Background Asthma poses a serious threat to children's growth, development, and mental health, thus there has been an increasing focus on the control of asthma morbidity in children and the assessment of its risk factors. A growing body of research has found that exposure to ambient air pollutants an significatly increase the risk of childhood asthma. Objective To understand the changes of ambient air pollutant concentrations in Nanjing and asthma outpatient visits to Nanjing Children's Hospital, and to quantitatively analyze the effects of exposure to different ambient air pollutants on children's asthma outpatient visits. Methods Daily data of ambient air pollutants fine particulate matter (PM_2.5), inhalable particle (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃), meteorological factors (air temperature & relative humidity), and outpatient visits due to asthma in the hospital from January 1, 2019 to December 31, 2023 were collected, and a generalized additive model based on quasi poisson distributions was used to quantitatively analyze the short-term effects of ambient air pollutant exposure on outpatient visits due to asthma in the hospital. Results The annual average concentrations of PM_2.5, PM₁₀, SO₂, and NO₂ in Nanjing from 2019 to 2023 did not exceed the national limits. For single-day lagged effects, the single-pollutant model showed that the effects of PM_2.5, PM₁₀, NO₂, and CO on children's asthma outpatient visits were greatest for every 10 units increase at lag0, with excess risk (ER) of 1.39% (95%CI: 0.65%, 2.14%), 1.46% (95%CI: 0.97%, 1.95%), 5.46% (95%CI: 4.36%, 6.57%), and 0.18% (95%CI: 0.11%, 0.26%), respectively, and SO₂ reached the maximum effect at lag1, with an ER of 23.15% (95%CI: 13.57%, 33.53%) for each 10 units increase in concentration. Different pollutants reached their maximum cumulative lag effects at different time. The PM₁₀, PM_2.5, SO₂, NO₂, and CO showed the largest cumulative lag effects at lag01, lag01, lag02, lag02, and lag03, respectively, with ERs of 1.35% (95%CI: 0.77%, 1.92%), 0.96% (95%CI: 0.10%, 1.83%), 28.50% (95%CI: 15.49%, 42.98%), 6.92% (95%CI: 5.53%, 8.33%), and 0.31% (95%CI: 0.20%, 0.42%), respectively. The influences of PM_2.5 and PM₁₀ on outpatient visits due to asthma in the hospital became more pronounced with advancing age, while the associations with NO₂, SO₂, and CO were weakened as children grew older. Conclusion Ambient air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO) can increase childhood asthma visits, and different pollutants have varied effects on the number of asthmatic children's visits at different ages.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Based on the interleukin-17(IL-17) signaling pathway, this study explores the effect and mechanism of Bufei Decoction on Klebsiella pneumoniae pneumonia in rats. SD rats were randomly divided into the control group, model group, Bufei Decoction low-dose group(6.68 g·kg~(-1)·d~(-1)), Bufei Decoction high-dose group(13.36 g·kg~(-1)·d~(-1)), and dexamethasone group(1.04 mg·kg~(-1)·d~(-1)), with 10 rats in each group. A pneumonia model was established by tracheal drip injection of K. pneumoniae. After successful model establishment, the improvement in lung tissue damage was observed following drug administration. Core targets and signaling pathways were screened using transcriptomics techniques. Real-time fluorescence quantitative polymerase chain reaction was used to detect the mRNA expression of core targets interleukin-6(IL-6), interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and chemokine CXC ligand 6(CXCL6). Western blot was used to assess key proteins in the IL-17 signaling pathway, including interleukin-17A(IL-17A), nuclear transcription factor-κB activator 1(Act1), tumor necrosis factor receptor-associated factor 6(TRAF6), and downstream phosphorylated p38 mitogen-activated protein kinase(p-p38 MAPK), and phosphorylated nuclear factor-κB p65(p-NF-κB p65). Apoptosis of lung tissue cells was detected by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling(TUNEL). The results showed that, compared with the control group, the model group exhibited significant pathological damage in lung tissue. The mRNA expression of IL-6, IL-1β, TNF-α, and CXCL6, as well as the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly increased, and the number of apoptotic cells was notably higher, indicating successful model establishment. Compared with the model group, both low-and high-dose groups of Bufei Decoction showed reduced pathological damage in lung tissue. The mRNA expression levels of IL-6, IL-1β, TNF-α, and CXCL6, and the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly decreased, with a significant reduction in apoptotic cells in the high-dose group. In conclusion, Bufei Decoction can effectively improve lung tissue damage and reduce inflammation in rats with K. pneumoniae. The mechanism may involve the regulation of the IL-17 signaling pathway and the reduction of apoptosis.
Animals ; Interleukin-17/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Rats ; Male ; Klebsiella pneumoniae/physiology* ; Klebsiella Infections/immunology* ; Humans ; Lung/drug effects*

Male factors contribute to 50% of infertility cases, with 20%-30% of cases being solely attributed to male infertility. Helicase for meiosis 1 ( HFM1 ) plays a crucial role in ensuring proper crossover formation and synapsis of homologous chromosomes during meiosis, an essential process in gametogenesis. HFM1 gene mutations are associated with male infertility, particularly in cases of non-obstructive azoospermia and severe oligozoospermia. However, the effects of intracytoplasmic sperm injection (ICSI) in HFM1 -related infertility cases remain inadequately explored. This study identified novel biallelic HFM1 variants through whole-exome sequencing (WES) in a Chinese patient with severe oligozoospermia, which was confirmed by Sanger sequencing. The pathogenicity of these variants was assessed using real-time quantitative polymerase chain reaction (RT-qPCR) and immunoblotting, which revealed a significant reduction in HFM1 mRNA and protein levels in spermatozoa compared to those in a healthy control. Transmission electron microscopy revealed morphological abnormalities in sperm cells, including defects in the head and flagellum. Despite these abnormalities, ICSI treatment resulted in a favorable fertility outcome for the patient, indicating that assisted reproductive techniques (ART) can be effective in managing HFM1 -related male infertility. These findings offer valuable insights into the management of such cases.
Humans ; Male ; Sperm Injections, Intracytoplasmic ; Oligospermia/therapy* ; Adult ; Spermatozoa/ultrastructure* ; Exome Sequencing ; Mutation

OBJECTIVE: To explore the expression regulation of lipid metabolism gene ABHD5 in testes. METHODS: Differential gene analysis was performed by integrating databases of TCGA and GTEx to identify the target gene ABHD5. The expression trends of ABHD5 gene in testicular carcinoma tissue were analyzed. Human testis single-cell atlases were obtained from the Human Protein Atlas and Male Health Atlas databases to determine the expression distribution of ABHD5 across different testicular cell types. Additionally, the GTEx database was utilized to visualize the expression pattern of ABHD5 in the testis, thereby enhancing the understanding of its transcriptional profile. The relationship between ABHD5 expression and age was assessed through integrated database analysis. Western blotting and immunofluorescence were performed to detect differential expressions of ABHD5 in testicular tissues of young and aged mice respectively. RESULTS: The TCGA database indicated that the expression of ABHD5 in human testicular carcinoma tissue was significantly lower than that in normal testicular tissue which showed a negative correlation with patient survival. ABHD5 was highly expressed in germ cells of the testis reveaked from Human Protein Atlas and Male Health Atlas databases. The stability of ABHD5 protein was crucial for testicular tissue, and its expression decreased with age. Furthermore, Western blot and immunofluorescence staining demonstrated that ABHD5 expression in the testicular tissue of aged mice was significantly lower than that in young mice. CONCLUSION ABHD5 plays an important role in testicular tissue, and may be inseparable from testicular tumors and reproductive aging. However, its mechanism of action remains to be further studied.
Male ; Animals ; Mice ; Testis/metabolism* ; Humans ; Lipid Metabolism/genetics* ; 1-Acylglycerol-3-Phosphate O-Acyltransferase/metabolism* ; Testicular Neoplasms/metabolism*

OBJECTIVE: Pneumoconiosis, a lung disease caused by irreversible fibrosis, represents a significant public health burden. This study investigates the causal relationships between gut microbiota, gene methylation, gene expression, protein levels, and pneumoconiosis using a multi-omics approach and Mendelian randomization (MR). METHODS: We analyzed gut microbiota data from MiBioGen and Esteban et al. to assess their potential causal effects on pneumoconiosis subtypes (asbestosis, silicosis, and inorganic pneumoconiosis) using conventional and summary-data-based MR (SMR). Gene methylation and expression data from Genotype-Tissue Expression and eQTLGen, along with protein level data from deCODE and UK Biobank Pharma Proteomics Project, were examined in relation to pneumoconiosis data from FinnGen. To validate our findings, we assessed self-measured gut flora from a pneumoconiosis cohort and performed fine mapping, drug prediction, molecular docking, and Phenome-Wide Association Studies to explore relevant phenotypes of key genes. RESULTS: Three core gut microorganisms were identified: Romboutsia ( OR = 0.249) as a protective factor against silicosis, Pasteurellaceae ( OR = 3.207) and Haemophilus parainfluenzae ( OR = 2.343) as risk factors for inorganic pneumoconiosis. Additionally, mapping and quantitative trait loci analyses revealed that the genes VIM, STX8, and MIF were significantly associated with pneumoconiosis risk. CONCLUSIONS This multi-omics study highlights the associations between gut microbiota and key genes ( VIM, STX8, MIF) with pneumoconiosis, offering insights into potential therapeutic targets and personalized treatment strategies.
Humans ; Male ; East Asian People/genetics* ; Europe ; Gastrointestinal Microbiome ; Lung ; Macrophage Migration-Inhibitory Factors/metabolism* ; Mendelian Randomization Analysis ; Multiomics ; Pneumoconiosis/microbiology* ; Intramolecular Oxidoreductases

This study explores the effects and possible mechanisms of nuclear factor E2 related factor 2 (NRF2) on ovarian granulosa cells, providing a scientific basis to prevent premature ovarian failure. An ovarian cell injury model was constructed by treating human ovarian granulosa cell (KGN cell) with 4-Vinylcyclohexene dioxide (VCD). Firstly, KGN cells were treated with different concentrations of VCD, and cell counting kit 8 (CCK-8) was used to detect ovarian cell proliferation. After determining IC 50 by CCK8, the levels of estradiol and progesterone in the cell supernatant were detected using enzyme-linked immunosorbent assay (ELISA), reactive oxygen species (ROS) assay kit was used to detect the content of ROS in ovarian cells, real-time fluorescence quantitative polymerase chain reaction (qRT PCR) was used to detect the mRNA expression level of NRF2, and Western blot was used to detect the protein expression level of NRF2. Further, NRF2 silence (siNRF2) and overexpression (NRF2-OE) cell models were constructed through lentivirus transfection, and the effects of regulating NRF2 on VCD treated cell models were investigated by detecting hormone levels, oxidative stress indicators (ROS, SOD, GSH-Px), and autophagy (LC3B level). The results showed that VCD intervention inhibited the proliferation of ovarian granulosa cells in a time-dependent and dose-dependent manner ( F>100, P<0.05), with an IC 50 of 1.2 mmol/L at 24 hours. After VCD treatment, the level of estradiol in the cell supernatant decreased from (56.32±10.18) ng/ml to (24.59±8.75) ng/ml ( t=5.78, P<0.05). Progesterone decreased from (50.25±7.03) ng/ml to (25.13±6.67) ng/ml ( t=6.54, P<0.05). After VCD treatment, the SOD of cells decreased from (44.47±7.71) ng/ml to (30.92±4.97) ng/ml ( t=3.61, P<0.05). GSH-Px decreased from (68.51±10.17) ng/ml to (35.19±6.59) ng/ml ( t=5.73, P<0.05). Simultaneously accompanied by an increase in autophagy and a decrease in NRF2. This study successfully constructed KGN cell models that silenced NRF2 and overexpressed NRF2. Subsequently, this study treated each group of cells with VCD and found that the cell proliferation activity of the siNRF2 group was significantly reduced ( t=8.37, P<0.05), while NRF2-OE could reverse the cell activity damage caused by VCD ( t=3.37, P<0.05). The siNRF2 group had the lowest level of estradiol ( t=5.78, P<0.05), while NRF2-OE could reverse the decrease in cellular estradiol levels caused by VCD ( t=5.58, P<0.05). The siNRF2 group had the lowest progesterone levels ( t=3.02, P<0.05), while NRF2-OE could reverse the decrease in cellular progesterone levels caused by VCD ( t=2.41, P<0.05). The ROS level in the siNRF2 group was the highest ( t=2.86, P<0.05), NRF2-OE could reverse the increase in ROS caused by VCD ( t=3.14, P<0.05), the SOD enzyme content in the siNRF2 group was the lowest ( t=2.98, P<0.05), and NRF2-OE could reverse the decrease in SOD enzyme content caused by VCD ( t=4.72, P<0.05). The GSH-Px enzyme content in the siNRF2 group was the lowest ( t=3.67, P<0.05), and NRF2-OE could reverse the decrease in antioxidant enzyme content caused by VCD ( t=2.71, P<0.05). The LC3B level was highest in the siNRF2 group ( t=2.45, P<0.05), and NRF2-OE was able to reverse the LC3B elevation caused by VCD ( t=9.64, P<0.05). In conclusion, NRF2 inhibits ROS induced autophagy, thereby playing a role in reducing ovarian granulosa cell damage, which may be a potential target for premature ovarian failure.

Objective:To evaluate the diagnostic efficacy and practicality of the Jaundice color card (JCard) as a screening tool for neonatal jaundice.Methods:Following the standards for reporting of diagnostic accuracy studies (STARD) statement, a multicenter prospective study was conducted in 9 hospitals in China from October 2019 to September 2021. A total of 845 newborns who were admitted to the hospital or outpatient department for liver function testing due to their own diseases. The inclusion criteria were a gestational age of ≥35 weeks, a birth weight of ≥2 000 g, and an age of ≤28 days. The neonate′s parents used the JCard to measure jaundice at the neonate′s cheek. Within 2 hours of the JCard measurement, transcutaneous bilirubin (TcB) was measured with a JH20-1B device and total serum bilirubin (TSB) was detected. The Pearson′s correlation analysis, Bland-Altman plots and the receiver operating characteristic (ROC) curve were used for statistic analysis.Results:Out of the 854 newborns, 445 were male and 409 were female; 46 were born at 35-36 weeks of gestational age and 808 were born at ≥37 weeks of gestational age. Additionally, 432 cases were aged 0-3 days, 236 cases were aged 4-7 days, and 186 cases were aged 8-28 days. The TSB level was (227.4±89.6) μmol/L, with a range of 23.7-717.0 μmol/L. The JCard level was (221.4±77.0) μmol/L and the TcB level was (252.5±76.0) μmol/L. Both the JCard and TcB values showed good correlation ( r=0.77 and 0.80, respectively) and agreements (96.0% (820/854) and 95.2% (813/854) of samples fell within the 95% limits of agreement, respectively) with TSB. The JCard value of 12 had a sensitivity of 0.93 and specificity of 0.75 for identifying a TSB ≥205.2?μmol/L, and a sensitivity of 1.00 and specificity of 0.35 for identifying a TSB ≥342.0?μmol/L. The TcB value of 205.2?μmol/L had a sensitivity of 0.97 and specificity of 0.60 for identifying TSB levels of 205.2 μmol/L, and a sensitivity of 1.00 and specificity of 0.26 for identifying TSB levels of 342.0 μmol/L. The areas under the ROC curve (AUC) of JCard for identifying TSB levels of 153.9, 205.2, 256.5, and 342.0 μmol/L were 0.96, 0.92, 0.83, and 0.83, respectively. The AUC of TcB were 0.94, 0.91, 0.86, and 0.87, respectively. There were both no significant differences between the AUC of JCard and TcB in identifying TSB levels of 153.9 and 205.2 μmol/L (both P>0.05). However, the AUC of JCard were both lower than those of TcB in identifying TSB levels of 256.5 and 342.0 μmol/L (both P<0.05). Conclusions:JCard can be used to classify different levels of bilirubin, but its diagnostic efficacy decreases with increasing bilirubin levels. When TSB level are ≤205.2 μmol/L, its diagnostic efficacy is equivalent to that of the JH20-1B. To prevent the misdiagnosis of severe jaundice, it is recommended that parents use a low JCard score, such as 12, to identify severe hyperbilirubinemia (TSB ≥342.0 μmol/L).

This study explores the effects and possible mechanisms of nuclear factor E2 related factor 2 (NRF2) on ovarian granulosa cells, providing a scientific basis to prevent premature ovarian failure. An ovarian cell injury model was constructed by treating human ovarian granulosa cell (KGN cell) with 4-Vinylcyclohexene dioxide (VCD). Firstly, KGN cells were treated with different concentrations of VCD, and cell counting kit 8 (CCK-8) was used to detect ovarian cell proliferation. After determining IC 50 by CCK8, the levels of estradiol and progesterone in the cell supernatant were detected using enzyme-linked immunosorbent assay (ELISA), reactive oxygen species (ROS) assay kit was used to detect the content of ROS in ovarian cells, real-time fluorescence quantitative polymerase chain reaction (qRT PCR) was used to detect the mRNA expression level of NRF2, and Western blot was used to detect the protein expression level of NRF2. Further, NRF2 silence (siNRF2) and overexpression (NRF2-OE) cell models were constructed through lentivirus transfection, and the effects of regulating NRF2 on VCD treated cell models were investigated by detecting hormone levels, oxidative stress indicators (ROS, SOD, GSH-Px), and autophagy (LC3B level). The results showed that VCD intervention inhibited the proliferation of ovarian granulosa cells in a time-dependent and dose-dependent manner ( F>100, P<0.05), with an IC 50 of 1.2 mmol/L at 24 hours. After VCD treatment, the level of estradiol in the cell supernatant decreased from (56.32±10.18) ng/ml to (24.59±8.75) ng/ml ( t=5.78, P<0.05). Progesterone decreased from (50.25±7.03) ng/ml to (25.13±6.67) ng/ml ( t=6.54, P<0.05). After VCD treatment, the SOD of cells decreased from (44.47±7.71) ng/ml to (30.92±4.97) ng/ml ( t=3.61, P<0.05). GSH-Px decreased from (68.51±10.17) ng/ml to (35.19±6.59) ng/ml ( t=5.73, P<0.05). Simultaneously accompanied by an increase in autophagy and a decrease in NRF2. This study successfully constructed KGN cell models that silenced NRF2 and overexpressed NRF2. Subsequently, this study treated each group of cells with VCD and found that the cell proliferation activity of the siNRF2 group was significantly reduced ( t=8.37, P<0.05), while NRF2-OE could reverse the cell activity damage caused by VCD ( t=3.37, P<0.05). The siNRF2 group had the lowest level of estradiol ( t=5.78, P<0.05), while NRF2-OE could reverse the decrease in cellular estradiol levels caused by VCD ( t=5.58, P<0.05). The siNRF2 group had the lowest progesterone levels ( t=3.02, P<0.05), while NRF2-OE could reverse the decrease in cellular progesterone levels caused by VCD ( t=2.41, P<0.05). The ROS level in the siNRF2 group was the highest ( t=2.86, P<0.05), NRF2-OE could reverse the increase in ROS caused by VCD ( t=3.14, P<0.05), the SOD enzyme content in the siNRF2 group was the lowest ( t=2.98, P<0.05), and NRF2-OE could reverse the decrease in SOD enzyme content caused by VCD ( t=4.72, P<0.05). The GSH-Px enzyme content in the siNRF2 group was the lowest ( t=3.67, P<0.05), and NRF2-OE could reverse the decrease in antioxidant enzyme content caused by VCD ( t=2.71, P<0.05). The LC3B level was highest in the siNRF2 group ( t=2.45, P<0.05), and NRF2-OE was able to reverse the LC3B elevation caused by VCD ( t=9.64, P<0.05). In conclusion, NRF2 inhibits ROS induced autophagy, thereby playing a role in reducing ovarian granulosa cell damage, which may be a potential target for premature ovarian failure.