To investigate the clinical characteristics and prognosis of extracranial malignant rhabdoid tumor (eMRT) in children, and to provide a reference for the clinical treatment of this disease.

Cardiovascular damage caused by cancer treatment has become an important cause of death for tumor survivors. With the recognition of cardiovascular diseases and cancer therapy-related cardiovascular toxicity (CTR-CVT) in tumor patients, noninvasive imaging technologies play pivotal roles in the risk stratification, early diagnosis, monitoring and follow-up for CTR-CVT. In recent years, the field of cardio-oncology has witnessed continual updates in diagnostic and therapeutic strategies, with several pertinent guidelines and expert consensus documents issued in China and abroad. However, there remains a conspicuous absence of systematic guidance documents on the application of imaging techniques in the clinical practice of cardio-oncology. Therefore, the Chinese Anti-Cancer Association Society of Integrative Cardio-oncology, the Ultrasound Branch of the Chinese Medical Association, and the Chinese Society of Echocardiography convened experts to formulate the "Chinese guideline for the clinical application of noninvasive imaging technology in accessing cancer therapy-related cardiovascular toxicity". Building upon the systematic evaluation of guidelines and the latest evidence-based medical research in the field of cardio-oncology domestically and abroad, and in conjunction with data derived from evidence-based medical research in China, this guideline proposes noninvasive imaging examination methods and monitoring strategies for CTR-CVT, aiming to further standardize and guide the clinical practice of multidisciplinary physicians specializing in cardio-oncology in China.

OBJECTIVE: To explore the functional roles and underlying mechanisms of neferine in the context of angiotensin II (Ang II)-induced hypertension and vascular dysfunction. METHODS: Male mice were infused with Ang II to induce hypertension and randomly divided into treatment groups receiving neferine or a control vehicle based on baseline blood pressure using a random number table method. The hypertensive mouse model was constructed by infusing Ang II via a micro-osmotic pump (500 ng/kg per minute), and neferine (0.1, 1, or 10 mg/kg), valsartan (10 mg/kg), or double distilled water was administered intragastrically once daily for 6 weeks. A non-invasive blood pressure system, ultrasound, and hematoxylin and eosin staining were performed to assess blood pressure and vascular changes. RNA sequencing and network pharmacology were employed to identify differentially expressed transcripts (DETs) and pathways. Vascular ring tension assay was used to test vascular function. A7R5 cells were incubated with neferine for 24 h and then treated with Ang II to record the real-time Ca²⁺ concentration by confocal microscope. Immunohistochemistry (IHC) and Western blot were used to evaluate vasorelaxation, calcium, and the extracellular signal-regulated kinase (ERK)1/2 pathway. RESULTS: Neferine treatment effectively mitigated the elevation in blood pressure, pulse wave velocity, aortic thickening in the abdominal aorta of Ang II-infused mice (P<0.05). RNA sequencing and network pharmacology analysis identified 355 DETs that were significantly reversed by neferine treatment, along with 25 potential target genes, which were further enriched in multiple pathways and biological processes, such as ERK1 and ERK2 cascade regulation, calcium pathway, and vascular smooth muscle contraction. Further investigation revealed that neferine treatment enhanced vasorelaxation and reduced Ca²⁺-dependent contraction of abdominal aortic rings, independent of endothelium function (P<0.05). The underlying mechanisms were mediated, at least in part, via suppression of receptor-operated channels, store-operated channels, or voltage-operated calcium channels. Neferine pre-treatment demonstrated a reduction in intracellular Ca²⁺ release in Ang II stimulated A7R5 cells. IHC staining and Western blot confirmed that neferine treatment effectively attenuated the upregulation of p-ERK1/2 both in vivo and in vitro, which was similar with treatment of ERK1/2 inhibitor PD98059 (P<0.05). CONCLUSIONS Neferine remarkably alleviates Ang II-induced elevation of blood pressure, vascular dysfunction, and pathological changes in the abdominal aorta. This beneficial effect is mediated by the modulation of multiple pathways, including calcium and ERK1/2 pathways.
Animals ; Angiotensin II ; Male ; Benzylisoquinolines/therapeutic use* ; Network Pharmacology ; Blood Pressure/drug effects* ; Sequence Analysis, RNA ; Mice ; Hypertension/chemically induced* ; Mice, Inbred C57BL ; Calcium/metabolism*

OBJECTIVE: Investigating the clinical efficacy of treating dorsally displaced distal radial double-column Die-punch fractures using a dorsal approach external fixator combined with Kirschner wires. METHODS: Retrospectively analyzed the clinical data of 15 patients with distal radial double-column Die-punch fractures treated with an external fixator combined with Kirschner wire between July 2020 and January 2023. There were 10 males and 5 females;6 cases on the left side and 9 on the right;age ranged from 22 to 76 years old. Recorded the preoperative and the final follow-up Cooney wrist function scores for the patients. The fracture healing time, and occurrence of complications were recorded. RESULTS: All 15 patients were followed up ranged from 12 to 16 months post-operation. All fractures achieved bony union, healing time ranging form 8 to 16 weeks. Not a single patient exhibited complications such as surgical site infection, fracture redislocation, or tendon injury. All individuals had their Kirschner wires and external fixation devices removed six weeks post-operatively and commenced rehabilitative therapy for wrist articulation. The Cooney wrist function scores at preoperative and ranged from 5 to 45 scores, at the latest follow-up ranged from 65 to 100 scores. At the final follow-up, the results were assessed as excellent in 10 patients, good in 4 patients, and fair in 1 patient. CONCLUSION The clinical efficacy of treating distal radial double-column Die-punch fractures using a dorsal approach external fixator combined with Kirschner wires is satisfactory.
Humans ; Male ; Female ; Middle Aged ; Adult ; External Fixators ; Bone Wires ; Aged ; Retrospective Studies ; Radius Fractures/physiopathology* ; Young Adult ; Fracture Fixation/methods*

Objective To construct a prognostic model of future asthma exacerbation risk in adults by com-bining novel biomarkers of serum chitinase-3-like protein 1(YKL-40),dipeptidyl peptidase-4(DPP4)and conventional predictors.Methods Patients with asthma in the non-acute exacerbation phase were recruited from the People's Hospital of Yubei District of Chongqing,from March 2022 to May 2023.Baseline clinical da-ta collected included medical history,forced expiratory volume in the first second(FEV1)/forced vital capacity(FVC),percentage of predicted forced expiratory volume in the first second(FEV1％pred),blood eosinophil count(EOS),blood neutrophil count(NEU),fractional exhaled nitric oxide(FeNO),serum YKL-40,and ser-um DPP4,etc.The patients were followed for one year to gather data on asthma acute exacerbations and their timings as defined in this study.A COX proportional hazards regression model was used to construct a prog-nostic model for future asthma exacerbations,with internal validation and results presentation.Results A to-tal of 224 patients with asthma completed the study.During the one-year follow-up period,102 patients experi-enced acute exacerbations as defined in this study.Based on univariate COX regression,stepwise regression for variable selection,clinical significance,and model simplicity,asthma control test(ACT)score group,number of asthma exacerbations in the past year group,log10(YKL-40),log10(FeNO),log10(EOS),and FEV1％pred were the following predictors were included in the final model.The overall C-statistic of the model was 0.795(95％CI:0.754-0.836),the area under the curve at the 52-week follow-up was 0.879(95％CI:0.834-0.924),and the Brier score at the 52-week follow-up was 0.142(95％CI:0.117-0.168).The calibration curve was close to a slope of 1,and bootstrap validation suggested good stability of the prediction model.The model was presented using a Nomogram and a dynamic scoring table in a web APP,which can be used to predict the risk of asthma exacerbations within 52 weeks for individual patients.Conclusion The prediction model based on serum YKL-40,EOS,FeNO,the number of asthma exacerbation in the past year group,FEV1％pred and ACT scores group can accurately predict the probability of acute attacks in 52 weeks of asthma patients.

Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
Deep Learning ; Biological Ontologies ; Humans ; Big Data ; Biomedical Research

Objective:To apply statistical process control (SPC) techniques to the quality assurance of non-normal radiotherapy plans through Johnson transformation, establishing patient-specific tolerance and action limits based on treatment sites and dose/distance assessment criteria, thereby enhancing the intensity-modulated radiation therapy (IMRT) verification accuracy and dose delivery precision.Methods:In this study, 951 gamma analysis data of patient-specific quality assurance (PSQA) executed on the Halcyon accelerator platform were selected and categorized into six groups based on treatment sites, including brain (102 cases), head and neck (100 cases), breast (229 cases), lung (154 cases), esophagus (223 cases), and pelvic (143 cases) groups. The six groups of data were statistically analyzed through Anderson-Darling normality tests ( α = 0.05) using Minitab 21 software. Non-normal data were transformed into normal data through Johnson transformation and then were used to establish treatment site-specific tolerance and action limits, which were compared with the Shewhart control charts based on normal distributions. Results:The PSQA result of the six groups all exhibited non-normal distributions ( P < 0.05). Through Johnson transformation, the tolerance and action limits for the head and neck, breast, lung, esophagus, and pelvic areas under the 3%/2 mm criterion ranged from 95.13% to 96.16% and 94.19% to 95.91%, respectively. In contrast, the tolerance and action limits ranged from 91.15% to 94.86% and 89.94% to 94.78% under the 2%/2 mm criterion. Directly applying Shewhart control charts without normality assumptions yielded higher tolerance limits compared to the application of Johnson transformation, increasing the false positive rate in the non-normal PSQA process. Conclusions:Applying the SPC techniques directly to a non-normal process can lead to an increased false alarm rate and wrong process interpretation. The SPC techniques combined with Johnson transformation enable more effective monitoring of a non-normal PSQA process, facilitating timely identification of potential factors that may lead to an out-of-control process based on the treatment site-specific limits.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

ObjectiveTo establish the multi-technique characteristic profiles of Alumen by X-ray diffraction（XRD）， Fourier-transform infrared spectroscopy（FTIR） and thermogravimetric-differential thermal analysis（TG-DTA）， and to explore the spectral characteristics for rapid identification of Alumen and its potential adulterant， Ammonium alum. MethodsA total of 27 batches of Alumen samples from 8 production regions were collected for preliminary identification based on visual characteristics. The PDF standard cards of XRD were used to differentiate Alumen from A. alum， and the XRD characteristic profiles of Alumen were established， and then the common peaks were screened. Based on hierarchical clustering analysis（HCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， the characteristic information that could be used for identification of Alumen was selected with variable importance in the projection（VIP） value>1. FTIR characteristic profiles of Alumen were established， and key wavenumbers for identification were screened by HCA and OPLS-DA with VIP value>1. Meanwhile， the thermogravimetric differences between Alumen and A. alum were analyzed by TG-DTA， and the thermogravimetric traits that could be used for identification were screened. ResultsAlumen and A. alum could not be effectively distinguished by traits alone. However， by comparing the PDF standard cards of XRD， 15 batches of Alumen and 12 batches of A. alum could be distinguished. In the XRD profiles， 10 characteristic peaks were confirmed， corresponding to diffraction angles of 14.560°， 24.316°， 12.620°， 32.122°， 17.898°， 34.642°， 27.496°， 46.048°， 40.697° and 21.973°. In the FTIR profiles， 4 wavenumber ranges（399.193-403.050， 1 186.010-1 471.420， 1 801.190-2 620.790， 3 612.020-3 997.710 cm^-1） and 12 characteristic wavenumbers（1 428.994， 1 430.922， 1 432.851， 1 434.779， 1 436.708， 1 438.636， 1 440.565， 1 442.493， 1 444.422， 1 446.350， 1 448.279， 1 450.207 cm^-1） were identified. In the TG-DTA profiles， there were characteristic decomposition peaks of ammonium ion and mass reduction features near 555.34 ℃ for A. alum. These characteristics could serve as important criteria for distinguishing the authenticity of Alumen. ConclusionXRD， FTIR and TG-DTA can be used to rapidly detect Alumen and A. alum， and combined with the discriminant features selected through chemometrics， the rapid and accurate identification of Alumen and A. alum can be achieved. The research findings provide new approaches for the rapid identification of Alumen.