ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

The pathogenesis of osteoarthritis (OA) is related to a variety of factors such as mechanical overload, metabolic dysfunction, aging, etc., and is a group of total joint diseases characterized by intra-articular chondrocyte apoptosis, cartilage fibrillations, synovial inflammation, and osteophyte formation. At present, the treatment methods for osteoarthritis include glucosamine, non-steroidal anti-inflammatory drugs, intra-articular injection of sodium hyaluronate, etc., which are difficult to take effect in a short period of time and require long-term treatment, so the patients struggle to adhere to doctor’s advice. Some methods can only provide temporary relief without chondrocyte protection, and some even increase the risk of cardiovascular disease and gastrointestinal disease. In the advanced stages of OA, patients often have to undergo joint replacement surgery due to pain and joint dysfunction. Mitochondrial dysfunction plays an important role in the development of OA. It is possible to improve mitochondrial biogenesis, quality control, autophagy balance, and oxidative stress levels, thereby exerting a protective effect on chondrocytes in OA. Therefore, compared to traditional treatments, improving mitochondrial function may be a potential treatment for OA. Here, we collected relevant literature on mitochondrial research in OA in recent years, summarized the potential pathogenic factors that affect the development of OA through mitochondrial pathways, and elaborated on relevant treatment methods, in order to provide new diagnostic and therapeutic ideas for the research field of osteoarthritis.

Objective To construct a diagnostic model based on the information of the TCM four diagnoses in hyperactive liver yang syndrome in patients with essential hypertension.Methods Syndromic investigation was carried out in patients with essential hypertension in some hospitals in Fujian and Beijing,and diagnostic information such as age,gender,symptoms,tongue and pulse were collected.On the basis of statistical analysis,this study adopted C5.0,CRT,CHAID and QUEST decision tree models respectively.After evaluating the stability and performance consistency of the models,the accuracy of the models was measured by diagnostic rate,and the optimal model of hyperactivity of liver yang in essential hypertension was selected.Results Totally 533 patients with essential hypertension were included,including 198 patients with hyperactive liver yang syndrome and 335 patients without hyperactive liver yang syndrome.The diagnostic rates of the four models were 75.2%,66.2%,67.7%and 65.0%,respectively.The diagnostic efficiency of C5.0 was better than that of CRT,CHAID and QUEST models."Aggravation after emotional excitement,poor complexion,string-like pulse,irritability,head swelling pain,bitter mouth,heavy pulse,fatigue,dark tongue,irritability,dizziness,thin pulse,yellow fur"could be regarded as the specific items of the syndrome model of hyperactive liver yang in essential hypertension.Conclusion The C5.0 decision tree model can clearly and intuitively identify the hyperactive liver yang syndrome in essential hypertension patients based on clinical TCM four diagnostic information data,and summarize diagnostic rules.

To evaluate the effectiveness and safety of implantable D-shant atrial shunt device in patients with severe pulmonary arterial hypertension(PAH)and right heart failure.A 53-year-old female patient diagnosed with severe idiopathic PAH and right heart failure,her WHO FC grade was Ⅳ.The right heart catheter and implantation of D-shant atrial shunt device were performed under local anesthesia on November 30,2021.A 6 mm×4 cm peripheral artery balloon was selected to dilate the atrial septum and a D-shant atrial shunt device with a fixed 4 mm diameter orifice was implanted into the heart.The clinical symptoms and hemodynamics of the patient was improved after the intervention.Implantation of atrial shunt device as a palliative therapy to established a right to left shunt is another strategy for treating patients with severe PAH in late period,which has good effectiveness and safety.It could be the last replacement therapy to improve symptoms and prolonged lives to drug resistant and severe PAH patients.

Objective:To explore the risk factors affecting the survival and efficacy of patients with acute myeloid leukemia with myelodysplasia-related changes(AML-MRC)transformed from myelodysplastic syndrome(MDS).Methods:The clinical data of 60 patients with AML-MRC transformed from MDS who hospitalized in The Third Affiliated Hospital of Soochow University from January 2010 to December 2021 were retrospectively analyzed.The demographic data and laboratory parameters,cytogenetic karyotypes,target genes of AML detected by next generation sequence,risk stratification,treatment regimen,therapeutic efficacy and survival outcome were documented.Rank sum test and Chi-square test or Fisher exact test were used to compare the survival and efficacy.The effects of clinical parameters,risk stratification and treatment regimens on the survival and efficacy of the AML-MRC patients were analyzed by univariate and multivariate analysis.Results:The median overall survival(OS)of the AML-MRC patients was 4.5 months,the 1-year OS rate was 28.3％,and the complete remission(CR)rate after treatment was 33.3％.The univariate analysis showed that age≥60 years,leukocytosis,severe thrombocytopenia,poor-risk group and only accepted hypomethylating agents(HMAs)or supportive therapy were the risk factors affecting OS.COX multivariate analysis showed that thrombocytopenia(HR=4.46),HMAs therapy(compared to transplantation,HR=10.47),supportive therapy(compared to transplantation,HR=25.80)and poor-risk group(compared to medium-risk group,HR=13.86)were independent hazard factors for median OS of patients with AML-MRC.The univariate analysis showed that the risk factors affecting 1-year OS in patients with AML-MRC were age ≥ 60 years,thrombocytopenia,time of transformation from MDS to AML(TTA)≥3 months,fibrinogen-albumin ratio index(FARI)≥ 0.07,CONUT score≥5,poor-risk group and supportive therapy.Binary logistic regression analysis showed that the independent risk factors for 1-year OS in AML-MRC patients were age ≥ 60 years(HR=11.23),thrombocytopenia(HR=8.71),FARI ≥ 0.07(HR=5.19)and poor-risk group(HR=14.00).The risk factors affecting CR of AML-MRC patients in univariate analysis were age ≥ 60 years,thrombocytopenia,FARI ≥ 0.1,CONUT score ≥ 5,poor-risk group and supportive therapy,while binary logistic regression analysis showed that age ≥ 60 years(HR=7.35),CONUT score ≥ 5(HR=9.60),thrombocytopenia(HR=12.05)and poor-risk group(HR=32.5)were independent risk factors affecting CR of the patients.Conclusion:The OS of AML-MRC patients is poor,old age(≥ 60 years old),supportive therapy,HMA therapy,poor-risk,thrombocytopenia,FARI ≥ 0.07 and CONUT score ≥ 5 may be associated with poor prognosis.

Objective To investigate the length of hospital stay,hospitalization expense and mortality attributable to the occurrence of carbapenem-resistant Enterobacterales(CRE)infection in patients in intensive care unit(ICU).Methods Patients admitted to the ICU in a tertiary first-class hospital from 2017 to 2022 were selected for the study.According to whether CRE infection occurred,patients were divided into infected group and non-infected group.Propensity score matching method was used to conduct a 1∶1 match between the infected group and non-infected group.Length of hospital stay,hospitalization expense and mortality of patients after matching were analyzed sta-tistically.A generalized linear model was established to recalculate the odds ratio(OR)of length of hospital stay,hospitalization expense and mortality of patients after matching.Results After propensity score matching,length of hospital stay of patients in the infected group extended by 10.56 days(P＜0.001),hospitalization expense in-creased by 36 021.02 Yuan(P＜0.001),and mortality increased by 6.70％(P=0.035).The results of the gene-ralized linear model indicated that OR for length of hospital stay,hospitalization expense,and mortality were 1.187(95％CI:1.013-1.393),1.134(95％CI:0.975-1.318),and 1.130(95％CI:1.049-1.218)respectively for CRE infected patients,compared with non-infected patients,except for hospitalization expense,length of hospital stay and mortality between two groups were statistically significant(both P＜0.05).Conclusion CRE infection in ICU patients will increase the length of hospital stay,economic burden,and mortality of patients.Measures should be taken to prevent and control CRE infection.

Objective:To evaluate the efficacy and safety of antaitasvir phosphate 100 mg or 200 mg combined with yiqibuvir for 12 weeks in patients with various genotypes of chronic hepatitis C, without cirrhosis or compensated stage cirrhosis.Methods:Patients with chronic hepatitis C (without cirrhosis or compensated stage cirrhosis) were randomly assigned to the antaitasvir phosphate 100 mg+yiqibuvir 600 mg group (100 mg group) or the antaitasvir phosphate 200 mg+yiqibuvir 600 mg group (200 mg group) in a 1∶1 ratio. The drugs were continuously administered once a day for 12 weeks and observed for 24 weeks after drug withdrawal. The drug safety profile was assessed concurrently with the observation of the sustained virological response (SVR12) in the two patient groups 12 weeks following the drug cessation. The intention-to-treat concept was used to define as closely as possible a full analysis set, including all randomized cases who received the experimental drug at least once. The safety set was collected from all subjects who received the experimental drug at least once (regardless of whether they participated in the randomization group) in this study. All efficacy endpoints and safety profile data were summarized using descriptive statistics. The primary efficacy endpoint was SVR12. The primary analysis was performed on a full analysis set. The frequency and proportion of cases were calculated in the experimental drug group (antaitasvir phosphate capsules combined with yiqibuvir tablets) that achieved "HCV RNA