Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Objective To identify and analyze risk factors for acute renal failure (ARF) following lung transplantation and to develop a predictive model. Methods Data for this study were obtained from the United Network for Organ Sharing (UNOS) database, encompassing patients who underwent unilateral or bilateral lung transplantation between 2015 and 2022. We analyzed both preoperative and postoperative clinical characteristics of the patients. A combined approach utilizing random forest and least absolute shrinkage and selection operator (LASSO) regression was employed to identify key factors associated with the incidence of ARF post-transplantation, based on which a nomogram model was developed. The predictive performance of the constructed model was evaluated in both training and validation sets, using receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics to verify and compare model effectiveness. Results A total of 15 110 lung transplantation patients were included in the study, consisting of6 041 males and 9 069 females, with a median age of 62.00 years (interquartile range: 54.00 to 67.00). The analysis revealed statistically significant differences between postoperative renal dialysis and non-dialysis patients regarding preoperative lung diagnosis, estimated glomerular filtration rate (eGFR), mechanical ventilation, preoperative ICU treatment, extracorporeal membrane oxygenation (ECMO) support, infections occurring within two weeks prior to transplantation, Karnofsky Performance Status (KPS) score, waitlist duration, double-lung transplantation, and ischemia time (P<0.05). Five key variables associated with ARF after lung transplantation were identified through random forest and LASSO regression: recipients’ eGFR, preoperative ICU treatment, ECMO support, bilateral lung transplantation, and ischemia time. A nomogram model was subsequently established. Model evaluation demonstrated that the constructed predictive model achieved high accuracy in both training and validation sets, with favorable AUC values, confirming its validity and reliability. Conclusion This study identifies common risk factors for ARF following lung transplantation and introduces an effective predictive model with potential clinical applications.

OBJECTIVE To provide a reference for pharmaceutical care in patients with hemorrhagic transformation after cerebral infarction complicated with atrial fibrillation. METHODS Clinical pharmacists participated in the treatment practice of a patient with hemorrhagic transformation after cerebral infarction complicated with atrial fibrillation. Because the patient had a hemorrhagic transformation after cerebral infarction after stent implantation and arterial thrombolysis， the clinical pharmacists recommended stopping antiplatelet drugs and giving plasma and cold precipitation； because D-dimer was significantly elevated， the clinical pharmacists recommended anticoagulant therapy with low-molecular heparin. Due to the patient’s recurrence of hemorrhagic transformation after cerebral infarction， the clinical pharmacists recommended discontinuing rivaroxaban and administering human prothrombin complex concentrate. RESULTS The physician adopted the clinical pharmacists’ recommendation. After treatment， the patient’s condition tended to improve steadily and was allowed to be discharged with medication. CONCLUSIONS The clinical pharmacists assessed the individualized risk and optimized the patient’s medication regimen by suggesting discontinuation of antiplatelet and anticoagulant drugs， carrying out pharmaceutical care such as coagulation index monitoring， determining the time and indications for restarting anticoagulation， and pharmaceutical monitoring， to ensure the safety and efficacy of the patient’s medication.

Objective To construct a Type 1 diabetes model in miniature pigs and explore postoperative care strategies for effectively prolonging the survival time of the model pigs.Methods Seven Banna miniature pigs were selected for pancreatectomy.Glucose,vitamins,and antibiotics were administered for 3-5 days after surgery to aid recovery.Blood glucose and urine glucose levels were measured twice a day in the morning and evening to adjust insulin supplementation accordingly.The model pigs were observed daily and records were kept,including orexis,psychosis,weakness,skin ulcer,and feces and urine.Body weight was measured weekly until the death of the model animals.Based on the model pigs'condition,glucose injection and Ringer's lactate solution were administered to supplement nutrition and correct electrolyte imbalances.Results All seven Banna miniature pigs showed typical symptoms of diabetes:random blood glucose levels higher than 11.1 mmol/L after pancreatectomy,far exceeding the average blood glucose level of 6.0 mmol/L in normal pigs;positive urine glucose;and progressive weight loss.These features indicated the successful construction of Type 1 diabetes model.Additionally,Type 1 diabetic pigs that survived more than 8 weeks showed progressive hair loss and skin ulceration.Euthanasia was performed on model pigs when they were unable to stand or even eat independently,and pathological examination and HE staining were conducted on tissues collected from affected organs such as the liver,kidneys,and skin.Pathological sections revealed liver congestion,massive glycogen accumulation,ballooning degeneration of hepatocytes,and progressive liver fibrosis,along with glomerular congestion,vacuolar degeneration in renal tubular epithelial cells,proteinuria,dermal congestion,thinning of vascular walls,and varying degrees of parakeratosis and dyskeratosis in the liver,kidneys,and skin tissues due to prolonged hyperglycemia.The average survival time of the constructed Banna miniature pig diabetes model was 44 d,with a maximum survival time of 121 d.Conclusion Type 1 diabetes model can be constructed successfully in Banna miniature pigs through pancreatectomy.With meticulous postoperative care,a long-term Type 1 diabetes model with significant complications can be achieved,providing a stable large-animal model for Type 1 diabetes treatment strategies.

In order to promote the development of hair transplantation, particularly the establishment of standards, the Hair Transplantation Expert Group of Plastic and Aesthetic National Medical Quality Control Center invited experts in the field of hair transplantation across China and formed a draft of the Expert Consensus on Standard Terminology for Hair Transplantation based on the collation of relevant literature and monographs. By combining Delphi methodology and consensus meetings, the expert group conducted several rounds of consultations on issues such as follicular unit anatomy, harvesting, dissection, and implantation, ultimately presenting 54 terminologies. This consensus considers the original Chinese and English meanings as well as the professional characteristics of terms used in hair transplantation, highlights the unique features and advantages of hair transplantation, and preserves its essence while promoting innovation, with the hope of providing valuable reference for clinical researchers engaged in hair transplantation both domestically and internationally, and better facilitating the standardization and advancement of the field.

Objective:We genetically modified our non-replicating vaccinia virus NTV to improve its immunogenicity.Methods:We constructed NTV-modified strain NTV-ΔF1L-C7L by homologous recombination of vaccinia virus based on CRISPR-Cas9 technology by inserting the C7L gene while deleting the F1L gene. The recombinant virus NTV-ΔF1L-C7L was then immunized with 10 7 PFU in BALB/c mice, and the levels of humoral and cellular immunity induced by NTV-ΔF1L-C7L were detected by ELISA and ELISpot method, respectively, and the levels of neutralizing antibodies were determined by the phage-reduced neutralization assay. Results:The PCR and western- blot identification proved that the F1L gene of the constructed NTV-modified strain NTV-ΔF1L-C7L was missing, while the C7L gene was inserted back in the region, and the C7L gene could be expressed normally, indicating that the recombinant virus was constructed correctly. After immunization of mice with NTV-ΔF1L-C7L, ELISA result showed that the recombinant virus NTV-ΔF1L-C7L induced a higher level of IgG antibody than NTV; ELISpot result also showed that the recombinant virus was able to induce a higher level of IFN-γ; and the result of plaque reduction neutralization test showed that the recombinant virus was able to induce a higher level of IFN-γ antibody than that of NTV.Conclusions:We correctly constructed the NTV gene-modified strain NTV-ΔF1L-C7L, which induced stronger humoral and cellular immunity compared with NTV, and provided reference data for the research and development of replacement products for smallpox or monkeypox vaccines.

Humans ; Waldenstrom Macroglobulinemia/genetics* ; East Asian People ; Chromosome Aberrations ; Lymphoma

Humans ; Artificial Intelligence ; Lung Neoplasms/pathology* ; Mediastinum/pathology* ; Lymph Nodes/pathology* ; Neoplasm Staging ; Lymph Node Excision