Harnessing the Power of Voice: A Deep Neural Network Model for Alzheimer's Disease Detection.

Background and Purpose: Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer's disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer's disease dementia (ADD). Methods: This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma. Results: A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%±1.6% when evaluated against unseen test dataset. Conclusions: Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.

Fate specification is spatially intermingled across planarian stem cells.

Regeneration requires mechanisms for producing a wide array of cell types. Neoblasts are stem cells in the planarian Schmidtea mediterranea that undergo fate specification to produce over 125 adult cell types. Fate specification in neoblasts can be regulated through expression of fate-specific transcription factors. We utilize multiplexed error-robust fluorescence in situ hybridization (MERFISH) and whole-mount FISH to characterize fate choice distribution of stem cells within planarians. Fate choices are often made distant from target tissues and in a highly intermingled manner, with neighboring neoblasts frequently making divergent fate choices for tissues of different location and function. We propose that pattern formation is driven primarily by the migratory assortment of progenitors from mixed and spatially distributed fate-specified stem cells and that fate choice involves stem-cell intrinsic processes.

Stricturing Crohn's Disease Single-Cell RNA Sequencing Reveals Fibroblast Heterogeneity and Intercellular Interactions.

BACKGROUND & AIMS: Fibroblasts play a key role in stricture formation in Crohn's disease (CD) but understanding its pathogenesis requires a systems-level investigation to uncover new treatment targets. We studied full-thickness CD tissues to characterize fibroblast heterogeneity and function by generating the first single-cell RNA sequencing (scRNAseq) atlas of strictured bowel and providing proof of principle for therapeutic target validation. METHODS: We performed scRNAseq of 13 fresh full-thickness CD resections containing noninvolved, inflamed nonstrictured, and strictured segments as well as 7 normal non-CD bowel segments. Each segment was separated into mucosa/submucosa or muscularis propria and analyzed separately for a total of 99 tissue samples and 409,001 cells. We validated cadherin-11 (CDH11) as a potential therapeutic target by using whole tissues, isolated intestinal cells, NanoString nCounter, next-generation sequencing, proteomics, and animal models. RESULTS: Our integrated dataset revealed fibroblast heterogeneity in strictured CD with the majority of stricture-selective changes detected in the mucosa/submucosa, but not the muscle layer. Cell-cell interaction modeling revealed CXCL14+ as well as MMP/WNT5A+ fibroblasts displaying a central signaling role in CD strictures. CDH11, a fibroblast cell-cell adhesion molecule, was broadly expressed and up-regulated, and its profibrotic function was validated using NanoString nCounter, RNA sequencing, tissue target expression, in vitro gain- and loss-of-function experiments, proteomics, and knock-out and antibody-mediated CDH11 blockade in experimental colitis. CONCLUSIONS: A full-thickness bowel scRNAseq atlas revealed previously unrecognized fibroblast heterogeneity and interactions in CD strictures and CDH11 was validated as a potential therapeutic target. These results provide a new resource for a better understanding of CD stricture formation and open potential therapeutic developments. This work has been posted as a preprint on Biorxiv under doi: 10.1101/2023.04.03.534781.

Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning.

MOTIVATION: Single-cell RNA sequencing enables researchers to study cellular heterogeneity at single-cell level. To this end, identifying cell types of cells with clustering techniques becomes an important task for downstream analysis. However, challenges of scRNA-seq data such as pervasive dropout phenomena hinder obtaining robust clustering outputs. Although existing studies try to alleviate these problems, they fall short of fully leveraging the relationship information and mainly rely on reconstruction-based losses that highly depend on the data quality, which is sometimes noisy. RESULTS: This work proposes a graph-based prototypical contrastive learning method, named scGPCL. Specifically, scGPCL encodes the cell representations using Graph Neural Networks on cell-gene graph that captures the relational information inherent in scRNA-seq data and introduces prototypical contrastive learning to learn cell representations by pushing apart semantically dissimilar pairs and pulling together similar ones. Through extensive experiments on both simulated and real scRNA-seq data, we demonstrate the effectiveness and efficiency of scGPCL. AVAILABILITY AND IMPLEMENTATION: Code is available at https://github.com/Junseok0207/scGPCL.

Stricturing Crohn's disease single-cell RNA sequencing reveals fibroblast heterogeneity and intercellular interactions.

Background: Fibroblasts play a key role in stricture formation in Crohn's disease (CD) but understanding it's pathogenesis requires a systems-level investigation to uncover new treatment targets. We studied full thickness CD tissues to characterize fibroblast heterogeneity and function by generating the first single cell RNA sequencing (scRNAseq) atlas of strictured bowel and providing proof of principle for therapeutic target validation. Methods: We performed scRNAseq of 13 fresh full thickness CD resections containing non-involved, inflamed non-strictured, and strictured segments as well as 7 normal non-CD bowel segments. Each segment was separated into mucosa/submucosa or muscularis propria and analyzed separately for a total of 99 tissue samples and 409,001 cells. We validated cadherin-11 (CDH11) as a potential therapeutic target by using whole tissues, isolated intestinal cells, NanoString nCounter, next generation sequencing, proteomics and animal models. Results: Our integrated dataset revealed fibroblast heterogeneity in strictured CD with the majority of stricture-selective changes detected in the mucosa/submucosa, but not the muscle layer. Cell-cell interaction modeling revealed CXCL14+ as well as MMP/WNT5A+ fibroblasts displaying a central signaling role in CD strictures. CDH11, a fibroblast cell-cell adhesion molecule, was broadly expressed and upregulated, and its pro-fibrotic function was validated by NanoString nCounter, RNA sequencing, tissue target expression, in vitro gain- and loss-of-function experiments, proteomics, and two animal models of experimental colitis. Conclusion: A full-thickness bowel scRNAseq atlas revealed previously unrecognized fibroblast heterogeneity and interactions in CD strictures and CDH11 was validated as a potential therapeutic target. These results provide a new resource for a better understanding of CD stricture formation and opens potential therapeutic developments.

Origami and Kirigami Structure for Impact Energy Absorption: Its Application to Drone Guards.

As the use of drones grows, so too does the demand for physical protection against drone damage resulting from collisions and falls. In addition, as the flight environment becomes more complicated, a shock absorption system is required, in which the protective structure can be deformed based on the circumstances. Here, we present an origami- and kirigami-based structure that provides protection from various directions. This research adds a deformation capacity to existing fixed-shape guards; by using shape memory alloys, the diameter and height of the protective structure are controlled. We present three protective modes (1: large diameter/low height; 2: small diameter/large height; and 3: lotus shaped) that mitigate drone falls and side collisions. From the result of the drop impact test, mode 2 showed a 78.2% reduction in the maximum impact force at side impact. We incorporated kirigami patterns into the origami structures in order to investigate the aerodynamic effects of the hollow patterns. Airflow experiments yielded a macro understanding of flow-through behaviors on each kirigami pattern. In the wind speed experiment, the change in airflow velocity induced by the penetration of the kirigami pattern was measured, and in the force measurement experiment, the air force applied to the structure was determined.

Strengthening deep-learning models for intracranial hemorrhage detection: strongly annotated computed tomography images and model ensembles.

Background and purpose: Multiple attempts at intracranial hemorrhage (ICH) detection using deep-learning techniques have been plagued by clinical failures. We aimed to compare the performance of a deep-learning algorithm for ICH detection trained on strongly and weakly annotated datasets, and to assess whether a weighted ensemble model that integrates separate models trained using datasets with different ICH improves performance. Methods: We used brain CT scans from the Radiological Society of North America (27,861 CT scans, 3,528 ICHs) and AI-Hub (53,045 CT scans, 7,013 ICHs) for training. DenseNet121, InceptionResNetV2, MobileNetV2, and VGG19 were trained on strongly and weakly annotated datasets and compared using independent external test datasets. We then developed a weighted ensemble model combining separate models trained on all ICH, subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), and small-lesion ICH cases. The final weighted ensemble model was compared to four well-known deep-learning models. After external testing, six neurologists reviewed 91 ICH cases difficult for AI and humans. Results: InceptionResNetV2, MobileNetV2, and VGG19 models outperformed when trained on strongly annotated datasets. A weighted ensemble model combining models trained on SDH, SAH, and small-lesion ICH had a higher AUC, compared with a model trained on all ICH cases only. This model outperformed four deep-learning models (AUC [95% C.I.]: Ensemble model, 0.953[0.938-0.965]; InceptionResNetV2, 0.852[0.828-0.873]; DenseNet121, 0.875[0.852-0.895]; VGG19, 0.796[0.770-0.821]; MobileNetV2, 0.650[0.620-0.680]; p < 0.0001). In addition, the case review showed that a better understanding and management of difficult cases may facilitate clinical use of ICH detection algorithms. Conclusion: We propose a weighted ensemble model for ICH detection, trained on large-scale, strongly annotated CT scans, as no model can capture all aspects of complex tasks.

Electrochemical Gene Amplification Signal Detection of Disposable Biochips Using Electrodes.

Real-time Polymerase Chain Reaction (RT-PCR), a molecular diagnostic technology, is spotlighted as one of the quickest and fastest diagnostic methods for the actual coronavirus (SARS-CoV-2). However, the fluorescent label-based technology of the RT-PCR technique requires expensive equipment and a sample pretreatment process for analysis. Therefore, this paper proposes a biochip based on Electrochemical Impedance Spectroscopy (EIS). In this paper, it was possible to see the change according to the concentration by measuring the impedance with a chip made of two electrodes with different shapes of sample DNA.

Compact Camera Fluorescence Detector for Parallel-Light Lens-Based Real-Time PCR System.

The polymerase chain reaction is an important technique in biological research. However, it is time consuming and has a number of disadvantages. Therefore, real-time PCR technology that can be used in real-time monitoring has emerged, and many studies are being conducted regarding its use. Real-time PCR requires many optical components and imaging devices such as expensive, high-performance cameras. Therefore, its cost and assembly process are limitations to its use. Currently, due to the development of smart camera devices, small, inexpensive cameras and various lenses are being developed. In this paper, we present a Compact Camera Fluorescence Detector for use in parallel-light lens-based real-time PCR devices. The proposed system has a simple optical structure, the system cost can be reduced, and the size can be miniaturized. This system only incorporates Fresnel lenses without additional optics in order for the same field of view to be achieved for 25 tubes. In the center of the Fresnel lens, one LED and a complementary metal-oxide semiconductor camera were placed in directions that were as similar as possible. In addition, to achieve the accurate analysis of the results, image processing was used to correct them. As a result of an experiment using a reference fluorescent substance and double-distilled water, it was confirmed that stable fluorescence detection was possible.

Association of Depression With Susceptibility and Adaptation to Seasickness in the Military Seafarers.

BACKGROUND: Although depression and motion sickness are prevalent in military personnel and seafarers, the association between depression and seasickness has been not yet elucidated. We aimed to evaluate the relationship of depression with initial susceptibility and adaptation to seasickness amongst military seafarers. METHODS: This retrospective cohort enrolled Navy seafarers who started seafaring between 2017 and 2019. Three groups were established according to the Beck Depression Inventory (BDI) score: no depression (BDI score of 0), minimal depression (BDI score 1-9), and mild-to-moderate depression (BDI score 10-29). The occurrence of seasickness requiring treatment was observed as the prescription of medication for the first 30 distant seafaring days. Considering adjustment period, the two different outcomes were defined. The susceptibility to seasickness was evaluated via at least one day suffered from seasickness requiring treatment during the early period (the first 5 seafaring days), and adaptation ability to seasickness was defined by more than 10% of the ratio, calculated days suffered from seasickness requiring treatment/days of seafaring during the late period (the 6-30th seafaring days). Binary logistic regression was further evaluated to estimate the odds of BDI groups and BDI score adjusted for age and workplace whether outside visual perception was possible. RESULTS: Among the 185 recruits, 179 participants (97%) sailed for more than 5 days were included in the study. Of the participants, 36% was susceptible to seasickness in the early and 17% was poorly adapted to seasickness in the late period. Multivariable model revealed that mild-to-moderate depression had elevated risk of poor adaptation (odds ratio [OR], 4.63; 95% confidence interval [CI], 1.31-16.98) whereas the results were not statistically significant for susceptibility to seasickness in the early period BDI score was independently associated with increased odds of poor adaptation (OR, 1.10; 95% CI, 1.04-1.18). CONCLUSION: The present study suggests that depression is associated with poor adaptation to seasickness in Navy seafarers. Depression screening tool might be helpful for providing preventable strategies for population at risk.

Noncanonical NF-κB factor p100/p52 regulates homologous recombination and modulates sensitivity to DNA-damaging therapy.

Homologous recombination (HR) serves multiple roles in DNA repair that are essential for maintaining genomic stability, including double-strand DNA break (DSB) repair. The central HR protein, RAD51, is frequently overexpressed in human malignancies, thereby elevating HR proficiency and promoting resistance to DNA-damaging therapies. Here, we find that the non-canonical NF-κB factors p100/52, but not RelB, control the expression of RAD51 in various human cancer subtypes. While p100/p52 depletion inhibits HR function in human tumor cells, it does not significantly influence the proficiency of non-homologous end joining, the other key mechanism of DSB repair. Clonogenic survival assays were performed using a pair DLD-1 cell lines that differ only in their expression of the key HR protein BRCA2. Targeted silencing of p100/p52 sensitizes the HR-competent cells to camptothecin, while sensitization is absent in HR-deficient control cells. These results suggest that p100/p52-dependent signaling specifically controls HR activity in cancer cells. Since non-canonical NF-κB signaling is known to be activated after various forms of genomic crisis, compensatory HR upregulation may represent a natural consequence of DNA damage. We propose that p100/p52-dependent signaling represents a promising oncologic target in combination with DNA-damaging treatments.

JULGI-mediated increment in phloem transport capacity relates to fruit yield in tomato.

The continuous growth of the global population and the increase in the amount of arid land has severely constrained agricultural crop production. To solve this problem, many researchers have attempted to increase productivity through the efficient distribution of energy; however, the direct relationship between the plant vasculature, specifically phloem development, and crop yield is not well established. Here, we demonstrate that an optimum increase in phloem-transportation capacity by reducing SIJUL expression leads to improved sink strength in tomato (Solanum lycopersicum L.). SIJUL, a negative regulator of phloem development, suppresses the translation of a positive regulator of phloem development, SlSMXL5. The suppression of SlJUL increases the number of phloem cells and sucrose transport, but only an optimal reduction of SlJUL function greatly enhances sink strength in tomato, improving fruit setting, and yield contents by 37% and 60%, respectively. We show that the increment in phloem cell number confers spare transport capacity. Our results suggest that the control of phloem-transport capacity within the threshold could enhance the commitment of photosynthates to instigate yield improvement.

Cost-Effective Multiplex Fluorescence Detection System for PCR Chip.

The lack of portability and high cost of multiplex real-time PCR systems limits the device to be used in POC. To overcome this issue, this paper proposes a compact and cost-effective fluorescence detection system that can be integrated to a multiplex real-time PCR equipment. An open platform camera with embedded lens was used instead of photodiodes or an industrial camera. A compact filter wheel using a sliding tape is integrated, and the excitation LEDs are fixed at a 45° angle near the PCR chip, eliminating the need of additional filter wheels. The results show precise positioning of the filter wheel with an error less than 20 µm. Fluorescence detection results using a reference dye and standard DNA amplification showed comparable performance to that of the photodiode system.

Cloud-Based Software Architecture for Fully Automated Point-of-Care Molecular Diagnostic Device.

This paper proposes a cloud-based software architecture for fully automated point-of-care molecular diagnostic devices. The target system operates a cartridge consisting of an extraction body for DNA extraction and a PCR chip for amplification and fluorescence detection. To facilitate control and monitoring via the cloud, a socket server was employed for fundamental molecular diagnostic functions such as DNA extraction, amplification, and fluorescence detection. The user interface for experimental control and monitoring was constructed with the RESTful application programming interface, allowing access from the terminal device, edge, and cloud. Furthermore, it can also be accessed through any web-based user interface on smart computing devices such as smart phones or tablets. An emulator with the proposed software architecture was fabricated to validate successful operation.

Multiple Compact Camera Fluorescence Detector for Real-Time PCR Devices.

The polymerase chain reaction is an important technique in biological research because it tests for diseases with a small amount of DNA. However, this process is time consuming and can lead to sample contamination. Recently, real-time PCR techniques have emerged which make it possible to monitor the amplification process for each cycle in real time. Existing camera-based systems that measure fluorescence after DNA amplification simultaneously process fluorescence excitation and emission for dozens of tubes. Therefore, there is a limit to the size, cost, and assembly of the optical element. In recent years, imaging devices for high-performance, open platforms have benefitted from significant innovations. In this paper, we propose a fluorescence detector for real-time PCR devices using an open platform camera. This system can reduce the cost, and can be miniaturized. To simplify the optical system, four low-cost, compact cameras were used. In addition, the field of view of the entire tube was minimized by dividing it into quadrants. An effective image processing method was used to compensate for the reduction in the signal-to-noise ratio. Using a reference fluorescence material, it was confirmed that the proposed system enables stable fluorescence detection according to the amount of DNA.

Open Platform Cameras Based Bio-Imaging Evaluation System.

With the active development of mobile devices, a variety of ultra-small, high-definition, and open platform-based cameras are being mass-produced. In this paper, we established an emulation system to verify the bio-imaging performance of the bulky and expensive high-performance cameras and various smartphone cameras that have been used in bio-imaging devices. In the proposed system, the linearity of the brightness gradient change of four types of cameras was compared and analyzed. Based on these results, three cameras were selected in order of excellent linearity, and gel image analysis results were compared.

Quantitative Analysis of Fluorescence Detection Using a Smartphone Camera for a PCR Chip.

Most existing commercial real-time polymerase chain reaction (RT-PCR) instruments are bulky because they contain expensive fluorescent detection sensors or complex optical structures. In this paper, we propose an RT-PCR system using a camera module for smartphones that is an ultra small, high-performance and low-cost sensor for fluorescence detection. The proposed system provides stable DNA amplification. A quantitative analysis of fluorescence intensity changes shows the camera's performance compared with that of commercial instruments. Changes in the performance between the experiments and the sets were also observed based on the threshold cycle values in a commercial RT-PCR system. The overall difference in the measured threshold cycles between the commercial system and the proposed camera was only 0.76 cycles, verifying the performance of the proposed system. The set calibration even reduced the difference to 0.41 cycles, which was less than the experimental variation in the commercial system, and there was no difference in performance.

High Chloride Burden and Clinical Outcomes in Critically Ill Patients With Large Hemispheric Infarction.

Background: In general, disease severity has been found to be associated with abnormal chloride levels in critically ill patients, but hyperchloremia is associated with mixed results regarding patient-centered clinical outcomes. We aimed to investigate the impact of maximum serum chloride concentration on the clinical outcomes of critically ill patients with large hemispheric infarction (LHI). Methods: We conducted a retrospective observational cohort study using prospective institutional neurocritical care registry data from 2013 to 2018. Patients with LHIs involving over two-thirds of middle cerebral artery territory, with or without infarction of other vascular territories, and a baseline National Institutes of Health Stroke Scale score of ≥13 were assessed. Those with a baseline creatinine clearance of <15 mL/min and required neurocritical care for <72 h were excluded. Primary outcome was in-hospital mortality. Secondary outcomes included 3-month mortality and acute kidney injury (AKI) occurrence. Outcomes were compared to different maximum serum chloride levels (5 mmol/L increases) during the entire hospitalization period using multivariable logistic regression analyses. Results: Of 90 patients, 20 (22.2%) died in-hospital. Patients who died in-hospital had significantly higher maximum serum chloride levels than did those who survived up to hospital discharge (139.7 ± 8.1 vs. 119.1 ± 10.4 mmol/L; p < 0.001). After adjusting for age, sex, and Glasgow coma scale score, each 5-mmol/L increase in maximum serum chloride concentration was independently associated with an increased risk of in-hospital mortality (adjusted odds ratio (aOR), 4.34; 95% confidence interval [CI], 1.98-9.50; p < 0.001). Maximum serum chloride level was also an independent risk factor for 3-month mortality (aOR, 1.99 [per 5 mmol/L increase]; 95% CI, 1.42-2.79; p < 0.001) and AKI occurrence (aOR, 1.57 [per 5 mmol/L increase]; 95% CI, 1.18-2.08; p = 0.002). Conclusions: High maximum serum chloride concentrations were associated with poor clinical outcomes in critically ill patients with LHI. This study highlights the importance of monitoring serum chloride levels and avoiding hyperchloremia in this patient population.

Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization.

The identification of potential microRNA (miRNA)-disease associations enables the elucidation of the pathogenesis of complex human diseases owing to the crucial role of miRNAs in various biologic processes and it yields insights into novel prognostic markers. In the consideration of the time and costs involved in wet experiments, computational models for finding novel miRNA-disease associations would be a great alternative. However, computational models, to date, are biased towards known miRNA-disease associations; this is not suitable for rare miRNAs (i.e., miRNAs with a few known disease associations) and uncommon diseases (i.e., diseases with a few known miRNA associations). This leads to poor prediction accuracies. The most straightforward way of improving the performance is by increasing the number of known miRNA-disease associations. However, due to lack of information, increasing attention has been paid to developing computational models that can handle insufficient data via a technical approach. In this paper, we present a general framework-improved prediction of miRNA-disease associations (IMDN)-based on matrix completion with network regularization to discover potential disease-related miRNAs. The success of adopting matrix factorization is demonstrated by its excellent performance in recommender systems. This approach considers a miRNA network as additional implicit feedback and makes predictions for disease associations relevant to a given miRNA based on its direct neighbors. Our experimental results demonstrate that IMDN achieved excellent performance with reliable area under the receiver operating characteristic (ROC) area under the curve (AUC) values of 0.9162 and 0.8965 in the frameworks of global and local leave-one-out cross-validations (LOOCV), respectively. Further, case studies demonstrated that our method can not only validate true miRNA-disease associations but also suggest novel disease-related miRNA candidates.

IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization.

Recently, increasing evidence have reported that microRNAs (miRNAs) play key roles in a variety of biological processes. Therefore, the identification of novel miRNA-disease associations can shed new light on disease etiology and pathogenesis. Till now, various computational methods have been proposed to predict potential miRNA-disease associations by reducing the experimental costs and time consumption. However, most existing methods are highly dependent on known miRNA-disease associations. Therefore, the prediction of new miRNAs (i.e., miRNAs without known associated diseases) and new diseases (i.e., diseases without known associated miRNAs) has become challenging. In this paper, we present IMIPMF, a novel method for predicting miRNA-disease associations using probabilistic matrix factorization (PMF), which is a machine learning technique that is widely used in recommender systems. Predicting the rating scores that a user may assign to each item in a recommender system is analogous to predicting miRNA-disease associations. By applying PMF, our model not only identifies novel miRNA-disease associations, but also overcomes the common problem of incompatibility with miRNAs without any known associated disease, which was a limitation of most previous computational methods. We demonstrated that our proposed model achieved a high performance with a reliable AUC value of 0.891 by performing 5-fold cross-validation. Overall, IMIPMF is a high-performance machine-learning-based model for predicting miRNA-disease associations, although it only considers known miRNA-disease associations and miRNA expression data.