Comparative estimation of the effects of antihypertensive medications on schizophrenia occurrence: a multinational observational cohort study.

BACKGROUND: The association between antihypertensive medication and schizophrenia has received increasing attention; however, evidence of the impact of antihypertensive medication on subsequent schizophrenia based on large-scale observational studies is limited. We aimed to compare the schizophrenia risk in large claims-based US and Korea cohort of patients with hypertension using angiotensin-converting enzyme (ACE) inhibitors versus those using angiotensin receptor blockers (ARBs) or thiazide diuretics. METHODS: Adults aged 18 years who were newly diagnosed with hypertension and received ACE inhibitors, ARBs, or thiazide diuretics as first-line antihypertensive medications were included. The study population was sub-grouped based on age (> 45 years). The comparison groups were matched using a large-scale propensity score (PS)-matching algorithm. The primary endpoint was incidence of schizophrenia. RESULTS: 5,907,522; 2,923,423; and 1,971,549 patients used ACE inhibitors, ARBs, and thiazide diuretics, respectively. After PS matching, the risk of schizophrenia was not significantly different among the groups (ACE inhibitor vs. ARB: summary hazard ratio [HR] 1.15 [95% confidence interval, CI, 0.99-1.33]; ACE inhibitor vs. thiazide diuretics: summary HR 0.91 [95% CI, 0.78-1.07]). In the older subgroup, there was no significant difference between ACE inhibitors and thiazide diuretics (summary HR, 0.91 [95% CI, 0.71-1.16]). The risk for schizophrenia was significantly higher in the ACE inhibitor group than in the ARB group (summary HR, 1.23 [95% CI, 1.05-1.43]). CONCLUSIONS: The risk of schizophrenia was not significantly different between the ACE inhibitor vs. ARB and ACE inhibitor vs. thiazide diuretic groups. Further investigations are needed to determine the risk of schizophrenia associated with antihypertensive drugs, especially in people aged > 45 years.

Long-term clinical outcomes of aspirin-exacerbated respiratory disease: Real-world data from an adult asthma cohort.

BACKGROUND: Aspirin-exacerbated respiratory disease (AERD) is a phenotype of severe asthma, but its disease course has not been well documented compared with that of aspirin-tolerant asthma (ATA). OBJECTIVES: This study aimed to investigate the long-term clinical outcomes between AERD and ATA. METHODS: AERD patients were identified by the diagnostic code and positive bronchoprovocation test in a real-world database. Longitudinal changes in lung function, blood eosinophil/neutrophil counts, and annual numbers of severe asthma exacerbations (AEx) were compared between the AERD and the ATA groups. Within a year after baseline, two or more severe AEx events indicated severe AERD, whereas less than two AEx events indicated nonsevere AERD. RESULTS: Among asthmatics, 353 had AERD in which 166 and 187 patients had severe and nonsevere AERD, respectively, and 717 had ATA. AERD patients had significantly lower FEV1%, higher blood neutrophil counts, and higher sputum eosinophils (%) (all p < .05) as well as higher levels of urinary LTE4 and serum periostin, and lower levels of serum myeloperoxidase and surfactant protein D (all p < .01) than those with ATA. In a 10-year follow-up, the severe AERD group maintained lower FEV1% with more severe AEs than the nonsevere AERD group. CONCLUSION AND CLINICAL RELEVANCE: We demonstrated that AERD patients presented poorer long-term clinical outcomes than ATA patients in real-world data analyses.

Safety outcomes of selective serotonin reuptake inhibitors in adolescent attention-deficit/hyperactivity disorder with comorbid depression: the ASSURE study.

BACKGROUND: Attention deficit-hyperactivity disorder (ADHD) is related to depressive disorder, and adolescents with both present poor outcomes. However, evidence for the safety of concomitantly using a methylphenidate (MPH) and a selective serotonin reuptake inhibitor (SSRI) among adolescent ADHD patients is limited, a literature gap aimed to address through this investigation. METHODS: We conducted a new-user cohort study using a nationwide claims database in South Korea. We identified a study population as adolescents who were diagnosed both ADHD and depressive disorder. MPH-only users were compared with patients who prescribed both a SSRI and a MPH. Fluoxetine and escitalopram users were also compared to find a preferable treatment option. Thirteen outcomes including neuropsychiatric, gastrointestinal, and other events were assessed, taking respiratory tract infection as a negative control outcome. We matched the study groups using a propensity score and used the Cox proportional hazard model to calculate the hazard ratio. Subgroup and sensitivity analyses were conducted in various epidemiologic settings. RESULTS: The risks of all the outcomes between the MPH-only and SSRI groups were not significantly different. Regarding SSRI ingredients, the risk of tic disorder was significantly lower in the fluoxetine group than the escitalopram group [HR 0.43 (0.25-0.71)]. However, there was no significant difference in other outcomes between the fluoxetine and escitalopram groups. CONCLUSION: The concomitant use of MPHs and SSRIs showed generally safe profiles in adolescent ADHD patients with depression. Most of the differences between fluoxetine and escitalopram, except those concerning tic disorder, were not significant.

Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study.

BACKGROUND: Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing. OBJECTIVE: This study aims to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model. METHODS: This retrospective study enrolled patients diagnosed with the first depressive episode at 5 tertiary hospitals in South Korea. We developed models for predicting bipolar transition by using data from 17,631 patients in 4 institutions. Further, we used data from 4541 patients for external validation from 1 institution. We created standardized pipelines to extract large-scale clinical features from the 4 institutions without any code modification. Moreover, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. Finally, we compared the federated and the 4 local models developed with each hospital's data on internal and external validation data sets. RESULTS: In the internal data set, 279 out of 17,631 patients showed bipolar disorder transition. In the external data set, 39 out of 4541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC] 0.726) and external validation (AUC 0.719) data sets was higher than that of the other locally developed models (AUC 0.642-0.707 and AUC 0.642-0.699, respectively). In the federated model, classifications were driven by several predictors such as the Charlson index (low scores were associated with bipolar transition, which may be due to younger age), severe depression, anxiolytics, young age, and visiting months (the bipolar transition was associated with seasonality, especially during the spring and summer months). CONCLUSIONS: We developed and validated a differentially private federated model by using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only.

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Focused-helium-ion-beam blow forming of nanostructures: radiation damage and nanofabrication.

Targeted irradiation of nanostructures by a finely focused ion beam provides routes to improved control of material modification and understanding of the physics of interactions between ion beams and nanomaterials. Here, we studied radiation damage in crystalline diamond and silicon nanostructures using a focused helium ion beam, with the former exhibiting extremely long-range ion propagation and large plastic deformation in a process visibly analogous to blow forming. We report the dependence of damage morphology on material, geometry, and irradiation conditions (ion dose, ion energy, ion species, and location). We anticipate that our method and findings will not only improve the understanding of radiation damage in isolated nanostructures, but will also support the design of new engineering materials and devices for current and future applications in nanotechnology.

Shape Memory Alloy (SMA)-Based Microscale Actuators with 60% Deformation Rate and 1.6 kHz Actuation Speed.

Shape memory alloys (SMAs) are widely utilized as an actuation source in microscale devices, since they have a simple actuation mechanism and high-power density. However, they have limitations in terms of strain range and actuation speed. High-speed microscale SMA actuators are developed having diamond-shaped frame structures with a diameter of 25 µm. These structures allow for a large elongation range compared with bulk SMA materials, with the aid of spring-like behavior under tensile deformation. These actuators are validated in terms of their applicability as an artificial muscle in microscale by investigating their behavior under mechanical deformation and changes in thermal conditions. The shape memory effect is triggered by delivering thermal energy with a laser. The fast heating and cooling phenomenon caused by the scale effect allows high-speed actuation up to 1600 Hz. It is expected that the proposed actuators will contribute to the development of soft robots and biomedical devices.

Superconductor-superconductor bilayers for enhancing single-photon detection.

Here, we optimized ultrathin films of granular NbN on SiO2 and of amorphous αW5Si3. We showed that hybrid superconducting nanowire single-photon detectors (SNSPDs) made of 2 nm thick αW5Si3 films over 2 nm thick NbN films exhibit advantageous coexistence of timing (<5 ns reset time and 52 ps timing jitter) and efficiency (>96% quantum efficiency) performance. We discuss the governing mechanism of this hybridization via the proximity effect. Our results demonstrate saturated SNSPDs performance at 1550 nm optical wavelength and suggest that such hybridization can significantly expand the range of available superconducting properties, impacting other nano-superconducting technologies. Lastly, this hybridization may be used to tune properties, such as the amorphous character of superconducting films.

The therapeutic decision making of the unilateral vocal cord palsy after thyroidectomy using thyroidectomy-related voice questionnaire (TVQ).

The objective of this study was to investigate the efficacy of early management of post-thyroidectomy unilateral vocal cord palsy (UVCP) and the clinical utility of the thyroidectomy-related voice questionnaire (TVQ) when planning UVCP treatment. The study group comprised 48 consecutive patients diagnosed with UVCP after thyroidectomy. Laryngoscopic examination and voice analysis were conducted, and the TVQ was administered pre-thyroidectomy and at 2 weeks and 1, 3, 6, and 12 months post-thyroidectomy. Twenty-five patients with aspiration symptoms and severe vocal difficulties received injection laryngoplasty, and 23 with no aspiration symptoms and relatively mild vocal difficulties underwent voice therapy. We performed a video fluoroscopic swallowing study on each patient 2 weeks after thyroidectomy and 1 month following the procedure. The average total TVQ scores 2 weeks post-thyroidectomy were 51.92 ± 11.42 in the injection laryngoplasty group and 35.78 ± 12.99 in the voice therapy group. Both subjective and objective parameters improved significantly at 1 month after treatment and continued to improve slowly over the next 12 months (p < 0.01) in both groups. TVQ scores were significantly lower in the injection laryngoplasty group than in the voice therapy group 1 month post-intervention (p < 0.01). At the study end point, the greatest improvement in subjective symptoms occurred in temporary VCP patients who underwent injection laryngoplasty. The optimal TVQ score cut-off distinguishing the two groups was 45 (68.0 % sensitivity, 78.3 % specificity). In conclusion, early management following timely diagnosis of post-thyroidectomy UVCP can improve symptoms within 1 month. Moreover, application of TVQ will aid clinicians to plan treatment for postoperative VCP patients.

Prognostic value of preoperative 18F-FDG PET/CT for primary head and neck squamous cell carcinoma.

(18)F-FDG PET/CT is clinically useful in the initial staging and follow-up of patients with head and neck squamous cell carcinoma (HNSCC). We studied the potential prognostic significance of preoperative (18)F-FDG PET/CT in HNSCC. The medical records of 294 patients who underwent preoperative (18)F-FDG PET/CT for HNSCC were retrospectively reviewed. The median SUVmax of the primary lesions (SUVmax-p) and cervical lymph nodes (SUVmax-n) was 7.98 ± 5.04 (range 1.2-28.7) and 3.34 ± 3.70 (range 1.0-20.4), respectively. There was a significant difference between with and without recurrence in SUVmax-p (11.14 ± 5.36 vs. 6.78 ± 4.35, p < 0.001) and SUVmax-n (5.60 ± 4.22 vs. 1.75 ± 1.46, p < 0.001). The cut-off values of SUVmax-p and SUVmax-n in the context of recurrence and cancer-related death were 8.5 and 3.5. The 5-year disease-free survival of patients with SUVmax-p < 8.5 and SUVmax-n < 3.5 was 79 and 79%, respectively, whereas that of patients with SUVmax-p ≥ 8.5 and SUVmax-n ≥ 3.5 was 39 and 30 %, respectively. Multivariate analysis confirmed the significant association between 5-year disease-free survival and SUVmax-p ≥ 8.5 (hazard ratio (HR) 2.68, p < 0.001) and SUVmax-n ≥ 3.5 (HR 2.29, p = 0.007). Furthermore, SUVmax-p ≥ 8.5 (HR 3.20, p = 0.012) and SUVmax-n ≥ 3.5 (HR 2.14, p < 0.001) were associated with 5-year overall survival. (18)F-FDG PET/CT cut-off values of SUVmax-p ≥ 8.5 or SUVmax-n ≥ 3.5 are associated with a recurrence and survival in HNSCC.

Age-related changes of the larynx and trachea assessed by three-dimensional computed tomography in children: Application to endotracheal intubation and bronchoscopy.

The purpose of this study was to establish normative data of laryngotracheal dimensions and shape and to evaluate differences associated with age and sex using three-dimensional (3D) imaging. A total of 120 patients (64 boys and 56 girls) were included. Subjects were divided into four groups: Group 1 (0-2 years), Group 2 (3-7 years), Group 3 (8-13 years), and Group 4 (14-20 years). Using 3D image processing software, the laryngeal volume (LV), tracheal volume (TV), anteroposterior diameter at the glottis, cross-sectional area (CSA) at the subglottis, and laryngeal angle (LA) of the thyroid laminae were measured. Parameters of laryngotracheal volume and size were positively correlated with age, whereas the LA was inversely correlated with age. The LV, TV, CSA-3 mm, and CSA-5 mm exhibited a growth spurt in Groups 2 and 3. The LA decreased at a faster rate in Group 1 (P = 0.012). In Groups 1 and 2, there were no differences between genders for each laryngotracheal segmentation or plane. However, gender differences in the TV of Group 3 were statistically significant (P = 0.030). In Group 4, gender differences of all airway parameters were evident. Volume and other dimensions of the laryngotracheal airway increase with age. There was a significant increase in the LV, TV, CSA-3 mm, and CSA-5 mm in Groups 2 and 3. The LA correlated negatively with age in the Group 1. Significant sex dimorphisms are evident in Group 4.

A De-Identification Model for Korean Clinical Notes: Using Deep Learning Models.

To extract information from free-text in clinical records due to the patient's protected health information PHI in the records pre-processing of de-identification is required. Therefore we aimed to identify PHI list and fine-tune the deep learning BERT model for developing de-identification model. The result of fine-tuning the model is strict F1 score of 0.924. Due to the convinced score the model can be used for the development of a de-identification model.

Prediction Models for Readmission Using Home Healthcare Notes and OMOP-CDM.

This study developed readmission prediction models using Home Healthcare (HHC) documents via natural language processing (NLP). An electronic health record of Ajou University Hospital was used to develop prediction models (A reference model using only structured data, and an NLP-enriched model with structured and unstructured data). Among 573 patients, 63 were readmitted to the hospital. Five topics were extracted from HHC documents and improved the model performance (AUROC 0.740).

Safety outcomes of antipsychotics classes in drug-naïve patients with first-episode schizophrenia: A nationwide cohort study in South Korea.

INTRODUCTION: Given the similar efficacies across antipsychotic medications for schizophrenia, understanding their safety profiles, particularly concerning receptor-binding differences, is crucial for optimal drug selection, especially for patients with first episode schizophrenia. We aimed to compare the safety outcomes of second-generation antipsychotics. METHODS: We conducted a retrospective cohort study with new user active comparator design using a nationwide claims database in South Korea. Participants were drug-naïve adult patients with first-episode schizophrenia. Three representative drugs with different pharmacologic profiles were compared: risperidone, olanzapine, and aripiprazole. Propensity scores were used to match the study groups, and the Cox proportional hazard model was used to calculate hazard ratios. Sensitivity analyses were performed in various epidemiological settings. Seventeen safety outcomes, including neuropsychiatric, cardiometabolic and gastrointestinal events, were assessed, with upper-respiratory-tract infection as a negative control outcome. RESULTS: A total of 1044, 2078, and 3634 participants were matched for olanzapine vs. risperidone, olanzapine vs. aripiprazole, and risperidone vs. aripiprazole comparisons, respectively. For parkinsonism, there was a significant difference in outcomes between the risperidone and aripiprazole groups (HR 1.80 [95% CI 1.13-2.91]), with consistent sensitivity analysis results. There were no significant differences in other neuropsychiatry outcomes or in the risk of cardiometabolic and gastrointestinal outcomes between any of the comparative group pairs. CONCLUSIONS: The risk of drug-induced parkinsonism was significantly higher with risperidone than with aripiprazole. Although olanzapine is known for its metabolic risk, there were no significant differences in risk between the other pairs.

Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study.

BACKGROUND: Hospital readmission is an important indicator of inpatient care quality and a significant driver of increasing medical costs. Therefore, it is important to explore the effects of postdischarge information, particularly from home healthcare notes, on enhancing readmission prediction models. Despite the use of Natural Language Processing (NLP) and machine learning in prediction model development, current studies often overlook insights from home healthcare notes. OBJECTIVE: This study aimed to develop prediction models for 30-day readmissions using home healthcare notes and structured data. In addition, it explored the development of 14- and 180-day prediction models using variables in the 30-day model. DESIGN: A retrospective observational cohort study. SETTING(S): This study was conducted at Ajou University School of Medicine in South Korea. PARTICIPANTS: Data from electronic health records, encompassing demographic characteristics of 1819 participants, along with information on conditions, drug, and home healthcare, were utilized. METHODS: Two distinct models were developed for each prediction window (30-, 14-, 180-day): the traditional model, which utilized structured variables alone, and the common data model (CDM)-NLP model, which incorporated structured and topic variables extracted from home healthcare notes. BERTopic facilitated topic generation and risk probability, representing the likelihood of documents being assigned to specific topics. Feature selection involved experimenting with various algorithms. The best-performing algorithm, determined using the area under the receiver operating characteristic curve (AUROC), was used for model development. Model performance was assessed using various learning metrics including AUROC. RESULTS: Among 1819 patients, 251 (13.80 %) experienced 30-day readmission. The least absolute shrinkage and selection operator was used for feature extraction and model development. The 15 structured features were used in the traditional model. Moreover, five additional topic variables from the home healthcare notes were applied in the CDM-NLP model. The AUROC of the traditional model was 0.739 (95 % CI: 0.672-0.807). The AUROC of the CDM-NLP model was high at 0.824 (95 % CI: 0.768-0.880), which indicated an outstanding performance. The topics in the CDM-NLP model included emotional distress, daily living functions, nutrition, postoperative status, and cardiorespiratory issues. In extended prediction model development for 14- and 180-day readmissions, the CDM-NLP consistently outperformed the traditional model. CONCLUSIONS: This study developed effective prediction models using both structured and unstructured data, thereby emphasizing the significance of postdischarge information from home healthcare notes in readmission prediction.

RHEA: Real-World Observational Health Data Exploration Application.

We developed a standardized framework named RHEA to represent longitudinal status of patient with cancer. RHEA generates a dashboard to visualize patients' data in the Observational Medical Outcomes Partnership-Common Data Model format. The generated dashboard consists of three main parts for providing the macroscopic characteristics of the patient: 1) cohort-level visualization, 2) individual-level visualization and 3) cohort generation.

Comparative Effectiveness and Safety Outcomes Between Denosumab and Bisphosphonate in South Korea.

Both bisphosphonates and denosumab are the mainstays of treatment for osteoporosis to prevent fractures. However, there are still few trials directly comparing the prevention of fractures and the safety of two drugs in the treatment of osteoporosis. We aimed to compare the efficacy and safety between denosumab and bisphosphonates using a nationwide claims database. The database was covered with ten million, 20% of the whole Korean population sampled by age and sex stratification of the Health Insurance Review and Assessment Service in South Korea. Among 228,367 subjects who were over 50 years of age and taking denosumab or bisphosphonate from Jan 2018 to April 2022, the analysis was performed on 91,460 subjects after 1: 1 propensity score matching. The primary outcome was treatment effectiveness; total fracture, major osteoporotic fracture, femur fracture, pelvic fracture, vertebral fracture, adverse drug reactions; acute kidney injury, chronic kidney disease, and atypical femoral fracture. Total fracture and osteoporotic major fracture, as the main outcomes of efficacy, were comparable in the denosumab and bisphosphonate group (HR 1.06, 95% CI 0.98-1.15, p=0.14; HR 1.13, 95% CI 0.97-1.32, p=0.12, respectively). Safety for acute kidney injury, chronic kidney disease, and atypical femoral fracture also did not show any differences between the two groups. In subgroup analysis according to ages, the denosumab group under 70 years of age had a significantly lower risk for occurrences of acute kidney injury compared to the bisphosphonate group under 70 years of age (HR 0.53, 95% CI 0.29-0.93, p=0.03). In real-world data reflecting clinical practice, denosumab, and bisphosphonate showed comparable effectiveness for total fracture and osteoporosis major fracture and safety for acute kidney injury, chronic kidney disease, and atypical femoral fracture.

This study compared the effectiveness and safety of denosumab and bisphosphonates, two primary treatments for osteoporosis, using a large South Korean nationwide claims database. Analysis of data from 91,460 individuals over 50 years old showed no significant difference in preventing fractures or in safety outcomes such as kidney injury and atypical femoral fractures between the two drugs. However, among patients under 70, denosumab was associated with a lower risk of acute kidney injury. Overall, both medications demonstrated similar effectiveness and safety in the real-world treatment of osteoporosis.

Fabrication of CP-Ti structure with controllable wettability using powder bed fusion and eco-friendly post-process.

Hydrophobic surfaces have a wide range of applications, such as water harvesting, self-cleaning, and anti-biofouling. However, traditional methods of achieving hydrophobicity often involve the use of toxic materials such as fluoropolymers. This study aims to create controllable wettability surfaces with a three-dimensional geometry using a laser base powder bed fusion (PBF) process with commercially pure titanium (CP-Ti) and silicone oil as non-toxic materials. The optimal PBF process parameters for fabricating micropillar structures, which are critical for obtaining the surface roughness necessary for achieving hydrophobic properties, were investigated experimentally. After fabricating the micropillar structures using PBF, their surface energy was reduced by treatment with silicone oil. Silicone oil provides a low-surface-energy coating that contributes to the water-repellent nature of hydrophobic surfaces. The wettability of the treated CP-Ti surfaces was evaluated based on the diameter of the pillars and the space between them. The structure with the optimal diameter and spacing of micropillars exhibited a high contact angle (156.15°). A pronounced petal effect (sliding angle of 25.9°) was achieved because of the morphology of the pillars, indicating the controllability of wetting. The micropillar diameter, spacing, and silicone oil played crucial roles in determining the water contact and sliding angle, which are key metrics for surface wettability.

Real-World Effectiveness of Statin Therapy in Adult Asthma.

BACKGROUND: Blood lipids affect airway inflammation in asthma. Although several studies have suggested anti-inflammatory effects of statins on asthmatic airways, further studies are needed to clarify the long-term effectiveness of statins on asthma control and whether they are an effective treatment option. OBJECTIVE: To evaluate the long-term effectiveness of statins in the chronic management of adult asthma in real-world practice. METHODS: Electronic medical record data spanning 28 years, collected from the Ajou University Medical Center in Korea, were used to conduct a retrospective study. Clinical outcomes were compared between patients with asthma who had maintained statin use (the statin group) and those not taking statins, whose blood lipid tests were always normal (the non-statin group). We performed propensity score matching and calculated hazard ratios with 95% CIs using the Cox proportional hazards model. Severe asthma exacerbation was the primary outcome; asthma exacerbation, asthma-related hospitalization, and new-onset type 2 diabetes mellitus and hypertension were secondary outcomes. RESULTS: After 1:1 propensity score matching, the statin and non-statin groups each included 545 adult patients with asthma. The risk of severe asthma exacerbations and asthma exacerbations was significantly lower in the statin group than in the non-statin group (hazard ratios [95% CI] = 0.57 [0.35-0.90] and 0.71 [0.52-0.96], respectively). There were no significant differences in the risk of asthma-related hospitalization or new-onset type 2 diabetes mellitus or hypertension between groups (0.76 [0.53-1.09], 2.33 [0.94-6.59], and 1.71 [0.95-3.17], respectively). CONCLUSION: Statin use is associated with a lower risk of asthma exacerbation, with better clinical outcomes in adult asthma.