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For wire-feed laser additive manufacturing (WLAM), the build geometrical parameters are one of the indicators of build quality; thus, it is crucial to monitor the geometrical parameters in real-time for quality assurance. However, the current research and development for in situ geometry monitoring are in the early phase due to interweaved correlation of the sensing data and their comprehensive effects on the bead geometry, as well as the high characterization cost to model these effects. This paper focuses on using machine learning techniques to enable in-process geometry monitoring by comprehensively modeling the correlation between the real-time molten pool sensing data and bead geometry properties. A deep learning-based multi-modality convolutional neural network (m-CNN) is trained to take the molten pool image and thermal profile as the input to comprehensively estimate the geometric properties of the build bead. The network is configured by the hyperparameter optimization process and experimentally validated by the real-time molten pool sensing data collected on a wire-feed laser additive manufacturing (AM) system. The effect of using the temperature data from the leading, center, and tailing positions of the molten pool on the prediction performance of the CNN model is studied and analyzed. The CNN model's performance is compared with a support vector regression model for comparison. The developed model represents an in-process monitoring framework for real-time estimation of post-processing bead geometric properties and takes a step towards developing in situ quality control strategy for the metal AM system. [ABSTRACT FROM AUTHOR]

To control part quality, it is critical to analyze pore generation mechanisms, laying theoretical foundation for future porosity control. Current porosity analysis models use machine setting parameters, such as laser angle and part pose. However, these setting-based models are machine dependent; hence, they often do not transfer to analysis of porosity for a different machine. To address the first problem, a physics-informed, data-driven model (PIM) is used, which instead of directly using machine setting parameters to predict porosity levels of printed parts, first interprets machine settings into physical effects, such as laser energy density and laser radiation pressure. Then, these physical, machine-independent effects are used to predict porosity levels according to “pass,” “flag,” and “fail” categories instead of focusing on quantitative pore size prediction. With six learning methods’ evaluation, PIM proved to achieve good performances with prediction error of 10∼\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$\sim $\end{document}26%. Finally, pore-encouraging influence and pore-suppressing influence were analyzed for quality analysis. [ABSTRACT FROM AUTHOR]

Introduction: Chronic hepatitis B virus (HBV) infection is associated with significant global morbidity and mortality. Low treatment rates are observed in patients living with HBV; the reasons for this are unclear. This study sought to describe patients' demographic, clinical and biochemical characteristics across three continents and their associated treatment need. Methods: This retrospective cross-sectional post hoc analysis of real-world data used four large electronic databases from the United States, United Kingdom and China (specifically Hong Kong and Fuzhou). Patients were identified by first evidence of chronic HBV infection in a given year (their index date) and characterized. An algorithm was designed and applied, wherein patients were categorized as treated, untreated but indicated for treatment and untreated and not indicated for treatment based on treatment status and demographic, clinical, biochemical and virological characteristics (age; evidence of fibrosis/cirrhosis; alanine aminotransferase [ALT] levels, HCV/HIV coinfection and HBV virology markers). Results: In total, 12,614 US patients, 503 UK patients, 34,135 patients from Hong Kong and 21,614 from Fuzhou were included. Adults (99.4%) and males (59.0%) predominated. Overall, 34.5% of patients were treated at index (range 15.9–49.6%), with nucleos(t)ide analogue monotherapy most commonly prescribed. The proportion of untreated-but-indicated patients ranged from 12.9% in Hong Kong to 18.2% in the UK; almost two-thirds of these patients (range 61.3–66.7%) had evidence of fibrosis/cirrhosis. A quarter (25.3%) of untreated-but-indicated patients were aged ≥ 65 years. Conclusion: This large real-world dataset demonstrates that chronic hepatitis B infection remains a global health concern; despite the availability of effective suppressive therapy, a considerable proportion of predominantly adult patients apparently indicated for treatment are currently untreated, including many patients with fibrosis/cirrhosis. Causes of disparity in treatment status warrant further investigation. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to construct a clinically practical model to precisely predict lymph node (LN) metastasis in bladder cancer patients. Methods: Four independent cohorts were included. The least absolute shrinkage and selection operator regression with multivariate logistic regression were applied. The diagnostic efficacy of LN score and CT/MRI was compared by accuracy, sensitivity, specificity, and area under curve (AUC). Results: A total of 606 patients were included to develop a basic prediction model. After multistep gene selection, the LN metastasis prediction model was constructed with 5 genes. The model can accurately predict LN metastasis with an AUC of 0.781. For clinically practical use, we transformed the model into a Fast LN Scoring System using the SYSMH cohort (n = 105). High LN score patients exhibited a 72.2% LN metastasis rate, while low LN score patients showed a 3.4% LN metastasis rate. The LN score achieved a superior accuracy than CT/MRI (0.882 vs. 0.727). Application of LN score can correct the diagnosis of 88% (22/25) patients who were misdiagnosed by CT/MRI. Discussion: The clinically practical LN score can precisely, rapidly, and conveniently predict LN status, which will assist preoperative diagnosis for LN metastasis and guide precise therapy. [ABSTRACT FROM AUTHOR]

HTO has proven to be a cost-effective surgical procedure in the treatment of KOA, but few investigations have studied radiological changes and clinical effectiveness of OWHTO in geriatric patients. 76 patients were recruited in this retrospective study. According to the age, patients were divided into two groups (≤ 60, Group "Young"; > 60, Group "Geriatric"). Demographic data, radiological imaging and postoperative complications were analyzed. Kellgren–Lawrence grade (K–L), weight-bearing line ratio (WBLR); posterior tibial slope angle (PTS); American knee score (AKS); Western Ontario and McMaster Universities Arthritis Index (WOMAC) and visual analog scale (VAS) were introduced to estimate the clinical outcome of OWHTO. There were 18 male and 58 female patients in the present study with a mean age of 58.5 ± 9.2 years (ranges from 40 to 82 years); the average age was 51.4 ± 4.1 years and 67.3 ± 4.9 years for group Y and G respectively, 44.7% and 31.5% patients were older than 60 and 65 years. BMI for the 76 patients was 26.6 ± 3.2 kg/m2, and geriatric patients were more likely accompanied by one or more comorbidities (70.6 vs. 45.2%). There were 34 and 42 patients in group Geriatric and group Young respectively, and no significant difference of MPTA, WBLR, PTS and WOMAC, VAS, AKS and ROM between the two group (P > 0.05) were found. After more than a two-year follow-up period, postoperative WBLR, AKS, WOMAC and VAS were much more desired than preoperative, and no significant difference of these variables between the young and geriatric group (P > 0.05), however, elderly patients were more likely to suffer from a longer bone union time. OWHTO can avoid geriatric patients from undergoing secondary knee surgery in the short term, however the survival rate of OWHTO in geriatric patients should be ultimately clarified by different studies. [ABSTRACT FROM AUTHOR]

Falls are a serious public health problem in the aging population because of the associated clinical and socioeconomic impact. Although previous studies have investigated fall-risk-increasing drugs (FRIDs), few studies have focused on dosage among adult inpatients. This study aimed to evaluate associations between fall risk and dosage of different FRIDs classes in hospital inpatients. Inpatients who experienced falls at medical or surgical wards of Changhua Christian Hospital from January 2017 to December 2021 were identified and matched by age, sex, and hospital ward to randomly selected controls (four per case). Anonymous patient data were extracted from the hospital medical data repository, including demographic characteristics, comorbidities, fall-risk scores, and drug prescriptions. Medication dosages were computed using the anatomical therapeutic chemical classification and the defined daily dose system of the World Health Organization. A total of 852 cases and 3408 controls were identified as eligible. Reducing the use of CNS-active medications, administering lower doses of sedative-hypnotics, prescribing sufficient dopaminergic anti-Parkinson agents, and using NSAIDs instead of opioids are imperative in preventing falls among hospitalized patients according to the findings in the study. [ABSTRACT FROM AUTHOR]

With the recent advance in genome-wide association studies (GWAS), disease-associated single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) have been extensively reported. Accordingly, the issue of incorrect identification of recombination events that can induce the distortion of multi-allelic or hemizygous variants has received more attention. However, the potential distorted calculation bias or significance of a detected association in a GWAS due to the coexistence of CNVs and SNPs in the same genomic region may remain under-recognized. Here we performed the association study within a congenital scoliosis (CS) cohort whose genetic etiology was recently elucidated as a compound inheritance model, including mostly one rare variant deletion CNV null allele and one common variant non-coding hypomorphic haplotype of the TBX6 gene. We demonstrated that the existence of a deletion in TBX6 led to an overestimation of the contribution of the SNPs on the hypomorphic allele. Furthermore, we generalized a model to explain the calculation bias, or distorted significance calculation for an association study, that can be ‘induced’ by CNVs at a locus. Meanwhile, overlapping between the disease-associated SNPs from published GWAS and common CNVs (overlap 10%) and pathogenic/likely pathogenic CNVs (overlap 99.69%) was significantly higher than the random distribution (p < 1 × 10−6 and p = 0.034, respectively), indicating that such co-existence of CNV and SNV alleles might generally influence data interpretation and potential outcomes of a GWAS. We also verified and assessed the influence of colocalizing CNVs to the detection sensitivity of disease-associated SNP variant alleles in another adolescent idiopathic scoliosis (AIS) genome-wide association study. We proposed that detecting co-existent CNVs when evaluating the association signals between SNPs and disease traits could improve genetic model analyses and better integrate GWAS with robust Mendelian principles. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Patient Biochemistry and Treatment Need in Chronic Hepatitis B Virus Infection Across Three Continents: Retrospective Cross-Sectional Cohort Studies." The original article was published without open access, but the error has been resolved and the article is now available under a Creative Commons Attribution 4.0 International License. The correction also includes the removal of a duplicate reference and the addition of an affiliation for one of the authors. The publisher, Springer Nature, remains neutral in terms of jurisdictional claims and institutional affiliations. [Extracted from the article]

Understanding the internal mechanism of CO2 adsorption and separation of COF adsorbent materials is of great significance for high-efficiency carbon capture and storage (CCS). Herein, polyarylether-based covalent organic frameworks (PAE-COFs) structures with functionalized –CN, –COOH, and –C(NH2)NOH groups were evaluated for CO2 adsorption and separation over N2 by using grand canonical Monte Carlo (GCMC) simulation and density functional theory (DFT). Our calculation results showed that the adsorption and separation performance of CO2 in COF frameworks was related to the physical characteristics of pores, the types of gas molecules, and the interaction between COFs and gas molecules. JUC-505 modified by –CN functional group provided a feasible microporous environment with moderate specific surface area and porosity, presenting a high CO2 adsorption capacity of 5.14 mmol/g at 273 K and 1 bar. The PAE-COFs structures had good CO2/N2 separation performance, in which the CO2/N2 selectivity of JUC-505-COOH reached up to 90 at 273 K and 1 bar. Interactions analysis showed that the van der Waals and Coulomb interactions between CO2 and the framework were significantly stronger than those between N2 and the framework. Therefore, CO2 had a higher adsorption heat, which led to the larger adsorption capacity and the high selectivity of CO2 over N2. The gas radial distribution functions and 2D density distribution displayed that the CO2 adsorption corresponded to a multi-layer adsorption in frameworks, leading to the high performances of PAE-COFs for CO2 capture and separation. [ABSTRACT FROM AUTHOR]

Highlights: A printable multifunctional electronic skin (e-skin) with an excellent self-healing ability was developed via a simple, clean and universally applicable method. Water served as both the only solvent throughout the fabrication process and the trigger of the self-healing process. Each e-skin component was biocompatible. Combined with conventional electronics, signals collected by the e-skin could be transmitted to smartphones via Bluetooth for post-processing.Electronic skins (e-skins) with an excellent sensing performance have been widely developed over the last few decades. However, wearability, biocompatibility, environmental friendliness and scalability have become new limitations. Self-healing ability can improve the long-term robustness and reliability of e-skins. However, self-healing ability and integration are hardly balanced in classical structures of self-healable devices. Here, cellulose nanofiber/poly(vinyl alcohol) (CNF/PVA), a biocompatible moisture-inspired self-healable composite, was applied both as the binder in functional layers and the substrate. Various functional layers comprising particular carbon materials and CNF/PVA were patterned on the substrate. A planar structure was beneficial for integration, and the active self-healing ability of the functional layers endowed self-healed e-skins with a higher toughness. Water served as both the only solvent throughout the fabrication process and the trigger of the self-healing process, which avoids the pollution and bioincompatibility caused by the application of noxious additives. Our e-skins could achieve real-time monitoring of whole-body physiological signals and environmental temperature and humidity. Cross-interference between different external stimuli was suppressed through reasonable material selection and structural design. Combined with conventional electronics, data could be transmitted to a nearby smartphone for post-processing. This work provides a previously unexplored strategy for multifunctional e-skins with an excellent practicality. [ABSTRACT FROM AUTHOR]

The FGFR3 gene encodes fibroblast growth factor receptor 3 protein, a negative regulator of chondrogenesis. Gain-of-function mutations result in constitutively activated FGFR3, leading to aberrant signal transduction, and accounting for inhibition of chondrocyte proliferation and differentiation. Generally, these pathogenic mutations maintain FGFR3 in an active state and cause diverse phenotypes in patients with skeletal dysplasia. For decades, studies have revealed the molecular mechanisms of constitutively activated FGFR3 and relevant therapeutic strategies. By modulating the FGFR3-induced signalling pathway with methods such as blocking binding between ligands and receptors, blocking tyrosine kinase activities, or antagonising the FGFR3 downstream signalling pathway, these strategies offer the possibility to ameliorate FGFR3 gene-related skeletal dysplasia phenotypes. In this review, we describe the mechanisms of potential therapeutic targets and underlying regulators and then systematically review molecular therapeutic strategies for FGFR3 gene-related skeletal dysplasia based on current knowledge. [ABSTRACT FROM AUTHOR]

Visual saliency is a type of visual feature which simulates visual attention selection mechanism in biological system and has better robustness and invariance. A rapid infrared image and visible light image registration method based on visual saliency and SIFT (scale invariant feature transform) is proposed in this paper. The method adopts amplitude modulation Fourier transform to construct saliency map, and the image salient points are achieved preliminarily by salient threshold. Subsequently, this method calculates the local entropy for these salient points because of entropy’s character for information measurement, then these points are reordered and screened based on the strategy of entropy priority. The screened results are thought as centers for salient regions. Morphological operation is used for growing and merging for neighbor salient scene region in image. Aiming at the abstracted salient region for image, PCA (principal component analysis)-SIFT algorithm is proposed, which can produce the compressed SIFT registration features and largely reduce the computational cost of image registration. The proposed algorithm adopts random sampling conformance method to remove the mistaken point pairs before calculating the model parameter of affine transformation for registration between infrared image and visible lights. The experimental results indicate that the method has good invariance under image scale, rotation, translation, and illumination variation and can realize effective registration between infrared image and visible lights. Compared with some classical algorithms, the proposed method has advantage in registration accuracy and registration speed obviously. [ABSTRACT FROM AUTHOR]