Prediction of small intracranial aneurysm rupture status based on combined Clinical-Radiomics model.

Background: Accumulating small unruptured intracranial aneurysms are detected due to the improved quality and higher frequency of cranial imaging, but treatment remains controversial. While surgery or endovascular treatment is effective for small aneurysms with a high risk of rupture, such interventions are unnecessary for aneurysms with a low risk of rupture. Consequently, it is imperative to accurately identify small aneurysms with a low risk of rupture. The purpose of this study was to develop a clinically practical model to predict small aneurysm ruptures based on a radiomics signature and clinical risk factors. Methods: A total of 293 patients having an aneurysm with a diameter of less than 5 mm, including 199 patients (67.9 %) with a ruptured aneurysm and 94 patients (32.1 %) without a ruptured aneurysm, were included in this study. Digital subtraction angiography or surgical treatment was required in all cases. Data on the clinical risk factors and the features on computed tomography angiography images associated with the aneurysm rupture status were collected simultaneously. We developed a clinical-radiomics model to predict aneurysm rupture status using multivariate logistic regression analysis. The combined clinical-radiomics model was constructed by nomogram analysis. The diagnostic performance, clinical utility, and model calibration were evaluated by operating characteristic curve analysis, decision curve analysis, and calibration analysis. Results: A combined clinical-radiomics model (Area Under Curve [AUC], 0.85; 95 % confidence interval [CI], 0.757-0.947) showed effective performance in the operating characteristic curve analysis. In the validation cohort, the performance of the combined model was better than that of the radiomics model (AUC, 0.75; 95 % CI, 0.645-0.865; Delong's test p-value = 0.01) and the clinical model (AUC, 0.74; 95 % CI, 0.625-0.851; Delong's test p-value <0.01) alone. The results of the decision curve, nomogram, and calibration analyses demonstrated the clinical utility and good fitness of the combined model. Conclusion: Our study demonstrated the effectiveness of a clinical-radiomics model for predicting rupture status in small aneurysms.

Pathomic model based on histopathological features and machine learning to predict IDO1 status and its association with breast cancer prognosis.

PURPOSE: To establish a pathomic model using histopathological image features for predicting indoleamine 2,3-dioxygenase 1 (IDO1) status and its relationship with overall survival (OS) in breast cancer. METHODS: A pathomic model was constructed using machine learning and histopathological images obtained from The Cancer Genome Atlas database to predict IDO1 expression. The model performance was evaluated based on the area under the curve, calibration curve, and decision curve analysis (DCA). Prediction scores (PSes) were generated from the model and applied to divide the patients into two groups. Survival outcomes, gene set enrichment, immune microenvironment, and tumor mutations were assessed between the two groups. RESULTS: Survival analysis followed by multivariate correction revealed that high IDO1 is a protective factor for OS. Further, the model was calibrated, and it exhibited good discrimination. Additionally, the DCA showed that the proposed model provided a good clinical net benefit. The Kaplan-Meier analysis revealed a positive correlation between high PS and improved OS. Univariate and multivariate Cox regression analyses demonstrated that PS is an independent protective factor for OS. Moreover, differentially expressed genes were enriched in various essential biological processes, including extracellular matrix receptor interaction, angiogenesis, transforming growth factor ß signaling, epithelial mesenchymal transition, cell junction, tryptophan metabolism, and heme metabolic processes. PS was positively correlated with M1 macrophages, CD8 + T cells, T follicular helper cells, and tumor mutational burden. CONCLUSION: These results indicate the potential ability of the proposed pathomic model to predict IDO1 status and the OS of breast cancer patients.

Bradykinin protects nucleus pulposus cells from tert-butyl hydroperoxide-induced damage and delays intervertebral disc degeneration.

Intervertebral disc degeneration (IVDD) is a leading cause of degenerative spinal disorders, involving complex biological processes. This study investigates the role of the kallikrein-kinin system (KKS) in IVDD, focusing on the protective effects of bradykinin (BK) on nucleus pulposus cells (NPCs) under oxidative stress. Clinical specimens were collected, and experiments were conducted using human and rat primary NPCs to elucidate BK's impact on tert-butyl hydroperoxide (TBHP)-induced oxidative stress and damage. The results demonstrate that BK significantly inhibits TBHP-induced NPC apoptosis and restores mitochondrial function. Further analysis reveals that this protective effect is mediated through the BK receptor 2 (B2R) and its downstream PI3K/AKT pathway. Additionally, BK/PLGA sustained-release microspheres were developed and validated in a rat model, highlighting their potential therapeutic efficacy for IVDD. Overall, this study sheds light on the crucial role of the KKS in IVDD pathogenesis and suggests targeting the B2R as a promising therapeutic strategy to delay IVDD progression and promote disc regeneration.

Added Value of Frequency of Imaging Markers for Prediction of Outcome After Intracerebral Hemorrhage: A Secondary Analysis of Existing Data.

BACKGROUND: Frequency of imaging markers (FIM) has been identified as an independent predictor of hematoma expansion in patients with intracerebral hemorrhage (ICH), but its impact on clinical outcome of ICH is yet to be determined. The aim of the present study was to investigate this association. METHODS: This study was a secondary analysis of our prior research. The data for this study were derived from six retrospective cohorts of ICH from January 2018 to August 2022. All consecutive study participants were examined within 6 h of stroke onset on neuroimaging. FIM was defined as the ratio of the number of imaging markers on noncontrast head tomography (i.e., hypodensities, blend sign, and island sign) to onset-to-neuroimaging time. The primary poor outcome was defined as a modified Rankin Scale score of 3-6 at 3 months. RESULTS: A total of 1253 patients with ICH were included for final analysis. Among those with available follow-up results, 713 (56.90%) exhibited a poor neurologic outcome at 3 months. In a univariate analysis, FIM was associated with poor prognosis (odds ratio 4.36; 95% confidence interval 3.31-5.74; p < 0.001). After adjustment for age, Glasgow Coma Scale score, systolic blood pressure, hematoma volume, and intraventricular hemorrhage, FIM was still an independent predictor of worse prognosis (odds ratio 3.26; 95% confidence interval 2.37-4.48; p < 0.001). Based on receiver operating characteristic curve analysis, a cutoff value of 0.28 for FIM was associated with 0.69 sensitivity, 0.66 specificity, 0.73 positive predictive value, 0.62 negative predictive value, and 0.71 area under the curve for the diagnosis of poor outcome. CONCLUSIONS: The metric of FIM is associated with 3-month poor outcome after ICH. The novel indicator that helps identify patients who are likely within the 6-h time window at risk for worse outcome would be a valuable addition to the clinical management of ICH.

Combining Non-Contrast CT Signs With Onset-to-Imaging Time to Predict the Evolution of Intracerebral Hemorrhage.

OBJECTIVE: This study aimed to determine the predictive performance of non-contrast CT (NCCT) signs for hemorrhagic growth after intracerebral hemorrhage (ICH) when stratified by onset-to-imaging time (OIT). MATERIALS AND METHODS: 1488 supratentorial ICH within 6 h of onset were consecutively recruited from six centers between January 2018 and August 2022. NCCT signs were classified according to density (hypodensities, swirl sign, black hole sign, blend sign, fluid level, and heterogeneous density) and shape (island sign, satellite sign, and irregular shape) features. Multivariable logistic regression was used to evaluate the association between NCCT signs and three types of hemorrhagic growth: hematoma expansion (HE), intraventricular hemorrhage growth (IVHG), and revised HE (RHE). The performance of the NCCT signs was evaluated using the positive predictive value (PPV) stratified by OIT. RESULTS: Multivariable analysis showed that hypodensities were an independent predictor of HE (adjusted odds ratio [95% confidence interval] of 7.99 [4.87-13.40]), IVHG (3.64 [2.15-6.24]), and RHE (7.90 [4.93-12.90]). Similarly, OIT (for a 1-h increase) was an independent inverse predictor of HE (0.59 [0.52-0.66]), IVHG (0.72 [0.64-0.81]), and RHE (0.61 [0.54-0.67]). Blend and island signs were independently associated with HE and RHE (10.60 [7.36-15.30] and 10.10 [7.10-14.60], respectively, for the blend sign and 2.75 [1.64-4.67] and 2.62 [1.60-4.30], respectively, for the island sign). Hypodensities demonstrated low PPVs of 0.41 (110/269) or lower for IVHG when stratified by OIT. When OIT was ≤ 2 h, the PPVs of hypodensities, blend sign, and island sign for RHE were 0.80 (215/269), 0.90 (142/157), and 0.83 (103/124), respectively. CONCLUSION: Hypodensities, blend sign, and island sign were the best NCCT predictors of RHE when OIT was ≤ 2 h. NCCT signs may assist in earlier recognition of the risk of hemorrhagic growth and guide early intervention to prevent neurological deterioration resulting from hemorrhagic growth.

Is the frequency of imaging markers still a predictor for revised intracerebral hemorrhage expansion?

INTRODUCTION: Frequency of imaging markers (FIM) has been described as a novel predictor for hematoma expansion after intracerebral hemorrhage (ICH). A revised definition of hematoma expansion that incorporates intraventricular hemorrhage (IVH) growth, that is, revised hematoma expansion (RHE), has also been proposed. Nevertheless, the associations between FIM and IVH growth or RHE remains unexplored. The objective of this study was to assess the influence and performance of the FIM on two types. MATERIALS AND METHODS: Patient selection and variables were based on our published protocol. FIM was defined as the ratio of the number of imaging markers to the onset-to-neuroimaging time. The association between FIM and two definitions was tested by multivariate analysis. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the FIM on two definitions were also evaluated. RESULTS: There were 303 (20.36%) and 583 (39.18%) subjects in the IVH growth and RHE, respectively. Multivariate analysis demonstrated that FIM was associated with both IVH growth and RHE (odds ratio [OR] = 1.96, 95% confidence interval [CI] = 1.60-2.39; OR = 15.01, 95% CI = 10.51-21.43, respectively). The optimal cutoff points for FIM to predict IVH growth and RHE were 0.63 and 0.62, with AUC, sensitivity, specificity, PPV, and NPV of 0.66, 0.50, 0.78, 0.36, and 0.86 versus 0.80, 0.60, 0.93, 0.84, and 0.78, respectively. DISCUSSION AND CONCLUSION: FIM was not only a predictor of IVH growth, but also of RHE. These findings may have important clinical implications for decision-making based on risk stratification of patients with ICH.

The frequency of imaging markers adjusted for time since symptom onset in intracerebral hemorrhage: A novel predictor for hematoma expansion.

BACKGROUND: Hematoma expansion (HE) is common in patients with intracerebral hemorrhage (ICH) and associated with a worse outcome. Imaging makers and shorter time from symptom onset are both associated with HE, but prognostic scores based on these parameters individually have not been satisfactory. We hypothesized that a score including both imaging markers of expansion, and time of onset, would improve prediction. METHODS: Patients with supratentorial ICH within 6 h after onset were consecutively recruited from six centers between January 2018 and August 2022. Three markers were used: hypodensities, the blend sign, and the island sign. We first defined frequency of imaging markers (FIM) as the relationship between the number of imaging markers and onset-to-CT time (OCT). The time-adjusted FIM was defined as the ratio of the number of imaging markers to the onset-to-initial imaging time. Multivariate analysis was performed to determine the relationship between FIM and HE. Receiver operating curve analysis was used to identify potential threshold values of FIM that optimally predict HE. In addition, the sensitivity, specificity, positive and negative predictive values (PPVs and NPVs), and the area under the curve (AUC) of the optimal cut-off in predicting HE were calculated. RESULTS: In total, 1488 patients were eligible for inclusion, of whom 418 had incident HE. Multivariate analysis showed that age, male sex, baseline Glasgow Coma Scale score, presence of intraventricular hemorrhage, and FIM were independent predictors of HE (odds ratio (OR) = 0.98, 95% confidence interval (CI) = 0.97-0.99; OR = 1.73, 95% CI = 1.28-2.35; OR = 0.87, 95% CI = 0.83-0.92; OR = 0.42, 95% CI = 0.28-0.62; OR = 7.82, 95% CI = 5.86-10.42, respectively). The optimal cut-off point for FIM in predicting HE was 0.63, with sensitivity, specificity, PPV, NPV, and AUC values of 0.69, 0.89, 0.71, 0.88, and 0.83, respectively. CONCLUSION: The FIM adjusted for time since symptom onset is a significant predictor of HE. Its use may allow improved prediction of those patients with ICH who develop HE, and the score may be clinically applicable in the management of patients with ICH.

Preparation and potential antitumor activity of alginate oligosaccharides degraded by alginate lyase from Cobetia marina.

It is of great significance to develop marine resources and study its potential biological activity by using alginate lyase produced by marine psychrophilic bacteria. In the previous study, a new marine psychrophilic bacterium (Cobetia marina HQZ08) was screened from the growth area of Laminaria japonica, and it was found that the strain could efficiently produce alginate-degrading enzyme (Aly30). In this paper, the ability of Aly30 to degrade alginate was optimized and the optimal degradation conditions were obtained. It was found that the main degradation product of alginate oligosaccharides was trisaccharide. In vitro cell experiments showed that the antitumor activity of low molecular weight alginate oligosaccharides was better than that of high molecular weight alginate oligosaccharides. In summary, Aly30 had the potential to produce alginate oligosaccharides with low degree of polymerization and antitumor activity, which provided a reference for the enzymatic preparation and application of alginate oligosaccharides.

Sex-specific disparities of serum pepsinogen I in relation to body mass index.

OBJECTIVES: The clinical significance of serum pepsinogen (PG) for screening gastric cancer has been a controversial topic. Serum PG I levels have been demonstrated to be correlated with age, sex, and the Helicobacter pylori (HP) infection. However, the underlying factors that influence serum PG I variations remain to be fully elucidated. We aimed to evaluate the impacts of sex and body mass index (BMI) on PG I in Chinese population. METHODS: The cross-sectional study recruited 4,299 apparently healthy participants in Fujian Province. Serum PG levels were automatically measured using ELISA method. Serum H. pylori-IgG antibody was detected by the colloidal gold immunoassay. Clinical characteristics were obtained by questionnaire. RESULTS: Totally, 2,263 participants who had tests of serum PG and anti-HP IgG antibody were enrolled. Increased BMI and serum uric acid were observed in males with low PG I value (<70 µg/L). Multiple logistic regression showed the presence of overweight was the independent risk factor for male participants with low PG I level (odds ratio [OR] 1.519; p=0.017). However, the association was not found in females. CONCLUSIONS: Sex-specific association of serum low PG I with overweight was observed in the southeast coastal areas of China. Thus, effects of sexual dimorphism should not be ignored during the clinical utilization of serum PG I.

Targeted therapy for intractable cancer on the basis of molecular profiles: An open-label, phase II basket trial (Long March Pathway).

Purpose: We evaluated he effects of molecular guided-targeted therapy for intractable cancer. Also, the epidemiology of druggable gene alterations in Chinese population was investigated. Materials and methods: The Long March Pathway (ClinicalTrials.gov identifier: NCT03239015) is a non-randomized, open-label, phase II trial consisting of several basket studies examining the molecular profiles of intractable cancers in the Chinese population. The trial aimed to 1) evaluate the efficacy of targeted therapy for intractable cancer and 2) identify the molecular epidemiology of the tier II gene alterations among Chinese pan-cancer patients. Results: In the first stage, molecular profiles of 520 intractable pan-cancer patients were identified, and 115 patients were identified to have tier II gene alterations. Then, 27 of these 115 patients received targeted therapy based on molecular profiles. The overall response rate (ORR) was 29.6% (8/27), and the disease control rate (DCR) was 44.4% (12/27). The median duration of response (DOR) was 4.80 months (95% CI, 3.33-27.2), and median progression-free survival (PFS) was 4.67 months (95% CI, 2.33-9.50). In the second stage, molecular epidemiology of 17,841 Chinese pan-cancer patients demonstrated that the frequency of tier II gene alterations across cancer types is 17.7%. Bladder cancer had the most tier-II alterations (26.1%), followed by breast cancer (22.4%), and non-small cell lung cancer (NSCLC; 20.2%). Conclusion: The Long March Pathway trial demonstrated a significant clinical benefit for intractable cancer from molecular-guided targeted therapy in the Chinese population. The frequency of tier II gene alterations across cancer types supports the feasibility of molecular-guided targeted therapy under basket trials.

The predictive value of the preoperative systemic immune-inflammation index in the occurrence of postoperative pneumonia in non-small cell lung cancer: A retrospective study based on 1486 cases.

BACKGROUND: To investigate the correlation between the preoperative systemic immune-inflammation index (pSII) and postoperative pneumonia (POP) in surgical non-small cell lung cancer patients. METHODS: Patients who underwent lung cancer surgery at West China Hospital of Sichuan University were retrospectively included. The indicators were collected, including basic information of patients, surgery-related variables and POP rate. The predictive value of the pSII in the occurrence of POP was analyzed. RESULTS: A total of 1486 patients (male: 748, 50.3%; female: 738, 49.7%; mean age: 58.2 ± 9.7 years; median age: 59 years old, interquartile range: 51-65 years old) were finally included in the study, of which 142 patients had POP with an incidence of 9.5% (142/1486), 9.2% (69/748) in males, and 9.9% (73/738) in females. The proportion of patients with diabetes in the pneumonia group was significantly higher than that in the nonpneumonia group (9.8%, 14/142 vs. 5.6%, 75/1344, p = 0.041). Compared with the nonpneumonia group, the level of the preoperative body mass index (24.2 [21.9, 26.1] vs. 23.1 [21.1, 25.2], p = 0.003) and SII (487 [350, 673] vs. 345 [230, 500], p < 0.001) in the pneumonia group were significantly higher. Multiple factor analysis showed that the pSII (odds ratio: 1.001, 95% confidence interval: 1.000-1.001, p < 0.001) was an independent risk factor for POP (487 [350, 673] vs. 345 [230, 500], p < 0.001); receiver operating characteristic curve analysis showed that the pSII was effective in predicting POP (area under curve: 0.751, p < 0.001). CONCLUSION: The pSII is closely related to and can effectively predict the occurrence of POP after lung cancer surgery.

Predicting Hemorrhage Progression in Deep Intracerebral Hemorrhage: A Multicenter Retrospective Cohort Study.

BACKGROUND: Hemorrhage progression in deep intracerebral hemorrhage (ICH) involves not only the growth of parenchymal hematoma but also an increase in intraventricular hemorrhage (IVH). The search for methods that predict both the increased risk of parenchymal hematoma and IVH growth is warranted. METHODS: We conducted a retrospective cohort study at multiple centers. Participants with deep ICH were enrolled from January 2018 to December 2021. Prediction models based on logistic regression analysis included clinical as well as routine radiographic and radiomics variables, separately or in combination. The performance of each model was evaluated using discrimination measures (e.g., area under the curve [AUC]). Evaluation of clinical utility was performed using decision curve analysis (DCA). RESULTS: Overall, 647 individuals across 4 stroke centers were included. A total of 429 (66%) patients from 3 centers were assigned to the primary cohort and 218 (34%) from another center were placed in the validation cohort. Multivariate analysis showed that the Glasgow Coma Scale score, baseline ICH volume, IVH, blend sign, and radiomics score were associated with hemorrhage progression in the primary cohort. The clinical-radiomics model (AUC = 0.852 and 0.835) improved the prediction performance of hemorrhage progression compared to the Noncontrast computed tomography signs model (AUC = 0.666 and 0.618) in both the primary and validation cohorts, with similar results in the decision curve analysis curves. CONCLUSIONS: The clinical-radiomics model outperformed the routine Noncontrast computed tomography signs model in predicting the progression of deep ICH. The clinical benefit of screening patients using this model may assist in risk stratification.

The safety and feasibility of preoperative induction therapy of Savolitinib in non-small cell lung cancer patients with MET exon 14 skipping mutation.

PURPOSE: Neoadjuvant therapy followed by surgical resection is one of the preferred treatment option for locally advanced non-small cell lung cancer (NSCLC). For patients with mesenchymal-epithelial transition (MET) factor exon 14 skipping (METex14) mutations, the use of MET-tyrosine kinase inhibitors (TKIs) showed high efficiency and reduced toxicity compared with first-line standard chemotherapy. However, it is unknown whether preoperative induction targeted therapy of MET-TKIs is feasible and safe. METHODS: Here, we reported 3 cases of locally advanced unresectable NSCLC with METex14 mutations receiving induction therapy of MET-TKI savolitinib as first-line therapy or second-line therapy when they experienced disease progression after preoperative chemotherapy. RESULTS: All these 3 patients achieved significant tumor size shrinkage and their unresectable tumors became resectable after the treatment of savolitinib. No serious adverse events were observed during the treatment. They recovered well postoperatively, and no significant events were identified. CONCLUSIONS: Preoperative induction treatment with MET-TKI savolitinib showed its safety and effectiveness and may be an alternative option for neoadjuvant therapy for NSCLC patients with METex14 mutations.

Periostin promotes nucleus pulposus cells apoptosis by activating the Wnt/ß-catenin signaling pathway.

Intervertebral disc (IVD) degeneration (IVDD) is closely linked to degenerative spinal disease, resulting in disability, poor quality of life, and financial burden. Apoptosis of nucleus pulposus (NP) cells (NPCs) is a key pathological basis of IVDD. Periostin (POSTN), an extracellular matrix protein, is expressed in many tissues, whereas its abnormal expression is associated with IVDD. The conventional Wnt/ß-catenin pathway is also involved in IVDD and contributes to NPCs apoptosis. However, research on the mechanisms of POSTN in IVDD is lacking. This study investigated the relationship between POSTN and ß-catenin expression in degenerated IVDs. We detected the expression of POSTN, ß-catenin, and cleaved-caspase-3 (C-caspase3) in degenerated and non-degenerated IVD tissues of different grades (n = 8) using RT-qPCR, immunohistochemical staining, and western blotting analysis. Next, we explored the effects of recombinant periostin (rPOSTN) and isoquercitrin (Iso), an inhibitor of the Wnt/ß-catenin pathway, on NPCs apoptosis. Finally, we inhibited the expression of POSTN in degenerated NPCs in vivo and investigated the anti-apoptotic effect. The expression of ß-catenin, POSTN, and C-caspase3 in severe degenerative IVDs was significantly higher than that in mild degenerative IVDs. These findings were confirmed in rat and cell-based degenerative models. When treated with rPOSTN, the Wnt/ß-catenin pathway activity and cell apoptosis were time- and dose-dependent. However, rPOSTN-induced NPCs apoptosis decreased after iso-induced inhibition of the Wnt/ß-catenin pathway. POSTN inhibition reduced apoptosis but was restored by rPOSTN re-addition. Lastly, POSTN inhibition ameliorated puncture-induced IVDD in vivo. Overall, our study demonstrated that POSTN promotes NPCs apoptosis and aggravates degeneration by activating the Wnt/ß-catenin pathway.

A Nomogram Based on Molecular Biomarkers and Radiomics to Predict Lymph Node Metastasis in Breast Cancer.

Background: The aim of this study was to explore the feasibility and efficacy of a non-invasive quantitative imaging evaluation model to assess the lymphatic metastasis of breast cancer based on a radiomics signature constructed using conventional T1-weighted image (T1WI) enhanced MRI and molecular biomarkers. Methods: Patients with breast cancer diagnosed via lymph biopsies between June 2015 and June 2019 were selected for the study. All patients underwent T1WI contrast-enhancement before treatment; lymph biopsy after surgery; and simultaneous Ki-67, COX-2, PR, Her2 and proliferating cell nuclear antigen detection. All images were imported into ITK-SNAP for whole tumor delineation, and AK software was used for radiomics feature extraction. Next, the radiomics signature Rad-score was constructed after reduction of specific radiomic features. A multiple regression logistic model was built by combining the Rad-score and molecular biomarkers based on the minimum AIC. Results: In all, 100 patients were enrolled in this study, including 45 with non-lymph node (LN) metastasis and 55 with LN metastasis. A total of 1,051 texture feature parameters were extracted, and LASSO was used to reduce the dimensionality of the radiomics features. The log(λ) was set to 0.002786, and 19 parameters were retained for the construction of the radiomics tag Rad-score. ROC was used to evaluate the diagnostic efficiency of Rad-score: the area under the ROC curve (AUC) of the Rad-score for identifying non-lymphatic and lymphatic metastases was 0.891 in the training cohort and 0.744 in the validation cohort. With the incorporation of tumor molecular markers, the AUCs of the training cohort and validation cohort of the nomogram were 0.936 and 0.793, respectively, which were notably higher than the AUCs of the clinical parameters in the training and validation cohorts (0.719 and 0.588, respectively). Conclusion: The combined model constructed using the Rad-score and molecular biomarkers can be used as an effective non-invasive method to assess LN metastasis of breast cancer. Furthermore, it can be used to quantitatively evaluate the risk of breast cancer LN metastasis before surgery.

Experimental study of ß-TCP scaffold loaded with VAN/PLGA microspheres in the treatment of infectious bone defects.

Antibiotic bone cement filling technology has been widely used in the treatment of infectious bone defects for decades. However, the current treatment requires multiple complicated procedures, which would lead to pain and financial burden for patients. Repairing bone defects and control infection at the same time is the pain spot of orthopaedic area. In this study, we develop a composite scaffold that aiming at effectively repair infectious bone defects simultaneously. Vancomycin hydrochloride(Van) /Poly(lactic-co-glycolic) acid(PLGA) microspheres prepared by double emulsion method were successfully loaded into ß-tricalcium phosphate scaffold through electrostatic and physical crosslinking. Full characterization, including mechanical properties, biocompatibility, in vitro release profile and antibacterial properties of the composite scaffolds(CPSFs) were performed. The rabbit osteomyelitis model based on big hole and small hole methods was established. Pharmacodynamics study, including the local bacteriostatic and osteogenic ability were evaluated by X-ray, Micro-CT and histopathology at 4 months after surgery. These findings indicate that a reliable rabbit model of local bone defect infection successfully established by big hole approach. The CPSFs with significant histocompatibility and biocompatibility could sustained release vancomycin for extended duration. It exhibited great application potential in clinical aim at the indication of local infectious bone defects.

[Study on the Benefit of Postoperative Exercise Rehabilitation in Patients with 
Lung Cancer Complicated with Chronic Obstructive Pulmonary Disease].

BACKGROUND: Chronic obstructive pulmonary diseases (COPD) affects 45%-63% of lung cancer patients worldwide. Lung cancer patients complicated with COPD have decreased cardiopulmonary function and increased perioperative risk, and their postoperative exercise endurance and lung function are significantly lower than those with conventional lung cancer. Previous studies have shown that postoperative exercise training can improve the exercise endurance of unselected lung cancer patients, but it is unclear whether lung cancer patients with COPD can also benefit from postoperative exercise training. This study intends to explore the effects of postoperative exercise training on exercise endurance, daily activity and lung function of lung cancer patients with COPD. METHODS: Seventy-four patients with non-small cell lung cancer (NSCLC) complicated with COPD who underwent pneumonectomy in the lung cancer center of West China Hospital of Sichuan University from August 5, 2020 to August 25, 2021 were prospectively analyzed. They were randomly divided into exercise group and control group; The patients in the two groups received routine postoperative rehabilitation in the first week after operation, and the control group was given routine nursing from the second week. On this basis, the exercise group received postoperative exercise rehabilitation training for two weeks. Baseline evaluation was performed 3 days before operation and endpoint evaluation was performed 3 weeks after operation. RESULTS: The exercise endurance, daily activity and pulmonary function test results of the two groups decreased from baseline to the end point. However, after the operation and intervention program, the maximum oxygen consumption of Cardiopulmonary Exercise Test and the walking distance of 6-Minute Walking Test in the exercise group were significantly better than those in the control group [(13.09±1.46) mL/kg/min vs (11.89±1.38) mL/kg/min, P=0.033; (297±46) m vs (243±43) m, P=0.041]. The average number of we-chat steps in the exercise group was also significantly better than that in the control group (4,381±397 vs 3,478±342, P=0.035). Forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) in the exercise group were better than those in the control group, but the difference did not reach a statistically significant level [(1.76±0.19) L vs (1.60±0.28) L, P=0.084; (1.01±0.17) L vs (0.96±0.21) L, P=0.467]. CONCLUSIONS: Postoperative exercise rehabilitation training can improve exercise endurance and daily activity ability of patients with lung cancer complicated with COPD and promote postoperative rehabilitation.

PAM-DenseNet: A Deep Convolutional Neural Network for Computer-Aided COVID-19 Diagnosis.

Currently, several convolutional neural network (CNN)-based methods have been proposed for computer-aided COVID-19 diagnosis based on lung computed tomography (CT) scans. However, the lesions of pneumonia in CT scans have wide variations in appearances, sizes, and locations in the lung regions, and the manifestations of COVID-19 in CT scans are also similar to other types of viral pneumonia, which hinders the further improvement of CNN-based methods. Delineating infection regions manually is a solution to this issue, while excessive workload of physicians during the epidemic makes it difficult for manual delineation. In this article, we propose a CNN called dense connectivity network with parallel attention module (PAM-DenseNet), which can perform well on coarse labels without manually delineated infection regions. The parallel attention module automatically learns to strengthen informative features from both channelwise and spatialwise simultaneously, which can make the network pay more attention to the infection regions without any manual delineation. The dense connectivity structure performs feature maps reuse by introducing direct connections from previous layers to all subsequent layers, which can extract representative features from fewer CT slices. The proposed network is first trained on 3530 lung CT slices selected from 382 COVID-19 lung CT scans, 372 lung CT scans infected by other pneumonia, and 200 normal lung CT scans to obtain a pretrained model for slicewise prediction. We then apply this pretrained model to a CT scans dataset containing 94 COVID-19 CT scans, 93 other pneumonia CT scans, and 93 normal lung scans, and achieve patientwise prediction through a voting mechanism. The experimental results show that the proposed network achieves promising results with an accuracy of 94.29%, a precision of 93.75%, a sensitivity of 95.74%, and a specificity of 96.77%, which is comparable to the methods that are based on manually delineated infection regions.

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease.

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis. Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71-1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03-1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73-1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05-1.40) with fungal pneumonia. Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.

Dissection of 4L lymph node for left-sided non-small cell lung cancer: a meta-analysis.

BACKGROUND: Whether dissection of left lower paratracheal (4L) lymph node has any impact on survival of patients with left-sided non-small cell lung cancer (NSCLC) remains unclear. We conducted the first meta-analysis to compare the survival of patients treated with 4L lymph node dissection (LND) and those without for left-sided NSCLC. METHODS: We systematically searched relevant studies from PubMed, Embase, and Web of Science on February 6, 2020. Data for analysis included 5-year overall survival (OS) and disease-free survival (DFS) rates, OS, and DFS. We calculated risk ratio (RR) for pooling 5-year OS and DFS rates and extracted hazard ratio (HR) from multivariate analysis for pooling OS and DFS. RESULTS: We finally included three retrospective cohort studies with propensity score-matched analysis consisting of 2103 patients. Meta-analysis showed that patients treated with 4L LND yielded significantly higher 5-year OS (67.7% vs. 54.6%; fixed effects models: RR = 0.75; 95% confidence interval [CI] = [0.67, 0.84]; p < 0.001; I2  = 0%) and DFS (53.3% vs. 44.8%; fixed effects models: RR = 0.85; 95% CI = [0.76, 0.95]; p = 0.003; I2  = 41.7%) rates than patients without 4L LNDS. Moreover, dissection of 4L lymph node was significantly associated with better OS (fixed effects model: HR = 0.66; 95% CI = [0.57, 0.76]; p < 0.001; I2  = 45.7%) and DFS (fixed effects model: HR = 0.67; 95% CI = [0.52, 0.87]; p = 0.003; I2  = 0%). No significant heterogeneities were observed. CONCLUSIONS: Dissection of 4L lymph node could significantly improve both 5-year OS and DFS rates and 4L LND was a favorable prognostic factor for patients with left-sided NSCLC.