Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study.

OBJECTIVE: To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment. METHODS: We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively. RESULTS: In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively). CONCLUSIONS: Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.

Topical Antibiotic Prophylaxis for Preventing Surgical Site Infections of Clean Wounds: A Systematic Review and Meta-Analysis.

Background: Topical antibiotic agents are not generally indicated for preventing of surgical site infections (SSIs) in clean incisions, and the drug concentrations that should be delivered to local incision sites remain uncertain. The aim of this study was to critically assess the efficacy of topical antibiotic agents in comparison with non-antibiotic agents for preventing SSIs in clean incisions by performing a systematic review and meta-analysis. Methods: We conducted a search of literature in PubMed, Embase, and Cochrane Databases and included randomized controlled trials (RCTs) on topical antibiotic use for patients with clean post-surgical incisions. The primary outcome was the incidence of SSI, presented as the event rate. Eleven RCTs were included. Results: Using random-effects modeling, the pooled risk ratio (RR) of developing a post-surgical incisions infection was 0.83 (95% confidence interval [CI], 0.61-1.16; I2, 0%). In subgroup analyses, no reductions in SSI were observed when topical antibiotic agents were used to treat incisions due to spinal (RR, 0.75; 95% CI, 0.40-1.38; I2, 0%), orthopedic (RR, 0.69; 95% CI, 0.37-1.29; I2, 0%), dermatologic (RR, 0.77; 95% CI, 0.39-1.55; I2, 65%), or cardiothoracic surgeries (RR, 1.31; 95% CI, 0.83-2.06; I2: 0%). The incidence of SSI across different operative phases did not differ for the application of topical antibiotic agents compared with non-antibiotic agents (RR, 0.80; 95% CI, 0.56-1.14; I2, 0%). Conclusions: The results of this meta-analysis show that topical antibiotic agents provide no clinical benefit for preventing SSI in clean incisions.

Impact of respiratory motion on 18 F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner.

PURPOSE: 18 F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18 F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18 F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner. MATERIALS AND METHODS: A total of 101 patients who underwent oncological 18 F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. 18 F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability. RESULTS: In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most 18 F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features. CONCLUSION: Respiratory motion has a significant impact on 18 F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.

A comparison of 18 F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study.

OBJECTIVE: The performance of 18 F-FDG PET-based radiomics and deep learning in detecting pathological regional nodal metastasis (pN+) in resectable lung adenocarcinoma varies, and their use across different generations of PET machines has not been thoroughly investigated. We compared handcrafted radiomics and deep learning using different PET scanners to predict pN+ in resectable lung adenocarcinoma. METHODS: We retrospectively analyzed pretreatment 18 F-FDG PET from 148 lung adenocarcinoma patients who underwent curative surgery. Patients were separated into analog (n = 131) and digital (n = 17) PET cohorts. Handcrafted radiomics and a ResNet-50 deep-learning model of the primary tumor were used to predict pN+ status. Models were trained in the analog PET cohort, and the digital PET cohort was used for cross-scanner validation. RESULTS: In the analog PET cohort, entropy, a handcrafted radiomics, independently predicted pN+. However, the areas under the receiver-operating-characteristic curves (AUCs) and accuracy for entropy were only 0.676 and 62.6%, respectively. The ResNet-50 model demonstrated a better AUC and accuracy of 0.929 and 94.7%, respectively. In the digital PET validation cohort, the ResNet-50 model also demonstrated better AUC (0.871 versus 0.697) and accuracy (88.2% versus 64.7%) than entropy. The ResNet-50 model achieved comparable specificity to visual interpretation but with superior sensitivity (83.3% versus 66.7%) in the digital PET cohort. CONCLUSION: Applying deep learning across different generations of PET scanners may be feasible and better predict pN+ than handcrafted radiomics. Deep learning may complement visual interpretation and facilitate tailored therapeutic strategies for resectable lung adenocarcinoma.

The Fast Growth and Quick Spread of Synchronous Tumors.

A 47-year-old man was diagnosed with left buccal squamous cell carcinoma using FDG PET/CT, by which focal lesions in the left buccal and left neck lymph node were found. Three months after the operation, CT images revealed a left lower lung lesion. Pathology indicated a left lower lung adenocarcinoma. Second FDG PET/CT was performed more than 11 days later, and lesions with intense FDG uptake were found in the left lower lung, metastatic to the lymph nodes, lungs, bones, and liver. The prior FDG PET/CT scan showed negative findings in the lungs. However, lung cancer with multiple metastases appeared 4 months later.

Genomic and Glycolytic Entropy Are Reliable Radiogenomic Heterogeneity Biomarkers for Non-Small Cell Lung Cancer.

Radiogenomic heterogeneity features in 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) have become popular in non-small cell lung cancer (NSCLC) research. However, the reliabilities of genomic heterogeneity features and of PET-based glycolytic features in different image matrix sizes have yet to be thoroughly tested. We conducted a prospective study with 46 NSCLC patients to assess the intra-class correlation coefficient (ICC) of different genomic heterogeneity features. We also tested the ICC of PET-based heterogeneity features from different image matrix sizes. The association of radiogenomic features with clinical data was also examined. The entropy-based genomic heterogeneity feature (ICC = 0.736) is more reliable than the median-based feature (ICC = -0.416). The PET-based glycolytic entropy was insensitive to image matrix size change (ICC = 0.958) and remained reliable in tumors with a metabolic volume of <10 mL (ICC = 0.894). The glycolytic entropy is also significantly associated with advanced cancer stages (p = 0.011). We conclude that the entropy-based radiogenomic features are reliable and may serve as ideal biomarkers for research and further clinical use for NSCLC.

Influence of the methodological aspects of the dichotomization of total metabolic tumor volume measured through baseline fluorine-18 fluorodeoxyglucose PET on survival prediction in lymphoma.

OBJECTIVE: The total metabolic tumor volume (TMTV) measured from fluorine-18 fluorodeoxyglucose (18F-FDG) PET can be useful for determining the prognosis of patients with lymphoma. Stratifying patients into high- and low-TMTV risk groups requires a cutoff point, which is determined through the dichotomization method. This study investigated whether different TMTV dichotomization methods influenced survival prediction in patients with lymphoma. METHODS: We retrospectively enrolled 129 patients with lymphoma who had undergone baseline 18F-FDG PET. TMTV was calculated using a fixed standardized uptake value threshold of 4.0. A total of six methods were employed to determine the optimal TMTV cutoff point using receiver-operating characteristic curve analyses, X-Tile bioinformatics software, and the Cutoff Finder web application. The prognostic performance of each method in survival prediction was examined. RESULTS: The median (interquartile range) TMTV was 123 cm3 (21-335 cm3). The optimal TMTV cutoff values for predicting progression-free survival (PFS) and overall survival (OS) were in the range of 144-748 cm3. The cutoff points were used to dichotomize patients into two groups with distinct prognoses. All TMTV dichotomizations were significantly predictive of PFS and OS. The survival curves showed significant differences between the high- and low-TMTV groups. The C-indices of the survival models did not significantly differ in any of the dichotomizations. CONCLUSION: The prognostic significance of TMTV was maintained regardless of the methodological aspects of dichotomization. However, the optimal TMTV cutoff point varied according to the chosen dichotomization method. Care should be taken when establishing an optimal TMTV cutoff point for clinical use.

Glucose metabolic heterogeneity correlates with pathological features and improves survival stratification of resectable lung adenocarcinoma.

OBJECTIVE: We investigated whether glycolytic heterogeneity correlated with histopathology, and further stratified the survival outcomes pertaining to resectable lung adenocarcinoma. METHODS: We retrospectively analyzed the 18F-fluorodeoxyglucose positron emission tomography-derived entropy and histopathology from 128 patients who had undergone curative surgery for lung adenocarcinoma. Disease-free survival (DFS) and overall survival (OS) were analyzed using univariate and multivariate Cox regression models. Independent predictors were used to construct survival prediction models. RESULTS: Entropy significantly correlated with histopathology, including tumor grades, lympho-vascular invasion, and visceral pleural invasion. Furthermore, entropy was an independent predictor of unfavorable DFS (p = 0.031) and OS (p = 0.004), while pathological nodal metastasis independently predicted DFS (p = 0.009). Our entropy-based models outperformed the traditional staging system (c-index = 0.694 versus 0.636, p = 0.010 for DFS; c-index = 0.704 versus 0.630, p = 0.233 for OS). The models provided further survival stratification in subgroups comprising different tumor grades (DFS: HR = 2.065, 1.315, and 1.408 for grade 1-3, p = 0.004, 0.001, and 0.039, respectively; OS: HR = 25.557, 6.484, and 2.570, for grade 1-3, p = 0.006, < 0.001, and = 0.224, respectively). CONCLUSION: The glycolytic heterogeneity portrayed by entropy is associated with aggressive histopathological characteristics. The proposed entropy-based models may provide more sophisticated survival stratification in addition to histopathology and may enable personalized treatment strategies for resectable lung cancer.

The combined tumor-nodal glycolytic entropy improves survival stratification in nonsmall cell lung cancer with locoregional disease.

OBJECTIVE: To investigate whether combining primary tumor and metastatic nodal glycolytic heterogeneity on 18 F-fluorodeoxyglucose PET ( 18 F-FDG PET) improves prognostic prediction in nonsmall cell lung cancer (NSCLC) with locoregional disease. METHODS: We retrospectively analyzed 18 F-FDG PET-derived features from 94 patients who had undergone curative treatments for regional nodal metastatic NSCLC. Overall survival (OS) and progression-free survival (PFS) were analyzed using univariate and multivariate Cox regression models. We used the independent prognosticators to construct models to predict survival. RESULTS: Combined entropy (entropy derived from the combination of the primary tumor and metastatic nodes) and age independently predicted OS (both P = 0.008) and PFS ( P = 0.007 and 0.050, respectively). At the same time, the Eastern Cooperative Oncology Group status was another independent risk factor for unfavorable OS ( P = 0.026). Our combined entropy-based models outperformed the traditional staging system (c-index = 0.725 vs. 0.540, P < 0.001 for OS; c-index = 0.638 vs. 0.511, P = 0.003 for PFS) and still showed prognostic value in subgroups according to sex, histopathology, and different initial curative treatment strategies. CONCLUSION: Combined primary tumor-nodal glycolytic heterogeneity independently predicted survival outcomes. In combination with clinical risk factors, our models provide better survival predictions and may enable tailored treatment strategies for NSCLC with locoregional disease.

Charge Carrier Dynamics of CsPbBr3/g-C3N4 Nanoheterostructures in Visible-Light-Driven CO2-to-CO Conversion.

The photon energy-dependent selectivity of photocatalytic CO2-to-CO conversion by CsPbBr3 nanocrystals (NCs) and CsPbBr3/g-C3N4 nanoheterostructures (NHSs) was demonstrated for the first time. The surficial capping ligands of CsPbBr3 NCs would adsorb CO2, resulting in the carboxyl intermediate to process the CO2-to-CO conversion via carbene pathways. The type-II energy band structure at the heterojunction of CsPbBr3/g-C3N4 NHSs would separate the charge carriers, promoting the efficiency in photocatalytic CO2-to-CO conversion. The electron consumption rate of CO2-to-CO conversion for CsPbBr3/g-C3N4 NHSs was found to intensively depend on the rate constant of interfacial hole transfer from CsPbBr3 to g-C3N4. An in situ transient absorption spectroscopy investigation revealed that the half-life time of photoexcited electrons in optimized CsPbBr3/g-C3N4 NHS was extended two times more than that in the CsPbBr3 NCs, resulting in the higher probability of charge carriers to carry out the CO2-to-CO conversion. The current work presents important and novel insights of semiconductor NHSs for solar energy-driven CO2 conversion.

A receptor-binding domain-based nanoparticle vaccine elicits durable neutralizing antibody responses against SARS-CoV-2 and variants of concern.

Numerous vaccines have been developed to address the current COVID-19 pandemic, but safety, cross-neutralizing efficacy, and long-term protectivity of currently approved vaccines are still important issues. In this study, we developed a subunit vaccine, ASD254, by using a nanoparticle vaccine platform to encapsulate the SARS-CoV-2 spike receptor-binding domain (RBD) protein. As compared with the aluminum-adjuvant RBD vaccine, ASD254 induced higher titers of RBD-specific antibodies and generated 10- to 30-fold more neutralizing antibodies. Mice vaccinated with ASD254 showed protective immune responses against SARS-CoV-2 challenge, with undetectable infectious viral loads and reduced typical lesions in lung. Besides, neutralizing antibodies in vaccinated mice lasted for at least one year and were effective against various SARS-CoV-2 variants of concern, including B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), B.1.617.2 (Delta), and B.1.1.529 (Omicron). Furthermore, particle size, polydispersity index, and zeta-potential of ASD254 remained stable after 8-month storage at 4°C. Thus, ASD254 is a promising nanoparticle vaccine with good immunogenicity and stability to be developed as an effective vaccine option in controlling upcoming waves of COVID-19.

Positively charged nanocomplex modulates dendritic cell differentiation to enhance Th1 immune response.

Most existing vaccines use activators that polarize the immune response to T-helper (Th) 2 response for antibody production. Our positively charged chitosan (Cs)-based nanocomplex (CNC) drives the Th1 response through unknown mechanisms. As receptors for the positively charged CNC are not determined, the physico-chemical properties are hypothesized to correlate with its immunomodulatory effects. To clarify the effects of surface charge and size on the immune response, smaller CNC and negatively charged CNC encapsulating ovalbumin are tested on dendritic cell (DC) 2.4 â€‹cells. The negatively charged CNC loses activity, but the smaller CNC does not. To further evaluate the material effects, we replace Cs by poly-amino acids. Compared with the negatively charged nanocomplex, the positively charged one preserves its activity. Using immature bone marrow-derived DCs (BMDC) enriched from BALB/c mice as a model to analyze DC differentiation, treatments with positively charged nanocomplexes evidently increase the proportions of Langerin+ dermal DC, CD11blo interstitial DC, and CD8a+ conventional DC. Additionally, vaccination with two doses containing positively charged nanocomplexes are safe and increase ovalbumin-specific IgG and recall T-cell responses in mice. Overall, a positive charge seems to contribute to the immunological effect of nanocomplexes on elevating the Th1 response by modulating DC differentiation.

Highly stable gold nanoparticle-antigen conjugates with self-adjuvanting property for induction of robust antigen-specific immune responses.

Poor long-term stability and formation of irreversible aggregates when subjected to a freeze-drying process greatly limits the clinical application of gold nanoparticles (GNPs) as a vaccine carrier. In this study, we synthesized a GNP-antigen conjugate with high colloidal stability by using a thiolated polyethylene glycol (PEG) linker to conjugate a model antigen (ovalbumin; OVA) onto the GNP surface (i.e. GNP-OVA) and demonstrated this conjugate had self-adjuvanting properties to augment antigen-specific immune responses. The synthesized GNP had an average hydrodynamic size of 13.8 ± 2.1 nm (n = 3); after conjugation of OVA, the diameter increased to 28.6 ± 7.3 nm (n = 3). The obtained GNP-OVA can maintain a stable dispersion state in aqueous solutions for at least 12 months and withstand stresses during lyophilization without creating irreversible aggregates. Compared with OVA alone or a mixture of PEG-functionalized GNP (GNP-PEG) and OVA (i.e. GNP-PEG/OVA), the chemical conjugation of OVA onto GNP-PEG substantially increased antigen uptake and upregulated major histocompatibility complex class II expression in macrophages. This indicated that the GNP can function as not only an adjuvant to promote the phagocytic activity of macrophages but also a carrier to deliver the conjugated antigen into the immune cells for the enhancement of its antigen presentation capability. Importantly, OVA-specific immunoglobulin G levels in the mice immunized with GNP-OVA were 4.1 and 2.9 times higher than those in the mice injected with OVA and GNP-PEG/OVA, respectively. These results demonstrated that the GNP-antigen conjugate exhibited remarkable stability either in liquid or freeze-dried form, which makes it attractive for further pharmaceutical applications. Moreover, covalently linking antigens onto the GNP surface was enabled to enhance the immunogenicity of antigens and boost immune responses, showing the potential of the GNP conjugation strategy for vaccine development.

Prognostic significance of lab data and performance comparison by validating survival prediction models for patients with spinal metastases after radiotherapy.

BACKGROUND AND PURPOSE: Well-performing survival prediction models (SPMs) help patients and healthcare professionals to choose treatment aligning with prognosis. This retrospective study aims to investigate the prognostic impacts of laboratory data and to compare the performances of Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy (METSSS) model, New England Spinal Metastasis Score (NESMS), and Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) for spinal metastases (SM). MATERIALS AND METHODS: From 2010 to 2018, patients who received radiotherapy (RT) for SM at a tertiary center were enrolled and the data were retrospectively collected. Multivariate logistic and Cox-proportional-hazard regression analyses were used to assess the association between laboratory values and survival. The area under receiver-operating characteristics curve (AUROC), calibration analysis, Brier score, and decision curve analysis were used to evaluate the performance of SPMs. RESULTS: A total of 2786 patients were included for analysis. The 90-day and 1-year survival rates after RT were 70.4% and 35.7%, respectively. Higher albumin, hemoglobin, or lymphocyte count were associated with better survival, while higher alkaline phosphatase, white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, or international normalized ratio were associated with poor prognosis. SORG-MLA has the best discrimination (AUROC 90-day, 0.78; 1-year 0.76), best calibrations, and the lowest Brier score (90-day 0.16; 1-year 0.18). The decision curve of SORG-MLA is above the other two competing models with threshold probabilities from 0.1 to 0.8. CONCLUSION: Laboratory data are of prognostic significance in survival prediction after RT for SM. Machine learning-based model SORG-MLA outperforms statistical regression-based model METSSS model and NESMS in survival predictions.

Oral Janus kinase inhibitors for treating hospitalized patients with COVID-19: An updated systematic review and meta-analysis of randomized controlled trials.

OBJECTIVES: This study investigated the clinical efficacy and safety of oral Janus kinase inhibitors (JAKis) in the treatment of hospitalized patients with COVID-19. METHODS: The PubMed, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases were searched for relevant articles written before January 29, 2022. Only randomized controlled trials (RCTs) that assessed the clinical efficacy and safety of oral JAKis in patients with COVID-19 were included. RESULTS: In the pooled analysis of the 7 RCTs, the all-cause 28-day mortality rate in the study group receiving JAKis was significantly lower than that in the control group (9.4% [183/1941] vs. 10.9% [184/1687], risk ratio [RR] = 0.69, 95% confidence interval [CI], 0.58-0.81, I2 = 0%). In addition, the risk of 14-day mortality was in the study group was lower than that in the control group (RR = 0.65, 95% CI, 0.46-0.92, I2 = 0%). Finally, the study group and the control group exhibited similar risks of any adverse events (RR = 0.96, 95% CI, 0.89-1.04, I2 = 0%). CONCLUSIONS: Oral JAKis can significantly reduce the risk of death among patients with COVID-19. In addition, JAKis are tolerable for hospitalized patients with COVID-19.

Clinical Efficacy and Safety of Reduced-Dose Prasugrel versus Clopidogrel in Patients Undergoing Percutaneous Coronary Intervention: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Objectives: This systematic review and meta-analysis of randomized controlled trials (RCTs) compared the clinical efficacy and safety of reduced-dose prasugrel (loading dose: 20 mg; daily maintenance dose: 3.75 mg) and clopidogrel in patients undergoing percutaneous coronary intervention (PCI). Methods: PubMed, Embase, and the Cochrane Library database were searched for relevant articles from inception to March 8, 2021. Only RCTs that compared the clinical efficacy and safety of reduced-dose prasugrel and clopidogrel treatment in adult patients undergoing PCI were included. The primary outcome was the risk of major cardiovascular events (MACEs). Results: Four RCTs involving 2464 patients were included. The overall risk of MACEs was 8.3% (102/1235) in the study group (reduced-dose prasugrel) and 9.8% (121/1229) in the control group (clopidogrel). No significant difference was observed in the risk of MACEs between the study and control groups (risk ratio: 0.84, 95% confidence interval: 0.65-1.08, I 2 = 0%). In addition, cardiovascular-related death, all-cause death, nonfatal myocardial infarction, nonfatal stroke, revascularization, and stent thrombosis did not differ significantly between the two groups. Apart from a higher risk of minor bleeding in the study group, reduced-dose prasugrel had a similar bleeding risk to clopidogrel. Conclusions: The clinical efficacy of reduced-dose prasugrel is comparable to that of clopidogrel; however, the risk of minor bleeding should be considered when prescribing this regimen for patients undergoing PCI.

Clinical efficacy of sofosbuvir/daclatasvir in patients with COVID-19: a systematic review and meta-analysis of randomized trials.

BACKGROUND: This study investigated the clinical efficacy sofosbuvir/daclatasvir (SOF-DCV) in patients with COVID-19. RESEARCH DESIGN AND METHODS: PubMed, Ovid MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases were searched for relevant articles written before January 6, 2022. Only randomized controlled trials (RCTs) comparing the clinical efficacy of SOF-DCV (study group) with alternative treatments (control group) in patients with COVID-19 were included. RESULTS: A total of 9 RCTs were included. The all-cause mortality rate in the study group was 10.7% (96/898), which was lower than that in the control group (12.3%, 108/871). However, this difference was not statistically significant (odds ratio [OR] = 0.83; 95% CI, 0.62-1.12; I2 = 49%). The overall clinical recovery rate was significantly higher in the study group than in the control group (OR = 2.34; 95% CI, 1.47-3.72; I2 = 20%). Furthermore, the average length of hospital stay was shorter in the study group than in the control group (mean deviation = -1.84; 95% CI, -3.42 to -0.26, I2 = 68%). CONCLUSIONS: Although SOF-DCV did not confer a survival benefit in patients with COVID-19, it may increase a patient's odds of clinical recovery, and shorten the length of their hospital stay.

Radiographic outcomes of the treatment of complex femoral shaft fractures (AO/OTA 32-C) with intramedullary nailing: a retrospective analysis of different techniques.

OBJECTIVES: To assess the results of open versus closed reduction in intramedullary nailing (IMN) for complex femoral fractures (Arbeitsgemeinschaft für Osteosynthesefragen Foundation/Orthopaedic Trauma Association [AO/OTA]: 32-C) and to determine the factors involved in bone healing. METHODS: This retrospective study involved 47 consecutive patients with complex femoral diaphyseal fractures who underwent reduction and fixation. RESULTS: All open-reduction and 12 closed-reduction patients (52.17%) had an anatomical-to-small gap. The closed-small group had the highest bone union rate (100%), followed by the open-reduction (79.17%) and closed-large groups (72.73%); intergroup differences were significant. The closed-small group had the shortest mean union time (7.31 months), followed by the open-reduction group (7.58 months). The closed-large group had a significantly longer union time (9.75 months) than those in the closed-small and open-reduction groups. Femoral radiographic union scores in the closed-small and open-reduction groups were similar at three timepoints; scores were higher than those in the closed-large group, with a significant difference 6 and 9 months post-operatively. CONCLUSION: IMN with closed reduction for complex femoral shaft fractures had better outcomes and fewer complications versus open reduction. For unsatisfactory closed reduction outcomes (i.e., residual gap >10 mm), minimally invasive techniques or open reduction with minimal stripping should be considered.

Systemic Inflammation Index and Tumor Glycolytic Heterogeneity Help Risk Stratify Patients with Advanced Epidermal Growth Factor Receptor-Mutated Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitor Therapy.

Tyrosine kinase inhibitors (TKIs) are the first-line treatment for patients with advanced epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma. Over half of patients failed to achieve prolonged survival benefits from TKI therapy. Awareness of a reliable prognostic tool may provide a valuable direction for tailoring individual treatments. We explored the prognostic power of the combination of systemic inflammation markers and tumor glycolytic heterogeneity to stratify patients in this clinical setting. One hundred and five patients with advanced EGFR-mutated lung adenocarcinoma treated with TKIs were retrospectively analyzed. Hematological variables as inflammation-induced biomarkers were collected, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and systemic inflammation index (SII). First-order entropy, as a marker of heterogeneity within the primary lung tumor, was obtained by analyzing 18F-fluorodeoxyglucose positron emission tomography images. In a univariate Cox regression analysis, sex, smoking status, NLR, LMR, PLR, SII, and entropy were associated with progression-free survival (PFS) and overall survival (OS). After adjusting for confounders in the multivariate analysis, smoking status, SII, and entropy, remained independent prognostic factors for PFS and OS. Integrating SII and entropy with smoking status represented a valuable prognostic scoring tool for improving the risk stratification of patients. The integrative model achieved a Harrell's C-index of 0.687 and 0.721 in predicting PFS and OS, respectively, outperforming the traditional TNM staging system (0.527 for PFS and 0.539 for OS, both p < 0.001). This risk-scoring model may be clinically helpful in tailoring treatment strategies for patients with advanced EGFR-mutated lung adenocarcinoma.