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Abstract Objective This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). Materials and methods The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. Results The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. Discussion The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system’s high reliability and suitability has been affirmed through empirical validation. Conclusion The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.

Abstract Background The aim of this study was to investigate the effects of introducing the Enhanced Recovery After Surgery (ERAS) nursing model on postoperative delirium occurrence and rehabilitation quality in elderly patients with femoral neck fractures. Methods A total of 160 elderly patients with femoral neck fractures, who met the inclusion criteria and were admitted between March 2021 to March 2023, were divided into two groups: the traditional care group and the ERAS nursing model group. In addition to traditional care measures, the ERAS nursing model group received interventions based on the principles of the ERAS nursing model. The occurrence of delirium and sleep quality were observed at 24, 48, and 72 h postoperatively, as well as during the overall hospital stay. The duration of hospitalization, time to first mobilization, and post-discharge follow-up on quality of life were compared between the two groups. Results The ERAS nursing model group exhibited a significant difference in the occurrence of delirium at 48 and 72 h postoperatively, as well as during the overall hospital stay (P 0.05). The sleep quality of the two groups showed a statistically significant difference (P

Introduction We explored the relationship and the predictive value of serum fibroblast growth factor 21 (FGF21) with all-cause mortality, major adverse cardiovascular events (MACEs) and pneumonia in hemodialysis (HD) patients.Methods A total of 388 Chinese HD patients from two HD centers were finally enrolled in this prospective cohort study (registration number: ChiCTR 1900028249) between January 2018 and December 2018. Serum FGF21 was detected. Patients were followed up with a median period of 47 months to record the MACEs and pneumonia until death or 31 December 2022.Results The incidence of all-cause mortality, MACEs and pneumonia in HD patients were 20.6%, 29.6%, and 34.8%, respectively. The optimal cutoffs for FGF21 to predict all-cause mortality, MACEs and pneumonia were 437.57 pg/mL, 216.99 pg/mL and 112.79 pg/mL. Multivariate Cox regression analyses showed that FGF21, as a categorical variable, was an independent predictor for all-cause mortality, MACEs and pneumonia (HR, 3.357, 95% CI, 2.128–5.295, p

BackgroundThe growing number of patients visiting pediatric emergency departments could have a detrimental impact on the care provided to children who are triaged as needing urgent attention. Therefore, it has become essential to continuously monitor and analyze the admissions and waiting times of pediatric emergency patients. Despite the significant challenge posed by the shortage of pediatric medical resources in China’s health care system, there have been few large-scale studies conducted to analyze visits to the pediatric emergency room. ObjectiveThis study seeks to examine the characteristics and admission patterns of patients in the pediatric emergency department using electronic medical record (EMR) data. Additionally, it aims to develop and assess machine learning models for predicting waiting times for pediatric emergency department visits. MethodsThis retrospective analysis involved patients who were admitted to the emergency department of Children’s Hospital Capital Institute of Pediatrics from January 1, 2021, to December 31, 2021. Clinical data from these admissions were extracted from the electronic medical records, encompassing various variables of interest such as patient demographics, clinical diagnoses, and time stamps of clinical visits. These indicators were collected and compared. Furthermore, we developed and evaluated several computational models for predicting waiting times. ResultsIn total, 183,024 eligible admissions from 127,368 pediatric patients were included. During the 12-month study period, pediatric emergency department visits were most frequent among children aged less than 5 years, accounting for 71.26% (130,423/183,024) of the total visits. Additionally, there was a higher proportion of male patients (104,147/183,024, 56.90%) compared with female patients (78,877/183,024, 43.10%). Fever (50,715/183,024, 27.71%), respiratory infection (43,269/183,024, 23.64%), celialgia (9560/183,024, 5.22%), and emesis (6898/183,024, 3.77%) were the leading causes of pediatric emergency room visits. The average daily number of admissions was 501.44, and 18.76% (34,339/183,204) of pediatric emergency department visits resulted in discharge without a prescription or further tests. The median waiting time from registration to seeing a doctor was 27.53 minutes. Prolonged waiting times were observed from April to July, coinciding with an increased number of arrivals, primarily for respiratory diseases. In terms of waiting time prediction, machine learning models, specifically random forest, LightGBM, and XGBoost, outperformed regression methods. On average, these models reduced the root-mean-square error by approximately 17.73% (8.951/50.481) and increased the R2 by approximately 29.33% (0.154/0.525). The SHAP method analysis highlighted that the features “wait.green” and “department” had the most significant influence on waiting times. ConclusionsThis study offers a contemporary exploration of pediatric emergency room visits, revealing significant variations in admission rates across different periods and uncovering certain admission patterns. The machine learning models, particularly ensemble methods, delivered more dependable waiting time predictions. Patient volume awaiting consultation or treatment and the triage status emerged as crucial factors contributing to prolonged waiting times. Therefore, strategies such as patient diversion to alleviate congestion in emergency departments and optimizing triage systems to reduce average waiting times remain effective approaches to enhance the quality of pediatric health care services in China.

Over the past few decades, micro liquid dispensing technology has been widely used in biology, chemistry, material and environmental sciences due to its efficacy in processing multiple samples. For practical applications, precise and effective droplet generation is very important. Despite numerous droplet generation methods, the implementation of droplet-on-demand still faces challenges concerning system complexity, precision, cost, and robustness. In this work, a novel on-demand contacting droplet generation method incorporated with model-based feedback control with an image processing unit as a sensor was proposed. By studying droplet identification using image processing techniques, the model of droplet formation was simplified. Then model-based feedback control was implemented using volumes of dispensed samples as sensing signals by tuning related parameters adaptively to resist disturbances. The proposed method was integrated and applied to a homebuilt automated micro liquid dispensing system with droplets ranging from 20 nanoliter to 200 nanoliter. The experimental results demonstrated a high degree of accuracy and precision. Additionally, the proposed system’s practical utility was evaluated by analyzing mutations in genes associated with sensorineural hearing loss, verifying its effectiveness.

Chylous blood, a kind of abnormal blood with an increasing proportion, should be excluded from unpaid blood donation. Therefore, it is important to determine the level of chylous blood, to judge that whether blood is applicable. In this manuscript, the image was preprocessed by the image acquisition device of the background board. Moreover, an improved ResNet-50 neural network model of chylous blood level based on images was established which decreased the parameters by about 44.5%, and thus greatly reducing the amount of calculation and improving the ability of the network to distinguish image details. It was found that the average values of precision, sensitivity, and F1-Score of the model were all above 0.95. Furthermore, the accuracy of the 5-level determination of the plasma chylous blood level was above 0.95. Compared with manual judgment, this method significantly improved the efficiency and accuracy of detection.

Abstract Serum uric acid (UA) is a traditional biomarker in the clinical diagnosis of gout and hyperuricemia. However, serum treatment and storage are cumbersome, and wounds are susceptible to infection. Therefore, in this study, a simple and noninvasive method was developed to detect the UA in human saliva to monitor the gout. An Inertsil ODS-3 column was used for the analysis under the condition of isocratic elution with the mixed solution phosphate buffer (74 mM, pH=2.2): Methanol=98:2 (v:v) and the UV detection at 284 nm. Using salivary UA data from healthy volunteers (HVs) (n=68) and gout patients (GPs) (n=14), we examined the salivary UA difference in their content. The intra-and inter-day accuracy and precision (RSD %) were less than 2.56%, the limit of detection (LOD) of UA was 5.0 ng/mL, the mean recoveries of the corresponding compounds were 102.48%. Saliva levels of UA in HVs and GPs were 35.26±14.06 µg/mL and 91.96±23.90 µg/mL, respectively. The concentrations of salivary UA in GPs were significantly higher than those in HVs ( p < 0.001). This method was also expected to monitor the hyperuricemia and other metabolic disorders in the future.

Abstract Background The coronavirus disease (COVID-19), a pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown its destructiveness with more than one million confirmed cases and dozens of thousands of death, which is highly contagious and still spreading globally. World-wide studies have been conducted aiming to understand the COVID-19 mechanism, transmission, clinical features, etc. A cross-language terminology of COVID-19 is essential for improving knowledge sharing and scientific discovery dissemination. Methods We developed a bilingual terminology of COVID-19 named COVID Term with mapping Chinese and English terms. The terminology was constructed as follows: (1) Classification schema design; (2) Concept representation model building; (3) Term source selection and term extraction; (4) Hierarchical structure construction; (5) Quality control (6) Web service. We built open access for the terminology, providing search, browse, and download services. Results The proposed COVID Term include 10 categories: disease, anatomic site, clinical manifestation, demographic and socioeconomic characteristics, living organism, qualifiers, psychological assistance, medical equipment, instruments and materials, epidemic prevention and control, diagnosis and treatment technique respectively. In total, COVID Terms covered 464 concepts with 724 Chinese terms and 887 English terms. All terms are openly available online (COVID Term URL: http://covidterm.imicams.ac.cn ). Conclusions COVID Term is a bilingual terminology focused on COVID-19, the epidemic pneumonia with a high risk of infection around the world. It will provide updated bilingual terms of the disease to help health providers and medical professionals retrieve and exchange information and knowledge in multiple languages. COVID Term was released in machine-readable formats (e.g., XML and JSON), which would contribute to the information retrieval, machine translation and advanced intelligent techniques application.

This article proposes an efficient XOR-based visual cryptography scheme with lossless reconfigurable algorithms, which includes an encryption and a decryption schemes for gray-scale and color secret images. The encryption scheme aims to encrypt secret images by mapping them to several shared images that do not suffer any pixel expansion problem and do not reveal any information of the secret images by considering a random column selection algorithm based on the 0-mapping and 1-mapping matrices, where the two matrices are dynamically generated in the encryption scheme. Moreover, the proposed decryption scheme can reconstruct the secret images with only a series of XOR operations. The reconstructed images do not suffer any contrast distortion and pixel expansion problems comparing with their original secret images. Finally, the proposed two schemes are illustrated with the practical examples.

Abstract Background The increasing global cancer incidence corresponds to serious health impact in countries worldwide. Knowledge-powered health system in different languages would enhance clinicians’ healthcare practice, patients’ health management and public health literacy. High-quality corpus containing cancer information is the necessary foundation of cancer education. Massive non-structural information resources exist in clinical narratives, electronic health records (EHR) etc. They can only be used for training AI models after being transformed into structured corpus. However, the scarcity of multilingual cancer corpus limits the intelligent processing, such as machine translation in medical scenarios. Thus, we created the cancer specific cross-lingual corpus and open it to the public for academic use. Methods Aiming to build an English-Chinese cancer parallel corpus, we developed a workflow of seven steps including data retrieval, data parsing, data processing, corpus implementation, assessment verification, corpus release, and application. We applied the workflow to a cross-lingual, comprehensive and authoritative cancer information resource, PDQ (Physician Data Query). We constructed, validated and released the parallel corpus named as ECCParaCorp, made it openly accessible online. Results The proposed English-Chinese Cancer Parallel Corpus (ECCParaCorp) consists of 6685 aligned text pairs in Xml, Excel, Csv format, containing 5190 sentence pairs, 1083 phrase pairs and 412 word pairs, which involved information of 6 cancers including breast cancer, liver cancer, lung cancer, esophageal cancer, colorectal cancer, and stomach cancer, and 3 cancer themes containing cancer prevention, screening, and treatment. All data in the parallel corpus are online, available for users to browse and download ( http://www.phoc.org.cn/ECCParaCorp/ ). Conclusions ECCParaCorp is a parallel corpus focused on cancer in a cross-lingual form, which is openly accessible. It would make up the imbalance of scarce multilingual corpus resources, bridge the gap between human readable information and machine understanding data resources, and would contribute to intelligent technology application as a preparatory data foundation e.g. cancer-related machine translation, cancer system development towards medical education, and disease-oriented knowledge extraction.

With the rapid development of biomedical technology, amounts of data in the field of precision medicine (PM) are growing exponentially. Valuable knowledge is included in scattered data in which meaningful biomedical entities and their semantic relationships are buried. Therefore, it is necessary to develop a knowledge representation model like ontology to formally represent the relationships among diseases, phenotypes, genes, mutations, drugs, etc. and achieve effective integration of heterogeneous data. On basis of existing work, our study focus on solving the following issues: (ⅰ) Selecting the primary entities in PM domain; (ⅱ) collecting and integrating biomedical vocabularies related to the above entities; (ⅲ) defining and normalizing semantic relationships among these entities. We proposed a semi-automated method which improved the original Ontology Development 101 method to build the Precision Medicine Ontology (PMO), including defining the scope of the PMO according to the definition of PM, collecting terms from different biomedical resources, integrating and normalizing the terms by a combination of machine and manual work, defining the annotation properties, reusing existing ontologies and taxonomies, defining semantic relationships, evaluating PMO and creating the PMO website. Finally, the Precision Medicine Vocabulary (PMV) contains 4.53 million terms collected from 62 biomedical vocabularies, and the PMO includes eleven branches of PM concepts such as disease, chemical and drug, phenotype, gene, mutation, gene product and cell, described by 93 semantic relationships among them. PMO is an open, extensible ontology of PM, all of the terms and relationships in which could be obtained from the PMO website (http://www.phoc.org.cn/pmo/). Compared to existing project, our work has brought a broader and deeper coverage of mutation, gene and gene product, which enriches the semantic type and vocabulary in PM domain and benefits all users in terms of medical literature annotation, text mining and knowledge base construction.

Nitrite and sulfite play important roles in human health and environmental science, so it is desired to develop a facile and efficient method to evaluate NO2- and SO32- concentrations. In this article, the use of green alternatives with the potential of multi-functionality has been synthesized to detect nitrite and sulfite based on fluorescent probe. The carbon dots (CDs) with starch as only raw materials show fluorescence turn “on-off-on” response towards NO2- and SO32- with the limits of detection of 0.425 and 0.243 μМ, respectively. Once nitrite was present in the solution, the fluorescence of CDs was quenched rapidly due to the charge transfer. When sulfite was introduced, the quenching fluorescence of CDs was effectively recovered because of the redox reaction between NO2- and SO32-, and thus providing a new way for NO2- and SO32- detection. Owing to their excellent analytical characteristics and low cytotoxicity, the “on-off-on” sensor was successfully employed for intracellular bioimaging of NO2- and SO32-.

Abstract Background To robustly identify synergistic combinations of drugs, high-throughput screenings are desirable. It will be of great help to automatically identify the relations in the published papers with machine learning based tools. To support the chemical disease semantic relation extraction especially for chronic diseases, a chronic disease specific corpus for combination therapy discovery in Chinese (RCorp) is manually annotated. Methods In this study, we extracted abstracts from a Chinese medical literature server and followed the annotation framework of the BioCreative CDR corpus, with the guidelines modified to make the combination therapy related relations available. An annotation tool was incorporated to the standard annotation process. Results The resulting RCorp consists of 339 Chinese biomedical articles with 2367 annotated chemicals, 2113 diseases, 237 symptoms, 164 chemical-induce-disease relations, 163 chemical-induce-symptom relations, and 805 chemical-treat-disease relations. Each annotation includes both the mention text spans and normalized concept identifiers. The corpus gets an inter-annotator agreement score of 0.883 for chemical entities, 0.791 for disease entities which are measured by F score. And the F score for chemical-treat-disease relations gets 0.788 after unifying the entity mentions. Conclusions We extracted and manually annotated a chronic disease specific corpus for combination therapy discovery in Chinese. The result analysis of the corpus proves its quality for the combination therapy related knowledge discovery task. Our annotated corpus would be a useful resource for the modelling of entity recognition and relation extraction tools. In the future, an evaluation based on the corpus will be held.

Diabetes mellitus (DM) has a high prevalence in patients with pancreatic cancer (PaC), but the prognostic value of DM in PaC remains controversial. Alterations of P-glycoprotein (P-gp) and cytochrome P450 3A4 (CYP3A4) contribute to multidrug resistance and intestinal metabolism in a variety of cancer types, which may be implicated in DM development. This study aimed to explore the potential prognostic value of P-gp and CYP3A4 in PaC patients in the context of DM through long-term follow-up. We retrospectively reviewed the medical records of patients with PaC admitted at The First People's Hospital of Changzhou, Jiangsu, China, from January 2011 to November 2019 and identified two cohorts of adult patients with PaC, including 24 with DM and 24 without DM (non-DM). The baseline clinical characteristics and outcomes were compared. Immunohistochemistry showed that protein expression of P-gp, but not CYP3A, in duodenum tissues was significantly upregulated in PaC patients with DM compared with those without DM. Kaplan-Meier analysis and log-rank test showed that the survival of patients with PaC and DM/high expression of P-gp was not significantly reduced compared with that of patients without DM/low expression of P-gp. These findings suggested that P-gp expression levels were different in the DM and non-DM groups of patients with PaC, but DM and duodenal P-gp levels were not associated with the long-term survival of patients with PaC. It appears that the presence of DM or P-gp expression levels may not serve as effective prognostic markers for PaC.

The aim of this paper is to fabricate micro-gears via laser shock punching with Spitlight 2000 Nd-YAG Laser, and to discuss effects of process parameters namely laser energy, soft punch properties and blank-holder on the quality of micro-gears deeply. Results show that dimensional accuracy is the best shocked at 1690 mJ. Tensile fracture instead of shear fracture is the main fracture mode under low laser energy. The soft punch might cause damage to punching quality when too high energy is employed. Appropriate thickness and hardness of soft punch is necessary. Silica gel with 200 µm in thickness is beneficial to not only homogenize energy but also propagate the shock wave. Polyurethane films need more energy than silica gel with the same thickness. In addition, blank-holders with different weight levels are used. A heavier blank-holder is more beneficial to improve the cutting quality. Furthermore, the simulation is conducted to reveal typical stages and the different deformation behavior under high and low pulse energy. The simulation results show that the fracture mode changes under lower energy.

Server load balancing technology makes services highly functional by distributing the incoming user requests to different servers. Thus, it plays a key role in data centers. However, most of the current server load balancing schemes are designed without considering the impact on the network. More specifically, when using these schemes, the server selection and routing path calculation are usually executed sequentially, which may result in inefficient use of network resources or even cause some issues in the network. As an emerging architecture, Software-Defined Networking (SDN) provides new solutions to overcome these shortcomings. Therefore, taking advantages of SDN, this paper proposes a Joint Server Selection and Traffic Routing algorithm (JSSTR) based on improving the Shuffle Frog Leaping Algorithm (SFLA) to achieve high network utilization, network load balancing and server load balancing. Evaluation results validate that the proposed algorithm can significantly improve network efficiency and balance the network load and server load.

Magnetoencephalography is a non-invasive technology for brain function imaging, which is of enormous value to brain science research and clinical application due to its ultra-high temporal and spatial trace resolution. In this paper, we introduce a self-built and atomic magnetometer based wearable magnetoencephalography system. By designing bi-planar coils system and combining with reference sensor array, the residual magnetic field in the subject's head movement area is controlled to be within ±1 nT, which ensures the sensors are maintained within their dynamic range during the moving measurement. At the same time, a virtual gradiometer-based noise reduction method is proposed to suppress the common-mode magnetic-field noise. Finally, the alpha rhythm and auditory evoked magnetic field signals with high signal-to-noise ratio are successfully detected under the subject's natural head movement and the effectiveness of the system is confirmed. This study could provide more possibilities for the application and promotion of moving wearable magnetoencephalography.

Two pairs of undescribed phenylpropanoids derivatives (±)-1 and (±)-2 together with sixteen known compounds (3–18) were isolated from the resin Bambusae concretio Silicea. Their structures were identified by spectroscopic analyses and computational methods. Furthermore, the anti-renal fibrosis activities of the new compounds were evaluated in transforming growth factor beta 1 (TGF-β1) stimulated NRK-52e cells by western blotting. As a result, compounds (±)-1 and (±)-2 were found to exhibit a dose-dependent inhibition of α-SMA, collagen I, and fibronectin expression in TGF-β1 stimulated NRK-52e cells. [Display omitted] • Two pairs of unknown phenylpropanoids derivatives were isolated from Bambusae concretio Silicea. • The structures were identified by spectroscopic analyses and computational methods. • Compounds (±)- 1 and (±)- 2 demonstrated activity against renal fibrosis. [ABSTRACT FROM AUTHOR]

In this work, we predict the light hadronic decay channels Y(4230)→ΛΛ¯ and Ξ−Ξ¯+ when treating the Y(4230) as a vector charmonium state. By the hadronic loop mechanism, the branching ratios of the Y(4230)→ΛΛ¯ and Ξ−Ξ¯+ processes are calculated. In addition, we discuss the possibility of carrying out the search for the signal of the Y(4230) through ΛΛ¯ and Ξ−Ξ¯+ channels from the e+e− annihilation. Assuming Y(4230) exist in ΛΛ¯ and Ξ−Ξ¯+ channel, we also present the time-like electromagnetic form factors (EMFFs) at s=mY(4230).

Background: Magnetoencephalography (MEG) is a non-invasive imaging technique for directly measuring the external magnetic field generated from synchronously activated pyramidal neurons in the brain. The optically pumped magnetometer (OPM) is known for its less expensive, non-cryogenic, movable and user-friendly custom-design provides the potential for a change in functional neuroimaging based on MEG. Methods: An array of OPMs covering the opposite sides of a subject's head is placed inside a magnetically shielded room (MSR) and responses evoked from the auditory cortices are measured. Results: High signal-to-noise ratio auditory evoked response fields (AEFs) were detected by a wearable OPM-MEG system in a MSR, for which a flexible helmet was specially designed to minimize the sensor-to-head distance, along with a set of bi-planar coils developed for background field and gradient nulling. Neuronal current sources activated in AEF experiments were localized and the auditory cortices showed the highest activities. Performance of the hybrid optically pumped magnetometer-magnetoencephalography/electroencephalography (OPM-MEG/EEG) system was also assessed. Conclusions: The multi-channel OPM-MEG system performs well in a custom built MSR equipped with bi-planar coils and detects human AEFs with a flexible helmet. Moreover, the similarities and differences of auditory evoked potentials (AEPs) and AEFs are discussed, while the operation of OPM-MEG sensors in conjunction with EEG electrodes provides an encouraging combination for the exploration of hybrid OPM-MEG/EEG systems. [ABSTRACT FROM AUTHOR]