Comparison of the Short-Term Clinical Effectiveness of 5% Dextrose Water, Platelet-rich Plasma and Corticosteroid Injections for Carpal Tunnel Syndrome: A Systematic Review and Network Meta-analysis of Randomized Controlled Trials.

OBJECTIVE: To compare the short-term effectiveness of corticosteroids, 5% dextrose (D5W), and platelet-rich plasma (PRP) injections for treating carpal tunnel syndrome (CTS). DATA SOURCES: Four databases (MEDLINE [PubMed], Embase, the Cochrane Controlled Trials Register, and Web of Science [WOS]) were researched from inception to the first of April 2022. STUDY SELECTION: Two authors independently screened the literature to identify the RCTs meeting the included criteria, which involved comparing corticosteroid, 5% dextrose water (D5W), and PRP injection with each other or placebo-controlled for treating CTS. DATA EXTRACTION: The 2 reviewers independently conducted information extraction, the utcomes included were the changes in Symptom Severity Scale, Functional Status Scale, and Visual Analog Scale at short-term follow-up after drug injection treatment and any adverse events reported. DATA SYNTHESIS: Twelve randomized controlled trials with 749 patients (817 hands) were included. The results of this study suggested that PRP injection was the most likely to relieve symptoms, improve functions, and alleviate pain, with the surface under the cumulative ranking curve being 91.5%, 92.7%, and 80.8%, respectively, after D5W injection (74.4%, 72.2%, 72.1%), and corticosteroid injection (33.7%, 31.9%, 46.2%). The injection of 3 drugs was significantly better than that of a placebo. CONCLUSIONS: From the results of the network meta-analysis, PRP injection is the most recommended treatment among the injection of corticosteroid, D5W, and PRP.

A nomogram based on pretreatment levels of serum bilirubin and total bile acid levels predicts survival in colorectal cancer patients.

BACKGROUND: Serum bilirubin and total bile acid (TBA) levels have been reported to be strongly associated with the risk and prognosis of certain cancers. Here, we aimed to investigate the effects of pretreatment levels of serum bilirubin and bile acids on the prognosis of patients with colorectal cancer (CRC). METHODS: A retrospective cohort of 1474 patients with CRC who underwent surgical resection between January 2015 and December 2017 was included in the study. Survival analysis was used to evaluate the predictive value of pretreatment levels of bilirubin and bile acids. X-Tile software was used to identify optimal cut-off values for total bilirubin (TBIL), direct bilirubin (DBIL) and TBA in terms of overall survival (OS) and disease-free survival (DFS). RESULTS: DBIL, TBIL, and TBA were validated as significant prognostic factors by univariate Cox regression analysis for both 3-year OS and DFS. Multivariate Cox regression analyses confirmed that high DBIL, TBIL and TBA levels were independent prognostic factors for both OS (HR: 0.435, 95% CI: 0.299-0.637, P < 0.001; HR: 0.436, 95% CI: 0.329-0.578, P < 0.001; HR: 0.206, 95% CI: 0.124-0.341, P < 0.001, respectively) and DFS (HR: 0.583, 95% CI: 0.391-0.871, P = 0.008; HR:0.437,95% CI: 0.292-0.655, P <0.001; HR: 0.634, 95% CI: 0.465-0.865, P = 0.004, respectively). In addition, nomograms for OS and DFS were established according to all significant factors, and the c-indexes were 0.819 (95% CI: 0.806-0.832) and 0.835 (95% CI: 0.822-0.849), respectively. CONCLUSIONS: TBIL, DBIL and TBA levels are independent prognostic factors in colorectal cancer patients. The nomograms based on OS and DFS can be used as a practical model for evaluating the prognosis of CRC patients.

The value of haematological parameters and serum tumour markers for predicting KRAS mutations in 784 Chinese colorectal cancer patients: a retrospective analysis.

BACKGROUND: Identifying the mutation status of KRAS is important for optimizing treatment in patients with colorectal cancer (CRC). The aim of this study was to investigate the predictive value of haematological parameters and serum tumour markers (STMs) for KRAS gene mutations. METHODS: The clinical data of patients with colorectal cancer from January 2014 to December 2018 were retrospectively collected, and the associations between KRAS mutations and other indicators were analysed. Receiver operating characteristic (ROC) curve analysis was performed to quantify the predictive value of these factors. Univariate and multivariate logistic regression models were applied to identify predictors of KRAS mutations by calculating the odds ratios (ORs) and their corresponding 95% confidence intervals (CIs). RESULTS: KRAS mutations were identified in 276 patients (35.2%). ROC analysis revealed that age, CA12-5, AFP, SCC, CA72-4, CA15-3, FERR, CYFRA21-1, MCHC, and tumor location could not predict KRAS mutations (P = 0.154, 0.177, 0.277, 0.350, 0.864, 0.941, 0.066, 0.279, 0.293, and 0.053 respectively), although CEA, CA19-9, NSE and haematological parameter values showed significant predictive value (P = 0.001, < 0.001, 0.043 and P = 0.003, < 0.001, 0.001, 0.031, 0.030, 0.016, 0.015, 0.019, and 0.006, respectively) but without large areas under the curve. Multivariate logistic regression analysis showed that CA19-9 was significantly associated with KRAS mutations and was the only independent predictor of KRAS positivity (P = 0.016). CONCLUSIONS: Haematological parameters and STMs were related to KRAS mutation status, and CA19-9 was an independent predictive factor for KRAS gene mutations. The combination of these clinical factors can improve the ability to identify KRAS mutations in CRC patients.

Perineural invasion is associated with poor prognosis of colorectal cancer: a retrospective cohort study.

PURPOSE: Perineural invasion (PNI) is associated with poor prognosis in a variety of cancers. Our aim was to determine the clinicopathological factors associated with PNI in colorectal cancer (CRC) and its impact on patient survival. MATERIAL AND METHODS: The clinical data of 1412 patients diagnosed with CRC from July 2013 to July 2016 were retrospectively collected. PNI was determined based on hematoxylin-eosin staining. The relationships of PNI with various clinicopathological factors and prognosis were analyzed. RESULTS: The incidence of PNI in the entire cohort was 21.5%. PNI was significantly more common in patients with lower tumor differentiation, higher tumor stage, vascular invasion, TNM stage, tumor diameter, MMR/KRAS/NRAS/BRAF mutation, and more positive lymph nodes. Logistic regression analysis showed that T stage, vascular invasion, tumor diameter, and MMR were the main influencing factors of PNI. Cox regression analysis showed that poor tumor differentiation, N stage, TNM stage, PNI, and BRAF status were independent prognostic factors for OS. The OS, CSS, and PFS rate of the PNI (-) group was higher than that of the PNI (+) group, and the difference was statistically significant (P < 0.001). CONCLUSION: PNI in patients with colorectal cancer is significantly associated with T stage, TNM stage, vessel invasion, tumor diameter, MMR status, and BRAF mutation. PNI status is an independent prognostic factor for CRC. Assessing the postoperative PNI status may help predict prognosis and determine further treatment options for these patients.

Colon metal stents as a bridge to surgery had no significant effects on the perineural invasion: a retrospective study.

PURPOSE: The long-term oncological effects of self-expandable metallic stent (SEMS) as a "bridge to surgery" are contradictory, and perineural invasion was supposed to be enhanced by the stenting. In this retrospective study, we compared the perineural invasion and the oncological outcomes between the stent as a bridge to surgery (SBTS)- and emergency surgery (ES)-treated patients to evaluate the results of stenting on the perineural invasion. METHODS: The clinical data of patients with acute intestinal obstruction caused by colorectal cancer from January 2013 to January 2017 were retrospectively collected. Forty-three patients underwent semi-elective curative resection after endoscopic SEMS insertion, and sixty-three underwent ES. The adverse events and long-term follow-up outcomes were assessed. The clinicopathological characteristics, perineural invasion rates, and survival rates were compared between the two patient groups. RESULTS: Stent insertion resulted in significantly lower stoma rate (32.6% vs 46%; P = 0.03), post-operative overall complication rate (11.6% vs 28.6%, P = 0.038), and total hospital stay (17.07 ± 5.544 days vs 20.48 ± 7.372 days, P = 0.042). Compared with the ES group, there was no significant increase in the incidence of peripheral invasion in the SBTS group (39.5% vs 47.6%, P = 0.411). No significant difference was noted in the survival rate and long-term prognosis between the SEMS and ES groups (P = 0.964). The technical success rate was 95.6%, and the clinical success rate was 97.7%. CONCLUSIONS: Preoperative colon stenting was an effective transitional method for colorectal cancer patients with complete obstruction. Short-term stent implantation had no significant effect on perineural invasion in patients with CRC.

Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.

Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predicting prognosis and explored the potential of ML models in clinical decision-making. From 2000 to 2018, 1243 patients with osteosarcoma were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Three ML methods were chosen for model development (DeepSurv, neural multi-task logistic regression [NMTLR]) and random survival forest [RSF]) and compared them with the traditional CoxPH model and TNM staging systems. 871 samples were used for model training, and the rest were used for model validation. The models' overall performance and predictive accuracy for 3- and 5-year survival were assessed by several metrics, including the concordance index (C-index), the Integrated Brier Score (IBS), receiver operating characteristic curves (ROC), area under the ROC curves (AUC), calibration curves, and decision curve analysis. The efficacy of personalized recommendations by ML models was evaluated by the survival curves. The performance was highest in the DeepSurv model (C-index, 0.77; IBS, 0.14; 3-year AUC, 0.80; 5-year AUC, 0.78) compared with other methods (C-index, 0.73-0.74; IBS, 0.16-0.17; 3-year AUC, 0.73-0.78; 5-year AUC, 0.72-0.78). There are also significant differences in survival outcomes between patients who align with the treatment option recommended by the DeepSurv model and those who do not (hazard ratio, 1.88; P < .05). The DeepSurv model is available in an approachable web app format at https://survivalofosteosarcoma.streamlit.app/. We developed ML models capable of accurately predicting the survival of osteosarcoma, which can provide useful information for decision-making regarding the appropriate treatment.

Accelerated, injectable, self-healing, scarless wound dressings using rGO reinforced dextran/chitosan hydrogels incorporated with PDA-loaded asiaticoside.

The process of wound healing is intricate and complex, necessitating the intricate coordination of various cell types and bioactive molecules. Despite significant advances, challenges persist in achieving accelerated healing and minimizing scar formation. Herein, a multifunctional hydrogel engineered via dynamic Schiff base crosslinking between oxidized dextran and quaternized chitosan, reinforced with reduced graphene oxide (rGO) is reported. The resulting OQG hydrogels demonstrated injectability to aid in conforming to irregular wound geometries, rapid self-healing to maintain structural integrity and adhesion for intimate integration with wound beds. Moreover, the developed hydrogels possessed antioxidant and antibacterial activities, mitigating inflammation and preventing infection. The incorporation of conductive rGO further facilitated the transmission of endogenous electrical signals, stimulating cell migration and tissue regeneration. In addition, the polydopamine-encapsulated asiaticoside (AC@PDA) nanoparticles were encapsulated in OQG hydrogels to reduce scar formation during in vivo evaluations. In vitro results confirmed the histocompatibility of the hydrogels to promote cell migration. The recovery of the full-thickness rat wounds revealed that these designed OQG hydrogels with the incorporation of AC@PDA nanoparticles could accelerate wound healing, reduce inflammation, facilitate angiogenesis, and minimize scarring when implemented. This multifunctional hydrogel system offers a promising strategy for enhanced wound management and scarless tissue regeneration, addressing the multifaceted challenges in wound care.

Machine learning based peri-surgical risk calculator for abdominal related emergency general surgery: a multicenter retrospective study.

BACKGROUND: Currently, there is a lack of ideal risk prediction tools in the field of emergency general surgery (EGS). The American Association for the Surgery of Trauma recommends developing risk assessment tools specifically for EGS-related diseases. In this study, we sought to utilize machine learning (ML) algorithms to explore and develop a web-based calculator for predicting five perioperative risk events of eight common operations in EGS. METHOD: This study focused on patients with EGS and utilized electronic medical record systems to obtain data retrospectively from five centers in China. Five ML algorithms, including Random Forest (RF), Support Vector Machine, Naive Bayes, XGBoost, and Logistic Regression, were employed to construct predictive models for postoperative mortality, pneumonia, surgical site infection, thrombosis, and mechanical ventilation >48 h. The optimal models for each outcome event were determined based on metrics, including the value of the Area Under the Curve, F1 score, and sensitivity. A comparative analysis was conducted between the optimal models and Emergency Surgery Score (ESS), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and American Society of Anesthesiologists (ASA) classification. A web-based calculator was developed to determine corresponding risk probabilities. RESULT: Based on 10 993 patients with EGS, we determined the optimal RF model. The RF model also exhibited strong predictive performance compared with the ESS, APACHE II score, and ASA classification. Using this optimal model, the authors developed an online calculator with a questionnaire-guided interactive interface, catering to both the preoperative and postoperative application scenarios. CONCLUSIONS: The authors successfully developed an ML-based calculator for predicting the risk of postoperative adverse events in patients with EGS. This calculator accurately predicted the occurrence risk of five outcome events, providing quantified risk probabilities for clinical diagnosis and treatment.

Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database.

BACKGROUND: The study aims to evaluate the performance of three advanced machine learning algorithms and a traditional Cox proportional hazard (CoxPH) model in predicting the overall survival (OS) of patients with pancreatic neuroendocrine neoplasms (PNENs). METHOD: The clinicopathological dataset obtained from the Surveillance, Epidemiology, and End Results database was randomly assigned to the training set and testing set at a ratio of 7:3. The concordance index (C-index) and integrated Brier score (IBS) were used to compare the predictive performance of the models. The accuracy of the model in predicting the 5-year and 10-year survival rates was compared using the receiver operating characteristic curve, decision curve analysis (DCA) and calibration curve. RESULTS: This study included 3239 patients with PNENs in total. The DeepSurv model had the highest C-index of 0.7882 in the testing set and training set and the lowest IBS of 0.1278 in the testing set compared with the CoxPH, neural multitask logistic and random survival forest models (C-index = 0.7501, 0.7616, and 0.7612, respectively; IBS = 0.1397, 0.1418, and 0.1432, respectively). Moreover, the DeepSurv model had the highest accuracy in predicting 5- and 10-year OS rates (area under the curve: 0.87 and 0.90). DCA showed that the DeepSurv model had high potential for clinical decisions in 5- and 10-year OS models. Finally, we developed an online application based on the DeepSurv model for clinical use (https://whuh-ml-neuroendocrinetumor-app-predict-oyw5km.streamlit.app/). CONCLUSIONS: All four models analyzed above can predict the prognosis of PNENs well, among which the DeepSurv model has the best prediction performance.

Machine learning methods for accurately predicting survival and guiding treatment in stage I and II hepatocellular carcinoma.

Accurately predicting survival in patients with early hepatocellular carcinoma (HCC) is essential for making informed decisions about treatment and prognosis. Herein, we have developed a machine learning (ML) model that can predict patient survival and guide treatment decisions. We obtained patient demographic information, tumor characteristics, and treatment details from the SEER database. To analyze the data, we employed a Cox proportional hazards (CoxPH) model as well as 3 ML algorithms: neural network multitask logistic regression (N-MLTR), DeepSurv, and random survival forest (RSF). Our evaluation relied on the concordance index (C-index) and Integrated Brier Score (IBS). Additionally, we provided personalized treatment recommendations regarding surgery and chemotherapy choices and validated models' efficacy. A total of 1136 patients with early-stage (I, II) hepatocellular carcinoma (HCC) who underwent liver resection or transplantation were randomly divided into training and validation cohorts at a ratio of 3:7. Feature selection was conducted using Cox regression analyses. The ML models (NMLTR: C-index = 0.6793; DeepSurv: C-index = 0.7028; RSF: C-index = 0.6890) showed better discrimination in predicting survival than the standard CoxPH model (C-index = 0.6696). Patients who received recommended treatments had higher survival rates than those who received unrecommended treatments. ML-based surgery treatment recommendations yielded higher hazard ratios (HRs): NMTLR HR = 0.36 (95% CI: 0.25-0.51, P < .001), DeepSurv HR = 0.34 (95% CI: 0.24-0.49, P < .001), and RSF HR = 0.37 (95% CI: 0.26-0.52, P = <.001). Chemotherapy treatment recommendations were associated with significantly improved survival for DeepSurv (HR: 0.57; 95% CI: 0.4-0.82, P = .002) and RSF (HR: 0.66; 95% CI: 0.46-0.94, P = .020). The ML survival model has the potential to benefit prognostic evaluation and treatment of HCC. This novel analytical approach could provide reliable information on individual survival and treatment recommendations.

Antibacterial and osteoconductive polycaprolactone/polylactic acid/nano-hydroxyapatite/Cu@ZIF-8 GBR membrane with asymmetric porous structure.

Repair of periodontal and maxillofacial bone defects is a major challenge in clinical. Guided bone regeneration (GBR) is considered one of the most effective methods. However, the efficacy of currently available GBR membranes for repair is frequently limited by their poor osteogenic potential and lack of antibacterial activity. The first step in this investigation was to construct a zinc-based zeolite-imidazolate framework loaded with copper ions (Cu@ZIF-8). Following that, a novel polycaprolactone/polylactic acid/nano-hydroxyapatite/Cu@ZIF-8 (PCL/PLA/n-HA/Cu@ZIF-8) GBR membrane was developed using a simple porogen with nonsolvent-induced phase separation (NIPS) approach. The produced membrane with asymmetric porous structure (one smooth side and one rough side) possesses hydrophilicity corresponding to the roughness of its two sides. The superior mechanical property, stability of degradation, and ion release capability of the membrane all contribute to the clinical feasibility. Additionally, in vitro biological experiments demonstrated that the PCL/PLA/n-HA/Cu@ZIF-8 membrane had favorable osteogenic and antibacterial properties, which suggests the high potential for application in the GBR procedure.

A prognostic NAD+ metabolism-related gene signature for predicting response to immune checkpoint inhibitor in glioma.

Background: Nicotinamide adenine dinucleotide (NAD+) metabolism is involved in a series of cancer pathogenesis processes, and is considered a promising therapeutic target for cancer treatment. However, a comprehensive analysis of NAD+ metabolism events on immune regulation and cancer survival has not yet been conducted. Here, we constructed a prognostic NAD+ metabolism-related gene signature (NMRGS) associated with immune checkpoint inhibitor (ICI) efficacy in glioma. Methods: 40 NAD+ metabolism-related genes (NMRGs) were obtained from the Reactome database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Glioma cases with transcriptome data and clinical information were obtained from Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). NMRGS was constructed based on the calculated risk score using univariate analysis, Kaplan-Meier analysis, multivariate Cox regression, and nomogram. This NMRGS was verified in training (CGGA693) and validation (TCGA and CGGA325) cohorts. The immune characteristics, mutation profile, and response to ICI therapy were subsequently analyzed for different NMRGS subgroups. Results: Six NAD+ metabolism-related genes, including CD38, nicotinamide adenine dinucleotide kinase (NADK), nicotinate phosphoribosyltransferase (NAPRT), nicotinamide/nicotinic acid mononucleotide adenylyltransferase 3 (NMNAT3), poly(ADP-Ribose) polymerase family member 6 (PARP6), and poly(ADP-Ribose) polymerase family member 9 (PARP9), were ultimately used to construct a comprehensive risk model for glioma patients. Patients in the NMRGS-high group showed a poorer survival outcome than those in the NMRGS-low group. The area under curve (AUC) indicated that NMRGS has good potential in glioma prognostic prediction. A nomogram with improved accuracy was established based on independent prognostic factors (NMRGS score, 1p19q codeletion status, and WHO grade). Furthermore, patients in the NMRGS-high group showed a more immunosuppressive microenvironment, higher tumor mutation burden (TMB), higher human leucocyte antigen (HLA) expression and a more therapeutic response to ICI therapy. Conclusions: This study constructed a prognostic NAD+ metabolism-related signature associated with the immune landscape in glioma, which can be used for guiding individualized ICI therapy.

CuS@TA-Fe Nanoparticle-Doped Multifunctional Hydrogel with Peroxide-Like Properties and Photothermal Properties for Synergistic Antimicrobial Repair of Infected Wounds.

Bacterial infection is a critical factor in wound healing. Due to the abuse of antibiotics, some pathogenic bacteria have developed resistance. Thus, there is an urgent need to develop a non-antibiotic-dependent multifunctional wound dressing for the treatment of bacteria-infected wounds. In this work, a multifunctional AOCuT hydrogel embedded with CuS@TA-Fe nanoparticles (NPs) through Schiff base reaction between gelatin quaternary ammonium salt - gallic acid (O-Gel-Ga) and sodium dialdehyde alginate (ADA) along with electrostatic interactions with CuS@TA-Fe NPs is prepared. These composite hydrogels possess favorable injectability, rapid shape adaptation, electrical conductivity, photothermal antimicrobial activity, and biocompatibility. Additionally, the doped NPs not only impart fast self-healing properties and excellent adhesion performance to the hydrogels, but also provide excellent peroxide-like properties, enabling them to scavenge free radicals and exhibit anti-inflammatory and antioxidant capabilities via photothermal (PTT) and photodynamic (PDT) effects. In an S. aureus infected wound model, the composite hydrogel effectively reduces the expression level of wound inflammatory factors and accelerates collagen deposition, epithelial tissue, and vascular regeneration, thereby promoting wound healing. This safe and synergistic therapeutic system holds great promise for clinical applications in the treatment of infectious wounds.

Development and Interpretation of a Clinicopathological-Based Model for the Identification of Microsatellite Instability in Colorectal Cancer.

Chemotherapy is not recommended for patients with deficient mismatch repair (dMMR) in colorectal cancer (CRC); therefore, assessing the status of MMR is crucial for the selection of subsequent treatment. This study is aimed at building predictive models to accurately and rapidly identify dMMR. A retrospective analysis was performed at Wuhan Union Hospital between May 2017 and December 2019 based on the clinicopathological data of patients with CRC. The variables were subjected to collinearity, least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) feature screening analyses. Four sets of machine learning models (extreme gradient boosting (XGBoost), support vector machine (SVM), naive Bayes (NB), and RF) and a conventional logistic regression (LR) model were built for model training and testing. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of the developed models. In total, 2279 patients were included in the study and were randomly divided into either the training or test group. Twelve clinicopathological features were incorporated into the development of the predictive models. The area under curve (AUC) values of the five predictive models were 0.8055 for XGBoost, 0.8174 for SVM, 0.7424 for NB, 8584 for RF, and 0.7835 for LR (Delong test, P value < 0.05). The results showed that the RF model exhibited the best recognition ability and outperformed the conventional LR method in identifying dMMR and proficient MMR (pMMR). Our predictive models based on routine clinicopathological data can significantly improve the diagnostic performance of dMMR and pMMR. The four machine learning models outperformed the conventional LR model.

Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis.

Background: Accurate prediction of prognosis is critical for therapeutic decisions in chondrosarcoma patients. Several prognostic models have been created utilizing multivariate Cox regression or binary classification-based machine learning approaches to predict the 3- and 5-year survival of patients with chondrosarcoma, but few studies have investigated the results of combining deep learning with time-to-event prediction. Compared with simplifying the prediction as a binary classification problem, modeling the probability of an event as a function of time by combining it with deep learning can provide better accuracy and flexibility. Materials and methods: Patients with the diagnosis of chondrosarcoma between 2000 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) registry. Three algorithms-two based on neural networks (DeepSurv, neural multi-task logistic regression [NMTLR]) and one on ensemble learning (random survival forest [RSF])-were selected for training. Meanwhile, a multivariate Cox proportional hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into training and testing datasets at a ratio of 7:3. Hyperparameter tuning was conducted through a 1000-repeated random search with 5-fold cross-validation on the training dataset. The model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-, 3-, 5- and 10-year survival was evaluated using receiver operating characteristic curves (ROC), calibration curves, and the area under the ROC curves (AUC). Results: A total of 3145 patients were finally enrolled in our study. The mean age at diagnosis was 52 ± 18 years, 1662 of the 3145 patients were male (53%), and mean survival time was 83 ± 67 months. Two deep learning models outperformed the RSF and classical CoxPH models, with the C-index on test datasets achieving values of 0.832 (DeepSurv) and 0.821 (NMTLR). The DeepSurv model produced better accuracy and calibrated survival estimates in predicting 1-, 3- 5- and 10-year survival (AUC:0.895-0.937). We deployed the DeepSurv model as a web application for use in clinical practice; it can be accessed through https://share.streamlit.io/whuh-ml/chondrosarcoma/Predict/app.py. Conclusions: Time-to-event prediction models based on deep learning algorithms are successful in predicting chondrosarcoma prognosis, with DeepSurv producing the best discriminative performance and calibration.

Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning.

Background: Most studies of molecular subtype prediction in breast cancer were mainly based on two-dimensional MRI images, the predictive value of three-dimensional volumetric features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting breast cancer molecular subtypes has not been thoroughly investigated. This study aimed to look into the role of features derived from DCE-MRI and how they could be combined with clinical data to predict invasive ductal breast cancer molecular subtypes. Methods: From January 2019 to December 2021, 190 Chinese women with invasive ductal breast cancer were studied (32 triple-negative, 59 HER2-enriched, and 99 luminal lesions) in this institutional review board-approved retrospective cohort study. The image processing software extracted 1130 quantitative radiomic features from the segmented lesion area, including shape-based, first-order statistical, texture, and wavelet features. Three binary classifications of the subtypes were performed: triple-negative vs. non-triple-negative, HER2-overexpressed vs. non-HER2-overexpressed, and luminal (A + B) vs. non-luminal. For the classification, five machine learning methods (random forest, logistic regression, support vector machine, naïve Bayes, and eXtreme Gradient Boosting) were employed. The classifiers were chosen using the least absolute shrinkage and selection operator method. The area evaluated classification performance under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean. Results: EXtreme Gradient Boosting model showed the best performance in luminal and non-luminal groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8282, 0.7524, 0.6542, 0.6964, 0.6086, 0.3458, 0.8524 and 0.7016, respectively. Meanwhile, the random forest model showed the best performance in HER2-overexpressed and non-HER2-overexpressed groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8054, 0.2941, 0.9744, 0.7679, 0.4348, 0.0256, 0.8333 and 0.5353, respectively. Furthermore, eXtreme Gradient Boosting model showed the best performance in the triple-negative and non-triple-negative groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.9031, 0.9362, 0.4444, 0.8571, 0.9167, 0.5556, 0.8980 and 0.6450. Conclusion: Clinical data and three-dimension imaging features from DCE-MRI were identified as potential biomarkers for distinguishing between three molecular subtypes of invasive ductal carcinomas breast cancer. In the future, more extensive studies will be required to evaluate the findings.

The Prognostic Significance of RIMKLB and Related Immune Infiltrates in Colorectal Cancers.

RimK-like family member B (RIMKLB) is an enzyme that post-translationally modulates ribosomal protein S6, which can affect the development of immune cells. Some studies have suggested its role in tumor progression. However, the relationships among RIMKLB expression, survival outcomes, and tumor-infiltrating immune cells (TIICs) in colorectal cancer (CRC) are still unknown. Therefore, we analyzed RIMKLB expression levels in CRC and normal tissues and investigated the correlations between RIMKLB and TIICs as well as the impact of RIMKLB expression on clinical prognosis in CRC using multiple databases, including the Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), PrognoScan, and UALCAN databases. Enrichment analysis was conducted with the cluster Profiler package in R software to explore the RIMKLB-related biological processes involved in CRC. The RIMKLB expression was significantly decreased in CRC compared to normal tissues, and correlated with histology, stage, lymphatic metastasis, and tumor status (p < 0.05). Patients with CRC with high expression of RIMKLB showed poorer overall survival (OS) (HR = 2.5,p = 0.00,042), and inferior disease-free survival (DFS) (HR = 1.9,p = 0.19) than those with low expression of RIMKLB. TIMER analysis indicated that RIMKLB transcription was closely related with several TIICs, including CD4+ and CD8+ T cells, B cells, tumor-associated macrophages (TAMs), monocytes, neutrophils, natural killer cells, dendritic cells, and subsets of T cells. Moreover, the expression of RIMKLB showed significant positive correlations with infiltrating levels of PD1 (r = 0.223, p = 1.31e-06; r = 0.249, p = 1.25e-03), PDL1 (r = 0.223, p = 6.03e-07; r = 0.41, p = 5.45e-08), and CTLA4 (r = 0.325, p = 9.68e-13; r = 0.41, p = 5.45e-08) in colon and rectum cancer, respectively. Enrichment analysis showed that the RIMKLB expression was positively related to extracellular matrix and immune inflammation-related pathways. In conclusion, RIMKLB expression is associated with survival outcomes and TIICs levels in patients with CRC, and therefore, might be a potential novel prognostic biomarker that reflects the immune infiltration status.

Clinical effects of the combination of Traditional Chinese and Western Medicines on coronavirus disease 2019: a systematic review and Meta-analysi.

OBJECTIVE: To explore the clinical efficacy of the combination of Traditional Chinese and Western Medicines for the treatment of coronavirus disease 2019 (COVID-19). METHODS: Studies were identified in six popular medical databases. RESULTS: Thirteen studies were included. The results showed that combined treatment with Traditional Chinese and Western Medicines can reduce the probability of progression from mild to severe disease [RR = 0.34, 95% confidence interval (CI) (0.18, 0.65)] (P = 0.001) and improve the clinical cure rate [RR = 0.17, 95% CI (0.05, 0.28)] (P = 0.004). The use of an integrated treatment strategy shortened the time to the remission of fever [WMD = －1.27, 95% CI (－1.67, －0.92)](P < 0.001) and improved the incidences of the disappearance of fever and fatigue [RR = 1.25, 95% CI (1.06, 1.47) (P = 0.007); RR = 1.49, 95% CI (1.13, 1.97) (P = 0.004)]. CONCLUSION: A combined treatment strategy is effective for COVID-19.

An Oxidative Stress Index-Based Score for Prognostic Prediction in Colorectal Cancer Patients Undergoing Surgery.

Oxidative stress plays an important role in the development of colorectal cancer (CRC). This study is aimed at developing and validating a novel scoring system, based on oxidative stress indexes, for prognostic prediction in CRC patients. A retrospective analysis of 1422 CRC patients who underwent surgical resection between January 2013 and December 2017 was performed. These patients were randomly assigned to the training set (n = 1022) or the validation set (n = 400). Cox regression model was used to analyze the laboratory parameters. The CRC-Integrated Oxidative Stress Score (CIOSS) was developed from albumin (ALB), direct bilirubin (DBIL), and blood urea nitrogen (BUN), which were significantly associated with survival in CRC patients. Furthermore, a survival nomogram was generated by combining the CIOSS with other beneficial clinical characteristics. The CIOSS generated was as follows: 0.074 × albumin (g/L), -0.094 × bilirubin (µmol/L), and -0.099 × blood urea nitrogen (mmol/L), based on the multivariable Cox regression analysis. Using 50% (0.1025) and 85% (0.481) of CIOSS as cutoff values, three prognostically distinct groups were formed. Patients with high CIOSS experienced worse overall survival (OS) (hazard ratio [HR] = 4.33; 95% confidence interval [CI], 2.80-6.68; P < 0.001) and worse disease-free survival (DFS) (HR = 3.02; 95% CI, 1.96-4.64; P < 0.001) compared to those with low CIOSS. This predictive nomogram had good calibration and discrimination. ROC analyses showed that the CIOSS possessed excellent performance (AUC = 0.818) in predicting DFS. The AUC of the OS nomogram based on CIOSS, TNM stage, T stage, and chemotherapy was 0.812, while that of the DFS nomogram based on CIOSS, T stage, and TNM stage was 0.855. Decision curve analysis showed that these two prediction models were clinically useful. CIOSS is a CRC-specific prognostic index based on the combination of available oxidative stress indexes. High CIOSS is a powerful indicator of poor prognosis. The CIOSS also showed better predictive performance compared to TNM stage in CRC patients.

Development and Validation of a Predictive Scoring System for Colorectal Cancer Patients With Liver Metastasis: A Population-Based Study.

Liver metastasis in colorectal cancer (CRC) is common and has an unfavorable prognosis. This study aimed to establish a functional nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer liver metastasis (CRCLM). A total of 9,736 patients with CRCLM from 2010 to 2016 were randomly assigned to training, internal validation, and external validation cohorts. Univariate and multivariate Cox analyses were performed to identify independent clinicopathologic predictive factors, and a nomogram was constructed to predict CSS and OS. Multivariate analysis demonstrated age, tumor location, differentiation, gender, TNM stage, chemotherapy, number of sampled lymph nodes, number of positive lymph nodes, tumor size, and metastatic surgery as independent predictors for CRCLM. A nomogram incorporating the 10 predictors was constructed. The nomogram showed favorable sensitivity at predicting 1-, 3-, and 5-year OS, with area under the receiver operating characteristic curve (AUROC) values of 0.816, 0.782, and 0.787 in the training cohort; 0.827, 0.769, and 0.774 in the internal validation cohort; and 0.819, 0.745, and 0.767 in the external validation cohort, respectively. For CSS, the values were 0.825, 0.771, and 0.772 in the training cohort; 0.828, 0.753, and 0.758 in the internal validation cohort; and 0.828, 0.737, and 0.772 in the external validation cohort, respectively. Calibration curves and ROC curves revealed that using our models to predict the OS and CSS would add more benefit than other single methods. In summary, the novel nomogram based on significant clinicopathological characteristics can be conveniently used to facilitate the postoperative individualized prediction of OS and CSS in CRCLM patients.