A review of disease risk prediction methods and applications in the omics era.

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Novel fluorescence strategy based on G-quadruplex structure-switching aptamer for enrofloxacin detection in food and environmental samples.

Enrofloxacin (ENR) is widely used in the prevention and treatment of animal infectious diseases, so it is necessary to strengthen the residue detection of this drug in animal-derived food and water environments. In this work, for the first time, we engineered assembly a split ENR aptamer into the G-quadruplex (G4) region to form a new aptamer (G4-ENRA) that provides a more sensitive signal-reporting function while retaining target-specific recognition ability of the aptamer. This rational design effectively overcomes the issue of difficulty in identification probe development. Under the optimized conditions, a response range of 0.05-20 µM and limit of detection of 26.7 nM were obtained by directly detecting fluorescence signals, displaying a comparative advantage over the previously reported methods. Moreover, this method demonstrated satisfactory performance for the ENR detection in various real food and environmental samples, with the detection recoveries ranging from 95.87 % to 104.36 %, illustrating promising applicability prospects.

Effects of genetic variation on the structure of RNA and protein.

Changes in the structure of RNA and protein, have an important impact on biological functions and are even important determinants of disease pathogenesis and treatment. Some genetic variations, including copy number variation, single nucleotide variation, and so on, can lead to changes in biological function and increased susceptibility to certain diseases by changing the structure of RNA or protein. With the development of structural biology and sequencing technology, a large amount of RNA and protein structure data and genetic variation data resources has emerged to be used to explain biological processes. Here, we reviewed the effects of genetic variation on the structure of RNAs and proteins, and investigated their impact on several diseases. An online resource (http://www.onethird-lab.com/gems/) to support convenient retrieval of common tools is also built. Finally, the challenges and future development of the effects of genetic variation on RNA and protein were discussed.

Genome-wide identification of m6A-associated single nucleotide polymorphisms in complex diseases of nervous system.

N6-methyladenosine (m6A) is one of the most abundant chemical modifications on RNA and can affect the occurrence and development of diseases. Some studies have shown that the expressions of some m6A-related genes are significantly regulated by single nucleotide variants (SNV). However, the function of m6A-associated single nucleotide polymorphisms (m6A-SNP) remains unclear in multiple sclerosis (MS), Alzheimer's disease (AD) and Parkinson's disease (PD). Here, we identified the disease-associated m6A-SNPs by integrating genome-wide association study (GWAS) and m6A-SNPs from the RMVar database, and confirmed the relationship between these identified m6A-SNPs and their target genes in eQTL analysis and gene differential expression analysis. Finally, 26 genes corresponding to 20 m6A-SNPs with eQTL signals were identified and differentially expressed (P < 0.05) in MS, 15 genes corresponding to 12 m6A-SNPs (P < 1e-04) were differentially expressed in AD, and 27 PD-associated m6A-SNPs that regulated the expression of 31 genes were identified. There were 5 HLA genes with eQTL signals (HLA-DQB1, HLA-DRB1, HLA-DQA1, HLA-DQA2 and HLA-DQB1-AS1) to be detected in the three diseases. In summary, our study provided new insights into understanding the potential roles of these m6A-SNPs in disease pathogenesis as well as therapeutic target.

A novel S,N-rich MOF for efficient recovery of Au(III): Performance and mechanism.

A novel S,N-rich MOF with adenine and 4,4'-thiodiphenol as organic ligands was synthesized via the one-step solvothermal method, and used for gold recovery. The pH impact, adsorption kinetics, isotherms, thermodynamics, selectivity, and reusability were investigated accordingly. The adsorption and desorption mechanism were also explored comprehensively. The electronic attraction, coordination, and in situ redox account for the Au(III) adsorption. The Au(III) adsorption is affected strongly by the pH of solutions, and best at pH of 2.57. The MOF exhibits exceptional adsorption capacity as high as 3680 mg/g at 55 °C, fast kinetics with 8 min for 9.6 mg/L Au(III), and excellent selectivity for gold ion in real e-waste leachates. The adsorption process of gold on the adsorbent is endothermic and spontaneous, and influenced visibly by temperature. The adsorption ratio still maintained 99% after seven adsorption-desorption cycles. The column adsorption experiments show that the MOF has outstanding selectivity for Au(III) with 100% of removal efficiency in a complex solution containing Au, Ni, Cu, Cd, Co, and Zn ions. A glorious adsorption with a breakthrough time of 532 mins was obtained for the breakthrough curve. This study not only provides an efficient adsorbent for gold recovery, but also guidance for designing new materials.

A label-free G-quadruplex aptamer fluorescent aptasensor for visual and real-time kanamycin detection in lake and human samples.

Antibiotic abuse is considered a serious problem affecting human health, necessitating that great attention be paid to explore robust, simple and sensitive methods for rapid evaluation. In this paper, we developed a fluorescent aptasensor for visual and real-time kanamycin detection by taking advantage of the label-free strategy based on H-aggregate disassembly of a chiral cyanine dye induced by a G-quadruplex aptamer. The good sensitivity and selectivity enabled this aptasensor to have a detection limit as low as 43 nM and have high specificity for kanamycin recognition. Furthermore, this assay was successfully applied for the detection of kanamycin in lake water and urine with excellent recoveries.

RABC: Rheumatoid Arthritis Bioinformatics Center.

Advances in sequencing technologies have led to the rapid growth of multi-omics data on rheumatoid arthritis (RA). However, a comprehensive database that systematically collects and classifies the scattered data is still lacking. Here, we developed the Rheumatoid Arthritis Bioinformatics Center (RABC, http://www.onethird-lab.com/RABC/), the first multi-omics data resource platform (data hub) for RA. There are four categories of data in RABC: (i) 175 multi-omics sample sets covering transcriptome, epigenome, genome, and proteome; (ii) 175 209 differentially expressed genes (DEGs), 105 differentially expressed microRNAs (DEMs), 18 464 differentially DNA methylated (DNAm) genes, 1 764 KEGG pathways, 30 488 GO terms, 74 334 SNPs, 242 779 eQTLs, 105 m6A-SNPs and 18 491 669 meta-mQTLs; (iii) prior knowledge on seven types of RA molecular markers from nine public and credible databases; (iv) 127 073 literature information from PubMed (from 1972 to March 2022). RABC provides a user-friendly interface for browsing, searching and downloading these data. In addition, a visualization module also supports users to generate graphs of analysis results by inputting personalized parameters. We believe that RABC will become a valuable resource and make a significant contribution to the study of RA.

Cr-Based MOF for Efficient Adsorption of Au at Low Concentrations.

The efficient enrichment and selection of Au are crucial for gold recovery. The adsorption technology is considered to have potential due to the advantages of operation simplicity and green processability. Nevertheless, the poor Au selectivity at low concentrations in complex solutions limits the further application of the adsorption technology. In this work, a novel Cr-based MOF adsorbent was successfully synthesized using 1,2,4-triazole and 4-aminobenzoic acid as ligands. Benefitting from the surface positive charge and extensive chelation and reduction sites, the novel Cr-based MOF exhibited a total adsorption capacity of up to 357 mg/g and excellent adsorption selectivity toward Au(III) in the complex metal mixed solutions, such as simulated sewage ash waste liquid and actual e-waste leachate. Furthermore, the adsorption kinetics, isotherms, and thermodynamics were discussed in depth for investigating the adsorption mechanisms of the MOF. The PXRD and XPS analyses reveal that the adsorption process involves complexation, redox, and electrostatic interactions. We believe that this study of novel Cr-based MOF adsorbents for efficient Au adsorption is meaningful for further application in the gold recovery technology from e-waste.

Mobile-Based and Self-Service Tool (iPed) to Collect, Manage, and Visualize Pedigree Data: Development Study.

BACKGROUND: Pedigree data (family history) are indispensable for genetics studies and the assessment of individuals' disease susceptibility. With the popularity of genetics testing, the collection of pedigree data is becoming more common. However, it can be time-consuming, laborious, and tedious for clinicians to investigate all pedigree data for each patient. A self-service robot could inquire about patients' family history in place of professional clinicians or genetic counselors. OBJECTIVE: The aim of this study was to develop a mobile-based and self-service tool to collect and visualize pedigree data, not only for professionals but also for those who know little about genetics. METHODS: There are 4 main aspects in the iPed construction, including interface building, data processing, data storage, and data visualization. The user interface was built using HTML, JavaScript libraries, and Cascading Style Sheets (version 3; Daniel Eden). Processing of the submitted data is carried out by PHP programming language. MySQL is used to document and manage the pedigree data. PHP calls the R script to accomplish the visualization. RESULTS: iPed is freely available to all users through the iPed website. No software is required to be installed, no pedigree files need to be prepared, and no knowledge of genetics or programs is required. The users can easily complete their pedigree data collection and visualization on their own and through a dialogue with iPed. Meanwhile, iPed provides a database that stores all users' information. Therefore, when the users need to construct new pedigree trees for other genetic traits or modify the pedigree trees that have already been created, unnecessary duplication of operations can be avoided. CONCLUSIONS: iPed is a mobile-based and self-service tool that could be used by both professionals and nonprofessionals at any time and from any place. It reduces the amount of time required to collect, manage, and visualize pedigree data.

The Identification of Elderly People with High Fall Risk Using Machine Learning Algorithms.

Falling is an important public health issue, and predicting the fall risk can reduce the incidence of injury events in the elderly. However, most of the existing studies may have additional human and financial costs for community workers and doctors. Therefore, it is socially important to identify elderly people who are at high fall risk through a reasonable and cost-effective method. We evaluated the potential of multifractal, machine learning algorithms to identify the elderly at high fall risk. We developed a 42-point calibration model of the human body and recorded the three-dimensional coordinate datasets. The stability of the motion trajectory is calculated by the multifractal algorithm and used as an input dimension to compare the performance of the six classifiers. The results showed that the instability of the faller group was significantly greater than that of the no-faller group in the male and female cohorts (p < 0.005), and the Gradient Boosting Decision Tree classifier showed the best performance. The findings could help elderly people at high fall risk to identify individualized risk factors and facilitate tailored fall interventions.

XAI Feature Detector for Ultrasound Feature Matching.

Feature matching is a crucial component of computer vision that has various applications. With the emergence of Computer-Aided Diagnosis (CAD), the need for feature matching has also emerged in the medical imaging field. In this paper, we proposed a novel algorithm using the Explainable Artificial Intelligence (XAI) [1] approach to achieve feature detection for ultrasound images based on the Deep Unfolding Super-resolution Network (USRNET). Based on the experimental results, our method shows higher interpretability and robustness than existing traditional feature extraction and matching algorithms. The proposed method provides a new insight for medical image processing, and may achieve better performance in the future with advancements of deep neural networks.

3D Reconstruction of Carotid Artery from Ultrasound Images.

3D reconstruction is an important area in computer vision, which can be applied to assist in medical diagnosis. Compared to observing 2D ultrasound images, 3D models are more suitable for diagnostic interpretation. In this paper, we describe an approach for 3D reconstruction of the carotid artery utilizing ultrasound images from the transverse and longitudinal views. We implement a human-computer interface to ensure the accuracy of the segmentation results by involving superpixels and ellipse fitting techniques. This approach is expected to achieve better accuracy to assist diagnostics in the future.

Effects of Jaw Clench Actions on Steady-State Visual Evoked Potential Detection at Some Typical Frequencies.

More and more hybrid brain-computer interfaces (BCI) supplement traditional single-modality BCI in practical applications. Combinations based on steady-state visual evoked potential (SSVEP) and electromyography (EMG) are the widely used hybrid BCIs. The EMG of jaw clench is commonly used together with SSVEP. This article explored the interference with SSVEP from occipital electrodes by the jaw clench-related EMG so that SSVEP with specific frequency can be identified even during occlusal movements. The experiment was divided into three sets base on the jaw clench patterns (no clenches, chew, and long clench). In each set, the subjects used the same visual stimuli, which were realized by the three flashing targets at different frequencies (6.2Hz, 9.8Hz, and 14.6Hz). After collecting the SSVEP at 4 sites in the occipital region, the SSVEP response spectrum of each stimulus was observed under the three jaw clench patterns. Then, the SSVEP signal was identified by the canonical correlation analysis method for accuracy statistics. Spectrum responses showed that the interference of the jaw clench EMG on SSVEP could be avoided when the stimulation frequency is lower than 20Hz. SSVEP could be identified based on the frequency domain characteristics of these signals. During steady-state visual stimulation with jaw clenches, the recognition rate of SSVEP was still high (no clenches: 100.0%, chew: 94.7%, and long clench: 100.0%). Through reasonable frequency selecting and signal processing, the influence of the jaw clench movement on the SSVEP could be reduced and a high recognition accuracy could be achieved, even the jaw clench actions and the SSVEP stimulation occur simultaneously.

Two-dimensional dynamic walking stability of elderly females with a history of falls.

Injuries related with falls are a major health risk for the elderly. Accurate evaluation of the dynamic walking stability of elderly people is the key to fall prevention. A two-dimensional (2-D) model is proposed in this study given that the custom method is mainly focused on the dynamic walking stability along the antero-posterior axis. An inverted pendulum model was utilised to calculate the region of stability at toe-off, and stability conditions were evaluated first along the antero-posterior and medio-lateral axes. The analysis was then extended to the 2-D plane. In the 2-D case, the region of stability was determined based on the use of the information of the envelope of the foot. Twenty-four female participants, categorised as healthy young, healthy elderly, and elderly with a history of falls, were examined. Significant differences among the three groups were demonstrated with the 2-D analysis method, but not in the antero-posterior or medio-lateral analyses. The centre-of-masses of elderly fallers were significantly closer to the foot-supporting boundary compared with that of healthy young and elderly adults at toe-off. A 2-D analysis method using the envelope-of-foot could evaluate the dynamic stability of elderly females based on a more accurate scale.

The Factors Affecting Volunteers' Willingness to Participate in Disaster Preparedness.

Disaster preparedness is crucial for providing an effective response to, and reducing the possible impacts of, disasters. Although volunteers' participation plays an important role in disaster preparedness, their actual participation in disaster preparedness activities is still low. To find ways to encourage more volunteers to participate, this study analyzed the social background and organizational and attitudinal factors affecting the volunteers' willingness to participate. Questionnaires were distributed to 990 registered disaster volunteers across Beijing and the data were analyzed using linear regression models. Results revealed a weak willingness to participate in disaster preparedness. Only 28.08% of the respondents indicated that they were "very ready" to participate in voluntary disaster preparedness, and 14.65% showed "a little bit" of interest. The following was concluded: (1) Disaster volunteers' social background variables were related to their willingness to participate in disaster preparedness. Compared to male volunteers, female volunteers were more willing to participate. Chinese Communist Party members were more willing to participate than non-members. (2) Providing accidental life insurance for the volunteers had a positive effect on their willingness to participate in disaster preparedness. Provision of more training had a negative effect on the volunteers' willingness to participate, indicating a low quality of training. (3) Organizational identification was positively related to the volunteers' willingness to participate. According to these results, we suggest that volunteer organizations should improve their standards and procedures for disaster volunteer recruitment and selection, and gain a deeper understanding of the needs of the disaster volunteers in order to better motivate them to participate.

Heterozygous PAX6 mutations may lead to hyper-proinsulinaemia and glucose intolerance: A case-control study in families with congenital aniridia.

AIM: PAX6 is a transcription factor involved in embryonic development of many organs, including the eyes and the pancreas. Mutations of PAX6 gene is the main cause of a rare disease, congenital aniridia (CA). This case-control study aims to investigate the effects of PAX6 mutations on glucose metabolism and insulin secretion in families with CA. METHODS: In all, 21 families with CA were screened by Sanger sequencing. Patients with PAX6 mutations and CA (cases) and age-matched healthy family members (controls) were enrolled. Oral glucose tolerance test (OGTT) was performed to detect diabetes or impaired glucose tolerance (IGT). Insulin and proinsulin secretion were evaluated. RESULTS: Among 21 CA families, heterozygous PAX6 mutations were detected in five families. Among cases (n = 10) from the five families, two were diagnosed with newly identified diabetes and another two were diagnosed with IGT. Among controls (n = 12), two had IGT. The levels of haemoglobin A1c were 36 ± 4 mmol/mol (5.57 ± 0.46%) and 32 ± 5 mmol/L (5.21 ± 0.54%) in the cases and the controls, respectively (p = 0.049). More importantly, levels of proinsulin in the cases were significantly higher than that of the controls, despite similar levels of total insulin. The areas under the curve of proinsulin in the cases (6425 ± 4390) were significantly higher than that of the controls (3709 ± 1769) (p = 0.032). CONCLUSION: PAX6 may participate in the production of proinsulin to insulin and heterozygous PAX6 mutations may be associated with glucose metabolism in CA patients.

Formation of a thin and continuous MOF membrane with 2-D MOF nanosheets as seeds via layer-by-layer growth.

Current MOF composite membranes usually suffer from unduly thick MOF layers and poor adhesion with the polymer substrate. We prepared a thin MOF film (440 nm) on a polymer support with 2-D MOF nanosheets as seeds via a layer-by-layer growth. These 2-D MOF seeds not only provided bonding between the MOF film and the polymer substrate but also facilitated the formation of a continuous and defect-free MOF membrane, which demonstrated excellent performance in gas separation.

Dynamic walking stability of elderly people with various BMIs.

BACKGROUND: Falls are one of the major causes of injury in the elderly. Obesity may be related to the risk of falling. Understanding the dynamic stability mechanisms of obese elderly people during gait is important as it may be associated with fall protection. RESEARCH QUESTION: Does obesity affect the dynamic walking stability of elderly people? METHODS: This is a prospective study. Fifty-three elderly participants, aged 60-82 years, were categorized into body mass index (BMI) groups. In single-limb support experiments, the center of mass velocity (COMv), center of mass acceleration (COMa), region of velocity stability (ROSv) and region of acceleration stability (ROSa) were calculated using kinematic data sampled from a motion analysis system. In addition, all participants were assessed for the dynamic balance ability test scale (DBATS). Statistical analyses were performed by one-way ANOVA, Kruskal-Wallis/Wilcoxon nonparametric tests, or bivariate Pearson/Spearman correlation analysis. RESULTS: During walking, peak COMv and COMa decreased with increasing BMI (Normal BMI: 1.20 ± 0.14 m/s, 1.66 ± 0.36 m/s2; High BMI: 1.14 ± 0.11 m/s, 1.56 ± 0.30 m/s2; Higher BMI: 1.04 ± 0.15 m/s, 1.47 ± 0.25 m/s2). At toe-off (TO), the normalized participants' center of mass (COM) is significantly more anterior in the Higher BMI group (Normal BMI: -0.30 ± 0.09, High BMI: -0.23 ± 0.07, Higher BMI: -0.16 ± 0.10), their normalized COMv and COMa (Normal BMI: 1.40 ± 0.16, 0.53 ± 0.11; High BMI: 1.33 ± 0.13, 0.49 ± 0.11; Higher BMI: 1.21 ± 0.16, 0.46 ± 0.11) are slower. The mean DBATS score of the Higher BMI group was the highest, indicating the weakest dynamic balance ability. SIGNIFICANCE: The COM dynamic stability parameters indicate that obesity may worsen balance, with the peak COMv and ROSv most affected. With increasing BMI, the dynamic stability and balance of elderly people both decreased.

Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms.

The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF) data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers) participated in functional movement tests, including walking and sit-to-stand (STS). A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN), pseudo nearest neighbor (PNN), local mean pseudo nearest neighbor (LMPNN) classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.

Relationship between the alignment of a non-mydriatic fundus camera, anterior chamber depth and axial length.

PURPOSE: To evaluate the relationship between the position of the focal adjustment knob of a fundus camera and refractive error and biometric data as measured in the same eye. METHODS: Normal eyes of patients presenting to clinics at the Beijing Tongren Hospital were examined with a non-mydriatic fundus camera. The position on the focal scale of a knob adjusting the distance between the camera lens and film plane, used to adjust focus the image of the patients fundus relative to the refractive power of the eye, was recorded in degrees. Ocular biometry and refractometry were performed on the same eyes. RESULTS: The study included 136 subjects with a mean age of 36.5±19.6 years and a mean refractive error of -1.31 ± 2.77 diopters. In univariate analysis, the position of the adjustment knob was significantly associated with refractive error (P<0.001; correlation coefficient r= -0.77), axial length (P<0.001; r= 0.65) and anterior chamber depth (P<0.001; r= 0.48). After adjustment for age, anterior chamber depth decreased by 0.01 mm (95% confidence interval: 0.003, 0.017) for change per degree in the position of the adjustment knob. CONCLUSION: A fundus camera can be used to estimate anterior chamber depth, axial length and refractive error. In a screening setting, a fundus camera operated by a technician may be helpful to detect a shallow anterior chamber and evaluate a potential risk factor for primary angle closure.