Differences in the risk of immune-related pneumonitis between PD-1 and PD-L1 inhibitors: a meta-analysis according to the new mirror-principle and PRISMA guidelines.

PURPOSE: To compare the risk of immune-associated pneumonitis between PD-1 and PD-L1 inhibitors, the meta-analysis was designed. METHOD: The difference in risk of immune-associated pneumonitis between PD-1 and PD-L1 inhibitors was assessed by two different meta-analysis methods, the Mirror-pairing and the PRISMA guidelines. RESULTS: A total of eighty-eight reports were used for meta-analysis, while thirty-two studies were used for the Mirror-pairing. Both PD-1 and PD-L1 inhibitors (used alone or combined with chemotherapy) increased the risk of developing immune-related pneumonitis (P < 0.00001; P < 0.00001). Based on indirect analyses results (subgroup analyses), the risk of PD-L1-induced pneumonitis was weaker than that of PD-1 inhibitors when the control group was chemotherapy (OR = 3.33 vs. 5.43) or placebo (OR = 2.53 vs. 3.19), while no obvious significant differences were found (P = 0.17; P = 0.53). For the Mirror-pairing-based meta-analysis, the risk of PD-1-induced pneumonitis was significantly higher than that of PD-L1 inhibitors (OR = 1.46, 95%CI [1.08, 1.98], I2 = 0%, Z = 2.47 (P = 0.01)). However, this difference was not significant, when they were combined with chemotherapy (OR = 1.05, 95%CI [0.68, 1.60], I2 = 38%, Z = 0.21 (P = 0.84)). CONCLUSION: Both PD-1 and PD-L1 inhibitors increased the risk of immune-related pneumonitis, while the risk of PD-1-induced pneumonitis was significantly higher than that of PD-L1 inhibitors.

Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization.

More and more evidence indicates that the dysregulations of microRNAs (miRNAs) lead to diseases through various kinds of underlying mechanisms. Identifying the multiple types of disease-related miRNAs plays an important role in studying the molecular mechanism of miRNAs in diseases. Moreover, compared with traditional biological experiments, computational models are time-saving and cost-minimized. However, most tensor-based computational models still face three main challenges: (i) easy to fall into bad local minima; (ii) preservation of high-order relations; (iii) false-negative samples. To this end, we propose a novel tensor completion framework integrating self-paced learning, hypergraph regularization and adaptive weight tensor into nonnegative tensor factorization, called SPLDHyperAWNTF, for the discovery of potential multiple types of miRNA-disease associations. We first combine self-paced learning with nonnegative tensor factorization to effectively alleviate the model from falling into bad local minima. Then, hypergraphs for miRNAs and diseases are constructed, and hypergraph regularization is used to preserve the high-order complex relations of these hypergraphs. Finally, we innovatively introduce adaptive weight tensor, which can effectively alleviate the impact of false-negative samples on the prediction performance. The average results of 5-fold and 10-fold cross-validation on four datasets show that SPLDHyperAWNTF can achieve better prediction performance than baseline models in terms of Top-1 precision, Top-1 recall and Top-1 F1. Furthermore, we implement case studies to further evaluate the accuracy of SPLDHyperAWNTF. As a result, 98 (MDAv2.0) and 98 (MDAv2.0-2) of top-100 are confirmed by HMDDv3.2 dataset. Moreover, the results of enrichment analysis illustrate that unconfirmed potential associations have biological significance.

Gram-Scale Room-Temperature Synthesis of Solid-State Fluorescent Carbon Nanodots for Bright Electroluminescent Light Emitting Diodes.

The reaction conditions of high temperature and high pressure will introduce structural defects, high energy consumption, and security risks, severely hindering the industrial application of organic carbon nanodots (CDs). Moreover, the aggregation caused quenching effect also fundamentally limits the CDs based electroluminescent light emitting diodes (LEDs). Herein, for the first time, a rapid one-step room temperature synthetic strategy is introduced to prepare highly emissive solid-state-fluorescent CDs (RT-CDs). A strong oxidizing agent, potassium periodate (KIO4 ), is adopted as a catalyst to facilitate the cyclization of o-phenylenediamine and 4-dimethylamino phenol in aqueous solution at room temperature for only 5 min. The resultant organic molecule, 2-(dimethylamino) phenazine, will self-assemble kinetically to generate supramolecular-structure CDs during crystallization. The elaborately arranged supramolecular structure (J aggregates) endows CDs with intense solid-state-fluorescence. Density functional theory (DFT) calculation shows that the excited state of RT-CDs exhibits charge transfer characteristic owing to the unique donor-Π-acceptor structure. A high-performance monochrome RT-CDs based electroluminescent LEDs (2967 cd m-2 and 1.38 cd A-1 ) were fabricated via systematic optimizations of device engineering. This work provides a concrete and feasible avenue for the rapid and massive preparation of CDs, advancing the commercialization of CDs based optoelectronic devices.

Palladium-Catalyzed Decarbonylative Annulation of 2-Arylbenzoic Acids with Internal Alkynes toward Phenanthrenes.

A palladium-catalyzed decarbonylative annulation of 2-arylbenzoic acids with internal alkynes via C(sp2)-H activation has been developed. A series of phenanthrenes were produced in moderate to good yield with good functional group tolerance. The mechanism study indicated that the C(sp2)-H activation should be the rate-determining step during the reaction.

Neurturin promotes tumor cell motility and angiogenesis in colorectal cancer.

Neurturin (NRTN) is one of the glial cell line-derived neurotrophic factor family ligands crucial for neuron growth, differentiation and maintenance. Recent studies showed NRTN promotes an aggressive pancreatic cancer phenotype, and predicts shorter survival in lung cancer patients. However, its expression and function in colorectal cancer (CRC) remain unclear. Herein, we found NRTN was enriched in CRC cells, and predicted poor patients outcomes. Upregulated NRTN enhanced the migration and invasion of CRC cells and vascularization of endothelial cells. In mechanism, NRTN promoted ZEB1/N-cadherin and vascular endothelial growth factor (VEGF)-A expression in CRC cells, which were responsible for tumor cell motility and angiogenesis, respectively. More importantly, NRTN inhibition prevented CRC metastasis and angiogenesis in vivo. In conclusion, NRTN promotes CRC cells motility and tumor angiogenesis via inducing ZEB1/N-cadherin and VEGF-A overexpression. It is a potential therapeutic target and negative prognostic biomarker for CRC patients.

Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning.

Precision oncology involves effectively selecting drugs for cancer patients and planning an effective treatment regimen. However, for Molecular targeted drug, using genomic state of the drug target to select drugs has limitations. Many patients who could benefit from molecularly targeted drugs, but they are being missed due to the insufficient labelling ability of the existing target genes. For non-specific chemotherapy drugs, most of the first-line anticancer drugs do not have biomarkers to guide doctor make treatment regimen. Furthermore, it is important to determine a long-term treatment plan based on the patient's genomic data during tumor evolution. Therefore, it is necessary to establish a tumor drug sensitivity prediction model, which can assist doctors in designing a personalized tumor treatment regimen. This paper proposed a novel model to predict tumor drug sensitivity including targeted drugs and non-specific chemotherapy drugs. This model uses statistical methods based on Bimodal distribution to select multimodal genetic data to solve dimensional challenges and reduce noise and to establish a classification model to predict the effectiveness of the drug in the tumor cell line using machine learning. The experimental test 87 molecular targeted drugs and non-specific chemotherapy drugs. The results show that the method can effectively predict the sensitivity of tumor drugs with an average sensitivity of 0.98 and specificity of 0.97. This model is worth to promotion. If it can be successfully used in clinical trials, it will effectively assist doctors to develop personalized cancer treatment programs and expand the application of molecularly targeted drugs.

An Auto-Switchable Dual-Mode Seawater Energy Extraction System Enabled by Metal-Organic Frameworks.

Harvesting energy directly in oceans by electrochemical devices is essential for driving underwater appliances such as underwater vehicles or detectors. Owing to the extreme undersea environment, it is important but difficult to use the devices with both a high energy density and power density simultaneously. Inspired by marine organisms that have switchable energy extraction modes (aerobic respiration for long-term living or anaerobic respiration to provide instantaneously high output power for fast movement), an auto-switchable dual-mode seawater energy extraction system is presented to provide high energy density and power density both by initiatively choosing different solutes in seawater as electron acceptors. With assistance from metal-organic frameworks, this device had a theoretical energy density of 3960 Wh kg-1 , and a high practical power density of 100±4 mW cm-2 with exceptional stability and low cost, making practical applications in seawater to be possible.

Structural, elastic, electronic, and optical properties of the tricycle-like phosphorene.

Phosphorene exhibits great potential applications in nanoelectronics due to its relatively large and direct band gap and good charge carrier mobility, and thus has attracted extensive attentions over the past few years. In this study, a novel hybrid phosphorene with a tricycle-like bulge is proposed using density functional theory calculations. Herein, structural stability, elastic, electronic, and optical properties have been addressed. It is found that all the hybrid phosphorenes are stable, and their cohesive energies are very close to that of black phosphorene monolayer. Due to the tricycle-like bulge, these hybrid layers are much softer than the black phosphorene. Their electronic band structures show that they are semiconductors with a robust indirect band gap, and their band gaps are strongly dependent on the sizes. Spatial charge distribution to the valence band maximum and the conduction band minimum is analyzed to explore the origin of the indirect band gap features. By calculating the complex dielectric function, optical properties have been discussed. Our results suggest that the hybrid phosphorenes with well structural stability, robust indirect band gaps, flexible property, and good optical absorption hold great promise for applications in the field of visible light harvesting and flexible nanoelectronic devices.

Robust indirect band gap and anisotropy of optical absorption in B-doped phosphorene.

A traditional doping technique plays an important role in the band structure engineering of two-dimensional nanostructures. Since electron interaction is changed by doping, the optical and electrochemical properties could also be significantly tuned. In this study, density functional theory calculations have been employed to explore the structural stability, and electronic and optical properties of B-doped phosphorene. The results show that all B-doped phosphorenes are stable with a relatively low binding energy. Of particular interest is that these B-doped systems exhibit an indirect band gap, which is distinct from the direct one of pure phosphorene. Despite the different concentrations and configurations of B dopants, such indirect band gaps are robust. The screened hybrid density functional HSE06 predicts that the band gap of B-doped phosphorene is slightly smaller than that of pure phosphorene. Spatial charge distributions at the valence band maximum (VBM) and the conduction band minimum (CBM) are analyzed to understand the features of an indirect band gap. By comparison with pure phosphorene, B-doped phosphorenes exhibit strong anisotropy and intensity of optical absorption. Moreover, B dopants could enhance the stability of Li adsorption on phosphorene with less sacrifice of the Li diffusion rate. Our results suggest that B-doping is an effective way of tuning the band gap, enhancing the intensity of optical absorption and improving the performances of Li adsorption, which could promote potential applications in novel optical devices and lithium-ion batteries.

[A capillary blood flow velocity detection system based on linear array charge-coupled devices].

In order to detect the flow characteristics of blood samples in the capillary, this paper introduces a blood flow velocity measurement system based on field-programmable gate array (FPGA), linear charge-coupled devices (CCD) and personal computer (PC) software structure. Based on the analysis of the TCD1703C and AD9826 device data sheets, Verilog HDL hardware description language was used to design and simulate the driver. Image signal acquisition and the extraction of the real-time edge information of the blood sample were carried out synchronously in the FPGA. Then a series of discrete displacement were performed in a differential operation to scan each of the blood samples displacement, so that the sample flow rate could be obtained. Finally, the feasibility of the blood flow velocity detection system was verified by simulation and debugging. After drawing the flow velocity curve and analyzing the velocity characteristics, the significance of measuring blood flow velocity is analyzed. The results show that the measurement of the system is less time-consuming and less complex than other flow rate monitoring schemes.

Structural and electronic properties of ZnO/GaN heterostructured nanowires from first-principles study.

ZnO/GaN alloys have exceptional photocatalytic applications owing to their suitable band gaps corresponding to the range of visible light wavelength and thus have attracted extensive attention over the past few years. In this study, the structural stabilities and electronic properties of core/shell, biaxial, and super-lattice ZnO/GaN heterostructured nanowires have been investigated by means of first-principles calculations based on the density functional theory. The effects of the nanowire size, the GaN ratio, and strain have been explored. It is found that all studied heterostructured nanowires are less stable than pure ZnO nanowires, exhibiting larger sized wires with better structural stabilities and inversely proportional relationship between structural stability and the GaN ratio. Electronic band structures imply that all heterostructured nanowires are semiconductors with the band gaps strongly depending on the GaN ratios as well as mechanical strain. Particularly, for the biaxial and the super-lattice nanowires, their band gaps decrease firstly and then increase with the increasing GaN ratios. Electronic contributions to the valence band maximum (VBM) and the conduction band minimum (CBM) are discussed for exploiting the potential photocatalytic applications.

Ultra-Low Carbon Emissions from Coal-Fired Power Plants through Bio-Oil Co-Firing and Biochar Sequestration.

This study investigates a novel strategy of reducing carbon emissions from coal-fired power plants through co-firing bio-oil and sequestering biochar in agricultural lands. The heavy end fraction of bio-oil recovered from corn stover fast pyrolysis is blended and co-fired with bituminous coal to form a bio-oil co-firing fuel (BCF). Life-cycle greenhouse gas (GHG) emissions per kWh electricity produced vary from 1.02 to 0.26 kg CO2-eq among different cases, with BCF heavy end fractions ranging from 10% to 60%, which corresponds to a GHG emissions reduction of 2.9% to 74.9% compared with that from traditional bituminous coal power plants. We found a heavy end fraction between 34.8% and 37.3% is required to meet the Clean Power Plan's emission regulation for new coal-fired power plants. The minimum electricity selling prices are predicted to increase from 8.8 to 14.9 cents/kWh, with heavy end fractions ranging from 30% to 60%. A minimum carbon price of $67.4 ± 13 per metric ton of CO2-eq was estimated to make BCF power commercially viable for the base case. These results suggest that BCF co-firing is an attractive pathway for clean power generation in existing power plants with a potential for significant reductions in carbon emissions.

Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information.

BACKGROUND: Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately. OBJECTIVE: We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance. METHODS: Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning. RESULTS: Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods. CONCLUSION: The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.

Prognostic value of body mass index for first-line chemoimmunotherapy combinations in advanced non-small cell lung cancer in Chinese population.

Background: Few studies have examined the correlation between body mass index (BMI) and effectiveness of first-line chemoimmunotherapy in patients with advanced non-small cell lung cancer (NSCLC); moreover, the conclusion remains elusive and no such studies have been conducted in the Chinese population. Our study aimed to validate the predictive significance of BMI in Chinese patients with advanced NSCLC receiving first-line chemoimmunotherapy combinations. Methods: Data of patients with advanced NSCLC treated with first-line chemoimmunotherapy between June 2018 and February 2022 at three centers were retrieved retrospectively. The association between baseline BMI with progression-free survival (PFS) and overall survival (OS) was evaluated using the Kaplan-Meier method and Cox regression models. BMI was categorized according to the World Health Organization criteria. Results: Of the included 805 patients, 5.3 % were underweight, 63.4 % had normal weight, 27.8 % were overweight, and 3.5 % were obese. Survival analysis showed that patients in the high BMI group had significantly better PFS (p = 0.012) and OS (p = 0.014) than those in the low BMI group. Further, patients in the overweight subgroup had better PFS (p = 0.036) and OS (p = 0.043) compared to the normal weight population. The results of Cox regression analysis confirmed the correlations between BMI and prognosis of advanced NSCLC patients receiving first-line chemoimmunotherapy combinations. Conclusions: Baseline BMI affected the clinical outcomes of first-line chemoimmunotherapy combinations in patients with advanced NSCLC, and was especially favorable for the overweight subgroup.

Efficacy of first-line treatment options beyond RET-TKIs in advanced RET-rearranged non-small cell lung cancer: A multi-center real-world study.

BACKGROUND: Although RET-tyrosine kinase inhibitors (RET-TKIs) are the preferred first-line therapy for advanced RET-arranged NSCLC, most patients cannot afford them. In this population, bevacizumab, immunotherapy, and chemotherapy are the most commonly used regimens. However, the optimal scheme beyond RET-TKIs has not been defined in the first-line setting. METHODS: This retrospective study included 86 stage IV NSCLC patients harboring RET rearrangement from six cancer centers between May 2017 and October 2022. RET-TKIs, chemotherapy, or one of the combination therapies (including immune checkpoint inhibitor (ICI) combined with chemotherapy (I + C), bevacizumab combined with chemotherapy (B + C), ICI and bevacizumab combined with chemotherapy (I + B + C)), were used as the first-line therapeutics. The clinical outcomes and safety were evaluated. RESULTS: Fourteen of the 86 patients received RET-TKIs, 57 received combination therapies, and 15 received chemotherapy alone. Their medium PFS (mPFS) were 16.92 months (95% CI: 5.9-27.9 months), 8.7 months (95% CI: 6.5-11.0 months), and 5.55 months (95% CI: 2.4-8.7 months) respectively. Among all the combination schemes, B + C (p = 0.007) or I + B + C (p = 0.025) gave beneficial PFS compared with chemotherapy, while I + C treatment (p = 0.169) generated comparable PFS with chemotherapy. In addition, I + B + C treatment had a numerically longer mPFS (12.21 months) compared with B + C (8.74 months) or I + C (7.89 months) schemes. In terms of safety, I + B + C treatment led to the highest frequency of hematological toxicity (50%) and vomiting (75%), but no ≥G3 adverse effect was observed. CONCLUSIONS: I + B + C might be a preferred option beyond RET-TKIs in the first-line therapy of RET-arranged NSCLC. Combination with Bevacizumab rather than with ICIs offered favorable survival compared with chemotherapy alone.

Synthesis of Fluoride-Substituted Layered Perovskites ZnMoO4 with an Enhanced Photocatalytic Activity.

Oxyfluorides possess considerable attention for their multiple excellent properties, but the conventional high-temperature solid-state syntheses have seen bottlenecks in the synthesis of new compounds. Herein, we report a novel layered oxyfluoride ZnMoO4:F, which is prepared by a facile hydrothermal method using ZnF2 as the fluoride source. The fluoride anions are successfully introduced into the oxygen sublattice, which is confirmed by a combined analysis using XRD, STEM, and TGA techniques. The as-synthesized ZnMoO4:F has an absorption edge at around 550 nm, indicating a red shift of Eg to the visible region compared to the oxide counterpart. The layered oxyfluoride exhibits an enhanced photocatalytic active for hydrogen evolution under simulated sunlight (λ > 350 nm), and the activity of ZnMoO4:F (651.9 µmol g-1) was 2 times higher than that of ZnMoO4 (309.7 µmol g-1). Further electrochemical analysis has shown that the conduction band position plays a critical role in the high performances of ZnMoO4:F. This work sheds new light on the future design and synthesis of novel fluoride-doped materials for photocatalysis applications.

Nonfunctional ectopic adrenocortical carcinoma in the lung: A case report and literature review.

Background: Ectopic adrenocortical tissues and neoplasms are rare and usually found in the genitourinary system and abdominal cavity. The thorax is an extremely rare ectopic site. Here, we report the first case of nonfunctional ectopic adrenocortical carcinoma (ACC) in the lung. Case presentation: A 71-year-old Chinese man presented with vague left-sided chest pain and irritable cough for 1 month. Thoracic computed tomography revealed a heterogeneously enhancing 5.3 × 5.8 × 6.0-cm solitary mass in the left lung. Radiological findings suggested a benign tumor. The tumor was surgically excised upon detection. Histopathological examination using hematoxylin and eosin staining showed that the cytoplasm of the tumor cells was rich and eosinophilic. Immunohistochemical profiles (inhibin-a+, melan-A+, Syn+) indicated that the tumor had an adrenocortical origin. The patient showed no symptoms of hormonal hypersecretion. The final pathological diagnosis was non-functional ectopic ACC. The patient was disease-free for 22 months and is still under follow-up. Conclusions: Nonfunctional ectopic ACC in the lung is an extremely rare neoplasm that can be easily misdiagnosed as primary lung cancer or lung metastasis, both preoperatively and on postoperative pathological examination. This report may provide clues to clinicians and pathologists regarding the diagnosis and treatment of nonfunctional ectopic ACC.

Improved Computational Drug-Repositioning by Self-Paced Non-Negative Matrix Tri-Factorization.

Drug repositioning (DR) is a strategy to find new targets for existing drugs, which plays an important role in reducing the costs, time, and risk of traditional drug development. Recently, the matrix factorization approach has been widely used in the field of DR prediction. Nevertheless, there are still two challenges: 1) Learning ability deficiencies, the model cannot accurately predict more potential associations. 2) Easy to fall into a bad local optimal solution, the model tends to get a suboptimal result. In this study, we propose a self-paced non-negative matrix tri-factorization (SPLNMTF) model, which integrates three types of different biological data from patients, genes, and drugs into a heterogeneous network through non-negative matrix tri-factorization, thereby learning more information to improve the learning ability of the model. In the meantime, the SPLNMTF model sequentially includes samples into training from easy (high-quality) to complex (low-quality) in the soft weighting way, which effectively alleviates falling into a bad local optimal solution to improve the prediction performance of the model. The experimental results on two real datasets of ovarian cancer and acute myeloid leukemia (AML) show that SPLNMTF outperforms the other eight state-of-the-art models and gets better prediction performance in drug repositioning. The data and source code are available at: https://github.com/qi0906/SPLNMTF.

Bias-free driven ion assisted photoelectrochemical system for sustainable wastewater treatment.

Photoelectrochemical (PEC) systems have emerged as a prominent renewable energy-based technology for wastewater treatment, offering sustainable advantages such as eliminating dependence on fossil fuels or grid electricity compared to traditional electrochemical treatment methods. However, previous PEC systems often overlook the potential of ions present in wastewater as an alternative to externally applied bias voltage for enhancing carrier separation efficiency. Here we report a bias-free driven ion assisted photoelectrochemical (IAPEC) system by integration of an electron-ion acceptor cathode, which leverages its fast ion-electron coupling capability to significantly enhance the separation of electrons and holes at the photoanode. We demonstrate that Prussian blue analogues (PBAs) can serve as robust and reversible electron-ion acceptors that provide reaction sites for photoelectron coupling cations, thus driving the hole oxidation to produce strong oxidant free radicals at photoanode. Our IAPEC system exhibits superior degradation performance in wastewater containing chloride medium. This indicates that, in addition to the cations (e.g., Na+) accelerating the electron transfer rate, the presence of Cl- ions further enhance efficient and sustainable wastewater treatment. This work highlights the potential of utilizing abundant sodium chloride in seawater as a cost-effective additive for wastewater treatment, offering crucial insights into the use of local materials for effective, low-carbon, and sustainable treatment processes.