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Background: With the prevalence of dietary supplements, the use of combinations of herbs and drugs is gradually increasing, together with the risk of drug interactions. In our clinical work, we unexpectedly found that the combination of Panax notoginseng and warfarin, which are herbs that activate blood circulation and remove blood stasis, showed antagonistic effects instead. The purpose of this study was to evaluate the drug interaction between Panax notoginseng saponins (PNS) and warfarin, the main active ingredient of Panax notoginseng, and to explore the interaction mechanism. Methods: The effects and mechanisms of PNS on the pharmacodynamics and pharmacokinetics of warfarin were explored mainly in Sprague–Dawley rats and HepG2 cells. Elisa was used to detect the concentrations of coagulation factors, HPLC-MS to detect the blood concentrations of warfarin in rats, immunoblotting was employed to examine protein levels, qRT-PCR to detect mRNA levels, cellular immunofluorescence to detect the localization of NR1I3, and dual luciferase to verify the binding of miR-214-3p and NR1I3. Results: PNS significantly accelerated warfarin metabolism and reduced its efficacy, accompanied by increased expression of NR1I3 and CYP2C9. Interference with NR1I3 rescued the accelerated metabolism of warfarin induce by PNS co-administration. In addition, we demonstrated that PNS significantly reduced miR-214-3p expression, whereas miR-214-3p overexpression reduced NR1I3 and CYP2C9 expression, resulting in a weakened antagonistic effect of PNS on warfarin. Additionally, we found that miR-214-3p bound directly to NR1I3 3′-UTR and significantly downregulated NR1I3 expression. Conclusion: Our study demonstrated that PNS accelerates warfarin metabolism and reduces its pharmacodynamics by downregulating miR-214-3p, leading to increased expression of its target gene NR1I3, these findings provide new insights for clinical drug applications to avoid adverse effects.

Pulmonary infections pose formidable challenges in clinical settings with high mortality rates across all age groups worldwide. Accurate diagnosis and early intervention are crucial to improve patient outcomes. Artificial intelligence (AI) has the capability to mine imaging features specific to different pathogens and fuse multimodal features to reach a synergistic diagnosis, enabling more precise investigation and individualized clinical management. In this study, we successfully developed a multimodal integration (MMI) pipeline to differentiate among bacterial, fungal, and viral pneumonia and pulmonary tuberculosis based on a real-world dataset of 24,107 patients. The area under the curve (AUC) of the MMI system comprising clinical text and computed tomography (CT) image scans yielded 0.910 (95% confidence interval [CI]: 0.904–0.916) and 0.887 (95% CI: 0.867–0.909) in the internal and external testing datasets respectively, which were comparable to those of experienced physicians. Furthermore, the MMI system was utilized to rapidly differentiate between viral subtypes with a mean AUC of 0.822 (95% CI: 0.805–0.837) and bacterial subtypes with a mean AUC of 0.803 (95% CI: 0.775–0.830). Here, the MMI system harbors the potential to guide tailored medication recommendations, thus mitigating the risk of antibiotic misuse. Additionally, the integration of multimodal factors in the AI-driven system also provided an evident advantage in predicting risks of developing critical illness, contributing to more informed clinical decision-making. To revolutionize medical care, embracing multimodal AI tools in pulmonary infections will pave the way to further facilitate early intervention and precise management in the foreseeable future.

Abstract Background Papillary thyroid carcinoma (PTC) is the most frequent histological type of thyroid carcinoma. Although an increasing number of diagnostic methods have recently been developed, the diagnosis of a few nodules is still unsatisfactory. Therefore, the present study aimed to develop and validate a comprehensive prediction model to optimize the diagnosis of PTC. Methods A total of 152 thyroid nodules that were evaluated by postoperative pathological examination were included in the development and validation cohorts recruited from two centres between August 2019 and February 2022. Patient data, including general information, cytopathology, imprinted gene detection, and ultrasound features, were obtained to establish a prediction model for PTC. Multivariate logistic regression analysis with a bidirectional elimination approach was performed to identify the predictors and develop the model. Results A comprehensive prediction model with predictors, such as component, microcalcification, imprinted gene detection, and cytopathology, was developed. The area under the curve (AUC), sensitivity, specificity, and accuracy of the developed model were 0.98, 97.0%, 89.5%, and 94.4%, respectively. The prediction model also showed satisfactory performance in both internal and external validations. Moreover, the novel method (imprinted gene detection) was demonstrated to play a role in improving the diagnosis of PTC. Conclusion The present study developed and validated a comprehensive prediction model for PTC, and a visualized nomogram based on the prediction model was provided for clinical application. The prediction model with imprinted gene detection effectively improves the diagnosis of PTCs that are undetermined by the current means.

Active power sharing and voltage regulation are two of the major control challenges in the operation of the voltage source converter based multi-terminal high-voltage DC (VSC-MTDC) system when integrating large-scale offshore wind farms (OWFs). This paper proposes two novel adaptive voltage reference based droop control methods to regulate pilot DC voltage and share the power burden autonomously. The proposed Method I utilizes DC grid lossy model with the local voltage droop control strategy, while the proposed Method II adopts a modified pilot voltage droop control (MPVDC) to avoid the large errors caused by the DC grid lossless model. Dynamic simulations of a five-terminal MTDC grid are carried out using MATLAB/Simulink SimPowerSystems /Specialized Technology to verify the proposed autonomous control methods under various types of disturbance and contingency. In addition, comparative study is implemented to demonstrate the advantages of the proposed methods.

Abstract Purposes The influence of gender on the epidemiology of and outcome from SA-AKI in ICU has not been fully clarified. Our aim is to elucidate these differences. Methods This study included adult patients with sepsis in MIMIC IV (V 2.2), and propensity matching analysis, cox regression and logistic regression were used to analyze gender differences in incidence, mortality and organ support rate. Results Of the 24,467 patients included in the cohort, 18,128 were retained after propensity score matching. In the matched cohort, the incidence of SA-AKI in males is higher than that in females (58.6% vs. 56.2%; P = 0.001).males were associated with a higher risk of SA-AKI (OR:1.07(1.01–1.14), P = 0.026;adjusted OR:1.07(1.01–1.14), P

Abstract Immune checkpoint inhibitors (ICIs) play a crucial role in the immunotherapy of malignant tumors, preventing immune evasion by tumor cells and activating autoimmune cells to eliminate the tumor. Despite their proven effectiveness in antitumor therapy, potential immune‐related adverse effects must be recognized, particularly ICI‐associated myocarditis (ICIAM). ICIAM is the most lethal form of organ immunotoxicity, with a significant impact on short‐term mortality. However, ICIAM is predominantly asymptomatic or mildly nonspecific. It is difficult to diagnose, especially due to the lack of unique molecular markers. This article aims to provide a comprehensive overview of the progress made in identifying molecular markers for ICIAM.

Abstract There have been hundreds of millions of cases of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the growing population of recovered patients, it is crucial to understand the long-term consequences of the disease and management strategies. Although COVID-19 was initially considered an acute respiratory illness, recent evidence suggests that manifestations including but not limited to those of the cardiovascular, respiratory, neuropsychiatric, gastrointestinal, reproductive, and musculoskeletal systems may persist long after the acute phase. These persistent manifestations, also referred to as long COVID, could impact all patients with COVID-19 across the full spectrum of illness severity. Herein, we comprehensively review the current literature on long COVID, highlighting its epidemiological understanding, the impact of vaccinations, organ-specific sequelae, pathophysiological mechanisms, and multidisciplinary management strategies. In addition, the impact of psychological and psychosomatic factors is also underscored. Despite these crucial findings on long COVID, the current diagnostic and therapeutic strategies based on previous experience and pilot studies remain inadequate, and well-designed clinical trials should be prioritized to validate existing hypotheses. Thus, we propose the primary challenges concerning biological knowledge gaps and efficient remedies as well as discuss the corresponding recommendations.

In randomized clinical trials with right-censored time-to-event outcomes, the popular log-rank test without adjusting for baseline covariates is asymptotically valid for treatment effect under simple randomization of treatments but is too conservative under covariate-adaptive randomization. The stratified log-rank test, which adjusts baseline covariates in the test procedure by stratification, is asymptotically valid regardless of what treatment randomization is applied. In the literature, however, under simple randomization there is no affirmative conclusion about whether the stratified log-rank test is asymptotically more powerful than the unstratified log-rank test. In this article we show when the stratified and unstratified log-rank tests aim for the same null hypothesis and that, under simple randomization, the stratified log-rank test is asymptotically more powerful than the unstratified log-rank test in the region of alternative hypothesis that is specified by a Cox proportional hazards model. We also provide some discussion about why we do not have an affirmative conclusion in general.

Abstract Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug‐resistant tuberculosis (DR‐TB) and drug‐sensitive tuberculosis (DS‐TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning‐based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR‐TB and classify crucial subtypes, including rifampicin‐resistant tuberculosis, multidrug‐resistant tuberculosis, and extensively drug‐resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR‐TB. Finally, DeepTB achieved an area under the curve (AUC) up to 0.930 for thoracic abnormality detection and 0.943 for DR‐TB diagnosis. Notably, the system demonstrated instructive value in DR‐TB subtype classification with AUCs ranging from 0.880 to 0.928. Meanwhile, class activation maps were generated to express a human‐understandable visual concept. Together, showing a prominent performance, DeepTB would be impactful in clinical decision‐making for DR‐TB.

The photoluminescence experiment was performed at 77 K on two types of heterostructures of In0.52Al0.48As/InP grown by molecular beam epitaxy. One type has the so-called direct interface formed with In0.52Al0.48As grown on the InP (100) substrate, and another is a double heterostructure type, i.e., InP/In0.52Al0.48As/InP, including the inverse interface formed with the InP cap. The thickness of both the as-grown InAlAs and InP layers is 2 µm. Under the excitation of 852 nm semiconductor laser, a single peak at 1.198 eV (P0) was observed for the direct interface whereas the inverse interface showed two peaks at 1.240 eV (P1) and 1.117 eV (P2). However, P2 manifests differently from P1 with two interesting features. First, it disappears when the excitation was replaced with the 532 nm laser. Second, when the cap layer of InP/In0.52Al0.48As/InP is thinned to 200 nm, P2 displays a large blueshift effect with the increase in laser intensity, but the extent of blueshift drops when the excitation is increased further into a higher power range. The phenomenon of P2 was attributed to the interface excitation-induced interplay between the luminescence with a carrier localization effect and that caused by the recombination of 2-dimensional electrons with the light-injected holes in the potential well at the interface.

Cardiac myxoma is a rare etiology of ischemic stroke, especially in young people. We report a case of multiple myxomas in left atrium and right ventricle inducing acute cerebral infarction. No significant abnormalities were detected in the patient's preoperative laboratory examination. Following emergency surgery, the patient's prognosis was satisfactory, providing valuable empirical insight for the surgical management of acute cerebral infarction in individuals diagnosed with cardiac myxoma. Our objective is to heighten awareness regarding the evaluation and treatment of patients with acute cerebral infarction subsequent to early diagnosis of cardiac myxoma.

Abstract Purpose To compare the therapeutic efficacy and safety of microwave ablation (MWA) and lauromacrogol injection for ablation (LIA) for benign predominantly cystic thyroid nodules. Materials and methods In this retrospective study, 85 patients with predominantly cystic thyroid nodules (PCTNs) who underwent microwave ablation (MWA) or lauromacrogol injection for ablation (LIA) between June 2019 and August 2022 at three hospitals were included in our research. Forty-six patients were treated with microwave ablation, and thirty-nine patients were treated with lauromacrogol injection for ablation. The baseline characteristics, nodal volume, volume reduction rate (VRR), and incidence of postoperative complications were compared between these two groups. Results After treatment, there were significant differences in the thyroid nodule volume and the volume reduction rate (VRR) at different follow-up times between the groups (p 0.05). Of note, no serious intraoperative or postoperative complications occurred in the corresponding group. Conclusion MWA and LIA are very effective and safe strategies for the treatment of predominantly cystic thyroid nodules. However, LIA is more advantageous in that it is less expensive and has a shorter length of hospital stay than MWA.

Abstract. Background:. Lung cancer prevails and induces high mortality around the world. This study provided real-world information on the evolution of clinicopathological profiles and survival outcomes of lung cancer, and provided survival information within stage I subtypes. Methods:. Patients pathologically confirmed with lung cancer between January 2009 and December 2018 were identified with complete clinicopathological information, molecular testing results, and follow-up data. Shifts in clinical characteristics were evaluated using x 2 tests. Overall survival (OS) was calculated through the Kaplan-Meier method. Results:. A total of 26,226 eligible lung cancer patients were included, among whom 62.55% were male and 52.89% were smokers. Non-smokers and elderly patients took increasingly larger proportions in the whole patient population. The proportion of adenocarcinoma increased from 51.63% to 71.80%, while that of squamous carcinoma decreased from 28.43% to 17.60%. Gene mutations including EGFR (52.14%), KRAS (12.14%), and ALK (8.12%) were observed. Female, younger, non-smoking, adenocarcinoma patients and those with mutated EGFR had better survival prognoses. Importantly, this study validated that early detection of early-stage lung cancer patients had contributed to pronounced survival benefits during the decade. Patients with stage I lung cancer, accounted for an increasingly considerable proportion, increasing from 15.28% to 40.25%, coinciding with the surgery rate increasing from 38.14% to 54.25%. Overall, period survival analyses found that 42.69% of patients survived 5 years, and stage I patients had a 5-year OS of 84.20%. Compared with that in 2009-2013, the prognosis of stage I patients in 2014-2018 was dramatically better, with 5-year OS increasing from 73.26% to 87.68%. Regarding the specific survival benefits among stage I patients, the 5-year survival rates were 95.28%, 93.25%, 82.08%, and 74.50% for stage IA1, IA2, IA3, and IB, respectively, far more promising than previous reports. Conclusions:. Crucial clinical and pathological changes have been observed in the past decade. Notably, the increased incidence of stage I lung cancer coincided with an improved prognosis, indicating actual benefits of early detection and management of lung cancer.

We consider maximum likelihood estimation with two or more datasets sampled from different populations with shared parameters. Although more datasets with shared parameters can increase statistical accuracy, this paper shows how to handle heterogeneity among different populations for correctness of estimation and inference. Asymptotic distributions of maximum likelihood estimators are derived under either regular cases where regularity conditions are satisfied or some non-regular situations. A bootstrap variance estimator for assessing performance of estimators and/or making large sample inference is also introduced and evaluated in a simulation study.

Data analysis in modern scientific research and practice has shifted from analysing a single dataset to coupling several datasets. We propose and study a kernel regression method that can handle the challenge of heterogeneous populations. It greatly extends the constrained kernel regression [Dai, C.-S., & Shao, J. (2023). Kernel regression utilizing external information as constraints. Statistica Sinica, 33, in press] that requires a homogeneous population of different datasets. The asymptotic normality of proposed estimators is established under some conditions and simulation results are presented to confirm our theory and to quantify the improvements from datasets with heterogeneous populations.

To improve the precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes, researchers and regulatory agencies recommend using g-computation as a reliable method of covariate adjustment. However, the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest. To fill this gap, we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.

Summary: Background: Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients. Methods: 20,333 CT images of 6371 patients from June 12, 2009 to March 24, 2022 in West China Hospital of Sichuan University, Mianzhu People's Hospital, Peking University People's Hospital, Chengdu Shangjin Nanfu Hospital and Guangan Peoples' Hospital were included in this retrospective study. We developed the IPES based on self-supervised pre-training and multi-task learning, which aimed to predict an overall survival (OS) risk for each patient. We further evaluated the prognostic accuracy of the IPES and its ability to stratify NSCLC patients with the same TNM stage and with the same EGFR genotype. Findings: The IPES was able to predict OS risk for stage I-III resected NSCLC patients in the training set (C-index 0.806; 95% CI: 0.744–0.846), internal validation set (0.783; 95% CI: 0.744–0.825) and external validation set (0.817; 95% CI: 0.786–0.849). In addition, IPES performed well in early-stage (stage I) and EGFR genotype prediction. Furthermore, by adopting IPES-based survival score (IPES-score), resected NSCLC patients in the same stage or with the same EGFR genotype could be divided into low- and high-risk subgroups with good and poor prognosis, respectively (p

Background The aim of this retrospective study was to construct and clinically apply a nomogram for cancer-specific survival (CSS) in patients diagnosed with base-of-tongue squamous cell carcinoma (BOTSCC) to predict their survival prognosis. Methods We collected 8448 patients diagnosed with BOTSCC during 2004–2015 from the Surveillance, Epidemiology, and End Results (SEER) database and divided 30% and 70% of them into validation and training cohorts, respectively. We utilized backward stepwise regression in the Cox model to select variables. Predictive variables were subsequently identified from the variables selected above by using multivariate Cox regression. The new survival model was compared with the American Joint Committee on Cancer (AJCC) prognosis model using the following variables: calibration curve, time-dependent area under the receiver operating characteristic curve (AUC), concordance index (C-index), integrated discrimination improvement (IDI), decision-curve analysis (DCA), and net reclassification improvement (NRI). Results A nomogram was established for predicting the CSS probability in patients with BOTSCC. Factors including sex, race, age at diagnosis, marital status, radiotherapy status, chemotherapy status, TNM AJCC stage, surgery status, tumor size, and months from diagnosis to treatment were selected through multivariate Cox regression as independent predictors of CSS. Calibration plots indicated that the model we established had satisfactory calibration ability. The AUC, C-index, IDI, DCA, and NRI results illustrated that the nomogram performed explicit prognoses more accurately than did the AJCC system alone. Conclusion We identified the relevant factors affecting the survival of BOTSCC patients and analyzed the data on patients suffering from BOTSCC in the SEER database. These factors were used to construct a new nomogram to give clinical staff a more-visual prediction model for the 3-, 5-, and 8-year probabilities of CSS for patients newly diagnosed with BOTSCC, thereby aiding clinical decision making.

As the smart oilfield has grown, various deep learning technologies are being utilized to recognize the graphic feature of the indicator diagram in order to detect the fault type of rod pump system, so as to maintain the oilfield's regular production. However, the original data for the indicator diagrams from various oil fields are influenced by different geographic conditions, sensor equipment, acquisition software, etc. and exhibit specific environmental characteristics. This poses difficulties for indicator diagram-based fault diagnosis methodologies and necessitates the use of a more generalizable diagnosis model. To address the issue, a multi-feature fusion fault diagnostic model is proposed. The model fuses the Fourier descriptor of the indicator diagram as a feature with the graphic feature to enhance the robustness of the feature. Firstly, the two backbone networks perform feature extraction on the single-modal input data of their own networks. Secondly, the information from the indicator diagram and the Fourier descriptor are learned together as features through the interactive fusion module. And finally the integrated features are used for feature classification to obtain the output of the network. The accuracy of the diagnostic model when using only a single feature is respectively: 0.8233(the graphic feature), 0.9422(the Fourier descriptor feature) according to the findings of the validation experiment, and when using the fusion of the two features is 0.9724. The results demonstrate that the suggested multi-input feature fusion model performs better than the single-input model. The technique makes use of the correlation between the features to realize their complementary benefits and enhance the effectiveness of the diagnostic model.

Abstract Background Anaplastic thyroid carcinoma (ATC) is one of the most aggressive malignancies, frequently accompanied by metastasis and aerobic glycolysis. Cancer cells adjust their metabolism by modulating the PKM alternative splicing and facilitating PKM2 isoform expression. Therefore, identifying factors and mechanisms that control PKM alternative splicing is significant for overcoming the current challenges in ATC treatment. Results In this study, the expression of RBX1 was largely enhanced in the ATC tissues. Our clinical tests suggested that high RBX1 expression was significantly related to poor survival. The functional analysis indicated that RBX1 facilitated the metastasis of ATC cells by enhancing the Warburg effect, and PKM2 played a key role in RBX1-mediated aerobic glycolysis. Furthermore, we confirmed that RBX1 regulates PKM alternative splicing and promotes the PKM2-mediated Warburg effect in ATC cells. Moreover, ATC cell migration and aerobic glycolysis induced by RBX1-mediated PKM alternative splicing are dependent on the destruction of the SMAR1/HDAC6 complex. RBX1, as an E3 ubiquitin ligase, degrades SMAR1 in ATC through the ubiquitin–proteasome pathway. Conclusion Overall, our study identified the mechanism underlying the regulation of PKM alternative splicing in ATC cells for the first time and provides evidence about the effect of RBX1 on cellular adaptation to metabolic stress.

Abstract Erectile dysfunction (ED) caused by cavernous nerve injury (CNI) is refractory to heal mainly ascribed to the adverse remodeling of the penis induced by ineffectual microvascular perfusion, fibrosis, and neurotrophins scarcity in cavernosum. Phosphodiesterase type V inhibitors (PDE5i) have been regarded as an alternative candidate drug for avoiding penile neuropathy. However, the therapeutic efficacy is severely limited due to poor accumulation under systemic medication and endogenous nitric oxide (NO) deficiency in cavernosum. Herein, an innovative liposomal microbubble (MB) loaded with both Sildenafil (one of PDE5i) and NO was designed. Ultrasound‐targeted MB destruction (UTMD)‐mediated efficient release and integration erectogenic agents into corpus cavernosum with high biosafety. On a bilateral CNI rat model, the multifunctional MB‐cooperated UTMD improved microvascular perfusion in penis, simultaneously, alleviated hypoxia and oxidative stress, indicating successful activation of NO–cyclic guanosine monophosphate pathway. Also, evaluation of the endothelial/muscular composition, intracavernosal pressure, and neural integrity in the penis proved that coordinated intervention reversed the abnormal structural remodeling and promoted the recovery of functional erection. Our work demonstrates that MB loading Sildenafil and NO combined with UTMD hold great promise to “awaken” the efficacy of PDE5i in neurogenic ED, which provided a superior option for ensuring penile rehabilitation.

Abstract Background The combination therapy of hydrocortisone, vitamin C, and thiamine has been proposed as a potential treatment in patients with sepsis and septic shock. However, subsequent trials have reported conflicting results in relation to survival outcomes. Hence, we performed this randomized controlled trial (RCT) to evaluate the efficacy and safety of early combination therapy among adult patients with septic shock. Methods This single-center, double-blind RCT enrolled adult patients with diagnosis of septic shock within 12 h from Northern Jiangsu People's Hospital between February 2019 and June 2021. Recruited patients were randomized 1:1 to receive intervention (hydrocortisone 200 mg daily, vitamin C 2 g every 6 h, and thiamine 200 mg every 12 h) or placebo (0.9% saline) for 5 days or until ICU discharge. The primary endpoint was 90-day mortality. The secondary endpoints included mortality at day 28, ICU discharge, and hospital discharge; shock reversal; 72-h Delta SOFA score; ICU-free days, vasopressor-free days, and ventilator support -free days up to day 28; ICU length of stay (LOS) and hospital LOS. Results Among 426 patients randomized, a total of 408 patients with septic shock were included in the per-protocol (PP) analysis, of which 203 were assigned to the intervention group and 205 to the placebo group. In the PP population, the primary outcome of 90-day mortality was 39.9% (81/203) and 39.0% (80/205) in the intervention and the placebo groups, respectively, and was not significantly different (P = 0.86). There was no significant difference between two groups in 28-day mortality (36.5% vs. 36.1%, P = 0.94) or the ICU mortality (31.5% vs. 28.8%, P = 0.55) and hospital mortality (34.5% vs. 33.2%, P = 0.78). No other secondary outcomes showed significant differences between two groups, including shock reversal, vasopressor-free days, and ICU LOS. Intention-to-treat analysis included all the 426 patients and confirmed these results (all P > 0.05). Conclusion Among adult patients with septic shock, early use of hydrocortisone, vitamin C, and thiamine combination therapy compared with placebo did not confer survival benefits. Trial registration ClinicalTrials.gov: NCT03872011 , registration date: March 12, 2019. Graphic Abstract

Abstract Respiratory diseases impose a tremendous global health burden on large patient populations. In this study, we aimed to develop DeepMRDTR, a deep learning-based medical image interpretation system for the diagnosis of major respiratory diseases based on the automated identification of a wide range of radiological abnormalities through computed tomography (CT) and chest X-ray (CXR) from real-world, large-scale datasets. DeepMRDTR comprises four networks (two CT-Nets and two CXR-Nets) that exploit contrastive learning to generate pre-training parameters that are fine-tuned on the retrospective dataset collected from a single institution. The performance of DeepMRDTR was evaluated for abnormality identification and disease diagnosis on data from two different institutions: one was an internal testing dataset from the same institution as the training data and the second was collected from an external institution to evaluate the model generalizability and robustness to an unrelated population dataset. In such a difficult multi-class diagnosis task, our system achieved the average area under the receiver operating characteristic curve (AUC) of 0.856 (95% confidence interval (CI):0.843–0.868) and 0.841 (95%CI:0.832–0.887) for abnormality identification, and 0.900 (95%CI:0.872–0.958) and 0.866 (95%CI:0.832–0.887) for major respiratory diseases’ diagnosis on CT and CXR datasets, respectively. Furthermore, to achieve a clinically actionable diagnosis, we deployed a preliminary version of DeepMRDTR into the clinical workflow, which was performed on par with senior experts in disease diagnosis, with an AUC of 0.890 and a Cohen’s k of 0.746–0.877 at a reasonable timescale; these findings demonstrate the potential to accelerate the medical workflow to facilitate early diagnosis as a triage tool for respiratory diseases which supports improved clinical diagnoses and decision-making.

Abstract A thorough interrogation of the immune landscape is crucial for immunotherapy strategy selection and prediction of clinical responses in non-small-cell lung cancer (NSCLC) patients. Single-cell RNA sequencing (scRNA-seq) techniques have prompted the opportunity to dissect the distinct immune signatures between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two major subtypes of NSCLC. Here, we performed scRNA-seq on 72,475 immune cells from 40 samples of tumor and matched adjacent normal tissues spanning 19 NSCLC patients, and drew a systematic immune cell transcriptome atlas. Joint analyses of the distinct cellular compositions, differentially expressed genes (DEGs), cell–cell interactions, pseudotime trajectory, transcriptomic factors and prognostic factors based on The Cancer Genome Atlas (TCGA), revealed the central roles of cytotoxic and effector T and NK cells and the distinct functional macrophages (Mφ) subtypes in the immune microenvironment heterogeneity between LUAD and LUSC. The dominant subtype of Mφ was FABP4-Mφ in LUAD and SPP1-Mφ in LUSC. Importantly, we identified a novel lymphocyte-related Mφ cluster, which we named SELENOP-Mφ, and further established its antitumor role in both types, especially in LUAD. Our comprehensive depiction of the immune heterogeneity and definition of Mφ clusters could help design personalized treatment for lung cancer patients in clinical practice.

Xanthine oxidase (XO), a form of xanthine oxidoreductase, is widely distributed in various human tissues. As a major source for the generation of superoxide radicals, XO is involved in the induction of oxidative stress and inflammation during ischemic and hypoxic tissue injury. Therefore, we designed this study to identify the role of serum XO levels in acute ischemic stroke (AIS) pathogenesis. In this single-center prospective study, 328 consecutive patients with AIS for the first time were included, and 107 age- and sex-matched healthy controls from a community-based stroke screening population were also included. The serum levels of XO and several conventional stroke risk factors were assessed. Multivariate analysis was applied to evaluate the relationship between serum levels of XO and clinical outcomes, and nomogram models were developed to predict the onset, progression and prognosis of AIS. Compared with the healthy control group, the serum level of XO was significantly higher in the AIS group (P

Abstract Background Acute right-sided colonic diverticulitis (RCD) is a common disease in Asian populations for which the optimal treatment remains controversial. The aim of this study was to investigate management and evaluate long-term outcomes of treatment in patients with acute RCD. Methods We retrospectively collected and analyzed clinical data for patients with acute RCD admitted to the Tongren Hospital, Shanghai Jiao Tong University School of Medicine from December 2015 to December 2020. The patients were divided into two groups, according to primary treatment strategy, which was either conservative treatment or surgical treatment. Results A total of 162 consecutive patients with acute RCD were enrolled in the study. There was no significant difference in age, sex, history of abdominal surgery, medical co-morbidities, fever, previous history of RCD, treatment success rate and incidence of complications between the conservative and surgery groups. However, the recurrence rate in conservative groups was significantly higher than in surgery groups (16.53% vs 2.44%, P = 0.020). And more frequent bowel movements and previous history of RCD increased the risk of recurrence of acute RCD. Moreover, there was no significant difference in either treatment success rate or the overall recurrence rate between the patients with uncomplicated diverticulitis and patients with complicated diverticulitis. Conclusions Surgical treatment is also safe and effective for acute RCD. Surgical treatment should mainly be considered for patients with acute RCD with recurrence risk factors (more frequent bowel movements and previous history of RCD) or with complicated acute RCD.

Objectives To identify the clinical risk factors that influence in-hospital mortality in elderly patients with persistent sepsis-associated acute kidney injury (S-AKI) and to establish and validate a nomogram to predict in-hospital mortality.Design Retrospective cohort analysis.Setting Data from critically ill patients at a US centre between 2008 and 2021 were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database (V.1.0).Participants Data from 1519 patients with persistent S-AKI were extracted from the MIMIC-IV database.Primary outcome All-cause in-hospital death from persistent S-AKI.Results Multiple logistic regression revealed that gender (OR 0.63, 95% CI 0.45–0.88), cancer (2.5, 1.69–3.71), respiratory rate (1.06, 1.01–1.12), AKI stage (2.01, 1.24–3.24), blood urea nitrogen (1.01, 1.01–1.02), Glasgow Coma Scale score (0.75, 0.70–0.81), mechanical ventilation (1.57, 1.01–2.46) and continuous renal replacement therapy within 48 hours (9.97, 3.39–33.9) were independent risk factors for mortality from persistent S-AKI. The consistency indices of the prediction and the validation cohorts were 0.780 (95% CI: 0.75–0.82) and 0.80 (95% CI: 0.75–0.85), respectively. The model’s calibration plot suggested excellent consistency between the predicted and actual probabilities.Conclusions This study’s prediction model demonstrated good discrimination and calibration abilities to predict in-hospital mortality of elderly patients with persistent S-AKI, although it warrants further external validation to verify its accuracy and applicability.

C-C motif ligand 2 (CCL2), also known as monocytic chemotactic protein 1 (MCP-1), is an integral chemotactic factor which recruits macrophages for the immune response. Together with its receptors (e.g., CCR2, ACKR1, and ACKR2), they exert noticeable influences on various diseases of different systems. At the maternal-fetal interface, CCL2 is detected to be expressed in trophoblasts, decidual tissue, the myometrium, and others. Meanwhile, existing reports have determined a series of physiological regulators of CCL2, which functions in maintaining normal recruitment of immunocytes, tissue remodeling, and angiogenesis. However, abnormal levels of CCL2 have also been reported to be associated with adverse pregnancy outcomes such as spontaneous abortion, preeclampsia and preterm labor. In this review, we concentrate on CCL2 expression at the maternal-fetal interface, as well as its precise regulatory mechanisms and classic signaling pathways, to reveal the multidimensional aspects of CCL2 in pregnancy.

To estimate unknown population parameters based on panel data having nonignorable item nonresponse, we propose an innovative data grouping approach according to the number of observed components in the multivariate outcome $ \boldsymbol {y} $ when the joint distribution of $ \boldsymbol {y} $ and associated covariate $ \boldsymbol {x} $ is nonparametric and the nonresponse probability conditional on $ \boldsymbol {y} $ and $ \boldsymbol {x} $ has a parametric form. To deal with the identifiability issue, we utilise a nonresponse instrument $ \boldsymbol {z} $ , an auxiliary variable related to $ \boldsymbol {y} $ but not related to the nonresponse probability conditional on $ \boldsymbol {y} $ and $ \boldsymbol {x} $ . We apply a modified generalised method of moments to obtain estimators of the parameters in the nonresponse probability, and a generalised regression estimation to utilise covariate information for efficient estimation of population parameters. Consistency and asymptotic normality of the proposed estimators of the population parameters are established. Simulation and real data results are presented.

Abstract Lung adenocarcinoma (LUAD) and squamous carcinoma (LUSC) are two major subtypes of non-small cell lung cancer with distinct pathologic features and treatment paradigms. The heterogeneity can be attributed to genetic, transcriptional, and epigenetic parameters. Here, we established a multi-omics atlas, integrating 52 single-cell RNA sequencing and 2342 public bulk RNA sequencing. We investigated their differences in genetic amplification, cellular compositions, and expression modules. We revealed that LUAD and LUSC contained amplifications occurring selectively in subclusters of AT2 and basal cells, and had distinct cellular composition modules associated with poor survival of lung cancer. Malignant and stage-specific gene analyses further uncovered critical transcription factors and genes in tumor progression. Moreover, we identified subclusters with proliferating and differentiating properties in AT2 and basal cells. Overexpression assays of ten genes, including sub-cluster markers AQP5 and KPNA2, further indicated their functional roles, providing potential targets for early diagnosis and treatment in lung cancer.

Abstract Background To date, there is no approved blood‐based biomarker for breast cancer detection. Herein, we aimed to assess semaphorin 4C (SEMA4C), a pivotal protein involved in breast cancer progression, as a serum diagnostic biomarker. Methods We included 6,213 consecutive inpatients from Tongji Hospital, Qilu Hospital, and Hubei Cancer Hospital. Training cohort and two validation cohorts were introduced for diagnostic exploration and validation. A pan‐cancer cohort was used to independently explore the diagnostic potential of SEMA4C among solid tumors. Breast cancer patients who underwent mass excision prior to modified radical mastectomy were also analyzed. We hypothesized that increased pre‐treatment serum SEMA4C levels, measured using optimized in‐house enzyme‐linked immunosorbent assay kits, could detect breast cancer. The endpoints were diagnostic performance, including area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Post‐surgery pathological diagnosis was the reference standard and breast cancer staging followed the TNM classification. There was no restriction on disease stage for eligibilities. Results We included 2667 inpatients with breast lesions, 2378 patients with other solid tumors, and 1168 healthy participants. Specifically, 118 patients with breast cancer were diagnosed with stage 0 (5.71%), 620 with stage I (30.00%), 966 with stage II (46.73%), 217 with stage III (10.50%), and 8 with stage IV (0.39%). Patients with breast cancer had significantly higher serum SEMA4C levels than benign breast tumor patients and normal controls (P < 0.001). Elevated serum SEMA4C levels had AUC of 0.920 (95% confidence interval [CI]: 0.900–0.941) and 0.932 (95%CI: 0.911–0.953) for breast cancer detection in the two validation cohorts. The AUCs for detecting early‐stage breast cancer (n = 366) and ductal carcinoma in situ (n = 85) were 0.931 (95%CI: 0.916–0.946) and 0.879 (95%CI: 0.832–0.925), respectively. Serum SEMA4C levels significantly decreased after surgery, and the reduction was more striking after modified radical mastectomy, compared with mass excision (P < 0.001). The positive rate of enhanced serum SEMA4C levels was 84.77% for breast cancer and below 20.75% for the other 14 solid tumors. Conclusions Serum SEMA4C demonstrated promising potential as a candidate biomarker for breast cancer diagnosis. However, validation in prospective settings and by other study groups is warranted.

Hydroxyl tagging velocimetry (HTV) technology is crucial in the velocimetry diagnosis of combustion flow fields. However, obtaining accurate HTV information in practical engineering applications is difficult because of complex flow fields and background noise interference. Therefore, for noise suppression, we proposed a generative adversarial network method for targeted network training based on the analysis of HTV image noise characteristics in a complex flow field and the construction of a high-confidence noise description model. The proposed method can effectively suppress noise in HTV experimental data, improve the signal-to-noise ratio of HTV images, and improve the accuracy of HTV measurement.

Tuberculosis, caused by Mycobacterium tuberculosis, engenders an onerous burden on public hygiene. Congenital and adaptive immunity in the human body act as robust defenses against the pathogens. However, in coevolution with humans, this microbe has gained multiple lines of mechanisms to circumvent the immune response to sustain its intracellular persistence and long-term survival inside a host. Moreover, emerging evidence has revealed that this stealthy bacterium can alter the expression of demic noncoding RNAs (ncRNAs), leading to dysregulated biological processes subsequently, which may be the rationale behind the pathogenesis of tuberculosis. Meanwhile, the differential accumulation in clinical samples endows them with the capacity to be indicators in the time of tuberculosis suffering. In this article, we reviewed the nearest insights into the impact of ncRNAs during Mycobacterium tuberculosis infection as realized via immune response modulation and their potential as biomarkers for the diagnosis, drug resistance identification, treatment evaluation, and adverse drug reaction prediction of tuberculosis, aiming to inspire novel and precise therapy development to combat this pathogen in the future.

Covariate-adaptive randomisation has a more than 45 years of history of applications in clinical trials, in order to balance treatment assignments across prognostic factors that may have influence on the outcomes of interest. However, almost no theory had been developed for covariate-adaptive randomisation until a paper on the theory of testing hypotheses published in 2010. In this article, we review aspects of methodology and theory developed in the last decade for statistical inference under covariate-adaptive randomisation. We focus on issues such as whether a conventional procedure valid under the assumption that treatments are assigned completely at random is still valid or conservative when the actual randomisation is covariate-adaptive, how a valid inference procedure can be obtained by modifying a conventional method or directly constructed by stratifying the covariates used in randomisation, whether inference procedures have different properties when covariate-adaptive randomisation schemes have different degrees of balancing assignments, and how to further adjust covariates in the inference procedures to gain more efficiency. Recommendations are made during the review and further research problems are discussed.

Background Onco-immunogenic molecule CD155 is overexpressed in various tumor microenvironments (TME) including in colorectal cancer (CRC). Tumor-associated macrophages (TAMs) are the most abundant immune cells in CRC TME and play a vital role in CRC progression and metastasis. Most studies have focused on investigating the role of CRC cell-specific CD155 on CRC progression, while the contribution of TAMs-specific CD155 is still unknown. Here, we sought to investigate the expression pattern of CD155 in CRC TAMs and its role in tumor immunity and progression.Methods CD155 expression patterns in CRC TAMs and macrophages in paratumor or adjacent normal tissue were analyzed in 50 patients with CRC using flow cytometry and in 141 patients with CRC using immunohistochemistry. The correlation of CD155 expression level in TAMs with M1 and M2 phenotypic transition was analyzed. The role of macrophage-specific CD155 in CRC progression and tumor immune response was investigated in vitro and in vivo. We further analyzed the effect of CRC cells on the regulation of CD155 expression in macrophages.Results CRC TAMs from clinical samples showed robustly higher expression of CD155 than macrophages from paratumor and adjacent normal tissues. The CD155 expression level was higher in TAMs of CRC at III/IV stages compared with the I/II stages and was negatively associated with the survival of patients with CRC. CD155+ TAMs showed an M2 phenotype and higher expression of interleukin (IL)-10 and transforming growth factor (TGF)-β. CD155+ macrophages promoted CRC cell migration, invasion, and tumor growth supporting the findings from the clinical tissue analysis. This effect was mainly regulated by TGF-β-induced STAT3 activation-mediated release of matrix metalloproteinases (MMP)2 and MMP9 in CRC cells. CD155–⁄– bone marrow transplantation in wild-type mice, as well as CD155– macrophages treatment, promoted the antitumor immune response in the mice ectopic CRC model. Additionally, CRC cells released IL-4 to trigger CD155 expression in macrophages indicating the regulatory role of CRC cells in the development of CD155+ TAMs.Conclusions These findings indicated that CD155+ TAMs are responsible for the M2-phenotype transition, immunosuppression, and tumor progression in CRC. The specific localization of CD155+ TAMs in CRC tissue could turn into a potential therapeutic target for CRC treatment.

Abstract Neurogenic erectile dysfunction (nED) is one of the most common and intractable postoperative complications of rectal and prostate cancer surgery and sometimes accompanies patients lifelong. The transplantation of stem cells has been proved a promising way for treatment. However, the therapeutic efficacy is severely impaired by excessive cell loss and death and poor accumulation in the injury site along with the traditional implantation strategy. Herein, an EPO‐loaded multifunctional hydrogel was designed. The hydrogels' adhesive property and mechanical strength were enhanced by adding catechol‐catechol adducts, thus significantly improving adipose‐derived stem cells (ADSC) retention and rescuing cell loss in the injury site. Meanwhile, the sustained release of EPO effectively ameliorated the viability and paracrine activity of ADSC, leading to enhanced migration of Schwann cells and differentiation of PC12 cells in vivo. On a bilateral cavernous nerve injury rat model, the present stem cell‐EPO‐hydrogel implanted strategy could significantly alleviate erectile dysfunction. The higher expression of Tuj1 and lower expression of GFAP in the major pelvic ganglia (MPG) indicated the acceleration of neural differentiation while the suppressing development of astrocytes. Also, the combined therapy restored the expression levels of eNOs, nNOs, and α‐SMA in penile tissues, suggesting the rehabilitation of the penis. Further analysis of Masson trichrome staining and apoptosis evaluation of the corpus cavernosum showed the preservation of vascular endothelium content and the prevention of penile fibrosis after denervation. Overall, we believe that this combined strategy presents a promising way not only for restoring neurogenic erectile function but also for the clinical translation of stem cell therapy.

Abstract Background Lung cancer is the common cause of cancer‐related deaths throughout the world, and brain is a frequent metastatic site of lung cancer. Aim This research sought to evaluate the impact of the number of brain metastases in prognosticating non‐small cell lung cancer (NSCLC) patients accounting to the role of epidermal growth factor receptor (EGFR) mutations. Methods and Results NSCLC patients with brain metastases diagnosed/treated in West China Hospital, Sichuan University between 2009 and 2017 were identified retrospectively. Kaplan–Meier approach was adopted to estimate OS. And we performed univariate and multivariate Cox proportional hazards regression analyses of characteristics related to overall survival (OS) in both EGFR‐mutated and wild‐type cohorts. In total, this study included 611 eligible NSCLC patients with brain metastases. Extracranial metastases and chemotherapy were independent prognostic factors of OS in both cohorts. As the disease progressed, EGFR‐mutated patients had brain metastasis significantly earlier (P

With the increasing incidence and mortality of pulmonary tuberculosis, in addition to tough and controversial disease management, time-wasting and resource-limited conventional approaches to the diagnosis and differential diagnosis of tuberculosis are still awkward issues, especially in countries with high tuberculosis burden and backwardness. In the meantime, the climbing proportion of drug-resistant tuberculosis poses a significant hazard to public health. Thus, auxiliary diagnostic tools with higher efficiency and accuracy are urgently required. Artificial intelligence (AI), which is not new but has recently grown in popularity, provides researchers with opportunities and technical underpinnings to develop novel, precise, rapid, and automated implements for pulmonary tuberculosis care, including but not limited to tuberculosis detection. In this review, we aimed to introduce representative AI methods, focusing on deep learning and radiomics, followed by definite descriptions of the state-of-the-art AI models developed using medical images and genetic data to detect pulmonary tuberculosis, distinguish the infection from other pulmonary diseases, and identify drug resistance of tuberculosis, with the purpose of assisting physicians in deciding the appropriate therapeutic schedule in the early stage of the disease. We also enumerated the challenges in maximizing the impact of AI in this field such as generalization and clinical utility of the deep learning models.

This study presents a glucose biosensor based on electrospun core–sheath nanofibers. Two types of film were fabricated using different electrospinning procedures. Film F1 was composed solely of core–sheath nanofibers fabricated using a modified coaxial electrospinning process. Film F2 was a double-layer hybrid film fabricated through a sequential electrospinning and blending process. The bottom layer of F2 comprised core–sheath nanofibers fabricated using a modified process, in which pure polymethacrylate type A (Eudragit L100) was used as the core section and water-soluble lignin (WSL) and phenol were loaded as the sheath section. The top layer of F2 contained glucose oxidase (GOx) and gold nanoparticles, which were distributed throughout the polyvinylpyrrolidone K90 (PVP K90) nanofibers through a single-fluid blending electrospinning process. The study investigated the sequential electrospinning process in detail. The experimental results demonstrated that the F2 hybrid film had a higher degradation efficiency of β-D-glucose than F1, reaching a maximum of over 70% after 12 h within the concentration range of 10–40 mmol/L. The hybrid film F2 is used for colorimetric sensing of β-D-glucose in the range of 1–15 mmol/L. The solution exhibited a color that deepened gradually with an increase in β-D-glucose concentration. Electrospinning is flexible in creating structures for bio-cascade reactions, and the double-layer hybrid film can provide a simple template for developing other sensing nanomaterials.

Aiming at the low stroke times motion characteristics badly of beam pumping units，a reversing mechanism for gear guide-rod pumping units is analyzed. The reversing mechanism is mainly composed of a long rod incomplete gear，a rack moving frame，a roller and the like. Firstly，the gear guide-rod pumping unit reversing mechanism and its principle are introduced，and then the mechanical model of the reversing mechanism is established. Then，based on the contact impact algorithm，the joint dynamics simulation of the reversing mechanism is carried out by using Solidworks and Adams. The results show that，the speed of the reversing mechanism is gentle on the upstroke，and there is a sudden increase in acceleration on the downstroke. Comparing the acceleration simulation curves of the pumping unit under three different strokes，the pumping unit meets the requirements of low-yield wells production requirements. Comparing the acceleration theoretical curves of beam pumping unit and gear guide-rod pumping unit，the acceleration of the suspension point of the upstroke of this pumping unit is less than that of beam pumping unit，has better motion characteristics，and is suitable for low production well oil extraction operation. The change of stroke times has a greater impact on the contact force of components. At the same time，it provides data support for the subsequent strength calculation of the reversing mechanism.

Background: The Global Health Security (GHS) Index has been developed to measure a country’s capacity to cope with a public health emergency; however, evidence for whether it corresponds to the response to a global pandemic is lacking. This study performed a multidimensional association analysis to explore the correlation between the GHS Index and COVID-19-associated morbidity, mortality, and disease increase rate (DIR) in 178 countries (regions). Methods: The GHS Index and COVID-19 pandemic data – including total cases per million (TCPM), total deaths per million (TDPM), and daily growth rate – were extracted from online databases. The Spearman correlation coefficient was applied to describe the strength of the association between the GHS Index, sociological characteristics, and the epidemic situation of COVID-19. DIRs were compared, and the impact of the GHS Index on the DIR by the time of “lockdown” was visualized. Results: The overall GHS Index was positively correlated with TCPM and TDPM, with coefficients of 0.34 and 0.41, respectively. Countries categorized into different GHS Indextiers had different DIRs before implementing lockdown measures. However, no significant difference was observed between countries in the middle and upper tiers after implementing lockdown measures. The correlation between GHS Index and DIR was positive five days before lockdown measures were taken, but it became negative 13 days later. Conclusions: The GHS Index has limited value in assessing a country's capacity to respond to a global pandemic. Nevertheless, it has potential value in determining the country’s ability to cope with a local epidemic situation.

BackgroundEndovascular repair of type B aortic dissection (TBAD) with aberrant right subclavian artery (ARSA) is challenging due to anatomical complexity. The embedded modular single-branched stent graft (EMSBSG) could solve this problem. However, the hemodynamic efficacy of this innovative technique has not been fully assessed. This study aimed to propose morphometric and functional indicators to quantify the outcomes of EMSBSG in treating TBAD with ARSA.Material and MethodsA patient who had TBAD with ARSA underwent EMSBSG implantation was admitted. Computational fluid dynamics (CFD) and three-dimensional structural analyses were conducted based on CTA datasets before the operation (Pre-1) and at 4 and 25 days after EMSBSG implantation (Post-1 and Post-2). Quantitative and qualitative functional analyses were conducted via pressure-, velocity- and wall shear stress (WSS) -based parameters, such as the luminal pressure difference (LPD), total energy loss, and flow distribution ratio. By precisely registering the aortas at the three time points, parameter variations in the EMSBSG region were also computed to investigate the prognostic improvement after EMSBSG implantation.ResultsThe first balance point of LPD distally shifted to the abdominal aorta in Post-1 by a distance of 20.172 cm, and shifted out of the dissected region in Post-2, indicating positive pressure recovery post EMSBSG. The flow distribution ratios of all aortic arch branches increased after EMSBSG implantation. A positive normal deformation index in the EMSBSG region confirmed true lumen expansion; dominant ARN (area ratio of negative value) of pressure and WSS-based parameters indicated an improved prognosis from Post-1 to Post-2.ConclusionsThe short-term results of EMSBSG in treating TBAD with ARSA proved to be promising, especially in EMSBSG region. Comprehensive evaluation could provide new insight into the therapy of TBAD with ARSA. Thus, it might guide the further management of complex aortic arch lesions.

ObjectivesDistinction of malignant pulmonary nodules from the benign ones based on computed tomography (CT) images can be time-consuming but significant in routine clinical management. The advent of artificial intelligence (AI) has provided an opportunity to improve the accuracy of cancer risk prediction.MethodsA total of 8950 detected pulmonary nodules with complete pathological results were retrospectively enrolled. The different radiological manifestations were identified mainly as various nodules densities and morphological features. Then, these nodules were classified into benign and malignant groups, both of which were subdivided into finer specific pathological types. Here, we proposed a deep convolutional neural network for the assessment of lung nodules named DeepLN to identify the radiological features and predict the pathologic subtypes of pulmonary nodules.ResultsIn terms of density, the area under the receiver operating characteristic curves (AUCs) of DeepLN were 0.9707 (95% confidence interval, CI: 0.9645-0.9765), 0.7789 (95%CI: 0.7569-0.7995), and 0.8950 (95%CI: 0.8822-0.9088) for the pure-ground glass opacity (pGGO), mixed-ground glass opacity (mGGO) and solid nodules. As for the morphological features, the AUCs were 0.8347 (95%CI: 0.8193-0.8499) and 0.9074 (95%CI: 0.8834-0.9314) for spiculation and lung cavity respectively. For the identification of malignant nodules, our DeepLN algorithm achieved an AUC of 0.8503 (95%CI: 0.8319-0.8681) in the test set. Pertaining to predicting the pathological subtypes in the test set, the multi-task AUCs were 0.8841 (95%CI: 0.8567-0.9083) for benign tumors, 0.8265 (95%CI: 0.8004-0.8499) for inflammation, and 0.8022 (95%CI: 0.7616-0.8445) for other benign ones, while AUCs were 0.8675 (95%CI: 0.8525-0.8813) for lung adenocarcinoma (LUAD), 0.8792 (95%CI: 0.8640-0.8950) for squamous cell carcinoma (LUSC), 0.7404 (95%CI: 0.7031-0.7782) for other malignant ones respectively in the malignant group.ConclusionsThe DeepLN based on deep learning algorithm represented a competitive performance to predict the imaging characteristics, malignancy and pathologic subtypes on the basis of non-invasive CT images, and thus had great possibility to be utilized in the routine clinical workflow.

BackgroundProgrammed death-ligand 1 (PD-L1) assessment of lung cancer in immunohistochemical assays was only approved diagnostic biomarker for immunotherapy. But the tumor proportion score (TPS) of PD-L1 was challenging owing to invasive sampling and intertumoral heterogeneity. There was a strong demand for the development of an artificial intelligence (AI) system to measure PD-L1 expression signature (ES) non-invasively.MethodsWe developed an AI system using deep learning (DL), radiomics and combination models based on computed tomography (CT) images of 1,135 non-small cell lung cancer (NSCLC) patients with PD-L1 status. The deep learning feature was obtained through a 3D ResNet as the feature map extractor and the specialized classifier was constructed for the prediction and evaluation tasks. Then, a Cox proportional-hazards model combined with clinical factors and PD-L1 ES was utilized to evaluate prognosis in survival cohort.ResultsThe combination model achieved a robust high-performance with area under the receiver operating characteristic curves (AUCs) of 0.950 (95% CI, 0.938–0.960), 0.934 (95% CI, 0.906–0.964), and 0.946 (95% CI, 0.933–0.958), for predicting PD-L1ES

With increasing appearances of high-dimensional data over the past two decades, variable selections through frequentist likelihood penalisation approaches and their Bayesian counterparts becomes a popular yet challenging research area in statistics. Under a normal linear model with shrinkage priors, we propose a benchmark variable approach for Bayesian variable selection. The benchmark variable serves as a standard and helps us to assess and rank the importance of each covariate based on the posterior distribution of the corresponding regression coefficient. For a sparse Bayesian analysis, we use the benchmark in conjunction with a modified BIC. We also develop our benchmark approach to accommodate models with covariates exhibiting group structures. Two simulation studies are carried out to assess and compare the performances among the proposed approach and other methods. Three real datasets are also analysed by using these methods for illustration.

Multiple daily injections via subcutaneous route are the primary modes of insulin delivery for patients with Diabetes Mellitus. While this process is invasive, painful and may cause patients to develop lipohypertrophy at injection site, the perception of fear surrounding this process causes patients to delay in initiation and remain persistent with insulin therapy over time. Moreover, poor glycemic control may often lead to acute complications, such as severe hypoglycemia and nocturnal hypoglycemia, especially in older patients with diabetes. To address the imperative need for a patient-convenient non-invasive insulin therapy, an insulin-loaded arginine-coated self-emulsifying nanoglobule system (INS-LANano) was developed for nasal delivery of insulin with a biodegradable cationic surfactant—Lauroyl Ethyl Arginate (LAE). Incorporation of LAE resulted in formation of positively charged nanoglobules with L-arginine oriented on the surface. LANano enabled binding of insulin molecules on the surface of nanoglobules via an electrostatic interaction between negatively charged α-helix and LAE molecules at physiological pH. INS-LANano showed a hydrodynamic diameter of 23.38 nm with a surface charge of +0.118 mV. The binding efficiency of insulin on LANano globules was confirmed by zeta potential, circular dichroism (CD) spectroscopy and centrifugal ultrafiltration studies. The attachment of insulin with permeation-enhancing nanoglobules demonstrated significantly higher in vitro permeability of insulin of 15.2% compared to insulin solution across human airway epithelial cell (Calu-3) monolayer. Upon intranasal administration of INS-LANano to diabetic rats at 2 IU/kg insulin dose, a rapid absorption of insulin with significantly higher Cmax of 14.3 mU/L and relative bioavailability (BA) of 23.3% was observed. Therefore, the INS-LANano formulation significant translational potential for intranasal delivery of insulin

BackgroundEpidermal growth factor receptor (EGFR) genotyping and programmed death ligand-1 (PD-L1) expressions are of paramount importance for treatment guidelines such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional identification of EGFR or PD-L1 status requires surgical or biopsied tumor specimens, which are obtained through invasive procedures associated with risk of morbidities and may be unavailable to access tissue samples. Here, we developed an artificial intelligence (AI) system that can predict EGFR and PD-L1 status in using non-invasive computed tomography (CT) images.MethodsA multitask AI system including deep learning (DL) module, radiomics (RA) module, and joint (JO) module combining the DL, RA, and clinical features was developed, trained, and optimized with CT images to predict the EGFR and PD-L1 status. We used feature selectors and feature fusion methods to find the best model among combinations of module types. The models were evaluated using the areas under the receiver operating characteristic curves (AUCs).ResultsOur multitask AI system yielded promising performance for gene expression status, subtype classification, and joint prediction. The AUCs of DL module achieved 0.842 (95% CI, 0.825–0.855) in the EGFR mutated status and 0.805 (95% CI, 0.779–0.829) in the mutated-EGFR subtypes discrimination (19Del, L858R, other mutations). DL module also demonstrated the AUCs of 0.799 (95% CI, 0.762–0.854) in the PD-L1 expression status and 0.837 (95% CI, 0.775–0.911) in the positive-PD-L1 subtypes (PD-L1 tumor proportion score, 1%–49% and ≥50%). Furthermore, the JO module of our AI system performed well in the EGFR and PD-L1 joint cohort, with an AUC of 0.928 (95% CI, 0.909–0.946) for distinguishing EGFR mutated status and 0.905 (95% CI, 0.886–0.930) for discriminating PD-L1 expression status.ConclusionOur AI system has demonstrated the encouraging results for identifying gene status and further assessing the genotypes. Both clinical indicators and radiomics features showed a complementary role in prediction and provided accurate estimates to predict EGFR and PD-L1 status. Furthermore, this non-invasive, high-throughput, and interpretable AI system can be used as an assistive tool in conjunction with or in lieu of ancillary tests and extensive diagnostic workups to facilitate early intervention.

Quantile treatment effects can be important causal estimands in evaluation of biomedical treatments or interventions for health outcomes such as medical cost and utilisation. We consider their estimation in observational studies with many possible covariates under the assumption that treatment and potential outcomes are independent conditional on all covariates. To obtain valid and efficient treatment effect estimators, we replace the set of all covariates by lower dimensional sets for estimation of the quantiles of potential outcomes. These lower dimensional sets are obtained using sufficient dimension reduction tools and are outcome specific. We justify our choice from efficiency point of view. We prove the asymptotic normality of our estimators and our theory is complemented by some simulation results and an application to data from the University of Wisconsin Health Accountable Care Organization.

Background:Myocardial impairment is a major complication and an important prognostic predictor of sepsis. Therefore, early and accurate diagnosis as well as timely management of septic cardiomyopathy is critical to achieve favorable outcomes.Aims:To investigate the risk factors of septic cardiomyopathy.Study Design:Cross-sectional study.Methods:This study performed between May 2016 and June 2018 recruited 93 septic patients from the intensive care unit. All patients received standardized treatments. Septic patients were divided into two groups: non cardiomyopathy (n=45) and septic cardiomyopathy group (n=48). Blood samples were collected and transthoracic echocardiography was performed within 24 hours of intensive care unit admission. Septic patients with one ultrasound abnormality but no history of heart disease were diagnosed as having septic cardiomyopathy. Plasma histones, cardiac troponin I, and N-terminal pro-brain natriuretic peptide were measured using ELISA. Sequential Organ Failure Assessment scores, vasopressor use, and the outcomes of intensive care unit stay were analyzed. Spearman rank analysis was used to determine the correlation between plasma histone H4 and other parameters. Binary logistic regression and receiver operating characteristic curve analysis were used to determine the risk factors for septic cardiomyopathy.Results:Compared with the non-cardiomyopathy group, the septic cardiomyopathy group had significantly higher plasma H4 and cardiac troponin I levels, a higher Sequential Organ Failure Assessment score, more frequent vasopressor use, and a higher mortality rate (p