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.

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.

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*

Objective To investigate the rate of germline variants in patients with pancreatic cancer and clinical characteristics related with germline variants.Methods A total of 271 patients diagnosed with pancreatic cancer were enrolled in this study.Germline variants of 21 tumor susceptibility genes were detected by next-generation sequencing,and the relationship between germline variants and clinical factors such as age of onset,family history and personal history was analyzed.Results The rate of germline P/LP variants was 6.3%in unselected pancreatic cancer patients,but was high as 17.1%in genetic high-risk group patients(those with a family or personal history of cancer,or early-onset).Genes with higher frequency of germline variants in pancreatic cancer patients were PALB2,BRCA2,and ATM.Conclusion The rate of germline variants in overall pancreatic cancer patients is not high,but it increases significantly in genetic high-risk group,proving the importance of clinical factors in the screening of hereditary pancreatic cancer.

Objective To evaluate the value of high-throughput sequencing(HTS)data reanalysis that does not include ERBB2 copy number variation(CNV)analysis,in identifying ERBB2 amplification in patients with colorectal cancer.Methods The HTS data of 252 cases of colorectal cancer diagnosed by pathological biopsy who received peripheral blood cfDNA HTS detection samples were retrospectively analyzed.According to the HTS data of ERBB2 non-amplified samples judged by immunohistochemistry(IHC)and/or fluorescence in situ hybridization(FISH),the number of chromosome 17(Chr17)reads in the total number of reads was calculated the range of the ratio was initially determined as the threshold for prompting ERBB2 amplification.Suspected positive samples were screened according to thresholds and verified by digital PCR,IHC and FISH.Results The proportion of the number of Chr17 reads accounts for the number of total reads in the 89 cases of ERBB2 non-amplified samples determined by IHC and/or FISH ranged from 0.188 to 0.299(0.239±0.192).Using 0.298(1.25 times the mean)as the threshold indicating ERBB2 amplification,the data of 163 samples were analyzed,of which 7 cases were suspected to be positive,and the ratio ranged from 0.302 to 0.853.Among them,5 cases were determined to be positive by IHC and/or FISH,and 6 cases were confirmed to be positive by digital PCR.The ratio of the number of Chr17 reads to the number of total reads was positively correlated with the ratio of ERBB2/EIF2C1,and the correlation was good(r2=0.909).Conclusion The high-throughput sequencing data that does not cover the ERBB2 CNV analysis has a certain hint value for ERBB2 amplification in patients with colorectal cancer.

CD200 and its receptor CD200R constitute an endogenous inhibitory signal. The binding of CD200 and CD200R can regulate the immune response to pathogenic stimuli, which has received much attention in recent years. It has been found that CD200-CD200R is involved in the regulation of many kinds of pathological inflammation, including autoimmune diseases, cardiac cerebrovascular disease, infection and tumor. This paper reviews the protein structure, distribution, expression, biological function of CD200-CD200R and the correlation with diseases, and analyses the current status and development ideas of CD200-CD200R as drug targets. It aims to provide theoretical support for new drug research and development based on this target.