Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

As traditional Chinese medicine （TCM） culture evolves， the academic thoughts of these practitioners， being a core component of TCM inheritance， are gradually shifting from traditional models to digital and intelligent approaches. However， this process faces challenges， including insufficient standardization of data collection and processing， low inheritance efficiency， and the risk of inheritance alienation. To address these issues， this paper proposed the construction of an intelligent platform following the "intelligence-transmission-modeling-linkage" path. "Intelligence" involves using smart perception technologies to accurately collect and classify diagnostic and therapeutic information from famous TCM practitioners， laying the foundation for digital inheritance； "transmission" focuses on leveraging artificial intelligence to mine and inherit the clinical experience of famous TCM practitioners， thereby establishing a "regional academic schools+group commonality" dynamic inheritance system； "modeling" integrates the academic thoughts and advantageous diseases of multiple schools to develop intelligent diagnostic and therapeutic models of famous TCM practitioners， resulting in personalized treatment plans； "linkage" involves constructing a clinical decision support system of famous TCM practitioners by integrating blockchain and generative intelligence， creating an AI digital avatar of TCM diagnostic and therapeutic knowledge. The "intelligence-transmission-modeling-linkage" intelligent inheritance model not only provides new ideas for the digital inheritance of TCM academic schools， but also offers strong support for the modernization and internationalization of TCM.

Compared to traditional suturing, lung stapling using automatic staplers offers advantages such as smaller trauma, faster wound healing, ease of operation, and lower complication rates, making it widely used in clinical practice. However, there are significant differences in bronchial tissue thickness at different anatomical locations, and the market is flooded with various types of staplers. Currently, there is a lack of recommended stapling schemes for bronchial staplers at different anatomical locations. This article reviews the development and application of automatic staplers and summarizes some types of staplers that are currently used in clinical practice, with the aim of promoting the formation of individualized stapler selection protocols for minimally invasive thoracic surgery based on the Chinese population.

Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) functions as a key regulator in inflammation and cell death and is involved in mediating a variety of inflammatory or degenerative diseases. A number of allosteric RIPK1 inhibitors (RIPK1i) have been developed, and some of them have already advanced into clinical evaluation. Recently, selective RIPK1i that interact with both the allosteric pocket and the ATP-binding site of RIPK1 have started to emerge. Here, we report the rational development of a new series of type-II RIPK1i based on the rediscovery of a reported but mechanistically atypical RIPK3i. We also describe the structure-guided lead optimization of a potent, selective, and orally bioavailable RIPK1i, 62, which exhibits extraordinary efficacies in mouse models of acute or chronic inflammatory diseases. Collectively, 62 provides a useful tool for evaluating RIPK1 in animal disease models and a promising lead for further drug development.

Objective To analyze the influencing factors of survival of patients with airway stenosis requiring clinical interventions after lung transplantation. Methods Clinical data of 66 patients with airway stenosis requiring clinical interventions after lung transplantation were retrospectively analyzed. Univariate and multivariate Cox’s regression models were adopted to analyze the influencing factors of survival of all patients with airway stenosis and those with early airway stenosis. Kaplan-Meier method was used to calculate the overall survival and delineate the survival curve. Results For 66 patients with airway stenosis, the median airway stenosis-free time was 72 (52,102) d, 27% (18/66) for central airway stenosis and 73% (48/66) for distal airway stenosis. Postoperative mechanical ventilation time [hazard ratio (HR) 1.037, 95% confidence interval (CI) 1.005-1.070, P=0.024] and type of surgery (HR 0.400, 95%CI 0.177-0.903, P=0.027) were correlated with the survival of patients with airway stenosis after lung transplantation. The longer the postoperative mechanical ventilation time, the higher the risk of mortality of the recipients. The overall survival of airway stenosis recipients undergoing bilateral lung transplantation was better than that of their counterparts after single lung transplantation. Subgroup analysis showed that grade 3 primary graft dysfunction (PGD) (HR 4.577, 95%CI 1.439-14.555, P=0.010) and immunosuppressive drugs (HR 0.079, 95%CI 0.022-0.287, P<0.001) were associated with the survival of patients with early airway stenosis after lung transplantation. The overall survival of patients with early airway stenosis after lung transplantation without grade 3 PGD was better compared with that of those with grade 3 PGD. The overall survival of patients with early airway stenosis after lung transplantation treated with tacrolimus was superior to that of their counterparts treated with cyclosporine. Conclusions Long postoperative mechanical ventilation time, single lung transplantation, grade 3 PGD and use of cyclosporine may affect the survival of patients with airway stenosis after lung transplantation.

Organ transplantation has become an effective treatment for multiple end-stage diseases. However, the recipients of organ transplantation need to take immunosuppressive drugs for a long time after operation, which leads to low immune function and relatively high incidence of bacterial, viral and fungal infections. Traditional microbial detection methods, such as pathogen culture, immunological detection and polymerase chain reaction, have been widely applied in infection detection, whereas these methods may cause problems, such as long detection time and presumed pathogens. Metagenomic next-generation sequencing has been widely adopted in infection prevention and control in organ transplantation in recent years due to high detection rate and comprehensive detection of pathogen spectrum. In this article, the application of metagenomic next-generation sequencing in the prevention and control of infection in solid organ transplantation was reviewed, aiming to provide reference for the diagnosis and treatment of transplantation-related infection.

Objective To investigate the epidemic features and pathogen spectrum distribution of diarrhea cases in Minhang District of Shanghai City so as to provide scientific evidence for developing prevention and control measures. Methods Surveillance on diarrhea was conducted in sentinel hospitals in Minghang District from 2018 to 2020. According to the quantity of outpatients in the monitoring hospital, the stool samples were collected by systematic sampling method according to the fixed interval proportion in the case queue which met the requirements of the monitored cases, and the pathogenic composition and epidemiological characteristics were analyzed. Results Among the 721 samples detected , 307(42.58%) were pathogen positive, The main positive bacteria was Vibrio parahaemolyticus, which accounted for 36.11%(39/108) among all positive bacteria.The main positive virus was norovirus GII, which accounted for 24.43%(75/307) among all positive virus. Positive cases were detected among all age groups. 81 positive cases (26.38%) were detected among 31-40 years old, with the highest detection rate. There was no difference in the positive detection rate between genders(χ²= 1.95, P = 0.16). The positive cases showed two peaks during the season of winter and spring. The positive rate of bacteria was highest in the third quarter and positive rate of viruses was highest in the first quarter. The mixed infection rate of bacteria and viruses was highest in the second quarter. Conclusions Diarrhea cases in Minhang District of Shanghai from 2018 to 2020 is caused by a variety of pathogens and related seasonality is obvious in Minghang District, Shanghai City in 2018-2020. It is necessary to take specific prevention based on various pathogens to reduce the incidence of diarrhea.