Real-world effectiveness of a single conventional disease-modifying anti-rheumatic drug (cDMARD) plus an anti-TNF agent versus multiple cDMARDs in rheumatoid arthritis: a prospective observational study.

Objective: The objective of this prospective, observational multicenter study (NCT03264703) was to compare the effectiveness of single conventional disease-modifying anti-rheumatic drug (cDMARD) plus anti-tumor necrosis factor (TNF) therapy versus multiple cDMARD treatments in patients with moderate-to-severe rheumatoid arthritis (RA) following cDMARD failure in the real-world setting in South Korea. Methods: At the treating physicians' discretion, patients received single cDMARD plus anti-TNF therapy or multiple cDMARDs. Changes from baseline in disease activity score 28-joint count with erythrocyte sedimentation rate (DAS28-ESR), corticosteroid use, and Korean Health Assessment Questionnaire (KHAQ-20) scores were evaluated at 3, 6, and 12 months. Results: Of 207 enrollees, the final analysis included 45 of 73 cDMARD plus anti-TNF and 91 of 134 multiple-cDMARD recipients. There were no significant between-group differences (BGDs) in ANCOVA-adjusted changes from baseline in DAS28-ESR at 3, 6 (primary endpoint), and 12 months (BGDs -0.18, -0.38, and -0.03, respectively). More cDMARD plus anti-TNF than multiple-cDMARD recipients achieved a >50% reduction from baseline in corticosteroid dosage at 12 months (35.7% vs 14.6%; p=0.007). Changes from baseline in KHAQ-20 scores at 3, 6, and 12 months were significantly better with cDMARD plus anti-TNF therapy than with multiple cDMARDs (BGD -0.18, -0.19, and -0.19 points, respectively; all p≤0.024). Conclusion: In the real-world setting, relative to multiple cDMARDs, single cDMARD plus anti-TNF therapy significantly improved quality-of-life scores and reduced corticosteroid use, with no significant BGD in disease activity, in RA patients in whom previous cDMARD therapy had failed.

Local Structures of Ex-Solved Nanoparticles Identified by Machine-Learned Potentials.

In this study, we identify the local structures of ex-solved nanoparticles using machine-learned potentials (MLPs). We develop a method for training machine-learned potentials by sampling local structures of heterointerface configurations as a training set with its efficacy tested on the Ni/MgO system, illustrating that the error in interface energy is only 0.004 eV/Å2. Using the developed scheme, we train an MLP for the Ni/La0.5Ca0.5TiO3 ex-solution system and identify the local structures for both exo- and endo-type particles. The established model aligns well with the experimental observations, accurately predicting a nucleation size of 0.45 nm. Lastly, the density functional theory calculations on the established atomistic model verify that the kinetic barrier for the dry reforming of methane are substantially reduced by 0.49 eV on the ex-solved catalysts compared to that on the impregnated catalysts. Our findings offer insights into the local structures, growth mechanisms, and underlying origin of the catalytic properties of ex-solved nanoparticles.

Assessment of radio-activation using spectroscopy in medical linear accelerators.

In radiotherapy, when photon energy exceeding 8 MV is utilized, photoneutrons can activate the components within the gantry of the linear accelerator (linac). At the end of the linac's lifecycle, radiation workers are tasked with its dismantling and disposal, potentially exposing them to unintentional radiation. This study aims to identify and measure the radioisotopes generated by this activation through spectroscopy, and to evaluate the effective dose rate. We selected nine medical linacs, considering various factors such as manufacturer (Siemens, Varian, and Elekta), model, energy, period of operation, and workload. We identified the radionuclides in the linac head by employing an in situ high-purity germanium (HPGe) detector. Spectroscopy and dose-rate measurements were conducted post-shutdown. We also measured the dose rates at the beam-exit window following irradiation with 10 MV and 15 MV photon beams. As a result of the spectroscopy, we identified approximately 20 nuclides including those with half-lives of 100 days or longer, such as 54Mn, 60Co, 65Zn, 122Sb, and 198Au. The dose rate measurements after 10 MV irradiation decreased to the background level in 10 min. By contrast, on 15 MV irradiation, the dose rate was 628 nSv/h after 10 min and decreased to 268 nSv/h after 1.5 hours. It was confirmed that the difference in the level of radiation and the type of nuclide depends on the period of use, energy, and workload. However, the type of nuclide does not differ significantly between the linacs. It is necessary to propose appropriate guidelines for the safety of workers, and disposal/move-install should be planned while taking into consideration the equipment's energy usage rate.

Qualitative evaluation of artificial intelligence-generated weight management diet plans.

Importance: The transformative potential of artificial intelligence (AI), particularly via large language models, is increasingly being manifested in healthcare. Dietary interventions are foundational to weight management efforts, but whether AI techniques are presently capable of generating clinically applicable diet plans has not been evaluated. Objective: Our study sought to evaluate the potential of personalized AI-generated weight-loss diet plans for clinical applications by employing a survey-based assessment conducted by experts in the fields of obesity medicine and clinical nutrition. Design setting and participants: We utilized ChatGPT (4.0) to create weight-loss diet plans and selected two control diet plans from tertiary medical centers for comparison. Dietitians, physicians, and nurse practitioners specializing in obesity medicine or nutrition were invited to provide feedback on the AI-generated plans. Each plan was assessed blindly based on its effectiveness, balanced-ness, comprehensiveness, flexibility, and applicability. Personalized plans for hypothetical patients with specific health conditions were also evaluated. Main outcomes and measures: The primary outcomes measured included the indistinguishability of the AI diet plan from human-created plans, and the potential of personalized AI-generated diet plans for real-world clinical applications. Results: Of 95 participants, 67 completed the survey and were included in the final analysis. No significant differences were found among the three weight-loss diet plans in any evaluation category. Among the 14 experts who believed that they could identify the AI plan, only five did so correctly. In an evaluation involving 57 experts, the AI-generated personalized weight-loss diet plan was assessed, with scores above neutral for all evaluation variables. Several limitations, of the AI-generated plans were highlighted, including conflicting dietary considerations, lack of affordability, and insufficient specificity in recommendations, such as exact portion sizes. These limitations suggest that refining inputs could enhance the quality and applicability of AI-generated diet plans. Conclusion: Despite certain limitations, our study highlights the potential of AI-generated diet plans for clinical applications. AI-generated dietary plans were frequently indistinguishable from diet plans widely used at major tertiary medical centers. Although further refinement and prospective studies are needed, these findings illustrate the potential of AI in advancing personalized weight-centric care.

Transcriptional Changes in Radiation-Induced Lung Injury: A Comparative Analysis of Two Radiation Doses for Preclinical Research.

In a recent stereotactic body radiation therapy animal model, radiation pneumonitis and radiation pulmonary fibrosis were observed at around 2 and 6 weeks, respectively. However, the molecular signature of this model remains unclear. This study aimed to examine the molecular characteristics at these two stages using RNA-seq analysis. Transcriptomic profiling revealed distinct transcriptional patterns for each stage. Inflammatory response and immune cell activation were involved in both stages. Cell cycle processes and response to type II interferons were observed during the inflammation stage. Extracellular matrix organization and immunoglobulin production were noted during the fibrosis stage. To investigate the impact of a 10 Gy difference on fibrosis progression, doses of 45, 55, and 65 Gy were tested. A dose of 65 Gy was selected and compared with 75 Gy. The 65 Gy dose induced inflammation and fibrosis as well as the 75 Gy dose, but with reduced lung damage, fewer inflammatory cells, and decreased collagen deposition, particularly during the inflammation stage. Transcriptomic analysis revealed significant overlap, but differences were observed and clarified in Gene Ontology and KEGG pathway analysis, potentially influenced by changes in interferon-gamma-mediated lipid metabolism. This suggests the suitability of 65 Gy for future preclinical basic and pharmaceutical research connected with radiation-induced lung injury.

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.

Optogenetic control of mRNA condensation reveals an intimate link between condensate material properties and functions.

Biomolecular condensates, often assembled through phase transition mechanisms, play key roles in organizing diverse cellular activities. The material properties of condensates, ranging from liquid droplets to solid-like glasses or gels, are key features impacting the way resident components associate with one another. However, it remains unclear whether and how different material properties would influence specific cellular functions of condensates. Here, we combine optogenetic control of phase separation with single-molecule mRNA imaging to study relations between phase behaviors and functional performance of condensates. Using light-activated condensation, we show that sequestering target mRNAs into condensates causes translation inhibition. Orthogonal mRNA imaging reveals highly transient nature of interactions between individual mRNAs and condensates. Tuning condensate composition and material property towards more solid-like states leads to stronger translational repression, concomitant with a decrease in molecular mobility. We further demonstrate that ß-actin mRNA sequestration in neurons suppresses spine enlargement during chemically induced long-term potentiation. Our work highlights how the material properties of condensates can modulate functions, a mechanism that may play a role in fine-tuning the output of condensate-driven cellular activities.

Comparative analysis of adverse drug reactions associated with new antiseizure medications from the Korea Adverse Event Reporting System database.

OBJECTIVE: This study aimed to compare and characterize the safety profiles of new antiseizure medications (ASMs) using a nationwide pharmacovigilance database from a long-term perspective in Korea. METHODS: We reviewed adverse event reports from the Korea Adverse Event Reporting System database between January 2013 and December 2022 for descriptive analysis of six new ASMs (lacosamide, levetiracetam, lamotrigine, oxcarbazepine, topiramate, and zonisamide). We investigated the frequency and characteristics of adverse drug reactions (ADRs) based on the MedDRA terminology, system organ classes, and modified WHO classification. RESULTS: We identified 5,733 reported cases of ADRs. The commonly reported ADRs associated with total ASMs were rash/urticaria (1,822, 31.8 %), dizziness (409, 7.1 %), somnolence/drowsiness (311, 5.4 %), and hepatotoxic effects (273, 4.8 %). Type B (idiosyncratic) effects (2,932; 51.1 %) were more commonly reported than Type A (related to known drug mechanisms) effects (2,613; 45.6 %). Skin and subcutaneous tissue disorders and type B effects were most commonly reported for lamotrigine and oxcarbazepine, whereas nervous system disorders and type A effects were most commonly reported for lacosamide, topiramate, and zonisamide. The pediatric group (<18 years) exhibited skin and subcutaneous tissue disorders and type B effects relatively more frequently than the adult and older adult groups. CONCLUSION: Hypersensitivity skin reactions and type B effects remained significant ADRs in the new ASMs; however, type A effects were more commonly reported in some ASMs. The pediatric group showed a higher rate of type B effects. Overall, new ASMs should also be used with caution.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders.

BACKGROUND: In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions. RESULTS: We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data. CONCLUSIONS: 3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

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.

Whole Process of Standardization of Diffusion-Weighted Imaging: Phantom Validation and Clinical Application According to the QIBA Profile.

Background: To use the apparent diffusion coefficient (ADC) as reliable biomarkers, validation of MRI equipment performance and clinical acquisition protocols should be performed prior to application in patients. This study aims to validate various MRI equipment and clinical brain protocols for diffusion weighted imaging (DWI) using commercial phantom, and confirm the validated protocols in patients' images. Methods: The performance of four different scanners and clinical brain protocols were validated using a Quantitative Imaging Biomarker Alliance (QIBA) diffusion phantom and cloud-based analysis tool. We evaluated the performance metrics regarding accuracy and repeatability of ADC measurement using QIBA profile. The validated clinical brain protocols were applied to 17 patients, and image quality and repeatability of ADC were assessed. Results: The MRI equipment performance of all four MRI scanners demonstrated high accuracy in ADC measurement (ADC bias, -2.3% to -0.4%), excellent linear correlation to the reference ADC value (slope, 0.9 to 1.0; R2, 0.999-1.000), and high short-term repeatability [within-subject-coefficient-of-variation (wCV), 0% to 0.3%]. The clinical protocols were also validated by fulfilling QIBA claims with high accuracy (ADC bias, -3.1% to -0.7%) and robust repeatability (wCV, 0% to 0.1%). Brain DWI acquired using the validated clinical protocols showed ideal image quality (mean score ≥ 2.9) and good repeatability (wCV, 1.8-2.2). Conclusions: The whole process of standardization of DWI demonstrated the robustness of ADC with high accuracy and repeatability across diverse MRI equipment and clinical protocols in accordance with the QIBA claims.

Brain-Decellularized ECM-Based 3D Myeloid Sarcoma Platform: Mimicking Adaptive Phenotypic Alterations in the Brain.

Leukemia circulates in the bloodstream and induces various symptoms and complications. Occasionally, these cells accumulate in non-marrow tissues, forming a tumor-like myeloid sarcoma (MS). When the blast-stage leukemia cells invade the brain parenchyma, intracranial MS occurs, leading to a challenging prognosis owing to the limited penetration of cytostatic drugs into the brain and the development of drug resistance. The scarcity of tissue samples from MS makes understanding the phenotypic changes occurring in leukemia cells within the brain environment challenging, thereby hindering development of effective treatment strategies for intracranial MS. This study presents a novel 3D in vitro model mimicking intracranial MS, employing a hydrogel scaffold derived from the brain-decellularized extracellular matrix in which suspended leukemia cells are embedded, simulating the formation of tumor masses in the brain parenchyma. This model reveals marked phenotypic changes in leukemia cells, including altered survival, proliferation, differentiation, and cell cycle regulation. Notably, proportion of dormant leukemia stem cells increases and expression of multidrug resistance genes is upregulated, leading to imatinib resistance, mirroring the pathological features of in vivo MS tissue. Furthermore, suppression of ferroptosis is identified as an important characteristic of intracranial MS, providing valuable insights for the development of targeted therapeutic strategies.

Harnessing Metabolic Indices as a Predictive Tool for Cardiovascular Disease in a Korean Population without Known Major Cardiovascular Event.

Background: This study evaluated the usefulness of indices for metabolic syndrome, non-alcoholic fatty liver disease (NAFLD), and insulin resistance (IR), as predictive tools for cardiovascular disease in middle-aged Korean adults. Methods: The prospective data obtained from the Ansan-Ansung cohort database, excluding patients with major adverse cardiac and cerebrovascular events (MACCE). The primary outcome was the incidence of MACCE during the follow-up period. Results: A total of 9,337 patients were included in the analysis, of whom 1,130 (12.1%) experienced MACCE during a median follow-up period of 15.5 years. The metabolic syndrome severity Z-score, metabolic syndrome severity score, hepatic steatosis index, and NAFLD liver fat score were found to significantly predict MACCE at values above the cut-off point and in the second and third tertiles. Among these indices, the hazard ratios of the metabolic syndrome severity score and metabolic syndrome severity Z-score were the highest after adjusting for confounding factors. The area under the receiver operating characteristic curve (AUC) of the 10-year atherosclerotic cardiovascular disease (ASCVD) score for predicting MACCE was 0.716, and the metabolic syndrome severity Z-score had an AUC of 0.619. Conclusion: The metabolic syndrome severity score is a highly reliable indicator and was closely associated with the 10-year ASCVD risk score in predicting MACCE in the general population. Given the specific characteristics and limitations of metabolic syndrome severity scores as well as the indices of NAFLD and IR, a more practical scoring system that considers these factors is essential to achieve greater accuracy in forecasting cardiovascular outcomes.

Research Note: Welfare and stress responses of broiler chickens raised in conventional and animal welfare-certified broiler farms.

The present study was conducted to assess the animal welfare status of broiler chickens raised in conventional and welfare-certified farms. One conventional farm (30,000 birds/house, 1,488 m2/house, 2 houses) and one animal welfare-certified farm (32,000 birds/house, 1,920 m2/house, 2 houses) were selected to measure productivity, stress responses, and animal welfare indicators in 3 broiler flocks (2 farms/season, n = 6 flocks/farm type) during summer, autumn, and spring. Upon farm visits, body weight, uniformity, and animal welfare indicators (i.e., fecal and feather corticosterone, footpad dermatitis, hock burn, feather dirtiness, and gait score) were measured at 26 d posthatch. Also, moisture, nitrogen, and pH of litter, light intensity, ammonia concentration, and body surface temperature of head, chest, and legs were measured. There was no difference in body weight and uniformity between farm types. Fecal corticosterone concentrations were higher (P = 0.021) in welfare-certified vs. conventional farm, but no significant difference was found in feather corticosterone. Welfare-certified vs. conventional farm had lower percentages of hock burn (P = 0.018), feather dirtiness scores (P = 0.009), and gait score (P = 0.040), and there was no difference in footpad dermatitis. Nitrogen content in litter samples tended to be higher in conventional vs. welfare-certified farms (P = 0.094), and there was no difference in moisture and pH between farm types. Ammonia concentration within the broiler houses was not different between 2 farms. However, animal welfare farm was found to be brighter than conventional farm (P < 0.001). The body surface temperature of head, chest, and legs was not different between farm types. In conclusion, the welfare-certified farm had higher welfare measures, including lower hock burn, feather dirtiness, and gait score, confirming an overall improvement in welfare indicators. However, the observation on the elevated feather corticosterone noted in welfare vs. conventionally raised chickens warrants further studies.

Identification of a complex intrachromosomal inverted insertion in the long arm of chromosome 9 as a cause of tuberous sclerosis complex in a Korean family.

BACKGROUND: Tuberous sclerosis complex (TSC) is an autosomal dominant multisystem disorder, caused by a loss-of-function of either TSC1 or TSC2 gene. However, in 10%-15% TSC patients there is no pathogenic variant identified in either TSC1 or TSC2 genes based on standard clinical testing. METHODS: In this study, genome sequencing was performed for families with clinical diagnosis of TSC with negative results from TSC1 and TSC2 single-gene tests. RESULTS: Herein, we report a family presenting a classical TSC phenotype with an unusual, complex structural variant involving the TSC1 gene: an intrachromosomal inverted insertion in the long arm of chromosome 9. We speculate that the inverted 9q33.3q34.13 region was inserted into the q31.2 region with the 3'-end of the breakpoint of the inversion being located within the TSC1 gene, resulting in premature termination of TSC1. CONCLUSIONS: In this study, we demonstrate the utility of genome sequencing for the identification of complex chromosomal rearrangement. Because the breakpoints are located within the deep intronic/intergenic region, this copy-neutral variant was missed by the TSC1 and TSC2 single-gene tests and contributed to an unknown etiology. Together, this finding suggests that complex structural variants may be underestimated causes for the etiology of TSC.

Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation.

Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is typically unavailable in the home environment. A key contribution of our work is to propose a deep learning-based assessment model trained only with video data. As all patients carry out identical tasks, a fine-grained assessment of task execution is required. Our model addresses this difficulty by learning RGB and optical flow data in a complementary manner. The correlation between the RGB and optical flow data is captured by a novel module for modality fusion using cross-attention with Transformers. Experiments showed that our model achieved higher accuracy in movement assessment than existing methods for action recognition. Based on the assessment model, we developed a patient-centered, solution-based mobile application for upper extremity exercises for hemiplegia, which can recommend 57 exercises with three levels of difficulty. A prototype of our application was evaluated by potential end-users and achieved a good quality score on the Mobile Application Rating Scale (MARS).

Ubiquitin ligase RNF20 coordinates sequential adipose thermogenesis with brown and beige fat-specific substrates.

In mammals, brown adipose tissue (BAT) and inguinal white adipose tissue (iWAT) execute sequential thermogenesis to maintain body temperature during cold stimuli. BAT rapidly generates heat through brown adipocyte activation, and further iWAT gradually stimulates beige fat cell differentiation upon prolonged cold challenges. However, fat depot-specific regulatory mechanisms for thermogenic activation of two fat depots are poorly understood. Here, we demonstrate that E3 ubiquitin ligase RNF20 orchestrates adipose thermogenesis with BAT- and iWAT-specific substrates. Upon cold stimuli, BAT RNF20 is rapidly downregulated, resulting in GABPα protein elevation by controlling protein stability, which stimulates thermogenic gene expression. Accordingly, BAT-specific Rnf20 suppression potentiates BAT thermogenic activity via GABPα upregulation. Moreover, upon prolonged cold stimuli, iWAT RNF20 is gradually upregulated to promote de novo beige adipogenesis. Mechanistically, iWAT RNF20 mediates NCoR1 protein degradation, rather than GABPα, to activate PPARγ. Together, current findings propose fat depot-specific regulatory mechanisms for temporal activation of adipose thermogenesis.

Effects of grain-based pecking blocks on productivity and welfare indicators in commercial broiler chickens.

OBJECTIVE: This experiment was conducted to investigate the effect of grain-based pecking blocks on productivity and welfare status at two commercial broiler welfare-certified farms. METHODS: Production and welfare indicators were assessed at two farms (designated Farm A and B). Both farms had two windowless houses with forced tunnel-type ventilation and housed broilers at stocking densities of approximately 16.7 birds/m2 (Farm A) and 16.8 birds/m2 (Farm B). Each house was divided into two or three equal sections and was provided with or without pecking blocks. Grain-based pecking blocks, measuring 25 × 25 × 25 cm, were given to broilers in both farms at 1 block per 1,000 birds. Various parameters including productivity (body weight and flock uniformity), corticosterone levels (in fecal droppings and feathers), footpad dermatitis, hock burn, feather dirtiness, gait score, litter quality, body surface temperature, and volatile fatty acids in fecal samples were assessed at 26 days of age, whereas litter quality was analyzed at 13 and 26 days of age. RESULTS: There were no significant effects of providing pecking blocks on productivity (body weight and uniformity), fecal and feather corticosterone, welfare indicators (i.e., footpad dermatitis, hock burn, feather cleanliness, and gait score), and litter quality (i.e., moisture, nitrogen, and pH). No differences in body surface temperature between the control and enrichment treatments were noted in Farm B, but body surface temperatures of the head (p = 0.029) and legs (p = 0.011) in the enrichment vs. control group were elevated in Farm A. Butyrate concentration in the enrichment vs control group was higher in Farm B (p = 0.023), but this effect was not detected in Farm A. CONCLUSION: It is concluded that grain-based pecking blocks did not affect performance and welfare indicators. Further studies are warranted to elucidate the potential impact of grain-based pecking blocks on gut health indicators.

Identification of acute myocardial infarction and stroke events using the National Health Insurance Service database in Korea.

OBJECTIVES: The escalating burden of cardiovascular disease (CVD) is a critical public health issue worldwide. CVD, especially acute myocardial infarction (AMI) and stroke, is the leading contributor to morbidity and mortality in Korea. We aimed to develop algorithms for identifying AMI and stroke events from the National Health Insurance Service (NHIS) database and validate these algorithms through medical record review. METHODS: We first established a concept and definition of "hospitalization episode," taking into account the unique features of health claims-based NHIS database. We then developed first and recurrent event identification algorithms, separately for AMI and stroke, to determine whether each hospitalization episode represents a true incident case of AMI or stroke. Finally, we assessed our algorithms' accuracy by calculating their positive predictive values (PPVs) based on medical records of algorithm- identified events. RESULTS: We developed identification algorithms for both AMI and stroke. To validate them, we conducted retrospective review of medical records for 3,140 algorithm-identified events (1,399 AMI and 1,741 stroke events) across 24 hospitals throughout Korea. The overall PPVs for the first and recurrent AMI events were around 92% and 78%, respectively, while those for the first and recurrent stroke events were around 88% and 81%, respectively. CONCLUSIONS: We successfully developed algorithms for identifying AMI and stroke events. The algorithms demonstrated high accuracy, with PPVs of approximately 90% for first events and 80% for recurrent events. These findings indicate that our algorithms hold promise as an instrumental tool for the consistent and reliable production of national CVD statistics in Korea.