Diagnosis and therapies for patients with cerebral palsy over the past 30 years: a bibliometric analysis.

Background: Currently, the incidence of cerebral palsy is high in newborns. However, the current methods for diagnosing and treating patients with cerebral palsy are complex and poorly targeted. Moreover, these studies lack the support of bibliometric analysis results. Objective: Our study focused on a bibliometric analysis of published papers on the diagnosis and treatment of patients with cerebral palsy. This study identified the primary authors, institutions, and countries involved in analyzing the status and trends of research on the diagnosis and treatment of patients with cerebral palsy. Additionally, the study also involved screening pathways related to cerebral palsy. Methods: The PubMed database was searched for publications on the diagnosis and treatment of patients with cerebral palsy between 1990 and 2023. R v4.2.2 and VOSviewer v1.6.18 software tools were utilized to perform bibliometric analysis and visualization. Results: There were 1,965 publications on cerebral palsy diagnosis and 5,418 articles on the qualified treatment strategies, and the annual number of publications also increased. The United States dominated in this field of research. Gregory Y.H. Lip and Patrizio Lancellotti published the most number of papers. The Cleveland Clinic published the most number of papers in the field. According to the analysis of the co-occurrence of keywords, we found that the main research directions were age, sex, disease diagnosis, and treatment. Newly emerging research has focused mainly on heart failure, which is related to valvular heart disease. Conclusion: The findings presented in this study offer valuable insights into ongoing research and potential future directions pertaining to cerebral palsy. These insights can assist researchers in identifying suitable collaborators and enhancing their investigations aimed at identifying the underlying molecular mechanisms associated with cerebral palsy, encompassing its etiology, preventive measures, and therapeutic interventions.

Attributes and Predictors of Opinion Leaders on Twitter: COVID-19 Childhood Vaccination Campaign.

Identifying attributes of the COVID-19 childhood vaccine message disseminators is beneficial to health campaign research. This study examines the We Can Do This COVID-19 childhood vaccination health campaign message disseminators on Twitter during 2021-2022. Guided by the diffusion of innovation theory and the theory of the two-step flow communication, we classify the disseminators (n=823) and examine their influence over the two-year period. A manual content analysis and social network analysis were conducted to measure the impact of disseminators on their networks through in-degree centrality assessment. Results revealed that experts in medical fields, industrial professionals, and social media influencers were the top three most common career titles disclosed in Twitter bios among individual accounts, whereas local nonprofit organizations, governments, and companies were the most common organizational accounts. Mann-Whitney U Tests indicated that in both years message disseminators with clinical medicine expertise were more influential than those who did not reveal such expertise. However, only in 2021 were campaign message disseminators with parental expertise more influential within the social network. We discuss the implications of disseminator classification as well as the significance of enhancing health campaigns by identifying who discusses them on social media.

Global responses of wetland methane emissions to extreme temperature and precipitation.

As global warming continues, events of extreme heat or heavy precipitation will become more frequent, while events of extreme cold will become less so. How wetlands around the globe will react to these extreme events is unclear yet critical, because they are among the greatest natural sources of methane(CH4). Here we use seven indices of extreme climate and the rate of methane emission from global wetlands(WME) during 2000-2019 simulated by 12 published models as input data. Our analyses suggest that extreme cold (particularly extreme low temperatures) inhibits WME, whereas extreme heat (particularly extreme high temperatures) accelerates WME. Our results also suggest that daily precipitation >10 mm accelerates WME, while much higher daily precipitation levels can slow WME. The correlation of extreme high temperature and precipitation with rate of WME became stronger during the study period, while the correlation between extreme low temperature and WME rate became weaker.

Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images.

Background: Pulmonary epithelioid hemangioendothelioma (PEH) is a rare vascular tumour, and its early diagnosis remains challenging. This study aims to comprehensively analyse the imaging features of PEH and develop a model for predicting PEH. Methods: Retrospective and pooled analyses of imaging findings were performed in PEH patients at our center (n=25) and in published cases (n=71), respectively. Relevant computed tomography (CT) images were extracted and used to build a deep learning model for PEH identification and differentiation from other diseases. Results: In this study, bilateral multiple nodules/masses (n=19) appeared to be more common with most nodules less than 2 cm. In addition to the common types and features, the pattern of mixed type (n=4) and isolated nodules (n=4), punctate calcifications (5/25) and lymph node enlargement were also observed (10/25). The presence of pleural effusion is associated with a poor prognosis in PEH. The deep learning model, with an area under the receiver operating characteristic curve (AUC) of 0.71 [95% confidence interval (CI): 0.69-0.72], has a differentiation accuracy of 100% and 74% for the training and test sets respectively. Conclusions: This study confirmed the heterogeneity of the imaging findings in PEH and showed several previously undescribed types and features. The current deep learning model based on CT has potential for clinical application and needs to be further explored in the future.

Imaging and Histopathological Features Of Primary Thymic Neuroendocrine Tumor.

OBJECTIVES: To investigate CT, MRI, and PET/CT features with histopathological findings of primary thymic neuroendocrine tumor. MATERIALS AND METHODS: All 9 cases with pathologically proven primary thymic neuroendocrine tumors were reviewed retrospectively. Among them, 7 underwent enhanced CT, 1 with MRI (enhanced) and another with PET/CT scan. Multiple characters were examined, including tumor location, contour, CT attenuation, enhancement pattern, involvement of surrounding structure and lymphadenopathy. RESULTS: Among 9 patients studied, 7 (77%) masses were located in the anterior superior mediastinum, 1 in the anterior superior-middle mediastinum, and 1 in the anterior and middle mediastinum. The maximum diameter (longitudinal) ranged from 4.2 to 23 cm (mean ± standard deviation, 9.5 cm ± 2.8). Four masses had irregular, 3 had lobulated, and 2 had smooth contours, while 8 masses had clear margins and 1 had an ill-defined margin. Six masses showed heterogeneous attenuation with necrotic/cystic component (n=5), calcification (n=2) and hemorrhage(n=1), and 3 showed homogeneous attenuation on the non-enhanced image. After contrast administration, 8 masses showed heterogeneous attenuation, and 1 showed homogeneous attenuation with tumor vessels visible in 4 masses. Among all, 8 masses showed strong enhancement, and 1 showed moderate enhancement in comparison to muscles in the anterior thoracic wall on enhanced images. Involvement of adjacent mediastinal structures was observed in 5 cases. Immunohistochemical analysis showed that the tumor cells were positive for CgA, Syn, CK, CD56 and EMA. CONCLUSION: Primary NETs are large masses located anterior superior mediastinum, irregular in contour, showing heterogeneous attenuation with necrotic/cystic component and strong heterogeneous enhancement with tumor vessels, compressing local mediastinal structures. In addition, immunohistochemical examination is required in such a diagnosis.

Structure and Functions of Endophytic Bacterial Communities Associated with Sphagnum Mosses and Their Drivers in Two Different Nutrient Types of Peatlands.

Sphagnum mosses are keystone plant species in the peatland ecosystems that play a crucial role in the formation of peat, which shelters a broad diversity of endophytic bacteria with important ecological functions. In particular, methanotrophic and nitrogen-fixing endophytic bacteria benefit Sphagnum moss hosts by providing both carbon and nitrogen. However, the composition and abundance of endophytic bacteria from different species of Sphagnum moss in peatlands of different nutrient statuses and their drivers remain unclear. This study used 16S rRNA gene amplicon sequencing to examine endophytic bacterial communities in Sphagnum mosses and measured the activity of methanotrophic microbial by the 13C-CH4 oxidation rate. According to the results, the endophytic bacterial community structure varied among Sphagnum moss species and Sphagnum capillifolium had the highest endophytic bacterial alpha diversity. Moreover, chlorophyll, phenol oxidase, carbon contents, and water retention capacity strongly shaped the communities of endophytic bacteria. Finally, Sphagnum palustre in Hani (SP) had a higher methane oxidation rate than S. palustre in Taishanmiao. This result is associated with the higher average relative abundance of Methyloferula an obligate methanotroph in SP. In summary, this work highlights the effects of Sphagnum moss characteristics on the endophytic bacteriome. The endophytic bacteriome is important for Sphagnum moss productivity, as well as for carbon and nitrogen cycles in Sphagnum moss peatlands.

Development and validation of a deep learning signature for predicting lymphovascular invasion and survival outcomes in clinical stage IA lung adenocarcinoma: A multicenter retrospective cohort study.

PURPOSE: The presence of lymphovascular invasion (LVI) influences the management and outcomes of patients with clinical stage IA lung adenocarcinoma. The objective was the development of a deep learning (DL) signature for the prediction of LVI and stratification of prognosis. METHODS: A total of 2077 patients from three centers were retrospectively enrolled and divided into a training set (n = 1515), an internal validation set (n = 381), and an external set (n = 181). A -three-dimensional residual neural network was used to extract the DL signature and three models, namely, the clinical, DL, and combined models, were developed. Diagnostic efficiency was assessed by ROC curves and AUC values. Kaplan-Meier curves and Cox proportional hazards regression analyses were conducted to evaluate links between various factors and disease-free survival. RESULTS: The DL model could effectively predict LVI, shown by AUC values of 0.72 (95 %CI: 0.68-0.76) and 0.63 (0.54-0.73) in the internal and external validation sets, respectively. The incorporation of DL signature and clinical-radiological factors increased the AUC to 0.74 (0.71-0.78) and 0.77 (0.70-0.84) in comparison with the DL and clinical models (AUC of 0.71 [0.68-0.75], 0.71 [0.61-0.81]) in the internal and external validation sets, respectively. Pathologic LVI, LVI predicted by both DL and combined models were associated with unfavorable prognosis (all p < 0.05). CONCLUSION: The effectiveness of the DL signature in the diagnosis of LVI and prognosis prediction in patients with clinical stage IA lung adenocarcinoma was demonstrated. These findings suggest the potential of the model in clinical decision-making.

An optimized water table depth detected for mitigating global warming potential of greenhouse gas emissions in wetland of Qinghai-Tibetan Plateau.

Climate change and human activities have intensified variations of water table depth (WTD) in wetlands around the world, which may strongly affect greenhouse gas emissions. Here, we analyzed how emissions of CO2, CH4, and N2O from the Zoige wetland on the Qinghai-Tibetan Plateau (QTP) vary with the WTD. Our data indicate that the wetland shows net positive global warming potential (11.72 tCO2-e ha-1 yr-1), and its emissions of greenhouse gases are driven primarily by WTD. Our analysis suggests that an optimal WTD exists, which at our study site was approximately 18 cm, for mitigating increases in global warming potential from the wetland. Our study provides insights into how climate change and human acitivies affect greenhouse gas emissions from alpine wetlands, and they suggest that water table management may be effective at mitigating future increases in emissions.

A CT-based deep learning model: visceral pleural invasion and survival prediction in clinical stage IA lung adenocarcinoma.

Pathologic visceral pleural invasion (VPI) in patients with early-stage lung cancer can result in the upstaging of T1 to T2, in addition to having implications for surgical resection and prognostic outcomes. This study was designed with the goal of establishing and validating a CT-based deep learning (DL) model capable of predicting VPI status and stratifying patients based on their prognostic outcomes. In total, 2077 patients from three centers with pathologically confirmed clinical stage IA lung adenocarcinoma were enrolled. DL signatures were extracted with a 3D residual neural network. DL model was able to effectively predict VPI status. VPI predicted by the DL models, as well as pathologic VPI, was associated with shorter disease-free survival. The established deep learning signature provides a tool capable of aiding the accurate prediction of VPI in patients with clinical stage IA lung adenocarcinoma, thus enabling prognostic stratification.

More enhanced non-growing season methane exchanges under warming on the Qinghai-Tibetan Plateau.

Uncertainty in methane (CH4) exchanges across wetlands and grasslands in the Qinghai-Tibetan Plateau (QTP) is projected to increase due to continuous permafrost degradation and asymmetrical seasonal warming. Temperature plays a vital role in regulating CH4 exchange, yet the seasonal patterns of temperature dependencies for CH4 fluxes over the wetlands and grasslands on the QTP remain poorly understood. Here, we demonstrated a stronger warming response of CH4 exchanges during the non-growing season compared to the growing season on the QTP. Analyzing 9745 daily observations and employing four methods -regression fitting of temperature-CH4 flux, temperature dependence calculations, field-based and model-based control experiments-we found that warming intensified CH4 emissions in wetlands and uptakes in grasslands. Specifically, the average reaction intensity in the non-growing season surpasses that in the growing season by 1.89 and 4.80 times, respectively. This stronger warming response of CH4 exchanges during the non-growing season significantly increases the regional CH4 exchange on the QTP. Our research reveals that CH4 exchanges in the QTP have a higher warming sensitivity in non-growing seasons, which meanwhile are dominated by a larger warming rate than the annual average. The combined effects of these two factors will significantly alter the CH4 source/sink on the QTP. Neglecting these impacts would lead to inaccurate estimations of CH4 source/sink over the QTP under climate warming.

Phylogeny and diversification of genus Sanicula L. (Apiaceae): novel insights from plastid phylogenomic analyses.

BACKGROUND: The genus Sanicula L. is a unique perennial herb that holds important medicinal values. Although the previous studies on Sanicula provided us with a good research basis, its taxonomic system and interspecific relationships have not been satisfactorily resolved, especially for those endemic to China. Moreover, the evolutionary history of this genus also remains inadequately understood. The plastid genomes possessing highly conserved structure and limited evolutionary rate have proved to be an effective tool for studying plant phylogeny and evolution. RESULTS: In the current study, we newly sequenced and assembled fifteen Sanicula complete plastomes. Combined with two previously reported plastomes, we performed comprehensively plastid phylogenomics analyses to gain novel insights into the evolutionary history of this genus. The comparative results indicated that the seventeen plastomes exhibited a high degree of conservation and similarity in terms of their structure, size, GC content, gene order, IR borders, codon bias patterns and SSRs profiles. Such as all of them displayed a typical quadripartite structure, including a large single copy region (LSC: 85,074-86,197 bp), a small single copy region (SSC: 17,047-17,132 bp) separated by a pair of inverted repeat regions (IRs: 26,176-26,334 bp). And the seventeen plastomes had similar IR boundaries and the adjacent genes were identical. The rps19 gene was located at the junction of the LSC/IRa, the IRa/SSC junction region was located between the trnN gene and ndhF gene, the ycf1 gene appeared in the SSC/IRb junction and the IRb/LSC boundary was located between rpl12 gene and trnH gene. Twelve specific mutation hotspots (atpF, cemA, accD, rpl22, rbcL, matK, ycf1, trnH-psbA, ycf4-cemA, rbcL-accD, trnE-trnT and trnG-trnR) were identified that can serve as potential DNA barcodes for species identification within the genus Sanicula. Furthermore, the plastomes data and Internal Transcribed Spacer (ITS) sequences were performed to reconstruct the phylogeny of Sanicula. Although the tree topologies of them were incongruent, both provided strong evidence supporting the monophyly of Saniculoideae and Apioideae. In addition, the sister groups between Saniculoideae and Apioideae were strongly suggested. The Sanicula species involved in this study were clustered into a clade, and the Eryngium species were also clustered together. However, it was clearly observed that the sections of Sanicula involved in the current study were not respectively recovered as monophyletic group. Molecular dating analysis explored that the origin of this genus was occurred during the late Eocene period, approximately 37.84 Ma (95% HPD: 20.33-52.21 Ma) years ago and the diversification of the genus was occurred in early Miocene 18.38 Ma (95% HPD: 10.68-25.28 Ma). CONCLUSION: The plastome-based tree and ITS-based tree generated incongruences, which may be attributed to the event of hybridization/introgression, incomplete lineage sorting (ILS) and chloroplast capture. Our study highlighted the power of plastome data to significantly improve the phylogenetic supports and resolutions, and to efficiently explore the evolutionary history of this genus. Molecular dating analysis explored that the diversification of the genus occurred in the early Miocene, which was largely influenced by the prevalence of the East Asian monsoon and the uplift of the Hengduan Mountains (HDM). In summary, our study provides novel insights into the plastome evolution, phylogenetic relationships, taxonomic framework and evolution of genus Sanicula.

Supervised training models with or without manual lesion delineation outperform clinicians in distinguishing pulmonary cryptococcosis from lung adenocarcinoma on chest CT.

BACKGROUND: The role of artificial intelligence (AI) in the discrimination between pulmonary cryptococcosis (PC) and lung adenocarcinoma (LA) warrants further research. OBJECTIVES: To compare the performances of AI models with clinicians in distinguishing PC from LA on chest CT. METHODS: Patients diagnosed with confirmed PC or LA were retrospectively recruited from three tertiary hospitals in Guangzhou. A deep learning framework was employed to develop two models: an undelineated supervised training (UST) model utilising original CT images, and a delineated supervised training (DST) model utilising CT images with manual lesion annotations provided by physicians. A subset of 20 cases was randomly selected from the entire dataset and reviewed by clinicians through a network questionnaire. The sensitivity, specificity and accuracy of the models and the clinicians were calculated. RESULTS: A total of 395 PC cases and 249 LA cases were included in the final analysis. The internal validation results for the UST model showed a sensitivity of 85.3%, specificity of 81.0%, accuracy of 83.6% and an area under the curve (AUC) of 0.93. Similarly, the DST model exhibited a sensitivity of 88.2%, specificity of 88.1%, accuracy of 88.2% and an AUC of 0.94. The external validation of the two models yielded AUC values of 0.74 and 0.77, respectively. The average sensitivity, specificity and accuracy of 102 clinicians were determined to be 63.1%, 53.7% and 59.3%, respectively. CONCLUSIONS: Both models outperformed the clinicians in distinguishing between PC and LA on chest CT, with the UST model exhibiting comparable performance to the DST model.

Weekly intraperitoneal steroid infusion to treat recurrent ascites in a patient with early stage of peritoneal dialysis-related encapsulating peritoneal sclerosis: A case report.

Encapsulating peritoneal sclerosis (EPS) is a rare and potentially fatal complication of long-term peritoneal dialysis (PD). EPS-induced large volume and recurrent ascites represents a challenging condition. We report a 51-year-old man with kidney failure treated with PD for 13 years who eventually developed early stage of EPS accompanied with poor intake and recurrent ascites. After management including discontinuing PD and switching to haemodialysis, as well as oral steroids and tamoxifen administration, the patient had refractory ascites. An intervention of weekly intraperitoneal steroid infusion with methylprednisolone was implemented for a year. Gradually, we observed a reduction in ascites drainage, an improvement of clinical symptoms and the patient's nutritional status. The PD catheter was successfully removed as there was no recurrence of ascites. Intraperitoneal corticosteroid administration represents a new intervention for patients with early stage of EPS and recurrent ascites after PD cessation.

Investigating Substance Use via Reddit: Systematic Scoping Review.

BACKGROUND: Reddit's (Reddit Inc) large user base, diverse communities, and anonymity make it a useful platform for substance use research. Despite a growing body of literature on substance use on Reddit, challenges and limitations must be carefully considered. However, no systematic scoping review has been conducted on the use of Reddit as a data source for substance use research. OBJECTIVE: This review aims to investigate the use of Reddit for studying substance use by examining previous studies' objectives, reasons, limitations, and methods for using Reddit. In addition, we discuss the implications and contributions of previous studies and identify gaps in the literature that require further attention. METHODS: A total of 7 databases were searched using keyword combinations including Reddit and substance-related keywords in April 2022. The initial search resulted in 456 articles, and 227 articles remained after removing duplicates. All included studies were peer reviewed, empirical, available in full text, and pertinent to Reddit and substance use, and they were all written in English. After screening, 60 articles met the eligibility criteria for the review, with 57 articles identified from the initial database search and 3 from the ancestry search. A codebook was developed, and qualitative content analysis was performed to extract relevant evidence related to the research questions. RESULTS: The use of Reddit for studying substance use has grown steadily since 2015, with a sharp increase in 2021. The primary objective was to identify tendencies and patterns in various types of substance use discussions (52/60, 87%). Reddit was also used to explore unique user experiences, propose methodologies, investigate user interactions, and develop interventions. A total of 9 reasons for using Reddit to study substance use were identified, such as the platform's anonymity, its widespread popularity, and the explicit topics of subreddits. However, 7 limitations were noted, including the platform's low representativeness of the general population with substance use and the lack of demographic information. Most studies use application programming interfaces for data collection and quantitative approaches for analysis, with few using qualitative approaches. Machine learning algorithms are commonly used for natural language processing tasks. The theoretical, methodological, and practical implications and contributions of the included articles are summarized and discussed. The most prevalent practical implications are investigating prevailing topics in Reddit discussions, providing recommendations for clinical practices and policies, and comparing Reddit discussions on substance use across various sources. CONCLUSIONS: This systematic scoping review provides an overview of Reddit's use as a data source for substance use research. Although the limitations of Reddit data must be considered, analyzing them can be useful for understanding patterns and user experiences related to substance use. Our review also highlights gaps in the literature and suggests avenues for future research.

Dual Strategies with Anion/Cation Co-Doping and Lithium Carbonate Coating to Enhance the Electrochemical Performance of Lithium-Rich Layered Oxides.

Lithium-rich layered oxides (LLOs, Li1.2 Mn0.54 Ni0.13 Co0.13 O2 ) are widely used as cathode materials for lithium-ion batteries due to its high specific capacity, high operating voltage and low cost. However, the LLOs are faced with rapid decay of charge/discharge capacity and voltage, as well as interface side reactions, which limit its electrochemical performance. Herein, the dual strategies of sulfite/sodium ion co-doping and lithium carbonate coating were used to improve it. It founds that modified LLOs achieve 88.74 % initial coulomb efficiency, 295.3 mAh g-1 first turn discharge capacity, in addition to 216.9 mAh g-1 at 1 C, and 87.23 % capacity retention after 100 cycles. Mechanism research indicated that the excellent electrochemical performance benefits from the doping of both Na+ and SO3 2- , and it could significantly reduce the migration energy barrier of Li+ and promote Li+ migration. Meanwhile, anion and cation are co-doped greatly reduces the band gap of LLOs and increase its electrical conductivity, and its binding effect on Li+ is weakened, making it easier for Li+ to shuttle through the material. In addition, the lithium carbonate coating significantly inhibits the occurrence of interfacial side reactions of LLOs. This work provides a theoretical basis and practical guidance for the further development of LLOs with higher electrochemical performance.

An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau.

Intense grazing may lead to grassland degradation on the Qinghai-Tibetan Plateau, but it is difficult to predict where this will occur and to quantify it. Based on a process-based ecosystem model, we define a productivity-based stocking rate threshold that induces extreme grassland degradation to assess whether and where the current grazing activity in the region is sustainable. We find that the current stocking rate is below the threshold in ~80% of grassland areas, but in 55% of these grasslands the stocking rate exceeds half the threshold. According to our model projections, positive effects of climate change including elevated CO2 can partly offset negative effects of grazing across nearly 70% of grasslands on the Plateau, but only in areas below the stocking rate threshold. Our analysis suggests that stocking rate that does not exceed 60% (within 50% to 70%) of the threshold may balance human demands with grassland protection in the face of climate change.

Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation.

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 1,986 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that: 1) challenge metric was permissive with the false positive predictions; and 2) size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download.

Pan-mediastinal neoplasm diagnosis via nationwide federated learning: a multicentre cohort study.

BACKGROUND: Mediastinal neoplasms are typical thoracic diseases with increasing incidence in the general global population and can lead to poor prognosis. In clinical practice, the mediastinum's complex anatomic structures and intertype confusion among different mediastinal neoplasm pathologies severely hinder accurate diagnosis. To solve these difficulties, we organised a multicentre national collaboration on the basis of privacy-secured federated learning and developed CAIMEN, an efficient chest CT-based artificial intelligence (AI) mediastinal neoplasm diagnosis system. METHODS: In this multicentre cohort study, 7825 mediastinal neoplasm cases and 796 normal controls were collected from 24 centres in China to develop CAIMEN. We further enhanced CAIMEN with several novel algorithms in a multiview, knowledge-transferred, multilevel decision-making pattern. CAIMEN was tested by internal (929 cases at 15 centres), external (1216 cases at five centres and a real-world cohort of 11 162 cases), and human-AI (60 positive cases from four centres and radiologists from 15 institutions) test sets to evaluate its detection, segmentation, and classification performance. FINDINGS: In the external test experiments, the area under the receiver operating characteristic curve for detecting mediastinal neoplasms of CAIMEN was 0·973 (95% CI 0·969-0·977). In the real-world cohort, CAIMEN detected 13 false-negative cases confirmed by radiologists. The dice score for segmenting mediastinal neoplasms of CAIMEN was 0·765 (0·738-0·792). The mediastinal neoplasm classification top-1 and top-3 accuracy of CAIMEN were 0·523 (0·497-0·554) and 0·799 (0·778-0·822), respectively. In the human-AI test experiments, CAIMEN outperformed clinicians with top-1 and top-3 accuracy of 0·500 (0·383-0·633) and 0·800 (0·700-0·900), respectively. Meanwhile, with assistance from the computer aided diagnosis software based on CAIMEN, the 46 clinicians improved their average top-1 accuracy by 19·1% (0·345-0·411) and top-3 accuracy by 13·0% (0·545-0·616). INTERPRETATION: For mediastinal neoplasms, CAIMEN can produce high diagnostic accuracy and assist the diagnosis of human experts, showing its potential for clinical practice. FUNDING: National Key R&D Program of China, National Natural Science Foundation of China, and Beijing Natural Science Foundation.

Injectable gelatin/glucosamine cryogel microbeads as scaffolds for chondrocyte delivery in cartilage tissue engineering.

In this study, we fabricate squeezable cryogel microbeads as injectable scaffolds for minimum invasive delivery of chondrocytes for cartilage tissue engineering applications. The microbeads with different glucosamine concentrations were prepared by combining the water-in-oil emulsion and cryogelation through crosslinking of gelatin with glutaraldehyde in the presence of glucosamine. The physicochemical characterization results show the successful preparation of cryogel microbeads with uniform shape and size, high porosity, large pore size, high water uptake capacity, and good injectability. In vitro analysis indicates proliferation, migration, and differentiated phenotype of rabbit chondrocytes in the cryogel scaffolds. The seeded chondrocytes in the cryogel scaffold can be delivered by injecting through an 18G needle to fully retain the cell viability. Furthermore, the incorporation of glucosamine in the cryogel promoted the differentiated phenotype of chondrocytes in a dose-dependent manner, from cartilage-specific gene expression and protein production. The in vivo study by injecting the cryogel microbeads into the subcutaneous pockets of nude mice indicates good retention ability as well as good biocompatibility and suitable biodegradability of the cryogel scaffold. Furthermore, the injected chondrocyte/cryogel microbead constructs can form ectopic functional neocartilage tissues following subcutaneous implantation in 21 days, as evidenced by histological and immunohistochemical analysis.