HDR syndrome presented with nephrotic syndrome in a Chinese boy: A case report.

BACKGROUND: HDR syndrome is a rare genetic disease caused by variants in the GATA3 gene and is phenotypically defined by the triad of hypoparathyroidism (H), deafness (D), and renal disease (R). Renal disorders of HDR are mainly developmental abnormalities, although renal functional abnormalities can also be observed. Nephrotic syndrome or nephrotic-level proteinuria is rare in HDR syndrome. Here, we report a Chinese infant with HDR syndrome who presented with early-onset nephrotic syndrome. We suggest that variants in the GATA3 gene might be associated with nephrotic syndrome. CASE SUMMARY: A 9-month-old boy was hospitalized with a complaint of diarrhea. Proteinuria was detected in the patient by routine testing for 3 days. No edema, oliguria, fever or abnormal urine color were observed. Routine urinary tests at a local hospital revealed proteinuria (protein 3 +) and microscopic hematuria (red blood cells 5-10/HP). The patient was born by cesarean delivery due to placental abruption at 35 weeks + 4 days of gestation. Intrauterine growth retardation was detected beginning at 6 months of gestation. His birth weight was 1.47 kg (< P3th), length was 39 cm (< P3th), and head circumference was 28 cm (< P3th). His motor developmental milestones were obviously delayed. Clinical data were analyzed, and genetic analysis for hereditary nephrotic syndrome was performed by next-generation sequencing. The clinical data showed that the boy exhibited growth retardation, early-onset nephrotic syndrome, microscopic hematuria, sensorineural deafness, T-cell immunodeficiency and congenital heart disease. Genetic tests revealed that the boy carried a de novo hemizygous variant, c.704C>T (p.Pro235 Leu), in exon 3 of the GATA3 gene. CONCLUSION: We report an infant with HDR syndrome who presented with early-onset nephrotic syndrome in China. We suggest that variants in the GATA3 gene might be associated with infant-onset nephrotic syndrome.

A staged approach using machine learning and uncertainty quantification to predict the risk of hip fracture.

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. The study cohort included 547 patients, with 94 experiencing hip fracture. To assess the risk of hip fracture, clinical variables and clinical variables combined with hip DXA imaging features were evaluated as predictors, followed by a novel staged approach. Hip DXA imaging features included those extracted by convolutional neural networks (CNNs), shape measurements, and texture features. Two ensemble machine learning models were evaluated: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and imaging features) using the logistic regression as the base classifier and bootstrapping for ensemble learning. The staged approach was developed using uncertainty quantification from Ensemble 1 which was used to decide if hip DXA imaging features were necessary to improve prediction for each subject. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.95, an accuracy of 0.92, a sensitivity of 0.81, and a specificity of 0.94. The staged model also performed well, with an AUC of 0.85, an accuracy of 0.86, a sensitivity of 0.56, and a specificity of 0.92, outperforming Ensemble 1, which had an AUC of 0.55, an accuracy of 0.73, a sensitivity of 0.20, and a specificity of 0.83. Furthermore, the staged model suggested that 54.49 % of patients did not require DXA scanning, effectively balancing accuracy and specificity, while offering a robust solution when DXA data acquisition is not feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patient health. It can identify individuals at risk of hip fracture with a high accuracy while reducing unnecessary DXA scans. This approach has great promise to guide the need for interventions to prevent hip fracture while reducing diagnostic cost and exposure to radiation.

Aggregated health risk assessment of perfluoroalkyl acids migrated from convenience food contact materials.

Ingestion of perfluoroalkyl acids (PFAAs) via contaminated food contact materials (FCMs) is an important human exposure source. This study adopts a toxicity equivalent approach to evaluate the collective health risk of multiple PFAAs in FCMs. A comprehensive extraction and analysis of 21 PFAAs in FCMs was performed. Among the analyzed substances, 15 PFAAs were detected. Migration experiment using three food simulants revealed the migration range of seven PFAAs from FCMs into the simulant to be 0.47-46.7 ng/cm2. The hazard quotient results suggest minimal health risk, except for 9% of packaged samples where perfluorooctanoic acid (PFOA) poses a higher risk. Utilizing PFOA toxic equivalent concentrations, comprehensive risk calculations showed ∼77% of samples potentially posing elevated health risks due to PFAA exposure. This emphasizes the substantial contribution of PFAAs beyond PFOA and underscores the importance of considering them in related assessments. The aggregated risk assessment reflects actual exposure circumstances more accurately.

Organophosphorus flame retardants in the Qiantang River of China: occurrence, source and ecological risk assessment.

In recent years, the prevalence and danger of organophosphorus flame retardants (OPFRs) have drawn attention from all around the world. This study examined twenty-five OPFRs observed in water and sediment samples from the Qiantang River in eastern China, as well as their occurrence, spatial distribution, possible origins, and ecological hazards. All the 25 OPFRs were detected in water and sediment samples. The levels of Σ25OPFRs in water and sediment were 35.5-192 ng/L and 8.84-48.5 ng/g dw, respectively. Chlorinated OPFRs were the main contributions in water, whereas alkyl-OPFRs were the most common congeners found in sediment. Spatial analysis revealed that sample locations in neighboring cities had somewhat higher water concentrations of OPFRs. Slowing down the river current and making the reservoir the main sink of OPFRs, the dam can prevent OPFRs from moving via the Qiantang River. Positive matrix factorization indicated that plasticizer in polyvinyl chloride, polyester resins, and polyurethane foam made the greatest contributions in water, whereas polyurethane foam and textile were the predominant source in sediment. Analysis of sediment-water exchange of OPFRs showed that twelve OPFRs in sediments can re-enter into the water body. The risk quotients showed the ecological risk was low to medium, but trixylyl phosphate exposures posed high ecological risk to aquatic organisms.

Genomic structural variations link multiple genes to bone mineral density in a multi-ethnic cohort study: Louisiana osteoporosis study.

Osteoporosis, characterized by low BMD, is a highly heritable metabolic bone disorder. Although single nucleotide variations (SNVs) have been extensively studied, they explain only a fraction of BMD heritability. Although genomic structural variations (SVs) are large-scale genomic alterations that contribute to genetic diversity in shaping phenotypic variations, the role of SVs in osteoporosis susceptibility remains poorly understood. This study aims to identify and prioritize genes that harbor BMD-related SVs. We performed whole genome sequencing on 4982 subjects from the Louisiana Osteoporosis Study. To obtain high-confidence SVs, the detection of SVs was performed using an ensemble approach. The SVs were tested for association with BMD variation at the hip (HIP), femoral neck (FNK), and lumbar spine (SPN), respectively. Additionally, we conducted co-occurrence analysis using multi-omics approaches to prioritize the identified genes based on their functional importance. Stratification was employed to explore the sex- and ethnicity-specific effects. We identified significant SV-BMD associations: 125 for FNK-BMD, 99 for SPN-BMD, and 83 for HIP-BMD. We observed SVs that were commonly associated with both FNK and HIP BMDs in our combined and stratified analyses. These SVs explain 13.3% to 19.1% of BMD variation. Novel bone-related genes emerged, including LINC02370, ZNF family genes, and ZDHHC family genes. Additionally, FMN2, carrying BMD-related deletions, showed associations with FNK or HIP BMDs, with sex-specific effects. The co-occurrence analysis prioritized an RNA gene LINC00494 and ZNF family genes positively associated with BMDs at different skeletal sites. Two potential causal genes, IBSP and SPP1, for osteoporosis were also identified. Our study uncovers new insights into genetic factors influencing BMD through SV analysis. We highlight BMD-related SVs, revealing a mix of shared and specific genetic influences across skeletal sites and gender or ethnicity. These findings suggest potential roles in osteoporosis pathophysiology, opening avenues for further research and therapeutic targets.

Aurantio­obtusin regulates lipogenesis and ferroptosis of liver cancer cells through inhibiting SCD1 and sensitizing RSL3.

Ferroptosis, characterized by iron­mediated non­apoptotic cell death and alterations in lipid redox metabolism, has emerged as a critical process implicated in various cellular functions, including cancer. Aurantio­obtusin (AO), a bioactive compound derived from Cassiae semen (the dried mature seeds of Cassie obtusifolia L. or Cassia toral L.), has anti­hyperlipidemic and antioxidant properties; however, to the best of our knowledge, the effect of AO on liver cancer cells remains unclear. The Cell Counting Kit­8, EdU staining and migration assays were employed to assess the anti­liver cancer activity of AO. Intracellular levels of glutathione peroxidase 4 protein and lipid peroxidation were measured as indicators of ferroptotic status. Immunohistochemical analyses, bioinformatics analyses and western blotting were conducted to evaluate the potential of stearoyl­CoA desaturase 1 (SCD1) in combination with ferroptosis inducers for the personalized treatment of liver cancer. The present study revealed that AO significantly inhibited the proliferation of liver cancer cells in vitro and in vivo. Mechanistically, AO inhibited AKT/mammalian target of rapamycin (mTOR) signaling, suppressed sterol regulatory element­binding protein 1 (SREBP1) expression, and downregulated fatty acid synthase expression, thereby inhibiting de novo fatty acid synthesis. Further investigations demonstrated that AO suppressed glutathione peroxidase 4 protein expression through the nuclear factor erythroid 2­related factor 2/heme oxygenase­1 pathway, induced ferroptosis in liver cancer cells, and simultaneously inhibited lipogenesis by suppressing SCD1 expression through the AKT/mTOR/SREBP1 pathway. Consequently, this increased the sensitivity of liver cancer cells to the ferroptosis inducer RSL3. Additionally, the enhanced effects of AO and RSL3, which resulted in significant tumor suppression, were confirmed in a xenograft mouse model. In conclusion, the present study demonstrated that AO induced ferroptosis, downregulated the expression of SCD1 and enhanced the sensitivity of liver cancer cells to the ferroptosis inducer RSL3. The synergistic use of AO and a ferroptosis inducer may have promising therapeutic effects in liver cancer cells.

Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

BACKGROUND: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

ResSAT: Enhancing Spatial Transcriptomics Prediction from H&E- Stained Histology Images with Interactive Spot Transformer.

Spatial transcriptomics (ST) revolutionizes RNA quantification with high spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. Current methods for predicting spatial gene expression from H&E images often overlook spatial relationships. We introduce ResSAT (Residual networks - Self-Attention Transformer), a framework generating spatially resolved gene expression profiles from H&E images by capturing tissue structures and using a self-attention transformer to enhance prediction.Benchmarking on 10× Visium datasets, ResSAT significantly outperformed existing methods, promising reduced ST profiling costs and rapid acquisition of numerous profiles.

An atlas of genetic effects on the monocyte methylome across European and African populations.

Elucidating the genetic architecture of DNA methylation (DNAm) is crucial for decoding the etiology of complex diseases. However, current epigenomic studies often suffer from incomplete coverage of methylation sites and the use of tissues containing heterogeneous cell populations. To address these challenges, we present a comprehensive human methylome atlas based on deep whole-genome bisulfite sequencing (WGBS) and whole-genome sequencing (WGS) of purified monocytes from 298 European Americans (EA) and 160 African Americans (AA) in the Louisiana Osteoporosis Study. Our atlas enables the analysis of over 25 million DNAm sites. We identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis -regions for EA and AA populations, respectively, with 880,108 sites shared between ancestries. While cis -meQTLs exhibited population-specific patterns, primarily due to differences in minor allele frequencies, shared cis -meQTLs showed high concordance across ancestries. Notably, cis -heritability estimates revealed significantly higher mean values in the AA population (0.09) compared to the EA population (0.04). Furthermore, we developed population-specific DNAm imputation models using Elastic Net, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. The performance of our MWAS models was validated through a systematic multi-ancestry analysis of 41 complex traits from the Million Veteran Program. Our findings bridge the gap between genomics and the monocyte methylome, uncovering novel methylation-phenotype associations and their transferability across diverse ancestries. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

Prevalence of subthreshold depression and its related factors in Chinese college students: A cross-sectional study.

Objective: To investigate the prevalence of subthreshold depression among Chinese college students and to explore the related factors. Methods: The research subjects were Chinese college students participating in the "2022 Psychology and Behavior Investigation of Chinese Residents (PBICR-2022)". Data on respondents' general characteristics, quality of life, perceived pressure, family communication, perceived social support, self-efficacy, and depression status were gathered. To investigate the association between each variable and the risk of subthreshold depression, statistical analyses, including chi-square tests and rank sum tests were conducted. Furthermore, a binary stepwise logistic regression was employed to establish the regression model of the factors related to subthreshold depression among Chinese college students. Results: A prevalence of subthreshold depression of about 39.7 % was found among the 8934 respondents. Logistic regression analysis revealed that respondents who are female, have chronic diseases, are in debt, experience significant impacts from epidemic control policies, have lower self-assessed quality of life, experience challenges in family communication, perceive lower social support, have lower self-efficacy, and feel higher perceived pressure are more likely to develop subthreshold depression compared to the control group. (P < 0.05). Conclusion: The prevalence rate of subthreshold depression among Chinese college students was found to be approximately 40 %. Female college students suffering from chronic diseases, with households in debt, greatly impacted by epidemic control policies, and experiencing high perceived stress, may be at risk for subthreshold depression among Chinese college students. On the other hand, strong family communication, perceived social support, and self-efficacy were identified as potential protective factors. In order to facilitate timely screening, diagnosis, and treatment of subthreshold depression in Chinese college students, it is crucial for the government, local communities, colleges, and families to prioritize the mental health of college students and implement targeted measures accordingly.

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

BACKGROUND: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Vancomycin-Loaded Sol-Gel System for In Situ Coating of Artificial Bone to Prevent Surgical Site Infections.

Surgical site infections (SSIs) related to implants have always been a major challenge for clinical doctors and patients. Clinically, doctors may directly apply antibiotics into the wound to prevent SSIs. However, this strategy is strongly associated with experience of doctors on the amount and the location of antibiotics. Herein, an in situ constructable sol-gel system is developed containing antibiotics during surgical process and validated the efficacy against SSIs in beagles. The system involves chitosan (CS), ß-glycerophosphate (ß-GP) and vancomycin (VAN), which can be adsorbed onto porous hydroxyapatite (HA) and form VAN-CS/ß-GP@HA hydrogel in a short time. The VAN concentration from VAN-CS/ß-GP@HA hydrogel is higher than minimum inhibitory concentration (MIC) against Staphylococcus aureus (S. aureus) at the 21st day in vitro. In an in vivo canine model for the prevention of SSIs in the femoral condyle, VAN-CS/ß-GP@HA exhibits excellent biocompatibility, antimicrobial properties, and promotion of bone healing. In all, the CS/ß-GP instant sol-gel system is able to in situ encapsulate antibiotics and adhere on artificial bone implants during the surgery, effectively preventing SSIs related to implants.

Efficacy and safety of fecal microbiota transplantation for treatment of ulcerative colitis: A post-consensus systematic review and meta-analysis.

BACKGROUND: Numerous studies have assessed the efficacy and safety of fecal microbiota transplantation (FMT) as a therapy for ulcerative colitis (UC). However, the treatment processes and outcomes of these studies vary. AIM: To evaluate the efficacy and safety of FMT for treating UC by conducting a systematic meta-analysis. METHODS: The inclusion criteria involved reports of adult patients with UC treated with FMT, while studies that did not report clinical outcomes or that included patients with infection were excluded. Clinical remission (CR) and endoscopic remission (ER) were the primary and secondary outcomes, respectively. RESULTS: We included nine studies retrieved from five electronic databases. The FMT group had better CR than the control group [relative risk (RR) = 1.53; 95% confidence interval (CI): 1.19-1.94; P < 0.0008]. ER was statistically significantly different between the two groups (RR = 2.80; 95%CI: 1.93-4.05; P < 0.00001). Adverse events did not differ significantly between the two groups. CONCLUSION: FMT demonstrates favorable performance and safety; however, well-designed randomized clinical trials are still needed before the widespread use of FMT can be recommended. Furthermore, standardizing the FMT process is urgently needed for improved safety and efficacy.

FOXO-regulated DEAF1 controls muscle regeneration through autophagy.

The commonality between various muscle diseases is the loss of muscle mass, function, and regeneration, which severely restricts mobility and impairs the quality of life. With muscle stem cells (MuSCs) playing a key role in facilitating muscle repair, targeting regulators of muscle regeneration has been shown to be a promising therapeutic approach to repair muscles. However, the underlying molecular mechanisms driving muscle regeneration are complex and poorly understood. Here, we identified a new regulator of muscle regeneration, Deaf1 (Deformed epidermal autoregulatory factor-1) - a transcriptional factor downstream of foxo signaling. We showed that Deaf1 is transcriptionally repressed by FOXOs and that DEAF1 targets to Pik3c3 and Atg16l1 promoter regions and suppresses their expression. Deaf1 depletion therefore induces macroautophagy/autophagy, which in turn blocks MuSC survival and differentiation. In contrast, Deaf1 overexpression inactivates autophagy in MuSCs, leading to increased protein aggregation and cell death. The fact that Deaf1 depletion and its overexpression both lead to defects in muscle regeneration highlights the importance of fine tuning DEAF1-regulated autophagy during muscle regeneration. We further showed that Deaf1 expression is altered in aging and cachectic MuSCs. Manipulation of Deaf1 expression can attenuate muscle atrophy and restore muscle regeneration in aged mice or mice with cachectic cancers. Together, our findings unveil an evolutionarily conserved role for DEAF1 in muscle regeneration, providing insights into the development of new therapeutic strategies against muscle atrophy.Abbreviations: DEAF1: Deformed epidermal autoregulatory factor-1; FOXO: Forkhead box O; MuSC: Muscle Stem Cell; PAX7: Paired box 7; PIK3C3: Phosphatidylinositol 3-kinase catalytic subunit type 3.

[Role and mechanism of epithelial-mesenchymal transition in a rat model of bronchopulmonary dysplasia induced by hyperoxia exposure]. / 上皮-间å è´¨è½¬åŒ–åœ¨é«˜æ°§è¯±å¯¼å¤§é¼ æ”¯æ°”ç®¡è‚ºå‘è‚²ä¸è‰¯æ¨¡åž‹ä¸­çš„ä½œç”¨åŠæœºåˆ¶ç ”ç©¶.

OBJECTIVES: To investigate the role and mechanism of epithelial-mesenchymal transition (EMT) in a rat model of bronchopulmonary dysplasia (BPD). METHODS: The experiment consisted of two parts. (1) Forty-eight preterm rats were randomly divided into a normoxia group and a hyperoxia group, with 24 rats in each group. The hyperoxia group was exposed to 85% oxygen to establish a BPD model, while the normoxia group was kept in room air at normal pressure. Lung tissue samples were collected on days 1, 4, 7, and 14 of the experiment. (2) Rat type II alveolar epithelial cells (RLE-6TN) were randomly divided into a normoxia group (cultured in air) and a hyperoxia group (cultured in 95% oxygen), and cell samples were collected 12, 24, and 48 hours after hyperoxia exposure. Hematoxylin-eosin staining was used to observe alveolarization in preterm rat lungs, and immunofluorescence was used to detect the co-localization of surfactant protein C (SPC) and α-smooth muscle actin (α-SMA) in preterm rat lung tissue and RLE-6TN cells. Quantitative real-time polymerase chain reaction and protein immunoblotting were used to detect the expression levels of EMT-related mRNA and proteins in preterm rat lung tissue and RLE-6TN cells. RESULTS: (1) Compared with the normoxia group, the hyperoxia group showed blocked alveolarization and simplified alveolar structure after 7 days of hyperoxia exposure. Co-localization of SPC and α-SMA was observed in lung tissue, with decreased SPC expression and increased α-SMA expression in the hyperoxia group at 7 and 14 days of hyperoxia exposure compared to the normoxia group. In the hyperoxia group, the mRNA and protein levels of TGF-ß1, α-SMA, and N-cadherin were increased, while the mRNA and protein levels of SPC and E-cadherin were decreased at 7 and 14 days of hyperoxia exposure compared to the normoxia group (P<0.05). (2) SPC and α-SMA was observed in RLE-6TN cells, with decreased SPC expression and increased α-SMA expression in the hyperoxia group at 24 and 48 hours of hyperoxia exposure compared to the normoxia group. Compared to the normoxia group, the mRNA and protein levels of SPC and E-cadherin in the hyperoxia group were decreased, while the mRNA and protein levels of TGF-ß1, α-SMA, and E-cadherin in the hyperoxia group increased at 48 hours of hyperoxia exposure (P<0.05). CONCLUSIONS: EMT disrupts the tight connections between alveolar epithelial cells in a preterm rat model of BPD, leading to simplified alveolar structure and abnormal development, and is involved in the development of BPD. Citation:Chinese Journal of Contemporary Pediatrics, 2024, 26(7): 765-773.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations.

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.

Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non-invasive and cost-effective detection method for early-stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on-skin sensor with low electrode-skin contact impedance for early-stage NAFLD detection is fabricated. A method is developed to synthesize platinum nanoparticles and reduced graphene quantum dots onto the on-skin sensor to reduce electrode-skin contact impedance by increasing double-layer capacitance, thereby enhancing detection accuracy. Furthermore, an attention-based deep learning algorithm is introduced to differentiate impedance signals associated with early-stage NAFLD in high-fat-diet-fed low-density lipoprotein receptor knockout (Ldlr-/-) mice compared to healthy controls. The integration of an adhesive, soft on-skin sensor with low electrode-skin contact impedance and the attention-based deep learning algorithm significantly enhances the detection accuracy for early-stage NAFLD, achieving a rate above 97.5% with an area under the receiver operating characteristic curve (AUC) of 1.0. The findings present a non-invasive approach for early-stage NAFLD detection and display a strategy for improved early detection through on-skin electronics and deep learning.

A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture.

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of the advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patients' health. It could identify individuals at risk with a high accuracy but reduce the unnecessary DXA scanning. Our approach has great promise to guide interventions to prevent hip fractures with reduced cost and radiation.

Identification of the serum metabolites associated with cow milk consumption in Chinese Peri-/Postmenopausal women.

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health.