Sepsis-associated encephalopathy (SAE) is a common and severe neurological complication of sepsis that markedly worsens long-term outcomes. Growing evidence suggest that metabolic reprogramming in microglia is a major driver of neuroinflammation in SAE; however, the molecular mechanisms that altered metabolism and inflammatory responses remain unclear. Transcriptomic data from public hippocampal datasets of SAE mice were analyzed to identify potential molecular drivers. We established a CLP-induced SAE model and performed AAV-mediated knockdown. For in vitro validation, BV2 microglia were treated with LPS to simulate neuroinflammation. Mechanistic validation was conducted using both genetic and pharmacological interventions. Cellular metabolism was examined through extracellular flux analysis and metabolite detection. Inflammatory responses were evaluated by cytokine profiling, and disease phenotypes were assessed using behavioral tests and histological analyses. S100A8 was markedly upregulated in activated microglia during SAE. Its knockdown reduced microglial activation, protected hippocampal neurons, and improved cognitive performance. Transcriptomic profiling identified PFKFB3 as a downstream glycolytic target of S100A8. Mechanistically, S100A8 activated the PI3K/AKT/HIF-1α signaling cascade, thereby upregulating PFKFB3 and promoting glycolytic reprogramming and cytokine release. Functionally, S100A8 knockdown lowered lactate production and LDH activity, while reducing TNF-α, IL-6, and IL-1β secretion. Rescue experiments confirmed that PFKFB3 mediates the glycolytic and pro-inflammatory effects of S100A8. This study demonstrates that S100A8 exacerbates SAE-related neuroinflammation and cognitive impairment by driving microglial metabolic reprogramming toward glycolysis via the PI3K/AKT/HIF-1α–PFKFB3 pathway. These findings highlight a mechanistic link between S100A8 and microglial metabolic reprogramming and neuroinflammation, and suggest that S100A8 could be a promising target for therapeutic intervention in SAE.

Objective: The current study aims to investigate the prognostic value of breast cancer integrated oxidative stress score (BCIOSS) in patients with breast cancer who received neoadjuvant chemotherapy (NACT). Methods: A retrospective analysis of 104 breast cancer patients who underwent NACT from June 2009 to December 2015 was performed. The differences of BCIOSS of breast cancers in regard to variables were analyzed using Chi-square test and Fisher's exact test. The Kaplan-Meier method was used to evaluate survival curve between low BCIOSS group and high BCIOSS group, and the two groups were compared by Log-rank tests at the individual index level. The univariate and multivariate Cox regression analyses were established by the important predictive factors determined based on univariate analysis. The nomograms were further conducted based on the factors by the multivariate analyses. Results: Patients were assigned to low BCIOSS group (BCIOSS≤2.54) or high BCIOSS group (BCIOSS>2.54) via ROC curve. BCIOSS was a latent prognostic factor for patient survival [DFS; hazard ratio (HR): 0.163, 95%CI: 0.045-0.596, P=0.006; and OS; HR: 0.168, 95%CI: 0.056-0.500, P=0.001]. Patients with a high BCIOSS had longer survival time than those with a low BCIOSS (DFS: χ2=7.317, P=0.0068; and OS: χ2=9.407, P=0.0022). Calibration curves shown that the predicted line conformed well to the reference line for the 5-year survival category. DCA revealed that the nomograms conducted had a better clinical predictive application than only by BCIOSS. Conclusion: BCIOSS is a latent prognostic factor, and patients with high oxidative stress scores have a better prognosis and longer survival time.

Sepsis-associated encephalopathy (SAE) is a common and serious complication of sepsis that leads to acute brain dysfunction and long-term cognitive impairment. We used widely targeted LC-MS/MS plasma metabolomics in 29 healthy controls, 32 sepsis patients, and 27 SAE patients, combined with machine learning, to define metabolic patterns across these groups. This approach identified 12 discriminatory metabolites, with succinate showing a stepwise increase from health to sepsis to SAE and associations with clinical severity scores. To test its functional relevance, we used a cecal ligation and puncture (CLP) mouse model and found that exogenous succinate supplementation aggravated cognitive deficits, neuronal injury, and microglial activation. Together, these findings link systemic metabolic remodeling to brain inflammation and dysfunction in sepsis and suggest that succinate and related pathways may help stratify SAE risk and provide mechanistic entry points for future therapeutic exploration.

Background:Patients undergoing maintenance hemodialysis face a high mortality rate, yet effective tools for predicting mortality risk in this population are lacking. This study aims to develop an interpretable machine learning model to predict mortality risk among maintenance hemodialysis patients. Methods:A retrospective analysis was conducted on clinical data from 512 maintenance hemodialysis patients treated at The Central Hospital of Wuhan between January 2021 and October 2024. The dataset included 50 feature variables. The data were split into a training set (70%) and a test set (30%). Five machine learning models-Random Forest, Extreme Gradient Boosting, Support Vector Machine, Logistic Regression, and K-Nearest Neighbor-were trained and evaluated for predicting patient mortality risk, using metrics such as the F1 score, precision, accuracy, AUC-ROC, and recall. SHAP values were used to assess the contribution of each feature in the best-performing model. Results:The K-Nearest Neighbor model achieved the highest AUC-ROC of 0.9792 (95% CI: 0.9600-0.9929). SHAP analysis identified key factors influencing predictions, including dialysis duration, creatinine levels, white blood cell ratio, blood phosphorus concentration, and unconjugated iron. Conclusion:The K-Nearest Neighbor model demonstrated high efficacy in predicting mortality risk among hemodialysis patients. SHAP analysis highlighted critical risk factors. While these findings show promise for future clinical research, they should be interpreted with caution due to the study's retrospective design and the need for external validation.

KRAS G12C inhibitors (G12Cis) have revolutionized the treatment of cancers driven by this historically undruggable mutation, offering unprecedented clinical responses in non-small cell lung cancer (NSCLC) and other malignancies. However, both primary and acquired resistance rapidly curtail their efficacy. Emerging clinical and preclinical evidence underscores the heterogeneity of resistance mechanisms. Strikingly, in KRAS-driven NSCLC, a common phenomenon is co-mutations in tumor suppressor genes (TSGs), which orchestrate resistance through multifaceted pathways such as sustained proliferation, metabolic reprogramming, phenotypic plasticity, and immune microenvironment remodeling. Accordingly, this review summarizes relevant reasons underlying diverse resistant mechanisms in KRAS G12C-mutated NSCLC, with an emphasis on deciphering the mechanism of tumor suppressor gene (TSG) alterations serving as key mediators linking oncogenic KRAS dependency to therapeutic resistance. Our research continued to discuss relevant preclinical models to facilitate the advancement of the study of these resistance mechanisms.

BACKGROUND:Arteriovenous fistula stenosis is a common complication in hemodialysis patients, yet effective predictive tools are lacking. This study aims to develop an interpretable machine learning model for stenosis risk prediction. METHODS:Clinical data from 974 patients (55 features) undergoing arteriovenous fistula dialysis at The Central Hospital of Wuhan (2017-2024) were analyzed retrospectively. The dataset was split into training (70%) and test (30%) sets. Seven models-Random Forest, XGBoost, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Artificial Neural Network, and Decision Tree-were trained. Performance was evaluated using F1 score, accuracy, specificity, precision, recall, and AUC-ROC. SHAP values identified key predictors in the optimal model. RESULTS:XGBoost achieved the highest AUC (0.829, 95% CI 0.785-0.880). SHAP analysis highlighted seven critical predictors: number of surgeries, prothrombin time activity, lymphocyte count, fistula duration, triglycerides, vitamin B12, and C-reactive protein. CONCLUSION:The XGBoost model effectively predicts arteriovenous fistula stenosis risk using clinical data. SHAP explanations enhance clinical interpretability, aiding personalized care strategies.

BACKGROUND:Thrombosis of arteriovenous fistulas represents a prevalent complication among patients undergoing hemodialysis, characterized by a notably high incidence rate. Presently, there is an absence of robust assessment tools capable of predicting thrombosis occurrence. This study seeks to develop an interpretable machine learning model to forecast the risk of arteriovenous fistula thrombosis. METHODS:Clinical data were retrospectively collected from 1,168 patients who received hemodialysis via arteriovenous fistulas at The Central Hospital of Wuhan between January 2017 and October 2024. A comprehensive analysis of 55 features was conducted utilizing Python. The dataset was partitioned into a training set and a test set, comprising 70% and 30% of the samples, respectively. Six distinct machine learning models-namely, Random Forest, Extreme Gradient Boosting, Decision Tree, Logistic Regression, K-Nearest Neighbors, and Naive Bayes-were constructed to predict the risk of thrombosis in arteriovenous fistulas. The performance of these models was assessed utilizing several metrics, including the F1 score, precision, specificity, accuracy, area under the receiver operating characteristic curve, and recall rate. The contribution of each feature within the most effective model was evaluated using SHAP values, and a specific case was selected to demonstrate the model's predictive capability. RESULTS:The study encompassed a cohort of 974 patients, each characterized by 55 clinical data features. Among the six machine learning models evaluated, the Random Forest model demonstrated superior performance, achieving an AUC-ROC of 0.984. SHAP visualization analysis identified the number of surgeries, stenosis, free fatty acids, platelet count, and C-reactive protein as the five most significant features influencing the risk of arteriovenous fistula thrombosis. CONCLUSION:We developed a Random Forest model based on patients' clinical data, which effectively predicts the risk of thrombosis in arteriovenous fistulas. SHAP analysis offers the potential to inform personalized and evidence-based nursing interventions for healthcare professionals.

Over the past decades, researchers have continuously investigated the potential functions of long-chain polyunsaturated fatty acids (LCPUFAs) in cancers, including lung cancer. The ω-3 LCPUFAs, primarily consisting of eicosapentaenoic acid and docosahexaenoic acid, were found to modify inflammatory tumor microenvironment, induce cancer cell apoptosis and autophagy, and suppress tumor development when administered alone or with other therapeutical strategies. Although the precise anti-tumor mechanism has not been elucidated yet, ω-3 LCPUFAs are often used in the nutritional treatment of patients with cancer due to their ability to significantly improve patient's nutritional status, increase the sensitivity of tumor cells to treatments, and alleviate cancer-related complications. Here we present the key roles of ω-3 LCPUFAs as dietary supplementations in lung cancer, comprehensively review the recent progress on the underlying mechanisms of cancer cell regulation by ω-3 LCPUFAs, and introduce the application of ω-3 LCPUFAs in the clinical management of lung cancer and its malignant complications.

BACKGROUND AND PURPOSE:Cerebrovascular events during thrombolysis in cardiac arrest (CA) caused by pulmonary embolism (PE) is a life-threatening condition. However, the balance between cerebrovascular events and thrombolytic therapy in PE-induced CA remains a great challenge. METHODS:In this study, we reported three unique cases regarding main concerns surrounding cerebrovascular events in thrombolytic therapy in PE-induced CA. RESULTS:The patient in the case 1 treated with thrombolysis during CPR and finally discharged neurologically intact. The patient in the case 2 received delayed thrombolysis and died eventually. The patient in the case 3 was contraindicated to thrombolysis due to the complication of subarachioid hemorrahage and died within days. CONCLUSIONS:Our case series highlights three proposed approaches to consider before administering thrombolysis as a treatment option in PE-induced CA patients: (1) prolonging the resuscitation, (2) administering thrombolysis promptly, and (3) ruling out cerebrovascular events.

BACKGROUND:Lung adenocarcinoma (LUAD) is a major type of lung cancer worldwide, and under the pandemic coronavirus disease 2019 (COVID-19), its cancer burden is enlarged. This study aimed to explore potential drug targets and potential drugs for developing effective treatments for patients with both lung cancer and COVID-19. METHODS:The interaction network of molecule compounds-target genes was constructed based on Traditional Chinese Medicines (TCMs) and gene expression data from public databases. The potential effectiveness of drugs was analyzed by molecular docking and molecular dynamics simulation. Western blot, transfection assay, Immunohistochemistry (IHC) staining, and flow cytometry were performed to investigate the function of HSP90AA1 in LUAD cells. RESULT:Eight target genes (GSK3B, HMOX1, HSP90AA1, ICAM1, MAPK1, PLAU, RELA and TNFSF15.) were identified, and two of them (HSP90AA1 and RELA) were significantly associated with LUAD prognosis. Luteolin was discovered to bind with HSP90AA1. Moreover, in vitro cell experiments demonstrated that HSP90AA1 had higher expression in A549 cells, promoted cell viability and suppressed apoptosis in A549 cells and H1299 cells. CONCLUSION:HSP90AA1 was a target gene for further designing effective drugs for LUAD patients. Luteolin was a potential drug for treating patients with both LUAD and COVID-19.

CLEC6A, (C-type lectin domain family 6, member A), plays a prominent role in regulating innate immunity and adaptive immunity. CLEC6A has shown great potential as a target for cancer immunotherapy. This study aims to explore the prognostic value of CLEC6A, and analyze the relationship associated with the common hematological parameters in breast cancer patients. We performed a retrospective analysis on 183 breast cancer patients data in hospital information system from January 2013 to December 2015. The expression of CLEC6A was recorded via semiquantitative immunohistochemistry in breast cancer. The association between expression of CLEC6A and relative parameters were performed by Chi-square test and Fisher's exact test. Kaplan-Meier assay and Log-rank test were performed to evaluate the survival time. The Cox proportional hazards regression analysis was applied to identify prognostic factors. Nomograms were conducted to predict 1-, 3-, and 5-year disease free survival (DFS) and overall survival (OS) for breast cancer, which could be a good reference in clinical practice. The nomogram model was estimated by calibration curve analysis for its function of discrimination. The accuracy and benefit of the nomogram model were appraised by comparing it to only CLEC6A via decision curve analysis (DCA). The prediction accuracy of CLEC6A was also determined by time-dependent receiver operating characteristics (TDROC) curves, and the area under the curve (AUC) for different survival time. There were 94 cases in the CLEC6A low-expression group and 89 cases in CLEC6A high-expression group. Compared to CLEC6A lowexpression group, the CLEC6A high-expression group had better survival (DFS: 56.95 vs. 70.81 months, P = 0.0078 and OS: 67.98 vs. 79.05 months, P = 0.0089). The CLEC6A was a potential prognostic factor in multivariate analysis (DFS: P = 0.023, hazard ratio (HR): 0.454, 95 % confidence interval (CI): 0.229-0.898; OS: P = 0.020, HR: 0.504, 95 %CI: 0.284-0.897). The nomogram in accordance with these potential prognostic factors was constructed to predict survival and the calibration curve analysis had indicated that the predicted line was well-matched with reference line in 1-, 3-, and 5-year DFS and OS category. The 1-, 3-, and 5-year DCA curves have revealed that nomogram model yielded larger net benefits than CLEC6A alone. Finally, the TDROC curve indicated that CLEC6A could better predict 1-year DFS and OS than others. Furthermore, we combined these potential independent prognostic factors to analyze the relationship among these hematologic index and oxidative stress indicators, and indicated that higher CLEC6A level, higher CO2 level or low CHOL level or high HDL-CHO level would have survived longer and better prognosis. In breast cancer, high expression of CLEC6A can independently predict better survival. Our nomogram consisted of CLEC6A and other indicators has good predictive performance and can facilitate clinical decision-making.

Objectives Our objective is to develop a prediction tool to predict the death after in-hospital cardiac arrest (IHCA).Design We conducted a retrospective double-centre observational study of IHCA patients from January 2015 to December 2021. Data including prearrest diagnosis, clinical features of the IHCA and laboratory results after admission were collected and analysed. Logistic regression analysis was used for multivariate analyses to identify the risk factors for death. A nomogram was formulated and internally evaluated by the boot validation and the area under the curve (AUC). Performance of the nomogram was further accessed by Kaplan-Meier survival curves for patients who survived the initial IHCA.Setting Intensive care unit, Tongji Hospital, China.Participants Adult patients (≥18 years) with IHCA after admission. Pregnant women, patients with ‘do not resuscitation’ order and patients treated with extracorporeal membrane oxygenation were excluded.Interventions None.Primary and secondary outcome measures The primary outcome was the death after IHCA.Results Patients (n=561) were divided into two groups: non-sustained return of spontaneous circulation (ROSC) group (n=241) and sustained ROSC group (n=320). Significant differences were found in sex (p=0.006), cardiopulmonary resuscitation (CPR) duration (p<0.001), total duration of CPR (p=0.014), rearrest (p<0.001) and length of stay (p=0.004) between two groups. Multivariate analysis identified that rearrest, duration of CPR and length of stay were independently associated with death. The nomogram including these three factors was well validated using boot calibration plot and exhibited excellent discriminative ability (AUC 0.88, 95% CI 0.83 to 0.93). The tertiles of patients in sustained ROSC group stratified by anticipated probability of death revealed significantly different survival rate (p<0.001).Conclusions Our proposed nomogram based on these three factors is a simple, robust prediction model to accurately predict the death after IHCA.

Background Nearly 10% to 20% of myasthenia gravis (MG) patients are refractory to conventional treatment for unclear reasons. The study aimed to explore the relationship between drug metabolism gene polymorphisms and refractory MG. Methods One hundred and thirty-one MG patients (33 in the refractory group; 98 in the non-refractory group) admitted to Tongji Hospital were included in this retrospective study. Improved multiplex ligation detection reaction (iMLDR) was used to genotype 13 polymorphisms (NR3C1 rs17209237, rs9324921; FKBP5 rs1360780, rs4713904, rs9296158; HSP90AA1 rs10873531, rs2298877, rs7160651; MDR1 rs1045642, rs1128503, rs2032582; CYP3A4 rs2242480; and CYP3A5 rs776746). We applied multivariable logistic regression to investigate the association between refractory MG and nucleotide polymorphisms. Generalized multifactor dimensionality reduction (GMDR) was used to examine gene-gene interactions. Results CC genotype of HSP90AA1 rs7160651 was associated with the increased risk of refractory MG than CT genotype [odds ratio (OR) =0.26; P=0.041] and CT + TT genotype (dominant model, OR =0.24; P=0.022). For CYP3A5 rs776746, AA genotype was associated with refractory MG compared with AG genotype (OR =0.11; P=0.017), GG genotype (OR =0.18; P=0.033), and AG + GG genotype (dominant model, OR =0.16; P=0.020). The frequency of CAT haplotype of HSP90AA1 rs10873531, rs2298877, rs7160651 was less common in refractory patients (OR =0.33; P=0.044). No significant gene-gene interactions were observed. Conclusions HSP90AA1 rs7160651 and CYP3A5 rs776746 were significantly associated with refractory MG. Further studies are warranted to confirm the results and investigate the use of polymorphisms for treatment individualization.

Cardiac arrest caused by pulmonary embolism can be lethal if not treated promptly. Thrombolysis during cardiac pulmonary resuscitation is an emergency treatment option to restore adequate circulation, but the benefit of which is controversial. We presented here three unique cases to illuminate the main concerns and to share our clinical experience on the treatment of thrombolysis in PE-induced CA. Our case series revealed that ruling out the contraindications, applying thrombolysis promptly during CPR and considering prolonged resuscitation were important aspects which should be taken into account before thrombolysis in PE-induced CA.

Background Cellular therapy based on mesenchymal stem cells (MSCs) is a promising novel therapeutic strategy for the osteonecrosis of the femoral head (ONFH), which is gradually becoming popular, particularly for early-stage ONFH. Nonetheless, the MSC-based therapy is challenging due to certain limitations, such as limited self-renewal capability of cells, availability of donor MSCs, and the costs involved in donor screening. As an alternative approach, MSCs derived from induced pluripotent stem cells (iPSCs), which may lead to further standardized-cell preparations. Methods In the present study, the bone marrow samples of patients with ONFH ( n = 16) and patients with the fracture of the femoral neck ( n = 12) were obtained during operation. The bone marrow-derived MSCs (BMSCs) were isolated by density gradient centrifugation. BMSCs of ONFH patients (ONFH-BMSCs) were reprogrammed to iPSCs, following which the iPSCs were differentiated into MSCs (iPSC-MSCs). Forty adult male rats were randomly divided into following groups ( n = 10 per group): (a) normal control group, (b) methylprednisolone (MPS) group, (c) MPS + BMSCs treated group, and (d) MPS + iPSC-MSC-treated group. Eight weeks after the establishment of the ONFH model, rats in BMSC-treated group and iPSC-MSC-treated group were implanted with BMSCs and iPSC-MSCs through intrabone marrow injection. Bone repair of the femoral head necrosis area was analyzed after MSC transplantation. Results The morphology, immunophenotype, in vitro differentiation potential, and DNA methylation patterns of iPSC-MSCs were similar to those of normal BMSCs, while the proliferation of iPSC-MSCs was higher and no tumorigenic ability was exhibited. Furthermore, comparing the effectiveness of iPSC-MSCs and the normal BMSCs in an ONFH rat model revealed that the iPSC-MSCs was equivalent to normal BMSCs in preventing bone loss and promoting bone repair in the necrosis region of the femoral head. Conclusion Reprogramming can reverse the abnormal proliferation, differentiation, and DNA methylation patterns of ONFH-BMSCs. Transplantation of iPSC-MSCs could effectively promote bone repair and angiogenesis in the necrosis area of the femoral head.

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan, People’s Republic of China, and has subsequently spread worldwide. Clinical information on patients who contracted severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the perioperative period is limited. Here, we report seven cases with confirmed SARS-CoV-2 infection in the perioperative period of lung resection. Retrospective analysis suggested that one patient had been infected with the SARS-CoV-2 infection before the surgery and the other six patients contracted the infection after the lung resection. Fever, lymphopenia, and ground-glass opacities revealed on computed tomography are the most common clinical manifestations of the patients who contracted COVID-19 after the lung resection. Pathologic studies of the specimens of these seven patients were performed. Pathologic examination of patient 1, who was infected with the SARS-CoV-2 infection before the surgery, revealed that apart from the tumor, there was a wide range of interstitial inflammation with plasma cell and macrophage infiltration. High density of macrophages and foam cells in the alveolar cavities, but no obvious proliferation of pneumocyte, was found. Three of seven patients died from COVID-19 pneumonia, suggesting lung resection surgery might be a risk factor for death in patients with COVID-19 in the perioperative period.

Background: The outbreak of 2019 novel coronavirus disease (COVID-19) in Wuhan, China imposes a major challenge in deciding and managing surgical operation on patients with lung cancer and other lung disorders. Here we reported the clinical characteristics of seven patients who underwent lung resection and contracted severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We also analyzed eight cases of nosocomial transmission of SARS-CoV-2 which occurred in the same department among health care staff. Methods: Clinical and laboratory data from seven patients with confirmed SARS-CoV-2 infection after lung resection and eight infected health care workers (HCWs) were retrospectively analyzed. Findings: The median age of the patients was 60 years (25th-75th percentile, 57-66 years), and five were male. Six patients were pathologically diagnosed with non-small cell lung cancer (NSCLC), and one patient had sclerosing pneumocytoma. Lung resection was successfully carried out in seven patients, including video-assisted thoracoscopic (VATS) lobectomy (four), VATS segmentectomy plus wedge resection (one), thoracotomy sleeve lobectomy (one) and lobectomy plus bronchus reconstruction (one). Seven patients presented the following symptoms: fever (seven), shortness of breath (five), nonproductive cough (four), fatigue (two), productive cough (one), myalgia (two), and diarrhea (one). Ground-glass opacity and/or patchy shadowing on chest computed tomography (CT) were presented in all seven patients. Lymphopenia was observed on the first day after surgery. Moreover, lymphocyte counts in the peripheral blood remained below preoperative level and decreased over time. The median age of HCWs was 34 years (25th-75th percentile, 28-47 years), and two out of total eight were male. Similar symptoms and signs, including fever, typical CT scan presentation and lymphopenia were also observed in HCWs. Two patients died from COVID-19 pneumonia. The fatality rate was 28·6%. Five HCWs has been cured and discharged from hospital. The rest five patients and three HCWs were remained hospitalized in stable condition. Interpretation: In the virus-infected patient cohort, a much higher fatality rate compared to ~3% among general infected public, suggests that surgery is a considerable risk factor, pointing to the necessity to postpone lung resection on patients during the epidemic.Funding Statement: The authors stated: "None."Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: The study was approved by the ethics committee of Tongji Hospital, Wuhan, China.

目的 总结双肺多发磨玻璃影(ground-glass opacity,GGO)患者同期行双侧单孔胸腔镜手术切除的经验.方法 回顾性分析2015年5月至2019年10月同期行双侧单孔胸腔镜肺GGO切除34例患者的临床资料,其中男6例、女28例,平均年龄41 ～ 69(57.9±6.7)岁.结果 术中平均出血量(120.9±67.7)mL,平均手术时间(140.0±74.8)min,术后平均胸腔引流时间(4.8±3.1)d,术后平均住院时间(7.2±4.3)d.术后并发症包括肺部感染2例,心房颤动3例,肺持续漏气＞3d5例,经治疗后均好转,无围手术期严重并发症及死亡病例.共切除GGO病灶76个,总恶性率为81.6％,其中纯GGO 40个,恶性28个(70.0％),平均直径(9.6±3.8)mm;混合GGO36个,恶性34个(94.4％),平均直径(15.6±6.6)mm.平均随访时间38.4个月,未发现术后转移及复发.结论 双肺多发GGO患者的病灶为恶性可能性大,在肺功能允许时可考虑同期双侧单孔胸腔镜多病灶切除,根据病灶位置、大小及术中快速病理结果可灵活采取亚肺叶或肺叶切除方法.双侧同期手术安全可行,不会增加术后并发症风险,短期预后良好.

Osteonecrosis of the femoral head (ONFH) is a common and disabling joint disease. Although there is no clear consensus on the complex pathogenic mechanism of ONFH, trauma, abuse of glucocorticoids, and alcoholism are implicated in its etiology. The therapeutic strategies are still limited, and the clinical outcomes are not satisfactory. Mesenchymal stem cells (MSCs) have been shown to exert a positive impact on ONFH in preclinical experiments and clinical trials. The beneficial properties of MSCs are due, at least in part, to their ability to home to the injured tissue, secretion of paracrine signaling molecules, and multipotentiality. Nevertheless, the regenerative capacity of transplanted cells is impaired by the hostile environment of necrotic tissue in vivo, limiting their clinical efficacy. Recently, genetic engineering has been introduced as an attractive strategy to improve the regenerative properties of MSCs in the treatment of early-stage ONFH. This review summarizes the function of several genes used in the engineering of MSCs for the treatment of ONFH. Further, current challenges and future perspectives of genetic manipulation of MSCs are discussed. The notion of genetically engineered MSCs functioning as a "factory" that can produce a significant amount of multipotent and patient-specific therapeutic product is emphasized.