Congenital blood group chimerism refers to the coexistence of two or more distinct blood types within an individual, resulting from the presence of hematopoietic cell populations with different genotypes. Consequently, red blood cells in such individuals may express different blood group antigens. Based on the timing and mechanism of formation, blood group chimerism can be classified as either congenital or acquired. Although congenital blood group chimerism is rare and involves complex mechanisms, it holds significant implications in transfusion medicine, transplantation, and obstetrics. This article reviews the formation mechanisms, detection methods, and clinical significance of congenital blood group chimerism in transfusion medicine. Particular emphasis is placed on the principles, advantages, and limitations of various detection techniques. Furthermore, the potential applications of these technologies in clinical diagnosis are discussed, providing a technical foundation for the development of precise transfusion strategies.

OBJECTIVE To evaluate the efficacy and safety of immune checkpoint inhibitors （ICIs） combined with neoadjuvant chemotherapy in the treatment of early triple-negative breast cancer （TNBC）. METHODS Randomized controlled trials （RCTs） comparing ICIs combined with neoadjuvant chemotherapy （experimental group） versus neoadjuvant chemotherapy alone （control group） were retrieved from PubMed， Cochrane Library， Embase， Web of Science， CNKI， Wanfang Data， and VIP databases， as well as relevant studies published at oncology academic conferences. The search period was from database inception to June 30， 2025. After literature screening， data extraction， and quality assessment， a meta-analysis was performed by using RevMan 5.4 software. RESULTS A total of 6 RCTs involving 3 786 patients were finally included. The meta-analysis results showed that the experimental group had superior event-free survival [HR＝0.73， 95%CI （0.62， 0.85）， P＜0.000 1]， overall survival [HR＝0.69， 95%CI （0.57， 0.84）， P＝0.000 3]， and pathological complete response （pCR） [OR＝1.57， 95%CI （1.37， 1.80）， P＜0.000 01] compared to the control group. The incidence of ≥grade 3 adverse event （AE）， severe AE （SAE）， and ≥ grade 3 immune-related adverse event （irAE） in the experimental group was significantly higher than that in the control group. There was no statistically significant difference between the two groups in the incidence of any AE or any irAE （P＞0.05）. Subgroup analysis revealed that， regardless of programmed cell death ligand 1 expression status （negative or positive），the pCR in the experimental group was significantly higher than that in the control group （P＜0.05）. Additionally， the pCR of the patients with positive lymph nodes in the experimental group was significantly higher to that in the ontrol group （P＜0.05）. There was no statistically significant difference in pCR between the two groups with negative lymph nodes （P＝0.09）. CONCLUSIONS ICIs combined with neoadjuvant chemotherapy can significantly improve event-free survival and overall survival in patients with TNBC， providing patients with long-term survival benefits. However， the risk of ≥ grade 3 AE， SAE and ≥ grade 3 irAE has increased.

ObjectiveTo investigate the clinical features of tumor-induced acute pancreatitis （TIAP）， to construct and validate a predictive model for TIAP， and to provide help for early identification in clinical practice. MethodsA retrospective analysis was performed for the clinical data of 3 051 patients with acute pancreatitis （AP） who were admitted to The First Affiliated Hospital of Xinjiang Medical University from January 2020 to January 2026， among whom there were 72 patients with TIAP. To reduce class imbalance， 216 patients with non-tumor-related AP （2 979 patients） were randomly selected as conventional group using sex-stratified sampling， resulting in a cohort of 288 patients， and this cohort was randomly divided into a training set with 201 patients and a test set with 87 patients at a ratio of 7∶3. The two groups were compared in terms of general information and laboratory markers. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. Recursive feature elimination and least absolute shrinkage and selection operator regression were used for screening of characteristic variables， and five machine learning models were constructed， i.e.， logistic regression model， random forest model， support vector machine model， extreme gradient boosting model， and light gradient boosting machine model. The receiver operating characteristic curve and the precision-recall curve were used to assess model performance； the calibration curve was used to assess goodness of fit； decision curve analysis was used to evaluate clinical applicability and practicality； Shapley additive explanations were used to assess model interpretability. ResultsIn the training set of 201 patients， there were 52 patients （25.9%） in the TIAP group， and in the test set of 87 patients， there were 20 patients （23.0%） in the TIAP group. Compared with the conventional group， the TIAP group had a significantly higher proportion of patients with pancreatic duct dilatation （χ²=79.474， P<0.05）， a significantly higher age （Z=-5.838， P<0.05）， and significantly lower levels of white blood cell count （Z=5.630， P<0.05）， amylase （Z=2.606， P<0.05）， hemoglobin （Z=5.038， P<0.05）， and neutrophil percentage （Z=5.269， P<0.05）， as well as a lower level of direct bilirubin （Z=0.936， P>0.05）. The five machine learning models constructed based on these seven variables had a certain predictive ability， among which the logistic regression model had the best performance in the test set， with an area under the curve of 0.893 （95% confidence interval： 0.821 — 0.953）， an average precision of 0.654 in the precision-recall curve， good calibration， and good clinical benefits based on the decision curve analysis. ConclusionThe predictive model for TIAP based on pancreatic duct dilatation， age， white blood cell count， amylase， hemoglobin， neutrophil percentage， and direct bilirubin shows good predictive performance and can provide important guidance for the early diagnosis of TIAP.

Objective To explore the method for establishing a pig left lung orthotopic transplantation model. Methods A total of 4 male Yorkshire pigs weighing (40.0±2.5) kg underwent surgical procedures, including anesthesia, tracheal intubation, donor lung procurement, and recipient lung transplantation. Histological morphology and blood gas analysis of the transplanted lungs were assessed 2 hours after reperfusion to evaluate the histological changes and function of the transplanted lungs. Results The pigs underwent left lung in situ transplantation, effectively simulating conditions relevant to human lung transplantation. Two hours after the transplantation, arterial blood gas analysis showed that arterial partial pressure of oxygen was 155.4-178.6 mm Hg, arterial partial pressure of carbon dioxide was 53.1-62.4 mm Hg, and the oxygenation index was 310.8-357.2 mm Hg. Hematoxylin and eosin staining indicated a low degree of pulmonary edema and minimal cellular infiltration. Conclusion The pig left lung orthotopic transplantation model possesses strong operability and stability. Researchers can replicate this model according to the described methods and further conduct basic research and explore clinical translational applications.

Fecal microbiota transplantation (FMT) technology originated in China during the Eastern Jin Dynasty and has rapidly developed over the past two decades, becoming a primary method for studying the causal relationship between gut microbiota and the occurrence and progression of diseases. At the same time, the therapeutic effects of FMT in the field of gastrointestinal diseases have gained widespread recognition and are gradually expanding into other disease areas. The FMT procedure is relatively complex, and there is currently no standardized method; its success is influenced by various factors, including the donor, recipient, processing of the fecal material, and the method of implantation. Given the increasingly recognized relationship between gut microbiota and various diseases, FMT has become a research hotspot in both scientific studies and clinical applications, achieving a series of significant advancements. To help researchers better understand this technology, this paper will outline the development history of FMT, summarize common operational methods in research and clinical settings, review its application progress, and look forward to future development directions.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.