Objective: To identify the mimicking autoantibodies using papain-enhanced erythrocyte agglutination in a case of repeated red blood cell transfusion refractoriness, aiming to explore the immune hematological methods for identifying mimicking alloantibodies, autoantibodies and alloantibodies, and to develop a safe and effective blood transfusion strategy based on the results. Methods: ABO, RhD and RhEeCc blood groups were detected using routine blood group serological method. The unexpected antibodies in plasma were screened and identified, followed by identification using a two-step papain-treated indirect antiglobulin test (enzyme-IAT). The cause of transfusion refractoriness of red blood cells was analyzed and summarized, and the prognosis was followed up. Results: The patient's blood type was type A, CCDee. The direct antiglobulin test (DAT) was positive. Unexpected antibody screening and identification using saline tube method, polybrene method, IAT were negative, and the release fluid (acid release) IAT identification was negative. Mimicking anti-e antibodies in plasma was identified by enzyme-ITA. The cause of red blood cell transfusion refractoriness in patients was autoimmune hemolysis caused by mimicking anti-e antibodies. After choosing to avoid antibodies corresponding to antigen-positive red blood cells, the patient's hemoglobin (Hb) increase value is in line with theoretical expectations, indicating effective red blood cell transfusion. Conclusion: Mimicking antibodies can cause immune destruction of red blood cells by non-specific binding to their own red blood cells and specific binding to red blood cells, resulting in red blood cell transfusion refractoriness.

Ureaplasma urealyticum (UU) is a common pathogen colonizing or infecting the neonatal respiratory tract. It can be vertically transmitted from mother to infant, irrespective of the duration of premature rupture of membranes and the mode of delivery. UU infection is an important factor contributing to preterm birth and low birth weight and is closely associated with adverse outcomes such as bronchopulmonary dysplasia and neurodevelopmental impairment. Given the immaturity of neonatal immune and organ systems, pharmacologic treatment must balance efficacy and safety. Currently, no unified standard regimen has been established for the treatment of neonatal UU infection. This review summarizes pharmacotherapeutic options for neonatal UU infection to inform clinical practice.
Humans ; Ureaplasma Infections/drug therapy* ; Infant, Newborn ; Ureaplasma urealyticum ; Anti-Bacterial Agents/therapeutic use*

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Objective:To investigate effect of quality improvement based on the Attitude-Skill-Knowledge(ASK)model on quality of life and emotion in patients with acute myocardial infarction(AMI).Methods:This random-ized controlled study enrolled 220 AMI patients admitted in Liyang People's Hospital between October 2020 and Oc-tober 2022.They were divided into control group(n=110,routine nursing)and intervention group(n=110,addi-tional quality improvement intervention based on ASK model).After 2-month intervention,scores of Patient Health Questionaire-9(PHQ-9),Beck Anxiety Inventory(BAI)and General Quality of life Inventory(GQOLI-74),cardiac function and satisfaction for nursing were compared between two groups.Results:After 2-month intervention,compared with patients in control group,those in intervention group had significant higher score of GQOLI-74[(372.82±23.07)points vs.(306.48±15.53)points],significant lower scores of PHQ-9[(9.58±2.41)points vs.(15.36±3.51)points]and BAI[(8.61±0.24)points vs.(16.81±3.54)points],levels of N-terminal pro brain natriuretic peptide(NT-proBNP)[(5.03±1.15)pg/ml vs.(13.11±1.22)pg/ml],cardiac troponin I(cTnⅠ)[(2.90±0.51)pg/ml vs.(4.76±1.32)pg/ml]and creatine kinase isoenzyme MB(CK-MB)[(8.70±2.31)ng/ml vs.(13.63±2.63)ng/ml](P＜0.001 all).Total satisfaction for nursing in intervention group was significantly higher than that of control group(94.55％vs.83.64％,P=0.009).Conclusion:Quality improvement based on Attitude-Skill-Knowledge model could improve cardiac function and quality of life as well as negative emotion in AMI patients with high satisfaction.

Objective To establish a stable liquid chromatography-tandem mass spectrometry(LC-MS/MS)method for detecting gamma-aminobutyric acid(GABA)in plasma,and to evaluate the value of GABA detection in the diagnosis of sleep disorders.Methods GABA was detected using a UPLC Xevo TQs system.The method was pre-validated and its performance was verified to establish a reference range for healthy individuals.The difference in plasma GABA levels between apparently healthy individuals and patients with sleep disorders was compared.Results We employed deuterated compounds as isotopic internal standards and utilized an Amide chromatographic column for separation.The mobile phase was 0.050%formic acid in water and 90%acetonitrile in water containing 0.175%formic acid and 5 mmol/L ammonium acetate with gradient elution in the column temperature of 35℃.The linear range for the detection of GABA by LC-MS/MS was 0.05-10.00 μmol/L,with a lower limit of quantification of 0.02 μmol/L,the inter-day CV<3.00%and intra assay CV<4.00%,respectively,and the recovery rate was 101.06%-109.02%.The reference ranges for plasma GABA were established by analyzing 300 healthy controls stratified by age:18-34 years(0.08-0.15 μmol/L),35-49 years(0.10-0.20 μmol/L),and≥50 years(0.12-0.23 μmol/L).Then plasma GABA was used as a biomarker for auxiliary diagnosis of sleep disorders in analyzing 221 patients and 300 healthy controls,which revealed that AUC values were 0.510(P=0.850),0.686(P=0.002),and 0.890(P<0.001)in the groups of 18-34 years,35-49 years,and≥50 years,respectively,with optimal cut-off values of 0.09,0.10 and 0.11 μmol/L.Conclusion A reliable LC-MS/MS method for detecting GABA has been established,which can detect plasma GABA levels sensitively and accurately and can be used in assisting the clinical diagnosis of sleep disorders.

Objective To establish a stable liquid chromatography-tandem mass spectrometry(LC-MS/MS)method for detecting gamma-aminobutyric acid(GABA)in plasma,and to evaluate the value of GABA detection in the diagnosis of sleep disorders.Methods GABA was detected using a UPLC Xevo TQs system.The method was pre-validated and its performance was verified to establish a reference range for healthy individuals.The difference in plasma GABA levels between apparently healthy individuals and patients with sleep disorders was compared.Results We employed deuterated compounds as isotopic internal standards and utilized an Amide chromatographic column for separation.The mobile phase was 0.050%formic acid in water and 90%acetonitrile in water containing 0.175%formic acid and 5 mmol/L ammonium acetate with gradient elution in the column temperature of 35℃.The linear range for the detection of GABA by LC-MS/MS was 0.05-10.00 μmol/L,with a lower limit of quantification of 0.02 μmol/L,the inter-day CV<3.00%and intra assay CV<4.00%,respectively,and the recovery rate was 101.06%-109.02%.The reference ranges for plasma GABA were established by analyzing 300 healthy controls stratified by age:18-34 years(0.08-0.15 μmol/L),35-49 years(0.10-0.20 μmol/L),and≥50 years(0.12-0.23 μmol/L).Then plasma GABA was used as a biomarker for auxiliary diagnosis of sleep disorders in analyzing 221 patients and 300 healthy controls,which revealed that AUC values were 0.510(P=0.850),0.686(P=0.002),and 0.890(P<0.001)in the groups of 18-34 years,35-49 years,and≥50 years,respectively,with optimal cut-off values of 0.09,0.10 and 0.11 μmol/L.Conclusion A reliable LC-MS/MS method for detecting GABA has been established,which can detect plasma GABA levels sensitively and accurately and can be used in assisting the clinical diagnosis of sleep disorders.