A Rational Design of Bio-Derived Disulfide CANs for Wearable Capacitive Pressure Sensor.

Classic approaches to integrate flexible capacitive sensor performance are to on-demand microstructuring dielectric layers and to adjust dielectric material compositions via the introduction of insoluble carbon additives (to increase sensitivity) or dynamic interactions (to achieve self-healing). However, the sensor's enhanced performances often come with increased material complexity, discouraging its circular economy. Herein, a new intrinsic self-healable, closed-loop recyclable dielectric layer material, a fully nature-derived dynamic covalent poly(disulfide) decorated with rich H bonding and metal-catechol complexations is introduced. The polymer network possesses a mechanically ductile character with an Arrhenius-type temperature-dependent viscoelasticity. The assembled capacitive pressure sensor is able to achieve a sensitivity of up to 9.26 kPa-1, fast response/recovery time of 32/24 ms, and can deliver consistent signals of continuous consecutive cycles even after being self-healed or closed-loop recycled for real-time detection of human motions. This is expected to be of high interest for current capacitive sensing research to move toward a life-like, high performance, and circular economy direction.

Atomic force microscopy correlates mechanical and electrical properties of HepG2 cells with curcumin concentration.

Hepatocellular carcinoma (HCC) is a highly prevalent cancer with a significant impact on human health. Curcumin, a natural compound, induces cytoskeletal changes in liver cancer cells and modifies the distribution of lipids, proteins, and polysaccharides on plasma membranes, affecting their mechanical and electrical properties. In this study, we used nanomechanical indentation techniques and Kelvin probe force microscopy (KPFM) based on atomic force microscopy (AFM) to investigate the changes in surface nanomechanical and electrical properties of nuclear and cytoplasmic regions of HepG2 cells in response to increasing curcumin concentrations. CCK-8 assays and flow cytometry results demonstrated time- and concentration-dependent inhibition of HepG2 cell proliferation by curcumin. Increasing curcumin concentration led to an initial increase and then decrease in the mechanical properties of nuclear and cytoplasmic regions of HepG2 cells, represented by the Young's modulus (E), as observed through nanoindentation. KPFM measurements indicated decreasing trends in both cell surface potential and height. Fluorescence microscopy results indicated a positive correlation between curcumin concentration and phosphatidylserine translocation from the inner to the outer membrane, which influenced the electrical properties of HepG2 cells. This study provides valuable insights into curcumin's mechanisms against cancer cells and aids nanoscale evaluation of therapeutic efficacy and drug screening.

Mechanically interlocked [c2]daisy chain backbone enabling advanced shape-memory polymeric materials.

The incorporation of mechanically interlocked structures into polymer backbones has been shown to confer remarkable functionalities to materials. In this work, a [c2]daisy chain unit based on dibenzo-24-crown-8 is covalently embedded into the backbone of a polymer network, resulting in a synthetic material possessing remarkable shape-memory properties under thermal control. By decoupling the molecular structure into three control groups, we demonstrate the essential role of the [c2]daisy chain crosslinks in driving the shape memory function. The mechanically interlocked topology is found to be an essential element for the increase of glass transition temperature and consequent gain of shape memory function. The supramolecular host-guest interactions within the [c2]daisy chain topology not only ensure robust mechanical strength and good network stability of the polymer, but also impart the shape memory polymer with remarkable shape recovery properties and fatigue resistance ability. The incorporation of the [c2]daisy chain unit as a building block has the potential to lay the groundwork for the development of a wide range of shape-memory polymer materials.

Detection of Dolichoectasia and Atherosclerosis by Automated MRA Tortuosity Metrics in a Population-Based Study.

BACKGROUND: Intracranial vessel tortuosity is a key component of dolichoectasia and has been associated with atherosclerosis and adverse neurologic outcomes. However, the evaluation of tortuosity is mainly a descriptive assessment. PURPOSE: To compare the performance of three automated tortuosity metrics (angle metric [AM], distance metric [DM], and distance-to-axis metric [DTA]) for detection of dolichoectasia and presence of segment-specific plaques. STUDY TYPE: Observational, cross-sectional metric assessment. POPULATION: 1899 adults from the general population; mean age = 76 years, female = 59%, and black = 29%. FIELD STRENGTH/SEQUENCE: 3-T, three-dimensional (3D) time-of-flight MRA and 3D vessel wall MRI. ASSESSMENT: Tortuosity metrics and mean luminal area were quantified for designated segments of the internal carotid artery, middle cerebral artery, anterior cerebral artery, posterior cerebral artery, vertebral artery, and entire length of basilar artery (BA). Qualitative interpretations of BA dolichoectasia were assessed based on Smoker's visual criteria. STATISTICAL TESTS: Descriptive statistics (2-sample t-tests, Pearson chi-square tests) for group comparisons. Receiver operating characteristics area under the curve (AUC) for detection of BA dolichoectasia or segment-specific plaque. Model inputs included 1) tortuosity metrics, 2) mean luminal area, and 3) demographics (age, race, and sex). RESULTS: Qualitative dolichoectasia was identified in 336 (18%) participants, and atherosclerotic plaques were detected in 192 (10%) participants. AM-, DM-, and DTA-calculated tortuosity were good individual discriminators of basilar dolichoectasia (AUCs: 0.76, 0.74, and 0.75, respectively), with model performance improving with the mean lumen area: (AUCs: 0.88, 0.87, and 0.87, respectively). Combined characteristics (tortuosity and mean luminal area) identified plaques with better performance in the anterior (AUCs ranging from 0.66 to 0.78) than posterior (AUCs ranging from 0.54 to 0.65) circulation, with all models improving by the addition of demographics (AUCs ranging from 0.62 to 0.84). DATA CONCLUSION: Quantitative vessel tortuosity metrics yield good diagnostic accuracy for the detection of dolichoectasia. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2.

Identifying Dynamic Patterns of Polypharmacy for Patients with Dementia from Primary Care Electronic Health Records: A Machine Learning Driven Longitudinal Study.

It is unclear how medication use evolved before diagnosis of dementia (DoD). This study aims to identify varied patterns of polypharmacy before DoD, their prevalence and possible complications. We collected primary care e-health records for 33,451 dementia patients in Wales from 1990 to 2015. The medication uses in every 5-year period along with 20-years prior to dementia diagnosis were considered. Exploratory factor analysis was used to identify clusters of medicines for every 5-year period. The prevalence of patients taking three or more medications was 82.16%, 69.7%, 41.1% and 5.5% in the Period 1 (0-5 years before DoD) ~ Period 4 (16-20 years before DoD) respectively. The Period 1 showed 3 clusters of polypharmacy - medicines for respiratory/urinary infections, arthropathies and rheumatism, and cardio-vascular disease (CVD) (66.55%); medicines for infections, arthropathies and rheumatism (AR), cardio-metabolic disease (CMD) and depression (22.02%); and medicines for arthropathies, rheumatism and osteoarthritis (2.6%). The Period 2 showed 4 clusters of polypharmacy - medicines for infections, arthropathies, and CVD (69.7%); medicines for CVD and depression (3%); medicines for CMD and arthropathies (0.3%); and medicines for AR, and CVD (2,5%). The Period 3 showed 6 clusters of polypharmacy - medicines for infections, arthropathies, and CVD (41.1%); medicines for CVD, acute-respiratory-infection (ARI), and arthropathies (1.25%); medicines for AR (1.16%); medicines for depression, anxiety (0.06%); medicines for CMD (1.4%); and medicines for dermatologic disorders (0.9%). The Period 4 showed 3 main clusters of polypharmacy - medicines for infections, arthropathy, and CVD (5.5%); medicines for anxiety, ARI (2.4%); and medicines for ARI and CVD (2.1%). As the development towards dementia progressed, the associative diseases tended to cluster with a larger prevalence in each cluster. Farther away before DoD, the clusters of polypharmacy tended to be clearly distinct between each other, resulting in an increasing number of patterns, but in a smaller prevalence.

Analysing patient-generated data to understand behaviours and characteristics of women with epilepsy of childbearing years: A prospective cohort study.

BACKGROUND: Women with epilepsy (WWE) are vulnerable in pregnancy, with increased risks to mother and baby including teratogenic risks, especially from valproate. The free EpSMon mobile-phone app allows self-monitoring to afford patient-centred feedback on seizure related risks, such as sudden death in epilepsy (SUDEP) to its users. We sought to generate insights into various seizure related risks and its treatments in WWE of childbearing age (16 to 60 years ) using EpSMon. METHODS: The study utilizes a prospective real-world cohort of 5.5 years. Patient reported data on demographics, medication taken, diagnoses, seizure types and recognised biological, psychological, and social factors of seizure related harm were extracted. Data was stratified according to frequent and infrequent users and those scoring lower and higher risk scores. Multivariate logistic regression and different statistical tests were conducted. FINDINGS: Data from 2158 WWE of childbearing age encompassing 4016 self-assessments were analysed. Overall risk awareness was 25.3% for pregnancy and 54.1% for SUDEP. Frequent users were more aware of pregnancy risks but not of SUDEP. Repeated EpSMon use increased SUDEP awareness but not pregnancy risks. Valproate was used by 11% of WWE, ranging from 6.5% of younger to 31.5% of older women. CONCLUSIONS: The awareness to risks to pregnancy, SUDEP and valproate is low. Valproate is being used by a significant minority. It is imperative risk communication continues for WWE based on their individual situation and need. This is unlikely to be delivered by current clinical models. Digital solutions hold promise but require work done to raise implementation and acceptability.

Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review.

The inclusion of machine-learning-derived models in systematic reviews of risk prediction models for colorectal cancer is rare. Whilst such reviews have highlighted methodological issues and limited performance of the models included, it is unclear why machine-learning-derived models are absent and whether such models suffer similar methodological problems. This scoping review aims to identify machine-learning models, assess their methodology, and compare their performance with that found in previous reviews. A literature search of four databases was performed for colorectal cancer prediction and prognosis model publications that included at least one machine-learning model. A total of 14 publications were identified for inclusion in the scoping review. Data was extracted using an adapted CHARM checklist against which the models were benchmarked. The review found similar methodological problems with machine-learning models to that observed in systematic reviews for non-machine-learning models, although model performance was better. The inclusion of machine-learning models in systematic reviews is required, as they offer improved performance despite similar methodological omissions; however, to achieve this the methodological issues that affect many prediction models need to be addressed.

Multicolor emission based on a N, N'-Disubstituted dihydrodibenzo [a, c] phenazine crown ether macrocycle.

Dynamic fluorophore 9,14-diphenyl-9,14-dihydrodibenzo[a,c]phenazine (DPAC) affords a new platform to produce diverse emission outputs. In this paper, a novel DPAC-containing crown ether macrocycle D-6 is synthesized and characterized. Host-guest interactions of D-6 with different ammonium guests produced a variety of fluorescence with hypsochromic shifts up to 130 nm, which are found to be affected by choice of solvent or guest and host/guest stoichiometry. Formation of supramolecular complexes were confirmed by UV-vis titration, 1H NMR and HRMS spectroscopy.

Concept Libraries for Repeatable and Reusable Research: Qualitative Study Exploring the Needs of Users.

BACKGROUND: Big data research in the field of health sciences is hindered by a lack of agreement on how to identify and define different conditions and their medications. This means that researchers and health professionals often have different phenotype definitions for the same condition. This lack of agreement makes it difficult to compare different study findings and hinders the ability to conduct repeatable and reusable research. OBJECTIVE: This study aims to examine the requirements of various users, such as researchers, clinicians, machine learning experts, and managers, in the development of a data portal for phenotypes (a concept library). METHODS: This was a qualitative study using interviews and focus group discussion. One-to-one interviews were conducted with researchers, clinicians, machine learning experts, and senior research managers in health data science (N=6) to explore their specific needs in the development of a concept library. In addition, a focus group discussion with researchers (N=14) working with the Secured Anonymized Information Linkage databank, a national eHealth data linkage infrastructure, was held to perform a SWOT (strengths, weaknesses, opportunities, and threats) analysis for the phenotyping system and the proposed concept library. The interviews and focus group discussion were transcribed verbatim, and 2 thematic analyses were performed. RESULTS: Most of the participants thought that the prototype concept library would be a very helpful resource for conducting repeatable research, but they specified that many requirements are needed before its development. Although all the participants stated that they were aware of some existing concept libraries, most of them expressed negative perceptions about them. The participants mentioned several facilitators that would stimulate them to share their work and reuse the work of others, and they pointed out several barriers that could inhibit them from sharing their work and reusing the work of others. The participants suggested some developments that they would like to see to improve reproducible research output using routine data. CONCLUSIONS: The study indicated that most interviewees valued a concept library for phenotypes. However, only half of the participants felt that they would contribute by providing definitions for the concept library, and they reported many barriers regarding sharing their work on a publicly accessible platform. Analysis of interviews and the focus group discussion revealed that different stakeholders have different requirements, facilitators, barriers, and concerns about a prototype concept library.

Self-Evolution of High Mechanical Strength Dry-Network Polythiourethane Thermosets into Neat Macroscopic Hollow Structures.

Organism-inspired hollow structures are attracting increasing interest for the construction of various bionic functional hollow materials. Next-generation self-evolution hollow materials tend to combine simple synthesis, high mechanical strength, and regular shape. In this study, we designed and synthesized a novel dry-network polythiourethane thermoset with excellent mechanical performance. The polymer film could evolve into a neat and well-organized object with a macroscopic hollow interior structure after being immersed in an aqueous NaOH solution. The self-evolution hollow structure originated from a hydrogen-bonded polymer network, which was later transformed into a network bearing both hydrogen bonds and ionic bonds. The swelling and thickness growth of this material could be controlled by the NaOH concentration and the immersion time. This unique self-evolution behavior was further utilized to produce a series of macroscopic 3D hollow-containing molds, which could be potentially applied in the production of smart materials.

Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective.

(1) Background: This study investigates influential risk factors for predicting 30-day readmission to hospital for Campylobacter infections (CI). (2) Methods: We linked general practitioner and hospital admission records of 13,006 patients with CI in Wales (1990-2015). An approach called TF-zR (term frequency-zRelevance) technique was presented to evaluates how relevant a clinical term is to a patient in a cohort characterized by coded health records. The zR is a supervised term-weighting metric to assign weight to a term based on relative frequencies of the term across different classes. Cost-sensitive classifier with swarm optimization and weighted subset learning was integrated to identify influential clinical signals as predictors and optimal model for readmission prediction. (3) Results: From a pool of up to 17,506 variables, 33 most predictive factors were identified, including age, gender, Townsend deprivation quintiles, comorbidities, medications, and procedures. The predictive model predicted readmission with 73% sensitivity and 54% specificity. Variables associated with readmission included male gender, recurrent tonsillitis, non-healing open wounds, operation for in-gown toenails. Cystitis, paracetamol/codeine use, age (21-25), and heliclear triple pack use, were associated with a lower risk of readmission. (4) Conclusions: This study gives a profile of clustered variables that are predictive of readmission associated with campylobacteriosis.

Automatically Generating Natural Language Descriptions of Images by a Deep Hierarchical Framework.

Automatically generating an accurate and meaningful description of an image is very challenging. However, the recent scheme of generating an image caption by maximizing the likelihood of target sentences lacks the capacity of recognizing the human-object interaction (HOI) and semantic relationship between HOIs and scenes, which are the essential parts of an image caption. This article proposes a novel two-phase framework to generate an image caption by addressing the above challenges: 1) a hybrid deep learning and 2) an image description generation. In the hybrid deep-learning phase, a novel factored three-way interaction machine was proposed to learn the relational features of the human-object pairs hierarchically. In this way, the image recognition problem is transformed into a latent structured labeling task. In the image description generation phase, a lexicalized probabilistic context-free tree growing scheme is innovatively integrated with a description generator to transform the descriptions generation task into a syntactic-tree generation process. Extensively comparing state-of-the-art image captioning methods on benchmark datasets, we demonstrated that our proposed framework outperformed the existing captioning methods in different ways, such as significantly improving the performance of the HOI and relationships between HOIs and scenes (RHIS) predictions, and quality of generated image captions in a semantically and structurally coherent manner.

[Prostatitis studies in China from 2000 to 2020: A Knowledge Atlas-based study on the development trend of contemporary disciplines].

OBJECTIVE: To review and analyze the trend of researches on prostatitis in China in the past two decades. METHODS: We searched the core collection of China National Knowledge Infrastructure (CNKI) for studies on prostatitis, and analyzed the data obtained using Excel, Citespace and VOSviewer. RESULTS: Totally, 1 216 original articles were identified, with 3 271 keywords, ≥3-time high-frequency keywords accounting for 12.9%, with "", "", "chronic prostatitis", "prostatitis", and "" as the top 5 ones, each with a centrality higher than 300. Major prostatitis-related studies focused on the 8 keywords, namely, prostatitis, prostatic fluid, rats, prostate, syndromes, efficacy observation, compound (in traditional Chinese medicine, TCM), and therapeutic application. The included literature involved 2 808 authors, with 402 involved more than twice and most of them in a scattered manner. The major topics of prostatitis studies varied in the past two decades, focusing on TCM therapies, promotion of blood circulation and stasis and comprehensive nursing in 2000－2001, on animal models, CD4+ lymphocytes and other experimental molecules in 2007－2010, on urodynamics, risk factors and specific antigens in 2013－2016, and on literature information resources in 2016. CONCLUSIONS: The immune mechanism remains a hot topic in the future researches on prostatitis. In terms of treatment of the disease, TCM has a potential value, and more practice and studies are required for an optimal combination of TCM and Western medicine. Strengthened collaborative efforts are needed to establish an authoritative source channel for the keywords, and incorporate it into the national standard system, and above all, to integrate the prostatitis study into multi-disciplinary researches, eliminate academic barriers, encourage collaborative innovation with multiple parties, and promote the exchanges and development in this field.

Identifying Prenatal and Postnatal Determinants of Infant Growth: A Structural Equation Modelling Based Cohort Analysis.

BACKGROUND: The growth and maturation of infants reflect their overall health and nutritional status. The purpose of this study is to examine the associations of prenatal and early postnatal factors with infant growth (IG). METHODS: A data-driven model was constructed by structural equation modelling to examine the relationships between pre- and early postnatal environmental factors and IG at age 12 months. The IG was a latent variable created from infant weight and waist circumference. Data were obtained on 274 mother-child pairs during pregnancy and the postnatal periods. RESULTS: Maternal pre-pregnancy BMI emerged as an important predictor of IG with both direct and indirect (mediated through infant birth weight) effects. Infants who gained more weight from birth to 6 months and consumed starchy foods daily at age 12 months, were more likely to be larger by age 12 months. Infant physical activity (PA) levels also emerged as a determinant. The constructed model provided a reasonable fit (χ2 (11) = 21.5, p < 0.05; RMSEA = 0.07; CFI = 0.94; SRMR = 0.05) to the data with significant pathways for all examined variables. CONCLUSION: Promoting healthy weight amongst women of child bearing age is important in preventing childhood obesity, and increasing daily infant PA is as important as a healthy infant diet.

Mining Primary Care Electronic Health Records for Automatic Disease Phenotyping: A Transparent Machine Learning Framework.

(1) Background: We aimed to develop a transparent machine-learning (ML) framework to automatically identify patients with a condition from electronic health records (EHRs) via a parsimonious set of features. (2) Methods: We linked multiple sources of EHRs, including 917,496,869 primary care records and 40,656,805 secondary care records and 694,954 records from specialist surgeries between 2002 and 2012, to generate a unique dataset. Then, we treated patient identification as a problem of text classification and proposed a transparent disease-phenotyping framework. This framework comprises a generation of patient representation, feature selection, and optimal phenotyping algorithm development to tackle the imbalanced nature of the data. This framework was extensively evaluated by identifying rheumatoid arthritis (RA) and ankylosing spondylitis (AS). (3) Results: Being applied to the linked dataset of 9657 patients with 1484 cases of rheumatoid arthritis (RA) and 204 cases of ankylosing spondylitis (AS), this framework achieved accuracy and positive predictive values of 86.19% and 88.46%, respectively, for RA and 99.23% and 97.75% for AS, comparable with expert knowledge-driven methods. (4) Conclusions: This framework could potentially be used as an efficient tool for identifying patients with a condition of interest from EHRs, helping clinicians in clinical decision-support process.

Concept libraries for automatic electronic health record based phenotyping: A review.

INTRODUCTION: Electronic health records (EHR) are linked together to examine disease history and to undertake research into the causes and outcomes of disease. However, the process of constructing algorithms for phenotyping (e.g., identifying disease characteristics) or health characteristics (e.g., smoker) is very time consuming and resource costly. In addition, results can vary greatly between researchers. Reusing or building on algorithms that others have created is a compelling solution to these problems. However, sharing algorithms is not a common practice and many published studies do not detail the clinical code lists used by the researchers in the disease/characteristic definition. To address these challenges, a number of centres across the world have developed health data portals which contain concept libraries (e.g., algorithms for defining concepts such as disease and characteristics) in order to facilitate disease phenotyping and health studies. OBJECTIVES: This study aims to review the literature of existing concept libraries, examine their utilities, identify the current gaps, and suggest future developments. METHODS: The five-stage framework of Arksey and O'Malley was used for the literature search. This approach included defining the research questions, identifying relevant studies through literature review, selecting eligible studies, charting and extracting data, and summarising and reporting the findings. RESULTS: This review identified seven publicly accessible Electronic Health data concept libraries which were developed in different countries including UK, USA, and Canada. The concept libraries (n = 7) investigated were either general libraries that hold phenotypes of multiple specialties (n = 4) or specialized libraries that manage only certain specialities such as rare diseases (n = 3). There were some clear differences between the general libraries such as archiving data from different electronic sources, and using a range of different types of coding systems. However, they share some clear similarities such as enabling users to upload their own code lists, and allowing users to use/download the publicly accessible code. In addition, there were some differences between the specialized libraries such as difference in ability to search, and if it was possible to use different searching queries such as simple or complex searches. Conversely, there were some similarities between the specialized libraries such as enabling users to upload their own concepts into the libraries and to show where they were published, which facilitates assessing the validity of the concepts. All the specialized libraries aimed to encourage the reuse of research methods such as lists of clinical code and/or metadata. CONCLUSION: The seven libraries identified have been developed independently and appear to replicate similar concepts but in different ways. Collaboration between similar libraries would greatly facilitate the use of these libraries for the user. The process of building code lists takes time and effort. Access to existing code lists increases consistency and accuracy of definitions across studies. Concept library developers should collaborate with each other to raise awareness of their existence and of their various functions, which could increase users' contributions to those libraries and promote their wide-ranging adoption.

Motorized Macrocycle: A Photo-responsive Host with Switchable and Stereoselective Guest Recognition.

Designing photo-responsive host-guest systems can provide versatile supramolecular tools for constructing smart systems and materials. We designed photo-responsive macrocyclic hosts, modulated by light-driven molecular rotary motors enabling switchable chiral guest recognition. The intramolecular cyclization of the two arms of a first-generation molecular motor with flexible oligoethylene glycol chains of different lengths resulted in crown-ether-like macrocycles with intrinsic motor function. The octaethylene glycol linkage enables the successful unidirectional rotation of molecular motors, simultaneously allowing the 1:1 host-guest interaction with ammonium salt guests. The binding affinity and stereoselectivity of the motorized macrocycle can be reversibly modulated, owing to the multi-state light-driven switching of geometry and helicity of the molecular motors. This approach provides an attractive strategy to construct stimuli-responsive host-guest systems and dynamic materials.

[Locking plate external fixation combined with membrane induction technology for the treatment of open and comminuted tibial fractures with bone defects].

OBJECTIVE: To explore clinical effect of locking plate external fixation combined with membrane induction technology in treating open and comminuted tibial fractures with bone defects. METHODS: Totally 92 patients of open and comminuted tibial fractures with bone defects were chosen form January 2018 to July 2019, and randomly divided into external fixation group and internal fixation group, 46 patients in each group. In external fixation group, there were 29 males and 17 females, aged from 25 to 62 years old, with an average of (37.45±10.92) years old;according to AO classification, 15 patients were type A, 22 patients were type B and 9 patients were type C;according to Gustilo classification, 21 patients were typeⅡ, 10 patients were type ⅢA, 10 patients were type ⅢB, 5 patients were type Ⅲ C;treated by fracture reduction with locking plate external fixation. In internal fixation group, there were 31 males and 15 females, aged from 23 to 60 years old, with an average of(36.88±10.64) years old;according to AO classification, 18 patients were type A, 20 patients were type B and 8 patients were type C; according to Gustilo classification, 22 patients were typeⅡ, 11 patients were type ⅢA, 7 patients were type ⅢB, 6 patients were type Ⅲ C;treated by traditional open reduction with plate internal fixation. Operation time, intraoperative blood loss, incision length, hospital stay, fracture healing time and lower limb full weight-bearing time and postoperative complications between two groups were observed and compared, bone mineral density, osteocalcin, blood calcium and phosphorus before operation and 1 month after operation. RESULTS: All patients were followed up from 12 to 18 months with an average of (14.92±2.46) months. Operation time, intraoperative blood loss, incision length, hospital stay, fracture healing time and lower limb full weight-bearing time of external fixation group were significantly better than that of internal fixation group(P<0.05). Postoperative bone mineral density, osteocalcin, blood calcium and phosphorus at 1 month in external group were higher than that of internal fixation group (P<0.05). Four patients in external fixation group occurred complications, 13 patients in internal fixtaion group, and occurrence rate of complications in external fixation group (8.70%) was lower than that of internal fixtaion group (28.26%)(χ2=4.618, P=0.032). CONCLUSION: Locking plate external fixation combined with membrane induction technology in treating open and comminuted tibial fractures with severe post-traumatic bone defects has advantages of less trauma, reliable fixation, shorter fracture healing time, and could improve bone metabolic activity with less postoperative complications.

Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients Using Primary Care Electronic Health Records.

A growing elderly population suffering from incurable, chronic conditions such as dementia present a continual strain on medical services due to mental impairment paired with high comorbidity resulting in increased hospitalization risk. The identification of at risk individuals allows for preventative measures to alleviate said strain. Electronic health records provide opportunity for big data analysis to address such applications. Such data however, provides a challenging problem space for traditional statistics and machine learning due to high dimensionality and sparse data elements. This article proposes a novel machine learning methodology: entropy regularization with ensemble deep neural networks (ECNN), which simultaneously provides high predictive performance of hospitalization of patients with dementia whilst enabling an interpretable heuristic analysis of the model architecture, able to identify individual features of importance within a large feature domain space. Experimental results on health records containing 54,647 features were able to identify 10 event indicators within a patient timeline: a collection of diagnostic events, medication prescriptions and procedural events, the highest ranked being essential hypertension. The resulting subset was still able to provide a highly competitive hospitalization prediction (Accuracy: 0.759) as compared to the full feature domain (Accuracy: 0.755) or traditional feature selection techniques (Accuracy: 0.737), a significant reduction in feature size. The discovery and heuristic evidence of correlation provide evidence for further clinical study of said medical events as potential novel indicators. There also remains great potential for adaption of ECNN within other medical big data domains as a data mining tool for novel risk factor identification.