Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.

OBJECTIVE: This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). MATERIALS AND METHODS: The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. RESULTS: The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. DISCUSSION: The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system's high reliability and suitability has been affirmed through empirical validation. CONCLUSION: The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.

Development and validation of a self-attention network-based algorithm to detect mediastinal lesions on computed tomography images.

Background: Diagnosis of mediastinal lesions on computed tomography (CT) images is challenging for radiologists, as numerous conditions can present as mass-like lesions at this site. This study aimed to develop a self-attention network-based algorithm to detect mediastinal lesions on CT images and to evaluate its efficacy in lesion detection. Methods: In this study, two separate large-scale open datasets [National Institutes of Health (NIH) DeepLesion and Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 Mediastinal Lesion Analysis (MELA) Challenge] were collected to develop a self-attention network-based algorithm for mediastinal lesion detection. We enrolled 921 abnormal CT images from the NIH DeepLesion dataset into the pretraining stage and 880 abnormal CT images from the MELA Challenge dataset into the model training and validation stages in a ratio of 8:2 at the patient level. The average precision (AP) and confidence score on lesion detection were evaluated in the validation set. Sensitivity to lesion detection was compared between the faster region-based convolutional neural network (R-CNN) model and the proposed model. Results: The proposed model achieved an 89.3% AP score in mediastinal lesion detection and could identify comparably large lesions with a high confidence score >0.8. Moreover, the proposed model achieved a performance boost of almost 2% in the competition performance metric (CPM) compared to the faster R-CNN model. In addition, the proposed model can ensure an outstanding sensitivity with a relatively low false-positive rate by setting appropriate threshold values. Conclusions: The proposed model showed excellent performance in detecting mediastinal lesions on CT. Thus, it can drastically reduce radiologists' workload, improve their performance, and speed up the reporting time in everyday clinical practice.

Does Antibiotic Use Increase the Risk of Post-Transplantation Diabetes Mellitus? A Retrospective Study of Renal Transplant Patients.

BACKGROUND This study aimed to investigate the incidence of post-transplant diabetes mellitus (PTDM) in renal transplant (RT) patients at our center and to explore new risk factors for PTDM. MATERIAL AND METHODS This retrospective study included RT patients from 2010 to 2022. Clinic data on RT patients were obtained from hospital electronic medical records. CYP3A5*3, POR*28, ABCB1 (3435 C>T), and ABCB1 (1236 C>T) were genotyped in RT patients. The associations between age, BMI, concentration of tacrolimus (TAC), polymorphism of genes, antibiotics (eg, penicillins, cephalosporins, oxazolidinones, quinolones), numbers and days of antibiotic use, and PTDM were analyzed. RESULTS In this study, 409 patients with RT were included. The cumulative incidence of PTDM in the first year after RT was 9.05%. The numbers and days of antibiotic use in PTDM patients were significantly higher than those in non-PTDM patients. Multivariate logistic regression analysis identified age (OR=1.047, P=0.014), body mass index (BMI) (OR=1.178, P=0.007), dose-adjusted trough concentration of TAC (TAC C0/D) at 7 days after RT (OR=1.159, P=0.042), trough concentration of TAC (TAC C0) at 28 days after RT (OR=1.094, P=0.042), and levofloxacin (OR=5.975, P=0.003) as independent risk factors for PTDM. CONCLUSIONS In addition to age, BMI, and TAC concentration after RT, antibiotic use may be a novel factor affecting PTDM. The use of antibiotics may influence the development of PTDM.

B(C6F5)3/Chiral Phosphoric Acid Catalyzed Asymmetric Aza-Diels-Alder Reaction of Imines and Unactivated Dienes.

Herein, we report an asymmetric aza-Diels-Alder reaction of quinoxalinones or benzoxazinones with unactivated dienes by utilizing a B(C6F5)3/chiral phosphoric acid catalyst to construct chiral six-membered N-heterocycles. Various quinoxalinones or benzoxazinones with electron-withdrawing and electron-donating groups and unactivated dienes were tolerated (up to 99% yield and 99% ee) in the methodology with only 2 mol % catalyst loading. Moreover, the luminescence mechanism and photophysical properties of the product were tested and used for anticounterfeiting of QR codes.

Modifiable Lifestyle, Sedentary Behaviors and the Risk of Frailty: A Univariate and Multivariate Mendelian Randomization Study.

This research conducted a two-sample univariate and multivariate Mendelian Randomization (MR) analysis to explore the causal link between different types of leisure sedentary behavior (LSB) and frailty. Independent instrumental variables significantly associated with sedentary behaviors (p < 5 × 10-8) are obtained from a genome-wide association study (GWAS) of 422,218 individuals, and Frailty Index (FI) are derived from the latest GWAS dataset of 175,226 individuals. MR analysis is conducted using inverse variance weighting, MR-Egger, weighted median, simple mode, and weighted mode, supplemented by MRAPSS. Univariate MR revealed that sedentary behaviors such as watching television increased the risk of frailty (OR, 1.271; 95% CI: 1.202-1.345; p = 6.952 × 10-17), as sedentary driving behaviors are done (OR, 1.436; 95% CI: 1.026-2.011; p = 0.035). Further validation through APSS, taking into account cryptic relatedness, stratification, and sample overlap, maintained the association between television viewing and increased frailty risk (OR, 1.394; 95% CI: 1.266-1.534; p = 1.143 × 10-11), while the association with driving dissipated. In multivariate inverse variance weighted (IVW) analysis, after adjusting for C-reactive protein (CRP) levels, television Sedentary behavior (SB) inversely affected frailty (OR, 0.782; 95% CI: 0.724-0.845; p = 4.820 × 10-10). This study indicates that televisio SB significantly increases the risk of frailty, suggesting potential biological heterogeneity behind specific sedentary activities. This process may interact with inflammation, influencing the development of frailty.

Gestational α-ketoglutarate supplementation ameliorates arsenic-induced hepatic lipid deposition via epigenetic reprogramming of ß-oxidation process in female offspring.

Inorganic trivalent arsenic (iAsⅢ) at environmentally relevant levels has been found to cause developmental toxicity. Maternal exposure to iAsⅢ leads to enduring hepatic lipid deposition in later adult life. However, the exact mechanism in iAsⅢ induced hepatic developmental hazards is still unclear. In this study, we initially found that gestational exposure to iAsⅢ at an environmentally relevant concentration disturbs lipid metabolism and reduces levels of alpha-ketoglutaric acid (α-KG), an important mitochondrial metabolite during the citric acid cycle, in fetal livers. Further, gestational supplementation of α-KG alleviated hepatic lipid deposition caused by early-life exposure to iAsⅢ. This beneficial effect was particularly pronounced in female offspring. α-KG partially restored the ß-oxidation process in hepatic tissues by hydroxymethylation modifications of carnitine palmitoyltransferase 1a (Cpt1a) gene during fetal development. Insufficient ß-oxidation capacities probably play a crucial role in hepatic lipid deposition in adulthood following in utero arsenite exposure, which can be efficiently counterbalanced by replenishing α-KG. These results suggest that gestational administration of α-KG can ameliorate hepatic lipid deposition caused by iAsⅢ in female adult offspring partially through epigenetic reprogramming of the ß-oxidation pathway. Furthermore, α-KG shows potential as an interventive target to mitigate the harmful effects of arsenic-induced hepatic developmental toxicity.

Effectiveness and safety of nirmatrelvir-ritonavir in kidney transplant recipients with severe kidney dysfunction infected with COVID-19.

Transplant patients face an elevated risk of coronavirus disease 2019 (COVID-19) morbidity and mortality and commonly encounter renal dysfunction. Nirmatrelvir is primarily excreted through the kidneys. The dosage of nirmatrelvir/ritonavir (NR) needs to be adjusted according to the degree of renal function impairment. Nevertheless, NR is not recommended for patients with severe renal impairment (estimated glomerular filtration rate < 30 mL/min) due to a dearth of associated research. In this study, we focus on kidney transplant patients and document and analyze the experiences of using NR in individuals with severe kidney dysfunction. This was a retrospective multicenter study that included transplant recipients hospitalized for COVID-19 in five major tertiary hospitals in China from December 2022 to June 2023. The outcomes consisted of the disease progression rate by day 28, individual disease progression events, safety outcomes, information on adverse events (AEs), and the blood drug concentrations of immunosuppressants. Data were presented with descriptive statistics. All analyses were performed using SPSS version 22. In total, 40 patients were included in the analysis. Considering the potential interaction between drugs, all patients temporarily discontinued their immunosuppressants during the NR treatment. None of the 32 moderate patients experienced disease progression. However, among the eight patients with critical COVID-19, unfortunately, two of them died. During the medication period, four patients experienced a total of six AEs associated with NR. None of them experienced AEs with a maximum grade of ≥3. Blood drug concentrations of immunosuppressants were monitored in 22 of 40 patients, and the blood drug concentrations of immunosuppressants did not show a significant increase, but some patients experienced lower blood drug concentrations. Our findings supported the use of NR therapy for the treatment of COVID-19 in transplant patients with severe renal insufficiency. A modified dose of NR was well-tolerated.

Barbier Polymerization-Induced Emission towards Fully Substituted Polyethylene Analogues with Non-Traditional Intrinsic Luminescence.

The properties of polyethylene are highly dependent on the variety and quantity of substitutions. Generally, polyethylene can only be fully substituted with fluorine atoms, mainly e. g., polytetrafluoroethylene and nafion, because atomic radius of fluorine atom is small enough. The preparation of fully substituted polyethylene analogues (FSPEA) and their non-traditional intrinsic luminescence (NTIL) are attractive, especially for substitutions with relatively larger atomic radii than a fluorine atom. Here, Barbier polymerization-induced emission (PIE) is demonstrated as a universal method for the molecular design of NTIL type FSPEAs with intriguing aggregation-induced emission (AIE) behaviors. Through Barbier polymerization of diphenyldichloromethane and different peroxyesters in the presence of Mg in one pot, a series of FSPEAs, including polytriphenylethanol (PTPE), polydiphenylfurylethanol (PDPFE), polydiphenylthiophenylethanol (PDPTE) and polydiphenylnaphthylethanol (PDPNE) have been successfully prepared. Further potential applications for explosive detection, artificial light-harvesting system and white phosphor-converted light-emitting diode are investigated. Therefore, this work opens up a new approach for the molecular design of FSPEA with non-conjugated luminescence, which may cause inspirations to different research fields like polyolefin and luminescent materials.

Rare 7,9'-dinorlignans with neuroprotective activity from the roots of Lindera aggregata (Sims) Kosterm.

Linderagatins C-F (1-4), the first examples of naturally occurring diaryltetrahydrofuran-type 7,9'-dinorlignans, were characterized from the roots of Lindera aggregata (Sims) Kosterm. The structures of these dinorlignans were elucidated by extensive spectroscopic analysis. The absolute configurations were determined based on calculated and experimental ECD data. A biosynthetic pathway for these dinorlignans was hypothetically proposed. Compounds 2 and 3 showed significant neuroprotective effects on erastin-induced ferroptosis in HT-22 cells with EC50 values of 23.4 and 21.8 µM, respectively.

The impact of perioperative enhanced recovery nursing model on postoperative delirium and rehabilitation quality in elderly patients with femoral neck fractures.

BACKGROUND: The aim of this study was to investigate the effects of introducing the Enhanced Recovery After Surgery (ERAS) nursing model on postoperative delirium occurrence and rehabilitation quality in elderly patients with femoral neck fractures. METHODS: A total of 160 elderly patients with femoral neck fractures, who met the inclusion criteria and were admitted between March 2021 to March 2023, were divided into two groups: the traditional care group and the ERAS nursing model group. In addition to traditional care measures, the ERAS nursing model group received interventions based on the principles of the ERAS nursing model. The occurrence of delirium and sleep quality were observed at 24, 48, and 72 h postoperatively, as well as during the overall hospital stay. The duration of hospitalization, time to first mobilization, and post-discharge follow-up on quality of life were compared between the two groups. RESULTS: The ERAS nursing model group exhibited a significant difference in the occurrence of delirium at 48 and 72 h postoperatively, as well as during the overall hospital stay (P < 0.05). However, there was no significant difference in the occurrence of delirium at 24 h postoperatively (P > 0.05). The sleep quality of the two groups showed a statistically significant difference (P < 0.05). The ERAS nursing model group had shorter time to first mobilization, reduced hospitalization duration, and higher Harris and SF-36 scores during post-discharge follow-up, compared to the traditional care group (P < 0.05). CONCLUSIONS: The implementation of the ERAS nursing model in elderly patients with femoral neck fractures improved postoperative sleep quality, reduced delirium occurrence, shortened average hospitalization duration, and enhanced patients' quality of life.

Characteristics and Admission Preferences of Pediatric Emergency Patients and Their Waiting Time Prediction Using Electronic Medical Record Data: Retrospective Comparative Analysis.

BACKGROUND: The growing number of patients visiting pediatric emergency departments could have a detrimental impact on the care provided to children who are triaged as needing urgent attention. Therefore, it has become essential to continuously monitor and analyze the admissions and waiting times of pediatric emergency patients. Despite the significant challenge posed by the shortage of pediatric medical resources in China's health care system, there have been few large-scale studies conducted to analyze visits to the pediatric emergency room. OBJECTIVE: This study seeks to examine the characteristics and admission patterns of patients in the pediatric emergency department using electronic medical record (EMR) data. Additionally, it aims to develop and assess machine learning models for predicting waiting times for pediatric emergency department visits. METHODS: This retrospective analysis involved patients who were admitted to the emergency department of Children's Hospital Capital Institute of Pediatrics from January 1, 2021, to December 31, 2021. Clinical data from these admissions were extracted from the electronic medical records, encompassing various variables of interest such as patient demographics, clinical diagnoses, and time stamps of clinical visits. These indicators were collected and compared. Furthermore, we developed and evaluated several computational models for predicting waiting times. RESULTS: In total, 183,024 eligible admissions from 127,368 pediatric patients were included. During the 12-month study period, pediatric emergency department visits were most frequent among children aged less than 5 years, accounting for 71.26% (130,423/183,024) of the total visits. Additionally, there was a higher proportion of male patients (104,147/183,024, 56.90%) compared with female patients (78,877/183,024, 43.10%). Fever (50,715/183,024, 27.71%), respiratory infection (43,269/183,024, 23.64%), celialgia (9560/183,024, 5.22%), and emesis (6898/183,024, 3.77%) were the leading causes of pediatric emergency room visits. The average daily number of admissions was 501.44, and 18.76% (34,339/183,204) of pediatric emergency department visits resulted in discharge without a prescription or further tests. The median waiting time from registration to seeing a doctor was 27.53 minutes. Prolonged waiting times were observed from April to July, coinciding with an increased number of arrivals, primarily for respiratory diseases. In terms of waiting time prediction, machine learning models, specifically random forest, LightGBM, and XGBoost, outperformed regression methods. On average, these models reduced the root-mean-square error by approximately 17.73% (8.951/50.481) and increased the R2 by approximately 29.33% (0.154/0.525). The SHAP method analysis highlighted that the features "wait.green" and "department" had the most significant influence on waiting times. CONCLUSIONS: This study offers a contemporary exploration of pediatric emergency room visits, revealing significant variations in admission rates across different periods and uncovering certain admission patterns. The machine learning models, particularly ensemble methods, delivered more dependable waiting time predictions. Patient volume awaiting consultation or treatment and the triage status emerged as crucial factors contributing to prolonged waiting times. Therefore, strategies such as patient diversion to alleviate congestion in emergency departments and optimizing triage systems to reduce average waiting times remain effective approaches to enhance the quality of pediatric health care services in China.

Model-Based Feedback Control for an Automated Micro Liquid Dispensing System Based on Contacting Droplet Generation through Image Sensing.

Over the past few decades, micro liquid dispensing technology has been widely used in biology, chemistry, material and environmental sciences due to its efficacy in processing multiple samples. For practical applications, precise and effective droplet generation is very important. Despite numerous droplet generation methods, the implementation of droplet-on-demand still faces challenges concerning system complexity, precision, cost, and robustness. In this work, a novel on-demand contacting droplet generation method incorporated with model-based feedback control with an image processing unit as a sensor was proposed. By studying droplet identification using image processing techniques, the model of droplet formation was simplified. Then model-based feedback control was implemented using volumes of dispensed samples as sensing signals by tuning related parameters adaptively to resist disturbances. The proposed method was integrated and applied to a homebuilt automated micro liquid dispensing system with droplets ranging from 20 nanoliter to 200 nanoliter. The experimental results demonstrated a high degree of accuracy and precision. Additionally, the proposed system's practical utility was evaluated by analyzing mutations in genes associated with sensorineural hearing loss, verifying its effectiveness.

Effects of Gestational Arsenic Exposures on Placental and Fetal Development in Mice: The Role of Cyr61 m6A.

BACKGROUND: Several epidemiological investigations demonstrated that maternal arsenic (As) exposure elevated risk of fetal growth restriction (FGR), but the mechanism remains unclear. OBJECTIVES: This study aimed to investigate the effects of gestational As exposure on placental and fetal development and its underlying mechanism. METHODS: Dams were exposed to 0.15, 1.5, and 15mg/L NaAsO2 throughout pregnancy via drinking water. Sizes of fetuses and placentas, placental histopathology, and glycogen content were measured. Placental RNA sequencing was conducted. Human trophoblasts were exposed to NaAsO2 (2µM) to establish an in vitro model of As exposure. The mRNA stability and protein level of genes identified through RNA sequencing were measured. N6-Methyladenosine (m6A) modification was detected by methylated RNA immunoprecipitation-quantitative real-time polymerase chain reason (qPCR). The binding ability of insulin-like growth factor 2 binding protein 2 to the gene of interest was detected by RNA-binding protein immunoprecipitation-qPCR. Intracellular S-adenosylmethionine (SAM) and methyltransferase activity were determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and colorimetry, respectively. In vitro As+3 methyltransferase (As3MT) knockdown or SAM supplementation and in vivo folic acid (FA) supplementation were used to evaluate the protective effect. A case-control study verified the findings. RESULTS: Sizes of fetuses (exposed to 1.5 and 15mg/L NaAsO2) and placentas (exposed to 15mg/L NaAsO2) were lower in As-exposed mice. More glycogen+ trophoblasts accumulated and the expression of markers of interstitial invasion was lower in the 15mg/L NaAsO2-exposed mouse group in comparison with control. Placental RNA sequencing identified cysteine-rich angiogenic inducer 61 (Cyr61) as a candidate gene of interest. Mechanistically, mice and cells exposed to As had lower protein expression of CYR61, and this was attributed to a lower incidence of Cyr61 m6A. Furthermore, cells exposed to As had lower methyltransferase activity, suggesting that this could be the mechanism by which Cyr61 m6A was affected. Depletion of intracellular SAM, a cofactor for m6A methyltransferase catalytic domain, partially contributed to As-induced methyltransferase activity reduction. Either As3MT knockdown or SAM supplementation attenuated As-induced Cyr61 m6A down-regulation. In mice, FA supplementation rescued As-induced defective trophoblastic invasion and FGR. In humans, a negative correlation between maternal urinary As and plasma CYR61 was observed in infants who were small for gestational age. DISCUSSION: Using in vitro and in vivo models, we found that intracellular SAM depletion-mediated Cyr61 m6A down-regulation partially contributed to As-induced defective trophoblastic invasion and FGR. https://doi.org/10.1289/EHP12207.

Elevated serum FGF21 is an independent predictor for adverse events in hemodialysis patients from two large centers: a prospective cohort study.

Introduction: We explored the relationship and the predictive value of serum fibroblast growth factor 21 (FGF21) with all-cause mortality, major adverse cardiovascular events (MACEs) and pneumonia in hemodialysis (HD) patients.Methods: A total of 388 Chinese HD patients from two HD centers were finally enrolled in this prospective cohort study (registration number: ChiCTR 1900028249) between January 2018 and December 2018. Serum FGF21 was detected. Patients were followed up with a median period of 47 months to record the MACEs and pneumonia until death or 31 December 2022.Results: The incidence of all-cause mortality, MACEs and pneumonia in HD patients were 20.6%, 29.6%, and 34.8%, respectively. The optimal cutoffs for FGF21 to predict all-cause mortality, MACEs and pneumonia were 437.57 pg/mL, 216.99 pg/mL and 112.79 pg/mL. Multivariate Cox regression analyses showed that FGF21, as a categorical variable, was an independent predictor for all-cause mortality, MACEs and pneumonia (HR, 3.357, 95% CI, 2.128-5.295, p < 0.001; HR, 1.575, 95% CI, 1.046-2.371, p = 0.029; HR, 1.784; 95% CI, 1.124-2.830; p = 0.014, respectively). The survival nomogram, MACEs-free survival nomogram and pneumonia-free survival nomogram based on FGF21 constructed for individualized assessment of HD patients had a high C-index with 0.841, 0.706 and 0.734.Conclusion: Higher serum FGF21 is an independent predictor of all-cause mortality, MACEs and pneumonia in HD patients.

Quantification of Multiplex miRNAs by Mass Spectrometry with Duplex-Specific Nuclease-Mediated Amplification.

Early quantification of multiplex biomarkers such as microRNAs (miRNAs) is critical during disease pathologic development and therapy. To tackle challenges of low abundance and multiplexing, we herein report a mass-encoded biosensing approach with duplex-specific nuclease (DSN) mediated signal amplification. Magnetic Fe3O4 cores are coated with small gold nanoparticles (AuNPs), which are applied to achieve facile DNA immobilization subsequent separation. This biosensor integrates multiple mass reporters corresponding to different targets (five miRNAs as examples). Due to the excellent resolution of mass spectrometry, these targets can be successfully distinguished in a single spectrum. Wide detection ranges from 10 fM to 1 nM are achieved, and the limits of detection are estimated to be 10 fM. High selectivity is promised due to the enzyme activity of DSN, and practical application in human serum samples performs satisfactorily. The number of targets to be tested can be further expanded by designing different specific mass tags in theory. Therefore, the proposed method can be utilized as an important and valuable tool to quantify multiplex miRNAs for disease screening as well as biomedical investigations.

Gestational 1-nitropyrene exposure causes anxiety-like behavior partially by altering hippocampal epigenetic reprogramming of synaptic plasticity in male adult offspring.

1-Nitropyrene (1-NP), a typical nitro-polycyclic aromatic hydrocarbon, is a developmental toxicant. This study was to evaluate gestational 1-NP-induced anxiety-like behavior in male adult offspring. Pregnant mice were orally administered to 1-NP daily throughout pregnancy. Anxiety-like behaviors, as determined by Elevated Plus-Maze (EPM) and Open-Field Test (OFT), were showed in male adult offspring whose mothers were exposed to 1-NP. Gestational 1-NP exposure reduced dendritic arborization, dendritic length and dendritic spine density in ventral hippocampus of male adult offspring. Additional experiments showed that gephyrin, an inhibitory synaptic marker, was reduced in fetal forebrain and hippocampus in male adult offspring. Nrg1 and Erbb4, two gephyrin-related genes, were reduced in 1-NP-exposed fetuses. Accordingly, 5hmC contents in two CpG sites (32008909 and 32009239) of Nrg1 gene and three CpG sites (69107743, 69107866 and 69107899) of Erbb4 gene were decreased in 1-NP-exposed fetuses. Mechanistically, ten-eleven translocation (TET) activity and alpha-ketoglutarate (α-KG) content were decreased in 1-NP-exposed fetal forebrain. Supplementation with α-KG alleviated 1-NP-induced downregulation of gephyrin-related genes, prevented hippocampal synaptic damage, and improved anxiety-like behavior in male adult offspring. These results indicate that early-life 1-NP exposure causes anxiety-like behavior in male adulthood partially by altering hippocampal epigenetic reprogramming of synaptic plasticity.

Associations between Dietary Patterns and Physical Activity with Physical Fitness among Adolescents in Shandong Province, China: A Cross-Sectional Study.

BACKGROUND: The trend of physical fitness (PF) and physical activity (PA) among Chinese adolescents is not optimistic, and unhealthy dietary behaviors are common. PA and dietary patterns (DPs) have been linked to PF in adolescents, but the associations between DPs and PF with PF in Chinese adolescents are rarely discussed. METHODS: A total of 8796 adolescents aged 11-18 were enrolled from Shandong Province, China. The CNSPFS battery was applied to assess PF. PA levels and diet quality were determined using the Physical Activity Questionnaire for Adolescents and the modified Chinese Diet Quality Questionnaire, respectively. This study used factor analysis to identify DPs and linear regression models to investigate the association between PF and related factors. RESULTS: The average PF score of the participants was 75.67. Adolescents who were girls, lived in rural areas and were active in PA performed better on the PF test (p < 0.05). Boys whose fathers were university educated or above had a higher probability of achieving higher PF scores (OR 4.36, 95% CI 1.32-14.36); however, if their mothers were university educated or above, they had a lower probability of achieving higher PF scores (OR 0.22, 95% CI 0.063-0.76). Unhealthy dietary pattern was negatively correlated with cardiorespiratory fitness in boys (OR 0.56, 95% CI 0.31-0.98). The association between unhealthy dietary pattern and girls' BMI became significant after adjustment for PA (p < 0.05). CONCLUSIONS: Girls performed better in PF than boys. Highly educated fathers could contribute to improve the PF performance in boys. There were four DPs among adolescents in Shandong Province, and different DPs may have different effects on PF in boys and girls.

Long-term cadmium exposure induces chronic obstructive pulmonary disease-like lung lesions in a mouse model.

Accumulating evidences demonstrate that long-term exposure to atmospheric fine particles and air pollutants elevates the risk of chronic obstructive pulmonary disease (COPD). Cadmium (Cd) is one of the important toxic substances in atmospheric fine particles and air pollutants. In this study, we aimed to establish a mouse model to evaluate whether respiratory Cd exposure induces COPD-like lung injury. Adult male C57BL/6 mice were exposed to CdCl2 (10 mg/L, 4 h per day) by inhaling aerosol for either 10 weeks (short-term) or 6 months (long-term). The mean serum Cd concentration was 6.26 µg/L in Cd-exposed mice. Lung weight and coefficient were elevated in long-term Cd-exposed mice. Pathological scores and alveolar destructive indices were increased in long-term Cd-exposed mouse lungs. Mean linear intercept and airway wall thickness were accordingly elevated in Cd-exposed mice. Inflammatory cell infiltration was obvious and inflammatory cytokines, including TNF-α, IL-1ß, IL-6, IL-8, IL-10 and TGF-ß, were up-regulated in Cd-exposed mouse lungs. α-SMA, N-cadherin and vimentin, epithelial-mesenchymal transition markers, and extracellular matrix collagen deposition around small airway, determined by Masson's trichrome staining, were shown in Cd-exposed mouse lungs. COPD-characteristic lung function decline was observed in long-term Cd-exposed mice. These outcomes show that long-term respiratory exposure to Cd induces COPD-like lung lesions for the first time.

Mining Potential Drug Targets for Osteoporosis Based on CeRNA Network.

OBJECTIVE: To identify key pathological hub genes, micro RNAs (miRNAs), and circular RNAs (circRNAs) of osteoporosis (OP) and construct their ceRNA network in an effort to explore the potential biomarkers and drug targets for OP therapy. METHODS: GSE7158, GSE201543, and GSE161361 microarray datasets were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by comparing OP patients with healthy controls and hub genes were screened by machine learning algorithms. Target miRNAs and circRNAs were predicted by FunRich and circbank, then ceRNA network were constructed by Cytoscape. Pathways affecting OP were identified by functional enrichment analysis. The hub genes were verified by receiver operating characteristic (ROC) curve and real time quantitative PCR (RT-qPCR). Potential drug molecules related to OP were predicted by DSigDB database and molecular docking was analyzed by autodock vina software. RESULTS: A total of 179 DEGs were identified. By combining three machine learning algorithms, BAG2, MME, SLC14A1, and TRIM44 were identified as hub genes. Three OP-associated target miRNAs and 362 target circRNAs were predicted to establish ceRNA network. The ROC curves showed that these four hub genes had good diagnostic performance and their differential expression was statistically significant in OP animal model. Benzo[a]pyrene was predicted which could successfully bind to protein receptors related to the hub genes and it was served as the potential drug molecules. CONCLUSION: An mRNA-miRNA-circRNA network is reported, which provides new ideas for exploring the pathogenesis of OP. Benzo[a]pyrene, as potential drug molecules for OP, may provide guidance for the clinical treatment.

Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov.

Artificial intelligence (AI) has driven innovative transformation in healthcare service patterns, despite a lack of understanding of its performance in clinical practice. We conducted a cross-sectional analysis of AI-related trials in healthcare based on ClinicalTrials.gov, intending to investigate the trial characteristics and AI's development status. Additionally, the Neo4j graph database and visualization technology were employed to construct an AI technology application graph, achieving a visual representation and analysis of research hotspots in healthcare AI. A total of 1725 eligible trials that were registered in ClinicalTrials.gov up to 31 March 2022 were included in this study. The number of trial registrations has dramatically grown each year since 2016. However, the AI-related trials had some design drawbacks and problems with poor-quality result reporting. The proportion of trials with prospective and randomized designs was insufficient, and most studies did not report results upon completion. Currently, most healthcare AI application studies are based on data-driven learning algorithms, covering various disease areas and healthcare scenarios. As few studies have publicly reported results on ClinicalTrials.gov, there is not enough evidence to support an assessment of AI's actual performance. The widespread implementation of AI technology in healthcare still faces many challenges and requires more high-quality prospective clinical validation.