Objective To investigate the effects of glycerol ingestion on pure tone audiometry(PTA),distor-tion products otoacoustic emission(DPOAE),and electrocochleography(ECochG)in patients with Ménière disease(MD).Methods Glycerol test was conducted in 50 patients with MD.PTA was performed in four series:before glycerol intake,1,2 and 3 hours after intake.DPOAE and ECochG were performed before glycerol intake and 2 hours after intake.All results were analyzed to assess the effect of glycerol on cochlear function of patients with MD.Results ① 55％of MD patients tested positive in PTA glycerol test,and the positive rate increased gradually after 1-3 hours of glycerin ingestion(P＜0.05).For the 33 positive ears,the pure tone threshold decreased the most between 1-2 hours and reached the lowest thresholds at 3 hours.Thresholds at 0.5 kHz,1 kHz,2 kHz dropped the most.② The positive rate of DPOAE glycerol test was 56.67％,with 34 positive ears showing a sig-nificant increase in amplitude between 0.75-2 kHz of f2.③ The positive rate of ECochG glycerin test was 13.64％.The decrease of-SP/AP ratio was not statistically significant before and after ingestion of glycerin(P＞0.05).Conclusion Ingestion of glycerin could alter to varying degrees of the results of PTA,DPOAE and ECo-chG,and influence the cochlear function to some extent.

OBJECTIVE To investigate ameliorative effects and mechanisms of two probiotics(Bacillus subtilis,Lactobacillus acidophilus)combined with Aurantii Fructus Immaturus(AFI)on functional dyspepsia(FD)in rats.METHODS Rats were randomly divided into blank group,model group,positive control group(domperidone group,2.7 mg/kg),AFI group(9 g/kg),L.acidophilus group(5×107 cfu/kg),B.subtilis group(5×107 cfu/kg),L.acidophilus+AFI group(L.acidophilus 5×107 cfu/kg+AFI 9 g/kg),and B.subtilis+AFI group(B.subtilis 5×107 cfu/kg+AFI 9 g/kg),with 8 rats in each group.Except for the blank group,FD model was established by tail-clamping stimulation+hunger and satiety disorder+swimming exhaustion in other groups.After modeling,each group was given the corresponding drug/probiotic suspensions/physiological saline intragastrically,once a day,for 14 consecutive days.After the last medication,gastric emptying rate and the rate of propulsion of the small intestine in rats were measured;the levels of brain-gut peptide-related indicators[gastrin(GAS),substance P(SP),vasoactive intestinal peptide(VIP),somatostatin(SS)and cholecystokinin(CCK)]in the serum of rats were measured.The pathological morphology of the gastric antrum tissue and duodenal tissue was observed.Cecal contents from the rats were collected for gut microbiota sequencing analysis.The protein expression levels of tyrosine kinase receptor c-Kit and stem cell factor(SCF)in the gastric antrum tissue,as well as Toll-like receptor 4(TLR4),myeloid differentiation factor 88(MyD88),and nuclear factor κB(NF-κB)in the duodenal tissue of the rats were detected.RESULTS Compared with the blank group,model group showed significantly lower gastric emptying rate,small intestinal propulsion rate,serum levels of GAS and SP,relative abundance of Firmicutes,Ace,Chao and Sobs indexes of the gut microbiota,and protein levels of SCF and c-Kit in gastric antrum(P＜0.05),while serum levels of VIP,SS and CCK,relative abundance of Bacteroidetes,as well as protein expressions of TLR4,MyD88,and NF-κB,were significantly higher(P＜0.05).The histological structure of the gastric antrum tissue appeared basically normal;however,abnormalities were observed in the duodenal structure,with a significant infiltration of inflammatory cells visible.Compared with the model group,all treatment groups significantly modulated most of the above indexes(P＜0.05).The histological structure of the gastric antrum tissue was normal.Except for the B.subtilis group and the B.subtilis+AFI group,the pathological states of the duodenum in the remaining rats gradually recovered.Compared with each single drug group,most of above indexes in rats from each combination group showed further improvement(P＜0.05).CONCLUSIONS The combination of AFI with two probiotics can improve gastrointestinal motility in FD rats,and the effect is superior to that of using the drugs alone.The specific underlying mechanisms may be related to the activation of the SCF/c-Kit signaling pathway and the inhibition of the TLR4/MyD88/NF-κB signaling pathway.

Objective:To develop and validate clinical predictive models for identifying poor short-term response to recombinant human growth hormone(rhGH) treatment in children with short stature.Methods:A retrospective analysis was conducted on 118 children diagnosed with growth hormone deficiency or idiopathic short stature who were treated at the First Affiliated Hospital of Zhengzhou University and two other hospitals between January 1, 2020, and January 1, 2024. A poor response to rhGH was defined as a height increase of less than 0.2 standard deviation score(SDS) after 6 months of rhGH treatment. LASSO regression was used to identify predictive variables from baseline and follow-up data. Two logistic regression models were conducted: Model A(incorporating baseline variables only) and model B(incorporating both baseline and follow-up variables), and nomograms were created for visualization. External data and internal resampling were used for dual validation of the models, and their performance was compared.Results:A total of 118 children with short stature were included. Six baseline predictive variables(diagnosis, initial height SDS, bone age, bone age-chronological age difference, rhGH dose, and gender) and one follow-up variable(height SDS after 3 months of rhGH treatment) were identified. Area under the curve values for Model A and Model B were 0.753(95% CI 0.696-0.811) and 0.930(95% CI 0.891-0.975), respectively. Calibration curves, decision curve analysis, and other evaluation metrics demonstrated good discrimination and clinical utility for both models. Model B, incorporating the 3-month follow-up variable, showed superior predictive performance compared to Model A. Conclusions:The clinical prediction models developed in this study(Model A and Model B) are practical and reliable tools for quantitatively, conveniently, and intuitively identifying children with short stature at risk of poor response to rhGH treatment.

Objective:To establish and optimize the cutoff values of D-dimer (D-D) for excluding suspected acute pulmonary embolism (APE).Methods:A retrospective cross-sectional study was conducted by recruiting a total of 428 patients with suspected APE complaining of chest pain, hemoptysis, dyspnea, etc., who underwent computed tomography pulmonary angiography (CTPA) in the Emergency Department of the First Hospital of Jilin University from January 1st, 2022, to October 31st, 2024, taken as observation group. The Median age was 64.0 (55.0, 72.0) years old with male and female 214 respectively. Data collection included clinical manifestations(hemoptysis, swelling and pain in the lower limbs), deep vein thrombosis (DVT) history, Wells scores, laboratory results, CTPA and vascular ultrasound foundings. According to CTPA results, observation group was divided into APE group (233 cases) and non-APE group (195 cases); according to Wells scoring, observation group was divided into APE at low, moderate, or high pre-test probability subgroups. Meanwhile, 196 healthy individuals in the same period were included as the health control group. D-D levels were compared among different groups. Receiver operating characteristic (ROC) curve analysis was used to determine the D-D cutoff values for excluding APE, and the area under the curve (AUC) and negative predictive value (NPV) were evaluated.Results:D-D levels in the CTPA-APE group, CTPA-non APE group, and the healthy control group were [7.77 (4.10, 16.58)] mg/L, [0.53 (0.24, 0.94)] mg/L, and [0.21 (0.15, 0.32)] mg/L, respectively, with statistically significant differences ( P<0.05). In the APE group, D-D levels within low-, moderate-, and high-probability subgroups were [7.48 (3.87, 15.85)] mg/L, [7.92 (4.08, 13.90)] mg/L, and [21.39 (7.92, 89.68)] mg/L, respectively, with statistically significant differences among subgroups ( P<0.05), while no significant difference between low-and moderate-probability subgroups ( P>0.05). For suspected APE with low-probability, exclusive D-D level was 0.62 mg/L with AUC and NPV at 1.000 and 100% taking healthy control group as negative control, and 1.65 mg/L with AUC and NPV at 0.989 and 94.00% taking non-APE group as negative control, while the optimized D-D level was 1.10 mg/L adjusted by NPV ≥98%. For suspected APE with low to moderate-probability, the exclusive D-D level was 0.55 mg/L with AUC and NPV at 0.997 and 99.00% taking healthy control group as negative control, and 1.64 mg/L with AUC and NPV at 0.979 and 92.60%, while the optimized D-D level was 0.55 mg/L adjusted by NPV ≥98%. Conclusion:This study established and optimized the exclusive diagnostic cutoff value of D-D for suspected APE in Emergency Department integrated with the Wells scoring, which may effectively reduce the false-positive rate while improve the clinical application for APE exclusion using D-D.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effec-tive MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models' generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer po-tential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.

Objective:To develop and validate clinical predictive models for identifying poor short-term response to recombinant human growth hormone(rhGH) treatment in children with short stature.Methods:A retrospective analysis was conducted on 118 children diagnosed with growth hormone deficiency or idiopathic short stature who were treated at the First Affiliated Hospital of Zhengzhou University and two other hospitals between January 1, 2020, and January 1, 2024. A poor response to rhGH was defined as a height increase of less than 0.2 standard deviation score(SDS) after 6 months of rhGH treatment. LASSO regression was used to identify predictive variables from baseline and follow-up data. Two logistic regression models were conducted: Model A(incorporating baseline variables only) and model B(incorporating both baseline and follow-up variables), and nomograms were created for visualization. External data and internal resampling were used for dual validation of the models, and their performance was compared.Results:A total of 118 children with short stature were included. Six baseline predictive variables(diagnosis, initial height SDS, bone age, bone age-chronological age difference, rhGH dose, and gender) and one follow-up variable(height SDS after 3 months of rhGH treatment) were identified. Area under the curve values for Model A and Model B were 0.753(95% CI 0.696-0.811) and 0.930(95% CI 0.891-0.975), respectively. Calibration curves, decision curve analysis, and other evaluation metrics demonstrated good discrimination and clinical utility for both models. Model B, incorporating the 3-month follow-up variable, showed superior predictive performance compared to Model A. Conclusions:The clinical prediction models developed in this study(Model A and Model B) are practical and reliable tools for quantitatively, conveniently, and intuitively identifying children with short stature at risk of poor response to rhGH treatment.

Objective: To analyze the nucleic acid load of human parvovirus B19 in major commercially available blood products in China, including human albumin, human intravenous immunoglobulin, human rabies immunoglobulin and various coagulation factor products, aiming to provide evidence for improving blood product manufacturing processes and quality control of source plasma. Methods: A total of 98 batches of coagulation factor products were tested for human parvovirus B19 nucleic acid using real-time fluorescent quantitative PCR, including 42 batches of human prothrombin complex, 35 batches of human coagulation factor Ⅷ, and 21 batches of human fibrinogen. Additionally, 6 batches of human albumin, 6 batches of human intravenous immunoglobulin, and 38 batches of human rabies immunoglobulin were tested for human parvovirus B19 nucleic acid. Results: Human parvovirus B19 nucleic acid were undetectable in human albumin, human intravenous immunoglobulin and human rabies immunoglobulin. Among the 98 batches of coagulation factor products tested for human parvovirus B19 nucleic acid, B19 nucleic acid reactivity rate was 69.0% (29/42) for human prothrombin complex batches, but nucleic acid concentration were all significantly lower than 10 IU/mL. The reactivity rate of B19 nucleic acid in 35 batches of human coagulation factor Ⅷ was 48.6% (17/35), with nucleic acid concentration all below 10 IU/mL. The reactivity rate of B19 nucleic acid in 21 batches of human fibrinogen was 61.9% (13/21), with nucleic acid concentration all below 10 IU/mL. Conclusion: No human parvovirus B19 has been detected in human albumin, human intravenous immunoglobulin, or human rabies immunoglobulin. Human parvovirus B19 nucleic acid may exist in commercially available coagulation factor products, highlighting the need for enhanced screening of human parvovirus B19 nucleic acid in these products. It is also recommended that B19 viral nucleic acid testing be conducted on source plasma, particularly for coagulation factor products.

Objective: To analyze the nucleic acid load of human parvovirus B19 in major commercially available blood products in China, including human albumin, human intravenous immunoglobulin, human rabies immunoglobulin and various coagulation factor products, aiming to provide evidence for improving blood product manufacturing processes and quality control of source plasma. Methods: A total of 98 batches of coagulation factor products were tested for human parvovirus B19 nucleic acid using real-time fluorescent quantitative PCR, including 42 batches of human prothrombin complex, 35 batches of human coagulation factor Ⅷ, and 21 batches of human fibrinogen. Additionally, 6 batches of human albumin, 6 batches of human intravenous immunoglobulin, and 38 batches of human rabies immunoglobulin were tested for human parvovirus B19 nucleic acid. Results: Human parvovirus B19 nucleic acid were undetectable in human albumin, human intravenous immunoglobulin and human rabies immunoglobulin. Among the 98 batches of coagulation factor products tested for human parvovirus B19 nucleic acid, B19 nucleic acid reactivity rate was 69.0% (29/42) for human prothrombin complex batches, but nucleic acid concentration were all significantly lower than 10 IU/mL. The reactivity rate of B19 nucleic acid in 35 batches of human coagulation factor Ⅷ was 48.6% (17/35), with nucleic acid concentration all below 10 IU/mL. The reactivity rate of B19 nucleic acid in 21 batches of human fibrinogen was 61.9% (13/21), with nucleic acid concentration all below 10 IU/mL. Conclusion: No human parvovirus B19 has been detected in human albumin, human intravenous immunoglobulin, or human rabies immunoglobulin. Human parvovirus B19 nucleic acid may exist in commercially available coagulation factor products, highlighting the need for enhanced screening of human parvovirus B19 nucleic acid in these products. It is also recommended that B19 viral nucleic acid testing be conducted on source plasma, particularly for coagulation factor products.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models' generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.

Objective To mine adverse drug event(ADE)signals of deucravacitinib,and to guide its rational clinical use.Methods ADE reports reported to the U.S.Food and Drug Administration Adverse Event Reporting System(FAERS)database from the third quarter of 2022 to the third quarter of 2024 were collected,ADE reports with deucravacitinib as the primary suspect drug were selected for analysis.ADE signals were identified using reporting adds ratios method and Bayesian confidence propagation neural networks method.Results A total of 1,777 ADE reports were collected involving 3,258 ADEs.Sixty-two ADE signals were identified,spanning 14 system-organ classifications.The top five ADE signals based on the number of reported cases were acne,oral ulcers,folliculitis,urticaria,and oral pain.The top five ADE signals based on signal intensity were cystic acne,hepatitis A,acne vulgaris,pustular acne,and folliculitis.ADE signals such as pigmenturia,hepatitis A,and gingival swelling were not included in the drug instructions.The median duration of ADEs associated with deucravacitinib was 22 days,with 58.33％occurring within the first month of treatment.Women may have a higher risk of developing acne than men.Conclusions When using deutericolaxitinib,healthcare professionals should focus on skin and subcutaneous tissue disorders,gastrointestinal system disorders,and infections and infestations to monitor the occurrence of acne in female patients.The latent ADEs that are not mentioned in the instructions should be remained vigilant to ensure safe drug use.