Perspective—Challenges in Developing Wearable Electrochemical Sensors for Longitudinal Health Monitoring

Wearable electrochemical sensors have the potential to overcome the problem of infrequent clinical visits that leads to transient events of potential diagnostic importance being unduly overlooked. The promise of real-time, personalized health care has driven multidisciplinary work on fabricating various forms of wearable sensors. Although remarkable advances in device form factor and integrated circuit design have been achieved, notable hurdles, such as shelf life, reuseability, flex and sweat resistance, and longitudinal performance, remain unaddressed. This perspective seeks to summarize major advances in current wearable electrochemical sensors and to highlight the most pressing challenges that will benefit from collective research endeavors

A rare early-onset neonatal case of Birk-Barel syndrome presenting severe obstructive sleep apnea: a case report

BackgroundBirk-Barel syndrome, also known as KCNK9 imprinting syndrome, is a rare fertility disorder. And the main clinical manifestations include congenital hypotonic, craniofacial malformation, developmental delay, and intellectual disability. Generally, such patients could be diagnosed beyond the infant period. Moreover, the delayed diagnosis might lead to a poor prognosis of rehabilitation therapy. However, neonatal obstructive sleep apnea (OSA) was seldom reported in Birk-Barel syndrome. Here, we reported a severe neonatal OSA case induced by Birk-Barel syndrome, resulting in an early diagnosis with improved outcomes by integrative management.Case presentationThe proband was a neonate presenting with recurrent severe OSA, with craniofacial deformity and congenital muscle hypotonia. Bronchoscopy examinations indicated a negative finding of pharyngeal and bronchus stenosis, while laryngomalacia had been observed. Whole exon sequencing demonstrated a c. 710C>A heterozygous variant resulting in a change of amino acid (p.A237D). This variant resulted in a change of amino acid sequence, affected protein features and changed splice site leading to a structural deformation in KCNK9 protein. This p.A237D variant also affected the crystal structure on the p.G129 site. Additionally, we used the mSCM tool to measure the free energy changes between wild-type and mutant protein, which indicated highly destabilizing (−2.622 kcal/mol).ConclusionThis case report expands the understanding of Birk-Barel syndrome and indicates that OSA could serve as the on-set manifestation of Birk-Barel syndrome. This case emphasized genetic variants which were associated with severe neonatal OSA. Adequate WES assessment promotes early intervention and improves the prognosis of neurological disorders in young children

State Estimation of Distributed Drive Electric Vehicle Based on Adaptive Kalman Filter

As a new type of transportation, the distributed drive electric vehicle is regarded as the main development direction of electric vehicles in the future. Due to the advantages of the independently controllable driving torque of each wheel, it provides more favorable conditions for vehicle active safety control. Acquiring accurate and real-time parameters such as vehicle speed and side slip angle is a prerequisite for vehicle active safety control. Therefore, relying on the National Natural Science Foundation of China, this paper takes the distributed drive electric vehicle in the form of four-wheel independent drive and steering as the research object. Taking the measurement data of low-cost vehicle sensors as input and adaptive Kalman filtering as theoretical support, the sub-filter of federal Kalman filtering adds a fuzzy controller on the basis of volumetric Kalman filtering, and designs the vehicle driving state estimation algorithm to realize the accurate estimation of driving state information. Finally, the typical experimental conditions are selected, and the designed algorithm is verified by the co-simulation of MATLAB/Simulink and CarSim. At the same time, the algorithm is further verified based on the driving simulator hardware-in-the-loop experimental platform. The results show that the designed estimation algorithm has good effects in terms of accuracy, stability, and real-time performance

Joint Estimation of Driving State and Road Surface Adhesion Coefficient of a Four-Wheel Independent and Steering-Drive Electric Vehicle

Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal Kalman filtering and an intelligent bionic antlion optimization algorithm. Firstly, according to the research purpose of the paper and the focus on the accuracy of the establishment of the three degrees of freedom dynamics model, fully considering the road conditions, the paper adopts the Dugoff tire model and finally completes the establishment of the vehicle state estimation model. Secondly, the drive state estimation algorithm is developed utilizing the principles of federal Kalman filtering and volume Kalman filtering. At the same time, robust estimation theory is introduced into the sub-filter, and the antlion optimization module is designed at the lower layer of the main filter to enhance the accuracy of estimates. It is easy to see that the design of the Antlion federal Kalman travel state estimation algorithm has noticeably enhanced accuracy and traceability, according to the result. Thirdly, a joint estimation algorithm of state estimation and road surface adhesion coefficient has been devised to enhance the stability and precision of the estimation process. Finally, the results showed that the joint estimation algorithm has high accuracy in estimating vehicle driving state parameters such as the center of mass lateral deflection angle and road surface adhesion coefficient by simulation

Multiform Informed Machine Learning Based on Piecewise and Weibull for Engine Remaining Useful Life Prediction

Informed machine learning (IML), which strengthens machine learning (ML) models by incorporating external knowledge, can get around issues like prediction outputs that do not follow natural laws and models, hitting optimization limits. It is therefore of significant importance to investigate how domain knowledge of equipment degradation or failure can be incorporated into machine learning models to achieve more accurate and more interpretable predictions of the remaining useful life (RUL) of equipment. Based on the informed machine learning process, the model proposed in this paper is divided into the following three steps: (1) determine the sources of the two types of knowledge based on the device domain knowledge, (2) express the two forms of knowledge formally in Piecewise and Weibull, respectively, and (3) select different ways of integrating them into the machine learning pipeline based on the results of the formal expression of the two types of knowledge in the previous step. The experimental results show that the model has a simpler and more general structure than existing machine learning models and that it has higher accuracy and more stable performance in most datasets, particularly those with complex operational conditions, which demonstrates the effectiveness of the method in this paper on the C-MAPSS dataset and assists scholars in properly using domain knowledge to deal with the problem of insufficient training data

Host-Induced Gene Silencing of a G Protein α Subunit Gene CsGpa1 Involved in Pathogen Appressoria Formation and Virulence Improves Tobacco Resistance to Ciboria shiraiana

Hypertrophy sorosis scleroteniosis caused by Ciboria shiraiana is the most devastating disease of mulberry fruit. However, few mulberry lines show any resistance to C. shiraiana. An increasing amount of research has shown that host-induced gene silencing (HIGS) is an effective strategy for enhancing plant tolerance to pathogens by silencing genes required for their pathogenicity. In this study, two G protein α subunit genes, CsGPA1 and CsGPA2, were identified from C. shiraiana. Silencing CsGPA1 and CsGPA2 had no effect on hyphal growth but reduced the number of sclerotia and increased the single sclerotium weight. Moreover, silencing CsGpa1 resulted in increased fungal resistance to osmotic and oxidative stresses. Compared with wild-type and empty vector strains, the number of appressoria was clearly lower in CsGPA1-silenced strains. Importantly, infection assays revealed that the virulence of CsGPA1-silenced strains was significantly reduced, which was accompanied by formation of fewer appressoria and decreased expression of several cAMP/PKA- or mitogen-activated protein-kinase-related genes. Additionally, transgenic Nicotiana benthamiana expressing double-stranded RNA targeted to CsGpa1 through the HIGS method significantly improved resistance to C. shiraiana. Our results indicate that CsGpa1 is an important regulator in appressoria formation and the pathogenicity of C. shiraiana. CsGpa1 is an efficient target to improve tolerance to C. shiraiana using HIGS technology

A novel ferroptosis-related gene signature for overall survival prediction in patients with gastric cancer

Abstract The global diagnosis rate and mortality of gastric cancer (GC) are among the highest. Ferroptosis and iron-metabolism have a profound impact on tumor development and are closely linked to cancer treatment and patient’s prognosis. In this study, we identified six PRDEGs (prognostic ferroptosis- and iron metabolism-related differentially expressed genes) using LASSO-penalized Cox regression analysis. The TCGA cohort was used to establish a prognostic risk model, which allowed us to categorize GC patients into the high- and the low-risk groups based on the median value of the risk scores. Our study demonstrated that patients in the low-risk group had a higher probability of survival compared to those in the high-risk group. Furthermore, the low-risk group exhibited a higher tumor mutation burden (TMB) and a longer 5-year survival period when compared to the high-risk group. In summary, the prognostic risk model, based on the six genes associated with ferroptosis and iron-metabolism, performs well in predicting the prognosis of GC patients

Berberine in the treatment of ulcerative colitis: A possible pathway through Tuft cells

Ulcerative colitis (UC) is an inflammatory bowel disease with complex pathogenesis, which is affected by genetic factors, intestinal immune status and intestinal microbial homeostasis. Intestinal epithelial barrier defect is crucial to the development of UC. Berberine, extracted from Chinese medicine, can identify bitter taste receptor on intestinal Tuft cells and activate IL-25-ILC2-IL-13 immune pathway to impair damaged intestinal tract by promoting differentiation of intestinal stem cells, which might be a potential approach for the treatment of UC

Immobilization of Nanobodies with Vapor-Deposited Polymer Encapsulation for Robust Biosensors

To produce next-generation, shelf-stable biosensors for point-of-care diagnostics, a combination of rugged biomolecular recognition elements, efficient encapsulants and innocuous deposition approaches are needed. Furthermore, to ensure that the sensitivity and specificity that is inherent to biological recognition elements is maintained in solid-state biosensing systems, site-specific immobilization chemistries must be invoked such that the function of the biomolecule remains unperturbed. In this work, we present a widely-applicable strategy to develop robust solid-state biosensors using emergent nanobody (Nb) recognition elements coupled with a vapor-deposited polymer encapsulation layer. As compared to conventional immunoglobulin G (IgG) antibodies, Nbs are smaller (12-15 kDa as opposed to ~150 kDa), have higher thermal stability and pH tolerance, boast greater ease of recombinant production, and are capable of binding antigens with high affinity and specificity. Photoinitiated chemical vapor deposition (piCVD) affords thin, protective polymer barrier layers over immobilized Nb arrays that allow for retention of Nb activity and specificity after both storage under ambient conditions and complete desiccation. Most importantly, we also demonstrate that vapor-deposited polymer encapsulation of nanobody arrays enables specific detection of target proteins in complex heterogenous samples, such as unpurified cell lysate, which is otherwise challenging to achieve with bare Nb arrays

Preparation of La(III), Fe(III) Modified Zeolite Molecular Sieves for the Removal of Fluorine from Water

Excessive fluoride in mine water has become a major concern because it can cause detrimental effects to human health. Nevertheless, the removal efficiency of traditional adsorbents is far from satisfactory. Herein, La and Fe bimetallic supported zeolite was synthesized by co-precipitation method, for efficient defluoridation. The defluoridation performance of La-Fe zeolite was studied by a batch adsorption experiment and dynamic adsorption column test. Results indicated that the removal efficiency of F− was 99.04% under the optimal conditions (4 h, adsorbent dosage 8.0 g/L, agitation rate 200 rpm/min, temperature 298K and pH = 6 ± 1) that were determined through the batch adsorption experiments. CO32− and HCO3− can inhibit the defluorination effect of La-Fe zeolite. The adsorption of fluoride ions on La-Fe zeolite can be well described by the Langmuir adsorption model, and the maximum fluoride ion adsorption capacity is 2.64 mg/g. The test of dynamic adsorption column shows that the adsorption efficiency of F− by La-Fe zeolite on was higher than 85% for continuous adsorption of 9 h, indicating that La-Fe zeolite has good practical applications. The mechanism analysis indicated that the adsorption of fluoride ion by La-Fe modified zeolite involves both ion exchange and complexation, which belongs to the physicochemical process