Objective: To investigate the serological identification and blood group gene sequencing analysis of two rare cases of K phenotype producing high-frequency antigen antibodies (anti-Ku), and to study the serological interrelationship between K cells and the high-frequency antigen antibody anti-KL. Methods: Serological methods were used to identify the antigen phenotypes of the ABO, Rh, and Kell blood group systems and to screen for and identify unexpected antibodies in the two patients. The characteristics of the unexpected antibodies were verified by the indirect antiglobulin test (IAT) using papain or dithiothreitol (DTT) -treated screening cells. The titer of anti-Ku was determined via the tube method using DTT-treated plasma. The Kell blood group genotype was determined by gene sequencing. The distinctive antigenicity of K cells was validated through their reactivity with anti-KL in IAT, and absorption-elution techniques were employed to corroborate the type of anti-KL. Results: Serological findings: Case 1 was blood group O, CCDee; Case 2 was blood group A, CCDee. Both cases exhibited the Kell phenotype: K-k-, Kp (a-b-). High-frequency antigen antibodies were detected in the plasma of both patients. The reactivity of these antibodies was slightly enhanced with papain-treated screening cells but became negative with DTT-treated cells. The anti-Ku (IgG) titer for Case 1 was 64. For Case 2, the anti-Ku (IgM) titer was<1, and the anti-Ku (IgG) titer was 32. Gene sequencing revealed that both cases harbored a homozygous c.715G>T mutation in the KEL gene, corresponding to the genotype KEL02N.24, consistent with the rare K phenotype. The unique high expression of the Kx antigen on K cells was confirmed through the antibody characteristics of anti-KL. Absorption-elution techniques demonstrated that K cells could separate anti-Km and anti-Kx, thereby supporting the classification of anti-KL. Conclusion: Serological and molecular biological assays identified both patients as having the rare Kell-null (K ) phenotype. If such rare blood types go undetected in transfusion medicine, the administration of standard blood products can readily induce the production of high-frequency antigen antibodies such as anti-Ku, potentially leading to a transfusion crisis due to the subsequent difficulty in finding compatible blood. The serological relationship between K cells and anti-KL clarified the characteristic high expression of the Kx antigen on K phenotype erythrocytes and concurrently supported the typological features of the rare high-frequency antibody anti-KL. This represents the first such verified report in China.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

ObjectiveColorectal cancer (CRC) is a highly heterogeneous malignancy, and traditional Chinese medicine (TCM) syndrome differentiation is widely used in its clinical management. However, the biological basis underlying different TCM syndromes remains insufficiently understood. This study aimed to investigate the proteomic differences between qi-blood deficiency syndrome (QBDS) and damp-heat syndrome (DHS) in CRC patients and to establish a “syndrome-differential protein-herbal component” network for exploring potential molecular mechanisms and therapeutic targets associated with syndrome-specific treatment strategies. MethodsTumor tissue samples were collected from 10 patients with pathologically confirmed CRC, including 5 patients diagnosed with QBDS and 5 with DHS according to standardized TCM syndrome differentiation criteria. Tandem mass tag (TMT)-based quantitative proteomics was employed to identify differentially expressed proteins (DEPs) between the two syndrome groups. Functional enrichment analyses, including Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed to characterize the biological functions and signaling pathways associated with the identified DEPs. Least absolute shrinkage and selection operator (LASSO) regression analysis was further applied to screen key syndrome-related proteins and construct a core protein prediction model. Subsequently, molecular docking was conducted to evaluate the binding affinities between representative herbal compounds, astragaloside IV and berberine, and the identified core proteins. Finally, cell counting kit-8 (CCK-8) assays were performed in HCT 116 and HT-29 colorectal cancer cell lines to validate the anti-proliferative activities of these compounds in vitro. ResultsA total of 44 DEPs were identified between QBDS and DHS tissues, including 21 upregulated and 23 downregulated proteins in the QBDS group relative to the DHS group. Functional enrichment analyses revealed that these proteins were primarily associated with antiviral immune responses, regulation of inflammatory signaling, NF‑κB signaling pathways, cholesterol metabolism, and other biological processes relevant to tumor progression and host immune regulation. LASSO regression analysis identified eight core proteins with potential syndrome-specific significance. Among them, ST7, GPCPD1, and PODXL were closely associated with QBDS, whereas FAT1, GRB7, KRT7, MAFF, and MMP8 were associated with DHS. Molecular docking demonstrated favorable interactions between astragaloside IV and QBDS-related proteins, with binding energies lower than -5 kcal/mol, indicating stable binding activity. Berberine exhibited even lower binding energies and stronger binding specificity toward DHS-related core proteins. In vitro experiments further confirmed that both compounds inhibited CRC cell proliferation, while berberine displayed significantly greater anti-proliferative efficacy. The lowest 48 h IC₅₀ value of berberine reached 15.21 mg/L in CRC cells, indicating a stronger growth-inhibitory effect than astragaloside IV. ConclusionCRC patients with QBDS and DHS exhibit distinct proteomic characteristics, suggesting that different TCM syndromes possess unique molecular signatures. The identified core proteins may serve as potential biomarkers for syndrome differentiation, while astragaloside IV and berberine may exert therapeutic effects through targeting syndrome-specific molecular networks. These findings provide preliminary proteomic evidence supporting syndrome-guided precision integrative medicine in CRC and offer new insights into the biological basis of TCM syndrome differentiation and individualized therapeutic strategies.

Objective To compare the efficacy of five Blastocystis staining method for staining cells with different morphologies and in different developmental stages to select appropriate staining techniques for specific scenarios and experimental purposes. Methods Blastocystis was stained using Wright-Giemsa, periodic acid-Schiff (PAS), Oil Red O, Nile Red, and Lugol's iodine solution respectively. The staining effects of the five method on Blastocystis were compared. Results Wright-Giemsa staining colored the cytoplasm and nucleus of Blastocystis bluish-purple, with other parts lightly stained; PAS staining rendered Blastocystis cells red; Oil Red O staining showed different structures within Blastocystis in varying intensities of orange-red; Nile Red enabled Blastocystis to fluoresce orange-yellow under a fluorescence microscope; Lugol's iodine solution stained the cytoplasm and nucleus a dark brownish-yellow. Conclusions Wright-Giemsa staining is recommended for clinical diagnosis. For metabolic studies, a combination of periodic acid-Schiff (PAS) staining and Oil Red O staining is suggested; PAS + Oil Red O staining can provide comprehensive morphological evidence for cell metabolism research. Nile Red staining is recommended for dynamic observation of live Blastocystis. Lugol's iodine staining is suitable for preliminary screening of large-scale samples.

ObjectiveTo systematically evaluate the measurement content, psychometric properties and quality of evidence of patient-reported outcome measures (PROMs) for adolescents with spinal dysfunction based on International Classification of Functioning, Disability and Health-Children and Youth (ICF-CY) framework, Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) and modified GRADE approach. MethodsDatabases including CNKI, Wanfang Data, PubMed, Embase and Web of Science were systematically searched for studies assessing psychometric properties of PROMs targeting adolescents with spinal dysfunction. The methodological quality of included studies was evaluated using COSMIN Risk of Bias checklist, the psychometric properties were rated according to COSMIN criteria, and evidence quality was graded using modified GRADE approach. Item content of each PROM was coded following the ICF-CY linking rules. ResultsA total of 23 studies involving eight PROMs were included. Brace Questionnaire (BrQ), Italian Spine Youth Quality of Life Questionnaire (ISYQOL), Quality of Life Profile for Spinal Deformities (QLPSD), Spinal Appearance Questionnaire (SAQ), Scoliosis Japanese Questionnaire-27 (SJ-27) and Scoliosis Research Society-22 Revised Questionnaire (SRS-22r) were rated as grade B, while EuroQol Five-Dimension Youth Five-Level Questionnaire (EQ-5D-Y-5L) and Young Spine Questionnaire (YSQ) were rated as grade C. The eight PROMs were linked to six to 24 ICF-CY categories. BrQ covered the largest number of categories (n = 24), followed by SJ-27 (n = 19) and SRS-22r (n = 16). Except for SAQ which mainly focused on dimension of body functions and structures, all other PROMs covered both dimension of body functions and structures, and activities and participation, while BrQ, ISYQOL, SRS-22r and YSQ also covered dimension of environmental factors. Major methodological limitations included insufficient evidence for content validity, inappropriate factor-analysis approaches, incomplete reporting for test-retest reliability, and overall scarcity of evidence concerning measurement error, minimal important change (MIC) and responsiveness. ConclusionThe quality of PROMs in adolescents with spinal dysfunction varies. BrQ, ISYQOL, QLPSD, SAQ, SJ-27 and SRS-22r show application potential, yet further high-quality studies are required to supplement their psychometric evidence. EQ-5D-Y-5L and YSQ exist high-quality insufficient evidence. Selection of PROMs should comprehensively consider target population, measurement purpose, content coverage and quality of evidence. Further researches are needed to improve reporting quality.

The recombinant human thyroid-stimulating hormone (rhTSH) can be used for differentiated thyroid cancer (DTC) preparation before ¹³¹I therapy, and follow-up. Since its approval by the American Food and Drug Administration (FDA) in 1998, rhTSH has been clinically used in European countries, the United States, and other countries for nearly 30 years. In April 2024, China’s first independently developed rhTSH agent was approved. Based on clinical experience and evidence from domestic and international studies, an expert consensus on the application of rhTSH has been developed following thorough consultations among experts. The consensus offers evidence-based recommendations to guide the appropriate use of rhTSH in clinical practice.

To explore the value of general information and rapid laboratory tests obtained from the emergency department in the early diagnosis and prevention of young patients with acute myocardial infarction and acute myocarditis, in order to prevent the disease from progressing to a critical stage. This study employs a retrospective observational study, compiling clinical data from young patients diagnosed with acute myocardial infarction or acute myocarditis who were admitted to the Department of Cardiology or Emergency Department of the Second Affiliated Hospital of Soochow University from January 2015 to September 2024. Demographic information and laboratory test results from both the outpatient and emergency departments were retrieved. The acute myocardial infarction group comprised 267 patients (257 males, 10 females) aged 23-44 ys, while the acute myocarditis group included 134 patients (93 males, 41 females) aged 18-44 ys. A comparative analysis of the clinical data between the two groups was conducted, encompassing variables such as age, gender, comorbidities, high-risk factors, emergency blood routine tests, high-sensitivity C-reactive protein levels, coagulation profiles, renal function tests, NT-proBNP levels, myocardial injury markers, electrocardiogram readings, blood pressure, and heart rate. The results showed that:Compared with the young myocarditis group, the myocardial infarction group was older (ys)[38(35, 42) vs 30(25, 37), U=7 893, P<0.001], more male [257(96.3%) vs 93(69.4%), χ2=57.95, P<0.001], more smoking [211(79.0%) vs 38(28.4%), χ2=97.32, P<0.001], drinking history [125(46.8%) vs 22(16.4%), χ2=35.51, P<0.001], family history of coronary heart disease [45(16.9%) vs 3(2.2%), χ2=18.09, P<0.001], hypertension [100(37.5%) vs 12(9.0%), χ2=36, P<0.001] and diabetes [42(15.7%) vs 4(3.0%), χ2=14.27, P<0.001]. Systolic blood pressure (mmHg)[126(114, 144) vs 119(101, 126), U=11 389.50, P<0.001], diastolic blood pressure (mmHg)[80(70, 93) vs 72(62, 81), U=12 220.50, P<0.001], total white blood cell count (10 9/L)[11.3(9.2, 14.1) vs 8.5(6.6, 11.2), U=10 825.50, P<0.001], hemoglobin (g/L)[157(147, 166) vs 143(129, 154), U=9 404.50, P<0.001], platelet count (10 9/L)[244(206, 297) vs 207(173, 253), U=11 680, P<0.001], uric acid (μmol/L)[380(315, 446) vs 347(265, 412), U=14 805.50, P=0.005], ST segment elevation [204(76.4%) vs 57(42.5%), χ2=73.03, P<0.001] and Q wave formation [76(28.5%) vs 17(12.7%), χ2=12.47, P<0.001] in ECG were higher than those in myocarditis group. The duration of onset (hs) [6(3, 25) vs 48(24, 73), U=27911, P<0.001], heart rate (beats/min)[82(74, 92) vs 92(78, 103), U=22 347, P<0.001], D-dimer (μg/ml)[0.23(0.17, 0.51) vs 0.61(0.30, 1.38), U=25 806, P<0.001], High-sensitivity troponin T/99th percentile upper reference limit [5(1, 36) vs 16(8, 39), U=22 577, P<0.001], NT-proBNP (pg/ml) [204(64, 644) vs 824(189, 4 043), U=25 134, P<0.001], C-reactive protein (mg/L)[6(3, 9) vs 24(6, 55), U=26 349.50, P<0.001] and body temperature (℃) [36.50(36.30, 36.60) vs 37.35(36.50, 38.50), U=26 961, P<0.001] were significantly lower than those in myocarditis group, the symptoms of chest pain in myocardial infarction group was significantly higher than those in myocarditis group [262(98.1%) vs 83(61.9%), χ2=97.24, P<0.001], and the history of prodromal infection [12(4.5%) vs 112(83.6%), χ2=261.26, P<0.001], syncope [11(4.1%) vs 18(13.4%), χ2=11.53, P<0.001] and shock [6(2.2%) vs 22(16.4%), χ2=27.59, P<0.001] in myocardial infarction group were significantly lower than those in myocarditis group. With acute myocardial infarction as the target outcome, 8 influencing factors selected by LASSO regression, and 5 independent influencing factors were found after multiple Logistic regression, those were age ( OR=1.21, 95% CI: 1.12-1.31; P<0.001), pre-infection ( OR=0.02, 95% CI: 0.01-0.06; P<0.001), body temperature ( OR=0.37, 95% CI: 0.18-0.77; P=0.008), chest pain ( OR=26.75, 95% CI: 5.87-121.81; P<0.001) and white blood cell count ( OR=1.27, 95% CI: 1.12-1.44; P<0.001). Younger age, high body temperature and pre-infection are independent predictors for acute myocarditis, while chest pain and elevated white blood cell count are independent predictors for acute myocardial infarction. The five influencing factors selected by multivariate logistic regression and their combined diagnostic model were subjected to ROC analysis. The AUC reached 0.969, sensitivity reached 0.940 and specificity reached 0.925. Calibration curve and decision curve analysis(DCA) demonstrate that the model possesses excellent clinical application value. In conclusion, age, chest pain, pre-infection, body temperature and white blood cell count were independent factors in distinguishing acute myocardial infarction and acute myocarditis in young people. The clinical differential diagnosis model based on 5 independent factors may has high efficiency and good clinical practicability.

Objective:To analyze the awareness of HCV infection status and willingness for HCV-RNA testing among hepatitis C cases in four provinces in China and to provide a reference for adjusting HCV prevention and control strategies.Methods:From September 2021 to September 2022, a cross-sectional survey was conducted using stratified random cluster sampling in four provinces (Jiangsu, Henan, Hubei, and Yunnan) in China, with an estimated sample size of 6 468 participants. The questionnaire included sociodemographic information, HCV infection awareness, willingness for HCV-RNA testing, and history of high-risk behaviors from the survey participants. Logistic regression models were used to analyze the factors associated with HCV infection awareness and willingness for HCV-RNA testing among hepatitis C cases. Statistical analysis was performed using R 4.1.3 software.Results:A total of 10 563 hepatitis C cases were surveyed. The awareness rate of HCV infection was 86.74% (9 162/10 563), and the willingness rate for HCV-RNA testing was 85.21% (9 001/10 563). Multivariate logistic regression models analysis showed that the awareness rate of HCV infection was lower among individuals aged ≥60 years, urban residents (with New Rural Cooperative Medical Insurance ), those without a history of blood transfusion, those without a history of paid blood donation, those without a history of injection drug use, and those without a family member with hepatitis C case.The awareness rate was higher among individuals with high or technical secondary school education, college education or above, and those married with a spouse (all P<0.05). In terms of willingness for HCV-RNA testing, it was lower among females, individuals aged ≥60 years, and those without a history of blood transfusion, paid blood donation, or injection drug use. The willingness was higher among farmers or migrant workers, employees of enterprises or institutions, and those in other occupations (all P<0.05). Conclusions:There was room for improvement in the awareness proportion of HCV infection and willingness for HCV-RNA testing among hepatitis C cases in the four provinces of China. More convenient policies and measures should be provided to increase the awareness rate of HCV infection and the willingness to undergo HCV-RNA testing in this population.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.