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

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