The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) is higher among individuals with inflammatory bowel disease (IBD) than in the general population. Emerging evidence indicates that the development of MASLD in patients with IBD may occur independently of traditional metabolic risk factors, suggesting a unique pathophysiological mechanism distinct from conventional MASLD pathways. Bile acids (BAs), which act as critical signaling molecules in enterohepatic circulation, play an essential role in maintaining gastrointestinal homeostasis through bidirectional gut–liver axis communication. These molecules are increasingly recognized as pivotal regulators in the progression of both MASLD and IBD. While previous studies have characterized the dynamics of BA in blood or fecal samples under both conditions, a comprehensive understanding of their metabolic profiles and the associated interactions within the gut–liver axis is still lacking. This review synthesizes current evidence from studies employing BA metabolomics to investigate IBD and MASLD. By focusing on BA signatures, we summarize conserved alterations between these two conditions and their potential mechanisms of disease progression. Our review advances understanding of BA-mediated pathways in IBD and MASLD, providing a foundation for assessing their roles in contributing to the pathogenesis of MASLD in patients with IBD.

BackgroundAttention deficit hyperactivity disorder （ADHD） is a neurodevelopmental disorder characterized by age-inappropriate inattention， excessive activities incongruous with setting， and emotional impulsivity. Subthreshold ADHD （sADHD） is clinically defined as the presence of ADHD symptoms that do not meet the full diagnostic criteria for ADHD. Children with sADHD exhibit deficits in executive function， demonstrate more conduct， learning， and anxiety-related problems compared to typically developing children， and show even poorer working memory performance than children diagnosed with ADHD. Currently， there is limited epidemiological research on sADHD in China， with few studies simultaneously investigating the prevalence of both ADHD and sADHD in children. ObjectiveTo investigate the prevalence of ADHD and sADHD among children aged 6–13 years in Chongqing， analyzing their distribution characteristics within this population， with the aim of providing references for developing preventive measures against both ADHD and sADHD. MethodsFrom October to November 2023， a total of 3 398 students in grades 1–6 from six primary schools in Jiangbei District， Chongqing were selected using a stratified cluster random sampling method. The occurrence of ADHD and sADHD was evaluated by using the short version （18-item version） of the Swanson， Nolan， and Pelham IV rating scales （SNAP-IV） and the Chinese vision of Schedule for Affective Disorder and Schizophrenia for School-aged Children-Present and Lifetime Version （K-SADS-PL）. ResultsThe ADHD detection rate among children in Chongqing was 1.90% （95% CI： 0.014–0.024）. Boys showed a significantly higher ADHD detection rate than girls （χ²=7.733， P=0.005）. No statistically significant differences were found in ADHD detection rates across different grades or age groups （χ²=7.347， 12.362， P>0.05）. The sADHD detection rate was 6.32% （95% CI： 0.054–0.072）. Similarly， boys exhibited significantly higher sADHD detection rates than girls （χ²=21.005， P<0.01）. Significant differences emerged across different grades （χ²=20.559， P=0.001）， while no statistically significant difference was observed in age groups （χ²=12.070， P=0.060）. ConclusionThe ADHD detection rates were comparable across all grade levels and age groups from 6–13 years old. Second-grade children demonstrated notably higher sADHD rates compared to other grades， while boys demonstrated higher prevalence rates than girls for both ADHD and sADHD. ［Funded by Science and Health Joint Medical Research Project in Jiangbei District， Chongqing City in the Second Half of 2023 （number， 2023JBKWLH022）］

In-depth study of the components of polymyxins is the key to controlling the quality of this class of antibiotics.Similarities and variations of components present significant analytical challenges.A two-dimensional(2D)liquid chromatography-mass spectrometry(LC-MS)method was established for screening and comprehensive profiling of compositions of the antibiotic colistimethate sodium(CMS).A high concentration of phosphate buffer mobile phase was used in the first-dimensional LC system to get the components well separated.For efficient and high-accuracy screening of CMS,a targeted method based on a self-constructed high resolution(HR)mass spectrum database of CMS components was established.The database was built based on the commercial MassHunter Personal Compound Database and Library(PCDL)software and its accuracy of the compound matching result was verified with six known components before being applied to genuine sample screening.On this basis,the unknown peaks in the CMS chromatograms were deduced and assigned.The molecular formula,group composition,and origins of a total of 99 compounds,of which the combined area percentage accounted for more than 95％of CMS components,were deduced by this 2D-LC-MS method combined with the MassHunter PCDL.This profiling method was highly efficient and could distinguish hundreds of components within 3 h,providing reliable results for quality control of this kind of complex drugs.

OBJECTIVES: To investigate the role and mechanism of copper overload-mediated endoplasmic reticulum stress (ERS) in vascular endothelial injury in Kawasaki disease (KD). METHODS: Four-week-old male C57BL/6 mice were randomly divided into four groups: control, KD, KD plus copper chelator tetrathiomolybdate (TTM), and KD plus ERS inhibitor AMG PERK 44 (AMG) (n=20 per group). A KD mouse model was established using Candida albicans extract. Human umbilical vein endothelial cells (HUVECs) were divided into control (intervention with healthy children's serum), KD (intervention with KD patients' serum), and KD+TTM (intervention with KD patients' serum plus 20 µmol/L TTM). Copper deposition in mouse heart tissue was assessed using rubeanic acid staining. Vascular pathological changes were observed using hematoxylin-eosin staining and measurement of abdominal aortic diameter and area. ERS activation was detected by transmission electron microscopy and immunofluorescence. HUVEC viability, apoptosis, and functional changes were evaluated using CCK8, flow cytometry, cell scratch assay, and angiogenesis experiments. ERS marker protein expression levels were measured by Western blot. RESULTS: Compared to the KD group, the KD+TTM and KD+AMG groups showed reduced copper deposition in the vascular wall, decreased swelling of coronary endothelial cells and endoplasmic reticulum, reduced inflammatory cell infiltration, and less abdominal aortic lesion expansion. The abdominal aortic diameter and area, and the fluorescence intensity of ERS marker proteins (GRP78 and CHOP) were significantly lower (P<0.05). Compared to the KD group, the KD+TTM group exhibited increased cell viability, tube number, and scratch healing rate, along with decreased apoptosis rate and expression of ERS marker proteins (GRP78, CHOP, ATF6, and p-PERK) (P<0.05). CONCLUSIONS Copper overload aggravates vascular endothelial injury in KD by activating the ERS pathway. TTM can exert protective effects on the endothelium by regulating copper metabolism and inhibiting the ERS pathway.
Endoplasmic Reticulum Stress ; Copper/toxicity* ; Male ; Mucocutaneous Lymph Node Syndrome/metabolism* ; Animals ; Humans ; Endoplasmic Reticulum Chaperone BiP ; Mice, Inbred C57BL ; Mice ; Human Umbilical Vein Endothelial Cells ; Apoptosis ; Endothelium, Vascular/injuries*

Spine fracture and dislocation are common traumatic spinal conditions that often require surgical intervention due to compromised spinal stability. Surgical approaches include anterior, posterior, and combined anterior-posterior spinal procedures. According to the specific surgical requirements, patients may be placed in the prone position or repositioned between prone and supine positions during surgery. Intraoperative repositioning has become an essential step in patient positioning. However, during repositioning, patients with spinal fracture and dislocation are at increased risk for complications such as hemodynamic instability, nerve injury, and pressure injuries to the skin and soft tissue. Notably, due to the instability of the spinal cord, even minor manipulations can further exacerbate the damage, potentially leading to severe outcomes like paraplegia. Although the current clinical guidelines provide instructive recommendations for standard position, there remains no specific protocols for intraoperative repositioning in patients with spine fracture and dislocation. With a concern for the lack of clinical studies on positioning techniques, risk prevention, and operational norms for special patients, no applicable guidelines or standards are available. A consensus was required to provide clinical reference, meet the requirements of surgical treatment, and minimize the safety risks of patients caused by improper placement of positions. Professional Committee of Operating Room Nursing of Shaanxi Nursing Association organized experts in nursing management and operating room nursing from major hospitals across China to formulate Expert consensus on intraoperative repositioning for patients with spinal fracture and dislocation ( version 2025). The consensus provides 11 recommendations covering pre-repositioning preparation, intraoperative maneuvers, and post-repositioning observation, aiming to provide references for clinical standardization of the intraoperative repositioning process and protection of patients′ safety.

With reference to the Information and Documentation-Resource Description (GB/T 3792-2021) and Bibliographical Description for Ancient Chinese Books (GB/T 3792.7-2008) and other cataloging standards and rules, drawing on the practical experience of cataloging ancient TCM books, Bibliographical Cataloging for Ancient TCM Books was formulated. This standard specifies the entry items and their order of ancient TCM books, cataloging identifier, cataloging text, cataloging information source, and cataloging item details. The standard can provide standardized and unified guiding principles and methods for the work of ancient TCM books, and promote the sharing and utilization of ancient TCM books.

Pancreatic cancer is characterized by significant drug resistance,and despite continuous advancements in treatment regimens,the 5-year survival rate of patients remains low.The nuclear factor-κB(NF-κB)signaling pathway,frequently mutated in tumors,has been identified as a critical factor in triggering drug resistance.Multiple studies have demonstrated that strategies targeting NF-κB signaling transduction exhibit promising outcomes in pancreatic cancer treatment.Therefore,exploring the relationship between the NF-κB signaling pathway and drug resistance in pancreatic cancer has become a research hotspot in pancreatic cancer treatment.This review summarizes recent advances in the relationship between NF-κB signaling pathway and tumor drug resistance,as well as its role in pancreatic cancer treatment.Specifically,the mechanisms by which the NF-κB signaling pathway mediates drug resistance in pancreatic cancer are elaborated from two perspectives:chemotherapy and immunotherapy,aiming to provide insights for pancreatic cancer treatment and future research.

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.

Objective:To investigate the nutritional status of esophageal cancer patients and analyze the ability of the modified Patient-Generated Subjective Global Assessment(mPG-SGA)to evaluate nutritional status against the original PG-SGA standard.Method:We enrolled 479 patients newly diagnosed with esophageal cancer at a provincial tertiary hospital in Shenyang from August 2022 to April 2024.Blood samples were analyzed for nutritional indicators,and nutritional status was assessed using both PG-SGA and mPG-SGA.The nutritional assessment ability of mPG-SGA was evaluated using specificity,sensitivity,area under the curve(AUC),weighted kappa coefficient,and correlation analysis.Results:Among the 479 patients,the incidence of moderate-to-severe malnutrition was 74.9%(by PG-SGA)and 72.9%(by mPG-SGA).Using PG-SGA as the standard,mPG-SGA demonstrated sensitivities of 95.4%,100%,and 100%;specificities of 98.5%,92.3%,and 89.5%;and AUC values of 0.997,0.994,and 0.989(all P<0.001)for distinguishing mild vs.moderate-to-severe malnutrition,mild-to-moderate malnutrition,and moderate-to-severe malnutrition,respectively.The overall weighted Kappa coefficient between the scales was 0.898(P<0.001).Conclusion:Malnutrition is highly prevalent among esophageal cancer patients.The mPG-SGA shows strong accuracy and consistency with PG-SGA in assessing nutritional status.Given its simplicity and efficiency,mPG-SGA may become a primary nutritional assessment tool for this population,though further multicenter validation with larger samples is warranted.

BACKGROUND: Non-invasive computed tomography angiography (CTA)-based fractional flow reserve (CT-FFR) could become a gatekeeper to invasive coronary angiography. Deep learning (DL)-based CT-FFR has shown promise when compared to invasive FFR. To evaluate the performance of a DL-based CT-FFR technique, DeepVessel FFR (DVFFR). METHODS: This retrospective study was designed for iScheMia Assessment based on a Retrospective, single-center Trial of CT-FFR (SMART). Patients suspected of stable coronary artery disease (CAD) and undergoing both CTA and invasive FFR examinations were consecutively selected from the Beijing Anzhen Hospital between January 1, 2016 to December 30, 2018. FFR obtained during invasive coronary angiography was used as the reference standard. DVFFR was calculated blindly using a DL-based CT-FFR approach that utilized the complete tree structure of the coronary arteries. RESULTS: Three hundred and thirty nine patients (60.5 ±10.0 years and 209 men) and 414 vessels with direct invasive FFR were included in the analysis. At per-vessel level, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of DVFFR were 94.7%, 88.6%, 90.8%, 82.7%, and 96.7%, respectively. The area under the receiver operating characteristics curve (AUC) was 0.95 for DVFFR and 0.56 for CTA-based assessment with a significant difference (P < 0.0001). At patient level, sensitivity, specificity, accuracy, PPV and NPV of DVFFR were 93.8%, 88.0%, 90.3%, 83.0%, and 95.8%, respectively. The computation for DVFFR was fast with the average time of 22.5 ± 1.9 s. CONCLUSIONS The results demonstrate that DVFFR was able to evaluate lesion hemodynamic significance accurately and effectively with improved diagnostic performance over CTA alone. Coronary artery disease (CAD) is a critical disease in which coronary artery luminal narrowing may result in myocardial ischemia. Early and effective assessment of myocardial ischemia is essential for optimal treatment planning so as to improve the quality of life and reduce medical costs.