Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability.

BACKGROUND: Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures. PURPOSE: By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability. METHODS: The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation. RESULTS: The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific. CONCLUSION: This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Radiomics-based hybrid model for predicting radiation pneumonitis: A systematic review and meta-analysis.

PURPOSE: This study reviewed and meta-analyzed evidence on radiomics-based hybrid models for predicting radiation pneumonitis (RP). These models are crucial for improving thoracic radiotherapy plans and mitigating RP, a common complication of thoracic radiotherapy. We examined and compared the RP prediction models developed in these studies with the radiomics features employed in RP models. METHODS: We systematically searched Google Scholar, Embase, PubMed, and MEDLINE for studies published up to April 19, 2024. Sixteen studies met the inclusion criteria. We compared the RP prediction models developed in these studies and the radiomics features employed. RESULTS: Radiomics, as a single-factor evaluation, achieved an area under the receiver operating characteristic curve (AUROC) of 0.73, accuracy of 0.69, sensitivity of 0.64, and specificity of 0.74. Dosiomics achieved an AUROC of 0.70. Clinical and dosimetric factors showed lower performance, with AUROCs of 0.59 and 0.58. Combining clinical and radiomic factors yielded an AUROC of 0.78, while combining dosiomic and radiomics factors produced an AUROC of 0.81. Triple combinations, including clinical, dosimetric, and radiomics factors, achieved an AUROC of 0.81. The study identifies key radiomics features, such as the Gray Level Co-occurrence Matrix (GLCM) and Gray Level Size Zone Matrix (GLSZM), which enhance the predictive accuracy of RP models. CONCLUSIONS: Radiomics-based hybrid models are highly effective in predicting RP. These models, combining traditional predictive factors with radiomic features, particularly GLCM and GLSZM, offer a clinically feasible approach for identifying patients at higher RP risk. This approach enhances clinical outcomes and improves patient quality of life. PROTOCOL REGISTRATION: The protocol of this study was registered on PROSPERO (CRD42023426565).

Prevalences and Interrelationships of Post COVID-19 Fatigue, Sleep Disturbances, and Depression in Healthy Young and Middle-Aged Adults.

Background: An evaluation of the persistence of symptoms following COVID-19 in economically active young and middle-aged adults is crucial due to its significant socioeconomic impact resulting from compromised work performance. Methods: A prospective, multicenter study at 12 South Korean hospitals from January to December 2022 involved telephone interviews along with validated questionnaires. Results: Among 696 participants with a median age of 32 and no prior diagnoses, 30% of participants experienced persistent fatigue, while 21.4% suffered from sleep disturbance at 6 months following infection. Additionally, approximately 25% of the participants exhibited depression that endured for up to 6 months. Symptomatic individuals at 3 months exhibited a significantly higher prevalence of persistent fatigue, sleep disturbances, and depression at 6 months compared to those who remained asymptomatic. Notably, sleep disturbance and persistent fatigue at 3 months emerged as significant independent predictors of the presence of depression at 6 months. Conclusions: Even among young and middle-aged healthy adults, prolonged fatigue, sleep disturbance, and depression exhibit a significant prevalence and persisted for up to 6 months. Therefore, implementing a workplace management protocol for these symptoms is essential to mitigate the socioeconomic burden caused by the impairment of work efficiency.

Dose-toxicity surface histogram-based prediction of radiation dermatitis severity and shape.

Objective.This study aimed to develop a new approach to predict radiation dermatitis (RD) by using the skin dose distribution in the actual area of RD occurrence to determine the predictive dose by grade.Approach.Twenty-three patients with head and neck cancer treated with volumetric modulated arc therapy were prospectively and retrospectively enrolled. A framework was developed to segment the RD occurrence area in skin photography by matching the skin surface image obtained using a 3D camera with the skin dose distribution. RD predictive doses were generated using the dose-toxicity surface histogram (DTH) calculated from the skin dose distribution within the segmented RD regions classified by severity. We then evaluated whether the developed DTH-based framework could visually predict RD grades and their occurrence areas and shapes according to severity.Main results.The developed framework successfully generated the DTH for three different RD severities: faint erythema (grade 1), dry desquamation (grade 2), and moist desquamation (grade 3); 48 DTHs were obtained from 23 patients: 23, 22, and 3 DTHs for grades 1, 2, and 3, respectively. The RD predictive doses determined using DTHs were 28.9 Gy, 38.1 Gy, and 54.3 Gy for grades 1, 2, and 3, respectively. The estimated RD occurrence area visualized by the DTH-based RD predictive dose showed acceptable agreement for all grades compared with the actual RD region in the patient. The predicted RD grade was accurate, except in two patients.Significance. The developed DTH-based framework can classify and determine RD predictive doses according to severity and visually predict the occurrence area and shape of different RD severities. The proposed approach can be used to predict the severity and shape of potential RD in patients and thus aid physicians in decision making.

Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.

This work aims to investigate the clinical feasibility of deep learning-based synthetic CT images for cervix cancer, comparing them to MR for calculating attenuation (MRCAT). Patient cohort with 50 pairs of T2-weighted MR and CT images from cervical cancer patients was split into 40 for training and 10 for testing phases. We conducted deformable image registration and Nyul intensity normalization for MR images to maximize the similarity between MR and CT images as a preprocessing step. The processed images were plugged into a deep learning model, generative adversarial network. To prove clinical feasibility, we assessed the accuracy of synthetic CT images in image similarity using structural similarity (SSIM) and mean-absolute-error (MAE) and dosimetry similarity using gamma passing rate (GPR). Dose calculation was performed on the true and synthetic CT images with a commercial Monte Carlo algorithm. Synthetic CT images generated by deep learning outperformed MRCAT images in image similarity by 1.5% in SSIM, and 18.5 HU in MAE. In dosimetry, the DL-based synthetic CT images achieved 98.71% and 96.39% in the GPR at 1% and 1 mm criterion with 10% and 60% cut-off values of the prescription dose, which were 0.9% and 5.1% greater GPRs over MRCAT images.

Quadriplegia, Dysphagia and Ataxia Manifested in a Child With COVID-19 Related Acute Necrotizing Encephalopathy: A Case Report.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, presents primarily with respiratory symptoms. However, children with COVID-19 are usually asymptomatic or mild acute symptoms and also neurological manifestations have also been observed. We report the case of a 7-year-old girl who presented with high fever and altered mental status, leading to a diagnosis of COVID-19 and acute necrotizing encephalopathy (ANE). The patient received intensive medical care in the intensive care unit and subsequently underwent rehabilitation programs due to neurological functional sequelae. Neurological complications in COVID-19, including ANE, may result from potential viral nerve involvement, cytokine storms, and the blood-brain barrier disruption. Early rehabilitation plays a pivotal role in managing COVID-19-related neurological complications and enhancing patients' functional outcomes. Further research is essential to gain a better understanding of the mechanisms and treatment strategies for neurological manifestations in pediatric COVID-19 patients, particularly those with multisystem inflammatory syndrome in child.

Endobronchial valves for emphysema and persistent air-leak: 10-year experience in an Asian country.

BACKGROUND: Endobronchial valve (EBV) therapy, a validated method for bronchoscopic lung volume reduction (BLVR) in severe emphysema, has been explored for persistent air-leak (PAL) management. However, its effectiveness and safety in the Asian population require further real-world evaluation. In this study, we assessed the outcomes of treatment with EBV within this demographic. METHODS: We conducted a retrospective analysis of medical records from 11 Korean centers. For the emphysema cohort, inclusion criteria were patients diagnosed with emphysema who underwent bronchoscopy intended for BLVR. We assessed these patients for clinical outcomes of chronic obstructive pulmonary disease. All patients with PAL who underwent treatment with EBV were included. We identified the underlying causes of PAL and evaluated clinical outcomes after the procedure. RESULTS: The severe emphysema cohort comprised 192 patients with an average age of 70.3 years, and 95.8% of them were men. Ultimately, 137 underwent treatment with EBV. Three months after the procedure, the BLVR group demonstrated a significant improvement in forced expiratory volume in 1 s (+160 mL vs. +30 mL; P = 0.009). Radiographic evidence of lung volume reduction 6 months after BLVR was significantly associated with improved survival (adjusted hazard ratio 0.020; 95% confidence interval 0.038-0.650; P = 0.010). Although pneumothorax was more common in the BLVR group (18.9% vs. 3.8%; P = 0.018), death was higher in the no-BLVR group (38.5% vs. 54.5%, P = 0.001), whereas other adverse events were comparable between the groups. Within the subset of 18 patients with PAL, the predominant causes of air-leak included spontaneous secondary pneumothorax (44.0%), parapneumonic effusion/empyema (22.2%), and post-lung resection surgery (16.7%). Following the treatment, the majority (77.8%) successfully had their chest tubes removed. Post-procedural complications were minimal, with two incidences of hemoptysis and one of empyema, all of which were effectively managed. CONCLUSIONS: Treatment with EBV provides substantial clinical benefits in the management of emphysema and PAL in the Asian population, suggesting a favorable outcome for this therapeutic approach.

Area-selective atomic layer deposition on 2D monolayer lateral superlattices.

The advanced patterning process is the basis of integration technology to realize the development of next-generation high-speed, low-power consumption devices. Recently, area-selective atomic layer deposition (AS-ALD), which allows the direct deposition of target materials on the desired area using a deposition barrier, has emerged as an alternative patterning process. However, the AS-ALD process remains challenging to use for the improvement of patterning resolution and selectivity. In this study, we report a superlattice-based AS-ALD (SAS-ALD) process using a two-dimensional (2D) MoS2-MoSe2 lateral superlattice as a pre-defining template. We achieved a minimum half pitch size of a sub-10 nm scale for the resulting AS-ALD on the 2D superlattice template by controlling the duration time of chemical vapor deposition (CVD) precursors. SAS-ALD introduces a mechanism that enables selectivity through the adsorption and diffusion processes of ALD precursors, distinctly different from conventional AS-ALD method. This technique facilitates selective deposition even on small pattern sizes and is compatible with the use of highly reactive precursors like trimethyl aluminum. Moreover, it allows for the selective deposition of a variety of materials, including Al2O3, HfO2, Ru, Te, and Sb2Se3.

Evaluation of the deliverability of dynamic conformal arc therapy (DCAT) by gantry wobble and its influence on dose.

We aimed to investigate the deliverability of dynamic conformal arc therapy (DCAT) by gantry wobble owing to the intrinsic inter-segment break of the Elekta linear accelerator (LINAC) and its adverse influence on the dose to the patient. The deliverability of DCAT was evaluated according to the plan parameters, which affect the gantry rotation speed and resultant positional inaccuracies; the deliverability according to the number of control points and dose rates was investigated by using treatment machine log files and dosimetry devices, respectively. A non-negligible degradation in DCAT deliverability due to gantry wobble was observed in both the treatment machine log files and dosimetry devices. The resulting dose-delivery error occurred below a certain number of control points or above a certain dose rate. Dose simulations in the patient domain showed a similar impact on deteriorated deliverability. For targets located primarily in the isocenter, the dose differences were negligible, whereas for organs at risk located mainly off-isocenter, the dose differences were significant up to - 8.77%. To ensure safe and accurate radiotherapy, optimal plan parameters should be selected, and gantry angle-specific validations should be conducted before treatment.

Identification and Antimicrobial Susceptibilities for Patients with Non-tuberculous Mycobacteria Infection in Jeju Island: Single-Center Retrospective Study.

BACKGROUND: The distribution of species and characteristics of non-tuberculous mycobacteria (NTM) differ, and surveillance data for changes in antimicrobial susceptibilities of NTM is insufficient. This study analyzed the changes in antimicrobial susceptibility trends across NTM species and assessed the appropriateness of empirical antimicrobial drugs for NTM. MATERIALS AND METHODS: We retrospectively analyzed the clinical characteristics, including demographics, distribution of NTM species, antimicrobial drug susceptibilities, and outcomes, at a teaching hospital in Jeju Island from 2009 - 2022. RESULTS: Overall, 342 patients were included in the analysis; 93.0% were classified into the pulmonary group (PG) and 7.0% into the extrapulmonary group (EPG). The isolation rate of Mycobacterium avium was significantly higher in PG (36.8% vs. 0%, P = 0.001), while that of Mycobacterium fortuitum was significantly higher in EPG (4.5% vs. 31.3%, P = 0.001). The antimicrobial susceptibility rate is higher against clarithromycin (89.9%) and amikacin (83.3%) and lower against rifampin (54.7%) and ethambutol (28.1%). The susceptibility rate to clarithromycin was over 80%, but those to rifampin and ethambutol showed decreasing annual trends. Of the 162 patients who received empirical antimicrobial therapy, actual antimicrobial susceptibility rates were high (90.1%) using empirical macrolide, and relatively low using ethambutol and rifampin (28.0% and 58.8%, respectively). CONCLUSION: This is the first study of analysis of the distribution, baseline characteristics, and antimicrobial susceptibility of isolated NTM species in pulmonary and extrapulmonary patients in Jeju Island over 10 years. Policies that continuously monitor changes in susceptibility rate are required to ensure effective treatment strategies.

Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention.

This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one breathing cycle into three types: break, normal, and wheeze, this study not only identifies abnormal sounds within each breath but also captures comprehensive data on their location, duration, and relationships within entire respiratory cycles, including atypical patterns. This innovative strategy is based on a combination of a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network model, enabling real-time analysis of respiratory sounds. Notably, it stands out for its capacity to handle continuous data, distinguishing it from conventional lung sound classification algorithms. The study utilizes a substantial dataset consisting of 535 respiration cycles from diverse sources, including the Child Sim Lung Sound Simulator, the EMTprep Open-Source Database, Clinical Patient Records, and the ICBHI 2017 Challenge Database. Achieving a classification accuracy of 90%, the exceptional result metrics encompass the identification of each breath cycle and simultaneous detection of the abnormal sound, enabling the real-time wheeze counting of all respirations. This innovative wheeze counter holds the promise of revolutionizing research on predicting lung diseases based on long-term breathing patterns and offers applicability in clinical and non-clinical settings for on-the-go detection and remote intervention of exacerbated respiratory symptoms.

Empiric Anti-Pseudomonal ß-Lactam Monotherapy Versus Fluoroquinolone Combination Therapy in Patients With Hospital-Acquired Pneumonia: A Multicenter Cohort Study With Propensity Score Matching.

BACKGROUND: There is insufficient data on the benefits of empiric antibiotic combinations for hospital-acquired pneumonia (HAP). We aimed to investigate whether empiric anti-pseudomonal combination therapy with fluoroquinolones decreases mortality in patients with HAP. METHODS: This multicenter, retrospective cohort study included adult patients admitted to 16 tertiary and general hospitals in Korea between January 1 and December 31, 2019. Patients with risk factors for combination therapy were divided into anti-pseudomonal non-carbapenem ß-lactam monotherapy and fluoroquinolone combination therapy groups. Primary outcome was 30-day mortality. Propensity score matching (PSM) was used to reduce selection bias. RESULTS: In total, 631 patients with HAP were enrolled. Monotherapy was prescribed in 54.7% (n = 345) of the patients, and combination therapy was prescribed in 45.3% (n = 286). There was no significant difference in 30-day mortality between the two groups (16.8% vs. 18.2%, P = 0.729) or even after the PSM (17.5% vs. 18.2%, P = 0.913). After the PSM, adjusted hazard ratio for 30-day mortality from the combination therapy was 1.646 (95% confidence interval, 0.782-3.461; P = 0.189) in the Cox proportional hazards model. Moreover, there was no significant difference in the appropriateness of initial empiric antibiotics between the two groups (55.0% vs. 56.8%, P = 0.898). The proportion of multidrug-resistant (MDR) pathogens was high in both groups. CONCLUSION: Empiric anti-pseudomonal fluoroquinolone combination therapy showed no survival benefit compared to ß-lactam monotherapy in patients with HAP. Caution is needed regarding the routine combination of fluoroquinolones in the empiric treatment of HAP patients with a high risk of MDR.

Microlens array camera with variable apertures for single-shot high dynamic range (HDR) imaging.

We report a microlens array camera with variable apertures (MACVA) for high dynamic range (HDR) imaging by using microlens arrays with various sizes of apertures. The MACVA comprises variable apertures, microlens arrays, gap spacers, and a CMOS image sensor. The microlenses with variable apertures capture low dynamic range (LDR) images with different f-stops under single-shot exposure. The reconstructed HDR images clearly exhibit expanded dynamic ranges surpassing LDR images as well as high resolution without motion artifacts, comparable to the maximum MTF50 value observed among the LDR images. This compact camera provides, what we believe to be, a new perspective for various machine vision or mobile devices applications.

Carbon Ion Radiotherapy in the Treatment of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly lethal cancer with limited treatment options and poor prognosis. Carbon ion radiotherapy (CIRT) has emerged as a promising treatment modality for HCC due to its unique physical and biological properties. CIRT uses carbon ions to target and destroy cancer cells with a high precision and efficacy. The Bragg Peak phenomenon allows precise dose delivery to the tumor while minimizing damage to healthy tissues. In addition, the high relative biological effectiveness of carbon ions can be shown against radioresistant and hypoxic tumor areas. CIRT also offers a shorter treatment schedule than conventional radiotherapy, which increases patient convenience and compliance. The clinical outcomes of CIRT for HCC have shown excellent local control rates with minimal side effects. Considering its physical and biological properties, CIRT may be a viable option for complex clinical scenarios such as patients with poor liver function, large tumors, re-irradiation cases, and tumors close to critical organs. Further research and larger studies are needed to establish definitive indications for CIRT and to compare its efficacy with that of other treatment modalities. Nevertheless, CIRT offers a potential breakthrough in HCC management, providing hope for improved therapeutic outcomes and reduced treatment-related toxicities.

Atomic Layer Deposition Route to Scalable, Electronic-Grade van der Waals Te Thin Films.

Scalable production and integration techniques for van der Waals (vdW) layered materials are vital for their implementation in next-generation nanoelectronics. Among available approaches, perhaps the most well-received is atomic layer deposition (ALD) due to its self-limiting layer-by-layer growth mode. However, ALD-grown vdW materials generally require high processing temperatures and/or additional postdeposition annealing steps for crystallization. Also, the collection of ALD-producible vdW materials is rather limited by the lack of a material-specific tailored process design. Here, we report the annealing-free wafer-scale growth of monoelemental vdW tellurium (Te) thin films using a rationally designed ALD process at temperatures as low as 50 °C. They exhibit exceptional homogeneity/crystallinity, precise layer controllability, and 100% step coverage, all of which are enabled by introducing a dual-function co-reactant and adopting a so-called repeating dosing technique. Electronically, vdW-coupled and mixed-dimensional vertical p-n heterojunctions with MoS2 and n-Si, respectively, are demonstrated with well-defined current rectification as well as spatial uniformity. Additionally, we showcase an ALD-Te-based threshold switching selector with fast switching time (∼40 ns), selectivity (∼104), and low Vth (∼1.3 V). This synthetic strategy allows the low-thermal-budget production of vdW semiconducting materials in a scalable fashion, thereby providing a promising approach for monolithic integration into arbitrary 3D device architectures.

Comparison of the end-of-life decisions of patients with hospital-acquired pneumonia after the enforcement of the life-sustaining treatment decision act in Korea.

BACKGROUND: Although the Life-Sustaining Treatment (LST) Decision Act was enforced in 2018 in Korea, data on whether it is well established in actual clinical settings are limited. Hospital-acquired pneumonia (HAP) is a common nosocomial infection with high mortality. However, there are limited data on the end-of-life (EOL) decision of patients with HAP. Therefore, we aimed to examine clinical characteristics and outcomes according to the EOL decision for patients with HAP. METHODS: This multicenter study enrolled patients with HAP at 16 referral hospitals retrospectively from January to December 2019. EOL decisions included do-not-resuscitate (DNR), withholding of LST, and withdrawal of LST. Descriptive and Kaplan-Meier curve analyses for survival were performed. RESULTS: Of 1,131 patients with HAP, 283 deceased patients with EOL decisions (105 cases of DNR, 108 cases of withholding of LST, and 70 cases of withdrawal of LST) were analyzed. The median age was 74 (IQR 63-81) years. The prevalence of solid malignant tumors was high (32.4% vs. 46.3% vs. 54.3%, P = 0.011), and the ICU admission rate was lower (42.9% vs. 35.2% vs. 24.3%, P = 0.042) in the withdrawal group. The prevalence of multidrug-resistant pathogens, impaired consciousness, and cough was significantly lower in the withdrawal group. Kaplan-Meier curve analysis revealed that 30-day and 60-day survival rates were higher in the withdrawal group than in the DNR and withholding groups (log-rank P = 0.021 and 0.018). The survival of the withdrawal group was markedly decreased after 40 days; thus, the withdrawal decision was made around this time. Among patients aged below 80 years, the rates of EOL decisions were not different (P = 0.430); however, mong patients aged over 80 years, the rate of withdrawal was significantly lower than that of DNR and withholding (P = 0.001). CONCLUSIONS: After the LST Decision Act was enforced in Korea, a DNR order was still common in EOL decisions. Baseline characteristics and outcomes were similar between the DNR and withholding groups; however, differences were observed in the withdrawal group. Withdrawal decisions seemed to be made at the late stage of dying. Therefore, advance care planning for patients with HAP is needed.

Application of error classification model using indices based on dose distribution for characteristics evaluation of multileaf collimator position errors.

This study aims to evaluate the specific characteristics of various multileaf collimator (MLC) position errors that are correlated with the indices using dose distribution. The dose distribution was investigated using the gamma, structural similarity, and dosiomics indices. Cases from the American Association of Physicists in Medicine Task Group 119 were planned, and systematic and random MLC position errors were simulated. The indices were obtained from distribution maps and statistically significant indices were selected. The final model was determined when all values of the area under the curve, accuracy, precision, sensitivity, and specificity were higher than 0.8 (p < 0.05). The dose-volume histogram (DVH) relative percentage difference between the error-free and error datasets was examined to investigate clinical relations. Seven multivariate predictive models were finalized. The common significant dosiomics indices (GLCM Energy and GLRLM_LRHGE) can characterize the MLC position error. In addition, the finalized logistic regression model for MLC position error prediction showed excellent performance with AUC > 0.9. Furthermore, the results of the DVH were related to dosiomics analysis in that it reflects the characteristics of the MLC position error. It was also shown that dosiomics analysis could provide important information on localized dose-distribution differences in addition to DVH information.

High urinary desmosine is associated with long-term mortality in patients with COPD.

COPD patients with high baseline urinary desmosines demonstrated significantly higher mortality than those with lower urinary desmosines. High urinary desmosine is independently associated with an increased risk of long-term mortality in COPD patients. https://bit.ly/4015xZ9.

Is hyper-selectivity a root of Asian American children's success?

Asian immigrants' children, even those from lower-backgrounds, tend to acquire higher levels of education than other ethnoracial groups, including White natives. Asian culture is often cited as a conventional explanation. The hyper-selectivity hypothesis challenges conventional wisdom by arguing that Asian American culture is an outcome of the community resources associated with hyper-selectivity. In this study, we assess the validity of the hyper-selectivity theory by examining the association between the magnitude of hyper-selectivity measured by the proportion of the BA + degree holders among the 1st generation Asian immigrants across communities and the likelihood of school enrollment for 1.5 and 2nd + generation Asian American children. Our results cast doubt on the hyper-selectivity theory. Asian American children's school enrollment is associated with the magnitude of educational selectivity among Asian immigrants for neither high school nor college. The benefits of hyper-selectivity do not seem to be cross-class or cross Asian ethnic groups. The higher the hyper-selectivity in a community is, the larger the education gap between upper- and lower-background Asian American children. The implications of these findings are discussed.