Robotic Versus Thoracoscopic Sub-lobar Resection for Octogenarians with Clinical Stage IA Non-small Cell Lung Cancer: A Propensity Score-Matched Real-World Study.

BACKGROUND: Minimally invasive sub-lobectomy is sufficient in treating small early-stage non-small cell lung cancer (NSCLC). However, comparison of the feasibility and oncologic efficacy between robot-assisted thoracoscopic surgery (RATS) and video-assisted thoracoscopic surgery (VATS) in performing sub-lobectomy for early-stage NSCLC patients age 80 years or older is scarce. METHODS: Octogenarians with clinical stage IA NSCLC (tumor size, ≤ 2 cm) undergoing minimally invasive wedge resection or segmentectomy at Shanghai Chest Hospital from 2011 to 2020 were retrospectively reviewed from a prospectively maintained database. Propensity score-matching (PSM) with a RATS versus VATS ratio of 1:4 was performed. Perioperative and long-term outcomes were analyzed. RESULTS: The study identified 594 patients (48 RATS and 546 VATS patients), and PSM resulted in 45 cases in the RATS group and 180 cases in the VATS group. The RATS patients experienced less intraoperative bleeding (60 mL [interquartile range (IQR), 50-100 mL] vs. 80 mL [IQR, 50-100 mL]; P = 0.027) and a shorter postoperative hospital stay (4 days [IQR, 3-5 days] vs. 5 days [IQR, 4-6 days]; P = 0.041) than the VATS patients. The two surgical approaches were comparable concerning other perioperative outcomes and postoperative complications (20.00% vs. 26.11%; P = 0.396). Additionally, during a median follow-up period of 66 months, RATS and VATS achieved comparable 5-year overall survival (90.48% vs. 87.93%; P = 0.891), recurrence-free survival (83.37% vs. 83.18%; P = 0.782), and cumulative incidence of death. Further subgroup comparison also demonstrated comparable long-term outcomes between the two approaches. Finally, multivariate Cox analysis indicated that the surgical approach was not independently correlated with long-term outcomes. CONCLUSIONS: The RATS approach shortened the postoperative hospital stay, reduced intraoperative bleeding by a statistically notable but clinically insignificant amount, and achieved long-term outcomes comparable with VATS in performing sub-lobectomy for octogenarians with early-stage small NSCLC.

Association between polycyclic aromatic hydrocarbons and periodontitis: Results from a large population-based study.

AIM: To explore the association between polycyclic aromatic hydrocarbons (PAHs) (measured using urinary metabolites) and periodontitis using data from the National Health and Nutrition Examination Survey 2009-2014. MATERIALS AND METHODS: Weighted binary logistic regression, Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression were used to evaluate independent and joint associations between the six urinary monohydroxylated metabolites of PAHs (OH-PAHs) and periodontitis. RESULTS: In all, 3413 participants were included in this study. All six urinary OH-PAHs were present at higher levels in the periodontitis group compared with the non-periodontitis group (p < .001). Fully adjusted multivariable logistic regressions showed positive associations between the six urinary OH-PAHs and periodontitis (p < .05). Higher concentrations of OH-PAHs were also positively associated with attachment loss, periodontal pocket depth (PPD) and the number of tooth loss. BKMR and WQS regression yielded similar positive associations between OH-PAH mixtures and periodontitis. CONCLUSIONS: PAHs and their mixture are positively associated with periodontitis, which may provide novel insights into periodontitis prevention from an environmental exposure perspective.

Non-linear modifications enhance prediction of pathological response to pre-operative PD-1 blockade in lung cancer: A longitudinal hybrid radiological model.

Major pathologic remission (MPR, residual tumor <10%) is a promising clinical endpoint for prognosis analysis in patients with lung cancer receiving pre-operative PD-1 blockade therapy. Most of the current biomarkers for predicting MPR such as PD-L1 and tumor mutation burden (TMB) need to be obtained invasively. They cannot overcome the spatiotemporal heterogeneity or provide dynamic monitoring solutions. Radiomics and artificial intelligence (AI) models provide a practical tool enabling non-invasive follow-up observation of tumor structural information through high-throughput data analysis. Currently, AI-based models mainly focus on the single baseline scan or pipeline, namely sole radiomics or deep learning (DL). This work merged the delta-radiomics based on the slope of classic radiomics indexes within a time interval and the features extracted by deep networks from the subtraction between the baseline and follow-up images. The subtracted images describing the tumor changes were based on the transformation generated by registration. Stepwise optimization of components was performed by repeating experiments among various combinations of DL networks, registration methods, feature selection algorithms, and classifiers. The optimized model could predict MPR with a cross-validation AUC of 0.91 and an external validation AUC of 0.85. A core set of 27 features (eight classic radiomics, 15 delta-radiomics, one classic DL features, and three delta-DL features) was identified. The changes in delta-radiomics indexes during the treatment were fitted with mathematic models. The fitting results revealed that over half of the features were of non-linear dynamics. Therefore, non-linear modifications were made on eight features by replacing the original features with non-linear fitting parameters, and the modified model achieved an improved power. The dynamic hybrid model serves as a novel and promising tool to predict the response of lesions to PD-1 blockade, which implies the importance of introducing the non-linear dynamic effects and DL approaches to the original delta-radiomics in the future.

Nanosilicate-functionalized nanofibrous membrane facilitated periodontal regeneration potential by harnessing periodontal ligament cell-mediated osteogenesis and immunomodulation.

Although various new biomaterials have enriched the methods for periodontal regeneration, their efficacy is still controversial, and the regeneration of damaged support tissue in the periodontium remains challenging. Laponite (LAP) nanosilicate is a layered two-dimensional nanoscale, ultrathin nanomaterial with a unique structure and brilliant biocompatibility and bioactivity. This study aimed to investigate the effects of nanosilicate-incorporated PCL (PCL/LAP) nanofibrous membranes on periodontal ligament cells (PDLCs) in vitro and periodontal regeneration in vivo. A PCL/LAP nanofibrous membrane was fabricated by an electrospinning method. The characterization of PCL/LAP nanofibrous membrane were determined by scanning electron microscopy (SEM), energy dispersive spectrum of X-ray (EDS), inductively coupled plasma mass spectrometry (ICP-MS) and tensile test. The proliferation and osteogenic differentiation of PDLCs on the PCL/LAP nanofibrous membrane were evaluated. A PDLCs and macrophage coculture system was used to explore the immunomodulatory effects of the PCL/LAP nanofibrous membrane. PCL/LAP nanofibrous membrane was implanted into rat calvarial and periodontal defects, and the regenerative potential was evaluated by microcomputed topography (micro-CT) and histological analysis. The PCL/LAP nanofibrous membrane showed good biocompatibility and bioactivity. It enhanced the proliferation and osteogenic differentiation of PDLCs. The PCL/LAP nanofibrous membrane also stimulated anti-inflammatory and pro-remodeling N2 neutrophil formation, regulated inflammatory responses and induced M2 macrophage polarization by orchestrating the immunomodulatory effects of PDLCs. The PCL/LAP nanofibrous membrane promoted rat calvarial defect repair and periodontal regeneration in vivo. LAP nanosilicate-incorporated PCL membrane is capable of mediating osteogenesis and immunomodulation of PDLCs in vitro and accelerating periodontal regeneration in vivo. It could be a promising biomaterial for periodontal regeneration therapy.

Biocompatible and Long-Term Monitoring Strategies of Wearable, Ingestible and Implantable Biosensors: Reform the Next Generation Healthcare.

Sensors enable the detection of physiological indicators and pathological markers to assist in the diagnosis, treatment, and long-term monitoring of diseases, in addition to playing an essential role in the observation and evaluation of physiological activities. The development of modern medical activities cannot be separated from the precise detection, reliable acquisition, and intelligent analysis of human body information. Therefore, sensors have become the core of new-generation health technologies along with the Internet of Things (IoTs) and artificial intelligence (AI). Previous research on the sensing of human information has conferred many superior properties on sensors, of which biocompatibility is one of the most important. Recently, biocompatible biosensors have developed rapidly to provide the possibility for the long-term and in-situ monitoring of physiological information. In this review, we summarize the ideal features and engineering realization strategies of three different types of biocompatible biosensors, including wearable, ingestible, and implantable sensors from the level of sensor designing and application. Additionally, the detection targets of the biosensors are further divided into vital life parameters (e.g., body temperature, heart rate, blood pressure, and respiratory rate), biochemical indicators, as well as physical and physiological parameters based on the clinical needs. In this review, starting from the emerging concept of next-generation diagnostics and healthcare technologies, we discuss how biocompatible sensors revolutionize the state-of-art healthcare system unprecedentedly, as well as the challenges and opportunities faced in the future development of biocompatible health sensors.

Global, regional, and national cancer burdens of respiratory and digestive tracts in 1990-2044: A cross-sectional and age-period-cohort forecast study.

BACKGROUND: Understanding the current status and future trends of cancer burdens by systems provides important information for specialists, policymakers, and specific risk populations. METHODS: The aim of this study was to compare the current and future cancer burdens of the gastrointestinal (GI) and respiratory tracts in terms of their magnitude and distribution. Data from a total of eight cancers of the digestive and respiratory tracts in the Global Burden of Disease (GBD) database were collected. The age-standardized incidence/death rates (ASIR/ASDRs), disability-adjusted life years (DALYs), and estimated annual percentage changes (EAPCs) were analyzed. Future trends were predicted with Bayesian age-period-cohort (BAPC) and NORDPRED models. RESULTS: In 2019, there was a significant increase in DALY for both digestive and respiratory tract cancers compared to 1990. Meanwhile, ASIR increased slightly and ASDR decreased notably. In 2019, the global cancer burdens of respiratory and digestive tracts were 38568363.53 and 66912328.72 in DALY, 34.28 and 55.32 in ASIR, and 656.82 and 808.22 in ASDR per 100,000 population with changes of +54.63% and +43.93%, +2.92% and +5.65%, and -17.39% and -26.83% compared to those in 1990, respectively. Significant cross-regional differences in the cancer burdens were observed among the regions. Compared to four representative chronic diseases, the burden of cancers showed less remission and greater global inequalities. The burdens of both digestive and respiratory tract cancers were higher in males than in females in terms of the ASIR, ASDR, and DALY. The incidence and mortality rates of respiratory tract cancers were up to 3-4 times higher in males than in females, whereas the difference between male and female rates of digestive tract cancers was relatively smaller. The main risk factor associated with all kinds of digestive and respiratory tract cancers is tobacco, leading to 18.5 in ASDR and 3.38×107 in DALY for respiratory tract cancers; 8.29 in ASDR and 1.60×107 in DALY for digestive tract cancers, in 2019. Additionally, alcohol use contributes to most digestive and respiratory tract cancers (1.23/1.03 in ASDR and 1.60×106/2.57×106 in DALY for respiratory tract cancers; 4.19/3.82 in ASDR and 4.49×106/8.06×106 in DALY for digestive tract cancers), except for stomach cancer and tracheal, bronchus, and lung cancer. The cancer burdens of respiratory and digestive tracts are likely to decrease substantially between 2020 and 2044. For most metrics, except for the ASIR and male-to-female ratios of ASDR and ASDALY in digestive tract cancers, the worldwide variances of burden metrics have been decreasing in the past decades and will possibly maintain stable trends in the future. CONCLUSIONS: The epidemiology of respiratory and GI tract cancers has common features and individual characteristics that are reflected in geography, age characteristics, and risk factors. Current epidemiological status, future trends, and the globalization of these disease burdens are important factors for making scientific planning of resources to minimize the cancer burden metrics and their cross-regional inequalities.

Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules.

INTRODUCTION: Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters for invasiveness prediction. METHODS: Patients with stage 0-IB NSCLC after surgical resection were retrospectively analysed. Preoperative CTs were evaluated with specialized software for nodule segmentation and CT quantification. Pathology was the reference for invasiveness. Univariate and multivariate logistic regression assessed predictors of high-risk SPN. RESULTS: Three hundred and fifty-five SPN were included. On multivariate analysis, CT value mean and nodule type (ground glass opacity vs. solid) were independent predictors of high-risk SPN. The area under the curve (AUC) was 0.811 for identifying high-risk nodules. CONCLUSIONS: Quantitative CT measures and nodule type correlated with invasiveness. Software-based CT assessment shows potential for noninvasive prediction to guide extent of resection. Further prospective validation is needed, including comparison with benign nodules.

Cisplatin-induced Pyroptosis Enhances the Efficacy of PD-L1 Inhibitor in Small-Cell Lung Cancer via GSDME/IL12/CD4Tem Axis.

The combination therapy of platinum-based chemotherapy and PD-L1 inhibitors but not the single anti-PD-L1 therapy has significantly improved the prognosis of patients with small-cell lung cancer (SCLC). However, the synergistic mechanism of combination therapy has not been fully elucidated. In this work, we identified a positive correlation between the expression of pyroptosis-related proteins Gasdermin E (GSDME) and the survival rates of patients with SCLC. Importantly, it was shown that human SCLC cell lines with high expression of GSDME showed more sensitivity to cisplatin, as well as cisplatin plus anti-PD-L1 treatment both in vitro and in vivo. Mechanically, cisplatin induced the activation of GSDME and the release of cytokines including IL-12, which enhance the expression of IFN-γ in T cells in the tumor immune microenvironment (TME) and subsequently improve anti-PD-L1 response. Altogether, our work demonstrates that cisplatin could induce GSDME-dependent cell pyroptosis to improve the response of anti-PD-L1 therapy though switching the TME from "cold" to "hot" in SCLC, indicating GSDME as a response biomarker for combination therapy of anti-PD-L1 and chemotherapy, as well as a potential target to sensitize the response to PD-L1 inhibitor therapy in future.

The multifaceted role of MUC1 in tumor therapy resistance.

Tumor therapeutic resistances are frequently linked to the recurrence and poor prognosis of cancers and have been a key bottleneck in clinical tumor treatment. Mucin1 (MUC1), a heterodimeric transmembrane glycoprotein, exhibits abnormally overexpression in a variety of human tumors and has been confirmed to be related to the formation of therapeutic resistance. In this review, the multifaceted roles of MUC1 in tumor therapy resistance are summarized from aspects of pan-cancer principles shared among therapies and individual mechanisms dependent on different therapies. Concretely, the common mechanisms of therapy resistance across cancers include interfering with gene expression, promoting genome instability, modifying tumor microenvironment, enhancing cancer heterogeneity and stemness, and activating evasion and metastasis. Moreover, the individual mechanisms of therapy resistance in chemotherapy, radiotherapy, and biotherapy are introduced. Last but not least, MUC1-involved therapy resistance in different types of cancers and MUC1-related clinical trials are summarized.

Cancer Burden Variations and Convergences in Globalization: A Comparative Study on the Tracheal, Bronchus, and Lung (TBL) and Liver Cancer Burdens Among WHO Regions from 1990 to 2019.

Lung cancer and liver cancer are the leading and third causes of cancer death, respectively. Both lung and liver cancer are with clear major risk factors. A thorough understanding of their burdens in the context of globalization, especially the convergences and variations among WHO regions, is useful in precision cancer prevention worldwide and understanding the changing epidemiological trends with the expanding globalization. The Global Burden of Disease (GBD) and WHO Global Health Observatory (GHO) database were analyzed to evaluate the burden metrics and risk factors of trachea, bronchus, and lung (TBL) cancer and liver cancer. Western Pacific Region (WPR) had the highest age-standardized incidence rate (ASIR) for both liver cancer (11.02 [9.62-12.61] per 100,000 population) and TBL cancer (38.82 [33.63-44.04] per 100,000 population) in 2019. Disability-adjusted life years (DALYs) for liver and TBL cancer elevated with the increasing sociodemographic index (SDI) level, except for liver cancer in WPR and TBL cancer in European Region (EUR). Region of the Americas (AMR) showed the biggest upward trends of liver cancer age-standardized rates (ASRs), as well as the biggest downward trends of TBL cancer ASRs, followed by Eastern Mediterranean Region (EMR). Alcohol use and smoking were the leading cause of liver and TBL cancer death in most WHO regions. Variances of ASRs for liver and TBL cancer among WHO memberships have been decreasing during the past decade. The homogenization and convergence of cancer burdens were also demonstrated in different agegroups and sexes and in the evolution of associated risk factors and etiology. In conclusion, our study reflects the variations and convergences in the liver and lung cancer burdens among the WHO regions with the developing globalization, which suggests that we need to be acutely aware of the global homogeneity of the disease burden that accompanies increasing globalization, including the global convergences in various populations, risk factors, and burden metrics.

Short- and long-term outcomes of robotic-assisted versus video-assisted thoracoscopic lobectomy in non-small cell lung cancer patients aged 35 years or younger: a real-world study with propensity score-matched analysis.

PURPOSE: This study compared short- and long-term outcomes of robotic-assisted thoracoscopic surgery (RATS) versus video-assisted thoracoscopic surgery (VATS) for lobectomy in young adults aged ≤ 35 years with non-small cell lung cancer (NSCLC), aiming to assess the superiority of RATS over VATS for this special group of patients. METHODS: A total of 1355 consecutive NSCLC cases aged 18-35 years undergoing RATS (n = 105) or VATS (n = 1250) between 2014 and 2021 were retrospectively identified from a prospectively maintained database. Propensity score matching (PSM) was applied to establish a 1:3 RATS versus VATS ratio. Baseline clinicopathological characteristics, perioperative outcomes, lymph node (LN) assessment, and long-term survival were investigated. RESULTS: Following PSM, 105 and 315 cases were in the RATS and VATS groups, respectively. RATS led to a shorter postoperative hospital stay than VATS (4.0 ± 1.5 vs 4.3 ± 1.7 days, p = 0.02). The two groups were comparable in other perioperative outcomes and postoperative complications (all p > 0.05). Moreover, RATS assessed more LNs (9.4 ± 4.4 vs 8.3 ± 3.6, p = 0.03), especially N1 LNs (4.2 ± 3.1 vs 3.5 ± 2.2, p = 0.02), than VATS. By comparison, no difference in 5-year recurrence-free survival (RFS), overall survival (OS), or recurrence or mortality patterns was found between the two groups (all p > 0.05). Further subgroup analyses also observed similar long-term outcomes between the two groups regarding age, gender, and smoking history. Finally, Cox's analyses found that the surgical approach was not independently correlated with RFS or OS. CONCLUSION: RATS shortened postoperative hospital stay, assessed more N1 and total LNs, and achieved comparable long-term outcomes to VATS for very young NSCLC patients.

Short-term outcomes of robot-assisted versus video-assisted thoracoscopic surgery for non-small cell lung cancer patients with neoadjuvant immunochemotherapy: a single-center retrospective study.

Background: Neoadjuvant immunochemotherapy has been increasingly applied to treat non-small cell lung cancer (NSCLC). However, the comparison between robotic-assisted thoracoscopic surgery (RATS) and video-assisted thoracoscopic surgery (VATS) in the feasibility and oncological efficacy following neoadjuvant immunochemotherapy is scarce. This study aims to assess the superiorities of RATS over (VATS) concerning short-term outcomes in treating NSCLC patients with neoadjuvant immunochemotherapy. Methods: NSCLC patients receiving RATS or VATS lobectomy following neoadjuvant immunochemotherapy at Shanghai Chest Hospital from 2019 to 2022 were retrospectively identified. Baseline clinical characteristics, perioperative outcomes, and survival profiles were analyzed. Results: Forty-six NSCLC patients with neoadjuvant immunochemotherapy were included and divided into the RATS (n=15) and VATS (n=31) groups. The baseline clinical characteristics and induction-related adverse events were comparable between the two groups (all p>0.050). The 30-day mortality in the RATS and VATS groups were 0% and 3.23%, respectively (p=1.000). Patients undergoing RATS were associated with reduced surgical-related intensive unit care (ICU) stay than those receiving VATS (0.0 [0.0-0.0] vs. 0.0 [0.0-1.0] days, p=0.026). Moreover, RATS assessed more N1 LNs (6.27 ± 1.94 vs 4.90 ± 1.92, p=0.042) and LN stations (3.07 ± 1.03 vs 2.52 ± 0.57, p=0.038) compared with VATS. By comparison, no difference was found in surgical outcomes, pathological results, and postoperative complications between the RATS and VATS groups (all p>0.050). Finally, RATS and VATS achieved comparable one-year recurrence-free survival (82.96% vs. 85.23%, p=0.821) and the timing of central nervous system, LN, and bone recurrences (all p>0.050). Conclusion: RATS is safe and feasible for NSCLC patients with neoadjuvant immunochemotherapy, reducing surgical-related ICU stay, assessing increased N1 LNs and stations, and achieving similar survival profiles to VATS.

Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: a multicenter cohort study.

BACKGROUND: Reliable pre-surgical prediction of spreading through air spaces (STAS) in primary lung cancer is essential for precision treatment and surgical decision-making. We aimed to develop and validate a dual-delta deep-learning and radiomics model based on pretreatment computed tomography (CT) image series to predict the STAS in patients with lung cancer. METHOD: Six hundred seventy-four patients with pre-surgery CT follow-up scans (with a minimum interval of two weeks) and primary lung cancer diagnosed by surgery were retrospectively recruited from three Chinese hospitals. The training cohort and internal validation cohort, comprising 509 and 76 patients respectively, were selected from Shanghai Chest Hospital; the external validation cohorts comprised 36 and 53 patients from two other centers, respectively. Four imaging signatures (classic radiomics features and deep learning [DL] features, delta-radiomics and delta-DL features) reflecting the STAS status were constructed from the pretreatment CT images by comprehensive methods including handcrafting, 3D views extraction, image registration and subtraction. A stepwise optimized three-step procedure, including feature extraction (by DL and time-base radiomics slope), feature selection (by reproducibility check and 45 selection algorithms), and classification (32 classifiers considered), was applied for signature building and methodology optimization. The interpretability of the proposed model was further assessed with Grad-CAM for DL-features and feature ranking for radiomics features. RESULTS: The dual-delta model showed satisfactory discrimination between STAS and non-STAS and yielded the areas under the receiver operating curve (AUCs) of 0.94 (95% CI, 0.92-0.96), 0.84 (95% CI, 0.82-0.86), and 0.84 (95% CI, 0.83-0.85) in the internal and two external validation cohorts, respectively, with interpretable core feature sets and feature maps. CONCLUSION: The coupling of delta-DL model with delta-radiomics features enriches information such as anisotropy of tumor growth and heterogeneous changes within the tumor during the radiological follow-up, which could provide valuable information for STAS prediction in primary lung cancer.

When artificial intelligence meets PD-1/PD-L1 inhibitors: Population screening, response prediction and efficacy evaluation.

Programmed cell death protein-1 (PD-1) and its ligand (programmed death ligand 1, PD-L1) inhibitors, as the rising stars of immunotherapy, have been widely used in clinical practice, and the corresponding population screening, response prediction and efficacy evaluation have become increasingly important in clinic. Artificial Intelligence (AI) can help us uncover effective information from clinical data such as medical history, images, laboratory results, sequencing data, etc., which can help us solve above problems and enrich the methodology of clinical research. In this way, AI researches related to PD-1/PD-L1 inhibitors have been emerging. Based on an introduction of AI fundamentals in medicine, this review systematically summarizes the existing AI studies related to PD-1/PD-L1 immunotherapy in three aspects: population screening, response prediction and efficacy evaluation, and briefly outlooks the development direction of AI studies related to PD-1/PD-L1 inhibitors.

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning.

The COVID-19 outbreak poses a huge challenge to international public health. Reliable forecast of the number of cases is of great significance to the planning of health resources and the investigation and evaluation of the epidemic situation. The data-driven machine learning models can adapt to complex changes in the epidemic situation without relying on correct physical dynamics modeling, which are sensitive and accurate in predicting the development of the epidemic. In this paper, an ensemble hybrid model based on Temporal Convolutional Networks (TCN), Gated Recurrent Unit (GRU), Deep Belief Networks (DBN), Q-learning, and Support Vector Machine (SVM) models, namely TCN-GRU-DBN-Q-SVM model, is proposed to achieve the forecasting of COVID-19 infections. Three widely-used predictors, TCN, GRU, and DBN are used as elements of the hybrid model ensembled by the weights provided by reinforcement learning method. Furthermore, an error predictor built by SVM, is trained with validation set, and the final prediction result could be obtained by combining the TCN-GRU-DBN-Q model with the SVM error predictor. In order to investigate the forecasting performance of the proposed hybrid model, several comparison models (TCN-GRU-DBN-Q, LSTM, N-BEATS, ANFIS, VMD-BP, WT-RVFL, and ARIMA models) are selected. The experimental results show that: (1) the prediction effect of the TCN-GRU-DBN-Q-SVM model on COVID-19 infection is satisfactory, which has been verified in three national infection data from the UK, India, and the US, and the proposed model has good generalization ability; (2) in the proposed hybrid model, SVM can efficiently predict the possible error of the predicted series given by TCN-GRU-DBN-Q components; (3) the integrated weights based on Q-learning can be adaptively adjusted according to the characteristics of the data in the forecasting tasks in different countries and multiple situations, which ensures the accuracy, robustness and generalization of the proposed model.

Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph.

BACKGROUND: Chest X-ray radiography (CXR) has been widely considered as an accessible, feasible, and convenient method to evaluate suspected patients' lung involvement during the COVID-19 pandemic. However, with the escalating number of suspected cases, traditional diagnosis via CXR fails to deliver results within a short period of time. Therefore, it is crucial to employ artificial intelligence (AI) to enhance CXRs for obtaining quick and accurate diagnoses. Previous studies have reported the feasibility of utilizing deep learning methods to screen for COVID-19 using CXR and CT results. However, these models only use a single deep learning network for chest radiograph detection; the accuracy of this approach required further improvement. METHODS: In this study, we propose a three-step hybrid ensemble model, including a feature extractor, a feature selector, and a classifier. First, a pre-trained AlexNet with an improved structure extracts the original image features. Then, the ReliefF algorithm is adopted to sort the extracted features, and a trial-and-error approach is used to select the n most important features to reduce the feature dimension. Finally, an SVM classifier provides classification results based on the n selected features. RESULTS: Compared to five existing models (InceptionV3: 97.916 ± 0.408%; SqueezeNet: 97.189 ± 0.526%; VGG19: 96.520 ± 1.220%; ResNet50: 97.476 ± 0.513%; ResNet101: 98.241 ± 0.209%), the proposed model demonstrated the best performance in terms of overall accuracy rate (98.642 ± 0.398%). Additionally, compared to the existing models, the proposed model demonstrates a considerable improvement in classification time efficiency (SqueezeNet: 6.602 ± 0.001s; InceptionV3: 12.376 ± 0.002s; ResNet50: 10.952 ± 0.001s; ResNet101: 18.040 ± 0.002s; VGG19: 16.632 ± 0.002s; proposed model: 5.917 ± 0.001s). CONCLUSION: The model proposed in this article is practical and effective, and can provide high-precision COVID-19 CXR detection. We demonstrated its suitability to aid medical professionals in distinguishing normal CXRs, viral pneumonia CXRs and COVID-19 CXRs efficiently on small sample sizes.

Efficient Toroidal Formation of Sorted Metallic and Semiconducting Single-Walled Carbon Nanotubes via General Pickering Emulsion.

Single-walled carbon nanotubes (SWNTs) with a toroidal/coiled geometry-shaped structure sustain innovative preference to future technology material. The toroidal shape can be used in designing nanoelectronic devices for various prospective applications such as tactile sensors, electromagnetic absorbers, and energy storage devices. In this study, we demonstrate the fabrication of toroidal geometry shapes of metallic (m-) and semiconducting (s-) SWNTs, which can be revealed by simply mixing a few solutions in the correct ratio, both oil-in-water (hydrophobic) and water-in-oil (hydrophilic) emulsion processes. Herein, the letter communicates the formation of pure m- and s-SWNTs (metallic and semiconducting) by annular, obtained from gel column chromatography, via the emulsion approach. We have also studied the surfactant sodium dodecyl sulfate removal of sorted species from a gel column by a simple method named as chloroform/methanol/water extraction.

Nano-Carriers of Combination Tumor Physical Stimuli-Responsive Therapies.

With the development of nanotechnology, Tumor Physical Stimuli-Responsive Therapies (TPSRTs) have reached a new stage because of the remarkable characteristics of nanocarriers. The nanocarriers enable such therapies to overcome the drawbacks of traditional therapies, such as radiotherapy or chemotherapy. To further explore the possibility of the nanocarrier-assisted TPSRTs, scientists have combined different TPSRTs via; the platform of nanocarriers into combination TPSRTs, which include Photothermal Therapy (PTT) with Magnetic Hyperthermia Therapy (MHT), PTT with Sonodynamic Therapy (SDT), MHT with Photodynamic Therapy (PDT), and PDT with PTT. To achieve such therapies, it requires to fully utilize the versatile functions of a specific nanocarrier, which depend on a pellucid understanding of the traits of those nanocarriers. This review covers the principles of different TPSRTs and their combinations, summarizes various types of combination TPSRTs nanocarriers and their therapeutic effects on tumors, and discusses the current disadvantages and future developments of these nanocarriers in the application of combination TPSRTs.

A simple MWCNTs@paper biosensor for CA19-9 detection and its long-term preservation by vacuum freeze drying.

This paper introduces a cheap simple MWCNTs@paper biosensor for the detection of CA19-9, which is a biomarker of pancreatic cancer. By adding the CA19-9 antibody to the surface of MWCNTs which are deposited on the microporous filter paper, the correlation between the concentration of CA19-9 and resistance of biosensor element was linear due to the site-specific binding of antigen and antibody. The detection range is wide (0 U/mL-at least 1000 U/mL), and even in the low concentration of CA19-9, the linearity remains satisfying. Based on this property, it could be used for the detection of early-stage pancreatic cancer. Besides, this research originally introduces a vacuum freeze-drying method for the long-term preservation of biosensor, prolonging its storage time from 3 h to at least 7 days, which signifcantly promoted its value in practical application. One piece of the MWCNTs@paper biosensor only cost $2 (about 30 times cheaper than ELISA) approximately, and the detection speed is satisfying (2 h, 12 times faster than ELISA), which will possibly increase its opportunity of mass production and clinical practice.

Enrichment of large-diameter semiconducting single-walled carbon nanotubes by a mixed-extractor strategy.

In this work, we report a new mixed-extractor strategy to improve the sorting yield of large-diameter semiconducting single-walled carbon nanotubes (s-SWCNTs) with high purity. In the new mixed-extractor strategy, two kinds of conjugated polymers with different rigidity, poly(9,9-n-dihexyl-2,7-fluorene-alt-9-phenyl-3,6-carbazole) (PDFP) and poly(9,9-dioctylfluorene-alt-benzothiadiazole) (P8BT), are used to sort large-diameter s-SWCNTs through two simple sonication processes. To our surprise, although PDFP itself shows no selectivity toward s-SWCNTs, it can greatly enhance the sorting yield of P8BT. Using the PDFP/P8BT mixed-extractor method, the yield of sorted s-SWCNTs has been enhanced by 5 times with a purity above 99 % in comparison to that using P8BT single-extractor method. In addition, the photoluminescence (PL) excitation maps shows that the PDFP/P8BT mixed-extractor system not only enhances the sorting yield substantially, but also tends to be enrichment of (15,4) SWCNTs with the diameter of 1.36 nm.