50 convolutional neural network and a selective CutMix training strategy involving a mixture of real and artificial examples. This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. In an additional test cohort of RP patients who were clinically followed over 30 years, quantitative Gleason pattern AI estimates achieved concordance indexes of 0.69, 0.72, and 0.64 for predicting RFS, MFS, and OS times, outperforming the control experiments and International Society of Urological Pathology system (ISUP) grading by pathologists. Finally, unsupervised clustering of test RP patient specimens into low-, medium-, and high-risk groups based on AI-estimated proportions of each Gleason pattern resulted in significantly improved RFS and MFS stratification compared with ISUP grading. In summary, deep learning-based quantitative Gleason scoring using a selective CutMix training strategy may improve prognostication after prostate cancer surgery.

Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review.

Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains, including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience and neurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, and surgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervous system's complexities. However, research on AI/ML in neuroregeneration is fragmented, necessitating a comprehensive review. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, 19 English-language papers focusing on AI/ML in neuroregeneration were selected from a total of 247. Two researchers independently conducted data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT) 2018. Eight studies were deemed high quality, 10 moderate, and four low. Primary goals included diagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12% each). Methods ranged from analyzing imaging data (24%) to animal models (24%) and electronic health records (12%). Deep learning accounted for 41% of AI/ML techniques, while standard ML algorithms constituted 29%. The review underscores the growing interest in AI/ML for neuroregenerative medicine, with increasing publications. These technologies aid in diagnosing diseases and facilitating functional recovery through robotics and targeted stimulation. AI-driven drug discovery holds promise for identifying neuroregenerative therapies. Nonetheless, addressing existing limitations remains crucial in this rapidly evolving field.

Glottis Recognition Software Development Using Artificial Intelligence.

The use of video laryngoscopes has enhanced the visualization of the vocal cords, thereby improving the accessibility of tracheal intubation. Employing artificial intelligence (AI) to recognize images obtained through video laryngoscopy, particularly when marking the epiglottis and vocal cords, may elucidate anatomical structures and enhance anatomical comprehension of anatomy. This study investigates the ability of an AI model to accurately identify the glottis in video laryngoscope images captured from a manikin. Tracheal intubation was conducted on a manikin using a bronchoscope with recording capabilities, and image data of the glottis was gathered for creating an AI model. Data preprocessing and annotation of the vocal cords, epiglottis, and glottis were performed, and human annotation of the vocal cords, epiglottis, and glottis was carried out. Based on the AI's determinations, anatomical structures were color-coded for identification. The recognition accuracy of the epiglottis and vocal cords recognized by the AI model was 0.9516, which was over 95%. The AI successfully marked the glottis, epiglottis, and vocal cords during the tracheal intubation process. These markings significantly aided in the visual identification of the respective structures with an accuracy of more than 95%. The AI demonstrated the ability to recognize the epiglottis, vocal cords, and glottis using an image recognition model of a manikin.

Open optimism as an "embodied-health" ethic for the information era.

This article forms part of a series on "openness," "non-linearity," and "embodied-health" in the post-physical, informational (virtual) era of society. This is vital given that the threats posed by advances in artificial intelligence call for a holistic, embodied approach. Typically, health is separated into different categories, for example, (psycho)mental health, biological/bodily health, genetic health, environmental health, or reproductive health. However, this separation only serves to undermine health; there can be no separation of health into subgroups (psychosomatics, for example). Embodied health contains no false divisions and relies on "optimism" as the key framing value. Optimism is only achieved through the mechanism/enabling condition of openness. Openness is vital to secure the embodied health for individuals and societies. Optimism demands that persons become active participants within their own lives and are not mere blank slates, painted in the colors of physical determinism (thus a move away from nihilism-which is the annihilation of freedom/autonomy/quality). To build an account of embodied health, the following themes/aims are analyzed, built, and validated: (1) a modern re-interpretation and validation of German idealism (the crux of many legal-ethical systems) and Freud; (2) ascertaining the bounded rationality and conceptual semantics of openness (which underlies thermodynamics, psychosocial relations, individual autonomy, ethics, and as being a central constitutional governmental value for many regulatory systems); (3) the link between openness and societal/individual embodied health, freedom, and autonomy; (4) securing the role of individualism/subjectivity in constituting openness; (5) the vital role of nonlinear dynamics in securing optimism and embodied health; (6) validation of arguments using the methodological scientific value of invariance (generalization value) by drawing evidence from (i) information and computer sciences, (ii) quantum theory, and (iii) bio-genetic evolutionary evidence; and (7) a validation and promotion of the inalienable role of theoretic philosophy in constituting embodied health, and how modern society denigrates embodied health, by misconstruing and undermining theoretics. Thus, this paper provides and defends an up-to-date non-physical account of embodied health by creating a psycho-physical-biological-computational-philosophical construction. Thus, this paper also brings invaluable coherence to legal and ethical debates on points of technicality from the empirical sciences, demonstrating that each field is saying the same thing.

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.

Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within their model. This fact, whether intentional or not, may ultimately lead to unintentional consequences in the clinical setting, potentially compromising patient outcomes. This concern is particularly important in medical imaging, where AI has been more progressively and widely embraced than any other medical field. A comprehensive understanding of bias at each stage of the AI pipeline is therefore essential to contribute to developing AI solutions that are not only less biased but also widely applicable. This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later. The authors began with the fundamentals of bias by explaining its different definitions and delineating various potential sources. Strategies for detecting and identifying bias were then outlined, followed by a review of techniques for its avoidance and mitigation. Moreover, ethical dimensions, challenges encountered, and prospects were discussed.

Identification of schizophrenia by applying interpretable radiomics modeling with structural magnetic resonance imaging of the cerebellum.

AIMS: The cerebellum is involved in higher-order mental processing as well as sensorimotor functions. Although structural abnormalities in the cerebellum have been demonstrated in schizophrenia, neuroimaging techniques are not yet applicable to identify them given the lack of biomarkers. We aimed to develop a robust diagnostic model for schizophrenia using radiomic features from T1-weighted magnetic resonance imaging (T1-MRI) of the cerebellum. METHODS: A total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1-MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability. RESULTS: We identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82-0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second-order size zone non-uniformity feature from the right lobule IX and first-order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia. CONCLUSION: The radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease-defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker-based decision-making in clinical practice.

Masquerade of authority: hijacked journals are gaining more credibility than original ones.

At the moment, the academic world is faced with various challenges that negatively impact science integrity. One is hijacked journals, a second, inauthentic website for indexed legitimate journals, managed by cybercriminals. These journals publish any manuscript by charging authors and pose a risk to scientific integrity. This piece compares a journal's original and hijacked versions regarding authority in search engines. A list of 16 medical journals, along with their hijacked versions, has been collected. The MOZ Domain Authority has been used to check the authority of both original and hijacked journals, and the results have been discussed. It indicates that hijacked journals are gaining more credibility than original ones. This should alarm academia and highlights a need for serious action against hijacked journals. The related policies should be planned, and tools should be developed to support easy detection of hijacked journals. On the publishers' side, the visibility of journals' websites must be enhanced to address this issue.

Optimizing discharge antimicrobial therapy: Evaluation of a transitions of care process and electronic scoring system for patients with community-acquired pneumonia or chronic obstructive pulmonary disease.

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE: Prescribing excess antibiotic duration at hospital discharge is common. A pharmacist-led Antimicrobial Stewardship Program Transition of Care (ASP TOC) intervention was associated with improved discharge prescribing. To improve the sustainability of this service, an electronic scoring system (ESS), which included the ASP TOC electronic variable, was implemented in the electronic medical record to prioritize pharmacist workload. The purpose of this study was to evaluate the implementation of the ASP TOC variable in the ESS in patients with community-acquired pneumonia (CAP) or chronic obstructive pulmonary disease (COPD). METHODS: This institutional review board-approved, retrospective quasi-experiment included patients discharged on oral antibiotics for CAP or COPD exacerbation (lower respiratory tract infection) from November 1, 2021, to March 1, 2022 (the preintervention period) and November 1, 2022, to March 1, 2023 (the postintervention period). The primary endpoint was optimized discharge antimicrobial regimen. A sample of at least 194 patients was required to achieve 80% power to detect a 20% difference in the frequency of optimized therapy. Multivariable logistic regression was used to identify factors associated with optimized regimens. RESULTS: Similar baseline characteristics were observed in both study groups (n = 100 for both groups). The frequency of optimized discharge regimens improved from 69% to 82% (P = 0.033). The percentage of ASP TOC interventions documented as completed by a pharmacist increased from 4% to 25% (P < 0.001). ASP TOC intervention, female gender, and COPD were independently associated with an optimized discharge regimen (adjusted odds ratios, 6.57, 1.61, and 3.89, respectively; 95% CI, 1.51-28.63, 0.81-3.17, and 1.85-8.20, respectively). CONCLUSION: After the launch of the ASP TOC variable, there was an increase in optimized discharge regimens and ASP TOC interventions completed. Pharmacists' use of the ASP TOC variable through an ESS can aid in improving discharge prescribing.

Enhancing the Diagnostic Yield of Esophageal Manometry Using Distension Contraction Plots of Peristalsis & Artificial Intelligence.

BACKGROUND: Our prior study reveal that the distension-contraction profiles using high-resolution manometry impedance (HRMZ) recordings can distinguish patients with dysphagia symptom but normal esophageal function testing ("functional dysphagia") from controls. AIMS: To determine the diagnostic value of the recording protocol used in our prior studies (10cc swallows with subjects in the Trendelenburg position) against the standard clinical protocol (5cc swallows with subject in the supine position). We used advanced machine learning techniques and robust metrics for the classification purposes. METHODS: Studies were performed in 30 healthy subjects and 30 patients with functional dysphagia. A custom-built software was used to extract the relevant distension-contraction features of esophageal peristalsis. Ensemble methods, i.e., gradient boost, support vector machines (SVM), and logit boost were used as the primary machine learning algorithms. RESULTS: While the individual contraction features were marginally different between the two groups, the distension features of peristalsis were significantly different. The ROC curves values for the standard recording protocol, for the distension features ranged from 0.74 to 0.82; they were significantly better for the protocol used in our prior studies, ranged from 0.81-0.91. The ROC curve values using 3 machine learning algorithms were far superior for the distension than the contraction features of esophageal peristalsis, revealing value of 0.95 for the SVM algorithm. CONCLUSIONS: Current patient classification based on the contraction phase of peristalsis misses large number of patients who have abnormality in the distension phase of peristalsis. Distension contraction plots should be the standard of assessing esophageal peristalsis in clinical practice.

Enhancing Photocathodic Performances of Particulate-CuGaS2-Based Photoelectrodes via Conjugation with Conductive Organic Polymers for Efficient Solar-Driven Hydrogen Production and CO2 Reduction.

Modification with conductive organic polymers consisting of a thiophane- or pyrrole-based backbone improved the cathodic photocurrent of a particulate-CuGaS2-based photoelectrode under simulated solar light. Among these polymers, poly(3,4-ethylenedioxythiophene) (PEDOT) was the most effective in the improvements, providing a photocurrent 670 times as high as that of the bare photocathode. An incident-photon-to-current efficiency (IPCE) for water reduction to form H2 under monochromatic light irradiation (450 nm at 0 V vs RHE) was ca. 11%. The most important point is that modification of the conductive organic polymers does not involve any vacuum processes. This importance lies in the use of an electrochemically oxidative polymerization, not in a physical process such as vapor deposition of metal conductors. This is expected to be advantageous in the large-scale application of photocathodes consisting of particulate photocatalyst materials toward industrial solar-hydrogen production using photoelectrochemical-cell-based devices. Artificial photosynthesis of water splitting and CO2 reduction under simulated solar light was demonstrated by combining the PEDOT-modified CuGaS2 photocathode with a CoOx-loaded BiVO4 photoanode. Furthermore, how the cathodic photocurrent of the particulate-CuGaS2-based photocathode was drastically improved by the modification was clarified based on various characterizations and control experiments as follows: (1) selectively filling cavities between the particulate CuGaS2 photocatalysts and a conductive substrate (FTO; fluorine-doped tin oxide) with the polymers and (2) using a large driving force for carrier transportation governed by the polymers' redox potentials adjusted by functional groups.