Accurate prognosis prediction is critical for individualized-therapy making of gastric cancer patients. We aimed to develop and test 6-month, 1-, 2-, 3-, 5-, and 10-year overall survival (OS) and cancer-specific survival (CSS) prediction models for gastric cancer patients following gastrectomy. We derived and tested Survival Quilts, a machine learning-based model, to develop 6-month, 1-, 2-, 3-, 5-, and 10-year OS and CSS prediction models. Gastrectomy patients in the development set (n = 20,583) and the internal validation set (n = 5,106) were recruited from the Surveillance, Epidemiology, and End Results (SEER) database, while those in the external validation set (n = 6,352) were recruited from the China National Cancer Center Gastric Cancer (NCCGC) database. Furthermore, we selected gastrectomy patients without neoadjuvant therapy as a subgroup to train and test the prognostic models in order to keep the accuracy of tumor-node-metastasis (TNM) stage. Prognostic performances of these OS and CSS models were assessed using the Concordance Index (C-index) and area under the curve (AUC) values. The machine learning model had a consistently high accuracy in predicting 6-month, 1-, 2-, 3-, 5-, and 10-year OS in the SEER development set (C-index = 0.861, 0.832, 0.789, 0.766, 0.740, and 0.709; AUC = 0.784, 0.828, 0.840, 0.849, 0.869, and 0.902, respectively), SEER validation set (C-index = 0.782, 0.739, 0.712, 0.698, 0.681, and 0.660; AUC = 0.751, 0.772, 0.767, 0.762, 0.766, and 0.787, respectively), and NCCGC set (C-index = 0.691, 0.756, 0.751, 0.737, 0.722, and 0.701; AUC = 0.769, 0.788, 0.790, 0.790, 0.787, and 0.788, respectively). The model was able to predict 6-month, 1-, 2-, 3-, 5-, and 10-year CSS in the SEER development set (C-index = 0.879, 0.858, 0.820, 0.802, 0.784, and 0.774; AUC = 0.756, 0.827, 0.852, 0.863, 0.874, and 0.884, respectively) and SEER validation set (C-index = 0.790, 0.763, 0.741, 0.729, 0.718, and 0.708; AUC = 0.706, 0.758, 0.767, 0.766, 0.766, and 0.764, respectively). In multivariate analysis, the high-risk group with risk score output by 5-year OS model was proved to be a strong survival predictor both in the SEER development set (hazard ratio [HR] = 14.59, 95% confidence interval [CI]: 1.872-2.774, P < 0.001), SEER validation set (HR = 2.28, 95% CI: 13.089-16.293, P < 0.001), and NCCGC set (HR = 1.98, 95% CI: 1.617-2.437, P < 0.001). We further explored the prognostic value of risk score resulted 5-year CSS model of gastrectomy patients, and found that high-risk group remained as an independent CSS factor in the SEER development set (HR = 12.81, 95% CI: 11.568-14.194, P < 0.001) and SEER validation set (HR = 1.61, 95% CI: 1.338-1.935, P < 0.001). Survival Quilts could allow accurate prediction of 6-month, 1-, 2-, 3-, 5-, and 10-year OS and CSS in gastric cancer patients following gastrectomy.
Prognosis prediction for high-risk patients undergoing invasive coronary angiography (ICA) is crucial for clinical decision-making. Despite machine learning (ML) advancements, time-to-event survival prediction remains limited. This study developed an ensemble ML model based on survival analysis to predict long-term outcomes in ICA patients. A total of 9517 ICA patients (2008-2020) were retrospectively analyzed. The primary outcome was all-cause mortality, with follow-up until December 31, 2021. Using 8 ML algorithms, we developed a model comprising 80 variables. Model performance was assessed using time-dependent C-index and Brier score, with variable importance analyzed using permutation-based and partial dependent plots. Survival Quilts model achieved the highest time-dependent C-index (0.920 at 30 days, 0.897 at 365 days), outperforming other ML algorithms. Time-dependent Brier scores generally increased, which remained stable. ICA-related characteristics had the greatest impact on mortality, while laboratory results, comorbidities, and patient characteristics gained influence over time. By day 365, patient characteristics and laboratory results became more prominent predictors. Among the domains, key variables included catheterization status, C-reactive protein, smoking, and chronic kidney disease. Survival analysis-based ensemble ML models, such as Survival Quilts, improve survival prediction by capturing time-varying influences of key predictors, offering a foundation for more precise cardiovascular care.
Precise prognostication is vital for guiding treatment decisions in people diagnosed with pancreatic cancer. Existing models depend on predetermined variables, constraining their effectiveness. Our objective was to explore a novel machine learning approach to enhance a prognostic model for predicting pancreatic cancer-specific mortality and, subsequently, to assess its performance against Cox regression models. Datasets were retrospectively collected and analyzed for 9,752 patients diagnosed with pancreatic cancer and with surgery performed. The primary outcomes were the mortality of patients with pancreatic carcinoma at one year, three years, and five years. Model discrimination was assessed using the concordance index (C-index), and calibration was assessed using Brier scores. The Survival Quilts model was compared with Cox regression models in clinical use, and decision curve analysis was done. The Survival Quilts model demonstrated robust discrimination for one-year (C-index 0.729), three-year (C-index 0.693), and five-year (C-index 0.672) pancreatic cancer-specific mortality. In comparison to Cox models, the Survival Quilts models exhibited a higher C-index up to 32 months but displayed inferior performance after 33 months. A subgroup analysis was conducted, revealing that within the subset of individuals without metastasis, the Survival Quilts models showcased a significant advantage over the Cox models. In the cohort with metastatic pancreatic cancer, Survival Quilts outperformed the Cox model before 24 months but exhibited a weaker performance after 25 months. This study has developed and validated a novel machine learning-based Survival Quilts model to predict pancreatic cancer-specific mortality that outperforms the Cox regression model.
Drawing on our lived experiences as public health doctoral scholars who grew up in rural communities in the United States of America-and as queer people, one Two-Spirit and one transgender-we used collaborative autoethnography to analyze our participation in an intergenerational 2S/LGBTQ+ storytelling event. Our analysis offered insight into how our intersecting identities shaped the quilt-square narratives we produced and deepened understanding of the cultural and structural forces shaping 2S/LGBTQ+ aging, intergenerational relationships, and the possibilities and constraints of queer solidarity amid an uncertain political climate. We generated four themes from our narrative quilts: (1) performing identity on someone else's stage to meet dominant expectations rather than personal truth; (2) masking, or how LGBTQ+ people across generations conceal aspects of themselves to navigate safety, belonging, and survival; (3) feeling like a fish out of water, capturing marginalization experienced by trans and Two-Spirit people even within 2S/LGBTQ+ spaces; and (4) questioning whose stories shape our futures, highlighting how racial and cultural erasure in queer aging spaces obscures BIPOC queer histories. Our findings illuminate the need for culturally inclusive 2S/LGBTQ+ spaces that promote solidarity, reduce social isolation, and advance affirming care across nursing, social work, and public health.
Low-coverage whole genome sequencing (lcWGS) combined with genotype imputation provides a cost-efficient alternative to high-coverage sequencing for large-scale genotyping. Although widely implemented in human and livestock genomics, this strategy has not yet been systematically optimized for insects of industrial importance. The black soldier fly (BSF, Hermetia illucens) is increasingly used in global waste bioconversion and sustainable protein production, but genomic resources remain limited. Here, we develop the first BSF haplotype reference panel, containing ∼29.8 million high-quality SNPs from 168 high-coverage genomes, and benchmark imputation performance using a validation experiment in which 33 high-coverage individuals were down-sampled to low coverage and imputed against a reference panel of 135 individuals. We evaluated the performance of three imputation tools, QUILT v1.0.5, GLIMPSE2, and STITCH v1.7.2, across multiple sequencing depths (0.5 × -3×) and allele frequency bins. Based on this validation, QUILT v1.0.5 achieved the highest accuracy overall, particularly for rare variants (MAF < 0.05), whereas GLIMPSE2 delivered comparable accuracy for common variants with approximately twofold faster runtimes. STITCH enabled reference-free imputation but exhibited reduced accuracy relative to reference-based approaches. We then applied the optimized framework to 180 low-coverage (∼1×) BSF genomes, demonstrating the practical utility of the reference panel for large-scale genotyping when true genotypes are unavailable. Together, the reference panel, benchmarking results, and accompanying lcWGS pipeline establish a validated framework for cost-effective BSF genotyping, enabling downstream applications in population monitoring, diversity assessment, and selective breeding.
In QUILT-3.032, the efficacy of interleukin-15 receptor agonist, nogapendekin alfa inbakicept (NAI), in combination with bacillus Calmette-Guérin (BCG) for BCG-unresponsive high-grade papillary-only nonmuscle-invasive bladder cancer was assessed. In this study, we report the 36-month follow-up among participants with BCG-unresponsive papillary disease (cohort B). NCT03022825 is an open-label, multicenter study of patients with BCG-unresponsive high-grade Ta/T1 papillary nonmuscle-invasive bladder cancer who received 400 μg NAI plus 50 mg BCG intravesically weekly for 6 consecutive weeks. The primary end point is disease-free survival (DFS) at 12 months. Progression-free survival (PFS), disease-specific survival (DSS), and cystectomy avoidance were assessed. Treatment-related adverse events were assessed. At July 15, 2024, data cutoff, the DFS rates at 12, 24, and 36 months were 58.2% (95% CI: 46.6, 68.2), 52.1% (95% CI: 40.3, 62.7), and 38.2% (95% CI: 25.6, 50.6), respectively. The PFS rates at 12 and 36 months were 94.9% (95% CI: 86.9, 98.0) and 83.1% (95% CI: 69.5, 91.0). The DSS rates at 12 and 36 months were 98.7% (95% CI: 91.4, 99.8) and 96.0% (95% CI: 88.2, 98.7). The median DSS has not been reached. Cystectomy avoidance rates at 12 and 36 months were 92.2% (95% CI: 83.4, 96.4) and 81.8% (95% CI: 68.1, 90.1), with median time to cystectomy not reached. Most treatment-related adverse events were grade 1 to 2 (61%) with 3% grade 3 and no grade 4 to 5. The 12-month and 36-month DFS, PFS, DSS, and cystectomy avoidance rates demonstrate the effectiveness and safety of NAI plus BCG in the management of BCG-unresponsive papillary disease. ClinicalTrials.gov Identifier: NCT03022825.
Sickle cell disease is a haematological disorder marked by recurrent events and progressive organ dysfunction, yet a standardised framework to assess disease severity is lacking. We developed a novel US-based severity grading system through identification of sickle cell disease-related complications mapping all to the Common Terminology Criteria For Adverse Events and applying a five-level severity scale. A modified Delphi process involving 29 US-based sickle cell disease experts was conducted over three virtual rounds (from Oct 20 to Nov 8, 2023; from June 26 to Aug 1, 2024; and from Nov 6, 2024, to Jan 7, 2025), with the use of a 9-point Likert scale ratings and iterative feedback. An in-person consensus workshop was then held in Memphis, TN, USA on Jan 16-17, 2025. This workshop was funded by the project's NIH grant (1R01CA270157-01). The final classification includes 53 clinical outcomes, each with diagnostic criteria, grades, and temporal patterns. This standardised, consensus-driven system provides a new benchmark for assessing sickle cell disease severity, with applications in clinical trials, disease burden assessment, and predictive modelling.
Sickle cell disease (SCD) is the most common inherited clinically relevant blood disorder. Although a deceptively simple monogenetic disorder, the associated complications have multiple downstream effects. In this review, we explore the many facets of SCD, with a particular focus on its impact on the vascular system. Despite progress in understanding the underlying mechanisms of SCD, including Hemoglobin S polymerization, microvascular occlusion, and inflammation, there are still many questions surrounding the condition, especially predicting which affected individuals will acquire specific complications in order to personalize treatments. While current standard of care treatments, including hydroxyurea and chronic red blood cell transfusions, have been proven to be disease-modifying, newer therapies like crizanlizumab and voxelotor have only proven to manage symptoms. Newer gene therapies have been approved; however, it is not clear what impact these will have long-term on the end-organ complications of SCD. There is still a significant need to understand how we optimize and personalize therapies to improve outcomes for patients. This review highlights the importance of recognizing SCD as a vascular disease to understand its multi-organ complications and heterogeneity of effects.
Inadvertent Perioperative Hypothermia (IPH) remains a common clinical problem. This study aimed to evaluate the efficacy of a multimodal temperature management strategy versus passive insulation in reducing the incidence of IPH among patients undergoing general anesthesia. This was a single-center, randomized, assessor-blinded randomized trial conducted at our hospital from November to December 2025. This study enrolled 206 patients scheduled for 4-8 h surgeries. They were randomly assigned to either a multimodal group (preoperative warming, forced-air warming blanket, fluid warming, increased ambient temperature, pharmacological intervention) or a control group (passive insulation with cotton quilts). Core body temperature was monitored at multiple perioperative time points. The primary outcome was the incidence of intraoperative hypothermia (core temperature < 36 °C). Secondary outcomes included inflammatory markers, blood gas parameters, fluid balance, pain scores, postoperative complications, and hospital length of stay. Compared with the control group, the multimodal group had a significantly lower incidence of intraoperative hypothermia starting at 90 min after surgery initiation (T5: control 60.6% vs. multimodal 48.0%; adjusted OR 0.37, 95% CI 0.15-0.93) and continuing to the end of surgery (T11: control 69.2% vs. multimodal 27.5%; adjusted OR 0.08, 95% CI 0.03-0.23). The multimodal intervention also reduced the incidence of hypothermia at operating room discharge (28% vs. 72%; adjusted OR 0.12, 95% CI 0.06-0.24) and suggest a potential reduction in the risk of postoperative cardiac adverse events (0% vs. 3.85%; P = 0.045). The multimodal group showed a greater increase in inflammatory markers (TNF-α, NLR, PLR), less pain at 48 h postoperatively, and a smaller reduction in pH. This multimodal temperature management bundle significantly lowers the incidence of perioperative hypothermia in patients undergoing prolonged surgery under general anesthesia, with benefits starting at 90 min and strengthening over time. It stabilizes metabolic parameters and may reduce cardiac complications and pain at 48 h postoperatively. https://www.chictr.org.cnChiCTR2500112554.Registered17/11/2025, first recruitment 20/11/2025.
Physicians are increasingly called to lead beyond clinical care, addressing system inefficiencies through innovation and entrepreneurial action. This qualitative multiple-case study examined how 21 physicians in the United States developed and applied "dual acumen," the integration of scientific expertise and entrepreneurial intelligence to advance healthcare innovation. Guided by effectuation theory, data were analyzed by NVivo 12 for thematic coding. Findings demonstrate how physician entrepreneurs navigated uncertainty through five effectual principles: bird-in-hand, affordable loss, crazy quilt, lemonade, and pilot-in-the-plane. These principles informed the development of the Dual Acumen Model, an empirically derived framework explaining how physicians translate clinical insights into entrepreneurial practice and system-level innovation. The study contributes empirical evidence that hybrid physician leaders advance healthcare improvement by integrating scientific and innovation competencies that build leadership capacity and organizational adaptability.
Ketamine is recommended as an opioid-sparing adjunct for sickle cell disease (SCD)-related pain management. Little is known regarding its use in hospitalized youth with SCD. We aimed to describe trends in ketamine administration and examine associations between ketamine administration and outcomes in hospitalized youth with SCD. We conducted a cross-sectional, multicenter study examining hospital admissions for youth with SCD from 44 children's hospitals in the United States from 2016 to 2023. Youth aged ≥6 months with SCD were identified using International Classification of Diseases tenth revision codes. Exposures included age, sex, race, payor, childhood opportunity index, hydroxyurea administration, and concomitant methadone, buprenorphine, or gabapentinoid administration. The primary outcome was ketamine administration during admission. Secondary outcomes included length of stay, days on IV opioids, all-cause 14-day readmission rates, and intensive care unit stays during admissions with ketamine administered during the first 3 days of hospitalization (early) and hospital day 4 or later (late). From 2016 to 2023, 4.5% (n = 3391) of admissions for patients with SCD included ketamine administration, with prevalence increasing from 2.3% in 2016 to 5.7% in 2023 (P< .001). Age groups ≥12 years and the year ≥2019 was associated with increased odds of ketamine administration. Admissions with ketamine administration were also more likely to have administration of methadone and hydroxyurea. Early vs late ketamine administration was associated with shorter length of stay and fewer parental opioid days, indicating randomized controlled studies are needed to determine not only which patients benefit from ketamine but also the impact of early administration.
To investigate the prevalence and types of artifacts in OCT angiography (OCTA) among patients with different glaucoma severities. Prospective cross-sectional study. Patients with open-angle glaucoma from a tertiary center were prospectively categorized into mild (mean deviation [MD] of 24-2 visual field ≥ -6 decibels [dB]), moderate (-6 to ≥ -12 dB), advanced (-12 to ≥ -20 dB), and severe glaucoma group (MD < -20 dB). AngioVue OCTA was performed three times within a single visit to obtain superficial and deep macular vessel density (VD) with 3 x 3-mm macular scans, and peripapillary VD with 4.5 x 4.5-mm scans centered on the optic disc. The intrasession variability was assessed by the coefficient of variation (CoV). Different types of image artifacts were identified. The prevalence of artifacts in patients with varying glaucoma severities, patient-related factors associated with artifact occurrence, and their impact on scan quality index (SQI) and variability of OCTA parameters. Among the 57 mild, 46 moderate, 46 advanced, and 39 severe glaucoma eyes, half of OCTA images exhibited artifacts. Their prevalence increased from 30% in mild to 67% in severe glaucoma (P < 0.001) for peripapillary scans and from 39% to 62% (P = 0.001) for macular scans. Defocus was the most common artifact (26%) and increased with worsening MD (P = 0.006), contributing to greater CoV of superficial (P = 0.043) and deep (P = 0.024) macular VD and reduced macular SQI (P = 0.018). Peripapillary scans were more affected by artifacts, with defocus (P < 0.001) and eye movement (P = 0.025) increasing as MD worsened, which reduced the peripapillary SQI (P = 0.003 and P < 0.001, respectively). Lower SQI (P < 0.001), eye movement (P = 0.042), and quilt (P = 0.047) were linked to greater CoV of peripapillary VD. Defocus is the most common OCTA artifact in glaucoma patients, increasing variability in OCTA metrics. Its prevalence rises with glaucoma severity and remains high even in scans with acceptable image quality, emphasizing the need for careful artifact assessment. The authors have no proprietary or commercial interest in any materials discussed in this article.
Background and Objective: Sickle cell disease (SCD) is an inherited blood disorder associated with recurrent painful crises. Sickle cell pain crises are a significant source of distress for patients and contribute substantially to hospital utilization among SCD populations. Many children with SCD also experience chronic pain, which is often multifactorial in nature. The management of both acute and chronic pain in SCD commonly relies on opioid medications. Acute and chronic use of opioids is associated with health risks and potential complications, which has raised interest in alternatives. Buprenorphine is a partial μ-receptor agonist with strong affinity that confers pain relief and may have an improved side-effect profile. While there is emerging evidence for its use in adult patients, the data is less developed in pediatrics. Methods: A scoping review was designed in accordance with PRISMA guidelines to systematically explore the literature on buprenorphine use in pain management for children with sickle cell disease (SCD). Results: This review shows that the published literature in this area is of low quality and extremely limited, and there is a lack of trials specifically designed to address the use of buprenorphine for this patient population. Studies are limited in their generalizability but suggest that buprenorphine may be useful in managing pain in this population. Conclusions: While promising, more data is required both retrospectively and prospectively to understand the utility of buprenorphine. The development of pediatric-specific protocols for transitioning from full µ-receptor agonist opioids to buprenorphine is also needed.
PurposeTo describe a novel tissue-sparing surgical technique-the Qidwai Quilt Pull-Over Patch-graft (Q-POP) technique-for the management of impending or established glaucoma drainage implant (GDI, ACP) tube exposure in high-risk eyes with conjunctival scarring and mechanical stress.MethodsThis report presents a single-case description of a monocular patient with severe nystagmus, extensive conjunctival fibrosis, and prior multiple ocular surgeries who developed progressive conjunctival thinning over a GDI (ACP) tube. A minimally invasive subconjunctival tunneling approach was used to deliver and position a scleral patch graft over the tube without a limbal-based peritomy.ResultsThe Q-POP technique provided stable tube coverage with minimal conjunctival manipulation. Postoperatively, the graft remained flat and well-positioned with no evidence of re-exposure, conjunctival dehiscence, inflammation, or intraocular pressure instability during follow-up.ConclusionsThe Q-POP technique offers a safe, reproducible, and cost-neutral alternative for managing tube exposure in eyes with compromised conjunctiva where conventional peritomy-based repairs carry high risk. This method may be particularly valuable in eyes subjected to mechanical stress like nystagmus. Larger case series are warranted to assess long-term outcomes and broader applicability.
Efforts to improve care for people living with sickle cell disease (SCD) have led to the development of several registries; however, many are dependent on time-limited funding and lack coordination. Consequently, existing data sets are fragmented and do not provide the comprehensive, longitudinal insights achievable through well-integrated registries. Although relevant data exist within electronic medical records across institutions, aggregation is limited by poor interoperability, inconsistent use of common data elements, and poor translation of natural language into codified data. These barriers hinder population-level research and contribute to gaps in understanding the lifelong progression of SCD. Creating a new common data system risk losing years of valuable data, highlighting the need to optimize existing data resources. This study aimed to develop a privacy-preserving method to securely link 3 of the largest SCD data collection efforts in the United States. Conducted at the University of Alabama at Birmingham Lifespan Sickle Cell Center, the study leveraged institutional review board-approved access to the Sickle Cell Data Collection project, the American Society of Hematology Research Collaborative Data Hub, and the Globin Research Network for Data and Discovery. Identity tokens were generated and hashed using Secure Hash Algorithm (SHA)-256 to enable secure linkage without sharing protected health information. A total of 8026 records were identified across the 3 registries. Deterministic matching of hashed tokens identified 1080 unique individuals appearing in at least 2 data sets. This study demonstrates the first privacy-preserving linkage of multiple SCD registries. Secure data integration enhances interoperability and enables richer longitudinal analyses critical for advancing SCD research and treatment development.
Ancient DNA research plays a pivotal role in reconstructing history and understanding biological evolution. Modern genotype imputation techniques, which leverage reference panels to predict missing genotypes, have emerged as powerful tools for enhancing genetic information retrieval from ancient DNA. In this review, we examine state-of-the-art computational tools and algorithms-including GLIMPSE, Beagle, and QUILT-that enable more comprehensive analysis of genetic architecture and evolutionary patterns in ancient humans and animals. While analytical accuracy can be affected by variables such as sampling strategies and reference panel composition, the field shows tremendous potential for growth. Key future directions include: (1) algorithmic refinements, (2) computational efficiency improvements, (3) integration with emerging technologies, and (4) expansion into novel research domains. These advances are expected to offer new perspectives for advancing ancient DNA research and understanding ancient life systems. 古DNA研究在历史重建和生物进化研究中发挥着关键作用，但样本降解和测序覆盖度低等问题制约着研究进展。基因型填充技术通过利用单倍型参考序列集预测缺失基因型，显著提升了古DNA遗传信息的获取能力。本文系统评述了GLIMPSE、Beagle和QUILT等前沿计算工具与算法，这些方法为解析古代人类与动物的遗传结构及演化模式提供了更全面的分析手段。尽管采样策略和单倍型参考序列集组成等因素可能影响分析准确性，但该领域仍展现出巨大的发展潜力。未来的重点发展方向包括：(1)算法优化；(2)计算效率提升；(3)新兴技术整合；(4)研究领域拓展。这些进展有望为古DNA研究的发展和古代生命系统的理解提供新的视角。.
Effects of lattice mismatch and twist on epitaxy in two-dimensional (2D) vertical heterostructures are well-explored, whereas the effects of in-plane anisotropy are largely unknown. Here, we report the synthesis of ReS2/MoS2 vertical heterostructures using chemical vapor deposition, combining anisotropic (triclinic) ReS2 and isotropic (hexagonal) MoS2. A combination of scanning/transmission electron microscopy (S/TEM) and density functional theory calculations are used to elucidate the microstructure of ReS2/MoS2 heterostructures and reveal a complex interplay of interfacial epitaxy, lattice mismatch strain, and anisotropy. We observe that the heterostructure system's ability to relax interlayer registry differs in the crystallographic directions of larger and smaller lattice mismatch. It nevertheless obtains an overall effective lattice registry on the longest length scales by means of variations in the Re chain direction that create a quilt-like pattern of stripe domains, a phenomenon we describe as mesoscale epitaxy.
As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths.
Unilateral pulmonary artery agenesis is a rare congenital malformation, typically observed in infancy or childhood, but rarely in adulthood. An elderly female patient admitted to our hospital with a chief complaint of acute chest pain is reported here. The patient experienced a sudden onset of chest pain during physical exertion (folding quilts) 4 days before admission, which progressively worsened. Thoracoabdominal computed tomography angiography (CTA) was performed to rule out acute aortic syndromes, revealing agenesis of the right pulmonary artery with systemic collateral circulation supplying the right lung. A further examination of the clinical history and symptoms uncovered a past medical history of chronic pulmonary disease lasting several decades. The patient's clinical manifestations had consistently presented as symptoms of common conditions such as chronic bronchitis, bronchiectasis, pneumonia, and pulmonary tuberculosis, and a definitive diagnosis of Isolated Unilateral Pulmonary Artery Agenesis (IUAPA) had not been established, nor had the association between this disorder and chronic pulmonary lesions been previously considered. Although follow-up examinations confirmed that the present episode of chest pain resulted from an osteoporotic vertebral fracture, further in-depth research is necessary to fully understand the relationship between the absence of the pulmonary artery and the chronic pulmonary lesions. This report, together with the literature review, discusses the key characteristics, misdiagnosis challenges, and strategies for improving the diagnosis of IUAPA.
We present a novel and cost-effective whole-greenhouse static chamber method for quantifying nighttime ecosystem respiration (carbon dioxide emissions in the dark) and nitrous oxide (N₂O) emissions in greenhouse cultivation systems. This method leverages routine nighttime sealing practices-such as closing vents and deploying thermal quilts-to create an enclosed environment for gas accumulation. Gas concentrations can be monitored either automatically using in situ sensors or manually via gas-tight sampling bags. In both approaches, linear regression of gas concentration versus time yields robust accumulation rates, which are then converted into fluxes based on greenhouse volume and temperature. Validation using a high-precision Picarro analyzer demonstrated strong linearity (R² > 0.95) in carbon dioxide (CO₂) and N₂O accumulation curves across 15 greenhouses, confirming the method's reliability. As both approaches produce comparable results, we recommend manual sampling for its simplicity and cost-efficiency. The method integrates emissions from soil, plants, and other surfaces, overcoming spatial limitations of small chambers. With minimal equipment and labor requirements-especially when using manual sampling-it offers a practical solution for routine monitoring of greenhouse gas (GHG) emissions in controlled-environment agriculture.•Allows for whole-ecosystem CO₂ and N₂O flux estimation using manual sampling and gas chromatography.•Demonstrates strong agreement with continuous online measurements in multi-site validation.•Provides a low-cost, scalable solution for GHG quantification in protected cropping systems.