Dependency of mitochondrial quantity on blastocyst timeline obscures its actual effect to pregnancy outcomes.

Objectives: To explore the correlation between mitochondrial quantity and the blastocyst development timeline as well as their respective contributions to early pregnancy. Methods: A retrospective study was conducted using a dataset comprising 2,633 embryos that underwent preimplantation genetic testing for aneuploidy (PGT-A) between January 2016 and December 2023. The study was divided into three subsets to address distinct aspects: the representativeness of a single trophectoderm (TE) biopsy for mitochondrial quantity (n=43), the correlation between morphokinetic features and mitochondrial quantity (n=307), and the association analysis among mitochondrial quantity, blastocyst timeline factor, and reproductive outcomes (n=2,283). Distribution assessment of mitochondrial quantity across an individual blastocyst involved the identification within multiple biopsies and spent culture media. Timeline evaluation included correlating mitochondrial quantity with time-lapse datasets. Finally, multivariate logistic regression models, incorporating potential effectors alongside mitochondrial quantity, were employed to analyze their respective contributions to early pregnancy endpoints. Results: Of distribution assessment, mitochondrial quantity exhibited an even distribution across the entire trophectoderm (Spearman's ρ=0.82), while no detectable mtDNAs in the corresponding spent culture media. Then the timeline correlation study revealed significant association between mitochondrial quantity and blastocyst features of both the day of expanded blastocyst formation (95% Confidence intervals, CIs: 0.27~4.89, p=0.03) and the timing of expanded blastocyst formation (tEB) (95% CIs: -0.24~-0.01, p=0.04) in the regression model, indicating a strong dependency between mitochondrial quantity and the blastocyst development timeline. For the contribution to early pregnancy, multivariate logistic regression models showed that the day of expanded blastocyst formation contributed to four endpoints persistently: positive for HCG (odd ratio, OR: 0.71, p=0.006), gestational sac (OR: 0.78, p=0.04), fetal heartbeat (OR: 0.71, p=0.004), and progression to 14 weeks (OR: 0.69, p=0.002). Contrastingly, no notable correlation was observed between the mitochondrial quantity and these endpoints. Conclusions: Strong interaction was observed between mitochondrial quantity and the blastocyst timeline, particularly the timing of expanded blastocyst formation. It suggests that the primary determinant influencing pregnancy outcomes lies in the time-dependent parameter of blastocyst rather than in the specific mitochondrial quantity.

Neonatal jaundice is associated with increased risks of congenital anomalies of the kidney and urinary tract and concomitant urinary tract infection.

The link between neonatal jaundice and urinary tract infection (UTI) remains debated, with congenital kidney and urinary tract anomalies (CAKUT) potentially playing a role. This population-based study aimed to analyze the correlations between neonatal jaundice, CAKUT, and concomitant UTI. The study cohort consisted of 2,078,122 live births from 2004 to 2014. We linked several population-based datasets in Taiwan to identify infants with unexplained neonatal jaundice and their mothers. The primary outcome was the rate of CAKUT occurring within 3 years after delivery, and the presence of concomitant UTI during neonatal jaundice hospitalization. Infants with neonatal jaundice had a significantly higher risk of CAKUT (adjusted odds ratio [aOR] 1.24, 95% confidence interval [CI] 1.11-1.39) during early childhood. Among the subtypes of CAKUT, obstructive uropathy, vesicoureteral reflux and other CAKUT were associated with an increased risk of neonatal jaundice. Infants who underwent intensive phototherapy, had a late diagnosis (> 14 days of postnatal age) or underwent a prolonged duration of phototherapy (> 3 days) exhibited a higher risk of concomitant UTI compared to other infants with jaundice. Our findings indicate a notable association between neonatal jaundice and increased risks of UTIs in the context of CAKUT. This study underscore the importance of vigilant monitoring and timely interventions for neonates presenting with jaundice, while acknowledging the complexity and variability in the progression of CAKUT and its potential connection to UTIs.

Deep Learning Model for Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Chest Radiographs.

Bronchopulmonary dysplasia (BPD) is common in preterm infants and may result in pulmonary vascular disease, compromising lung function. This study aimed to employ artificial intelligence (AI) techniques to help physicians accurately diagnose BPD in preterm infants in a timely and efficient manner. This retrospective study involves two datasets: a lung region segmentation dataset comprising 1491 chest radiographs of infants, and a BPD prediction dataset comprising 1021 chest radiographs of preterm infants. Transfer learning of a pre-trained machine learning model was employed for lung region segmentation and image fusion for BPD prediction to enhance the performance of the AI model. The lung segmentation model uses transfer learning to achieve a dice score of 0.960 for preterm infants with ≤ 168 h postnatal age. The BPD prediction model exhibited superior diagnostic performance compared to that of experts and demonstrated consistent performance for chest radiographs obtained at ≤ 24 h postnatal age, and those obtained at 25 to 168 h postnatal age. This study is the first to use deep learning on preterm chest radiographs for lung segmentation to develop a BPD prediction model with an early detection time of less than 24 h. Additionally, this study compared the model's performance according to both NICHD and Jensen criteria for BPD. Results demonstrate that the AI model surpasses the diagnostic accuracy of experts in predicting lung development in preterm infants.

Corrected QT Interval and Outcomes of Dialysis Patients with Symptomatic Peripheral Artery Disease: A Prospective Cohort Study.

BACKGROUND: Peripheral artery disease (PAD) is common and associated with a higher risk of cardiovascular morbidity and mortality in dialysis patients. A longer corrected QT (QTc) interval has been associated with adverse cardiovascular events and mortality in the general population and patients with end-stage kidney disease. However, little evidence is available on the predictive value of QTc in dialysis patients with PAD. METHODS: We conducted a prospective cohort study of 356 dialysis patients with symptomatic PAD undergoing endovascular therapy. We performed the resting 12-lead electrocardiogram (ECG) at baseline. Cox regression analyses were used to assess the association of QTc with all-cause mortality and major adverse cardiovascular events (MACEs), defined as non-fatal myocardial infarction, non-fatal stroke, and cardiovascular death. RESULTS: The mean age was 67.3 ± 11.5 years; 41.6% of participants were women. The median QTc was 471 (interquartile ranges 448-491) milliseconds (ms). During a median follow-up of 2.2 years, 188 (52.8%) patients died, and MACEs occurred in 119 (33.4%) patients. In multivariable-adjusted models, patients in tertile 3 of QTc levels had a significantly greater risk of all-cause mortality (hazard ratio [HR] 2.41, 95% confidence intervals [CI] 1.58-3.69) and MACEs (HR 1.90, 95% CI 1.15-3.13) than those in tertile 1. Similarly, each 10-ms increase in the baseline QTc predicted a higher risk of all-cause death (HR 1.15, 95% CI 1.09-1.21) and MACEs (HR 1.15, 95% CI 1.07-1.23). CONCLUSIONS: QTc prolongation was independently associated with adverse outcomes among dialysis patients with symptomatic PAD.

Associations between maternal chronic diseases and congenital anomalies of the kidney and urinary tract in offspring: a population-based cohort study.

Background: The relationship between maternal chronic diseases and congenital anomalies of the kidneys and urinary tract (CAKUT) in offspring still needs elucidation. This study aimed to comprehensively evaluate the associations between maternal chronic disease and CAKUT in their offspring. Methods: Data of mothers and children were extracted from the Taiwan Maternal and Child Health Database and National Health Insurance Research Database. The concept of developmental origins of health and disease (DOHaD) was used to select maternal chronic diseases. Results: The study cohort included 1 196 175 mothers and 1 628 706 offspring. Analysis showed that maternal chronic diseases, especially type 1 diabetes, type 2 diabetes, gestational diabetes, connective tissue disorders and CAKUT were highly associated with CAKUT in the offspring. Higher maternal age, abnormal birthweight (>3500 g or <2500 g), gestational age <36 weeks and birth order <2 were all associated with a higher risk of CAKUT. Maternal chronic hypertension and taking angiotensin-related drugs increased the odds ratios of obstructive kidney disease in the offspring. Offspring tended to have the same type of CAKUT as their mothers. Conclusion: Maternal chronic diseases, older maternal age and abnormal birthweight are risk factors for CAKUT. Also, a percentage of patients with CAKUT were not full-term newborns. Results support prenatal counselling and health management of pregnant women with chronic diseases and extra care for infants with a high risk of anomalies. It is strongly recommended that prevention of CAKUT in offspring should start with care of the mothers' prenatal chronic diseases.

A Latent Activated Olfactory Stem Cell State Revealed by Single Cell Transcriptomic and Epigenomic Profiling.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration after injury-induced cell death. In this study, we used single cell sequencing to identify major regulatory players in determining olfactory epithelial stem cell fate after acute injury. We combined gene expression and accessible chromatin profiles of individual lineage traced olfactory stem cells to predict transcription factor activity specific to different lineages and stages of recovery. We further identified a discrete stem cell state that appears poised for activation, characterized by accessible chromatin around wound response and lineage specific genes prior to their later expression in response to injury. Together these results provide evidence that a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images.

BACKGROUND: Many methodologies for selecting histopathological images, such as sample image patches or segment histology from regions of interest (ROIs) or whole-slide images (WSIs), have been utilized to develop survival models. With gigapixel WSIs exhibiting diverse histological appearances, obtaining clinically prognostic and explainable features remains challenging. Therefore, we propose a novel deep learning-based algorithm combining tissue areas with histopathological features to predict cancer survival. METHODS: The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset was used in this investigation. A deep convolutional survival model (DeepConvSurv) extracted histopathological information from the image patches of nine different tissue types, including tumors, lymphocytes, stroma, and mucus. The tissue map of the WSIs was segmented using image processing techniques that involved localizing and quantifying the tissue region. Six survival models with the concordance index (C-index) were used as the evaluation metrics. RESULTS: We extracted 128 histopathological features from four histological types and five tissue area features from WSIs to predict colorectal cancer survival. Our method performed better in six distinct survival models than the Whole Slide Histopathological Images Survival Analysis framework (WSISA), which adaptively sampled patches using K-means from WSIs. The best performance using histopathological features was 0.679 using LASSO-Cox. Compared to histopathological features alone, tissue area features increased the C-index by 2.5%. Based on histopathological features and tissue area features, our approach achieved performance of 0.704 with RIDGE-Cox. CONCLUSIONS: A deep learning-based algorithm combining histopathological features with tissue area proved clinically relevant and effective for predicting cancer survival.

High-throughput transcriptome analyses from ASPIRO, a phase 1/2/3 study of gene replacement therapy for X-linked myotubular myopathy.

X-linked myotubular myopathy (XLMTM) is a severe congenital disease characterized by profound muscle weakness, respiratory failure, and early death. No approved therapy for XLMTM is currently available. Adeno-associated virus (AAV)-mediated gene replacement therapy has shown promise as an investigational therapeutic strategy. We aimed to characterize the transcriptomic changes in muscle biopsies of individuals with XLMTM who received resamirigene bilparvovec (AT132; rAAV8-Des-hMTM1) in the ASPIRO clinical trial and to identify potential biomarkers that correlate with therapeutic outcome. We leveraged RNA-sequencing data from the muscle biopsies of 15 study participants and applied differential expression analysis, gene co-expression analysis, and machine learning to characterize the transcriptomic changes at baseline (pre-dose) and at 24 and 48 weeks after resamirigene bilparvovec dosing. As expected, MTM1 expression levels were significantly increased after dosing (p < 0.0001). Differential expression analysis identified upregulated genes after dosing that were enriched in several pathways, including lipid metabolism and inflammatory response pathways, and downregulated genes were enriched in cell-cell adhesion and muscle development pathways. Genes involved in inflammatory and immune pathways were differentially expressed between participants exhibiting ventilator support reduction of either greater or less than 6 h/day after gene therapy compared to pre-dosing. Co-expression analysis identified similarly regulated genes, which were grouped into modules. Finally, the machine learning model identified five genes, including MTM1, as potential RNA biomarkers to monitor the progress of AAV gene replacement therapy. These findings further extend our understanding of AAV-mediated gene therapy in individuals with XLMTM at the transcriptomic level.

Validation of Gait Measurements on Short-Distance Walkways Using Azure Kinect DK in Patients Receiving Chronic Hemodialysis.

Muscle dysfunction, skeletal muscle fibrosis, and disability are associated with weakness in patients with end-stage renal disease. The main purpose of this study was to validate the effectiveness of a proposed system for gait monitoring on short-distance 1.5 m walkways in a dialysis center. Gaits with reduced speed and stride length, long sit-to-stand time (SST), two forward angles, and two unbalanced gait regions are defined in the proposed Kinect v3 gait measurement and analysis system (K3S) and have been considered clinical features in end-stage renal disease (ESRD) associated with poor dialysis outcomes. The stride and pace calibrations of the Kinect v3 system are based on the Zeno Walkway. Its single rating intraclass correlation (ICC) for the stride is 0.990, and its single rating ICC for the pace is 0.920. The SST calibration of Kinect v3 is based on a pressure insole; its single rating ICC for the SST is 0.871. A total of 75 patients on chronic dialysis underwent gait measurement and analysis during walking and weighing actions. After dialysis, patients demonstrated a smaller stride (p < 0.001) and longer SST (p < 0.001). The results demonstrate that patients' physical fitness was greatly reduced after dialysis. This study ensures patients' adequate physical gait strength to cope with the dialysis-associated physical exhaustion risk by tracing gait outliers. As decreased stride and pace are associated with an increased risk of falls, further studies are warranted to evaluate the clinical benefits of monitoring gait with the proposed reliable and valid system in order to reduce fall risk in hemodialysis patients.

Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients.

Cardiomegaly is associated with poor clinical outcomes and is assessed by routine monitoring of the cardiothoracic ratio (CTR) from chest X-rays (CXRs). Judgment of the margins of the heart and lungs is subjective and may vary between different operators. METHODS: Patients aged > 19 years in our hemodialysis unit from March 2021 to October 2021 were enrolled. The borders of the lungs and heart on CXRs were labeled by two nephrologists as the ground truth (nephrologist-defined mask). We implemented AlbuNet-34, a U-Net variant, to predict the heart and lung margins from CXR images and to automatically calculate the CTRs. RESULTS: The coefficient of determination (R2) obtained using the neural network model was 0.96, compared with an R2 of 0.90 obtained by nurse practitioners. The mean difference between the CTRs calculated by the nurse practitioners and senior nephrologists was 1.52 ± 1.46%, and that between the neural network model and the nephrologists was 0.83 ± 0.87% (p < 0.001). The mean CTR calculation duration was 85 s using the manual method and less than 2 s using the automated method (p < 0.001). CONCLUSIONS: Our study confirmed the validity of automated CTR calculations. By achieving high accuracy and saving time, our model can be implemented in clinical practice.

Association between corrected QT interval and long-term cardiovascular outcomes in elderly patients who had undergone endovascular therapy for lower extremity arterial disease.

Background: Population-based studies have reported the association between prolonged corrected QT (QTc) intervals and an increased risk of adverse cardiovascular events. Data regarding the association between longer QTc intervals and incident cardiovascular outcomes in patients with lower extremity arterial disease (LEAD) are scarce. Objective: To examine the impact of QTc interval on long-term cardiovascular outcomes in elderly patients with symptomatic LEAD. Methods: This cohort study extracted data from the Tzu-chi Registry of ENDovascular Intervention for Peripheral Artery Disease (TRENDPAD) and enrolled 504 patients aged ≥ 70 treated with endovascular therapy for atherosclerotic LEAD from July 1, 2005, to December 31, 2019. The main outcomes of interest were all-cause mortality and major adverse cardiovascular events (MACE). Multivariate analysis was conducted using the Cox proportional hazard model to determine independent variables. We performed interaction analysis between corrected QT and other covariates and Kaplan-Meier analysis to compare the outcome of interest among the groups stratified by the tercile of QTc intervals. Results: A total of 504 patients [235 men (46.6%); mean age, 79.9 ± 6.2 years; mean QTc interval, 459 ± 33 msec] entered the final data analysis. We categorized the baseline patient characteristics according to terciles of QTc intervals. During the median follow-up time of 3.15 (interquartile ranges, 1.65-5.42) years, we noted 264 deaths and 145 MACEs. The 5-year rates of freedom from all-cause mortality (71% vs. 57% vs. 31%, P < 0.001) and MACEs (83% vs. 67% vs. 46%, P < 0.001) were significantly different among the tercile groups. Multivariate analysis showed that a 1-SD increase in the QTc interval increased the risk of all-cause mortality [hazard ratio (HR) 1.49, P < 0.001] and MACEs (HR 1.59, P < 0.001) after adjusting for other covariates. The interaction analysis showed that QTc interval and C-reactive protein levels were most strongly associated with death (HR = 4.88, 95% CI 3.09-7.73, interaction P < 0.001) and MACEs (HR = 7.83, 95% CI 4.14-14.79, interaction P < 0.001). Conclusions: In elderly patients with symptomatic atherosclerotic LEAD, a prolonged QTc interval is associated with advanced limb ischemia, multiple medical comorbidities, increased risk of MACEs, and all-cause mortality.

Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN.

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models.

Repetition of Paclitaxel-Coated Devices for the Treatment of Lower Extremity Artery Disease: Mortality Outcomes and Predictors.

Background: A recent meta-analysis reported late excess mortality in patients treated with paclitaxel-coated devices (PCDs) for symptomatic femoropopliteal disease. However, this finding is controversial. Objectives: To investigate the impact on mortality and predictors of repeat exposure to PCDs in patients with lower extremity peripheral arterial disease (LE-PAD). Methods: We analyzed registry patient-level data from two centers. A total of 214 patients were enrolled, and stratified based on terciles of cumulative dose of paclitaxel. We treated 134 patients with a single PCD exposure and 80 with multiple PCD exposures. We used the follow-up index (FUI) in Kaplan-Meier survival estimates to minimize potential selection bias. We used Cox proportional hazard and splines models to determine the predictors of mortality and assess their relationships with mortality. Results: The mean cumulative dose of paclitaxel was significantly different among groups (6.40 mg vs. 15.06 mg vs. 38.57 mg, p < 0.001). The 5-year FUI (0.93 ± 0.19 vs. 0.94 ± 0.18 vs. 0.95 ± 0.15, p = 0.836) and survival rates were not different (65.4% vs. 51.9% vs. 72.0%, p = 0.148). There was no dose-response association between paclitaxel dosage and death (p = 0.297). The predictors of death were congestive heart failure, stroke, dialysis dependence, neutrophil-lymphocyte ratio (NLR) > 3, age > 71 years, and body mass index (BMI) < 20 kg/m2. Spline model analysis validated the non-linear associations between mortality, age, BMI, and NLR. Conclusions: Repeated PCD exposure for LE-PAD did not result in excess late mortality. Predictors of mortality might change over time, and continuous variables had non-linear relationships with death.

Differential Effects of ABCG5/G8 Gene Region Variants on Lipid Profile, Blood Pressure Status, and Gallstone Disease History in Taiwan.

ABCG5 and ABCG8 are two key adenosine triphosphate-binding cassette (ABC) proteins that regulate whole-body sterol trafficking. This study aimed to elucidate the association between ABCG5/G8 gene region variants and lipid profile, cardiometabolic traits, and gallstone disease history in Taiwan. A total of 1494 Taiwan Biobank participants with whole-genome sequencing data and 117,679 participants with Axiom Genome-Wide CHB Array data were enrolled for analysis. Using genotype-phenotype and stepwise linear regression analyses, we found independent associations of four Asian-specific ABCG5 variants, rs119480069, rs199984328, rs560839317, and rs748096191, with total, low-density lipoprotein (LDL), and non-high-density lipoprotein (HDL) cholesterol levels (all p ≤ 0.0002). Four other variants, which were in nearly complete linkage disequilibrium, exhibited genome-wide significant associations with gallstone disease history, and the ABCG8 rs11887534 variant showed a trend of superiority for gallstone disease history in a nested logistic regression model (p = 0.074). Through regional association analysis of various other cardiometabolic traits, two variants of the PLEKHH2, approximately 50 kb from the ABCG5/G8 region, exhibited significant associations with blood pressure status (p < 10-6). In conclusion, differential effects of ABCG5/G8 region variants were noted for lipid profile, blood pressure status, and gallstone disease history in Taiwan. These results indicate the crucial role of individualized assessment of ABCG5/G8 variants for different cardiometabolic phenotypes.

Pathogenicity Prediction of Single Amino Acid Variants With Machine Learning Model Based on Protein Structural Energies.

The most popular tools for predicting pathogenicity of single amino acid variants (SAVs) were developed based on sequence-based techniques. SAVs may change protein structure and function. In the context of van der Waals force and disulfide bridge calculations, no method directly predicts the impact of mutations on the energies of the protein structure. Here, we combined machine learning methods and energy scores of protein structures calculated by Rosetta Energy Function 2015 to predict SAV pathogenicity. The accuracy level of our model (0.76) is higher than that of six prediction tools. Further analyses revealed that the differential reference energies, attractive energies, and solvation of polar atoms between wildtype and mutant side-chains played essential roles in distinguishing benign from pathogenic variants. These features indicated the physicochemical properties of amino acids, which were observed in 3D structures instead of sequences. We added 16 features to Rhapsody (the prediction tool we used for our data set) and consequently improved its performance. The results indicated that these energy scores were more appropriate and more detailed representations of the pathogenicity of SAVs.

Detecting Hydronephrosis Through Ultrasound Images Using State-of-the-Art Deep Learning Models.

The goal of this study was to assess the feasibility of three models for detecting hydronephrosis through ultrasound images using state-of-the-art deep learning algorithms. The diagnosis of hydronephrosis is challenging because of varying and non-specific presentations. With the characteristics of ready accessibility, no radiation exposure and repeated assessments, point-of-care ultrasound becomes a complementary diagnostic tool for hydronephrosis; however, inter-observer variability still exists after time-consuming training. Artificial intelligence has the potential to overcome the human limitations. A total of 3462 ultrasound frames for 97 patients with hydronephrosis confirmed by the expert nephrologists were included. One thousand six hundred twenty-eight ultrasound frames were also extracted from the 265 controls who had normal renal ultrasonography. We built three deep learning models based on U-Net, Res-UNet and UNet++ and compared their performance. We applied pre-processing techniques including wiping the background to lessen interference by YOLOv4 and standardizing image sizes. Also, post-processing techniques such as adding filter for filtering the small effusion areas were used. The Res-UNet algorithm had the best performance with an accuracy of 94.6% for moderate/severe hydronephrosis with substantial recall rate, specificity, precision, F1 measure and intersection over union. The Res-UNet algorithm has the best performance in detection of moderate/severe hydronephrosis. It would decrease variability among sonographers and improve efficiency under clinical conditions.

Organic ultraviolet filters regulate hyaluronan metabolism in human epidermal keratinocytes through the phosphatidylinositol 3-kinase pathway.

Chronic exposure of skin to ultraviolet (UV) radiation is responsible for skin ageing, which includes degradation of the epidermal and dermal layers. Filtering UV light is key in the sunscreen industry. We studied the effects of organic UV filters on hyaluronan (HA) metabolism and skin hydration in human HaCaT keratinocytes. The gene expression of HA receptors, HA synthase (HAS), hyaluronidase (HYAL), and water channel aquaporin 3 (AQP3) was evaluated by quantitative RT-PCR. The state of oxidative stress was determined by measuring the intracellular levels of reactive oxygen species (ROS). The results showed that five organic UV filters reduced the extracellular contents of HA, and a phosphatidylinositol 3-kinase (PI3K) inhibitor partially restored the decreased HA levels after octinoxate, octocrylene, and oxybenzone treatment. The expression levels of HA receptors, including cluster of differentiation 44 (CD44), receptor for hyaluronic acid-mediated motility (RHAMM), and toll-like receptors (TLRs), were determined. Avobenzone, octinoxate, oxybenzone, and padimate O exerted inhibitory effects on RHAMM expression. Oxybenzone led to a significant increase in CD44 and AQP3 expression. Both octinoxate and octocrylene increased TLR4 expression but decreased ROS accumulation by activating the PI3K pathway. However, the organic UV filters differentially regulated the mRNA expression of HAS and HYAL. Taken together, these results suggest that certain organic UV filters regulate HA metabolism in human keratinocytes in a PI3K pathway-dependent manner.

Identification of fish species through tRNA-based primer design.

BACKGROUND: The correct establishment of the barcode classification system for fish can facilitate biotaxonomists to distinguish fish species, and it can help the government to verify the authenticity of the ingredients of fish products or identify unknown fish related samples. The Cytochrome c oxidation I (COI) gene sequence in the mitochondria of each species possesses unique characteristics, which has been widely used as barcodes in identifying species in recent years. Instead of using COI gene sequences for primer design, flanking tRNA segments of COI genes from 2618 complete fish mitochondrial genomes were analyzed to discover suitable primers for fish classification at taxonomic family level. The minimal number of primer sets is designed to effectively distinguish various clustered groups of fish species for identification applications. Sequence alignment analysis and cross tRNA segment comparisons were applied to check and ensure the primers for each cluster group are exclusive. RESULTS: Two approaches were applied to improve primer design and re-cluster fish species. The results have shown that exclusive primers for 2618 fish species were successfully discovered through in silico analysis. In addition, we applied sequence alignment analysis to confirm that each pair of primers can successfully identify all collected fish species at the taxonomic family levels. CONCLUSIONS: This study provided a practical strategy to discover unique primers for each fishery species and a comprehensive list of exclusive primers for extracting COI barcode sequences of all known fishery species. Various applications of verification of fish products or identification of unknown fish species could be effectively achieved.

Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects.

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of epigenetic regulation of gene expression by assessing chromatin configuration for an entire genome. Despite its popularity, there have been limited studies investigating the analytical challenges related to ATAC-seq data, with most studies leveraging tools developed for bulk transcriptome sequencing. Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins and evaluate it together with 11 different normalization procedures on 8 public ATAC-seq datasets. Accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, improving accuracy and interpretability. Through case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.

Prognostic Factors for Clinical Outcomes in Patients with Newly Diagnosed Advanced-stage Hodgkin Lymphoma: A Nationwide Retrospective Study.

INTRODUCTION: While Hodgkin lymphoma (HL) is mostly curable, outcomes for advanced-stage HL remain unsatisfactory. The International Prognostic Score and its modifications were developed to predict HL prognosis; however, more straightforward prognostic factors are needed. This study aimed to identify simpler prognostic factors for advanced-stage newly diagnosed HL (NDHL). METHODS: This retrospective study used the Taiwan National Health Insurance Research Database and the Taiwan Cancer Registry. Patients with advanced-stage NDHL receiving ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) or ABVD-like regimens between 2009 and 2016 were enrolled. Cox proportional hazards models were used to identify prognostic factors for the time to next treatment (TTNT) and overall survival (OS). We used the time-dependent area under the receiver operating characteristic curve (AUROC) to evaluate model performance. RESULTS: The study included 459 patients with advanced-stage NDHL. A bimodal age distribution (peaks 20-44 and >65 years) was observed. Over a median follow-up of 4.7 years, the complete remission and OS rates were 52% and 76%, respectively. Age ≥60 years (adjusted hazard ratio [aHR]: 1.73, 95% confidence interval [CI]: 1.23-2.43), extranodal involvement (1.40, 1.05-1.87), B symptoms (1.53, 1.13-2.06), and Charlson Comorbidity Index (CCI) ≥1 (1.49, 1.08-2.06) were significantly associated with a shorter TTNT. The time-dependent AUROC was .65. With a time-dependent AUROC of .81, age ≥60 years (4.55, 2.90-7.15) and CCI ≥1 (1.86, 1.18-2.91) were risk factors for worse OS. CONCLUSION: Older age and more comorbidities were risk factors for an inferior OS in advanced-stage NDHL, while older age, extranodal involvement, B-symptoms, and higher CCI were significantly associated with disease relapse.