Automated Type 2 Diabetes Case and Control Identification from the MIMIC-IV Database

Phenotyping for Type 2 Diabetes (T2DM) is needed due to the increasing demand for T2DM research on electronic health records (EHRs). eMERGE is a reliable and interpretable rule-based algorithm for the identification of T2DM cases and controls in EHRs. MIMIC-IV, an extension of MIMIC-III, contains more than 520,000 hospital admissions and has become a valuable EHR database for secondary medical research. However, there was no prior work to extract T2DM cases and controls from MIMIC-IV, which requires a comprehensive knowledge of the database. Our work provided insight into the structure and data elements in MIMIC-IV and adapted eMERGE to accomplish the task. The results included MIMIC-IV\u27s data tables and elements used, 12,735 cases and 9,828 controls of T2DM, and summary statistics of the cohorts in comparison with those on other EHR databases. They could be used for the development of statistical and machine learning models in future studies about the disease

Winner's Curse Free Robust Mendelian Randomization with Summary Data

In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental variables, are known for their robustness against reverse causation bias and unmeasured confounders. Nevertheless, classical MR analyses utilizing summary data may still produce biased causal effect estimates due to the winner's curse and pleiotropic issues. To address these two issues and establish valid causal conclusions, we propose a unified robust Mendelian Randomization framework with summary data, which systematically removes the winner's curse and screens out invalid genetic instruments with pleiotropic effects. Different from existing robust MR literature, our framework delivers valid statistical inference on the causal effect neither requiring the genetic pleiotropy effects to follow any parametric distribution nor relying on perfect instrument screening property. Under appropriate conditions, we show that our proposed estimator converges to a normal distribution and its variance can be well estimated. We demonstrate the performance of our proposed estimator through Monte Carlo simulations and two case studies. The codes implementing the procedures are available at https://github.com/ChongWuLab/CARE/

Unsupervised Deep Representation Learning Enables Phenotype Discovery For Genetic association Studies of Brain Imaging

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants\u27 T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes

Frequency Characteristic of Resonant Micro Fluidic Chip for Oil Detection Based on Resistance Parameter

Monitoring the working condition of hydraulic equipment is significance in industrial fields. The abnormal wear of the hydraulic system can be revealed by detecting the variety and size of micro metal debris in the hydraulic oil. We thus present the design and implementation of a micro detection system of hydraulic oil metal debris based on inductor capacitor (LC) resonant circuit in this paper. By changing the resonant frequency of the micro fluidic chip, we can detect the metal debris of hydraulic oil and analyze the sensitivity of the micro fluidic chip at different resonant frequencies. We then obtained the most suitable resonant frequency. The chip would generate a positive resistance pulse when the iron particles pass through the detection area and the sensitivity of the chip decreased with resonant frequency. The chip would generate a negative resistance pulse when the copper particles pass through the detection area and the sensitivity of the chip increased with resonant frequency. The experimental results show that the change of resonant frequency has a great effect on the copper particles and little on the iron particles. Thus, a relatively big resonant frequency can be selected for chip designing and testing. In practice, we can choose a relatively big resonant frequency in this micro fluidic chip designing. The resonant micro fluidic chip is capable of detecting 20–30 μm iron particles and 70–80 μm copper particles at 0.9 MHz resonant frequency

Efficacy and safety of modular versus monoblock stems in revision total hip arthroplasty: a systematic review and meta-analysis

Abstract Background Both modular and monoblock tapered fluted titanium (TFT) stems are increasingly being used for revision total hip arthroplasty (rTHA). However, the differences between the two designs in clinical outcomes and complications are not yet clear. Here, we intend to compare the efficacy and safety of modular versus monoblock TFT stems in rTHA. Methods PubMed, Embase, Web of Science, and Cochrane Library databases were searched to include studies comparing modular and monoblock implants in rTHA. Data on the survivorship of stems, postoperative hip function, and complications were extracted following inclusion criteria. Inverse variance and Mantel–Haenszel methods in Review Manager (version 5.3 from Cochrane Collaboration) were used to evaluate differences between the two groups. Results Ten studies with a total of 2188 hips (1430 modular and 758 monoblock stems) were finally included. The main reason for the revision was aseptic loosening. Paprosky type III was the most common type in both groups. Both stems showed similar re-revision rates (modular vs monoblock: 10.3% vs 9.5%, P = 0.80) and Harris Hip Scores (WMD = 0.43, P = 0.46) for hip function. The intraoperative fracture rate was 11.6% and 5.0% (P = 0.0004) for modular and monoblock stems, respectively. The rate of subsidence > 10 mm was significantly higher in the monoblock group (4.5% vs 1.0%, P = 0.003). The application of extended trochanteric osteotomy was more popular in monoblock stems (22.7% vs 17.5%, P = 0.003). The incidence of postoperative complications such as periprosthetic femoral fracture and dislocation was similar between both stems. Conclusions No significant difference was found between modular and monoblock tapered stems as regards postoperative hip function, re-revision rates, and complications. Severe subsidence was more frequent in monoblock stems while modular ones were at higher risk of intraoperative fracture. Level of evidence: Level III, systematic review of randomized control and non-randomized studies. Trial Registration: We registered our study in the international prospective register of systematic reviews (PROSPERO) (CRD42020213642)

A PDA‐Functionalized 3D Lung Scaffold Bioplatform to Construct Complicated Breast Tumor Microenvironment for Anticancer Drug Screening and Immunotherapy

Abstract 2D cell culture occupies an important place in cancer progression and drug discovery research. However, it limitedly models the “true biology” of tumors in vivo. 3D tumor culture systems can better mimic tumor characteristics for anticancer drug discovery but still maintain great challenges. Herein, polydopamine (PDA)‐modified decellularized lung scaffolds are designed and can serve as a functional biosystem to study tumor progression and anticancer drug screening, as well as mimic the tumor microenvironment. PDA‐modified scaffolds with strong hydrophilicity and excellent cell compatibility can promote cell growth and proliferation. After 96 h treatment with 5‐FU, cisplatin, and DOX, higher survival rates in PDA‐modified scaffolds are observed compared to nonmodified scaffolds and 2D systems. The E‐cadhesion formation, HIF‐1α‐mediated senescence decrease, and tumor stemness enhancement can drive drug resistance and antitumor drug screening of breast cancer cells. Moreover, there is a higher survival rate of CD45+/CD3+/CD4+/CD8+ T cells in PDA‐modified scaffolds for potential cancer immunotherapy drug screening. This PDA‐modified tumor bioplatform will supply some promising information for studying tumor progression, overcoming tumor resistance, and screening tumor immunotherapy drugs

Individual Manhattan Plots and QQ Plots

Attached is the individual Manhattan plots and QQ plots for T1 and T2 derived 128 dimensional UDIPs.Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6,130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9,457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.</p

Unsupervised Deep Representation Learning Enables Phenotype Discovery for Genetic Association Studies of Brain Imaging

Attached is the github repository for the paper: Unsupervised Deep Representation Learning Enables Phenotype Discovery for Genetic Association Studies of Brain ImagingUnderstanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6,130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9,457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.</p

T2 UDIPs t-maps

These are 128 UDIPs t-maps derived from T2-FLAIR through PerDI. UDIPs learn representation all over the brain. To identify regions of the brain represented by a specific UDIP, we develop Perturbation-based Decoder Interpretation (PerDI). The regions of the brain of that specific UDIP can then be associated with the SNPs identified by the same UDIP. We add one standard deviation (σ) as noise to the specific UDIP we are trying to interpret while keeping other UDIPs constant. The original decoder is used to reconstruct images from the perturbed UDIPs (perturbed reconstructed images). The process is repeated for 500 MRIs from 500 randomly selected individuals for improving the robustness of the result. Paired t-test is carried out between the 500 original reconstructed images and 500 perturbed reconstructed images. Absolute t-map is obtained. Gaussian filter (σ=3) is used to smoothen the final t-map. Using Gaussian blur reduces the impact of not using non-linear registration.</p