Discovery of Ketal-Ester Ionizable Lipid Nanoparticle with Reduced Hepatotoxicity, Enhanced Spleen Tropism for mRNA Vaccine Delivery.

The safety and efficacy of the lipid nanoparticle (LNP) delivery system are crucial for the successful development of messenger RNA vaccines. We designed and synthesized a series of ketal ester lipids (KELs), featuring a biodegradable ketal moiety in the linker and ester segments in the tail. Through iterative optimization of the head and tail groups of KELs, we tuned the pKa and molecular shapes, and identified (4S)-KEL12 as a safe and efficient ionizable lipid for mRNA delivery. (4S)-KEL12 LNP showed significantly higher delivery efficacy and lower toxicity than the DLin-MC3-DMA LNP. In comparison to SM-102 LNP, (4S)-KEL12 LNP exhibited better spleen tropism, reduced liver tropism, and hepatotoxicity. Additionally, (4S)-KEL12 demonstrated good biodegradability following intramuscular or intravenous injection. Notably, (4S)-KEL12 LNP encapsulated with a therapeutic mRNA cancer vaccine elicited robust cellular immune responses leading to substantial tumor regression along with prolonged survival in tumor-bearing mice. Our results suggest that (4S)-KEL12 LNP holds great promise for mRNA vaccine delivery. The comprehensive analysis of the structure-activity relationship, toxicity, biodegradability, distribution, expression, efficacy, and stereochemistry of these LNPs will greatly contribute to the rational design and discovery of novel lipid-based delivery systems.

Design, synthesis and antimycobacterial activity of novel benzothiazinones with improved water solubility.

Nitrobenzothiazinones (BTZs) represent a novel type of antitubercular agents targeting DprE1. Two clinical candidates BTZ043 and PBTZ169, as well as many other BTZs showed potent anti-TB activity, but they are all highly lipophilic and their poor aqueous solubility is still a serious issue need to be addressed. Here, we designed and synthesized a series of new BTZ derivatives, wherein a hydrophilic COOH or NH2 group is directly attached to the oxime moiety of TZY-5-84 discovered in our lab, through various linkers. Two compounds 1a and 3 were first reported to possess excellent activity against MTB H37Rv and MDR-MTB strains (MIC: <0.029-0.095 µM), low toxicity and acceptable oral PK profiles, as well as significantly improved water solubility (1200 and > 2000 µg/mL, respectively), suggesting they may serve as promising hydrophilic BTZs for further antitubercular drug discovery.

Learning Motion Primitives for the Quantification and Diagnosis of Mobility Deficits.

The severity of mobility deficits is one of the most critical parameters in the diagnosis and rehabilitation of Parkinson's disease (PD). The current approach for severity evaluation is clinical scaling that relies on a clinician's subjective observations and experience, and the observation in laboratories or clinics may not suffice to reflect the severity of motion deficits as compared to daily living activities. The paper presents an approach to modeling and quantifying the severity of mobility deficits from motion data by using nonintrusive wearable physio-biological sensors. The approach provides a user-specific metric that measures mobility deficits in terms of the quantities of motion primitives that are learned from motion tracking data. The proposed method achieved 99.84% prediction accuracy on laboratory data and 93.95% prediction accuracy on clinical data. This approach presents the potential to supplant traditional observation-based clinical scaling, providing an avenue for real-time feedback to fortify positive progression throughout the course of rehabilitation.

Optimizing Human-Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis.

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human-robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions-categorized as accurate performance or inaccurate performance due to high/low task load-are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human-robot collaboration.

The use of ecological momentary assessment for family caregivers of adults with chronic conditions: A systematic review.

Background: Ecological momentary assessment (EMA) as a real-time data collection method can provide insight into the daily experiences of family caregivers. Purpose: This systematic review aimed to synthesize studies involving EMA completed by family caregivers of adults with chronic conditions. Methods: A systematic search was conducted within six databases for articles published from the inception of the database through September 2023. We extracted the characteristics of the included studies and data on EMA-specific methods to determine the quality of the included studies. Results: A total of 12 studies involving EMA completed by family caregivers of adults with chronic conditions were identified, with almost all studies focused on caregivers of persons with Alzheimer's or dementia-related conditions. The average compliance rate across the included studies was 75%, below the recommended rate. In addition, most of the included studies did not collect the family caregivers' daily activities and care contexts in their responses (i.e., affect, stress, well-being, care demand, and fatigue) to the EMA prompts. Discussion: This review showed that using EMA to collect information on family caregivers of adults with chronic health conditions appeared feasible and acceptable. However, the methodology or design of using EMA to collect caregiver information in this population is still preliminary. The limited number of existing studies that have used EMA to capture the daily experiences of family caregivers does not provide key information that could improve understanding of caregivers' emotional experiences and well-being in real-life situations. We identified gaps in the literature that warrant additional EMA studies for this population.

Identification of dihydroquinolizinone derivatives with nitrogen heterocycle moieties as new anti-HBV agents.

The sustained loss of HBsAg is considered a pivotal indicator for achieving functional cure of HBV. Dihydroquinolizinone derivatives (DHQs) have demonstrated remarkable inhibitory activity against HBsAg both in vitro and in vivo. However, the reported neurotoxicity associated with RG7834 has raised concerns regarding the development of DHQs. In this study, we designed and synthesized a series of DHQs incorporating nitrogen heterocycle moieties. Almost all of these compounds exhibited potent inhibition activity against HBsAg, with IC50 values at the nanomolar level. Impressively, the compound (S)-2a (10 µM) demonstrated a comparatively reduced impact on the neurite outgrowth of HT22 cells and isolated mouse DRG neurons in comparison to RG7834, thereby indicating a decrease in neurotoxicity. Furthermore, (S)-2a exhibited higher drug exposures than RG7834. The potent anti-HBV activity, reduced neurotoxicity, and favorable pharmacokinetic profiles underscore its promising potential as a lead compound for future anti-HBV drug discovery.

Radiogenomic analysis of ultrasound phenotypic features coupled to proteomes predicts metastatic risk in primary prostate cancer.

BACKGROUND: Primary prostate cancer with metastasis has a poor prognosis, so assessing its risk of metastasis is essential. METHODS: This study combined comprehensive ultrasound features with tissue proteomic analysis to obtain biomarkers and practical diagnostic image features that signify prostate cancer metastasis. RESULTS: In this study, 17 ultrasound image features of benign prostatic hyperplasia (BPH), primary prostate cancer without metastasis (PPCWOM), and primary prostate cancer with metastasis (PPCWM) were comprehensively analyzed and combined with the corresponding tissue proteome data to perform weighted gene co-expression network analysis (WGCNA), which resulted in two modules highly correlated with the ultrasound phenotype. We screened proteins with temporal expression trends based on the progression of the disease from BPH to PPCWOM and ultimately to PPCWM from two modules and obtained a protein that can promote prostate cancer metastasis. Subsequently, four ultrasound image features significantly associated with the metastatic biomarker HNRNPC (Heterogeneous nuclear ribonucleoprotein C) were identified by analyzing the correlation between the protein and ultrasound image features. The biomarker HNRNPC showed a significant difference in the five-year survival rate of prostate cancer patients (p < 0.0053). On the other hand, we validated the diagnostic efficiency of the four ultrasound image features in clinical data from 112 patients with PPCWOM and 150 patients with PPCWM, obtaining a combined diagnostic AUC of 0.904. In summary, using ultrasound imaging features for predicting whether prostate cancer is metastatic has many applications. CONCLUSION: The above study reveals noninvasive ultrasound image biomarkers and their underlying biological significance, which provide a basis for early diagnosis, treatment, and prognosis of primary prostate cancer with metastasis.

Multilevel resistive switching in stable all-inorganic n-i-p double perovskite memristor.

Memristors are promising information storage devices for commercial applications because of their long endurance and low power consumption. Particularly, perovskite memristors have revealed excellent resistive switching (RS) properties owing to the fast ion migration and solution fabrication process. Here, an n-i-p type double perovskite memristor with "ITO/SnO2/Cs2AgBiBr6/NiOx/Ag" architecture was developed and demonstrated to reveal three resistance states because of the p-n junction electric field coupled with ion migration. The devices exhibited reliable filamentary with an on/off ratio exceeding 50. The RS characteristics remained unchanged after 1000 s read and 300 switching cycles. The synaptic functions were examined through long-term depression and potentiation measurements. Significantly, the device still worked after one year to reveal long-term stability because of the all-inorganic layers. This work indicates a novel idea for designing a multistate memristor by utilizing the p-n junction unidirectional conductivity during the forward and reverse scanning.

Experiment protocols for brain-body imaging of locomotion: A systematic review.

Introduction: Human locomotion is affected by several factors, such as growth and aging, health conditions, and physical activity levels for maintaining overall health and well-being. Notably, impaired locomotion is a prevalent cause of disability, significantly impacting the quality of life of individuals. The uniqueness and high prevalence of human locomotion have led to a surge of research to develop experimental protocols for studying the brain substrates, muscle responses, and motion signatures associated with locomotion. However, from a technical perspective, reproducing locomotion experiments has been challenging due to the lack of standardized protocols and benchmarking tools, which impairs the evaluation of research quality and the validation of previous findings. Methods: This paper addresses the challenges by conducting a systematic review of existing neuroimaging studies on human locomotion, focusing on the settings of experimental protocols, such as locomotion intensity, duration, distance, adopted brain imaging technologies, and corresponding brain activation patterns. Also, this study provides practical recommendations for future experiment protocols. Results: The findings indicate that EEG is the preferred neuroimaging sensor for detecting brain activity patterns, compared to fMRI, fNIRS, and PET. Walking is the most studied human locomotion task, likely due to its fundamental nature and status as a reference task. In contrast, running has received little attention in research. Additionally, cycling on an ergometer at a speed of 60 rpm using fNIRS has provided some research basis. Dual-task walking tasks are typically used to observe changes in cognitive function. Moreover, research on locomotion has primarily focused on healthy individuals, as this is the scenario most closely resembling free-living activity in real-world environments. Discussion: Finally, the paper outlines the standards and recommendations for setting up future experiment protocols based on the review findings. It discusses the impact of neurological and musculoskeletal factors, as well as the cognitive and locomotive demands, on the experiment design. It also considers the limitations imposed by the sensing techniques used, including the acceptable level of motion artifacts in brain-body imaging experiments and the effects of spatial and temporal resolutions on brain sensor performance. Additionally, various experiment protocol constraints that need to be addressed and analyzed are explained.

Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling.

Locomotor impairment is a highly prevalent and significant source of disability and significantly impacts the quality of life of a large portion of the population. Despite decades of research on human locomotion, challenges remain in simulating human movement to study the features of musculoskeletal drivers and clinical conditions. Most recent efforts to utilize reinforcement learning (RL) techniques are promising in the simulation of human locomotion and reveal musculoskeletal drives. However, these simulations often fail to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR) and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Inertial Moment Unit (IMU) sensor. The sensor was equipped on the participants' pelvis to capture reference motion data. We also adapted the reward function by leveraging previous research on walking simulations for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means that the simulated human locomotion was more realistic. As a bio-inspired defined cost, IMU data enhanced the agent's capacity to converge during the training process. As a result, the models' convergence was faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quickly and in a broader range of environments, with a better simulation performance.

Proteomics reveals MRPL4 as a high-risk factor and a potential diagnostic biomarker for prostate cancer.

Through digital rectal examinations (DRE) and routine prostate-specific antigen (PSA) screening, early prostate cancer (PC) treatment has become possible. However, PC is a complex and heterogeneous disease. In vivo, cancer cells can invade adjacent tissues and metastasize to other tissues resulting in hard cures. Therefore, the key to improving PC patients' survival time is preventing cancer cells' metastasis. We used mass spectrometry to profile primary PC in patients with versus without metastatic PC. We named these two groups of PC patients as high-risk primary PC (n = 11) and low-risk primary PC (n = 7), respectively. At the same time, patients with benign prostatic hyperplasia (BPH, n = 6) were used as controls to explore the possible factors driving PC metastasis. Based on comprehensive mass spectrometry analysis and biological validation, we found significant upregulation of MRPL4 expression in high-risk primary PC relative to low-risk primary PC and BPH. Further, through research of the extensive clinical cohort data in the database, we discovered that MRPL4 could be a high-risk factor for PC and serve as a potential diagnostic biomarker. The MRPL4 might be used as an auxiliary indicator for clinical status/stage of primary PC to predict patient survival time.

Rehabilitation Tracking of Athletes Post Anterior Cruciate Ligament Reconstruction (ACL-R) Surgery Through Causal Analysis of Gait Data & Computational Modeling.

Early identification of motion disparities in Anterior Cruciate Ligament reconstructed (ACL-R) athletes may better post-operative decision making when returning athletes to sport. Existing return to play assessments consist of assessments of muscle strength, functional tasks, patient-reported outcomes, and 3D coordinate tracking. However, these methods primarily depend on the medical provider's intuition to release them to participate in an unrestricted activity after ACL-R that may cause reinjury or long-term impacts. This study proposes a wearable sensor-based system that helps track athlete rehabilitation progress and return to sport decision making. For this, we capture gait data from 89 ACL-R athletes during their walking and jogging trials. The raw gyroscope data collected from this system is used to extract causal features based on Nolte's phase slope index. Features extracted from this study are used to develop computational models that classify ACL-R athletes based on their reconstructed knee during two visits (3-6 months & 9 months) post ACL-R surgery. The classifier's performance degradation in detecting ACL-R athletes injured knee during multiple visits supports athletic trainers and physicians' decision-making process to confirm an athlete's safe return to sport.Clinical Relevance- This study develops computational models based on causal analysis of gait data to support athletic trainers and medical practitioners' decision to return athletes to sport post ACL-R surgery.

Theory-Guided Randomized Neural Networks for Decoding Medication-Taking Behavior.

Long-term endocrine therapy (e.g. Tamoxifen, aromatase inhibitors) is crucial to prevent breast cancer recurrence, yet rates of adherence to these medications are low. To develop, evaluate, and sustain future interventions, individual-level modeling can be used to understand breast cancer survivors' behavioral mechanisms of medication-taking. This paper presents interdisciplinary research, wherein a model employing randomized neural networks was developed to predict breast cancer survivors' daily medication-taking behavior based on their survey data over three time periods (baseline, 4 months, 8 months). The neural network structure was guided by random utility theory developed in psychology and behavioral economics. Comparative analysis indicates that the proposed model outperforms existing computational models in terms of prediction accuracy under conditions of randomness.

cncRNAdb: a manually curated resource of experimentally supported RNAs with both protein-coding and noncoding function.

RNA endowed with both protein-coding and noncoding functions is referred to as 'dual-function RNA', 'binary functional RNA (bifunctional RNA)' or 'cncRNA (coding and noncoding RNA)'. Recently, an increasing number of cncRNAs have been identified, including both translated ncRNAs (ncRNAs with coding functions) and untranslated mRNAs (mRNAs with noncoding functions). However, an appropriate database for storing and organizing cncRNAs is still lacking. Here, we developed cncRNAdb, a manually curated database of experimentally supported cncRNAs, which aims to provide a resource for efficient manipulation, browsing and analysis of cncRNAs. The current version of cncRNAdb documents about 2600 manually curated entries of cncRNA functions with experimental evidence, involving more than 2,000 RNAs (including over 1300 translated ncRNAs and over 600 untranslated mRNAs) across over 20 species. In summary, we believe that cncRNAdb will help elucidate the functions and mechanisms of cncRNAs and develop new prediction methods. The database is available at http://www.rna-society.org/cncrnadb/.

Artificial Intelligence in Health Care: Bibliometric Analysis.

BACKGROUND: As a critical driving power to promote health care, the health care-related artificial intelligence (AI) literature is growing rapidly. OBJECTIVE: The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care-related AI publications. METHODS: The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. RESULTS: The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. CONCLUSIONS: This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care-related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

Validation of a Virtual Reality Buffet environment to assess food selection processes among emerging adults.

Emerging adulthood is a critical developmental period for examining food- and eating-related behaviors as long-term weight-related behavioral patterns are established. Virtual reality (VR) technology is a promising tool for basic and applied research on eating and food-related processes. Thus, the present study tested the validity and user perceptions of a highly immersive and realistic VR food buffet by: (1) comparing participants' food selections made in the VR buffet and a real-world (RW) food buffet cafeteria one-week apart, and (2) assessing participants' rated perceptions of their VR experience (0-100 scale). Participants comprised an ethnically diverse sample of emerging adults (N = 35, Mage = 20.49, SD = 2.17). Results revealed that participants' food selections in the VR and RW food buffets were significantly and positively correlated in Kcals, grams, carbohydrates, and protein (all p's < 0.05). Moreover, participants perceived that: (a) the VR buffet was natural (M = 70.97, SD = 20.92), (b) their lunch selection in the VR buffet represented a lunch they would select on an average day (M = 84.11, SD = 15.92); and (c) their selection represented a lunch they would select if the same foods were available (M = 91.29, SD = 11.00). Our findings demonstrated the validity and acceptability of our highly immersive and realistic VR buffet for assessing food selection that is generalizable to RW food settings one-week apart without precisely matched foods. The findings of this study support the utility of VR as a validated tool for research on psychological and behavioral food-related processes and training interventions among emerging adults.

Leveraging Mobile Sensing to Understand and Develop Intervention Strategies to Improve Medication Adherence.

Interventions to improve medication adherence have had limited success and can require significant human resources to implement. Research focused on improving medication adherence has undergone a paradigm shift, of late, with a shift towards developing personalized, theory-driven interventions. The current research integrates foundational and translational science to implement a mechanisms-focused, context-aware approach. Increasing adoption of mobile and wearable sensing systems presents new opportunities for understanding how medication-taking behaviors unfold in natural settings, especially in populations who have difficulty adhering to medications. When combined with survey and ecological momentary assessment data, these mobile and wearable sensing systems can directly capture the context of medication adherence in situ, including personal, behavioral, and environmental factors. The purpose of this paper is to present a new transdisciplinary research framework in medication adherence, highlight critical advances in this rapidly-evolving research field, and outline potential future directions for both research and clinical applications.

USP8 maintains embryonic stem cell stemness via deubiquitination of EPG5.

Embryonic stem cells (ESCs) can propagate in an undifferentiated state indefinitely in culture and retain the potential to differentiate into any somatic lineage as well as germ cells. The catabolic process autophagy has been reported to be involved in ESC identity regulation, but the underlying mechanism is still largely unknown. Here we show that EPG5, a eukaryotic-specific autophagy regulator which mediates autophagosome/lysosome fusion, is highly expressed in ESCs and contributes to ESC identity maintenance. We identify that the deubiquitinating enzyme USP8 binds to the Coiled-coil domain of EPG5. Mechanistically, USP8 directly removes non-classical K63-linked ubiquitin chains from EPG5 at Lysine 252, leading to enhanced interaction between EPG5 and LC3. We propose that deubiquitination of EPG5 by USP8 guards the autophagic flux in ESCs to maintain their stemness. This work uncovers a novel crosstalk pathway between ubiquitination and autophagy through USP8-EPG5 interaction to regulate the stemness of ESCs.

Use of Body Sensors to Examine Nocturnal Agitation, Sleep, and Urinary Incontinence in Individuals With Alzheimer's Disease.

Nighttime agitation, sleep disturbances, and urinary incontinence (UI) occur frequently in individuals with dementia and can add additional burden to family caregivers, although the co-occurrence of these symptoms is not well understood. The purpose of the current study was to determine the feasibility and acceptability of using passive body sensors in community-dwelling individuals with Alzheimer's disease (AD) by family caregivers and the correlates among these distressing symptoms. A single-group, descriptive design with convenience sampling of participants with AD and their family caregivers was undertaken to address the study aims. Results showed that using body sensors was feasible and acceptable and that patterns of nocturnal agitation, sleep, and UI could be determined and were correlated in study participants. Using data from body sensors may be useful to develop and implement targeted, individualized interventions to lessen these distressing symptoms and decrease caregiver burden. Further study in this field is warranted. [Journal of Gerontological Nursing, 44(8), 19-26.].

A Social Cognitive Theory-based Framework for Monitoring Medication Adherence Applied to Endocrine Therapy in Breast Cancer Survivors.

Poor adherence to long-term therapies for chronic diseases, such as cancer, compromises effectiveness of treatment and increases the likelihood of disease progression, making medication adherence a critical issue in population health. While the field has documented many eers to adherence to medication, it has also come up with few efficacious solutions to medication adherence, indicating that new and innovative approaches are needed. In this paper, we evaluate medication-taking behaviors based on social cognitive theory (SCT), presenting patterns of adherence stratified across SCT constructs in 33 breast cancer survivors over an 8-month period. Findings indicate that medication adherence is a very personal experience influenced by many simultaneously interacting factors, and a deeper contextual understanding is needed to understand and develop interventions targeting non-adherence.