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Socio-demographic, Clinical, and Genetic Determinants of Quality of Life in Lung Cancer Patients.
Patient reported health-related quality of life (QOL) is a major component of the overall well-being of cancer patients, with links to prognosis. In 6,420 lung cancer patients, we identified patient characteristics and genetic determinants of QOL. Patient responses from the SF-12 questionnaire was used to calculate normalized Physical Component Summary (PCS) and Mental Component Summary (MCS) scores. Further, we analyzed 218 single nucleotide polymorphisms (SNPs) in the p38 MAPK signaling pathway, a key mediator of response to cellular and environmental stress, as genetic determinants of QOL in a subset of the study population (N = 641). Trends among demographic factors for mean PCS and MCS included smoking status (PCS Ptrend < 0.001, MCS Ptrend < 0.001) and education (PCS Ptrend < 0.001, MCS Ptrend < 0.001). Similar relationships were seen for MCS. The homozygous rare genotype of MEF2B: rs2040562 showed an increased risk of a poor MCS (OR: 3.06, 95% CI: 1.05-8.92, P = 0.041). Finally, survival analysis showed that a low PCS or a MCS was associated with increased risks of five-year mortality (HR = 1.63, 95% CI: 1.51-1.77, HR = 1.23, 95% CI: 1.16-1.32, respectively) and there was a significant reduction in median survival time (Plog-rank < 0.001). These findings suggest that multiple factors contribute to QOL in lung cancer patients, and baseline QOL can impact survival
Design and Optimization of Functionally-graded Triangular Lattices for Multiple Loading Conditions
Aligning lattices based on local stress distribution is crucial for achieving
exceptional structural stiffness. However, this aspect has primarily been
investigated under a single load condition, where stress in 2D can be described
by two orthogonal principal stress directions. In this paper, we introduce a
novel approach for designing and optimizing triangular lattice structures to
accommodate multiple loading conditions, which means multiple stress fields.
Our method comprises two main steps: homogenization-based topology optimization
and geometry-based de-homogenization. To ensure the geometric regularity of
triangular lattices, we propose a simplified version of the general rank-
laminate and parameterize the design domain using equilateral triangles with
unique thickness per edge. During optimization, the thicknesses and orientation
of each equilateral triangle are adjusted based on the homogenized properties
of triangular lattices. Our numerical findings demonstrate that this proposed
simplification results in only a slight decrease in stiffness, while achieving
triangular lattice structures with a compelling geometric regularity. In
geometry-based de-homogenization, we adopt a field-aligned triangulation
approach to generate a globally consistent triangle mesh, with each triangle
oriented according to the optimized orientation field. Our approach for
handling multiple loading conditions, akin to de-homogenization techniques for
single loading conditions, yields highly detailed, optimized, spatially varying
lattice structures. The method is computationally efficient, as simulations and
optimizations are conducted at a low-resolution discretization of the design
domain. Furthermore, since our approach is geometry-based, obtained structures
are encoded into a compact geometric format that facilitates downstream
operations such as editing and fabrication
Application of an ultrasound-guided bilateral erector spinae plane block after the Nuss procedure for pectus excavatum in children: a retrospective cohort study with propensity score matching
ObjectiveTo retrospectively analyze the effect of applying an ultrasound-guided bilateral erector spine plane block (ESPB) after the Nuss procedure for surgical repair of pectus excavatum (PE) in children.MethodsThe subjects of the study were patients with severe PE who received the Nuss procedure in our hospital between 1 January 2019 and 30 November 2021. According to different methods for postoperative pain management, the enrolled patients were divided into two groups, the ultrasound-guided ESPB group and the thoracic epidural analgesia (TEA) group. The primary outcome of this study was analgesic drug dosage and the secondary outcome was numerical rating scales (NRSs) between the two groups.ResultsThere was no significant difference between the two groups in terms of demographic, preoperative clinical evaluation, or surgical characteristics (P > 0.05). The catheter duration in the TEA group was significantly shorter than that in the ESPB group (P < 0.05), while the hospitalization time in the ESPB group was significantly shorter than that in the TEA group (P < 0.05). In terms of oral morphine equivalent comparison, the required dose of the TEA group was lower than that of the ESPB group on the 1st and 2nd day after the operation (P < 0.05), and there was no statistical difference between the two groups on the 3rd and 4th day after the operation (P > 0.05). The number of patients with an S-NRS ≥ 7 and D-NRS ≥ 7 in the TEA group at day 1 was lower than that in the ESPB group (P < 0.05). There was no significant difference between the two groups at other time points (P > 0.05),ConclusionAn ultrasound-guided ESPB used in Nuss surgery for children with funnel chest can provide good analgesia for surgery and shorten the postoperative rehabilitation and hospitalization time of patients. It is a safe and effective alternative to TEA
Provably Robust Semi-Infinite Program Under Collision Constraints via Subdivision
We present a semi-infinite program (SIP) solver for trajectory optimizations
of general articulated robots. These problems are more challenging than
standard Nonlinear Program (NLP) by involving an infinite number of non-convex,
collision constraints. Prior SIP solvers based on constraint sampling cannot
guarantee the satisfaction of all constraints. Instead, our method uses a
conservative bound on articulated body motions to ensure the solution
feasibility throughout the optimization procedure. We further use subdivision
to adaptively reduce the error in conservative motion estimation. Combined, we
prove that our SIP solver guarantees feasibility while approaches the critical
point of SIP problems up to arbitrary user-provided precision. We have verified
our method on a row of trajectory optimization problems involving industrial
robot arms and UAVs, where our method can generate collision-free, locally
optimal trajectories within a couple minutes
Personalized Risk Assessment in Never, Light, and Heavy Smokers in a prospective cohort in Taiwan.
The objective of this study was to develop markedly improved risk prediction models for lung cancer using a prospective cohort of 395,875 participants in Taiwan. Discriminatory accuracy was measured by generation of receiver operator curves and estimation of area under the curve (AUC). In multivariate Cox regression analysis, age, gender, smoking pack-years, family history of lung cancer, personal cancer history, BMI, lung function test, and serum biomarkers such as carcinoembryonic antigen (CEA), bilirubin, alpha fetoprotein (AFP), and c-reactive protein (CRP) were identified and included in an integrative risk prediction model. The AUC in overall population was 0.851 (95% CI = 0.840-0.862), with never smokers 0.806 (95% CI = 0.790-0.819), light smokers 0.847 (95% CI = 0.824-0.871), and heavy smokers 0.732 (95% CI = 0.708-0.752). By integrating risk factors such as family history of lung cancer, CEA and AFP for light smokers, and lung function test (Maximum Mid-Expiratory Flow, MMEF25-75%), AFP and CEA for never smokers, light and never smokers with cancer risks as high as those within heavy smokers could be identified. The risk model for heavy smokers can allow us to stratify heavy smokers into subgroups with distinct risks, which, if applied to low-dose computed tomography (LDCT) screening, may greatly reduce false positives
Visual-Guided Mesh Repair
Mesh repair is a long-standing challenge in computer graphics and related
fields. Converting defective meshes into watertight manifold meshes can greatly
benefit downstream applications such as geometric processing, simulation,
fabrication, learning, and synthesis. In this work, we first introduce three
visual measures for visibility, orientation, and openness, based on
ray-tracing. We then present a novel mesh repair framework that incorporates
visual measures with several critical steps, i.e., open surface closing, face
reorientation, and global optimization, to effectively repair defective meshes,
including gaps, holes, self-intersections, degenerate elements, and
inconsistent orientations. Our method reduces unnecessary mesh complexity
without compromising geometric accuracy or visual quality while preserving
input attributes such as UV coordinates for rendering. We evaluate our approach
on hundreds of models randomly selected from ShapeNet and Thingi10K,
demonstrating its effectiveness and robustness compared to existing approaches
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