Validity of the Chinese multimorbidity-weighted index in measuring disease burden using health check-ups data in primary care.

BACKGROUND: As multimorbidity becomes common that imposes a considerable burden to patients, but the extent to which widely-used multimorbidity indexes can be applied to quantify disease burden using primary care data in China is not clear. We applied the Chinese Multimorbidity-Weighted Index (CMWI) to health check-ups data routinely collected among older adults by primary care, to examine its validity in measuring multimorbidity associated risks of disability and mortality in annual follow-ups. METHODS: The study utilized data from annual health check-ups of older adults, which included information on individual age, sex, and 14 health conditions at primary care in a district of Guangzhou, Guangdong, China. The risk of CMWI for mortality was analysed in a total sample of 45,009 persons 65 years and older between 2014 and 2020 (average 2.70-year follow-up), and the risk for disability was in a subsample of 18,320 older adults free of physical impairment in 2019 and followed-up in 2020. Risk of death and disability were assessed with Cox proportional hazard regression and binary logistic regression, respectively, with both models adjusted for age and sex variables. The model fit was assessed by the Akaike information criterion (AIC), and C-statistic or the area under the receiver operating characteristic curve (AUC). RESULTS: One unit increase in baseline-CMWI (Median= 1.70, IQR: 1.30-3.00) was associated with higher risk in subsequent disability (OR = 1.12, 95%CI = 1.05,1.20) and mortality (OR = 1.18, 95%CI = 1.14, 1.22). Participants in the top tertile of CMWI had 99% and 152% increased risks of disability and mortality than their counterparts in the bottom tertile. Model fit was satisfied with adequate AUC (0.84) or C-statistic (0.76) for both outcomes. CONCLUSIONS: CMWI, calculated based on primary care's routine health check-ups data, provides valid estimates of disability and mortality risks in older adults. This validated tool can be used to quantity and monitor older patients' health risks in primary care.

Altered Brain Glymphatic Function at Diffusion-Tensor MRI in Pre-cirrhotic Metabolic Dysfunction-Associated Fatty Liver Disease.

RATIONALE AND OBJECTIVES: To evaluate glymphatic function changes and their relationships with clinical features in patients with metabolic dysfunction-associated fatty liver disease (MAFLD), thereby facilitating early intervention before this disease progresses to cirrhosis. MATERIALS AND METHODS: A cross-sectional cohort of 46 pre-cirrhotic MAFLD patients and 30 age-, sex-, and education-matched controls was enrolled, with diffusion-tensor imaging (DTI) data, laboratory and neurocognitive scores collected. The DTI analysis along the perivascular space (DTI-ALPS) index was computed for qualifying glymphatic function. Generalized linear model and partial correlation analyses were applied to evaluate relationships between the ALPS index and clinical variables. RESULTS: MAFLD group exhibited a decreased ALPS index and increased diffusivity along the y-axis in the projection fiber compared to the controls. The altered ALPS index was associated with clock drawing test (CDT) score (3.931 [0.914, 6.947], P = 0.011) and was correlated with diastolic pressure level (r = -0.315, P = 0.033) in MAFLD group. The relationships of ALPS index with CDT score (6.263 [2.069, 10.458], P = 0.003) and diastolic pressure level (r = -0.518, P = 0.014) remained in the MAFLD with metabolic syndrome (MetS) group. Furthermore, the ALPS index was even associated with Auditory Verbal Learning Test-Immediate recall score (-23.853 [-45.417, -2.289], P = 0.030) in MAFLD with MetS group. CONCLUSION: MAFLD patients may have a glymphatic dysfunction prior to cirrhosis, and this alteration may be related to cognition and diastolic pressure. Glymphatic dysfunction has a more severe impact on cognition when MAFLD patient is accompanied by MetS.

Preclinical evaluation of [18F]FP-CIT, the radiotracer targeting dopamine transporter for diagnosing Parkinson's disease: pharmacokinetic and efficacy analysis.

BACKGROUND: N-(3-fluoropropyl)-2ß-carboxymethoxy-3ß-(4-iodophenyl) nortropane (FP-CIT), the representative cocaine derivative used in dopamine transporter imaging, is a promising biomarker, as it reflects the severity of Parkinson's disease (PD). 123I- and 18F-labeled FP-CIT has been used for PD diagnosis. However, preclinical studies evaluating [18F]FP-CIT as a potential diagnostic biomarker are scarce. Among translational research advancements from bench to bedside, translating preclinical findings into clinical practice is one-directional. The aim of this study is to employ a circular approach, beginning back from the preclinical stage, progressing to the supplementation of [18F]FP-CIT, and subsequently returning to clinical application. We investigated the pharmacokinetic properties of [18F]FP-CIT and its efficacy for PD diagnosis using murine models. RESULTS: Biodistribution, metabolite and excretion analyses were performed in mice and PD models were induced in rats using 6-hydroxydopamine (6-OHDA). The targeting efficiency of [18F]FP-CIT for the dopamine receptor was assessed through animal PET/CT imaging. Subsequently, correlation analysis was conducted between animal PET/CT imaging results and immunohistochemistry (IHC) targeting tyrosine hydroxylase. Rapid circulation was confirmed after [18F]FP-CIT injection. [18F]FP-CIT reached the highest uptake of 23.50 ± 12.46%ID/g in the striatum 1 min after injection, and it was rapidly excreted within 60 min. The major metabolic organs of [18F]FP-CIT were confirmed to be the intestines, liver, and kidneys. Its uptake in the intestine was approximately 5% ID/g. The uptake in the liver gradually increased, with excretion beginning after reaching a maximum after 60 min. The kidneys exhibited rapid elimination after 10 min. In the excretion study, rapid elimination was verified, with 21.46 ± 9.53% of the compound excreted within a 6 h period. Additionally, the efficacy of [18F]FP-CIT PET was demonstrated in the PD model, with a high correlation with IHC for both the absolute value (R = 0.803, p = 0.0017) and the ratio value (R = 0.973, p = 0.0011). CONCLUSIONS: This study fills the gap regarding insufficient preclinical studies on [18F]FP-CIT, including its ADME, metabolites, and efficiency. The pharmacological results, including accurate diagnosis, rapid circulation, and [18F]FP-CIT excretion, provide complementary evidence that [18F]FP-CIT can be used safely and efficiently to diagnose PD in clinics, although it is already used in clinics.

Prediction Model for Chronological Weight Loss After Bariatric Surgery in Korean Patients.

Purpose: This study aimed to develop a predictive model for monitoring chronological weight loss during the early postoperative period following bariatric surgery in Korean patients with morbid obesity. Materials and Methods: The baseline characteristics and postoperative weight loss outcomes were collected for up to 24 months after surgery in patients who underwent sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB). The factors influencing weight loss outcomes were analyzed, and longitudinal percentile charts were plotted using quantile regression models adjusted for the identified independent factors. Results: The analysis included 491 and 274 patients who underwent SG and RYGB, respectively, of whom 225 (29.4%) were men. A positive association was found between the maximum percentage of total weight loss (%TWL) and female sex, body mass index (BMI) ≥40, and age <40 years. Among patients who reached nadir BMI or had at least 12 months of follow-up data (n=304), 7.6% exhibited inadequate weight loss (TWL <20%). The predictors of insufficient weight loss were older age (>40 years), male sex, and psychological problems. Centile charts were generated for the entire cohort, incorporating age, sex, and the type of procedure as covariates. Conclusion: The percentile charts proposed in the present study can assist surgeons and healthcare providers in gauging patients' progress toward their weight loss goals and determining the timing of adjunctive intervention in poor responders during early postoperative follow-up.

Urine osmolality assessment through the integration of urea hydrolysis and impedance measurement.

We present the development and validation of an impedance-based urine osmometer for accurate and portable measurement of urine osmolality. The urine osmolality of a urine sample can be estimated by determining the concentrations of the conductive solutes and urea, which make up approximately 94% of the urine composition. Our method utilizes impedance measurements to determine the conductive solutes and urea after hydrolysis with urease enzyme. We built an impedance model using sodium chloride (NaCl) and urea at various known concentrations. In this work, we validated the accuracy of the impedance-based urine osmometer by developing a proof-of-concept first prototype and an integrated urine dipstick second prototype, where both prototypes exhibit an average accuracy of 95.5 ± 2.4% and 89.9 ± 9.1%, respectively in comparison to a clinical freezing point osmometer in the hospital laboratory. While the integrated dipstick design exhibited a slightly lower accuracy than the first prototype, it eliminated the need for pre-mixing or manual pipetting. Impedance calibration curves for conductive and non-conductive solutes consistently yielded results for NaCl but underscored challenges in achieving uniform urease enzyme coating on the dipstick. We also investigated the impact of storing urine at room temperature for 24 hours, demonstrating negligible differences in osmolality values. Overall, our impedance-based urine osmometer presents a promising tool for point-of-care urine osmolality measurements, addressing the demand for a portable, accurate, and user-friendly device with potential applications in clinical and home settings.

Automated segmentation of MRI white matter hyperintensities in 8,421 patients with acute ischemic stroke.

BACKGROUND AND PURPOSE: To date, only a few small studies have attempted deep learning-based automatic segmentation of white matter hyperintensity (WMH) lesions in patients with cerebral infarction, which is complicated because stroke-related lesions can obscure WMH borders. We developed and validated deep learning algorithms to segment WMH lesions accurately in patients with cerebral infarction, using multisite datasets involving 8,421 patients with acute ischemic stroke. MATERIALS AND METHODS: We included 8,421 stroke patients from 9 centers in Korea. 2D UNet and SE-Unet models were trained using 2,408 FLAIR MRI from 3 hospitals and validated using 6,013 FLAIR MRIs from 6 hospitals. WMH segmentation performance was assessed by calculating DSC, correlation coefficient, and concordance correlation coefficient compared to a human-segmented gold standard. In addition, we obtained an uncertainty index that represents overall ambiguity in the voxel classification for WMH segmentation in each patient based on the Kullback-Leibler divergence. RESULTS: In the training dataset, the mean age was 67.4±13.0 years and 60.4% were men. The mean (95% CI) DSCs for Unet in internal testing and external validation were respectively 0.659 (0.649-0.669) and 0.710 (0.707-0.714), which were slightly lower than the reliability between humans (DSC=0.744; 95% CI=0.738-0.751; P=.031). Compared with the Unet, the SE-Unet demonstrated better performance, achieving a mean DSC of 0.675 (0.666-0.685; P<.001) in the internal testing and 0.722 (0.719-0.726; P<.001) in the external validation; moreover, it achieved high DSC values (ranging from 0.672 to 0.744) across multiple validation datasets. We observed a significant correlation between WMH volumes that were segmented automatically and manually for the Unet (r=0.917, P<.0001) and even stronger for the SE-Unet (r=0.933, P<.0001). The SE-Unet also attained a high concordance correlation coefficient (ranging from 0.841 to 0.956) in external test datasets. In addition, the uncertainty indices in the majority of patients (86%) in the external datasets were below 0.35, with an average DSC of 0.744 in these patients. CONCLUSIONS: We developed and validated deep learning algorithms to segment WMH in patients with acute cerebral infarction using the largest-ever MRI datasets. In addition, we showed that the uncertainty index can be used to identify cases where automatic WMH segmentation is less accurate and requires human review. ABBREVIATIONS: WMH = white matter hyperintensity; CNN = convolutional neural networks; SE = squeeze-and-excitation; KL = Kullback-Leibler; ReLU = rectified linear unit; LKW = last known well; mRS = modified Rankin Scale; NIHSS = National Institute of Health Stroke Scale; LAA = large artery atherosclerosis; SVO = small vessel occlusion; CE = cardioembolism.

Rapid Plaque Progression Is Independently Associated With Hyperglycemia and Low HDL Cholesterol in Patients With Stable Coronary Artery Disease: A PARADIGM Study.

BACKGROUND: We assessed whether combinations of cardiometabolic risk factors independently predict coronary plaque progression (PP) and major adverse cardiovascular events in patients with stable coronary artery disease. METHODS: Patients with known or suspected stable coronary artery disease (60.9±9.3 years, 55.4% male) undergoing serial coronary computed tomography angiographies (≥2 years apart), with clinical characterization and follow-up (N=1200), were analyzed from the PARADIGM study (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging). Plaque volumes measured in coronary segments (≥2 mm in diameter) were summed to provide whole heart plaque volume (mm3) and percent atheroma volume (plaque volume/vessel volume×100; %) per patient at baseline and follow-up. Rapid PP was defined as a percent atheroma volume increase of ≥1.0%/y. Major adverse cardiovascular events included nonfatal myocardial infarction, death, and unplanned coronary revascularization. RESULTS: In an interscan period of 3.2 years (interquartile range, 1.9), rapid PP occurred in 341 patients (28%). At multivariable analysis, the combination of cardiometabolic risk factors defined as metabolic syndrome predicted rapid PP (odds ratio, 1.51 [95% CI, 1.12-2.03]; P=0.007) together with older age, smoking habits, and baseline percent atheroma volume. Among single cardiometabolic variables, high fasting plasma glucose (diabetes or fasting plasma glucose >100 mg/dL) and low HDL-C (high-density lipoprotein cholesterol; <40 mg/dL in males and <50 mg/dL in females) were independently associated with rapid PP, in particular when combined (odds ratio, 2.37 [95% CI, 1.56-3.61]; P<0.001). In a follow-up of 8.23 years (interquartile range, 5.92-9.53), major adverse cardiovascular events occurred in 201 patients (17%). At multivariable Cox analysis, the combination of high fasting plasma glucose with high systemic blood pressure (treated hypertension or systemic blood pressure >130/85 mm Hg) was an independent predictor of events (hazard ratio, 1.79 [95% CI, 1.10-2.90]; P=0.018) together with family history, baseline percent atheroma volume, and rapid PP. CONCLUSIONS: In patients with stable coronary artery disease, the combination of hyperglycemia with low HDL-C is associated with rapid PP independently of other risk factors, baseline plaque burden, and treatment. The combination of hyperglycemia with high systemic blood pressure independently predicts the worse outcome beyond PP. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02803411.

FEditNet++: Few-Shot Editing of Latent Semantics in GAN Spaces with Correlated Attribute Disentanglement.

Generative Adversarial Networks have achieved significant advancements in generating and editing high-resolution images. However, most methods suffer from either requiring extensive labeled datasets or strong prior knowledge. It is also challenging for them to disentangle correlated attributes with few-shot data. In this paper, we propose FEditNet++, a GAN-based approach to explore latent semantics. It aims to enable attribute editing with limited labeled data and disentangle the correlated attributes. We propose a layer-wise feature contrastive objective, which takes into consideration content consistency and facilitates the invariance of the unrelated attributes before and after editing. Furthermore, we harness the knowledge from the pretrained discriminative model to prevent overfitting. In particular, to solve the entanglement problem between the correlated attributes from data and semantic latent correlation, we extend our model to jointly optimize multiple attributes and propose a novel decoupling loss and cross-assessment loss to disentangle them from both latent and image space. We further propose a novel-attribute disentanglement strategy to enable editing of novel attributes with unknown entanglements. Finally, we extend our model to accurately edit the fine-grained attributes. Qualitative and quantitative assessments demonstrate that our method outperforms state-of-the-art approaches across various datasets, including CelebA-HQ, RaFD, Danbooru2018 and LSUN Church.

Time-Resolved Evaluation of L-Dopa Metabolism in Bacteria-Host Symbiotic System and the Effect on Parkinson's Molecular Pathology.

The gut microbiome influences drug metabolism and therapeutic efficacy. Still, the lack of a general label-free approach for monitoring bacterial or host metabolic contribution hampers deeper insights. Here, a 2D nuclear magnetic resonance (NMR) approach is introduced that enables real-time monitoring of the metabolism of Levodopa (L-dopa), an anti-Parkinson drug, in both live bacteria and bacteria-host (Caenorhabditis elegans) symbiotic systems. The quantitative method reveals that discrete Enterococcus faecalis substrains produce different amounts of dopamine in live hosts, even though they are a single species and all have the Tyrosine decarboxylase (TyrDC) gene involved in L-dopa metabolism. The differential bacterial metabolic activity correlates with differing Parkinson's molecular pathology concerning alpha-synuclein aggregation as well as behavioral phenotypes. The gene's existence or expression is not an indicator of metabolic activity is also shown, underscoring the significance of quantitative metabolic estimation in vivo. This simple approach is widely adaptable to any chemical drug to elucidate pharmacomicrobiomic relationships and may help rapidly screen bacterial metabolic effects in drug development.

Stacked generalization as a computational method for the genomic selection.

As genomic selection emerges as a promising breeding method for both plants and animals, numerous methods have been introduced and applied to various real and simulated data sets. Research suggests that no single method is universally better than others; rather, performance is highly dependent on the characteristics of the data and the nature of the prediction task. This implies that each method has its strengths and weaknesses. In this study, we exploit this notion and propose a different approach. Rather than comparing multiple methods to determine the best one for a particular study, we advocate combining multiple methods to achieve better performance than each method in isolation. In pursuit of this goal, we introduce and develop a computational method of the stacked generalization within ensemble methods. In this method, the meta-model merges predictions from multiple base models to achieve improved performance. We applied this method to plant and animal data and compared its performance with currently available methods using standard performance metrics. We found that the proposed method yielded a lower or comparable mean squared error in predicting phenotypes compared to the current methods. In addition, the proposed method showed greater resistance to overfitting compared to the current methods. Further analysis included statistical hypothesis testing, which showed that the proposed method outperformed or matched the current methods. In summary, the proposed stacked generalization integrates currently available methods to achieve stable and better performance. In this context, our study provides general recommendations for effective practices in genomic selection.

177Lu Anti-Angiogenic Radioimmunotherapy Targeting ATP Synthase in Gastric Cancer Model.

This study investigated a novel radioimmunotherapy strategy for targeting tumor angiogenesis. We developed a radiopharmaceutical complex by labeling an anti-adenosine triphosphate synthase (ATPS) monoclonal antibody (mAb) with the radioisotope 177Lu using DOTA as a chelating agent. 177Lu-DOTA-ATPS mAb demonstrated high labeling efficiency (99.0%) and stability in serum. MKN-45 cancer cells exhibited the highest cellular uptake, which could be specifically blocked by unlabeled ATPS mAb. In mice, 177Lu-DOTA-ATPS mAb accumulated significantly in tumors, with a tumor uptake of 16.0 ± 1.5%ID/g on day 7. 177Lu-DOTA-ATPS mAb treatment significantly reduced the viability of MKN-45 cells in a dose-dependent manner. In a xenograft tumor model, this radioimmunotherapy strategy led to substantial tumor growth inhibition (82.8%). Furthermore, combining 177Lu-DOTA-ATPS mAb with sunitinib, an anti-angiogenic drug, enhanced the therapeutic efficacy of sunitinib in the mouse model. Our study successfully developed 177Lu-DOTA-ATPS mAb, a radioimmunotherapy agent targeting tumor blood vessels. This approach demonstrates significant promise for inhibiting tumor growth, both as a single therapy and in combination with other anti-cancer drugs.

Association of Fibrate use with clinical expression of hypertrophic cardiomyopathy.

AIMS: An association between obesity, metabolic abnormalities and clinical hypertrophic cardiomyopathy (HCM) expression has been reported. We investigated whether managing dyslipidaemia with fibrates could affect the clinical expression of HCM. METHODS: We screened patients who used fibrates between 2010 and 2017 from a nationwide database. After excluding patients with a history of HCM, we identified fibrate-user group (n = 412 823). We then constructed a 1:1 matched cohort of fibrate-naïve participants (n = 412 823). After a 1 year lag period, we identified the incident HCM cases for the following 5 years. RESULTS: During a median follow-up period of 3.96 years, we identified 454 incident clinical HCM cases. After adjusting for covariates, fibrate use was associated with a lower risk of clinical HCM expression [hazard ratio (HR) 95% confidence interval (CI): 0.763 (0.630-0.924)]. In subgroup analyses, fibrate use was associated with a reduced risk of clinical HCM expression in patients with a body mass index ≥25 kg/m2 and those with abdominal obesity [HR (95% CI): 0.719 (0.553-0.934) and 0.655 (0.492-0.872)], but not in those without obesity. Fibrate use was also associated with lower risks of incident clinical HCM in patients with triglyceride levels ≥150 mg/dL and those with metabolic syndrome [HR (95% CI): 0.741 (0.591-0.929) and 0.750 (0.609-0.923)], but not in their counterparts. Regarding lifestyle behaviours, fibrate use appeared to provide more prognostic benefits in patients who currently smoked, consumed alcohol or did not engage in regular physical activities. CONCLUSION: The use of fibrates is associated with a lower incidence of clinical HCM expression. This association was also more prominent in those with obesity, unhealthy metabolic profiles and poor lifestyle behaviours.

A Comparative Metagenomic Analysis of Specified Microorganisms in Groundwater for Non-Sterilized Pharmaceutical Products.

In pharmaceutical manufacturing, ensuring product safety involves the detection and identification of microorganisms with human pathogenic potential, including Burkholderia cepacia complex (BCC), Escherichia coli, Pseudomonas aeruginosa, Salmonella enterica, Staphylococcus aureus, Clostridium sporogenes, Candida albicans, and Mycoplasma spp., some of which may be missed or not identified by traditional culture-dependent methods. In this study, we employed a metagenomic approach to detect these taxa, avoiding the limitations of conventional cultivation methods. We assessed the groundwater microbiome's taxonomic and functional features from samples collected at two locations in the spring and summer. All datasets comprised 436-557 genera with Proteobacteria, Bacteroidota, Firmicutes, Actinobacteria, and Cyanobacteria accounting for > 95% of microbial DNA sequences. The aforementioned species constituted less than 18.3% of relative abundance. Escherichia and Salmonella were mainly detected in Hot Springs, relative to Jefferson, while Clostridium and Pseudomonas were mainly found in Jefferson relative to Hot Springs. Multidrug resistance efflux pumps and BlaR1 family regulatory sensor-transducer disambiguation dominated in Hot Springs and in Jefferson. These initial results provide insight into the detection of specified microorganisms and could constitute a framework for the establishment of comprehensive metagenomic analysis for the microbiological evaluation of pharmaceutical-grade water and other non-sterile pharmaceutical products, ensuring public safety.

2023 Korean Society of Echocardiography position paper for the diagnosis and management of valvular heart disease, part II: mitral and tricuspid valve disease.

This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases. This position paper focuses on the diagnosis and management of valvular heart diseases with referring to the guidelines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee sought to reflect national data on the topic of valvular heart diseases published to date through a systematic literature search based on validity and relevance. In the part II of this article, we intend to present recommendations for diagnosis and treatment of mitral valve disease and tricuspid valve disease.

Sparse reconstruction of sound field using pattern-coupled Bayesian compressive sensing.

Conventional near-field acoustic holography based on compressive sensing either does not fully exploit the underlying block-sparse structures of the signal or suffers from a mismatch between the actual and predefined block structure due to the lack of prior information about block partitions, resulting in poor accuracy in sound field reconstruction. In this paper, a pattern-coupled Bayesian compressive sensing method is proposed for sparse reconstruction of sound fields. The proposed method establishes a hierarchical Gaussian-Gamma probability model with a pattern-coupled prior based on the equivalent source method, transforming the sound field reconstruction problem into recovering the sparse coefficient vector of the equivalent source strengths within the compressive sensing framework. A set of hyperparameters is introduced to control the sparsity of each element in the sparse coefficient vector of the equivalent source strengths, where the sparsity of each element is determined by both its own hyperparameters and those of its immediate neighbors. This approach enables the promotion of block sparse solutions and achieves better performance in solving for the sparse coefficient vector of the equivalent source strengths without prior information of block partitions. The effectiveness and superiority of the proposed method in reconstructing sound fields are verified by simulations and experiments.

Tricaprylin-based drug crystalline suspension for intramuscular long-acting delivery of entecavir with alleviated local inflammation.

In order to ensure prolonged pharmacokinetic profile along with local tolerability at the injection site, tricaprylin-based drug crystalline suspension (TS) was designed and its local distribution, pharmacokinetics, and inflammatory response, were evaluated with conventional aqueous suspension (AS). As model drug particles, entecavir 3-palmitate (EV-P), an ester lipidic prodrug for entecavir (EV), was employed. The EV-P-loaded TS was prepared by ultra-sonication method. Prepared TS and conventional AS exhibited comparable morphology (rod or rectangular), median diameter (2.7 and 2.6 µm), crystallinity (melting point of 160-165°C), and in vitro dissolution profile. However, in vivo performances of drug microparticles were markedly different, depending on delivery vehicle. At AS-injected site, drug aggregates of up to 500 µm were formed upon intramuscular injection, and were surrounded with inflammatory cells and fibroblastic bands. In contrast, no distinct particle aggregation and adjacent granulation was observed at TS-injected site, with >4 weeks remaining of the oily vehicle in micro-computed tomographic observation. Surprisingly, TS exhibited markedly alleviated local inflammation compared to AS, endowing markedly lessened necrosis, fibrosis thickness, inflammatory area, and macrophage infiltration. The higher initial systemic exposure was observed with TS compared to AS, but TS provided prolonged delivery of EV for 3 weeks. Therefore, we suggest that the novel TS system can be a promising tool in designing parenteral long-acting delivery, with improved local tolerability.

Mitral Annular Tissue Velocity Predicts Survival in Patients With Primary Mitral Regurgitation.

BACKGROUND AND OBJECTIVES: Early diastolic mitral annular tissue (e') velocity is a commonly used marker of left ventricular (LV) diastolic function. This study aimed to investigate the prognostic implications of e' velocity in patients with mitral regurgitation (MR). METHODS: This retrospective cohort study included 1,536 consecutive patients aged <65 years with moderate or severe chronic primary MR diagnosed between 2009 and 2018. The primary and secondary outcomes were all-cause and cardiovascular mortality, respectively. According to the current guidelines, the cut-off value of e' velocity was defined as 7 cm/s. RESULTS: A total of 404 individuals were enrolled (median age, 51.0 years; 64.1% male; 47.8% severe MR). During a median 6.0-year follow-up, there were 40 all-cause mortality and 16 cardiovascular deaths. Multivariate analysis revealed a significant association between e' velocity and all-cause death (adjusted hazard ratio [aHR], 0.770; 95% confidence interval [CI], 0.634-0.935; p=0.008) and cardiovascular death (aHR, 0.690; 95% CI, 0.477-0.998; p=0.049). Abnormal e' velocity (≤7 cm/s) independently predicted all-cause death (aHR, 2.467; 95% CI, 1.170-5.200; p=0.018) and cardiovascular death (aHR, 5.021; 95% CI, 1.189-21.211; p=0.028), regardless of symptoms, LV dimension and ejection fraction. Subgroup analysis according to sex, MR severity, mitral valve replacement/repair, and symptoms, showed no significant interactions. Including e' velocity in the 10-year risk score improved reclassification for mortality (net reclassification improvement [NRI], 0.154; 95% CI, 0.308-0.910; p<0.001) and cardiovascular death (NRI, 1.018; 95% CI, 0.680-1.356; p<0.001). CONCLUSIONS: In patients aged <65 years with primary MR, e' velocity served as an independent predictor of all-cause and cardiovascular deaths.

Air pollution and survival in patients with malignant mesothelioma and asbestos-related lung cancer: a follow-up study of 1591 patients in South Korea.

BACKGROUND: Despite significant advancements in treatments such as surgery, radiotherapy, and chemotherapy, the survival rate for patients with asbestos-related cancers remains low. Numerous studies have provided evidence suggesting that air pollution induces oxidative stress and inflammation, affecting acute respiratory diseases, lung cancer, and overall mortality. However, because of the high case fatality rate, there is limited knowledge regarding the effects of air pollution exposures on survival following a diagnosis of asbestos-related cancers. This study aimed to determine the effect of air pollution on the survival of patients with malignant mesothelioma and asbestos-related lung cancer. METHODS: We followed up with 593 patients with malignant mesothelioma and 998 patients with lung cancer identified as asbestos victims between 2009 and 2022. Data on five air pollutants-sulfur dioxide, carbon monoxide, nitrogen dioxide, fine particulate matter with a diameter < 10 µm, and fine particulate matter with a diameter < 2.5 µm-were obtained from nationwide atmospheric monitoring stations. Cox proportional hazard models were used to estimate the association of cumulative air pollutant exposure with patient mortality, while adjusting for potential confounders. Quantile-based g-computation was used to assess the combined effect of the air pollutant mixture on mortality. RESULTS: The 1-, 3-, and 5-year survival rates for both cancer types decreased with increasing exposure to all air pollutants. The estimated hazard ratios rose significantly with a 1-standard deviation increase in each pollutant exposure level. A quartile increase in the pollutant mixture was associated with a 1.99-fold increase in the risk of malignant mesothelioma-related mortality (95% confidence interval: 1.62, 2.44). For lung cancer, a quartile increase in the pollutant mixture triggered a 1.87-fold increase in the mortality risk (95% confidence interval: 1.53, 2.30). CONCLUSION: These findings support the hypothesis that air pollution exposure after an asbestos-related cancer diagnosis can negatively affect patient survival.

Utilizing deep learning for automated detection of oral lesions: A multicenter study.

OBJECTIVES: We aim to develop a YOLOX-based convolutional neural network model for the precise detection of multiple oral lesions, including OLP, OLK, and OSCC, in patient photos. MATERIALS AND METHODS: We collected 1419 photos for model development and evaluation, conducting both a comparative analysis to gauge the model's capabilities and a multicenter evaluation to assess its diagnostic aid, where 24 participants from 14 centers across the nation were invited. We further integrated this model into a mobile application for rapid and accurate diagnostics. RESULTS: In the comparative analysis, our model overperformed the senior group (comprising three most experienced experts with more than 10 years of experience) in macro-average recall (85 % vs 77.5 %), precision (87.02 % vs 80.29 %), and specificity (95 % vs 92.5 %). In the multicenter model-assisted diagnosis evaluation, the dental, general, and community hospital groups showed significant improvement when aided by the model, reaching a level comparable to the senior group, with all macro-average metrics closely aligning or even surpassing with those of the latter (recall of 78.67 %, 74.72 %, 83.54 % vs 77.5 %, precision of 80.56 %, 76.42 %, 85.15 % vs 80.29 %, specificity of 92.89 %, 91.57 %, 94.51 % vs 92.5 %). CONCLUSION: Our model exhibited a high proficiency in detection of oral lesions, surpassing the performance of highly experienced specialists. The model can also help specialists and general dentists from dental and community hospitals in diagnosing oral lesions, reaching the level of highly experienced specialists. Moreover, our model's integration into a mobile application facilitated swift and precise diagnostic procedures.

Impact of Aeroallergen Sensitization on Chronic Rhinosinusitis.

PURPOSE: This study investigated the impact of aeroallergens on the development and progression of chronic rhinosinusitis (CRS), with a focus on the specific associations between aeroallergens and CRS according to allergen type, number, and extent of sensitization. METHODS: The medical records of 256 CRS patients were retrospectively analyzed. All were divided into nonallergic, house dust mite (HDM)-allergic, pollen-allergic, and double allergic groups via specific immunoglobulin E (IgE) testing. Clinical characteristics, computed tomography (CT) scores, olfactory functions, and demographic data were compared. Correlation analysis was performed to explore the relationships between the extent of allergen sensitization and CRS severity. Binary logistic regression analysis was used to identify risk factors for hyposmia and anosmia. RESULTS: The allergic group exhibited higher total CT scores than the nonallergic group (P = 0.001). Sensitivity to HDM or pollen allergens alone was not significantly associated with increased CRS severity. No significant differences were observed between the effects of HDM and pollen allergens on CRS severity. However, the double allergic group exhibited significantly higher CT scores (P < 0.001, < 0.001, and 0.003) than the other groups. Although the prevalence rates of anosmia and hyposmia were notably higher in the double allergic group, the difference was not statistically significant. The maximum specific IgE levels to HDM and pollen allergens positively correlated with the CT scores (P = 0.001 and 0.001, respectively). CONCLUSIONS: Allergen sensitization, particularly to multiple common allergens, contributed to CRS severity. CRS patients sensitized to both HDM and pollen allergens tended to experience the diminished olfactory function. These findings underscore the importance of considering the allergen sensitization pattern when assessing CRS severity and its potential progression.