Liver transplantation is a key treatment for end-stage liver disease and primary liver cancer, yet acute rejection remains a major factor affecting the prognosis of recipients. At present, the diagnosis of acute rejection mainly relies on liver biopsy, but it is traumatic and has diagnostic errors. Non-invasive detection methods such as ultrasound elastography and cytokine assays can assist in the diagnosis of acute rejection, but a single method cannot completely replace liver biopsy. Combining multiple non-invasive methods to predict and diagnose rejection after liver transplantation is likely the direction of future research. Therefore, this article reviews non-invasive approaches for acute rejection after liver transplantation, including imaging evaluation and various biomarkers, in order to achieve personalized immunosuppressive management and improve the prognosis of liver transplant recipients.

ObjectiveTo investigate the application value of a machine learning model based on preoperative clinical indicators in predicting the risk of hypoalbuminemia after partial hepatectomy. MethodsA retrospective analysis was performed for the clinical data of 700 patients who underwent partial hepatectomy in Nanfang Hospital， Southern Medical University， from January 2018 to January 2023， including demographic data， history of underlying diseases， tumor characteristics， preoperative laboratory markers， and perioperative indicators. The research data were divided into a training set and a test set at a ratio of 7∶3. The two-independent-samples t test was used for comparison of normally distributed continuous data between two groups； the two-independent-samples Wilcoxon rank-sum test was used for comparison of continuous data with skewed distribution between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. The least absolute shrinkage and selection operator （LASSO） regression analysis was used to identify characteristic variables， and 7 machine learning algorithms were used to construct predictive models， i.e.， logistic regression， decision tree， artificial neural network， K-nearest neighbors （KNN）， support vector machine， eXtreme gradient boosting， and light gradient boosting machine. The receiver operating characteristic （ROC） curve and the area under the ROC curve （AUC） were used to assess the discriminatory ability of models， and the DeLong test was used for comparison of AUC. The calibration curve and decision curve analysis were used to assess the calibration and clinical practicability of models， and the models were compared with albumin-bilirubin （ALBI） score and Model for End-Stage Liver Disease （MELD） score. SHapley Additive exPlanations （SHAP） were used to interpret the key influencing factors for the optimal model. ResultsA total of 700 patients were finally enrolled， 283 （40.42%） developed hypoalbuminemia after surgery. The LASSO regression analysis identified 8 predictive factors of age， hepatitis B， fatty liver， blockade time， preoperative albumin （Alb）， time of operation， intraoperative blood loss， and preoperative aspartate aminotransferase （AST）. Among the 7 machine learning models， the KNN model showed the best overall predictive performance， with an AUC of 0.835 （95% confidence interval： 0.781 — 0.889）， a sensitivity of 84.0%， and a specificity of 65.5% in the test set. ALBI and MELD scores had an AUC of 0.652 and 0.524， respectively， and the KNN model had a better predictive performance than these two scores （Z=5.309 and 8.945， both P <0.001）. The calibration curve showed good consistency between predicted probabilities and actual incidence rates， and the decision curve analysis showed that the KNN model had net clinical benefit across a wide threshold range. The SHAP analysis showed that preoperative Alb， hepatitis B， time of operation， and age were the most significant influencing factors， and a synergistic effect was observed between hepatitis B and age/time of operation. ConclusionThe KNN machine learning model constructed based on preoperative clinical indicators can effectively predict the risk of hypoalbuminemia after partial hepatectomy and has a better performance than traditional scoring models， which provides a reference for the early identification of high-risk patients in clinical practice.

Objective:To understand the epidemiological and clinical characteristics of children with severe and critical hemorrhagic fever with renal syndrome(HFRS), and to accumulate experience in the diagnosis and treatment of severe and critical HFRS in children.Methods:A retrospective analysis method was used to collect the clinical data of 49 children diagnosed with HFRS, who were admitted to the Department of Infectious Diseases, Children′s Hospital Affiliated to Xi′an Jiaotong University from January 2019 to December 2021 and classified as severe or critical group.The epidemiological data, characteristics, clinical manifestations, laboratory examinations, diagnosis and treatment outcomes of the children were analyzed.Results:Among the 49 children with severe HFRS, the ratio of male∶female was 3.45∶1(38∶11), the median age was 10 years and 8 months old, and the highest proportion was 6-12 years old.The peak incidence was in November and December(75.51%).Most of them lived in rural areas, with a total of 39 cases(79.59%).All patients had fever onset, gastrointestinal symptoms(vomiting, abdominal pain, diarrhea, 81.63%)and hyperemia of skin and mucous membranes(77.55%)were common, but typical headache, backache pain and orbital pain(three pains)only accounted for 15 cases(30.6%).Laboratory test results: the white blood cells in routine blood increased in 42 cases(85.71%), while the platelets decreased in 47 cases(95.91%).The procalcitonin was increased in 48 cases(97.95%).The alanine aminotransferase was elevated in 49 cases(100%), while the albumin of 38 cases(77.55%)were lower than 30 g/L.The urea nitrogen increased in 34 cases(69.38%), and the creatinine increased in 47 cases(95.91%).The creatine kinase isozyme was elevated in 41 cases(83.67%).Forty-nine children had different degrees of electrolyte imbalance, among which low sodium(91.83%)and low calcium(85.71%)were the most common.The urine protein was positive in 47 cases(95.91%), and the urine red blood cell was positive in 38 cases(77.55%).Ultrasonography of the urinary system revealed abnormalities in the kidneys and surrounding kidneys in 43 cases(91.48%).Twenty-eight(58.33%)patients had abnormal electrocardiogram.All the 49 patients were clinically cured.Conclusion:Severe HFRS is mainly in rural male children aged 6-12 years, mainly with fever and gastrointestinal symptoms, lack of typical three pain symptoms.When white blood cell count and procalcitonin significantly increased, platelet count significantly decreased, liver and kidney function impaired and electrolyte imbalance, severe cases should be highly suspected.Early identification of critically ill children and active treatment are critical to their prognosis.

Objective:To summarize the clinical characteristics, causes of misdiagnosis and preventive measures of infectious mononucleosis (IM) in children, and to improve the ability of clinicians in early diagnosis of IM in children.Methods:The clinical data of 468 children with IM in Xi′an Children′s Hospital from January 2018 to December 2021 were retrospectively analyzed, including general situation, disease onset, diagnosis and misdiagnosis.Results:Among the 468 children, 33 cases were clinically diagnosed and 435 cases were a definitely diagnosed; 281 males (60.04%) and 187 females (39.96%); the incidence rate was highest in preschool children (43.80%, 205/468) and in autumn (33.12%, 155/468). The first symptoms were fever (52.99%, 248/468), eyelid edema (15.38%,72/468) and neck mass (14.96%, 70/468). The fever rate was 90.38% (423/468), and the median time of first fever appearance was on the first (first, second) day of disease course, and the median duration of fever was 6 (4, 8) d. The median time of first visit was on the third (first, fifth) day of disease course, and the time of diagnosis was on the seventh (fifth, ninth) day of disease course. Blood routine examination showed that the proportion of white blood cell count increased was 51.92% (243/468), the proportion of lymphocytes increased was 61.75% (289/468), and the proportion of abnormal lymphocytes increased (≥10%) in peripheral blood was 58.97% (276/468). The lymphocyte subsets of 364 children were detected, the rate of helper T lymphocytes (Th cells) decreased was 80.22% (292/364), the rate of suppressor T lymphocytes (Ts cells) increased was 99.45% (362/364), the value and decreased rate of Th cells/Ts cells were 0.24 (0.16, 0.40) and 100.00% (364/364), rate of B lymphocytes decreased was 93.96% (342/364), rates of natural killer cells decreased and increased were 35.16% (128/364) and 0.55% (2/364). The misdiagnosis rate was 55.13% (258/468), and the misdiagnosis time was on the fifth (fourth, seventh) day of disease course. Among the 258 misdiagnosed children, 105 cases (40.70%) were misdiagnosed as upper respiratory tract infection, 65 cases (25.19%) as acute suppurative tonsillitis, 27 cases (10.47%) as acute cervical lymphadenitis or neck mass.Conclusions:Due to the complex and diverse clinical manifestations of IM in children, it is easy to be misdiagnosed in the early stage of the disease. So, it is necessary for clinicians to master the clinical characteristics of IM in children, constantly improve the level of diagnosis and treatment, and reduce the misdiagnosis rate.