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Abstract The advent of immunotherapy has greatly improved the prognosis of non-small cell lung (NSCLC) patients. However, given its low response rate and high cost of treatment, the search for valuable predictive markers of treatment efficacy is necessary. Considering the complexity and heterogeneity of the tumour and tumour microenvironment, the construction of a multi-dimensional prediction model is necessary. Therefore, we aimed to integrate clinical parameters, radiomic features, and immune signature data from NSCLC patients receiving immunotherapy to construct a multi-dimensional prediction model to better predict the efficacy of immunotherapy. The current study enrolled 137 NSCLC patients who received immunotherapy. We collected baseline clinical information, CT images, and tumour tissue specimens. Using 3D-Slicer software, radiomic features were extracted from patient CT images, and tumor tissue samples obtained before immunotherapy were subjected to immunohistochemical staining. Then, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to downscale the data, and the radiomic features and immune signatures associated with the prognosis of immunotherapy patients were identified. The modified lung immune predictive index (mLIPI), radiomics score (Radioscore), immune score and multi-dimensional model nomogram were constructed. The C-index and area under the curve (AUC) were applied to evaluate the predictive efficacy of the models. Three radiomic features and three immune signatures that could predict the efficacy of immunotherapy were eventually screened. Multivariate analysis showed that the mLIPI, Radioscore, and immune score were independent predictive factors for PFS and OS (P

Due to real-time fluctuations in wind farm output, large-scale renewable energy (RE) generation poses significant challenges to power system stability. To address this issue, this paper proposes a deep reinforcement learning (DRL)-based electric hydrogen hybrid storage (EHHS) strategy to mitigate wind power fluctuations (WPFs). First, a wavelet packet power decomposition algorithm based on variable frequency entropy improvement is proposed. This algorithm characterizes the energy characteristics of the original wind power in different frequency bands. Second, to minimize WPF and the comprehensive operating cost of EHHS, an optimization model for suppressing wind power in the integrated power and hydrogen system (IPHS) is constructed. Next, considering the real-time and stochastic characteristics of wind power, the wind power smoothing model is transformed into a Markov decision process. A modified proximal policy optimization (MPPO) based on wind power deviation is proposed for training and solving. Based on the DRL agent’s real-time perception of wind power energy characteristics and the IPHS operation status, a WPF smoothing strategy is formulated. Finally, a numerical analysis based on a specific wind farm is conducted. The simulation results based on MATLAB R2021b show that the proposed strategy effectively suppresses WPF and demonstrates excellent convergence stability. The comprehensive performance of the MPPO is improved by 21.25% compared with the proximal policy optimization (PPO) and 42.52% compared with MPPO.

Abstract Background Immune checkpoint inhibitors (ICIs) combined with chemotherapy have been recommended as the standard treatment for advanced NSCLC patients without driver‐gene mutations. However, there are different genetic characteristics and biological traits of tumors between non‐East Asian (nEA) and East Asian (EA) patients with NSCLC, which may contribute to differences in the efficacy of ICIs in different ethnic populations. Previous findings regarding differences in the efficacy of ICIs among ethnic groups have been inconsistent. Therefore, we performed a meta‐analysis by collecting published data to investigate the clinical outcomes of ICIs for EA NSCLC patients compared to nEA patients. Methods Overall survival (OS) and progression‐free survival (PFS) were used to access the difference in survival outcomes between the two populations. Subgroup analyses were performed based on the line of ICIs, the use of ICIs alone or in combination, and the type of ICIs. Results A total of 9826 NSCLC patients from 21 randomized controlled trials (RCTs) with 4064 EAs were included, which involved PD‐1, PD‐L1, and CTLA‐4 inhibitors. EA NSCLC patients who received ICIs‐based therapy were associated with significantly improved survival benefits in OS (p = 0.02) compared with nEA patients. Subgroup analysis indicated that EA patients receiving first‐line ICIs showed significantly superior OS compared with nEA patients (p = 0.007). Chemo‐ICIs treatment showed significant advantages in terms of OS (p = 0.002) and PFS (p = 0.02) among EA patients compared to nEA patients. In addition, PD‐1 inhibitors were associated with improved OS among both EA patients and nEA patients compared with PD‐L1 inhibitors. Conclusion EA NSCLC patients who received ICIs‐based therapy were associated with significantly improved survival benefits compared with nEA NSCLC patients. Earlier intervention with ICIs and combination treatment was more recommended for EA NSCLC patients. Moreover, PD‐1 inhibitors are associated with prolonged survival among both EA and nEA patients.

Abstract Cancer treatment is gradually entering an era of precision, with multitude studies in gene testing and immunotherapy. Tumor cells can be recognized and eliminated by the immune system through the expression of tumor-associated antigens, but when the cancer escapes or otherwise suppresses immunity, the balance between cancer cell proliferation and immune-induced cancer cell killing may be interrupted, resulting in tumor proliferation and progression. There has been significant attention to combining conventional cancer therapies (i.e., radiotherapy) with immunotherapy as opposed to treatment alone. The combination of radio-immunotherapy has been demonstrated in both basic research and clinical trials to provide more effective anti-tumor responses. However, the absolute benefits of radio-immunotherapy are dependent on individual characteristics and not all patients can benefit from radio-immunotherapy. At present, there are numerous articles about exploring the optimal models for combination radio-immunotherapy, but the factors affecting the efficacy of the combination, especially with regard to radiosensitivity remain inconclusive. Radiosensitivity is a measure of the response of cells, tissues, or individuals to ionizing radiation, and various studies have shown that the radiosensitivity index (RSI) will be a potential biomarker for predicting the efficacy of combination radio-immunotherapy. The purpose of this review is to focus on the factors that influence and predict the radiosensitivity of tumor cells, and to evaluate the impact and predictive significance of radiosensitivity on the efficacy of radio-immunotherapy combination.

Abstract Background Accurate prognostic prediction plays a crucial role in the clinical setting. However, the TNM staging system fails to provide satisfactory individual survival prediction for stage III non‐small cell lung cancer (NSCLC). The performance of the deep learning network for survival prediction in stage III NSCLC has not been explored. Objectives This study aimed to develop a deep learning‐based prognostic system that could achieve better predictive performance than the existing staging system for stage III NSCLC. Methods In this study, a deep survival learning model (DSLM) for stage III NSCLC was developed based on the Surveillance, Epidemiology, and End Results (SEER) database and was independently tested with another external cohort from our institute. DSLM was compared with the Cox proportional hazard (CPH) and random survival forest (RSF) models. A new prognostic system for stage III NSCLC was also proposed based on the established deep learning model. Results The study included 16,613 patients with stage III NSCLC from the SEER database. DSLM showed the best performance in survival prediction, with a C‐index of 0.725 in the validation set, followed by RSF (0.688) and CPH (0.683). DSLM also showed C‐indices of 0.719 and 0.665 in the internal and real‐world external testing datasets, respectively. In addition, the new prognostic system based on DSLM (AUROC = 0.744) showed better performance than the TNM staging system (AUROC = 0.561). Conclusion In this study, a new, integrated deep learning‐based prognostic model was developed and evaluated for stage III NSCLC. This novel approach may be valuable in improving patient stratification and potentially provide meaningful prognostic information that contributes to personalized therapy.

Abstract Background The efficacy of bevacizumab in non-small cell lung cancer (NSCLC) patients is unsatisfactory, and the selection of suitable patients is still challenging. Given the epigenetic modifications can contribute to an aberrant regulation of angiogenesis and microenvironment, we investigated DNA methylation profiles to determine clinical benefit of bevacizumab in NSCLC patients. Methods Genome-wide DNA methylation profiling was performed in NSCLC patients treated with chemotherapy in combination with bevacizumab. Patients were divided into better prognosis group (A group) and inferior prognosis group (B group) based on their survival. The difference of methylation patterns and respective functional enrichment analysis were performed between two groups. Prognostic DNA methylation signature for bevacizumab was established with the least absolute shrinkage and selection operator regression analyses. TISIDB database was further used to infer immunological relationship for prognostic related DNA methylation. Results Twenty patients were included in this study, and significantly distinct methylation patterns were observed between patients with different prognosis. Related genes of different methylation regions were significantly enriched in the biological process of cell projection assembly, neutrophil mediated immunity, and pathway of VEGFA-VEGFR2 signaling pathway, neutrophil degranulation. A 10-gene DNA methylation signature for prognosis prediction was established with the C-index of 0.76. And host genes of signature were found to be related to the abundance of ActCD4, Th1, ActCD8, NKT and neutrophil cells. Conclusion The 10-gene DNA methylation signature could serve as a novel biomarker to predict the clinical benefit of bevacizumab therapy and improve this anti-tumor approach for NSCLC patients.

Background Many tools have been developed to predict the efficacy of immunotherapy, such as lung immune prognostic index (LIPI), EPSILoN [Eastern Cooperative Oncology Group performance status (ECOG PS), smoking, liver metastases, lactate dehydrogenase (LDH), neutrophil‐to‐lymphocyte ratio (NLR)], and modified lung immune predictive index (mLIPI) scores. The aim of this study was to determine the ability of three predictive scores to predict the outcomes in Chinese advanced non‐small cell lung cancer (aNSCLC) patients treated with immune checkpoint inhibitors (ICIs). Methods We retrospectively analyzed 429 patients with aNSCLC treated with ICIs at our institution. The predictive ability of these models was evaluated using area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis. Calibration was assessed using the Hosmer–Lemeshow test (H–L test) and Spearman's correlation coefficient. Progression‐free survival (PFS) and overall survival (OS) curves were generated using the Kaplan–Meier method. Results The AUC values of LIPI, mLIPI, and EPSILoN scores predicting PFS at 6 months were 0.642 [95% confidence interval (CI):0.590–0.694], 0.720 (95% CI: 0.675–0.762), and 0.633 (95% CI: 0.585–0.679), respectively (p

Background and PurposeThis study aims to develop a risk model to predict esophageal fistula in esophageal cancer (EC) patients by learning from both clinical data and computerized tomography (CT) radiomic features.Materials and MethodsIn this retrospective study, computerized tomography (CT) images and clinical data of 186 esophageal fistula patients and 372 controls (1:2 matched by the diagnosis time of EC, sex, marriage, and race) were collected. All patients had esophageal cancer and did not receive esophageal surgery. 70% patients were assigned into training set randomly and 30% into validation set. We firstly use a novel attentional convolutional neural network for radiographic descriptor extraction from nine views of planes of contextual CT, segmented tumor and neighboring structures. Then clinical factors including general, diagnostic, pathologic, therapeutic and hematological parameters are fed into neural network for high-level latent representation. The radiographic descriptors and latent clinical factor representations are finally associated by a fully connected layer for patient level risk prediction using SoftMax classifier.Results512 deep radiographic features and 32 clinical features were extracted. The integrative deep learning model achieved C-index of 0.901, sensitivity of 0.835, and specificity of 0.918 on validation set with superior performance than non-integrative model using CT imaging alone (C-index = 0.857) or clinical data alone (C-index = 0.780).ConclusionThe integration of radiomic descriptors from CT and clinical data significantly improved the esophageal fistula prediction. We suggest that this model has the potential to support individualized stratification and treatment planning for EC patients.

Abstract Objectives We aimed to identify the risk factors and provide a nomogram for the prediction of radiotherapy-related esophageal fistula in patients with esophageal cancer (EC) using a case-control study. Patients and methods Patients with esophageal fistula who received radiotherapy or chemoradiotherapy between 2003 and 2017 were retrospectively collected in two institutions. In the training cohort (TC), clinical, pathologic, and serum data of 136 patients (cases) who developed esophageal fistula during or after radiotherapy were enrolled and compared with 272 controls (1:2 matched with the diagnosis time of EC, sex, marriage, and race). After the univariable and multivariable logistic regression analyses, the independent risk factors were identified and incorporated into a nomogram. Then the nomogram for the risk prediction was externally validated in the validation cohort (VC; 47 cases and 94 controls) using bootstrap resampling. Results Multivariable analyses demonstrated that ECOG PS, BMI, T4, N2/3 and re-radiotherapy were independent factors for esophageal fistula. Then a nomogram was constructed with the C-index of 0.805 (95% CI, 0.762–0.848) for predicting the risk of developing esophageal fistula in EC patients receiving radiotherapy. Importantly, the C-index maintained 0.764 (95% CI, 0.683–0.845) after the external validation. Conclusions We created and externally validated the first risk nomogram of esophageal fistula associated with radiotherapy. This will aid individual risk stratification of patients with EC developing esophageal fistula.

Abstract Introduction Locally advanced nonsmall‐cell lung cancer (LA‐NSCLC) represented a highly heterogeneous group, with more than half of the patients aged ≥65 years at the time of diagnosis. However, the optimal treatment for elderly LA‐NSCLC patients was still not defined. Methods A total of 33530 elderly patients (≥65 years) diagnosed with LA‐NSCLC from 2004 to 2014 were identified from Surveillance, Epidemiology, and End Results (SEER) database. Results Locally advanced nonsmall‐cell lung cancer patients aged 65‐74 years were more frequently treated with chemoradiotherapy (CRT) (40%), while patients aged ≥75 years received more best supportive care (BSC) (36%). For age group of 65‐74 years, patients who had surgery with or without (neo)adjuvant therapy had a median survival of 28 months, CRT 15 months, radiotherapy (RT) alone 6 months, chemotherapy alone 11 months, and BSC 3 months; while for patients aged ≥ 75 years, the median OS was 20, 13, 7, 9, and 2, respectively. Besides, independent clinicopathological factors were integrated into nomograms for OS and CSS prediction, C‐indexes achieved 0.692 and 0.698, respectively. Importantly, the discrimination of nomogram was superior to that of the American Joint Committee on Cancer TNM classification (0.742 vs 0.572 for training set and 0.731 vs 0.565 for validation set). Conclusion For elderly patients with LA‐NSCLC, the curative‐intent treatment (surgery or CRT) conferred better survival compared to chemotherapy alone, RT alone and BSC. The proposed nomograms based on independent clinicopathological variables may be practical and helpful for precise evaluation of patient prognosis, and guiding the individualized treatment for elderly LA‐NSCLC.

Schistosoma japonicum (S. japonicum) is one of the etiological agents of schistosomiasis, a widespread zoonotic parasitic disease. However, the mechanism of the balanced co-existence between the host immune system and S. japonicum as well as their complex interaction remains unclear. In this study, 16S rRNA gene sequencing, combined with metagenomic sequencing approach as well as ultraperformance liquid chromatography–mass spectrometry metabolic profiling, was applied to demonstrate changes in the gut microbiome community structure during schistosomiasis progression, the functional interactions between the gut bacteria and S. japonicum infection in BALB/c mice, and the dynamic metabolite changes of the host. The results showed that both gut microbiome and the metabolites were significantly altered at different time points after the infection. Decrease in richness and diversity as well as differed composition of the gut microbiota was observed in the infected status when compared with the uninfected status. At the phylum level, the gut microbial communities in all samples were dominated by Firmicutes, Bacteroidetes, Proteobacteria, and Deferribacteres, while at the genus level, Lactobacillus, Lachnospiraceae NK4A136 group, Bacteroides, Staphylococcus, and Alloprevotella were the most abundant. After exposure, Roseburia, and Ruminococcaceae UCG-014 decreased, while Staphylococcus, Alistipes, and Parabacteroides increased, which could raise the risk of infections. Furthermore, LEfSe demonstrated several bacterial taxa that could discriminate between each time point of S. japonicum infection. Besides that, metagenomic analysis illuminated that the AMP-activated protein kinase (AMPK) signaling pathway and the chemokine signaling pathway were significantly perturbed after the infection. Phosphatidylcholine and colfosceril palmitate in serum as well as xanthurenic acid, naphthalenesulfonic acid, and pimelylcarnitine in urine might be metabolic biomarkers due to their promising diagnostic potential at the early stage of the infection. Alterations of glycerophospholipid and purine metabolism were also discovered in the infection. The present study might provide further understanding of the mechanisms during schistosome infection in aspects of gut microbiome and metabolites, and facilitate the discovery of new targets for early diagnosis and prognostic purposes. Further validations of potential biomarkers in human populations are necessary, and the exploration of interactions among S. japonicum, gut microbiome, and metabolites is to be deepened in the future.

It has long been a puzzle in cancer treatment that despite the initial appearance of apoptosis, the process could be reversed in some cancer cells and often results in more aggressive tumors and metastasis. The mechanism for this recurrence is yet unknown. Here we report that human mammary carcinoma cells induced to undergo apoptosis could recover with increased tumorigenicity in vitro and in vivo, and induced lymph node metastasis. Specifically, the reversed cells underwent epithelial-to-mesenchymal transitions in the primary tumors in situ, and mesenchymal-to-epithelial transitions in the metastatic cells. Flow cytometry confirmed an elevated percentage of cells carrying cancer stem cells (CSCs) markers (CD44+/CD24-) in the reversed breast cancer cell population, with hypomethylated CD44 promoters and hypermethylated CD24 promoters. More importantly, CSCs were generated anew from non-stem cancer cells after apoptosis reversal possibly through epigenetic modifications. The results from this study can open doors to discovering more effective cancer treatments by suppressing apoptosis reversal.

Abstract Background Schistosomiasis is considered second only to malaria as the most devastating parasitic disease in tropical countries. Schistosome cercariae invade the host by penetrating the skin and migrate though the lungs and portal circulation to their final destination in the hepatic portal system and eventually the mesenteric veins. Previous studies have shown that the cytotoxic pathways that target schistosomulum in the lung-stage involve nitric oxide (NO) produced by macrophages. By contrast, skin-stage schistosomulas can evade clearance, indicating that they might be freed from macrophage NO-mediated cytotoxicity to achieve immune evasion; however, the critical molecules and mechanisms involved remain unknown. Methods Recombinant SjCa8 (rSjCa8), an 8-kDa calcium-binding protein that is stage-specifically expressed in cercaria and early skin-stage schistosomulas of Schistosoma japonicum, was incubated with mouse RAW264.7 macrophages. Effects on macrophage proliferation were determined using Cell Counting Kit-8. Next, transwell assay was carried out to further investigate the role of rSjCa8 in macrophage migration. The effects of rSjCa8 on macrophage apoptosis were evaluated using confocal microscopy and flow cytometry. Additional impacts of rSjCa8 on NO release by lipopolysaccharide (LPS)-stimulated macrophages as well as the underlying mechanisms were explored using fluorescent probe, nitric oxide signaling pathway microarray, quantitative real-time PCR, mutagenesis, and neutralizing antibody approaches. Results rSjCa8 exhibited a striking inhibitory effect on macrophage migration, but did not markedly increase cell proliferation or apoptosis. Additionally, rSjCa8 potently inhibited NO release by LPS-stimulated macrophages in a dose- and time-dependent manner, and the inhibitory mechanism was closely associated with intracellular Ca2+ levels, the up-regulation of catalase expression, and the down-regulation of the expression of 47 genes, including Myc, Gadd45a, Txnip, Fas, Sod2, Nos2, and Hmgb1. Vaccination with rSjCa8 increased NO concentration in the challenging skin area of infected mice and reduced the number of migrated schistosomula after skin penetration by cercariae. Conclusions Our findings indicate that SjCa8 might be a novel molecule that plays a critical role in immune evasion by S. japonicum cercaria during the process of skin penetration. The inhibitory impacts of rSjCa8 on macrophage migration and [Ca2+]i-dependent NO release suggest it might represent a novel vaccine candidate and chemotherapeutic target for the prevention and treatment of schistosomiasis.

Integrating large numbers of intermittent energy resources, such as photovoltaic generations and wind turbines, brings huge uncertainty to the operation of distribution network. In this paper, an interval power flow method via multi-stage affine arithmetic is proposed to address the impact of distributed generation output power and load uncertainty on the power flow solution for the unbalanced distribution network, which acquires affine expression of bus voltage by solving the fixed component, first-order perturbation and non-linear perturbation of the bus complex voltage respectively. Multi-numerical results based on modified IEEE 123 nodes test feeder are presented and discussed, demonstrating that the proposed method achieves much better performance than existing ones in both precision and computation aspects. [ABSTRACT FROM AUTHOR]

Background: Angiostrongylus cantonensis has been the only parasite among Angiostrongylidae to cause human central nervous system infection characterized by eosinophilic meningitis or meningoencephalitis. The mechanism of the extensive neurological impairments of hosts caused by A. cantonensis larvae remains unclear. The aim of the present study was to investigate apoptosis, necroptosis and autophagy in the brains of mice infected with A. cantonensis, which will be valuable for better understanding the pathogenesis of angiostrongyliasis cantonensis., Methods: Functional and histological neurological impairments of brain tissues from mice infected with A. cantonensis were measured by the Morris water maze test and haematoxylin and eosin (H&E) staining, respectively. The transcriptional and translational levels of apoptosis-, necroptosis- and autophagy-related genes were quantified by quantitative real-time polymerase chain reaction (RT-PCR), and assessed by western blot and immunohistochemistry (IHC) analysis. Apoptotic and necroptotic cells and their distributions in infected brain tissues were analysed by flow cytometry and transmission electron microscopy (TEM)., Results: Inflammatory response in the central nervous system deteriorated as A. cantonensis infection evolved, as characterized by abundant inflammatory cell infiltration underneath the meninges, which peaked at 21 days post-infection (dpi). The learning and memory capacities of the mice were significantly decreased at 14 dpi, indicating prominent impairment of their cognitive functions. Compared with those of the control group, the mRNA levels of caspase-3, -4, -6, and RIP3 and the protein levels of caspase-4, cleaved caspase-3, cleaved caspase-6, RIP3, and pRIP3 were obviously elevated. However, no changes in the mRNA or protein levels of FADD, Beclin-1 or LC3B were evident, indicating that apoptosis and necroptosis, but not autophagy, occurred in the brain tissues of mice infected with A. cantonensis. The quantitative RT-PCR, western blot, IHC, flow cytometry and TEM results further revealed the apoptotic and necroptotic microglia, astrocytes and neurons in the parenchymal and hippocampal regions of infected mice., Conclusions: To our knowledge, we showed for the first time that A. cantonensis infection causes the apoptosis and necroptosis of microglia and astrocytes in the parenchymal and hippocampal regions of host brain tissues, further demonstrating the pathogenesis of A. cantonensis infection and providing potential therapeutic targets for the management of angiostrongyliasis.