Background/Aims: Distal mean nocturnal baseline impedance (MNBI) measuring via pH-impedance may be valuable in diagnosing patients with suspected laryngopharyngeal reflux (LPR). However, its wide adoption is hindered by cost and invasiveness. This study investigates whether baseline impedance measured during high-resolution impedance manometry (HRIM-BI) can predict pathological MNBI. Methods: A cross-sectional study in Taiwan included 74 subjects suspected of LPR, who underwent HRIM (MMS) and pH-impedance testing (Diversatek), after stopping proton pump inhibitors for more than 7 days. Subjects with grade C or D esophagitis or Barrett’s esophagus were excluded. The cohort was divided into 2 groups: those with concomitant typical reflux symptoms (CTRS, n = 28) and those with isolated LPR symptoms (ILPRS, n = 46). HRIM-BI measurements focused on both distal and proximal esophagi. Pathological MNBI was identified as values below 2065 Ω, measured 3 cm above the lower esophageal sphincter. Results: In all subjects, distal HRIM-BI values correlated weakly with distal MNBI(r = 0.34-0.39, P < 0.005). However, in patients with ILPRS, distal HRIM-BI corelated moderately with distal MNBI(r = 0.43-0.48, P < 0.005). The areas under the receiver operating characteristic curve was 0.78 (P = 0.001) with a sensitivity of 0.83 and a specificity of 0.68. No correlation exists between distal HRIM-BI and distal MNBI in patients with CTRS, and between proximal HRIM-BI and proximal MNBI in both groups. Conclusions Distal HRIM-BI from HRIM may potentially predict pathological MNBI in patients with ILPRS, but not in those with CTRS. Future outcome studies linked to the metric are warranted.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective To establish and evaluate a nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer.Methods A retrospective study was conducted on 1298 patients with HBV-related primary liver cancer hospitalized in the Kunming Third People's Hospital from January 2012 to December 2022.General data and serological indicators were collected,and patients were divided into infection group(n=262)and control group(n=1036)based on the occurrence of spontaneous peritonitis.Univariate and LASSO regression analyses were used to screen variables,followed by binary logistic regression to analyze the influencing factors of spontaneous peritonitis in HBV-related primary liver cancer patients,leading to the establishment of a nomogram prediction model.Finally,the Hosmer-lemeshow(H-L)goodness of fit test,receiver operating characteristic(ROC)curve,calibration curve,decision curve analysis(DCA)and clinical impact curve(CIC)were utilized to evaluate the fit degree,accuracy,calibration,and clinical practicability of the nomogram prediction model.Results Single factor analysis revealed significant differences between infection group and control group in portal vein cancer thrombus(PVTT),Child-Pugh grade,China Liver Cancer Staging(CNLC)stage,alcohol consumption history,smoking history,white blood cell count(WBC),neutrophil count(NE),hemoglobin(Hb),fibrinogen(FIB),abnormal prothrombin(PIVKA-Ⅱ),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total protein(TP),prealbumin(PA),γ-glutamyltransferase(GGT),alkaline phosphatase(ALP),cholinesterase(CHE),total bile acid(TBA),total cholesterol(TC),low density lipoprotein(LDL),creatinine(Cr),HBV DNA,CD3+T cells count,CD4+T cells count,CD8+T cells count,CD4+T cells/CD8+T cells ratio,procalcitonin(PCT),serum amyloid A(SAA),interleukin-6(IL-6),high-sensitivity C-reactive protein(hs-CRP),alpha-fetoprotein(AFP),and IL-4(P＜0.05).LASSO regression analysis identified 5 variables:Child-Pugh grade,PVTT,WBC,CHE and hs-CRP.Binary logistic regression analysis indicated that Child-Pugh grade(Grade B:OR=5.780,95％CI 3.271-10.213,P＜0.001;Grade C:OR=14.818,95％CI 7.697-28.526,P＜0.001),PVTT(OR=2.893,95％CI 2.037-4.108,P＜0.001),WBC(OR=1.088,95％CI 1.031-1.148,P=0.002),and hs-CRP(OR=1.005,95％CI 1.001-1.010,P=0.026)were the independent risk factors of spontaneous peritonitis in HBV-related primary liver cancer patients.Using these 4 variables,a nomogram prediction model was constructed and evaluated.The P-value of the H-L goodness of fit test was 0.760.Moreover,the area under ROC curve(AUC)was 0.866,with a sensitivity of 0.870 and a specificity of 0.716.The average absolute error of the calibration curve is 0.022.DCA and CIC analyses demonstrated that the nomogram prediction model possessed some clinical utility.Conclusion The nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer patients,constructed using Child-Pugh grade,PVTT,WBC and hs-CRP,exhibits a high fitting degree and accuracy,with the prediction probability highly consistent with the actual occurrence probability,and possesses certain clinical practicability.

Objective To investigate the expression of myelin transcription factor 1-like(MYT1L)during amyotrophic lateral sclerosis(ALS)progression and its association with neuronal degeneration through bioinformatics analysis combined with in vivo and in vitro experiments.Methods Bioinformatics analysis of the GSE106803 dataset from the Gene Expression Omnibus(GEO)database revealed significant down-regulation of MYT1L in spinal cords of ALS transgenic mice carrying the human superoxide dismutase 1 mutant gene(hSOD1G93A)compared to the wild-type(WT)mice.hSOD1G93A transgenic mice and their WT littermates were selected to analyze MYT1L mRNA and protein changes in spinal cord tissues at different disease stages using Real-time PCR and Western blotting.Double immunofluorescent staining was used to determine the distribution and cellular localization of MYT1L in the spinal cord of mice at the middle stage of the disease.An ALS cellular model was established using hSOD1G93A mutant NSC34 cells,with hSOD1WT NSC34 cells as controls.MYT1L expression and distribution were assessed in these cells via Real-time PCR,Western blotting,and immunofluorescent staining.Based on the GSE76220 dataset from the GEO database,differentially expressed genes(DEGs)between MYT1L high-and low-expression groups in lumbar spinal motor neurons of ALS patients were identified,followed by Gene Ontology(GO)functional enrichment analysis.MYT1L overexpression was induced in the ALS cellular model to evaluate alterations in cell viability and neurite outgrowth.Results In the GSE106803 dataset,MYT1L expression was significantly down-regulated in the spinal cord of ALS mice.Animal experiments confirmed progressive reductions in MYT1L mRNA and protein levels in spinal cord tissues of ALS mice during mid-and late-disease stages.Compared to the WT group,MYT1L expression decreased in motor neurons of the lumbar spinal cord gray matter anterior horn in ALS mice,while it increased in astrocytes.In vitro,hSOD1G93Amutant NSC34 cells exhibited significantly reduced MYT1L expression than controls,with MYT1L localized to both the cytoplasm and nucleus.DEGs between MYT1L high-and low-expression groups in lumbar spinal cord motor neurons of ALS patients(GSE76220 dataset)were enriched in synaptic-related functions through GO analysis.Overexpression of MYT1L in hSOD1G93A mutant NSC34 cells enhanced cell viability and promoted neurite outgrowth.Conclusion Aberrantly low expression of MYT1L is closely associated with ALS pathogenesis.Overexpression of MYT1L promotes neurite growth and exerts protective effects on ALS motor neurons,suggesting its therapeutic potential.

Suhuang Zhike Capsules are widely used in clinical practice for the treatment of respiratory diseases and have been included in Medicine Catalogue for National Basic Medical Insurance, Work Injury Insurance, and Maternity Insurance and National Essential Medicines List. However, problems remain, such as unclear definitions of treatment courses and unidentified contraindications for certain populations. Therefore, this consensus was developed collaboratively by clinical experts in traditional Chinese medicine(TCM) related to pulmonary diseases, respiratory, and critical care medicine, as well as methodology and pharmacy experts, adhering strictly to the consensus development procedures established by the China Association of Chinese Medicine for clinical application of Chinese patent medicines, with the aim to guide the correct clinical use of Suhuang Zhike Capsules for the treatment of cough variant asthma, post-infectious cough, and other respiratory diseases. This consensus employed questionnaire surveys and expert interviews to identify clinical concerns based on the PICOS principle and conduct evidence evaluation and GRADE grading. Utilizing nominal group techniques and GRADE networking methods, it resulted in 17 recommendations and consensus suggestions. The consensus further clarifies the indications, TCM syndromes, usage, and clinical safety of Suhuang Zhike Capsules in the treatment of cough variant asthma and post-infectious cough, aiming to promote standardized medication use and facilitate the rational clinical application of Suhuang Zhike Capsules.
Humans ; Drugs, Chinese Herbal/administration & dosage* ; Consensus ; Capsules ; Respiratory Tract Diseases/drug therapy* ; Medicine, Chinese Traditional

This study aims to explore the pharmacodynamic material basis of Jinbei Oral Liquid(JBOL) against idiopathic pulmonary fibrosis(IPF) based on serum pharmacochemistry and network pharmacology. The ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UHPLC-Q-TOF-MS/MS) technology was employed to analyze and identify the components absorbed into rat blood after oral administration of JBOL. Combined with network pharmacology, the study explored the pharmacodynamic material basis and potential mechanism of JBOL against IPF through protein-protein interaction(PPI) network construction, "component-target-pathway" analysis, Gene Ontology(GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis. First, a total of 114 compounds were rapidly identified in JBOL extract according to the exact relative molecular mass, fragment ions, and other information of the compounds with the use of reference substances and a self-built compound database. Second, on this basis, 70 prototype components in blood were recognized by comparing blank serum with drug-containing serum samples, including 28 flavonoids, 25 organic acids, 4 saponins, 4 alkaloids, and 9 others. Finally, using these components absorbed into blood as candidates, the study obtained 212 potential targets of JBOL against IPF. The anti-IPF mechanism might involve the action of active ingredients such as glycyrrhetinic acid, cryptotanshinone, salvianolic acid B, and forsythoside A on core targets like AKT1, TNF, and ALB and thereby the regulation of multiple signaling pathways including PI3K/AKT, HIF-1, and TNF. In conclusion, JBOL exerts the anti-IPF effect through multiple components, targets, and pathways. The results would provide a reference for further study on pharmacodynamic material basis and pharmacological mechanism of JBOL.
Drugs, Chinese Herbal/pharmacokinetics* ; Animals ; Tandem Mass Spectrometry ; Network Pharmacology ; Rats ; Chromatography, High Pressure Liquid ; Rats, Sprague-Dawley ; Male ; Idiopathic Pulmonary Fibrosis/metabolism* ; Humans ; Administration, Oral ; Protein Interaction Maps/drug effects* ; Signal Transduction/drug effects*

Epilepsy is a chronic neurological disease caused by abnormal synchronous discharge of the brain, which is characterized by recurrent and transient neurological abnormalities, mainly manifested as loss of consciousness and limb convulsions, and can occur in people of all ages. At present, anti-epileptic drugs (AEDs) are still the main means of treatment, but their efficacy is limited by the problem of drug resistance, and long-term use can cause serious side effects, such as cognitive dysfunction and vital organ damage. Although surgical resection of epileptic lesions has achieved certain results in some patients, the high cost and potential risk of neurological damage limit its scope of application. Therefore, the development of safe, accurate and personalized non-invasive treatment strategies has become one of the key directions of epilepsy research. In recent years, photobiomodulation (PBM) has gained significant attention as a promising non-invasive therapeutic approach. PBM uses light of specific wavelengths to penetrate tissues and interact with photosensitive molecules within cells, thereby modulating cellular metabolic processes. Research has shown that PBM can enhance mitochondrial function, promote ATP production, improve meningeal lymphatic drainage, reduce neuroinflammation, and stimulate the growth of neurons and synapses. These biological effects suggest that PBM not only holds the potential to reduce the frequency of seizures but also to improve the metabolic state and network function of neurons, providing a novel therapeutic avenue for epilepsy treatment. Compared to traditional treatment methods, PBM is non-invasive and avoids the risks associated with surgical interventions. Its low risk of significant side effects makes it particularly suitable for patients with drug-resistant epilepsy, offering new therapeutic options for those who have not responded to conventional treatments. Furthermore, PBM’s multi-target mechanism enables it to address a variety of complex etiologies of epilepsy, demonstrating its potential in precision medicine. In contrast to therapies targeting a single pathological mechanism, PBM’s multifaceted approach makes it highly adaptable to different types of epilepsy, positioning it as a promising supplementary or alternative treatment. Although animal studies and preliminary clinical trials have shown positive outcomes with PBM, its clinical application remains in the exploratory phase. Future research should aim to elucidate the precise mechanisms of PBM, optimize light parameters, such as wavelength, dose, and frequency, and investigate potential synergistic effects with other therapeutic modalities. These efforts will be crucial for enhancing the therapeutic efficacy of PBM and ensuring its safety and consistency in clinical settings. This review summarizes the types of epilepsy, diagnostic biomarkers, the advantages of PBM, and its mechanisms and potential applications in epilepsy treatment. The unique value of PBM lies not only in its multi-target therapeutic effects but also in its adaptability to the diverse etiologies of epilepsy. The combination of PBM with traditional treatments, such as pharmacotherapy and neuroregulatory techniques, holds promise for developing a more comprehensive and multidimensional treatment strategy, ultimately alleviating the treatment burden on patients. PBM has also shown beneficial effects on neural network plasticity in various neurodegenerative diseases. The dynamic remodeling of neural networks plays a critical role in the pathogenesis and treatment of epilepsy, and PBM’s multi-target mechanism may promote brain function recovery by facilitating neural network remodeling. In this context, optimizing optical parameters remains a key area of research. By adjusting parameters such as wavelength, dose, and frequency, researchers aim to further enhance the therapeutic effects of PBM while maintaining its safety and stability. Looking forward, interdisciplinary collaboration, particularly in the fields of neuroscience, optical engineering, and clinical medicine, will drive the development of PBM technology and facilitate its transition from laboratory research to clinical application. With the advancement of portable devices, PBM is expected to provide safer and more effective treatments for epilepsy patients and make a significant contribution to personalized medicine, positioning it as a critical component of precision therapeutic strategies.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.